diff --git a/README.md b/README.md index ef5bdc66ef03131318e1dde627e0224cca9137fd..3cdb6e478ddf4f18af7f81bb3e321510903beb9d 100644 --- a/README.md +++ b/README.md @@ -22,6 +22,10 @@ organization for the purposes of conducting machine learning and deep neural networks research. The system is general enough to be applicable in a wide variety of other domains, as well. +Keep up to date with release announcements and security updates by +subscribing to +[announce@tensorflow.org](https://groups.google.com/a/tensorflow.org/forum/#!forum/announce). + ## Installation *See [Installing TensorFlow](https://www.tensorflow.org/get_started/os_setup.html) for instructions on how to install our release binaries or how to build from source.* diff --git a/tensorflow/SECURITY.md b/SECURITY.md similarity index 97% rename from tensorflow/SECURITY.md rename to SECURITY.md index fea24b273920885ba8a1ae96aafbf7710df46e1f..378e77696725e338e8289cda84dbc543303ae053 100644 --- a/tensorflow/SECURITY.md +++ b/SECURITY.md @@ -6,7 +6,7 @@ report vulnerabilities in TensorFlow. ## TensorFlow models are programs -TensorFlow's runtime system interprets and executes programs. What machine +TensorFlow's runtime system interprets and executes programs. What machine learning practitioners term [**models**](https://developers.google.com/machine-learning/glossary/#model) are expressed as programs that TensorFlow executes. TensorFlow programs are encoded @@ -28,12 +28,12 @@ data you supply to TensorFlow to train a model, or to use a model to run inference on the data. **TensorFlow models are programs, and need to be treated as such from a security -perspective.** +perspective.** ## Running untrusted models As a general rule: **Always** execute untrusted models inside a sandbox (e.g., -[nsjail](https://github.com/google/nsjail)). +[nsjail](https://github.com/google/nsjail)). There are several ways in which a model could become untrusted. Obviously, if an untrusted party supplies TensorFlow kernels, arbitrary code may be executed. @@ -109,11 +109,11 @@ graphs known to the `ModelServer`. This means that an attacker may run graphs using untrusted inputs as described above, but they would not be able to execute arbitrary graphs. It is possible to safely expose a `ModelServer` directly to an untrusted network, **but only if the graphs it is configured to -use have been carefully audited to be safe**. +use have been carefully audited to be safe**. Similar to best practices for other servers, we recommend running any `ModelServer` with appropriate privileges (i.e., using a separate user with -reduced permisisons). In the spirit of defense in depth, we recommend +reduced permissions). In the spirit of defense in depth, we recommend authenticating requests to any TensorFlow server connected to an untrusted network, as well as sandboxing the server to minimize the adverse effects of any breach. @@ -129,11 +129,11 @@ with specially crafted inputs. ### What is a vulnerability? Given TensorFlow's flexibility, it is possible to specify computation graphs -which exhibit unexpected or unwanted behaviors. The fact that TensorFlow models +which exhibit unexpected or unwanted behavior. The fact that TensorFlow models can perform arbitrary computations means that they may read and write files, communicate via the network, produce deadlocks and infinite loops, or run out of memory. It is only when these behaviors are outside the specifications of the -operations involved that such behavior is a vulnerability. +operations involved that such behavior is a vulnerability. A `FileWriter` writing a file is not unexpected behavior and therefore is not a vulnerability in TensorFlow. A `MatMul` allowing arbitrary binary code execution @@ -168,7 +168,7 @@ below). Please use a descriptive subject line for your report email. After the initial reply to your report, the security team will endeavor to keep you informed of -the progress being made towards a fix and announcement. +the progress being made towards a fix and announcement. If you believe that an existing (public) issue is security-related, please send an email to `security@tensorflow.org`. The email should include the issue ID and diff --git a/configure.py b/configure.py index 7d2e30cd8af53b74c9274e28c4b95bdd01e9d41a..d14edef1be9e31137c96bed7aebf7ba158b3274f 100644 --- a/configure.py +++ b/configure.py @@ -250,7 +250,11 @@ def reset_tf_configure_bazelrc(workspace_path): if _TF_BAZELRC_FILENAME in l: continue f.write('%s\n' % l) - f.write('import %s\n' % _TF_BAZELRC) + if is_windows(): + tf_bazelrc_path = _TF_BAZELRC.replace("\\", "/") + else: + tf_bazelrc_path = _TF_BAZELRC + f.write('import %s\n' % tf_bazelrc_path) def cleanup_makefile(): @@ -444,7 +448,7 @@ def check_bazel_version(min_version): if which('bazel') is None: print('Cannot find bazel. Please install bazel.') sys.exit(0) - curr_version = run_shell(['bazel', '--batch', 'version']) + curr_version = run_shell(['bazel', '--batch', '--bazelrc=/dev/null', 'version']) for line in curr_version.split('\n'): if 'Build label: ' in line: @@ -1044,7 +1048,10 @@ def set_tf_tensorrt_install_path(environ_cp): for lib_file in possible_files: if is_compatible(lib_file, cuda_ver, cudnn_ver): - ver_str = nvinfer_pattern.search(lib_file).group(1) + matches = nvinfer_pattern.search(lib_file) + if len(matches.groups()) == 0: + continue + ver_str = matches.group(1) ver = convert_version_to_int(ver_str) if len(ver_str) else 0 if ver > highest_ver[0]: highest_ver = [ver, ver_str, lib_file] @@ -1373,7 +1380,7 @@ def main(): # environment variables. environ_cp = dict(os.environ) - check_bazel_version('0.5.4') + check_bazel_version('0.10.0') reset_tf_configure_bazelrc(args.workspace) cleanup_makefile() diff --git a/tensorflow/BUILD b/tensorflow/BUILD index dc995d231d3e591771f801e28024a76610cdba26..9932e5607685b5b8f5900bdfa42363151e57d3f1 100644 --- a/tensorflow/BUILD +++ b/tensorflow/BUILD @@ -415,6 +415,17 @@ py_library( deps = ["//tensorflow/python"], ) +py_library( + name = "experimental_tensorflow_py", + srcs = ["experimental_api.py"], + srcs_version = "PY2AND3", + visibility = ["//tensorflow/tools/api/tests:__subpackages__"], + deps = [ + "//tensorflow/python", + "//tensorflow/tools/api/generator:python_api", + ], +) + filegroup( name = "all_opensource_files", data = [ @@ -441,6 +452,7 @@ filegroup( "//tensorflow/compiler/xla:all_files", "//tensorflow/compiler/xla/client:all_files", "//tensorflow/compiler/xla/client/lib:all_files", + "//tensorflow/compiler/xla/client/xla_client:all_files", "//tensorflow/compiler/xla/legacy_flags:all_files", "//tensorflow/compiler/xla/python:all_files", "//tensorflow/compiler/xla/service:all_files", @@ -787,6 +799,7 @@ tf_cc_shared_object( }), deps = [ "//tensorflow/c:c_api", + "//tensorflow/c:c_api_experimental", "//tensorflow/c:exported_symbols.lds", "//tensorflow/c:version_script.lds", "//tensorflow/c/eager:c_api", diff --git a/tensorflow/c/BUILD b/tensorflow/c/BUILD index 5dfb743681255d8c03e91ea43fd441d94fdee59d..29ed957c9aa8cbe515f5f43bdccbf8c94f47c459 100644 --- a/tensorflow/c/BUILD +++ b/tensorflow/c/BUILD @@ -17,7 +17,10 @@ load( filegroup( name = "headers", - srcs = ["c_api.h"], + srcs = [ + "c_api.h", + "c_api_experimental.h", + ], visibility = ["//tensorflow:__subpackages__"], ) diff --git a/tensorflow/c/c_api.cc b/tensorflow/c/c_api.cc index 85f1d1639b4d09f2de77d326481a86ec246270d0..778cb667e2c0015c6a768ecf3b12b82601764117 100644 --- a/tensorflow/c/c_api.cc +++ b/tensorflow/c/c_api.cc @@ -30,6 +30,7 @@ limitations under the License. #endif #include "tensorflow/c/c_api_internal.h" #include "tensorflow/core/common_runtime/device_mgr.h" +#include "tensorflow/core/common_runtime/eval_const_tensor.h" #include "tensorflow/core/common_runtime/shape_refiner.h" #include "tensorflow/core/framework/allocation_description.pb.h" #include "tensorflow/core/framework/log_memory.h" @@ -62,6 +63,7 @@ limitations under the License. // brain namespace because we are defining 'extern "C"' functions. using tensorflow::AllocationDescription; using tensorflow::DataType; +using tensorflow::ExtendSessionGraphHelper; using tensorflow::Graph; using tensorflow::GraphDef; using tensorflow::mutex_lock; @@ -73,6 +75,7 @@ using tensorflow::NodeBuilder; using tensorflow::NodeDef; using tensorflow::OpDef; using tensorflow::OpRegistry; +using tensorflow::OutputTensor; using tensorflow::PartialTensorShape; using tensorflow::RunMetadata; using tensorflow::RunOptions; @@ -638,11 +641,11 @@ Status MessageToBuffer(const tensorflow::protobuf::Message& in, } void RecordMutation(TF_Graph* graph, const TF_Operation& op, - const char* mutation_type) - EXCLUSIVE_LOCKS_REQUIRED(graph->mu) { + const char* mutation_type) { // If any session has already run this node_id, mark this session as // unrunnable. for (auto it : graph->sessions) { + mutex_lock session_lock(it.first->mu); if (it.first->last_num_graph_nodes > op.node.id()) { it.second = FailedPrecondition( "Operation '", op.node.DebugString(), "' was changed by ", @@ -708,6 +711,60 @@ void TF_GraphSetOutputHandleShapesAndTypes(TF_Graph* graph, TF_Output output, Status LoadLibrary(const char* library_filename, void** result, const void** buf, size_t* len); +// TODO(josh11b,mrry): Change Session to be able to use a Graph* +// directly, instead of requiring us to serialize to a GraphDef and +// call Session::Extend(). +bool ExtendSessionGraphHelper(TF_Session* session, TF_Status* status) { + if (session->graph != nullptr) { + // Take the graph lock before the session lock to avoid deadlock. This is + // safe since session->graph does not change. + session->graph->mu.lock(); + mutex_lock session_lock(session->mu); + const Graph& graph = session->graph->graph; + + status->status = session->graph->sessions[session]; + if (!status->status.ok()) { + session->graph->mu.unlock(); + return false; + } + + const auto num_nodes = graph.num_node_ids(); + if (session->last_num_graph_nodes < num_nodes) { + status->status = tensorflow::ValidateNoCycles(session->graph->graph); + if (!status->status.ok()) { + session->graph->mu.unlock(); + return false; + } + + GraphDef graph_def; + *graph_def.mutable_versions() = graph.versions(); + // Fill graph_def with nodes with ids in the range + // [session->last_num_graph_nodes, num_nodes), that is the nodes + // added since the last TF_SessionRun() call. + for (auto id = session->last_num_graph_nodes; id < num_nodes; ++id) { + Node* const node = graph.FindNodeId(id); + if (node != nullptr && node->IsOp()) { + NodeDef* const node_def = graph_def.add_node(); + *node_def = node->def(); + } + } + *graph_def.mutable_library() = graph.flib_def().ToProto(); + session->graph->mu.unlock(); + status->status = session->session->Extend(graph_def); + if (!status->status.ok()) { + // Contract is we always delete input_values[i]. + return false; + } + // Note: session->session is not modified if Extend() fails, so + // we only set last_num_graph_nodes if it succeeds. + session->last_num_graph_nodes = num_nodes; + } else { + session->graph->mu.unlock(); + } + } + return true; +} + } // namespace tensorflow static void TF_Run_Setup(int noutputs, TF_Tensor** c_outputs, @@ -2408,11 +2465,7 @@ void TF_AddGradients(TF_Graph* g, TF_Output* y, int ny, TF_Output* x, int nx, // TF_Session functions ---------------------------------------------- TF_Session::TF_Session(tensorflow::Session* s, TF_Graph* g) - : session(s), graph(g), last_num_graph_nodes(0), device_mgr(nullptr) { - if (s->LocalDeviceManager(&device_mgr).ok()) { - devices = device_mgr->ListDevices(); - } -} + : session(s), graph(g), last_num_graph_nodes(0), extend_before_run(true) {} TF_Session* TF_NewSession(TF_Graph* graph, const TF_SessionOptions* opt, TF_Status* status) { @@ -2512,58 +2565,6 @@ void TF_DeleteSession(TF_Session* s, TF_Status* status) { delete s; } -// TODO(josh11b,mrry): Change Session to be able to use a Graph* -// directly, instead of requiring us to serialize to a GraphDef and -// call Session::Extend(). -static bool ExtendSessionGraphHelper(TF_Session* session, TF_Status* status) { - if (session->graph != nullptr) { - mutex_lock session_lock(session->mu); - session->graph->mu.lock(); - const Graph& graph = session->graph->graph; - - status->status = session->graph->sessions[session]; - if (!status->status.ok()) { - session->graph->mu.unlock(); - return false; - } - - const auto num_nodes = graph.num_node_ids(); - if (session->last_num_graph_nodes < num_nodes) { - status->status = tensorflow::ValidateNoCycles(session->graph->graph); - if (!status->status.ok()) { - session->graph->mu.unlock(); - return false; - } - - GraphDef graph_def; - *graph_def.mutable_versions() = graph.versions(); - // Fill graph_def with nodes with ids in the range - // [session->last_num_graph_nodes, num_nodes), that is the nodes - // added since the last TF_SessionRun() call. - for (auto id = session->last_num_graph_nodes; id < num_nodes; ++id) { - Node* const node = graph.FindNodeId(id); - if (node != nullptr && node->IsOp()) { - NodeDef* const node_def = graph_def.add_node(); - *node_def = node->def(); - } - } - *graph_def.mutable_library() = graph.flib_def().ToProto(); - session->graph->mu.unlock(); - status->status = session->session->Extend(graph_def); - if (!status->status.ok()) { - // Contract is we always delete input_values[i]. - return false; - } - // Note: session->session is not modified if Extend() fails, so - // we only set last_num_graph_nodes if it succeeds. - session->last_num_graph_nodes = num_nodes; - } else { - session->graph->mu.unlock(); - } - } - return true; -} - void TF_SessionRun(TF_Session* session, const TF_Buffer* run_options, const TF_Output* inputs, TF_Tensor* const* input_values, int ninputs, const TF_Output* outputs, @@ -2573,7 +2574,8 @@ void TF_SessionRun(TF_Session* session, const TF_Buffer* run_options, // TODO(josh11b,mrry): Change Session to be able to use a Graph* // directly, instead of requiring us to serialize to a GraphDef and // call Session::Extend(). - if (!ExtendSessionGraphHelper(session, status)) { + if (session->extend_before_run && + !ExtendSessionGraphHelper(session, status)) { return; } @@ -2610,7 +2612,8 @@ void TF_SessionPRunSetup(TF_Session* session, const TF_Output* inputs, const char** handle, TF_Status* status) { *handle = nullptr; - if (!ExtendSessionGraphHelper(session, status)) { + if (session->extend_before_run && + !ExtendSessionGraphHelper(session, status)) { return; } @@ -2653,7 +2656,8 @@ void TF_SessionPRun(TF_Session* session, const char* handle, // TODO(josh11b,mrry): Change Session to be able to use a Graph* // directly, instead of requiring us to serialize to a GraphDef and // call Session::Extend(). - if (!ExtendSessionGraphHelper(session, status)) { + if (session->extend_before_run && + !ExtendSessionGraphHelper(session, status)) { return; } @@ -2682,6 +2686,24 @@ void TF_SessionPRun(TF_Session* session, const char* handle, output_values, target_names, nullptr, status); } +unsigned char TF_TryEvaluateConstant(TF_Graph* graph, TF_Output output, + TF_Tensor** result, TF_Status* status) { + *result = nullptr; + mutex_lock l(graph->mu); + OutputTensor tensor(&output.oper->node, output.index); + bool evaluated; + Tensor result_tensor; + status->status = EvaluateConstantTensor( + tensor, graph->refiner, *graph->graph.op_registry(), + graph->graph.versions().producer(), &evaluated, &result_tensor); + if (evaluated) { + DCHECK(status->status.ok()); + *result = TF_TensorFromTensor(result_tensor, status); + if (!status->status.ok()) evaluated = false; + } + return evaluated; +} + TF_ApiDefMap* TF_NewApiDefMap(TF_Buffer* op_list_buffer, TF_Status* status) { tensorflow::OpList op_list; if (!op_list.ParseFromArray(op_list_buffer->data, op_list_buffer->length)) { diff --git a/tensorflow/c/c_api.h b/tensorflow/c/c_api.h index ad592ef70961ef427bfe9fd322a82bd64df7f9f1..b32f574628c4d1dc5c3bb3f1265a1b12adee28bc 100644 --- a/tensorflow/c/c_api.h +++ b/tensorflow/c/c_api.h @@ -1275,13 +1275,22 @@ TF_CAPI_EXPORT extern void TF_FunctionGetAttrValueProto( // Deleting a function does not remove it from any graphs it was copied to. TF_CAPI_EXPORT extern void TF_DeleteFunction(TF_Function* func); +// Attempts to evaluate `output`. This will only be possible if `output` doesn't +// depend on any graph inputs (this function is safe to call if this isn't the +// case though). +// +// If the evaluation is successful, this function returns true and `output`s +// value is returned in `result`. Otherwise returns false. An error status is +// returned if something is wrong with the graph or input. Note that this may +// return false even if no error status is set. +TF_CAPI_EXPORT extern unsigned char TF_TryEvaluateConstant(TF_Graph* graph, + TF_Output output, + TF_Tensor** result, + TF_Status* status); + // TODO(josh11b): Register OpDef, available to all operations added // to this graph. -// The following two may both benefit from a subgraph-definition API -// that re-uses most of the graph-definition API. -// TODO(andydavis): Add functions to a graph. - // -------------------------------------------------------------------------- // API for driving Graph execution. diff --git a/tensorflow/c/c_api_function_test.cc b/tensorflow/c/c_api_function_test.cc index 7ca50119eafe299b307f06c555aec1388e7e82e2..610274696f5940c063e68f2310cfd9cc1e0bd964 100644 --- a/tensorflow/c/c_api_function_test.cc +++ b/tensorflow/c/c_api_function_test.cc @@ -20,6 +20,7 @@ limitations under the License. #include "tensorflow/core/framework/op_def.pb.h" #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/lib/hash/hash.h" +#include "tensorflow/core/lib/strings/proto_serialization.h" #include "tensorflow/core/lib/strings/str_util.h" #include "tensorflow/core/lib/strings/strcat.h" #include "tensorflow/core/platform/logging.h" diff --git a/tensorflow/c/c_api_internal.h b/tensorflow/c/c_api_internal.h index 91667056e0eeb224b4b8a034766f11a123cd1a03..e885a699274cfae04d5a17c736da1acfddcc7b3b 100644 --- a/tensorflow/c/c_api_internal.h +++ b/tensorflow/c/c_api_internal.h @@ -124,15 +124,16 @@ struct TF_Session { TF_Session(tensorflow::Session* s, TF_Graph* g); tensorflow::Session* session; - TF_Graph* graph; + TF_Graph* const graph; - tensorflow::mutex mu; + tensorflow::mutex mu ACQUIRED_AFTER(TF_Graph::mu); int last_num_graph_nodes; - // NOTE(ashankar): Experimental fields to help keep the - // buffers of a TF_Tensor pinned in device memory. - const tensorflow::DeviceMgr* device_mgr; // Owned by session. - std::vector devices; // Owned by device_mgr. + // If true, TF_SessionRun and similar methods will call + // ExtendSessionGraphHelper before running the graph (this is the default + // public behavior). Can be set to false if the caller needs to call + // ExtendSessionGraphHelper manually. + std::atomic extend_before_run; }; struct TF_ImportGraphDefOptions { @@ -210,7 +211,11 @@ void TF_GraphSetOutputHandleShapesAndTypes(TF_Graph* graph, TF_Output output, TF_Status* status); void RecordMutation(TF_Graph* graph, const TF_Operation& op, - const char* mutation_type); + const char* mutation_type) + EXCLUSIVE_LOCKS_REQUIRED(graph->mu); + +bool ExtendSessionGraphHelper(TF_Session* session, TF_Status* status) + LOCKS_EXCLUDED(session->graph->mu, session->mu); } // end namespace tensorflow diff --git a/tensorflow/c/c_test_util.cc b/tensorflow/c/c_test_util.cc index 3db2852ce6560ba493d60ef54a110161c112d110..22f77e7b874a13b3b6e0fbe981b4188c634db439 100644 --- a/tensorflow/c/c_test_util.cc +++ b/tensorflow/c/c_test_util.cc @@ -34,6 +34,10 @@ static void DoubleDeallocator(void* data, size_t, void* arg) { delete[] static_cast(data); } +static void FloatDeallocator(void* data, size_t, void* arg) { + delete[] static_cast(data); +} + TF_Tensor* Int8Tensor(const int64_t* dims, int num_dims, const char* values) { int64_t num_values = 1; for (int i = 0; i < num_dims; ++i) { @@ -78,21 +82,30 @@ TF_Tensor* DoubleTensor(double v) { &DoubleDeallocator, nullptr); } +TF_Tensor* FloatTensor(float v) { + const int num_bytes = sizeof(float); + float* values = new float[1]; + values[0] = v; + return TF_NewTensor(TF_FLOAT, nullptr, 0, values, num_bytes, + &FloatDeallocator, nullptr); +} + // All the *Helper methods are used as a workaround for the restrictions that // one cannot call ASSERT_* methods in non-void-returning functions (when // exceptions are disabled during compilation) void PlaceholderHelper(TF_Graph* graph, TF_Status* s, const char* name, - TF_Operation** op) { + TF_DataType dtype, TF_Operation** op) { TF_OperationDescription* desc = TF_NewOperation(graph, "Placeholder", name); - TF_SetAttrType(desc, "dtype", TF_INT32); + TF_SetAttrType(desc, "dtype", dtype); *op = TF_FinishOperation(desc, s); ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); ASSERT_NE(*op, nullptr); } -TF_Operation* Placeholder(TF_Graph* graph, TF_Status* s, const char* name) { +TF_Operation* Placeholder(TF_Graph* graph, TF_Status* s, const char* name, + TF_DataType dtype) { TF_Operation* op; - PlaceholderHelper(graph, s, name, &op); + PlaceholderHelper(graph, s, name, dtype, &op); return op; } @@ -126,6 +139,12 @@ TF_Operation* ScalarConst(double v, TF_Graph* graph, TF_Status* s, return Const(tensor.get(), graph, s, name); } +TF_Operation* ScalarConst(float v, TF_Graph* graph, TF_Status* s, + const char* name) { + unique_tensor_ptr tensor(FloatTensor(v), TF_DeleteTensor); + return Const(tensor.get(), graph, s, name); +} + void AddOpHelper(TF_Operation* l, TF_Operation* r, TF_Graph* graph, TF_Status* s, const char* name, TF_Operation** op, bool check) { diff --git a/tensorflow/c/c_test_util.h b/tensorflow/c/c_test_util.h index 2a70177c724c569844a5d8ad42b99bed20209946..d87c57fd5193129665ca65761872a38131ee532b 100644 --- a/tensorflow/c/c_test_util.h +++ b/tensorflow/c/c_test_util.h @@ -44,8 +44,11 @@ TF_Tensor* Int32Tensor(int32_t v); TF_Tensor* DoubleTensor(double v); +TF_Tensor* FloatTensor(float v); + TF_Operation* Placeholder(TF_Graph* graph, TF_Status* s, - const char* name = "feed"); + const char* name = "feed", + TF_DataType dtype = TF_INT32); TF_Operation* Const(TF_Tensor* t, TF_Graph* graph, TF_Status* s, const char* name = "const"); @@ -56,6 +59,9 @@ TF_Operation* ScalarConst(int32_t v, TF_Graph* graph, TF_Status* s, TF_Operation* ScalarConst(double v, TF_Graph* graph, TF_Status* s, const char* name = "scalar"); +TF_Operation* ScalarConst(float v, TF_Graph* graph, TF_Status* s, + const char* name = "scalar"); + TF_Operation* Add(TF_Operation* l, TF_Operation* r, TF_Graph* graph, TF_Status* s, const char* name = "add"); diff --git a/tensorflow/c/eager/BUILD b/tensorflow/c/eager/BUILD index e55cb672e97e1403a3dd864c91c176426eb3f067..3046d9064a6d4b39cd8a7209d7f20e1e779c2847 100644 --- a/tensorflow/c/eager/BUILD +++ b/tensorflow/c/eager/BUILD @@ -58,6 +58,7 @@ tf_cuda_library( "//tensorflow/core:framework", "//tensorflow/core:framework_internal", "//tensorflow/core:framework_lite", + "//tensorflow/core:lib", "//tensorflow/core:lib_internal", ], ) diff --git a/tensorflow/c/eager/c_api.cc b/tensorflow/c/eager/c_api.cc index 98ef6f0d0ab094eae3e2e21624c3a4ba30d1c3d3..0811bd363f2aecedc94488b6ee87fac3f4b2af14 100644 --- a/tensorflow/c/eager/c_api.cc +++ b/tensorflow/c/eager/c_api.cc @@ -31,8 +31,10 @@ limitations under the License. #include "tensorflow/core/common_runtime/copy_tensor.h" #include "tensorflow/core/common_runtime/device_factory.h" #include "tensorflow/core/common_runtime/device_mgr.h" +#include "tensorflow/core/common_runtime/device_set.h" #include "tensorflow/core/common_runtime/function.h" #include "tensorflow/core/common_runtime/rendezvous_mgr.h" +#include "tensorflow/core/framework/node_def_util.h" #include "tensorflow/core/framework/rendezvous.h" #include "tensorflow/core/framework/tensor_shape.pb.h" #include "tensorflow/core/framework/types.h" @@ -40,6 +42,7 @@ limitations under the License. #include "tensorflow/core/lib/gtl/flatmap.h" #include "tensorflow/core/lib/gtl/map_util.h" #include "tensorflow/core/lib/gtl/stl_util.h" +#include "tensorflow/core/platform/env.h" #include "tensorflow/core/platform/mutex.h" #include "tensorflow/core/platform/thread_annotations.h" #include "tensorflow/core/public/version.h" @@ -65,8 +68,21 @@ string DeviceName(const tensorflow::Device* d) { #ifdef TENSORFLOW_EAGER_USE_XLA std::atomic_int_fast64_t func_id_generator(0); #endif // TENSORFLOW_EAGER_USE_XLA + } // namespace +TFE_ContextDevicePlacementPolicy PlacementPolicy( + bool soft_placement, TFE_ContextDevicePlacementPolicy original_policy) { + if (!soft_placement) { + return original_policy; + } + if (original_policy == TFE_DEVICE_PLACEMENT_EXPLICIT || + original_policy == TFE_DEVICE_PLACEMENT_SILENT_FOR_INT32) { + return TFE_DEVICE_PLACEMENT_SILENT; + } + return original_policy; +} + extern "C" { TFE_ContextOptions* TFE_NewContextOptions() { return new TFE_ContextOptions; } @@ -76,47 +92,64 @@ void TFE_ContextOptionsSetConfig(TFE_ContextOptions* options, const void* proto, TF_SetConfig(&options->session_options, proto, proto_len, status); } +void TFE_ContextOptionsSetAsync(TFE_ContextOptions* options, + unsigned char async) { + options->async = async; +} void TFE_ContextOptionsSetDevicePlacementPolicy( TFE_ContextOptions* options, TFE_ContextDevicePlacementPolicy policy) { options->policy = policy; } +TF_CAPI_EXPORT extern void TFE_ContextSetAsyncForThread(TFE_Context* ctx, + unsigned char async, + TF_Status* status) { + { + tensorflow::mutex_lock l(ctx->async_map_mu); + ctx->thread_local_async[std::this_thread::get_id()] = async; + } + if (async) { + ctx->executor.EnableAsync(); + } else { + // TODO(agarwal): Currently we add a wait here to handle cases where a sync + // op has a control dependency on an async op, and the latter has not + // executed yet. This wait can be removed by storing all the control inputs + // and waiting for them when executing ops. + status->status = ctx->executor.WaitForAllPendingNodes(); + } +} + void TFE_DeleteContextOptions(TFE_ContextOptions* options) { delete options; } TFE_Context* TFE_NewContext(const TFE_ContextOptions* opts, TF_Status* status) { - TF_Graph* graph = TF_NewGraph(); - TF_Session* session = TF_NewSession(graph, &opts->session_options, status); - if (status->status.ok()) { - if (session->device_mgr == nullptr || session->devices.empty()) { - status->status = tensorflow::errors::InvalidArgument( - "Provided TF_SessionOptions are not compatible with eager execution " - "(perhaps the TF_SessionOptions alluded to session execution in a " - "remote address space?)"); - } - } + std::vector devices; + status->status = tensorflow::DeviceFactory::AddDevices( + opts->session_options.options, "/job:localhost/replica:0/task:0", + &devices); if (!status->status.ok()) { - TF_DeleteGraph(graph); return nullptr; } - - return new TFE_Context(*opts, session); + std::unique_ptr device_mgr( + new tensorflow::DeviceMgr(devices)); + tensorflow::Rendezvous* r = + new tensorflow::IntraProcessRendezvous(device_mgr.get()); + return new TFE_Context(*opts, std::move(device_mgr), r); } void TFE_DeleteContext(TFE_Context* ctx, TF_Status* status) { - status->status = tensorflow::Status::OK(); + status->status = ctx->executor.WaitForAllPendingNodes(); { tensorflow::mutex_lock ml(ctx->cache_mu); tensorflow::gtl::STLDeleteValues(&ctx->kernel_cache); } - TF_Graph* graph = ctx->session->graph; - TF_DeleteSession(ctx->session, status); - TF_DeleteGraph(graph); ctx->rendezvous->Unref(); delete ctx; } TF_DeviceList* TFE_ContextListDevices(TFE_Context* ctx, TF_Status* status) { - return TF_SessionListDevices(ctx->session, status); + TF_DeviceList* list = new TF_DeviceList; + ctx->device_manager->ListDeviceAttributes(&list->response); + return list; } void TFE_ContextClearCaches(TFE_Context* ctx) { @@ -130,6 +163,9 @@ void TFE_ContextSetThreadLocalDevicePlacementPolicy( ctx->thread_local_policies[std::this_thread::get_id()] = policy; } +// Note: this function looks up a thread local policy. So it should be called in +// the appropriate client thread. In particular, in async mode, it may not be +// safe to call this function from the async TFE_Executor threads. extern TFE_ContextDevicePlacementPolicy TFE_ContextGetDevicePlacementPolicy( TFE_Context* ctx) { tensorflow::mutex_lock ml(ctx->policy_map_mu); @@ -141,59 +177,89 @@ extern TFE_ContextDevicePlacementPolicy TFE_ContextGetDevicePlacementPolicy( return ctx->policy; } +void TFE_ContextAsyncWait(TFE_Context* ctx, TF_Status* status) { + status->status = ctx->executor.WaitForAllPendingNodes(); +} + +void TFE_ContextGetStatus(TFE_Context* ctx, TF_Status* status) { + status->status = ctx->executor.status(); +} + +void TFE_ContextAsyncClearError(TFE_Context* ctx) { + ctx->executor.ClearError(); +} + TFE_TensorHandle* TFE_NewTensorHandle(TF_Tensor* t, TF_Status* status) { tensorflow::Tensor tensor; status->status = tensorflow::TF_TensorToTensor(t, &tensor); if (!status->status.ok()) return nullptr; - return new TFE_TensorHandle(tensor, nullptr); + return new TFE_TensorHandle(tensor, nullptr, nullptr); } -void TFE_DeleteTensorHandle(TFE_TensorHandle* h) { delete h; } +void TFE_DeleteTensorHandle(TFE_TensorHandle* h) { + DCHECK(h); + h->Unref(); +} TF_DataType TFE_TensorHandleDataType(TFE_TensorHandle* h) { - return static_cast(h->t.dtype()); + return static_cast(h->dtype); } -int TFE_TensorHandleNumDims(TFE_TensorHandle* h) { return h->t.dims(); } +int TFE_TensorHandleNumDims(TFE_TensorHandle* h, TF_Status* status) { + const tensorflow::Tensor* t = nullptr; + status->status = h->Tensor(&t); + return t == nullptr ? 0 : t->dims(); +} -int64_t TFE_TensorHandleDim(TFE_TensorHandle* h, int dim_index) { - return h->t.dim_size(dim_index); +int64_t TFE_TensorHandleDim(TFE_TensorHandle* h, int dim_index, + TF_Status* status) { + const tensorflow::Tensor* t = nullptr; + status->status = h->Tensor(&t); + return t == nullptr ? 0 : t->dim_size(dim_index); } -const char* TFE_TensorHandleDeviceName(TFE_TensorHandle* h) { - // TODO(apassos) this will be potentially incorrect in the distributed case as - // our local device will have a name which depends on the ClusterSpec and - // hence will require the context to resolve. - return (h->d == nullptr) ? "/job:localhost/replica:0/task:0/device:CPU:0" - : h->d->name().c_str(); +const char* TFE_TensorHandleDeviceName(TFE_TensorHandle* h, TF_Status* status) { + tensorflow::Device* d = nullptr; + status->status = h->OpDevice(&d); + return (d == nullptr) ? "/job:localhost/replica:0/task:0/device:CPU:0" + : d->name().c_str(); } TF_Tensor* TFE_TensorHandleResolve(TFE_TensorHandle* h, TF_Status* status) { - if (!IsCPU(h->d)) { + // TODO(agarwal): move this implementation inside TFE_TensorHandle. + tensorflow::Device* d = nullptr; + tensorflow::Device* op_device = nullptr; + const tensorflow::Tensor* t = nullptr; + status->status = h->TensorAndDevice(&t, &d, &op_device); + if (!status->status.ok()) return nullptr; + if (!IsCPU(d)) { TF_SetStatus(status, TF_UNIMPLEMENTED, tensorflow::strings::StrCat( "TFE_TensorHandle can be resolved iff it is on CPU (this " "handle is on ", - h->d->name(), + d->name(), "). Consider using TFE_TensorHandleCopyToDevice to get a " "copy of the tensor on CPU") .c_str()); return nullptr; } - return tensorflow::TF_TensorFromTensor(h->t, status); + return tensorflow::TF_TensorFromTensor(*t, status); } +} // extern "C" -TFE_TensorHandle* TFE_TensorHandleCopyToDevice(TFE_TensorHandle* h, - TFE_Context* ctx, - const char* device_name, - TF_Status* status) { - tensorflow::Device* dstd = ctx->devices()[0]; - if (device_name != nullptr && strlen(device_name) > 0) { - status->status = ctx->session->device_mgr->LookupDevice(device_name, &dstd); - if (!status->status.ok()) return nullptr; - } +namespace { - tensorflow::Device* srcd = h->d == nullptr ? ctx->devices()[0] : h->d; +tensorflow::Status TensorHandleCopyToDevice(TFE_TensorHandle* h, + TFE_Context* ctx, + tensorflow::Device* dstd, + TFE_TensorHandle** output) { + const tensorflow::Tensor* src = nullptr; + tensorflow::Device* srcd = nullptr; + // TODO(agarwal): src_opd is unused. Perhaps allow TensorAndDevice to accept + // nullptr. + tensorflow::Device* src_opd = nullptr; + TF_RETURN_IF_ERROR(h->TensorAndDevice(&src, &srcd, &src_opd)); + if (srcd == nullptr) srcd = ctx->devices[0]; bool is_same_device = (srcd == dstd) || (DeviceName(srcd) == DeviceName(dstd)); const bool dst_cpu = IsCPU(dstd); @@ -202,18 +268,16 @@ TFE_TensorHandle* TFE_TensorHandleCopyToDevice(TFE_TensorHandle* h, // 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) { - return new TFE_TensorHandle(h->t, dst_cpu ? nullptr : dstd); + dstd = dst_cpu ? nullptr : dstd; + *output = new TFE_TensorHandle(*src, dstd, dstd); + return tensorflow::Status::OK(); } - tensorflow::Tensor* src = &(h->t); if (!dst_cpu && (src->dtype() != tensorflow::DT_VARIANT && !tensorflow::DataTypeCanUseMemcpy(src->dtype()))) { - TF_SetStatus( - status, TF_INVALID_ARGUMENT, - tensorflow::strings::StrCat("Can't copy Tensor with type ", - tensorflow::DataTypeString(src->dtype()), - " to device ", DeviceName(dstd), ".") - .c_str()); - return nullptr; + return tensorflow::errors::InvalidArgument( + "Can't copy Tensor with type ", + tensorflow::DataTypeString(src->dtype()), " to device ", + DeviceName(dstd), "."); } tensorflow::AllocatorAttributes attr; if (src->dtype() == tensorflow::DT_VARIANT) { @@ -221,7 +285,9 @@ TFE_TensorHandle* TFE_TensorHandleCopyToDevice(TFE_TensorHandle* h, } tensorflow::Tensor dst(dstd->GetAllocator(attr), src->dtype(), src->shape()); if (src->shape().num_elements() == 0) { - return new TFE_TensorHandle(dst, dst_cpu ? nullptr : dstd); + dstd = dst_cpu ? nullptr : dstd; + *output = new TFE_TensorHandle(dst, dstd, dstd); + return tensorflow::Status::OK(); } tensorflow::DeviceContext* src_device_context = nullptr; if (!src_cpu) { @@ -238,20 +304,26 @@ TFE_TensorHandle* TFE_TensorHandleCopyToDevice(TFE_TensorHandle* h, // With that setup, Sync()ing across all 3 streams should be sufficient // but more than necessary (since it waits for operations that might have // nothing to do with this tensor to complete). - status->status = srcd->Sync(); + TF_RETURN_IF_ERROR(srcd->Sync()); tensorflow::Notification n; + tensorflow::Status status; tensorflow::CopyTensor::ViaDMA("copy", src_device_context, dst_device_context, srcd, dstd, tensorflow::AllocatorAttributes(), tensorflow::AllocatorAttributes(), src, &dst, - [status, &n](const tensorflow::Status& s) { - status->status = s; + [&status, &n](const tensorflow::Status& s) { + status = s; n.Notify(); }); n.WaitForNotification(); - return (TF_GetCode(status) == TF_OK) - ? new TFE_TensorHandle(dst, dst_cpu ? nullptr : dstd) - : nullptr; + if (status.ok()) { + dstd = dst_cpu ? nullptr : dstd; + *output = new TFE_TensorHandle(dst, dstd, dstd); + } + return status; } +} // namespace + +extern "C" { TFE_Op* TFE_NewOp(TFE_Context* ctx, const char* op_or_function_name, TF_Status* status) { @@ -274,8 +346,7 @@ void TFE_DeleteOp(TFE_Op* op) { delete op; } void TFE_OpSetDevice(TFE_Op* op, const char* device_name, TF_Status* status) { tensorflow::Device* d = nullptr; if (device_name != nullptr && strlen(device_name) > 0) { - status->status = - op->ctx->session->device_mgr->LookupDevice(device_name, &d); + status->status = op->ctx->device_manager->LookupDevice(device_name, &d); if (!status->status.ok()) return; } op->device = d; @@ -283,7 +354,7 @@ void TFE_OpSetDevice(TFE_Op* op, const char* device_name, TF_Status* status) { const char* TFE_OpGetDevice(TFE_Op* op, TF_Status* status) { tensorflow::Device* device = - (op->device == nullptr) ? op->ctx->devices()[0] : op->device; + (op->device == nullptr) ? op->ctx->devices[0] : op->device; return device->name().c_str(); } @@ -296,17 +367,19 @@ void TFE_OpSetXLACompilation(TFE_Op* op, unsigned char enable) { } void TFE_OpAddInput(TFE_Op* op, TFE_TensorHandle* h, TF_Status* status) { - // Questionable heuristic ... - // - // Motivation: After an 'op' is placed on GPU because some of its earlier - // inputs are on GPU, we want to keep the 'op' there, even if some later - // inputs of it are not on GPU. - if (IsCPU(op->device) && !IsCPU(h->d)) { - op->device = h->d; + if (op->device == nullptr) { + // Questionable heuristic ... + // - If a device was explicitly set on the op, always use that. + // - If not, place on the first non-host device seen. + tensorflow::Device* d = nullptr; + // TODO(agarwal): This call may block if h is not ready. Avoid this if + // possible. + status->status = h->Device(&d); + if (!status->status.ok()) return; + if (!IsCPU(d)) op->device = d; } - if (!status->status.ok()) return; - op->inputs.push_back(h->t); - op->input_devices.push_back(h->d); + h->Ref(); + op->inputs.push_back(h); op->attrs.NumInputs(op->inputs.size()); } @@ -468,14 +541,14 @@ void TFE_OpSetAttrFunctionList(TFE_Op* op, const char* attr_name, tensorflow::gtl::ArraySlice( funcs.get(), num_values)); } +} // extern "C" namespace { tensorflow::Status ValidateInputTypeAndPlacement( TFE_Context* ctx, tensorflow::Device* host_device, tensorflow::Device* op_device, TFE_Op* op, - const tensorflow::OpKernel* kernel, - std::vector* copied_tensors) { + const tensorflow::OpKernel* kernel) { const tensorflow::MemoryTypeVector& memtypes = kernel->input_memory_types(); if (memtypes.size() != op->inputs.size()) { return tensorflow::errors::InvalidArgument( @@ -484,14 +557,17 @@ tensorflow::Status ValidateInputTypeAndPlacement( for (int i = 0; i < op->inputs.size(); ++i) { const tensorflow::Device* expected_device = memtypes[i] == tensorflow::HOST_MEMORY ? host_device : op_device; + TFE_TensorHandle* handle = op->inputs[i]; + tensorflow::Device* handle_device = nullptr; + TF_RETURN_IF_ERROR(handle->Device(&handle_device)); const tensorflow::Device* actual_device = - op->input_devices[i] == nullptr ? host_device : op->input_devices[i]; + handle_device == nullptr ? host_device : handle_device; if (expected_device != actual_device) { switch (TFE_ContextGetDevicePlacementPolicy(ctx)) { case TFE_DEVICE_PLACEMENT_SILENT_FOR_INT32: // TODO(xpan): See if we could bubble python related error up // to python level. - if (op->inputs[i].dtype() == tensorflow::DT_INT32) { + if (handle->dtype == tensorflow::DT_INT32) { // Note: enabling silent copies of int32 tensors to match behavior // of graph mode. break; @@ -522,35 +598,242 @@ tensorflow::Status ValidateInputTypeAndPlacement( } // We are only here if the policy is warn or silent copies, so we should // trigger a copy. - TFE_TensorHandle original{op->inputs[i], op->input_devices[i]}; TF_Status* s = TF_NewStatus(); TFE_TensorHandle* copied_tensor = TFE_TensorHandleCopyToDevice( - &original, ctx, expected_device->name().c_str(), s); - if (!s->status.ok()) { - tensorflow::Status status = s->status; - delete s; + handle, ctx, expected_device->name().c_str(), s); + tensorflow::Status status = s->status; + TF_DeleteStatus(s); + if (!status.ok()) { + if (copied_tensor != nullptr) copied_tensor->Unref(); return tensorflow::errors::Internal( "Failed copying input tensor from ", actual_device->name(), " to ", expected_device->name(), " in order to run ", op->name, ": ", status.error_message()); } - op->inputs[i] = copied_tensor->t; - copied_tensors->push_back(copied_tensor); - op->input_devices[i] = copied_tensor->d; - delete s; + handle->Unref(); + handle = copied_tensor; + op->inputs[i] = copied_tensor; } - if (op->inputs[i].dtype() != kernel->input_type(i)) { + if (handle->dtype != kernel->input_type(i)) { return tensorflow::errors::InvalidArgument( "cannot compute ", op->name, " as input #", i, " was expected to be a ", tensorflow::DataTypeString(kernel->input_type(i)), - " tensor but is a ", - tensorflow::DataTypeString(op->inputs[i].dtype()), " tensor"); + " tensor but is a ", tensorflow::DataTypeString(handle->dtype), + " tensor"); + } + } + return tensorflow::Status::OK(); +} + +tensorflow::Device* SelectDevice(const tensorflow::NodeDef& ndef, + TFE_Context* ctx, TF_Status* status) { + tensorflow::DeviceSet ds; + for (tensorflow::Device* d : ctx->devices) { + ds.AddDevice(d); + } + tensorflow::DeviceTypeVector final_devices; + status->status = tensorflow::SupportedDeviceTypesForNode( + ds.PrioritizedDeviceTypeList(), ndef, &final_devices); + if (!status->status.ok()) { + return nullptr; + } + if (final_devices.empty()) { + status->status = tensorflow::errors::Internal( + "Could not find valid device for node ", ndef.DebugString()); + return nullptr; + } + for (tensorflow::Device* d : ctx->devices) { + if (d->device_type() == final_devices[0].type_string()) { + return d; + } + } + status->status = tensorflow::errors::Unknown( + "Could not find a device for node ", ndef.DebugString()); + return nullptr; +} + +tensorflow::Status Execute( + TFE_Context* ctx, tensorflow::Device* device, + const tensorflow::gtl::InlinedVector& op_inputs, + tensorflow::KernelAndDevice* kernel, tensorflow::NodeExecStats* maybe_stats, + TFE_TensorHandle** retvals, int num_retvals) { + if (!ctx->soft_placement && device == nullptr) { + // TODO(ashankar): ASSUMPTION: ctx->devices[0] is always CPU + device = ctx->devices[0]; + } + + if (device == nullptr) { + // TODO(apassos) debug how the assignment below might return a different + // device from the one requested above. + device = kernel->device(); + } + + std::vector outputs(1); + const tensorflow::MemoryTypeVector* output_memory_types = nullptr; + output_memory_types = &kernel->kernel()->output_memory_types(); + std::vector inputs(op_inputs.size()); + for (int i = 0; i < op_inputs.size(); ++i) { + const tensorflow::Tensor* input_tensor = nullptr; + TF_RETURN_IF_ERROR(op_inputs[i]->Tensor(&input_tensor)); + inputs[i] = *input_tensor; + } + // WARNING: kernel->Run utilizes the FunctionLibraryRuntime + // (ctx->func_lib(device)), which in turn holds a pointer to func_lib_def, + // which is GUARDED_BY(ctx->functions_mu). But knowledge of the implementation + // of FunctionLibraryRuntime tells us that func_lib_def is not accessed by + // FunctionLibraryRuntime::Run(), so there is no thread-safety concern here. + // This is quite subtle. Re-work things to make this better? (Would it make + // sense for FunctionLibraryRuntime to ensure thread-safe access to + // FunctionLibraryDefinition?). TODO(apassos) figure out how to record stats + // for ops which are a part of functions. + // TODO(agarwal): change Run to take vector of handles ? + TF_RETURN_IF_ERROR(kernel->Run(&inputs, &outputs, maybe_stats)); + if (maybe_stats != nullptr) { + maybe_stats->set_op_end_rel_micros(tensorflow::Env::Default()->NowMicros() - + maybe_stats->all_start_micros()); + tensorflow::mutex_lock ml(ctx->metadata_mu); + if (ctx->should_store_metadata.load()) { + auto* step_stats = ctx->run_metadata.mutable_step_stats(); + // Lazily initialize the RunMetadata with information about all devices if + // this is the first call. + while (step_stats->dev_stats_size() < ctx->devices.size()) { + step_stats->add_dev_stats(); + } + // Find the current device's index. + int device_idx = 0; + for (int i = 0; i < ctx->devices.size(); ++i) { + if (ctx->devices[i] == device) { + device_idx = i; + break; + } + } + // Populate the device stats for this device. + auto* dev_stats = step_stats->mutable_dev_stats(device_idx); + dev_stats->set_device(device->name()); + *dev_stats->add_node_stats() = *maybe_stats; + } + } + DCHECK_EQ(num_retvals, outputs.size()); + tensorflow::Device* op_device = IsCPU(device) ? nullptr : device; + for (int i = 0; i < num_retvals; ++i) { + tensorflow::Device* d = op_device; + if (d != nullptr && output_memory_types != nullptr && + (*output_memory_types)[i] == tensorflow::HOST_MEMORY) { + d = nullptr; + } + if (retvals[i] == nullptr) { + retvals[i] = new TFE_TensorHandle(outputs[i], d, op_device); + } else { + retvals[i]->SetTensorAndDevice(outputs[i], d, op_device); } } return tensorflow::Status::OK(); } +// TODO(agarwal): move TFE_Executor and TFE_Node related code to a separate +// file. +class ExecuteNode : public TFE_Node { + public: + ExecuteNode(TFE_Op* op, tensorflow::KernelAndDevice* kernel, + tensorflow::NodeExecStats* maybe_stats, + const tensorflow::DataTypeVector& output_dtypes, + TFE_TensorHandle** retvals, int num_retvals) + : TFE_Node(op->ctx->executor.NextId()), + ctx_(op->ctx), + op_device_(op->device), + inputs_(op->inputs), + kernel_(kernel), + maybe_stats_(maybe_stats), + retvals_(num_retvals) { + for (auto handle : inputs_) { + handle->Ref(); + } + TFE_Context* ctx = op->ctx; + for (int i = 0; i < num_retvals; ++i) { + TFE_TensorHandle* h = new TFE_TensorHandle(id, output_dtypes[i], ctx); + h->Ref(); + retvals[i] = h; + retvals_[i] = h; + } + } + + ~ExecuteNode() override { + for (auto handle : inputs_) { + handle->Unref(); + } + for (auto handle : retvals_) { + handle->Unref(); + } + } + + tensorflow::Status Run() override { + const tensorflow::Status status = + Execute(ctx_, op_device_, inputs_, kernel_, maybe_stats_.get(), + retvals_.begin(), retvals_.size()); + if (status.ok()) { + return status; + } else { + return tensorflow::Status( + status.code(), + tensorflow::strings::StrCat("Got error, \"", status.error_message(), + "\" while executing kernel ", + kernel_->kernel()->def().DebugString())); + } + } + + private: + TFE_Context* ctx_; + tensorflow::Device* op_device_; + tensorflow::gtl::InlinedVector inputs_; + tensorflow::KernelAndDevice* kernel_; + std::unique_ptr maybe_stats_; + tensorflow::gtl::InlinedVector retvals_; +}; + +class CopyToDeviceNode : public TFE_Node { + public: + CopyToDeviceNode(TFE_TensorHandle* src, tensorflow::Device* dstd, + TFE_Context* ctx) + : TFE_Node(ctx->executor.NextId()), + src_(src), + dstd_(dstd), + ctx_(ctx), + dst_(new TFE_TensorHandle(id, src_->dtype, ctx)) { + src_->Ref(); + dst_->Ref(); + } + + ~CopyToDeviceNode() override { + src_->Unref(); + dst_->Unref(); + } + + tensorflow::Status Run() override { + TFE_TensorHandle* temp = nullptr; + TF_RETURN_IF_ERROR(TensorHandleCopyToDevice(src_, ctx_, dstd_, &temp)); + const tensorflow::Tensor* tensor = nullptr; + tensorflow::Device* device = nullptr; + tensorflow::Device* op_device = nullptr; + tensorflow::Status status = + temp->TensorAndDevice(&tensor, &device, &op_device); + // `temp` is a ready handle. So the following call should return OK. + TF_DCHECK_OK(status) << status.error_message(); + DCHECK(tensor); + dst_->SetTensorAndDevice(*tensor, device, op_device); + temp->Unref(); + return tensorflow::Status::OK(); + } + + TFE_TensorHandle* dst() { return dst_; } + + private: + TFE_TensorHandle* src_; + tensorflow::Device* dstd_; + TFE_Context* ctx_; + TFE_TensorHandle* dst_; +}; + #ifdef TENSORFLOW_EAGER_USE_XLA // Synthesizes and returns a wrapper function over `op`, which must be a // primitive op (e.g. matmul). @@ -616,7 +899,7 @@ const tensorflow::FunctionDef* OpToFunction( (*op_input_to_func_input)[i] = const_index; func_input_arg = signature->mutable_input_arg(const_index++); const_input_types->push_back( - static_cast(op->inputs[i].dtype())); + static_cast(op->inputs[i]->dtype)); } else if (op_input_arg.type() == tensorflow::DT_RESOURCE) { VLOG(1) << "For resource input, mapping op input " << i << " to func input " << resource_index; @@ -628,11 +911,11 @@ const tensorflow::FunctionDef* OpToFunction( (*op_input_to_func_input)[i] = arg_index; func_input_arg = signature->mutable_input_arg(arg_index++); arg_input_types->push_back( - static_cast(op->inputs[i].dtype())); + static_cast(op->inputs[i]->dtype)); } func_input_arg->set_name(op_input_arg.name()); - func_input_arg->set_type(op->inputs[i].dtype()); + func_input_arg->set_type(op->inputs[i]->dtype); } VLOG(1) << "Added OpDef Inputs: " << fdef.DebugString(); @@ -725,21 +1008,16 @@ std::unique_ptr BuildXlaLaunch(TFE_Op* op, TF_Status* status) { // Since input param reordering may have occurred between `op` and `launch_op` // via `op_input_to_func_input`, adjust the actual inputs accordingly. launch_op->inputs = op->inputs; - launch_op->input_devices = op->input_devices; + for (TFE_TensorHandle* h : launch_op->inputs) { + h->Ref(); + } if (!op_input_to_func_input.empty()) { DCHECK_EQ(op->inputs.size(), op_input_to_func_input.size()); - if (!op->input_devices.empty()) { - DCHECK_EQ(op->input_devices.size(), op_input_to_func_input.size()); - } for (int i = 0; i < op_input_to_func_input.size(); ++i) { VLOG(1) << "mapping op input " << i << " to func input " << op_input_to_func_input[i]; launch_op->inputs[op_input_to_func_input[i]] = op->inputs[i]; - if (!op->input_devices.empty()) { - launch_op->input_devices[op_input_to_func_input[i]] = - op->input_devices[i]; - } } } launch_op->attrs.NumInputs(op->inputs.size()); @@ -772,15 +1050,18 @@ std::unique_ptr BuildXlaLaunch(TFE_Op* op, TF_Status* status) { return launch_op; } #endif // TENSORFLOW_EAGER_USE_XLA + } // namespace +extern "C" { + void TFE_Execute(TFE_Op* op, TFE_TensorHandle** retvals, int* num_retvals, TF_Status* status) { TFE_Context* ctx = op->ctx; - // TODO(ashankar): ASSUMPTION: ctx->devices()[0] is always CPU - tensorflow::Device* device = - (op->device == nullptr) ? ctx->devices()[0] : op->device; - + status->status = ctx->executor.status(); + if (!status->status.ok()) { + return; + } #ifdef TENSORFLOW_EAGER_USE_XLA std::unique_ptr xla_launch_op; if (op->use_xla && op->name != "_XlaLaunch") { @@ -791,10 +1072,31 @@ void TFE_Execute(TFE_Op* op, TFE_TensorHandle** retvals, int* num_retvals, op = xla_launch_op.get(); } #endif // TENSORFLOW_EAGER_USE_XLA + // Ensure all resource-touching ops run in the device the resource is, + // regardless of anything else that has been specified. This is identical to + // the graph mode behavior. + for (int i = 0; i < op->inputs.size(); ++i) { + tensorflow::Device* input_op_device = nullptr; + status->status = op->inputs[i]->OpDevice(&input_op_device); + if (!status->status.ok()) return; + if (op->inputs[i]->dtype == tensorflow::DT_RESOURCE && + input_op_device != op->device) { + tensorflow::Device* d = + input_op_device == nullptr ? ctx->devices[0] : input_op_device; + VLOG(1) << "Changing device of operation " << op->name << " to " + << d->name() << " because input #" << i + << " is a resource in this device."; + op->device = d; + } + } + tensorflow::Device* device = op->device; + if (!ctx->soft_placement && device == nullptr) { + // TODO(ashankar): ASSUMPTION: ctx->devices[0] is always CPU + device = ctx->devices[0]; + } - std::vector outputs(1); - const tensorflow::MemoryTypeVector* output_memory_types = nullptr; - tensorflow::Fprint128 cache_key = op->attrs.CacheKey(device->name()); + tensorflow::Fprint128 cache_key = + op->attrs.CacheKey(device == nullptr ? "unspecified" : device->name()); tensorflow::KernelAndDevice* kernel; { tensorflow::tf_shared_lock l(ctx->cache_mu); @@ -802,12 +1104,23 @@ void TFE_Execute(TFE_Op* op, TFE_TensorHandle** retvals, int* num_retvals, } if (kernel == nullptr) { const tensorflow::NodeDef& ndef = op->attrs.BuildNodeDef(); + if (ctx->soft_placement && device == nullptr) { + device = SelectDevice(ndef, ctx, status); + if (!status->status.ok()) { + return; + } + } + CHECK(device != nullptr); + if (ctx->log_device_placement) { + LOG(INFO) << "Executing op " << ndef.op() << " in device " + << device->name(); + } kernel = new tensorflow::KernelAndDevice(ctx->rendezvous); // Knowledge of the implementation of Init (and in-turn // FunctionLibraryRuntime::CreateKernel) tells us that ctx->func_lib_def // will be accessed, so grab on to the lock. - // See WARNING comment below - would be nice to rework to avoid this - // subtlety. + // See WARNING comment in Execute (before kernel->Run) - would be nice to + // rework to avoid this subtlety. tensorflow::tf_shared_lock l(ctx->functions_mu); status->status = tensorflow::KernelAndDevice::Init(ndef, ctx->func_lib(device), kernel); @@ -815,19 +1128,47 @@ void TFE_Execute(TFE_Op* op, TFE_TensorHandle** retvals, int* num_retvals, delete kernel; return; } + // Update output_dtypes inside `kernel`. + const tensorflow::OpDef* op_def = nullptr; + const tensorflow::FunctionDef* function_def = + ctx->func_lib_def.Find(ndef.op()); + if (function_def != nullptr) { + op_def = &(function_def->signature()); + } + if (op_def == nullptr) { + status->status = OpDefForOp(ndef.op().c_str(), &op_def); + if (!status->status.ok()) { + return; + } + } + tensorflow::DataTypeVector input_dtypes; + status->status = InOutTypesForNode(ndef, *op_def, &input_dtypes, + kernel->mutable_output_dtypes()); + if (!status->status.ok()) { + return; + } tensorflow::mutex_lock ml(ctx->cache_mu); tensorflow::gtl::InsertOrUpdate(&(ctx->kernel_cache), cache_key, kernel); } - std::vector copied_tensors; - status->status = ValidateInputTypeAndPlacement( - ctx, ctx->devices()[0], device, op, kernel->kernel(), &copied_tensors); - output_memory_types = &kernel->kernel()->output_memory_types(); - if (!status->status.ok()) { - for (auto* t : copied_tensors) { - TFE_DeleteTensorHandle(t); - } + const tensorflow::DataTypeVector& output_dtypes = kernel->output_dtypes(); + const int output_dtypes_size = output_dtypes.size(); + if (output_dtypes_size > *num_retvals) { + TF_SetStatus(status, TF_INVALID_ARGUMENT, + tensorflow::strings::StrCat("Expecting ", output_dtypes.size(), + " outputs, but *num_retvals is ", + *num_retvals) + .c_str()); return; } + *num_retvals = output_dtypes_size; + if (device == nullptr) { + // TODO(apassos) debug how the assignment below might return a different + // device from the one requested above. + device = kernel->device(); + } + status->status = ValidateInputTypeAndPlacement(ctx, ctx->devices[0], device, + op, kernel->kernel()); + if (!status->status.ok()) return; std::unique_ptr maybe_stats; if (ctx->should_store_metadata.load()) { maybe_stats.reset(new tensorflow::NodeExecStats); @@ -837,53 +1178,47 @@ void TFE_Execute(TFE_Op* op, TFE_TensorHandle** retvals, int* num_retvals, maybe_stats->set_scheduled_micros(tensorflow::Env::Default()->NowMicros()); // TODO(apassos) track referenced tensors } - // WARNING: kernel->Run utilizes the FunctionLibraryRuntime - // (ctx->func_lib(device)), which in turn holds a pointer to func_lib_def, - // which is GUARDED_BY(ctx->functions_mu). But knowledge of the implementation - // of FunctionLibraryRuntime tells us that func_lib_def is not accessed by - // FunctionLibraryRuntime::Run(), so there is no thread-safety concern here. - // This is quite subtle. Re-work things to make this better? (Would it make - // sense for FunctionLibraryRuntime to ensure thread-safe access to - // FunctionLibraryDefinition?). TODO(apassos) figure out how to record stats - // for ops which are a part of functions. - status->status = kernel->Run(&op->inputs, &outputs, maybe_stats.get()); - for (auto* t : copied_tensors) { - TFE_DeleteTensorHandle(t); - } - if (!status->status.ok()) return; - if (maybe_stats != nullptr) { - maybe_stats->set_op_end_rel_micros(tensorflow::Env::Default()->NowMicros() - - maybe_stats->all_start_micros()); - tensorflow::mutex_lock ml(ctx->metadata_mu); - if (ctx->should_store_metadata.load()) { - auto* step_stats = ctx->run_metadata.mutable_step_stats(); - // Lazily initialize the RunMetadata with information about all devices if - // this is the first call. - while (step_stats->dev_stats_size() < ctx->devices().size()) { - step_stats->add_dev_stats(); - } - // Find the current device's index. - int device_idx = 0; - for (int i = 0; i < ctx->devices().size(); ++i) { - if (ctx->devices()[i] == device) { - device_idx = i; - break; - } - } - // Populate the device stats for this device. - auto* dev_stats = step_stats->mutable_dev_stats(device_idx); - dev_stats->set_device(device->name()); - *dev_stats->add_node_stats() = *maybe_stats; + if (ctx->Async()) { + // Note that for async mode, execution order will make sure that all + // input handles are ready before executing them. + // TODO(agarwal): Consider executing "cheap" kernels inline for performance. + TFE_Node* node = new ExecuteNode(op, kernel, maybe_stats.release(), + output_dtypes, retvals, *num_retvals); + ctx->executor.Add(node); + } else { + // Execute checks if retvals[i] is nullptr or not to figure if it needs to + // allocate it. + for (int i = 0; i < *num_retvals; ++i) { + retvals[i] = nullptr; } + status->status = Execute(op->ctx, op->device, op->inputs, kernel, + maybe_stats.get(), retvals, *num_retvals); } - *num_retvals = std::min(*num_retvals, outputs.size()); - for (int i = 0; i < *num_retvals; ++i) { - tensorflow::Device* d = IsCPU(device) ? nullptr : device; - if (d != nullptr && output_memory_types != nullptr && - (*output_memory_types)[i] == tensorflow::HOST_MEMORY) { - d = nullptr; - } - retvals[i] = new TFE_TensorHandle(outputs[i], d); +} + +TFE_TensorHandle* TFE_TensorHandleCopyToDevice(TFE_TensorHandle* h, + TFE_Context* ctx, + const char* device_name, + TF_Status* status) { + status->status = ctx->executor.status(); + if (!status->status.ok()) { + return nullptr; + } + tensorflow::Device* dstd = ctx->devices[0]; + if (device_name != nullptr && strlen(device_name) > 0) { + status->status = ctx->device_manager->LookupDevice(device_name, &dstd); + if (!status->status.ok()) return nullptr; + } + if (ctx->Async()) { + // Note that `h` may not be currently ready. However execution order will + // make sure that `h` is ready before the copy is actually done. + CopyToDeviceNode* node = new CopyToDeviceNode(h, dstd, ctx); + ctx->executor.Add(node); + return node->dst(); + } else { + TFE_TensorHandle* output = nullptr; + status->status = TensorHandleCopyToDevice(h, ctx, dstd, &output); + return output; } } @@ -906,36 +1241,319 @@ void TFE_ContextAddFunction(TFE_Context* ctx, TF_Function* function, status->status = ctx->func_lib_def.AddFunctionDef(function->fdef); } +void TFE_ContextEnableRunMetadata(TFE_Context* ctx) { + ctx->should_store_metadata.store(true); +} + +void TFE_ContextDisableRunMetadata(TFE_Context* ctx) { + tensorflow::mutex_lock ml(ctx->metadata_mu); + ctx->should_store_metadata.store(false); + ctx->run_metadata.Clear(); +} + } // extern "C" TFE_TensorHandle* TFE_NewTensorHandle(const tensorflow::Tensor& t) { - return new TFE_TensorHandle(t, nullptr); + return new TFE_TensorHandle(t, nullptr, nullptr); } const tensorflow::Tensor* TFE_TensorHandleUnderlyingTensorInHostMemory( TFE_TensorHandle* h, TF_Status* status) { - if (h->d != nullptr) { + tensorflow::Device* d = nullptr; + tensorflow::Device* op_device = nullptr; + const tensorflow::Tensor* t = nullptr; + status->status = h->TensorAndDevice(&t, &d, &op_device); + if (!status->status.ok()) return nullptr; + if (d != nullptr) { status->status = tensorflow::errors::FailedPrecondition( "TFE_TensorHandle is placed in device (not host) memory. Cannot return " "a tensorflow::Tensor"); return nullptr; } - return &h->t; -} - -void TFE_ContextEnableRunMetadata(TFE_Context* ctx) { - ctx->should_store_metadata.store(true); -} - -void TFE_ContextDisableRunMetadata(TFE_Context* ctx) { - tensorflow::mutex_lock ml(ctx->metadata_mu); - ctx->should_store_metadata.store(false); - ctx->run_metadata.Clear(); + return t; } void TFE_ContextExportRunMetadata(TFE_Context* ctx, TF_Buffer* buf, TF_Status* status) { + TFE_ContextAsyncWait(ctx, status); + if (!status->status.ok()) return; tensorflow::mutex_lock ml(ctx->metadata_mu); status->status = MessageToBuffer(ctx->run_metadata, buf); ctx->run_metadata.Clear(); } + +namespace { +TFE_Op* GetFunc(TFE_Context* ctx, const tensorflow::NameAttrList& func, + TF_Status* status) { + TFE_Op* func_op = TFE_NewOp(ctx, func.name().data(), status); + for (const auto& attr : func.attr()) { + if (TF_GetCode(status) != TF_OK) return nullptr; + SetOpAttrValueScalar(ctx, func_op, attr.second, attr.first.data(), status); + if (TF_GetCode(status) != TF_OK) return nullptr; + } + return func_op; +} +} // namespace + +namespace tensorflow { +void SetOpAttrValueScalar(TFE_Context* ctx, TFE_Op* op, + const tensorflow::AttrValue& default_value, + const char* attr_name, TF_Status* status) { + switch (default_value.value_case()) { + case tensorflow::AttrValue::kS: + TFE_OpSetAttrString(op, attr_name, default_value.s().data()); + break; + case tensorflow::AttrValue::kI: + TFE_OpSetAttrInt(op, attr_name, static_cast(default_value.i())); + break; + case tensorflow::AttrValue::kF: + TFE_OpSetAttrFloat(op, attr_name, default_value.f()); + break; + case tensorflow::AttrValue::kB: + TFE_OpSetAttrBool(op, attr_name, default_value.b()); + break; + case tensorflow::AttrValue::kType: + TFE_OpSetAttrType(op, attr_name, + static_cast(default_value.type())); + break; + case tensorflow::AttrValue::kShape: { + const auto& tensor_shape = default_value.shape(); + if (tensor_shape.unknown_rank()) { + TFE_OpSetAttrShape(op, attr_name, nullptr, -1, status); + } else { + const auto num_dims = tensor_shape.dim_size(); + std::unique_ptr dims(new int64_t[num_dims]); + for (int i = 0; i < num_dims; ++i) { + dims[i] = tensor_shape.dim(i).size(); + } + TFE_OpSetAttrShape(op, attr_name, dims.get(), num_dims, status); + } + } break; + case tensorflow::AttrValue::kFunc: { + const auto func_op = GetFunc(ctx, default_value.func(), status); + if (TF_GetCode(status) != TF_OK) return; + // TODO(nareshmodi): TFE_OpSetAttrFunction and TFE_OpSetAttrFunctionList + // require TFE_Op* and just convert it internally a NameAttrValue, so + // consider adding an overload to the C API to make this case easier. + TFE_OpSetAttrFunction(op, attr_name, func_op); + } break; + case tensorflow::AttrValue::kList: + TF_FALLTHROUGH_INTENDED; + case tensorflow::AttrValue::kTensor: + TF_FALLTHROUGH_INTENDED; + case tensorflow::AttrValue::kPlaceholder: + TF_FALLTHROUGH_INTENDED; + case tensorflow::AttrValue::VALUE_NOT_SET: + TF_SetStatus( + status, TF_UNIMPLEMENTED, + tensorflow::strings::StrCat("Unable to get setfor default value: ", + default_value.DebugString()) + .data()); + } +} +} // namespace tensorflow + +TFE_Node::TFE_Node(tensorflow::uint64 id) : id(id) {} + +TFE_Executor::~TFE_Executor() { + tensorflow::mutex_lock l(node_queue_mutex_); + thread_done_ = true; + nodes_pending_.notify_all(); +} + +tensorflow::uint64 TFE_Executor::NextId() { + tensorflow::mutex_lock l(next_id_mutex_); + return next_id_++; +} + +void TFE_Executor::EnableAsync() { + tensorflow::mutex_lock l(node_queue_mutex_); + if (thread_ == nullptr) { + thread_.reset(tensorflow::Env::Default()->StartThread( + tensorflow::ThreadOptions(), "eager_async_executor", + std::bind(&TFE_Executor::Run, this))); + } +} + +void TFE_Executor::Add(TFE_Node* node) { + tensorflow::mutex_lock l(node_queue_mutex_); + DCHECK(thread_) << "EnableAsync should have been called before Add"; + if (!status_.ok()) { + delete node; + return; + } + int qlen = node_queue_.size(); + if (qlen > 0) { + if (node_queue_.back()->id >= node->id) { + status_ = tensorflow::errors::InvalidArgument( + "Inserting TFE_Node with non-increasing ids:", node_queue_.back()->id, + " vs ", node->id); + delete node; + return; + } + node_queue_.push(node); + } else { + node_queue_.push(node); + nodes_pending_.notify_all(); + } +} + +tensorflow::Status TFE_Executor::WaitFor(tensorflow::uint64 node_id) { + return WaitImpl(false, node_id); +} + +tensorflow::Status TFE_Executor::WaitForAllPendingNodes() { + return WaitImpl(true, 0); +} + +tensorflow::Status TFE_Executor::WaitImpl(bool wait_all, + tensorflow::uint64 node_id) { + tensorflow::condition_variable cond; + tensorflow::mutex_lock l(node_queue_mutex_); + // Don't wait if an error is already set. + if (!status_.ok()) return status_; + if (node_queue_.empty()) return tensorflow::Status::OK(); + if (wait_all) { + node_id = node_queue_.back()->id; + } else if (node_id < node_queue_.front()->id) { + // Note that we are relying on the ops being dispatched sequentially from + // the queue. + return tensorflow::Status::OK(); + } + node_done_notifications_.insert(std::make_pair(node_id, &cond)); + cond.wait(l); + // Note that we could be woken up if an error occurs, even though the node has + // not actually executed. + return status_; +} + +void TFE_Executor::ClearError() { + tensorflow::mutex_lock l(node_queue_mutex_); + if (status_.ok()) return; + // If an error was set, node_done_notifications_ and node_queue_ should have + // been cleared, and no new entries should have been added since. + DCHECK(node_done_notifications_.empty()); + DCHECK(node_queue_.empty()); + status_ = tensorflow::Status::OK(); + nodes_pending_.notify_all(); +} + +tensorflow::Status TFE_Executor::status() { + tensorflow::mutex_lock l(node_queue_mutex_); + return status_; +} + +void TFE_Executor::Run() { + while (true) { + std::unique_ptr curr_node; + { + tensorflow::mutex_lock l(node_queue_mutex_); + while (node_queue_.empty() || !status_.ok()) { + if (thread_done_) return; + nodes_pending_.wait(l); + } + curr_node.reset(node_queue_.front()); + } + tensorflow::Status status = curr_node->Run(); + const bool ok = status.ok(); + tensorflow::mutex_lock l(node_queue_mutex_); + node_queue_.pop(); + if (!ok) { + status_ = status; + // TODO(agarwal): mark all affected handles as corrupted before clearing + // this queue. + // We remove any pending ops so that we don't try to execute them if + // ClearError is called. + for (int i = 0; i < node_queue_.size(); ++i) { + delete node_queue_.front(); + node_queue_.pop(); + } + } + if (!node_done_notifications_.empty()) { + tensorflow::uint64 node_id = curr_node->id; + // Note that we notify all waiting threads in case an error has occurred. + // These calling threads are responsible for checking status_ before + // proceeding. + const auto range = ok ? node_done_notifications_.equal_range(node_id) + : make_pair(node_done_notifications_.begin(), + node_done_notifications_.end()); + for (auto it = range.first; it != range.second; ++it) { + it->second->notify_all(); + } + node_done_notifications_.erase(range.first, range.second); + } + } +} + +bool TFE_Context::Async() const { + tensorflow::mutex_lock l(async_map_mu); + return tensorflow::gtl::FindWithDefault( + thread_local_async, std::this_thread::get_id(), async_default); +} + +bool TFE_TensorHandle::IsReady() { + if (node_id == 0) return true; + tensorflow::mutex_lock l(ctx_mutex_); + return ctx_ == nullptr; +} + +tensorflow::Status TFE_TensorHandle::WaitReady() { + if (node_id == 0) return tensorflow::Status::OK(); + TFE_Executor* executor = nullptr; + { + tensorflow::mutex_lock l(ctx_mutex_); + if (ctx_ == nullptr) return tensorflow::Status::OK(); + executor = &ctx_->executor; + } + return executor->WaitFor(node_id); +} + +tensorflow::Status TFE_TensorHandle::Tensor(const tensorflow::Tensor** t) { + TF_RETURN_IF_ERROR(WaitReady()); + DCHECK(IsReady()); + *t = &tensor_; + return tensorflow::Status::OK(); +} + +tensorflow::Status TFE_TensorHandle::Device(tensorflow::Device** d) { + TF_RETURN_IF_ERROR(WaitReady()); + DCHECK(IsReady()); + *d = device_; + return tensorflow::Status::OK(); +} + +tensorflow::Status TFE_TensorHandle::OpDevice(tensorflow::Device** d) { + TF_RETURN_IF_ERROR(WaitReady()); + DCHECK(IsReady()); + *d = op_device_; + return tensorflow::Status::OK(); +} + +tensorflow::Status TFE_TensorHandle::TensorAndDevice( + const tensorflow::Tensor** tensor, tensorflow::Device** device, + tensorflow::Device** op_device) { + TF_RETURN_IF_ERROR(WaitReady()); + DCHECK(IsReady()); + *tensor = &tensor_; + *device = device_; + *op_device = op_device_; + return tensorflow::Status::OK(); +} + +void TFE_TensorHandle::SetTensorAndDevice(const tensorflow::Tensor& tensor, + tensorflow::Device* device, + tensorflow::Device* op_device) { + tensorflow::mutex_lock l(ctx_mutex_); + DCHECK(node_id > 0 && ctx_) << "SetTensorAndDevice should be only called " + << "on non-ready handles."; + ctx_ = nullptr; + tensor_ = tensor; + device_ = device; + op_device_ = op_device; +} + +TFE_Op::~TFE_Op() { + for (TFE_TensorHandle* h : inputs) { + h->Unref(); + } +} diff --git a/tensorflow/c/eager/c_api.h b/tensorflow/c/eager/c_api.h index 7a321b54da343fd2b8912187bc620c1e7456db0c..a5029bf2115c7dac54d03b8bc6397bc63349c068 100644 --- a/tensorflow/c/eager/c_api.h +++ b/tensorflow/c/eager/c_api.h @@ -61,7 +61,8 @@ TF_CAPI_EXPORT extern void TFE_ContextOptionsSetConfig( // Controls how to act when we try to run an operation on a given device but // some input tensors are not on that device. typedef enum TFE_ContextDevicePlacementPolicy { - // Running operations with input tensors on the wrong device will fail. + // Running operations with input tensors on the wrong device will fail. When + // soft placement is enabled acts like TFE_DEVICE_PLACEMENT_SILENT. TFE_DEVICE_PLACEMENT_EXPLICIT = 0, // Copy the tensor to the right device but log a warning. TFE_DEVICE_PLACEMENT_WARN = 1, @@ -69,10 +70,16 @@ typedef enum TFE_ContextDevicePlacementPolicy { // operation will be blocked till the copy completes. TFE_DEVICE_PLACEMENT_SILENT = 2, // Default placement policy which silently copies int32 tensors but not other - // dtypes. + // dtypes. When soft placement is enabled acts like + // TFE_DEVICE_PLACEMENT_SILENT. TFE_DEVICE_PLACEMENT_SILENT_FOR_INT32 = 3, } TFE_ContextDevicePlacementPolicy; +// Sets the default execution mode (sync/async). Note that this can be +// overridden per thread using TFE_ContextSetAsyncForThread. +TF_CAPI_EXPORT extern void TFE_ContextOptionsSetAsync(TFE_ContextOptions*, + unsigned char async); + TF_CAPI_EXPORT extern void TFE_ContextOptionsSetDevicePlacementPolicy( TFE_ContextOptions*, TFE_ContextDevicePlacementPolicy); @@ -108,6 +115,30 @@ TF_CAPI_EXPORT extern void TFE_ContextSetThreadLocalDevicePlacementPolicy( TF_CAPI_EXPORT extern TFE_ContextDevicePlacementPolicy TFE_ContextGetDevicePlacementPolicy(TFE_Context*); +// Overrides the execution mode (sync/async) for the current thread. +TF_CAPI_EXPORT extern void TFE_ContextSetAsyncForThread(TFE_Context*, + unsigned char async, + TF_Status* status); + +// Causes the calling thread to block till all ops dispatched in async mode +// have been executed. Note that "execution" here refers to kernel execution / +// scheduling of copies, etc. Similar to sync execution, it doesn't guarantee +// that lower level device queues (like GPU streams) have been flushed. +// +// This call may not block for execution of ops enqueued concurrently with this +// call. +TF_CAPI_EXPORT extern void TFE_ContextAsyncWait(TFE_Context*, + TF_Status* status); + +// When an error happens, any pending operations are discarded and newly issued +// ops return an error. This call clears the error state and re-enables +// execution of newly issued ops. +// +// Note that outputs of discarded ops remain in a corrupt state and should not +// be used for future calls. +// TODO(agarwal): mark the affected handles and raise errors if they are used. +TF_CAPI_EXPORT extern void TFE_ContextAsyncClearError(TFE_Context*); + // A handle to a tensor on a device. // // Like a TF_Tensor, a TFE_TensorHandle refers to a tensor with a value, shape, @@ -117,13 +148,21 @@ typedef struct TFE_TensorHandle TFE_TensorHandle; TF_CAPI_EXPORT extern TFE_TensorHandle* TFE_NewTensorHandle(TF_Tensor* t, TF_Status* status); +// Indicates that the caller will not be using `h` any more. TF_CAPI_EXPORT extern void TFE_DeleteTensorHandle(TFE_TensorHandle* h); TF_CAPI_EXPORT extern TF_DataType TFE_TensorHandleDataType(TFE_TensorHandle* h); -TF_CAPI_EXPORT extern int TFE_TensorHandleNumDims(TFE_TensorHandle* h); +// This function will block till the operation that produces `h` has completed. +TF_CAPI_EXPORT extern int TFE_TensorHandleNumDims(TFE_TensorHandle* h, + TF_Status* status); +// This function will block till the operation that produces `h` has completed. TF_CAPI_EXPORT extern int64_t TFE_TensorHandleDim(TFE_TensorHandle* h, - int dim_index); + int dim_index, + TF_Status* status); +// This function will block till the operation that produces `h` has completed. TF_CAPI_EXPORT extern const char* TFE_TensorHandleDeviceName( - TFE_TensorHandle* h); + TFE_TensorHandle* h, TF_Status* status); + +// This function will block till the operation that produces `h` has completed. TF_CAPI_EXPORT extern TF_Tensor* TFE_TensorHandleResolve(TFE_TensorHandle* h, TF_Status* status); @@ -133,6 +172,9 @@ TF_CAPI_EXPORT extern TF_Tensor* TFE_TensorHandleResolve(TFE_TensorHandle* h, // that shares the underlying buffer. Otherwise, it currently requires at least // one of the source or destination devices to be CPU (i.e., for the source or // destination tensor to be placed in host memory). +// If async execution is enabled, the copy may be enqueued and the call will +// return "non-ready" handle. Else, this function returns after the copy has +// been done. TF_CAPI_EXPORT extern TFE_TensorHandle* TFE_TensorHandleCopyToDevice( TFE_TensorHandle* h, TFE_Context* ctx, const char* device_name, TF_Status* status); @@ -153,6 +195,7 @@ typedef struct TFE_Op TFE_Op; TF_CAPI_EXPORT extern TFE_Op* TFE_NewOp(TFE_Context* ctx, const char* op_or_function_name, TF_Status* status); + TF_CAPI_EXPORT extern void TFE_DeleteOp(TFE_Op* op); TF_CAPI_EXPORT extern void TFE_OpSetDevice(TFE_Op* op, const char* device_name, @@ -238,13 +281,21 @@ TF_CAPI_EXPORT extern void TFE_OpSetAttrFunctionList(TFE_Op* op, int num_values); // Execute the operation defined by 'op' and return handles to computed -// tensors in 'retvals'. +// tensors in `retvals`. +// +// 'retvals' must point to a pre-allocated array of TFE_TensorHandle* and +// '*num_retvals' should be set to the size of this array. It is an error if +// the size of 'retvals' is less than the number of outputs. This call sets +// *num_retvals to the number of outputs. // -// 'retvals' must point to a pre-allocated array of TFE_TensorHandle* -// and '*num_retvals' should be set to the size of this array. +// If async execution is enabled, the call may simply enqueue the execution +// and return "non-ready" handles in `retvals`. Note that any handles contained +// in 'op' should not be mutated till the kernel execution actually finishes. // -// On return, 'num_retvals' will be set to the actual number of outputs -// returned by the operation. +// For sync execution, if any of the inputs to `op` are not ready, this call +// will block till they become ready and then return when the kernel execution +// is done. +// TODO(agarwal): change num_retvals to int from int*. TF_CAPI_EXPORT extern void TFE_Execute(TFE_Op* op, TFE_TensorHandle** retvals, int* num_retvals, TF_Status* status); @@ -270,6 +321,8 @@ TF_CAPI_EXPORT extern void TFE_ContextDisableRunMetadata(TFE_Context* ctx); // Populates the passed-in buffer with a serialized RunMetadata protocol buffer // containing any run metadata information accumulated so far and clears this // information. +// If async mode is enabled, this call blocks till all currently pending ops are +// done. TF_CAPI_EXPORT extern void TFE_ContextExportRunMetadata(TFE_Context* ctx, TF_Buffer* buf, TF_Status* status); diff --git a/tensorflow/c/eager/c_api_internal.h b/tensorflow/c/eager/c_api_internal.h index 7b9f1db02ed9c53a280c7bd1284165cac4fb6353..8dba12f47b580c33041cc134c6f07a1fafff7453 100644 --- a/tensorflow/c/eager/c_api_internal.h +++ b/tensorflow/c/eager/c_api_internal.h @@ -19,7 +19,9 @@ limitations under the License. #include #include +#include #include +#include #include #include #include @@ -31,27 +33,140 @@ limitations under the License. #include "tensorflow/core/common_runtime/function.h" #include "tensorflow/core/common_runtime/rendezvous_mgr.h" #include "tensorflow/core/framework/rendezvous.h" +#include "tensorflow/core/lib/gtl/inlined_vector.h" #include "tensorflow/core/lib/gtl/map_util.h" #include "tensorflow/core/lib/gtl/stl_util.h" #include "tensorflow/core/platform/mutex.h" #include "tensorflow/core/platform/thread_annotations.h" #include "tensorflow/core/public/version.h" +// A unit of execution for the TFE_Executor class below. Example subclasses +// encapsulate execution of a TFE_Op, or copying a TFE_TensorHandle from one +// device to another. +class TFE_Node { + public: + explicit TFE_Node(tensorflow::uint64 id); + + virtual ~TFE_Node() {} + + // Runs the computation corresponding to this node and blocks till the + // execution is done. + virtual tensorflow::Status Run() = 0; + + // An id unique to the TFE_Context under which this node is created. Allocated + // monotonically. + const tensorflow::uint64 id; +}; + +// A class for handling async execution (see TFE_ContextSetAsync). +// Note that this class is thread-safe. +// TODO(agarwal): TFE_OpAddInput may currently block if it tries to access the +// device of the input handle. Fix that. +// TODO(agarwal): On error, mark all affected handles as corrupted. +// TODO(agarwal): Implement support for control dependencies. +// TODO(agarwal): Support out-of-order execution and dispatching multiple +// TFE_Node in parallel. +// TODO(agarwal): Implement optimizations over TFE_Node traces. +class TFE_Executor { + public: + ~TFE_Executor(); + + // This is called whenever async mode is enabled. Note that it may be called + // multiple times as different calling threads may switch async mode on or off + // independently. + void EnableAsync(); + + // Helper function to create monotonically increasing ids unique to this + // object. + tensorflow::uint64 NextId(); + + // Schedules `node` for execution. + // Note that Add must be called in monotonically increasing order of node->id. + void Add(TFE_Node* node); + + // Causes the caller to block till node with id `node_id` has finished + // execution. + tensorflow::Status WaitFor(tensorflow::uint64 node_id); + + // Blocks till all currently pending ops are done. + tensorflow::Status WaitForAllPendingNodes(); + + // Clears all currently set errors which re-enables async execution. + void ClearError(); + + // Returns Status based on any errors that occurred during async execution. + tensorflow::Status status(); + + private: + // Starts execution of pending TFE_Nodes. This function loops till + // thread_done_ is set to true. If any errors are encontered, these are set + // inside `status_`. The loop blocks anytime there are no pending nodes, or if + // `status_` is not ok. + void Run(); + + tensorflow::Status WaitImpl(bool wait_all, tensorflow::uint64 node_id); + + tensorflow::mutex node_queue_mutex_; + + // Used to signal that some TFE_Nodes are pending execution. + tensorflow::condition_variable nodes_pending_ GUARDED_BY(node_queue_mutex_); + + // Queue of pending TFE_Nodes. + std::queue node_queue_ GUARDED_BY(node_queue_mutex_); + + // `status_` is set based on any errors raised during execution of a TFE_Node. + // It remains set until ClearError is called. + tensorflow::Status status_ GUARDED_BY(node_queue_mutex_); + + // Map from id of a TFE_Node to condition_variables (not owned by the map). + // These condition_variables are notified and removed when that TFE_Node is + // done executing, or if an error is found in execution of any TFE_Node. + std::multimap + node_done_notifications_ GUARDED_BY(node_queue_mutex_); + + // Thread object that calls the `Run` method. Currently we use only one thread + // for executing the TFE_Nodes one-by-one. + std::unique_ptr thread_ GUARDED_BY(node_queue_mutex_); + + // Indicates that `thread_` should stop as soon as it is done executing the + // current TFE_Node. + bool thread_done_ GUARDED_BY(node_queue_mutex_) = false; + + tensorflow::mutex next_id_mutex_; + tensorflow::uint64 next_id_ GUARDED_BY(next_id_mutex_) = 1; +}; + struct TFE_ContextOptions { TF_SessionOptions session_options; + // true if async execution is enabled. + bool async = false; TFE_ContextDevicePlacementPolicy policy{ TFE_DEVICE_PLACEMENT_SILENT_FOR_INT32}; }; +TFE_ContextDevicePlacementPolicy PlacementPolicy( + bool soft_placement, TFE_ContextDevicePlacementPolicy original_policy); + struct TFE_Context { - explicit TFE_Context(const TFE_ContextOptions& opts, TF_Session* s) - : policy(opts.policy), - session(s), - rendezvous(new tensorflow::IntraProcessRendezvous(s->device_mgr)), + explicit TFE_Context(const TFE_ContextOptions& opts, + std::unique_ptr device_mgr, + tensorflow::Rendezvous* rendezvous) + : soft_placement( + opts.session_options.options.config.allow_soft_placement()), + policy(PlacementPolicy(soft_placement, opts.policy)), + device_manager(std::move(device_mgr)), + devices(device_manager->ListDevices()), + rendezvous(rendezvous), pflr(new tensorflow::ProcessFunctionLibraryRuntime( - session->device_mgr, opts.session_options.options.env, - TF_GRAPH_DEF_VERSION, &func_lib_def, {})) {} + device_manager.get(), opts.session_options.options.env, + TF_GRAPH_DEF_VERSION, &func_lib_def, {})), + log_device_placement( + opts.session_options.options.config.log_device_placement()), + async_default(opts.async) { + if (async_default) executor.EnableAsync(); + } + const bool soft_placement; const TFE_ContextDevicePlacementPolicy policy; // Note: we cannot use C++11 thread_local here as there is no concept of a @@ -60,8 +175,9 @@ struct TFE_Context { std::unordered_map thread_local_policies GUARDED_BY(policy_map_mu); - // TFE_Context is an extension of TF_Session. And TF_Session needs a TF_Graph. - TF_Session* const session; + std::unique_ptr device_manager; + // Devices owned by device_manager + const std::vector devices; tensorflow::Rendezvous* const rendezvous; tensorflow::mutex functions_mu; @@ -82,28 +198,103 @@ struct TFE_Context { return pflr->GetFLR(d->name()); } - const std::vector& devices() { return session->devices; } - // Whether we should compute RunMetadata. std::atomic should_store_metadata{false}; tensorflow::mutex metadata_mu; tensorflow::RunMetadata run_metadata GUARDED_BY(metadata_mu); + const bool log_device_placement; + // TFE_Executor for async execution. + TFE_Executor executor; + + // True if running in asynchronous mode. + bool Async() const; + + // True if the default value for execution mode is async. Note that this value + // can be overridden per thread based on `thread_local_async` overrides. + const bool async_default; + mutable tensorflow::mutex async_map_mu; + std::unordered_map thread_local_async + GUARDED_BY(async_map_mu); }; -struct TFE_TensorHandle { - TFE_TensorHandle(const tensorflow::Tensor& t, tensorflow::Device* d) - : t(t), d(d) {} +struct TFE_TensorHandle : public tensorflow::core::RefCounted { + public: + TFE_TensorHandle(const tensorflow::Tensor& t, tensorflow::Device* d, + tensorflow::Device* op_device) + : dtype(t.dtype()), + node_id(0), + tensor_(t), + device_(d), + op_device_(op_device), + ctx_(nullptr) {} + + TFE_TensorHandle(tensorflow::uint64 node_id, tensorflow::DataType dtype, + TFE_Context* ctx) + : dtype(dtype), + node_id(node_id), + tensor_(dtype), + device_(nullptr), + op_device_(nullptr), + ctx_(ctx) { + DCHECK_GT(node_id, 0); + } + + ~TFE_TensorHandle() override {} + + tensorflow::Status Tensor(const tensorflow::Tensor** t); + + tensorflow::Status Device(tensorflow::Device** d); + + tensorflow::Status OpDevice(tensorflow::Device** d); + + tensorflow::Status TensorAndDevice(const tensorflow::Tensor** tensor, + tensorflow::Device** device, + tensorflow::Device** op_device); - tensorflow::Tensor t; - // TODO(ashankar): d == nullptr iff local CPU - // This was expedient, but perhaps worth revisiting ('d' should always be a - // valid pointer?) + // Note that this can be called at most once, and only on non-ready handles, + // and makes them ready. + void SetTensorAndDevice(const tensorflow::Tensor& tensor, + tensorflow::Device* device, + tensorflow::Device* op_device); + + // dtype for the handle. It must be the same as t.dtype() once the handle is + // ready. + const tensorflow::DataType dtype; + + private: + // If the contents of the Tensor pointed to by this handle is yet to be + // computed by a TFE_Node, this function will block till that compuatation is + // done and the handle is "ready". + tensorflow::Status WaitReady(); + + bool IsReady(); + + // Id for the TFE_Node that will compute the value pointed to by this handle. + // If the value is 0, the handle is already ready, but not vice-versa. + const tensorflow::uint64 node_id; + + tensorflow::Tensor tensor_; + + // TODO(ashankar): device_ == nullptr iff local CPU + // This was expedient, but perhaps worth revisiting ('device_' should always + // be a valid pointer?) // This can be done if TFE_NewOp() and the TFE_TensorHandle constructors are // provided with the appropriate TFE_Context. // - // TODO(ashankar): Reference count TFE_Context to ensure that 'd' of a + // TODO(ashankar): Reference count TFE_Context to ensure that 'device_' of a // TFE_TensorHandle does not outlive the TFE_Context from which it came? - tensorflow::Device* d; + tensorflow::Device* device_; + + // Device in which the op producing this tensor was executed. Equals to + // device_ for constant tensors. + tensorflow::Device* op_device_; + + tensorflow::mutex ctx_mutex_; + + // `ctx` is only guaranteed to be set if the handle is not "ready". This is + // typically true when the handle was produced during async execution. + // `ctx` object is not owned and should outlive this handle. + TFE_Context* ctx_ GUARDED_BY(ctx_mutex_); }; struct TFE_Op { @@ -112,16 +303,24 @@ struct TFE_Op { TFE_Op(TFE_Context* ctx, const char* op, const tensorflow::AttrTypeMap* t) : ctx(ctx), name(op), attrs(op), attr_types(t), device(nullptr) {} + ~TFE_Op(); + bool const is_function() const { return attr_types == nullptr; } TFE_Context* ctx; // Must outlive the TFE_Op. const tensorflow::string name; tensorflow::AttrBuilder attrs; const tensorflow::AttrTypeMap* attr_types; - std::vector inputs; - std::vector input_devices; + tensorflow::gtl::InlinedVector inputs; tensorflow::Device* device; bool use_xla = false; }; +namespace tensorflow { +// Set an AttrValue on the op. Doesn't handle the list types. +void SetOpAttrValueScalar(TFE_Context* ctx, TFE_Op* op, + const tensorflow::AttrValue& default_value, + const char* attr_name, TF_Status* status); +} // namespace tensorflow + #endif // TENSORFLOW_C_EAGER_C_API_INTERNAL_H_ diff --git a/tensorflow/c/eager/c_api_test.cc b/tensorflow/c/eager/c_api_test.cc index 4a3ecbc0abb16296a84c0d2184dc3fc9f7f3ebb4..2268aba90d60b7b2f10e99f64fd7aa3ae719badb 100644 --- a/tensorflow/c/eager/c_api_test.cc +++ b/tensorflow/c/eager/c_api_test.cc @@ -29,6 +29,20 @@ using tensorflow::string; namespace { +TFE_TensorHandle* DoubleTestMatrixTensorHandle() { + int64_t dims[] = {2, 2}; + double data[] = {1.0, 2.0, 3.0, 4.0}; + TF_Tensor* t = TF_AllocateTensor( + TF_DOUBLE, &dims[0], sizeof(dims) / sizeof(int64_t), sizeof(data)); + memcpy(TF_TensorData(t), &data[0], TF_TensorByteSize(t)); + TF_Status* status = TF_NewStatus(); + TFE_TensorHandle* th = TFE_NewTensorHandle(t, status); + CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); + TF_DeleteTensor(t); + TF_DeleteStatus(status); + return th; +} + TFE_TensorHandle* TestMatrixTensorHandle() { int64_t dims[] = {2, 2}; float data[] = {1.0f, 2.0f, 3.0f, 4.0f}; @@ -43,6 +57,20 @@ TFE_TensorHandle* TestMatrixTensorHandle() { return th; } +TFE_TensorHandle* TestMatrixTensorHandle3X2() { + int64_t dims[] = {3, 2}; + double data[] = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0}; + TF_Tensor* t = TF_AllocateTensor( + TF_FLOAT, &dims[0], sizeof(dims) / sizeof(int64_t), sizeof(data)); + memcpy(TF_TensorData(t), &data[0], TF_TensorByteSize(t)); + TF_Status* status = TF_NewStatus(); + TFE_TensorHandle* th = TFE_NewTensorHandle(t, status); + CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); + TF_DeleteTensor(t); + TF_DeleteStatus(status); + return th; +} + TFE_Op* MatMulOp(TFE_Context* ctx, TFE_TensorHandle* a, TFE_TensorHandle* b) { TF_Status* status = TF_NewStatus(); @@ -139,10 +167,12 @@ void BM_InitOp(int iters) { } BENCHMARK(BM_InitOp); -void BM_Execute(int iters) { +void BM_Execute(int iters, int async) { tensorflow::testing::StopTiming(); + tensorflow::testing::SetLabel(async ? "ExecuteAsync" : "Execute"); TF_Status* status = TF_NewStatus(); TFE_ContextOptions* opts = TFE_NewContextOptions(); + TFE_ContextOptionsSetAsync(opts, static_cast(async)); TFE_Context* ctx = TFE_NewContext(opts, status); CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TFE_DeleteContextOptions(opts); @@ -156,6 +186,9 @@ void BM_Execute(int iters) { TFE_Execute(matmul, &retvals[0], &num_retvals, status); CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); } + if (async) { + TFE_ContextAsyncWait(ctx, status); + } tensorflow::testing::StopTiming(); TFE_DeleteOp(matmul); TFE_DeleteTensorHandle(m); @@ -163,7 +196,7 @@ void BM_Execute(int iters) { CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TF_DeleteStatus(status); } -BENCHMARK(BM_Execute); +BENCHMARK(BM_Execute)->Arg(0)->Arg(1); TEST(CAPI, Context) { TF_Status* status = TF_NewStatus(); @@ -205,10 +238,11 @@ TEST(CAPI, TensorHandle) { TFE_DeleteTensorHandle(h); } -TEST(CAPI, TensorHandleCopyBetweenDevices) { +void TensorHandleCopyBetweenDevices(bool async) { std::unique_ptr status( TF_NewStatus(), TF_DeleteStatus); TFE_ContextOptions* opts = TFE_NewContextOptions(); + TFE_ContextOptionsSetAsync(opts, static_cast(async)); TFE_Context* ctx = TFE_NewContext(opts, status.get()); TFE_DeleteContextOptions(opts); ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get()); @@ -274,10 +308,56 @@ TEST(CAPI, TensorHandleCopyBetweenDevices) { EXPECT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get()); } -TEST(CAPI, TensorHandleCopyBetweenTwoGPUDevices) { +TEST(CAPI, TensorHandleCopyBetweenDevices) { + TensorHandleCopyBetweenDevices(false); +} + +TEST(CAPI, TensorHandleCopyBetweenDevicesAsync) { + TensorHandleCopyBetweenDevices(true); +} + +void TensorHandleCopyBetweenDevicesError(bool async) { + std::unique_ptr status( + TF_NewStatus(), TF_DeleteStatus); + TFE_ContextOptions* opts = TFE_NewContextOptions(); + TFE_ContextOptionsSetAsync(opts, static_cast(async)); + TFE_Context* ctx = TFE_NewContext(opts, status.get()); + TFE_DeleteContextOptions(opts); + ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get()); + TFE_TensorHandle* hcpu = TestMatrixTensorHandle(); + const char* kErrorDevice = "NoSuchDevice:0"; + TFE_TensorHandle* hdevice = + TFE_TensorHandleCopyToDevice(hcpu, ctx, kErrorDevice, status.get()); + EXPECT_NE(TF_OK, TF_GetCode(status.get())); + const char* msg = "NoSuchDevice:0 unknown device"; + EXPECT_TRUE(strstr(TF_Message(status.get()), msg) != nullptr) + << TF_Message(status.get()); + TF_SetStatus(status.get(), TF_OK, ""); + const char* kCPUDevice = "CPU:0"; + TFE_TensorHandle* hcopy = + TFE_TensorHandleCopyToDevice(hcpu, ctx, kCPUDevice, status.get()); + EXPECT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get()); + TFE_ContextAsyncWait(ctx, status.get()); + EXPECT_EQ(TF_OK, TF_GetCode(status.get())); + TFE_DeleteTensorHandle(hcopy); + TFE_DeleteTensorHandle(hcpu); + if (hdevice != nullptr) TFE_DeleteTensorHandle(hdevice); + TFE_DeleteContext(ctx, status.get()); +} + +TEST(CAPI, TensorHandleCopyBetweenDevicesError) { + TensorHandleCopyBetweenDevicesError(false); +} + +TEST(CAPI, TensorHandleCopyBetweenDevicesErrorAsync) { + TensorHandleCopyBetweenDevicesError(true); +} + +void TensorHandleCopyBetweenTwoGPUDevices(bool async) { std::unique_ptr status( TF_NewStatus(), TF_DeleteStatus); TFE_ContextOptions* opts = TFE_NewContextOptions(); + TFE_ContextOptionsSetAsync(opts, static_cast(async)); TFE_Context* ctx = TFE_NewContext(opts, status.get()); TFE_DeleteContextOptions(opts); ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get()); @@ -332,11 +412,20 @@ TEST(CAPI, TensorHandleCopyBetweenTwoGPUDevices) { EXPECT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get()); } -TEST(CAPI, TensorHandleSilentCopy) { +TEST(CAPI, TensorHandleCopyBetweenTwoGPUDevices) { + TensorHandleCopyBetweenTwoGPUDevices(false); +} + +TEST(CAPI, TensorHandleCopyBetweenTwoGPUDevicesAsync) { + TensorHandleCopyBetweenTwoGPUDevices(true); +} + +void TensorHandleSilentCopy(bool async) { std::unique_ptr status( TF_NewStatus(), TF_DeleteStatus); TFE_ContextOptions* opts = TFE_NewContextOptions(); TFE_ContextOptionsSetDevicePlacementPolicy(opts, TFE_DEVICE_PLACEMENT_SILENT); + TFE_ContextOptionsSetAsync(opts, static_cast(async)); TFE_Context* ctx = TFE_NewContext(opts, status.get()); TFE_DeleteContextOptions(opts); ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get()); @@ -366,14 +455,20 @@ TEST(CAPI, TensorHandleSilentCopy) { TF_DeleteTensor(t); TFE_DeleteTensorHandle(hcpu); + TFE_ContextAsyncWait(ctx, status.get()); + EXPECT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get()); TFE_DeleteContext(ctx, status.get()); EXPECT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get()); } -TEST(CAPI, TensorHandleSilentCopyLocal) { +TEST(CAPI, TensorHandleSilentCopy) { TensorHandleSilentCopy(false); } +TEST(CAPI, TensorHandleSilentCopyAsync) { TensorHandleSilentCopy(true); } + +void TensorHandleSilentCopyLocal(bool async) { std::unique_ptr status( TF_NewStatus(), TF_DeleteStatus); TFE_ContextOptions* opts = TFE_NewContextOptions(); + TFE_ContextOptionsSetAsync(opts, static_cast(async)); TFE_ContextOptionsSetDevicePlacementPolicy(opts, TFE_DEVICE_PLACEMENT_EXPLICIT); TFE_Context* ctx = TFE_NewContext(opts, status.get()); @@ -407,11 +502,17 @@ TEST(CAPI, TensorHandleSilentCopyLocal) { TF_DeleteTensor(t); TFE_DeleteTensorHandle(hcpu); + TFE_ContextAsyncWait(ctx, status.get()); + EXPECT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get()); TFE_DeleteContext(ctx, status.get()); EXPECT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get()); } +TEST(CAPI, TensorHandleSilentCopyLocal) { TensorHandleSilentCopyLocal(false); } +TEST(CAPI, TensorHandleSilentCopyLocalAsync) { + TensorHandleSilentCopyLocal(true); +} -TEST(CAPI, SetAndGetOpDevices) { +void SetAndGetOpDevices(bool async) { TF_Status* status = TF_NewStatus(); TFE_ContextOptions* opts = TFE_NewContextOptions(); TFE_Context* ctx = TFE_NewContext(opts, status); @@ -442,27 +543,28 @@ TEST(CAPI, SetAndGetOpDevices) { TF_DeleteStatus(status); } -TEST(CAPI, Execute_MatMul_CPU) { +void Execute_MatMul_CPU(bool async) { TF_Status* status = TF_NewStatus(); TFE_ContextOptions* opts = TFE_NewContextOptions(); + TFE_ContextOptionsSetAsync(opts, static_cast(async)); TFE_Context* ctx = TFE_NewContext(opts, status); CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TFE_DeleteContextOptions(opts); TFE_TensorHandle* m = TestMatrixTensorHandle(); TFE_Op* matmul = MatMulOp(ctx, m, m); - TFE_TensorHandle* retvals[2] = {nullptr}; - int num_retvals = 2; // Should be reduced to 1 by the TFE_Execute call. + TFE_TensorHandle* retvals[2] = {nullptr, nullptr}; + int num_retvals = 2; TFE_Execute(matmul, &retvals[0], &num_retvals, status); + EXPECT_EQ(1, num_retvals); EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TFE_DeleteOp(matmul); TFE_DeleteTensorHandle(m); - TFE_DeleteContext(ctx, status); - ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); - ASSERT_EQ(1, num_retvals); TF_Tensor* t = TFE_TensorHandleResolve(retvals[0], status); + ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TFE_DeleteTensorHandle(retvals[0]); + TFE_DeleteContext(ctx, status); ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); float product[4] = {0}; EXPECT_EQ(sizeof(product), TF_TensorByteSize(t)); @@ -474,7 +576,101 @@ TEST(CAPI, Execute_MatMul_CPU) { EXPECT_EQ(22, product[3]); TF_DeleteStatus(status); } +TEST(CAPI, Execute_MatMul_CPU) { Execute_MatMul_CPU(false); } +TEST(CAPI, Execute_MatMul_CPUAsync) { Execute_MatMul_CPU(true); } + +void Execute_MatMul_CPU_Runtime_Error(bool async) { + TF_Status* status = TF_NewStatus(); + TFE_ContextOptions* opts = TFE_NewContextOptions(); + TFE_ContextOptionsSetAsync(opts, static_cast(async)); + TFE_Context* ctx = TFE_NewContext(opts, status); + CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); + TFE_DeleteContextOptions(opts); + + TFE_TensorHandle* m1 = TestMatrixTensorHandle(); + TFE_TensorHandle* m2 = TestMatrixTensorHandle3X2(); + TFE_Op* matmul = MatMulOp(ctx, m1, m2); + TFE_Op* matmul2 = MatMulOp(ctx, m1, m1); + TFE_TensorHandle* retvals[1] = {nullptr}; + int num_retvals = 1; + TFE_Execute(matmul, &retvals[0], &num_retvals, status); + TFE_DeleteOp(matmul); + if (!async) { + EXPECT_NE(TF_OK, TF_GetCode(status)); + } else { + TF_Tensor* t = TFE_TensorHandleResolve(retvals[0], status); + EXPECT_NE(TF_OK, TF_GetCode(status)); + EXPECT_EQ(nullptr, t); + const char* msg = "Matrix size-incompatible: In[0]: [2,2], In[1]: [3,2]"; + EXPECT_TRUE(strstr(TF_Message(status), msg) != nullptr) + << TF_Message(status); + // Since error is not cleared, the following copy with correct device will + // still fail. + TF_SetStatus(status, TF_OK, ""); + TFE_DeleteTensorHandle(retvals[0]); + retvals[0] = nullptr; + TFE_Execute(matmul2, &retvals[0], &num_retvals, status); + EXPECT_NE(TF_OK, TF_GetCode(status)); + TFE_ContextAsyncClearError(ctx); + TFE_ContextAsyncWait(ctx, status); + EXPECT_EQ(TF_OK, TF_GetCode(status)); + } + // Following works in async mode since TFE_ContextAsyncClearError was called. + TF_SetStatus(status, TF_OK, ""); + if (retvals[0] != nullptr) { + TFE_DeleteTensorHandle(retvals[0]); + } + retvals[0] = nullptr; + TFE_Execute(matmul2, &retvals[0], &num_retvals, status); + EXPECT_EQ(TF_OK, TF_GetCode(status)); + TF_Tensor* t = TFE_TensorHandleResolve(retvals[0], status); + EXPECT_EQ(TF_OK, TF_GetCode(status)); + TF_DeleteTensor(t); + TFE_DeleteOp(matmul2); + TFE_DeleteTensorHandle(m1); + TFE_DeleteTensorHandle(m2); + TFE_DeleteTensorHandle(retvals[0]); + TFE_DeleteContext(ctx, status); + TF_DeleteStatus(status); +} +TEST(CAPI, Execute_MatMul_CPU_Runtime_Error) { + Execute_MatMul_CPU_Runtime_Error(false); +} +TEST(CAPI, Execute_MatMul_CPU_Runtime_ErrorAsync) { + Execute_MatMul_CPU_Runtime_Error(true); +} + +void Execute_MatMul_CPU_Type_Error(bool async) { + TF_Status* status = TF_NewStatus(); + TFE_ContextOptions* opts = TFE_NewContextOptions(); + TFE_ContextOptionsSetAsync(opts, static_cast(async)); + TFE_Context* ctx = TFE_NewContext(opts, status); + CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); + TFE_DeleteContextOptions(opts); + + TFE_TensorHandle* m1 = TestMatrixTensorHandle(); + TFE_TensorHandle* m2 = DoubleTestMatrixTensorHandle(); + TFE_Op* matmul = MatMulOp(ctx, m1, m2); + TFE_TensorHandle* retvals[1] = {nullptr}; + int num_retvals = 1; + TFE_Execute(matmul, &retvals[0], &num_retvals, status); + EXPECT_NE(TF_OK, TF_GetCode(status)); + TFE_DeleteOp(matmul); + TFE_DeleteTensorHandle(m1); + TFE_DeleteTensorHandle(m2); + if (retvals[0] != nullptr) { + TFE_DeleteTensorHandle(retvals[0]); + } + TFE_DeleteContext(ctx, status); + TF_DeleteStatus(status); +} +TEST(CAPI, Execute_MatMul_CPU_Type_Error) { + Execute_MatMul_CPU_Type_Error(false); +} +TEST(CAPI, Execute_MatMul_CPU_Type_ErrorAsync) { + Execute_MatMul_CPU_Type_Error(true); +} TEST(CAPI, Execute_Min_CPU) { TF_Status* status = TF_NewStatus(); TFE_ContextOptions* opts = TFE_NewContextOptions(); @@ -485,8 +681,8 @@ TEST(CAPI, Execute_Min_CPU) { TFE_TensorHandle* input = TestMatrixTensorHandle(); TFE_TensorHandle* axis = TestAxisTensorHandle(); TFE_Op* minOp = MinOp(ctx, input, axis); - TFE_TensorHandle* retvals[2] = {nullptr}; - int num_retvals = 2; // Should be reduced to 1 by the TFE_Execute call. + TFE_TensorHandle* retvals[1] = {nullptr}; + int num_retvals = 1; TFE_Execute(minOp, &retvals[0], &num_retvals, status); EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TFE_DeleteOp(minOp); @@ -509,9 +705,10 @@ TEST(CAPI, Execute_Min_CPU) { } #ifdef TENSORFLOW_EAGER_USE_XLA -TEST(CAPI, Execute_MatMul_XLA_CPU) { +void Execute_MatMul_XLA_CPU(bool async) { TF_Status* status = TF_NewStatus(); TFE_ContextOptions* opts = TFE_NewContextOptions(); + TFE_ContextOptionsSetAsync(opts, static_cast(async)); TFE_Context* ctx = TFE_NewContext(opts, status); CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TFE_DeleteContextOptions(opts); @@ -521,15 +718,14 @@ TEST(CAPI, Execute_MatMul_XLA_CPU) { TFE_OpSetXLACompilation(matmul, true); - TFE_TensorHandle* retvals[2] = {nullptr}; - int num_retvals = 2; // Should be reduced to 1 by the TFE_Execute call. + TFE_TensorHandle* retvals[1] = {nullptr}; + int num_retvals = 1; TFE_Execute(matmul, &retvals[0], &num_retvals, status); // Running a primitive TF operator via XLA is not yet supported. ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TFE_DeleteOp(matmul); TFE_DeleteTensorHandle(m); - TFE_DeleteContext(ctx, status); ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); EXPECT_EQ(1, num_retvals); @@ -545,13 +741,16 @@ TEST(CAPI, Execute_MatMul_XLA_CPU) { EXPECT_EQ(10, product[1]); EXPECT_EQ(15, product[2]); EXPECT_EQ(22, product[3]); - + TFE_DeleteContext(ctx, status); TF_DeleteStatus(status); } +TEST(CAPI, Execute_MatMul_XLA_CPU) { Execute_MatMul_XLA_CPU(false); } +TEST(CAPI, Execute_MatMul_XLA_CPUAsync) { Execute_MatMul_XLA_CPU(true); } -TEST(CAPI, Execute_Min_XLA_CPU) { +void Execute_Min_XLA_CPU(bool async) { TF_Status* status = TF_NewStatus(); TFE_ContextOptions* opts = TFE_NewContextOptions(); + TFE_ContextOptionsSetAsync(opts, static_cast(async)); TFE_Context* ctx = TFE_NewContext(opts, status); CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TFE_DeleteContextOptions(opts); @@ -562,14 +761,13 @@ TEST(CAPI, Execute_Min_XLA_CPU) { TFE_OpSetXLACompilation(minOp, true); - TFE_TensorHandle* retvals[2] = {nullptr}; - int num_retvals = 2; // Should be reduced to 1 by the TFE_Execute call. + TFE_TensorHandle* retvals[1] = {nullptr}; + int num_retvals = 1; TFE_Execute(minOp, &retvals[0], &num_retvals, status); EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TFE_DeleteOp(minOp); TFE_DeleteTensorHandle(input); TFE_DeleteTensorHandle(axis); - TFE_DeleteContext(ctx, status); ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); ASSERT_EQ(1, num_retvals); @@ -582,13 +780,17 @@ TEST(CAPI, Execute_Min_XLA_CPU) { TF_DeleteTensor(t); EXPECT_EQ(1, output[0]); EXPECT_EQ(3, output[1]); + TFE_DeleteContext(ctx, status); TF_DeleteStatus(status); } +TEST(CAPI, Execute_Min_XLA_CPU) { Execute_Min_XLA_CPU(false); } +TEST(CAPI, Execute_Min_XLA_CPUAsync) { Execute_Min_XLA_CPU(true); } #endif // TENSORFLOW_EAGER_USE_XLA -TEST(CAPI, ExecuteWithTracing) { +void ExecuteWithTracing(bool async) { TF_Status* status = TF_NewStatus(); TFE_ContextOptions* opts = TFE_NewContextOptions(); + TFE_ContextOptionsSetAsync(opts, static_cast(async)); TFE_Context* ctx = TFE_NewContext(opts, status); TFE_ContextEnableRunMetadata(ctx); CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); @@ -596,8 +798,8 @@ TEST(CAPI, ExecuteWithTracing) { TFE_TensorHandle* m = TestMatrixTensorHandle(); TFE_Op* matmul = MatMulOp(ctx, m, m); - TFE_TensorHandle* retvals[2] = {nullptr}; - int num_retvals = 2; // Should be reduced to 1 by the TFE_Execute call. + TFE_TensorHandle* retvals[1] = {nullptr}; + int num_retvals = 1; TFE_Execute(matmul, &retvals[0], &num_retvals, status); EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TFE_DeleteOp(matmul); @@ -609,12 +811,12 @@ TEST(CAPI, ExecuteWithTracing) { EXPECT_TRUE( rm.ParseFromString({reinterpret_cast(b->data), b->length})); TF_DeleteBuffer(b); - TFE_DeleteContext(ctx, status); ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); ASSERT_EQ(1, num_retvals); TF_Tensor* t = TFE_TensorHandleResolve(retvals[0], status); TFE_DeleteTensorHandle(retvals[0]); + TFE_DeleteContext(ctx, status); ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); float product[4] = {0}; EXPECT_EQ(sizeof(product), TF_TensorByteSize(t)); @@ -626,6 +828,8 @@ TEST(CAPI, ExecuteWithTracing) { EXPECT_EQ(22, product[3]); TF_DeleteStatus(status); } +TEST(CAPI, ExecuteWithTracing) { ExecuteWithTracing(false); } +TEST(CAPI, ExecuteWithTracingAsync) { ExecuteWithTracing(true); } TEST(CAPI, Function_ident_CPU) { // First create a simple identity function. @@ -657,32 +861,37 @@ TEST(CAPI, Function_ident_CPU) { ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status); TF_DeleteFunction(fn); - TF_Tensor* t = - TF_AllocateTensor(TF_INT32, nullptr, 0, 1 * sizeof(tensorflow::int32)); - *reinterpret_cast(TF_TensorData(t)) = 42; - TFE_TensorHandle* h = TFE_NewTensorHandle(t, status); - ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status); - TF_DeleteTensor(t); + for (bool async : {false, true, false}) { + TFE_ContextSetAsyncForThread(ctx, static_cast(async), + status); + ASSERT_TRUE(TF_GetCode(status) == TF_OK); + TF_Tensor* t = + TF_AllocateTensor(TF_INT32, nullptr, 0, 1 * sizeof(tensorflow::int32)); + *reinterpret_cast(TF_TensorData(t)) = 42; + TFE_TensorHandle* h = TFE_NewTensorHandle(t, status); + ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status); + TF_DeleteTensor(t); - TFE_Op* op = TFE_NewOp(ctx, "ident", status); - ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status); - TFE_OpAddInput(op, h, status); - ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status); + TFE_Op* op = TFE_NewOp(ctx, "ident", status); + ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status); + TFE_OpAddInput(op, h, status); + ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status); - std::vector result; - result.push_back(nullptr); - int num_retvals = 1; - TFE_Execute(op, result.data(), &num_retvals, status); - TFE_DeleteOp(op); - ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status); - ASSERT_EQ(num_retvals, 1); + std::vector result; + result.push_back(nullptr); + int num_retvals = 1; + TFE_Execute(op, result.data(), &num_retvals, status); + TFE_DeleteOp(op); + ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status); + ASSERT_EQ(num_retvals, 1); - TF_Tensor* r = TFE_TensorHandleResolve(result[0], status); - ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status); - EXPECT_EQ(*reinterpret_cast(TF_TensorData(r)), 42); - TFE_DeleteTensorHandle(h); - TF_DeleteTensor(r); - TFE_DeleteTensorHandle(result[0]); + TF_Tensor* r = TFE_TensorHandleResolve(result[0], status); + ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status); + EXPECT_EQ(*reinterpret_cast(TF_TensorData(r)), 42); + TFE_DeleteTensorHandle(h); + TF_DeleteTensor(r); + TFE_DeleteTensorHandle(result[0]); + } TFE_DeleteContext(ctx, status); ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status); TF_DeleteStatus(status); @@ -719,35 +928,40 @@ TEST(CAPI, Function_ident_XLA_CPU) { ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status); TF_DeleteFunction(fn); - TF_Tensor* t = - TF_AllocateTensor(TF_INT32, nullptr, 0, 1 * sizeof(tensorflow::int32)); - *reinterpret_cast(TF_TensorData(t)) = 42; - TFE_TensorHandle* h = TFE_NewTensorHandle(t, status); - ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status); - TF_DeleteTensor(t); + for (bool async : {false, true, false}) { + TFE_ContextSetAsyncForThread(ctx, static_cast(async), + status); + ASSERT_TRUE(TF_GetCode(status) == TF_OK); + TF_Tensor* t = + TF_AllocateTensor(TF_INT32, nullptr, 0, 1 * sizeof(tensorflow::int32)); + *reinterpret_cast(TF_TensorData(t)) = 42; + TFE_TensorHandle* h = TFE_NewTensorHandle(t, status); + ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status); + TF_DeleteTensor(t); - TFE_Op* op = TFE_NewOp(ctx, "ident", status); - ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status); - TFE_OpAddInput(op, h, status); - ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status); + TFE_Op* op = TFE_NewOp(ctx, "ident", status); + ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status); + TFE_OpAddInput(op, h, status); + ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status); - // Now run it via XLA. - TFE_OpSetXLACompilation(op, true); + // Now run it via XLA. + TFE_OpSetXLACompilation(op, true); - std::vector result; - result.push_back(nullptr); - int num_retvals = 1; - TFE_Execute(op, result.data(), &num_retvals, status); - TFE_DeleteOp(op); - ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status); - ASSERT_EQ(num_retvals, 1); + std::vector result; + result.push_back(nullptr); + int num_retvals = 1; + TFE_Execute(op, result.data(), &num_retvals, status); + TFE_DeleteOp(op); + ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status); + ASSERT_EQ(num_retvals, 1); - TF_Tensor* r = TFE_TensorHandleResolve(result[0], status); - ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status); - EXPECT_EQ(*reinterpret_cast(TF_TensorData(r)), 42); - TFE_DeleteTensorHandle(h); - TF_DeleteTensor(r); - TFE_DeleteTensorHandle(result[0]); + TF_Tensor* r = TFE_TensorHandleResolve(result[0], status); + ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status); + EXPECT_EQ(*reinterpret_cast(TF_TensorData(r)), 42); + TFE_DeleteTensorHandle(h); + TF_DeleteTensor(r); + TFE_DeleteTensorHandle(result[0]); + } TFE_DeleteContext(ctx, status); ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status); TF_DeleteStatus(status); @@ -788,9 +1002,10 @@ string MatMulFunction() { return def.SerializeAsString(); } -TEST(CAPI, FunctionDefAndExecute) { +void FunctionDefAndExecute(bool async) { TF_Status* status = TF_NewStatus(); TFE_ContextOptions* opts = TFE_NewContextOptions(); + TFE_ContextOptionsSetAsync(opts, static_cast(async)); TFE_Context* ctx = TFE_NewContext(opts, status); CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TFE_DeleteContextOptions(opts); @@ -827,11 +1042,16 @@ TEST(CAPI, FunctionDefAndExecute) { EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TF_DeleteStatus(status); } +TEST(CAPI, FunctionDefAndExecute) { FunctionDefAndExecute(false); } +TEST(CAPI, FunctionDefAndExecuteAsync) { FunctionDefAndExecute(true); } -void BM_ExecuteFunction(int iters) { +void BM_ExecuteFunction(int iters, int async) { tensorflow::testing::StopTiming(); + tensorflow::testing::SetLabel(async ? "ExecuteFunctionAsync" + : "ExecuteFunction"); TF_Status* status = TF_NewStatus(); TFE_ContextOptions* opts = TFE_NewContextOptions(); + TFE_ContextOptionsSetAsync(opts, static_cast(async)); TFE_Context* ctx = TFE_NewContext(opts, status); CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TFE_DeleteContextOptions(opts); @@ -853,6 +1073,9 @@ void BM_ExecuteFunction(int iters) { TFE_Execute(matmul, &retval[0], &num_retvals, status); CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); } + if (async) { + TFE_ContextAsyncWait(ctx, status); + } tensorflow::testing::StopTiming(); TFE_DeleteTensorHandle(m); TFE_DeleteTensorHandle(retval[0]); @@ -860,7 +1083,7 @@ void BM_ExecuteFunction(int iters) { EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TF_DeleteStatus(status); } -BENCHMARK(BM_ExecuteFunction); +BENCHMARK(BM_ExecuteFunction)->Arg(0)->Arg(1); TFE_TensorHandle* CreateVariable(TFE_Context* ctx, float value, TF_Status* status) { @@ -932,7 +1155,8 @@ TEST(CAPI, Variables) { ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); ASSERT_EQ(1, num_retvals); EXPECT_EQ(TF_FLOAT, TFE_TensorHandleDataType(value_handle)); - EXPECT_EQ(0, TFE_TensorHandleNumDims(value_handle)); + EXPECT_EQ(0, TFE_TensorHandleNumDims(value_handle, status)); + ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); float value = 0.0f; TF_Tensor* t = TFE_TensorHandleResolve(value_handle, status); ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); @@ -974,7 +1198,8 @@ void BM_ReadVariable(int iters) { CHECK_EQ(1, num_retvals); CHECK(h); CHECK_EQ(TF_FLOAT, TFE_TensorHandleDataType(h)); - CHECK_EQ(0, TFE_TensorHandleNumDims(h)); + CHECK_EQ(0, TFE_TensorHandleNumDims(h, status)); + CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); h = nullptr; } tensorflow::testing::StopTiming(); diff --git a/tensorflow/c/eager/runtime.cc b/tensorflow/c/eager/runtime.cc index f77a937f1ffc2d146224cb3191a5ca127daefc22..9b46cf8245901934c9c4d41a2b7c10c1c5bf7cbd 100644 --- a/tensorflow/c/eager/runtime.cc +++ b/tensorflow/c/eager/runtime.cc @@ -41,17 +41,26 @@ const uint32 kIsList = 1U << 31; } // namespace +Status OpDefForOp(const char* op_name, const OpDef** op_def) { + const OpRegistrationData* op_reg_data = nullptr; + Status s = OpRegistry::Global()->LookUp(op_name, &op_reg_data); + if (s.ok()) { + *op_def = &op_reg_data->op_def; + } + return s; +} + Status AttrTypeMapForOp(const char* op_name, const AttrTypeMap** out) { mutex_lock l(g_op_name_to_attr_type_map_lock); *out = gtl::FindPtrOrNull(*OpNameToAttrTypeMap(), op_name); if (*out != nullptr) return Status::OK(); - const OpRegistrationData* op_reg_data = nullptr; - Status s = OpRegistry::Global()->LookUp(op_name, &op_reg_data); + const OpDef* op_def = nullptr; + Status s = OpDefForOp(op_name, &op_def); if (!s.ok()) return s; std::unique_ptr m(new AttrTypeMap); // TODO(agarwal): Avoid having to create this "registry" at runtime, // perhaps can be done at op registration time? - for (const auto& attr : op_reg_data->op_def.attr()) { + for (const auto& attr : op_def->attr()) { string type = attr.type(); const bool is_list = (type.length() > 6 && type.compare(0, 4, "list") == 0); if (is_list) { @@ -293,7 +302,18 @@ Status KernelAndDevice::Run(std::vector* input_tensors, params.runner = &runner; OpKernelContext context(¶ms); - device_->Compute(kernel_.get(), &context); + + if (kernel_->def().op() == "_Recv") { + // TODO(apassos) do not special-case _Recv. Currently the GPU device fails + // if trying to run _Recv->Compute(), specifically checking for _Recv. To go + // around this we call _Recv->ComputeAsync, to mimic graph mode behavior. + AsyncOpKernel* async = kernel_->AsAsync(); + Notification done; + device_->ComputeAsync(async, &context, [&done]() { done.Notify(); }); + done.WaitForNotification(); + } else { + device_->Compute(kernel_.get(), &context); + } if (!context.status().ok()) return context.status(); output_tensors->clear(); diff --git a/tensorflow/c/eager/runtime.h b/tensorflow/c/eager/runtime.h index 4d20b5244a46fcde2eed0a429dced2a77b86aedd..ad16f65495f8a8193b685c2b13a099232d03a505 100644 --- a/tensorflow/c/eager/runtime.h +++ b/tensorflow/c/eager/runtime.h @@ -39,6 +39,9 @@ namespace tensorflow { // represent the TF_AttrType type of the values in the list. typedef std::unordered_map AttrTypeMap; +// Look up OpDef for `op_name`. +Status OpDefForOp(const char* op_name, const OpDef** op_def); + // Returns the AttrTypeMap for the TensorFlow operation named op_name. Status AttrTypeMapForOp(const char* op_name, const AttrTypeMap** out); @@ -180,12 +183,18 @@ class KernelAndDevice { const OpKernel* kernel() const { return kernel_.get(); } + Device* device() const { return device_; } + + DataTypeVector* mutable_output_dtypes() { return &output_dtypes_; } + const DataTypeVector& output_dtypes() { return output_dtypes_; } + private: std::unique_ptr kernel_; Device* device_; FunctionLibraryRuntime* flib_; checkpoint::TensorSliceReaderCacheWrapper slice_reader_cache_; Rendezvous* rendez_; + DataTypeVector output_dtypes_; }; } // namespace tensorflow diff --git a/tensorflow/c/eager/runtime_test.cc b/tensorflow/c/eager/runtime_test.cc index 643153058ce3d6f0c88dd23a0dec4c6eff060319..4f75d278878d7c8ff6a5e48e5b4e633aa13aedc5 100644 --- a/tensorflow/c/eager/runtime_test.cc +++ b/tensorflow/c/eager/runtime_test.cc @@ -41,7 +41,7 @@ class TestEnv { device_mgr_.reset(new DeviceMgr({device})); flib_runtime_ = NewFunctionLibraryRuntime(device_mgr_.get(), Env::Default(), device, TF_GRAPH_DEF_VERSION, - &flib_def_, {}, nullptr); + &flib_def_, nullptr, {}, nullptr); } FunctionLibraryRuntime* function_library_runtime() const { diff --git a/tensorflow/c/python_api.cc b/tensorflow/c/python_api.cc index f553142d15f476ad2c1af68016a4254ed211b9b2..cd604538f1fa142c6fe6a76624c048baddaa52fb 100644 --- a/tensorflow/c/python_api.cc +++ b/tensorflow/c/python_api.cc @@ -104,4 +104,9 @@ void SetRequireShapeInferenceFns(TF_Graph* graph, bool require) { graph->refiner.set_require_shape_inference_fns(require); } +void ExtendSession(TF_Session* session, TF_Status* status) { + ExtendSessionGraphHelper(session, status); + session->extend_before_run = false; +} + } // namespace tensorflow diff --git a/tensorflow/c/python_api.h b/tensorflow/c/python_api.h index 542d70f42c2a5df8309a722b32d850dd249e496f..13b680b3a24afa2d285ea18207578aff4350f6d5 100644 --- a/tensorflow/c/python_api.h +++ b/tensorflow/c/python_api.h @@ -41,6 +41,16 @@ void RemoveAllControlInputs(TF_Graph* graph, TF_Operation* op); // error. The default is true. void SetRequireShapeInferenceFns(TF_Graph* graph, bool require); +// Extends `session` with any new operations added to its associated graph. +// Usually this happens automatically in TF_SessionRun. After this is called, +// TF_SessionRun will no longer extend the session on every call. +// +// We expose this here to allow fine-grained synchronization in multi-threaded +// workloads, which is required since the Python implementation depends on the +// above mutation methods. This allows us to prevent modifications to nodes in +// the graph after the session has been made aware of them. +void ExtendSession(TF_Session* session, TF_Status* status); + } // namespace tensorflow #endif // TENSORFLOW_C_PYTHON_API_H_ diff --git a/tensorflow/cc/framework/cc_op_gen.cc b/tensorflow/cc/framework/cc_op_gen.cc index a40ad1ffc3b262840e6ca0043139b1b61e04510d..d73121c7b701ec06c03836d1a765f4b35d88fe92 100644 --- a/tensorflow/cc/framework/cc_op_gen.cc +++ b/tensorflow/cc/framework/cc_op_gen.cc @@ -28,6 +28,7 @@ limitations under the License. #include "tensorflow/core/framework/types.pb_text.h" #include "tensorflow/core/lib/gtl/map_util.h" #include "tensorflow/core/lib/gtl/stl_util.h" +#include "tensorflow/core/lib/hash/hash.h" #include "tensorflow/core/lib/strings/str_util.h" #include "tensorflow/core/lib/strings/strcat.h" #include "tensorflow/core/platform/env.h" @@ -697,7 +698,8 @@ string OpInfo::GetOpAttrStruct() const { attr_comment = MakeComment(attr_comment, " "); strings::StrAppend(&setters, attr_comment); - strings::StrAppend(&setters, " Attrs ", attr_func_def, " x) {\n"); + strings::StrAppend(&setters, " TF_MUST_USE_RESULT Attrs ", attr_func_def, + " x) {\n"); strings::StrAppend(&setters, " Attrs ret = *this;\n"); strings::StrAppend(&setters, " ret.", api_def_attr.rename_to(), "_ = x;\n"); diff --git a/tensorflow/cc/framework/while_gradients.cc b/tensorflow/cc/framework/while_gradients.cc index 0734075fc6144d7c9f4fdb48c5e097faa58b8355..81870a0efa309ae6dbd5cc05a5dbe8c3e2d437c8 100644 --- a/tensorflow/cc/framework/while_gradients.cc +++ b/tensorflow/cc/framework/while_gradients.cc @@ -72,9 +72,9 @@ Status AddForwardLoopCounter(WhileContext* while_ctx, const Scope& scope, }; // Body function that adds one to input. - BodyGraphBuilderFn body_fn = [while_ctx](const Scope& scope, - const std::vector& inputs, - std::vector* outputs) { + BodyGraphBuilderFn body_fn = [](const Scope& scope, + const std::vector& inputs, + std::vector* outputs) { DCHECK_EQ(inputs.size(), 1); outputs->emplace_back(ops::Add(scope, inputs[0], 1)); return scope.status(); diff --git a/tensorflow/cc/gradients/nn_grad.cc b/tensorflow/cc/gradients/nn_grad.cc index 13a3bba5e6d5ca19ff3f0eca76665ba7d3ab628d..0cb3132e94e381f672d69aefe4a199d2b590830c 100644 --- a/tensorflow/cc/gradients/nn_grad.cc +++ b/tensorflow/cc/gradients/nn_grad.cc @@ -48,8 +48,8 @@ Status SoftmaxGrad(const Scope& scope, const Operation& op, REGISTER_GRADIENT_OP("Softmax", SoftmaxGrad); Status LogSoftmaxGrad(const Scope& scope, const Operation& op, - const std::vector& grad_inputs, - std::vector* grad_outputs) { + const std::vector& grad_inputs, + std::vector* grad_outputs) { auto softmax = Exp(scope, op.output(0)); auto sum = Sum(scope, grad_inputs[0], {1}, Sum::KeepDims(true)); auto mul = Mul(scope, sum, softmax); @@ -107,11 +107,10 @@ Status BiasAddGradHelper(const Scope& scope, const Operation& op, const std::vector& grad_inputs, std::vector* grad_outputs) { string data_format; - BiasAddGrad::Attrs input_attrs; TF_RETURN_IF_ERROR( GetNodeAttr(op.output(0).node()->attrs(), "data_format", &data_format)); - input_attrs.DataFormat(data_format); - auto dx_1 = BiasAddGrad(scope, grad_inputs[0], input_attrs); + auto dx_1 = + BiasAddGrad(scope, grad_inputs[0], BiasAddGrad::DataFormat(data_format)); grad_outputs->push_back(Identity(scope, grad_inputs[0])); grad_outputs->push_back(dx_1); return scope.status(); @@ -130,19 +129,16 @@ Status Conv2DGrad(const Scope& scope, const Operation& op, TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "padding", &padding)); TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "strides", &strides)); TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "use_cudnn_on_gpu", &use_cudnn_on_gpu)); - Conv2DBackpropInput::Attrs input_attrs; - input_attrs.DataFormat(data_format); - input_attrs.UseCudnnOnGpu(use_cudnn_on_gpu); - auto dx_1 = Conv2DBackpropInput(scope, Shape(scope, op.input(0)), - op.input(1), grad_inputs[0], - strides, padding, input_attrs); + auto dx_1 = Conv2DBackpropInput(scope, Shape(scope, op.input(0)), op.input(1), + grad_inputs[0], strides, padding, + Conv2DBackpropInput::DataFormat(data_format) + .UseCudnnOnGpu(use_cudnn_on_gpu)); grad_outputs->push_back(dx_1); - Conv2DBackpropFilter::Attrs filter_attrs; - filter_attrs.DataFormat(data_format); - filter_attrs.UseCudnnOnGpu(use_cudnn_on_gpu); - auto dx_2 = Conv2DBackpropFilter(scope, op.input(0), - Shape(scope, op.input(1)), grad_inputs[0], - strides, padding, filter_attrs); + auto dx_2 = + Conv2DBackpropFilter(scope, op.input(0), Shape(scope, op.input(1)), + grad_inputs[0], strides, padding, + Conv2DBackpropFilter::DataFormat(data_format) + .UseCudnnOnGpu(use_cudnn_on_gpu)); grad_outputs->push_back(dx_2); return scope.status(); } @@ -160,13 +156,9 @@ Status MaxPoolGradHelper(const Scope& scope, const Operation& op, TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "ksize", &ksize)); TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "padding", &padding)); TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "strides", &strides)); - internal::MaxPoolGrad::Attrs grad_attrs; - grad_attrs.DataFormat(data_format); - auto dx = internal::MaxPoolGrad(scope, op.input(0), - op.output(0), - grad_inputs[0], - ksize, strides, - padding, grad_attrs); + auto dx = internal::MaxPoolGrad( + scope, op.input(0), op.output(0), grad_inputs[0], ksize, strides, padding, + internal::MaxPoolGrad::DataFormat(data_format)); grad_outputs->push_back(dx); return scope.status(); } @@ -180,15 +172,9 @@ Status MaxPoolGradV2Helper(const Scope& scope, const Operation& op, auto attrs = op.output(0).node()->attrs(); TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "data_format", &data_format)); TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "padding", &padding)); - MaxPoolGradV2::Attrs grad_attrs; - grad_attrs.DataFormat(data_format); - auto dx = MaxPoolGradV2(scope, op.input(0), - op.output(0), - grad_inputs[0], - op.input(1), - op.input(2), - padding, - grad_attrs); + auto dx = MaxPoolGradV2(scope, op.input(0), op.output(0), grad_inputs[0], + op.input(1), op.input(2), padding, + MaxPoolGradV2::DataFormat(data_format)); grad_outputs->push_back(dx); grad_outputs->push_back(NoGradient()); grad_outputs->push_back(NoGradient()); @@ -196,13 +182,74 @@ Status MaxPoolGradV2Helper(const Scope& scope, const Operation& op, } REGISTER_GRADIENT_OP("MaxPoolV2", MaxPoolGradV2Helper); +Status MaxPool3DGradHelper(const Scope& scope, const Operation& op, + const std::vector& grad_inputs, + std::vector* grad_outputs) { + std::vector ksize; + std::vector strides; + string padding; + string data_format; + auto attrs = op.output(0).node()->attrs(); + TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "ksize", &ksize)); + TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "strides", &strides)); + TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "padding", &padding)); + TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "data_format", &data_format)); + MaxPool3DGrad::Attrs grad_attrs; + auto dx = MaxPool3DGrad(scope, op.input(0), op.output(0), grad_inputs[0], + ksize, strides, padding, + grad_attrs.DataFormat(data_format)); + grad_outputs->push_back(dx); + return scope.status(); +} +REGISTER_GRADIENT_OP("MaxPool3D", MaxPool3DGradHelper); + +Status AvgPoolGradHelper(const Scope& scope, const Operation& op, + const std::vector& grad_inputs, + std::vector* grad_outputs) { + std::vector ksize; + std::vector strides; + string padding; + string data_format; + auto attrs = op.output(0).node()->attrs(); + TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "ksize", &ksize)); + TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "strides", &strides)); + TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "padding", &padding)); + TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "data_format", &data_format)); + internal::AvgPoolGrad::Attrs grad_attrs; + auto dx = + internal::AvgPoolGrad(scope, Shape(scope, op.input(0)), grad_inputs[0], + ksize, strides, padding, + grad_attrs.DataFormat(data_format)); + grad_outputs->push_back(dx); + return scope.status(); +} +REGISTER_GRADIENT_OP("AvgPool", AvgPoolGradHelper); + +Status AvgPool3DGradHelper(const Scope& scope, const Operation& op, + const std::vector& grad_inputs, + std::vector* grad_outputs) { + std::vector ksize; + std::vector strides; + string padding; + string data_format; + auto attrs = op.output(0).node()->attrs(); + TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "ksize", &ksize)); + TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "strides", &strides)); + TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "padding", &padding)); + TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "data_format", &data_format)); + AvgPool3DGrad::Attrs grad_attrs; + auto dx = AvgPool3DGrad(scope, Shape(scope, op.input(0)), grad_inputs[0], + ksize, strides, padding, + grad_attrs.DataFormat(data_format)); + grad_outputs->push_back(dx); + return scope.status(); +} +REGISTER_GRADIENT_OP("AvgPool3D", AvgPool3DGradHelper); + Status LRNGradHelper(const Scope& scope, const Operation& op, const std::vector& grad_inputs, - std::vector* grad_outputs){ - internal::LRNGrad::Attrs grad_attrs; - - auto dx = internal::LRNGrad(scope, grad_inputs[0], op.input(0), op.output(0), - grad_attrs); + std::vector* grad_outputs) { + auto dx = internal::LRNGrad(scope, grad_inputs[0], op.input(0), op.output(0)); grad_outputs->push_back(dx); return scope.status(); } diff --git a/tensorflow/cc/gradients/nn_grad_test.cc b/tensorflow/cc/gradients/nn_grad_test.cc index 0cfe5f6e3c49f7c4a3cafbf48ff4e54a0ffd0d47..c4eba7ecb017fe4628140d75a63bc7f0f09deb7f 100644 --- a/tensorflow/cc/gradients/nn_grad_test.cc +++ b/tensorflow/cc/gradients/nn_grad_test.cc @@ -31,8 +31,11 @@ using ops::Elu; using ops::L2Loss; using ops::LogSoftmax; using ops::LRN; +using ops::AvgPool; +using ops::AvgPool3D; using ops::MaxPool; using ops::MaxPoolV2; +using ops::MaxPool3D; using ops::Placeholder; using ops::Relu; using ops::Relu6; @@ -70,9 +73,9 @@ class NNGradTest : public ::testing::Test { // Sets tensor with random values, ensuring that the max value is largest by // a reasonable amount. - // This is an issue for MaxPool and MaxPoolV2, in which perturbations by the - // numeric gradient computation in the gradient checker can change the max - // value if values are too close together. + // This is an issue for MaxPool, MaxPoolV2 and MaxPool3D, in which + // perturbations by the numeric gradient computation in the gradient checker + // can change the max value if values are too close together. template void SetRandomValuesWithBumpedMax(Tensor* tensor) { auto tensor_flat = tensor->flat(); @@ -203,6 +206,41 @@ TEST_F(NNGradTest, MaxPoolGradV2Helper) { RunTest(x, x_init_value, y, y_shape); } +TEST_F(NNGradTest, MaxPool3DGradHelper) { + TensorShape x_shape({1, 3, 3, 3, 1}); + TensorShape y_shape({1, 1, 1, 1, 1}); + auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape)); + // Setup window and strides so that we only do one MaxPool3D. + const std::vector ksize{1, 3, 3, 3, 1}; + const std::vector strides{1, 3, 3, 3, 1}; + auto y = MaxPool3D(scope_, x, ksize, strides, "VALID"); + Tensor x_init_value = Tensor(DT_FLOAT, x_shape); + SetRandomValuesWithBumpedMax(&x_init_value); + RunTest(x, x_init_value, y, y_shape); +} + +TEST_F(NNGradTest, AvgPoolGradHelper) { + TensorShape x_shape({1, 2, 2, 1}); + TensorShape y_shape({1, 1, 1, 1}); + auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape)); + // Setup window and strides so that we only do one AvgPool. + const std::vector ksize{1, 2, 2, 1}; + const std::vector strides{1, 2, 2, 1}; + auto y = AvgPool(scope_, x, ksize, strides, "SAME"); + RunTest(x, x_shape, y, y_shape); +} + +TEST_F(NNGradTest, AvgPool3DGradHelper) { + TensorShape x_shape({1, 3, 3, 3, 1}); + TensorShape y_shape({1, 1, 1, 1, 1}); + auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape)); + // Setup window and strides so that we only do one AvgPool3D. + const std::vector ksize{1, 3, 3, 3, 1}; + const std::vector strides{1, 3, 3, 3, 1}; + auto y = AvgPool3D(scope_, x, ksize, strides, "SAME"); + RunTest(x, x_shape, y, y_shape); +} + TEST_F(NNGradTest, LRN){ TensorShape x_shape({1, 1, 2, 1}); auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape)); diff --git a/tensorflow/cc/profiler/profiler.h b/tensorflow/cc/profiler/profiler.h index 6077c45c5854fd5812ccb7c91522f93ed4e54883..64edbb5766c3604fbe0f15c2299843718381aa3f 100644 --- a/tensorflow/cc/profiler/profiler.h +++ b/tensorflow/cc/profiler/profiler.h @@ -61,18 +61,18 @@ class Profiler { /// Adds tracing information `run_meta` to profiler. A `run_meta` is /// generated by a TensorFlow session run call. `step` is the key /// to the `run_meta`. When calling ProfileXXX methods, caller can specify - /// `step` in `options` to seletively profile the corresponding `run_meta`. + /// `step` in `options` to selectively profile the corresponding `run_meta`. /// Multiple different `run_meta` can be keyed by the same `step` in order /// to group them together. void AddStep(int64 step, const RunMetadata& run_meta); /// Profiles the model by organizing nodes in graph structure. - /// Each node is an op and the nodes are contected by the op inputs/outputs. + /// Each node is an op and the nodes are connected by the op inputs/outputs. GraphNodeProto ProfileGraph(const Options& options); /// Profiles the model by organizing nodes in name scope structure. /// Each node is an op, and nodes are organized by the ops' name - /// scope, similar to a filesystem tree. + /// scope, similar to a file system tree. /// E.g. /foo is the root of operation /foo/matmul_1 and foo/conv_2. GraphNodeProto ProfileNameScope(const Options& options); diff --git a/tensorflow/cc/tools/BUILD b/tensorflow/cc/tools/BUILD index 97f66e79b8ad9f383b22f56e9385fc6d2080e1f8..f413a5cc52e9eb4bc393b8186f5b591681fa2e5e 100644 --- a/tensorflow/cc/tools/BUILD +++ b/tensorflow/cc/tools/BUILD @@ -32,6 +32,7 @@ tf_cc_test( deps = [ ":freeze_saved_model", "//tensorflow/cc:cc_ops", + "//tensorflow/cc:resource_variable_ops", "//tensorflow/core:core_cpu", "//tensorflow/core:framework_internal", "//tensorflow/core:protos_all_cc", diff --git a/tensorflow/cc/tools/freeze_saved_model.cc b/tensorflow/cc/tools/freeze_saved_model.cc index ddf372cdef21e1b3892c9a03714478d5a5785517..4ddddcb5863c9ffb1e5367db750b0d2ffd29cd5e 100644 --- a/tensorflow/cc/tools/freeze_saved_model.cc +++ b/tensorflow/cc/tools/freeze_saved_model.cc @@ -75,16 +75,13 @@ void GetNodeNameToNodeDefMap( // variable nodes to convert. void GetReachableNodesAndVariables( GraphDef* graph_def, const std::unordered_set& outputs, + const std::unordered_map& name_to_node_map, std::unordered_set* reachable_node_names, std::unordered_set* variable_node_names) { // TODO(suharshs): Add support for ResourceVariables. static const std::unordered_set* kVariableTypes = - new std::unordered_set({"Variable", "VariableV2"}); - // name_to_node_map is needed to get the inputs from the NodeDef corresponding - // the a string node name. These inputs are used when doing our backwards - // traversal. - std::unordered_map name_to_node_map; - GetNodeNameToNodeDefMap(graph_def, &name_to_node_map); + new std::unordered_set({"Variable", "VariableV2", "VarHandleOp"}); + std::queue nodes_to_visit; for (const string& tensor_name : outputs) { // We need to strip off the tensor part to get the node name. @@ -99,7 +96,7 @@ void GetReachableNodesAndVariables( continue; } reachable_node_names->insert(node_name); - NodeDef* node = name_to_node_map[node_name]; + NodeDef* node = name_to_node_map.at(node_name); if (kVariableTypes->find(node->op()) != kVariableTypes->end()) { variable_node_names->insert(node->name()); } @@ -111,7 +108,9 @@ void GetReachableNodesAndVariables( // Gets a map from variable name to variable value. Status GetVariableNameToTensorMap( - Session* session, std::unordered_set variable_names_set, + Session* session, + const std::unordered_map& name_to_node_map, + std::unordered_set variable_names_set, std::unordered_map* variable_name_to_value_map) { if (variable_names_set.empty()) { return Status::OK(); @@ -120,8 +119,14 @@ Status GetVariableNameToTensorMap( std::vector tensor_names; for (const string& node_name : variable_names_set) { variable_names.push_back(node_name); - // We need to run tensors, so append ":0". - tensor_names.push_back(node_name + ":0"); + NodeDef* node_def = name_to_node_map.at(node_name); + if (node_def->op() == "VarHandleOp") { + // If this is a resource variable, we have to run the corresponding + // ReadVariableOp. + tensor_names.push_back(node_name + "/Read/ReadVariableOp:0"); + } else { + tensor_names.push_back(node_name + ":0"); + } } std::vector outputs; TF_RETURN_IF_ERROR( @@ -143,6 +148,15 @@ void ConvertVariableToConstant(const NodeDef& variable_node, (*const_node->mutable_attr())["value"].mutable_tensor()); } +// Converts a ReadVariableOp NodeDef to an Identity NodeDef. +void ConvertReadVariableOpToIdentity(const NodeDef& node, + NodeDef* identity_node) { + identity_node->set_name(node.name()); + identity_node->set_op("Identity"); + (*identity_node->mutable_attr())["T"] = node.attr().at("dtype"); + identity_node->add_input(node.input(0)); +} + // Freezes the subgraph of all nodes needed by `outputs`. Status FreezeGraphDef(const SavedModelBundle& saved_model_bundle, const std::unordered_set& outputs, @@ -155,14 +169,19 @@ Status FreezeGraphDef(const SavedModelBundle& saved_model_bundle, if (graph_def.node_size() == 0) { return Status::OK(); } + // name_to_node_map is needed to get the inputs from the NodeDef corresponding + // the a string node name. These inputs are used when doing our backwards + // traversal. + std::unordered_map name_to_node_map; + GetNodeNameToNodeDefMap(&graph_def, &name_to_node_map); std::unordered_set reachable_node_names; std::unordered_set variable_node_names; - GetReachableNodesAndVariables(&graph_def, outputs, &reachable_node_names, - &variable_node_names); + GetReachableNodesAndVariables(&graph_def, outputs, name_to_node_map, + &reachable_node_names, &variable_node_names); std::unordered_map variable_to_value_map; - TF_RETURN_IF_ERROR( - GetVariableNameToTensorMap(saved_model_bundle.session.get(), - variable_node_names, &variable_to_value_map)); + TF_RETURN_IF_ERROR(GetVariableNameToTensorMap( + saved_model_bundle.session.get(), name_to_node_map, variable_node_names, + &variable_to_value_map)); // We copy the nodes in the same order they were in the original graph_def. for (const NodeDef& node : graph_def.node()) { if (reachable_node_names.find(node.name()) == reachable_node_names.end()) { @@ -171,6 +190,12 @@ Status FreezeGraphDef(const SavedModelBundle& saved_model_bundle, if (variable_node_names.find(node.name()) != variable_node_names.end()) { ConvertVariableToConstant(node, variable_to_value_map[node.name()], frozen_graph_def->add_node()); + } else if (node.op() == "ReadVariableOp" && + variable_node_names.find(node.input(0)) != + variable_node_names.end()) { + // If the node is a ReadVariableOp, its input VarHandleOp will be + // converted to a Constant, so we will need to convert it to an Identity. + ConvertReadVariableOpToIdentity(node, frozen_graph_def->add_node()); } else { // If the node isn't a variable, just copy the node as-is. *frozen_graph_def->add_node() = node; diff --git a/tensorflow/cc/tools/freeze_saved_model_test.cc b/tensorflow/cc/tools/freeze_saved_model_test.cc index 52a81a50284aec36bba4e56a0232c886cb0cb6cf..cd35fd3b95deec669218cfa4f25fea2c3ac9e56e 100644 --- a/tensorflow/cc/tools/freeze_saved_model_test.cc +++ b/tensorflow/cc/tools/freeze_saved_model_test.cc @@ -15,6 +15,7 @@ limitations under the License. #include "tensorflow/cc/tools/freeze_saved_model.h" +#include "tensorflow/cc/ops/resource_variable_ops.h" #include "tensorflow/cc/ops/standard_ops.h" #include "tensorflow/core/framework/function_testlib.h" #include "tensorflow/core/framework/graph.pb.h" @@ -113,6 +114,160 @@ class FreezeTest : public ::testing::Test { test::ExpectTensorEqual(unfrozen_outputs[0], frozen_outputs[0]); } + + void TestFreezeGraphWithoutDependentVariables(bool use_resource) { + // Test freezing a graph with variables that are not needed by the outputs + // in the SignatureDef. The resulting graph shouldn't be frozen, but + // non-dependent nodes should be pruned. + SavedModelBundle saved_model_bundle; + GraphDef graph_def; + Scope scope = Scope::NewRootScope(); + Output a = ops::Const(scope.WithOpName("a"), 10.0f, {}); + Output b = ops::Const(scope.WithOpName("b"), 10.0f, {}); + Output c = ops::Mul(scope.WithOpName("c"), a, b); + if (use_resource) { + Output var = + ops::VarHandleOp(scope.WithOpName("var"), DataType::DT_FLOAT, {}); + Output read_var = ops::ReadVariableOp( + scope.WithOpName("var/Read/ReadVariableOp"), var, DataType::DT_FLOAT); + auto assign = ops::AssignVariableOp(scope.WithOpName("assign"), var, a); + } else { + Output var = + ops::Variable(scope.WithOpName("var"), {}, DataType::DT_FLOAT); + Output assign = ops::Assign(scope.WithOpName("assign"), var, a); + } + + TF_ASSERT_OK(scope.ToGraphDef(&graph_def)); + // "c" isnt dependent on the variable, so nothing should be frozen. + TF_ASSERT_OK(AddGraphDefWithOutputsToSavedModelBundle( + graph_def, {"c:0"}, "assign", &saved_model_bundle)); + + GraphDef frozen_graph_def; + std::unordered_set inputs; + std::unordered_set outputs; + TF_ASSERT_OK(FreezeSavedModel(saved_model_bundle, &frozen_graph_def, + &inputs, &outputs)); + + GraphDef expected_graph_def; + Scope expected_scope = Scope::NewRootScope(); + Output expected_a = ops::Const(expected_scope.WithOpName("a"), 10.0f, {}); + Output expected_b = ops::Const(expected_scope.WithOpName("b"), 10.0f, {}); + Output expected_c = + ops::Mul(expected_scope.WithOpName("c"), expected_a, expected_b); + TF_ASSERT_OK(expected_scope.ToGraphDef(&expected_graph_def)); + + GraphDefEqual(frozen_graph_def, expected_graph_def); + + RunAndCompareFrozenAndUnfrozenGraphs(saved_model_bundle.session.get(), + frozen_graph_def, "c:0"); + } + + void TestFreezeGraphWithDependentVariables(bool use_resource) { + // Test freezing a graph with variables that are needed by outputs in the + // SignatureDef. The variables should be frozen. + SavedModelBundle saved_model_bundle; + GraphDef graph_def; + Scope scope = Scope::NewRootScope(); + Output a = ops::Const(scope.WithOpName("a"), 10.0f, {}); + Output read_var; + if (use_resource) { + Output var = + ops::VarHandleOp(scope.WithOpName("var"), DataType::DT_FLOAT, {}); + read_var = ops::ReadVariableOp( + scope.WithOpName("var/Read/ReadVariableOp"), var, DataType::DT_FLOAT); + auto assign = ops::AssignVariableOp(scope.WithOpName("assign"), var, a); + } else { + Output read_var = + ops::Variable(scope.WithOpName("var"), {}, DataType::DT_FLOAT); + Output assign = ops::Assign(scope.WithOpName("assign"), read_var, a); + } + Output c = ops::Mul(scope.WithOpName("c"), a, read_var); + TF_ASSERT_OK(scope.ToGraphDef(&graph_def)); + // "c" isnt dependent on the variable, so nothing should be frozen. + TF_ASSERT_OK(AddGraphDefWithOutputsToSavedModelBundle( + graph_def, {"c:0"}, "assign", &saved_model_bundle)); + + GraphDef frozen_graph_def; + std::unordered_set inputs; + std::unordered_set outputs; + TF_ASSERT_OK(FreezeSavedModel(saved_model_bundle, &frozen_graph_def, + &inputs, &outputs)); + + // If using normal variables there should be 3 nodes in the resulting + // graph_def. If using resource variables there should be 4 nodes in the + // resulting graph_def. + // In both cases, none should be variables. + size_t expected_nodes = use_resource ? 4 : 3; + EXPECT_EQ(frozen_graph_def.node_size(), expected_nodes); + for (const NodeDef& node : frozen_graph_def.node()) { + EXPECT_NE(node.op(), "Variable") << node.name(); + EXPECT_NE(node.op(), "VariableV2") << node.name(); + EXPECT_NE(node.op(), "VarHandleOp") << node.name(); + EXPECT_NE(node.op(), "ReadVariableOp") << node.name(); + } + + RunAndCompareFrozenAndUnfrozenGraphs(saved_model_bundle.session.get(), + frozen_graph_def, "c:0"); + } + + void TestFreezeGraphWithAndWithoutDependentVariables(bool use_resource) { + // Test freezing a graph with some variables that are needed and not needed + // by + // the outputs in the SignatureDef. The resulting graph should only freeze + // dependent variables. + SavedModelBundle saved_model_bundle; + GraphDef graph_def; + Scope scope = Scope::NewRootScope(); + Output a = ops::Const(scope.WithOpName("a"), 10.0f, {}); + Output read_var; + + if (use_resource) { + Output var = + ops::VarHandleOp(scope.WithOpName("var"), DataType::DT_FLOAT, {}); + read_var = ops::ReadVariableOp( + scope.WithOpName("var/Read/ReadVariableOp"), var, DataType::DT_FLOAT); + auto assign = ops::AssignVariableOp(scope.WithOpName("assign"), var, a); + Output var_1 = + ops::VarHandleOp(scope.WithOpName("var_1"), DataType::DT_FLOAT, {}); + Output read_var_1 = + ops::ReadVariableOp(scope.WithOpName("var_1/Read/ReadVariableOp"), + var, DataType::DT_FLOAT); + auto assign_1 = + ops::AssignVariableOp(scope.WithOpName("assign_1"), var_1, a); + } else { + read_var = ops::Variable(scope.WithOpName("var"), {}, DataType::DT_FLOAT); + Output assign = ops::Assign(scope.WithOpName("assign"), read_var, a); + Output var_1 = + ops::Variable(scope.WithOpName("var_1"), {}, DataType::DT_FLOAT); + Output assign_1 = ops::Assign(scope.WithOpName("assign_1"), var_1, a); + } + + Output c = ops::Mul(scope.WithOpName("c"), a, read_var); + TF_ASSERT_OK(scope.ToGraphDef(&graph_def)); + // "c" isnt dependent on the variable, so nothing should be frozen. + TF_ASSERT_OK(AddGraphDefWithOutputsToSavedModelBundle( + graph_def, {"c:0"}, "assign", &saved_model_bundle)); + + GraphDef frozen_graph_def; + std::unordered_set inputs; + std::unordered_set outputs; + TF_ASSERT_OK(FreezeSavedModel(saved_model_bundle, &frozen_graph_def, + &inputs, &outputs)); + + // There should be 3 nodes in the resulting graph_def, and none should be + // variables. + size_t expected_nodes = use_resource ? 4 : 3; + EXPECT_EQ(frozen_graph_def.node_size(), expected_nodes); + for (const NodeDef& node : frozen_graph_def.node()) { + EXPECT_NE(node.op(), "Variable") << node.name(); + EXPECT_NE(node.op(), "VariableV2") << node.name(); + EXPECT_NE(node.op(), "VarHandleOp") << node.name(); + EXPECT_NE(node.op(), "ReadVariableOp") << node.name(); + } + + RunAndCompareFrozenAndUnfrozenGraphs(saved_model_bundle.session.get(), + frozen_graph_def, "c:0"); + } }; TEST_F(FreezeTest, InputsAndOutputsSingleSignatureDef) { @@ -196,111 +351,28 @@ TEST_F(FreezeTest, GraphDefWithNoVariables) { GraphDefEqual(frozen_graph_def, graph_def); } -TEST_F(FreezeTest, GraphDefWithVariablesNotNeededByOutputs) { - // Test freezing a graph with variables that are not needed by the outputs in - // the SignatureDef. The resulting graph shouldn't be frozen, but - // non-dependent nodes should be pruned. - SavedModelBundle saved_model_bundle; - GraphDef graph_def; - Scope scope = Scope::NewRootScope(); - Output a = ops::Const(scope.WithOpName("a"), 10.0f, {}); - Output b = ops::Const(scope.WithOpName("b"), 10.0f, {}); - Output c = ops::Mul(scope.WithOpName("c"), a, b); - Output var = ops::Variable(scope.WithOpName("var"), {}, DataType::DT_FLOAT); - Output assign = ops::Assign(scope.WithOpName("assign"), var, a); - TF_ASSERT_OK(scope.ToGraphDef(&graph_def)); - // "c" isnt dependent on the variable, so nothing should be frozen. - TF_ASSERT_OK(AddGraphDefWithOutputsToSavedModelBundle( - graph_def, {"c:0"}, assign.name(), &saved_model_bundle)); - - GraphDef frozen_graph_def; - std::unordered_set inputs; - std::unordered_set outputs; - TF_ASSERT_OK(FreezeSavedModel(saved_model_bundle, &frozen_graph_def, &inputs, - &outputs)); - - GraphDef expected_graph_def; - Scope expected_scope = Scope::NewRootScope(); - Output expected_a = ops::Const(expected_scope.WithOpName("a"), 10.0f, {}); - Output expected_b = ops::Const(expected_scope.WithOpName("b"), 10.0f, {}); - Output expected_c = - ops::Mul(expected_scope.WithOpName("c"), expected_a, expected_b); - TF_ASSERT_OK(expected_scope.ToGraphDef(&expected_graph_def)); - - GraphDefEqual(frozen_graph_def, expected_graph_def); - - RunAndCompareFrozenAndUnfrozenGraphs(saved_model_bundle.session.get(), - frozen_graph_def, "c:0"); +TEST_F(FreezeTest, GraphDefWithoutDependentVariables) { + TestFreezeGraphWithoutDependentVariables(false); } -TEST_F(FreezeTest, GraphDefWithVariablesNeededByOutputs) { - // Test freezing a graph with variables that are needed by outputs in the - // SignatureDef. The variables should be frozen. - SavedModelBundle saved_model_bundle; - GraphDef graph_def; - Scope scope = Scope::NewRootScope(); - Output a = ops::Const(scope.WithOpName("a"), 10.0f, {}); - Output var = ops::Variable(scope.WithOpName("var"), {}, DataType::DT_FLOAT); - Output c = ops::Mul(scope.WithOpName("c"), a, var); - Output assign = ops::Assign(scope.WithOpName("assign"), var, a); - TF_ASSERT_OK(scope.ToGraphDef(&graph_def)); - // "c" isnt dependent on the variable, so nothing should be frozen. - TF_ASSERT_OK(AddGraphDefWithOutputsToSavedModelBundle( - graph_def, {"c:0"}, assign.name(), &saved_model_bundle)); - - GraphDef frozen_graph_def; - std::unordered_set inputs; - std::unordered_set outputs; - TF_ASSERT_OK(FreezeSavedModel(saved_model_bundle, &frozen_graph_def, &inputs, - &outputs)); - - // There should be 3 nodes in the resulting graph_def, and none should be - // variables. - EXPECT_EQ(frozen_graph_def.node_size(), 3); - for (const NodeDef& node : frozen_graph_def.node()) { - EXPECT_NE(node.op(), "Variable") << node.name(); - EXPECT_NE(node.op(), "VariableV2") << node.name(); - } - - RunAndCompareFrozenAndUnfrozenGraphs(saved_model_bundle.session.get(), - frozen_graph_def, "c:0"); +TEST_F(FreezeTest, GraphDefWithoutDependentResourceVariables) { + TestFreezeGraphWithoutDependentVariables(true); } -TEST_F(FreezeTest, GraphDefWithVariablesNeededAndNotNeededByOutputs) { - // Test freezing a graph with some variables that are needed and not needed by - // the outputs in the SignatureDef. The resulting graph should only freeze - // dependent variables. - SavedModelBundle saved_model_bundle; - GraphDef graph_def; - Scope scope = Scope::NewRootScope(); - Output a = ops::Const(scope.WithOpName("a"), 10.0f, {}); - Output var = ops::Variable(scope.WithOpName("var"), {}, DataType::DT_FLOAT); - Output c = ops::Mul(scope.WithOpName("c"), a, var); - Output assign = ops::Assign(scope.WithOpName("assign"), var, a); - Output var_1 = - ops::Variable(scope.WithOpName("var_1"), {}, DataType::DT_FLOAT); - Output assign_1 = ops::Assign(scope.WithOpName("assign_1"), var, a); - TF_ASSERT_OK(scope.ToGraphDef(&graph_def)); - // "c" isnt dependent on the variable, so nothing should be frozen. - TF_ASSERT_OK(AddGraphDefWithOutputsToSavedModelBundle( - graph_def, {"c:0"}, assign.name(), &saved_model_bundle)); +TEST_F(FreezeTest, GraphDefWithDependentVariables) { + TestFreezeGraphWithDependentVariables(false); +} - GraphDef frozen_graph_def; - std::unordered_set inputs; - std::unordered_set outputs; - TF_ASSERT_OK(FreezeSavedModel(saved_model_bundle, &frozen_graph_def, &inputs, - &outputs)); +TEST_F(FreezeTest, GraphDefWithDependentResourceVariables) { + TestFreezeGraphWithDependentVariables(true); +} - // There should be 3 nodes in the resulting graph_def, and none should be - // variables. - EXPECT_EQ(frozen_graph_def.node_size(), 3); - for (const NodeDef& node : frozen_graph_def.node()) { - EXPECT_NE(node.op(), "Variable") << node.name(); - EXPECT_NE(node.op(), "VariableV2") << node.name(); - } +TEST_F(FreezeTest, GraphDefWithAndWithoutDependentVariables) { + TestFreezeGraphWithAndWithoutDependentVariables(false); +} - RunAndCompareFrozenAndUnfrozenGraphs(saved_model_bundle.session.get(), - frozen_graph_def, "c:0"); +TEST_F(FreezeTest, GraphDefWithAndWithoutDependentResourceVariables) { + TestFreezeGraphWithAndWithoutDependentVariables(true); } } // namespace diff --git a/tensorflow/compiler/aot/BUILD b/tensorflow/compiler/aot/BUILD index 0900e87ebabd378e6237b77ca0ef01677c07c244..ffa2d088295375bbbcd2cdd9365982907f2bf480 100644 --- a/tensorflow/compiler/aot/BUILD +++ b/tensorflow/compiler/aot/BUILD @@ -72,6 +72,7 @@ cc_library( "//tensorflow/core:core_cpu_internal", "//tensorflow/core:framework_internal", "//tensorflow/core:lib", + "//tensorflow/core:lib_internal", "//tensorflow/core:protos_all_cc", ], ) diff --git a/tensorflow/compiler/aot/compile.cc b/tensorflow/compiler/aot/compile.cc index c87f2b75dfa18ad5c3eda4bd6fcbcb3083ef73fd..7c833878818022c86fd3171ec9cef9fcd3217a24 100644 --- a/tensorflow/compiler/aot/compile.cc +++ b/tensorflow/compiler/aot/compile.cc @@ -32,6 +32,7 @@ limitations under the License. #include "tensorflow/core/framework/graph.pb.h" #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/lib/io/path.h" +#include "tensorflow/core/lib/strings/proto_serialization.h" #include "tensorflow/core/platform/env.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/types.h" diff --git a/tensorflow/compiler/jit/BUILD b/tensorflow/compiler/jit/BUILD index a711319607f4ff2b83aa0ebe50e215b3d0e2258e..c4a2d4ab0321bbf9db91f5e4387084c27e576b87 100644 --- a/tensorflow/compiler/jit/BUILD +++ b/tensorflow/compiler/jit/BUILD @@ -29,7 +29,10 @@ load("@local_config_cuda//cuda:build_defs.bzl", "if_cuda_is_configured") # Target that bundles up the XLA CPU and GPU JIT devices. cc_library( name = "jit", - visibility = [":friends"], + visibility = [ + ":friends", + "//learning/tfx:__subpackages__", + ], deps = [ ":xla_cpu_device", ":xla_cpu_jit", @@ -102,12 +105,17 @@ cc_library( cc_library( name = "xla_interpreter_device", srcs = ["xla_interpreter_device.cc"], + visibility = [":friends"], deps = [ + ":jit_compilation_passes", ":xla_device", "//tensorflow/compiler/jit/kernels:xla_launch_op", "//tensorflow/compiler/tf2xla:xla_compiler", + "//tensorflow/compiler/tf2xla/kernels:xla_ops", + "//tensorflow/compiler/xla/service:interpreter_plugin", # buildcleaner: keep + "//tensorflow/core:lib", ], - alwayslink = True, + alwayslink = 1, ) cc_library( @@ -127,6 +135,7 @@ cc_library( deps = [ ":common", ":jit_compilation_passes", + ":xla_launch_util", "//tensorflow/compiler/jit/ops:xla_ops", "//tensorflow/compiler/tf2xla:common", "//tensorflow/compiler/tf2xla:dump_graph", @@ -166,6 +175,28 @@ cc_library( visibility = [":friends"], ) +cc_library( + name = "xla_launch_util", + srcs = ["xla_launch_util.cc"], + hdrs = ["xla_launch_util.h"], + deps = [ + ":common", + ":xla_compilation_cache", + "//tensorflow/compiler/tf2xla:xla_compiler", + "//tensorflow/compiler/xla:status_macros", + "//tensorflow/compiler/xla:statusor", + "//tensorflow/compiler/xla/client:client_library", + "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/core:core_cpu", + "//tensorflow/core:core_cpu_internal", + "//tensorflow/core:framework", + "//tensorflow/core:lib", + "//tensorflow/core:lib_internal", + "//tensorflow/core:protos_all_cc", + "//tensorflow/core/kernels:variable_ops", + ], +) + cc_library( name = "xla_compilation_cache", srcs = ["xla_compilation_cache.cc"], @@ -200,6 +231,7 @@ cc_library( name = "graph_to_functiondef", srcs = ["graph_to_functiondef.cc"], hdrs = ["graph_to_functiondef.h"], + visibility = [":friends"], deps = [ "//tensorflow/core:core_cpu", "//tensorflow/core:framework", diff --git a/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc b/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc index 9c372a012789fc25ca0a711349c09ca62edc6754..2d175c40f9dfaef4e5024b77a6ecb8d6022e7a56 100644 --- a/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc +++ b/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc @@ -381,12 +381,24 @@ class Encapsulator { Node* send_from_host = nullptr; }; + // Creates an outside_compilation subgraph for outside_compilation_id if + // none exists yet. Returns the (possible newly created) subgraph for + // outside_compilation_id. + OutsideCompilationSubgraph* LookupOrCreateOutsideCompilationSubgraph( + const string& outside_compilation_id); + // Builds a ParallelCheck op that compares the output of the original // subgraph with the encapsulated subgraph. Status BuildParallelCheckOp( const std::unordered_map& node_images, Graph* graph_out); + // Builds a placeholder node used to provide the key input to a RecvAtHost + // or SendFromHost node. This placeholder node will be removed by a later + // pass. + Status AddHostComputeKeyPlaceholder(OutsideCompilationSubgraph* oc_subgraph, + Graph* graph_out); + // Builds a _RecvAtHost node producing all the inputs of an // outside_compilation subgraph and stores it in oc_subgraph.recv_at_host. Status AddRecvAtHostNode(const string& subgraph_name, @@ -413,6 +425,10 @@ class Encapsulator { // NodeDef for the function call node. NodeDef call_node_def_; + // Placeholder node simulating the host compute key in the output graph. + // Not owned. + Node* host_compute_key_placeholder_ = nullptr; + // Function call node(s) in the output graph. Not owned. // If parallel_checking is enabled, 'call_node_inputs' is the function call // node to which inputs should be fed, and 'call_node_outputs' is the @@ -712,39 +728,44 @@ Status Encapsulator::Subgraph::RecordResult( return Status::OK(); } -void Encapsulator::Subgraph::RecordOutsideCompilationInputOrControl( - const string& outside_compilation_id, const Edge* edge) { +Encapsulator::Subgraph::OutsideCompilationSubgraph* +Encapsulator::Subgraph::LookupOrCreateOutsideCompilationSubgraph( + const string& outside_compilation_id) { auto iter = outside_compilation_subgraphs_ .emplace(outside_compilation_id, OutsideCompilationSubgraph()) .first; - OutsideCompilationSubgraph& outside_subgraph = iter->second; + OutsideCompilationSubgraph* outside_subgraph = &iter->second; + return outside_subgraph; +} + +void Encapsulator::Subgraph::RecordOutsideCompilationInputOrControl( + const string& outside_compilation_id, const Edge* edge) { + OutsideCompilationSubgraph* outside_subgraph = + LookupOrCreateOutsideCompilationSubgraph(outside_compilation_id); if (edge->IsControlEdge()) { - outside_subgraph.control_inputs.insert(edge->src()); + outside_subgraph->control_inputs.insert(edge->src()); } else { - int input_index = outside_subgraph.inputs.size(); - outside_subgraph.inputs.emplace(NodeSlot(edge->src(), edge->src_output()), - input_index); + int input_index = outside_subgraph->inputs.size(); + outside_subgraph->inputs.emplace(NodeSlot(edge->src(), edge->src_output()), + input_index); } } void Encapsulator::Subgraph::RecordOutsideCompilationOutputOrControl( const string& outside_compilation_id, const Edge* edge) { - auto subgraph_iter = - outside_compilation_subgraphs_ - .emplace(outside_compilation_id, OutsideCompilationSubgraph()) - .first; - OutsideCompilationSubgraph& outside_subgraph = subgraph_iter->second; + OutsideCompilationSubgraph* outside_subgraph = + LookupOrCreateOutsideCompilationSubgraph(outside_compilation_id); if (edge->IsControlEdge()) { - outside_subgraph.control_outputs.insert(edge->dst()); + outside_subgraph->control_outputs.insert(edge->dst()); } else { DataType dtype = edge->dst()->input_type(edge->dst_input()); auto output_iter = - outside_subgraph.outputs_by_src + outside_subgraph->outputs_by_src .emplace(NodeSlot(edge->src(), edge->src_output(), dtype), - outside_subgraph.outputs_by_src.size()) + outside_subgraph->outputs_by_src.size()) .first; int output_index = output_iter->second; - outside_subgraph.outputs_by_dst[NodeSlot(edge->dst(), edge->dst_input())] = + outside_subgraph->outputs_by_dst[NodeSlot(edge->dst(), edge->dst_input())] = output_index; } } @@ -1060,9 +1081,36 @@ Status Encapsulator::Subgraph::AddFunctionCallNode( return Status::OK(); } +Status Encapsulator::Subgraph::AddHostComputeKeyPlaceholder( + OutsideCompilationSubgraph* oc_subgraph, Graph* graph_out) { + TensorShapeProto shape_proto; + TensorShape shape({2}); + shape.AsProto(&shape_proto); + GraphDefBuilder::Options options(graph_out, /*status=*/nullptr); + NodeDef key_def; + NodeDefBuilder builder( + strings::StrCat(call_node_def_.name(), "_key_placeholder"), + "Placeholder"); + builder.Attr("dtype", DT_STRING); + builder.Attr("shape", shape_proto); + builder.Attr("_host_compute_call_node", call_node_def_.name()); + Status s = builder.Finalize(&key_def); + if (!s.ok()) return s; + + host_compute_key_placeholder_ = graph_out->AddNode(key_def, &s); + if (!s.ok()) return s; + host_compute_key_placeholder_->set_assigned_device_name(device_); + + return Status::OK(); +} + Status Encapsulator::Subgraph::AddRecvAtHostNode( const string& subgraph_name, const string& oc_subgraph_name, OutsideCompilationSubgraph* oc_subgraph, Graph* graph_out) { + if (host_compute_key_placeholder_ == nullptr) { + TF_RETURN_IF_ERROR(AddHostComputeKeyPlaceholder(oc_subgraph, graph_out)); + } + std::vector dtypes(oc_subgraph->inputs.size(), DT_INVALID); for (const auto& input : oc_subgraph->inputs) { @@ -1078,15 +1126,21 @@ Status Encapsulator::Subgraph::AddRecvAtHostNode( NodeDefBuilder builder(strings::StrCat("outside_compilation_", subgraph_name, "_", oc_subgraph_name, "_recv"), kRecvAtHostOp); + builder.Device(device_); builder.Attr("Toutputs", dtypes); + // TODO(misard) For now we only support TPU device 0. + builder.Attr("device_ordinal", 0); builder.Attr("key", strings::StrCat("host_compute_channel_", subgraph_name, "_", oc_subgraph_name)); + builder.Input(host_compute_key_placeholder_->name(), 0, DT_STRING); Status s = builder.Finalize(&recv_def); if (!s.ok()) return s; oc_subgraph->recv_at_host = graph_out->AddNode(recv_def, &s); if (!s.ok()) return s; oc_subgraph->recv_at_host->set_assigned_device_name(device_); + graph_out->AddEdge(host_compute_key_placeholder_, 0, + oc_subgraph->recv_at_host, 0); // Add a control dependency forcing the RecvAtHost to run before the subgraph // completes. This has no effect on execution order but prevents the @@ -1101,6 +1155,10 @@ Status Encapsulator::Subgraph::AddSendFromHostNode( const std::unordered_map& node_images, const string& subgraph_name, const string& oc_subgraph_name, OutsideCompilationSubgraph* oc_subgraph, Graph* graph_out) { + if (host_compute_key_placeholder_ == nullptr) { + TF_RETURN_IF_ERROR(AddHostComputeKeyPlaceholder(oc_subgraph, graph_out)); + } + std::vector dtypes(oc_subgraph->outputs_by_src.size(), DT_INVALID); std::vector inputs( oc_subgraph->outputs_by_src.size()); @@ -1120,16 +1178,22 @@ Status Encapsulator::Subgraph::AddSendFromHostNode( NodeDefBuilder builder(strings::StrCat("outside_compilation_", subgraph_name, "_", oc_subgraph_name, "_send"), kSendFromHostOp); + builder.Device(device_); builder.Attr("Tinputs", dtypes); builder.Attr("key", strings::StrCat("host_compute_channel_", subgraph_name, "_", oc_subgraph_name)); + // TODO(misard) For now we only support TPU device 0. + builder.Attr("device_ordinal", 0); builder.Input(inputs); + builder.Input(host_compute_key_placeholder_->name(), 0, DT_STRING); Status s = builder.Finalize(&send_def); if (!s.ok()) return s; oc_subgraph->send_from_host = graph_out->AddNode(send_def, &s); if (!s.ok()) return s; oc_subgraph->send_from_host->set_assigned_device_name(device_); + graph_out->AddEdge(host_compute_key_placeholder_, 0, + oc_subgraph->send_from_host, inputs.size()); // Add a control dependency forcing the SendFromHost to run before the // subgraph completes. This has no effect on execution order but prevents the @@ -1709,7 +1773,9 @@ Status Encapsulator::DoStaticShapeInferenceForOutsideCompilationSend( std::unique_ptr graph_out(new Graph(graph_in.op_registry())); graph_out->set_versions(graph_in.versions()); - static_shape_out->resize(send_node->num_inputs()); + // The final input to the send node is the dynamic key, which we don't include + // in the static shapes. + static_shape_out->resize(send_node->num_inputs() - 1); // We don't use the standard ReverseDFS because we want to cut off traversal // whenever we find an output with fully defined shape. @@ -1750,9 +1816,14 @@ Status Encapsulator::DoStaticShapeInferenceForOutsideCompilationSend( // continue. TensorShapeProto proto; context->ShapeHandleToProto(shape, &proto); - dummy_node_images[src_node] = AddDummyShapedNode( - src_node->output_type(src_port), proto, graph_out.get()); - if (n == send_node) { + if (dummy_node_images.find(src_node) == dummy_node_images.end()) { + dummy_node_images[src_node] = AddDummyShapedNode( + src_node->output_type(src_port), proto, graph_out.get()); + } + // The final input to the send node is the dynamic key, which we + // don't include in the static shapes. + if (n == send_node && + in_edge->dst_input() < static_shape_out->size()) { (*static_shape_out)[in_edge->dst_input()] = proto; } } else { diff --git a/tensorflow/compiler/jit/encapsulate_subgraphs_pass_test.cc b/tensorflow/compiler/jit/encapsulate_subgraphs_pass_test.cc index aed9cae0f1799c4524da8ee309344849798755d5..d7bea56a7244665c571c23d49c6769a163b86e9e 100644 --- a/tensorflow/compiler/jit/encapsulate_subgraphs_pass_test.cc +++ b/tensorflow/compiler/jit/encapsulate_subgraphs_pass_test.cc @@ -246,26 +246,32 @@ bool EqualFunctionDefLibrary(const FunctionDefLibrary& expected, << diff << "\nActual: " << actual.DebugString(); \ } while (false) -// TODO(misard): remove these fake registrations once there are real Ops to be -// compiled. +// These dummy Op registrations are here because the real Op registrations live +// in contrib and there can't be a dependence from this test to contrib. REGISTER_OP("_XlaHostCompute") .Input("inputs: Tinputs") .Output("outputs: Toutputs") .Attr("Tinputs: list(type) >= 0") .Attr("Toutputs: list(type) >= 0") .Attr("key: string") + .Attr("shape_inference_graph: string = ''") + .Attr("shapes: list(shape) >= 0") .SetShapeFn(::tensorflow::shape_inference::UnknownShape); REGISTER_OP("_XlaSendFromHost") - .Input("input: Tinputs") + .Input("inputs: Tinputs") + .Input("dynamic_key: string") .Attr("Tinputs: list(type) >= 0") .Attr("key: string") + .Attr("device_ordinal: int") .SetShapeFn(::tensorflow::shape_inference::UnknownShape); REGISTER_OP("_XlaRecvAtHost") - .Output("output: Toutputs") + .Input("dynamic_key: string") + .Output("outputs: Toutputs") .Attr("Toutputs: list(type) >= 0") .Attr("key: string") + .Attr("device_ordinal: int") .SetShapeFn(::tensorflow::shape_inference::UnknownShape); REGISTER_OP("InputTest") @@ -327,43 +333,71 @@ Node* InputShaped(const GraphDefBuilder::Options& opts) { return ops::SourceOp("InputTestShaped", opts); } -Node* KnownShape(const gtl::ArraySlice& shape, - const GraphDefBuilder::Options& opts) { +Node* KnownShapeBase(DataType dtype, const gtl::ArraySlice& shape, + const GraphDefBuilder::Options& opts) { if (opts.HaveError()) return nullptr; NodeBuilder node_builder(opts.GetNameForOp("Const"), "Const", opts.op_registry()); TensorProto value; - value.set_dtype(DT_FLOAT); + value.set_dtype(dtype); for (int dim : shape) { value.mutable_tensor_shape()->add_dim()->set_size(dim); } return opts.WithAttr("value", value) - .WithAttr("dtype", DT_FLOAT) + .WithAttr("dtype", dtype) + .FinalizeBuilder(&node_builder); +} + +Node* KnownShape(const gtl::ArraySlice& shape, + const GraphDefBuilder::Options& opts) { + return KnownShapeBase(DT_FLOAT, shape, opts); +} + +Node* KeyPlaceholderShape(const GraphDefBuilder::Options& opts) { + return KnownShapeBase(DT_STRING, {2}, opts); +} + +Node* KeyPlaceholder(const string& call_node, + const GraphDefBuilder::Options& opts) { + if (opts.HaveError()) return nullptr; + NodeBuilder node_builder(opts.GetNameForOp("Placeholder"), "Placeholder", + opts.op_registry()); + TensorShapeProto shape; + shape.add_dim()->set_size(2); + return opts.WithAttr("shape", shape) + .WithAttr("dtype", DT_STRING) + .WithAttr("_host_compute_call_node", call_node) .FinalizeBuilder(&node_builder); } -Node* RecvAtHost(const string& key, const gtl::ArraySlice& dtypes, +Node* RecvAtHost(ops::NodeOut key_input, const string& key, + const gtl::ArraySlice& dtypes, const GraphDefBuilder::Options& opts) { if (opts.HaveError()) return nullptr; NodeBuilder node_builder(opts.GetNameForOp("_XlaRecvAtHost"), "_XlaRecvAtHost", opts.op_registry()); + node_builder.Input(std::move(key_input)); return opts.WithAttr("Toutputs", dtypes) .WithAttr("key", key) + .WithAttr("device_ordinal", 0) .FinalizeBuilder(&node_builder); } -Node* SendFromHost(const string& key, const std::vector& inputs, +Node* SendFromHost(ops::NodeOut key_input, const string& key, + const std::vector& inputs, const GraphDefBuilder::Options& opts) { if (opts.HaveError()) return nullptr; NodeBuilder node_builder(opts.GetNameForOp("_XlaSendFromHost"), "_XlaSendFromHost", opts.op_registry()); node_builder.Input(inputs); + node_builder.Input(std::move(key_input)); std::vector dtypes; for (const auto& node : inputs) { dtypes.push_back(node.dt); } - return opts.WithAttr("key", key) - .WithAttr("Tinputs", dtypes) + return opts.WithAttr("Tinputs", dtypes) + .WithAttr("key", key) + .WithAttr("device_ordinal", 0) .FinalizeBuilder(&node_builder); } @@ -809,13 +843,16 @@ TEST(EncapsulateSubgraphsTest, OneFunctionOneOutside) { string shape_string_expected; { GraphDefBuilder shape(GraphDefBuilder::kFailImmediately); + Node* key_constant = + KeyPlaceholderShape(shape.opts().WithName("KnownShape/_0")); Node* recv = - RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT, DT_FLOAT}, + RecvAtHost(ops::NodeOut(key_constant, 0), "host_compute_channel_F1_O1", + {DT_FLOAT, DT_FLOAT}, shape.opts().WithName("outside_compilation_F1_O1_recv")); Node* e = Binary(ops::NodeOut(recv, 0), ops::NodeOut(recv, 1), shape.opts().WithName("E")); - SendFromHost("host_compute_channel_F1_O1", {e}, - shape.opts().WithName("outside_compilation_F1_O1_send")); + SendFromHost(ops::NodeOut(key_constant, 0), "host_compute_channel_F1_O1", + {e}, shape.opts().WithName("outside_compilation_F1_O1_send")); GraphDef shape_graph; TF_EXPECT_OK(shape.ToGraphDef(&shape_graph)); EXPECT_TRUE(shape_graph.SerializeToString(&shape_string_expected)); @@ -855,12 +892,16 @@ TEST(EncapsulateSubgraphsTest, OneFunctionOneOutside) { node_builder.Input(a).Input(b); Node* call = b2.opts().FinalizeBuilder(&node_builder); + Node* key_constant = + KeyPlaceholder("F1", b2.opts().WithName("F1_key_placeholder")); Node* recv = - RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT, DT_FLOAT}, + RecvAtHost(ops::NodeOut(key_constant, 0), "host_compute_channel_F1_O1", + {DT_FLOAT, DT_FLOAT}, b2.opts().WithName("outside_compilation_F1_O1_recv")); Node* e = Binary(ops::NodeOut(recv, 0), ops::NodeOut(recv, 1), b2.opts().WithName("E").WithControlInputs({recv, b})); - Node* send = SendFromHost("host_compute_channel_F1_O1", {e}, + Node* send = SendFromHost(ops::NodeOut(key_constant, 0), + "host_compute_channel_F1_O1", {e}, b2.opts() .WithName("outside_compilation_F1_O1_send") .WithControlInput(e)); @@ -921,13 +962,16 @@ TEST(EncapsulateSubgraphsTest, OneFunctionTwoOutside) { string shape_string_expected_1; { GraphDefBuilder shape1(GraphDefBuilder::kFailImmediately); + Node* key_constant = + KeyPlaceholderShape(shape1.opts().WithName("KnownShape/_0")); Node* recv = - RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT, DT_FLOAT}, + RecvAtHost(ops::NodeOut(key_constant, 0), "host_compute_channel_F1_O1", + {DT_FLOAT, DT_FLOAT}, shape1.opts().WithName("outside_compilation_F1_O1_recv")); Node* e = Binary(ops::NodeOut(recv, 0), ops::NodeOut(recv, 1), shape1.opts().WithName("E")); - SendFromHost("host_compute_channel_F1_O1", {e}, - shape1.opts().WithName("outside_compilation_F1_O1_send")); + SendFromHost(ops::NodeOut(key_constant, 0), "host_compute_channel_F1_O1", + {e}, shape1.opts().WithName("outside_compilation_F1_O1_send")); GraphDef shape1_graph; TF_EXPECT_OK(shape1.ToGraphDef(&shape1_graph)); EXPECT_TRUE(shape1_graph.SerializeToString(&shape_string_expected_1)); @@ -936,17 +980,21 @@ TEST(EncapsulateSubgraphsTest, OneFunctionTwoOutside) { string shape_string_expected_2; { GraphDefBuilder shape2(GraphDefBuilder::kFailImmediately); + Node* key_constant = + KeyPlaceholderShape(shape2.opts().WithName("KnownShape/_0")); Node* recv1 = - RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT, DT_FLOAT}, + RecvAtHost(ops::NodeOut(key_constant, 0), "host_compute_channel_F1_O1", + {DT_FLOAT, DT_FLOAT}, shape2.opts().WithName("outside_compilation_F1_O1_recv")); Node* e = Binary(ops::NodeOut(recv1, 0), ops::NodeOut(recv1, 1), shape2.opts().WithName("E")); Node* recv2 = - RecvAtHost("host_compute_channel_F1_O2", {DT_FLOAT, DT_FLOAT}, + RecvAtHost(ops::NodeOut(key_constant, 0), "host_compute_channel_F1_O2", + {DT_FLOAT, DT_FLOAT}, shape2.opts().WithName("outside_compilation_F1_O2_recv")); Node* h = Binary(ops::NodeOut(recv2, 0), e, shape2.opts().WithName("H")); - SendFromHost("host_compute_channel_F1_O2", {h}, - shape2.opts().WithName("outside_compilation_F1_O2_send")); + SendFromHost(ops::NodeOut(key_constant, 0), "host_compute_channel_F1_O2", + {h}, shape2.opts().WithName("outside_compilation_F1_O2_send")); GraphDef shape2_graph; TF_EXPECT_OK(shape2.ToGraphDef(&shape2_graph)); EXPECT_TRUE(shape2_graph.SerializeToString(&shape_string_expected_2)); @@ -997,25 +1045,30 @@ TEST(EncapsulateSubgraphsTest, OneFunctionTwoOutside) { node_builder.Input(a).Input(b); Node* call = b2.opts().FinalizeBuilder(&node_builder); + Node* key_constant = + KeyPlaceholder("F1", b2.opts().WithName("F1_key_placeholder")); Node* recv1 = - RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT, DT_FLOAT}, + RecvAtHost(ops::NodeOut(key_constant, 0), "host_compute_channel_F1_O1", + {DT_FLOAT, DT_FLOAT}, b2.opts().WithName("outside_compilation_F1_O1_recv")); Node* e = Binary(ops::NodeOut(recv1, 0), ops::NodeOut(recv1, 1), b2.opts().WithName("E").WithControlInputs({recv1, b})); - Node* send1 = SendFromHost("host_compute_channel_F1_O1", {e}, + Node* send1 = SendFromHost(ops::NodeOut(key_constant, 0), + "host_compute_channel_F1_O1", {e}, b2.opts() .WithName("outside_compilation_F1_O1_send") .WithControlInput(e)); Node* recv2 = - RecvAtHost("host_compute_channel_F1_O2", {DT_FLOAT, DT_FLOAT}, + RecvAtHost(ops::NodeOut(key_constant, 0), "host_compute_channel_F1_O2", + {DT_FLOAT, DT_FLOAT}, b2.opts().WithName("outside_compilation_F1_O2_recv")); Node* g = Binary(e, ops::NodeOut(recv2, 1), b2.opts().WithName("G").WithControlInputs({recv2, e})); Node* h = Binary(ops::NodeOut(recv2, 0), e, b2.opts().WithName("H")); - Node* send2 = - SendFromHost("host_compute_channel_F1_O2", {h}, - b2.opts().WithName("outside_compilation_F1_O2_send")); + Node* send2 = SendFromHost( + ops::NodeOut(key_constant, 0), "host_compute_channel_F1_O2", {h}, + b2.opts().WithName("outside_compilation_F1_O2_send")); Node* s = NoOp(b2.opts() .WithName("F1_sequencer") @@ -1073,13 +1126,16 @@ TEST(EncapsulateSubgraphsTest, TwoFunctionsTwoOutside) { string shape_string_expected; { GraphDefBuilder shape(GraphDefBuilder::kFailImmediately); + Node* key_constant = + KeyPlaceholderShape(shape.opts().WithName("KnownShape/_0")); Node* recv = - RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT, DT_FLOAT}, + RecvAtHost(ops::NodeOut(key_constant, 0), "host_compute_channel_F1_O1", + {DT_FLOAT, DT_FLOAT}, shape.opts().WithName("outside_compilation_F1_O1_recv")); Node* e = Binary(ops::NodeOut(recv, 0), ops::NodeOut(recv, 1), shape.opts().WithName("E")); - SendFromHost("host_compute_channel_F1_O1", {e}, - shape.opts().WithName("outside_compilation_F1_O1_send")); + SendFromHost(ops::NodeOut(key_constant, 0), "host_compute_channel_F1_O1", + {e}, shape.opts().WithName("outside_compilation_F1_O1_send")); GraphDef shape_graph; TF_EXPECT_OK(shape.ToGraphDef(&shape_graph)); EXPECT_TRUE(shape_graph.SerializeToString(&shape_string_expected)); @@ -1138,12 +1194,16 @@ TEST(EncapsulateSubgraphsTest, TwoFunctionsTwoOutside) { Node* a = InputShaped(b2.opts().WithName("A")); Node* b = InputShaped(b2.opts().WithName("B")); + Node* key_constant1 = + KeyPlaceholder("F1", b2.opts().WithName("F1_key_placeholder")); Node* recv1 = - RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT, DT_FLOAT}, + RecvAtHost(ops::NodeOut(key_constant1, 0), "host_compute_channel_F1_O1", + {DT_FLOAT, DT_FLOAT}, b2.opts().WithName("outside_compilation_F1_O1_recv")); Node* e = Binary(ops::NodeOut(recv1, 0), ops::NodeOut(recv1, 1), b2.opts().WithName("E").WithControlInputs({recv1, b})); - Node* send1 = SendFromHost("host_compute_channel_F1_O1", {e}, + Node* send1 = SendFromHost(ops::NodeOut(key_constant1, 0), + "host_compute_channel_F1_O1", {e}, b2.opts() .WithName("outside_compilation_F1_O1_send") .WithControlInput(e)); @@ -1153,14 +1213,16 @@ TEST(EncapsulateSubgraphsTest, TwoFunctionsTwoOutside) { Node* s1 = NoOp( b2.opts().WithName("F1_sequencer").WithControlInputs({recv1, send1})); - Node* recv2 = - RecvAtHost("host_compute_channel_F2_O1", {DT_FLOAT}, - b2.opts().WithName("outside_compilation_F2_O1_recv")); + Node* key_constant2 = + KeyPlaceholder("F2", b2.opts().WithName("F2_key_placeholder")); + Node* recv2 = RecvAtHost( + ops::NodeOut(key_constant2, 0), "host_compute_channel_F2_O1", + {DT_FLOAT}, b2.opts().WithName("outside_compilation_F2_O1_recv")); Node* h = Binary(ops::NodeOut(call1, 1), recv2, b2.opts().WithName("H").WithControlInput(s1)); - Node* send2 = - SendFromHost("host_compute_channel_F2_O1", {h}, - b2.opts().WithName("outside_compilation_F2_O1_send")); + Node* send2 = SendFromHost( + ops::NodeOut(key_constant2, 0), "host_compute_channel_F2_O1", {h}, + b2.opts().WithName("outside_compilation_F2_O1_send")); NodeBuilder node_builder2("F2", "F2", lib_def.get()); node_builder2.Input(e).Input(call1); @@ -1237,9 +1299,11 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationNoInputs) { Node* b = Input(b2.opts().WithName("B")); Node* e = Unary(a, b2.opts().WithName("E")); - Node* send1 = - SendFromHost("host_compute_channel_F1_O1", {e}, - b2.opts().WithName("outside_compilation_F1_O1_send")); + Node* key_constant = + KeyPlaceholder("F1", b2.opts().WithName("F1_key_placeholder")); + Node* send1 = SendFromHost( + ops::NodeOut(key_constant, 0), "host_compute_channel_F1_O1", {e}, + b2.opts().WithName("outside_compilation_F1_O1_send")); NodeBuilder node_builder1("F1", "F1", lib_def.get()); node_builder1.Input(a).Input(b); Node* call1 = b2.opts().FinalizeBuilder(&node_builder1); @@ -1313,13 +1377,15 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationControlInput) { Node* a = InputShaped(b2.opts().WithName("A")); Node* b = Input(b2.opts().WithName("B")); + Node* key_constant = + KeyPlaceholder("F1", b2.opts().WithName("F1_key_placeholder")); Node* recv1 = - RecvAtHost("host_compute_channel_F1_O1", {}, - b2.opts().WithName("outside_compilation_F1_O1_recv")); + RecvAtHost(ops::NodeOut(key_constant, 0), "host_compute_channel_F1_O1", + {}, b2.opts().WithName("outside_compilation_F1_O1_recv")); Node* e = Unary(a, b2.opts().WithName("E").WithControlInput(recv1)); - Node* send1 = - SendFromHost("host_compute_channel_F1_O1", {e}, - b2.opts().WithName("outside_compilation_F1_O1_send")); + Node* send1 = SendFromHost( + ops::NodeOut(key_constant, 0), "host_compute_channel_F1_O1", {e}, + b2.opts().WithName("outside_compilation_F1_O1_send")); NodeBuilder node_builder1("F1", "F1", lib_def.get()); node_builder1.Input(a).Input(b); Node* call1 = b2.opts().FinalizeBuilder(&node_builder1); @@ -1385,9 +1451,11 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationNoOutputs) { Node* a = Input(b2.opts().WithName("A")); Node* b = Input(b2.opts().WithName("B")); - Node* recv1 = - RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT}, - b2.opts().WithName("outside_compilation_F1_O1_recv")); + Node* key_constant = + KeyPlaceholder("F1", b2.opts().WithName("F1_key_placeholder")); + Node* recv1 = RecvAtHost( + ops::NodeOut(key_constant, 0), "host_compute_channel_F1_O1", {DT_FLOAT}, + b2.opts().WithName("outside_compilation_F1_O1_recv")); Node* e = Unary(recv1, b2.opts().WithName("E")); NodeBuilder node_builder1("F1", "F1", lib_def.get()); node_builder1.Input(a).Input(b); @@ -1458,11 +1526,14 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationControlOutput) { Node* a = Input(b2.opts().WithName("A")); Node* b = Input(b2.opts().WithName("B")); - Node* recv1 = - RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT}, - b2.opts().WithName("outside_compilation_F1_O1_recv")); + Node* key_constant = + KeyPlaceholder("F1", b2.opts().WithName("F1_key_placeholder")); + Node* recv1 = RecvAtHost( + ops::NodeOut(key_constant, 0), "host_compute_channel_F1_O1", {DT_FLOAT}, + b2.opts().WithName("outside_compilation_F1_O1_recv")); Node* e = Unary(recv1, b2.opts().WithName("E")); - Node* send1 = SendFromHost("host_compute_channel_F1_O1", {}, + Node* send1 = SendFromHost(ops::NodeOut(key_constant, 0), + "host_compute_channel_F1_O1", {}, b2.opts() .WithName("outside_compilation_F1_O1_send") .WithControlInput(e)); @@ -1572,13 +1643,15 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationShapeInference) { string shape_string_expected; { GraphDefBuilder shape(GraphDefBuilder::kFailImmediately); - Node* known = KnownShape({2}, shape.opts().WithName("KnownShape/_0")); - Node* recv = - RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT}, - shape.opts().WithName("outside_compilation_F1_O1_recv")); + Node* key_constant = + KeyPlaceholderShape(shape.opts().WithName("KnownShape/_0")); + Node* known = KnownShape({2}, shape.opts().WithName("KnownShape/_1")); + Node* recv = RecvAtHost( + ops::NodeOut(key_constant, 0), "host_compute_channel_F1_O1", {DT_FLOAT}, + shape.opts().WithName("outside_compilation_F1_O1_recv")); Node* e = BinaryUnknownShape(known, recv, shape.opts().WithName("E")); - SendFromHost("host_compute_channel_F1_O1", {e}, - shape.opts().WithName("outside_compilation_F1_O1_send")); + SendFromHost(ops::NodeOut(key_constant, 0), "host_compute_channel_F1_O1", + {e}, shape.opts().WithName("outside_compilation_F1_O1_send")); GraphDef shape_graph; TF_EXPECT_OK(shape.ToGraphDef(&shape_graph)); EXPECT_TRUE(shape_graph.SerializeToString(&shape_string_expected)); @@ -1619,13 +1692,16 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationShapeInference) { Node* call = b2.opts().WithControlInputs({c}).FinalizeBuilder(&node_builder); - Node* recv = - RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT}, - b2.opts().WithName("outside_compilation_F1_O1_recv")); + Node* key_constant = + KeyPlaceholder("F1", b2.opts().WithName("F1_key_placeholder")); + Node* recv = RecvAtHost( + ops::NodeOut(key_constant, 0), "host_compute_channel_F1_O1", {DT_FLOAT}, + b2.opts().WithName("outside_compilation_F1_O1_recv")); Node* e = BinaryUnknownShape( c, ops::NodeOut(recv, 0), b2.opts().WithName("E").WithControlInputs({recv, b})); - Node* send = SendFromHost("host_compute_channel_F1_O1", {e}, + Node* send = SendFromHost(ops::NodeOut(key_constant, 0), + "host_compute_channel_F1_O1", {e}, b2.opts() .WithName("outside_compilation_F1_O1_send") .WithControlInput(e)); diff --git a/tensorflow/compiler/jit/kernels/BUILD b/tensorflow/compiler/jit/kernels/BUILD index 9bea5663319c8a25249fdc265cee0191556a7c04..616a7f8f1541d3debff97a90bd390c76c665d196 100644 --- a/tensorflow/compiler/jit/kernels/BUILD +++ b/tensorflow/compiler/jit/kernels/BUILD @@ -14,6 +14,7 @@ cc_library( "//tensorflow/compiler/jit:common", "//tensorflow/compiler/jit:xla_compilation_cache", "//tensorflow/compiler/jit:xla_device", + "//tensorflow/compiler/jit:xla_launch_util", "//tensorflow/compiler/tf2xla:common", "//tensorflow/compiler/tf2xla:xla_compiler", "//tensorflow/compiler/xla:statusor", diff --git a/tensorflow/compiler/jit/kernels/xla_launch_op.cc b/tensorflow/compiler/jit/kernels/xla_launch_op.cc index 6353149e4afdf739fe44dd5c76502ef5d98b8477..cd7f8dd779120637c96d6af041b0afcc734e5eff 100644 --- a/tensorflow/compiler/jit/kernels/xla_launch_op.cc +++ b/tensorflow/compiler/jit/kernels/xla_launch_op.cc @@ -17,6 +17,7 @@ limitations under the License. #include "tensorflow/compiler/jit/defs.h" #include "tensorflow/compiler/jit/xla_device.h" +#include "tensorflow/compiler/jit/xla_launch_util.h" #include "tensorflow/compiler/tf2xla/shape_util.h" #include "tensorflow/compiler/tf2xla/xla_compiler.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" @@ -40,111 +41,6 @@ namespace gpu = perftools::gputools; namespace tensorflow { -// Adapter class that wraps a Tensorflow allocator as an XLA allocator. -// Assumes that the Tensorflow allocator permits asynchronous deallocation: -// see comment on `AllowsAsynchronousDeallocation()`. -class XlaAllocator : public xla::DeviceMemoryAllocator { - public: - XlaAllocator(const gpu::Platform* platform, OpKernelContext* op_context); - ~XlaAllocator() override; - xla::StatusOr Allocate(int device_ordinal, uint64 size, - bool retry_on_failure) override; - Status Deallocate(int device_ordinal, gpu::DeviceMemoryBase* mem) override; - - // Register an Tensor (input or resource variable) with the allocator. If - // the operation returns an alias to one of its inputs, then the allocator - // needs to be able to handle it. - Status RegisterArgument(const Tensor* t); - - // Makes 'tensor' a wrapper around the data buffer at 'ptr'. The buffer is - // interpreted as having data type 'dtype' and shape 'shape'. - Status MakeTensorFromBuffer(gpu::DeviceMemoryBase buffer, DataType dtype, - const TensorShape& shape, Tensor* tensor) const; - - // The Tensorflow BFC allocator used on GPU allows host-side deallocation - // before GPU execution takes place. Tensorflow uses the ordering of the main - // compute stream to enforce a happens-before relationship between a memory - // allocation and code that reuses the same memory. If Tensorflow adds - // support for multiple GPU streams or allocators with different ordering - // requirements, this code may need to change. - // (This attribute has no effect on CPU.) - bool AllowsAsynchronousDeallocation() const override { return true; } - - private: - OpKernelContext* const op_context_; - - // Map from pointer address to the owning Tensor; used by - // MakeTensorFromBuffer. Also used to automatically release Tensors when the - // allocator is freed. - std::unordered_map tensors_; -}; - -XlaAllocator::XlaAllocator(const gpu::Platform* platform, - OpKernelContext* op_context) - : xla::DeviceMemoryAllocator(platform), op_context_(op_context) {} - -XlaAllocator::~XlaAllocator() = default; - -xla::StatusOr XlaAllocator::Allocate( - int device_ordinal, uint64 size, bool retry_on_failure) { - AllocatorAttributes allocator_attrs; - allocator_attrs.set_on_host(false); - - AllocationAttributes allocation_attrs; - allocation_attrs.no_retry_on_failure = !retry_on_failure; - - Tensor t; - Status status = op_context_->allocate_temp( - DT_UINT8, TensorShape({static_cast(size)}), &t, allocator_attrs, - allocation_attrs); - if (!status.ok()) { - VLOG(2) << "Allocation failed " << size; - return status; - } - void* data = - reinterpret_cast(const_cast(t.tensor_data().data())); - tensors_[data] = t; - return gpu::DeviceMemoryBase(data, size); -} - -Status XlaAllocator::RegisterArgument(const Tensor* t) { - void* data = - reinterpret_cast(const_cast(t->tensor_data().data())); - tensors_[data] = *t; - return Status::OK(); -} - -Status XlaAllocator::Deallocate(int device_ordinal, - gpu::DeviceMemoryBase* mem) { - if (mem->opaque() != nullptr) { - if (tensors_.erase(mem->opaque()) == 0) { - return tensorflow::errors::InvalidArgument("Unknown tensor address"); - } - } - return Status::OK(); -} - -Status XlaAllocator::MakeTensorFromBuffer(gpu::DeviceMemoryBase buffer, - DataType dtype, - const TensorShape& shape, - Tensor* out_tensor) const { - void* ptr = const_cast(buffer.opaque()); - auto it = tensors_.find(ptr); - if (it == tensors_.end()) { - return errors::InvalidArgument("Unknown tensor address"); - } - const Tensor& tensor = it->second; - - int64 output_size = DataTypeSize(dtype) * shape.num_elements(); - if (tensor.TotalBytes() == output_size) { - out_tensor->UnsafeCopyFromInternal(tensor, dtype, shape); - } else { - Tensor slice = tensor.Slice(0, output_size); - out_tensor->UnsafeCopyFromInternal(slice, dtype, shape); - } - return Status::OK(); -} - XlaLocalLaunchOp::XlaLocalLaunchOp(OpKernelConstruction* ctx) : OpKernel(ctx), device_type_(ctx->device_type()) { const NameAttrList* func; @@ -196,23 +92,6 @@ Status XlaLocalLaunchOp::BuildCompilationCache(OpKernelContext* ctx, return Status::OK(); } -std::vector SnapshotResourceVariables(OpKernelContext* ctx, - int num_variables) { - std::vector snapshot(num_variables); - int first_variable = ctx->num_inputs() - num_variables; - for (int i = 0; i < num_variables; ++i) { - Var* variable = nullptr; - ResourceHandle handle = HandleFromInput(ctx, first_variable + i); - if (LookupResource(ctx, handle, &variable).ok()) { - tf_shared_lock lock(*variable->mu()); - snapshot[i].name = handle.name(); - snapshot[i].present = true; - snapshot[i].value = *variable->tensor(); - } - } - return snapshot; -} - void XlaLocalLaunchOp::Compute(OpKernelContext* ctx) { VLOG(1) << "XlaLocalLaunchOp::Compute " << Canonicalize(function_.name(), AttrSlice(&function_.attr())); @@ -244,7 +123,7 @@ void XlaLocalLaunchOp::Compute(OpKernelContext* ctx) { } } - std::vector variables = + std::map variables = SnapshotResourceVariables(ctx, num_resource_args_); xla::LocalClient* client = static_cast(cache->client()); @@ -269,43 +148,9 @@ void XlaLocalLaunchOp::Compute(OpKernelContext* ctx) { VLOG(1) << "Executing XLA Computation..."; - std::unique_ptr output; - // Build xla::ShapedBuffers that point directly to the Tensor buffers. - std::vector> arg_buffers; - arg_buffers.reserve(kernel->xla_input_shapes.size() + 1); - arg_buffers.resize(kernel->xla_input_shapes.size()); - std::vector arg_ptrs(arg_buffers.size()); - - const int first_variable_arg = ctx->num_inputs() - num_resource_args_; - // Pass remaining parameters. - const Tensor* t; - for (int i = 0; i < kernel->xla_input_shapes.size(); ++i) { - int arg_num = kernel->input_mapping[i]; - const xla::Shape& shape = kernel->xla_input_shapes[i]; - if (arg_num >= first_variable_arg) { - t = &(variables[arg_num - first_variable_arg].value); - } else { - t = &(ctx->input(arg_num)); - } - - gpu::DeviceMemoryBase dmem = gpu::DeviceMemoryBase( - const_cast(t->tensor_data().data()), t->tensor_data().size()); - - const xla::Shape on_device_shape = - client->backend().transfer_manager()->HostShapeToDeviceShape(shape); - CHECK(xla::ShapeUtil::Equal(shape, on_device_shape)) - << "On-device shape " - << xla::ShapeUtil::HumanStringWithLayout(on_device_shape) - << " not the same as on-host shape " - << xla::ShapeUtil::HumanStringWithLayout(shape); - arg_buffers[i] = xla::MakeUnique( - /*on_host_shape=*/shape, /*on_device_shape=*/shape, client->platform(), - client->default_device_ordinal()); - arg_buffers[i]->set_buffer(dmem, /*index=*/{}); - arg_ptrs[i] = arg_buffers[i].get(); - - OP_REQUIRES_OK(ctx, xla_allocator.RegisterArgument(t)); - } + XlaComputationLaunchContext launch_context(num_resource_args_, client, + &xla_allocator); + launch_context.PopulateInputs(ctx, kernel, variables); // Execute the computation. VLOG(2) << "Executing computation."; @@ -315,93 +160,14 @@ void XlaLocalLaunchOp::Compute(OpKernelContext* ctx) { run_options.set_intra_op_thread_pool(&ctx->eigen_cpu_device()); Env* env = Env::Default(); auto start_time = env->NowMicros(); - auto run_result = executable->Run(arg_ptrs, run_options); + auto run_result = executable->Run(launch_context.arguments(), run_options); OP_REQUIRES(ctx, run_result.ok(), run_result.status()); - output = run_result.ConsumeValueOrDie()->release(); auto elapsed = env->NowMicros() - start_time; VLOG(2) << "Elapsed time: " << elapsed << "us"; - // Computation output should always be a tuple. - if (VLOG_IS_ON(2)) { - VLOG(2) << "Result tuple shape: " << output->on_host_shape().DebugString(); - } - CHECK_EQ(ctx->num_outputs(), kernel->outputs.size()); - - // Copy XLA results to the OpOutputList. - int output_num = 0; - for (int i = 0; i < ctx->num_outputs(); ++i) { - if (kernel->outputs[i].is_constant) { - // Output is a constant. - const Tensor& const_tensor = kernel->outputs[i].constant_value; - const size_t total_bytes = const_tensor.TotalBytes(); - if (stream && total_bytes > 0) { - // Copy host -> device. (Empty tensors don't have backing buffers.) - VLOG(1) << "Constant output tensor on device"; - Tensor* output_tensor; - TF_CHECK_OK( - ctx->allocate_output(i, const_tensor.shape(), &output_tensor)); - - const void* src_ptr = DMAHelper::base(&const_tensor); - void* dst_ptr = DMAHelper::base(output_tensor); - gpu::DeviceMemoryBase gpu_dst_ptr(dst_ptr, total_bytes); - stream->ThenMemcpy(&gpu_dst_ptr, src_ptr, total_bytes); - } else { - // No copy required. - ctx->set_output(i, const_tensor); - } - } else { - const TensorShape& shape = kernel->outputs[i].shape; - VLOG(2) << "Retval " << i << " shape " << shape.DebugString(); - - gpu::DeviceMemoryBase buffer = output->buffer({output_num}); - Tensor output_tensor; - // Looks up the owning Tensor by buffer address. - OP_REQUIRES_OK(ctx, xla_allocator.MakeTensorFromBuffer( - buffer, ctx->expected_output_dtype(i), shape, - &output_tensor)); - ctx->set_output(i, output_tensor); - ++output_num; - } - - if (VLOG_IS_ON(3)) { - VLOG(3) << ctx->mutable_output(i)->DebugString(); - } - } - - // Apply variable updates, if any. - VLOG(2) << "Applying variable updates"; - for (int i = 0; i < kernel->resource_updates.size(); ++i) { - const XlaCompiler::ResourceUpdate& write = kernel->resource_updates[i]; - OP_REQUIRES(ctx, - write.input_index >= 0 && write.input_index < ctx->num_inputs(), - errors::Internal("Invalid input index for variable write.")); - - gpu::DeviceMemoryBase buffer = output->buffer({output_num}); - - Var* variable = nullptr; - // TODO(b/35625933): tensorflow::Var should contain a PersistentTensor, not - // a Tensor. - OP_REQUIRES_OK(ctx, LookupOrCreateResource( - ctx, HandleFromInput(ctx, write.input_index), - &variable, [this, ctx, &write](Var** ptr) { - *ptr = new Var(write.type); - return Status::OK(); - })); - - core::ScopedUnref s(variable); - - mutex_lock ml(*variable->mu()); - OP_REQUIRES(ctx, variable->tensor()->dtype() == write.type, - errors::Internal("Mismatched type in variable write")); - - // Looks up the owning Tensor by buffer address. - OP_REQUIRES_OK( - ctx, xla_allocator.MakeTensorFromBuffer(buffer, write.type, write.shape, - variable->tensor())); - ++output_num; - } - + launch_context.PopulateOutputs(ctx, kernel, + run_result.ConsumeValueOrDie()->release()); VLOG(1) << "Done"; } diff --git a/tensorflow/compiler/jit/kernels/xla_launch_op.h b/tensorflow/compiler/jit/kernels/xla_launch_op.h index 47fd912b12abbbe876e933ab57f6f586fd299909..c6cc0986af0300c51283d432c671e92a1e4d8145 100644 --- a/tensorflow/compiler/jit/kernels/xla_launch_op.h +++ b/tensorflow/compiler/jit/kernels/xla_launch_op.h @@ -26,14 +26,6 @@ limitations under the License. namespace tensorflow { -// Takes a snapshot of the values of resource variable arguments, which are -// the last `num_variables` arguments. We snapshot tensors that back -// resource variables since concurrent updates may modify the shape, and it is -// important that the shapes used for compilation match the true shapes of the -// buffers. -std::vector SnapshotResourceVariables(OpKernelContext* ctx, - int num_variables); - // XlaLocalLaunchOp is used to replace a region of the TensorFlow graph // which will be compiled and executed using XLA. The XlaLocalLaunchOp is // responsible for handling interactions with the TensorFlow executor. diff --git a/tensorflow/compiler/jit/legacy_flags/mark_for_compilation_pass_flags.cc b/tensorflow/compiler/jit/legacy_flags/mark_for_compilation_pass_flags.cc index 4bc209b7ecf499d82e7567f7eff12b17cefa9863..51384ac2fe6fa70c8a723097093a0a29e7ad2c6b 100644 --- a/tensorflow/compiler/jit/legacy_flags/mark_for_compilation_pass_flags.cc +++ b/tensorflow/compiler/jit/legacy_flags/mark_for_compilation_pass_flags.cc @@ -40,6 +40,7 @@ static void AllocateFlags() { flags->tf_xla_max_cluster_size = std::numeric_limits::max(); flags->tf_xla_clustering_debug = false; flags->tf_xla_cpu_global_jit = false; + flags->tf_xla_clustering_fuel = std::numeric_limits::max(); flag_list = new std::vector( {Flag("tf_xla_auto_jit", &flags->tf_xla_auto_jit, "Control compilation of operators into XLA computations on CPU and " @@ -55,7 +56,10 @@ static void AllocateFlags() { Flag("tf_xla_clustering_debug", &flags->tf_xla_clustering_debug, "Dump graphs during XLA compilation."), Flag("tf_xla_cpu_global_jit", &flags->tf_xla_cpu_global_jit, - "Enables global JIT compilation for CPU via SessionOptions.")}); + "Enables global JIT compilation for CPU via SessionOptions."), + Flag("tf_xla_clustering_fuel", &flags->tf_xla_clustering_fuel, + "Places an artificial limit on the number of ops marked as " + "eligible for clustering.")}); xla::legacy_flags::ParseFlagsFromEnv(*flag_list); } diff --git a/tensorflow/compiler/jit/legacy_flags/mark_for_compilation_pass_flags.h b/tensorflow/compiler/jit/legacy_flags/mark_for_compilation_pass_flags.h index e1ccd7ddb8706ca445b6811ca1fec369af7cd5d5..170b89c987f30f985f981d7835b4af455922594e 100644 --- a/tensorflow/compiler/jit/legacy_flags/mark_for_compilation_pass_flags.h +++ b/tensorflow/compiler/jit/legacy_flags/mark_for_compilation_pass_flags.h @@ -48,6 +48,9 @@ typedef struct { bool tf_xla_clustering_debug; // Dump graphs during XLA compilation. bool tf_xla_cpu_global_jit; // Enables global JIT compilation for CPU // via SessionOptions. + int64 tf_xla_clustering_fuel; // "Compiler fuel" for clustering. Only this + // many ops will be marked as eligible for + // clustering. } MarkForCompilationPassFlags; // Return a pointer to the MarkForCompilationPassFlags struct; diff --git a/tensorflow/compiler/jit/mark_for_compilation_pass.cc b/tensorflow/compiler/jit/mark_for_compilation_pass.cc index a0211acbbe9eec77d30c7d14293650de8826f41c..57fb8d242208318a608b1f356bef7a8d39dbdc83 100644 --- a/tensorflow/compiler/jit/mark_for_compilation_pass.cc +++ b/tensorflow/compiler/jit/mark_for_compilation_pass.cc @@ -174,7 +174,9 @@ bool HasResourceInputOrOutput(const Node& node) { } struct NodeCompare { - bool operator()(const Node* a, const Node* b) { return a->id() < b->id(); } + bool operator()(const Node* a, const Node* b) const { + return a->id() < b->id(); + } }; using OrderedNodeSet = std::set; @@ -189,7 +191,27 @@ Status FindCompilationCandidates( FunctionLibraryRuntime* lib_runtime = pflr->GetFLR(ProcessFunctionLibraryRuntime::kDefaultFLRDevice); + int64& fuel = + legacy_flags::GetMarkForCompilationPassFlags()->tf_xla_clustering_fuel; + + // Iterate over nodes in sorted order so that compiler fuel is deterministic. + // We can't simply pass op_nodes().begin() and op_nodes().end to the + // std::vector constructor because they're not proper iterators, with + // iterator_traits defined and so on. + std::vector sorted_nodes; for (Node* node : graph.op_nodes()) { + sorted_nodes.push_back(node); + } + std::sort(sorted_nodes.begin(), sorted_nodes.end(), NodeCompare()); + + for (Node* node : sorted_nodes) { + VLOG(2) << "Fuel: " << fuel; + if (fuel <= 0) { + VLOG(2) + << "Hit fuel limit; not marking any remaining ops as clusterable."; + break; + } + VLOG(2) << "FindCompilationCandidates(): Processing " << node->DebugString(); @@ -234,7 +256,9 @@ Status FindCompilationCandidates( continue; } candidates->insert(node); + --fuel; } + VLOG(2) << "candidates->size() = " << candidates->size(); return Status::OK(); } diff --git a/tensorflow/compiler/jit/xla_compilation_cache.cc b/tensorflow/compiler/jit/xla_compilation_cache.cc index 6d854a920eb0b4c01b09024ceaef5035e847d392..8cc79a9bd0b7aa2098ce177a9d7749f4e6c6ac27 100644 --- a/tensorflow/compiler/jit/xla_compilation_cache.cc +++ b/tensorflow/compiler/jit/xla_compilation_cache.cc @@ -93,7 +93,7 @@ uint64 XlaCompilationCache::Signature::Hash::operator()( Status XlaCompilationCache::BuildSignature( const NameAttrList& function, int num_constant_args, - const std::vector& variable_args, OpKernelContext* ctx, + const std::map& variable_args, OpKernelContext* ctx, Signature* signature) { signature->name = Canonicalize(function.name(), AttrSlice(&function.attr())); signature->arg_values.resize(num_constant_args); @@ -115,7 +115,8 @@ Status XlaCompilationCache::BuildSignature( } // For variable signatures, use the type and shape of the variable's // current value. - for (const OptionalTensor& variable : variable_args) { + for (auto& iterator : variable_args) { + const OptionalTensor& variable = iterator.second; TF_RET_CHECK(input_num < ctx->num_inputs()); if (variable.present) { signature->arg_types.emplace_back(variable.value.dtype(), @@ -133,7 +134,7 @@ namespace { // Builds a XlaCompiler::Argument vector from the arguments to the _XlaLaunch // op. The first `num_constant_args` arguments must be host-memory Tensors. Status BuildArguments(int num_constant_args, - const std::vector& variable_args, + const std::map& variable_args, OpKernelContext* ctx, std::vector* args) { args->resize(ctx->num_inputs()); @@ -175,17 +176,17 @@ Status BuildArguments(int num_constant_args, // Handles resource variables. TF_RET_CHECK(input_num + num_variable_args == ctx->num_inputs()); - for (int variable_id = 0; variable_id < num_variable_args; ++variable_id) { + for (auto& iterator : variable_args) { const Tensor& input = ctx->input(input_num); TF_RET_CHECK(input.dtype() == DT_RESOURCE); XlaCompiler::Argument& arg = (*args)[input_num]; - arg.name = variable_args[variable_id].name; + arg.name = iterator.second.name; arg.kind = XlaCompiler::Argument::kResource; arg.resource_kind = XlaResource::kVariable; - if (variable_args[variable_id].present) { - const Tensor& value = variable_args[variable_id].value; + if (iterator.second.present) { + const Tensor& value = iterator.second.value; arg.type = value.dtype(); arg.shape = value.shape(); arg.initialized = true; @@ -233,7 +234,7 @@ Status XlaCompilationCache::BuildExecutable( Status XlaCompilationCache::Compile( const XlaCompiler::Options& options, const NameAttrList& function, - int num_constant_args, const std::vector& variable_args, + int num_constant_args, const std::map& variable_args, OpKernelContext* ctx, const XlaCompiler::CompilationResult** compilation_result, xla::LocalExecutable** executable, @@ -250,10 +251,12 @@ Status XlaCompilationCache::Compile( << " present=" << ctx->has_input(i) << " shape=" << shape.DebugString(); } - for (const OptionalTensor& variable : variable_args) { + for (auto& iterator : variable_args) { + const OptionalTensor& variable = iterator.second; VLOG(2) << "variable present=" << variable.present << " type=" << DataTypeString(variable.value.dtype()) - << " shape=" << variable.value.shape().DebugString(); + << " shape=" << variable.value.shape().DebugString() + << " TF arg= " << iterator.first; } VLOG(2) << "num_outputs = " << ctx->num_outputs(); for (int i = 0; i < ctx->num_outputs(); i++) { diff --git a/tensorflow/compiler/jit/xla_compilation_cache.h b/tensorflow/compiler/jit/xla_compilation_cache.h index 0858020716fcf4763e42dc0699ad22cfda756942..d5063783140205db54e673a7c7fd8f94b8aa2c65 100644 --- a/tensorflow/compiler/jit/xla_compilation_cache.h +++ b/tensorflow/compiler/jit/xla_compilation_cache.h @@ -63,7 +63,7 @@ class XlaCompilationCache : public ResourceBase { // outputs. Status Compile(const XlaCompiler::Options& options, const NameAttrList& function, int num_constant_args, - const std::vector& variable_args, + const std::map& variable_args, OpKernelContext* ctx, const XlaCompiler::CompilationResult** compilation_result, xla::LocalExecutable** executable, @@ -105,7 +105,7 @@ class XlaCompilationCache : public ResourceBase { // Builds the signature for a compilation. Status BuildSignature(const NameAttrList& function, int num_constant_args, - const std::vector& variable_args, + const std::map& variable_args, OpKernelContext* ctx, Signature* signature); // The value associated with a cache entry. diff --git a/tensorflow/compiler/jit/xla_interpreter_device.cc b/tensorflow/compiler/jit/xla_interpreter_device.cc index 2614deefd8823dcb8f38e9e22ae4e78145d0d96a..a329451b14a785b17913e3838a6571b62b422804 100644 --- a/tensorflow/compiler/jit/xla_interpreter_device.cc +++ b/tensorflow/compiler/jit/xla_interpreter_device.cc @@ -25,8 +25,8 @@ namespace tensorflow { const char* const DEVICE_XLA_INTERPRETER = "XLA_INTERPRETER"; const char* const DEVICE_INTERPRETER_XLA_JIT = "XLA_INTERPRETER_JIT"; -constexpr std::array kExecAllTypes = { - {DT_INT32, DT_FLOAT, DT_BOOL, DT_DOUBLE, DT_INT64}}; +constexpr std::array kExecAllTypes = { + {DT_INT32, DT_INT64, DT_FLOAT, DT_DOUBLE, DT_COMPLEX64, DT_BOOL}}; class XlaInterpreterDeviceFactory : public DeviceFactory { public: diff --git a/tensorflow/compiler/jit/xla_launch_util.cc b/tensorflow/compiler/jit/xla_launch_util.cc new file mode 100644 index 0000000000000000000000000000000000000000..8322dd2e829a850413f8eee843b78052f6aad549 --- /dev/null +++ b/tensorflow/compiler/jit/xla_launch_util.cc @@ -0,0 +1,255 @@ +/* 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/jit/xla_launch_util.h" + +#include "tensorflow/compiler/jit/defs.h" +#include "tensorflow/compiler/tf2xla/xla_compiler.h" +#include "tensorflow/compiler/xla/client/client_library.h" +#include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/compiler/xla/statusor.h" +#include "tensorflow/core/common_runtime/dma_helper.h" +#include "tensorflow/core/common_runtime/function.h" +#include "tensorflow/core/framework/allocator.h" +#include "tensorflow/core/framework/node_def_util.h" +#include "tensorflow/core/framework/op.h" +#include "tensorflow/core/framework/op_kernel.h" +#include "tensorflow/core/framework/tensor.h" +#include "tensorflow/core/framework/types.h" +#include "tensorflow/core/util/stream_executor_util.h" + +namespace gpu = perftools::gputools; + +namespace tensorflow { + +std::map SnapshotResourceVariables(OpKernelContext* ctx, + int num_variables) { + std::map snapshot; + int first_variable = ctx->num_inputs() - num_variables; + for (int i = 0; i < num_variables; ++i) { + Var* variable = nullptr; + ResourceHandle handle = HandleFromInput(ctx, first_variable + i); + OptionalTensor& tensor = snapshot[first_variable + i]; + if (LookupResource(ctx, handle, &variable).ok()) { + tf_shared_lock lock(*variable->mu()); + tensor.name = handle.name(); + tensor.present = true; + tensor.value = *variable->tensor(); + } + } + return snapshot; +} + +XlaAllocator::XlaAllocator(const gpu::Platform* platform, + OpKernelContext* op_context) + : xla::DeviceMemoryAllocator(platform), op_context_(op_context) {} + +XlaAllocator::~XlaAllocator() = default; + +xla::StatusOr XlaAllocator::Allocate( + int device_ordinal, uint64 size, bool retry_on_failure) { + AllocatorAttributes allocator_attrs; + allocator_attrs.set_on_host(false); + + AllocationAttributes allocation_attrs; + allocation_attrs.no_retry_on_failure = !retry_on_failure; + + Tensor t; + Status status = op_context_->allocate_temp( + DT_UINT8, TensorShape({static_cast(size)}), &t, allocator_attrs, + allocation_attrs); + if (!status.ok()) { + VLOG(2) << "Allocation failed " << size; + return status; + } + void* data = + reinterpret_cast(const_cast(t.tensor_data().data())); + tensors_[data] = t; + return gpu::DeviceMemoryBase(data, size); +} + +Status XlaAllocator::RegisterArgument(const Tensor* t) { + void* data = + reinterpret_cast(const_cast(t->tensor_data().data())); + tensors_[data] = *t; + return Status::OK(); +} + +Status XlaAllocator::Deallocate(int device_ordinal, + gpu::DeviceMemoryBase* mem) { + if (mem->opaque() != nullptr) { + if (tensors_.erase(mem->opaque()) == 0) { + return tensorflow::errors::InvalidArgument("Unknown tensor address"); + } + } + return Status::OK(); +} + +Status XlaAllocator::MakeTensorFromBuffer(gpu::DeviceMemoryBase buffer, + DataType dtype, + const TensorShape& shape, + Tensor* out_tensor) const { + void* ptr = const_cast(buffer.opaque()); + auto it = tensors_.find(ptr); + if (it == tensors_.end()) { + return errors::InvalidArgument("Unknown tensor address"); + } + const Tensor& tensor = it->second; + + int64 output_size = DataTypeSize(dtype) * shape.num_elements(); + if (tensor.TotalBytes() == output_size) { + out_tensor->UnsafeCopyFromInternal(tensor, dtype, shape); + } else { + Tensor slice = tensor.Slice(0, output_size); + out_tensor->UnsafeCopyFromInternal(slice, dtype, shape); + } + return Status::OK(); +} + +XlaComputationLaunchContext::XlaComputationLaunchContext( + int64 num_resource_args, xla::LocalClient* client, + XlaAllocator* xla_allocator) + : num_resource_args_(num_resource_args), + client_(client), + xla_allocator_(xla_allocator) {} + +void XlaComputationLaunchContext::PopulateInputs( + OpKernelContext* ctx, const XlaCompiler::CompilationResult* kernel, + const std::map& variables) { + // Build xla::ShapedBuffers that point directly to the Tensor buffers. + arg_buffers_.reserve(kernel->xla_input_shapes.size() + 1); + arg_buffers_.resize(kernel->xla_input_shapes.size()); + arg_ptrs_ = std::vector(arg_buffers_.size()); + + // Pass remaining parameters. + const Tensor* t; + for (int i = 0; i < kernel->xla_input_shapes.size(); ++i) { + int arg_num = kernel->input_mapping[i]; + const xla::Shape& shape = kernel->xla_input_shapes[i]; + if (variables.count(arg_num)) { + t = &(variables.at(arg_num).value); + CHECK(t); + } else { + t = &(ctx->input(arg_num)); + } + + gpu::DeviceMemoryBase dmem = gpu::DeviceMemoryBase( + const_cast(t->tensor_data().data()), t->tensor_data().size()); + + const xla::Shape on_device_shape = + client_->backend().transfer_manager()->HostShapeToDeviceShape(shape); + CHECK(xla::ShapeUtil::Equal(shape, on_device_shape)) + << "On-device shape " + << xla::ShapeUtil::HumanStringWithLayout(on_device_shape) + << " not the same as on-host shape " + << xla::ShapeUtil::HumanStringWithLayout(shape); + arg_buffers_[i] = xla::MakeUnique( + /*on_host_shape=*/shape, /*on_device_shape=*/shape, client_->platform(), + client_->default_device_ordinal()); + arg_buffers_[i]->set_buffer(dmem, /*index=*/{}); + arg_ptrs_[i] = arg_buffers_[i].get(); + + OP_REQUIRES_OK(ctx, xla_allocator_->RegisterArgument(t)); + } +} + +void XlaComputationLaunchContext::PopulateOutputs( + OpKernelContext* ctx, const XlaCompiler::CompilationResult* kernel, + std::unique_ptr output) { + gpu::Stream* stream = + ctx->op_device_context() ? ctx->op_device_context()->stream() : nullptr; + + // Computation output should always be a tuple. + if (VLOG_IS_ON(2)) { + VLOG(2) << "Result tuple shape: " << output->on_host_shape().DebugString(); + } + CHECK_EQ(ctx->num_outputs(), kernel->outputs.size()); + + // Copy XLA results to the OpOutputList. + int output_num = 0; + for (int i = 0; i < ctx->num_outputs(); ++i) { + if (kernel->outputs[i].is_constant) { + // Output is a constant. + const Tensor& const_tensor = kernel->outputs[i].constant_value; + const size_t total_bytes = const_tensor.TotalBytes(); + if (stream && total_bytes > 0) { + // Copy host -> device. (Empty tensors don't have backing buffers.) + VLOG(1) << "Constant output tensor on device"; + Tensor* output_tensor; + TF_CHECK_OK( + ctx->allocate_output(i, const_tensor.shape(), &output_tensor)); + + const void* src_ptr = DMAHelper::base(&const_tensor); + void* dst_ptr = DMAHelper::base(output_tensor); + gpu::DeviceMemoryBase gpu_dst_ptr(dst_ptr, total_bytes); + stream->ThenMemcpy(&gpu_dst_ptr, src_ptr, total_bytes); + } else { + // No copy required. + ctx->set_output(i, const_tensor); + } + } else { + const TensorShape& shape = kernel->outputs[i].shape; + VLOG(2) << "Retval " << i << " shape " << shape.DebugString(); + + gpu::DeviceMemoryBase buffer = output->buffer({output_num}); + Tensor output_tensor; + // Looks up the owning Tensor by buffer address. + OP_REQUIRES_OK(ctx, xla_allocator_->MakeTensorFromBuffer( + buffer, ctx->expected_output_dtype(i), shape, + &output_tensor)); + ctx->set_output(i, output_tensor); + ++output_num; + } + + if (VLOG_IS_ON(3)) { + VLOG(3) << ctx->mutable_output(i)->DebugString(); + } + } + + // Apply variable updates, if any. + VLOG(2) << "Applying variable updates"; + for (int i = 0; i < kernel->resource_updates.size(); ++i) { + const XlaCompiler::ResourceUpdate& write = kernel->resource_updates[i]; + OP_REQUIRES(ctx, + write.input_index >= 0 && write.input_index < ctx->num_inputs(), + errors::Internal("Invalid input index for variable write.")); + + gpu::DeviceMemoryBase buffer = output->buffer({output_num}); + + Var* variable = nullptr; + // TODO(b/35625933): tensorflow::Var should contain a PersistentTensor, + // not a Tensor. + OP_REQUIRES_OK(ctx, LookupOrCreateResource( + ctx, HandleFromInput(ctx, write.input_index), + &variable, [this, ctx, &write](Var** ptr) { + *ptr = new Var(write.type); + return Status::OK(); + })); + + core::ScopedUnref s(variable); + + mutex_lock ml(*variable->mu()); + OP_REQUIRES(ctx, variable->tensor()->dtype() == write.type, + errors::Internal("Mismatched type in variable write")); + + // Looks up the owning Tensor by buffer address. + OP_REQUIRES_OK(ctx, + xla_allocator_->MakeTensorFromBuffer( + buffer, write.type, write.shape, variable->tensor())); + ++output_num; + } +} + +} // namespace tensorflow diff --git a/tensorflow/compiler/jit/xla_launch_util.h b/tensorflow/compiler/jit/xla_launch_util.h new file mode 100644 index 0000000000000000000000000000000000000000..9fd356fce5896c317196cb31fd5248b6bc3427a8 --- /dev/null +++ b/tensorflow/compiler/jit/xla_launch_util.h @@ -0,0 +1,116 @@ +/* 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. +==============================================================================*/ + +// Contains utilities for launching compiled XLA kernels for a KernelContext. + +#ifndef TENSORFLOW_COMPILER_JIT_XLA_LAUNCH_UTIL_H_ +#define TENSORFLOW_COMPILER_JIT_XLA_LAUNCH_UTIL_H_ + +#include "tensorflow/compiler/jit/xla_compilation_cache.h" +#include "tensorflow/compiler/tf2xla/xla_compiler.h" +#include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/core/framework/tensor.h" +#include "tensorflow/core/framework/types.h" +#include "tensorflow/core/kernels/variable_ops.h" +#include "tensorflow/core/lib/core/status.h" + +namespace tensorflow { +class XlaAllocator; + +// Takes a snapshot of the values of resource variable arguments, which are +// the last `num_variables` arguments. We snapshot tensors that back +// resource variables since concurrent updates may modify the shape, and it is +// important that the shapes used for compilation match the true shapes of the +// buffers. +// +// Returns a map of TensorFlow argument index to resource variable. +std::map SnapshotResourceVariables(OpKernelContext* ctx, + int num_variables); + +// Adapter class that wraps a Tensorflow allocator as an XLA allocator. +// Assumes that the Tensorflow allocator permits asynchronous deallocation: +// see comment on `AllowsAsynchronousDeallocation()`. +class XlaAllocator : public xla::DeviceMemoryAllocator { + public: + XlaAllocator(const perftools::gputools::Platform* platform, + OpKernelContext* op_context); + ~XlaAllocator() override; + xla::StatusOr Allocate( + int device_ordinal, uint64 size, bool retry_on_failure) override; + Status Deallocate(int device_ordinal, + perftools::gputools::DeviceMemoryBase* mem) override; + + // Register an Tensor (input or resource variable) with the allocator. If + // the operation returns an alias to one of its inputs, then the allocator + // needs to be able to handle it. + Status RegisterArgument(const Tensor* t); + + // Makes 'tensor' a wrapper around the data buffer at 'ptr'. The buffer is + // interpreted as having data type 'dtype' and shape 'shape'. + Status MakeTensorFromBuffer(perftools::gputools::DeviceMemoryBase buffer, + DataType dtype, const TensorShape& shape, + Tensor* out_tensor) const; + + // The Tensorflow BFC allocator used on GPU allows host-side deallocation + // before GPU execution takes place. Tensorflow uses the ordering of the main + // compute stream to enforce a happens-before relationship between a memory + // allocation and code that reuses the same memory. If Tensorflow adds + // support for multiple GPU streams or allocators with different ordering + // requirements, this code may need to change. + // (This attribute has no effect on CPU.) + bool AllowsAsynchronousDeallocation() const override { return true; } + + private: + OpKernelContext* const op_context_; + + // Map from pointer address to the owning Tensor; used by + // MakeTensorFromBuffer. Also used to automatically release Tensors when the + // allocator is freed. + std::unordered_map tensors_; +}; + +// Helper class to perform the marshalling of TensorFlow inputs and outputs to +// ShapedBuffers suitable for passing to an XLA computation. +class XlaComputationLaunchContext { + public: + XlaComputationLaunchContext(int64 num_resource_args, xla::LocalClient* client, + XlaAllocator* xla_allocator); + + // Add all inputs within `ctx` as XLA arguments (returned by arguments()). + // `variables` is a map from TensorFlow argument number to resource variable. + void PopulateInputs(OpKernelContext* ctx, + const XlaCompiler::CompilationResult* kernel, + const std::map& variables); + + // Given the XLA output in `output`, populate all outputs of `ctx`. + void PopulateOutputs(OpKernelContext* ctx, + const XlaCompiler::CompilationResult* kernel, + std::unique_ptr output); + + // Return the argument list. Only valid after PopulateInputs() has been + // called. + const std::vector& arguments() const { return arg_ptrs_; } + + private: + int64 num_resource_args_; + xla::LocalClient* client_; + XlaAllocator* xla_allocator_; + std::vector> arg_buffers_; + std::vector arg_ptrs_; +}; + +} // namespace tensorflow + +#endif diff --git a/tensorflow/compiler/tests/BUILD b/tensorflow/compiler/tests/BUILD index 782bf82d4149968d5e5fbfb93bbd4ff1dcd75494..85a2adab283c273af607b6b80fb4fd76f8dac2b2 100644 --- a/tensorflow/compiler/tests/BUILD +++ b/tensorflow/compiler/tests/BUILD @@ -98,7 +98,7 @@ tf_xla_py_test( tf_xla_py_test( name = "binary_ops_test", - size = "small", + size = "medium", srcs = ["binary_ops_test.py"], shard_count = 5, tags = [ @@ -537,6 +537,7 @@ tf_xla_py_test( size = "medium", srcs = ["spacetobatch_op_test.py"], shard_count = 3, + tags = ["notsan"], deps = [ ":xla_test", "//tensorflow/python:array_ops", diff --git a/tensorflow/compiler/tests/binary_ops_test.py b/tensorflow/compiler/tests/binary_ops_test.py index 30a6d3a74d64f90ad33062df6d1e16e3a575bd63..ba7b9bacd2b794c74409d517a9c05bfbb14a845f 100644 --- a/tensorflow/compiler/tests/binary_ops_test.py +++ b/tensorflow/compiler/tests/binary_ops_test.py @@ -71,7 +71,7 @@ class BinaryOpsTest(XLATestCase): expected=np.array([[[[False, True], [True, False]]]], dtype=dtype)) self._testBinary( - gen_math_ops._real_div, + gen_math_ops.real_div, np.array([3, 3, -1.5, -8, 44], dtype=dtype), np.array([2, -2, 7, -4, 0], dtype=dtype), expected=np.array( @@ -108,57 +108,57 @@ class BinaryOpsTest(XLATestCase): [0, np.pi / 4, np.pi / 2, np.pi * 3 / 4, np.pi], dtype=dtype)) self._testBinary( - gen_math_ops._reciprocal_grad, + gen_math_ops.reciprocal_grad, np.array([4, -3, -2, 1], dtype=dtype), np.array([5, -6, 7, -8], dtype=dtype), expected=np.array([-80, 54, -28, 8], dtype=dtype)) self._testBinary( - gen_math_ops._sigmoid_grad, + gen_math_ops.sigmoid_grad, np.array([4, 3, 2, 1], dtype=dtype), np.array([5, 6, 7, 8], dtype=dtype), expected=np.array([-60, -36, -14, 0], dtype=dtype)) self._testBinary( - gen_math_ops._rsqrt_grad, + gen_math_ops.rsqrt_grad, np.array([4, 3, 2, 1], dtype=dtype), np.array([5, 6, 7, 8], dtype=dtype), expected=np.array([-160, -81, -28, -4], dtype=dtype)) self._testBinary( - gen_math_ops._sqrt_grad, + gen_math_ops.sqrt_grad, np.array([4, 3, 2, 1], dtype=dtype), np.array([5, 6, 7, 8], dtype=dtype), expected=np.array([0.625, 1, 1.75, 4], dtype=dtype)) self._testBinary( - gen_nn_ops._softplus_grad, + gen_nn_ops.softplus_grad, np.array([4, 3, 2, 1], dtype=dtype), np.array([5, 6, 7, 8], dtype=dtype), expected=np.array( [3.97322869, 2.99258232, 1.99817801, 0.99966466], dtype=dtype)) self._testBinary( - gen_nn_ops._softsign_grad, + gen_nn_ops.softsign_grad, np.array([4, 3, 2, 1], dtype=dtype), np.array([5, 6, 7, 8], dtype=dtype), expected=np.array( [0.11111111, 0.06122449, 0.03125, 0.01234568], dtype=dtype)) self._testBinary( - gen_math_ops._tanh_grad, + gen_math_ops.tanh_grad, np.array([4, 3, 2, 1], dtype=dtype), np.array([5, 6, 7, 8], dtype=dtype), expected=np.array([-75, -48, -21, 0], dtype=dtype)) self._testBinary( - gen_nn_ops._elu_grad, + gen_nn_ops.elu_grad, np.array([1, 2, 3, 4, 5, 6], dtype=dtype), np.array([-.6, -.4, -.2, 0, .2, .4], dtype=dtype), expected=np.array([0.4, 1.2, 2.4, 4, 5, 6], dtype=dtype)) self._testBinary( - gen_nn_ops._selu_grad, + gen_nn_ops.selu_grad, np.array([1, 2, 3, 4, 5, 6], dtype=dtype), np.array([-.6, -.4, -.2, .2, .4, .6], dtype=dtype), expected=np.array( @@ -166,20 +166,20 @@ class BinaryOpsTest(XLATestCase): 4.202803949422, 5.2535049367774, 6.30420592413], dtype=dtype)) self._testBinary( - gen_nn_ops._relu_grad, + gen_nn_ops.relu_grad, np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], dtype=dtype), np.array([0, 0, 0, 0, 0, 0.1, 0.3, 0.5, 0.7, 0.9], dtype=dtype), expected=np.array([0, 0, 0, 0, 0, 6, 7, 8, 9, 10], dtype=dtype)) self._testBinary( - gen_nn_ops._relu6_grad, + gen_nn_ops.relu6_grad, np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=dtype), np.array( [0, 0, 0, 0, 0, 0.1, 0.3, 0.5, 0.7, 0.9, 6.1, 10.0], dtype=dtype), expected=np.array([0, 0, 0, 0, 0, 6, 7, 8, 9, 10, 0, 0], dtype=dtype)) self._testBinary( - gen_nn_ops._softmax_cross_entropy_with_logits, + gen_nn_ops.softmax_cross_entropy_with_logits, np.array([[1, 2, 3, 4], [5, 6, 7, 8]], dtype=dtype), np.array([[0.1, 0.2, 0.3, 0.4], [0.4, 0.3, 0.2, 0.1]], dtype=dtype), expected=[ @@ -191,7 +191,7 @@ class BinaryOpsTest(XLATestCase): equality_test=self.ListsAreClose) self._testBinary( - gen_nn_ops._sparse_softmax_cross_entropy_with_logits, + gen_nn_ops.sparse_softmax_cross_entropy_with_logits, np.array([[0.1, 0.2, 0.3, 0.4], [0.5, 0.6, 0.7, 0.8], [0.9, 1.0, 1.1, 1.2]], dtype=dtype), np.array([2, 1, 7], dtype=np.int32), @@ -207,7 +207,7 @@ class BinaryOpsTest(XLATestCase): def testIntOps(self): for dtype in self.int_types: self._testBinary( - gen_math_ops._truncate_div, + gen_math_ops.truncate_div, np.array([3, 3, -1, -9, -8], dtype=dtype), np.array([2, -2, 7, 2, -4], dtype=dtype), expected=np.array([1, -1, 0, -4, 2], dtype=dtype)) @@ -232,11 +232,16 @@ class BinaryOpsTest(XLATestCase): expected=np.right_shift(lhs, rhs)) if dtype in [np.int8, np.int16, np.int32, np.int64]: - lhs = np.array([-1, -5, -3, -14], dtype=dtype) - rhs = np.array([5, 0, 1, 11], dtype=dtype) - self._testBinary( - bitwise_ops.right_shift, lhs, rhs, - expected=np.right_shift(lhs, rhs)) + lhs = np.array([-1, -5, -3, -14, -2], dtype=dtype) + rhs = np.array([5, 0, 1, 11, 36], dtype=dtype) + # HLO has saturating shift behavior. + bits = np.ceil( + np.log(np.iinfo(dtype).max - np.iinfo(dtype).min) / np.log(2)) + expected = [ + np.right_shift(l, r) if r < bits else np.sign(l) + for l, r in zip(lhs, rhs) + ] + self._testBinary(bitwise_ops.right_shift, lhs, rhs, expected=expected) def testNumericOps(self): for dtype in self.numeric_types: @@ -255,12 +260,18 @@ class BinaryOpsTest(XLATestCase): np.array([[1], [2]], dtype=dtype), dtype(7), expected=np.array([[8], [9]], dtype=dtype)) + self._testBinary( + math_ops.add, + np.array([0xffffffff, 0xfffffffff, 1, 1], dtype=np.int64), + np.array([1, 1, 0xffffffff, 0xfffffffff], dtype=np.int64), + expected=np.array( + [1 << 32, 1 << 36, 1 << 32, 1 << 36], dtype=np.int64)) self._testBinary( math_ops.subtract, - np.array([1, 2], dtype=dtype), - np.array([10, 20], dtype=dtype), - expected=np.array([-9, -18], dtype=dtype)) + np.array([1, 2, 100], dtype=dtype), + np.array([10, 20, -1], dtype=dtype), + expected=np.array([-9, -18, 101], dtype=dtype)) self._testBinary( math_ops.subtract, dtype(5), @@ -369,7 +380,7 @@ class BinaryOpsTest(XLATestCase): expected=np.array([[[[False, True], [True, False]]]], dtype=dtype)) self._testBinary( - gen_math_ops._real_div, + gen_math_ops.real_div, np.array([3, 3j, -1.5j, -8, 2 + 3j, 2 + 4j], dtype=dtype), np.array([2, -2, 7j, -4j, 4 - 6j, 1 + 2j], dtype=dtype), expected=np.array( @@ -378,7 +389,7 @@ class BinaryOpsTest(XLATestCase): # Test inf/nan scenarios. self._testBinary( - gen_math_ops._real_div, + gen_math_ops.real_div, np.array([4 + 3j, 4, 3j, -4, -4j, 2 - 3j], dtype=dtype), np.array([0, 0, 0, 0, 0, 0], dtype=dtype), expected=np.array( @@ -418,19 +429,19 @@ class BinaryOpsTest(XLATestCase): lhs = np.array([4 + 2j, -3 - 1j, 2j, 1], dtype=dtype) rhs = np.array([5, -6j, 7 - 3j, -8j], dtype=dtype) self._testBinary( - gen_math_ops._reciprocal_grad, lhs, rhs, expected=-rhs * lhs * lhs) + gen_math_ops.reciprocal_grad, lhs, rhs, expected=-rhs * lhs * lhs) self._testBinary( - gen_math_ops._sigmoid_grad, lhs, rhs, expected=rhs * lhs * (1 - lhs)) + gen_math_ops.sigmoid_grad, lhs, rhs, expected=rhs * lhs * (1 - lhs)) self._testBinary( - gen_math_ops._rsqrt_grad, lhs, rhs, expected=lhs**3 * rhs / -2) + gen_math_ops.rsqrt_grad, lhs, rhs, expected=lhs**3 * rhs / -2) self._testBinary( - gen_math_ops._sqrt_grad, lhs, rhs, expected=rhs / (2 * lhs)) + gen_math_ops.sqrt_grad, lhs, rhs, expected=rhs / (2 * lhs)) self._testBinary( - gen_math_ops._tanh_grad, lhs, rhs, expected=rhs * (1 - lhs * lhs)) + gen_math_ops.tanh_grad, lhs, rhs, expected=rhs * (1 - lhs * lhs)) def testComplexMath(self): for dtype in self.complex_types: @@ -538,7 +549,7 @@ class BinaryOpsTest(XLATestCase): if dtype not in self.complex_types: # floordiv unsupported for complex. self._testBinary( - gen_math_ops._floor_div, + gen_math_ops.floor_div, np.array([3, 3, -1, -9, -8], dtype=dtype), np.array([2, -2, 7, 2, -4], dtype=dtype), expected=np.array([1, -2, -1, -5, 2], dtype=dtype)) @@ -554,12 +565,12 @@ class BinaryOpsTest(XLATestCase): def _testRemainder(self, dtype): """Test cases for remainder operators.""" self._testBinary( - gen_math_ops._floor_mod, + gen_math_ops.floor_mod, np.array([3, 3, -1, -8], dtype=dtype), np.array([2, -2, 7, -4], dtype=dtype), expected=np.array([1, -1, 6, 0], dtype=dtype)) self._testBinary( - gen_math_ops._truncate_mod, + gen_math_ops.truncate_mod, np.array([3, 3, -1, -8], dtype=dtype), np.array([2, -2, 7, -4], dtype=dtype), expected=np.array([1, 1, -1, 0], dtype=dtype)) @@ -668,6 +679,11 @@ class BinaryOpsTest(XLATestCase): np.array([[10], [7], [2]], dtype=np.float32), np.float32(7), expected=np.array([[False], [False], [True]], dtype=np.bool)) + self._testBinary( + less_op, + np.array([[10], [7], [2], [-1]], dtype=np.int64), + np.int64(7), + expected=np.array([[False], [False], [True], [True]], dtype=np.bool)) for less_equal_op in [math_ops.less_equal, (lambda x, y: x <= y)]: self._testBinary( @@ -686,6 +702,80 @@ class BinaryOpsTest(XLATestCase): np.float32(7), expected=np.array([[False], [True], [True]], dtype=np.bool)) + def testS64Comparisons(self): + for op in [(lambda x, y: x < y), (lambda x, y: x <= y), + (lambda x, y: x >= y), (lambda x, y: x > y)]: + lhs = np.array( + [ + np.int64(0x000000007FFFFFFF), + np.int64(0x000000007FFFFFFF), + np.int64(0x0000000080000000), + np.int64(0x0000000080000000), + np.int64(0x0000000080000001), + np.int64(0x00000000FFFF0000), + np.int64(0x00000000FFFF0000), + np.int64(0x00000000FFFFFFFE), + np.int64(0x00000000FFFFFFFF), + np.int64(0x00000000FFFFFFFF), + np.int64(0x0000000100000000), + np.int64(0x0000000200000002), + np.int64(0x0000000200000002), + np.int64(0x0000000200000002), + np.int64(0x0000000200000002), + np.int64(0x0000000200000002), + np.int64(0x0000000200000002), + np.int64(0x0000000200000002), + np.int64(0x0000000200000002), + np.int64(0x0000000200000002), + np.int64(-0x7FFFFFFF00000002), + np.int64(-0x7FFFFFFF00000002), + np.int64(-0x7FFFFFFF00000001), + np.int64(-0x7FFFFFFF00000001), + np.int64(-0x7FFFFFFF00000001), + np.int64(-0x7FFFFFFF00000001), + np.int64(0x7ffffffefff00010), + np.int64(0x7ffffffefff00010), + np.int64(-1), + np.int64(-1) + ], + dtype=np.int64) + rhs = np.array( + [ + np.int64(0x000000007FFFFFFE), + np.int64(0x000000007FFFFFFF), + np.int64(0x000000007FFFFFFF), + np.int64(0x0000000080000000), + np.int64(0x0000000080000001), + np.int64(0x00000000FFFF0000), + np.int64(0x00000000FFFF0001), + np.int64(0x00000000FFFFFFFF), + np.int64(0x00000000FFFFFFFE), + np.int64(0x00000000FFFFFFFF), + np.int64(0x00000000FFFFFFFF), + np.int64(0x0000000100000001), + np.int64(0x0000000100000002), + np.int64(0x0000000100000003), + np.int64(0x0000000200000001), + np.int64(0x0000000200000002), + np.int64(0x0000000200000003), + np.int64(0x0000000300000001), + np.int64(0x0000000300000002), + np.int64(0x0000000300000003), + np.int64(0x00000000FFFFFFFF), + np.int64(-0x7FFFFFFF00000001), + np.int64(0x00000000FFFFFFFE), + np.int64(0x00000000FFFFFFFF), + np.int64(-0x7FFFFFFF00000002), + np.int64(-0x7FFFFFFF00000001), + np.int64(0x00000000FFFFFFFF), + np.int64(-0x7FFFFFFF00000001), + np.int64(-2), + np.int64(-1) + ], + dtype=np.int64) + expected = np.array([op(l, r) for l, r in zip(lhs, rhs)], dtype=np.bool) + self._testBinary(op, lhs, rhs, expected=expected) + def testBroadcasting(self): """Tests broadcasting behavior of an operator.""" @@ -1045,6 +1135,20 @@ class BinaryOpsTest(XLATestCase): ], equality_test=self.ListsAreClose) + def splitvOp(x, y): # pylint: disable=invalid-name + return array_ops.split(value=y, num_or_size_splits=[2, 3], axis=x) + for axis in [1, -1]: + self._testBinary( + splitvOp, + np.int32(axis), + np.array([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]], + dtype=dtype), + expected=[ + np.array([[0, 1], [5, 6]], dtype=dtype), + np.array([[2, 3, 4], [7, 8, 9]], dtype=dtype), + ], + equality_test=self.ListsAreClose) + def testTile(self): for dtype in self.numeric_types: self._testBinary( diff --git a/tensorflow/compiler/tests/concat_ops_test.py b/tensorflow/compiler/tests/concat_ops_test.py index 81734082d9aab86f8bc763681265ef64ef32bd31..f10973e19f1945515b776cf86349445ed7334629 100644 --- a/tensorflow/compiler/tests/concat_ops_test.py +++ b/tensorflow/compiler/tests/concat_ops_test.py @@ -301,7 +301,7 @@ class ConcatOffsetTest(XLATestCase): s0 = constant_op.constant([2, 3, 5], dtypes.int32) s1 = constant_op.constant([2, 7, 5], dtypes.int32) s2 = constant_op.constant([2, 20, 5], dtypes.int32) - off = gen_array_ops._concat_offset(cdim, [s0, s1, s2]) + off = gen_array_ops.concat_offset(cdim, [s0, s1, s2]) ans = sess.run(off) self.assertAllEqual(ans, [[0, 0, 0], [0, 3, 0], [0, 10, 0]]) diff --git a/tensorflow/compiler/tests/image_ops_test.py b/tensorflow/compiler/tests/image_ops_test.py index 538fa8e8e570b83ed681ecc0501285520cabdecb..3bc41b7cfd72bec7572097f8c53eef314a4369f6 100644 --- a/tensorflow/compiler/tests/image_ops_test.py +++ b/tensorflow/compiler/tests/image_ops_test.py @@ -426,7 +426,7 @@ class ResizeBilinearTest(XLATestCase): with self.test_session() as sess, self.test_scope(): dtype = dtype or np.float32 grads = array_ops.placeholder(np.float32) - resized = gen_image_ops._resize_bilinear_grad( + resized = gen_image_ops.resize_bilinear_grad( grads, np.zeros([1, input_shape[0], input_shape[1], 1], dtype=dtype), align_corners=True) diff --git a/tensorflow/compiler/tests/lrn_ops_test.py b/tensorflow/compiler/tests/lrn_ops_test.py index 5d8d89224d4a778d84803811710bb095872e86b2..69bd8f7230d4394c45764d02a88fb0ec097c5756 100644 --- a/tensorflow/compiler/tests/lrn_ops_test.py +++ b/tensorflow/compiler/tests/lrn_ops_test.py @@ -115,11 +115,11 @@ class LRNTest(XLATestCase): out_image = constant_op.constant(out_image_vals, shape=shape) out_grads = constant_op.constant(out_grads_vals, shape=shape) with ops.device(CPU_DEVICE): - expected = gen_nn_ops._lrn_grad(out_grads, in_image, out_image, - depth_radius, bias, alpha, beta) + expected = gen_nn_ops.lrn_grad(out_grads, in_image, out_image, + depth_radius, bias, alpha, beta) with self.test_scope(): - actual = gen_nn_ops._lrn_grad(out_grads, in_image, out_image, - depth_radius, bias, alpha, beta) + actual = gen_nn_ops.lrn_grad(out_grads, in_image, out_image, + depth_radius, bias, alpha, beta) expected_val = expected.eval() actual_val = actual.eval() self.assertAllClose(actual_val, expected_val, rtol=1e-3) diff --git a/tensorflow/compiler/tests/pooling_ops_3d_test.py b/tensorflow/compiler/tests/pooling_ops_3d_test.py index eb48fe555a0b182ea7983cbd8c3b217d56350408..4eed903963a34a253ea5c409782d9a89a97a4fdf 100644 --- a/tensorflow/compiler/tests/pooling_ops_3d_test.py +++ b/tensorflow/compiler/tests/pooling_ops_3d_test.py @@ -33,7 +33,7 @@ from tensorflow.python.platform import test # MaxPoolGrad. def _AvgPoolGrad(inputs, outputs, output_gradients, ksize, strides, padding): del outputs # Unused by average-pooling gradients. - return gen_nn_ops._avg_pool3d_grad( + return gen_nn_ops.avg_pool3d_grad( inputs.get_shape().as_list(), output_gradients, ksize=ksize, @@ -263,7 +263,7 @@ class Pooling3DTest(XLATestCase): def testMaxPoolGradValidPadding1_1_3d(self): self._VerifyGradient( nn_ops.max_pool3d, - gen_nn_ops._max_pool3d_grad, + gen_nn_ops.max_pool3d_grad, input_sizes=[1, 3, 3, 3, 1], ksize=[1, 1, 1], strides=[1, 1, 1], @@ -272,7 +272,7 @@ class Pooling3DTest(XLATestCase): def testMaxPoolGradValidPadding2_1_6_3d(self): self._VerifyGradient( nn_ops.max_pool3d, - gen_nn_ops._max_pool3d_grad, + gen_nn_ops.max_pool3d_grad, input_sizes=[2, 3, 3, 6, 3], ksize=[2, 2, 2], strides=[1, 1, 1], @@ -281,7 +281,7 @@ class Pooling3DTest(XLATestCase): def testMaxPoolGradValidPadding2_1_7_3d(self): self._VerifyGradient( nn_ops.max_pool3d, - gen_nn_ops._max_pool3d_grad, + gen_nn_ops.max_pool3d_grad, input_sizes=[2, 3, 5, 7, 3], ksize=[2, 2, 2], strides=[1, 1, 1], @@ -290,7 +290,7 @@ class Pooling3DTest(XLATestCase): def testMaxPoolGradValidPadding2_2_3d(self): self._VerifyGradient( nn_ops.max_pool3d, - gen_nn_ops._max_pool3d_grad, + gen_nn_ops.max_pool3d_grad, input_sizes=[2, 2, 2, 2, 3], ksize=[2, 2, 2], strides=[2, 2, 2], @@ -299,7 +299,7 @@ class Pooling3DTest(XLATestCase): def testMaxPoolGradSamePadding1_1_3d(self): self._VerifyGradient( nn_ops.max_pool3d, - gen_nn_ops._max_pool3d_grad, + gen_nn_ops.max_pool3d_grad, input_sizes=[2, 3, 2, 4, 1], ksize=[1, 1, 1], strides=[1, 1, 1], @@ -308,7 +308,7 @@ class Pooling3DTest(XLATestCase): def testMaxPoolGradSamePadding2_1_3d(self): self._VerifyGradient( nn_ops.max_pool3d, - gen_nn_ops._max_pool3d_grad, + gen_nn_ops.max_pool3d_grad, input_sizes=[2, 3, 2, 4, 1], ksize=[2, 2, 2], strides=[1, 1, 1], @@ -317,7 +317,7 @@ class Pooling3DTest(XLATestCase): def testMaxPoolGradSamePadding2_2_3d(self): self._VerifyGradient( nn_ops.max_pool3d, - gen_nn_ops._max_pool3d_grad, + gen_nn_ops.max_pool3d_grad, input_sizes=[2, 5, 2, 4, 3], ksize=[2, 2, 2], strides=[2, 2, 2], @@ -326,7 +326,7 @@ class Pooling3DTest(XLATestCase): def testMaxPoolGradSamePadding3_1_3d(self): self._VerifyGradient( nn_ops.max_pool3d, - gen_nn_ops._max_pool3d_grad, + gen_nn_ops.max_pool3d_grad, input_sizes=[1, 3, 3, 7, 1], ksize=[3, 3, 3], strides=[1, 1, 1], diff --git a/tensorflow/compiler/tests/pooling_ops_test.py b/tensorflow/compiler/tests/pooling_ops_test.py index 7c19a99c4eb4be3ca34b3ce949216e557b0a681d..fe270af3d636c0824621f36360ce9e7d14d8fc91 100644 --- a/tensorflow/compiler/tests/pooling_ops_test.py +++ b/tensorflow/compiler/tests/pooling_ops_test.py @@ -292,8 +292,15 @@ class PoolGradTest(XLATestCase): CPU_DEVICE = "/job:localhost/replica:0/task:0/cpu:0" - def _VerifyOneTest(self, pool_func, pool_grad_func, input_sizes, ksize, - strides, padding, data_format): + def _VerifyOneTest(self, + pool_func, + pool_grad_func, + input_sizes, + ksize, + strides, + padding, + data_format, + pool_grad_grad_func=None): """Verifies the output values of the pooling gradient function. Args: @@ -304,9 +311,19 @@ class PoolGradTest(XLATestCase): strides: The stride dimensions padding: Padding type. data_format: The data format we use to run the pooling operation. + pool_grad_grad_func: Second-order gradient function, if available. """ total_size = np.prod(input_sizes) - x = np.arange(1, total_size + 1, dtype=np.float32).reshape(input_sizes) + # TODO(b/73062247): MaxPoolGradGrad can confuse gradients when x is equally + # maximal at 16 bits. Switch to np.random.randn when resolved. + x = np.arange(1, total_size + 1, dtype=np.float32) + x *= (np.random.randint(2, size=total_size) * 2 - 1) # Flip signs randomly + # Verify some specifically interesting values... + x[np.random.choice(total_size)] = np.inf + x[np.random.choice(total_size)] = -np.inf + # TODO(b/74222344): Fix nan handling for max pool grad. + # x[np.random.choice(total_size)] = np.nan + x = x.reshape(input_sizes) with self.test_session() as sess: # Use the forward pool function to compute some corresponding outputs # (needed for the CPU device, and we need the shape in both cases). @@ -323,6 +340,8 @@ class PoolGradTest(XLATestCase): output_gradient_vals = np.arange( 1, output_vals.size + 1, dtype=np.float32) output_gradient_vals = output_gradient_vals.reshape(output_vals.shape) + output_grad_grad_vals = np.arange(1, x.size + 1, dtype=np.float32) + output_grad_grad_vals = output_grad_grad_vals.reshape(x.shape) # Use the Tensorflow CPU pooling gradient to compute the expected input # gradients. @@ -342,18 +361,36 @@ class PoolGradTest(XLATestCase): {inputs: x, output_gradients: output_gradient_vals}) + output_grad_gradients = array_ops.placeholder( + dtypes.float32, shape=expected_input_gradient_vals.shape) + if pool_grad_grad_func is not None: + expected_grad_gradients = pool_grad_grad_func( + inputs, + outputs, + output_grad_gradients, + ksize=ksize, + strides=strides, + padding=padding, + data_format="NHWC") + expected_grad_gradients_vals = sess.run(expected_grad_gradients, { + inputs: x, + output_grad_gradients: output_grad_grad_vals + }) + # Run the gradient op on the XLA device with self.test_scope(): outputs = array_ops.placeholder(dtypes.float32, shape=output_vals.shape) xla_inputs = inputs xla_outputs = outputs xla_output_gradients = output_gradients + xla_output_grad_gradients = output_grad_gradients xla_ksize = ksize xla_strides = strides if data_format == "NCHW": xla_inputs = NHWCToNCHW(inputs) xla_outputs = NHWCToNCHW(outputs) xla_output_gradients = NHWCToNCHW(output_gradients) + xla_output_grad_gradients = NHWCToNCHW(output_grad_gradients) xla_ksize = NHWCToNCHW(ksize) xla_strides = NHWCToNCHW(strides) actual_input_gradients = pool_grad_func( @@ -366,22 +403,54 @@ class PoolGradTest(XLATestCase): data_format=data_format) if data_format == "NCHW": actual_input_gradients = NCHWToNHWC(actual_input_gradients) - actual = sess.run(actual_input_gradients, { + if pool_grad_grad_func is not None: + actual_grad_gradients = pool_grad_grad_func( + xla_inputs, + xla_outputs, + xla_output_grad_gradients, + ksize=xla_ksize, + strides=xla_strides, + padding=padding, + data_format=data_format) + if data_format == "NCHW": + actual_grad_gradients = NCHWToNHWC(actual_grad_gradients) + actual_input_gradients_vals = sess.run(actual_input_gradients, { inputs: x, outputs: output_vals, output_gradients: output_gradient_vals }) - # Compare the Tensorflow and XLA results. self.assertAllClose( - expected_input_gradient_vals.flatten(), - actual.flatten(), + expected_input_gradient_vals, + actual_input_gradients_vals, rtol=1e-4, atol=1e-6) - self.assertShapeEqual(actual, inputs) - - def _VerifyValues(self, pool_func, pool_grad_func, input_sizes, ksize, - strides, padding): + self.assertShapeEqual(actual_input_gradients_vals, inputs) + + if pool_grad_grad_func is not None: + actual_grad_gradients_vals = sess.run( + actual_grad_gradients, { + inputs: x, + outputs: output_vals, + output_grad_gradients: output_grad_grad_vals + }) + + # Compare the Tensorflow and XLA results. + self.assertAllClose( + expected_grad_gradients_vals, + actual_grad_gradients_vals, + rtol=1e-4, + atol=1e-6) + self.assertShapeEqual(actual_grad_gradients_vals, outputs) + + def _VerifyValues(self, + pool_func, + pool_grad_func, + input_sizes, + ksize, + strides, + padding, + pool_grad_grad_func=None): """Verifies the output values of the pooling function. Args: @@ -391,12 +460,20 @@ class PoolGradTest(XLATestCase): ksize: The kernel size dimensions strides: The stride dimensions padding: Padding type. + pool_grad_grad_func: Second-order gradient function, if available. """ for data_format in GetTestConfigs(): - self._VerifyOneTest(pool_func, pool_grad_func, input_sizes, ksize, - strides, padding, data_format) - - def _TestPooling(self, forward_op, backward_op): + self._VerifyOneTest( + pool_func, + pool_grad_func, + input_sizes, + ksize, + strides, + padding, + data_format, + pool_grad_grad_func=pool_grad_grad_func) + + def _TestPooling(self, forward_op, backward_op, pool_grad_grad_func=None): # VALID padding self._VerifyValues( forward_op, @@ -404,7 +481,8 @@ class PoolGradTest(XLATestCase): input_sizes=[1, 3, 3, 3], ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], - padding="VALID") + padding="VALID", + pool_grad_grad_func=pool_grad_grad_func) # SAME padding self._VerifyValues( @@ -413,7 +491,8 @@ class PoolGradTest(XLATestCase): input_sizes=[1, 2, 3, 3], ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], - padding="SAME") + padding="SAME", + pool_grad_grad_func=pool_grad_grad_func) # SAME padding, non square window self._VerifyValues( @@ -422,7 +501,8 @@ class PoolGradTest(XLATestCase): input_sizes=[1, 2, 2, 1], ksize=[1, 1, 2, 1], strides=[1, 1, 1, 1], - padding="SAME") + padding="SAME", + pool_grad_grad_func=pool_grad_grad_func) # VALID padding, uneven stride self._VerifyValues( @@ -431,14 +511,16 @@ class PoolGradTest(XLATestCase): input_sizes=[1, 4, 4, 1], ksize=[1, 2, 2, 1], strides=[1, 1, 2, 1], - padding="VALID") + padding="VALID", + pool_grad_grad_func=pool_grad_grad_func) self._VerifyValues( forward_op, backward_op, input_sizes=[1, 4, 4, 1], ksize=[1, 2, 2, 1], strides=[1, 2, 1, 1], - padding="VALID") + padding="VALID", + pool_grad_grad_func=pool_grad_grad_func) # SAME padding, size 4 input self._VerifyValues( @@ -447,7 +529,8 @@ class PoolGradTest(XLATestCase): input_sizes=[1, 4, 4, 4], ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], - padding="SAME") + padding="SAME", + pool_grad_grad_func=pool_grad_grad_func) # SAME padding, size 8 input self._VerifyValues( @@ -456,10 +539,14 @@ class PoolGradTest(XLATestCase): input_sizes=[1, 8, 8, 8], ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], - padding="SAME") + padding="SAME", + pool_grad_grad_func=pool_grad_grad_func) def testMaxPool(self): - self._TestPooling(nn_ops.max_pool, gen_nn_ops._max_pool_grad) + self._TestPooling( + nn_ops.max_pool, + gen_nn_ops.max_pool_grad, + pool_grad_grad_func=gen_nn_ops.max_pool_grad_grad) def testAvgPool(self): # Wrapper around AvgPoolGrad that ignores extra arguments needed by @@ -467,7 +554,7 @@ class PoolGradTest(XLATestCase): def AvgPoolGrad(inputs, outputs, output_gradients, ksize, strides, padding, data_format): del outputs # Unused by average-pooling gradients. - return gen_nn_ops._avg_pool_grad( + return gen_nn_ops.avg_pool_grad( inputs.get_shape().as_list(), output_gradients, ksize=ksize, @@ -483,7 +570,7 @@ class PoolGradTest(XLATestCase): def testMaxPoolKernelSmallerThanStrideValid(self): self._VerifyValues( nn_ops.max_pool, - gen_nn_ops._max_pool_grad, + gen_nn_ops.max_pool_grad, input_sizes=[1, 7, 7, 1], ksize=[1, 2, 2, 1], strides=[1, 3, 3, 1], @@ -492,7 +579,7 @@ class PoolGradTest(XLATestCase): def testMaxPoolKernelSmallerThanStrideSame(self): self._VerifyValues( nn_ops.max_pool, - gen_nn_ops._max_pool_grad, + gen_nn_ops.max_pool_grad, input_sizes=[1, 3, 3, 1], ksize=[1, 1, 1, 1], strides=[1, 2, 2, 1], @@ -500,7 +587,7 @@ class PoolGradTest(XLATestCase): self._VerifyValues( nn_ops.max_pool, - gen_nn_ops._max_pool_grad, + gen_nn_ops.max_pool_grad, input_sizes=[1, 4, 4, 1], ksize=[1, 1, 1, 1], strides=[1, 2, 2, 1], diff --git a/tensorflow/compiler/tests/reduce_ops_test.py b/tensorflow/compiler/tests/reduce_ops_test.py index 965fdf684b973498d0b3c3cde17711cca7279705..2c084b04fa2f67ad0d86508109522d7bead206eb 100644 --- a/tensorflow/compiler/tests/reduce_ops_test.py +++ b/tensorflow/compiler/tests/reduce_ops_test.py @@ -18,6 +18,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import functools import numpy as np from tensorflow.compiler.tests.xla_test import XLATestCase @@ -30,8 +31,13 @@ from tensorflow.python.platform import googletest class ReduceOpsTest(XLATestCase): - def _testReduction(self, tf_reduce_fn, np_reduce_fn, dtype, test_inputs, - rtol=1e-4, atol=1e-4): + def _testReduction(self, + tf_reduce_fn, + np_reduce_fn, + dtype, + test_inputs, + rtol=1e-4, + atol=1e-4): """Tests that the output of 'tf_reduce_fn' matches numpy's output.""" for test_input in test_inputs: @@ -41,16 +47,16 @@ class ReduceOpsTest(XLATestCase): index = array_ops.placeholder(dtypes.int32) out = tf_reduce_fn(a, index) result = sess.run(out, {a: test_input, index: [0]}) - self.assertAllClose(result, np_reduce_fn(test_input, axis=0), - rtol=rtol, atol=atol) + self.assertAllClose( + result, np_reduce_fn(test_input, axis=0), rtol=rtol, atol=atol) result = sess.run(out, {a: test_input, index: [1]}) - self.assertAllClose(result, np_reduce_fn(test_input, axis=1), - rtol=rtol, atol=atol) + self.assertAllClose( + result, np_reduce_fn(test_input, axis=1), rtol=rtol, atol=atol) result = sess.run(out, {a: test_input, index: [-1]}) - self.assertAllClose(result, np_reduce_fn(test_input, axis=1), - rtol=rtol, atol=atol) + self.assertAllClose( + result, np_reduce_fn(test_input, axis=1), rtol=rtol, atol=atol) with self.assertRaisesWithPredicateMatch( errors_impl.InvalidArgumentError, 'Invalid reduction dim'): @@ -60,7 +66,7 @@ class ReduceOpsTest(XLATestCase): errors_impl.InvalidArgumentError, 'Invalid reduction dim'): sess.run(out, {a: test_input, index: [2]}) - FLOAT_DATA = [ + REAL_DATA = [ np.zeros(shape=(2, 0)), np.zeros(shape=(0, 30)), np.arange(1, 7).reshape(2, 3), @@ -74,7 +80,7 @@ class ReduceOpsTest(XLATestCase): np.arange(-14, -2, dtype=np.float32).view(np.complex64).reshape(2, 3), np.arange(-4, 8, dtype=np.float32).view(np.complex64).reshape(2, 3), ] - NONEMPTY_FLOAT_DATA = [x for x in FLOAT_DATA if np.size(x) > 0] + NONEMPTY_REAL_DATA = [x for x in REAL_DATA if np.size(x) > 0] NONEMPTY_COMPLEX_DATA = [x for x in COMPLEX_DATA if np.size(x) > 0] BOOL_DATA = [ np.array([], dtype=np.bool).reshape(2, 0), @@ -83,8 +89,7 @@ class ReduceOpsTest(XLATestCase): ] def testReduceSumF32(self): - self._testReduction(math_ops.reduce_sum, np.sum, np.float32, - self.FLOAT_DATA) + self._testReduction(math_ops.reduce_sum, np.sum, np.float32, self.REAL_DATA) def testReduceSumC64(self): self._testReduction(math_ops.reduce_sum, np.sum, np.complex64, @@ -92,7 +97,7 @@ class ReduceOpsTest(XLATestCase): def testReduceProdF32(self): self._testReduction(math_ops.reduce_prod, np.prod, np.float32, - self.FLOAT_DATA) + self.REAL_DATA) def testReduceProdC64(self): self._testReduction(math_ops.reduce_prod, np.prod, np.complex64, @@ -100,31 +105,44 @@ class ReduceOpsTest(XLATestCase): def testReduceMin(self): - def reference_min(inp, axis): + def reference_min(dtype, inp, axis): """Wrapper around np.amin that returns +infinity for an empty input.""" if inp.shape[axis] == 0: - return np.full(inp.shape[0:axis] + inp.shape[axis + 1:], float('inf')) + if np.issubdtype(dtype, np.floating): + return np.full(inp.shape[0:axis] + inp.shape[axis + 1:], float('inf')) + return np.full(inp.shape[0:axis] + inp.shape[axis + 1:], + np.iinfo(dtype).max) return np.amin(inp, axis) - self._testReduction(math_ops.reduce_min, reference_min, np.float32, - self.FLOAT_DATA) + for dtype in set(self.all_types).intersection( + [np.float32, np.int32, np.int64]): + self._testReduction(math_ops.reduce_min, + functools.partial(reference_min, dtype), dtype, + self.REAL_DATA) def testReduceMax(self): - def reference_max(inp, axis): + def reference_max(dtype, inp, axis): """Wrapper around np.amax that returns -infinity for an empty input.""" if inp.shape[axis] == 0: - return np.full(inp.shape[0:axis] + inp.shape[axis + 1:], float('-inf')) + if np.issubdtype(dtype, np.floating): + return np.full(inp.shape[0:axis] + inp.shape[axis + 1:], + float('-inf')) + return np.full(inp.shape[0:axis] + inp.shape[axis + 1:], + np.iinfo(dtype).min) return np.amax(inp, axis) - self._testReduction(math_ops.reduce_max, reference_max, np.float32, - self.FLOAT_DATA) + for dtype in set(self.all_types).intersection( + [np.float32, np.int32, np.int64]): + self._testReduction(math_ops.reduce_max, + functools.partial(reference_max, dtype), dtype, + self.REAL_DATA) def testReduceMeanF32(self): # TODO(phawkins): mean on XLA currently returns 0 instead of NaN when # reducing across zero inputs. self._testReduction(math_ops.reduce_mean, np.mean, np.float32, - self.NONEMPTY_FLOAT_DATA) + self.NONEMPTY_REAL_DATA) def testReduceMeanC64(self): self._testReduction(math_ops.reduce_mean, np.mean, np.complex64, diff --git a/tensorflow/compiler/tests/spacetobatch_op_test.py b/tensorflow/compiler/tests/spacetobatch_op_test.py index c013f4b50a4cf95be8028248c52b10b1c3be2bd3..92518aadc4bf5c601cfb4192c093799784b6aa72 100644 --- a/tensorflow/compiler/tests/spacetobatch_op_test.py +++ b/tensorflow/compiler/tests/spacetobatch_op_test.py @@ -75,11 +75,11 @@ class SpaceToBatchTest(XLATestCase): for dtype in self.float_types: # outputs = space_to_batch(inputs) placeholder = array_ops.placeholder(dtype) - x_tf = gen_array_ops._space_to_batch( + x_tf = gen_array_ops.space_to_batch( placeholder, paddings, block_size=block_size) self.assertAllEqual(sess.run(x_tf, {placeholder: inputs}), outputs) # inputs = batch_to_space(outputs) - x_tf = gen_array_ops._batch_to_space( + x_tf = gen_array_ops.batch_to_space( placeholder, paddings, block_size=block_size) self.assertAllEqual(sess.run(x_tf, {placeholder: outputs}), inputs) diff --git a/tensorflow/compiler/tests/stack_ops_test.py b/tensorflow/compiler/tests/stack_ops_test.py index 2b9c2279737ccee531d488d27ccdb0cafa1dc8fc..94342f9567ca71274609e63b0482d55637c98d51 100644 --- a/tensorflow/compiler/tests/stack_ops_test.py +++ b/tensorflow/compiler/tests/stack_ops_test.py @@ -34,33 +34,33 @@ class StackOpTest(XLATestCase): with self.test_session(), self.test_scope(): size = array_ops.placeholder(dtypes.int32) v = array_ops.placeholder(dtypes.float32) - h = gen_data_flow_ops._stack_v2(size, dtypes.float32, stack_name="foo") - c = gen_data_flow_ops._stack_push_v2(h, v) + h = gen_data_flow_ops.stack_v2(size, dtypes.float32, stack_name="foo") + c = gen_data_flow_ops.stack_push_v2(h, v) with ops.control_dependencies([c]): - c1 = gen_data_flow_ops._stack_pop_v2(h, dtypes.float32) + c1 = gen_data_flow_ops.stack_pop_v2(h, dtypes.float32) self.assertAllClose([[4.0, 5.0]], c1.eval({size: 5, v: [[4.0, 5.0]]})) def testStackPushPopSwap(self): with self.test_session(), self.test_scope(): a = np.arange(2000) x = array_ops.placeholder(dtypes.float32) - h = gen_data_flow_ops._stack_v2(5, dtypes.float32, stack_name="foo") - c = gen_data_flow_ops._stack_push_v2(h, x, swap_memory=True) + h = gen_data_flow_ops.stack_v2(5, dtypes.float32, stack_name="foo") + c = gen_data_flow_ops.stack_push_v2(h, x, swap_memory=True) with ops.control_dependencies([c]): - c1 = gen_data_flow_ops._stack_pop_v2(h, dtypes.float32) + c1 = gen_data_flow_ops.stack_pop_v2(h, dtypes.float32) self.assertAllClose(a, c1.eval({x: a})) def testMultiStack(self): with self.test_session(), self.test_scope(): v = array_ops.placeholder(dtypes.float32) - h1 = gen_data_flow_ops._stack_v2(5, dtypes.float32, stack_name="foo") - c1 = gen_data_flow_ops._stack_push_v2(h1, v) + h1 = gen_data_flow_ops.stack_v2(5, dtypes.float32, stack_name="foo") + c1 = gen_data_flow_ops.stack_push_v2(h1, v) with ops.control_dependencies([c1]): - c1 = gen_data_flow_ops._stack_pop_v2(h1, dtypes.float32) - h2 = gen_data_flow_ops._stack_v2(5, dtypes.float32, stack_name="bar") - c2 = gen_data_flow_ops._stack_push_v2(h2, 5.0) + c1 = gen_data_flow_ops.stack_pop_v2(h1, dtypes.float32) + h2 = gen_data_flow_ops.stack_v2(5, dtypes.float32, stack_name="bar") + c2 = gen_data_flow_ops.stack_push_v2(h2, 5.0) with ops.control_dependencies([c2]): - c2 = gen_data_flow_ops._stack_pop_v2(h2, dtypes.float32) + c2 = gen_data_flow_ops.stack_pop_v2(h2, dtypes.float32) r = c1 + c2 self.assertAllClose(9.0, r.eval({v: 4.0})) @@ -69,15 +69,15 @@ class StackOpTest(XLATestCase): with self.test_session() as sess, self.test_scope(): v1 = array_ops.placeholder(dtypes.float32) v2 = array_ops.placeholder(dtypes.float32) - h1 = gen_data_flow_ops._stack_v2(5, dtypes.float32, stack_name="foo") - h2 = gen_data_flow_ops._stack_v2(5, dtypes.float32, stack_name="foo") + h1 = gen_data_flow_ops.stack_v2(5, dtypes.float32, stack_name="foo") + h2 = gen_data_flow_ops.stack_v2(5, dtypes.float32, stack_name="foo") - c1 = gen_data_flow_ops._stack_push_v2(h1, v1) + c1 = gen_data_flow_ops.stack_push_v2(h1, v1) with ops.control_dependencies([c1]): - c2 = gen_data_flow_ops._stack_push_v2(h2, v2) + c2 = gen_data_flow_ops.stack_push_v2(h2, v2) with ops.control_dependencies([c2]): - pop1 = gen_data_flow_ops._stack_pop_v2(h1, dtypes.float32) - pop2 = gen_data_flow_ops._stack_pop_v2(h2, dtypes.float32) + pop1 = gen_data_flow_ops.stack_pop_v2(h1, dtypes.float32) + pop2 = gen_data_flow_ops.stack_pop_v2(h2, dtypes.float32) out1, out2 = sess.run([pop1, pop2], {v1: 4.0, v2: 5.0}) self.assertAllClose(out1, 4.0) @@ -86,17 +86,17 @@ class StackOpTest(XLATestCase): def testCloseStack(self): with self.test_session() as sess, self.test_scope(): size = array_ops.placeholder(dtypes.int32) - h = gen_data_flow_ops._stack_v2(size, dtypes.float32, stack_name="foo") - c1 = gen_data_flow_ops._stack_close_v2(h) + h = gen_data_flow_ops.stack_v2(size, dtypes.float32, stack_name="foo") + c1 = gen_data_flow_ops.stack_close_v2(h) sess.run(c1, {size: 5}) def testPushCloseStack(self): with self.test_session() as sess, self.test_scope(): v = array_ops.placeholder(dtypes.float32) - h = gen_data_flow_ops._stack_v2(5, dtypes.float32, stack_name="foo") - c = gen_data_flow_ops._stack_push_v2(h, v) + h = gen_data_flow_ops.stack_v2(5, dtypes.float32, stack_name="foo") + c = gen_data_flow_ops.stack_push_v2(h, v) with ops.control_dependencies([c]): - c1 = gen_data_flow_ops._stack_close_v2(h) + c1 = gen_data_flow_ops.stack_close_v2(h) sess.run(c1, {v: [[4.0, 5.0]]}) diff --git a/tensorflow/compiler/tests/tensor_array_ops_test.py b/tensorflow/compiler/tests/tensor_array_ops_test.py index a62925a1818da00cb0a9e82e1281db20fb38b208..7624d6e4b2e2ece6a61155743fc8b866f6903f32 100644 --- a/tensorflow/compiler/tests/tensor_array_ops_test.py +++ b/tensorflow/compiler/tests/tensor_array_ops_test.py @@ -338,7 +338,7 @@ class TensorArrayTest(xla_test.XLATestCase): w0 = ta.write(0, [[4.0, 5.0]]) # Test reading wrong datatype. - r0_bad = gen_data_flow_ops._tensor_array_read_v3( + r0_bad = gen_data_flow_ops.tensor_array_read_v3( handle=w0.handle, index=0, dtype=dtype2, flow_in=w0.flow) with self.assertRaisesOpError("TensorArray dtype is "): r0_bad.eval() diff --git a/tensorflow/compiler/tests/xla_test.py b/tensorflow/compiler/tests/xla_test.py index 7e1f5c76ed65946363cc3c113ab1a9862f87b289..cc778f1c3c0098da5ab933f9b4674890a724d160 100644 --- a/tensorflow/compiler/tests/xla_test.py +++ b/tensorflow/compiler/tests/xla_test.py @@ -71,14 +71,14 @@ class XLATestCase(test.TestCase): self._all_types = set( [dtype.as_numpy_dtype for dtype in self._all_tf_types]) - self.int_types = set([dtype.as_numpy_dtype for dtype in self.int_tf_types]) + self._int_types = set([dtype.as_numpy_dtype for dtype in self.int_tf_types]) self._float_types = set( [dtype.as_numpy_dtype for dtype in self._float_tf_types]) self.complex_types = set([ dtype.as_numpy_dtype for dtype in self.complex_tf_types ]) - self._numeric_types = set( - self.int_types | self._float_types | self.complex_types) + self._numeric_types = set(self._int_types | self._float_types + | self.complex_types) # Parse the manifest file, if any, into a regex identifying tests to # disable @@ -130,6 +130,11 @@ class XLATestCase(test.TestCase): name = '{}.{}'.format(type(self).__name__, self._testMethodName) return self._float_tf_types - self._method_types_filter.get(name, set()) + @property + def int_types(self): + name = '{}.{}'.format(type(self).__name__, self._testMethodName) + return self._int_types - self._method_types_filter.get(name, set()) + @property def numeric_tf_types(self): name = '{}.{}'.format(type(self).__name__, self._testMethodName) diff --git a/tensorflow/compiler/tf2xla/BUILD b/tensorflow/compiler/tf2xla/BUILD index fb82c2601c432cee425a46a3b6dc2c55febeda87..eb20ca501c80b01c76198e1ad54173f1c601714d 100644 --- a/tensorflow/compiler/tf2xla/BUILD +++ b/tensorflow/compiler/tf2xla/BUILD @@ -58,6 +58,15 @@ xla_proto_library( ], ) +xla_proto_library( + name = "host_compute_metadata_proto", + srcs = ["host_compute_metadata.proto"], + visibility = ["//visibility:public"], + deps = [ + "//tensorflow/core:protos_all_cc", + ], +) + cc_library( name = "tf2xla", srcs = ["tf2xla.cc"], @@ -149,6 +158,7 @@ cc_library( ":common", ":dump_graph", ":functionalize_control_flow", + ":host_compute_metadata_proto", ":sharding_util", ":tf2xla_util", "//tensorflow/compiler/tf2xla/lib:util", diff --git a/tensorflow/compiler/tf2xla/const_analysis.cc b/tensorflow/compiler/tf2xla/const_analysis.cc index 82923722c54d235716b9138d95a75a441df924ca..6f46532419d3389bafe8c3bf41fa41e8a3e173b7 100644 --- a/tensorflow/compiler/tf2xla/const_analysis.cc +++ b/tensorflow/compiler/tf2xla/const_analysis.cc @@ -37,7 +37,7 @@ Status BackwardsConstAnalysis(const Graph& g, }; Status status; - std::unordered_set must_be_const; + std::unordered_set must_be_const; auto visit = [&status, &metadata_ops, &must_be_const, compile_time_const_args](Node* node) { if (!status.ok()) return; @@ -55,7 +55,7 @@ Status BackwardsConstAnalysis(const Graph& g, compile_time_const_args->at(index) = true; return; } - for (Node* pred : node->in_nodes()) { + for (const Node* pred : node->in_nodes()) { must_be_const.insert(pred); } return; diff --git a/tensorflow/compiler/tf2xla/g3doc/cpu_supported_ops.md b/tensorflow/compiler/tf2xla/g3doc/cpu_supported_ops.md index 91351421bcacd26c41b5c9f98ea833730e4aef30..20179b67991d3d23d678cf1df2642e029ea037fd 100644 --- a/tensorflow/compiler/tf2xla/g3doc/cpu_supported_ops.md +++ b/tensorflow/compiler/tf2xla/g3doc/cpu_supported_ops.md @@ -3,6 +3,7 @@ Operator | Type Constraint ------------------------------------- | --------------- `Abs` | `T={double,float,int32,int64}` +`Acos` | `T={complex64,double,float,int32,int64}` `Acosh` | `T={complex64,double,float}` `Add` | `T={complex64,double,float,int32,int64}` `AddN` | `T={complex64,double,float,int32,int64,uint32,uint64}` @@ -15,10 +16,12 @@ Operator | Type Constraint `ApproximateEqual` | `T={complex64,double,float,int32,int64,uint32,uint64}` `ArgMax` | `Tidx={int32,int64}`
`output_type={int32,int64}`
`T={float}` `ArgMin` | `Tidx={int32,int64}`
`output_type={int32,int64}`
`T={complex64,double,float,int32,int64,uint32,uint64}` +`Asin` | `T={complex64,double,float,int32,int64}` `Asinh` | `T={complex64,double,float}` `AssignAddVariableOp` | `dtype={complex64,double,float,int32,int64,uint32,uint64}` `AssignSubVariableOp` | `dtype={complex64,double,float,int32,int64,uint32,uint64}` `AssignVariableOp` | `dtype={bool,complex64,double,float,int32,int64,uint32,uint64}` +`Atan` | `T={complex64,double,float,int32,int64}` `Atan2` | `T={double,float}` `Atanh` | `T={complex64,double,float}` `AvgPool` | `T={double,float}` @@ -75,6 +78,10 @@ Operator | Type Constraint `FFT` | `FFT2D` | `FFT3D` | +`FakeQuantWithMinMaxArgs` | +`FakeQuantWithMinMaxArgsGradient` | +`FakeQuantWithMinMaxVars` | +`FakeQuantWithMinMaxVarsGradient` | `Fill` | `index_type={int32,int64}`
`T={bool,complex64,double,float,int32,int64,uint32,uint64}` `Floor` | `T={double,float}` `FloorDiv` | `T={complex64,double,float,int32,int64}` @@ -84,6 +91,7 @@ Operator | Type Constraint `FusedBatchNormGradV2` | `U={float}`
`T={float}` `FusedBatchNormV2` | `U={float}`
`T={float}` `Gather` | `Tindices={int32,int64}`
`Tparams={bool,complex64,double,float,int32,int64,uint32,uint64}` +`GatherNd` | `Tindices={int32,int64}`
`Tparams={bool,complex64,double,float,int32,int64,uint32,uint64}` `GatherV2` | `Taxis={int32,int64}`
`Tindices={int32,int64}`
`Tparams={bool,complex64,double,float,int32,int64,uint32,uint64}` `Greater` | `T={double,float,int32,int64,uint32,uint64}` `GreaterEqual` | `T={double,float,int32,int64,uint32,uint64}` @@ -117,14 +125,18 @@ Operator | Type Constraint `LogicalNot` | `LogicalOr` | `MatMul` | `T={complex64,double,float}` +`MatrixBandPart` | `Tindex={int32,int64}`
`T={bool,complex64,double,float,int32,int64,uint32,uint64}` `MatrixDiag` | `T={bool,complex64,double,float,int32,int64,uint32,uint64}` `MatrixDiagPart` | `T={bool,complex64,double,float,int32,int64,uint32,uint64}` +`MatrixSetDiag` | `T={bool,complex64,double,float,int32,int64,uint32,uint64}` `MatrixTriangularSolve` | `T={complex64,double,float}` `Max` | `Tidx={int32,int64}`
`T={complex64,double,float,int32,int64,uint32,uint64}` `MaxPool` | `T={double,float,int32,int64}` `MaxPool3D` | `T={float}` `MaxPool3DGrad` | `TInput={float}`
`T={float}` `MaxPoolGrad` | `T={double,float,int32,int64,uint32,uint64}` +`MaxPoolGradGrad` | `T={float}` +`MaxPoolGradGradV2` | `T={float}` `MaxPoolGradV2` | `T={double,float,int32,int64,uint32,uint64}` `MaxPoolV2` | `T={double,float,int32,int64}` `Maximum` | `T={double,float,int32,int64}` @@ -186,6 +198,7 @@ Operator | Type Constraint `Round` | `T={complex64,double,float,int32,int64}` `Rsqrt` | `T={complex64,double,float}` `RsqrtGrad` | `T={complex64,double,float}` +`ScatterNd` | `Tindices={int32,int64}`
`T={bool,complex64,double,float,int32,int64,uint32,uint64}` `Select` | `T={bool,complex64,double,float,int32,int64,uint32,uint64}` `Selu` | `T={double,float}` `SeluGrad` | `T={double,float}` @@ -198,6 +211,7 @@ Operator | Type Constraint `Sinh` | `T={complex64,double,float}` `Size` | `out_type={int32,int64}`
`T={bool,complex64,double,float,int32,int64,uint32,uint64}` `Slice` | `Index={int32,int64}`
`T={bool,complex64,double,float,int32,int64,uint32,uint64}` +`Snapshot` | `T={bool,complex64,double,float,int32,int64,uint32,uint64}` `Softmax` | `T={double,float}` `SoftmaxCrossEntropyWithLogits` | `T={double,float}` `Softplus` | `T={double,float,int32,int64,uint32,uint64}` diff --git a/tensorflow/compiler/tf2xla/g3doc/gpu_supported_ops.md b/tensorflow/compiler/tf2xla/g3doc/gpu_supported_ops.md index b9bdb829d773825005a8921f48d28b6892d8f0cd..55f0538dba7c1941dfea88e0631cd299e51f76d0 100644 --- a/tensorflow/compiler/tf2xla/g3doc/gpu_supported_ops.md +++ b/tensorflow/compiler/tf2xla/g3doc/gpu_supported_ops.md @@ -3,6 +3,7 @@ Operator | Type Constraint ------------------------------------- | --------------- `Abs` | `T={double,float,int32,int64}` +`Acos` | `T={complex64,double,float,int32,int64}` `Acosh` | `T={complex64,double,float}` `Add` | `T={complex64,double,float,int32,int64}` `AddN` | `T={complex64,double,float,int32,int64,uint32,uint64}` @@ -15,10 +16,12 @@ Operator | Type Constraint `ApproximateEqual` | `T={complex64,double,float,int32,int64,uint32,uint64}` `ArgMax` | `Tidx={int32,int64}`
`output_type={int32,int64}`
`T={complex64,double,float,int32,int64,uint32,uint64}` `ArgMin` | `Tidx={int32,int64}`
`output_type={int32,int64}`
`T={complex64,double,float,int32,int64,uint32,uint64}` +`Asin` | `T={complex64,double,float,int32,int64}` `Asinh` | `T={complex64,double,float}` `AssignAddVariableOp` | `dtype={complex64,double,float,int32,int64,uint32,uint64}` `AssignSubVariableOp` | `dtype={complex64,double,float,int32,int64,uint32,uint64}` `AssignVariableOp` | `dtype={bool,complex64,double,float,int32,int64,uint32,uint64}` +`Atan` | `T={complex64,double,float,int32,int64}` `Atan2` | `T={double,float}` `Atanh` | `T={complex64,double,float}` `AvgPool` | `T={double,float}` @@ -75,6 +78,10 @@ Operator | Type Constraint `FFT` | `FFT2D` | `FFT3D` | +`FakeQuantWithMinMaxArgs` | +`FakeQuantWithMinMaxArgsGradient` | +`FakeQuantWithMinMaxVars` | +`FakeQuantWithMinMaxVarsGradient` | `Fill` | `index_type={int32,int64}`
`T={bool,complex64,double,float,int32,int64,uint32,uint64}` `Floor` | `T={double,float}` `FloorDiv` | `T={complex64,double,float,int32,int64}` @@ -84,6 +91,7 @@ Operator | Type Constraint `FusedBatchNormGradV2` | `U={float}`
`T={float}` `FusedBatchNormV2` | `U={float}`
`T={float}` `Gather` | `Tindices={int32,int64}`
`Tparams={bool,complex64,double,float,int32,int64,uint32,uint64}` +`GatherNd` | `Tindices={int32,int64}`
`Tparams={bool,complex64,double,float,int32,int64,uint32,uint64}` `GatherV2` | `Taxis={int32,int64}`
`Tindices={int32,int64}`
`Tparams={bool,complex64,double,float,int32,int64,uint32,uint64}` `Greater` | `T={double,float,int32,int64,uint32,uint64}` `GreaterEqual` | `T={double,float,int32,int64,uint32,uint64}` @@ -117,14 +125,18 @@ Operator | Type Constraint `LogicalNot` | `LogicalOr` | `MatMul` | `T={complex64,double,float}` +`MatrixBandPart` | `Tindex={int32,int64}`
`T={bool,complex64,double,float,int32,int64,uint32,uint64}` `MatrixDiag` | `T={bool,complex64,double,float,int32,int64,uint32,uint64}` `MatrixDiagPart` | `T={bool,complex64,double,float,int32,int64,uint32,uint64}` +`MatrixSetDiag` | `T={bool,complex64,double,float,int32,int64,uint32,uint64}` `MatrixTriangularSolve` | `T={complex64,double,float}` `Max` | `Tidx={int32,int64}`
`T={complex64,double,float,int32,int64,uint32,uint64}` `MaxPool` | `T={double,float,int32,int64}` `MaxPool3D` | `T={float}` `MaxPool3DGrad` | `TInput={float}`
`T={float}` `MaxPoolGrad` | `T={double,float,int32,int64,uint32,uint64}` +`MaxPoolGradGrad` | `T={float}` +`MaxPoolGradGradV2` | `T={float}` `MaxPoolGradV2` | `T={double,float,int32,int64,uint32,uint64}` `MaxPoolV2` | `T={double,float,int32,int64}` `Maximum` | `T={double,float,int32,int64}` @@ -183,6 +195,7 @@ Operator | Type Constraint `Round` | `T={complex64,double,float,int32,int64}` `Rsqrt` | `T={complex64,double,float}` `RsqrtGrad` | `T={complex64,double,float}` +`ScatterNd` | `Tindices={int32,int64}`
`T={bool,complex64,double,float,int32,int64,uint32,uint64}` `Select` | `T={bool,complex64,double,float,int32,int64,uint32,uint64}` `Selu` | `T={double,float}` `SeluGrad` | `T={double,float}` @@ -195,6 +208,7 @@ Operator | Type Constraint `Sinh` | `T={complex64,double,float}` `Size` | `out_type={int32,int64}`
`T={bool,complex64,double,float,int32,int64,uint32,uint64}` `Slice` | `Index={int32,int64}`
`T={bool,complex64,double,float,int32,int64,uint32,uint64}` +`Snapshot` | `T={bool,complex64,double,float,int32,int64,uint32,uint64}` `Softmax` | `T={double,float}` `SoftmaxCrossEntropyWithLogits` | `T={double,float}` `Softplus` | `T={double,float,int32,int64,uint32,uint64}` diff --git a/tensorflow/compiler/tf2xla/graph_compiler.cc b/tensorflow/compiler/tf2xla/graph_compiler.cc index 058a1f2621c64a735bd9d9c9d0ae007f93aa4dea..b20c1ffc7d8956f3f5530ee63e9b711a26439be5 100644 --- a/tensorflow/compiler/tf2xla/graph_compiler.cc +++ b/tensorflow/compiler/tf2xla/graph_compiler.cc @@ -130,7 +130,7 @@ Status GraphCompiler::Compile() { // Set up inputs from outputs of previous nodes. for (auto* e : n->in_edges()) { if (e->IsControlEdge()) continue; - Node* src = e->src(); + const Node* src = e->src(); TF_RET_CHECK(src->id() < output_registry.size()); const NodeOutputs& src_outputs = output_registry[src->id()]; diff --git a/tensorflow/compiler/tf2xla/host_compute_metadata.proto b/tensorflow/compiler/tf2xla/host_compute_metadata.proto new file mode 100644 index 0000000000000000000000000000000000000000..43ab371a217e6c4521a160715104c96e3c8782c6 --- /dev/null +++ b/tensorflow/compiler/tf2xla/host_compute_metadata.proto @@ -0,0 +1,38 @@ +syntax = "proto3"; + +package tensorflow.tf2xla; +option cc_enable_arenas = true; +option java_outer_classname = "Tf2XlaProtos"; +option java_multiple_files = true; +option java_package = "org.tensorflow.tf2xla"; + +import "tensorflow/core/framework/tensor_shape.proto"; +import "tensorflow/core/framework/types.proto"; + +// TensorMetadata indicates the type and shape of a Tensor that is +// part of a host compute transfer. +message TensorMetadata { + DataType type = 1; + TensorShapeProto shape = 2; +} + +// HostTransferMetadata describes a transfer either from host to device +// or device to host. It has a key that is unique to the computation, +// and metadata about the list of tensors being transferred. +message HostTransferMetadata { + // The key used to identify this transfer. + string key = 1; + + // For each Tensor being transferred, its type and shape. + repeated TensorMetadata metadata = 2; +} + +// HostComputeMetadata describes all the sends and recvs +// from all host compute transfer ops in a computation. +message HostComputeMetadata { + // Metadata about each device_to_host transfer + repeated HostTransferMetadata device_to_host = 1; + + // Metadata about each host_to_device transfer + repeated HostTransferMetadata host_to_device = 2; +} diff --git a/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc b/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc index d4fb5dd4e06c7c70591262c0d63a91c383a2a6e0..086a9491aa93ebfae99f296dd355ae2e322084ec 100644 --- a/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc @@ -525,5 +525,172 @@ class AvgPool3DGradOp : public AvgPoolGradOp { REGISTER_XLA_OP(Name("AvgPool3DGrad").CompileTimeConstInput("orig_input_shape"), AvgPool3DGradOp); +class MaxPoolGradGradOp : public XlaOpKernel { + public: + MaxPoolGradGradOp(OpKernelConstruction* ctx, int num_spatial_dims) + : XlaOpKernel(ctx), num_spatial_dims_(num_spatial_dims) { + if (ctx->num_inputs() == 3) { + OP_REQUIRES_OK(ctx, ctx->GetAttr("ksize", &ksize_)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("strides", &stride_)); + } + OP_REQUIRES_OK(ctx, ctx->GetAttr("padding", &padding_)); + } + + int num_dims() const { return num_spatial_dims_ + 2; } + + void Compile(XlaOpKernelContext* ctx) override { + if (ctx->num_inputs() != 3) { + OP_REQUIRES( + ctx, ctx->num_inputs() == 5, + errors::InvalidArgument("Must supply ksize and stride arguments.")); + const TensorShape ksize_shape = ctx->InputShape(3); + // Validate input sizes. + OP_REQUIRES(ctx, TensorShapeUtils::IsVector(ksize_shape), + errors::InvalidArgument("ksize must be a vector, not shape ", + ksize_shape.DebugString())); + OP_REQUIRES_OK(ctx, ctx->ConstantInputAsIntVector(3, &ksize_)); + + const TensorShape stride_shape = ctx->InputShape(4); + // Validate input sizes. + OP_REQUIRES(ctx, TensorShapeUtils::IsVector(stride_shape), + errors::InvalidArgument("stride must be a vector, not shape ", + stride_shape.DebugString())); + OP_REQUIRES_OK(ctx, ctx->ConstantInputAsIntVector(4, &stride_)); + } + + OP_REQUIRES(ctx, ksize_.size() == num_dims(), + errors::InvalidArgument("Sliding window ksize field must " + "specify ", + num_dims(), " dimensions")); + OP_REQUIRES(ctx, stride_.size() == num_dims(), + errors::InvalidArgument("Sliding window strides field must " + "specify ", + num_dims(), " dimensions")); + + const TensorShape tensor_in_shape = ctx->InputShape(0); + const TensorShape tensor_out_shape = ctx->InputShape(1); + const TensorShape out_backprop_shape = ctx->InputShape(2); + + // For maxpooling, tensor_in should have num_dims() dimensions. + OP_REQUIRES(ctx, tensor_in_shape.dims() == num_dims(), + errors::InvalidArgument("tensor_in must be ", num_dims(), + "-dimensional")); + OP_REQUIRES(ctx, tensor_out_shape.dims() == num_dims(), + errors::InvalidArgument("tensor_out must be ", num_dims(), + "-dimensional")); + // For maxpooling, out_backprop should have num_dims() dimensions. + OP_REQUIRES(ctx, out_backprop_shape.dims() == num_dims(), + errors::InvalidArgument("out_backprop must be ", num_dims(), + "-dimensional")); + + // What we want to compute: + // Given y = MaxPool(x), and xs_grad = MaxPoolGrad(x, y, ys_grad) + // MaxPoolGradGrad computes {ys_grad}_grad given x, y, and {xs_grad}_grad. + // + // In the regular TF op, this amounts to selecting for each window the + // incoming backprop value from xs_grad_grad that corresponds to the maximal + // value in the corresponding window of x. + // + // TODO(b/73062247): What we really want is a ReduceWindow with different + // arrays for index selection vs return value selection--a select-to-gather. + // + // Here, we implement a bitwise hack: we use the hi 16 bits of input for + // separate max pooling alongside each of the hi and lo 16 bits of + // out_backprop packed into 16 lo bits, which we then glue back together at + // the end to get a full 32 bits of gradient. + // + // This could select the wrong backprop value for two x values that are + // equally maximal up to the first 16 bits, in which case we are taking the + // latter. + // + // Note that in principle we could use 32 separate maxpools to recover each + // of 32 bits of the gradient while preserving 31 bits of input for the max + // pooling criteria; here, we just truncate to the first 16 bits of input. + + auto input = ctx->Input(0); + auto out_backprop = ctx->Input(2); + + auto b = ctx->builder(); + + auto sixteen = b->ConstantR0(16); + // in (f32) -> round to bf16 -> f32 for correct bitwidth -> 16-high-bit u32 + auto in_hi = b->BitcastConvertType( + b->ConvertElementType(b->ConvertElementType(input, xla::BF16), + xla::F32), + xla::U32); + auto bp_int = b->BitcastConvertType(out_backprop, xla::U32); + auto bp_hi = b->ShiftRightLogical(bp_int, sixteen); + auto bp_lo = b->ShiftRightLogical(b->ShiftLeft(bp_int, sixteen), sixteen); + auto in_hi_bp_hi = b->Add(in_hi, bp_hi); // Want an unsigned add. + auto in_hi_bp_lo = b->Add(in_hi, bp_lo); // Want an unsigned add. + + auto init_value = XlaHelpers::MinValue(b, DT_FLOAT); + // We will reduce by taking the maximal value up to 16 bits (ignoring the lo + // 16 bits of packed-in hi/lo backprop value). + auto rb = b->CreateSubBuilder("GreaterOrEqOf_ByFirst16Bits"); + { + // F32 parameters to satisfy lowering type restriction for reduce opcode. + const xla::Shape scalar = xla::ShapeUtil::MakeShape(xla::F32, {}); + auto lhs = rb->Parameter(0, scalar, "lhs"); + auto rhs = rb->Parameter(1, scalar, "rhs"); + auto sixteen = rb->ConstantR0(16); + auto lhs_criteria = rb->ShiftLeft( + rb->ShiftRightLogical(rb->BitcastConvertType(lhs, xla::S32), sixteen), + sixteen); + auto rhs_criteria = rb->ShiftLeft( + rb->ShiftRightLogical(rb->BitcastConvertType(rhs, xla::S32), sixteen), + sixteen); + // Must use a F32 comparison, because S32 would not work for negatives. + rb->Select(rb->Ge(rb->BitcastConvertType(lhs_criteria, xla::F32), + rb->BitcastConvertType(rhs_criteria, xla::F32)), + lhs, rhs); + } + auto reduce = rb->BuildAndNoteError(); + xla::Padding xla_padding = + (padding_ == VALID) ? xla::Padding::kValid : xla::Padding::kSame; + auto pooled_hi = + b->ReduceWindow(b->BitcastConvertType(in_hi_bp_hi, xla::F32), + init_value, reduce, ksize_, stride_, xla_padding); + auto pooled_lo = + b->ReduceWindow(b->BitcastConvertType(in_hi_bp_lo, xla::F32), + init_value, reduce, ksize_, stride_, xla_padding); + auto grads_hi = + b->ShiftLeft(b->BitcastConvertType(pooled_hi, xla::U32), sixteen); + auto grads_lo = b->ShiftRightLogical( + b->ShiftLeft(b->BitcastConvertType(pooled_lo, xla::U32), sixteen), + sixteen); + auto grads = b->Add(grads_hi, grads_lo); // Want an unsigned add. + + xla::PrimitiveType element_type; + OP_REQUIRES_OK(ctx, DataTypeToPrimitiveType(input_type(2), &element_type)); + ctx->SetOutput(0, b->BitcastConvertType(grads, element_type)); + } + + protected: + const int num_spatial_dims_; + std::vector ksize_; + std::vector stride_; + Padding padding_; + TensorFormat data_format_ = FORMAT_NHWC; +}; + +class MaxPool2DGradGradOp : public MaxPoolGradGradOp { + public: + explicit MaxPool2DGradGradOp(OpKernelConstruction* ctx) + : MaxPoolGradGradOp(ctx, /*num_spatial_dims=*/2) { + string data_format; + OP_REQUIRES_OK(ctx, ctx->GetAttr("data_format", &data_format)); + OP_REQUIRES(ctx, FormatFromString(data_format, &data_format_), + errors::InvalidArgument("Invalid data format")); + } +}; +REGISTER_XLA_OP(Name("MaxPoolGradGrad").TypeConstraint("T", DT_FLOAT), + MaxPool2DGradGradOp); +REGISTER_XLA_OP(Name("MaxPoolGradGradV2") + .TypeConstraint("T", DT_FLOAT) + .CompileTimeConstInput("ksize") + .CompileTimeConstInput("strides"), + MaxPool2DGradGradOp); + } // anonymous namespace } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/kernels/split_op.cc b/tensorflow/compiler/tf2xla/kernels/split_op.cc index 79c435c90a1f57250be90c2c2523bf3d7d231461..43c15e753805352875034dfd2c70a2a1ed9a4114 100644 --- a/tensorflow/compiler/tf2xla/kernels/split_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/split_op.cc @@ -111,27 +111,24 @@ class SplitVOp : public XlaOpKernel { void Compile(XlaOpKernelContext* ctx) override { const int32 num_split = num_outputs(); + const TensorShape input_shape = ctx->InputShape(0); const TensorShape index_shape = ctx->InputShape(2); - xla::Literal literal_index; - OP_REQUIRES_OK(ctx, ctx->ConstantInput(2, &literal_index)); - int32 split_dim; - OP_REQUIRES(ctx, index_shape.dims() == 0, - errors::InvalidArgument("split_dim input to Split Op must be a " - "scalar")); - split_dim = literal_index.Get({}); + int64 split_dim_orig; + OP_REQUIRES_OK(ctx, ctx->ConstantInputAsIntScalar(2, &split_dim_orig)); + int64 split_dim = split_dim_orig < 0 ? split_dim_orig + input_shape.dims() + : split_dim_orig; + OP_REQUIRES(ctx, 0 <= split_dim && split_dim < input_shape.dims(), + errors::InvalidArgument("-input rank(-", input_shape.dims(), + ") <= split_dim < input rank (", + input_shape.dims(), "), but got ", + split_dim_orig)); xla::ComputationDataHandle input = ctx->Input(0); - const TensorShape input_shape = ctx->InputShape(0); OP_REQUIRES(ctx, input_shape.dims() > 0, errors::InvalidArgument("Can't split a 0 dimensional input")); - OP_REQUIRES( - ctx, 0 <= split_dim && split_dim < input_shape.dims(), - errors::InvalidArgument("0 <= split_dim < number of input dimensions (", - input_shape.dims(), "), but got ", split_dim)); - OP_REQUIRES( ctx, num_split > 0, errors::InvalidArgument( diff --git a/tensorflow/compiler/tf2xla/xla_compiler.cc b/tensorflow/compiler/tf2xla/xla_compiler.cc index 5ec05c4121e059ad2b1307376766a41916fe61ae..0dc5118c9c659cc1529515f34c9eb43fd07a69e8 100644 --- a/tensorflow/compiler/tf2xla/xla_compiler.cc +++ b/tensorflow/compiler/tf2xla/xla_compiler.cc @@ -674,6 +674,14 @@ Status XlaCompiler::CompileGraph(const XlaCompiler::CompileOptions& options, VLOG(2) << "XLA output shape: " << xla::ShapeUtil::HumanString(result->xla_output_shape); + // Copy the host transfer metadata to the result. + for (const auto& send : host_compute_sends_) { + *result->host_compute_metadata.add_device_to_host() = send.second; + } + for (const auto& recv : host_compute_recvs_) { + *result->host_compute_metadata.add_host_to_device() = recv.second; + } + // Tensorflow expects a major-to-minor order of results. xla::LayoutUtil::SetToDefaultLayout(&result->xla_output_shape); @@ -708,4 +716,59 @@ Status XlaCompiler::GetChannelHandle(const string& key, return Status::OK(); } +namespace { + +void SetTransfer(const string& key, const std::vector& types, + const std::vector& shapes, + tf2xla::HostTransferMetadata* transfer) { + transfer->set_key(key); + CHECK(types.size() == shapes.size()); + for (int i = 0; i < types.size(); ++i) { + tf2xla::TensorMetadata* metadata = transfer->add_metadata(); + metadata->set_type(types[i]); + shapes[i].AsProto(metadata->mutable_shape()); + } +} + +} // namespace + +Status XlaCompiler::SetDeviceToHostMetadata( + const string& key, const std::vector& types, + const std::vector& shapes) { + if (host_compute_sends_.find(key) != host_compute_sends_.end()) { + return errors::InvalidArgument( + "Duplicate calls to SetDeviceToHostMetadata with key ", key); + } + tf2xla::HostTransferMetadata& transfer = host_compute_sends_[key]; + SetTransfer(key, types, shapes, &transfer); + return Status::OK(); +} + +Status XlaCompiler::GetDeviceToHostShapes( + const string& key, std::vector* shapes) const { + const auto iter = host_compute_sends_.find(key); + if (iter == host_compute_sends_.end()) { + return errors::InvalidArgument( + "No host compute send shapes registered for key ", key); + } + shapes->clear(); + for (int i = 0; i < iter->second.metadata_size(); ++i) { + TensorShape shape(iter->second.metadata(i).shape()); + shapes->push_back(shape); + } + return Status::OK(); +} + +Status XlaCompiler::SetHostToDeviceMetadata( + const string& key, const std::vector& types, + const std::vector& shapes) { + if (host_compute_recvs_.find(key) != host_compute_sends_.end()) { + return errors::InvalidArgument( + "Duplicate calls to SetHostToDeviceMetadata with key ", key); + } + tf2xla::HostTransferMetadata& transfer = host_compute_recvs_[key]; + SetTransfer(key, types, shapes, &transfer); + return Status::OK(); +} + } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/xla_compiler.h b/tensorflow/compiler/tf2xla/xla_compiler.h index c4449bc4be06daff856eff70c6d89be6ddbcf0ee..a70d2637e0b578ddb57dc990cd9550798e675e1d 100644 --- a/tensorflow/compiler/tf2xla/xla_compiler.h +++ b/tensorflow/compiler/tf2xla/xla_compiler.h @@ -16,6 +16,7 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_TF2XLA_XLA_COMPILER_H_ #define TENSORFLOW_COMPILER_TF2XLA_XLA_COMPILER_H_ +#include "tensorflow/compiler/tf2xla/host_compute_metadata.pb.h" #include "tensorflow/compiler/tf2xla/xla_compilation_device.h" #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/core/common_runtime/device.h" @@ -216,6 +217,10 @@ class XlaCompiler { // containing both constant and non-constant results. std::vector outputs; + // TensorFlow shapes and types of sends/recvs from HostCompute Ops to their + // matching RecvAtHost/SendFromHost Ops in the outer graph. + tf2xla::HostComputeMetadata host_compute_metadata; + // Resources whose values were updated by the computation, ordered // by return value position. Resource updates follow the non-constant // results in the outputs of XLA computation. @@ -296,6 +301,22 @@ class XlaCompiler { // same XlaCompiler. Status GetChannelHandle(const string& key, xla::ChannelHandle* channel); + // Sets the shapes and types for the device to host transfer associated with + // 'key'. + Status SetDeviceToHostMetadata(const string& key, + const std::vector& types, + const std::vector& shapes); + + // Gets the shapes the device to host transfer associated with 'key'. + Status GetDeviceToHostShapes(const string& key, + std::vector* shapes) const; + + // Sets the shapes and types for the host to device transfer associated with + // 'key'. + Status SetHostToDeviceMetadata(const string& key, + const std::vector& types, + const std::vector& shapes); + const Options& options() const { return options_; } xla::Client* client() const { return options_.client; } FunctionLibraryRuntime* flib_runtime() const { return flib_runtime_; } @@ -359,6 +380,9 @@ class XlaCompiler { std::unordered_map channels_; + std::unordered_map host_compute_sends_; + std::unordered_map host_compute_recvs_; + TF_DISALLOW_COPY_AND_ASSIGN(XlaCompiler); }; diff --git a/tensorflow/compiler/xla/BUILD b/tensorflow/compiler/xla/BUILD index c7cb69215fb051b7f87c3be3b0b419b9c1b8998c..cd13db4d300bb5bba21a734173b6afb9223539d8 100644 --- a/tensorflow/compiler/xla/BUILD +++ b/tensorflow/compiler/xla/BUILD @@ -52,6 +52,7 @@ xla_proto_library( visibility = ["//visibility:public"], deps = [ ":xla_data_proto", + "//tensorflow/compiler/xla/service:hlo_proto", "//tensorflow/compiler/xla/service:session_proto", ], ) diff --git a/tensorflow/compiler/xla/array.h b/tensorflow/compiler/xla/array.h index 46ee4e64c9ae7ca111d9d04bedcb74ff02a42386..24b58bec11bd8d8b5c79ac84c5f43c509644b51d 100644 --- a/tensorflow/compiler/xla/array.h +++ b/tensorflow/compiler/xla/array.h @@ -121,10 +121,31 @@ class Array { CHECK(idx == num_elements()); } - // Creates a 2D array of Eigen::half from the given nested initializer list of - // float values. + // Creates a 1D array of a floating-point type (half, bfloat16, float, + // or double) from an initializer list of float values. template ::value && + (std::is_same::value || + std::is_same::value || + std::is_same::value || + std::is_same::value) && + std::is_same::value>::type> + Array(std::initializer_list values) + : Array(ToInt64Vector({values.size()})) { + int64 idx = 0; + for (const auto& it1 : values) { + values_[idx] = static_cast(it1); + ++idx; + } + CHECK(idx == num_elements()); + } + + // Creates a 2D array of a floating-point type (half, bfloat16, float, + // or double) from an initializer list of float values. + template ::value || + std::is_same::value || + std::is_same::value || + std::is_same::value) && std::is_same::value>::type> Array(std::initializer_list> values) : Array(ToInt64Vector({values.size(), values.begin()->size()})) { @@ -155,10 +176,13 @@ class Array { CHECK(idx == num_elements()); } - // Creates a 3D array of Eigen::half from the given nested initializer list of - // float values. + // Creates a 3D array of a floating-point type (half, bfloat16, float, + // or double) from an initializer list of float values. template ::value && + (std::is_same::value || + std::is_same::value || + std::is_same::value || + std::is_same::value) && std::is_same::value>::type> Array(std::initializer_list>> values) @@ -196,10 +220,13 @@ class Array { CHECK(idx == num_elements()); } - // Creates a 4D array of Eigen::half from the given nested initializer list of - // float values. + // Creates a 4D array of a floating-point type (half, bfloat16, float, + // or double) from an initializer list of float values. template ::value && + (std::is_same::value || + std::is_same::value || + std::is_same::value || + std::is_same::value) && std::is_same::value>::type> Array(std::initializer_list< std::initializer_list>>> diff --git a/tensorflow/compiler/xla/array2d.h b/tensorflow/compiler/xla/array2d.h index d30e78ecde45cfcfcfdaac6c13c9d87ab5630c57..a17e81f44832f272fd93dce9f854042b4a84fde4 100644 --- a/tensorflow/compiler/xla/array2d.h +++ b/tensorflow/compiler/xla/array2d.h @@ -53,10 +53,13 @@ class Array2D : public Array { Array2D(std::initializer_list> values) : Array(values) {} - // Creates an array of Eigen::half from the given nested initializer list of - // float values. + // Creates an array of a floating-point type (half, bfloat16, float, + // or double) from the given nested initializer list of float values. template ::value && + (std::is_same::value || + std::is_same::value || + std::is_same::value || + std::is_same::value) && std::is_same::value>::type> Array2D(std::initializer_list> values) : Array(values) {} @@ -100,14 +103,16 @@ std::unique_ptr> MakeLinspaceArray2D(double from, double to, int64 n1, int64 n2) { auto array = MakeUnique>(n1, n2); int64 count = n1 * n2; - NativeT step = (count > 1) ? (to - from) / (count - 1) : 0.0f; + NativeT step = + static_cast((count > 1) ? (to - from) / (count - 1) : 0); auto set = [&array, n1, n2](int64 index, NativeT value) { (*array)(index / n2, index % n2) = value; }; for (int64 i = 0; i < count - 1; ++i) { - set(i, static_cast(from + i * step)); + set(i, (static_cast(from) + + static_cast(i) * static_cast(step))); } - set(count - 1, to); + set(count - 1, static_cast(to)); return array; } } // namespace xla diff --git a/tensorflow/compiler/xla/array3d.h b/tensorflow/compiler/xla/array3d.h index e5eb235d45d160d486d1499db665ed14a8509043..0e9a0722ae43e1dc6ecddde9cbc3daf1db058840 100644 --- a/tensorflow/compiler/xla/array3d.h +++ b/tensorflow/compiler/xla/array3d.h @@ -57,10 +57,13 @@ class Array3D : public Array { values) : Array(values) {} - // Creates an array of Eigen::half from the given nested initializer list of - // float values. + // Creates an array of a floating-point type (half, bfloat16, float, + // or double) from the given nested initializer list of float values. template ::value && + (std::is_same::value || + std::is_same::value || + std::is_same::value || + std::is_same::value) && std::is_same::value>::type> Array3D( std::initializer_list>> diff --git a/tensorflow/compiler/xla/array4d.h b/tensorflow/compiler/xla/array4d.h index cff70e54bad0116bdd08674b626b3bf99dc89e1f..a75fffc605aa0df3e1e2eeb6d3129718cbbba0e4 100644 --- a/tensorflow/compiler/xla/array4d.h +++ b/tensorflow/compiler/xla/array4d.h @@ -82,10 +82,13 @@ class Array4D : public Array { values) : Array(values) {} - // Creates an array of Eigen::half from the given nested initializer list of - // float values. + // Creates an array of a floating-point type (half, bfloat16, float, + // or double) from the given nested initializer list of float values. template ::value && + (std::is_same::value || + std::is_same::value || + std::is_same::value || + std::is_same::value) && std::is_same::value>::type> Array4D(std::initializer_list>>> diff --git a/tensorflow/compiler/xla/client/compile_only_client.cc b/tensorflow/compiler/xla/client/compile_only_client.cc index c7e2c4367b89ca2112022fa40449ae3ebe28463e..59662c95ac15e7c23790c5b5ff5d75a694613aeb 100644 --- a/tensorflow/compiler/xla/client/compile_only_client.cc +++ b/tensorflow/compiler/xla/client/compile_only_client.cc @@ -39,16 +39,15 @@ CompileOnlyClient::CompileAheadOfTime( return compiler_service_->CompileAheadOfTime(service_instances, options); } -int64 CompileOnlyClient::PointerSizeForTriple( - tensorflow::StringPiece target_triple) { - llvm::Triple triple(llvm::Triple::normalize( - llvm::StringRef(target_triple.data(), target_triple.size()))); - if (triple.isArch64Bit()) { +int64 CompileOnlyClient::PointerSizeForTriple(tensorflow::StringPiece triple) { + llvm::Triple llvm_triple( + llvm::Triple::normalize(llvm::StringRef(triple.data(), triple.size()))); + if (llvm_triple.isArch64Bit()) { return 8; - } else if (triple.isArch32Bit()) { + } else if (llvm_triple.isArch32Bit()) { return 4; } else { - CHECK(triple.isArch16Bit()); + CHECK(llvm_triple.isArch16Bit()); return 2; } } diff --git a/tensorflow/compiler/xla/client/computation_builder.cc b/tensorflow/compiler/xla/client/computation_builder.cc index 2a6e02649d15bc9fd47a893c41f9c8a62ac076c6..39d02f0863f78d4094f2cc4805f534713fb7e929 100644 --- a/tensorflow/compiler/xla/client/computation_builder.cc +++ b/tensorflow/compiler/xla/client/computation_builder.cc @@ -408,7 +408,7 @@ ComputationDataHandle ComputationBuilder::Reshape( ComputationDataHandle ComputationBuilder::Collapse( const ComputationDataHandle& operand, - tensorflow::gtl::ArraySlice dims_to_collapse) { + tensorflow::gtl::ArraySlice dimensions) { if (!first_error_.ok()) { return ComputationDataHandle(); } @@ -416,8 +416,8 @@ ComputationDataHandle ComputationBuilder::Collapse( // Don't support out-of-order collapse here. // Checks that the collapsed dimensions are in order and consecutive. for (tensorflow::gtl::ArraySlice::size_type i = 1; - i < dims_to_collapse.size(); ++i) { - if (dims_to_collapse[i] - 1 != dims_to_collapse[i - 1]) { + i < dimensions.size(); ++i) { + if (dimensions[i] - 1 != dimensions[i - 1]) { NoteError(InvalidArgument( "Collapsed dimensions are not in order and consecutive.")); return ComputationDataHandle(); @@ -434,9 +434,9 @@ ComputationDataHandle ComputationBuilder::Collapse( VLOG(3) << "original shape: " << ShapeUtil::HumanString(*original_shape); VLOG(3) << "dims to collapse: " - << tensorflow::str_util::Join(dims_to_collapse, ","); + << tensorflow::str_util::Join(dimensions, ","); - if (dims_to_collapse.size() <= 1) { + if (dimensions.size() <= 1) { // Not collapsing anything, trivially we can return the operand versus // enqueueing a trivial reshape. return operand; @@ -444,7 +444,7 @@ ComputationDataHandle ComputationBuilder::Collapse( std::vector new_sizes; for (int i = 0; i < ShapeUtil::Rank(*original_shape); ++i) { - if (i <= dims_to_collapse.front() || i > dims_to_collapse.back()) { + if (i <= dimensions.front() || i > dimensions.back()) { new_sizes.push_back(original_shape->dimensions(i)); } else { new_sizes.back() *= original_shape->dimensions(i); @@ -753,13 +753,13 @@ ComputationDataHandle ComputationBuilder::Infeed(const Shape& shape, } void ComputationBuilder::Outfeed(const ComputationDataHandle& operand, - const Shape& shape, + const Shape& shape_with_layout, const string& outfeed_config) { OpRequest op_request; OutfeedRequest* request = op_request.mutable_outfeed_request(); request->set_outfeed_config(outfeed_config); *request->mutable_operand() = operand; - *request->mutable_shape() = shape; + *request->mutable_shape() = shape_with_layout; RunOpAndNoteError(&op_request); } @@ -868,6 +868,14 @@ ComputationDataHandle ComputationBuilder::Or( return BinaryOp(BINOP_OR, lhs, rhs, broadcast_dimensions); } +// TODO(b/65209188): Create a dedicated lowering for Xor +ComputationDataHandle ComputationBuilder::Xor( + const ComputationDataHandle& lhs, const ComputationDataHandle& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions) { + return Or(And(Not(lhs), rhs, broadcast_dimensions), + And(lhs, Not(rhs), broadcast_dimensions)); +} + ComputationDataHandle ComputationBuilder::Not( const ComputationDataHandle& operand) { return UnaryOp(UNOP_NOT, operand); @@ -1382,15 +1390,16 @@ ComputationDataHandle ComputationBuilder::BatchNormInference( ComputationDataHandle ComputationBuilder::BatchNormGrad( const ComputationDataHandle& operand, const ComputationDataHandle& scale, - const ComputationDataHandle& mean, const ComputationDataHandle& var, + const ComputationDataHandle& batch_mean, + const ComputationDataHandle& batch_var, const ComputationDataHandle& grad_output, float epsilon, int64 feature_index) { OpRequest op_request; BatchNormGradRequest* request = op_request.mutable_batch_norm_grad_request(); *request->mutable_operand() = operand; *request->mutable_scale() = scale; - *request->mutable_mean() = mean; - *request->mutable_variance() = var; + *request->mutable_mean() = batch_mean; + *request->mutable_variance() = batch_var; *request->mutable_grad_output() = grad_output; request->set_epsilon(epsilon); request->set_feature_index(feature_index); diff --git a/tensorflow/compiler/xla/client/computation_builder.h b/tensorflow/compiler/xla/client/computation_builder.h index 377b6716399ea87b12bd0bd8a9486d4476e3cbf0..2141ebc2065a1a80d2fe820a7b6fe15434c89e28 100644 --- a/tensorflow/compiler/xla/client/computation_builder.h +++ b/tensorflow/compiler/xla/client/computation_builder.h @@ -512,6 +512,10 @@ class ComputationBuilder { const ComputationDataHandle& lhs, const ComputationDataHandle& rhs, tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + ComputationDataHandle Xor( + const ComputationDataHandle& lhs, const ComputationDataHandle& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + ComputationDataHandle Not(const ComputationDataHandle& operand); ComputationDataHandle ShiftLeft( @@ -872,7 +876,7 @@ class ComputationBuilder { Window* window); // Internal helper method that does the building for an arbitrary unary op. - ComputationDataHandle UnaryOp(UnaryOperation binop, + ComputationDataHandle UnaryOp(UnaryOperation unop, const ComputationDataHandle& operand); // Internal helper method that does the building for an arbitrary binary op. diff --git a/tensorflow/compiler/xla/client/local_client.h b/tensorflow/compiler/xla/client/local_client.h index b52a30f5a0b92e0094e6b0de3241c10a5a909cad..de0ed13c43f87966c272102b2e9af9ff3be63aea 100644 --- a/tensorflow/compiler/xla/client/local_client.h +++ b/tensorflow/compiler/xla/client/local_client.h @@ -69,7 +69,7 @@ class LocalExecutable { // of the computation. tensorflow::Status ValidateExecutionOptions( const tensorflow::gtl::ArraySlice arguments, - const ExecutableRunOptions& options, const Backend& backend); + const ExecutableRunOptions& run_options, const Backend& backend); // Records the computation in a SessionModule proto with the arguments used to // invoke it, and the result. Enabled by flag: --tla_dump_executions_to. diff --git a/tensorflow/compiler/xla/client/xla_client/BUILD b/tensorflow/compiler/xla/client/xla_client/BUILD new file mode 100644 index 0000000000000000000000000000000000000000..b912889e2627aa01e5a7441e71e6bf002916ba5e --- /dev/null +++ b/tensorflow/compiler/xla/client/xla_client/BUILD @@ -0,0 +1,77 @@ +# Description: +# The new XLA client libraries. +# +# This is NOT YET ready to use. + +licenses(["notice"]) # Apache 2.0 + +package(default_visibility = [":friends"]) + +package_group( + name = "friends", + includes = [ + "//tensorflow/compiler/xla:friends", + ], +) + +# Filegroup used to collect source files for dependency checking. +filegroup( + name = "c_srcs", + data = glob([ + "**/*.cc", + "**/*.h", + ]), +) + +load("//tensorflow:tensorflow.bzl", "tf_cc_test") + +# TODO(b/74197823): Replace computation_builder with xla_builder. +cc_library( + name = "xla_builder", + srcs = ["xla_builder.cc"], + hdrs = ["xla_builder.h"], + deps = [ + "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:status_macros", + "//tensorflow/compiler/xla:statusor", + "//tensorflow/compiler/xla:types", + "//tensorflow/compiler/xla:util", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/service:hlo", + "//tensorflow/compiler/xla/service:hlo_proto", + "//tensorflow/compiler/xla/service:shape_inference", + "//tensorflow/core:lib", + ], +) + +tf_cc_test( + name = "xla_builder_test", + srcs = ["xla_builder_test.cc"], + deps = [ + ":xla_builder", + "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:status_macros", + "//tensorflow/compiler/xla:test", + "//tensorflow/compiler/xla:test_helpers", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/service:hlo", + "//tensorflow/compiler/xla/service:hlo_matchers", + "//tensorflow/core:test", + ], +) + +# ----------------------------------------------------------------------------- + +filegroup( + name = "all_files", + srcs = glob( + ["**/*"], + exclude = [ + "**/METADATA", + "**/OWNERS", + ], + ), + visibility = ["//tensorflow:__subpackages__"], +) diff --git a/tensorflow/compiler/xla/client/xla_client/xla_builder.cc b/tensorflow/compiler/xla/client/xla_client/xla_builder.cc new file mode 100644 index 0000000000000000000000000000000000000000..6328a4f350fc70efaa96102f8202fb00b88b51f2 --- /dev/null +++ b/tensorflow/compiler/xla/client/xla_client/xla_builder.cc @@ -0,0 +1,253 @@ +/* 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/xla_client/xla_builder.h" + +#include +#include + +#include "tensorflow/compiler/xla/service/hlo_opcode.h" +#include "tensorflow/compiler/xla/service/shape_inference.h" +#include "tensorflow/compiler/xla/util.h" +#include "tensorflow/core/lib/strings/strcat.h" +#include "tensorflow/core/platform/mutex.h" + +namespace xla { + +using tensorflow::strings::StrCat; + +namespace { + +int64 GetUniqueId() { + static tensorflow::mutex mu(tensorflow::LINKER_INITIALIZED); + static int64 built_counter = 0; + tensorflow::mutex_lock loc(mu); + const int64 id = built_counter++; + return id; +} + +// Returns true if an instruction with the given opcode can be the root of the +// computation. +bool CanBeRoot(HloOpcode opcode) { + switch (opcode) { + case HloOpcode::kSend: + case HloOpcode::kOutfeed: + case HloOpcode::kTrace: + return false; + default: + return true; + } +} + +void SetOpcode(HloInstructionProto* instr, HloOpcode opcode) { + instr->set_opcode(HloOpcodeString(opcode)); +} + +} // namespace + +StatusOr> XlaBuilder::GetShape(const XlaOp& op) const { + TF_ASSIGN_OR_RETURN(auto instr, LookUpInstruction(op)); + return MakeUnique(instr->shape()); +} + +StatusOr XlaOp::GetShape() const { + TF_RET_CHECK(builder_ != nullptr); + TF_ASSIGN_OR_RETURN(auto shape, builder_->GetShape(*this)); + return *shape; +} + +XlaBuilder::XlaBuilder(const string& computation_name) + : name_(computation_name) {} + +XlaBuilder::~XlaBuilder() {} + +void XlaBuilder::NoteError(const Status& error) { + CHECK(!error.ok()); + if (die_immediately_on_error_) { + LOG(FATAL) << "error building computation: " << error; + } + + if (first_error_.ok()) { + first_error_ = error; + first_error_backtrace_.CreateCurrent(/*skip_count=*/1); + } +} + +StatusOr XlaBuilder::Build() { + if (!first_error_.ok()) { + string backtrace; + first_error_backtrace_.Dump(tensorflow::DebugWriteToString, &backtrace); + return AppendStatus(first_error_, backtrace); + } + + HloComputationProto entry; + ProgramShape* program_shape = entry.mutable_program_shape(); + + entry.set_name(name_); + + // Not all instructions can be roots. Walk backwards from the last added + // instruction until a valid root is found. + for (int64 i = instructions_.size() - 1; i >= 0; i--) { + TF_ASSIGN_OR_RETURN(HloOpcode opcode, + StringToHloOpcode(instructions_[i].opcode())); + if (CanBeRoot(opcode)) { + entry.set_root_name(instructions_[i].name()); + *program_shape->mutable_result() = instructions_[i].shape(); + break; + } + } + if (entry.root_name().empty()) { + return FailedPrecondition("no root instruction was found"); + } + + // Check that the parameter numbers are continuous from 0, and add parameter + // shapes and names to the program shape. + const int64 param_count = parameter_numbers_.size(); + for (int64 i = 0; i < param_count; i++) { + program_shape->add_parameters(); + program_shape->add_parameter_names(); + } + for (const HloInstructionProto& instr : instructions_) { + // Parameter number uniqueness is guaranteed in XlaBuilder::Parameter(). So + // to verify continuity, we just need to verify that every parameter is in + // the right range. + if (instr.opcode() == HloOpcodeString(HloOpcode::kParameter)) { + const int64 index = instr.parameter_number(); + TF_RET_CHECK(index >= 0 && index < param_count) + << "invalid parameter number: " << index; + *program_shape->mutable_parameters(index) = instr.shape(); + *program_shape->mutable_parameter_names(index) = instr.name(); + } + } + + for (auto& instruction : instructions_) { + entry.add_instructions()->Swap(&instruction); + } + + const int64 id = GetUniqueId(); + entry.set_id(id); + XlaComputation computation(id); + HloModuleProto* module = computation.mutable_proto(); + module->set_name(entry.name()); + module->set_entry_computation_name(entry.name()); + *module->mutable_program_shape() = entry.program_shape(); + for (auto& e : embedded_) { + module->add_computations()->Swap(&e.second); + } + module->add_computations()->Swap(&entry); + + return std::move(computation); +} + +XlaOp XlaBuilder::Add(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions) { + auto op = [&]() -> StatusOr { + HloInstructionProto instr; + SetOpcode(&instr, HloOpcode::kAdd); + TF_ASSIGN_OR_RETURN(const auto* lhs_instr, LookUpInstruction(lhs)); + TF_ASSIGN_OR_RETURN(const auto* rhs_instr, LookUpInstruction(rhs)); + TF_ASSIGN_OR_RETURN(*instr.mutable_shape(), + ShapeInference::InferBinaryOpShape( + HloOpcode::kAdd, lhs_instr->shape(), + rhs_instr->shape(), broadcast_dimensions)); + instr.add_operand_names(lhs_instr->name()); + instr.add_operand_names(rhs_instr->name()); + return AddInstruction(std::move(instr)); + }; + return NoteErrorOrReturn(op()); +} + +XlaOp XlaBuilder::ConstantLiteral(const Literal& literal) { + HloInstructionProto instr; + SetOpcode(&instr, HloOpcode::kConstant); + *instr.mutable_shape() = literal.shape(); + *instr.mutable_literal() = literal.ToProto(); + return AddInstruction(std::move(instr)); +} + +XlaOp XlaBuilder::Call(const XlaComputation& computation, + tensorflow::gtl::ArraySlice operands) { + auto op = [&]() -> StatusOr { + HloInstructionProto instr; + SetOpcode(&instr, HloOpcode::kCall); + std::vector operand_shapes; + for (const auto& operand : operands) { + TF_ASSIGN_OR_RETURN(const auto* input, LookUpInstruction(operand)); + operand_shapes.push_back(&input->shape()); + } + TF_ASSIGN_OR_RETURN(*instr.mutable_shape(), + ShapeInference::InferCallShape( + operand_shapes, + /*to_apply=*/computation.GetProgramShape())); + + // Add input operands. + for (const auto& operand : operands) { + TF_ASSIGN_OR_RETURN(auto operand_instr, LookUpInstruction(operand)); + instr.add_operand_names(operand_instr->name()); + } + + // Add called computation. + *instr.add_called_computation_names() = computation.proto().name(); + for (const HloComputationProto& e : computation.proto().computations()) { + embedded_.insert({e.id(), e}); + } + + return AddInstruction(std::move(instr)); + }; + return NoteErrorOrReturn(op()); +} + +XlaOp XlaBuilder::Parameter(int64 parameter_number, const Shape& shape, + const string& name) { + auto op = [&]() -> StatusOr { + HloInstructionProto instr; + SetOpcode(&instr, HloOpcode::kParameter); + if (parameter_numbers_.find(parameter_number) != parameter_numbers_.end()) { + return InvalidArgument("parameter %lld already registered", + parameter_number); + } + parameter_numbers_.insert(parameter_number); + instr.set_parameter_number(parameter_number); + instr.set_name(name); + *instr.mutable_shape() = shape; + return AddInstruction(std::move(instr)); + }; + return NoteErrorOrReturn(op()); +} + +XlaOp XlaBuilder::AddInstruction(HloInstructionProto&& instr) { + const int64 handle = instructions_.size(); + if (instr.name().empty()) { + instr.set_name(StrCat(instr.opcode(), ".", handle)); + } else { + // Append the handle to make sure the name is unique. + instr.set_name(StrCat(instr.name(), ".", handle)); + } + instructions_.push_back(instr); + + XlaOp op(handle, this); + return op; +} + +StatusOr XlaBuilder::LookUpInstruction( + const XlaOp& op) const { + TF_RET_CHECK(op.builder_ == this); + if (op.handle() >= instructions_.size() || op.handle() < 0) { + return InvalidArgument("no XlaOp value %lld", op.handle()); + } + return &instructions_[op.handle()]; +} + +} // namespace xla diff --git a/tensorflow/compiler/xla/client/xla_client/xla_builder.h b/tensorflow/compiler/xla/client/xla_client/xla_builder.h new file mode 100644 index 0000000000000000000000000000000000000000..7632bd289d792ef487fb667de3cea335e06778bf --- /dev/null +++ b/tensorflow/compiler/xla/client/xla_client/xla_builder.h @@ -0,0 +1,216 @@ +/* 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. +==============================================================================*/ + +// TODO(b/74197823): Replace computation_builder.h with this file. +// +// This is NOT YET ready to use. + +#ifndef TENSORFLOW_COMPILER_XLA_CLIENT_XLA_CLIENT_XLA_BUILDER_H_ +#define TENSORFLOW_COMPILER_XLA_CLIENT_XLA_CLIENT_XLA_BUILDER_H_ + +#include +#include +#include + +#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/service/hlo.pb.h" +#include "tensorflow/compiler/xla/shape_util.h" +#include "tensorflow/compiler/xla/status_macros.h" +#include "tensorflow/compiler/xla/statusor.h" +#include "tensorflow/compiler/xla/types.h" +#include "tensorflow/compiler/xla/xla_data.pb.h" +#include "tensorflow/core/lib/core/stringpiece.h" +#include "tensorflow/core/lib/gtl/array_slice.h" +#include "tensorflow/core/lib/gtl/flatset.h" +#include "tensorflow/core/platform/macros.h" +#include "tensorflow/core/platform/stacktrace.h" +#include "tensorflow/core/platform/types.h" + +namespace xla { + +class XlaBuilder; + +// This represents an instruction that has been enqueued using the XlaBuilder. +// This is used to pass to subsequent computations that depends upon the +// instruction as an operand. +// +// TODO(b/74197823): Replace xla::ComputationDataHandle with this one. +class XlaOp { + public: + StatusOr GetShape() const; + + private: + XlaOp() : handle_(0), builder_(nullptr) {} + XlaOp(int64 handle, XlaBuilder* builder) + : handle_(handle), builder_(builder) {} + + int64 handle() const { return handle_; } + friend class XlaBuilder; + + int64 handle_; + XlaBuilder* builder_; // Not owned. +}; + +// The computation graph that the user builds up with the XlaBuilder. +// +// TODO(b/74197823): Replace xla::Computation with this one. +class XlaComputation { + public: + XlaComputation(const XlaComputation&) = delete; + XlaComputation& operator=(const XlaComputation&) = delete; + + XlaComputation(XlaComputation&& from) { *this = std::move(from); } + + XlaComputation& operator=(XlaComputation&& from) { + proto_ = std::move(from.proto()); + unique_id_ = from.unique_id_; + return *this; + } + + // Returns the "program shape" (parameter and return shapes) for this + // computation. + const ProgramShape& GetProgramShape() const { return proto_.program_shape(); } + + const HloModuleProto& proto() const { return proto_; } + + private: + // Creates a null Computation. + XlaComputation(const int64 unique_id) : unique_id_(unique_id) {} + HloModuleProto* mutable_proto() { return &proto_; } + friend class XlaBuilder; + + int64 unique_id_; + HloModuleProto proto_; +}; + +// A convenient interface for building up computations. +// +// Thread-compatible. +// +// TODO(b/74197823): Replace xla::ComputationBuilder with this one. +class XlaBuilder { + public: + // computation_name: name to use for the built computation. + XlaBuilder(const string& computation_name); + + XlaBuilder(const XlaBuilder&) = delete; + XlaBuilder& operator=(const XlaBuilder&) = delete; + + ~XlaBuilder(); + + // Returns the computation name. + const string& name() const { return name_; } + + // Sets the builder to a mode where it will die immediately when an error is + // encountered, rather than producing it in a deferred fashion when Build() is + // called (which is the default). + void set_die_immediately_on_error(bool enabled) { + die_immediately_on_error_ = enabled; + } + + // Enqueues an add instruction onto the computation. + XlaOp Add(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + + // Enqueues a call instruction onto the computation. + XlaOp Call(const XlaComputation& computation, + tensorflow::gtl::ArraySlice operands); + + // Enqueues a "retrieve parameter value" instruction for a parameter that was + // passed to the computation. + XlaOp Parameter(int64 parameter_number, const Shape& shape, + const string& name); + + // Enqueues a constant with the value of the given literal onto the + // computation. + XlaOp ConstantLiteral(const Literal& literal); + + // Enqueues a constant onto the computation. Methods are templated on the + // native host type (NativeT) which corresponds to a specific XLA + // PrimitiveType as given in the following table: + // + // Native Type PrimitiveType + // ----------------------------- + // bool PRED + // int32 S32 + // int64 S64 + // uint32 U32 + // uint64 U64 + // float F32 + // double F64 + // + // Note: not all primitive types defined in xla_data.proto have a + // corresponding native type yet. + template + XlaOp ConstantR0(NativeT value); + + // Returns the shape of the given op. + StatusOr> GetShape(const XlaOp& op) const; + + // Builds the computation with the requested operations, or returns a non-ok + // status. + StatusOr Build(); + + private: + XlaOp AddInstruction(HloInstructionProto&& instr); + + // Notes that the error occurred by: + // * storing it internally and capturing a backtrace if it's the first error + // (this deferred value will be produced on the call to Build()) + // * dying if die_immediately_on_error_ is true + void NoteError(const Status& error); + + XlaOp NoteErrorOrReturn(StatusOr&& op) { + if (!op.ok()) { + NoteError(op.status()); + return XlaOp(); + } + return op.ConsumeValueOrDie(); + } + + StatusOr LookUpInstruction(const XlaOp& op) const; + + string name_; // Name to use for the built computation. + + // The first error encountered while building the computation. + // This is OK until the first error is encountered. + Status first_error_; + + // The saved stack trace from the point at which the first error occurred. + tensorflow::SavedStackTrace first_error_backtrace_; + + // The instructions of this computation. + std::vector instructions_; + + // The embedded computations used by this computation. Each computation was + // the entry computation of some XlaComputation, the key is the unique id of + // that XlaComputation. + std::map embedded_; + + // The unique parameter numbers. + tensorflow::gtl::FlatSet parameter_numbers_; + + // Mode bit that indicates whether to die when a first error is encountered. + bool die_immediately_on_error_ = false; +}; + +template +XlaOp XlaBuilder::ConstantR0(NativeT value) { + return ConstantLiteral(*Literal::CreateR0(value)); +} + +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_CLIENT_XLA_CLIENT_XLA_BUILDER_H_ diff --git a/tensorflow/compiler/xla/client/xla_client/xla_builder_test.cc b/tensorflow/compiler/xla/client/xla_client/xla_builder_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..a400e4e78b044ae633a0135b0011d5267eacc115 --- /dev/null +++ b/tensorflow/compiler/xla/client/xla_client/xla_builder_test.cc @@ -0,0 +1,137 @@ +/* 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/xla_client/xla_builder.h" + +#include + +#include "tensorflow/compiler/xla/service/hlo_matchers.h" +#include "tensorflow/compiler/xla/service/hlo_module.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/xla_data.pb.h" + +namespace xla { + +namespace { + +namespace op = xla::testing::opcode_matchers; + +using ::testing::HasSubstr; + +// TODO(b/74197823): Move the tests to service/. +class XlaBuilderTest : public ::testing::Test { + protected: + StatusOr> BuildHloModule(XlaBuilder* b) { + TF_ASSIGN_OR_RETURN(XlaComputation computation, b->Build()); + const HloModuleProto& proto = computation.proto(); + TF_ASSIGN_OR_RETURN(const auto& config, + HloModule::CreateModuleConfigFromProto(proto)); + return HloModule::CreateFromProto(proto, config); + } + + // Returns the name of the test currently being run. + string TestName() const { + return ::testing::UnitTest::GetInstance()->current_test_info()->name(); + } +}; + +TEST_F(XlaBuilderTest, OnePlusTwo) { + XlaBuilder b(TestName()); + b.Add(b.ConstantR0(1.0), b.ConstantR0(2.0)); + TF_ASSERT_OK_AND_ASSIGN(auto module, BuildHloModule(&b)); + auto root = module->entry_computation()->root_instruction(); + EXPECT_THAT(root, op::Add(op::Constant(), op::Constant())); +} + +TEST_F(XlaBuilderTest, ParamPlusConstant) { + XlaBuilder b(TestName()); + auto x = b.Parameter(0, ShapeUtil::MakeShape(F32, {3, 5}), "x"); + b.Add(x, b.ConstantR0(1.0)); + TF_ASSERT_OK_AND_ASSIGN(auto module, BuildHloModule(&b)); + auto root = module->entry_computation()->root_instruction(); + EXPECT_THAT(root, op::Add(op::Parameter(), op::Constant())); +} + +TEST_F(XlaBuilderTest, ParamPlusParam) { + XlaBuilder b(TestName()); + const auto& x_shape = ShapeUtil::MakeShape(S32, {2, 4, 6}); + const auto& y_shape = ShapeUtil::MakeShape(S32, {2, 4}); + auto x = b.Parameter(0, x_shape, "x"); + auto y = b.Parameter(1, y_shape, "y"); + auto add = b.Add(x, y, /*broadcast_dimensions=*/{0, 1}); + + TF_ASSERT_OK_AND_ASSIGN(auto add_shape, add.GetShape()); + EXPECT_TRUE(ShapeUtil::Equal(add_shape, x_shape)); + + TF_ASSERT_OK_AND_ASSIGN(auto module, BuildHloModule(&b)); + auto root = module->entry_computation()->root_instruction(); + EXPECT_THAT(root, op::Add(op::Parameter(0), op::Parameter(1))); +} + +TEST_F(XlaBuilderTest, XPlusX) { + XlaBuilder b(TestName()); + auto x = b.Parameter(0, ShapeUtil::MakeShape(S32, {1, 3, 5, 7}), "x"); + b.Add(x, x); + TF_ASSERT_OK_AND_ASSIGN(auto module, BuildHloModule(&b)); + auto root = module->entry_computation()->root_instruction(); + EXPECT_THAT(root, op::Add(op::Parameter(0), op::Parameter(0))); +} + +TEST_F(XlaBuilderTest, ShapeInferenceError) { + XlaBuilder b(TestName()); + auto x = b.Parameter(0, ShapeUtil::MakeShape(U32, {2, 4, 6}), "x"); + auto y = b.Parameter(1, ShapeUtil::MakeShape(U32, {2, 4}), "y"); + b.Add(x, y); + auto statusor = BuildHloModule(&b); + ASSERT_FALSE(statusor.ok()); + EXPECT_THAT(statusor.status().error_message(), HasSubstr("shape inference")); +} + +TEST_F(XlaBuilderTest, ParameterAlreadyRegistered) { + XlaBuilder b_call("add"); + b_call.Parameter(0, ShapeUtil::MakeShape(PRED, {}), "x"); + + XlaBuilder b(TestName()); + auto x = b.Parameter(0, ShapeUtil::MakeShape(PRED, {}), "x"); + auto y = b.Parameter(0, ShapeUtil::MakeShape(PRED, {}), "y"); + b.Add(x, y); + auto statusor = BuildHloModule(&b); + ASSERT_FALSE(statusor.ok()); + EXPECT_THAT(statusor.status().error_message(), + HasSubstr("parameter 0 already registered")); +} + +TEST_F(XlaBuilderTest, Call) { + XlaBuilder b_call("the_only_to_apply"); + auto p0 = b_call.Parameter(0, ShapeUtil::MakeShape(F32, {}), "p0"); + auto p1 = b_call.Parameter(1, ShapeUtil::MakeShape(F32, {}), "p1"); + b_call.Add(p0, p1); + TF_ASSERT_OK_AND_ASSIGN(auto call, b_call.Build()); + XlaBuilder b(TestName()); + auto x = b.Parameter(0, ShapeUtil::MakeShape(F32, {}), "x"); + auto y = b.Parameter(1, ShapeUtil::MakeShape(F32, {}), "y"); + auto one = b.ConstantR0(1); + auto two = b.ConstantR0(2); + b.Add(b.Call(call, {x, y}), b.Call(call, {one, two})); + TF_ASSERT_OK_AND_ASSIGN(auto module, BuildHloModule(&b)); + auto root = module->entry_computation()->root_instruction(); + EXPECT_THAT(root, op::Add(op::Call(op::Parameter(), op::Parameter()), + op::Call(op::Constant(), op::Constant()))); +} + +} // namespace +} // namespace xla diff --git a/tensorflow/compiler/xla/literal_util.cc b/tensorflow/compiler/xla/literal_util.cc index 823da43b5ab2e9c8e80181efc993735877a2c363..0a24db046a390eb447bc3518476c3dd9897d973c 100644 --- a/tensorflow/compiler/xla/literal_util.cc +++ b/tensorflow/compiler/xla/literal_util.cc @@ -223,7 +223,7 @@ Status Literal::CopySliceFromInternal( Literal::StrideConfig stride_config(src_literal.shape(), shape(), copy_size); - auto copy_proc = [&](const std::vector& indexes) { + auto copy_proc = [&](tensorflow::gtl::ArraySlice indexes) { // Map from multi-dimensional index, to source index. std::transform(indexes.begin(), indexes.end(), src_base.begin(), src_indexes.begin(), std::plus()); @@ -248,6 +248,28 @@ Status Literal::CopySliceFromInternal( return Status::OK(); } +Status Literal::CopyElementFrom(const Literal& src_literal, + tensorflow::gtl::ArraySlice src_index, + tensorflow::gtl::ArraySlice dest_index) { + DCHECK_EQ(shape().element_type(), src_literal.shape().element_type()); + const int64 src_linear_index = IndexUtil::MultidimensionalIndexToLinearIndex( + src_literal.shape(), src_index); + const int64 dest_linear_index = + IndexUtil::MultidimensionalIndexToLinearIndex(shape(), dest_index); + const int64 primitive_size = + ShapeUtil::ByteSizeOfPrimitiveType(shape().element_type()); + + char* dest_address = + static_cast(untyped_data()) + dest_linear_index * primitive_size; + const char* source_address = + static_cast(src_literal.untyped_data()) + + src_linear_index * primitive_size; + if (dest_address != source_address) { + memcpy(dest_address, source_address, primitive_size); + } + return Status::OK(); +} + std::vector Literal::DecomposeTuple() { CHECK(ShapeUtil::IsTuple(shape())); std::vector elements; @@ -343,7 +365,7 @@ Status Literal::Piece::CopyFrom(const Literal::Piece& src) { #undef COPY_ELEMENTS default: return Unimplemented( - "Unhandled primitive type %s", + "Copying a Literal object with element type %s is not implemented.", PrimitiveType_Name(subshape().element_type()).c_str()); } } @@ -491,7 +513,10 @@ Status Literal::CopySliceFrom(const Literal& src_literal, default: break; } - return Unimplemented("Unhandled primitive type %d", shape().element_type()); + return Unimplemented( + "Copying a slice from a Literal object with element type %d is not " + "implemented.", + shape().element_type()); } /* static */ Literal Literal::Zero(PrimitiveType primitive_type) { @@ -808,9 +833,10 @@ std::unique_ptr Literal::Slice( DimensionVector result_dimensions; for (int64 dnum = 0; dnum < ShapeUtil::Rank(shape()); ++dnum) { CHECK_GE(start_indices[dnum], 0); - CHECK_LE(limit_indices[dnum], shape().dimensions(dnum)); + CHECK_LE(limit_indices[dnum], shape().dimensions(dnum)) + << "dnum = " << dnum; int64 dimension = limit_indices[dnum] - start_indices[dnum]; - CHECK_GT(dimension, 0); + CHECK_GE(dimension, 0) << "dnum = " << dnum; result_dimensions.push_back(dimension); } const auto result_shape = @@ -997,6 +1023,36 @@ StatusOr Literal::GetIntegralAsS64( } } +Status Literal::SetIntegralAsS64(tensorflow::gtl::ArraySlice multi_index, + int64 value) { + CHECK(LayoutUtil::IsDenseArray(shape())); + switch (shape().element_type()) { + case PRED: + Set(multi_index, value); + break; + case U8: + Set(multi_index, value); + break; + case S32: + Set(multi_index, value); + break; + case S64: + Set(multi_index, value); + break; + case U32: + Set(multi_index, value); + break; + case U64: + Set(multi_index, value); + break; + default: + return FailedPrecondition( + "Array element type is not integral: %s", + PrimitiveType_Name(shape().element_type()).c_str()); + } + return Status::OK(); +} + tensorflow::gtl::ArraySlice Literal::GetSparseIndex( int64 sparse_element_number, const ShapeIndex& shape_index) const { const Piece& p = piece(shape_index); @@ -1394,8 +1450,8 @@ StatusOr> ConvertIfDestTypeMatches( return ConvertToC64(src_literal); // Other types are not yet supported. default: - return InvalidArgument( - "Unimplemented: Convert from type %s to type %s", + return Unimplemented( + "Converting from type %s to type %s is not implemented.", PrimitiveType_Name(src_literal.shape().element_type()).c_str(), PrimitiveType_Name(primitive_dest_type).c_str()); } @@ -1424,10 +1480,29 @@ StatusOr> Literal::Convert( #undef CONVERT_IF_DEST_TYPE_MATCHES // Other types are not yet supported. default: - return InvalidArgument("Unimplemented: Convert from type %s to type %s", - PrimitiveType_Name(shape().element_type()).c_str(), - PrimitiveType_Name(primitive_dest_type).c_str()); + return Unimplemented( + "Converting from type %s to type %s is not implemented.", + PrimitiveType_Name(shape().element_type()).c_str(), + PrimitiveType_Name(primitive_dest_type).c_str()); + } +} + +StatusOr> Literal::ConvertToShape( + const Shape& dest_shape) const { + if (!ShapeUtil::IsTuple(dest_shape)) { + return Convert(dest_shape.element_type()); } + std::vector elements; + for (int i = 0; i < ShapeUtil::TupleElementCount(shape()); ++i) { + auto element = LiteralView::Create(*this, {i}); + TF_ASSIGN_OR_RETURN( + auto new_element, + element.ConvertToShape(ShapeUtil::GetSubshape(dest_shape, {i}))); + elements.push_back(std::move(*new_element)); + } + auto converted = MakeUnique(); + *converted = Literal::MoveIntoTuple(&elements); + return std::move(converted); } template diff --git a/tensorflow/compiler/xla/literal_util.h b/tensorflow/compiler/xla/literal_util.h index d5ae3fd72322fe243f0156dfbe236b6d62ab8c9d..e24f5285d9a14cf26216e4a16c6d1e516afc413f 100644 --- a/tensorflow/compiler/xla/literal_util.h +++ b/tensorflow/compiler/xla/literal_util.h @@ -262,6 +262,11 @@ class Literal { tensorflow::gtl::ArraySlice dest_base, tensorflow::gtl::ArraySlice copy_size); + // Copies one element from src_literal[src_index] to (*this)[dest_index]. + Status CopyElementFrom(const Literal& src_literal, + tensorflow::gtl::ArraySlice src_index, + tensorflow::gtl::ArraySlice dest_index); + // Returns a vector containing the tuple elements of this Literal as separate // Literals. This Literal must be tuple-shaped and can be a nested tuple. The // elements are moved into the new Literals; no data is copied. Upon return @@ -333,6 +338,11 @@ class Literal { StatusOr> Convert( PrimitiveType primitive_dest_type) const; + // Converts this literal to the given shape. Returns an error is the + // conversion is not possible. + StatusOr> ConvertToShape( + const Shape& dest_shape) const; + // Creates a scalar literal value zero of the given primitive type. static Literal Zero(PrimitiveType primitive_type); @@ -469,6 +479,11 @@ class Literal { StatusOr GetIntegralAsS64( tensorflow::gtl::ArraySlice multi_index) const; + // As Set(), but truncates `value` to the literal element type before storing. + // This literal must be an array. + Status SetIntegralAsS64(tensorflow::gtl::ArraySlice multi_index, + int64 value); + // Returns an identity matrix (rank 2) with the given row and column count. template static std::unique_ptr MakeIdentityR2(int64 size); @@ -1269,7 +1284,7 @@ Status Literal::Populate(const FnType& generator) { int64 minor_dimension_size = ShapeUtil::GetDimension(this_shape, stride_config.minor_dimension); - auto init_function = [&](const std::vector& indexes) { + auto init_function = [&](tensorflow::gtl::ArraySlice indexes) { const int64 index = IndexUtil::MultidimensionalIndexToLinearIndex(shape(), indexes); std::copy(indexes.begin(), indexes.end(), minor_scan_indexes.begin()); diff --git a/tensorflow/compiler/xla/literal_util_test.cc b/tensorflow/compiler/xla/literal_util_test.cc index ee2f4fe87440428c7364fe2924003c5124f4eaa2..04e45f00491b0bef94f3c0af1c875b2d007194fd 100644 --- a/tensorflow/compiler/xla/literal_util_test.cc +++ b/tensorflow/compiler/xla/literal_util_test.cc @@ -30,6 +30,7 @@ limitations under the License. namespace xla { namespace { +using tensorflow::gtl::ArraySlice; using ::testing::ElementsAre; using ::testing::HasSubstr; @@ -214,11 +215,11 @@ TEST_F(LiteralUtilTest, CreateSparse) { std::vector expected_values = {8, 9, 7, 10}; EXPECT_EQ(literal->sparse_indices()->data(), - tensorflow::gtl::ArraySlice( - expected_indices.data(), expected_indices.num_elements())); - EXPECT_EQ(tensorflow::gtl::ArraySlice(literal->data().data(), - expected_values.size()), - tensorflow::gtl::ArraySlice(expected_values)); + ArraySlice(expected_indices.data(), + expected_indices.num_elements())); + EXPECT_EQ( + ArraySlice(literal->data().data(), expected_values.size()), + ArraySlice(expected_values)); } TEST_F(LiteralUtilTest, LiteralR4F32ProjectedStringifies) { @@ -290,7 +291,7 @@ TEST_F(LiteralUtilTest, EachCellR2F32) { // clang-format on std::vector> seen; literal->EachCellAsString( - [&seen](tensorflow::gtl::ArraySlice indices, const string& value) { + [&seen](ArraySlice indices, const string& value) { seen.emplace_back(indices[0], indices[1], value); }); @@ -622,11 +623,10 @@ TEST_F(LiteralUtilTest, TransposeR4) { // clang-format on auto reshape = original->Transpose(/*permutation=*/{2, 3, 0, 1}); - reshape->EachCell( - [&](tensorflow::gtl::ArraySlice indices, float value) { - EXPECT_EQ(value, original->Get( - {indices[2], indices[3], indices[0], indices[1]})); - }); + reshape->EachCell([&](ArraySlice indices, float value) { + EXPECT_EQ(value, original->Get( + {indices[2], indices[3], indices[0], indices[1]})); + }); } TEST_F(LiteralUtilTest, TestR4RelayoutEquivalence) { @@ -863,7 +863,7 @@ TEST_F(LiteralUtilTest, CopySliceFrom) { const int64 zero_base[] = {0, 0, 0, 0}; const int64 step[] = {1, 1, 1, 1}; uint32 seqnr = 0; - auto init_proc = [&](const std::vector& indexes) { + auto init_proc = [&](ArraySlice indexes) { source->Set(indexes, ++seqnr); return true; }; @@ -879,7 +879,7 @@ TEST_F(LiteralUtilTest, CopySliceFrom) { std::vector source_indexes(TF_ARRAYSIZE(dimensions), 0); std::vector blank_indexes(TF_ARRAYSIZE(dimensions), 0); bool matched = true; - auto check_proc = [&](const std::vector& indexes) { + auto check_proc = [&](ArraySlice indexes) { std::copy(indexes.begin(), indexes.end(), source_indexes.begin()); std::transform(source_indexes.begin(), source_indexes.end(), src_base, source_indexes.begin(), std::plus()); @@ -1067,7 +1067,7 @@ TEST_F(LiteralUtilTest, Populate) { primitive_util::NativeToPrimitiveType(), data.dimensions, data.layout); auto literal = Literal::CreateFromShape(shape); - auto generator = [&](tensorflow::gtl::ArraySlice indexes) -> uint32 { + auto generator = [&](ArraySlice indexes) -> uint32 { // Offsets from linear index just to avoid R0 literals to be initialized // with zero. return IndexUtil::MultidimensionalIndexToLinearIndex(literal->shape(), @@ -1079,7 +1079,7 @@ TEST_F(LiteralUtilTest, Populate) { std::vector zero_base(data.dimensions.size(), 0); std::vector step(data.dimensions.size(), 1); bool matched = true; - auto check_function = [&](const std::vector& indexes) { + auto check_function = [&](ArraySlice indexes) { auto value = literal->Get(indexes); matched = matched && (value == generator(indexes)); return matched; @@ -1232,15 +1232,15 @@ TEST_F(LiteralUtilTest, ConvertIfTypesMatch) { EXPECT_EQ(*conv, *c64); EXPECT_EQ(s32->Convert(TUPLE).status().code(), - tensorflow::error::INVALID_ARGUMENT); + tensorflow::error::UNIMPLEMENTED); EXPECT_EQ(s32->Convert(S16).status().code(), - tensorflow::error::INVALID_ARGUMENT); + tensorflow::error::UNIMPLEMENTED); EXPECT_EQ(s32->Convert(U16).status().code(), - tensorflow::error::INVALID_ARGUMENT); + tensorflow::error::UNIMPLEMENTED); EXPECT_EQ(c64->Convert(F32).status().code(), - tensorflow::error::INVALID_ARGUMENT); + tensorflow::error::UNIMPLEMENTED); EXPECT_EQ(c64->Convert(S32).status().code(), - tensorflow::error::INVALID_ARGUMENT); + tensorflow::error::UNIMPLEMENTED); } TEST_F(LiteralUtilTest, CopyFromProto_Bool) { diff --git a/tensorflow/compiler/xla/python/xla_client.py b/tensorflow/compiler/xla/python/xla_client.py index 3b8ec851d5aa032ebbf4f6cfc7e12f5a03539cbd..90cda42f3227c80826ffbf4e5473647c2795544d 100644 --- a/tensorflow/compiler/xla/python/xla_client.py +++ b/tensorflow/compiler/xla/python/xla_client.py @@ -30,9 +30,9 @@ from tensorflow.compiler.xla import xla_data_pb2 from tensorflow.compiler.xla.python import pywrap_xla as c_api -# Most functions are snake_case for consistency with other modules, -# whereas method names of ComputationBuilder and LocalComputation are -# CamelCase for consistency with XLA. +# Most functions are snake_case for consistency with other modules, whereas +# method names of ComputationBuilder and LocalComputation are CamelCase for +# consistency with XLA. # pylint: disable=invalid-name @@ -123,24 +123,34 @@ _BINARY_OPS = [ 'Pow', ] + XLA_ELEMENT_TYPE_TO_DTYPE = { - xla_data_pb2.F32: np.dtype(np.float32), - xla_data_pb2.F64: np.dtype(np.float64), - xla_data_pb2.S32: np.dtype(np.int32), - xla_data_pb2.S64: np.dtype(np.int64), - xla_data_pb2.U32: np.dtype(np.uint32), - xla_data_pb2.U64: np.dtype(np.uint64), - xla_data_pb2.PRED: np.dtype(np.bool), + xla_data_pb2.PRED: np.dtype('bool'), + xla_data_pb2.S8: np.dtype('int8'), + xla_data_pb2.S16: np.dtype('int16'), + xla_data_pb2.S32: np.dtype('int32'), + xla_data_pb2.S64: np.dtype('int64'), + xla_data_pb2.U8: np.dtype('uint8'), + xla_data_pb2.U16: np.dtype('uint16'), + xla_data_pb2.U32: np.dtype('uint32'), + xla_data_pb2.U64: np.dtype('uint64'), + xla_data_pb2.F16: np.dtype('float16'), + xla_data_pb2.F32: np.dtype('float32'), + xla_data_pb2.F64: np.dtype('float64'), + xla_data_pb2.C64: np.dtype('complex64'), xla_data_pb2.TUPLE: np.dtype(np.object), } # Note the conversion on the key. Numpy has a known issue wherein dtype hashing # doesn't work as expected (https://github.com/numpy/numpy/issues/7242). Thus, # when keying by dtype in this dict, we use the string form of dtypes. -DTYPE_TO_XLA_ELEMENT_TYPE = { - str(v): k - for k, v in XLA_ELEMENT_TYPE_TO_DTYPE.items() -} +DTYPE_TO_XLA_ELEMENT_TYPE = {str(dt): et + for et, dt in XLA_ELEMENT_TYPE_TO_DTYPE.items()} + + +def dtype_to_etype(dtype): + """Convenience function for reading DTYPE_TO_XLA_ELEMENT_TYPE.""" + return DTYPE_TO_XLA_ELEMENT_TYPE[str(np.dtype(dtype))] class LocalBuffer(object): diff --git a/tensorflow/compiler/xla/reference_util.cc b/tensorflow/compiler/xla/reference_util.cc index a9acdae380af5b7f9efb3d08302fc717108f5e40..ad3a28e11939d6259ebd75d544a950ba7abd741f 100644 --- a/tensorflow/compiler/xla/reference_util.cc +++ b/tensorflow/compiler/xla/reference_util.cc @@ -30,29 +30,23 @@ limitations under the License. namespace xla { -/* static */ std::unique_ptr> ReferenceUtil::TransposeArray2D( - const Array2D& operand) { - auto result = MakeUnique>(operand.width(), operand.height()); - for (int64 w = 0; w < operand.width(); ++w) { - for (int64 h = 0; h < operand.height(); ++h) { - (*result)(w, h) = operand(h, w); - } - } - - return result; -} - -/* static */ std::unique_ptr> ReferenceUtil::MatmulArray2D( - const Array2D& lhs, const Array2D& rhs) { +namespace { + +template +std::unique_ptr> MatmulArray2DImpl( + const Array2D& lhs, const Array2D& rhs, + const std::function& impl_fn) { CHECK_EQ(lhs.width(), rhs.height()); int m = lhs.height(); int n = rhs.width(); int k = lhs.width(); - auto result = MakeUnique>(m, n); + auto result = MakeUnique>(m, n); // Because Eigen is a header-oriented library, make sure that the Eigen code // is the same as the code used by the CPU backend (otherwise the linker will // randomly pick *some* definition). - __xla_cpu_runtime_EigenSingleThreadedMatMulF32( + impl_fn( /*run_options_ptr=*/nullptr, result->data(), rhs.data(), lhs.data(), n, m, k, /*transpose_lhs=*/0, @@ -60,22 +54,24 @@ namespace xla { return result; } +} // namespace + +/* static */ std::unique_ptr> ReferenceUtil::MatmulArray2D( + const Array2D& lhs, const Array2D& rhs) { + return MatmulArray2DImpl( + lhs, rhs, __xla_cpu_runtime_EigenSingleThreadedMatMulF16); +} + +/* static */ std::unique_ptr> ReferenceUtil::MatmulArray2D( + const Array2D& lhs, const Array2D& rhs) { + return MatmulArray2DImpl( + lhs, rhs, __xla_cpu_runtime_EigenSingleThreadedMatMulF32); +} + /* static */ std::unique_ptr> ReferenceUtil::MatmulArray2D( const Array2D& lhs, const Array2D& rhs) { - CHECK_EQ(lhs.width(), rhs.height()); - int m = lhs.height(); - int n = rhs.width(); - int k = lhs.width(); - auto result = MakeUnique>(m, n); - // Because Eigen is a header-oriented library, make sure that the Eigen code - // is the same as the code used by the CPU backend (otherwise the linker will - // randomly pick *some* definition). - __xla_cpu_runtime_EigenSingleThreadedMatMulF64( - /*run_options_ptr=*/nullptr, result->data(), rhs.data(), lhs.data(), n, m, - k, - /*transpose_lhs=*/0, - /*transpose_rhs=*/0); - return result; + return MatmulArray2DImpl( + lhs, rhs, __xla_cpu_runtime_EigenSingleThreadedMatMulF64); } /* static */ std::unique_ptr> ReferenceUtil::Array2DF32ToF64( @@ -188,18 +184,6 @@ ReferenceUtil::SeparableConvArray4D(const Array4D& input, return tensorflow::MathUtil::CeilOfRatio(unpadded_width, stride); } -/* static */ std::unique_ptr> -ReferenceUtil::ReduceWindow1DGeneric( - const tensorflow::gtl::ArraySlice& operand, float init, - const std::function& reduce_func, - const tensorflow::gtl::ArraySlice& window, - const tensorflow::gtl::ArraySlice& stride, Padding padding) { - std::vector dim_lengths{static_cast(operand.size())}; - return ReduceWindow1DGeneric( - operand, init, reduce_func, window, stride, - xla::MakePadding(dim_lengths, window, stride, padding)); -} - /* static */ std::unique_ptr> ReferenceUtil::ReduceWindow1DGeneric( const tensorflow::gtl::ArraySlice& operand, float init, @@ -239,23 +223,28 @@ ReferenceUtil::ReduceWindow1DAdd( const tensorflow::gtl::ArraySlice& window, const tensorflow::gtl::ArraySlice& stride, Padding padding) { const auto add_reduce = [](float arg1, float arg2) { return arg1 + arg2; }; - return ReduceWindow1DGeneric(operand, init, add_reduce, window, stride, - padding); + std::vector dim_lengths{static_cast(operand.size())}; + return ReduceWindow1DGeneric( + operand, init, add_reduce, window, stride, + xla::MakePadding(dim_lengths, window, stride, padding)); } -/* static */ std::unique_ptr> ReferenceUtil::ReduceWindow2DAdd( +/* static */ std::unique_ptr> +ReferenceUtil::ReduceWindow2DGeneric( const Array2D& operand, float init, + const std::function& reduce_func, const tensorflow::gtl::ArraySlice& window, - const tensorflow::gtl::ArraySlice& stride, Padding padding) { + const tensorflow::gtl::ArraySlice& stride, + const tensorflow::gtl::ArraySlice>& padding) { std::vector dim_lengths{operand.height(), operand.width()}; - auto padding_both = xla::MakePadding(dim_lengths, window, stride, padding); std::vector window_counts(window.size(), 0); std::vector pad_low(window.size(), 0); for (int64 i = 0; i < window.size(); ++i) { + int64 padded_width = padding[i].first + dim_lengths[i] + padding[i].second; window_counts[i] = - WindowCount(dim_lengths[i], window[i], stride[i], padding); - pad_low[i] = padding_both[i].first; + window_util::StridedBound(padded_width, window[i], stride[i]); + pad_low[i] = padding[i].first; } auto result = MakeUnique>(window_counts[0], window_counts[1]); @@ -271,7 +260,7 @@ ReferenceUtil::ReduceWindow1DAdd( if (i0_base + i0_win >= 0 && i1_base + i1_win >= 0 && i0_base + i0_win < operand.n1() && i1_base + i1_win < operand.n2()) { - val += operand(i0_base + i0_win, i1_base + i1_win); + val = reduce_func(val, operand(i0_base + i0_win, i1_base + i1_win)); } } } @@ -281,6 +270,17 @@ ReferenceUtil::ReduceWindow1DAdd( return result; } +/* static */ std::unique_ptr> ReferenceUtil::ReduceWindow2DAdd( + const Array2D& operand, float init, + const tensorflow::gtl::ArraySlice& window, + const tensorflow::gtl::ArraySlice& stride, Padding padding) { + const auto add_reduce = [](float arg1, float arg2) { return arg1 + arg2; }; + std::vector dim_lengths{operand.height(), operand.width()}; + return ReduceWindow2DGeneric( + operand, init, add_reduce, window, stride, + xla::MakePadding(dim_lengths, window, stride, padding)); +} + /* static */ std::unique_ptr> ReferenceUtil::ReduceWindow3DAdd( const Array3D& operand, float init, const tensorflow::gtl::ArraySlice& window, @@ -472,7 +472,7 @@ ReferenceUtil::SelectAndScatter4DGePlus( i3_base + i3_win < operand.n4()) { float tmp = operand(i0_base + i0_win, i1_base + i1_win, i2_base + i2_win, i3_base + i3_win); - if (tmp >= val) { + if (tmp > val) { val = tmp; scatter_0 = i0_base + i0_win; scatter_1 = i1_base + i1_win; diff --git a/tensorflow/compiler/xla/reference_util.h b/tensorflow/compiler/xla/reference_util.h index 3ec96f2f38b8f91e1549419b60481327fa9bbd5f..28d6a8c3fe85fa4179bf2f41c82ad4eb93a045fe 100644 --- a/tensorflow/compiler/xla/reference_util.h +++ b/tensorflow/compiler/xla/reference_util.h @@ -39,10 +39,22 @@ namespace xla { class ReferenceUtil { public: // Returns the result of a transpose operation on the input matrix. - static std::unique_ptr> TransposeArray2D( - const Array2D& operand); + template + static std::unique_ptr> TransposeArray2D( + const Array2D& operand) { + auto result = MakeUnique>(operand.width(), operand.height()); + for (int64 w = 0; w < operand.width(); ++w) { + for (int64 h = 0; h < operand.height(); ++h) { + (*result)(w, h) = operand(h, w); + } + } + + return result; + } // Returns the result of a matrix multiply `lhs x rhs`. + static std::unique_ptr> MatmulArray2D( + const Array2D& lhs, const Array2D& rhs); static std::unique_ptr> MatmulArray2D( const Array2D& lhs, const Array2D& rhs); static std::unique_ptr> MatmulArray2D( @@ -187,9 +199,10 @@ class ReferenceUtil { const tensorflow::gtl::ArraySlice& operand, float init, const std::function& reduce_func, const tensorflow::gtl::ArraySlice& window, - const tensorflow::gtl::ArraySlice& stride, Padding padding); - static std::unique_ptr> ReduceWindow1DGeneric( - const tensorflow::gtl::ArraySlice& operand, float init, + const tensorflow::gtl::ArraySlice& stride, + const tensorflow::gtl::ArraySlice>& padding); + static std::unique_ptr> ReduceWindow2DGeneric( + const Array2D& operand, float init, const std::function& reduce_func, const tensorflow::gtl::ArraySlice& window, const tensorflow::gtl::ArraySlice& stride, @@ -215,6 +228,7 @@ class ReferenceUtil { // Performs select and scatter with Greater Than or equal as the select, plus // as the scatter, and Same Padding. + // TODO(b/74533103) Switch tests to evaluator and remove this implementation. static std::unique_ptr> SelectAndScatter4DGePlus( const Array4D& operand, const Array4D& source, float init, const tensorflow::gtl::ArraySlice& window, diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD index 37ca1b893abdd914ca21ddaa970b821f157810f9..fba20c94cafea587bffcd766d1122d6327f32182 100644 --- a/tensorflow/compiler/xla/service/BUILD +++ b/tensorflow/compiler/xla/service/BUILD @@ -106,6 +106,7 @@ tf_cc_test( ":bfloat16_normalization", ":bfloat16_support", ":hlo", + ":hlo_verifier", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:test", @@ -126,7 +127,10 @@ cc_library( ":bfloat16_support", ":hlo", ":hlo_dataflow_analysis", + ":hlo_dce", ":hlo_pass", + ":tuple_simplifier", + "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:shape_tree", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:util", @@ -146,6 +150,7 @@ tf_cc_test( "//tensorflow/compiler/xla:test_helpers", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/tests:hlo_test_base", + "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", # fixdeps: keep ], ) @@ -984,6 +989,7 @@ tf_cc_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", + "//tensorflow/compiler/xla/tools/parser:hlo_parser", "//tensorflow/core:lib", ], ) @@ -1060,6 +1066,38 @@ tf_cc_test( ], ) +cc_library( + name = "hlo_module_group_metadata", + srcs = ["hlo_module_group_metadata.cc"], + hdrs = ["hlo_module_group_metadata.h"], + deps = [ + ":hlo", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:status", + "//tensorflow/compiler/xla:status_macros", + "//tensorflow/compiler/xla:statusor", + "//tensorflow/compiler/xla:util", + "//tensorflow/core:lib", + ], +) + +cc_library( + name = "hlo_module_group_util", + srcs = ["hlo_module_group_util.cc"], + hdrs = ["hlo_module_group_util.h"], + deps = [ + ":hlo", + ":hlo_module_group_metadata", + ":hlo_reachability", + "//tensorflow/compiler/xla:status", + "//tensorflow/compiler/xla:status_macros", + "//tensorflow/compiler/xla:statusor", + "//tensorflow/compiler/xla:types", + "//tensorflow/compiler/xla:util", + "//tensorflow/core:lib", + ], +) + cc_library( name = "hlo_scheduling", srcs = ["hlo_scheduling.cc"], @@ -1128,6 +1166,19 @@ tf_cc_test( ], ) +cc_library( + name = "hlo_creation_utils", + srcs = ["hlo_creation_utils.cc"], + hdrs = ["hlo_creation_utils.h"], + deps = [ + ":hlo", + ":shape_inference", + "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:statusor", + "//tensorflow/compiler/xla:util", + ], +) + cc_library( name = "batchnorm_expander", srcs = ["batchnorm_expander.cc"], @@ -1136,7 +1187,6 @@ cc_library( ":hlo", ":hlo_pass", ":hlo_query", - ":shape_inference", "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", @@ -1148,6 +1198,20 @@ cc_library( ], ) +cc_library( + name = "gather_expander", + srcs = ["gather_expander.cc"], + hdrs = ["gather_expander.h"], + deps = [ + ":hlo", + ":hlo_creation_utils", + ":hlo_pass", + ":while_util", + "//tensorflow/compiler/xla:statusor", + "//tensorflow/compiler/xla:util", + ], +) + tf_cc_test( name = "batchnorm_expander_test", size = "small", @@ -1175,9 +1239,9 @@ cc_library( hdrs = ["algebraic_simplifier.h"], deps = [ ":hlo", + ":hlo_creation_utils", ":hlo_pass", ":hlo_query", - ":shape_inference", "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", @@ -1211,6 +1275,41 @@ tf_cc_test( ], ) +cc_library( + name = "conditional_simplifier", + srcs = ["conditional_simplifier.cc"], + hdrs = ["conditional_simplifier.h"], + deps = [ + ":call_inliner", + ":hlo", + ":hlo_pass", + "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:status_macros", + "//tensorflow/compiler/xla:statusor", + "//tensorflow/compiler/xla:types", + "//tensorflow/compiler/xla:util", + "//tensorflow/core:lib", + ], +) + +tf_cc_test( + name = "conditional_simplifier_test", + srcs = ["conditional_simplifier_test.cc"], + deps = [ + ":conditional_simplifier", + ":hlo", + ":hlo_matchers", + "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:test", + "//tensorflow/compiler/xla:types", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/tests:hlo_verified_test_base", + "//tensorflow/core:lib", + "//tensorflow/core:test", + ], +) + cc_library( name = "while_loop_simplifier", srcs = ["while_loop_simplifier.cc"], @@ -1233,6 +1332,7 @@ tf_cc_test( ":while_loop_simplifier", "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla/tests:hlo_verified_test_base", + "//tensorflow/core:lib", "//tensorflow/core:test", ], ) @@ -2347,6 +2447,24 @@ cc_library( ":hlo", ":hlo_proto", "//tensorflow/compiler/xla:status", + "//tensorflow/compiler/xla:util", + ], +) + +tf_cc_test( + name = "hlo_proto_util_test", + srcs = ["hlo_proto_util_test.cc"], + deps = [ + ":hlo", + ":hlo_proto", + ":hlo_proto_util", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:status_macros", + "//tensorflow/compiler/xla:test", + "//tensorflow/compiler/xla:types", + "//tensorflow/compiler/xla/tests:hlo_test_base", + "//tensorflow/compiler/xla/tests:xla_internal_test_main", + "//tensorflow/core:lib", ], ) @@ -2445,7 +2563,9 @@ cc_library( deps = [ ":call_inliner", ":hlo", + ":hlo_creation_utils", ":tuple_util", + "//tensorflow/core:lib", ], ) diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier.cc b/tensorflow/compiler/xla/service/algebraic_simplifier.cc index 4391462c1cbb3b4333d2a129efbd53e9faf02db7..be7aa307d2c9f70ba8d334b842a4ff29a49687f9 100644 --- a/tensorflow/compiler/xla/service/algebraic_simplifier.cc +++ b/tensorflow/compiler/xla/service/algebraic_simplifier.cc @@ -26,10 +26,10 @@ limitations under the License. #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" +#include "tensorflow/compiler/xla/service/hlo_creation_utils.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/service/hlo_opcode.h" #include "tensorflow/compiler/xla/service/hlo_query.h" -#include "tensorflow/compiler/xla/service/shape_inference.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/types.h" @@ -122,6 +122,8 @@ class AlgebraicSimplifierVisitor : public DfsHloVisitorWithDefault { Status HandleBitcast(HloInstruction* bitcast) override; + Status HandleBitcastConvert(HloInstruction* bitcast) override; + Status HandleBroadcast(HloInstruction* broadcast) override; Status HandleConcatenate(HloInstruction* concatenate) override; @@ -381,13 +383,9 @@ Status AlgebraicSimplifierVisitor::HandleAdd(HloInstruction* add) { !lhs->operand(0)->IsConstant() && lhs->operand(1)->IsConstant()) { auto* c1 = lhs->mutable_operand(1); auto* c2 = rhs; - TF_ASSIGN_OR_RETURN( - Shape sum_of_constants_shape, - ShapeInference::InferBinaryOpShape(HloOpcode::kAdd, c1, c2)); - auto* sum_of_constants = - computation_->AddInstruction(HloInstruction::CreateBinary( - sum_of_constants_shape, HloOpcode::kAdd, c1, c2)); + TF_ASSIGN_OR_RETURN(auto* sum_of_constants, + CreateBinaryHlo(HloOpcode::kAdd, c1, c2)); return ReplaceWithNewInstruction( add, HloInstruction::CreateBinary(add->shape(), HloOpcode::kAdd, lhs->mutable_operand(0), @@ -411,6 +409,13 @@ Status AlgebraicSimplifierVisitor::HandleBitcast(HloInstruction* bitcast) { return Status::OK(); } +Status AlgebraicSimplifierVisitor::HandleBitcastConvert( + HloInstruction* bitcast) { + // Eliminate bitcast converts between same shape. + ReplaceInstructionIfSameShape(bitcast, bitcast->mutable_operand(0)); + return Status::OK(); +} + Status AlgebraicSimplifierVisitor::HandleCopy(HloInstruction* copy) { // If a copy feeds a copy, make it a single copy. if (copy->operand(0)->opcode() == HloOpcode::kCopy) { @@ -632,31 +637,24 @@ Status AlgebraicSimplifierVisitor::HandleDivide(HloInstruction* divide) { if (lhs->opcode() == HloOpcode::kDivide && rhs->opcode() == HloOpcode::kDivide) { TF_ASSIGN_OR_RETURN( - const Shape a_times_d_shape, - ShapeInference::InferBinaryOpShape(HloOpcode::kMultiply, - lhs->operand(0), rhs->operand(1))); - auto a_times_d = computation_->AddInstruction(HloInstruction::CreateBinary( - a_times_d_shape, HloOpcode::kMultiply, lhs->mutable_operand(0), - rhs->mutable_operand(1))); + auto a_times_d, + CreateBinaryHlo(HloOpcode::kMultiply, lhs->mutable_operand(0), + rhs->mutable_operand(1))); TF_ASSIGN_OR_RETURN( - const Shape b_times_c_shape, - ShapeInference::InferBinaryOpShape(HloOpcode::kMultiply, - lhs->operand(1), rhs->operand(0))); - auto b_times_c = computation_->AddInstruction(HloInstruction::CreateBinary( - b_times_c_shape, HloOpcode::kMultiply, lhs->mutable_operand(1), - rhs->mutable_operand(0))); - return ReplaceWithNewInstruction( - divide, HloInstruction::CreateBinary( - divide->shape(), HloOpcode::kDivide, a_times_d, b_times_c)); + auto b_times_c, + CreateBinaryHlo(HloOpcode::kMultiply, lhs->mutable_operand(1), + rhs->mutable_operand(0))); + TF_ASSIGN_OR_RETURN(auto new_divide, CreateBinaryHlo(HloOpcode::kDivide, + a_times_d, b_times_c)); + + return ReplaceInstruction(divide, new_divide); } // (A / B) / C => A / (B * C) if (lhs->opcode() == HloOpcode::kDivide) { - TF_ASSIGN_OR_RETURN(const Shape b_times_c_shape, - ShapeInference::InferBinaryOpShape( - HloOpcode::kMultiply, lhs->operand(1), rhs)); - auto b_times_c = computation_->AddInstruction(HloInstruction::CreateBinary( - b_times_c_shape, HloOpcode::kMultiply, lhs->mutable_operand(1), rhs)); + TF_ASSIGN_OR_RETURN( + auto b_times_c, + CreateBinaryHlo(HloOpcode::kMultiply, lhs->mutable_operand(1), rhs)); return ReplaceWithNewInstruction( divide, HloInstruction::CreateBinary(divide->shape(), HloOpcode::kDivide, @@ -665,11 +663,9 @@ Status AlgebraicSimplifierVisitor::HandleDivide(HloInstruction* divide) { // A / (B / C) => (A*C) / B if (rhs->opcode() == HloOpcode::kDivide) { - TF_ASSIGN_OR_RETURN(const Shape a_times_c_shape, - ShapeInference::InferBinaryOpShape( - HloOpcode::kMultiply, lhs, rhs->operand(1))); - auto a_times_c = computation_->AddInstruction(HloInstruction::CreateBinary( - a_times_c_shape, HloOpcode::kMultiply, lhs, rhs->mutable_operand(1))); + TF_ASSIGN_OR_RETURN( + auto a_times_c, + CreateBinaryHlo(HloOpcode::kMultiply, lhs, rhs->mutable_operand(1))); return ReplaceWithNewInstruction( divide, HloInstruction::CreateBinary(divide->shape(), HloOpcode::kDivide, @@ -1302,17 +1298,14 @@ Status AlgebraicSimplifierVisitor::HandlePad(HloInstruction* pad) { padding_dimension->set_edge_padding_high(0); } } - TF_ASSIGN_OR_RETURN(Shape nonzero_pad_shape, - ShapeInference::InferPadShape(pad->operand(0)->shape(), - pad->operand(1)->shape(), - nonzero_padding)); + + TF_ASSIGN_OR_RETURN(HloInstruction * nonzero_pad, + CreatePadHlo(pad->mutable_operand(0), + pad->mutable_operand(1), nonzero_padding)); // Copy the layout from the original pad instructions. The new pad and the // slice instruction should all have the same layout. - TF_RETURN_IF_ERROR( - LayoutUtil::CopyLayoutBetweenShapes(pad->shape(), &nonzero_pad_shape)); - HloInstruction* nonzero_pad = computation_->AddInstruction( - HloInstruction::CreatePad(nonzero_pad_shape, pad->mutable_operand(0), - pad->mutable_operand(1), nonzero_padding)); + TF_RETURN_IF_ERROR(LayoutUtil::CopyLayoutBetweenShapes( + pad->shape(), nonzero_pad->mutable_shape())); // Second, construct the slice instruction to perform the negative padding. std::vector start_indices; @@ -1325,7 +1318,7 @@ Status AlgebraicSimplifierVisitor::HandlePad(HloInstruction* pad) { if (padding_dimension.edge_padding_low() < 0) { start = -1 * padding_dimension.edge_padding_low(); } - int64 end = nonzero_pad_shape.dimensions(i); + int64 end = nonzero_pad->shape().dimensions(i); if (padding_dimension.edge_padding_high() < 0) { end += padding_dimension.edge_padding_high(); } @@ -1334,16 +1327,14 @@ Status AlgebraicSimplifierVisitor::HandlePad(HloInstruction* pad) { strides.push_back(1); } - // Verify that the slice shape matches the pad shape. TF_ASSIGN_OR_RETURN( - Shape inferred_slice_shape, - ShapeInference::InferSliceShape(nonzero_pad_shape, start_indices, - end_indices, strides)); - TF_RET_CHECK(ShapeUtil::Compatible(inferred_slice_shape, pad->shape())); + HloInstruction * slice, + CreateSliceHlo(nonzero_pad, start_indices, end_indices, strides)); + + // Verify that the slice shape matches the pad shape. + TF_RET_CHECK(ShapeUtil::Compatible(slice->shape(), pad->shape())); - std::unique_ptr slice = HloInstruction::CreateSlice( - pad->shape(), nonzero_pad, start_indices, end_indices, strides); - return ReplaceWithNewInstruction(pad, std::move(slice)); + return ReplaceInstruction(pad, slice); } return Status::OK(); @@ -1616,6 +1607,14 @@ Status AlgebraicSimplifierVisitor::HandleDynamicUpdateSlice( if (IsAll(start_indices, 0) && SameShape(dynamic_update_slice, update)) { return ReplaceInstruction(dynamic_update_slice, update); } + + // If any dimension of update is 0, elide the DynamicUpdateSlice. This + // optimization becomes invalid should we later prefer to warn about out of + // bound indices. + if (ShapeUtil::HasZeroElements(update->shape())) { + return ReplaceInstruction(dynamic_update_slice, + dynamic_update_slice->mutable_operand(0)); + } return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc b/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc index 667ae01993ebf0feeab89e0b5afaf7c7c8c99ab9..451294ef5d8367686d7fc22b7f5ebfde89d14d42 100644 --- a/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc +++ b/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc @@ -2800,6 +2800,29 @@ DotOfConcatTestSpec kDotOfConcatTestSpecs[] = { {/*m=*/1, /*k=*/16, /*n=*/1}, // }; +// Test that DynamicUpdateSlice update param with any dimension equal to zero +// gets removed. +TEST_F(AlgebraicSimplifierTest, DynamicUpdateSliceZeroUpdate) { + HloComputation::Builder builder(TestName()); + const Shape dslice_shape = ShapeUtil::MakeShape(F32, {10}); + HloInstruction* const operand = builder.AddInstruction( + HloInstruction::CreateParameter(0, dslice_shape, "operand")); + const Shape update_shape = ShapeUtil::MakeShape(F32, {0}); + HloInstruction* const update = builder.AddInstruction( + HloInstruction::CreateParameter(1, update_shape, "update")); + HloInstruction* const start_indices = builder.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR1({0}))); + builder.AddInstruction(HloInstruction::CreateDynamicUpdateSlice( + dslice_shape, operand, update, start_indices)); + const HloComputation* const computation = + module().AddEntryComputation(builder.Build()); + + AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, + non_bitcasting_callback()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); + EXPECT_THAT(computation->root_instruction(), operand); +} + INSTANTIATE_TEST_CASE_P(DotOfConcatSimplificationTestInstantiation, DotOfConcatSimplificationTest, ::testing::ValuesIn(kDotOfConcatTestSpecs)); diff --git a/tensorflow/compiler/xla/service/allocation_tracker.cc b/tensorflow/compiler/xla/service/allocation_tracker.cc index 4e80679c11dfdf7fdf8077a9f354139a4cab6803..4f819a743c48f30df8dde00ece72a0b4e1748802 100644 --- a/tensorflow/compiler/xla/service/allocation_tracker.cc +++ b/tensorflow/compiler/xla/service/allocation_tracker.cc @@ -34,40 +34,54 @@ StatusOr AllocationTracker::Register( std::unique_ptr shaped_buffer, const string& tag) { tensorflow::mutex_lock lock(mutex_); VLOG(2) << "Register"; - return RegisterInternal(std::move(shaped_buffer), tag); + std::vector> replicated_buffers; + replicated_buffers.emplace_back(std::move(shaped_buffer)); + return RegisterInternal(std::move(replicated_buffers), tag); +} + +StatusOr AllocationTracker::RegisterReplicatedBuffers( + std::vector> replicated_buffers, + const string& tag) { + tensorflow::mutex_lock lock(mutex_); + VLOG(2) << "RegisterReplicatedBuffers"; + return RegisterInternal(std::move(replicated_buffers), tag); } StatusOr AllocationTracker::RegisterInternal( - std::unique_ptr shaped_buffer, const string& tag) { + std::vector> replicated_buffers, + const string& tag) { VLOG(2) << "RegisterInternal(" - << "tag: \"" << tag << "\" " - << "shaped_buffer: " << *shaped_buffer; - if (shaped_buffer->platform() != backend_->platform()) { - return InvalidArgument( - "AllocationTracker for platform %s cannot register buffer from " - "platform %s", - backend_->platform()->Name().c_str(), - shaped_buffer->platform()->Name().c_str()); + << "tag: \"" << tag << "\" with " << replicated_buffers.size() + << " shaped_buffers."; + for (const auto& shaped_buffer : replicated_buffers) { + VLOG(2) << "shaped_buffer:" << *shaped_buffer; + if (shaped_buffer->platform() != backend_->platform()) { + return InvalidArgument( + "AllocationTracker for platform %s cannot register buffer from " + "platform %s", + backend_->platform()->Name().c_str(), + shaped_buffer->platform()->Name().c_str()); + } } int64 handle = next_handle_++; - std::vector shape_indices; - ShapeUtil::ForEachSubshape(shaped_buffer->on_device_shape(), - [this, &shape_indices](const Shape& /*subshape*/, - const ShapeIndex& index) { - shape_indices.push_back(index); - }); - for (const ShapeIndex& index : shape_indices) { - AddAllocationOrIncrementRefCount(shaped_buffer->buffer(index), - shaped_buffer->device_ordinal()); + for (auto& shaped_buffer : replicated_buffers) { + std::vector shape_indices; + ShapeUtil::ForEachSubshape(shaped_buffer->on_device_shape(), + [this, &shape_indices](const Shape& /*subshape*/, + const ShapeIndex& index) { + shape_indices.push_back(index); + }); + for (const ShapeIndex& index : shape_indices) { + AddAllocationOrIncrementRefCount(shaped_buffer->buffer(index), + shaped_buffer->device_ordinal()); + } + handle_to_shaped_buffers_[handle].emplace_back(std::move(shaped_buffer)); } + GlobalDataHandle result; result.set_handle(handle); - - handle_to_shaped_buffer_[handle] = std::move(shaped_buffer); - VLOG(2) << "handle: " << handle; - return result; } @@ -75,23 +89,35 @@ tensorflow::Status AllocationTracker::Unregister(const GlobalDataHandle& data) { tensorflow::mutex_lock lock(mutex_); VLOG(2) << "Unregister(" << "handle: " << data.handle() << ")"; - TF_ASSIGN_OR_RETURN(ShapedBuffer * shaped_buffer, ResolveInternal(data)); - std::vector shape_indices; - ShapeUtil::ForEachSubshape(shaped_buffer->on_device_shape(), - [this, &shape_indices](const Shape& /*subshape*/, - const ShapeIndex& index) { - shape_indices.push_back(index); - }); - for (const ShapeIndex& index : shape_indices) { - TF_RETURN_IF_ERROR(DecrementRefCount(shaped_buffer->buffer(index), - shaped_buffer->device_ordinal())); + TF_ASSIGN_OR_RETURN(std::vector replicated_buffers, + ResolveInternal(data)); + for (const auto& shaped_buffer : replicated_buffers) { + std::vector shape_indices; + ShapeUtil::ForEachSubshape(shaped_buffer->on_device_shape(), + [this, &shape_indices](const Shape& /*subshape*/, + const ShapeIndex& index) { + shape_indices.push_back(index); + }); + for (const ShapeIndex& index : shape_indices) { + TF_RETURN_IF_ERROR(DecrementRefCount(shaped_buffer->buffer(index), + shaped_buffer->device_ordinal())); + } } + return Reset(data); +} - // Keep a nullptr as a tombstone for unregistered handles. This enables better - // error messages. That is, "handle has been deallocated" versus "handle does - // not exist". - handle_to_shaped_buffer_.at(data.handle()).reset(); - +Status AllocationTracker::Reset(const GlobalDataHandle& data) { + // Keep a nullptr as a tombstone for unregistered handles. This enables + // better error messages. That is, "handle has been deallocated" versus + // "handle does not exist". + auto it = handle_to_shaped_buffers_.find(data.handle()); + if (it == handle_to_shaped_buffers_.end()) { + return NotFound("no allocation record for global data handle: %lld", + data.handle()); + } + for (auto& shaped_buffer : it->second) { + shaped_buffer.reset(); + } return tensorflow::Status::OK(); } @@ -99,7 +125,11 @@ StatusOr> AllocationTracker::DeconstructTuple( const GlobalDataHandle& data) { tensorflow::mutex_lock lock(mutex_); - TF_ASSIGN_OR_RETURN(ShapedBuffer * shaped_buffer, ResolveInternal(data)); + TF_ASSIGN_OR_RETURN(std::vector replicated_buffers, + ResolveInternal(data)); + // We only need to care about replica id 0 here, since the GlobalDataHandle is + // the same for all buffers across replicas. + const ShapedBuffer* shaped_buffer = replicated_buffers[0]; if (!ShapeUtil::IsTuple(shaped_buffer->on_host_shape())) { return InvalidArgument("global data handle %lld is not a tuple", data.handle()); @@ -109,7 +139,7 @@ StatusOr> AllocationTracker::DeconstructTuple( TF_RET_CHECK(ShapeUtil::IsTuple(shaped_buffer->on_device_shape())); if (ShapeUtil::IsNestedTuple(shaped_buffer->on_device_shape())) { - return Unimplemented("deconstructing nested tuples not yet supported"); + return Unimplemented("Deconstructing nested tuples is not implemented."); } std::vector element_handles; @@ -122,37 +152,55 @@ StatusOr> AllocationTracker::DeconstructTuple( shaped_buffer->platform(), shaped_buffer->device_ordinal()); element_buffer->set_buffer(shaped_buffer->buffer(/*index=*/{i}), /*index=*/{}); + std::vector> replicated_buffers; + replicated_buffers.emplace_back(std::move(element_buffer)); TF_ASSIGN_OR_RETURN( GlobalDataHandle element_handle, - RegisterInternal(std::move(element_buffer), "deconstructed tuple")); + RegisterInternal(std::move(replicated_buffers), "deconstructed tuple")); element_handles.push_back(element_handle); } return std::move(element_handles); } -StatusOr AllocationTracker::Resolve( +StatusOr> AllocationTracker::Resolve( const GlobalDataHandle& data) { tensorflow::mutex_lock lock(mutex_); return AllocationTracker::ResolveInternal(data); } -StatusOr AllocationTracker::ResolveInternal( +StatusOr AllocationTracker::ResolveForReplica( + const GlobalDataHandle& data, int replica_id) { + tensorflow::mutex_lock lock(mutex_); + TF_ASSIGN_OR_RETURN(std::vector replicated_buffers, + ResolveInternal(data)); + if (replica_id >= replicated_buffers.size()) { + return InvalidArgument( + "Requesting buffer for replica %d, but found buffers only for %lu " + "replicas.", + replica_id, replicated_buffers.size()); + } + return replicated_buffers[replica_id]; +} + +StatusOr> AllocationTracker::ResolveInternal( const GlobalDataHandle& data) { VLOG(2) << "resolve:" << data.handle(); - auto it = handle_to_shaped_buffer_.find(data.handle()); - if (it == handle_to_shaped_buffer_.end()) { + auto it = handle_to_shaped_buffers_.find(data.handle()); + if (it == handle_to_shaped_buffers_.end()) { return NotFound("no allocation record for global data handle: %lld", data.handle()); } - ShapedBuffer* shaped_buffer = it->second.get(); - - if (shaped_buffer == nullptr) { - return InvalidArgument("global data handle %lld was previously deallocated", - data.handle()); + std::vector replicated_buffers; + for (const auto& shaped_buffer : it->second) { + if (shaped_buffer == nullptr) { + return InvalidArgument( + "global data handle %lld was previously deallocated", data.handle()); + } + replicated_buffers.push_back(shaped_buffer.get()); } - return shaped_buffer; + return replicated_buffers; } void AllocationTracker::AddAllocationOrIncrementRefCount( diff --git a/tensorflow/compiler/xla/service/allocation_tracker.h b/tensorflow/compiler/xla/service/allocation_tracker.h index 807af8694972083d097604a67ee46d2f73d9545a..038aee8541b297d6f91fe2b3bce7455fd9a7084e 100644 --- a/tensorflow/compiler/xla/service/allocation_tracker.h +++ b/tensorflow/compiler/xla/service/allocation_tracker.h @@ -43,10 +43,17 @@ class AllocationTracker { AllocationTracker(Backend* backend) : backend_(backend), next_handle_(1) {} // Registers a shaped buffer of device memory, and returns a corresponding - // handle that can be used for talking to XLA clients. + // handle that can be used for talking to XLA clients. The given shaped buffer + // will be treated as the buffer corresponding to the only replica. StatusOr Register( std::unique_ptr shaped_buffer, const string& tag); + // Registers a vector of shaped buffers of device memory, one per replica, and + // returns a corresponding handle that can be used for talking to XLA clients. + StatusOr RegisterReplicatedBuffers( + std::vector> replicated_buffers, + const string& tag); + // Unregister the allocation for the given data handle. Status Unregister(const GlobalDataHandle& data); @@ -54,9 +61,17 @@ class AllocationTracker { StatusOr> DeconstructTuple( const GlobalDataHandle& Data); - // Resolve a handle from an XLA client to a shaped buffer, or provide an error - // status to say whether it was not found (or found, but found deallocated). - StatusOr Resolve(const GlobalDataHandle& data); + // Resolve a handle from an XLA client to a vector of shaped buffers, one per + // replica, or provide an error status to say whether any of those buffers + // were not found (or found, but found deallocated). + StatusOr> Resolve( + const GlobalDataHandle& data); + + // Resolves a handle from an XLA client and replica id to a shaped buffer, or + // provide an error status to say whether it was not found (or found, but + // found deallocated). + StatusOr ResolveForReplica(const GlobalDataHandle& data, + int replica_id); private: // Data structure encapsulating single memory allocation on the device. @@ -74,13 +89,17 @@ class AllocationTracker { // Internal helper which resolves the given GlobalDataHandle to a // ShapedBuffer. - StatusOr ResolveInternal(const GlobalDataHandle& data) - EXCLUSIVE_LOCKS_REQUIRED(mutex_); + StatusOr> ResolveInternal( + const GlobalDataHandle& data) EXCLUSIVE_LOCKS_REQUIRED(mutex_); - // Internal helper which registers a shaped buffer. + // Internal helper which registers a vector of shaped buffers, one per + // replica. StatusOr RegisterInternal( - std::unique_ptr shaped_buffer, const string& tag) - EXCLUSIVE_LOCKS_REQUIRED(mutex_); + std::vector> replicated_buffers, + const string& tag) EXCLUSIVE_LOCKS_REQUIRED(mutex_); + + // Resets the shaped buffers corresponding to the given handle. + Status Reset(const GlobalDataHandle& data) EXCLUSIVE_LOCKS_REQUIRED(mutex_); // Adds the given device address to the allocation tracker, or if it already // exists, then increment it's reference count. @@ -111,9 +130,10 @@ class AllocationTracker { tensorflow::gtl::FlatMap opaque_to_allocation_map_ GUARDED_BY(mutex_); - // A map from data handle to ShapedBuffer. - tensorflow::gtl::FlatMap> - handle_to_shaped_buffer_ GUARDED_BY(mutex_); + // A map from data handle to a vector of shaped buffers that represent the + // buffers for different replicas. + tensorflow::gtl::FlatMap>> + handle_to_shaped_buffers_ GUARDED_BY(mutex_); TF_DISALLOW_COPY_AND_ASSIGN(AllocationTracker); }; diff --git a/tensorflow/compiler/xla/service/batchnorm_expander.cc b/tensorflow/compiler/xla/service/batchnorm_expander.cc index 84c9db32932becd9b701929b392efa4998d03067..38086bd7e121847be6b6b69415cfe87814e7fc24 100644 --- a/tensorflow/compiler/xla/service/batchnorm_expander.cc +++ b/tensorflow/compiler/xla/service/batchnorm_expander.cc @@ -30,7 +30,6 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/service/hlo_opcode.h" #include "tensorflow/compiler/xla/service/hlo_query.h" -#include "tensorflow/compiler/xla/service/shape_inference.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/types.h" diff --git a/tensorflow/compiler/xla/service/bfloat16_conversion_folding.cc b/tensorflow/compiler/xla/service/bfloat16_conversion_folding.cc index cde990e176ddb57a8e93ecc3c60260b2dbae32a8..432448e9bbc7db30ed67a0130d52b060032362d5 100644 --- a/tensorflow/compiler/xla/service/bfloat16_conversion_folding.cc +++ b/tensorflow/compiler/xla/service/bfloat16_conversion_folding.cc @@ -147,6 +147,10 @@ Status BFloat16ConversionFoldingVisitor::DefaultAction(HloInstruction* hlo) { hlo->opcode() == HloOpcode::kGetTupleElement || // hlo->opcode() == HloOpcode::kInfeed || // hlo->opcode() == HloOpcode::kOutfeed || // + hlo->opcode() == HloOpcode::kSend || // + hlo->opcode() == HloOpcode::kSendDone || // + hlo->opcode() == HloOpcode::kRecv || // + hlo->opcode() == HloOpcode::kRecvDone || // hlo->opcode() == HloOpcode::kConstant || // hlo->opcode() == HloOpcode::kParameter || // hlo->opcode() == HloOpcode::kFusion || // diff --git a/tensorflow/compiler/xla/service/bfloat16_normalization.cc b/tensorflow/compiler/xla/service/bfloat16_normalization.cc index b032c040e8aff49f9e0fc1ff9a1c1e79ea4bb77f..14c54ddd135af024327f63418b410da1ed3c4fd4 100644 --- a/tensorflow/compiler/xla/service/bfloat16_normalization.cc +++ b/tensorflow/compiler/xla/service/bfloat16_normalization.cc @@ -152,44 +152,64 @@ Status BFloat16NormalizationVisitor::HandleCrossReplicaSum( std::vector operand_types(crs->operand_count()); std::vector output_types(crs->operand_count()); - bool has_f32 = false; - bool has_bf16 = false; - bool has_bf16_output = false; + int64 f32_count = 0; + int64 bf16_count = 0; + bool has_unsupported_bf16_operand = false; + bool has_unsupported_bf16_output = false; for (int64 i = 0; i < crs->operand_count(); ++i) { operand_types[i] = crs->operand(i)->shape().element_type(); output_types[i] = ShapeUtil::GetSubshape(crs->shape(), {i}).element_type(); - if (operand_types[i] == F32 || output_types[i] == F32) { - has_f32 = true; + if (operand_types[i] == F32) { + f32_count += 1; } else if (operand_types[i] == BF16) { - has_bf16 = true; + bf16_count += 1; + if (!bfloat16_support_->SupportsBF16Operand(*crs, i)) { + has_unsupported_bf16_operand = true; + } } - if (output_types[i] == BF16) { - has_bf16 = true; - has_bf16_output = true; + if (output_types[i] == F32) { + f32_count += 1; + } else if (output_types[i] == BF16) { + bf16_count += 1; + if (!bfloat16_support_->SupportsBF16Output(*crs)) { + has_unsupported_bf16_output = true; + } } } - for (int64 i = 0; i < crs->operand_count(); ++i) { + if (bf16_count == 0) { + return Status::OK(); + } + + auto should_convert_operand = [&](int64 i) { if (operand_types[i] != BF16) { - continue; + return false; } - if (bfloat16_support_->SupportsBF16Operand(*crs, i) && - (bfloat16_support_->SupportsMixedPrecisions(*crs) || !has_f32)) { - continue; + if (!bfloat16_support_->SupportsBF16Operand(*crs, i)) { + return true; } - TF_RETURN_IF_ERROR(InsertConvertBeforeOperand(crs, i, F32, computation_)); - has_f32 = true; - } + if (bfloat16_support_->SupportsMixedPrecisions(*crs)) { + return false; + } + return has_unsupported_bf16_operand || has_unsupported_bf16_output || + f32_count > 0; + }; - if (!has_bf16_output) { - return Status::OK(); + for (int64 i = 0; i < crs->operand_count(); ++i) { + if (should_convert_operand(i)) { + TF_RETURN_IF_ERROR(InsertConvertBeforeOperand(crs, i, F32, computation_)); + f32_count += 1; + bf16_count -= 1; + } } - if (bfloat16_support_->SupportsBF16Output(*crs) && - (bfloat16_support_->SupportsMixedPrecisions(*crs) || !has_f32)) { + if (!has_unsupported_bf16_output && + (bfloat16_support_->SupportsMixedPrecisions(*crs) || f32_count == 0 || + bf16_count == 0)) { return Status::OK(); } + std::vector materialized_users = crs->users(); std::vector output_elements(crs->operand_count()); auto original_shape = crs->shape(); for (int64 i = 0; i < crs->operand_count(); ++i) { @@ -209,7 +229,6 @@ Status BFloat16NormalizationVisitor::HandleCrossReplicaSum( auto tuple = computation_->AddInstruction( HloInstruction::CreateTuple(output_elements)); - std::vector materialized_users = crs->users(); // Use the crs' shape temporarily, in order to pass checks in // ReplaceUseWith. *tuple->mutable_shape() = crs->shape(); @@ -221,41 +240,37 @@ Status BFloat16NormalizationVisitor::HandleCrossReplicaSum( } Status BFloat16NormalizationVisitor::HandleInstruction(HloInstruction* hlo) { - std::vector bf16_operands; - std::vector f32_operands; - bool has_f32 = false; - bool has_bf16 = false; + int f32_count = 0; + int bf16_count = 1; for (int64 i = 0; i < hlo->operand_count(); ++i) { if (hlo->operand(i)->shape().element_type() == F32) { - f32_operands.push_back(i); - has_f32 = true; + f32_count += 1; } else if (hlo->operand(i)->shape().element_type() == BF16) { - bf16_operands.push_back(i); - has_bf16 = true; + bf16_count += 1; } } if (hlo->shape().element_type() == F32) { - has_f32 = true; + f32_count += 1; } else if (hlo->shape().element_type() == BF16) { - has_bf16 = true; + bf16_count += 1; } std::vector bf16_called_comps; for (auto* comp : hlo->called_computations()) { bool comp_has_bf16 = false; if (comp->root_instruction()->shape().element_type() == F32) { - has_f32 = true; + f32_count += 1; } else if (comp->root_instruction()->shape().element_type() == BF16) { - has_bf16 = true; + bf16_count += 1; comp_has_bf16 = true; } for (auto* param : comp->parameter_instructions()) { if (param->shape().element_type() == F32) { - has_f32 = true; + f32_count += 1; } else if (param->shape().element_type() == BF16) { - has_bf16 = true; + bf16_count += 1; comp_has_bf16 = true; } } @@ -264,54 +279,69 @@ Status BFloat16NormalizationVisitor::HandleInstruction(HloInstruction* hlo) { } } - if (!bfloat16_support_->SupportsMixedPrecisions(*hlo) && has_bf16 && - has_f32) { - // Resolve unsupported mixed precision. - // - // See if we can change everything to BF16. - if (hlo->called_computations().empty() && - hlo->shape().element_type() == BF16) { - bool can_use_bf16 = true; - for (int i : f32_operands) { - if (bfloat16_support_->EffectiveOperandPrecisionIsOutputPrecision(*hlo, - i) && - bfloat16_support_->SupportsBF16Operand(*hlo, i)) { - continue; - } - can_use_bf16 = false; - break; - } - if (can_use_bf16) { - for (int i : f32_operands) { - TF_RETURN_IF_ERROR( - InsertConvertBeforeOperand(hlo, i, BF16, computation_)); - } - return Status::OK(); - } - } - if (hlo->shape().element_type() == BF16) { - TF_RETURN_IF_ERROR( - ChangeOutputTypeThenInsertConvertBack(hlo, F32, computation_)); - } - for (int i : bf16_operands) { - TF_RETURN_IF_ERROR(InsertConvertBeforeOperand(hlo, i, F32, computation_)); - } - return ConvertCalledComputations(hlo, bf16_called_comps); - } - - for (int i : bf16_operands) { - if (!bfloat16_support_->SupportsBF16Operand(*hlo, i)) { + // Resolve unsupported BF16 operands. + for (int i = 0; i < hlo->operand_count(); ++i) { + if (hlo->operand(i)->shape().element_type() == BF16 && + !bfloat16_support_->SupportsBF16Operand(*hlo, i)) { TF_RETURN_IF_ERROR(InsertConvertBeforeOperand(hlo, i, F32, computation_)); + bf16_count -= 1; + f32_count += 1; } } + // Resolve unsupported BF16 output. if (hlo->shape().element_type() == BF16 && !bfloat16_support_->SupportsBF16Output(*hlo)) { TF_RETURN_IF_ERROR( ChangeOutputTypeThenInsertConvertBack(hlo, F32, computation_)); + bf16_count -= 1; + f32_count += 1; } - return Status::OK(); + // Resolve unsupported mixed precision after resolving unsupported BF16 + // operands and output, because the numbers of BF16 operands/output and F32 + // operands/output may have changed. + if (bfloat16_support_->SupportsMixedPrecisions(*hlo) || bf16_count == 0 || + f32_count == 0) { + return Status::OK(); + } + // See if we can change everything to BF16. + if (hlo->called_computations().empty() && + hlo->shape().element_type() == BF16) { + bool can_use_bf16 = true; + for (int i = 0; i < hlo->operand_count(); ++i) { + if (hlo->operand(i)->shape().element_type() == BF16) { + continue; + } + if ((bfloat16_support_->EffectiveOperandPrecisionIsBF16(*hlo, i) || + bfloat16_support_->EffectiveOperandPrecisionIsOutputPrecision(*hlo, + i)) && + bfloat16_support_->SupportsBF16Operand(*hlo, i)) { + continue; + } + can_use_bf16 = false; + break; + } + if (can_use_bf16) { + for (int i = 0; i < hlo->operand_count(); ++i) { + if (hlo->operand(i)->shape().element_type() == F32) { + TF_RETURN_IF_ERROR( + InsertConvertBeforeOperand(hlo, i, BF16, computation_)); + } + } + return Status::OK(); + } + } + if (hlo->shape().element_type() == BF16) { + TF_RETURN_IF_ERROR( + ChangeOutputTypeThenInsertConvertBack(hlo, F32, computation_)); + } + for (int i = 0; i < hlo->operand_count(); ++i) { + if (hlo->operand(i)->shape().element_type() == BF16) { + TF_RETURN_IF_ERROR(InsertConvertBeforeOperand(hlo, i, F32, computation_)); + } + } + return ConvertCalledComputations(hlo, bf16_called_comps); } Status BFloat16NormalizationVisitor::DefaultAction(HloInstruction* hlo) { diff --git a/tensorflow/compiler/xla/service/bfloat16_normalization_test.cc b/tensorflow/compiler/xla/service/bfloat16_normalization_test.cc index 66c3085842c4afe7ffc4d5891883e4cce9389d45..1afaefd9df9c5771fb9e134ae9050f3abb00ea4a 100644 --- a/tensorflow/compiler/xla/service/bfloat16_normalization_test.cc +++ b/tensorflow/compiler/xla/service/bfloat16_normalization_test.cc @@ -19,6 +19,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/service/hlo_module.h" #include "tensorflow/compiler/xla/service/hlo_opcode.h" +#include "tensorflow/compiler/xla/service/hlo_verifier.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/test.h" #include "tensorflow/compiler/xla/test_helpers.h" @@ -41,13 +42,17 @@ class TestBFloat16Support : public BFloat16Support { hlo.opcode() == HloOpcode::kGetTupleElement) { return true; } + if (hlo.opcode() == HloOpcode::kDot) { + // Test that only the first operand of kDot supports BF16. + return operand_index == 0; + } return false; } bool SupportsBF16Output(const HloInstruction& hlo) const override { if (hlo.opcode() == HloOpcode::kAdd || hlo.opcode() == HloOpcode::kReduce || hlo.opcode() == HloOpcode::kSubtract || - hlo.opcode() == HloOpcode::kTuple || + hlo.opcode() == HloOpcode::kDot || hlo.opcode() == HloOpcode::kTuple || hlo.opcode() == HloOpcode::kGetTupleElement) { return true; } @@ -70,6 +75,10 @@ class BFloat16NormalizationTest : public HloTestBase { BFloat16Normalization normalization(&bfloat16_support_); StatusOr result = normalization.Run(module); EXPECT_IS_OK(result.status()); + + HloVerifier verifier(/*allow_mixed_precision=*/true); + EXPECT_IS_OK(verifier.Run(module).status()); + return result.ValueOrDie(); } }; @@ -166,7 +175,7 @@ TEST_F(BFloat16NormalizationTest, ResolveUnsupportedMixedPrecisionReduce) { Shape f32_input_shape = ShapeUtil::MakeShape(F32, {2, 4}); Shape f32_output_shape = ShapeUtil::MakeShape(F32, {4}); - Shape bf16_scalar_shape = ShapeUtil::MakeShape(BF16, {2, 4}); + Shape bf16_scalar_shape = ShapeUtil::MakeShape(BF16, {}); auto reduce_comp_builder = HloComputation::Builder("reduce_comp"); auto reduce_comp_param0 = reduce_comp_builder.AddInstruction( @@ -245,4 +254,34 @@ TEST_F(BFloat16NormalizationTest, ResolveMixedPrecisionTupleCrossReplicaSum) { EXPECT_EQ(ShapeUtil::GetSubshape(crs->shape(), {1}).element_type(), F32); } +// Tests that the normalization should not cause unsupported mixed precision due +// to resolving unsupported BF16 operand. +TEST_F(BFloat16NormalizationTest, DoNotAddUnsupportedMixedPrecision) { + auto builder = HloComputation::Builder(TestName()); + Shape bf16_shape = ShapeUtil::MakeShape(BF16, {4, 4}); + + HloInstruction* a = builder.AddInstruction( + HloInstruction::CreateParameter(0, bf16_shape, "a")); + HloInstruction* b = builder.AddInstruction( + HloInstruction::CreateParameter(1, bf16_shape, "b")); + + DotDimensionNumbers dot_dnums; + dot_dnums.add_lhs_contracting_dimensions(1); + dot_dnums.add_rhs_contracting_dimensions(0); + HloInstruction* dot = builder.AddInstruction( + HloInstruction::CreateDot(bf16_shape, a, b, dot_dnums)); + + auto module = CreateNewModule(); + auto computation = module->AddEntryComputation(builder.Build()); + + EXPECT_TRUE(Normalize(module.get())); + + EXPECT_EQ(computation->root_instruction()->opcode(), HloOpcode::kConvert); + EXPECT_EQ(dot->shape().element_type(), F32); + EXPECT_EQ(dot->operand(0)->shape().element_type(), F32); + EXPECT_EQ(dot->operand(0)->opcode(), HloOpcode::kConvert); + EXPECT_EQ(dot->operand(1)->shape().element_type(), F32); + EXPECT_EQ(dot->operand(1)->opcode(), HloOpcode::kConvert); +} + } // namespace xla diff --git a/tensorflow/compiler/xla/service/bfloat16_propagation.cc b/tensorflow/compiler/xla/service/bfloat16_propagation.cc index 9246cb25d28b53008af092197a132db91236d61c..531f36e8c5473ef684e654ed6b89c4d5ef04b401 100644 --- a/tensorflow/compiler/xla/service/bfloat16_propagation.cc +++ b/tensorflow/compiler/xla/service/bfloat16_propagation.cc @@ -15,10 +15,13 @@ limitations under the License. #include "tensorflow/compiler/xla/service/bfloat16_propagation.h" +#include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/map_util.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" +#include "tensorflow/compiler/xla/service/hlo_dce.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/service/hlo_module.h" +#include "tensorflow/compiler/xla/service/tuple_simplifier.h" #include "tensorflow/compiler/xla/shape_tree.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/core/lib/gtl/cleanup.h" @@ -66,33 +69,53 @@ void BFloat16Propagation::DetermineAndMutateFusionComputationPrecision( for (auto inst_it = insts.rbegin(); inst_it != insts.rend(); ++inst_it) { DetermineAndMutateInstructionPrecision(*inst_it, /*skip_parameters=*/false); } + computations_visited_in_mutation_pass_.insert( + fusion->fused_instructions_computation()); } -void BFloat16Propagation::AdjustFusionParameters(HloInstruction* fusion) { - CHECK_EQ(fusion->fused_parameters().size(), fusion->operand_count()); - for (int64 i = 0; i < fusion->operand_count(); ++i) { - auto parameter = fusion->fused_parameter(i); - ShapeUtil::ForEachMutableSubshape( - parameter->mutable_shape(), - [&](Shape* subshape, const ShapeIndex& index) { - if (!ShapeUtil::IsLeafIndex(parameter->shape(), index)) { - return; - } - PrimitiveType operand_type = - ShapeUtil::GetSubshape(fusion->operand(i)->shape(), index) - .element_type(); - if (subshape->element_type() == operand_type) { - return; - } - CHECK(operand_type == F32 || operand_type == BF16); - subshape->set_element_type(operand_type); +void BFloat16Propagation::DetermineAndMutateWhileComputationsPrecision( + HloInstruction* while_hlo) { + CHECK_EQ(while_hlo->opcode(), HloOpcode::kWhile); + + // We are depending on the while node itself having already been analyzed for + // whether it can output BF16 and this has been adjusted in the output shape, + // and now we're looking to update the body and condition computations to + // match the new output shape, as well as recursively process the whole while + // node even if the output shape was not modified. + HloComputation* body = while_hlo->while_body(); + auto body_root = body->root_instruction(); + HloComputation* condition = while_hlo->while_condition(); + + ShapeUtil::ForEachMutableSubshape( + body_root->mutable_shape(), + [this, while_hlo, body_root](Shape* subshape, const ShapeIndex& index) { + if (subshape->element_type() != F32) { + return; + } + if (ShapeUtil::GetSubshape(while_hlo->shape(), index).element_type() == + BF16) { + subshape->set_element_type(BF16); changed_ = true; - VLOG(2) << "Fused parameter " << parameter->ToString() + VLOG(2) << "While body root " << body_root->ToString() << " at shape index " << index - << " adjusted to match operand in fusion " - << fusion->ToString(); - }); + << " changed to BF16 precision for while " + << while_hlo->ToString(); + } + }); + + auto body_insts = body->MakeInstructionPostOrder(); + for (auto inst_it = body_insts.rbegin(); inst_it != body_insts.rend(); + ++inst_it) { + DetermineAndMutateInstructionPrecision(*inst_it, /*skip_parameters=*/false); + } + computations_visited_in_mutation_pass_.insert(body); + + auto condition_insts = condition->MakeInstructionPostOrder(); + for (auto inst_it = condition_insts.rbegin(); + inst_it != condition_insts.rend(); ++inst_it) { + DetermineAndMutateInstructionPrecision(*inst_it, /*skip_parameters=*/false); } + computations_visited_in_mutation_pass_.insert(condition); } bool BFloat16Propagation::AllUsersConsumeBF16(const HloInstruction& hlo, @@ -106,14 +129,45 @@ bool BFloat16Propagation::AllUsersConsumeBF16(const HloInstruction& hlo, continue; } for (const HloUse& use : value->uses()) { + if (!ContainsKey(instructions_visited_in_mutation_pass_, + use.instruction)) { + // We don't know yet whether use.instruction will consume BF16 since it + // hasn't been visited. Although we visit instructions in reverse + // topological order, this is still possible because there may be + // unvisited instruction that alias the same buffer. In this case, we + // aggressively skip this use, and if this causes inconsistency (e.g., + // one use is in BF16 but another use is in F32), it will be resolved at + // the end of the BFloat16Propagation pass. + continue; + } + // Any visited user that can accept BF16 has already been updated if + // necessary, e.g., the output has been changed to BF16 if it propagates + // precision, or a called computation's parameters have been changed to + // BF16 for fusions or whiles. if (use.instruction->opcode() == HloOpcode::kFusion) { - auto fused_parameter = + const auto* fused_parameter = use.instruction->fused_parameter(use.operand_number); if (ShapeUtil::GetSubshape(fused_parameter->shape(), use.operand_index) .element_type() != BF16) { return false; } continue; + } else if (use.instruction->opcode() == HloOpcode::kWhile) { + const auto* cond_parameter = + use.instruction->while_condition()->parameter_instruction( + use.operand_number); + if (ShapeUtil::GetSubshape(cond_parameter->shape(), use.operand_index) + .element_type() != BF16) { + return false; + } + const auto* body_parameter = + use.instruction->while_body()->parameter_instruction( + use.operand_number); + if (ShapeUtil::GetSubshape(body_parameter->shape(), use.operand_index) + .element_type() != BF16) { + return false; + } + continue; } if (bfloat16_support_->EffectiveOperandPrecisionIsBF16( *use.instruction, use.operand_number)) { @@ -147,24 +201,40 @@ bool BFloat16Propagation::AllUsersConsumeBF16(const HloInstruction& hlo, void BFloat16Propagation::DetermineAndMutateInstructionPrecision( HloInstruction* hlo, bool skip_parameters) { - // We handle any fusion computation after the instruction is handled, because - // we need to know a fusion's output shape before propagating inside its fused - // computation. - auto cleaner = tensorflow::gtl::MakeCleanup([this, hlo] { - if (hlo->opcode() == HloOpcode::kFusion) { - DetermineAndMutateFusionComputationPrecision(hlo); - } - }); + // We handle any fusion computation or while body/condition after the + // instruction is handled, because we need to know the output shape of a + // fusion or while before propagating inside its computations. + bool postpone_processing_called_computations = false; + auto cleaner = tensorflow::gtl::MakeCleanup( + [this, hlo, &postpone_processing_called_computations] { + if (!postpone_processing_called_computations) { + if (hlo->opcode() == HloOpcode::kFusion) { + DetermineAndMutateFusionComputationPrecision(hlo); + } else if (hlo->opcode() == HloOpcode::kWhile) { + DetermineAndMutateWhileComputationsPrecision(hlo); + } + } + instructions_visited_in_mutation_pass_.insert(hlo); + }); + + if (hlo->opcode() == HloOpcode::kWhile && + (caller_counts_[hlo->while_condition()] > 1 || + caller_counts_[hlo->while_body()] > 1)) { + postpone_processing_called_computations = true; + return; + } // Do not change precision for instructions related to entry and exit of a // computation, and control flow, because this pass might break the interfaces // or assumptions for them. if (hlo->opcode() == HloOpcode::kInfeed || // hlo->opcode() == HloOpcode::kOutfeed || // - hlo->opcode() == HloOpcode::kConstant || // + hlo->opcode() == HloOpcode::kSend || // + hlo->opcode() == HloOpcode::kSendDone || // + hlo->opcode() == HloOpcode::kRecv || // + hlo->opcode() == HloOpcode::kRecvDone || // hlo->opcode() == HloOpcode::kCustomCall || // hlo->opcode() == HloOpcode::kCall || // - hlo->opcode() == HloOpcode::kWhile || // hlo->opcode() == HloOpcode::kConditional || // (hlo->opcode() == HloOpcode::kParameter && skip_parameters)) { return; @@ -229,6 +299,334 @@ bool BFloat16Propagation::InstructionIsCandidateForBF16Output( return true; } +void BFloat16Propagation::AdjustCalledComputationParameters( + HloInstruction* hlo) { + auto adjust_computation = + [this, hlo](HloComputation* computation, + tensorflow::gtl::ArraySlice operands) { + // Adjust parameters. + CHECK_EQ(operands.size(), computation->num_parameters()); + for (int64 i = 0; i < operands.size(); ++i) { + auto parameter = computation->parameter_instruction(i); + ShapeUtil::ForEachMutableSubshape( + parameter->mutable_shape(), + [this, i, hlo, &operands, parameter](Shape* subshape, + const ShapeIndex& index) { + if (!ShapeUtil::IsLeafIndex(parameter->shape(), index)) { + return; + } + PrimitiveType operand_type = + ShapeUtil::GetSubshape(operands[i]->shape(), index) + .element_type(); + if (subshape->element_type() == operand_type) { + return; + } + CHECK(operand_type == F32 || operand_type == BF16); + subshape->set_element_type(operand_type); + changed_ = true; + VLOG(2) << "Called computation parameter " + << parameter->ToString() << " at shape index " << index + << " adjusted to match operand in HLO " + << hlo->ToString(); + }); + } + }; + + switch (hlo->opcode()) { + case HloOpcode::kFusion: + adjust_computation(hlo->fused_instructions_computation(), + hlo->operands()); + break; + case HloOpcode::kWhile: + adjust_computation(hlo->while_condition(), hlo->operands()); + adjust_computation(hlo->while_body(), hlo->operands()); + break; + default: + break; + } +} + +void BFloat16Propagation::AdjustCalledComputationRoot(HloInstruction* hlo) { + auto adjust_computation = [this, hlo](HloComputation* computation, + const Shape& output_shape) { + // Adjust root. + HloInstruction* root = computation->root_instruction(); + ShapeUtil::ForEachMutableSubshape( + root->mutable_shape(), [this, hlo, root, &output_shape]( + Shape* subshape, const ShapeIndex& index) { + if (!ShapeUtil::IsLeafIndex(hlo->shape(), index)) { + return; + } + const PrimitiveType output_type = + ShapeUtil::GetSubshape(output_shape, index).element_type(); + if (subshape->element_type() == output_type) { + return; + } + CHECK(output_type == F32 || output_type == BF16); + subshape->set_element_type(output_type); + // It's possible that output_type is F32, but the root instruction's + // type is BF16; e.g., a fusion node's output was changed to BF16 + // initially but then adjusted back to F32, and the fusion computation + // is now being adjusted after the fusion node. + if (output_type == F32) { + for (const auto* value : + dataflow_->GetValueSet(root, index).values()) { + // We rely on the fact that this adjustment works in reverse + // topological order so that called computation will be + // processed later. Adding the value to + // values_that_must_be_kept_as_f32_ will ensure the + // correctness of the adjustment for HLOs that will be + // processed later. + values_that_must_be_kept_as_f32_.insert(value); + } + } + changed_ = true; + VLOG(2) << "Called computation root " << root->ToString() + << " at shape index " << index + << " adjusted to match output shape of " << hlo->ToString(); + }); + }; + + switch (hlo->opcode()) { + case HloOpcode::kFusion: + adjust_computation(hlo->fused_instructions_computation(), hlo->shape()); + break; + case HloOpcode::kWhile: + adjust_computation(hlo->while_condition(), hlo->shape()); + adjust_computation(hlo->while_body(), hlo->shape()); + break; + default: + break; + } +} + +bool BFloat16Propagation::ResolveInconsistencyOfAliasingBuffersHelper( + HloComputation* computation, + tensorflow::gtl::FlatSet* visited_computations) { + bool parameter_changed = false; + auto insts = computation->MakeInstructionPostOrder(); + // Do the adjustment on each instruction in the computation in reverse + // topological order. + for (auto inst_it = insts.rbegin(); inst_it != insts.rend(); ++inst_it) { + auto hlo = *inst_it; + auto adjust_hlo_output = [this, hlo, ¶meter_changed]( + Shape* subshape, const ShapeIndex& index) { + if (subshape->element_type() != F32 && subshape->element_type() != BF16) { + return; + } + PrimitiveType type = BF16; + for (const auto* value : dataflow_->GetValueSet(hlo, index).values()) { + if (value->shape().element_type() == BF16) { + continue; + } + CHECK_EQ(value->shape().element_type(), F32); + type = F32; + break; + } + // It's possible that a user has been changed from BF16 to F32 + // during this final adjustment pass, so we need to check + // AllUsersConsumeBF16() again. + if (type == BF16 && !AllUsersConsumeBF16(*hlo, index)) { + type = F32; + } + if (type == F32) { + for (const auto* value : dataflow_->GetValueSet(hlo, index).values()) { + // We rely on the fact that this adjustment works in reverse + // topological order. Adding the value to + // values_that_must_be_kept_as_f32_ will ensure the correctness + // of the adjustment for HLOs that will be processed later. + values_that_must_be_kept_as_f32_.insert(value); + } + } + if (type != subshape->element_type()) { + subshape->set_element_type(type); + VLOG(2) << "HloInstruction output at shape index " << index + << " adjusted to " << *subshape << ": " << hlo->ToString(); + if (hlo->opcode() == HloOpcode::kParameter) { + parameter_changed = true; + } + } + }; + ShapeUtil::ForEachMutableSubshape(hlo->mutable_shape(), adjust_hlo_output); + AdjustCalledComputationRoot(hlo); + if (hlo->opcode() == HloOpcode::kWhile) { + // We need to run on the while body and condition repeatedly until a fixed + // point is reached, i.e., the parameters do not change any more. We may + // need more than one iteration because the while input and output alias + // each other, so changing one input parameter requires changing the + // corresponding output element and thus may transitively require changing + // another input parameter. A fixed point will be reached because the + // parameters can only be changed from BF16 to F32, not the other way + // around. + tensorflow::gtl::FlatSet visited_in_while; + while (ResolveInconsistencyOfAliasingBuffersHelper(hlo->while_condition(), + &visited_in_while) || + ResolveInconsistencyOfAliasingBuffersHelper(hlo->while_body(), + &visited_in_while)) { + visited_in_while.clear(); + ShapeUtil::ForEachMutableSubshape(hlo->mutable_shape(), + adjust_hlo_output); + AdjustCalledComputationRoot(hlo); + } + visited_computations->insert(visited_in_while.begin(), + visited_in_while.end()); + } + } + // Now adjust parameters of called computations. + for (auto inst_it = insts.rbegin(); inst_it != insts.rend(); ++inst_it) { + AdjustCalledComputationParameters(*inst_it); + } + return parameter_changed; +} + +Status BFloat16Propagation::ResolveInconsistencyOfAliasingBuffers( + HloModule* module) { + std::list computations_topological_order = + module->MakeComputationPostOrder(); + tensorflow::gtl::FlatSet resolved; + for (auto comp_it = computations_topological_order.rbegin(); + comp_it != computations_topological_order.rend(); ++comp_it) { + if (ContainsKey(resolved, *comp_it)) { + continue; + } + ResolveInconsistencyOfAliasingBuffersHelper(*comp_it, &resolved); + } + + // We could have changed a fusion computation's root shape to have a different + // precision than the fusion node's output, if the fusion root does not + // define a buffer (e.g., a tuple). Now we add conversions after such fusion + // roots to make them match the fusion output. If the fusion output is a + // (possibly nested) tuple, we first create get-tuple-elements, then convert + // the unmatching leaf nodes, and finally create a new tuple as the fusion + // computation's root. If tuples and get-tuple-elements are created, we will + // run tuple simplifier and dead code elimination at the end (dead code is not + // allowed in fusion computation). E.g., + // + // (1) (2) (3) + // a b a b a b + // |\ | |\ | |\ | + // \ add -> |add -> | add + // \ | \ | convert | + // tuple tuple \ | + // / \ tuple + // gte gte + // | | + // convert | + // \ / + // tuple + // (1) a is F32 but tuple is BF16 + // (2) after adding conversion + // (3) after tuple simplifier and DCE. + bool needs_tuple_simplifier = false; + for (auto computation : computations_topological_order) { + auto insts = computation->MakeInstructionPostOrder(); + for (auto inst_it = insts.rbegin(); inst_it != insts.rend(); ++inst_it) { + auto hlo = *inst_it; + if (hlo->opcode() != HloOpcode::kFusion) { + continue; + } + auto fusion_computation = hlo->fused_instructions_computation(); + auto fusion_root = fusion_computation->root_instruction(); + if (ShapeUtil::Compatible(fusion_root->shape(), hlo->shape())) { + continue; + } + ShapeTree converted_outputs(hlo->shape()); + // Iterate through nodes in the shape tree in pre-order and initialize + // each non-root node with a corresponding get-tuple-element. For a leaf + // node, if its shape does not match the fusion output, create a + // conversion node to overwrite the node value. + for (auto it = converted_outputs.begin(); it != converted_outputs.end(); + ++it) { + ShapeIndex output_index = it->first; + HloInstruction*& output = it->second; + const Shape subshape = + ShapeUtil::GetSubshape(hlo->shape(), output_index); + if (output_index.empty()) { + output = fusion_root; + } else { + ShapeIndex parent_index = output_index; + parent_index.pop_back(); + output = fusion_computation->AddInstruction( + HloInstruction::CreateGetTupleElement( + subshape, converted_outputs.element(parent_index), + output_index.back())); + } + if (ShapeUtil::IsTuple(subshape)) { + continue; + } + if (!ShapeUtil::Compatible( + subshape, + ShapeUtil::GetSubshape(fusion_root->shape(), output_index))) { + output = fusion_computation->AddInstruction( + HloInstruction::CreateConvert(subshape, output)); + } + } + // Iterate through nodes in the shape tree in reverse pre-order and create + // a tuple instruction for each non-leaf node where the elements are the + // values of its child nodes. + for (auto it = converted_outputs.rbegin(); it != converted_outputs.rend(); + ++it) { + ShapeIndex output_index = it->first; + HloInstruction*& output = it->second; + const Shape& subshape = + ShapeUtil::GetSubshape(hlo->shape(), output_index); + if (!ShapeUtil::IsTuple(subshape)) { + continue; + } + std::vector elements( + ShapeUtil::TupleElementCount(subshape)); + ShapeIndex child_index = output_index; + for (int64 i = 0; i < elements.size(); ++i) { + child_index.push_back(i); + elements[i] = converted_outputs.element(child_index); + child_index.pop_back(); + } + output = fusion_computation->AddInstruction( + HloInstruction::CreateTuple(elements)); + } + fusion_computation->set_root_instruction(converted_outputs.element({})); + needs_tuple_simplifier |= ShapeUtil::IsTuple(hlo->shape()); + } + } + + // We may have converted some constants from F32 to BF16, so adjust the + // constant literals in such cases. We do this here instead of when the + // constant node's is changed because 1) the HloInstruction interface does not + // allow resetting the literal so we have to create a new kConstant + // instruction to replace the old one, which invalidates dataflow analysis, + // and 2) it's possible that a kConstant's output gets changed to BF16 at the + // beginning but later on adjusted back to F32, so converting literals here + // can avoid repeated conversions. + // + // TODO(b/73833576): Consider resetting literal in HloInstruction. + bool needs_dce = needs_tuple_simplifier; + for (auto computation : computations_topological_order) { + for (auto hlo : computation->MakeInstructionPostOrder()) { + if (hlo->opcode() != HloOpcode::kConstant) { + continue; + } + if (!ShapeUtil::Equal(hlo->literal().shape(), hlo->shape())) { + TF_ASSIGN_OR_RETURN(auto converted_literal, + hlo->literal().ConvertToShape(hlo->shape())); + auto new_constant = computation->AddInstruction( + HloInstruction::CreateConstant(std::move(converted_literal))); + TF_RETURN_IF_ERROR(hlo->ReplaceAllUsesWith(new_constant)); + needs_dce = true; + } + } + } + + if (needs_tuple_simplifier) { + TupleSimplifier tuple_simplifier; + TF_RETURN_IF_ERROR(tuple_simplifier.Run(module).status()); + } + if (needs_dce) { + HloDCE dce; + TF_RETURN_IF_ERROR(dce.Run(module).status()); + } + return Status::OK(); +} + // The algorithm first does a forward pass (parameters to root) to determine a // set of instructions to consider using bfloat16, then does a backward pass to // determine the precisions of those instructions according to the need of @@ -278,56 +676,7 @@ StatusOr BFloat16Propagation::Run(HloModule* module) { // It's possible that an instruction does not define a buffer, but the // defining instruction's shape has changed. So we need to adjust the output // shapes of instructions according to the HLO values they refer to. - for (auto comp_it = computations_topological_order.rbegin(); - comp_it != computations_topological_order.rend(); ++comp_it) { - auto insts = (*comp_it)->MakeInstructionPostOrder(); - // Do the adjustment on each instruction in the computation in reverse - // topological order. - for (auto inst_it = insts.rbegin(); inst_it != insts.rend(); ++inst_it) { - auto hlo = *inst_it; - auto adjust_buffer = [this, hlo](Shape* subshape, - const ShapeIndex& index) { - if (subshape->element_type() != F32 && - subshape->element_type() != BF16) { - return; - } - PrimitiveType type = BF16; - for (const auto* value : dataflow_->GetValueSet(hlo, index).values()) { - if (value->shape().element_type() == BF16) { - continue; - } - CHECK_EQ(value->shape().element_type(), F32); - type = F32; - break; - } - // It's possible that a user has been changed from BF16 to F32 - // during this final adjustment pass, so we need to check - // AllUsersConsumeBF16() again. - if (type == BF16 && !AllUsersConsumeBF16(*hlo, index)) { - type = F32; - } - if (type == F32) { - for (const auto* value : - dataflow_->GetValueSet(hlo, index).values()) { - // We rely on the fact that this adjustment works in reverse - // topological order. Adding the value to - // values_that_must_be_kept_as_f32_ will ensure the correctness - // of the adjustment for HLOs that will be processed later. - values_that_must_be_kept_as_f32_.insert(value); - } - } - subshape->set_element_type(type); - }; - ShapeUtil::ForEachMutableSubshape(hlo->mutable_shape(), adjust_buffer); - } - // Now adjust parameters of fusions inside this computation. - for (auto inst_it = insts.rbegin(); inst_it != insts.rend(); ++inst_it) { - auto hlo = *inst_it; - if (hlo->opcode() == HloOpcode::kFusion) { - AdjustFusionParameters(hlo); - } - } - } + TF_RETURN_IF_ERROR(ResolveInconsistencyOfAliasingBuffers(module)); return true; } diff --git a/tensorflow/compiler/xla/service/bfloat16_propagation.h b/tensorflow/compiler/xla/service/bfloat16_propagation.h index aa81dde3b0e6964576a056cd0f579a8ec9540d64..89a5ac5db1549877a135182ae8df57fa6bf9d579 100644 --- a/tensorflow/compiler/xla/service/bfloat16_propagation.h +++ b/tensorflow/compiler/xla/service/bfloat16_propagation.h @@ -38,7 +38,8 @@ namespace xla { // be bitwise identical to that without this pass; this is possible if the // backend already reduces precision to BF16 on some HLO instructions. // -// This pass will not modify the signature of any non-fusion computation. +// This pass will not modify the signature of a computation, unless it is a +// fusion computation or its only caller is a while. // // !!! WARNING !!! This pass can introduce mixed precision in individual HLOs, // which has two issues: @@ -92,11 +93,48 @@ class BFloat16Propagation : public HloPassInterface { bool skip_parameters); // Special handling in the mutation pass for fusion computations. + // + // Precondition: hlo->opcode() == kFusion void DetermineAndMutateFusionComputationPrecision(HloInstruction* fusion); - // Makes the fusion parameters match the precision of the actual parameters - // passed to the fusion node. - void AdjustFusionParameters(HloInstruction* fusion); + // Special handling in the mutation pass for while computations. + // + // Precondition: hlo->opcode() == kWhile + void DetermineAndMutateWhileComputationsPrecision(HloInstruction* while_hlo); + + // The set of HloInstructions that have been visited in the mutation pass. + tensorflow::gtl::FlatSet + instructions_visited_in_mutation_pass_; + + // The set of HloComputations that have been visited in the mutation pass. + tensorflow::gtl::FlatSet + computations_visited_in_mutation_pass_; + + // *************************** + // Functions called by the final inconsistency resolving pass. + + // Adjusts the output shapes of HloInstructions such that if two + // HloInstructions have aliasing buffers in their outputs, they must have the + // same precision. + Status ResolveInconsistencyOfAliasingBuffers(HloModule* module); + + // Resolves inconsistency of aliasing buffers for the given computation, and + // recursively runs on a while instruction's condition and body until a fixed + // point is reached. + bool ResolveInconsistencyOfAliasingBuffersHelper( + HloComputation* computation, + tensorflow::gtl::FlatSet* visited_computations); + + // Makes the parameters of called computations match how they are called by + // the given HLO. + void AdjustCalledComputationParameters(HloInstruction* hlo); + + // Makes the root instructions of called computations match how they are used + // by the given HLO. + void AdjustCalledComputationRoot(HloInstruction* hlo); + + // *************************** + // Functions called and state used by two or more passes. // Returns whether all uses of the given HloInstruction can consume BF16 // input. @@ -106,8 +144,10 @@ class BFloat16Propagation : public HloPassInterface { // The set of F32 HLO values that must be kept in F32. tensorflow::gtl::FlatSet values_that_must_be_kept_as_f32_; - // *************************** - // State used by both passes. + // Mapping from each HloComputation to the number of callers to it in the + // module. Populated at the beginning of this pass. + tensorflow::gtl::FlatMap caller_counts_; + const BFloat16Support* bfloat16_support_; std::unique_ptr dataflow_; diff --git a/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc b/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc index 4c86c6b26e362c62804b627036e6ba7c078b402a..5950b004b3da439c442eec6e5e09ea2307fcb018 100644 --- a/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc +++ b/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc @@ -23,6 +23,7 @@ limitations under the License. #include "tensorflow/compiler/xla/test.h" #include "tensorflow/compiler/xla/test_helpers.h" #include "tensorflow/compiler/xla/tests/hlo_test_base.h" +#include "tensorflow/compiler/xla/tests/literal_test_util.h" #include "tensorflow/compiler/xla/xla_data.pb.h" namespace xla { @@ -68,7 +69,7 @@ class BFloat16PropagationTest : public HloTestBase { // Returns whether the given HloInstruction's output element type is BF16 or // the only use of it is converting to BF16. - bool OutputsBF16(HloInstruction* inst) { + bool OutputsBF16(const HloInstruction* inst) { if (inst->shape().element_type() == BF16) { return true; } @@ -121,6 +122,41 @@ TEST_F(BFloat16PropagationTest, PropagateThroughSelectButNotAdd) { EXPECT_FALSE(OutputsBF16(c)); } +// Tests that if a constant is converted to BF16 then its literal must also be +// converted. +TEST_F(BFloat16PropagationTest, ConvertConstantLiteral) { + auto builder = HloComputation::Builder(TestName()); + Shape shape = ShapeUtil::MakeShape(F32, {4, 4}); + Array2D array_a(4, 4); + array_a.FillUnique(1.0f); + Array2D array_b(4, 4); + array_b.FillUnique(10.0f); + + HloInstruction* a = builder.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateFromArray(array_a))); + HloInstruction* b = builder.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateFromArray(array_b))); + HloInstruction* dot = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kDot, a, b)); + + auto module = CreateNewModule(); + auto computation = module->AddEntryComputation(builder.Build()); + + EXPECT_TRUE(PropagatePrecision(module.get())); + + EXPECT_EQ(computation->root_instruction(), dot); + EXPECT_TRUE(OutputsBF16(dot->operand(0))); + EXPECT_TRUE(OutputsBF16(dot->operand(1))); + EXPECT_EQ(dot->operand(0)->opcode(), HloOpcode::kConstant); + EXPECT_EQ(dot->operand(1)->opcode(), HloOpcode::kConstant); + LiteralTestUtil::ExpectEqual( + dot->operand(0)->literal(), + *LiteralTestUtil::ConvertF32ToBF16(*Literal::CreateFromArray(array_a))); + LiteralTestUtil::ExpectEqual( + dot->operand(1)->literal(), + *LiteralTestUtil::ConvertF32ToBF16(*Literal::CreateFromArray(array_b))); +} + // Tests that BF16 can be propagated through nested tuples. TEST_F(BFloat16PropagationTest, PropagateThroughTuples) { auto builder = HloComputation::Builder(TestName()); @@ -287,6 +323,64 @@ TEST_F(BFloat16PropagationTest, PropagateThroughFusion) { EXPECT_TRUE(OutputsBF16(b_f1)); } +// Tests that if 1) the root instruction of a fusion is a tuple, 2) the fusion +// outputs are only used by a dot, and 3) one element of the tuple is used by +// an add in the fusion computation, then the propagation pass should create a +// convert in the fusion computation to keep the add's operand in F32 but change +// the fusion output to BF16. E.g., the following fusion computation +// (F32, F32) fusion_computation(F32 a, F32 b) +// = tuple(F32 a, F32 add(F32 a, F32 b)) +// will be changed to +// (BF16, BF16) fusion_computation(F32 a, F32 b) +// = tuple(BF16 convert(a), BF16 add(F32 a, F32 b)) +TEST_F(BFloat16PropagationTest, ConvertTupleFusionElementIfUsedByAdd) { + auto module = CreateNewModule(); + auto builder = HloComputation::Builder(TestName()); + Shape shape = ShapeUtil::MakeShape(F32, {4, 4}); + + HloInstruction* param = builder.AddInstruction( + HloInstruction::CreateParameter(0, shape, "param")); + HloInstruction* add = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, param, param)); + + auto builder_f = HloComputation::Builder("fusion0"); + HloInstruction* a_f = + builder_f.AddInstruction(HloInstruction::CreateParameter(0, shape, "a")); + HloInstruction* b_f = + builder_f.AddInstruction(HloInstruction::CreateParameter(1, shape, "b")); + HloInstruction* add_f = builder_f.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, a_f, b_f)); + HloInstruction* tuple_f = + builder_f.AddInstruction(HloInstruction::CreateTuple({a_f, add_f})); + auto comp_f = module->AddEmbeddedComputation(builder_f.Build()); + auto fusion = builder.AddInstruction(HloInstruction::CreateFusion( + tuple_f->shape(), HloInstruction::FusionKind::kCustom, {add, add}, + comp_f)); + + HloInstruction* gte0 = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, fusion, 0)); + HloInstruction* gte1 = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, fusion, 1)); + HloInstruction* dot = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kDot, gte0, gte1)); + + auto computation = module->AddEntryComputation(builder.Build()); + + EXPECT_TRUE(PropagatePrecision(module.get())); + + EXPECT_EQ(computation->root_instruction(), dot); + EXPECT_TRUE(OutputsBF16(gte0)); + EXPECT_TRUE(OutputsBF16(gte1)); + EXPECT_FALSE(OutputsBF16(a_f)); + EXPECT_FALSE(OutputsBF16(b_f)); + EXPECT_TRUE(OutputsBF16(add_f)); + auto new_fusion_root = comp_f->root_instruction(); + EXPECT_EQ(new_fusion_root->opcode(), HloOpcode::kTuple); + EXPECT_EQ(new_fusion_root->operand(1), add_f); + EXPECT_EQ(new_fusion_root->operand(0)->opcode(), HloOpcode::kConvert); + EXPECT_TRUE(OutputsBF16(new_fusion_root->operand(0))); +} + // A select over tuples does not define the leaf buffers, so the types in // on_true and on_false must match, so that as long as one of them is F32, the // other must be F32 as well. @@ -332,4 +426,195 @@ TEST_F(BFloat16PropagationTest, SelectOverTuples) { EXPECT_TRUE(OutputsBF16(xpose)); } +// Tests that BF16 is propagated properly through while computations. +TEST_F(BFloat16PropagationTest, PropagateThroughWhile) { + auto module = CreateNewModule(); + auto builder = HloComputation::Builder(TestName()); + Shape shape = ShapeUtil::MakeShape(F32, {4, 4}); + + HloInstruction* param0 = builder.AddInstruction( + HloInstruction::CreateParameter(0, shape, "param0")); + HloInstruction* param1 = builder.AddInstruction( + HloInstruction::CreateParameter(1, shape, "param1")); + HloInstruction* add0 = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, param0, param1)); + HloInstruction* add1 = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, param0, param1)); + HloInstruction* tuple = + builder.AddInstruction(HloInstruction::CreateTuple({add0, add1})); + + auto builder_cond = HloComputation::Builder("cond"); + auto cond_param = builder_cond.AddInstruction( + HloInstruction::CreateParameter(0, tuple->shape(), "cond_param")); + auto cond_lhs = builder_cond.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, cond_param, 0)); + auto cond_rhs = builder_cond.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, cond_param, 1)); + // This add should prevent RHS from using BF16 + auto cond_add_rhs = builder_cond.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, cond_rhs, cond_rhs)); + auto cond_dot = builder_cond.AddInstruction(HloInstruction::CreateBinary( + shape, HloOpcode::kDot, cond_lhs, cond_add_rhs)); + builder_cond.AddInstruction(HloInstruction::CreateBinary( + ShapeUtil::MakeShape(PRED, {}), HloOpcode::kGt, + builder_cond.AddInstruction(HloInstruction::CreateSlice( + ShapeUtil::MakeShape(F32, {}), cond_dot, {0, 0}, {1, 1}, {1, 1})), + builder_cond.AddInstruction(HloInstruction::CreateSlice( + ShapeUtil::MakeShape(F32, {}), cond_dot, {1, 1}, {2, 2}, {1, 1})))); + auto cond = module->AddEmbeddedComputation(builder_cond.Build()); + + auto builder_body = HloComputation::Builder("body"); + auto body_param = builder_body.AddInstruction( + HloInstruction::CreateParameter(0, tuple->shape(), "body_param")); + auto body_lhs = builder_body.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, body_param, 0)); + auto body_rhs = builder_body.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, body_param, 1)); + auto body_dot = builder_body.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kDot, body_lhs, body_rhs)); + builder_body.AddInstruction( + HloInstruction::CreateTuple({body_dot, body_rhs})); + auto body = module->AddEmbeddedComputation(builder_body.Build()); + + auto while_hlo = builder.AddInstruction( + HloInstruction::CreateWhile(tuple->shape(), cond, body, tuple)); + + auto lhs = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, while_hlo, 0)); + auto rhs = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, while_hlo, 1)); + auto dot = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kDot, lhs, rhs)); + auto computation = module->AddEntryComputation(builder.Build()); + + EXPECT_TRUE(PropagatePrecision(module.get())); + + EXPECT_EQ(computation->root_instruction(), dot); + EXPECT_TRUE(OutputsBF16(lhs)); + EXPECT_FALSE(OutputsBF16(rhs)); + EXPECT_TRUE(OutputsBF16(body_dot)); + EXPECT_TRUE(OutputsBF16(body_lhs)); + EXPECT_FALSE(OutputsBF16(body_rhs)); + EXPECT_TRUE(OutputsBF16(cond_lhs)); + EXPECT_FALSE(OutputsBF16(cond_rhs)); + EXPECT_TRUE(OutputsBF16(add0)); + EXPECT_FALSE(OutputsBF16(add1)); +} + +// Tests that BF16 is not propagated through multiple whiles that invoke the +// same computation as long as one while prevents the propagation. +TEST_F(BFloat16PropagationTest, DoNotPropagateWhilesCallingSameComputation) { + auto module = CreateNewModule(); + auto builder = HloComputation::Builder(TestName()); + Shape shape = ShapeUtil::MakeShape(F32, {4, 4}); + + HloInstruction* param0 = builder.AddInstruction( + HloInstruction::CreateParameter(0, shape, "param0")); + HloInstruction* param1 = builder.AddInstruction( + HloInstruction::CreateParameter(1, shape, "param1")); + HloInstruction* add0 = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, param0, param1)); + HloInstruction* add1 = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, param0, param1)); + HloInstruction* add2 = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, param0, param1)); + HloInstruction* add3 = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, param0, param1)); + HloInstruction* tuple0 = + builder.AddInstruction(HloInstruction::CreateTuple({add0, add1})); + HloInstruction* tuple1 = + builder.AddInstruction(HloInstruction::CreateTuple({add2, add3})); + + // Condition computation for the first while. + auto builder_cond0 = HloComputation::Builder("cond0"); + auto cond0_param = builder_cond0.AddInstruction( + HloInstruction::CreateParameter(0, tuple0->shape(), "cond0_param")); + auto cond0_lhs = builder_cond0.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, cond0_param, 0)); + auto cond0_rhs = builder_cond0.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, cond0_param, 1)); + // This add should prevent RHS from using BF16 + auto cond0_add_rhs = + builder_cond0.AddInstruction(HloInstruction::CreateBinary( + shape, HloOpcode::kAdd, cond0_rhs, cond0_rhs)); + auto cond0_dot = builder_cond0.AddInstruction(HloInstruction::CreateBinary( + shape, HloOpcode::kDot, cond0_lhs, cond0_add_rhs)); + builder_cond0.AddInstruction(HloInstruction::CreateBinary( + ShapeUtil::MakeShape(PRED, {}), HloOpcode::kGt, + builder_cond0.AddInstruction(HloInstruction::CreateSlice( + ShapeUtil::MakeShape(F32, {}), cond0_dot, {0, 0}, {1, 1}, {1, 1})), + builder_cond0.AddInstruction(HloInstruction::CreateSlice( + ShapeUtil::MakeShape(F32, {}), cond0_dot, {1, 1}, {2, 2}, {1, 1})))); + auto cond0 = module->AddEmbeddedComputation(builder_cond0.Build()); + + // Condition computation for the second while. + auto builder_cond1 = HloComputation::Builder("cond1"); + auto cond1_param = builder_cond1.AddInstruction( + HloInstruction::CreateParameter(0, tuple1->shape(), "cond1_param")); + auto cond1_lhs = builder_cond1.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, cond1_param, 0)); + auto cond1_rhs = builder_cond1.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, cond1_param, 1)); + // This add should prevent LHS from using BF16 + auto cond1_add_lhs = + builder_cond1.AddInstruction(HloInstruction::CreateBinary( + shape, HloOpcode::kAdd, cond1_lhs, cond1_lhs)); + auto cond1_dot = builder_cond1.AddInstruction(HloInstruction::CreateBinary( + shape, HloOpcode::kDot, cond1_add_lhs, cond1_rhs)); + builder_cond1.AddInstruction(HloInstruction::CreateBinary( + ShapeUtil::MakeShape(PRED, {}), HloOpcode::kGt, + builder_cond1.AddInstruction(HloInstruction::CreateSlice( + ShapeUtil::MakeShape(F32, {}), cond1_dot, {0, 0}, {1, 1}, {1, 1})), + builder_cond1.AddInstruction(HloInstruction::CreateSlice( + ShapeUtil::MakeShape(F32, {}), cond1_dot, {1, 1}, {2, 2}, {1, 1})))); + auto cond1 = module->AddEmbeddedComputation(builder_cond1.Build()); + + // Body computation shared by both whiles. + auto builder_body = HloComputation::Builder("body"); + auto body_param = builder_body.AddInstruction( + HloInstruction::CreateParameter(0, tuple0->shape(), "body_param")); + auto body_lhs = builder_body.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, body_param, 0)); + auto body_rhs = builder_body.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, body_param, 1)); + auto body_dot = builder_body.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kDot, body_lhs, body_rhs)); + builder_body.AddInstruction( + HloInstruction::CreateTuple({body_dot, body_rhs})); + auto body = module->AddEmbeddedComputation(builder_body.Build()); + + auto while0 = builder.AddInstruction( + HloInstruction::CreateWhile(tuple0->shape(), cond0, body, tuple0)); + auto while1 = builder.AddInstruction( + HloInstruction::CreateWhile(tuple1->shape(), cond1, body, tuple1)); + + auto lhs = builder.AddInstruction(HloInstruction::CreateBinary( + shape, HloOpcode::kDot, + builder.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, while0, 0)), + builder.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, while0, 1)))); + auto rhs = builder.AddInstruction(HloInstruction::CreateBinary( + shape, HloOpcode::kDot, + builder.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, while1, 0)), + builder.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, while1, 1)))); + auto dot = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kDot, lhs, rhs)); + auto computation = module->AddEntryComputation(builder.Build()); + + EXPECT_TRUE(PropagatePrecision(module.get())); + EXPECT_FALSE(OutputsBF16(body_dot)); + EXPECT_FALSE(OutputsBF16(body_rhs)); + EXPECT_FALSE(OutputsBF16(body_lhs)); + EXPECT_FALSE(OutputsBF16(cond0_lhs)); + EXPECT_FALSE(OutputsBF16(cond0_rhs)); + EXPECT_FALSE(OutputsBF16(cond1_lhs)); + EXPECT_FALSE(OutputsBF16(cond1_rhs)); + EXPECT_TRUE(OutputsBF16(cond0_add_rhs)); + EXPECT_TRUE(OutputsBF16(cond1_add_lhs)); + EXPECT_EQ(computation->root_instruction(), dot); +} + } // namespace xla diff --git a/tensorflow/compiler/xla/service/bfloat16_support.cc b/tensorflow/compiler/xla/service/bfloat16_support.cc index 3fd9e24601f27633c8063e4574c7c4f91f30dcff..07b4b14b5ec1bdbc01345091105df69368b0b2fb 100644 --- a/tensorflow/compiler/xla/service/bfloat16_support.cc +++ b/tensorflow/compiler/xla/service/bfloat16_support.cc @@ -79,6 +79,7 @@ bool BFloat16Support::EffectiveOperandPrecisionIsOutputPrecision( case HloOpcode::kBroadcast: case HloOpcode::kClamp: case HloOpcode::kConcatenate: + case HloOpcode::kConvert: case HloOpcode::kCopy: case HloOpcode::kGetTupleElement: case HloOpcode::kMaximum: diff --git a/tensorflow/compiler/xla/service/buffer_assignment.cc b/tensorflow/compiler/xla/service/buffer_assignment.cc index b1e693da9d5af4babe619b8796007f2da318f6a8..dbe45e932cdeed00e959355d5b3199d2e858148f 100644 --- a/tensorflow/compiler/xla/service/buffer_assignment.cc +++ b/tensorflow/compiler/xla/service/buffer_assignment.cc @@ -48,6 +48,183 @@ using ::tensorflow::strings::HumanReadableNumBytes; using ::tensorflow::strings::Printf; using ::tensorflow::strings::StrAppend; +namespace { + +template +string ColocatedBufferSetsToString(const T& container, const char* title) { + string result; + StrAppend(&result, title, "\n"); + for (const auto& it : container) { + StrAppend(&result, "\t", it->ToString(), "\n"); + } + return result; +} + +// Walk the call graph of the HLO module and place each computation into either +// thread_local_computations or global_computations depending upon whether the +// computation requires thread-local allocations or global allocations. The +// elements in thread_local_computations and global_computations are in post +// order (if computation A has an instruction which calls computation B, then A +// will appear after B in the vector). +Status GatherComputationsByAllocationType( + const HloModule* module, + std::vector* thread_local_computations, + std::vector* global_computations) { + // Create a worklist of computations paired with whether the allocation must + // be thread-local. + std::deque> worklist; + worklist.push_back(std::make_pair(module->entry_computation(), + /*is_thread_local*/ false)); + + // Sets for quickly checking membership. Computations are returned in vectors + // for stable iteration. + FlatSet thread_local_set; + FlatSet global_set; + + while (!worklist.empty()) { + auto worklist_front = worklist.front(); + worklist.pop_front(); + const HloComputation* computation = worklist_front.first; + bool is_thread_local = worklist_front.second; + bool in_thread_local_set = thread_local_set.count(computation) > 0; + bool in_global_set = global_set.count(computation) > 0; + + // If the computation has already been added to the respective set, then + // nothing to do. + if ((is_thread_local && in_thread_local_set) || + (!is_thread_local && in_global_set)) { + continue; + } + + // If the computation has already been added to the other set this is an + // error condition because the global call to the computation (eg, + // while/call) may return a reference to one of the thread-local buffers to + // the calling computation which will become a dangling reference when the + // thread-local is deallocated with the call return. + if ((is_thread_local && in_global_set) || + (!is_thread_local && in_thread_local_set)) { + return InvalidArgument( + "computation %s has conflicting allocation requirements (global " + "and thread-local)", + computation->name().c_str()); + } + + if (is_thread_local) { + thread_local_set.insert(computation); + } else { + global_set.insert(computation); + } + + for (auto* instruction : computation->instructions()) { + for (HloComputation* subcomputation : + instruction->called_computations()) { + switch (instruction->opcode()) { + case HloOpcode::kCall: + case HloOpcode::kConditional: + case HloOpcode::kWhile: + // Call and while must be called from a computation with global + // allocations as they may return references to buffers inside the + // called computation which cannot be thread-local. + if (is_thread_local) { + return InvalidArgument( + "computation %s cannot contain call/while op because it " + "requires thread-local buffer allocations", + computation->name().c_str()); + } + worklist.push_back(std::make_pair(subcomputation, + false)); // Not thread local. + break; + case HloOpcode::kMap: + case HloOpcode::kReduce: + case HloOpcode::kReduceWindow: + case HloOpcode::kSelectAndScatter: + case HloOpcode::kFusion: + // Map/reduce etc computations are always thread-local. + worklist.push_back(std::make_pair(subcomputation, + true)); // Thread local. + break; + default: + return InternalError( + "Unexpected calling opcode: %s", + HloOpcodeString(instruction->opcode()).c_str()); + } + } + } + } + + // Add the computations to the vectors in post order. + for (auto* computation : module->MakeComputationPostOrder()) { + if (thread_local_set.count(computation) > 0) { + thread_local_computations->push_back(computation); + } else if (global_set.count(computation) > 0) { + global_computations->push_back(computation); + } + // If the computation is not reachable from the entry computation, then it + // will not appear in either thread_local_set or global_set. We don't bother + // assigning buffers for these. + } + return Status::OK(); +} + +// Checks that points-to set of 'instruction' is unambiguous and distinct +// (ensured by CopyInsertion), then adds the buffer from the points-to set at +// 'index' to 'colocated_set'. +const LogicalBuffer* AddBufferToColocatedSet( + const HloInstruction* instruction, const ShapeIndex& index, + const TuplePointsToAnalysis& points_to_analysis, + std::vector* colocated_set) { + // CopyInsertion ensures root points-to set is unambiguous and distinct. + const auto& points_to = points_to_analysis.GetPointsToSet(instruction); + DCHECK(!points_to.IsAmbiguous()); + colocated_set->push_back(points_to.element(index)[0]); + return colocated_set->back(); +} + +// Given the interference map of a graph (the list of interfering node indices +// for each node), perform graph coloring such that interfering nodes are +// assigned to different colors. Returns the assigned color of the nodes, where +// the colors are represented as integer values [0, color_count). +std::vector ColorInterferenceGraph( + const std::vector>& interference_map) { + const int64 node_count = interference_map.size(); + + // Sort the nodes such that we assign nodes with more interference first. This + // relies on the common heuristic of assigning the most constrained node + // first, but it would be good to investigate other ordering heuristics too. + std::vector nodes(node_count); + std::iota(nodes.begin(), nodes.end(), 0); + std::sort(nodes.begin(), nodes.end(), + [&interference_map](const int64 i, const int64 j) { + return interference_map[i].size() > interference_map[j].size(); + }); + + const int64 kColorUnassigned = -1; + std::vector assigned_colors(node_count, kColorUnassigned); + for (int64 node : nodes) { + // Mark the colors that are already assigned to the neighbors. + std::vector available_colors(node_count, true); + for (int64 neighbor : interference_map[node]) { + int64 color = assigned_colors[neighbor]; + if (color != kColorUnassigned) { + available_colors[color] = false; + } + } + + // Find the color that is not yet assigned to the neighbors. + int64 color = kColorUnassigned; + for (color = 0; color < available_colors.size(); ++color) { + if (available_colors[color]) { + break; + } + } + CHECK_NE(color, kColorUnassigned); + assigned_colors[node] = color; + } + return assigned_colors; +} + +} // namespace + size_t BufferAllocation::Slice::Hasher::operator()(Slice s) const { uint64 h = std::hash()(s.index()); h = tensorflow::Hash64Combine(h, std::hash()(s.offset())); @@ -115,6 +292,112 @@ BufferAllocationProto BufferAllocation::ToProto() const { return proto; } +std::pair> +BufferAllocation::ComputePeakMemoryLogicalBuffers() const { + if (HeapTraces().empty()) { + // Just return the largest LogicalBuffer in the allocation. + const LogicalBuffer* largest_buffer = nullptr; + int64 largest_size = 0; + for (const auto& pair : assigned_buffers()) { + const LogicalBuffer* buffer = pair.first; + int64 size = pair.second.size; + if (largest_buffer == nullptr) { + largest_buffer = buffer; + largest_size = size; + continue; + } + // Tie-break with LogicalBuffer::Id so the return value is stable relative + // to changing addresses. + if (size > largest_size || + ((size == largest_size) && (largest_buffer->id() > buffer->id()))) { + largest_buffer = buffer; + largest_size = size; + } + } + CHECK(largest_buffer != nullptr) + << "No logical buffers in allocation: " << ToString(); + return {largest_size, {largest_buffer}}; + } + + // Create a map from LogicalBuffer::Id to LogicalBuffer* for the logical + // buffers in this allocation. + tensorflow::gtl::FlatMap + id_to_buffer; + tensorflow::gtl::FlatMap buffer_sizes; + for (const auto& pair : assigned_buffers()) { + const LogicalBuffer* buffer = pair.first; + const OffsetSize& offset_size = pair.second; + id_to_buffer[buffer->id()] = buffer; + buffer_sizes[buffer] = offset_size.size; + } + + // Returns how much the given event increases the total size of live + // buffers. Can be negative. + auto memory_delta = [this, &id_to_buffer, &buffer_sizes]( + const HeapSimulatorTrace::Event& event) -> int64 { + const LogicalBuffer* buffer = id_to_buffer.at(event.buffer_id()); + const int64 buffer_size = buffer_sizes.at(buffer); + if (event.kind() == HeapSimulatorTrace::Event::ALLOC) { + return buffer_size; + } else if (event.kind() == HeapSimulatorTrace::Event::SHARE_WITH) { + // Sharing a buffer does not change the live set size for the purposes of + // the heap simulator. Even though the shared-with buffer may be smaller, + // the entire allocation remains live. + return 0; + } else if (event.kind() == HeapSimulatorTrace::Event::FREE) { + return -1 * buffer_size; + } + LOG(FATAL) << "Unknown event kind: " << event.kind(); + }; + + int64 total_max_live_size = 0; + std::vector live_buffers_vector; + for (const HeapSimulatorTrace& heap_trace : HeapTraces()) { + // First compute the size of the maximal live set. + int64 max_live_size = 0; + int64 live_size = 0; + for (const auto& event : heap_trace.events()) { + live_size += memory_delta(event); + if (max_live_size < live_size) { + max_live_size = live_size; + } + } + + // Next gather the set of logical buffers live at the earliest point of + // maximal live set size. + tensorflow::gtl::FlatSet live_buffers; + live_size = 0; + for (const auto& event : heap_trace.events()) { + const LogicalBuffer* buffer = id_to_buffer.at(event.buffer_id()); + if (event.kind() == HeapSimulatorTrace::Event::ALLOC) { + InsertOrDie(&live_buffers, buffer); + } else if (event.kind() == HeapSimulatorTrace::Event::SHARE_WITH) { + // Nothing to do. + } else if (event.kind() == HeapSimulatorTrace::Event::FREE) { + CHECK(ContainsKey(live_buffers, buffer)); + live_buffers.erase(buffer); + } + + live_size += memory_delta(event); + if (live_size == max_live_size) { + break; + } + } + CHECK_EQ(live_size, max_live_size); + total_max_live_size += max_live_size; + + live_buffers_vector.insert(live_buffers_vector.end(), live_buffers.begin(), + live_buffers.end()); + } + + // Stabily sort the live buffers. + std::sort(live_buffers_vector.begin(), live_buffers_vector.end(), + [](const LogicalBuffer* a, const LogicalBuffer* b) { + return a->id() < b->id(); + }); + return {total_max_live_size, live_buffers_vector}; +} + string BufferAllocation::ToString() const { string output; Appendf(&output, "allocation %lld: %p, size %lld", index_, this, size()); @@ -348,6 +631,7 @@ void BufferAssignment::AddAssignment(BufferAllocation* allocation, // Combines allocations of temporary buffers of the same color into one big // BufferAllocation. void BufferAssignment::CombineTempAllocations() { + VLOG(1) << "CombineTempAllocations()"; FlatMap combined_allocation_map; @@ -369,11 +653,16 @@ void BufferAssignment::CombineTempAllocations() { if (combined_it == combined_allocation_map.end()) { // We have found the first temp allocation of this color. Collect // the other temp allocations of the same color into it. + VLOG(1) << "Combined temp allocation for color " << color + << " is: " << temp_allocation; combined_allocation_map.emplace(color, temp_allocation); continue; } auto* combined_allocation = &combined_it->second; + VLOG(1) << "Combined allocation absorbing temp allocation: " + << temp_allocation; + // Each temp allocation is placed end-to-end, accounting for alignment. // The offset of each buffer in the combined allocation is computed from // the base offset of the allocation. @@ -387,6 +676,10 @@ void BufferAssignment::CombineTempAllocations() { const int64 size = buffer_offset_size.second.size; combined_allocation->AddAssignment(*buffer, base + offset, size); } + if (!temp_allocation.HeapTraces().empty()) { + CHECK_EQ(temp_allocation.HeapTraces().size(), 1); + combined_allocation->AddHeapTrace(temp_allocation.HeapTraces().front()); + } } // Replace all existing temporary allocations with the new combined // allocations. @@ -516,123 +809,13 @@ BufferAssignmentProto BufferAssignment::ToProto() const { for (const BufferAllocation& allocation : Allocations()) { BufferAllocationProto proto_allocation = allocation.ToProto(); proto.add_buffer_allocations()->Swap(&proto_allocation); - } - for (const HeapSimulatorTrace& trace : heap_simulator_traces_) { - *proto.add_heap_simulator_traces() = trace; - } - return proto; -} - -namespace { - -// Walk the call graph of the HLO module and place each computation into either -// thread_local_computations or global_computations depending upon whether the -// computation requires thread-local allocations or global allocations. The -// elements in thread_local_computations and global_computations are in post -// order (if computation A has an instruction which calls computation B, then A -// will appear after B in the vector). -Status GatherComputationsByAllocationType( - const HloModule* module, - std::vector* thread_local_computations, - std::vector* global_computations) { - // Create a worklist of computations paired with whether the allocation must - // be thread-local. - std::deque> worklist; - worklist.push_back(std::make_pair(module->entry_computation(), - /*is_thread_local*/ false)); - - // Sets for quickly checking membership. Computations are returned in vectors - // for stable iteration. - FlatSet thread_local_set; - FlatSet global_set; - - while (!worklist.empty()) { - auto worklist_front = worklist.front(); - worklist.pop_front(); - const HloComputation* computation = worklist_front.first; - bool is_thread_local = worklist_front.second; - bool in_thread_local_set = thread_local_set.count(computation) > 0; - bool in_global_set = global_set.count(computation) > 0; - - // If the computation has already been added to the respective set, then - // nothing to do. - if ((is_thread_local && in_thread_local_set) || - (!is_thread_local && in_global_set)) { - continue; + for (const HeapSimulatorTrace& heap_trace : allocation.HeapTraces()) { + *proto.add_heap_simulator_traces() = heap_trace; } - - // If the computation has already been added to the other set this is an - // error condition because the global call to the computation (eg, - // while/call) may return a reference to one of the thread-local buffers to - // the calling computation which will become a dangling reference when the - // thread-local is deallocated with the call return. - if ((is_thread_local && in_global_set) || - (!is_thread_local && in_thread_local_set)) { - return InvalidArgument( - "computation %s has conflicting allocation requirements (global " - "and thread-local)", - computation->name().c_str()); - } - - if (is_thread_local) { - thread_local_set.insert(computation); - } else { - global_set.insert(computation); - } - - for (auto* instruction : computation->instructions()) { - for (HloComputation* subcomputation : - instruction->called_computations()) { - switch (instruction->opcode()) { - case HloOpcode::kCall: - case HloOpcode::kConditional: - case HloOpcode::kWhile: - // Call and while must be called from a computation with global - // allocations as they may return references to buffers inside the - // called computation which cannot be thread-local. - if (is_thread_local) { - return InvalidArgument( - "computation %s cannot contain call/while op because it " - "requires thread-local buffer allocations", - computation->name().c_str()); - } - worklist.push_back(std::make_pair(subcomputation, - false)); // Not thread local. - break; - case HloOpcode::kMap: - case HloOpcode::kReduce: - case HloOpcode::kReduceWindow: - case HloOpcode::kSelectAndScatter: - case HloOpcode::kFusion: - // Map/reduce etc computations are always thread-local. - worklist.push_back(std::make_pair(subcomputation, - true)); // Thread local. - break; - default: - return InternalError( - "Unexpected calling opcode: %s", - HloOpcodeString(instruction->opcode()).c_str()); - } - } - } - } - - // Add the computations to the vectors in post order. - for (auto* computation : module->MakeComputationPostOrder()) { - if (thread_local_set.count(computation) > 0) { - thread_local_computations->push_back(computation); - } else if (global_set.count(computation) > 0) { - global_computations->push_back(computation); - } - // If the computation is not reachable from the entry computation, then it - // will not appear in either thread_local_set or global_set. We don't bother - // assigning buffers for these. } - return Status::OK(); + return proto; } -} // namespace - /* static */ StatusOr> BufferAssigner::Run( const HloModule* module, std::unique_ptr hlo_ordering, @@ -1064,7 +1247,8 @@ void BufferAssigner::AssignBuffersFromHeapSimulator( assignment->AddAssignment(allocation, buffer, chunk.offset, chunk.size); } - assignment->heap_simulator_traces_.push_back(result.debug_trace); + VLOG(1) << "Ran heap simulation for allocation: " << allocation->ToString(); + allocation->AddHeapTrace(result.debug_trace); } // Adds the 'colocated_set' of buffers to 'colocated_buffer_sets', maintaining @@ -1085,7 +1269,8 @@ void BufferAssigner::AddSetToColocatedBufferSets( if (colocated_set.empty()) { return; } - + VLOG(5) << ColocatedBufferSetsToString(colocated_set, + "Adding colocated buffer set"); // Find existing sets that overlap with at least one buffer from the // colocated_set. The resulting 'overlap_set_indices' will have at most // colocated_buffer_sets->size() entries, and will be in increasing order. @@ -1093,6 +1278,10 @@ void BufferAssigner::AddSetToColocatedBufferSets( for (size_t index = 0; index < colocated_buffer_sets->size(); ++index) { for (const LogicalBuffer* buffer : colocated_set) { if ((*colocated_buffer_sets)[index].count(buffer) > 0) { + VLOG(5) << "Found overlap with existing set on buffer " + << buffer->ToString() << "\n" + << ColocatedBufferSetsToString((*colocated_buffer_sets)[index], + "Overlapping set"); overlap_set_indices.push_back(index); break; } @@ -1104,6 +1293,7 @@ void BufferAssigner::AddSetToColocatedBufferSets( colocated_buffer_sets->emplace_back(); colocated_buffer_sets->back().insert(colocated_set.begin(), colocated_set.end()); + VLOG(5) << "No overlap found, new group created"; return; } @@ -1115,6 +1305,8 @@ void BufferAssigner::AddSetToColocatedBufferSets( first->insert(overlap_set.begin(), overlap_set.end()); } first->insert(colocated_set.begin(), colocated_set.end()); + VLOG(5) << ColocatedBufferSetsToString( + *first, "Result of the colocated buffer set merging"); // Remove overlap sets that we just merged. The offset accounts for the fact // that as elements are erased, the indices need to be adjusted. Keep in mind @@ -1125,67 +1317,6 @@ void BufferAssigner::AddSetToColocatedBufferSets( } } -namespace { - -// Checks that points-to set of 'instruction' is unambiguous and distinct -// (ensured by CopyInsertion), then adds the buffer from the points-to set at -// 'index' to 'colocated_set'. -const LogicalBuffer* AddBufferToColocatedSet( - const HloInstruction* instruction, const ShapeIndex& index, - const TuplePointsToAnalysis& points_to_analysis, - std::vector* colocated_set) { - // CopyInsertion ensures root points-to set is unambiguous and distinct. - const auto& points_to = points_to_analysis.GetPointsToSet(instruction); - DCHECK(!points_to.IsAmbiguous()); - colocated_set->push_back(points_to.element(index)[0]); - return colocated_set->back(); -} - -// Given the interference map of a graph (the list of interfering node indices -// for each node), perform graph coloring such that interfering nodes are -// assigned to different colors. Returns the assigned color of the nodes, where -// the colors are represented as integer values [0, color_count). -std::vector ColorInterferenceGraph( - const std::vector>& interference_map) { - const int64 node_count = interference_map.size(); - - // Sort the nodes such that we assign nodes with more interference first. This - // relies on the common heuristic of assigning the most constrained node - // first, but it would be good to investigate other ordering heuristics too. - std::vector nodes(node_count); - std::iota(nodes.begin(), nodes.end(), 0); - std::sort(nodes.begin(), nodes.end(), - [&interference_map](const int64 i, const int64 j) { - return interference_map[i].size() > interference_map[j].size(); - }); - - const int64 kColorUnassigned = -1; - std::vector assigned_colors(node_count, kColorUnassigned); - for (int64 node : nodes) { - // Mark the colors that are already assigned to the neighbors. - std::vector available_colors(node_count, true); - for (int64 neighbor : interference_map[node]) { - int64 color = assigned_colors[neighbor]; - if (color != kColorUnassigned) { - available_colors[color] = false; - } - } - - // Find the color that is not yet assigned to the neighbors. - int64 color = kColorUnassigned; - for (color = 0; color < available_colors.size(); ++color) { - if (available_colors[color]) { - break; - } - } - CHECK_NE(color, kColorUnassigned); - assigned_colors[node] = color; - } - return assigned_colors; -} - -} // namespace - std::vector BufferAssigner::MergeColocatedBufferSets( const std::vector& colocated_buffer_sets, @@ -1208,26 +1339,35 @@ BufferAssigner::MergeColocatedBufferSets( auto cannot_merge_buffer_sets = [&colocated_buffer_sets, &buffer_liveness, &buffer_size, &is_entry_parameter](int64 i, int64 j) { - for (auto& buffer_a : colocated_buffer_sets[i]) { - for (auto& buffer_b : colocated_buffer_sets[j]) { - // Do not merge if the set includes live outs or entry parameters. - if ((buffer_liveness.MaybeLiveOut(*buffer_a) && - is_entry_parameter(*buffer_b)) || - (buffer_liveness.MaybeLiveOut(*buffer_b) && - is_entry_parameter(*buffer_a))) { + // Do not merge if one of the sets includes live outs or entry parameters. + for (int64 key : {i, j}) { + for (auto& buffer : colocated_buffer_sets[key]) { + if (buffer_liveness.MaybeLiveOut(*buffer) || + is_entry_parameter(*buffer)) { return true; } - // Do not merge if the buffers interfere with each other. + } + } + + // Colocated sets satisfy the invariant that all buffers within a set have + // the same size. That means we need to check whether the size is the same + // between the two sets, but also that it's enough to look at just one + // buffer within each set. + if (buffer_size(**colocated_buffer_sets[i].begin()) != + buffer_size(**colocated_buffer_sets[j].begin())) { + return true; + } + + // Do not merge if some pair of buffers interferes with each other. + for (auto& buffer_a : colocated_buffer_sets[i]) { + for (auto& buffer_b : colocated_buffer_sets[j]) { if (buffer_a->id() != buffer_b->id() && buffer_liveness.MayInterfere(*buffer_a, *buffer_b)) { return true; } - // Do not merge if the buffer sizes are different. - if (buffer_size(*buffer_a) != buffer_size(*buffer_b)) { - return true; - } } } + return false; }; diff --git a/tensorflow/compiler/xla/service/buffer_assignment.h b/tensorflow/compiler/xla/service/buffer_assignment.h index 6b7fd0014d103ef0617afcc5cb3f663554a01aa4..3086d0e2ca0026547134285b8ceb357390fc7ece 100644 --- a/tensorflow/compiler/xla/service/buffer_assignment.h +++ b/tensorflow/compiler/xla/service/buffer_assignment.h @@ -192,6 +192,37 @@ class BufferAllocation { !is_thread_local(); } + // Add a heap trace which was used to assign slices to logical buffers in this + // allocation. A single BufferAllocation may include multiple heap traces + // in the case of the temporary block where there is a heap trace per + // computation. + void AddHeapTrace(const HeapSimulatorTrace& heap_trace) { + heap_traces_.push_back(heap_trace); + } + + // Return the set of heap traces used to assign slices to logical buffers in + // this allocation. + const std::vector HeapTraces() const { + return heap_traces_; + } + + // Compute and return the LogicalBuffers which are live at the point of peak + // memory usage for the given allocation. The point of peak memory usage is + // the point at which the total size of all live logical buffers is + // maximal. If peak memory is reached at multiple points, the set of logical + // buffers live at the earliest maximal point is returned. The vector is + // stabily asserted by LogicalBuffer::Index. + // + // The return value is a pair of total size of the logical buffers at peak, + // and the buffers themselves. + std::pair> + ComputePeakMemoryLogicalBuffers() const; + + // Get the number of bytes lost to fragmentation. This is equal to the + // difference between the size of the allocation and the size of the maximal + // live set. + int64 fragmentation_bytes() const { return fragmentation_bytes_; } + bool operator==(const BufferAllocation& other) const { return index_ == other.index_; } @@ -257,6 +288,9 @@ class BufferAllocation { // Mapping from the set of buffers assigned to this allocation to their // logical offsets and sizes. tensorflow::gtl::FlatMap assigned_buffers_; + + int64 fragmentation_bytes_ = 0; + std::vector heap_traces_; }; // Add stream operators for nicer output of CHECK/RET_CHECK failures. @@ -441,7 +475,6 @@ class BufferAssignment { LogicalBuffer::AlignmentFunction color_alignment_; Stats stats_; - std::vector heap_simulator_traces_; TF_DISALLOW_COPY_AND_ASSIGN(BufferAssignment); }; diff --git a/tensorflow/compiler/xla/service/buffer_assignment_test.cc b/tensorflow/compiler/xla/service/buffer_assignment_test.cc index cd73654b8f666c4b96c000235cc3ad2cd0a46c17..513a8785bbd52b0a3bfa3642bbfc62b1035ffb17 100644 --- a/tensorflow/compiler/xla/service/buffer_assignment_test.cc +++ b/tensorflow/compiler/xla/service/buffer_assignment_test.cc @@ -37,14 +37,16 @@ limitations under the License. #include "tensorflow/compiler/xla/test.h" #include "tensorflow/compiler/xla/test_helpers.h" #include "tensorflow/compiler/xla/tests/hlo_test_base.h" +#include "tensorflow/compiler/xla/tools/parser/hlo_parser.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/platform/macros.h" namespace xla { - namespace { +using ::testing::UnorderedElementsAre; + // DFS visitor that collects the instructions referenced by a computation // without descending into nested computations, i.e., only from the operands. class InstructionListVisitor : public DfsHloVisitorWithDefault { @@ -101,6 +103,22 @@ class BufferAssignmentTest : public HloTestBase { .ConsumeValueOrDie(); } + std::unique_ptr RunBufferAssignmentWithInstructionSequence( + HloModule* module, + tensorflow::gtl::ArraySlice instruction_sequence, + int64 alignment = 1) { + SequentialHloOrdering::HloModuleSequence module_sequence; + module_sequence[module->entry_computation()] = + std::vector(instruction_sequence.begin(), + instruction_sequence.end()); + return BufferAssigner::Run( + module, + xla::MakeUnique(module, module_sequence), + backend().compiler()->BufferSizeBytesFunction(), + [alignment](LogicalBuffer::Color) { return alignment; }) + .ConsumeValueOrDie(); + } + // Builds an x+1.0 computation to use in a Map. std::unique_ptr BuildMapComputationPlus1(const string& name) { auto builder = HloComputation::Builder(name); @@ -1370,7 +1388,7 @@ TEST_F(BufferAssignmentTest, AmbiguousBufferAsOutput) { auto element_slices = assignment->GetAllSlices(select, /*index=*/{0}); EXPECT_EQ(2, element_slices.size()); EXPECT_THAT(element_slices, - ::testing::UnorderedElementsAre( + UnorderedElementsAre( assignment->GetUniqueSlice(tuple_param0, /*index=*/{0}) .ConsumeValueOrDie(), assignment->GetUniqueSlice(tuple_param1, /*index=*/{0}) @@ -1473,6 +1491,98 @@ TEST_F(BufferAssignmentTest, OneTempAllocation) { } } +TEST_F(BufferAssignmentTest, TrivialPeakBuffers) { + // paramscalar ------- (mul) -- (add) -- (sub) + // / / / + // param0[100] -------/ / / + // / / + // param1[100] --------------/--------/ + auto builder = HloComputation::Builder(TestName()); + auto paramscalar = + builder.AddInstruction(HloInstruction::CreateParameter(0, r0f32_, "")); + auto param0 = builder.AddInstruction( + HloInstruction::CreateParameter(1, f32vec100_, "")); + auto param1 = builder.AddInstruction( + HloInstruction::CreateParameter(2, f32vec100_, "")); + auto mul = builder.AddInstruction(HloInstruction::CreateBinary( + f32vec100_, HloOpcode::kMultiply, paramscalar, param0)); + auto add = builder.AddInstruction( + HloInstruction::CreateBinary(f32vec100_, HloOpcode::kAdd, mul, param1)); + builder.AddInstruction(HloInstruction::CreateBinary( + f32vec100_, HloOpcode::kSubtract, add, param1)); + auto module = CreateNewModule(); + module->AddEntryComputation(builder.Build()); + + auto buffers = RunBufferAssignment(module.get()); + + // Trivially, the set of peak memory logical buffer(s) of an allocation with a + // single logical buffer should be exactly the logical buffer in that + // allocation. + const BufferAllocation& mul_buffer = GetTopLevelAllocation(*buffers, mul); + int64 peak_size; + std::vector peak_buffers; + + std::tie(peak_size, peak_buffers) = + mul_buffer.ComputePeakMemoryLogicalBuffers(); + EXPECT_EQ(peak_size, ShapeUtil::ByteSizeOf(f32vec100_)); + ASSERT_EQ(peak_buffers.size(), 1); + EXPECT_EQ(peak_buffers[0]->instruction(), mul); +} + +TEST_F(BufferAssignmentTest, PeakBuffers) { + // Compute the peak liveness buffers of the following sequence: + // + // %param = ... + // %log = log(%param) + // %rev = reverse(%log) + // %neg = neg(%param) + // %concat = concat(%rev, %neg) + // ROOT %root = slice(concat) + // + // In the temporary block, the set of live buffers at peak memory use should + // be {%rev, %neg, %concat}. This occurs right at the concat itself. + auto builder = HloComputation::Builder(TestName()); + auto param = builder.AddInstruction( + HloInstruction::CreateParameter(0, f32vec100_, "")); + auto log = builder.AddInstruction( + HloInstruction::CreateUnary(f32vec100_, HloOpcode::kLog, param)); + auto rev = builder.AddInstruction( + HloInstruction::CreateReverse(f32vec100_, log, {0})); + auto neg = builder.AddInstruction( + HloInstruction::CreateUnary(f32vec100_, HloOpcode::kNegate, param)); + const Shape concat_shape = ShapeUtil::MakeShape(F32, {200}); + auto concat = builder.AddInstruction( + HloInstruction::CreateConcatenate(concat_shape, {rev, neg}, 0)); + // Make the root tiny so no interior nodes can share its buffer. + auto root = builder.AddInstruction(HloInstruction::CreateSlice( + ShapeUtil::MakeShape(F32, {1}), concat, {0}, {1}, {1})); + + auto module = CreateNewModule(); + module->AddEntryComputation(builder.Build()); + + auto buffers = RunBufferAssignmentWithInstructionSequence( + module.get(), {param, log, rev, neg, concat, root}); + + // The temporary buffer should hold the 4 interior instructions. + const BufferAllocation& buffer = GetTopLevelAllocation(*buffers, concat); + EXPECT_FALSE(buffer.IsInputOrOutput()); + EXPECT_TRUE(buffer.IsPreallocatedTempBuffer()); + ASSERT_EQ(buffer.assigned_buffers().size(), 4); + + int64 peak_size; + std::vector peak_buffers; + std::tie(peak_size, peak_buffers) = buffer.ComputePeakMemoryLogicalBuffers(); + + // The peak live set should be concat and its inputs. + EXPECT_EQ(peak_size, ShapeUtil::ByteSizeOf(ShapeUtil::MakeShape(F32, {400}))); + ASSERT_EQ(peak_buffers.size(), 3); + std::vector peak_instructions; + for (const LogicalBuffer* logical_buffer : peak_buffers) { + peak_instructions.push_back(logical_buffer->instruction()); + } + EXPECT_THAT(peak_instructions, UnorderedElementsAre(rev, neg, concat)); +} + class WhileBufferAssignmentTest : public HloTestBase { protected: std::unique_ptr BuildWhileConditionComputation( @@ -1587,6 +1697,81 @@ TEST_F(WhileBufferAssignmentTest, TwoForwardWhileLoops) { assignment->GetUniqueSlice(while1, {1}).ConsumeValueOrDie()); } +// Tests that two colocated buffer sets are not merged if an entry parameter +// buffer belongs to either of the colocation sets (b/73267882). +// +// %param --> %while.0 --> %mul --> %while.1 --> %broadcast +// +// %while.0 body just forwards the init value, so the loop carried variable +// remains the constant, whereas %while.1 changes the loop carried variable. +TEST_F(WhileBufferAssignmentTest, ColocatedBufferWithEntryParameter) { + const Shape r0s32 = ShapeUtil::MakeShape(S32, {}); + + const char* module_str = R"( +HloModule test_module + +%cond.v0 { + %param = s32[] parameter(0) + ROOT %constant = pred[] constant(true) +} + +%cond.v1 { + %param.0 = s32[] parameter(0) + ROOT %constant.0 = pred[] constant(true) +} + +%body.v0 { + ROOT %param.1 = s32[] parameter(0) +} + +%body.v1 { + %param.2 = s32[] parameter(0) + ROOT add = s32[] add(%param.2, %param.2) +} + +ENTRY %test_module { + %param.3 = s32[] parameter(0) + %while.0 = s32[] while(%param.3), condition=%cond.v0, body=%body.v0 + %mul = s32[] multiply(%while.0, %while.0) + %while.1 = s32[] while(%mul), condition=%cond.v1, body=%body.v1 + ROOT %bcast = s32[1024,1024]{1,0} broadcast(s32[] %while.1), dimensions={} +})"; + + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module, + tools::Parse(module_str)); + + // Run CopyInsertion and check if the graph constructed above doesn't need + // any copies inserted for BufferAssignment to run. + int64 instruction_count = module->instruction_count(); + CopyInsertion copy_insertion; + ASSERT_IS_OK(copy_insertion.Run(module.get()).status()); + ASSERT_EQ(instruction_count, module->instruction_count()); + + // Get the instructions in the module. + const HloInstruction* bcast = module->entry_computation()->root_instruction(); + const HloInstruction* param = + module->entry_computation()->parameter_instruction(0); + ASSERT_EQ(bcast->opcode(), HloOpcode::kBroadcast); + const HloInstruction* while1 = bcast->operand(0); + ASSERT_EQ(while1->opcode(), HloOpcode::kWhile); + const HloInstruction* while0 = while1->operand(0)->operand(0); + ASSERT_EQ(while0->opcode(), HloOpcode::kWhile); + + // Run buffer assignment. + auto assignment = RunBufferAssignment(module.get()); + TF_ASSERT_OK_AND_ASSIGN(auto slice_param, + assignment->GetUniqueSlice(param, {})); + TF_ASSERT_OK_AND_ASSIGN(auto slice_while0, + assignment->GetUniqueSlice(while0, {})); + TF_ASSERT_OK_AND_ASSIGN(auto slice_while1, + assignment->GetUniqueSlice(while1, {})); + + // The parameter slice is part of the while0's colocation set (init value), + // but not merged into the while1's colocation set. + EXPECT_EQ(slice_param, slice_while0); + EXPECT_NE(slice_param, slice_while1); +} + // Tests that the colocated buffers for while instructions are properly assigned // during buffer assignment such that the result tuple elements are not assigned // to the same buffer. diff --git a/tensorflow/compiler/xla/service/compile_only_service.cc b/tensorflow/compiler/xla/service/compile_only_service.cc index dab73596e1639eed62151197048ee8d29570b20a..6664496ab6c603c35c7dce923fcf94c54d1ce714 100644 --- a/tensorflow/compiler/xla/service/compile_only_service.cc +++ b/tensorflow/compiler/xla/service/compile_only_service.cc @@ -72,8 +72,7 @@ CompileOnlyService::CompileAheadOfTime( VersionedComputationHandle versioned_handle = user_computation->GetVersionedHandle(); - // TODO(b/63773457): Track DebugOptions in AotCompilationOptions. - DebugOptions debug_options = legacy_flags::GetDebugOptionsFromFlags(); + const DebugOptions& debug_options = options.debug_options(); // Dump computation proto state if flag is set. const string& directory_path = debug_options.xla_dump_computations_to(); diff --git a/tensorflow/compiler/xla/service/compiler.cc b/tensorflow/compiler/xla/service/compiler.cc index e2e9d2a0c048fec6c6ffbeef1223ae0e6aef50d1..0392d4af48a040c4a648f7bf9bf21a62ce03a990 100644 --- a/tensorflow/compiler/xla/service/compiler.cc +++ b/tensorflow/compiler/xla/service/compiler.cc @@ -86,4 +86,7 @@ Compiler::GetPlatformCompilers() { return compilers->at(platform->id()).get(); } +AotCompilationOptions::AotCompilationOptions() + : debug_options_(legacy_flags::GetDebugOptionsFromFlags()) {} + } // namespace xla diff --git a/tensorflow/compiler/xla/service/compiler.h b/tensorflow/compiler/xla/service/compiler.h index 74fd24edf88d44b2dfdc87556b0af43987e69e08..33e19efc72c6d30ccd7e0b3a13f664a4f42208bf 100644 --- a/tensorflow/compiler/xla/service/compiler.h +++ b/tensorflow/compiler/xla/service/compiler.h @@ -79,11 +79,15 @@ class AotCompilationOptions { device_allocator_ = device_allocator; } + const DebugOptions& debug_options() const { return debug_options_; } + DebugOptions* mutable_debug_options() { return &debug_options_; } + protected: - AotCompilationOptions() = default; + AotCompilationOptions(); private: DeviceMemoryAllocator* device_allocator_ = nullptr; + DebugOptions debug_options_; }; // Abstract compiler interface that is subclassed for compilation on a diff --git a/tensorflow/compiler/xla/service/conditional_simplifier.cc b/tensorflow/compiler/xla/service/conditional_simplifier.cc new file mode 100644 index 0000000000000000000000000000000000000000..f35de080853f7ec986565cb2df1050946ac3f244 --- /dev/null +++ b/tensorflow/compiler/xla/service/conditional_simplifier.cc @@ -0,0 +1,106 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/conditional_simplifier.h" + +#include +#include +#include + +#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/service/call_inliner.h" +#include "tensorflow/compiler/xla/service/hlo_computation.h" +#include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/compiler/xla/service/hlo_opcode.h" +#include "tensorflow/compiler/xla/status_macros.h" +#include "tensorflow/compiler/xla/types.h" +#include "tensorflow/compiler/xla/util.h" +#include "tensorflow/core/lib/core/errors.h" +#include "tensorflow/core/lib/strings/str_util.h" +#include "tensorflow/core/lib/strings/strcat.h" + +namespace xla { + +// Tries to replace a conditional with a call operation of the corresponding +// computation. If the given conditional has a constant predicate, tries to +// replace it with a call to its true/false computation as appropirate and then +// inline that computation. +// +// Returns true if it made a change to the graph. +static StatusOr TryRemoveConditional(HloInstruction* conditional) { + CHECK_EQ(conditional->opcode(), HloOpcode::kConditional); + // Do not remove conditionals that contain side-effecting instructions or + // have control predecessors/successors in either true/false computation. + if (!conditional->parent()->IsRemovable(conditional) || + conditional->HasSideEffect()) { + VLOG(2) << "Not attempting to remove conditional as it is not removable or " + "has side effect: " + << conditional->ToShortString(); + return false; + } + + if (conditional->operand(0)->opcode() != HloOpcode::kConstant) { + VLOG(2) << "Not attempting to remove conditional as its predicate is not a " + "compile-time constant: " + << conditional->ToShortString(); + return false; + } + + auto computation = conditional->parent(); + HloInstruction* call_op; + if (conditional->operand(0)->literal().Get({})) { + call_op = computation->AddInstruction(HloInstruction::CreateCall( + conditional->shape(), {conditional->mutable_operand(1)}, + conditional->true_computation())); + } else { + call_op = computation->AddInstruction(HloInstruction::CreateCall( + conditional->shape(), {conditional->mutable_operand(2)}, + conditional->false_computation())); + } + + TF_RETURN_IF_ERROR(computation->ReplaceInstruction(conditional, call_op)); + TF_RETURN_IF_ERROR(CallInliner::Inline(call_op).status()); + + return true; +} + +StatusOr ConditionalSimplifier::Run(HloModule* module) { + XLA_VLOG_LINES( + 3, "ConditionalSimplifier::Run(), before:\n" + module->ToString()); + bool changed = false; + + // Gather all the conditional ops in our module. We do this ahead of time so + // we don't have to worry about mutating the lists of computations or + // instructions as we iterate. + std::vector conditional_ops; + for (auto* comp : module->computations()) { + for (auto* instr : comp->instructions()) { + if (instr->opcode() == HloOpcode::kConditional) { + conditional_ops.push_back(instr); + } + } + } + + for (HloInstruction* conditional_op : conditional_ops) { + TF_ASSIGN_OR_RETURN(bool result, TryRemoveConditional(conditional_op)); + changed |= result; + } + + XLA_VLOG_LINES(3, + "ConditionalSimplifier::Run(), after:\n" + module->ToString()); + return changed; +} + +} // namespace xla diff --git a/tensorflow/compiler/xla/service/conditional_simplifier.h b/tensorflow/compiler/xla/service/conditional_simplifier.h new file mode 100644 index 0000000000000000000000000000000000000000..063261e26d06e21a297e8e3c405898a17221b7ca --- /dev/null +++ b/tensorflow/compiler/xla/service/conditional_simplifier.h @@ -0,0 +1,38 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_CONDITIONAL_SIMPLIFIER_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_CONDITIONAL_SIMPLIFIER_H_ + +#include "tensorflow/compiler/xla/service/hlo_module.h" +#include "tensorflow/compiler/xla/service/hlo_pass_interface.h" +#include "tensorflow/compiler/xla/statusor.h" +#include "tensorflow/core/lib/core/stringpiece.h" + +namespace xla { + +// HLO pass that removes kConditional with a constant predicate, replacing them +// with their true or false computation as appropriate. +class ConditionalSimplifier : public HloPassInterface { + public: + tensorflow::StringPiece name() const override { + return "simplify-conditional"; + } + StatusOr Run(HloModule* module) override; +}; + +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_CONDITIONAL_SIMPLIFIER_H_ diff --git a/tensorflow/compiler/xla/service/conditional_simplifier_test.cc b/tensorflow/compiler/xla/service/conditional_simplifier_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..868348547d9f5cbdc7576c7fc0697d72c3a3e557 --- /dev/null +++ b/tensorflow/compiler/xla/service/conditional_simplifier_test.cc @@ -0,0 +1,153 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/conditional_simplifier.h" + +#include +#include + +#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/service/hlo_computation.h" +#include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/compiler/xla/service/hlo_matchers.h" +#include "tensorflow/compiler/xla/service/hlo_opcode.h" +#include "tensorflow/compiler/xla/shape_util.h" +#include "tensorflow/compiler/xla/test.h" +#include "tensorflow/compiler/xla/tests/hlo_verified_test_base.h" +#include "tensorflow/compiler/xla/types.h" +#include "tensorflow/compiler/xla/xla_data.pb.h" +#include "tensorflow/core/lib/core/status.h" +#include "tensorflow/core/lib/core/status_test_util.h" +#include "tensorflow/core/platform/types.h" + +namespace xla { +namespace { + +namespace op = xla::testing::opcode_matchers; + +class ConditionalSimplifierTest : public HloVerifiedTestBase { + public: + // Makes a computation that contains a conditional with constant predicate. + HloComputation* MakeConditional(HloModule* module); +}; + +HloComputation* ConditionalSimplifierTest::MakeConditional(HloModule* module) { + HloComputation::Builder builder(TestName()); + + // true_computation returns param+1. + HloComputation* true_computation; + { + HloComputation::Builder true_computation_builder(TestName() + + ".true_computation"); + auto param = + true_computation_builder.AddInstruction(HloInstruction::CreateParameter( + 0, ShapeUtil::MakeShape(S32, {}), "param")); + auto one = true_computation_builder.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR0(1))); + + true_computation_builder.AddInstruction(HloInstruction::CreateBinary( + ShapeUtil::MakeShape(S32, {}), HloOpcode::kAdd, param, one)); + + true_computation = + module->AddEmbeddedComputation(true_computation_builder.Build()); + } + + // false_computation returns param+42. + HloComputation* false_computation; + { + HloComputation::Builder false_computation_builder(TestName() + + ".false_computation"); + auto param = false_computation_builder.AddInstruction( + HloInstruction::CreateParameter(0, ShapeUtil::MakeShape(S32, {}), + "param")); + auto forty_two = false_computation_builder.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR0(42))); + + false_computation_builder.AddInstruction(HloInstruction::CreateBinary( + ShapeUtil::MakeShape(S32, {}), HloOpcode::kAdd, param, forty_two)); + false_computation = + module->AddEmbeddedComputation(false_computation_builder.Build()); + } + + auto false_instrn = builder.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR0(false))); + auto false_param = builder.AddInstruction(HloInstruction::CreateParameter( + 0, ShapeUtil::MakeShape(S32, {}), "false_param")); + auto one = builder.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR0(1))); + + builder.AddInstruction(HloInstruction::CreateConditional( + ShapeUtil::MakeShape(S32, {}), false_instrn, one, true_computation, + false_param, false_computation)); + + return module->AddEntryComputation(builder.Build()); +} + +TEST_F(ConditionalSimplifierTest, ConditionalGetsInlined) { + HloComputation* computation = MakeConditional(&module()); + ASSERT_TRUE(ConditionalSimplifier().Run(&module()).ValueOrDie()); + EXPECT_THAT(computation->root_instruction(), + op::Add(op::Parameter(), op::Constant())); +} + +TEST_F(ConditionalSimplifierTest, ConditionalWithControlDependency) { + HloComputation* computation = MakeConditional(&module()); + + auto* true_op = computation->AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR0(true))); + TF_ASSERT_OK( + true_op->AddControlDependencyTo(computation->root_instruction())); + + EXPECT_FALSE(ConditionalSimplifier().Run(&module()).ValueOrDie()); +} + +TEST_F(ConditionalSimplifierTest, NotRemovedIfContainsSend) { + HloComputation* computation = MakeConditional(&module()); + auto* conditional = computation->root_instruction(); + ASSERT_EQ(conditional->opcode(), HloOpcode::kConditional); + + auto* true_computation = conditional->true_computation(); + auto* send = true_computation->AddInstruction(HloInstruction::CreateSend( + true_computation->AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR0(true))), + /*channel_id=*/0)); + true_computation->AddInstruction(HloInstruction::CreateSendDone(send)); + EXPECT_FALSE(ConditionalSimplifier().Run(&module()).ValueOrDie()); +} + +TEST_F(ConditionalSimplifierTest, NotRemovedIfContainsRecv) { + HloComputation* computation = MakeConditional(&module()); + auto* conditional = computation->root_instruction(); + ASSERT_EQ(conditional->opcode(), HloOpcode::kConditional); + + auto* true_computation = conditional->true_computation(); + auto* recv = true_computation->AddInstruction(HloInstruction::CreateRecv( + ShapeUtil::MakeShape(F32, {1}), /*channel_id=*/0)); + true_computation->AddInstruction(HloInstruction::CreateRecvDone(recv)); + EXPECT_FALSE(ConditionalSimplifier().Run(&module()).ValueOrDie()); +} + +TEST_F(ConditionalSimplifierTest, NotRemovedIfContainsNonRemovableInstruction) { + HloComputation* computation = MakeConditional(&module()); + auto* conditional = computation->root_instruction(); + ASSERT_EQ(conditional->opcode(), HloOpcode::kConditional); + auto* false_computation = conditional->false_computation(); + false_computation->AddInstruction( + HloInstruction::CreateInfeed(ShapeUtil::MakeShape(F32, {1}), "config")); + EXPECT_FALSE(ConditionalSimplifier().Run(&module()).ValueOrDie()); +} + +} // namespace +} // namespace xla diff --git a/tensorflow/compiler/xla/service/copy_insertion.cc b/tensorflow/compiler/xla/service/copy_insertion.cc index cc195879a6bb490a9b49ad962aa9326cb51d9b0a..e9c974a0461da4b79b4d4cf7a15f407ead5eb4bb 100644 --- a/tensorflow/compiler/xla/service/copy_insertion.cc +++ b/tensorflow/compiler/xla/service/copy_insertion.cc @@ -58,6 +58,45 @@ bool ValueIsReadOnly(const HloValue& value) { return IsConstantValue(value) || IsEntryParameterValue(value); } +// Data structure describing the action which should be taken on parts of a +// computation buffers, with respect to the adding of special case copies. +struct SpecialCaseCopyPolicy { + // Insert a copy if the same buffer is found at multiple indices within the + // output tuple. + bool copy_root_replicated_buffers = false; + // If true, insert a copy if a buffer coming from a constant or a parameter + // is found wihtin the output tuple. + bool copy_parameters_and_constants = false; +}; + +SpecialCaseCopyPolicy GetSpecialCaseCopyPolicy(const CallGraphNode& node, + HloModule* module, + HloComputation* computation) { + SpecialCaseCopyPolicy policy; + if (computation == module->entry_computation()) { + policy.copy_parameters_and_constants = true; + policy.copy_root_replicated_buffers = true; + } + for (const CallSite& site : node.caller_callsites()) { + // The kWhile instruction does not have an handling here, as the + // AddCopiesForWhile() API takes care of adding its own copies. + if (site.instruction()->opcode() == HloOpcode::kConditional) { + policy.copy_parameters_and_constants = true; + policy.copy_root_replicated_buffers = true; + } + } + return policy; +} + +bool ShouldCopyRootValue(const HloValue& value, + const SpecialCaseCopyPolicy& policy) { + if (policy.copy_parameters_and_constants) { + return IsConstantValue(value) || + value.defining_instruction()->opcode() == HloOpcode::kParameter; + } + return false; +} + // Deep copy the given instructions 'from' and 'to' at the ShapeIndexes given in // 'indices_to_copy'. Add control edges from the respective kCopy instructions // in deep copy of 'from' to the respective kCopy instruction in the deep copy @@ -921,7 +960,7 @@ Status AddSpecialCaseCopies(const CallGraph& call_graph, HloModule* module) { // Identify which shape indices of which instructions need to be copied. Store // these results in 'instructions_to_copy'. - std::unordered_map> instructions_to_copy; + HloInstructionMap> instructions_to_copy; auto add_index_to_copy = [&instructions_to_copy](HloInstruction* instruction, const ShapeIndex& index) { auto it = instructions_to_copy.find(instruction); @@ -957,7 +996,8 @@ Status AddSpecialCaseCopies(const CallGraph& call_graph, HloModule* module) { } TF_RET_CHECK(node.context() == CallContext::kSequential); - const bool is_entry = computation == module->entry_computation(); + SpecialCaseCopyPolicy policy = + GetSpecialCaseCopyPolicy(node, module, computation); HloInstruction* root = computation->root_instruction(); // Mark nondistinct/ambiguous indices. @@ -970,27 +1010,26 @@ Status AddSpecialCaseCopies(const CallGraph& call_graph, HloModule* module) { for (const HloBuffer* buffer : buffers_at_index) { buffer_seen_before |= !seen.insert(buffer).second; } - if (buffers_at_index.size() > 1 || (buffer_seen_before && is_entry)) { - VLOG(2) << "Index " << index << " of root of computation " + if (buffers_at_index.size() > 1 || + (buffer_seen_before && policy.copy_root_replicated_buffers)) { + VLOG(2) << "Index " << index << " of computation " << computation->name() << " (" << root->name() << ") has ambiguous or non-distinct buffer. Copying."; add_index_to_copy(root, index); } }); - // For entry instructions, mark any parameter or constant values. - if (is_entry) { - for (const auto& pair : - alias_analysis->dataflow_analysis().GetInstructionValueSet(root)) { - const ShapeIndex& index = pair.first; - const HloValueSet& value_set = pair.second; - for (const HloValue* value : value_set.values()) { - if (ValueIsReadOnly(*value)) { - VLOG(2) << "Root of entry computation (" << root->name() - << ") has constant or entry parameter value at index " - << index << ". Copying."; - add_index_to_copy(root, index); - } + for (const auto& pair : + alias_analysis->dataflow_analysis().GetInstructionValueSet(root)) { + const ShapeIndex& index = pair.first; + const HloValueSet& value_set = pair.second; + for (const HloValue* value : value_set.values()) { + if (ShouldCopyRootValue(*value, policy)) { + VLOG(2) << "Root of (" << root->name() << ") of computation(" + << computation->name() + << ") has constant or parameter value at index " << index + << ". Copying."; + add_index_to_copy(root, index); } } } @@ -1012,7 +1051,6 @@ Status AddSpecialCaseCopies(const CallGraph& call_graph, HloModule* module) { instruction->parent()->set_root_instruction(deep_copy); } } - return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD index 32be0b0c968f2d24f460fc8377c458f2da282112..91ae66ece11e70459db9a62782d3c24a303829c2 100644 --- a/tensorflow/compiler/xla/service/cpu/BUILD +++ b/tensorflow/compiler/xla/service/cpu/BUILD @@ -105,9 +105,11 @@ cc_library( "//tensorflow/compiler/xla/service:buffer_assignment", "//tensorflow/compiler/xla/service:buffer_liveness", "//tensorflow/compiler/xla/service:call_inliner", + "//tensorflow/compiler/xla/service:conditional_simplifier", "//tensorflow/compiler/xla/service:dot_decomposer", "//tensorflow/compiler/xla/service:executable", "//tensorflow/compiler/xla/service:flatten_call_graph", + "//tensorflow/compiler/xla/service:gather_expander", "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/service:hlo_constant_folding", "//tensorflow/compiler/xla/service:hlo_cse", @@ -514,7 +516,6 @@ cc_library( cc_library( name = "runtime_matvec", - srcs = ["runtime_matvec.cc"], hdrs = ["runtime_matvec.h"], copts = runtime_copts(), deps = [ diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc index 387806e24aad0d5f28cb104507ef6cc136ffd779..0a966fd5a7c1ce2c4e367b26701c9186ab2ebf74 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc @@ -47,6 +47,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/buffer_assignment.h" #include "tensorflow/compiler/xla/service/buffer_liveness.h" #include "tensorflow/compiler/xla/service/call_inliner.h" +#include "tensorflow/compiler/xla/service/conditional_simplifier.h" #include "tensorflow/compiler/xla/service/cpu/compiler_functor.h" #include "tensorflow/compiler/xla/service/cpu/conv_canonicalization.h" #include "tensorflow/compiler/xla/service/cpu/cpu_copy_insertion.h" @@ -66,6 +67,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h" #include "tensorflow/compiler/xla/service/dot_decomposer.h" #include "tensorflow/compiler/xla/service/flatten_call_graph.h" +#include "tensorflow/compiler/xla/service/gather_expander.h" #include "tensorflow/compiler/xla/service/hlo.pb.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_constant_folding.h" @@ -260,6 +262,7 @@ Status CpuCompiler::RunHloPasses(HloModule* module, bool is_aot_compile) { /*rewrite_inference_op=*/true, /*rewrite_grad_op=*/true, /*use_fusion=*/false); + pipeline.AddPass(); pass.AddPass( /*is_layout_sensitive=*/false, [](const Shape&, const Shape&) { return false; }, @@ -275,6 +278,7 @@ Status CpuCompiler::RunHloPasses(HloModule* module, bool is_aot_compile) { pass.AddPass(); pass.AddPass(); pass.AddPass(); + pass.AddPass(); } pipeline.AddPass( [](const HloInstruction& dot, diff --git a/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion.cc b/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion.cc index 482e04052d5a914eab0e5bff2c7a83f3b698052f..0fc5a746bbbc7685ff5d4647111a750e7d7b1c19 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion.cc @@ -30,7 +30,6 @@ bool CanBeLoopFused(const HloInstruction& hlo) { // These are the only ones we fuse since we rely on effective elemental IR // generation. return hlo.IsElementwise() || // - hlo.opcode() == HloOpcode::kBitcast || hlo.opcode() == HloOpcode::kBroadcast || hlo.opcode() == HloOpcode::kConcatenate || hlo.opcode() == HloOpcode::kDynamicSlice || diff --git a/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion_test.cc b/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion_test.cc index 595c3f55b321f47e2312b93e0c238c7637495d77..6ed1cd31b18f6360bdd7fd41bd5be2e657b310a5 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion_test.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion_test.cc @@ -77,7 +77,7 @@ TEST_F(InstructionFusionTest, DotOperationFusion_Basic_1) { EXPECT_THAT(computation->root_instruction(), op::Fusion()); } -TEST_F(InstructionFusionTest, DotOperationFusion_Bitcast) { +TEST_F(InstructionFusionTest, DotOperationNoFusion_Bitcast) { HloComputation::Builder builder(TestName()); HloInstruction* arg0 = builder.AddInstruction(HloInstruction::CreateParameter( 0, ShapeUtil::MakeShape(F32, {2, 512, 2, 128}), "arg0")); @@ -94,8 +94,7 @@ TEST_F(InstructionFusionTest, DotOperationFusion_Bitcast) { auto module = CreateNewModule(); auto computation = module->AddEntryComputation(builder.Build()); EXPECT_EQ(dot, computation->root_instruction()); - EXPECT_TRUE(CpuInstructionFusion().Run(module.get()).ValueOrDie()); - EXPECT_THAT(computation->root_instruction(), op::Fusion()); + EXPECT_FALSE(CpuInstructionFusion().Run(module.get()).ValueOrDie()); } TEST_F(InstructionFusionTest, DotOperationFusion_Reshape) { @@ -244,35 +243,33 @@ class OpcodeFusionTest : public InstructionFusionTest { } }; -TEST_F(OpcodeFusionTest, Exponential_Bitcast_Negate) { +TEST_F(OpcodeFusionTest, Exponential_Reshape_Negate) { HloComputation::Builder builder(TestName()); Shape param_shape = ShapeUtil::MakeShape(F32, {1, 4}); Shape result_shape = ShapeUtil::MakeShape(F32, {4}); HloInstruction* param0 = builder.AddInstruction( HloInstruction::CreateParameter(0, param_shape, "param")); - // InstructionFusion::ShouldFuse() precludes fusing a bitcast whose operand - // is a parameter, so create an operand between the parameter and bitcast. HloInstruction* exp1 = builder.AddInstruction( HloInstruction::CreateUnary(param_shape, HloOpcode::kExp, param0)); - HloInstruction* bitcast2 = builder.AddInstruction( - HloInstruction::CreateUnary(result_shape, HloOpcode::kBitcast, exp1)); + HloInstruction* reshape2 = + builder.AddInstruction(HloInstruction::CreateReshape(result_shape, exp1)); builder.AddInstruction( - HloInstruction::CreateUnary(result_shape, HloOpcode::kNegate, bitcast2)); + HloInstruction::CreateUnary(result_shape, HloOpcode::kNegate, reshape2)); auto module = CreateNewModule(); module->AddEntryComputation(builder.Build()); RunFusionAndCheckOpcodesWereFused( - module.get(), {HloOpcode::kNegate, HloOpcode::kBitcast, HloOpcode::kExp, + module.get(), {HloOpcode::kNegate, HloOpcode::kReshape, HloOpcode::kExp, HloOpcode::kParameter}); } -TEST_F(OpcodeFusionTest, Broadcast_Bitcast_DynamicSlice_Tanh) { +TEST_F(OpcodeFusionTest, Broadcast_Reshape_DynamicSlice_Tanh) { HloComputation::Builder builder(TestName()); Shape param_shape = ShapeUtil::MakeShape(F32, {8}); Shape starts_shape = ShapeUtil::MakeShape(F32, {2}); Shape broadcast_shape = ShapeUtil::MakeShape(F32, {1, 8, 8}); - Shape bitcast_shape = ShapeUtil::MakeShape(F32, {8, 8}); + Shape reshape_shape = ShapeUtil::MakeShape(F32, {8, 8}); Shape dynamic_slice_shape = ShapeUtil::MakeShape(F32, {4, 4}); HloInstruction* param0 = builder.AddInstruction( HloInstruction::CreateParameter(0, param_shape, "param")); @@ -280,11 +277,11 @@ TEST_F(OpcodeFusionTest, Broadcast_Bitcast_DynamicSlice_Tanh) { HloInstruction::CreateParameter(1, starts_shape, "starts")); HloInstruction* broadcast2 = builder.AddInstruction( HloInstruction::CreateBroadcast(broadcast_shape, param0, {1})); - HloInstruction* bitcast3 = builder.AddInstruction(HloInstruction::CreateUnary( - bitcast_shape, HloOpcode::kBitcast, broadcast2)); + HloInstruction* reshape3 = builder.AddInstruction( + HloInstruction::CreateReshape(reshape_shape, broadcast2)); HloInstruction* dynamic_slice4 = builder.AddInstruction(HloInstruction::CreateDynamicSlice( - dynamic_slice_shape, bitcast3, param1, {4, 4})); + dynamic_slice_shape, reshape3, param1, {4, 4})); builder.AddInstruction(HloInstruction::CreateUnary( dynamic_slice_shape, HloOpcode::kTanh, dynamic_slice4)); @@ -293,7 +290,7 @@ TEST_F(OpcodeFusionTest, Broadcast_Bitcast_DynamicSlice_Tanh) { RunFusionAndCheckOpcodesWereFused( module.get(), - {HloOpcode::kTanh, HloOpcode::kDynamicSlice, HloOpcode::kBitcast, + {HloOpcode::kTanh, HloOpcode::kDynamicSlice, HloOpcode::kReshape, HloOpcode::kBroadcast, HloOpcode::kParameter, HloOpcode::kParameter}); } diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc b/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc index 40ace963270e8cead47cc731cc326351178dff7d..9a3bd68c80c6e8bcdb231c63ba025d1f73619eb7 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc @@ -31,6 +31,8 @@ XfeedManager* GetXfeedManager() { return manager; } +extern const char* const kEigenMatMulF16SymbolName = + "__xla_cpu_runtime_EigenMatMulF16"; extern const char* const kEigenMatMulF32SymbolName = "__xla_cpu_runtime_EigenMatMulF32"; extern const char* const kEigenMatMulF64SymbolName = @@ -40,6 +42,8 @@ extern const char* const kEigenConvF16SymbolName = extern const char* const kEigenConvF32SymbolName = "__xla_cpu_runtime_EigenConvF32"; extern const char* const kEigenFftSymbolName = "__xla_cpu_runtime_EigenFft"; +extern const char* const kEigenSingleThreadedMatMulF16SymbolName = + "__xla_cpu_runtime_EigenSingleThreadedMatMulF16"; extern const char* const kEigenSingleThreadedMatMulF32SymbolName = "__xla_cpu_runtime_EigenSingleThreadedMatMulF32"; extern const char* const kEigenSingleThreadedMatMulF64SymbolName = diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime.h b/tensorflow/compiler/xla/service/cpu/cpu_runtime.h index 2141dfe1cedd6f9674acc348152574b4fd30895b..e61d6ea28b633398863357541e056ee887582f9c 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_runtime.h +++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime.h @@ -41,11 +41,13 @@ namespace runtime { // the actual symbol. // 2. When using ahead-of-time compilation, the linker can resolve the name // because it is a symbol in the cpu_runtime library. +extern const char* const kEigenMatMulF16SymbolName; extern const char* const kEigenMatMulF32SymbolName; extern const char* const kEigenMatMulF64SymbolName; extern const char* const kEigenConvF16SymbolName; extern const char* const kEigenConvF32SymbolName; extern const char* const kEigenFftSymbolName; +extern const char* const kEigenSingleThreadedMatMulF16SymbolName; extern const char* const kEigenSingleThreadedMatMulF32SymbolName; extern const char* const kEigenSingleThreadedMatMulF64SymbolName; extern const char* const kEigenSingleThreadedConvF16SymbolName; diff --git a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc index cfe7c9c3af0be109ac8a86753e880e2bcbceba41..6f06256e08e8e3342e77c7c79a2a47465b89eca3 100644 --- a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc +++ b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc @@ -919,6 +919,12 @@ tensorflow::Status DotOpEmitter::EmitCallToRuntime() { llvm::Type* float_type; const char* fn_name; switch (type) { + case F16: + fn_name = multi_threaded_eigen + ? runtime::kEigenMatMulF16SymbolName + : runtime::kEigenSingleThreadedMatMulF16SymbolName; + float_type = ir_builder_->getHalfTy(); + break; case F32: fn_name = multi_threaded_eigen ? runtime::kEigenMatMulF32SymbolName @@ -1051,7 +1057,8 @@ static bool AreValidGemmShapes(const Shape& lhs_shape, const Shape& rhs_shape, // The inputs and the output must // 1) be matrices with no padding, and // 2) have an allowed element type. - return output_shape.element_type() == F32 && + PrimitiveType output_primitive_type = output_shape.element_type(); + return (output_primitive_type == F32 || output_primitive_type == F16) && IsRank2WithNoPadding(lhs_shape) && IsRank2WithNoPadding(rhs_shape) && IsRank2WithNoPadding(output_shape); } diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc index 4dffaee87f6b33933b58c8c58478eec918569197..3b8056d50500cac381a1c5ad6b05028476504a47 100644 --- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc +++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc @@ -2074,7 +2074,7 @@ Status IrEmitter::HandleFusion(HloInstruction* fusion) { TF_RETURN_IF_ERROR(ElementTypesSameAndSupported( /*instruction=*/*root, /*operands=*/{lhs, rhs}, - /*supported_types=*/{F32})); + /*supported_types=*/{F16, F32})); llvm_ir::IrArray lhs_array(GetIrArrayFor(lhs)); llvm_ir::IrArray rhs_array(GetIrArrayFor(rhs)); diff --git a/tensorflow/compiler/xla/service/cpu/runtime_conv2d.h b/tensorflow/compiler/xla/service/cpu/runtime_conv2d.h index 39e20ed45639040110b99ddb52eb6f6dab26dfaa..7337c907f5c83d608641b7382e75902e6f6c05d4 100644 --- a/tensorflow/compiler/xla/service/cpu/runtime_conv2d.h +++ b/tensorflow/compiler/xla/service/cpu/runtime_conv2d.h @@ -16,6 +16,7 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_XLA_SERVICE_CPU_RUNTIME_CONV2D_H_ #define TENSORFLOW_COMPILER_XLA_SERVICE_CPU_RUNTIME_CONV2D_H_ +#include "third_party/eigen3/Eigen/Core" #include "tensorflow/core/platform/types.h" extern "C" { diff --git a/tensorflow/compiler/xla/service/cpu/runtime_matmul.cc b/tensorflow/compiler/xla/service/cpu/runtime_matmul.cc index bff57d33ae23fbba8c664cbd18df77e4c35eb592..39b13183ff093611a42b3931d45f64eadb420622 100644 --- a/tensorflow/compiler/xla/service/cpu/runtime_matmul.cc +++ b/tensorflow/compiler/xla/service/cpu/runtime_matmul.cc @@ -63,30 +63,41 @@ void MatMul(const void* run_options_ptr, T* out, T* lhs, T* rhs, int64 m, C.device(*run_options->intra_op_thread_pool()) = A.contract(B, dims); } +template +void MatMulImpl(const void* run_options_ptr, T* out, T* lhs, T* rhs, int64 m, + int64 n, int64 k, int32 transpose_lhs, int32 transpose_rhs) { + if (m == 1 || n == 1) { + // Despite being single threaded, this version of matrix * vector is faster. + xla::EigenMatVec(out, lhs, rhs, m, n, k, transpose_lhs, transpose_rhs); + } else { + MatMul(run_options_ptr, out, lhs, rhs, m, n, k, transpose_lhs, + transpose_rhs); + } +} + } // namespace +void __xla_cpu_runtime_EigenMatMulF16(const void* run_options_ptr, + Eigen::half* out, Eigen::half* lhs, + Eigen::half* rhs, int64 m, int64 n, + int64 k, int32 transpose_lhs, + int32 transpose_rhs) { + MatMulImpl(run_options_ptr, out, lhs, rhs, m, n, k, + transpose_lhs, transpose_rhs); +} + void __xla_cpu_runtime_EigenMatMulF32(const void* run_options_ptr, float* out, float* lhs, float* rhs, int64 m, int64 n, int64 k, int32 transpose_lhs, int32 transpose_rhs) { - if (m == 1 || n == 1) { - // Despite being single threaded, this version of matrix * vector is faster. - xla::EigenMatVecF32(out, lhs, rhs, m, n, k, transpose_lhs, transpose_rhs); - } else { - MatMul(run_options_ptr, out, lhs, rhs, m, n, k, transpose_lhs, - transpose_rhs); - } + MatMulImpl(run_options_ptr, out, lhs, rhs, m, n, k, transpose_lhs, + transpose_rhs); } void __xla_cpu_runtime_EigenMatMulF64(const void* run_options_ptr, double* out, double* lhs, double* rhs, int64 m, int64 n, int64 k, int32 transpose_lhs, int32 transpose_rhs) { - if (m == 1 || n == 1) { - // Despite being single threaded, this version of matrix * vector is faster. - xla::EigenMatVecF64(out, lhs, rhs, m, n, k, transpose_lhs, transpose_rhs); - } else { - MatMul(run_options_ptr, out, lhs, rhs, m, n, k, transpose_lhs, - transpose_rhs); - } + MatMulImpl(run_options_ptr, out, lhs, rhs, m, n, k, transpose_lhs, + transpose_rhs); } diff --git a/tensorflow/compiler/xla/service/cpu/runtime_matmul.h b/tensorflow/compiler/xla/service/cpu/runtime_matmul.h index fdb644651dd5d0fa0345580f52ed0fb051672285..d96fe3d58bd5ffbad347e3ede3534d1d47be697a 100644 --- a/tensorflow/compiler/xla/service/cpu/runtime_matmul.h +++ b/tensorflow/compiler/xla/service/cpu/runtime_matmul.h @@ -16,6 +16,7 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_XLA_SERVICE_CPU_RUNTIME_MATMUL_H_ #define TENSORFLOW_COMPILER_XLA_SERVICE_CPU_RUNTIME_MATMUL_H_ +#include "third_party/eigen3/Eigen/Core" #include "tensorflow/core/platform/types.h" extern "C" { @@ -25,6 +26,12 @@ extern "C" { // order. 'out' is a pointer to a buffer sufficiently large to hold the result // of the operation. Following standard nomenclature: lhs is m x k, // rhs is k x n, and out is m x n. +extern void __xla_cpu_runtime_EigenMatMulF16( + const void* /* xla::ExecutableRunOptions* */ run_options_ptr, + Eigen::half* out, Eigen::half* lhs, Eigen::half* rhs, tensorflow::int64 m, + tensorflow::int64 n, tensorflow::int64 k, tensorflow::int32 transpose_lhs, + tensorflow::int32 transpose_rhs); + extern void __xla_cpu_runtime_EigenMatMulF32( const void* /* xla::ExecutableRunOptions* */ run_options_ptr, float* out, float* lhs, float* rhs, tensorflow::int64 m, tensorflow::int64 n, diff --git a/tensorflow/compiler/xla/service/cpu/runtime_matvec.cc b/tensorflow/compiler/xla/service/cpu/runtime_matvec.cc deleted file mode 100644 index 435820cdd36e2a906d9dfbe2555f4c0df623c729..0000000000000000000000000000000000000000 --- a/tensorflow/compiler/xla/service/cpu/runtime_matvec.cc +++ /dev/null @@ -1,110 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include -#include - -#include "third_party/eigen3/Eigen/Core" -#include "tensorflow/compiler/xla/service/cpu/runtime_matvec.h" - -using tensorflow::int32; -using tensorflow::int64; - -namespace { - -// Does mat * x or mat^T * x. -template -void MatVec(T* out_buf, T* mat_buf, T* x_buf, int64 rows, int64 cols, - int32 transpose) { - // Use an Eigen Matrix instead of a Tensor, as the GEMV from Matrix seems to - // be faster (b/30223679). See also: the matmul op kernel in TensorFlow, - // which implements the same optimization. - using Matrix = Eigen::Matrix; - using MatrixMap = Eigen::Map; - - using Vector = Eigen::Matrix; - using VectorMap = Eigen::Map; - - auto x = VectorMap(x_buf, cols); - auto out = VectorMap(out_buf, rows); - - int64 mat_rows = rows; - int64 mat_cols = cols; - - if (transpose) { - std::swap(mat_rows, mat_cols); - } - - auto mat = MatrixMap(mat_buf, mat_rows, mat_cols); - - if (transpose) { - out = mat.transpose() * x; - } else { - out = mat * x; - } -} - -// Converts matmul-style args to matvec. -template -void DispatchMatVec(T* out, T* lhs, T* rhs, int64 m, int64 n, int64 k, - int32 transpose_lhs, int32 transpose_rhs) { - // If the input is in the form x * A, where x is the vector, then bring A back - // over to the left hand side. We make use of the identity - // - // (x * A)^T = A^T * x^T - // - // We do not need to take the transpose of x or of the result since taking - // the transpose of a vector does not change the memory layout. - const int64 cols = k; - - T* mat; - T* vec; - int64 rows; - bool transpose_mat; - - bool is_mat_vec = (n == 1); - - if (is_mat_vec) { - mat = lhs; - vec = rhs; - rows = m; - transpose_mat = transpose_lhs; - } else { - mat = rhs; - vec = lhs; - rows = n; - transpose_mat = !transpose_rhs; - } - - MatVec(out, mat, vec, rows, cols, transpose_mat); -} - -} // namespace - -namespace xla { - -void EigenMatVecF32(float* out, float* lhs, float* rhs, int64 m, int64 n, - int64 k, int32 transpose_lhs, int32 transpose_rhs) { - assert((m == 1 || n == 1) && "not a matrix-vector multiply"); - DispatchMatVec(out, lhs, rhs, m, n, k, transpose_lhs, transpose_rhs); -} - -void EigenMatVecF64(double* out, double* lhs, double* rhs, int64 m, int64 n, - int64 k, int32 transpose_lhs, int32 transpose_rhs) { - assert((m == 1 || n == 1) && "not a matrix-vector multiply"); - DispatchMatVec(out, lhs, rhs, m, n, k, transpose_lhs, transpose_rhs); -} - -} // namespace xla diff --git a/tensorflow/compiler/xla/service/cpu/runtime_matvec.h b/tensorflow/compiler/xla/service/cpu/runtime_matvec.h index 1bd8dfb377acc1f7cfbe9a92773f87f0ef25de3a..70eb98c54169824e220d9287753c0849362eade6 100644 --- a/tensorflow/compiler/xla/service/cpu/runtime_matvec.h +++ b/tensorflow/compiler/xla/service/cpu/runtime_matvec.h @@ -16,10 +16,86 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_XLA_SERVICE_CPU_RUNTIME_MATVEC_H_ #define TENSORFLOW_COMPILER_XLA_SERVICE_CPU_RUNTIME_MATVEC_H_ +#include "third_party/eigen3/Eigen/Core" + #include "tensorflow/core/platform/types.h" namespace xla { +namespace detail { + +using tensorflow::int32; +using tensorflow::int64; + +// Does mat * x or mat^T * x. +template +void MatVec(T* out_buf, T* mat_buf, T* x_buf, int64 rows, int64 cols, + int32 transpose) { + // Use an Eigen Matrix instead of a Tensor, as the GEMV from Matrix seems to + // be faster (b/30223679). See also: the matmul op kernel in TensorFlow, + // which implements the same optimization. + using Matrix = Eigen::Matrix; + using MatrixMap = Eigen::Map; + + using Vector = Eigen::Matrix; + using VectorMap = Eigen::Map; + + auto x = VectorMap(x_buf, cols); + auto out = VectorMap(out_buf, rows); + + int64 mat_rows = rows; + int64 mat_cols = cols; + + if (transpose) { + std::swap(mat_rows, mat_cols); + } + + auto mat = MatrixMap(mat_buf, mat_rows, mat_cols); + + if (transpose) { + out = mat.transpose() * x; + } else { + out = mat * x; + } +} + +// Converts matmul-style args to matvec. +template +void DispatchMatVec(T* out, T* lhs, T* rhs, int64 m, int64 n, int64 k, + int32 transpose_lhs, int32 transpose_rhs) { + // If the input is in the form x * A, where x is the vector, then bring A back + // over to the left hand side. We make use of the identity + // + // (x * A)^T = A^T * x^T + // + // We do not need to take the transpose of x or of the result since taking + // the transpose of a vector does not change the memory layout. + const int64 cols = k; + + T* mat; + T* vec; + int64 rows; + bool transpose_mat; + + bool is_mat_vec = (n == 1); + + if (is_mat_vec) { + mat = lhs; + vec = rhs; + rows = m; + transpose_mat = transpose_lhs; + } else { + mat = rhs; + vec = lhs; + rows = n; + transpose_mat = !transpose_rhs; + } + + MatVec(out, mat, vec, rows, cols, transpose_mat); +} + +} // namespace detail + // Performs a matrix-vector multiplication using Eigen. 'lhs' and 'rhs' are // pointers to buffers containing input matrices in column-major order. 'out' is // a pointer to a buffer sufficiently large to hold the result of the @@ -30,15 +106,15 @@ namespace xla { // // TODO(b/64684907): Compare runtime performance of these functions with dot // simplification. -void EigenMatVecF32(float* out, float* lhs, float* rhs, tensorflow::int64 m, - tensorflow::int64 n, tensorflow::int64 k, - tensorflow::int32 transpose_lhs, - tensorflow::int32 transpose_rhs); - -void EigenMatVecF64(double* out, double* lhs, double* rhs, tensorflow::int64 m, - tensorflow::int64 n, tensorflow::int64 k, - tensorflow::int32 transpose_lhs, - tensorflow::int32 transpose_rhs); +template +void EigenMatVec(T* out, T* lhs, T* rhs, tensorflow::int64 m, + tensorflow::int64 n, tensorflow::int64 k, + tensorflow::int32 transpose_lhs, + tensorflow::int32 transpose_rhs) { + assert((m == 1 || n == 1) && "not a matrix-vector multiply"); + detail::DispatchMatVec(out, lhs, rhs, m, n, k, transpose_lhs, + transpose_rhs); +} } // namespace xla diff --git a/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_conv2d.h b/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_conv2d.h index f216bd0152aa93b8753d881938c63a9cabea899b..44b201725b2c724f48c1a3f0373c41e76211e0c2 100644 --- a/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_conv2d.h +++ b/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_conv2d.h @@ -16,6 +16,7 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_XLA_SERVICE_CPU_RUNTIME_SINGLE_THREADED_CONV2D_H_ #define TENSORFLOW_COMPILER_XLA_SERVICE_CPU_RUNTIME_SINGLE_THREADED_CONV2D_H_ +#include "third_party/eigen3/Eigen/Core" #include "tensorflow/core/platform/types.h" extern "C" { diff --git a/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.cc b/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.cc index ee8eb081556d60fcf6537b1036a4a5825c4c7bf6..17303e2f0d34e531a3a56aa147608b949e0f43ae 100644 --- a/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.cc +++ b/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.cc @@ -57,26 +57,38 @@ void MatMul(const void* run_options_ptr, T* out, T* lhs, T* rhs, int64 m, C = A.contract(B, dims); } +template +void SingleThreadedMatMul(const void* run_options_ptr, T* out, T* lhs, T* rhs, + int64 m, int64 n, int64 k, int32 transpose_lhs, + int32 transpose_rhs) { + if (m == 1 || n == 1) { + xla::EigenMatVec(out, lhs, rhs, m, n, k, transpose_lhs, transpose_rhs); + } else { + MatMul(run_options_ptr, out, lhs, rhs, m, n, k, transpose_lhs, + transpose_rhs); + } +} + } // namespace +void __xla_cpu_runtime_EigenSingleThreadedMatMulF16( + const void* run_options_ptr, Eigen::half* out, Eigen::half* lhs, + Eigen::half* rhs, int64 m, int64 n, int64 k, int32 transpose_lhs, + int32 transpose_rhs) { + SingleThreadedMatMul(run_options_ptr, out, lhs, rhs, m, n, k, + transpose_lhs, transpose_rhs); +} + void __xla_cpu_runtime_EigenSingleThreadedMatMulF32( const void* run_options_ptr, float* out, float* lhs, float* rhs, int64 m, int64 n, int64 k, int32 transpose_lhs, int32 transpose_rhs) { - if (m == 1 || n == 1) { - xla::EigenMatVecF32(out, lhs, rhs, m, n, k, transpose_lhs, transpose_rhs); - } else { - MatMul(run_options_ptr, out, lhs, rhs, m, n, k, transpose_lhs, - transpose_rhs); - } + SingleThreadedMatMul(run_options_ptr, out, lhs, rhs, m, n, k, + transpose_lhs, transpose_rhs); } void __xla_cpu_runtime_EigenSingleThreadedMatMulF64( const void* run_options_ptr, double* out, double* lhs, double* rhs, int64 m, int64 n, int64 k, int32 transpose_lhs, int32 transpose_rhs) { - if (m == 1 || n == 1) { - xla::EigenMatVecF64(out, lhs, rhs, m, n, k, transpose_lhs, transpose_rhs); - } else { - MatMul(run_options_ptr, out, lhs, rhs, m, n, k, transpose_lhs, - transpose_rhs); - } + SingleThreadedMatMul(run_options_ptr, out, lhs, rhs, m, n, k, + transpose_lhs, transpose_rhs); } diff --git a/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.h b/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.h index 029eb9514287d8c69cde2cfb06e0d56e78d6f165..82a1fcce594fa5b04f4fe459870991863c32a91a 100644 --- a/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.h +++ b/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.h @@ -16,6 +16,7 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_XLA_SERVICE_CPU_RUNTIME_SINGLE_THREADED_MATMUL_H_ #define TENSORFLOW_COMPILER_XLA_SERVICE_CPU_RUNTIME_SINGLE_THREADED_MATMUL_H_ +#include "third_party/eigen3/Eigen/Core" #include "tensorflow/core/platform/types.h" extern "C" { @@ -25,6 +26,12 @@ extern "C" { // 'out' is a pointer to a buffer sufficiently large to hold the result of the // operation. Following standard nomenclature: lhs is m x k, rhs is k x n, and // out is m x n. +extern void __xla_cpu_runtime_EigenSingleThreadedMatMulF16( + const void* /* xla::ExecutableRunOptions* */ run_options_ptr, + Eigen::half* out, Eigen::half* lhs, Eigen::half* rhs, tensorflow::int64 m, + tensorflow::int64 n, tensorflow::int64 k, tensorflow::int32 transpose_lhs, + tensorflow::int32 transpose_rhs); + extern void __xla_cpu_runtime_EigenSingleThreadedMatMulF32( const void* /* xla::ExecutableRunOptions* */ run_options_ptr, float* out, float* lhs, float* rhs, tensorflow::int64 m, tensorflow::int64 n, diff --git a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc index e8a375d63791cd9a94f77af4ef5e74d2cb7e4361..80c24eaccfc2a83f8f3f311d60860715668d0c08 100644 --- a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc +++ b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc @@ -181,10 +181,12 @@ bool RegisterKnownJITSymbols() { REGISTER_CPU_RUNTIME_SYMBOL(EigenConvF16); REGISTER_CPU_RUNTIME_SYMBOL(EigenConvF32); REGISTER_CPU_RUNTIME_SYMBOL(EigenFft); + REGISTER_CPU_RUNTIME_SYMBOL(EigenMatMulF16); REGISTER_CPU_RUNTIME_SYMBOL(EigenMatMulF32); REGISTER_CPU_RUNTIME_SYMBOL(EigenMatMulF64); REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedConvF16); REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedConvF32); + REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedMatMulF16); REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedMatMulF32); REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedMatMulF64); REGISTER_CPU_RUNTIME_SYMBOL(ParallelForkJoin); diff --git a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc index c732974995f70d9ba1b46e18aa4cc2c6ab467182..b6a0903b0eeaa04d8bc1488378c148b2016c5d48 100644 --- a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc +++ b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc @@ -1522,15 +1522,12 @@ llvm_ir::ElementGenerator ElementalIrEmitter::MakeElementGenerator( case HloOpcode::kBroadcast: return [this, hlo, &operand_to_generator]( const IrArray::Index& target_index) -> StatusOr { + const HloInstruction* operand = hlo->operand(0); // The `dimensions` member of the broadcast instruction maps from // input dimensions to output dimensions. - const HloInstruction* operand = hlo->operand(0); - int64 rank = ShapeUtil::Rank(operand->shape()); - IrArray::Index source_index(rank); - for (int64 i = 0; i < rank; ++i) { - source_index[i] = target_index[hlo->dimensions(i)]; - } - return operand_to_generator.at(operand)(source_index); + return operand_to_generator.at( + operand)(target_index.SourceIndexOfBroadcast( + hlo->shape(), operand->shape(), hlo->dimensions(), ir_builder_)); }; case HloOpcode::kSlice: return [this, hlo, &operand_to_generator]( @@ -1722,6 +1719,14 @@ llvm_ir::ElementGenerator ElementalIrEmitter::MakeElementGenerator( SetToFirstInsertPoint(if_data.after_block, ir_builder_); return ir_builder_->CreateLoad(ret_value_addr); }; + case HloOpcode::kBitcast: + CHECK_EQ(ShapeUtil::ElementsIn(hlo->shape()), + ShapeUtil::ElementsIn(hlo->operand(0)->shape())); + return [this, hlo, &operand_to_generator](const IrArray::Index& index) { + const HloInstruction* operand = hlo->operand(0); + return operand_to_generator.at(operand)(index.SourceIndexOfBitcast( + hlo->shape(), operand->shape(), ir_builder_)); + }; case HloOpcode::kReshape: CHECK_EQ(ShapeUtil::ElementsIn(hlo->shape()), ShapeUtil::ElementsIn(hlo->operand(0)->shape())); diff --git a/tensorflow/compiler/xla/service/executable.cc b/tensorflow/compiler/xla/service/executable.cc index 90481c7a88f90edea5399ee44aee2d2c77fc115f..be92b1629a2d8dae57b315751bd4f7f9ccddf171 100644 --- a/tensorflow/compiler/xla/service/executable.cc +++ b/tensorflow/compiler/xla/service/executable.cc @@ -21,6 +21,7 @@ limitations under the License. #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/core/lib/hash/hash.h" #include "tensorflow/core/lib/io/path.h" +#include "tensorflow/core/lib/strings/proto_serialization.h" #include "tensorflow/core/lib/strings/stringprintf.h" #include "tensorflow/core/platform/env.h" diff --git a/tensorflow/compiler/xla/service/gather_expander.cc b/tensorflow/compiler/xla/service/gather_expander.cc new file mode 100644 index 0000000000000000000000000000000000000000..a133d810675814f6be7da23a2335fb19f3ff47fc --- /dev/null +++ b/tensorflow/compiler/xla/service/gather_expander.cc @@ -0,0 +1,359 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include + +#include "tensorflow/compiler/xla/service/gather_expander.h" +#include "tensorflow/compiler/xla/service/hlo_creation_utils.h" +#include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/compiler/xla/service/while_util.h" +#include "tensorflow/compiler/xla/statusor.h" +#include "tensorflow/compiler/xla/util.h" + +namespace xla { +using tensorflow::gtl::ArraySlice; + +static StatusOr TransposeIndexVectorDimToLast( + HloInstruction* gather_indices, int64 index_vector_dim) { + const Shape& gather_indices_shape = gather_indices->shape(); + if (index_vector_dim == (gather_indices_shape.dimensions_size() - 1)) { + return gather_indices; + } + std::vector permutation; + permutation.reserve(gather_indices_shape.dimensions_size()); + for (int64 i = 0, e = gather_indices_shape.dimensions_size(); i < e; i++) { + if (i != index_vector_dim) { + permutation.push_back(i); + } + } + permutation.push_back(index_vector_dim); + return CreateTransposeHlo(gather_indices, permutation); +} + +// If the gather_indices holds scalar indices (i.e. gather_indices has rank N +// and index_vector_dim is N) then reshape it to have a trailing degenerate +// dimension. This makes the code for slicing out the index vector more +// uniform. +static StatusOr DeScalarizeGatherIndices( + HloInstruction* gather_indices, int64 index_vector_dim) { + const Shape& gather_indices_shape = gather_indices->shape(); + if (index_vector_dim != gather_indices_shape.dimensions_size()) { + return gather_indices; + } + + int64 last_index = gather_indices_shape.dimensions( + gather_indices_shape.dimensions_size() - 1); + return ExpandLastDimIntoNDims(gather_indices, {last_index, 1}); +} + +// Canonicalizes the gather_indices tensors so that we only have deal with some +// specific cases in the while loop that does the heavy lifting. +// +// See the "High Level Algorithm" section for a broader picture. +static StatusOr CanonicalizeGatherIndices( + HloInstruction* gather_indices, int64 index_vector_dim) { + // If gather_indices holds scalar indices, normalize it to hold index vectors + // of size 1. + TF_ASSIGN_OR_RETURN( + HloInstruction * descalarized_gather_indices, + DeScalarizeGatherIndices(gather_indices, index_vector_dim)); + + // Transpose the non-index-vector dimensions to the front. + TF_ASSIGN_OR_RETURN(HloInstruction * transposed_gather_indices, + TransposeIndexVectorDimToLast(descalarized_gather_indices, + index_vector_dim)); + + // If there is only one index (i.e. gather_indices has rank 1 and this gather + // is really just a dynamic slice) add a leading degenerate dimension for + // uniformity. Otherwise create a "collapsed" leading dimension that subsumes + // all of the non-index-vector dimensions. + const Shape& shape = transposed_gather_indices->shape(); + if (shape.dimensions_size() == 1) { + return ExpandFirstDimIntoNDims(gather_indices, {1, shape.dimensions(0)}); + } else { + return CollapseFirstNDims(transposed_gather_indices, + shape.dimensions_size() - 1); + } +} + +// Expands out the gather dimensions in the accumulator produced by the while +// loop. +static StatusOr ExpandGatherDimsInAccumulator( + const Shape& gather_indices_shape, HloInstruction* accumulator, + int64 index_vector_dim) { + std::vector output_gather_dim_bounds; + output_gather_dim_bounds.reserve(gather_indices_shape.dimensions_size()); + for (int64 i = 0, e = gather_indices_shape.dimensions_size(); i < e; i++) { + if (i != index_vector_dim) { + output_gather_dim_bounds.push_back(gather_indices_shape.dimensions(i)); + } + } + + if (output_gather_dim_bounds.empty()) { + // If output_gather_dim_bounds is empty we must be lowering a (effectively) + // dynamic-slice. + CHECK_EQ(accumulator->shape().dimensions(0), 1); + return CollapseFirstNDims(accumulator, 2); + } + + return ExpandFirstDimIntoNDims(accumulator, output_gather_dim_bounds); +} + +// Expand an index vector from the gather_indices tensor into a vector that can +// be used to dynamic-slice out of the gather operand. +static StatusOr ExpandIndexVectorIntoOperandSpace( + HloInstruction* index_vector, const GatherDimensionNumbers& dim_numbers, + int64 operand_rank) { + HloComputation* computation = index_vector->parent(); + const Shape& index_shape = index_vector->shape(); + HloInstruction* zero = + computation->AddInstruction(HloInstruction::CreateConstant( + Literal::CreateFromDimensions(index_shape.element_type(), {1}))); + + // We extract out individual components from the smaller index and concatenate + // them (interspersing zeros as needed) into the larger index. + std::vector expanded_index_components; + + for (int i = 0; i < operand_rank; i++) { + int64 index_vector_dim_index = + FindIndex(dim_numbers.gather_dims_to_operand_dims(), i); + if (index_vector_dim_index != + dim_numbers.gather_dims_to_operand_dims_size()) { + TF_ASSIGN_OR_RETURN( + HloInstruction * component_to_concat, + CreateSliceHlo( + index_vector, /*start_indices=*/{index_vector_dim_index}, + /*limit_indices=*/{index_vector_dim_index + 1}, /*strides=*/{1})); + expanded_index_components.push_back(component_to_concat); + } else { + expanded_index_components.push_back(zero); + } + } + + return CreateConcatHlo(expanded_index_components, /*dimension=*/0); +} + +// This generates the body of the while that implements the main data movement +// behavior of gather using dynamic-slice and dynamic-update-slice. +static StatusOr> GatherLoopBody( + const HloInstruction& gather, HloInstruction* induction_var, + const std::vector& incoming_loop_state) { + CHECK_EQ(incoming_loop_state.size(), 3); + HloInstruction* const operand = incoming_loop_state[0]; + HloInstruction* const gather_indices = incoming_loop_state[1]; + HloInstruction* const output_accumulator = incoming_loop_state[2]; + + int64 index_vector_size = gather_indices->shape().dimensions(1); + + TF_ASSIGN_OR_RETURN( + HloInstruction * induction_var_as_vector, + CreateBroadcastHlo(induction_var, /*broadcast_dimensions=*/{}, + /*result_shape_bounds=*/{1})); + + TF_ASSIGN_OR_RETURN( + HloInstruction * index_into_gather_indices, + PadVectorWithZeros(induction_var_as_vector, + /*zeros_to_prepend=*/0, /*zeros_to_append=*/1)); + + TF_ASSIGN_OR_RETURN( + HloInstruction * index_vector_2d, + CreateDynamicSliceHlo(gather_indices, index_into_gather_indices, + {1, index_vector_size})); + + TF_ASSIGN_OR_RETURN(HloInstruction * index_vector, + ElideDegenerateDims(index_vector_2d, {0})); + + TF_ASSIGN_OR_RETURN(HloInstruction * gathered_slice_start, + ExpandIndexVectorIntoOperandSpace( + index_vector, gather.gather_dimension_numbers(), + operand->shape().dimensions_size())); + + TF_ASSIGN_OR_RETURN(HloInstruction * gathered_slice, + CreateDynamicSliceHlo(operand, gathered_slice_start, + gather.gather_window_bounds())); + + TF_ASSIGN_OR_RETURN( + HloInstruction * gathered_slice_for_update, + ExpandFirstDimIntoNDims(gathered_slice, + {1, gathered_slice->shape().dimensions(0)})); + + TF_ASSIGN_OR_RETURN( + HloInstruction * index_vector_into_accumulator, + PadVectorWithZeros( + induction_var_as_vector, /*zeros_to_prepend=*/0, + /*zeros_to_append=*/gathered_slice->shape().dimensions_size())); + + TF_ASSIGN_OR_RETURN( + HloInstruction * updated_accumulator, + CreateDynamicUpdateSliceHlo(output_accumulator, gathered_slice_for_update, + index_vector_into_accumulator)); + + // New loop state -- only the accumulator has changed. The + // WhileUtil::MakeCountedLoop functions takes care of the induction variable + // and the while loop exit condition. + return StatusOr>{ + {operand, gather_indices, updated_accumulator}}; +} + +static StatusOr CreateGatherLoopAccumulatorInitValue( + HloComputation* computation, PrimitiveType element_type, + ArraySlice window_bounds, int64 gather_loop_trip_count) { + std::vector accumulator_state_shape_dims; + accumulator_state_shape_dims.reserve(1 + window_bounds.size()); + accumulator_state_shape_dims.push_back(gather_loop_trip_count); + c_copy(window_bounds, std::back_inserter(accumulator_state_shape_dims)); + return BroadcastZeros(computation, element_type, + accumulator_state_shape_dims); +} + +static StatusOr ElideWindowDimsFromAccumulator( + HloInstruction* accumulator, const GatherDimensionNumbers& dim_numbers) { + std::vector dims_to_elide; + dims_to_elide.reserve(dim_numbers.elided_window_dims_size()); + for (int64 elided_window_dim : dim_numbers.elided_window_dims()) { + dims_to_elide.push_back(elided_window_dim + 1); + } + + return ElideDegenerateDims(accumulator, dims_to_elide); +} + +// `accumulator` is almost the tensor the gather operation would have produced, +// except that it has the dimensions in the wrong order -- the gather dimensions +// are the major dimensions and the window dimensions are the minor dimensions. +// Fix this up with a transpose. +static StatusOr PermuteGatherAndWindowDims( + HloInstruction* accumulator, ArraySlice output_window_dims, + int64 output_rank) { + std::vector permutation; + permutation.reserve(output_rank); + + int64 gather_idx_counter = 0; + int64 window_idx_counter = output_rank - output_window_dims.size(); + for (int64 i = 0; i < output_rank; i++) { + bool is_window_dim = c_binary_search(output_window_dims, i); + if (is_window_dim) { + permutation.push_back(window_idx_counter++); + } else { + permutation.push_back(gather_idx_counter++); + } + } + + return CreateTransposeHlo(accumulator, permutation); +} + +// High Level Algorithm +// +// We follow the following steps in sequence: +// +// 1. We canonicalize the gather_indices tensor such that it has rank +// 2 (i.e. is a matrix) where each row is an index vector into the +// operand. +// 2. We iterate over the set of indices in the canonicalized +// gather_indices tensor using a while loop, accumulating slices +// of the operand tensor into an accumulator using +// DynamicUpdateSlice. +// 3. The accumulator result from the while loop from (2) is then +// reshaped to split out all the individual gather dimensions and +// then transposed to give the final result. +// +// As an example, if we started with the following operation: +// +// HloModule TensorFlowGatherMultipleBatchDims +// +// ENTRY main { +// operand = s32[3,3] parameter(0) +// indices = s32[2,2] parameter(1) +// ROOT gather = s32[2,3,2] gather(operand, indices), +// output_window_dims={1}, +// elided_window_dims={1}, +// gather_dims_to_operand_dims={1}, +// index_vector_dim=2, +// window_bounds={3, 1} +// } +// +// We'd first reshape indices to s32[4,1], where each row is an index +// into operand. We'd then run a loop to slice out 4 tensors of shape +// [3,1] out of operand into an accumulator of shape [4,3,1]. We then +// reshape this result to [2,2,3] and finally transpose it to [2,3,2]. + +StatusOr GatherExpander::ExpandGather( + HloInstruction* gather_instr) { + HloComputation* computation = gather_instr->parent(); + HloInstruction* operand = gather_instr->mutable_operand(0); + HloInstruction* gather_indices = gather_instr->mutable_operand(1); + const Shape& output_shape = gather_instr->shape(); + int64 output_rank = output_shape.dimensions_size(); + + const GatherDimensionNumbers& dim_numbers = + gather_instr->gather_dimension_numbers(); + + TF_ASSIGN_OR_RETURN(HloInstruction * canonical_gather_indices, + CanonicalizeGatherIndices( + gather_indices, dim_numbers.index_vector_dim())); + + const int64 gather_loop_trip_count = + canonical_gather_indices->shape().dimensions(0); + + TF_ASSIGN_OR_RETURN( + HloInstruction * accumulator_init, + CreateGatherLoopAccumulatorInitValue( + computation, output_shape.element_type(), + gather_instr->gather_window_bounds(), gather_loop_trip_count)); + + StatusOr> gather_loop_result_or_error = + WhileUtil::MakeCountedLoop( + computation, gather_loop_trip_count, + {operand, canonical_gather_indices, accumulator_init}, + [&](HloInstruction* indvar, + const std::vector& loop_state) { + return GatherLoopBody(*gather_instr, indvar, loop_state); + }); + + TF_ASSIGN_OR_RETURN(std::vector gather_loop_result, + gather_loop_result_or_error); + + HloInstruction* accumulator_result = gather_loop_result.back(); + TF_ASSIGN_OR_RETURN( + HloInstruction * accumulator_with_window_dims_elided, + ElideWindowDimsFromAccumulator(accumulator_result, dim_numbers)); + + TF_ASSIGN_OR_RETURN( + HloInstruction * accumulator_with_output_gather_dims_decanonicalized, + ExpandGatherDimsInAccumulator(gather_indices->shape(), + accumulator_with_window_dims_elided, + dim_numbers.index_vector_dim())); + + return PermuteGatherAndWindowDims( + accumulator_with_output_gather_dims_decanonicalized, + AsInt64Slice(dim_numbers.output_window_dims()), output_rank); +} + +StatusOr GatherExpander::Run(HloModule* module) { + std::vector gather_instrs; + for (HloComputation* computation : module->MakeNonfusionComputations()) { + c_copy_if(computation->instructions(), std::back_inserter(gather_instrs), + [](HloInstruction* inst) { + return inst->opcode() == HloOpcode::kGather; + }); + } + + for (HloInstruction* inst : gather_instrs) { + TF_ASSIGN_OR_RETURN(HloInstruction * expanded_root, ExpandGather(inst)); + TF_RETURN_IF_ERROR(inst->parent()->ReplaceInstruction(inst, expanded_root)); + } + + return !gather_instrs.empty(); +} +} // namespace xla diff --git a/tensorflow/compiler/xla/service/gather_expander.h b/tensorflow/compiler/xla/service/gather_expander.h new file mode 100644 index 0000000000000000000000000000000000000000..c1fc8574da99fff223c7dbb570b4533f76905b9a --- /dev/null +++ b/tensorflow/compiler/xla/service/gather_expander.h @@ -0,0 +1,37 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_GATHER_EXPANDER_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_GATHER_EXPANDER_H_ + +#include "tensorflow/compiler/xla/service/hlo_pass_interface.h" + +namespace xla { + +// This pass rewrites gather operations into (roughly) while loops of dynamic +// slices. This lets backends that don't support gather directly to +// nevertheless have a minimum level of support. +class GatherExpander : public HloPassInterface { + public: + tensorflow::StringPiece name() const override { return "gather_expander"; } + StatusOr Run(HloModule* module) override; + + private: + StatusOr ExpandGather(HloInstruction* gather_instr); +}; + +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_GATHER_EXPANDER_H_ diff --git a/tensorflow/compiler/xla/service/generic_transfer_manager.cc b/tensorflow/compiler/xla/service/generic_transfer_manager.cc index 78dc0ad4fcd167c93f19d0c2b18ea72d666897ef..a99e2b7794a399047fb5a77a140bd333214e3f23 100644 --- a/tensorflow/compiler/xla/service/generic_transfer_manager.cc +++ b/tensorflow/compiler/xla/service/generic_transfer_manager.cc @@ -38,14 +38,7 @@ namespace xla { GenericTransferManager::GenericTransferManager(se::Platform::Id platform_id, size_t pointer_size) - : platform_id_(platform_id), pointer_size_(pointer_size) { - // We currently only support kHostPlatformId for CPU, kCudaPlatformId for - // GPU and kInterpreterPlatformId for Interpreter. Before supporting other - // platforms, we need to test this transfer manager on them. - CHECK(platform_id_ == se::host::kHostPlatformId || - platform_id_ == se::interpreter::kInterpreterPlatformId || - platform_id_ == se::cuda::kCudaPlatformId); -} + : platform_id_(platform_id), pointer_size_(pointer_size) {} se::Platform::Id GenericTransferManager::PlatformId() const { return platform_id_; diff --git a/tensorflow/compiler/xla/service/gpu/BUILD b/tensorflow/compiler/xla/service/gpu/BUILD index 9da4fb97fa27a238fead74985cb481a9be1f4a65..a3b7e10ae8df080879ce98b02b83f246bb19204b 100644 --- a/tensorflow/compiler/xla/service/gpu/BUILD +++ b/tensorflow/compiler/xla/service/gpu/BUILD @@ -397,6 +397,7 @@ tf_cc_test( "//tensorflow/compiler/xla/service:hlo_matchers", "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", + "//tensorflow/compiler/xla/tools/parser:hlo_parser", ], ) @@ -437,8 +438,10 @@ tf_cc_test( ":fusion_merger", ":instruction_fusion", "//tensorflow/compiler/xla:test_helpers", + "//tensorflow/compiler/xla/service:hlo_matchers", "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", + "//tensorflow/compiler/xla/tools/parser:hlo_parser", ], ) @@ -452,6 +455,7 @@ cc_library( "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:window_util", "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/service:hlo_creation_utils", "//tensorflow/compiler/xla/service:hlo_pass", "//tensorflow/compiler/xla/service:shape_inference", ], @@ -510,9 +514,11 @@ cc_library( "//tensorflow/compiler/xla/service:buffer_assignment", "//tensorflow/compiler/xla/service:buffer_liveness", "//tensorflow/compiler/xla/service:call_inliner", + "//tensorflow/compiler/xla/service:conditional_simplifier", "//tensorflow/compiler/xla/service:dot_decomposer", "//tensorflow/compiler/xla/service:executable", "//tensorflow/compiler/xla/service:flatten_call_graph", + "//tensorflow/compiler/xla/service:gather_expander", "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/service:hlo_constant_folding", "//tensorflow/compiler/xla/service:hlo_cse", diff --git a/tensorflow/compiler/xla/service/gpu/fusion_merger.cc b/tensorflow/compiler/xla/service/gpu/fusion_merger.cc index c137fbc97e29e24edb3603c611a5c8f093bc62a6..3cd30b754c3242f00c704de1afab2282ed827b41 100644 --- a/tensorflow/compiler/xla/service/gpu/fusion_merger.cc +++ b/tensorflow/compiler/xla/service/gpu/fusion_merger.cc @@ -45,6 +45,7 @@ void MaybeResolveTupleElements(HloInstruction* instruction, // Returns the bytes read by fusion parameter 'param', by returning the byte // size of 'param' shape (or the cumulative byte sizes of all leaf tuple // elements if 'param' is tuple-shaped). +// // In the special case where all users of 'param' (or all users of a leaf // tuple element if 'param' is tuple-shaped) are Slice instructions, the size // of each slice instruction is accumulated instead, to give a more accurate @@ -63,11 +64,10 @@ double CalculateBytesReadByFusionParameter(HloInstruction* param) { // Slice for a more accurate estimate of bytes read. double bytes = 0.0; for (auto& instruction : instructions) { - if (std::all_of(instruction->users().begin(), instruction->users().end(), - [](const HloInstruction* instruction) { - return instruction->opcode() == HloOpcode::kSlice || - instruction->opcode() == HloOpcode::kDynamicSlice; - })) { + if (c_all_of(instruction->users(), [](const HloInstruction* instruction) { + return instruction->opcode() == HloOpcode::kSlice || + instruction->opcode() == HloOpcode::kDynamicSlice; + })) { // All users are slice: accumulate bytes of all user slice instructions. for (auto& user : instruction->users()) { bytes += ShapeUtil::ByteSizeOf(user->shape()); @@ -199,6 +199,7 @@ Status FusionInstructionMerger::HandleFusion(HloInstruction* fusion) { ++total_visited_; // Skip 'fusion' instruction if there are no users into which we can merge. if (fusion->users().empty()) { + VLOG(3) << "Not merging " << fusion->name() << ": Has no users."; ++num_fail_no_users_; return Status::OK(); } @@ -208,24 +209,27 @@ Status FusionInstructionMerger::HandleFusion(HloInstruction* fusion) { // Input fusion instructions need to be rooted at a particular HLO (e.g. // kReduce), so they shouldn't be further fused either. if (fusion->fusion_kind() != HloInstruction::FusionKind::kLoop) { + VLOG(3) << "Not merging " << fusion->name() << ": Is not loop fusion."; ++num_fail_not_loop_fusion_; return Status::OK(); } // Skip multiple output fusion. It's not yet supported. if (fusion->IsMultiOutputFusion()) { + VLOG(3) << "Not merging " << fusion->name() << ": Is multi-output fusion."; ++num_fail_not_loop_fusion_; return Status::OK(); } // Skip 'fusion' instruction if we cannot merge into all of its users. // Merging into all users enables the removal of 'fusion' from the // computation. - if (!std::all_of(fusion->users().begin(), fusion->users().end(), - [](const HloInstruction* instruction) { - return instruction->opcode() == HloOpcode::kFusion && - instruction->fusion_kind() == - HloInstruction::FusionKind::kLoop; - })) { + if (!c_all_of(fusion->users(), [](const HloInstruction* user) { + return user->opcode() == HloOpcode::kFusion && + (user->fusion_kind() == HloInstruction::FusionKind::kLoop || + user->fusion_kind() == HloInstruction::FusionKind::kInput); + })) { + VLOG(3) << "Not merging " << fusion->name() + << ": Some of its users are not loop/input fusion kernels."; ++num_fail_merge_all_users_; return Status::OK(); } @@ -233,18 +237,17 @@ Status FusionInstructionMerger::HandleFusion(HloInstruction* fusion) { // Skip 'fusion' instruction if any of its fused instructions are expensive. // This is done to avoid the duplication of expensive instructions, which // would occur if 'fusion' were merged into multiple users. + // // If 'fusion' has just one user, then an earlier fusion pass chose not to // fuse this producer/comsumer pair (likely because of expensive instruction // re-use by the consumer), and so we honor that choice here as well. - if (!std::all_of(fusion->fused_instructions().begin(), - fusion->fused_instructions().end(), - [](const HloInstruction* instruction) { - if (instruction->opcode() != HloOpcode::kParameter && - GpuInstructionFusion::IsExpensive(*instruction)) { - return false; - } - return true; - })) { + if (c_any_of(fusion->fused_instructions(), + [](const HloInstruction* instruction) { + return instruction->opcode() != HloOpcode::kParameter && + GpuInstructionFusion::IsExpensive(*instruction); + })) { + VLOG(3) << "Not merging " << fusion->name() + << ": Contains one or more expensive instructions."; ++num_fail_expensive_fused_instruction_; return Status::OK(); } @@ -253,6 +256,8 @@ Status FusionInstructionMerger::HandleFusion(HloInstruction* fusion) { // exceeds the threshold value. if (CalculateFlopsToBytesRatio(fusion) > FusionMerger::GetThresholdFlopsToBytesRatio()) { + VLOG(3) << "Not merging " << fusion->name() + << ": flops-to-bytes ratio is not favorable."; ++num_fail_flops_to_byte_ratio_; return Status::OK(); } @@ -265,6 +270,9 @@ Status FusionInstructionMerger::HandleFusion(HloInstruction* fusion) { const double merged_to_current_bytes_ratio = merged_bytes_transferred / std::max(1.0, current_bytes_transferred); if (merged_to_current_bytes_ratio > 1.10) { + VLOG(3) << "Not merging " << fusion->name() + << ": merged-to-current-bytes-ratio of " + << merged_to_current_bytes_ratio << " is not favorable."; ++num_fail_net_bytes_transferred_ratio_; return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/gpu/fusion_merger_test.cc b/tensorflow/compiler/xla/service/gpu/fusion_merger_test.cc index deef5966b80d1b7f16e9982eed9ac5c7131e9d73..2217776c7d5a5f92c520d56222988f80401be9e4 100644 --- a/tensorflow/compiler/xla/service/gpu/fusion_merger_test.cc +++ b/tensorflow/compiler/xla/service/gpu/fusion_merger_test.cc @@ -16,257 +16,21 @@ limitations under the License. #include "tensorflow/compiler/xla/service/gpu/fusion_merger.h" #include "tensorflow/compiler/xla/service/gpu/instruction_fusion.h" +#include "tensorflow/compiler/xla/service/hlo_matchers.h" #include "tensorflow/compiler/xla/test_helpers.h" #include "tensorflow/compiler/xla/tests/hlo_test_base.h" +#include "tensorflow/compiler/xla/tools/parser/hlo_parser.h" namespace xla { namespace gpu { namespace { -class FusionMergerTest : public HloTestBase { - protected: - FusionMergerTest() : module_(CreateNewModule()) {} - - // Builds the following computation: - // - // Param - // / | \ - // / | \ - // OnesVec GTE(0) GTE(1) GTE(2) - // \ / \ / - // Add Add OnesVec - // \ / \ / - // \ Add Mul OnesVec - // \ | | / - // \ Mul Add - // \ | / - // \ | / - // Tuple - // - HloComputation* BuildComputation0() { - auto builder = HloComputation::Builder(TestName() + ".Computation0"); - // Create param instruction to access computation state. - auto param = builder.AddInstruction( - HloInstruction::CreateParameter(0, tuple_shape3_, "param")); - - // Create GetTupleElement instructions for each tuple element. - auto gte0 = builder.AddInstruction( - HloInstruction::CreateGetTupleElement(data_shape_, param, 0)); - auto gte1 = builder.AddInstruction( - HloInstruction::CreateGetTupleElement(data_shape_, param, 1)); - auto gte2 = builder.AddInstruction( - HloInstruction::CreateGetTupleElement(data_shape_, param, 2)); - - // Create const vector of ones to be used in element-wise computations. - auto one_vec = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR1({1.f, 1.f, 1.f, 1.f}))); - - // Create simple fusable computation for tuple element 0 (wont get merged). - auto out0 = builder.AddInstruction(HloInstruction::CreateBinary( - data_shape_, HloOpcode::kAdd, one_vec, gte0)); - - // Create fusable computation which is dependent on second and third tuple - // elements (will initially be fused on its own). - auto add1 = builder.AddInstruction( - HloInstruction::CreateBinary(data_shape_, HloOpcode::kAdd, gte1, gte2)); - - // Create two sub-computations, both of which are users of 'add1'. - - // First sub-computation: out1 = Mul(Add(add1, one_vec), one_vec) - auto add2 = builder.AddInstruction(HloInstruction::CreateBinary( - data_shape_, HloOpcode::kAdd, add1, one_vec)); - auto out1 = builder.AddInstruction(HloInstruction::CreateBinary( - data_shape_, HloOpcode::kMultiply, add2, one_vec)); - - // Second sub-computation: out2 = Add(Mul(add1, one_vec), one_vec) - auto mul0 = builder.AddInstruction(HloInstruction::CreateBinary( - data_shape_, HloOpcode::kMultiply, add1, one_vec)); - auto out2 = builder.AddInstruction(HloInstruction::CreateBinary( - data_shape_, HloOpcode::kAdd, mul0, one_vec)); - - // Create output Tuple. - builder.AddInstruction(HloInstruction::CreateTuple({out0, out1, out2})); - return module_->AddEntryComputation(builder.Build()); - } - - // Builds the following computation: - // - // Param - // / \ - // GTE(0) GTE(1) - // | | \ / - // | | Mul - // \ \ | - // \ Mul - // \ | - // OnesVec Mul OnesVec - // \ / \ / - // OnesVec Add Mul OnesVec - // \ | | / - // Mul Add - // \ / - // \ / - // Tuple - // - HloComputation* BuildComputation1() { - auto builder = HloComputation::Builder(TestName() + ".Computation1"); - Shape tuple_shape2_ = ShapeUtil::MakeTupleShape({data_shape_, data_shape_}); - // Create param instruction to access computation state. - auto state = builder.AddInstruction( - HloInstruction::CreateParameter(0, tuple_shape2_, "state")); - - // Create shared sub-computation (will initially be fused on its own). - auto gte0 = builder.AddInstruction( - HloInstruction::CreateGetTupleElement(data_shape_, state, 0)); - auto gte1 = builder.AddInstruction( - HloInstruction::CreateGetTupleElement(data_shape_, state, 2)); - // Calculate the flops we need to generate for this shared computation - // to exceed the threshold flops_to_bytes_ratio. - // Note that bytes transferred is multiplied by 3 because there are two - // operands and one output of size 'data_shape_'. - const int64 flops_needed = FusionMerger::GetThresholdFlopsToBytesRatio() * - ShapeUtil::ByteSizeOf(data_shape_) * 3; - const int64 vec_elements = ShapeUtil::ElementsIn(data_shape_); - const int64 iters = (flops_needed + vec_elements - 1) / vec_elements; - - auto mul0 = builder.AddInstruction(HloInstruction::CreateBinary( - data_shape_, HloOpcode::kMultiply, gte0, gte1)); - for (int i = 0; i < iters; ++i) { - mul0 = builder.AddInstruction(HloInstruction::CreateBinary( - data_shape_, HloOpcode::kMultiply, gte0, mul0)); - } - - // Create two sub-computations, both of which are users of 'mul0'. - auto one_vec = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR1({1.f, 1.f, 1.f, 1.f}))); - - // First sub-computation: out0 = Mul(Add(mul0, one_vec), one_vec) - auto add0 = builder.AddInstruction(HloInstruction::CreateBinary( - data_shape_, HloOpcode::kAdd, mul0, one_vec)); - auto out0 = builder.AddInstruction(HloInstruction::CreateBinary( - data_shape_, HloOpcode::kMultiply, add0, one_vec)); - - // Second sub-computation: out1 = Add(Mul(mul0, one_vec), one_vec) - auto mul1 = builder.AddInstruction(HloInstruction::CreateBinary( - data_shape_, HloOpcode::kMultiply, mul0, one_vec)); - auto out1 = builder.AddInstruction(HloInstruction::CreateBinary( - data_shape_, HloOpcode::kAdd, mul1, one_vec)); - - // Create output Tuple. - builder.AddInstruction(HloInstruction::CreateTuple({out0, out1})); - return module_->AddEntryComputation(builder.Build()); - } - - // Builds the following computation: - // - // Param - // / | | \ - // / | | \ - // / | | \ - // GTE(0) GTE(1) GTE(2) GTE(3) - // \ / / / - // Add / / - // \ / / - // Add / - // \ / - // \ / - // OnesVec Add OnesVec - // \ / \ / - // OnesVec Add Mul OnesVec - // \ | | / - // Mul Add - // \ / - // \ / - // Tuple - // - HloComputation* BuildComputation2(bool add_extra_input) { - auto builder = HloComputation::Builder(TestName() + ".Computation2"); - Shape state_shape = add_extra_input ? tuple_shape4_ : tuple_shape3_; - // Create param instruction to access computation state. - auto state = builder.AddInstruction( - HloInstruction::CreateParameter(0, state_shape, "state")); - - // Create GetTupleElement instructions for each tuple element. - auto gte0 = builder.AddInstruction( - HloInstruction::CreateGetTupleElement(data_shape_, state, 0)); - auto gte1 = builder.AddInstruction( - HloInstruction::CreateGetTupleElement(data_shape_, state, 1)); - auto gte2 = builder.AddInstruction( - HloInstruction::CreateGetTupleElement(data_shape_, state, 2)); - - // Create shared fusable computation that reduces its operands. - auto reduce0 = builder.AddInstruction( - HloInstruction::CreateBinary(data_shape_, HloOpcode::kAdd, gte0, gte1)); - auto reduce_out = builder.AddInstruction(HloInstruction::CreateBinary( - data_shape_, HloOpcode::kAdd, reduce0, gte2)); - if (add_extra_input) { - auto gte3 = builder.AddInstruction( - HloInstruction::CreateGetTupleElement(data_shape_, state, 3)); - reduce_out = builder.AddInstruction(HloInstruction::CreateBinary( - data_shape_, HloOpcode::kAdd, reduce_out, gte3)); - } +namespace op = xla::testing::opcode_matchers; - // Create two fusable sub-computations which are dependent on shared - // computation 'reduce_out'. - auto one_vec = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR1({1.f, 1.f, 1.f, 1.f}))); - - // First sub-computation: out0 = Mul(Add(reduce_out, one_vec), one_vec) - auto add2 = builder.AddInstruction(HloInstruction::CreateBinary( - data_shape_, HloOpcode::kAdd, reduce_out, one_vec)); - auto out0 = builder.AddInstruction(HloInstruction::CreateBinary( - data_shape_, HloOpcode::kMultiply, add2, one_vec)); - - // Second sub-computation: out1 = Add(Mul(reduce_out, one_vec), one_vec) - auto mul0 = builder.AddInstruction(HloInstruction::CreateBinary( - data_shape_, HloOpcode::kMultiply, reduce_out, one_vec)); - auto out1 = builder.AddInstruction(HloInstruction::CreateBinary( - data_shape_, HloOpcode::kAdd, mul0, one_vec)); - - // Create output Tuple. - builder.AddInstruction(HloInstruction::CreateTuple({out0, out1})); - return module_->AddEntryComputation(builder.Build()); - } - - Shape data_shape_ = ShapeUtil::MakeShape(F32, {4}); - Shape tuple_shape2_ = ShapeUtil::MakeTupleShape({data_shape_, data_shape_}); - Shape tuple_shape3_ = - ShapeUtil::MakeTupleShape({data_shape_, data_shape_, data_shape_}); - Shape tuple_shape4_ = ShapeUtil::MakeTupleShape( - {data_shape_, data_shape_, data_shape_, data_shape_}); - - std::unique_ptr module_; -}; +class FusionMergerTest : public HloTestBase {}; // Tests that we can merge a fusion instruction that is below threshold. // -// Original computation: -// -// Param -// / | \ -// / | \ -// OnesVec GTE(0) GTE(1) GTE(2) -// \ / \ / -// Add Add OnesVec -// \ / \ / -// \ Add Mul OnesVec -// \ | | / -// \ Mul Add -// \ | / -// \ | / -// Tuple -// -// Computation after fusion passes: -// -// Param -// / \ -// Fusion3 Fusion2 -// | / \ -// \ Fusion0 Fusion1 -// \ | / -// \ | / -// Tuple -// // Computation after fusion merger pass (Fusion2 is merged into Fusion0 and // Fusion1): // Param @@ -276,19 +40,50 @@ class FusionMergerTest : public HloTestBase { // Tuple // TEST_F(FusionMergerTest, MergeSharedFusionInstruction) { - auto computation = BuildComputation0(); - // Run standard fusion passes. - EXPECT_TRUE(GpuInstructionFusion(/*may_duplicate=*/false) - .Run(module_.get()) - .ValueOrDie()); - EXPECT_FALSE(GpuInstructionFusion(/*may_duplicate=*/true) - .Run(module_.get()) - .ValueOrDie()); - // Run fusion merger pass, which should merge the shared fusion instruction - // into its two users. - EXPECT_TRUE(FusionMerger().Run(module_.get()).ValueOrDie()); - - auto* root = computation->root_instruction(); + auto module = tools::Parse(R"( +HloModule MergeSharedFusionInstruction + +comp.3 { + constant.param_0 = f32[4]{0} parameter(0) + param.param_1.2 = (f32[4]{0}, f32[4]{0}, f32[4]{0}) parameter(1) + get-tuple-element.6 = f32[4]{0} get-tuple-element(param.param_1.2), index=0 + ROOT add.7 = f32[4]{0} add(constant.param_0, get-tuple-element.6) +} + +comp.2 { + param.param_1.1 = (f32[4]{0}, f32[4]{0}, f32[4]{0}) parameter(0) + get-tuple-element.4 = f32[4]{0} get-tuple-element(param.param_1.1), index=1 + get-tuple-element.5 = f32[4]{0} get-tuple-element(param.param_1.1), index=2 + ROOT add.6 = f32[4]{0} add(get-tuple-element.4, get-tuple-element.5) +} + +comp.1 { + add.1.param_1.1 = f32[4]{0} parameter(1) + constant.param_1.3 = f32[4]{0} parameter(0) + add.5 = f32[4]{0} add(add.1.param_1.1, constant.param_1.3) + ROOT multiply.3 = f32[4]{0} multiply(add.5, constant.param_1.3) +} + +comp { + add.1.param_1 = f32[4]{0} parameter(1) + constant.param_1.1 = f32[4]{0} parameter(0) + multiply.2 = f32[4]{0} multiply(add.1.param_1, constant.param_1.1) + ROOT add.4 = f32[4]{0} add(multiply.2, constant.param_1.1) +} + +ENTRY MergeSharedFusionInstruction.Computation0 { + constant = f32[4]{0} constant({1, 1, 1, 1}) + param = (f32[4]{0}, f32[4]{0}, f32[4]{0}) parameter(0) + fusion.3 = f32[4]{0} fusion(constant, param), kind=kLoop, calls=comp.3 + fusion.4 = f32[4]{0} fusion(param), kind=kLoop, calls=comp.2 + fusion.5 = f32[4]{0} fusion(constant, fusion.4), kind=kLoop, calls=comp.1 + fusion.6 = f32[4]{0} fusion(constant, fusion.4), kind=kLoop, calls=comp + ROOT tuple = (f32[4]{0}, f32[4]{0}, f32[4]{0}) tuple(fusion.3, fusion.5, fusion.6) +})") + .ValueOrDie(); + EXPECT_TRUE(FusionMerger().Run(module.get()).ValueOrDie()); + + auto* root = module->entry_computation()->root_instruction(); EXPECT_EQ(HloOpcode::kTuple, root->opcode()); // Check operand 0 (not merged). Should have 4 instructions. auto* operand0 = root->operand(0); @@ -307,156 +102,188 @@ TEST_F(FusionMergerTest, MergeSharedFusionInstruction) { // Tests that we do not merge a fusion instruction that above flops to bytes // threshold. // -// Original computation: -// -// Param -// / \ -// GTE(0) GTE(1) -// | | \ / -// | | Mul -// \ \ | -// \ Mul -// \ | -// OnesVec Mul OnesVec -// \ / \ / -// OnesVec Add Mul OnesVec -// \ | | / -// Mul Add -// \ / -// \ / -// Tuple -// -// Computation after fusion passes and fusion merger pass (Fusion2 is not -// merged because it exceeds the threshold flops to bytes ratio). -// -// Param -// | -// Fusion2 -// / \ -// Fusion0 Fusion1 -// \ / -// Tuple -// +// Fusion2 is not merged because it exceeds the threshold flops-to-bytes ratio. TEST_F(FusionMergerTest, FlopsToBytesRatioThresholdExceeded) { - BuildComputation1(); - // Run standard fusion passes. - EXPECT_TRUE(GpuInstructionFusion(/*may_duplicate=*/false) - .Run(module_.get()) - .ValueOrDie()); - EXPECT_FALSE(GpuInstructionFusion(/*may_duplicate=*/true) - .Run(module_.get()) - .ValueOrDie()); + auto module = tools::Parse(R"( +HloModule FlopsToBytesRatioThresholdExceeded + +comp.2 { + state.param_1.1 = (f32[4]{0}, f32[4]{0}) parameter(0) + get-tuple-element.3 = f32[4]{0} get-tuple-element(state.param_1.1), index=0 + get-tuple-element.4 = f32[4]{0} get-tuple-element(state.param_1.1), index=2 + multiply.29 = f32[4]{0} multiply(get-tuple-element.3, get-tuple-element.4) + multiply.30 = f32[4]{0} multiply(get-tuple-element.3, multiply.29) + multiply.31 = f32[4]{0} multiply(get-tuple-element.3, multiply.30) + multiply.32 = f32[4]{0} multiply(get-tuple-element.3, multiply.31) + multiply.33 = f32[4]{0} multiply(get-tuple-element.3, multiply.32) + multiply.34 = f32[4]{0} multiply(get-tuple-element.3, multiply.33) + multiply.35 = f32[4]{0} multiply(get-tuple-element.3, multiply.34) + multiply.36 = f32[4]{0} multiply(get-tuple-element.3, multiply.35) + multiply.37 = f32[4]{0} multiply(get-tuple-element.3, multiply.36) + multiply.38 = f32[4]{0} multiply(get-tuple-element.3, multiply.37) + multiply.39 = f32[4]{0} multiply(get-tuple-element.3, multiply.38) + multiply.40 = f32[4]{0} multiply(get-tuple-element.3, multiply.39) + ROOT multiply.41 = f32[4]{0} multiply(get-tuple-element.3, multiply.40) +} + +comp.1 { + multiply.12.param_1.1 = f32[4]{0} parameter(1) + constant.param_1.3 = f32[4]{0} parameter(0) + add.3 = f32[4]{0} add(multiply.12.param_1.1, constant.param_1.3) + ROOT multiply.16 = f32[4]{0} multiply(add.3, constant.param_1.3) +} + +comp { + multiply.12.param_1 = f32[4]{0} parameter(1) + constant.param_1.1 = f32[4]{0} parameter(0) + multiply.15 = f32[4]{0} multiply(multiply.12.param_1, constant.param_1.1) + ROOT add.2 = f32[4]{0} add(multiply.15, constant.param_1.1) +} + +ENTRY FlopsToBytesRatioThresholdExceeded.Computation1 { + constant = f32[4]{0} constant({1, 1, 1, 1}) + state = (f32[4]{0}, f32[4]{0}) parameter(0) + fusion.2 = f32[4]{0} fusion(state), kind=kLoop, calls=comp.2 + fusion.3 = f32[4]{0} fusion(constant, fusion.2), kind=kLoop, calls=comp.1 + fusion.4 = f32[4]{0} fusion(constant, fusion.2), kind=kLoop, calls=comp + ROOT tuple = (f32[4]{0}, f32[4]{0}) tuple(fusion.3, fusion.4) +})") + .ValueOrDie(); // Run fusion merger pass, which should detect that the flops/bytes of the // shared fusion instruction exceeds the threshold ratio, and therefore // cannot be merged with other fusion instructions. - EXPECT_FALSE(FusionMerger().Run(module_.get()).ValueOrDie()); + EXPECT_FALSE(FusionMerger().Run(module.get()).ValueOrDie()); } // Tests that threshold for bytes transferred if merged is exceeded. // -// Original computation: -// -// Param -// / | | \ -// / | | \ -// / | | \ -// GTE(0) GTE(1) GTE(2) GTE(3) -// \ / / / -// Add / / -// \ / / -// Add / -// \ / -// \ / -// OnesVec Add OnesVec -// \ / \ / -// OnesVec Add Mul OnesVec -// \ | | / -// Mul Add -// \ / -// \ / -// Tuple -// -// Computation after fusion passes and fusion merger pass. Fusion2 is not -// merged because it exceeds the threshold bytes transferred. This is because -// the bytes read by Fusion2 (when replicated if the instruction is merged -// into Fusion0 and Fusion1) would exceed the bytes transferred threshold. -// -// Param -// | -// Fusion2 -// / \ -// Fusion0 Fusion1 -// \ / -// Tuple -// +// Fusion2 is not merged because it exceeds the threshold bytes transferred. +// This is because the bytes read by Fusion2 (when replicated if the instruction +// is merged into Fusion0 and Fusion1) would exceed the bytes transferred +// threshold. TEST_F(FusionMergerTest, BytesTransferredThresholdExeceeded) { - BuildComputation2(/*add_extra_input=*/true); - // Run standard fusion passes. - EXPECT_TRUE(GpuInstructionFusion(/*may_duplicate=*/false) - .Run(module_.get()) - .ValueOrDie()); - EXPECT_FALSE(GpuInstructionFusion(/*may_duplicate=*/true) - .Run(module_.get()) - .ValueOrDie()); + auto module = tools::Parse(R"( +HloModule BytesTransferredThresholdExeceeded + +comp.2 { + state.param_1.1 = (f32[4]{0}, f32[4]{0}, f32[4]{0}, f32[4]{0}) parameter(0) + get-tuple-element.7 = f32[4]{0} get-tuple-element(state.param_1.1), index=0 + get-tuple-element.8 = f32[4]{0} get-tuple-element(state.param_1.1), index=1 + add.9 = f32[4]{0} add(get-tuple-element.7, get-tuple-element.8) + get-tuple-element.9 = f32[4]{0} get-tuple-element(state.param_1.1), index=2 + add.10 = f32[4]{0} add(add.9, get-tuple-element.9) + get-tuple-element.10 = f32[4]{0} get-tuple-element(state.param_1.1), index=3 + ROOT add.11 = f32[4]{0} add(add.10, get-tuple-element.10) +} + +comp.1 { + add.2.param_1.1 = f32[4]{0} parameter(1) + constant.param_1.3 = f32[4]{0} parameter(0) + add.6 = f32[4]{0} add(add.2.param_1.1, constant.param_1.3) + ROOT multiply.3 = f32[4]{0} multiply(add.6, constant.param_1.3) +} + +comp { + add.2.param_1 = f32[4]{0} parameter(1) + constant.param_1.1 = f32[4]{0} parameter(0) + multiply.2 = f32[4]{0} multiply(add.2.param_1, constant.param_1.1) + ROOT add.5 = f32[4]{0} add(multiply.2, constant.param_1.1) +} + +ENTRY BytesTransferredThresholdExeceeded.Computation2 { + constant = f32[4]{0} constant({1, 1, 1, 1}) + state = (f32[4]{0}, f32[4]{0}, f32[4]{0}, f32[4]{0}) parameter(0) + fusion.2 = f32[4]{0} fusion(state), kind=kLoop, calls=comp.2 + fusion.3 = f32[4]{0} fusion(constant, fusion.2), kind=kLoop, calls=comp.1 + fusion.4 = f32[4]{0} fusion(constant, fusion.2), kind=kLoop, calls=comp + ROOT tuple = (f32[4]{0}, f32[4]{0}) tuple(fusion.3, fusion.4) +})") + .ValueOrDie(); // Run fusion merger pass, which should detect that the net bytes transferred // (if merged) would increase. - EXPECT_FALSE(FusionMerger().Run(module_.get()).ValueOrDie()); + EXPECT_FALSE(FusionMerger().Run(module.get()).ValueOrDie()); } // Tests that threshold for bytes transferred if merged is not exceeded. // -// Original computation: -// -// Param -// / | \ -// / | \ -// / | \ -// GTE(0) GTE(1) GTE(2) -// \ / / -// Add / -// \ / -// OnesVec Add OnesVec -// \ / \ / -// OnesVec Add Mul OnesVec -// \ / \ / -// Mul Add -// \ / -// \ / -// Tuple -// -// Computation after fusion passes: -// -// Param -// | -// Fusion2 -// / \ -// Fusion0 Fusion1 -// \ / -// Tuple -// -// Computation after fusion merger pass (Fusion2 is merged into Fusion0 and -// Fusion1, because bytes read from Param by Fusion2 is reduced for this test -// which makes the merge operation into its operand below the bytes -// transferred threshold. -// -// Param -// / \ -// Fusion0 Fusion1 -// \ / -// Tuple -// +// Fusion2 is merged into Fusion0 and Fusion1, because bytes read from Param by +// Fusion2 is reduced for this test which makes the merge operation into its +// operand below the bytes transferred threshold. TEST_F(FusionMergerTest, BytesTransferredThresholdNotExeceeded) { - BuildComputation2(/*add_extra_input=*/false); - // Run standard fusion passes. - EXPECT_TRUE(GpuInstructionFusion(/*may_duplicate=*/false) - .Run(module_.get()) - .ValueOrDie()); - EXPECT_FALSE(GpuInstructionFusion(/*may_duplicate=*/true) - .Run(module_.get()) - .ValueOrDie()); + auto module = tools::Parse(R"( +HloModule BytesTransferredThresholdNotExeceeded + +comp.2 { + state.param_1.1 = (f32[4]{0}, f32[4]{0}, f32[4]{0}) parameter(0) + get-tuple-element.5 = f32[4]{0} get-tuple-element(state.param_1.1), index=0 + get-tuple-element.6 = f32[4]{0} get-tuple-element(state.param_1.1), index=1 + add.7 = f32[4]{0} add(get-tuple-element.5, get-tuple-element.6) + get-tuple-element.7 = f32[4]{0} get-tuple-element(state.param_1.1), index=2 + ROOT add.8 = f32[4]{0} add(add.7, get-tuple-element.7) +} + +comp.1 { + add.1.param_1.1 = f32[4]{0} parameter(1) + constant.param_1.3 = f32[4]{0} parameter(0) + add.5 = f32[4]{0} add(add.1.param_1.1, constant.param_1.3) + ROOT multiply.3 = f32[4]{0} multiply(add.5, constant.param_1.3) +} + +comp { + add.1.param_1 = f32[4]{0} parameter(1) + constant.param_1.1 = f32[4]{0} parameter(0) + multiply.2 = f32[4]{0} multiply(add.1.param_1, constant.param_1.1) + ROOT add.4 = f32[4]{0} add(multiply.2, constant.param_1.1) +} + +ENTRY BytesTransferredThresholdNotExeceeded.Computation2 { + constant = f32[4]{0} constant({1, 1, 1, 1}) + state = (f32[4]{0}, f32[4]{0}, f32[4]{0}) parameter(0) + fusion.2 = f32[4]{0} fusion(state), kind=kLoop, calls=comp.2 + fusion.3 = f32[4]{0} fusion(constant, fusion.2), kind=kLoop, calls=comp.1 + fusion.4 = f32[4]{0} fusion(constant, fusion.2), kind=kLoop, calls=comp + ROOT tuple = (f32[4]{0}, f32[4]{0}) tuple(fusion.3, fusion.4) +})") + .ValueOrDie(); // Run fusion merger pass, which should detect that the net bytes transferred // (if merged) would not increase. - EXPECT_TRUE(FusionMerger().Run(module_.get()).ValueOrDie()); + EXPECT_TRUE(FusionMerger().Run(module.get()).ValueOrDie()); +} + +// Check that we're willing to merge f1_computation into f2_computation, even +// though f2 is an input fusion node. +TEST_F(FusionMergerTest, WillMergeIntoInputFusion) { + auto module = tools::Parse(R"( + HloModule m + + f1_computation { + f1_p0 = f32[10]{0} parameter(0) + ROOT f1_root = f32[10]{0} add(f1_p0, f1_p0) + } + + add_computation { + add_lhs = f32[] parameter(0) + add_rhs = f32[] parameter(1) + ROOT add_root = f32[] add(add_lhs, add_rhs) + } + + f2_computation { + f2_p0 = f32[10]{0} parameter(0) + f2_mul = f32[10]{0} multiply(f2_p0, f2_p0) + f2_zero = f32[] constant(0) + ROOT f2_root = f32[] reduce(f2_mul, f2_zero), dimensions={0}, + to_apply=add_computation + } + + ENTRY entry { + p0 = f32[10]{0} parameter(0) + f1 = f32[10]{0} fusion(p0), kind=kLoop, calls=f1_computation + ROOT f2 = f32[] fusion(f1), kind=kInput, calls=f2_computation + })") + .ValueOrDie(); + EXPECT_TRUE(FusionMerger().Run(module.get()).ValueOrDie()); + EXPECT_THAT(module->entry_computation()->root_instruction(), + op::Fusion(op::Parameter())); } } // namespace diff --git a/tensorflow/compiler/xla/service/gpu/gemm_thunk.cc b/tensorflow/compiler/xla/service/gpu/gemm_thunk.cc index ba482793e7632f0f423cc9da0dd9620bdf29c642..38668ff455a44c7ef99b57b750f1a3b18a90bd2c 100644 --- a/tensorflow/compiler/xla/service/gpu/gemm_thunk.cc +++ b/tensorflow/compiler/xla/service/gpu/gemm_thunk.cc @@ -49,7 +49,7 @@ struct MatrixDescriptor { // rhs_matrix, and stores the result to output_matrix. template bool DoGemm(MatrixDescriptor lhs_matrix, MatrixDescriptor rhs_matrix, - MatrixDescriptor output_matrix, se::Stream* stream) { + MatrixDescriptor output_matrix, double alpha, se::Stream* stream) { DCHECK(!output_matrix.transpose); se::DeviceMemory lhs_data(lhs_matrix.data); @@ -65,7 +65,7 @@ bool DoGemm(MatrixDescriptor lhs_matrix, MatrixDescriptor rhs_matrix, return stream ->ThenBlasGemm( lhs_transpose, rhs_transpose, output_matrix.num_rows, - output_matrix.num_cols, /*size of reduce dim=*/k, /*alpha=*/1.0, + output_matrix.num_cols, /*size of reduce dim=*/k, /*alpha=*/alpha, lhs_data, /*leading dim of LHS=*/lhs_matrix.num_rows, rhs_data, /*leading dim of RHS=*/rhs_matrix.num_rows, /*beta=*/0.0, &output_data, /*leading dim of output=*/output_matrix.num_rows) @@ -89,7 +89,7 @@ bool DoGemm(MatrixDescriptor lhs_matrix, MatrixDescriptor rhs_matrix, template bool DoGemmWithAlgorithm(MatrixDescriptor lhs_matrix, MatrixDescriptor rhs_matrix, - MatrixDescriptor output_matrix, + MatrixDescriptor output_matrix, double alpha, se::blas::ComputationType computation_type, se::blas::AlgorithmType algorithm, se::Stream* stream, se::blas::ProfileResult* output_profile_result) { @@ -108,11 +108,13 @@ bool DoGemmWithAlgorithm(MatrixDescriptor lhs_matrix, return stream ->ThenBlasGemmWithAlgorithm( lhs_transpose, rhs_transpose, output_matrix.num_rows, - output_matrix.num_cols, /*size of reduce dim=*/k, /*alpha=*/1.0, - lhs_data, /*leading dim of LHS=*/lhs_matrix.num_rows, rhs_data, - /*leading dim of RHS=*/rhs_matrix.num_rows, /*beta=*/0.0, - &output_data, /*leading dim of output=*/output_matrix.num_rows, - computation_type, algorithm, output_profile_result) + output_matrix.num_cols, /*size of reduce dim=*/k, + /*alpha=*/static_cast(alpha), lhs_data, + /*leading dim of LHS=*/lhs_matrix.num_rows, rhs_data, + /*leading dim of RHS=*/rhs_matrix.num_rows, + /*beta=*/static_cast(0.0f), &output_data, + /*leading dim of output=*/output_matrix.num_rows, computation_type, + algorithm, output_profile_result) .ok(); } @@ -125,8 +127,8 @@ bool DoGemmWithAlgorithm(MatrixDescriptor lhs_matrix, template StatusOr DoGemmAutotune( MatrixDescriptor lhs_matrix, MatrixDescriptor rhs_matrix, - MatrixDescriptor output_matrix, se::blas::ComputationType computation_type, - se::Stream* stream) { + MatrixDescriptor output_matrix, double alpha, + se::blas::ComputationType computation_type, se::Stream* stream) { std::vector algorithms; CHECK(stream->parent()->GetBlasGemmAlgorithms(&algorithms)); @@ -138,8 +140,8 @@ StatusOr DoGemmAutotune( // non-null ProfileResult, DoGemmWithAlgorithm should always return true, // and the actual success-ness is returned in ProfileResult::is_valid. CHECK(DoGemmWithAlgorithm(lhs_matrix, rhs_matrix, output_matrix, - computation_type, algorithm, stream, - &profile_result)); + alpha, computation_type, algorithm, + stream, &profile_result)); if (profile_result.is_valid() && profile_result.elapsed_time_in_ms() < best_result.elapsed_time_in_ms()) { @@ -161,6 +163,8 @@ StatusOr DoGemmAutotune( // DoGemm/DoGemmWithAlgorithm/DoGemmAutotune. auto GetGemmFn(PrimitiveType type) -> decltype(&DoGemm) { switch (type) { + case F16: + return &DoGemm; case F32: return &DoGemm; case F64: @@ -172,6 +176,8 @@ auto GetGemmFn(PrimitiveType type) -> decltype(&DoGemm) { auto GetGemmWithAlgorithmFn(PrimitiveType type) -> decltype(&DoGemmWithAlgorithm) { switch (type) { + case F16: + return &DoGemmWithAlgorithm; case F32: return &DoGemmWithAlgorithm; case F64: @@ -182,6 +188,8 @@ auto GetGemmWithAlgorithmFn(PrimitiveType type) } auto GetGemmAutotuneFn(PrimitiveType type) -> decltype(&DoGemmAutotune) { switch (type) { + case F16: + return &DoGemmAutotune; case F32: return &DoGemmAutotune; case F64: @@ -196,6 +204,10 @@ auto GetGemmAutotuneFn(PrimitiveType type) -> decltype(&DoGemmAutotune) { // separately from the precision of the inputs and result. se::blas::ComputationType GetBlasComputationType(PrimitiveType type) { switch (type) { + case F16: + // Use F32 as computation type for F16 as we currently only implement the + // cuDNN pseudo half configuration for half precision. + return se::blas::ComputationType::kF32; case F32: return se::blas::ComputationType::kF32; case F64: @@ -212,7 +224,8 @@ GemmThunk::GemmThunk(const BufferAllocation::Slice& lhs_buffer, const BufferAllocation::Slice& output_buffer, const Shape& lhs_shape, const Shape& rhs_shape, const Shape& output_shape, bool transpose_lhs, - bool transpose_rhs, const HloInstruction* hlo_instruction) + bool transpose_rhs, double alpha, + const HloInstruction* hlo_instruction) : Thunk(Kind::kGemm, hlo_instruction), lhs_buffer_(lhs_buffer), rhs_buffer_(rhs_buffer), @@ -221,7 +234,8 @@ GemmThunk::GemmThunk(const BufferAllocation::Slice& lhs_buffer, rhs_shape_(rhs_shape), output_shape_(output_shape), transpose_lhs_(transpose_lhs), - transpose_rhs_(transpose_rhs) {} + transpose_rhs_(transpose_rhs), + alpha_(alpha) {} tensorflow::Status GemmThunk::ExecuteOnStream( const BufferAllocations& buffer_allocations, se::Stream* stream) { @@ -290,7 +304,7 @@ tensorflow::Status GemmThunk::ExecuteOnStream( if (autotune_it == autotune_results_.end()) { StatusOr best_algorithm = GetGemmAutotuneFn(element_type)(lhs_matrix, rhs_matrix, output_matrix, - computation_type, stream); + alpha_, computation_type, stream); autotune_it = autotune_results_.insert({device_name, best_algorithm}).first; @@ -311,12 +325,15 @@ tensorflow::Status GemmThunk::ExecuteOnStream( VLOG(2) << "Using algorithm " << algorithm << " chosen by autotuning on GemmThunk " << this; return GetGemmWithAlgorithmFn(element_type)( - lhs_matrix, rhs_matrix, output_matrix, computation_type, algorithm, - stream, + lhs_matrix, rhs_matrix, output_matrix, alpha_, computation_type, + algorithm, stream, /*output_profile_result=*/nullptr); } + + // Autotune will fail when CUDA 8 and GPU sm_50 or older are used. + // Use the older Gemm API in this case. return GetGemmFn(element_type)(lhs_matrix, rhs_matrix, output_matrix, - stream); + alpha_, stream); }; bool launch_ok; diff --git a/tensorflow/compiler/xla/service/gpu/gemm_thunk.h b/tensorflow/compiler/xla/service/gpu/gemm_thunk.h index 8c6a1f51a8a09ef78950dfe7e89994a3fe247f49..df3edcefef898d465cd5ddc53e5d06a966a31f88 100644 --- a/tensorflow/compiler/xla/service/gpu/gemm_thunk.h +++ b/tensorflow/compiler/xla/service/gpu/gemm_thunk.h @@ -34,15 +34,16 @@ namespace gpu { // This is thread-compatible. class GemmThunk : public Thunk { public: - // Constructs a thunk that computes "output = lhs rhs" using BLAS gemm. - // transpose_lhs and transpose_rhs indicate whether gemm should transpose the - // lhs and rhs operand. hlo_instruction is as in Thunk. + // Constructs a thunk that computes "output = (lhs rhs) * alpha" using + // BLAS gemm. transpose_lhs and transpose_rhs indicate whether gemm should + // transpose the lhs and rhs operand. hlo_instruction is as in Thunk. alpha is + // a constant. GemmThunk(const BufferAllocation::Slice& lhs_buffer, const BufferAllocation::Slice& rhs_buffer, const BufferAllocation::Slice& output_buffer, const Shape& lhs_shape, const Shape& rhs_shape, const Shape& output_shape, bool transpose_lhs, bool transpose_rhs, - const HloInstruction* hlo_instruction); + double alpha, const HloInstruction* hlo_instruction); GemmThunk(const GemmThunk&) = delete; GemmThunk& operator=(const GemmThunk&) = delete; @@ -72,6 +73,7 @@ class GemmThunk : public Thunk { const bool transpose_lhs_; const bool transpose_rhs_; + const double alpha_; // Maps device names (StreamExecutor::DeviceDescription::name()) to autotune // results. The map's value is the best algorithm we've found for this thunk diff --git a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc index 28ebd034ee0c89137f4e6eb417d8a37f4a00af7a..c67b552abbdc971351f99ec89536af78479b87c1 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc +++ b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc @@ -33,8 +33,10 @@ limitations under the License. #include "tensorflow/compiler/xla/service/buffer_assignment.h" #include "tensorflow/compiler/xla/service/buffer_liveness.h" #include "tensorflow/compiler/xla/service/call_inliner.h" +#include "tensorflow/compiler/xla/service/conditional_simplifier.h" #include "tensorflow/compiler/xla/service/dot_decomposer.h" #include "tensorflow/compiler/xla/service/flatten_call_graph.h" +#include "tensorflow/compiler/xla/service/gather_expander.h" #include "tensorflow/compiler/xla/service/gpu/cudnn_batchnorm_rewriter.h" #include "tensorflow/compiler/xla/service/gpu/cudnn_convolution_algorithm_picker.h" #include "tensorflow/compiler/xla/service/gpu/cudnn_convolution_rewriter.h" @@ -164,6 +166,9 @@ tensorflow::Status OptimizeHloModule(HloModule* hlo_module, /*rewrite_grad_op=*/true, /*use_fusion=*/false); + // Rewrite gather ops into smaller ones. + pass.AddPass(); + // BatchNormExpander can create zero-sized ops, so zero-sized HLO // elimination has to come after that pass. pipeline.AddPass(); @@ -176,6 +181,7 @@ tensorflow::Status OptimizeHloModule(HloModule* hlo_module, pass.AddPass(); pass.AddPass(); pass.AddPass(); + pass.AddPass(); } pipeline.AddPass( @@ -241,6 +247,22 @@ tensorflow::Status OptimizeHloModule(HloModule* hlo_module, TF_RETURN_IF_ERROR(pipeline.Run(hlo_module).status()); } + { + HloPassPipeline pipeline("layout_assignment"); + pipeline.AddPass( + hlo_module->mutable_entry_computation_layout()); + + // The LayoutAssignment pass may leave behind kCopy instructions which are + // duplicate or NOPs, so remove them with algebraic simplification and CSE. + pipeline.AddPass>( + /*is_layout_sensitive=*/true, + /*valid_bitcast_callback=*/[](const Shape&, const Shape&) { + return true; + }); + pipeline.AddPass(/*is_layout_sensitive=*/true); + TF_RETURN_IF_ERROR(pipeline.Run(hlo_module).status()); + } + { HloPassFix fusion("fusion"); fusion.AddInvariantChecker(); @@ -277,15 +299,6 @@ tensorflow::Status PrepareHloModuleForIrEmitting(HloModule* hlo_module) { HloPassPipeline pipeline("GPU-ir-emit-prepare"); pipeline.AddInvariantChecker(); - pipeline.AddPass( - hlo_module->mutable_entry_computation_layout()); - - // The LayoutAssignment pass may leave behind kCopy instructions which are - // duplicate or NOPs, so remove them with algebraic simplification and CSE. - pipeline.AddPass>( - /*is_layout_sensitive=*/true, - [](const Shape&, const Shape&) { return true; }); - pipeline.AddPass(/*is_layout_sensitive=*/true); // Copy insertion should be performed immediately before IR emission to avoid // inserting unnecessary copies (later pass adds an instruction which // materializes the value) or missing a necessary copy (later pass removes an diff --git a/tensorflow/compiler/xla/service/gpu/gpu_executable.cc b/tensorflow/compiler/xla/service/gpu/gpu_executable.cc index 623d6714de501000e38b7698620925f66425f157..04b37d913e0bc8f8226057f107da05fd1e675010 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_executable.cc +++ b/tensorflow/compiler/xla/service/gpu/gpu_executable.cc @@ -46,12 +46,14 @@ namespace { class HloExecutionProfiler { public: // If profiling is enabled, start an execution timer running. - explicit HloExecutionProfiler(bool do_profile, HloExecutionProfile* profile, - se::Stream* stream, - const HloComputation* computation) + explicit HloExecutionProfiler( + bool do_profile, HloExecutionProfile* profile, se::Stream* stream, + const std::vector::SmartPtr>& sub_streams, + const HloComputation* computation) : do_profile_(do_profile), profile_(profile), stream_(stream), + sub_streams_(sub_streams), computation_(computation) { if (do_profile_) { clock_rate_ghz_ = @@ -70,6 +72,7 @@ class HloExecutionProfiler { CHECK(!finished_execution_) << "Call FinishExecution only once!"; finished_execution_ = true; if (do_profile_) { + stream_->ThenWaitFor(&sub_streams_); stream_->ThenStopTimer(execution_timer_.get()); stream_->BlockHostUntilDone().IgnoreError(); profile_->set_total_cycles_executed( @@ -88,6 +91,7 @@ class HloExecutionProfiler { // that the hlo_instruction took to execute in the profile. void FinishOperation(const HloInstruction* hlo_instruction) { if (do_profile_) { + stream_->ThenWaitFor(&sub_streams_); stream_->ThenStopTimer(per_op_timer_.get()); stream_->BlockHostUntilDone().IgnoreError(); profile_->SetCyclesTakenBy( @@ -100,6 +104,7 @@ class HloExecutionProfiler { double clock_rate_ghz_; HloExecutionProfile* profile_; se::Stream* stream_; + const std::vector::SmartPtr>& sub_streams_; const HloComputation* computation_; std::unique_ptr execution_timer_; std::unique_ptr per_op_timer_; @@ -147,13 +152,9 @@ Status GpuExecutable::ExecuteThunks( LOG(WARNING) << "PROFILING: profiling is enabled"; } - HloExecutionProfiler profiler(do_profile, hlo_execution_profile, main_stream, - hlo_module_->entry_computation()); - - uint64 start_micros = tensorflow::Env::Default()->NowMicros(); - // Stream 0 indicates `main_stream` and substreams start from stream 1. std::vector::SmartPtr> sub_streams; + sub_streams.reserve(thunk_schedule_->StreamCount() - 1); while (sub_streams.size() + 1 < thunk_schedule_->StreamCount()) { sub_streams.emplace_back(); TF_ASSIGN_OR_RETURN( @@ -161,6 +162,10 @@ Status GpuExecutable::ExecuteThunks( run_options->BorrowStream(main_stream->parent()->device_ordinal())); } + HloExecutionProfiler profiler(do_profile, hlo_execution_profile, main_stream, + sub_streams, hlo_module_->entry_computation()); + uint64 start_micros = tensorflow::Env::Default()->NowMicros(); + // The next event enqueued on stream N must not run until the thunk at // last_blocking_thunk_for_stream[N] completes. std::map last_blocking_thunk_for_stream; diff --git a/tensorflow/compiler/xla/service/gpu/instruction_fusion.cc b/tensorflow/compiler/xla/service/gpu/instruction_fusion.cc index b5962f069bf499c913bd5479f263a7cb77c00555..85ecbe8fdb34700ca738b99ddd9ea615afc35da3 100644 --- a/tensorflow/compiler/xla/service/gpu/instruction_fusion.cc +++ b/tensorflow/compiler/xla/service/gpu/instruction_fusion.cc @@ -25,13 +25,19 @@ namespace gpu { namespace { bool IsFusile(const HloInstruction& hlo) { + // Don't fuse get-tuple-element on GPU: We can, but it's slower than not + // fusing. We never generate kernels for unfused GTEs. Instead, if an + // unfused GTE is an input to a kernel (including a fusion kernel), we + // compute the address of the GTE at the top of the kernel. Often we know the + // address of the GTE result statically, so we can do this without chasing any + // pointers. return (hlo.IsElementwise() && hlo.operand_count() > 0) || + hlo.opcode() == HloOpcode::kBitcast || hlo.opcode() == HloOpcode::kBroadcast || hlo.opcode() == HloOpcode::kConcatenate || hlo.opcode() == HloOpcode::kDynamicSlice || hlo.opcode() == HloOpcode::kDynamicUpdateSlice || hlo.opcode() == HloOpcode::kFusion || - hlo.opcode() == HloOpcode::kGetTupleElement || hlo.opcode() == HloOpcode::kPad || hlo.opcode() == HloOpcode::kReduce || hlo.opcode() == HloOpcode::kReduceWindow || @@ -46,6 +52,34 @@ bool GpuInstructionFusion::ShouldFuse(HloInstruction* consumer, int64 operand_index) { HloInstruction* producer = consumer->mutable_operand(operand_index); + // Check if we can use output fusion for (A @ B) * alpha + if (producer->opcode() == HloOpcode::kDot) { + if (consumer->opcode() == HloOpcode::kMultiply) { + CHECK_EQ(consumer->operand_count(), 2); + int64 other_operand_index = 1 - operand_index; + const HloInstruction* alpha = consumer->operand(other_operand_index); + if (alpha->opcode() == HloOpcode::kConstant && + ShapeUtil::IsScalar(alpha->shape())) { + return true; + } + } + } + + // Only allow to fuse transpose into an output fusion. + if (consumer->opcode() == HloOpcode::kFusion && + consumer->fusion_kind() == HloInstruction::FusionKind::kOutput) { + if (producer->opcode() != HloOpcode::kTranspose) { + return false; + } + // Check that the transpose is the operand of a dot. + auto producer_operand_index = consumer->operand_index(producer); + auto fused_parameter = consumer->fused_parameter(producer_operand_index); + const std::vector& fused_parameter_users = + fused_parameter->users(); + return (fused_parameter_users.size() == 1 && + fused_parameter_users[0]->opcode() == HloOpcode::kDot); + } + // Output fusion is not currently supported on GPUs. if (producer->opcode() == HloOpcode::kFusion) { return false; @@ -70,17 +104,6 @@ bool GpuInstructionFusion::ShouldFuse(HloInstruction* consumer, return false; } - // We may need to know original operand layout to emit input fusion, and so - // far, we merely use the layout of an operand of the fusion node, which means - // we must fuse only elementwise operations. This restriction should be lifted - // later if we need to fuse other operations, e.g. transpose, for performance. - if ((IsReductionToVector(*consumer) || - (HloOpcode::kFusion == consumer->opcode() && - HloInstruction::FusionKind::kInput == consumer->fusion_kind())) && - !producer->IsElementwise()) { - return false; - } - // Cost condition: not fuse (simple, expensive producers) and (consumers who // reuse operand elements). if (producer->opcode() != HloOpcode::kFusion && @@ -98,6 +121,9 @@ HloInstruction::FusionKind GpuInstructionFusion::ChooseKind( if (IsReductionToVector(*consumer)) { return HloInstruction::FusionKind::kInput; } + if (producer->opcode() == HloOpcode::kDot) { + return HloInstruction::FusionKind::kOutput; + } if (HloOpcode::kFusion == consumer->opcode()) { return consumer->fusion_kind(); } diff --git a/tensorflow/compiler/xla/service/gpu/instruction_fusion_test.cc b/tensorflow/compiler/xla/service/gpu/instruction_fusion_test.cc index 2d6dad27a59978da6e4719afc50ebee5e641dde0..4b231c449f8f101127b4d30bfff20c69d8cef5c1 100644 --- a/tensorflow/compiler/xla/service/gpu/instruction_fusion_test.cc +++ b/tensorflow/compiler/xla/service/gpu/instruction_fusion_test.cc @@ -17,6 +17,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_matchers.h" #include "tensorflow/compiler/xla/tests/hlo_test_base.h" +#include "tensorflow/compiler/xla/tools/parser/hlo_parser.h" namespace op = xla::testing::opcode_matchers; @@ -137,30 +138,119 @@ TEST_F(InstructionFusionTest, PotentialBitcastTransposeOfDotUnfused) { .ValueOrDie()); } -TEST_F(InstructionFusionTest, GetTupleElementFused) { - HloComputation::Builder builder(TestName()); - Shape data_shape = ShapeUtil::MakeShape(F32, {8}); - Shape tuple_shape = ShapeUtil::MakeTupleShape({data_shape, data_shape}); - auto param = builder.AddInstruction( - HloInstruction::CreateParameter(0, tuple_shape, "param")); - auto gte0 = builder.AddInstruction( - HloInstruction::CreateGetTupleElement(data_shape, param, 0)); - auto gte1 = builder.AddInstruction( - HloInstruction::CreateGetTupleElement(data_shape, param, 1)); - builder.AddInstruction( - HloInstruction::CreateBinary(data_shape, HloOpcode::kAdd, gte0, gte1)); - auto module = CreateNewModule(); - auto computation = module->AddEntryComputation(builder.Build()); +// Tests that broadcasts fused into a fusion with a reduce root. +TEST_F(InstructionFusionTest, BroadcastIntoReduce) { + auto module = tools::Parse(R"( + HloModule test_module + + add { + lhs = f32[] parameter(0) + rhs = f32[] parameter(1) + ROOT add = f32[] add(lhs, rhs) + } + + ENTRY BroadcastIntoReduce { + constant = f32[] constant(1) + broadcast = f32[16,16,16,16]{3,2,1,0} broadcast(constant), dimensions={} + constant.1 = f32[] constant(0) + ROOT reduce = f32[] reduce(broadcast, constant.1), dimensions={0,1,2,3}, + to_apply=add + })") + .ValueOrDie(); + + EXPECT_TRUE(GpuInstructionFusion(/*may_duplicate=*/true) + .Run(module.get()) + .ValueOrDie()); + + HloInstruction* root = module->entry_computation()->root_instruction(); + EXPECT_THAT(root, op::Fusion()); + EXPECT_THAT(root->fused_expression_root(), + op::Reduce(op::Broadcast(op::Parameter()), op::Parameter())); +} + +TEST_F(InstructionFusionTest, BitcastIntoAdd) { + auto module = tools::Parse(R"( + HloModule test_module + + ENTRY BroadcastIntoAdd { + p0 = f32[4,1,1]{2,1,0} parameter(0) + p1 = f32[4,1]{1,0} parameter(1) + bitcast = f32[4,1]{1,0} bitcast(p0) + ROOT add = f32[4,1] add(bitcast, p1) + })") + .ValueOrDie(); + + EXPECT_TRUE(GpuInstructionFusion(/*may_duplicate=*/true) + .Run(module.get()) + .ValueOrDie()); + + HloInstruction* root = module->entry_computation()->root_instruction(); + EXPECT_THAT(root, op::Fusion()); + EXPECT_THAT(root->fused_expression_root(), + op::Add(op::Bitcast(op::Parameter()), op::Parameter())); +} + +TEST_F(InstructionFusionTest, AddIntoBitcast) { + auto module = tools::Parse(R"( + HloModule test_module + + ENTRY BroadcastIntoAdd { + p0 = f32[4,1,1]{2,1,0} parameter(0) + p1 = f32[4,1]{1,0} parameter(1) + add = f32[4,1] add(p0, p1) + ROOT bitcast = f32[4,1,1] bitcast(add) + })") + .ValueOrDie(); + + EXPECT_TRUE(GpuInstructionFusion(/*may_duplicate=*/true) + .Run(module.get()) + .ValueOrDie()); + + HloInstruction* root = module->entry_computation()->root_instruction(); + EXPECT_THAT(root, op::Fusion()); + EXPECT_THAT(root->fused_expression_root(), + op::Bitcast(op::Add(op::Parameter(), op::Parameter()))); +} + +TEST_F(InstructionFusionTest, DontFuseGTE) { + auto module = tools::Parse(R"( + HloModule test_module + ENTRY DontFuseGTE { + p0 = (f32[10], f32[10]) parameter(0) + gte0 = f32[10] get-tuple-element(p0), index=0 + gte1 = f32[10] get-tuple-element(p0), index=1 + ROOT add = f32[10] add(gte0, gte1) + })") + .ValueOrDie(); + + EXPECT_FALSE(GpuInstructionFusion(/*may_duplicate=*/true) + .Run(module.get()) + .ValueOrDie()); +} + +TEST_F(InstructionFusionTest, DotOutputFusion) { + auto module = tools::Parse(R"( + HloModule test_module + ENTRY OutputFusion { + constant = f32[] constant(3) + p0 = f32[4,3]{1,0} parameter(0) + p1 = f32[4,3]{1,0} parameter(1) + transpose = f32[3,4]{1,0} transpose(p1), dimensions={1, 0} + dot = f32[4,4]{1,0} dot(p0, transpose) + ROOT mul = f32[4,4] multiply(constant, dot) + })") + .ValueOrDie(); + EXPECT_TRUE(GpuInstructionFusion(/*may_duplicate=*/true) .Run(module.get()) .ValueOrDie()); - HloInstruction* root = computation->root_instruction(); - EXPECT_EQ(HloOpcode::kFusion, root->opcode()); - HloInstruction* fused_root = root->fused_expression_root(); - EXPECT_EQ(HloOpcode::kAdd, fused_root->opcode()); - // Check that operands of 'fused_root' are GTE. - EXPECT_EQ(HloOpcode::kGetTupleElement, fused_root->operand(0)->opcode()); - EXPECT_EQ(HloOpcode::kGetTupleElement, fused_root->operand(1)->opcode()); + + HloInstruction* root = module->entry_computation()->root_instruction(); + EXPECT_THAT(root, op::Fusion()); + EXPECT_THAT( + root->fused_expression_root(), + op::Multiply(op::Parameter(), + op::Dot(op::Parameter(), op::Transpose(op::Parameter())))); } } // namespace gpu diff --git a/tensorflow/compiler/xla/service/gpu/ir_emission_utils.cc b/tensorflow/compiler/xla/service/gpu/ir_emission_utils.cc index 2f65edffea81db7dba1f8545f92b27ea622044e7..32413f975a40c1abc334b16e81097bb44f56a44a 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emission_utils.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emission_utils.cc @@ -49,8 +49,10 @@ bool AreValidGemmShapes(const Shape& lhs_shape, const Shape& rhs_shape, // The inputs and the output must // 1) be matrices with no padding and a non-zero number of elements, // 2) have an allowed element type. - bool type_is_allowed = (output_shape.element_type() == F32 || - output_shape.element_type() == F64); + PrimitiveType output_primitive_type = output_shape.element_type(); + bool type_is_allowed = + (output_primitive_type == F16 || output_primitive_type == F32 || + output_primitive_type == F64); return type_is_allowed && IsRank2WithNoPadding(lhs_shape) && IsRank2WithNoPadding(rhs_shape) && IsRank2WithNoPadding(output_shape) && @@ -87,6 +89,19 @@ bool ImplementedAsGemm(const HloInstruction& hlo) { return true; } + if (hlo.opcode() == HloOpcode::kFusion && + hlo.fusion_kind() == HloInstruction::FusionKind::kOutput && + hlo.fused_expression_root()->opcode() == HloOpcode::kMultiply) { + // Try to find the dot inside the output fusion node. + const HloInstruction* dot = hlo.fused_expression_root()->operand(0); + if (dot->opcode() != HloOpcode::kDot) { + dot = hlo.fused_expression_root()->operand(1); + } + if (dot->opcode() == HloOpcode::kDot) { + return ImplementedAsGemm(*dot); + } + } + return false; } diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter.cc index a3df67a87344d6ece2ea9047321ad9542c13f8cf..1e0db2821a2c212d0f212ae94ab69231bc6053ea 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter.cc @@ -17,6 +17,7 @@ limitations under the License. #include #include +#include #include "tensorflow/core/platform/logging.h" // IWYU pragma: no_include "llvm/IR/Intrinsics.gen.inc" @@ -438,6 +439,32 @@ Status IrEmitter::HandleSelect(HloInstruction* select) { return IrEmitter::DefaultAction(select); } +namespace { +llvm::Value* Real(llvm::Value* x, llvm::IRBuilder<>* ir_builder) { + return ir_builder->CreateExtractValue(x, {0}); +} + +llvm::Value* Imag(llvm::Value* x, llvm::IRBuilder<>* ir_builder) { + return ir_builder->CreateExtractValue(x, {1}); +} + +std::pair MultiplyComplex( + llvm::Value* lhs_value, llvm::Value* rhs_value, + llvm::IRBuilder<>* ir_builder) { + llvm::Value* lhs_real = Real(lhs_value, ir_builder); + llvm::Value* lhs_imag = Imag(lhs_value, ir_builder); + llvm::Value* rhs_real = Real(rhs_value, ir_builder); + llvm::Value* rhs_imag = Imag(rhs_value, ir_builder); + llvm::Value* real_result1 = ir_builder->CreateFMul(lhs_real, rhs_real); + llvm::Value* real_result2 = ir_builder->CreateFMul(lhs_imag, rhs_imag); + llvm::Value* real_result = ir_builder->CreateFSub(real_result1, real_result2); + llvm::Value* imag_result1 = ir_builder->CreateFMul(lhs_real, rhs_imag); + llvm::Value* imag_result2 = ir_builder->CreateFMul(lhs_imag, rhs_real); + llvm::Value* imag_result = ir_builder->CreateFAdd(imag_result1, imag_result2); + return {real_result, imag_result}; +} +} // namespace + Status IrEmitter::HandleDot(HloInstruction* dot) { auto lhs_instruction = dot->operand(0); auto rhs_instruction = dot->operand(1); @@ -456,21 +483,10 @@ Status IrEmitter::HandleDot(HloInstruction* dot) { rhs_array.EmitReadArrayElement(/*index=*/{}, &ir_builder_); llvm::Value* result; if (ShapeUtil::ElementIsComplex(lhs_shape)) { - auto real = [&](llvm::Value* x) { - return ir_builder_.CreateExtractValue(x, {0}); - }; - auto imag = [&](llvm::Value* x) { - return ir_builder_.CreateExtractValue(x, {1}); - }; - llvm::Value* real_result = ir_builder_.CreateFSub( - ir_builder_.CreateFMul(real(lhs_value), real(rhs_value)), - ir_builder_.CreateFMul(imag(lhs_value), imag(rhs_value))); - llvm::Value* imag_result = ir_builder_.CreateFAdd( - ir_builder_.CreateFMul(real(lhs_value), imag(rhs_value)), - ir_builder_.CreateFMul(imag(lhs_value), real(rhs_value))); + auto value = MultiplyComplex(lhs_value, rhs_value, &ir_builder_); result = llvm::ConstantAggregateZero::get(lhs_array.GetElementLlvmType()); - result = ir_builder_.CreateInsertValue(result, real_result, {0}); - result = ir_builder_.CreateInsertValue(result, imag_result, {1}); + result = ir_builder_.CreateInsertValue(result, value.first, {0}); + result = ir_builder_.CreateInsertValue(result, value.second, {1}); } else { result = ir_builder_.CreateFMul(lhs_value, rhs_value); } @@ -548,20 +564,13 @@ Status IrEmitter::HandleDot(HloInstruction* dot) { llvm::Value* accum = ir_builder_.CreateLoad(accum_address); llvm::Value* updated_accum; if (ShapeUtil::ElementIsComplex(lhs_shape)) { -#define REAL(x) ir_builder_.CreateExtractValue(x, {0}) -#define IMAG(x) ir_builder_.CreateExtractValue(x, {1}) - llvm::Value* product_real = ir_builder_.CreateFSub( - ir_builder_.CreateFMul(REAL(lhs_element), REAL(rhs_element)), - ir_builder_.CreateFMul(IMAG(lhs_element), IMAG(rhs_element))); - llvm::Value* product_imag = ir_builder_.CreateFAdd( - ir_builder_.CreateFMul(REAL(lhs_element), IMAG(rhs_element)), - ir_builder_.CreateFMul(IMAG(lhs_element), REAL(rhs_element))); - updated_accum = ir_builder_.CreateInsertValue( - accum, ir_builder_.CreateFAdd(REAL(accum), product_real), {0}); - updated_accum = ir_builder_.CreateInsertValue( - updated_accum, ir_builder_.CreateFAdd(IMAG(accum), product_imag), {1}); -#undef IMAG -#undef REAL + auto value = MultiplyComplex(lhs_element, rhs_element, &ir_builder_); + llvm::Value* accum_real = Real(accum, &ir_builder_); + llvm::Value* real_sum = ir_builder_.CreateFAdd(accum_real, value.first); + updated_accum = ir_builder_.CreateInsertValue(accum, real_sum, {0}); + llvm::Value* accum_imag = Imag(accum, &ir_builder_); + llvm::Value* imag_sum = ir_builder_.CreateFAdd(accum_imag, value.second); + updated_accum = ir_builder_.CreateInsertValue(updated_accum, imag_sum, {1}); } else { llvm::Value* product = ir_builder_.CreateFMul(lhs_element, rhs_element); updated_accum = ir_builder_.CreateFAdd(accum, product); diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc index 30c88c0a5d38f6ea3f94d3b47b7b69c7122bf6ac..2381d7a7d59ba2777e711138779b4493b8037f3d 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc @@ -517,39 +517,6 @@ Status IrEmitterUnnested::HandleFusion(HloInstruction* fusion) { TF_RETURN_IF_ERROR(root->Accept(&fused_emitter)); Shape input_shape = root->operand(0)->shape(); - // EmitReductionToVector requires the input shape to have a layout, but - // fused instructions don't have one. So we determine its layout from - // the fusion's operands. The choice of the layout only affects - // performance but not correctness. - auto choose_input_layout = []( - tensorflow::gtl::ArraySlice operands, - Shape* input_shape) -> Status { - // Prefer the layout of an operand whose shape is compatible with - // input_shape. - for (const HloInstruction* operand : operands) { - if (ShapeUtil::Compatible(*input_shape, operand->shape())) { - return LayoutUtil::CopyLayoutBetweenShapes(operand->shape(), - input_shape); - } - } - // If no operand has a compatible shape, prefer an operand that has - // the same rank at least. - for (const HloInstruction* operand : operands) { - if (ShapeUtil::Rank(*input_shape) == - ShapeUtil::Rank(operand->shape())) { - // Do not use CopyLayoutBetweenShapes because input_shape and - // operand->shape() may be incompatible. - *input_shape->mutable_layout() = operand->shape().layout(); - return Status::OK(); - } - } - // When all the above fails, which is rare, set the default layout. - LayoutUtil::SetToDefaultLayout(input_shape); - return Status::OK(); - }; - TF_RETURN_IF_ERROR( - choose_input_layout(fusion->operands(), &input_shape)); - return EmitReductionToVector( root, input_shape, fused_emitter.GetGenerator(root->operand(0)), fused_emitter.GetGenerator(root->operand(1)), root->dimensions(), @@ -2221,31 +2188,63 @@ std::unique_ptr IrEmitterUnnested::BuildGemmThunk( inst->shape(), // The shape of the output. false, // Do not transpose LHS. false, // Do not transpose RHS. + 1.0, // alpha. inst); } if (inst->opcode() == HloOpcode::kFusion) { - const HloInstruction* dot = inst->fused_expression_root(); - DCHECK(dot->opcode() == HloOpcode::kDot); - const HloInstruction* lhs_parameter = StripTranspose(*dot->operand(0)); - const HloInstruction* rhs_parameter = StripTranspose(*dot->operand(1)); - DCHECK(lhs_parameter->opcode() == HloOpcode::kParameter && - rhs_parameter->opcode() == HloOpcode::kParameter); - const HloInstruction* lhs = - inst->operand(lhs_parameter->parameter_number()); - const HloInstruction* rhs = - inst->operand(rhs_parameter->parameter_number()); - - return MakeUnique( - GetAllocationSlice(*lhs), // The buffer assigned to LHS. - GetAllocationSlice(*rhs), // The buffer assigned to RHS. - GetAllocationSlice(*inst), // The output buffer. - lhs->shape(), // The shape of LHS. - rhs->shape(), // The shape of RHS. - inst->shape(), // The shape of the output. - dot->operand(0)->IsRank2Transpose(), // Transpose LHS. - dot->operand(1)->IsRank2Transpose(), // Trasnpose RHS. - inst); + if (inst->fusion_kind() == HloInstruction::FusionKind::kOutput) { + const HloInstruction* mul = inst->fused_expression_root(); + const HloInstruction* dot = mul->operand(0); + const HloInstruction* alpha = mul->operand(1); + if (dot->opcode() != HloOpcode::kDot) { + std::swap(dot, alpha); + } + DCHECK(dot->opcode() == HloOpcode::kDot); + const HloInstruction* lhs_parameter = StripTranspose(*dot->operand(0)); + const HloInstruction* rhs_parameter = StripTranspose(*dot->operand(1)); + DCHECK(lhs_parameter->opcode() == HloOpcode::kParameter && + rhs_parameter->opcode() == HloOpcode::kParameter); + const HloInstruction* lhs = + inst->operand(lhs_parameter->parameter_number()); + const HloInstruction* rhs = + inst->operand(rhs_parameter->parameter_number()); + + return MakeUnique( + GetAllocationSlice(*lhs), // The buffer assigned to LHS. + GetAllocationSlice(*rhs), // The buffer assigned to RHS. + GetAllocationSlice(*mul), // The output buffer. + lhs->shape(), // The shape of LHS. + rhs->shape(), // The shape of RHS. + inst->shape(), // The shape of the output. + dot->operand(0)->IsRank2Transpose(), // Transpose LHS. + dot->operand(1)->IsRank2Transpose(), // Transpose RHS. + alpha->literal().Get({0}), // alpha. + inst); + } else { + const HloInstruction* dot = inst->fused_expression_root(); + DCHECK(dot->opcode() == HloOpcode::kDot); + const HloInstruction* lhs_parameter = StripTranspose(*dot->operand(0)); + const HloInstruction* rhs_parameter = StripTranspose(*dot->operand(1)); + DCHECK(lhs_parameter->opcode() == HloOpcode::kParameter && + rhs_parameter->opcode() == HloOpcode::kParameter); + const HloInstruction* lhs = + inst->operand(lhs_parameter->parameter_number()); + const HloInstruction* rhs = + inst->operand(rhs_parameter->parameter_number()); + + return MakeUnique( + GetAllocationSlice(*lhs), // The buffer assigned to LHS. + GetAllocationSlice(*rhs), // The buffer assigned to RHS. + GetAllocationSlice(*inst), // The output buffer. + lhs->shape(), // The shape of LHS. + rhs->shape(), // The shape of RHS. + inst->shape(), // The shape of the output. + dot->operand(0)->IsRank2Transpose(), // Transpose LHS. + dot->operand(1)->IsRank2Transpose(), // Transpose RHS. + 1.0, // Alpha. + inst); + } } LOG(FATAL) << "Cannot build a GemmThunk for " << inst->ToString(); diff --git a/tensorflow/compiler/xla/service/gpu/pad_insertion.cc b/tensorflow/compiler/xla/service/gpu/pad_insertion.cc index 25846dc6cd4633c7becb6e62d6bc9585348a6eac..fa405b9329a327a70161821212db4d3213e834b7 100644 --- a/tensorflow/compiler/xla/service/gpu/pad_insertion.cc +++ b/tensorflow/compiler/xla/service/gpu/pad_insertion.cc @@ -17,6 +17,7 @@ limitations under the License. #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/service/gpu/ir_emission_utils.h" +#include "tensorflow/compiler/xla/service/hlo_creation_utils.h" #include "tensorflow/compiler/xla/service/shape_inference.h" #include "tensorflow/compiler/xla/util.h" #include "tensorflow/compiler/xla/window_util.h" @@ -68,13 +69,7 @@ HloInstruction* MaybePaddedAndSlicedInput( HloInstruction* padding = computation->AddInstruction(HloInstruction::CreateConstant( MakeUnique(Literal::Zero(element_type)))); - input = computation->AddInstruction(HloInstruction::CreatePad( - ShapeInference::InferPadShape( - /*operand_shape=*/input->shape(), - /*padding_value_shape=*/ShapeUtil::MakeShape(element_type, {}), - padding_config) - .ConsumeValueOrDie(), - input, padding, padding_config)); + input = CreatePadHlo(input, padding, padding_config).ValueOrDie(); } if (window_util::HasNegativePadding(conv_window)) { @@ -97,11 +92,8 @@ HloInstruction* MaybePaddedAndSlicedInput( std::max(0LL, -conv_window.dimensions(i).padding_high()); } - input = computation->AddInstruction(HloInstruction::CreateSlice( - ShapeInference::InferSliceShape(input->shape(), start_indices, - limit_indices, strides) - .ConsumeValueOrDie(), - input, start_indices, limit_indices, strides)); + input = CreateSliceHlo(input, start_indices, limit_indices, strides) + .ValueOrDie(); } return input; @@ -134,13 +126,7 @@ HloInstruction* MaybePaddedKernel(const Window& conv_window, HloInstruction* padding = computation->AddInstruction(HloInstruction::CreateConstant( MakeUnique(Literal::Zero(element_type)))); - return computation->AddInstruction(HloInstruction::CreatePad( - ShapeInference::InferPadShape( - /*operand_shape=*/kernel->shape(), - /*padding_value_shape=*/ShapeUtil::MakeShape(element_type, {}), - padding_config) - .ConsumeValueOrDie(), - kernel, padding, padding_config)); + return CreatePadHlo(kernel, padding, padding_config).ValueOrDie(); } } // namespace @@ -252,11 +238,7 @@ bool PadInsertion::CanonicalizeBackwardFilterConvolution( computation->AddInstruction(HloInstruction::CreateConstant( MakeUnique(Literal::Zero(input->shape().element_type())))); HloInstruction* padded_input = - computation->AddInstruction(HloInstruction::CreatePad( - ShapeInference::InferPadShape(input->shape(), padding->shape(), - input_padding_config) - .ConsumeValueOrDie(), - input, padding, input_padding_config)); + CreatePadHlo(input, padding, input_padding_config).ValueOrDie(); // The shape of the backward_conv CustomCall is a tuple (conv_result, // scratch_buffer). Extract out the shape of conv_result. diff --git a/tensorflow/compiler/xla/service/heap_simulator.cc b/tensorflow/compiler/xla/service/heap_simulator.cc index a2d13c013c56059148ccd04dba2137a5b2badc42..3dd4c4a0794e5c41b877078c4e69c6c9584ce6c0 100644 --- a/tensorflow/compiler/xla/service/heap_simulator.cc +++ b/tensorflow/compiler/xla/service/heap_simulator.cc @@ -27,38 +27,6 @@ namespace xla { using tensorflow::gtl::FlatMap; using tensorflow::gtl::FlatSet; -namespace { - -// Returns the set of buffers that may be sources of all operands of the given -// instruction. The returned buffers are guaranteed to have no duplicates, and -// to be sorted in a deterministic order. -std::vector UniqueOperandSourceBuffers( - const HloInstruction* instruction, - const TuplePointsToAnalysis& points_to_analysis) { - std::vector buffers; - for (const HloInstruction* operand : instruction->operands()) { - points_to_analysis.GetPointsToSet(operand).ForEachElement( - [&](const ShapeIndex& /*index*/, - const PointsToSet::BufferList& points_to) { - buffers.insert(buffers.end(), points_to.begin(), points_to.end()); - }); - } - - // Sort and then remove duplicates from buffers. - std::sort(buffers.begin(), buffers.end(), - [](const LogicalBuffer* a, const LogicalBuffer* b) { - return a->id() < b->id(); - }); - buffers.erase(std::unique(buffers.begin(), buffers.end(), - [](const LogicalBuffer* a, const LogicalBuffer* b) { - return a->id() == b->id(); - }), - buffers.end()); - return buffers; -} - -} // namespace - /*static*/ StatusOr HeapSimulator::Run( std::unique_ptr algorithm, const HloModule& module, @@ -93,6 +61,7 @@ Status HeapSimulator::RunComputation( const HloComputation& computation, const std::vector& instruction_sequence, const TuplePointsToAnalysis& points_to_analysis) { + VLOG(3) << "Computation:\n" << computation.ToString(); // The goal here is to minimize memory usage, assuming the given sequential // ordering of instructions. The strategy is to walk through the instruction // sequence, calling Alloc and Free on the underlying heap algorithm. The @@ -101,7 +70,51 @@ Status HeapSimulator::RunComputation( // 'live_buffers' tracks the liveness of each buffer that we assign, by // associating it with a set of HloInstructions that need to be visited. When // the set becomes empty, the buffer is no longer used, and can be freed. + // 'used_buffers' is the reverse map - it tracks which buffers were used by an + // instruction, so that we can remove the instructions from a buffer's live + // set after they are visited. FlatMap> live_buffers; + FlatMap> used_buffers; + auto add_user_to_buffer = [this, &live_buffers, &used_buffers]( + const HloInstruction* user, + const LogicalBuffer* buffer) { + if (!IgnoreBuffer(buffer)) { + VLOG(4) << " Adding user " << user->name() << " to buffer " + << buffer->ToString(); + live_buffers[buffer].insert(user); + used_buffers[user].insert(buffer); + } + }; + + // Initialize live_buffers for each buffer that we're going to assign. The + // set of instructions that need to be visited contains all users of all + // aliases, that is, all users of all instructions that have the buffer + // contained in their points-to set. + for (const HloInstruction* instruction : instruction_sequence) { + const PointsToSet& points_to = + points_to_analysis.GetPointsToSet(instruction); + const PointsToSet::BufferSet& buffer_set = points_to.CreateFlattenedSet(); + for (const HloInstruction* user : instruction->users()) { + if (user->opcode() != HloOpcode::kGetTupleElement) { + for (const LogicalBuffer* buffer : buffer_set) { + add_user_to_buffer(user, buffer); + } + } else { + // A GetTupleElement doesn't need to keep all of its operand's buffers + // alive. It only needs the buffers that relate to the element its + // extracting, and the tuple it's extracting from, but not the buffers + // for the other elements. + for (const LogicalBuffer* buffer : points_to.element({})) { + add_user_to_buffer(user, buffer); + } + const PointsToSet& gte_points_to = + points_to_analysis.GetPointsToSet(user); + for (const LogicalBuffer* buffer : gte_points_to.CreateFlattenedSet()) { + add_user_to_buffer(user, buffer); + } + } + } + } const HloInstruction* root = computation.root_instruction(); auto output_source_buffers = @@ -114,34 +127,17 @@ Status HeapSimulator::RunComputation( buffers_defined_by_instruction = points_to_analysis.GetBuffersDefinedByInstruction(instruction); - // Initialize live_buffers for each buffer that we're going to assign. The - // set of instructions that need to be visited contains all users of all - // aliases. The alias itself is not necessary; if it has users, the users - // are necessarily scheduled after the alias. And if it has no users, it is - // either a dead value or an output, both of which are handled below. - // - // We ignore control dependencies here. The reasoning is that the control - // dependencies have already been accounted for in the ordering of the given - // 'instruction_sequence', and should not otherwise artificially extend the - // lifetime of buffers that aren't already connected by a data dependency. + VLOG(3) << "Instruction: " << instruction->ToString(); + for (const LogicalBuffer* buffer : buffers_defined_by_instruction) { + VLOG(4) << " Defines: " << buffer->ToString() + << (IgnoreBuffer(buffer) ? " (Ignored)" : ""); + } + dead_buffers_to_free.clear(); for (const LogicalBuffer* buffer : buffers_defined_by_instruction) { if (IgnoreBuffer(buffer)) { continue; } - FlatSet* live_set = nullptr; - for (const BufferAlias& alias : - points_to_analysis.GetBufferAliases(*buffer)) { - const std::vector& users = - alias.instruction()->users(); - if (!users.empty()) { - if (live_set == nullptr) { - live_set = &live_buffers[buffer]; - } - live_set->insert(users.begin(), users.end()); - } - } - // Add a nullptr sentry to ensure entry parameters and output source // buffers are not freed until the very end. const bool entry_parameter = @@ -165,11 +161,12 @@ Status HeapSimulator::RunComputation( // have no instructions left to visit are moved from live_buffers to // operand_buffers_to_free. operand_buffers_to_free.clear(); - for (const LogicalBuffer* operand_buffer : - UniqueOperandSourceBuffers(instruction, points_to_analysis)) { + for (const LogicalBuffer* operand_buffer : used_buffers[instruction]) { if (IgnoreBuffer(operand_buffer)) { continue; } + VLOG(4) << " Removing user " << instruction->name() << " from buffer " + << operand_buffer->ToString(); auto it = live_buffers.find(operand_buffer); FlatSet* live_set = &it->second; live_set->erase(instruction); @@ -178,6 +175,11 @@ Status HeapSimulator::RunComputation( operand_buffers_to_free.push_back(operand_buffer); } } + // Sort to get a deterministic iteration order. + std::sort(operand_buffers_to_free.begin(), operand_buffers_to_free.end(), + [](const LogicalBuffer* x, const LogicalBuffer* y) { + return x->id() < y->id(); + }); // Allocate buffers defined by this instruction. This is the latest point // that we can allocate; right before the buffer is first used. This must @@ -203,6 +205,8 @@ Status HeapSimulator::RunComputation( CanShareOperandBufferWithUser( operand_buffer->instruction(), operand_buffer->index(), buffer->instruction(), buffer->index(), points_to_analysis)) { + VLOG(3) << " Sharing: " << buffer->ToString() << " with " + << operand_buffer->ToString(); ShareBuffer(buffer, operand_buffer, instruction); shared = true; break; @@ -211,6 +215,7 @@ Status HeapSimulator::RunComputation( } if (!shared) { + VLOG(3) << " Allocating: " << buffer->ToString(); Alloc(buffer, instruction); } } @@ -244,20 +249,34 @@ Status HeapSimulator::RunComputation( // Free buffers that are no longer live. This is the earliest point that we // can de-allocate; right after the last use of the buffer. for (const LogicalBuffer* buffer : dead_buffers_to_free) { + VLOG(3) << " Freeing dead: " << buffer->ToString(); Free(buffer, instruction); } for (const LogicalBuffer* buffer : operand_buffers_to_free) { + VLOG(3) << " Freeing operand: " << buffer->ToString(); Free(buffer, instruction); } } // Any remaining live buffers must be entry parameters or output source - // buffers, which had a nullptr sentry added. Free them now. + // buffers, which had a nullptr sentry added. Free them now, in a + // deterministic order. + std::vector to_free; + to_free.reserve(live_buffers.size()); for (const auto& buffer_pending : live_buffers) { const LogicalBuffer* buffer = buffer_pending.first; const FlatSet& pending = buffer_pending.second; CHECK_EQ(pending.size(), 1) << *buffer; CHECK(*pending.begin() == nullptr) << *buffer; + to_free.push_back(buffer); + } + + std::sort(to_free.begin(), to_free.end(), + [](const LogicalBuffer* x, const LogicalBuffer* y) { + return x->id() < y->id(); + }); + for (const LogicalBuffer* buffer : to_free) { + VLOG(3) << "Freeing pending: " << buffer->ToString(); Free(buffer, root); } diff --git a/tensorflow/compiler/xla/service/heap_simulator_test.cc b/tensorflow/compiler/xla/service/heap_simulator_test.cc index 387b649a731ebcbfd8307807469f39f22d192b06..688a271712ac243666ba4ff02932aa4f7f7ed21c 100644 --- a/tensorflow/compiler/xla/service/heap_simulator_test.cc +++ b/tensorflow/compiler/xla/service/heap_simulator_test.cc @@ -410,6 +410,56 @@ TEST_F(HeapSimulatorTest, MultiplyDotDotTuple) { }); } +TEST_F(HeapSimulatorTest, IndependentTupleElements) { + auto builder = HloComputation::Builder(TestName()); + auto paramA = builder.AddInstruction( + HloInstruction::CreateParameter(0, f32scalar_, "paramA")); + auto paramB = builder.AddInstruction( + HloInstruction::CreateParameter(1, f32scalar_, "paramB")); + auto mul = builder.AddInstruction(HloInstruction::CreateBinary( + f32scalar_, HloOpcode::kMultiply, paramA, paramB)); + auto add = builder.AddInstruction(HloInstruction::CreateBinary( + f32scalar_, HloOpcode::kAdd, paramA, paramB)); + auto tuple = builder.AddInstruction(HloInstruction::CreateTuple({mul, add})); + auto element0 = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(f32scalar_, tuple, 0)); + auto broadcast = builder.AddInstruction( + HloInstruction::CreateBroadcast(f32vec4_, element0, {0})); + auto sub = builder.AddInstruction(HloInstruction::CreateBinary( + f32scalar_, HloOpcode::kSubtract, paramA, paramB)); + auto element1 = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(f32scalar_, tuple, 1)); + auto output = builder.AddInstruction( + HloInstruction::CreateTuple({broadcast, sub, element1})); + + HeapSimulatorTracker tracker(TestName(), builder.Build(), + {paramA, paramB, mul, add, tuple, element0, + broadcast, sub, element1, output}); + tracker.ExpectCallSequence({ + {kAlloc, tracker.BufferAt(paramA, {})}, + {kAlloc, tracker.BufferAt(paramB, {})}, + {kAlloc, tracker.BufferAt(mul, {})}, + {kAlloc, tracker.BufferAt(add, {})}, + {kAlloc, tracker.BufferAt(tuple, {})}, + {kAlloc, tracker.BufferAt(broadcast, {})}, + // The mul can be freed right after the broadcast happens, even though + // The other GetTupleElement is still alive. + {kFree, tracker.BufferAt(mul, {})}, + {kAlloc, tracker.BufferAt(sub, {})}, + // The temporary tuple is now dead. + {kFree, tracker.BufferAt(tuple, {})}, + {kAlloc, tracker.BufferAt(output, {})}, + // All params and outputs are freed at the end. + {kFree, tracker.BufferAt(paramA, {})}, + {kFree, tracker.BufferAt(paramB, {})}, + {kFree, tracker.BufferAt(add, {})}, + {kFree, tracker.BufferAt(broadcast, {})}, + {kFree, tracker.BufferAt(sub, {})}, + {kFree, tracker.BufferAt(output, {})}, + {kFinish, nullptr}, + }); +} + TEST_F(HeapSimulatorTest, WholeModule) { HeapSimulatorTracker tracker(TestName()); diff --git a/tensorflow/compiler/xla/service/hlo.proto b/tensorflow/compiler/xla/service/hlo.proto index a43785b4a9701369ae315f67d4d64d03dc6c081d..bf903d6a390fe2951d33942dfc2e124868c9fdb5 100644 --- a/tensorflow/compiler/xla/service/hlo.proto +++ b/tensorflow/compiler/xla/service/hlo.proto @@ -133,6 +133,9 @@ message HloInstructionProto { // Gather dimension numbers. xla.GatherDimensionNumbers gather_dimension_numbers = 33; repeated int64 gather_window_bounds = 34; + + // The id of this instruction. + int64 id = 35; } // Serialization of HloComputation. @@ -145,6 +148,12 @@ message HloComputationProto { // The name of the root of the computation. string root_name = 3; + + // The program shape (with layout) of this computation. + xla.ProgramShape program_shape = 4; + + // The id of this computation. + int64 id = 5; } // Serialization of HloModule. @@ -155,6 +164,12 @@ message HloModuleProto { // The array of computations is always in a valid dependency order, where // callees appear before their callers. repeated HloComputationProto computations = 3; + + // The program shape (with layout) of the entry computation. + xla.ProgramShape program_shape = 4; + + // The id of this module. + int64 id = 5; } // Serialization of HloOrdering. diff --git a/tensorflow/compiler/xla/service/hlo_computation.cc b/tensorflow/compiler/xla/service/hlo_computation.cc index 21e6b2ca730f6347af902097e6496826b861e8a3..f99c7cf5e495eaf83e0dda859ef31a7487bc6ffe 100644 --- a/tensorflow/compiler/xla/service/hlo_computation.cc +++ b/tensorflow/compiler/xla/service/hlo_computation.cc @@ -399,6 +399,7 @@ HloComputationProto HloComputation::ToProto() const { proto.add_instructions()->Swap(&instruction_proto); } proto.set_root_name(root_instruction()->name()); + *proto.mutable_program_shape() = ComputeProgramShape(); return proto; } @@ -532,7 +533,6 @@ ProgramShape HloComputation::ComputeProgramShape() const { } *program_shape.mutable_result() = root_instruction_->shape(); - LayoutUtil::ClearLayout(&program_shape); return program_shape; } diff --git a/tensorflow/compiler/xla/service/hlo_computation.h b/tensorflow/compiler/xla/service/hlo_computation.h index 39d864efcb70382b6f8e631d7e6e452ea6410104..dd9d346999f0eae448d74628278c802ccd3f51b4 100644 --- a/tensorflow/compiler/xla/service/hlo_computation.h +++ b/tensorflow/compiler/xla/service/hlo_computation.h @@ -248,7 +248,7 @@ class HloComputation { ShapeTree* copies_added = nullptr); // Computes and returns the ProgramShape of this computation (shape of - // parameters and result without layout). + // parameters and result with layout). ProgramShape ComputeProgramShape() const; // Return whether `*this` and `other` are functionally equivalent. diff --git a/tensorflow/compiler/xla/service/hlo_constant_folding.cc b/tensorflow/compiler/xla/service/hlo_constant_folding.cc index 53450991b6fad5b9651d9d23b55c908e6b68e5dd..35ecd4428d0dfde2de445ea34472d2c78148c6c9 100644 --- a/tensorflow/compiler/xla/service/hlo_constant_folding.cc +++ b/tensorflow/compiler/xla/service/hlo_constant_folding.cc @@ -35,7 +35,10 @@ limitations under the License. namespace xla { StatusOr HloConstantFolding::Run(HloModule* module) { - auto evaluator = MakeUnique(); + // Limit the constant folding to 0 iterations to skip folding loops. This + // retains the behavior from before while loop support in HloEvaluator and may + // be revised. + auto evaluator = MakeUnique(/*max_loop_iterations=*/0); XLA_VLOG_LINES(2, "HloConstantFolding::Run(), before:\n" + module->ToString()); diff --git a/tensorflow/compiler/xla/service/hlo_creation_utils.cc b/tensorflow/compiler/xla/service/hlo_creation_utils.cc new file mode 100644 index 0000000000000000000000000000000000000000..4585bffa42fd001c7375b778c5e1c42e58d17692 --- /dev/null +++ b/tensorflow/compiler/xla/service/hlo_creation_utils.cc @@ -0,0 +1,295 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/hlo_creation_utils.h" +#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/ptr_util.h" +#include "tensorflow/compiler/xla/service/shape_inference.h" +#include "tensorflow/compiler/xla/util.h" + +namespace xla { +using tensorflow::gtl::ArraySlice; +using tensorflow::strings::StrCat; + +StatusOr CreateBinaryHlo(HloOpcode opcode, HloInstruction* lhs, + HloInstruction* rhs) { + HloComputation* computation = lhs->parent(); + CHECK_EQ(computation, rhs->parent()); + TF_ASSIGN_OR_RETURN(Shape binary_op_shape, + ShapeInference::InferBinaryOpShape(opcode, lhs, rhs)); + return computation->AddInstruction( + HloInstruction::CreateBinary(binary_op_shape, opcode, lhs, rhs)); +} + +StatusOr CreatePadHlo(HloInstruction* operand, + HloInstruction* padding_value, + const PaddingConfig& padding_config) { + HloComputation* computation = operand->parent(); + CHECK_EQ(computation, padding_value->parent()); + TF_ASSIGN_OR_RETURN( + Shape pad_shape, + ShapeInference::InferPadShape(operand->shape(), padding_value->shape(), + padding_config)); + return computation->AddInstruction(HloInstruction::CreatePad( + pad_shape, operand, padding_value, padding_config)); +} + +StatusOr CreateSliceHlo(HloInstruction* operand, + ArraySlice start_indices, + ArraySlice limit_indices, + ArraySlice strides) { + HloComputation* computation = operand->parent(); + TF_ASSIGN_OR_RETURN(Shape slice_shape, ShapeInference::InferSliceShape( + operand->shape(), start_indices, + limit_indices, strides)); + return computation->AddInstruction(HloInstruction::CreateSlice( + slice_shape, operand, start_indices, limit_indices, strides)); +} + +StatusOr CreateConvolveHlo( + HloInstruction* lhs, HloInstruction* rhs, const Window& window, + const ConvolutionDimensionNumbers& dimension_numbers) { + HloComputation* computation = lhs->parent(); + CHECK_EQ(computation, rhs->parent()); + TF_ASSIGN_OR_RETURN(Shape convolve_shape, ShapeInference::InferConvolveShape( + lhs->shape(), rhs->shape(), + window, dimension_numbers)); + return computation->AddInstruction(HloInstruction::CreateConvolve( + convolve_shape, lhs, rhs, window, dimension_numbers)); +} + +StatusOr CreateTransposeHlo(HloInstruction* operand, + ArraySlice dimensions) { + HloComputation* computation = operand->parent(); + TF_ASSIGN_OR_RETURN( + Shape transpose_shape, + ShapeInference::InferTransposeShape(operand->shape(), dimensions)); + return computation->AddInstruction( + HloInstruction::CreateTranspose(transpose_shape, operand, dimensions)); +} + +StatusOr CreateReshapeHlo(const Shape& result_shape, + HloInstruction* operand) { + HloComputation* computation = operand->parent(); + return computation->AddInstruction( + HloInstruction::CreateReshape(result_shape, operand)); +} + +StatusOr CreateReshapeHlo( + ArraySlice result_shape_dim_bounds, HloInstruction* operand) { + Shape new_shape = ShapeUtil::MakeShape(operand->shape().element_type(), + result_shape_dim_bounds); + return CreateReshapeHlo(new_shape, operand); +} + +StatusOr CreateDynamicSliceHlo(HloInstruction* operand, + HloInstruction* start_indices, + ArraySlice slice_sizes) { + HloComputation* computation = operand->parent(); + CHECK_EQ(computation, start_indices->parent()); + TF_ASSIGN_OR_RETURN( + Shape dynamic_slice_shape, + ShapeInference::InferDynamicSliceShape( + operand->shape(), start_indices->shape(), slice_sizes)); + return computation->AddInstruction(HloInstruction::CreateDynamicSlice( + dynamic_slice_shape, operand, start_indices, slice_sizes)); +} + +StatusOr CreateDynamicUpdateSliceHlo( + HloInstruction* operand, HloInstruction* update, + HloInstruction* start_indices) { + HloComputation* computation = operand->parent(); + CHECK_EQ(computation, update->parent()); + CHECK_EQ(computation, start_indices->parent()); + TF_ASSIGN_OR_RETURN( + Shape dynamic_update_slice_shape, + ShapeInference::InferDynamicUpdateSliceShape( + operand->shape(), update->shape(), start_indices->shape())); + return computation->AddInstruction(HloInstruction::CreateDynamicUpdateSlice( + dynamic_update_slice_shape, operand, update, start_indices)); +} + +StatusOr CreateBroadcastHlo( + HloInstruction* operand, ArraySlice broadcast_dimensions, + ArraySlice result_shape_bounds) { + HloComputation* computation = operand->parent(); + Shape broadcast_shape = ShapeUtil::MakeShape(operand->shape().element_type(), + result_shape_bounds); + + return computation->AddInstruction(HloInstruction::CreateBroadcast( + broadcast_shape, operand, broadcast_dimensions)); +} + +StatusOr CreateGetTupleElementHlo(HloInstruction* operand, + int64 index) { + HloComputation* computation = operand->parent(); + + TF_ASSIGN_OR_RETURN( + Shape gte_shape, + ShapeInference::InferGetTupleElementShape(operand->shape(), index)); + return computation->AddInstruction( + HloInstruction::CreateGetTupleElement(gte_shape, operand, index)); +} + +StatusOr CreateConcatHlo(ArraySlice operands, + int64 dimension) { + CHECK_GT(operands.size(), 0); + + HloComputation* computation = operands[0]->parent(); + CHECK(c_all_of(operands, [&](HloInstruction* instr) { + return instr->parent() == computation; + })); + + std::vector operand_shapes; + c_transform(operands, std::back_inserter(operand_shapes), + [](HloInstruction* instr) { return &instr->shape(); }); + + TF_ASSIGN_OR_RETURN(Shape concat_shape, ShapeInference::InferConcatOpShape( + operand_shapes, dimension)); + return computation->AddInstruction( + HloInstruction::CreateConcatenate(concat_shape, operands, dimension)); +} + +StatusOr CollapseFirstNDims(HloInstruction* operand, int64 n) { + const Shape& operand_shape = operand->shape(); + CHECK_GE(operand_shape.dimensions_size(), n); + int64 new_shape_leading_bound = 1; + for (int64 i = 0; i < n; i++) { + new_shape_leading_bound *= operand_shape.dimensions(i); + } + + std::vector new_shape_dims; + new_shape_dims.reserve(operand_shape.dimensions_size() - n + 1); + new_shape_dims.push_back(new_shape_leading_bound); + + std::copy(operand_shape.dimensions().begin() + n, + operand_shape.dimensions().end(), + std::back_inserter(new_shape_dims)); + + Shape output_shape = + ShapeUtil::MakeShape(operand_shape.element_type(), new_shape_dims); + + return CreateReshapeHlo(output_shape, operand); +} + +StatusOr ExpandFirstDimIntoNDims( + HloInstruction* operand, ArraySlice expanded_dims) { + CHECK_GT(operand->shape().dimensions_size(), 0); + CHECK_EQ(operand->shape().dimensions(0), Product(expanded_dims)); + + std::vector expanded_shape_dim_bounds; + expanded_shape_dim_bounds.reserve(expanded_dims.size() + + operand->shape().dimensions_size() - 1); + c_copy(expanded_dims, std::back_inserter(expanded_shape_dim_bounds)); + std::copy(operand->shape().dimensions().begin() + 1, + operand->shape().dimensions().end(), + std::back_inserter(expanded_shape_dim_bounds)); + Shape new_shape = ShapeUtil::MakeShape(operand->shape().element_type(), + expanded_shape_dim_bounds); + return CreateReshapeHlo(new_shape, operand); +} + +StatusOr ExpandLastDimIntoNDims( + HloInstruction* operand, ArraySlice expanded_dims) { + CHECK_GT(operand->shape().dimensions_size(), 0); + CHECK_EQ(operand->shape().dimensions(operand->shape().dimensions_size() - 1), + Product(expanded_dims)); + + std::vector expanded_shape_dim_bounds; + expanded_shape_dim_bounds.reserve(expanded_dims.size() + + operand->shape().dimensions_size() - 1); + std::copy(operand->shape().dimensions().begin(), + operand->shape().dimensions().end() - 1, + std::back_inserter(expanded_shape_dim_bounds)); + c_copy(expanded_dims, std::back_inserter(expanded_shape_dim_bounds)); + Shape new_shape = ShapeUtil::MakeShape(operand->shape().element_type(), + expanded_shape_dim_bounds); + return CreateReshapeHlo(new_shape, operand); +} + +StatusOr ElideDegenerateDims(HloInstruction* operand, + ArraySlice dims_to_elide) { + CHECK(c_is_sorted(dims_to_elide)); + + const Shape& input_shape = operand->shape(); + // First accumulate in reverse + std::vector new_shape_dim_bounds; + new_shape_dim_bounds.reserve(input_shape.dimensions_size() - + dims_to_elide.size()); + int64 dims_to_elide_idx = dims_to_elide.size() - 1; + for (int64 i = input_shape.dimensions_size() - 1; i >= 0; i--) { + if (dims_to_elide_idx >= 0 && i == dims_to_elide[dims_to_elide_idx]) { + CHECK_EQ(input_shape.dimensions(i), 1); + dims_to_elide_idx--; + } else { + new_shape_dim_bounds.push_back(input_shape.dimensions(i)); + } + } + + c_reverse(new_shape_dim_bounds); + Shape output_shape = + ShapeUtil::MakeShape(input_shape.element_type(), new_shape_dim_bounds); + return CreateReshapeHlo(output_shape, operand); +} + +StatusOr PadVectorWithZeros(HloInstruction* operand, + int64 zeros_to_prepend, + int64 zeros_to_append) { + HloComputation* computation = operand->parent(); + CHECK_EQ(operand->shape().dimensions_size(), 1); + PaddingConfig padding_config; + PaddingConfig::PaddingConfigDimension padding_config_dim; + padding_config_dim.set_edge_padding_low(zeros_to_prepend); + padding_config_dim.set_edge_padding_high(zeros_to_append); + *padding_config.add_dimensions() = padding_config_dim; + + HloInstruction* zero = + computation->AddInstruction(HloInstruction::CreateConstant( + MakeUnique(Literal::Zero(operand->shape().element_type())))); + return CreatePadHlo(operand, zero, padding_config); +} + +StatusOr BroadcastZeros( + HloComputation* computation, PrimitiveType element_type, + ArraySlice broadcast_dimensions) { + HloInstruction* zero = + computation->AddInstruction(HloInstruction::CreateConstant( + MakeUnique(Literal::Zero(element_type)))); + return CreateBroadcastHlo(zero, /*broadcast_dimensions=*/{}, + /*result_shape_bounds=*/broadcast_dimensions); +} + +StatusOr> CreateComputationWithSignature( + ArraySlice domain, const Shape& range, + tensorflow::StringPiece name) { + HloComputation::Builder b(name.ToString()); + int64 param_idx = 0; + for (const Shape* param_shape : domain) { + b.AddInstruction(HloInstruction::CreateParameter( + param_idx, *param_shape, StrCat("param.", param_idx))); + param_idx++; + } + + // We can't change the root type of a computation once it is created so create + // a dummy root instruction to give the computation the right root shape. In + // the future we may want to use a (recursive) broadcast here to avoid + // creating large constants. + b.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateFromShape(range))); + + return b.Build(); +} + +} // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_creation_utils.h b/tensorflow/compiler/xla/service/hlo_creation_utils.h new file mode 100644 index 0000000000000000000000000000000000000000..2b03a849cff35008a96eaedd212ab1aa24695822 --- /dev/null +++ b/tensorflow/compiler/xla/service/hlo_creation_utils.h @@ -0,0 +1,163 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_HLO_CREATION_UTILS_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_HLO_CREATION_UTILS_H_ + +#include "tensorflow/compiler/xla/service/hlo_computation.h" +#include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/compiler/xla/statusor.h" + +namespace xla { + +// Some lightweight utilities intended to make HLO instruction creation more +// ergonomic. We don't have a complete set of helpers yet -- I expect we'll +// expand this interface as needed on an ad-hoc basis. + +// Creates a binary HLO instruction and adds it to the computation containing +// `lhs` and `rhs` (`lhs` and `rhs` must be in the same computation). +StatusOr CreateBinaryHlo(HloOpcode opcode, HloInstruction* lhs, + HloInstruction* rhs); + +// Creates a pad HLO instruction and adds it to the computation containing +// `operand` and `padding_value` (`operand` and `padding_value` must be in the +// same computation). +StatusOr CreatePadHlo(HloInstruction* operand, + HloInstruction* padding_value, + const PaddingConfig& padding_config); + +// Creates a slice HLO instruction and adds it to the computation containing +// `operand`. +StatusOr CreateSliceHlo( + HloInstruction* operand, tensorflow::gtl::ArraySlice start_indices, + tensorflow::gtl::ArraySlice limit_indices, + tensorflow::gtl::ArraySlice strides); + +// Creates a convolution HLO instruction and adds it to the computation +// containing `lhs` and `rhs` (`lhs` and `rhs` must be in the same computation). +StatusOr CreateConvolveHlo( + HloInstruction* lhs, HloInstruction* rhs, const Window& window, + const ConvolutionDimensionNumbers& dimension_numbers); + +// Creates a transpose HLO instruction and adds it to the computation containing +// `operand`. +StatusOr CreateTransposeHlo( + HloInstruction* operand, tensorflow::gtl::ArraySlice dimensions); + +// Creates a reshape HLO instruction and adds it to the computation containing +// `operand`. +StatusOr CreateReshapeHlo(const Shape& result_shape, + HloInstruction* operand); + +StatusOr CreateReshapeHlo( + tensorflow::gtl::ArraySlice result_shape_dim_bounds, + HloInstruction* operand); + +// Creates a dynamic-slice HLO instruction and adds it to the computation +// containing `operand` and `start_indices` (`operand` and `start_indices` must +// be in the same computation). +StatusOr CreateDynamicSliceHlo( + HloInstruction* operand, HloInstruction* start_indices, + tensorflow::gtl::ArraySlice slice_sizes); + +// Creates a dynamic-update-slice HLO instruction and adds it to the computation +// containing `operand`, `update` and `start_indices` (`operand`, `update` and +// `start_indices` must be in the same computation). +StatusOr CreateDynamicUpdateSliceHlo( + HloInstruction* operand, HloInstruction* update, + HloInstruction* start_indices); + +// Creates a broadcast HLO instruction and adds it to the computation containing +// `operand`. +StatusOr CreateBroadcastHlo( + HloInstruction* operand, + tensorflow::gtl::ArraySlice broadcast_dimensions, + tensorflow::gtl::ArraySlice result_shape_bounds); + +// Creates a GetTupleElement HLO instruction and adds it to the computation +// containing `operand`. +StatusOr CreateGetTupleElementHlo(HloInstruction* operand, + int64 index); + +// Creates a Concatenate HLO instruction and adds it to the computation +// containing `operands` (`operands` must be non-empty and every element must be +// contained in the same computation). +StatusOr CreateConcatHlo( + tensorflow::gtl::ArraySlice operands, int64 dimension); + +// ----------------------------------------------------------------------------- +// Some other miscellaneous helpers to generate common HLO patterns. All of +// these add all the instructions they generate into the computation containing +// their operand(s). + +// Collapses (via reshape) the first N (logical) dimensions of `operand` into a +// single leading dimension. `operand` must have rank > n. +// +// For instance if `operand` has shape f32[7,8,9] and n is 2 then the output is +// the `operand` reshaped to [56,9]. +StatusOr CollapseFirstNDims(HloInstruction* operand, int64 n); + +// Expands (via reshape) the first (logical) dimension of `operand` into a +// sequence of `expanded_dims` dimensions. `operand` must at least be of rank 1 +// and the number of elements in its first dimension must be equal to the +// product of `expanded_dims`. +// +// For instance if `operand` has shape f32[200,9,7] and expanded_dims is +// {2,5,20} the result is `operand` reshaped to [2,5,20,9,7]. +StatusOr ExpandFirstDimIntoNDims( + HloInstruction* operand, tensorflow::gtl::ArraySlice expanded_dims); + +// Expands (via reshape) the last (logical) dimension of `operand` into a +// sequence of `expanded_dims` dimensions. `operand` must at least be of rank 1 +// and the number of elements in its last dimension must be equal to the +// product of `expanded_dims`. +// +// For instance if `operand` has shape f32[9,7,200] and expanded_dims is +// {2,5,20} the result is `operand` reshaped to [9,7,2,5,20]. +StatusOr ExpandLastDimIntoNDims( + HloInstruction* operand, tensorflow::gtl::ArraySlice expanded_dims); + +// Elides (via reshape) a set of degenerate dimensions (dimensions containing +// exactly one element), `dims_to_elide` from `operand`. Every dimension in +// `dims_to_elide` must be a degenerate dimension. `dims_to_elide` must be +// sorted and not contain duplicates. +// +// For example if `operand` is of shape f32[19,1,20,1,7,1,9] and dims_to_elide +// is {1,5} then the result is `operand` reshaped to [19,20,1,7,9]. +StatusOr ElideDegenerateDims( + HloInstruction* operand, tensorflow::gtl::ArraySlice dims_to_elide); + +// Pads `operand` (which must have rank 1) with `zeros_to_prepend` zeros in the +// front and `zeros_to_append` zeros in the back. +StatusOr PadVectorWithZeros(HloInstruction* operand, + int64 zeros_to_prepend, + int64 zeros_to_append); + +// Broadcasts a zero value of type `element_type` into a tensor with element +// type `element_type` and dimension bounds `broadcast_dimensions`. The +// broadcast instruction is emitted into `computation`. +StatusOr BroadcastZeros( + HloComputation* computation, PrimitiveType element_type, + tensorflow::gtl::ArraySlice broadcast_dimensions); + +// Creates a HLO computation that takes arguments of type `domain` and produces +// a value of type `range`. +StatusOr> CreateComputationWithSignature( + tensorflow::gtl::ArraySlice domain, const Shape& range, + tensorflow::StringPiece name); + +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_HLO_CREATION_UTILS_H_ diff --git a/tensorflow/compiler/xla/service/hlo_dce.cc b/tensorflow/compiler/xla/service/hlo_dce.cc index 1e5f0f797a13fd7e7ce1cc934387a274a74153bc..fcd723af146e2227b8661b1a4993f1338f7de389 100644 --- a/tensorflow/compiler/xla/service/hlo_dce.cc +++ b/tensorflow/compiler/xla/service/hlo_dce.cc @@ -40,7 +40,7 @@ StatusOr HloDCE::Run(HloModule* module) { VLOG(2) << "Before dce:"; XLA_VLOG_LINES(2, module->ToString()); - for (auto* computation : module->MakeNonfusionComputations()) { + for (auto* computation : module->MakeComputationPostOrder()) { std::unordered_set live_instructions; TF_RETURN_IF_ERROR(computation->root_instruction()->Accept( [&live_instructions](HloInstruction* instruction) { diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.cc b/tensorflow/compiler/xla/service/hlo_evaluator.cc index 15ae53128aa5dfe706daa6d47dc6d842fd78e26c..91341b5d35d85b904715fb5a059f51fff13ac4da 100644 --- a/tensorflow/compiler/xla/service/hlo_evaluator.cc +++ b/tensorflow/compiler/xla/service/hlo_evaluator.cc @@ -51,12 +51,22 @@ namespace xla { namespace { +using tensorflow::gtl::ArraySlice; +using tensorflow::gtl::FlatSet; +using tensorflow::gtl::optional; + template struct is_complex_t : public std::false_type {}; template <> struct is_complex_t : public std::true_type {}; +template +struct is_complex64_t : public std::false_type {}; + +template <> +struct is_complex64_t : public std::true_type {}; + template StatusOr> Compare(const Shape& shape, HloOpcode opcode, const Literal& lhs_literal, @@ -99,11 +109,10 @@ StatusOr> Compare(const Shape& shape, HloOpcode opcode, } auto result = Literal::CreateFromShape(shape); - TF_RETURN_IF_ERROR(result->Populate( - [&](tensorflow::gtl::ArraySlice multi_index) { - return compare_op(lhs_literal.Get(multi_index), - rhs_literal.Get(multi_index)); - })); + TF_RETURN_IF_ERROR(result->Populate([&](ArraySlice multi_index) { + return compare_op(lhs_literal.Get(multi_index), + rhs_literal.Get(multi_index)); + })); return std::move(result); } @@ -130,11 +139,10 @@ StatusOr> Compare( } auto result = Literal::CreateFromShape(shape); - TF_RETURN_IF_ERROR(result->Populate( - [&](tensorflow::gtl::ArraySlice multi_index) { - return compare_op(lhs_literal.Get(multi_index), - rhs_literal.Get(multi_index)); - })); + TF_RETURN_IF_ERROR(result->Populate([&](ArraySlice multi_index) { + return compare_op(lhs_literal.Get(multi_index), + rhs_literal.Get(multi_index)); + })); return std::move(result); } @@ -159,8 +167,8 @@ StatusOr> ElementWiseUnaryOpImpl( auto result = Literal::CreateFromShape(shape); - TF_RETURN_IF_ERROR(result->Populate( - [&](tensorflow::gtl::ArraySlice multi_index) { + TF_RETURN_IF_ERROR( + result->Populate([&](ArraySlice multi_index) { return unary_op(operand_literal.Get(multi_index)); })); return std::move(result); @@ -172,7 +180,7 @@ StatusOr> ElementWiseUnaryOpImpl( // with the base index. void IterateThroughWindow( const Shape& window_shape, const Window& window, const Shape& base_shape, - const tensorflow::gtl::ArraySlice& window_count_index, + const ArraySlice& window_count_index, const std::function&)>& f) { const int64 rank = ShapeUtil::Rank(base_shape); DimensionVector window_index(rank); @@ -248,17 +256,37 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { template < typename NativeT, - typename std::enable_if::value || - is_complex_t::value>::type* = nullptr> + typename std::enable_if::value>::type* = nullptr> Status HandleAbs(HloInstruction* abs) { TF_ASSIGN_OR_RETURN(parent_->evaluated_[abs], - ElementWiseUnaryOp(abs, [](ElementwiseT elem_operand) { + ElementWiseUnaryOp(abs, [](NativeT elem_operand) { return std::abs(elem_operand); })); return Status::OK(); } + template < + typename NativeT, + typename std::enable_if::value>::type* = nullptr> + Status HandleAbs(HloInstruction* abs) { + const Literal& operand_literal = + parent_->GetEvaluatedLiteralFor(abs->operand(0)); + TF_ASSIGN_OR_RETURN( + parent_->evaluated_[abs], + (ElementWiseUnaryOpImpl( + abs, [](NativeT elem_operand) { return std::abs(elem_operand); }, + operand_literal))); + + return Status::OK(); + } + Status HandleAbs(HloInstruction* abs) override { + // If the operand is of C64 type, the return type of abs will be F32. + // However, ElementwiseT would still be the return type, F32, and thus + // specifying the ElementwiseT explicitly as C64 is needed below. + if (abs->operand(0)->shape().element_type() == C64) { + return HandleAbs(abs); + } return HandleAbs(abs); } @@ -306,13 +334,12 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { operand_to_broadcast.shape().dimensions(i)); } - return output->Populate( - [&](tensorflow::gtl::ArraySlice multi_index) { - for (int64 i = 0; i < broadcast->dimensions().size(); ++i) { - broadcast_indices[i] = multi_index[broadcast->dimensions(i)]; - } - return operand_to_broadcast.Get(broadcast_indices); - }); + return output->Populate([&](ArraySlice multi_index) { + for (int64 i = 0; i < broadcast->dimensions().size(); ++i) { + broadcast_indices[i] = multi_index[broadcast->dimensions(i)]; + } + return operand_to_broadcast.Get(broadcast_indices); + }); } template < @@ -586,14 +613,25 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { return Status::OK(); } - template < - typename NativeT, - typename std::enable_if::value>::type* = nullptr> + template ::value>::type* = + nullptr> + Status HandleMaximum(HloInstruction* maximum) { + TF_ASSIGN_OR_RETURN( + parent_->evaluated_[maximum], + ElementWiseBinaryOp(maximum, [](ElementwiseT lhs, ElementwiseT rhs) { + return std::max(lhs, rhs); + })); + return Status::OK(); + } + + template ::value>::type* = nullptr> Status HandleMaximum(HloInstruction* maximum) { TF_ASSIGN_OR_RETURN( parent_->evaluated_[maximum], ElementWiseBinaryOp(maximum, [](ElementwiseT lhs, ElementwiseT rhs) { - return std::fmax(lhs, rhs); + return ((lhs >= rhs) || std::isnan(lhs)) ? lhs : rhs; })); return Status::OK(); } @@ -609,18 +647,30 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { return HandleMaximum(maximum); } - template < - typename NativeT, - typename std::enable_if::value>::type* = nullptr> + template ::value>::type* = + nullptr> Status HandleMinimum(HloInstruction* minimum) { TF_ASSIGN_OR_RETURN(parent_->evaluated_[minimum], ElementWiseBinaryOp(minimum, [](ElementwiseT lhs_el, ElementwiseT rhs_el) { - return std::fmin(lhs_el, rhs_el); + return std::min(lhs_el, rhs_el); })); return Status::OK(); } + template ::value>::type* = nullptr> + Status HandleMinimum(HloInstruction* minimum) { + TF_ASSIGN_OR_RETURN( + parent_->evaluated_[minimum], + ElementWiseBinaryOp(minimum, [](ElementwiseT lhs_el, + ElementwiseT rhs_el) { + return ((lhs_el <= rhs_el) || std::isnan(lhs_el)) ? lhs_el : rhs_el; + })); + return Status::OK(); + } + template < typename NativeT, typename std::enable_if::value>::type* = nullptr> @@ -825,7 +875,7 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { Status HandleClamp(HloInstruction* clamp) { std::function clamp_op = [](ElementwiseT low, ElementwiseT value, ElementwiseT high) { - return std::fmax(low, std::fmin(value, high)); + return std::fmin(high, std::fmax(value, low)); }; TF_ASSIGN_OR_RETURN( parent_->evaluated_[clamp], @@ -846,6 +896,7 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { } Status HandleSelect(HloInstruction* select) override { + CHECK(!ShapeUtil::IsScalar(select->operand(0)->shape())); CHECK(!ShapeUtil::IsTuple(select->shape())); std::function select_op = [](bool pred, ReturnT on_true, ReturnT on_false) { @@ -876,8 +927,8 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { const Literal& operand_literal = parent_->GetEvaluatedLiteralFor(operand); auto result = Literal::CreateFromShape(result_shape); - TF_RETURN_IF_ERROR(result->Populate( - [&](tensorflow::gtl::ArraySlice out_index) { + TF_RETURN_IF_ERROR( + result->Populate([&](ArraySlice out_index) { std::vector from_index(out_index.begin(), out_index.end()); for (const int64 dim : reverse_dimensions) { from_index[dim] = result_shape.dimensions(dim) - 1 - out_index[dim]; @@ -952,7 +1003,7 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { DimensionVector rhs_index(rhs_rank); DimensionVector rhs_spatial_index(dnums.kernel_spatial_dimensions_size()); - auto func = [&](tensorflow::gtl::ArraySlice out_index) { + auto func = [&](ArraySlice out_index) { ElementwiseT result_val = static_cast(0); std::fill(lhs_index.begin(), lhs_index.end(), 0); @@ -1074,9 +1125,8 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { } std::vector rhs_non_batch_non_contracting_dims; - tensorflow::gtl::FlatSet batch_dims_set( - dnums.rhs_batch_dimensions().begin(), - dnums.rhs_batch_dimensions().end()); + FlatSet batch_dims_set(dnums.rhs_batch_dimensions().begin(), + dnums.rhs_batch_dimensions().end()); for (int64 i = 0; i < rhs_rank; i++) { if (i != rhs_contracting_dimension && batch_dims_set.count(i) == 0) { rhs_non_batch_non_contracting_dims.push_back(i); @@ -1088,8 +1138,8 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { DimensionVector lhs_index(lhs_rank); DimensionVector rhs_index(rhs_rank); - TF_RETURN_IF_ERROR(result->Populate( - [&](tensorflow::gtl::ArraySlice result_index) { + TF_RETURN_IF_ERROR( + result->Populate([&](ArraySlice result_index) { ElementwiseT result_val = static_cast(0); // Find the corresponding non-contracting indices for lhs and rhs. @@ -1183,9 +1233,7 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { parent_->GetEvaluatedLiteralFor(pad->operand(1)).Get({}); auto result = Literal::CreateFromShape(pad->shape()); TF_RETURN_IF_ERROR(result->Populate( - [&scalar](tensorflow::gtl::ArraySlice multi_index) { - return scalar; - })); + [&scalar](ArraySlice multi_index) { return scalar; })); const Literal& evaluated_operand = parent_->GetEvaluatedLiteralFor(pad->operand(0)); @@ -1198,7 +1246,7 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { // corresponding index of the resulting padded literal. const PaddingConfig& pad_config = pad->padding_config(); - auto func = [&](const std::vector& input_index) { + auto func = [&](ArraySlice input_index) { for (auto i = 0; i < input_index.size(); ++i) { // Interior padding occurs logically before edge padding, so in the case // of negative edge padding elements are removed from the @@ -1348,9 +1396,9 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { auto result = Literal::CreateFromShape(map->shape()); - HloEvaluator embedded_evaluator; - TF_RETURN_IF_ERROR(result->Populate( - [&](tensorflow::gtl::ArraySlice multi_index) { + HloEvaluator embedded_evaluator(parent_->max_loop_iterations_); + TF_RETURN_IF_ERROR( + result->Populate([&](ArraySlice multi_index) { std::vector> arg_literals; arg_literals.reserve(operands.size()); @@ -1440,7 +1488,7 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { Status HandleReduce(HloInstruction* reduce) override { auto arg = reduce->operand(0); auto init_value = reduce->operand(1); - tensorflow::gtl::ArraySlice dimensions(reduce->dimensions()); + ArraySlice dimensions(reduce->dimensions()); HloComputation* function = reduce->to_apply(); TF_RET_CHECK(ShapeUtil::Rank(reduce->shape()) == ShapeUtil::Rank(arg->shape()) - dimensions.size()); @@ -1483,10 +1531,10 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { } } - HloEvaluator embedded_evaluator; + HloEvaluator embedded_evaluator(parent_->max_loop_iterations_); // For each resulting dimension, calculate and assign computed value. - TF_RETURN_IF_ERROR(result->Populate( - [&](tensorflow::gtl::ArraySlice multi_index) { + TF_RETURN_IF_ERROR( + result->Populate([&](ArraySlice multi_index) { ReturnT result_val = init_scalar; std::vector base(arg_dimensions.size()); @@ -1494,7 +1542,7 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { base[result_to_arg_index[i]] = multi_index[i]; } - auto func = [&](const std::vector& input_index) { + auto func = [&](ArraySlice input_index) { auto curr_val = arg_literal.Get(input_index); // Evaluate computation with specified literal operands. @@ -1540,9 +1588,7 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { // Initialize result array with the init value. TF_RETURN_IF_ERROR(result->Populate( - [&](tensorflow::gtl::ArraySlice output_index) { - return init_scalar; - })); + [&](ArraySlice output_index) { return init_scalar; })); std::vector window_dimension_sizes; for (const auto& window_dimension : window.dimensions()) { @@ -1559,7 +1605,7 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { int64 rank = ShapeUtil::Rank(operand_literal.shape()); - HloEvaluator embedded_evaluator; + HloEvaluator embedded_evaluator(parent_->max_loop_iterations_); DimensionVector source_index(rank); std::fill(source_index.begin(), source_index.end(), 0); @@ -1575,8 +1621,8 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { // 2. Using the selected index, scatter value from `source` to result. We // do this by iterating through the window, and compare each index with // the selected index. - tensorflow::gtl::optional selected_val; - tensorflow::gtl::optional> selected_index; + optional selected_val; + optional> selected_index; IterateThroughWindow( window_shape, window, operand_literal.shape(), source_index, @@ -1591,11 +1637,11 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { Literal::CreateR0(*selected_val); const std::vector args = { - curr_val_literal.get(), selected_val_literal.get()}; + selected_val_literal.get(), curr_val_literal.get()}; std::unique_ptr computed_result = embedded_evaluator.Evaluate(*select, args) .ConsumeValueOrDie(); - bool selected = computed_result->Get({}); + bool selected = !computed_result->Get({}); if (selected) { selected_val = curr_val; selected_index = operand_index; @@ -1670,10 +1716,10 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { DimensionVector window_index(window.dimensions_size()); DimensionVector operand_index(ShapeUtil::Rank(operand_literal.shape())); - HloEvaluator embedded_evaluator; + HloEvaluator embedded_evaluator(parent_->max_loop_iterations_); // For each resulting dimension, calculate and assign computed value. - TF_RETURN_IF_ERROR(result->Populate( - [&](tensorflow::gtl::ArraySlice output_index) { + TF_RETURN_IF_ERROR( + result->Populate([&](ArraySlice output_index) { ReturnT result_val = init_scalar; std::fill(window_index.begin(), window_index.end(), 0); @@ -1723,7 +1769,7 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { const int64 rank = ShapeUtil::Rank(operand->shape()); const Literal& operand_literal = parent_->GetEvaluatedLiteralFor(operand); - auto func = [&](tensorflow::gtl::ArraySlice out_index) { + auto func = [&](ArraySlice out_index) { DimensionVector operand_index(rank); for (int64 i = 0; i < rank; ++i) { operand_index[i] = @@ -1904,8 +1950,8 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { std::vector operand_indices(start.size()); auto result = Literal::CreateFromShape(result_shape); - TF_RETURN_IF_ERROR(result->Populate( - [&](tensorflow::gtl::ArraySlice multi_index) { + TF_RETURN_IF_ERROR( + result->Populate([&](ArraySlice multi_index) { for (int64 i = 0; i < operand_indices.size(); ++i) { CHECK_GE(multi_index[i] + start[i], 0); // Mod is only used here to be consistent with the existing @@ -1925,17 +1971,26 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { StatusOr> DynamicUpdateSlice( const Literal& operand_literal, const Literal& update_literal, const Literal& start_indices_literal) { - auto start_indices_typed = start_indices_literal.data(); - const std::vector start(start_indices_typed.begin(), - start_indices_typed.end()); - auto result = operand_literal.CloneToUnique(); - std::vector result_index(ShapeUtil::Rank(result->shape()), 0); + auto start_indices_typed = start_indices_literal.data(); + const auto rank = ShapeUtil::Rank(result->shape()); + std::vector start(rank, 0); + for (int64 i = 0; i < rank; ++i) { + // All other implementations currently wrap-around the index, so this + // should do so as well. + start[i] = (start_indices_typed[i] % result->shape().dimensions(i)); + start[i] += (start[i] < 0) * result->shape().dimensions(i); + } + std::vector result_index(rank, 0); - auto func = [&](const std::vector& update_index) { + auto func = [&](ArraySlice update_index) { std::transform(update_index.begin(), update_index.end(), start.begin(), result_index.begin(), std::plus()); - + // Same as above, wrap-around only to match other implementations' + // semantics. + std::transform(result_index.begin(), result_index.end(), + result->shape().dimensions().begin(), result_index.begin(), + std::modulus()); result->Set(result_index, update_literal.Get(update_index)); return true; @@ -1988,8 +2043,8 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { auto result = Literal::CreateFromShape(shape); - TF_RETURN_IF_ERROR(result->Populate( - [&](tensorflow::gtl::ArraySlice multi_index) { + TF_RETURN_IF_ERROR( + result->Populate([&](ArraySlice multi_index) { return ConvertBinaryFunction(binary_op)( lhs_literal.Get(multi_index), rhs_literal.Get(multi_index)); @@ -2026,8 +2081,8 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { auto result = Literal::CreateFromShape(shape); - TF_RETURN_IF_ERROR(result->Populate( - [&](tensorflow::gtl::ArraySlice multi_index) { + TF_RETURN_IF_ERROR( + result->Populate([&](ArraySlice multi_index) { return ternary_op(lhs_literal.Get(multi_index), rhs_literal.Get(multi_index), ehs_literal.Get(multi_index)); @@ -2047,17 +2102,20 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { HloEvaluator* parent_; }; // class HloEvaluator::TypedVisitor -HloEvaluator::HloEvaluator() { +HloEvaluator::HloEvaluator(int64 max_loop_iterations) + : max_loop_iterations_(max_loop_iterations) { typed_visitors_[PRED] = MakeUnique>(this); typed_visitors_[U8] = MakeUnique>(this); typed_visitors_[U16] = MakeUnique([](HloInstruction*) { - return Unimplemented("HloEvaluator: unhandled primitive type: U16."); + return Unimplemented( + "HloEvaluator::TypedVisitor: unhandled primitive type: U16."); }); typed_visitors_[U32] = MakeUnique>(this); typed_visitors_[U64] = MakeUnique>(this); typed_visitors_[S8] = MakeUnique>(this); typed_visitors_[S16] = MakeUnique([](HloInstruction*) { - return Unimplemented("HloEvaluator: unhandled primitive type: S16."); + return Unimplemented( + "HloEvaluator::TypedVisitor: unhandled primitive type: S16."); }); typed_visitors_[S32] = MakeUnique>(this); typed_visitors_[S64] = MakeUnique>(this); @@ -2071,18 +2129,20 @@ HloEvaluator::HloEvaluator() { // elementwise computations to be done in F32 and do BF16<->F32 conversion // around the input and the output of the computations. typed_visitors_[BF16] = MakeUnique>(this); + typed_visitors_[TUPLE] = MakeUnique([](HloInstruction*) { - return Unimplemented("HloEvaluator: unhandled primitive type: TUPLE."); + return Unimplemented( + "HloEvaluator::TypedVistor: unhandled primitive type: TUPLE."); }); typed_visitors_[OPAQUE] = MakeUnique([](HloInstruction*) { - return Unimplemented("HloEvaluator: unhandled primitive type: OPAQUE."); + return Unimplemented( + "HloEvaluator::TypedVisitor: unhandled primitive type: OPAQUE."); }); } template StatusOr> HloEvaluator::Evaluate( - const HloModule& module, - tensorflow::gtl::ArraySlice arg_literals) { + const HloModule& module, ArraySlice arg_literals) { XLA_VLOG_LINES(2, "HloEvaluator::Evaluate module:\n" + module.ToString()); evaluated_.clear(); @@ -2099,8 +2159,8 @@ StatusOr> HloEvaluator::Evaluate( template StatusOr> HloEvaluator::Evaluate( - const HloComputation& computation, - tensorflow::gtl::ArraySlice arg_literals) { + const HloComputation& computation, ArraySlice arg_literals) { + CHECK(computation.parent() != nullptr); XLA_VLOG_LINES( 2, "HloEvaluator::Evaluate computation:\n" + computation.ToString()); @@ -2116,8 +2176,7 @@ StatusOr> HloEvaluator::Evaluate( template StatusOr> HloEvaluator::Evaluate( - HloInstruction* instruction, - tensorflow::gtl::ArraySlice arg_literals) { + HloInstruction* instruction, ArraySlice arg_literals) { TF_RET_CHECK(hlo_query::AllOperandsAreParametersOrConstants(*instruction)); TF_RETURN_IF_ERROR(ShapeUtil::ValidateShape(instruction->shape())); @@ -2242,8 +2301,7 @@ Status HloEvaluator::HandleTranspose(HloInstruction* transpose) { } Status HloEvaluator::HandleConcatenate(HloInstruction* concatenate) { - tensorflow::gtl::ArraySlice operands( - concatenate->operands()); + ArraySlice operands(concatenate->operands()); // The result concatenate dimension is going to be the sum of all // concatenate dimensions of the operands taking part of the operation. const Shape& reference_shape = operands[0]->shape(); @@ -2415,6 +2473,340 @@ Status HloEvaluator::HandleTuple(HloInstruction* tuple) { return Status::OK(); } +// Returns an ShapeUtil::IndexIterationSpace that iterates over the output +// gather dimensions while keeping the rest of the output dimensions clamped to +// 0. +ShapeUtil::IndexIterationSpace IterationSpaceForOutputGatherIndices( + const Shape& output_shape, const GatherDimensionNumbers& dim_numbers) { + int64 output_rank = output_shape.dimensions_size(); + std::vector index_base(output_rank, 0); + std::vector index_count; + index_count.reserve(output_rank); + for (int64 i = 0; i < output_rank; i++) { + bool is_output_gather_dim = + !c_binary_search(dim_numbers.output_window_dims(), i); + index_count.push_back(is_output_gather_dim ? output_shape.dimensions(i) + : 1); + } + + return {std::move(index_base), std::move(index_count), + std::vector(output_rank, 1)}; +} + +// Return an ShapeUtil::IndexIterationSpace that iterates over the output window +// dimensions while keeping the rest of the output dimensions clamped to 0. +ShapeUtil::IndexIterationSpace IterationSpaceForOutputWindowIndices( + int64 output_rank, ArraySlice window_bounds, + const GatherDimensionNumbers& dim_numbers) { + std::vector index_base(output_rank, 0); + std::vector index_count(output_rank, 1); + int64 window_bounds_idx = 0; + for (int64 i = 0; i < output_rank; i++) { + bool is_output_window_dim = + c_binary_search(dim_numbers.output_window_dims(), i); + if (is_output_window_dim) { + while (c_binary_search(dim_numbers.elided_window_dims(), + window_bounds_idx)) { + window_bounds_idx++; + } + index_count[i] = window_bounds[window_bounds_idx++]; + } + } + + return {std::move(index_base), std::move(index_count), + std::vector(output_rank, 1)}; +} + +// This functor computes the contribution of gather_indices to an input index +// corresponding to an output index. That is, given an output index I, it picks +// out the gather output indices in I and uses them to look up a gather index, +// G, from the gather indices tensor, and expands G into the input space +// according to gather_dims_to_operand_dims. +class OutputGatherIndexToInputIndex { + public: + // The constructor does some setup work that is amortized across all + // iterations. + explicit OutputGatherIndexToInputIndex( + const GatherDimensionNumbers* dim_numbers, const Shape& input_shape, + const Shape& output_shape, const Literal* gather_indices) + : dim_numbers_(*dim_numbers), gather_indices_(*gather_indices) { + for (int64 i = 0; i < output_shape.dimensions_size(); i++) { + output_dim_is_gather_dims_.push_back( + !c_binary_search(dim_numbers_.output_window_dims(), i)); + } + + for (int64 i = 0; i < input_shape.dimensions_size(); i++) { + int64 index_of_input_dim_in_index_vector = + std::distance(dim_numbers_.gather_dims_to_operand_dims().begin(), + c_find(dim_numbers_.gather_dims_to_operand_dims(), i)); + if (index_of_input_dim_in_index_vector == + dim_numbers_.gather_dims_to_operand_dims_size()) { + input_dim_value_to_index_vector_.push_back(-1); + } else { + input_dim_value_to_index_vector_.push_back( + index_of_input_dim_in_index_vector); + } + } + + index_vector_index_.resize(gather_indices_.shape().dimensions_size()); + input_index_.resize(input_shape.dimensions_size()); + int64 index_vector_size = + gather_indices_.shape().dimensions(dim_numbers_.index_vector_dim()); + index_vector_.resize(index_vector_size); + } + + // Returns the contribution of gather_indices to the input index corresponding + // to output_index. See gather_inner_loop_body. + // + // This is conceptually a stateless transformation from output_index to the + // gather input index, but: + // + // - Instead of allocating memory to represent the gather input index on + // every invocation we reuse the same storage for the result + // (input_index_), mutating it in place. + // - Instead of allocating buffers for temporary values like + // index_vector_index_ and index_vector on every invocation, we reuse the + // same storage for all invocations. + // + // This returns an arrayslice into memory owned by the class. + StatusOr> operator()(ArraySlice output_index) { + PropagateOutputIndexGatherDimsToIndexVectorIndex(output_index); + TF_RETURN_IF_ERROR(FetchIndexVector()); + PropagateIndexVectorToInputIndex(); + return ArraySlice(input_index_); + } + + private: + // Propagates the gather index dimensions from the output index into + // index_vector_index_ by mutating index_vector_index_ in place. Does not + // update the dim_numbers.index_vector_dim() dimension -- that's the dimension + // we iterate over in FetchIndexVector. + void PropagateOutputIndexGatherDimsToIndexVectorIndex( + ArraySlice output_index) { + int64 index_vector_index_i = 0; + for (int64 i = 0, e = output_index.size(); i < e; i++) { + if (!output_dim_is_gather_dims_[i]) { + continue; + } + + if (index_vector_index_i == dim_numbers_.index_vector_dim()) { + index_vector_index_i++; + } + + index_vector_index_[index_vector_index_i++] = output_index[i]; + } + } + + // Populates index_vector_ by iterating over gather_indices_ according to + // index_vector_index_. + Status FetchIndexVector() { + int64 index_vector_dim = dim_numbers_.index_vector_dim(); + for (int64 i = 0, e = index_vector_.size(); i < e; i++) { + index_vector_index_[index_vector_dim] = i; + TF_ASSIGN_OR_RETURN(index_vector_[i], gather_indices_.GetIntegralAsS64( + index_vector_index_)); + } + return Status::OK(); + } + + // Populates input_index_. + void PropagateIndexVectorToInputIndex() { + for (int64 i = 0, e = input_index_.size(); i < e; i++) { + if (input_dim_value_to_index_vector_[i] != -1) { + input_index_[i] = index_vector_[input_dim_value_to_index_vector_[i]]; + } + + // If input_dim_value_to_index_vector_[i] == -1 then input_index_[i] + // remains 0, as set by the constructor. + } + } + + // input_dim_value_to_index_vector_[i] tells us how to compute dimension i of + // the input index from the index vector. See + // PropagateIndexVectorToInputIndex. + std::vector input_dim_value_to_index_vector_; + + // output_dim_is_gather_dims_[i] is true iff the output index i is a gather + // dimension. + std::vector output_dim_is_gather_dims_; + + // The buffer into which we construct an index into gather_indices_ to fetch + // the index vector. + std::vector index_vector_index_; + + // The index vector fetched from gather_indices_. + std::vector index_vector_; + + // The result computed by this functor. operator() returns an ArraySlice into + // this vector. + std::vector input_index_; + + const GatherDimensionNumbers& dim_numbers_; + const Literal& gather_indices_; +}; + +// This functor computes the contribution of the window indices in an output +// index to an input index. That is, given an output index I it picks out the +// output window indices in I and expands it into a window index into the input +// shape. +class OutputWindowIndexToInputIndex { + public: + // The constructor does some setup work that is amortized across all + // iterations. + explicit OutputWindowIndexToInputIndex( + const GatherDimensionNumbers& dim_numbers, const Shape& input_shape, + const Shape& output_shape) { + std::vector window_index_to_output_index; + int64 output_index_count = 0; + for (int64 i = 0; i < output_shape.dimensions_size(); i++) { + if (c_binary_search(dim_numbers.output_window_dims(), i)) { + window_index_to_output_index.push_back(output_index_count++); + } else { + output_index_count++; + } + } + + int64 window_dim_count = 0; + for (int64 i = 0; i < input_shape.dimensions_size(); i++) { + if (c_binary_search(dim_numbers.elided_window_dims(), i)) { + input_dim_value_to_output_index_.push_back(-1); + } else { + input_dim_value_to_output_index_.push_back( + window_index_to_output_index[window_dim_count++]); + } + } + + input_index_.resize(input_shape.dimensions_size()); + } + + // Returns the contribution of the window indices to the input index + // corresponding to output_index. See gather_inner_loop_body. + // + // This is conceptually a stateless transformation from output_index to the + // window input index, but instead of allocating memory to represent the + // gather input index on every invocation we reuse the same storage for the + // result (input_index_), mutating it in place. + // + // This returns an arrayslice into memory owned by the class. + StatusOr> operator()(ArraySlice output_index) { + PropagateOutputIndexWindowDimsToInputIndex(output_index); + return ArraySlice(input_index_); + } + + private: + // Propagates window dimensions from the output index to input_index_ by + // mutating input_index_ in place. + void PropagateOutputIndexWindowDimsToInputIndex( + ArraySlice output_index) { + for (int64 i = 0, e = input_index_.size(); i < e; i++) { + if (input_dim_value_to_output_index_[i] != -1) { + input_index_[i] = output_index[input_dim_value_to_output_index_[i]]; + } + + // If input_dim_value_to_index_vector_[i] == -1 then input_index_[i] + // remains 0, as set by the constructor. + } + } + + // input_dim_value_to_index_vector_[i] tells us how to compute dimension i of + // the input index from the output index. See + // PropagateOutputIndexToInputIndex. + std::vector input_dim_value_to_output_index_; + + // The result computed by this functor. operator() returns an ArraySlice into + // this vector. + std::vector input_index_; +}; + +// Rehapes the gather indices input to have a trailing degenerate `1` dimension +// if necessary. Hands over the ownership of the newly created literal (if +// there is one) to `reshaped_gather_indices`. +static StatusOr> ReshapedGatherIndices( + int64 index_vector_dim, const Literal& gather_indices, + std::unique_ptr* reshaped_gather_indices) { + if (gather_indices.shape().dimensions_size() != index_vector_dim) { + return std::cref(gather_indices); + } + + std::vector new_shape(gather_indices.shape().dimensions().begin(), + gather_indices.shape().dimensions().end()); + new_shape.push_back(1); + TF_ASSIGN_OR_RETURN(*reshaped_gather_indices, + gather_indices.Reshape(new_shape)); + return std::cref(**reshaped_gather_indices); +} + +Status HloEvaluator::HandleGather(HloInstruction* gather) { + std::unique_ptr result = Literal::CreateFromShape(gather->shape()); + const Shape& shape = gather->shape(); + const GatherDimensionNumbers& dim_numbers = + gather->gather_dimension_numbers(); + const Literal& operand = GetEvaluatedLiteralFor(gather->operand(0)); + std::unique_ptr reshaped_gather_indices; + TF_ASSIGN_OR_RETURN( + const Literal& gather_indices, + ReshapedGatherIndices(dim_numbers.index_vector_dim(), + GetEvaluatedLiteralFor(gather->operand(1)), + &reshaped_gather_indices)); + + // We iterate over the gather dimensions in the output shape in an outer loop + // nest, and iterate over the window dimensions in the output shape in an + // inner loop nest. + + ShapeUtil::IndexIterationSpace gather_indices_iteration_space = + IterationSpaceForOutputGatherIndices(shape, dim_numbers); + ShapeUtil::IndexIterationSpace window_indices_iteration_space = + IterationSpaceForOutputWindowIndices( + shape.dimensions_size(), gather->gather_window_bounds(), dim_numbers); + + // Scratch buffers that hold an index in the output shape and the + // corresponding index in the input shape. + std::vector input_index(operand.shape().dimensions_size()); + std::vector output_index(gather->shape().dimensions_size()); + + OutputGatherIndexToInputIndex output_gather_index_to_input_index( + &gather->gather_dimension_numbers(), /*input_shape=*/operand.shape(), + /*output_shape=*/shape, &gather_indices); + OutputWindowIndexToInputIndex output_window_index_to_input_index( + gather->gather_dimension_numbers(), /*input_shape=*/operand.shape(), + /*output_shape=*/shape); + + auto gather_inner_loop_body = + [&](ArraySlice output_window_index, + ArraySlice input_gather_index, + ArraySlice output_gather_index) -> StatusOr { + TF_ASSIGN_OR_RETURN( + ArraySlice input_window_index, + output_window_index_to_input_index(output_window_index)); + for (int i = 0, e = output_index.size(); i < e; i++) { + output_index[i] = output_gather_index[i] + output_window_index[i]; + } + for (int i = 0, e = input_index.size(); i < e; i++) { + input_index[i] = input_gather_index[i] + input_window_index[i]; + } + TF_RETURN_IF_ERROR( + result->CopyElementFrom(operand, input_index, output_index)); + return true; + }; + + auto gather_outer_loop_body = + [&](ArraySlice output_gather_index) -> StatusOr { + TF_ASSIGN_OR_RETURN( + ArraySlice input_gather_index, + output_gather_index_to_input_index(output_gather_index)); + TF_RETURN_IF_ERROR(ShapeUtil::ForEachIndexWithStatus( + shape, window_indices_iteration_space, + std::bind(gather_inner_loop_body, std::placeholders::_1, + input_gather_index, output_gather_index))); + return true; + }; + + TF_RETURN_IF_ERROR(ShapeUtil::ForEachIndexWithStatus( + shape, gather_indices_iteration_space, gather_outer_loop_body)); + evaluated_[gather] = std::move(result); + return Status::OK(); +} + Status HloEvaluator::HandleGetTupleElement(HloInstruction* get_tuple_element) { const auto result_shape = get_tuple_element->shape(); const int64 index = get_tuple_element->tuple_index(); @@ -2445,6 +2837,135 @@ Status HloEvaluator::HandleCopy(HloInstruction* copy) { return Status::OK(); } +Status HloEvaluator::HandleCall(HloInstruction* call) { + auto* computation = call->to_apply(); + auto operands = call->operands(); + + std::vector arg_literals; + arg_literals.reserve(operands.size()); + for (auto operand : operands) { + const Literal& arg_literal = GetEvaluatedLiteralFor(operand); + arg_literals.push_back(&arg_literal); + } + + HloEvaluator embedded_evaluator; + std::unique_ptr result = + embedded_evaluator.Evaluate(*computation, arg_literals) + .ConsumeValueOrDie(); + + evaluated_[call] = std::move(result); + return Status::OK(); +} + +Status HloEvaluator::HandleFusion(HloInstruction* fusion) { + // Attach cloned computation to an empty HLO module so the existing ones are + // not modified. + HloModule empty_hlo_module("EmptyModuleForFusion"); + auto cloned_fused_computation = + fusion->fused_instructions_computation()->Clone( + /*suffix=*/"clone_with_layout", &empty_hlo_module); + for (auto* instruction : cloned_fused_computation->instructions()) { + LayoutUtil::SetToDefaultLayout(instruction->mutable_shape()); + } + auto readded_computation = + empty_hlo_module.AddEntryComputation(std::move(cloned_fused_computation)); + + auto operands = fusion->operands(); + std::vector arg_literals; + arg_literals.reserve(operands.size()); + for (auto operand : operands) { + const Literal& arg_literal = GetEvaluatedLiteralFor(operand); + arg_literals.push_back(&arg_literal); + } + + HloEvaluator embedded_evaluator; + std::unique_ptr result = + embedded_evaluator + .Evaluate(*readded_computation, arg_literals) + .ConsumeValueOrDie(); + + evaluated_[fusion] = std::move(result); + return Status::OK(); +} + +Status HloEvaluator::HandleConditional(HloInstruction* conditional) { + const auto& pred = GetEvaluatedLiteralFor(conditional->operand(0)); + const auto& true_computation_arg = + GetEvaluatedLiteralFor(conditional->operand(1)); + const auto& false_computation_arg = + GetEvaluatedLiteralFor(conditional->operand(2)); + + auto* true_computation = conditional->true_computation(); + auto* false_computation = conditional->false_computation(); + + auto result = Literal::CreateFromShape(conditional->shape()); + HloEvaluator embedded_evaluator; + if (pred.Get({})) { + result = embedded_evaluator + .Evaluate(*true_computation, + {&true_computation_arg}) + .ConsumeValueOrDie(); + } else { + result = embedded_evaluator + .Evaluate(*false_computation, + {&false_computation_arg}) + .ConsumeValueOrDie(); + } + + evaluated_[conditional] = std::move(result); + return Status::OK(); +} + +Status HloEvaluator::HandleSelect(HloInstruction* select) { + const auto& pred = GetEvaluatedLiteralFor(select->operand(0)); + const auto& on_true = GetEvaluatedLiteralFor(select->operand(1)); + const auto& on_false = GetEvaluatedLiteralFor(select->operand(2)); + + // If predicate is of scalar type, no element-wise selection would be needed. + // This would also handle output array of tuple types as the DefaultAction + // would go through the TypedVisitor which doesn't handle tuples. + if (ShapeUtil::IsScalar(pred.shape())) { + if (pred.Get({})) { + evaluated_[select] = on_true.CloneToUnique(); + } else { + evaluated_[select] = on_false.CloneToUnique(); + } + return Status::OK(); + } + + return DefaultAction(select); +} + +Status HloEvaluator::HandleWhile(HloInstruction* while_hlo) { + HloComputation* cond_comp = while_hlo->while_condition(); + HloComputation* body_comp = while_hlo->while_body(); + // Initialize the loop carried valued with the input to the While instruction. + auto lcv = GetEvaluatedLiteralFor(while_hlo->operand(0)).CloneToUnique(); + bool keep_going = true; + int64 iteration_count = 0; + HloEvaluator cond_evaluator(max_loop_iterations_); + HloEvaluator loop_body_evaluator(max_loop_iterations_); + while (keep_going) { + if (max_loop_iterations_ >= 0 && iteration_count++ > max_loop_iterations_) { + return InvalidArgument("Loop %s exceeded loop iteration limit (%lld).", + while_hlo->name().c_str(), max_loop_iterations_); + } + TF_ASSIGN_OR_RETURN(auto cond_val, cond_evaluator.Evaluate( + *cond_comp, {lcv.get()})); + keep_going = cond_val->GetFirstElement(); + if (keep_going) { + TF_ASSIGN_OR_RETURN(auto body_val, loop_body_evaluator.Evaluate( + *body_comp, {lcv.get()})); + VLOG(3) << "Loop iteration result: " << body_val->ToString(); + lcv = std::move(body_val); + cond_evaluator.ResetVisitStates(); + loop_body_evaluator.ResetVisitStates(); + } + } + evaluated_[while_hlo] = std::move(lcv); + return Status::OK(); +} + Status HloEvaluator::Preprocess(HloInstruction* hlo) { VLOG(2) << "About to visit HLO: " << hlo->ToString(); return Status::OK(); @@ -2458,28 +2979,27 @@ Status HloEvaluator::Postprocess(HloInstruction* hlo) { // Explicit instantiation of templatized Evaluate* methods. // -template StatusOr> HloEvaluator::Evaluate< - const Literal*>(const HloModule& module, - tensorflow::gtl::ArraySlice arg_literals); +template StatusOr> +HloEvaluator::Evaluate(const HloModule& module, + ArraySlice arg_literals); template StatusOr> HloEvaluator::Evaluate>( - const HloModule& module, - tensorflow::gtl::ArraySlice> arg_literals); + const HloModule& module, ArraySlice> arg_literals); -template StatusOr> HloEvaluator::Evaluate< - const Literal*>(const HloComputation& computation, - tensorflow::gtl::ArraySlice arg_literals); +template StatusOr> +HloEvaluator::Evaluate(const HloComputation& computation, + ArraySlice arg_literals); template StatusOr> HloEvaluator::Evaluate>( const HloComputation& computation, - tensorflow::gtl::ArraySlice> arg_literals); + ArraySlice> arg_literals); -template StatusOr> HloEvaluator::Evaluate< - const Literal*>(HloInstruction* instruction, - tensorflow::gtl::ArraySlice arg_literals); +template StatusOr> +HloEvaluator::Evaluate(HloInstruction* instruction, + ArraySlice arg_literals); template StatusOr> HloEvaluator::Evaluate>( HloInstruction* instruction, - tensorflow::gtl::ArraySlice> arg_literals); + ArraySlice> arg_literals); } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.h b/tensorflow/compiler/xla/service/hlo_evaluator.h index 3b2b697e492a78a06a4e5ae6bf056ff8676f2ff5..c0dcee0c3e382f74de72a2b89f39e06f042e2b80 100644 --- a/tensorflow/compiler/xla/service/hlo_evaluator.h +++ b/tensorflow/compiler/xla/service/hlo_evaluator.h @@ -36,7 +36,10 @@ namespace xla { // This class is not thread-safe. class HloEvaluator : public DfsHloVisitorWithDefault { public: - HloEvaluator(); + // Only evaluate up to max_loop_iterations per while-loop execution if + // specified. + explicit HloEvaluator(int64 max_loop_iterations = -1); + // Evaluates an HLO module and an array of pointers to literals. // Returns the evaluated result as a literal if successful. // Precondition: The indices of arg_literals correspond to the parameter @@ -149,10 +152,22 @@ class HloEvaluator : public DfsHloVisitorWithDefault { Status HandleTuple(HloInstruction* tuple) override; + Status HandleGather(HloInstruction* gather) override; + Status HandleGetTupleElement(HloInstruction* get_tuple_element) override; Status HandleCopy(HloInstruction* copy) override; + Status HandleConditional(HloInstruction* conditional) override; + + Status HandleCall(HloInstruction* call) override; + + Status HandleFusion(HloInstruction* fusion) override; + + Status HandleWhile(HloInstruction* while_hlo) override; + + Status HandleSelect(HloInstruction* select) override; + private: // Returns the already-evaluated literal result for the instruction. // A Constant instruction is considered evaluated and its literal will be @@ -190,6 +205,9 @@ class HloEvaluator : public DfsHloVisitorWithDefault { // Must be cleared for each evaluation. std::vector arg_literals_; + // Max loop iterations to execute with no maximum if negative. + int64 max_loop_iterations_; + TF_DISALLOW_COPY_AND_ASSIGN(HloEvaluator); }; diff --git a/tensorflow/compiler/xla/service/hlo_evaluator_test.cc b/tensorflow/compiler/xla/service/hlo_evaluator_test.cc index 97765d65909cee192f65069777f8f195081603b2..685cacd7f74c00789296dee16f0a6a94c35a4393 100644 --- a/tensorflow/compiler/xla/service/hlo_evaluator_test.cc +++ b/tensorflow/compiler/xla/service/hlo_evaluator_test.cc @@ -1729,6 +1729,207 @@ TEST_P(HloEvaluatorTest, EvaluateWithSubstitutionsWithConstantOperand) { *result.ValueOrDie()); } +TEST_P(HloEvaluatorTest, EvaluateGather_TensorFlowGatherV1) { + const char* hlo_text = R"( +HloModule TensorFlowGatherV1 + +ENTRY main { + operand = s32[3,3] parameter(0) + indices = s32[2] parameter(1) + ROOT gather = s32[2,3] gather(operand, indices), + output_window_dims={1}, + elided_window_dims={0}, + gather_dims_to_operand_dims={0}, + index_vector_dim=1, + window_bounds={1, 3} +} +)"; + ParseAndVerifyModule(hlo_text); + std::unique_ptr operand = + Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr gather_indices = Literal::CreateR1({0, 2}); + LiteralTestUtil::ExpectEqual( + *Literal::CreateR2({{1, 2, 3}, {7, 8, 9}}), + *Evaluate({operand.get(), gather_indices.get()})); +} + +TEST_P(HloEvaluatorTest, EvaluateGather_TensorFlowGatherV2) { + const char* hlo_text = R"( +HloModule TensorFlowGatherV2 + +ENTRY main { + operand = s32[3,3] parameter(0) + indices = s32[2] parameter(1) + ROOT gather = s32[3,2] gather(operand, indices), + output_window_dims={0}, + elided_window_dims={1}, + gather_dims_to_operand_dims={1}, + index_vector_dim=1, + window_bounds={3, 1} +} +)"; + ParseAndVerifyModule(hlo_text); + std::unique_ptr operand = + Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr gather_indices = Literal::CreateR1({0, 2}); + LiteralTestUtil::ExpectEqual( + *Literal::CreateR2({{1, 3}, {4, 6}, {7, 9}}), + *Evaluate({operand.get(), gather_indices.get()})); +} + +TEST_P(HloEvaluatorTest, EvaluateGather_TensorFlowGatherMultipleBatchDims) { + const char* hlo_text = R"( +HloModule TensorFlowGatherMultipleBatchDims + +ENTRY main { + operand = s32[3,3] parameter(0) + indices = s32[2,2] parameter(1) + ROOT gather = s32[2,3,2] gather(operand, indices), + output_window_dims={1}, + elided_window_dims={1}, + gather_dims_to_operand_dims={1}, + index_vector_dim=2, + window_bounds={3, 1} +} +)"; + ParseAndVerifyModule(hlo_text); + std::unique_ptr operand = + Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr gather_indices = + Literal::CreateR2({{0, 2}, {2, 1}}); + LiteralTestUtil::ExpectEqual( + *Literal::CreateR3( + {{{1, 3}, {4, 6}, {7, 9}}, {{3, 2}, {6, 5}, {9, 8}}}), + *Evaluate({operand.get(), gather_indices.get()})); +} + +TEST_P(HloEvaluatorTest, EvaluateGather_TensorFlowGatherNd) { + const char* hlo_text = R"( +HloModule TensorFlowGatherNd + +ENTRY main { + operand = s32[3,3,2] parameter(0) + indices = s32[2,2] parameter(1) + ROOT gather = s32[2,2] gather(operand, indices), + output_window_dims={1}, + elided_window_dims={0,1}, + gather_dims_to_operand_dims={0,1}, + index_vector_dim=1, + window_bounds={1,1,2} +} +)"; + ParseAndVerifyModule(hlo_text); + std::unique_ptr operand = + Literal::CreateR3({{{-1, 1}, {-2, 2}, {-3, 3}}, // + {{-4, 4}, {-5, 5}, {-6, 6}}, // + {{-7, 7}, {-8, 8}, {-9, 9}}}); + std::unique_ptr gather_indices = + Literal::CreateR2({{0, 0}, {1, 0}}); + LiteralTestUtil::ExpectEqual( + *Literal::CreateR2({{-1, 1}, {-4, 4}}), + *Evaluate({operand.get(), gather_indices.get()})); +} + +TEST_P(HloEvaluatorTest, + EvaluateGather_TensorFlowGatherNdNonDefaultIndexVectorDim) { + const char* hlo_text = R"( +HloModule TensorFlowGatherNd + +ENTRY main { + operand = s32[3,3,2] parameter(0) + indices = s32[2,2] parameter(1) + ROOT gather = s32[2,2] gather(operand, indices), + output_window_dims={1}, + elided_window_dims={0,1}, + gather_dims_to_operand_dims={0,1}, + index_vector_dim=0, + window_bounds={1,1,2} +} +)"; + ParseAndVerifyModule(hlo_text); + std::unique_ptr operand = + Literal::CreateR3({{{-1, 1}, {-2, 2}, {-3, 3}}, // + {{-4, 4}, {-5, 5}, {-6, 6}}, // + {{-7, 7}, {-8, 8}, {-9, 9}}}); + std::unique_ptr gather_indices = + Literal::CreateR2({{0, 0}, {1, 0}}); + LiteralTestUtil::ExpectEqual( + *Literal::CreateR2({{-2, 2}, {-1, 1}}), + *Evaluate({operand.get(), gather_indices.get()})); +} + +TEST_P(HloEvaluatorTest, EvaluateGather_DynamicSlice) { + const char* hlo_text = R"( +HloModule DynamicSlice + +ENTRY main { + operand = s32[3,3] parameter(0) + indices = s32[2] parameter(1) + ROOT gather = s32[1,1] gather(operand, indices), + output_window_dims={0,1}, + elided_window_dims={}, + gather_dims_to_operand_dims={0,1}, + index_vector_dim=0, + window_bounds={1,1} +} +)"; + ParseAndVerifyModule(hlo_text); + std::unique_ptr operand = + Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr gather_indices = Literal::CreateR1({1, 1}); + LiteralTestUtil::ExpectEqual( + *Literal::CreateR2({{5}}), + *Evaluate({operand.get(), gather_indices.get()})); +} + +TEST_P(HloEvaluatorTest, EvaluateGather_BatchDynamicSlice) { + const char* hlo_text = R"( +HloModule BatchDynamicSlice + +ENTRY main { + operand = s32[3,3] parameter(0) + indices = s32[2,2] parameter(1) + ROOT gather = s32[2,1,1] gather(operand, indices), + output_window_dims={1,2}, + elided_window_dims={}, + gather_dims_to_operand_dims={0,1}, + index_vector_dim=0, + window_bounds={1,1} +} +)"; + ParseAndVerifyModule(hlo_text); + std::unique_ptr operand = + Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr gather_indices = + Literal::CreateR2({{2, 1}, {1, 1}}); + LiteralTestUtil::ExpectEqual( + *Literal::CreateR3({{{8}}, {{5}}}), + *Evaluate({operand.get(), gather_indices.get()})); +} + +TEST_P(HloEvaluatorTest, EvaluateGather_ZeroDimBounds) { + const char* hlo_text = R"( +HloModule TensorFlowGatherV1 + +ENTRY main { + operand = s32[3,0] parameter(0) + indices = s32[2] parameter(1) + ROOT gather = s32[2,0] gather(operand, indices), + output_window_dims={1}, + elided_window_dims={0}, + gather_dims_to_operand_dims={0}, + index_vector_dim=1, + window_bounds={1, 0} +} +)"; + ParseAndVerifyModule(hlo_text); + std::unique_ptr operand = Literal::CreateR2({{}, {}, {}}); + std::unique_ptr gather_indices = Literal::CreateR1({0, 2}); + LiteralTestUtil::ExpectEqual( + *Literal::CreateR2({{}, {}}), + *Evaluate({operand.get(), gather_indices.get()})); +} + INSTANTIATE_TEST_CASE_P(HloEvaluatorTest_Instantiation, HloEvaluatorTest, ::testing::ValuesIn(use_bf16_params)); diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc index 2861fec39ef0c92fdfbcee04584f9bd36d3cb4d8..1dc72355cf179e996caab4d6b52068dc99d02244 100644 --- a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc +++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc @@ -157,52 +157,60 @@ enum ColorScheme { kDashedBorder, }; +// Graphviz attributes/colors that make up a color scheme. +struct NodeColors { + const char* style; + const char* fill_color; + const char* stroke_color; + const char* font_color; +}; + +NodeColors NodeColorsForScheme(ColorScheme color) { + switch (color) { + case kBlue: + return NodeColors{"filled", "#bbdefb", "#8aacc8", "black"}; + case kBrown: + return NodeColors{"filled", "#bcaaa4", "#8c7b75", "black"}; + case kDarkBlue: + return NodeColors{"filled", "#1565c0", "#003c8f", "white"}; + case kDarkGreen: + return NodeColors{"filled", "#2e7d32", "#005005", "white"}; + case kDarkRed: + return NodeColors{"filled", "#b71c1c", "#7f0000", "white"}; + case kGray: + return NodeColors{"filled", "#cfd8dc", "#9ea7aa", "black"}; + case kGreen: + return NodeColors{"filled", "#c8e6c9", "#97b498", "black"}; + case kOrange: + return NodeColors{"filled", "#ffe0b2", "#cbae82", "black"}; + case kPurple: + return NodeColors{"filled", "#e1bee7", "#af8eb5", "black"}; + case kRed: + return NodeColors{"filled", "#ffcdd2", "#cb9ca1", "black"}; + case kWhite: + return NodeColors{"filled", "white", "black", "black"}; + case kYellow: + return NodeColors{"filled", "#fff9c4", "#cbc693", "black"}; + case kDashedBorder: + // "filled,dashed" looks the same as "dashed", since we have a white + // background. But we use "filled,dashed" so that when you hover over + // any part of the node (not just the text inside the node), our css + // :hover rule is triggered. + return NodeColors{"filled,dashed", "white", "#757575", "#757575"}; + } +} + // Given a ColorScheme, returns an attribute string for a node of that color. // Sets the node's style and fill/stroke/text colors. // // Colors are from https://material.io/color. string NodeColorAttributes(ColorScheme color) { - using std::make_tuple; - - const char *style, *fill_color, *stroke_color, *font_color; - std::tie(style, fill_color, stroke_color, font_color) = [color] { - switch (color) { - case kBlue: - return make_tuple("filled", "#bbdefb", "#8aacc8", "black"); - case kBrown: - return make_tuple("filled", "#bcaaa4", "#8c7b75", "black"); - case kDarkBlue: - return make_tuple("filled", "#1565c0", "#003c8f", "white"); - case kDarkGreen: - return make_tuple("filled", "#2e7d32", "#005005", "white"); - case kDarkRed: - return make_tuple("filled", "#b71c1c", "#7f0000", "white"); - case kGray: - return make_tuple("filled", "#cfd8dc", "#9ea7aa", "black"); - case kGreen: - return make_tuple("filled", "#c8e6c9", "#97b498", "black"); - case kOrange: - return make_tuple("filled", "#ffe0b2", "#cbae82", "black"); - case kPurple: - return make_tuple("filled", "#e1bee7", "#af8eb5", "black"); - case kRed: - return make_tuple("filled", "#ffcdd2", "#cb9ca1", "black"); - case kWhite: - return make_tuple("filled", "white", "black", "black"); - case kYellow: - return make_tuple("filled", "#fff9c4", "#cbc693", "black"); - case kDashedBorder: - // "filled,dashed" looks the same as "dashed", since we have a white - // background. But we use "filled,dashed" so that when you hover over - // any part of the node (not just the text inside the node), our css - // :hover rule is triggered. - return make_tuple("filled,dashed", "white", "#757575", "#757575"); - } - }(); + NodeColors node_colors = NodeColorsForScheme(color); return Printf( - R"(style="%s", fontcolor="%s", color="%s", fillcolor="%s")", style, - font_color, stroke_color, fill_color); + R"(style="%s", fontcolor="%s", color="%s", fillcolor="%s")", + node_colors.style, node_colors.font_color, node_colors.stroke_color, + node_colors.fill_color); } // Replaces <> with <>, so that this string is safe(er) for use in a @@ -604,11 +612,21 @@ tooltip = " "; StrAppend(&subcomp_label, "
", extra_info); } - // Subcomputation's fill/stroke color is light/dark red/gray, depending on - // whether or not the subcomputation's fusion node is highlighted. bool highlight = filter_.Highlight(parent_instr); - const char* fillcolor = highlight ? "#ffcdd2" : "#f5f5f5"; - const char* strokecolor = highlight ? "#b71c1c" : "#c2c2c2"; + const char* fillcolor; + const char* strokecolor; + if (debug_options_.xla_hlo_graph_sharding_color() && !highlight) { + // Use the sharding color, if the node isn't highlighted. + NodeColors node_colors = + NodeColorsForScheme(GetInstructionColor(parent_instr)); + fillcolor = node_colors.fill_color; + strokecolor = node_colors.stroke_color; + } else { + // Subcomputation's fill/stroke color is light/dark red/gray, depending on + // whether or not the subcomputation's fusion node is highlighted. + fillcolor = highlight ? "#ffcdd2" : "#f5f5f5"; + strokecolor = highlight ? "#b71c1c" : "#c2c2c2"; + } style = Printf(R"(style="rounded,filled,bold"; fillcolor="%s"; color="%s;")", fillcolor, strokecolor); @@ -782,6 +800,14 @@ string HloDotDumper::GetInstructionNodeInlinedOperands( auto stringify_constant = [](const HloInstruction* constant) { const auto& shape = constant->shape(); + // If the shape has a dimension of size zero, print it as e.g. + // "{} (f32[42, 0, 10])". The alternative, calling Literal::ToString(), + // enumerates all of its empty dimensions (e.g. "{ { {}, {} }, ..."), which + // is just noise. + if (ShapeUtil::HasZeroElements(shape)) { + return Printf("{} (%s)", ShapeUtil::HumanString(constant->shape())); + } + // Print the literal value of constants with <= K elements. optional elem_count; if (!ShapeUtil::IsOpaque(shape) && !ShapeUtil::IsTuple(shape)) { diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc index b7dd055d7cd78eb759a2b24bcbbbc948159f9425..d33add23d07b52cb56e4b212a29b415259af7694 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction.cc +++ b/tensorflow/compiler/xla/service/hlo_instruction.cc @@ -182,6 +182,7 @@ StatusOr> HloInstruction::CreateFromProto( /* static */ std::unique_ptr HloInstruction::CreateGetTupleElement(const Shape& shape, HloInstruction* operand, int64 index) { + CHECK(ShapeUtil::IsTuple(operand->shape())); auto instruction = WrapUnique(new HloInstruction(HloOpcode::kGetTupleElement, shape)); instruction->tuple_index_ = index; @@ -1172,7 +1173,8 @@ bool HloInstruction::HasSideEffect() const { /* static */ GatherDimensionNumbers HloInstruction::MakeGatherDimNumbers( tensorflow::gtl::ArraySlice output_window_dims, tensorflow::gtl::ArraySlice elided_window_dims, - tensorflow::gtl::ArraySlice gather_dims_to_operand_dims) { + tensorflow::gtl::ArraySlice gather_dims_to_operand_dims, + int64 index_vector_dim) { GatherDimensionNumbers gather_dim_numbers; for (int64 output_window_dim : output_window_dims) { gather_dim_numbers.add_output_window_dims(output_window_dim); @@ -1184,6 +1186,7 @@ bool HloInstruction::HasSideEffect() const { gather_dim_numbers.add_gather_dims_to_operand_dims(gather_dim_to_input_dim); } + gather_dim_numbers.set_index_vector_dim(index_vector_dim); return gather_dim_numbers; } @@ -2680,8 +2683,10 @@ Status HloInstruction::Visit(DfsHloVisitorBase* visitor) { case HloOpcode::kTrace: break; } - return Unimplemented("unhandled HloOpcode for DfsHloVisitor: %s", - HloOpcodeString(opcode_).c_str()); + return InternalError( + "Unhandled HloOpcode for DfsHloVisitor: %s. This should not happen - " + "please file a bug for XLA.", + HloOpcodeString(opcode_).c_str()); } // Explicit instantiations. @@ -3369,9 +3374,12 @@ string HloInstruction::GatherDimensionNumbersToString() const { string gather_dims_to_operand_dims = StrCat( "gather_dims_to_operand_dims={", Join(gather_dimension_numbers_->gather_dims_to_operand_dims(), ","), "}"); + string index_vector_dim = StrCat( + "index_vector_dim=", gather_dimension_numbers_->index_vector_dim()); return Join>( - {output_window_dims, elided_window_dims, gather_dims_to_operand_dims}, + {output_window_dims, elided_window_dims, gather_dims_to_operand_dims, + index_vector_dim}, ", "); } diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h index e4d22e5703811dc9b5f3ea3ee1ca85fd848f88b2..e4c86214c2014095b2e171ff10691e1221574cb7 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction.h +++ b/tensorflow/compiler/xla/service/hlo_instruction.h @@ -502,7 +502,8 @@ class HloInstruction { static GatherDimensionNumbers MakeGatherDimNumbers( tensorflow::gtl::ArraySlice output_window_dims, tensorflow::gtl::ArraySlice elided_window_dims, - tensorflow::gtl::ArraySlice gather_dims_to_operand_dims); + tensorflow::gtl::ArraySlice gather_dims_to_operand_dims, + int64 index_vector_dim); // Returns the opcode for this instruction. HloOpcode opcode() const { return opcode_; } diff --git a/tensorflow/compiler/xla/service/hlo_instruction_test.cc b/tensorflow/compiler/xla/service/hlo_instruction_test.cc index 32d3ed272bd6b239918076999ecae6c1b3ded2fd..f2980d309d01fdf3b3e601bc260a0ad0895b3064 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction_test.cc +++ b/tensorflow/compiler/xla/service/hlo_instruction_test.cc @@ -1271,7 +1271,7 @@ TEST_F(HloInstructionTest, Stringification) { "true_computation=%TransposeDot, false_computation=%TransposeDot"); } -TEST_F(HloInstructionTest, StringifyGather) { +TEST_F(HloInstructionTest, StringifyGather_0) { Shape input_tensor_shape = ShapeUtil::MakeShape(F32, {50, 49, 48, 47, 46}); Shape gather_indices_tensor_shape = ShapeUtil::MakeShape(S64, {10, 9, 8, 7, 5}); @@ -1291,7 +1291,8 @@ TEST_F(HloInstructionTest, StringifyGather) { HloInstruction::MakeGatherDimNumbers( /*output_window_dims=*/{4, 5, 6, 7, 8}, /*elided_window_dims=*/{}, - /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}), + /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, + /*index_vector_dim=*/4), /*window_bounds=*/{30, 29, 28, 27, 26})); HloModule module(TestName()); @@ -1303,7 +1304,43 @@ TEST_F(HloInstructionTest, StringifyGather) { "s64[10,9,8,7,5]{4,3,2,1,0} %gather_indices), " "output_window_dims={4,5,6,7,8}, elided_window_dims={}, " "gather_dims_to_operand_dims={0,1,2,3,4}, " - "window_bounds={30,29,28,27,26}"); + "index_vector_dim=4, window_bounds={30,29,28,27,26}"); +} + +TEST_F(HloInstructionTest, StringifyGather_1) { + Shape input_tensor_shape = ShapeUtil::MakeShape(F32, {50, 49, 48, 47, 46}); + Shape gather_indices_tensor_shape = + ShapeUtil::MakeShape(S64, {10, 9, 5, 7, 6}); + Shape gather_result_shape = + ShapeUtil::MakeShape(F32, {10, 9, 7, 6, 30, 29, 28, 27, 26}); + + HloComputation::Builder builder("Gather"); + HloInstruction* input = builder.AddInstruction( + HloInstruction::CreateParameter(0, input_tensor_shape, "input_tensor")); + HloInstruction* gather_indices = + builder.AddInstruction(HloInstruction::CreateParameter( + 1, gather_indices_tensor_shape, "gather_indices")); + + HloInstruction* gather_instruction = + builder.AddInstruction(HloInstruction::CreateGather( + gather_result_shape, input, gather_indices, + HloInstruction::MakeGatherDimNumbers( + /*output_window_dims=*/{4, 5, 6, 7, 8}, + /*elided_window_dims=*/{}, + /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, + /*index_vector_dim=*/2), + /*window_bounds=*/{30, 29, 28, 27, 26})); + + HloModule module(TestName()); + module.AddEntryComputation(builder.Build()); + + EXPECT_EQ(gather_instruction->ToString(), + "%gather = f32[10,9,7,6,30,29,28,27,26]{8,7,6,5,4,3,2,1,0} " + "gather(f32[50,49,48,47,46]{4,3,2,1,0} %input_tensor, " + "s64[10,9,5,7,6]{4,3,2,1,0} %gather_indices), " + "output_window_dims={4,5,6,7,8}, elided_window_dims={}, " + "gather_dims_to_operand_dims={0,1,2,3,4}, " + "index_vector_dim=2, window_bounds={30,29,28,27,26}"); } } // namespace diff --git a/tensorflow/compiler/xla/service/hlo_module.cc b/tensorflow/compiler/xla/service/hlo_module.cc index cb2fe9f874012a51e1e6cbd1dd086dbb26994bde..cdea3d597824d155241a544d226aa18d3b0b0274 100644 --- a/tensorflow/compiler/xla/service/hlo_module.cc +++ b/tensorflow/compiler/xla/service/hlo_module.cc @@ -213,74 +213,23 @@ HloModuleProto HloModule::ToProto() const { continue; } HloComputationProto computation_proto = computation->ToProto(); + if (computation->name() == entry_computation_->name()) { + *proto.mutable_program_shape() = computation_proto.program_shape(); + } proto.add_computations()->Swap(&computation_proto); } return proto; } -namespace { - -// Construct a ProgramShape matching the shape of the parameters and root of the -// given module's entry computation. -StatusOr ProgramShapeFromProto(const HloModuleProto& module) { - const HloComputationProto* entry_computation = nullptr; - for (const HloComputationProto& computation : module.computations()) { - if (computation.name() == module.entry_computation_name()) { - entry_computation = &computation; - break; - } - } - TF_RET_CHECK(entry_computation != nullptr) - << "No computation with entry computation name" - << module.entry_computation_name(); - - tensorflow::gtl::FlatMap> parameters; - const HloInstructionProto* root = nullptr; - for (const HloInstructionProto& instruction : - entry_computation->instructions()) { - if (instruction.name() == entry_computation->root_name()) { - TF_RET_CHECK(root == nullptr) << "Entry computation has more than " - "one instruction with (root) name " - << instruction.name(); - root = &instruction; - } - if (instruction.opcode() == HloOpcodeString(HloOpcode::kParameter)) { - TF_RET_CHECK(!ContainsKey(parameters, instruction.parameter_number())) - << "Entry computation has more than one parameter instruction " - "with parameter number " - << instruction.parameter_number(); - parameters[instruction.parameter_number()] = {instruction.name(), - &instruction.shape()}; - } - } - TF_RET_CHECK(root != nullptr) - << "Entry computation is missing root instruction named " - << entry_computation->root_name(); - - ProgramShape program_shape; - *program_shape.mutable_result() = root->shape(); - for (int64 i = 0; i < parameters.size(); ++i) { - TF_RET_CHECK(ContainsKey(parameters, i)) - << "Entry computation missing parameter number " << i; - const string& name = parameters.at(i).first; - const Shape& shape = *parameters.at(i).second; - *program_shape.add_parameters() = shape; - program_shape.add_parameter_names(name); - } - - return std::move(program_shape); -} - -} // namespace - /* static */ StatusOr> HloModule::CreateFromProto( const HloModuleProto& proto, const HloModuleConfig& module_config, const VersionedComputationHandle& entry_computation_handle) { // The ProgramShape in the passed in module config must match the shapes of // the entry parameters and root. - TF_ASSIGN_OR_RETURN(ProgramShape expected_program_shape, - ProgramShapeFromProto(proto)); + TF_RET_CHECK(proto.has_program_shape()) + << "No program shape found in the proto"; + const auto& expected_program_shape = proto.program_shape(); TF_RET_CHECK(expected_program_shape.parameters_size() == module_config.entry_computation_layout().parameter_count()); for (int i = 0; i < expected_program_shape.parameters_size(); ++i) { @@ -354,8 +303,9 @@ StatusOr> HloModule::CreateFromProto( /* static */ StatusOr HloModule::CreateModuleConfigFromProto( const HloModuleProto& module) { - TF_ASSIGN_OR_RETURN(ProgramShape program_shape, - ProgramShapeFromProto(module)); + TF_RET_CHECK(module.has_program_shape()) + << "No program shape found in the proto"; + const auto& program_shape = module.program_shape(); HloModuleConfig module_config(program_shape); diff --git a/tensorflow/compiler/xla/service/hlo_module.h b/tensorflow/compiler/xla/service/hlo_module.h index 06d92f94fd6f62162b22575e9cc341f2906cd0db..755bbd359f7b95e7f3f3cbee1b46df85908202c6 100644 --- a/tensorflow/compiler/xla/service/hlo_module.h +++ b/tensorflow/compiler/xla/service/hlo_module.h @@ -103,7 +103,7 @@ class HloModule { return config_.mutable_entry_computation_layout(); } - ComputationLayout entry_computation_layout() const { + const ComputationLayout& entry_computation_layout() const { return config_.entry_computation_layout(); } @@ -187,11 +187,6 @@ class HloModule { // Returns a randomly generated uint64. uint64 RandomNew64() const; - // Returns the unique name for a computation in this module. - string GetUniqueCompuationName(const string& prefix) { - return computation_name_uniquer_.GetUniqueName(prefix); - } - // Returns the NameUniquer for uniquing instruction names in this module. NameUniquer& instruction_name_uniquer() { return instruction_name_uniquer_; } diff --git a/tensorflow/compiler/xla/service/hlo_module_config.cc b/tensorflow/compiler/xla/service/hlo_module_config.cc index 822e2f1f53e5ee460b88c2241ecf7f6b91ef608b..4205b0402cb8b2c31141d65be652cd84c22e7262 100644 --- a/tensorflow/compiler/xla/service/hlo_module_config.cc +++ b/tensorflow/compiler/xla/service/hlo_module_config.cc @@ -40,7 +40,7 @@ void HloModuleConfig::SetDefaultComputationLayout( string HloModuleConfig::compilation_cache_key() const { string key = - tensorflow::strings::StrCat("profiling=", hlo_profiling_enabled_); + tensorflow::strings::StrCat("profiling=", hlo_profiling_enabled()); StrAppend(&key, "::("); std::vector params; for (const ShapeLayout& param_layout : diff --git a/tensorflow/compiler/xla/service/hlo_module_config.h b/tensorflow/compiler/xla/service/hlo_module_config.h index d3c1fae592bb465609ffbde2d0262e2600912e63..586a03d412681cacdd780f48e77baf4cd4c51415 100644 --- a/tensorflow/compiler/xla/service/hlo_module_config.h +++ b/tensorflow/compiler/xla/service/hlo_module_config.h @@ -63,9 +63,10 @@ class HloModuleConfig { return &(*entry_computation_layout_); } - // Sets/returns whether to enable HLO-level profiling. - bool hlo_profiling_enabled() const { return hlo_profiling_enabled_; } - void enable_hlo_profiling(bool enabled) { hlo_profiling_enabled_ = enabled; } + // Returns whether to enable HLO-level profiling. + bool hlo_profiling_enabled() const { + return debug_options_.xla_hlo_profile(); + } // Sets/returns whether this is a "host module". Host modules are used to // record the data- and control-flow dependencies of host side computation @@ -110,9 +111,6 @@ class HloModuleConfig { tensorflow::gtl::optional entry_computation_layout_; - // Whether to enable HLO-level profiling. - bool hlo_profiling_enabled_ = false; - // Whether this is a 'host module'. bool is_host_module_ = false; diff --git a/tensorflow/compiler/xla/service/hlo_module_group_metadata.cc b/tensorflow/compiler/xla/service/hlo_module_group_metadata.cc new file mode 100644 index 0000000000000000000000000000000000000000..fa5dcb0b369d17c70c64c67b9f11640c93fb4278 --- /dev/null +++ b/tensorflow/compiler/xla/service/hlo_module_group_metadata.cc @@ -0,0 +1,350 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/hlo_module_group_metadata.h" + +#include +#include + +#include "tensorflow/compiler/xla/ptr_util.h" +#include "tensorflow/compiler/xla/shape_util.h" +#include "tensorflow/compiler/xla/status_macros.h" +#include "tensorflow/compiler/xla/util.h" +#include "tensorflow/core/lib/core/errors.h" +#include "tensorflow/core/platform/logging.h" +#include "tensorflow/core/platform/types.h" + +namespace xla { + +string HloModuleGroupMetadata::TrackedInstruction::ToString() const { + string repr = + (instruction_ != nullptr) ? instruction_->ToShortString() : "NULL"; + switch (kind_) { + case ComputationKind::kInvalid: + repr += ":INVALID"; + break; + case ComputationKind::kWhileCondition: + repr += ":WHILE_CONDITION"; + break; + case ComputationKind::kWhileBody: + repr += ":WHILE_BODY"; + break; + case ComputationKind::kConditionalTrue: + repr += ":CONDITIONAL_TRUE"; + break; + case ComputationKind::kConditionalFalse: + repr += ":CONDITIONAL_FALSE"; + break; + } + return repr; +} + +/* static */ StatusOr> +HloModuleGroupMetadata::Build(const std::vector& modules) { + auto metadata = absl::make_unique(modules); + TF_RETURN_IF_ERROR(metadata->Build()); + return std::move(metadata); +} + +Status HloModuleGroupMetadata::Build() { + TF_RETURN_IF_ERROR(RecordInstructions()); + TF_RETURN_IF_ERROR(VerifyChannelInstructions()); + + // Record all companion while instructions. + const auto visitor = [this](HloInstruction* hlo) -> Status { + // We only need to process if the instruction is within the computation + // of a companion instruction, like in the condition or body computation + // of a While. + const TrackedInstruction* tracked = GetTrackedInstruction(hlo->parent()); + if (tracked == nullptr) { + return Status::OK(); + } + // Add the parent computation of this channel instruction and its peer + // computation (both must be while computations) as companions. + if (IsChannelInstruction(hlo)) { + HloComputation* peer_computation = PeerComputation(hlo); + const TrackedInstruction* peer_tracked = + GetTrackedInstruction(peer_computation); + TF_RET_CHECK(peer_tracked != nullptr) + << "Peer instruction is not a possible companion"; + TF_RET_CHECK(*tracked == *peer_tracked) + << "Peer instruction does not match the computation kind"; + TF_RETURN_IF_ERROR( + AddCompanion(tracked->instruction(), peer_tracked->instruction())); + } + + // Add the parents of companion instructions (they must be all of the same + // kind of instructions, opcode wise) as companions. + if (IsCompanionInstruction(hlo)) { + for (HloInstruction* companion : Companions(hlo)) { + const TrackedInstruction* companion_tracked = + GetTrackedInstruction(companion->parent()); + TF_RET_CHECK(companion_tracked != nullptr); + TF_RET_CHECK(*tracked == *companion_tracked); + TF_RETURN_IF_ERROR(AddCompanion(tracked->instruction(), + companion_tracked->instruction())); + } + } + return Status::OK(); + }; + + // Visit the computations in postorder so that the companion information grows + // from inner computations to outer ones. + for (HloModule* module : modules_) { + for (HloComputation* computation : module->MakeComputationPostOrder()) { + TF_RETURN_IF_ERROR(computation->Accept(visitor)); + } + } + return Status::OK(); +} + +bool HloModuleGroupMetadata::IsChannelInstruction( + const HloInstruction* instruction) const { + switch (instruction->opcode()) { + case HloOpcode::kSend: + case HloOpcode::kRecv: + case HloOpcode::kSendDone: + case HloOpcode::kRecvDone: + return true; + default: + return false; + } +} + +bool HloModuleGroupMetadata::IsCompanionInstruction(HloInstruction* hlo) const { + return companion_set_index_.count(hlo) > 0; +} + +bool HloModuleGroupMetadata::InstructionCommunicates( + HloInstruction* hlo) const { + return IsChannelInstruction(hlo) || IsCompanionInstruction(hlo); +} + +const HloModuleGroupMetadata::Channel& HloModuleGroupMetadata::GetChannel( + int64 channel_id) const { + CHECK(channel_id_map_.find(channel_id) != channel_id_map_.end()); + return channels_[channel_id_map_.at(channel_id)]; +} + +HloComputation* HloModuleGroupMetadata::PeerComputation( + const HloInstruction* instruction) const { + CHECK(IsChannelInstruction(instruction)); + const Channel& channel = GetChannel(instruction->channel_id()); + switch (instruction->opcode()) { + case HloOpcode::kSend: + case HloOpcode::kSendDone: + return channel.recv->parent(); + case HloOpcode::kRecv: + case HloOpcode::kRecvDone: + return channel.send->parent(); + default: + LOG(FATAL) << "opcode not supported"; + } +} + +std::vector +HloModuleGroupMetadata::GetCompanionsPath(const HloInstruction* hlo) const { + std::vector path; + const HloComputation* parent = hlo->parent(); + const TrackedInstruction* companion; + while ((companion = GetTrackedInstruction(parent)) != nullptr) { + parent = companion->instruction()->parent(); + path.push_back(*companion); + } + return path; +} + +bool HloModuleGroupMetadata::CheckCompanionPathsCompatibility( + const std::vector& path0, + const std::vector& path1) const { + if (path0.size() != path1.size()) { + VLOG(5) << "Companion path size do not match: " << path0.size() + << " != " << path1.size(); + return false; + } + for (int64 i = 0; i < path0.size(); ++i) { + if (path0[i] != path1[i]) { + VLOG(5) << "Companion instructions at path index " << i + << " do not have the same opcode: " << path0[i].ToString() + << " vs " << path1[i].ToString(); + return false; + } + } + return true; +} + +int64 HloModuleGroupMetadata::GetModuleId(const HloModule* module) const { + for (int64 i = 0; i < modules_.size(); ++i) { + if (modules_[i] == module) { + return i; + } + } + LOG(FATAL) << "unknown module"; +} + +Status HloModuleGroupMetadata::RecordInstructions() { + const auto visitor = [this](HloInstruction* hlo) -> Status { + if (hlo->opcode() == HloOpcode::kWhile) { + tracked_instructions_[hlo->while_condition()] = + TrackedInstruction(hlo, ComputationKind::kWhileCondition); + tracked_instructions_[hlo->while_body()] = + TrackedInstruction(hlo, ComputationKind::kWhileBody); + } else if (hlo->opcode() == HloOpcode::kConditional) { + tracked_instructions_[hlo->true_computation()] = + TrackedInstruction(hlo, ComputationKind::kConditionalTrue); + tracked_instructions_[hlo->false_computation()] = + TrackedInstruction(hlo, ComputationKind::kConditionalFalse); + } + if (!IsChannelInstruction(hlo)) { + return Status::OK(); + } + + // Add a new channel if needed. + if (channel_id_map_.find(hlo->channel_id()) == channel_id_map_.end()) { + channels_.emplace_back(); + channels_.back().id = hlo->channel_id(); + channel_id_map_[hlo->channel_id()] = channels_.size() - 1; + max_channel_id_ = std::max(max_channel_id_, hlo->channel_id()); + } + Channel& channel = channels_[channel_id_map_[hlo->channel_id()]]; + + if (hlo->opcode() == HloOpcode::kSend) { + TF_RET_CHECK(channel.send == nullptr) + << "channel id " << hlo->channel_id() + << " is used by multiple send instructions"; + channel.send = hlo; + } + if (hlo->opcode() == HloOpcode::kRecv) { + TF_RET_CHECK(channel.recv == nullptr) + << "channel id " << hlo->channel_id() + << " is used by multiple recv instructions"; + channel.recv = hlo; + } + if (hlo->opcode() == HloOpcode::kSendDone) { + TF_RET_CHECK(channel.send_done == nullptr) + << "channel id " << hlo->channel_id() + << " is used by multiple send-done instructions"; + channel.send_done = hlo; + } + if (hlo->opcode() == HloOpcode::kRecvDone) { + TF_RET_CHECK(channel.recv_done == nullptr) + << "channel id " << hlo->channel_id() + << " is used by multiple recv-done instructions"; + channel.recv_done = hlo; + } + return Status::OK(); + }; + + for (HloModule* module : modules_) { + for (auto* computation : module->computations()) { + TF_RETURN_IF_ERROR(computation->Accept(visitor)); + } + } + return Status::OK(); +} + +Status HloModuleGroupMetadata::AddCompanion(HloInstruction* instruction1, + HloInstruction* instruction2) { + TF_RET_CHECK(instruction1->opcode() == HloOpcode::kWhile || + instruction1->opcode() == HloOpcode::kConditional); + VLOG(2) << "adding as companions:" << instruction1->ToString() << " and " + << instruction2->ToString(); + + if (!ContainsKey(companion_set_index_, instruction1) && + !ContainsKey(companion_set_index_, instruction2)) { + companion_sets_.push_back( + absl::make_unique>()); + auto companion_set = companion_sets_.back().get(); + companion_set->insert(instruction1); + companion_set->insert(instruction2); + companion_set_index_[instruction1] = companion_sets_.size() - 1; + companion_set_index_[instruction2] = companion_sets_.size() - 1; + } else if (!ContainsKey(companion_set_index_, instruction1)) { + companion_sets_[companion_set_index_[instruction2]]->insert(instruction1); + companion_set_index_[instruction1] = companion_set_index_[instruction2]; + } else if (!ContainsKey(companion_set_index_, instruction2)) { + companion_sets_[companion_set_index_[instruction1]]->insert(instruction2); + companion_set_index_[instruction2] = companion_set_index_[instruction1]; + } else if (companion_set_index_[instruction1] != + companion_set_index_[instruction2]) { + companion_sets_[companion_set_index_[instruction1]]->insert( + Companions(instruction2).begin(), Companions(instruction2).end()); + int64 index_to_remove = companion_set_index_[instruction2]; + for (HloInstruction* hlo : Companions(instruction2)) { + companion_set_index_[hlo] = companion_set_index_[instruction1]; + } + companion_sets_.erase(companion_sets_.begin() + index_to_remove); + } + return Status::OK(); +} + +Status HloModuleGroupMetadata::VerifyChannelInstructions() { + for (const Channel& channel : channels_) { + if (channel.send == nullptr) { + return FailedPrecondition("missing send for id : %lld", channel.id); + } + if (channel.recv == nullptr) { + return FailedPrecondition("missing recv for id : %lld", channel.id); + } + if (channel.send_done == nullptr) { + return FailedPrecondition("missing send-done for id : %lld", channel.id); + } + if (channel.recv_done == nullptr) { + return FailedPrecondition("missing recv-done for id : %lld", channel.id); + } + } + + // Check if the shapes match for each channel. + for (const Channel& channel : channels_) { + const Shape& send_shape = channel.send->operand(0)->shape(); + const Shape& recv_shape = channel.recv_done->shape(); + if (!ShapeUtil::Compatible(send_shape, recv_shape)) { + return FailedPrecondition("send/recv shapes do not match"); + } + } + + // Check if channel instructions are used only in allowed computations. + const auto allowed = [this](HloInstruction* hlo) { + HloComputation* computation = hlo->parent(); + const HloModule* module = computation->parent(); + if (module->entry_computation() == computation || + tracked_instructions_.count(computation) > 0) { + return true; + } + return false; + }; + for (const Channel& channel : channels_) { + if (!allowed(channel.send) || !allowed(channel.send_done) || + !allowed(channel.recv) || !allowed(channel.recv_done)) { + return FailedPrecondition("channel is used in disallowed computation"); + } + } + // Check if the nest levels match for each channel. + for (const Channel& channel : channels_) { + std::vector path = GetCompanionsPath(channel.send); + if (!CheckCompanionPathsCompatibility( + path, GetCompanionsPath(channel.send_done)) || + !CheckCompanionPathsCompatibility(path, + GetCompanionsPath(channel.recv)) || + !CheckCompanionPathsCompatibility( + path, GetCompanionsPath(channel.recv_done))) { + return FailedPrecondition( + "Nest companion paths do not match for channel %lld", channel.id); + } + } + return Status::OK(); +} + +} // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_module_group_metadata.h b/tensorflow/compiler/xla/service/hlo_module_group_metadata.h new file mode 100644 index 0000000000000000000000000000000000000000..c48a7ab0b59269474f7406ef24a249355528e085 --- /dev/null +++ b/tensorflow/compiler/xla/service/hlo_module_group_metadata.h @@ -0,0 +1,239 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_HLO_MODULE_GROUP_METADATA_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_HLO_MODULE_GROUP_METADATA_H_ + +#include +#include +#include +#include +#include + +#include "tensorflow/compiler/xla/service/hlo_computation.h" +#include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/compiler/xla/service/hlo_module.h" +#include "tensorflow/compiler/xla/status.h" +#include "tensorflow/compiler/xla/statusor.h" +#include "tensorflow/core/lib/core/status.h" +#include "tensorflow/core/lib/gtl/flatmap.h" +#include "tensorflow/core/platform/types.h" + +namespace xla { + +// Class for bookkeeping the information on the given modules, in particular on +// the interaction between computations. +// +// Companion instructions are one of the information collected as we build the +// metadata. For example, for each While instruction, companion instructions +// refer to a set of While instructions in other computations that communicate +// with each other. +// In the example below with 3 modules, {While_0, While_2, While_5}, {While_1, +// While_4}, {While_3, While_6} are companion sets. +// +// +// While_0() { While_2() { While_5() { +// While_1() { Send(0) } While_3() { Send(1) } While_6() { Recv(1) } +// } While_4() { Recv(0) } +// } +// +// Companion instructions are used to detect cycles in the graph and also for +// global scheduling. +class HloModuleGroupMetadata { + public: + // The kind of companion computation a given instruction can be within. + enum class ComputationKind { + kInvalid, + kWhileCondition, + kWhileBody, + kConditionalTrue, + kConditionalFalse, + }; + + // Tracks the instruction mapped to a given computation, and the computation + // kind. + // For example, a body computation of a while instruction, will generate a + // TrackedInstruction with instruction being the while instruction, and + // kind being ComputationKind::kWhileBody. + class TrackedInstruction { + public: + TrackedInstruction() = default; + TrackedInstruction(HloInstruction* instruction, ComputationKind kind) + : instruction_(instruction), kind_(kind) {} + + bool operator==(const TrackedInstruction& rhs) const { + return instruction_->opcode() == rhs.instruction_->opcode() && + kind_ == rhs.kind_; + } + bool operator!=(const TrackedInstruction& rhs) const { + return !operator==(rhs); + } + + HloInstruction* instruction() const { return instruction_; } + + string ToString() const; + + private: + HloInstruction* instruction_ = nullptr; + ComputationKind kind_ = ComputationKind::kInvalid; + }; + + // Represents a channel and the 4 instructions that form the channel. + struct Channel { + int64 id = -1; + HloInstruction* send = nullptr; + HloInstruction* recv = nullptr; + HloInstruction* send_done = nullptr; + HloInstruction* recv_done = nullptr; + }; + + explicit HloModuleGroupMetadata(const std::vector& modules) + : modules_(modules) {} + + ~HloModuleGroupMetadata() = default; + + // Build and return the metadata for the given modules. + static StatusOr> Build( + const std::vector& modules); + + // Returns true if the instruction is one of the 4 channel instructions (Send, + // Recv, SendDone, RecvDone). + bool IsChannelInstruction(const HloInstruction* instruction) const; + + // Returns true if the instruction is a companion instruction. See the class + // comment above on companion instructions. + bool IsCompanionInstruction(HloInstruction* hlo) const; + + // Returns true if the instruction is either a channel instruction or a + // companion instruction. + bool InstructionCommunicates(HloInstruction* hlo) const; + + // Returns the Channel instance for the given channel id. + const Channel& GetChannel(int64 channel_id) const; + + // Returns the computation that contains the peer channel instructions for + // the given instruction. + // + // Precondition: IsChannelInstruction(instruction) is true. + HloComputation* PeerComputation(const HloInstruction* instruction) const; + + // Returns the path of the nested companion instructions, in terms of HLO + // instructions. The path goes from inner to outer companions. + // The returned path does not include the input hlo instruction, in case it + // is a companion instruction. + std::vector GetCompanionsPath( + const HloInstruction* hlo) const; + + // Checks whether two companion paths (as returned by the GetCompanionsPath() + // API) are compatible. The two paths are compatible if the sequence of + // opcodes, and the companion kinds, of the two paths matches. + bool CheckCompanionPathsCompatibility( + const std::vector& path0, + const std::vector& path1) const; + + // Returns the unique integer for each module. The returned id is the index of + // the module in the module vector. + int64 GetModuleId(const HloModule* module) const; + + // Returns the companion instructions for the given instruction. + // + // Precondition: IsCompanionWhile(instruction) is true. + const std::unordered_set& Companions( + HloInstruction* instruction) const { + CHECK_EQ(companion_set_index_.count(instruction), 1); + return companion_set(companion_set_index_.at(instruction)); + } + + // Returns the companion set at the given index. + const std::unordered_set& companion_set(int64 index) const { + CHECK_LT(index, companion_sets_.size()); + return *companion_sets_[index]; + } + + // Returns the companion set index of the given instruction. + int64 companion_set_index(HloInstruction* instruction) const { + return companion_set_index_.at(instruction); + } + + // Returns the list of all companion sets in the HLO module group. + const std::vector>>& + companion_sets() const { + return companion_sets_; + } + + // Returns all channels in the module group. + const std::vector& channels() const { return channels_; } + + // Returns the maximum channel id used in the module group. + int64 max_channel_id() const { return max_channel_id_; } + + private: + Status Build(); + + // Record all channel instructions and While instructions. + Status RecordInstructions(); + + // Verifies the given HloModules are well-formed and follow the specification, + // in particular with respect to using channel instructions. + // + // * Each channel has all 4 instructions (Send, Recv, SendDone, RecvDone). + // * The shape of channel instructions match. + // * The nest level of channel instructions match. + // * Channel instructions are used in allowed computations; i.e., in the + // entry computation of the module or condition/body of While computations. + // + // TODO(b/62064342): Currently, HloModuleGroupScheduler checks if there is a + // cycle in the graph, but it would be good to verify here. + Status VerifyChannelInstructions(); + + // Adds metadata that the given two instructions are companions. + Status AddCompanion(HloInstruction* instruction1, + HloInstruction* instruction2); + + // Retrieves a pointer to the stored TrackedInstruction associated with a + // tracked computation, or nullptr in case such computation is not tracked. + const TrackedInstruction* GetTrackedInstruction( + const HloComputation* computation) const { + auto it = tracked_instructions_.find(computation); + return it != tracked_instructions_.end() ? &it->second : nullptr; + } + + // List of all companion instructions sets in the module. + std::vector>> + companion_sets_; + + // Map from each companion while instruction to the index into companion_set_. + tensorflow::gtl::FlatMap companion_set_index_; + + // Map from computation to the instruction using it (a kWhile, kConditional). + tensorflow::gtl::FlatMap + tracked_instructions_; + + // All channels in the module. + std::vector channels_; + + // Map from channel ids to the index in channels_. + tensorflow::gtl::FlatMap channel_id_map_; + + // The maximum channel id used in the module group. + int64 max_channel_id_ = -1; + + // The modules that this metadata was built from. + const std::vector& modules_; +}; + +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_HLO_MODULE_GROUP_METADATA_H_ diff --git a/tensorflow/compiler/xla/service/hlo_module_group_util.cc b/tensorflow/compiler/xla/service/hlo_module_group_util.cc new file mode 100644 index 0000000000000000000000000000000000000000..289c96b0a7b90c5f8a122cd3fc327a5762099106 --- /dev/null +++ b/tensorflow/compiler/xla/service/hlo_module_group_util.cc @@ -0,0 +1,316 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/hlo_module_group_util.h" + +#include +#include +#include +#include +#include +#include + +#include "tensorflow/compiler/xla/service/hlo_opcode.h" +#include "tensorflow/compiler/xla/service/hlo_reachability.h" +#include "tensorflow/compiler/xla/status_macros.h" +#include "tensorflow/compiler/xla/types.h" +#include "tensorflow/compiler/xla/util.h" +#include "tensorflow/core/lib/core/errors.h" +#include "tensorflow/core/lib/strings/strcat.h" +#include "tensorflow/core/platform/logging.h" +#include "tensorflow/core/platform/types.h" + +namespace xla { + +std::vector HloModuleGroupUtil::GlobalPredecessors( + HloInstruction* instruction) { + std::vector predecessors; + + // Adds to the unique predecessors list and also add companion instructions + // if the given predecessor has those. + auto add_unique_predecessor = [&](HloInstruction* predecessor) { + if (std::find(predecessors.begin(), predecessors.end(), predecessor) != + predecessors.end()) { + return; + } + if (!metadata_.IsCompanionInstruction(predecessor)) { + predecessors.push_back(predecessor); + return; + } + for (HloInstruction* companion : metadata_.Companions(predecessor)) { + predecessors.push_back(companion); + } + }; + + // If the given instruction is a companion instruction, we need to find the + // predecessors of all of its companion instructions. + std::vector instruction_group; + if (metadata_.IsCompanionInstruction(instruction)) { + for (HloInstruction* companion : metadata_.Companions(instruction)) { + instruction_group.push_back(companion); + } + } else { + instruction_group.push_back(instruction); + } + + for (HloInstruction* hlo : instruction_group) { + for (HloInstruction* operand : hlo->operands()) { + add_unique_predecessor(operand); + } + for (HloInstruction* control_predecessor : hlo->control_predecessors()) { + add_unique_predecessor(control_predecessor); + } + } + if (instruction->opcode() == HloOpcode::kRecvDone) { + // Send is a remote predecessor of RecvDone. + HloInstruction* send = metadata_.GetChannel(instruction->channel_id()).send; + add_unique_predecessor(send); + } + if (instruction->opcode() == HloOpcode::kSend) { + // Recv is a remote predecessor of Send. + HloInstruction* recv_done = + metadata_.GetChannel(instruction->channel_id()).recv_done; + CHECK(recv_done->opcode() == HloOpcode::kRecvDone); + CHECK_EQ(recv_done->operand_count(), 1); + HloInstruction* recv = recv_done->mutable_operand(0); + add_unique_predecessor(recv); + } + return predecessors; +} + +std::vector HloModuleGroupUtil::GlobalSuccessors( + HloInstruction* instruction) { + std::vector successors; + + // Adds to the unique successors list and also add companion instructions + // if the given successor has those. + auto add_unique_successor = [&](HloInstruction* successor) { + if (std::find(successors.begin(), successors.end(), successor) != + successors.end()) { + return; + } + if (!metadata_.IsCompanionInstruction(successor)) { + successors.push_back(successor); + return; + } + for (HloInstruction* companion : metadata_.Companions(successor)) { + successors.push_back(companion); + } + }; + + // If the given instruction is a companion instruction, we need to find the + // successors of all of its companion instructions. + std::vector instruction_group; + if (metadata_.IsCompanionInstruction(instruction)) { + for (HloInstruction* companion : metadata_.Companions(instruction)) { + instruction_group.push_back(companion); + } + } else { + instruction_group.push_back(instruction); + } + + for (HloInstruction* hlo : instruction_group) { + for (HloInstruction* user : hlo->users()) { + add_unique_successor(user); + } + for (HloInstruction* control_successor : hlo->control_successors()) { + add_unique_successor(control_successor); + } + } + if (instruction->opcode() == HloOpcode::kRecv) { + // Send is a remote successor of Recv. + const HloInstruction* recv_done = instruction->users().front(); + CHECK(recv_done->opcode() == HloOpcode::kRecvDone); + HloInstruction* send = metadata_.GetChannel(instruction->channel_id()).send; + add_unique_successor(send); + } + if (instruction->opcode() == HloOpcode::kSend) { + // RecvDone is a remote successor of Send. + HloInstruction* recv_done = + metadata_.GetChannel(instruction->channel_id()).recv_done; + add_unique_successor(recv_done); + } + return successors; +} + +std::vector HloModuleGroupUtil::RootInstructions( + tensorflow::gtl::ArraySlice computations) { + std::vector roots; + for (HloComputation* computation : computations) { + for (HloInstruction* instruction : computation->instructions()) { + if (GlobalSuccessors(instruction).empty()) { + roots.push_back(instruction); + } + } + } + return roots; +} + +Status HloModuleGroupUtil::VisitTopologicalOrder( + VisitStates* visit_state, const VisitFunction& visit_function, + HloInstruction* root) { + // Stack of HLO instructions visited in DFS order. + std::stack stack; + stack.push(root); + + while (!stack.empty()) { + HloInstruction* hlo = stack.top(); + + // Find the instruction group of the currently visited instruction. The + // instruction group represents all companion instructions of the + // current instruction, and are considered to be a single entity for the + // purpose of the traversal (i.e., they must always be in the same visit + // state). + std::vector instruction_group; + if (metadata_.IsCompanionInstruction(hlo)) { + for (HloInstruction* companion : metadata_.Companions(hlo)) { + instruction_group.push_back(companion); + } + } else { + instruction_group.push_back(hlo); + } + + if ((*visit_state)[hlo] == VisitState::kVisited) { + // All instructions in the group must be in the same state. + for (HloInstruction* instruction : instruction_group) { + TF_RET_CHECK((*visit_state)[instruction] == VisitState::kVisited); + } + stack.pop(); + continue; + } + + if ((*visit_state)[hlo] == VisitState::kVisiting) { + TF_RETURN_IF_ERROR(visit_function(hlo, instruction_group)); + + // Set the visit state of all instructions in the group to kVisited. + for (HloInstruction* instruction : instruction_group) { + TF_RET_CHECK((*visit_state)[instruction] == VisitState::kVisiting); + (*visit_state)[instruction] = VisitState::kVisited; + } + stack.pop(); + continue; + } + + // Set the visit state of all instructions in the group to kVisiting. + for (HloInstruction* instruction : instruction_group) { + TF_RET_CHECK((*visit_state)[instruction] == VisitState::kNotVisited) + << instruction->ToString(); + (*visit_state)[instruction] = VisitState::kVisiting; + } + + // For each instruction in the group, visit its predecessors (operands, + // control predecessors and remote predecessors). + for (HloInstruction* instruction : instruction_group) { + for (HloInstruction* predecessor : GlobalPredecessors(instruction)) { + // Visiting a node that is already being visited implies that there is + // a cycle. Generate an error with the list of instructions in the + // cycle. + if ((*visit_state)[predecessor] == VisitState::kVisiting) { + string cyclic_instructions; + for (const auto& state : *visit_state) { + if (state.second == VisitState::kVisiting) { + tensorflow::strings::StrAppend(&cyclic_instructions, + state.first->ToString(), "\n"); + } + } + // TODO(b/64305524): Improve the error message to print out the + // instructions in a deterministic order that forms the cycle. + return FailedPrecondition( + "Cross-computation cycle detected via communicating nodes. The " + "cycle contains the node %s. The cycle is found among the " + "following nodes. Note that the order of the nodes is arbitrary " + "and that the list may include nodes that are not part of the " + "cycle.\n%s", + predecessor->ToString().c_str(), cyclic_instructions.c_str()); + } + stack.push(predecessor); + } + } + } + + return Status::OK(); +} + +Status HloModuleGroupUtil::VerifyComputations( + tensorflow::gtl::ArraySlice computations) { + auto visit_function = + [&](HloInstruction* instruction, + const std::vector& instruction_group) { + return Status::OK(); + }; + int64 instructions_count = 0; + VisitStates visit_states; + for (HloComputation* computation : computations) { + // Visit all instructions, and not just from the root instruction of the + // computation. This allows us to detect dead cycles (i.e., cycles that + // are not reachable from the root) or to enforce an order for the + // communication instructions that are not reachable from any roots. + for (HloInstruction* instruction : computation->instructions()) { + TF_RETURN_IF_ERROR( + VisitTopologicalOrder(&visit_states, visit_function, instruction)); + } + instructions_count += computation->instruction_count(); + } + + // Check if all instructions are visited and are in the visited state. + TF_RET_CHECK(visit_states.size() == instructions_count); + for (auto& state : visit_states) { + TF_RET_CHECK(state.second == VisitState::kVisited); + } + + return Status::OK(); +} + +StatusOr> +HloModuleGroupUtil::ComputeReachability( + tensorflow::gtl::ArraySlice computations) { + std::list post_order; + auto visit_function = + [&](HloInstruction* instruction, + const std::vector& instruction_group) { + post_order.insert(post_order.end(), instruction_group.begin(), + instruction_group.end()); + return Status::OK(); + }; + HloModuleGroupUtil::VisitStates visit_states; + for (HloInstruction* root : RootInstructions(computations)) { + TF_RETURN_IF_ERROR( + VisitTopologicalOrder(&visit_states, visit_function, root)); + } + auto reachability = absl::make_unique(post_order); + for (HloInstruction* hlo : post_order) { + reachability->SetReachabilityToUnion(GlobalPredecessors(hlo), hlo); + } + return std::move(reachability); +} + +void HloModuleGroupUtil::UpdateReachabilityThroughInstruction( + HloInstruction* instruction, HloReachabilityMap* reachability_map) { + std::queue worklist; + worklist.push(instruction); + + while (!worklist.empty()) { + HloInstruction* item = worklist.front(); + worklist.pop(); + if (reachability_map->SetReachabilityToUnion(GlobalPredecessors(item), + item)) { + for (HloInstruction* successor : GlobalSuccessors(item)) { + worklist.push(successor); + } + } + } +} + +} // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_module_group_util.h b/tensorflow/compiler/xla/service/hlo_module_group_util.h new file mode 100644 index 0000000000000000000000000000000000000000..c25ca1aff50b288f3ac3885cbed53e7ba9768430 --- /dev/null +++ b/tensorflow/compiler/xla/service/hlo_module_group_util.h @@ -0,0 +1,117 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_HLO_MODULE_GROUP_UTIL_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_HLO_MODULE_GROUP_UTIL_H_ + +#include +#include +#include + +#include "tensorflow/compiler/xla/service/hlo_computation.h" +#include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/compiler/xla/service/hlo_module_group_metadata.h" +#include "tensorflow/compiler/xla/service/hlo_reachability.h" +#include "tensorflow/compiler/xla/status.h" +#include "tensorflow/compiler/xla/statusor.h" +#include "tensorflow/core/lib/core/status.h" +#include "tensorflow/core/lib/gtl/array_slice.h" +#include "tensorflow/core/lib/gtl/flatmap.h" + +namespace xla { + +// Collection of utilities for handling HloModuleGroups. +class HloModuleGroupUtil { + public: + explicit HloModuleGroupUtil(const HloModuleGroupMetadata& metadata) + : metadata_(metadata) {} + + // Returns all unique predecessors of the instruction. This includes: + // * predecessors in the same computation: operands and control predecessors + // * Recv is a predecessor of Send + // * Send is a predecessor of RecvDone + // * predecessors of companions (if the instruction is a companion while) + // * predecessors' companions (for any predecessor that is a companion while) + std::vector GlobalPredecessors(HloInstruction* instruction); + + // Returns all unique successors of the instruction. This includes: + // * successors in the same computation: users and control successors + // * Send is a successor of Recv + // * RecvDone is a predecessor of Send + // * successors of companions (if the instruction is a companion while) + // * successors' companions (for any successor that is a companion while) + std::vector GlobalSuccessors(HloInstruction* instruction); + + // Returns the root instructions of the computations. + std::vector RootInstructions( + tensorflow::gtl::ArraySlice computations); + + // Visit state of each instruction during DFS traversal. + enum VisitState { + kNotVisited = 0, + kVisiting, + kVisited, + }; + + // Function called on each instruction group during the DFS traversal. See the + // comment for VisitTopologicalOrder()). + using VisitFunction = std::function& instruction_group)>; + + // Given the hlo instruction as the root, recursively visits all its + // predecessor instructions in DFS order to visit nodes in topological order. + // + // Note that the DFS traversal does not only visit nodes in the same + // computation (parent of the root instruction), but also visits nodes in + // different computations connected via communication instructions. During the + // traversal, companion While instructions (see the class comment in + // HloModuleGroupMetadata) are treated as a single instruction (called + // instruction group, which contains only a single instruction if the visiting + // node is not a companion while) -- visiting one of the instructions in the + // group effectively visits all other instructions in the group, and then all + // predecessor instructions of the group are visited. + // + // * visit_state: map from each instruction to its visit state. + // * visit_function: function called when each instruction group. + // * root: the root instruction of the traversal. + using VisitStates = tensorflow::gtl::FlatMap; + Status VisitTopologicalOrder(VisitStates* visit_state, + const VisitFunction& visit_function, + HloInstruction* root); + + // Verifies that the computations are well-formed (e.g., no cycles). + Status VerifyComputations( + tensorflow::gtl::ArraySlice computations); + + // Below Reachability utils resemble those in HloComputation, except that + // they can handle instructions across multiple computations. + // + // Creates the reachability map for the instructions in the computations. + StatusOr> ComputeReachability( + tensorflow::gtl::ArraySlice computations); + + // Updates the reachability of the given instruction, taking the global + // predeccessorss and successors into account. + void UpdateReachabilityThroughInstruction( + HloInstruction* instruction, HloReachabilityMap* reachability_map); + + private: + const HloModuleGroupMetadata& metadata_; +}; + +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_HLO_MODULE_GROUP_UTIL_H_ diff --git a/tensorflow/compiler/xla/service/hlo_proto_util.cc b/tensorflow/compiler/xla/service/hlo_proto_util.cc index 78e6a101c10a1e812e3e2631d520139fd0bc425c..3460679558d185d1e022660d9a1d23176d0d96bf 100644 --- a/tensorflow/compiler/xla/service/hlo_proto_util.cc +++ b/tensorflow/compiler/xla/service/hlo_proto_util.cc @@ -15,6 +15,10 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_proto_util.h" +#include + +#include "tensorflow/compiler/xla/util.h" + namespace xla { HloProto MakeHloProto(const HloModule& module, @@ -35,4 +39,35 @@ HloProto MakeHloProto(const HloModule& module) { return proto; } +StatusOr> EntryComputationParameterShapes( + const HloProto& hlo_proto) { + if (!hlo_proto.has_hlo_module()) { + return NotFound("HloProto missing HloModuleProto."); + } + if (!hlo_proto.hlo_module().has_program_shape()) { + return NotFound("HloProto missing program shape."); + } + + std::vector parameter_shapes; + const auto& program_shape = hlo_proto.hlo_module().program_shape(); + for (const Shape& shape : program_shape.parameters()) { + parameter_shapes.push_back(&shape); + } + return parameter_shapes; +} + +StatusOr EntryComputationOutputShape(const HloProto& hlo_proto) { + if (!hlo_proto.has_hlo_module()) { + return NotFound("HloProto missing HloModuleProto."); + } + if (!hlo_proto.hlo_module().has_program_shape()) { + return NotFound("HloProto missing program shape."); + } + if (!hlo_proto.hlo_module().program_shape().has_result()) { + return NotFound("HloProto missing result in its program shape"); + } + + return &hlo_proto.hlo_module().program_shape().result(); +} + } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_proto_util.h b/tensorflow/compiler/xla/service/hlo_proto_util.h index 320288fdb9aa0810b306b1d78bd1ff4cfc366ed2..3d9c375cd5d26f92cf8316f78789daf4fc08c927 100644 --- a/tensorflow/compiler/xla/service/hlo_proto_util.h +++ b/tensorflow/compiler/xla/service/hlo_proto_util.h @@ -35,6 +35,15 @@ HloProto MakeHloProto(const HloModule& module, // will not be included in the output. HloProto MakeHloProto(const HloModule& module); +// Returns the shapes of the parameters of the entry computation. Shape pointers +// refer to shapes inside of the given HloProto. +StatusOr> EntryComputationParameterShapes( + const HloProto& hlo_proto); + +// Returns the shape of the output of the entry computation. The shape pointer +// refers to the output shape inside of the given HloProto. +StatusOr EntryComputationOutputShape(const HloProto& hlo_proto); + } // namespace xla #endif // TENSORFLOW_COMPILER_XLA_SERVICE_HLO_PROTO_UTIL_H_ diff --git a/tensorflow/compiler/xla/service/hlo_proto_util_test.cc b/tensorflow/compiler/xla/service/hlo_proto_util_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..b9cca138703c8fa61aadf69dd7304a215a9f4be2 --- /dev/null +++ b/tensorflow/compiler/xla/service/hlo_proto_util_test.cc @@ -0,0 +1,53 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/hlo_proto_util.h" + +#include "tensorflow/compiler/xla/service/hlo.pb.h" +#include "tensorflow/compiler/xla/service/hlo_opcode.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/tests/hlo_test_base.h" +#include "tensorflow/compiler/xla/types.h" +#include "tensorflow/core/lib/strings/str_util.h" + +namespace xla { +namespace { + +class HloProtoUtilTest : public ::testing::Test {}; + +TEST_F(HloProtoUtilTest, ParamsAndOutputShapeMissingModule) { + HloProto hlo_proto; + + auto status = EntryComputationParameterShapes(hlo_proto).status(); + ASSERT_FALSE(status.ok()); + ASSERT_THAT(status.error_message(), + ::testing::HasSubstr("missing HloModuleProto")); +} + +TEST_F(HloProtoUtilTest, MissingProgramShape) { + HloProto hlo_proto; + HloModuleProto* module = hlo_proto.mutable_hlo_module(); + module->set_name("entry"); + + auto status = EntryComputationParameterShapes(hlo_proto).status(); + ASSERT_FALSE(status.ok()); + ASSERT_THAT(status.error_message(), + ::testing::HasSubstr("missing program shape")); +} + +} // namespace +} // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_runner.cc b/tensorflow/compiler/xla/service/hlo_runner.cc index 41b079eb799d06321a31f7d7ae0630dc8d58c46b..e5b1c2efa3fc25d23531df298e125521c002dba1 100644 --- a/tensorflow/compiler/xla/service/hlo_runner.cc +++ b/tensorflow/compiler/xla/service/hlo_runner.cc @@ -110,7 +110,7 @@ HloRunner::HloRunner(se::Platform* platform) { HloRunner::~HloRunner() {} -StatusOr> HloRunner::ExecuteInternal( +StatusOr> HloRunner::Execute( std::unique_ptr module, const tensorflow::gtl::ArraySlice arguments, bool run_hlo_passes) { @@ -158,8 +158,8 @@ StatusOr> HloRunner::ExecuteInternal( TF_ASSIGN_OR_RETURN( std::unique_ptr result, - executable->ExecuteOnStream(&service_run_options, argument_buffer_ptrs, - /*hlo_execution_profile=*/nullptr)); + executable->ExecuteOnStreamWrapper( + &service_run_options, /*profile=*/nullptr, argument_buffer_ptrs)); // Create a ScopedShapedBuffer of the result to manage deallocation. This will // deallocate all the device memory when it goes out of scope. diff --git a/tensorflow/compiler/xla/service/hlo_runner.h b/tensorflow/compiler/xla/service/hlo_runner.h index cbaebc68bee708090b8ccb2eae19b556c4d6d453..06ce22a5b9fc7b3d6c10857c84196094c0eed303 100644 --- a/tensorflow/compiler/xla/service/hlo_runner.h +++ b/tensorflow/compiler/xla/service/hlo_runner.h @@ -27,6 +27,7 @@ limitations under the License. #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/statusor.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/gtl/array_slice.h" #include "tensorflow/core/platform/stream_executor_no_cuda.h" @@ -64,17 +65,27 @@ class HloRunner { const std::string& filename, const DebugOptions& debug_options); // Executes the given module with given literals as input and returns the - // result as a Literal. The LiteralPtr type accepts Literal* or - // std::unique_ptr. + // result as a Literal. // // If run_hlo_passes is false, the module will be executed without Hlo // optimization. - template StatusOr> Execute( std::unique_ptr module, - const tensorflow::gtl::ArraySlice arguments, + const tensorflow::gtl::ArraySlice arguments, bool run_hlo_passes = true); + StatusOr> Execute( + std::unique_ptr module, + const tensorflow::gtl::ArraySlice> arguments, + bool run_hlo_passes = true) { + // Construct a vector of plain pointers for the arguments. + std::vector argument_pointers; + c_transform( + arguments, std::back_inserter(argument_pointers), + [](const std::unique_ptr& literal) { return literal.get(); }); + return Execute(std::move(module), argument_pointers, run_hlo_passes); + } + // If backend is not created in the constructor, creates and returns the // default backend. If creation fails, crashes the program. // @@ -83,11 +94,6 @@ class HloRunner { Backend& backend(); private: - StatusOr> ExecuteInternal( - std::unique_ptr module, - const tensorflow::gtl::ArraySlice arguments, - bool run_hlo_passes = true); - struct EigenThreadPoolWrapper; std::unique_ptr thread_pool_wrapper_; @@ -95,19 +101,6 @@ class HloRunner { std::unique_ptr backend_; }; -template -StatusOr> HloRunner::Execute( - std::unique_ptr module, - const tensorflow::gtl::ArraySlice arguments, - bool run_hlo_passes) { - // Construct a vector of plain pointers for the arguments. - std::vector argument_pointers; - for (const auto& argument : arguments) { - argument_pointers.push_back(&*argument); - } - return ExecuteInternal(std::move(module), argument_pointers, run_hlo_passes); -} - } // namespace xla #endif // TENSORFLOW_COMPILER_XLA_SERVICE_HLO_RUNNER_H_ diff --git a/tensorflow/compiler/xla/service/hlo_scheduling.cc b/tensorflow/compiler/xla/service/hlo_scheduling.cc index f6e33403f538bd8492b04c34d46a458f7f06cc06..da448ed71ab470e0c4d72e234bf1f1087d3ea7b4 100644 --- a/tensorflow/compiler/xla/service/hlo_scheduling.cc +++ b/tensorflow/compiler/xla/service/hlo_scheduling.cc @@ -348,6 +348,7 @@ StatusOr> RunDFSMemoryScheduler( // simply users-1 for each instruction. By subtracting 1, we're saying that // instructions with no users or a single user don't count; instructions with // lots of fan-out will be visited earlier. + int64 cumulative_total_size = 0; tensorflow::gtl::FlatMap extra_users; tensorflow::gtl::FlatMap total_sizes; for (const HloInstruction* hlo : computation.MakeInstructionPostOrder()) { @@ -357,14 +358,17 @@ StatusOr> RunDFSMemoryScheduler( continue; } extra_users[hlo] = hlo->users().empty() ? 0 : hlo->users().size() - 1; - total_sizes[hlo] = SumLogicalBufferSizes( + int64 logical_buffer_size = SumLogicalBufferSizes( points_to_analysis.GetBuffersDefinedByInstruction(hlo), size_function); + total_sizes[hlo] = logical_buffer_size; + cumulative_total_size += logical_buffer_size; tensorflow::gtl::FlatSet unique_operands( hlo->operands().begin(), hlo->operands().end()); for (const HloInstruction* operand : unique_operands) { extra_users[hlo] += extra_users[operand]; total_sizes[hlo] += total_sizes[operand]; } + total_sizes[hlo] = std::min(total_sizes[hlo], cumulative_total_size); } CHECK_EQ(extra_users.size(), computation.instruction_count()); CHECK_EQ(total_sizes.size(), computation.instruction_count()); diff --git a/tensorflow/compiler/xla/service/hlo_sharding.cc b/tensorflow/compiler/xla/service/hlo_sharding.cc index afe79c9f17befdcb2812c0a08b205f21b0715b19..aa9ff89e983aa5d35a18906afca1c6e8eeaefa06 100644 --- a/tensorflow/compiler/xla/service/hlo_sharding.cc +++ b/tensorflow/compiler/xla/service/hlo_sharding.cc @@ -348,4 +348,30 @@ OpSharding HloSharding::ToProto() const { return result; } +HloSharding HloSharding::TransformShardedTileShape( + const Shape& new_shape, + const std::function& transform) const { + CHECK(!IsTuple()); + if (IsTileMaximal()) { + return *this; + } + CHECK_EQ(ShapeUtil::Rank(new_shape), ShapeUtil::Rank(tile_shape())); + Shape new_tile_shape; + new_tile_shape.set_element_type(tile_shape().element_type()); + for (int64 i = 0; i < ShapeUtil::Rank(new_shape); ++i) { + int64 dim; + if (tile_assignment().dim(i) == 1) { + dim = new_shape.dimensions(i); + } else if (transform) { + dim = transform(i, tile_shape().dimensions(i)); + } else { + dim = tile_shape().dimensions(i); + } + new_tile_shape.add_dimensions(dim); + } + TF_CHECK_OK( + LayoutUtil::CopyLayoutBetweenShapes(tile_shape_, &new_tile_shape)); + return HloSharding::Tile(new_tile_shape, tile_assignment()); +} + } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_sharding.h b/tensorflow/compiler/xla/service/hlo_sharding.h index 7263198385cf0c84b1dac1e15177dcac99adaafb..e715dff9a0b8fcc2301a1581919dba384206923c 100644 --- a/tensorflow/compiler/xla/service/hlo_sharding.h +++ b/tensorflow/compiler/xla/service/hlo_sharding.h @@ -207,6 +207,19 @@ class HloSharding { // REQUIRES: !IsReplicated() && !IsTuple() const Array& tile_assignment() const { return tile_assignment_; } + // Return a new sharding that can apply to the given new shape. + // If this sharding is tile-maximal, the returned sharding will be the same as + // this sharding. If this sharding is not tile-maximal, the returned + // sharding's tile size will differ: + // - Non-sharded dimensions will be adapted to be the same as `new_shape`; + // tile_dimension(i) = new_shape.dimensions(i); + // - Sharded dimensions will be kept the same unless `transform` is supplied + // in which case tile_dimension(i) = transform(i, tile_dimension(i)); + // REQUIRES: !IsTuple(). + HloSharding TransformShardedTileShape( + const Shape& new_shape, + const std::function& transform = nullptr) const; + private: HloSharding() : replicated_(true), diff --git a/tensorflow/compiler/xla/service/hlo_sharding_test.cc b/tensorflow/compiler/xla/service/hlo_sharding_test.cc index 0c7487b3ac77ff181d44dd55ebcf2608feaf02ea..07fc4687cc1c0518b3ab2a86c62464fc54082a01 100644 --- a/tensorflow/compiler/xla/service/hlo_sharding_test.cc +++ b/tensorflow/compiler/xla/service/hlo_sharding_test.cc @@ -269,5 +269,18 @@ TEST_F(HloShardingTest, Hash) { } } +TEST_F(HloShardingTest, TransformShardedTileShapeTest) { + HloSharding sharding = + HloSharding::Tile(ShapeUtil::MakeShape(F32, {3, 5, 7, 11}), + Array4D({{{{0, 1}, {2, 3}}}})); + HloSharding result = sharding.TransformShardedTileShape( + ShapeUtil::MakeShape(F32, {13, 15, 17, 19}), + [](int dim, int value) { return dim * 111; }); + HloSharding expected = + HloSharding::Tile(ShapeUtil::MakeShape(F32, {13, 15, 222, 333}), + Array4D({{{{0, 1}, {2, 3}}}})); + EXPECT_EQ(result, expected); +} + } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_verifier.cc b/tensorflow/compiler/xla/service/hlo_verifier.cc index b1fd068115e1d104a11d880675ef84e07d6d5602..8c875698eb1992719d504d272ca338b05b60e36b 100644 --- a/tensorflow/compiler/xla/service/hlo_verifier.cc +++ b/tensorflow/compiler/xla/service/hlo_verifier.cc @@ -762,11 +762,14 @@ StatusOr HloVerifier::Run(HloModule* module) { } else if (instruction->opcode() == HloOpcode::kBroadcast) { // If you see this failure then someone has confused the difference // between the HLO broadcast op, and the UserComputation broadcast - // op. See https://groups.google.com/forum/#!topic/xla-dev/9LqijHmTt_I + // op. See https://groups.google.com/forum/#!topic/xla-dev/9LqijHmTt_I // or ComputationLowerer::Visit() TF_RET_CHECK(instruction->dimensions().size() == ShapeUtil::Rank(instruction->operand(0)->shape())) - << "Broadcast HLO has invalid number of dimensions."; + << "Broadcast HLO (" << instruction->ToShortString() + << ") has invalid number of dimensions: " + << instruction->dimensions().size() + << " != " << ShapeUtil::Rank(instruction->operand(0)->shape()); } else if (instruction->opcode() == HloOpcode::kWhile) { auto* while_cond = instruction->while_condition(); auto* while_body = instruction->while_body(); diff --git a/tensorflow/compiler/xla/service/instruction_fusion.cc b/tensorflow/compiler/xla/service/instruction_fusion.cc index f494748e17fc2d0de74dec67f7414d4791f76a07..d69ad80bdb4d2eab2d34228be026d7bc0b76efc0 100644 --- a/tensorflow/compiler/xla/service/instruction_fusion.cc +++ b/tensorflow/compiler/xla/service/instruction_fusion.cc @@ -302,7 +302,7 @@ StatusOr InstructionFusion::Run(HloModule* module) { // Consider each operand of this instruction for fusion into this // instruction. We want to consider the operands in a particular order to - // avoid created duplicate instruction clones in the fusion instruction. + // avoid creating duplicate instruction clones in the fusion instruction. // For example, consider the following expression: // // A = ... @@ -377,7 +377,7 @@ StatusOr InstructionFusion::Run(HloModule* module) { changed = true; if (operand->user_count() == 0) { - // Operand is now dead. Remove from post order by setting it's + // Operand is now dead. Remove from post order by setting its // location to nullptr. post_order[FindOrDie(post_order_index, operand)] = nullptr; post_order_index.erase(operand); diff --git a/tensorflow/compiler/xla/service/interpreter/executable.cc b/tensorflow/compiler/xla/service/interpreter/executable.cc index 0cb9b5d8107cd8bf468b07d5fe2a22930d9e8b8c..883063d0f075f5b0d79edc01bcd27a7c579272f4 100644 --- a/tensorflow/compiler/xla/service/interpreter/executable.cc +++ b/tensorflow/compiler/xla/service/interpreter/executable.cc @@ -93,7 +93,7 @@ StatusOr> InterpreterExecutable::ExecuteOnStream( TF_ASSIGN_OR_RETURN(std::unique_ptr result, transfer_manager->AllocateShapedBuffer( result_literal->shape(), run_options->allocator(), - run_options->device_ordinal())); + executor->device_ordinal())); TF_RETURN_IF_ERROR(transfer_manager->TransferLiteralToDevice( executor, *result_literal, *result)); diff --git a/tensorflow/compiler/xla/service/layout_assignment.cc b/tensorflow/compiler/xla/service/layout_assignment.cc index 0668f66051ce96292c3c85bac7e649d89914106c..39f9120e552f014dd2759bff2892157402d9c47a 100644 --- a/tensorflow/compiler/xla/service/layout_assignment.cc +++ b/tensorflow/compiler/xla/service/layout_assignment.cc @@ -192,17 +192,34 @@ LayoutConstraints::LayoutConstraints( } } +PointsToSet::BufferSet* LayoutConstraints::GetBufferSet( + const HloInstruction* instruction) const { + auto it = buffer_sets_cache_.find(instruction); + if (it != buffer_sets_cache_.end()) { + return it->second.get(); + } + auto& buffer_set = + buffer_sets_cache_ + .emplace(instruction, MakeUnique()) + .first->second; + const auto& points_to_set = points_to_analysis_.GetPointsToSet(instruction); + points_to_set.ForEachElement( + [&buffer_set](const ShapeIndex& /*index*/, + const PointsToSet::BufferList& buffers) { + buffer_set->insert(buffers.begin(), buffers.end()); + }); + return buffer_set.get(); +} + bool LayoutConstraints::OperandBufferForwarded( const HloInstruction* instruction, int64 operand_no) const { // The operand is potentially forwarded if the intersection of points-to sets // of the operand and the instruction is non-empty. - auto output_buffers = - points_to_analysis_.GetPointsToSet(instruction).CreateFlattenedSet(); - auto operand_buffers = - points_to_analysis_.GetPointsToSet(instruction->operand(operand_no)) - .CreateFlattenedSet(); - for (const LogicalBuffer* output_buffer : output_buffers) { - if (operand_buffers.count(output_buffer) > 0) { + PointsToSet::BufferSet* output_buffers = GetBufferSet(instruction); + PointsToSet::BufferSet* operand_buffers = + GetBufferSet(instruction->operand(operand_no)); + for (const LogicalBuffer* output_buffer : *output_buffers) { + if (operand_buffers->count(output_buffer) > 0) { return true; } } @@ -1544,6 +1561,13 @@ StatusOr LayoutAssignment::Run(HloModule* module) { // infeeds. Clearing the layouts here avoids hiding potential bugs in the // layout assignment pass that may accidently use the existing layout. for (HloInstruction* instruction : computation->instructions()) { + if (instruction->opcode() == HloOpcode::kBitcast) { + // bitcasts are inherently layout sensitive and so a bitcast instruction + // present in the IR before layout assignment is a bug. + return InternalError( + "Unexpected bitcast operation seen during layout assignment: %s.", + instruction->ToString().c_str()); + } if (instruction->opcode() != HloOpcode::kInfeed) { LayoutUtil::ClearLayout(instruction->mutable_shape()); } diff --git a/tensorflow/compiler/xla/service/layout_assignment.h b/tensorflow/compiler/xla/service/layout_assignment.h index 29018584487cabfd740d7914625c2a50f552d6ff..680f88048a1f0cd5ede7991640003ef407d4facf 100644 --- a/tensorflow/compiler/xla/service/layout_assignment.h +++ b/tensorflow/compiler/xla/service/layout_assignment.h @@ -38,6 +38,7 @@ limitations under the License. #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/lib/core/status.h" +#include "tensorflow/core/lib/gtl/flatmap.h" #include "tensorflow/core/platform/types.h" namespace xla { @@ -199,6 +200,11 @@ class LayoutConstraints { string ToString() const; private: + // Find a bufferset in the bufferset cache. This is useful since we can + // currently create the flattened buffer set for the same instruction many + // times, which is often slow. + PointsToSet::BufferSet* GetBufferSet(const HloInstruction* instruction) const; + // The set of BufferLayoutConstraints applied to the computation. std::unordered_map buffer_constraints_; @@ -221,6 +227,10 @@ class LayoutConstraints { // Array-shaped buffers which have not yet been constrained. std::set unconstrained_buffer_ids_; + mutable tensorflow::gtl::FlatMap> + buffer_sets_cache_; + HloComputation* computation_; }; @@ -393,7 +403,6 @@ class LayoutAssignment : public HloPassInterface { Status CheckLayouts(HloModule* module); ComputationLayout* entry_computation_layout_; - ChannelLayoutConstraints* channel_layout_constraints_; protected: // Map containing the layouts of all computations assigned so @@ -401,6 +410,7 @@ class LayoutAssignment : public HloPassInterface { // handled before their caller instructions so the layouts of caller // instructions can be set to match the computation. std::map computation_layouts_; + ChannelLayoutConstraints* channel_layout_constraints_; }; } // namespace xla diff --git a/tensorflow/compiler/xla/service/layout_assignment_test.cc b/tensorflow/compiler/xla/service/layout_assignment_test.cc index 88e5caaf478bc99ecf93ab00ddba4637397b9d78..4b1c9bad41de8030cf14bc6d1c0db21b9c56c3bf 100644 --- a/tensorflow/compiler/xla/service/layout_assignment_test.cc +++ b/tensorflow/compiler/xla/service/layout_assignment_test.cc @@ -590,6 +590,85 @@ TEST_F(LayoutAssignmentTest, TransposeToBitcastToUser) { transpose->shape(), {2, 3, 0, 1})); } +// TransposeIsBitcast shouldn't be called without layout information. +TEST_F(LayoutAssignmentTest, TransposeIsBitcastFail) { + auto builder = HloComputation::Builder(TestName()); + Shape input_shape = ShapeUtil::MakeShape(F32, {2, 2, 2}); + Shape input_shape_with_layout(input_shape); + *input_shape_with_layout.mutable_layout() = LayoutUtil::MakeLayout({2, 1, 0}); + auto param = builder.AddInstruction( + HloInstruction::CreateParameter(0, input_shape_with_layout, "param")); + auto hlo = builder.AddInstruction( + HloInstruction::CreateTranspose(input_shape, param, {0, 2, 1})); + // Clear the default layout assigned to the instruction. + LayoutUtil::ClearLayout(hlo->mutable_shape()); + EXPECT_DEATH(ShapeUtil::TransposeIsBitcast(hlo->operand(0)->shape(), + hlo->shape(), hlo->dimensions()), + "LayoutUtil::HasLayout"); +} + +// ReshapeIsBitcast shouldn't be called without layout information. +TEST_F(LayoutAssignmentTest, ReshapeIsBitcastFail) { + auto builder = HloComputation::Builder(TestName()); + Shape input_shape = ShapeUtil::MakeShape(F32, {2, 2, 2}); + Shape input_shape_with_layout(input_shape); + *input_shape_with_layout.mutable_layout() = LayoutUtil::MakeLayout({2, 1, 0}); + auto param = builder.AddInstruction( + HloInstruction::CreateParameter(0, input_shape_with_layout, "param")); + auto hlo = + builder.AddInstruction(HloInstruction::CreateReshape(input_shape, param)); + // Clear the default layout assigned to the instruction. + LayoutUtil::ClearLayout(hlo->mutable_shape()); + EXPECT_DEATH( + ShapeUtil::ReshapeIsBitcast(hlo->operand(0)->shape(), hlo->shape()), + "LayoutUtil::HasLayout"); +} + +// Check that the computation below doesn't crash the compiler. +// +// Within a fusion computation, only the parameters and result get assigned a +// layout. When we run the algebraic simplifier on this computation post layout +// assignment, it should not call TransposeIsBitcast on the `transpose` node +// inside the fusion computation as TransposeIsBitcast checks both input_shape +// and output_shape have layouts. +TEST_F(LayoutAssignmentTest, TransposeWithinFusionDoesNotCrash) { + const char* module_str = R"( + HloModule test_module + + fused_computation { + param_1 = f32[2,2,2]{2,1,0} parameter(1) + transpose = f32[2,2,2]{2,1,0} transpose(param_1), dimensions={0,2,1} + reduce_1 = f32[] parameter(0) + broadcast_1 = f32[2,2,2]{2,1,0} broadcast(reduce_1), dimensions={} + ROOT divide_1 = f32[2,2,2]{2,1,0} divide(transpose, broadcast_1) + } + + ENTRY entry_computation { + fusion.1 = f32[2,2,2]{2,1,0} parameter(1) + reduce.1 = f32[] parameter(0) + fusion.2 = f32[2,2,2]{2,1,0} fusion(reduce.1, fusion.1), kind=kLoop, calls=fused_computation + ROOT tuple.1 = (f32[2,2,2]{2,1,0}) tuple(fusion.2) + } + )"; + + auto module = tools::Parse(module_str).ValueOrDie(); + + module = + backend() + .compiler() + ->RunHloPasses(std::move(module), backend().default_stream_executor(), + /*device_allocator=*/nullptr) + .ConsumeValueOrDie(); + + EXPECT_EQ( + ::tensorflow::Status::OK(), + backend() + .compiler() + ->RunBackend(std::move(module), backend().default_stream_executor(), + /*device_allocator=*/nullptr) + .status()); +} + // A GTE inside of a fusion node inherits the layout of its operand (which // should, if we keep following operands, eventually be a parameter). TEST_F(LayoutAssignmentTest, GTEInheritsLayoutFromOperand) { @@ -717,5 +796,26 @@ TEST_F(LayoutAssignmentTest, ConditionalAsymmetricLayout) { EXPECT_THAT(false_result->opcode(), HloOpcode::kCopy); } +TEST_F(LayoutAssignmentTest, InternalErrorOnBitcast) { + auto builder = HloComputation::Builder(TestName()); + auto constant0 = builder.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR2WithLayout( + {{1.0, 2.0}, {3.0, 4.0}}, LayoutUtil::MakeLayout({0, 1})))); + builder.AddInstruction(HloInstruction::CreateUnary( + constant0->shape(), HloOpcode::kBitcast, constant0)); + auto module = CreateNewModule(); + module->AddEntryComputation(builder.Build()); + + ComputationLayout computation_layout( + module->entry_computation()->ComputeProgramShape()); + LayoutAssignment layout_assignment(&computation_layout); + Status error_status = layout_assignment.Run(module.get()).status(); + EXPECT_FALSE(error_status.ok()); + EXPECT_THAT( + error_status.error_message(), + ::testing::HasSubstr( + "Unexpected bitcast operation seen during layout assignment")); +} + } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc b/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc index 6384c7f46f5ebbedaeda232b40095611a5d738a4..3312a888443233139841ce7a5e3173f907605e1d 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc @@ -29,18 +29,13 @@ limitations under the License. namespace xla { namespace llvm_ir { -IrArray::Index::Index(llvm::Value* linear, const Shape& shape, - llvm::IRBuilder<>* ir_builder) - : multidim_(ShapeUtil::Rank(shape)), - linear_(linear), - layout_(shape.layout()), - dims_(shape.dimensions().begin(), shape.dimensions().end()) { - CHECK(LayoutUtil::HasLayout(shape)) - << "Shape " << ShapeUtil::HumanStringWithLayout(shape) - << " should have a layout."; +static void Delinearize(std::vector* multidim, + llvm::Value* linear, const Shape& shape, + llvm::IRBuilder<>* ir_builder) { int64 divisor = 1; - for (int64 i = 0; i < layout_.minor_to_major_size(); ++i) { - int64 dimension = layout_.minor_to_major(i); + const Layout& layout = shape.layout(); + for (int64 i = 0; i < layout.minor_to_major_size(); ++i) { + int64 dimension = layout.minor_to_major(i); int64 size_of_current_dimension = shape.dimensions(dimension); // If i is not the last dimension, compute @@ -54,16 +49,28 @@ IrArray::Index::Index(llvm::Value* linear, const Shape& shape, // memory lives in one big allocation, so cuda-memcheck can't detect // out-of-bounds accesses. auto* quot = ir_builder->CreateUDiv(linear, ir_builder->getInt64(divisor)); - if (i < layout_.minor_to_major_size() - 1) { - multidim_[dimension] = ir_builder->CreateURem( + if (i < layout.minor_to_major_size() - 1) { + (*multidim)[dimension] = ir_builder->CreateURem( quot, ir_builder->getInt64(size_of_current_dimension)); } else { - multidim_[dimension] = quot; + (*multidim)[dimension] = quot; } divisor *= size_of_current_dimension; } } +IrArray::Index::Index(llvm::Value* linear, const Shape& shape, + llvm::IRBuilder<>* ir_builder) + : multidim_(ShapeUtil::Rank(shape)), + linear_(linear), + layout_(shape.layout()), + dims_(shape.dimensions().begin(), shape.dimensions().end()) { + CHECK(LayoutUtil::HasLayout(shape)) + << "Shape " << ShapeUtil::HumanStringWithLayout(shape) + << " should have a layout."; + Delinearize(&multidim_, linear, shape, ir_builder); +} + IrArray::Index::Index(tensorflow::gtl::ArraySlice multidim, llvm::Value* linear, const Shape& shape) : multidim_(multidim.begin(), multidim.end()), @@ -83,7 +90,6 @@ IrArray::Index::Index(tensorflow::gtl::ArraySlice multidim, dims_(shape.dimensions().begin(), shape.dimensions().end()) { CHECK_EQ(shape.dimensions_size(), multidim.size()); CHECK(LayoutUtil::HasLayout(shape)); - linear_ = Linearize(AsInt64Slice(shape.dimensions()), ir_builder); } IrArray::IrArray(llvm::Value* base_ptr, const Shape& shape) @@ -106,16 +112,13 @@ IrArray::IrArray(llvm::Value* base_ptr, const Shape& shape) } } -// Returns whether given linear index valid on given shape. +// Returns whether the given linear index is valid on the given shape. bool IrArray::Index::LinearValidOnShape(const Shape& a) const { - auto b = ShapeUtil::MakeShape(PRED /* irrelevant */, dims_); + auto b = ShapeUtil::MakeShape(a.element_type(), dims_); *b.mutable_layout() = layout_; return linear_ != nullptr && - ContainersEqual( - ShapeUtil::StripDegenerateDimensions(a).dimensions(), - ShapeUtil::StripDegenerateDimensions(b).dimensions()) && - LayoutUtil::Equal(ShapeUtil::StripDegenerateDimensions(a).layout(), - ShapeUtil::StripDegenerateDimensions(b).layout()); + ShapeUtil::ElementsIn(a) == ShapeUtil::ElementsIn(b) && + ShapeUtil::ReshapeIsBitcast(a, b); } IrArray::Index IrArray::Index::SourceIndexOfReshape( @@ -160,7 +163,8 @@ IrArray::Index IrArray::Index::SourceIndexOfReshape( } } - if (linear() != nullptr && + if (linear() != nullptr && LayoutUtil::HasLayout(input_shape) && + LayoutUtil::HasLayout(output_shape) && ShapeUtil::ReshapeIsBitcast(input_shape, output_shape)) { return Index(source_multidim_index, linear(), input_shape); } @@ -195,13 +199,111 @@ IrArray::Index IrArray::Index::SourceIndexOfTranspose( llvm::IRBuilder<>* builder) const { std::vector operand_multidim_index = Permute(dimension_mapping, multidim()); - if (linear() != nullptr && + + if (linear() != nullptr && LayoutUtil::HasLayout(operand_shape) && + LayoutUtil::HasLayout(shape) && ShapeUtil::TransposeIsBitcast(operand_shape, shape, dimension_mapping)) { return Index(operand_multidim_index, linear(), operand_shape); } + return Index(operand_multidim_index); } +IrArray::Index IrArray::Index::SourceIndexOfBitcast( + const Shape& shape, const Shape& operand_shape, + llvm::IRBuilder<>* builder) const { + CHECK(LayoutUtil::HasLayout(shape) && LayoutUtil::HasLayout(operand_shape)); + // In case the bitcast is just a reshape, we can use SourceIndexOfReshape() + // instead. This will reuse linear() if possible, so we don't have to build a + // new 'linear_index'. + if (ShapeUtil::ReshapeIsBitcast(operand_shape, shape)) { + return SourceIndexOfReshape(shape, operand_shape, builder); + } + + // First linearize the index coming from the output of the bitcast. We want + // the physical index of the element in the buffer. This is like Linearize, + // but takes the layout into account. + int64 scale = 1; + llvm::Value* linear_index = builder->getInt64(0); + for (auto dimension : LayoutUtil::MinorToMajor(shape)) { + linear_index = builder->CreateAdd( + linear_index, + builder->CreateMul(multidim_[dimension], builder->getInt64(scale), "", + /*HasNUW=*/true, /*HasNSW=*/true), + "", /*HasNUW=*/true, /*HasNSW=*/true); + scale *= shape.dimensions(dimension); + } + + // Now delinearize it for the input of the bitcast. + std::vector multi_index(operand_shape.dimensions_size()); + Delinearize(&multi_index, linear_index, operand_shape, builder); + + return Index(multi_index, linear_index, operand_shape); +} + +IrArray::Index IrArray::Index::SourceIndexOfBroadcast( + const Shape& shape, const Shape& operand_shape, + tensorflow::gtl::ArraySlice dimension_mapping, + llvm::IRBuilder<>* builder) const { + int64 rank = ShapeUtil::Rank(operand_shape); + std::vector source_index(rank); + for (int64 i = 0; i < rank; ++i) { + source_index[i] = multidim_[dimension_mapping[i]]; + } + if (linear_ == nullptr || !LayoutUtil::HasLayout(operand_shape) || + !LayoutUtil::HasLayout(shape)) { + return Index(source_index); + } + // High-level idea: we can reuse the linear index if the broadcasted + // dimensions are contiguous, and this part of the operation is a bitcast. + // The other dimensions can be masked out with a div and a mod operation. + std::vector logical_to_physical = + LayoutUtil::MakeLogicalToPhysical(shape.layout()); + int64 output_rank = ShapeUtil::Rank(shape); + // The minimum physical dimension that is broadcasted. + int64 min_broadcasted_dimension = output_rank; + // The maximum physical dimension that is broadcasted. + int64 max_broadcasted_dimension = -1; + for (int64 i = 0; i < rank; ++i) { + int64 physical_dim = logical_to_physical[dimension_mapping[i]]; + min_broadcasted_dimension = + std::min(min_broadcasted_dimension, physical_dim); + max_broadcasted_dimension = + std::max(max_broadcasted_dimension, physical_dim); + } + bool contiguous_broadcast_dimensions = + max_broadcasted_dimension - min_broadcasted_dimension == rank - 1; + if (!contiguous_broadcast_dimensions) { + return Index(source_index); + } + // Check if the mapped dimensions are a bitcast. + std::vector operand_logical_to_physical = + LayoutUtil::MakeLogicalToPhysical(operand_shape.layout()); + for (int64 i = 0; i < rank; ++i) { + if (operand_logical_to_physical[i] != + logical_to_physical[dimension_mapping[i]] - min_broadcasted_dimension) { + return Index(source_index); + } + } + llvm::Value* linear = linear_; + int64 divisor = 1; + for (int64 i = max_broadcasted_dimension + 1; i < output_rank; ++i) { + divisor *= shape.dimensions(LayoutUtil::Major(shape.layout(), i)); + } + if (divisor > 1) { + linear = builder->CreateUDiv(linear, builder->getInt64(divisor)); + } + if (min_broadcasted_dimension > 0) { + int64 mod = 1; + for (int64 i = min_broadcasted_dimension; i <= max_broadcasted_dimension; + ++i) { + mod *= shape.dimensions(LayoutUtil::Major(shape.layout(), i)); + } + linear = builder->CreateURem(linear, builder->getInt64(mod)); + } + return Index(source_index, linear, operand_shape); +} + llvm::Value* IrArray::Index::Linearize( tensorflow::gtl::ArraySlice dimensions, llvm::IRBuilder<>* builder) const { diff --git a/tensorflow/compiler/xla/service/llvm_ir/ir_array.h b/tensorflow/compiler/xla/service/llvm_ir/ir_array.h index 387d4629125cbb791840e943013188d14159908a..06cfb2a36c56c5fdece7140e469379f8394111fa 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/ir_array.h +++ b/tensorflow/compiler/xla/service/llvm_ir/ir_array.h @@ -76,8 +76,7 @@ class IrArray { llvm::IRBuilder<>* ir_builder); // Constructs an index from the given multi-dimensional index and the shape - // that it indexes into. Also, computes the linear index according to - // "shape". + // that it indexes into. // // Precondition: "shape" has a layout. Index(tensorflow::gtl::ArraySlice multidim, @@ -134,6 +133,18 @@ class IrArray { tensorflow::gtl::ArraySlice dimension_mapping, llvm::IRBuilder<>* builder) const; + // Given that "this" is the target index of a bitcast from `operand_shape` + // to `shape`, returns the source index. + Index SourceIndexOfBitcast(const Shape& shape, const Shape& operand_shape, + llvm::IRBuilder<>* builder) const; + + // Given that "this" is the target index of a broadcast from `operand_shape` + // to `shape` with the given dimension mapping, returns the source index. + Index SourceIndexOfBroadcast( + const Shape& shape, const Shape& operand_shape, + tensorflow::gtl::ArraySlice dimension_mapping, + llvm::IRBuilder<>* builder) const; + // Linearizes the index into the given shape, i.e. reshapes it to rank-1 and // returns the index into the sole dimension 0 of the new shape. llvm::Value* Linearize(tensorflow::gtl::ArraySlice dimensions, diff --git a/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc b/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc index 5c1866311d1ae1e0c33ab061ee326d86d647a908..2a282f3be79f847a6569416794d1a2a3fcd69148 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc @@ -106,8 +106,10 @@ llvm::Value* EmitFloatMax(llvm::Value* lhs_value, llvm::Value* rhs_value, auto cmp = ir_builder->CreateFCmpUGE(lhs_value, rhs_value); return ir_builder->CreateSelect(cmp, lhs_value, rhs_value); } else { - return EmitCallToIntrinsic(llvm::Intrinsic::maxnum, {lhs_value, rhs_value}, - {lhs_value->getType()}, ir_builder); + auto cmp_ge = ir_builder->CreateFCmpOGE(lhs_value, rhs_value); + auto lhs_is_nan = ir_builder->CreateFCmpUNE(lhs_value, lhs_value); + auto sel_lhs = ir_builder->CreateOr(cmp_ge, lhs_is_nan); + return ir_builder->CreateSelect(sel_lhs, lhs_value, rhs_value); } } @@ -117,8 +119,10 @@ llvm::Value* EmitFloatMin(llvm::Value* lhs_value, llvm::Value* rhs_value, auto cmp = ir_builder->CreateFCmpULE(lhs_value, rhs_value); return ir_builder->CreateSelect(cmp, lhs_value, rhs_value); } else { - return EmitCallToIntrinsic(llvm::Intrinsic::minnum, {lhs_value, rhs_value}, - {lhs_value->getType()}, ir_builder); + auto cmp_le = ir_builder->CreateFCmpOLE(lhs_value, rhs_value); + auto lhs_is_nan = ir_builder->CreateFCmpUNE(lhs_value, lhs_value); + auto sel_lhs = ir_builder->CreateOr(cmp_le, lhs_is_nan); + return ir_builder->CreateSelect(sel_lhs, lhs_value, rhs_value); } } diff --git a/tensorflow/compiler/xla/service/service.cc b/tensorflow/compiler/xla/service/service.cc index 43d0f605985819afdaf2db2309a0bfb86f230fe3..0becc9d8f8ed22b2d7174b76ce775efec4b646f5 100644 --- a/tensorflow/compiler/xla/service/service.cc +++ b/tensorflow/compiler/xla/service/service.cc @@ -232,10 +232,14 @@ tensorflow::Status Service::ValidateResultShapeWithLayout( return ShapeUtil::ValidateShape(shape_with_layout); } -StatusOr> Service::ResolveAndValidateArguments( +StatusOr>> +Service::ResolveAndValidateArguments( tensorflow::gtl::ArraySlice arguments, - int device_ordinal) { - std::vector shaped_buffers; + tensorflow::gtl::ArraySlice + stream_executors) { + CHECK_EQ(options_.number_of_replicas(), stream_executors.size()); + std::vector> replicated_arguments; + replicated_arguments.resize(options_.number_of_replicas()); for (size_t i = 0; i < arguments.size(); ++i) { auto buffer_status = allocation_tracker_.Resolve(*arguments[i]); if (!buffer_status.ok()) { @@ -243,22 +247,25 @@ StatusOr> Service::ResolveAndValidateArguments( StrCat(buffer_status.status().error_message(), ", ", "failed to resolve allocation for parameter ", i)); } - const ShapedBuffer* shaped_buffer = buffer_status.ValueOrDie(); - - // Verify allocation is same platform and device as the execution. - if (shaped_buffer->platform() != execute_backend_->platform() || - shaped_buffer->device_ordinal() != device_ordinal) { - return InvalidArgument( - "argument %lu is on device %s:%d but computation will be executed " - "on device %s", - i, shaped_buffer->platform()->Name().c_str(), - shaped_buffer->device_ordinal(), - execute_backend_->device_name(device_ordinal).c_str()); + auto replicated_buffers = buffer_status.ValueOrDie(); + CHECK_EQ(options_.number_of_replicas(), replicated_buffers.size()); + for (int replica = 0; replica < options_.number_of_replicas(); ++replica) { + const ShapedBuffer* shaped_buffer = replicated_buffers[replica]; + int replica_device_ordinal = stream_executors[replica]->device_ordinal(); + // Verify allocation is same platform and device as the execution. + if (shaped_buffer->platform() != execute_backend_->platform() || + shaped_buffer->device_ordinal() != replica_device_ordinal) { + return InvalidArgument( + "argument %lu is on device %s:%d but computation will be executed " + "on device %s", + i, shaped_buffer->platform()->Name().c_str(), + shaped_buffer->device_ordinal(), + execute_backend_->device_name(replica_device_ordinal).c_str()); + } + replicated_arguments[replica].push_back(shaped_buffer); } - - shaped_buffers.push_back(shaped_buffer); } - return shaped_buffers; + return replicated_arguments; } StatusOr> Service::CreateModuleConfig( @@ -307,8 +314,6 @@ StatusOr> Service::CreateModuleConfig( if (execution_options != nullptr) { config->set_seed(execution_options->seed()); config->set_debug_options(execution_options->debug_options()); - config->enable_hlo_profiling( - execution_options->debug_options().xla_hlo_profile()); } else { config->set_debug_options(legacy_flags::GetDebugOptionsFromFlags()); } @@ -490,7 +495,8 @@ StatusOr> Service::BuildAndCacheExecutable( StatusOr> Service::ExecuteParallelAndRegisterResult( tensorflow::gtl::ArraySlice executables, - tensorflow::gtl::ArraySlice> arguments, + tensorflow::gtl::ArraySlice>> + arguments, Backend* backend, tensorflow::gtl::ArraySlice device_handles, tensorflow::gtl::ArraySlice result_tags, ExecutionProfile* profile) { @@ -513,6 +519,8 @@ Service::ExecuteParallelAndRegisterResult( for (int64 i = 0; i < executables.size(); i++) { // Stream executors for the replicas of the current computation. TF_ASSIGN_OR_RETURN(auto replicas, Replicas(*backend, device_handles[i])); + CHECK_EQ(replicas.size(), arguments[i].size()); + std::vector> result_buffers; for (int64 replica = 0; replica < replicas.size(); ++replica) { TF_ASSIGN_OR_RETURN(Pool::SmartPtr stream, backend->BorrowStream(replicas[replica])); @@ -545,23 +553,20 @@ Service::ExecuteParallelAndRegisterResult( backend->StreamBorrower()); // Asynchronously launch the computation. - TF_ASSIGN_OR_RETURN( - std::unique_ptr result, - executables[i]->ExecuteAsyncOnStream(&run_options, arguments[i])); + TF_ASSIGN_OR_RETURN(std::unique_ptr result, + executables[i]->ExecuteAsyncOnStream( + &run_options, arguments[i][replica])); if (replica == 0 && profile != nullptr) { streams.back()->ThenStopTimer(timers.back().get()); } - // All replicas share the same device address for the result allocation, - // so only one of the replicas need to register the result handle. - if (replica == 0) { - TF_ASSIGN_OR_RETURN( - GlobalDataHandle handle, - allocation_tracker_.Register(std::move(result), result_tags[i])); - result_handles.push_back(handle); - } + result_buffers.emplace_back(std::move(result)); } + TF_ASSIGN_OR_RETURN(GlobalDataHandle handle, + allocation_tracker_.RegisterReplicatedBuffers( + std::move(result_buffers), result_tags[i])); + result_handles.push_back(handle); } // Wait for all executions to complete. @@ -627,9 +632,9 @@ Service::ExecuteParallelAndRegisterResult( StatusOr Service::ExecuteAndRegisterResult( Executable* executable, - const tensorflow::gtl::ArraySlice arguments, - Backend* backend, perftools::gputools::StreamExecutor* executor, - const string& result_tag, ExecutionProfile* profile) { + const tensorflow::gtl::ArraySlice> + arguments, + Backend* backend, const string& result_tag, ExecutionProfile* profile) { // Set up streams. std::vector::SmartPtr> streams; @@ -662,21 +667,26 @@ StatusOr Service::ExecuteAndRegisterResult( backend->inter_op_thread_pool()); } - std::unique_ptr result; if (options_.number_of_replicas() == 1) { - TF_ASSIGN_OR_RETURN(result, executable->ExecuteOnStreamWrapper( - &run_options[0], profile, arguments)); - } else { - // TODO(b/69985541): Support profiling also on this path. - std::vector> - repeated_arguments(options_.number_of_replicas(), arguments); - - TF_ASSIGN_OR_RETURN(auto results, executable->ExecuteOnStreams( - run_options, repeated_arguments)); - TF_RET_CHECK(!results.empty()); - result = std::move(results[0]); + TF_ASSIGN_OR_RETURN( + auto result, executable->ExecuteOnStreamWrapper(&run_options[0], + profile, arguments[0])); + return allocation_tracker_.Register(std::move(result), result_tag); } - return allocation_tracker_.Register(std::move(result), result_tag); + + // TODO(b/69985541): Support profiling also on this path. + + std::vector> + replicated_arguments; + for (const auto& arg : arguments) { + replicated_arguments.emplace_back(arg); + } + + TF_ASSIGN_OR_RETURN(auto results, executable->ExecuteOnStreams( + run_options, replicated_arguments)); + TF_RET_CHECK(!results.empty()); + return allocation_tracker_.RegisterReplicatedBuffers(std::move(results), + result_tag); } tensorflow::Status Service::SetReturnValue(const SetReturnValueRequest* arg, @@ -690,7 +700,7 @@ tensorflow::Status Service::ExecuteParallel(const ExecuteParallelRequest* arg, ExecuteParallelResponse* result) { VLOG(1) << "running execute-parallel request: " << arg->ShortDebugString(); - std::vector> all_arguments; + std::vector>> all_arguments; std::vector> all_executors; std::vector versioned_handles; std::vector> module_configs; @@ -718,6 +728,14 @@ tensorflow::Status Service::ExecuteParallel(const ExecuteParallelRequest* arg, return FailedPrecondition( "device handles must be given to execute parallel computations"); } + if (arg->requests_size() > 1 && + execution_options.device_handles_size() > 1) { + return InvalidArgument( + "Parallel requests with multiple device handles is not supported. " + "Found %d parallel requests, with request %lld containing %d device " + "handles.", + arg->requests_size(), i, execution_options.device_handles_size()); + } std::vector executors; for (const auto& device_handle : execution_options.device_handles()) { TF_ASSIGN_OR_RETURN(auto replicas, @@ -747,22 +765,26 @@ tensorflow::Status Service::ExecuteParallel(const ExecuteParallelRequest* arg, // In the case of partitioned computations, assume all arguments go on the // zeroth core. TF_ASSIGN_OR_RETURN( - std::vector arguments, - ResolveAndValidateArguments(request.arguments(), - executors[0]->device_ordinal())); + auto replicas, + Replicas(*execute_backend_, execution_options.device_handles(0))); + TF_ASSIGN_OR_RETURN( + std::vector> replicated_arguments, + ResolveAndValidateArguments(request.arguments(), replicas)); // Create an HloModuleConfig object for the computation, given the shape of - // the program and the argument allocations. + // the program and the argument allocations. Here, we care only about the + // shapes of the arguments, so, it is sufficient to use the arguments of + // replica 0. TF_ASSIGN_OR_RETURN( std::unique_ptr module_config, - CreateModuleConfig(*program_shape, arguments, + CreateModuleConfig(*program_shape, replicated_arguments.front(), request.execution_options(), *user_computation)); VLOG(3) << "ExecuteParallel created HloModuleConfig computation layout: " << module_config->entry_computation_layout().ToString(); // Adds to the vectors to build and execute the computations after the loop. - all_arguments.push_back(arguments); - all_arguments.insert(all_arguments.end(), executors.size() - 1, {}); + all_arguments.push_back(replicated_arguments); + all_arguments.insert(all_arguments.end(), executors.size() - 1, {{}}); versioned_handles.push_back(versioned_handle); module_configs.push_back(std::move(module_config)); computation_names.insert(computation_names.end(), executors.size(), @@ -861,15 +883,18 @@ tensorflow::Status Service::Execute(const ExecuteRequest* arg, std::shared_ptr program_shape, user_computation->ComputeProgramShape(versioned_handle.version)); + TF_ASSIGN_OR_RETURN(auto replicas, Replicas(*execute_backend_, + SingleComputationDeviceHandle())); TF_ASSIGN_OR_RETURN( - std::vector arguments, - ResolveAndValidateArguments(arg->arguments(), - execute_backend_->default_device_ordinal())); + std::vector> replicated_arguments, + ResolveAndValidateArguments(arg->arguments(), replicas)); + // Since we care only about the shapes of the arguments, it is sufficient to + // use the arguments of replica 0. TF_ASSIGN_OR_RETURN( std::unique_ptr module_config, - CreateModuleConfig(*program_shape, arguments, arg->execution_options(), - *user_computation)); + CreateModuleConfig(*program_shape, replicated_arguments.front(), + arg->execution_options(), *user_computation)); VLOG(3) << "Execute created HloModuleConfig computation layout: " << module_config->entry_computation_layout().ToString(); @@ -885,20 +910,21 @@ tensorflow::Status Service::Execute(const ExecuteRequest* arg, executable->session_module()->set_execution_platform( execute_backend_->platform()->Name()); TF_RETURN_IF_ERROR(RecordArguments( - arguments, execute_backend_->default_stream_executor(), + replicated_arguments.front(), + execute_backend_->default_stream_executor(), execute_backend_->transfer_manager(), executable->session_module())); } TF_ASSIGN_OR_RETURN( *result->mutable_output(), ExecuteAndRegisterResult( - executable.get(), arguments, execute_backend_.get(), - execute_backend_->default_stream_executor(), + executable.get(), replicated_arguments, execute_backend_.get(), "result of " + user_computation->name(), result->mutable_profile())); if (executable->dumping()) { - TF_ASSIGN_OR_RETURN(const ShapedBuffer* result_buffer, - allocation_tracker_.Resolve(result->output())); + TF_ASSIGN_OR_RETURN( + const ShapedBuffer* result_buffer, + allocation_tracker_.ResolveForReplica(result->output(), 0)); TF_RETURN_IF_ERROR(RecordResult( *result_buffer, execute_backend_->default_stream_executor(), execute_backend_->transfer_manager(), executable->session_module())); @@ -909,6 +935,11 @@ tensorflow::Status Service::Execute(const ExecuteRequest* arg, return tensorflow::Status::OK(); } +tensorflow::Status Service::ExecuteGraph(const ExecuteGraphRequest* /*arg*/, + ExecuteResponse* /*result*/) { + return Unimplemented("execute-graph is not yet implemented"); +} + tensorflow::Status Service::ExecuteAsync(const ExecuteAsyncRequest* arg, ExecuteAsyncResponse* result) { VLOG(1) << "running execute-async request: " << arg->ShortDebugString(); @@ -926,15 +957,17 @@ tensorflow::Status Service::ExecuteAsync(const ExecuteAsyncRequest* arg, std::shared_ptr program_shape, user_computation->ComputeProgramShape(versioned_handle.version)); + TF_ASSIGN_OR_RETURN(auto replicas, Replicas(*execute_backend_, + SingleComputationDeviceHandle())); + TF_RET_CHECK(!replicas.empty()); TF_ASSIGN_OR_RETURN( - std::vector arguments, - ResolveAndValidateArguments(arg->arguments(), - execute_backend_->default_device_ordinal())); + std::vector> replicated_arguments, + ResolveAndValidateArguments(arg->arguments(), replicas)); TF_ASSIGN_OR_RETURN( std::unique_ptr module_config, - CreateModuleConfig(*program_shape, arguments, arg->execution_options(), - *user_computation)); + CreateModuleConfig(*program_shape, replicated_arguments.front(), + arg->execution_options(), *user_computation)); VLOG(3) << "ExecuteAsync created HloModuleConfig computation layout: " << module_config->entry_computation_layout().ToString(); @@ -947,21 +980,17 @@ tensorflow::Status Service::ExecuteAsync(const ExecuteAsyncRequest* arg, versioned_handle, std::move(module_config), execute_backend_.get(), execute_backend_->default_stream_executor(), &profile)); - TF_ASSIGN_OR_RETURN(auto replicas, Replicas(*execute_backend_, - SingleComputationDeviceHandle())); - TF_RET_CHECK(!replicas.empty()); - // Set up streams. std::vector::SmartPtr> streams; - for (se::StreamExecutor* executor : replicas) { TF_ASSIGN_OR_RETURN(Pool::SmartPtr stream, execute_backend_->BorrowStream(executor)); streams.push_back(std::move(stream)); } - std::unique_ptr result_buffer; - for (const Pool::SmartPtr& stream : streams) { + std::vector> result_buffers; + for (size_t i = 0; i < streams.size(); ++i) { + const auto& stream = streams[i]; ExecutableRunOptions options; options.set_stream(stream.get()); options.set_allocator(execute_backend_->memory_allocator()); @@ -972,20 +1001,17 @@ tensorflow::Status Service::ExecuteAsync(const ExecuteAsyncRequest* arg, ServiceExecutableRunOptions service_options( options, execute_backend_->StreamBorrower()); - TF_ASSIGN_OR_RETURN( - std::unique_ptr this_result_buffer, - executable->ExecuteAsyncOnStream(&service_options, arguments)); + TF_ASSIGN_OR_RETURN(std::unique_ptr this_result_buffer, + executable->ExecuteAsyncOnStream( + &service_options, replicated_arguments[i])); - // Take the first result. - if (result_buffer == nullptr) { - result_buffer = std::move(this_result_buffer); - } + result_buffers.emplace_back(std::move(this_result_buffer)); } TF_ASSIGN_OR_RETURN( GlobalDataHandle output, - allocation_tracker_.Register(std::move(result_buffer), - "result of " + user_computation->name())); + allocation_tracker_.RegisterReplicatedBuffers( + std::move(result_buffers), "result of " + user_computation->name())); *result->mutable_execution() = execution_tracker_.Register( execute_backend_.get(), std::move(streams), profile, output); @@ -1013,7 +1039,7 @@ tensorflow::Status Service::WaitForExecution(const WaitForExecutionRequest* arg, tensorflow::Status Service::TransferToClient(const TransferToClientRequest* arg, TransferToClientResponse* result) { TF_ASSIGN_OR_RETURN(const ShapedBuffer* shaped_buffer, - allocation_tracker_.Resolve(arg->data())); + allocation_tracker_.ResolveForReplica(arg->data(), 0)); const Shape* return_shape; if (arg->has_shape_with_layout()) { @@ -1074,37 +1100,24 @@ tensorflow::Status Service::TransferToServer(const TransferToServerRequest* arg, replicas, Replicas(*execute_backend_, SingleComputationDeviceHandle())); } - // All memory allocation is done on the first replica. The allocations in all - // other replicas mirror the firsts'. - int master_device_ordinal = replicas[0]->device_ordinal(); - TF_ASSIGN_OR_RETURN( - std::unique_ptr shaped_buffer, - execute_backend_->transfer_manager()->AllocateShapedBuffer( - shape, execute_backend_->memory_allocator(), master_device_ordinal)); - - // Transfer the data to the replicas. + // Allocate memory in each replica and transfer the data to all replicas. + std::vector> replicated_buffers; for (se::StreamExecutor* executor : replicas) { - if (executor->device_ordinal() == master_device_ordinal) { - TF_RETURN_IF_ERROR( - execute_backend_->transfer_manager()->TransferLiteralToDevice( - executor, *literal, *shaped_buffer)); - } else { - // The replica is not the master. Create an cloned shaped buffer with - // the replica's device ordinal. This is required because - // TransferLiteralToDevice verifies that the device ordinal of the shaped - // buffer matches that of the executor. - std::unique_ptr clone = - CloneShapedBufferOnDevice(*shaped_buffer, executor->device_ordinal()); - TF_RETURN_IF_ERROR( - execute_backend_->transfer_manager()->TransferLiteralToDevice( - executor, *literal, *clone)); - } + TF_ASSIGN_OR_RETURN( + std::unique_ptr shaped_buffer, + execute_backend_->transfer_manager()->AllocateShapedBuffer( + shape, execute_backend_->memory_allocator(), + executor->device_ordinal())); + TF_RETURN_IF_ERROR( + execute_backend_->transfer_manager()->TransferLiteralToDevice( + executor, *literal, *shaped_buffer)); + replicated_buffers.emplace_back(std::move(shaped_buffer)); } - TF_ASSIGN_OR_RETURN( - *result->mutable_data(), - allocation_tracker_.Register(std::move(shaped_buffer), - StrCat("TransferToServer literal of shape ", - ShapeUtil::HumanString(shape)))); + TF_ASSIGN_OR_RETURN(*result->mutable_data(), + allocation_tracker_.RegisterReplicatedBuffers( + std::move(replicated_buffers), + StrCat("TransferToServer literal of shape ", + ShapeUtil::HumanString(shape)))); return tensorflow::Status::OK(); } @@ -1287,7 +1300,7 @@ tensorflow::Status Service::ComputeConstant(const ComputeConstantRequest* arg, tensorflow::Status Service::GetShape(const GetShapeRequest* arg, GetShapeResponse* result) { TF_ASSIGN_OR_RETURN(const ShapedBuffer* buffer, - allocation_tracker_.Resolve(arg->data())); + allocation_tracker_.ResolveForReplica(arg->data(), 0)); *result->mutable_shape() = buffer->on_host_shape(); return tensorflow::Status::OK(); } diff --git a/tensorflow/compiler/xla/service/service.h b/tensorflow/compiler/xla/service/service.h index 6ce241971156599aaa25aea1b0caac0e1bd5379c..96352d9096e6aeeb33f84c7b6fc42c28820e5e84 100644 --- a/tensorflow/compiler/xla/service/service.h +++ b/tensorflow/compiler/xla/service/service.h @@ -112,6 +112,12 @@ class Service : public ServiceInterface { tensorflow::Status Execute(const ExecuteRequest* arg, ExecuteResponse* result) override; + // Executes a computation with the provided global data passed as + // immutable arguments. The request contains the whole computation graph. + // Returns global data output and execution timing. + tensorflow::Status ExecuteGraph(const ExecuteGraphRequest* arg, + ExecuteResponse* result) override; + // Executes one or more computations in parallel with the provided global data // passed as immutable arguments. Returns global data output for each // computation. @@ -265,11 +271,14 @@ class Service : public ServiceInterface { static StatusOr> CreateComputeConstantBackend(); // Resolves the given argument handles in the allocation tracker and returns - // the corresponding allocations. The function also verifies that each - // allocation matches the execution platform and device ordinal. - StatusOr> ResolveAndValidateArguments( + // the corresponding allocations for every replica. The function also verifies + // that each allocation matches the execution platform and device ordinal of + // the corresponding replica. + StatusOr>> + ResolveAndValidateArguments( tensorflow::gtl::ArraySlice arguments, - int device_ordinal); + tensorflow::gtl::ArraySlice + stream_executors); // Create a Hlo module config for the given program shape and arguments. // execution_options is optional; if not given a default is used. @@ -314,16 +323,17 @@ class Service : public ServiceInterface { // ExecutionProfile object which will be filled in with profile data. StatusOr ExecuteAndRegisterResult( Executable* executable, - const tensorflow::gtl::ArraySlice arguments, - Backend* backend, perftools::gputools::StreamExecutor* executor, - const string& result_tag, ExecutionProfile* profile); + const tensorflow::gtl::ArraySlice> + arguments, + Backend* backend, const string& result_tag, ExecutionProfile* profile); // Runs the given executables with the given arguments and register the result // from each executable in the allocation tracker. The handles of the result // from the tracker are returned. StatusOr> ExecuteParallelAndRegisterResult( tensorflow::gtl::ArraySlice executables, - tensorflow::gtl::ArraySlice> arguments, + tensorflow::gtl::ArraySlice>> + arguments, Backend* backend, tensorflow::gtl::ArraySlice device_handles, tensorflow::gtl::ArraySlice result_tags, diff --git a/tensorflow/compiler/xla/service/shape_inference.cc b/tensorflow/compiler/xla/service/shape_inference.cc index c9692757b27980b10a5ca562223c3d0f6462d820..8c8bd6d73ad41db7d609ac91c7bdfc4703f364e1 100644 --- a/tensorflow/compiler/xla/service/shape_inference.cc +++ b/tensorflow/compiler/xla/service/shape_inference.cc @@ -169,11 +169,11 @@ bool AllUnique(tensorflow::gtl::ArraySlice slice) { tensorflow::Status ExpectNotTupleOrOpaque(const Shape& shape, tensorflow::StringPiece op_type) { if (ShapeUtil::IsTuple(shape)) { - return InvalidArgument("Expected non-tuple argument for %s. Got: %s", + return InvalidArgument("Expected non-tuple argument for %s, but got %s.", op_type.ToString().c_str(), ShapeUtil::HumanString(shape).c_str()); } else if (ShapeUtil::IsOpaque(shape)) { - return InvalidArgument("Expected non-opaque argument for %s. Got: %s", + return InvalidArgument("Expected non-opaque argument for %s, but got %s.", op_type.ToString().c_str(), ShapeUtil::HumanString(shape).c_str()); } else { @@ -193,8 +193,10 @@ tensorflow::Status VerifyReducerShape(const ProgramShape& reducer_shape, const Shape& accumulator_shape = reducer_shape.result(); if (ShapeUtil::Rank(accumulator_shape) != 0) { - return Unimplemented( - "Reduction function currently must have rank-0 result."); + return InvalidArgument( + "Reduction function must have rank 0 (rank %lld reduction function " + "given).", + ShapeUtil::Rank(accumulator_shape)); } // Check that the accumulator can be passed in as the first argument. @@ -235,8 +237,8 @@ tensorflow::Status VerifyReducerShape(const ProgramShape& reducer_shape, if (!ShapeUtil::CompatibleIgnoringFpPrecision(accumulator_shape, reducer_shape.parameters(1))) { return InvalidArgument( - "Reduction function's second parameter shape currently must " - "match the result shape. Got %s vs %s", + "Reduction function's second parameter shape must " + "match the result shape, but got %s vs %s.", ShapeUtil::HumanString(reducer_shape.parameters(1)).c_str(), ShapeUtil::HumanString(accumulator_shape).c_str()); } @@ -258,29 +260,29 @@ StatusOr InferWindowOutputShape(const Shape& base_shape, for (int64 i = 0; i < window.dimensions_size(); ++i) { const auto& dim = window.dimensions(i); if (dim.size() <= 0) { - return InvalidArgument("Window has a non-positive dimension. Window: %s", + return InvalidArgument("Window %s has a non-positive dimension.", window.DebugString().c_str()); } if (dim.stride() <= 0) { - return InvalidArgument("Window has a non-positive stride. Window: %s", + return InvalidArgument("Window %s has a non-positive stride.", window.DebugString().c_str()); } if (!allow_negative_padding && dim.padding_low() < 0) { - return InvalidArgument("Window has a negative low padding. Window: %s", + return InvalidArgument("Window %s has a negative low padding.", window.DebugString().c_str()); } if (!allow_negative_padding && dim.padding_high() < 0) { - return InvalidArgument("Window has a negative high padding. Window: %s", + return InvalidArgument("Window %s has a negative high padding.", window.DebugString().c_str()); } if (dim.base_dilation() < 1) { return InvalidArgument( - "Window has a non-positive base area dilation factor. Window: %s", + "Window %s has a non-positive base area dilation factor.", window.DebugString().c_str()); } if (dim.window_dilation() < 1) { return InvalidArgument( - "Window has a non-positive window dilation factor. Window: %s", + "Window %s has a non-positive window dilation factor.", window.DebugString().c_str()); } @@ -320,8 +322,8 @@ StatusOr InferWindowOutputShape(const Shape& base_shape, case UNOP_CEIL: if (!ShapeUtil::ElementIsFloating(arg)) { return InvalidArgument( - "expected element type in shape to be floating for floor/ceil " - "operation; got %s", + "Expected element type in shape to be floating for floor/ceil " + "operation; got %s.", PrimitiveType_Name(arg.element_type()).c_str()); } return arg; @@ -333,8 +335,8 @@ StatusOr InferWindowOutputShape(const Shape& base_shape, if (!ShapeUtil::ElementIsFloating(arg) && !ShapeUtil::ElementIsComplex(arg)) { return InvalidArgument( - "expected element type in shape to be floating or complex for " - "sin/cos/exp/log/tanh operation; got %s", + "Expected element type in shape to be floating or complex for " + "sin/cos/exp/log/tanh operation; got %s.", PrimitiveType_Name(arg.element_type()).c_str()); } return arg; @@ -342,8 +344,8 @@ StatusOr InferWindowOutputShape(const Shape& base_shape, case UNOP_IMAG: if (!ShapeUtil::ElementIsComplex(arg)) { return InvalidArgument( - "expected element type in shape to be complex for real/imag " - "operation; got %s", + "Expected element type in shape to be complex for real/imag " + "operation; got %s.", PrimitiveType_Name(arg.element_type()).c_str()); } return ShapeUtil::ChangeElementType(arg, F32); @@ -363,8 +365,8 @@ StatusOr InferWindowOutputShape(const Shape& base_shape, if (arg.element_type() != PRED && !primitive_util::IsIntegralType(arg.element_type())) { return InvalidArgument( - "expected pred or an integral element type in argument to not " - "operation; got %s", + "Expected pred or an integral element type in argument to Not " + "operation; got %s.", PrimitiveType_Name(arg.element_type()).c_str()); } return arg; @@ -372,8 +374,8 @@ StatusOr InferWindowOutputShape(const Shape& base_shape, case UNOP_IS_FINITE: if (!ShapeUtil::ElementIsFloating(arg)) { return InvalidArgument( - "expected element type in shape to be floating point for IsFinite " - "operation; got %s", + "Expected element type in shape to be floating point for IsFinite " + "operation; got %s.", PrimitiveType_Name(arg.element_type()).c_str()); } return ShapeUtil::ChangeElementType(arg, PRED); @@ -389,10 +391,10 @@ StatusOr InferWindowOutputShape(const Shape& base_shape, tensorflow::gtl::ArraySlice arg_shapes, const int64 dimension) { if (arg_shapes.empty()) { - return InvalidArgument("Concatenate expects at least one argument"); + return InvalidArgument("Concatenate expects at least one argument."); } if (dimension < 0 || dimension >= ShapeUtil::Rank(*arg_shapes[0])) { - return InvalidArgument("dimension to concatenate along out of bounds: %lld", + return InvalidArgument("Concatenate dimension out of bounds: %lld.", dimension); } const Shape* arg_shape = nullptr; @@ -408,14 +410,14 @@ StatusOr InferWindowOutputShape(const Shape& base_shape, if (ShapeUtil::Rank(*arg_shape) != ShapeUtil::Rank(*shape)) { return InvalidArgument( "Cannot concatenate arrays with different ranks: %lld (%s) vs %lld " - "(%s)", + "(%s).", ShapeUtil::Rank(*arg_shape), ShapeUtil::HumanString(*arg_shape).c_str(), ShapeUtil::Rank(*shape), ShapeUtil::HumanString(*shape).c_str()); } if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(*arg_shape, *shape)) { return InvalidArgument( - "cannot concatenate arrays with different element types: %s vs %s", + "Cannot concatenate arrays with different element types: %s vs %s.", PrimitiveType_Name(arg_shape->element_type()).c_str(), PrimitiveType_Name(shape->element_type()).c_str()); } @@ -428,9 +430,9 @@ StatusOr InferWindowOutputShape(const Shape& base_shape, // concatenating. } return InvalidArgument( - "cannot concatenate arrays that differ in dimensions other than " + "Cannot concatenate arrays that differ in dimensions other than " "the one being concatenated (the other array dimensions must be " - "the same): %s vs %s in dimension %lld", + "the same): %s vs %s in dimension %lld.", ShapeUtil::HumanString(*arg_shape).c_str(), ShapeUtil::HumanString(*shape).c_str(), dimension); } @@ -452,7 +454,7 @@ StatusOr InferWindowOutputShape(const Shape& base_shape, if (primitive_util::IsComplexType(old_element_type) && !primitive_util::IsComplexType(new_element_type)) { return Unimplemented( - "Unsupported conversion from complex to real type: %s => %s", + "Conversion from complex to real type %s => %s is not implemented.", ShapeUtil::HumanString(operand_shape).c_str(), PrimitiveType_Name(new_element_type).c_str()); } @@ -461,7 +463,7 @@ StatusOr InferWindowOutputShape(const Shape& base_shape, // future, by recursing into the tuple elements to check all sub-conversions // are valid. For now we just reject them, though. return InvalidArgument( - "cannot convert from or to tuple type; requested conversion: %s => %s", + "Convert does not allow tuples, so cannot convert from %s to %s.", ShapeUtil::HumanString(operand_shape).c_str(), PrimitiveType_Name(new_element_type).c_str()); } @@ -474,24 +476,23 @@ StatusOr InferWindowOutputShape(const Shape& base_shape, auto old_element_type = operand_shape.element_type(); if (primitive_util::IsComplexType(old_element_type) != primitive_util::IsComplexType(new_element_type)) { - return Unimplemented( - "Unsupported conversion between real and complex types: %s => %s", - ShapeUtil::HumanString(operand_shape).c_str(), - PrimitiveType_Name(new_element_type).c_str()); + return InvalidArgument("Conversion from complex to real type %s => %s.", + ShapeUtil::HumanString(operand_shape).c_str(), + PrimitiveType_Name(new_element_type).c_str()); } if (ShapeUtil::IsTuple(operand_shape) || new_element_type == TUPLE) { // Note: we may want to support tuple conversions via this operation in the // future, by recursing into the tuple elements to check all sub-conversions // are valid. For now we just reject them, though. return InvalidArgument( - "cannot convert from or to tuple type; requested conversion: %s => %s", + "Cannot convert from or to tuple type; requested conversion: %s => %s.", ShapeUtil::HumanString(operand_shape).c_str(), PrimitiveType_Name(new_element_type).c_str()); } if (primitive_util::BitWidth(old_element_type) != primitive_util::BitWidth(new_element_type)) { return InvalidArgument( - "cannot bitcast types with different bit-widths: %s => %s", + "Cannot bitcast types with different bit-widths: %s => %s.", PrimitiveType_Name(old_element_type).c_str(), PrimitiveType_Name(new_element_type).c_str()); } @@ -504,20 +505,20 @@ StatusOr InferWindowOutputShape(const Shape& base_shape, const int mantissa_bits) { if (!ShapeUtil::ElementIsFloating(operand_shape)) { return InvalidArgument( - "expected element type in shape to be floating point for " - "ReducePrecision operation; got %s", + "Expected element type in shape to be floating point for " + "ReducePrecision operation; got %s.", PrimitiveType_Name(operand_shape.element_type()).c_str()); } if (exponent_bits < 1) { // One exponent bit is necessary to distinguish 0 from infinity. Having // no exponent bits doesn't produce a sensible number, so we require at // least one. - return InvalidArgument("expected exponent_bits >= 1; got %d", + return InvalidArgument("Expected exponent_bits >= 1; got %d.", exponent_bits); } if (mantissa_bits < 0) { // A number with no mantissa bits is still meaningful, however. - return InvalidArgument("expected non-negative mantissa_bits; got %d", + return InvalidArgument("Expected non-negative mantissa_bits; got %d.", mantissa_bits); } return operand_shape; @@ -528,23 +529,23 @@ StatusOr InferWindowOutputShape(const Shape& base_shape, const PaddingConfig& padding_config) { if (ShapeUtil::IsTuple(operand_shape)) { return InvalidArgument( - "pad operation does not support tuple-shape operands"); + "Pad operation does not support tuple-shape operands."); } if (!ShapeUtil::IsScalar(padding_value_shape)) { return InvalidArgument( - "pad operation does not support non-scalar padding values"); + "Pad operation does not support non-scalar padding values."); } if (ShapeUtil::Rank(operand_shape) != padding_config.dimensions_size()) { return InvalidArgument( "The rank of the operand and the padding configuration do not match: " - "%s vs %s", + "%s vs %s.", ShapeUtil::HumanString(operand_shape).c_str(), padding_config.ShortDebugString().c_str()); } if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(operand_shape, padding_value_shape)) { return InvalidArgument( - "the element types of the operands to pad do not match"); + "The element types of the operands to Pad do not match."); } std::vector dimensions(ShapeUtil::Rank(operand_shape)); for (int64 i = 0; i < operand_shape.dimensions_size(); ++i) { @@ -605,7 +606,7 @@ Status ValidateDotDimensionNumbers( lhs_batch_dimensions) || !dims_in_range(ShapeUtil::Rank(rhs), rhs_contracting_dimensions, rhs_batch_dimensions)) { - return InvalidArgument("A dimension number is out of range in dot: %s", + return InvalidArgument("A dimension number is out of range in Dot: %s.", dimension_numbers.DebugString().c_str()); } @@ -623,7 +624,7 @@ Status ValidateDotDimensionNumbers( if (!dims_unique(lhs_contracting_dimensions, lhs_batch_dimensions) || !dims_unique(rhs_contracting_dimensions, rhs_batch_dimensions)) { - return InvalidArgument("A dimension number is not unique in dot: %s", + return InvalidArgument("A dimension number is not unique in Dot: %s.", dimension_numbers.DebugString().c_str()); } @@ -641,8 +642,7 @@ Status ValidateDotDimensionNumbers( rhs_non_contracting_non_batch_dims < 0 || rhs_non_contracting_non_batch_dims > 1) { return InvalidArgument( - "batch and contracting dimension number mismatch " - "with rank "); + "Batch and contracting dimension number mismatch with rank."); } // Check that batch dimension numbers are ordered before all others, and @@ -654,7 +654,7 @@ Status ValidateDotDimensionNumbers( !std::equal(batch_dim_numbers.begin(), batch_dim_numbers.end(), rhs_batch_dimensions.begin())) { return InvalidArgument( - "batch dimension numbers must precede non-batch dimensions and be" + "Batch dimension numbers must precede non-batch dimensions and be" "monotonically increasing."); } @@ -671,22 +671,22 @@ Status ValidateDotDimensionNumbers( auto fail = [lhs, rhs](const string& addendum) -> Status { string message = tensorflow::strings::Printf( - "cannot infer shape for dot operation: %s %s", + "Cannot infer shape for dot operation: %s %s.", ShapeUtil::HumanString(lhs).c_str(), ShapeUtil::HumanString(rhs).c_str()); if (!addendum.empty()) { - message += ": " + addendum; + message += " " + addendum; } return InvalidArgument("%s", message.c_str()); }; // Check if both element types are the same. if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(lhs, rhs)) { - return fail("element types do not match"); + return fail("Element types do not match."); } if ((ShapeUtil::Rank(lhs) < 1) || (ShapeUtil::Rank(rhs) < 1)) { - return fail("dot only supports rank 1 or above."); + return fail("Dot only supports rank 1 or above."); } // Validate basic properties of dot dimension numbers. @@ -696,7 +696,7 @@ Status ValidateDotDimensionNumbers( if (dimension_numbers.lhs_contracting_dimensions_size() != dimension_numbers.rhs_contracting_dimensions_size() || dimension_numbers.lhs_contracting_dimensions_size() != 1) { - return fail("must specify one contracting dimension for both lhs and rhs."); + return fail("Must specify one contracting dimension for both lhs and rhs."); } // Check that contracting dimension sizes match. @@ -706,13 +706,13 @@ Status ValidateDotDimensionNumbers( dimension_numbers.rhs_contracting_dimensions(0); if (lhs.dimensions(lhs_contracting_dimension) != rhs.dimensions(rhs_contracting_dimension)) { - return fail("contracting dimension sizes do not match."); + return fail("Contracting dimension sizes do not match."); } // Check that number of batch dimensions match. if (dimension_numbers.lhs_batch_dimensions_size() != dimension_numbers.rhs_batch_dimensions_size()) { - return fail("must the same number of batch dimensions for lhs and rhs."); + return fail("Must the same number of batch dimensions for lhs and rhs."); } // Check that batch dimension numbers and sizes match. @@ -721,7 +721,7 @@ Status ValidateDotDimensionNumbers( dimension_numbers.rhs_batch_dimensions(i) || lhs.dimensions(dimension_numbers.lhs_batch_dimensions(i)) != rhs.dimensions(dimension_numbers.rhs_batch_dimensions(i))) { - return fail("batch dimension numbers and sizes must match for lhs/rhs."); + return fail("Batch dimension numbers and sizes must match for lhs/rhs."); } } @@ -770,10 +770,11 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( } else if (rhs.dimensions(i) == 1) { output_dimensions[i] = lhs.dimensions(i); } else { - return InvalidArgument("binary op %s with incompatible shapes: %s and %s", - BinaryOperation_Name(operation).c_str(), - ShapeUtil::HumanString(lhs).c_str(), - ShapeUtil::HumanString(rhs).c_str()); + return InvalidArgument( + "Binary op %s with incompatible shapes: %s and %s.", + BinaryOperation_Name(operation).c_str(), + ShapeUtil::HumanString(lhs).c_str(), + ShapeUtil::HumanString(rhs).c_str()); } } return ShapeUtil::MakeShape(ShapeUtil::HigherPrecisionElementType(lhs, rhs), @@ -788,15 +789,15 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( // Reject "magic" inference for binops on different shapes, requiring // the user to provide an explicit broadcast dimension in this case. // See b/25177275 for more details. - return InvalidArgument("automatic shape inference not supported: %s and %s", + return InvalidArgument("Automatic shape inference not supported: %s and %s", ShapeUtil::HumanString(smaller_shape).c_str(), ShapeUtil::HumanString(larger_shape).c_str()); } else if (broadcast_dimensions.size() != ShapeUtil::Rank(smaller_shape)) { return InvalidArgument( - "size of broadcast_dimensions has to match lower-rank operand's " + "Size of broadcast_dimensions has to match lower-rank operand's " "rank; " " lower-rank operand's rank is %lld, size of broadcast_dimensions is " - "%zu", + "%zu.", ShapeUtil::Rank(smaller_shape), broadcast_dimensions.size()); } @@ -846,13 +847,13 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( int64 dimension_to_match = broadcast_dimensions.at(i); if (dimension_to_match < 0) { return InvalidArgument( - "broadcast dimension number (%lld) cannot be negative", + "Broadcast dimension number (%lld) cannot be negative.", dimension_to_match); } if (dimension_to_match >= larger_shape.dimensions_size()) { return InvalidArgument( - "broadcast dimension number (%lld) too large; higher-rank " - "operand has rank %d", + "Broadcast dimension number (%lld) too large; higher-rank " + "operand has rank %d.", dimension_to_match, larger_shape.dimensions_size()); } int64 small_dimension_size = smaller_shape.dimensions(i); @@ -863,7 +864,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( if (small_dimension_size != large_dimension_size && small_dimension_size != 1 && large_dimension_size != 1) { return InvalidArgument( - "broadcast dimension %d mismatch: %lld != %lld; %s and %s", i, + "Broadcast dimension %d mismatch: %lld != %lld; %s and %s.", i, small_dimension_size, large_dimension_size, ShapeUtil::HumanString(smaller_shape).c_str(), ShapeUtil::HumanString(larger_shape).c_str()); @@ -872,7 +873,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( // order. if (i > 0 && broadcast_dimensions.at(i - 1) >= dimension_to_match) { return InvalidArgument( - "broadcast dimensions order is wrong: %lld comes after %lld", + "Broadcast dimensions order is wrong: %lld comes after %lld.", dimension_to_match, broadcast_dimensions.at(i - 1)); } @@ -892,7 +893,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(lhs, rhs)) { return InvalidArgument( - "binary op %s with different element types: %s and %s", + "Binary op %s with different element types: %s and %s.", BinaryOperation_Name(operation).c_str(), ShapeUtil::HumanString(lhs).c_str(), ShapeUtil::HumanString(rhs).c_str()); @@ -904,8 +905,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( if (!broadcast_dimensions.empty() && broadcast_dimensions != identity_dims) { return InvalidArgument( - "broadcast dimensions field must either be not set or be the " - "identity on binary operations with operands of the same rank"); + "Broadcast dimensions field must either be not set or be the " + "identity on binary operations with operands of the same rank."); } } @@ -943,6 +944,13 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( rhs->shape(), /*broadcast_dimensions=*/{}); } +/* static */ StatusOr ShapeInference::InferBinaryOpShape( + HloOpcode opcode, const Shape& lhs, const Shape& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions) { + return InferBinaryOpShape(OpcodeToBinaryOperation(opcode), lhs, rhs, + broadcast_dimensions); +} + /* static */ StatusOr ShapeInference::InferBinaryOpShape( BinaryOperation operation, const Shape& lhs, const Shape& rhs, tensorflow::gtl::ArraySlice broadcast_dimensions) { @@ -979,8 +987,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( case BINOP_COMPLEX: { if (!ShapeUtil::ElementIsFloating(lhs)) { return InvalidArgument( - "expected element type in shape to be floating for complex compose " - "operation; got %s", + "Expected element type in shape to be floating for complex compose " + "operation; got %s.", PrimitiveType_Name(lhs.element_type()).c_str()); } TF_ASSIGN_OR_RETURN(const Shape& shape, @@ -989,7 +997,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( if (lhs.element_type() == F32 && rhs.element_type() == F32) { return ShapeUtil::ChangeElementType(shape, C64); } else { - return Unimplemented("complex component type not supported"); + return Unimplemented("Complex component type is not implemented."); } } case BINOP_AND: @@ -997,8 +1005,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( if (lhs.element_type() != PRED && !primitive_util::IsIntegralType(lhs.element_type())) { return InvalidArgument( - "expected pred or integral type in argument to and/or operation; " - "got %s", + "Expected pred or integral type in argument to and/or operation; " + "got %s.", PrimitiveType_Name(lhs.element_type()).c_str()); } return InferElementwiseBinaryOpShape(operation, lhs, rhs, @@ -1016,7 +1024,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( } default: return Unimplemented( - "not yet implemented; infer binary op shape: %s; lhs: %s; rhs: %s", + "Binary op shape inference: %s; lhs: %s; rhs: %s is not implemented.", BinaryOperation_Name(operation).c_str(), lhs.ShortDebugString().c_str(), rhs.ShortDebugString().c_str()); } @@ -1041,7 +1049,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( case TRIOP_SELECT: return InferSelectShape(lhs, rhs, ehs); default: - return InvalidArgument("unknown operation %s", + return InvalidArgument("Unknown operation %s.", TernaryOperation_Name(operation).c_str()); } } @@ -1072,7 +1080,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return result; } default: - return InvalidArgument("unknown operation %s", + return InvalidArgument("Unknown operation %s.", VariadicOperation_Name(operation).c_str()); } } @@ -1082,7 +1090,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( const ProgramShape& to_apply, tensorflow::gtl::ArraySlice dimensions) { if (arg_shapes.empty()) { - return InvalidArgument("Map expects at least one argument"); + return InvalidArgument("Map expects at least one argument."); } // All arguments must have the same shape. @@ -1113,7 +1121,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( } return InvalidArgument( "Map operation requires all operands to have the same shape; got: " - "%s", + "%s.", Join(pieces, ", ").c_str()); } @@ -1122,7 +1130,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( if (dimensions.size() != arg_shape->dimensions_size()) { return InvalidArgument( "Map applied to a subset of dimensions currently not supported: " - "arg_dimension_size: %d, requested_map_dimensions_size: %zu", + "arg_dimension_size: %d, requested_map_dimensions_size: %zu.", arg_shape->dimensions_size(), dimensions.size()); } @@ -1130,7 +1138,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( for (int i = 0; i < dimensions.size(); ++i) { if (dimensions[i] != i) { return InvalidArgument( - "Map requires monotonically increasing dimension numbers, found: %s ", + "Map requires monotonically increasing dimension numbers; got: %s.", Join(dimensions, ", ").c_str()); } } @@ -1139,7 +1147,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( if (arg_shapes.size() != to_apply.parameters_size()) { return InvalidArgument( "Map applied function arity must match number of arguments; got: " - "arity: %d, arguments: %zu", + "arity: %d, arguments: %zu.", to_apply.parameters_size(), arg_shapes.size()); } @@ -1147,8 +1155,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( const Shape& output_shape = to_apply.result(); if (!ShapeUtil::IsScalar(output_shape)) { return InvalidArgument( - "mapped computation's result has to be a scalar; " - "got: %s", + "Mapped computation's result has to be a scalar; got: %s.", ShapeUtil::HumanString(output_shape).c_str()); } @@ -1157,16 +1164,16 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( if (!ShapeUtil::IsScalar(parameter_shape)) { return InvalidArgument( - "mapped computation's parameter has to be a scalar; " - "got parameter %d shape: %s", + "Mapped computation's parameter has to be a scalar; " + "got parameter %d shape: %s.", i, ShapeUtil::HumanString(parameter_shape).c_str()); } if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(parameter_shape, *arg_shape)) { return InvalidArgument( - "mapped computation's parameter type has to match argument element " - "type; got parameter %d shape: %s, argument shape: %s", + "Mapped computation's parameter type has to match argument element " + "type; got parameter %d shape: %s, argument shape: %s.", i, ShapeUtil::HumanString(parameter_shape).c_str(), ShapeUtil::HumanString(*arg_shape).c_str()); } @@ -1197,21 +1204,21 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return InvalidArgument( "Expected feature_index of batch-norm-training to be " "smaller than the rank of operand_shape; " - "got feature_index %lld, and rank %lld", + "got feature_index %lld, and rank %lld.", feature_index, ShapeUtil::Rank(operand_shape)); } if (feature_index < 0) { return InvalidArgument( "Expected feature_index of batch-norm-training to " - "be a non-negative number, got %lld", + "be a non-negative number, got %lld.", feature_index); } if (ShapeUtil::Rank(operand_shape) < 1) { return InvalidArgument( "Expected the rank of operand to " - "batch-norm-training to be at least 1; got %lld", + "batch-norm-training to be at least 1; got %lld.", ShapeUtil::Rank(operand_shape)); } @@ -1232,7 +1239,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( if (!ShapeUtil::ElementIsFloating(operand_shape)) { return InvalidArgument( "The operand to batch-norm-training must have a floating point " - "element type, but the shape is %s", + "element type, but the shape is %s.", PrimitiveType_Name(operand_shape.element_type()).c_str()); } @@ -1241,7 +1248,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return InvalidArgument( "The inputs should have the same element type for batch-norm-training, " "but the shape of offset factor is %s " - "and the shape of operand is %s", + "and the shape of operand is %s.", PrimitiveType_Name(offset_shape.element_type()).c_str(), PrimitiveType_Name(operand_shape.element_type()).c_str()); } @@ -1251,7 +1258,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return InvalidArgument( "The inputs should have the same element type for batch-norm-training, " "but the shape of scale factor is %s " - "and the shape of operand is %s", + "and the shape of operand is %s.", PrimitiveType_Name(scale_shape.element_type()).c_str(), PrimitiveType_Name(operand_shape.element_type()).c_str()); } @@ -1264,7 +1271,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return InvalidArgument( "The size of offset factor should be the same as feature count," "but the size of offset factor is %lld " - "and the feature count is %lld", + "and the feature count is %lld.", ShapeUtil::GetDimension(offset_shape, 0), feature_count); } @@ -1272,7 +1279,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return InvalidArgument( "The size of scale factor should be the same as feature count," "but the size of scale factor is %lld " - "and the feature count is %lld", + "and the feature count is %lld.", ShapeUtil::GetDimension(scale_shape, 0), feature_count); } @@ -1307,21 +1314,21 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return InvalidArgument( "Expected feature_index of batch-norm-inference to be " "smaller than the rank of operand_shape; " - "got feature_index %lld, and rank %lld", + "got feature_index %lld, and rank %lld.", feature_index, ShapeUtil::Rank(operand_shape)); } if (feature_index < 0) { return InvalidArgument( "Expected feature_index of batch-norm-inference to " - "be a non-negative number, got %lld", + "be a non-negative number, got %lld.", feature_index); } if (ShapeUtil::Rank(operand_shape) < 1) { return InvalidArgument( "Expected the rank of operand to " - "batch-norm-inference to be at least 1; got %lld", + "batch-norm-inference to be at least 1; got %lld.", ShapeUtil::Rank(operand_shape)); } @@ -1342,7 +1349,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( if (!ShapeUtil::ElementIsFloating(operand_shape)) { return InvalidArgument( "The operand to batch-norm-inference must have a floating point " - "element type, but the shape is %s", + "element type, but the shape is %s.", PrimitiveType_Name(operand_shape.element_type()).c_str()); } @@ -1352,7 +1359,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( "The inputs should have the same element type for " "batch-norm-inference, " "but the shape of offset factor is %s " - "and the shape of operand is %s", + "and the shape of operand is %s.", PrimitiveType_Name(offset_shape.element_type()).c_str(), PrimitiveType_Name(operand_shape.element_type()).c_str()); } @@ -1363,7 +1370,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( "The inputs should have the same element type for " "batch-norm-inference, " "but the shape of scale factor is %s " - "and the shape of operand is %s", + "and the shape of operand is %s.", PrimitiveType_Name(scale_shape.element_type()).c_str(), PrimitiveType_Name(operand_shape.element_type()).c_str()); } @@ -1374,7 +1381,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( "The inputs should have the same element type for " "batch-norm-inference, " "but the shape of mean is %s " - "and the shape of operand is %s", + "and the shape of operand is %s.", PrimitiveType_Name(mean_shape.element_type()).c_str(), PrimitiveType_Name(operand_shape.element_type()).c_str()); } @@ -1385,7 +1392,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( "The inputs should have the same element type for " "batch-norm-inference, " "but the shape of variance is %s " - "and the shape of operand is %s", + "and the shape of operand is %s.", PrimitiveType_Name(mean_shape.element_type()).c_str(), PrimitiveType_Name(variance_shape.element_type()).c_str()); } @@ -1398,7 +1405,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return InvalidArgument( "The size of offset factor should be the same as feature count," "but the size of offset factor is %lld " - "and the feature count is %lld", + "and the feature count is %lld.", ShapeUtil::GetDimension(offset_shape, 0), feature_count); } @@ -1406,7 +1413,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return InvalidArgument( "The size of scale factor should be the same as feature count," "but the size of scale factor is %lld " - "and the feature count is %lld", + "and the feature count is %lld.", ShapeUtil::GetDimension(scale_shape, 0), feature_count); } @@ -1414,7 +1421,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return InvalidArgument( "The size of mean should be the same as feature count," "but the size of mean is %lld " - "and the feature count is %lld", + "and the feature count is %lld.", ShapeUtil::GetDimension(mean_shape, 0), feature_count); } @@ -1422,7 +1429,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return InvalidArgument( "The size of variance should be the same as feature count," "but the size of variance is %lld " - "and the feature count is %lld", + "and the feature count is %lld.", ShapeUtil::GetDimension(variance_shape, 0), feature_count); } @@ -1455,7 +1462,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return InvalidArgument( "Expected feature_index of batch-norm-grad to be " "smaller than the rank of operand_shape; " - "got feature_index %lld, and rank %lld", + "got feature_index %lld, and rank %lld.", feature_index, ShapeUtil::Rank(operand_shape)); } @@ -1463,7 +1470,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return InvalidArgument( "Expected operand_shape of batch-norm-grad to have the same rank as" " output_grad_shape; got rank(oprand_shape) %lld, and" - " rank(output_grad_shape) %lld", + " rank(output_grad_shape) %lld.", ShapeUtil::Rank(operand_shape), ShapeUtil::Rank(output_grad_shape)); } @@ -1491,14 +1498,14 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( if (!ShapeUtil::ElementIsFloating(operand_shape)) { return InvalidArgument( "The operand to batch-norm-grad must have a floating point " - "element type, but the shape is %s", + "element type, but the shape is %s.", PrimitiveType_Name(operand_shape.element_type()).c_str()); } if (!ShapeUtil::ElementIsFloating(output_grad_shape)) { return InvalidArgument( "The output_grad to batch-norm-grad must have a floating point " - "element type, but the shape is %s", + "element type, but the shape is %s.", PrimitiveType_Name(output_grad_shape.element_type()).c_str()); } @@ -1507,7 +1514,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return InvalidArgument( "The inputs should have the same element type for batch-norm-grad, " "but the element type of output_grad is %s " - "and the element type of operand is %s", + "and the element type of operand is %s.", PrimitiveType_Name(output_grad_shape.element_type()).c_str(), PrimitiveType_Name(operand_shape.element_type()).c_str()); } @@ -1517,7 +1524,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return InvalidArgument( "The inputs should have the same element type for batch-norm-grad, " "but the element type of scale factor is %s " - "and the element type of operand is %s", + "and the element type of operand is %s.", PrimitiveType_Name(scale_shape.element_type()).c_str(), PrimitiveType_Name(operand_shape.element_type()).c_str()); } @@ -1527,7 +1534,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return InvalidArgument( "The inputs should have the same element type for batch-norm-grad, " "but the element type of mean is %s " - "and the element type of operand is %s", + "and the element type of operand is %s.", PrimitiveType_Name(mean_shape.element_type()).c_str(), PrimitiveType_Name(operand_shape.element_type()).c_str()); } @@ -1537,7 +1544,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return InvalidArgument( "The inputs should have the same element type for batch-norm-grad, " "but the element type of mean is %s " - "and the element type of operand is %s", + "and the element type of operand is %s.", PrimitiveType_Name(mean_shape.element_type()).c_str(), PrimitiveType_Name(operand_shape.element_type()).c_str()); } @@ -1551,7 +1558,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return InvalidArgument( "The size of mean should be the same as feature count," "but the size of offset factor is %lld " - "and the feature count is %lld", + "and the feature count is %lld.", ShapeUtil::GetDimension(mean_shape, 0), feature_count); } @@ -1559,7 +1566,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return InvalidArgument( "The size of scale factor should be the same as feature count," "but the size of scale factor is %lld " - "and the feature count is %lld", + "and the feature count is %lld.", ShapeUtil::GetDimension(scale_shape, 0), feature_count); } @@ -1567,7 +1574,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return InvalidArgument( "The size of variance should be the same as feature count," "but the size of variance is %lld " - "and the feature count is %lld", + "and the feature count is %lld.", ShapeUtil::GetDimension(var_shape, 0), feature_count); } @@ -1578,7 +1585,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return InvalidArgument( "The bounds of operand shape should be the same as output_grad's," "but the bound of operand_shape at dimension %lld is %lld " - "and the bound of output_grad_shape is %lld", + "and the bound of output_grad_shape is %lld.", i, ShapeUtil::GetDimension(operand_shape, i), ShapeUtil::GetDimension(output_grad_shape, i)); } @@ -1596,7 +1603,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(lhs, rhs)) { return InvalidArgument( - "Convolution with different element types: %s and %s", + "Convolution with different element types: %s and %s.", ShapeUtil::HumanString(lhs).c_str(), ShapeUtil::HumanString(rhs).c_str()); } @@ -1612,21 +1619,19 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( if (window.dimensions_size() != num_spatial_dims) { return InvalidArgument( "Window must have same number of dimensions as dimension numbers.\n" - "Window: %s\nDimension numbers: %s", + "Window: %s\nDimension numbers: %s.", window.DebugString().c_str(), dnums.DebugString().c_str()); } const int num_dims = num_spatial_dims + 2; if (ShapeUtil::Rank(lhs) != num_dims) { return InvalidArgument( - "The LHS argument to a convolution should have rank %d.\n" - "lhs: %s", + "The LHS argument to a convolution should have rank %d; lhs: %s.", num_dims, ShapeUtil::HumanString(lhs).c_str()); } if (ShapeUtil::Rank(rhs) != num_dims) { return InvalidArgument( - "The RHS argument to a convolution should have rank %d.\n" - "lhs: %s", + "The RHS argument to a convolution should have rank %d; lhs: %s.", num_dims, ShapeUtil::HumanString(lhs).c_str()); } TF_DCHECK_OK(ShapeUtil::ValidateShapeWithOptionalLayout(lhs)); @@ -1663,26 +1668,26 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( !std::all_of(window_dnums.begin(), window_dnums.end(), in_range) || !std::all_of(output_dnums.begin(), output_dnums.end(), in_range)) { return InvalidArgument( - "A dimension number is out of range in convolution: %s", + "A dimension number is out of range in convolution: %s.", dnums.DebugString().c_str()); } if (input_dnums != expected_dnums) { return InvalidArgument( "Input dimensions of convolution must contain each dimension exactly " - "once: %s", + "once: %s.", dnums.DebugString().c_str()); } if (window_dnums != expected_dnums) { return InvalidArgument( "Window dimensions of convolution must contain each dimension exactly " - "once: %s", + "once: %s.", dnums.DebugString().c_str()); } if (output_dnums != expected_dnums) { return InvalidArgument( "Output dimensions of convolution must contain each dimension exactly " - "once: %s", + "once: %s.", dnums.DebugString().c_str()); } @@ -1706,7 +1711,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return InvalidArgument( "Expected LHS feature dimension (value %lld) to match RHS " "input feature dimension (value %lld); got (%s, %s)\n" - "Dimension numbers: {%s}", + "Dimension numbers: {%s}.", input_features, kernel_input_features, ShapeUtil::HumanString(lhs).c_str(), ShapeUtil::HumanString(rhs).c_str(), dnums.DebugString().c_str()); @@ -1720,7 +1725,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( "Window dimensions do not match RHS shape:\n\t" "RHS shape: %s\n\t" "Window: {%s}\n\t" - "Dimension numbers: {%s}", + "Dimension numbers: {%s}.", ShapeUtil::HumanString(rhs).c_str(), window.ShortDebugString().c_str(), dnums.ShortDebugString().c_str()); } @@ -1748,8 +1753,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( const tensorflow::gtl::ArraySlice fft_length) { const int64 fft_rank = fft_length.size(); if (fft_rank < 1 || fft_rank > 3) { - return InvalidArgument("FFT only supports ranks 1-3, but got %lld", - fft_rank); + return InvalidArgument("FFT only supports ranks 1-3; got %lld.", fft_rank); } #define RET_CHECK_RANK(x) \ if (x.dimensions_size() < fft_rank) { \ @@ -1762,7 +1766,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( case FFT: case IFFT: if (in.element_type() != C64) { - return InvalidArgument("%s requires C64 input type, found %s", + return InvalidArgument("%s requires C64 input type, found %s.", FftType_Name(fft_type).c_str(), PrimitiveType_Name(in.element_type()).c_str()); } @@ -1770,7 +1774,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return in; case RFFT: { if (in.element_type() != F32) { - return InvalidArgument("RFFT requires F32 input type, found %s", + return InvalidArgument("RFFT requires F32 input type, found %s.", PrimitiveType_Name(in.element_type()).c_str()); } RET_CHECK_RANK(in); @@ -1779,7 +1783,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( fft_length[i]) { return InvalidArgument( "RFFT requires innermost dimensions match fft_length but " - "dimension %lld is %lld and should be %lld", + "dimension %lld is %lld and should be %lld.", in.dimensions_size() - fft_rank + i, in.dimensions(in.dimensions_size() - fft_rank + i), fft_length[i]); @@ -1792,7 +1796,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( } case IRFFT: { if (in.element_type() != C64) { - return InvalidArgument("IRFFT requires C64 input type, found %s", + return InvalidArgument("IRFFT requires C64 input type, found %s.", PrimitiveType_Name(in.element_type()).c_str()); } RET_CHECK_RANK(in); @@ -1802,7 +1806,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( fft_length[i]) { return InvalidArgument( "IRFFT requires all but one innermost dimensions match " - "fft_length, but dimension %lld is %lld and should be %lld", + "fft_length, but dimension %lld is %lld and should be %lld.", in.dimensions_size() - fft_rank + i, in.dimensions(in.dimensions_size() - fft_rank + i), fft_length[i]); @@ -1812,7 +1816,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( fft_length[fft_rank - 1] / 2 + 1) { return InvalidArgument( "IRFFT requires innermost dimension matches fft_length/2+1, but " - "dimension %d is %lld and should be %lld", + "dimension %d is %lld and should be %lld.", in.dimensions_size() - 1, in.dimensions(in.dimensions_size() - 1), fft_length[fft_rank - 1] / 2 + 1); } @@ -1850,8 +1854,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( for (int64 dimension : dimensions_to_reduce) { if (dimension >= ShapeUtil::Rank(arg) || dimension < 0) { return InvalidArgument( - "attempting to reduce out-of-bounds dimension %lld in shape %s", - dimension, ShapeUtil::HumanString(arg).c_str()); + "Reducing out-of-bounds dimension %lld in shape %s.", dimension, + ShapeUtil::HumanString(arg).c_str()); } } TF_RETURN_IF_ERROR( @@ -1891,30 +1895,30 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( // Check if the select function has a proper shape of (T,T) -> PRED. if (select_shape.parameters_size() != 2) { return InvalidArgument( - "select function must take 2 parameters, but " + "Select function must take 2 parameters, but " "takes %d parameter(s).", select_shape.parameters_size()); } const Shape& select_result_shape = select_shape.result(); if (!ShapeUtil::Compatible(select_result_shape, ShapeUtil::MakeShape(PRED, {}))) { - return Unimplemented("select function must have rank-0 PRED result."); + return InvalidArgument("Select function must have rank-0 PRED result."); } const Shape& operand_element_shape = ShapeUtil::MakeShape(operand_shape.element_type(), {}); if (!ShapeUtil::CompatibleIgnoringFpPrecision(operand_element_shape, select_shape.parameters(0))) { return InvalidArgument( - "select function's first parameter shape currently must " - "match the operand element shape. Got %s vs %s", + "Select function's first parameter shape currently must " + "match the operand element shape, but got %s vs %s.", ShapeUtil::HumanString(select_shape.parameters(0)).c_str(), ShapeUtil::HumanString(operand_element_shape).c_str()); } if (!ShapeUtil::CompatibleIgnoringFpPrecision(operand_element_shape, select_shape.parameters(1))) { return InvalidArgument( - "select function's second parameter shape currently must " - "match the operand element shape. Got %s vs %s", + "Select function's second parameter shape currently must " + "match the operand element shape, but got %s vs %s.", ShapeUtil::HumanString(select_shape.parameters(1)).c_str(), ShapeUtil::HumanString(operand_element_shape).c_str()); } @@ -1931,8 +1935,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( if (!ShapeUtil::CompatibleIgnoringFpPrecision(source_shape, window_result_shape)) { return InvalidArgument( - "source shape does not match the shape of window-reduced operand: " - "source(%s), window-reduced operand(%s)", + "Source shape does not match the shape of window-reduced operand: " + "source(%s), window-reduced operand(%s).", ShapeUtil::HumanString(source_shape).c_str(), ShapeUtil::HumanString(window_result_shape).c_str()); } @@ -1946,7 +1950,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( auto error = [&](const string& message) { return InvalidArgument( "%s in slice operation; argument shape: %s; starts: {%s}; limits: " - "{%s}; strides: {%s}", + "{%s}; strides: {%s}.", message.c_str(), ShapeUtil::HumanString(arg).c_str(), Join(starts, ",").c_str(), Join(limits, ",").c_str(), Join(strides, ",").c_str()); @@ -1969,7 +1973,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( if (starts.size() != ShapeUtil::Rank(arg)) { return InvalidArgument( - "slice index count does not match argument rank: %zu vs %lld", + "Slice index count does not match argument rank: %zu vs %lld.", starts.size(), ShapeUtil::Rank(arg)); } @@ -1979,7 +1983,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( int64 limit_index = limits[dimension]; int64 stride = strides[dimension]; if (start_index < 0) { - return InvalidArgument("negative start index to slice: %lld", + return InvalidArgument("Negative start index to slice: %lld.", start_index); } if (limit_index > arg.dimensions(dimension)) { @@ -1999,7 +2003,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( limit_index, start_index)); } if (stride <= 0) { - return InvalidArgument("stride (%lld) must be positive", stride); + return InvalidArgument("Stride (%lld) must be positive.", stride); } sizes.push_back((limit_index - start_index + stride - 1) / stride); } @@ -2023,20 +2027,20 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( if (ShapeUtil::Rank(start_indices_shape) != 1) { return InvalidArgument( - "dynamic slice start indices of rank %lld must be rank1.", + "Dynamic slice start indices of rank %lld must be rank1.", ShapeUtil::Rank(start_indices_shape)); } if (!ShapeUtil::ElementIsIntegral(start_indices_shape)) { return InvalidArgument( - "dynamic slice start indices must be of integral type."); + "Dynamic slice start indices must be of integral type."); } const int64 start_num_dims = start_indices_shape.dimensions(0); if (ShapeUtil::Rank(operand_shape) != start_num_dims) { return InvalidArgument( - "dynamic slice start number of dimensions %lld (%s) must match rank " - "%lld of slice input (%s)", + "Dynamic slice start number of dimensions %lld (%s) must match rank " + "%lld of slice input (%s).", start_num_dims, ShapeUtil::HumanString(start_indices_shape).c_str(), ShapeUtil::Rank(operand_shape), ShapeUtil::HumanString(operand_shape).c_str()); @@ -2044,7 +2048,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( if (slice_sizes.size() != ShapeUtil::Rank(operand_shape)) { return InvalidArgument( - "dynamic slice index count does not match argument rank: %zu vs %lld", + "Dynamic slice index count does not match argument rank: %zu vs %lld.", slice_sizes.size(), ShapeUtil::Rank(operand_shape)); } @@ -2052,12 +2056,12 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( const int64 input_dim_size = operand_shape.dimensions(dim); const int64 slice_dim_size = slice_sizes[dim]; if (slice_dim_size < 0) { - return InvalidArgument("negative size index to dynamic slice: %lld", + return InvalidArgument("Negative size index to dynamic slice: %lld.", slice_dim_size); } if (slice_dim_size > input_dim_size) { return InvalidArgument( - "slice dim size %lld greater than dynamic slice dimension: %lld", + "Slice dim size %lld greater than dynamic slice dimension: %lld.", slice_dim_size, input_dim_size); } VLOG(2) << tensorflow::strings::Printf("slice_sizes[%lld] = %lld", dim, @@ -2086,20 +2090,20 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( if (ShapeUtil::Rank(start_indices_shape) != 1) { return InvalidArgument( - "dynamic update slice start indices of rank %lld must be rank1.", + "Dynamic update slice start indices of rank %lld must be rank1.", ShapeUtil::Rank(start_indices_shape)); } if (!ShapeUtil::ElementIsIntegral(start_indices_shape)) { return InvalidArgument( - "dynamic update slice start indices must be of integral type."); + "Dynamic update slice start indices must be of integral type."); } const int64 start_num_dims = start_indices_shape.dimensions(0); if (ShapeUtil::Rank(operand_shape) != start_num_dims) { return InvalidArgument( - "dynamic slice start number of dimensions %lld (%s) must match rank " - "%lld of slice input (%s)", + "Dynamic update slice start number of dimensions %lld (%s) must match " + "rank %lld of slice input (%s).", start_num_dims, ShapeUtil::HumanString(start_indices_shape).c_str(), ShapeUtil::Rank(operand_shape), ShapeUtil::HumanString(operand_shape).c_str()); @@ -2107,16 +2111,16 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( if (ShapeUtil::Rank(update_shape) != ShapeUtil::Rank(operand_shape)) { return InvalidArgument( - "dynamic update slice update rank does not match argument rank: " - "%lld vs %lld", + "Dynamic update slice update rank does not match argument rank: " + "%lld vs %lld.", ShapeUtil::Rank(update_shape), ShapeUtil::Rank(operand_shape)); } if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(operand_shape, update_shape)) { return InvalidArgument( - "dynamic update slice update element type does not match argument. " - "operand.element_type: %s vs update.element_type: %s", + "Dynamic update slice update element type does not match argument. " + "operand.element_type: %s vs update.element_type: %s.", PrimitiveType_Name(operand_shape.element_type()).c_str(), PrimitiveType_Name(update_shape.element_type()).c_str()); } @@ -2126,12 +2130,12 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( const int64 update_dim_size = update_shape.dimensions(dim); if (update_dim_size < 0) { return InvalidArgument( - "size index %lld to dynamic update slice must be >= 0", + "Size index %lld to dynamic update slice must be >= 0.", update_dim_size); } if (update_dim_size > input_dim_size) { return InvalidArgument( - "update dim size %lld greater than dynamic slice dimension: %lld", + "Update dim size %lld greater than dynamic slice dimension: %lld.", update_dim_size, input_dim_size); } VLOG(2) << tensorflow::strings::Printf("update_sizes[%lld] = %lld", dim, @@ -2151,7 +2155,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( for (int64 dimension : dimensions) { if (dimension >= ShapeUtil::Rank(operand_shape) || dimension < 0) { return InvalidArgument( - "one of the reverse dimensions (%lld) is out-of-bounds in shape %s", + "One of the reverse dimensions (%lld) is out-of-bounds in shape %s.", dimension, ShapeUtil::HumanString(operand_shape).c_str()); } } @@ -2162,14 +2166,14 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( const Shape& arg, int64 index) { if (!ShapeUtil::IsTuple(arg)) { return InvalidArgument( - "cannot infer shape: attempting to index into non-tuple: %s", + "Cannot infer shape: attempting to index into non-tuple: %s.", ShapeUtil::HumanString(arg).c_str()); } if (index >= arg.tuple_shapes_size()) { return InvalidArgument( - "cannot infer shape: attempt to index out of tuple bounds: %lld " - ">= %d in shape %s", + "Cannot infer shape: attempt to index out of tuple bounds: %lld " + ">= %d in shape %s.", index, arg.tuple_shapes_size(), ShapeUtil::HumanString(arg).c_str()); } @@ -2181,17 +2185,17 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( const Shape& init) { // Check the number of parameters for given computations. if (condition.parameters_size() != 1) { - return InvalidArgument("condition must take 1 arguments; got %d", + return InvalidArgument("Condition must take 1 arguments; got %d.", condition.parameters_size()); } if (body.parameters_size() != 1) { - return InvalidArgument("body must take 1 arguments; got %d", + return InvalidArgument("Body must take 1 arguments; got %d.", body.parameters_size()); } auto shape_string = [&]() { return tensorflow::strings::Printf( - "condition: %s; body: %s; init: %s", + "Condition: %s; body: %s; init: %s.", ShapeUtil::HumanString(condition).c_str(), ShapeUtil::HumanString(body).c_str(), ShapeUtil::HumanString(init).c_str()); @@ -2199,15 +2203,15 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( // Check the shapes of computation parameters and return types. if (!ShapeUtil::ShapeIs(condition.result(), PRED, {})) { - return InvalidArgument("condition must return a boolean; got %s", + return InvalidArgument("Condition must return a boolean; got %s.", shape_string().c_str()); } if (!ShapeUtil::Compatible(body.result(), condition.parameters(0)) || !ShapeUtil::Compatible(body.result(), body.parameters(0)) || !ShapeUtil::Compatible(body.result(), init)) { return InvalidArgument( - "the parameter of condition and body, the result of the body, and init " - "must all have the same shape; got %s", + "The parameter of condition and body, the result of the body, and init " + "must all have the same shape; got %s.", shape_string().c_str()); } @@ -2219,7 +2223,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( const Shape& false_operand, const ProgramShape& true_computation, const ProgramShape& false_computation) { if (!ShapeUtil::ShapeIs(predicate, PRED, {})) { - return InvalidArgument("predicate must be a boolean; got %s.", + return InvalidArgument("Predicate must be a boolean; got %s.", ShapeUtil::HumanString(predicate).c_str()); } @@ -2302,8 +2306,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( if (ShapeUtil::ElementsIn(operand) != ShapeUtil::ElementsIn(inferred_shape)) { return InvalidArgument( - "reshape operation has mismatched element counts: from=%lld (%s) " - "to=%lld (%s)", + "Reshape operation has mismatched element counts: from=%lld (%s) " + "to=%lld (%s).", ShapeUtil::ElementsIn(operand), ShapeUtil::HumanString(operand).c_str(), ShapeUtil::ElementsIn(inferred_shape), ShapeUtil::HumanString(inferred_shape).c_str()); @@ -2351,7 +2355,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(max, "clamp max")); if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(min, operand) || !ShapeUtil::SameElementTypeIgnoringFpPrecision(max, operand)) { - return InvalidArgument("clamp op with different operand types: %s, %s, %s", + return InvalidArgument("Clamp with different operand types: %s, %s, %s.", ShapeUtil::HumanString(min).c_str(), ShapeUtil::HumanString(operand).c_str(), ShapeUtil::HumanString(max).c_str()); @@ -2372,7 +2376,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( } } return Unimplemented( - "not yet implemented: %s, %s %s", min.ShortDebugString().c_str(), + "%s, %s %s is not implemented.", min.ShortDebugString().c_str(), max.ShortDebugString().c_str(), operand.ShortDebugString().c_str()); } @@ -2391,25 +2395,26 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( } if (!compatible) { return InvalidArgument( - "operands to select must be the same shape; got %s and %s", + "Operands to select must be the same shape; got %s and %s.", ShapeUtil::HumanString(on_true).c_str(), ShapeUtil::HumanString(on_false).c_str()); } if (pred.element_type() != PRED) { return InvalidArgument( - "select's pred operand must have PRED element type; got %s", + "Select's pred operand must have PRED element type; got %s.", ShapeUtil::HumanString(pred).c_str()); } - if (ShapeUtil::SameDimensions(pred, on_true) || ShapeUtil::Rank(pred) == 0) { + if (ShapeUtil::CompatibleIgnoringElementType(pred, on_true) || + ShapeUtil::Rank(pred) == 0) { // By this stage we know that pred's element type is PRED. Therefore, this // check restricts pred to be a PRED scalar, or a PRED array with the same // dimensions as on_true and on_false. return ShapeUtil::ChangeElementType( on_true, ShapeUtil::HigherPrecisionElementType(on_true, on_false)); } else { - return Unimplemented( - "select operation with non-scalar predicate with dimensionality " - " different from the other operands: %s", + return InvalidArgument( + "Select operation with non-scalar predicate with dimensionality " + " different from the other operands: %s.", ShapeUtil::HumanString(pred).c_str()); } } @@ -2427,7 +2432,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return InvalidArgument( "Call applied function arity must match number of arguments; got: " "arity: %d, arguments: %zu; computation signature: %s; argument " - "shapes: [%s]", + "shapes: [%s].", to_apply.parameters_size(), arg_shapes.size(), computation_signature.c_str(), argument_shapes.c_str()); } @@ -2439,7 +2444,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( if (!ShapeUtil::Compatible(arg_shape, param_shape)) { return InvalidArgument( "Call parameter must match argument; got parameter %d shape: %s, " - "argument shape: %s", + "argument shape: %s.", i, ShapeUtil::HumanString(param_shape).c_str(), ShapeUtil::HumanString(arg_shape).c_str()); } @@ -2454,40 +2459,40 @@ static Status ValidateGatherDimensionNumbers( const GatherDimensionNumbers& dim_numbers) { if (!c_is_sorted(dim_numbers.output_window_dims())) { return InvalidArgument( - "Output window dimensions in gather op must be ascending; got: %s", + "Output window dimensions in gather op must be ascending; got: %s.", Join(dim_numbers.output_window_dims(), ", ").c_str()); } if (c_adjacent_find(dim_numbers.output_window_dims()) != dim_numbers.output_window_dims().end()) { return InvalidArgument( - "Output window dimensions in gather op must not repeat; got: %s", + "Output window dimensions in gather op must not repeat; got: %s.", Join(dim_numbers.output_window_dims(), ", ").c_str()); } const int64 output_window_dim_count = dim_numbers.output_window_dims_size(); const int64 output_shape_rank = - output_window_dim_count + gather_indices_shape.size(); + output_window_dim_count + gather_indices_shape.size() - 1; for (int i = 0; i < dim_numbers.output_window_dims_size(); ++i) { int64 window_index = dim_numbers.output_window_dims(i); if (window_index < 0 || window_index >= output_shape_rank) { return InvalidArgument( "Window index %d in gather op is out of bounds; got %lld, but should " - "have been in" - "[0,%lld)", + "have been in [0,%lld).", i, window_index, output_shape_rank); } } if (dim_numbers.gather_dims_to_operand_dims_size() != - gather_indices_shape.back()) { + gather_indices_shape[dim_numbers.index_vector_dim()]) { return InvalidArgument( - "There must be exactly as many elements in gather_dims_to_operand_dims " - "as there are elements in the last dimension of %%gather_indices; got: " - "%d, expected %lld", + "Gather op has %d elements in gather_dims_to_operand_dims and the " + "bound of dimension index_vector_dim=%lld of gather_indices is " + "%lld. These two numbers must be equal.", dim_numbers.gather_dims_to_operand_dims_size(), - gather_indices_shape.back()); + dim_numbers.index_vector_dim(), + gather_indices_shape[dim_numbers.index_vector_dim()]); } for (int i = 0; i < dim_numbers.gather_dims_to_operand_dims_size(); i++) { @@ -2496,7 +2501,7 @@ static Status ValidateGatherDimensionNumbers( gather_dim_to_input_dim >= input_shape.dimensions_size()) { return InvalidArgument( "Invalid gather_dims_to_operand_dims mapping; domain is [0, %d), " - "got: %d->%lld", + "got: %d->%lld.", input_shape.dimensions_size(), i, gather_dim_to_input_dim); } } @@ -2511,7 +2516,7 @@ static Status ValidateGatherDimensionNumbers( sorted_gather_dims_to_operand_dims.end()) { return InvalidArgument( "Repeated dimensions are not allowed in gather_dims_to_operand_dims; " - "got: %s", + "got: %s.", Join(dim_numbers.gather_dims_to_operand_dims(), ", ").c_str()); } @@ -2519,7 +2524,7 @@ static Status ValidateGatherDimensionNumbers( if (elided_dim < 0 || elided_dim >= input_shape.dimensions_size()) { return InvalidArgument( "Invalid elided_window_dims set in gather op; valid range is [0, " - "%d), got: %lld", + "%d), got: %lld.", input_shape.dimensions_size(), elided_dim); } } @@ -2534,7 +2539,7 @@ static Status ValidateGatherDimensionNumbers( dim_numbers.elided_window_dims().end()) { return InvalidArgument( "Repeated dimensions not allowed in elided_window_dims in gather op; " - "got: %s", + "got: %s.", Join(dim_numbers.elided_window_dims(), ", ").c_str()); } @@ -2550,24 +2555,33 @@ static Status ValidateGatherDimensionNumbers( TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque( gather_indices_shape, "gather indices operand of gather op")); - if (gather_indices_shape.dimensions_size() < 1) { + if (!ShapeUtil::ElementIsIntegral(gather_indices_shape)) { return InvalidArgument( - "Gather indices parameter must at least of rank 1; got %s", + "Gather indices parameter must be an integral tensor; got %s.", ShapeUtil::HumanString(gather_indices_shape).c_str()); } - if (!ShapeUtil::ElementIsIntegral(gather_indices_shape)) { + // We implicitly reshape gather indices of shape P[A,B,C] to P[A,B,C,1] if + // index_vector_dim is rank(P). The bounds of this expanded shape is + // stored in expanded_gather_indices_shape. + + if (gather_indices_shape.dimensions_size() < + gather_dim_numbers.index_vector_dim() || + gather_dim_numbers.index_vector_dim() < 0) { return InvalidArgument( - "Gather indices parameter must be an integral tensor; got %s", - ShapeUtil::HumanString(gather_indices_shape).c_str()); + "Gather index leaf dimension must be within [0, rank(gather_indices) + " + "1). rank(gather_indices) is %d and gather index leaf dimension is " + "%lld.", + gather_indices_shape.dimensions_size(), + gather_dim_numbers.index_vector_dim()); } std::vector expanded_gather_indices_shape; - // We implicitly reshape gather indices of shape P[N] to P[N,1]. expanded_gather_indices_shape.reserve(gather_indices_shape.dimensions_size()); c_copy(gather_indices_shape.dimensions(), std::back_inserter(expanded_gather_indices_shape)); - if (expanded_gather_indices_shape.size() == 1) { + if (expanded_gather_indices_shape.size() == + gather_dim_numbers.index_vector_dim()) { expanded_gather_indices_shape.push_back(1); } @@ -2577,7 +2591,7 @@ static Status ValidateGatherDimensionNumbers( if (window_bounds.size() != input_shape.dimensions_size()) { return InvalidArgument( "Gather op must have one window bound for every input dimension; got: " - "len(window_bounds)=%lu, input_shape.rank=%d", + "len(window_bounds)=%lu, input_shape.rank=%d.", window_bounds.size(), input_shape.dimensions_size()); } @@ -2587,7 +2601,7 @@ static Status ValidateGatherDimensionNumbers( return InvalidArgument( "All components of the window index in a gather op must either be a " "output window index or explicitly elided; got len(window_bounds)=%lu, " - "output_window_bounds=%s, elided_window_bounds=%s", + "output_window_bounds=%s, elided_window_bounds=%s.", window_bounds.size(), Join(gather_dim_numbers.output_window_dims(), ",").c_str(), Join(gather_dim_numbers.elided_window_dims(), ",").c_str()); @@ -2600,7 +2614,7 @@ static Status ValidateGatherDimensionNumbers( return InvalidArgument( "Window bound at index %d in gather op is out of range, must be " "within " - "[0, %lld), got %lld", + "[0, %lld), got %lld.", i, corresponding_input_bound + 1, window_bound); } } @@ -2609,7 +2623,7 @@ static Status ValidateGatherDimensionNumbers( if (window_bounds[gather_dim_numbers.elided_window_dims(i)] != 1) { return InvalidArgument( "Gather op can only elide window indices with bound 1, but bound is " - "%lld for index %lld at position %d", + "%lld for index %lld at position %d.", window_bounds[gather_dim_numbers.elided_window_dims(i)], gather_dim_numbers.elided_window_dims(i), i); } @@ -2632,6 +2646,9 @@ static Status ValidateGatherDimensionNumbers( } current_bound = window_bounds[window_dims_seen++]; } else { + if (gather_dims_seen == gather_dim_numbers.index_vector_dim()) { + gather_dims_seen++; + } current_bound = expanded_gather_indices_shape[gather_dims_seen++]; } diff --git a/tensorflow/compiler/xla/service/shape_inference.h b/tensorflow/compiler/xla/service/shape_inference.h index 0d3045213db2230da3e18ffcb1a9923250560b64..085fdac60c6de161c457dff672175e82f4f4da51 100644 --- a/tensorflow/compiler/xla/service/shape_inference.h +++ b/tensorflow/compiler/xla/service/shape_inference.h @@ -56,6 +56,9 @@ class ShapeInference { static StatusOr InferBinaryOpShape( BinaryOperation operation, const Shape& lhs, const Shape& rhs, tensorflow::gtl::ArraySlice broadcast_dimensions); + static StatusOr InferBinaryOpShape( + HloOpcode opcode, const Shape& lhs, const Shape& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); static StatusOr InferBinaryOpShape(HloOpcode opcode, const HloInstruction* lhs, const HloInstruction* rhs); diff --git a/tensorflow/compiler/xla/service/shape_inference_test.cc b/tensorflow/compiler/xla/service/shape_inference_test.cc index 7eb120843fd841d841048eeaefd895fde96d133c..0e61994a786b53a295ef9c9c2287b28fbf754d9b 100644 --- a/tensorflow/compiler/xla/service/shape_inference_test.cc +++ b/tensorflow/compiler/xla/service/shape_inference_test.cc @@ -135,7 +135,7 @@ TEST_F(ShapeInferenceTest, SelectBadShapes) { TernaryOperation::TRIOP_SELECT, pred_, matrix_64_48_, matrix_32_64_); ASSERT_FALSE(inferred_status_error1.ok()); ASSERT_THAT(inferred_status_error1.status().error_message(), - HasSubstr("operands to select must be the same shape")); + HasSubstr("Operands to select must be the same shape")); auto inferred_status_error2 = ShapeInference::InferTernaryOpShape( TernaryOperation::TRIOP_SELECT, s32_, matrix_64_48_, matrix_64_48_); @@ -340,7 +340,7 @@ TEST_F(SelectAndScatterShapeInferenceTest, SelectAndScatterWrongSourceShape) { init_value_shape_, scatter_program_shape_); ASSERT_FALSE(inferred_status_fail.ok()); ASSERT_THAT(inferred_status_fail.status().error_message(), - HasSubstr("source shape does not match")); + HasSubstr("Source shape does not match")); } TEST_F(SelectAndScatterShapeInferenceTest, SelectAndScatterWrongSelectShape1) { @@ -351,7 +351,7 @@ TEST_F(SelectAndScatterShapeInferenceTest, SelectAndScatterWrongSelectShape1) { init_value_shape_, scatter_program_shape_); ASSERT_FALSE(inferred_status_fail.ok()); ASSERT_THAT(inferred_status_fail.status().error_message(), - HasSubstr("select function must take 2 parameters")); + HasSubstr("Select function must take 2 parameters")); } TEST_F(SelectAndScatterShapeInferenceTest, SelectAndScatterWrongSelectShape2) { @@ -362,7 +362,7 @@ TEST_F(SelectAndScatterShapeInferenceTest, SelectAndScatterWrongSelectShape2) { init_value_shape_, scatter_program_shape_); ASSERT_FALSE(inferred_status_fail.ok()); ASSERT_THAT(inferred_status_fail.status().error_message(), - HasSubstr("select function must have rank-0 PRED")); + HasSubstr("Select function must have rank-0 PRED")); } TEST_F(SelectAndScatterShapeInferenceTest, SelectAndScatterWrongSelectShape3) { @@ -373,7 +373,7 @@ TEST_F(SelectAndScatterShapeInferenceTest, SelectAndScatterWrongSelectShape3) { init_value_shape_, scatter_program_shape_); ASSERT_FALSE(inferred_status_fail.ok()); ASSERT_THAT(inferred_status_fail.status().error_message(), - HasSubstr("select function's first parameter")); + HasSubstr("Select function's first parameter")); } TEST_F(SelectAndScatterShapeInferenceTest, SelectAndScatterWrongSelectShape4) { @@ -384,7 +384,7 @@ TEST_F(SelectAndScatterShapeInferenceTest, SelectAndScatterWrongSelectShape4) { init_value_shape_, scatter_program_shape_); ASSERT_FALSE(inferred_status_fail.ok()); ASSERT_THAT(inferred_status_fail.status().error_message(), - HasSubstr("select function's second parameter")); + HasSubstr("Select function's second parameter")); } TEST_F(ShapeInferenceTest, Convolve) { @@ -906,7 +906,7 @@ TEST_F(ShapeInferenceTest, ScalarDotVector) { ShapeInference::InferDotOpShape(f32_, vector_32_, dot_dnums); ASSERT_FALSE(inferred_status.ok()); ASSERT_THAT(inferred_status.status().error_message(), - HasSubstr("dot only supports rank")); + HasSubstr("Dot only supports rank")); } // 3D 2D: error @@ -918,7 +918,7 @@ TEST_F(ShapeInferenceTest, DotWithRankHigherThanTwo) { ShapeUtil::MakeShape(F32, {32, 32, 32}), matrix_32_64_, dot_dnums); ASSERT_FALSE(inferred_status.ok()); ASSERT_THAT(inferred_status.status().error_message(), - HasSubstr("batch and contracting dimension number mismatch")); + HasSubstr("Batch and contracting dimension number mismatch")); } // vector vector -> scalar @@ -1024,7 +1024,7 @@ TEST_F(ShapeInferenceTest, DotWithTwoContractingDimsFails) { ShapeInference::InferDotOpShape(lhs_shape, rhs_shape, dot_dnums); ASSERT_FALSE(inferred_status.ok()); ASSERT_THAT(inferred_status.status().error_message(), - HasSubstr("must specify one contracting dimension for both " + HasSubstr("Must specify one contracting dimension for both " "lhs and rhs")); } @@ -1044,7 +1044,7 @@ TEST_F(ShapeInferenceTest, DotWithMisatchedBatchDimSizesFails) { ShapeInference::InferDotOpShape(lhs_shape, rhs_shape, dot_dnums); ASSERT_FALSE(inferred_status.ok()); ASSERT_THAT(inferred_status.status().error_message(), - HasSubstr("batch dimension numbers and sizes must match")); + HasSubstr("Batch dimension numbers and sizes must match")); } // BatchMatMul with different batch dimension numbers fails. @@ -1063,7 +1063,7 @@ TEST_F(ShapeInferenceTest, DotWithMisatchedBatchDimNumbersFails) { ShapeInference::InferDotOpShape(lhs_shape, rhs_shape, dot_dnums); ASSERT_FALSE(inferred_status.ok()); ASSERT_THAT(inferred_status.status().error_message(), - HasSubstr("batch dimension numbers must precede non-batch")); + HasSubstr("Batch dimension numbers must precede non-batch")); } // BatchMatMul with out-of-range dimension numbers fails. @@ -1166,42 +1166,42 @@ TEST_F(ShapeInferenceTest, BinOpBroadcastBadDimension) { BinaryOperation::BINOP_ADD, tensor, vec8, {}); ASSERT_FALSE(inferred_status_error1.ok()); ASSERT_THAT(inferred_status_error1.status().error_message(), - HasSubstr("automatic")); + HasSubstr("Automatic")); // broadcast_dimension out of bounds for tensor's rank auto inferred_status_error2 = ShapeInference::InferBinaryOpShape( BinaryOperation::BINOP_ADD, tensor, vec8, {3}); ASSERT_FALSE(inferred_status_error2.ok()); ASSERT_THAT(inferred_status_error2.status().error_message(), - ContainsRegex("broadcast dimension number .* too large")); + ContainsRegex("Broadcast dimension number .* too large")); // broadcast_dimension doesn't match corresponding dimension auto inferred_status_error3 = ShapeInference::InferBinaryOpShape( BinaryOperation::BINOP_ADD, tensor, vec8, {0}); ASSERT_FALSE(inferred_status_error3.ok()); ASSERT_THAT(inferred_status_error3.status().error_message(), - HasSubstr("broadcast dimension 0 mismatch")); + HasSubstr("Broadcast dimension 0 mismatch")); // broadcast_dimensions list too long auto inferred_status_error4 = ShapeInference::InferBinaryOpShape( BinaryOperation::BINOP_ADD, tensor, matrix8_4, {0, 1, 2}); ASSERT_FALSE(inferred_status_error4.ok()); ASSERT_THAT(inferred_status_error4.status().error_message(), - HasSubstr("size of broadcast_dimensions has to match")); + HasSubstr("broadcast_dimensions has to match")); // there's a dimension above the rank of the tensor auto inferred_status_error5 = ShapeInference::InferBinaryOpShape( BinaryOperation::BINOP_ADD, tensor, matrix8_4, {3, 0}); ASSERT_FALSE(inferred_status_error5.ok()); ASSERT_THAT(inferred_status_error5.status().error_message(), - ContainsRegex("broadcast dimension number .* too large")); + ContainsRegex("dimension number .* too large")); // broadcasting dimensions don't match in this order auto inferred_status_error6 = ShapeInference::InferBinaryOpShape( BinaryOperation::BINOP_ADD, tensor, matrix8_4, {2, 1}); ASSERT_FALSE(inferred_status_error6.ok()); ASSERT_THAT(inferred_status_error6.status().error_message(), - HasSubstr("broadcast dimension 0 mismatch")); + HasSubstr("dimension 0 mismatch")); // The following two tests make sure that broadcasting dimensions are listed // in a proper (strictly increasing) order, even if the lower-rank array @@ -1210,13 +1210,13 @@ TEST_F(ShapeInferenceTest, BinOpBroadcastBadDimension) { BinaryOperation::BINOP_ADD, tensor8_8_8, matrix8_8, {0, 0}); ASSERT_FALSE(inferred_status_error7.ok()); ASSERT_THAT(inferred_status_error7.status().error_message(), - HasSubstr("broadcast dimensions order is wrong")); + HasSubstr("dimensions order is wrong")); auto inferred_status_error8 = ShapeInference::InferBinaryOpShape( BinaryOperation::BINOP_ADD, tensor8_8_8, matrix8_8, {1, 0}); ASSERT_FALSE(inferred_status_error8.ok()); ASSERT_THAT(inferred_status_error8.status().error_message(), - HasSubstr("broadcast dimensions order is wrong")); + HasSubstr("dimensions order is wrong")); } // Tests for the while instruction with proper shapes. @@ -1242,7 +1242,7 @@ TEST_F(ShapeInferenceTest, WhileWithBadShapes) { ShapeInference::InferWhileShape(bad_shape_1, body, result_shape); ASSERT_FALSE(inferred_status_error1.ok()); ASSERT_THAT(inferred_status_error1.status().error_message(), - HasSubstr("condition must take 1 arguments")); + HasSubstr("Condition must take 1 arguments")); auto bad_shape_2 = ShapeUtil::MakeProgramShape({s32_, result_shape}, result_shape); @@ -1250,14 +1250,14 @@ TEST_F(ShapeInferenceTest, WhileWithBadShapes) { ShapeInference::InferWhileShape(cond, bad_shape_2, result_shape); ASSERT_FALSE(inferred_status_error2.ok()); ASSERT_THAT(inferred_status_error2.status().error_message(), - HasSubstr("body must take 1 arguments")); + HasSubstr("Body must take 1 arguments")); auto bad_shape_3 = ShapeUtil::MakeProgramShape({result_shape}, s32_); auto inferred_status_error3 = ShapeInference::InferWhileShape(bad_shape_3, body, result_shape); ASSERT_FALSE(inferred_status_error3.ok()); ASSERT_THAT(inferred_status_error3.status().error_message(), - HasSubstr("condition must return a boolean")); + HasSubstr("Condition must return a boolean")); auto bad_shape_4 = ShapeUtil::MakeProgramShape({result_shape}, vector_32_); auto inferred_status_error4 = @@ -1301,13 +1301,13 @@ TEST_F(ShapeInferenceTest, ConcatenateWithBadShapes) { ShapeInference::InferConcatOpShape({&vector_32_}, /*dimension=*/-1); ASSERT_FALSE(inferred_status_error2.ok()); ASSERT_THAT(inferred_status_error2.status().error_message(), - HasSubstr("dimension to concatenate along out of bounds: -1")); + HasSubstr("dimension out of bounds: -1")); auto inferred_status_error3 = ShapeInference::InferConcatOpShape({&vector_32_}, /*dimension=*/1); ASSERT_FALSE(inferred_status_error3.ok()); ASSERT_THAT(inferred_status_error3.status().error_message(), - HasSubstr("dimension to concatenate along out of bounds: 1")); + HasSubstr("dimension out of bounds: 1")); Shape tuple = ShapeUtil::MakeTupleShape({vector_32_}); auto inferred_status_error4 = ShapeInference::InferConcatOpShape( @@ -1315,21 +1315,20 @@ TEST_F(ShapeInferenceTest, ConcatenateWithBadShapes) { ASSERT_FALSE(inferred_status_error4.ok()); ASSERT_THAT( inferred_status_error4.status().error_message(), - HasSubstr("Expected non-tuple argument for operand of concatenation.")); + HasSubstr("Expected non-tuple argument for operand of concatenation")); const Shape vector_s32 = ShapeUtil::MakeShape(S32, {32}); auto inferred_status_error5 = ShapeInference::InferConcatOpShape( {&vector_32_, &vector_s32}, /*dimension=*/0); ASSERT_FALSE(inferred_status_error5.ok()); - ASSERT_THAT( - inferred_status_error5.status().error_message(), - HasSubstr("cannot concatenate arrays with different element types")); + ASSERT_THAT(inferred_status_error5.status().error_message(), + HasSubstr("concatenate arrays with different element types")); auto inferred_status_error6 = ShapeInference::InferConcatOpShape( {&matrix_32_48_, &matrix_32_64_}, /*dimension=*/0); ASSERT_FALSE(inferred_status_error6.ok()); ASSERT_THAT(inferred_status_error6.status().error_message(), - HasSubstr("cannot concatenate arrays that differ in " + HasSubstr("concatenate arrays that differ in " "dimensions other than the one being " "concatenated")); } @@ -1467,7 +1466,7 @@ TEST_F(ShapeInferenceTest, Conditional) { ShapeUtil::MakeProgramShape({vector_64_}, f32_)); EXPECT_FALSE(inferred_status_error0.ok()); EXPECT_THAT(inferred_status_error0.status().error_message(), - HasSubstr("predicate must be a boolean")); + HasSubstr("Predicate must be a boolean")); auto inferred_status_error1 = ShapeInference::InferConditionalShape( pred_, ShapeUtil::MakeTupleShape({f32_, vector_32_}), matrix_32_48_, @@ -1530,11 +1529,17 @@ TEST_F(ShapeInferenceTest, BadSlice) { class GatherShapeInferenceTest : public ShapeInferenceTest { protected: + const Shape s64_scalar_ = ShapeUtil::MakeShape(S64, {}); + const Shape s64_vector_5_ = ShapeUtil::MakeShape(S64, {5}); const Shape s64_vector_32_ = ShapeUtil::MakeShape(S64, {32}); const Shape s64_4d_tensor_10_9_8_7_1_ = ShapeUtil::MakeShape(S64, {10, 9, 8, 7, 1}); const Shape s64_4d_tensor_10_9_8_7_5_ = ShapeUtil::MakeShape(S64, {10, 9, 8, 7, 5}); + const Shape s64_4d_tensor_5_10_9_7_6_ = + ShapeUtil::MakeShape(S64, {5, 10, 9, 7, 6}); + const Shape s64_4d_tensor_10_9_5_7_6_ = + ShapeUtil::MakeShape(S64, {10, 9, 5, 7, 6}); const Shape f32_5d_tensor_50_49_48_47_46_ = ShapeUtil::MakeShape(F32, {50, 49, 48, 47, 46}); const Shape tuple_shape_ = ShapeUtil::MakeTupleShape( @@ -1548,7 +1553,8 @@ TEST_F(GatherShapeInferenceTest, TensorFlowGather) { HloInstruction::MakeGatherDimNumbers( /*output_window_dims=*/{0}, /*elided_window_dims=*/{1}, - /*gather_dims_to_operand_dims=*/{1}), + /*gather_dims_to_operand_dims=*/{1}, + /*index_vector_dim=*/1), /*window_bounds=*/{64, 1})); EXPECT_TRUE( ShapeUtil::Equal(gather_shape, ShapeUtil::MakeShape(F32, {64, 32}))) @@ -1562,7 +1568,8 @@ TEST_F(GatherShapeInferenceTest, TensorFlowGatherV2) { HloInstruction::MakeGatherDimNumbers( /*output_window_dims=*/{1}, /*elided_window_dims=*/{0}, - /*gather_dims_to_operand_dims=*/{0}), + /*gather_dims_to_operand_dims=*/{0}, + /*index_vector_dim=*/1), /*window_bounds=*/{1, 48})); EXPECT_TRUE( ShapeUtil::Equal(gather_shape, ShapeUtil::MakeShape(F32, {32, 48}))) @@ -1576,7 +1583,8 @@ TEST_F(GatherShapeInferenceTest, TensorFlowGatherNd) { HloInstruction::MakeGatherDimNumbers( /*output_window_dims=*/{4}, /*elided_window_dims=*/{0}, - /*gather_dims_to_operand_dims=*/{0}), + /*gather_dims_to_operand_dims=*/{0}, + /*index_vector_dim=*/4), /*window_bounds=*/{1, 48})); EXPECT_TRUE(ShapeUtil::Equal(gather_shape, ShapeUtil::MakeShape(F32, {10, 9, 8, 7, 48}))) @@ -1591,7 +1599,8 @@ TEST_F(GatherShapeInferenceTest, TensorFlowBatchDynamicSlice) { HloInstruction::MakeGatherDimNumbers( /*output_window_dims=*/{4, 5, 6, 7, 8}, /*elided_window_dims=*/{}, - /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}), + /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, + /*index_vector_dim=*/4), /*window_bounds=*/{30, 29, 28, 27, 26})); EXPECT_TRUE(ShapeUtil::Equal( gather_shape, @@ -1599,12 +1608,85 @@ TEST_F(GatherShapeInferenceTest, TensorFlowBatchDynamicSlice) { << ShapeUtil::HumanString(gather_shape); } +TEST_F(GatherShapeInferenceTest, NonDefaultGatherIndicesLeafDim_A) { + TF_ASSERT_OK_AND_ASSIGN( + Shape gather_shape, + ShapeInference::InferGatherShape( + f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_5_7_6_, + HloInstruction::MakeGatherDimNumbers( + /*output_window_dims=*/{4, 5, 6, 7, 8}, + /*elided_window_dims=*/{}, + /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, + /*index_vector_dim=*/2), + /*window_bounds=*/{30, 29, 28, 27, 26})); + + EXPECT_TRUE(ShapeUtil::Equal( + gather_shape, + ShapeUtil::MakeShape(F32, {10, 9, 7, 6, 30, 29, 28, 27, 26}))) + << ShapeUtil::HumanString(gather_shape); +} + +TEST_F(GatherShapeInferenceTest, NonDefaultGatherIndicesLeafDim_B) { + TF_ASSERT_OK_AND_ASSIGN( + Shape gather_shape, + ShapeInference::InferGatherShape( + f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_5_10_9_7_6_, + HloInstruction::MakeGatherDimNumbers( + /*output_window_dims=*/{4, 5, 6, 7, 8}, + /*elided_window_dims=*/{}, + /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, + /*index_vector_dim=*/0), + /*window_bounds=*/{30, 29, 28, 27, 26})); + + EXPECT_TRUE(ShapeUtil::Equal( + gather_shape, + ShapeUtil::MakeShape(F32, {10, 9, 7, 6, 30, 29, 28, 27, 26}))) + << ShapeUtil::HumanString(gather_shape); +} + +TEST_F(GatherShapeInferenceTest, NoOutputGatherDims) { + // This is equivalent to a dynamic slice. + TF_ASSERT_OK_AND_ASSIGN( + Shape gather_shape, + ShapeInference::InferGatherShape( + f32_5d_tensor_50_49_48_47_46_, s64_vector_5_, + HloInstruction::MakeGatherDimNumbers( + /*output_window_dims=*/{0, 1, 2, 3, 4}, + /*elided_window_dims=*/{}, + /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, + /*index_vector_dim=*/0), + /*window_bounds=*/{30, 29, 28, 27, 26})); + + EXPECT_TRUE(ShapeUtil::Equal(gather_shape, + ShapeUtil::MakeShape(F32, {30, 29, 28, 27, 26}))) + << ShapeUtil::HumanString(gather_shape); +} + +TEST_F(GatherShapeInferenceTest, ScalarGatherIndices) { + // The gather indices "tensor" is a scalar S here that's used to slice out + // [S,0,0,0,0]..[S,30,29,28,27] into a [30,29,28,27] shaped result. + TF_ASSERT_OK_AND_ASSIGN(Shape gather_shape, + ShapeInference::InferGatherShape( + f32_5d_tensor_50_49_48_47_46_, s64_scalar_, + HloInstruction::MakeGatherDimNumbers( + /*output_window_dims=*/{0, 1, 2, 3}, + /*elided_window_dims=*/{0}, + /*gather_dims_to_operand_dims=*/{0}, + /*index_vector_dim=*/0), + /*window_bounds=*/{1, 30, 29, 28, 27})); + + EXPECT_TRUE(ShapeUtil::Equal(gather_shape, + ShapeUtil::MakeShape(F32, {30, 29, 28, 27}))) + << ShapeUtil::HumanString(gather_shape); +} + TEST_F(GatherShapeInferenceTest, TupleShapedTensorInput) { StatusOr statusor = ShapeInference::InferGatherShape( tuple_shape_, s64_vector_32_, HloInstruction::MakeGatherDimNumbers(/*output_window_dims=*/{0}, /*elided_window_dims=*/{1}, - /*gather_dims_to_operand_dims=*/{1}), + /*gather_dims_to_operand_dims=*/{1}, + /*index_vector_dim=*/1), /*window_bounds=*/{64, 1}); ASSERT_FALSE(statusor.ok()); EXPECT_THAT(statusor.status().error_message(), @@ -1617,7 +1699,8 @@ TEST_F(GatherShapeInferenceTest, TupleShapedGatherIndicesInput) { s64_vector_32_, tuple_shape_, HloInstruction::MakeGatherDimNumbers(/*output_window_dims=*/{0}, /*elided_window_dims=*/{1}, - /*gather_dims_to_operand_dims=*/{1}), + /*gather_dims_to_operand_dims=*/{1}, + /*index_vector_dim=*/0), /*window_bounds=*/{64, 1}); ASSERT_FALSE(statusor.ok()); EXPECT_THAT(statusor.status().error_message(), @@ -1625,25 +1708,13 @@ TEST_F(GatherShapeInferenceTest, TupleShapedGatherIndicesInput) { << statusor.status(); } -TEST_F(GatherShapeInferenceTest, ScalarGatherIndicesInput) { - StatusOr statusor = ShapeInference::InferGatherShape( - s64_vector_32_, s32_, - HloInstruction::MakeGatherDimNumbers(/*output_window_dims=*/{0}, - /*elided_window_dims=*/{1}, - /*gather_dims_to_operand_dims=*/{1}), - /*window_bounds=*/{64, 1}); - ASSERT_FALSE(statusor.ok()); - EXPECT_THAT(statusor.status().error_message(), - HasSubstr("Gather indices parameter must at least of rank 1")) - << statusor.status(); -} - TEST_F(GatherShapeInferenceTest, FloatingPointGatherIndicesInput) { StatusOr statusor = ShapeInference::InferGatherShape( s64_vector_32_, vector_32_, HloInstruction::MakeGatherDimNumbers(/*output_window_dims=*/{0}, /*elided_window_dims=*/{1}, - /*gather_dims_to_operand_dims=*/{1}), + /*gather_dims_to_operand_dims=*/{1}, + /*index_vector_dim=*/0), /*window_bounds=*/{64, 1}); ASSERT_FALSE(statusor.ok()); EXPECT_THAT(statusor.status().error_message(), @@ -1658,7 +1729,8 @@ TEST_F(GatherShapeInferenceTest, HloInstruction::MakeGatherDimNumbers( /*output_window_dims=*/{4, 5, 6, 8, 7}, /*elided_window_dims=*/{}, - /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}), + /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, + /*index_vector_dim=*/4), /*window_bounds=*/{30, 29, 28, 27, 26}); ASSERT_FALSE(statusor.ok()); EXPECT_THAT( @@ -1674,7 +1746,8 @@ TEST_F(GatherShapeInferenceTest, HloInstruction::MakeGatherDimNumbers( /*output_window_dims=*/{4, 5, 6, 7, 7}, /*elided_window_dims=*/{}, - /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}), + /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, + /*index_vector_dim=*/4), /*window_bounds=*/{30, 29, 28, 27, 26}); ASSERT_FALSE(statusor.ok()); EXPECT_THAT( @@ -1690,7 +1763,8 @@ TEST_F(GatherShapeInferenceTest, HloInstruction::MakeGatherDimNumbers( /*output_window_dims=*/{4, 5, 99, 100, 101}, /*elided_window_dims=*/{}, - /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}), + /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, + /*index_vector_dim=*/4), /*window_bounds=*/{30, 29, 28, 27, 26}); ASSERT_FALSE(statusor.ok()); EXPECT_THAT(statusor.status().error_message(), @@ -1698,6 +1772,22 @@ TEST_F(GatherShapeInferenceTest, << statusor.status(); } +TEST_F(GatherShapeInferenceTest, + InvalidGatherDimNumbers_WindowIndexBarelyOutOfBounds) { + StatusOr statusor = ShapeInference::InferGatherShape( + f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_, + HloInstruction::MakeGatherDimNumbers( + /*output_window_dims=*/{4, 5, 6, 7, 9}, + /*elided_window_dims=*/{}, + /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, + /*index_vector_dim=*/4), + /*window_bounds=*/{30, 29, 28, 27, 26}); + ASSERT_FALSE(statusor.ok()); + EXPECT_THAT(statusor.status().error_message(), + HasSubstr("Window index 4 in gather op is out of bounds")) + << statusor.status(); +} + TEST_F(GatherShapeInferenceTest, InvalidGatherDimNumbers_MismatchingElidedWindowDims) { StatusOr statusor = ShapeInference::InferGatherShape( @@ -1705,7 +1795,8 @@ TEST_F(GatherShapeInferenceTest, HloInstruction::MakeGatherDimNumbers( /*output_window_dims=*/{4, 5, 6, 7, 8}, /*elided_window_dims=*/{4}, - /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}), + /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, + /*index_vector_dim=*/4), /*window_bounds=*/{30, 29, 28, 27, 26}); ASSERT_FALSE(statusor.ok()); EXPECT_THAT( @@ -1722,7 +1813,8 @@ TEST_F(GatherShapeInferenceTest, HloInstruction::MakeGatherDimNumbers( /*output_window_dims=*/{4, 5, 6, 7, 8}, /*elided_window_dims=*/{0, 1, 2, 3, 19}, - /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}), + /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, + /*index_vector_dim=*/4), /*window_bounds=*/{30, 29, 28, 27, 26}); ASSERT_FALSE(statusor.ok()); EXPECT_THAT(statusor.status().error_message(), @@ -1738,7 +1830,8 @@ TEST_F(GatherShapeInferenceTest, HloInstruction::MakeGatherDimNumbers( /*output_window_dims=*/{4, 5, 6, 7, 8}, /*elided_window_dims=*/{0, 1, 2, 3, 3}, - /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}), + /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, + /*index_vector_dim=*/4), /*window_bounds=*/{30, 29, 28, 27, 26}); ASSERT_FALSE(statusor.ok()); EXPECT_THAT( @@ -1755,15 +1848,15 @@ TEST_F(GatherShapeInferenceTest, HloInstruction::MakeGatherDimNumbers( /*output_window_dims=*/{4, 5, 6, 7, 8}, /*elided_window_dims=*/{}, - /*gather_dims_to_operand_dims=*/{0, 1, 2, 3}), + /*gather_dims_to_operand_dims=*/{0, 1, 2, 3}, + /*index_vector_dim=*/4), /*window_bounds=*/{30, 29, 28, 27, 26}); ASSERT_FALSE(statusor.ok()); EXPECT_THAT( statusor.status().error_message(), - HasSubstr( - "There must be exactly as many elements in " - "gather_dims_to_operand_dims " - "as there are elements in the last dimension of %gather_indices")) + HasSubstr("Gather op has 4 elements in gather_dims_to_operand_dims and " + "the bound of dimension index_vector_dim=4 of " + "gather_indices is 5. These two numbers must be equal.")) << statusor.status(); } @@ -1774,7 +1867,8 @@ TEST_F(GatherShapeInferenceTest, HloInstruction::MakeGatherDimNumbers( /*output_window_dims=*/{4, 5, 6, 7, 8}, /*elided_window_dims=*/{}, - /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 7}), + /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 7}, + /*index_vector_dim=*/4), /*window_bounds=*/{30, 29, 28, 27, 26}); ASSERT_FALSE(statusor.ok()); EXPECT_THAT( @@ -1791,7 +1885,8 @@ TEST_F(GatherShapeInferenceTest, HloInstruction::MakeGatherDimNumbers( /*output_window_dims=*/{4, 5, 6, 7, 8}, /*elided_window_dims=*/{}, - /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 3}), + /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 3}, + /*index_vector_dim=*/4), /*window_bounds=*/{30, 29, 28, 27, 26}); ASSERT_FALSE(statusor.ok()); EXPECT_THAT( @@ -1808,7 +1903,8 @@ TEST_F(GatherShapeInferenceTest, HloInstruction::MakeGatherDimNumbers( /*output_window_dims=*/{4, 5, 6, 7, 8}, /*elided_window_dims=*/{2, 1}, - /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}), + /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, + /*index_vector_dim=*/4), /*window_bounds=*/{1, 1, 28, 27, 26}); ASSERT_FALSE(statusor.ok()); EXPECT_THAT(statusor.status().error_message(), @@ -1822,7 +1918,8 @@ TEST_F(GatherShapeInferenceTest, InvalidGatherDimNumbers_WindowBoundsTooLarge) { HloInstruction::MakeGatherDimNumbers( /*output_window_dims=*/{4, 5, 6, 7}, /*elided_window_dims=*/{2}, - /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}), + /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, + /*index_vector_dim=*/4), /*window_bounds=*/{30, 29, 1, 300, 26}); ASSERT_FALSE(statusor.ok()); EXPECT_THAT(statusor.status().error_message(), @@ -1838,7 +1935,8 @@ TEST_F(GatherShapeInferenceTest, HloInstruction::MakeGatherDimNumbers( /*output_window_dims=*/{4, 5, 6, 7, 8}, /*elided_window_dims=*/{}, - /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}), + /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, + /*index_vector_dim=*/4), /*window_bounds=*/{30, 29, 28, 26}); ASSERT_FALSE(statusor.ok()); EXPECT_THAT( @@ -1855,7 +1953,8 @@ TEST_F(GatherShapeInferenceTest, HloInstruction::MakeGatherDimNumbers( /*output_window_dims=*/{4, 5, 6, 7}, /*elided_window_dims=*/{1}, - /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}), + /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, + /*index_vector_dim=*/4), /*window_bounds=*/{30, 29, 28, 26, 20}); ASSERT_FALSE(statusor.ok()); EXPECT_THAT(statusor.status().error_message(), @@ -1864,5 +1963,22 @@ TEST_F(GatherShapeInferenceTest, << statusor.status(); } +TEST_F(GatherShapeInferenceTest, OutOfBoundsGatherIndicesLeafDim) { + StatusOr statusor = ShapeInference::InferGatherShape( + f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_5_7_6_, + HloInstruction::MakeGatherDimNumbers( + /*output_window_dims=*/{4, 5, 6, 7, 8}, + /*elided_window_dims=*/{}, + /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, + /*index_vector_dim=*/32), + /*window_bounds=*/{30, 29, 28, 27, 26}); + + ASSERT_FALSE(statusor.ok()); + EXPECT_THAT(statusor.status().error_message(), + HasSubstr("Gather index leaf dimension must be within [0, " + "rank(gather_indices) + 1)")) + << statusor.status(); +} + } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/service/user_computation.cc b/tensorflow/compiler/xla/service/user_computation.cc index 06735e9442942f3c69d1cd679857fe22f2fa6756..0dca30a804005c6f536aca5b54af24eb08d4560b 100644 --- a/tensorflow/compiler/xla/service/user_computation.cc +++ b/tensorflow/compiler/xla/service/user_computation.cc @@ -3315,20 +3315,23 @@ void ComputationLowerer::Visit( HloInstruction* rhs = lookup_instruction(ternary_op_request.rhs()); HloInstruction* ehs = lookup_instruction(ternary_op_request.ehs()); auto hlo_opcode = TernaryOperationToHloOpcode(ternary_op_request.triop()); - - if (debug_options_.xla_eliminate_hlo_implicit_broadcast()) { - if (!ShapeUtil::SameDimensions(request.output_shape(), lhs->shape())) { + if (debug_options_.xla_eliminate_hlo_implicit_broadcast() && + !ShapeUtil::IsTuple(request.output_shape())) { + if (!ShapeUtil::IsTuple(lhs->shape()) && + !ShapeUtil::SameDimensions(request.output_shape(), lhs->shape())) { // lhs side is being implicitly broadcast. Change to explicit. lhs = ImplicitBroadcastToExplicitBroadcast(lhs, request.output_shape()); } - if (!ShapeUtil::SameDimensions(request.output_shape(), rhs->shape())) { + if (!ShapeUtil::IsTuple(rhs->shape()) && + !ShapeUtil::SameDimensions(request.output_shape(), rhs->shape())) { rhs = ImplicitBroadcastToExplicitBroadcast(rhs, request.output_shape()); } - if (!ShapeUtil::SameDimensions(request.output_shape(), ehs->shape())) { + if (!ShapeUtil::IsTuple(ehs->shape()) && + !ShapeUtil::SameDimensions(request.output_shape(), ehs->shape())) { ehs = ImplicitBroadcastToExplicitBroadcast(ehs, request.output_shape()); } diff --git a/tensorflow/compiler/xla/service/while_loop_invariant_code_motion.cc b/tensorflow/compiler/xla/service/while_loop_invariant_code_motion.cc index a5f9b01f011ce04f1114c74391a967c62f015221..3ef0cdff6751258e4489ce350deb0931fdf69ef9 100644 --- a/tensorflow/compiler/xla/service/while_loop_invariant_code_motion.cc +++ b/tensorflow/compiler/xla/service/while_loop_invariant_code_motion.cc @@ -106,20 +106,12 @@ static bool NotWorthHoistingIndividually(const HloInstruction& instruction) { case HloOpcode::kBitcast: case HloOpcode::kBroadcast: case HloOpcode::kConstant: + case HloOpcode::kReshape: case HloOpcode::kReverse: case HloOpcode::kSlice: + case HloOpcode::kTranspose: case HloOpcode::kTuple: return true; - - case HloOpcode::kTranspose: - return ShapeUtil::TransposeIsBitcast( - /*input_shape=*/instruction.operand(0)->shape(), - /*output_shape=*/instruction.shape(), instruction.dimensions()); - - case HloOpcode::kReshape: - return ShapeUtil::ReshapeIsBitcast( - /*input_shape=*/instruction.operand(0)->shape(), - /*output_shape=*/instruction.shape()); } } diff --git a/tensorflow/compiler/xla/service/while_loop_simplifier.cc b/tensorflow/compiler/xla/service/while_loop_simplifier.cc index 981de9b2200a9ae8938db21299580f510834d2f0..ec05a74e286c89dd8db5ae07580e461938d7c087 100644 --- a/tensorflow/compiler/xla/service/while_loop_simplifier.cc +++ b/tensorflow/compiler/xla/service/while_loop_simplifier.cc @@ -16,6 +16,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/while_loop_simplifier.h" #include "tensorflow/compiler/xla/service/call_inliner.h" #include "tensorflow/compiler/xla/service/hlo_evaluator.h" +#include "tensorflow/core/lib/gtl/flatmap.h" #include "tensorflow/core/lib/gtl/optional.h" #include "tensorflow/core/lib/strings/str_util.h" #include "tensorflow/core/lib/strings/strcat.h" @@ -212,7 +213,7 @@ static optional GetLoopTripCount(HloInstruction* while_op) { // Now that we know the index of the induction variable, we can we can try to // compute how many times the loop executes. Start by computing the induction // variable's initial value. - HloEvaluator evaluator; + HloEvaluator evaluator(/*max_loop_iterations=*/0); auto* while_init = while_op->mutable_operand(0); auto* indvar_init = while_init->mutable_operand(*indvar_tuple_idx); StatusOr> indvar_init_result = @@ -605,6 +606,78 @@ static StatusOr TryRemoveWhileLoop(HloInstruction* while_op) { return false; } +static StatusOr TryPropagateConstant(HloInstruction* while_op) { + auto while_init = while_op->operand(0); + if (while_init->opcode() != HloOpcode::kTuple) { + return false; + } + + auto while_body = while_op->while_body(); + auto while_body_root = while_body->root_instruction(); + if (while_body_root->opcode() != HloOpcode::kTuple) { + return false; + } + + auto while_body_param = while_body->parameter_instruction(0); + const HloInstruction::InstructionVector& root_operands = + while_body_root->operands(); + + // Find the loop invariant tuple elements with scalar constant init value and + // build a map from the tuple element index to the constant value. Limit this + // to scalar constant values because propagating array constants can regress + // performance by forcing us to copy constants. + tensorflow::gtl::FlatMap index_to_constant; + for (int i = 0; i < root_operands.size(); i++) { + HloInstruction* instr = root_operands[i]; + if (instr->opcode() == HloOpcode::kGetTupleElement && + instr->tuple_index() == i && instr->operand(0) == while_body_param && + ShapeUtil::IsScalar(instr->shape())) { + auto tuple_element = while_init->operand(i); + if (tuple_element->IsConstant()) { + VLOG(3) << "Found loop invariant tuple element " << i << " " + << tuple_element->ToString(); + index_to_constant[i] = tuple_element; + } + } + } + + if (index_to_constant.empty()) { + return false; + } + + // Replace the use of each constant tuple element in the loop_condition and + // loop_body with the corresponding constant value. + auto propagate_constant = [&](HloComputation* computation) -> StatusOr { + HloInstruction* param = computation->parameter_instruction(0); + bool changed = false; + for (auto instr : param->users()) { + // Since only a while-loop with a tuple result reaches here, we can safely + // assume that `param` is a tuple and the first operand of the + // GetTupleElement instruction is a use of `param`. + if (instr->opcode() == HloOpcode::kGetTupleElement) { + VLOG(3) << "tuple index " << instr->tuple_index() << " " + << instr->ToString(); + auto iter = index_to_constant.find(instr->tuple_index()); + if (iter != index_to_constant.end()) { + const HloInstruction* hlo_constant = (*iter).second; + VLOG(3) << "Replace use of " << instr->ToString() << " with " + << hlo_constant->ToString(); + TF_RETURN_IF_ERROR(instr->ReplaceAllUsesWith( + computation->AddInstruction(hlo_constant->Clone()))); + changed = true; + } + } + } + return changed; + }; + + TF_ASSIGN_OR_RETURN(bool changed_cond, + propagate_constant(while_op->while_condition())); + TF_ASSIGN_OR_RETURN(bool changed_body, propagate_constant(while_body)); + + return changed_cond || changed_body; +} + StatusOr WhileLoopSimplifier::Run(HloModule* module) { XLA_VLOG_LINES(3, "WhileLoopSimplifier::Run(), before:\n" + module->ToString()); @@ -635,7 +708,11 @@ StatusOr WhileLoopSimplifier::Run(HloModule* module) { continue; } - StatusOr result = TryRemoveWhileLoop(while_op); + StatusOr result = TryPropagateConstant(while_op); + TF_RETURN_IF_ERROR(result.status()); + changed |= result.ValueOrDie(); + + result = TryRemoveWhileLoop(while_op); TF_RETURN_IF_ERROR(result.status()); if (result.ValueOrDie()) { changed = true; diff --git a/tensorflow/compiler/xla/service/while_loop_simplifier_test.cc b/tensorflow/compiler/xla/service/while_loop_simplifier_test.cc index c5183f8d3aee99696ed4114c3f7e451888222137..f1fea6d7634f2060abe18c0fdd51a3391dcb5ae3 100644 --- a/tensorflow/compiler/xla/service/while_loop_simplifier_test.cc +++ b/tensorflow/compiler/xla/service/while_loop_simplifier_test.cc @@ -19,6 +19,7 @@ limitations under the License. #include "tensorflow/compiler/xla/test.h" #include "tensorflow/compiler/xla/tests/hlo_verified_test_base.h" #include "tensorflow/core/lib/core/status_test_util.h" +#include "tensorflow/core/lib/strings/str_util.h" namespace xla { namespace { @@ -26,112 +27,140 @@ namespace { namespace op = xla::testing::opcode_matchers; class WhileLoopSimplifierTest : public HloVerifiedTestBase { - public: - // Makes a computation that contains a loop that runs num_iters times. - HloComputation* MakeSimpleLoop(int num_iters, HloModule* module); - - // Makes a computation which has one parameter, of the given shape, and always - // returns PRED[]{true}. This is useful as a dummy loop condition. - HloComputation* MakeAlwaysTrueComputation(const Shape& param_shape, - HloModule* module); + protected: + // Makes an HloModule that contains a loop with `num_iters` iteration. + void MakeModuleWithSimpleLoop(int num_iters); + + // Similar to MakeModuleWithSimpleLoop except that the loop bound is passed to + // the loop-condition through an element of a tuple which is the + // loop-condition parameter. + void MakeModuleWithSimpleLoopTupleElementLoopBound(int num_iters); }; -HloComputation* WhileLoopSimplifierTest::MakeSimpleLoop(int num_iters, - HloModule* module) { - HloComputation::Builder builder(TestName()); - - auto loop_iter_init = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42))); - auto loop_data_init = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({0, 1, 2}))); - auto loop_init = builder.AddInstruction( - HloInstruction::CreateTuple({loop_iter_init, loop_data_init})); - - HloComputation* condition; - { - HloComputation::Builder cond_builder(TestName() + ".condition"); - auto loop_var = cond_builder.AddInstruction( - HloInstruction::CreateParameter(0, loop_init->shape(), "loop_var")); - auto loop_induction_var = - cond_builder.AddInstruction(HloInstruction::CreateGetTupleElement( - ShapeUtil::MakeShape(S32, {}), loop_var, 0)); - auto limit = cond_builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR0(42 + num_iters))); - cond_builder.AddInstruction(HloInstruction::CreateBinary( - ShapeUtil::MakeShape(PRED, {}), HloOpcode::kLt, loop_induction_var, - limit)); - condition = module->AddEmbeddedComputation(cond_builder.Build()); +void WhileLoopSimplifierTest::MakeModuleWithSimpleLoop(int num_iters) { + string hlo_string_template = R"( + HloModule SimpleLoop + SimpleLoop.body { + loop_var.1 = (s32[], s32[3]{0}) parameter(0) + get-tuple-element.1 = s32[] get-tuple-element(loop_var.1), index=0 + constant.1 = s32[] constant(1) + add = s32[] add(get-tuple-element.1, constant.1) + get-tuple-element.2 = s32[3]{0} get-tuple-element(loop_var.1), index=1 + multiply = s32[3]{0} multiply(get-tuple-element.2, get-tuple-element.2) + ROOT tuple = (s32[], s32[3]{0}) tuple(add, multiply) } - - HloComputation* body; - { - HloComputation::Builder body_builder(TestName() + ".body"); - auto loop_var = body_builder.AddInstruction( - HloInstruction::CreateParameter(0, loop_init->shape(), "loop_var")); - auto loop_induction_var = - body_builder.AddInstruction(HloInstruction::CreateGetTupleElement( - ShapeUtil::MakeShape(S32, {}), loop_var, 0)); - auto new_loop_induction_var = - body_builder.AddInstruction(HloInstruction::CreateBinary( - loop_induction_var->shape(), HloOpcode::kAdd, loop_induction_var, - body_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1))))); - auto loop_data = - body_builder.AddInstruction(HloInstruction::CreateGetTupleElement( - loop_data_init->shape(), loop_var, 1)); - auto new_loop_data = - body_builder.AddInstruction(HloInstruction::CreateBinary( - loop_data_init->shape(), HloOpcode::kMultiply, loop_data, - loop_data)); - body_builder.AddInstruction( - HloInstruction::CreateTuple({new_loop_induction_var, new_loop_data})); - body = module->AddEmbeddedComputation(body_builder.Build()); + SimpleLoop.condition { + loop_var.2 = (s32[], s32[3]{0}) parameter(0) + get-tuple-element.3 = s32[] get-tuple-element(loop_var.2), index=0 + constant.2 = s32[] constant({{LOOP_BOUND}}) + ROOT less-than = pred[] less-than(get-tuple-element.3, constant.2) } + ENTRY SimpleLoop { + constant.3 = s32[] constant(42) + constant.4 = s32[3]{0} constant({0, 1, 2}) + tuple.1 = (s32[], s32[3]{0}) tuple(constant.3, constant.4) + ROOT while = (s32[], s32[3]{0}) while(tuple.1), condition= + SimpleLoop.condition, body=SimpleLoop.body + } + )"; - builder.AddInstruction(HloInstruction::CreateWhile( - loop_init->shape(), condition, body, loop_init)); - - return module->AddEntryComputation(builder.Build()); + string hlo_string = tensorflow::str_util::StringReplace( + hlo_string_template, "{{LOOP_BOUND}}", + tensorflow::strings::StrCat(42 + num_iters), + /*replace_all=*/true); + ParseAndVerifyModule(hlo_string.c_str()); } -HloComputation* WhileLoopSimplifierTest::MakeAlwaysTrueComputation( - const Shape& param_shape, HloModule* module) { - HloComputation::Builder builder(TestName() + ".always_true"); - builder.AddInstruction( - HloInstruction::CreateParameter(0, param_shape, "param")); - builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(true))); - return module->AddEmbeddedComputation(builder.Build()); +void WhileLoopSimplifierTest::MakeModuleWithSimpleLoopTupleElementLoopBound( + int num_iters) { + string hlo_string_template = R"( + HloModule SimpleLoopWithIndirectLoopBound + SimpleLoopWithIndirectLoopBound.body { + loop_var.1 = (s32[], s32[3]{0}, s32[]) parameter(0) + get-tuple-element.1 = s32[] get-tuple-element(loop_var.1), index=0 + constant.1 = s32[] constant(1) + add = s32[] add(get-tuple-element.1, constant.1) + get-tuple-element.2 = s32[3]{0} get-tuple-element(loop_var.1), index=1 + multiply = s32[3]{0} multiply(get-tuple-element.2, get-tuple-element.2) + limit = s32[] get-tuple-element(loop_var.1), index=2 + ROOT tuple = (s32[], s32[3]{0}, s32[]) tuple(add, multiply, limit) + } + SimpleLoopWithIndirectLoopBound.condition { + loop_var.2 = (s32[], s32[3]{0}, s32[]) parameter(0) + get-tuple-element.3 = s32[] get-tuple-element(loop_var.2), index=0 + get-tuple-element.4 = s32[] get-tuple-element(loop_var.2), index=2 + ROOT less-than = pred[] less-than(get-tuple-element.3, get-tuple-element.4) + } + ENTRY SimpleLoopWithIndirectLoopBound { + constant.3 = s32[] constant(42) + constant.4 = s32[3]{0} constant({0, 1, 2}) + constant.2 = s32[] constant({{LOOP_BOUND}}) + tuple.1 = (s32[], s32[3]{0}, s32[]) tuple(constant.3, constant.4, + constant.2) + ROOT while = (s32[], s32[3]{0}, s32[]) while(tuple.1), + condition=SimpleLoopWithIndirectLoopBound.condition, + body=SimpleLoopWithIndirectLoopBound.body + } + )"; + + string hlo_string = tensorflow::str_util::StringReplace( + hlo_string_template, "{{LOOP_BOUND}}", + tensorflow::strings::StrCat(42 + num_iters), + /*replace_all=*/true); + ParseAndVerifyModule(hlo_string.c_str()); } -TEST_F(WhileLoopSimplifierTest, WhileLoopWithZeroIterations) { - HloComputation* computation = MakeSimpleLoop(/*num_iters=*/0, &module()); - ASSERT_TRUE(WhileLoopSimplifier().Run(&module()).ValueOrDie()); - EXPECT_THAT(computation->root_instruction(), +TEST_F(WhileLoopSimplifierTest, LoopWithZeroIterationSimiplified) { + MakeModuleWithSimpleLoop(/*num_iters=*/0); + HloModule* the_module = &module(); + ASSERT_TRUE(WhileLoopSimplifier().Run(the_module).ValueOrDie()); + EXPECT_THAT(the_module->entry_computation()->root_instruction(), op::Tuple(op::Constant(), op::Constant())); } -TEST_F(WhileLoopSimplifierTest, WhileLoopWithOneIteration) { - HloComputation* computation = MakeSimpleLoop(/*num_iters=*/1, &module()); - ASSERT_TRUE(WhileLoopSimplifier().Run(&module()).ValueOrDie()); - EXPECT_THAT(computation->root_instruction(), +TEST_F(WhileLoopSimplifierTest, + LoopWithZeroIterationTupleElementLoopBoundSimplified) { + MakeModuleWithSimpleLoopTupleElementLoopBound(/*num_iters=*/0); + HloModule* the_module = &module(); + ASSERT_TRUE(WhileLoopSimplifier().Run(the_module).ValueOrDie()); + EXPECT_THAT(the_module->entry_computation()->root_instruction(), + op::Tuple(op::Constant(), op::Constant(), op::Constant())); +} + +TEST_F(WhileLoopSimplifierTest, LoopWithOneIterationSimplified) { + MakeModuleWithSimpleLoop(/*num_iters=*/1); + HloModule* the_module = &module(); + ASSERT_TRUE(WhileLoopSimplifier().Run(the_module).ValueOrDie()); + EXPECT_THAT(the_module->entry_computation()->root_instruction(), op::Tuple(op::Add(), op::Multiply())); } -TEST_F(WhileLoopSimplifierTest, WhileLoopWithTwoIterations) { - MakeSimpleLoop(/*num_iters=*/2, &module()); +TEST_F(WhileLoopSimplifierTest, + LoopWithOneIterationTupleELementLoopBoundSimplified) { + MakeModuleWithSimpleLoopTupleElementLoopBound(/*num_iters=*/1); + HloModule* the_module = &module(); + ASSERT_TRUE(WhileLoopSimplifier().Run(the_module).ValueOrDie()); + EXPECT_THAT(the_module->entry_computation()->root_instruction(), + op::Tuple(op::Add(), op::Multiply(), op::Constant())); +} + +TEST_F(WhileLoopSimplifierTest, LoopWithTwoIterationsNotSimplified) { + MakeModuleWithSimpleLoop(/*num_iters=*/2); EXPECT_FALSE(WhileLoopSimplifier().Run(&module()).ValueOrDie()); } -TEST_F(WhileLoopSimplifierTest, WhileLoopWithControlDependency) { - HloComputation* computation = MakeSimpleLoop(/*num_iters=*/1, &module()); +TEST_F(WhileLoopSimplifierTest, + LoopWithControlDependencySimplifiedDependencyPreserved) { + MakeModuleWithSimpleLoop(/*num_iters=*/1); + HloModule* the_module = &module(); + HloComputation* computation = the_module->entry_computation(); auto* while_op = computation->root_instruction(); ASSERT_EQ(while_op->opcode(), HloOpcode::kWhile); auto* true_op = while_op->while_body()->AddInstruction( HloInstruction::CreateConstant(Literal::CreateR0(true))); TF_ASSERT_OK(true_op->AddControlDependencyTo( while_op->while_body()->root_instruction())); - ASSERT_TRUE(WhileLoopSimplifier().Run(&module()).ValueOrDie()); + ASSERT_TRUE(WhileLoopSimplifier().Run(the_module).ValueOrDie()); EXPECT_THAT(computation->root_instruction()->control_predecessors(), ElementsAre(op::Constant())) << computation->ToString(); @@ -139,8 +168,10 @@ TEST_F(WhileLoopSimplifierTest, WhileLoopWithControlDependency) { // Loops that contain send/recv nodes can't be simplified; the loop structure // around send/recv nodes must be preserved. -TEST_F(WhileLoopSimplifierTest, NotRemovedIfContainsSend) { - HloComputation* computation = MakeSimpleLoop(/*num_iters=*/1, &module()); +TEST_F(WhileLoopSimplifierTest, LoopWithSendNotSimplified) { + MakeModuleWithSimpleLoop(/*num_iters=*/1); + HloModule* the_module = &module(); + HloComputation* computation = the_module->entry_computation(); auto* while_op = computation->root_instruction(); ASSERT_EQ(while_op->opcode(), HloOpcode::kWhile); auto* while_body = while_op->while_body(); @@ -149,11 +180,13 @@ TEST_F(WhileLoopSimplifierTest, NotRemovedIfContainsSend) { HloInstruction::CreateConstant(Literal::CreateR0(true))), /*channel_id=*/0)); while_body->AddInstruction(HloInstruction::CreateSendDone(send)); - EXPECT_FALSE(WhileLoopSimplifier().Run(&module()).ValueOrDie()); + EXPECT_FALSE(WhileLoopSimplifier().Run(the_module).ValueOrDie()); } -TEST_F(WhileLoopSimplifierTest, NotRemovedIfContainsRecv) { - HloComputation* computation = MakeSimpleLoop(/*num_iters=*/1, &module()); +TEST_F(WhileLoopSimplifierTest, LoopWithRecvNotSimplified) { + MakeModuleWithSimpleLoop(/*num_iters=*/1); + HloModule* the_module = &module(); + HloComputation* computation = the_module->entry_computation(); auto* while_op = computation->root_instruction(); ASSERT_EQ(while_op->opcode(), HloOpcode::kWhile); auto* while_body = while_op->while_body(); @@ -161,247 +194,217 @@ TEST_F(WhileLoopSimplifierTest, NotRemovedIfContainsRecv) { HloInstruction::CreateRecv(ShapeUtil::MakeShape(F32, {1}), /*channel_id=*/0)); while_body->AddInstruction(HloInstruction::CreateRecvDone(recv)); - EXPECT_FALSE(WhileLoopSimplifier().Run(&module()).ValueOrDie()); + EXPECT_FALSE(WhileLoopSimplifier().Run(the_module).ValueOrDie()); } // The limitation on not being able to simplify loops that contain infeeds (and // other non-removable instructions) isn't fundamental -- it just stems from the // fact that our infrastructure sees simplifying such a loop as tantamount to // removing the non-removable instruction. -TEST_F(WhileLoopSimplifierTest, NotRemovedIfContainsNonRemovableInstruction) { - HloComputation* computation = MakeSimpleLoop(/*num_iters=*/1, &module()); +TEST_F(WhileLoopSimplifierTest, LoopWithInfeedNotSimplified) { + MakeModuleWithSimpleLoop(/*num_iters=*/1); + HloModule* the_module = &module(); + HloComputation* computation = the_module->entry_computation(); auto* while_op = computation->root_instruction(); ASSERT_EQ(while_op->opcode(), HloOpcode::kWhile); auto* while_body = while_op->while_body(); while_body->AddInstruction( HloInstruction::CreateInfeed(ShapeUtil::MakeShape(F32, {1}), "config")); - EXPECT_FALSE(WhileLoopSimplifier().Run(&module()).ValueOrDie()); + EXPECT_FALSE(WhileLoopSimplifier().Run(the_module).ValueOrDie()); } -// Check that we don't crash when given a loop whose shape is not a tuple. -TEST_F(WhileLoopSimplifierTest, IgnoreNonTupleShapedLoop) { - HloComputation::Builder builder(TestName()); - auto loop_init = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42))); - - HloComputation* condition; - { - HloComputation::Builder cond_builder(TestName() + ".condition"); - auto param = cond_builder.AddInstruction( - HloInstruction::CreateParameter(0, loop_init->shape(), "loop_var")); - cond_builder.AddInstruction(HloInstruction::CreateBinary( - ShapeUtil::MakeShape(PRED, {}), HloOpcode::kLt, param, - cond_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(100))))); - condition = module().AddEmbeddedComputation(cond_builder.Build()); +// A non-tuple shaped loop shouldn't be simplified or crash the compiler. +TEST_F(WhileLoopSimplifierTest, NonTupleShapedLoopNotSimplified) { + const string hlo_string = R"( + HloModule NonTupleShapedLoop + NonTupleShapedLoop.body { + loop_var.1 = s32[] parameter(0) + constant.1 = s32[] constant(-1) + ROOT add = s32[] add(s32[] loop_var.1, s32[] constant.1) + } + NonTupleShapedLoop.condition { + loop_var = s32[] parameter(0) + constant = s32[] constant(100) + ROOT less-than = pred[] less-than(s32[] loop_var, s32[] constant) + } + ENTRY INonTupleShapedLoop { + constant.2 = s32[] constant(42) + ROOT while = s32[] while(s32[] constant.2), + condition=NonTupleShapedLoop.condition, + body=NonTupleShapedLoop.body } + )"; - HloComputation* body; - { - HloComputation::Builder body_builder(TestName() + ".body"); - auto param = body_builder.AddInstruction( - HloInstruction::CreateParameter(0, loop_init->shape(), "loop_var")); - body_builder.AddInstruction(HloInstruction::CreateBinary( - ShapeUtil::MakeShape(S32, {}), HloOpcode::kAdd, param, - body_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(-1))))); - body = module().AddEmbeddedComputation(body_builder.Build()); - } - - builder.AddInstruction(HloInstruction::CreateWhile( - loop_init->shape(), condition, body, loop_init)); - - module().AddEntryComputation(builder.Build()); + ParseAndVerifyModule(hlo_string.c_str()); EXPECT_FALSE(WhileLoopSimplifier().Run(&module()).ValueOrDie()); } -// Construct a loop where we swap the tuple elements in each iteration. -// Although the tuple elements aren't used in the loop, we don't eliminate them, -// because the swapping side-effect is visible to users of the loop. -TEST_F(WhileLoopSimplifierTest, SwapTupleIndices) { - HloComputation::Builder builder(TestName()); - auto loop_init = builder.AddInstruction(HloInstruction::CreateTuple({ - builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0))), - builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1))), - })); - - HloComputation* condition = - MakeAlwaysTrueComputation(loop_init->shape(), &module()); - HloComputation* body; - { - HloComputation::Builder body_builder(TestName() + ".body"); - auto param = body_builder.AddInstruction( - HloInstruction::CreateParameter(0, loop_init->shape(), "loop_var")); - auto scalar_s32 = ShapeUtil::MakeShape(S32, {}); - body_builder.AddInstruction(HloInstruction::CreateTuple({ - body_builder.AddInstruction( - HloInstruction::CreateGetTupleElement(scalar_s32, param, 1)), - body_builder.AddInstruction( - HloInstruction::CreateGetTupleElement(scalar_s32, param, 0)), - })); - body = module().AddEmbeddedComputation(body_builder.Build()); +// A while loop that does nothing else besides swapping tuple elements +// can't be simplified as the result of the swapping is visible to users of the +// loop. +TEST_F(WhileLoopSimplifierTest, LoopSwappingTupleElementsNotSimplified) { + const string hlo_string = R"( + HloModule SwappingTupleElements + SwappingTupleElements.body { + loop_var = (s32[], s32[]) parameter(0) + get-tuple-element = s32[] get-tuple-element((s32[], s32[]) loop_var),index=1 + get-tuple-element.1 = s32[] get-tuple-element((s32[], s32[]) loop_var), + index=0 + ROOT tuple = (s32[], s32[]) tuple(s32[] get-tuple-element, + s32[] get-tuple-element.1) } + SwappingTupleElements.always_true { + param = (s32[], s32[]) parameter(0) + ROOT constant = pred[] constant(true) + } + ENTRY SwappingTupleElements { + x = s32[] parameter(0) + y = s32[] parameter(1) + tuple.1 = (s32[], s32[]) tuple(s32[] x, s32[] y) + ROOT while = (s32[], s32[]) while((s32[], s32[]) tuple.1), + condition=SwappingTupleElements.always_true, + body=SwappingTupleElements.body + } + )"; - builder.AddInstruction(HloInstruction::CreateWhile( - loop_init->shape(), condition, body, loop_init)); - - module().AddEntryComputation(builder.Build()); + ParseAndVerifyModule(hlo_string.c_str()); EXPECT_FALSE(WhileLoopSimplifier().Run(&module()).ValueOrDie()); } // Construct a loop where we assign a constant to tuple element 0 in each // iteration. We can't eliminate tuple element 0, even though we never use its // value. -TEST_F(WhileLoopSimplifierTest, UnusedButModifiedTupleElement) { - HloComputation::Builder builder(TestName()); - auto loop_init = builder.AddInstruction( - HloInstruction::CreateTuple({builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0)))})); - - HloComputation* condition = - MakeAlwaysTrueComputation(loop_init->shape(), &module()); - HloComputation* body; - { - HloComputation::Builder body_builder(TestName() + ".body"); - body_builder.AddInstruction( - HloInstruction::CreateParameter(0, loop_init->shape(), "loop_var")); - body_builder.AddInstruction(HloInstruction::CreateTuple({ - body_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1))), - })); - body = module().AddEmbeddedComputation(body_builder.Build()); +TEST_F(WhileLoopSimplifierTest, + LoopWithUnusedButModifiedTupleElementNotSimplified) { + const string hlo_string = R"( + HloModule UnusedButModifiedTupleElement + UnusedButModifiedTupleElement.body { + loop_var = (s32[]) parameter(0) + constant.1 = s32[] constant(1) + ROOT tuple = (s32[]) tuple(s32[] constant.1) } + UnusedButModifiedTupleElement.always_true { + param = (s32[]) parameter(0) + ROOT constant = pred[] constant(true) + } + ENTRY UnusedButModifiedTupleElement { + constant.2 = s32[] constant(0) + tuple.1 = (s32[]) tuple(s32[] constant.2) + ROOT while = (s32[]) while((s32[]) tuple.1), + condition=UnusedButModifiedTupleElement.always_true, + body=UnusedButModifiedTupleElement.body + } + )"; - builder.AddInstruction(HloInstruction::CreateWhile( - loop_init->shape(), condition, body, loop_init)); - - module().AddEntryComputation(builder.Build()); + ParseAndVerifyModule(hlo_string.c_str()); EXPECT_FALSE(WhileLoopSimplifier().Run(&module()).ValueOrDie()); } // Nothing to simplify in a while loop whose tuple has 0 elements. -TEST_F(WhileLoopSimplifierTest, EmptyTuple) { - HloComputation::Builder builder(TestName()); - auto loop_init = builder.AddInstruction(HloInstruction::CreateTuple({})); - - HloComputation* condition = - MakeAlwaysTrueComputation(loop_init->shape(), &module()); - HloComputation* body; - { - HloComputation::Builder body_builder(TestName() + ".body"); - body_builder.AddInstruction( - HloInstruction::CreateParameter(0, loop_init->shape(), "loop_var")); - body_builder.AddInstruction(HloInstruction::CreateTuple({})); - body = module().AddEmbeddedComputation(body_builder.Build()); +TEST_F(WhileLoopSimplifierTest, LoopWithEmptyTupleNotSimplified) { + const string hlo_string = R"( + HloModule EmptyTuple + EmptyTuple.body { + loop_var = () parameter(0) + ROOT tuple = () tuple() + } + EmptyTuple.always_true { + param = () parameter(0) + ROOT constant = pred[] constant(true) + } + ENTRY EmptyTuple { + tuple.1 = () tuple() + ROOT while = () while(() tuple.1), condition=EmptyTuple.always_true, + body=EmptyTuple.body } + )"; - builder.AddInstruction(HloInstruction::CreateWhile( - loop_init->shape(), condition, body, loop_init)); - module().AddEntryComputation(builder.Build()); + ParseAndVerifyModule(hlo_string.c_str()); EXPECT_FALSE(WhileLoopSimplifier().Run(&module()).ValueOrDie()); } // While loop where one tuple element is used twice in the body, and thus can't // be simplified away. -TEST_F(WhileLoopSimplifierTest, ElemUsedTwice) { - HloComputation::Builder builder(TestName()); - auto loop_init = builder.AddInstruction(HloInstruction::CreateTuple({ - builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0))), - builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1))), - })); - - HloComputation* condition = - MakeAlwaysTrueComputation(loop_init->shape(), &module()); - - auto scalar_s32 = ShapeUtil::MakeShape(S32, {}); - HloComputation* body; - { - HloComputation::Builder body_builder(TestName() + ".body"); - auto* param = body_builder.AddInstruction( - HloInstruction::CreateParameter(0, loop_init->shape(), "param0")); - auto* gte0 = body_builder.AddInstruction( - HloInstruction::CreateGetTupleElement(scalar_s32, param, /*index=*/0)); - // get0 is used twice in the loop body's tuple. - body_builder.AddInstruction(HloInstruction::CreateTuple({gte0, gte0})); - body = module().AddEmbeddedComputation(body_builder.Build()); +TEST_F(WhileLoopSimplifierTest, LoopWithElemUsedTwiceNotSimplified) { + const string hlo_string = R"( + HloModule ElemUsedTwice + ElemUsedTwice.body { + param0 = (s32[], s32[]) parameter(0) + get-tuple-element = s32[] get-tuple-element((s32[], s32[]) param0), index=0 + ROOT tuple = (s32[], s32[]) tuple(s32[] get-tuple-element, + s32[] get-tuple-element) + } + ElemUsedTwice.always_true { + param = (s32[], s32[]) parameter(0) + ROOT constant = pred[] constant(true) } + ENTRY ElemUsedTwice { + x = s32[] parameter(0) + y = s32[] parameter(1) + tuple.1 = (s32[], s32[]) tuple(s32[] x, s32[] y) + ROOT while = (s32[], s32[]) while((s32[], s32[]) tuple.1), + condition=ElemUsedTwice.always_true, body=ElemUsedTwice.body + } + )"; - builder.AddInstruction(HloInstruction::CreateWhile( - loop_init->shape(), condition, body, loop_init)); - module().AddEntryComputation(builder.Build()); + ParseAndVerifyModule(hlo_string.c_str()); EXPECT_FALSE(WhileLoopSimplifier().Run(&module()).ValueOrDie()); } // This while loop has three tuple elements. Element 0 is unused and should be // removed. Element 1 is used by the loop body, and element 2 is used by the // loop condition; these two should stay. -TEST_F(WhileLoopSimplifierTest, RemoveUnusedOperand) { - HloComputation::Builder builder(TestName()); - auto loop_init = builder.AddInstruction(HloInstruction::CreateTuple({ - builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0))), - builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0))), - builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0))), - })); - auto loop_shape = loop_init->shape(); - auto scalar_s32 = ShapeUtil::MakeShape(S32, {}); - - HloComputation* condition; - { - HloComputation::Builder cond_builder(TestName() + ".loop_condition"); - auto param = cond_builder.AddInstruction( - HloInstruction::CreateParameter(0, loop_shape, "param0")); - cond_builder.AddInstruction(HloInstruction::CreateBinary( - ShapeUtil::MakeShape(PRED, {}), HloOpcode::kEq, - cond_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0))), - cond_builder.AddInstruction(HloInstruction::CreateGetTupleElement( - scalar_s32, param, /*index=*/2)))); - condition = module().AddEmbeddedComputation(cond_builder.Build()); +TEST_F(WhileLoopSimplifierTest, RemoveUnusedLoopOperands) { + const string hlo_string = R"( + HloModule RemoveUnusedOperands + RemoveUnusedOperands.body { + loop_var = (s32[], s32[], s32[]) parameter(0) + get-tuple-element.1 = s32[] get-tuple-element((s32[], s32[], + s32[]) loop_var), index=0 + get-tuple-element.2 = s32[] get-tuple-element((s32[], s32[], + s32[]) loop_var), index=1 + constant.1 = s32[] constant(1) + add = s32[] add(s32[] get-tuple-element.2, s32[] constant.1) + get-tuple-element.3 = s32[] get-tuple-element((s32[], s32[], s32[]) + loop_var), index=2 + ROOT tuple = (s32[], s32[], s32[]) tuple(s32[] get-tuple-element.1, + s32[] add, s32[] get-tuple-element.3) } - - HloComputation* body; - { - HloComputation::Builder body_builder(TestName() + ".body"); - auto* param = body_builder.AddInstruction( - HloInstruction::CreateParameter(0, loop_shape, "loop_var")); - - auto* tuple0 = body_builder.AddInstruction( - HloInstruction::CreateGetTupleElement(scalar_s32, param, /*index=*/0)); - auto* tuple1 = body_builder.AddInstruction(HloInstruction::CreateBinary( - scalar_s32, HloOpcode::kAdd, - body_builder.AddInstruction(HloInstruction::CreateGetTupleElement( - scalar_s32, param, /*index=*/1)), - body_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1))))); - auto* tuple2 = body_builder.AddInstruction( - HloInstruction::CreateGetTupleElement(scalar_s32, param, /*index=*/2)); - body_builder.AddInstruction( - HloInstruction::CreateTuple({tuple0, tuple1, tuple2})); - - body = module().AddEmbeddedComputation(body_builder.Build()); + RemoveUnusedOperands.loop_condition { + constant.2 = s32[] constant(0) + param0 = (s32[], s32[], s32[]) parameter(0) + get-tuple-element = s32[] get-tuple-element((s32[], s32[], s32[]) param0), + index=2 + ROOT equal-to = pred[] equal-to(s32[] constant.2, s32[] get-tuple-element) } + ENTRY RemoveUnusedOperands { + x = s32[] parameter(0) + constant.3 = s32[] constant(0) + y = s32[] parameter(1) + tuple.1 = (s32[], s32[], s32[]) tuple(s32[] x, s32[] constant.3, + s32[] y) + ROOT while = (s32[], s32[], s32[]) while((s32[], s32[], s32[]) tuple.1), + condition=RemoveUnusedOperands.loop_condition, + body=RemoveUnusedOperands.body + } + )"; + + ParseAndVerifyModule(hlo_string.c_str()); + HloModule* the_module = &module(); + EXPECT_TRUE(WhileLoopSimplifier().Run(the_module).ValueOrDie()); + + // The original while instruction is still left in the module as a dead + // instruction, find a while instruction with a different name as the new + // while instruction. + HloInstruction* new_while_op = + *std::find_if(the_module->entry_computation()->instructions().begin(), + the_module->entry_computation()->instructions().end(), + [&](const HloInstruction* instr) { + return (instr->opcode() == HloOpcode::kWhile && + instr->name() != "while"); + }); - auto* while_op = builder.AddInstruction(HloInstruction::CreateWhile( - loop_init->shape(), condition, body, loop_init)); - - module().AddEntryComputation(builder.Build()); - EXPECT_TRUE(WhileLoopSimplifier().Run(&module()).ValueOrDie()); - - // We leave most of the checking to HloVerifiedTestBase, which runs the - // verifier on module() at the end of this test. - HloInstruction* new_while_op = *std::find_if( - module().entry_computation()->instructions().begin(), - module().entry_computation()->instructions().end(), - [&](const HloInstruction* instr) { - return instr != while_op && instr->opcode() == HloOpcode::kWhile; - }); + auto scalar_s32 = ShapeUtil::MakeShape(S32, {}); EXPECT_TRUE( ShapeUtil::Equal(new_while_op->shape(), ShapeUtil::MakeTupleShape({scalar_s32, scalar_s32}))) @@ -418,31 +421,91 @@ TEST_F(WhileLoopSimplifierTest, RemoveUnusedOperand) { op::GetTupleElement(op::Parameter(0), /*tuple_index=*/1))); } -TEST_F(WhileLoopSimplifierTest, BodyHasNonTupleRoot) { - auto scalar_s32 = ShapeUtil::MakeShape(S32, {}); - Shape while_shape = ShapeUtil::MakeTupleShape({scalar_s32, scalar_s32}); - - HloComputation* while_body = [&]() { - HloComputation::Builder builder(TestName() + ".passthrough"); - HloInstruction* param = builder.AddInstruction( - HloInstruction::CreateParameter(0, while_shape, "param")); - HloComputation* result = module().AddEmbeddedComputation(builder.Build()); - - result->AddInstruction( - HloInstruction::CreateGetTupleElement(scalar_s32, param, 1)); - return result; - }(); - - HloComputation::Builder builder(TestName()); - auto* init_value = builder.AddInstruction( - HloInstruction::CreateParameter(0, while_shape, "init_value")); - builder.AddInstruction(HloInstruction::CreateWhile( - while_shape, MakeAlwaysTrueComputation(while_shape, &module()), - while_body, init_value)); - module().AddEntryComputation(builder.Build()); - TF_ASSERT_OK_AND_ASSIGN(bool simplified_loop, - WhileLoopSimplifier{}.Run(&module())); - EXPECT_FALSE(simplified_loop); +TEST_F(WhileLoopSimplifierTest, LoopWithNonTupleBodyShapeNotSimplified) { + const string hlo_string = R"( + HloModule BodyHasNonTupleRoot + BodyHasNonTupleRoot.passthrough { + ROOT param = (s32[], s32[]) parameter(0) + } + BodyHasNonTupleRoot.always_true { + param.1 = (s32[], s32[]) parameter(0) + ROOT constant = pred[] constant(true) + } + ENTRY BodyHasNonTupleRoot { + init_value = (s32[], s32[]) parameter(0) + ROOT while = (s32[], s32[]) while((s32[], s32[]) init_value), + condition=BodyHasNonTupleRoot.always_true, + body=BodyHasNonTupleRoot.passthrough + } + )"; + + ParseAndVerifyModule(hlo_string.c_str()); + EXPECT_FALSE(WhileLoopSimplifier().Run(&module()).ValueOrDie()); +} + +TEST_F(WhileLoopSimplifierTest, + LoopWithNonTupleBodyRootInstructionNotSimplified) { + const string hlo_string = R"( + HloModule SimpleLoop + SimpleLoop.body { + loop_var.1 = (s32[], s32[3]{0}) parameter(0) + get-tuple-element.1 = s32[] get-tuple-element(loop_var.1), index=0 + constant.1 = s32[] constant(1) + add = s32[] add(get-tuple-element.1, constant.1) + get-tuple-element.2 = s32[3]{0} get-tuple-element(loop_var.1), index=1 + multiply = s32[3]{0} multiply(get-tuple-element.2, get-tuple-element.2) + ROOT custom-call = (s32[], s32[3]{0}) custom-call(add, multiply), + custom_call_target="x" + } + SimpleLoop.condition { + loop_var.2 = (s32[], s32[3]{0}) parameter(0) + get-tuple-element.3 = s32[] get-tuple-element(loop_var.2), index=0 + constant.2 = s32[] constant(44) + ROOT less-than = pred[] less-than(get-tuple-element.3, constant.2) + } + ENTRY SimpleLoop { + constant.3 = s32[] constant(42) + constant.4 = s32[3]{0} constant({0, 1, 2}) + tuple.1 = (s32[], s32[3]{0}) tuple(constant.3, constant.4) + ROOT while = (s32[], s32[3]{0}) while(tuple.1), condition= + SimpleLoop.condition, body=SimpleLoop.body + } + )"; + + ParseAndVerifyModule(hlo_string.c_str()); + EXPECT_FALSE(WhileLoopSimplifier().Run(&module()).ValueOrDie()); +} + +TEST_F(WhileLoopSimplifierTest, LoopWithArrayConstantNotSimplified) { + const string hlo_string = R"( + HloModule SimpleLoop + SimpleLoop.body { + loop_var.1 = (s32[], s32[3]{0}, s32[3]{0}) parameter(0) + get-tuple-element.1 = s32[] get-tuple-element(loop_var.1), index=0 + constant.1 = s32[] constant(1) + add = s32[] add(get-tuple-element.1, constant.1) + get-tuple-element.2 = s32[3]{0} get-tuple-element(loop_var.1), index=1 + get-tuple-element.3 = s32[3]{0} get-tuple-element(loop_var.1), index=2 + add.2 = s32[3]{0} add(get-tuple-element.2, get-tuple-element.3) + ROOT tuple = (s32[], s32[3]{0}) tuple(add, add.2, get-tuple-element.3) + } + SimpleLoop.condition { + loop_var.2 = (s32[], s32[3]{0}, s32[3]{0}) parameter(0) + get-tuple-element.4 = s32[] get-tuple-element(loop_var.2), index=0 + constant.2 = s32[] constant(47) + ROOT less-than = pred[] less-than(get-tuple-element.4, constant.2) + } + ENTRY SimpleLoop { + constant.3 = s32[] constant(42) + constant.4 = s32[3]{0} constant({0, 1, 2}) + tuple.1 = (s32[], s32[3]{0}) tuple(constant.3, constant.4, constant.4) + ROOT while = (s32[], s32[3]{0}, s32[3]{0}) while(tuple.1), condition= + SimpleLoop.condition, body=SimpleLoop.body + } + )"; + + ParseAndVerifyModule(hlo_string.c_str()); + EXPECT_FALSE(WhileLoopSimplifier().Run(&module()).ValueOrDie()); } } // namespace diff --git a/tensorflow/compiler/xla/service/while_util.cc b/tensorflow/compiler/xla/service/while_util.cc index e20b25e4a08a946f6b58575a4d4e557744f8035c..7441a7ad395bf185cd31de7d4b57beae66cc3063 100644 --- a/tensorflow/compiler/xla/service/while_util.cc +++ b/tensorflow/compiler/xla/service/while_util.cc @@ -15,18 +15,21 @@ limitations under the License. #include "tensorflow/compiler/xla/service/while_util.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" +#include "tensorflow/compiler/xla/service/hlo_creation_utils.h" #include "tensorflow/compiler/xla/service/tuple_util.h" +#include "tensorflow/core/lib/strings/strcat.h" namespace xla { +using tensorflow::strings::StrCat; + static StatusOr WidenWhileCondition( HloComputation* narrow_condition, const Shape& wide_shape) { const Shape& narrow_shape = narrow_condition->parameter_instruction(0)->shape(); HloComputation* wide_while_cond = [&]() { - HloComputation::Builder builder( - tensorflow::strings::StrCat("wide.", narrow_condition->name())); + HloComputation::Builder builder(StrCat("wide.", narrow_condition->name())); builder.AddInstruction( HloInstruction::CreateParameter(0, wide_shape, "wide_param")); @@ -57,8 +60,7 @@ WidenWhileBody(HloComputation* narrow_body, const Shape& wide_shape) { const Shape& narrow_shape = narrow_body->parameter_instruction(0)->shape(); HloComputation* wide_while_body = [&]() { - HloComputation::Builder builder( - tensorflow::strings::StrCat("wide.", narrow_body->name())); + HloComputation::Builder builder(StrCat("wide.", narrow_body->name())); builder.AddInstruction( HloInstruction::CreateParameter(0, wide_shape, "wide_param")); return narrow_body->parent()->AddEmbeddedComputation(builder.Build()); @@ -137,4 +139,108 @@ WhileUtil::MakeInstructionsLiveIn( return std::move(result); } + +static StatusOr> +MakeCountedLoopConditionComputation(const Shape& loop_state_shape, + int64 trip_count) { + Shape scalar_pred = ShapeUtil::MakeShape(PRED, {}); + Shape scalar_s64 = ShapeUtil::MakeShape(S64, {}); + + TF_ASSIGN_OR_RETURN(std::unique_ptr cond_computation, + CreateComputationWithSignature( + {&loop_state_shape}, scalar_pred, "while_cond")); + + HloInstruction* trip_count_constant = cond_computation->AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR0(trip_count))); + + HloInstruction* param = cond_computation->parameter_instruction(0); + TF_ASSIGN_OR_RETURN(HloInstruction * counter, + CreateGetTupleElementHlo(param, 0)); + TF_ASSIGN_OR_RETURN( + HloInstruction * compare, + CreateBinaryHlo(HloOpcode::kLt, counter, trip_count_constant)); + cond_computation->set_root_instruction(compare); + return std::move(cond_computation); +} + +static StatusOr> MakeCountedLoopBodyComputation( + const Shape& loop_state_shape, + const std::function( + HloInstruction*, const WhileUtil::LoopStateTy&)>& loop_body_generator) { + TF_ASSIGN_OR_RETURN(std::unique_ptr body_computation, + CreateComputationWithSignature( + {&loop_state_shape}, loop_state_shape, "while_body")); + HloInstruction* one = body_computation->AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR0(1))); + + HloInstruction* param = body_computation->parameter_instruction(0); + TF_ASSIGN_OR_RETURN(HloInstruction * indvar, + CreateGetTupleElementHlo(param, 0)); + TF_ASSIGN_OR_RETURN(HloInstruction * next_indvar, + CreateBinaryHlo(HloOpcode::kAdd, indvar, one)); + + std::vector loop_body_generator_args; + for (int64 i = 1, e = loop_state_shape.tuple_shapes_size(); i < e; i++) { + TF_ASSIGN_OR_RETURN(HloInstruction * tuple_element, + CreateGetTupleElementHlo(param, i)); + loop_body_generator_args.push_back(tuple_element); + } + TF_ASSIGN_OR_RETURN(std::vector next_state, + loop_body_generator(indvar, loop_body_generator_args)); + next_state.insert(next_state.begin(), next_indvar); + HloInstruction* next_state_tuple = + body_computation->AddInstruction(HloInstruction::CreateTuple(next_state)); + body_computation->set_root_instruction(next_state_tuple); + + return std::move(body_computation); +} + +static StatusOr MakeInitTupleFromInitValues( + HloComputation* computation, const WhileUtil::LoopStateTy& init_values) { + std::vector init_values_with_indvar; + init_values_with_indvar.reserve(init_values.size() + 1); + HloInstruction* zero = computation->AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR0(0))); + init_values_with_indvar.push_back(zero); + c_copy(init_values, std::back_inserter(init_values_with_indvar)); + return computation->AddInstruction( + HloInstruction::CreateTuple(init_values_with_indvar)); +} + +static Shape MakeLoopStateShape(const WhileUtil::LoopStateTy& init_values) { + std::vector loop_state_shape_components; + loop_state_shape_components.reserve(init_values.size() + 1); + loop_state_shape_components.push_back(ShapeUtil::MakeShape(S64, {})); + c_transform(init_values, std::back_inserter(loop_state_shape_components), + [](HloInstruction* instr) { return instr->shape(); }); + return ShapeUtil::MakeTupleShape(loop_state_shape_components); +} + +/*static*/ StatusOr WhileUtil::MakeCountedLoop( + HloComputation* computation, int64 trip_count, + const WhileUtil::LoopStateTy& init_values, + const WhileUtil::LoopBodyGeneratorTy& loop_body_generator) { + Shape loop_state_shape = MakeLoopStateShape(init_values); + TF_ASSIGN_OR_RETURN( + std::unique_ptr cond, + MakeCountedLoopConditionComputation(loop_state_shape, trip_count)); + TF_ASSIGN_OR_RETURN( + std::unique_ptr body, + MakeCountedLoopBodyComputation(loop_state_shape, loop_body_generator)); + TF_ASSIGN_OR_RETURN(HloInstruction * init_tuple, + MakeInitTupleFromInitValues(computation, init_values)); + HloModule* module = computation->parent(); + HloInstruction* while_instr = + computation->AddInstruction(HloInstruction::CreateWhile( + loop_state_shape, module->AddEmbeddedComputation(std::move(cond)), + module->AddEmbeddedComputation(std::move(body)), init_tuple)); + + std::vector result; + for (int64 i = 0, e = init_values.size(); i < e; i++) { + TF_ASSIGN_OR_RETURN(HloInstruction * user_state, + CreateGetTupleElementHlo(while_instr, i + 1)); + result.push_back(user_state); + } + return result; +} } // namespace xla diff --git a/tensorflow/compiler/xla/service/while_util.h b/tensorflow/compiler/xla/service/while_util.h index 3600b5a80d26e37fdb7d5173c3b8743734306390..80f7e16e64f4d1b1faa73f4fb9b4dd6443bf488b 100644 --- a/tensorflow/compiler/xla/service/while_util.h +++ b/tensorflow/compiler/xla/service/while_util.h @@ -52,6 +52,28 @@ class WhileUtil { static StatusOr MakeInstructionsLiveIn( HloInstruction* while_instr, tensorflow::gtl::ArraySlice instructions); + + using LoopStateTy = std::vector; + using LoopBodyGeneratorTy = std::function( + HloInstruction* /*induction_var*/, + const LoopStateTy& /*current_values*/)>; + + // Creates a while loop in `computation` that runs for `trip_count` + // iterations. The structure of the while loop is as follows, in pseudocode: + // + // loop_state while_loop() { + // indvar = 0; + // loop_state = init_values + // while (indvar < trip_count) { + // loop_state = loop_body_generator(loop_state) + // indvar++; + // } + // return loop_state; + // } + static StatusOr MakeCountedLoop( + HloComputation* computation, int64 trip_count, + const LoopStateTy& init_values, + const LoopBodyGeneratorTy& loop_body_generator); }; } // namespace xla diff --git a/tensorflow/compiler/xla/service_interface.h b/tensorflow/compiler/xla/service_interface.h index 809941d8fe1f63d66bf104e66eea66167a0f509d..d8235113dd800f7bab5ceb70272a598b9dcb1fbe 100644 --- a/tensorflow/compiler/xla/service_interface.h +++ b/tensorflow/compiler/xla/service_interface.h @@ -54,6 +54,9 @@ class ServiceInterface { virtual tensorflow::Status Execute(const ExecuteRequest* arg, ExecuteResponse* result) = 0; + virtual tensorflow::Status ExecuteGraph(const ExecuteGraphRequest* arg, + ExecuteResponse* result) = 0; + virtual tensorflow::Status ExecuteParallel( const ExecuteParallelRequest* arg, ExecuteParallelResponse* result) = 0; diff --git a/tensorflow/compiler/xla/shape_tree.h b/tensorflow/compiler/xla/shape_tree.h index 280f02e88675381bd75108bfae0dd22c462ba718..ffaa40c2d673a2365342371ed8dab59565d1d08f 100644 --- a/tensorflow/compiler/xla/shape_tree.h +++ b/tensorflow/compiler/xla/shape_tree.h @@ -53,7 +53,7 @@ struct ShapeTreeNode { ShapeTreeNode(const ShapeTreeNode& other) : data(other.data), children(other.children.size()) { for (size_t i = 0; i < children.size(); ++i) { - children[i] = MakeUnique(*other.children[i]); + children[i] = ::xla::MakeUnique(*other.children[i]); } } @@ -62,7 +62,7 @@ struct ShapeTreeNode { data = other.data; children.resize(other.children.size()); for (size_t i = 0; i < children.size(); ++i) { - children[i] = MakeUnique(*other.children[i]); + children[i] = ::xla::MakeUnique(*other.children[i]); } } return *this; @@ -445,7 +445,7 @@ class ShapeTreeIterator : public std::iterator(index, node_->data); + current_ = ::xla::MakeUnique(index, node_->data); return *current_; } @@ -492,7 +492,7 @@ void ShapeTree::InitChildren(const Shape& shape, Node* node) { template ShapeTree::ShapeTree(Shape shape) : root_(), - shape_storage_(MakeUnique(std::move(shape))), + shape_storage_(::xla::MakeUnique(std::move(shape))), shape_(shape_storage_.get()) { // The shape_ field is just used to hold the structure of the shape. // It should not be relied upon to store layout information. @@ -508,7 +508,7 @@ ShapeTree::ShapeTree(const Shape* shape) : root_(), shape_(shape) { template ShapeTree::ShapeTree(Shape shape, const T& init_value) : root_(init_value), - shape_storage_(MakeUnique(std::move(shape))), + shape_storage_(::xla::MakeUnique(std::move(shape))), shape_(shape_storage_.get()) { // The shape_ field is just used to hold the structure of the shape. // It should not be relied upon to store layout information. diff --git a/tensorflow/compiler/xla/shape_util.cc b/tensorflow/compiler/xla/shape_util.cc index 604e0173e789348923316174873f58058eaf2815..4f604e6f7cb18c1aaf844967d54e3b0e07e54b34 100644 --- a/tensorflow/compiler/xla/shape_util.cc +++ b/tensorflow/compiler/xla/shape_util.cc @@ -609,6 +609,8 @@ StatusOr ParseShapeStringInternal(tensorflow::StringPiece* s) { /* static */ bool ShapeUtil::SameDimensions(const Shape& lhs, const Shape& rhs) { + CHECK(ShapeUtil::IsArray(lhs)); + CHECK(ShapeUtil::IsArray(rhs)); return ContainersEqual(lhs.dimensions(), rhs.dimensions()); } @@ -617,7 +619,10 @@ StatusOr ParseShapeStringInternal(tensorflow::StringPiece* s) { return rhs.element_type() == TUPLE && ContainersEqual(lhs.tuple_shapes(), rhs.tuple_shapes(), Compatible); } - return SameDimensions(lhs, rhs) && SameElementType(lhs, rhs); + if (lhs.element_type() == OPAQUE) { + return rhs.element_type() == OPAQUE; + } + return SameElementType(lhs, rhs) && SameDimensions(lhs, rhs); } /* static */ bool ShapeUtil::CompatibleIgnoringElementType(const Shape& lhs, @@ -627,7 +632,10 @@ StatusOr ParseShapeStringInternal(tensorflow::StringPiece* s) { ContainersEqual(lhs.tuple_shapes(), rhs.tuple_shapes(), CompatibleIgnoringElementType); } - return SameDimensions(lhs, rhs); + if (lhs.element_type() == OPAQUE) { + return rhs.element_type() == OPAQUE; + } + return ShapeUtil::IsArray(rhs) && SameDimensions(lhs, rhs); } /* static */ bool ShapeUtil::CompatibleIgnoringFpPrecision(const Shape& lhs, @@ -637,6 +645,9 @@ StatusOr ParseShapeStringInternal(tensorflow::StringPiece* s) { ContainersEqual(lhs.tuple_shapes(), rhs.tuple_shapes(), CompatibleIgnoringFpPrecision); } + if (lhs.element_type() == OPAQUE) { + return rhs.element_type() == OPAQUE; + } if (SameElementTypeIgnoringFpPrecision(lhs, rhs)) { return CompatibleIgnoringElementType(lhs, rhs); } @@ -1073,9 +1084,10 @@ ShapeUtil::DimensionsUnmodifiedByReshape(const Shape& input_shape, /* static */ bool ShapeUtil::TransposeIsBitcast( const Shape& input_shape, const Shape& output_shape, tensorflow::gtl::ArraySlice dimension_mapping) { - // Can't insert bitcasts without layout information. - if (!LayoutUtil::HasLayout(input_shape) && - !LayoutUtil::HasLayout(output_shape)) { + CHECK(LayoutUtil::HasLayout(input_shape) && + LayoutUtil::HasLayout(output_shape)); + + if (!SameElementType(input_shape, output_shape)) { return false; } @@ -1106,9 +1118,10 @@ ShapeUtil::DimensionsUnmodifiedByReshape(const Shape& input_shape, /* static */ bool ShapeUtil::ReshapeIsBitcast(const Shape& input_shape, const Shape& output_shape) { - // Can't convert reshapes into bitcasts without layout information. - if (!LayoutUtil::HasLayout(input_shape) || - !LayoutUtil::HasLayout(output_shape)) { + CHECK(LayoutUtil::HasLayout(input_shape) && + LayoutUtil::HasLayout(output_shape)); + + if (!SameElementType(input_shape, output_shape)) { return false; } diff --git a/tensorflow/compiler/xla/shape_util.h b/tensorflow/compiler/xla/shape_util.h index 19b1aa93bd373ebd5f502d0dca56c9b31ab4fd7f..3e130a02e2ce853ee157e46afb9760f5ff5a5026 100644 --- a/tensorflow/compiler/xla/shape_util.h +++ b/tensorflow/compiler/xla/shape_util.h @@ -24,6 +24,7 @@ limitations under the License. #include "tensorflow/compiler/xla/layout_util.h" #include "tensorflow/compiler/xla/primitive_util.h" +#include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/xla_data.pb.h" @@ -208,6 +209,7 @@ class ShapeUtil { // Returns whether the LHS and RHS shapes have the same dimensions; note: does // not check element type. + // Precondition: IsArray(lhs) && IsArray(rhs) static bool SameDimensions(const Shape& lhs, const Shape& rhs); // Returns whether the lhs and rhs shapes have the same element type. @@ -320,6 +322,15 @@ class ShapeUtil { static Shape MakeShape(PrimitiveType element_type, tensorflow::gtl::ArraySlice dimensions); + // Creates a Shape with element type corresponding to T and the given + // dimensions + template + static Shape MakeShapeWithType( + tensorflow::gtl::ArraySlice dimensions) { + return ShapeUtil::MakeShape(primitive_util::NativeToPrimitiveType(), + dimensions); + } + // Constructs a new shape with the given minor_to_major order in its Layout. // Returns a value shape such that shape.has_layout(). static Shape MakeShapeWithLayout( @@ -522,12 +533,16 @@ class ShapeUtil { // Returns whether a transpose from input_shape to output_shape with dimension // mapping "dimension_mapping" produces a result which is bit-wise identical // to its input and thus may be replaced with a bitcast. + // + // Precondition: Both input_shape and output_shape have explicit layouts. static bool TransposeIsBitcast( const Shape& input_shape, const Shape& output_shape, tensorflow::gtl::ArraySlice dimension_mapping); // Returns whether a reshape from "input_shape" to "output_shape" is a // bitcast. + // + // Precondition: Both input_shape and output_shape have explicit layouts. static bool ReshapeIsBitcast(const Shape& input_shape, const Shape& output_shape); @@ -560,16 +575,16 @@ class ShapeUtil { // The visitor_function visitor function should return true if it wants to // continue, or false otherwise. // - // visitor_function must be a callable of type bool(const std::vector&) - // or compatible. + // visitor_function must be a callable of type + // StatusOr(ArraySlice) or compatible. template - static void ForEachIndex(const Shape& shape, - tensorflow::gtl::ArraySlice base, - tensorflow::gtl::ArraySlice count, - tensorflow::gtl::ArraySlice incr, - const FnType& visitor_function) { + static Status ForEachIndexWithStatus(const Shape& shape, + tensorflow::gtl::ArraySlice base, + tensorflow::gtl::ArraySlice count, + tensorflow::gtl::ArraySlice incr, + const FnType& visitor_function) { if (ShapeUtil::HasZeroElements(shape)) { - return; + return Status::OK(); } CHECK_EQ(Rank(shape), base.size()); CHECK_EQ(incr.size(), base.size()); @@ -579,7 +594,11 @@ class ShapeUtil { // once with the proper empty indexes. int64 n = -1; std::vector indexes(base.begin(), base.end()); - while (n < rank && visitor_function(indexes)) { + while (n < rank) { + TF_ASSIGN_OR_RETURN(bool should_continue, visitor_function(indexes)); + if (!should_continue) { + break; + } // Increments dimensions in minor to major order. for (n = 0; n < rank; ++n) { int64 dim = LayoutUtil::Minor(shape.layout(), n); @@ -590,6 +609,37 @@ class ShapeUtil { indexes[dim] = base[dim]; } } + + return Status::OK(); + } + + // Simple ergonomic wrapper around ShapeUtil::ForEachIndexWithStatus. + struct IndexIterationSpace { + std::vector index_base; + std::vector index_count; + std::vector index_incr; + }; + + template + static Status ForEachIndexWithStatus( + const Shape& shape, const IndexIterationSpace& iteration_space, + FnTy&& function) { + return ShapeUtil::ForEachIndexWithStatus( + shape, iteration_space.index_base, iteration_space.index_count, + iteration_space.index_incr, std::forward(function)); + } + + template + static void ForEachIndex(const Shape& shape, + tensorflow::gtl::ArraySlice base, + tensorflow::gtl::ArraySlice count, + tensorflow::gtl::ArraySlice incr, + const FnType& visitor_function) { + ForEachIndexWithStatus(shape, base, count, incr, + [&](tensorflow::gtl::ArraySlice indices) { + return StatusOr(visitor_function(indices)); + }) + .IgnoreError(); } private: diff --git a/tensorflow/compiler/xla/shape_util_test.cc b/tensorflow/compiler/xla/shape_util_test.cc index 4db97d45b20b86dc60531845c6e28a223203ff7f..424cfe37ea44d64884e08695fd1f49ca1970ca62 100644 --- a/tensorflow/compiler/xla/shape_util_test.cc +++ b/tensorflow/compiler/xla/shape_util_test.cc @@ -238,6 +238,18 @@ TEST(ShapeUtilTest, IncompatibleTuplesWithDifferentDimensions) { EXPECT_FALSE(ShapeUtil::Compatible(tuple1, tuple2)); } +TEST(ShapeUtilTest, IncompatibleScalarVsTuple) { + Shape shape1 = ShapeUtil::MakeShape(F32, {}); + Shape shape2 = ShapeUtil::MakeTupleShape( + {ShapeUtil::MakeShape(F32, {3, 2}), ShapeUtil::MakeShape(U32, {})}); + EXPECT_FALSE(ShapeUtil::Compatible(shape1, shape2)); + EXPECT_FALSE(ShapeUtil::Compatible(shape2, shape1)); + EXPECT_FALSE(ShapeUtil::CompatibleIgnoringElementType(shape1, shape2)); + EXPECT_FALSE(ShapeUtil::CompatibleIgnoringElementType(shape2, shape1)); + EXPECT_FALSE(ShapeUtil::CompatibleIgnoringFpPrecision(shape1, shape2)); + EXPECT_FALSE(ShapeUtil::CompatibleIgnoringFpPrecision(shape2, shape1)); +} + TEST(ShapeUtilTest, CompareShapesWithPaddedDimensionsMismatch) { Shape shape1 = ShapeUtil::MakeShape(F32, {20, 30}); shape1.mutable_layout()->add_padded_dimensions(10); @@ -573,10 +585,11 @@ TEST(ShapeUtilTest, ForEachIndex) { Shape shape = ShapeUtil::MakeShape(F32, data.dimensions); // Increments at every invocation. int invocations = 0; - auto increment_func = [&invocations](const std::vector& indexes) { - invocations++; - return true; - }; + auto increment_func = + [&invocations](tensorflow::gtl::ArraySlice indexes) { + invocations++; + return true; + }; std::vector zero_base(data.dimensions.size(), 0); std::vector step(data.dimensions.size(), 1); @@ -588,6 +601,29 @@ TEST(ShapeUtilTest, ForEachIndex) { } } +TEST(ShapeUtilTest, ForEachIndexWithStatus) { + Shape shape = ShapeUtil::MakeShape(F32, {10, 10}); + // Increments at every invocation. + int invocations = 0; + auto increment_func = + [&invocations]( + tensorflow::gtl::ArraySlice indexes) -> StatusOr { + if (++invocations == 5) { + return Unimplemented("Cannot increment beyond 5."); + } + return true; + }; + + Status error_status = ShapeUtil::ForEachIndexWithStatus( + shape, /*base=*/{0, 0}, /*count=*/{10, 10}, /*incr=*/{0, 1}, + increment_func); + + EXPECT_FALSE(error_status.ok()); + EXPECT_THAT(error_status.error_message(), + ::testing::HasSubstr("Cannot increment beyond 5.")); + EXPECT_EQ(invocations, 5); +} + TEST(ShapeUtilTest, DimensionsUnmodifiedByReshape_1x1x1x1_to_1x1x1) { // All output dimensions should be unmodified. One of the input dimensions is // modified because the input rank is larger by one. diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD index 1958e5abf6b9ea971e6e4c498ff291f822ca930c..025ac129d7040a007493cbb222d07c6cf323567f 100644 --- a/tensorflow/compiler/xla/tests/BUILD +++ b/tensorflow/compiler/xla/tests/BUILD @@ -44,6 +44,7 @@ cc_library( "//tensorflow/core:lib", "//tensorflow/core:test", ], + alwayslink = True, ) cc_library( @@ -138,6 +139,7 @@ cc_library( "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/service:hlo_verifier", + "//tensorflow/compiler/xla/tools/parser:hlo_parser", "//tensorflow/core:lib", "//tensorflow/core:test", ], @@ -334,6 +336,9 @@ xla_test( xla_test( name = "while_test", srcs = ["while_test.cc"], + tags = [ + "enable_for_xla_interpreter", + ], deps = [ "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:shape_util", @@ -478,6 +483,7 @@ xla_test( xla_test( name = "conditional_test", srcs = ["conditional_test.cc"], + tags = ["enable_for_xla_interpreter"], deps = [ "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:computation_builder", @@ -494,6 +500,7 @@ xla_test( xla_test( name = "unary_op_test", srcs = ["unary_op_test.cc"], + tags = ["enable_for_xla_interpreter"], deps = [ "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:computation_builder", @@ -550,6 +557,9 @@ xla_test( xla_test( name = "deconstruct_tuple_test", srcs = ["deconstruct_tuple_test.cc"], + tags = [ + "enable_for_xla_interpreter", + ], deps = [ "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:shape_util", @@ -662,6 +672,18 @@ xla_test( ], ) +xla_test( + name = "gather_operation_test", + srcs = ["gather_operation_test.cc"], + deps = [ + ":hlo_test_base", + "//tensorflow/compiler/xla:status_macros", + "//tensorflow/compiler/xla:test", + "//tensorflow/compiler/xla/tests:xla_internal_test_main", + "//tensorflow/compiler/xla/tools/parser:hlo_parser", + ], +) + # Repeat dot_operation_runtime_test with single-threaded eigen. xla_test( name = "dot_operation_single_threaded_runtime_test", @@ -942,6 +964,9 @@ xla_test( name = "dynamic_ops_test", timeout = "moderate", srcs = ["dynamic_ops_test.cc"], + tags = [ + "enable_for_xla_interpreter", + ], deps = [ "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla:reference_util", @@ -968,6 +993,9 @@ xla_test( xla_test( name = "tuple_test", srcs = ["tuple_test.cc"], + tags = [ + "enable_for_xla_interpreter", + ], deps = [ "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla:literal_util", @@ -1011,6 +1039,7 @@ xla_test( shard_count = 40, tags = [ "enable_for_xla_interpreter", + "optonly", ], deps = [ "//tensorflow/compiler/xla:array2d", @@ -1142,6 +1171,9 @@ xla_test( xla_test( name = "call_test", srcs = ["call_test.cc"], + tags = [ + "enable_for_xla_interpreter", + ], deps = [ "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:shape_util", @@ -1290,6 +1322,7 @@ xla_test( "//tensorflow/compiler/xla/client:local_client", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", + "//tensorflow/compiler/xla/tests:test_utils", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:lib", "//tensorflow/core:test", @@ -1645,6 +1678,9 @@ xla_test( xla_test( name = "fusion_test", srcs = ["fusion_test.cc"], + tags = [ + "enable_for_xla_interpreter", + ], deps = [ "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla:literal_util", diff --git a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc index 8b35259013200e96807446803c696451a8db80a9..6e21dda25d8e5151b31b8c2328253260595a94c4 100644 --- a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc +++ b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc @@ -1648,33 +1648,15 @@ XLA_TEST_F(ArrayElementwiseOpTest, SquareIn4DZeroElements) { ComputeAndCompareR4(&builder, expected, {}, error_spec_); } -// GPU backend emits nvvm intrinsic for fmin and fmax, whose semantics is NOT -// such -// * fmin(NaN, x) = x -// * fmax(NaN, x) = x -// so we only test NAN on CPU. -// -// TODO(b/28180546): Make this compile in a way that is consistent -// among backends. XLA_TEST_F(ArrayElementwiseOpTest, MinF32s) { ComputationBuilder builder(client_, TestName()); -#if !defined(XLA_TEST_BACKEND_CPU) - auto lhs = builder.ConstantR1({1.0f, 1.0f, 2.25f}); - auto rhs = builder.ConstantR1({2.0f, -5.0f, 1.0f}); -#else SetFastMathDisabled(true); auto lhs = builder.ConstantR1({1.0f, 1.0f, 2.25f, NAN, 6.0f}); auto rhs = builder.ConstantR1({2.0f, -5.0f, 1.0f, 10.0f, NAN}); -#endif auto minimum = builder.Min(lhs, rhs); - ComputeAndCompareR1(&builder, -#if !defined(XLA_TEST_BACKEND_CPU) - {1.0f, -5.0f, 1.0f}, -#else - {1.0f, -5.0f, 1.0f, 10.0f, 6.0f}, -#endif - {}, error_spec_); + ComputeAndCompareR1(&builder, {1.0f, -5.0f, 1.0f, NAN, NAN}, {}, + error_spec_); } XLA_TEST_F(ArrayElementwiseOpTest, MinZeroElementF32s) { @@ -1685,50 +1667,26 @@ XLA_TEST_F(ArrayElementwiseOpTest, MinZeroElementF32s) { ComputeAndCompareR1(&builder, {}, {}, error_spec_); } -// TODO(b/28180546): Make this compile in a way that is consistent -// among backends. See comment on MinF32s test above. XLA_TEST_F(ArrayElementwiseOpTest, MinF64s) { ComputationBuilder builder(client_, TestName()); -#if !defined(XLA_TEST_BACKEND_CPU) - auto lhs = builder.ConstantR1({1.0, 1.0, 2.25}); - auto rhs = builder.ConstantR1({2.0, -5.0, 1.0}); -#else SetFastMathDisabled(true); auto lhs = builder.ConstantR1({1.0, 1.0, 2.25, NAN, 6.0}); auto rhs = builder.ConstantR1({2.0, -5.0, 1.0, 10.0, NAN}); -#endif auto minimum = builder.Min(lhs, rhs); - ComputeAndCompareR1(&builder, -#if !defined(XLA_TEST_BACKEND_CPU) - {1.0, -5.0, 1.0}, -#else - {1.0, -5.0, 1.0, 10.0, 6.0}, -#endif - {}, error_spec_); + ComputeAndCompareR1(&builder, {1.0, -5.0, 1.0, NAN, NAN}, {}, + error_spec_); } -// TODO(b/28180546): Make this compile in a way that is consistent -// among backends. See comment on MinF32s test above. XLA_TEST_F(ArrayElementwiseOpTest, MaxF32s) { ComputationBuilder builder(client_, TestName()); -#if !defined(XLA_TEST_BACKEND_CPU) - auto lhs = builder.ConstantR1({1.0f, 1.0f, 2.25f}); - auto rhs = builder.ConstantR1({2.0f, -5.0f, 1.0f}); -#else SetFastMathDisabled(true); auto lhs = builder.ConstantR1({1.0f, 1.0f, 2.25f, NAN, 6.0f}); auto rhs = builder.ConstantR1({2.0f, -5.0f, 1.0f, 10.0f, NAN}); -#endif auto maximum = builder.Max(lhs, rhs); - ComputeAndCompareR1(&builder, -#if !defined(XLA_TEST_BACKEND_CPU) - {2.0f, 1.0f, 2.25f}, -#else - {2.0f, 1.0f, 2.25f, 10.0f, 6.0f}, -#endif - {}, error_spec_); + ComputeAndCompareR1(&builder, {2.0f, 1.0f, 2.25f, NAN, NAN}, {}, + error_spec_); } XLA_TEST_F(ArrayElementwiseOpTest, MaxZeroElementF32s) { @@ -1739,27 +1697,15 @@ XLA_TEST_F(ArrayElementwiseOpTest, MaxZeroElementF32s) { ComputeAndCompareR1(&builder, {}, {}, error_spec_); } -// TODO(b/28180546): Make this compile in a way that is consistent -// among backends. See comment on MinF32s test above. XLA_TEST_F(ArrayElementwiseOpTest, MaxF64s) { ComputationBuilder builder(client_, TestName()); -#if !defined(XLA_TEST_BACKEND_CPU) - auto lhs = builder.ConstantR1({1.0, 1.0, 2.25}); - auto rhs = builder.ConstantR1({2.0, -5.0, 1.0}); -#else SetFastMathDisabled(true); auto lhs = builder.ConstantR1({1.0, 1.0, 2.25, NAN, 6.0}); auto rhs = builder.ConstantR1({2.0, -5.0, 1.0, 10.0, NAN}); -#endif auto maximum = builder.Max(lhs, rhs); - ComputeAndCompareR1(&builder, -#if !defined(XLA_TEST_BACKEND_CPU) - {2.0, 1.0, 2.25}, -#else - {2.0, 1.0, 2.25, 10.0, 6.0}, -#endif - {}, error_spec_); + ComputeAndCompareR1(&builder, {2.0, 1.0, 2.25, NAN, NAN}, {}, + error_spec_); } XLA_TEST_F(ArrayElementwiseOpTest, MaxS32s) { diff --git a/tensorflow/compiler/xla/tests/broadcast_simple_test.cc b/tensorflow/compiler/xla/tests/broadcast_simple_test.cc index 03f5e08315bfed2bcb43ebb7098aaa0b97228605..97095f1cc427789845051a8fea24c95475286fe2 100644 --- a/tensorflow/compiler/xla/tests/broadcast_simple_test.cc +++ b/tensorflow/compiler/xla/tests/broadcast_simple_test.cc @@ -662,7 +662,7 @@ XLA_TEST_F(BroadcastSimpleTest, InvalidBinaryAndDegenerateBroadcasting) { auto result_status = Execute(&b, {}); EXPECT_FALSE(result_status.ok()); EXPECT_THAT(result_status.status().error_message(), - HasSubstr("broadcast dimension 0 mismatch")); + HasSubstr("dimension 0 mismatch")); } XLA_TEST_F(BroadcastSimpleTest, InvalidInDimensionBroadcasting) { @@ -675,7 +675,7 @@ XLA_TEST_F(BroadcastSimpleTest, InvalidInDimensionBroadcasting) { auto result_status = Execute(&b, {}); EXPECT_FALSE(result_status.ok()); EXPECT_THAT(result_status.status().error_message(), - HasSubstr("binary op BINOP_ADD with incompatible shapes")); + HasSubstr("op BINOP_ADD with incompatible shapes")); } XLA_TEST_F(BroadcastSimpleTest, InvalidDegenerateBroadcasting) { @@ -688,7 +688,7 @@ XLA_TEST_F(BroadcastSimpleTest, InvalidDegenerateBroadcasting) { auto result_status = Execute(&b, {}); EXPECT_FALSE(result_status.ok()); EXPECT_THAT(result_status.status().error_message(), - HasSubstr("binary op BINOP_ADD with incompatible shapes")); + HasSubstr("op BINOP_ADD with incompatible shapes")); } } // namespace diff --git a/tensorflow/compiler/xla/tests/concat_test.cc b/tensorflow/compiler/xla/tests/concat_test.cc index 1bcad5a3f37a37c9d482f3a5a899ac527666cca3..fb0e9c724a69b61801e6e0c2d07ef75b63a00465 100644 --- a/tensorflow/compiler/xla/tests/concat_test.cc +++ b/tensorflow/compiler/xla/tests/concat_test.cc @@ -75,7 +75,7 @@ XLA_TEST_F(ConcatTest, CannotConcatR0WithR0) { StatusOr computation_status = builder.Build(); ASSERT_FALSE(computation_status.ok()); EXPECT_THAT(computation_status.status().ToString(), - HasSubstr("dimension to concatenate along out of bounds: 0")); + HasSubstr("out of bounds: 0")); } XLA_TEST_F(ConcatTest, Concat_R1_L0_With_R1_L0) { diff --git a/tensorflow/compiler/xla/tests/convolution_test.cc b/tensorflow/compiler/xla/tests/convolution_test.cc index e2b5c91653fa6db5df86404c6c5f9158b0d484e1..72715398dea468d0000144759454c5f8d8673516 100644 --- a/tensorflow/compiler/xla/tests/convolution_test.cc +++ b/tensorflow/compiler/xla/tests/convolution_test.cc @@ -53,26 +53,12 @@ class ConvolutionTest : public ClientLibraryTestBase { #endif }; -#if (XLA_TEST_BACKEND_GPU || XLA_TEST_BACKEND_CPU) -using TestTypes = ::testing::Types; -#else +#ifdef XLA_BACKEND_DOES_NOT_SUPPORT_FLOAT16 using TestTypes = ::testing::Types; +#else +using TestTypes = ::testing::Types; #endif -template -Shape MakeShapeWrapper(tensorflow::gtl::ArraySlice dimensions); - -template <> -Shape MakeShapeWrapper(tensorflow::gtl::ArraySlice dimensions) { - return ShapeUtil::MakeShape(F32, dimensions); -} - -template <> -Shape MakeShapeWrapper( - tensorflow::gtl::ArraySlice dimensions) { - return ShapeUtil::MakeShape(F16, dimensions); -} - template class ForwardPassConvolution_3x3x256_256_OutputZ_Iota : public ConvolutionTest { public: @@ -121,8 +107,8 @@ class Convolve_1x1x1x2_1x1x1x2_Valid : public ConvolutionTest { public: void RunTest() { ComputationBuilder builder(client_, TestName()); - Shape input_shape = MakeShapeWrapper({1, 1, 1, 2}); - Shape filter_shape = MakeShapeWrapper({1, 1, 1, 2}); + Shape input_shape = ShapeUtil::MakeShapeWithType({1, 1, 1, 2}); + Shape filter_shape = ShapeUtil::MakeShapeWithType({1, 1, 1, 2}); auto input = builder.Parameter(0, input_shape, "input"); auto filter = builder.Parameter(1, filter_shape, "filter"); auto conv = builder.Conv(input, filter, {1, 1}, Padding::kValid); @@ -152,8 +138,8 @@ class Convolve_1x1x4x4_1x1x2x2_Valid : public ConvolutionTest { public: void RunTest() { ComputationBuilder builder(client_, TestName()); - Shape input_shape = MakeShapeWrapper({1, 1, 4, 4}); - Shape filter_shape = MakeShapeWrapper({1, 1, 2, 2}); + Shape input_shape = ShapeUtil::MakeShapeWithType({1, 1, 4, 4}); + Shape filter_shape = ShapeUtil::MakeShapeWithType({1, 1, 2, 2}); auto input = builder.Parameter(0, input_shape, "input"); auto filter = builder.Parameter(1, filter_shape, "filter"); auto conv = builder.Conv(input, filter, {1, 1}, Padding::kValid); @@ -186,8 +172,8 @@ class Convolve_1x1x4x4_1x1x2x2_Same : public ConvolutionTest { public: void RunTest() { ComputationBuilder builder(client_, TestName()); - Shape input_shape = MakeShapeWrapper({1, 1, 4, 4}); - Shape filter_shape = MakeShapeWrapper({1, 1, 2, 2}); + Shape input_shape = ShapeUtil::MakeShapeWithType({1, 1, 4, 4}); + Shape filter_shape = ShapeUtil::MakeShapeWithType({1, 1, 2, 2}); auto input = builder.Parameter(0, input_shape, "input"); auto filter = builder.Parameter(1, filter_shape, "filter"); auto conv = builder.Conv(input, filter, {1, 1}, Padding::kSame); @@ -222,8 +208,8 @@ class Convolve_1x1x4x4_1x1x3x3_Same : public ConvolutionTest { public: void RunTest() { ComputationBuilder builder(client_, TestName()); - Shape input_shape = MakeShapeWrapper({1, 1, 4, 4}); - Shape filter_shape = MakeShapeWrapper({1, 1, 3, 3}); + Shape input_shape = ShapeUtil::MakeShapeWithType({1, 1, 4, 4}); + Shape filter_shape = ShapeUtil::MakeShapeWithType({1, 1, 3, 3}); auto input = builder.Parameter(0, input_shape, "input"); auto filter = builder.Parameter(1, filter_shape, "filter"); auto conv = builder.Conv(input, filter, {1, 1}, Padding::kSame); @@ -280,8 +266,8 @@ class Convolve1D_1x2x5_1x2x2_WithRHSDilation : public ConvolutionTest { void RunTest() { ComputationBuilder builder(client_, TestName()); { - Shape input_shape = MakeShapeWrapper({1, 2, 5}); - Shape filter_shape = MakeShapeWrapper({1, 2, 2}); + Shape input_shape = ShapeUtil::MakeShapeWithType({1, 2, 5}); + Shape filter_shape = ShapeUtil::MakeShapeWithType({1, 2, 2}); auto input = builder.Parameter(0, input_shape, "input"); auto filter = builder.Parameter(1, filter_shape, "filter"); // Convolution dimensions are bf0_oi0->bo0. @@ -381,8 +367,8 @@ class Convolve1D_1x2x5_1x2x2_WithPadding : public ConvolutionTest { void RunTest() { ComputationBuilder builder(client_, TestName()); { - Shape input_shape = MakeShapeWrapper({1, 2, 5}); - Shape filter_shape = MakeShapeWrapper({1, 2, 2}); + Shape input_shape = ShapeUtil::MakeShapeWithType({1, 2, 5}); + Shape filter_shape = ShapeUtil::MakeShapeWithType({1, 2, 2}); auto input = builder.Parameter(0, input_shape, "input"); auto filter = builder.Parameter(1, filter_shape, "filter"); // Convolution dimensions are bf0_oi0->bo0. @@ -486,8 +472,8 @@ class Convolve2D_1x3x3x5_3x3x5x5_Valid : public ConvolutionTest { ComputationBuilder builder(client_, TestName()); std::vector input_dims = {1, 3, 3, 5}; std::vector filter_dims = {3, 3, 5, 3}; - Shape input_shape = MakeShapeWrapper(input_dims); - Shape filter_shape = MakeShapeWrapper(filter_dims); + Shape input_shape = ShapeUtil::MakeShapeWithType(input_dims); + Shape filter_shape = ShapeUtil::MakeShapeWithType(filter_dims); { auto input = builder.Parameter(0, input_shape, "input"); auto filter = builder.Parameter(1, filter_shape, "filter"); @@ -611,8 +597,8 @@ class Convolve1D1WindowTestBase input_feature}; std::vector filter_dims = {window_size, input_feature, output_feature}; - Shape input_shape = MakeShapeWrapper(input_dims); - Shape filter_shape = MakeShapeWrapper(filter_dims); + Shape input_shape = ShapeUtil::MakeShapeWithType(input_dims); + Shape filter_shape = ShapeUtil::MakeShapeWithType(filter_dims); { auto input = builder.Parameter(0, input_shape, "input"); auto filter = builder.Parameter(1, filter_shape, "filter"); @@ -737,7 +723,7 @@ INSTANTIATE_TEST_CASE_P( ); #endif -TEST_F(ConvolutionTest, Convolve_bf16_1x1x1x2_1x1x1x2_Valid) { +XLA_TEST_F(ConvolutionTest, Convolve_bf16_1x1x1x2_1x1x1x2_Valid) { ComputationBuilder builder(client_, TestName()); Shape input_shape = ShapeUtil::MakeShape(BF16, {1, 1, 1, 2}); Shape filter_shape = ShapeUtil::MakeShape(BF16, {1, 1, 1, 2}); diff --git a/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc b/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc index 032c06cd3c9f872f57674d3d7b5adc201c91ea77..3ab0ea4ad48c00724d48e7d285ec024e10d5db31 100644 --- a/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc +++ b/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc @@ -195,7 +195,7 @@ XLA_TEST_F(DeconstructTupleTest, DeconstructNestedTuple) { auto result_status = client_->DeconstructTuple(*global_data); EXPECT_FALSE(result_status.ok()); EXPECT_THAT(result_status.status().error_message(), - HasSubstr("deconstructing nested tuples not yet supported")); + HasSubstr("Deconstructing nested tuples is not implemented")); } } // namespace diff --git a/tensorflow/compiler/xla/tests/dot_operation_test.cc b/tensorflow/compiler/xla/tests/dot_operation_test.cc index 815962094ae476c4b15713ad2c1e4f1e0d140fd9..09b1dd283e4d026a2f0007240d88cd9ac38acb19 100644 --- a/tensorflow/compiler/xla/tests/dot_operation_test.cc +++ b/tensorflow/compiler/xla/tests/dot_operation_test.cc @@ -34,169 +34,194 @@ limitations under the License. namespace xla { namespace { -// TODO(b/34468543): use GUnit typed tests when we can do all tests on all -// backends. class DotOperationTest : public ClientLibraryTestBase { public: ErrorSpec error_spec_{0.0001, 1e-5}; - - protected: - template - void TestOneElementVectorDot(); - template - void TestVectorDot(); - template - void TestSquareMatrixDot(bool lhs_row_major = false, - bool rhs_row_major = false); - template - void TestNonsquareMatrixDot(bool lhs_row_major = false, - bool rhs_row_major = false); }; -XLA_TEST_F(DotOperationTest, ZeroElementVectorDotF32) { - ComputationBuilder builder(client_, TestName()); - auto lhs = builder.ConstantR1({}); - auto rhs = builder.ConstantR1({}); +#if defined(XLA_BACKEND_DOES_NOT_SUPPORT_FLOAT16) && \ + defined(XLA_BACKEND_DOES_NOT_SUPPORT_FLOAT64) +using TypesF16F32 = ::testing::Types; +using TypesF16F32F64 = ::testing::Types; +using TypesF16F32F64CF64 = ::testing::Types; +#elif !defined(XLA_BACKEND_DOES_NOT_SUPPORT_FLOAT16) && \ + !defined(XLA_BACKEND_DOES_NOT_SUPPORT_FLOAT64) +using TypesF16F32 = ::testing::Types; +using TypesF16F32F64 = ::testing::Types; +using TypesF16F32F64CF64 = + ::testing::Types; +#else +#error "Situation not handled yet" +#endif + +template +class DotOperationTest_F16F32F64CF64 : public DotOperationTest {}; +TYPED_TEST_CASE(DotOperationTest_F16F32F64CF64, TypesF16F32F64CF64); + +XLA_TYPED_TEST(DotOperationTest_F16F32F64CF64, ZeroElementVectorDot) { + using T = TypeParam; + ComputationBuilder builder(this->client_, this->TestName()); + + auto lhs = builder.ConstantR1({}); + auto rhs = builder.ConstantR1({}); auto result = builder.Dot(lhs, rhs); - ComputeAndCompareR0(&builder, 0.0, {}, error_spec_); + this->template ComputeAndCompareR0(&builder, static_cast(0.0), {}, + this->error_spec_); } -XLA_TEST_F(DotOperationTest, TrivialMatrixVectorDotF32) { - ComputationBuilder builder(client_, TestName()); - auto lhs = builder.ConstantR2({{3.0, 4.0}}); - auto rhs = builder.ConstantR1({3.0, 4.0}); - auto result = builder.Dot(lhs, rhs); +template +class DotOperationTest_F16F32F64 : public DotOperationTest {}; +TYPED_TEST_CASE(DotOperationTest_F16F32F64, TypesF16F32F64); - ComputeAndCompareR1(&builder, {25.0}, {}, error_spec_); -} - -template -void DotOperationTest::TestOneElementVectorDot() { - ComputationBuilder builder(client_, TestName()); - auto lhs = builder.ConstantR1({2.0}); - auto rhs = builder.ConstantR1({3.0}); +XLA_TYPED_TEST(DotOperationTest_F16F32F64, TrivialMatrixVectorDot) { + using T = TypeParam; + ComputationBuilder builder(this->client_, this->TestName()); + auto lhs = builder.ConstantR2FromArray2D({{3.0f, 4.0f}}); + auto rhs = builder.ConstantFromArray({3.0f, 4.0f}); auto result = builder.Dot(lhs, rhs); - ComputeAndCompareR0(&builder, 6.0, {}, error_spec_); + this->template ComputeAndCompareR1(&builder, {static_cast(25.0f)}, {}, + this->error_spec_); } -XLA_TEST_F(DotOperationTest, OneElementVectorDotF32) { - TestOneElementVectorDot(); -} +XLA_TYPED_TEST(DotOperationTest_F16F32F64, OneElementVectorDot) { + using T = TypeParam; + ComputationBuilder builder(this->client_, this->TestName()); + auto lhs = builder.ConstantR1({static_cast(2.0f)}); + auto rhs = builder.ConstantR1({static_cast(3.0f)}); + auto result = builder.Dot(lhs, rhs); -XLA_TEST_F(DotOperationTest, OneElementVectorDotF64) { - TestOneElementVectorDot(); + this->template ComputeAndCompareR0(&builder, static_cast(6.0f), {}, + this->error_spec_); } -template -void DotOperationTest::TestVectorDot() { - ComputationBuilder builder(client_, TestName()); - auto lhs = builder.ConstantR1({1.0, 2.5, 42.0}); - auto rhs = builder.ConstantR1({11.0, -1.0, 0.5}); +XLA_TYPED_TEST(DotOperationTest_F16F32F64, VectorDot) { + using T = TypeParam; + ComputationBuilder builder(this->client_, this->TestName()); + auto lhs = builder.ConstantFromArray({1.0f, 2.5f, 42.0f}); + auto rhs = builder.ConstantFromArray({11.0f, -1.0f, 0.5f}); auto result = builder.Dot(lhs, rhs); - ComputeAndCompareR0(&builder, 29.5, {}, error_spec_); + this->template ComputeAndCompareR0(&builder, static_cast(29.5f), {}, + this->error_spec_); } -XLA_TEST_F(DotOperationTest, VectorDotF32) { TestVectorDot(); } - -XLA_TEST_F(DotOperationTest, VectorDotF64) { TestVectorDot(); } - -namespace { - std::vector MinorToMajorForIsRowMajor(bool row_major) { return {row_major ? 1 : 0, row_major ? 0 : 1}; } -} // namespace - -XLA_TEST_F(DotOperationTest, Dot_0x2_2x0) { - ComputationBuilder builder(client_, TestName()); - auto lhs = builder.ConstantR2FromArray2D(Array2D(0, 2)); - auto rhs = builder.ConstantR2FromArray2D(Array2D(2, 0)); +XLA_TYPED_TEST(DotOperationTest_F16F32F64, Dot_0x2_2x0) { + using T = TypeParam; + ComputationBuilder builder(this->client_, this->TestName()); + auto lhs = builder.ConstantR2FromArray2D(Array2D(0, 2)); + auto rhs = builder.ConstantR2FromArray2D(Array2D(2, 0)); auto result = builder.Dot(lhs, rhs); - ComputeAndCompareR2(&builder, Array2D(0, 0), {}, error_spec_); + this->template ComputeAndCompareR2(&builder, Array2D(0, 0), {}, + this->error_spec_); } -XLA_TEST_F(DotOperationTest, Dot_0x2_2x3) { - ComputationBuilder builder(client_, TestName()); - auto lhs = builder.ConstantR2FromArray2D(Array2D(0, 2)); - auto rhs = builder.ConstantR2({{7.0, 8.0, 9.0}, {42.0, 77.0, 101.0}}); +XLA_TYPED_TEST(DotOperationTest_F16F32F64, Dot_0x2_2x3) { + using T = TypeParam; + ComputationBuilder builder(this->client_, this->TestName()); + auto lhs = builder.ConstantR2FromArray2D(Array2D(0, 2)); + auto rhs = builder.ConstantR2FromArray2D( + {{7.0f, 8.0f, 9.0f}, {42.0f, 77.0f, 101.0f}}); auto result = builder.Dot(lhs, rhs); - ComputeAndCompareR2(&builder, Array2D(0, 3), {}, error_spec_); + this->template ComputeAndCompareR2(&builder, Array2D(0, 3), {}, + this->error_spec_); } -XLA_TEST_F(DotOperationTest, Dot_3x2_2x0) { - ComputationBuilder builder(client_, TestName()); - auto lhs = - builder.ConstantR2({{7.0, 8.0}, {9.0, 42.0}, {77.0, 101.0}}); - auto rhs = builder.ConstantR2FromArray2D(Array2D(2, 0)); +XLA_TYPED_TEST(DotOperationTest_F16F32F64, Dot_3x2_2x0) { + using T = TypeParam; + ComputationBuilder builder(this->client_, this->TestName()); + auto lhs = builder.ConstantR2FromArray2D( + {{7.0f, 8.0f}, {9.0f, 42.0f}, {77.0f, 101.0f}}); + auto rhs = builder.ConstantR2FromArray2D(Array2D(2, 0)); auto result = builder.Dot(lhs, rhs); - ComputeAndCompareR2(&builder, Array2D(3, 0), {}, error_spec_); + this->template ComputeAndCompareR2(&builder, Array2D(3, 0), {}, + this->error_spec_); } -XLA_TEST_F(DotOperationTest, Dot_2x0_0x2) { - ComputationBuilder builder(client_, TestName()); - auto lhs = builder.ConstantR2FromArray2D(Array2D(2, 0)); - auto rhs = builder.ConstantR2FromArray2D(Array2D(0, 2)); +XLA_TYPED_TEST(DotOperationTest_F16F32F64, Dot_2x0_0x2) { + using T = TypeParam; + ComputationBuilder builder(this->client_, this->TestName()); + auto lhs = builder.ConstantR2FromArray2D(Array2D(2, 0)); + auto rhs = builder.ConstantR2FromArray2D(Array2D(0, 2)); auto result = builder.Dot(lhs, rhs); - ComputeAndCompareR2(&builder, Array2D(2, 2, 0.0f), {}, - error_spec_); + this->template ComputeAndCompareR2( + &builder, Array2D(2, 2, static_cast(0.0f)), {}, this->error_spec_); } -XLA_TEST_F(DotOperationTest, FusedDot) { - ComputationBuilder builder(client_, TestName()); - auto param0 = builder.Parameter(0, ShapeUtil::MakeShape(F32, {2, 4}), "arg0"); - auto param1 = builder.Parameter(1, ShapeUtil::MakeShape(F32, {4, 1}), "arg1"); +XLA_TYPED_TEST(DotOperationTest_F16F32F64, FusedDot) { + using T = TypeParam; + ComputationBuilder builder(this->client_, this->TestName()); + auto param0 = + builder.Parameter(0, ShapeUtil::MakeShapeWithType({2, 4}), "arg0"); + auto param1 = + builder.Parameter(1, ShapeUtil::MakeShapeWithType({4, 1}), "arg1"); auto exp0 = builder.Exp(param0); auto result = builder.Dot(exp0, param1); - auto lhs_handle = client_ - ->TransferToServer(*Literal::CreateR2( - {{1.0, 2.0, 3.0, 4.0}, {-1.0, -2.0, -3.0, -4.0}})) - .ConsumeValueOrDie(); - auto rhs_handle = client_ - ->TransferToServer(*Literal::CreateR2( - {{1.0}, {2.0}, {3.0}, {4.0}})) - .ConsumeValueOrDie(); - - ComputeAndCompareR2( - &builder, Array2D({{296.14560492846033}, {0.8611737683031964}}), - {lhs_handle.get(), rhs_handle.get()}, error_spec_); -} - -template -void DotOperationTest::TestSquareMatrixDot(bool lhs_row_major, - bool rhs_row_major) { auto lhs_handle = - client_ - ->TransferToServer(*Literal::CreateR2WithLayout( - {{1.0, 2.0}, {3.0, -4.0}}, - LayoutUtil::MakeLayout(MinorToMajorForIsRowMajor(lhs_row_major)))) - .ConsumeValueOrDie(); - auto rhs_handle = - client_ - ->TransferToServer(*Literal::CreateR2WithLayout( - {{1.0, 6.0}, {7.0, -4.0}}, - LayoutUtil::MakeLayout(MinorToMajorForIsRowMajor(rhs_row_major)))) + this->client_ + ->TransferToServer(*Literal::CreateR2FromArray2D( + {{1.0f, 2.0f, 3.0f, 4.0f}, {-1.0f, -2.0f, -3.0f, -4.0f}})) .ConsumeValueOrDie(); + auto rhs_handle = this->client_ + ->TransferToServer(*Literal::CreateR2FromArray2D( + {{1.0f}, {2.0f}, {3.0f}, {4.0f}})) + .ConsumeValueOrDie(); - ComputationBuilder builder(client_, TestName()); - auto prim_type = primitive_util::NativeToPrimitiveType(); - auto result = builder.Dot( - builder.Parameter(0, ShapeUtil::MakeShape(prim_type, {2, 2}), "lhs"), - builder.Parameter(1, ShapeUtil::MakeShape(prim_type, {2, 2}), "rhs")); + if (std::is_same::value) { + this->error_spec_ = ErrorSpec{0.0001, 1e-3}; + } - Array2D expected({{15.0, -2.0}, {-25.0, 34.0}}); - ComputeAndCompareR2( - &builder, expected, {lhs_handle.get(), rhs_handle.get()}, error_spec_); + this->template ComputeAndCompareR2( + &builder, Array2D({{296.14560492846033f}, {0.8611737683031964f}}), + {lhs_handle.get(), rhs_handle.get()}, this->error_spec_); } +template +class SquareMatrixDot : public DotOperationTest { + public: + void TestImpl(bool lhs_row_major, bool rhs_row_major) { + auto lhs_handle = + client_ + ->TransferToServer(*Literal::CreateFromArrayWithLayout( + {{1.0f, 2.0f}, {3.0f, -4.0f}}, + LayoutUtil::MakeLayout( + MinorToMajorForIsRowMajor(lhs_row_major)))) + .ConsumeValueOrDie(); + auto rhs_handle = + client_ + ->TransferToServer(*Literal::CreateFromArrayWithLayout( + {{1.0f, 6.0f}, {7.0f, -4.0f}}, + LayoutUtil::MakeLayout( + MinorToMajorForIsRowMajor(rhs_row_major)))) + .ConsumeValueOrDie(); + ComputationBuilder builder(client_, TestName()); + auto prim_type = primitive_util::NativeToPrimitiveType(); + auto result = builder.Dot( + builder.Parameter(0, ShapeUtil::MakeShape(prim_type, {2, 2}), "lhs"), + builder.Parameter(1, ShapeUtil::MakeShape(prim_type, {2, 2}), "rhs")); + + Array2D expected({{15.0f, -2.0f}, {-25.0f, 34.0f}}); + ComputeAndCompareR2(&builder, expected, + {lhs_handle.get(), rhs_handle.get()}, error_spec_); + } +}; + +TYPED_TEST_CASE(SquareMatrixDot, TypesF16F32F64CF64); +XLA_TYPED_TEST(SquareMatrixDot, TypesFF) { this->TestImpl(false, false); } +XLA_TYPED_TEST(SquareMatrixDot, TypesFT) { this->TestImpl(false, true); } +XLA_TYPED_TEST(SquareMatrixDot, TypesTF) { this->TestImpl(true, false); } +XLA_TYPED_TEST(SquareMatrixDot, TypesTT) { this->TestImpl(true, true); } + struct DotTestParam { int m; int k; @@ -302,14 +327,13 @@ void ParametricDotTest::TestImpl() { if (param.has_addend) { args.push_back(addend_handle.get()); } - - ComputeAndCompareR2(&builder, *expected, args, ErrorSpec(0.3, 3e-3)); + ErrorSpec error_spec(0.3, 3e-3); + if (std::is_same::value) { + error_spec = ErrorSpec(0.3, 5e-3); + } + ComputeAndCompareR2(&builder, *expected, args, error_spec); } -XLA_TEST_P(ParametricDotTest, TestF32) { TestImpl(); } - -XLA_TEST_P(ParametricDotTest, TestF64) { TestImpl(); } - std::vector CreateDotTestParameters() { std::vector params; @@ -331,6 +355,12 @@ std::vector CreateDotTestParameters() { return params; } +#ifndef XLA_BACKEND_DOES_NOT_SUPPORT_FLOAT16 +XLA_TEST_P(ParametricDotTest, TestF16) { TestImpl(); } +#endif +XLA_TEST_P(ParametricDotTest, TestF32) { TestImpl(); } +XLA_TEST_P(ParametricDotTest, TestF64) { TestImpl(); } + INSTANTIATE_TEST_CASE_P(DotTests, ParametricDotTest, ::testing::ValuesIn(CreateDotTestParameters()), PrintDotTestParam); @@ -343,14 +373,6 @@ class ParametricDotTestWithoutLayoutAssignment : public ParametricDotTest { } }; -XLA_TEST_P(ParametricDotTestWithoutLayoutAssignment, TestF32) { - TestImpl(); -} - -XLA_TEST_P(ParametricDotTestWithoutLayoutAssignment, TestF64) { - TestImpl(); -} - std::vector CreateNoLayoutAssignmentDotTestParameters() { std::vector params; @@ -407,110 +429,60 @@ std::vector CreateNoLayoutAssignmentDotTestParameters() { return params; } -INSTANTIATE_TEST_CASE_P( - DotTests, ParametricDotTestWithoutLayoutAssignment, - ::testing::ValuesIn(CreateNoLayoutAssignmentDotTestParameters()), - PrintDotTestParam); - -XLA_TEST_F(DotOperationTest, SquareMatrixDotF32MinorToMajorFF) { - TestSquareMatrixDot(false, false); -} - -XLA_TEST_F(DotOperationTest, SquareMatrixDotF32MinorToMajorFT) { - TestSquareMatrixDot(false, true); -} - -XLA_TEST_F(DotOperationTest, SquareMatrixDotF32MinorToMajorTF) { - TestSquareMatrixDot(true, false); -} - -XLA_TEST_F(DotOperationTest, SquareMatrixDotF32MinorToMajorTT) { - TestSquareMatrixDot(true, true); +#ifndef XLA_BACKEND_DOES_NOT_SUPPORT_FLOAT16 +XLA_TEST_P(ParametricDotTestWithoutLayoutAssignment, TestF16) { + TestImpl(); } - -XLA_TEST_F(DotOperationTest, SquareMatrixDotC64MinorToMajorFF) { - TestSquareMatrixDot(false, false); -} - -XLA_TEST_F(DotOperationTest, SquareMatrixDotC64MinorToMajorFT) { - TestSquareMatrixDot(false, true); -} - -XLA_TEST_F(DotOperationTest, SquareMatrixDotC64MinorToMajorTF) { - TestSquareMatrixDot(true, false); -} - -XLA_TEST_F(DotOperationTest, SquareMatrixDotC64MinorToMajorTT) { - TestSquareMatrixDot(true, true); -} - -XLA_TEST_F(DotOperationTest, SquareMatrixDotF64) { - TestSquareMatrixDot(); -} - -template -void DotOperationTest::TestNonsquareMatrixDot(bool lhs_row_major, - bool rhs_row_major) { - auto lhs_handle = - client_ - ->TransferToServer(*Literal::CreateR2WithLayout( - {{1.0, 2.0, 3.0}, {3.0, -4.0, -1.0}}, - LayoutUtil::MakeLayout(MinorToMajorForIsRowMajor(lhs_row_major)))) - .ConsumeValueOrDie(); - auto rhs_handle = - client_ - ->TransferToServer(*Literal::CreateR2WithLayout( - {{1.0, 6.0}, {2.0, 3.0}, {7.0, -4.0}}, - LayoutUtil::MakeLayout(MinorToMajorForIsRowMajor(rhs_row_major)))) - .ConsumeValueOrDie(); - - ComputationBuilder builder(client_, TestName()); - auto prim_type = primitive_util::NativeToPrimitiveType(); - auto result = builder.Dot( - builder.Parameter(0, ShapeUtil::MakeShape(prim_type, {2, 3}), "lhs"), - builder.Parameter(1, ShapeUtil::MakeShape(prim_type, {3, 2}), "rhs")); - - Array2D expected({{26.0, 0.0}, {-12.0, 10.0}}); - - ComputeAndCompareR2( - &builder, expected, {lhs_handle.get(), rhs_handle.get()}, error_spec_); -} - -XLA_TEST_F(DotOperationTest, NonsquareMatrixDotF32MajorToMinorFF) { - TestNonsquareMatrixDot(false, false); -} - -XLA_TEST_F(DotOperationTest, NonsquareMatrixDotF32MajorToMinorFT) { - TestNonsquareMatrixDot(false, true); -} - -XLA_TEST_F(DotOperationTest, NonsquareMatrixDotF32MajorToMinorTF) { - TestNonsquareMatrixDot(true, false); -} - -XLA_TEST_F(DotOperationTest, NonsquareMatrixDotF32MajorToMinorTT) { - TestNonsquareMatrixDot(true, true); -} - -XLA_TEST_F(DotOperationTest, NonsquareMatrixDotF64) { - TestNonsquareMatrixDot(); +#endif +XLA_TEST_P(ParametricDotTestWithoutLayoutAssignment, TestF32) { + TestImpl(); } - -XLA_TEST_F(DotOperationTest, NonsquareMatrixDotC64MajorToMinorFF) { - TestNonsquareMatrixDot(false, false); +XLA_TEST_P(ParametricDotTestWithoutLayoutAssignment, TestF64) { + TestImpl(); } -XLA_TEST_F(DotOperationTest, NonsquareMatrixDotC64MajorToMinorFT) { - TestNonsquareMatrixDot(false, true); -} +INSTANTIATE_TEST_CASE_P( + DotTests, ParametricDotTestWithoutLayoutAssignment, + ::testing::ValuesIn(CreateNoLayoutAssignmentDotTestParameters()), + PrintDotTestParam); -XLA_TEST_F(DotOperationTest, NonsquareMatrixDotC64MajorToMinorTF) { - TestNonsquareMatrixDot(true, false); -} +template +class NonsquareMatrixDot : public DotOperationTest { + public: + void TestImpl(bool lhs_row_major, bool rhs_row_major) { + auto lhs_handle = + client_ + ->TransferToServer(*Literal::CreateFromArrayWithLayout( + {{1.0f, 2.0f, 3.0f}, {3.0f, -4.0f, -1.0f}}, + LayoutUtil::MakeLayout( + MinorToMajorForIsRowMajor(lhs_row_major)))) + .ConsumeValueOrDie(); + auto rhs_handle = + client_ + ->TransferToServer(*Literal::CreateFromArrayWithLayout( + {{1.0f, 6.0f}, {2.0f, 3.0f}, {7.0f, -4.0f}}, + LayoutUtil::MakeLayout( + MinorToMajorForIsRowMajor(rhs_row_major)))) + .ConsumeValueOrDie(); + + ComputationBuilder builder(client_, TestName()); + auto prim_type = primitive_util::NativeToPrimitiveType(); + auto result = builder.Dot( + builder.Parameter(0, ShapeUtil::MakeShape(prim_type, {2, 3}), "lhs"), + builder.Parameter(1, ShapeUtil::MakeShape(prim_type, {3, 2}), "rhs")); + + Array2D expected({{26.0f, 0.0f}, {-12.0f, 10.0f}}); + + ComputeAndCompareR2(&builder, expected, + {lhs_handle.get(), rhs_handle.get()}, error_spec_); + } +}; -XLA_TEST_F(DotOperationTest, NonsquareMatrixDotC64MajorToMinorTT) { - TestNonsquareMatrixDot(true, true); -} +TYPED_TEST_CASE(NonsquareMatrixDot, TypesF16F32F64CF64); +XLA_TYPED_TEST(NonsquareMatrixDot, TestFF) { this->TestImpl(false, false); } +XLA_TYPED_TEST(NonsquareMatrixDot, TestFT) { this->TestImpl(false, true); } +XLA_TYPED_TEST(NonsquareMatrixDot, TestTF) { this->TestImpl(true, false); } +XLA_TYPED_TEST(NonsquareMatrixDot, TestTT) { this->TestImpl(true, true); } XLA_TEST_F(DotOperationTest, MatrixVectorC64) { auto lhs_handle = @@ -537,25 +509,35 @@ XLA_TEST_F(DotOperationTest, MatrixVectorC64) { &builder, expected, {lhs_handle.get(), rhs_handle.get()}, error_spec_); } -XLA_TEST_F(DotOperationTest, ConcurrentMatMul) { - ComputationBuilder builder(client_, TestName()); - auto matrix1 = builder.ConstantR2({{1.0, 2.0}, {3.0, 4.0}}); - auto matrix2 = builder.ConstantR2({{5.0, 6.0}, {7.0, 8.0}}); +XLA_TYPED_TEST(DotOperationTest_F16F32F64, ConcurrentMatMult) { + using T = TypeParam; + + ComputationBuilder builder(this->client_, this->TestName()); + auto matrix1 = builder.ConstantR2FromArray2D({{1.0f, 2.0f}, {3.0f, 4.0f}}); + auto matrix2 = builder.ConstantR2FromArray2D({{5.0f, 6.0f}, {7.0f, 8.0f}}); auto matrix12 = builder.Dot(matrix1, matrix2); auto matrix21 = builder.Dot(matrix2, matrix1); builder.Add(matrix12, matrix21); - Array2D expected({{42.0, 56.0}, {74.0, 96.0}}); - ComputeAndCompareR2(&builder, expected, {}, error_spec_); + Array2D expected({{42.0f, 56.0f}, {74.0f, 96.0f}}); + this->template ComputeAndCompareR2(&builder, expected, {}, + this->error_spec_); } +template +class DotOperationTestForBatchMatMul : public DotOperationTest {}; +TYPED_TEST_CASE(DotOperationTestForBatchMatMul, TypesF16F32F64); + // Regression test for b/32055648. The root of the graph is a kFusion of 4 // bitcasts. Although bitcasts don't map to thunks, the root should still be // sync-dependent on bitcasts' operands. -XLA_TEST_F(DotOperationTest, BatchMatMul) { - ComputationBuilder builder(client_, TestName()); - auto x = builder.Parameter(0, ShapeUtil::MakeShape(F32, {2, 2, 2, 2}), "x"); - auto y = builder.Parameter(1, ShapeUtil::MakeShape(F32, {2, 2, 2, 2}), "y"); +XLA_TYPED_TEST(DotOperationTestForBatchMatMul, Types) { + using T = TypeParam; + ComputationBuilder builder(this->client_, this->TestName()); + auto x = + builder.Parameter(0, ShapeUtil::MakeShapeWithType({2, 2, 2, 2}), "x"); + auto y = + builder.Parameter(1, ShapeUtil::MakeShapeWithType({2, 2, 2, 2}), "y"); auto x_flat = builder.Reshape(x, {0, 1, 2, 3}, {4, 2, 2}); auto y_flat = builder.Reshape(y, {0, 1, 2, 3}, {4, 2, 2}); @@ -576,29 +558,42 @@ XLA_TEST_F(DotOperationTest, BatchMatMul) { auto out_flat = builder.ConcatInDim(out_slices, 0); builder.Reshape(out_flat, {0, 1, 2}, {2, 2, 2, 2}); - auto x_data = client_ - ->TransferToServer(*Literal::CreateR4( - {{{{1000, 100}, {10, 1}}, {{2000, 200}, {20, 2}}}, - {{{3000, 300}, {30, 3}}, {{4000, 400}, {40, 4}}}})) - .ConsumeValueOrDie(); - auto y_data = client_ - ->TransferToServer(*Literal::CreateR4( - {{{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}, - {{{11, 22}, {33, 44}}, {{55, 66}, {77, 88}}}})) + auto x_data = this->client_ + ->TransferToServer(*Literal::CreateR4FromArray4D( + {{{{1000.0f, 100.0f}, {10.0f, 1.0f}}, + {{2000.0f, 200.0f}, {20.0f, 2.0f}}}, + {{{3000.0f, 300.0f}, {30.0f, 3.0f}}, + {{4000.0f, 400.0f}, {40.0f, 4.0f}}}})) .ConsumeValueOrDie(); + auto y_data = + this->client_ + ->TransferToServer(*Literal::CreateR4FromArray4D( + {{{{1.0f, 2.0f}, {3.0f, 4.0f}}, {{5.0f, 6.0f}, {7.0f, 8.0f}}}, + {{{11.0f, 22.0f}, {33.0f, 44.0f}}, + {{55.0f, 66.0f}, {77.0f, 88.0f}}}})) + .ConsumeValueOrDie(); - ComputeAndCompareR4( + if (std::is_same::value) { + this->error_spec_ = ErrorSpec{0.0001, 1e-3}; + } + this->template ComputeAndCompareR4( &builder, /*expected=*/ - {{{{1300, 2400}, {13, 24}}, {{11400, 13600}, {114, 136}}}, - {{{42900, 79200}, {429, 792}}, {{250800, 299200}, {2508, 2992}}}}, - {x_data.get(), y_data.get()}, error_spec_); + {{{{1300.0f, 2400.0f}, {13.0f, 24.0f}}, + {{11400.0f, 13600.0f}, {114.0f, 136.0f}}}, + {{{42900.0f, 79200.0f}, {429.0f, 792.0f}}, + {{250800.0f, 299200.0f}, {2508.0f, 2992.0f}}}}, + {x_data.get(), y_data.get()}, this->error_spec_); } -XLA_TEST_F(DotOperationTest, GeneralMatMul) { - ComputationBuilder builder(client_, TestName()); - auto x = builder.Parameter(0, ShapeUtil::MakeShape(F32, {2, 2, 2}), "x"); - auto y = builder.Parameter(1, ShapeUtil::MakeShape(F32, {2, 2, 2}), "y"); +XLA_TYPED_TEST(DotOperationTest_F16F32F64, GeneralMatMul) { + using T = TypeParam; + + ComputationBuilder builder(this->client_, this->TestName()); + auto x = + builder.Parameter(0, ShapeUtil::MakeShapeWithType({2, 2, 2}), "x"); + auto y = + builder.Parameter(1, ShapeUtil::MakeShapeWithType({2, 2, 2}), "y"); DotDimensionNumbers dnums; dnums.add_lhs_contracting_dimensions(2); @@ -608,31 +603,34 @@ XLA_TEST_F(DotOperationTest, GeneralMatMul) { auto out = builder.DotGeneral(x, y, dnums); - auto x_data = client_ - ->TransferToServer(*Literal::CreateR3( - {{{1.0, 2.0}, {3.0, 4.0}}, {{5.0, 6.0}, {7.0, 8.0}}})) - .ConsumeValueOrDie(); + auto x_data = + this->client_ + ->TransferToServer(*Literal::CreateR3FromArray3D( + {{{1.0f, 2.0f}, {3.0f, 4.0f}}, {{5.0f, 6.0f}, {7.0f, 8.0f}}})) + .ConsumeValueOrDie(); - auto y_data = client_ - ->TransferToServer(*Literal::CreateR3( - {{{1.0, 0.0}, {0.0, 1.0}}, {{1.0, 0.0}, {0.0, 1.0}}})) - .ConsumeValueOrDie(); + auto y_data = + this->client_ + ->TransferToServer(*Literal::CreateR3FromArray3D( + {{{1.0f, 0.0f}, {0.0f, 1.0f}}, {{1.0f, 0.0f}, {0.0f, 1.0f}}})) + .ConsumeValueOrDie(); - ComputeAndCompareR3( + this->template ComputeAndCompareR3( &builder, /*expected=*/ - {{{1.0, 2.0}, {3.0, 4.0}}, {{5.0, 6.0}, {7.0, 8.0}}}, - {x_data.get(), y_data.get()}, error_spec_); + {{{1.0f, 2.0f}, {3.0f, 4.0f}}, {{5.0f, 6.0f}, {7.0f, 8.0f}}}, + {x_data.get(), y_data.get()}, this->error_spec_); } -TEST_F(DotOperationTest, TransposeFolding) { +XLA_TYPED_TEST(DotOperationTest_F16F32F64, TransposeFolding) { + using T = TypeParam; for (bool transpose_lhs : {false, true}) { for (bool transpose_rhs : {false, true}) { for (bool row_major : {false, true}) { - std::unique_ptr> lhs( - new Array2D({{1.0, 2.0, 3.0}, {3.0, -4.0, -1.0}})); - std::unique_ptr> rhs( - new Array2D({{1.0, 6.0}, {2.0, 3.0}, {7.0, -4.0}})); + std::unique_ptr> lhs( + new Array2D({{1.0f, 2.0f, 3.0f}, {3.0f, -4.0f, -1.0f}})); + std::unique_ptr> rhs( + new Array2D({{1.0f, 6.0f}, {2.0f, 3.0f}, {7.0f, -4.0f}})); if (transpose_lhs) { lhs = ReferenceUtil::TransposeArray2D(*lhs); @@ -641,22 +639,20 @@ TEST_F(DotOperationTest, TransposeFolding) { rhs = ReferenceUtil::TransposeArray2D(*rhs); } auto lhs_handle = - client_ - ->TransferToServer( - *Literal::CreateR2FromArray2DWithLayout( - *lhs, LayoutUtil::MakeLayout( - MinorToMajorForIsRowMajor(row_major)))) + this->client_ + ->TransferToServer(*Literal::CreateR2FromArray2DWithLayout( + *lhs, LayoutUtil::MakeLayout( + MinorToMajorForIsRowMajor(row_major)))) .ConsumeValueOrDie(); auto rhs_handle = - client_ - ->TransferToServer( - *Literal::CreateR2FromArray2DWithLayout( - *rhs, LayoutUtil::MakeLayout( - MinorToMajorForIsRowMajor(row_major)))) + this->client_ + ->TransferToServer(*Literal::CreateR2FromArray2DWithLayout( + *rhs, LayoutUtil::MakeLayout( + MinorToMajorForIsRowMajor(row_major)))) .ConsumeValueOrDie(); - ComputationBuilder builder(client_, TestName()); - auto prim_type = primitive_util::NativeToPrimitiveType(); + ComputationBuilder builder(this->client_, this->TestName()); + auto prim_type = primitive_util::NativeToPrimitiveType(); auto lhs_arg = builder.Parameter( 0, ShapeUtil::MakeShape(prim_type, {lhs->height(), lhs->width()}), "lhs"); @@ -671,24 +667,27 @@ TEST_F(DotOperationTest, TransposeFolding) { } auto result = builder.Dot(lhs_arg, rhs_arg); - Array2D expected({{26.0, 0.0}, {-12.0, 10.0}}); + Array2D expected({{26.0f, 0.0f}, {-12.0f, 10.0f}}); VLOG(1) << "TestTransposeFolding " << transpose_lhs << " " << transpose_rhs << " " << row_major; - ComputeAndCompareR2(&builder, expected, - {lhs_handle.get(), rhs_handle.get()}, - error_spec_); + this->template ComputeAndCompareR2( + &builder, expected, {lhs_handle.get(), rhs_handle.get()}, + this->error_spec_); } } } } -TEST_F(DotOperationTest, DotOfConcatOptimizationWithConstLHS) { - auto prim_type = primitive_util::NativeToPrimitiveType(); +XLA_TYPED_TEST(DotOperationTest_F16F32F64, + DotOfConcatOptimizationWithConstLHS) { + using T = TypeParam; + auto prim_type = primitive_util::NativeToPrimitiveType(); - std::unique_ptr> constant_lhs_array(new Array2D( - {{1.0, 2.0, 3.0, 4.0, 5.0, 6.0}, {6.0, 5.0, 4.0, 3.0, 2.0, 1.0}})); + std::unique_ptr> constant_lhs_array( + new Array2D({{1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f}, + {6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}})); - ComputationBuilder builder(client_, TestName()); + ComputationBuilder builder(this->client_, this->TestName()); auto lhs_constant = builder.ConstantR2FromArray2D(*constant_lhs_array); auto rhs_arg_0 = builder.Parameter(0, ShapeUtil::MakeShape(prim_type, {2, 2}), "rhs_arg_0"); @@ -699,78 +698,80 @@ TEST_F(DotOperationTest, DotOfConcatOptimizationWithConstLHS) { auto result = builder.Dot( lhs_constant, builder.ConcatInDim({rhs_arg_0, rhs_arg_1, rhs_arg_2}, 0)); - std::unique_ptr> arg_0_value_array( - new Array2D({{1.0, 2.0}, {3.0, 4.0}})); - std::unique_ptr> arg_1_value_array( - new Array2D({{1.0, 2.0}, {3.0, 4.0}, {5.0, 6.0}})); - std::unique_ptr> arg_2_value_array( - new Array2D({{1.0, 2.0}})); + std::unique_ptr> arg_0_value_array( + new Array2D({{1.0f, 2.0f}, {3.0f, 4.0f}})); + std::unique_ptr> arg_1_value_array( + new Array2D({{1.0f, 2.0f}, {3.0f, 4.0f}, {5.0f, 6.0f}})); + std::unique_ptr> arg_2_value_array(new Array2D({{1.0f, 2.0f}})); TF_ASSERT_OK_AND_ASSIGN( auto arg_0_value, - client_->TransferToServer( - *Literal::CreateR2FromArray2D(*arg_0_value_array))); + this->client_->TransferToServer( + *Literal::CreateR2FromArray2D(*arg_0_value_array))); TF_ASSERT_OK_AND_ASSIGN( auto arg_1_value, - client_->TransferToServer( - *Literal::CreateR2FromArray2D(*arg_1_value_array))); + this->client_->TransferToServer( + *Literal::CreateR2FromArray2D(*arg_1_value_array))); TF_ASSERT_OK_AND_ASSIGN( auto arg_2_value, - client_->TransferToServer( - *Literal::CreateR2FromArray2D(*arg_2_value_array))); + this->client_->TransferToServer( + *Literal::CreateR2FromArray2D(*arg_2_value_array))); - Array2D expected({{53.0, 74.0}, {45.0, 66.0}}); - ComputeAndCompareR2( + Array2D expected({{53.0f, 74.0f}, {45.0f, 66.0f}}); + this->template ComputeAndCompareR2( &builder, expected, - {arg_0_value.get(), arg_1_value.get(), arg_2_value.get()}, error_spec_); -} - -TEST_F(DotOperationTest, DotOfConcatOptimizationWithConstRHS) { - auto prim_type = primitive_util::NativeToPrimitiveType(); - - std::unique_ptr> constant_rhs_array( - new Array2D({{1.0, 2.0}, - {3.0, 4.0}, - {5.0, 6.0}, - {6.0, 5.0}, - {4.0, 3.0}, - {2.0, 1.0}})); - - ComputationBuilder builder(client_, TestName()); + {arg_0_value.get(), arg_1_value.get(), arg_2_value.get()}, + this->error_spec_); +} + +XLA_TYPED_TEST(DotOperationTest_F16F32F64, + DotOfConcatOptimizationWithConstRHS) { + using T = TypeParam; + std::unique_ptr> constant_rhs_array( + new Array2D({{1.0f, 2.0f}, + {3.0f, 4.0f}, + {5.0f, 6.0f}, + {6.0f, 5.0f}, + {4.0f, 3.0f}, + {2.0f, 1.0f}})); + + ComputationBuilder builder(this->client_, this->TestName()); auto rhs_constant = builder.ConstantR2FromArray2D(*constant_rhs_array); - auto lhs_arg_0 = builder.Parameter(0, ShapeUtil::MakeShape(prim_type, {2, 2}), + auto lhs_arg_0 = builder.Parameter(0, ShapeUtil::MakeShapeWithType({2, 2}), "lhs_arg_0"); - auto lhs_arg_1 = builder.Parameter(1, ShapeUtil::MakeShape(prim_type, {2, 3}), + auto lhs_arg_1 = builder.Parameter(1, ShapeUtil::MakeShapeWithType({2, 3}), "lhs_arg_1"); - auto lhs_arg_2 = builder.Parameter(2, ShapeUtil::MakeShape(prim_type, {2, 1}), + auto lhs_arg_2 = builder.Parameter(2, ShapeUtil::MakeShapeWithType({2, 1}), "lhs_arg_2"); auto result = builder.Dot( builder.ConcatInDim({lhs_arg_0, lhs_arg_1, lhs_arg_2}, 1), rhs_constant); - std::unique_ptr> arg_0_value_array( - new Array2D({{1.0, 2.0}, {3.0, 4.0}})); - std::unique_ptr> arg_1_value_array( - new Array2D({{1.0, 2.0, 3.0}, {4.0, 5.0, 6.0}})); - std::unique_ptr> arg_2_value_array( - new Array2D({{1.0}, {2.0}})); + std::unique_ptr> arg_0_value_array( + new Array2D({{1.0f, 2.0f}, {3.0f, 4.0f}})); + std::unique_ptr> arg_1_value_array( + new Array2D({{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}})); + std::unique_ptr> arg_2_value_array( + new Array2D({{1.0f}, {2.0f}})); TF_ASSERT_OK_AND_ASSIGN( auto arg_0_value, - client_->TransferToServer( - *Literal::CreateR2FromArray2D(*arg_0_value_array))); + this->client_->TransferToServer( + *Literal::CreateR2FromArray2D(*arg_0_value_array))); TF_ASSERT_OK_AND_ASSIGN( auto arg_1_value, - client_->TransferToServer( - *Literal::CreateR2FromArray2D(*arg_1_value_array))); + this->client_->TransferToServer( + *Literal::CreateR2FromArray2D(*arg_1_value_array))); TF_ASSERT_OK_AND_ASSIGN( auto arg_2_value, - client_->TransferToServer( - *Literal::CreateR2FromArray2D(*arg_2_value_array))); + this->client_->TransferToServer( + *Literal::CreateR2FromArray2D(*arg_2_value_array))); - Array2D expected({{38.0, 36.0}, {93.0, 91.0}}); - ComputeAndCompareR2( + Array2D expected({{38.0f, 36.0f}, {93.0f, 91.0f}}); + this->template ComputeAndCompareR2( &builder, expected, - {arg_0_value.get(), arg_1_value.get(), arg_2_value.get()}, error_spec_); + {arg_0_value.get(), arg_1_value.get(), arg_2_value.get()}, + this->error_spec_); } + } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/tests/dynamic_ops_test.cc b/tensorflow/compiler/xla/tests/dynamic_ops_test.cc index 877dc7db0eec229a7119b3627f177a33ed0d971b..4f354e6aefe70a51c09be1c0ca151af2bb9f0a2c 100644 --- a/tensorflow/compiler/xla/tests/dynamic_ops_test.cc +++ b/tensorflow/compiler/xla/tests/dynamic_ops_test.cc @@ -206,19 +206,19 @@ XLA_TEST_F(DynamicSliceTest, Int32R1BF16) { TestR1(); } XLA_TEST_F(DynamicSliceTest, Int32R1) { TestR1(); } XLA_TEST_F(DynamicSliceTest, Int32R1Wrap) { TestR1Wrap(); } XLA_TEST_F(DynamicSliceTest, Int64R1) { TestR1(); } -XLA_TEST_F(DynamicSliceTest, UInt64R1) { TestR1(); } +XLA_TEST_F(DynamicSliceTest, UInt64R1) { TestR1(); } XLA_TEST_F(DynamicSliceTest, Int32R2BF16) { TestR2(); } XLA_TEST_F(DynamicSliceTest, Int32R2) { TestR2(); } XLA_TEST_F(DynamicSliceTest, Int32R2Wrap) { TestR2Wrap(); } -XLA_TEST_F(DynamicSliceTest, Int64R2) { TestR2(); } +XLA_TEST_F(DynamicSliceTest, Int64R2) { TestR2(); } XLA_TEST_F(DynamicSliceTest, UInt64R2) { TestR2(); } XLA_TEST_F(DynamicSliceTest, Int32R3BF16) { TestR3(); } XLA_TEST_F(DynamicSliceTest, Int32R3) { TestR3(); } XLA_TEST_F(DynamicSliceTest, Int32R3Wrap) { TestR3Wrap(); } XLA_TEST_F(DynamicSliceTest, Int64R3) { TestR3(); } -XLA_TEST_F(DynamicSliceTest, UInt64R3) { TestR3(); } +XLA_TEST_F(DynamicSliceTest, UInt64R3) { TestR3(); } XLA_TEST_F(DynamicSliceTest, Int32R1Pred) { // Slice at dimension start. @@ -506,7 +506,7 @@ XLA_TEST_F(DynamicUpdateSliceTest, DISABLED_ON_CPU_PARALLEL(Int32R1BF16)) { } XLA_TEST_F(DynamicUpdateSliceTest, Int32R1) { TestR1(); } XLA_TEST_F(DynamicUpdateSliceTest, Int64R1) { TestR1(); } -XLA_TEST_F(DynamicUpdateSliceTest, UInt64R1) { TestR1(); } +XLA_TEST_F(DynamicUpdateSliceTest, UInt64R1) { TestR1(); } // TODO(b/71820067): The CPU parallel backend failed for this on 2018-01-10. XLA_TEST_F(DynamicUpdateSliceTest, DISABLED_ON_CPU_PARALLEL(Int32R2BF16)) { diff --git a/tensorflow/compiler/xla/tests/gather_operation_test.cc b/tensorflow/compiler/xla/tests/gather_operation_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..4e2f19ade10794fd159ff89807d6ab34630dbb43 --- /dev/null +++ b/tensorflow/compiler/xla/tests/gather_operation_test.cc @@ -0,0 +1,263 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/status_macros.h" +#include "tensorflow/compiler/xla/test.h" +#include "tensorflow/compiler/xla/tests/hlo_test_base.h" +#include "tensorflow/compiler/xla/tests/test_macros.h" +#include "tensorflow/compiler/xla/tools/parser/hlo_parser.h" + +// NB! TODO(b/74360564): These tests do not test out of bounds behavior since +// that hasn't been specced yet. + +namespace xla { +namespace { + +using tensorflow::gtl::nullopt; + +class GatherOperationTest : public HloTestBase { + protected: + void RunTest(const string& hlo_text, Literal* operand, + Literal* gather_indices) { + HloModuleConfig config; + config.set_debug_options(GetDebugOptionsForTest()); + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module, + tools::Parse(hlo_text, config)); + EXPECT_TRUE( + RunAndCompare(std::move(module), {operand, gather_indices}, nullopt)); + } +}; + +XLA_TEST_F(GatherOperationTest, TensorFlowGatherV1) { + const string hlo_text = R"( +HloModule TensorFlowGatherV1 + +ENTRY main { + operand = s32[3,3] parameter(0) + indices = s32[2] parameter(1) + ROOT gather = s32[2,3] gather(operand, indices), + output_window_dims={1}, + elided_window_dims={0}, + gather_dims_to_operand_dims={0}, + index_vector_dim=1, + window_bounds={1, 3} +} +)"; + std::unique_ptr operand = + Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr gather_indices = Literal::CreateR1({0, 2}); + RunTest(hlo_text, operand.get(), gather_indices.get()); +} + +XLA_TEST_F(GatherOperationTest, TensorFlowGatherV2) { + const string hlo_text = R"( +HloModule TensorFlowGatherV2 + +ENTRY main { + operand = s32[3,3] parameter(0) + indices = s32[2] parameter(1) + ROOT gather = s32[3,2] gather(operand, indices), + output_window_dims={0}, + elided_window_dims={1}, + gather_dims_to_operand_dims={1}, + index_vector_dim=1, + window_bounds={3, 1} +} +)"; + std::unique_ptr operand = + Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr gather_indices = Literal::CreateR1({0, 2}); + RunTest(hlo_text, operand.get(), gather_indices.get()); +} + +XLA_TEST_F(GatherOperationTest, TensorFlowGatherMultipleBatchDims) { + const string hlo_text = R"( +HloModule TensorFlowGatherMultipleBatchDims + +ENTRY main { + operand = s32[3,3] parameter(0) + indices = s32[2,2] parameter(1) + ROOT gather = s32[2,3,2] gather(operand, indices), + output_window_dims={1}, + elided_window_dims={1}, + gather_dims_to_operand_dims={1}, + index_vector_dim=2, + window_bounds={3, 1} +} +)"; + std::unique_ptr operand = + Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr gather_indices = + Literal::CreateR2({{0, 2}, {2, 1}}); + RunTest(hlo_text, operand.get(), gather_indices.get()); +} + +XLA_TEST_F(GatherOperationTest, TensorFlowGatherNdMultipleBatchDims_0) { + const string hlo_text = R"( +HloModule TensorFlowGatherNdMultipleBatchDims + +ENTRY main { + operand = s32[3,3] parameter(0) + indices = s32[2,2,2] parameter(1) + ROOT gather = s32[2,2] gather(operand, indices), + output_window_dims={}, + elided_window_dims={0,1}, + gather_dims_to_operand_dims={0,1}, + index_vector_dim=2, + window_bounds={1, 1} +} +)"; + std::unique_ptr operand = + Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr gather_indices = + Literal::CreateR3({{{0, 2}, {2, 1}}, {{1, 2}, {2, 0}}}); + RunTest(hlo_text, operand.get(), gather_indices.get()); +} + +XLA_TEST_F(GatherOperationTest, TensorFlowGatherNdMultipleBatchDims_1) { + const string hlo_text = R"( +HloModule TensorFlowGatherNdMultipleBatchDims + +ENTRY main { + operand = s32[3,3] parameter(0) + indices = s32[2,2,2] parameter(1) + ROOT gather = s32[2,1,1,2] gather(operand, indices), + output_window_dims={1,2}, + elided_window_dims={}, + gather_dims_to_operand_dims={0,1}, + index_vector_dim=2, + window_bounds={1, 1} +} +)"; + std::unique_ptr operand = + Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr gather_indices = + Literal::CreateR3({{{0, 2}, {2, 1}}, {{1, 2}, {2, 0}}}); + RunTest(hlo_text, operand.get(), gather_indices.get()); +} + +XLA_TEST_F(GatherOperationTest, TensorFlowGatherNd) { + const string hlo_text = R"( +HloModule TensorFlowGatherNd + +ENTRY main { + operand = s32[3,3,2] parameter(0) + indices = s32[2,2] parameter(1) + ROOT gather = s32[2,2] gather(operand, indices), + output_window_dims={1}, + elided_window_dims={0,1}, + gather_dims_to_operand_dims={0,1}, + index_vector_dim=1, + window_bounds={1,1,2} +} +)"; + std::unique_ptr operand = + Literal::CreateR3({{{-1, 1}, {-2, 2}, {-3, 3}}, // + {{-4, 4}, {-5, 5}, {-6, 6}}, // + {{-7, 7}, {-8, 8}, {-9, 9}}}); + std::unique_ptr gather_indices = + Literal::CreateR2({{0, 0}, {1, 0}}); + RunTest(hlo_text, operand.get(), gather_indices.get()); +} + +XLA_TEST_F(GatherOperationTest, TensorFlowGatherNdNonDefaultIndexVectorDim) { + const string hlo_text = R"( +HloModule TensorFlowGatherNd + +ENTRY main { + operand = s32[3,3,2] parameter(0) + indices = s32[2,2] parameter(1) + ROOT gather = s32[2,2] gather(operand, indices), + output_window_dims={1}, + elided_window_dims={0,1}, + gather_dims_to_operand_dims={0,1}, + index_vector_dim=0, + window_bounds={1,1,2} +} +)"; + std::unique_ptr operand = + Literal::CreateR3({{{-1, 1}, {-2, 2}, {-3, 3}}, // + {{-4, 4}, {-5, 5}, {-6, 6}}, // + {{-7, 7}, {-8, 8}, {-9, 9}}}); + std::unique_ptr gather_indices = + Literal::CreateR2({{0, 0}, {1, 0}}); + RunTest(hlo_text, operand.get(), gather_indices.get()); +} + +XLA_TEST_F(GatherOperationTest, DynamicSlice) { + const char* hlo_text = R"( +HloModule DynamicSlice + +ENTRY main { + operand = s32[3,3] parameter(0) + indices = s32[2] parameter(1) + ROOT gather = s32[1,1] gather(operand, indices), + output_window_dims={0,1}, + elided_window_dims={}, + gather_dims_to_operand_dims={0,1}, + index_vector_dim=0, + window_bounds={1,1} +} +)"; + std::unique_ptr operand = + Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr gather_indices = Literal::CreateR1({1, 1}); + RunTest(hlo_text, operand.get(), gather_indices.get()); +} + +XLA_TEST_F(GatherOperationTest, BatchDynamicSlice) { + const string hlo_text = R"( +HloModule BatchDynamicSlice + +ENTRY main { + operand = s32[3,3] parameter(0) + indices = s32[2,2] parameter(1) + ROOT gather = s32[2,1,1] gather(operand, indices), + output_window_dims={1,2}, + elided_window_dims={}, + gather_dims_to_operand_dims={0,1}, + index_vector_dim=0, + window_bounds={1,1} +} +)"; + std::unique_ptr operand = + Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr gather_indices = + Literal::CreateR2({{2, 1}, {1, 1}}); + RunTest(hlo_text, operand.get(), gather_indices.get()); +} + +XLA_TEST_F(GatherOperationTest, ZeroDimBounds) { + const char* hlo_text = R"( +HloModule TensorFlowGatherV1 + +ENTRY main { + operand = s32[3,0] parameter(0) + indices = s32[2] parameter(1) + ROOT gather = s32[2,0] gather(operand, indices), + output_window_dims={1}, + elided_window_dims={0}, + gather_dims_to_operand_dims={0}, + index_vector_dim=1, + window_bounds={1, 0} +} +)"; + std::unique_ptr operand = Literal::CreateR2({{}, {}, {}}); + std::unique_ptr gather_indices = Literal::CreateR1({0, 2}); + RunTest(hlo_text, operand.get(), gather_indices.get()); +} + +} // namespace +} // namespace xla diff --git a/tensorflow/compiler/xla/tests/hlo_test_base.cc b/tensorflow/compiler/xla/tests/hlo_test_base.cc index 6723c99edb945492abfbac159bed1959d551ec57..5f62c44f25dd62b563bd8ce02477bd741f264182 100644 --- a/tensorflow/compiler/xla/tests/hlo_test_base.cc +++ b/tensorflow/compiler/xla/tests/hlo_test_base.cc @@ -140,15 +140,10 @@ StatusOr> HloTestBase::MakeReferenceModule( return std::move(reference_module); } -template StatusOr<::testing::AssertionResult> HloTestBase::RunAndCompareInternal( - std::unique_ptr module, const ArraySlice arguments, + std::unique_ptr module, const ArraySlice arguments, const optional& error, bool run_hlo_passes, const std::function& reference_preprocessor) { - static_assert( - std::is_same::value || - std::is_same, LiteralPtr>::value, - "The LiteralPtr type only accepts Literal* or std::unique_ptr."); TF_RETURN_IF_ERROR( VerifyHloModule(*test_runner_.backend().platform(), module.get())); TF_ASSIGN_OR_RETURN(auto reference_module, @@ -165,9 +160,8 @@ StatusOr<::testing::AssertionResult> HloTestBase::RunAndCompareInternal( error); } -template ::testing::AssertionResult HloTestBase::RunAndCompare( - std::unique_ptr module, const ArraySlice arguments, + std::unique_ptr module, const ArraySlice arguments, const optional& error, const std::function& reference_preprocessor) { auto result = @@ -179,9 +173,8 @@ template return result.ValueOrDie(); } -template ::testing::AssertionResult HloTestBase::RunAndCompareNoHloPasses( - std::unique_ptr module, const ArraySlice arguments, + std::unique_ptr module, const ArraySlice arguments, const optional& error, const std::function& reference_preprocessor) { auto result = @@ -198,8 +191,14 @@ template const std::function& reference_preprocessor) { const auto& fake_arguments = MakeFakeArguments(module.get()).ConsumeValueOrDie(); - return RunAndCompare>( - std::move(module), fake_arguments, error, reference_preprocessor); + + std::vector fake_argument_ptrs; + c_transform( + fake_arguments, std::back_inserter(fake_argument_ptrs), + [](const std::unique_ptr& literal) { return literal.get(); }); + + return RunAndCompare(std::move(module), fake_argument_ptrs, error, + reference_preprocessor); } ::testing::AssertionResult HloTestBase::RunAndCompareNoHloPasses( @@ -207,8 +206,13 @@ template const std::function& reference_preprocessor) { const auto& fake_arguments = MakeFakeArguments(module.get()).ConsumeValueOrDie(); - return RunAndCompareNoHloPasses>( - std::move(module), fake_arguments, error, reference_preprocessor); + std::vector fake_argument_ptrs; + c_transform( + fake_arguments, std::back_inserter(fake_argument_ptrs), + [](const std::unique_ptr& literal) { return literal.get(); }); + + return RunAndCompareNoHloPasses(std::move(module), fake_argument_ptrs, error, + reference_preprocessor); } ::testing::AssertionResult HloTestBase::RunAndCompare( diff --git a/tensorflow/compiler/xla/tests/hlo_test_base.h b/tensorflow/compiler/xla/tests/hlo_test_base.h index 413bb213fdcb1303f396308d13d9d0b96b47b71f..e375f13a44e0618f2a498325859d928c1adb830e 100644 --- a/tensorflow/compiler/xla/tests/hlo_test_base.h +++ b/tensorflow/compiler/xla/tests/hlo_test_base.h @@ -44,7 +44,7 @@ namespace xla { // enables, for one, explicitly building a graph of HLO instructions to run. // // This can also be used to write text/file-based test cases. Note that the test -// target is responsible for linking the needed backends. A covenient way to do +// target is responsible for linking the needed backends. A convenient way to do // this is to make it an xla_test: it will generate test targets linking with // the respective backends, which will be used as the test backend; the // interpreter backend is already linked with hlo_test_base so it will be the @@ -104,8 +104,7 @@ class HloTestBase : public ::testing::Test { // Executes the given hlo module on two backends and compares results. // - // 'arguments': the input of the hlo module. The LiteralPtr type accepts - // Literal* or std::unique_ptr. + // 'arguments': the input of the hlo module. // // 'error': if has value, expects the results to be near (within the error // bound). Otherwise, expects the results to be equal. @@ -114,20 +113,18 @@ class HloTestBase : public ::testing::Test { // backend, but it might need to be tailored so that it is able to run on the // reference backend. Note that the program shape of the module must not be // modified. - template ::testing::AssertionResult RunAndCompare( std::unique_ptr module, - const tensorflow::gtl::ArraySlice arguments, + const tensorflow::gtl::ArraySlice arguments, const tensorflow::gtl::optional& error, const std::function& reference_preprocessor = nullptr) TF_MUST_USE_RESULT; // Same as above, except that the module will be executed without Hlo // optimization. - template ::testing::AssertionResult RunAndCompareNoHloPasses( std::unique_ptr module, - const tensorflow::gtl::ArraySlice arguments, + const tensorflow::gtl::ArraySlice arguments, const tensorflow::gtl::optional& error, const std::function& reference_preprocessor = nullptr) TF_MUST_USE_RESULT; @@ -232,10 +229,9 @@ class HloTestBase : public ::testing::Test { // Runs the module on two platforms with or without running hlo passes and // compares the results. Returns whether the results are near or equal. If any // error happens before the results are computed, returns the error status. - template StatusOr<::testing::AssertionResult> RunAndCompareInternal( std::unique_ptr module, - const tensorflow::gtl::ArraySlice arguments, + const tensorflow::gtl::ArraySlice arguments, const tensorflow::gtl::optional& error, bool run_hlo_passes, const std::function& reference_preprocessor); }; diff --git a/tensorflow/compiler/xla/tests/hlo_verified_test_base.cc b/tensorflow/compiler/xla/tests/hlo_verified_test_base.cc index 506091ddd8d1d8e6519525bb7031f4e8b296b5fb..641907acf260c099a5ac885c362d92a0b6d78a42 100644 --- a/tensorflow/compiler/xla/tests/hlo_verified_test_base.cc +++ b/tensorflow/compiler/xla/tests/hlo_verified_test_base.cc @@ -18,6 +18,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_verifier.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" +#include "tensorflow/compiler/xla/tools/parser/hlo_parser.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/test.h" @@ -40,18 +41,22 @@ void HloVerifiedTestBase::TearDown() { << "TearDown called more than once; it should be called exactly once."; tear_down_called_ = true; if (module_) { - HloVerifier verifier; - xla::StatusOr mutated = verifier.Run(module_.get()); - if (!mutated.ok()) { - ADD_FAILURE() << "HloVerifier failed: " << mutated.status(); - } else { - EXPECT_FALSE(mutated.ValueOrDie()) - << "HloVerifier should never mutate the HloModule"; - } + VerifyModule(); } HloTestBase::TearDown(); } +void HloVerifiedTestBase::VerifyModule() { + HloVerifier verifier; + xla::StatusOr mutated = verifier.Run(module_.get()); + if (!mutated.ok()) { + ADD_FAILURE() << "HloVerifier failed: " << mutated.status(); + } else { + EXPECT_FALSE(mutated.ValueOrDie()) + << "HloVerifier should never mutate the HloModule"; + } +} + HloModule& HloVerifiedTestBase::module() { if (!module_) { module_ = CreateNewModule(); @@ -59,4 +64,9 @@ HloModule& HloVerifiedTestBase::module() { return *module_; } +void HloVerifiedTestBase::ParseAndVerifyModule(const char* hlo_text) { + CHECK(!module_) << "Called ParseModule when test already has a module."; + TF_ASSERT_OK_AND_ASSIGN(module_, tools::Parse(hlo_text)); + VerifyModule(); +} } // namespace xla diff --git a/tensorflow/compiler/xla/tests/hlo_verified_test_base.h b/tensorflow/compiler/xla/tests/hlo_verified_test_base.h index 492688bf7d682cf991cb8c09399492a0437f651b..c0cb12bc93f56a5cb5ebdac94488369331f0cea6 100644 --- a/tensorflow/compiler/xla/tests/hlo_verified_test_base.h +++ b/tensorflow/compiler/xla/tests/hlo_verified_test_base.h @@ -44,6 +44,7 @@ class HloVerifiedTestBase : public HloTestBase { // Returns the default HloModule, lazily creating it if necessary via // HloTestBase::CreateNewModule(). HloModule& module(); + void ParseAndVerifyModule(const char* hlo_text); // Sets the shape-size function used during hlo verification. If this isn't // called, a default ShapeVerifier is used instead. @@ -55,6 +56,7 @@ class HloVerifiedTestBase : public HloTestBase { std::unique_ptr module_; // Lazily populated. Access via module(). std::unique_ptr shape_verifier_; bool tear_down_called_ = false; + void VerifyModule(); }; } // namespace xla diff --git a/tensorflow/compiler/xla/tests/llvm_irgen_test_base.cc b/tensorflow/compiler/xla/tests/llvm_irgen_test_base.cc index 99514baf23cafe61adc28a30dfdfe2691ab82d32..3023df47cda33f5d11abc921fd0355d48f761107 100644 --- a/tensorflow/compiler/xla/tests/llvm_irgen_test_base.cc +++ b/tensorflow/compiler/xla/tests/llvm_irgen_test_base.cc @@ -20,6 +20,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h" #include "tensorflow/compiler/xla/tests/filecheck.h" +#include "tensorflow/core/lib/core/status_test_util.h" #include "tensorflow/core/platform/test.h" namespace xla { @@ -49,11 +50,11 @@ void LLVMIRGenTestBase::CompileAndVerifyIr( std::unique_ptr hlo_module, const string& pattern, bool match_optimized_ir) { SetIrHook(match_optimized_ir); - ASSERT_TRUE(CompileToExecutable(std::move(hlo_module)).ok()); + TF_ASSERT_OK(CompileToExecutable(std::move(hlo_module)).status()); ResetIrHook(); StatusOr filecheck_result = RunFileCheck(ir_, pattern); - ASSERT_TRUE(filecheck_result.ok()); + TF_ASSERT_OK(filecheck_result.status()); EXPECT_TRUE(filecheck_result.ValueOrDie()); } diff --git a/tensorflow/compiler/xla/tests/map_test.cc b/tensorflow/compiler/xla/tests/map_test.cc index 2b0f7e6e80c48435ca55432a2afa3b6d69162625..0cd812fd1b4bc69c34b70d3ca0fd0aa6cf57fa4c 100644 --- a/tensorflow/compiler/xla/tests/map_test.cc +++ b/tensorflow/compiler/xla/tests/map_test.cc @@ -531,7 +531,7 @@ TEST_F(MapTest, MapOperantionWithBuildError) { ASSERT_TRUE(!computation_status.ok()); EXPECT_THAT( computation_status.status().ToString(), - ::testing::HasSubstr("error from: ErrorAdd: binary op BINOP_ADD with " + ::testing::HasSubstr("error from: ErrorAdd: Binary op BINOP_ADD with " "different element types: f32[] and u16[]")); } diff --git a/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc b/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc index 6c86dd5b9ef673c9facffafa37e00a859ce82010..c42f71388baba73e08a361d817e41b03e03bf133 100644 --- a/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc +++ b/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc @@ -29,6 +29,8 @@ limitations under the License. #include "tensorflow/compiler/xla/test_helpers.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/tests/test_utils.h" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/lib/strings/stringprintf.h" #include "tensorflow/core/platform/logging.h" @@ -38,258 +40,223 @@ limitations under the License. namespace xla { namespace { -class MatOpsSimpleTest : public ClientLibraryTestBase { - protected: - Computation BuildSum() { - // sum(x, y) = x + y - ComputationBuilder builder(client_, "sum"); - auto x_value = - builder.Parameter(0, ShapeUtil::MakeShape(F32, {}), "x_value"); - auto y_value = - builder.Parameter(1, ShapeUtil::MakeShape(F32, {}), "y_value"); - builder.Add(x_value, y_value); - auto computation_status = builder.Build(); - TF_CHECK_OK(computation_status.status()); - return computation_status.ConsumeValueOrDie(); - } - - void TestLinspaceMax(int64 rows, int64 cols) { - float from = -128.0, to = 256.0; - std::unique_ptr> alhs = - MakeLinspaceArray2D(from, to, rows, cols); - auto arhs = MakeUnique>(rows, cols, 1.0); - - ComputationBuilder builder( - client_, - tensorflow::strings::Printf("max_%lldx%lld_linspace", rows, cols)); - auto lhs = builder.ConstantR2FromArray2D(*alhs); - auto rhs = builder.ConstantR2FromArray2D(*arhs); - auto max = builder.Max(lhs, rhs); - - Array2D aexpected(rows, cols); - for (int row = 0; row < rows; ++row) { - for (int col = 0; col < cols; ++col) { - aexpected(row, col) = std::max((*alhs)(row, col), (*arhs)(row, col)); - } - } - - ComputeAndCompareR2(&builder, aexpected, {}, ErrorSpec(1e-6)); - } -}; - -TEST_F(MatOpsSimpleTest, ExpTwoByTwoValues) { - ComputationBuilder builder(client_, "exp_2x2"); - auto data = builder.ConstantR2({ - {1.0, 0.0}, // row 0 - {-1.0, 0.5}, // row 1 +#ifdef XLA_BACKEND_DOES_NOT_SUPPORT_FLOAT16 +using TypesF16F32 = ::testing::Types; +#else +using TypesF16F32 = ::testing::Types; +#endif + +class MatOpsSimpleTest : public ClientLibraryTestBase {}; + +template +class MatOpsSimpleTest_F16F32 : public MatOpsSimpleTest {}; + +// TODO(bixia): This test for F16 failed on GPU 02-25-2018. +#ifdef XLA_TEST_BACKEND_GPU +TYPED_TEST_CASE(MatOpsSimpleTest_F16F32, ::testing::Types); +#else +TYPED_TEST_CASE(MatOpsSimpleTest_F16F32, TypesF16F32); +#endif + +XLA_TYPED_TEST(MatOpsSimpleTest_F16F32, ExpTwoByTwoValues) { + using T = TypeParam; + ComputationBuilder builder(this->client_, "exp_2x2"); + auto data = builder.ConstantR2FromArray2D({ + {1.0f, 0.0f}, // row 0 + {-1.0f, 0.5f}, // row 1 }); builder.Exp(data); std::unique_ptr expected = - Literal::CreateR2({{2.71828, 1.00000}, // row 0 - {0.36788, 1.64872}}); // row 1 + Literal::CreateR2FromArray2D({{2.71828f, 1.00000f}, // row 0 + {0.36788f, 1.64872f}}); // row 1 - ComputeAndCompareLiteral(&builder, *expected, {}, ErrorSpec(1e-5)); + this->template ComputeAndCompareLiteral(&builder, *expected, {}, + ErrorSpec(1e-5)); } -TEST_F(MatOpsSimpleTest, MapTwoByTwo) { +XLA_TYPED_TEST(MatOpsSimpleTest_F16F32, MapTwoByTwo) { + using T = TypeParam; Computation add_half; { // add_half(x) = x + 0.5 - ComputationBuilder builder(client_, "add_half"); + ComputationBuilder builder(this->client_, "add_half"); auto x_value = - builder.Parameter(0, ShapeUtil::MakeShape(F32, {}), "x_value"); - auto half = builder.ConstantR0(0.5); + builder.Parameter(0, ShapeUtil::MakeShapeWithType({}), "x_value"); + auto half = builder.ConstantR0(static_cast(0.5)); builder.Add(x_value, half); auto computation_status = builder.Build(); ASSERT_IS_OK(computation_status.status()); add_half = computation_status.ConsumeValueOrDie(); } - ComputationBuilder builder(client_, "map_2x2"); - auto data = builder.ConstantR2({ - {1.0, 0.0}, // row 0 - {-1.0, 0.5}, // row 1 + ComputationBuilder builder(this->client_, "map_2x2"); + auto data = builder.ConstantR2FromArray2D({ + {1.0f, 0.0f}, // row 0 + {-1.0f, 0.5f}, // row 1 }); auto map = builder.Map({data}, add_half, {0, 1}); std::unique_ptr expected = - Literal::CreateR2({{1.5, 0.5}, // row 0 - {-0.5, 1.0}}); // row 1 - ComputeAndCompareLiteral(&builder, *expected, {}, ErrorSpec(1e-5)); + Literal::CreateR2FromArray2D({{1.5f, 0.5f}, // row 0 + {-0.5f, 1.0f}}); // row 1 + this->template ComputeAndCompareLiteral(&builder, *expected, {}, + ErrorSpec(1e-5)); } -TEST_F(MatOpsSimpleTest, MaxTwoByTwoValues) { - ComputationBuilder builder(client_, "max_2x2"); - auto lhs = builder.ConstantR2({ - {7.0, 2.0}, // row 0 - {3.0, -4.0}, // row 1 +XLA_TYPED_TEST(MatOpsSimpleTest_F16F32, MaxTwoByTwoValues) { + using T = TypeParam; + ComputationBuilder builder(this->client_, "max_2x2"); + auto lhs = builder.ConstantR2FromArray2D({ + {7.0f, 2.0f}, // row 0 + {3.0f, -4.0f}, // row 1 }); - auto rhs = builder.ConstantR2({ - {5.0, 6.0}, // row 0 - {1.0, -8.0}, // row 1 + auto rhs = builder.ConstantR2FromArray2D({ + {5.0f, 6.0f}, // row 0 + {1.0f, -8.0f}, // row 1 }); auto max = builder.Max(lhs, rhs); std::unique_ptr expected = - Literal::CreateR2({{7.0, 6.0}, // row 0 - {3.0, -4.0}}); // row 1 - ComputeAndCompareLiteral(&builder, *expected, {}, ErrorSpec(1e-6)); + Literal::CreateR2FromArray2D({{7.0f, 6.0f}, // row 0 + {3.0f, -4.0f}}); // row 1 + this->template ComputeAndCompareLiteral(&builder, *expected, {}, + ErrorSpec(1e-6)); } -TEST_F(MatOpsSimpleTest, Max1x1Linspace) { TestLinspaceMax(1, 1); } - -TEST_F(MatOpsSimpleTest, Max2x2Linspace) { TestLinspaceMax(2, 2); } - -TEST_F(MatOpsSimpleTest, Max3x3Linspace) { TestLinspaceMax(3, 3); } - -TEST_F(MatOpsSimpleTest, Max4x4Linspace) { TestLinspaceMax(4, 4); } - -TEST_F(MatOpsSimpleTest, Max6x6Linspace) { TestLinspaceMax(6, 6); } - -TEST_F(MatOpsSimpleTest, Max8x8Linspace) { TestLinspaceMax(8, 8); } - -TEST_F(MatOpsSimpleTest, Max12x12Linspace) { TestLinspaceMax(12, 12); } - -TEST_F(MatOpsSimpleTest, Max16x16Linspace) { TestLinspaceMax(16, 16); } +struct TestLinspaceMaxParam { + int64 rows; + int64 cols; +}; -TEST_F(MatOpsSimpleTest, Max32x8Linspace) { TestLinspaceMax(32, 8); } +class TestLinspaceMaxParametric + : public MatOpsSimpleTest, + public ::testing::WithParamInterface { + public: + template + void TestImpl() { + TestLinspaceMaxParam param = GetParam(); + int64 rows = param.rows; + int64 cols = param.cols; + float from = -128.0, to = 256.0; + std::unique_ptr> alhs = + MakeLinspaceArray2D(from, to, rows, cols); + auto arhs = MakeUnique>(rows, cols, static_cast(1.0f)); -TEST_F(MatOpsSimpleTest, Max64x8Linspace) { TestLinspaceMax(64, 8); } + ComputationBuilder builder( + client_, + tensorflow::strings::Printf("max_%lldx%lld_linspace", rows, cols)); + auto lhs = builder.ConstantR2FromArray2D(*alhs); + auto rhs = builder.ConstantR2FromArray2D(*arhs); + auto max = builder.Max(lhs, rhs); -class MatOpsDotAddTest - : public ClientLibraryTestBase, - public ::testing::WithParamInterface> {}; - -TEST_P(MatOpsDotAddTest, Dot_Add_2x2_2x2) { - bool row_major = std::get<0>(GetParam()); - bool add_lhs = std::get<1>(GetParam()); - bool transpose = std::get<2>(GetParam()); - Array2D lhs({{1.0, 2.0}, {3.0, 4.0}}); - Array2D rhs({{10.0, 11.0}, {12.0, 13.0}}); - - auto minor_to_major = [](bool row_major) -> std::vector { - return {row_major ? 1 : 0, row_major ? 0 : 1}; - }; - - auto prim_type = primitive_util::NativeToPrimitiveType(); - Shape lhs_shape = - ShapeUtil::MakeShape(prim_type, {lhs.height(), lhs.width()}); - Shape rhs_shape = - ShapeUtil::MakeShape(prim_type, {rhs.height(), rhs.width()}); - - TF_ASSERT_OK_AND_ASSIGN( - auto lhs_handle, - client_->TransferToServer(*Literal::CreateR2FromArray2DWithLayout( - lhs, LayoutUtil::MakeLayout(minor_to_major(row_major))))); - TF_ASSERT_OK_AND_ASSIGN( - auto rhs_handle, - client_->TransferToServer(*Literal::CreateR2FromArray2DWithLayout( - rhs, LayoutUtil::MakeLayout(minor_to_major(row_major))))); - - ComputationBuilder builder(client_, TestName()); - auto lhs_arg = builder.Parameter(0, lhs_shape, "lhs"); - auto lhs_mat_arg = lhs_arg; - if (transpose) { - lhs_mat_arg = builder.Transpose(lhs_mat_arg, {1, 0}); - } - auto rhs_arg = builder.Parameter(1, rhs_shape, "rhs"); - auto result = builder.Dot(lhs_mat_arg, rhs_arg); - Array2D expected; - if (add_lhs) { - result = builder.Add(result, lhs_arg); - if (transpose) { - expected = Array2D({{47, 52}, {71, 78}}); - } else { - expected = Array2D({{35, 39}, {81, 89}}); + Array2D expected(rows, cols); + for (int row = 0; row < rows; ++row) { + for (int col = 0; col < cols; ++col) { + expected(row, col) = std::max((*alhs)(row, col), (*arhs)(row, col)); + } } - } else { - result = builder.Add(result, rhs_arg); - if (transpose) { - expected = Array2D({{56, 61}, {80, 87}}); - } else { - expected = Array2D({{44, 48}, {90, 98}}); + ErrorSpec error_spec(1e-6); + if (std::is_same::value) { + error_spec = ErrorSpec(1e-6, 2e-4); } + ComputeAndCompareR2(&builder, expected, {}, error_spec); } +}; - ComputeAndCompareR2(&builder, expected, - {lhs_handle.get(), rhs_handle.get()}, - ErrorSpec(1e-6)); +string PrintTestLinspaceMaxParam( + const ::testing::TestParamInfo& test_param) { + const TestLinspaceMaxParam& param = test_param.param; + return tensorflow::strings::StrCat(param.rows, "r", param.cols, "c"); } -INSTANTIATE_TEST_CASE_P(MatOpsDotAddTestInstances, MatOpsDotAddTest, - ::testing::Combine(::testing::Bool(), ::testing::Bool(), - ::testing::Bool())); +#ifndef XLA_BACKEND_DOES_NOT_SUPPORT_FLOAT16 +// TODO(bixia): This test failed on GPU 02-25-2018 +#ifdef XLA_TEST_BACKEND_CPU +XLA_TEST_P(TestLinspaceMaxParametric, TestF16) { TestImpl(); } +#endif +#endif +XLA_TEST_P(TestLinspaceMaxParametric, TestF32) { TestImpl(); } + +INSTANTIATE_TEST_CASE_P( + TestLinspaceMax, TestLinspaceMaxParametric, + ::testing::Values(TestLinspaceMaxParam{1, 1}, TestLinspaceMaxParam{2, 2}, + TestLinspaceMaxParam{3, 3}, TestLinspaceMaxParam{4, 4}, + TestLinspaceMaxParam{6, 6}, TestLinspaceMaxParam{8, 8}, + TestLinspaceMaxParam{12, 12}, + TestLinspaceMaxParam{16, 16}, TestLinspaceMaxParam{32, 8}, + TestLinspaceMaxParam{64, 8}), + PrintTestLinspaceMaxParam); -class MatOpsDotAddTest_bf16 +class MatOpsDotAddTest : public ClientLibraryTestBase, - public ::testing::WithParamInterface> {}; - -TEST_P(MatOpsDotAddTest_bf16, Dot_Add_2x2_2x2) { - bool row_major = std::get<0>(GetParam()); - bool add_lhs = std::get<1>(GetParam()); - bool transpose = std::get<2>(GetParam()); - Array2D lhs( - {{bfloat16(1.0f), bfloat16(2.0f)}, {bfloat16(3.0), bfloat16(4.0)}}); - Array2D rhs( - {{bfloat16(10.0f), bfloat16(11.0f)}, {bfloat16(12.0f), bfloat16(13.0f)}}); - - auto minor_to_major = [](bool row_major) -> std::vector { - return {row_major ? 1 : 0, row_major ? 0 : 1}; - }; - - auto prim_type = primitive_util::NativeToPrimitiveType(); - Shape lhs_shape = - ShapeUtil::MakeShape(prim_type, {lhs.height(), lhs.width()}); - Shape rhs_shape = - ShapeUtil::MakeShape(prim_type, {rhs.height(), rhs.width()}); - - TF_ASSERT_OK_AND_ASSIGN( - auto lhs_handle, - client_->TransferToServer( - *Literal::CreateR2FromArray2DWithLayout( - lhs, LayoutUtil::MakeLayout(minor_to_major(row_major))))); - TF_ASSERT_OK_AND_ASSIGN( - auto rhs_handle, - client_->TransferToServer( - *Literal::CreateR2FromArray2DWithLayout( - rhs, LayoutUtil::MakeLayout(minor_to_major(row_major))))); - - ComputationBuilder builder(client_, TestName()); - auto lhs_arg = builder.Parameter(0, lhs_shape, "lhs"); - auto lhs_mat_arg = lhs_arg; - if (transpose) { - lhs_mat_arg = builder.Transpose(lhs_mat_arg, {1, 0}); - } - auto rhs_arg = builder.Parameter(1, rhs_shape, "rhs"); - auto result = builder.Dot(lhs_mat_arg, rhs_arg); - Array2D expected; - if (add_lhs) { - result = builder.Add(result, lhs_arg); + public ::testing::WithParamInterface> { + public: + template + void TestImpl() { + bool row_major = std::get<0>(GetParam()); + bool add_lhs = std::get<1>(GetParam()); + bool transpose = std::get<2>(GetParam()); + Array2D lhs({{1.0f, 2.0f}, {3.0f, 4.0f}}); + Array2D rhs({{10.0f, 11.0f}, {12.0f, 13.0f}}); + + auto minor_to_major = [](bool row_major) -> std::vector { + return {row_major ? 1 : 0, row_major ? 0 : 1}; + }; + + auto prim_type = primitive_util::NativeToPrimitiveType(); + Shape lhs_shape = + ShapeUtil::MakeShape(prim_type, {lhs.height(), lhs.width()}); + Shape rhs_shape = + ShapeUtil::MakeShape(prim_type, {rhs.height(), rhs.width()}); + + TF_ASSERT_OK_AND_ASSIGN( + auto lhs_handle, + client_->TransferToServer(*Literal::CreateR2FromArray2DWithLayout( + lhs, LayoutUtil::MakeLayout(minor_to_major(row_major))))); + TF_ASSERT_OK_AND_ASSIGN( + auto rhs_handle, + client_->TransferToServer(*Literal::CreateR2FromArray2DWithLayout( + rhs, LayoutUtil::MakeLayout(minor_to_major(row_major))))); + + ComputationBuilder builder(client_, TestName()); + auto lhs_arg = builder.Parameter(0, lhs_shape, "lhs"); + auto lhs_mat_arg = lhs_arg; if (transpose) { - expected = Array2D( - {{bfloat16(47), bfloat16(52)}, {bfloat16(71), bfloat16(78)}}); - } else { - expected = Array2D( - {{bfloat16(35), bfloat16(39)}, {bfloat16(81), bfloat16(89)}}); + lhs_mat_arg = builder.Transpose(lhs_mat_arg, {1, 0}); } - } else { - result = builder.Add(result, rhs_arg); - if (transpose) { - expected = Array2D( - {{bfloat16(56), bfloat16(61)}, {bfloat16(80), bfloat16(87)}}); + auto rhs_arg = builder.Parameter(1, rhs_shape, "rhs"); + auto result = builder.Dot(lhs_mat_arg, rhs_arg); + Array2D expected; + if (add_lhs) { + result = builder.Add(result, lhs_arg); + if (transpose) { + expected = Array2D({{47.0f, 52.0f}, {71.0f, 78.0f}}); + } else { + expected = Array2D({{35.0f, 39.0f}, {81.0f, 89.0f}}); + } } else { - expected = Array2D( - {{bfloat16(44), bfloat16(48)}, {bfloat16(90), bfloat16(98)}}); + result = builder.Add(result, rhs_arg); + if (transpose) { + expected = Array2D({{56.0f, 61.0f}, {80.0f, 87.0f}}); + } else { + expected = Array2D({{44.0f, 48.0f}, {90.0f, 98.0f}}); + } } + + ComputeAndCompareR2(&builder, expected, + {lhs_handle.get(), rhs_handle.get()}, + ErrorSpec(1e-6)); } +}; - ComputeAndCompareR2(&builder, expected, - {lhs_handle.get(), rhs_handle.get()}, - ErrorSpec(1e-6)); -} +XLA_TEST_P(MatOpsDotAddTest, Dot_Add_2x2_2x2BF16) { TestImpl(); } +#ifndef XLA_BACKEND_DOES_NOT_SUPPORT_FLOAT16 +XLA_TEST_P(MatOpsDotAddTest, Dot_Add_2x2_2x2F16) { TestImpl(); } +#endif +XLA_TEST_P(MatOpsDotAddTest, Dot_Add_2x2_2x2F32) { TestImpl(); } -INSTANTIATE_TEST_CASE_P(MatOpsDotAddTestInstances, MatOpsDotAddTest_bf16, +INSTANTIATE_TEST_CASE_P(MatOpsDotAddTestInstances, MatOpsDotAddTest, ::testing::Combine(::testing::Bool(), ::testing::Bool(), ::testing::Bool())); diff --git a/tensorflow/compiler/xla/tests/reduce_window_test.cc b/tensorflow/compiler/xla/tests/reduce_window_test.cc index b11b64e40a582150d6adf29e915cd70b4bcb982b..8b736f62f045bb913ac09add2f00d5edb0692d83 100644 --- a/tensorflow/compiler/xla/tests/reduce_window_test.cc +++ b/tensorflow/compiler/xla/tests/reduce_window_test.cc @@ -960,45 +960,67 @@ struct R2ReduceWindowTestData { int64 base_bounds[2]; int64 window_bounds[2]; int64 strides[2]; + int64 pad_low[2]; + int64 pad_high[2]; int64 layout[2]; - Padding padding; Reducer reducer; } kR2TestCases[] = { {/*base_bounds=*/{4, 18}, /*window_bounds=*/{2, 4}, - /*strides=*/{1, 2}, /*layout=*/{0, 1}, - /*padding=*/Padding::kSame, /*reducer=*/Reducer::kAdd}, + /*strides=*/{1, 2}, /*pad_low=*/{0, 1}, /*pad_high=*/{1, 1}, + /*layout=*/{0, 1}, + /*reducer=*/Reducer::kAdd}, {/*base_bounds=*/{2, 5}, /*window_bounds=*/{2, 4}, - /*strides=*/{1, 1}, /*layout=*/{0, 1}, - /*padding=*/Padding::kSame, /*reducer=*/Reducer::kAdd}, + /*strides=*/{1, 1}, /*pad_low=*/{0, 1}, /*pad_high=*/{1, 2}, + /*layout=*/{0, 1}, + /*reducer=*/Reducer::kAdd}, {/*base_bounds=*/{1, 3}, /*window_bounds=*/{2, 3}, - /*strides=*/{1, 1}, /*layout=*/{0, 1}, - /*padding=*/Padding::kSame, /*reducer=*/Reducer::kAdd}, + /*strides=*/{1, 1}, /*pad_low=*/{0, 1}, /*pad_high=*/{1, 1}, + /*layout=*/{0, 1}, + /*reducer=*/Reducer::kAdd}, {/*base_bounds=*/{3, 129}, /*window_bounds=*/{1, 100}, - /*strides=*/{2, 99}, /*layout=*/{0, 1}, - /*padding=*/Padding::kSame, /*reducer=*/Reducer::kAdd}, + /*strides=*/{2, 99}, /*pad_low=*/{0, 0}, /*pad_high=*/{35, 35}, + /*layout=*/{0, 1}, + /*reducer=*/Reducer::kAdd}, +// TODO(b/74260408): This test last failed on GPU on 2018-03-08, likely due to a +// ptxas bug. +#ifndef XLA_TEST_BACKEND_GPU {/*base_bounds=*/{6, 152}, /*window_bounds=*/{2, 25}, - /*strides=*/{5, 4}, /*layout=*/{0, 1}, - /*padding=*/Padding::kSame, /*reducer=*/Reducer::kAdd}, + /*strides=*/{5, 4}, /*pad_low=*/{0, 1}, /*pad_high=*/{10, 11}, + /*layout=*/{0, 1}, + /*reducer=*/Reducer::kAdd}, +#endif {/*base_bounds=*/{6, 4}, /*window_bounds=*/{4, 2}, - /*strides=*/{3, 3}, /*layout=*/{0, 1}, - /*padding=*/Padding::kSame, /*reducer=*/Reducer::kAdd}, + /*strides=*/{3, 3}, /*pad_low=*/{0, 1}, /*pad_high=*/{0, 1}, + /*layout=*/{0, 1}, + /*reducer=*/Reducer::kAdd}, {/*base_bounds=*/{5, 147}, /*window_bounds=*/{1, 36}, - /*strides=*/{4, 5}, /*layout=*/{1, 0}, - /*padding=*/Padding::kSame, /*reducer=*/Reducer::kAdd}, + /*strides=*/{4, 5}, /*pad_low=*/{0, 0}, /*pad_high=*/{17, 17}, + /*layout=*/{1, 0}, + /*reducer=*/Reducer::kAdd}, {/*base_bounds=*/{4, 153}, /*window_bounds=*/{2, 93}, - /*strides=*/{1, 1}, /*layout=*/{1, 0}, - /*padding=*/Padding::kSame, /*reducer=*/Reducer::kAdd}, + /*strides=*/{1, 1}, /*pad_low=*/{0, 1}, /*pad_high=*/{46, 46}, + /*layout=*/{1, 0}, + /*reducer=*/Reducer::kAdd}, // Regression test for a bug that appeared in Inception (b/34784899). {/*base_bounds=*/{28, 28}, /*window_bounds=*/{3, 3}, - /*strides=*/{1, 1}, /*layout=*/{1, 0}, - /*padding=*/Padding::kSame, /*reducer=*/Reducer::kAdd}, + /*strides=*/{1, 1}, /*pad_low=*/{1, 1}, /*pad_high=*/{1, 1}, + /*layout=*/{1, 0}, + /*reducer=*/Reducer::kAdd}, + {/*base_bounds=*/{4, 4}, /*window_bounds=*/{2, 2}, + /*strides=*/{1, 1}, /*pad_low=*/{0, 0}, /*pad_high=*/{0, 0}, + /*layout=*/{1, 0}, + /*reducer=*/Reducer::kAdd}, // Regression test for a bug that appeared in Inception (b/34784899). {/*base_bounds=*/{4, 32}, /*window_bounds=*/{2, 2}, - /*strides=*/{2, 2}, /*layout=*/{1, 0}, - /*padding=*/Padding::kValid, /*reducer=*/Reducer::kAdd}, - {/*base_bounds=*/{4, 4}, /*window_bounds=*/{2, 2}, - /*strides=*/{1, 1}, /*layout=*/{1, 0}, - /*padding=*/Padding::kValid, /*reducer=*/Reducer::kAdd}, + /*strides=*/{2, 2}, /*pad_low=*/{0, 0}, /*pad_high=*/{0, 0}, + /*layout=*/{1, 0}, + /*reducer=*/Reducer::kAdd}, + // Regression test for b/73903312: bf16 lacks precision to store result of + // very large windows. Testing with a reasonable window larger than 128. + {/*base_bounds=*/{8, 130}, /*window_bounds=*/{1, 130}, + /*strides=*/{1, 1}, /*pad_low=*/{0, 130}, /*pad_high=*/{0, 0}, + /*layout=*/{1, 0}, + /*reducer=*/Reducer::kAdd}, }; string R2ReduceWindowTestDataToString( @@ -1008,10 +1030,11 @@ string R2ReduceWindowTestDataToString( string str = tensorflow::strings::StrCat( "base_bounds_", tensorflow::str_util::Join(param.base_bounds, "x"), // "__window_bounds_", - tensorflow::str_util::Join(param.window_bounds, "x"), // - "__strides_", tensorflow::str_util::Join(param.strides, "x"), // - "__padding_", param.padding == Padding::kSame ? "same" : "valid", // - "__layout_", param.layout[0], "_", param.layout[1], // + tensorflow::str_util::Join(param.window_bounds, "x"), // + "__strides_", tensorflow::str_util::Join(param.strides, "x"), // + "__pad_low_", tensorflow::str_util::Join(param.pad_low, "x"), + "__pad_high_", tensorflow::str_util::Join(param.pad_high, "x"), + "__layout_", param.layout[0], "_", param.layout[1], // "__reducer_", param.reducer == kAdd ? "add" : "max"); if (::testing::get<1>(data.param)) { str = tensorflow::strings::StrCat(str, "_bfloat16"); @@ -1039,17 +1062,29 @@ class R2ReduceWindowTest : public ReduceWindowTestBase, ComputationDataHandle parameter; auto input_arg = CreateParameterAndTransferLiteral(0, *input_literal, "p0", &b, ¶meter); + std::vector> padding(2); + for (int i = 0; i < 2; ++i) { + padding[i] = {param.pad_low[i], param.pad_high[i]}; + } + auto computation = param.reducer == kAdd + ? CreateScalarAddComputation(FloatType(), &b) + : CreateScalarMaxComputation(FloatType(), &b); auto init_value = CreateConstantFromLiteral(*Literal::CreateR0(kInitValue), &b); - b.ReduceWindow(/*operand=*/parameter, - /*init_value=*/init_value, - /*computation=*/CreateScalarAddComputation(FloatType(), &b), - /*window_dimensions=*/param.window_bounds, - /*window_strides=*/param.strides, /*padding=*/param.padding); + b.ReduceWindowWithGeneralPadding( + /*operand=*/parameter, + /*init_value=*/init_value, + /*computation=*/computation, + /*window_dimensions=*/param.window_bounds, + /*window_strides=*/param.strides, /*padding=*/padding); - auto expected = ReferenceUtil::ReduceWindow2DAdd( - /*operand=*/input, /*init=*/kInitValue, /*window=*/param.window_bounds, - /*stride=*/param.strides, /*padding=*/param.padding); + auto reduce_func = param.reducer == kAdd + ? +[](float a, float b) { return a + b; } + : +[](float a, float b) { return std::max(a, b); }; + auto expected = ReferenceUtil::ReduceWindow2DGeneric( + /*operand=*/input, /*init=*/kInitValue, /*reduce_func=*/reduce_func, + /*window=*/param.window_bounds, + /*stride=*/param.strides, /*padding=*/padding); ComputeAndCompareLiteral(&b, *Literal::CreateFromArray(*expected), {input_arg.get()}, DefaultErrorSpec()); @@ -1074,8 +1109,9 @@ XLA_TEST_P(R2ReduceWindowFailingCpuGpuBf16Test, const R2ReduceWindowTestData kR2FailingValuesCpuGpuBf16Test[] = { {/*base_bounds=*/{8, 128}, /*window_bounds=*/{8, 128}, - /*strides=*/{1, 1}, /*layout=*/{1, 0}, - /*padding=*/Padding::kValid, /*reducer=*/Reducer::kAdd}, + /*strides=*/{1, 1}, /*pad_low=*/{0, 0}, /*pad_high=*/{0, 0}, + /*layout=*/{1, 0}, + /*reducer=*/Reducer::kAdd}, }; INSTANTIATE_TEST_CASE_P( diff --git a/tensorflow/compiler/xla/tests/scalar_computations_test.cc b/tensorflow/compiler/xla/tests/scalar_computations_test.cc index d7bda77e87f33938162f94dbee42b160906b4087..0c88bef69dfc522fef52422b0bd3a825fa173d44 100644 --- a/tensorflow/compiler/xla/tests/scalar_computations_test.cc +++ b/tensorflow/compiler/xla/tests/scalar_computations_test.cc @@ -860,6 +860,12 @@ XLA_TEST_F(ScalarComputationsTest, MinF32Below) { TestMinMax(-100.1f, 3.1f, -100.1f, &ComputationBuilder::Min); } +XLA_TEST_F(ScalarComputationsTest, MinPropagatesNan) { + SetFastMathDisabled(true); + TestMinMax(NAN, 3.1f, NAN, &ComputationBuilder::Min); + TestMinMax(-3.1f, NAN, NAN, &ComputationBuilder::Min); +} + XLA_TEST_F(ScalarComputationsTest, MaxF32Above) { TestMinMax(10.1f, 3.1f, 10.1f, &ComputationBuilder::Max); } @@ -868,6 +874,12 @@ XLA_TEST_F(ScalarComputationsTest, MaxF32Below) { TestMinMax(-100.1f, 3.1f, 3.1f, &ComputationBuilder::Max); } +XLA_TEST_F(ScalarComputationsTest, MaxPropagatesNan) { + SetFastMathDisabled(true); + TestMinMax(NAN, 3.1f, NAN, &ComputationBuilder::Max); + TestMinMax(-3.1f, NAN, NAN, &ComputationBuilder::Max); +} + XLA_TEST_F(ScalarComputationsTest, ComplicatedArithmeticExpressionF32) { // Compute the expression (1 * (3 - 1) * (7 + 0) - 4) / 20. ComputationBuilder b(client_, TestName()); diff --git a/tensorflow/compiler/xla/tests/select_and_scatter_test.cc b/tensorflow/compiler/xla/tests/select_and_scatter_test.cc index 9ee94b8571e5fc8789b60501462986967ce909a0..d268fdcacebcb162bf61bc7dd4b208f4db6c4a5f 100644 --- a/tensorflow/compiler/xla/tests/select_and_scatter_test.cc +++ b/tensorflow/compiler/xla/tests/select_and_scatter_test.cc @@ -252,6 +252,21 @@ XLA_TEST_F(SelectAndScatterTest, R2S32) { ComputeAndCompareR2(&builder_, expected, {}); } +// Test for tie breaking rule in ge_f32_. When a tie is present, the operand +// that has the lower lexicographical order (smaller index) should be chosen. +XLA_TEST_F(SelectAndScatterTest, R2F32Tie) { + const auto operand = builder_.ConstantR2( + {{0.f, 0.f, 0.f}, {0.f, 0.f, 0.f}, {0.f, 0.f, 0.f}}); + const auto source = builder_.ConstantR2( + {{1.0f, 2.0f, 3.0f}, {4.f, 5.0f, 6.0f}, {7.0f, 8.0f, 9.0f}}); + Array2D expected( + {{12.f, 9.f, 0.f}, {15.f, 9.f, 0.f}, {0.f, 0.f, 0.f}}); + builder_.SelectAndScatter(operand, ge_f32_, /*window_dimensions=*/{3, 3}, + /*window_strides=*/{1, 1}, Padding::kSame, source, + builder_.ConstantR0(0.0f), add_f32_); + ComputeAndCompareR2(&builder_, expected, {}, ErrorSpec(1e-7)); +} + // Similar to SelectAndScatterTest.R2S32 but the input is transposed. XLA_TEST_F(SelectAndScatterTest, ReshapeR2S32) { const auto operand = builder_.ConstantR2( diff --git a/tensorflow/compiler/xla/tests/while_test.cc b/tensorflow/compiler/xla/tests/while_test.cc index 52157b837c383205f77a030ef98b2fd03a41aff5..33d457c70bac84c2da10e3cf9302c2c952cf1bc2 100644 --- a/tensorflow/compiler/xla/tests/while_test.cc +++ b/tensorflow/compiler/xla/tests/while_test.cc @@ -910,7 +910,7 @@ XLA_TEST_F(WhileTest, WhileWithDynamicUpdateSlice) { // Per backend the values generated can be different as the different backends // use different random number generators. // TODO(b/32240857): Extend test to verify outputs. -TEST_F(WhileTest, WhileWithPrngScalarResult) { +TEST_F(WhileTest, DISABLED_ON_INTERPRETER(WhileWithPrngScalarResult)) { auto v6s32 = ShapeUtil::MakeShape(S32, {6}); // Create a computation for the condition: repeat for count iterations. @@ -1166,7 +1166,7 @@ XLA_TEST_F(WhileTest, NestedWhileWithScalarResult) { // while (f(result).get<0>()) { // result = result + 1; // } -TEST_F(WhileTest, WhileWithCallInsideCondition) { +TEST_F(WhileTest, DISABLED_ON_INTERPRETER(WhileWithCallInsideCondition)) { auto result_shape = ShapeUtil::MakeShape(S32, {}); // Create a computation for the condition: repeat for 5 iterations. diff --git a/tensorflow/compiler/xla/tools/parser/hlo_parser.cc b/tensorflow/compiler/xla/tools/parser/hlo_parser.cc index cd2b843ad36013ae83818ecbc184fb823093f037..e60a5a4919f2207939821e787c3c59a08ff3ba4e 100644 --- a/tensorflow/compiler/xla/tools/parser/hlo_parser.cc +++ b/tensorflow/compiler/xla/tools/parser/hlo_parser.cc @@ -1049,9 +1049,40 @@ bool HloParser::ParseInstruction(HloComputation::Builder* builder, HloInstruction::CreateDot(shape, operands[0], operands[1], dnum)); break; } - case HloOpcode::kGather: - // TODO(b/72710576): HLO parsing is not implemented for Gather. - return TokenError("HLO parsing is not implemented for Gather"); + case HloOpcode::kGather: { + optional> output_window_dims; + attrs["output_window_dims"] = { + /*required=*/true, AttrTy::kBracedInt64List, &output_window_dims}; + optional> elided_window_dims; + attrs["elided_window_dims"] = { + /*required=*/true, AttrTy::kBracedInt64List, &elided_window_dims}; + optional> gather_dims_to_operand_dims; + attrs["gather_dims_to_operand_dims"] = {/*required=*/true, + AttrTy::kBracedInt64List, + &gather_dims_to_operand_dims}; + optional index_vector_dim; + attrs["index_vector_dim"] = {/*required=*/true, AttrTy::kInt64, + &index_vector_dim}; + optional> window_bounds; + attrs["window_bounds"] = {/*required=*/true, AttrTy::kBracedInt64List, + &window_bounds}; + + if (!ParseOperands(&operands, /*expected_size=*/2) || + !ParseAttributes(attrs)) { + return false; + } + + GatherDimensionNumbers dim_numbers = HloInstruction::MakeGatherDimNumbers( + /*output_window_dims=*/*output_window_dims, + /*elided_window_dims=*/*elided_window_dims, + /*gather_dims_to_operand_dims=*/*gather_dims_to_operand_dims, + /*index_vector_dim=*/*index_vector_dim); + + instruction = builder->AddInstruction(HloInstruction::CreateGather( + shape, /*operand=*/operands[0], /*gather_indices=*/operands[1], + dim_numbers, *window_bounds)); + break; + } case HloOpcode::kTrace: return TokenError(StrCat("parsing not yet implemented for op: ", HloOpcodeString(opcode))); diff --git a/tensorflow/compiler/xla/tools/parser/hlo_parser_test.cc b/tensorflow/compiler/xla/tools/parser/hlo_parser_test.cc index b8c6b59204f897c7dc07b846370b5b776a19a808..863081d654390440aa6506bab4576b3cc5c1cbd1 100644 --- a/tensorflow/compiler/xla/tools/parser/hlo_parser_test.cc +++ b/tensorflow/compiler/xla/tools/parser/hlo_parser_test.cc @@ -716,6 +716,18 @@ ENTRY %sparse_f32_r1 () -> f32[9] { ROOT %foo = f32[9]sparse{10} constant(f32[9]{1: 2, 3: 4, 5: 6}) } +)" +}, +{ +"gather", +R"(HloModule StringifyGather + +ENTRY %Gather (input_tensor: f32[50,49,48,47,46], gather_indices: s64[10,9,8,7,5]) -> f32[10,9,8,7,30,29,28,27,26] { + %input_tensor = f32[50,49,48,47,46]{4,3,2,1,0} parameter(0) + %gather_indices = s64[10,9,8,7,5]{4,3,2,1,0} parameter(1) + ROOT %gather = f32[10,9,8,7,30,29,28,27,26]{8,7,6,5,4,3,2,1,0} gather(f32[50,49,48,47,46]{4,3,2,1,0} %input_tensor, s64[10,9,8,7,5]{4,3,2,1,0} %gather_indices), output_window_dims={4,5,6,7,8}, elided_window_dims={}, gather_dims_to_operand_dims={0,1,2,3,4}, index_vector_dim=4, window_bounds={30,29,28,27,26} +} + )" }, }); @@ -860,6 +872,18 @@ ENTRY dot { ROOT dot = f32[2,3]{1,0} dot(a, b), lhs_batch_dims={0}, lhs_contracting_dims={1}, rhs_contracting_dims={0} } +)" +}, +{ +"gather", +R"(HloModule gather + +ENTRY Gather { + input_tensor = f32[50,49,48,47,46]{4,3,2,1,0} parameter(0) + gather_indices = s64[10,9,8,7,5]{4,3,2,1,0} parameter(1) + ROOT gather = f32[10,9,8,7,30,29,28,27,26]{8,7,6,5,4,3,2,1,0} gather(input_tensor, gather_indices), output_window_dims={4,5,6,7,8}, elided_window_dims={}, gather_dims_to_operand_dims={0,1,2,3,4}, index_vector_dim=4, window_bounds={30,29,28,27,26} +} + )" }, }); diff --git a/tensorflow/compiler/xla/util.cc b/tensorflow/compiler/xla/util.cc index 1f0c626bbb2d64ef4e67c9ec51485ae96ae73d04..dc4f7a1cb436183f5acfa360fb092795258b6a75 100644 --- a/tensorflow/compiler/xla/util.cc +++ b/tensorflow/compiler/xla/util.cc @@ -15,7 +15,6 @@ limitations under the License. #include "tensorflow/compiler/xla/util.h" -#include #include #include @@ -292,7 +291,8 @@ void LogLines(int sev, tensorflow::StringPiece text, const char* fname, } int64 Product(tensorflow::gtl::ArraySlice xs) { - return std::accumulate(xs.begin(), xs.end(), 1, std::multiplies()); + return std::accumulate(xs.begin(), xs.end(), static_cast(1), + std::multiplies()); } std::vector> CommonFactors( diff --git a/tensorflow/compiler/xla/util.h b/tensorflow/compiler/xla/util.h index e14c8cefa1d16e0a749e7a2c022a24a1c5083b15..ff99d3728d1c3b58fc94d3eb3de78be23407edc9 100644 --- a/tensorflow/compiler/xla/util.h +++ b/tensorflow/compiler/xla/util.h @@ -21,6 +21,7 @@ limitations under the License. #include #include +#include #include #include "tensorflow/compiler/xla/status.h" @@ -427,30 +428,37 @@ std::vector> CommonFactors( string SanitizeFileName(string file_name); template -bool c_all_of(Container container, Predicate&& predicate) { +bool c_all_of(const Container& container, Predicate&& predicate) { return std::all_of(std::begin(container), std::end(container), std::forward(predicate)); } +template +bool c_any_of(const Container& container, Predicate&& predicate) { + return std::any_of(std::begin(container), std::end(container), + std::forward(predicate)); +} + template -OutputIterator c_transform(InputContainer input_container, +OutputIterator c_transform(const InputContainer& input_container, OutputIterator output_iterator, - UnaryOperation unary_op) { + UnaryOperation&& unary_op) { return std::transform(std::begin(input_container), std::end(input_container), - output_iterator, unary_op); + output_iterator, + std::forward(unary_op)); } template -OutputIterator c_copy_if(InputContainer input_container, +OutputIterator c_copy_if(const InputContainer& input_container, OutputIterator output_iterator, - UnaryPredicate predicate) { + UnaryPredicate&& predicate) { return std::copy_if(std::begin(input_container), std::end(input_container), - output_iterator, predicate); + output_iterator, std::forward(predicate)); } template -OutputIterator c_copy(InputContainer input_container, +OutputIterator c_copy(const InputContainer& input_container, OutputIterator output_iterator) { return std::copy(std::begin(input_container), std::end(input_container), output_iterator); @@ -468,7 +476,7 @@ void c_sort(InputContainer& input_container, Comparator&& comparator) { } template -bool c_binary_search(Sequence& sequence, T&& value) { +bool c_binary_search(const Sequence& sequence, T&& value) { return std::binary_search(std::begin(sequence), std::end(sequence), std::forward(value)); } @@ -487,6 +495,30 @@ template auto c_find_if(const C& c, Pred&& pred) -> decltype(std::begin(c)) { return std::find_if(std::begin(c), std::end(c), std::forward(pred)); } + +template +auto c_find(const C& c, Value&& value) -> decltype(std::begin(c)) { + return std::find(std::begin(c), std::end(c), std::forward(value)); +} + +template +void c_reverse(Sequence& sequence) { + std::reverse(std::begin(sequence), std::end(sequence)); +} + +template +typename std::decay::type c_accumulate(const Sequence& sequence, T&& init, + BinaryOp&& binary_op) { + return std::accumulate(std::begin(sequence), std::end(sequence), + std::forward(init), + std::forward(binary_op)); +} + +template +int64 FindIndex(const C& c, Value&& value) { + auto it = c_find(c, std::forward(value)); + return std::distance(c.begin(), it); +} } // namespace xla #define XLA_LOG_LINES(SEV, STRING) \ diff --git a/tensorflow/compiler/xla/xla.proto b/tensorflow/compiler/xla/xla.proto index 56162ab44e2e0e3e4478fe631888f243332dc1d8..edf1b07af82b5d43fe67c6efdabdb0a9b4b1edea 100644 --- a/tensorflow/compiler/xla/xla.proto +++ b/tensorflow/compiler/xla/xla.proto @@ -16,6 +16,7 @@ limitations under the License. syntax = "proto3"; import "tensorflow/compiler/xla/xla_data.proto"; +import "tensorflow/compiler/xla/service/hlo.proto"; import "tensorflow/compiler/xla/service/session.proto"; package xla; @@ -342,6 +343,14 @@ message ExecuteRequest { ExecutionOptions execution_options = 5; } +message ExecuteGraphRequest { + HloModuleProto computation = 1; + repeated GlobalDataHandle arguments = 2; + + // Options that affect how XLA compiles and runs code to service this request. + ExecutionOptions execution_options = 3; +} + message ExecuteParallelRequest { repeated ExecuteRequest requests = 1; } diff --git a/tensorflow/compiler/xla/xla_data.proto b/tensorflow/compiler/xla/xla_data.proto index 28620c3b86349281573eaf57d2838bee1488d838..1f16e6d25178fd9c10a30b0c500e090ee2e08117 100644 --- a/tensorflow/compiler/xla/xla_data.proto +++ b/tensorflow/compiler/xla/xla_data.proto @@ -418,6 +418,10 @@ message GatherDimensionNumbers { // transforms the gather index looked up from the gather_indices tensor into // the starting index in the input space. repeated int64 gather_dims_to_operand_dims = 3; + + // The dimension in the gather_indices input that contains the starting + // indices. + int64 index_vector_dim = 4; } // Operation requests that are all collected as a tagged union with a oneof diff --git a/tensorflow/contrib/BUILD b/tensorflow/contrib/BUILD index bab37e8906e5c648acdc1556da7e5f4601776ff5..c2663c5e83352a1088166dc7581a0346c7b104a4 100644 --- a/tensorflow/contrib/BUILD +++ b/tensorflow/contrib/BUILD @@ -8,6 +8,7 @@ package(default_visibility = ["//tensorflow:__subpackages__"]) load("//third_party/mpi:mpi.bzl", "if_mpi") load("@local_config_cuda//cuda:build_defs.bzl", "if_cuda") load("@local_config_tensorrt//:build_defs.bzl", "if_tensorrt") +load("//tensorflow:tensorflow.bzl", "if_not_windows") py_library( name = "contrib_py", @@ -39,7 +40,6 @@ py_library( "//tensorflow/contrib/estimator:estimator_py", "//tensorflow/contrib/factorization:factorization_py", "//tensorflow/contrib/feature_column:feature_column_py", - "//tensorflow/contrib/ffmpeg:ffmpeg_ops_py", "//tensorflow/contrib/framework:framework_py", "//tensorflow/contrib/fused_conv:fused_conv_py", "//tensorflow/contrib/gan", @@ -51,7 +51,6 @@ py_library( "//tensorflow/contrib/image:single_image_random_dot_stereograms_py", "//tensorflow/contrib/input_pipeline:input_pipeline_py", "//tensorflow/contrib/integrate:integrate_py", - "//tensorflow/contrib/kafka", "//tensorflow/contrib/keras", "//tensorflow/contrib/kernel_methods", "//tensorflow/contrib/kfac", @@ -63,7 +62,6 @@ py_library( "//tensorflow/contrib/linalg:linalg_py", "//tensorflow/contrib/linear_optimizer:sdca_estimator_py", "//tensorflow/contrib/linear_optimizer:sdca_ops_py", - "//tensorflow/contrib/lite/python:lite", "//tensorflow/contrib/lookup:lookup_py", "//tensorflow/contrib/losses:losses_py", "//tensorflow/contrib/losses:metric_learning_py", @@ -110,6 +108,10 @@ py_library( "//tensorflow/python:util", ] + if_mpi(["//tensorflow/contrib/mpi_collectives:mpi_collectives_py"]) + if_tensorrt([ "//tensorflow/contrib/tensorrt:init_py", + ]) + if_not_windows([ + "//tensorflow/contrib/ffmpeg:ffmpeg_ops_py", # unix dependency, need to fix code + "//tensorflow/contrib/lite/python:lite", # unix dependency, need to fix code + "//tensorflow/contrib/kafka", # has some linking issue on opensssl. ]), ) @@ -121,6 +123,7 @@ cc_library( "//tensorflow/contrib/coder:all_kernels", "//tensorflow/contrib/cudnn_rnn:cudnn_rnn_kernels", "//tensorflow/contrib/data/kernels:dataset_kernels", + "//tensorflow/contrib/kafka:dataset_kernels", "//tensorflow/contrib/factorization/kernels:all_kernels", "//tensorflow/contrib/input_pipeline:input_pipeline_ops_kernels", "//tensorflow/contrib/layers:sparse_feature_cross_op_kernel", @@ -147,7 +150,7 @@ cc_library( "//tensorflow/contrib/factorization:all_ops", "//tensorflow/contrib/framework:all_ops", "//tensorflow/contrib/input_pipeline:input_pipeline_ops_op_lib", - "//tensorflow/contrib/kafka:kafka_ops_op_lib", + "//tensorflow/contrib/kafka:dataset_ops_op_lib", "//tensorflow/contrib/layers:sparse_feature_cross_op_op_lib", "//tensorflow/contrib/nccl:nccl_ops_op_lib", "//tensorflow/contrib/nearest_neighbor:nearest_neighbor_ops_op_lib", diff --git a/tensorflow/contrib/__init__.py b/tensorflow/contrib/__init__.py index 4f6f539027b040de7554d09fe9118ff97aa006f8..669d611b01b585d91ab48921b7ba17703dd6bc98 100644 --- a/tensorflow/contrib/__init__.py +++ b/tensorflow/contrib/__init__.py @@ -18,6 +18,8 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import os + # Add projects here, they will show up under tf.contrib. from tensorflow.contrib import batching from tensorflow.contrib import bayesflow @@ -83,7 +85,8 @@ from tensorflow.contrib import tpu from tensorflow.contrib import training from tensorflow.contrib import util from tensorflow.contrib.eager.python import tfe as eager -from tensorflow.contrib.lite.python import lite +if os.name != 'nt': + from tensorflow.contrib.lite.python import lite from tensorflow.contrib.receptive_field import receptive_field_api as receptive_field from tensorflow.contrib.remote_fused_graph import pylib as remote_fused_graph from tensorflow.contrib.specs import python as specs @@ -92,6 +95,7 @@ from tensorflow.contrib.summary import summary from tensorflow.python.util.lazy_loader import LazyLoader ffmpeg = LazyLoader("ffmpeg", globals(), "tensorflow.contrib.ffmpeg") +del os del LazyLoader del absolute_import diff --git a/tensorflow/contrib/bayesflow/BUILD b/tensorflow/contrib/bayesflow/BUILD index d7beb26e1b77c44d45a21a8c2bc752c93bf5b313..c6feec68e0104ff33451bbb6fa7de51d13e0a43c 100644 --- a/tensorflow/contrib/bayesflow/BUILD +++ b/tensorflow/contrib/bayesflow/BUILD @@ -39,7 +39,7 @@ py_library( cuda_py_test( name = "metropolis_hastings_test", - size = "medium", + size = "large", srcs = ["python/kernel_tests/metropolis_hastings_test.py"], additional_deps = [ ":bayesflow_py", @@ -56,117 +56,6 @@ cuda_py_test( ], ) -cuda_py_test( - name = "csiszar_divergence_test", - size = "medium", - srcs = ["python/kernel_tests/csiszar_divergence_test.py"], - additional_deps = [ - ":bayesflow_py", - "//third_party/py/numpy", - "//tensorflow/contrib/distributions:distributions_py", - "//tensorflow/python/ops/distributions", - "//tensorflow/python:array_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:gradients", - "//tensorflow/python:linalg_ops", - "//tensorflow/python:math_ops", - "//tensorflow/python:nn_ops", - ], - tags = [ - "manual", # b/64490288 - "notap", - ], -) - -cuda_py_test( - name = "custom_grad_test", - size = "small", - srcs = ["python/kernel_tests/custom_grad_test.py"], - additional_deps = [ - ":bayesflow_py", - "//third_party/py/numpy", - "//tensorflow/contrib/layers:layers_py", - "//tensorflow/python:array_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:framework_test_lib", - "//tensorflow/python:gradients", - "//tensorflow/python:init_ops", - "//tensorflow/python:platform_test", - "//tensorflow/python:variable_scope", - "//tensorflow/python:variables", - ], -) - -cuda_py_test( - name = "docstring_util_test", - size = "small", - srcs = ["python/kernel_tests/docstring_util_test.py"], - additional_deps = [ - ":bayesflow_py", - "//tensorflow/python:client_testlib", - ], -) - -cuda_py_test( - name = "layers_conv_variational_test", - size = "small", - srcs = ["python/kernel_tests/layers_conv_variational_test.py"], - additional_deps = [ - ":bayesflow_py", - "//third_party/py/numpy", - "//tensorflow/contrib/distributions:distributions_py", - "//tensorflow/python/ops/distributions", - "//tensorflow/python:array_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:gradients", - "//tensorflow/python:linalg_ops", - "//tensorflow/python:math_ops", - "//tensorflow/python:nn_ops", - ], -) - -cuda_py_test( - name = "layers_dense_variational_test", - size = "small", - srcs = ["python/kernel_tests/layers_dense_variational_test.py"], - additional_deps = [ - ":bayesflow_py", - "//third_party/py/numpy", - "//tensorflow/contrib/distributions:distributions_py", - "//tensorflow/python/ops/distributions", - "//tensorflow/python:array_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:gradients", - "//tensorflow/python:linalg_ops", - "//tensorflow/python:math_ops", - "//tensorflow/python:nn_ops", - ], -) - -cuda_py_test( - name = "mcmc_diagnostics_test", - size = "small", - srcs = ["python/kernel_tests/mcmc_diagnostics_test.py"], - additional_deps = [ - ":bayesflow_py", - "//third_party/py/numpy", - "//tensorflow/python:spectral_ops_test_util", - "//tensorflow/contrib/distributions:distributions_py", - "//tensorflow/python/ops/distributions", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:framework_test_lib", - "//tensorflow/python:math_ops", - "//tensorflow/python:platform_test", - "//tensorflow/python:random_seed", - ], -) - cuda_py_test( name = "monte_carlo_test", size = "small", @@ -188,29 +77,9 @@ cuda_py_test( ], ) -cuda_py_test( - name = "halton_sequence_test", - size = "small", - srcs = ["python/kernel_tests/halton_sequence_test.py"], - additional_deps = [ - ":bayesflow_py", - "//third_party/py/numpy", - "//tensorflow/python:array_ops", - "//tensorflow/python:math_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:platform_test", - "//tensorflow/python:random_ops", - "//tensorflow/python:variable_scope", - "//tensorflow/python:variables", - ], - tags = ["no_mac"], # b/73192243 -) - cuda_py_test( name = "hmc_test", - size = "medium", + size = "large", srcs = ["python/kernel_tests/hmc_test.py"], additional_deps = [ ":bayesflow_py", @@ -227,67 +96,7 @@ cuda_py_test( "//tensorflow/python:platform_test", "//tensorflow/python:random_seed", ], -) - -cuda_py_test( - name = "sgld_optimizer_test", - size = "small", - srcs = ["python/kernel_tests/sgld_optimizer_test.py"], - additional_deps = [ - ":bayesflow_py", - "//third_party/py/numpy", - "//tensorflow/contrib/distributions:distributions_py", - "//tensorflow/contrib/layers:layers_py", - "//tensorflow/python/ops/distributions", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:framework_test_lib", - "//tensorflow/python:gradients", - "//tensorflow/python:math_ops", - "//tensorflow/python:platform_test", - "//tensorflow/python:random_seed", - ], - tags = ["notsan"], -) - -cuda_py_test( - name = "variable_utils_test", - size = "small", - srcs = ["python/kernel_tests/variable_utils_test.py"], - additional_deps = [ - ":bayesflow_py", - "//third_party/py/numpy", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:framework_test_lib", - "//tensorflow/python:gradients", - "//tensorflow/python:math_ops", - "//tensorflow/python:platform_test", - ], -) - -cuda_py_test( - name = "variational_sgd_optimizer_test", - size = "small", - srcs = ["python/kernel_tests/variational_sgd_optimizer_test.py"], - additional_deps = [ - ":bayesflow_py", - "//third_party/py/numpy", - "//tensorflow/contrib/distributions:distributions_py", - "//tensorflow/contrib/layers:layers_py", - "//tensorflow/python/ops/distributions", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:framework_test_lib", - "//tensorflow/python:gradients", - "//tensorflow/python:math_ops", - "//tensorflow/python:platform_test", - "//tensorflow/python:random_seed", - ], - tags = ["notsan"], + tags = ["nomsan"], ) filegroup( diff --git a/tensorflow/contrib/bayesflow/__init__.py b/tensorflow/contrib/bayesflow/__init__.py index 528c4fbacd06c7b0defa0e32bd24a98b2bc07b64..f86820382682f79e85e6a92c7f63fa15bb8be1a3 100644 --- a/tensorflow/contrib/bayesflow/__init__.py +++ b/tensorflow/contrib/bayesflow/__init__.py @@ -21,35 +21,21 @@ from __future__ import division from __future__ import print_function # pylint: disable=unused-import,line-too-long -from tensorflow.contrib.bayesflow.python.ops import csiszar_divergence -from tensorflow.contrib.bayesflow.python.ops import custom_grad -from tensorflow.contrib.bayesflow.python.ops import halton_sequence from tensorflow.contrib.bayesflow.python.ops import hmc -from tensorflow.contrib.bayesflow.python.ops import layers -from tensorflow.contrib.bayesflow.python.ops import mcmc_diagnostics from tensorflow.contrib.bayesflow.python.ops import metropolis_hastings from tensorflow.contrib.bayesflow.python.ops import monte_carlo -from tensorflow.contrib.bayesflow.python.ops import optimizers -from tensorflow.contrib.bayesflow.python.ops import variable_utils # pylint: enable=unused-import,line-too-long from tensorflow.python.util.all_util import remove_undocumented _allowed_symbols = [ - 'csiszar_divergence', - 'custom_grad', 'entropy', - 'halton_sequence', 'hmc', - 'layers', 'metropolis_hastings', - 'mcmc_diagnostics', 'monte_carlo', - 'optimizers', 'special_math', 'stochastic_variables', - 'variable_utils', 'variational_inference', ] diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/csiszar_divergence_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/csiszar_divergence_test.py deleted file mode 100644 index 2e94b7206de4f7c40c89f083f3bfa2a22bb7b917..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/bayesflow/python/kernel_tests/csiszar_divergence_test.py +++ /dev/null @@ -1,1004 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for Csiszar Divergence Ops.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import numpy as np - -from tensorflow.contrib.bayesflow.python.ops import csiszar_divergence_impl -from tensorflow.contrib.distributions.python.ops import mvn_diag as mvn_diag_lib -from tensorflow.contrib.distributions.python.ops import mvn_full_covariance as mvn_full_lib -from tensorflow.python.framework import ops -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import gradients_impl -from tensorflow.python.ops import linalg_ops -from tensorflow.python.ops import math_ops -from tensorflow.python.ops import nn_ops -from tensorflow.python.ops.distributions import kullback_leibler -from tensorflow.python.ops.distributions import normal as normal_lib -from tensorflow.python.platform import test - - -cd = csiszar_divergence_impl - - -def tridiag(d, diag_value, offdiag_value): - """d x d matrix with given value on diag, and one super/sub diag.""" - diag_mat = linalg_ops.eye(d) * (diag_value - offdiag_value) - three_bands = array_ops.matrix_band_part( - array_ops.fill([d, d], offdiag_value), 1, 1) - return diag_mat + three_bands - - -class AmariAlphaTest(test.TestCase): - - def setUp(self): - self._logu = np.linspace(-10., 10, 100) - self._u = np.exp(self._logu) - - def test_at_zero(self): - for alpha in [-1., 0., 1., 2.]: - for normalized in [True, False]: - with self.test_session(graph=ops.Graph()): - self.assertAllClose( - cd.amari_alpha(0., alpha=alpha, - self_normalized=normalized).eval(), - 0.) - - def test_correct_when_alpha0(self): - with self.test_session(): - self.assertAllClose( - cd.amari_alpha(self._logu, alpha=0.).eval(), - -self._logu) - - self.assertAllClose( - cd.amari_alpha(self._logu, alpha=0., self_normalized=True).eval(), - -self._logu + (self._u - 1.)) - - def test_correct_when_alpha1(self): - with self.test_session(): - self.assertAllClose( - cd.amari_alpha(self._logu, alpha=1.).eval(), - self._u * self._logu) - - self.assertAllClose( - cd.amari_alpha(self._logu, alpha=1., self_normalized=True).eval(), - self._u * self._logu - (self._u - 1.)) - - def test_correct_when_alpha_not_01(self): - for alpha in [-2, -1., -0.5, 0.5, 2.]: - with self.test_session(graph=ops.Graph()): - self.assertAllClose( - cd.amari_alpha(self._logu, - alpha=alpha, - self_normalized=False).eval(), - ((self._u**alpha - 1)) / (alpha * (alpha - 1.))) - - self.assertAllClose( - cd.amari_alpha(self._logu, - alpha=alpha, - self_normalized=True).eval(), - ((self._u**alpha - 1.) - - alpha * (self._u - 1)) / (alpha * (alpha - 1.))) - - -class KLReverseTest(test.TestCase): - - def setUp(self): - self._logu = np.linspace(-10., 10, 100) - self._u = np.exp(self._logu) - - def test_at_zero(self): - for normalized in [True, False]: - with self.test_session(graph=ops.Graph()): - self.assertAllClose( - cd.kl_reverse(0., self_normalized=normalized).eval(), - 0.) - - def test_correct(self): - with self.test_session(): - self.assertAllClose( - cd.kl_reverse(self._logu).eval(), - -self._logu) - - self.assertAllClose( - cd.kl_reverse(self._logu, self_normalized=True).eval(), - -self._logu + (self._u - 1.)) - - -class KLForwardTest(test.TestCase): - - def setUp(self): - self._logu = np.linspace(-10., 10, 100) - self._u = np.exp(self._logu) - - def test_at_zero(self): - for normalized in [True, False]: - with self.test_session(graph=ops.Graph()): - self.assertAllClose( - cd.kl_forward(0., self_normalized=normalized).eval(), - 0.) - - def test_correct(self): - with self.test_session(): - self.assertAllClose( - cd.kl_forward(self._logu).eval(), - self._u * self._logu) - - self.assertAllClose( - cd.kl_forward(self._logu, self_normalized=True).eval(), - self._u * self._logu - (self._u - 1.)) - - -class JensenShannonTest(test.TestCase): - - def setUp(self): - self._logu = np.linspace(-10., 10, 100) - self._u = np.exp(self._logu) - - def test_at_zero(self): - with self.test_session(): - self.assertAllClose(cd.jensen_shannon(0.).eval(), np.log(0.25)) - - def test_symmetric(self): - with self.test_session(): - self.assertAllClose( - cd.jensen_shannon(self._logu).eval(), - cd.symmetrized_csiszar_function( - self._logu, cd.jensen_shannon).eval()) - - self.assertAllClose( - cd.jensen_shannon(self._logu, self_normalized=True).eval(), - cd.symmetrized_csiszar_function( - self._logu, - lambda x: cd.jensen_shannon(x, self_normalized=True)).eval()) - - def test_correct(self): - with self.test_session(): - self.assertAllClose( - cd.jensen_shannon(self._logu).eval(), - (self._u * self._logu - - (1 + self._u) * np.log1p(self._u))) - - self.assertAllClose( - cd.jensen_shannon(self._logu, self_normalized=True).eval(), - (self._u * self._logu - - (1 + self._u) * np.log((1 + self._u) / 2))) - - -class ArithmeticGeometricMeanTest(test.TestCase): - - def setUp(self): - self._logu = np.linspace(-10., 10, 100) - self._u = np.exp(self._logu) - - def test_at_zero(self): - with self.test_session(): - self.assertAllClose(cd.arithmetic_geometric(0.).eval(), np.log(4)) - self.assertAllClose( - cd.arithmetic_geometric(0., self_normalized=True).eval(), 0.) - - def test_symmetric(self): - with self.test_session(): - self.assertAllClose( - cd.arithmetic_geometric(self._logu).eval(), - cd.symmetrized_csiszar_function( - self._logu, cd.arithmetic_geometric).eval()) - - def test_correct(self): - with self.test_session(): - self.assertAllClose( - cd.arithmetic_geometric(self._logu).eval(), - (1. + self._u) * np.log((1. + self._u) / np.sqrt(self._u))) - - self.assertAllClose( - cd.arithmetic_geometric(self._logu, self_normalized=True).eval(), - (1. + self._u) * np.log(0.5 * (1. + self._u) / np.sqrt(self._u))) - - -class TotalVariationTest(test.TestCase): - - def setUp(self): - self._logu = np.linspace(-10., 10, 100) - self._u = np.exp(self._logu) - - def test_at_zero(self): - with self.test_session(): - self.assertAllClose(cd.total_variation(0.).eval(), 0.) - - def test_correct(self): - with self.test_session(): - self.assertAllClose( - cd.total_variation(self._logu).eval(), - 0.5 * np.abs(self._u - 1)) - - -class PearsonTest(test.TestCase): - - def setUp(self): - self._logu = np.linspace(-10., 10, 100) - self._u = np.exp(self._logu) - - def test_at_zero(self): - with self.test_session(): - self.assertAllClose(cd.pearson(0.).eval(), 0.) - - def test_correct(self): - with self.test_session(): - self.assertAllClose( - cd.pearson(self._logu).eval(), - np.square(self._u - 1)) - - -class SquaredHellingerTest(test.TestCase): - - def setUp(self): - self._logu = np.linspace(-10., 10, 100) - self._u = np.exp(self._logu) - - def test_at_zero(self): - with self.test_session(): - self.assertAllClose(cd.squared_hellinger(0.).eval(), 0.) - - def test_symmetric(self): - with self.test_session(): - self.assertAllClose( - cd.squared_hellinger(self._logu).eval(), - cd.symmetrized_csiszar_function( - self._logu, cd.squared_hellinger).eval()) - - def test_correct(self): - with self.test_session(): - self.assertAllClose( - cd.squared_hellinger(self._logu).eval(), - np.square(np.sqrt(self._u) - 1)) - - -class TriangularTest(test.TestCase): - - def setUp(self): - self._logu = np.linspace(-10., 10, 100) - self._u = np.exp(self._logu) - - def test_at_zero(self): - with self.test_session(): - self.assertAllClose(cd.triangular(0.).eval(), 0.) - - def test_symmetric(self): - with self.test_session(): - self.assertAllClose( - cd.triangular(self._logu).eval(), - cd.symmetrized_csiszar_function( - self._logu, cd.triangular).eval()) - - def test_correct(self): - with self.test_session(): - self.assertAllClose( - cd.triangular(self._logu).eval(), - np.square(self._u - 1) / (1 + self._u)) - - -class TPowerTest(test.TestCase): - - def setUp(self): - self._logu = np.linspace(-10., 10, 100) - self._u = np.exp(self._logu) - - def test_at_zero(self): - with self.test_session(): - self.assertAllClose(cd.t_power(0., t=-0.1).eval(), 0.) - self.assertAllClose(cd.t_power(0., t=0.5).eval(), 0.) - self.assertAllClose(cd.t_power(0., t=1.1).eval(), 0.) - self.assertAllClose( - cd.t_power(0., t=-0.1, self_normalized=True).eval(), 0.) - self.assertAllClose( - cd.t_power(0., t=0.5, self_normalized=True).eval(), 0.) - self.assertAllClose( - cd.t_power(0., t=1.1, self_normalized=True).eval(), 0.) - - def test_correct(self): - with self.test_session(): - self.assertAllClose( - cd.t_power(self._logu, t=np.float64(-0.1)).eval(), - self._u ** -0.1 - 1.) - self.assertAllClose( - cd.t_power(self._logu, t=np.float64(0.5)).eval(), - -self._u ** 0.5 + 1.) - self.assertAllClose( - cd.t_power(self._logu, t=np.float64(1.1)).eval(), - self._u ** 1.1 - 1.) - - def test_correct_self_normalized(self): - with self.test_session(): - self.assertAllClose( - cd.t_power(self._logu, t=np.float64(-0.1), - self_normalized=True).eval(), - self._u ** -0.1 - 1. + 0.1 * (self._u - 1.)) - self.assertAllClose( - cd.t_power(self._logu, t=np.float64(0.5), - self_normalized=True).eval(), - -self._u ** 0.5 + 1. + 0.5 * (self._u - 1.)) - self.assertAllClose( - cd.t_power(self._logu, t=np.float64(1.1), - self_normalized=True).eval(), - self._u ** 1.1 - 1. - 1.1 * (self._u - 1.)) - - -class Log1pAbsTest(test.TestCase): - - def setUp(self): - self._logu = np.linspace(-10., 10, 100) - self._u = np.exp(self._logu) - - def test_at_zero(self): - with self.test_session(): - self.assertAllClose(cd.log1p_abs(0.).eval(), 0.) - - def test_correct(self): - with self.test_session(): - self.assertAllClose( - cd.log1p_abs(self._logu).eval(), - self._u**(np.sign(self._u - 1)) - 1) - - -class JeffreysTest(test.TestCase): - - def setUp(self): - self._logu = np.linspace(-10., 10, 100) - self._u = np.exp(self._logu) - - def test_at_zero(self): - with self.test_session(): - self.assertAllClose(cd.jeffreys(0.).eval(), 0.) - - def test_symmetric(self): - with self.test_session(): - self.assertAllClose( - cd.jeffreys(self._logu).eval(), - cd.symmetrized_csiszar_function( - self._logu, cd.jeffreys).eval()) - - def test_correct(self): - with self.test_session(): - self.assertAllClose( - cd.jeffreys(self._logu).eval(), - 0.5 * (self._u * self._logu - self._logu)) - - -class ChiSquareTest(test.TestCase): - - def setUp(self): - self._logu = np.linspace(-10., 10, 100) - self._u = np.exp(self._logu) - - def test_at_zero(self): - with self.test_session(): - self.assertAllClose(cd.chi_square(0.).eval(), 0.) - - def test_correct(self): - with self.test_session(): - self.assertAllClose( - cd.chi_square(self._logu).eval(), - self._u**2 - 1) - - -class ModifiedGanTest(test.TestCase): - - def setUp(self): - self._logu = np.linspace(-10., 10, 100) - self._u = np.exp(self._logu) - - def test_at_zero(self): - with self.test_session(): - self.assertAllClose( - cd.modified_gan(0.).eval(), np.log(2)) - self.assertAllClose( - cd.modified_gan(0., self_normalized=True).eval(), np.log(2)) - - def test_correct(self): - with self.test_session(): - self.assertAllClose( - cd.modified_gan(self._logu).eval(), - np.log1p(self._u) - self._logu) - - self.assertAllClose( - cd.modified_gan(self._logu, self_normalized=True).eval(), - np.log1p(self._u) - self._logu + 0.5 * (self._u - 1)) - - -class SymmetrizedCsiszarFunctionTest(test.TestCase): - - def setUp(self): - self._logu = np.linspace(-10., 10., 100) - self._u = np.exp(self._logu) - - def test_jensen_shannon(self): - with self.test_session(): - - # The following functions come from the claim made in the - # symmetrized_csiszar_function docstring. - def js1(logu): - return (-logu - - (1. + math_ops.exp(logu)) * ( - nn_ops.softplus(logu))) - - def js2(logu): - return 2. * (math_ops.exp(logu) * ( - logu - nn_ops.softplus(logu))) - - self.assertAllClose( - cd.symmetrized_csiszar_function(self._logu, js1).eval(), - cd.jensen_shannon(self._logu).eval()) - - self.assertAllClose( - cd.symmetrized_csiszar_function(self._logu, js2).eval(), - cd.jensen_shannon(self._logu).eval()) - - def test_jeffreys(self): - with self.test_session(): - self.assertAllClose( - cd.symmetrized_csiszar_function(self._logu, cd.kl_reverse).eval(), - cd.jeffreys(self._logu).eval()) - - self.assertAllClose( - cd.symmetrized_csiszar_function(self._logu, cd.kl_forward).eval(), - cd.jeffreys(self._logu).eval()) - - -class DualCsiszarFunctionTest(test.TestCase): - - def setUp(self): - self._logu = np.linspace(-10., 10., 100) - self._u = np.exp(self._logu) - - def test_kl_forward(self): - with self.test_session(): - self.assertAllClose( - cd.dual_csiszar_function(self._logu, cd.kl_forward).eval(), - cd.kl_reverse(self._logu).eval()) - - def test_kl_reverse(self): - with self.test_session(): - self.assertAllClose( - cd.dual_csiszar_function(self._logu, cd.kl_reverse).eval(), - cd.kl_forward(self._logu).eval()) - - -class MonteCarloCsiszarFDivergenceTest(test.TestCase): - - def test_kl_forward(self): - with self.test_session() as sess: - q = normal_lib.Normal( - loc=np.ones(6), - scale=np.array([0.5, 1.0, 1.5, 2.0, 2.5, 3.0])) - - p = normal_lib.Normal(loc=q.loc + 0.1, scale=q.scale - 0.2) - - approx_kl = cd.monte_carlo_csiszar_f_divergence( - f=cd.kl_forward, - p_log_prob=p.log_prob, - q=q, - num_draws=int(1e5), - seed=1) - - approx_kl_self_normalized = cd.monte_carlo_csiszar_f_divergence( - f=lambda logu: cd.kl_forward(logu, self_normalized=True), - p_log_prob=p.log_prob, - q=q, - num_draws=int(1e5), - seed=1) - - exact_kl = kullback_leibler.kl_divergence(p, q) - - [approx_kl_, approx_kl_self_normalized_, exact_kl_] = sess.run([ - approx_kl, approx_kl_self_normalized, exact_kl]) - - self.assertAllClose(approx_kl_, exact_kl_, - rtol=0.08, atol=0.) - - self.assertAllClose(approx_kl_self_normalized_, exact_kl_, - rtol=0.02, atol=0.) - - def test_kl_reverse(self): - with self.test_session() as sess: - - q = normal_lib.Normal( - loc=np.ones(6), - scale=np.array([0.5, 1.0, 1.5, 2.0, 2.5, 3.0])) - - p = normal_lib.Normal(loc=q.loc + 0.1, scale=q.scale - 0.2) - - approx_kl = cd.monte_carlo_csiszar_f_divergence( - f=cd.kl_reverse, - p_log_prob=p.log_prob, - q=q, - num_draws=int(1e5), - seed=1) - - approx_kl_self_normalized = cd.monte_carlo_csiszar_f_divergence( - f=lambda logu: cd.kl_reverse(logu, self_normalized=True), - p_log_prob=p.log_prob, - q=q, - num_draws=int(1e5), - seed=1) - - exact_kl = kullback_leibler.kl_divergence(q, p) - - [approx_kl_, approx_kl_self_normalized_, exact_kl_] = sess.run([ - approx_kl, approx_kl_self_normalized, exact_kl]) - - self.assertAllClose(approx_kl_, exact_kl_, - rtol=0.07, atol=0.) - - self.assertAllClose(approx_kl_self_normalized_, exact_kl_, - rtol=0.02, atol=0.) - - def test_kl_reverse_multidim(self): - - with self.test_session() as sess: - d = 5 # Dimension - - p = mvn_full_lib.MultivariateNormalFullCovariance( - covariance_matrix=tridiag(d, diag_value=1, offdiag_value=0.5)) - - q = mvn_diag_lib.MultivariateNormalDiag(scale_diag=[0.5]*d) - - approx_kl = cd.monte_carlo_csiszar_f_divergence( - f=cd.kl_reverse, - p_log_prob=p.log_prob, - q=q, - num_draws=int(1e5), - seed=1) - - approx_kl_self_normalized = cd.monte_carlo_csiszar_f_divergence( - f=lambda logu: cd.kl_reverse(logu, self_normalized=True), - p_log_prob=p.log_prob, - q=q, - num_draws=int(1e5), - seed=1) - - exact_kl = kullback_leibler.kl_divergence(q, p) - - [approx_kl_, approx_kl_self_normalized_, exact_kl_] = sess.run([ - approx_kl, approx_kl_self_normalized, exact_kl]) - - self.assertAllClose(approx_kl_, exact_kl_, - rtol=0.02, atol=0.) - - self.assertAllClose(approx_kl_self_normalized_, exact_kl_, - rtol=0.08, atol=0.) - - def test_kl_forward_multidim(self): - - with self.test_session() as sess: - d = 5 # Dimension - - p = mvn_full_lib.MultivariateNormalFullCovariance( - covariance_matrix=tridiag(d, diag_value=1, offdiag_value=0.5)) - - # Variance is very high when approximating Forward KL, so we make - # scale_diag larger than in test_kl_reverse_multidim. This ensures q - # "covers" p and thus Var_q[p/q] is smaller. - q = mvn_diag_lib.MultivariateNormalDiag(scale_diag=[1.]*d) - - approx_kl = cd.monte_carlo_csiszar_f_divergence( - f=cd.kl_forward, - p_log_prob=p.log_prob, - q=q, - num_draws=int(1e5), - seed=1) - - approx_kl_self_normalized = cd.monte_carlo_csiszar_f_divergence( - f=lambda logu: cd.kl_forward(logu, self_normalized=True), - p_log_prob=p.log_prob, - q=q, - num_draws=int(1e5), - seed=1) - - exact_kl = kullback_leibler.kl_divergence(p, q) - - [approx_kl_, approx_kl_self_normalized_, exact_kl_] = sess.run([ - approx_kl, approx_kl_self_normalized, exact_kl]) - - self.assertAllClose(approx_kl_, exact_kl_, - rtol=0.06, atol=0.) - - self.assertAllClose(approx_kl_self_normalized_, exact_kl_, - rtol=0.05, atol=0.) - - def test_score_trick(self): - - with self.test_session() as sess: - d = 5 # Dimension - num_draws = int(1e5) - seed = 1 - - p = mvn_full_lib.MultivariateNormalFullCovariance( - covariance_matrix=tridiag(d, diag_value=1, offdiag_value=0.5)) - - # Variance is very high when approximating Forward KL, so we make - # scale_diag larger than in test_kl_reverse_multidim. This ensures q - # "covers" p and thus Var_q[p/q] is smaller. - s = array_ops.constant(1.) - q = mvn_diag_lib.MultivariateNormalDiag( - scale_diag=array_ops.tile([s], [d])) - - approx_kl = cd.monte_carlo_csiszar_f_divergence( - f=cd.kl_reverse, - p_log_prob=p.log_prob, - q=q, - num_draws=num_draws, - seed=seed) - - approx_kl_self_normalized = cd.monte_carlo_csiszar_f_divergence( - f=lambda logu: cd.kl_reverse(logu, self_normalized=True), - p_log_prob=p.log_prob, - q=q, - num_draws=num_draws, - seed=seed) - - approx_kl_score_trick = cd.monte_carlo_csiszar_f_divergence( - f=cd.kl_reverse, - p_log_prob=p.log_prob, - q=q, - num_draws=num_draws, - use_reparametrization=False, - seed=seed) - - approx_kl_self_normalized_score_trick = ( - cd.monte_carlo_csiszar_f_divergence( - f=lambda logu: cd.kl_reverse(logu, self_normalized=True), - p_log_prob=p.log_prob, - q=q, - num_draws=num_draws, - use_reparametrization=False, - seed=seed)) - - exact_kl = kullback_leibler.kl_divergence(q, p) - - grad_sum = lambda fs: gradients_impl.gradients(fs, s)[0] - - [ - approx_kl_grad_, - approx_kl_self_normalized_grad_, - approx_kl_score_trick_grad_, - approx_kl_self_normalized_score_trick_grad_, - exact_kl_grad_, - approx_kl_, - approx_kl_self_normalized_, - approx_kl_score_trick_, - approx_kl_self_normalized_score_trick_, - exact_kl_, - ] = sess.run([ - grad_sum(approx_kl), - grad_sum(approx_kl_self_normalized), - grad_sum(approx_kl_score_trick), - grad_sum(approx_kl_self_normalized_score_trick), - grad_sum(exact_kl), - approx_kl, - approx_kl_self_normalized, - approx_kl_score_trick, - approx_kl_self_normalized_score_trick, - exact_kl, - ]) - - # Test average divergence. - self.assertAllClose(approx_kl_, exact_kl_, - rtol=0.02, atol=0.) - - self.assertAllClose(approx_kl_self_normalized_, exact_kl_, - rtol=0.08, atol=0.) - - self.assertAllClose(approx_kl_score_trick_, exact_kl_, - rtol=0.02, atol=0.) - - self.assertAllClose(approx_kl_self_normalized_score_trick_, exact_kl_, - rtol=0.08, atol=0.) - - # Test average gradient-divergence. - self.assertAllClose(approx_kl_grad_, exact_kl_grad_, - rtol=0.007, atol=0.) - - self.assertAllClose(approx_kl_self_normalized_grad_, exact_kl_grad_, - rtol=0.011, atol=0.) - - self.assertAllClose(approx_kl_score_trick_grad_, exact_kl_grad_, - rtol=0.018, atol=0.) - - self.assertAllClose( - approx_kl_self_normalized_score_trick_grad_, exact_kl_grad_, - rtol=0.017, atol=0.) - - -class CsiszarVIMCOTest(test.TestCase): - - def _csiszar_vimco_helper(self, logu): - """Numpy implementation of `csiszar_vimco_helper`.""" - - # Since this is a naive/intuitive implementation, we compensate by using the - # highest precision we can. - logu = np.float128(logu) - n = logu.shape[0] - u = np.exp(logu) - loogeoavg_u = [] # Leave-one-out geometric-average of exp(logu). - for j in range(n): - loogeoavg_u.append(np.exp(np.mean( - [logu[i, ...] for i in range(n) if i != j], - axis=0))) - loogeoavg_u = np.array(loogeoavg_u) - - loosum_u = [] # Leave-one-out sum of exp(logu). - for j in range(n): - loosum_u.append(np.sum( - [u[i, ...] for i in range(n) if i != j], - axis=0)) - loosum_u = np.array(loosum_u) - - # Natural log of the average u except each is swapped-out for its - # leave-`i`-th-out Geometric average. - log_sooavg_u = np.log(loosum_u + loogeoavg_u) - np.log(n) - - log_avg_u = np.log(np.mean(u, axis=0)) - return log_avg_u, log_sooavg_u - - def _csiszar_vimco_helper_grad(self, logu, delta): - """Finite difference approximation of `grad(csiszar_vimco_helper, logu)`.""" - - # This code actually estimates the sum of the Jacobiab because that's what - # TF's `gradients` does. - np_log_avg_u1, np_log_sooavg_u1 = self._csiszar_vimco_helper( - logu[..., None] + np.diag([delta]*len(logu))) - np_log_avg_u, np_log_sooavg_u = self._csiszar_vimco_helper( - logu[..., None]) - return [ - (np_log_avg_u1 - np_log_avg_u) / delta, - np.sum(np_log_sooavg_u1 - np_log_sooavg_u, axis=0) / delta, - ] - - def test_vimco_helper_1(self): - """Tests that function calculation correctly handles batches.""" - - logu = np.linspace(-100., 100., 100).reshape([10, 2, 5]) - with self.test_session() as sess: - np_log_avg_u, np_log_sooavg_u = self._csiszar_vimco_helper(logu) - [log_avg_u, log_sooavg_u] = sess.run(cd.csiszar_vimco_helper(logu)) - self.assertAllClose(np_log_avg_u, log_avg_u, - rtol=1e-8, atol=0.) - self.assertAllClose(np_log_sooavg_u, log_sooavg_u, - rtol=1e-8, atol=0.) - - def test_vimco_helper_2(self): - """Tests that function calculation correctly handles overflow.""" - - # Using 700 (rather than 1e3) since naive numpy version can't handle higher. - logu = np.float32([0., 700, -1, 1]) - with self.test_session() as sess: - np_log_avg_u, np_log_sooavg_u = self._csiszar_vimco_helper(logu) - [log_avg_u, log_sooavg_u] = sess.run(cd.csiszar_vimco_helper(logu)) - self.assertAllClose(np_log_avg_u, log_avg_u, - rtol=1e-6, atol=0.) - self.assertAllClose(np_log_sooavg_u, log_sooavg_u, - rtol=1e-5, atol=0.) - - def test_vimco_helper_3(self): - """Tests that function calculation correctly handles underlow.""" - - logu = np.float32([0., -1000, -1, 1]) - with self.test_session() as sess: - np_log_avg_u, np_log_sooavg_u = self._csiszar_vimco_helper(logu) - [log_avg_u, log_sooavg_u] = sess.run(cd.csiszar_vimco_helper(logu)) - self.assertAllClose(np_log_avg_u, log_avg_u, - rtol=1e-5, atol=0.) - self.assertAllClose(np_log_sooavg_u, log_sooavg_u, - rtol=1e-4, atol=1e-15) - - def test_vimco_helper_gradient_using_finite_difference_1(self): - """Tests that gradient calculation correctly handles batches.""" - - logu_ = np.linspace(-100., 100., 100).reshape([10, 2, 5]) - with self.test_session() as sess: - logu = array_ops.constant(logu_) - - grad = lambda flogu: gradients_impl.gradients(flogu, logu)[0] - log_avg_u, log_sooavg_u = cd.csiszar_vimco_helper(logu) - - [ - grad_log_avg_u, - grad_log_sooavg_u, - ] = sess.run([grad(log_avg_u), grad(log_sooavg_u)]) - - # We skip checking against finite-difference approximation since it - # doesn't support batches. - - # Verify claim in docstring. - self.assertAllClose( - np.ones_like(grad_log_avg_u.sum(axis=0)), - grad_log_avg_u.sum(axis=0)) - self.assertAllClose( - np.ones_like(grad_log_sooavg_u.mean(axis=0)), - grad_log_sooavg_u.mean(axis=0)) - - def test_vimco_helper_gradient_using_finite_difference_2(self): - """Tests that gradient calculation correctly handles overflow.""" - - delta = 1e-3 - logu_ = np.float32([0., 1000, -1, 1]) - with self.test_session() as sess: - logu = array_ops.constant(logu_) - - [ - np_grad_log_avg_u, - np_grad_log_sooavg_u, - ] = self._csiszar_vimco_helper_grad(logu_, delta) - - grad = lambda flogu: gradients_impl.gradients(flogu, logu)[0] - log_avg_u, log_sooavg_u = cd.csiszar_vimco_helper(logu) - - [ - grad_log_avg_u, - grad_log_sooavg_u, - ] = sess.run([grad(log_avg_u), grad(log_sooavg_u)]) - - self.assertAllClose(np_grad_log_avg_u, grad_log_avg_u, - rtol=delta, atol=0.) - self.assertAllClose(np_grad_log_sooavg_u, grad_log_sooavg_u, - rtol=delta, atol=0.) - # Verify claim in docstring. - self.assertAllClose( - np.ones_like(grad_log_avg_u.sum(axis=0)), - grad_log_avg_u.sum(axis=0)) - self.assertAllClose( - np.ones_like(grad_log_sooavg_u.mean(axis=0)), - grad_log_sooavg_u.mean(axis=0)) - - def test_vimco_helper_gradient_using_finite_difference_3(self): - """Tests that gradient calculation correctly handles underlow.""" - - delta = 1e-3 - logu_ = np.float32([0., -1000, -1, 1]) - with self.test_session() as sess: - logu = array_ops.constant(logu_) - - [ - np_grad_log_avg_u, - np_grad_log_sooavg_u, - ] = self._csiszar_vimco_helper_grad(logu_, delta) - - grad = lambda flogu: gradients_impl.gradients(flogu, logu)[0] - log_avg_u, log_sooavg_u = cd.csiszar_vimco_helper(logu) - - [ - grad_log_avg_u, - grad_log_sooavg_u, - ] = sess.run([grad(log_avg_u), grad(log_sooavg_u)]) - - self.assertAllClose(np_grad_log_avg_u, grad_log_avg_u, - rtol=delta, atol=0.) - self.assertAllClose(np_grad_log_sooavg_u, grad_log_sooavg_u, - rtol=delta, atol=0.) - # Verify claim in docstring. - self.assertAllClose( - np.ones_like(grad_log_avg_u.sum(axis=0)), - grad_log_avg_u.sum(axis=0)) - self.assertAllClose( - np.ones_like(grad_log_sooavg_u.mean(axis=0)), - grad_log_sooavg_u.mean(axis=0)) - - def test_vimco_and_gradient(self): - - with self.test_session() as sess: - dims = 5 # Dimension - num_draws = int(20) - num_batch_draws = int(3) - seed = 1 - - f = lambda logu: cd.kl_reverse(logu, self_normalized=False) - np_f = lambda logu: -logu - - p = mvn_full_lib.MultivariateNormalFullCovariance( - covariance_matrix=tridiag(dims, diag_value=1, offdiag_value=0.5)) - - # Variance is very high when approximating Forward KL, so we make - # scale_diag larger than in test_kl_reverse_multidim. This ensures q - # "covers" p and thus Var_q[p/q] is smaller. - s = array_ops.constant(1.) - q = mvn_diag_lib.MultivariateNormalDiag( - scale_diag=array_ops.tile([s], [dims])) - - vimco = cd.csiszar_vimco( - f=f, - p_log_prob=p.log_prob, - q=q, - num_draws=num_draws, - num_batch_draws=num_batch_draws, - seed=seed) - - x = q.sample(sample_shape=[num_draws, num_batch_draws], - seed=seed) - x = array_ops.stop_gradient(x) - logu = p.log_prob(x) - q.log_prob(x) - f_log_sum_u = f(cd.csiszar_vimco_helper(logu)[0]) - - grad_sum = lambda fs: gradients_impl.gradients(fs, s)[0] - - def jacobian(x): - # Warning: this function is slow and may not even finish if prod(shape) - # is larger than, say, 100. - shape = x.shape.as_list() - assert all(s is not None for s in shape) - x = array_ops.reshape(x, shape=[-1]) - r = [grad_sum(x[i]) for i in range(np.prod(shape))] - return array_ops.reshape(array_ops.stack(r), shape=shape) - - [ - logu_, - jacobian_logqx_, - vimco_, - grad_vimco_, - f_log_sum_u_, - grad_mean_f_log_sum_u_, - ] = sess.run([ - logu, - jacobian(q.log_prob(x)), - vimco, - grad_sum(vimco), - f_log_sum_u, - grad_sum(f_log_sum_u) / num_batch_draws, - ]) - - np_log_avg_u, np_log_sooavg_u = self._csiszar_vimco_helper(logu_) - - # Test VIMCO loss is correct. - self.assertAllClose(np_f(np_log_avg_u).mean(axis=0), vimco_, - rtol=1e-5, atol=0.) - - # Test gradient of VIMCO loss is correct. - # - # To make this computation we'll inject two gradients from TF: - # - grad[mean(f(log(sum(p(x)/q(x)))))] - # - jacobian[log(q(x))]. - # - # We now justify why using these (and only these) TF values for - # ground-truth does not undermine the completeness of this test. - # - # Regarding `grad_mean_f_log_sum_u_`, note that we validate the - # correctness of the zero-th order derivative (for each batch member). - # Since `cd.csiszar_vimco_helper` itself does not manipulate any gradient - # information, we can safely rely on TF. - self.assertAllClose(np_f(np_log_avg_u), f_log_sum_u_, rtol=1e-4, atol=0.) - # - # Regarding `jacobian_logqx_`, note that testing the gradient of - # `q.log_prob` is outside the scope of this unit-test thus we may safely - # use TF to find it. - - # The `mean` is across batches and the `sum` is across iid samples. - np_grad_vimco = ( - grad_mean_f_log_sum_u_ - + np.mean( - np.sum( - jacobian_logqx_ * (np_f(np_log_avg_u) - - np_f(np_log_sooavg_u)), - axis=0), - axis=0)) - - self.assertAllClose(np_grad_vimco, grad_vimco_, - rtol=1e-5, atol=0.) - - -if __name__ == "__main__": - test.main() diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/custom_grad_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/custom_grad_test.py deleted file mode 100644 index a95df31ac1fd9f5038abe779391ccba5f7fe408d..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/bayesflow/python/kernel_tests/custom_grad_test.py +++ /dev/null @@ -1,157 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for Custom Gradient Ops.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import numpy as np - -from tensorflow.contrib.bayesflow.python.ops import custom_grad_impl -from tensorflow.python.framework import constant_op -from tensorflow.python.framework import dtypes -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import gradients_impl -from tensorflow.python.ops import init_ops -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 test - - -cg = custom_grad_impl - - -class CustomGradientTest(test.TestCase): - - def test_works_correctly(self): - with self.test_session() as sess: - f = lambda x: x**2 / 2 - g = lambda x: (x - 1)**3 / 3 - x_ = np.linspace(-100, 100, int(1e4)) + [0.] - - x = constant_op.constant(x_) - fx = cg.custom_gradient(f(x), g(x), x) - gx = gradients_impl.gradients(fx, x)[0] - [fx_, gx_] = sess.run([fx, gx]) - - self.assertAllClose(f(x_), fx_) - self.assertAllClose(g(x_), gx_) - - def test_works_correctly_both_f_g_zero(self): - with self.test_session() as sess: - f = lambda x: x**2 / 2 - g = lambda x: x**3 / 3 - x_ = np.linspace(-100, 100, int(1e4)) + [0.] - - x = constant_op.constant(x_) - fx = cg.custom_gradient(f(x), g(x), x) - gx = gradients_impl.gradients(fx, x)[0] - [fx_, gx_] = sess.run([fx, gx]) - - self.assertAllClose(f(x_), fx_) - self.assertAllClose(g(x_), gx_) - - def test_works_correctly_vector_of_vars(self): - with self.test_session() as sess: - x = variable_scope.get_variable( - name="x", - shape=[], - dtype=dtypes.float32, - initializer=init_ops.constant_initializer(2)) - y = variable_scope.get_variable( - name="y", - shape=[], - dtype=dtypes.float32, - initializer=init_ops.constant_initializer(3)) - sess.run([variables.global_variables_initializer()]) - - f = lambda z: z[0] * z[1] - g = lambda z: z[0]**2 * z[1]**2 / 2 - - z = array_ops.stack([x, y]) - fz = cg.custom_gradient(f(z), g(z), z, axis=0) - gz = gradients_impl.gradients(fz, variables.trainable_variables()) - [z_, fz_, gx_, gy_] = sess.run([z, fz, gz[0], gz[1]]) - - self.assertEqual(f(z_), fz_) - self.assertEqual(g(z_), gx_) - self.assertEqual(g(z_), gy_) - - def test_works_correctly_side_vars(self): - with self.test_session() as sess: - x_ = np.float32(2.1) # Adding extra tenth to force imprecision. - y_ = np.float32(3.1) - x = variable_scope.get_variable( - name="x", - shape=[], - dtype=dtypes.float32, - initializer=init_ops.constant_initializer(x_)) - y = variable_scope.get_variable( - name="y", - shape=[], - dtype=dtypes.float32, - initializer=init_ops.constant_initializer(y_)) - sess.run([variables.global_variables_initializer()]) - - f = lambda x: x * y - g = lambda z: math_ops.square(x) * y - - fx = cg.custom_gradient(f(x), g(x), x) - gx = gradients_impl.gradients(fx, variables.trainable_variables()) - [x_, fx_, gx_] = sess.run([x, fx, gx[0]]) - gy_ = gx[1] - - self.assertEqual(x_ * y_, fx_) - self.assertEqual(np.square(x_) * y_, gx_) - self.assertEqual(None, gy_) - - def test_works_correctly_fx_gx_manually_stopped(self): - with self.test_session() as sess: - x_ = np.float32(2.1) # Adding extra tenth to force imprecision. - y_ = np.float32(3.1) - x = variable_scope.get_variable( - name="x", - shape=[], - dtype=dtypes.float32, - initializer=init_ops.constant_initializer(x_)) - y = variable_scope.get_variable( - name="y", - shape=[], - dtype=dtypes.float32, - initializer=init_ops.constant_initializer(y_)) - sess.run([variables.global_variables_initializer()]) - - stop = array_ops.stop_gradient # For readability. - - # Basically we need to stop the `x` portion of `f`. And when we supply the - # arg to `custom_gradient` we need to stop the complement, i.e., the `y` - # part. - f = lambda x: stop(x) * y - g = lambda x: stop(math_ops.square(x)) * y - fx = cg.custom_gradient(f(x), g(x), x + stop(y), - fx_gx_manually_stopped=True) - - gx = gradients_impl.gradients(fx, variables.trainable_variables()) - [x_, fx_, gx_, gy_] = sess.run([x, fx, gx[0], gx[1]]) - - self.assertEqual(x_ * y_, fx_) - self.assertEqual(np.square(x_) * y_, gx_) - self.assertEqual(x_, gy_) - - -if __name__ == "__main__": - test.main() diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/docstring_util_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/docstring_util_test.py deleted file mode 100644 index 8ed500b19d8dd72795758a2920119e3680576697..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/bayesflow/python/kernel_tests/docstring_util_test.py +++ /dev/null @@ -1,87 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for docstring utilities.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from tensorflow.contrib.bayesflow.python.ops import docstring_util -from tensorflow.python.platform import test - - -class DocstringUtil(test.TestCase): - - def _testFunction(self): - doc_args = """x: Input to return as output. - y: Baz.""" - @docstring_util.expand_docstring(args=doc_args) - def foo(x): - # pylint: disable=g-doc-args - """Hello world. - - Args: - @{args} - - Returns: - x. - """ - # pylint: enable=g-doc-args - return x - - true_docstring = """Hello world. - - Args: - x: Input to return as output. - y: Baz. - - Returns: - x. - """ - self.assertEqual(foo.__doc__, true_docstring) - - def _testClassInit(self): - doc_args = """x: Input to return as output. - y: Baz.""" - - class Foo(object): - - @docstring_util.expand_docstring(args=doc_args) - def __init__(self, x, y): - # pylint: disable=g-doc-args - """Hello world. - - Args: - @{args} - - Bar. - """ - # pylint: enable=g-doc-args - pass - - true_docstring = """Hello world. - - Args: - x: Input to return as output. - y: Baz. - - Bar. - """ - self.assertEqual(Foo.__doc__, true_docstring) - - -if __name__ == "__main__": - test.main() diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/halton_sequence_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/halton_sequence_test.py deleted file mode 100644 index 0a85862abfd744a86b9a38e10dbb5b985d0a0e94..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/bayesflow/python/kernel_tests/halton_sequence_test.py +++ /dev/null @@ -1,131 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for halton_sequence.py.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import numpy as np - -from tensorflow.contrib.bayesflow.python.ops import halton_sequence as halton -from tensorflow.contrib.bayesflow.python.ops import monte_carlo_impl as monte_carlo_lib -from tensorflow.python.framework import dtypes -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import math_ops -from tensorflow.python.ops.distributions import normal as normal_lib -from tensorflow.python.platform import test - - -mc = monte_carlo_lib - - -class HaltonSequenceTest(test.TestCase): - - def test_known_values_small_bases(self): - with self.test_session(): - # The first five elements of the Halton sequence with base 2 and 3 - expected = np.array(((1. / 2, 1. / 3), - (1. / 4, 2. / 3), - (3. / 4, 1. / 9), - (1. / 8, 4. / 9), - (5. / 8, 7. / 9)), dtype=np.float32) - sample = halton.sample(2, num_samples=5) - self.assertAllClose(expected, sample.eval(), rtol=1e-6) - - def test_sample_indices(self): - with self.test_session(): - dim = 5 - indices = math_ops.range(10, dtype=dtypes.int32) - sample_direct = halton.sample(dim, num_samples=10) - sample_from_indices = halton.sample(dim, sample_indices=indices) - self.assertAllClose(sample_direct.eval(), sample_from_indices.eval(), - rtol=1e-6) - - def test_dtypes_works_correctly(self): - with self.test_session(): - dim = 3 - sample_float32 = halton.sample(dim, num_samples=10, dtype=dtypes.float32) - sample_float64 = halton.sample(dim, num_samples=10, dtype=dtypes.float64) - self.assertEqual(sample_float32.eval().dtype, np.float32) - self.assertEqual(sample_float64.eval().dtype, np.float64) - - def test_normal_integral_mean_and_var_correctly_estimated(self): - n = int(1000) - # This test is almost identical to the similarly named test in - # monte_carlo_test.py. The only difference is that we use the Halton - # samples instead of the random samples to evaluate the expectations. - # MC with pseudo random numbers converges at the rate of 1/ Sqrt(N) - # (N=number of samples). For QMC in low dimensions, the expected convergence - # rate is ~ 1/N. Hence we should only need 1e3 samples as compared to the - # 1e6 samples used in the pseudo-random monte carlo. - with self.test_session(): - mu_p = array_ops.constant([-1.0, 1.0], dtype=dtypes.float64) - mu_q = array_ops.constant([0.0, 0.0], dtype=dtypes.float64) - sigma_p = array_ops.constant([0.5, 0.5], dtype=dtypes.float64) - sigma_q = array_ops.constant([1.0, 1.0], dtype=dtypes.float64) - p = normal_lib.Normal(loc=mu_p, scale=sigma_p) - q = normal_lib.Normal(loc=mu_q, scale=sigma_q) - - cdf_sample = halton.sample(2, num_samples=n, dtype=dtypes.float64) - q_sample = q.quantile(cdf_sample) - - # Compute E_p[X]. - e_x = mc.expectation_importance_sampler( - f=lambda x: x, log_p=p.log_prob, sampling_dist_q=q, z=q_sample, - seed=42) - - # Compute E_p[X^2]. - e_x2 = mc.expectation_importance_sampler( - f=math_ops.square, log_p=p.log_prob, sampling_dist_q=q, z=q_sample, - seed=42) - - stddev = math_ops.sqrt(e_x2 - math_ops.square(e_x)) - # Keep the tolerance levels the same as in monte_carlo_test.py. - self.assertEqual(p.batch_shape, e_x.get_shape()) - self.assertAllClose(p.mean().eval(), e_x.eval(), rtol=0.01) - self.assertAllClose(p.stddev().eval(), stddev.eval(), rtol=0.02) - - def test_docstring_example(self): - # Produce the first 1000 members of the Halton sequence in 3 dimensions. - num_samples = 1000 - dim = 3 - with self.test_session(): - sample = halton.sample(dim, num_samples=num_samples) - - # Evaluate the integral of x_1 * x_2^2 * x_3^3 over the three dimensional - # hypercube. - powers = math_ops.range(1.0, limit=dim + 1) - integral = math_ops.reduce_mean( - math_ops.reduce_prod(sample ** powers, axis=-1)) - true_value = 1.0 / math_ops.reduce_prod(powers + 1.0) - - # Produces a relative absolute error of 1.7%. - self.assertAllClose(integral.eval(), true_value.eval(), rtol=0.02) - - # Now skip the first 1000 samples and recompute the integral with the next - # thousand samples. The sample_indices argument can be used to do this. - - sample_indices = math_ops.range(start=1000, limit=1000 + num_samples, - dtype=dtypes.int32) - sample_leaped = halton.sample(dim, sample_indices=sample_indices) - - integral_leaped = math_ops.reduce_mean( - math_ops.reduce_prod(sample_leaped ** powers, axis=-1)) - self.assertAllClose(integral_leaped.eval(), true_value.eval(), rtol=0.001) - - -if __name__ == '__main__': - test.main() diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/hmc_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/hmc_test.py index 819095a060b5f4cf18df6e7e4e4556e50ae44dd3..dabadfc7b6a3da8786e88d559fe2d05b44599ca0 100644 --- a/tensorflow/contrib/bayesflow/python/kernel_tests/hmc_test.py +++ b/tensorflow/contrib/bayesflow/python/kernel_tests/hmc_test.py @@ -462,138 +462,6 @@ class HMCTest(test.TestCase): def testKernelLeavesTargetInvariant3(self): self._kernel_leaves_target_invariant_wrapper(3) - def _ais_gets_correct_log_normalizer(self, init, independent_chain_ndims, - sess, feed_dict=None): - counter = collections.Counter() - - def proposal_log_prob(x): - counter["proposal_calls"] += 1 - event_dims = math_ops.range(independent_chain_ndims, array_ops.rank(x)) - return -0.5 * math_ops.reduce_sum(x**2. + np.log(2 * np.pi), - axis=event_dims) - - def target_log_prob(x): - counter["target_calls"] += 1 - event_dims = math_ops.range(independent_chain_ndims, array_ops.rank(x)) - return self._log_gamma_log_prob(x, event_dims) - - if feed_dict is None: - feed_dict = {} - - num_steps = 200 - - _, ais_weights, _ = hmc.sample_annealed_importance_chain( - proposal_log_prob_fn=proposal_log_prob, - num_steps=num_steps, - target_log_prob_fn=target_log_prob, - step_size=0.5, - current_state=init, - num_leapfrog_steps=2, - seed=45) - - # We have three calls because the calculation of `ais_weights` entails - # another call to the `convex_combined_log_prob_fn`. We could refactor - # things to avoid this, if needed (eg, b/72994218). - self.assertAllEqual(dict(target_calls=3, proposal_calls=3), counter) - - event_shape = array_ops.shape(init)[independent_chain_ndims:] - event_size = math_ops.reduce_prod(event_shape) - - log_true_normalizer = ( - -self._shape_param * math_ops.log(self._rate_param) - + math_ops.lgamma(self._shape_param)) - log_true_normalizer *= math_ops.cast(event_size, log_true_normalizer.dtype) - - log_estimated_normalizer = (math_ops.reduce_logsumexp(ais_weights) - - np.log(num_steps)) - - ratio_estimate_true = math_ops.exp(ais_weights - log_true_normalizer) - ais_weights_size = array_ops.size(ais_weights) - standard_error = math_ops.sqrt( - _reduce_variance(ratio_estimate_true) - / math_ops.cast(ais_weights_size, ratio_estimate_true.dtype)) - - [ - ratio_estimate_true_, - log_true_normalizer_, - log_estimated_normalizer_, - standard_error_, - ais_weights_size_, - event_size_, - ] = sess.run([ - ratio_estimate_true, - log_true_normalizer, - log_estimated_normalizer, - standard_error, - ais_weights_size, - event_size, - ], feed_dict) - - logging_ops.vlog(1, " log_true_normalizer: {}\n" - " log_estimated_normalizer: {}\n" - " ais_weights_size: {}\n" - " event_size: {}\n".format( - log_true_normalizer_, - log_estimated_normalizer_, - ais_weights_size_, - event_size_)) - self.assertNear(ratio_estimate_true_.mean(), 1., 4. * standard_error_) - - def _ais_gets_correct_log_normalizer_wrapper(self, independent_chain_ndims): - """Tests that AIS yields reasonable estimates of normalizers.""" - with self.test_session(graph=ops.Graph()) as sess: - x_ph = array_ops.placeholder(np.float32, name="x_ph") - initial_draws = np.random.normal(size=[30, 2, 1]) - self._ais_gets_correct_log_normalizer( - x_ph, - independent_chain_ndims, - sess, - feed_dict={x_ph: initial_draws}) - - def testAIS1(self): - self._ais_gets_correct_log_normalizer_wrapper(1) - - def testAIS2(self): - self._ais_gets_correct_log_normalizer_wrapper(2) - - def testAIS3(self): - self._ais_gets_correct_log_normalizer_wrapper(3) - - def testSampleAIChainSeedReproducibleWorksCorrectly(self): - with self.test_session(graph=ops.Graph()) as sess: - independent_chain_ndims = 1 - x = np.random.rand(4, 3, 2) - - def proposal_log_prob(x): - event_dims = math_ops.range(independent_chain_ndims, array_ops.rank(x)) - return -0.5 * math_ops.reduce_sum(x**2. + np.log(2 * np.pi), - axis=event_dims) - - def target_log_prob(x): - event_dims = math_ops.range(independent_chain_ndims, array_ops.rank(x)) - return self._log_gamma_log_prob(x, event_dims) - - ais_kwargs = dict( - proposal_log_prob_fn=proposal_log_prob, - num_steps=200, - target_log_prob_fn=target_log_prob, - step_size=0.5, - current_state=x, - num_leapfrog_steps=2, - seed=53) - - _, ais_weights0, _ = hmc.sample_annealed_importance_chain( - **ais_kwargs) - - _, ais_weights1, _ = hmc.sample_annealed_importance_chain( - **ais_kwargs) - - [ais_weights0_, ais_weights1_] = sess.run([ - ais_weights0, ais_weights1]) - - self.assertAllClose(ais_weights0_, ais_weights1_, - atol=1e-5, rtol=1e-5) - def testNanRejection(self): """Tests that an update that yields NaN potentials gets rejected. diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/layers_conv_variational_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/layers_conv_variational_test.py deleted file mode 100644 index 750afb6654311fea30a1dc6b31b20aa3b4160ae2..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/bayesflow/python/kernel_tests/layers_conv_variational_test.py +++ /dev/null @@ -1,521 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for convolutional Bayesian layers.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import numpy as np - -from tensorflow.contrib.bayesflow.python.ops import layers_conv_variational as prob_layers_lib -from tensorflow.contrib.bayesflow.python.ops import layers_util as prob_layers_util -from tensorflow.contrib.distributions.python.ops import independent as independent_lib -from tensorflow.python.framework import dtypes -from tensorflow.python.framework import ops -from tensorflow.python.framework import tensor_shape -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import math_ops -from tensorflow.python.ops import nn -from tensorflow.python.ops import nn_ops -from tensorflow.python.ops import random_ops -from tensorflow.python.ops.distributions import normal as normal_lib -from tensorflow.python.ops.distributions import util as distribution_util -from tensorflow.python.platform import test - - -class Counter(object): - """Helper class to manage incrementing a counting `int`.""" - - def __init__(self): - self._value = -1 - - @property - def value(self): - return self._value - - def __call__(self): - self._value += 1 - return self._value - - -class MockDistribution(independent_lib.Independent): - """Monitors layer calls to the underlying distribution.""" - - def __init__(self, result_sample, result_log_prob, loc=None, scale=None): - self.result_sample = result_sample - self.result_log_prob = result_log_prob - self.result_loc = loc - self.result_scale = scale - self.result_distribution = normal_lib.Normal(loc=0.0, scale=1.0) - if loc is not None and scale is not None: - self.result_distribution = normal_lib.Normal(loc=self.result_loc, - scale=self.result_scale) - self.called_log_prob = Counter() - self.called_sample = Counter() - self.called_loc = Counter() - self.called_scale = Counter() - - def log_prob(self, *args, **kwargs): - self.called_log_prob() - return self.result_log_prob - - def sample(self, *args, **kwargs): - self.called_sample() - return self.result_sample - - @property - def distribution(self): # for dummy check on Independent(Normal) - return self.result_distribution - - @property - def loc(self): - self.called_loc() - return self.result_loc - - @property - def scale(self): - self.called_scale() - return self.result_scale - - -class MockKLDivergence(object): - """Monitors layer calls to the divergence implementation.""" - - def __init__(self, result): - self.result = result - self.args = [] - self.called = Counter() - - def __call__(self, *args, **kwargs): - self.called() - self.args.append(args) - return self.result - - -class ConvVariational(test.TestCase): - - def _testKLPenaltyKernel(self, layer_class): - with self.test_session(): - layer = layer_class(filters=2, kernel_size=3) - if layer_class in (prob_layers_lib.Conv1DReparameterization, - prob_layers_lib.Conv1DFlipout): - inputs = random_ops.random_uniform([2, 3, 1], seed=1) - elif layer_class in (prob_layers_lib.Conv2DReparameterization, - prob_layers_lib.Conv2DFlipout): - inputs = random_ops.random_uniform([2, 3, 3, 1], seed=1) - elif layer_class in (prob_layers_lib.Conv3DReparameterization, - prob_layers_lib.Conv3DFlipout): - inputs = random_ops.random_uniform([2, 3, 3, 3, 1], seed=1) - - # No keys. - losses = ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES) - self.assertEqual(len(losses), 0) - self.assertListEqual(layer.losses, losses) - - _ = layer(inputs) - - # Yes keys. - losses = ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES) - self.assertEqual(len(losses), 1) - self.assertListEqual(layer.losses, losses) - - def _testKLPenaltyBoth(self, layer_class): - def _make_normal(dtype, *args): # pylint: disable=unused-argument - return normal_lib.Normal( - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)) - with self.test_session(): - layer = layer_class( - filters=2, - kernel_size=3, - bias_posterior_fn=prob_layers_util.default_mean_field_normal_fn(), - bias_prior_fn=_make_normal) - if layer_class in (prob_layers_lib.Conv1DReparameterization, - prob_layers_lib.Conv1DFlipout): - inputs = random_ops.random_uniform([2, 3, 1], seed=1) - elif layer_class in (prob_layers_lib.Conv2DReparameterization, - prob_layers_lib.Conv2DFlipout): - inputs = random_ops.random_uniform([2, 3, 3, 1], seed=1) - elif layer_class in (prob_layers_lib.Conv3DReparameterization, - prob_layers_lib.Conv3DFlipout): - inputs = random_ops.random_uniform([2, 3, 3, 3, 1], seed=1) - - # No keys. - losses = ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES) - self.assertEqual(len(losses), 0) - self.assertListEqual(layer.losses, losses) - - _ = layer(inputs) - - # Yes keys. - losses = ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES) - self.assertEqual(len(losses), 2) - self.assertListEqual(layer.losses, losses) - - def _testConvSetUp(self, layer_class, batch_size, depth=None, - height=None, width=None, channels=None, filters=None, - **kwargs): - seed = Counter() - if layer_class in (prob_layers_lib.Conv1DReparameterization, - prob_layers_lib.Conv1DFlipout): - inputs = random_ops.random_uniform( - [batch_size, width, channels], seed=seed()) - kernel_size = (2,) - elif layer_class in (prob_layers_lib.Conv2DReparameterization, - prob_layers_lib.Conv2DFlipout): - inputs = random_ops.random_uniform( - [batch_size, height, width, channels], seed=seed()) - kernel_size = (2, 2) - elif layer_class in (prob_layers_lib.Conv3DReparameterization, - prob_layers_lib.Conv3DFlipout): - inputs = random_ops.random_uniform( - [batch_size, depth, height, width, channels], seed=seed()) - kernel_size = (2, 2, 2) - - kernel_shape = kernel_size + (channels, filters) - kernel_posterior = MockDistribution( - loc=random_ops.random_uniform(kernel_shape, seed=seed()), - scale=random_ops.random_uniform(kernel_shape, seed=seed()), - result_log_prob=random_ops.random_uniform(kernel_shape, seed=seed()), - result_sample=random_ops.random_uniform(kernel_shape, seed=seed())) - kernel_prior = MockDistribution( - result_log_prob=random_ops.random_uniform(kernel_shape, seed=seed()), - result_sample=random_ops.random_uniform(kernel_shape, seed=seed())) - kernel_divergence = MockKLDivergence( - result=random_ops.random_uniform(kernel_shape, seed=seed())) - - bias_size = (filters,) - bias_posterior = MockDistribution( - result_log_prob=random_ops.random_uniform(bias_size, seed=seed()), - result_sample=random_ops.random_uniform(bias_size, seed=seed())) - bias_prior = MockDistribution( - result_log_prob=random_ops.random_uniform(bias_size, seed=seed()), - result_sample=random_ops.random_uniform(bias_size, seed=seed())) - bias_divergence = MockKLDivergence( - result=random_ops.random_uniform(bias_size, seed=seed())) - - layer = layer_class( - filters=filters, - kernel_size=kernel_size, - padding="SAME", - kernel_posterior_fn=lambda *args: kernel_posterior, - kernel_posterior_tensor_fn=lambda d: d.sample(seed=42), - kernel_prior_fn=lambda *args: kernel_prior, - kernel_divergence_fn=kernel_divergence, - bias_posterior_fn=lambda *args: bias_posterior, - bias_posterior_tensor_fn=lambda d: d.sample(seed=43), - bias_prior_fn=lambda *args: bias_prior, - bias_divergence_fn=bias_divergence, - **kwargs) - - outputs = layer(inputs) - - kl_penalty = ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES) - return (kernel_posterior, kernel_prior, kernel_divergence, - bias_posterior, bias_prior, bias_divergence, - layer, inputs, outputs, kl_penalty, kernel_shape) - - def _testConvReparameterization(self, layer_class): - batch_size, depth, height, width, channels, filters = 2, 4, 4, 4, 3, 5 - with self.test_session() as sess: - (kernel_posterior, kernel_prior, kernel_divergence, - bias_posterior, bias_prior, bias_divergence, layer, inputs, - outputs, kl_penalty, kernel_shape) = self._testConvSetUp( - layer_class, batch_size, - depth=depth, height=height, width=width, channels=channels, - filters=filters) - - convolution_op = nn_ops.Convolution( - tensor_shape.TensorShape(inputs.shape), - filter_shape=tensor_shape.TensorShape(kernel_shape), - padding="SAME") - expected_outputs = convolution_op(inputs, kernel_posterior.result_sample) - expected_outputs = nn.bias_add(expected_outputs, - bias_posterior.result_sample, - data_format="NHWC") - - [ - expected_outputs_, actual_outputs_, - expected_kernel_, actual_kernel_, - expected_kernel_divergence_, actual_kernel_divergence_, - expected_bias_, actual_bias_, - expected_bias_divergence_, actual_bias_divergence_, - ] = sess.run([ - expected_outputs, outputs, - kernel_posterior.result_sample, layer.kernel_posterior_tensor, - kernel_divergence.result, kl_penalty[0], - bias_posterior.result_sample, layer.bias_posterior_tensor, - bias_divergence.result, kl_penalty[1], - ]) - - self.assertAllClose( - expected_kernel_, actual_kernel_, - rtol=1e-6, atol=0.) - self.assertAllClose( - expected_bias_, actual_bias_, - rtol=1e-6, atol=0.) - self.assertAllClose( - expected_outputs_, actual_outputs_, - rtol=1e-6, atol=0.) - self.assertAllClose( - expected_kernel_divergence_, actual_kernel_divergence_, - rtol=1e-6, atol=0.) - self.assertAllClose( - expected_bias_divergence_, actual_bias_divergence_, - rtol=1e-6, atol=0.) - - self.assertAllEqual( - [[kernel_posterior.distribution, - kernel_prior.distribution, - kernel_posterior.result_sample]], - kernel_divergence.args) - - self.assertAllEqual( - [[bias_posterior.distribution, - bias_prior.distribution, - bias_posterior.result_sample]], - bias_divergence.args) - - def _testConvFlipout(self, layer_class): - batch_size, depth, height, width, channels, filters = 2, 4, 4, 4, 3, 5 - with self.test_session() as sess: - (kernel_posterior, kernel_prior, kernel_divergence, - bias_posterior, bias_prior, bias_divergence, layer, inputs, - outputs, kl_penalty, kernel_shape) = self._testConvSetUp( - layer_class, batch_size, - depth=depth, height=height, width=width, channels=channels, - filters=filters, seed=44) - - convolution_op = nn_ops.Convolution( - tensor_shape.TensorShape(inputs.shape), - filter_shape=tensor_shape.TensorShape(kernel_shape), - padding="SAME") - - expected_kernel_posterior_affine = normal_lib.Normal( - loc=array_ops.zeros_like(kernel_posterior.result_loc), - scale=kernel_posterior.result_scale) - expected_kernel_posterior_affine_tensor = ( - expected_kernel_posterior_affine.sample(seed=42)) - - expected_outputs = convolution_op( - inputs, kernel_posterior.distribution.loc) - - input_shape = array_ops.shape(inputs) - output_shape = array_ops.shape(expected_outputs) - batch_shape = array_ops.expand_dims(input_shape[0], 0) - channels = input_shape[-1] - rank = len(inputs.get_shape()) - 2 - - sign_input = random_ops.random_uniform( - array_ops.concat([batch_shape, - array_ops.expand_dims(channels, 0)], 0), - minval=0, - maxval=2, - dtype=dtypes.int32, - seed=layer.seed) - sign_input = math_ops.cast(2 * sign_input - 1, inputs.dtype) - sign_output = random_ops.random_uniform( - array_ops.concat([batch_shape, - array_ops.expand_dims(filters, 0)], 0), - minval=0, - maxval=2, - dtype=dtypes.int32, - seed=distribution_util.gen_new_seed( - layer.seed, salt="conv_flipout")) - sign_output = math_ops.cast(2 * sign_output - 1, inputs.dtype) - for _ in range(rank): - sign_input = array_ops.expand_dims(sign_input, 1) # 2D ex: (B, 1, 1, C) - sign_output = array_ops.expand_dims(sign_output, 1) - - sign_input = array_ops.tile( # tile for element-wise op broadcasting - sign_input, - [1] + [input_shape[i + 1] for i in range(rank)] + [1]) - sign_output = array_ops.tile( - sign_output, - [1] + [output_shape[i + 1] for i in range(rank)] + [1]) - - perturbed_inputs = convolution_op( - inputs * sign_input, expected_kernel_posterior_affine_tensor) - perturbed_inputs *= sign_output - - expected_outputs += perturbed_inputs - expected_outputs = nn.bias_add(expected_outputs, - bias_posterior.result_sample, - data_format="NHWC") - - [ - expected_outputs_, actual_outputs_, - expected_kernel_divergence_, actual_kernel_divergence_, - expected_bias_, actual_bias_, - expected_bias_divergence_, actual_bias_divergence_, - ] = sess.run([ - expected_outputs, outputs, - kernel_divergence.result, kl_penalty[0], - bias_posterior.result_sample, layer.bias_posterior_tensor, - bias_divergence.result, kl_penalty[1], - ]) - - self.assertAllClose( - expected_bias_, actual_bias_, - rtol=1e-6, atol=0.) - self.assertAllClose( - expected_outputs_, actual_outputs_, - rtol=1e-6, atol=0.) - self.assertAllClose( - expected_kernel_divergence_, actual_kernel_divergence_, - rtol=1e-6, atol=0.) - self.assertAllClose( - expected_bias_divergence_, actual_bias_divergence_, - rtol=1e-6, atol=0.) - - self.assertAllEqual( - [[kernel_posterior.distribution, kernel_prior.distribution, None]], - kernel_divergence.args) - - self.assertAllEqual( - [[bias_posterior.distribution, - bias_prior.distribution, - bias_posterior.result_sample]], - bias_divergence.args) - - def _testRandomConvFlipout(self, layer_class): - batch_size, depth, height, width, channels, filters = 2, 4, 4, 4, 3, 5 - with self.test_session() as sess: - seed = Counter() - if layer_class in (prob_layers_lib.Conv1DReparameterization, - prob_layers_lib.Conv1DFlipout): - inputs = random_ops.random_uniform( - [batch_size, width, channels], seed=seed()) - kernel_size = (2,) - elif layer_class in (prob_layers_lib.Conv2DReparameterization, - prob_layers_lib.Conv2DFlipout): - inputs = random_ops.random_uniform( - [batch_size, height, width, channels], seed=seed()) - kernel_size = (2, 2) - elif layer_class in (prob_layers_lib.Conv3DReparameterization, - prob_layers_lib.Conv3DFlipout): - inputs = random_ops.random_uniform( - [batch_size, depth, height, width, channels], seed=seed()) - kernel_size = (2, 2, 2) - - kernel_shape = kernel_size + (channels, filters) - bias_size = (filters,) - - kernel_posterior = MockDistribution( - loc=random_ops.random_uniform( - kernel_shape, seed=seed()), - scale=random_ops.random_uniform( - kernel_shape, seed=seed()), - result_log_prob=random_ops.random_uniform( - kernel_shape, seed=seed()), - result_sample=random_ops.random_uniform( - kernel_shape, seed=seed())) - bias_posterior = MockDistribution( - loc=random_ops.random_uniform( - bias_size, seed=seed()), - scale=random_ops.random_uniform( - bias_size, seed=seed()), - result_log_prob=random_ops.random_uniform( - bias_size, seed=seed()), - result_sample=random_ops.random_uniform( - bias_size, seed=seed())) - layer_one = layer_class( - filters=filters, - kernel_size=kernel_size, - padding="SAME", - kernel_posterior_fn=lambda *args: kernel_posterior, - kernel_posterior_tensor_fn=lambda d: d.sample(seed=42), - bias_posterior_fn=lambda *args: bias_posterior, - bias_posterior_tensor_fn=lambda d: d.sample(seed=43), - seed=44) - layer_two = layer_class( - filters=filters, - kernel_size=kernel_size, - padding="SAME", - kernel_posterior_fn=lambda *args: kernel_posterior, - kernel_posterior_tensor_fn=lambda d: d.sample(seed=42), - bias_posterior_fn=lambda *args: bias_posterior, - bias_posterior_tensor_fn=lambda d: d.sample(seed=43), - seed=45) - - outputs_one = layer_one(inputs) - outputs_two = layer_two(inputs) - - outputs_one_, outputs_two_ = sess.run([ - outputs_one, outputs_two]) - - self.assertLess(np.sum(np.isclose(outputs_one_, outputs_two_)), - np.prod(outputs_one_.shape)) - - def testKLPenaltyKernelConv1DReparameterization(self): - self._testKLPenaltyKernel(prob_layers_lib.Conv1DReparameterization) - - def testKLPenaltyKernelConv2DReparameterization(self): - self._testKLPenaltyKernel(prob_layers_lib.Conv2DReparameterization) - - def testKLPenaltyKernelConv3DReparameterization(self): - self._testKLPenaltyKernel(prob_layers_lib.Conv3DReparameterization) - - def testKLPenaltyKernelConv1DFlipout(self): - self._testKLPenaltyKernel(prob_layers_lib.Conv1DFlipout) - - def testKLPenaltyKernelConv2DFlipout(self): - self._testKLPenaltyKernel(prob_layers_lib.Conv2DFlipout) - - def testKLPenaltyKernelConv3DFlipout(self): - self._testKLPenaltyKernel(prob_layers_lib.Conv3DFlipout) - - def testKLPenaltyBothConv1DReparameterization(self): - self._testKLPenaltyBoth(prob_layers_lib.Conv1DReparameterization) - - def testKLPenaltyBothConv2DReparameterization(self): - self._testKLPenaltyBoth(prob_layers_lib.Conv2DReparameterization) - - def testKLPenaltyBothConv3DReparameterization(self): - self._testKLPenaltyBoth(prob_layers_lib.Conv3DReparameterization) - - def testKLPenaltyBothConv1DFlipout(self): - self._testKLPenaltyBoth(prob_layers_lib.Conv1DFlipout) - - def testKLPenaltyBothConv2DFlipout(self): - self._testKLPenaltyBoth(prob_layers_lib.Conv2DFlipout) - - def testKLPenaltyBothConv3DFlipout(self): - self._testKLPenaltyBoth(prob_layers_lib.Conv3DFlipout) - - def testConv1DReparameterization(self): - self._testConvReparameterization(prob_layers_lib.Conv1DReparameterization) - - def testConv2DReparameterization(self): - self._testConvReparameterization(prob_layers_lib.Conv2DReparameterization) - - def testConv3DReparameterization(self): - self._testConvReparameterization(prob_layers_lib.Conv3DReparameterization) - - def testConv1DFlipout(self): - self._testConvFlipout(prob_layers_lib.Conv1DFlipout) - - def testConv2DFlipout(self): - self._testConvFlipout(prob_layers_lib.Conv2DFlipout) - - def testConv3DFlipout(self): - self._testConvFlipout(prob_layers_lib.Conv3DFlipout) - - def testRandomConv1DFlipout(self): - self._testRandomConvFlipout(prob_layers_lib.Conv1DFlipout) - - -if __name__ == "__main__": - test.main() diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/layers_dense_variational_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/layers_dense_variational_test.py deleted file mode 100644 index 342f38ccec7ec74db1b393d6cdc22300205cc547..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/bayesflow/python/kernel_tests/layers_dense_variational_test.py +++ /dev/null @@ -1,443 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for dense Bayesian layers.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import numpy as np - -from tensorflow.contrib.bayesflow.python.ops import layers_dense_variational as prob_layers_lib -from tensorflow.contrib.bayesflow.python.ops import layers_util as prob_layers_util -from tensorflow.contrib.distributions.python.ops import independent as independent_lib -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 random_ops -from tensorflow.python.ops.distributions import normal as normal_lib -from tensorflow.python.ops.distributions import util as distribution_util -from tensorflow.python.platform import test - - -class Counter(object): - """Helper class to manage incrementing a counting `int`.""" - - def __init__(self): - self._value = -1 - - @property - def value(self): - return self._value - - def __call__(self): - self._value += 1 - return self._value - - -class MockDistribution(independent_lib.Independent): - """Monitors layer calls to the underlying distribution.""" - - def __init__(self, result_sample, result_log_prob, loc=None, scale=None): - self.result_sample = result_sample - self.result_log_prob = result_log_prob - self.result_loc = loc - self.result_scale = scale - self.result_distribution = normal_lib.Normal(loc=0.0, scale=1.0) - if loc is not None and scale is not None: - self.result_distribution = normal_lib.Normal(loc=self.result_loc, - scale=self.result_scale) - self.called_log_prob = Counter() - self.called_sample = Counter() - self.called_loc = Counter() - self.called_scale = Counter() - - def log_prob(self, *args, **kwargs): - self.called_log_prob() - return self.result_log_prob - - def sample(self, *args, **kwargs): - self.called_sample() - return self.result_sample - - @property - def distribution(self): # for dummy check on Independent(Normal) - return self.result_distribution - - @property - def loc(self): - self.called_loc() - return self.result_loc - - @property - def scale(self): - self.called_scale() - return self.result_scale - - -class MockKLDivergence(object): - """Monitors layer calls to the divergence implementation.""" - - def __init__(self, result): - self.result = result - self.args = [] - self.called = Counter() - - def __call__(self, *args, **kwargs): - self.called() - self.args.append(args) - return self.result - - -class DenseVariational(test.TestCase): - - def _testKLPenaltyKernel(self, layer_class): - with self.test_session(): - layer = layer_class(units=2) - inputs = random_ops.random_uniform([2, 3], seed=1) - - # No keys. - losses = ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES) - self.assertEqual(len(losses), 0) - self.assertListEqual(layer.losses, losses) - - _ = layer(inputs) - - # Yes keys. - losses = ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES) - self.assertEqual(len(losses), 1) - self.assertListEqual(layer.losses, losses) - - def _testKLPenaltyBoth(self, layer_class): - def _make_normal(dtype, *args): # pylint: disable=unused-argument - return normal_lib.Normal( - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)) - with self.test_session(): - layer = layer_class( - units=2, - bias_posterior_fn=prob_layers_util.default_mean_field_normal_fn(), - bias_prior_fn=_make_normal) - inputs = random_ops.random_uniform([2, 3], seed=1) - - # No keys. - losses = ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES) - self.assertEqual(len(losses), 0) - self.assertListEqual(layer.losses, losses) - - _ = layer(inputs) - - # Yes keys. - losses = ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES) - self.assertEqual(len(losses), 2) - self.assertListEqual(layer.losses, losses) - - def _testDenseSetUp(self, layer_class, batch_size, in_size, out_size, - **kwargs): - seed = Counter() - inputs = random_ops.random_uniform([batch_size, in_size], seed=seed()) - - kernel_size = [in_size, out_size] - kernel_posterior = MockDistribution( - loc=random_ops.random_uniform(kernel_size, seed=seed()), - scale=random_ops.random_uniform(kernel_size, seed=seed()), - result_log_prob=random_ops.random_uniform(kernel_size, seed=seed()), - result_sample=random_ops.random_uniform(kernel_size, seed=seed())) - kernel_prior = MockDistribution( - result_log_prob=random_ops.random_uniform(kernel_size, seed=seed()), - result_sample=random_ops.random_uniform(kernel_size, seed=seed())) - kernel_divergence = MockKLDivergence( - result=random_ops.random_uniform(kernel_size, seed=seed())) - - bias_size = [out_size] - bias_posterior = MockDistribution( - result_log_prob=random_ops.random_uniform(bias_size, seed=seed()), - result_sample=random_ops.random_uniform(bias_size, seed=seed())) - bias_prior = MockDistribution( - result_log_prob=random_ops.random_uniform(bias_size, seed=seed()), - result_sample=random_ops.random_uniform(bias_size, seed=seed())) - bias_divergence = MockKLDivergence( - result=random_ops.random_uniform(bias_size, seed=seed())) - - layer = layer_class( - units=out_size, - kernel_posterior_fn=lambda *args: kernel_posterior, - kernel_posterior_tensor_fn=lambda d: d.sample(seed=42), - kernel_prior_fn=lambda *args: kernel_prior, - kernel_divergence_fn=kernel_divergence, - bias_posterior_fn=lambda *args: bias_posterior, - bias_posterior_tensor_fn=lambda d: d.sample(seed=43), - bias_prior_fn=lambda *args: bias_prior, - bias_divergence_fn=bias_divergence, - **kwargs) - - outputs = layer(inputs) - - kl_penalty = ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES) - return (kernel_posterior, kernel_prior, kernel_divergence, - bias_posterior, bias_prior, bias_divergence, - layer, inputs, outputs, kl_penalty) - - def testKLPenaltyKernelReparameterization(self): - self._testKLPenaltyKernel(prob_layers_lib.DenseReparameterization) - - def testKLPenaltyKernelLocalReparameterization(self): - self._testKLPenaltyKernel(prob_layers_lib.DenseLocalReparameterization) - - def testKLPenaltyKernelFlipout(self): - self._testKLPenaltyKernel(prob_layers_lib.DenseFlipout) - - def testKLPenaltyBothReparameterization(self): - self._testKLPenaltyBoth(prob_layers_lib.DenseReparameterization) - - def testKLPenaltyBothLocalReparameterization(self): - self._testKLPenaltyBoth(prob_layers_lib.DenseLocalReparameterization) - - def testKLPenaltyBothFlipout(self): - self._testKLPenaltyBoth(prob_layers_lib.DenseFlipout) - - def testDenseReparameterization(self): - batch_size, in_size, out_size = 2, 3, 4 - with self.test_session() as sess: - (kernel_posterior, kernel_prior, kernel_divergence, - bias_posterior, bias_prior, bias_divergence, layer, inputs, - outputs, kl_penalty) = self._testDenseSetUp( - prob_layers_lib.DenseReparameterization, - batch_size, in_size, out_size) - - expected_outputs = ( - math_ops.matmul(inputs, kernel_posterior.result_sample) + - bias_posterior.result_sample) - - [ - expected_outputs_, actual_outputs_, - expected_kernel_, actual_kernel_, - expected_kernel_divergence_, actual_kernel_divergence_, - expected_bias_, actual_bias_, - expected_bias_divergence_, actual_bias_divergence_, - ] = sess.run([ - expected_outputs, outputs, - kernel_posterior.result_sample, layer.kernel_posterior_tensor, - kernel_divergence.result, kl_penalty[0], - bias_posterior.result_sample, layer.bias_posterior_tensor, - bias_divergence.result, kl_penalty[1], - ]) - - self.assertAllClose( - expected_kernel_, actual_kernel_, - rtol=1e-6, atol=0.) - self.assertAllClose( - expected_bias_, actual_bias_, - rtol=1e-6, atol=0.) - self.assertAllClose( - expected_outputs_, actual_outputs_, - rtol=1e-6, atol=0.) - self.assertAllClose( - expected_kernel_divergence_, actual_kernel_divergence_, - rtol=1e-6, atol=0.) - self.assertAllClose( - expected_bias_divergence_, actual_bias_divergence_, - rtol=1e-6, atol=0.) - - self.assertAllEqual( - [[kernel_posterior.distribution, - kernel_prior.distribution, - kernel_posterior.result_sample]], - kernel_divergence.args) - - self.assertAllEqual( - [[bias_posterior.distribution, - bias_prior.distribution, - bias_posterior.result_sample]], - bias_divergence.args) - - def testDenseLocalReparameterization(self): - batch_size, in_size, out_size = 2, 3, 4 - with self.test_session() as sess: - (kernel_posterior, kernel_prior, kernel_divergence, - bias_posterior, bias_prior, bias_divergence, layer, inputs, - outputs, kl_penalty) = self._testDenseSetUp( - prob_layers_lib.DenseLocalReparameterization, - batch_size, in_size, out_size) - - expected_kernel_posterior_affine = normal_lib.Normal( - loc=math_ops.matmul(inputs, kernel_posterior.result_loc), - scale=math_ops.matmul( - inputs**2., kernel_posterior.result_scale**2)**0.5) - expected_kernel_posterior_affine_tensor = ( - expected_kernel_posterior_affine.sample(seed=42)) - expected_outputs = (expected_kernel_posterior_affine_tensor + - bias_posterior.result_sample) - - [ - expected_outputs_, actual_outputs_, - expected_kernel_divergence_, actual_kernel_divergence_, - expected_bias_, actual_bias_, - expected_bias_divergence_, actual_bias_divergence_, - ] = sess.run([ - expected_outputs, outputs, - kernel_divergence.result, kl_penalty[0], - bias_posterior.result_sample, layer.bias_posterior_tensor, - bias_divergence.result, kl_penalty[1], - ]) - - self.assertAllClose( - expected_bias_, actual_bias_, - rtol=1e-6, atol=0.) - self.assertAllClose( - expected_outputs_, actual_outputs_, - rtol=1e-6, atol=0.) - self.assertAllClose( - expected_kernel_divergence_, actual_kernel_divergence_, - rtol=1e-6, atol=0.) - self.assertAllClose( - expected_bias_divergence_, actual_bias_divergence_, - rtol=1e-6, atol=0.) - - self.assertAllEqual( - [[kernel_posterior.distribution, - kernel_prior.distribution, - None]], - kernel_divergence.args) - - self.assertAllEqual( - [[bias_posterior.distribution, - bias_prior.distribution, - bias_posterior.result_sample]], - bias_divergence.args) - - def testDenseFlipout(self): - batch_size, in_size, out_size = 2, 3, 4 - with self.test_session() as sess: - (kernel_posterior, kernel_prior, kernel_divergence, - bias_posterior, bias_prior, bias_divergence, layer, inputs, - outputs, kl_penalty) = self._testDenseSetUp( - prob_layers_lib.DenseFlipout, - batch_size, in_size, out_size, seed=44) - - expected_kernel_posterior_affine = normal_lib.Normal( - loc=array_ops.zeros_like(kernel_posterior.result_loc), - scale=kernel_posterior.result_scale) - expected_kernel_posterior_affine_tensor = ( - expected_kernel_posterior_affine.sample(seed=42)) - - sign_input = random_ops.random_uniform( - [batch_size, in_size], - minval=0, - maxval=2, - dtype=dtypes.int32, - seed=layer.seed) - sign_input = math_ops.cast(2 * sign_input - 1, inputs.dtype) - sign_output = random_ops.random_uniform( - [batch_size, out_size], - minval=0, - maxval=2, - dtype=dtypes.int32, - seed=distribution_util.gen_new_seed( - layer.seed, salt="dense_flipout")) - sign_output = math_ops.cast(2 * sign_output - 1, inputs.dtype) - perturbed_inputs = math_ops.matmul( - inputs * sign_input, expected_kernel_posterior_affine_tensor) - perturbed_inputs *= sign_output - - expected_outputs = math_ops.matmul(inputs, kernel_posterior.result_loc) - expected_outputs += perturbed_inputs - expected_outputs += bias_posterior.result_sample - - [ - expected_outputs_, actual_outputs_, - expected_kernel_divergence_, actual_kernel_divergence_, - expected_bias_, actual_bias_, - expected_bias_divergence_, actual_bias_divergence_, - ] = sess.run([ - expected_outputs, outputs, - kernel_divergence.result, kl_penalty[0], - bias_posterior.result_sample, layer.bias_posterior_tensor, - bias_divergence.result, kl_penalty[1], - ]) - - self.assertAllClose( - expected_bias_, actual_bias_, - rtol=1e-6, atol=0.) - self.assertAllClose( - expected_outputs_, actual_outputs_, - rtol=1e-6, atol=0.) - self.assertAllClose( - expected_kernel_divergence_, actual_kernel_divergence_, - rtol=1e-6, atol=0.) - self.assertAllClose( - expected_bias_divergence_, actual_bias_divergence_, - rtol=1e-6, atol=0.) - - self.assertAllEqual( - [[kernel_posterior.distribution, kernel_prior.distribution, None]], - kernel_divergence.args) - - self.assertAllEqual( - [[bias_posterior.distribution, - bias_prior.distribution, - bias_posterior.result_sample]], - bias_divergence.args) - - def testRandomDenseFlipout(self): - batch_size, in_size, out_size = 2, 3, 4 - with self.test_session() as sess: - seed = Counter() - inputs = random_ops.random_uniform([batch_size, in_size], seed=seed()) - - kernel_posterior = MockDistribution( - loc=random_ops.random_uniform( - [in_size, out_size], seed=seed()), - scale=random_ops.random_uniform( - [in_size, out_size], seed=seed()), - result_log_prob=random_ops.random_uniform( - [in_size, out_size], seed=seed()), - result_sample=random_ops.random_uniform( - [in_size, out_size], seed=seed())) - bias_posterior = MockDistribution( - loc=random_ops.random_uniform( - [out_size], seed=seed()), - scale=random_ops.random_uniform( - [out_size], seed=seed()), - result_log_prob=random_ops.random_uniform( - [out_size], seed=seed()), - result_sample=random_ops.random_uniform( - [out_size], seed=seed())) - layer_one = prob_layers_lib.DenseFlipout( - units=out_size, - kernel_posterior_fn=lambda *args: kernel_posterior, - kernel_posterior_tensor_fn=lambda d: d.sample(seed=42), - bias_posterior_fn=lambda *args: bias_posterior, - bias_posterior_tensor_fn=lambda d: d.sample(seed=43), - seed=44) - layer_two = prob_layers_lib.DenseFlipout( - units=out_size, - kernel_posterior_fn=lambda *args: kernel_posterior, - kernel_posterior_tensor_fn=lambda d: d.sample(seed=42), - bias_posterior_fn=lambda *args: bias_posterior, - bias_posterior_tensor_fn=lambda d: d.sample(seed=43), - seed=45) - - outputs_one = layer_one(inputs) - outputs_two = layer_two(inputs) - - outputs_one_, outputs_two_ = sess.run([ - outputs_one, outputs_two]) - - self.assertLess(np.sum(np.isclose(outputs_one_, outputs_two_)), out_size) - - -if __name__ == "__main__": - test.main() diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/mcmc_diagnostics_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/mcmc_diagnostics_test.py deleted file mode 100644 index 52e36e135d95c1ec919c710f35d59073c2134d05..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/bayesflow/python/kernel_tests/mcmc_diagnostics_test.py +++ /dev/null @@ -1,445 +0,0 @@ -# Copyright 2018 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for MCMC diagnostic utilities.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import numpy as np - -from tensorflow.contrib.bayesflow.python.ops import mcmc_diagnostics_impl as mcmc_diagnostics -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import spectral_ops_test_util -from tensorflow.python.platform import test - -rng = np.random.RandomState(42) - - -class _EffectiveSampleSizeTest(object): - - @property - def use_static_shape(self): - raise NotImplementedError( - "Subclass failed to implement `use_static_shape`.") - - def _check_versus_expected_effective_sample_size(self, - x_, - expected_ess, - sess, - atol=1e-2, - rtol=1e-2, - filter_threshold=None, - filter_beyond_lag=None): - x = array_ops.placeholder_with_default( - input=x_, shape=x_.shape if self.use_static_shape else None) - ess = mcmc_diagnostics.effective_sample_size( - x, - filter_threshold=filter_threshold, - filter_beyond_lag=filter_beyond_lag) - if self.use_static_shape: - self.assertAllEqual(x.shape[1:], ess.shape) - - ess_ = sess.run(ess) - - self.assertAllClose( - np.ones_like(ess_) * expected_ess, ess_, atol=atol, rtol=rtol) - - def testIidRank1NormalHasFullEssMaxLags10(self): - # With a length 5000 iid normal sequence, and filter_beyond_lag = 10, we - # should have a good estimate of ESS, and it should be close to the full - # sequence length of 5000. - # The choice of filter_beyond_lag = 10 is a short cutoff, reasonable only - # since we know the correlation length should be zero right away. - with self.test_session() as sess: - with spectral_ops_test_util.fft_kernel_label_map(): - self._check_versus_expected_effective_sample_size( - x_=rng.randn(5000).astype(np.float32), - expected_ess=5000, - sess=sess, - filter_beyond_lag=10, - filter_threshold=None, - rtol=0.3) - - def testIidRank2NormalHasFullEssMaxLags10(self): - # See similar test for Rank1Normal for reasoning. - with self.test_session() as sess: - with spectral_ops_test_util.fft_kernel_label_map(): - self._check_versus_expected_effective_sample_size( - x_=rng.randn(5000, 2).astype(np.float32), - expected_ess=5000, - sess=sess, - filter_beyond_lag=10, - filter_threshold=None, - rtol=0.3) - - def testIidRank1NormalHasFullEssMaxLagThresholdZero(self): - # With a length 5000 iid normal sequence, and filter_threshold = 0, - # we should have a super-duper estimate of ESS, and it should be very close - # to the full sequence length of 5000. - # The choice of filter_beyond_lag = 0 means we cutoff as soon as the - # auto-corris below zero. This should happen very quickly, due to the fact - # that the theoretical auto-corr is [1, 0, 0,...] - with self.test_session() as sess: - with spectral_ops_test_util.fft_kernel_label_map(): - self._check_versus_expected_effective_sample_size( - x_=rng.randn(5000).astype(np.float32), - expected_ess=5000, - sess=sess, - filter_beyond_lag=None, - filter_threshold=0., - rtol=0.1) - - def testIidRank2NormalHasFullEssMaxLagThresholdZero(self): - # See similar test for Rank1Normal for reasoning. - with self.test_session() as sess: - with spectral_ops_test_util.fft_kernel_label_map(): - self._check_versus_expected_effective_sample_size( - x_=rng.randn(5000, 2).astype(np.float32), - expected_ess=5000, - sess=sess, - filter_beyond_lag=None, - filter_threshold=0., - rtol=0.1) - - def testLength10CorrelationHasEssOneTenthTotalLengthUsingMaxLags50(self): - # Create x_, such that - # x_[i] = iid_x_[0], i = 0,...,9 - # x_[i] = iid_x_[1], i = 10,..., 19, - # and so on. - iid_x_ = rng.randn(5000, 1).astype(np.float32) - x_ = (iid_x_ * np.ones((5000, 10)).astype(np.float32)).reshape((50000,)) - with self.test_session() as sess: - with spectral_ops_test_util.fft_kernel_label_map(): - self._check_versus_expected_effective_sample_size( - x_=x_, - expected_ess=50000 // 10, - sess=sess, - filter_beyond_lag=50, - filter_threshold=None, - rtol=0.2) - - def testLength10CorrelationHasEssOneTenthTotalLengthUsingMaxLagsThresholdZero( - self): - # Create x_, such that - # x_[i] = iid_x_[0], i = 0,...,9 - # x_[i] = iid_x_[1], i = 10,..., 19, - # and so on. - iid_x_ = rng.randn(5000, 1).astype(np.float32) - x_ = (iid_x_ * np.ones((5000, 10)).astype(np.float32)).reshape((50000,)) - with self.test_session() as sess: - with spectral_ops_test_util.fft_kernel_label_map(): - self._check_versus_expected_effective_sample_size( - x_=x_, - expected_ess=50000 // 10, - sess=sess, - filter_beyond_lag=None, - filter_threshold=0., - rtol=0.1) - - def testListArgs(self): - # x_ has correlation length 10 ==> ESS = N / 10 - # y_ has correlation length 1 ==> ESS = N - iid_x_ = rng.randn(5000, 1).astype(np.float32) - x_ = (iid_x_ * np.ones((5000, 10)).astype(np.float32)).reshape((50000,)) - y_ = rng.randn(50000).astype(np.float32) - states = [x_, x_, y_, y_] - filter_threshold = [0., None, 0., None] - filter_beyond_lag = [None, 5, None, 5] - - # See other tests for reasoning on tolerance. - with self.test_session() as sess: - with spectral_ops_test_util.fft_kernel_label_map(): - ess = mcmc_diagnostics.effective_sample_size( - states, - filter_threshold=filter_threshold, - filter_beyond_lag=filter_beyond_lag) - ess_ = sess.run(ess) - self.assertAllEqual(4, len(ess_)) - - self.assertAllClose(50000 // 10, ess_[0], rtol=0.3) - self.assertAllClose(50000 // 10, ess_[1], rtol=0.3) - self.assertAllClose(50000, ess_[2], rtol=0.1) - self.assertAllClose(50000, ess_[3], rtol=0.1) - - def testMaxLagsThresholdLessThanNeg1SameAsNone(self): - # Setting both means we filter out items R_k from the auto-correlation - # sequence if k > filter_beyond_lag OR k >= j where R_j < filter_threshold. - - # x_ has correlation length 10. - iid_x_ = rng.randn(500, 1).astype(np.float32) - x_ = (iid_x_ * np.ones((500, 10)).astype(np.float32)).reshape((5000,)) - with self.test_session() as sess: - with spectral_ops_test_util.fft_kernel_label_map(): - x = array_ops.placeholder_with_default( - input=x_, shape=x_.shape if self.use_static_shape else None) - - ess_none_none = mcmc_diagnostics.effective_sample_size( - x, filter_threshold=None, filter_beyond_lag=None) - ess_none_200 = mcmc_diagnostics.effective_sample_size( - x, filter_threshold=None, filter_beyond_lag=200) - ess_neg2_200 = mcmc_diagnostics.effective_sample_size( - x, filter_threshold=-2., filter_beyond_lag=200) - ess_neg2_none = mcmc_diagnostics.effective_sample_size( - x, filter_threshold=-2., filter_beyond_lag=None) - ess_none_none_, ess_none_200_, ess_neg2_200_, ess_neg2_none_ = sess.run( - [ess_none_none, ess_none_200, ess_neg2_200, ess_neg2_none]) - - # filter_threshold=-2 <==> filter_threshold=None. - self.assertAllClose(ess_none_none_, ess_neg2_none_) - self.assertAllClose(ess_none_200_, ess_neg2_200_) - - def testMaxLagsArgsAddInAnOrManner(self): - # Setting both means we filter out items R_k from the auto-correlation - # sequence if k > filter_beyond_lag OR k >= j where R_j < filter_threshold. - - # x_ has correlation length 10. - iid_x_ = rng.randn(500, 1).astype(np.float32) - x_ = (iid_x_ * np.ones((500, 10)).astype(np.float32)).reshape((5000,)) - with self.test_session() as sess: - with spectral_ops_test_util.fft_kernel_label_map(): - x = array_ops.placeholder_with_default( - input=x_, shape=x_.shape if self.use_static_shape else None) - - ess_1_9 = mcmc_diagnostics.effective_sample_size( - x, filter_threshold=1., filter_beyond_lag=9) - ess_1_none = mcmc_diagnostics.effective_sample_size( - x, filter_threshold=1., filter_beyond_lag=None) - ess_none_9 = mcmc_diagnostics.effective_sample_size( - x, filter_threshold=1., filter_beyond_lag=9) - ess_1_9_, ess_1_none_, ess_none_9_ = sess.run( - [ess_1_9, ess_1_none, ess_none_9]) - - # Since R_k = 1 for k < 10, and R_k < 1 for k >= 10, - # filter_threshold = 1 <==> filter_beyond_lag = 9. - self.assertAllClose(ess_1_9_, ess_1_none_) - self.assertAllClose(ess_1_9_, ess_none_9_) - - -class EffectiveSampleSizeStaticTest(test.TestCase, _EffectiveSampleSizeTest): - - @property - def use_static_shape(self): - return True - - -class EffectiveSampleSizeDynamicTest(test.TestCase, _EffectiveSampleSizeTest): - - @property - def use_static_shape(self): - return False - - -class _PotentialScaleReductionTest(object): - - @property - def use_static_shape(self): - raise NotImplementedError( - "Subclass failed to impliment `use_static_shape`.") - - def testListOfStatesWhereFirstPassesSecondFails(self): - """Simple test showing API with two states. Read first!.""" - n_samples = 1000 - - # state_0 is two scalar chains taken from iid Normal(0, 1). Will pass. - state_0 = rng.randn(n_samples, 2) - - # state_1 is three 4-variate chains taken from Normal(0, 1) that have been - # shifted. Since every chain is shifted, they are not the same, and the - # test should fail. - offset = np.array([1., -1., 2.]).reshape(3, 1) - state_1 = rng.randn(n_samples, 3, 4) + offset - - rhat = mcmc_diagnostics.potential_scale_reduction( - chains_states=[state_0, state_1], independent_chain_ndims=1) - - self.assertIsInstance(rhat, list) - with self.test_session() as sess: - rhat_0_, rhat_1_ = sess.run(rhat) - - # r_hat_0 should be close to 1, meaning test is passed. - self.assertAllEqual((), rhat_0_.shape) - self.assertAllClose(1., rhat_0_, rtol=0.02) - - # r_hat_1 should be greater than 1.2, meaning test has failed. - self.assertAllEqual((4,), rhat_1_.shape) - self.assertAllEqual(np.ones_like(rhat_1_).astype(bool), rhat_1_ > 1.2) - - def check_results(self, state_, independent_chain_shape, should_pass): - sample_ndims = 1 - independent_chain_ndims = len(independent_chain_shape) - with self.test_session(): - state = array_ops.placeholder_with_default( - input=state_, shape=state_.shape if self.use_static_shape else None) - - rhat = mcmc_diagnostics.potential_scale_reduction( - state, independent_chain_ndims=independent_chain_ndims) - - if self.use_static_shape: - self.assertAllEqual( - state_.shape[sample_ndims + independent_chain_ndims:], rhat.shape) - - rhat_ = rhat.eval() - if should_pass: - self.assertAllClose(np.ones_like(rhat_), rhat_, atol=0, rtol=0.02) - else: - self.assertAllEqual(np.ones_like(rhat_).astype(bool), rhat_ > 1.2) - - def iid_normal_chains_should_pass_wrapper(self, - sample_shape, - independent_chain_shape, - other_shape, - dtype=np.float32): - """Check results with iid normal chains.""" - - state_shape = sample_shape + independent_chain_shape + other_shape - state_ = rng.randn(*state_shape).astype(dtype) - - # The "other" dimensions do not have to be identical, just independent, so - # force them to not be identical. - if other_shape: - state_ *= rng.rand(*other_shape).astype(dtype) - - self.check_results(state_, independent_chain_shape, should_pass=True) - - def testPassingIIDNdimsAreIndependentOneOtherZero(self): - self.iid_normal_chains_should_pass_wrapper( - sample_shape=[10000], independent_chain_shape=[4], other_shape=[]) - - def testPassingIIDNdimsAreIndependentOneOtherOne(self): - self.iid_normal_chains_should_pass_wrapper( - sample_shape=[10000], independent_chain_shape=[3], other_shape=[7]) - - def testPassingIIDNdimsAreIndependentOneOtherTwo(self): - self.iid_normal_chains_should_pass_wrapper( - sample_shape=[10000], independent_chain_shape=[2], other_shape=[5, 7]) - - def testPassingIIDNdimsAreIndependentTwoOtherTwo64Bit(self): - self.iid_normal_chains_should_pass_wrapper( - sample_shape=[10000], - independent_chain_shape=[2, 3], - other_shape=[5, 7], - dtype=np.float64) - - def offset_normal_chains_should_fail_wrapper( - self, sample_shape, independent_chain_shape, other_shape): - """Check results with normal chains that are offset from each other.""" - - state_shape = sample_shape + independent_chain_shape + other_shape - state_ = rng.randn(*state_shape) - - # Add a significant offset to the different (formerly iid) chains. - offset = np.linspace( - 0, 2, num=np.prod(independent_chain_shape)).reshape([1] * len( - sample_shape) + independent_chain_shape + [1] * len(other_shape)) - state_ += offset - - self.check_results(state_, independent_chain_shape, should_pass=False) - - def testFailingOffsetNdimsAreSampleOneIndependentOneOtherOne(self): - self.offset_normal_chains_should_fail_wrapper( - sample_shape=[10000], independent_chain_shape=[2], other_shape=[5]) - - -class PotentialScaleReductionStaticTest(test.TestCase, - _PotentialScaleReductionTest): - - @property - def use_static_shape(self): - return True - - def testIndependentNdimsLessThanOneRaises(self): - with self.assertRaisesRegexp(ValueError, "independent_chain_ndims"): - mcmc_diagnostics.potential_scale_reduction( - rng.rand(2, 3, 4), independent_chain_ndims=0) - - -class PotentialScaleReductionDynamicTest(test.TestCase, - _PotentialScaleReductionTest): - - @property - def use_static_shape(self): - return False - - -class _ReduceVarianceTest(object): - - @property - def use_static_shape(self): - raise NotImplementedError( - "Subclass failed to impliment `use_static_shape`.") - - def check_versus_numpy(self, x_, axis, biased, keepdims): - with self.test_session(): - x_ = np.asarray(x_) - x = array_ops.placeholder_with_default( - input=x_, shape=x_.shape if self.use_static_shape else None) - var = mcmc_diagnostics._reduce_variance( - x, axis=axis, biased=biased, keepdims=keepdims) - np_var = np.var(x_, axis=axis, ddof=0 if biased else 1, keepdims=keepdims) - - if self.use_static_shape: - self.assertAllEqual(np_var.shape, var.shape) - - var_ = var.eval() - # We will mask below, which changes shape, so check shape explicitly here. - self.assertAllEqual(np_var.shape, var_.shape) - - # We get NaN when we divide by zero due to the size being the same as ddof - nan_mask = np.isnan(np_var) - if nan_mask.any(): - self.assertTrue(np.isnan(var_[nan_mask]).all()) - self.assertAllClose(np_var[~nan_mask], var_[~nan_mask], atol=0, rtol=0.02) - - def testScalarBiasedTrue(self): - self.check_versus_numpy(x_=-1.234, axis=None, biased=True, keepdims=False) - - def testScalarBiasedFalse(self): - # This should result in NaN. - self.check_versus_numpy(x_=-1.234, axis=None, biased=False, keepdims=False) - - def testShape2x3x4AxisNoneBiasedFalseKeepdimsFalse(self): - self.check_versus_numpy( - x_=rng.randn(2, 3, 4), axis=None, biased=True, keepdims=False) - - def testShape2x3x4Axis1BiasedFalseKeepdimsTrue(self): - self.check_versus_numpy( - x_=rng.randn(2, 3, 4), axis=1, biased=True, keepdims=True) - - def testShape2x3x4x5Axis13BiasedFalseKeepdimsTrue(self): - self.check_versus_numpy( - x_=rng.randn(2, 3, 4, 5), axis=1, biased=True, keepdims=True) - - def testShape2x3x4x5Axis13BiasedFalseKeepdimsFalse(self): - self.check_versus_numpy( - x_=rng.randn(2, 3, 4, 5), axis=1, biased=False, keepdims=False) - - -class ReduceVarianceTestStaticShape(test.TestCase, _ReduceVarianceTest): - - @property - def use_static_shape(self): - return True - - -class ReduceVarianceTestDynamicShape(test.TestCase, _ReduceVarianceTest): - - @property - def use_static_shape(self): - return False - - -if __name__ == "__main__": - test.main() diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/sgld_optimizer_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/sgld_optimizer_test.py deleted file mode 100644 index 756c25683bd4b0c8c77e9e28485ca2a85582999c..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/bayesflow/python/kernel_tests/sgld_optimizer_test.py +++ /dev/null @@ -1,212 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Functional test for GradientDescent.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function -import math -from tensorflow.contrib.bayesflow.python.ops.optimizers import SGLDOptimizer -from tensorflow.python.framework import constant_op -from tensorflow.python.framework import dtypes -from tensorflow.python.framework import ops -from tensorflow.python.ops import variables -from tensorflow.python.platform import test - - -class SGLDOptimizerTest(test.TestCase): - - def testBasic(self): - for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: - with self.test_session(): - var0 = variables.Variable([1.1, 2.1], dtype=dtype) - var1 = variables.Variable([3.0, 4.0], dtype=dtype) - grads0 = constant_op.constant([0.1, 0.1], dtype=dtype) - grads1 = constant_op.constant([0.01, 0.01], dtype=dtype) - decay_rate = 0.53 - sgd_optimizer = SGLDOptimizer(3.0, preconditioner_decay_rate=decay_rate) - sgd_op = sgd_optimizer.apply_gradients( - zip([grads0, grads1], [var0, var1])) - variables.global_variables_initializer().run() - # Fetch params to validate initial values - self.assertAllCloseAccordingToType([1.1, 2.1], var0.eval()) - self.assertAllCloseAccordingToType([3.0, 4.0], var1.eval()) - # Run 1 step of sgd - sgd_op.run() - # Validate updated params - grads_scaled = (0.5 * 0.1 / math.sqrt(decay_rate + - (1 - decay_rate) * 0.1**2 + 1e-8)) - self.assertAllCloseAccordingToType( - [1.1 - 3.0 * grads_scaled, 2.1 - 3.0 * grads_scaled], var0.eval()) - grads_scaled = (0.5 * 0.01 / math.sqrt( - decay_rate + (1 - decay_rate) * 0.01**2 + 1e-8)) - self.assertAllCloseAccordingToType( - [3.0 - 3.0 * grads_scaled, 4.0 - 3.0 * grads_scaled], var1.eval()) - self.assertAllCloseAccordingToType(1, sgd_optimizer._counter.eval()) - - def testBasicMultiInstance(self): - for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: - with self.test_session(): - var0 = variables.Variable([1.1, 2.1], dtype=dtype) - var1 = variables.Variable([3.0, 4.0], dtype=dtype) - grads0 = constant_op.constant([0.1, 0.1], dtype=dtype) - grads1 = constant_op.constant([0.01, 0.01], dtype=dtype) - vara = variables.Variable([1.1, 2.1], dtype=dtype) - varb = variables.Variable([3.0, 4.0], dtype=dtype) - gradsa = constant_op.constant([0.1, 0.1], dtype=dtype) - gradsb = constant_op.constant([0.01, 0.01], dtype=dtype) - decay_rate = 0.5 - sgd_optimizer = SGLDOptimizer(3.0, preconditioner_decay_rate=decay_rate) - sgd_op = sgd_optimizer.apply_gradients( - zip([grads0, grads1], [var0, var1])) - sgd_optimizer2 = SGLDOptimizer( - 3.0, preconditioner_decay_rate=decay_rate) - sgd_op2 = sgd_optimizer2.apply_gradients( - zip([gradsa, gradsb], [vara, varb])) - variables.global_variables_initializer().run() - # Fetch params to validate initial values - self.assertAllCloseAccordingToType([1.1, 2.1], var0.eval()) - self.assertAllCloseAccordingToType([3.0, 4.0], var1.eval()) - self.assertAllCloseAccordingToType([1.1, 2.1], vara.eval()) - self.assertAllCloseAccordingToType([3.0, 4.0], varb.eval()) - - # Run 1 step of sgd - sgd_op.run() - sgd_op2.run() - # Validate updated params - grads_scaled = (0.5 * 0.1 / math.sqrt(decay_rate + - (1 - decay_rate) * 0.1**2 + 1e-8)) - self.assertAllCloseAccordingToType( - [1.1 - 3.0 * grads_scaled, 2.1 - 3.0 * grads_scaled], var0.eval()) - self.assertAllCloseAccordingToType( - [1.1 - 3.0 * grads_scaled, 2.1 - 3.0 * grads_scaled], vara.eval()) - - grads_scaled = (0.5 * 0.01 / math.sqrt( - decay_rate + (1 - decay_rate) * 0.01**2 + 1e-8)) - self.assertAllCloseAccordingToType( - [3.0 - 3.0 * grads_scaled, 4.0 - 3.0 * grads_scaled], var1.eval()) - self.assertAllCloseAccordingToType( - [3.0 - 3.0 * grads_scaled, 4.0 - 3.0 * grads_scaled], varb.eval()) - self.assertNotEqual(sgd_optimizer.variable_scope, - sgd_optimizer2.variable_scope) - self.assertNotEqual(sgd_optimizer.variable_scope.name, - sgd_optimizer2.variable_scope.name) - self.assertAllCloseAccordingToType(1, sgd_optimizer._counter.eval()) - self.assertAllCloseAccordingToType(1, sgd_optimizer2._counter.eval()) - - def testTensorLearningRate(self): - for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: - with self.test_session(): - var0 = variables.Variable([1.1, 2.1], dtype=dtype) - var1 = variables.Variable([3.0, 4.0], dtype=dtype) - grads0 = constant_op.constant([0.1, 0.1], dtype=dtype) - grads1 = constant_op.constant([0.01, 0.01], dtype=dtype) - lrate = constant_op.constant(3.0) - decay_rate = 0.5 - sgd_op = SGLDOptimizer( - lrate, preconditioner_decay_rate=constant_op.constant( - decay_rate)).apply_gradients( - zip([grads0, grads1], [var0, var1])) - variables.global_variables_initializer().run() - # Fetch params to validate initial values - self.assertAllCloseAccordingToType([1.1, 2.1], var0.eval()) - self.assertAllCloseAccordingToType([3.0, 4.0], var1.eval()) - # Run 1 step of sgd - sgd_op.run() - # Validate updated params - grads_scaled = (0.5 * 0.1 / math.sqrt(decay_rate + - (1 - decay_rate) * 0.1**2 + 1e-8)) - self.assertAllCloseAccordingToType( - [1.1 - 3.0 * grads_scaled, 2.1 - 3.0 * grads_scaled], var0.eval()) - grads_scaled = (0.5 * 0.01 / math.sqrt( - decay_rate + (1 - decay_rate) * 0.01**2 + 1e-8)) - self.assertAllCloseAccordingToType( - [3.0 - 3.0 * grads_scaled, 4.0 - 3.0 * grads_scaled], var1.eval()) - - def testGradWrtRef(self): - for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: - with self.test_session(): - opt = SGLDOptimizer(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_) - variables.global_variables_initializer().run() - for grad, _ in grads_and_vars: - self.assertAllCloseAccordingToType([1.0], grad.eval()) - - def testWithGlobalStep(self): - for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: - with self.test_session(): - global_step = variables.Variable(0, trainable=False) - var0 = variables.Variable([1.1, 2.1], dtype=dtype) - var1 = variables.Variable([3.0, 4.0], dtype=dtype) - grads0 = constant_op.constant([0.1, 0.1], dtype=dtype) - grads1 = constant_op.constant([0.01, 0.01], dtype=dtype) - decay_rate = 0.1 - sgd_op = SGLDOptimizer( - 3.0, preconditioner_decay_rate=decay_rate).apply_gradients( - zip([grads0, grads1], [var0, var1]), global_step=global_step) - variables.global_variables_initializer().run() - # Fetch params to validate initial values - self.assertAllCloseAccordingToType([1.1, 2.1], var0.eval()) - self.assertAllCloseAccordingToType([3.0, 4.0], var1.eval()) - # Run 1 step of sgd - sgd_op.run() - - # Validate updated params and global_step - grads_scaled = (0.5 * 0.1 / math.sqrt(decay_rate + - (1 - decay_rate) * 0.1**2 + 1e-8)) - self.assertAllCloseAccordingToType( - [1.1 - 3.0 * grads_scaled, 2.1 - 3.0 * grads_scaled], var0.eval()) - grads_scaled = (0.5 * 0.01 / math.sqrt( - decay_rate + (1 - decay_rate) * 0.01**2 + 1e-8)) - self.assertAllCloseAccordingToType( - [3.0 - 3.0 * grads_scaled, 4.0 - 3.0 * grads_scaled], var1.eval()) - self.assertAllCloseAccordingToType(1, global_step.eval()) - - def testSparseBasic(self): - for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: - with self.test_session(): - var0 = variables.Variable([[1.1], [2.1]], dtype=dtype) - var1 = variables.Variable([[3.0], [4.0]], dtype=dtype) - grads0 = ops.IndexedSlices( - constant_op.constant([0.1], shape=[1, 1], dtype=dtype), - constant_op.constant([0]), constant_op.constant([2, 1])) - grads1 = ops.IndexedSlices( - constant_op.constant([0.01], shape=[1, 1], dtype=dtype), - constant_op.constant([1]), constant_op.constant([2, 1])) - decay_rate = 0.9 - sgd_op = SGLDOptimizer( - 3.0, preconditioner_decay_rate=decay_rate).apply_gradients( - zip([grads0, grads1], [var0, var1])) - variables.global_variables_initializer().run() - # Fetch params to validate initial values - self.assertAllCloseAccordingToType([[1.1], [2.1]], var0.eval()) - self.assertAllCloseAccordingToType([[3.0], [4.0]], var1.eval()) - # Run 1 step of sgd - sgd_op.run() - # Validate updated params - grads_scaled = (0.5 * 0.1 / math.sqrt(decay_rate + - (1 - decay_rate) * 0.1**2 + 1e-8)) - self.assertAllCloseAccordingToType([[1.1 - 3.0 * grads_scaled], [2.1]], - var0.eval()) - grads_scaled = (0.5 * 0.01 / math.sqrt( - decay_rate + (1 - decay_rate) * 0.01**2 + 1e-8)) - self.assertAllCloseAccordingToType( - [[3.0 - 3.0 * 0], [4.0 - 3.0 * grads_scaled]], var1.eval()) - - -if __name__ == "__main__": - test.main() diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/variable_utils_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/variable_utils_test.py deleted file mode 100644 index f978cf86417dc5ff5412a3eee584330a266e0964..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/bayesflow/python/kernel_tests/variable_utils_test.py +++ /dev/null @@ -1,135 +0,0 @@ -# Copyright 2018 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for utility functions related to managing `tf.Variable`s.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import warnings - -import numpy as np - -from tensorflow.contrib.bayesflow.python.ops import variable_utils - -from tensorflow.python.framework import constant_op -from tensorflow.python.framework import ops -from tensorflow.python.ops import variable_scope as varscope_ops -from tensorflow.python.ops import variables as variables_ops -from tensorflow.python.platform import test - - -def test_fn(x): - x = ops.convert_to_tensor(x, name="x") - dtype = x.dtype.as_numpy_dtype - s = x.shape.as_list() - z = varscope_ops.get_variable( - name="z", - dtype=dtype, - initializer=np.arange(np.prod(s)).reshape(s).astype(dtype)) - y = varscope_ops.get_variable( - name="y", - dtype=dtype, - initializer=np.arange(np.prod(s)).reshape(s).astype(dtype)**2) - return x + y + z - - -class _WrapCallableTest(object): - - def testDefaultArgsWorkCorrectly(self): - with self.test_session(): - x = constant_op.constant(self.dtype([0.1, 0.2])) - wrapped_fn, vars_args = variable_utils.externalize_variables_as_args( - test_fn, [x]) - - varscope_ops.get_variable_scope().reuse_variables() - - result = wrapped_fn(self.dtype(2), [3, 4, 5], 0.5) - - y_actual = varscope_ops.get_variable("y", dtype=self.dtype) - z_actual = varscope_ops.get_variable("z", dtype=self.dtype) - - variables_ops.global_variables_initializer().run() - result_ = result.eval() - - self.assertEqual(self.dtype, result_.dtype) - self.assertAllEqual([5.5, 6.5, 7.5], result_) - self.assertAllEqual([y_actual, z_actual], vars_args) - - def testNonDefaultArgsWorkCorrectly(self): - with self.test_session(): - x = constant_op.constant(self.dtype([0.1, 0.2])) - - _ = test_fn(self.dtype([0., 0.])) # Needed to create vars. - varscope_ops.get_variable_scope().reuse_variables() - - y_actual = varscope_ops.get_variable("y", dtype=self.dtype) - - wrapped_fn, vars_args = variable_utils.externalize_variables_as_args( - test_fn, [x], possible_ancestor_vars=[y_actual]) - - result = wrapped_fn(self.dtype([2, 3]), 0.5) # x, y - - variables_ops.global_variables_initializer().run() - result_ = result.eval() - - self.assertEqual(self.dtype, result_.dtype) - self.assertAllEqual([2.5, 4.5], result_) - self.assertAllEqual([y_actual], vars_args) - - def testWarnings(self): - with self.test_session(): - x = constant_op.constant(self.dtype([0.1, 0.2])) - wrapped_fn, _ = variable_utils.externalize_variables_as_args( - test_fn, [x], possible_ancestor_vars=[]) - varscope_ops.get_variable_scope().reuse_variables() - with warnings.catch_warnings(record=True) as w: - wrapped_fn(self.dtype(2)) - w = sorted(w, key=lambda w: str(w.message)) - self.assertEqual(2, len(w)) - self.assertRegexpMatches( - str(w[0].message), - r"Variable .* 'y:0' .* not found in bypass dict.") - self.assertRegexpMatches( - str(w[1].message), - r"Variable .* 'z:0' .* not found in bypass dict.") - - def testExceptions(self): - with self.test_session(): - x = constant_op.constant(self.dtype([0.1, 0.2])) - wrapped_fn, _ = variable_utils.externalize_variables_as_args( - test_fn, - [x], - possible_ancestor_vars=[], - assert_variable_override=True) - varscope_ops.get_variable_scope().reuse_variables() - with self.assertRaisesRegexp(ValueError, r"not found"): - wrapped_fn(self.dtype(2)) - - -class WrapCallableTest16(test.TestCase, _WrapCallableTest): - dtype = np.float16 - - -class WrapCallableTest32(test.TestCase, _WrapCallableTest): - dtype = np.float32 - - -class WrapCallableTest64(test.TestCase, _WrapCallableTest): - dtype = np.float64 - - -if __name__ == "__main__": - test.main() diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/variational_sgd_optimizer_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/variational_sgd_optimizer_test.py deleted file mode 100644 index 83c64dbe0fd586edcb784a5c09a4c133aaa99cff..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/bayesflow/python/kernel_tests/variational_sgd_optimizer_test.py +++ /dev/null @@ -1,268 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Functional test for GradientDescent.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function -from tensorflow.contrib.bayesflow.python.ops.optimizers import VariationalSGDOptimizer -from tensorflow.python.framework import constant_op -from tensorflow.python.framework import dtypes -from tensorflow.python.framework import errors -from tensorflow.python.framework import ops -from tensorflow.python.ops import variables -from tensorflow.python.platform import test - - -class VariationalSGDOptimizerTest(test.TestCase): - - def testBasic(self): - for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: - with self.test_session(): - var0 = variables.Variable([1.1, 2.1], dtype=dtype) - var1 = variables.Variable([3.0, 4.0], dtype=dtype) - grads0 = constant_op.constant([0.1, 0.1], dtype=dtype) - grads1 = constant_op.constant([0.01, 0.01], dtype=dtype) - decay_rate = 0.53 - sgd_op = VariationalSGDOptimizer( - 1, - 1, - preconditioner_decay_rate=decay_rate, - max_learning_rate=3.0, - burnin_max_learning_rate=3.0, - use_single_learning_rate=True).apply_gradients( - zip([grads0, grads1], [var0, var1])) - variables.global_variables_initializer().run() - # Fetch params to validate initial values - self.assertAllCloseAccordingToType([1.1, 2.1], var0.eval()) - self.assertAllCloseAccordingToType([3.0, 4.0], var1.eval()) - # Run 1 step of sgd - sgd_op.run() - self.assertAllCloseAccordingToType([1.1 - 3.0 * 0.1, 2.1 - 3.0 * 0.1], - var0.eval()) - self.assertAllCloseAccordingToType([3.0 - 3.0 * 0.01, 4.0 - 3.0 * 0.01], - var1.eval()) - - def testBasicMultiInstance(self): - for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: - with self.test_session(): - var0 = variables.Variable([1.1, 2.1], dtype=dtype) - var1 = variables.Variable([3.0, 4.0], dtype=dtype) - grads0 = constant_op.constant([0.1, 0.1], dtype=dtype) - grads1 = constant_op.constant([0.01, 0.01], dtype=dtype) - vara = variables.Variable([1.1, 2.1], dtype=dtype) - varb = variables.Variable([3.0, 4.0], dtype=dtype) - gradsa = constant_op.constant([0.1, 0.1], dtype=dtype) - gradsb = constant_op.constant([0.01, 0.01], dtype=dtype) - decay_rate = 0.5 - batch_size = 2 - total_num_examples = 10 - optimizer = VariationalSGDOptimizer( - batch_size, - total_num_examples, - max_learning_rate=1.0, - burnin_max_learning_rate=3.0, - preconditioner_decay_rate=decay_rate) - sgd_op = optimizer.apply_gradients( - zip([grads0, grads1], [var0, var1])) - optimizer2 = VariationalSGDOptimizer( - batch_size, - total_num_examples, - max_learning_rate=1.0, - burnin_max_learning_rate=10.0, - burnin=0, - preconditioner_decay_rate=decay_rate) - sgd_op2 = optimizer2.apply_gradients( - zip([gradsa, gradsb], [vara, varb])) - variables.global_variables_initializer().run() - # Fetch params to validate initial values - self.assertAllCloseAccordingToType([1.1, 2.1], var0.eval()) - self.assertAllCloseAccordingToType([3.0, 4.0], var1.eval()) - self.assertAllCloseAccordingToType([1.1, 2.1], vara.eval()) - self.assertAllCloseAccordingToType([3.0, 4.0], varb.eval()) - - # Run 1 step of sgd - sgd_op.run() - sgd_op2.run() - # Validate updated params - self.assertAllCloseAccordingToType([1.1 - 3. * 0.1, 2.1 - 3. * 0.1], - var0.eval()) - self.assertAllCloseAccordingToType([1.1 - 0.1, 2.1 - 0.1], vara.eval()) - - self.assertAllCloseAccordingToType([3.0 - 3. * 0.01, 4.0 - 3. * 0.01], - var1.eval()) - self.assertAllCloseAccordingToType([3.0 - 0.01, 4.0 - 0.01], - varb.eval()) - self.assertNotEqual(optimizer.variable_scope, - optimizer2.variable_scope) - self.assertNotEqual(optimizer.variable_scope.name, - optimizer2.variable_scope.name) - self.assertAllCloseAccordingToType(1, optimizer._counter.eval()) - self.assertAllCloseAccordingToType(1, optimizer2._counter.eval()) - - def testTensorLearningRate(self): - for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: - with self.test_session(): - var0 = variables.Variable([1.1, 2.1], dtype=dtype) - var1 = variables.Variable([3.0, 4.0], dtype=dtype) - grads0 = constant_op.constant([0.1, 0.1], dtype=dtype) - grads1 = constant_op.constant([0.01, 0.01], dtype=dtype) - lrate = constant_op.constant(3.0) - decay_rate = 0.5 - batch_size = 2 - total_num_examples = 10 - sgd_op = VariationalSGDOptimizer( - batch_size, - total_num_examples, - max_learning_rate=lrate, - burnin=0, - preconditioner_decay_rate=decay_rate).apply_gradients( - zip([grads0, grads1], [var0, var1])) - variables.global_variables_initializer().run() - # Fetch params to validate initial values - self.assertAllCloseAccordingToType([1.1, 2.1], var0.eval()) - self.assertAllCloseAccordingToType([3.0, 4.0], var1.eval()) - # Run 1 step of sgd - sgd_op.run() - # Validate updated params - self.assertAllCloseAccordingToType([1.1 - 3.0 * 0.1, 2.1 - 3.0 * 0.1], - var0.eval()) - self.assertAllCloseAccordingToType([3.0 - 3.0 * 0.01, 4.0 - 3.0 * 0.01], - var1.eval()) - - def testTensorDecayLearningRate(self): - for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: - with self.test_session(): - var0 = variables.Variable([1.1, 2.1], dtype=dtype) - var1 = variables.Variable([3.0, 4.0], dtype=dtype) - grads0 = constant_op.constant([0.1, 0.1], dtype=dtype) - grads1 = constant_op.constant([0.01, 0.01], dtype=dtype) - lrate = variables.Variable(3.0) - lrate_decay_op = lrate.assign_add(-3.) - decay_rate = 0.5 - batch_size = 2 - total_num_examples = 10 - optimizer = VariationalSGDOptimizer( - batch_size, - total_num_examples, - max_learning_rate=lrate, - burnin=0, - preconditioner_decay_rate=decay_rate) - sgd_op = optimizer.apply_gradients(zip([grads0, grads1], [var0, var1])) - variables.global_variables_initializer().run() - # Fetch params to validate initial values - self.assertAllCloseAccordingToType([1.1, 2.1], var0.eval()) - self.assertAllCloseAccordingToType([3.0, 4.0], var1.eval()) - # Run 1 step of sgd - sgd_op.run() - # Validate updated params - self.assertAllCloseAccordingToType([1.1 - 3.0 * 0.1, 2.1 - 3.0 * 0.1], - var0.eval()) - self.assertAllCloseAccordingToType([3.0 - 3.0 * 0.01, 4.0 - 3.0 * 0.01], - var1.eval()) - # Update learning rate to 0 - lrate_decay_op.eval() - sgd_op.run() - # Validate params haven't changed - self.assertAllCloseAccordingToType([1.1 - 3.0 * 0.1, 2.1 - 3.0 * 0.1], - var0.eval()) - self.assertAllCloseAccordingToType([3.0 - 3.0 * 0.01, 4.0 - 3.0 * 0.01], - var1.eval()) - lrate_decay_op.eval() - - with self.assertRaises(errors.InvalidArgumentError): - sgd_op.run() - - def testGradWrtRef(self): - for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: - with self.test_session(): - opt = VariationalSGDOptimizer(1, 1, max_learning_rate=1.0) - 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_) - variables.global_variables_initializer().run() - for grad, _ in grads_and_vars: - self.assertAllCloseAccordingToType([1.0], grad.eval()) - - def testWithGlobalStep(self): - for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: - with self.test_session(): - global_step = variables.Variable(0, trainable=False) - var0 = variables.Variable([1.1, 2.1], dtype=dtype) - var1 = variables.Variable([3.0, 4.0], dtype=dtype) - grads0 = constant_op.constant([0.1, 0.1], dtype=dtype) - grads1 = constant_op.constant([0.01, 0.01], dtype=dtype) - decay_rate = 0.1 - batch_size = 2 - total_num_examples = 10 - sgd_optimizer = VariationalSGDOptimizer( - batch_size, - total_num_examples, - max_learning_rate=3.0, - burnin=0, - preconditioner_decay_rate=decay_rate) - sgd_op = sgd_optimizer.apply_gradients( - zip([grads0, grads1], [var0, var1]), global_step=global_step) - variables.global_variables_initializer().run() - # Fetch params to validate initial values - self.assertAllCloseAccordingToType([1.1, 2.1], var0.eval()) - self.assertAllCloseAccordingToType([3.0, 4.0], var1.eval()) - # Run 1 step of sgd - sgd_op.run() - - # Validate updated params and global_step - self.assertAllCloseAccordingToType([1.1 - 3.0 * 0.1, 2.1 - 3.0 * 0.1], - var0.eval()) - self.assertAllCloseAccordingToType([3.0 - 3.0 * 0.01, 4.0 - 3.0 * 0.01], - var1.eval()) - self.assertAllCloseAccordingToType(1, global_step.eval()) - self.assertAllCloseAccordingToType(1, sgd_optimizer._counter.eval()) - - def testSparseBasic(self): - for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: - with self.test_session(): - var0 = variables.Variable([[1.1], [2.1]], dtype=dtype) - var1 = variables.Variable([[3.0], [4.0]], dtype=dtype) - grads0 = ops.IndexedSlices( - constant_op.constant([0.1], shape=[1, 1], dtype=dtype), - constant_op.constant([0]), constant_op.constant([2, 1])) - grads1 = ops.IndexedSlices( - constant_op.constant([0.01], shape=[1, 1], dtype=dtype), - constant_op.constant([1]), constant_op.constant([2, 1])) - decay_rate = 0.1 - batch_size = 2 - total_num_examples = 10 - sgd_op = VariationalSGDOptimizer( - batch_size, - total_num_examples, - max_learning_rate=3.0, - burnin=0, - preconditioner_decay_rate=decay_rate).apply_gradients( - zip([grads0, grads1], [var0, var1])) - variables.global_variables_initializer().run() - # Fetch params to validate initial values - self.assertAllCloseAccordingToType([[1.1], [2.1]], var0.eval()) - self.assertAllCloseAccordingToType([[3.0], [4.0]], var1.eval()) - # Run 1 step of sgd - sgd_op.run() - # Validate updated params - self.assertAllCloseAccordingToType([[1.1 - 3.0 * 0.1], [2.1]], - var0.eval()) - self.assertAllCloseAccordingToType( - [[3.0 - 3.0 * 0], [4.0 - 3.0 * 0.01]], var1.eval()) - - -if __name__ == "__main__": - test.main() diff --git a/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence.py b/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence.py deleted file mode 100644 index 9f7a95f138f7fd3e726f095dc16f41abb6182e17..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence.py +++ /dev/null @@ -1,51 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Csiszar f-Divergence and helpers. - -See ${python/contrib.bayesflow.csiszar_divergence}. -""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -# go/tf-wildcard-import -# pylint: disable=wildcard-import -from tensorflow.contrib.bayesflow.python.ops.csiszar_divergence_impl import * -# pylint: enable=wildcard-import -from tensorflow.python.util.all_util import remove_undocumented - -_allowed_symbols = [ - 'amari_alpha', - 'arithmetic_geometric', - 'chi_square', - 'csiszar_vimco', - 'dual_csiszar_function', - 'jeffreys', - 'jensen_shannon', - 'kl_forward', - 'kl_reverse', - 'log1p_abs', - 'modified_gan', - 'monte_carlo_csiszar_f_divergence', - 'pearson', - 'squared_hellinger', - 'symmetrized_csiszar_function', - 'total_variation', - 't_power', - 'triangular', -] - -remove_undocumented(__name__, _allowed_symbols) diff --git a/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py b/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py deleted file mode 100644 index 8efd59d6516924bea538717d45bb4ae303583421..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/bayesflow/python/ops/csiszar_divergence_impl.py +++ /dev/null @@ -1,1105 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Csiszar f-Divergence and helpers. - -@@amari_alpha -@@arithmetic_geometric -@@chi_square -@@csiszar_vimco -@@dual_csiszar_function -@@jeffreys -@@jensen_shannon -@@kl_forward -@@kl_reverse -@@log1p_abs -@@modified_gan -@@monte_carlo_csiszar_f_divergence -@@pearson -@@squared_hellinger -@@symmetrized_csiszar_function -@@total_variation -@@triangular - -""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import numpy as np - -from tensorflow.contrib import framework as contrib_framework -from tensorflow.contrib.bayesflow.python.ops import monte_carlo_impl as monte_carlo -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 nn_ops -from tensorflow.python.ops.distributions import distribution -from tensorflow.python.ops.distributions import util as distribution_util - - -def amari_alpha(logu, alpha=1., self_normalized=False, name=None): - """The Amari-alpha Csiszar-function in log-space. - - A Csiszar-function is a member of, - - ```none - F = { f:R_+ to R : f convex }. - ``` - - When `self_normalized = True`, the Amari-alpha Csiszar-function is: - - ```none - f(u) = { -log(u) + (u - 1), alpha = 0 - { u log(u) - (u - 1), alpha = 1 - { [(u**alpha - 1) - alpha (u - 1)] / (alpha (alpha - 1)), otherwise - ``` - - When `self_normalized = False` the `(u - 1)` terms are omitted. - - Warning: when `alpha != 0` and/or `self_normalized = True` this function makes - non-log-space calculations and may therefore be numerically unstable for - `|logu| >> 0`. - - For more information, see: - A. Cichocki and S. Amari. "Families of Alpha-Beta-and GammaDivergences: - Flexible and Robust Measures of Similarities." Entropy, vol. 12, no. 6, pp. - 1532-1568, 2010. - - Args: - logu: `float`-like `Tensor` representing `log(u)` from above. - alpha: `float`-like Python scalar. (See Mathematical Details for meaning.) - self_normalized: Python `bool` indicating whether `f'(u=1)=0`. When - `f'(u=1)=0` the implied Csiszar f-Divergence remains non-negative even - when `p, q` are unnormalized measures. - name: Python `str` name prefixed to Ops created by this function. - - Returns: - amari_alpha_of_u: `float`-like `Tensor` of the Csiszar-function evaluated - at `u = exp(logu)`. - - Raises: - TypeError: if `alpha` is `None` or a `Tensor`. - TypeError: if `self_normalized` is `None` or a `Tensor`. - """ - with ops.name_scope(name, "amari_alpha", [logu]): - if alpha is None or contrib_framework.is_tensor(alpha): - raise TypeError("`alpha` cannot be `None` or `Tensor` type.") - if self_normalized is None or contrib_framework.is_tensor(self_normalized): - raise TypeError("`self_normalized` cannot be `None` or `Tensor` type.") - - logu = ops.convert_to_tensor(logu, name="logu") - - if alpha == 0.: - f = -logu - elif alpha == 1.: - f = math_ops.exp(logu) * logu - else: - f = math_ops.expm1(alpha * logu) / (alpha * (alpha - 1.)) - - if not self_normalized: - return f - - if alpha == 0.: - return f + math_ops.expm1(logu) - elif alpha == 1.: - return f - math_ops.expm1(logu) - else: - return f - math_ops.expm1(logu) / (alpha - 1.) - - -def kl_reverse(logu, self_normalized=False, name=None): - """The reverse Kullback-Leibler Csiszar-function in log-space. - - A Csiszar-function is a member of, - - ```none - F = { f:R_+ to R : f convex }. - ``` - - When `self_normalized = True`, the KL-reverse Csiszar-function is: - - ```none - f(u) = -log(u) + (u - 1) - ``` - - When `self_normalized = False` the `(u - 1)` term is omitted. - - Observe that as an f-Divergence, this Csiszar-function implies: - - ```none - D_f[p, q] = KL[q, p] - ``` - - The KL is "reverse" because in maximum likelihood we think of minimizing `q` - as in `KL[p, q]`. - - Warning: when self_normalized = True` this function makes non-log-space - calculations and may therefore be numerically unstable for `|logu| >> 0`. - - Args: - logu: `float`-like `Tensor` representing `log(u)` from above. - self_normalized: Python `bool` indicating whether `f'(u=1)=0`. When - `f'(u=1)=0` the implied Csiszar f-Divergence remains non-negative even - when `p, q` are unnormalized measures. - name: Python `str` name prefixed to Ops created by this function. - - Returns: - kl_reverse_of_u: `float`-like `Tensor` of the Csiszar-function evaluated at - `u = exp(logu)`. - - Raises: - TypeError: if `self_normalized` is `None` or a `Tensor`. - """ - - with ops.name_scope(name, "kl_reverse", [logu]): - return amari_alpha(logu, alpha=0., self_normalized=self_normalized) - - -def kl_forward(logu, self_normalized=False, name=None): - """The forward Kullback-Leibler Csiszar-function in log-space. - - A Csiszar-function is a member of, - - ```none - F = { f:R_+ to R : f convex }. - ``` - - When `self_normalized = True`, the KL-forward Csiszar-function is: - - ```none - f(u) = u log(u) - (u - 1) - ``` - - When `self_normalized = False` the `(u - 1)` term is omitted. - - Observe that as an f-Divergence, this Csiszar-function implies: - - ```none - D_f[p, q] = KL[p, q] - ``` - - The KL is "forward" because in maximum likelihood we think of minimizing `q` - as in `KL[p, q]`. - - Warning: this function makes non-log-space calculations and may therefore be - numerically unstable for `|logu| >> 0`. - - Args: - logu: `float`-like `Tensor` representing `log(u)` from above. - self_normalized: Python `bool` indicating whether `f'(u=1)=0`. When - `f'(u=1)=0` the implied Csiszar f-Divergence remains non-negative even - when `p, q` are unnormalized measures. - name: Python `str` name prefixed to Ops created by this function. - - Returns: - kl_forward_of_u: `float`-like `Tensor` of the Csiszar-function evaluated at - `u = exp(logu)`. - - Raises: - TypeError: if `self_normalized` is `None` or a `Tensor`. - """ - - with ops.name_scope(name, "kl_forward", [logu]): - return amari_alpha(logu, alpha=1., self_normalized=self_normalized) - - -def jensen_shannon(logu, self_normalized=False, name=None): - """The Jensen-Shannon Csiszar-function in log-space. - - A Csiszar-function is a member of, - - ```none - F = { f:R_+ to R : f convex }. - ``` - - When `self_normalized = True`, the Jensen-Shannon Csiszar-function is: - - ```none - f(u) = u log(u) - (1 + u) log(1 + u) + (u + 1) log(2) - ``` - - When `self_normalized = False` the `(u + 1) log(2)` term is omitted. - - Observe that as an f-Divergence, this Csiszar-function implies: - - ```none - D_f[p, q] = KL[p, m] + KL[q, m] - m(x) = 0.5 p(x) + 0.5 q(x) - ``` - - In a sense, this divergence is the "reverse" of the Arithmetic-Geometric - f-Divergence. - - This Csiszar-function induces a symmetric f-Divergence, i.e., - `D_f[p, q] = D_f[q, p]`. - - Warning: this function makes non-log-space calculations and may therefore be - numerically unstable for `|logu| >> 0`. - - For more information, see: - Lin, J. "Divergence measures based on the Shannon entropy." IEEE Trans. - Inf. Th., 37, 145-151, 1991. - - Args: - logu: `float`-like `Tensor` representing `log(u)` from above. - self_normalized: Python `bool` indicating whether `f'(u=1)=0`. When - `f'(u=1)=0` the implied Csiszar f-Divergence remains non-negative even - when `p, q` are unnormalized measures. - name: Python `str` name prefixed to Ops created by this function. - - Returns: - jensen_shannon_of_u: `float`-like `Tensor` of the Csiszar-function - evaluated at `u = exp(logu)`. - """ - - with ops.name_scope(name, "jensen_shannon", [logu]): - logu = ops.convert_to_tensor(logu, name="logu") - npdt = logu.dtype.as_numpy_dtype - y = nn_ops.softplus(logu) - if self_normalized: - y -= np.log(2).astype(npdt) - return math_ops.exp(logu) * logu - (1. + math_ops.exp(logu)) * y - - -def arithmetic_geometric(logu, self_normalized=False, name=None): - """The Arithmetic-Geometric Csiszar-function in log-space. - - A Csiszar-function is a member of, - - ```none - F = { f:R_+ to R : f convex }. - ``` - - When `self_normalized = True` the Arithmetic-Geometric Csiszar-function is: - - ```none - f(u) = (1 + u) log( (1 + u) / sqrt(u) ) - (1 + u) log(2) - ``` - - When `self_normalized = False` the `(1 + u) log(2)` term is omitted. - - Observe that as an f-Divergence, this Csiszar-function implies: - - ```none - D_f[p, q] = KL[m, p] + KL[m, q] - m(x) = 0.5 p(x) + 0.5 q(x) - ``` - - In a sense, this divergence is the "reverse" of the Jensen-Shannon - f-Divergence. - - This Csiszar-function induces a symmetric f-Divergence, i.e., - `D_f[p, q] = D_f[q, p]`. - - Warning: this function makes non-log-space calculations and may therefore be - numerically unstable for `|logu| >> 0`. - - Args: - logu: `float`-like `Tensor` representing `log(u)` from above. - self_normalized: Python `bool` indicating whether `f'(u=1)=0`. When - `f'(u=1)=0` the implied Csiszar f-Divergence remains non-negative even - when `p, q` are unnormalized measures. - name: Python `str` name prefixed to Ops created by this function. - - Returns: - arithmetic_geometric_of_u: `float`-like `Tensor` of the - Csiszar-function evaluated at `u = exp(logu)`. - """ - - with ops.name_scope(name, "arithmetic_geometric", [logu]): - logu = ops.convert_to_tensor(logu, name="logu") - y = nn_ops.softplus(logu) - 0.5 * logu - if self_normalized: - y -= np.log(2.).astype(logu.dtype.as_numpy_dtype) - return (1. + math_ops.exp(logu)) * y - - -def total_variation(logu, name=None): - """The Total Variation Csiszar-function in log-space. - - A Csiszar-function is a member of, - - ```none - F = { f:R_+ to R : f convex }. - ``` - - The Total-Variation Csiszar-function is: - - ```none - f(u) = 0.5 |u - 1| - ``` - - Warning: this function makes non-log-space calculations and may therefore be - numerically unstable for `|logu| >> 0`. - - Args: - logu: `float`-like `Tensor` representing `log(u)` from above. - name: Python `str` name prefixed to Ops created by this function. - - Returns: - total_variation_of_u: `float`-like `Tensor` of the Csiszar-function - evaluated at `u = exp(logu)`. - """ - - with ops.name_scope(name, "total_variation", [logu]): - logu = ops.convert_to_tensor(logu, name="logu") - return 0.5 * math_ops.abs(math_ops.expm1(logu)) - - -def pearson(logu, name=None): - """The Pearson Csiszar-function in log-space. - - A Csiszar-function is a member of, - - ```none - F = { f:R_+ to R : f convex }. - ``` - - The Pearson Csiszar-function is: - - ```none - f(u) = (u - 1)**2 - ``` - - Warning: this function makes non-log-space calculations and may therefore be - numerically unstable for `|logu| >> 0`. - - Args: - logu: `float`-like `Tensor` representing `log(u)` from above. - name: Python `str` name prefixed to Ops created by this function. - - Returns: - pearson_of_u: `float`-like `Tensor` of the Csiszar-function evaluated at - `u = exp(logu)`. - """ - - with ops.name_scope(name, "pearson", [logu]): - logu = ops.convert_to_tensor(logu, name="logu") - return math_ops.square(math_ops.expm1(logu)) - - -def squared_hellinger(logu, name=None): - """The Squared-Hellinger Csiszar-function in log-space. - - A Csiszar-function is a member of, - - ```none - F = { f:R_+ to R : f convex }. - ``` - - The Squared-Hellinger Csiszar-function is: - - ```none - f(u) = (sqrt(u) - 1)**2 - ``` - - This Csiszar-function induces a symmetric f-Divergence, i.e., - `D_f[p, q] = D_f[q, p]`. - - Warning: this function makes non-log-space calculations and may therefore be - numerically unstable for `|logu| >> 0`. - - Args: - logu: `float`-like `Tensor` representing `log(u)` from above. - name: Python `str` name prefixed to Ops created by this function. - - Returns: - squared_hellinger_of_u: `float`-like `Tensor` of the Csiszar-function - evaluated at `u = exp(logu)`. - """ - - with ops.name_scope(name, "squared_hellinger", [logu]): - logu = ops.convert_to_tensor(logu, name="logu") - return pearson(0.5 * logu) - - -def triangular(logu, name=None): - """The Triangular Csiszar-function in log-space. - - A Csiszar-function is a member of, - - ```none - F = { f:R_+ to R : f convex }. - ``` - - The Triangular Csiszar-function is: - - ```none - f(u) = (u - 1)**2 / (1 + u) - ``` - - This Csiszar-function induces a symmetric f-Divergence, i.e., - `D_f[p, q] = D_f[q, p]`. - - Warning: this function makes non-log-space calculations and may therefore be - numerically unstable for `|logu| >> 0`. - - Args: - logu: `float`-like `Tensor` representing `log(u)` from above. - name: Python `str` name prefixed to Ops created by this function. - - Returns: - triangular_of_u: `float`-like `Tensor` of the Csiszar-function evaluated - at `u = exp(logu)`. - """ - - with ops.name_scope(name, "triangular", [logu]): - logu = ops.convert_to_tensor(logu, name="logu") - return pearson(logu) / (1. + math_ops.exp(logu)) - - -def t_power(logu, t, self_normalized=False, name=None): - """The T-Power Csiszar-function in log-space. - - A Csiszar-function is a member of, - - ```none - F = { f:R_+ to R : f convex }. - ``` - - When `self_normalized = True` the T-Power Csiszar-function is: - - ```none - f(u) = s [ u**t - 1 - t(u - 1) ] - s = { -1 0 < t < 1 - { +1 otherwise - ``` - - When `self_normalized = False` the `- t(u - 1)` term is omitted. - - This is similar to the `amari_alpha` Csiszar-function, with the associated - divergence being the same up to factors depending only on `t`. - - Args: - logu: `float`-like `Tensor` representing `log(u)` from above. - t: `Tensor` of same `dtype` as `logu` and broadcastable shape. - self_normalized: Python `bool` indicating whether `f'(u=1)=0`. - name: Python `str` name prefixed to Ops created by this function. - - Returns: - t_power_of_u: `float`-like `Tensor` of the Csiszar-function evaluated - at `u = exp(logu)`. - """ - with ops.name_scope(name, "t_power", [logu, t]): - logu = ops.convert_to_tensor(logu, name="logu") - t = ops.convert_to_tensor(t, dtype=logu.dtype.base_dtype, name="t") - fu = math_ops.expm1(t * logu) - if self_normalized: - fu -= t * math_ops.expm1(logu) - fu *= array_ops.where(math_ops.logical_and(0. < t, t < 1.), - -array_ops.ones_like(t), - array_ops.ones_like(t)) - return fu - - -def log1p_abs(logu, name=None): - """The log1p-abs Csiszar-function in log-space. - - A Csiszar-function is a member of, - - ```none - F = { f:R_+ to R : f convex }. - ``` - - The Log1p-Abs Csiszar-function is: - - ```none - f(u) = u**(sign(u-1)) - 1 - ``` - - This function is so-named because it was invented from the following recipe. - Choose a convex function g such that g(0)=0 and solve for f: - - ```none - log(1 + f(u)) = g(log(u)). - <=> - f(u) = exp(g(log(u))) - 1 - ``` - - That is, the graph is identically `g` when y-axis is `log1p`-domain and x-axis - is `log`-domain. - - Warning: this function makes non-log-space calculations and may therefore be - numerically unstable for `|logu| >> 0`. - - Args: - logu: `float`-like `Tensor` representing `log(u)` from above. - name: Python `str` name prefixed to Ops created by this function. - - Returns: - log1p_abs_of_u: `float`-like `Tensor` of the Csiszar-function evaluated - at `u = exp(logu)`. - """ - - with ops.name_scope(name, "log1p_abs", [logu]): - logu = ops.convert_to_tensor(logu, name="logu") - return math_ops.expm1(math_ops.abs(logu)) - - -def jeffreys(logu, name=None): - """The Jeffreys Csiszar-function in log-space. - - A Csiszar-function is a member of, - - ```none - F = { f:R_+ to R : f convex }. - ``` - - The Jeffreys Csiszar-function is: - - ```none - f(u) = 0.5 ( u log(u) - log(u) ) - = 0.5 kl_forward + 0.5 kl_reverse - = symmetrized_csiszar_function(kl_reverse) - = symmetrized_csiszar_function(kl_forward) - ``` - - This Csiszar-function induces a symmetric f-Divergence, i.e., - `D_f[p, q] = D_f[q, p]`. - - Warning: this function makes non-log-space calculations and may therefore be - numerically unstable for `|logu| >> 0`. - - Args: - logu: `float`-like `Tensor` representing `log(u)` from above. - name: Python `str` name prefixed to Ops created by this function. - - Returns: - jeffreys_of_u: `float`-like `Tensor` of the Csiszar-function evaluated - at `u = exp(logu)`. - """ - - with ops.name_scope(name, "jeffreys", [logu]): - logu = ops.convert_to_tensor(logu, name="logu") - return 0.5 * math_ops.expm1(logu) * logu - - -def chi_square(logu, name=None): - """The chi-Square Csiszar-function in log-space. - - A Csiszar-function is a member of, - - ```none - F = { f:R_+ to R : f convex }. - ``` - - The Chi-square Csiszar-function is: - - ```none - f(u) = u**2 - 1 - ``` - - Warning: this function makes non-log-space calculations and may therefore be - numerically unstable for `|logu| >> 0`. - - Args: - logu: `float`-like `Tensor` representing `log(u)` from above. - name: Python `str` name prefixed to Ops created by this function. - - Returns: - chi_square_of_u: `float`-like `Tensor` of the Csiszar-function evaluated - at `u = exp(logu)`. - """ - - with ops.name_scope(name, "chi_square", [logu]): - logu = ops.convert_to_tensor(logu, name="logu") - return math_ops.expm1(2. * logu) - - -def modified_gan(logu, self_normalized=False, name=None): - """The Modified-GAN Csiszar-function in log-space. - - A Csiszar-function is a member of, - - ```none - F = { f:R_+ to R : f convex }. - ``` - - When `self_normalized = True` the modified-GAN (Generative/Adversarial - Network) Csiszar-function is: - - ```none - f(u) = log(1 + u) - log(u) + 0.5 (u - 1) - ``` - - When `self_normalized = False` the `0.5 (u - 1)` is omitted. - - The unmodified GAN Csiszar-function is identical to Jensen-Shannon (with - `self_normalized = False`). - - Warning: this function makes non-log-space calculations and may therefore be - numerically unstable for `|logu| >> 0`. - - Args: - logu: `float`-like `Tensor` representing `log(u)` from above. - self_normalized: Python `bool` indicating whether `f'(u=1)=0`. When - `f'(u=1)=0` the implied Csiszar f-Divergence remains non-negative even - when `p, q` are unnormalized measures. - name: Python `str` name prefixed to Ops created by this function. - - Returns: - chi_square_of_u: `float`-like `Tensor` of the Csiszar-function evaluated - at `u = exp(logu)`. - """ - - with ops.name_scope(name, "chi_square", [logu]): - logu = ops.convert_to_tensor(logu, name="logu") - y = nn_ops.softplus(logu) - logu - if self_normalized: - y += 0.5 * math_ops.expm1(logu) - return y - - -def dual_csiszar_function(logu, csiszar_function, name=None): - """Calculates the dual Csiszar-function in log-space. - - A Csiszar-function is a member of, - - ```none - F = { f:R_+ to R : f convex }. - ``` - - The Csiszar-dual is defined as: - - ```none - f^*(u) = u f(1 / u) - ``` - - where `f` is some other Csiszar-function. - - For example, the dual of `kl_reverse` is `kl_forward`, i.e., - - ```none - f(u) = -log(u) - f^*(u) = u f(1 / u) = -u log(1 / u) = u log(u) - ``` - - The dual of the dual is the original function: - - ```none - f^**(u) = {u f(1/u)}^*(u) = u (1/u) f(1/(1/u)) = f(u) - ``` - - Warning: this function makes non-log-space calculations and may therefore be - numerically unstable for `|logu| >> 0`. - - Args: - logu: `float`-like `Tensor` representing `log(u)` from above. - csiszar_function: Python `callable` representing a Csiszar-function over - log-domain. - name: Python `str` name prefixed to Ops created by this function. - - Returns: - dual_f_of_u: `float`-like `Tensor` of the result of calculating the dual of - `f` at `u = exp(logu)`. - """ - - with ops.name_scope(name, "dual_csiszar_function", [logu]): - return math_ops.exp(logu) * csiszar_function(-logu) - - -def symmetrized_csiszar_function(logu, csiszar_function, name=None): - """Symmetrizes a Csiszar-function in log-space. - - A Csiszar-function is a member of, - - ```none - F = { f:R_+ to R : f convex }. - ``` - - The symmetrized Csiszar-function is defined as: - - ```none - f_g(u) = 0.5 g(u) + 0.5 u g (1 / u) - ``` - - where `g` is some other Csiszar-function. - - We say the function is "symmetrized" because: - - ```none - D_{f_g}[p, q] = D_{f_g}[q, p] - ``` - - for all `p << >> q` (i.e., `support(p) = support(q)`). - - There exists alternatives for symmetrizing a Csiszar-function. For example, - - ```none - f_g(u) = max(f(u), f^*(u)), - ``` - - where `f^*` is the dual Csiszar-function, also implies a symmetric - f-Divergence. - - Example: - - When either of the following functions are symmetrized, we obtain the - Jensen-Shannon Csiszar-function, i.e., - - ```none - g(u) = -log(u) - (1 + u) log((1 + u) / 2) + u - 1 - h(u) = log(4) + 2 u log(u / (1 + u)) - ``` - - implies, - - ```none - f_g(u) = f_h(u) = u log(u) - (1 + u) log((1 + u) / 2) - = jensen_shannon(log(u)). - ``` - - Warning: this function makes non-log-space calculations and may therefore be - numerically unstable for `|logu| >> 0`. - - Args: - logu: `float`-like `Tensor` representing `log(u)` from above. - csiszar_function: Python `callable` representing a Csiszar-function over - log-domain. - name: Python `str` name prefixed to Ops created by this function. - - Returns: - symmetrized_g_of_u: `float`-like `Tensor` of the result of applying the - symmetrization of `g` evaluated at `u = exp(logu)`. - """ - - with ops.name_scope(name, "symmetrized_csiszar_function", [logu]): - logu = ops.convert_to_tensor(logu, name="logu") - return 0.5 * (csiszar_function(logu) - + dual_csiszar_function(logu, csiszar_function)) - - -def monte_carlo_csiszar_f_divergence( - f, - p_log_prob, - q, - num_draws, - use_reparametrization=None, - seed=None, - name=None): - """Monte-Carlo approximation of the Csiszar f-Divergence. - - A Csiszar-function is a member of, - - ```none - F = { f:R_+ to R : f convex }. - ``` - - The Csiszar f-Divergence for Csiszar-function f is given by: - - ```none - D_f[p(X), q(X)] := E_{q(X)}[ f( p(X) / q(X) ) ] - ~= m**-1 sum_j^m f( p(x_j) / q(x_j) ), - where x_j ~iid q(X) - ``` - - Tricks: Reparameterization and Score-Gradient - - When q is "reparameterized", i.e., a diffeomorphic transformation of a - parameterless distribution (e.g., - `Normal(Y; m, s) <=> Y = sX + m, X ~ Normal(0,1)`), we can swap gradient and - expectation, i.e., - `grad[Avg{ s_i : i=1...n }] = Avg{ grad[s_i] : i=1...n }` where `S_n=Avg{s_i}` - and `s_i = f(x_i), x_i ~iid q(X)`. - - However, if q is not reparameterized, TensorFlow's gradient will be incorrect - since the chain-rule stops at samples of unreparameterized distributions. In - this circumstance using the Score-Gradient trick results in an unbiased - gradient, i.e., - - ```none - grad[ E_q[f(X)] ] - = grad[ int dx q(x) f(x) ] - = int dx grad[ q(x) f(x) ] - = int dx [ q'(x) f(x) + q(x) f'(x) ] - = int dx q(x) [q'(x) / q(x) f(x) + f'(x) ] - = int dx q(x) grad[ f(x) q(x) / stop_grad[q(x)] ] - = E_q[ grad[ f(x) q(x) / stop_grad[q(x)] ] ] - ``` - - Unless `q.reparameterization_type != distribution.FULLY_REPARAMETERIZED` it is - usually preferable to set `use_reparametrization = True`. - - Example Application: - - The Csiszar f-Divergence is a useful framework for variational inference. - I.e., observe that, - - ```none - f(p(x)) = f( E_{q(Z | x)}[ p(x, Z) / q(Z | x) ] ) - <= E_{q(Z | x)}[ f( p(x, Z) / q(Z | x) ) ] - := D_f[p(x, Z), q(Z | x)] - ``` - - The inequality follows from the fact that the "perspective" of `f`, i.e., - `(s, t) |-> t f(s / t))`, is convex in `(s, t)` when `s/t in domain(f)` and - `t` is a real. Since the above framework includes the popular Evidence Lower - BOund (ELBO) as a special case, i.e., `f(u) = -log(u)`, we call this framework - "Evidence Divergence Bound Optimization" (EDBO). - - Args: - f: Python `callable` representing a Csiszar-function in log-space, i.e., - takes `p_log_prob(q_samples) - q.log_prob(q_samples)`. - p_log_prob: Python `callable` taking (a batch of) samples from `q` and - returning the natural-log of the probability under distribution `p`. - (In variational inference `p` is the joint distribution.) - q: `tf.Distribution`-like instance; must implement: - `reparameterization_type`, `sample(n, seed)`, and `log_prob(x)`. - (In variational inference `q` is the approximate posterior distribution.) - num_draws: Integer scalar number of draws used to approximate the - f-Divergence expectation. - use_reparametrization: Python `bool`. When `None` (the default), - automatically set to: - `q.reparameterization_type == distribution.FULLY_REPARAMETERIZED`. - When `True` uses the standard Monte-Carlo average. When `False` uses the - score-gradient trick. (See above for details.) When `False`, consider - using `csiszar_vimco`. - seed: Python `int` seed for `q.sample`. - name: Python `str` name prefixed to Ops created by this function. - - Returns: - monte_carlo_csiszar_f_divergence: `float`-like `Tensor` Monte Carlo - approximation of the Csiszar f-Divergence. - - Raises: - ValueError: if `q` is not a reparameterized distribution and - `use_reparametrization = True`. A distribution `q` is said to be - "reparameterized" when its samples are generated by transforming the - samples of another distribution which does not depend on the - parameterization of `q`. This property ensures the gradient (with respect - to parameters) is valid. - TypeError: if `p_log_prob` is not a Python `callable`. - """ - with ops.name_scope(name, "monte_carlo_csiszar_f_divergence", [num_draws]): - if use_reparametrization is None: - use_reparametrization = (q.reparameterization_type - == distribution.FULLY_REPARAMETERIZED) - elif (use_reparametrization and - q.reparameterization_type != distribution.FULLY_REPARAMETERIZED): - # TODO(jvdillon): Consider only raising an exception if the gradient is - # requested. - raise ValueError( - "Distribution `q` must be reparameterized, i.e., a diffeomorphic " - "transformation of a parameterless distribution. (Otherwise this " - "function has a biased gradient.)") - if not callable(p_log_prob): - raise TypeError("`p_log_prob` must be a Python `callable` function.") - return monte_carlo.expectation( - f=lambda q_samples: f(p_log_prob(q_samples) - q.log_prob(q_samples)), - samples=q.sample(num_draws, seed=seed), - log_prob=q.log_prob, # Only used if use_reparametrization=False. - use_reparametrization=use_reparametrization) - - -def csiszar_vimco(f, - p_log_prob, - q, - num_draws, - num_batch_draws=1, - seed=None, - name=None): - """Use VIMCO to lower the variance of gradient[csiszar_function(Avg(logu))]. - - This function generalizes "Variational Inference for Monte Carlo Objectives" - (VIMCO), i.e., https://arxiv.org/abs/1602.06725, to Csiszar f-Divergences. - - Note: if `q.reparameterization_type = distribution.FULLY_REPARAMETERIZED`, - consider using `monte_carlo_csiszar_f_divergence`. - - The VIMCO loss is: - - ```none - vimco = f(Avg{logu[i] : i=0,...,m-1}) - where, - logu[i] = log( p(x, h[i]) / q(h[i] | x) ) - h[i] iid~ q(H | x) - ``` - - Interestingly, the VIMCO gradient is not the naive gradient of `vimco`. - Rather, it is characterized by: - - ```none - grad[vimco] - variance_reducing_term - where, - variance_reducing_term = Sum{ grad[log q(h[i] | x)] * - (vimco - f(log Avg{h[j;i] : j=0,...,m-1})) - : i=0, ..., m-1 } - h[j;i] = { u[j] j!=i - { GeometricAverage{ u[k] : k!=i} j==i - ``` - - (We omitted `stop_gradient` for brevity. See implementation for more details.) - - The `Avg{h[j;i] : j}` term is a kind of "swap-out average" where the `i`-th - element has been replaced by the leave-`i`-out Geometric-average. - - This implementation prefers numerical precision over efficiency, i.e., - `O(num_draws * num_batch_draws * prod(batch_shape) * prod(event_shape))`. - (The constant may be fairly large, perhaps around 12.) - - Args: - f: Python `callable` representing a Csiszar-function in log-space. - p_log_prob: Python `callable` representing the natural-log of the - probability under distribution `p`. (In variational inference `p` is the - joint distribution.) - q: `tf.Distribution`-like instance; must implement: `sample(n, seed)`, and - `log_prob(x)`. (In variational inference `q` is the approximate posterior - distribution.) - num_draws: Integer scalar number of draws used to approximate the - f-Divergence expectation. - num_batch_draws: Integer scalar number of draws used to approximate the - f-Divergence expectation. - seed: Python `int` seed for `q.sample`. - name: Python `str` name prefixed to Ops created by this function. - - Returns: - vimco: The Csiszar f-Divergence generalized VIMCO objective. - - Raises: - ValueError: if `num_draws < 2`. - """ - with ops.name_scope(name, "csiszar_vimco", [num_draws, num_batch_draws]): - if num_draws < 2: - raise ValueError("Must specify num_draws > 1.") - stop = array_ops.stop_gradient # For readability. - x = stop(q.sample(sample_shape=[num_draws, num_batch_draws], - seed=seed)) - logqx = q.log_prob(x) - logu = p_log_prob(x) - logqx - f_log_avg_u, f_log_sooavg_u = [f(r) for r in csiszar_vimco_helper(logu)] - dotprod = math_ops.reduce_sum( - logqx * stop(f_log_avg_u - f_log_sooavg_u), - axis=0) # Sum over iid samples. - # We now rewrite f_log_avg_u so that: - # `grad[f_log_avg_u] := grad[f_log_avg_u + dotprod]`. - # To achieve this, we use a trick that - # `f(x) - stop(f(x)) == zeros_like(f(x))` - # but its gradient is grad[f(x)]. - # Note that IEEE754 specifies that `x - x == 0.` and `x + 0. == x`, hence - # this trick loses no precision. For more discussion regarding the relevant - # portions of the IEEE754 standard, see the StackOverflow question, - # "Is there a floating point value of x, for which x-x == 0 is false?" - # http://stackoverflow.com/q/2686644 - f_log_avg_u += dotprod - stop(dotprod) # Add zeros_like(dot_prod). - return math_ops.reduce_mean(f_log_avg_u, axis=0) # Avg over batches. - - -def csiszar_vimco_helper(logu, name=None): - """Helper to `csiszar_vimco`; computes `log_avg_u`, `log_sooavg_u`. - - `axis = 0` of `logu` is presumed to correspond to iid samples from `q`, i.e., - - ```none - logu[j] = log(u[j]) - u[j] = p(x, h[j]) / q(h[j] | x) - h[j] iid~ q(H | x) - ``` - - Args: - logu: Floating-type `Tensor` representing `log(p(x, h) / q(h | x))`. - name: Python `str` name prefixed to Ops created by this function. - - Returns: - log_avg_u: `logu.dtype` `Tensor` corresponding to the natural-log of the - average of `u`. The sum of the gradient of `log_avg_u` is `1`. - log_sooavg_u: `logu.dtype` `Tensor` characterized by the natural-log of the - average of `u`` except that the average swaps-out `u[i]` for the - leave-`i`-out Geometric-average. The mean of the gradient of - `log_sooavg_u` is `1`. Mathematically `log_sooavg_u` is, - ```none - log_sooavg_u[i] = log(Avg{h[j ; i] : j=0, ..., m-1}) - h[j ; i] = { u[j] j!=i - { GeometricAverage{u[k] : k != i} j==i - ``` - - """ - with ops.name_scope(name, "csiszar_vimco_helper", [logu]): - logu = ops.convert_to_tensor(logu, name="logu") - - n = logu.shape.with_rank_at_least(1)[0].value - if n is None: - n = array_ops.shape(logu)[0] - log_n = math_ops.log(math_ops.cast(n, dtype=logu.dtype)) - nm1 = math_ops.cast(n - 1, dtype=logu.dtype) - else: - log_n = np.log(n).astype(logu.dtype.as_numpy_dtype) - nm1 = np.asarray(n - 1, dtype=logu.dtype.as_numpy_dtype) - - # Throughout we reduce across axis=0 since this is presumed to be iid - # samples. - - log_max_u = math_ops.reduce_max(logu, axis=0) - log_sum_u_minus_log_max_u = math_ops.reduce_logsumexp( - logu - log_max_u, axis=0) - - # log_loosum_u[i] = - # = logsumexp(logu[j] : j != i) - # = log( exp(logsumexp(logu)) - exp(logu[i]) ) - # = log( exp(logsumexp(logu - logu[i])) exp(logu[i]) - exp(logu[i])) - # = logu[i] + log(exp(logsumexp(logu - logu[i])) - 1) - # = logu[i] + log(exp(logsumexp(logu) - logu[i]) - 1) - # = logu[i] + softplus_inverse(logsumexp(logu) - logu[i]) - d = log_sum_u_minus_log_max_u + (log_max_u - logu) - # We use `d != 0` rather than `d > 0.` because `d < 0.` should never - # happens; if it does we want to complain loudly (which `softplus_inverse` - # will). - d_ok = math_ops.not_equal(d, 0.) - safe_d = array_ops.where(d_ok, d, array_ops.ones_like(d)) - d_ok_result = logu + distribution_util.softplus_inverse(safe_d) - - inf = np.array(np.inf, dtype=logu.dtype.as_numpy_dtype) - - # When not(d_ok) and is_positive_and_largest then we manually compute the - # log_loosum_u. (We can efficiently do this for any one point but not all, - # hence we still need the above calculation.) This is good because when - # this condition is met, we cannot use the above calculation; its -inf. - # We now compute the log-leave-out-max-sum, replicate it to every - # point and make sure to select it only when we need to. - is_positive_and_largest = math_ops.logical_and( - logu > 0., - math_ops.equal(logu, log_max_u[array_ops.newaxis, ...])) - log_lomsum_u = math_ops.reduce_logsumexp( - array_ops.where(is_positive_and_largest, - array_ops.fill(array_ops.shape(logu), -inf), - logu), - axis=0, keep_dims=True) - log_lomsum_u = array_ops.tile( - log_lomsum_u, - multiples=1 + array_ops.pad([n-1], [[0, array_ops.rank(logu)-1]])) - - d_not_ok_result = array_ops.where( - is_positive_and_largest, - log_lomsum_u, - array_ops.fill(array_ops.shape(d), -inf)) - - log_loosum_u = array_ops.where(d_ok, d_ok_result, d_not_ok_result) - - # The swap-one-out-sum ("soosum") is n different sums, each of which - # replaces the i-th item with the i-th-left-out average, i.e., - # soo_sum_u[i] = [exp(logu) - exp(logu[i])] + exp(mean(logu[!=i])) - # = exp(log_loosum_u[i]) + exp(looavg_logu[i]) - looavg_logu = (math_ops.reduce_sum(logu, axis=0) - logu) / nm1 - log_soosum_u = math_ops.reduce_logsumexp( - array_ops.stack([log_loosum_u, looavg_logu]), - axis=0) - - log_avg_u = log_sum_u_minus_log_max_u + log_max_u - log_n - log_sooavg_u = log_soosum_u - log_n - - log_avg_u.set_shape(logu.shape.with_rank_at_least(1)[1:]) - log_sooavg_u.set_shape(logu.shape) - - return log_avg_u, log_sooavg_u diff --git a/tensorflow/contrib/bayesflow/python/ops/custom_grad_impl.py b/tensorflow/contrib/bayesflow/python/ops/custom_grad_impl.py deleted file mode 100644 index d44fe6529a7ff0da0c6747e193fdb98a272a8da3..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/bayesflow/python/ops/custom_grad_impl.py +++ /dev/null @@ -1,110 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Functions for specifying custom gradients. - -@@custom_gradient - -""" - -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 math_ops - -__all__ = [ - "custom_gradient", -] - - -def custom_gradient(fx, gx, x, axis=(), fx_gx_manually_stopped=False, - name=None): - """Enables specifying a custom gradient. - - This function works by clever application of `stop_gradient`. I.e., observe - that: - - ```none - h(x) = x * stop_gradient(g(x)) + stop_gradient(f(x) - x * g(x)) - ``` - - is such that `h(x) = stop_gradient(f(x))` and `grad[h(x), x] = - stop_gradient(g(x)).` - - In addition to scalar-domain/scalar-range functions, this function also - supports tensor-domain/scalar-range functions. However, in the latter case it - is necessary to reduce `x` to a scalar. This can be done by indicating the - `axis` over which `f` operates or by appropriately `reduce_sum`-ing `x`, prior - to calling this function. - - Partial Custom Gradient: - - Suppose `h(x) = htilde(x, y)`. Note that `dh/dx = stop(g(x))` but `dh/dy = - None`. This is because a `Tensor` cannot have only a portion of its gradient - stopped. To circumvent this issue, one must manually `stop_gradient` the - relevant portions of `f`, `g`. For example see the unit-test, - `test_works_correctly_fx_gx_manually_stopped`. - - Args: - fx: `Tensor`. Output of function evaluated at `x`. - gx: `Tensor`. Gradient of function evaluated at `x`. - x: `Tensor`. Point of evaluation for `f, g`. - axis: 1D `int` `Tensor` representing dimensions of `x` which are the domain - of `f`. If `()` (the default), `f` is assumed scalar-domain/scalar-range. - If `None` `f` is assumed to render one scalar given all of `x`. Otherwise - `f` is assumed to output one scalar for each of `axis` dimensions of `x`. - fx_gx_manually_stopped: Python `bool` indicating that `fx`, `gx` manually - have `stop_gradient` applied. - name: Python `str` name prefixed to Ops created by this function. - - Returns: - fx: Floating-type `Tensor` equal to `f(x)` but which has gradient - `stop_gradient(g(x))`. - """ - with ops.name_scope(name, "custom_gradient", [fx, gx, x]): - fx = ops.convert_to_tensor(fx, name="fx") - # We don't want to bother eagerly computing `gx` since we may not even need - # it. - with ops.control_dependencies([fx]): - gx = ops.convert_to_tensor(gx, dtype=fx.dtype, name="gx") - gx = array_ops.identity(gx, name="gx") - # Proof of correctness: - # - # f(x) = x * stop[gx] + stop[fx - x * gx] - # = stop[fx] - # - # g(x) = grad[fx] - # = stop[gx] + grad[stop[fx - x * gx]] - # = stop[gx] + 0 - # - # Notice that when x is zero it still works: - # grad[x * stop(gx) + stop(fx - x * gx)] = 1 * stop[gx] + 0 = stop[gx] - # - # The proof is similar for the tensor-domain case, except that `x` is - # replaced by `reduce_sum(x)`. - sum_x = math_ops.reduce_sum(x, axis=axis, name="sum_x") - if not fx_gx_manually_stopped: - fx = array_ops.stop_gradient(fx) - gx = array_ops.stop_gradient(gx) - # IEEE754 ensures `(x-x)==0.` and that `0.*x==0.` so we make sure to write - # the code this way, rather than, e.g., - # `sum_x * stop(gx) + stop(fx - sum_x * gx)`. - # For more discussion regarding the relevant portions of the IEEE754 - # standard, see the StackOverflow question, - # "Is there a floating point value of x, for which x-x == 0 is false?" - # http://stackoverflow.com/q/2686644 - return (sum_x - array_ops.stop_gradient(sum_x)) * gx + fx diff --git a/tensorflow/contrib/bayesflow/python/ops/docstring_util.py b/tensorflow/contrib/bayesflow/python/ops/docstring_util.py deleted file mode 100644 index 081f2d5a8bfd437fd173f63b4226fb7df6ca921c..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/bayesflow/python/ops/docstring_util.py +++ /dev/null @@ -1,88 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Utilities for programmable docstrings. -""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import re -import six - - -def expand_docstring(**kwargs): - """Decorator to programmatically expand the docstring. - - Args: - **kwargs: Keyword arguments to set. For each key-value pair `k` and `v`, - the key is found as `@{k}` in the docstring and replaced with `v`. - - Returns: - Decorated function. - """ - def _fn_wrapped(fn): - """Original function with modified `__doc__` attribute.""" - doc = _trim(fn.__doc__) - for k, v in six.iteritems(kwargs): - # Capture each @{k} reference to replace with v. - # We wrap the replacement in a function so no backslash escapes - # are processed. - pattern = r'@\{' + str(k) + r'\}' - doc = re.sub(pattern, lambda match: v, doc) # pylint: disable=cell-var-from-loop - fn.__doc__ = doc - return fn - return _fn_wrapped - - -def _trim(docstring): - """Trims docstring indentation. - - In general, multi-line docstrings carry their level of indentation when - defined under a function or class method. This function standardizes - indentation levels by removing them. Taken from PEP 257 docs. - - Args: - docstring: Python string to trim indentation. - - Returns: - Trimmed docstring. - """ - if not docstring: - return '' - # Convert tabs to spaces (following the normal Python rules) - # and split into a list of lines: - lines = docstring.expandtabs().splitlines() - # Determine minimum indentation (first line doesn't count): - indent = None - for line in lines[1:]: - stripped = line.lstrip() - if stripped: - if indent is None: - indent = len(line) - len(stripped) - else: - indent = min(indent, len(line) - len(stripped)) - # Remove indentation (first line is special): - trimmed = [lines[0].strip()] - if indent is not None: - for line in lines[1:]: - trimmed.append(line[indent:].rstrip()) - # Strip off trailing and leading blank lines: - while trimmed and not trimmed[-1]: - trimmed.pop() - while trimmed and not trimmed[0]: - trimmed.pop(0) - # Return a single string: - return '\n'.join(trimmed) diff --git a/tensorflow/contrib/bayesflow/python/ops/halton_sequence_impl.py b/tensorflow/contrib/bayesflow/python/ops/halton_sequence_impl.py deleted file mode 100644 index 8cabf18903b5f15002470acdfb8fdd3ec31a7413..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/bayesflow/python/ops/halton_sequence_impl.py +++ /dev/null @@ -1,264 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Quasi Monte Carlo support: Halton sequence. - -@@sample -""" - -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.ops import array_ops -from tensorflow.python.ops import math_ops - - -__all__ = [ - 'sample', -] - - -# The maximum dimension we support. This is limited by the number of primes -# in the _PRIMES array. -_MAX_DIMENSION = 1000 - - -def sample(dim, num_samples=None, sample_indices=None, dtype=None, name=None): - r"""Returns a sample from the `m` dimensional Halton sequence. - - Warning: The sequence elements take values only between 0 and 1. Care must be - taken to appropriately transform the domain of a function if it differs from - the unit cube before evaluating integrals using Halton samples. It is also - important to remember that quasi-random numbers are not a replacement for - pseudo-random numbers in every context. Quasi random numbers are completely - deterministic and typically have significant negative autocorrelation (unless - randomized). - - Computes the members of the low discrepancy Halton sequence in dimension - `dim`. The d-dimensional sequence takes values in the unit hypercube in d - dimensions. Currently, only dimensions up to 1000 are supported. The prime - base for the `k`-th axes is the k-th prime starting from 2. For example, - if dim = 3, then the bases will be [2, 3, 5] respectively and the first - element of the sequence will be: [0.5, 0.333, 0.2]. For a more complete - description of the Halton sequences see: - https://en.wikipedia.org/wiki/Halton_sequence. For low discrepancy sequences - and their applications see: - https://en.wikipedia.org/wiki/Low-discrepancy_sequence. - - The user must supply either `num_samples` or `sample_indices` but not both. - The former is the number of samples to produce starting from the first - element. If `sample_indices` is given instead, the specified elements of - the sequence are generated. For example, sample_indices=tf.range(10) is - equivalent to specifying n=10. - - Example Use: - - ```python - bf = tf.contrib.bayesflow - - # Produce the first 1000 members of the Halton sequence in 3 dimensions. - num_samples = 1000 - dim = 3 - sample = bf.halton_sequence.sample(dim, num_samples=num_samples) - - # Evaluate the integral of x_1 * x_2^2 * x_3^3 over the three dimensional - # hypercube. - powers = tf.range(1.0, limit=dim + 1) - integral = tf.reduce_mean(tf.reduce_prod(sample ** powers, axis=-1)) - true_value = 1.0 / tf.reduce_prod(powers + 1.0) - with tf.Session() as session: - values = session.run((integral, true_value)) - - # Produces a relative absolute error of 1.7%. - print ("Estimated: %f, True Value: %f" % values) - - # Now skip the first 1000 samples and recompute the integral with the next - # thousand samples. The sample_indices argument can be used to do this. - - - sample_indices = tf.range(start=1000, limit=1000 + num_samples, - dtype=tf.int32) - sample_leaped = halton.sample(dim, sample_indices=sample_indices) - - integral_leaped = tf.reduce_mean(tf.reduce_prod(sample_leaped ** powers, - axis=-1)) - with tf.Session() as session: - values = session.run((integral_leaped, true_value)) - # Now produces a relative absolute error of 0.05%. - print ("Leaped Estimated: %f, True Value: %f" % values) - ``` - - Args: - dim: Positive Python `int` representing each sample's `event_size.` Must - not be greater than 1000. - num_samples: (Optional) positive Python `int`. The number of samples to - generate. Either this parameter or sample_indices must be specified but - not both. If this parameter is None, then the behaviour is determined by - the `sample_indices`. - sample_indices: (Optional) `Tensor` of dtype int32 and rank 1. The elements - of the sequence to compute specified by their position in the sequence. - The entries index into the Halton sequence starting with 0 and hence, - must be whole numbers. For example, sample_indices=[0, 5, 6] will produce - the first, sixth and seventh elements of the sequence. If this parameter - is None, then the `num_samples` parameter must be specified which gives - the number of desired samples starting from the first sample. - dtype: (Optional) The dtype of the sample. One of `float32` or `float64`. - Default is `float32`. - name: (Optional) Python `str` describing ops managed by this function. If - not supplied the name of this function is used. - - Returns: - halton_elements: Elements of the Halton sequence. `Tensor` of supplied dtype - and `shape` `[num_samples, dim]` if `num_samples` was specified or shape - `[s, dim]` where s is the size of `sample_indices` if `sample_indices` - were specified. - - Raises: - ValueError: if both `sample_indices` and `num_samples` were specified or - if dimension `dim` is less than 1 or greater than 1000. - """ - if dim < 1 or dim > _MAX_DIMENSION: - raise ValueError( - 'Dimension must be between 1 and {}. Supplied {}'.format(_MAX_DIMENSION, - dim)) - if (num_samples is None) == (sample_indices is None): - raise ValueError('Either `num_samples` or `sample_indices` must be' - ' specified but not both.') - - dtype = dtype or dtypes.float32 - if not dtype.is_floating: - raise ValueError('dtype must be of `float`-type') - - with ops.name_scope(name, 'sample', values=[sample_indices]): - # Here and in the following, the shape layout is as follows: - # [sample dimension, event dimension, coefficient dimension]. - # The coefficient dimension is an intermediate axes which will hold the - # weights of the starting integer when expressed in the (prime) base for - # an event dimension. - indices = _get_indices(num_samples, sample_indices, dtype) - radixes = array_ops.constant(_PRIMES[0:dim], dtype=dtype, shape=[dim, 1]) - - max_sizes_by_axes = _base_expansion_size(math_ops.reduce_max(indices), - radixes) - - max_size = math_ops.reduce_max(max_sizes_by_axes) - - # The powers of the radixes that we will need. Note that there is a bit - # of an excess here. Suppose we need the place value coefficients of 7 - # in base 2 and 3. For 2, we will have 3 digits but we only need 2 digits - # for base 3. However, we can only create rectangular tensors so we - # store both expansions in a [2, 3] tensor. This leads to the problem that - # we might end up attempting to raise large numbers to large powers. For - # example, base 2 expansion of 1024 has 10 digits. If we were in 10 - # dimensions, then the 10th prime (29) we will end up computing 29^10 even - # though we don't need it. We avoid this by setting the exponents for each - # axes to 0 beyond the maximum value needed for that dimension. - exponents_by_axes = array_ops.tile([math_ops.range(max_size)], [dim, 1]) - weight_mask = exponents_by_axes > max_sizes_by_axes - capped_exponents = array_ops.where( - weight_mask, array_ops.zeros_like(exponents_by_axes), exponents_by_axes) - weights = radixes ** capped_exponents - coeffs = math_ops.floor_div(indices, weights) - coeffs *= 1 - math_ops.cast(weight_mask, dtype) - coeffs = (coeffs % radixes) / radixes - return math_ops.reduce_sum(coeffs / weights, axis=-1) - - -def _get_indices(n, sample_indices, dtype, name=None): - """Generates starting points for the Halton sequence procedure. - - The k'th element of the sequence is generated starting from a positive integer - which must be distinct for each `k`. It is conventional to choose the starting - point as `k` itself (or `k+1` if k is zero based). This function generates - the starting integers for the required elements and reshapes the result for - later use. - - Args: - n: Positive `int`. The number of samples to generate. If this - parameter is supplied, then `sample_indices` should be None. - sample_indices: `Tensor` of dtype int32 and rank 1. The entries - index into the Halton sequence starting with 0 and hence, must be whole - numbers. For example, sample_indices=[0, 5, 6] will produce the first, - sixth and seventh elements of the sequence. If this parameter is not None - then `n` must be None. - dtype: The dtype of the sample. One of `float32` or `float64`. - Default is `float32`. - name: Python `str` name which describes ops created by this function. - - Returns: - indices: `Tensor` of dtype `dtype` and shape = `[n, 1, 1]`. - """ - with ops.name_scope(name, 'get_indices', [n, sample_indices]): - if sample_indices is None: - sample_indices = math_ops.range(n, dtype=dtype) - else: - sample_indices = math_ops.cast(sample_indices, dtype) - - # Shift the indices so they are 1 based. - indices = sample_indices + 1 - - # Reshape to make space for the event dimension and the place value - # coefficients. - return array_ops.reshape(indices, [-1, 1, 1]) - - -def _base_expansion_size(num, bases): - """Computes the number of terms in the place value expansion. - - Let num = a0 + a1 b + a2 b^2 + ... ak b^k be the place value expansion of - `num` in base b (ak <> 0). This function computes and returns `k` for each - base `b` specified in `bases`. - - This can be inferred from the base `b` logarithm of `num` as follows: - $$k = Floor(log_b (num)) + 1 = Floor( log(num) / log(b)) + 1$$ - - Args: - num: Scalar `Tensor` of dtype either `float32` or `float64`. The number to - compute the base expansion size of. - bases: `Tensor` of the same dtype as num. The bases to compute the size - against. - - Returns: - Tensor of same dtype and shape as `bases` containing the size of num when - written in that base. - """ - return math_ops.floor(math_ops.log(num) / math_ops.log(bases)) + 1 - - -def _primes_less_than(n): - # Based on - # https://stackoverflow.com/questions/2068372/fastest-way-to-list-all-primes-below-n-in-python/3035188#3035188 - """Returns sorted array of primes such that `2 <= prime < n`.""" - small_primes = np.array((2, 3, 5)) - if n <= 6: - return small_primes[small_primes < n] - sieve = np.ones(n // 3 + (n % 6 == 2), dtype=np.bool) - sieve[0] = False - m = int(n ** 0.5) // 3 + 1 - for i in range(m): - if not sieve[i]: - continue - k = 3 * i + 1 | 1 - sieve[k ** 2 // 3::2 * k] = False - sieve[(k ** 2 + 4 * k - 2 * k * (i & 1)) // 3::2 * k] = False - return np.r_[2, 3, 3 * np.nonzero(sieve)[0] + 1 | 1] - -_PRIMES = _primes_less_than(7919+1) - -assert len(_PRIMES) == _MAX_DIMENSION diff --git a/tensorflow/contrib/bayesflow/python/ops/hmc.py b/tensorflow/contrib/bayesflow/python/ops/hmc.py index 7fd5652c5c3e085b23c05baef6e3a42b7a42e08f..c8a5a195d3d709ded7afd09287255deab2ac2f3c 100644 --- a/tensorflow/contrib/bayesflow/python/ops/hmc.py +++ b/tensorflow/contrib/bayesflow/python/ops/hmc.py @@ -24,7 +24,6 @@ from tensorflow.python.util import all_util _allowed_symbols = [ "sample_chain", - "sample_annealed_importance_chain", "kernel", ] diff --git a/tensorflow/contrib/bayesflow/python/ops/hmc_impl.py b/tensorflow/contrib/bayesflow/python/ops/hmc_impl.py index 82693c2b7bcdbca9f6f4a1d799be5728bb5d36bf..66afcc749746ab5c04114e585c5f93a3f3354d86 100644 --- a/tensorflow/contrib/bayesflow/python/ops/hmc_impl.py +++ b/tensorflow/contrib/bayesflow/python/ops/hmc_impl.py @@ -15,7 +15,6 @@ """Hamiltonian Monte Carlo, a gradient-based MCMC algorithm. @@sample_chain -@@sample_annealed_importance_chain @@kernel """ @@ -38,7 +37,6 @@ from tensorflow.python.ops.distributions import util as distributions_util __all__ = [ "sample_chain", - "sample_annealed_importance_chain", "kernel", ] @@ -330,221 +328,6 @@ def sample_chain( return functional_ops.scan(**scan_kwargs) -def sample_annealed_importance_chain( - proposal_log_prob_fn, - num_steps, - target_log_prob_fn, - current_state, - step_size, - num_leapfrog_steps, - seed=None, - name=None): - """Runs annealed importance sampling (AIS) to estimate normalizing constants. - - This function uses Hamiltonian Monte Carlo to sample from a series of - distributions that slowly interpolates between an initial "proposal" - distribution: - - `exp(proposal_log_prob_fn(x) - proposal_log_normalizer)` - - and the target distribution: - - `exp(target_log_prob_fn(x) - target_log_normalizer)`, - - accumulating importance weights along the way. The product of these - importance weights gives an unbiased estimate of the ratio of the - normalizing constants of the initial distribution and the target - distribution: - - `E[exp(ais_weights)] = exp(target_log_normalizer - proposal_log_normalizer)`. - - Note: `proposal_log_prob_fn` and `target_log_prob_fn` are called exactly three - times (although this may be reduced to two times, in the future). - - #### Examples: - - ##### Estimate the normalizing constant of a log-gamma distribution. - - ```python - tfd = tf.contrib.distributions - - # Run 100 AIS chains in parallel - num_chains = 100 - dims = 20 - dtype = np.float32 - - proposal = tfd.MultivatiateNormalDiag( - loc=tf.zeros([dims], dtype=dtype)) - - target = tfd.TransformedDistribution( - distribution=tfd.Gamma(concentration=dtype(2), - rate=dtype(3)), - bijector=tfd.bijectors.Invert(tfd.bijectors.Exp()), - event_shape=[dims]) - - chains_state, ais_weights, kernels_results = ( - hmc.sample_annealed_importance_chain( - proposal_log_prob_fn=proposal.log_prob, - num_steps=1000, - target_log_prob_fn=target.log_prob, - step_size=0.2, - current_state=proposal.sample(num_chains), - num_leapfrog_steps=2)) - - log_estimated_normalizer = (tf.reduce_logsumexp(ais_weights) - - np.log(num_chains)) - log_true_normalizer = tf.lgamma(2.) - 2. * tf.log(3.) - ``` - - ##### Estimate marginal likelihood of a Bayesian regression model. - - ```python - tfd = tf.contrib.distributions - - def make_prior(dims, dtype): - return tfd.MultivariateNormalDiag( - loc=tf.zeros(dims, dtype)) - - def make_likelihood(weights, x): - return tfd.MultivariateNormalDiag( - loc=tf.tensordot(weights, x, axes=[[0], [-1]])) - - # Run 100 AIS chains in parallel - num_chains = 100 - dims = 10 - dtype = np.float32 - - # Make training data. - x = np.random.randn(num_chains, dims).astype(dtype) - true_weights = np.random.randn(dims).astype(dtype) - y = np.dot(x, true_weights) + np.random.randn(num_chains) - - # Setup model. - prior = make_prior(dims, dtype) - def target_log_prob_fn(weights): - return prior.log_prob(weights) + make_likelihood(weights, x).log_prob(y) - - proposal = tfd.MultivariateNormalDiag( - loc=tf.zeros(dims, dtype)) - - weight_samples, ais_weights, kernel_results = ( - hmc.sample_annealed_importance_chain( - num_steps=1000, - proposal_log_prob_fn=proposal.log_prob, - target_log_prob_fn=target_log_prob_fn - current_state=tf.zeros([num_chains, dims], dtype), - step_size=0.1, - num_leapfrog_steps=2)) - log_normalizer_estimate = (tf.reduce_logsumexp(ais_weights) - - np.log(num_chains)) - ``` - - Args: - proposal_log_prob_fn: Python callable that returns the log density of the - initial distribution. - num_steps: Integer number of Markov chain updates to run. More - iterations means more expense, but smoother annealing between q - and p, which in turn means exponentially lower variance for the - normalizing constant estimator. - target_log_prob_fn: Python callable which takes an argument like - `current_state` (or `*current_state` if it's a list) and returns its - (possibly unnormalized) log-density under the target distribution. - current_state: `Tensor` or Python `list` of `Tensor`s representing the - current state(s) of the Markov chain(s). The first `r` dimensions index - independent chains, `r = tf.rank(target_log_prob_fn(*current_state))`. - step_size: `Tensor` or Python `list` of `Tensor`s representing the step size - for the leapfrog integrator. Must broadcast with the shape of - `current_state`. Larger step sizes lead to faster progress, but too-large - step sizes make rejection exponentially more likely. When possible, it's - often helpful to match per-variable step sizes to the standard deviations - of the target distribution in each variable. - num_leapfrog_steps: Integer number of steps to run the leapfrog integrator - for. Total progress per HMC step is roughly proportional to `step_size * - num_leapfrog_steps`. - seed: Python integer to seed the random number generator. - name: Python `str` name prefixed to Ops created by this function. - Default value: `None` (i.e., "hmc_sample_annealed_importance_chain"). - - Returns: - next_state: `Tensor` or Python list of `Tensor`s representing the - state(s) of the Markov chain(s) at the final iteration. Has same shape as - input `current_state`. - ais_weights: Tensor with the estimated weight(s). Has shape matching - `target_log_prob_fn(current_state)`. - kernel_results: `collections.namedtuple` of internal calculations used to - advance the chain. - """ - def make_convex_combined_log_prob_fn(iter_): - def _fn(*args): - p = proposal_log_prob_fn(*args) - t = target_log_prob_fn(*args) - dtype = p.dtype.base_dtype - beta = (math_ops.cast(iter_ + 1, dtype) - / math_ops.cast(num_steps, dtype)) - return (1. - beta) * p + beta * t - return _fn - - with ops.name_scope( - name, "hmc_sample_annealed_importance_chain", - [num_steps, current_state, step_size, num_leapfrog_steps, seed]): - with ops.name_scope("initialize"): - [ - current_state, - step_size, - current_log_prob, - current_grads_log_prob, - ] = _prepare_args( - make_convex_combined_log_prob_fn(iter_=0), - current_state, - step_size, - description="convex_combined_log_prob") - num_steps = ops.convert_to_tensor( - num_steps, - dtype=dtypes.int32, - name="num_steps") - num_leapfrog_steps = ops.convert_to_tensor( - num_leapfrog_steps, - dtype=dtypes.int32, - name="num_leapfrog_steps") - def _loop_body(iter_, ais_weights, current_state, kernel_results): - """Closure which implements `tf.while_loop` body.""" - current_state_parts = (list(current_state) - if _is_list_like(current_state) - else [current_state]) - # TODO(b/72994218): Consider refactoring things to avoid this unecessary - # call. - ais_weights += ((target_log_prob_fn(*current_state_parts) - - proposal_log_prob_fn(*current_state_parts)) - / math_ops.cast(num_steps, ais_weights.dtype)) - return [iter_ + 1, ais_weights] + list(kernel( - make_convex_combined_log_prob_fn(iter_), - current_state, - step_size, - num_leapfrog_steps, - seed, - kernel_results.current_target_log_prob, - kernel_results.current_grads_target_log_prob)) - - while_loop_kwargs = dict( - cond=lambda iter_, *args: iter_ < num_steps, - body=_loop_body, - loop_vars=[ - np.int32(0), # iter_ - array_ops.zeros_like(current_log_prob), # ais_weights - current_state, - _make_dummy_kernel_results(current_state, - current_log_prob, - current_grads_log_prob), - ]) - if seed is not None: - while_loop_kwargs["parallel_iterations"] = 1 - - [ais_weights, current_state, kernel_results] = control_flow_ops.while_loop( - **while_loop_kwargs)[1:] # Lop-off "iter_". - - return [current_state, ais_weights, kernel_results] - - def kernel(target_log_prob_fn, current_state, step_size, diff --git a/tensorflow/contrib/bayesflow/python/ops/layers.py b/tensorflow/contrib/bayesflow/python/ops/layers.py deleted file mode 100644 index a742b7c1aa593d6c08bf9d8d597c99c9fc4e7aed..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/bayesflow/python/ops/layers.py +++ /dev/null @@ -1,67 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Probabilistic neural layers. - -See ${python/contrib.bayesflow.layers}. -""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -# go/tf-wildcard-import -# pylint: disable=wildcard-import -from tensorflow.contrib.bayesflow.python.ops.layers_conv_variational import * -from tensorflow.contrib.bayesflow.python.ops.layers_dense_variational import * -from tensorflow.contrib.bayesflow.python.ops.layers_util import * -# pylint: enable=wildcard-import -from tensorflow.python.util.all_util import remove_undocumented - -_allowed_symbols = [ - 'Convolution1DReparameterization', - 'Convolution2DReparameterization', - 'Convolution3DReparameterization', - 'Convolution1DFlipout', - 'Convolution2DFlipout', - 'Convolution3DFlipout', - 'Conv1DReparameterization', - 'Conv2DReparameterization', - 'Conv3DReparameterization', - 'Conv1DFlipout', - 'Conv2DFlipout', - 'Conv3DFlipout', - 'convolution1d_reparameterization', - 'convolution2d_reparameterization', - 'convolution3d_reparameterization', - 'convolution1d_flipout', - 'convolution2d_flipout', - 'convolution3d_flipout', - 'conv1d_reparameterization', - 'conv2d_reparameterization', - 'conv3d_reparameterization', - 'conv1d_flipout', - 'conv2d_flipout', - 'conv3d_flipout', - 'DenseReparameterization', - 'DenseLocalReparameterization', - 'DenseFlipout', - 'dense_reparameterization', - 'dense_local_reparameterization', - 'dense_flipout', - 'default_loc_scale_fn', - 'default_mean_field_normal_fn', -] - -remove_undocumented(__name__, _allowed_symbols) diff --git a/tensorflow/contrib/bayesflow/python/ops/layers_conv_variational.py b/tensorflow/contrib/bayesflow/python/ops/layers_conv_variational.py deleted file mode 100644 index cb80718f719ff31fb8ba5066170342fc69630780..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/bayesflow/python/ops/layers_conv_variational.py +++ /dev/null @@ -1,2486 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Convolutional variational layer classes and their functional aliases. -""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from tensorflow.contrib.bayesflow.python.ops import docstring_util -from tensorflow.contrib.bayesflow.python.ops import layers_util -from tensorflow.contrib.distributions.python.ops import independent as independent_lib -from tensorflow.python.framework import dtypes -from tensorflow.python.framework import ops -from tensorflow.python.framework import tensor_shape -from tensorflow.python.layers import base as layers_lib -from tensorflow.python.layers import utils -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import nn -from tensorflow.python.ops import nn_ops -from tensorflow.python.ops import standard_ops -from tensorflow.python.ops.distributions import kullback_leibler as kl_lib -from tensorflow.python.ops.distributions import normal as normal_lib -from tensorflow.python.ops.distributions import util as distribution_util - -doc_args = """activation: Activation function. Set it to None to maintain a - linear activation. - activity_regularizer: Optional regularizer function for the output. - trainable: Boolean, if `True` also add variables to the graph collection - `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). - kernel_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `kernel` parameter. Default value: - `default_mean_field_normal_fn()`. - kernel_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - kernel_prior_fn: Python `callable` which creates `tf.distributions` - instance. See `default_mean_field_normal_fn` docstring for required - parameter signature. - Default value: `tf.distributions.Normal(loc=0., scale=1.)`. - kernel_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - sample is a `Tensor`. - bias_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `bias` parameter. Default value: - `default_mean_field_normal_fn(is_singular=True)` (which creates an - instance of `tf.distributions.Deterministic`). - bias_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - bias_prior_fn: Python `callable` which creates `tf.distributions` instance. - See `default_mean_field_normal_fn` docstring for required parameter - signature. Default value: `None` (no prior, no variational inference) - bias_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - sample is a `Tensor`. - name: A string, the name of the layer.""" - - -class _ConvVariational(layers_lib.Layer): - """Abstract nD convolution layer (private, used as implementation base). - - This layer creates a convolution kernel that is convolved - (actually cross-correlated) with the layer input to produce a tensor of - outputs. It may also include a bias addition and activation function - on the outputs. It assumes the `kernel` and/or `bias` are drawn from - distributions. - - By default, the layer implements a stochastic forward pass via - sampling from the kernel and bias posteriors, - ```none - outputs = f(inputs; kernel, bias), kernel, bias ~ posterior - ``` - where f denotes the layer's calculation. - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Properties: - rank: Python integer, dimensionality of convolution. - filters: Python integer, dimensionality of the output space. - kernel_size: Size of the convolution window. - strides: Stride length of convolution. - padding: Python string describing padding approach. - data_format: Python string describing input data's dimensions. - dilation_rate: Dilation rate for an atrous convolution. - activation: Activation function (`callable`). - activity_regularizer: Regularizer function for the output. - kernel_posterior_fn: `callable` returning posterior. - kernel_posterior_tensor_fn: `callable` operating on posterior. - kernel_prior_fn: `callable` returning prior. - kernel_divergence_fn: `callable` returning divergence. - bias_posterior_fn: `callable` returning posterior. - bias_posterior_tensor_fn: `callable` operating on posterior. - bias_prior_fn: `callable` returning prior. - bias_divergence_fn: `callable` returning divergence. - """ - - @docstring_util.expand_docstring(args=doc_args) - def __init__( - self, - rank, - filters, - kernel_size, - strides=1, - padding="valid", - data_format="channels_last", - dilation_rate=1, - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn(is_singular=True), # pylint: disable=line-too-long - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - name=None, - **kwargs): - # pylint: disable=g-doc-args - """Construct layer. - - Args: - rank: An integer, the rank of the convolution, e.g. "2" for 2D - convolution. - filters: Integer, the dimensionality of the output space (i.e. the number - of filters in the convolution). - kernel_size: An integer or tuple/list of n integers, specifying the - length of the convolution window. - strides: An integer or tuple/list of n integers, - specifying the stride length of the convolution. - Specifying any stride value != 1 is incompatible with specifying - any `dilation_rate` value != 1. - padding: One of `"valid"` or `"same"` (case-insensitive). - data_format: A string, one of `channels_last` (default) or - `channels_first`. The ordering of the dimensions in the inputs. - `channels_last` corresponds to inputs with shape `(batch, ..., - channels)` while `channels_first` corresponds to inputs with shape - `(batch, channels, ...)`. - dilation_rate: An integer or tuple/list of n integers, specifying - the dilation rate to use for dilated convolution. - Currently, specifying any `dilation_rate` value != 1 is - incompatible with specifying any `strides` value != 1. - @{args} - """ - # pylint: enable=g-doc-args - super(_ConvVariational, self).__init__( - trainable=trainable, - name=name, - activity_regularizer=activity_regularizer, - **kwargs) - self.rank = rank - self.filters = filters - self.kernel_size = utils.normalize_tuple(kernel_size, rank, "kernel_size") - self.strides = utils.normalize_tuple(strides, rank, "strides") - self.padding = utils.normalize_padding(padding) - self.data_format = utils.normalize_data_format(data_format) - self.dilation_rate = utils.normalize_tuple( - dilation_rate, rank, "dilation_rate") - self.activation = activation - self.input_spec = layers_lib.InputSpec(ndim=self.rank + 2) - self.kernel_posterior_fn = kernel_posterior_fn - self.kernel_posterior_tensor_fn = kernel_posterior_tensor_fn - self.kernel_prior_fn = kernel_prior_fn - self.kernel_divergence_fn = kernel_divergence_fn - self.bias_posterior_fn = bias_posterior_fn - self.bias_posterior_tensor_fn = bias_posterior_tensor_fn - self.bias_prior_fn = bias_prior_fn - self.bias_divergence_fn = bias_divergence_fn - - def build(self, input_shape): - input_shape = tensor_shape.TensorShape(input_shape) - if self.data_format == "channels_first": - channel_axis = 1 - else: - channel_axis = -1 - if input_shape[channel_axis].value is None: - raise ValueError("The channel dimension of the inputs " - "should be defined. Found `None`.") - input_dim = input_shape[channel_axis].value - kernel_shape = self.kernel_size + (input_dim, self.filters) - dtype = dtypes.as_dtype(self.dtype) - - # Must have a posterior kernel. - self.kernel_posterior = self.kernel_posterior_fn( - dtype, kernel_shape, "kernel_posterior", - self.trainable, self.add_variable) - - if self.kernel_prior_fn is None: - self.kernel_prior = None - else: - self.kernel_prior = self.kernel_prior_fn( - dtype, kernel_shape, "kernel_prior", - self.trainable, self.add_variable) - self._built_kernel_divergence = False - - if self.bias_posterior_fn is None: - self.bias_posterior = None - else: - self.bias_posterior = self.bias_posterior_fn( - dtype, (self.filters,), "bias_posterior", - self.trainable, self.add_variable) - - if self.bias_prior_fn is None: - self.bias_prior = None - else: - self.bias_prior = self.bias_prior_fn( - dtype, (self.filters,), "bias_prior", - self.trainable, self.add_variable) - self._built_bias_divergence = False - - self.input_spec = layers_lib.InputSpec(ndim=self.rank + 2, - axes={channel_axis: input_dim}) - self._convolution_op = nn_ops.Convolution( - input_shape, - filter_shape=tensor_shape.TensorShape(kernel_shape), - dilation_rate=self.dilation_rate, - strides=self.strides, - padding=self.padding.upper(), - data_format=utils.convert_data_format(self.data_format, - self.rank + 2)) - - self.built = True - - def call(self, inputs): - inputs = ops.convert_to_tensor(inputs, dtype=self.dtype) - - outputs = self._apply_variational_kernel(inputs) - outputs = self._apply_variational_bias(outputs) - if self.activation is not None: - outputs = self.activation(outputs) - if not self._built_kernel_divergence: - kernel_posterior = self.kernel_posterior - kernel_prior = self.kernel_prior - if isinstance(self.kernel_posterior, independent_lib.Independent): - kernel_posterior = kernel_posterior.distribution - if isinstance(self.kernel_prior, independent_lib.Independent): - kernel_prior = kernel_prior.distribution - self._apply_divergence(self.kernel_divergence_fn, - kernel_posterior, - kernel_prior, - self.kernel_posterior_tensor, - name="divergence_kernel") - self._built_kernel_divergence = True - if not self._built_bias_divergence: - bias_posterior = self.bias_posterior - bias_prior = self.bias_prior - if isinstance(self.bias_posterior, independent_lib.Independent): - bias_posterior = bias_posterior.distribution - if isinstance(self.bias_prior, independent_lib.Independent): - bias_prior = bias_prior.distribution - self._apply_divergence(self.bias_divergence_fn, - bias_posterior, - bias_prior, - self.bias_posterior_tensor, - name="divergence_bias") - self._built_bias_divergence = True - return outputs - - def _apply_variational_bias(self, inputs): - if self.bias_posterior is None: - self.bias_posterior_tensor = None - return inputs - self.bias_posterior_tensor = self.bias_posterior_tensor_fn( - self.bias_posterior) - outputs = inputs - if self.data_format == "channels_first": - if self.rank == 1: - # nn.bias_add does not accept a 1D input tensor. - bias = array_ops.reshape(self.bias_posterior_tensor, - (1, self.filters, 1)) - outputs += bias - if self.rank == 2: - outputs = nn.bias_add(outputs, - self.bias_posterior_tensor, - data_format="NCHW") - if self.rank == 3: - # As of Mar 2017, direct addition is significantly slower than - # bias_add when computing gradients. To use bias_add, we collapse Z - # and Y into a single dimension to obtain a 4D input tensor. - outputs_shape = outputs.shape.as_list() - outputs_4d = array_ops.reshape(outputs, - [outputs_shape[0], outputs_shape[1], - outputs_shape[2] * outputs_shape[3], - outputs_shape[4]]) - outputs_4d = nn.bias_add(outputs_4d, - self.bias_posterior_tensor, - data_format="NCHW") - outputs = array_ops.reshape(outputs_4d, outputs_shape) - else: - outputs = nn.bias_add(outputs, - self.bias_posterior_tensor, - data_format="NHWC") - return outputs - - def _apply_divergence(self, divergence_fn, posterior, prior, - posterior_tensor, name): - if (divergence_fn is None or - posterior is None or - prior is None): - divergence = None - return - divergence = standard_ops.identity( - divergence_fn( - posterior, prior, posterior_tensor), - name=name) - self.add_loss(divergence) - - def _compute_output_shape(self, input_shape): - input_shape = tensor_shape.TensorShape(input_shape).as_list() - if self.data_format == "channels_last": - space = input_shape[1:-1] - new_space = [] - for i in range(len(space)): - new_dim = utils.conv_output_length( - space[i], - self.kernel_size[i], - padding=self.padding, - stride=self.strides[i], - dilation=self.dilation_rate[i]) - new_space.append(new_dim) - return tensor_shape.TensorShape([input_shape[0]] + new_space + - [self.filters]) - else: - space = input_shape[2:] - new_space = [] - for i in range(len(space)): - new_dim = utils.conv_output_length( - space[i], - self.kernel_size[i], - padding=self.padding, - stride=self.strides[i], - dilation=self.dilation_rate[i]) - new_space.append(new_dim) - return tensor_shape.TensorShape([input_shape[0], self.filters] + - new_space) - - -class _ConvReparameterization(_ConvVariational): - """Abstract nD convolution layer (private, used as implementation base). - - This layer creates a convolution kernel that is convolved - (actually cross-correlated) with the layer input to produce a tensor of - outputs. It may also include a bias addition and activation function - on the outputs. It assumes the `kernel` and/or `bias` are drawn from - distributions. - - By default, the layer implements a stochastic forward pass via - sampling from the kernel and bias posteriors, - ```none - outputs = f(inputs; kernel, bias), kernel, bias ~ posterior - ``` - where f denotes the layer's calculation. It uses the reparameterization - estimator [1], which performs a Monte Carlo approximation of the - distribution integrating over the `kernel` and `bias`. - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Properties: - rank: Python integer, dimensionality of convolution. - filters: Python integer, dimensionality of the output space. - kernel_size: Size of the convolution window. - strides: Stride length of convolution. - padding: Python string describing padding approach. - data_format: Python string describing input data's dimensions. - dilation_rate: Dilation rate for an atrous convolution. - activation: Activation function (`callable`). - activity_regularizer: Regularizer function for the output. - kernel_posterior_fn: `callable` returning posterior. - kernel_posterior_tensor_fn: `callable` operating on posterior. - kernel_prior_fn: `callable` returning prior. - kernel_divergence_fn: `callable` returning divergence. - bias_posterior_fn: `callable` returning posterior. - bias_posterior_tensor_fn: `callable` operating on posterior. - bias_prior_fn: `callable` returning prior. - bias_divergence_fn: `callable` returning divergence. - - [1]: "Auto-Encoding Variational Bayes." - Diederik P. Kingma, Max Welling. - International Conference on Learning Representations, 2014. - """ - - @docstring_util.expand_docstring(args=doc_args) - def __init__( - self, - rank, - filters, - kernel_size, - strides=1, - padding="valid", - data_format="channels_last", - dilation_rate=1, - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn(is_singular=True), # pylint: disable=line-too-long - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - name=None, - **kwargs): - # pylint: disable=g-doc-args - """Construct layer. - - Args: - rank: An integer, the rank of the convolution, e.g. "2" for 2D - convolution. - filters: Integer, the dimensionality of the output space (i.e. the number - of filters in the convolution). - kernel_size: An integer or tuple/list of n integers, specifying the - length of the convolution window. - strides: An integer or tuple/list of n integers, - specifying the stride length of the convolution. - Specifying any stride value != 1 is incompatible with specifying - any `dilation_rate` value != 1. - padding: One of `"valid"` or `"same"` (case-insensitive). - data_format: A string, one of `channels_last` (default) or - `channels_first`. The ordering of the dimensions in the inputs. - `channels_last` corresponds to inputs with shape `(batch, ..., - channels)` while `channels_first` corresponds to inputs with shape - `(batch, channels, ...)`. - dilation_rate: An integer or tuple/list of n integers, specifying - the dilation rate to use for dilated convolution. - Currently, specifying any `dilation_rate` value != 1 is - incompatible with specifying any `strides` value != 1. - @{args} - """ - # pylint: enable=g-doc-args - super(_ConvReparameterization, self).__init__( - rank=rank, - filters=filters, - kernel_size=kernel_size, - strides=strides, - padding=padding, - data_format=data_format, - dilation_rate=dilation_rate, - activation=activation, - activity_regularizer=activity_regularizer, - trainable=trainable, - kernel_posterior_fn=kernel_posterior_fn, - kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, - kernel_prior_fn=kernel_prior_fn, - kernel_divergence_fn=kernel_divergence_fn, - bias_posterior_fn=bias_posterior_fn, - bias_posterior_tensor_fn=bias_posterior_tensor_fn, - bias_prior_fn=bias_prior_fn, - bias_divergence_fn=bias_divergence_fn, - name=name, **kwargs) - - def _apply_variational_kernel(self, inputs): - self.kernel_posterior_tensor = self.kernel_posterior_tensor_fn( - self.kernel_posterior) - self.kernel_posterior_affine = None - self.kernel_posterior_affine_tensor = None - outputs = self._convolution_op(inputs, self.kernel_posterior_tensor) - return outputs - - -class Conv1DReparameterization(_ConvReparameterization): - """1D convolution layer (e.g. temporal convolution). - - This layer creates a convolution kernel that is convolved - (actually cross-correlated) with the layer input to produce a tensor of - outputs. It may also include a bias addition and activation function - on the outputs. It assumes the `kernel` and/or `bias` are drawn from - distributions. - - By default, the layer implements a stochastic forward pass via - sampling from the kernel and bias posteriors, - ```none - outputs = f(inputs; kernel, bias), kernel, bias ~ posterior - ``` - where f denotes the layer's calculation. It uses the reparameterization - estimator [1], which performs a Monte Carlo approximation of the - distribution integrating over the `kernel` and `bias`. - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Properties: - filters: Python integer, dimensionality of the output space. - kernel_size: Size of the convolution window. - strides: Stride length of convolution. - padding: Python string describing padding approach. - data_format: Python string describing input data's dimensions. - dilation_rate: Dilation rate for an atrous convolution. - activation: Activation function (`callable`). - activity_regularizer: Regularizer function for the output. - kernel_posterior_fn: `callable` returning posterior. - kernel_posterior_tensor_fn: `callable` operating on posterior. - kernel_prior_fn: `callable` returning prior. - kernel_divergence_fn: `callable` returning divergence. - bias_posterior_fn: `callable` returning posterior. - bias_posterior_tensor_fn: `callable` operating on posterior. - bias_prior_fn: `callable` returning prior. - bias_divergence_fn: `callable` returning divergence. - - #### Examples - - We illustrate a Bayesian neural network with [variational inference]( - https://en.wikipedia.org/wiki/Variational_Bayesian_methods), - assuming a dataset of `features` and `labels`. - - ```python - tfp = tf.contrib.bayesflow - - net = tf.reshape(features, [-1, 128, 1]) - net = tfp.layers.Conv1DReparameterization(64, - kernel_size=5, - padding="SAME", - activation=tf.nn.relu)(net) - net = tf.reshape(net, [-1, 128 * 64]) - logits = tfp.layers.DenseReparameterization(10)(net) - neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits( - labels=labels, logits=logits) - kl = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) - loss = neg_log_likelihood + kl - train_op = tf.train.AdamOptimizer().minimize(loss) - ``` - - It uses reparameterization gradients to minimize the - Kullback-Leibler divergence up to a constant, also known as the - negative Evidence Lower Bound. It consists of the sum of two terms: - the expected negative log-likelihood, which we approximate via - Monte Carlo; and the KL divergence, which is added via regularizer - terms which are arguments to the layer. - - [1]: "Auto-Encoding Variational Bayes." - Diederik P. Kingma, Max Welling. - International Conference on Learning Representations, 2014. - """ - - @docstring_util.expand_docstring(args=doc_args) - def __init__( - self, - filters, - kernel_size, - strides=1, - padding="valid", - data_format="channels_last", - dilation_rate=1, - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn(is_singular=True), # pylint: disable=line-too-long - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - name=None, - **kwargs): - # pylint: disable=g-doc-args - """Construct layer. - - Args: - filters: Integer, the dimensionality of the output space (i.e. the number - of filters in the convolution). - kernel_size: An integer or tuple/list of a single integer, specifying the - length of the 1D convolution window. - strides: An integer or tuple/list of a single integer, - specifying the stride length of the convolution. - Specifying any stride value != 1 is incompatible with specifying - any `dilation_rate` value != 1. - padding: One of `"valid"` or `"same"` (case-insensitive). - data_format: A string, one of `channels_last` (default) or - `channels_first`. The ordering of the dimensions in the inputs. - `channels_last` corresponds to inputs with shape `(batch, length, - channels)` while `channels_first` corresponds to inputs with shape - `(batch, channels, length)`. - dilation_rate: An integer or tuple/list of a single integer, specifying - the dilation rate to use for dilated convolution. - Currently, specifying any `dilation_rate` value != 1 is - incompatible with specifying any `strides` value != 1. - @{args} - """ - # pylint: enable=g-doc-args - super(Conv1DReparameterization, self).__init__( - rank=1, - filters=filters, - kernel_size=kernel_size, - strides=strides, - padding=padding, - data_format=data_format, - dilation_rate=dilation_rate, - activation=activation, - activity_regularizer=activity_regularizer, - trainable=trainable, - kernel_posterior_fn=kernel_posterior_fn, - kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, - kernel_prior_fn=kernel_prior_fn, - kernel_divergence_fn=kernel_divergence_fn, - bias_posterior_fn=bias_posterior_fn, - bias_posterior_tensor_fn=bias_posterior_tensor_fn, - bias_prior_fn=bias_prior_fn, - bias_divergence_fn=bias_divergence_fn, - name=name, **kwargs) - - -@docstring_util.expand_docstring(args=doc_args) -def conv1d_reparameterization( - inputs, - filters, - kernel_size, - strides=1, - padding="valid", - data_format="channels_last", - dilation_rate=1, - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn(is_singular=True), # pylint: disable=line-too-long - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - name=None, - reuse=None): - # pylint: disable=g-doc-args - """Functional interface for 1D convolution layer (e.g. temporal convolution). - - This layer creates a convolution kernel that is convolved - (actually cross-correlated) with the layer input to produce a tensor of - outputs. It may also include a bias addition and activation function - on the outputs. It assumes the `kernel` and/or `bias` are drawn from - distributions. - - By default, the layer implements a stochastic forward pass via - sampling from the kernel and bias posteriors, - ```none - outputs = f(inputs; kernel, bias), kernel, bias ~ posterior - ``` - where f denotes the layer's calculation. It uses the reparameterization - estimator [1], which performs a Monte Carlo approximation of the - distribution integrating over the `kernel` and `bias`. - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Args: - inputs: Tensor input. - filters: Integer, the dimensionality of the output space (i.e. the number - of filters in the convolution). - kernel_size: An integer or tuple/list of a single integer, specifying the - length of the 1D convolution window. - strides: An integer or tuple/list of a single integer, - specifying the stride length of the convolution. - Specifying any stride value != 1 is incompatible with specifying - any `dilation_rate` value != 1. - padding: One of `"valid"` or `"same"` (case-insensitive). - data_format: A string, one of `channels_last` (default) or `channels_first`. - The ordering of the dimensions in the inputs. - `channels_last` corresponds to inputs with shape - `(batch, length, channels)` while `channels_first` corresponds to - inputs with shape `(batch, channels, length)`. - dilation_rate: An integer or tuple/list of a single integer, specifying - the dilation rate to use for dilated convolution. - Currently, specifying any `dilation_rate` value != 1 is - incompatible with specifying any `strides` value != 1. - @{args} - reuse: Boolean, whether to reuse the weights of a previous layer - by the same name. - - Returns: - Output tensor. - - Raises: - ValueError: if eager execution is enabled. - - #### Examples - - We illustrate a Bayesian neural network with [variational inference]( - https://en.wikipedia.org/wiki/Variational_Bayesian_methods), - assuming a dataset of `features` and `labels`. - - ```python - tfp = tf.contrib.bayesflow - - net = tf.reshape(features, [-1, 128, 1]) - net = tfp.layers.conv1d_reparameterization(net, - filters=64, - kernel_size=5, - padding="SAME", - activation=tf.nn.relu) - net = tf.reshape(net, [-1, 128 * 64]) - logits = tfp.layers.dense_reparameterization(net, 10) - neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits( - labels=labels, logits=logits) - kl = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) - loss = neg_log_likelihood + kl - train_op = tf.train.AdamOptimizer().minimize(loss) - ``` - - It uses reparameterization gradients to minimize the - Kullback-Leibler divergence up to a constant, also known as the - negative Evidence Lower Bound. It consists of the sum of two terms: - the expected negative log-likelihood, which we approximate via - Monte Carlo; and the KL divergence, which is added via regularizer - terms which are arguments to the layer. - - [1]: "Auto-Encoding Variational Bayes." - Diederik P. Kingma, Max Welling. - International Conference on Learning Representations, 2014. - """ - # pylint: enable=g-doc-args - layer = Conv1DReparameterization( - filters=filters, - kernel_size=kernel_size, - strides=strides, - padding=padding, - data_format=data_format, - dilation_rate=dilation_rate, - activation=activation, - activity_regularizer=activity_regularizer, - trainable=trainable, - kernel_posterior_fn=kernel_posterior_fn, - kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, - kernel_prior_fn=kernel_prior_fn, - kernel_divergence_fn=kernel_divergence_fn, - bias_posterior_fn=bias_posterior_fn, - bias_posterior_tensor_fn=bias_posterior_tensor_fn, - bias_prior_fn=bias_prior_fn, - bias_divergence_fn=bias_divergence_fn, - name=name, - dtype=inputs.dtype.base_dtype, - _scope=name, - _reuse=reuse) - return layer.apply(inputs) - - -class Conv2DReparameterization(_ConvReparameterization): - """2D convolution layer (e.g. spatial convolution over images). - - This layer creates a convolution kernel that is convolved - (actually cross-correlated) with the layer input to produce a tensor of - outputs. It may also include a bias addition and activation function - on the outputs. It assumes the `kernel` and/or `bias` are drawn from - distributions. - - By default, the layer implements a stochastic forward pass via - sampling from the kernel and bias posteriors, - ```none - outputs = f(inputs; kernel, bias), kernel, bias ~ posterior - ``` - where f denotes the layer's calculation. It uses the reparameterization - estimator [1], which performs a Monte Carlo approximation of the - distribution integrating over the `kernel` and `bias`. - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Properties: - filters: Python integer, dimensionality of the output space. - kernel_size: Size of the convolution window. - strides: Stride length of convolution. - padding: Python string describing padding approach. - data_format: Python string describing input data's dimensions. - dilation_rate: Dilation rate for an atrous convolution. - activation: Activation function (`callable`). - activity_regularizer: Regularizer function for the output. - kernel_posterior_fn: `callable` returning posterior. - kernel_posterior_tensor_fn: `callable` operating on posterior. - kernel_prior_fn: `callable` returning prior. - kernel_divergence_fn: `callable` returning divergence. - bias_posterior_fn: `callable` returning posterior. - bias_posterior_tensor_fn: `callable` operating on posterior. - bias_prior_fn: `callable` returning prior. - bias_divergence_fn: `callable` returning divergence. - - #### Examples - - We illustrate a Bayesian neural network with [variational inference]( - https://en.wikipedia.org/wiki/Variational_Bayesian_methods), - assuming a dataset of `features` and `labels`. - - ```python - tfp = tf.contrib.bayesflow - - net = tf.reshape(features, [-1, 32, 32, 3]) - net = tfp.layers.Conv2DReparameterization(64, - kernel_size=5, - padding="SAME", - activation=tf.nn.relu)(net) - net = tf.layers.MaxPooling2D(pool_size=2, - strides=2, - padding="SAME")(net) - net = tf.reshape(net, [-1, 8 * 8 * 64]) - logits = tfp.layers.DenseReparameterization(10)(net) - neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits( - labels=labels, logits=logits) - kl = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) - loss = neg_log_likelihood + kl - train_op = tf.train.AdamOptimizer().minimize(loss) - ``` - - It uses reparameterization gradients to minimize the - Kullback-Leibler divergence up to a constant, also known as the - negative Evidence Lower Bound. It consists of the sum of two terms: - the expected negative log-likelihood, which we approximate via - Monte Carlo; and the KL divergence, which is added via regularizer - terms which are arguments to the layer. - - [1]: "Auto-Encoding Variational Bayes." - Diederik P. Kingma, Max Welling. - International Conference on Learning Representations, 2014. - """ - - @docstring_util.expand_docstring(args=doc_args) - def __init__( - self, - filters, - kernel_size, - strides=(1, 1), - padding="valid", - data_format="channels_last", - dilation_rate=(1, 1), - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn(is_singular=True), # pylint: disable=line-too-long - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - name=None, - **kwargs): - # pylint: disable=g-doc-args - """Construct layer. - - Args: - filters: Integer, the dimensionality of the output space (i.e. the number - of filters in the convolution). - kernel_size: An integer or tuple/list of 2 integers, specifying the - height and width of the 2D convolution window. - Can be a single integer to specify the same value for - all spatial dimensions. - strides: An integer or tuple/list of 2 integers, - specifying the strides of the convolution along the height and width. - Can be a single integer to specify the same value for - all spatial dimensions. - Specifying any stride value != 1 is incompatible with specifying - any `dilation_rate` value != 1. - padding: One of `"valid"` or `"same"` (case-insensitive). - data_format: A string, one of `channels_last` (default) or - `channels_first`. The ordering of the dimensions in the inputs. - `channels_last` corresponds to inputs with shape `(batch, height, - width, channels)` while `channels_first` corresponds to inputs with - shape `(batch, channels, height, width)`. - dilation_rate: An integer or tuple/list of 2 integers, specifying - the dilation rate to use for dilated convolution. - Can be a single integer to specify the same value for - all spatial dimensions. - Currently, specifying any `dilation_rate` value != 1 is - incompatible with specifying any stride value != 1. - @{args} - """ - # pylint: enable=g-doc-args - super(Conv2DReparameterization, self).__init__( - rank=2, - filters=filters, - kernel_size=kernel_size, - strides=strides, - padding=padding, - data_format=data_format, - dilation_rate=dilation_rate, - activation=activation, - activity_regularizer=activity_regularizer, - trainable=trainable, - kernel_posterior_fn=kernel_posterior_fn, - kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, - kernel_prior_fn=kernel_prior_fn, - kernel_divergence_fn=kernel_divergence_fn, - bias_posterior_fn=bias_posterior_fn, - bias_posterior_tensor_fn=bias_posterior_tensor_fn, - bias_prior_fn=bias_prior_fn, - bias_divergence_fn=bias_divergence_fn, - name=name, **kwargs) - - -@docstring_util.expand_docstring(args=doc_args) -def conv2d_reparameterization( - inputs, - filters, - kernel_size, - strides=(1, 1), - padding="valid", - data_format="channels_last", - dilation_rate=(1, 1), - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn(is_singular=True), # pylint: disable=line-too-long - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - name=None, - reuse=None): - # pylint: disable=g-doc-args - """Functional interface for the 2D convolution layer. - - This layer creates a convolution kernel that is convolved - (actually cross-correlated) with the layer input to produce a tensor of - outputs. It may also include a bias addition and activation function - on the outputs. It assumes the `kernel` and/or `bias` are drawn from - distributions. - - By default, the layer implements a stochastic forward pass via - sampling from the kernel and bias posteriors, - ```none - outputs = f(inputs; kernel, bias), kernel, bias ~ posterior - ``` - where f denotes the layer's calculation. It uses the reparameterization - estimator [1], which performs a Monte Carlo approximation of the - distribution integrating over the `kernel` and `bias`. - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Args: - inputs: Tensor input. - filters: Integer, the dimensionality of the output space (i.e. the number - of filters in the convolution). - kernel_size: An integer or tuple/list of 2 integers, specifying the - height and width of the 2D convolution window. - Can be a single integer to specify the same value for - all spatial dimensions. - strides: An integer or tuple/list of 2 integers, - specifying the strides of the convolution along the height and width. - Can be a single integer to specify the same value for - all spatial dimensions. - Specifying any stride value != 1 is incompatible with specifying - any `dilation_rate` value != 1. - padding: One of `"valid"` or `"same"` (case-insensitive). - data_format: A string, one of `channels_last` (default) or `channels_first`. - The ordering of the dimensions in the inputs. - `channels_last` corresponds to inputs with shape - `(batch, height, width, channels)` while `channels_first` corresponds to - inputs with shape `(batch, channels, height, width)`. - dilation_rate: An integer or tuple/list of 2 integers, specifying - the dilation rate to use for dilated convolution. - Can be a single integer to specify the same value for - all spatial dimensions. - Currently, specifying any `dilation_rate` value != 1 is - incompatible with specifying any stride value != 1. - @{args} - reuse: Boolean, whether to reuse the weights of a previous layer - by the same name. - - Returns: - Output tensor. - - Raises: - ValueError: if eager execution is enabled. - - #### Examples - - We illustrate a Bayesian neural network with [variational inference]( - https://en.wikipedia.org/wiki/Variational_Bayesian_methods), - assuming a dataset of `features` and `labels`. - - ```python - tfp = tf.contrib.bayesflow - - net = tf.reshape(features, [-1, 32, 32, 3]) - net = tfp.layers.conv2d_reparameterization(net, - filters=64, - kernel_size=5, - padding="SAME", - activation=tf.nn.relu) - net = tf.layers.max_pooling2d(net, - pool_size=2, - strides=2, - padding="SAME") - net = tf.reshape(net, [-1, 8 * 8 * 64]) - logits = tfp.layers.dense_reparameterization(net, 10) - neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits( - labels=labels, logits=logits) - kl = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) - loss = neg_log_likelihood + kl - train_op = tf.train.AdamOptimizer().minimize(loss) - ``` - - It uses reparameterization gradients to minimize the - Kullback-Leibler divergence up to a constant, also known as the - negative Evidence Lower Bound. It consists of the sum of two terms: - the expected negative log-likelihood, which we approximate via - Monte Carlo; and the KL divergence, which is added via regularizer - terms which are arguments to the layer. - - [1]: "Auto-Encoding Variational Bayes." - Diederik P. Kingma, Max Welling. - International Conference on Learning Representations, 2014. - """ - # pylint: enable=g-doc-args - layer = Conv2DReparameterization( - filters=filters, - kernel_size=kernel_size, - strides=strides, - padding=padding, - data_format=data_format, - dilation_rate=dilation_rate, - activation=activation, - activity_regularizer=activity_regularizer, - trainable=trainable, - kernel_posterior_fn=kernel_posterior_fn, - kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, - kernel_prior_fn=kernel_prior_fn, - kernel_divergence_fn=kernel_divergence_fn, - bias_posterior_fn=bias_posterior_fn, - bias_posterior_tensor_fn=bias_posterior_tensor_fn, - bias_prior_fn=bias_prior_fn, - bias_divergence_fn=bias_divergence_fn, - name=name, - dtype=inputs.dtype.base_dtype, - _scope=name, - _reuse=reuse) - return layer.apply(inputs) - - -class Conv3DReparameterization(_ConvReparameterization): - """3D convolution layer (e.g. spatial convolution over volumes). - - This layer creates a convolution kernel that is convolved - (actually cross-correlated) with the layer input to produce a tensor of - outputs. It may also include a bias addition and activation function - on the outputs. It assumes the `kernel` and/or `bias` are drawn from - distributions. - - By default, the layer implements a stochastic forward pass via - sampling from the kernel and bias posteriors, - ```none - outputs = f(inputs; kernel, bias), kernel, bias ~ posterior - ``` - where f denotes the layer's calculation. It uses the reparameterization - estimator [1], which performs a Monte Carlo approximation of the - distribution integrating over the `kernel` and `bias`. - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Properties: - filters: Python integer, dimensionality of the output space. - kernel_size: Size of the convolution window. - strides: Stride length of convolution. - padding: Python string describing padding approach. - data_format: Python string describing input data's dimensions. - dilation_rate: Dilation rate for an atrous convolution. - activation: Activation function (`callable`). - activity_regularizer: Regularizer function for the output. - kernel_posterior_fn: `callable` returning posterior. - kernel_posterior_tensor_fn: `callable` operating on posterior. - kernel_prior_fn: `callable` returning prior. - kernel_divergence_fn: `callable` returning divergence. - bias_posterior_fn: `callable` returning posterior. - bias_posterior_tensor_fn: `callable` operating on posterior. - bias_prior_fn: `callable` returning prior. - bias_divergence_fn: `callable` returning divergence. - - #### Examples - - We illustrate a Bayesian neural network with [variational inference]( - https://en.wikipedia.org/wiki/Variational_Bayesian_methods), - assuming a dataset of `features` and `labels`. - - ```python - tfp = tf.contrib.bayesflow - - net = tf.reshape(features, [-1, 256, 32, 32, 3]) - net = tfp.layers.Conv3DReparameterization(64, - kernel_size=5, - padding="SAME", - activation=tf.nn.relu)(net) - net = tf.layers.MaxPooling2D(pool_size=2, - strides=2, - padding="SAME")(net) - net = tf.reshape(net, [-1, 256 * 8 * 8 * 64]) - logits = tfp.layers.DenseReparameterization(10)(net) - neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits( - labels=labels, logits=logits) - kl = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) - loss = neg_log_likelihood + kl - train_op = tf.train.AdamOptimizer().minimize(loss) - ``` - - It uses reparameterization gradients to minimize the - Kullback-Leibler divergence up to a constant, also known as the - negative Evidence Lower Bound. It consists of the sum of two terms: - the expected negative log-likelihood, which we approximate via - Monte Carlo; and the KL divergence, which is added via regularizer - terms which are arguments to the layer. - - [1]: "Auto-Encoding Variational Bayes." - Diederik P. Kingma, Max Welling. - International Conference on Learning Representations, 2014. - """ - - @docstring_util.expand_docstring(args=doc_args) - def __init__( - self, - filters, - kernel_size, - strides=(1, 1, 1), - padding="valid", - data_format="channels_last", - dilation_rate=(1, 1, 1), - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn(is_singular=True), # pylint: disable=line-too-long - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - name=None, - **kwargs): - # pylint: disable=g-doc-args - """Construct layer. - - Args: - filters: Integer, the dimensionality of the output space (i.e. the number - of filters in the convolution). - kernel_size: An integer or tuple/list of 3 integers, specifying the - depth, height and width of the 3D convolution window. - Can be a single integer to specify the same value for - all spatial dimensions. - strides: An integer or tuple/list of 3 integers, - specifying the strides of the convolution along the depth, - height and width. - Can be a single integer to specify the same value for - all spatial dimensions. - Specifying any stride value != 1 is incompatible with specifying - any `dilation_rate` value != 1. - padding: One of `"valid"` or `"same"` (case-insensitive). - data_format: A string, one of `channels_last` (default) or - `channels_first`. The ordering of the dimensions in the inputs. - `channels_last` corresponds to inputs with shape `(batch, depth, - height, width, channels)` while `channels_first` corresponds to inputs - with shape `(batch, channels, depth, height, width)`. - dilation_rate: An integer or tuple/list of 3 integers, specifying - the dilation rate to use for dilated convolution. - Can be a single integer to specify the same value for - all spatial dimensions. - Currently, specifying any `dilation_rate` value != 1 is - incompatible with specifying any stride value != 1. - @{args} - """ - # pylint: enable=g-doc-args - super(Conv3DReparameterization, self).__init__( - rank=3, - filters=filters, - kernel_size=kernel_size, - strides=strides, - padding=padding, - data_format=data_format, - dilation_rate=dilation_rate, - activation=activation, - activity_regularizer=activity_regularizer, - trainable=trainable, - kernel_posterior_fn=kernel_posterior_fn, - kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, - kernel_prior_fn=kernel_prior_fn, - kernel_divergence_fn=kernel_divergence_fn, - bias_posterior_fn=bias_posterior_fn, - bias_posterior_tensor_fn=bias_posterior_tensor_fn, - bias_prior_fn=bias_prior_fn, - bias_divergence_fn=bias_divergence_fn, - name=name, **kwargs) - - -@docstring_util.expand_docstring(args=doc_args) -def conv3d_reparameterization( - inputs, - filters, - kernel_size, - strides=(1, 1, 1), - padding="valid", - data_format="channels_last", - dilation_rate=(1, 1, 1), - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn(is_singular=True), # pylint: disable=line-too-long - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - name=None, - reuse=None): - # pylint: disable=g-doc-args - """Functional interface for the 3D convolution layer. - - This layer creates a convolution kernel that is convolved - (actually cross-correlated) with the layer input to produce a tensor of - outputs. It may also include a bias addition and activation function - on the outputs. It assumes the `kernel` and/or `bias` are drawn from - distributions. - - By default, the layer implements a stochastic forward pass via - sampling from the kernel and bias posteriors, - ```none - outputs = f(inputs; kernel, bias), kernel, bias ~ posterior - ``` - where f denotes the layer's calculation. It uses the reparameterization - estimator [1], which performs a Monte Carlo approximation of the - distribution integrating over the `kernel` and `bias`. - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Args: - inputs: Tensor input. - filters: Integer, the dimensionality of the output space (i.e. the number - of filters in the convolution). - kernel_size: An integer or tuple/list of 3 integers, specifying the - depth, height and width of the 3D convolution window. - Can be a single integer to specify the same value for - all spatial dimensions. - strides: An integer or tuple/list of 3 integers, - specifying the strides of the convolution along the depth, - height and width. - Can be a single integer to specify the same value for - all spatial dimensions. - Specifying any stride value != 1 is incompatible with specifying - any `dilation_rate` value != 1. - padding: One of `"valid"` or `"same"` (case-insensitive). - data_format: A string, one of `channels_last` (default) or `channels_first`. - The ordering of the dimensions in the inputs. - `channels_last` corresponds to inputs with shape - `(batch, depth, height, width, channels)` while `channels_first` - corresponds to inputs with shape - `(batch, channels, depth, height, width)`. - dilation_rate: An integer or tuple/list of 3 integers, specifying - the dilation rate to use for dilated convolution. - Can be a single integer to specify the same value for - all spatial dimensions. - Currently, specifying any `dilation_rate` value != 1 is - incompatible with specifying any stride value != 1. - @{args} - reuse: Boolean, whether to reuse the weights of a previous layer - by the same name. - - Returns: - Output tensor. - - Raises: - ValueError: if eager execution is enabled. - - #### Examples - - We illustrate a Bayesian neural network with [variational inference]( - https://en.wikipedia.org/wiki/Variational_Bayesian_methods), - assuming a dataset of `features` and `labels`. - - ```python - tfp = tf.contrib.bayesflow - - net = tf.reshape(features, [-1, 256, 32, 32, 3]) - net = tfp.layers.conv3d_reparameterization(net, - filters=64, - kernel_size=5, - padding="SAME", - activation=tf.nn.relu) - net = tf.layers.max_pooling2d(net, - pool_size=2, - strides=2, - padding="SAME") - net = tf.reshape(net, [-1, 256 * 8 * 8 * 64]) - logits = tfp.layers.dense_reparameterization(net, 10) - neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits( - labels=labels, logits=logits) - kl = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) - loss = neg_log_likelihood + kl - train_op = tf.train.AdamOptimizer().minimize(loss) - ``` - - It uses reparameterization gradients to minimize the - Kullback-Leibler divergence up to a constant, also known as the - negative Evidence Lower Bound. It consists of the sum of two terms: - the expected negative log-likelihood, which we approximate via - Monte Carlo; and the KL divergence, which is added via regularizer - terms which are arguments to the layer. - - [1]: "Auto-Encoding Variational Bayes." - Diederik P. Kingma, Max Welling. - International Conference on Learning Representations, 2014. - """ - # pylint: enable=g-doc-args - layer = Conv3DReparameterization( - filters=filters, - kernel_size=kernel_size, - strides=strides, - padding=padding, - data_format=data_format, - dilation_rate=dilation_rate, - activation=activation, - activity_regularizer=activity_regularizer, - trainable=trainable, - kernel_posterior_fn=kernel_posterior_fn, - kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, - kernel_prior_fn=kernel_prior_fn, - kernel_divergence_fn=kernel_divergence_fn, - bias_posterior_fn=bias_posterior_fn, - bias_posterior_tensor_fn=bias_posterior_tensor_fn, - bias_prior_fn=bias_prior_fn, - bias_divergence_fn=bias_divergence_fn, - name=name, - dtype=inputs.dtype.base_dtype, - _scope=name, - _reuse=reuse) - return layer.apply(inputs) - - -class _ConvFlipout(_ConvVariational): - """Abstract nD convolution layer (private, used as implementation base). - - This layer creates a convolution kernel that is convolved - (actually cross-correlated) with the layer input to produce a tensor of - outputs. It may also include a bias addition and activation function - on the outputs. It assumes the `kernel` and/or `bias` are drawn from - distributions. - - By default, the layer implements a stochastic forward pass via - sampling from the kernel and bias posteriors, - ```none - outputs = f(inputs; kernel, bias), kernel, bias ~ posterior - ``` - where f denotes the layer's calculation. It uses the Flipout - estimator [1], which performs a Monte Carlo approximation of the - distribution integrating over the `kernel` and `bias`. Flipout uses - roughly twice as many floating point operations as the - reparameterization estimator but has the advantage of significantly - lower variance. - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Properties: - rank: Python integer, dimensionality of convolution. - filters: Python integer, dimensionality of the output space. - kernel_size: Size of the convolution window. - strides: Stride length of convolution. - padding: Python string describing padding approach. - data_format: Python string describing input data's dimensions. - dilation_rate: Dilation rate for an atrous convolution. - activation: Activation function (`callable`). - activity_regularizer: Regularizer function for the output. - kernel_posterior_fn: `callable` returning posterior. - kernel_posterior_tensor_fn: `callable` operating on posterior. - kernel_prior_fn: `callable` returning prior. - kernel_divergence_fn: `callable` returning divergence. - bias_posterior_fn: `callable` returning posterior. - bias_posterior_tensor_fn: `callable` operating on posterior. - bias_prior_fn: `callable` returning prior. - bias_divergence_fn: `callable` returning divergence. - seed: Python integer, used to create random seeds. - - [1]: "Flipout: Efficient Pseudo-Independent Weight Perturbations on - Mini-Batches." - Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse. - International Conference on Learning Representations, 2018. - """ - - @docstring_util.expand_docstring(args=doc_args) - def __init__( - self, - rank, - filters, - kernel_size, - strides=1, - padding="valid", - data_format="channels_last", - dilation_rate=1, - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn(is_singular=True), # pylint: disable=line-too-long - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - seed=None, - name=None, - **kwargs): - # pylint: disable=g-doc-args - """Construct layer. - - Args: - rank: An integer, the rank of the convolution, e.g. "2" for 2D - convolution. - filters: Integer, the dimensionality of the output space (i.e. the number - of filters in the convolution). - kernel_size: An integer or tuple/list of n integers, specifying the - length of the convolution window. - strides: An integer or tuple/list of n integers, - specifying the stride length of the convolution. - Specifying any stride value != 1 is incompatible with specifying - any `dilation_rate` value != 1. - padding: One of `"valid"` or `"same"` (case-insensitive). - data_format: A string, one of `channels_last` (default) or - `channels_first`. The ordering of the dimensions in the inputs. - `channels_last` corresponds to inputs with shape `(batch, ..., - channels)` while `channels_first` corresponds to inputs with shape - `(batch, channels, ...)`. - dilation_rate: An integer or tuple/list of n integers, specifying - the dilation rate to use for dilated convolution. - Currently, specifying any `dilation_rate` value != 1 is - incompatible with specifying any `strides` value != 1. - @{args} - """ - # pylint: enable=g-doc-args - super(_ConvFlipout, self).__init__( - rank=rank, - filters=filters, - kernel_size=kernel_size, - strides=strides, - padding=padding, - data_format=data_format, - dilation_rate=dilation_rate, - activation=activation, - activity_regularizer=activity_regularizer, - trainable=trainable, - kernel_posterior_fn=kernel_posterior_fn, - kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, - kernel_prior_fn=kernel_prior_fn, - kernel_divergence_fn=kernel_divergence_fn, - bias_posterior_fn=bias_posterior_fn, - bias_posterior_tensor_fn=bias_posterior_tensor_fn, - bias_prior_fn=bias_prior_fn, - bias_divergence_fn=bias_divergence_fn, - name=name, **kwargs) - self.seed = seed - - def _apply_variational_kernel(self, inputs): - if (not isinstance(self.kernel_posterior, independent_lib.Independent) or - not isinstance(self.kernel_posterior.distribution, normal_lib.Normal)): - raise TypeError( - "`{}` requires " - "`kernel_posterior_fn` produce an instance of " - "`tf.distributions.Independent(tf.distributions.Normal)` " - "(saw: \"{}\").".format( - type(self).__name__, self.kernel_posterior.name)) - self.kernel_posterior_affine = normal_lib.Normal( - loc=array_ops.zeros_like(self.kernel_posterior.distribution.loc), - scale=self.kernel_posterior.distribution.scale) - self.kernel_posterior_affine_tensor = ( - self.kernel_posterior_tensor_fn(self.kernel_posterior_affine)) - self.kernel_posterior_tensor = None - - outputs = self._convolution_op( - inputs, self.kernel_posterior.distribution.loc) - - input_shape = array_ops.shape(inputs) - output_shape = array_ops.shape(outputs) - batch_shape = array_ops.expand_dims(input_shape[0], 0) - channels = input_shape[-1] - - sign_input = layers_util.random_sign( - array_ops.concat([batch_shape, - array_ops.expand_dims(channels, 0)], 0), - dtype=inputs.dtype, - seed=self.seed) - sign_output = layers_util.random_sign( - array_ops.concat([batch_shape, - array_ops.expand_dims(self.filters, 0)], 0), - dtype=inputs.dtype, - seed=distribution_util.gen_new_seed( - self.seed, salt="conv_flipout")) - for _ in range(self.rank): - sign_input = array_ops.expand_dims(sign_input, 1) # 2D ex: (B, 1, 1, C) - sign_output = array_ops.expand_dims(sign_output, 1) - - sign_input = array_ops.tile( # tile for element-wise op broadcasting - sign_input, - [1] + [input_shape[i + 1] for i in range(self.rank)] + [1]) - sign_output = array_ops.tile( - sign_output, - [1] + [output_shape[i + 1] for i in range(self.rank)] + [1]) - - perturbed_inputs = self._convolution_op( - inputs * sign_input, self.kernel_posterior_affine_tensor) * sign_output - - outputs += perturbed_inputs - return outputs - - -class Conv1DFlipout(_ConvFlipout): - """1D convolution layer (e.g. temporal convolution) with Flipout. - - This layer creates a convolution kernel that is convolved - (actually cross-correlated) with the layer input to produce a tensor of - outputs. It may also include a bias addition and activation function - on the outputs. It assumes the `kernel` and/or `bias` are drawn from - distributions. - - By default, the layer implements a stochastic forward pass via - sampling from the kernel and bias posteriors, - ```none - outputs = f(inputs; kernel, bias), kernel, bias ~ posterior - ``` - where f denotes the layer's calculation. It uses the Flipout - estimator [1], which performs a Monte Carlo approximation of the - distribution integrating over the `kernel` and `bias`. Flipout uses - roughly twice as many floating point operations as the - reparameterization estimator but has the advantage of significantly - lower variance. - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Properties: - filters: Python integer, dimensionality of the output space. - kernel_size: Size of the convolution window. - strides: Stride length of convolution. - padding: Python string describing padding approach. - data_format: Python string describing input data's dimensions. - dilation_rate: Dilation rate for an atrous convolution. - activation: Activation function (`callable`). - activity_regularizer: Regularizer function for the output. - kernel_posterior_fn: `callable` returning posterior. - kernel_posterior_tensor_fn: `callable` operating on posterior. - kernel_prior_fn: `callable` returning prior. - kernel_divergence_fn: `callable` returning divergence. - bias_posterior_fn: `callable` returning posterior. - bias_posterior_tensor_fn: `callable` operating on posterior. - bias_prior_fn: `callable` returning prior. - bias_divergence_fn: `callable` returning divergence. - seed: Python integer, used to create random seeds. - - #### Examples - - We illustrate a Bayesian neural network with [variational inference]( - https://en.wikipedia.org/wiki/Variational_Bayesian_methods), - assuming a dataset of `features` and `labels`. - - ```python - tfp = tf.contrib.bayesflow - - net = tf.reshape(features, [-1, 128, 1]) - net = tfp.layers.Conv1DFlipout(64, - kernel_size=5, - padding="SAME", - activation=tf.nn.relu)(net) - net = tf.reshape(net, [-1, 128 * 64]) - logits = tfp.layers.DenseFlipout(10)(net) - neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits( - labels=labels, logits=logits) - kl = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) - loss = neg_log_likelihood + kl - train_op = tf.train.AdamOptimizer().minimize(loss) - ``` - - It uses the Flipout gradient estimator to minimize the - Kullback-Leibler divergence up to a constant, also known as the - negative Evidence Lower Bound. It consists of the sum of two terms: - the expected negative log-likelihood, which we approximate via - Monte Carlo; and the KL divergence, which is added via regularizer - terms which are arguments to the layer. - - [1]: "Flipout: Efficient Pseudo-Independent Weight Perturbations on - Mini-Batches." - Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse. - International Conference on Learning Representations, 2018. - """ - - @docstring_util.expand_docstring(args=doc_args) - def __init__( - self, - filters, - kernel_size, - strides=1, - padding="valid", - data_format="channels_last", - dilation_rate=1, - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn(is_singular=True), # pylint: disable=line-too-long - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - seed=None, - name=None, - **kwargs): - # pylint: disable=g-doc-args - """Construct layer. - - Args: - filters: Integer, the dimensionality of the output space (i.e. the number - of filters in the convolution). - kernel_size: An integer or tuple/list of a single integer, specifying the - length of the 1D convolution window. - strides: An integer or tuple/list of a single integer, - specifying the stride length of the convolution. - Specifying any stride value != 1 is incompatible with specifying - any `dilation_rate` value != 1. - padding: One of `"valid"` or `"same"` (case-insensitive). - data_format: A string, one of `channels_last` (default) or - `channels_first`. The ordering of the dimensions in the inputs. - `channels_last` corresponds to inputs with shape `(batch, length, - channels)` while `channels_first` corresponds to inputs with shape - `(batch, channels, length)`. - dilation_rate: An integer or tuple/list of a single integer, specifying - the dilation rate to use for dilated convolution. - Currently, specifying any `dilation_rate` value != 1 is - incompatible with specifying any `strides` value != 1. - @{args} - """ - # pylint: enable=g-doc-args - super(Conv1DFlipout, self).__init__( - rank=1, - filters=filters, - kernel_size=kernel_size, - strides=strides, - padding=padding, - data_format=data_format, - dilation_rate=dilation_rate, - activation=activation, - activity_regularizer=activity_regularizer, - trainable=trainable, - kernel_posterior_fn=kernel_posterior_fn, - kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, - kernel_prior_fn=kernel_prior_fn, - kernel_divergence_fn=kernel_divergence_fn, - bias_posterior_fn=bias_posterior_fn, - bias_posterior_tensor_fn=bias_posterior_tensor_fn, - bias_prior_fn=bias_prior_fn, - bias_divergence_fn=bias_divergence_fn, - seed=seed, - name=name, **kwargs) - - -@docstring_util.expand_docstring(args=doc_args) -def conv1d_flipout( - inputs, - filters, - kernel_size, - strides=1, - padding="valid", - data_format="channels_last", - dilation_rate=1, - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn(is_singular=True), # pylint: disable=line-too-long - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - seed=None, - name=None, - reuse=None): - # pylint: disable=g-doc-args - """Functional interface for 1D convolution layer (e.g. temporal convolution). - - This layer creates a convolution kernel that is convolved - (actually cross-correlated) with the layer input to produce a tensor of - outputs. It may also include a bias addition and activation function - on the outputs. It assumes the `kernel` and/or `bias` are drawn from - distributions. - - By default, the layer implements a stochastic forward pass via - sampling from the kernel and bias posteriors, - ```none - outputs = f(inputs; kernel, bias), kernel, bias ~ posterior - ``` - where f denotes the layer's calculation. It uses the Flipout - estimator [1], which performs a Monte Carlo approximation of the - distribution integrating over the `kernel` and `bias`. Flipout uses - roughly twice as many floating point operations as the - reparameterization estimator but has the advantage of significantly - lower variance. - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Args: - inputs: Tensor input. - filters: Integer, the dimensionality of the output space (i.e. the number - of filters in the convolution). - kernel_size: An integer or tuple/list of a single integer, specifying the - length of the 1D convolution window. - strides: An integer or tuple/list of a single integer, - specifying the stride length of the convolution. - Specifying any stride value != 1 is incompatible with specifying - any `dilation_rate` value != 1. - padding: One of `"valid"` or `"same"` (case-insensitive). - data_format: A string, one of `channels_last` (default) or `channels_first`. - The ordering of the dimensions in the inputs. - `channels_last` corresponds to inputs with shape - `(batch, length, channels)` while `channels_first` corresponds to - inputs with shape `(batch, channels, length)`. - dilation_rate: An integer or tuple/list of a single integer, specifying - the dilation rate to use for dilated convolution. - Currently, specifying any `dilation_rate` value != 1 is - incompatible with specifying any `strides` value != 1. - @{args} - reuse: Boolean, whether to reuse the weights of a previous layer - by the same name. - - Returns: - Output tensor. - - Raises: - ValueError: if eager execution is enabled. - - #### Examples - - We illustrate a Bayesian neural network with [variational inference]( - https://en.wikipedia.org/wiki/Variational_Bayesian_methods), - assuming a dataset of `features` and `labels`. - - ```python - tfp = tf.contrib.bayesflow - - net = tf.reshape(features, [-1, 128, 1]) - net = tfp.layers.conv1d_flipout(net, - filters=64, - kernel_size=5, - padding="SAME", - activation=tf.nn.relu) - net = tf.reshape(net, [-1, 128 * 64]) - logits = tfp.layers.dense_flipout(net, 10) - neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits( - labels=labels, logits=logits) - kl = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) - loss = neg_log_likelihood + kl - train_op = tf.train.AdamOptimizer().minimize(loss) - ``` - - It uses the Flipout gradient estimator to minimize the - Kullback-Leibler divergence up to a constant, also known as the - negative Evidence Lower Bound. It consists of the sum of two terms: - the expected negative log-likelihood, which we approximate via - Monte Carlo; and the KL divergence, which is added via regularizer - terms which are arguments to the layer. - - [1]: "Flipout: Efficient Pseudo-Independent Weight Perturbations on - Mini-Batches." - Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse. - International Conference on Learning Representations, 2018. - """ - # pylint: enable=g-doc-args - layer = Conv1DFlipout( - filters=filters, - kernel_size=kernel_size, - strides=strides, - padding=padding, - data_format=data_format, - dilation_rate=dilation_rate, - activation=activation, - activity_regularizer=activity_regularizer, - trainable=trainable, - kernel_posterior_fn=kernel_posterior_fn, - kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, - kernel_prior_fn=kernel_prior_fn, - kernel_divergence_fn=kernel_divergence_fn, - bias_posterior_fn=bias_posterior_fn, - bias_posterior_tensor_fn=bias_posterior_tensor_fn, - bias_prior_fn=bias_prior_fn, - bias_divergence_fn=bias_divergence_fn, - seed=seed, - name=name, - dtype=inputs.dtype.base_dtype, - _scope=name, - _reuse=reuse) - return layer.apply(inputs) - - -class Conv2DFlipout(_ConvFlipout): - """2D convolution layer (e.g. spatial convolution over images) with Flipout. - - This layer creates a convolution kernel that is convolved - (actually cross-correlated) with the layer input to produce a tensor of - outputs. It may also include a bias addition and activation function - on the outputs. It assumes the `kernel` and/or `bias` are drawn from - distributions. - - By default, the layer implements a stochastic forward pass via - sampling from the kernel and bias posteriors, - ```none - outputs = f(inputs; kernel, bias), kernel, bias ~ posterior - ``` - where f denotes the layer's calculation. It uses the Flipout - estimator [1], which performs a Monte Carlo approximation of the - distribution integrating over the `kernel` and `bias`. Flipout uses - roughly twice as many floating point operations as the - reparameterization estimator but has the advantage of significantly - lower variance. - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Properties: - filters: Python integer, dimensionality of the output space. - kernel_size: Size of the convolution window. - strides: Stride length of convolution. - padding: Python string describing padding approach. - data_format: Python string describing input data's dimensions. - dilation_rate: Dilation rate for an atrous convolution. - activation: Activation function (`callable`). - activity_regularizer: Regularizer function for the output. - kernel_posterior_fn: `callable` returning posterior. - kernel_posterior_tensor_fn: `callable` operating on posterior. - kernel_prior_fn: `callable` returning prior. - kernel_divergence_fn: `callable` returning divergence. - bias_posterior_fn: `callable` returning posterior. - bias_posterior_tensor_fn: `callable` operating on posterior. - bias_prior_fn: `callable` returning prior. - bias_divergence_fn: `callable` returning divergence. - seed: Python integer, used to create random seeds. - - #### Examples - - We illustrate a Bayesian neural network with [variational inference]( - https://en.wikipedia.org/wiki/Variational_Bayesian_methods), - assuming a dataset of `features` and `labels`. - - ```python - tfp = tf.contrib.bayesflow - - net = tf.reshape(features, [-1, 32, 32, 3]) - net = tfp.layers.Conv2DFlipout(64, - kernel_size=5, - padding="SAME", - activation=tf.nn.relu)(net) - net = tf.layers.MaxPooling2D(pool_size=2, - strides=2, - padding="SAME")(net) - net = tf.reshape(net, [-1, 8 * 8 * 64]) - logits = tfp.layers.DenseFlipout(10)(net) - neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits( - labels=labels, logits=logits) - kl = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) - loss = neg_log_likelihood + kl - train_op = tf.train.AdamOptimizer().minimize(loss) - ``` - - It uses the Flipout gradient estimator to minimize the - Kullback-Leibler divergence up to a constant, also known as the - negative Evidence Lower Bound. It consists of the sum of two terms: - the expected negative log-likelihood, which we approximate via - Monte Carlo; and the KL divergence, which is added via regularizer - terms which are arguments to the layer. - - [1]: "Flipout: Efficient Pseudo-Independent Weight Perturbations on - Mini-Batches." - Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse. - International Conference on Learning Representations, 2018. - """ - - @docstring_util.expand_docstring(args=doc_args) - def __init__( - self, - filters, - kernel_size, - strides=(1, 1), - padding="valid", - data_format="channels_last", - dilation_rate=(1, 1), - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn(is_singular=True), # pylint: disable=line-too-long - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - seed=None, - name=None, - **kwargs): - # pylint: disable=g-doc-args - """Construct layer. - - Args: - filters: Integer, the dimensionality of the output space (i.e. the number - of filters in the convolution). - kernel_size: An integer or tuple/list of 2 integers, specifying the - height and width of the 2D convolution window. - Can be a single integer to specify the same value for - all spatial dimensions. - strides: An integer or tuple/list of 2 integers, - specifying the strides of the convolution along the height and width. - Can be a single integer to specify the same value for - all spatial dimensions. - Specifying any stride value != 1 is incompatible with specifying - any `dilation_rate` value != 1. - padding: One of `"valid"` or `"same"` (case-insensitive). - data_format: A string, one of `channels_last` (default) or - `channels_first`. The ordering of the dimensions in the inputs. - `channels_last` corresponds to inputs with shape `(batch, height, - width, channels)` while `channels_first` corresponds to inputs with - shape `(batch, channels, height, width)`. - dilation_rate: An integer or tuple/list of 2 integers, specifying - the dilation rate to use for dilated convolution. - Can be a single integer to specify the same value for - all spatial dimensions. - Currently, specifying any `dilation_rate` value != 1 is - incompatible with specifying any stride value != 1. - @{args} - """ - # pylint: enable=g-doc-args - super(Conv2DFlipout, self).__init__( - rank=2, - filters=filters, - kernel_size=kernel_size, - strides=strides, - padding=padding, - data_format=data_format, - dilation_rate=dilation_rate, - activation=activation, - activity_regularizer=activity_regularizer, - trainable=trainable, - kernel_posterior_fn=kernel_posterior_fn, - kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, - kernel_prior_fn=kernel_prior_fn, - kernel_divergence_fn=kernel_divergence_fn, - bias_posterior_fn=bias_posterior_fn, - bias_posterior_tensor_fn=bias_posterior_tensor_fn, - bias_prior_fn=bias_prior_fn, - bias_divergence_fn=bias_divergence_fn, - seed=seed, - name=name, **kwargs) - - -@docstring_util.expand_docstring(args=doc_args) -def conv2d_flipout( - inputs, - filters, - kernel_size, - strides=(1, 1), - padding="valid", - data_format="channels_last", - dilation_rate=(1, 1), - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn(is_singular=True), # pylint: disable=line-too-long - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - seed=None, - name=None, - reuse=None): - # pylint: disable=g-doc-args - """Functional interface for the 2D convolution layer. - - This layer creates a convolution kernel that is convolved - (actually cross-correlated) with the layer input to produce a tensor of - outputs. It may also include a bias addition and activation function - on the outputs. It assumes the `kernel` and/or `bias` are drawn from - distributions. - - By default, the layer implements a stochastic forward pass via - sampling from the kernel and bias posteriors, - ```none - outputs = f(inputs; kernel, bias), kernel, bias ~ posterior - ``` - where f denotes the layer's calculation. It uses the Flipout - estimator [1], which performs a Monte Carlo approximation of the - distribution integrating over the `kernel` and `bias`. Flipout uses - roughly twice as many floating point operations as the - reparameterization estimator but has the advantage of significantly - lower variance. - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Args: - inputs: Tensor input. - filters: Integer, the dimensionality of the output space (i.e. the number - of filters in the convolution). - kernel_size: An integer or tuple/list of 2 integers, specifying the - height and width of the 2D convolution window. - Can be a single integer to specify the same value for - all spatial dimensions. - strides: An integer or tuple/list of 2 integers, - specifying the strides of the convolution along the height and width. - Can be a single integer to specify the same value for - all spatial dimensions. - Specifying any stride value != 1 is incompatible with specifying - any `dilation_rate` value != 1. - padding: One of `"valid"` or `"same"` (case-insensitive). - data_format: A string, one of `channels_last` (default) or `channels_first`. - The ordering of the dimensions in the inputs. - `channels_last` corresponds to inputs with shape - `(batch, height, width, channels)` while `channels_first` corresponds to - inputs with shape `(batch, channels, height, width)`. - dilation_rate: An integer or tuple/list of 2 integers, specifying - the dilation rate to use for dilated convolution. - Can be a single integer to specify the same value for - all spatial dimensions. - Currently, specifying any `dilation_rate` value != 1 is - incompatible with specifying any stride value != 1. - @{args} - reuse: Boolean, whether to reuse the weights of a previous layer - by the same name. - - Returns: - Output tensor. - - Raises: - ValueError: if eager execution is enabled. - - #### Examples - - We illustrate a Bayesian neural network with [variational inference]( - https://en.wikipedia.org/wiki/Variational_Bayesian_methods), - assuming a dataset of `features` and `labels`. - - ```python - tfp = tf.contrib.bayesflow - - net = tf.reshape(features, [-1, 32, 32, 3]) - net = tfp.layers.conv2d_flipout(net, - filters=64, - kernel_size=5, - padding="SAME", - activation=tf.nn.relu) - net = tf.layers.max_pooling2d(net, - pool_size=2, - strides=2, - padding="SAME") - net = tf.reshape(net, [-1, 8 * 8 * 64]) - logits = tfp.layers.dense_flipout(net, 10) - neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits( - labels=labels, logits=logits) - kl = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) - loss = neg_log_likelihood + kl - train_op = tf.train.AdamOptimizer().minimize(loss) - ``` - - It uses the Flipout gradient estimator to minimize the - Kullback-Leibler divergence up to a constant, also known as the - negative Evidence Lower Bound. It consists of the sum of two terms: - the expected negative log-likelihood, which we approximate via - Monte Carlo; and the KL divergence, which is added via regularizer - terms which are arguments to the layer. - - [1]: "Flipout: Efficient Pseudo-Independent Weight Perturbations on - Mini-Batches." - Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse. - International Conference on Learning Representations, 2018. - """ - # pylint: enable=g-doc-args - layer = Conv2DFlipout( - filters=filters, - kernel_size=kernel_size, - strides=strides, - padding=padding, - data_format=data_format, - dilation_rate=dilation_rate, - activation=activation, - activity_regularizer=activity_regularizer, - trainable=trainable, - kernel_posterior_fn=kernel_posterior_fn, - kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, - kernel_prior_fn=kernel_prior_fn, - kernel_divergence_fn=kernel_divergence_fn, - bias_posterior_fn=bias_posterior_fn, - bias_posterior_tensor_fn=bias_posterior_tensor_fn, - bias_prior_fn=bias_prior_fn, - bias_divergence_fn=bias_divergence_fn, - seed=seed, - name=name, - dtype=inputs.dtype.base_dtype, - _scope=name, - _reuse=reuse) - return layer.apply(inputs) - - -class Conv3DFlipout(_ConvFlipout): - """3D convolution layer (e.g. spatial convolution over volumes) with Flipout. - - This layer creates a convolution kernel that is convolved - (actually cross-correlated) with the layer input to produce a tensor of - outputs. It may also include a bias addition and activation function - on the outputs. It assumes the `kernel` and/or `bias` are drawn from - distributions. - - By default, the layer implements a stochastic forward pass via - sampling from the kernel and bias posteriors, - ```none - outputs = f(inputs; kernel, bias), kernel, bias ~ posterior - ``` - where f denotes the layer's calculation. It uses the Flipout - estimator [1], which performs a Monte Carlo approximation of the - distribution integrating over the `kernel` and `bias`. Flipout uses - roughly twice as many floating point operations as the - reparameterization estimator but has the advantage of significantly - lower variance. - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Properties: - filters: Python integer, dimensionality of the output space. - kernel_size: Size of the convolution window. - strides: Stride length of convolution. - padding: Python string describing padding approach. - data_format: Python string describing input data's dimensions. - dilation_rate: Dilation rate for an atrous convolution. - activation: Activation function (`callable`). - activity_regularizer: Regularizer function for the output. - kernel_posterior_fn: `callable` returning posterior. - kernel_posterior_tensor_fn: `callable` operating on posterior. - kernel_prior_fn: `callable` returning prior. - kernel_divergence_fn: `callable` returning divergence. - bias_posterior_fn: `callable` returning posterior. - bias_posterior_tensor_fn: `callable` operating on posterior. - bias_prior_fn: `callable` returning prior. - bias_divergence_fn: `callable` returning divergence. - seed: Python integer, used to create random seeds. - - #### Examples - - We illustrate a Bayesian neural network with [variational inference]( - https://en.wikipedia.org/wiki/Variational_Bayesian_methods), - assuming a dataset of `features` and `labels`. - - ```python - tfp = tf.contrib.bayesflow - - net = tf.reshape(features, [-1, 256, 32, 32, 3]) - net = tfp.layers.Conv3DFlipout(64, - kernel_size=5, - padding="SAME", - activation=tf.nn.relu)(net) - net = tf.layers.MaxPooling2D(pool_size=2, - strides=2, - padding="SAME")(net) - net = tf.reshape(net, [-1, 256 * 8 * 8 * 64]) - logits = tfp.layers.DenseFlipout(10)(net) - neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits( - labels=labels, logits=logits) - kl = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) - loss = neg_log_likelihood + kl - train_op = tf.train.AdamOptimizer().minimize(loss) - ``` - - It uses the Flipout gradient estimator to minimize the - Kullback-Leibler divergence up to a constant, also known as the - negative Evidence Lower Bound. It consists of the sum of two terms: - the expected negative log-likelihood, which we approximate via - Monte Carlo; and the KL divergence, which is added via regularizer - terms which are arguments to the layer. - - [1]: "Flipout: Efficient Pseudo-Independent Weight Perturbations on - Mini-Batches." - Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse. - International Conference on Learning Representations, 2018. - """ - - @docstring_util.expand_docstring(args=doc_args) - def __init__( - self, - filters, - kernel_size, - strides=(1, 1, 1), - padding="valid", - data_format="channels_last", - dilation_rate=(1, 1, 1), - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn(is_singular=True), # pylint: disable=line-too-long - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - seed=None, - name=None, - **kwargs): - # pylint: disable=g-doc-args - """Construct layer. - - Args: - filters: Integer, the dimensionality of the output space (i.e. the number - of filters in the convolution). - kernel_size: An integer or tuple/list of 3 integers, specifying the - depth, height and width of the 3D convolution window. - Can be a single integer to specify the same value for - all spatial dimensions. - strides: An integer or tuple/list of 3 integers, - specifying the strides of the convolution along the depth, - height and width. - Can be a single integer to specify the same value for - all spatial dimensions. - Specifying any stride value != 1 is incompatible with specifying - any `dilation_rate` value != 1. - padding: One of `"valid"` or `"same"` (case-insensitive). - data_format: A string, one of `channels_last` (default) or - `channels_first`. The ordering of the dimensions in the inputs. - `channels_last` corresponds to inputs with shape `(batch, depth, - height, width, channels)` while `channels_first` corresponds to inputs - with shape `(batch, channels, depth, height, width)`. - dilation_rate: An integer or tuple/list of 3 integers, specifying - the dilation rate to use for dilated convolution. - Can be a single integer to specify the same value for - all spatial dimensions. - Currently, specifying any `dilation_rate` value != 1 is - incompatible with specifying any stride value != 1. - @{args} - """ - # pylint: enable=g-doc-args - super(Conv3DFlipout, self).__init__( - rank=3, - filters=filters, - kernel_size=kernel_size, - strides=strides, - padding=padding, - data_format=data_format, - dilation_rate=dilation_rate, - activation=activation, - activity_regularizer=activity_regularizer, - trainable=trainable, - kernel_posterior_fn=kernel_posterior_fn, - kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, - kernel_prior_fn=kernel_prior_fn, - kernel_divergence_fn=kernel_divergence_fn, - bias_posterior_fn=bias_posterior_fn, - bias_posterior_tensor_fn=bias_posterior_tensor_fn, - bias_prior_fn=bias_prior_fn, - bias_divergence_fn=bias_divergence_fn, - seed=seed, - name=name, **kwargs) - - -@docstring_util.expand_docstring(args=doc_args) -def conv3d_flipout( - inputs, - filters, - kernel_size, - strides=(1, 1, 1), - padding="valid", - data_format="channels_last", - dilation_rate=(1, 1, 1), - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn(is_singular=True), # pylint: disable=line-too-long - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - seed=None, - name=None, - reuse=None): - # pylint: disable=g-doc-args - """Functional interface for the 3D convolution layer. - - This layer creates a convolution kernel that is convolved - (actually cross-correlated) with the layer input to produce a tensor of - outputs. It may also include a bias addition and activation function - on the outputs. It assumes the `kernel` and/or `bias` are drawn from - distributions. - - By default, the layer implements a stochastic forward pass via - sampling from the kernel and bias posteriors, - ```none - outputs = f(inputs; kernel, bias), kernel, bias ~ posterior - ``` - where f denotes the layer's calculation. It uses the Flipout - estimator [1], which performs a Monte Carlo approximation of the - distribution integrating over the `kernel` and `bias`. Flipout uses - roughly twice as many floating point operations as the - reparameterization estimator but has the advantage of significantly - lower variance. - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Args: - inputs: Tensor input. - filters: Integer, the dimensionality of the output space (i.e. the number - of filters in the convolution). - kernel_size: An integer or tuple/list of 3 integers, specifying the - depth, height and width of the 3D convolution window. - Can be a single integer to specify the same value for - all spatial dimensions. - strides: An integer or tuple/list of 3 integers, - specifying the strides of the convolution along the depth, - height and width. - Can be a single integer to specify the same value for - all spatial dimensions. - Specifying any stride value != 1 is incompatible with specifying - any `dilation_rate` value != 1. - padding: One of `"valid"` or `"same"` (case-insensitive). - data_format: A string, one of `channels_last` (default) or `channels_first`. - The ordering of the dimensions in the inputs. - `channels_last` corresponds to inputs with shape - `(batch, depth, height, width, channels)` while `channels_first` - corresponds to inputs with shape - `(batch, channels, depth, height, width)`. - dilation_rate: An integer or tuple/list of 3 integers, specifying - the dilation rate to use for dilated convolution. - Can be a single integer to specify the same value for - all spatial dimensions. - Currently, specifying any `dilation_rate` value != 1 is - incompatible with specifying any stride value != 1. - @{args} - reuse: Boolean, whether to reuse the weights of a previous layer - by the same name. - - Returns: - Output tensor. - - Raises: - ValueError: if eager execution is enabled. - - #### Examples - - We illustrate a Bayesian neural network with [variational inference]( - https://en.wikipedia.org/wiki/Variational_Bayesian_methods), - assuming a dataset of `features` and `labels`. - - ```python - tfp = tf.contrib.bayesflow - - net = tf.reshape(features, [-1, 256, 32, 32, 3]) - net = tfp.layers.conv3d_flipout(net, - filters=64, - kernel_size=5, - padding="SAME", - activation=tf.nn.relu) - net = tf.layers.max_pooling2d(net, - pool_size=2, - strides=2, - padding="SAME") - net = tf.reshape(net, [-1, 256 * 8 * 8 * 64]) - logits = tfp.layers.dense_flipout(net, 10) - neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits( - labels=labels, logits=logits) - kl = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) - loss = neg_log_likelihood + kl - train_op = tf.train.AdamOptimizer().minimize(loss) - ``` - - It uses the Flipout gradient estimator to minimize the - Kullback-Leibler divergence up to a constant, also known as the - negative Evidence Lower Bound. It consists of the sum of two terms: - the expected negative log-likelihood, which we approximate via - Monte Carlo; and the KL divergence, which is added via regularizer - terms which are arguments to the layer. - - [1]: "Flipout: Efficient Pseudo-Independent Weight Perturbations on - Mini-Batches." - Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse. - International Conference on Learning Representations, 2018. - """ - # pylint: enable=g-doc-args - layer = Conv3DFlipout( - filters=filters, - kernel_size=kernel_size, - strides=strides, - padding=padding, - data_format=data_format, - dilation_rate=dilation_rate, - activation=activation, - activity_regularizer=activity_regularizer, - trainable=trainable, - kernel_posterior_fn=kernel_posterior_fn, - kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, - kernel_prior_fn=kernel_prior_fn, - kernel_divergence_fn=kernel_divergence_fn, - bias_posterior_fn=bias_posterior_fn, - bias_posterior_tensor_fn=bias_posterior_tensor_fn, - bias_prior_fn=bias_prior_fn, - bias_divergence_fn=bias_divergence_fn, - seed=seed, - name=name, - dtype=inputs.dtype.base_dtype, - _scope=name, - _reuse=reuse) - return layer.apply(inputs) - - -# Aliases - -Convolution1DReparameterization = Conv1DReparameterization -Convolution2DReparameterization = Conv2DReparameterization -Convolution3DReparameterization = Conv3DReparameterization -convolution1d_reparameterization = conv1d_reparameterization -convolution2d_reparameterization = conv2d_reparameterization -convolution3d_reparameterization = conv3d_reparameterization -Convolution1DFlipout = Conv1DFlipout -Convolution2DFlipout = Conv2DFlipout -Convolution3DFlipout = Conv3DFlipout -convolution1d_flipout = conv1d_flipout -convolution2d_flipout = conv2d_flipout -convolution3d_flipout = conv3d_flipout diff --git a/tensorflow/contrib/bayesflow/python/ops/layers_dense_variational.py b/tensorflow/contrib/bayesflow/python/ops/layers_dense_variational.py deleted file mode 100644 index 1f1d8fda2a5db4db33a2b6e5d7f027c4b509011a..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/bayesflow/python/ops/layers_dense_variational.py +++ /dev/null @@ -1,955 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Dense Bayesian layer using KL-divergence based variational inference. -""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from tensorflow.contrib.bayesflow.python.ops import docstring_util -from tensorflow.contrib.bayesflow.python.ops import layers_util -from tensorflow.contrib.distributions.python.ops import independent as independent_lib -from tensorflow.python.framework import dtypes -from tensorflow.python.framework import ops -from tensorflow.python.framework import tensor_shape -from tensorflow.python.layers import base as layers_lib -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import nn -from tensorflow.python.ops import standard_ops -from tensorflow.python.ops.distributions import kullback_leibler as kl_lib -from tensorflow.python.ops.distributions import normal as normal_lib -from tensorflow.python.ops.distributions import util as distribution_util - - -doc_args = """units: Integer or Long, dimensionality of the output space. - activation: Activation function (`callable`). Set it to None to maintain a - linear activation. - activity_regularizer: Regularizer function for the output. - trainable: Boolean, if `True` also add variables to the graph collection - `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). - kernel_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `kernel` parameter. Default value: - `default_mean_field_normal_fn()`. - kernel_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - kernel_prior_fn: Python `callable` which creates `tf.distributions` - instance. See `default_mean_field_normal_fn` docstring for required - parameter signature. - Default value: `tf.distributions.Normal(loc=0., scale=1.)`. - kernel_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - sample is a `Tensor`. - bias_posterior_fn: Python `callable` which creates - `tf.distributions.Distribution` instance representing the surrogate - posterior of the `bias` parameter. Default value: - `default_mean_field_normal_fn(is_singular=True)` (which creates an - instance of `tf.distributions.Deterministic`). - bias_posterior_tensor_fn: Python `callable` which takes a - `tf.distributions.Distribution` instance and returns a representative - value. Default value: `lambda d: d.sample()`. - bias_prior_fn: Python `callable` which creates `tf.distributions` instance. - See `default_mean_field_normal_fn` docstring for required parameter - signature. Default value: `None` (no prior, no variational inference) - bias_divergence_fn: Python `callable` which takes the surrogate posterior - distribution, prior distribution and random variate sample(s) from the - surrogate posterior and computes or approximates the KL divergence. The - distributions are `tf.distributions.Distribution`-like instances and the - sample is a `Tensor`. - seed: Python scalar `int` which initializes the random number - generator. Default value: `None` (i.e., use global seed). - name: Python `str`, the name of the layer. Layers with the same name will - share `tf.Variable`s, but to avoid mistakes we require `reuse=True` in - such cases. - reuse: Python `bool`, whether to reuse the `tf.Variable`s of a previous - layer by the same name.""" - - -class _DenseVariational(layers_lib.Layer): - """Abstract densely-connected class (private, used as implementation base). - - This layer implements the Bayesian variational inference analogue to - a dense layer by assuming the `kernel` and/or the `bias` are drawn - from distributions. By default, the layer implements a stochastic - forward pass via sampling from the kernel and bias posteriors, - - ```none - kernel, bias ~ posterior - outputs = activation(matmul(inputs, kernel) + bias) - ``` - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Properties: - units: Python integer, dimensionality of the output space. - activation: Activation function (`callable`). - activity_regularizer: Regularizer function for the output. - kernel_posterior_fn: `callable` returning posterior. - kernel_posterior_tensor_fn: `callable` operating on posterior. - kernel_prior_fn: `callable` returning prior. - kernel_divergence_fn: `callable` returning divergence. - bias_posterior_fn: `callable` returning posterior. - bias_posterior_tensor_fn: `callable` operating on posterior. - bias_prior_fn: `callable` returning prior. - bias_divergence_fn: `callable` returning divergence. - """ - - @docstring_util.expand_docstring(args=doc_args) - def __init__( - self, - units, - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn(is_singular=True), # pylint: disable=line-too-long - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - name=None, - **kwargs): - # pylint: disable=g-doc-args - """Construct layer. - - Args: - @{args} - """ - # pylint: enable=g-doc-args - super(_DenseVariational, self).__init__( - trainable=trainable, - name=name, - activity_regularizer=activity_regularizer, - **kwargs) - self.units = units - self.activation = activation - self.input_spec = layers_lib.InputSpec(min_ndim=2) - self.kernel_posterior_fn = kernel_posterior_fn - self.kernel_posterior_tensor_fn = kernel_posterior_tensor_fn - self.kernel_prior_fn = kernel_prior_fn - self.kernel_divergence_fn = kernel_divergence_fn - self.bias_posterior_fn = bias_posterior_fn - self.bias_posterior_tensor_fn = bias_posterior_tensor_fn - self.bias_prior_fn = bias_prior_fn - self.bias_divergence_fn = bias_divergence_fn - - def build(self, input_shape): - input_shape = tensor_shape.TensorShape(input_shape) - in_size = input_shape.with_rank_at_least(2)[-1].value - if in_size is None: - raise ValueError("The last dimension of the inputs to `Dense` " - "should be defined. Found `None`.") - self._input_spec = layers_lib.InputSpec(min_ndim=2, axes={-1: in_size}) - dtype = dtypes.as_dtype(self.dtype) - - # Must have a posterior kernel. - self.kernel_posterior = self.kernel_posterior_fn( - dtype, [in_size, self.units], "kernel_posterior", - self.trainable, self.add_variable) - - if self.kernel_prior_fn is None: - self.kernel_prior = None - else: - self.kernel_prior = self.kernel_prior_fn( - dtype, [in_size, self.units], "kernel_prior", - self.trainable, self.add_variable) - self._built_kernel_divergence = False - - if self.bias_posterior_fn is None: - self.bias_posterior = None - else: - self.bias_posterior = self.bias_posterior_fn( - dtype, [self.units], "bias_posterior", - self.trainable, self.add_variable) - - if self.bias_prior_fn is None: - self.bias_prior = None - else: - self.bias_prior = self.bias_prior_fn( - dtype, [self.units], "bias_prior", - self.trainable, self.add_variable) - self._built_bias_divergence = False - - self.built = True - - def call(self, inputs): - inputs = ops.convert_to_tensor(inputs, dtype=self.dtype) - - outputs = self._apply_variational_kernel(inputs) - outputs = self._apply_variational_bias(outputs) - if self.activation is not None: - outputs = self.activation(outputs) # pylint: disable=not-callable - if not self._built_kernel_divergence: - kernel_posterior = self.kernel_posterior - kernel_prior = self.kernel_prior - if isinstance(self.kernel_posterior, independent_lib.Independent): - kernel_posterior = kernel_posterior.distribution - if isinstance(self.kernel_prior, independent_lib.Independent): - kernel_prior = kernel_prior.distribution - self._apply_divergence(self.kernel_divergence_fn, - kernel_posterior, - kernel_prior, - self.kernel_posterior_tensor, - name="divergence_kernel") - self._built_kernel_divergence = True - if not self._built_bias_divergence: - bias_posterior = self.bias_posterior - bias_prior = self.bias_prior - if isinstance(self.bias_posterior, independent_lib.Independent): - bias_posterior = bias_posterior.distribution - if isinstance(self.bias_prior, independent_lib.Independent): - bias_prior = bias_prior.distribution - self._apply_divergence(self.bias_divergence_fn, - bias_posterior, - bias_prior, - self.bias_posterior_tensor, - name="divergence_bias") - self._built_bias_divergence = True - return outputs - - def _apply_variational_bias(self, inputs): - if self.bias_posterior is None: - self.bias_posterior_tensor = None - return inputs - self.bias_posterior_tensor = self.bias_posterior_tensor_fn( - self.bias_posterior) - return nn.bias_add(inputs, self.bias_posterior_tensor) - - def _apply_divergence(self, divergence_fn, posterior, prior, - posterior_tensor, name): - if (divergence_fn is None or - posterior is None or - prior is None): - divergence = None - return - divergence = standard_ops.identity( - divergence_fn( - posterior, prior, posterior_tensor), - name=name) - self.add_loss(divergence) - - def _matmul(self, inputs, kernel): - if inputs.shape.ndims <= 2: - return standard_ops.matmul(inputs, kernel) - # To handle broadcasting, we must use `tensordot`. - return standard_ops.tensordot(inputs, kernel, axes=[[-1], [0]]) - - def _compute_output_shape(self, input_shape): - input_shape = tensor_shape.TensorShape(input_shape).with_rank_at_least(2) - if input_shape[-1].value is None: - raise ValueError( - "The innermost dimension of input_shape must be defined, " - "but saw: {}".format(input_shape)) - return input_shape[:-1].concatenate(self.units) - - -class DenseReparameterization(_DenseVariational): - """Densely-connected layer class with reparameterization estimator. - - This layer implements the Bayesian variational inference analogue to - a dense layer by assuming the `kernel` and/or the `bias` are drawn - from distributions. By default, the layer implements a stochastic - forward pass via sampling from the kernel and bias posteriors, - - ```none - kernel, bias ~ posterior - outputs = activation(matmul(inputs, kernel) + bias) - ``` - - It uses the reparameterization estimator [1], which performs a Monte Carlo - approximation of the distribution integrating over the `kernel` and - `bias`. - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Properties: - units: Python integer, dimensionality of the output space. - activation: Activation function (`callable`). - activity_regularizer: Regularizer function for the output. - kernel_posterior_fn: `callable` returning posterior. - kernel_posterior_tensor_fn: `callable` operating on posterior. - kernel_prior_fn: `callable` returning prior. - kernel_divergence_fn: `callable` returning divergence. - bias_posterior_fn: `callable` returning posterior. - bias_posterior_tensor_fn: `callable` operating on posterior. - bias_prior_fn: `callable` returning prior. - bias_divergence_fn: `callable` returning divergence. - - #### Examples - - We illustrate a Bayesian neural network with [variational inference]( - https://en.wikipedia.org/wiki/Variational_Bayesian_methods), - assuming a dataset of `features` and `labels`. - - ```python - tfp = tf.contrib.bayesflow - - net = tfp.layers.DenseReparameterization( - 512, activation=tf.nn.relu)(features) - logits = tfp.layers.DenseReparameterization(10)(net) - neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits( - labels=labels, logits=logits) - kl = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) - loss = neg_log_likelihood + kl - train_op = tf.train.AdamOptimizer().minimize(loss) - ``` - - It uses reparameterization gradients to minimize the - Kullback-Leibler divergence up to a constant, also known as the - negative Evidence Lower Bound. It consists of the sum of two terms: - the expected negative log-likelihood, which we approximate via - Monte Carlo; and the KL divergence, which is added via regularizer - terms which are arguments to the layer. - - [1]: "Auto-Encoding Variational Bayes." - Diederik P. Kingma, Max Welling. - International Conference on Learning Representations, 2014. - """ - - @docstring_util.expand_docstring(args=doc_args) - def __init__( - self, - units, - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn( - is_singular=True), - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - name=None, - **kwargs): - # pylint: disable=g-doc-args - """Construct layer. - - Args: - @{args} - """ - # pylint: enable=g-doc-args - super(DenseReparameterization, self).__init__( - units=units, - activation=activation, - activity_regularizer=activity_regularizer, - trainable=trainable, - kernel_posterior_fn=kernel_posterior_fn, - kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, - kernel_prior_fn=kernel_prior_fn, - kernel_divergence_fn=kernel_divergence_fn, - bias_posterior_fn=bias_posterior_fn, - bias_posterior_tensor_fn=bias_posterior_tensor_fn, - bias_prior_fn=bias_prior_fn, - bias_divergence_fn=bias_divergence_fn, - name=name, - **kwargs) - - def _apply_variational_kernel(self, inputs): - self.kernel_posterior_tensor = self.kernel_posterior_tensor_fn( - self.kernel_posterior) - self.kernel_posterior_affine = None - self.kernel_posterior_affine_tensor = None - return self._matmul(inputs, self.kernel_posterior_tensor) - - -@docstring_util.expand_docstring(args=doc_args) -def dense_reparameterization( - inputs, - units, - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn(is_singular=True), # pylint: disable=line-too-long - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - name=None, - reuse=None): - # pylint: disable=g-doc-args - """Densely-connected layer with reparameterization estimator. - - This layer implements the Bayesian variational inference analogue to - a dense layer by assuming the `kernel` and/or the `bias` are drawn - from distributions. By default, the layer implements a stochastic - forward pass via sampling from the kernel and bias posteriors, - - ```none - kernel, bias ~ posterior - outputs = activation(matmul(inputs, kernel) + bias) - ``` - - It uses the reparameterization estimator [1], which performs a Monte Carlo - approximation of the distribution integrating over the `kernel` and - `bias`. - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Args: - inputs: Tensor input. - @{args} - - Returns: - output: `Tensor` representing a the affine transformed input under a random - draw from the surrogate posterior distribution. - - #### Examples - - We illustrate a Bayesian neural network with [variational inference]( - https://en.wikipedia.org/wiki/Variational_Bayesian_methods), - assuming a dataset of `features` and `labels`. - - ```python - tfp = tf.contrib.bayesflow - - net = tfp.layers.dense_reparameterization( - features, 512, activation=tf.nn.relu) - logits = tfp.layers.dense_reparameterization(net, 10) - neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits( - labels=labels, logits=logits) - kl = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) - loss = neg_log_likelihood + kl - train_op = tf.train.AdamOptimizer().minimize(loss) - ``` - - It uses reparameterization gradients to minimize the - Kullback-Leibler divergence up to a constant, also known as the - negative Evidence Lower Bound. It consists of the sum of two terms: - the expected negative log-likelihood, which we approximate via - Monte Carlo; and the KL divergence, which is added via regularizer - terms which are arguments to the layer. - - [1]: "Auto-Encoding Variational Bayes." - Diederik P. Kingma, Max Welling. - International Conference on Learning Representations, 2014. - """ - # pylint: enable=g-doc-args - layer = DenseReparameterization( - units, - activation=activation, - activity_regularizer=activity_regularizer, - trainable=trainable, - kernel_posterior_fn=kernel_posterior_fn, - kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, - kernel_prior_fn=kernel_prior_fn, - kernel_divergence_fn=kernel_divergence_fn, - bias_posterior_fn=bias_posterior_fn, - bias_posterior_tensor_fn=bias_posterior_tensor_fn, - bias_prior_fn=bias_prior_fn, - bias_divergence_fn=bias_divergence_fn, - name=name, - dtype=inputs.dtype.base_dtype, - _scope=name, - _reuse=reuse) - return layer.apply(inputs) - - -class DenseLocalReparameterization(_DenseVariational): - """Densely-connected layer class with local reparameterization estimator. - - This layer implements the Bayesian variational inference analogue to - a dense layer by assuming the `kernel` and/or the `bias` are drawn - from distributions. By default, the layer implements a stochastic - forward pass via sampling from the kernel and bias posteriors, - - ```none - kernel, bias ~ posterior - outputs = activation(matmul(inputs, kernel) + bias) - ``` - - It uses the local reparameterization estimator [1], which performs a - Monte Carlo approximation of the distribution on the hidden units - induced by the `kernel` and `bias`. - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Properties: - units: Python integer, dimensionality of the output space. - activation: Activation function (`callable`). - activity_regularizer: Regularizer function for the output. - kernel_posterior_fn: `callable` returning posterior. - kernel_posterior_tensor_fn: `callable` operating on posterior. - kernel_prior_fn: `callable` returning prior. - kernel_divergence_fn: `callable` returning divergence. - bias_posterior_fn: `callable` returning posterior. - bias_posterior_tensor_fn: `callable` operating on posterior. - bias_prior_fn: `callable` returning prior. - bias_divergence_fn: `callable` returning divergence. - - #### Examples - - We illustrate a Bayesian neural network with [variational inference]( - https://en.wikipedia.org/wiki/Variational_Bayesian_methods), - assuming a dataset of `features` and `labels`. - - ```python - tfp = tf.contrib.bayesflow - - net = tfp.layers.DenseLocalReparameterization( - 512, activation=tf.nn.relu)(features) - logits = tfp.layers.DenseLocalReparameterization(10)(net) - neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits( - labels=labels, logits=logits) - kl = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) - loss = neg_log_likelihood + kl - train_op = tf.train.AdamOptimizer().minimize(loss) - ``` - - It uses local reparameterization gradients to minimize the - Kullback-Leibler divergence up to a constant, also known as the - negative Evidence Lower Bound. It consists of the sum of two terms: - the expected negative log-likelihood, which we approximate via - Monte Carlo; and the KL divergence, which is added via regularizer - terms which are arguments to the layer. - - [1]: "Variational Dropout and the Local Reparameterization Trick." - Diederik P. Kingma, Tim Salimans, Max Welling. - Neural Information Processing Systems, 2015. - """ - - @docstring_util.expand_docstring(args=doc_args) - def __init__( - self, - units, - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn( - is_singular=True), - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - name=None, - **kwargs): - # pylint: disable=g-doc-args - """Construct layer. - - Args: - @{args} - """ - # pylint: enable=g-doc-args - super(DenseLocalReparameterization, self).__init__( - units=units, - activation=activation, - activity_regularizer=activity_regularizer, - trainable=trainable, - kernel_posterior_fn=kernel_posterior_fn, - kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, - kernel_prior_fn=kernel_prior_fn, - kernel_divergence_fn=kernel_divergence_fn, - bias_posterior_fn=bias_posterior_fn, - bias_posterior_tensor_fn=bias_posterior_tensor_fn, - bias_prior_fn=bias_prior_fn, - bias_divergence_fn=bias_divergence_fn, - name=name, - **kwargs) - - def _apply_variational_kernel(self, inputs): - if (not isinstance(self.kernel_posterior, independent_lib.Independent) or - not isinstance(self.kernel_posterior.distribution, normal_lib.Normal)): - raise TypeError( - "`DenseLocalReparameterization` requires " - "`kernel_posterior_fn` produce an instance of " - "`tf.distributions.Independent(tf.distributions.Normal)` " - "(saw: \"{}\").".format(self.kernel_posterior.name)) - self.kernel_posterior_affine = normal_lib.Normal( - loc=self._matmul(inputs, self.kernel_posterior.distribution.loc), - scale=standard_ops.sqrt(self._matmul( - standard_ops.square(inputs), - standard_ops.square(self.kernel_posterior.distribution.scale)))) - self.kernel_posterior_affine_tensor = ( - self.kernel_posterior_tensor_fn(self.kernel_posterior_affine)) - self.kernel_posterior_tensor = None - return self.kernel_posterior_affine_tensor - - -@docstring_util.expand_docstring(args=doc_args) -def dense_local_reparameterization( - inputs, - units, - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn( - is_singular=True), - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - name=None, - reuse=None): - # pylint: disable=g-doc-args - """Densely-connected layer with local reparameterization estimator. - - This layer implements the Bayesian variational inference analogue to - a dense layer by assuming the `kernel` and/or the `bias` are drawn - from distributions. By default, the layer implements a stochastic - forward pass via sampling from the kernel and bias posteriors, - - ```none - kernel, bias ~ posterior - outputs = activation(matmul(inputs, kernel) + bias) - ``` - - It uses the local reparameterization estimator [1], which performs a - Monte Carlo approximation of the distribution on the hidden units - induced by the `kernel` and `bias`. - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Args: - inputs: Tensor input. - @{args} - - Returns: - output: `Tensor` representing a the affine transformed input under a random - draw from the surrogate posterior distribution. - - #### Examples - - We illustrate a Bayesian neural network with [variational inference]( - https://en.wikipedia.org/wiki/Variational_Bayesian_methods), - assuming a dataset of `features` and `labels`. - - ```python - tfp = tf.contrib.bayesflow - - net = tfp.layers.dense_local_reparameterization( - features, 512, activation=tf.nn.relu) - logits = tfp.layers.dense_local_reparameterization(net, 10) - neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits( - labels=labels, logits=logits) - kl = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) - loss = neg_log_likelihood + kl - train_op = tf.train.AdamOptimizer().minimize(loss) - ``` - - It uses local reparameterization gradients to minimize the - Kullback-Leibler divergence up to a constant, also known as the - negative Evidence Lower Bound. It consists of the sum of two terms: - the expected negative log-likelihood, which we approximate via - Monte Carlo; and the KL divergence, which is added via regularizer - terms which are arguments to the layer. - - [1]: "Variational Dropout and the Local Reparameterization Trick." - Diederik P. Kingma, Tim Salimans, Max Welling. - Neural Information Processing Systems, 2015. - """ - # pylint: enable=g-doc-args - layer = DenseLocalReparameterization( - units, - activation=activation, - activity_regularizer=activity_regularizer, - trainable=trainable, - kernel_posterior_fn=kernel_posterior_fn, - kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, - kernel_prior_fn=kernel_prior_fn, - kernel_divergence_fn=kernel_divergence_fn, - bias_posterior_fn=bias_posterior_fn, - bias_posterior_tensor_fn=bias_posterior_tensor_fn, - bias_prior_fn=bias_prior_fn, - bias_divergence_fn=bias_divergence_fn, - name=name, - dtype=inputs.dtype.base_dtype, - _scope=name, - _reuse=reuse) - return layer.apply(inputs) - - -class DenseFlipout(_DenseVariational): - """Densely-connected layer class with Flipout estimator. - - This layer implements the Bayesian variational inference analogue to - a dense layer by assuming the `kernel` and/or the `bias` are drawn - from distributions. By default, the layer implements a stochastic - forward pass via sampling from the kernel and bias posteriors, - - ```none - kernel, bias ~ posterior - outputs = activation(matmul(inputs, kernel) + bias) - ``` - - It uses the Flipout estimator [1], which performs a Monte Carlo - approximation of the distribution integrating over the `kernel` and - `bias`. Flipout uses roughly twice as many floating point operations - as the reparameterization estimator but has the advantage of - significantly lower variance. - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Properties: - units: Python integer, dimensionality of the output space. - activation: Activation function (`callable`). - activity_regularizer: Regularizer function for the output. - kernel_posterior_fn: `callable` returning posterior. - kernel_posterior_tensor_fn: `callable` operating on posterior. - kernel_prior_fn: `callable` returning prior. - kernel_divergence_fn: `callable` returning divergence. - bias_posterior_fn: `callable` returning posterior. - bias_posterior_tensor_fn: `callable` operating on posterior. - bias_prior_fn: `callable` returning prior. - bias_divergence_fn: `callable` returning divergence. - seed: Python integer, used to create random seeds. - - #### Examples - - We illustrate a Bayesian neural network with [variational inference]( - https://en.wikipedia.org/wiki/Variational_Bayesian_methods), - assuming a dataset of `features` and `labels`. - - ```python - tfp = tf.contrib.bayesflow - - net = tfp.layers.DenseFlipout( - 512, activation=tf.nn.relu)(features) - logits = tfp.layers.DenseFlipout(10)(net) - neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits( - labels=labels, logits=logits) - kl = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) - loss = neg_log_likelihood + kl - train_op = tf.train.AdamOptimizer().minimize(loss) - ``` - - It uses the Flipout gradient estimator to minimize the - Kullback-Leibler divergence up to a constant, also known as the - negative Evidence Lower Bound. It consists of the sum of two terms: - the expected negative log-likelihood, which we approximate via - Monte Carlo; and the KL divergence, which is added via regularizer - terms which are arguments to the layer. - - [1]: "Flipout: Efficient Pseudo-Independent Weight Perturbations on - Mini-Batches." - Anonymous. OpenReview, 2017. - https://openreview.net/forum?id=rJnpifWAb - """ - - @docstring_util.expand_docstring(args=doc_args) - def __init__( - self, - units, - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn( - is_singular=True), - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - seed=None, - name=None, - **kwargs): - # pylint: disable=g-doc-args - """Construct layer. - - Args: - @{args} - """ - # pylint: enable=g-doc-args - super(DenseFlipout, self).__init__( - units=units, - activation=activation, - activity_regularizer=activity_regularizer, - trainable=trainable, - kernel_posterior_fn=kernel_posterior_fn, - kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, - kernel_prior_fn=kernel_prior_fn, - kernel_divergence_fn=kernel_divergence_fn, - bias_posterior_fn=bias_posterior_fn, - bias_posterior_tensor_fn=bias_posterior_tensor_fn, - bias_prior_fn=bias_prior_fn, - bias_divergence_fn=bias_divergence_fn, - name=name, - **kwargs) - self.seed = seed - - def _apply_variational_kernel(self, inputs): - if (not isinstance(self.kernel_posterior, independent_lib.Independent) or - not isinstance(self.kernel_posterior.distribution, normal_lib.Normal)): - raise TypeError( - "`DenseFlipout` requires " - "`kernel_posterior_fn` produce an instance of " - "`tf.distributions.Independent(tf.distributions.Normal)` " - "(saw: \"{}\").".format(self.kernel_posterior.name)) - self.kernel_posterior_affine = normal_lib.Normal( - loc=array_ops.zeros_like(self.kernel_posterior.distribution.loc), - scale=self.kernel_posterior.distribution.scale) - self.kernel_posterior_affine_tensor = ( - self.kernel_posterior_tensor_fn(self.kernel_posterior_affine)) - self.kernel_posterior_tensor = None - - input_shape = array_ops.shape(inputs) - batch_shape = input_shape[:-1] - - sign_input = layers_util.random_sign( - input_shape, - dtype=inputs.dtype, - seed=self.seed) - sign_output = layers_util.random_sign( - array_ops.concat([batch_shape, - array_ops.expand_dims(self.units, 0)], 0), - dtype=inputs.dtype, - seed=distribution_util.gen_new_seed( - self.seed, salt="dense_flipout")) - perturbed_inputs = self._matmul( - inputs * sign_input, self.kernel_posterior_affine_tensor) * sign_output - - outputs = self._matmul(inputs, self.kernel_posterior.distribution.loc) - outputs += perturbed_inputs - return outputs - - -@docstring_util.expand_docstring(args=doc_args) -def dense_flipout( - inputs, - units, - activation=None, - activity_regularizer=None, - trainable=True, - kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), - kernel_posterior_tensor_fn=lambda d: d.sample(), - kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda - loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), - kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - bias_posterior_fn=layers_util.default_mean_field_normal_fn( - is_singular=True), - bias_posterior_tensor_fn=lambda d: d.sample(), - bias_prior_fn=None, - bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), - seed=None, - name=None, - reuse=None): - # pylint: disable=g-doc-args - """Densely-connected layer with Flipout estimator. - - This layer implements the Bayesian variational inference analogue to - a dense layer by assuming the `kernel` and/or the `bias` are drawn - from distributions. By default, the layer implements a stochastic - forward pass via sampling from the kernel and bias posteriors, - - ```none - kernel, bias ~ posterior - outputs = activation(matmul(inputs, kernel) + bias) - ``` - - It uses the Flipout estimator [1], which performs a Monte Carlo - approximation of the distribution integrating over the `kernel` and - `bias`. Flipout uses roughly twice as many floating point operations - as the reparameterization estimator but has the advantage of - significantly lower variance. - - The arguments permit separate specification of the surrogate posterior - (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` - distributions. - - Args: - inputs: Tensor input. - @{args} - - Returns: - output: `Tensor` representing a the affine transformed input under a random - draw from the surrogate posterior distribution. - - #### Examples - - We illustrate a Bayesian neural network with [variational inference]( - https://en.wikipedia.org/wiki/Variational_Bayesian_methods), - assuming a dataset of `features` and `labels`. - - ```python - tfp = tf.contrib.bayesflow - - net = tfp.layers.dense_flipout( - features, 512, activation=tf.nn.relu) - logits = tfp.layers.dense_flipout(net, 10) - neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits( - labels=labels, logits=logits) - kl = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) - loss = neg_log_likelihood + kl - train_op = tf.train.AdamOptimizer().minimize(loss) - ``` - - It uses the Flipout gradient estimator to minimize the - Kullback-Leibler divergence up to a constant, also known as the - negative Evidence Lower Bound. It consists of the sum of two terms: - the expected negative log-likelihood, which we approximate via - Monte Carlo; and the KL divergence, which is added via regularizer - terms which are arguments to the layer. - - [1]: "Flipout: Efficient Pseudo-Independent Weight Perturbations on - Mini-Batches." - Anonymous. OpenReview, 2017. - https://openreview.net/forum?id=rJnpifWAb - """ - # pylint: enable=g-doc-args - layer = DenseFlipout( - units, - activation=activation, - activity_regularizer=activity_regularizer, - trainable=trainable, - kernel_posterior_fn=kernel_posterior_fn, - kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, - kernel_prior_fn=kernel_prior_fn, - kernel_divergence_fn=kernel_divergence_fn, - bias_posterior_fn=bias_posterior_fn, - bias_posterior_tensor_fn=bias_posterior_tensor_fn, - bias_prior_fn=bias_prior_fn, - bias_divergence_fn=bias_divergence_fn, - seed=seed, - name=name, - dtype=inputs.dtype.base_dtype, - _scope=name, - _reuse=reuse) - return layer.apply(inputs) diff --git a/tensorflow/contrib/bayesflow/python/ops/layers_util.py b/tensorflow/contrib/bayesflow/python/ops/layers_util.py deleted file mode 100644 index 8c1fb203f7328e8260e49b4326d813fbe133613e..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/bayesflow/python/ops/layers_util.py +++ /dev/null @@ -1,191 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Utilities for probabilistic layers. -""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import numpy as np - -from tensorflow.contrib.distributions.python.ops import deterministic as deterministic_lib -from tensorflow.contrib.distributions.python.ops import independent as independent_lib -from tensorflow.python.framework import dtypes -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import init_ops -from tensorflow.python.ops import math_ops -from tensorflow.python.ops import nn_ops -from tensorflow.python.ops import random_ops -from tensorflow.python.ops.distributions import normal as normal_lib - - -def default_loc_scale_fn( - is_singular=False, - loc_initializer=init_ops.random_normal_initializer(stddev=0.1), - untransformed_scale_initializer=init_ops.random_normal_initializer( - mean=-3., stddev=0.1), - loc_regularizer=None, - untransformed_scale_regularizer=None, - loc_constraint=None, - untransformed_scale_constraint=None): - """Makes closure which creates `loc`, `scale` params from `tf.get_variable`. - - This function produces a closure which produces `loc`, `scale` using - `tf.get_variable`. The closure accepts the following arguments: - - dtype: Type of parameter's event. - shape: Python `list`-like representing the parameter's event shape. - name: Python `str` name prepended to any created (or existing) - `tf.Variable`s. - trainable: Python `bool` indicating all created `tf.Variable`s should be - added to the graph collection `GraphKeys.TRAINABLE_VARIABLES`. - add_variable_fn: `tf.get_variable`-like `callable` used to create (or - access existing) `tf.Variable`s. - - Args: - is_singular: Python `bool` indicating if `scale is None`. Default: `False`. - loc_initializer: Initializer function for the `loc` parameters. - The default is `tf.random_normal_initializer(mean=0., stddev=0.1)`. - untransformed_scale_initializer: Initializer function for the `scale` - parameters. Default value: `tf.random_normal_initializer(mean=-3., - stddev=0.1)`. This implies the softplus transformed result has mean - approximately `0.05` and std. deviation approximately `0.005`. - loc_regularizer: Regularizer function for the `loc` parameters. - The default (`None`) is to use the `tf.get_variable` default. - untransformed_scale_regularizer: Regularizer function for the `scale` - parameters. The default (`None`) is to use the `tf.get_variable` default. - loc_constraint: An optional projection function to be applied to the - loc after being updated by an `Optimizer`. The function must take as input - the unprojected variable and must return the projected variable (which - must have the same shape). Constraints are not safe to use when doing - asynchronous distributed training. - The default (`None`) is to use the `tf.get_variable` default. - untransformed_scale_constraint: An optional projection function to be - applied to the `scale` parameters after being updated by an `Optimizer` - (e.g. used to implement norm constraints or value constraints). The - function must take as input the unprojected variable and must return the - projected variable (which must have the same shape). Constraints are not - safe to use when doing asynchronous distributed training. The default - (`None`) is to use the `tf.get_variable` default. - - Returns: - default_loc_scale_fn: Python `callable` which instantiates `loc`, `scale` - parameters from args: `dtype, shape, name, trainable, add_variable_fn`. - """ - def _fn(dtype, shape, name, trainable, add_variable_fn): - """Creates `loc`, `scale` parameters.""" - loc = add_variable_fn( - name=name + "_loc", - shape=shape, - initializer=loc_initializer, - regularizer=loc_regularizer, - constraint=loc_constraint, - dtype=dtype, - trainable=trainable) - if is_singular: - return loc, None - untransformed_scale = add_variable_fn( - name=name + "_untransformed_scale", - shape=shape, - initializer=untransformed_scale_initializer, - regularizer=untransformed_scale_regularizer, - constraint=untransformed_scale_constraint, - dtype=dtype, - trainable=trainable) - scale = (np.finfo(dtype.as_numpy_dtype).eps + - nn_ops.softplus(untransformed_scale)) - return loc, scale - return _fn - - -def default_mean_field_normal_fn( - is_singular=False, - loc_initializer=None, - untransformed_scale_initializer=None, - loc_regularizer=None, - untransformed_scale_regularizer=None, - loc_constraint=None, - untransformed_scale_constraint=None): - """Creates a function to build Normal distributions with trainable params. - - This function produces a closure which produces `tf.distributions.Normal` - parameterized by a loc` and `scale` each created using `tf.get_variable`. The - produced closure accepts the following arguments: - - name: Python `str` name prepended to any created (or existing) - `tf.Variable`s. - shape: Python `list`-like representing the parameter's event shape. - dtype: Type of parameter's event. - trainable: Python `bool` indicating all created `tf.Variable`s should be - added to the graph collection `GraphKeys.TRAINABLE_VARIABLES`. - add_variable_fn: `tf.get_variable`-like `callable` used to create (or - access existing) `tf.Variable`s. - - Args: - is_singular: Python `bool` if `True`, forces the special case limit of - `scale->0`, i.e., a `Deterministic` distribution. - loc_initializer: Initializer function for the `loc` parameters. - If `None` (default), values are initialized using the default - initializer used by `tf.get_variable`. - untransformed_scale_initializer: Initializer function for the `scale` - parameters. If `None` (default), values are initialized using the default - initializer used by `tf.get_variable`. - loc_regularizer: Regularizer function for the `loc` parameters. - untransformed_scale_regularizer: Regularizer function for the `scale` - parameters. - loc_constraint: An optional projection function to be applied to the - loc after being updated by an `Optimizer`. The function must take as input - the unprojected variable and must return the projected variable (which - must have the same shape). Constraints are not safe to use when doing - asynchronous distributed training. - untransformed_scale_constraint: An optional projection function to be - applied to the `scale` parameters after being updated by an `Optimizer` - (e.g. used to implement norm constraints or value constraints). The - function must take as input the unprojected variable and must return the - projected variable (which must have the same shape). Constraints are not - safe to use when doing asynchronous distributed training. - - Returns: - make_normal_fn: Python `callable` which creates a `tf.distributions.Normal` - using from args: `dtype, shape, name, trainable, add_variable_fn`. - """ - loc_scale_fn_ = default_loc_scale_fn( - is_singular, - loc_initializer, - untransformed_scale_initializer, - loc_regularizer, - untransformed_scale_regularizer, - loc_constraint, - untransformed_scale_constraint) - def _fn(dtype, shape, name, trainable, add_variable_fn): - """Creates multivariate `Deterministic` or `Normal` distribution.""" - loc, scale = loc_scale_fn_(dtype, shape, name, trainable, add_variable_fn) - if scale is None: - dist = deterministic_lib.Deterministic(loc=loc) - else: - dist = normal_lib.Normal(loc=loc, scale=scale) - reinterpreted_batch_ndims = array_ops.shape(dist.batch_shape_tensor())[0] - return independent_lib.Independent( - dist, reinterpreted_batch_ndims=reinterpreted_batch_ndims) - return _fn - - -def random_sign(shape, dtype=dtypes.float32, seed=None): - """Draw values from {-1, 1} uniformly, i.e., Rademacher distribution.""" - random_bernoulli = random_ops.random_uniform(shape, minval=0, maxval=2, - dtype=dtypes.int32, - seed=seed) - return math_ops.cast(2 * random_bernoulli - 1, dtype) diff --git a/tensorflow/contrib/bayesflow/python/ops/mcmc_diagnostics.py b/tensorflow/contrib/bayesflow/python/ops/mcmc_diagnostics.py deleted file mode 100644 index f3a645eafc249d1c39e0d4a238ae7ec8755c78d8..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/bayesflow/python/ops/mcmc_diagnostics.py +++ /dev/null @@ -1,32 +0,0 @@ -# Copyright 2018 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Utilities for Markov Chain Monte Carlo (MCMC) sampling.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -# go/tf-wildcard-import -# pylint: disable=wildcard-import -from tensorflow.contrib.bayesflow.python.ops.mcmc_diagnostics_impl import * -# pylint: enable=wildcard-import -from tensorflow.python.util.all_util import remove_undocumented - -_allowed_symbols = [ - "effective_sample_size", - "potential_scale_reduction", -] - -remove_undocumented(__name__, _allowed_symbols) diff --git a/tensorflow/contrib/bayesflow/python/ops/mcmc_diagnostics_impl.py b/tensorflow/contrib/bayesflow/python/ops/mcmc_diagnostics_impl.py deleted file mode 100644 index 0424b6952bc89ce7fe5b00b0135c9a5fe1faa8cf..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/bayesflow/python/ops/mcmc_diagnostics_impl.py +++ /dev/null @@ -1,400 +0,0 @@ -# Copyright 2018 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Utilities for Markov Chain Monte Carlo (MCMC) sampling. - -@@effective_sample_size -@@potential_scale_reduction -""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from tensorflow.contrib.distributions.python.ops import sample_stats -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 - -__all__ = [ - "effective_sample_size", - "potential_scale_reduction", -] - - -def effective_sample_size(states, - filter_threshold=0., - filter_beyond_lag=None, - name=None): - """Estimate a lower bound on effective sample size for each independent chain. - - Roughly speaking, "effective sample size" (ESS) is the size of an iid sample - with the same variance as `state`. - - More precisely, given a stationary sequence of possibly correlated random - variables `X_1, X_2,...,X_N`, each identically distributed ESS is the number - such that - - ```Variance{ N**-1 * Sum{X_i} } = ESS**-1 * Variance{ X_1 }.``` - - If the sequence is uncorrelated, `ESS = N`. In general, one should expect - `ESS <= N`, with more highly correlated sequences having smaller `ESS`. - - #### Example of using ESS to estimate standard error. - - ``` - tfd = tf.contrib.distributions - tfb = tf.contrib.bayesflow - - target = tfd.MultivariateNormalDiag(scale_diag=[1., 2.]) - - # Get 1000 states from one chain. - states = tfb.hmc.sample_chain( - num_results=1000, - target_log_prob_fn=target.log_prob, - current_state=tf.constant([0., 0.]), - step_size=0.05, - num_leapfrog_steps=20, - num_burnin_steps=200) - states.shape - ==> (1000, 2) - - ess = effective_sample_size(states) - ==> Shape (2,) Tensor - - mean, variance = tf.nn.moments(states, axis=0) - standard_error = tf.sqrt(variance / ess) - ``` - - Some math shows that, with `R_k` the auto-correlation sequence, - `R_k := Covariance{X_1, X_{1+k}} / Variance{X_1}`, we have - - ```ESS(N) = N / [ 1 + 2 * ( (N - 1) / N * R_1 + ... + 1 / N * R_{N-1} ) ]``` - - This function estimates the above by first estimating the auto-correlation. - Since `R_k` must be estimated using only `N - k` samples, it becomes - progressively noisier for larger `k`. For this reason, the summation over - `R_k` should be truncated at some number `filter_beyond_lag < N`. Since many - MCMC methods generate chains where `R_k > 0`, a reasonable critera is to - truncate at the first index where the estimated auto-correlation becomes - negative. - - The arguments `filter_beyond_lag`, `filter_threshold` are filters intended to - remove noisy tail terms from `R_k`. They combine in an "OR" manner meaning - terms are removed if they were to be filtered under the `filter_beyond_lag` OR - `filter_threshold` criteria. - - Args: - states: `Tensor` or list of `Tensor` objects. Dimension zero should index - identically distributed states. - filter_threshold: `Tensor` or list of `Tensor` objects. - Must broadcast with `state`. The auto-correlation sequence is truncated - after the first appearance of a term less than `filter_threshold`. - Setting to `None` means we use no threshold filter. Since `|R_k| <= 1`, - setting to any number less than `-1` has the same effect. - filter_beyond_lag: `Tensor` or list of `Tensor` objects. Must be - `int`-like and scalar valued. The auto-correlation sequence is truncated - to this length. Setting to `None` means we do not filter based on number - of lags. - name: `String` name to prepend to created ops. - - Returns: - ess: `Tensor` or list of `Tensor` objects. The effective sample size of - each component of `states`. Shape will be `states.shape[1:]`. - - Raises: - ValueError: If `states` and `filter_threshold` or `states` and - `filter_beyond_lag` are both lists with different lengths. - """ - states_was_list = _is_list_like(states) - - # Convert all args to lists. - if not states_was_list: - states = [states] - - filter_beyond_lag = _broadcast_maybelist_arg(states, filter_beyond_lag, - "filter_beyond_lag") - filter_threshold = _broadcast_maybelist_arg(states, filter_threshold, - "filter_threshold") - - # Process items, one at a time. - with ops.name_scope(name, "effective_sample_size"): - ess_list = [ - _effective_sample_size_single_state(s, ml, mlt) - for (s, ml, mlt) in zip(states, filter_beyond_lag, filter_threshold) - ] - - if states_was_list: - return ess_list - return ess_list[0] - - -def _effective_sample_size_single_state(states, filter_beyond_lag, - filter_threshold): - """ESS computation for one single Tensor argument.""" - - with ops.name_scope( - "effective_sample_size_single_state", - values=[states, filter_beyond_lag, filter_threshold]): - - states = ops.convert_to_tensor(states, name="states") - dt = states.dtype - - # filter_beyond_lag == None ==> auto_corr is the full sequence. - auto_corr = sample_stats.auto_correlation( - states, axis=0, max_lags=filter_beyond_lag) - if filter_threshold is not None: - filter_threshold = ops.convert_to_tensor( - filter_threshold, dtype=dt, name="filter_threshold") - # Get a binary mask to zero out values of auto_corr below the threshold. - # mask[i, ...] = 1 if auto_corr[j, ...] > threshold for all j <= i, - # mask[i, ...] = 0, otherwise. - # So, along dimension zero, the mask will look like [1, 1, ..., 0, 0,...] - # Building step by step, - # Assume auto_corr = [1, 0.5, 0.0, 0.3], and filter_threshold = 0.2. - # Step 1: mask = [False, False, True, False] - mask = auto_corr < filter_threshold - # Step 2: mask = [0, 0, 1, 1] - mask = math_ops.cast(mask, dtype=dt) - # Step 3: mask = [0, 0, 1, 2] - mask = math_ops.cumsum(mask, axis=0) - # Step 4: mask = [1, 1, 0, 0] - mask = math_ops.maximum(1. - mask, 0.) - auto_corr *= mask - - # With R[k] := auto_corr[k, ...], - # ESS = N / {1 + 2 * Sum_{k=1}^N (N - k) / N * R[k]} - # = N / {-1 + 2 * Sum_{k=0}^N (N - k) / N * R[k]} (since R[0] = 1) - # approx N / {-1 + 2 * Sum_{k=0}^M (N - k) / N * R[k]} - # where M is the filter_beyond_lag truncation point chosen above. - - # Get the factor (N - k) / N, and give it shape [M, 1,...,1], having total - # ndims the same as auto_corr - n = _axis_size(states, axis=0) - k = math_ops.range(0., _axis_size(auto_corr, axis=0)) - nk_factor = (n - k) / n - if auto_corr.shape.ndims is not None: - new_shape = [-1] + [1] * (auto_corr.shape.ndims - 1) - else: - new_shape = array_ops.concat( - ([-1], - array_ops.ones([array_ops.rank(auto_corr) - 1], dtype=dtypes.int32)), - axis=0) - nk_factor = array_ops.reshape(nk_factor, new_shape) - - return n / (-1 + 2 * math_ops.reduce_sum(nk_factor * auto_corr, axis=0)) - - -def potential_scale_reduction(chains_states, - independent_chain_ndims=1, - name=None): - """Gelman and Rubin's potential scale reduction factor for chain convergence. - - Given `N > 1` states from each of `C > 1` independent chains, the potential - scale reduction factor, commonly referred to as R-hat, measures convergence of - the chains (to the same target) by testing for equality of means. - Specifically, R-hat measures the degree to which variance (of the means) - between chains exceeds what one would expect if the chains were identically - distributed. See [1], [2]. - - Some guidelines: - - * The initial state of the chains should be drawn from a distribution - overdispersed with respect to the target. - * If all chains converge to the target, then as `N --> infinity`, R-hat --> 1. - Before that, R-hat > 1 (except in pathological cases, e.g. if the chain - paths were identical). - * The above holds for any number of chains `C > 1`. Increasing `C` does - improves effectiveness of the diagnostic. - * Sometimes, R-hat < 1.2 is used to indicate approximate convergence, but of - course this is problem depedendent. See [2]. - * R-hat only measures non-convergence of the mean. If higher moments, or other - statistics are desired, a different diagnostic should be used. See [2]. - - #### Examples - - Diagnosing convergence by monitoring 10 chains that each attempt to - sample from a 2-variate normal. - - ```python - tfd = tf.contrib.distributions - tfb = tf.contrib.bayesflow - - target = tfd.MultivariateNormalDiag(scale_diag=[1., 2.]) - - # Get 10 (2x) overdispersed initial states. - initial_state = target.sample(10) * 2. - ==> (10, 2) - - # Get 1000 samples from the 10 independent chains. - chains_states, _ = tfb.hmc.sample_chain( - num_results=1000, - target_log_prob_fn=target.log_prob, - current_state=initial_state, - step_size=0.05, - num_leapfrog_steps=20, - num_burnin_steps=200) - chains_states.shape - ==> (1000, 10, 2) - - rhat = tfb.mcmc_diagnostics.potential_scale_reduction( - chains_states, independent_chain_ndims=1) - - # The second dimension needed a longer burn-in. - rhat.eval() - ==> [1.05, 1.3] - ``` - - To see why R-hat is reasonable, let `X` be a random variable drawn uniformly - from the combined states (combined over all chains). Then, in the limit - `N, C --> infinity`, with `E`, `Var` denoting expectation and variance, - - ```R-hat = ( E[Var[X | chain]] + Var[E[X | chain]] ) / E[Var[X | chain]].``` - - Using the law of total variance, the numerator is the variance of the combined - states, and the denominator is the total variance minus the variance of the - the individual chain means. If the chains are all drawing from the same - distribution, they will have the same mean, and thus the ratio should be one. - - [1] "Inference from Iterative Simulation Using Multiple Sequences" - Andrew Gelman and Donald B. Rubin - Statist. Sci. Volume 7, Number 4 (1992), 457-472. - [2] "General Methods for Monitoring Convergence of Iterative Simulations" - Stephen P. Brooks and Andrew Gelman - Journal of Computational and Graphical Statistics, 1998. Vol 7, No. 4. - - Args: - chains_states: `Tensor` or Python `list` of `Tensor`s representing the - state(s) of a Markov Chain at each result step. The `ith` state is - assumed to have shape `[Ni, Ci1, Ci2,...,CiD] + A`. - Dimension `0` indexes the `Ni > 1` result steps of the Markov Chain. - Dimensions `1` through `D` index the `Ci1 x ... x CiD` independent - chains to be tested for convergence to the same target. - The remaining dimensions, `A`, can have any shape (even empty). - independent_chain_ndims: Integer type `Tensor` with value `>= 1` giving the - number of giving the number of dimensions, from `dim = 1` to `dim = D`, - holding independent chain results to be tested for convergence. - name: `String` name to prepend to created ops. Default: - `potential_scale_reduction`. - - Returns: - `Tensor` or Python `list` of `Tensor`s representing the R-hat statistic for - the state(s). Same `dtype` as `state`, and shape equal to - `state.shape[1 + independent_chain_ndims:]`. - - Raises: - ValueError: If `independent_chain_ndims < 1`. - """ - chains_states_was_list = _is_list_like(chains_states) - if not chains_states_was_list: - chains_states = [chains_states] - - # tensor_util.constant_value returns None iff a constant value (as a numpy - # array) is not efficiently computable. Therefore, we try constant_value then - # check for None. - icn_const_ = tensor_util.constant_value( - ops.convert_to_tensor(independent_chain_ndims)) - if icn_const_ is not None: - independent_chain_ndims = icn_const_ - if icn_const_ < 1: - raise ValueError( - "Argument `independent_chain_ndims` must be `>= 1`, found: {}".format( - independent_chain_ndims)) - - with ops.name_scope(name, "potential_scale_reduction"): - rhat_list = [ - _potential_scale_reduction_single_state(s, independent_chain_ndims) - for s in chains_states - ] - - if chains_states_was_list: - return rhat_list - return rhat_list[0] - - -def _potential_scale_reduction_single_state(state, independent_chain_ndims): - """potential_scale_reduction for one single state `Tensor`.""" - with ops.name_scope( - "potential_scale_reduction_single_state", - values=[state, independent_chain_ndims]): - # We assume exactly one leading dimension indexes e.g. correlated samples - # from each Markov chain. - state = ops.convert_to_tensor(state, name="state") - sample_ndims = 1 - - sample_axis = math_ops.range(0, sample_ndims) - chain_axis = math_ops.range(sample_ndims, - sample_ndims + independent_chain_ndims) - sample_and_chain_axis = math_ops.range( - 0, sample_ndims + independent_chain_ndims) - - n = _axis_size(state, sample_axis) - m = _axis_size(state, chain_axis) - - # In the language of [2], - # B / n is the between chain variance, the variance of the chain means. - # W is the within sequence variance, the mean of the chain variances. - b_div_n = _reduce_variance( - math_ops.reduce_mean(state, sample_axis, keepdims=True), - sample_and_chain_axis, - biased=False) - w = math_ops.reduce_mean( - _reduce_variance(state, sample_axis, keepdims=True, biased=True), - sample_and_chain_axis) - - # sigma^2_+ is an estimate of the true variance, which would be unbiased if - # each chain was drawn from the target. c.f. "law of total variance." - sigma_2_plus = w + b_div_n - - return ((m + 1.) / m) * sigma_2_plus / w - (n - 1.) / (m * n) - - -# TODO(b/72873233) Move some variant of this to sample_stats. -def _reduce_variance(x, axis=None, biased=True, keepdims=False): - with ops.name_scope("reduce_variance"): - x = ops.convert_to_tensor(x, name="x") - mean = math_ops.reduce_mean(x, axis=axis, keepdims=True) - biased_var = math_ops.reduce_mean( - math_ops.squared_difference(x, mean), axis=axis, keepdims=keepdims) - if biased: - return biased_var - n = _axis_size(x, axis) - return (n / (n - 1.)) * biased_var - - -def _axis_size(x, axis=None): - """Get number of elements of `x` in `axis`, as type `x.dtype`.""" - if axis is None: - return math_ops.cast(array_ops.size(x), x.dtype) - return math_ops.cast( - math_ops.reduce_prod(array_ops.gather(array_ops.shape(x), axis)), x.dtype) - - -def _is_list_like(x): - """Helper which returns `True` if input is `list`-like.""" - return isinstance(x, (tuple, list)) - - -def _broadcast_maybelist_arg(states, secondary_arg, name): - """Broadcast a listable secondary_arg to that of states.""" - if _is_list_like(secondary_arg): - if len(secondary_arg) != len(states): - raise ValueError("Argument `%s` was a list of different length ({}) than " - "`states` ({})".format(name, len(states))) - else: - secondary_arg = [secondary_arg] * len(states) - - return secondary_arg diff --git a/tensorflow/contrib/bayesflow/python/ops/sgld_optimizer.py b/tensorflow/contrib/bayesflow/python/ops/sgld_optimizer.py deleted file mode 100644 index 7786656398e3c87704227be95b3cd23a38785249..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/bayesflow/python/ops/sgld_optimizer.py +++ /dev/null @@ -1,220 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""An optimizer module for stochastic gradient Langevin dynamics.""" -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 check_ops -from tensorflow.python.ops import control_flow_ops -from tensorflow.python.ops import init_ops -from tensorflow.python.ops import math_ops -from tensorflow.python.ops import random_ops -from tensorflow.python.ops import variable_scope as varscope_ops -from tensorflow.python.training import optimizer -from tensorflow.python.training import training_ops - - -class SGLDOptimizer(optimizer.Optimizer): - """An optimizer module for stochastic gradient Langevin dynamics. - - This implements the preconditioned Stochastic Gradient Langevin Dynamics - optimizer [1]. The optimization variable is regarded as a sample from the - posterior under Stochastic Gradient Langevin Dynamics with noise rescaled in - each dimension according to RMSProp [2]. - - Note: If a prior is included in the loss, it should be scaled by - `1/num_pseudo_batches`, where num_pseudo_batches is the number of minibatches - in the data. I.e., it should be divided by the `num_pseudo_batches` term - described below. - - [1]: "Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural - Networks." Chunyuan Li, Changyou Chen, David Carlson, Lawrence Carin. - ArXiv:1512.07666, 2015. https://arxiv.org/abs/1512.07666 - [2]: http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf - - Args: - learning_rate: Scalar `float`-like `Tensor`. The base learning rate for the - optimizer. Must be tuned to the specific function being minimized. - preconditioner_decay_rate: Scalar `float`-like `Tensor`. The exponential - decay rate of the rescaling of the preconditioner (RMSprop). (This is - "alpha" in [1]). Should be smaller than but nearly `1` to approximate - sampling from the posterior. (Default: `0.95`) - num_pseudo_batches: Scalar `int`-like `Tensor`. The effective number of - minibatches in the data set. Trades off noise and prior with the SGD - likelihood term. Note: Assumes the loss is taken as the mean over a - minibatch. Otherwise if the sum was taken, divide this number by the - batch size. (Default: `1`) - burnin: Scalar `int`-like `Tensor`. The number of iterations to collect - gradient statistics to update the preconditioner before starting to draw - noisy samples. (Default: `25`) - diagonal_bias: Scalar `float`-like `Tensor`. Term added to the diagonal of - the preconditioner to prevent the preconditioner from degenerating. - (Default: `1e-8`) - name: Python `str` describing ops managed by this function. - (Default: `"SGLDOptimizer"`) - variable_scope: Variable scope used for calls to `tf.get_variable`. - If `None`, a new variable scope is created using name - `ops.get_default_graph().unique_name(name or default_name)`. - - Raises: - InvalidArgumentError: If preconditioner_decay_rate is a `Tensor` not in - `(0,1]`. - """ - - def __init__(self, - learning_rate, - preconditioner_decay_rate=0.95, - num_pseudo_batches=1, - burnin=25, - diagonal_bias=1e-8, - name=None, - variable_scope=None): - default_name = 'SGLDOptimizer' - with ops.name_scope(name, default_name, [ - learning_rate, preconditioner_decay_rate, num_pseudo_batches, burnin, - diagonal_bias - ]): - if variable_scope is None: - var_scope_name = ops.get_default_graph().unique_name( - name or default_name) - with varscope_ops.variable_scope(var_scope_name) as scope: - self._variable_scope = scope - else: - self._variable_scope = variable_scope - - self._preconditioner_decay_rate = ops.convert_to_tensor( - preconditioner_decay_rate, name='preconditioner_decay_rate') - self._num_pseudo_batches = ops.convert_to_tensor( - num_pseudo_batches, name='num_pseudo_batches') - self._burnin = ops.convert_to_tensor(burnin, name='burnin') - self._diagonal_bias = ops.convert_to_tensor( - diagonal_bias, name='diagonal_bias') - self._learning_rate = ops.convert_to_tensor( - learning_rate, name='learning_rate') - - with varscope_ops.variable_scope(self._variable_scope): - self._counter = varscope_ops.get_variable( - 'counter', initializer=0, trainable=False) - - self._preconditioner_decay_rate = control_flow_ops.with_dependencies([ - check_ops.assert_non_negative( - self._preconditioner_decay_rate, - message='`preconditioner_decay_rate` must be non-negative'), - check_ops.assert_less_equal( - self._preconditioner_decay_rate, - 1., - message='`preconditioner_decay_rate` must be at most 1.'), - ], self._preconditioner_decay_rate) - - self._num_pseudo_batches = control_flow_ops.with_dependencies([ - check_ops.assert_greater( - self._num_pseudo_batches, - 0, - message='`num_pseudo_batches` must be greater than zero') - ], self._num_pseudo_batches) - - self._burnin = control_flow_ops.with_dependencies([ - check_ops.assert_non_negative( - self._burnin, message='`burnin` must be non-negative'), - check_ops.assert_integer( - self._burnin, message='`burnin` must be an integer') - ], self._burnin) - - self._diagonal_bias = control_flow_ops.with_dependencies([ - check_ops.assert_non_negative( - self._diagonal_bias, - message='`diagonal_bias` must be non-negative') - ], self._diagonal_bias) - - super(SGLDOptimizer, self).__init__(use_locking=False, - name=name or default_name) - - def _create_slots(self, var_list): - for v in var_list: - init_rms = init_ops.ones_initializer(dtype=v.dtype) - self._get_or_make_slot_with_initializer(v, init_rms, v.get_shape(), - v.dtype, 'rms', self._name) - - def _prepare(self): - # We need to put the conversion and check here because a user will likely - # want to decay the learning rate dynamically. - self._learning_rate_tensor = control_flow_ops.with_dependencies([ - check_ops.assert_non_negative( - self._learning_rate, message='`learning_rate` must be non-negative') - ], ops.convert_to_tensor(self._learning_rate, name='learning_rate_tensor')) - self._decay_tensor = ops.convert_to_tensor( - self._preconditioner_decay_rate, name='preconditioner_decay_rate') - - super(SGLDOptimizer, self)._prepare() - - def _apply_dense(self, grad, var): - rms = self.get_slot(var, 'rms') - - with ops.control_dependencies([ - self._update_momentum(rms, grad, math_ops.cast(self._decay_tensor, - var.dtype.base_dtype))]): - new_grad = self._apply_noisy_update(rms, grad) - - return training_ops.apply_gradient_descent( - var, - math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype), - new_grad, - use_locking=self._use_locking).op - - def _apply_sparse(self, grad, var): - rms = self.get_slot(var, 'rms') - - with ops.control_dependencies([ - self._update_momentum(rms, grad, math_ops.cast(self._decay_tensor, - var.dtype.base_dtype))]): - new_grad = self._apply_noisy_update(rms, grad) - - return training_ops.apply_gradient_descent( - var, - math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype), - new_grad, - use_locking=self._use_locking).op - - def _finish(self, update_ops, name_scope): - update_ops.append([self._counter.assign_add(1)]) - return control_flow_ops.group(*update_ops, name=name_scope) - - @property - def variable_scope(self): - """Variable scope of all calls to `tf.get_variable`.""" - return self._variable_scope - - def _apply_noisy_update(self, mom, grad): - # Compute and apply the gradient update following - # preconditioned Langevin dynamics - stddev = array_ops.where( - array_ops.squeeze(self._counter > self._burnin), - math_ops.cast(math_ops.rsqrt(self._learning_rate), grad.dtype), - array_ops.zeros([], grad.dtype)) - - preconditioner = math_ops.rsqrt( - mom + math_ops.cast(self._diagonal_bias, grad.dtype)) - return ( - 0.5 * preconditioner * grad * math_ops.cast(self._num_pseudo_batches, - grad.dtype) + - random_ops.random_normal(array_ops.shape(grad), 1.0, dtype=grad.dtype) * - stddev * math_ops.sqrt(preconditioner)) - - def _update_momentum(self, mom, grad, decay): - # Keep an exponentially weighted moving average of squared gradients. - # Not thread safe - return mom.assign_add((1.0 - decay) * (math_ops.square(grad) - mom)) diff --git a/tensorflow/contrib/bayesflow/python/ops/variable_utils_impl.py b/tensorflow/contrib/bayesflow/python/ops/variable_utils_impl.py deleted file mode 100644 index ca3d75b5bfee093449026c7d1d62e3bdeff6b096..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/bayesflow/python/ops/variable_utils_impl.py +++ /dev/null @@ -1,157 +0,0 @@ -# Copyright 2018 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Utility functions related to managing `tf.Variable`s. - -@@externalize_variables_as_args -""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import warnings - -from tensorflow.python.framework import ops -from tensorflow.python.ops import gradients_impl as gradients_ops -from tensorflow.python.ops import variable_scope as varscope_ops -from tensorflow.python.ops import variables as variables_ops - -__all__ = [ - "externalize_variables_as_args", -] - - -# Cause all warnings to always be triggered. -# Not having this means subsequent calls wont trigger the warning. -warnings.simplefilter("always") - - -def externalize_variables_as_args(fn, - fn_args=(), - ancestor_variables=None, - possible_ancestor_vars=None, - assert_variable_override=False, - name=None): - """"Converts variables within a callable into explicit args. - - Makes a new callable from `fn` which has arguments `list(fn_args) + - list(ancestor_variables)`. If `ancestor_variables` is not specified, it is - inferred by checking which of `possible_ancestor_vars` actually influences the - return value of `fn` (concretely, gradient of `fn(*fn_args)` is not `None`). - By default `possible_ancestor_vars` is `tf.trainable_variables() + - tf.get_collection(tf.GraphKeys.TRAINABLE_RESOURCE_VARIABLES)`. - - #### Examples: - - ```python - num_samples = 2 - num_dims = 1 - dtype = np.float32 - - def foo(x): - x = tf.convert_to_tensor(x, dtype=dtype, name="x") - s = x.shape.as_list() - y = tf.get_variable( - name="y", - dtype=dtype, - initializer=np.arange(np.prod(s)).reshape(s).astype(dtype)) - return x + y - - x = tf.constant(dtype([0.1, 0.2])) - - wrapped_foo, discovered_ancestor_variables = ( - externalize_variables_as_args(foo, [x])) - - new_x = dtype([[1.], [2.]]) - new_y = dtype([[3.], [4.]]) - new_result = wrapped_foo(new_x, new_y) - # ==> [[4.], [6.]] - - discovered_ancestor_variables == [tf.get_variable("y", dtype)] - # ==> [True] - ``` - - Args: - fn: Python callable which returns a `Tensor` and accepts `*fn_args`. - fn_args: Python list of args to `fn`. Represents dummy arguments passed to - `fn` to trace its execution; actual values are unimportant. These args are - only used to construct the output of `fn` and to resolve the ancestor - `tf.Variable`s. - Default value: `()` (i.e., `fn` takes no args). - ancestor_variables: Python list of `tf.Variable`s. When `None` the list is - expanded to non-`None` gradients of `fn(*fn_args)`. By directly providing - the `ancestor_variables` the internal call to `fn` is avoided. - Default value: `None` (i.e., `tf.Variable` dependencies are discovered). - possible_ancestor_vars: Python list of possible `tf.Variable`s which might - be a dependency of computing `fn(*fn_args)`. - Default value: `None` (i.e., expanded as described above). - assert_variable_override: Python `bool` indicating that not finding a - `tf.Variable` in the override list is an exception. - Default value: `False` (i.e., missing a `Variable` triggers a `warning`). - name: Python `str` name prefixed to Ops created by this function. - Default value: `None` (i.e., "externalize_variables_as_args"). - - Returns: - wrapped_fn: Python callable taking arguments like - `*(list(fn_args) + discovered_ancestor_variables)`. - discovered_ancestor_variables: Python list of `tf.Variable`s known to be a - dependency of `fn(*fn_args)`. - - Raises: - ValueError: if `assert_variable_override` is `True` and `Variable` is - requested but not overridden. - """ - def _make_bypassing_custom_getter_fn(new_var_dict): - """Return dict value rather than what would otherwise be dict key.""" - def _custom_getter(getter, *args, **kwargs): - v = getter(*args, **kwargs) - new_v = new_var_dict.get(v, None) - if new_v is None: - msg = "Variable \"{}\" not found in bypass dict.".format(v) - if assert_variable_override: - raise ValueError(msg) - warnings.warn(msg) - return v - return new_v - return _custom_getter - - with ops.name_scope(name, "externalize_variables_as_args"): - if ancestor_variables is not None and not ancestor_variables: - return fn, () - if ancestor_variables is None: - y = fn(*fn_args) # Side-effect: adds trainable vars. - if possible_ancestor_vars is None: - possible_ancestor_vars = ( - variables_ops.trainable_variables() + - ops.get_collection(ops.GraphKeys.TRAINABLE_RESOURCE_VARIABLES)) - # TODO(b/72873296): Add a dedicated op for identifying ancestors. - ancestors = [v for g, v - in zip(gradients_ops.gradients(y, possible_ancestor_vars), - possible_ancestor_vars) - if g is not None] - ancestor_variables = sorted(ancestors, key=lambda v: v.name) - n = len(fn_args) - def _fn(*args): - with ops.name_scope("wrapped_fn"): - vars_dict = dict( - (k, ops.convert_to_tensor( - v, dtype=k.dtype.base_dtype, name=k.op.name)) - for k, v in zip(ancestor_variables, args[n:])) - with varscope_ops.variable_scope( - varscope_ops.get_variable_scope(), - reuse=True, - custom_getter=_make_bypassing_custom_getter_fn(vars_dict)): - return fn(*args[:n]) - return _fn, ancestor_variables diff --git a/tensorflow/contrib/bayesflow/python/ops/variational_sgd_optimizer.py b/tensorflow/contrib/bayesflow/python/ops/variational_sgd_optimizer.py deleted file mode 100644 index 4d5f0cfe9713a011b32c5aba8d429847d81f33e2..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/bayesflow/python/ops/variational_sgd_optimizer.py +++ /dev/null @@ -1,279 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""An optimizer module for constant stochastic gradient descent.""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function -from tensorflow.python.framework import errors -from tensorflow.python.framework import ops -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import check_ops -from tensorflow.python.ops import clip_ops -from tensorflow.python.ops import control_flow_ops -from tensorflow.python.ops import init_ops -from tensorflow.python.ops import math_ops -from tensorflow.python.ops import state_ops -from tensorflow.python.ops import variable_scope as varscope_ops -from tensorflow.python.training import optimizer -from tensorflow.python.training import training_ops - - -class VariationalSGDOptimizer(optimizer.Optimizer): - """An optimizer module for constant stochastic gradient descent. - - This implements an optimizer module for the constant stochastic gradient - descent algorithm [1]. The optimization variable is regarded as an - approximate sample from the posterior . - - Note: If a prior is included in the loss, it should be scaled by - `1/num_pseudo_batches`, where num_pseudo_batches is the number of minibatches - in the data. I.e., it should be divided by the `num_pseudo_batches` term - described below. - - [1]: "Stochastic Gradient Descent as Approximate Bayesian Inference - Stephan Mandt, Matthew D. Hoffman, David M. Blei. - ArXiv:1704.04289, 2017. https://arxiv.org/abs/1704.04289 - - Args: - batch_size: Scalar `int`-like `Tensor`. The number of examples in a - minibatch in the data set. Note: Assumes the loss is taken as the mean - over a minibatch. Otherwise if the sum was taken set this to 1. - total_num_examples: Scalar `int`-like `Tensor`. The total number of examples - in the data set. - max_learning_rate: Scalar `float`-like `Tensor`. A maximum allowable - effective coordinate-wise learning rate. The algorithm scales down any - effective learning rate (i.e. after preconditioning) that is larger than - this. (Default: `1`) - preconditioner_decay_rate: Scalar `float`-like `Tensor`. The exponential - decay rate of the rescaling of the preconditioner (RMSprop). (This is - "alpha" in [1]). Should be smaller than but nearly `1` to approximate - sampling from the posterior. (Default: `0.95`) - burnin: Scalar `int`-like `Tensor`. The number of iterations to collect - gradient statistics to update the preconditioner before starting to draw - noisy samples. (Default: `25`) - burnin_max_learning_rate: Scalar `float`-like `Tensor`. Maximum learning - rate to use during the burnin period. - (Default: `1e-8`) - use_single_learning_rate: Boolean Indicates whether one single learning - rate is used or coordinate_wise learning rates are used. - (Default: `False`) - name: Python `str` describing ops managed by this function. - (Default: `"VariationalSGDOptimizer"`) - variable_scope: Variable scope used for calls to `tf.get_variable`. - If `None`, a new variable scope is created using name - `ops.get_default_graph().unique_name(name or default_name)`. - - Raises: - InvalidArgumentError: If preconditioner_decay_rate is a `Tensor` not in - `(0,1]`. - """ - - def __init__(self, - batch_size, - total_num_examples, - max_learning_rate=1.0, - preconditioner_decay_rate=0.95, - burnin=25, - burnin_max_learning_rate=1e-6, - use_single_learning_rate=False, - name=None, - variable_scope=None): - default_name = 'VariationalSGDOptimizer' - with ops.name_scope(name, default_name, [ - max_learning_rate, preconditioner_decay_rate, batch_size, burnin, - burnin_max_learning_rate - ]): - if variable_scope is None: - var_scope_name = ops.get_default_graph().unique_name( - name or default_name) - with varscope_ops.variable_scope(var_scope_name) as scope: - self._variable_scope = scope - else: - self._variable_scope = variable_scope - - self._preconditioner_decay_rate = ops.convert_to_tensor( - preconditioner_decay_rate, name='preconditioner_decay_rate') - self._batch_size = ops.convert_to_tensor(batch_size, name='batch_size') - self._total_num_examples = ops.convert_to_tensor( - total_num_examples, name='total_num_examples') - self._burnin = ops.convert_to_tensor(burnin, name='burnin') - self._burnin_max_learning_rate = ops.convert_to_tensor( - burnin_max_learning_rate, name='burnin_max_learning_rate') - self._max_learning_rate = ops.convert_to_tensor( - max_learning_rate, name='max_learning_rate') - self._use_single_learning_rate = use_single_learning_rate - - with varscope_ops.variable_scope(self._variable_scope): - self._counter = varscope_ops.get_variable( - 'counter', initializer=0, trainable=False) - - self._preconditioner_decay_rate = control_flow_ops.with_dependencies([ - check_ops.assert_non_negative( - self._preconditioner_decay_rate, - message='`preconditioner_decay_rate` must be non-negative'), - check_ops.assert_less_equal( - self._preconditioner_decay_rate, - 1., - message='`preconditioner_decay_rate` must be at most 1.'), - ], self._preconditioner_decay_rate) - - self._batch_size = control_flow_ops.with_dependencies([ - check_ops.assert_greater( - self._batch_size, - 0, - message='`batch_size` must be greater than zero') - ], self._batch_size) - - self._total_num_examples = control_flow_ops.with_dependencies([ - check_ops.assert_greater( - self._total_num_examples, - 0, - message='`total_num_examples` must be greater than zero') - ], self._total_num_examples) - - self._burnin = control_flow_ops.with_dependencies([ - check_ops.assert_non_negative( - self._burnin, message='`burnin` must be non-negative'), - check_ops.assert_integer( - self._burnin, message='`burnin` must be an integer') - ], self._burnin) - - self._burnin_max_learning_rate = control_flow_ops.with_dependencies([ - check_ops.assert_non_negative( - self._burnin_max_learning_rate, - message='`burnin_max_learning_rate` must be non-negative') - ], self._burnin_max_learning_rate) - - self._max_learning_rate = control_flow_ops.with_dependencies([ - check_ops.assert_non_negative( - self._max_learning_rate, - message='`max_learning_rate` must be non-negative') - ], self._max_learning_rate) - - super(VariationalSGDOptimizer, self).__init__( - use_locking=False, name=name or default_name) - - def _create_slots(self, var_list): - for v in var_list: - init_moment = init_ops.zeros_initializer(dtype=v.dtype) - self._get_or_make_slot_with_initializer( - v, init_moment, v.get_shape(), v.dtype, 'first_moment', self._name) - self._get_or_make_slot_with_initializer( - v, init_moment, v.get_shape(), v.dtype, 'second_moment', self._name) - - def _prepare(self): - self._decay_tensor = ops.convert_to_tensor( - self._preconditioner_decay_rate, name='preconditioner_decay_rate') - self._batch_size_tensor = ops.convert_to_tensor( - self._batch_size, name='batch_size_tensor') - - super(VariationalSGDOptimizer, self)._prepare() - - def _get_coordinatewise_learning_rate(self, grad, var): - # Compute the learning rate using a moving average for the diagonal of BB^T - avg_first = self.get_slot(var, 'first_moment') - avg_second = self.get_slot(var, 'second_moment') - decay_tensor = math_ops.cast(self._decay_tensor, var.dtype) - batch_size = math_ops.cast(self._batch_size_tensor, var.dtype) - - # Create an estimator for the moving average of gradient mean and variance - # via Welford's algorithm - if isinstance(grad, ops.Tensor): - delta = grad - avg_first - first_moment_update = avg_first.assign_add( - array_ops.where(self._counter < 1, math_ops.cast(1, var.dtype), - 1. - decay_tensor) * delta) - - with ops.control_dependencies([first_moment_update]): - second_moment_update = avg_second.assign_add( - math_ops.cast(self._counter < 1, var.dtype) * - -(1. - decay_tensor) * ( - avg_second - decay_tensor * math_ops.square(delta))) - diag_preconditioner = control_flow_ops.with_dependencies( - [second_moment_update], - clip_ops.clip_by_value(avg_second, 1e-12, 1e12)) - elif isinstance(grad, ops.IndexedSlices): - delta = grad.values - array_ops.gather_nd(avg_first, grad.indices) - first_moment_update = state_ops.scatter_add( - avg_first, - grad.indices, - array_ops.where(self._counter < 1, - math_ops.cast(1., var.dtype), - 1. - decay_tensor) * delta) - - with ops.control_dependencies([first_moment_update]): - avg_second = state_ops.scatter_add( - avg_second, - grad.indices, - math_ops.cast(self._counter < 1, var.dtype) * - -(1. - decay_tensor) * ( - array_ops.gather_nd(avg_second, grad.indices) - decay_tensor * - math_ops.square(delta))) - avg_second = array_ops.gather_nd(avg_second, grad.indices) - # TODO(b/70783772) - diag_preconditioner = clip_ops.clip_by_value(avg_second, 1e-12, 1e12) - else: - raise errors.InvalidArgumentError( - None, None, 'grad must of type Tensor or IndexedSlice') - - diag_preconditioner *= batch_size - - if self._use_single_learning_rate: - diag_preconditioner = math_ops.reduce_mean(diag_preconditioner) - - # From Theorem 2 Corollary 1 of Mandt et al. 2017 - return 2. * batch_size / ( - math_ops.cast(self._total_num_examples, var.dtype.base_dtype) * - diag_preconditioner) - - def _apply_dense(self, grad, var): - - max_learning_rate = array_ops.where(self._counter < self._burnin, - self._burnin_max_learning_rate, - self._max_learning_rate) - - learn_rates = clip_ops.clip_by_value( - self._get_coordinatewise_learning_rate(grad, var), 0.0, - math_ops.cast(max_learning_rate, var.dtype.base_dtype)) - - newgrad = grad * learn_rates - return training_ops.apply_gradient_descent( - var, - math_ops.cast(1.0, var.dtype), - newgrad, - use_locking=self._use_locking).op - - def _apply_sparse(self, grad, var): - - max_learning_rate = array_ops.where(self._counter < self._burnin, - self._burnin_max_learning_rate, - self._max_learning_rate) - - learn_rate = clip_ops.clip_by_value( - self._get_coordinatewise_learning_rate(grad, var), 0.0, - math_ops.cast(max_learning_rate, var.dtype)) - delta = grad.values * learn_rate - - return state_ops.scatter_sub(var, grad.indices, delta, - use_locking=self._use_locking) - - def _finish(self, update_ops, name_scope): - update_ops.append([self._counter.assign_add(1)]) - return control_flow_ops.group(*update_ops, name=name_scope) - - @property - def variable_scope(self): - """Variable scope of all calls to `tf.get_variable`.""" - return self._variable_scope diff --git a/tensorflow/contrib/boosted_trees/kernels/model_ops.cc b/tensorflow/contrib/boosted_trees/kernels/model_ops.cc index 754b7bc3270d647fc381033b769eadd7b791771e..3bf33186ec13f5ff991db938d59849c0124a30a0 100644 --- a/tensorflow/contrib/boosted_trees/kernels/model_ops.cc +++ b/tensorflow/contrib/boosted_trees/kernels/model_ops.cc @@ -137,6 +137,61 @@ class TreeEnsembleDeserializeOp : public OpKernel { } }; +class TreeEnsembleUsedHandlersOp : public OpKernel { + public: + explicit TreeEnsembleUsedHandlersOp(OpKernelConstruction* context) + : OpKernel(context) { + OP_REQUIRES_OK(context, + context->GetAttr("num_all_handlers", &num_handlers_)); + } + + void Compute(OpKernelContext* context) override { + boosted_trees::models::DecisionTreeEnsembleResource* ensemble_resource; + + OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0), + &ensemble_resource)); + tf_shared_lock l(*ensemble_resource->get_mutex()); + core::ScopedUnref unref_me(ensemble_resource); + + // Get the stamp token. + const Tensor* stamp_token_t; + OP_REQUIRES_OK(context, context->input("stamp_token", &stamp_token_t)); + int64 stamp_token = stamp_token_t->scalar()(); + + // Only the Chief should run this Op and it is guaranteed to be in + // a consistent state so the stamps must always match. + CHECK(ensemble_resource->is_stamp_valid(stamp_token)); + + Tensor* output_used_handlers_t = nullptr; + OP_REQUIRES_OK( + context, context->allocate_output("used_handlers_mask", {num_handlers_}, + &output_used_handlers_t)); + auto output_used_handlers = output_used_handlers_t->vec(); + + Tensor* output_num_used_handlers_t = nullptr; + OP_REQUIRES_OK(context, + context->allocate_output("num_used_handlers", {}, + &output_num_used_handlers_t)); + int handler_idx = 0; + std::vector used_handlers = ensemble_resource->GetUsedHandlers(); + output_num_used_handlers_t->scalar()() = used_handlers.size(); + for (int64 i = 0; i < num_handlers_; ++i) { + if (handler_idx >= used_handlers.size() || + used_handlers[handler_idx] > i) { + output_used_handlers(i) = false; + } else { + OP_REQUIRES(context, used_handlers[handler_idx] == i, + errors::InvalidArgument("Handler IDs should be sorted.")); + ++handler_idx; + output_used_handlers(i) = true; + } + } + } + + private: + int64 num_handlers_; +}; + REGISTER_RESOURCE_HANDLE_KERNEL(DecisionTreeEnsembleResource); REGISTER_KERNEL_BUILDER( @@ -155,5 +210,7 @@ REGISTER_KERNEL_BUILDER(Name("TreeEnsembleSerialize").Device(DEVICE_CPU), REGISTER_KERNEL_BUILDER(Name("TreeEnsembleDeserialize").Device(DEVICE_CPU), TreeEnsembleDeserializeOp); +REGISTER_KERNEL_BUILDER(Name("TreeEnsembleUsedHandlers").Device(DEVICE_CPU), + TreeEnsembleUsedHandlersOp); } // namespace boosted_trees } // namespace tensorflow diff --git a/tensorflow/contrib/boosted_trees/kernels/training_ops.cc b/tensorflow/contrib/boosted_trees/kernels/training_ops.cc index 7f8dea1d3c2a04b725843f6e2932a0cdfbc7733c..1bfeed306641111718984b2097512e5ec3fa8630 100644 --- a/tensorflow/contrib/boosted_trees/kernels/training_ops.cc +++ b/tensorflow/contrib/boosted_trees/kernels/training_ops.cc @@ -361,27 +361,10 @@ class GrowTreeEnsembleOp : public OpKernel { // Increment attempt stats. ensemble_resource->IncrementAttempts(); - // In case we want to do feature selection and we have reached the limit, - // build a list of handlers used so far to avoid adding new features. - std::vector allowed_handlers; - if (learner_config_.constraints().max_number_of_unique_feature_columns() > - 0) { - allowed_handlers = ensemble_resource->GetUsedHandlers(); - // TODO(soroush): We can disable handlers that are not going to be used to - // avoid unnecessary computations. - if (allowed_handlers.size() < - learner_config_.constraints() - .max_number_of_unique_feature_columns()) { - // We have not reached the limit yet. Empty the list of allow features - // which means we can keep adding new features. - allowed_handlers.clear(); - } - } - // Find best splits for each active partition. std::map best_splits; - FindBestSplitsPerPartition(context, allowed_handlers, partition_ids_list, - gains_list, splits_list, &best_splits); + FindBestSplitsPerPartition(context, partition_ids_list, gains_list, + splits_list, &best_splits); // No-op if no new splits can be considered. if (best_splits.empty()) { @@ -422,19 +405,12 @@ class GrowTreeEnsembleOp : public OpKernel { // and finds the best split for each partition. void FindBestSplitsPerPartition( OpKernelContext* const context, - const std::vector& allowed_handlers, // Empty means all handlers. const OpInputList& partition_ids_list, const OpInputList& gains_list, const OpInputList& splits_list, std::map* best_splits) { // Find best split per partition going through every feature candidate. // TODO(salehay): Is this worth parallelizing? for (int64 handler_id = 0; handler_id < num_handlers_; ++handler_id) { - if (!allowed_handlers.empty()) { - if (!std::binary_search(allowed_handlers.begin(), - allowed_handlers.end(), handler_id)) { - continue; - } - } const auto& partition_ids = partition_ids_list[handler_id].vec(); const auto& gains = gains_list[handler_id].vec(); const auto& splits = splits_list[handler_id].vec(); diff --git a/tensorflow/contrib/boosted_trees/lib/learner/common/stats/node-stats.h b/tensorflow/contrib/boosted_trees/lib/learner/common/stats/node-stats.h index cd925f6b65e569538212e9c26aef0abc8482960b..794ba2bcb0aafa26c5e1c90fcd66caf9dd5bf7d5 100644 --- a/tensorflow/contrib/boosted_trees/lib/learner/common/stats/node-stats.h +++ b/tensorflow/contrib/boosted_trees/lib/learner/common/stats/node-stats.h @@ -137,7 +137,7 @@ struct NodeStats { Eigen::MatrixXf hessian = TensorToEigenMatrix(grad_stats.second.t, grad_dim, grad_dim); // I is an identity matrix. - // The gain in general form is -g^T (H+l2 I)^-1 g. + // The gain in general form is g^T (H+l2 I)^-1 g. // The node weights are -(H+l2 I)^-1 g. Eigen::MatrixXf identity; identity.setIdentity(grad_dim, grad_dim); @@ -240,7 +240,7 @@ struct NodeStats { // given regularized Hessian and gradient vector g. void CalculateWeightAndGain(const Eigen::MatrixXf& hessian_and_reg, const Eigen::VectorXf& g) { - // The gain in general form is -g^T (Hessian_and_regularization)^-1 g. + // The gain in general form is g^T (Hessian_and_regularization)^-1 g. // The node weights are -(Hessian_and_regularization)^-1 g. Eigen::VectorXf weight; // If we want to calculate x = K^-1 v, instead of explicitly calculating diff --git a/tensorflow/contrib/boosted_trees/lib/utils/batch_features.h b/tensorflow/contrib/boosted_trees/lib/utils/batch_features.h index da5e7448519cb7f4092f7bbbe1b526271008ec22..7815fa049aa165a944c45872c762b7a5bf91b316 100644 --- a/tensorflow/contrib/boosted_trees/lib/utils/batch_features.h +++ b/tensorflow/contrib/boosted_trees/lib/utils/batch_features.h @@ -48,9 +48,9 @@ class BatchFeatures { Status GetFeatureColumnSizes(int64* const num_dense_float_features, int64* const num_sparse_float_features, int64* const num_sparse_int_features) const { - QCHECK_NE(num_dense_float_features, nullptr); - QCHECK_NE(num_sparse_float_features, nullptr); - QCHECK_NE(num_sparse_int_features, nullptr); + QCHECK_NE(num_dense_float_features, (int64*) nullptr); + QCHECK_NE(num_sparse_float_features, (int64*) nullptr); + QCHECK_NE(num_sparse_int_features, (int64*) nullptr); *num_dense_float_features = dense_float_feature_columns_.size(); *num_sparse_float_features = sparse_float_feature_columns_.size(); *num_sparse_int_features = sparse_int_feature_columns_.size(); diff --git a/tensorflow/contrib/boosted_trees/ops/model_ops.cc b/tensorflow/contrib/boosted_trees/ops/model_ops.cc index 0786c4166410720e8d4d70960e5747ff111076d8..9d6343c7e80f369bf6a5465821c5f4bacb984cd0 100644 --- a/tensorflow/contrib/boosted_trees/ops/model_ops.cc +++ b/tensorflow/contrib/boosted_trees/ops/model_ops.cc @@ -110,5 +110,32 @@ stamp_token: Token to use as the new value of the resource stamp. tree_ensemble_config: Serialized proto of the ensemble. )doc"); +REGISTER_OP("TreeEnsembleUsedHandlers") + .Attr("num_all_handlers: int >= 0") + .Input("tree_ensemble_handle: resource") + .Input("stamp_token: int64") + .Output("num_used_handlers: int64") + .Output("used_handlers_mask: bool") + .SetShapeFn([](shape_inference::InferenceContext* c) { + shape_inference::ShapeHandle unused_input; + TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_input)); + c->set_output(0, c->Scalar()); + int num_all_handlers; + c->GetAttr("num_all_handlers", &num_all_handlers).IgnoreError(); + c->set_output(1, {c->Vector(num_all_handlers)}); + + return Status::OK(); + }) + .Doc(R"doc( +Returns the mask of used handlers along with the number of non-zero elements in +this mask. Used in feature selection. + +tree_ensemble_handle: Handle to the tree ensemble. +stamp_token: Token to use as the new value of the resource stamp. +num_used_handlers: number of feature column handlers used in the model. +used_handlers_mask: A boolean vector of showing which handlers are used in the + model. +)doc"); + } // namespace boosted_trees } // namespace tensorflow diff --git a/tensorflow/contrib/boosted_trees/ops/quantile_ops.cc b/tensorflow/contrib/boosted_trees/ops/quantile_ops.cc index ae99d53a2cf805d70d60746cd44f73f7fd9dc6e2..6aa52463987b55a54b7308765920cbe94c15b8d1 100644 --- a/tensorflow/contrib/boosted_trees/ops/quantile_ops.cc +++ b/tensorflow/contrib/boosted_trees/ops/quantile_ops.cc @@ -272,6 +272,20 @@ REGISTER_OP("Quantiles") .Input("sparse_indices: num_sparse_features * int64") .Output("dense_quantiles: num_dense_features * int32") .Output("sparse_quantiles: num_sparse_features * int32") + .SetShapeFn([](InferenceContext* c) { + int num_dense_features; + TF_RETURN_IF_ERROR(c->GetAttr("num_dense_features", &num_dense_features)); + int num_sparse_features; + TF_RETURN_IF_ERROR( + c->GetAttr("num_sparse_features", &num_sparse_features)); + // Set output shapes (dense_quantiles and sparse_quantiles) by the + // relevant inputs (dense_values and sparse_values). Note that the output + // has an additional dimension for dimension_ids. + for (int i = 0; i < num_dense_features + num_sparse_features; ++i) { + c->set_output(i, c->MakeShape({c->Dim(c->input(i), 0), 2})); + } + return Status::OK(); + }) .Doc(R"doc( Computes quantile for each a given list of dense and sparse feature values using the given buckets. diff --git a/tensorflow/contrib/boosted_trees/python/kernel_tests/model_ops_test.py b/tensorflow/contrib/boosted_trees/python/kernel_tests/model_ops_test.py index 27c288bbf78b3b593d0807e92ac7fd9afc4d2725..63b9c5fddf0d9967d53077608664b59d9ae00481 100644 --- a/tensorflow/contrib/boosted_trees/python/kernel_tests/model_ops_test.py +++ b/tensorflow/contrib/boosted_trees/python/kernel_tests/model_ops_test.py @@ -310,6 +310,22 @@ class ModelOpsTest(test_util.TensorFlowTestCase): # The third tree was added after the save. self.assertAllClose(result.eval(), [[-1.1], [-1.1]]) + def testUsedHandlers(self): + with self.test_session(): + tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() + tree_ensemble_config.growing_metadata.used_handler_ids.append(1) + tree_ensemble_config.growing_metadata.used_handler_ids.append(5) + stamp_token = 3 + tree_ensemble_handle = model_ops.tree_ensemble_variable( + stamp_token=stamp_token, + tree_ensemble_config=tree_ensemble_config.SerializeToString(), + name="create_tree") + resources.initialize_resources(resources.shared_resources()).run() + result = model_ops.tree_ensemble_used_handlers( + tree_ensemble_handle, stamp_token, num_all_handlers=6) + self.assertAllEqual([0, 1, 0, 0, 0, 1], result.used_handlers_mask.eval()) + self.assertEqual(2, result.num_used_handlers.eval()) + if __name__ == "__main__": googletest.main() diff --git a/tensorflow/contrib/boosted_trees/python/kernel_tests/training_ops_test.py b/tensorflow/contrib/boosted_trees/python/kernel_tests/training_ops_test.py index 8ca1aabacaf53b66aaba184962922294427d6803..3e524efbeac74ff754d63cae92b3e194411cb2de 100644 --- a/tensorflow/contrib/boosted_trees/python/kernel_tests/training_ops_test.py +++ b/tensorflow/contrib/boosted_trees/python/kernel_tests/training_ops_test.py @@ -1588,7 +1588,7 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): self.assertEqual( 2, tree_ensemble_config.tree_metadata[2].num_tree_weight_updates) - def testGrowExistingEnsembleTreeWithFeatureSelectionCanStillGrow(self): + def testGrowExistingEnsembleTreeWithFeatureSelectionUsedHandlers(self): """Test growing a tree with feature selection.""" with self.test_session() as session: # Create existing ensemble with one root split and one bias tree. @@ -1649,7 +1649,6 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): num_trees_attempted: 2 num_layers_attempted: 2 used_handler_ids: 2 - used_handler_ids: 5 } """, tree_ensemble_config) tree_ensemble_handle = model_ops.tree_ensemble_variable( @@ -1668,183 +1667,8 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): min_node_weight=0, pruning_mode=learner_pb2.LearnerConfig.PRE_PRUNE, growing_mode=learner_pb2.LearnerConfig.WHOLE_TREE) - # There are 2 handler_ids in used_handler_ids already but one of them - # is handler 2, so we can still grow trees. - learner_config.constraints.max_number_of_unique_feature_columns = 2 - learner_config = learner_config.SerializeToString() - # Prepare handler inputs. - handler1_partitions = np.array([0], dtype=np.int32) - handler1_gains = np.array([7.62], dtype=np.float32) - handler1_split = [_gen_dense_split_info(5, 0.52, -4.375, 7.143)] - handler2_partitions = np.array([0], dtype=np.int32) - handler2_gains = np.array([0.63], dtype=np.float32) - handler2_split = [_gen_dense_split_info(2, 0.23, -0.6, 0.24)] - handler3_partitions = np.array([0], dtype=np.int32) - handler3_gains = np.array([7.62], dtype=np.float32) - handler3_split = [_gen_categorical_split_info(8, 7, -4.375, 7.143)] - - # Grow tree ensemble. - grow_op = training_ops.grow_tree_ensemble( - tree_ensemble_handle, - stamp_token=0, - next_stamp_token=1, - learning_rate=1, - partition_ids=[ - handler1_partitions, handler2_partitions, handler3_partitions - ], - gains=[handler1_gains, handler2_gains, handler3_gains], - splits=[handler1_split, handler2_split, handler3_split], - learner_config=learner_config, - dropout_seed=123, - center_bias=True) - session.run(grow_op) - - # Expect a new tree to be added with the split from handler 1. - _, serialized = session.run( - model_ops.tree_ensemble_serialize(tree_ensemble_handle)) - tree_ensemble_config.ParseFromString(serialized) - self.assertEqual(3, len(tree_ensemble_config.trees)) - self.assertEqual( - 2, len(tree_ensemble_config.growing_metadata.used_handler_ids)) - - def testGrowExistingEnsembleTreeWithFeatureSelectionEmptyEnsemble(self): - """Test growing a tree with feature selection with empty ensemble.""" - with self.test_session() as session: - # Create existing ensemble with one root split and one bias tree. - tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() - tree_ensemble_handle = model_ops.tree_ensemble_variable( - stamp_token=0, - tree_ensemble_config=tree_ensemble_config.SerializeToString(), - name="tree_ensemble") - resources.initialize_resources(resources.shared_resources()).run() - - # Prepare learner config. - learner_config = _gen_learner_config( - num_classes=2, - l1_reg=0, - l2_reg=0, - tree_complexity=0, - max_depth=1, - min_node_weight=0, - pruning_mode=learner_pb2.LearnerConfig.PRE_PRUNE, - growing_mode=learner_pb2.LearnerConfig.WHOLE_TREE) - learner_config.constraints.max_number_of_unique_feature_columns = 2 - learner_config = learner_config.SerializeToString() - # Prepare handler inputs. - handler1_partitions = np.array([0], dtype=np.int32) - handler1_gains = np.array([7.62], dtype=np.float32) - handler1_split = [_gen_dense_split_info(5, 0.52, -4.375, 7.143)] - handler2_partitions = np.array([0], dtype=np.int32) - handler2_gains = np.array([0.63], dtype=np.float32) - handler2_split = [_gen_dense_split_info(2, 0.23, -0.6, 0.24)] - handler3_partitions = np.array([0], dtype=np.int32) - handler3_gains = np.array([7.62], dtype=np.float32) - handler3_split = [_gen_categorical_split_info(8, 7, -4.375, 7.143)] - - # Grow tree ensemble. - grow_op = training_ops.grow_tree_ensemble( - tree_ensemble_handle, - stamp_token=0, - next_stamp_token=1, - learning_rate=1, - partition_ids=[ - handler1_partitions, handler2_partitions, handler3_partitions - ], - gains=[handler1_gains, handler2_gains, handler3_gains], - splits=[handler1_split, handler2_split, handler3_split], - learner_config=learner_config, - dropout_seed=123, - center_bias=True) - session.run(grow_op) - - _, serialized = session.run( - model_ops.tree_ensemble_serialize(tree_ensemble_handle)) - tree_ensemble_config.ParseFromString(serialized) - self.assertEqual(1, len(tree_ensemble_config.trees)) - self.assertEqual( - 1, len(tree_ensemble_config.growing_metadata.used_handler_ids)) - - def testGrowExistingEnsembleTreeWithFeatureSelectionCantGrow(self): - """Test growing a tree with feature selection with empty ensemble.""" - with self.test_session() as session: - # Create existing ensemble with one root split and one bias tree. - tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() - text_format.Merge(""" - trees { - nodes { - leaf { - vector { - value: -0.32 - value: 0.28 - } - } - } - } - trees { - nodes { - categorical_id_binary_split { - feature_column: 3 - feature_id: 7 - left_id: 1 - right_id: 2 - } - node_metadata { - gain: 1.3 - } - } - nodes { - leaf { - sparse_vector { - index: 0 - value: 2.3 - } - } - } - nodes { - leaf { - sparse_vector { - index: 0 - value: -0.9 - } - } - } - } - tree_weights: 0.7 - tree_weights: 1 - tree_metadata { - num_tree_weight_updates: 1 - num_layers_grown: 1 - is_finalized: true - } - tree_metadata { - num_tree_weight_updates: 5 - num_layers_grown: 1 - is_finalized: true - } - growing_metadata { - num_trees_attempted: 2 - num_layers_attempted: 2 - used_handler_ids: 4 - used_handler_ids: 5 - } - """, tree_ensemble_config) - tree_ensemble_handle = model_ops.tree_ensemble_variable( - stamp_token=0, - tree_ensemble_config=tree_ensemble_config.SerializeToString(), - name="tree_ensemble") - resources.initialize_resources(resources.shared_resources()).run() - # Prepare learner config. - learner_config = _gen_learner_config( - num_classes=2, - l1_reg=0, - l2_reg=0, - tree_complexity=0, - max_depth=1, - min_node_weight=0, - pruning_mode=learner_pb2.LearnerConfig.PRE_PRUNE, - growing_mode=learner_pb2.LearnerConfig.WHOLE_TREE) - learner_config.constraints.max_number_of_unique_feature_columns = 2 + learner_config.constraints.max_number_of_unique_feature_columns = 3 learner_config = learner_config.SerializeToString() # Prepare handler inputs. handler1_partitions = np.array([0], dtype=np.int32) @@ -1876,12 +1700,10 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): _, serialized = session.run( model_ops.tree_ensemble_serialize(tree_ensemble_handle)) tree_ensemble_config.ParseFromString(serialized) - # We can't grow a tree since we have reached the limit of 2 unique - # features [4, 5] and the only available splits are from - # handlers [0, 1, 2]. - self.assertEqual(2, len(tree_ensemble_config.trees)) - self.assertEqual( - 2, len(tree_ensemble_config.growing_metadata.used_handler_ids)) + self.assertEqual(3, len(tree_ensemble_config.trees)) + # 2 was already used. handler 0 is being added in this tree. + self.assertAllEqual( + [0, 2], tree_ensemble_config.growing_metadata.used_handler_ids) if __name__ == "__main__": diff --git a/tensorflow/contrib/boosted_trees/python/ops/model_ops.py b/tensorflow/contrib/boosted_trees/python/ops/model_ops.py index 7a5f509047d46549ba81039a23d29ec987ca7920..25b2c9e2fd72bd018717e8a87fce726f26bad968 100644 --- a/tensorflow/contrib/boosted_trees/python/ops/model_ops.py +++ b/tensorflow/contrib/boosted_trees/python/ops/model_ops.py @@ -25,6 +25,7 @@ from tensorflow.contrib.boosted_trees.python.ops.gen_model_ops import tree_ensem from tensorflow.contrib.boosted_trees.python.ops.gen_model_ops import tree_ensemble_serialize # pylint: disable=unused-import from tensorflow.contrib.boosted_trees.python.ops.gen_model_ops import tree_ensemble_stamp_token +from tensorflow.contrib.boosted_trees.python.ops.gen_model_ops import tree_ensemble_used_handlers # pylint: enable=unused-import from tensorflow.python.framework import ops diff --git a/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py b/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py index f0b66dcbbe1c5167b9993e66b30b1dc8a839c380..85b909e4f2556c520a5bffe46d5954683d9dda5a 100644 --- a/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py +++ b/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py @@ -57,6 +57,8 @@ PREDICTIONS = "predictions" PARTITION_IDS = "partition_ids" NUM_LAYERS_ATTEMPTED = "num_layers" NUM_TREES_ATTEMPTED = "num_trees" +NUM_USED_HANDLERS = "num_used_handlers" +USED_HANDLERS_MASK = "used_handlers_mask" _FEATURE_NAME_TEMPLATE = "%s_%d" @@ -70,7 +72,8 @@ def _get_column_by_index(tensor, indices): return array_ops.reshape(array_ops.gather(p_flat, i_flat), [shape[0], -1]) -def _make_predictions_dict(stamp, logits, partition_ids, ensemble_stats): +def _make_predictions_dict(stamp, logits, partition_ids, ensemble_stats, + used_handlers): """Returns predictions for the given logits and n_classes. Args: @@ -79,6 +82,8 @@ def _make_predictions_dict(stamp, logits, partition_ids, ensemble_stats): that contains predictions when no dropout was applied. partition_ids: A rank 1 `Tensor` with shape [batch_size]. ensemble_stats: A TreeEnsembleStatsOp result tuple. + used_handlers: A TreeEnsembleUsedHandlerOp result tuple of an int and a + boolean mask.. Returns: A dict of predictions. @@ -89,6 +94,8 @@ def _make_predictions_dict(stamp, logits, partition_ids, ensemble_stats): result[PARTITION_IDS] = partition_ids result[NUM_LAYERS_ATTEMPTED] = ensemble_stats.attempted_layers result[NUM_TREES_ATTEMPTED] = ensemble_stats.attempted_trees + result[NUM_USED_HANDLERS] = used_handlers.num_used_handlers + result[USED_HANDLERS_MASK] = used_handlers.used_handlers_mask return result @@ -361,6 +368,13 @@ class GradientBoostedDecisionTreeModel(object): """ ensemble_stats = training_ops.tree_ensemble_stats(ensemble_handle, ensemble_stamp) + num_handlers = ( + len(self._dense_floats) + len(self._sparse_float_shapes) + + len(self._sparse_int_shapes)) + # Used during feature selection. + used_handlers = model_ops.tree_ensemble_used_handlers( + ensemble_handle, ensemble_stamp, num_all_handlers=num_handlers) + # We don't need dropout info - we can always restore it based on the # seed. apply_dropout, seed = _dropout_params(mode, ensemble_stats) @@ -395,7 +409,7 @@ class GradientBoostedDecisionTreeModel(object): use_locking=True) return _make_predictions_dict(ensemble_stamp, predictions, partition_ids, - ensemble_stats) + ensemble_stats, used_handlers) def predict(self, mode): """Returns predictions given the features and mode. @@ -710,12 +724,28 @@ class GradientBoostedDecisionTreeModel(object): active_handlers_current_layer = ( active_handlers_current_layer < self._learner_config.feature_fraction_per_tree) - active_handlers = array_ops.stack(active_handlers_current_layer, - array_ops.ones( - [len(handlers)], dtype=dtypes.bool)) + active_handlers = array_ops.stack([ + active_handlers_current_layer, + array_ops.ones([len(handlers)], dtype=dtypes.bool)], axis=1) else: active_handlers = array_ops.ones([len(handlers), 2], dtype=dtypes.bool) + if self._learner_config.constraints.max_number_of_unique_feature_columns: + target = ( + self._learner_config.constraints.max_number_of_unique_feature_columns) + + def _feature_selection_active_handlers(): + # The active list for current and the next iteration. + used_handlers = array_ops.reshape(predictions_dict[USED_HANDLERS_MASK], + [-1, 1]) + used_handlers = array_ops.concat([used_handlers, used_handlers], axis=1) + return math_ops.logical_and(used_handlers, active_handlers) + + active_handlers = ( + control_flow_ops.cond(predictions_dict[NUM_USED_HANDLERS] >= target, + _feature_selection_active_handlers, + lambda: active_handlers)) + # Prepare empty gradients and hessians when handlers are not ready. empty_hess_shape = [1] + hessian_shape.as_list() empty_grad_shape = [1] + gradient_shape.as_list() diff --git a/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch_test.py b/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch_test.py index dba51d4f527792d2a8dedc693f74c07119fd231d..6411f57a5419123e799af9231a04fce8ae7724d4 100644 --- a/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch_test.py +++ b/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch_test.py @@ -47,6 +47,38 @@ def _squared_loss(label, unused_weights, predictions): return loss +def _append_to_leaf(leaf, c_id, w): + """Helper method for building tree leaves. + + Appends weight contributions for the given class index to a leaf node. + + Args: + leaf: leaf node to append to. + c_id: class Id for the weight update. + w: weight contribution value. + """ + leaf.sparse_vector.index.append(c_id) + leaf.sparse_vector.value.append(w) + + +def _set_float_split(split, feat_col, thresh, l_id, r_id): + """Helper method for building tree float splits. + + Sets split feature column, threshold and children. + + Args: + split: split node to update. + feat_col: feature column for the split. + thresh: threshold to split on forming rule x <= thresh. + l_id: left child Id. + r_id: right child Id. + """ + split.feature_column = feat_col + split.threshold = thresh + split.left_id = l_id + split.right_id = r_id + + class GbdtTest(test_util.TensorFlowTestCase): def setUp(self): @@ -917,6 +949,350 @@ class GbdtTest(test_util.TensorFlowTestCase): output.trees[0].nodes[2].leaf.sparse_vector.value[0], atol=1e-4, rtol=1e-4) + def testTrainFnChiefFeatureSelectionReachedLimitNoGoodSplit(self): + """Tests the train function running on chief with feature selection.""" + with self.test_session() as sess: + ensemble_handle = model_ops.tree_ensemble_variable( + stamp_token=0, tree_ensemble_config="", name="tree_ensemble") + learner_config = learner_pb2.LearnerConfig() + learner_config.learning_rate_tuner.fixed.learning_rate = 0.1 + learner_config.num_classes = 2 + learner_config.regularization.l1 = 0 + learner_config.regularization.l2 = 0 + learner_config.constraints.max_tree_depth = 1 + learner_config.constraints.max_number_of_unique_feature_columns = 1 + learner_config.constraints.min_node_weight = 0 + features = {} + features["dense_float_0"] = array_ops.ones([4, 1], dtypes.float32) + # Feature 1 is predictive but it won't be used because we have reached the + # limit of num_used_handlers >= max_number_of_unique_feature_columns + features["dense_float_1"] = array_ops.constant([0, 0, 1, 1], + dtypes.float32) + + gbdt_model = gbdt_batch.GradientBoostedDecisionTreeModel( + is_chief=True, + num_ps_replicas=0, + center_bias=False, + ensemble_handle=ensemble_handle, + examples_per_layer=1, + learner_config=learner_config, + logits_dimension=1, + features=features) + + predictions = array_ops.constant( + [[0.0], [1.0], [0.0], [2.0]], dtype=dtypes.float32) + partition_ids = array_ops.zeros([4], dtypes.int32) + ensemble_stamp = variables.Variable( + initial_value=0, + name="ensemble_stamp", + trainable=False, + dtype=dtypes.int64) + + predictions_dict = { + "predictions": + predictions, + "predictions_no_dropout": + predictions, + "partition_ids": + partition_ids, + "ensemble_stamp": + ensemble_stamp, + "num_trees": + 12, + "num_used_handlers": + array_ops.constant(1, dtype=dtypes.int64), + "used_handlers_mask": + array_ops.constant([True, False], dtype=dtypes.bool), + } + + labels = array_ops.constant([0, 0, 1, 1], dtypes.float32) + weights = array_ops.ones([4, 1], dtypes.float32) + # Create train op. + train_op = gbdt_model.train( + loss=math_ops.reduce_mean( + _squared_loss(labels, weights, predictions)), + predictions_dict=predictions_dict, + labels=labels) + variables.global_variables_initializer().run() + resources.initialize_resources(resources.shared_resources()).run() + + # On first run, expect no splits to be chosen because the quantile + # buckets will not be ready. + train_op.run() + stamp_token, serialized = model_ops.tree_ensemble_serialize( + ensemble_handle) + output = tree_config_pb2.DecisionTreeEnsembleConfig() + output.ParseFromString(serialized.eval()) + self.assertEquals(len(output.trees), 0) + self.assertEquals(len(output.tree_weights), 0) + self.assertEquals(stamp_token.eval(), 1) + + # Update the stamp to be able to run a second time. + sess.run([ensemble_stamp.assign_add(1)]) + + # On second run, expect a trivial split to be chosen to basically + # predict the average. + train_op.run() + stamp_token, serialized = model_ops.tree_ensemble_serialize( + ensemble_handle) + output = tree_config_pb2.DecisionTreeEnsembleConfig() + output.ParseFromString(serialized.eval()) + self.assertEquals(len(output.trees), 1) + self.assertAllClose(output.tree_weights, [0.1]) + self.assertEquals(stamp_token.eval(), 2) + expected_tree = """ + nodes { + dense_float_binary_split { + feature_column: 0 + threshold: 1.0 + left_id: 1 + right_id: 2 + } + node_metadata { + gain: 0 + } + } + nodes { + leaf { + vector { + value: -0.25 + } + } + } + nodes { + leaf { + vector { + value: 0.0 + } + } + }""" + self.assertProtoEquals(expected_tree, output.trees[0]) + + def testTrainFnChiefFeatureSelectionWithGoodSplits(self): + """Tests the train function running on chief with feature selection.""" + with self.test_session() as sess: + ensemble_handle = model_ops.tree_ensemble_variable( + stamp_token=0, tree_ensemble_config="", name="tree_ensemble") + learner_config = learner_pb2.LearnerConfig() + learner_config.learning_rate_tuner.fixed.learning_rate = 0.1 + learner_config.num_classes = 2 + learner_config.regularization.l1 = 0 + learner_config.regularization.l2 = 0 + learner_config.constraints.max_tree_depth = 1 + learner_config.constraints.max_number_of_unique_feature_columns = 1 + learner_config.constraints.min_node_weight = 0 + features = {} + features["dense_float_0"] = array_ops.ones([4, 1], dtypes.float32) + # Feature 1 is predictive and is in our selected features so it will be + # used even when we're at the limit. + features["dense_float_1"] = array_ops.constant([0, 0, 1, 1], + dtypes.float32) + + gbdt_model = gbdt_batch.GradientBoostedDecisionTreeModel( + is_chief=True, + num_ps_replicas=0, + center_bias=False, + ensemble_handle=ensemble_handle, + examples_per_layer=1, + learner_config=learner_config, + logits_dimension=1, + features=features) + + predictions = array_ops.constant( + [[0.0], [1.0], [0.0], [2.0]], dtype=dtypes.float32) + partition_ids = array_ops.zeros([4], dtypes.int32) + ensemble_stamp = variables.Variable( + initial_value=0, + name="ensemble_stamp", + trainable=False, + dtype=dtypes.int64) + + predictions_dict = { + "predictions": + predictions, + "predictions_no_dropout": + predictions, + "partition_ids": + partition_ids, + "ensemble_stamp": + ensemble_stamp, + "num_trees": + 12, + "num_used_handlers": + array_ops.constant(1, dtype=dtypes.int64), + "used_handlers_mask": + array_ops.constant([False, True], dtype=dtypes.bool), + } + + labels = array_ops.constant([0, 0, 1, 1], dtypes.float32) + weights = array_ops.ones([4, 1], dtypes.float32) + # Create train op. + train_op = gbdt_model.train( + loss=math_ops.reduce_mean( + _squared_loss(labels, weights, predictions)), + predictions_dict=predictions_dict, + labels=labels) + variables.global_variables_initializer().run() + resources.initialize_resources(resources.shared_resources()).run() + + # On first run, expect no splits to be chosen because the quantile + # buckets will not be ready. + train_op.run() + stamp_token, serialized = model_ops.tree_ensemble_serialize( + ensemble_handle) + output = tree_config_pb2.DecisionTreeEnsembleConfig() + output.ParseFromString(serialized.eval()) + self.assertEquals(len(output.trees), 0) + self.assertEquals(len(output.tree_weights), 0) + self.assertEquals(stamp_token.eval(), 1) + + # Update the stamp to be able to run a second time. + sess.run([ensemble_stamp.assign_add(1)]) + + train_op.run() + stamp_token, serialized = model_ops.tree_ensemble_serialize( + ensemble_handle) + output = tree_config_pb2.DecisionTreeEnsembleConfig() + output.ParseFromString(serialized.eval()) + + self.assertEquals(len(output.trees), 1) + self.assertAllClose(output.tree_weights, [0.1]) + self.assertEquals(stamp_token.eval(), 2) + expected_tree = """ + nodes { + dense_float_binary_split { + feature_column: 1 + left_id: 1 + right_id: 2 + } + node_metadata { + gain: 0.5 + } + } + nodes { + leaf { + vector { + value: 0.0 + } + } + } + nodes { + leaf { + vector { + value: -0.5 + } + } + }""" + self.assertProtoEquals(expected_tree, output.trees[0]) + + def testTrainFnChiefFeatureSelectionReachedLimitIncrementAttemptedLayer(self): + """Tests the train function running on chief with feature selection.""" + with self.test_session() as sess: + tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() + tree = tree_ensemble_config.trees.add() + + _set_float_split(tree.nodes.add() + .sparse_float_binary_split_default_right.split, 2, 4.0, + 1, 2) + _append_to_leaf(tree.nodes.add().leaf, 0, 0.5) + _append_to_leaf(tree.nodes.add().leaf, 1, 1.2) + tree_ensemble_config.tree_weights.append(1.0) + metadata = tree_ensemble_config.tree_metadata.add() + metadata.is_finalized = False + metadata.num_layers_grown = 1 + tree_ensemble_config = tree_ensemble_config.SerializeToString() + ensemble_handle = model_ops.tree_ensemble_variable( + stamp_token=0, tree_ensemble_config=tree_ensemble_config, + name="tree_ensemble") + learner_config = learner_pb2.LearnerConfig() + learner_config.learning_rate_tuner.fixed.learning_rate = 0.1 + learner_config.num_classes = 2 + learner_config.regularization.l1 = 0 + learner_config.regularization.l2 = 0 + learner_config.constraints.max_tree_depth = 1 + learner_config.constraints.max_number_of_unique_feature_columns = 1 + learner_config.constraints.min_node_weight = 0 + features = {} + # Both features will be disabled since the feature selection limit is + # already reached. + features["dense_float_0"] = array_ops.ones([4, 1], dtypes.float32) + features["dense_float_1"] = array_ops.constant([0, 0, 1, 1], + dtypes.float32) + + gbdt_model = gbdt_batch.GradientBoostedDecisionTreeModel( + is_chief=True, + num_ps_replicas=0, + center_bias=False, + ensemble_handle=ensemble_handle, + examples_per_layer=1, + learner_config=learner_config, + logits_dimension=1, + features=features) + + predictions = array_ops.constant( + [[0.0], [1.0], [0.0], [2.0]], dtype=dtypes.float32) + partition_ids = array_ops.zeros([4], dtypes.int32) + ensemble_stamp = variables.Variable( + initial_value=0, + name="ensemble_stamp", + trainable=False, + dtype=dtypes.int64) + + predictions_dict = { + "predictions": + predictions, + "predictions_no_dropout": + predictions, + "partition_ids": + partition_ids, + "ensemble_stamp": + ensemble_stamp, + "num_trees": + 12, + # We have somehow reached our limit 1. Both of the handlers will be + # disabled. + "num_used_handlers": + array_ops.constant(1, dtype=dtypes.int64), + "used_handlers_mask": + array_ops.constant([False, False], dtype=dtypes.bool), + } + + labels = array_ops.constant([0, 0, 1, 1], dtypes.float32) + weights = array_ops.ones([4, 1], dtypes.float32) + # Create train op. + train_op = gbdt_model.train( + loss=math_ops.reduce_mean( + _squared_loss(labels, weights, predictions)), + predictions_dict=predictions_dict, + labels=labels) + variables.global_variables_initializer().run() + resources.initialize_resources(resources.shared_resources()).run() + + # On first run, expect no splits to be chosen because the quantile + # buckets will not be ready. + train_op.run() + stamp_token, serialized = model_ops.tree_ensemble_serialize( + ensemble_handle) + output = tree_config_pb2.DecisionTreeEnsembleConfig() + output.ParseFromString(serialized.eval()) + self.assertEquals(len(output.trees), 1) + self.assertEquals(output.growing_metadata.num_layers_attempted, 1) + self.assertEquals(stamp_token.eval(), 1) + + # Update the stamp to be able to run a second time. + sess.run([ensemble_stamp.assign_add(1)]) + + train_op.run() + stamp_token, serialized = model_ops.tree_ensemble_serialize( + ensemble_handle) + output = tree_config_pb2.DecisionTreeEnsembleConfig() + output.ParseFromString(serialized.eval()) + # Make sure the trees are not modified, but the num_layers_attempted is + # incremented so that eventually the training stops. + self.assertEquals(len(output.trees), 1) + self.assertEquals(len(output.trees[0].nodes), 3) + + self.assertEquals(output.growing_metadata.num_layers_attempted, 2) if __name__ == "__main__": googletest.main() diff --git a/tensorflow/contrib/cluster_resolver/BUILD b/tensorflow/contrib/cluster_resolver/BUILD index 6b03df2b8eb636ad888d050a3b2b29eabc3f8934..1a124eca364424b651de86bfaac6f33ad131804b 100644 --- a/tensorflow/contrib/cluster_resolver/BUILD +++ b/tensorflow/contrib/cluster_resolver/BUILD @@ -110,5 +110,6 @@ tf_py_test( "//tensorflow/python:platform_test", "//tensorflow/python:training", ], + grpc_enabled = True, main = "python/training/tpu_cluster_resolver_test.py", ) diff --git a/tensorflow/contrib/cluster_resolver/python/training/cluster_resolver.py b/tensorflow/contrib/cluster_resolver/python/training/cluster_resolver.py index b04822fa9d66465e34a545d3b00c399bbb196514..1c480b25134b1e54200e0ddb780bd7bb0f122341 100644 --- a/tensorflow/contrib/cluster_resolver/python/training/cluster_resolver.py +++ b/tensorflow/contrib/cluster_resolver/python/training/cluster_resolver.py @@ -53,11 +53,16 @@ class ClusterResolver(object): raise NotImplementedError( 'cluster_spec is not implemented for {}.'.format(self)) + @abc.abstractmethod + def master(self): + """...""" + raise NotImplementedError('master is not implemented for {}.'.format(self)) + class SimpleClusterResolver(ClusterResolver): """Simple implementation of ClusterResolver that accepts a ClusterSpec.""" - def __init__(self, cluster_spec): + def __init__(self, cluster_spec, master=''): """Creates a SimpleClusterResolver from a ClusterSpec.""" super(SimpleClusterResolver, self).__init__() @@ -65,10 +70,18 @@ class SimpleClusterResolver(ClusterResolver): raise TypeError('cluster_spec must be a ClusterSpec.') self._cluster_spec = cluster_spec + if not isinstance(master, str): + raise TypeError('master must be a string.') + self._master = master + def cluster_spec(self): """Returns the ClusterSpec passed into the constructor.""" return self._cluster_spec + def master(self): + """Returns the master address to use when creating a session.""" + return self._master + class UnionClusterResolver(ClusterResolver): """Performs a union on underlying ClusterResolvers. @@ -87,9 +100,13 @@ class UnionClusterResolver(ClusterResolver): Raises: TypeError: If any argument is not a subclass of `ClusterResolvers`. + ValueError: If there are no arguments passed. """ super(UnionClusterResolver, self).__init__() + if not args: + raise ValueError('At least one ClusterResolver is required.') + for cluster_resolver in args: if not isinstance(cluster_resolver, ClusterResolver): raise TypeError('All arguments must be a sub-class of ' @@ -169,3 +186,7 @@ class UnionClusterResolver(ClusterResolver): merged_cluster[job_name].update(task_dict) return ClusterSpec(merged_cluster) + + def master(self): + """master returns the master address from the first cluster resolver.""" + return self._cluster_resolvers[0].master() diff --git a/tensorflow/contrib/cluster_resolver/python/training/cluster_resolver_test.py b/tensorflow/contrib/cluster_resolver/python/training/cluster_resolver_test.py index dbfb77723cdaab66e29bb41b764593bb5fd61b35..d9c97d53eb3663f6ab2f7b40395592dc7638b896 100644 --- a/tensorflow/contrib/cluster_resolver/python/training/cluster_resolver_test.py +++ b/tensorflow/contrib/cluster_resolver/python/training/cluster_resolver_test.py @@ -234,5 +234,7 @@ class UnionClusterResolverTest(test.TestCase): self._verifyClusterSpecEquality(cluster_spec, expected_proto) +# TODO(saeta): Include tests for master resolution + if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/cluster_resolver/python/training/gce_cluster_resolver.py b/tensorflow/contrib/cluster_resolver/python/training/gce_cluster_resolver.py index d6f2eced93ba4fda5ac27f9412b6f729981f4f40..3f5824128948453634bc5e5a7d6fdeedae60f5bd 100644 --- a/tensorflow/contrib/cluster_resolver/python/training/gce_cluster_resolver.py +++ b/tensorflow/contrib/cluster_resolver/python/training/gce_cluster_resolver.py @@ -134,3 +134,6 @@ class GceClusterResolver(ClusterResolver): worker_list.sort() return ClusterSpec({self._job_name: worker_list}) + + def master(self): + return '' diff --git a/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver.py b/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver.py index a6a6e642e4e4c721b94821a70d55d6fe931347d6..300b19733e2b4d1b912f966e94ae0286ed9c694d 100644 --- a/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver.py +++ b/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver.py @@ -18,12 +18,14 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import os from six.moves.urllib.request import Request from six.moves.urllib.request import urlopen from tensorflow.contrib.cluster_resolver.python.training.cluster_resolver import ClusterResolver -from tensorflow.python.training.server_lib import ClusterSpec +from tensorflow.python.training import server_lib +from tensorflow.python.util import compat _GOOGLE_API_CLIENT_INSTALLED = True try: @@ -33,6 +35,9 @@ except ImportError: _GOOGLE_API_CLIENT_INSTALLED = False +_GKE_ENV_VARIABLE = 'KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS' + + class TPUClusterResolver(ClusterResolver): """Cluster Resolver for Google Cloud TPUs. @@ -46,13 +51,30 @@ class TPUClusterResolver(ClusterResolver): req = Request('http://metadata/computeMetadata/v1/%s' % path, headers={'Metadata-Flavor': 'Google'}) resp = urlopen(req) - return resp.read() + return compat.as_bytes(resp.read()) + + def _shouldResolve(self): + if (self._tpu == compat.as_bytes('') or + self._tpu == compat.as_bytes('local') or + self._tpu.startswith(compat.as_bytes('/bns')) or + self._tpu.startswith(compat.as_bytes('grpc://'))): + return False + return True + + def _inGke(self): + """When running in GKE, the environment variable will be set.""" + return _GKE_ENV_VARIABLE in os.environ + + def _gkeMaster(self): + return os.environ[_GKE_ENV_VARIABLE].split(',')[0] def __init__(self, - tpu_names, + tpu=None, zone=None, project=None, - job_name='tpu_worker', + job_name='worker', + coordinator_name='coordinator', + coordinator_address=None, credentials='default', service=None): """Creates a new TPUClusterResolver object. @@ -61,7 +83,11 @@ class TPUClusterResolver(ClusterResolver): for the IP addresses and ports of each Cloud TPU listed. Args: - tpu_names: A list of names of the target Cloud TPUs. + tpu: Either a string, or a list of strings corresponding to the TPUs to + use. If the single string is the empty string, the string 'local', or a + string that begins with 'grpc://' or '/bns', then it is assumed to not + correspond with a Cloud TPU and will instead be passed as the session + master and no ClusterSpec propagation will be done. zone: Zone where the TPUs are located. If omitted or empty, we will assume that the zone of the TPU is the same as the zone of the GCE VM, which we will try to discover from the GCE metadata service. @@ -69,6 +95,12 @@ class TPUClusterResolver(ClusterResolver): empty, we will try to discover the project name of the GCE VM from the GCE metadata service. job_name: Name of the TensorFlow job the TPUs belong to. + coordinator_name: The name to use for the coordinator. Set to None if the + coordinator should not be included in the computed ClusterSpec. + coordinator_address: The address of the coordinator (typically an ip:port + pair). If set to None, a TF server will be started. If coordinator_name + is None, a TF server will not be started even if coordinator_address is + None. credentials: GCE Credentials. If None, then we use default credentials from the oauth2client service: The GCE API object returned by the googleapiclient.discovery @@ -77,29 +109,47 @@ class TPUClusterResolver(ClusterResolver): Raises: ImportError: If the googleapiclient is not installed. + ValueError: If no TPUs are specified. """ + if isinstance(tpu, list): + if not tpu: + raise ValueError('At least one TPU must be specified.') + if len(tpu) != 1: + raise NotImplementedError( + 'Using multiple TPUs in a single session is not yet implemented') + tpu = tpu[0] + + # When using GKE with Cloud TPUs, the env variable will be set. + if tpu is None and self._inGke(): + tpu = self._gkeMaster() + + self._tpu = compat.as_bytes(tpu) # self._tpu is always bytes + self._job_name = job_name + self._credentials = credentials + + should_resolve = self._shouldResolve() - if not project: - project = self._requestComputeMetadata('/project/project-id') + if not project and should_resolve: + project = compat.as_str( + self._requestComputeMetadata('project/project-id')) - if not zone: - zone_path = self._requestComputeMetadata('/instance/zone') + if not zone and should_resolve: + zone_path = compat.as_str(self._requestComputeMetadata('instance/zone')) zone = zone_path.split('/')[-1] self._project = project self._zone = zone - self._tpu_names = tpu_names - self._job_name = job_name - self._credentials = credentials - if credentials == 'default': + if credentials == 'default' and should_resolve: if _GOOGLE_API_CLIENT_INSTALLED: self._credentials = GoogleCredentials.get_application_default() - if service is None: + if service is None and should_resolve: if not _GOOGLE_API_CLIENT_INSTALLED: raise ImportError('googleapiclient must be installed before using the ' - 'TPU cluster resolver') + 'TPU cluster resolver. Execute: `pip install ' + '--upgrade google-api-python-client` to install with ' + 'pip.') self._service = discovery.build( 'tpu', 'v1alpha1', @@ -107,25 +157,41 @@ class TPUClusterResolver(ClusterResolver): else: self._service = service - def get_master(self): - """Get the ClusterSpec grpc master path. + self._coordinator_name = coordinator_name + if coordinator_name and not coordinator_address and should_resolve: + self._start_local_server() + else: + self._coordinator_address = coordinator_address - This returns the grpc path (grpc://1.2.3.4:8470) of first instance in the - ClusterSpec returned by the cluster_spec function. This is suitable for use - for the `master` argument in tf.Session() when you are using one TPU. + def master(self): + """Get the Master string to be used for the session. + + In the normal case, this returns the grpc path (grpc://1.2.3.4:8470) of + first instance in the ClusterSpec returned by the cluster_spec function. + + If a non-TPU name is used when constructing a TPUClusterResolver, that will + be returned instead (e.g. If the tpus argument's value when constructing + this TPUClusterResolver was 'grpc://10.240.1.2:8470', + 'grpc://10.240.1.2:8470' will be returned). Returns: - string, the grpc path of the first instance in the ClusterSpec. + string, the connection string to use when creating a session. Raises: ValueError: If none of the TPUs specified exists. """ + if not self._shouldResolve(): + return self._tpu + job_tasks = self.cluster_spec().job_tasks(self._job_name) if not job_tasks: raise ValueError('No TPUs exists with the specified names exist.') return 'grpc://' + job_tasks[0] + def get_master(self): + return self.master() + def cluster_spec(self): """Returns a ClusterSpec object based on the latest TPU information. @@ -134,17 +200,54 @@ class TPUClusterResolver(ClusterResolver): Returns: A ClusterSpec containing host information returned from Cloud TPUs. - """ - worker_list = [] - for tpu_name in self._tpu_names: - full_name = 'projects/%s/locations/%s/nodes/%s' % ( - self._project, self._zone, tpu_name) - request = self._service.projects().locations().nodes().get(name=full_name) - response = request.execute() - - if 'health' in response and response['health'] == 'HEALTHY': - instance_url = '%s:%s' % (response['ipAddress'], response['port']) - worker_list.append(instance_url) - - return ClusterSpec({self._job_name: worker_list}) + Raises: + RuntimeError: If the provided TPU is not healthy. + """ + if not self._shouldResolve(): + return server_lib.ClusterSpec({}) + + full_name = 'projects/%s/locations/%s/nodes/%s' % ( + self._project, self._zone, compat.as_text(self._tpu)) + request = self._service.projects().locations().nodes().get(name=full_name) + response = request.execute() + + if 'health' in response and response['health'] != 'HEALTHY': + raise RuntimeError('TPU "%s" is unhealthy: "%s"' % (self._tpu, + response['health'])) + + if 'networkEndpoints' in response: + worker_list = [ + '%s:%s' % (endpoint['ipAddress'], endpoint['port']) + for endpoint in response['networkEndpoints'] + ] + else: + # Fall back to the deprecated response format + instance_url = '%s:%s' % (response['ipAddress'], response['port']) + worker_list = [instance_url] + + cluster_spec = {self._job_name: worker_list} + + if self._coordinator_address: + cluster_spec[self._coordinator_name] = [self._coordinator_address] + + return server_lib.ClusterSpec(cluster_spec) + + def _start_local_server(self): + address = self._requestComputeMetadata('instance/network-interfaces/0/ip') + self._server = server_lib.Server( + { + 'local': ['0.0.0.0:0'] + }, protocol='grpc', config=None, start=True) + # self._server.target is of the form: grpc://ipaddress:port + target = compat.as_bytes(self._server.target) + splits = target.split(compat.as_bytes(':')) + assert len(splits) == 3, self._server.target + assert splits[0] == compat.as_bytes('grpc'), self._server.target + self._coordinator_port = compat.as_text(splits[2]) + self._coordinator_address = '%s:%s' % ( + address, compat.as_text(self._coordinator_port)) + + def __deepcopy__(self, memo): + # TODO(b/73668574): Remove this once RunConfig avoids performing deepcopy. + return self diff --git a/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver_test.py b/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver_test.py index 4fd34629cf74f90869c77b8cb098d3c585a49404..48c3f6bb4f2d1643982e03d9ed68db14c10c184a 100644 --- a/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver_test.py +++ b/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver_test.py @@ -18,10 +18,12 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import os + from tensorflow.contrib.cluster_resolver.python.training.tpu_cluster_resolver import TPUClusterResolver from tensorflow.python.platform import test from tensorflow.python.training import server_lib - +from tensorflow.python.util import compat mock = test.mock @@ -50,10 +52,12 @@ class MockNodeClass(object): def mock_request_compute_metadata(cls, *args, **kwargs): del cls, kwargs # Unused. - if args[0] == '/project/project-id': + if args[0] == 'project/project-id': return 'test-project' - elif args[0] == '/instance/zone': + elif args[0] == 'instance/zone': return 'projects/test-project/locations/us-central1-c' + elif args[0] == 'instance/network-interfaces/0/ip': + return '10.128.1.2' return '' @@ -71,18 +75,17 @@ class TPUClusterResolverTest(test.TestCase): expected_proto: Expected protobuf """ 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()) + 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 mock_service_client( - self, - tpu_map=None): + def mock_service_client(self, tpu_map=None): if tpu_map is None: tpu_map = {} @@ -98,8 +101,7 @@ class TPUClusterResolverTest(test.TestCase): return mock_client - @mock.patch.object(TPUClusterResolver, - '_requestComputeMetadata', + @mock.patch.object(TPUClusterResolver, '_requestComputeMetadata', mock_request_compute_metadata) def testRetrieveProjectAndZoneFromMetadata(self): tpu_map = { @@ -113,17 +115,26 @@ class TPUClusterResolverTest(test.TestCase): tpu_cluster_resolver = TPUClusterResolver( project=None, zone=None, - tpu_names=['test-tpu-1'], + tpu=['test-tpu-1'], credentials=None, service=self.mock_service_client(tpu_map=tpu_map)) actual_cluster_spec = tpu_cluster_resolver.cluster_spec() expected_proto = """ - job { name: 'tpu_worker' tasks { key: 0 value: '10.1.2.3:8470' } } - """ - self._verifyClusterSpecEquality(actual_cluster_spec, expected_proto) + job { + name: 'coordinator' + tasks { key: 0 value: '10.128.1.2:%s' } + } + job { + name: 'worker' + tasks { key: 0 value: '10.1.2.3:8470' } + } + """ % tpu_cluster_resolver._coordinator_port + self._verifyClusterSpecEquality(actual_cluster_spec, str(expected_proto)) - def testSimpleSuccessfulRetrieval(self): + @mock.patch.object(TPUClusterResolver, '_requestComputeMetadata', + mock_request_compute_metadata) + def testRetrieveProjectAndZoneFromMetadataNoCoordinator(self): tpu_map = { 'projects/test-project/locations/us-central1-c/nodes/test-tpu-1': { 'ipAddress': '10.1.2.3', @@ -133,116 +144,230 @@ class TPUClusterResolverTest(test.TestCase): } tpu_cluster_resolver = TPUClusterResolver( - project='test-project', - zone='us-central1-c', - tpu_names=['test-tpu-1'], + project=None, + zone=None, + tpu=['test-tpu-1'], + coordinator_name=None, credentials=None, service=self.mock_service_client(tpu_map=tpu_map)) actual_cluster_spec = tpu_cluster_resolver.cluster_spec() expected_proto = """ - job { name: 'tpu_worker' tasks { key: 0 value: '10.1.2.3:8470' } } + job { name: 'worker' tasks { key: 0 value: '10.1.2.3:8470' } } """ self._verifyClusterSpecEquality(actual_cluster_spec, expected_proto) - def testMultipleSuccessfulRetrieval(self): + def testSimpleSuccessfulRetrieval(self): tpu_map = { 'projects/test-project/locations/us-central1-c/nodes/test-tpu-1': { 'ipAddress': '10.1.2.3', 'port': '8470', 'health': 'HEALTHY' - }, - 'projects/test-project/locations/us-central1-c/nodes/test-tpu-2': { - 'ipAddress': '10.4.5.6', - 'port': '8470', - 'health': 'HEALTHY' } } tpu_cluster_resolver = TPUClusterResolver( project='test-project', zone='us-central1-c', - tpu_names=['test-tpu-2', 'test-tpu-1'], + tpu=['test-tpu-1'], + coordinator_address='10.128.1.5:10203', credentials=None, service=self.mock_service_client(tpu_map=tpu_map)) actual_cluster_spec = tpu_cluster_resolver.cluster_spec() expected_proto = """ - job { name: 'tpu_worker' tasks { key: 0 value: '10.4.5.6:8470' } - tasks { key: 1 value: '10.1.2.3:8470' } } + 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) - def testHealthyTpuNodeRetrieval(self): + def testNewNetworkEndpointFormat(self): tpu_map = { 'projects/test-project/locations/us-central1-c/nodes/test-tpu-1': { - 'ipAddress': '10.1.2.3', - 'port': '8470', - 'health': 'HEALTHY' - }, - 'projects/test-project/locations/us-central1-c/nodes/test-tpu-2': { - 'ipAddress': '10.4.5.6', - 'port': '8470', - }, - 'projects/test-project/locations/us-central1-c/nodes/test-tpu-3': { - 'ipAddress': '10.7.8.9', - 'port': '8470', - 'health': 'UNHEALTHY' + 'health': 'HEALTHY', + 'networkEndpoints': [{ + 'ipAddress': '10.2.3.4', + 'port': 8470, + }] } } tpu_cluster_resolver = TPUClusterResolver( project='test-project', zone='us-central1-c', - tpu_names=['test-tpu-2', 'test-tpu-1', 'test-tpu-3'], + tpu='test-tpu-1', + coordinator_address='10.128.1.5:10203', credentials=None, service=self.mock_service_client(tpu_map=tpu_map)) actual_cluster_spec = tpu_cluster_resolver.cluster_spec() expected_proto = """ - job { - name: 'tpu_worker' - tasks { - key: 0 - value: '10.1.2.3:8470' - } - } + 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()) - def testGetMasterMultipleEntries(self): + @mock.patch.object(TPUClusterResolver, '_requestComputeMetadata', + mock_request_compute_metadata) + def testPodResolution(self): tpu_map = { 'projects/test-project/locations/us-central1-c/nodes/test-tpu-1': { - 'ipAddress': '10.1.2.3', - 'port': '8470', - 'health': 'HEALTHY' - }, - 'projects/test-project/locations/us-central1-c/nodes/test-tpu-2': { - 'ipAddress': '10.4.5.6', - 'port': '8470', - 'health': 'HEALTHY' + 'health': + 'HEALTHY', + 'networkEndpoints': [ + { + 'ipAddress': '10.2.3.4', + 'port': 8470, + }, + { + 'ipAddress': '10.2.3.5', + 'port': 8470, + }, + { + 'ipAddress': '10.2.3.6', + 'port': 8470, + }, + { + 'ipAddress': '10.2.3.7', + 'port': 8470, + }, + ] + } + } + + tpu_cluster_resolver = TPUClusterResolver( + tpu='test-tpu-1', + credentials=None, + service=self.mock_service_client(tpu_map=tpu_map)) + + actual_cluster_spec = tpu_cluster_resolver.cluster_spec() + expected_proto = """ + job { + name: 'coordinator', + tasks { key: 0 value: '10.128.1.2:%s'} + } + job { + name: 'worker' + tasks { key: 0 value: '10.2.3.4:8470' } + tasks { key: 1 value: '10.2.3.5:8470' } + tasks { key: 2 value: '10.2.3.6:8470' } + tasks { key: 3 value: '10.2.3.7:8470' } + } + """ % tpu_cluster_resolver._coordinator_port + self._verifyClusterSpecEquality(actual_cluster_spec, str(expected_proto)) + + def testPodResolutionNoCoordinator(self): + tpu_map = { + 'projects/test-project/locations/us-central1-c/nodes/test-tpu-1': { + 'health': + 'HEALTHY', + 'networkEndpoints': [ + { + 'ipAddress': '10.2.3.4', + 'port': 8470, + }, + { + 'ipAddress': '10.2.3.5', + 'port': 8470, + }, + { + 'ipAddress': '10.2.3.6', + 'port': 8470, + }, + { + 'ipAddress': '10.2.3.7', + 'port': 8470, + }, + ] } } tpu_cluster_resolver = TPUClusterResolver( project='test-project', zone='us-central1-c', - tpu_names=['test-tpu-2', 'test-tpu-1'], + tpu='test-tpu-1', + coordinator_name=None, credentials=None, service=self.mock_service_client(tpu_map=tpu_map)) - self.assertEqual('grpc://10.4.5.6:8470', tpu_cluster_resolver.get_master()) + + actual_cluster_spec = tpu_cluster_resolver.cluster_spec() + expected_proto = """ + job { + name: 'worker' + tasks { key: 0 value: '10.2.3.4:8470' } + tasks { key: 1 value: '10.2.3.5:8470' } + tasks { key: 2 value: '10.2.3.6:8470' } + tasks { key: 3 value: '10.2.3.7:8470' } + } + """ + self._verifyClusterSpecEquality(actual_cluster_spec, expected_proto) def testGetMasterNoEntries(self): tpu_map = {} + with self.assertRaises(ValueError): + TPUClusterResolver( + project='test-project', + zone='us-central1-c', + tpu=[], + coordinator_name=None, + credentials=None, + service=self.mock_service_client(tpu_map=tpu_map)) + + # TODO(saeta): Convert to parameterized test when included in OSS TF. + def verifyShouldResolve(self, tpu, should_resolve): tpu_cluster_resolver = TPUClusterResolver( project='test-project', zone='us-central1-c', - tpu_names=[], + tpu=tpu, + coordinator_name=None, credentials=None, - service=self.mock_service_client(tpu_map=tpu_map)) - with self.assertRaises(ValueError): - tpu_cluster_resolver.get_master() + service=self.mock_service_client(tpu_map={})) + self.assertEqual(should_resolve, tpu_cluster_resolver._shouldResolve(), + "TPU: '%s'" % tpu) + + def testShouldResolveNoName(self): + self.verifyShouldResolve('', False) + + def testShouldResolveLocal(self): + self.verifyShouldResolve('local', False) + + def testShouldResolveGrpc(self): + self.verifyShouldResolve('grpc://10.1.2.3:8470', False) + + def testShouldResolveBns(self): + self.verifyShouldResolve('/bns/foo/bar', False) + + def testShouldResolveName(self): + self.verifyShouldResolve('mytpu', True) + + def testShouldResolveList(self): + self.verifyShouldResolve(['myothertpu'], True) + + def testShouldResolveGrpcPrefix(self): + self.verifyShouldResolve('grpctpu', True) + + def testNoCallComputeMetadata(self): + tpu_cluster_resolver = TPUClusterResolver(tpu='/bns/foo/bar') + self.assertEqual( + compat.as_bytes('/bns/foo/bar'), tpu_cluster_resolver.master()) + self.assertEqual( + server_lib.ClusterSpec({}), tpu_cluster_resolver.cluster_spec()) + + def testGkeEnvironment(self): + os.environ['KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS'] = 'grpc://10.120.27.5:8470' + self.assertTrue('KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS' in os.environ) + tpu_cluster_resolver = TPUClusterResolver() + self.assertTrue(tpu_cluster_resolver._inGke()) + self.assertEqual( + compat.as_bytes('grpc://10.120.27.5:8470'), + compat.as_bytes(tpu_cluster_resolver._gkeMaster())) + self.assertEqual( + compat.as_bytes('grpc://10.120.27.5:8470'), + compat.as_bytes(tpu_cluster_resolver.get_master())) + del os.environ['KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS'] + if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/cmake/README.md b/tensorflow/contrib/cmake/README.md index 8f85a75ee466dbac524a1266dc2522109ca77cd5..fe83bb32046cd75328c92a74cdb4fdb6ce44560e 100644 --- a/tensorflow/contrib/cmake/README.md +++ b/tensorflow/contrib/cmake/README.md @@ -26,7 +26,7 @@ The CMake files in this directory can build the core TensorFlow runtime, an example C++ binary, and a PIP package containing the runtime and Python bindings. -### Pre-requisites +### Prerequisites * CMake version 3.5 or later. @@ -34,14 +34,16 @@ bindings. * [SWIG](http://www.swig.org/download.html) -* Additional pre-requisites for Microsoft Windows: +* Additional prerequisites for Microsoft Windows: - Visual Studio 2015 - Python 3.5 - - NumPy 1.11.0 or later -* Additional pre-requisites for Linux: +* Additional prerequisites for Linux: - Python 2.7 or later - [Docker](https://www.docker.com/) (for automated testing) + +* Python dependencies: + - wheel - NumPy 1.11.0 or later ### Known-good configurations @@ -102,7 +104,7 @@ ops or APIs. Step-by-step Windows build ========================== -1. Install the pre-requisites detailed above, and set up your environment. +1. Install the prerequisites detailed above, and set up your environment. * The following commands assume that you are using the Windows Command Prompt (`cmd.exe`). You will need to set up your environment to use the diff --git a/tensorflow/contrib/cmake/external/cub.cmake b/tensorflow/contrib/cmake/external/cub.cmake index 836889895567f679d9960e29ece1600d1a7a58eb..98a8c7e736e5c8c407b90e8eac440cdc7ab21579 100644 --- a/tensorflow/contrib/cmake/external/cub.cmake +++ b/tensorflow/contrib/cmake/external/cub.cmake @@ -14,8 +14,8 @@ # ============================================================================== include (ExternalProject) -set(cub_URL https://mirror.bazel.build/github.com/NVlabs/cub/archive/1.7.4.zip) -set(cub_HASH SHA256=20a1a39fd97e5da7f40f5f2e7fd73fd2ea59f9dc4bb8a6c5f228aa543e727e31) +set(cub_URL https://mirror.bazel.build/github.com/NVlabs/cub/archive/1.8.0.zip) +set(cub_HASH SHA256=6bfa06ab52a650ae7ee6963143a0bbc667d6504822cbd9670369b598f18c58c3) set(cub_BUILD ${CMAKE_CURRENT_BINARY_DIR}/cub/src/cub) set(cub_INCLUDE_DIR ${CMAKE_CURRENT_BINARY_DIR}/cub/src/cub) set(cub_ARCHIVE_DIR ${CMAKE_CURRENT_BINARY_DIR}/external/cub_archive) diff --git a/tensorflow/contrib/cmake/external/grpc.cmake b/tensorflow/contrib/cmake/external/grpc.cmake index a9f43a3ecba4830533efcc13f8c4c1c61fe1ef78..17f65999faaf5c0ca39bfbc968a9140dbff49c2e 100644 --- a/tensorflow/contrib/cmake/external/grpc.cmake +++ b/tensorflow/contrib/cmake/external/grpc.cmake @@ -35,6 +35,7 @@ else() set(grpc_STATIC_LIBRARIES ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/libgrpc++_unsecure.a ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/libgrpc_unsecure.a + ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/third_party/cares/cares/lib/libcares.a ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/libgpr.a) endif() diff --git a/tensorflow/contrib/cmake/external/protobuf.cmake b/tensorflow/contrib/cmake/external/protobuf.cmake index aba8a5244e17d717293deec6d9b6e8e725ef010e..ab464bc99a43138130bb2758ae28ecef29805c31 100644 --- a/tensorflow/contrib/cmake/external/protobuf.cmake +++ b/tensorflow/contrib/cmake/external/protobuf.cmake @@ -16,7 +16,7 @@ include (ExternalProject) set(PROTOBUF_INCLUDE_DIRS ${CMAKE_CURRENT_BINARY_DIR}/protobuf/src/protobuf/src) set(PROTOBUF_URL https://github.com/google/protobuf.git) -set(PROTOBUF_TAG 396336eb961b75f03b25824fe86cf6490fb75e3a) +set(PROTOBUF_TAG b04e5cba356212e4e8c66c61bbe0c3a20537c5b9) if(WIN32) if(${CMAKE_GENERATOR} MATCHES "Visual Studio.*") diff --git a/tensorflow/contrib/cmake/python_modules.txt b/tensorflow/contrib/cmake/python_modules.txt index bfe53c01b3b5fb9db8a5d8fa280d1d7f98974882..0d2a6a23db26af2fb9498849aa93e74379915fe3 100644 --- a/tensorflow/contrib/cmake/python_modules.txt +++ b/tensorflow/contrib/cmake/python_modules.txt @@ -165,6 +165,7 @@ tensorflow/contrib/distributions/python tensorflow/contrib/distributions/python/ops tensorflow/contrib/distributions/python/ops/bijectors tensorflow/contrib/eager +tensorflow/contrib/eager/proto tensorflow/contrib/eager/python tensorflow/contrib/estimator tensorflow/contrib/estimator/python diff --git a/tensorflow/contrib/cmake/python_protos.txt b/tensorflow/contrib/cmake/python_protos.txt index 8a9c406d8b118c10ddcaafb0e4fc242aa79cdb57..c03c0c80fe62a4f95d0fcf240ee25725a19d86f0 100644 --- a/tensorflow/contrib/cmake/python_protos.txt +++ b/tensorflow/contrib/cmake/python_protos.txt @@ -4,6 +4,7 @@ tensorflow/python tensorflow/contrib/boosted_trees/proto tensorflow/contrib/cloud/kernels tensorflow/contrib/decision_trees/proto +tensorflow/contrib/eager/proto tensorflow/contrib/gdr tensorflow/contrib/lite/toco tensorflow/contrib/mpi diff --git a/tensorflow/contrib/cmake/tf_core_cpu.cmake b/tensorflow/contrib/cmake/tf_core_cpu.cmake index 96ac60d095dbc84470ff1be92f4bf52bb420fc52..a54cbff33b66d63d7229fa2f50b8a4ca962111ed 100644 --- a/tensorflow/contrib/cmake/tf_core_cpu.cmake +++ b/tensorflow/contrib/cmake/tf_core_cpu.cmake @@ -63,6 +63,12 @@ file(GLOB_RECURSE tf_core_cpu_exclude_srcs "${tensorflow_source_dir}/tensorflow/core/grappler/inputs/trivial_test_graph_input_yielder.h" "${tensorflow_source_dir}/tensorflow/core/grappler/inputs/trivial_test_graph_input_yielder.cc" ) +file(GLOB_RECURSE tf_core_cpu_whitelisted_srcs + "${tensorflow_source_dir}/tensorflow/core/common_runtime/gpu/gpu_id.h" + "${tensorflow_source_dir}/tensorflow/core/common_runtime/gpu/gpu_id.cc" + "${tensorflow_source_dir}/tensorflow/core/common_runtime/gpu/gpu_id_manager.cc" +) +list(REMOVE_ITEM tf_core_cpu_exclude_srcs ${tf_core_cpu_whitelisted_srcs}) list(REMOVE_ITEM tf_core_cpu_srcs ${tf_core_cpu_exclude_srcs}) if (tensorflow_ENABLE_GPU) @@ -79,6 +85,7 @@ if (tensorflow_ENABLE_GPU) "${tensorflow_source_dir}/tensorflow/core/*test*.cc" ) list(REMOVE_ITEM tf_core_gpu_srcs ${tf_core_gpu_exclude_srcs}) + list(REMOVE_ITEM tf_core_gpu_srcs ${tf_core_cpu_whitelisted_srcs}) list(APPEND tf_core_cpu_srcs ${tf_core_gpu_srcs}) endif() diff --git a/tensorflow/contrib/cmake/tf_tests.cmake b/tensorflow/contrib/cmake/tf_tests.cmake index 1c4ebd7f0c1113bcd0857fb0858df2248499f920..b3e5b30826097d6c747245fec975fcbea3785d15 100644 --- a/tensorflow/contrib/cmake/tf_tests.cmake +++ b/tensorflow/contrib/cmake/tf_tests.cmake @@ -222,6 +222,7 @@ if (tensorflow_BUILD_PYTHON_TESTS) "${tensorflow_source_dir}/tensorflow/python/debug/cli/curses_ui_test.py" # TFDBG grpc:// mode is not yet available on Windows. "${tensorflow_source_dir}/tensorflow/python/debug/lib/dist_session_debug_grpc_test.py" + "${tensorflow_source_dir}/tensorflow/python/debug/lib/grpc_large_data_test.py" "${tensorflow_source_dir}/tensorflow/python/debug/lib/session_debug_grpc_test.py" "${tensorflow_source_dir}/tensorflow/python/debug/lib/source_remote_test.py" # stl on windows handles overflows different @@ -475,6 +476,10 @@ if (tensorflow_BUILD_CC_TESTS) "${tensorflow_source_dir}/tensorflow/core/profiler/internal/advisor/*_test.cc" ) + list(REMOVE_ITEM tf_test_src_simple + ${tf_core_profiler_test_srcs} + ) + set(tf_test_lib tf_test_lib) add_library(${tf_test_lib} STATIC ${tf_src_testlib}) diff --git a/tensorflow/contrib/data/__init__.py b/tensorflow/contrib/data/__init__.py index fcdccdd26ca1824bf13f1fd0cfd80b20ca8a10c3..9212b69700941c190df1d44ed308147105c56fba 100644 --- a/tensorflow/contrib/data/__init__.py +++ b/tensorflow/contrib/data/__init__.py @@ -23,12 +23,15 @@ removing existing functionality. See the @{$datasets$Importing Data} Programmer's Guide for an overview. @@Counter +@@SqlDataset @@batch_and_drop_remainder +@@bucket_by_sequence_length @@dense_to_sparse_batch @@enumerate_dataset @@group_by_window @@ignore_errors +@@make_batched_features_dataset @@make_saveable_from_iterator @@map_and_batch @@padded_batch_and_drop_remainder @@ -37,6 +40,7 @@ See the @{$datasets$Importing Data} Programmer's Guide for an overview. @@rejection_resample @@scan @@shuffle_and_repeat +@@sliding_window_batch @@sloppy_interleave @@unbatch @@ -58,15 +62,20 @@ from tensorflow.contrib.data.python.ops.counter import Counter from tensorflow.contrib.data.python.ops.enumerate_ops import enumerate_dataset from tensorflow.contrib.data.python.ops.error_ops import ignore_errors from tensorflow.contrib.data.python.ops.get_single_element import get_single_element +from tensorflow.contrib.data.python.ops.grouping import bucket_by_sequence_length from tensorflow.contrib.data.python.ops.grouping import group_by_window from tensorflow.contrib.data.python.ops.interleave_ops import parallel_interleave from tensorflow.contrib.data.python.ops.interleave_ops import sloppy_interleave from tensorflow.contrib.data.python.ops.iterator_ops import make_saveable_from_iterator +from tensorflow.contrib.data.python.ops.readers import make_batched_features_dataset from tensorflow.contrib.data.python.ops.readers import read_batch_features from tensorflow.contrib.data.python.ops.readers import SqlDataset from tensorflow.contrib.data.python.ops.resampling import rejection_resample from tensorflow.contrib.data.python.ops.scan_ops import scan from tensorflow.contrib.data.python.ops.shuffle_ops import shuffle_and_repeat +from tensorflow.contrib.data.python.ops.sliding import sliding_window_batch +from tensorflow.python.data.ops.iterator_ops import Iterator +from tensorflow.python.ops.parsing_ops import parse_single_example_v2 as parse_single_example # pylint: enable=unused-import from tensorflow.python.util.all_util import remove_undocumented diff --git a/tensorflow/contrib/data/kernels/BUILD b/tensorflow/contrib/data/kernels/BUILD index 9bd6a42da2d93263e84a759cffdc5a9e8f9742fd..c87da7dfaa5943f7918c370f63362673844c7f0e 100644 --- a/tensorflow/contrib/data/kernels/BUILD +++ b/tensorflow/contrib/data/kernels/BUILD @@ -10,6 +10,7 @@ cc_library( name = "prefetching_kernels", srcs = ["prefetching_kernels.cc"], deps = [ + "//tensorflow/core:core_cpu_headers_lib", "//tensorflow/core:framework_headers_lib", "//third_party/eigen3", "@protobuf_archive//:protobuf_headers", diff --git a/tensorflow/contrib/data/kernels/prefetching_kernels.cc b/tensorflow/contrib/data/kernels/prefetching_kernels.cc index d3df14bdd03476e9ee4015b374512e5bb9893a63..1baac3ea5239659e65881e5b2dea4fe1a8c49d1b 100644 --- a/tensorflow/contrib/data/kernels/prefetching_kernels.cc +++ b/tensorflow/contrib/data/kernels/prefetching_kernels.cc @@ -14,6 +14,7 @@ limitations under the License. ==============================================================================*/ #include +#include "tensorflow/core/common_runtime/process_function_library_runtime.h" #include "tensorflow/core/framework/function.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/resource_op_kernel.h" @@ -35,12 +36,14 @@ using FunctionBufferCallback = std::function; class FunctionBufferingResource : public ResourceBase { public: FunctionBufferingResource(FunctionLibraryRuntime* lib, + std::unique_ptr pflr, const NameAttrList& func, int64 buffer_size, const string& source_device, const string& target_device, const std::vector& func_args, int64 thread_pool_size) : lib_(lib), + pflr_(std::move(pflr)), func_(func), buffer_size_(buffer_size), source_device_(source_device), @@ -222,6 +225,7 @@ class FunctionBufferingResource : public ResourceBase { mutex mu_; FunctionLibraryRuntime* lib_; + std::unique_ptr pflr_; NameAttrList func_; const int64 buffer_size_; const string source_device_; @@ -241,7 +245,7 @@ class FunctionBufferingResource : public ResourceBase { class FunctionBufferResourceHandleOp : public OpKernel { public: explicit FunctionBufferResourceHandleOp(OpKernelConstruction* ctx) - : OpKernel(ctx) { + : OpKernel(ctx), flib_def_(nullptr) { OP_REQUIRES_OK(ctx, ctx->GetAttr("f", &func_)); OP_REQUIRES_OK(ctx, ctx->GetAttr("buffer_size", &buffer_size_)); OP_REQUIRES_OK(ctx, ctx->GetAttr("container", &container_)); @@ -249,6 +253,17 @@ class FunctionBufferResourceHandleOp : public OpKernel { OP_REQUIRES_OK(ctx, ctx->GetAttr("thread_pool_size", &thread_pool_size_)); } + ~FunctionBufferResourceHandleOp() override { + if (cinfo_.resource_is_private_to_kernel()) { + if (!cinfo_.resource_manager() + ->Delete(cinfo_.container(), + cinfo_.name()) + .ok()) { + // Do nothing; the resource can have been deleted by session resets. + } + } + } + void Compute(OpKernelContext* ctx) override { const Tensor* string_arg; OP_REQUIRES_OK(ctx, ctx->input("string_arg", &string_arg)); @@ -267,28 +282,39 @@ class FunctionBufferResourceHandleOp : public OpKernel { const string& source_device = ctx->device()->name(); - ContainerInfo cinfo; - OP_REQUIRES_OK(ctx, cinfo.Init(ctx->resource_manager(), def())); - // Create the resource. - FunctionBufferingResource* buffer; - OP_REQUIRES_OK( - ctx, ctx->resource_manager()->LookupOrCreate( - cinfo.container(), cinfo.name(), &buffer, - [lib, &source_device, &target_device, func_args, - this](FunctionBufferingResource** ptr) { - *ptr = new FunctionBufferingResource( - lib, func_, buffer_size_, source_device, target_device, - func_args, thread_pool_size_); - return Status::OK(); - })); - OP_REQUIRES_OK(ctx, buffer->Instantiate()); + mutex_lock l(mu_); + if (!initialized_) { + OP_REQUIRES_OK(ctx, cinfo_.Init(ctx->resource_manager(), def())); + FunctionLibraryRuntime* clone_lib; + std::unique_ptr pflr; + OP_REQUIRES_OK(ctx, lib->Clone(&flib_def_, &pflr, &clone_lib)); + // Create the resource. + FunctionBufferingResource* buffer; + OP_REQUIRES_OK( + ctx, + ctx->resource_manager()->LookupOrCreate( + cinfo_.container(), cinfo_.name(), &buffer, + [clone_lib, &pflr, &source_device, &target_device, func_args, + this](FunctionBufferingResource** ptr) { + *ptr = new FunctionBufferingResource( + clone_lib, std::move(pflr), func_, buffer_size_, + source_device, target_device, func_args, thread_pool_size_); + return Status::OK(); + })); + OP_REQUIRES_OK(ctx, buffer->Instantiate()); + initialized_ = true; + } OP_REQUIRES_OK(ctx, MakeResourceHandleToOutput( - ctx, 0, cinfo.container(), cinfo.name(), + ctx, 0, cinfo_.container(), cinfo_.name(), MakeTypeIndex())); } private: + mutex mu_; + ContainerInfo cinfo_ GUARDED_BY(mu_); + bool initialized_ GUARDED_BY(mu_) = false; + std::unique_ptr flib_def_; NameAttrList func_; int64 buffer_size_; string container_; diff --git a/tensorflow/contrib/data/kernels/threadpool_dataset_op.cc b/tensorflow/contrib/data/kernels/threadpool_dataset_op.cc index 4b3edde85fc755f1c7694a555b867317e81f149d..63e19ae3f837c9d3cfb1221df64360ee74117f13 100644 --- a/tensorflow/contrib/data/kernels/threadpool_dataset_op.cc +++ b/tensorflow/contrib/data/kernels/threadpool_dataset_op.cc @@ -166,14 +166,10 @@ class ThreadPoolDatasetOp : public UnaryDatasetOpKernel { params.runner = [pool](std::function c) { pool->Schedule(std::move(c)); }; - params.stats_aggregator_getter = [ctx]() { - return ctx->stats_aggregator(); - }; + params.stats_aggregator_getter = ctx->stats_aggregator_getter(); params.lib = ctx->lib(); params.function_library = ctx->function_library(); - params.allocator_getter = [ctx](AllocatorAttributes attrs) { - return ctx->allocator(attrs); - }; + params.allocator_getter = ctx->allocator_getter(); IteratorContext threadpool_ctx(params); return input_impl_->GetNext(&threadpool_ctx, out_tensors, end_of_sequence); diff --git a/tensorflow/contrib/data/python/kernel_tests/BUILD b/tensorflow/contrib/data/python/kernel_tests/BUILD index 82cd276ce8073b1e66bbc620fa845733aaaca4d4..2c4d4adfdad6d2b3268896cb91cd0357b2b814d9 100644 --- a/tensorflow/contrib/data/python/kernel_tests/BUILD +++ b/tensorflow/contrib/data/python/kernel_tests/BUILD @@ -168,8 +168,10 @@ py_test( srcs = ["interleave_dataset_op_test.py"], srcs_version = "PY2AND3", tags = [ + "manual", "no_oss", "no_pip", + "notap", ], deps = [ ":dataset_serialization_test", @@ -292,9 +294,12 @@ py_test( "//tensorflow/python:errors", "//tensorflow/python:framework_ops", "//tensorflow/python:lib", + "//tensorflow/python:math_ops", "//tensorflow/python:parsing_ops", + "//tensorflow/python:string_ops", "//tensorflow/python:util", "//tensorflow/python/data/ops:iterator_ops", + "//third_party/py/numpy", ], ) @@ -493,6 +498,23 @@ py_test( ], ) +tf_py_test( + name = "slide_dataset_op_test", + size = "small", + srcs = ["slide_dataset_op_test.py"], + additional_deps = [ + "//tensorflow/contrib/data/python/ops:dataset_ops", + "//tensorflow/contrib/data/python/ops:transformation_ops", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:dtypes", + "//tensorflow/python:errors", + "//tensorflow/python:math_ops", + "//tensorflow/python:sparse_tensor", + "//third_party/py/numpy", + ], +) + filegroup( name = "all_files", srcs = glob( diff --git a/tensorflow/contrib/data/python/kernel_tests/bucketing_test.py b/tensorflow/contrib/data/python/kernel_tests/bucketing_test.py index f1b494e1a620992365ed75613b508e32f94b40a4..94f800e8a58bc34eef3034cd976b931528c01940 100644 --- a/tensorflow/contrib/data/python/kernel_tests/bucketing_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/bucketing_test.py @@ -17,6 +17,8 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import random + import numpy as np from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base @@ -379,5 +381,93 @@ class BucketTest(test.TestCase): self.assertEqual(batches, 15) +class BucketBySequenceLength(test.TestCase): + + def testBucket(self): + + boundaries = [10, 20, 30] + batch_sizes = [10, 8, 4, 2] + lengths = [8, 13, 25, 35] + + def element_gen(): + # Produce 1 batch for each bucket + elements = [] + for batch_size, length in zip(batch_sizes, lengths): + for _ in range(batch_size): + elements.append([1] * length) + random.shuffle(elements) + for el in elements: + yield (el,) + + element_len = lambda el: array_ops.shape(el)[0] + dataset = dataset_ops.Dataset.from_generator( + element_gen, (dtypes.int64,), ([None],)).apply( + grouping.bucket_by_sequence_length( + element_len, boundaries, batch_sizes)) + batch, = dataset.make_one_shot_iterator().get_next() + + with self.test_session() as sess: + batches = [] + for _ in range(4): + batches.append(sess.run(batch)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(batch) + batch_sizes_val = [] + lengths_val = [] + for batch in batches: + batch_size = batch.shape[0] + length = batch.shape[1] + batch_sizes_val.append(batch_size) + lengths_val.append(length) + self.assertEqual(sum(batch_sizes_val), sum(batch_sizes)) + self.assertEqual(sorted(batch_sizes), sorted(batch_sizes_val)) + self.assertEqual(sorted(lengths), sorted(lengths_val)) + + def testPadToBoundary(self): + + boundaries = [10, 20, 30] + batch_sizes = [10, 8, 4, 2] + lengths = [8, 13, 25] + + def element_gen(): + # Produce 1 batch for each bucket + elements = [] + for batch_size, length in zip(batch_sizes[:-1], lengths): + for _ in range(batch_size): + elements.append([1] * length) + random.shuffle(elements) + for el in elements: + yield (el,) + for _ in range(batch_sizes[-1]): + el = [1] * (boundaries[-1] + 5) + yield (el,) + + element_len = lambda el: array_ops.shape(el)[0] + dataset = dataset_ops.Dataset.from_generator( + element_gen, (dtypes.int64,), ([None],)).apply( + grouping.bucket_by_sequence_length( + element_len, boundaries, batch_sizes, + pad_to_bucket_boundary=True)) + batch, = dataset.make_one_shot_iterator().get_next() + + with self.test_session() as sess: + batches = [] + for _ in range(3): + batches.append(sess.run(batch)) + with self.assertRaisesOpError("bucket_boundaries"): + sess.run(batch) + batch_sizes_val = [] + lengths_val = [] + for batch in batches: + batch_size = batch.shape[0] + length = batch.shape[1] + batch_sizes_val.append(batch_size) + lengths_val.append(length) + batch_sizes = batch_sizes[:-1] + self.assertEqual(sum(batch_sizes_val), sum(batch_sizes)) + self.assertEqual(sorted(batch_sizes), sorted(batch_sizes_val)) + self.assertEqual(sorted(boundaries), sorted(lengths_val)) + + if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/get_single_element_test.py b/tensorflow/contrib/data/python/kernel_tests/get_single_element_test.py index 32ea44f7c7ba329dc253bb9fbbcac0a1ed16aec7..87b7c6ddb7afcbaaf8fe97cd8be87e6f5af8cd4d 100644 --- a/tensorflow/contrib/data/python/kernel_tests/get_single_element_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/get_single_element_test.py @@ -22,6 +22,7 @@ from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors +from tensorflow.python.framework import sparse_tensor from tensorflow.python.ops import array_ops from tensorflow.python.platform import test @@ -33,17 +34,25 @@ class GetSingleElementTest(test.TestCase): take_value = array_ops.placeholder_with_default( constant_op.constant(1, dtype=dtypes.int64), shape=[]) + def make_sparse(x): + x_1d = array_ops.reshape(x, [1]) + x_2d = array_ops.reshape(x, [1, 1]) + return sparse_tensor.SparseTensor(x_2d, x_1d, x_1d) + dataset = (dataset_ops.Dataset.range(100) .skip(skip_value) - .map(lambda x: x * x) + .map(lambda x: (x * x, make_sparse(x))) .take(take_value)) element = get_single_element.get_single_element(dataset) with self.test_session() as sess: - self.assertEqual(0, sess.run(element, feed_dict={skip_value: 0})) - self.assertEqual(25, sess.run(element, feed_dict={skip_value: 5})) - self.assertEqual(100, sess.run(element, feed_dict={skip_value: 10})) + for x in [0, 5, 10]: + dense_val, sparse_val = sess.run(element, feed_dict={skip_value: x}) + self.assertEqual(x * x, dense_val) + self.assertAllEqual([[x]], sparse_val.indices) + self.assertAllEqual([x], sparse_val.values) + self.assertAllEqual([x], sparse_val.dense_shape) with self.assertRaisesRegexp(errors.InvalidArgumentError, "Dataset was empty."): diff --git a/tensorflow/contrib/data/python/kernel_tests/reader_dataset_ops_test.py b/tensorflow/contrib/data/python/kernel_tests/reader_dataset_ops_test.py index 6efe97444a375febc550ff3a3ea04bcd9330a3a5..699e8e7865502facd05e0c4d6d4f01b80f7c050c 100644 --- a/tensorflow/contrib/data/python/kernel_tests/reader_dataset_ops_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/reader_dataset_ops_test.py @@ -21,6 +21,8 @@ import gzip import os import zlib +import numpy as np + from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base from tensorflow.contrib.data.python.ops import readers from tensorflow.core.example import example_pb2 @@ -33,7 +35,9 @@ from tensorflow.python.framework import errors from tensorflow.python.framework import ops from tensorflow.python.lib.io import python_io from tensorflow.python.ops import array_ops +from tensorflow.python.ops import math_ops from tensorflow.python.ops import parsing_ops +from tensorflow.python.ops import string_ops from tensorflow.python.platform import test from tensorflow.python.util import compat @@ -262,12 +266,19 @@ class ReadBatchFeaturesTest(test.TestCase): self._num_records = 7 self.test_filenames = self._createFiles() - def _read_batch_features(self, filenames, num_epochs, batch_size): + def _read_batch_features(self, + filenames, + num_epochs, + batch_size, + reader_num_threads=1, + parser_num_threads=1, + shuffle=False, + shuffle_seed=None): self.filenames = filenames self.num_epochs = num_epochs self.batch_size = batch_size - return readers.read_batch_features( + return readers.make_batched_features_dataset( file_pattern=self.filenames, batch_size=self.batch_size, features={ @@ -276,8 +287,12 @@ class ReadBatchFeaturesTest(test.TestCase): "keywords": parsing_ops.VarLenFeature(dtypes.string) }, reader=core_readers.TFRecordDataset, - randomize_input=False, - num_epochs=self.num_epochs) + num_epochs=self.num_epochs, + shuffle=shuffle, + shuffle_seed=shuffle_seed, + reader_num_threads=reader_num_threads, + parser_num_threads=parser_num_threads).make_one_shot_iterator( + ).get_next() def _record(self, f, r): example = example_pb2.Example(features=feature_pb2.Features( @@ -312,24 +327,35 @@ class ReadBatchFeaturesTest(test.TestCase): writer.close() return filenames - def _next_actual_batch(self, sess): - file_op = self.outputs["file"] - keywords_indices_op = self.outputs["keywords"].indices - keywords_values_op = self.outputs["keywords"].values - keywords_dense_shape_op = self.outputs["keywords"].dense_shape - record_op = self.outputs["record"] + def _run_actual_batch(self, outputs, sess): + file_op = outputs["file"] + keywords_indices_op = outputs["keywords"].indices + keywords_values_op = outputs["keywords"].values + keywords_dense_shape_op = outputs["keywords"].dense_shape + record_op = outputs["record"] return sess.run([ file_op, keywords_indices_op, keywords_values_op, keywords_dense_shape_op, record_op ]) - def _next_expected_batch(self, file_indices, batch_size, num_epochs): + def _next_actual_batch(self, sess): + return self._run_actual_batch(self.outputs, sess) + + def _next_expected_batch(self, + file_indices, + batch_size, + num_epochs, + cycle_length=1): def _next_record(file_indices): for j in file_indices: for i in range(self._num_records): yield j, i + def _next_record_interleaved(file_indices, cycle_length): + return self._interleave([_next_record([i]) for i in file_indices], + cycle_length) + file_batch = [] keywords_batch_indices = [] keywords_batch_values = [] @@ -337,7 +363,11 @@ class ReadBatchFeaturesTest(test.TestCase): record_batch = [] batch_index = 0 for _ in range(num_epochs): - for record in _next_record(file_indices): + if cycle_length == 1: + next_records = _next_record(file_indices) + else: + next_records = _next_record_interleaved(file_indices, cycle_length) + for record in next_records: f = record[0] r = record[1] file_batch.append(f) @@ -365,14 +395,41 @@ class ReadBatchFeaturesTest(test.TestCase): [len(file_batch), keywords_batch_max_len], record_batch ] - def _verify_records(self, sess, batch_size, file_index=None, num_epochs=1): + def _interleave(self, iterators, cycle_length): + pending_iterators = iterators + open_iterators = [] + num_open = 0 + for i in range(cycle_length): + if pending_iterators: + open_iterators.append(pending_iterators.pop(0)) + num_open += 1 + + while num_open: + for i in range(min(cycle_length, len(open_iterators))): + if open_iterators[i] is None: + continue + try: + yield next(open_iterators[i]) + except StopIteration: + if pending_iterators: + open_iterators[i] = pending_iterators.pop(0) + else: + open_iterators[i] = None + num_open -= 1 + + def _verify_records(self, + sess, + batch_size, + file_index=None, + num_epochs=1, + interleave_cycle_length=1): if file_index is not None: file_indices = [file_index] else: file_indices = range(self._num_files) - for expected_batch in self._next_expected_batch(file_indices, batch_size, - num_epochs): + for expected_batch in self._next_expected_batch( + file_indices, batch_size, num_epochs, interleave_cycle_length): actual_batch = self._next_actual_batch(sess) for i in range(len(expected_batch)): self.assertAllEqual(expected_batch[i], actual_batch[i]) @@ -435,6 +492,302 @@ class ReadBatchFeaturesTest(test.TestCase): with self.assertRaises(errors.OutOfRangeError): sess.run(next_element) + def testReadWithFusedShuffleRepeatDataset(self): + num_epochs = 5 + total_records = num_epochs * self._num_records + for batch_size in [1, 2]: + # Test that shuffling with same seed produces the same result. + with ops.Graph().as_default() as g: + with self.test_session(graph=g) as sess: + outputs1 = self._read_batch_features( + filenames=self.test_filenames[0], + num_epochs=num_epochs, + batch_size=batch_size, + shuffle=True, + shuffle_seed=5) + outputs2 = self._read_batch_features( + filenames=self.test_filenames[0], + num_epochs=num_epochs, + batch_size=batch_size, + shuffle=True, + shuffle_seed=5) + for _ in range(total_records // batch_size): + batch1 = self._run_actual_batch(outputs1, sess) + batch2 = self._run_actual_batch(outputs2, sess) + for i in range(len(batch1)): + self.assertAllEqual(batch1[i], batch2[i]) + + # Test that shuffling with different seeds produces a different order. + with ops.Graph().as_default() as g: + with self.test_session(graph=g) as sess: + outputs1 = self._read_batch_features( + filenames=self.test_filenames[0], + num_epochs=num_epochs, + batch_size=batch_size, + shuffle=True, + shuffle_seed=5) + outputs2 = self._read_batch_features( + filenames=self.test_filenames[0], + num_epochs=num_epochs, + batch_size=batch_size, + shuffle=True, + shuffle_seed=15) + all_equal = True + for _ in range(total_records // batch_size): + batch1 = self._run_actual_batch(outputs1, sess) + batch2 = self._run_actual_batch(outputs2, sess) + for i in range(len(batch1)): + all_equal = all_equal and np.array_equal(batch1[i], batch2[i]) + self.assertFalse(all_equal) + + def testParallelReadersAndParsers(self): + num_epochs = 5 + for batch_size in [1, 2]: + for reader_num_threads in [2, 4]: + for parser_num_threads in [2, 4]: + with ops.Graph().as_default() as g: + with self.test_session(graph=g) as sess: + self.outputs = self._read_batch_features( + filenames=self.test_filenames, + num_epochs=num_epochs, + batch_size=batch_size, + reader_num_threads=reader_num_threads, + parser_num_threads=parser_num_threads) + self._verify_records( + sess, + batch_size, + num_epochs=num_epochs, + interleave_cycle_length=reader_num_threads) + with self.assertRaises(errors.OutOfRangeError): + self._next_actual_batch(sess) + + +class MakeCsvDatasetTest(test.TestCase): + + COLUMN_TYPES = [ + dtypes.int32, dtypes.int64, dtypes.float32, dtypes.float64, dtypes.string + ] + COLUMNS = ["col%d" % i for i in range(len(COLUMN_TYPES))] + LABEL = COLUMNS[0] + + def setUp(self): + super(MakeCsvDatasetTest, self).setUp() + self._num_files = 2 + self._num_records = 7 + self._test_filenames = self._create_files() + + def _csv_values(self, fileno, recordno): + return [ + fileno, + recordno, + fileno * recordno * 0.5, + fileno * recordno + 0.5, + "record %d" % recordno if recordno % 2 == 1 else "", + ] + + def _csv_record(self, fileno, recordno): + return ",".join(str(v) for v in self._csv_values(fileno, recordno)) + + def _create_files(self): + filenames = [] + for i in range(self._num_files): + fn = os.path.join(self.get_temp_dir(), "csv_file%d.csv" % i) + filenames.append(fn) + f = open(fn, "w") + f.write(",".join(self.COLUMNS) + "\n") # header line + for j in range(self._num_records): + f.write(self._csv_record(i, j) + "\n") + f.write("# Some comment goes here. Should be ignored!\n") + f.close() + return filenames + + def _make_csv_dataset(self, + filenames, + defaults, + label_key=LABEL, + batch_size=1, + num_epochs=1, + shuffle=False, + shuffle_seed=None): + return readers.make_csv_dataset( + filenames, + column_keys=self.COLUMNS, + column_defaults=defaults, + label_key=label_key, + batch_size=batch_size, + num_epochs=num_epochs, + shuffle=shuffle, + shuffle_seed=shuffle_seed, + skip=1, + filter_fn= + lambda line: math_ops.not_equal(string_ops.substr(line, 0, 1), "#"), + ) + + def _next_actual_batch(self, file_indices, batch_size, num_epochs): + features = {col: list() for col in self.COLUMNS} + for _ in range(num_epochs): + for i in file_indices: + for j in range(self._num_records): + values = self._csv_values(i, j) + if not values[-1]: + values[-1] = "NULL" # null values in csv are interpreted as default + values[-1] = values[-1].encode("utf-8") + + # Regroup lists by column instead of row + for n, col in enumerate(self.COLUMNS): + features[col].append(values[n]) + if len(list(features.values())[0]) == batch_size: + yield features + features = {col: list() for col in self.COLUMNS} + + def _run_actual_batch(self, outputs, sess): + features, labels = sess.run(outputs) + batch = [features[k] for k in self.COLUMNS if k != self.LABEL] + batch.append(labels) + return batch + + def _verify_records( + self, + sess, + dataset, + file_indices, + label_key=LABEL, + batch_size=1, + num_epochs=1, + ): + iterator = dataset.make_one_shot_iterator() + get_next = iterator.get_next() + + for expected_features in self._next_actual_batch(file_indices, batch_size, + num_epochs): + actual_features = sess.run(get_next) + + if label_key is not None: + expected_labels = expected_features.pop(label_key) + # Compare labels + self.assertAllEqual(expected_labels, actual_features[1]) + actual_features = actual_features[0] # Extract features dict from tuple + + for k in expected_features.keys(): + # Compare features + self.assertAllEqual(expected_features[k], actual_features[k]) + + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + + def test_make_csv_dataset(self): + defaults = [ + constant_op.constant([], dtype=d) for d in self.COLUMN_TYPES[:-1] + ] + defaults.append(constant_op.constant(["NULL"], dtype=dtypes.string)) + + with ops.Graph().as_default() as g: + with self.test_session(graph=g) as sess: + # Basic test: read from file 0. + dataset = self._make_csv_dataset(self._test_filenames[0], defaults) + self._verify_records(sess, dataset, [0]) + with ops.Graph().as_default() as g: + with self.test_session(graph=g) as sess: + # Basic test: read from file 1. + dataset = self._make_csv_dataset(self._test_filenames[1], defaults) + self._verify_records(sess, dataset, [1]) + with ops.Graph().as_default() as g: + with self.test_session(graph=g) as sess: + # Read from both files. + dataset = self._make_csv_dataset(self._test_filenames, defaults) + self._verify_records(sess, dataset, range(self._num_files)) + with ops.Graph().as_default() as g: + with self.test_session(graph=g) as sess: + # Read from both files. Exercise the `batch` and `num_epochs` parameters + # of make_csv_dataset and make sure they work. + dataset = self._make_csv_dataset( + self._test_filenames, defaults, batch_size=2, num_epochs=10) + self._verify_records( + sess, dataset, range(self._num_files), batch_size=2, num_epochs=10) + + def test_make_csv_dataset_with_no_label(self): + defaults = [ + constant_op.constant([], dtype=d) for d in self.COLUMN_TYPES[:-1] + ] + defaults.append(constant_op.constant(["NULL"], dtype=dtypes.string)) + with ops.Graph().as_default() as g: + with self.test_session(graph=g) as sess: + # Read from both files. Make sure this works with no label key supplied. + dataset = self._make_csv_dataset( + self._test_filenames, + defaults, + batch_size=2, + num_epochs=10, + label_key=None) + self._verify_records( + sess, + dataset, + range(self._num_files), + batch_size=2, + num_epochs=10, + label_key=None) + + def test_make_csv_dataset_with_types(self): + defaults = [d for d in self.COLUMN_TYPES[:-1]] + defaults.append(constant_op.constant(["NULL"], dtype=dtypes.string)) + with ops.Graph().as_default() as g: + with self.test_session(graph=g) as sess: + dataset = self._make_csv_dataset(self._test_filenames, defaults) + self._verify_records(sess, dataset, range(self._num_files)) + + def test_make_csv_dataset_with_shuffle(self): + total_records = self._num_files * self._num_records + defaults = [d for d in self.COLUMN_TYPES[:-1]] + defaults.append(constant_op.constant(["NULL"], dtype=dtypes.string)) + for batch_size in [1, 2]: + with ops.Graph().as_default() as g: + with self.test_session(graph=g) as sess: + # Test that shuffling with the same seed produces the same result + dataset1 = self._make_csv_dataset( + self._test_filenames, + defaults, + batch_size=batch_size, + shuffle=True, + shuffle_seed=5) + dataset2 = self._make_csv_dataset( + self._test_filenames, + defaults, + batch_size=batch_size, + shuffle=True, + shuffle_seed=5) + outputs1 = dataset1.make_one_shot_iterator().get_next() + outputs2 = dataset2.make_one_shot_iterator().get_next() + for _ in range(total_records // batch_size): + batch1 = self._run_actual_batch(outputs1, sess) + batch2 = self._run_actual_batch(outputs2, sess) + for i in range(len(batch1)): + self.assertAllEqual(batch1[i], batch2[i]) + + with ops.Graph().as_default() as g: + with self.test_session(graph=g) as sess: + # Test that shuffling with a different seed produces different results + dataset1 = self._make_csv_dataset( + self._test_filenames, + defaults, + batch_size=batch_size, + shuffle=True, + shuffle_seed=5) + dataset2 = self._make_csv_dataset( + self._test_filenames, + defaults, + batch_size=batch_size, + shuffle=True, + shuffle_seed=6) + outputs1 = dataset1.make_one_shot_iterator().get_next() + outputs2 = dataset2.make_one_shot_iterator().get_next() + all_equal = False + for _ in range(total_records // batch_size): + batch1 = self._run_actual_batch(outputs1, sess) + batch2 = self._run_actual_batch(outputs2, sess) + for i in range(len(batch1)): + all_equal = all_equal and np.array_equal(batch1[i], batch2[i]) + self.assertFalse(all_equal) + if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/slide_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/slide_dataset_op_test.py new file mode 100644 index 0000000000000000000000000000000000000000..33c48e20bea53b88d69a59e715af38b22dd2cbd4 --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/slide_dataset_op_test.py @@ -0,0 +1,242 @@ +# 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 experimental input pipeline ops.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.data.python.ops import sliding +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import errors +from tensorflow.python.framework import sparse_tensor +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.platform import test + + +class SlideDatasetTest(test.TestCase): + + def testSlideDataset(self): + """Test an dataset that maps a TF function across its input elements.""" + 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=[]) + window_size = array_ops.placeholder(dtypes.int64, shape=[]) + stride = array_ops.placeholder(dtypes.int64, shape=[]) + + def _map_fn(x, y, z): + return math_ops.square(x), math_ops.square(y), math_ops.square(z) + + # The pipeline is TensorSliceDataset -> MapDataset(square_3) -> + # RepeatDataset(count) -> _SlideDataset(window_size, stride). + iterator = (dataset_ops.Dataset.from_tensor_slices(components) + .map(_map_fn) + .repeat(count) + .apply(sliding.sliding_window_batch(window_size, stride)) + .make_initializable_iterator()) + init_op = iterator.initializer + get_next = iterator.get_next() + + self.assertEqual([[None] + list(c.shape[1:]) for c in components], + [t.shape.as_list() for t in get_next]) + + with self.test_session() as sess: + # Slide over a finite input, where the window_size divides the + # total number of elements. + sess.run(init_op, feed_dict={count: 20, window_size: 14, stride: 7}) + # Same formula with convolution layer. + num_batches = (20 * 7 - 14) // 7 + 1 + for i in range(num_batches): + result = sess.run(get_next) + for component, result_component in zip(components, result): + for j in range(14): + self.assertAllEqual(component[(i*7 + j) % 7]**2, + result_component[j]) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + + # Slide over a finite input, where the window_size does not + # divide the total number of elements. + sess.run(init_op, feed_dict={count: 20, window_size: 17, stride: 9}) + + num_batches = (20 * 7 - 17) // 9 + 1 + for i in range(num_batches): + result = sess.run(get_next) + for component, result_component in zip(components, result): + for j in range(17): + self.assertAllEqual(component[(i*9 + j) % 7]**2, + result_component[j]) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + + # Slide over a finite input, which is less than window_size, + # should fail straight away. + sess.run(init_op, feed_dict={count: 1, window_size: 10, stride: 4}) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + + sess.run(init_op, feed_dict={count: 1, window_size: 10, stride: 8}) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + + # Slide over an empty input should fail straight away. + sess.run(init_op, feed_dict={count: 0, window_size: 8, stride: 4}) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + + # Empty window_size should be an initialization time error. + with self.assertRaises(errors.InvalidArgumentError): + sess.run(init_op, feed_dict={count: 14, window_size: 0, stride: 0}) + + # Invalid stride should be an initialization time error. + with self.assertRaises(errors.InvalidArgumentError): + sess.run(init_op, feed_dict={count: 14, window_size: 3, stride: 0}) + with self.assertRaises(errors.InvalidArgumentError): + sess.run(init_op, feed_dict={count: 14, window_size: 3, stride: 3}) + with self.assertRaises(errors.InvalidArgumentError): + sess.run(init_op, feed_dict={count: 14, window_size: 3, stride: 5}) + + def assertSparseValuesEqual(self, a, b): + self.assertAllEqual(a.indices, b.indices) + self.assertAllEqual(a.values, b.values) + self.assertAllEqual(a.dense_shape, b.dense_shape) + + def testSlideSparse(self): + + def _sparse(i): + return sparse_tensor.SparseTensorValue( + indices=[[0]], values=(i * [1]), dense_shape=[1]) + + iterator = dataset_ops.Dataset.range(10).map(_sparse).apply( + sliding.sliding_window_batch(5, 3)).make_initializable_iterator() + init_op = iterator.initializer + get_next = iterator.get_next() + + with self.test_session() as sess: + sess.run(init_op) + num_batches = (10 - 5) // 3 + 1 + for i in range(num_batches): + actual = sess.run(get_next) + expected = sparse_tensor.SparseTensorValue( + indices=[[0, 0], [1, 0], [2, 0], [3, 0], [4, 0]], + values=[i * 3, i * 3 + 1, i * 3 + 2, i * 3 + 3, i * 3 + 4], + dense_shape=[5, 1]) + self.assertTrue(sparse_tensor.is_sparse(actual)) + self.assertSparseValuesEqual(actual, expected) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + + def testSlideSparseWithDifferentDenseShapes(self): + + def _sparse(i): + return sparse_tensor.SparseTensorValue( + indices=array_ops.expand_dims( + math_ops.range(i, dtype=dtypes.int64), 1), + values=array_ops.fill([math_ops.to_int32(i)], i), + dense_shape=[i]) + + iterator = dataset_ops.Dataset.range(10).map(_sparse).apply( + sliding.sliding_window_batch(5, 3)).make_initializable_iterator() + init_op = iterator.initializer + get_next = iterator.get_next() + + with self.test_session() as sess: + sess.run(init_op) + num_batches = (10 - 5) // 3 + 1 + for i in range(num_batches): + actual = sess.run(get_next) + expected_indices = [] + expected_values = [] + for j in range(5): + for k in range(i * 3 + j): + expected_indices.append([j, k]) + expected_values.append(i * 3 + j) + expected = sparse_tensor.SparseTensorValue( + indices=expected_indices, + values=expected_values, + dense_shape=[5, i * 3 + 5 - 1]) + self.assertTrue(sparse_tensor.is_sparse(actual)) + self.assertSparseValuesEqual(actual, expected) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + + def testNestedSlideSparse(self): + + def _sparse(i): + return sparse_tensor.SparseTensorValue( + indices=[[0]], values=(i * [1]), dense_shape=[1]) + + iterator = (dataset_ops.Dataset.range(10) + .map(_sparse) + .apply(sliding.sliding_window_batch(4, 2)) + .apply(sliding.sliding_window_batch(3, 1)) + .make_initializable_iterator()) + init_op = iterator.initializer + get_next = iterator.get_next() + + with self.test_session() as sess: + sess.run(init_op) + # Slide: 1st batch. + actual = sess.run(get_next) + expected = sparse_tensor.SparseTensorValue( + indices=[[0, 0, 0], [0, 1, 0], [0, 2, 0], [0, 3, 0], + [1, 0, 0], [1, 1, 0], [1, 2, 0], [1, 3, 0], + [2, 0, 0], [2, 1, 0], [2, 2, 0], [2, 3, 0]], + values=[0, 1, 2, 3, 2, 3, 4, 5, 4, 5, 6, 7], + dense_shape=[3, 4, 1]) + self.assertTrue(sparse_tensor.is_sparse(actual)) + self.assertSparseValuesEqual(actual, expected) + # Slide: 2nd batch. + actual = sess.run(get_next) + expected = sparse_tensor.SparseTensorValue( + indices=[[0, 0, 0], [0, 1, 0], [0, 2, 0], [0, 3, 0], + [1, 0, 0], [1, 1, 0], [1, 2, 0], [1, 3, 0], + [2, 0, 0], [2, 1, 0], [2, 2, 0], [2, 3, 0]], + values=[2, 3, 4, 5, 4, 5, 6, 7, 6, 7, 8, 9], + dense_shape=[3, 4, 1]) + self.assertTrue(sparse_tensor.is_sparse(actual)) + self.assertSparseValuesEqual(actual, expected) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + + def testSlideShapeError(self): + + def generator(): + yield [1.0, 2.0, 3.0] + yield [4.0, 5.0, 6.0] + yield [7.0, 8.0, 9.0, 10.0] + + iterator = (dataset_ops.Dataset.from_generator(generator, dtypes.float32, + output_shapes=[None]) + .apply(sliding.sliding_window_batch(3, 1)) + .make_initializable_iterator()) + next_element = iterator.get_next() + + with self.test_session() as sess: + sess.run(iterator.initializer) + with self.assertRaisesRegexp( + errors.InvalidArgumentError, + r"Cannot batch tensors with different shapes in component 0. " + r"First element had shape \[3\] and element 2 had shape \[4\]."): + sess.run(next_element) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/ops/BUILD b/tensorflow/contrib/data/python/ops/BUILD index 789cb9c99a6bba06a1e3bd3371d1378065f49f46..c3331e963602d60fe27dd44b0cc06dfb20ca2b6a 100644 --- a/tensorflow/contrib/data/python/ops/BUILD +++ b/tensorflow/contrib/data/python/ops/BUILD @@ -67,6 +67,8 @@ py_library( srcs_version = "PY2AND3", deps = [ ":dataset_ops", + ":shuffle_ops", + "//tensorflow/python:constant_op", "//tensorflow/python:dataset_ops_gen", "//tensorflow/python:dtypes", "//tensorflow/python:framework_ops", @@ -104,6 +106,7 @@ py_library( "interleave_ops.py", "resampling.py", "scan_ops.py", + "sliding.py", "stats_ops.py", "threadpool.py", "unique.py", @@ -126,6 +129,7 @@ py_library( "//tensorflow/python:tensor_util", "//tensorflow/python:util", "//tensorflow/python/data/ops:dataset_ops", + "//tensorflow/python/data/ops:readers", "//tensorflow/python/data/util:convert", "//tensorflow/python/data/util:nest", "//tensorflow/python/data/util:sparse", diff --git a/tensorflow/contrib/data/python/ops/get_single_element.py b/tensorflow/contrib/data/python/ops/get_single_element.py index a817b45b71b608810a9d7536ec123ab84f7cdc3b..3a07df572748e464284f580d67e3a664e71acdfe 100644 --- a/tensorflow/contrib/data/python/ops/get_single_element.py +++ b/tensorflow/contrib/data/python/ops/get_single_element.py @@ -19,6 +19,7 @@ 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 @@ -59,9 +60,14 @@ def get_single_element(dataset): """ if not isinstance(dataset, dataset_ops.Dataset): raise TypeError("`dataset` must be a `tf.data.Dataset` object.") - return nest.pack_sequence_as( - dataset.output_types, - gen_dataset_ops.dataset_to_single_element( + + nested_ret = nest.pack_sequence_as( + dataset.output_types, gen_dataset_ops.dataset_to_single_element( dataset._as_variant_tensor(), # pylint: disable=protected-access - output_types=nest.flatten(dataset.output_types), - output_shapes=nest.flatten(dataset.output_shapes))) + output_types=nest.flatten(sparse.as_dense_types( + dataset.output_types, dataset.output_classes)), + output_shapes=nest.flatten(sparse.as_dense_shapes( + dataset.output_shapes, dataset.output_classes)))) + return sparse.deserialize_sparse_tensors( + nested_ret, dataset.output_types, dataset.output_shapes, + dataset.output_classes) diff --git a/tensorflow/contrib/data/python/ops/grouping.py b/tensorflow/contrib/data/python/ops/grouping.py index 67b085002aa7797d858837fea4646fb968ad5d97..a19be222545ef0242502ec07badbdae5c7634a0c 100644 --- a/tensorflow/contrib/data/python/ops/grouping.py +++ b/tensorflow/contrib/data/python/ops/grouping.py @@ -17,13 +17,20 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import numpy as np + from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.util import nest from tensorflow.python.data.util import sparse +from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import function from tensorflow.python.framework import ops +from tensorflow.python.framework import tensor_shape +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import check_ops from tensorflow.python.ops import gen_dataset_ops +from tensorflow.python.ops import math_ops def group_by_window(key_func, @@ -85,6 +92,114 @@ def group_by_window(key_func, return _apply_fn +def bucket_by_sequence_length(element_length_func, + bucket_boundaries, + bucket_batch_sizes, + padded_shapes=None, + padding_values=None, + pad_to_bucket_boundary=False): + """A transformation that buckets elements in a `Dataset` by length. + + Elements of the `Dataset` are grouped together by length and then are padded + and batched. + + This is useful for sequence tasks in which the elements have variable length. + Grouping together elements that have similar lengths reduces the total + fraction of padding in a batch which increases training step efficiency. + + Args: + element_length_func: function from element in `Dataset` to `tf.int64`, + determines the length of the element, which will determine the bucket it + goes into. + bucket_boundaries: `list`, upper length boundaries of the buckets. + bucket_batch_sizes: `list`, batch size per bucket. Length should be + `len(bucket_boundaries) + 1`. + padded_shapes: Nested structure of `tf.TensorShape` to pass to + @{tf.data.Dataset.padded_batch}. If not provided, will use + `dataset.output_shapes`, which will result in variable length dimensions + being padded out to the maximum length in each batch. + padding_values: Values to pad with, passed to + @{tf.data.Dataset.padded_batch}. Defaults to padding with 0. + pad_to_bucket_boundary: bool, if `False`, will pad dimensions with unknown + size to maximum length in batch. If `True`, will pad dimensions with + unknown size to bucket boundary, and caller must ensure that the source + `Dataset` does not contain any elements with length longer than + `max(bucket_boundaries)`. + + Returns: + A `Dataset` transformation function, which can be passed to + @{tf.data.Dataset.apply}. + + Raises: + ValueError: if `len(bucket_batch_sizes) != len(bucket_boundaries) + 1`. + """ + with ops.name_scope("bucket_by_seq_length"): + if len(bucket_batch_sizes) != (len(bucket_boundaries) + 1): + raise ValueError( + "len(bucket_batch_sizes) must equal len(bucket_boundaries) + 1") + + batch_sizes = constant_op.constant(bucket_batch_sizes, dtype=dtypes.int64) + + def element_to_bucket_id(element): + """Return int64 id of the length bucket for this element.""" + seq_length = element_length_func(element) + + boundaries = list(bucket_boundaries) + buckets_min = [np.iinfo(np.int32).min] + boundaries + buckets_max = boundaries + [np.iinfo(np.int32).max] + conditions_c = math_ops.logical_and( + math_ops.less_equal(buckets_min, seq_length), + math_ops.less(seq_length, buckets_max)) + bucket_id = math_ops.reduce_min(array_ops.where(conditions_c)) + + return bucket_id + + def window_size_fn(bucket_id): + # The window size is set to the batch size for this bucket + window_size = batch_sizes[bucket_id] + return window_size + + def make_padded_shapes(shapes, none_filler=None): + padded = [] + for shape in nest.flatten(shapes): + shape = tensor_shape.TensorShape(shape) + shape = [ + none_filler if d.value is None else d + for d in shape + ] + padded.append(shape) + return nest.pack_sequence_as(shapes, padded) + + def batching_fn(bucket_id, grouped_dataset): + """Batch elements in dataset.""" + batch_size = batch_sizes[bucket_id] + none_filler = None + if pad_to_bucket_boundary: + err_msg = ("When pad_to_bucket_boundary=True, elements must have " + "length <= max(bucket_boundaries).") + check = check_ops.assert_less( + bucket_id, + constant_op.constant(len(bucket_batch_sizes) - 1, + dtype=dtypes.int64), + message=err_msg) + with ops.control_dependencies([check]): + boundaries = constant_op.constant(bucket_boundaries, + dtype=dtypes.int64) + bucket_boundary = boundaries[bucket_id] + none_filler = bucket_boundary + shapes = make_padded_shapes( + padded_shapes or grouped_dataset.output_shapes, + none_filler=none_filler) + return grouped_dataset.padded_batch(batch_size, shapes, padding_values) + + def _apply_fn(dataset): + return dataset.apply( + group_by_window(element_to_bucket_id, batching_fn, + window_size_func=window_size_fn)) + + return _apply_fn + + class _VariantDataset(dataset_ops.Dataset): """A Dataset wrapper for a tf.variant-typed function argument.""" diff --git a/tensorflow/contrib/data/python/ops/interleave_ops.py b/tensorflow/contrib/data/python/ops/interleave_ops.py index 3124ca1d1540e12d949dded88ce1c66181be3595..91f19da02d4a479820782822475d9121125fc38e 100644 --- a/tensorflow/contrib/data/python/ops/interleave_ops.py +++ b/tensorflow/contrib/data/python/ops/interleave_ops.py @@ -17,101 +17,10 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.python.data.ops import dataset_ops -from tensorflow.python.data.util import convert -from tensorflow.python.data.util import nest -from tensorflow.python.data.util import sparse -from tensorflow.python.framework import dtypes -from tensorflow.python.framework import function -from tensorflow.python.framework import ops -from tensorflow.python.ops import gen_dataset_ops +from tensorflow.python.data.ops import readers from tensorflow.python.util import deprecation -class ParallelInterleaveDataset(dataset_ops.Dataset): - """A `Dataset` that maps a function over its input and flattens the result.""" - - def __init__(self, input_dataset, map_func, cycle_length, block_length, - sloppy, buffer_output_elements, prefetch_input_elements): - """See `tf.contrib.data.parallel_interleave()` for details.""" - super(ParallelInterleaveDataset, self).__init__() - self._input_dataset = input_dataset - - @function.Defun(*nest.flatten( - sparse.as_dense_types(input_dataset.output_types, - input_dataset.output_classes))) - def tf_map_func(*args): - """A wrapper for Defun that facilitates shape inference.""" - # Pass in shape information from the input_dataset. - dense_shapes = sparse.as_dense_shapes(input_dataset.output_shapes, - input_dataset.output_classes) - for arg, shape in zip(args, nest.flatten(dense_shapes)): - arg.set_shape(shape) - - nested_args = nest.pack_sequence_as(input_dataset.output_types, args) - nested_args = sparse.deserialize_sparse_tensors( - nested_args, input_dataset.output_types, input_dataset.output_shapes, - input_dataset.output_classes) - if dataset_ops._should_unpack_args(nested_args): # pylint: disable=protected-access - dataset = map_func(*nested_args) - else: - dataset = map_func(nested_args) - - if not isinstance(dataset, dataset_ops.Dataset): - raise TypeError("`map_func` must return a `Dataset` object.") - - self._output_classes = dataset.output_classes - self._output_types = dataset.output_types - self._output_shapes = dataset.output_shapes - - return dataset._as_variant_tensor() # pylint: disable=protected-access - - self._map_func = tf_map_func - self._map_func.add_to_graph(ops.get_default_graph()) - - self._cycle_length = ops.convert_to_tensor( - cycle_length, dtype=dtypes.int64, name="cycle_length") - self._block_length = ops.convert_to_tensor( - block_length, dtype=dtypes.int64, name="block_length") - self._sloppy = ops.convert_to_tensor( - sloppy, dtype=dtypes.bool, name="sloppy") - self._buffer_output_elements = convert.optional_param_to_tensor( - "buffer_output_elements", - buffer_output_elements, - argument_default=2 * block_length) - self._prefetch_input_elements = convert.optional_param_to_tensor( - "prefetch_input_elements", - prefetch_input_elements, - argument_default=2 * cycle_length) - - def _as_variant_tensor(self): - return gen_dataset_ops.parallel_interleave_dataset( - self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access - self._map_func.captured_inputs, - self._cycle_length, - self._block_length, - self._sloppy, - self._buffer_output_elements, - self._prefetch_input_elements, - f=self._map_func, - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes)), - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes))) - - @property - def output_classes(self): - return self._output_classes - - @property - def output_shapes(self): - return self._output_shapes - - @property - def output_types(self): - return self._output_types - - def parallel_interleave(map_func, cycle_length, block_length=1, @@ -162,7 +71,7 @@ def parallel_interleave(map_func, @{tf.data.Dataset.apply}. """ def _apply_fn(dataset): - return ParallelInterleaveDataset( + return readers.ParallelInterleaveDataset( dataset, map_func, cycle_length, block_length, sloppy, buffer_output_elements, prefetch_input_elements) @@ -221,7 +130,7 @@ def sloppy_interleave(map_func, cycle_length, block_length=1): @{tf.data.Dataset.apply}. """ def _apply_fn(dataset): - return ParallelInterleaveDataset( + return readers.ParallelInterleaveDataset( dataset, map_func, cycle_length, diff --git a/tensorflow/contrib/data/python/ops/prefetching_ops.py b/tensorflow/contrib/data/python/ops/prefetching_ops.py index 96a9e9ed6649444dac5e56d7dd2fcdb62fc56459..7059b358f349e0ec847e85c37652012d48ed910a 100644 --- a/tensorflow/contrib/data/python/ops/prefetching_ops.py +++ b/tensorflow/contrib/data/python/ops/prefetching_ops.py @@ -25,12 +25,14 @@ from tensorflow.contrib.data.python.ops import gen_dataset_ops # method and provides a get_next() that calls the prefetch op. def function_buffering_resource(string_arg, target_device, - shared_name, f, buffer_size, thread_pool_size=1, container="", + shared_name=None, name=None): + if shared_name is None: + shared_name = "" return gen_dataset_ops.function_buffering_resource( string_arg=string_arg, target_device=target_device, diff --git a/tensorflow/contrib/data/python/ops/random_ops.py b/tensorflow/contrib/data/python/ops/random_ops.py index 7d727165feabb101549567f28a2dfa07083de244..28ef5e50f39dd7d1b6f124e58e068fc968ddd6dc 100644 --- a/tensorflow/contrib/data/python/ops/random_ops.py +++ b/tensorflow/contrib/data/python/ops/random_ops.py @@ -19,11 +19,10 @@ from __future__ import print_function from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.util import nest +from tensorflow.python.data.util import random_seed from tensorflow.python.data.util import sparse -from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops -from tensorflow.python.framework import random_seed from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import gen_dataset_ops @@ -34,16 +33,7 @@ class RandomDataset(dataset_ops.Dataset): def __init__(self, seed=None): """A `Dataset` of pseudorandom values.""" super(RandomDataset, self).__init__() - seed, seed2 = random_seed.get_seed(seed) - if seed is None: - self._seed = constant_op.constant(0, dtype=dtypes.int64, name="seed") - else: - self._seed = ops.convert_to_tensor(seed, dtype=dtypes.int64, name="seed") - if seed2 is None: - self._seed2 = constant_op.constant(0, dtype=dtypes.int64, name="seed2") - else: - self._seed2 = ops.convert_to_tensor( - seed2, dtype=dtypes.int64, name="seed2") + self._seed, self._seed2 = random_seed.get_seed(seed) def _as_variant_tensor(self): return gen_dataset_ops.random_dataset( diff --git a/tensorflow/contrib/data/python/ops/readers.py b/tensorflow/contrib/data/python/ops/readers.py index 57f30102778f3bac47580f9bdf94e411dfe1b621..f70f9c881df168564cbf2431bbc2ebdf7e7f7ded 100644 --- a/tensorflow/contrib/data/python/ops/readers.py +++ b/tensorflow/contrib/data/python/ops/readers.py @@ -17,20 +17,289 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.contrib.data.python.ops import interleave_ops +from tensorflow.contrib.data.python.ops import shuffle_ops from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.data.ops import readers as core_readers from tensorflow.python.data.util import nest +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.ops import gen_dataset_ops from tensorflow.python.ops import parsing_ops from tensorflow.python.platform import gfile +from tensorflow.python.util import deprecation +_ACCEPTABLE_CSV_TYPES = (dtypes.float32, dtypes.float64, dtypes.int32, + dtypes.int64, dtypes.string) + +def make_csv_dataset( + file_pattern, + batch_size, + column_keys, + column_defaults, + label_key=None, + field_delim=",", + use_quote_delim=True, + skip=0, + filter_fn=None, + num_epochs=None, + shuffle=True, + shuffle_buffer_size=10000, + shuffle_seed=None, + prefetch_buffer_size=1, +): + """Reads CSV files into a dataset. + + Reads CSV files into a dataset, where each element is a (features, labels) + tuple that corresponds to a batch of CSV rows. The features dictionary + maps feature column names to `Tensor`s containing the corresponding + feature data, and labels is a `Tensor` containing the batch's label data. + + Args: + file_pattern: List of files or patterns of file paths containing CSV + records. See @{tf.gfile.Glob} for pattern rules. + batch_size: An int representing the number of consecutive elements of this + dataset to combine in a single batch. + column_keys: A list of strings that corresponds to the CSV columns, in + order. One per column of the input record. + column_defaults: A list of default values for the CSV fields. One item per + column of the input record. Each item in the list is either one of the + following dtypes: float32, float64, int32, int64, or string, or a + `Tensor` with one of the aforementioned types. One item per column of + the input record, with either scalar default value for that column if it + is required, or, if the column is required, an empty `Tensor` or a dtype. + label_key: A optional string corresponding to the label column. If provided, + the data for this column is returned as a separate `Tensor` from the + features dictionary, so that the dataset complies with the format expected + by a `tf.Estimator.train` or `tf.Estimator.evaluate` input function. + field_delim: An optional `string`. Defaults to `","`. Char delimiter to + separate fields in a record. + use_quote_delim: An optional bool. Defaults to `True`. If false, treats + double quotation marks as regular characters inside of the string fields. + skip: An integer that corresponds to the number of lines to skip at the + head of each CSV file. Defaults to 0. + filter_fn: A callable function that takes in a CSV string and returns a + boolean that corresponds to whether the record should be included. If + None, does not filter records. + num_epochs: An int specifying the number of times this dataset is repeated. + If None, cycles through the dataset forever. + shuffle: A bool that indicates whether the input should be shuffled. + shuffle_buffer_size: Buffer size to use for shuffling. A large buffer size + ensures better shuffling, but would increase memory usage and startup + time. + shuffle_seed: Randomization seed to use for shuffling. + prefetch_buffer_size: An int specifying the number of feature batches to + prefetch for performance improvement. Recommended value is the number of + batches consumed per training step. + + Returns: + A dataset, where each element is a (features, labels) tuple that corresponds + to a batch of `batch_size` CSV rows. The features dictionary maps feature + column names to `Tensor`s containing the corresponding column data, and + labels is a `Tensor` containing the column data for the label column + specified by `label_key`. + """ + filenames = _get_file_names(file_pattern, False) + column_defaults = [ + constant_op.constant([], dtype=x) if x in _ACCEPTABLE_CSV_TYPES else x + for x in column_defaults + ] + + dataset = dataset_ops.Dataset.from_tensor_slices(filenames) + if label_key is not None: + assert label_key in column_keys + + def filename_to_dataset(filename): + ds = core_readers.TextLineDataset(filename) + if skip > 0: + ds = ds.skip(skip) + if filter_fn is not None: + ds = ds.filter(filter_fn) + return ds + + def decode_csv(line): + """Decodes csv line into features. + + Args: + line: String tensor corresponding to one csv record. + Returns: + A dictionary of feature names to values for that particular record. If + label_key is provided, extracts the label feature to be returned as the + second element of the tuple. + """ + columns = parsing_ops.decode_csv( + line, + column_defaults, + field_delim=field_delim, + use_quote_delim=use_quote_delim) + features = dict(zip(column_keys, columns)) + if label_key is not None: + label = features.pop(label_key) + return features, label + return features + + # TODO(rachelim): interleave records from files for better shuffling + dataset = dataset.flat_map(filename_to_dataset) + # TODO(rachelim): use fused shuffle_and_repeat for perf + if shuffle: + dataset = dataset.shuffle(shuffle_buffer_size, shuffle_seed) + if num_epochs != 1: + dataset = dataset.repeat(num_epochs) + + dataset = dataset.batch(batch_size) + dataset = dataset.map(decode_csv) + dataset = dataset.prefetch(prefetch_buffer_size) + return dataset + + +def make_batched_features_dataset(file_pattern, + batch_size, + features, + reader=core_readers.TFRecordDataset, + reader_args=None, + num_epochs=None, + shuffle=True, + shuffle_buffer_size=10000, + shuffle_seed=None, + prefetch_buffer_size=1, + reader_num_threads=1, + parser_num_threads=2, + sloppy_ordering=False): + """Returns a `Dataset` of feature dictionaries from `Example` protos. + + Example: + + ``` + serialized_examples = [ + features { + feature { key: "age" value { int64_list { value: [ 0 ] } } } + feature { key: "gender" value { bytes_list { value: [ "f" ] } } } + feature { key: "kws" value { bytes_list { value: [ "code", "art" ] } } } + }, + features { + feature { key: "age" value { int64_list { value: [] } } } + feature { key: "gender" value { bytes_list { value: [ "f" ] } } } + feature { key: "kws" value { bytes_list { value: [ "sports" ] } } } + } + ] + ``` + + We can use arguments: + + ``` + features: { + "age": FixedLenFeature([], dtype=tf.int64, default_value=-1), + "gender": FixedLenFeature([], dtype=tf.string), + "kws": VarLenFeature(dtype=tf.string), + } + ``` + + And the expected output is: + + ```python + { + "age": [[0], [-1]], + "gender": [["f"], ["f"]], + "kws": SparseTensor( + indices=[[0, 0], [0, 1], [1, 0]], + values=["code", "art", "sports"] + dense_shape=[2, 2]), + } + ``` + + Args: + file_pattern: List of files or patterns of file paths containing + `Example` records. See `tf.gfile.Glob` for pattern rules. + batch_size: An int representing the number of consecutive elements of this + dataset to combine in a single batch. + features: A `dict` mapping feature keys to `FixedLenFeature` or + `VarLenFeature` values. See `tf.parse_example`. + reader: A function or class that can be + called with a `filenames` tensor and (optional) `reader_args` and returns + a `Dataset` of `Example` tensors. Defaults to `tf.data.TFRecordDataset`. + reader_args: Additional arguments to pass to the reader class. + num_epochs: Integer specifying the number of times to read through the + dataset. If None, cycles through the dataset forever. Defaults to `None`. + shuffle: A boolean, indicates whether the input should be shuffled. Defaults + to `True`. + shuffle_buffer_size: Buffer size of the ShuffleDataset. A large capacity + ensures better shuffling but would increase memory usage and startup time. + shuffle_seed: Randomization seed to use for shuffling. + prefetch_buffer_size: Number of feature batches to prefetch in order to + improve performance. Recommended value is the number of batches consumed + per training step (default is 1). + reader_num_threads: Number of threads used to read `Example` records. If >1, + the results will be interleaved. + parser_num_threads: Number of threads to use for parsing `Example` tensors + into a dictionary of `Feature` tensors. + sloppy_ordering: If `True`, reading performance will be improved at + the cost of non-deterministic ordering. If `False`, the order of elements + produced is deterministic prior to shuffling (elements are still + randomized if `shuffle=True`. Note that if the seed is set, then order + of elements after shuffling is deterministic). Defaults to `False`. + + Returns: + A dataset of `dict` elements. Each `dict` maps feature keys to + `Tensor` or `SparseTensor` objects. + """ + # Create dataset of all matching filenames + if shuffle: + dataset = dataset_ops.Dataset.list_files(file_pattern, shuffle=True) + else: + # TODO(b/73959787): Use Dataset.list_files() once ordering is deterministic. + filenames = _get_file_names(file_pattern, shuffle) + dataset = dataset_ops.Dataset.from_tensor_slices(filenames) + + # Read `Example` records from files as tensor objects. + if reader_args is None: + reader_args = [] + + # Read files sequentially (if reader_num_threads=1) or in parallel + dataset = dataset.apply( + interleave_ops.parallel_interleave( + lambda filename: reader(filename, *reader_args), + cycle_length=reader_num_threads, + sloppy=sloppy_ordering)) + + # Extract values if the `Example` tensors are stored as key-value tuples. + if dataset.output_types == (dtypes.string, dtypes.string): + dataset = dataset.map(lambda _, v: v) + + # Apply dataset repeat and shuffle transformations. + repeat_dataset = (num_epochs != 1) + if repeat_dataset and shuffle: + # Used fused shuffle_and_repeat operation for better performance + dataset = dataset.apply( + shuffle_ops.shuffle_and_repeat(shuffle_buffer_size, num_epochs, + shuffle_seed)) + elif repeat_dataset: + dataset = dataset.repeat(num_epochs) + elif shuffle: + dataset = dataset.shuffle(shuffle_buffer_size, shuffle_seed) + + dataset = dataset.batch(batch_size) + + # Parse `Example` tensors to a dictionary of `Feature` tensors. + dataset = dataset.map( + lambda x: parsing_ops.parse_example(x, features), + num_parallel_calls=parser_num_threads) + + # TODO(rachelim): Add an optional label_key argument for extracting the label + # from the features dictionary, to comply with the type expected by the + # input_fn to a `tf.Estimator.train` or `tf.Estimator.evaluate` function. + dataset = dataset.prefetch(prefetch_buffer_size) + return dataset + + +@deprecation.deprecated(None, + "Use `tf.contrib.data.make_batched_features_dataset`") def read_batch_features(file_pattern, batch_size, features, - reader, + reader=core_readers.TFRecordDataset, reader_args=None, randomize_input=True, num_epochs=None, @@ -84,43 +353,38 @@ def read_batch_features(file_pattern, dataset to combine in a single batch. features: A `dict` mapping feature keys to `FixedLenFeature` or `VarLenFeature` values. See `tf.parse_example`. - reader: A function or class that can be called with a `filenames` tensor - and (optional) `reader_args` and returns a `Dataset` of Examples. + reader: A function or class that can be + called with a `filenames` tensor and (optional) `reader_args` and returns + a `Dataset` of `Example` tensors. Defaults to `tf.data.TFRecordDataset`. reader_args: Additional arguments to pass to the reader class. randomize_input: Whether the input should be randomized. num_epochs: Integer specifying the number of times to read through the dataset. If None, cycles through the dataset forever. - capacity: Capacity of the ShuffleDataset. A large capacity ensures better + capacity: Buffer size of the ShuffleDataset. A large capacity ensures better shuffling but would increase memory usage and startup time. - Returns: A dict from keys in features to `Tensor` or `SparseTensor` objects. """ - filenames = _get_file_names(file_pattern, randomize_input) - if reader_args: - dataset = reader(filenames, *reader_args) - else: - dataset = reader(filenames) - if dataset.output_types == (dtypes.string, dtypes.string): - dataset = dataset.map(lambda _, v: v) - if num_epochs != 1: - dataset = dataset.repeat(num_epochs) - if randomize_input: - dataset = dataset.shuffle(capacity) - dataset = dataset.batch(batch_size) - dataset = dataset.map(lambda x: parsing_ops.parse_example(x, features)) - dataset = dataset.prefetch(1) + dataset = make_batched_features_dataset( + file_pattern, + batch_size, + features, + reader=reader, + reader_args=reader_args, + shuffle=randomize_input, + num_epochs=num_epochs, + shuffle_buffer_size=capacity) iterator = dataset.make_one_shot_iterator() outputs = iterator.get_next() return outputs -def _get_file_names(file_pattern, randomize_input): +def _get_file_names(file_pattern, shuffle): """Parse list of file names from pattern, optionally shuffled. Args: file_pattern: File glob pattern, or list of glob patterns. - randomize_input: Whether to shuffle the order of file names. + shuffle: Whether to shuffle the order of file names. Returns: List of file names matching `file_pattern`. @@ -141,7 +405,7 @@ def _get_file_names(file_pattern, randomize_input): raise ValueError("No files match %s." % file_pattern) # Sort files so it will be deterministic for unit tests. - if not randomize_input: + if not shuffle: file_names = sorted(file_names) return file_names diff --git a/tensorflow/contrib/data/python/ops/shuffle_ops.py b/tensorflow/contrib/data/python/ops/shuffle_ops.py index 99bb79bc06a421f811869ca9169aaa11deaca2f3..f35795abd38000b13cec0f08596e2ff66e86286c 100644 --- a/tensorflow/contrib/data/python/ops/shuffle_ops.py +++ b/tensorflow/contrib/data/python/ops/shuffle_ops.py @@ -19,11 +19,11 @@ from __future__ import print_function from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.util import nest +from tensorflow.python.data.util import random_seed from tensorflow.python.data.util import sparse from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops -from tensorflow.python.framework import random_seed from tensorflow.python.ops import gen_dataset_ops @@ -45,17 +45,7 @@ class _ShuffleAndRepeatDataset(dataset_ops.Dataset): else: self._count = ops.convert_to_tensor( count, dtype=dtypes.int64, name="count") - - seed, seed2 = random_seed.get_seed(seed) - if seed is None: - self._seed = constant_op.constant(0, dtype=dtypes.int64, name="seed") - else: - self._seed = ops.convert_to_tensor(seed, dtype=dtypes.int64, name="seed") - if seed2 is None: - self._seed2 = constant_op.constant(0, dtype=dtypes.int64, name="seed2") - else: - self._seed2 = ops.convert_to_tensor( - seed2, dtype=dtypes.int64, name="seed2") + self._seed, self._seed2 = random_seed.get_seed(seed) def _as_variant_tensor(self): # pylint: disable=protected-access diff --git a/tensorflow/contrib/data/python/ops/sliding.py b/tensorflow/contrib/data/python/ops/sliding.py new file mode 100644 index 0000000000000000000000000000000000000000..19cc3cb89fc5c494f79ce1d25ed57c92099c8bd2 --- /dev/null +++ b/tensorflow/contrib/data/python/ops/sliding.py @@ -0,0 +1,102 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Sliding dataset transformations.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.data.util import nest +from tensorflow.python.data.util import sparse +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.framework import tensor_shape +from tensorflow.python.ops import gen_dataset_ops + + +class _SlideDataset(dataset_ops.Dataset): + """A `Dataset` that passes a sliding window over its input.""" + + def __init__(self, input_dataset, window_size, stride=1): + """See `sliding_window_batch` for details.""" + super(_SlideDataset, self).__init__() + self._input_dataset = input_dataset + self._window_size = ops.convert_to_tensor( + window_size, dtype=dtypes.int64, name="window_size") + self._stride = ops.convert_to_tensor( + stride, dtype=dtypes.int64, name="stride") + + def _as_variant_tensor(self): + return gen_dataset_ops.slide_dataset( + self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access + window_size=self._window_size, + stride=self._stride, + output_shapes=nest.flatten( + sparse.as_dense_shapes(self.output_shapes, self.output_classes)), + output_types=nest.flatten( + sparse.as_dense_types(self.output_types, self.output_classes))) + + @property + def output_classes(self): + return self._input_dataset.output_classes + + @property + def output_shapes(self): + input_shapes = self._input_dataset.output_shapes + return nest.pack_sequence_as(input_shapes, [ + tensor_shape.vector(None).concatenate(s) + for s in nest.flatten(self._input_dataset.output_shapes) + ]) + + @property + def output_types(self): + return self._input_dataset.output_types + + +def sliding_window_batch(window_size, stride=1): + """A sliding window with size of `window_size` and step of `stride`. + + This transformation passes a sliding window over this dataset. The + window size is `window_size` and step size is `stride`. If the left + elements cannot fill up the sliding window, this transformation will + drop the final smaller element. For example: + + ```python + # NOTE: The following examples use `{ ... }` to represent the + # contents of a dataset. + a = { [1], [2], [3], [4], [5], [6] } + + a.apply(tf.contrib.data.sliding_window_batch(window_size=3, stride=2)) == + { + [[1], [2], [3]], + [[3], [4], [5]], + } + ``` + + Args: + window_size: A `tf.int64` scalar `tf.Tensor`, representing the number of + elements in the sliding window. + stride: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the + steps moving the sliding window forward for one iteration. The default + is `1`. It must be in `[1, window_size)`. + + Returns: + A `Dataset` transformation function, which can be passed to + @{tf.data.Dataset.apply}. + """ + def _apply_fn(dataset): + return _SlideDataset(dataset, window_size, stride) + + return _apply_fn diff --git a/tensorflow/contrib/data/python/ops/stats_ops.py b/tensorflow/contrib/data/python/ops/stats_ops.py index 9cd1701c397b5a0bf5cc47c1bcab033704794d80..b5cf0fcfe91ebc22444302fca5d488a278ef2994 100644 --- a/tensorflow/contrib/data/python/ops/stats_ops.py +++ b/tensorflow/contrib/data/python/ops/stats_ops.py @@ -47,7 +47,7 @@ class StatsAggregator(object): dataset = ... iterator = dataset.make_one_shot_iterator() stats_aggregator = stats_ops.StatsAggregator() - set_op = stats_op.set_stats_aggregator_op(iterator, stats_aggregator) + set_op = stats_aggregator.subscribe(iterator) with tf.Session() as sess: # Running `set_op` will associate `iterator` with `stats_aggregator`. diff --git a/tensorflow/contrib/data/python/ops/threadpool.py b/tensorflow/contrib/data/python/ops/threadpool.py index 3f85aa84cd53fcf5e21480aac96e067766ad1b65..56f67e1766bbaff680bdff6b939df0c3ba68c679 100644 --- a/tensorflow/contrib/data/python/ops/threadpool.py +++ b/tensorflow/contrib/data/python/ops/threadpool.py @@ -44,7 +44,7 @@ class PrivateThreadPool(object): def __init__(self, num_threads, display_name=None): """Creates a `PrivateThreadPool` with the given number of threads.""" - if context.in_eager_mode(): + if context.executing_eagerly(): shared_name = _generate_shared_name("privatethreadpool") self._resource = gen_dataset_ops.thread_pool_handle( num_threads=num_threads, diff --git a/tensorflow/contrib/decision_trees/proto/BUILD b/tensorflow/contrib/decision_trees/proto/BUILD index f6de5998d73a4869d2444cd90c9b64d1a2c889ac..ae3847b8b62452b1afbe472fcb6369181ec60b73 100644 --- a/tensorflow/contrib/decision_trees/proto/BUILD +++ b/tensorflow/contrib/decision_trees/proto/BUILD @@ -25,7 +25,6 @@ tf_proto_library( name = "generic_tree_model", srcs = ["generic_tree_model.proto"], cc_api_version = 2, - go_api_version = 2, java_api_version = 2, visibility = ["//visibility:public"], ) @@ -34,7 +33,6 @@ tf_proto_library( name = "generic_tree_model_extensions", srcs = ["generic_tree_model_extensions.proto"], cc_api_version = 2, - go_api_version = 2, protodeps = [":generic_tree_model"], visibility = ["//visibility:public"], ) diff --git a/tensorflow/contrib/distributions/BUILD b/tensorflow/contrib/distributions/BUILD index 35dd2ee439be401bdc0ce971a9dd71ed0cb411d0..1bd73ee7044de34988144196f53299db2fb80fcf 100644 --- a/tensorflow/contrib/distributions/BUILD +++ b/tensorflow/contrib/distributions/BUILD @@ -251,6 +251,21 @@ cuda_py_test( ], ) +cuda_py_test( + name = "kumaraswamy_test", + srcs = ["python/kernel_tests/kumaraswamy_test.py"], + additional_deps = [ + ":distributions_py", + "//third_party/py/numpy", + "//tensorflow/python:client", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:framework_test_lib", + "//tensorflow/python:nn_ops", + "//tensorflow/python:platform_test", + ], +) + cuda_py_test( name = "moving_stats_test", size = "small", @@ -335,6 +350,7 @@ cuda_py_test( "//tensorflow/python:nn_ops", "//tensorflow/python:platform_test", ], + tags = ["nomsan"], ) cuda_py_test( @@ -438,6 +454,7 @@ cuda_py_test( "//tensorflow/python:framework_test_lib", "//tensorflow/python:platform_test", ], + tags = ["no_windows"], # TODO: needs investigation on Windows ) cuda_py_test( @@ -459,6 +476,24 @@ cuda_py_test( tags = ["nomsan"], # disable to avoid false positives from scipy. ) +cuda_py_test( + name = "statistical_testing_test", + size = "medium", + srcs = [ + "python/kernel_tests/statistical_testing_test.py", + ], + additional_deps = [ + ":distributions_py", + "//third_party/py/numpy", + "//tensorflow/python:client_testlib", + ], + tags = [ + "manual", + "noasan", + "noguitar", + ], +) + cuda_py_test( name = "vector_sinh_arcsinh_diag_test", size = "medium", @@ -782,6 +817,25 @@ cuda_py_test( tags = ["noasan"], # times out b/63678675 ) +cuda_py_test( + name = "affine_scalar_test", + size = "small", + srcs = ["python/kernel_tests/bijectors/affine_scalar_test.py"], + additional_deps = [ + ":bijectors_py", + ":distributions_py", + "//third_party/py/numpy", + "@six_archive//:six", + "//tensorflow/contrib/linalg:linalg_py", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:framework_test_lib", + "//tensorflow/python:math_ops", + "//tensorflow/python:platform_test", + ], +) + cuda_py_test( name = "affine_linear_operator_test", size = "small", @@ -801,6 +855,22 @@ cuda_py_test( ], ) +cuda_py_test( + name = "batch_normalization_test", + size = "small", + srcs = ["python/kernel_tests/bijectors/batch_normalization_test.py"], + additional_deps = [ + ":bijectors_py", + ":distributions_py", + "//third_party/py/numpy", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:framework_test_lib", + "//tensorflow/python:platform_test", + ], +) + cuda_py_test( name = "chain_test", size = "small", @@ -915,6 +985,25 @@ cuda_py_test( ], ) +cuda_py_test( + name = "kumaraswamy_bijector_test", + size = "small", + srcs = ["python/kernel_tests/bijectors/kumaraswamy_bijector_test.py"], + additional_deps = [ + ":bijectors_py", + ":distributions_py", + "//third_party/py/numpy", + "@six_archive//:six", + "//tensorflow/contrib/linalg:linalg_py", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:framework_test_lib", + "//tensorflow/python:math_ops", + "//tensorflow/python:platform_test", + ], +) + cuda_py_test( name = "masked_autoregressive_test", size = "small", @@ -1055,6 +1144,7 @@ cuda_py_test( "//tensorflow/python:math_ops", "//tensorflow/python:platform_test", ], + tags = ["no_windows"], # TODO: needs investigation on Windows ) cuda_py_test( @@ -1095,6 +1185,25 @@ cuda_py_test( ], ) +cuda_py_test( + name = "square_test", + size = "small", + srcs = ["python/kernel_tests/bijectors/square_test.py"], + additional_deps = [ + ":bijectors_py", + ":distributions_py", + "//third_party/py/numpy", + "@six_archive//:six", + "//tensorflow/contrib/linalg:linalg_py", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:framework_test_lib", + "//tensorflow/python:math_ops", + "//tensorflow/python:platform_test", + ], +) + cuda_py_test( name = "weibull_test", size = "small", diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/affine_scalar_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/affine_scalar_test.py new file mode 100644 index 0000000000000000000000000000000000000000..16173a166fd943413345036df12245c2a4ab8343 --- /dev/null +++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/affine_scalar_test.py @@ -0,0 +1,153 @@ +# 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. +# ============================================================================== +"""Affine Scalar Tests.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.distributions.python.ops.bijectors.affine_scalar import AffineScalar +from tensorflow.python.framework import dtypes +from tensorflow.python.ops import array_ops +from tensorflow.python.ops.distributions.bijector_test_util import assert_scalar_congruency +from tensorflow.python.platform import test + + +class AffineScalarBijectorTest(test.TestCase): + """Tests correctness of the Y = scale @ x + shift transformation.""" + + def testProperties(self): + with self.test_session(): + mu = -1. + # scale corresponds to 1. + bijector = AffineScalar(shift=mu) + self.assertEqual("affine_scalar", bijector.name) + + def testNoBatchScalar(self): + with self.test_session() as sess: + + def static_run(fun, x): + return fun(x).eval() + + def dynamic_run(fun, x_value): + x_value = np.array(x_value) + x = array_ops.placeholder(dtypes.float32, name="x") + return sess.run(fun(x), feed_dict={x: x_value}) + + for run in (static_run, dynamic_run): + mu = -1. + # Corresponds to scale = 2 + bijector = AffineScalar(shift=mu, scale=2.) + x = [1., 2, 3] # Three scalar samples (no batches). + self.assertAllClose([1., 3, 5], run(bijector.forward, x)) + self.assertAllClose([1., 1.5, 2.], run(bijector.inverse, x)) + self.assertAllClose([-np.log(2.)] * 3, + run(bijector.inverse_log_det_jacobian, x)) + + def testOneBatchScalarViaIdentityIn64BitUserProvidesShiftOnly(self): + with self.test_session() as sess: + + def static_run(fun, x): + return fun(x).eval() + + def dynamic_run(fun, x_value): + x_value = np.array(x_value).astype(np.float64) + x = array_ops.placeholder(dtypes.float64, name="x") + return sess.run(fun(x), feed_dict={x: x_value}) + + for run in (static_run, dynamic_run): + mu = np.float64([1.]) + # One batch, scalar. + # Corresponds to scale = 1. + bijector = AffineScalar(shift=mu) + x = np.float64([1.]) # One sample from one batches. + self.assertAllClose([2.], run(bijector.forward, x)) + self.assertAllClose([0.], run(bijector.inverse, x)) + self.assertAllClose([0.], run(bijector.inverse_log_det_jacobian, x)) + + def testOneBatchScalarViaIdentityIn64BitUserProvidesScaleOnly(self): + with self.test_session() as sess: + + def static_run(fun, x): + return fun(x).eval() + + def dynamic_run(fun, x_value): + x_value = np.array(x_value).astype(np.float64) + x = array_ops.placeholder(dtypes.float64, name="x") + return sess.run(fun(x), feed_dict={x: x_value}) + + for run in (static_run, dynamic_run): + multiplier = np.float64([2.]) + # One batch, scalar. + # Corresponds to scale = 2, shift = 0. + bijector = AffineScalar(scale=multiplier) + x = np.float64([1.]) # One sample from one batches. + self.assertAllClose([2.], run(bijector.forward, x)) + self.assertAllClose([0.5], run(bijector.inverse, x)) + self.assertAllClose([np.log(0.5)], + run(bijector.inverse_log_det_jacobian, x)) + + def testTwoBatchScalarIdentityViaIdentity(self): + with self.test_session() as sess: + + def static_run(fun, x): + return fun(x).eval() + + def dynamic_run(fun, x_value): + x_value = np.array(x_value) + x = array_ops.placeholder(dtypes.float32, name="x") + return sess.run(fun(x), feed_dict={x: x_value}) + + for run in (static_run, dynamic_run): + mu = [1., -1] + # Univariate, two batches. + # Corresponds to scale = 1. + bijector = AffineScalar(shift=mu) + x = [1., 1] # One sample from each of two batches. + self.assertAllClose([2., 0], run(bijector.forward, x)) + self.assertAllClose([0., 2], run(bijector.inverse, x)) + self.assertAllClose([0., 0.], run(bijector.inverse_log_det_jacobian, x)) + + def testTwoBatchScalarIdentityViaScale(self): + with self.test_session() as sess: + + def static_run(fun, x): + return fun(x).eval() + + def dynamic_run(fun, x_value): + x_value = np.array(x_value) + x = array_ops.placeholder(dtypes.float32, name="x") + return sess.run(fun(x), feed_dict={x: x_value}) + + for run in (static_run, dynamic_run): + mu = [1., -1] + # Univariate, two batches. + # Corresponds to scale = 1. + bijector = AffineScalar(shift=mu, scale=[2., 1]) + x = [1., 1] # One sample from each of two batches. + self.assertAllClose([3., 0], run(bijector.forward, x)) + self.assertAllClose([0., 2], run(bijector.inverse, x)) + self.assertAllClose( + [-np.log(2), 0.], run(bijector.inverse_log_det_jacobian, x)) + + def testScalarCongruency(self): + with self.test_session(): + bijector = AffineScalar(shift=3.6, scale=0.42) + assert_scalar_congruency(bijector, lower_x=-2., upper_x=2.) + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/affine_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/affine_test.py index c9158117f7a982e37047e8dd2b534a30040a87d9..077e6176b4e7aecb28369d49edad6d1367cc7259 100644 --- a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/affine_test.py +++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/affine_test.py @@ -25,7 +25,6 @@ import numpy as np from tensorflow.contrib.distributions.python.ops.bijectors.affine import Affine from tensorflow.python.framework import dtypes from tensorflow.python.ops import array_ops -from tensorflow.python.ops.distributions.bijector_test_util import assert_scalar_congruency from tensorflow.python.platform import test @@ -36,192 +35,9 @@ class AffineBijectorTest(test.TestCase): with self.test_session(): mu = -1. # scale corresponds to 1. - bijector = Affine(shift=mu, event_ndims=0) + bijector = Affine(shift=mu) self.assertEqual("affine", bijector.name) - def testNoBatchScalarViaIdentity(self): - with self.test_session() as sess: - - def static_run(fun, x): - return fun(x).eval() - - def dynamic_run(fun, x_value): - x_value = np.array(x_value) - x = array_ops.placeholder(dtypes.float32, name="x") - return sess.run(fun(x), feed_dict={x: x_value}) - - for run in (static_run, dynamic_run): - mu = -1. - # Corresponds to scale = 2 - bijector = Affine( - shift=mu, scale_identity_multiplier=2., event_ndims=0) - self.assertEqual(0, bijector.event_ndims.eval()) # "is scalar" - x = [1., 2, 3] # Three scalar samples (no batches). - self.assertAllClose([1., 3, 5], run(bijector.forward, x)) - self.assertAllClose([1., 1.5, 2.], run(bijector.inverse, x)) - self.assertAllClose(-np.log(2.), - run(bijector.inverse_log_det_jacobian, x)) - - def testNoBatchScalarViaDiag(self): - with self.test_session() as sess: - - def static_run(fun, x): - return fun(x).eval() - - def dynamic_run(fun, x_value): - x_value = np.array(x_value) - x = array_ops.placeholder(dtypes.float32, name="x") - return sess.run(fun(x), feed_dict={x: x_value}) - - for run in (static_run, dynamic_run): - mu = -1. - # Corresponds to scale = 2 - bijector = Affine(shift=mu, scale_identity_multiplier=2., event_ndims=0) - self.assertEqual(0, bijector.event_ndims.eval()) # "is scalar" - x = [1., 2, 3] # Three scalar samples (no batches). - self.assertAllClose([1., 3, 5], run(bijector.forward, x)) - self.assertAllClose([1., 1.5, 2.], run(bijector.inverse, x)) - self.assertAllClose(-np.log(2.), - run(bijector.inverse_log_det_jacobian, x)) - - def testWeirdSampleNoBatchScalarViaDiagMultiplier(self): - with self.test_session() as sess: - - def static_run(fun, x): - return fun(x).eval() - - def dynamic_run(fun, x_value): - x_value = np.array(x_value) - x = array_ops.placeholder(dtypes.float32, name="x") - return sess.run(fun(x), feed_dict={x: x_value}) - - for run in (static_run, dynamic_run): - mu = -1. - # Corresponds to scale = 2. - bijector = Affine( - shift=mu, scale_identity_multiplier=2., event_ndims=0) - self.assertEqual(0, bijector.event_ndims.eval()) # "is scalar" - x = [[1., 2, 3], [4, 5, 6]] # Weird sample shape. - self.assertAllClose([[1., 3, 5], - [7, 9, 11]], - run(bijector.forward, x)) - self.assertAllClose([[1., 1.5, 2.], - [2.5, 3, 3.5]], - run(bijector.inverse, x)) - self.assertAllClose(-np.log(2.), - run(bijector.inverse_log_det_jacobian, x)) - - def testOneBatchScalarViaIdentityIn64BitUserProvidesShiftOnly(self): - with self.test_session() as sess: - - def static_run(fun, x): - return fun(x).eval() - - def dynamic_run(fun, x_value): - x_value = np.array(x_value).astype(np.float64) - x = array_ops.placeholder(dtypes.float64, name="x") - return sess.run(fun(x), feed_dict={x: x_value}) - - for run in (static_run, dynamic_run): - mu = np.float64([1.]) - # One batch, scalar. - # Corresponds to scale = 1. - bijector = Affine(shift=mu, event_ndims=0) - self.assertEqual(0, bijector.event_ndims.eval()) # "is scalar" - x = np.float64([1.]) # One sample from one batches. - self.assertAllClose([2.], run(bijector.forward, x)) - self.assertAllClose([0.], run(bijector.inverse, x)) - self.assertAllClose(0., run(bijector.inverse_log_det_jacobian, x)) - - def testOneBatchScalarViaIdentityIn64BitUserProvidesMultiplierOnly(self): - with self.test_session() as sess: - - def static_run(fun, x): - return fun(x).eval() - - def dynamic_run(fun, x_value): - x_value = np.array(x_value).astype(np.float64) - x = array_ops.placeholder(dtypes.float64, name="x") - return sess.run(fun(x), feed_dict={x: x_value}) - - for run in (static_run, dynamic_run): - multiplier = np.float64([2.]) - # One batch, scalar. - # Corresponds to scale = 2, shift = 0. - bijector = Affine(scale_identity_multiplier=multiplier, event_ndims=0) - self.assertEqual(0, bijector.event_ndims.eval()) # "is scalar" - x = np.float64([1.]) # One sample from one batches. - self.assertAllClose([2.], run(bijector.forward, x)) - self.assertAllClose([0.5], run(bijector.inverse, x)) - self.assertAllClose([np.log(0.5)], - run(bijector.inverse_log_det_jacobian, x)) - - def testOneBatchScalarViaDiagMultiplier(self): - with self.test_session() as sess: - - def static_run(fun, x): - return fun(x).eval() - - def dynamic_run(fun, x_value): - x_value = np.array(x_value) - x = array_ops.placeholder(dtypes.float32, name="x") - return sess.run(fun(x), feed_dict={x: x_value}) - - for run in (static_run, dynamic_run): - mu = [1.] - # One batch, scalar. - # Corresponds to scale = 1. - bijector = Affine(shift=mu, scale_identity_multiplier=1., event_ndims=0) - self.assertEqual(0, bijector.event_ndims.eval()) # "is scalar" - x = [1.] # One sample from one batches. - self.assertAllClose([2.], run(bijector.forward, x)) - self.assertAllClose([0.], run(bijector.inverse, x)) - self.assertAllClose(0., run(bijector.inverse_log_det_jacobian, x)) - - def testTwoBatchScalarIdentityViaIdentity(self): - with self.test_session() as sess: - - def static_run(fun, x): - return fun(x).eval() - - def dynamic_run(fun, x_value): - x_value = np.array(x_value) - x = array_ops.placeholder(dtypes.float32, name="x") - return sess.run(fun(x), feed_dict={x: x_value}) - - for run in (static_run, dynamic_run): - mu = [1., -1] - # Univariate, two batches. - # Corresponds to scale = 1. - bijector = Affine(shift=mu, event_ndims=0) - self.assertEqual(0, bijector.event_ndims.eval()) # "is scalar" - x = [1., 1] # One sample from each of two batches. - self.assertAllClose([2., 0], run(bijector.forward, x)) - self.assertAllClose([0., 2], run(bijector.inverse, x)) - self.assertAllClose(0., run(bijector.inverse_log_det_jacobian, x)) - - def testTwoBatchScalarIdentityViaDiagMultiplier(self): - with self.test_session() as sess: - - def static_run(fun, x): - return fun(x).eval() - - def dynamic_run(fun, x_value): - x_value = np.array(x_value) - x = array_ops.placeholder(dtypes.float32, name="x") - return sess.run(fun(x), feed_dict={x: x_value}) - - for run in (static_run, dynamic_run): - mu = [1., -1] - # Univariate, two batches. - # Corresponds to scale = 1. - bijector = Affine(shift=mu, scale_identity_multiplier=1., event_ndims=0) - self.assertEqual(0, bijector.event_ndims.eval()) # "is scalar" - x = [1., 1] # One sample from each of two batches. - self.assertAllClose([2., 0], run(bijector.forward, x)) - self.assertAllClose([0., 2], run(bijector.inverse, x)) - self.assertAllClose(0., run(bijector.inverse_log_det_jacobian, x)) - def testNoBatchMultivariateIdentity(self): with self.test_session() as sess: @@ -238,7 +54,6 @@ class AffineBijectorTest(test.TestCase): # Multivariate # Corresponds to scale = [[1., 0], [0, 1.]] bijector = Affine(shift=mu) - self.assertEqual(1, bijector.event_ndims.eval()) # "is vector" x = [1., 1] # matmul(sigma, x) + shift # = [-1, -1] + [1, -1] @@ -269,7 +84,6 @@ class AffineBijectorTest(test.TestCase): # Multivariate # Corresponds to scale = [[2., 0], [0, 1.]] bijector = Affine(shift=mu, scale_diag=[2., 1]) - self.assertEqual(1, bijector.event_ndims.eval()) # "is vector" x = [1., 1] # matmul(sigma, x) + shift # = [-1, -1] + [1, -1] @@ -297,22 +111,17 @@ class AffineBijectorTest(test.TestCase): x = array_ops.placeholder(dtypes.float32, name="x") mu = array_ops.placeholder(dtypes.float32, name="mu") scale_diag = array_ops.placeholder(dtypes.float32, name="scale_diag") - event_ndims = array_ops.placeholder(dtypes.int32, name="event_ndims") x_value = np.array([[1., 1]], dtype=np.float32) mu_value = np.array([1., -1], dtype=np.float32) scale_diag_value = np.array([2., 2], dtype=np.float32) - event_ndims_value = np.array(1, dtype=np.int32) feed_dict = { x: x_value, mu: mu_value, scale_diag: scale_diag_value, - event_ndims: event_ndims_value } - bijector = Affine( - shift=mu, scale_diag=scale_diag, event_ndims=event_ndims) - self.assertEqual(1, sess.run(bijector.event_ndims, feed_dict)) + bijector = Affine(shift=mu, scale_diag=scale_diag) self.assertAllClose([[3., 1]], sess.run(bijector.forward(x), feed_dict)) self.assertAllClose([[0., 1]], sess.run(bijector.inverse(x), feed_dict)) self.assertAllClose( @@ -335,7 +144,6 @@ class AffineBijectorTest(test.TestCase): # Corresponds to 1 2x2 matrix, with twos on the diagonal. scale = 2. bijector = Affine(shift=mu, scale_identity_multiplier=scale) - self.assertEqual(1, bijector.event_ndims.eval()) # "is vector" x = [[[1., 1]]] self.assertAllClose([[[3., 1]]], run(bijector.forward, x)) self.assertAllClose([[[0., 1]]], run(bijector.inverse, x)) @@ -358,7 +166,6 @@ class AffineBijectorTest(test.TestCase): # Corresponds to 1 2x2 matrix, with twos on the diagonal. scale_diag = [[2., 2]] bijector = Affine(shift=mu, scale_diag=scale_diag) - self.assertEqual(1, bijector.event_ndims.eval()) # "is vector" x = [[[1., 1]]] self.assertAllClose([[[3., 1]]], run(bijector.forward, x)) self.assertAllClose([[[0., 1]]], run(bijector.inverse, x)) @@ -370,23 +177,18 @@ class AffineBijectorTest(test.TestCase): x = array_ops.placeholder(dtypes.float32, name="x") mu = array_ops.placeholder(dtypes.float32, name="mu") scale_diag = array_ops.placeholder(dtypes.float32, name="scale_diag") - event_ndims = array_ops.placeholder(dtypes.int32, name="event_ndims") x_value = np.array([[[1., 1]]], dtype=np.float32) mu_value = np.array([[1., -1]], dtype=np.float32) scale_diag_value = np.array([[2., 2]], dtype=np.float32) - event_ndims_value = 1 feed_dict = { x: x_value, mu: mu_value, scale_diag: scale_diag_value, - event_ndims: event_ndims_value } - bijector = Affine( - shift=mu, scale_diag=scale_diag, event_ndims=event_ndims) - self.assertEqual(1, sess.run(bijector.event_ndims, feed_dict)) + bijector = Affine(shift=mu, scale_diag=scale_diag) self.assertAllClose([[[3., 1]]], sess.run(bijector.forward(x), feed_dict)) self.assertAllClose([[[0., 1]]], sess.run(bijector.inverse(x), feed_dict)) self.assertAllClose([-np.log(4)], @@ -410,9 +212,7 @@ class AffineBijectorTest(test.TestCase): bijector = Affine( shift=mu, scale_identity_multiplier=1., - scale_diag=[1., 1., 1.], - event_ndims=1) - self.assertEqual(1, bijector.event_ndims.eval()) # "is vector" + scale_diag=[1., 1., 1.]) x = [1., 2, 3] # Three scalar samples (no batches). self.assertAllClose([1., 3, 5], run(bijector.forward, x)) self.assertAllClose([1., 1.5, 2.], run(bijector.inverse, x)) @@ -437,7 +237,6 @@ class AffineBijectorTest(test.TestCase): shift=mu, scale_identity_multiplier=1., scale_tril=[[1., 0], [2., 1]]) - self.assertEqual(1, bijector.event_ndims.eval()) # "is vector" x = [[1., 2]] # One multivariate sample. self.assertAllClose([[1., 5]], run(bijector.forward, x)) self.assertAllClose([[1., 0.5]], run(bijector.inverse, x)) @@ -460,7 +259,6 @@ class AffineBijectorTest(test.TestCase): # scale = [[2., 0], [2, 3]] bijector = Affine( shift=mu, scale_diag=[1., 2.], scale_tril=[[1., 0], [2., 1]]) - self.assertEqual(1, bijector.event_ndims.eval()) # "is vector" x = [[1., 2]] # One multivariate sample. self.assertAllClose([[1., 7]], run(bijector.forward, x)) self.assertAllClose([[1., 1 / 3.]], run(bijector.inverse, x)) @@ -486,7 +284,6 @@ class AffineBijectorTest(test.TestCase): scale_identity_multiplier=1.0, scale_diag=[1., 2.], scale_tril=[[1., 0], [2., 1]]) - self.assertEqual(1, bijector.event_ndims.eval()) # "is vector" x = [[1., 2]] # One multivariate sample. self.assertAllClose([[2., 9]], run(bijector.forward, x)) self.assertAllClose([[2 / 3., 5 / 12.]], run(bijector.inverse, x)) @@ -514,7 +311,6 @@ class AffineBijectorTest(test.TestCase): scale_perturb_factor=[[2., 0], [0., 0], [0, 1]]) bijector_ref = Affine(shift=mu, scale_diag=[10., 2, 3]) - self.assertEqual(1, bijector.event_ndims.eval()) # "is vector" x = [1., 2, 3] # Vector. self.assertAllClose([9., 3, 8], run(bijector.forward, x)) self.assertAllClose( @@ -550,7 +346,6 @@ class AffineBijectorTest(test.TestCase): scale_perturb_factor=[[2., 0], [0., 0], [0, 1]]) bijector_ref = Affine(shift=mu, scale_diag=[10., 3, 5]) - self.assertEqual(1, bijector.event_ndims.eval()) # "is vector" x = [1., 2, 3] # Vector. self.assertAllClose([9., 5, 14], run(bijector.forward, x)) self.assertAllClose( @@ -586,7 +381,6 @@ class AffineBijectorTest(test.TestCase): bijector_ref = Affine( shift=mu, scale_tril=[[10., 0, 0], [1, 3, 0], [2, 3, 5]]) - self.assertEqual(1, bijector.event_ndims.eval()) # "is vector" x = [1., 2, 3] # Vector. self.assertAllClose([9., 6, 22], run(bijector.forward, x)) self.assertAllClose( @@ -622,7 +416,6 @@ class AffineBijectorTest(test.TestCase): bijector_ref = Affine( shift=mu, scale_tril=[[6., 0, 0], [1, 3, 0], [2, 3, 5]]) - self.assertEqual(1, bijector.event_ndims.eval()) # "is vector" x = [1., 2, 3] # Vector. self.assertAllClose([5., 6, 22], run(bijector.forward, x)) self.assertAllClose( @@ -647,38 +440,6 @@ class AffineBijectorTest(test.TestCase): with self.assertRaisesOpError("diagonal part must be non-zero"): bijector.forward([1., 1.]).eval() - def testEventNdimsLargerThanOneRaises(self): - with self.test_session(): - mu = [1., -1] - with self.assertRaisesRegexp( - ValueError, (r"event_ndims\(2\) was not 0 or 1")): - # Scale corresponds to 2x2 identity matrix. - bijector = Affine(shift=mu, event_ndims=2, validate_args=True) - bijector.forward([1., 1.]).eval() - - def testScaleZeroScalarRaises(self): - with self.test_session(): - mu = -1. - # Check Identity matrix with zero scaling. - bijector = Affine( - shift=mu, - scale_identity_multiplier=0., - event_ndims=0, - validate_args=True) - with self.assertRaisesOpError("identity_multiplier should be non-zero"): - bijector.forward(1.).eval() - - def testScaleDiagAndEventNdimsZeroRaises(self): - # Check Diag matrix with zero scaling. - with self.assertRaisesRegexp(ValueError, "only scale argument"): - Affine(shift=None, scale_diag=[0.0], event_ndims=0, validate_args=True) - - def testScalarCongruency(self): - with self.test_session(): - bijector = Affine( - shift=3.6, scale_identity_multiplier=0.42, event_ndims=0) - assert_scalar_congruency(bijector, lower_x=-2., upper_x=2.) - def _makeScale(self, x, scale_identity_multiplier=None, @@ -747,14 +508,12 @@ class AffineBijectorTest(test.TestCase): scale_args = dict({"x": x}, **args) scale = self._makeScale(**scale_args) - bijector_args = dict({"event_ndims": 1}, **args) - # We haven't specified enough information for the scale. if scale is None: with self.assertRaisesRegexp(ValueError, ("must be specified.")): - bijector = Affine(shift=shift, **bijector_args) + bijector = Affine(shift=shift, **args) else: - bijector = Affine(shift=shift, **bijector_args) + bijector = Affine(shift=shift, **args) np_x = x # For the case a vector is passed in, we need to make the shape # match the matrix for matmul to work. @@ -829,15 +588,5 @@ class AffineBijectorTest(test.TestCase): x=np.array( [1., 2], dtype=np.float32)) - def testScalarEventIdentityScale(self): - with self.test_session() as sess: - doubler = Affine( - scale_identity_multiplier=2., - event_ndims=0) - doubler2 = doubler.inverse_log_det_jacobian(2.) - doubler2_ildj_ = sess.run([doubler2]) - self.assertAllClose([-np.log(2.)], doubler2_ildj_) - - if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/batch_normalization_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/batch_normalization_test.py new file mode 100644 index 0000000000000000000000000000000000000000..a215a4a2b1ffbea7951bdb9b4352ed567e0b1e41 --- /dev/null +++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/batch_normalization_test.py @@ -0,0 +1,236 @@ +# 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 BatchNorm Bijector.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib import distributions +from tensorflow.contrib.distributions.python.ops import test_util +from tensorflow.contrib.distributions.python.ops.bijectors.batch_normalization import BatchNormalization +from tensorflow.contrib.distributions.python.ops.bijectors.invert import Invert +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import ops +from tensorflow.python.layers import normalization +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import variables +from tensorflow.python.ops.distributions import normal as normal_lib +from tensorflow.python.ops.distributions import transformed_distribution as transformed_distribution_lib +from tensorflow.python.platform import test +from tensorflow.python.training import adam + + +class BatchNormTest(test_util.VectorDistributionTestHelpers, + test.TestCase): + + def _reduction_axes(self, input_shape, event_dims): + if isinstance(event_dims, int): + event_dims = [event_dims] + ndims = len(input_shape) + # Convert event_dims to non-negative indexing. + event_dims = list(event_dims) + for idx, x in enumerate(event_dims): + if x < 0: + event_dims[idx] = ndims + x + return tuple(i for i in range(ndims) if i not in event_dims) + + def testForwardInverse(self): + """Tests forward and backward passes with different event shapes. + + input_shape: Tuple of shapes for input tensor. + event_dims: Tuple of dimension indices that will be normalized. + training: Boolean of whether bijector runs in training or inference mode. + """ + params = [ + ((5*2, 4), [-1], False), + ((5, 2, 4), [-1], False), + ((5, 2, 4), [1, 2], False), + ((5, 2, 4), [0, 1], False), + ((5*2, 4), [-1], True), + ((5, 2, 4), [-1], True), + ((5, 2, 4), [1, 2], True), + ((5, 2, 4), [0, 1], True) + ] + for input_shape, event_dims, training in params: + x_ = np.arange(5 * 4 * 2).astype(np.float32).reshape(input_shape) + with self.test_session() as sess: + x = constant_op.constant(x_) + # When training, memorize the exact mean of the last + # minibatch that it normalized (instead of moving average assignment). + layer = normalization.BatchNormalization( + axis=event_dims, momentum=0., epsilon=0.) + batch_norm = BatchNormalization( + batchnorm_layer=layer, training=training) + # Minibatch statistics are saved only after norm_x has been computed. + norm_x = batch_norm.inverse(x) + with ops.control_dependencies(batch_norm.batchnorm.updates): + moving_mean = array_ops.identity(batch_norm.batchnorm.moving_mean) + moving_var = array_ops.identity(batch_norm.batchnorm.moving_variance) + denorm_x = batch_norm.forward(array_ops.identity(norm_x)) + fldj = batch_norm.forward_log_det_jacobian(x) + # Use identity to invalidate cache. + ildj = batch_norm.inverse_log_det_jacobian( + array_ops.identity(denorm_x)) + variables.global_variables_initializer().run() + # Update variables. + norm_x_ = sess.run(norm_x) + [ + norm_x_, + moving_mean_, + moving_var_, + denorm_x_, + ildj_, + fldj_, + ] = sess.run([ + norm_x, + moving_mean, + moving_var, + denorm_x, + ildj, + fldj, + ]) + self.assertEqual("batch_normalization", batch_norm.name) + + reduction_axes = self._reduction_axes(input_shape, event_dims) + keepdims = len(event_dims) > 1 + + expected_batch_mean = np.mean( + x_, axis=reduction_axes, keepdims=keepdims) + expected_batch_var = np.var(x_, axis=reduction_axes, keepdims=keepdims) + + if training: + # When training=True, values become normalized across batch dim and + # original values are recovered after de-normalizing. + zeros = np.zeros_like(norm_x_) + self.assertAllClose(np.mean(zeros, axis=reduction_axes), + np.mean(norm_x_, axis=reduction_axes)) + + self.assertAllClose(expected_batch_mean, moving_mean_) + self.assertAllClose(expected_batch_var, moving_var_) + self.assertAllClose(x_, denorm_x_, atol=1e-5) + # Since moving statistics are set to batch statistics after + # normalization, ildj and -fldj should match. + self.assertAllClose(ildj_, -fldj_) + # ildj is computed with minibatch statistics. + expected_ildj = np.sum(np.log(1.) - .5 * np.log( + expected_batch_var + batch_norm.batchnorm.epsilon)) + self.assertAllClose(expected_ildj, ildj_) + else: + # When training=False, moving_mean, moving_var remain at their + # initialized values (0., 1.), resulting in no scale/shift (a small + # shift occurs if epsilon > 0.) + self.assertAllClose(x_, norm_x_) + self.assertAllClose(x_, denorm_x_, atol=1e-5) + # ildj is computed with saved statistics. + expected_ildj = np.sum( + np.log(1.) - .5 * np.log(1. + batch_norm.batchnorm.epsilon)) + self.assertAllClose(expected_ildj, ildj_) + + def testMaximumLikelihoodTraining(self): + # Test Maximum Likelihood training with default bijector. + with self.test_session() as sess: + base_dist = distributions.MultivariateNormalDiag(loc=[0., 0.]) + batch_norm = BatchNormalization(training=True) + dist = transformed_distribution_lib.TransformedDistribution( + distribution=base_dist, + bijector=batch_norm) + target_dist = distributions.MultivariateNormalDiag(loc=[1., 2.]) + target_samples = target_dist.sample(100) + dist_samples = dist.sample(3000) + loss = -math_ops.reduce_mean(dist.log_prob(target_samples)) + with ops.control_dependencies(batch_norm.batchnorm.updates): + train_op = adam.AdamOptimizer(1e-2).minimize(loss) + moving_mean = array_ops.identity(batch_norm.batchnorm.moving_mean) + moving_var = array_ops.identity(batch_norm.batchnorm.moving_variance) + variables.global_variables_initializer().run() + for _ in range(3000): + sess.run(train_op) + [ + dist_samples_, + moving_mean_, + moving_var_ + ] = sess.run([ + dist_samples, + moving_mean, + moving_var + ]) + self.assertAllClose([1., 2.], np.mean(dist_samples_, axis=0), atol=5e-2) + self.assertAllClose([1., 2.], moving_mean_, atol=5e-2) + self.assertAllClose([1., 1.], moving_var_, atol=5e-2) + + def testLogProb(self): + with self.test_session() as sess: + layer = normalization.BatchNormalization(epsilon=0.) + batch_norm = BatchNormalization(batchnorm_layer=layer, training=False) + base_dist = distributions.MultivariateNormalDiag(loc=[0., 0.]) + dist = transformed_distribution_lib.TransformedDistribution( + distribution=base_dist, + bijector=batch_norm, + validate_args=True) + samples = dist.sample(int(1e5)) + # No volume distortion since training=False, bijector is initialized + # to the identity transformation. + base_log_prob = base_dist.log_prob(samples) + dist_log_prob = dist.log_prob(samples) + variables.global_variables_initializer().run() + base_log_prob_, dist_log_prob_ = sess.run([base_log_prob, dist_log_prob]) + self.assertAllClose(base_log_prob_, dist_log_prob_) + + def testMutuallyConsistent(self): + # BatchNorm bijector is only mutually consistent when training=False. + dims = 4 + with self.test_session() as sess: + layer = normalization.BatchNormalization(epsilon=0.) + batch_norm = BatchNormalization(batchnorm_layer=layer, training=False) + dist = transformed_distribution_lib.TransformedDistribution( + distribution=normal_lib.Normal(loc=0., scale=1.), + bijector=batch_norm, + event_shape=[dims], + validate_args=True) + self.run_test_sample_consistent_log_prob( + sess_run_fn=sess.run, + dist=dist, + num_samples=int(1e5), + radius=2., + center=0., + rtol=0.02) + + def testInvertMutuallyConsistent(self): + # BatchNorm bijector is only mutually consistent when training=False. + dims = 4 + with self.test_session() as sess: + layer = normalization.BatchNormalization(epsilon=0.) + batch_norm = Invert( + BatchNormalization(batchnorm_layer=layer, training=False)) + dist = transformed_distribution_lib.TransformedDistribution( + distribution=normal_lib.Normal(loc=0., scale=1.), + bijector=batch_norm, + event_shape=[dims], + validate_args=True) + self.run_test_sample_consistent_log_prob( + sess_run_fn=sess.run, + dist=dist, + num_samples=int(1e5), + radius=2., + center=0., + rtol=0.02) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/cholesky_outer_product_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/cholesky_outer_product_test.py index 0ff35304283fce9ce3f9e5d31b1258394e384d7b..f392e83d2c3da9dac43c2e87070e952ae2060b34 100644 --- a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/cholesky_outer_product_test.py +++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/cholesky_outer_product_test.py @@ -18,70 +18,111 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import numpy as np + from tensorflow.contrib.distributions.python.ops import bijectors -from tensorflow.python.framework import tensor_shape +from tensorflow.python.framework import dtypes from tensorflow.python.ops import array_ops -from tensorflow.python.ops.distributions import gamma as gamma_lib -from tensorflow.python.ops.distributions import transformed_distribution as transformed_distribution_lib -from tensorflow.python.ops.distributions.bijector_test_util import assert_scalar_congruency from tensorflow.python.platform import test -class InvertBijectorTest(test.TestCase): - """Tests the correctness of the Y = Invert(bij) transformation.""" +class CholeskyOuterProductBijectorTest(test.TestCase): + """Tests the correctness of the Y = X @ X.T transformation.""" - def testBijector(self): + def testBijectorMatrix(self): with self.test_session(): - for fwd in [ - bijectors.Identity(), - bijectors.Exp(event_ndims=1), - bijectors.Affine( - shift=[0., 1.], scale_diag=[2., 3.], event_ndims=1), - bijectors.Softplus(event_ndims=1), - bijectors.SoftmaxCentered(event_ndims=1), - bijectors.SigmoidCentered(), - ]: - rev = bijectors.Invert(fwd) - self.assertEqual("_".join(["invert", fwd.name]), rev.name) - x = [[[1., 2.], - [2., 3.]]] - self.assertAllClose(fwd.inverse(x).eval(), rev.forward(x).eval()) - self.assertAllClose(fwd.forward(x).eval(), rev.inverse(x).eval()) - self.assertAllClose( - fwd.forward_log_det_jacobian(x).eval(), - rev.inverse_log_det_jacobian(x).eval()) - self.assertAllClose( - fwd.inverse_log_det_jacobian(x).eval(), - rev.forward_log_det_jacobian(x).eval()) + bijector = bijectors.CholeskyOuterProduct(validate_args=True) + self.assertEqual("cholesky_outer_product", bijector.name) + x = [[[1., 0], [2, 1]], [[np.sqrt(2.), 0], [np.sqrt(8.), 1]]] + y = np.matmul(x, np.transpose(x, axes=(0, 2, 1))) + # Fairly easy to compute differentials since we have 2x2. + dx_dy = [[[2. * 1, 0, 0], + [2, 1, 0], + [0, 2 * 2, 2 * 1]], + [[2 * np.sqrt(2.), 0, 0], + [np.sqrt(8.), np.sqrt(2.), 0], + [0, 2 * np.sqrt(8.), 2 * 1]]] + ildj = -np.sum( + np.log(np.asarray(dx_dy).diagonal( + offset=0, axis1=1, axis2=2)), + axis=1) + self.assertAllEqual((2, 2, 2), bijector.forward(x).get_shape()) + self.assertAllEqual((2, 2, 2), bijector.inverse(y).get_shape()) + self.assertAllClose(y, bijector.forward(x).eval()) + self.assertAllClose(x, bijector.inverse(y).eval()) + self.assertAllClose( + ildj, bijector.inverse_log_det_jacobian(y).eval(), atol=0., rtol=1e-7) + self.assertAllClose( + -bijector.inverse_log_det_jacobian(y).eval(), + bijector.forward_log_det_jacobian(x).eval(), + atol=0., + rtol=1e-7) - def testScalarCongruency(self): - with self.test_session(): - bijector = bijectors.Invert(bijectors.Exp()) - assert_scalar_congruency( - bijector, lower_x=1e-3, upper_x=1.5, rtol=0.05) + def testNoBatchStatic(self): + x = np.array([[1., 0], [2, 1]]) # np.linalg.cholesky(y) + y = np.array([[1., 2], [2, 5]]) # np.matmul(x, x.T) + with self.test_session() as sess: + y_actual = bijectors.CholeskyOuterProduct().forward(x=x) + x_actual = bijectors.CholeskyOuterProduct().inverse(y=y) + [y_actual_, x_actual_] = sess.run([y_actual, x_actual]) + self.assertAllEqual([2, 2], y_actual.get_shape()) + self.assertAllEqual([2, 2], x_actual.get_shape()) + self.assertAllClose(y, y_actual_) + self.assertAllClose(x, x_actual_) - def testShapeGetters(self): - with self.test_session(): - bijector = bijectors.Invert(bijectors.SigmoidCentered(validate_args=True)) - x = tensor_shape.TensorShape([2]) - y = tensor_shape.TensorShape([]) - self.assertAllEqual(y, bijector.forward_event_shape(x)) - self.assertAllEqual( - y.as_list(), - bijector.forward_event_shape_tensor(x.as_list()).eval()) - self.assertAllEqual(x, bijector.inverse_event_shape(y)) - self.assertAllEqual( - x.as_list(), - bijector.inverse_event_shape_tensor(y.as_list()).eval()) + def testNoBatchDeferred(self): + x = np.array([[1., 0], [2, 1]]) # np.linalg.cholesky(y) + y = np.array([[1., 2], [2, 5]]) # np.matmul(x, x.T) + with self.test_session() as sess: + x_pl = array_ops.placeholder(dtypes.float32) + y_pl = array_ops.placeholder(dtypes.float32) + y_actual = bijectors.CholeskyOuterProduct().forward(x=x_pl) + x_actual = bijectors.CholeskyOuterProduct().inverse(y=y_pl) + [y_actual_, x_actual_] = sess.run([y_actual, x_actual], + feed_dict={x_pl: x, y_pl: y}) + self.assertEqual(None, y_actual.get_shape()) + self.assertEqual(None, x_actual.get_shape()) + self.assertAllClose(y, y_actual_) + self.assertAllClose(x, x_actual_) - def testDocstringExample(self): - with self.test_session(): - exp_gamma_distribution = ( - transformed_distribution_lib.TransformedDistribution( - distribution=gamma_lib.Gamma(concentration=1., rate=2.), - bijector=bijectors.Invert(bijectors.Exp()))) - self.assertAllEqual( - [], array_ops.shape(exp_gamma_distribution.sample()).eval()) + def testBatchStatic(self): + x = np.array([[[1., 0], + [2, 1]], + [[3., 0], + [1, 2]]]) # np.linalg.cholesky(y) + y = np.array([[[1., 2], + [2, 5]], + [[9., 3], + [3, 5]]]) # np.matmul(x, x.T) + with self.test_session() as sess: + y_actual = bijectors.CholeskyOuterProduct().forward(x=x) + x_actual = bijectors.CholeskyOuterProduct().inverse(y=y) + [y_actual_, x_actual_] = sess.run([y_actual, x_actual]) + self.assertEqual([2, 2, 2], y_actual.get_shape()) + self.assertEqual([2, 2, 2], x_actual.get_shape()) + self.assertAllClose(y, y_actual_) + self.assertAllClose(x, x_actual_) + + def testBatchDeferred(self): + x = np.array([[[1., 0], + [2, 1]], + [[3., 0], + [1, 2]]]) # np.linalg.cholesky(y) + y = np.array([[[1., 2], + [2, 5]], + [[9., 3], + [3, 5]]]) # np.matmul(x, x.T) + with self.test_session() as sess: + x_pl = array_ops.placeholder(dtypes.float32) + y_pl = array_ops.placeholder(dtypes.float32) + y_actual = bijectors.CholeskyOuterProduct().forward(x=x_pl) + x_actual = bijectors.CholeskyOuterProduct().inverse(y=y_pl) + [y_actual_, x_actual_] = sess.run([y_actual, x_actual], + feed_dict={x_pl: x, y_pl: y}) + self.assertEqual(None, y_actual.get_shape()) + self.assertEqual(None, x_actual.get_shape()) + self.assertAllClose(y, y_actual_) + self.assertAllClose(x, x_actual_) if __name__ == "__main__": diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/invert_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/invert_test.py index 0ff35304283fce9ce3f9e5d31b1258394e384d7b..28e3e3135455348debb002b7d457e785799e1564 100644 --- a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/invert_test.py +++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/invert_test.py @@ -35,8 +35,7 @@ class InvertBijectorTest(test.TestCase): for fwd in [ bijectors.Identity(), bijectors.Exp(event_ndims=1), - bijectors.Affine( - shift=[0., 1.], scale_diag=[2., 3.], event_ndims=1), + bijectors.Affine(shift=[0., 1.], scale_diag=[2., 3.]), bijectors.Softplus(event_ndims=1), bijectors.SoftmaxCentered(event_ndims=1), bijectors.SigmoidCentered(), diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/kumaraswamy_bijector_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/kumaraswamy_bijector_test.py new file mode 100644 index 0000000000000000000000000000000000000000..ad11d9f2484c4b08c67c5f82aec1320475d1d983 --- /dev/null +++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/kumaraswamy_bijector_test.py @@ -0,0 +1,80 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for Kumaraswamy Bijector.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.distributions.python.ops.bijectors.kumaraswamy import Kumaraswamy +from tensorflow.python.ops.distributions.bijector_test_util import assert_bijective_and_finite +from tensorflow.python.ops.distributions.bijector_test_util import assert_scalar_congruency +from tensorflow.python.platform import test + + +class KumaraswamyBijectorTest(test.TestCase): + """Tests correctness of the Kumaraswamy bijector.""" + + def testBijector(self): + with self.test_session(): + a = 2. + b = 0.3 + bijector = Kumaraswamy( + concentration1=a, concentration0=b, + event_ndims=0, validate_args=True) + self.assertEqual("kumaraswamy", bijector.name) + x = np.array([[[0.1], [0.2], [0.3], [0.4], [0.5]]], dtype=np.float32) + # Kumaraswamy cdf. This is the same as inverse(x). + y = 1. - (1. - x ** a) ** b + self.assertAllClose(y, bijector.inverse(x).eval()) + self.assertAllClose(x, bijector.forward(y).eval()) + kumaraswamy_log_pdf = (np.log(a) + np.log(b) + (a - 1) * np.log(x) + + (b - 1) * np.log1p(-x ** a)) + + self.assertAllClose( + # We should lose a dimension from calculating the determinant of the + # jacobian. + kumaraswamy_log_pdf, + bijector.inverse_log_det_jacobian(x).eval()) + self.assertAllClose( + -bijector.inverse_log_det_jacobian(x).eval(), + bijector.forward_log_det_jacobian(y).eval(), + rtol=1e-4, + atol=0.) + + def testScalarCongruency(self): + with self.test_session(): + assert_scalar_congruency( + Kumaraswamy(concentration1=0.5, concentration0=1.1), + lower_x=0., upper_x=1., n=int(10e3), rtol=0.02) + + def testBijectiveAndFinite(self): + with self.test_session(): + concentration1 = 1.2 + concentration0 = 2. + bijector = Kumaraswamy( + concentration1=concentration1, + concentration0=concentration0, validate_args=True) + # Omitting the endpoints 0 and 1, since idlj will be inifinity at these + # endpoints. + y = np.linspace(.01, 0.99, num=10).astype(np.float32) + x = 1 - (1 - y ** concentration1) ** concentration0 + assert_bijective_and_finite(bijector, x, y, rtol=1e-3) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/softmax_centered_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/softmax_centered_test.py index 62e3869db090e9c9327bc552d10234ff76ba28fd..4a7679daad6f6acc632eb9133078499dda89e43d 100644 --- a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/softmax_centered_test.py +++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/softmax_centered_test.py @@ -21,7 +21,9 @@ from __future__ import print_function import numpy as np from tensorflow.contrib.distributions.python.ops.bijectors.softmax_centered import SoftmaxCentered +from tensorflow.python.framework import dtypes from tensorflow.python.framework import tensor_shape +from tensorflow.python.ops import array_ops from tensorflow.python.ops.distributions.bijector_test_util import assert_bijective_and_finite from tensorflow.python.platform import test @@ -76,6 +78,32 @@ class SoftmaxCenteredBijectorTest(test.TestCase): atol=0., rtol=1e-7) + def testBijectorUnknownShape(self): + with self.test_session(): + softmax = SoftmaxCentered(event_ndims=1) + self.assertEqual("softmax_centered", softmax.name) + x = array_ops.placeholder(shape=[2, None], dtype=dtypes.float32) + real_x = np.log([[2., 3, 4], [4., 8, 12]]) + y = array_ops.placeholder(shape=[2, None], dtype=dtypes.float32) + real_y = [[0.2, 0.3, 0.4, 0.1], [0.16, 0.32, 0.48, 0.04]] + self.assertAllClose(real_y, softmax.forward(x).eval( + feed_dict={x: real_x})) + self.assertAllClose(real_x, softmax.inverse(y).eval( + feed_dict={y: real_y})) + self.assertAllClose( + -np.sum(np.log(real_y), axis=1), + softmax.inverse_log_det_jacobian(y).eval( + feed_dict={y: real_y}), + atol=0., + rtol=1e-7) + self.assertAllClose( + -softmax.inverse_log_det_jacobian(y).eval( + feed_dict={y: real_y}), + softmax.forward_log_det_jacobian(x).eval( + feed_dict={x: real_x}), + atol=0., + rtol=1e-7) + def testShapeGetters(self): with self.test_session(): for x, y, b in ((tensor_shape.TensorShape([]), diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/square_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/square_test.py new file mode 100644 index 0000000000000000000000000000000000000000..f03d6f1343a11ae4517f9034ceb0c99ca6fe7fa2 --- /dev/null +++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/square_test.py @@ -0,0 +1,58 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for Bijector.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.distributions.python.ops import bijectors +from tensorflow.python.ops.distributions.bijector_test_util import assert_scalar_congruency +from tensorflow.python.platform import test + + +class SquareBijectorTest(test.TestCase): + """Tests the correctness of the Y = X ** 2 transformation.""" + + def testBijectorScalar(self): + with self.test_session(): + bijector = bijectors.Square(validate_args=True) + self.assertEqual("square", bijector.name) + x = [[[1., 5], + [2, 1]], + [[np.sqrt(2.), 3], + [np.sqrt(8.), 1]]] + y = np.square(x) + ildj = -np.log(2.) - np.log(x) + self.assertAllClose(y, bijector.forward(x).eval()) + self.assertAllClose(x, bijector.inverse(y).eval()) + self.assertAllClose( + ildj, bijector.inverse_log_det_jacobian(y).eval(), atol=0., rtol=1e-7) + self.assertAllClose( + -bijector.inverse_log_det_jacobian(y).eval(), + bijector.forward_log_det_jacobian(x).eval(), + atol=0., + rtol=1e-7) + + def testScalarCongruency(self): + with self.test_session(): + bijector = bijectors.Square(validate_args=True) + assert_scalar_congruency(bijector, lower_x=1e-3, upper_x=1.5, rtol=0.05) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/distributions/python/kernel_tests/independent_test.py b/tensorflow/contrib/distributions/python/kernel_tests/independent_test.py index 06318ca09dec851cf025fa35c83732b85824cbee..6a69f9e60b99a17c657f074597a075890265a93b 100644 --- a/tensorflow/contrib/distributions/python/kernel_tests/independent_test.py +++ b/tensorflow/contrib/distributions/python/kernel_tests/independent_test.py @@ -27,6 +27,7 @@ from tensorflow.python.framework import dtypes from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops.distributions import bernoulli as bernoulli_lib +from tensorflow.python.ops.distributions import kullback_leibler from tensorflow.python.ops.distributions import normal as normal_lib from tensorflow.python.platform import test from tensorflow.python.platform import tf_logging @@ -126,6 +127,100 @@ class ProductDistributionTest(test.TestCase): self.assertAllClose(sample_entropy_, actual_entropy_, rtol=0.01, atol=0.) self.assertAllClose(loc, actual_mode_, rtol=1e-6, atol=0.) + def testKLRaises(self): + ind1 = independent_lib.Independent( + distribution=normal_lib.Normal( + loc=np.float32([-1., 1]), + scale=np.float32([0.1, 0.5])), + reinterpreted_batch_ndims=1) + ind2 = independent_lib.Independent( + distribution=normal_lib.Normal( + loc=np.float32(-1), + scale=np.float32(0.5)), + reinterpreted_batch_ndims=0) + + with self.assertRaisesRegexp( + ValueError, "Event shapes do not match"): + kullback_leibler.kl_divergence(ind1, ind2) + + ind1 = independent_lib.Independent( + distribution=normal_lib.Normal( + loc=np.float32([-1., 1]), + scale=np.float32([0.1, 0.5])), + reinterpreted_batch_ndims=1) + ind2 = independent_lib.Independent( + distribution=mvn_diag_lib.MultivariateNormalDiag( + loc=np.float32([-1., 1]), + scale_diag=np.float32([0.1, 0.5])), + reinterpreted_batch_ndims=0) + + with self.assertRaisesRegexp( + NotImplementedError, "different event shapes"): + kullback_leibler.kl_divergence(ind1, ind2) + + def testKLScalarToMultivariate(self): + normal1 = normal_lib.Normal( + loc=np.float32([-1., 1]), + scale=np.float32([0.1, 0.5])) + ind1 = independent_lib.Independent( + distribution=normal1, reinterpreted_batch_ndims=1) + + normal2 = normal_lib.Normal( + loc=np.float32([-3., 3]), + scale=np.float32([0.3, 0.3])) + ind2 = independent_lib.Independent( + distribution=normal2, reinterpreted_batch_ndims=1) + + normal_kl = kullback_leibler.kl_divergence(normal1, normal2) + ind_kl = kullback_leibler.kl_divergence(ind1, ind2) + self.assertAllClose( + self.evaluate(math_ops.reduce_sum(normal_kl, axis=-1)), + self.evaluate(ind_kl)) + + def testKLIdentity(self): + normal1 = normal_lib.Normal( + loc=np.float32([-1., 1]), + scale=np.float32([0.1, 0.5])) + # This is functionally just a wrapper around normal1, + # and doesn't change any outputs. + ind1 = independent_lib.Independent( + distribution=normal1, reinterpreted_batch_ndims=0) + + normal2 = normal_lib.Normal( + loc=np.float32([-3., 3]), + scale=np.float32([0.3, 0.3])) + # This is functionally just a wrapper around normal2, + # and doesn't change any outputs. + ind2 = independent_lib.Independent( + distribution=normal2, reinterpreted_batch_ndims=0) + + normal_kl = kullback_leibler.kl_divergence(normal1, normal2) + ind_kl = kullback_leibler.kl_divergence(ind1, ind2) + self.assertAllClose( + self.evaluate(normal_kl), self.evaluate(ind_kl)) + + def testKLMultivariateToMultivariate(self): + # (1, 1, 2) batch of MVNDiag + mvn1 = mvn_diag_lib.MultivariateNormalDiag( + loc=np.float32([[[[-1., 1, 3.], [2., 4., 3.]]]]), + scale_diag=np.float32([[[0.2, 0.1, 5.], [2., 3., 4.]]])) + ind1 = independent_lib.Independent( + distribution=mvn1, reinterpreted_batch_ndims=2) + + # (1, 1, 2) batch of MVNDiag + mvn2 = mvn_diag_lib.MultivariateNormalDiag( + loc=np.float32([[[[-2., 3, 2.], [1., 3., 2.]]]]), + scale_diag=np.float32([[[0.1, 0.5, 3.], [1., 2., 1.]]])) + + ind2 = independent_lib.Independent( + distribution=mvn2, reinterpreted_batch_ndims=2) + + mvn_kl = kullback_leibler.kl_divergence(mvn1, mvn2) + ind_kl = kullback_leibler.kl_divergence(ind1, ind2) + self.assertAllClose( + self.evaluate(math_ops.reduce_sum(mvn_kl, axis=[-1, -2])), + self.evaluate(ind_kl)) + def _testMnistLike(self, static_shape): sample_shape = [4, 5] batch_shape = [10] diff --git a/tensorflow/contrib/distributions/python/kernel_tests/kumaraswamy_test.py b/tensorflow/contrib/distributions/python/kernel_tests/kumaraswamy_test.py index ea3c86b5c0f42b64fc6e4e362cbcc162bccf74a2..2980e2bfe93b2e2aa01d38fc9fa4650a015efc06 100644 --- a/tensorflow/contrib/distributions/python/kernel_tests/kumaraswamy_test.py +++ b/tensorflow/contrib/distributions/python/kernel_tests/kumaraswamy_test.py @@ -130,10 +130,8 @@ class KumaraswamyTest(test.TestCase): dist.prob([.1, .3, .6]).eval() dist.prob([.2, .3, .5]).eval() # Either condition can trigger. - with self.assertRaisesOpError("sample must be positive"): + with self.assertRaisesOpError("sample must be non-negative"): dist.prob([-1., 0.1, 0.5]).eval() - with self.assertRaisesOpError("sample must be positive"): - dist.prob([0., 0.1, 0.5]).eval() with self.assertRaisesOpError("sample must be no larger than `1`"): dist.prob([.1, .2, 1.2]).eval() @@ -249,13 +247,13 @@ class KumaraswamyTest(test.TestCase): a = np.array([1., 2, 3]) b = np.array([2., 4, 1.2]) dist = kumaraswamy_lib.Kumaraswamy(a, b, allow_nan_stats=False) - with self.assertRaisesOpError("Condition x < y.*"): + with self.assertRaisesOpError("Mode undefined for concentration1 <= 1."): dist.mode().eval() a = np.array([2., 2, 3]) b = np.array([1., 4, 1.2]) dist = kumaraswamy_lib.Kumaraswamy(a, b, allow_nan_stats=False) - with self.assertRaisesOpError("Condition x < y.*"): + with self.assertRaisesOpError("Mode undefined for concentration0 <= 1."): dist.mode().eval() def testKumaraswamyModeEnableAllowNanStats(self): diff --git a/tensorflow/contrib/distributions/python/kernel_tests/statistical_testing_test.py b/tensorflow/contrib/distributions/python/kernel_tests/statistical_testing_test.py new file mode 100644 index 0000000000000000000000000000000000000000..3548ac18078a0b40f117c2bf9e2b34d20cee163b --- /dev/null +++ b/tensorflow/contrib/distributions/python/kernel_tests/statistical_testing_test.py @@ -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. +# ============================================================================== +"""Tests for the statistical testing library.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.distributions.python.ops import statistical_testing as st +from tensorflow.python.framework import errors +from tensorflow.python.ops import check_ops +from tensorflow.python.platform import test + + +class StatisticalTestingTest(test.TestCase): + + def test_dkwm_design_mean_one_sample_soundness(self): + numbers = [1e-5, 1e-2, 1.1e-1, 0.9, 1., 1.02, 2., 10., 1e2, 1e5, 1e10] + rates = [1e-6, 1e-3, 1e-2, 1.1e-1, 0.2, 0.5, 0.7, 1.] + with self.test_session() as sess: + for ff in rates: + for fp in rates: + sufficient_n = st.min_num_samples_for_dkwm_mean_test( + numbers, 0., 1., false_fail_rate=ff, false_pass_rate=fp) + detectable_d = st.min_discrepancy_of_true_means_detectable_by_dkwm( + sufficient_n, 0., 1., false_fail_rate=ff, false_pass_rate=fp) + sess.run(check_ops.assert_less_equal(detectable_d, numbers)) + + def test_dkwm_design_mean_two_sample_soundness(self): + numbers = [1e-5, 1e-2, 1.1e-1, 0.9, 1., 1.02, 2., 10., 1e2, 1e5, 1e10] + rates = [1e-6, 1e-3, 1e-2, 1.1e-1, 0.2, 0.5, 0.7, 1.] + with self.test_session() as sess: + for ff in rates: + for fp in rates: + (sufficient_n1, + sufficient_n2) = st.min_num_samples_for_dkwm_mean_two_sample_test( + numbers, 0., 1., 0., 1., + false_fail_rate=ff, false_pass_rate=fp) + d_fn = st.min_discrepancy_of_true_means_detectable_by_dkwm_two_sample + detectable_d = d_fn( + sufficient_n1, 0., 1., sufficient_n2, 0., 1., + false_fail_rate=ff, false_pass_rate=fp) + sess.run(check_ops.assert_less_equal(detectable_d, numbers)) + + def test_true_mean_confidence_interval_by_dkwm_one_sample(self): + rng = np.random.RandomState(seed=0) + + num_samples = 5000 + # 5000 samples is chosen to be enough to find discrepancies of + # size 0.1 or more with assurance 1e-6, as confirmed here: + with self.test_session() as sess: + d = st.min_discrepancy_of_true_means_detectable_by_dkwm( + num_samples, 0., 1., false_fail_rate=1e-6, false_pass_rate=1e-6) + d = sess.run(d) + self.assertLess(d, 0.1) + + # Test that the confidence interval computed for the mean includes + # 0.5 and excludes 0.4 and 0.6. + with self.test_session() as sess: + samples = rng.uniform(size=num_samples).astype(np.float32) + (low, high) = st.true_mean_confidence_interval_by_dkwm( + samples, 0., 1., error_rate=1e-6) + low, high = sess.run([low, high]) + self.assertGreater(low, 0.4) + self.assertLess(low, 0.5) + self.assertGreater(high, 0.5) + self.assertLess(high, 0.6) + + def test_dkwm_mean_one_sample_assertion(self): + rng = np.random.RandomState(seed=0) + num_samples = 5000 + + # Test that the test assertion agrees that the mean of the standard + # uniform distribution is 0.5. + samples = rng.uniform(size=num_samples).astype(np.float32) + with self.test_session() as sess: + sess.run(st.assert_true_mean_equal_by_dkwm( + samples, 0., 1., 0.5, false_fail_rate=1e-6)) + + # Test that the test assertion confirms that the mean of the + # standard uniform distribution is not 0.4. + with self.assertRaises(errors.InvalidArgumentError): + sess.run(st.assert_true_mean_equal_by_dkwm( + samples, 0., 1., 0.4, false_fail_rate=1e-6)) + + # Test that the test assertion confirms that the mean of the + # standard uniform distribution is not 0.6. + with self.assertRaises(errors.InvalidArgumentError): + sess.run(st.assert_true_mean_equal_by_dkwm( + samples, 0., 1., 0.6, false_fail_rate=1e-6)) + + def test_dkwm_mean_two_sample_assertion(self): + rng = np.random.RandomState(seed=0) + num_samples = 15000 + + # 15000 samples is chosen to be enough to find discrepancies of + # size 0.1 or more with assurance 1e-6, as confirmed here: + with self.test_session() as sess: + d = st.min_discrepancy_of_true_means_detectable_by_dkwm_two_sample( + num_samples, 0., 1., num_samples, 0., 1., + false_fail_rate=1e-6, false_pass_rate=1e-6) + d = sess.run(d) + self.assertLess(d, 0.1) + + # Test that the test assertion agrees that the standard + # uniform distribution has the same mean as itself. + samples1 = rng.uniform(size=num_samples).astype(np.float32) + samples2 = rng.uniform(size=num_samples).astype(np.float32) + with self.test_session() as sess: + sess.run(st.assert_true_mean_equal_by_dkwm_two_sample( + samples1, 0., 1., samples2, 0., 1., false_fail_rate=1e-6)) + + # Test that the test assertion confirms that the mean of the + # standard uniform distribution is different from the mean of beta(2, 1). + beta_high_samples = rng.beta(2, 1, size=num_samples).astype(np.float32) + with self.assertRaises(errors.InvalidArgumentError): + sess.run(st.assert_true_mean_equal_by_dkwm_two_sample( + samples1, 0., 1., + beta_high_samples, 0., 1., + false_fail_rate=1e-6)) + + # Test that the test assertion confirms that the mean of the + # standard uniform distribution is different from the mean of beta(1, 2). + beta_low_samples = rng.beta(1, 2, size=num_samples).astype(np.float32) + with self.assertRaises(errors.InvalidArgumentError): + sess.run(st.assert_true_mean_equal_by_dkwm_two_sample( + samples1, 0., 1., + beta_low_samples, 0., 1., + false_fail_rate=1e-6)) + + def test_dkwm_argument_validity_checking(self): + rng = np.random.RandomState(seed=0) + samples = rng.uniform(size=5000).astype(np.float32) + + # Test that the test library complains if the given samples fall + # outside the purported bounds. + with self.test_session() as sess: + with self.assertRaises(errors.InvalidArgumentError): + sess.run(st.true_mean_confidence_interval_by_dkwm( + samples, 0., 0.5, error_rate=0.5)) + with self.assertRaises(errors.InvalidArgumentError): + sess.run(st.true_mean_confidence_interval_by_dkwm( + samples, 0.5, 1., error_rate=0.5)) + + # But doesn't complain if they don't. + op = st.true_mean_confidence_interval_by_dkwm( + samples, 0., 1., error_rate=0.5) + _ = sess.run(op) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/distributions/python/kernel_tests/transformed_distribution_test.py b/tensorflow/contrib/distributions/python/kernel_tests/transformed_distribution_test.py index cbaf74d3f66253ae5727e1ba579e2d49235b748e..af13553c32bdb6ef4038daa5e4bbef3251cff2f3 100644 --- a/tensorflow/contrib/distributions/python/kernel_tests/transformed_distribution_test.py +++ b/tensorflow/contrib/distributions/python/kernel_tests/transformed_distribution_test.py @@ -245,9 +245,8 @@ class TransformedDistributionTest(test.TestCase): with self.test_session() as sess: exp2 = self._cls()( ds.Exponential(rate=0.25), - bijector=ds.bijectors.Affine( - scale_identity_multiplier=2., - event_ndims=0)) + bijector=ds.bijectors.AffineScalar(scale=2.) + ) log_prob = exp2.log_prob(1.) log_prob_ = sess.run(log_prob) base_log_prob = -0.5 * 0.25 + np.log(0.25) diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/__init__.py b/tensorflow/contrib/distributions/python/ops/bijectors/__init__.py index 93923c3f083c7f5136b55e9021cbd6323684b976..452f1caa30fdbf5442274cbcc7f3549081b80ae9 100644 --- a/tensorflow/contrib/distributions/python/ops/bijectors/__init__.py +++ b/tensorflow/contrib/distributions/python/ops/bijectors/__init__.py @@ -17,7 +17,9 @@ @@AbsoluteValue @@Affine @@AffineLinearOperator +@@AffineScalar @@Bijector +@@BatchNormalization @@Chain @@CholeskyOuterProduct @@ConditionalBijector @@ -26,6 +28,7 @@ @@Identity @@Inline @@Invert +@@Kumaraswamy @@MaskedAutoregressiveFlow @@Permute @@PowerTransform @@ -36,6 +39,7 @@ @@SinhArcsinh @@SoftmaxCentered @@Softplus +@@Square @@Weibull @@masked_autoregressive_default_template @@ -52,6 +56,8 @@ from __future__ import print_function from tensorflow.contrib.distributions.python.ops.bijectors.absolute_value import * from tensorflow.contrib.distributions.python.ops.bijectors.affine import * from tensorflow.contrib.distributions.python.ops.bijectors.affine_linear_operator import * +from tensorflow.contrib.distributions.python.ops.bijectors.affine_scalar import * +from tensorflow.contrib.distributions.python.ops.bijectors.batch_normalization import * from tensorflow.contrib.distributions.python.ops.bijectors.chain import * from tensorflow.contrib.distributions.python.ops.bijectors.cholesky_outer_product import * from tensorflow.contrib.distributions.python.ops.bijectors.conditional_bijector import * @@ -59,6 +65,7 @@ from tensorflow.contrib.distributions.python.ops.bijectors.exp import * from tensorflow.contrib.distributions.python.ops.bijectors.gumbel import * from tensorflow.contrib.distributions.python.ops.bijectors.inline import * from tensorflow.contrib.distributions.python.ops.bijectors.invert import * +from tensorflow.contrib.distributions.python.ops.bijectors.kumaraswamy import * from tensorflow.contrib.distributions.python.ops.bijectors.masked_autoregressive import * from tensorflow.contrib.distributions.python.ops.bijectors.permute import * from tensorflow.contrib.distributions.python.ops.bijectors.power_transform import * @@ -69,6 +76,7 @@ from tensorflow.contrib.distributions.python.ops.bijectors.sigmoid_centered impo from tensorflow.contrib.distributions.python.ops.bijectors.sinh_arcsinh import * from tensorflow.contrib.distributions.python.ops.bijectors.softmax_centered import * from tensorflow.contrib.distributions.python.ops.bijectors.softplus import * +from tensorflow.contrib.distributions.python.ops.bijectors.square import * from tensorflow.python.ops.distributions.bijector import * from tensorflow.python.ops.distributions.identity_bijector import Identity diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/affine.py b/tensorflow/contrib/distributions/python/ops/bijectors/affine.py index 05bb9c2f9bdf35e222c94db3491157893da64ebd..7fe73ada4466d38a7d352f23a55d6b90ed38c84a 100644 --- a/tensorflow/contrib/distributions/python/ops/bijectors/affine.py +++ b/tensorflow/contrib/distributions/python/ops/bijectors/affine.py @@ -104,7 +104,6 @@ class Affine(bijector.Bijector): scale_tril=None, scale_perturb_factor=None, scale_perturb_diag=None, - event_ndims=1, validate_args=False, name="affine"): """Instantiates the `Affine` bijector. @@ -157,8 +156,6 @@ class Affine(bijector.Bijector): matrix. `scale_perturb_diag` has shape [N1, N2, ... r], which represents an `r x r` diagonal matrix. When `None` low rank updates will take the form `scale_perturb_factor * scale_perturb_factor.T`. - event_ndims: Scalar `int` `Tensor` indicating the number of dimensions - associated with a particular draw from the distribution. Must be 0 or 1. validate_args: Python `bool` indicating whether arguments should be checked for correctness. name: Python `str` name given to ops managed by this object. @@ -187,23 +184,6 @@ class Affine(bijector.Bijector): with self._name_scope("init", values=[ shift, scale_identity_multiplier, scale_diag, scale_tril, scale_perturb_diag, scale_perturb_factor]): - event_ndims = ops.convert_to_tensor(event_ndims, name="event_ndims") - event_ndims_const = tensor_util.constant_value(event_ndims) - if event_ndims_const is not None and event_ndims_const not in (0, 1): - raise ValueError("event_ndims(%s) was not 0 or 1" % event_ndims_const) - else: - if validate_args: - # Shape tool will catch if event_ndims is negative. - event_ndims = control_flow_ops.with_dependencies( - [check_ops.assert_less( - event_ndims, 2, message="event_ndims must be 0 or 1")], - event_ndims) - - if event_ndims_const == 0 and not self._is_only_identity_multiplier: - raise ValueError( - "If event_ndims == 0, the only scale argument you can pass is " - "scale_identity_multiplier. All others operate on vectors.") - # In the absence of `loc` and `scale`, we'll assume `dtype` is `float32`. dtype = dtypes.float32 @@ -251,12 +231,11 @@ class Affine(bijector.Bijector): self._scale = scale self._shaper = _DistributionShape( batch_ndims=batch_ndims, - event_ndims=event_ndims, + event_ndims=1, validate_args=validate_args) super(Affine, self).__init__( - event_ndims=event_ndims, + event_ndims=1, graph_parents=( - [event_ndims] + [self._scale] if tensor_util.is_tensor(self._scale) else self._scale.graph_parents + [self._shift] if self._shift is not None else []), @@ -388,9 +367,7 @@ class Affine(bijector.Bijector): if self._is_only_identity_multiplier: # We don't pad in this case and instead let the fldj be applied # via broadcast. - event_size = distribution_util.pick_vector( - math_ops.equal(self._shaper.event_ndims, 0), - [1], array_ops.shape(x))[-1] + event_size = array_ops.shape(x)[-1] event_size = math_ops.cast(event_size, dtype=self._scale.dtype) return math_ops.log(math_ops.abs(self._scale)) * event_size return self.scale.log_abs_determinant() diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/affine_scalar.py b/tensorflow/contrib/distributions/python/ops/bijectors/affine_scalar.py new file mode 100644 index 0000000000000000000000000000000000000000..8adaa54c843d1b243a02967402a37b7c63fabbdf --- /dev/null +++ b/tensorflow/contrib/distributions/python/ops/bijectors/affine_scalar.py @@ -0,0 +1,138 @@ +# 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. +# ============================================================================== +"""Affine bijector.""" + +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 check_ops +from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops.distributions import bijector + + +__all__ = [ + "AffineScalar", +] + + +class AffineScalar(bijector.Bijector): + """Compute `Y = g(X; shift, scale) = scale * X + shift`. + + Examples: + + ```python + # Y = X + b = AffineScalar() + + # Y = X + shift + b = AffineScalar(shift=[1., 2, 3]) + + # Y = 2 * X + shift + b = AffineScalar( + shift=[1., 2, 3], + scale=2.) + ``` + + """ + + def __init__(self, + shift=None, + scale=None, + validate_args=False, + name="affine_scalar"): + """Instantiates the `AffineScalar` bijector. + + This `Bijector` is initialized with `shift` `Tensor` and `scale` arguments, + giving the forward operation: + + ```none + Y = g(X) = scale * X + shift + ``` + + if `scale` is not specified, then the bijector has the semantics of + `scale = 1.`. Similarly, if `shift` is not specified, then the bijector + has the semantics of `shift = 0.`. + + Args: + shift: Floating-point `Tensor`. If this is set to `None`, no shift is + applied. + scale: Floating-point `Tensor`. If this is set to `None`, no scale is + applied. + validate_args: Python `bool` indicating whether arguments should be + checked for correctness. + name: Python `str` name given to ops managed by this object. + """ + self._graph_parents = [] + self._name = name + self._validate_args = validate_args + + with self._name_scope("init", values=[scale, shift]): + self._shift = shift + self._scale = scale + + if self._shift is not None: + self._shift = ops.convert_to_tensor(shift, name="shift") + + if self._scale is not None: + self._scale = ops.convert_to_tensor(self._scale, name="scale") + if validate_args: + self._scale = control_flow_ops.with_dependencies( + [check_ops.assert_none_equal( + self._scale, + array_ops.zeros([], dtype=self._scale.dtype))], + self._scale) + + super(AffineScalar, self).__init__( + event_ndims=0, + is_constant_jacobian=True, + validate_args=validate_args, + name=name) + + @property + def shift(self): + """The `shift` `Tensor` in `Y = scale @ X + shift`.""" + return self._shift + + @property + def scale(self): + """The `scale` `LinearOperator` in `Y = scale @ X + shift`.""" + return self._scale + + def _forward(self, x): + y = array_ops.identity(x) + if self.scale is not None: + y *= self.scale + if self.shift is not None: + y += self.shift + return y + + def _inverse(self, y): + x = array_ops.identity(y) + if self.shift is not None: + x -= self.shift + if self.scale is not None: + x /= self.scale + return x + + def _forward_log_det_jacobian(self, x): + log_det_jacobian = array_ops.zeros_like(x) + if self.scale is None: + return log_det_jacobian + log_det_jacobian += math_ops.log(math_ops.abs(self.scale)) + return log_det_jacobian diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/batch_normalization.py b/tensorflow/contrib/distributions/python/ops/bijectors/batch_normalization.py new file mode 100644 index 0000000000000000000000000000000000000000..be72ff3081225b9f9fdb6541322b7fc3d4aaa41e --- /dev/null +++ b/tensorflow/contrib/distributions/python/ops/bijectors/batch_normalization.py @@ -0,0 +1,258 @@ +# 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. +# ============================================================================== +"""Batch Norm bijector.""" + +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.layers import normalization +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import nn +from tensorflow.python.ops.distributions import bijector + + +__all__ = [ + "BatchNormalization", +] + + +def _undo_batch_normalization(x, + mean, + variance, + offset, + scale, + variance_epsilon, + name=None): + r"""Inverse of tf.nn.batch_normalization. + + Args: + x: Input `Tensor` of arbitrary dimensionality. + mean: A mean `Tensor`. + variance: A variance `Tensor`. + offset: An offset `Tensor`, often denoted `beta` in equations, or + None. If present, will be added to the normalized tensor. + scale: A scale `Tensor`, often denoted `gamma` in equations, or + `None`. If present, the scale is applied to the normalized tensor. + variance_epsilon: A small `float` added to the minibatch `variance` to + prevent dividing by zero. + name: A name for this operation (optional). + + Returns: + batch_unnormalized: The de-normalized, de-scaled, de-offset `Tensor`. + """ + with ops.name_scope( + name, "undo_batchnorm", [x, mean, variance, scale, offset]): + # inv = math_ops.rsqrt(variance + variance_epsilon) + # if scale is not None: + # inv *= scale + # return x * inv + ( + # offset - mean * inv if offset is not None else -mean * inv) + rescale = math_ops.sqrt(variance + variance_epsilon) + if scale is not None: + rescale /= scale + batch_unnormalized = x * rescale + ( + mean - offset * rescale if offset is not None else mean) + return batch_unnormalized + + +class BatchNormalization(bijector.Bijector): + """Compute `Y = g(X) s.t. X = g^-1(Y) = (Y - mean(Y)) / std(Y)`. + + Applies Batch Normalization [1] to samples from a data distribution. This can + be used to stabilize training of normalizing flows [2, 3]. + + When training Deep Neural Networks (DNNs), it is common practice to + normalize or whiten features by shifting them to have zero mean and + scaling them to have unit variance. + + The `inverse()` method of the BatchNorm bijector, which is used in the + log-likelihood computation of data samples, implements the normalization + procedure (shift-and-scale) using the mean and standard deviation of the + current minibatch. + + Conversely, the `forward()` method of the bijector de-normalizes samples (e.g. + `X*std(Y) + mean(Y)` with the running-average mean and standard deviation + computed at training-time. De-normalization is useful for sampling. + + + ```python + + dist = tfd.TransformedDistribution( + distribution=tfd.Normal()), + bijector=tfb.BatchNorm()) + + y = tfd.MultivariateNormalDiag(loc=1., scale=2.).sample(100) # ~ N(1, 2) + x = dist.bijector.inverse(y) # ~ N(0, 1) + y = dist.sample() # ~ N(1, 2) + ``` + + During training time, `BatchNorm.inverse` and `BatchNorm.forward` are not + guaranteed to be inverses of each other because `inverse(y)` uses statistics + of the current minibatch, while `forward(x)` uses running-average statistics + accumulated from training. In other words, + `BatchNorm.inverse(BatchNorm.forward(...))` and + `BatchNorm.forward(BatchNorm.inverse(...))` will be identical when + `training=False` but may be different when `training=True`. + + [1]: "Batch Normalization: Accelerating Deep Network Training by Reducing + Internal Covariate Shift." + Sergey Ioffe, Christian Szegedy. Arxiv. 2015. + https://arxiv.org/abs/1502.03167 + + [2]: "Density Estimation using Real NVP." + Laurent Dinh, Jascha Sohl-Dickstein, Samy Bengio. ICLR. 2017. + https://arxiv.org/abs/1605.08803 + + [3]: "Masked Autoregressive Flow for Density Estimation." + George Papamakarios, Theo Pavlakou, Iain Murray. Arxiv. 2017. + https://arxiv.org/abs/1705.07057 + + """ + + def __init__(self, + batchnorm_layer=None, + training=True, + validate_args=False, + name="batch_normalization"): + """Instantiates the `BatchNorm` bijector. + + Args: + batchnorm_layer: `tf.layers.BatchNormalization` layer object. If `None`, + defaults to + `tf.layers.BatchNormalization(gamma_constraint=nn_ops.relu(x) + 1e-6)`. + This ensures positivity of the scale variable. + + training: If True, updates running-average statistics during call to + `inverse()`. + validate_args: Python `bool` indicating whether arguments should be + checked for correctness. + name: Python `str` name given to ops managed by this object. + Raises: + ValueError: If bn_layer is not an instance of + `tf.layers.BatchNormalization`, or if it is specified with `renorm=True` + or a virtual batch size. + """ + # Scale must be positive. + g_constraint = lambda x: nn.relu(x) + 1e-6 + self.batchnorm = batchnorm_layer or normalization.BatchNormalization( + gamma_constraint=g_constraint) + self._validate_bn_layer(self.batchnorm) + self._training = training + super(BatchNormalization, self).__init__( + validate_args=validate_args, name=name) + + def _validate_bn_layer(self, layer): + """Check for valid BatchNormalization layer. + + Args: + layer: Instance of `tf.layers.BatchNormalization`. + Raises: + ValueError: If batchnorm_layer argument is not an instance of + `tf.layers.BatchNormalization`, or if `batchnorm_layer.renorm=True` or + if `batchnorm_layer.virtual_batch_size` is specified. + """ + if not isinstance(layer, normalization.BatchNormalization): + raise ValueError( + "batchnorm_layer must be an instance of BatchNormalization layer.") + if layer.renorm: + raise ValueError("BatchNorm Bijector does not support renormalization.") + if layer.virtual_batch_size: + raise ValueError( + "BatchNorm Bijector does not support virtual batch sizes.") + + def _get_broadcast_fn(self, x): + # Compute shape to broadcast scale/shift parameters to. + if not x.shape.is_fully_defined(): + raise ValueError("Input must have shape known at graph construction.") + input_shape = np.int32(x.shape.as_list()) + + ndims = len(input_shape) + # event_dims = self._compute_event_dims(x) + reduction_axes = [i for i in range(ndims) if i not in self.batchnorm.axis] + # Broadcasting only necessary for single-axis batch norm where the axis is + # not the last dimension + broadcast_shape = [1] * ndims + broadcast_shape[self.batchnorm.axis[0]] = ( + input_shape[self.batchnorm.axis[0]]) + def _broadcast(v): + if (v is not None and + len(v.get_shape()) != ndims and + reduction_axes != list(range(ndims - 1))): + return array_ops.reshape(v, broadcast_shape) + return v + return _broadcast + + def _normalize(self, y): + return self.batchnorm.apply(y, training=self._training) + + def _de_normalize(self, x): + # Uses the saved statistics. + if not self.batchnorm.built: + input_shape = x.get_shape() + self.batchnorm.build(input_shape) + broadcast_fn = self._get_broadcast_fn(x) + mean = broadcast_fn(self.batchnorm.moving_mean) + variance = broadcast_fn(self.batchnorm.moving_variance) + beta = broadcast_fn(self.batchnorm.beta) if self.batchnorm.center else None + gamma = broadcast_fn(self.batchnorm.gamma) if self.batchnorm.scale else None + return _undo_batch_normalization( + x, mean, variance, beta, gamma, self.batchnorm.epsilon) + + def _forward(self, x): + return self._de_normalize(x) + + def _inverse(self, y): + return self._normalize(y) + + def _forward_log_det_jacobian(self, x): + # Uses saved statistics to compute volume distortion. + return -self._inverse_log_det_jacobian(x, use_saved_statistics=True) + + def _inverse_log_det_jacobian(self, y, use_saved_statistics=False): + if not y.shape.is_fully_defined(): + raise ValueError("Input must have shape known at graph construction.") + input_shape = np.int32(y.shape.as_list()) + + if not self.batchnorm.built: + # Create variables. + self.batchnorm.build(input_shape) + + event_dims = self.batchnorm.axis + reduction_axes = [i for i in range(len(input_shape)) if i not in event_dims] + + if use_saved_statistics or not self._training: + log_variance = math_ops.log( + self.batchnorm.moving_variance + self.batchnorm.epsilon) + else: + # At training-time, ildj is computed from the mean and log-variance across + # the current minibatch. + _, v = nn.moments(y, axes=reduction_axes, keep_dims=True) + log_variance = math_ops.log(v + self.batchnorm.epsilon) + + # `gamma` and `log Var(y)` reductions over event_dims. + # Log(total change in area from gamma term). + log_total_gamma = math_ops.reduce_sum(math_ops.log(self.batchnorm.gamma)) + + # Log(total change in area from log-variance term). + log_total_variance = math_ops.reduce_sum(log_variance) + # The ildj is scalar, as it does not depend on the values of x and are + # constant across minibatch elements. + return log_total_gamma - 0.5 * log_total_variance diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/cholesky_outer_product.py b/tensorflow/contrib/distributions/python/ops/bijectors/cholesky_outer_product.py index cbd60f92a60612c6cf791b2c7708a3310c6e2b6b..43208ff088b469b70ebc08757daac277d4432b37 100644 --- a/tensorflow/contrib/distributions/python/ops/bijectors/cholesky_outer_product.py +++ b/tensorflow/contrib/distributions/python/ops/bijectors/cholesky_outer_product.py @@ -20,8 +20,6 @@ from __future__ import print_function import numpy as np -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 check_ops from tensorflow.python.ops import control_flow_ops @@ -39,8 +37,6 @@ __all__ = [ class CholeskyOuterProduct(bijector.Bijector): """Compute `g(X) = X @ X.T`; X is lower-triangular, positive-diagonal matrix. - `event_ndims` must be 0 or 2, i.e., scalar or matrix. - Note: the upper-triangular part of X is ignored (whether or not its zero). The surjectivity of g as a map from the set of n x n positive-diagonal @@ -64,46 +60,31 @@ class CholeskyOuterProduct(bijector.Bijector): Examples: ```python - bijector.CholeskyOuterProduct(event_ndims=2).forward(x=[[1., 0], [2, 1]]) + bijector.CholeskyOuterProduct().forward(x=[[1., 0], [2, 1]]) # Result: [[1., 2], [2, 5]], i.e., x @ x.T - bijector.CholeskyOuterProduct(event_ndims=2).inverse(y=[[1., 2], [2, 5]]) + bijector.CholeskyOuterProduct().inverse(y=[[1., 2], [2, 5]]) # Result: [[1., 0], [2, 1]], i.e., cholesky(y). ``` """ - def __init__(self, event_ndims=2, validate_args=False, - name="cholesky_outer_product"): + def __init__(self, validate_args=False, name="cholesky_outer_product"): """Instantiates the `CholeskyOuterProduct` bijector. Args: - event_ndims: `constant` `int32` scalar `Tensor` indicating the number of - dimensions associated with a particular draw from the distribution. Must - be 0 or 2. validate_args: Python `bool` indicating whether arguments should be checked for correctness. name: Python `str` name given to ops managed by this object. - - Raises: - ValueError: if event_ndims is neither 0 or 2. """ self._graph_parents = [] self._name = name - with self._name_scope("init", values=[event_ndims]): - event_ndims = ops.convert_to_tensor(event_ndims, name="event_ndims") - event_ndims = tensor_util.constant_value(event_ndims) - if event_ndims is None or event_ndims not in [0, 2]: - raise ValueError("`event_ndims` must be a TF constant which is 0 or 2") - self._static_event_ndims = event_ndims super(CholeskyOuterProduct, self).__init__( - event_ndims=event_ndims, + event_ndims=2, validate_args=validate_args, name=name) def _forward(self, x): - if self._static_event_ndims == 0: - return math_ops.square(x) if self.validate_args: is_matrix = check_ops.assert_rank_at_least(x, 2) shape = array_ops.shape(x) @@ -114,11 +95,7 @@ class CholeskyOuterProduct(bijector.Bijector): return math_ops.matmul(x, x, adjoint_b=True) def _inverse(self, y): - return (math_ops.sqrt(y) if self._static_event_ndims == 0 - else linalg_ops.cholesky(y)) - - def _inverse_log_det_jacobian(self, y): - return -self._forward_log_det_jacobian(x=self._inverse(y)) + return linalg_ops.cholesky(y) def _forward_log_det_jacobian(self, x): # Let Y be a symmetric, positive definite matrix and write: @@ -161,13 +138,6 @@ class CholeskyOuterProduct(bijector.Bijector): # Since there is a 2 X[j,j] term for every lower-triangular element of X we # conclude: # |Jac(d vec[Y]/d vec[X])| = 2^p prod_{j=0}^{p-1} X[j,j]^{p-j}. - if self._static_event_ndims == 0: - if self.validate_args: - is_positive = check_ops.assert_positive( - x, message="All elements must be positive.") - x = control_flow_ops.with_dependencies([is_positive], x) - return np.log(2.) + math_ops.log(x) - diag = array_ops.matrix_diag_part(x) # We now ensure diag is columnar. Eg, if `diag = [1, 2, 3]` then the output diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/kumaraswamy.py b/tensorflow/contrib/distributions/python/ops/bijectors/kumaraswamy.py new file mode 100644 index 0000000000000000000000000000000000000000..f5de052c9ed18b1ebf4c174aeea3a951b1ddcd9d --- /dev/null +++ b/tensorflow/contrib/distributions/python/ops/bijectors/kumaraswamy.py @@ -0,0 +1,153 @@ +# 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. +# ============================================================================== +"""Kumaraswamy bijector.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +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 check_ops +from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops.distributions import bijector + +__all__ = [ + "Kumaraswamy", +] + + +class Kumaraswamy(bijector.Bijector): + """Compute `Y = g(X) = (1 - (1 - X)**(1 / b))**(1 / a), X in [0, 1]`. + + This bijector maps inputs from `[0, 1]` to [0, 1]`. The inverse of the + bijector applied to a uniform random variable `X ~ U(0, 1) gives back a + random variable with the [Kumaraswamy distribution]( + https://en.wikipedia.org/wiki/Kumaraswamy_distribution): + + ```none + Y ~ Kumaraswamy(a, b) + pdf(y; a, b, 0 <= y <= 1) = a * b * y ** (a - 1) * (1 - y**a) ** (b - 1) + ``` + """ + + def __init__(self, + concentration1=None, + concentration0=None, + event_ndims=0, + validate_args=False, + name="kumaraswamy"): + """Instantiates the `Kumaraswamy` bijector. + + Args: + concentration1: Python `float` scalar indicating the transform power, + i.e., `Y = g(X) = (1 - (1 - X)**(1 / b))**(1 / a)` where `a` is + `concentration1`. + concentration0: Python `float` scalar indicating the transform power, + i.e., `Y = g(X) = (1 - (1 - X)**(1 / b))**(1 / a)` where `b` is + `concentration0`. + event_ndims: Python scalar indicating the number of dimensions associated + with a particular draw from the distribution. Currently only zero is + supported. + validate_args: Python `bool` indicating whether arguments should be + checked for correctness. + name: Python `str` name given to ops managed by this object. + + Raises: + ValueError: If `event_ndims` is not zero. + """ + self._graph_parents = [] + self._name = name + self._validate_args = validate_args + + event_ndims = ops.convert_to_tensor(event_ndims, name="event_ndims") + event_ndims_const = tensor_util.constant_value(event_ndims) + if event_ndims_const is not None and event_ndims_const not in (0,): + raise ValueError("event_ndims(%s) was not 0" % event_ndims_const) + else: + if validate_args: + event_ndims = control_flow_ops.with_dependencies( + [check_ops.assert_equal( + event_ndims, 0, message="event_ndims was not 0")], + event_ndims) + + with self._name_scope("init", values=[concentration1, concentration0]): + concentration1 = self._maybe_assert_valid_concentration( + ops.convert_to_tensor(concentration1, name="concentration1"), + validate_args=validate_args) + concentration0 = self._maybe_assert_valid_concentration( + ops.convert_to_tensor(concentration0, name="concentration0"), + validate_args=validate_args) + + self._concentration1 = concentration1 + self._concentration0 = concentration0 + super(Kumaraswamy, self).__init__( + event_ndims=0, + validate_args=validate_args, + name=name) + + @property + def concentration1(self): + """The `a` in: `Y = g(X) = (1 - (1 - X)**(1 / b))**(1 / a)`.""" + return self._concentration1 + + @property + def concentration0(self): + """The `b` in: `Y = g(X) = (1 - (1 - X)**(1 / b))**(1 / a)`.""" + return self._concentration0 + + def _forward(self, x): + x = self._maybe_assert_valid(x) + return math_ops.exp( + math_ops.log1p(-math_ops.exp(math_ops.log1p(-x) / self.concentration0)) + / self.concentration1) + + def _inverse(self, y): + y = self._maybe_assert_valid(y) + return math_ops.exp(math_ops.log1p( + -(1 - y**self.concentration1)**self.concentration0)) + + def _inverse_log_det_jacobian(self, y): + y = self._maybe_assert_valid(y) + event_dims = self._event_dims_tensor(y) + return math_ops.reduce_sum( + math_ops.log(self.concentration1) + math_ops.log(self.concentration0) + + (self.concentration1 - 1) * math_ops.log(y) + + (self.concentration0 - 1) * math_ops.log1p(-y**self.concentration1), + axis=event_dims) + + def _maybe_assert_valid_concentration(self, concentration, validate_args): + """Checks the validity of a concentration parameter.""" + if not validate_args: + return concentration + return control_flow_ops.with_dependencies([ + check_ops.assert_positive( + concentration, + message="Concentration parameter must be positive."), + ], concentration) + + def _maybe_assert_valid(self, x): + if not self.validate_args: + return x + return control_flow_ops.with_dependencies([ + check_ops.assert_non_negative( + x, + message="sample must be non-negative"), + check_ops.assert_less_equal( + x, array_ops.ones([], self.concentration0.dtype), + message="sample must be no larger than `1`."), + ], x) diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/softmax_centered.py b/tensorflow/contrib/distributions/python/ops/bijectors/softmax_centered.py index a9dcce6c526600f3b26c6bceb730417000917ce7..24add40445c60db533aac6d0c8eb537774895c65 100644 --- a/tensorflow/contrib/distributions/python/ops/bijectors/softmax_centered.py +++ b/tensorflow/contrib/distributions/python/ops/bijectors/softmax_centered.py @@ -18,8 +18,6 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import numpy as np - from tensorflow.contrib.distributions.python.ops import distribution_util from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops @@ -161,33 +159,16 @@ class SoftmaxCentered(bijector.Bijector): # x[i] = log(exp(x[i])) - log(y[end]) - log(normalization) # = log(exp(x[i])/normalization) - log(y[end]) # = log(y[i]) - log(y[end]) - shape = (np.asarray(y.shape.as_list(), dtype=np.int32) - if y.shape.is_fully_defined() - else array_ops.shape(y, name="shape")) - ndims = distribution_util.prefer_static_rank(y) # Do this first to make sure CSE catches that it'll happen again in # _inverse_log_det_jacobian. x = math_ops.log(y) - # We now extract the last coordinate of the rightmost dimension. - # Our trick is to slice from [0,0,...,shape[-1]-1] to shape[:-1]+[1]. - begin = array_ops.one_hot(indices=ndims-1, - depth=ndims, - on_value=shape[-1]-np.array(1, dtype=shape.dtype), - dtype=shape.dtype) - size = array_ops.concat([shape[:-1], np.asarray([1], dtype=shape.dtype)], 0) - log_normalization = -array_ops.strided_slice(x, begin, begin + size) - - # Here we slice out all but the last coordinate; see above for idea. - begin = array_ops.zeros_like(shape) - size = array_ops.concat([shape[:-1], [shape[-1] - 1]], 0) - x = array_ops.strided_slice(x, begin, begin + size) - - x += log_normalization + log_normalization = (-x[..., -1])[..., array_ops.newaxis] + x = x[..., :-1] + log_normalization if self._static_event_ndims == 0: - x = array_ops.squeeze(x, squeeze_dims=[ndims-1]) + x = array_ops.squeeze(x, squeeze_dims=-1) # Set shape hints. if y.shape.ndims is not None: diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/square.py b/tensorflow/contrib/distributions/python/ops/bijectors/square.py new file mode 100644 index 0000000000000000000000000000000000000000..2831a92df8e0ad2bf681f13533cdb6f5d2089a57 --- /dev/null +++ b/tensorflow/contrib/distributions/python/ops/bijectors/square.py @@ -0,0 +1,84 @@ +# 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. +# ============================================================================== +"""Square bijector.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +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.distributions import bijector + + +__all__ = [ + "Square", +] + + +class Square(bijector.Bijector): + """Compute `g(X) = X^2`; X is a positive real number. + + g is a bijection between the non-negative real numbers (R_+) and the + non-negative real numbers. + + Examples: + + ```python + bijector.Square().forward(x=[[1., 0], [2, 1]]) + # Result: [[1., 0], [4, 1]], i.e., x^2 + + bijector.Square().inverse(y=[[1., 4], [9, 1]]) + # Result: [[1., 2], [3, 1]], i.e., sqrt(y). + ``` + + """ + + def __init__(self, validate_args=False, name="square"): + """Instantiates the `Square` bijector. + + Args: + validate_args: Python `bool` indicating whether arguments should be + checked for correctness. + name: Python `str` name given to ops managed by this object. + """ + self._name = name + super(Square, self).__init__( + event_ndims=0, + validate_args=validate_args, + name=name) + + def _forward(self, x): + x = self._maybe_assert_valid(x) + return math_ops.square(x) + + def _inverse(self, y): + y = self._maybe_assert_valid(y) + return math_ops.sqrt(y) + + def _forward_log_det_jacobian(self, x): + x = self._maybe_assert_valid(x) + return np.log(2.) + math_ops.log(x) + + def _maybe_assert_valid(self, t): + if not self.validate_args: + return t + is_valid = check_ops.assert_non_negative( + t, message="All elements must be non-negative.") + return control_flow_ops.with_dependencies([is_valid], t) + diff --git a/tensorflow/contrib/distributions/python/ops/chi2.py b/tensorflow/contrib/distributions/python/ops/chi2.py index bdd5571c966a74e58e4f9f8eed2628f131a1b92e..e610f469e5d5f446b75c734cc39811de30a8cb9a 100644 --- a/tensorflow/contrib/distributions/python/ops/chi2.py +++ b/tensorflow/contrib/distributions/python/ops/chi2.py @@ -21,6 +21,8 @@ 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.ops import array_ops +from tensorflow.python.ops import check_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops.distributions import gamma @@ -87,7 +89,11 @@ class Chi2(gamma.Gamma): # allow_nan_stats=True # through to the parent class results in unnecessary asserts. with ops.name_scope(name, values=[df]): - self._df = ops.convert_to_tensor(df, name="df") + with ops.control_dependencies([ + check_ops.assert_positive(df), + ] if validate_args else []): + self._df = array_ops.identity(df, name="df") + super(Chi2, self).__init__( concentration=0.5 * self._df, rate=constant_op.constant(0.5, dtype=self._df.dtype), diff --git a/tensorflow/contrib/distributions/python/ops/gumbel.py b/tensorflow/contrib/distributions/python/ops/gumbel.py index d0efaefb8e78ddf4436e9e5a112d2c1cdddaf3b5..8d05ad6b8032fb8bada99389959091fb1c28beda 100644 --- a/tensorflow/contrib/distributions/python/ops/gumbel.py +++ b/tensorflow/contrib/distributions/python/ops/gumbel.py @@ -190,9 +190,6 @@ class _Gumbel(distribution.Distribution): def _log_prob(self, x): return self._log_unnormalized_prob(x) - self._log_normalization() - def _prob(self, x): - return math_ops.exp(self._log_prob(x)) - def _log_cdf(self, x): return -math_ops.exp(-self._z(x)) diff --git a/tensorflow/contrib/distributions/python/ops/independent.py b/tensorflow/contrib/distributions/python/ops/independent.py index cbce005013281ff3c58c94d525d5ce7a865d725a..7dcb3e3ac4db1855adacb7ec0fa8554c45d9c859 100644 --- a/tensorflow/contrib/distributions/python/ops/independent.py +++ b/tensorflow/contrib/distributions/python/ops/independent.py @@ -28,6 +28,7 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import check_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops.distributions import distribution as distribution_lib +from tensorflow.python.ops.distributions import kullback_leibler class Independent(distribution_lib.Distribution): @@ -254,3 +255,58 @@ class Independent(distribution_lib.Distribution): else: which_maximum = np.maximum return which_maximum(0, ndims - 1) + + +@kullback_leibler.RegisterKL(Independent, Independent) +def _kl_independent(a, b, name="kl_independent"): + """Batched KL divergence `KL(a || b)` for Independent distributions. + + We can leverage the fact that + ``` + KL(Independent(a) || Independent(b)) = sum(KL(a || b)) + ``` + where the sum is over the `reinterpreted_batch_ndims`. + + Args: + a: Instance of `Independent`. + b: Instance of `Independent`. + name: (optional) name to use for created ops. Default "kl_independent". + + Returns: + Batchwise `KL(a || b)`. + + Raises: + ValueError: If the event space for `a` and `b`, or their underlying + distributions don't match. + """ + p = a.distribution + q = b.distribution + + # The KL between any two (non)-batched distributions is a scalar. + # Given that the KL between two factored distributions is the sum, i.e. + # KL(p1(x)p2(y) || q1(x)q2(y)) = KL(p1 || q1) + KL(q1 || q2), we compute + # KL(p || q) and do a `reduce_sum` on the reinterpreted batch dimensions. + if a.event_shape.is_fully_defined() and b.event_shape.is_fully_defined(): + if a.event_shape == b.event_shape: + if p.event_shape == q.event_shape: + num_reduce_dims = a.event_shape.ndims - p.event_shape.ndims + reduce_dims = [-i - 1 for i in range(0, num_reduce_dims)] + + return math_ops.reduce_sum( + kullback_leibler.kl_divergence(p, q, name=name), axis=reduce_dims) + else: + raise NotImplementedError("KL between Independents with different " + "event shapes not supported.") + else: + raise ValueError("Event shapes do not match.") + else: + with ops.control_dependencies([ + check_ops.assert_equal(a.event_shape_tensor(), b.event_shape_tensor()), + check_ops.assert_equal(p.event_shape_tensor(), q.event_shape_tensor()) + ]): + num_reduce_dims = ( + array_ops.shape(a.event_shape_tensor()[0]) - + array_ops.shape(p.event_shape_tensor()[0])) + reduce_dims = math_ops.range(-num_reduce_dims - 1, -1, 1) + return math_ops.reduce_sum( + kullback_leibler.kl_divergence(p, q, name=name), axis=reduce_dims) diff --git a/tensorflow/contrib/distributions/python/ops/inverse_gamma.py b/tensorflow/contrib/distributions/python/ops/inverse_gamma.py index ee4d86867d48b20e97757bcec57d452085814b80..51ac61dcf640ca89f22c47127bda71316a179ca4 100644 --- a/tensorflow/contrib/distributions/python/ops/inverse_gamma.py +++ b/tensorflow/contrib/distributions/python/ops/inverse_gamma.py @@ -192,12 +192,6 @@ class InverseGamma(distribution.Distribution): def _log_prob(self, x): return self._log_unnormalized_prob(x) - self._log_normalization() - def _prob(self, x): - return math_ops.exp(self._log_prob(x)) - - def _log_cdf(self, x): - return math_ops.log(self._cdf(x)) - def _cdf(self, x): x = self._maybe_assert_valid_sample(x) # Note that igammac returns the upper regularized incomplete gamma diff --git a/tensorflow/contrib/distributions/python/ops/kumaraswamy.py b/tensorflow/contrib/distributions/python/ops/kumaraswamy.py index 74d5d8773cf3e69a52554c87d656fea2835c8354..120b38db3cf72e8fce56a7e9293cdf25e75784e2 100644 --- a/tensorflow/contrib/distributions/python/ops/kumaraswamy.py +++ b/tensorflow/contrib/distributions/python/ops/kumaraswamy.py @@ -20,15 +20,17 @@ from __future__ import print_function import numpy as np +from tensorflow.contrib.distributions.python.ops import bijectors +from tensorflow.contrib.distributions.python.ops import distribution_util +from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import check_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops -from tensorflow.python.ops import random_ops from tensorflow.python.ops import special_math_ops -from tensorflow.python.ops.distributions import beta from tensorflow.python.ops.distributions import distribution -from tensorflow.python.ops.distributions import util as distribution_util +from tensorflow.python.ops.distributions import transformed_distribution +from tensorflow.python.ops.distributions import uniform from tensorflow.python.util.tf_export import tf_export __all__ = [ @@ -60,7 +62,7 @@ def _harmonic_number(x): @tf_export("distributions.Kumaraswamy") -class Kumaraswamy(beta.Beta): +class Kumaraswamy(transformed_distribution.TransformedDistribution): """Kumaraswamy distribution. The Kumaraswamy distribution is defined over the `(0, 1)` interval using @@ -151,59 +153,32 @@ class Kumaraswamy(beta.Beta): more of the statistic's batch members are undefined. name: Python `str` name prefixed to Ops created by this class. """ + concentration1 = ops.convert_to_tensor( + concentration1, name="concentration1") + concentration0 = ops.convert_to_tensor( + concentration0, name="concentration0") super(Kumaraswamy, self).__init__( - concentration1=concentration1, - concentration0=concentration0, - validate_args=validate_args, - allow_nan_stats=allow_nan_stats, + distribution=uniform.Uniform( + low=array_ops.zeros([], dtype=concentration1.dtype), + high=array_ops.ones([], dtype=concentration1.dtype), + allow_nan_stats=allow_nan_stats), + bijector=bijectors.Kumaraswamy( + concentration1=concentration1, concentration0=concentration0, + validate_args=validate_args), + batch_shape=distribution_util.get_broadcast_shape( + concentration1, concentration0), name=name) self._reparameterization_type = distribution.FULLY_REPARAMETERIZED - def _sample_n(self, n, seed=None): - expanded_concentration1 = array_ops.ones_like( - self.total_concentration, dtype=self.dtype) * self.concentration1 - expanded_concentration0 = array_ops.ones_like( - self.total_concentration, dtype=self.dtype) * self.concentration0 - shape = array_ops.concat([[n], self.batch_shape_tensor()], 0) - uniform_sample = random_ops.random_uniform( - shape=shape, minval=0.0, maxval=1.0, dtype=self.dtype, seed=seed) - - kumaraswamy_sample = (1 - uniform_sample**(1. / expanded_concentration0))**( - 1. / expanded_concentration1) - return kumaraswamy_sample - - @distribution_util.AppendDocstring(_kumaraswamy_sample_note) - def _log_cdf(self, x): - a = self.concentration1 - b = self.concentration0 - return math_ops.log1p(-(1 - x**a)**b) + @property + def concentration1(self): + """Concentration parameter associated with a `1` outcome.""" + return self.bijector.concentration1 - @distribution_util.AppendDocstring(_kumaraswamy_sample_note) - def _cdf(self, x): - a = self.concentration1 - b = self.concentration0 - return 1 - (1 - x**a)**b - - def _survival_function(self, x): - a = self.concentration1 - b = self.concentration0 - return (1 - x**a)**b - - def _log_survival_function(self, x): - a = self.concentration1 - b = self.concentration0 - return b * math_ops.log1p(-x**a) - - def _log_unnormalized_prob(self, x): - x = self._maybe_assert_valid_sample(x) - a = self.concentration1 - b = self.concentration0 - return (a - 1) * math_ops.log(x) + (b - 1) * math_ops.log1p(-x**a) - - def _log_normalization(self): - a = self.concentration1 - b = self.concentration0 - return -(math_ops.log(a) + math_ops.log(b)) + @property + def concentration0(self): + """Concentration parameter associated with a `0` outcome.""" + return self.bijector.concentration0 def _entropy(self): a = self.concentration1 @@ -213,10 +188,11 @@ class Kumaraswamy(beta.Beta): def _moment(self, n): """Compute the n'th (uncentered) moment.""" + total_concentration = self.concentration1 + self.concentration0 expanded_concentration1 = array_ops.ones_like( - self.total_concentration, dtype=self.dtype) * self.concentration1 + total_concentration, dtype=self.dtype) * self.concentration1 expanded_concentration0 = array_ops.ones_like( - self.total_concentration, dtype=self.dtype) * self.concentration0 + total_concentration, dtype=self.dtype) * self.concentration0 beta_arg0 = 1 + n / expanded_concentration1 beta_arg = array_ops.stack([beta_arg0, expanded_concentration0], -1) log_moment = math_ops.log(expanded_concentration0) + special_math_ops.lbeta( @@ -246,13 +222,14 @@ class Kumaraswamy(beta.Beta): name="nan") is_defined = (self.concentration1 > 1.) & (self.concentration0 > 1.) return array_ops.where(is_defined, mode, nan) + return control_flow_ops.with_dependencies([ check_ops.assert_less( - array_ops.ones([], dtype=self.dtype), + array_ops.ones([], dtype=self.concentration1.dtype), self.concentration1, message="Mode undefined for concentration1 <= 1."), check_ops.assert_less( - array_ops.ones([], dtype=self.dtype), + array_ops.ones([], dtype=self.concentration0.dtype), self.concentration0, message="Mode undefined for concentration0 <= 1.") ], mode) diff --git a/tensorflow/contrib/distributions/python/ops/logistic.py b/tensorflow/contrib/distributions/python/ops/logistic.py index 473677f8d91b184e029f345bb05f5c5d63df7a40..68e6bca5a554b29a450911073eb5c4fe55f313c6 100644 --- a/tensorflow/contrib/distributions/python/ops/logistic.py +++ b/tensorflow/contrib/distributions/python/ops/logistic.py @@ -185,9 +185,6 @@ class Logistic(distribution.Distribution): def _log_prob(self, x): return self._log_unnormalized_prob(x) - self._log_normalization() - def _prob(self, x): - return math_ops.exp(self._log_prob(x)) - def _log_cdf(self, x): return -nn_ops.softplus(-self._z(x)) diff --git a/tensorflow/contrib/distributions/python/ops/onehot_categorical.py b/tensorflow/contrib/distributions/python/ops/onehot_categorical.py index b76cebf79fad09ebec68f2459c6fe80794ea81c0..46c2cc8b7a8c536a90176fbb2b2d52fed61e4705 100644 --- a/tensorflow/contrib/distributions/python/ops/onehot_categorical.py +++ b/tensorflow/contrib/distributions/python/ops/onehot_categorical.py @@ -203,9 +203,6 @@ class OneHotCategorical(distribution.Distribution): ret = array_ops.reshape(ret, logits_shape) return ret - def _prob(self, x): - return math_ops.exp(self._log_prob(x)) - def _entropy(self): return -math_ops.reduce_sum( nn_ops.log_softmax(self.logits) * self.probs, axis=-1) diff --git a/tensorflow/contrib/distributions/python/ops/relaxed_onehot_categorical.py b/tensorflow/contrib/distributions/python/ops/relaxed_onehot_categorical.py index 2aa771a71efe52c8d86d459f090ea8ee137c4487..ff33f327c7a77597e516208cacad8c4aed65d1c9 100644 --- a/tensorflow/contrib/distributions/python/ops/relaxed_onehot_categorical.py +++ b/tensorflow/contrib/distributions/python/ops/relaxed_onehot_categorical.py @@ -285,9 +285,6 @@ class ExpRelaxedOneHotCategorical(distribution.Distribution): ret = array_ops.reshape(log_prob, logits_shape) return ret - def _prob(self, x): - return math_ops.exp(self._log_prob(x)) - def _assert_valid_sample(self, x): if not self.validate_args: return x diff --git a/tensorflow/contrib/distributions/python/ops/sinh_arcsinh.py b/tensorflow/contrib/distributions/python/ops/sinh_arcsinh.py index c4b8f055b7fbc3f0835b503eddd7617610326d8c..0d8a1926913766da374cb65767dccfa28bf75579 100644 --- a/tensorflow/contrib/distributions/python/ops/sinh_arcsinh.py +++ b/tensorflow/contrib/distributions/python/ops/sinh_arcsinh.py @@ -174,13 +174,12 @@ class SinhArcsinh(transformed_distribution.TransformedDistribution): skewness=skewness.dtype.as_numpy_dtype(0.), tailweight=tailweight, event_ndims=0) - # Make the Affine bijector, Z --> loc + scale * Z (2 / F_0(2)) + # Make the AffineScalar bijector, Z --> loc + scale * Z (2 / F_0(2)) c = 2 * scale / f_noskew.forward(ops.convert_to_tensor(2, dtype=dtype)) - affine = bijectors.Affine( + affine = bijectors.AffineScalar( shift=loc, - scale_identity_multiplier=c, - validate_args=validate_args, - event_ndims=0) + scale=c, + validate_args=validate_args) bijector = bijectors.Chain([affine, f]) diff --git a/tensorflow/contrib/distributions/python/ops/statistical_testing.py b/tensorflow/contrib/distributions/python/ops/statistical_testing.py new file mode 100644 index 0000000000000000000000000000000000000000..d66c34cc1a45cc09da5138a5f72ae3817690db49 --- /dev/null +++ b/tensorflow/contrib/distributions/python/ops/statistical_testing.py @@ -0,0 +1,728 @@ +# 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. +# ============================================================================== +"""Statistical test assertions calibrated for their error rates.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import itertools + +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import check_ops +from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import gen_math_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import nn_ops + +__all__ = [ + "true_mean_confidence_interval_by_dkwm", + "assert_true_mean_equal_by_dkwm", + "min_discrepancy_of_true_means_detectable_by_dkwm", + "min_num_samples_for_dkwm_mean_test", + "assert_true_mean_equal_by_dkwm_two_sample", + "min_discrepancy_of_true_means_detectable_by_dkwm_two_sample", + "min_num_samples_for_dkwm_mean_two_sample_test", +] + + +def _batch_sort_vector(x, ascending=True, name=None): + with ops.name_scope(name, "sort_each_row", [x]): + x = ops.convert_to_tensor(x, name="x") + n = array_ops.shape(x)[-1] + if ascending: + y, _ = nn_ops.top_k(-x, k=n, sorted=True) + y = -y + else: + y, _ = nn_ops.top_k(x, k=n, sorted=True) + y.set_shape(x.shape) + return y + + +def _do_maximum_mean(samples, envelope, high, name=None): + """Common code between maximum_mean and minimum_mean.""" + with ops.name_scope(name, "do_maximum_mean", [samples, envelope, high]): + n = array_ops.rank(samples) + # Move the batch dimension of `samples` to the rightmost position, + # where the _batch_sort_vector function wants it. + perm = array_ops.concat([math_ops.range(1, n), [0]], axis=0) + samples = array_ops.transpose(samples, perm) + + samples = _batch_sort_vector(samples) + batch_shape = array_ops.shape(samples)[:-1] + n = array_ops.shape(samples)[-1] + step = 1. / math_ops.cast(n, dtype=samples.dtype.base_dtype) + + def _loop_body(iter_, total, to_skip): + total = array_ops.where( + step <= to_skip, + total, + array_ops.where( + to_skip > 0., + total + (step - to_skip) * samples[..., iter_], + total + step * samples[..., iter_])) + to_skip = array_ops.where(step <= to_skip, to_skip - step, 0.) + return [iter_ + 1, total, to_skip] + + _, total, _ = control_flow_ops.while_loop( + cond=lambda iter_, *args: iter_ < n, + body=_loop_body, + loop_vars=[ + 0, + array_ops.zeros(batch_shape, dtype=samples.dtype.base_dtype), + envelope, # to_skip + ]) + + return total + envelope * high + + +def _maximum_mean(samples, envelope, high, name=None): + """Returns a stochastic upper bound on the mean of a scalar distribution. + + The idea is that if the true CDF is within an `eps`-envelope of the + empirical CDF of the samples, and the support is bounded above, then + the mean is bounded above as well. In symbols, + + ```none + sup_x(|F_n(x) - F(x)|) < eps + ``` + + The 0th dimension of `samples` is interpreted as independent and + identically distributed samples. The remaining dimensions are + broadcast together with `envelope` and `high`, and operated on + separately. + + Args: + samples: Floating-point tensor of samples from the distribution(s) + of interest. Entries are assumed IID across the 0th dimension. + The other dimensions must broadcast with `envelope` and `high`. + envelope: Floating-point tensor of sizes of admissible CDF + envelopes (i.e., the `eps` above). + high: Floating-point tensor of upper bounds on the distributions' + supports. + name: A name for this operation (optional). + + Returns: + bound: Floating-point tensor of upper bounds on the true means. + + Raises: + InvalidArgumentError: If some `sample` is found to be larger than + the corresponding `high`. + """ + with ops.name_scope(name, "maximum_mean", [samples, envelope, high]): + samples = ops.convert_to_tensor(samples, name="samples") + envelope = ops.convert_to_tensor(envelope, name="envelope") + high = ops.convert_to_tensor(high, name="high") + + xmax = math_ops.reduce_max(samples, axis=[-1]) + msg = "Given sample maximum value exceeds expectations" + check_op = check_ops.assert_less_equal(xmax, high, message=msg) + with ops.control_dependencies([check_op]): + return array_ops.identity(_do_maximum_mean(samples, envelope, high)) + + +def _minimum_mean(samples, envelope, low, name=None): + """Returns a stochastic lower bound on the mean of a scalar distribution. + + The idea is that if the true CDF is within an `eps`-envelope of the + empirical CDF of the samples, and the support is bounded below, then + the mean is bounded below as well. In symbols, + + ```none + sup_x(|F_n(x) - F(x)|) < eps + ``` + + The 0th dimension of `samples` is interpreted as independent and + identically distributed samples. The remaining dimensions are + broadcast together with `envelope` and `low`, and operated on + separately. + + Args: + samples: Floating-point tensor of samples from the distribution(s) + of interest. Entries are assumed IID across the 0th dimension. + The other dimensions must broadcast with `envelope` and `low`. + envelope: Floating-point tensor of sizes of admissible CDF + envelopes (i.e., the `eps` above). + low: Floating-point tensor of lower bounds on the distributions' + supports. + name: A name for this operation (optional). + + Returns: + bound: Floating-point tensor of lower bounds on the true means. + + Raises: + InvalidArgumentError: If some `sample` is found to be smaller than + the corresponding `low`. + """ + with ops.name_scope(name, "minimum_mean", [samples, envelope, low]): + samples = ops.convert_to_tensor(samples, name="samples") + envelope = ops.convert_to_tensor(envelope, name="envelope") + low = ops.convert_to_tensor(low, name="low") + + xmin = math_ops.reduce_min(samples, axis=[-1]) + msg = "Given sample minimum value falls below expectations" + check_op = check_ops.assert_greater_equal(xmin, low, message=msg) + with ops.control_dependencies([check_op]): + return - _do_maximum_mean(-samples, envelope, -low) + + +def _dkwm_cdf_envelope(n, error_rate, name=None): + """Computes the CDF envelope that the DKWM inequality licenses. + + The [Dvoretzky-Kiefer-Wolfowitz-Massart inequality] + (https://en.wikipedia.org/wiki/CDF-based_nonparametric_confidence_interval) + gives a stochastic bound on the distance between the true cumulative + distribution function (CDF) of any distribution and its empirical + CDF. To wit, for `n` iid samples from any distribution with CDF F, + + ```none + P(sup_x |F_n(x) - F(x)| > eps) < 2exp(-2n eps^2) + ``` + + This function computes the envelope size `eps` as a function of the + number of samples `n` and the desired limit on the left-hand + probability above. + + Args: + n: Tensor of numbers of samples drawn. + error_rate: Floating-point tensor of admissible rates of mistakes. + name: A name for this operation (optional). + + Returns: + eps: Tensor of maximum distances the true CDF can be from the + empirical CDF. This scales as `O(sqrt(-log(error_rate)))` and + as `O(1 / sqrt(n))`. The shape is the broadcast of `n` and + `error_rate`. + """ + with ops.name_scope(name, "dkwm_cdf_envelope", [n, error_rate]): + n = math_ops.cast(n, dtype=error_rate.dtype) + return math_ops.sqrt(-gen_math_ops.log(error_rate / 2.) / (2. * n)) + + +def _check_shape_dominates(tensor, tensors): + """Check that broadcasting `tensor` against `tensors` does not expand it. + + Why? Because I want to be very sure that the samples tensor is not + accidentally enlarged by broadcasting against tensors that are + supposed to be describing the distribution(s) sampled from, lest the + sample counts end up inflated. + + Args: + tensor: A Tensor whose shape is to be protected against broadcasting. + tensors: A list of Tensors to check + + Returns: + tensor: `tf.identity(tensor)` with control dependencies attached; + be sure to use that downstream. + """ + def check(t): + target = array_ops.shape(tensor)[1:] + result = array_ops.broadcast_dynamic_shape(target, array_ops.shape(t)) + # This rank check ensures that I don't get a wrong answer from the + # _shapes_ broadcasting against each other. + gt = check_ops.assert_greater(array_ops.rank(target), array_ops.rank(t)) + eq = check_ops.assert_equal(target, result) + return gt, eq + checks = list(itertools.chain(*[check(t) for t in tensors])) + with ops.control_dependencies(checks): + return array_ops.identity(array_ops.identity(tensor)) + + +def true_mean_confidence_interval_by_dkwm( + samples, low, high, error_rate=1e-6, name=None): + """Computes a confidence interval for the mean of a scalar distribution. + + In batch mode, computes confidence intervals for all distributions + in the batch (which need not be identically distributed). + + Relies on the [Dvoretzky-Kiefer-Wolfowitz-Massart inequality] + (https://en.wikipedia.org/wiki/CDF-based_nonparametric_confidence_interval). + + The probability (over the randomness of drawing the given samples) + that any true mean is outside the corresponding returned interval is + no more than the given `error_rate`. The size of the intervals + scale as + `O(1 / sqrt(#samples))`, as `O(high - low)`, and as `O(-log(error_rate))`. + + Note that `error_rate` is a total error rate for all the confidence + intervals in the batch. As such, if the batch is nontrivial, the + error rate is not broadcast but divided (evenly) among the batch + members. + + Args: + samples: Floating-point tensor of samples from the distribution(s) + of interest. Entries are assumed IID across the 0th dimension. + The other dimensions must broadcast with `low` and `high`. + low: Floating-point tensor of lower bounds on the distributions' + supports. + high: Floating-point tensor of upper bounds on the distributions' + supports. + error_rate: *Scalar* admissible total rate of mistakes. + name: A name for this operation (optional). + + Returns: + low: A floating-point tensor of stochastic lower bounds on the true means. + high: A floating-point tensor of stochastic upper bounds on the true means. + """ + with ops.name_scope( + name, "true_mean_confidence_interval_by_dkwm", + [samples, low, high, error_rate]): + samples = ops.convert_to_tensor(samples, name="samples") + low = ops.convert_to_tensor(low, name="low") + high = ops.convert_to_tensor(high, name="high") + error_rate = ops.convert_to_tensor(error_rate, name="error_rate") + samples = _check_shape_dominates(samples, [low, high]) + check_ops.assert_scalar(error_rate) # Static shape + error_rate = _itemwise_error_rate(error_rate, [low, high], samples) + n = array_ops.shape(samples)[0] + envelope = _dkwm_cdf_envelope(n, error_rate) + min_mean = _minimum_mean(samples, envelope, low) + max_mean = _maximum_mean(samples, envelope, high) + return min_mean, max_mean + + +def _itemwise_error_rate( + total_error_rate, param_tensors, sample_tensor=None, name=None): + with ops.name_scope( + name, "itemwise_error_rate", + [total_error_rate, param_tensors, sample_tensor]): + result_shape = [1] + for p_tensor in param_tensors: + result_shape = array_ops.broadcast_dynamic_shape( + array_ops.shape(p_tensor), result_shape) + if sample_tensor is not None: + result_shape = array_ops.broadcast_dynamic_shape( + array_ops.shape(sample_tensor)[1:], result_shape) + num_items = math_ops.reduce_prod(result_shape) + return total_error_rate / math_ops.cast( + num_items, dtype=total_error_rate.dtype) + + +def assert_true_mean_equal_by_dkwm( + samples, low, high, expected, false_fail_rate=1e-6, name=None): + """Asserts the mean of the given distribution is as expected. + + More precisely, fails if there is enough evidence (using the + [Dvoretzky-Kiefer-Wolfowitz-Massart inequality] + (https://en.wikipedia.org/wiki/CDF-based_nonparametric_confidence_interval)) + that the true mean of some distribution from which the given samples are + drawn is _not_ the given expected mean with statistical significance + `false_fail_rate` or stronger, otherwise passes. If you also want to + check that you are gathering enough evidence that a pass is not + spurious, see `min_num_samples_for_dkwm_mean_test` and + `min_discrepancy_of_true_means_detectable_by_dkwm`. + + Note that `false_fail_rate` is a total false failure rate for all + the assertions in the batch. As such, if the batch is nontrivial, + the assertion will insist on stronger evidence to fail any one member. + + Args: + samples: Floating-point tensor of samples from the distribution(s) + of interest. Entries are assumed IID across the 0th dimension. + The other dimensions must broadcast with `low` and `high`. + low: Floating-point tensor of lower bounds on the distributions' + supports. + high: Floating-point tensor of upper bounds on the distributions' + supports. + expected: Floating-point tensor of expected true means. + false_fail_rate: *Scalar* admissible total rate of mistakes. + name: A name for this operation (optional). + + Returns: + check: Op that raises `InvalidArgumentError` if any expected mean is + outside the corresponding confidence interval. + """ + with ops.name_scope( + name, "assert_true_mean_equal_by_dkwm", + [samples, low, high, expected, false_fail_rate]): + samples = ops.convert_to_tensor(samples, name="samples") + low = ops.convert_to_tensor(low, name="low") + high = ops.convert_to_tensor(high, name="high") + expected = ops.convert_to_tensor(expected, name="expected") + false_fail_rate = ops.convert_to_tensor( + false_fail_rate, name="false_fail_rate") + samples = _check_shape_dominates(samples, [low, high, expected]) + min_mean, max_mean = true_mean_confidence_interval_by_dkwm( + samples, low, high, error_rate=false_fail_rate) + less_op = check_ops.assert_less( + min_mean, expected, message="Mean confidence interval too high") + with ops.control_dependencies([less_op]): + return check_ops.assert_greater( + max_mean, expected, message="Mean confidence interval too low") + + +def min_discrepancy_of_true_means_detectable_by_dkwm( + n, low, high, false_fail_rate, false_pass_rate, name=None): + """Returns the minimum mean discrepancy that a DKWM-based test can detect. + + DKWM is the [Dvoretzky-Kiefer-Wolfowitz-Massart inequality] + (https://en.wikipedia.org/wiki/CDF-based_nonparametric_confidence_interval). + + Note that `false_fail_rate` is a total false failure rate for all + the tests in the batch. As such, if the batch is nontrivial, each + member will demand more samples. The `false_pass_rate` is also + interpreted as a total, but is treated asymmetrically: If each test + in the batch detects its corresponding discrepancy with probability + at least `1 - false_pass_rate`, then running all those tests and + failing if any one fails will jointly detect all those discrepancies + with the same `false_pass_rate`. + + Args: + n: Tensor of numbers of samples to be drawn from the distributions + of interest. + low: Floating-point tensor of lower bounds on the distributions' + supports. + high: Floating-point tensor of upper bounds on the distributions' + supports. + false_fail_rate: *Scalar* admissible total rate of false failures. + false_pass_rate: *Scalar* admissible rate of false passes. + name: A name for this operation (optional). + + Returns: + discr: Tensor of lower bounds on the distances between true + means detectable by a DKWM-based test. + + For each batch member `i`, of `K` total, drawing `n[i]` samples from + some scalar distribution supported on `[low[i], high[i]]` is enough + to detect a difference in means of size `discr[i]` or more. + Specifically, we guarantee that (a) if the true mean is the expected + mean, `assert_true_mean_equal_by_dkwm` will fail with probability at + most `false_fail_rate / K` (which amounts to `false_fail_rate` if + applied to the whole batch at once), and (b) if the true mean + differs from the expected mean by at least `discr[i]`, + `assert_true_mean_equal_by_dkwm` will pass with probability at most + `false_pass_rate`. + + The detectable discrepancy scales as + + - `O(high[i] - low[i])`, + - `O(1 / sqrt(n[i]))`, + - `O(-log(false_fail_rate/K))`, and + - `O(-log(false_pass_rate))`. + """ + with ops.name_scope( + name, "min_discrepancy_of_true_means_detectable_by_dkwm", + [n, low, high, false_fail_rate, false_pass_rate]): + n = ops.convert_to_tensor(n, name="n") + low = ops.convert_to_tensor(low, name="low") + high = ops.convert_to_tensor(high, name="high") + false_fail_rate = ops.convert_to_tensor( + false_fail_rate, name="false_fail_rate") + false_pass_rate = ops.convert_to_tensor( + false_pass_rate, name="false_pass_rate") + # Algorithm: Assume a true CDF F. The DKWM inequality gives a + # stochastic bound on how far the observed empirical CDF F_n can be. + # Then, using the DKWM inequality again gives a stochastic bound on + # the farthest candidate true CDF F' that + # true_mean_confidence_interval_by_dkwm might consider. At worst, these + # errors may go in the same direction, so the distance between F and + # F' is bounded by the sum. + # On batching: false fail rates sum, so I need to reduce + # the input to account for the batching. False pass rates + # max, so I don't. + sampling_envelope = _dkwm_cdf_envelope(n, false_pass_rate) + false_fail_rate = _itemwise_error_rate(false_fail_rate, [n, low, high]) + analysis_envelope = _dkwm_cdf_envelope(n, false_fail_rate) + return (high - low) * (sampling_envelope + analysis_envelope) + + +def min_num_samples_for_dkwm_mean_test( + discrepancy, low, high, + false_fail_rate=1e-6, false_pass_rate=1e-6, name=None): + """Returns how many samples suffice for a one-sample DKWM mean test. + + To wit, returns an upper bound on the number of samples necessary to + guarantee detecting a mean difference of at least the given + `discrepancy`, with the given `false_fail_rate` and `false_pass_rate`, + using the [Dvoretzky-Kiefer-Wolfowitz-Massart inequality] + (https://en.wikipedia.org/wiki/CDF-based_nonparametric_confidence_interval) + on a scalar distribution supported on `[low, high]`. + + Args: + discrepancy: Floating-point tensor of desired upper limits on mean + differences that may go undetected with probability higher than + `1 - false_pass_rate`. + low: Tensor of lower bounds on the distributions' support. + high: Tensor of upper bounds on the distributions' support. + false_fail_rate: *Scalar* admissible total rate of false failures. + false_pass_rate: *Scalar* admissible rate of false passes. + name: A name for this operation (optional). + + Returns: + n: Tensor of numbers of samples to be drawn from the distributions + of interest. + + The `discrepancy`, `low`, and `high` tensors must have + broadcast-compatible shapes. + + For each batch member `i`, of `K` total, drawing `n[i]` samples from + some scalar distribution supported on `[low[i], high[i]]` is enough + to detect a difference in means of size `discrepancy[i]` or more. + Specifically, we guarantee that (a) if the true mean is the expected + mean, `assert_true_mean_equal_by_dkwm` will fail with probability at + most `false_fail_rate / K` (which amounts to `false_fail_rate` if + applied to the whole batch at once), and (b) if the true mean + differs from the expected mean by at least `discrepancy[i]`, + `assert_true_mean_equal_by_dkwm` will pass with probability at most + `false_pass_rate`. + + The required number of samples scales + as `O((high[i] - low[i])**2)`, `O(-log(false_fail_rate/K))`, + `O(-log(false_pass_rate))`, and `O(1 / discrepancy[i]**2)`. + """ + with ops.name_scope( + name, "min_num_samples_for_dkwm_mean_test", + [low, high, false_fail_rate, false_pass_rate, discrepancy]): + discrepancy = ops.convert_to_tensor( + discrepancy, name="discrepancy") + low = ops.convert_to_tensor(low, name="low") + high = ops.convert_to_tensor(high, name="high") + false_fail_rate = ops.convert_to_tensor( + false_fail_rate, name="false_fail_rate") + false_pass_rate = ops.convert_to_tensor( + false_pass_rate, name="false_pass_rate") + # Could choose to cleverly allocate envelopes, but this is sound. + envelope1 = discrepancy / (2. * (high - low)) + envelope2 = envelope1 + false_fail_rate = _itemwise_error_rate( + false_fail_rate, [low, high, discrepancy]) + n1 = -math_ops.log(false_fail_rate / 2.) / (2. * envelope1**2) + n2 = -math_ops.log(false_pass_rate / 2.) / (2. * envelope2**2) + return math_ops.maximum(n1, n2) + + +def assert_true_mean_equal_by_dkwm_two_sample( + samples1, low1, high1, samples2, low2, high2, + false_fail_rate=1e-6, name=None): + """Asserts the means of the given distributions are equal. + + More precisely, fails if there is enough evidence (using the + [Dvoretzky-Kiefer-Wolfowitz-Massart inequality] + (https://en.wikipedia.org/wiki/CDF-based_nonparametric_confidence_interval)) + that the means of the distributions from which the given samples are + drawn are _not_ equal with statistical significance `false_fail_rate` + or stronger, otherwise passes. If you also want to check that you + are gathering enough evidence that a pass is not spurious, see + `min_num_samples_for_dkwm_mean_two_sample_test` and + `min_discrepancy_of_true_means_detectable_by_dkwm_two_sample`. + + Note that `false_fail_rate` is a total false failure rate for all + the assertions in the batch. As such, if the batch is nontrivial, + the assertion will insist on stronger evidence to fail any one member. + + Args: + samples1: Floating-point tensor of samples from the + distribution(s) A. Entries are assumed IID across the 0th + dimension. The other dimensions must broadcast with `low1`, + `high1`, `low2`, and `high2`. + low1: Floating-point tensor of lower bounds on the supports of the + distributions A. + high1: Floating-point tensor of upper bounds on the supports of + the distributions A. + samples2: Floating-point tensor of samples from the + distribution(s) B. Entries are assumed IID across the 0th + dimension. The other dimensions must broadcast with `low1`, + `high1`, `low2`, and `high2`. + low2: Floating-point tensor of lower bounds on the supports of the + distributions B. + high2: Floating-point tensor of upper bounds on the supports of + the distributions B. + false_fail_rate: *Scalar* admissible total rate of mistakes. + name: A name for this operation (optional). + + Returns: + check: Op that raises `InvalidArgumentError` if any pair of confidence + intervals true for corresponding true means do not overlap. + """ + with ops.name_scope( + name, "assert_true_mean_equal_by_dkwm_two_sample", + [samples1, low1, high1, samples2, low2, high2, false_fail_rate]): + samples1 = ops.convert_to_tensor(samples1, name="samples1") + low1 = ops.convert_to_tensor(low1, name="low1") + high1 = ops.convert_to_tensor(high1, name="high1") + samples2 = ops.convert_to_tensor(samples2, name="samples2") + low2 = ops.convert_to_tensor(low2, name="low2") + high2 = ops.convert_to_tensor(high2, name="high2") + false_fail_rate = ops.convert_to_tensor( + false_fail_rate, name="false_fail_rate") + samples1 = _check_shape_dominates(samples1, [low1, high1]) + samples2 = _check_shape_dominates(samples2, [low2, high2]) + compatible_samples = check_ops.assert_equal( + array_ops.shape(samples1)[1:], array_ops.shape(samples2)[1:]) + with ops.control_dependencies([compatible_samples]): + # Could in principle play games with cleverly allocating + # significance instead of the even split below. It may be possible + # to get tighter intervals, in order to obtain a higher power test. + # Any allocation strategy that depends only on the support bounds + # and sample counts should be valid; however, because the intervals + # scale as O(-log(false_fail_rate)), there doesn't seem to be much + # room to win. + min_mean_1, max_mean_1 = true_mean_confidence_interval_by_dkwm( + samples1, low1, high1, false_fail_rate / 2.) + min_mean_2, max_mean_2 = true_mean_confidence_interval_by_dkwm( + samples2, low2, high2, false_fail_rate / 2.) + # I want to assert + # not (max_mean_1 < min_mean_2 or min_mean_1 > max_mean_2), + # but I think I only have and-combination of asserts, so use DeMorgan. + clause1_op = check_ops.assert_greater_equal(max_mean_1, min_mean_2) + with ops.control_dependencies([clause1_op]): + return check_ops.assert_less_equal(min_mean_1, max_mean_2) + + +def min_discrepancy_of_true_means_detectable_by_dkwm_two_sample( + n1, low1, high1, n2, low2, high2, + false_fail_rate, false_pass_rate, name=None): + """Returns the minimum mean discrepancy for a two-sample DKWM-based test. + + DKWM is the [Dvoretzky-Kiefer-Wolfowitz-Massart inequality] + (https://en.wikipedia.org/wiki/CDF-based_nonparametric_confidence_interval). + + Note that `false_fail_rate` is a total false failure rate for all + the tests in the batch. As such, if the batch is nontrivial, each + member will demand more samples. The `false_pass_rate` is also + interpreted as a total, but is treated asymmetrically: If each test + in the batch detects its corresponding discrepancy with probability + at least `1 - false_pass_rate`, then running all those tests and + failing if any one fails will jointly detect all those discrepancies + with the same `false_pass_rate`. + + Args: + n1: Tensor of numbers of samples to be drawn from the distributions A. + low1: Floating-point tensor of lower bounds on the supports of the + distributions A. + high1: Floating-point tensor of upper bounds on the supports of + the distributions A. + n2: Tensor of numbers of samples to be drawn from the distributions B. + low2: Floating-point tensor of lower bounds on the supports of the + distributions B. + high2: Floating-point tensor of upper bounds on the supports of + the distributions B. + false_fail_rate: *Scalar* admissible total rate of false failures. + false_pass_rate: *Scalar* admissible rate of false passes. + name: A name for this operation (optional). + + Returns: + discr: Tensor of lower bounds on the distances between true means + detectable by a two-sample DKWM-based test. + + For each batch member `i`, of `K` total, drawing `n1[i]` samples + from scalar distribution A supported on `[low1[i], high1[i]]` and `n2[i]` + samples from scalar distribution B supported on `[low2[i], high2[i]]` + is enough to detect a difference in their true means of size + `discr[i]` or more. Specifically, we guarantee that (a) if their + true means are equal, `assert_true_mean_equal_by_dkwm_two_sample` + will fail with probability at most `false_fail_rate/K` (which + amounts to `false_fail_rate` if applied to the whole batch at once), + and (b) if their true means differ by at least `discr[i]`, + `assert_true_mean_equal_by_dkwm_two_sample` will pass with + probability at most `false_pass_rate`. + + The detectable distribution scales as + + - `O(high1[i] - low1[i])`, `O(high2[i] - low2[i])`, + - `O(1 / sqrt(n1[i]))`, `O(1 / sqrt(n2[i]))`, + - `O(-log(false_fail_rate/K))`, and + - `O(-log(false_pass_rate))`. + """ + with ops.name_scope( + name, "min_discrepancy_of_true_means_detectable_by_dkwm_two_sample", + [n1, low1, high1, n2, low2, high2, false_fail_rate, false_pass_rate]): + n1 = ops.convert_to_tensor(n1, name="n1") + low1 = ops.convert_to_tensor(low1, name="low1") + high1 = ops.convert_to_tensor(high1, name="high1") + n2 = ops.convert_to_tensor(n2, name="n2") + low2 = ops.convert_to_tensor(low2, name="low2") + high2 = ops.convert_to_tensor(high2, name="high2") + false_fail_rate = ops.convert_to_tensor( + false_fail_rate, name="false_fail_rate") + false_pass_rate = ops.convert_to_tensor( + false_pass_rate, name="false_pass_rate") + det_disc1 = min_discrepancy_of_true_means_detectable_by_dkwm( + n1, low1, high1, false_fail_rate / 2., false_pass_rate / 2.) + det_disc2 = min_discrepancy_of_true_means_detectable_by_dkwm( + n2, low2, high2, false_fail_rate / 2., false_pass_rate / 2.) + return det_disc1 + det_disc2 + + +def min_num_samples_for_dkwm_mean_two_sample_test( + discrepancy, low1, high1, low2, high2, + false_fail_rate=1e-6, false_pass_rate=1e-6, name=None): + """Returns how many samples suffice for a two-sample DKWM mean test. + + DKWM is the [Dvoretzky-Kiefer-Wolfowitz-Massart inequality] + (https://en.wikipedia.org/wiki/CDF-based_nonparametric_confidence_interval). + + Args: + discrepancy: Floating-point tensor of desired upper limits on mean + differences that may go undetected with probability higher than + `1 - false_pass_rate`. + low1: Floating-point tensor of lower bounds on the supports of the + distributions A. + high1: Floating-point tensor of upper bounds on the supports of + the distributions A. + low2: Floating-point tensor of lower bounds on the supports of the + distributions B. + high2: Floating-point tensor of upper bounds on the supports of + the distributions B. + false_fail_rate: *Scalar* admissible total rate of false failures. + false_pass_rate: *Scalar* admissible rate of false passes. + name: A name for this operation (optional). + + Returns: + n1: Tensor of numbers of samples to be drawn from the distributions A. + n2: Tensor of numbers of samples to be drawn from the distributions B. + + For each batch member `i`, of `K` total, drawing `n1[i]` samples + from scalar distribution A supported on `[low1[i], high1[i]]` and `n2[i]` + samples from scalar distribution B supported on `[low2[i], high2[i]]` + is enough to detect a difference in their true means of size + `discr[i]` or more. Specifically, we guarantee that (a) if their + true means are equal, `assert_true_mean_equal_by_dkwm_two_sample` + will fail with probability at most `false_fail_rate/K` (which + amounts to `false_fail_rate` if applied to the whole batch at once), + and (b) if their true means differ by at least `discr[i]`, + `assert_true_mean_equal_by_dkwm_two_sample` will pass with + probability at most `false_pass_rate`. + + The required number of samples scales as + + - `O((high1[i] - low1[i])**2)`, `O((high2[i] - low2[i])**2)`, + - `O(-log(false_fail_rate/K))`, + - `O(-log(false_pass_rate))`, and + - `O(1 / discrepancy[i]**2)`. + """ + with ops.name_scope( + name, "min_num_samples_for_dkwm_mean_two_sample_test", + [low1, high1, low2, high2, + false_fail_rate, false_pass_rate, discrepancy]): + discrepancy = ops.convert_to_tensor(discrepancy, name="discrepancy") + low1 = ops.convert_to_tensor(low1, name="low1") + high1 = ops.convert_to_tensor(high1, name="high1") + low2 = ops.convert_to_tensor(low2, name="low2") + high2 = ops.convert_to_tensor(high2, name="high2") + false_fail_rate = ops.convert_to_tensor( + false_fail_rate, name="false_fail_rate") + false_pass_rate = ops.convert_to_tensor( + false_pass_rate, name="false_pass_rate") + # Could choose to cleverly allocate discrepancy tolerances and + # failure probabilities, but this is sound. + n1 = min_num_samples_for_dkwm_mean_test( + discrepancy / 2., low1, high1, + false_fail_rate / 2., false_pass_rate / 2.) + n2 = min_num_samples_for_dkwm_mean_test( + discrepancy / 2., low2, high2, + false_fail_rate / 2., false_pass_rate / 2.) + return n1, n2 diff --git a/tensorflow/contrib/distributions/python/ops/vector_sinh_arcsinh_diag.py b/tensorflow/contrib/distributions/python/ops/vector_sinh_arcsinh_diag.py index e1ccf116457a97261b9ce3965552764771d3bdd2..003c66b9413fdcad20fbcc8b4bf47259692932e7 100644 --- a/tensorflow/contrib/distributions/python/ops/vector_sinh_arcsinh_diag.py +++ b/tensorflow/contrib/distributions/python/ops/vector_sinh_arcsinh_diag.py @@ -227,7 +227,7 @@ class VectorSinhArcsinhDiag(transformed_distribution.TransformedDistribution): c = 2 * scale_diag_part / f_noskew.forward( ops.convert_to_tensor(2, dtype=dtype)) affine = bijectors.Affine( - shift=loc, scale_diag=c, validate_args=validate_args, event_ndims=1) + shift=loc, scale_diag=c, validate_args=validate_args) bijector = bijectors.Chain([affine, f]) diff --git a/tensorflow/contrib/eager/python/BUILD b/tensorflow/contrib/eager/python/BUILD index a26ec8513f4b7b9c278edddc95e6acd2523194f2..384ef7f9630647714b77825b54b3b8a3abdfa6f3 100644 --- a/tensorflow/contrib/eager/python/BUILD +++ b/tensorflow/contrib/eager/python/BUILD @@ -11,12 +11,14 @@ py_library( srcs_version = "PY2AND3", visibility = ["//visibility:public"], deps = [ + ":checkpointable_utils", ":datasets", ":metrics", ":network", ":saver", "//tensorflow/python:framework_ops", "//tensorflow/python:framework_test_lib", + "//tensorflow/python:gradients", "//tensorflow/python:numerics", "//tensorflow/python:resource_variable_ops", "//tensorflow/python:script_ops", @@ -26,7 +28,6 @@ py_library( "//tensorflow/python/eager:backprop", "//tensorflow/python/eager:context", "//tensorflow/python/eager:core", - "//tensorflow/python/eager:custom_gradient", "//tensorflow/python/eager:execution_callbacks", "//tensorflow/python/eager:function", ], @@ -116,6 +117,7 @@ py_library( srcs_version = "PY2AND3", visibility = ["//tensorflow:internal"], deps = [ + "//tensorflow/contrib/eager/python:checkpointable_utils", "//tensorflow/contrib/summary:summary_ops", "//tensorflow/python:array_ops", "//tensorflow/python:control_flow_ops", @@ -241,13 +243,13 @@ py_library( ], ) -py_test( +cuda_py_test( name = "checkpointable_utils_test", srcs = ["checkpointable_utils_test.py"], - srcs_version = "PY2AND3", - deps = [ + additional_deps = [ ":checkpointable_utils", ":network", + "@six_archive//:six", "//tensorflow/python:constant_op", "//tensorflow/python:dtypes", "//tensorflow/python:framework_ops", @@ -262,7 +264,11 @@ py_test( "//tensorflow/python:variables", "//tensorflow/python/eager:context", "//tensorflow/python/eager:test", - "@six_archive//:six", + "//tensorflow/python/keras", + ], + tags = [ + "no_windows", # TODO: needs investigation on Windows + "notsan", ], ) diff --git a/tensorflow/contrib/eager/python/checkpointable_utils.py b/tensorflow/contrib/eager/python/checkpointable_utils.py index d9648ffb03d19429911b23d8c40c532e75e1cdfd..d07121df635cc95402a4811f810007807dfa0c37 100644 --- a/tensorflow/contrib/eager/python/checkpointable_utils.py +++ b/tensorflow/contrib/eager/python/checkpointable_utils.py @@ -17,6 +17,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import abc import collections import weakref @@ -26,6 +27,7 @@ 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 +from tensorflow.python.framework import errors_impl from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import control_flow_ops @@ -37,6 +39,7 @@ from tensorflow.python.training import checkpointable as core_checkpointable from tensorflow.python.training import checkpointable_utils as core_checkpointable_utils from tensorflow.python.training import optimizer as optimizer_lib from tensorflow.python.training import saver as saver_lib +from tensorflow.python.util import deprecation _ESCAPE_CHAR = "." # For avoiding conflicts with user-specified names. @@ -278,6 +281,37 @@ def _serialize_object_graph(root_checkpointable): slot_variables=slot_variables) +def gather_initializers(root_checkpointable): + """Traverse the object graph and find initialization ops. + + Looks for `Checkpointable` objects which are dependencies of + `root_checkpointable` and which have an `initializer` property. Includes + initializers for slot variables only if the variable they are slotting for and + the optimizer are dependencies of `root_checkpointable` (i.e. if they would be + saved with a checkpoint). + + Args: + root_checkpointable: A `Checkpointable` object to gather initializers for. + Returns: + A list of initialization ops. + """ + # TODO(allenl): Extract out gathering logic so the naming logic doesn't have + # to run. + checkpointable_objects, path_to_root = ( + _breadth_first_checkpointable_traversal(root_checkpointable)) + object_names = { + obj: _object_prefix_from_path(path) + for obj, path in path_to_root.items()} + node_ids = {node: node_id for node_id, node + in enumerate(checkpointable_objects)} + _serialize_slot_variables( + checkpointable_objects=checkpointable_objects, + node_ids=node_ids, + object_names=object_names) + return [c.initializer for c in checkpointable_objects + if hasattr(c, "initializer") and c.initializer is not None] + + class _NoRestoreSaveable(saver_lib.BaseSaverBuilder.SaveableObject): def __init__(self, tensor, name): @@ -288,7 +322,26 @@ class _NoRestoreSaveable(saver_lib.BaseSaverBuilder.SaveableObject): return control_flow_ops.no_op() -class CheckpointLoadStatus(object): +class _LoadStatus(object): + """Abstract base for load status callbacks.""" + + @abc.abstractmethod + def assert_consumed(self): + """Raises an exception unless a non-trivial restoration has completed.""" + pass + + @abc.abstractmethod + def run_restore_ops(self, session=None): + """Runs restore ops from the checkpoint. Requires a valid checkpoint.""" + pass + + @abc.abstractmethod + def initialize_or_restore(self, session=None): + """Runs restore ops from the checkpoint, or initializes variables.""" + pass + + +class CheckpointLoadStatus(_LoadStatus): """Checks the status of checkpoint loading and manages restore ops. Returned from `Saver.restore`. Since `restore` may defer the loading of values @@ -342,12 +395,112 @@ class CheckpointLoadStatus(object): def run_restore_ops(self, session=None): """Run operations to restore objects in the dependency graph.""" - if context.in_eager_mode(): + if context.executing_eagerly(): return # Run eagerly if session is None: session = ops.get_default_session() session.run(self._checkpoint.restore_ops, feed_dict=self._feed_dict) + def initialize_or_restore(self, session=None): + """Alias for `run_restore_ops`. + + This method has a sibling in `InitializationOnlyStatus` which instead + initializes variables. That type is returned if no checkpoint is specified + in `Saver.restore`. + + Args: + session: The session to run restore ops in. If `None`, uses the default + session. + """ + self.run_restore_ops(session=session) + + +class InitializationOnlyStatus(_LoadStatus): + """Returned from `Saver.restore` when no checkpoint has been specified. + + Objects of this type have the same `assert_consumed` method as + `CheckpointLoadStatus`, but it always fails. However, + `initialize_or_restore` works on objects of both types, and will + initialize variables in `InitializationOnlyStatus` objects or restore them + otherwise. + """ + + def __init__(self, root_checkpointable): + self._root_checkpointable = root_checkpointable + + def assert_consumed(self): + """Assertion for consistency with `CheckpointLoadStatus`. Always fails.""" + raise AssertionError( + "No checkpoint specified (save_path=None); nothing is being restored.") + + def run_restore_ops(self, session=None): + """For consistency with `CheckpointLoadStatus`. + + Use `initialize_or_restore` for initializing if no checkpoint was passed + to `Saver.restore` and restoring otherwise. + + Args: + session: Not used. + """ + raise AssertionError( + "No checkpoint specified, so no restore ops are available " + "(save_path=None to Saver.restore).") + + def initialize_or_restore(self, session=None): + """Runs initialization ops for variables. + + Only objects which would be saved by `Saver.save` will be initialized. See + `gather_initializers` for details. + + This method does nothing when executing eagerly (initializers get run + eagerly). + + Args: + session: The session to run initialization ops in. If `None`, uses the + default session. + """ + if context.executing_eagerly(): + return # run eagerly + if session is None: + session = ops.get_default_session() + session.run(gather_initializers(self._root_checkpointable)) + + +_DEPRECATED_RESTORE_INSTRUCTIONS = ( + "Restoring a name-based tf.train.Saver checkpoint using the object-based " + "restore API. This mode uses global names to match variables, and so is " + "somewhat fragile. It also adds new restore ops to the graph each time it " + "is called. Prefer re-encoding training checkpoints in the object-based " + "format: run save() on the object-based saver (the same one this message " + "is coming from) and use that checkpoint in the future.") + + +class NameBasedSaverStatus(_LoadStatus): + """Status for loading a name-based training checkpoint.""" + + def __init__(self, object_saver, save_path): + self._object_saver = object_saver + self._save_path = save_path + + def assert_consumed(self): + """Assertion for consistency with `CheckpointLoadStatus`. Always fails.""" + raise AssertionError( + "Restoring a name-based checkpoint. No load status is available.") + + @deprecation.deprecated( + date=None, instructions=_DEPRECATED_RESTORE_INSTRUCTIONS) + def run_restore_ops(self, session=None): + """Load the name-based training checkpoint using a new `tf.train.Saver`.""" + if session is None and not context.executing_eagerly(): + session = ops.get_default_session() + with ops.device("/cpu:0"): + saver_lib.Saver(self._object_saver._global_variable_names()).restore( # pylint: disable=protected-access + sess=session, save_path=self._save_path) + + def initialize_or_restore(self, session=None): + """Alias for `run_restore_ops`.""" + self.run_restore_ops(session=session) + class _SessionWithFeedDictAdditions(session_lib.SessionInterface): """Pretends to be a session, inserts extra feeds on run().""" @@ -366,7 +519,7 @@ class _SessionWithFeedDictAdditions(session_lib.SessionInterface): fetches=fetches, feed_dict=feed_dict, **kwargs) -class Saver(object): +class CheckpointableSaver(object): """Saves and restores a `Checkpointable` object and its dependencies. See `Checkpointable` for details of dependency management. `Saver` wraps @@ -396,8 +549,9 @@ class Saver(object): # Allow passing in a weak reference to avoid reference cycles when # `Checkpointable` objects save themselves. self._root_checkpointable_ref = root_checkpointable - if context.in_graph_mode(): - self._file_prefix_placeholder = constant_op.constant("model") + if not context.executing_eagerly(): + with ops.device("/cpu:0"): + self._file_prefix_placeholder = constant_op.constant("model") else: self._file_prefix_placeholder = None @@ -429,7 +583,7 @@ class Saver(object): Args: file_prefix: A prefix to use for the checkpoint filenames (/path/to/directory/and_a_prefix). Names are generated based on this - prefix and the global step, if provided. + prefix and `checkpoint_number`, if provided. checkpoint_number: An integer variable or Tensor, used to number checkpoints. Typically this value is saved along with other variables in training checkpoints, which will happen automatically if it was created @@ -444,19 +598,21 @@ class Saver(object): """ named_variables, graph_proto = _serialize_object_graph( self._root_checkpointable) - in_graph_mode = context.in_graph_mode() + in_graph_mode = not context.executing_eagerly() if in_graph_mode: if session is None: session = ops.get_default_session() if self._object_graph_feed_tensor is None: - self._object_graph_feed_tensor = constant_op.constant( - "", dtype=dtypes.string) + with ops.device("/cpu:0"): + self._object_graph_feed_tensor = constant_op.constant( + "", dtype=dtypes.string) object_graph_tensor = self._object_graph_feed_tensor feed_additions = {object_graph_tensor: graph_proto.SerializeToString()} else: session = None - object_graph_tensor = constant_op.constant( - graph_proto.SerializeToString(), dtype=dtypes.string) + with ops.device("/cpu:0"): + object_graph_tensor = constant_op.constant( + graph_proto.SerializeToString(), dtype=dtypes.string) feed_additions = None assert _OBJECT_GRAPH_PROTO_KEY not in named_variables named_variables[_OBJECT_GRAPH_PROTO_KEY] = _NoRestoreSaveable( @@ -475,14 +631,26 @@ class Saver(object): self._last_save_object_graph = graph_proto else: saver = self._last_save_saver - save_path = saver.save( - sess=_SessionWithFeedDictAdditions( - session=session, feed_additions=feed_additions), - save_path=file_prefix, - write_meta_graph=False, - global_step=checkpoint_number) + 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, + global_step=checkpoint_number) return save_path + def _global_variable_names(self): + """Generate a `tf.train.Saver`-style `var_list` using `variable.name`s.""" + named_saveables, graph_proto = _serialize_object_graph( + self._root_checkpointable) + saver_names = {} + for object_proto in graph_proto.nodes: + for attribute_proto in object_proto.attributes: + saver_names[attribute_proto.full_name] = named_saveables[ + attribute_proto.checkpoint_key] + return saver_names + def restore(self, save_path, session=None): """Restore a training checkpoint. @@ -518,21 +686,36 @@ class Saver(object): If the checkpoint has not been consumed completely, then the list of restore ops will grow as more objects are added to the dependency graph. + Name-based `tf.train.Saver` checkpoints can be loaded using this + method. There is no deferred loading, and names are used to match + variables. No restore ops are created/run until `run_restore_ops()` or + `initialize_or_restore()` are called on the returned status object, even + when executing eagerly. Re-encode name-based checkpoints using this + object-based `Saver.save` as soon as possible. + Args: save_path: The path to the checkpoint, as returned by `save` or `tf.train.latest_checkpoint`. If None (as when there is no latest - checkpoint for `tf.train.latest_checkpoint` to return), does nothing. + checkpoint for `tf.train.latest_checkpoint` to return), returns an + object which may run initializers for objects in the dependency + graph. If the checkpoint was written by the name-based `tf.train.Saver`, + names are used to match variables. session: The session to retrieve metadata with. Ignored when executing eagerly. If not provided when graph building, the default session is used. Returns: - A `CheckpointLoadStatus` object, which can be used to make assertions - about the status of checkpoint restoration and run restore ops. + A load status object, which can be used to make assertions about the + status of checkpoint restoration and run initialization/restore ops + (of type `CheckpointLoadStatus`, or `InitializationOnlyStatus` if + `save_path` is `None`). + + If `save_path` points to a name-based checkpoint, a `NameBasedSaverStatus` + object is returned which runs restore ops from a name-based saver. """ if save_path is None: - return - in_graph_mode = context.in_graph_mode() + return InitializationOnlyStatus(self._root_checkpointable) + in_graph_mode = not context.executing_eagerly() if in_graph_mode: if session is None: session = ops.get_default_session() @@ -540,23 +723,31 @@ class Saver(object): file_prefix_feed_dict = {self._file_prefix_placeholder: save_path} else: session = None - file_prefix_tensor = constant_op.constant(save_path) + with ops.device("/cpu:0"): + file_prefix_tensor = constant_op.constant(save_path) file_prefix_feed_dict = None - if not in_graph_mode or self._object_graph_restore_tensor is None: - object_graph_string, = io_ops.restore_v2( - prefix=file_prefix_tensor, - tensor_names=[_OBJECT_GRAPH_PROTO_KEY], - shape_and_slices=[""], - dtypes=[dtypes.string], - name="object_graph_proto_read") + try: + if not in_graph_mode or self._object_graph_restore_tensor is None: + with ops.device("/cpu:0"): + object_graph_string, = io_ops.restore_v2( + prefix=file_prefix_tensor, + tensor_names=[_OBJECT_GRAPH_PROTO_KEY], + shape_and_slices=[""], + dtypes=[dtypes.string], + name="object_graph_proto_read") + if in_graph_mode: + self._object_graph_restore_tensor = object_graph_string if in_graph_mode: - self._object_graph_restore_tensor = object_graph_string - if in_graph_mode: - object_graph_string = session.run( - self._object_graph_restore_tensor, - feed_dict=file_prefix_feed_dict) - else: - object_graph_string = object_graph_string.numpy() + object_graph_string = session.run( + self._object_graph_restore_tensor, + feed_dict=file_prefix_feed_dict) + else: + object_graph_string = object_graph_string.numpy() + except errors_impl.NotFoundError: + # The object graph proto does not exist in this checkpoint. Try again with + # name-based saving. + return NameBasedSaverStatus(self, save_path) + object_graph_proto = ( checkpointable_object_graph_pb2.CheckpointableObjectGraph()) object_graph_proto.ParseFromString(object_graph_string) @@ -586,3 +777,103 @@ class Saver(object): load_status = CheckpointLoadStatus( checkpoint, feed_dict=file_prefix_feed_dict) return load_status + + +class Checkpoint(core_checkpointable.Checkpointable): + """A utility class which groups `Checkpointable` objects. + + Accepts arbitrary keyword arguments to its constructor and saves those values + with a checkpoint. Maintains a `save_counter` for numbering checkpoints. + + Example usage: + + ```python + import tensorflow as tf + import tensorflow.contrib.eager as tfe + import os + + checkpoint_directory = "/tmp/training_checkpoints" + checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") + + root = tfe.Checkpoint(optimizer=optimizer, model=model) + root.restore(tf.train.latest_checkpoint(checkpoint_directory)) + for _ in range(num_training_steps): + optimizer.minimize( ... ) + root.save(file_prefix=checkpoint_prefix) + ``` + + For more manual control over saving, use `tfe.CheckpointableSaver` directly. + + Attributes: + save_counter: Incremented when `save()` is called. Used to number + checkpoints. + """ + + def __init__(self, **kwargs): + """Group objects into a training checkpoint. + + Args: + **kwargs: Keyword arguments are set as attributes of this object, and are + saved with the checkpoint. Attribute values must derive from + `CheckpointableBase`. + Raises: + ValueError: If objects in `kwargs` are not Checkpointable. + """ + super(Checkpoint, self).__init__() + for k, v in sorted(kwargs.items(), key=lambda item: item[0]): + if not isinstance(v, core_checkpointable.CheckpointableBase): + raise ValueError( + ("`Checkpoint` was expecting an object derived from " + "`CheckpointableBase`, got %s.") % (v,)) + setattr(self, k, v) + self._save_counter = None # Created lazily for restore-on-create. + self._saver = CheckpointableSaver(weakref.ref(self)) + + def _maybe_create_save_counter(self): + """Create a save counter if it does not yet exist.""" + if self._save_counter is None: + # Initialized to 0 and incremented before saving. + with ops.device("/cpu:0"): + self._save_counter = add_variable( + self, name="save_counter", initializer=0, dtype=dtypes.int64) + + @property + def save_counter(self): + """An integer variable which starts at zero and is incremented on save. + + Used to number checkpoints. + + Returns: + The save counter variable. + """ + self._maybe_create_save_counter() + return self._save_counter + + def save(self, file_prefix, session=None): + """Save a checkpoint. Wraps `tfe.CheckpointableSaver.save`.""" + in_graph_mode = not context.executing_eagerly() + if in_graph_mode: + if session is None: + session = ops.get_default_session() + if self._save_counter is None: + # When graph building, if this is a new save counter variable then it + # needs to be initialized before assign_add. This is only an issue if + # restore() has not been called first. + session.run(self.save_counter.initializer) + with ops.colocate_with(self.save_counter): + assign_op = self.save_counter.assign_add(1) + if in_graph_mode: + session.run(assign_op) + return self._saver.save( + file_prefix=file_prefix, + checkpoint_number=self.save_counter, + session=session) + + def restore(self, save_path): + """Restore a checkpoint. Wraps `tfe.CheckpointableSaver.restore`.""" + status = self._saver.restore(save_path=save_path) + # Create the save counter now so it gets initialized with other variables + # when graph building. Creating it earlier would lead to double + # initialization when executing eagerly. + self._maybe_create_save_counter() + return status diff --git a/tensorflow/contrib/eager/python/checkpointable_utils_test.py b/tensorflow/contrib/eager/python/checkpointable_utils_test.py index b7554defde57201d21eefe70cbf4e4e98651e175..2054878bf861553bb6cfa8d3730fa2070cf6b8bb 100644 --- a/tensorflow/contrib/eager/python/checkpointable_utils_test.py +++ b/tensorflow/contrib/eager/python/checkpointable_utils_test.py @@ -18,98 +18,30 @@ from __future__ import print_function import functools import os -import weakref import six from tensorflow.contrib.eager.python import checkpointable_utils -from tensorflow.contrib.eager.python import network as network_lib +from tensorflow.python.client import session as session_lib from tensorflow.python.eager import context 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.layers import base +from tensorflow.python.keras._impl.keras.engine import training from tensorflow.python.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.ops import variables from tensorflow.python.training import adam from tensorflow.python.training import checkpointable from tensorflow.python.training import saver as core_saver from tensorflow.python.training import training_util -class CheckpointableDenseLayer(core.Dense, checkpointable.Checkpointable): - - def __init__(self, *args, **kwargs): - checkpointable.Checkpointable.__init__(self) - core.Dense.__init__(self, *args, **kwargs) - - def add_variable(self, name, shape, **kwargs): - # Calls both Checkpointable._add_variable and Layer.add_variable. Eventually - # Layer.add_variable should inherit from Checkpointable and simply call - # super and then do post-processing. - return checkpointable.Checkpointable._add_variable_with_custom_getter( - self, - name=name, - shape=shape, - getter=functools.partial(core.Dense.add_variable, self), - **kwargs) - - -# pylint: disable=not-callable -class CheckpointableNetwork(network_lib.Network, checkpointable.Checkpointable): - - def __setattr__(self, name, value): - if isinstance(value, base.Layer): - self.track_layer(value, name=name) - # Checkpointable is next in the method resolution order, so this will catch - # Checkpointable objects which aren't Layers. - super(CheckpointableNetwork, self).__setattr__(name, value) - - def track_layer(self, layer, name): - self._track_checkpointable(layer, name=name) - return super(CheckpointableNetwork, self).track_layer(layer) - - -class CheckpointableAdam(adam.AdamOptimizer, checkpointable.Checkpointable): - - # NOTE: Copied from Optimizer with modifications to use add_variable - # for non-slot variables. These contortions are necessary to maintain - # checkpoint compatibility with variable.name based saving. - # TODO(allenl): Make this cleaner. - def _create_non_slot_variable(self, initial_value, name, colocate_with): - """Add an extra variable, not associated with a slot.""" - if context.in_graph_mode(): - graph = colocate_with.graph - else: - graph = None - - key = (name, graph) - v = self._non_slot_dict.get(key, None) - if v is None: - with ops.colocate_with(colocate_with): - def _variable_getter(name, shape, dtype, initializer): - del shape, dtype # not used, but there for compatibility - return variable_scope.variable( - name=name, initial_value=initializer, trainable=False) - - initial_value = ops.convert_to_tensor(initial_value) - v = self._add_variable_with_custom_getter( - name=name, - shape=initial_value.get_shape(), - initializer=initial_value, - getter=_variable_getter) - - self._non_slot_dict[key] = v - - return v - - class NonLayerCheckpointable(checkpointable.Checkpointable): def __init__(self): @@ -118,60 +50,20 @@ class NonLayerCheckpointable(checkpointable.Checkpointable): self, name="a_variable", shape=[]) -class MyNetwork(CheckpointableNetwork): - """A concrete Network for testing.""" +# pylint: disable=not-callable +class MyModel(training.Model): + """A concrete Model for testing.""" def __init__(self): - super(MyNetwork, self).__init__() - self._named_dense = CheckpointableDenseLayer(1, use_bias=True) - self._via_track_layer = self.track_layer( - CheckpointableDenseLayer(1, use_bias=False), name="via_track_layer") + 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): - return self._via_track_layer(self._named_dense(values)) - - -class Checkpoint(checkpointable.Checkpointable): - """A utility class which groups `Checkpointable` objects.""" - - def __init__(self, **kwargs): - super(Checkpoint, self).__init__() - for k, v in sorted(kwargs.items(), key=lambda item: item[0]): - setattr(self, k, v) - self._save_counter = None - self._saver = checkpointable_utils.Saver(weakref.ref(self)) - - @property - def save_counter(self): - """An integer variable which starts at zero and is incremented on save. - - Used to number checkpoints. - - Returns: - The save counter variable. - """ - if self._save_counter is None: - # Initialized to 0 and incremented before saving. - self._save_counter = checkpointable_utils.add_variable( - self, name="save_counter", initializer=0, dtype=dtypes.int64) - return self._save_counter - - def save(self, file_prefix, session=None): - assign_op = self.save_counter.assign_add(1) - if context.in_graph_mode(): - if session is None: - session = ops.get_default_session() - session.run(assign_op) - return self._saver.save( - file_prefix=file_prefix, - checkpoint_number=self.save_counter, - session=session) - - def restore(self, save_path): - return self._saver.restore( - save_path=save_path) + ret = self._second(self._named_dense(values)) + return ret class InterfaceTests(test.TestCase): @@ -206,8 +98,7 @@ class InterfaceTests(test.TestCase): with self.assertRaisesRegexp(ValueError, "'duplicate' already exists"): checkpointable_utils.add_variable(obj, name="duplicate", shape=[]) - if context.in_graph_mode(): - self.evaluate(variables.global_variables_initializer()) + self.evaluate(checkpointable_utils.gather_initializers(obj)) self.assertEqual("constant_initializer:0", constant_initializer.name) self.assertEqual(1, self.evaluate(constant_initializer)) self.assertEqual("some_variable_scope/ones_initializer:0", @@ -217,14 +108,14 @@ class InterfaceTests(test.TestCase): [0., 0.]], self.evaluate(bare_initializer)) self.assertEqual("a_variable:0", obj.a_variable.name) self.assertEqual("duplicate:0", other_duplicate.name) - if context.in_graph_mode(): - # The .name attribute may be globally influenced, but the checkpoint name - # won't be (tested below). - self.assertEqual("duplicate_1:0", duplicate.name) - else: + if context.executing_eagerly(): # When executing eagerly, there's no uniquification of variable names. The # checkpoint name will be the same. self.assertEqual("duplicate:0", duplicate.name) + else: + # The .name attribute may be globally influenced, but the checkpoint name + # won't be (tested below). + self.assertEqual("duplicate_1:0", duplicate.name) named_variables, _ = checkpointable_utils._serialize_object_graph(obj) expected_checkpoint_names = ( "a_variable/.ATTRIBUTES/VARIABLE_VALUE", @@ -266,52 +157,50 @@ 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.]]) - network = MyNetwork() - # A nuisance Network using the same optimizer. Its slot variables should not + 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_network = MyNetwork() - optimizer = CheckpointableAdam(0.001) + other_model = MyModel() + optimizer = adam.AdamOptimizer(0.001) optimizer_step = training_util.get_or_create_global_step() - root_checkpointable = Checkpoint( - optimizer=optimizer, network=network, optimizer_step=optimizer_step) - if context.in_eager_mode(): + root_checkpointable = checkpointable_utils.Checkpoint( + optimizer=optimizer, model=model, optimizer_step=optimizer_step) + if context.executing_eagerly(): optimizer.minimize( - lambda: network(input_value), + lambda: model(input_value), global_step=optimizer_step) optimizer.minimize( - lambda: other_network(input_value), + lambda: other_model(input_value), global_step=optimizer_step) else: train_op = optimizer.minimize( - network(input_value), global_step=optimizer_step) + model(input_value), global_step=optimizer_step) optimizer.minimize( - other_network(input_value), + other_model(input_value), global_step=optimizer_step) - self.evaluate(variables.global_variables_initializer()) + self.evaluate(checkpointable_utils.gather_initializers( + root_checkpointable)) self.evaluate(train_op) named_variables, serialized_graph = ( checkpointable_utils._serialize_object_graph(root_checkpointable)) expected_checkpoint_names = ( # Created in the root node, so no prefix. "optimizer_step", - # No name provided to track_checkpointable(), so the position is used - # instead (one-based). - "network/via_track_layer/kernel", - # track_checkpointable() with a name provided, so that's used - "network/_named_dense/kernel", - "network/_named_dense/bias", - # non-Layer dependency of the network - "network/_non_layer/a_variable", + "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 - "network/via_track_layer/kernel/.OPTIMIZER_SLOT/optimizer/m", - "network/via_track_layer/kernel/.OPTIMIZER_SLOT/optimizer/v", - "network/_named_dense/kernel/.OPTIMIZER_SLOT/optimizer/m", - "network/_named_dense/kernel/.OPTIMIZER_SLOT/optimizer/v", - "network/_named_dense/bias/.OPTIMIZER_SLOT/optimizer/m", - "network/_named_dense/bias/.OPTIMIZER_SLOT/optimizer/v", + "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 = [ @@ -323,11 +212,11 @@ class CheckpointingTests(test.TestCase): "global_step:0", named_variables["optimizer_step" + suffix].name) self.assertEqual( - "my_network/checkpointable_dense_layer_1/kernel:0", - named_variables["network/via_track_layer/kernel" + suffix].name) + "my_model/dense_1/kernel:0", + named_variables["model/_second/kernel" + suffix].name) self.assertEqual( - "my_network/checkpointable_dense_layer/kernel:0", - named_variables["network/_named_dense/kernel" + suffix].name) + "my_model/dense/kernel:0", + named_variables["model/_named_dense/kernel" + suffix].name) self.assertEqual( "beta1_power:0", named_variables["optimizer/beta1_power" + suffix].name) @@ -345,78 +234,80 @@ class CheckpointingTests(test.TestCase): serialized_graph.nodes[optimizer_node.children[0].node_id] .attributes[0].full_name) self.assertEqual( - "my_network/checkpointable_dense_layer/kernel", + "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_network/checkpointable_dense_layer/kernel/Adam", + "my_model/dense/kernel/Adam", serialized_graph.nodes[optimizer_node.slot_variables[0] .slot_variable_node_id] .attributes[0].full_name) self.assertEqual( - "my_network/checkpointable_dense_layer/kernel/Adam:0", + "my_model/dense/kernel/Adam:0", optimizer.get_slot( - var=named_variables["network/_named_dense/kernel" + suffix], + var=named_variables["model/_named_dense/kernel" + suffix], name="m").name) self.assertEqual( - "network/_named_dense/kernel" + suffix, + "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( - "network/_named_dense/kernel/.OPTIMIZER_SLOT/optimizer/m" + suffix, + "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): - network = MyNetwork() - optimizer = CheckpointableAdam(0.001) - root_checkpointable = Checkpoint(optimizer=optimizer, network=network) + model = MyModel() + optimizer = adam.AdamOptimizer(0.001) + root_checkpointable = checkpointable_utils.Checkpoint( + optimizer=optimizer, model=model) input_value = constant_op.constant([[3.]]) - if context.in_eager_mode(): + if context.executing_eagerly(): optimizer.minimize( - lambda: network(input_value)) + lambda: model(input_value)) else: - train_op = optimizer.minimize(network(input_value)) + 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(variables.global_variables_initializer()) + 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(network._named_dense.variables[1], [42.])) - m_bias_slot = optimizer.get_slot(network._named_dense.variables[1], "m") + 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(network._named_dense.variables[1], [43.])) + 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(network._named_dense.variables[1])) + 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 context.in_graph_mode(): + if not context.executing_eagerly(): return # Restore-on-create is only supported when executing eagerly - on_create_network = MyNetwork() - on_create_optimizer = CheckpointableAdam(0.001) - on_create_root = Checkpoint( - optimizer=on_create_optimizer, network=on_create_network) + on_create_model = MyModel() + on_create_optimizer = adam.AdamOptimizer(0.001) + 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) - on_create_network(constant_op.constant([[3.]])) # create variables + 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_network._named_dense.variables[1])) + on_create_model._named_dense.variables[1])) on_create_m_bias_slot = on_create_optimizer.get_slot( - on_create_network._named_dense.variables[1], "m") + on_create_model._named_dense.variables[1], "m") # Optimizer slot variables are created when the original variable is # restored. self.assertAllEqual([1.5], self.evaluate(on_create_m_bias_slot)) @@ -429,23 +320,24 @@ class CheckpointingTests(test.TestCase): 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): - network = MyNetwork() - optimizer = CheckpointableAdam(0.001) - root = Checkpoint( - optimizer=optimizer, network=network, + 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(core_saver.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: network(input_value), # pylint: disable=cell-var-from-loop + 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, @@ -459,37 +351,66 @@ class CheckpointingTests(test.TestCase): checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") for training_continuation in range(3): with ops.Graph().as_default(): - network = MyNetwork() - optimizer = CheckpointableAdam(0.001) - root = Checkpoint( - optimizer=optimizer, network=network, + 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( - network(input_value), + model(input_value), global_step=root.global_step) - root.save_counter # pylint: disable=pointless-statement - init_op = variables.global_variables_initializer() checkpoint_path = core_saver.latest_checkpoint(checkpoint_directory) with self.test_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) - session.run(init_op) - # Another alternative would be to run initializers automatically - # if no checkpoint is being loaded. This would make deferred - # loading a bit more useful with graph execution. + with self.assertRaises(AssertionError): + status.assert_consumed() else: - status = root.restore(save_path=checkpoint_path).assert_consumed() - status.run_restore_ops() + status.assert_consumed() for _ in range(num_training_steps): session.run(train_op) - root.save(file_prefix=checkpoint_prefix, - session=session) + 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() + checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") + for training_continuation in range(3): + with ops.Graph().as_default(), self.test_session( + graph=ops.get_default_graph()), 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()) + checkpoint_path = core_saver.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() + for _ in range(num_training_steps): + train_fn() + 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)) + def _get_checkpoint_name(self, name): root = checkpointable.Checkpointable() checkpointable_utils.add_variable( @@ -551,9 +472,11 @@ class CheckpointingTests(test.TestCase): self.evaluate(state_ops.assign(original.dep.var, 123.)) checkpoint_directory = self.get_temp_dir() checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") - save_path = checkpointable_utils.Saver(original).save(checkpoint_prefix) + save_path = checkpointable_utils.CheckpointableSaver( + original).save(checkpoint_prefix) load_into = LateDependencies() - status = checkpointable_utils.Saver(load_into).restore(save_path) + status = checkpointable_utils.CheckpointableSaver( + load_into).restore(save_path) with self.assertRaises(AssertionError): status.assert_consumed() load_into.add_dep() @@ -582,11 +505,12 @@ class CheckpointingTests(test.TestCase): self.evaluate(state_ops.assign(dep_after_var.dep.var, -14.)) checkpoint_directory = self.get_temp_dir() checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") - save_path = checkpointable_utils.Saver(dep_after_var).save( + save_path = checkpointable_utils.CheckpointableSaver(dep_after_var).save( checkpoint_prefix) loaded_dep_after_var = DepAfterVar() - status = checkpointable_utils.Saver(loaded_dep_after_var).restore(save_path) + status = checkpointable_utils.CheckpointableSaver( + loaded_dep_after_var).restore(save_path) loaded_dep_after_var.add_dep() status.assert_consumed() status.run_restore_ops() @@ -599,27 +523,33 @@ class CheckpointingTests(test.TestCase): root = checkpointable.Checkpointable() root.var = checkpointable_utils.add_variable( root, name="var", initializer=0.) - optimizer = CheckpointableAdam(0.1) - if context.in_graph_mode(): + optimizer = adam.AdamOptimizer(0.1) + if context.executing_eagerly(): + optimizer.minimize(root.var.read_value) + else: train_op = optimizer.minimize(root.var) - self.evaluate(variables.global_variables_initializer()) + # 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) - else: - optimizer.minimize(root.var.read_value) self.evaluate(state_ops.assign(root.var, 12.)) - no_slots_path = checkpointable_utils.Saver(root).save( + 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.Saver(root).save( + slots_path = checkpointable_utils.CheckpointableSaver(root).save( os.path.join(checkpoint_directory, "with_slots")) new_root = checkpointable.Checkpointable() # Load the slot-containing checkpoint (deferred), then immediately overwrite # the non-slot variable (also deferred). - slot_status = checkpointable_utils.Saver(new_root).restore(slots_path) - no_slot_status = checkpointable_utils.Saver(new_root).restore(no_slots_path) + 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( @@ -627,11 +557,11 @@ 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 = CheckpointableAdam(0.1) + new_root.optimizer = adam.AdamOptimizer(0.1) with self.assertRaisesRegexp(AssertionError, "beta1_power"): slot_status.assert_consumed() self.assertEqual(12., self.evaluate(new_root.var)) - if context.in_eager_mode(): + if context.executing_eagerly(): # Slot variables are only created with restoring initializers when # executing eagerly. self.assertEqual(14., self.evaluate( @@ -639,7 +569,9 @@ class CheckpointingTests(test.TestCase): else: self.assertIs(new_root.optimizer.get_slot(name="m", var=new_root.var), None) - if context.in_graph_mode(): + 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. @@ -647,8 +579,6 @@ class CheckpointingTests(test.TestCase): self.assertEqual(14., self.evaluate( new_root.optimizer.get_slot(name="m", var=new_root.var))) self.evaluate(train_op) - else: - new_root.optimizer.minimize(new_root.var.read_value) slot_status.assert_consumed() @test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True) @@ -659,15 +589,17 @@ class CheckpointingTests(test.TestCase): save_root.dep.var = checkpointable_utils.add_variable( save_root.dep, name="var", initializer=0.) self.evaluate(state_ops.assign(save_root.dep.var, 12.)) - saver = checkpointable_utils.Saver(save_root) + saver = checkpointable_utils.CheckpointableSaver(save_root) first_path = saver.save(os.path.join(checkpoint_directory, "first")) self.evaluate(state_ops.assign(save_root.dep.var, 13.)) second_path = saver.save(os.path.join(checkpoint_directory, "second")) first_root = checkpointable.Checkpointable() second_root = checkpointable.Checkpointable() - first_status = checkpointable_utils.Saver(first_root).restore(first_path) - second_status = checkpointable_utils.Saver(second_root).restore(second_path) + first_status = checkpointable_utils.CheckpointableSaver( + first_root).restore(first_path) + second_status = checkpointable_utils.CheckpointableSaver( + second_root).restore(second_path) load_dep = checkpointable.Checkpointable() load_dep.var = checkpointable_utils.add_variable( load_dep, name="var", shape=[]) @@ -684,8 +616,10 @@ class CheckpointingTests(test.TestCase): # determines the final value. first_root = checkpointable.Checkpointable() second_root = checkpointable.Checkpointable() - second_status = checkpointable_utils.Saver(second_root).restore(second_path) - first_status = checkpointable_utils.Saver(first_root).restore(first_path) + second_status = checkpointable_utils.CheckpointableSaver( + second_root).restore(second_path) + first_status = checkpointable_utils.CheckpointableSaver( + first_root).restore(first_path) load_dep = checkpointable.Checkpointable() load_dep.var = checkpointable_utils.add_variable( load_dep, name="var", shape=[]) @@ -709,11 +643,11 @@ class CheckpointingTests(test.TestCase): save_root.dep_one.dep_three = dep_three save_root.dep_two.dep_three = dep_three checkpointable_utils.add_variable(dep_three, name="var", initializer=0.) - self.evaluate(variables.global_variables_initializer()) - save_path = checkpointable_utils.Saver(save_root).save( + self.evaluate(checkpointable_utils.gather_initializers(save_root)) + save_path = checkpointable_utils.CheckpointableSaver(save_root).save( os.path.join(checkpoint_directory, "ckpt")) load_root = checkpointable.Checkpointable() - checkpointable_utils.Saver(load_root).restore(save_path) + checkpointable_utils.CheckpointableSaver(load_root).restore(save_path) load_root.dep_one = checkpointable.Checkpointable() load_root.dep_two = checkpointable.Checkpointable() load_root.dep_one.dep_three = checkpointable.Checkpointable() @@ -732,8 +666,8 @@ class CheckpointingTests(test.TestCase): save_root.dep_one, name="var1", initializer=32., dtype=dtypes.float64) checkpointable_utils.add_variable( save_root.dep_two, name="var2", initializer=64., dtype=dtypes.float64) - self.evaluate(variables.global_variables_initializer()) - save_path = checkpointable_utils.Saver(save_root).save( + self.evaluate(checkpointable_utils.gather_initializers(save_root)) + save_path = checkpointable_utils.CheckpointableSaver(save_root).save( os.path.join(checkpoint_directory, "ckpt")) load_root = checkpointable.Checkpointable() load_root.dep_one = checkpointable.Checkpointable() @@ -742,7 +676,7 @@ class CheckpointingTests(test.TestCase): load_root.dep_one, name="var1", shape=[], dtype=dtypes.float64) v2 = checkpointable_utils.add_variable( load_root.dep_one, name="var2", shape=[], dtype=dtypes.float64) - status = checkpointable_utils.Saver(load_root).restore( + status = checkpointable_utils.CheckpointableSaver(load_root).restore( save_path).assert_consumed() status.run_restore_ops() self.assertEqual(32., self.evaluate(v1)) @@ -760,14 +694,15 @@ class CheckpointingTests(test.TestCase): first, "v1", initializer=[3., 1., 4.]) second.v = checkpointable_utils.add_variable( second, "v2", initializer=[1., 1., 2., 3.]) - self.evaluate(variables.global_variables_initializer()) + self.evaluate(checkpointable_utils.gather_initializers(first)) checkpoint_directory = self.get_temp_dir() - save_path = checkpointable_utils.Saver(first).save( + save_path = checkpointable_utils.CheckpointableSaver(first).save( os.path.join(checkpoint_directory, "ckpt")) # Test deferred loading first_load = checkpointable.Checkpointable() - status = checkpointable_utils.Saver(first_load).restore(save_path) + status = checkpointable_utils.CheckpointableSaver( + first_load).restore(save_path) second_load = checkpointable.Checkpointable() first_load.second = second_load second_load.first = first_load @@ -787,7 +722,7 @@ class CheckpointingTests(test.TestCase): self.assertAllEqual([2., 7., 1.], self.evaluate(first_load.v)) self.evaluate(second_load.v.assign([2., 7., 1., 8.])) self.assertAllEqual([2., 7., 1., 8.], self.evaluate(second_load.v)) - status = checkpointable_utils.Saver(first_load).restore( + status = checkpointable_utils.CheckpointableSaver(first_load).restore( save_path).assert_consumed() status.run_restore_ops() self.assertAllEqual([3., 1., 4.], self.evaluate(first_load.v)) @@ -806,14 +741,15 @@ class CheckpointingTests(test.TestCase): name="blah", initializer=0.) self.evaluate(first.var1.assign(4.)) self.evaluate(first.var2.assign(8.)) - save_path = checkpointable_utils.Saver(first).save( + save_path = checkpointable_utils.CheckpointableSaver(first).save( checkpoint_prefix) restore_graph = ops.Graph() with restore_graph.as_default(), self.test_session(restore_graph): second = checkpointable.Checkpointable() second.var2 = variable_scope.get_variable( name="blah", initializer=0.) - status = checkpointable_utils.Saver(second).restore(save_path) + status = checkpointable_utils.CheckpointableSaver( + second).restore(save_path) recreated_var1 = variable_scope.get_variable( name="outside_var", initializer=0.) status.run_restore_ops() @@ -833,10 +769,10 @@ class CheckpointingTests(test.TestCase): checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") obj = checkpointable.Checkpointable() obj.var = variable_scope.get_variable(name="v", initializer=0.) - obj.opt = CheckpointableAdam(0.1) + obj.opt = adam.AdamOptimizer(0.1) obj.opt.minimize(obj.var.read_value()) - self.evaluate(variables.global_variables_initializer()) - saver = checkpointable_utils.Saver(obj) + 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) @@ -851,15 +787,263 @@ class CheckpointingTests(test.TestCase): checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") obj = checkpointable.Checkpointable() obj.var = variable_scope.get_variable(name="v", initializer=0.) - obj.opt = CheckpointableAdam(0.1) + obj.opt = adam.AdamOptimizer(0.1) obj.opt.minimize(obj.var.read_value()) - self.evaluate(variables.global_variables_initializer()) - saver = checkpointable_utils.Saver(obj) + 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)) + + +class TemplateTests(test.TestCase): + + @test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True) + def test_checkpointable_save_restore(self): + + def _templated(): + v = variable_scope.get_variable( + "v", shape=[1], initializer=init_ops.zeros_initializer()) + v2 = variable_scope.get_variable( + "v2", shape=[1], initializer=init_ops.zeros_initializer()) + return v, v + 1., v2 + + save_template = template.make_template("s1", _templated) + save_root = checkpointable_utils.Checkpoint(my_template=save_template) + v1_save, _, v2_save = save_template() + 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_root = checkpointable_utils.Checkpoint(my_template=load_template) + status = load_root.restore(save_path) + var, var_plus_one, var2 = load_template() + self.assertEqual(2, len(load_template._checkpoint_dependencies)) + self.assertEqual("v", load_template._checkpoint_dependencies[0].name) + self.assertEqual("v2", load_template._checkpoint_dependencies[1].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)) + + @test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True) + def test_checkpointable_save_restore_nested(self): + + def _inner_template(): + v = variable_scope.get_variable( + "v", shape=[1], initializer=init_ops.zeros_initializer()) + return v + + def _outer_template(): + first_inner = template.make_template("i1", _inner_template) + second_inner = template.make_template("i2", _inner_template) + v1 = first_inner() + v2 = second_inner() + v3 = second_inner() + return (first_inner, second_inner), (v1, v2, v3) + + with variable_scope.variable_scope("ignored"): + save_template = template.make_template("s1", _outer_template) + save_root = checkpointable_utils.Checkpoint(my_template=save_template) + (inner_template_one, inner_template_two), _ = save_template() + self.evaluate(inner_template_one.variables[0].assign([20.])) + self.evaluate(inner_template_two.variables[0].assign([25.])) + 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", _outer_template) + load_root = checkpointable_utils.Checkpoint(my_template=load_template) + 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.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.assertEqual("v", inner_template_one._checkpoint_dependencies[0].name) + self.assertEqual(1, len(inner_template_two._checkpoint_dependencies)) + self.assertEqual("v", inner_template_two._checkpoint_dependencies[0].name) + status.assert_consumed().run_restore_ops() + self.assertAllEqual([20.], self.evaluate(v1)) + self.assertAllEqual([25.], self.evaluate(v2)) + self.assertAllEqual([25.], self.evaluate(v3)) + + +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.test_session( + graph=save_graph) as session: + root = self._initialized_model() + name_saver = core_saver.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) + status = object_saver.restore(save_path) + with self.assertRaises(AssertionError): + status.assert_consumed() + status.run_restore_ops() + self._check_sentinels(root) + self._set_sentinels(root) + status.initialize_or_restore() + self._check_sentinels(root) + + # TODO(allenl): Test for the core name-based saver loading object-based + # checkpoints once object-based checkpointing is in core. + + 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.test_session( + graph=save_graph) as session: + root = self._initialized_model() + object_saver = checkpointable_utils.CheckpointableSaver(root) + save_path = object_saver.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() + object_saver = checkpointable_utils.CheckpointableSaver(root) + save_path = object_saver.save(file_prefix=checkpoint_prefix) + with context.graph_mode(): + save_graph = ops.Graph() + with save_graph.as_default(), self.test_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/contrib/eager/python/datasets.py b/tensorflow/contrib/eager/python/datasets.py index d177bfeab2d1fdc05d7ced54df8723fae2c77fdb..332bada57b42fe53fe6be0de1b39c905c0b32579 100644 --- a/tensorflow/contrib/eager/python/datasets.py +++ b/tensorflow/contrib/eager/python/datasets.py @@ -27,7 +27,6 @@ from tensorflow.python.data.util import sparse 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 function from tensorflow.python.framework import ops from tensorflow.python.ops import gen_dataset_ops @@ -45,8 +44,13 @@ def _generate_shared_name(prefix): return "{}{}".format(prefix, uid) -class Iterator(object): - """An iterator producing tf.Tensor objects from a tf.data.Dataset.""" +class Iterator(iterator_ops.EagerIterator): + """An iterator producing tf.Tensor objects from a tf.data.Dataset. + + NOTE: Unlike the iterator created by the + @{tf.data.Dataset.make_one_shot_iterator} method, this class enables + additional experimental functionality, such as prefetching to the GPU. + """ def __init__(self, dataset): """Creates a new iterator over the given dataset. @@ -67,37 +71,12 @@ class Iterator(object): Raises: RuntimeError: When invoked without eager execution enabled. """ - - if not context.in_eager_mode(): - raise RuntimeError( - "{} objects can only be used when eager execution is enabled, use " - "tf.data.Dataset.make_iterator or " - "tf.data.Dataset.make_one_shot_iterator for graph construction". - format(type(self))) - with ops.device("/device:CPU:0"): - ds_variant = dataset._as_variant_tensor() # pylint: disable=protected-access - self._output_classes = dataset.output_classes - self._output_types = dataset.output_types - self._output_shapes = dataset.output_shapes - self._flat_output_types = nest.flatten( - sparse.as_dense_types(self._output_types, self._output_classes)) - self._flat_output_shapes = nest.flatten( - sparse.as_dense_shapes(self._output_shapes, self._output_classes)) - self._resource = gen_dataset_ops.iterator( - shared_name="", - container=_generate_shared_name("eageriterator"), - output_types=self._flat_output_types, - output_shapes=self._flat_output_shapes) - gen_dataset_ops.make_iterator(ds_variant, self._resource) - # Delete the resource when this object is deleted - self._resource_deleter = resource_variable_ops.EagerResourceDeleter( - handle=self._resource, handle_device="/device:CPU:0") - self._device = context.context().device_name - self._buffer_resource_handle = None + super(Iterator, self).__init__(dataset) if not context.context().device_spec.device_type: is_remote_device = False else: is_remote_device = context.context().device_spec.device_type != "CPU" + self._buffer_resource_handle = None if is_remote_device: with ops.device("/device:CPU:0"): iter_string_handle = gen_dataset_ops.iterator_to_string_handle( @@ -106,7 +85,7 @@ class Iterator(object): @function.Defun(dtypes.string) def remote_fn(h): remote_iterator = iterator_ops.Iterator.from_string_handle( - h, self._output_types, self._output_shapes) + h, self.output_types, self.output_shapes, self.output_classes) return remote_iterator.get_next() remote_fn.add_to_graph(None) @@ -124,89 +103,16 @@ class Iterator(object): handle=self._buffer_resource_handle, handle_device=self._device) - def __iter__(self): - return self - - def __next__(self): # For Python 3 compatibility - return self.next() - def _next_internal(self): """Returns a nested structure of `tf.Tensor`s containing the next element. """ - with ops.device(self._device): - if self._buffer_resource_handle is not None: + if self._buffer_resource_handle is not None: + with ops.device(self._device): ret = prefetching_ops.function_buffering_resource_get_next( function_buffer_resource=self._buffer_resource_handle, output_types=self._flat_output_types) - else: - # TODO(ashankar): Consider removing this ops.device() contextmanager - # and instead mimic ops placement in graphs: Operations on resource - # handles execute on the same device as where the resource is placed. - # NOTE(mrry): Here we use the "_sync" variant of `iterator_get_next` - # because in eager mode this code will run synchronously on the calling - # thread. Therefore we do not need to make a defensive context switch - # to a background thread, and can achieve a small constant performance - # boost by invoking the iterator synchronously. - ret = gen_dataset_ops.iterator_get_next_sync( - self._resource, - output_types=self._flat_output_types, - output_shapes=self._flat_output_shapes) - - return sparse.deserialize_sparse_tensors( - nest.pack_sequence_as(self._output_types, ret), self._output_types, - self._output_shapes, self._output_classes) - - def next(self): - """Returns a nested structure of `tf.Tensor`s containing the next element. - """ - try: - return self._next_internal() - except errors.OutOfRangeError: - raise StopIteration - - @property - def output_classes(self): - """Returns the class of each component of an element of this iterator. - - The expected values are `tf.Tensor` and `tf.SparseTensor`. - - Returns: - A nested structure of Python `type` objects corresponding to each - component of an element of this dataset. - """ - return self._output_classes - - @property - def output_shapes(self): - """Returns the shape of each component of an element of this iterator. - - Returns: - A nested structure of `tf.TensorShape` objects corresponding to each - component of an element of this dataset. - """ - return self._output_shapes - - @property - def output_types(self): - """Returns the type of each component of an element of this iterator. - - Returns: - A nested structure of `tf.DType` objects corresponding to each component - of an element of this dataset. - """ - return self._output_types - - def get_next(self, name=None): - """Returns a nested structure of `tf.Tensor`s containing the next element. - - Args: - name: (Optional.) A name for the created operation. Currently unused. - - Returns: - A nested structure of `tf.Tensor` objects. - - Raises: - `tf.errors.OutOfRangeError`: If the end of the dataset has been reached. - """ - del name - return self._next_internal() + return sparse.deserialize_sparse_tensors( + nest.pack_sequence_as(self._output_types, ret), self._output_types, + self._output_shapes, self._output_classes) + else: + return super(Iterator, self)._next_internal() diff --git a/tensorflow/contrib/eager/python/datasets_test.py b/tensorflow/contrib/eager/python/datasets_test.py index 35c3c5d3fad0a84bbe4d24c7bb17878583bded4b..4afadd88f59a79dde4f3af5175adbbbb18557ced 100644 --- a/tensorflow/contrib/eager/python/datasets_test.py +++ b/tensorflow/contrib/eager/python/datasets_test.py @@ -44,6 +44,18 @@ class IteratorTest(test.TestCase): got.append(t.numpy()) self.assertAllEqual([0, 1, 2, 3], got) + def testBasicOneShotIterator(self): + got = [] + for t in Dataset.range(4).make_one_shot_iterator(): + got.append(t.numpy()) + self.assertAllEqual([0, 1, 2, 3], got) + + def testBasicImplicitIterator(self): + got = [] + for t in Dataset.range(4): + got.append(t.numpy()) + self.assertAllEqual([0, 1, 2, 3], got) + def testGetNext(self): iterator = datasets.Iterator(Dataset.range(4)) self.assertEqual(0, iterator.get_next().numpy()) @@ -53,6 +65,15 @@ class IteratorTest(test.TestCase): with self.assertRaises(errors.OutOfRangeError): iterator.get_next() + def testGetNextOneShotIterator(self): + iterator = Dataset.range(4).make_one_shot_iterator() + self.assertEqual(0, iterator.get_next().numpy()) + self.assertEqual(1, iterator.get_next().numpy()) + self.assertEqual(2, iterator.get_next().numpy()) + self.assertEqual(3, iterator.get_next().numpy()) + with self.assertRaises(errors.OutOfRangeError): + iterator.get_next() + def testMultipleIteratorsOnTheSameDataset(self): ds = Dataset.range(4) it1 = datasets.Iterator(ds) diff --git a/tensorflow/contrib/eager/python/evaluator.py b/tensorflow/contrib/eager/python/evaluator.py index 68e7b5421fec7f73f10e381ca45f9d900de299d7..37c8f0d47adbde6932bf409cdcae9a1845d700b5 100644 --- a/tensorflow/contrib/eager/python/evaluator.py +++ b/tensorflow/contrib/eager/python/evaluator.py @@ -57,7 +57,7 @@ class Evaluator(object): self._model = model self._metrics = {} self._evaluators = {} - if context.in_graph_mode(): + if not context.executing_eagerly(): self.call = function.defun(self.call) # ---- API for users ---- @@ -90,7 +90,7 @@ class Evaluator(object): Only for graph execution. @end_compatibility """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError("Evaluator.init_variables() not needed when " "eager execution is enabled.") return control_flow_ops.group([m.init_variables() for _, m in self.metrics]) @@ -113,7 +113,8 @@ class Evaluator(object): with summary_ops.create_file_writer( summary_logdir).as_default(), summary_ops.always_record_summaries(): return self._all_metric_results() - if context.in_eager_mode(): + + if context.executing_eagerly(): return f() else: return function.defun(f)() @@ -158,16 +159,16 @@ class Evaluator(object): @end_compatibility """ summary_logdir = kwargs.pop("summary_logdir", None) - if context.in_graph_mode(): - call_op = self.__call__(dataset.make_one_shot_iterator().get_next(), - *args, **kwargs) - init_op = self.init_variables() - results_op = self.all_metric_results(summary_logdir) - return (init_op, call_op, results_op) - # Eager case - for example in datasets.Iterator(dataset): - self.__call__(example, *args, **kwargs) - return self.all_metric_results(summary_logdir) + if context.executing_eagerly(): + for example in datasets.Iterator(dataset): + self.__call__(example, *args, **kwargs) + return self.all_metric_results(summary_logdir) + # Graph construction + call_op = self.__call__(dataset.make_one_shot_iterator().get_next(), *args, + **kwargs) + init_op = self.init_variables() + results_op = self.all_metric_results(summary_logdir) + return (init_op, call_op, results_op) @staticmethod def run_evaluation(init_op, call_op, results_op, sess=None): @@ -192,7 +193,7 @@ class Evaluator(object): Only for graph execution. @end_compatibility """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError("Evaluator.run_evaluation() not supported when " "eager execution is enabled.") sess = sess or ops.get_default_session() diff --git a/tensorflow/contrib/eager/python/examples/gan/mnist.py b/tensorflow/contrib/eager/python/examples/gan/mnist.py index b9ac79f46c83bb709918e3b72830b90ddcfd71b4..2b7e199fad08c9a5e320b51b3a4de92c2d7dbb1a 100644 --- a/tensorflow/contrib/eager/python/examples/gan/mnist.py +++ b/tensorflow/contrib/eager/python/examples/gan/mnist.py @@ -35,7 +35,7 @@ from tensorflow.examples.tutorials.mnist import input_data FLAGS = None -class Discriminator(tfe.Network): +class Discriminator(tf.keras.Model): """GAN Discriminator. A network to differentiate between generated and real handwritten digits. @@ -56,19 +56,15 @@ class Discriminator(tfe.Network): else: assert data_format == 'channels_last' self._input_shape = [-1, 28, 28, 1] - self.conv1 = self.track_layer(tf.layers.Conv2D(64, 5, padding='SAME', - data_format=data_format, - activation=tf.tanh)) - self.pool1 = self.track_layer( - tf.layers.AveragePooling2D(2, 2, data_format=data_format)) - self.conv2 = self.track_layer(tf.layers.Conv2D(128, 5, - data_format=data_format, - activation=tf.tanh)) - self.pool2 = self.track_layer( - tf.layers.AveragePooling2D(2, 2, data_format=data_format)) - self.flatten = self.track_layer(tf.layers.Flatten()) - self.fc1 = self.track_layer(tf.layers.Dense(1024, activation=tf.tanh)) - self.fc2 = self.track_layer(tf.layers.Dense(1, activation=None)) + self.conv1 = tf.layers.Conv2D( + 64, 5, padding='SAME', data_format=data_format, activation=tf.tanh) + self.pool1 = tf.layers.AveragePooling2D(2, 2, data_format=data_format) + self.conv2 = tf.layers.Conv2D( + 128, 5, data_format=data_format, activation=tf.tanh) + self.pool2 = tf.layers.AveragePooling2D(2, 2, data_format=data_format) + self.flatten = tf.layers.Flatten() + self.fc1 = tf.layers.Dense(1024, activation=tf.tanh) + self.fc2 = tf.layers.Dense(1, activation=None) def call(self, inputs): """Return two logits per image estimating input authenticity. @@ -95,7 +91,7 @@ class Discriminator(tfe.Network): return x -class Generator(tfe.Network): +class Generator(tf.keras.Model): """Generator of handwritten digits similar to the ones in the MNIST dataset. """ @@ -116,18 +112,17 @@ class Generator(tfe.Network): else: assert data_format == 'channels_last' self._pre_conv_shape = [-1, 6, 6, 128] - self.fc1 = self.track_layer(tf.layers.Dense(6 * 6 * 128, - activation=tf.tanh)) + self.fc1 = tf.layers.Dense(6 * 6 * 128, activation=tf.tanh) # In call(), we reshape the output of fc1 to _pre_conv_shape # Deconvolution layer. Resulting image shape: (batch, 14, 14, 64) - self.conv1 = self.track_layer(tf.layers.Conv2DTranspose( - 64, 4, strides=2, activation=None, data_format=data_format)) + self.conv1 = tf.layers.Conv2DTranspose( + 64, 4, strides=2, activation=None, data_format=data_format) # Deconvolution layer. Resulting image shape: (batch, 28, 28, 1) - self.conv2 = self.track_layer(tf.layers.Conv2DTranspose( - 1, 2, strides=2, activation=tf.nn.sigmoid, data_format=data_format)) + self.conv2 = tf.layers.Conv2DTranspose( + 1, 2, strides=2, activation=tf.nn.sigmoid, data_format=data_format) def call(self, inputs): """Return a batch of generated images. @@ -168,7 +163,8 @@ def discriminator_loss(discriminator_real_outputs, discriminator_gen_outputs): """ loss_on_real = tf.losses.sigmoid_cross_entropy( - tf.ones_like(discriminator_real_outputs), discriminator_real_outputs, + tf.ones_like(discriminator_real_outputs), + discriminator_real_outputs, label_smoothing=0.25) loss_on_generated = tf.losses.sigmoid_cross_entropy( tf.zeros_like(discriminator_gen_outputs), discriminator_gen_outputs) @@ -198,9 +194,9 @@ def generator_loss(discriminator_gen_outputs): return loss -def train_one_epoch(generator, discriminator, - generator_optimizer, discriminator_optimizer, - dataset, log_interval, noise_dim): +def train_one_epoch(generator, discriminator, generator_optimizer, + discriminator_optimizer, dataset, step_counter, + log_interval, noise_dim): """Trains `generator` and `discriminator` models on `dataset`. Args: @@ -209,7 +205,8 @@ def train_one_epoch(generator, discriminator, generator_optimizer: Optimizer to use for generator. discriminator_optimizer: Optimizer to use for discriminator. dataset: Dataset of images to train on. - log_interval: How many global steps to wait between logging and collecting + step_counter: An integer variable, used to write summaries regularly. + log_interval: How many steps to wait between logging and collecting summaries. noise_dim: Dimension of noise vector to use. """ @@ -218,18 +215,23 @@ def train_one_epoch(generator, discriminator, total_discriminator_loss = 0.0 for (batch_index, images) in enumerate(tfe.Iterator(dataset)): with tf.device('/cpu:0'): - tf.assign_add(tf.train.get_global_step(), 1) + tf.assign_add(step_counter, 1) - with tf.contrib.summary.record_summaries_every_n_global_steps(log_interval): + with tf.contrib.summary.record_summaries_every_n_global_steps( + log_interval, global_step=step_counter): current_batch_size = images.shape[0] - noise = tf.random_uniform(shape=[current_batch_size, noise_dim], - minval=-1., maxval=1., seed=batch_index) + noise = tf.random_uniform( + shape=[current_batch_size, noise_dim], + minval=-1., + maxval=1., + seed=batch_index) with tfe.GradientTape(persistent=True) as g: generated_images = generator(noise) - tf.contrib.summary.image('generated_images', - tf.reshape(generated_images, [-1, 28, 28, 1]), - max_images=10) + tf.contrib.summary.image( + 'generated_images', + tf.reshape(generated_images, [-1, 28, 28, 1]), + max_images=10) discriminator_gen_outputs = discriminator(generated_images) discriminator_real_outputs = discriminator(images) @@ -244,18 +246,16 @@ def train_one_epoch(generator, discriminator, discriminator_grad = g.gradient(discriminator_loss_val, discriminator.variables) - with tf.variable_scope('generator'): - generator_optimizer.apply_gradients(zip(generator_grad, - generator.variables)) - with tf.variable_scope('discriminator'): - discriminator_optimizer.apply_gradients(zip(discriminator_grad, - discriminator.variables)) + generator_optimizer.apply_gradients( + zip(generator_grad, generator.variables)) + discriminator_optimizer.apply_gradients( + zip(discriminator_grad, discriminator.variables)) if log_interval and batch_index > 0 and batch_index % log_interval == 0: print('Batch #%d\tAverage Generator Loss: %.6f\t' - 'Average Discriminator Loss: %.6f' % ( - batch_index, total_generator_loss/batch_index, - total_discriminator_loss/batch_index)) + 'Average Discriminator Loss: %.6f' % + (batch_index, total_generator_loss / batch_index, + total_discriminator_loss / batch_index)) def main(_): @@ -266,18 +266,18 @@ def main(_): # Load the datasets data = input_data.read_data_sets(FLAGS.data_dir) - dataset = (tf.data.Dataset - .from_tensor_slices(data.train.images) - .shuffle(60000) - .batch(FLAGS.batch_size)) - - # Create the models and optimizers - generator = Generator(data_format) - discriminator = Discriminator(data_format) - with tf.variable_scope('generator'): - generator_optimizer = tf.train.AdamOptimizer(FLAGS.lr) - with tf.variable_scope('discriminator'): - discriminator_optimizer = tf.train.AdamOptimizer(FLAGS.lr) + dataset = ( + tf.data.Dataset.from_tensor_slices(data.train.images).shuffle(60000) + .batch(FLAGS.batch_size)) + + # Create the models and optimizers. + model_objects = { + 'generator': Generator(data_format), + 'discriminator': Discriminator(data_format), + 'generator_optimizer': tf.train.AdamOptimizer(FLAGS.lr), + 'discriminator_optimizer': tf.train.AdamOptimizer(FLAGS.lr), + 'step_counter': tf.train.get_or_create_global_step(), + } # Prepare summary writer and checkpoint info summary_writer = tf.contrib.summary.create_summary_file_writer( @@ -286,28 +286,22 @@ def main(_): latest_cpkt = tf.train.latest_checkpoint(FLAGS.checkpoint_dir) if latest_cpkt: print('Using latest checkpoint at ' + latest_cpkt) + checkpoint = tfe.Checkpoint(**model_objects) + # Restore variables on creation if a checkpoint exists. + checkpoint.restore(latest_cpkt) with tf.device(device): - for epoch in range(1, 101): - with tfe.restore_variables_on_create(latest_cpkt): - global_step = tf.train.get_or_create_global_step() - start = time.time() - with summary_writer.as_default(): - train_one_epoch(generator, discriminator, generator_optimizer, - discriminator_optimizer, - dataset, FLAGS.log_interval, FLAGS.noise) - end = time.time() - print('\nTrain time for epoch #%d (global step %d): %f' % ( - epoch, global_step.numpy(), end - start)) - - all_variables = ( - generator.variables - + discriminator.variables - + generator_optimizer.variables() - + discriminator_optimizer.variables() - + [global_step]) - tfe.Saver(all_variables).save( - checkpoint_prefix, global_step=global_step) + for _ in range(100): + start = time.time() + with summary_writer.as_default(): + train_one_epoch(dataset=dataset, log_interval=FLAGS.log_interval, + noise_dim=FLAGS.noise, **model_objects) + end = time.time() + checkpoint.save(checkpoint_prefix) + print('\nTrain time for epoch #%d (step %d): %f' % + (checkpoint.save_counter.numpy(), + checkpoint.step_counter.numpy(), + end - start)) if __name__ == '__main__': diff --git a/tensorflow/contrib/eager/python/examples/gan/mnist_test.py b/tensorflow/contrib/eager/python/examples/gan/mnist_test.py index 4a3ca8d82bc2619b05a734f6d2e58431c1a45995..bd35e50c1f434d167c5a8c5aa7d224912523ce28 100644 --- a/tensorflow/contrib/eager/python/examples/gan/mnist_test.py +++ b/tensorflow/contrib/eager/python/examples/gan/mnist_test.py @@ -62,7 +62,7 @@ class MnistEagerGanBenchmark(tf.test.Benchmark): for _ in range(measure_batches)] measure_dataset = tf.data.Dataset.from_tensor_slices(measure_images) - tf.train.get_or_create_global_step() + step_counter = tf.train.get_or_create_global_step() with tf.device(device()): # Create the models and optimizers generator = mnist.Generator(data_format()) @@ -78,13 +78,15 @@ class MnistEagerGanBenchmark(tf.test.Benchmark): # warm up mnist.train_one_epoch(generator, discriminator, generator_optimizer, discriminator_optimizer, - burn_dataset, log_interval=SUMMARY_INTERVAL, + burn_dataset, step_counter, + log_interval=SUMMARY_INTERVAL, noise_dim=NOISE_DIM) # measure start = time.time() mnist.train_one_epoch(generator, discriminator, generator_optimizer, discriminator_optimizer, - measure_dataset, log_interval=SUMMARY_INTERVAL, + measure_dataset, step_counter, + log_interval=SUMMARY_INTERVAL, noise_dim=NOISE_DIM) self._report('train', start, measure_batches, batch_size) diff --git a/tensorflow/contrib/eager/python/examples/linear_regression/BUILD b/tensorflow/contrib/eager/python/examples/linear_regression/BUILD index f86331af6f7928f0f86c888e22706c6e0a5978b2..2f6cfdf31e852d5d69a7a87980c9a441da504cf2 100644 --- a/tensorflow/contrib/eager/python/examples/linear_regression/BUILD +++ b/tensorflow/contrib/eager/python/examples/linear_regression/BUILD @@ -22,6 +22,7 @@ cuda_py_test( ":linear_regression", "//tensorflow:tensorflow_py", ], + tags = ["no_windows"], # TODO: needs investigation on Windows ) cuda_py_test( diff --git a/tensorflow/contrib/eager/python/examples/linear_regression/linear_regression.py b/tensorflow/contrib/eager/python/examples/linear_regression/linear_regression.py index 6ce4de6ee0bf50400eff339ac04e132252a2b53e..6ab847cb78a09ab0a38beefff56f87d8314c0713 100644 --- a/tensorflow/contrib/eager/python/examples/linear_regression/linear_regression.py +++ b/tensorflow/contrib/eager/python/examples/linear_regression/linear_regression.py @@ -33,23 +33,13 @@ import tensorflow as tf import tensorflow.contrib.eager as tfe -class LinearModel(tfe.Network): - """A TensorFlow linear regression model. - - Uses TensorFlow's eager execution. - - For those familiar with TensorFlow graphs, notice the absence of - `tf.Session`. The `forward()` method here immediately executes and - returns output values. The `loss()` method immediately compares the - output of `forward()` with the target and returns the MSE loss value. - The `fit()` performs gradient-descent training on the model's weights - and bias. - """ +class LinearModel(tf.keras.Model): + """A TensorFlow linear regression model.""" def __init__(self): """Constructs a LinearModel object.""" super(LinearModel, self).__init__() - self._hidden_layer = self.track_layer(tf.layers.Dense(1)) + self._hidden_layer = tf.layers.Dense(1) def call(self, xs): """Invoke the linear model. @@ -64,7 +54,7 @@ class LinearModel(tfe.Network): def mean_square_loss(model, xs, ys): - return tf.reduce_mean(tf.square(model(xs) - ys)) + return tf.reduce_mean(tf.square(tf.subtract(model(xs), ys))) def fit(model, dataset, optimizer, verbose=False, logdir=None): diff --git a/tensorflow/contrib/eager/python/examples/resnet50/resnet50.py b/tensorflow/contrib/eager/python/examples/resnet50/resnet50.py index 9982fdb07eefa665379e7be095f4f8017d92cf97..6b59413141f78fc85474850e109454ecdeb68cd3 100644 --- a/tensorflow/contrib/eager/python/examples/resnet50/resnet50.py +++ b/tensorflow/contrib/eager/python/examples/resnet50/resnet50.py @@ -27,10 +27,9 @@ from __future__ import print_function import functools import tensorflow as tf -import tensorflow.contrib.eager as tfe -class _IdentityBlock(tfe.Network): +class _IdentityBlock(tf.keras.Model): """_IdentityBlock is the block that has no conv layer at shortcut. Args: @@ -50,31 +49,24 @@ class _IdentityBlock(tfe.Network): bn_name_base = 'bn' + str(stage) + block + '_branch' bn_axis = 1 if data_format == 'channels_first' else 3 - self.conv2a = self.track_layer( - tf.layers.Conv2D( - filters1, (1, 1), - name=conv_name_base + '2a', - data_format=data_format)) - self.bn2a = self.track_layer( - tf.layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')) - - self.conv2b = self.track_layer( - tf.layers.Conv2D( - filters2, - kernel_size, - padding='same', - data_format=data_format, - name=conv_name_base + '2b')) - self.bn2b = self.track_layer( - tf.layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')) - - self.conv2c = self.track_layer( - tf.layers.Conv2D( - filters3, (1, 1), - name=conv_name_base + '2c', - data_format=data_format)) - self.bn2c = self.track_layer( - tf.layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')) + self.conv2a = tf.layers.Conv2D( + filters1, (1, 1), name=conv_name_base + '2a', data_format=data_format) + self.bn2a = tf.layers.BatchNormalization( + axis=bn_axis, name=bn_name_base + '2a') + + self.conv2b = tf.layers.Conv2D( + filters2, + kernel_size, + padding='same', + data_format=data_format, + name=conv_name_base + '2b') + self.bn2b = tf.layers.BatchNormalization( + axis=bn_axis, name=bn_name_base + '2b') + + self.conv2c = tf.layers.Conv2D( + filters3, (1, 1), name=conv_name_base + '2c', data_format=data_format) + self.bn2c = tf.layers.BatchNormalization( + axis=bn_axis, name=bn_name_base + '2c') def call(self, input_tensor, training=False): x = self.conv2a(input_tensor) @@ -92,7 +84,7 @@ class _IdentityBlock(tfe.Network): return tf.nn.relu(x) -class _ConvBlock(tfe.Network): +class _ConvBlock(tf.keras.Model): """_ConvBlock is the block that has a conv layer at shortcut. Args: @@ -121,41 +113,35 @@ class _ConvBlock(tfe.Network): bn_name_base = 'bn' + str(stage) + block + '_branch' bn_axis = 1 if data_format == 'channels_first' else 3 - self.conv2a = self.track_layer( - tf.layers.Conv2D( - filters1, (1, 1), - strides=strides, - name=conv_name_base + '2a', - data_format=data_format)) - self.bn2a = self.track_layer( - tf.layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')) - - self.conv2b = self.track_layer( - tf.layers.Conv2D( - filters2, - kernel_size, - padding='same', - name=conv_name_base + '2b', - data_format=data_format)) - self.bn2b = self.track_layer( - tf.layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')) - - self.conv2c = self.track_layer( - tf.layers.Conv2D( - filters3, (1, 1), - name=conv_name_base + '2c', - data_format=data_format)) - self.bn2c = self.track_layer( - tf.layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')) - - self.conv_shortcut = self.track_layer( - tf.layers.Conv2D( - filters3, (1, 1), - strides=strides, - name=conv_name_base + '1', - data_format=data_format)) - self.bn_shortcut = self.track_layer( - tf.layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '1')) + self.conv2a = tf.layers.Conv2D( + filters1, (1, 1), + strides=strides, + name=conv_name_base + '2a', + data_format=data_format) + self.bn2a = tf.layers.BatchNormalization( + axis=bn_axis, name=bn_name_base + '2a') + + self.conv2b = tf.layers.Conv2D( + filters2, + kernel_size, + padding='same', + name=conv_name_base + '2b', + data_format=data_format) + self.bn2b = tf.layers.BatchNormalization( + axis=bn_axis, name=bn_name_base + '2b') + + self.conv2c = tf.layers.Conv2D( + filters3, (1, 1), name=conv_name_base + '2c', data_format=data_format) + self.bn2c = tf.layers.BatchNormalization( + axis=bn_axis, name=bn_name_base + '2c') + + self.conv_shortcut = tf.layers.Conv2D( + filters3, (1, 1), + strides=strides, + name=conv_name_base + '1', + data_format=data_format) + self.bn_shortcut = tf.layers.BatchNormalization( + axis=bn_axis, name=bn_name_base + '1') def call(self, input_tensor, training=False): x = self.conv2a(input_tensor) @@ -176,7 +162,8 @@ class _ConvBlock(tfe.Network): return tf.nn.relu(x) -class ResNet50(tfe.Network): +# pylint: disable=not-callable +class ResNet50(tf.keras.Model): """Instantiates the ResNet50 architecture. Args: @@ -220,32 +207,28 @@ class ResNet50(tfe.Network): self.include_top = include_top def conv_block(filters, stage, block, strides=(2, 2)): - l = _ConvBlock( + return _ConvBlock( 3, filters, stage=stage, block=block, data_format=data_format, strides=strides) - return self.track_layer(l) def id_block(filters, stage, block): - l = _IdentityBlock( + return _IdentityBlock( 3, filters, stage=stage, block=block, data_format=data_format) - return self.track_layer(l) - - self.conv1 = self.track_layer( - tf.layers.Conv2D( - 64, (7, 7), - strides=(2, 2), - data_format=data_format, - padding='same', - name='conv1')) + + self.conv1 = tf.layers.Conv2D( + 64, (7, 7), + strides=(2, 2), + data_format=data_format, + padding='same', + name='conv1') bn_axis = 1 if data_format == 'channels_first' else 3 - self.bn_conv1 = self.track_layer( - tf.layers.BatchNormalization(axis=bn_axis, name='bn_conv1')) - self.max_pool = self.track_layer( - tf.layers.MaxPooling2D((3, 3), strides=(2, 2), data_format=data_format)) + self.bn_conv1 = tf.layers.BatchNormalization(axis=bn_axis, name='bn_conv1') + self.max_pool = tf.layers.MaxPooling2D( + (3, 3), strides=(2, 2), data_format=data_format) self.l2a = conv_block([64, 64, 256], stage=2, block='a', strides=(1, 1)) self.l2b = id_block([64, 64, 256], stage=2, block='b') @@ -267,13 +250,11 @@ class ResNet50(tfe.Network): self.l5b = id_block([512, 512, 2048], stage=5, block='b') self.l5c = id_block([512, 512, 2048], stage=5, block='c') - self.avg_pool = self.track_layer( - tf.layers.AveragePooling2D( - (7, 7), strides=(7, 7), data_format=data_format)) + self.avg_pool = tf.layers.AveragePooling2D( + (7, 7), strides=(7, 7), data_format=data_format) if self.include_top: - self.fc1000 = self.track_layer( - tf.layers.Dense(classes, name='fc1000')) + self.fc1000 = tf.layers.Dense(classes, name='fc1000') else: reduction_indices = [1, 2] if data_format == 'channels_last' else [2, 3] reduction_indices = tf.constant(reduction_indices) @@ -288,7 +269,7 @@ class ResNet50(tfe.Network): else: self.global_pooling = None - def call(self, input_tensor, training=False): + def call(self, input_tensor, training): x = self.conv1(input_tensor) x = self.bn_conv1(x, training=training) x = tf.nn.relu(x) diff --git a/tensorflow/contrib/eager/python/examples/resnet50/resnet50_graph_test.py b/tensorflow/contrib/eager/python/examples/resnet50/resnet50_graph_test.py index 23317886e712323f4b520000e0fd372734fc53a1..551c76b0df71c88919df9cd6d81b4176b23b0ba3 100644 --- a/tensorflow/contrib/eager/python/examples/resnet50/resnet50_graph_test.py +++ b/tensorflow/contrib/eager/python/examples/resnet50/resnet50_graph_test.py @@ -55,7 +55,7 @@ class ResNet50GraphTest(tf.test.TestCase): with tf.Graph().as_default(): images = tf.placeholder(tf.float32, image_shape(None)) model = resnet50.ResNet50(data_format()) - predictions = model(images) + predictions = model(images, training=False) init = tf.global_variables_initializer() @@ -114,7 +114,7 @@ class ResNet50Benchmarks(tf.test.Benchmark): with tf.Graph().as_default(): images = tf.placeholder(tf.float32, image_shape(None)) model = resnet50.ResNet50(data_format()) - predictions = model(images) + predictions = model(images, training=False) init = tf.global_variables_initializer() diff --git a/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py b/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py index 0ff8746884c288f824f5f22ab4c550370d0e0302..65dcc53aab39670cae10846b6996c17d7b4c5ba8 100644 --- a/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py +++ b/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py @@ -71,7 +71,7 @@ class ResNet50Test(tf.test.TestCase): model.call = tfe.defun(model.call) with tf.device(device): images, _ = random_batch(2) - output = model(images) + output = model(images, training=False) self.assertEqual((2, 1000), output.shape) def test_apply(self): @@ -85,7 +85,7 @@ class ResNet50Test(tf.test.TestCase): model = resnet50.ResNet50(data_format, include_top=False) with tf.device(device): images, _ = random_batch(2) - output = model(images) + output = model(images, training=False) output_shape = ((2, 2048, 1, 1) if data_format == 'channels_first' else (2, 1, 1, 2048)) self.assertEqual(output_shape, output.shape) @@ -95,7 +95,7 @@ class ResNet50Test(tf.test.TestCase): model = resnet50.ResNet50(data_format, include_top=False, pooling='avg') with tf.device(device): images, _ = random_batch(2) - output = model(images) + output = model(images, training=False) self.assertEqual((2, 2048), output.shape) def test_train(self): @@ -194,11 +194,11 @@ class ResNet50Benchmarks(tf.test.Benchmark): with tf.device(device): images, _ = random_batch(batch_size) for _ in xrange(num_burn): - model(images).cpu() + model(images, training=False).cpu() gc.collect() start = time.time() for _ in xrange(num_iters): - model(images).cpu() + model(images, training=False).cpu() self._report(label, start, num_iters, device, batch_size, data_format) def benchmark_eager_apply(self): diff --git a/tensorflow/contrib/eager/python/examples/rnn_colorbot/rnn_colorbot.py b/tensorflow/contrib/eager/python/examples/rnn_colorbot/rnn_colorbot.py index aa87b94e7b0876e65405f6bcb2d6aabde36582bf..29f02324544ede172500f799cd84068984d7d87b 100644 --- a/tensorflow/contrib/eager/python/examples/rnn_colorbot/rnn_colorbot.py +++ b/tensorflow/contrib/eager/python/examples/rnn_colorbot/rnn_colorbot.py @@ -109,7 +109,7 @@ def load_dataset(data_dir, url, batch_size): # pylint: disable=not-callable -class RNNColorbot(tfe.Network): +class RNNColorbot(tf.keras.Model): """Multi-layer (LSTM) RNN that regresses on real-valued vector labels. """ @@ -127,23 +127,20 @@ class RNNColorbot(tfe.Network): self.label_dimension = label_dimension self.keep_prob = keep_prob - # Note the calls to `track_layer` below; these calls register the layers as - # network components that house trainable variables. - self.cells = [ - self.track_layer(tf.nn.rnn_cell.BasicLSTMCell(size)) - for size in rnn_cell_sizes - ] - self.relu = self.track_layer( - tf.layers.Dense(label_dimension, activation=tf.nn.relu, name="relu")) + self.cells = self._add_cells( + [tf.nn.rnn_cell.BasicLSTMCell(size) for size in rnn_cell_sizes]) + self.relu = tf.layers.Dense( + label_dimension, activation=tf.nn.relu, name="relu") - def call(self, chars, sequence_length, training=False): + def call(self, inputs, training=False): """Implements the RNN logic and prediction generation. Args: - chars: a Tensor of dimension [batch_size, time_steps, 256] holding a - batch of one-hot encoded color names - sequence_length: a Tensor of dimension [batch_size] holding the length - of each character sequence (i.e., color name) + inputs: A tuple (chars, sequence_length), where chars is a batch of + one-hot encoded color names represented as a Tensor with dimensions + [batch_size, time_steps, 256] and sequence_length holds the length + of each character sequence (color name) as a Tensor with dimension + [batch_size]. training: whether the invocation is happening during training Returns: @@ -151,6 +148,7 @@ class RNNColorbot(tfe.Network): passing chars through a multi-layer RNN and applying a ReLU to the final hidden state. """ + (chars, sequence_length) = inputs # Transpose the first and second dimensions so that chars is of shape # [time_steps, batch_size, dimension]. chars = tf.transpose(chars, [1, 0, 2]) @@ -181,6 +179,14 @@ class RNNColorbot(tfe.Network): hidden_states = tf.gather_nd(chars, indices) return self.relu(hidden_states) + def _add_cells(self, cells): + # "Magic" required for keras.Model classes to track all the variables in + # a list of tf.layers.Layer objects. + # TODO(ashankar): Figure out API so user code doesn't have to do this. + for i, c in enumerate(cells): + setattr(self, "cell-%d" % i, c) + return cells + def loss(labels, predictions): """Computes mean squared loss.""" @@ -191,7 +197,7 @@ def test(model, eval_data): """Computes the average loss on eval_data, which should be a Dataset.""" avg_loss = tfe.metrics.Mean("loss") for (labels, chars, sequence_length) in tfe.Iterator(eval_data): - predictions = model(chars, sequence_length, training=False) + predictions = model((chars, sequence_length), training=False) avg_loss(loss(labels, predictions)) print("eval/loss: %.6f\n" % avg_loss.result()) with tf.contrib.summary.always_record_summaries(): @@ -204,7 +210,7 @@ def train_one_epoch(model, optimizer, train_data, log_interval=10): tf.train.get_or_create_global_step() def model_loss(labels, chars, sequence_length): - predictions = model(chars, sequence_length, training=True) + predictions = model((chars, sequence_length), training=True) loss_value = loss(labels, predictions) tf.contrib.summary.scalar("loss", loss_value) return loss_value @@ -277,7 +283,7 @@ def main(_): (chars, length) = (tf.identity(chars), tf.identity(length)) chars = tf.expand_dims(chars, 0) length = tf.expand_dims(length, 0) - preds = tf.unstack(model(chars, length, training=False)[0]) + preds = tf.unstack(model((chars, length), training=False)[0]) # Predictions cannot be negative, as they are generated by a ReLU layer; # they may, however, be greater than 1. diff --git a/tensorflow/contrib/eager/python/examples/rnn_ptb/rnn_ptb.py b/tensorflow/contrib/eager/python/examples/rnn_ptb/rnn_ptb.py index 5c5c59c87744f4ffa6db90e5d8d3aa3bc8132756..69cd16d12c32c8c7c4744d8f0b4b1feedf946aa1 100644 --- a/tensorflow/contrib/eager/python/examples/rnn_ptb/rnn_ptb.py +++ b/tensorflow/contrib/eager/python/examples/rnn_ptb/rnn_ptb.py @@ -39,21 +39,23 @@ from tensorflow.contrib.cudnn_rnn.python.layers import cudnn_rnn from tensorflow.contrib.eager.python import tfe -class RNN(tfe.Network): +class RNN(tf.keras.Model): """A static RNN. - Similar to tf.nn.static_rnn, implemented as a tf.layer.Layer. + Similar to tf.nn.static_rnn, implemented as a class. """ def __init__(self, hidden_dim, num_layers, keep_ratio): super(RNN, self).__init__() self.keep_ratio = keep_ratio - for _ in range(num_layers): - self.track_layer(tf.nn.rnn_cell.BasicLSTMCell(num_units=hidden_dim)) + self.cells = self._add_cells([ + tf.nn.rnn_cell.BasicLSTMCell(num_units=hidden_dim) + for _ in range(num_layers) + ]) def call(self, input_seq, training): batch_size = int(input_seq.shape[1]) - for c in self.layers: + for c in self.cells: state = c.zero_state(batch_size, tf.float32) outputs = [] input_seq = tf.unstack(input_seq, num=int(input_seq.shape[0]), axis=0) @@ -64,7 +66,19 @@ class RNN(tfe.Network): input_seq = tf.stack(outputs, axis=0) if training: input_seq = tf.nn.dropout(input_seq, self.keep_ratio) - return input_seq, None + # Returning a list instead of a single tensor so that the line: + # y = self.rnn(y, ...)[0] + # in PTBModel.call works for both this RNN and CudnnLSTM (which returns a + # tuple (output, output_states). + return [input_seq] + + def _add_cells(self, cells): + # "Magic" required for keras.Model classes to track all the variables in + # a list of tf.layers.Layer objects. + # TODO(ashankar): Figure out API so user code doesn't have to do this. + for i, c in enumerate(cells): + setattr(self, "cell-%d" % i, c) + return cells class Embedding(tf.layers.Layer): @@ -87,7 +101,8 @@ class Embedding(tf.layers.Layer): return tf.nn.embedding_lookup(self.embedding, x) -class PTBModel(tfe.Network): +# pylint: disable=not-callable +class PTBModel(tf.keras.Model): """LSTM for word language modeling. Model described in: @@ -109,19 +124,16 @@ class PTBModel(tfe.Network): self.keep_ratio = 1 - dropout_ratio self.use_cudnn_rnn = use_cudnn_rnn - self.embedding = self.track_layer(Embedding(vocab_size, embedding_dim)) + self.embedding = Embedding(vocab_size, embedding_dim) if self.use_cudnn_rnn: self.rnn = cudnn_rnn.CudnnLSTM( num_layers, hidden_dim, dropout=dropout_ratio) else: self.rnn = RNN(hidden_dim, num_layers, self.keep_ratio) - self.track_layer(self.rnn) - self.linear = self.track_layer( - tf.layers.Dense( - vocab_size, - kernel_initializer=tf.random_uniform_initializer(-0.1, 0.1))) + self.linear = tf.layers.Dense( + vocab_size, kernel_initializer=tf.random_uniform_initializer(-0.1, 0.1)) self._output_shape = [-1, embedding_dim] def call(self, input_seq, training): @@ -136,7 +148,7 @@ class PTBModel(tfe.Network): y = self.embedding(input_seq) if training: y = tf.nn.dropout(y, self.keep_ratio) - y, _ = self.rnn(y, training=training) + y = self.rnn(y, training=training)[0] return self.linear(tf.reshape(y, self._output_shape)) @@ -148,7 +160,7 @@ def clip_gradients(grads_and_vars, clip_ratio): def loss_fn(model, inputs, targets, training): labels = tf.reshape(targets, [-1]) - outputs = model(inputs, training) + outputs = model(inputs, training=training) return tf.reduce_mean( tf.nn.sparse_softmax_cross_entropy_with_logits( labels=labels, logits=outputs)) diff --git a/tensorflow/contrib/eager/python/examples/spinn/BUILD b/tensorflow/contrib/eager/python/examples/spinn/BUILD index a1f8a759e2a556bc219f0aa13942f293c4f34cfa..98d01ad1d5a70788d2d4cb07031a8d76a6bf628f 100644 --- a/tensorflow/contrib/eager/python/examples/spinn/BUILD +++ b/tensorflow/contrib/eager/python/examples/spinn/BUILD @@ -38,5 +38,8 @@ cuda_py_test( "//tensorflow/python:client_testlib", "//tensorflow/python:framework_test_lib", ], - tags = ["no_pip"], # because spinn.py is under third_party/. + tags = [ + "no_cuda_on_cpu_tap", + "no_pip", # because spinn.py is under third_party/. + ], ) diff --git a/tensorflow/contrib/eager/python/g3doc/guide.md b/tensorflow/contrib/eager/python/g3doc/guide.md index ebb05051f27841f1cd3d21b6218986e774ed4c9f..b73dc17e5f9cb15a51426f85e966a49604145f1d 100644 --- a/tensorflow/contrib/eager/python/g3doc/guide.md +++ b/tensorflow/contrib/eager/python/g3doc/guide.md @@ -574,49 +574,45 @@ repository](https://github.com/tensorflow/models/tree/master/official/mnist/mnis ### Checkpointing trained variables -TensorFlow Variables (`tfe.Variable`) provides a way to represent shared, -persistent state of your model. The `tfe.Saver` class (which is a thin wrapper -over the -[`tf.train.Saver`](https://www.tensorflow.org/api_docs/python/tf/train/Saver) -class) provides a means to save and restore variables to and from _checkpoints_. +TensorFlow Variables (`tfe.Variable`) provide a way to represent shared, +persistent state of your model. The `tfe.Checkpoint` class provides a means to +save and restore variables to and from _checkpoints_. For example: ```python # Create variables. -x = tfe.Variable(10., name='x') -y = tfe.Variable(5., name='y') +x = tfe.Variable(10.) +y = tfe.Variable(5.) -# Create a Saver. -saver = tfe.Saver([x, y]) +# Indicate that the variables should be saved as "x" and "y". +checkpoint = tfe.Checkpoint(x=x, y=y) # Assign new values to the variables and save. x.assign(2.) -saver.save('/tmp/ckpt') +checkpoint.save('/tmp/ckpt') # Change the variable after saving. x.assign(11.) assert 16. == (x + y).numpy() # 11 + 5 # Restore the values in the checkpoint. -saver.restore('/tmp/ckpt') +checkpoint.restore('/tmp/ckpt-1') assert 7. == (x + y).numpy() # 2 + 5 ``` -### `tfe.Network` +### `tf.keras.Model` You may often want to organize your models using classes, like the `MNISTModel` -class described above. We recommend inheriting from the `tfe.Network` class as -it provides conveniences like keeping track of all model variables and methods -to save and restore from checkpoints. +class described above. We recommend inheriting from the `tf.keras.Model` class +as it provides conveniences like keeping track of all model variables. -Sub-classes of `tfe.Network` may register `Layer`s (like classes in -[`tf.layers`](https://www.tensorflow.org/api_docs/python/tf/layers), -or [Keras -layers](https://www.tensorflow.org/api_docs/python/tf/keras/layers)) -using a call to `self.track_layer()` and define the computation in an -implementation of `call()`. +Sub-classes of `tf.keras.Model` may register `Layer`s (like classes in +[`tf.layers`](https://www.tensorflow.org/api_docs/python/tf/layers), or [Keras +layers](https://www.tensorflow.org/api_docs/python/tf/keras/layers)) by +assigning them to attributes (`self.name = layer_object`) and define the +computation in an implementation of `call()`. Note that `tf.layers.Layer` objects (like `tf.layers.Dense`) create variables lazily, when the first input is encountered. @@ -624,12 +620,11 @@ lazily, when the first input is encountered. For example, consider the following two-layer neural network: ```python -class TwoLayerNet(tfe.Network): +class TwoLayerNet(tf.keras.Model): def __init__(self): super(TwoLayerNet, self).__init__() - self.layer1 = self.track_layer( - tf.layers.Dense(2, activation=tf.nn.relu, use_bias=False)) - self.layer2 = self.track_layer(tf.layers.Dense(3, use_bias=False)) + self.layer1 = tf.layers.Dense(2, activation=tf.nn.relu, use_bias=False) + self.layer2 = tf.layers.Dense(3, use_bias=False) def call(self, x): return self.layer2(self.layer1(x)) @@ -653,15 +648,16 @@ assert [1, 2] == net.variables[0].shape.as_list() # weights of layer1. assert [2, 3] == net.variables[1].shape.as_list() # weights of layer2. ``` -The `tfe.Network` class is itself a sub-class of `tf.layers.Layer`. This allows -instances of `tfe.Network` to be embedded in other networks. For example: +The `tf.keras.Model` class is itself a sub-class of `tf.layers.Layer`. This +allows instances of `tf.keras.Model` to be embedded in other models. For +example: ```python -class ThreeLayerNet(tfe.Network): +class ThreeLayerNet(tf.keras.Model): def __init__(self): super(ThreeLayerNet, self).__init__() - self.a = self.track_layer(TwoLayerNet()) - self.b = self.track_layer(tf.layers.Dense(4, use_bias=False)) + self.a = TwoLayerNet() + self.b = tf.layers.Dense(4, use_bias=False) def call(self, x): return self.b(self.a(x)) @@ -678,9 +674,8 @@ assert [3, 4] == net.variables[2].shape.as_list() See more examples in [`tensorflow/contrib/eager/python/examples`](https://www.tensorflow.org/code/tensorflow/contrib/eager/python/examples). -`tfe.Saver` in combination with `tfe.restore_variables_on_create` provides a -convenient way to save and load checkpoints without changing the program once -the checkpoint has been created. For example, we can set an objective for the +`tfe.Checkpoint` provides a convenient way to save and load training +checkpoints. Let's define something simple to train. We set an objective for the output of our network, choose an optimizer, and a location for the checkpoint: ```python @@ -691,30 +686,27 @@ checkpoint_prefix = os.path.join(checkpoint_directory, 'ckpt') net = ThreeLayerNet() ``` -Note that variables have not been created yet. We want them to be restored from -a checkpoint, if one exists, so we create them inside a -`tfe.restore_variables_on_create` context manager. Then our training loop is the -same whether starting training or resuming from a previous checkpoint: +We group them in a `tfe.Checkpoint` and request that it be restored. This +ensures that variables created by these objects are restored before their values +are used. Our training loop is the same whether starting training or resuming +from a previous checkpoint: ```python -with tfe.restore_variables_on_create( - tf.train.latest_checkpoint(checkpoint_directory)): - global_step = tf.train.get_or_create_global_step() - for _ in range(100): - loss_fn = lambda: tf.norm(net(inp) - objective) - optimizer.minimize(loss_fn, global_step=global_step) - if tf.equal(global_step % 20, 0): - print("Step %d, output %s" % (global_step.numpy(), - net(inp).numpy())) - all_variables = ( - net.variables - + optimizer.variables() - + [global_step]) - # Save the checkpoint. - tfe.Saver(all_variables).save(checkpoint_prefix, global_step=global_step) -``` - -The first time it runs, `Network` variables are initialized randomly. Then the +global_step = tf.train.get_or_create_global_step() +checkpoint = tfe.Checkpoint( + global_step=global_step, optimizer=optimizer, network=net) +checkpoint.restore(tf.train.latest_checkpoint(checkpoint_directory)) +for _ in range(100): + loss_fn = lambda: tf.norm(net(inp) - objective) + optimizer.minimize(loss_fn, global_step=global_step) + if tf.equal(global_step % 20, 0): + print("Step %d, output %s" % (global_step.numpy(), + net(inp).numpy())) + # Save the checkpoint. + checkpoint.save(checkpoint_prefix) +``` + +The first time it runs, `Model` variables are initialized randomly. Then the output is trained to match the objective we've set: ``` diff --git a/tensorflow/contrib/eager/python/metrics_impl.py b/tensorflow/contrib/eager/python/metrics_impl.py index ea8dbf2b46ea4bd0e33645ae3c590c4dd13f7a52..1490c2ccacd55156bcc1cf8c07d9941336e18e1b 100644 --- a/tensorflow/contrib/eager/python/metrics_impl.py +++ b/tensorflow/contrib/eager/python/metrics_impl.py @@ -30,12 +30,12 @@ from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import variable_scope - +from tensorflow.python.training import checkpointable _to_replace = re.compile("[^A-Za-z0-9.]") -class Metric(object): +class Metric(checkpointable.CheckpointableBase): """A metric holds state for aggregating statistics over an evaluation run. Example use with eager execution: @@ -93,11 +93,12 @@ class Metric(object): `aggregate()`, it is for use by TensorFlow infrastructure. """ - def __init__(self, name=None): + def __init__(self, name=None, use_global_variables=False): self._built = False self._vars = [] self._initial_values = {} self._updates = [] + self._use_global_variables = use_global_variables name = name or self.__class__.__name__ # Replace things like spaces in name to create a valid scope name. scope_name = _to_replace.sub("_", name) @@ -108,13 +109,13 @@ class Metric(object): pos = scope.name.rfind(scope_name) self._name = name + scope.name[pos + len(scope_name):] self._scope = scope - if context.in_graph_mode(): + if context.executing_eagerly(): + self._construction_scope = context.eager_mode + else: # We make self.call() into a graph callable here, so that we can # return a single op that performs all of the variable updates. self._construction_scope = ops.get_default_graph().as_default self.call = function.defun(self.call) - else: - self._construction_scope = context.eager_mode # ---- API for users ---- def __call__(self, *args, **kwargs): @@ -155,10 +156,11 @@ class Metric(object): initialization. Under eager execution, the variables are reset to their initial values as a side effect and this function returns None. """ - if context.in_graph_mode(): + if context.executing_eagerly(): + for v in self._vars: + v.assign(self._initial_values[v]) + else: return control_flow_ops.group([v.initializer for v in self._vars]) - for v in self._vars: - v.assign(self._initial_values[v]) # ---- To be implemented by descendants --- def build(self, *args, **kwargs): @@ -200,10 +202,10 @@ class Metric(object): def value(self): """In graph mode returns the result Tensor while in eager the callable.""" - if context.in_graph_mode(): - return self.result() - else: + if context.executing_eagerly(): return self.result + else: + return self.result() # We can support two different strategies of for doing data-parallel # distributed metric computations: @@ -245,19 +247,31 @@ class Metric(object): """***Only for use by descendants of Metric***.""" if self._built: raise RuntimeError("Can't call add_variable() except in build().") - collections = None if context.in_eager_mode() else [ - ops.GraphKeys.LOCAL_VARIABLES, ops.GraphKeys.METRIC_VARIABLES - ] - v = variable_scope.get_variable( - name, - shape, - dtype, - initializer, + if context.executing_eagerly(): + collections = None + else: + if self._use_global_variables: + collections = [ops.GraphKeys.GLOBAL_VARIABLES] + else: + collections = [ops.GraphKeys.LOCAL_VARIABLES] + collections += [ops.GraphKeys.METRIC_VARIABLES] + # Variables are Checkpointable dependencies of Metrics regardless of the + # global/local distinction. Users can avoid saving variables by not adding a + # dependency on the Metric. + v = self._add_variable_with_custom_getter( + name=name, + shape=shape, + dtype=dtype, + initializer=initializer, trainable=False, collections=collections, - use_resource=True) + use_resource=True, + getter=variable_scope.get_variable, + # Raise duplicate variable exceptions from get_variable rather than + # Checkpointable. + overwrite=True) self._vars.append(v) - if context.in_eager_mode(): + if context.executing_eagerly(): self._initial_values[v] = v.value() return v @@ -267,8 +281,10 @@ class Mean(Metric): # TODO(josh11b): Maybe have a dtype argument that defaults to tf.float64? # Or defaults to type of the input if it is tf.float32, else tf.float64? - def __init__(self, name=None, dtype=dtypes.float64): - super(Mean, self).__init__(name=name) + def __init__(self, name=None, dtype=dtypes.float64, + use_global_variables=False): + super(Mean, self).__init__(name=name, + use_global_variables=use_global_variables) self.dtype = dtype def build(self, *args, **kwargs): diff --git a/tensorflow/contrib/eager/python/metrics_test.py b/tensorflow/contrib/eager/python/metrics_test.py index a9ecaa3f8bced3043ea0eb0ac3aa8bfa65e9e1ff..6b5450ba89bdfa6e0195f488b75f596b58c463d5 100644 --- a/tensorflow/contrib/eager/python/metrics_test.py +++ b/tensorflow/contrib/eager/python/metrics_test.py @@ -18,8 +18,10 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import os import tempfile +from tensorflow.contrib.eager.python import checkpointable_utils from tensorflow.contrib.eager.python import metrics from tensorflow.contrib.summary import summary_ops from tensorflow.contrib.summary import summary_test_util @@ -50,6 +52,19 @@ class MetricsTest(test.TestCase): self.assertEqual( set(m.variables), set(ops.get_collection(ops.GraphKeys.LOCAL_VARIABLES))) + self.assertEqual(ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES), []) + self.assertEqual( + set(m.variables), + set(ops.get_collection(ops.GraphKeys.METRIC_VARIABLES))) + + def testUseGlobalVariablesCollections(self): + with context.graph_mode(), ops.Graph().as_default(): + m = metrics.Mean(use_global_variables=True) + m(1000) + self.assertEqual( + set(m.variables), + set(ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES))) + self.assertEqual(ops.get_collection(ops.GraphKeys.LOCAL_VARIABLES), []) self.assertEqual( set(m.variables), set(ops.get_collection(ops.GraphKeys.METRIC_VARIABLES))) @@ -193,6 +208,31 @@ class MetricsTest(test.TestCase): self.assertAllEqual(m2.result().eval(), 2.0) self.assertAllEqual(m1.result().eval(), 1.0) + @test_util.run_in_graph_and_eager_modes() + def testSaveRestore(self): + checkpoint_directory = self.get_temp_dir() + checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") + mean = metrics.Mean() + checkpoint = checkpointable_utils.Checkpoint(mean=mean) + mean.build() + mean._built = True + self.evaluate(mean.init_variables()) + self.evaluate(mean(100.)) + self.evaluate(mean(200.)) + save_path = checkpoint.save(checkpoint_prefix) + self.evaluate(mean(1000.)) + checkpoint.restore(save_path).assert_consumed().run_restore_ops() + self.evaluate(mean(300.)) + self.assertAllEqual(200., self.evaluate(mean.value())) + + restore_mean = metrics.Mean() + restore_checkpoint = checkpointable_utils.Checkpoint(mean=restore_mean) + status = restore_checkpoint.restore(save_path) + restore_update = restore_mean(300.) + status.assert_consumed().run_restore_ops() + self.evaluate(restore_update) + self.assertAllEqual(200., self.evaluate(restore_mean.value())) + self.assertEqual(3, self.evaluate(restore_mean.denom)) if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/eager/python/network.py b/tensorflow/contrib/eager/python/network.py index e3c13cbd2e8ccd2ab79da74e0e97905c6ed5c02d..4c937716e8df7c8cda26d6431885ce33346b77fb 100644 --- a/tensorflow/contrib/eager/python/network.py +++ b/tensorflow/contrib/eager/python/network.py @@ -639,7 +639,7 @@ def _make_custom_getter_for_deferred_restorations(): # Mark as already restored from this checkpoint. delayed_restoration.checkpointed_variables_to_restore[ checkpoint_name] = None - if context.in_graph_mode(): + if not context.executing_eagerly(): delayed_restoration.session.run(variable.initializer) if found_value: # Error checking should run even if we've already restored a value. @@ -772,7 +772,7 @@ def save_network_checkpoint( variable_map[mapped_name]._shared_name, variable._shared_name, network.scope_name)) - if context.in_eager_mode(): + if context.executing_eagerly(): sess = None else: sess = ops.get_default_session() @@ -853,7 +853,7 @@ def _restore_existing_variables(network, save_path, map_func, user_map_func): network_name=network.name, network_scope_name=network.scope_name)) if existing_variables_by_checkpoint_name: - if context.in_eager_mode(): + if context.executing_eagerly(): sess = None else: sess = ops.get_default_session() @@ -880,7 +880,7 @@ def _set_restore_on_create(network, save_path, map_func, user_map_func, # _DeferredRestoration objects once a Network has been built (so that # restoring in a loop does not take increasing amounts of memory). if checkpointed_variables_to_restore: - if context.in_eager_mode(): + if context.executing_eagerly(): sess = None else: sess = ops.get_default_session() diff --git a/tensorflow/contrib/eager/python/saver.py b/tensorflow/contrib/eager/python/saver.py index 62421849c766a1124c726812428985c913c653a3..fdaca90fd13576e6ca8a3408aaf528dbc2384b0c 100644 --- a/tensorflow/contrib/eager/python/saver.py +++ b/tensorflow/contrib/eager/python/saver.py @@ -73,7 +73,7 @@ def restore_variables_on_create(save_path, map_func=None): NotFoundError: If the variable is not found in checkpoint. ValueError: If not used in eager mode or map_func is not callable. """ - if context.in_graph_mode(): + if not context.executing_eagerly(): raise ValueError( "Currently, restore_variables_on_create can only be used with " "eager execution enabled.") @@ -131,7 +131,7 @@ class Saver(object): Raises: RuntimeError: if invoked when eager execution has not been enabled. """ - if context.in_graph_mode(): + if not context.executing_eagerly(): raise RuntimeError("tfe.Saver can only be used when eager " "execution is enabled. Use tf.train.Saver when " "building graphs.") diff --git a/tensorflow/contrib/eager/python/tfe.py b/tensorflow/contrib/eager/python/tfe.py index d32bebf90c1e768d1efec26b3b78bf1a522a8f00..5aabc9aae868021284e83a4c4d80d65c2ee63fca 100644 --- a/tensorflow/contrib/eager/python/tfe.py +++ b/tensorflow/contrib/eager/python/tfe.py @@ -56,8 +56,12 @@ To use, at program startup, call `tfe.enable_eager_execution()`. @@save_network_checkpoint @@restore_network_checkpoint +@@Checkpoint +@@Checkpointable +@@CheckpointableSaver + +@@executing_eagerly @@in_eager_mode -@@in_graph_mode @@run_test_in_graph_and_eager_modes @@ -74,6 +78,8 @@ from __future__ import print_function # pylint:disable=g-bad-import-order,g-import-not-at-top,unused-import # from tensorflow.contrib.eager.python import metrics +from tensorflow.contrib.eager.python.checkpointable_utils import CheckpointableSaver +from tensorflow.contrib.eager.python.checkpointable_utils import Checkpoint from tensorflow.contrib.eager.python.datasets import Iterator from tensorflow.contrib.eager.python.network import Network from tensorflow.contrib.eager.python.network import Sequential @@ -87,11 +93,9 @@ from tensorflow.python.eager import function from tensorflow.python.eager.context import DEVICE_PLACEMENT_EXPLICIT from tensorflow.python.eager.context import DEVICE_PLACEMENT_WARN from tensorflow.python.eager.context import DEVICE_PLACEMENT_SILENT -from tensorflow.python.eager.context import in_eager_mode -from tensorflow.python.eager.context import in_graph_mode +from tensorflow.python.eager.context import executing_eagerly from tensorflow.python.eager.context import list_devices from tensorflow.python.eager.context import num_gpus -from tensorflow.python.eager.custom_gradient import custom_gradient 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 inf_callback @@ -101,10 +105,12 @@ from tensorflow.python.eager.execution_callbacks import seterr from tensorflow.python.framework.ops import enable_eager_execution from tensorflow.python.framework.ops import eager_run as run from tensorflow.python.framework.test_util import run_in_graph_and_eager_modes as run_test_in_graph_and_eager_modes +from tensorflow.python.ops.custom_gradient import custom_gradient from tensorflow.python.ops.resource_variable_ops import ResourceVariable as Variable from tensorflow.python.ops.variable_scope import EagerVariableStore from tensorflow.python.ops import script_ops from tensorflow.python.ops import template +from tensorflow.python.training.checkpointable import Checkpointable from tensorflow.python.util.all_util import remove_undocumented py_func = script_ops.eager_py_func @@ -115,5 +121,6 @@ implicit_value_and_gradients = backprop.implicit_val_and_grad gradients_function = backprop.gradients_function value_and_gradients_function = backprop.val_and_grad_function GradientTape = backprop.GradientTape # pylint: disable=invalid-name +in_eager_mode = executing_eagerly remove_undocumented(__name__) diff --git a/tensorflow/contrib/eager/python/tfe_test.py b/tensorflow/contrib/eager/python/tfe_test.py index b6659c2a1797feab261d756e78b45231dbea5a02..e80ccbb74d8623e977a98cb7fa5eb41f3c9bf250 100644 --- a/tensorflow/contrib/eager/python/tfe_test.py +++ b/tensorflow/contrib/eager/python/tfe_test.py @@ -47,7 +47,8 @@ class TFETest(test_util.TensorFlowTestCase): def testVariableError(self): with self.assertRaisesRegexp( - RuntimeError, r'Variable not supported in Eager mode'): + RuntimeError, + r'Variable not supported when eager execution is enabled'): variables.Variable(initial_value=1.0) def testGradients(self): diff --git a/tensorflow/contrib/estimator/BUILD b/tensorflow/contrib/estimator/BUILD index ddccfce3c07d20bde78de297db25437a347d75cb..773c6ab6c79217698c7c598a133082e2553f28f6 100644 --- a/tensorflow/contrib/estimator/BUILD +++ b/tensorflow/contrib/estimator/BUILD @@ -170,6 +170,7 @@ py_library( "//tensorflow/python:lookup_ops", "//tensorflow/python:math_ops", "//tensorflow/python:metrics", + "//tensorflow/python:nn", "//tensorflow/python:sparse_ops", "//tensorflow/python:sparse_tensor", "//tensorflow/python:summary", @@ -192,6 +193,7 @@ py_test( ":head", "//tensorflow/core:protos_all_py", "//tensorflow/python:array_ops", + "//tensorflow/python:check_ops", "//tensorflow/python:client_testlib", "//tensorflow/python:constant_op", "//tensorflow/python:control_flow_ops", diff --git a/tensorflow/contrib/estimator/__init__.py b/tensorflow/contrib/estimator/__init__.py index 0f75b77050b0ba4c752a6a74fdc7024170b6f318..6b9f9575b606f1822d760e8597c55994dd8af04c 100644 --- a/tensorflow/contrib/estimator/__init__.py +++ b/tensorflow/contrib/estimator/__init__.py @@ -39,6 +39,7 @@ _allowed_symbols = [ 'multi_class_head', 'multi_head', 'multi_label_head', + 'poisson_regression_head', 'regression_head', 'DNNEstimator', 'DNNLinearCombinedEstimator', diff --git a/tensorflow/contrib/estimator/python/estimator/extenders.py b/tensorflow/contrib/estimator/python/estimator/extenders.py index c99bf8badb35e6fffb7cae8761db9d402b8b3a8f..2b6881b81487dfdb682d5d6261a0318c59d461f6 100644 --- a/tensorflow/contrib/estimator/python/estimator/extenders.py +++ b/tensorflow/contrib/estimator/python/estimator/extenders.py @@ -33,7 +33,7 @@ _VALID_METRIC_FN_ARGS = set(['features', 'labels', 'predictions', 'config']) def add_metrics(estimator, metric_fn): - """Creates a new ${tf.estimator.Estimator} which has given metrics. + """Creates a new @{tf.estimator.Estimator} which has given metrics. Example: @@ -60,7 +60,7 @@ def add_metrics(estimator, metric_fn): ``` Args: - estimator: A ${tf.estimator.Estimator} object. + estimator: A @{tf.estimator.Estimator} object. metric_fn: A function which should obey the following signature: - Args: can only have following four arguments in any order: * predictions: Predictions `Tensor` or dict of `Tensor` created by given @@ -78,7 +78,7 @@ def add_metrics(estimator, metric_fn): function, namely a `(metric_tensor, update_op)` tuple. Returns: - A new ${tf.estimator.Estimator} which has a union of original metrics with + A new @{tf.estimator.Estimator} which has a union of original metrics with given ones. """ _verify_metric_fn_args(metric_fn) @@ -161,14 +161,14 @@ def forward_features(estimator, keys=None): ``` Args: - estimator: A ${tf.estimator.Estimator} object. + estimator: A @{tf.estimator.Estimator} object. keys: a `string` or a `list` of `string`. If it is `None`, all of the `features` in `dict` is forwarded to the `predictions`. If it is a `string`, only given key is forwarded. If it is a `list` of strings, all the given `keys` are forwarded. Returns: - A new ${tf.estimator.Estimator} which forwards features to predictions. + A new @{tf.estimator.Estimator} which forwards features to predictions. Raises: ValueError: diff --git a/tensorflow/contrib/estimator/python/estimator/head.py b/tensorflow/contrib/estimator/python/estimator/head.py index a45f6934cc5b9bb7bccf148edbd7553b702c2127..f95fcc8039cb54c26543781b31013a7676168b0b 100644 --- a/tensorflow/contrib/estimator/python/estimator/head.py +++ b/tensorflow/contrib/estimator/python/estimator/head.py @@ -31,6 +31,7 @@ from tensorflow.python.ops import check_ops from tensorflow.python.ops import lookup_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import metrics as metrics_lib +from tensorflow.python.ops import nn from tensorflow.python.ops import sparse_ops from tensorflow.python.ops.losses import losses from tensorflow.python.saved_model import signature_constants @@ -237,6 +238,66 @@ def regression_head(weight_column=None, name=name) +def poisson_regression_head( + weight_column=None, + label_dimension=1, + loss_reduction=losses.Reduction.SUM, + compute_full_loss=True, + name=None): + """Creates a `_Head` for poisson regression using `tf.nn.log_poisson_loss`. + + The loss is the weighted sum over all input dimensions. Namely, if the input + labels have shape `[batch_size, label_dimension]`, the loss is the weighted + sum over both `batch_size` and `label_dimension`. + + The head expects `logits` with shape `[D0, D1, ... DN, label_dimension]`. + In many applications, the shape is `[batch_size, label_dimension]`. + + The `labels` shape must match `logits`, namely + `[D0, D1, ... DN, label_dimension]`. If `label_dimension=1`, shape + `[D0, D1, ... DN]` is also supported. + + If `weight_column` is specified, weights must be of shape + `[D0, D1, ... DN]`, `[D0, D1, ... DN, 1]` or + `[D0, D1, ... DN, label_dimension]`. + + This is implemented as a generalized linear model, see + https://en.wikipedia.org/wiki/Generalized_linear_model. + + Args: + weight_column: A string or a `_NumericColumn` created by + `tf.feature_column.numeric_column` defining feature column representing + weights. It is used to down weight or boost examples during training. It + will be multiplied by the loss of the example. + label_dimension: Number of regression labels per example. This is the size + of the last dimension of the labels `Tensor` (typically, this has shape + `[batch_size, label_dimension]`). + loss_reduction: One of `tf.losses.Reduction` except `NONE`. Describes how to + reduce training loss over batch. Defaults to `SUM`. + compute_full_loss: Whether to include the constant `log(z!)` term in + computing the poisson loss. See `tf.nn.log_poisson_loss` for the full + documentation. + name: name of the head. If provided, summary and metrics keys will be + suffixed by `"/" + name`. Also used as `name_scope` when creating ops. + + Returns: + An instance of `_Head` for poisson regression. + + Raises: + ValueError: If `label_dimension` or `loss_reduction` is invalid. + """ + def _poisson_loss(labels, logits): + return nn.log_poisson_loss( + targets=labels, log_input=logits, compute_full_loss=compute_full_loss) + return head_lib._regression_head_with_mean_squared_error_loss( # pylint:disable=protected-access + weight_column=weight_column, + label_dimension=label_dimension, + loss_reduction=loss_reduction, + loss_fn=_poisson_loss, + inverse_link_fn=math_ops.exp, + name=name) + + def multi_label_head(n_classes, weight_column=None, thresholds=None, diff --git a/tensorflow/contrib/estimator/python/estimator/head_test.py b/tensorflow/contrib/estimator/python/estimator/head_test.py index 1411635228457218578c0297d4d901e9c86ca91a..dc30dde877ab5f912e3f6a724d481b151a3ed044 100644 --- a/tensorflow/contrib/estimator/python/estimator/head_test.py +++ b/tensorflow/contrib/estimator/python/estimator/head_test.py @@ -32,6 +32,7 @@ from tensorflow.python.framework import errors from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor from tensorflow.python.ops import array_ops +from tensorflow.python.ops import check_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import string_ops @@ -446,7 +447,7 @@ class MultiLabelHead(test.TestCase): # auc and auc_pr cannot be reliably calculated for only 4 samples, but # this assert tests that the algorithm remains consistent. keys.AUC: 0.3333, - keys.AUC_PR: 0.5972, + keys.AUC_PR: 0.7639, } self._test_eval( head=head, @@ -478,7 +479,7 @@ class MultiLabelHead(test.TestCase): # auc and auc_pr cannot be reliably calculated for only 4 samples, but # this assert tests that the algorithm remains consistent. keys.AUC: 0.3333, - keys.AUC_PR: 0.5972, + keys.AUC_PR: 0.7639, } self._test_eval( head=head, @@ -509,7 +510,7 @@ class MultiLabelHead(test.TestCase): # auc and auc_pr cannot be reliably calculated for only 4 samples, but # this assert tests that the algorithm remains consistent. keys.AUC: 0.3333, - keys.AUC_PR: 0.5972, + keys.AUC_PR: 0.7639, } self._test_eval( head=head, @@ -543,7 +544,7 @@ class MultiLabelHead(test.TestCase): # auc and auc_pr cannot be reliably calculated for only 4 samples, but # this assert tests that the algorithm remains consistent. keys.AUC: 0.3333, - keys.AUC_PR: 0.5972, + keys.AUC_PR: 0.7639, } self._test_eval( head=head, @@ -573,7 +574,7 @@ class MultiLabelHead(test.TestCase): # auc and auc_pr cannot be reliably calculated for only 4 samples, but # this assert tests that the algorithm remains consistent. keys.AUC: 0.3333, - keys.AUC_PR: 0.5972, + keys.AUC_PR: 0.7639, keys.ACCURACY_AT_THRESHOLD % thresholds[0]: 2. / 4., keys.PRECISION_AT_THRESHOLD % thresholds[0]: 2. / 3., keys.RECALL_AT_THRESHOLD % thresholds[0]: 2. / 3., @@ -621,7 +622,7 @@ class MultiLabelHead(test.TestCase): # auc and auc_pr cannot be reliably calculated for only 4 samples, but # this assert tests that the algorithm remains consistent. keys.AUC: 0.2000, - keys.AUC_PR: 0.5833, + keys.AUC_PR: 0.7833, } # Assert spec contains expected tensors. @@ -1095,7 +1096,7 @@ class MultiLabelHead(test.TestCase): # auc and auc_pr cannot be reliably calculated for only 4 samples, but # this assert tests that the algorithm remains consistent. keys.AUC: 0.4977, - keys.AUC_PR: 0.4037, + keys.AUC_PR: 0.6645, } self._test_eval( head=head, @@ -1106,5 +1107,75 @@ class MultiLabelHead(test.TestCase): expected_metrics=expected_metrics) +class PoissonRegressionHead(test.TestCase): + + def setUp(self): + ops.reset_default_graph() + + def test_train(self): + head = head_lib.poisson_regression_head() + + # Create estimator spec. + logits = np.array([[0], [-1], [1]], dtype=np.float32) + labels = np.array([[1], [2], [3]], dtype=np.int32) + # With x = exp(logits), z = labels. + # loss = -ln(exp(-x) * (x^z) / z!) + # = x - z * ln(x) + ln(z!) + # = exp(logits) - labels * logits - ln(labels!) + # But for ln(z!) and z > 1, the Stirling approximation is used + # ln(z!) = z*ln(z) - z + 0.5*ln(2*pi*z) + # loss = [exp(0) - 1 * 0 + ln(1!), + # exp(-1) - 2 * (-1) + 2*ln(2) - 2 + 0.5*ln(2*pi*2), + # exp(1) - 3 * 1 + 3*ln(3) - 3 + 0.5*ln(2*pi*3)] + # = [1.0, 3.020, 1.482] + # sum_loss = 5.502 + expected_loss = 5.502 + atol = 0.001 + expected_train_result = b'my_train_op' + def _train_op_fn(loss): + with ops.control_dependencies((check_ops.assert_near( + math_ops.to_float(expected_loss), math_ops.to_float(loss), + atol=atol, name='assert_loss'),)): + return constant_op.constant(expected_train_result) + + spec = head.create_estimator_spec( + features={'x': np.array(((42.,),), dtype=np.int32)}, + mode=model_fn.ModeKeys.TRAIN, + logits=logits, + labels=labels, + train_op_fn=_train_op_fn) + + with self.test_session() as sess: + _initialize_variables(self, spec.scaffold) + loss, train_result = sess.run([spec.loss, spec.train_op]) + self.assertAlmostEqual(expected_loss, loss, delta=atol) + self.assertEqual(expected_train_result, train_result) + + def test_predict(self): + head = head_lib.poisson_regression_head() + + # Create estimator spec. + logits = np.array([[0], [-1], [1]], dtype=np.float32) + expected_predictions = np.exp(logits) + spec = head.create_estimator_spec( + features={'x': np.array(((42.,),), dtype=np.int32)}, + mode=model_fn.ModeKeys.PREDICT, + logits=logits) + + # Assert spec contains expected tensors. + keys = prediction_keys.PredictionKeys + self.assertItemsEqual( + (keys.PREDICTIONS, keys.LOGITS), spec.predictions.keys()) + self.assertEqual(dtypes.float32, spec.predictions[keys.PREDICTIONS].dtype) + self.assertEqual(dtypes.float32, spec.predictions[keys.LOGITS].dtype) + + # Assert predictions. + with self.test_session(): + _initialize_variables(self, spec.scaffold) + self.assertAllClose( + expected_predictions, spec.predictions[keys.PREDICTIONS].eval()) + self.assertAllClose(logits, spec.predictions[keys.LOGITS].eval()) + + if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/estimator/python/estimator/multi_head_test.py b/tensorflow/contrib/estimator/python/estimator/multi_head_test.py index e47a6788f3b5440c4906b9f0430c802cf73237e3..65ea89ba1b9236d0bf4d2de430fab168ef50bf97 100644 --- a/tensorflow/contrib/estimator/python/estimator/multi_head_test.py +++ b/tensorflow/contrib/estimator/python/estimator/multi_head_test.py @@ -306,8 +306,8 @@ class MultiHeadTest(test.TestCase): # this assert tests that the algorithm remains consistent. keys.AUC + '/head1': 0.1667, keys.AUC + '/head2': 0.3333, - keys.AUC_PR + '/head1': 0.49999964, - keys.AUC_PR + '/head2': 0.33333313, + keys.AUC_PR + '/head1': 0.6667, + keys.AUC_PR + '/head2': 0.5000, } # Assert spec contains expected tensors. diff --git a/tensorflow/contrib/factorization/BUILD b/tensorflow/contrib/factorization/BUILD index 180f1b68f3b56113dfbbfc100bd04efc3bb8b31f..c56c92a0a4a01218d1da5a6b366df3272d14b861 100644 --- a/tensorflow/contrib/factorization/BUILD +++ b/tensorflow/contrib/factorization/BUILD @@ -223,7 +223,10 @@ py_test( srcs = ["python/ops/kmeans_test.py"], shard_count = 4, srcs_version = "PY2AND3", - tags = ["notsan"], # b/67512932 + tags = [ + "nomac", # b/73741358 + "notsan", # b/67512932 + ], deps = [ ":factorization_py", ":factorization_py_CYCLIC_DEPENDENCIES_THAT_NEED_TO_GO", diff --git a/tensorflow/contrib/factorization/python/ops/kmeans.py b/tensorflow/contrib/factorization/python/ops/kmeans.py index c861cfff544a78617aa1ace730b50c094cf16330..7319eaa7de8db8e4677bdf64af3b0a72c1007a90 100644 --- a/tensorflow/contrib/factorization/python/ops/kmeans.py +++ b/tensorflow/contrib/factorization/python/ops/kmeans.py @@ -61,8 +61,8 @@ class _LossRelativeChangeHook(session_run_hook.SessionRunHook): loss = run_values.results assert loss is not None if self._prev_loss: - relative_change = (abs(loss - self._prev_loss) / - (1 + abs(self._prev_loss))) + relative_change = ( + abs(loss - self._prev_loss) / (1 + abs(self._prev_loss))) if relative_change < self._tolerance: run_context.request_stop() self._prev_loss = loss @@ -233,7 +233,57 @@ class _ModelFn(object): # TODO(agarwal,ands): support sharded input. class KMeansClustering(estimator.Estimator): - """An Estimator for K-Means clustering.""" + """An Estimator for K-Means clustering. + + Example: + ``` + import numpy as np + import tensorflow as tf + + num_points = 100 + dimensions = 2 + points = np.random.uniform(0, 1000, [num_points, dimensions]) + + def input_fn(): + return tf.train.limit_epochs( + tf.convert_to_tensor(points, dtype=tf.float32), num_epochs=1) + + num_clusters = 5 + kmeans = tf.contrib.factorization.KMeansClustering( + num_clusters=num_clusters, use_mini_batch=False) + + # train + num_iterations = 10 + previous_centers = None + for _ in xrange(num_iterations): + kmeans.train(input_fn) + cluster_centers = kmeans.cluster_centers() + if previous_centers is not None: + print 'delta:', cluster_centers - previous_centers + previous_centers = cluster_centers + print 'score:', kmeans.score(input_fn) + print 'cluster centers:', cluster_centers + + # map the input points to their clusters + cluster_indices = list(kmeans.predict_cluster_index(input_fn)) + for i, point in enumerate(points): + cluster_index = cluster_indices[i] + center = cluster_centers[cluster_index] + print 'point:', point, 'is in cluster', cluster_index, 'centered at', center + ``` + + The `SavedModel` saved by the `export_savedmodel` method does not include the + cluster centers. However, the cluster centers may be retrieved by the + latest checkpoint saved during training. Specifically, + ``` + kmeans.cluster_centers() + ``` + is equivalent to + ``` + tf.train.load_variable( + kmeans.model_dir, KMeansClustering.CLUSTER_CENTERS_VAR_NAME) + ``` + """ # Valid values for the distance_metric constructor argument. SQUARED_EUCLIDEAN_DISTANCE = clustering_ops.SQUARED_EUCLIDEAN_DISTANCE @@ -253,6 +303,9 @@ class KMeansClustering(estimator.Estimator): CLUSTER_INDEX = 'cluster_index' ALL_DISTANCES = 'all_distances' + # Variable name used by cluster_centers(). + CLUSTER_CENTERS_VAR_NAME = clustering_ops.CLUSTERS_VAR_NAME + def __init__(self, num_clusters, model_dir=None, @@ -406,4 +459,4 @@ class KMeansClustering(estimator.Estimator): def cluster_centers(self): """Returns the cluster centers.""" - return self.get_variable_value(clustering_ops.CLUSTERS_VAR_NAME) + return self.get_variable_value(KMeansClustering.CLUSTER_CENTERS_VAR_NAME) diff --git a/tensorflow/contrib/feature_column/BUILD b/tensorflow/contrib/feature_column/BUILD index 6fc053759c58d30c24657dd22e7d12be46fc7a7e..3614b2b15a6cbdd73f9f24c7e4e4534228d31499 100644 --- a/tensorflow/contrib/feature_column/BUILD +++ b/tensorflow/contrib/feature_column/BUILD @@ -25,13 +25,42 @@ py_library( srcs = ["__init__.py"], srcs_version = "PY2AND3", deps = [ - ":sequential_feature_column", + ":sequence_feature_column", + "//tensorflow/python:util", ], ) py_library( - name = "sequential_feature_column", - srcs = ["python/feature_column/sequential_feature_column.py"], + name = "sequence_feature_column", + srcs = ["python/feature_column/sequence_feature_column.py"], srcs_version = "PY2AND3", - deps = [], + deps = [ + "//tensorflow/python:array_ops", + "//tensorflow/python:check_ops", + "//tensorflow/python:dtypes", + "//tensorflow/python:framework_ops", + "//tensorflow/python:parsing_ops", + "//tensorflow/python:sparse_ops", + "//tensorflow/python:tensor_shape", + "//tensorflow/python:variable_scope", + "//tensorflow/python/feature_column", + ], +) + +py_test( + name = "sequence_feature_column_test", + srcs = ["python/feature_column/sequence_feature_column_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":sequence_feature_column", + "//tensorflow/python:client_testlib", + "//tensorflow/python:dtypes", + "//tensorflow/python:errors", + "//tensorflow/python:framework_ops", + "//tensorflow/python:sparse_tensor", + "//tensorflow/python:training", + "//tensorflow/python/feature_column", + "//third_party/py/numpy", + ], ) diff --git a/tensorflow/contrib/feature_column/__init__.py b/tensorflow/contrib/feature_column/__init__.py index 6da7b126931effae9cc97091a27070d7013450d4..baa8c1567a5aeb39976ab04c54ae2728ba050a7c 100644 --- a/tensorflow/contrib/feature_column/__init__.py +++ b/tensorflow/contrib/feature_column/__init__.py @@ -19,12 +19,18 @@ from __future__ import division from __future__ import print_function # pylint: disable=unused-import,line-too-long,wildcard-import -from tensorflow.contrib.feature_column.python.feature_column.sequential_feature_column import * +from tensorflow.contrib.feature_column.python.feature_column.sequence_feature_column import * from tensorflow.python.util.all_util import remove_undocumented # pylint: enable=unused-import,line-too-long,wildcard-import _allowed_symbols = [ + 'sequence_categorical_column_with_hash_bucket', + 'sequence_categorical_column_with_identity', + 'sequence_categorical_column_with_vocabulary_list', + 'sequence_categorical_column_with_vocabulary_file', + 'sequence_input_layer', + 'sequence_numeric_column', ] remove_undocumented(__name__, allowed_exception_list=_allowed_symbols) diff --git a/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py new file mode 100644 index 0000000000000000000000000000000000000000..e60116966fc8d8bb0745f50a0238f10f02af4167 --- /dev/null +++ b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py @@ -0,0 +1,417 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Experimental methods for tf.feature_column sequence input.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + + +import collections + + +from tensorflow.python.feature_column import feature_column as fc +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.framework import tensor_shape +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import check_ops +from tensorflow.python.ops import parsing_ops +from tensorflow.python.ops import sparse_ops +from tensorflow.python.ops import variable_scope + +# pylint: disable=protected-access +# TODO(b/73827486): Support SequenceExample. + + +def sequence_input_layer( + features, + feature_columns, + weight_collections=None, + trainable=True): + """"Builds input layer for sequence input. + + All `feature_columns` must be sequence dense columns with the same + `sequence_length`. The output of this method can be fed into sequence + networks, such as RNN. + + The output of this method is a 3D `Tensor` of shape `[batch_size, T, D]`. + `T` is the maximum sequence length for this batch, which could differ from + batch to batch. + + If multiple `feature_columns` are given with `Di` `num_elements` each, their + outputs are concatenated. So, the final `Tensor` has shape + `[batch_size, T, D0 + D1 + ... + Dn]`. + + Example: + + ```python + rating = sequence_numeric_column('rating') + watches = sequence_categorical_column_with_identity( + 'watches', num_buckets=1000) + watches_embedding = embedding_column(watches, dimension=10) + columns = [rating, watches] + + features = tf.parse_example(..., features=make_parse_example_spec(columns)) + input_layer, sequence_length = sequence_input_layer(features, columns) + + rnn_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size) + outputs, state = tf.nn.dynamic_rnn( + rnn_cell, inputs=input_layer, sequence_length=sequence_length) + ``` + + Args: + features: A dict mapping keys to tensors. + feature_columns: An iterable of dense sequence columns. Valid columns are + - `embedding_column` that wraps a `sequence_categorical_column_with_*` + - `sequence_numeric_column`. + weight_collections: A list of collection names to which the Variable will be + added. Note that variables will also be added to collections + `tf.GraphKeys.GLOBAL_VARIABLES` and `ops.GraphKeys.MODEL_VARIABLES`. + trainable: If `True` also add the variable to the graph collection + `GraphKeys.TRAINABLE_VARIABLES`. + + Returns: + An `(input_layer, sequence_length)` tuple where: + - input_layer: A float `Tensor` of shape `[batch_size, T, D]`. + `T` is the maximum sequence length for this batch, which could differ + from batch to batch. `D` is the sum of `num_elements` for all + `feature_columns`. + - sequence_length: An int `Tensor` of shape `[batch_size]`. The sequence + length for each example. + + Raises: + ValueError: If any of the `feature_columns` is the wrong type. + """ + feature_columns = fc._clean_feature_columns(feature_columns) + for c in feature_columns: + if not isinstance(c, fc._SequenceDenseColumn): + raise ValueError( + 'All feature_columns must be of type _SequenceDenseColumn. ' + 'You can wrap a sequence_categorical_column with an embedding_column ' + 'or indicator_column. ' + 'Given (type {}): {}'.format(type(c), c)) + + with variable_scope.variable_scope( + None, default_name='sequence_input_layer', values=features.values()): + builder = fc._LazyBuilder(features) + output_tensors = [] + sequence_lengths = [] + ordered_columns = [] + for column in sorted(feature_columns, key=lambda x: x.name): + ordered_columns.append(column) + with variable_scope.variable_scope( + None, default_name=column._var_scope_name): + dense_tensor, sequence_length = column._get_sequence_dense_tensor( + builder, + weight_collections=weight_collections, + trainable=trainable) + # Flattens the final dimension to produce a 3D Tensor. + num_elements = column._variable_shape.num_elements() + shape = array_ops.shape(dense_tensor) + output_tensors.append( + array_ops.reshape( + dense_tensor, + shape=array_ops.concat([shape[:2], [num_elements]], axis=0))) + sequence_lengths.append(sequence_length) + fc._verify_static_batch_size_equality(output_tensors, ordered_columns) + fc._verify_static_batch_size_equality(sequence_lengths, ordered_columns) + sequence_length = _assert_all_equal_and_return(sequence_lengths) + return array_ops.concat(output_tensors, -1), sequence_length + + +def sequence_categorical_column_with_identity( + key, num_buckets, default_value=None): + """Returns a feature column that represents sequences of integers. + + Pass this to `embedding_column` or `indicator_column` to convert sequence + categorical data into dense representation for input to sequence NN, such as + RNN. + + Example: + + ```python + watches = sequence_categorical_column_with_identity( + 'watches', num_buckets=1000) + watches_embedding = embedding_column(watches, dimension=10) + columns = [watches_embedding] + + features = tf.parse_example(..., features=make_parse_example_spec(columns)) + input_layer, sequence_length = sequence_input_layer(features, columns) + + rnn_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size) + outputs, state = tf.nn.dynamic_rnn( + rnn_cell, inputs=input_layer, sequence_length=sequence_length) + ``` + + Args: + key: A unique string identifying the input feature. + num_buckets: Range of inputs. Namely, inputs are expected to be in the + range `[0, num_buckets)`. + default_value: If `None`, this column's graph operations will fail for + out-of-range inputs. Otherwise, this value must be in the range + `[0, num_buckets)`, and will replace out-of-range inputs. + + Returns: + A `_SequenceCategoricalColumn`. + """ + return fc._SequenceCategoricalColumn( + fc.categorical_column_with_identity( + key=key, + num_buckets=num_buckets, + default_value=default_value)) + + +def sequence_categorical_column_with_hash_bucket( + key, hash_bucket_size, dtype=dtypes.string): + """A sequence of categorical terms where ids are set by hashing. + + Pass this to `embedding_column` or `indicator_column` to convert sequence + categorical data into dense representation for input to sequence NN, such as + RNN. + + Example: + + ```python + tokens = sequence_categorical_column_with_hash_bucket( + 'tokens', hash_bucket_size=1000) + tokens_embedding = embedding_column(tokens, dimension=10) + columns = [tokens_embedding] + + features = tf.parse_example(..., features=make_parse_example_spec(columns)) + input_layer, sequence_length = sequence_input_layer(features, columns) + + rnn_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size) + outputs, state = tf.nn.dynamic_rnn( + rnn_cell, inputs=input_layer, sequence_length=sequence_length) + ``` + + Args: + key: A unique string identifying the input feature. + hash_bucket_size: An int > 1. The number of buckets. + dtype: The type of features. Only string and integer types are supported. + + Returns: + A `_SequenceCategoricalColumn`. + """ + return fc._SequenceCategoricalColumn( + fc.categorical_column_with_hash_bucket( + key=key, + hash_bucket_size=hash_bucket_size, + dtype=dtype)) + + +def sequence_categorical_column_with_vocabulary_file( + key, vocabulary_file, vocabulary_size=None, num_oov_buckets=0, + default_value=None, dtype=dtypes.string): + """A sequence of categorical terms where ids use a vocabulary file. + + Pass this to `embedding_column` or `indicator_column` to convert sequence + categorical data into dense representation for input to sequence NN, such as + RNN. + + Example: + + ```python + states = sequence_categorical_column_with_vocabulary_file( + key='states', vocabulary_file='/us/states.txt', vocabulary_size=50, + num_oov_buckets=5) + states_embedding = embedding_column(states, dimension=10) + columns = [states_embedding] + + features = tf.parse_example(..., features=make_parse_example_spec(columns)) + input_layer, sequence_length = sequence_input_layer(features, columns) + + rnn_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size) + outputs, state = tf.nn.dynamic_rnn( + rnn_cell, inputs=input_layer, sequence_length=sequence_length) + ``` + + Args: + key: A unique string identifying the input feature. + vocabulary_file: The vocabulary file name. + vocabulary_size: Number of the elements in the vocabulary. This must be no + greater than length of `vocabulary_file`, if less than length, later + values are ignored. If None, it is set to the length of `vocabulary_file`. + num_oov_buckets: Non-negative integer, the number of out-of-vocabulary + buckets. All out-of-vocabulary inputs will be assigned IDs in the range + `[vocabulary_size, vocabulary_size+num_oov_buckets)` based on a hash of + the input value. A positive `num_oov_buckets` can not be specified with + `default_value`. + default_value: The integer ID value to return for out-of-vocabulary feature + values, defaults to `-1`. This can not be specified with a positive + `num_oov_buckets`. + dtype: The type of features. Only string and integer types are supported. + + Returns: + A `_SequenceCategoricalColumn`. + """ + return fc._SequenceCategoricalColumn( + fc.categorical_column_with_vocabulary_file( + key=key, + vocabulary_file=vocabulary_file, + vocabulary_size=vocabulary_size, + num_oov_buckets=num_oov_buckets, + default_value=default_value, + dtype=dtype)) + + +def sequence_categorical_column_with_vocabulary_list( + key, vocabulary_list, dtype=None, default_value=-1, num_oov_buckets=0): + """A sequence of categorical terms where ids use an in-memory list. + + Pass this to `embedding_column` or `indicator_column` to convert sequence + categorical data into dense representation for input to sequence NN, such as + RNN. + + Example: + + ```python + colors = sequence_categorical_column_with_vocabulary_list( + key='colors', vocabulary_list=('R', 'G', 'B', 'Y'), + num_oov_buckets=2) + colors_embedding = embedding_column(colors, dimension=3) + columns = [colors_embedding] + + features = tf.parse_example(..., features=make_parse_example_spec(columns)) + input_layer, sequence_length = sequence_input_layer(features, columns) + + rnn_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size) + outputs, state = tf.nn.dynamic_rnn( + rnn_cell, inputs=input_layer, sequence_length=sequence_length) + ``` + + Args: + key: A unique string identifying the input feature. + vocabulary_list: An ordered iterable defining the vocabulary. Each feature + is mapped to the index of its value (if present) in `vocabulary_list`. + Must be castable to `dtype`. + dtype: The type of features. Only string and integer types are supported. + If `None`, it will be inferred from `vocabulary_list`. + default_value: The integer ID value to return for out-of-vocabulary feature + values, defaults to `-1`. This can not be specified with a positive + `num_oov_buckets`. + num_oov_buckets: Non-negative integer, the number of out-of-vocabulary + buckets. All out-of-vocabulary inputs will be assigned IDs in the range + `[len(vocabulary_list), len(vocabulary_list)+num_oov_buckets)` based on a + hash of the input value. A positive `num_oov_buckets` can not be specified + with `default_value`. + + Returns: + A `_SequenceCategoricalColumn`. + """ + return fc._SequenceCategoricalColumn( + fc.categorical_column_with_vocabulary_list( + key=key, + vocabulary_list=vocabulary_list, + dtype=dtype, + default_value=default_value, + num_oov_buckets=num_oov_buckets)) + + +def sequence_numeric_column( + key, + shape=(1,), + default_value=0., + dtype=dtypes.float32): + """Returns a feature column that represents sequences of numeric data. + + Example: + + ```python + temperature = sequence_numeric_column('temperature') + columns = [temperature] + + features = tf.parse_example(..., features=make_parse_example_spec(columns)) + input_layer, sequence_length = sequence_input_layer(features, columns) + + rnn_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size) + outputs, state = tf.nn.dynamic_rnn( + rnn_cell, inputs=input_layer, sequence_length=sequence_length) + ``` + + Args: + key: A unique string identifying the input features. + shape: The shape of the input data per sequence id. E.g. if `shape=(2,)`, + each example must contain `2 * sequence_length` values. + default_value: A single value compatible with `dtype` that is used for + padding the sparse data into a dense `Tensor`. + dtype: The type of values. + + Returns: + A `_SequenceNumericColumn`. + """ + # TODO(b/73160931): Add validations. + return _SequenceNumericColumn( + key, + shape=shape, + default_value=default_value, + dtype=dtype) + + +def _assert_all_equal_and_return(tensors, name=None): + """Asserts that all tensors are equal and returns the first one.""" + with ops.name_scope(name, 'assert_all_equal', values=tensors): + if len(tensors) == 1: + return tensors[0] + assert_equal_ops = [] + for t in tensors[1:]: + assert_equal_ops.append(check_ops.assert_equal(tensors[0], t)) + with ops.control_dependencies(assert_equal_ops): + return array_ops.identity(tensors[0]) + + +class _SequenceNumericColumn( + fc._SequenceDenseColumn, + collections.namedtuple( + '_SequenceNumericColumn', + ['key', 'shape', 'default_value', 'dtype'])): + """Represents sequences of numeric data.""" + + @property + def name(self): + return self.key + + @property + def _parse_example_spec(self): + return {self.key: parsing_ops.VarLenFeature(self.dtype)} + + def _transform_feature(self, inputs): + return inputs.get(self.key) + + @property + def _variable_shape(self): + return tensor_shape.TensorShape(self.shape) + + def _get_sequence_dense_tensor( + self, inputs, weight_collections=None, trainable=None): + # Do nothing with weight_collections and trainable since no variables are + # created in this function. + del weight_collections + del trainable + sp_tensor = inputs.get(self) + dense_tensor = sparse_ops.sparse_tensor_to_dense( + sp_tensor, default_value=self.default_value) + # Reshape into [batch_size, T, variable_shape]. + dense_shape = array_ops.concat( + [array_ops.shape(dense_tensor)[:1], [-1], self._variable_shape], + axis=0) + dense_tensor = array_ops.reshape(dense_tensor, shape=dense_shape) + sequence_length = fc._sequence_length_from_sparse_tensor( + sp_tensor, num_elements=self._variable_shape.num_elements()) + return fc._SequenceDenseColumn.TensorSequenceLengthPair( + dense_tensor=dense_tensor, sequence_length=sequence_length) + +# pylint: enable=protected-access diff --git a/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py new file mode 100644 index 0000000000000000000000000000000000000000..b64f086376dad65c1f32bee4bfce9334a60fd24a --- /dev/null +++ b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py @@ -0,0 +1,790 @@ +# 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 sequential_feature_column.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import numpy as np + +from tensorflow.contrib.feature_column.python.feature_column import sequence_feature_column as sfc +from tensorflow.python.feature_column import feature_column as fc +from tensorflow.python.feature_column.feature_column import _LazyBuilder +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.platform import test +from tensorflow.python.training import monitored_session + + +class SequenceInputLayerTest(test.TestCase): + + def test_embedding_column(self): + vocabulary_size = 3 + sparse_input_a = sparse_tensor.SparseTensorValue( + # example 0, ids [2] + # example 1, ids [0, 1] + indices=((0, 0), (1, 0), (1, 1)), + values=(2, 0, 1), + dense_shape=(2, 2)) + sparse_input_b = sparse_tensor.SparseTensorValue( + # example 0, ids [1] + # example 1, ids [2, 0] + indices=((0, 0), (1, 0), (1, 1)), + values=(1, 2, 0), + dense_shape=(2, 2)) + + embedding_dimension_a = 2 + embedding_values_a = ( + (1., 2.), # id 0 + (3., 4.), # id 1 + (5., 6.) # id 2 + ) + embedding_dimension_b = 3 + embedding_values_b = ( + (11., 12., 13.), # id 0 + (14., 15., 16.), # id 1 + (17., 18., 19.) # id 2 + ) + def _get_initializer(embedding_dimension, embedding_values): + def _initializer(shape, dtype, partition_info): + self.assertAllEqual((vocabulary_size, embedding_dimension), shape) + self.assertEqual(dtypes.float32, dtype) + self.assertIsNone(partition_info) + return embedding_values + return _initializer + + expected_input_layer = [ + # example 0, ids_a [2], ids_b [1] + [[5., 6., 14., 15., 16.], [0., 0., 0., 0., 0.]], + # example 1, ids_a [0, 1], ids_b [2, 0] + [[1., 2., 17., 18., 19.], [3., 4., 11., 12., 13.]], + ] + expected_sequence_length = [1, 2] + + categorical_column_a = sfc.sequence_categorical_column_with_identity( + key='aaa', num_buckets=vocabulary_size) + embedding_column_a = fc.embedding_column( + categorical_column_a, dimension=embedding_dimension_a, + initializer=_get_initializer(embedding_dimension_a, embedding_values_a)) + categorical_column_b = sfc.sequence_categorical_column_with_identity( + key='bbb', num_buckets=vocabulary_size) + embedding_column_b = fc.embedding_column( + categorical_column_b, dimension=embedding_dimension_b, + initializer=_get_initializer(embedding_dimension_b, embedding_values_b)) + + input_layer, sequence_length = sfc.sequence_input_layer( + features={ + 'aaa': sparse_input_a, + 'bbb': sparse_input_b, + }, + # Test that columns are reordered alphabetically. + feature_columns=[embedding_column_b, embedding_column_a]) + + global_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES) + self.assertItemsEqual( + ('sequence_input_layer/aaa_embedding/embedding_weights:0', + 'sequence_input_layer/bbb_embedding/embedding_weights:0'), + tuple([v.name for v in global_vars])) + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual(embedding_values_a, global_vars[0].eval(session=sess)) + self.assertAllEqual(embedding_values_b, global_vars[1].eval(session=sess)) + self.assertAllEqual(expected_input_layer, input_layer.eval(session=sess)) + self.assertAllEqual( + expected_sequence_length, sequence_length.eval(session=sess)) + + def test_embedding_column_with_non_sequence_categorical(self): + """Tests that error is raised for non-sequence categorical column.""" + vocabulary_size = 3 + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, ids [2] + # example 1, ids [0, 1] + indices=((0, 0), (1, 0), (1, 1)), + values=(2, 0, 1), + dense_shape=(2, 2)) + + categorical_column_a = fc.categorical_column_with_identity( + key='aaa', num_buckets=vocabulary_size) + embedding_column_a = fc.embedding_column( + categorical_column_a, dimension=2) + + with self.assertRaisesRegexp( + ValueError, + r'In embedding_column: aaa_embedding\. categorical_column must be of ' + r'type _SequenceCategoricalColumn to use sequence_input_layer\.'): + _, _ = sfc.sequence_input_layer( + features={'aaa': sparse_input}, + feature_columns=[embedding_column_a]) + + def test_indicator_column(self): + vocabulary_size_a = 3 + sparse_input_a = sparse_tensor.SparseTensorValue( + # example 0, ids [2] + # example 1, ids [0, 1] + indices=((0, 0), (1, 0), (1, 1)), + values=(2, 0, 1), + dense_shape=(2, 2)) + vocabulary_size_b = 2 + sparse_input_b = sparse_tensor.SparseTensorValue( + # example 0, ids [1] + # example 1, ids [1, 0] + indices=((0, 0), (1, 0), (1, 1)), + values=(1, 1, 0), + dense_shape=(2, 2)) + + expected_input_layer = [ + # example 0, ids_a [2], ids_b [1] + [[0., 0., 1., 0., 1.], [0., 0., 0., 0., 0.]], + # example 1, ids_a [0, 1], ids_b [1, 0] + [[1., 0., 0., 0., 1.], [0., 1., 0., 1., 0.]], + ] + expected_sequence_length = [1, 2] + + categorical_column_a = sfc.sequence_categorical_column_with_identity( + key='aaa', num_buckets=vocabulary_size_a) + indicator_column_a = fc.indicator_column(categorical_column_a) + categorical_column_b = sfc.sequence_categorical_column_with_identity( + key='bbb', num_buckets=vocabulary_size_b) + indicator_column_b = fc.indicator_column(categorical_column_b) + input_layer, sequence_length = sfc.sequence_input_layer( + features={ + 'aaa': sparse_input_a, + 'bbb': sparse_input_b, + }, + # Test that columns are reordered alphabetically. + feature_columns=[indicator_column_b, indicator_column_a]) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual(expected_input_layer, input_layer.eval(session=sess)) + self.assertAllEqual( + expected_sequence_length, sequence_length.eval(session=sess)) + + def test_indicator_column_with_non_sequence_categorical(self): + """Tests that error is raised for non-sequence categorical column.""" + vocabulary_size = 3 + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, ids [2] + # example 1, ids [0, 1] + indices=((0, 0), (1, 0), (1, 1)), + values=(2, 0, 1), + dense_shape=(2, 2)) + + categorical_column_a = fc.categorical_column_with_identity( + key='aaa', num_buckets=vocabulary_size) + indicator_column_a = fc.indicator_column(categorical_column_a) + + with self.assertRaisesRegexp( + ValueError, + r'In indicator_column: aaa_indicator\. categorical_column must be of ' + r'type _SequenceCategoricalColumn to use sequence_input_layer\.'): + _, _ = sfc.sequence_input_layer( + features={'aaa': sparse_input}, + feature_columns=[indicator_column_a]) + + def test_numeric_column(self): + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, values [[0.], [1]] + # example 1, [[10.]] + indices=((0, 0), (0, 1), (1, 0)), + values=(0., 1., 10.), + dense_shape=(2, 2)) + expected_input_layer = [ + [[0.], [1.]], + [[10.], [0.]], + ] + expected_sequence_length = [2, 1] + numeric_column = sfc.sequence_numeric_column('aaa') + + input_layer, sequence_length = sfc.sequence_input_layer( + features={'aaa': sparse_input}, + feature_columns=[numeric_column]) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual(expected_input_layer, input_layer.eval(session=sess)) + self.assertAllEqual( + expected_sequence_length, sequence_length.eval(session=sess)) + + def test_numeric_column_multi_dim(self): + """Tests sequence_input_layer for multi-dimensional numeric_column.""" + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, values [[[0., 1.], [2., 3.]], [[4., 5.], [6., 7.]]] + # example 1, [[[10., 11.], [12., 13.]]] + indices=((0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (0, 7), + (1, 0), (1, 1), (1, 2), (1, 3)), + values=(0., 1., 2., 3., 4., 5., 6., 7., 10., 11., 12., 13.), + dense_shape=(2, 8)) + # The output of numeric_column._get_dense_tensor should be flattened. + expected_input_layer = [ + [[0., 1., 2., 3.], [4., 5., 6., 7.]], + [[10., 11., 12., 13.], [0., 0., 0., 0.]], + ] + expected_sequence_length = [2, 1] + numeric_column = sfc.sequence_numeric_column('aaa', shape=(2, 2)) + + input_layer, sequence_length = sfc.sequence_input_layer( + features={'aaa': sparse_input}, + feature_columns=[numeric_column]) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual(expected_input_layer, input_layer.eval(session=sess)) + self.assertAllEqual( + expected_sequence_length, sequence_length.eval(session=sess)) + + def test_sequence_length_not_equal(self): + """Tests that an error is raised when sequence lengths are not equal.""" + # Input a with sequence_length = [2, 1] + sparse_input_a = sparse_tensor.SparseTensorValue( + indices=((0, 0), (0, 1), (1, 0)), + values=(0., 1., 10.), + dense_shape=(2, 2)) + # Input b with sequence_length = [1, 1] + sparse_input_b = sparse_tensor.SparseTensorValue( + indices=((0, 0), (1, 0)), + values=(1., 10.), + dense_shape=(2, 2)) + numeric_column_a = sfc.sequence_numeric_column('aaa') + numeric_column_b = sfc.sequence_numeric_column('bbb') + + _, sequence_length = sfc.sequence_input_layer( + features={ + 'aaa': sparse_input_a, + 'bbb': sparse_input_b, + }, + feature_columns=[numeric_column_a, numeric_column_b]) + + with monitored_session.MonitoredSession() as sess: + with self.assertRaisesRegexp( + errors.InvalidArgumentError, + r'\[Condition x == y did not hold element-wise:\] ' + r'\[x \(sequence_input_layer/aaa/sequence_length:0\) = \] \[2 1\] ' + r'\[y \(sequence_input_layer/bbb/sequence_length:0\) = \] \[1 1\]'): + sess.run(sequence_length) + + +class InputLayerTest(test.TestCase): + """Tests input_layer with sequence feature columns.""" + + def test_embedding_column(self): + """Tests that error is raised for sequence embedding column.""" + vocabulary_size = 3 + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, ids [2] + # example 1, ids [0, 1] + indices=((0, 0), (1, 0), (1, 1)), + values=(2, 0, 1), + dense_shape=(2, 2)) + + categorical_column_a = sfc.sequence_categorical_column_with_identity( + key='aaa', num_buckets=vocabulary_size) + embedding_column_a = fc.embedding_column( + categorical_column_a, dimension=2) + + with self.assertRaisesRegexp( + ValueError, + r'In embedding_column: aaa_embedding\. categorical_column must not be ' + r'of type _SequenceCategoricalColumn\.'): + _ = fc.input_layer( + features={'aaa': sparse_input}, + feature_columns=[embedding_column_a]) + + def test_indicator_column(self): + """Tests that error is raised for sequence indicator column.""" + vocabulary_size = 3 + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, ids [2] + # example 1, ids [0, 1] + indices=((0, 0), (1, 0), (1, 1)), + values=(2, 0, 1), + dense_shape=(2, 2)) + + categorical_column_a = sfc.sequence_categorical_column_with_identity( + key='aaa', num_buckets=vocabulary_size) + indicator_column_a = fc.indicator_column(categorical_column_a) + + with self.assertRaisesRegexp( + ValueError, + r'In indicator_column: aaa_indicator\. categorical_column must not be ' + r'of type _SequenceCategoricalColumn\.'): + _ = fc.input_layer( + features={'aaa': sparse_input}, + feature_columns=[indicator_column_a]) + + +def _assert_sparse_tensor_value(test_case, expected, actual): + _assert_sparse_tensor_indices_shape(test_case, expected, actual) + + test_case.assertEqual( + np.array(expected.values).dtype, np.array(actual.values).dtype) + test_case.assertAllEqual(expected.values, actual.values) + + +def _assert_sparse_tensor_indices_shape(test_case, expected, actual): + test_case.assertEqual(np.int64, np.array(actual.indices).dtype) + test_case.assertAllEqual(expected.indices, actual.indices) + + test_case.assertEqual(np.int64, np.array(actual.dense_shape).dtype) + test_case.assertAllEqual(expected.dense_shape, actual.dense_shape) + + +class SequenceCategoricalColumnWithIdentityTest(test.TestCase): + + def test_get_sparse_tensors(self): + column = sfc.sequence_categorical_column_with_identity( + 'aaa', num_buckets=3) + inputs = sparse_tensor.SparseTensorValue( + indices=((0, 0), (1, 0), (1, 1)), + values=(1, 2, 0), + dense_shape=(2, 2)) + expected_sparse_ids = sparse_tensor.SparseTensorValue( + indices=((0, 0, 0), (1, 0, 0), (1, 1, 0)), + values=np.array((1, 2, 0), dtype=np.int64), + dense_shape=(2, 2, 1)) + + id_weight_pair = column._get_sparse_tensors(_LazyBuilder({'aaa': inputs})) + + self.assertIsNone(id_weight_pair.weight_tensor) + with monitored_session.MonitoredSession() as sess: + _assert_sparse_tensor_value( + self, + expected_sparse_ids, + id_weight_pair.id_tensor.eval(session=sess)) + + def test_get_sparse_tensors_inputs3d(self): + """Tests _get_sparse_tensors when the input is already 3D Tensor.""" + column = sfc.sequence_categorical_column_with_identity( + 'aaa', num_buckets=3) + inputs = sparse_tensor.SparseTensorValue( + indices=((0, 0, 0), (1, 0, 0), (1, 1, 0)), + values=(1, 2, 0), + dense_shape=(2, 2, 1)) + + with self.assertRaisesRegexp( + errors.InvalidArgumentError, + r'Column aaa expected ID tensor of rank 2\.\s*' + r'id_tensor shape:\s*\[2 2 1\]'): + id_weight_pair = column._get_sparse_tensors( + _LazyBuilder({'aaa': inputs})) + with monitored_session.MonitoredSession() as sess: + id_weight_pair.id_tensor.eval(session=sess) + + +class SequenceCategoricalColumnWithHashBucketTest(test.TestCase): + + def test_get_sparse_tensors(self): + column = sfc.sequence_categorical_column_with_hash_bucket( + 'aaa', hash_bucket_size=10) + inputs = sparse_tensor.SparseTensorValue( + indices=((0, 0), (1, 0), (1, 1)), + values=('omar', 'stringer', 'marlo'), + dense_shape=(2, 2)) + + expected_sparse_ids = sparse_tensor.SparseTensorValue( + indices=((0, 0, 0), (1, 0, 0), (1, 1, 0)), + # Ignored to avoid hash dependence in test. + values=np.array((0, 0, 0), dtype=np.int64), + dense_shape=(2, 2, 1)) + + id_weight_pair = column._get_sparse_tensors(_LazyBuilder({'aaa': inputs})) + + self.assertIsNone(id_weight_pair.weight_tensor) + with monitored_session.MonitoredSession() as sess: + _assert_sparse_tensor_indices_shape( + self, + expected_sparse_ids, + id_weight_pair.id_tensor.eval(session=sess)) + + +class SequenceCategoricalColumnWithVocabularyFileTest(test.TestCase): + + def _write_vocab(self, vocab_strings, file_name): + vocab_file = os.path.join(self.get_temp_dir(), file_name) + with open(vocab_file, 'w') as f: + f.write('\n'.join(vocab_strings)) + return vocab_file + + def setUp(self): + super(SequenceCategoricalColumnWithVocabularyFileTest, self).setUp() + + vocab_strings = ['omar', 'stringer', 'marlo'] + self._wire_vocabulary_file_name = self._write_vocab(vocab_strings, + 'wire_vocabulary.txt') + self._wire_vocabulary_size = 3 + + def test_get_sparse_tensors(self): + column = sfc.sequence_categorical_column_with_vocabulary_file( + key='aaa', + vocabulary_file=self._wire_vocabulary_file_name, + vocabulary_size=self._wire_vocabulary_size) + inputs = sparse_tensor.SparseTensorValue( + indices=((0, 0), (1, 0), (1, 1)), + values=('marlo', 'skywalker', 'omar'), + dense_shape=(2, 2)) + expected_sparse_ids = sparse_tensor.SparseTensorValue( + indices=((0, 0, 0), (1, 0, 0), (1, 1, 0)), + values=np.array((2, -1, 0), dtype=np.int64), + dense_shape=(2, 2, 1)) + + id_weight_pair = column._get_sparse_tensors(_LazyBuilder({'aaa': inputs})) + + self.assertIsNone(id_weight_pair.weight_tensor) + with monitored_session.MonitoredSession() as sess: + _assert_sparse_tensor_value( + self, + expected_sparse_ids, + id_weight_pair.id_tensor.eval(session=sess)) + + +class SequenceCategoricalColumnWithVocabularyListTest(test.TestCase): + + def test_get_sparse_tensors(self): + column = sfc.sequence_categorical_column_with_vocabulary_list( + key='aaa', + vocabulary_list=('omar', 'stringer', 'marlo')) + inputs = sparse_tensor.SparseTensorValue( + indices=((0, 0), (1, 0), (1, 1)), + values=('marlo', 'skywalker', 'omar'), + dense_shape=(2, 2)) + expected_sparse_ids = sparse_tensor.SparseTensorValue( + indices=((0, 0, 0), (1, 0, 0), (1, 1, 0)), + values=np.array((2, -1, 0), dtype=np.int64), + dense_shape=(2, 2, 1)) + + id_weight_pair = column._get_sparse_tensors(_LazyBuilder({'aaa': inputs})) + + self.assertIsNone(id_weight_pair.weight_tensor) + with monitored_session.MonitoredSession() as sess: + _assert_sparse_tensor_value( + self, + expected_sparse_ids, + id_weight_pair.id_tensor.eval(session=sess)) + + +class SequenceEmbeddingColumnTest(test.TestCase): + + def test_get_sequence_dense_tensor(self): + vocabulary_size = 3 + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, ids [2] + # example 1, ids [0, 1] + # example 2, ids [] + # example 3, ids [1] + indices=((0, 0), (1, 0), (1, 1), (3, 0)), + values=(2, 0, 1, 1), + dense_shape=(4, 2)) + + embedding_dimension = 2 + embedding_values = ( + (1., 2.), # id 0 + (3., 5.), # id 1 + (7., 11.) # id 2 + ) + def _initializer(shape, dtype, partition_info): + self.assertAllEqual((vocabulary_size, embedding_dimension), shape) + self.assertEqual(dtypes.float32, dtype) + self.assertIsNone(partition_info) + return embedding_values + + expected_lookups = [ + # example 0, ids [2] + [[7., 11.], [0., 0.]], + # example 1, ids [0, 1] + [[1., 2.], [3., 5.]], + # example 2, ids [] + [[0., 0.], [0., 0.]], + # example 3, ids [1] + [[3., 5.], [0., 0.]], + ] + + categorical_column = sfc.sequence_categorical_column_with_identity( + key='aaa', num_buckets=vocabulary_size) + embedding_column = fc.embedding_column( + categorical_column, dimension=embedding_dimension, + initializer=_initializer) + + embedding_lookup, _ = embedding_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + global_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES) + self.assertItemsEqual( + ('embedding_weights:0',), tuple([v.name for v in global_vars])) + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual(embedding_values, global_vars[0].eval(session=sess)) + self.assertAllEqual(expected_lookups, embedding_lookup.eval(session=sess)) + + def test_sequence_length(self): + vocabulary_size = 3 + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, ids [2] + # example 1, ids [0, 1] + indices=((0, 0), (1, 0), (1, 1)), + values=(2, 0, 1), + dense_shape=(2, 2)) + expected_sequence_length = [1, 2] + + categorical_column = sfc.sequence_categorical_column_with_identity( + key='aaa', num_buckets=vocabulary_size) + embedding_column = fc.embedding_column( + categorical_column, dimension=2) + + _, sequence_length = embedding_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + with monitored_session.MonitoredSession() as sess: + sequence_length = sess.run(sequence_length) + self.assertAllEqual(expected_sequence_length, sequence_length) + self.assertEqual(np.int64, sequence_length.dtype) + + def test_sequence_length_with_empty_rows(self): + """Tests _sequence_length when some examples do not have ids.""" + vocabulary_size = 3 + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, ids [] + # example 1, ids [2] + # example 2, ids [0, 1] + # example 3, ids [] + # example 4, ids [1] + # example 5, ids [] + indices=((1, 0), (2, 0), (2, 1), (4, 0)), + values=(2, 0, 1, 1), + dense_shape=(6, 2)) + expected_sequence_length = [0, 1, 2, 0, 1, 0] + + categorical_column = sfc.sequence_categorical_column_with_identity( + key='aaa', num_buckets=vocabulary_size) + embedding_column = fc.embedding_column( + categorical_column, dimension=2) + + _, sequence_length = embedding_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual( + expected_sequence_length, sequence_length.eval(session=sess)) + + +class SequenceIndicatorColumnTest(test.TestCase): + + def test_get_sequence_dense_tensor(self): + vocabulary_size = 3 + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, ids [2] + # example 1, ids [0, 1] + # example 2, ids [] + # example 3, ids [1] + indices=((0, 0), (1, 0), (1, 1), (3, 0)), + values=(2, 0, 1, 1), + dense_shape=(4, 2)) + + expected_lookups = [ + # example 0, ids [2] + [[0., 0., 1.], [0., 0., 0.]], + # example 1, ids [0, 1] + [[1., 0., 0.], [0., 1., 0.]], + # example 2, ids [] + [[0., 0., 0.], [0., 0., 0.]], + # example 3, ids [1] + [[0., 1., 0.], [0., 0., 0.]], + ] + + categorical_column = sfc.sequence_categorical_column_with_identity( + key='aaa', num_buckets=vocabulary_size) + indicator_column = fc.indicator_column(categorical_column) + + indicator_tensor, _ = indicator_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual(expected_lookups, indicator_tensor.eval(session=sess)) + + def test_sequence_length(self): + vocabulary_size = 3 + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, ids [2] + # example 1, ids [0, 1] + indices=((0, 0), (1, 0), (1, 1)), + values=(2, 0, 1), + dense_shape=(2, 2)) + expected_sequence_length = [1, 2] + + categorical_column = sfc.sequence_categorical_column_with_identity( + key='aaa', num_buckets=vocabulary_size) + indicator_column = fc.indicator_column(categorical_column) + + _, sequence_length = indicator_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + with monitored_session.MonitoredSession() as sess: + sequence_length = sess.run(sequence_length) + self.assertAllEqual(expected_sequence_length, sequence_length) + self.assertEqual(np.int64, sequence_length.dtype) + + def test_sequence_length_with_empty_rows(self): + """Tests _sequence_length when some examples do not have ids.""" + vocabulary_size = 3 + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, ids [] + # example 1, ids [2] + # example 2, ids [0, 1] + # example 3, ids [] + # example 4, ids [1] + # example 5, ids [] + indices=((1, 0), (2, 0), (2, 1), (4, 0)), + values=(2, 0, 1, 1), + dense_shape=(6, 2)) + expected_sequence_length = [0, 1, 2, 0, 1, 0] + + categorical_column = sfc.sequence_categorical_column_with_identity( + key='aaa', num_buckets=vocabulary_size) + indicator_column = fc.indicator_column(categorical_column) + + _, sequence_length = indicator_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual( + expected_sequence_length, sequence_length.eval(session=sess)) + + +class SequenceNumericColumnTest(test.TestCase): + + def test_get_sequence_dense_tensor(self): + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, values [[0.], [1]] + # example 1, [[10.]] + indices=((0, 0), (0, 1), (1, 0)), + values=(0., 1., 10.), + dense_shape=(2, 2)) + expected_dense_tensor = [ + [[0.], [1.]], + [[10.], [0.]], + ] + numeric_column = sfc.sequence_numeric_column('aaa') + + dense_tensor, _ = numeric_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual( + expected_dense_tensor, dense_tensor.eval(session=sess)) + + def test_get_sequence_dense_tensor_with_shape(self): + """Tests get_sequence_dense_tensor with shape !=(1,).""" + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, values [[0., 1., 2.], [3., 4., 5.]] + # example 1, [[10., 11., 12.]] + indices=((0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), + (1, 0), (1, 1), (1, 2)), + values=(0., 1., 2., 3., 4., 5., 10., 11., 12.), + dense_shape=(2, 6)) + expected_dense_tensor = [ + [[0., 1., 2.], [3., 4., 5.]], + [[10., 11., 12.], [0., 0., 0.]], + ] + numeric_column = sfc.sequence_numeric_column('aaa', shape=(3,)) + + dense_tensor, _ = numeric_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual( + expected_dense_tensor, dense_tensor.eval(session=sess)) + + def test_get_dense_tensor_multi_dim(self): + """Tests get_sequence_dense_tensor for multi-dim numeric_column.""" + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, values [[[0., 1.], [2., 3.]], [[4., 5.], [6., 7.]]] + # example 1, [[[10., 11.], [12., 13.]]] + indices=((0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (0, 7), + (1, 0), (1, 1), (1, 2), (1, 3)), + values=(0., 1., 2., 3., 4., 5., 6., 7., 10., 11., 12., 13.), + dense_shape=(2, 8)) + expected_dense_tensor = [ + [[[0., 1.], [2., 3.]], [[4., 5.], [6., 7.]]], + [[[10., 11.], [12., 13.]], [[0., 0.], [0., 0.]]], + ] + numeric_column = sfc.sequence_numeric_column('aaa', shape=(2, 2)) + + dense_tensor, _ = numeric_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual( + expected_dense_tensor, dense_tensor.eval(session=sess)) + + def test_sequence_length(self): + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, values [[0., 1., 2.], [3., 4., 5.]] + # example 1, [[10., 11., 12.]] + indices=((0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), + (1, 0), (1, 1), (1, 2)), + values=(0., 1., 2., 3., 4., 5., 10., 11., 12.), + dense_shape=(2, 6)) + expected_sequence_length = [2, 1] + numeric_column = sfc.sequence_numeric_column('aaa', shape=(3,)) + + _, sequence_length = numeric_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + with monitored_session.MonitoredSession() as sess: + sequence_length = sess.run(sequence_length) + self.assertAllEqual(expected_sequence_length, sequence_length) + self.assertEqual(np.int64, sequence_length.dtype) + + def test_sequence_length_with_shape(self): + """Tests _sequence_length with shape !=(1,).""" + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, values [[0.], [1]] + # example 1, [[10.]] + indices=((0, 0), (0, 1), (1, 0)), + values=(0., 1., 10.), + dense_shape=(2, 2)) + expected_sequence_length = [2, 1] + numeric_column = sfc.sequence_numeric_column('aaa') + + _, sequence_length = numeric_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual( + expected_sequence_length, sequence_length.eval(session=sess)) + + def test_sequence_length_with_empty_rows(self): + """Tests _sequence_length when some examples do not have ids.""" + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, values [] + # example 1, values [[0.], [1.]] + # example 2, [[2.]] + # example 3, values [] + # example 4, [[3.]] + # example 5, values [] + indices=((1, 0), (1, 1), (2, 0), (4, 0)), + values=(0., 1., 2., 3.), + dense_shape=(6, 2)) + expected_sequence_length = [0, 2, 1, 0, 1, 0] + numeric_column = sfc.sequence_numeric_column('aaa') + + _, sequence_length = numeric_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual( + expected_sequence_length, sequence_length.eval(session=sess)) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/feature_column/python/feature_column/sequential_feature_column.py b/tensorflow/contrib/feature_column/python/feature_column/sequential_feature_column.py index 690a44ff4368663306733300a1ea70397fb93e1e..4ed7268e7a921284eed7767d870e56ecac39a3b1 100644 --- a/tensorflow/contrib/feature_column/python/feature_column/sequential_feature_column.py +++ b/tensorflow/contrib/feature_column/python/feature_column/sequential_feature_column.py @@ -12,8 +12,314 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Experimental methods for tf.feature_column sequential input.""" +"""Experimental methods for tf.feature_column sequence input.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function + + +import abc +import collections + + +from tensorflow.python.feature_column import feature_column as fc +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.framework import tensor_shape +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import check_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import parsing_ops +from tensorflow.python.ops import sparse_ops +from tensorflow.python.ops import variable_scope + +# TODO(b/73160931): Fix pydoc. +# pylint: disable=g-doc-args,missing-docstring,protected-access +# TODO(b/73827486): Support SequenceExample. + + +def sequence_input_layer( + features, + feature_columns, + weight_collections=None, + trainable=True, + scope=None): + """"Builds input layer for sequence input. + + All `feature_columns` must be sequence dense columns with the same + `sequence_length`. The output of this method can be fed into sequence + networks, such as RNN. + + The output of this method is a 3D `Tensor` of shape `[batch_size, T, D]`. + `T` is the maximum sequence length for this batch, which could differ from + batch to batch. + + If multiple `feature_columns` are given with `Di` `num_elements` each, their + outputs are concatenated. So, the final `Tensor` has shape + `[batch_size, T, D0 + D1 + ... + Dn]`. + + Example: + + ```python + rating = sequence_numeric_column('rating') + watches = sequence_categorical_column_with_identity( + 'watches', num_buckets=1000) + watches_embedding = embedding_column(watches, dimension=10) + columns = [rating, watches] + + features = tf.parse_example(..., features=make_parse_example_spec(columns)) + input_layer, sequence_length = sequence_input_layer(features, columns) + + rnn_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size) + outputs, state = tf.nn.dynamic_rnn( + rnn_cell, inputs=input_layer, sequence_length=sequence_length) + ``` + + Returns: + An `(input_layer, sequence_length)` tuple where: + - input_layer: A float `Tensor` of shape `[batch_size, T, D]`. + `T` is the maximum sequence length for this batch, which could differ + from batch to batch. `D` is the sum of `num_elements` for all + `feature_columns`. + - sequence_length: An int `Tensor` of shape `[batch_size]`. The sequence + length for each example. + Raises: + ValueError: If any of the `feature_columns` is the wrong type. + """ + feature_columns = fc._clean_feature_columns(feature_columns) + for c in feature_columns: + if not isinstance(c, _SequenceDenseColumn): + raise ValueError( + 'All feature_columns must be of type _SequenceDenseColumn. ' + 'Given (type {}): {}'.format(type(c), c)) + + with variable_scope.variable_scope( + scope, default_name='sequence_input_layer', values=features.values()): + builder = fc._LazyBuilder(features) + output_tensors = [] + sequence_lengths = [] + ordered_columns = [] + for column in sorted(feature_columns, key=lambda x: x.name): + ordered_columns.append(column) + with variable_scope.variable_scope( + None, default_name=column._var_scope_name): + dense_tensor, sequence_length = column._get_sequence_dense_tensor( + builder, + weight_collections=weight_collections, + trainable=trainable) + # Flattens the final dimension to produce a 3D Tensor. + num_elements = column._variable_shape.num_elements() + shape = array_ops.shape(dense_tensor) + output_tensors.append( + array_ops.reshape( + dense_tensor, + shape=array_ops.concat([shape[:2], [num_elements]], axis=0))) + sequence_lengths.append(sequence_length) + fc._verify_static_batch_size_equality(output_tensors, ordered_columns) + # TODO(b/73160931): Verify sequence_length equality. + return array_ops.concat(output_tensors, -1), sequence_lengths[0] + + +# TODO(b/73160931): Add remaining categorical columns. +def sequence_categorical_column_with_identity( + key, num_buckets, default_value=None): + return _SequenceCategoricalColumn( + fc.categorical_column_with_identity( + key=key, + num_buckets=num_buckets, + default_value=default_value)) + + +# TODO(b/73160931): Merge with embedding_column +def _sequence_embedding_column( + categorical_column, dimension, initializer=None, ckpt_to_load_from=None, + tensor_name_in_ckpt=None, max_norm=None, trainable=True): + if not isinstance(categorical_column, _SequenceCategoricalColumn): + raise ValueError( + 'categorical_column must be of type _SequenceCategoricalColumn. ' + 'Given (type {}): {}'.format( + type(categorical_column), categorical_column)) + return _SequenceEmbeddingColumn( + fc.embedding_column( + categorical_column, + dimension=dimension, + initializer=initializer, + ckpt_to_load_from=ckpt_to_load_from, + tensor_name_in_ckpt=tensor_name_in_ckpt, + max_norm=max_norm, + trainable=trainable)) + + +def sequence_numeric_column( + key, + shape=(1,), + default_value=0., + dtype=dtypes.float32): + # TODO(b/73160931): Add validations. + return _SequenceNumericColumn( + key, + shape=shape, + default_value=default_value, + dtype=dtype) + + +class _SequenceDenseColumn(fc._FeatureColumn): + """Represents dense sequence data.""" + + __metaclass__ = abc.ABCMeta + + TensorSequenceLengthPair = collections.namedtuple( # pylint: disable=invalid-name + 'TensorSequenceLengthPair', ['dense_tensor', 'sequence_length']) + + @abc.abstractproperty + def _variable_shape(self): + """`TensorShape` without batch and sequence dimensions.""" + pass + + @abc.abstractmethod + def _get_sequence_dense_tensor( + self, inputs, weight_collections=None, trainable=None): + """Returns a `TensorSequenceLengthPair`.""" + pass + + +def _sequence_length_from_sparse_tensor(sp_tensor, num_elements=1): + with ops.name_scope(None, 'sequence_length') as name_scope: + row_ids = sp_tensor.indices[:, 0] + column_ids = sp_tensor.indices[:, 1] + column_ids += array_ops.ones_like(column_ids) + seq_length = ( + math_ops.segment_max(column_ids, segment_ids=row_ids) / num_elements) + # If the last n rows do not have ids, seq_length will have shape + # [batch_size - n]. Pad the remaining values with zeros. + n_pad = array_ops.shape(sp_tensor)[:1] - array_ops.shape(seq_length)[:1] + padding = array_ops.zeros(n_pad, dtype=seq_length.dtype) + return array_ops.concat([seq_length, padding], axis=0, name=name_scope) + + +class _SequenceCategoricalColumn( + fc._CategoricalColumn, + collections.namedtuple( + '_SequenceCategoricalColumn', ['categorical_column'])): + + @property + def name(self): + return self.categorical_column.name + + @property + def _parse_example_spec(self): + return self.categorical_column._parse_example_spec + + def _transform_feature(self, inputs): + return self.categorical_column._transform_feature(inputs) + + @property + def _num_buckets(self): + return self.categorical_column._num_buckets + + def _get_sparse_tensors(self, inputs, weight_collections=None, + trainable=None): + sparse_tensors = self.categorical_column._get_sparse_tensors(inputs) + id_tensor = sparse_tensors.id_tensor + weight_tensor = sparse_tensors.weight_tensor + # Expands final dimension, so that embeddings are not combined during + # embedding lookup. + check_id_rank = check_ops.assert_equal( + array_ops.rank(id_tensor), 2, + data=[ + 'Column {} expected ID tensor of rank 2. '.format(self.name), + 'id_tensor shape: ', array_ops.shape(id_tensor)]) + with ops.control_dependencies([check_id_rank]): + id_tensor = sparse_ops.sparse_reshape( + id_tensor, + shape=array_ops.concat([id_tensor.dense_shape, [1]], axis=0)) + if weight_tensor is not None: + check_weight_rank = check_ops.assert_equal( + array_ops.rank(weight_tensor), 2, + data=[ + 'Column {} expected weight tensor of rank 2.'.format(self.name), + 'weight_tensor shape:', array_ops.shape(weight_tensor)]) + with ops.control_dependencies([check_weight_rank]): + weight_tensor = sparse_ops.sparse_reshape( + weight_tensor, + shape=array_ops.concat([weight_tensor.dense_shape, [1]], axis=0)) + return fc._CategoricalColumn.IdWeightPair(id_tensor, weight_tensor) + + def _sequence_length(self, inputs): + sparse_tensors = self.categorical_column._get_sparse_tensors(inputs) + return _sequence_length_from_sparse_tensor(sparse_tensors.id_tensor) + + +class _SequenceEmbeddingColumn( + _SequenceDenseColumn, + collections.namedtuple('_SequenceEmbeddingColumn', ['embedding_column'])): + + @property + def name(self): + return self.embedding_column.name + + @property + def _parse_example_spec(self): + return self.embedding_column._parse_example_spec + + def _transform_feature(self, inputs): + return self.embedding_column._transform_feature(inputs) + + @property + def _variable_shape(self): + return self.embedding_column._variable_shape + + def _get_sequence_dense_tensor( + self, inputs, weight_collections=None, trainable=None): + dense_tensor = self.embedding_column._get_dense_tensor( + inputs=inputs, + weight_collections=weight_collections, + trainable=trainable) + sequence_length = self.embedding_column.categorical_column._sequence_length( + inputs) + return _SequenceDenseColumn.TensorSequenceLengthPair( + dense_tensor=dense_tensor, sequence_length=sequence_length) + + +class _SequenceNumericColumn( + _SequenceDenseColumn, + collections.namedtuple( + '_SequenceNumericColumn', + ['key', 'shape', 'default_value', 'dtype'])): + + @property + def name(self): + return self.key + + @property + def _parse_example_spec(self): + return {self.key: parsing_ops.VarLenFeature(self.dtype)} + + def _transform_feature(self, inputs): + return inputs.get(self.key) + + @property + def _variable_shape(self): + return tensor_shape.TensorShape(self.shape) + + def _get_sequence_dense_tensor( + self, inputs, weight_collections=None, trainable=None): + # Do nothing with weight_collections and trainable since no variables are + # created in this function. + del weight_collections + del trainable + sp_tensor = inputs.get(self) + dense_tensor = sparse_ops.sparse_tensor_to_dense( + sp_tensor, default_value=self.default_value) + # Reshape into [batch_size, T, variable_shape]. + dense_shape = array_ops.concat( + [array_ops.shape(dense_tensor)[:1], [-1], self._variable_shape], + axis=0) + dense_tensor = array_ops.reshape(dense_tensor, shape=dense_shape) + sequence_length = _sequence_length_from_sparse_tensor( + sp_tensor, num_elements=self._variable_shape.num_elements()) + return _SequenceDenseColumn.TensorSequenceLengthPair( + dense_tensor=dense_tensor, sequence_length=sequence_length) + +# pylint: enable=g-doc-args,missing-docstring,protected-access diff --git a/tensorflow/contrib/feature_column/python/feature_column/sequential_feature_column_test.py b/tensorflow/contrib/feature_column/python/feature_column/sequential_feature_column_test.py new file mode 100644 index 0000000000000000000000000000000000000000..59674869a27c3a40ab9cb3dcede384d1cda7ce27 --- /dev/null +++ b/tensorflow/contrib/feature_column/python/feature_column/sequential_feature_column_test.py @@ -0,0 +1,471 @@ +# 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 sequential_feature_column.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.feature_column.python.feature_column import sequential_feature_column as sfc +from tensorflow.python.feature_column.feature_column import _LazyBuilder +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.platform import test +from tensorflow.python.training import monitored_session + + +class SequenceInputLayerTest(test.TestCase): + + def test_embedding_column(self): + vocabulary_size = 3 + sparse_input_a = sparse_tensor.SparseTensorValue( + # example 0, ids [2] + # example 1, ids [0, 1] + indices=((0, 0), (1, 0), (1, 1)), + values=(2, 0, 1), + dense_shape=(2, 2)) + sparse_input_b = sparse_tensor.SparseTensorValue( + # example 0, ids [1] + # example 1, ids [2, 0] + indices=((0, 0), (1, 0), (1, 1)), + values=(1, 2, 0), + dense_shape=(2, 2)) + + embedding_dimension_a = 2 + embedding_values_a = ( + (1., 2.), # id 0 + (3., 4.), # id 1 + (5., 6.) # id 2 + ) + embedding_dimension_b = 3 + embedding_values_b = ( + (11., 12., 13.), # id 0 + (14., 15., 16.), # id 1 + (17., 18., 19.) # id 2 + ) + def _get_initializer(embedding_dimension, embedding_values): + def _initializer(shape, dtype, partition_info): + self.assertAllEqual((vocabulary_size, embedding_dimension), shape) + self.assertEqual(dtypes.float32, dtype) + self.assertIsNone(partition_info) + return embedding_values + return _initializer + + expected_input_layer = [ + # example 0, ids_a [2], ids_b [1] + [[5., 6., 14., 15., 16.], [0., 0., 0., 0., 0.]], + # example 1, ids_a [0, 1], ids_b [2, 0] + [[1., 2., 17., 18., 19.], [3., 4., 11., 12., 13.]], + ] + expected_sequence_length = [1, 2] + + categorical_column_a = sfc.sequence_categorical_column_with_identity( + key='aaa', num_buckets=vocabulary_size) + embedding_column_a = sfc._sequence_embedding_column( + categorical_column_a, dimension=embedding_dimension_a, + initializer=_get_initializer(embedding_dimension_a, embedding_values_a)) + categorical_column_b = sfc.sequence_categorical_column_with_identity( + key='bbb', num_buckets=vocabulary_size) + embedding_column_b = sfc._sequence_embedding_column( + categorical_column_b, dimension=embedding_dimension_b, + initializer=_get_initializer(embedding_dimension_b, embedding_values_b)) + + input_layer, sequence_length = sfc.sequence_input_layer( + features={ + 'aaa': sparse_input_a, + 'bbb': sparse_input_b, + }, + # Test that columns are reordered alphabetically. + feature_columns=[embedding_column_b, embedding_column_a]) + + global_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES) + self.assertItemsEqual( + ('sequence_input_layer/aaa_embedding/embedding_weights:0', + 'sequence_input_layer/bbb_embedding/embedding_weights:0'), + tuple([v.name for v in global_vars])) + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual(embedding_values_a, global_vars[0].eval(session=sess)) + self.assertAllEqual(embedding_values_b, global_vars[1].eval(session=sess)) + self.assertAllEqual(expected_input_layer, input_layer.eval(session=sess)) + self.assertAllEqual( + expected_sequence_length, sequence_length.eval(session=sess)) + + def test_numeric_column(self): + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, values [[0.], [1]] + # example 1, [[10.]] + indices=((0, 0), (0, 1), (1, 0)), + values=(0., 1., 10.), + dense_shape=(2, 2)) + expected_input_layer = [ + [[0.], [1.]], + [[10.], [0.]], + ] + expected_sequence_length = [2, 1] + numeric_column = sfc.sequence_numeric_column('aaa') + + input_layer, sequence_length = sfc.sequence_input_layer( + features={'aaa': sparse_input}, + feature_columns=[numeric_column]) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual(expected_input_layer, input_layer.eval(session=sess)) + self.assertAllEqual( + expected_sequence_length, sequence_length.eval(session=sess)) + + def test_numeric_column_multi_dim(self): + """Tests sequence_input_layer for multi-dimensional numeric_column.""" + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, values [[[0., 1.], [2., 3.]], [[4., 5.], [6., 7.]]] + # example 1, [[[10., 11.], [12., 13.]]] + indices=((0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (0, 7), + (1, 0), (1, 1), (1, 2), (1, 3)), + values=(0., 1., 2., 3., 4., 5., 6., 7., 10., 11., 12., 13.), + dense_shape=(2, 8)) + # The output of numeric_column._get_dense_tensor should be flattened. + expected_input_layer = [ + [[0., 1., 2., 3.], [4., 5., 6., 7.]], + [[10., 11., 12., 13.], [0., 0., 0., 0.]], + ] + expected_sequence_length = [2, 1] + numeric_column = sfc.sequence_numeric_column('aaa', shape=(2, 2)) + + input_layer, sequence_length = sfc.sequence_input_layer( + features={'aaa': sparse_input}, + feature_columns=[numeric_column]) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual(expected_input_layer, input_layer.eval(session=sess)) + self.assertAllEqual( + expected_sequence_length, sequence_length.eval(session=sess)) + + +def _assert_sparse_tensor_value(test_case, expected, actual): + test_case.assertEqual(np.int64, np.array(actual.indices).dtype) + test_case.assertAllEqual(expected.indices, actual.indices) + + test_case.assertEqual( + np.array(expected.values).dtype, np.array(actual.values).dtype) + test_case.assertAllEqual(expected.values, actual.values) + + test_case.assertEqual(np.int64, np.array(actual.dense_shape).dtype) + test_case.assertAllEqual(expected.dense_shape, actual.dense_shape) + + +class SequenceCategoricalColumnWithIdentityTest(test.TestCase): + + def test_get_sparse_tensors(self): + column = sfc.sequence_categorical_column_with_identity( + 'aaa', num_buckets=3) + inputs = sparse_tensor.SparseTensorValue( + indices=((0, 0), (1, 0), (1, 1)), + values=(1, 2, 0), + dense_shape=(2, 2)) + expected_sparse_ids = sparse_tensor.SparseTensorValue( + indices=((0, 0, 0), (1, 0, 0), (1, 1, 0)), + values=np.array((1, 2, 0), dtype=np.int64), + dense_shape=(2, 2, 1)) + + id_weight_pair = column._get_sparse_tensors(_LazyBuilder({'aaa': inputs})) + + self.assertIsNone(id_weight_pair.weight_tensor) + with monitored_session.MonitoredSession() as sess: + _assert_sparse_tensor_value( + self, + expected_sparse_ids, + id_weight_pair.id_tensor.eval(session=sess)) + + def test_get_sparse_tensors_inputs3d(self): + """Tests _get_sparse_tensors when the input is already 3D Tensor.""" + column = sfc.sequence_categorical_column_with_identity( + 'aaa', num_buckets=3) + inputs = sparse_tensor.SparseTensorValue( + indices=((0, 0, 0), (1, 0, 0), (1, 1, 0)), + values=(1, 2, 0), + dense_shape=(2, 2, 1)) + + with self.assertRaisesRegexp( + errors.InvalidArgumentError, + r'Column aaa expected ID tensor of rank 2\.\s*' + r'id_tensor shape:\s*\[2 2 1\]'): + id_weight_pair = column._get_sparse_tensors( + _LazyBuilder({'aaa': inputs})) + with monitored_session.MonitoredSession() as sess: + id_weight_pair.id_tensor.eval(session=sess) + + def test_sequence_length(self): + column = sfc.sequence_categorical_column_with_identity( + 'aaa', num_buckets=3) + inputs = sparse_tensor.SparseTensorValue( + indices=((0, 0), (1, 0), (1, 1)), + values=(1, 2, 0), + dense_shape=(2, 2)) + expected_sequence_length = [1, 2] + + sequence_length = column._sequence_length(_LazyBuilder({'aaa': inputs})) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual( + expected_sequence_length, sequence_length.eval(session=sess)) + + def test_sequence_length_with_zeros(self): + column = sfc.sequence_categorical_column_with_identity( + 'aaa', num_buckets=3) + inputs = sparse_tensor.SparseTensorValue( + indices=((1, 0), (3, 0), (3, 1)), + values=(1, 2, 0), + dense_shape=(5, 2)) + expected_sequence_length = [0, 1, 0, 2, 0] + + sequence_length = column._sequence_length(_LazyBuilder({'aaa': inputs})) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual( + expected_sequence_length, sequence_length.eval(session=sess)) + + +class SequenceEmbeddingColumnTest(test.TestCase): + + def test_get_sequence_dense_tensor(self): + vocabulary_size = 3 + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, ids [2] + # example 1, ids [0, 1] + # example 2, ids [] + # example 3, ids [1] + indices=((0, 0), (1, 0), (1, 1), (3, 0)), + values=(2, 0, 1, 1), + dense_shape=(4, 2)) + + embedding_dimension = 2 + embedding_values = ( + (1., 2.), # id 0 + (3., 5.), # id 1 + (7., 11.) # id 2 + ) + def _initializer(shape, dtype, partition_info): + self.assertAllEqual((vocabulary_size, embedding_dimension), shape) + self.assertEqual(dtypes.float32, dtype) + self.assertIsNone(partition_info) + return embedding_values + + expected_lookups = [ + # example 0, ids [2] + [[7., 11.], [0., 0.]], + # example 1, ids [0, 1] + [[1., 2.], [3., 5.]], + # example 2, ids [] + [[0., 0.], [0., 0.]], + # example 3, ids [1] + [[3., 5.], [0., 0.]], + ] + + categorical_column = sfc.sequence_categorical_column_with_identity( + key='aaa', num_buckets=vocabulary_size) + embedding_column = sfc._sequence_embedding_column( + categorical_column, dimension=embedding_dimension, + initializer=_initializer) + + embedding_lookup, _ = embedding_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + global_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES) + self.assertItemsEqual( + ('embedding_weights:0',), tuple([v.name for v in global_vars])) + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual(embedding_values, global_vars[0].eval(session=sess)) + self.assertAllEqual(expected_lookups, embedding_lookup.eval(session=sess)) + + def test_sequence_length(self): + vocabulary_size = 3 + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, ids [2] + # example 1, ids [0, 1] + indices=((0, 0), (1, 0), (1, 1)), + values=(2, 0, 1), + dense_shape=(2, 2)) + expected_sequence_length = [1, 2] + + categorical_column = sfc.sequence_categorical_column_with_identity( + key='aaa', num_buckets=vocabulary_size) + embedding_column = sfc._sequence_embedding_column( + categorical_column, dimension=2) + + _, sequence_length = embedding_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual( + expected_sequence_length, sequence_length.eval(session=sess)) + + def test_sequence_length_with_empty_rows(self): + """Tests _sequence_length when some examples do not have ids.""" + vocabulary_size = 3 + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, ids [] + # example 1, ids [2] + # example 2, ids [0, 1] + # example 3, ids [] + # example 4, ids [1] + # example 5, ids [] + indices=((1, 0), (2, 0), (2, 1), (4, 0)), + values=(2, 0, 1, 1), + dense_shape=(6, 2)) + expected_sequence_length = [0, 1, 2, 0, 1, 0] + + categorical_column = sfc.sequence_categorical_column_with_identity( + key='aaa', num_buckets=vocabulary_size) + embedding_column = sfc._sequence_embedding_column( + categorical_column, dimension=2) + + _, sequence_length = embedding_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual( + expected_sequence_length, sequence_length.eval(session=sess)) + + +class SequenceNumericColumnTest(test.TestCase): + + def test_get_sequence_dense_tensor(self): + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, values [[0.], [1]] + # example 1, [[10.]] + indices=((0, 0), (0, 1), (1, 0)), + values=(0., 1., 10.), + dense_shape=(2, 2)) + expected_dense_tensor = [ + [[0.], [1.]], + [[10.], [0.]], + ] + numeric_column = sfc.sequence_numeric_column('aaa') + + dense_tensor, _ = numeric_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual( + expected_dense_tensor, dense_tensor.eval(session=sess)) + + def test_get_sequence_dense_tensor_with_shape(self): + """Tests get_sequence_dense_tensor with shape !=(1,).""" + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, values [[0., 1., 2.], [3., 4., 5.]] + # example 1, [[10., 11., 12.]] + indices=((0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), + (1, 0), (1, 1), (1, 2)), + values=(0., 1., 2., 3., 4., 5., 10., 11., 12.), + dense_shape=(2, 6)) + expected_dense_tensor = [ + [[0., 1., 2.], [3., 4., 5.]], + [[10., 11., 12.], [0., 0., 0.]], + ] + numeric_column = sfc.sequence_numeric_column('aaa', shape=(3,)) + + dense_tensor, _ = numeric_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual( + expected_dense_tensor, dense_tensor.eval(session=sess)) + + def test_get_dense_tensor_multi_dim(self): + """Tests get_sequence_dense_tensor for multi-dim numeric_column.""" + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, values [[[0., 1.], [2., 3.]], [[4., 5.], [6., 7.]]] + # example 1, [[[10., 11.], [12., 13.]]] + indices=((0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (0, 7), + (1, 0), (1, 1), (1, 2), (1, 3)), + values=(0., 1., 2., 3., 4., 5., 6., 7., 10., 11., 12., 13.), + dense_shape=(2, 8)) + expected_dense_tensor = [ + [[[0., 1.], [2., 3.]], [[4., 5.], [6., 7.]]], + [[[10., 11.], [12., 13.]], [[0., 0.], [0., 0.]]], + ] + numeric_column = sfc.sequence_numeric_column('aaa', shape=(2, 2)) + + dense_tensor, _ = numeric_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual( + expected_dense_tensor, dense_tensor.eval(session=sess)) + + def test_sequence_length(self): + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, values [[0., 1., 2.], [3., 4., 5.]] + # example 1, [[10., 11., 12.]] + indices=((0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), + (1, 0), (1, 1), (1, 2)), + values=(0., 1., 2., 3., 4., 5., 10., 11., 12.), + dense_shape=(2, 6)) + expected_sequence_length = [2, 1] + numeric_column = sfc.sequence_numeric_column('aaa', shape=(3,)) + + _, sequence_length = numeric_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual( + expected_sequence_length, sequence_length.eval(session=sess)) + + def test_sequence_length_with_shape(self): + """Tests _sequence_length with shape !=(1,).""" + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, values [[0.], [1]] + # example 1, [[10.]] + indices=((0, 0), (0, 1), (1, 0)), + values=(0., 1., 10.), + dense_shape=(2, 2)) + expected_sequence_length = [2, 1] + numeric_column = sfc.sequence_numeric_column('aaa') + + _, sequence_length = numeric_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual( + expected_sequence_length, sequence_length.eval(session=sess)) + + def test_sequence_length_with_empty_rows(self): + """Tests _sequence_length when some examples do not have ids.""" + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, values [] + # example 1, values [[0.], [1.]] + # example 2, [[2.]] + # example 3, values [] + # example 4, [[3.]] + # example 5, values [] + indices=((1, 0), (1, 1), (2, 0), (4, 0)), + values=(0., 1., 2., 3.), + dense_shape=(6, 2)) + expected_sequence_length = [0, 2, 1, 0, 1, 0] + numeric_column = sfc.sequence_numeric_column('aaa') + + _, sequence_length = numeric_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual( + expected_sequence_length, sequence_length.eval(session=sess)) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/ffmpeg/default/ffmpeg_lib.cc b/tensorflow/contrib/ffmpeg/default/ffmpeg_lib.cc index e61221a6b0d34373279a379f356c99c379488182..35341406a08dc681c861aea30fcff784e3b963ef 100644 --- a/tensorflow/contrib/ffmpeg/default/ffmpeg_lib.cc +++ b/tensorflow/contrib/ffmpeg/default/ffmpeg_lib.cc @@ -256,6 +256,9 @@ Status ReadInfoFile(const string& filename, uint32* width, uint32* height, if (p != std::string::npos) { string rgb24 = line.substr(p + 9, line.find(" ", p + 9)); rgb24 = rgb24.substr(0, rgb24.find(",")); + // Strip anything after " ", in case the format is + // `640x360 [SAR 1:1 DAR 16:9]` + rgb24 = rgb24.substr(0, rgb24.find(" ")); string rgb24_width = rgb24.substr(0, rgb24.find("x")); string rgb24_height = rgb24.substr(rgb24_width.length() + 1); if (strings::safe_strtou32(rgb24_width, &width_value) && @@ -270,8 +273,10 @@ Status ReadInfoFile(const string& filename, uint32* width, uint32* height, // We only look for the first stream mapping to have the number of the // frames. // Once processed we will not further process stream mapping section. - if (line.find("frame= ") == 0) { - string number = line.substr(8, line.find(" ", 8)); + if (line.find("frame=") == 0) { + // The format might be `frame= 166 ` or `frame=12488 ` + string number = line.substr(6); + number = number.substr(number.find_first_not_of(" ")); number = number.substr(0, number.find(" ")); if (strings::safe_strtou32(number, &frames_value)) { in_mapping = false; diff --git a/tensorflow/contrib/framework/BUILD b/tensorflow/contrib/framework/BUILD index dbdb5cfaaca1a687fefb81cee200295d5cbb7fd5..ac043fda0638e61f422e769ab3047a53a1b377bd 100644 --- a/tensorflow/contrib/framework/BUILD +++ b/tensorflow/contrib/framework/BUILD @@ -28,7 +28,6 @@ tf_custom_op_py_library( "python/framework/graph_util.py", "python/framework/tensor_util.py", "python/ops/__init__.py", - "python/ops/accumulate_n_v2.py", "python/ops/arg_scope.py", "python/ops/audio_ops.py", "python/ops/checkpoint_ops.py", @@ -63,7 +62,9 @@ tf_custom_op_py_library( "//tensorflow/python:math_ops", "//tensorflow/python:platform", "//tensorflow/python:pywrap_tensorflow", + "//tensorflow/python:resource_variable_ops", "//tensorflow/python:script_ops", + "//tensorflow/python:smart_cond", "//tensorflow/python:sparse_tensor", "//tensorflow/python:state_ops", "//tensorflow/python:state_ops_gen", @@ -161,23 +162,6 @@ py_test( ], ) -py_test( - name = "accumulate_n_v2_test", - size = "small", - srcs = ["python/ops/accumulate_n_v2_test.py"], - srcs_version = "PY2AND3", - deps = [ - ":framework_py", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:framework_test_lib", - "//tensorflow/python:gradients", - "//tensorflow/python:platform_test", - "//tensorflow/python:variables", - "//third_party/py/numpy", - ], -) - cuda_py_test( name = "critical_section_test", size = "medium", @@ -196,26 +180,6 @@ cuda_py_test( ], ) -py_test( - name = "accumulate_n_v2_eager_test", - size = "small", - srcs = ["python/ops/accumulate_n_v2_eager_test.py"], - srcs_version = "PY2AND3", - deps = [ - ":framework_py", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:framework_test_lib", - "//tensorflow/python:gradients", - "//tensorflow/python:math_ops", - "//tensorflow/python:resource_variable_ops", - "//tensorflow/python/eager:backprop", - "//tensorflow/python/eager:context", - "//tensorflow/python/eager:tape", - "//third_party/py/numpy", - ], -) - py_test( name = "ops_test", size = "small", diff --git a/tensorflow/contrib/framework/__init__.py b/tensorflow/contrib/framework/__init__.py index deeb5bec79341f3e0468a127aeead69f960114d8..3398b3fd1c1036091bfadf548f7d44dbf9eb1046 100644 --- a/tensorflow/contrib/framework/__init__.py +++ b/tensorflow/contrib/framework/__init__.py @@ -71,6 +71,7 @@ See the @{$python/contrib.framework} guide. @@model_variable @@variable @@VariableDeviceChooser +@@convolutional_delta_orthogonal @@zero_initializer @@load_checkpoint @@ -87,6 +88,10 @@ See the @{$python/contrib.framework} guide. @@get_placeholders +@@smart_cond +@@smart_constant_value +@@smart_case + @@CriticalSection @@BoundedTensorSpec @@ -104,10 +109,12 @@ from tensorflow.contrib.framework.python.ops import * from tensorflow.python.framework.ops import prepend_name_scope from tensorflow.python.framework.ops import strip_name_scope +from tensorflow.python.framework.smart_cond import smart_case +from tensorflow.python.framework.smart_cond import smart_cond +from tensorflow.python.framework.smart_cond import smart_constant_value from tensorflow.python.framework.tensor_spec import BoundedTensorSpec from tensorflow.python.framework.tensor_spec import TensorSpec -from tensorflow.python.ops.control_flow_ops import smart_cond -from tensorflow.python.ops.control_flow_ops import smart_constant_value +from tensorflow.python.ops.init_ops import convolutional_delta_orthogonal from tensorflow.python.util.all_util import remove_undocumented _allowed_symbols = ['nest'] diff --git a/tensorflow/contrib/framework/python/framework/experimental_test.py b/tensorflow/contrib/framework/python/framework/experimental_test.py index 8e54e09e04ee3c0ddbd4fa84cc0912cb70c93e62..cfdc7df7d8fd4c1406bf447a79038ac33b11e047 100644 --- a/tensorflow/contrib/framework/python/framework/experimental_test.py +++ b/tensorflow/contrib/framework/python/framework/experimental_test.py @@ -49,7 +49,6 @@ class ExperimentalTest(test.TestCase): "\nTHIS FUNCTION IS EXPERIMENTAL. It may change or " "be removed at any time, and without warning." "\n" - "\n" "\nArgs:" "\n arg0: Arg 0." "\n arg1: Arg 1." diff --git a/tensorflow/contrib/framework/python/framework/graph_util.py b/tensorflow/contrib/framework/python/framework/graph_util.py index 49eec3a3f1a0f357ea3adfade51e71cb0f89942d..2703224b1bf62831b6088558d4f93950fe938c10 100644 --- a/tensorflow/contrib/framework/python/framework/graph_util.py +++ b/tensorflow/contrib/framework/python/framework/graph_util.py @@ -85,14 +85,19 @@ def fuse_op(graph_def, input_nodes, output_nodes, output_dtypes, if n not in reachable_by_input and n not in output_nodes_set: # n is between input and output, i.e., part of the fused op next_to_visit = [n] + visited = set() while next_to_visit: cur_node = next_to_visit[0] + visited.add(cur_node) del next_to_visit[0] if cur_node in reachable_by_input and cur_node not in input_nodes_set: raise TypeError("Node %s uses input %s not in input_nodes." % (n, cur_node)) if cur_node not in input_nodes_set: - next_to_visit += name_to_input_name[cur_node] + next_to_visit += [ + input_node for input_node in name_to_input_name[cur_node] + if input_node not in visited + ] elif n not in reachable_by_input: nodes_post_output.append(n) diff --git a/tensorflow/contrib/framework/python/framework/graph_util_test.py b/tensorflow/contrib/framework/python/framework/graph_util_test.py index b8a6d109e19211d271c2b15bac66ddacd38fe395..812c5fbd8cb759aef6eb1aad532c03794b2ceaf4 100644 --- a/tensorflow/contrib/framework/python/framework/graph_util_test.py +++ b/tensorflow/contrib/framework/python/framework/graph_util_test.py @@ -42,7 +42,8 @@ class GraphUtilTest(test.TestCase): graph_def = graph_pb2.GraphDef() node_a = GetNewNode('A', 'Placeholder', []) node_b = GetNewNode('B', 'Op1', ['A']) - node_c = GetNewNode('C', 'Op1', ['B']) + # A loop in the part that will be fused. + node_c = GetNewNode('C', 'Op1', ['B', 'C']) node_d = GetNewNode('D', 'Op1', ['C']) node_e = GetNewNode('E', 'Op1', ['D']) graph_def.node.extend([node_a, node_b, node_c, node_d, node_e]) diff --git a/tensorflow/contrib/framework/python/framework/tensor_util_test.py b/tensorflow/contrib/framework/python/framework/tensor_util_test.py index 8cdb340f2ddd9b3a7f55c1937ef045f4627e99be..a2834b648933772cab53002462c3edbe9a553e94 100644 --- a/tensorflow/contrib/framework/python/framework/tensor_util_test.py +++ b/tensorflow/contrib/framework/python/framework/tensor_util_test.py @@ -209,6 +209,7 @@ class WithShapeTest(test.TestCase): self.assertRaisesRegexp(errors_impl.OpError, "Wrong shape", tensor_2x2.eval, {tensor_no_shape: [42.0]}) + @test_util.enable_c_shapes def test_with_shape_partial(self): with self.test_session(): tensor_partial_shape = array_ops.placeholder(dtypes.float32) diff --git a/tensorflow/contrib/framework/python/ops/accumulate_n_v2.py b/tensorflow/contrib/framework/python/ops/accumulate_n_v2.py deleted file mode 100644 index 476528b0dd3df05239d5dc402b466e06dd789985..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/framework/python/ops/accumulate_n_v2.py +++ /dev/null @@ -1,111 +0,0 @@ -# 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. -# ============================================================================== -"""Ops that will eventually be folded into tensorflow/python/ops/math_ops.py -""" -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 ops -from tensorflow.python.framework import tensor_shape -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import gen_math_ops -from tensorflow.python.ops import math_ops - - - -def accumulate_n_v2(inputs, shape=None, tensor_dtype=None, name=None): - """Returns the element-wise sum of a list of tensors. - - Optionally, pass `shape` and `tensor_dtype` for shape and type checking, - otherwise, these are inferred. - - `tf.accumulate_n_v2` performs the same operation as `tf.add_n`, but does not - wait for all of its inputs to be ready before beginning to sum. This can - save memory if inputs are ready at different times, since minimum temporary - storage is proportional to the output size rather than the inputs size. - - Unlike the original `accumulate_n`, `accumulate_n_v2` is differentiable. - - For example: - - ```python - a = tf.constant([[1, 2], [3, 4]]) - b = tf.constant([[5, 0], [0, 6]]) - tf.accumulate_n_v2([a, b, a]) # [[7, 4], [6, 14]] - - # Explicitly pass shape and type - tf.accumulate_n_v2([a, b, a], shape=[2, 2], tensor_dtype=tf.int32) - # [[7, 4], - # [6, 14]] - ``` - - Args: - inputs: A list of `Tensor` objects, each with same shape and type. - shape: Shape of elements of `inputs`. - tensor_dtype: The type of `inputs`. - name: A name for the operation (optional). - - Returns: - A `Tensor` of same shape and type as the elements of `inputs`. - - Raises: - ValueError: If `inputs` don't all have same shape and dtype or the shape - cannot be inferred. - """ - _INPUTS_ERR_MSG = ValueError("inputs must be a list of at least one Tensor" - "with the same dtype and shape") - if not inputs or not isinstance(inputs, (list, tuple)): - raise _INPUTS_ERR_MSG - inputs = ops.convert_n_to_tensor_or_indexed_slices(inputs) - if not all(isinstance(x, ops.Tensor) for x in inputs): - raise _INPUTS_ERR_MSG - if not all(x.dtype == inputs[0].dtype for x in inputs): - raise _INPUTS_ERR_MSG - if shape is not None: - shape = tensor_shape.as_shape(shape) - else: - shape = tensor_shape.unknown_shape() - for input_tensor in inputs: - if isinstance(input_tensor, ops.Tensor): - shape = shape.merge_with(input_tensor.get_shape()) - - # tensor_dtype is for safety only; operator's output type computed in C++ - if tensor_dtype is not None and tensor_dtype != inputs[0].dtype: - raise TypeError("tensor_dtype is {}, but input is of type {}" - .format(tensor_dtype, inputs[0].dtype)) - - if len(inputs) == 1 and name is None: - return inputs[0] - elif len(inputs) == 1 and name is not None: - return array_ops.identity(inputs[0], name=name) - elif context.in_eager_mode(): - # TemporaryVariable not currently supported in eager mode; fall back - # onto AddN for now. - # TODO(frreiss) remove this once the lifetime of eager variables gets - # addressed - return math_ops.add_n(inputs, name=name) - else: - return gen_math_ops._accumulate_nv2(inputs, name=name, shape=shape) - -# The following code should eventually be merged into -# tensorflow/python/ops/math_grad.py -@ops.RegisterGradient("AccumulateNV2") -def _AddNGrad(op, grad): - """Same as gradient for AddN. Copies the gradient to all inputs.""" - # Not broadcasting. - return [grad] * len(op.inputs) diff --git a/tensorflow/contrib/framework/python/ops/arg_scope.py b/tensorflow/contrib/framework/python/ops/arg_scope.py index 409657fe1da0e5540cd2ad6070d86737c039e91f..3cad1fee1984042e3a9ab91a0af70cbaca25cece 100644 --- a/tensorflow/contrib/framework/python/ops/arg_scope.py +++ b/tensorflow/contrib/framework/python/ops/arg_scope.py @@ -142,7 +142,7 @@ def arg_scope(list_ops_or_scope, **kwargs): else: # Assumes that list_ops_or_scope is a list/tuple of ops with kwargs. if not isinstance(list_ops_or_scope, (list, tuple)): - raise TypeError('list_ops_or_scope must either be a list/tuple or reused' + raise TypeError('list_ops_or_scope must either be a list/tuple or reused ' 'scope (i.e. dict)') try: current_scope = current_arg_scope().copy() diff --git a/tensorflow/contrib/framework/python/ops/critical_section_ops.py b/tensorflow/contrib/framework/python/ops/critical_section_ops.py index 3c5c55ed656432a33f19462130a9e58c2ab14efb..cc19372acf956371c2d029c7b8eb5534c3789413 100644 --- a/tensorflow/contrib/framework/python/ops/critical_section_ops.py +++ b/tensorflow/contrib/framework/python/ops/critical_section_ops.py @@ -143,7 +143,7 @@ class CriticalSection(object): def _init_from_args(self, name, shared_name): # pylint: disable=invalid-name """Initialize the CriticalSection from constructor arguments.""" with ops.name_scope(name, "CriticalSection", []) as name: - with ops.control_dependencies(None): + with ops.init_scope(): # pylint: disable=protected-access container = ops.get_default_graph()._container # pylint: enable=protected-access @@ -154,7 +154,7 @@ class CriticalSection(object): self._handle = gen_resource_variable_ops.mutex_v2( shared_name=shared_name, container=container, name=name) - if context.in_graph_mode(): + if not context.executing_eagerly(): ops.add_to_collections(CRITICAL_SECTIONS, self) @property @@ -221,12 +221,14 @@ class CriticalSection(object): "This is illegal and would cause deadlocks. " "CriticalSection: %s." % self._handle) - if context.in_graph_mode(): + if not context.executing_eagerly(): # Collections and op introspection does not work in eager # mode. This is generally ok; since eager mode (as of # writing) executes sequentially anyway. for sg in ops.get_collection(CRITICAL_SECTION_EXECUTIONS): - if sg.handle.name == self._handle.name: + sg_handle_name = ops.convert_to_tensor(sg.handle).name + self_handle_name = ops.convert_to_tensor(self._handle).name + if sg_handle_name == self_handle_name: # Other executions in the same critical section are allowed. continue if not (exclusive_resource_access or sg.exclusive_resource_access): @@ -248,7 +250,7 @@ class CriticalSection(object): return x.identity() elif isinstance(x, ops.Operation): return control_flow_ops.group(x) - elif context.in_eager_mode() and x is None: + elif context.executing_eagerly() and x is None: return None else: return array_ops.identity(x) @@ -272,7 +274,7 @@ class CriticalSection(object): with ops.control_dependencies([ensure_lock_exists]): outputs = nest.map_structure(identity, r) - if context.in_graph_mode(): + if not context.executing_eagerly(): signature = _ExecutionSignature( op=lock.op, handle=self._handle, diff --git a/tensorflow/contrib/gan/BUILD b/tensorflow/contrib/gan/BUILD index 0eb0e3cbe20f5804db5476c08167d4e1c9080cfa..ff6f3b744190c9a7c74fb88878e5f13412251e79 100644 --- a/tensorflow/contrib/gan/BUILD +++ b/tensorflow/contrib/gan/BUILD @@ -354,6 +354,7 @@ py_test( name = "classifier_metrics_test", srcs = ["python/eval/python/classifier_metrics_test.py"], srcs_version = "PY2AND3", + tags = ["no_windows"], # TODO: needs investigation on Windows deps = [ ":classifier_metrics", "//tensorflow/core:protos_all_py", diff --git a/tensorflow/contrib/gan/python/eval/python/classifier_metrics_impl.py b/tensorflow/contrib/gan/python/eval/python/classifier_metrics_impl.py index fdfabd07c13f689d075ecbb8786d725fa8a62d01..4811edcbcfa63e99210b3c2f416b71bb83915869 100644 --- a/tensorflow/contrib/gan/python/eval/python/classifier_metrics_impl.py +++ b/tensorflow/contrib/gan/python/eval/python/classifier_metrics_impl.py @@ -317,7 +317,7 @@ def classifier_score(images, classifier_fn, num_batches=1): NOTE: This function consumes images, computes their logits, and then computes the classifier score. If you would like to precompute many logits for - large batches, use clasifier_score_from_logits(), which this method also + large batches, use classifier_score_from_logits(), which this method also uses. Args: @@ -450,7 +450,7 @@ def frechet_classifier_distance(real_images, This technique is described in detail in https://arxiv.org/abs/1706.08500. Given two Gaussian distribution with means m and m_w and covariance matrices - C and C_w, this function calcuates + C and C_w, this function calculates |m - m_w|^2 + Tr(C + C_w - 2(C * C_w)^(1/2)) @@ -463,7 +463,7 @@ def frechet_classifier_distance(real_images, Frechet distance is biased. It is more biased for small sample sizes. (e.g. even if the two distributions are the same, for a small sample size, the expected Frechet distance is large). It is important to use the same - sample size to compute frechet classifier distance when comparing two + sample size to compute Frechet classifier distance when comparing two generative models. NOTE: This function consumes images, computes their activations, and then @@ -523,7 +523,7 @@ def frechet_classifier_distance_from_activations( This technique is described in detail in https://arxiv.org/abs/1706.08500. Given two Gaussian distribution with means m and m_w and covariance matrices - C and C_w, this function calcuates + C and C_w, this function calculates |m - m_w|^2 + Tr(C + C_w - 2(C * C_w)^(1/2)) diff --git a/tensorflow/contrib/gan/python/eval/python/sliced_wasserstein_impl.py b/tensorflow/contrib/gan/python/eval/python/sliced_wasserstein_impl.py index 9bebcacbe46d85fc4226c4275b71b3ecbde57a97..4b10bc0f8e607c02763d8ea622d6f8f2572c586d 100644 --- a/tensorflow/contrib/gan/python/eval/python/sliced_wasserstein_impl.py +++ b/tensorflow/contrib/gan/python/eval/python/sliced_wasserstein_impl.py @@ -212,7 +212,7 @@ def sliced_wasserstein_distance(real_images, Args: real_images: (tensor) Real images (batch, height, width, channels). fake_images: (tensor) Fake images (batch, height, width, channels). - resolution_min: (int) Minimum resolution for the Laplacion pyramid. + resolution_min: (int) Minimum resolution for the Laplacian pyramid. patches_per_image: (int) Number of patches to extract per image per Laplacian level. patch_size: (int) Width of a square patch. @@ -221,7 +221,7 @@ def sliced_wasserstein_distance(real_images, use_svd: experimental method to compute a more accurate distance. Returns: List of tuples (distance_real, distance_fake) for each level of the - Laplacian pyramid from the highest resoluion to the lowest. + Laplacian pyramid from the highest resolution to the lowest. distance_real is the Wasserstein distance between real images distance_fake is the Wasserstein distance between real and fake images. Raises: 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 cd31c62667fc048b1003d334377405b284f32af5..e2594faf85bcf91cbe09f266e4d4211d20bdee17 100644 --- a/tensorflow/contrib/gan/python/features/python/conditioning_utils_impl.py +++ b/tensorflow/contrib/gan/python/features/python/conditioning_utils_impl.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Miscellanous utilities for TFGAN code and examples. +"""Miscellaneous utilities for TFGAN code and examples. Includes: 1) Conditioning the value of a Tensor, based on techniques from diff --git a/tensorflow/contrib/gan/python/features/python/random_tensor_pool_impl.py b/tensorflow/contrib/gan/python/features/python/random_tensor_pool_impl.py index 4cfae0de4451880cf8229903b0eb74b1c6e2e04d..9e4ec59e7098443efc53506a4ba159e84b5c1618 100644 --- a/tensorflow/contrib/gan/python/features/python/random_tensor_pool_impl.py +++ b/tensorflow/contrib/gan/python/features/python/random_tensor_pool_impl.py @@ -17,7 +17,7 @@ We use this to keep a history of values created by a generator, such that a discriminator can randomly be trained on some older samples, not just the current one. This can help to not let the discriminator get too far ahead of the -generator and also to keep the system from oscilating, if the discriminator +generator and also to keep the system from oscillating, if the discriminator forgets too fast what past samples from the generator looked like. See the following papers for more details. @@ -97,7 +97,7 @@ def tensor_pool(input_values, dtypes=[v.dtype for v in input_values], shapes=None) - # In pseudeo code this code does the following: + # In pseudo code this code does the following: # if not pool_full: # enqueue(input_values) # return input_values diff --git a/tensorflow/contrib/gan/python/features/python/virtual_batchnorm_test.py b/tensorflow/contrib/gan/python/features/python/virtual_batchnorm_test.py index 845f89827b6e60eda41a55a80671f43460247b05..2fe06a287284ff994326d5a977a2e4d4634268ae 100644 --- a/tensorflow/contrib/gan/python/features/python/virtual_batchnorm_test.py +++ b/tensorflow/contrib/gan/python/features/python/virtual_batchnorm_test.py @@ -148,7 +148,7 @@ class VirtualBatchnormTest(test.TestCase): self.assertAllClose(bn_np[i, ...], vb_np) def test_minibatch_independent(self): - """Test that virtual batch normalized exampels are independent. + """Test that virtual batch normalized examples are independent. Unlike batch normalization, virtual batch normalization has the property that the virtual batch normalized value of an example is independent of the diff --git a/tensorflow/contrib/graph_editor/reroute.py b/tensorflow/contrib/graph_editor/reroute.py index 7ffdbb7139281734917fdb715601b317eb58b82f..95c02a64d47c26e731ef2628fb551529e9bc3f4d 100644 --- a/tensorflow/contrib/graph_editor/reroute.py +++ b/tensorflow/contrib/graph_editor/reroute.py @@ -471,9 +471,10 @@ def remove_control_inputs(op, cops): if cop not in op.control_inputs: raise ValueError("{} is not a control_input of {}".format(op.name, cop.name)) + control_inputs = [cop for cop in op.control_inputs if cop not in cops] # pylint: disable=protected-access - op._control_inputs = [cop for cop in op._control_inputs if cop not in cops] - op._recompute_node_def() + op._remove_all_control_inputs() + op._add_control_inputs(control_inputs) # pylint: enable=protected-access @@ -496,9 +497,6 @@ def add_control_inputs(op, cops): if cop in op.control_inputs: raise ValueError("{} is already a control_input of {}".format(cop.name, op.name)) - # pylint: disable=protected-access - op._control_inputs += cops - op._recompute_node_def() - # pylint: enable=protected-access + op._add_control_inputs(cops) # pylint: disable=protected-access remove_undocumented(__name__, _allowed_symbols) diff --git a/tensorflow/contrib/grid_rnn/python/ops/grid_rnn_cell.py b/tensorflow/contrib/grid_rnn/python/ops/grid_rnn_cell.py index 252788140f8c1906718c150574b963385b6ecfa1..bcd2a34c4e791a2ab66a439109145d6b78c14e22 100644 --- a/tensorflow/contrib/grid_rnn/python/ops/grid_rnn_cell.py +++ b/tensorflow/contrib/grid_rnn/python/ops/grid_rnn_cell.py @@ -110,7 +110,7 @@ class GridRNNCell(rnn.RNNCell): logging.warning('%s: Using a concatenated state is slower and will ' 'soon be deprecated. Use state_is_tuple=True.', self) if not output_is_tuple: - logging.warning('%s: Using a concatenated output is slower and will' + logging.warning('%s: Using a concatenated output is slower and will ' 'soon be deprecated. Use output_is_tuple=True.', self) if num_dims < 1: diff --git a/tensorflow/contrib/image/kernels/segmentation_ops.cc b/tensorflow/contrib/image/kernels/segmentation_ops.cc index fe8bf6e21c7b7310527668324571774e8bc50893..93722896233f0278c6cbb44af7203345e58c3172 100644 --- a/tensorflow/contrib/image/kernels/segmentation_ops.cc +++ b/tensorflow/contrib/image/kernels/segmentation_ops.cc @@ -101,8 +101,8 @@ struct ImageConnectedComponentsFunctor { int cost = (union_find.block_height() + union_find.block_width()) * 20; Shard(worker_threads->num_threads, worker_threads->workers, num_images * num_blocks_vertically * num_blocks_horizontally, cost, - [&union_find, num_images, num_blocks_vertically, - num_blocks_horizontally](int64 start_block, int64 limit_block) { + [&union_find, num_blocks_vertically, num_blocks_horizontally]( + int64 start_block, int64 limit_block) { for (int64 i = start_block; i < limit_block; i++) { int64 block_x = i % num_blocks_horizontally; int64 block_y = diff --git a/tensorflow/contrib/kafka/BUILD b/tensorflow/contrib/kafka/BUILD index efb403462a6e5df5b69ac0735ffc03f40d4a252c..1c3974871c62911c0cb47677eb92d28286837142 100644 --- a/tensorflow/contrib/kafka/BUILD +++ b/tensorflow/contrib/kafka/BUILD @@ -1,66 +1,93 @@ -package( - default_visibility = ["//visibility:private"], -) +package(default_visibility = ["//tensorflow:internal"]) licenses(["notice"]) # Apache 2.0 exports_files(["LICENSE"]) -load("//tensorflow:tensorflow.bzl", "tf_gen_op_libs") -load("//tensorflow:tensorflow.bzl", "tf_gen_op_wrapper_py") -load("//tensorflow:tensorflow.bzl", "tf_kernel_library") -load("//tensorflow:tensorflow.bzl", "tf_py_test") +load( + "//tensorflow:tensorflow.bzl", + "tf_gen_op_wrapper_py", + "tf_kernel_library", + "tf_custom_op_library", + "tf_custom_op_py_library", + "tf_gen_op_libs", + "tf_py_test", +) -tf_kernel_library( - name = "kafka_kernels", +py_library( + name = "kafka", + srcs = ["__init__.py"], + srcs_version = "PY2AND3", + deps = [ + ":dataset_ops", + ], +) + +tf_custom_op_library( + name = "_dataset_ops.so", + srcs = ["ops/dataset_ops.cc"], + deps = [":dataset_kernels"], +) + +tf_gen_op_libs( + op_lib_names = ["dataset_ops"], +) + +cc_library( + name = "dataset_kernels", srcs = ["kernels/kafka_dataset_ops.cc"], - visibility = ["//visibility:public"], deps = [ - "//tensorflow/core:framework", - "//tensorflow/core:lib", - "//tensorflow/core:lib_internal", - "//tensorflow/core/kernels:bounds_check_lib", - "//tensorflow/core/kernels:dataset", + "//tensorflow/core:framework_headers_lib", "//third_party/eigen3", "@kafka", + "@protobuf_archive//:protobuf_headers", ], + alwayslink = 1, ) -tf_gen_op_libs( - op_lib_names = ["kafka_ops"], +py_library( + name = "dataset_ops", + srcs = [ + "python/ops/kafka_dataset_ops.py", + ], + srcs_version = "PY2AND3", deps = [ - "//tensorflow/core:lib", + ":kafka_op_loader", + "//tensorflow/python:dataset_ops_gen", + "//tensorflow/python:util", + "//tensorflow/python/data/ops:dataset_ops", + "//tensorflow/python/data/util:nest", ], ) tf_gen_op_wrapper_py( - name = "gen_kafka_ops", - out = "python/ops/gen_kafka_ops.py", - require_shape_functions = True, - deps = [":kafka_ops_op_lib"], + name = "gen_dataset_ops", + out = "python/ops/gen_dataset_ops.py", + deps = ["//tensorflow/contrib/kafka:dataset_ops_op_lib"], ) -py_library( - name = "kafka", - srcs = [ - "__init__.py", - "python/ops/kafka_dataset_ops.py", +tf_kernel_library( + name = "dataset_ops_kernels", + deps = [ + ":dataset_kernels", + "//tensorflow/core:framework", + ], + alwayslink = 1, +) + +tf_custom_op_py_library( + name = "kafka_op_loader", + srcs = ["python/ops/kafka_op_loader.py"], + dso = ["//tensorflow/contrib/kafka:_dataset_ops.so"], + kernels = [ + ":dataset_ops_kernels", + "//tensorflow/contrib/kafka:dataset_ops_op_lib", ], srcs_version = "PY2AND3", - visibility = ["//visibility:public"], deps = [ - ":gen_kafka_ops", + ":gen_dataset_ops", "//tensorflow/contrib/util:util_py", - "//tensorflow/python:array_ops", - "//tensorflow/python:control_flow_ops", - "//tensorflow/python:framework", - "//tensorflow/python:framework_for_generated_wrappers", "//tensorflow/python:platform", - "//tensorflow/python:state_ops", - "//tensorflow/python:training", - "//tensorflow/python/data/ops:dataset_ops", - "//tensorflow/python/data/ops:iterator_ops", - "//tensorflow/python/data/ops:readers", ], ) @@ -88,6 +115,7 @@ tf_py_test( ], tags = [ "manual", + "no_windows", "notap", ], ) @@ -95,7 +123,9 @@ tf_py_test( filegroup( name = "all_files", srcs = glob( - ["**/*"], + include = [ + "**/*", + ], exclude = [ "**/METADATA", "**/OWNERS", diff --git a/tensorflow/contrib/kafka/kernels/kafka_dataset_ops.cc b/tensorflow/contrib/kafka/kernels/kafka_dataset_ops.cc index 88ef5f357113372b0a2d0cb13382ac980a61252d..a4cd4a2cc4b99b5906185bd2b942ed15c1ddf5e4 100644 --- a/tensorflow/contrib/kafka/kernels/kafka_dataset_ops.cc +++ b/tensorflow/contrib/kafka/kernels/kafka_dataset_ops.cc @@ -13,9 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/core/kernels/dataset.h" - -#include "tensorflow/core/framework/tensor.h" +#include "tensorflow/core/framework/dataset.h" #include "src-cpp/rdkafkacpp.h" diff --git a/tensorflow/contrib/kafka/ops/kafka_ops.cc b/tensorflow/contrib/kafka/ops/dataset_ops.cc similarity index 100% rename from tensorflow/contrib/kafka/ops/kafka_ops.cc rename to tensorflow/contrib/kafka/ops/dataset_ops.cc diff --git a/tensorflow/contrib/kafka/python/ops/kafka_dataset_ops.py b/tensorflow/contrib/kafka/python/ops/kafka_dataset_ops.py index 8e51d27a342359881de072c3979a2b5a7fc034ea..a1624614d1ab1be31463c5cdc0b4cfb653165a0c 100644 --- a/tensorflow/contrib/kafka/python/ops/kafka_dataset_ops.py +++ b/tensorflow/contrib/kafka/python/ops/kafka_dataset_ops.py @@ -17,8 +17,9 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.kafka.python.ops import gen_kafka_ops -from tensorflow.python.data.ops.readers import Dataset +from tensorflow.contrib.kafka.python.ops import kafka_op_loader # pylint: disable=unused-import +from tensorflow.contrib.kafka.python.ops import gen_dataset_ops +from tensorflow.python.data.ops.dataset_ops import Dataset from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape @@ -58,8 +59,8 @@ class KafkaDataset(Dataset): timeout, dtype=dtypes.int64, name="timeout") def _as_variant_tensor(self): - return gen_kafka_ops.kafka_dataset(self._topics, self._servers, self._group, - self._eof, self._timeout) + return gen_dataset_ops.kafka_dataset(self._topics, self._servers, + self._group, self._eof, self._timeout) @property def output_classes(self): diff --git a/tensorflow/contrib/bayesflow/python/ops/variable_utils.py b/tensorflow/contrib/kafka/python/ops/kafka_op_loader.py similarity index 66% rename from tensorflow/contrib/bayesflow/python/ops/variable_utils.py rename to tensorflow/contrib/kafka/python/ops/kafka_op_loader.py index eadf6f4d5fa1c776e2c71c66c4b64b8f5ac98359..ec2fdea962ef946d3f8f32b9e630b92649d612fe 100644 --- a/tensorflow/contrib/bayesflow/python/ops/variable_utils.py +++ b/tensorflow/contrib/kafka/python/ops/kafka_op_loader.py @@ -12,18 +12,13 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Utility functions related to managing `tf.Variable`s.""" - +"""Python helper for loading kafka ops and kernels.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function -# go/tf-wildcard-import -from tensorflow.contrib.bayesflow.python.ops.variable_utils_impl import * # pylint: disable=wildcard-import,unused-wildcard-import,g-importing-member -from tensorflow.python.util import all_util - -_allowed_symbols = [ - "externalize_variables_as_args", -] +from tensorflow.contrib.util import loader +from tensorflow.python.platform import resource_loader -all_util.remove_undocumented(__name__, _allowed_symbols) +_dataset_ops = loader.load_op_library( + resource_loader.get_path_to_datafile("../../_dataset_ops.so")) diff --git a/tensorflow/contrib/kfac/python/kernel_tests/BUILD b/tensorflow/contrib/kfac/python/kernel_tests/BUILD index f4ed978174a9ddd8b54a88e60bfb48a67a2e76d2..d1c449402a697dd5f8876c82a6682dde2d18b4df 100644 --- a/tensorflow/contrib/kfac/python/kernel_tests/BUILD +++ b/tensorflow/contrib/kfac/python/kernel_tests/BUILD @@ -36,6 +36,7 @@ py_test( srcs = ["fisher_factors_test.py"], srcs_version = "PY2AND3", deps = [ + "//tensorflow/contrib/kfac/python/ops:fisher_blocks", "//tensorflow/contrib/kfac/python/ops:fisher_factors", "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", @@ -113,6 +114,7 @@ py_test( name = "utils_test", srcs = ["utils_test.py"], srcs_version = "PY2AND3", + tags = ["no_windows"], # TODO: needs investigation on Windows deps = [ "//tensorflow/contrib/kfac/python/ops:utils", "//tensorflow/contrib/tpu", diff --git a/tensorflow/contrib/kfac/python/kernel_tests/estimator_test.py b/tensorflow/contrib/kfac/python/kernel_tests/estimator_test.py index b12f7be76907dc206667eb8ee0c750f3b8db57fc..30c5404e03910eedb48132b0d69b2eabb89a9149 100644 --- a/tensorflow/contrib/kfac/python/kernel_tests/estimator_test.py +++ b/tensorflow/contrib/kfac/python/kernel_tests/estimator_test.py @@ -90,59 +90,83 @@ class EstimatorTest(test.TestCase): def testEstimatorInitManualRegistration(self): with self._graph.as_default(): # We should be able to build an estimator for only the registered vars. - estimator.FisherEstimator(lambda: 0.2, [self.weights], 0.1, + estimator.FisherEstimator([self.weights], 0.1, 0.2, self.layer_collection) # Check that we throw an error if we try to build an estimator for vars # that were not manually registered. with self.assertRaises(ValueError): - estimator.FisherEstimator(lambda: 0.2, [self.weights, self.bias], 0.1, - self.layer_collection) + est = estimator.FisherEstimator([self.weights, self.bias], 0.1, 0.2, + self.layer_collection) + est.make_ops_and_vars() # Check that we throw an error if we don't include registered variables, # i.e. self.weights with self.assertRaises(ValueError): - estimator.FisherEstimator(lambda: 0.2, [], 0.1, self.layer_collection) + est = estimator.FisherEstimator([], 0.1, 0.2, self.layer_collection) + est.make_ops_and_vars() @test.mock.patch.object(utils.SubGraph, "variable_uses", return_value=42) def testVariableWrongNumberOfUses(self, mock_uses): with self.assertRaises(ValueError): - estimator.FisherEstimator(lambda: 0.2, [self.weights], 0.1, - self.layer_collection) + est = estimator.FisherEstimator([self.weights], 0.1, 0.2, + self.layer_collection) + est.make_ops_and_vars() def testInvalidEstimationMode(self): with self.assertRaises(ValueError): - estimator.FisherEstimator(lambda: 0.2, [self.weights], 0.1, - self.layer_collection, "not_a_real_mode") + est = estimator.FisherEstimator([self.weights], 0.1, 0.2, + self.layer_collection, + estimation_mode="not_a_real_mode") + est.make_ops_and_vars() - def testModeListCorrect(self): + def testGradientsModeBuild(self): with self._graph.as_default(): - est = estimator.FisherEstimator(lambda: 0.2, [self.weights], 0.1, - self.layer_collection) - self.assertItemsEqual(_ALL_ESTIMATION_MODES, est._gradient_fns.keys()) + est = estimator.FisherEstimator([self.weights], 0.1, 0.2, + self.layer_collection, + estimation_mode="gradients") + est.make_ops_and_vars() + + def testEmpiricalModeBuild(self): + with self._graph.as_default(): + est = estimator.FisherEstimator([self.weights], 0.1, 0.2, + self.layer_collection, + estimation_mode="empirical") + est.make_ops_and_vars() + + def testCurvaturePropModeBuild(self): + with self._graph.as_default(): + est = estimator.FisherEstimator([self.weights], 0.1, 0.2, + self.layer_collection, + estimation_mode="curvature_prop") + est.make_ops_and_vars() - def testAllModesBuild(self): - for mode in _ALL_ESTIMATION_MODES: - with self._graph.as_default(): - estimator.FisherEstimator(lambda: 0.2, [self.weights], 0.1, - self.layer_collection, mode) + def testExactModeBuild(self): + with self._graph.as_default(): + est = estimator.FisherEstimator([self.weights], 0.1, 0.2, + self.layer_collection, + estimation_mode="exact") + est.make_ops_and_vars() def test_cov_update_thunks(self): """Ensures covariance update ops run once per global_step.""" with self._graph.as_default(), self.test_session() as sess: fisher_estimator = estimator.FisherEstimator( - damping_fn=lambda: 0.2, variables=[self.weights], layer_collection=self.layer_collection, + damping=0.2, cov_ema_decay=0.0) # Construct an op that executes one covariance update per step. global_step = training_util.get_or_create_global_step() + (cov_variable_thunks, cov_update_op_thunks, + _, _) = fisher_estimator.create_ops_and_vars_thunks() + for thunk in cov_variable_thunks: + thunk() cov_matrices = [ fisher_factor.get_cov() for fisher_factor in self.layer_collection.get_factors() ] - cov_update_op_thunks = fisher_estimator.cov_update_thunks cov_update_op = control_flow_ops.case( [(math_ops.equal(global_step, i), thunk) for i, thunk in enumerate(cov_update_op_thunks)]) @@ -178,19 +202,24 @@ class EstimatorTest(test.TestCase): """Ensures inverse update ops run once per global_step.""" with self._graph.as_default(), self.test_session() as sess: fisher_estimator = estimator.FisherEstimator( - damping_fn=lambda: 0.2, variables=[self.weights], layer_collection=self.layer_collection, + damping=0.2, cov_ema_decay=0.0) # Construct op that updates one inverse per global step. global_step = training_util.get_or_create_global_step() + (cov_variable_thunks, _, inv_variable_thunks, + inv_update_op_thunks) = fisher_estimator.create_ops_and_vars_thunks() + for thunk in cov_variable_thunks: + thunk() + for thunk in inv_variable_thunks: + thunk() inv_matrices = [ matrix for fisher_factor in self.layer_collection.get_factors() - for matrix in fisher_factor._inverses_by_damping.values() + for matrix in fisher_factor._matpower_by_exp_and_damping.values() ] - inv_update_op_thunks = fisher_estimator.inv_update_thunks inv_update_op = control_flow_ops.case( [(math_ops.equal(global_step, i), thunk) for i, thunk in enumerate(inv_update_op_thunks)]) diff --git a/tensorflow/contrib/kfac/python/kernel_tests/fisher_blocks_test.py b/tensorflow/contrib/kfac/python/kernel_tests/fisher_blocks_test.py index fb4b3a241c1e9fd82e7bf630fd57295917048fbd..b70c700f0936c2d8a2eca6e0836a3ee4ffe4e46d 100644 --- a/tensorflow/contrib/kfac/python/kernel_tests/fisher_blocks_test.py +++ b/tensorflow/contrib/kfac/python/kernel_tests/fisher_blocks_test.py @@ -94,6 +94,9 @@ class FullFBTest(test.TestCase): block.register_additional_minibatch(32) grads = (params[0]**2, math_ops.sqrt(params[1])) block.instantiate_factors((grads,), 0.5) + block._factor.instantiate_cov_variables() + block.register_inverse() + block._factor.instantiate_inv_variables() # Make sure our inverse is something other than the identity. sess.run(tf_variables.global_variables_initializer()) @@ -112,6 +115,9 @@ class FullFBTest(test.TestCase): block.register_additional_minibatch(32) grads = params**2 block.instantiate_factors((grads,), 0.5) + block._factor.instantiate_cov_variables() + block.register_inverse() + block._factor.instantiate_inv_variables() # Make sure our inverse is something other than the identity. sess.run(tf_variables.global_variables_initializer()) @@ -131,6 +137,9 @@ class FullFBTest(test.TestCase): grads = (array_ops.constant([2., 3.]), array_ops.constant(4.)) damping = 0.5 block.instantiate_factors((grads,), damping) + block._factor.instantiate_cov_variables() + block.register_inverse() + block._factor.instantiate_inv_variables() # Make sure our inverse is something other than the identity. sess.run(state_ops.assign(block._factor._cov, _make_psd(3))) @@ -185,6 +194,7 @@ class NaiveDiagonalFBTest(test.TestCase): block.register_additional_minibatch(32) grads = (params[0]**2, math_ops.sqrt(params[1])) block.instantiate_factors((grads,), 0.5) + block._factor.instantiate_cov_variables() # Make sure our inverse is something other than the identity. sess.run(tf_variables.global_variables_initializer()) @@ -203,6 +213,7 @@ class NaiveDiagonalFBTest(test.TestCase): block.register_additional_minibatch(32) grads = params**2 block.instantiate_factors((grads,), 0.5) + block._factor.instantiate_cov_variables() # Make sure our inverse is something other than the identity. sess.run(tf_variables.global_variables_initializer()) @@ -221,6 +232,7 @@ class NaiveDiagonalFBTest(test.TestCase): grads = (params[0]**2, math_ops.sqrt(params[1])) damping = 0.5 block.instantiate_factors((grads,), damping) + block._factor.instantiate_cov_variables() cov = array_ops.reshape(array_ops.constant([2., 3., 4.]), [-1, 1]) sess.run(state_ops.assign(block._factor._cov, cov)) @@ -367,6 +379,7 @@ class FullyConnectedDiagonalFBTest(test.TestCase): block.register_additional_minibatch(i, o) block.instantiate_factors((output_grads,), damping=0.0) + block._factor.instantiate_cov_variables() sess.run(tf_variables.global_variables_initializer()) sess.run(block._factor.make_covariance_update_op(0.0)) @@ -394,7 +407,7 @@ class EmbeddingKFACFBTest(test.TestCase): # Instantiate factor's variables. Ensure it doesn't fail. grads = outputs**2. damping = array_ops.constant(0.) - block.instantiate_factors(([grads],), damping) + block.instantiate_factors(((grads,),), damping) def testMultiplyInverse(self): with ops.Graph().as_default(), self.test_session() as sess: @@ -412,7 +425,12 @@ class EmbeddingKFACFBTest(test.TestCase): # Instantiate factor's variables. Ensure it doesn't fail. grads = outputs**2. damping = array_ops.constant(0.) - block.instantiate_factors(([grads],), damping) + block.instantiate_factors(((grads,),), damping) + block._input_factor.instantiate_cov_variables() + block._output_factor.instantiate_cov_variables() + block.register_inverse() + block._input_factor.instantiate_inv_variables() + block._output_factor.instantiate_inv_variables() # Create a sparse update. indices = array_ops.constant([1, 3, 4]) @@ -456,7 +474,7 @@ class FullyConnectedKFACBasicFBTest(test.TestCase): block.register_additional_minibatch(inputs, outputs) grads = outputs**2 - block.instantiate_factors(([grads],), 0.5) + block.instantiate_factors(((grads,),), 0.5) def testInstantiateFactorsNoBias(self): with ops.Graph().as_default(): @@ -467,7 +485,7 @@ class FullyConnectedKFACBasicFBTest(test.TestCase): block.register_additional_minibatch(inputs, outputs) grads = outputs**2 - block.instantiate_factors(([grads],), 0.5) + block.instantiate_factors(((grads,),), 0.5) def testMultiplyInverseTuple(self): with ops.Graph().as_default(), self.test_session() as sess: @@ -477,7 +495,13 @@ class FullyConnectedKFACBasicFBTest(test.TestCase): block = fb.FullyConnectedKFACBasicFB(lc.LayerCollection(), has_bias=False) block.register_additional_minibatch(inputs, outputs) grads = outputs**2 - block.instantiate_factors(([grads],), 0.5) + block.instantiate_factors(((grads,),), 0.5) + + block._input_factor.instantiate_cov_variables() + block._output_factor.instantiate_cov_variables() + block.register_inverse() + block._input_factor.instantiate_inv_variables() + block._output_factor.instantiate_inv_variables() # Make sure our inverse is something other than the identity. sess.run(tf_variables.global_variables_initializer()) @@ -503,7 +527,12 @@ class FullyConnectedKFACBasicFBTest(test.TestCase): block = fb.FullyConnectedKFACBasicFB(lc.LayerCollection(), has_bias=False) block.register_additional_minibatch(inputs, outputs) grads = outputs**2 - block.instantiate_factors(([grads],), 0.5) + block.instantiate_factors(((grads,),), 0.5) + block._input_factor.instantiate_cov_variables() + block._output_factor.instantiate_cov_variables() + block.register_inverse() + block._input_factor.instantiate_inv_variables() + block._output_factor.instantiate_inv_variables() # Make sure our inverse is something other than the identity. sess.run(tf_variables.global_variables_initializer()) @@ -527,10 +556,17 @@ class FullyConnectedKFACBasicFBTest(test.TestCase): block.register_additional_minibatch(inputs, outputs) grads = outputs**2 damping = 0. # This test is only valid without damping. - block.instantiate_factors(([grads],), damping) + block.instantiate_factors(((grads,),), damping) + block._input_factor.instantiate_cov_variables() + block._output_factor.instantiate_cov_variables() sess.run(state_ops.assign(block._input_factor._cov, _make_psd(3))) sess.run(state_ops.assign(block._output_factor._cov, _make_psd(2))) + + block.register_inverse() + block._input_factor.instantiate_inv_variables() + block._output_factor.instantiate_inv_variables() + sess.run(block._input_factor.make_inverse_update_ops()) sess.run(block._output_factor.make_inverse_update_ops()) @@ -718,6 +754,7 @@ class ConvDiagonalFBTest(test.TestCase): block.register_additional_minibatch(i, o) block.instantiate_factors((output_grads,), damping=0.0) + block._factor.instantiate_cov_variables() sess.run(tf_variables.global_variables_initializer()) sess.run(block._factor.make_covariance_update_op(0.0)) @@ -727,6 +764,54 @@ class ConvDiagonalFBTest(test.TestCase): return multiply_result, multiply_inverse_result +class DepthwiseConvKFCBasicFBTest(test.TestCase): + + def testInstantiateFactors(self): + with ops.Graph().as_default(): + random_seed.set_random_seed(200) + params = random_ops.random_normal((3, 3, 8, 2)) + inputs = random_ops.random_normal((32, 5, 5, 8)) + outputs = random_ops.random_normal((32, 5, 5, 16)) + layer_collection = lc.LayerCollection() + block = fb.DepthwiseConvKFCBasicFB( + layer_collection, params=params, strides=[1, 1, 1, 1], padding='SAME') + block.register_additional_minibatch(inputs, outputs) + grads = outputs**2 + block.instantiate_factors(([grads],), 0.5) + + def testMultiplyInverse(self): + with ops.Graph().as_default(), self.test_session() as sess: + random_seed.set_random_seed(200) + params = random_ops.random_normal((3, 3, 8, 2)) + inputs = random_ops.random_normal((32, 5, 5, 8)) + outputs = random_ops.random_normal((32, 5, 5, 16)) + layer_collection = lc.LayerCollection() + block = fb.DepthwiseConvKFCBasicFB( + layer_collection, params=params, strides=[1, 1, 1, 1], padding='SAME') + block.register_additional_minibatch(inputs, outputs) + grads = outputs**2 + block.instantiate_factors(([grads],), 0.5) + block._input_factor.instantiate_cov_variables() + block._output_factor.instantiate_cov_variables() + block.register_inverse() + block._input_factor.instantiate_inv_variables() + block._output_factor.instantiate_inv_variables() + + # Ensure inverse update op doesn't crash. + sess.run(tf_variables.global_variables_initializer()) + sess.run([ + factor.make_inverse_update_ops() + for factor in layer_collection.get_factors() + ]) + + # Ensure inverse-vector multiply doesn't crash. + output = block.multiply_inverse(params) + sess.run(output) + + # Ensure same shape. + self.assertAllEqual(output.shape, params.shape) + + class ConvKFCBasicFBTest(test.TestCase): def _testConvKFCBasicFBInitParams(self, params): @@ -738,16 +823,17 @@ class ConvKFCBasicFBTest(test.TestCase): params = array_ops.constant(params) inputs = random_ops.random_normal((2, 2, 2)) outputs = random_ops.random_normal((2, 2, 2)) - block = fb.ConvKFCBasicFB(lc.LayerCollection(), params, [1, 1, 1], 'SAME') + block = fb.ConvKFCBasicFB( + lc.LayerCollection(), params=params, padding='SAME') block.register_additional_minibatch(inputs, outputs) self.assertAllEqual([outputs], block.tensors_to_compute_grads()) def testConvKFCBasicFBInitParamsParamsTuple(self): - self._testConvKFCBasicFBInitParams([np.array([1., 2.]), np.array(3.)]) + self._testConvKFCBasicFBInitParams([np.ones([1, 2, 2]), np.ones([2])]) def testConvKFCBasicFBInitParamsParamsSingle(self): - self._testConvKFCBasicFBInitParams([np.array([1., 2.])]) + self._testConvKFCBasicFBInitParams([np.ones([1, 2, 2])]) def testMultiplyInverseTuple(self): with ops.Graph().as_default(), self.test_session() as sess: @@ -755,11 +841,16 @@ class ConvKFCBasicFBTest(test.TestCase): params = random_ops.random_normal((2, 2, 2, 2)) inputs = random_ops.random_normal((2, 2, 2, 2)) outputs = random_ops.random_normal((2, 2, 2, 2)) - block = fb.ConvKFCBasicFB(lc.LayerCollection(), params, (1, 1, 1, 1), - 'SAME') + block = fb.ConvKFCBasicFB( + lc.LayerCollection(), params=params, padding='SAME') block.register_additional_minibatch(inputs, outputs) grads = outputs**2 - block.instantiate_factors(([grads],), 0.5) + block.instantiate_factors(((grads,),), 0.5) + block._input_factor.instantiate_cov_variables() + block._output_factor.instantiate_cov_variables() + block.register_inverse() + block._input_factor.instantiate_inv_variables() + block._output_factor.instantiate_inv_variables() # Make sure our inverse is something other than the identity. sess.run(tf_variables.global_variables_initializer()) @@ -781,12 +872,17 @@ class ConvKFCBasicFBTest(test.TestCase): params = random_ops.random_normal((2, 2, 2, 2)) inputs = random_ops.random_normal((2, 2, 2, 2)) outputs = random_ops.random_normal((2, 2, 2, 2)) - block = fb.ConvKFCBasicFB(lc.LayerCollection(), params, (1, 1, 1, 1), - 'SAME') + block = fb.ConvKFCBasicFB( + lc.LayerCollection(), params=params, padding='SAME') block.register_additional_minibatch(inputs, outputs) self.assertFalse(block._has_bias) grads = outputs**2 - block.instantiate_factors(([grads],), 0.5) + block.instantiate_factors(((grads,),), 0.5) + block._input_factor.instantiate_cov_variables() + block._output_factor.instantiate_cov_variables() + block.register_inverse() + block._input_factor.instantiate_inv_variables() + block._output_factor.instantiate_inv_variables() # Make sure our inverse is something other than the identity. sess.run(tf_variables.global_variables_initializer()) @@ -804,12 +900,17 @@ class ConvKFCBasicFBTest(test.TestCase): params = [random_ops.random_normal((2, 2, 2, 2))] inputs = random_ops.random_normal((2, 2, 2, 2)) outputs = random_ops.random_normal((2, 2, 2, 2)) - block = fb.ConvKFCBasicFB(lc.LayerCollection(), params, (1, 1, 1, 1), - 'SAME') + block = fb.ConvKFCBasicFB( + lc.LayerCollection(), params=params, padding='SAME') block.register_additional_minibatch(inputs, outputs) self.assertTrue(block._has_bias) grads = outputs**2 - block.instantiate_factors(([grads],), 0.5) + block.instantiate_factors(((grads,),), 0.5) + block._input_factor.instantiate_cov_variables() + block._output_factor.instantiate_cov_variables() + block.register_inverse() + block._input_factor.instantiate_inv_variables() + block._output_factor.instantiate_inv_variables() # Make sure our inverse is something other than the identity. sess.run(tf_variables.global_variables_initializer()) @@ -827,12 +928,17 @@ class ConvKFCBasicFBTest(test.TestCase): params = array_ops.zeros((2, 2, 2, 2)) inputs = array_ops.zeros((2, 2, 2, 2)) outputs = array_ops.zeros((2, 2, 2, 2)) - block = fb.ConvKFCBasicFB(lc.LayerCollection(), params, (1, 1, 1, 1), - 'SAME') + block = fb.ConvKFCBasicFB( + lc.LayerCollection(), params=params, padding='SAME') block.register_additional_minibatch(inputs, outputs) grads = outputs**2 damping = 0. # This test is only valid without damping. - block.instantiate_factors(([grads],), damping) + block.instantiate_factors(((grads,),), damping) + block._input_factor.instantiate_cov_variables() + block._output_factor.instantiate_cov_variables() + block.register_inverse() + block._input_factor.instantiate_inv_variables() + block._output_factor.instantiate_inv_variables() sess.run(state_ops.assign(block._input_factor._cov, _make_psd(8))) sess.run(state_ops.assign(block._output_factor._cov, _make_psd(2))) @@ -857,9 +963,9 @@ class FullyConnectedSeriesFBTest(test.TestCase): random_seed.set_random_seed(200) inputs = array_ops.constant([1., 2.]) outputs = array_ops.constant([3., 4.]) - block = fb.FullyConnectedSeriesFB( - lc.LayerCollection(), inputs=[inputs], outputs=[outputs]) - self.assertAllEqual([outputs], block.tensors_to_compute_grads()) + block = fb.FullyConnectedSeriesFB(lc.LayerCollection()) + block.register_additional_minibatch([inputs], [outputs]) + self.assertAllEqual([[outputs]], block.tensors_to_compute_grads()) def testInstantiateFactorsHasBias(self): with ops.Graph().as_default(): @@ -868,11 +974,10 @@ class FullyConnectedSeriesFBTest(test.TestCase): outputs = array_ops.constant([[3., 4.], [5., 6.]]) block = fb.FullyConnectedSeriesFB( lc.LayerCollection(), - inputs=[inputs], - outputs=[outputs], has_bias=True) + block.register_additional_minibatch([inputs], [outputs]) grads = outputs**2 - block.instantiate_factors(((grads,),), 0.5) + block.instantiate_factors((((grads,),),), 0.5) def testInstantiateFactorsNoBias(self): with ops.Graph().as_default(): @@ -881,11 +986,10 @@ class FullyConnectedSeriesFBTest(test.TestCase): outputs = array_ops.constant([[3., 4.], [5., 6.]]) block = fb.FullyConnectedSeriesFB( lc.LayerCollection(), - inputs=[inputs], - outputs=[outputs], has_bias=False) + block.register_additional_minibatch([inputs], [outputs]) grads = outputs**2 - block.instantiate_factors(((grads,),), 0.5) + block.instantiate_factors((((grads,),),), 0.5) def as_tensors(tensor_or_tuple): diff --git a/tensorflow/contrib/kfac/python/kernel_tests/fisher_factors_test.py b/tensorflow/contrib/kfac/python/kernel_tests/fisher_factors_test.py index 66e18974abfadaad5d7a20b40d0b1352bfda67ee..16f02f1199ad8a404b0e6944fc89df32ce08609c 100644 --- a/tensorflow/contrib/kfac/python/kernel_tests/fisher_factors_test.py +++ b/tensorflow/contrib/kfac/python/kernel_tests/fisher_factors_test.py @@ -21,6 +21,7 @@ from __future__ import print_function import numpy as np import numpy.random as npr +from tensorflow.contrib.kfac.python.ops import fisher_blocks as fb from tensorflow.contrib.kfac.python.ops import fisher_factors as ff from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes @@ -29,36 +30,13 @@ from tensorflow.python.framework import random_seed from tensorflow.python.ops import array_ops from tensorflow.python.ops import gradients_impl from tensorflow.python.ops import math_ops +from tensorflow.python.ops import random_ops from tensorflow.python.ops import variables as tf_variables from tensorflow.python.platform import test -class MaybeColocateTest(test.TestCase): - - def setUp(self): - self._colocate_cov_ops_with_inputs = ff.COLOCATE_COV_OPS_WITH_INPUTS - - def tearDown(self): - ff.set_global_constants( - colocate_cov_ops_with_inputs=self._colocate_cov_ops_with_inputs) - - def testFalse(self): - ff.set_global_constants(colocate_cov_ops_with_inputs=False) - with tf_ops.Graph().as_default(): - a = constant_op.constant([2.0], name='a') - with ff.maybe_colocate_with(a): - b = constant_op.constant(3.0, name='b') - self.assertEqual([b'loc:@a'], a.op.colocation_groups()) - self.assertEqual([b'loc:@b'], b.op.colocation_groups()) - - def testTrue(self): - ff.set_global_constants(colocate_cov_ops_with_inputs=True) - with tf_ops.Graph().as_default(): - a = constant_op.constant([2.0], name='a') - with ff.maybe_colocate_with(a): - b = constant_op.constant(3.0, name='b') - self.assertEqual([b'loc:@a'], a.op.colocation_groups()) - self.assertEqual([b'loc:@a'], b.op.colocation_groups()) +def make_damping_func(damping): + return fb._package_func(lambda: damping, damping) class FisherFactorTestingDummy(ff.FisherFactor): @@ -98,10 +76,13 @@ class FisherFactorTestingDummy(ff.FisherFactor): def right_multiply(self, x, damping): return NotImplementedError - def left_multiply_inverse(self, x, damping): + def left_multiply_matpower(self, x, exp, damping): + return NotImplementedError + + def right_multiply_matpower(self, x, exp, damping): return NotImplementedError - def right_multiply_inverse(self, x, damping): + def instantiate_inv_variables(self): return NotImplementedError @@ -246,21 +227,24 @@ class InverseProvidingFactorTest(test.TestCase): factor = InverseProvidingFactorTestingDummy(shape) factor_var_scope = 'dummy/a_b_c' - dampings = 0.1, 1e-1, 0.00001, 1e-5 + damping_funcs = [make_damping_func(0.1), + make_damping_func(0.1), + make_damping_func(1e-5), + make_damping_func(1e-5)] + for damping_func in damping_funcs: + factor.register_inverse(damping_func) - for damping in dampings: - factor.register_damped_inverse(damping) + factor.instantiate_inv_variables() - self.assertEqual(set(dampings), set(factor._inverses_by_damping.keys())) - inv = factor._inverses_by_damping[dampings[0]] - self.assertEqual(inv, factor._inverses_by_damping[dampings[1]]) - self.assertNotEqual(inv, factor._inverses_by_damping[dampings[2]]) - self.assertEqual(factor._inverses_by_damping[dampings[2]], - factor._inverses_by_damping[dampings[3]]) + inv = factor.get_inverse(damping_funcs[0]) + self.assertEqual(inv, factor.get_inverse(damping_funcs[1])) + self.assertNotEqual(inv, factor.get_inverse(damping_funcs[2])) + self.assertEqual(factor.get_inverse(damping_funcs[2]), + factor.get_inverse(damping_funcs[3])) factor_vars = tf_ops.get_collection(tf_ops.GraphKeys.GLOBAL_VARIABLES, factor_var_scope) - self.assertListEqual([inv, factor._inverses_by_damping[dampings[2]]], - factor_vars) + self.assertEqual(set([inv, factor.get_inverse(damping_funcs[2])]), + set(factor_vars)) self.assertEqual(shape, inv.get_shape()) def testRegisterMatpower(self): @@ -270,17 +254,22 @@ class InverseProvidingFactorTest(test.TestCase): factor = InverseProvidingFactorTestingDummy(shape) factor_var_scope = 'dummy/a_b_c' - factor.register_matpower(1, 0.5) - factor.register_matpower(2, 0.5) + # TODO(b/74201126): Change to using the same func for both once + # Topohash is in place. + damping_func_1 = make_damping_func(0.5) + damping_func_2 = make_damping_func(0.5) + + factor.register_matpower(-0.5, damping_func_1) + factor.register_matpower(2, damping_func_2) + + factor.instantiate_inv_variables() - self.assertEqual( - set([(1, 0.5), (2, 0.5)]), - set(factor._matpower_by_exp_and_damping.keys())) factor_vars = tf_ops.get_collection(tf_ops.GraphKeys.GLOBAL_VARIABLES, factor_var_scope) - matpower1 = factor.get_matpower(1, 0.5) - matpower2 = factor.get_matpower(2, 0.5) - self.assertListEqual([matpower1, matpower2], factor_vars) + matpower1 = factor.get_matpower(-0.5, damping_func_1) + matpower2 = factor.get_matpower(2, damping_func_2) + + self.assertEqual(set([matpower1, matpower2]), set(factor_vars)) self.assertEqual(shape, matpower1.get_shape()) self.assertEqual(shape, matpower2.get_shape()) @@ -299,17 +288,24 @@ class InverseProvidingFactorTest(test.TestCase): factor = InverseProvidingFactorTestingDummy(cov.shape) factor._cov = array_ops.constant(cov, dtype=dtypes.float32) + damping_funcs = [] for i in range(1, ff.EIGENVALUE_DECOMPOSITION_THRESHOLD + 1): - factor.register_damped_inverse(1. / i) + damping_funcs.append(make_damping_func(1./i)) + + for i in range(ff.EIGENVALUE_DECOMPOSITION_THRESHOLD): + factor.register_inverse(damping_funcs[i]) + + factor.instantiate_inv_variables() ops = factor.make_inverse_update_ops() self.assertEqual(1, len(ops)) sess.run(tf_variables.global_variables_initializer()) new_invs = [] sess.run(ops) - for i in range(1, ff.EIGENVALUE_DECOMPOSITION_THRESHOLD + 1): + for i in range(ff.EIGENVALUE_DECOMPOSITION_THRESHOLD): # The inverse op will assign the damped inverse of cov to the inv var. - new_invs.append(sess.run(factor._inverses_by_damping[1. / i])) + new_invs.append(sess.run(factor.get_inverse(damping_funcs[i]))) + # We want to see that the new invs are all different from each other. for i in range(len(new_invs)): for j in range(i + 1, len(new_invs)): @@ -324,14 +320,16 @@ class InverseProvidingFactorTest(test.TestCase): factor._cov = array_ops.constant(cov, dtype=dtypes.float32) exp = 2 # NOTE(mattjj): must be int to test with np.linalg.matrix_power damping = 0.5 + damping_func = make_damping_func(damping) - factor.register_matpower(exp, damping) + factor.register_matpower(exp, damping_func) + factor.instantiate_inv_variables() ops = factor.make_inverse_update_ops() self.assertEqual(1, len(ops)) sess.run(tf_variables.global_variables_initializer()) sess.run(ops[0]) - matpower = sess.run(factor._matpower_by_exp_and_damping[(exp, damping)]) + matpower = sess.run(factor.get_matpower(exp, damping_func)) matpower_np = np.linalg.matrix_power(cov + np.eye(2) * damping, exp) self.assertAllClose(matpower, matpower_np) @@ -342,18 +340,21 @@ class InverseProvidingFactorTest(test.TestCase): factor = InverseProvidingFactorTestingDummy(cov.shape) factor._cov = array_ops.constant(cov, dtype=dtypes.float32) - factor.register_damped_inverse(0) + damping_func = make_damping_func(0) + + factor.register_inverse(damping_func) + factor.instantiate_inv_variables() ops = factor.make_inverse_update_ops() self.assertEqual(1, len(ops)) sess.run(tf_variables.global_variables_initializer()) # The inverse op will assign the damped inverse of cov to the inv var. - old_inv = sess.run(factor._inverses_by_damping[0]) + old_inv = sess.run(factor.get_inverse(damping_func)) self.assertAllClose( sess.run(ff.inverse_initializer(cov.shape, dtypes.float32)), old_inv) sess.run(ops) - new_inv = sess.run(factor._inverses_by_damping[0]) + new_inv = sess.run(factor.get_inverse(damping_func)) self.assertAllClose(new_inv, np.linalg.inv(cov)) @@ -364,6 +365,7 @@ class FullFactorTest(test.TestCase): random_seed.set_random_seed(200) tensor = array_ops.ones((2, 3), name='a/b/c') factor = ff.FullFactor((tensor,), 32) + factor.instantiate_cov_variables() self.assertEqual([6, 6], factor.get_cov().get_shape().as_list()) def testFullFactorInitFloat64(self): @@ -372,6 +374,7 @@ class FullFactorTest(test.TestCase): random_seed.set_random_seed(200) tensor = array_ops.ones((2, 3), dtype=dtype, name='a/b/c') factor = ff.FullFactor((tensor,), 32) + factor.instantiate_cov_variables() cov = factor.get_cov() self.assertEqual(cov.dtype, dtype) self.assertEqual([6, 6], cov.get_shape().as_list()) @@ -381,6 +384,7 @@ class FullFactorTest(test.TestCase): random_seed.set_random_seed(200) tensor = array_ops.constant([1., 2.], name='a/b/c') factor = ff.FullFactor((tensor,), 2) + factor.instantiate_cov_variables() sess.run(tf_variables.global_variables_initializer()) new_cov = sess.run(factor.make_covariance_update_op(.5)) @@ -394,6 +398,7 @@ class NaiveDiagonalFactorTest(test.TestCase): random_seed.set_random_seed(200) tensor = array_ops.ones((2, 3), name='a/b/c') factor = ff.NaiveDiagonalFactor((tensor,), 32) + factor.instantiate_cov_variables() self.assertEqual([6, 1], factor.get_cov_var().get_shape().as_list()) def testNaiveDiagonalFactorInitFloat64(self): @@ -402,6 +407,7 @@ class NaiveDiagonalFactorTest(test.TestCase): random_seed.set_random_seed(200) tensor = array_ops.ones((2, 3), dtype=dtype, name='a/b/c') factor = ff.NaiveDiagonalFactor((tensor,), 32) + factor.instantiate_cov_variables() cov = factor.get_cov_var() self.assertEqual(cov.dtype, dtype) self.assertEqual([6, 1], cov.get_shape().as_list()) @@ -411,6 +417,7 @@ class NaiveDiagonalFactorTest(test.TestCase): random_seed.set_random_seed(200) tensor = array_ops.constant([1., 2.], name='a/b/c') factor = ff.NaiveDiagonalFactor((tensor,), 2) + factor.instantiate_cov_variables() sess.run(tf_variables.global_variables_initializer()) new_cov = sess.run(factor.make_covariance_update_op(.5)) @@ -423,7 +430,8 @@ class EmbeddingInputKroneckerFactorTest(test.TestCase): with tf_ops.Graph().as_default(): input_ids = array_ops.constant([[0], [1], [4]]) vocab_size = 5 - factor = ff.EmbeddingInputKroneckerFactor((input_ids,), vocab_size) + factor = ff.EmbeddingInputKroneckerFactor(input_ids, vocab_size) + factor.instantiate_cov_variables() cov = factor.get_cov_var() self.assertEqual(cov.shape.as_list(), [vocab_size]) @@ -431,7 +439,8 @@ class EmbeddingInputKroneckerFactorTest(test.TestCase): with tf_ops.Graph().as_default(): input_ids = array_ops.constant([[0], [1], [4]]) vocab_size = 5 - factor = ff.EmbeddingInputKroneckerFactor((input_ids,), vocab_size) + factor = ff.EmbeddingInputKroneckerFactor(input_ids, vocab_size) + factor.instantiate_cov_variables() cov_update_op = factor.make_covariance_update_op(0.0) with self.test_session() as sess: @@ -440,6 +449,117 @@ class EmbeddingInputKroneckerFactorTest(test.TestCase): self.assertAllClose(np.array([1., 1., 0., 0., 1.]) / 3., new_cov) +class ConvDiagonalFactorTest(test.TestCase): + + def setUp(self): + self.batch_size = 10 + self.height = self.width = 32 + self.in_channels = 3 + self.out_channels = 1 + self.kernel_height = self.kernel_width = 3 + self.strides = [1, 2, 2, 1] + self.data_format = 'NHWC' + self.padding = 'SAME' + self.kernel_shape = [ + self.kernel_height, self.kernel_width, self.in_channels, + self.out_channels + ] + + def testInit(self): + with tf_ops.Graph().as_default(): + inputs = random_ops.random_uniform( + [self.batch_size, self.height, self.width, self.in_channels]) + outputs_grads = [ + random_ops.random_uniform([ + self.batch_size, self.height // self.strides[1], + self.width // self.strides[2], self.out_channels + ]) for _ in range(3) + ] + + factor = ff.ConvDiagonalFactor( + inputs, + outputs_grads, + self.kernel_shape, + self.strides, + self.padding, + data_format=self.data_format) + factor.instantiate_cov_variables() + + # Ensure covariance matrix's shape makes sense. + self.assertEqual([ + self.kernel_height * self.kernel_width * self.in_channels, + self.out_channels + ], + factor.get_cov_var().shape.as_list()) + + def testMakeCovarianceUpdateOp(self): + with tf_ops.Graph().as_default(): + # Construct all arguments such that convolution kernel is applied in + # exactly one spatial location. + inputs = np.random.randn( + 1, # batch_size + self.kernel_height, + self.kernel_width, + self.in_channels) # in_channels + outputs_grad = np.random.randn( + 1, # batch_size + 1, # output_height + 1, # output_width + self.out_channels) + + factor = ff.ConvDiagonalFactor( + constant_op.constant(inputs), [constant_op.constant(outputs_grad)], + self.kernel_shape, + strides=[1, 1, 1, 1], + padding='VALID') + factor.instantiate_cov_variables() + + # Completely forget initial value on first update. + cov_update_op = factor.make_covariance_update_op(0.0) + + # Ensure new covariance value is same as outer-product of inputs/outputs + # vectorized, squared. + with self.test_session() as sess: + sess.run(tf_variables.global_variables_initializer()) + cov = sess.run(cov_update_op) + expected_cov = np.outer(inputs.flatten(), outputs_grad.flatten())**2 + self.assertAllClose(expected_cov, cov) + + def testHasBias(self): + with tf_ops.Graph().as_default(): + inputs = random_ops.random_uniform( + [self.batch_size, self.height, self.width, self.in_channels]) + outputs_grads = [ + random_ops.random_uniform([ + self.batch_size, self.height // self.strides[1], + self.width // self.strides[2], self.out_channels + ]) for _ in range(3) + ] + + factor = ff.ConvDiagonalFactor( + inputs, + outputs_grads, + self.kernel_shape, + self.strides, + self.padding, + data_format=self.data_format, + has_bias=True) + factor.instantiate_cov_variables() + + # Ensure shape accounts for bias. + self.assertEqual([ + self.kernel_height * self.kernel_width * self.in_channels + 1, + self.out_channels + ], + factor.get_cov_var().shape.as_list()) + + # Ensure update op doesn't crash. + cov_update_op = factor.make_covariance_update_op(0.0) + with self.test_session() as sess: + sess.run(tf_variables.global_variables_initializer()) + sess.run(cov_update_op) + + class FullyConnectedKroneckerFactorTest(test.TestCase): def _testFullyConnectedKroneckerFactorInit(self, @@ -450,6 +570,7 @@ class FullyConnectedKroneckerFactorTest(test.TestCase): random_seed.set_random_seed(200) tensor = array_ops.ones((2, 3), dtype=dtype, name='a/b/c') factor = ff.FullyConnectedKroneckerFactor((tensor,), has_bias=has_bias) + factor.instantiate_cov_variables() cov = factor.get_cov() self.assertEqual(cov.dtype, dtype) self.assertEqual(final_shape, cov.get_shape().as_list()) @@ -467,6 +588,7 @@ class FullyConnectedKroneckerFactorTest(test.TestCase): random_seed.set_random_seed(200) tensor = array_ops.constant([[1., 2.], [3., 4.]], name='a/b/c') factor = ff.FullyConnectedKroneckerFactor((tensor,), has_bias=True) + factor.instantiate_cov_variables() sess.run(tf_variables.global_variables_initializer()) new_cov = sess.run(factor.make_covariance_update_op(.5)) @@ -477,39 +599,170 @@ class FullyConnectedKroneckerFactorTest(test.TestCase): random_seed.set_random_seed(200) tensor = array_ops.constant([[1., 2.], [3., 4.]], name='a/b/c') factor = ff.FullyConnectedKroneckerFactor((tensor,)) + factor.instantiate_cov_variables() sess.run(tf_variables.global_variables_initializer()) new_cov = sess.run(factor.make_covariance_update_op(.5)) self.assertAllClose([[3, 3.5], [3.5, 5.5]], new_cov) -class ConvInputKroneckerFactorTest(test.TestCase): +class ConvFactorTestCase(test.TestCase): + + def assertMatrixRank(self, rank, matrix, atol=1e-5): + assert rank <= matrix.shape[0], 'Rank cannot be larger than matrix size.' + eigvals = np.linalg.eigvals(matrix) + nnz_eigvals = np.sum(eigvals > atol) + self.assertEqual( + rank, + nnz_eigvals, + msg=('Found %d of %d expected non-zero eigenvalues: %s.' % + (nnz_eigvals, rank, eigvals))) + + +class ConvInputKroneckerFactorTest(ConvFactorTestCase): + + def test3DConvolution(self): + with tf_ops.Graph().as_default(): + batch_size = 1 + width = 3 + in_channels = 3**3 + out_channels = 4 + + factor = ff.ConvInputKroneckerFactor( + inputs=random_ops.random_uniform( + (batch_size, width, width, width, in_channels), seed=0), + filter_shape=(width, width, width, in_channels, out_channels), + padding='SAME', + strides=(2, 2, 2), + extract_patches_fn='extract_convolution_patches', + has_bias=False) + factor.instantiate_cov_variables() + + # Ensure shape of covariance matches input size of filter. + input_size = in_channels * (width**3) + self.assertEqual([input_size, input_size], + factor.get_cov_var().shape.as_list()) + + # Ensure cov_update_op doesn't crash. + with self.test_session() as sess: + sess.run(tf_variables.global_variables_initializer()) + sess.run(factor.make_covariance_update_op(0.0)) + cov = sess.run(factor.get_cov_var()) + + # Cov should be rank-8, as the filter will be applied at each corner of + # the 4-D cube. + self.assertMatrixRank(8, cov) + + def testPointwiseConv2d(self): + with tf_ops.Graph().as_default(): + batch_size = 1 + width = 3 + in_channels = 3**2 + out_channels = 4 + + factor = ff.ConvInputKroneckerFactor( + inputs=random_ops.random_uniform( + (batch_size, width, width, in_channels), seed=0), + filter_shape=(1, 1, in_channels, out_channels), + padding='SAME', + strides=(1, 1, 1, 1), + extract_patches_fn='extract_pointwise_conv2d_patches', + has_bias=False) + factor.instantiate_cov_variables() + + # Ensure shape of covariance matches input size of filter. + self.assertEqual([in_channels, in_channels], + factor.get_cov_var().shape.as_list()) + + # Ensure cov_update_op doesn't crash. + with self.test_session() as sess: + sess.run(tf_variables.global_variables_initializer()) + sess.run(factor.make_covariance_update_op(0.0)) + cov = sess.run(factor.get_cov_var()) + + # Cov should be rank-9, as the filter will be applied at each location. + self.assertMatrixRank(9, cov) + + def testStrides(self): + with tf_ops.Graph().as_default(): + batch_size = 1 + width = 3 + in_channels = 3**2 + out_channels = 4 + + factor = ff.ConvInputKroneckerFactor( + inputs=random_ops.random_uniform( + (batch_size, width, width, in_channels), seed=0), + filter_shape=(1, 1, in_channels, out_channels), + padding='SAME', + strides=(1, 2, 1, 1), + extract_patches_fn='extract_image_patches', + has_bias=False) + factor.instantiate_cov_variables() + + with self.test_session() as sess: + sess.run(tf_variables.global_variables_initializer()) + sess.run(factor.make_covariance_update_op(0.0)) + cov = sess.run(factor.get_cov_var()) + + # Cov should be the sum of 3 * 2 = 6 outer products. + self.assertMatrixRank(6, cov) + + def testDilationRate(self): + with tf_ops.Graph().as_default(): + batch_size = 1 + width = 3 + in_channels = 2 + out_channels = 4 + + factor = ff.ConvInputKroneckerFactor( + inputs=random_ops.random_uniform( + (batch_size, width, width, in_channels), seed=0), + filter_shape=(3, 3, in_channels, out_channels), + padding='SAME', + extract_patches_fn='extract_image_patches', + strides=(1, 1, 1, 1), + dilation_rate=(1, width, width, 1), + has_bias=False) + factor.instantiate_cov_variables() + + with self.test_session() as sess: + sess.run(tf_variables.global_variables_initializer()) + sess.run(factor.make_covariance_update_op(0.0)) + cov = sess.run(factor.get_cov_var()) + + # Cov should be rank = in_channels, as only the center of the filter + # receives non-zero input for each input channel. + self.assertMatrixRank(in_channels, cov) def testConvInputKroneckerFactorInitNoBias(self): with tf_ops.Graph().as_default(): - random_seed.set_random_seed(200) - tensor = array_ops.ones((2, 3), name='a/b/c') + tensor = array_ops.ones((64, 1, 2, 3), name='a/b/c') factor = ff.ConvInputKroneckerFactor( - tensor, (1, 2, 3, 4), 3, 2, has_bias=False) + inputs=tensor, + filter_shape=(1, 2, 3, 4), + padding='SAME', + has_bias=False) + factor.instantiate_cov_variables() self.assertEqual([1 * 2 * 3, 1 * 2 * 3], factor.get_cov().get_shape().as_list()) def testConvInputKroneckerFactorInit(self): with tf_ops.Graph().as_default(): - random_seed.set_random_seed(200) - tensor = array_ops.ones((2, 3), name='a/b/c') + tensor = array_ops.ones((64, 1, 2, 3), name='a/b/c') factor = ff.ConvInputKroneckerFactor( - tensor, (1, 2, 3, 4), 3, 2, has_bias=True) + tensor, filter_shape=(1, 2, 3, 4), padding='SAME', has_bias=True) + factor.instantiate_cov_variables() self.assertEqual([1 * 2 * 3 + 1, 1 * 2 * 3 + 1], factor.get_cov().get_shape().as_list()) def testConvInputKroneckerFactorInitFloat64(self): with tf_ops.Graph().as_default(): dtype = dtypes.float64_ref - random_seed.set_random_seed(200) - tensor = array_ops.ones((2, 3), dtype=dtype, name='a/b/c') + tensor = array_ops.ones((64, 1, 2, 3), name='a/b/c', dtype=dtypes.float64) factor = ff.ConvInputKroneckerFactor( - tensor, (1, 2, 3, 4), 3, 2, has_bias=True) + tensor, filter_shape=(1, 2, 3, 4), padding='SAME', has_bias=True) + factor.instantiate_cov_variables() cov = factor.get_cov() self.assertEqual(cov.dtype, dtype) self.assertEqual([1 * 2 * 3 + 1, 1 * 2 * 3 + 1], @@ -517,37 +770,67 @@ class ConvInputKroneckerFactorTest(test.TestCase): def testMakeCovarianceUpdateOpWithBias(self): with tf_ops.Graph().as_default(), self.test_session() as sess: - random_seed.set_random_seed(200) + input_shape = (2, 1, 1, 1) tensor = array_ops.constant( - np.arange(1., 17.).reshape(2, 2, 2, 2), dtype=dtypes.float32) + np.arange(1, 1 + np.prod(input_shape)).reshape(input_shape).astype( + np.float32)) factor = ff.ConvInputKroneckerFactor( - tensor, (1, 2, 1, 1), [1, 1, 1, 1], 'SAME', has_bias=True) + tensor, filter_shape=(1, 1, 1, 1), padding='SAME', has_bias=True) + factor.instantiate_cov_variables() sess.run(tf_variables.global_variables_initializer()) - new_cov = sess.run(factor.make_covariance_update_op(.5)) - self.assertAllClose([[34.375, 37, 3.125], [37, 41, 3.5], [3.125, 3.5, 1]], - new_cov) + new_cov = sess.run(factor.make_covariance_update_op(0.)) + self.assertAllClose( + [ + [(1. + 4.) / 2., (1. + 2.) / 2.], # + [(1. + 2.) / 2., (1. + 1.) / 2.] + ], # + new_cov) def testMakeCovarianceUpdateOpNoBias(self): with tf_ops.Graph().as_default(), self.test_session() as sess: - random_seed.set_random_seed(200) + input_shape = (2, 1, 1, 1) tensor = array_ops.constant( - np.arange(1., 17.).reshape(2, 2, 2, 2), dtype=dtypes.float32) - factor = ff.ConvInputKroneckerFactor(tensor, (1, 2, 1, 1), [1, 1, 1, 1], - 'SAME') + np.arange(1, 1 + np.prod(input_shape)).reshape(input_shape).astype( + np.float32)) + factor = ff.ConvInputKroneckerFactor( + tensor, filter_shape=(1, 1, 1, 1), padding='SAME') + factor.instantiate_cov_variables() sess.run(tf_variables.global_variables_initializer()) - new_cov = sess.run(factor.make_covariance_update_op(.5)) - self.assertAllClose([[34.375, 37], [37, 41]], new_cov) + new_cov = sess.run(factor.make_covariance_update_op(0.)) + self.assertAllClose([[(1. + 4.) / 2.]], new_cov) -class ConvOutputKroneckerFactorTest(test.TestCase): +class ConvOutputKroneckerFactorTest(ConvFactorTestCase): + + def test3DConvolution(self): + with tf_ops.Graph().as_default(): + batch_size = 1 + width = 3 + out_channels = width**3 + + factor = ff.ConvOutputKroneckerFactor(outputs_grads=[ + random_ops.random_uniform( + (batch_size, width, width, width, out_channels), seed=0) + ]) + factor.instantiate_cov_variables() + + with self.test_session() as sess: + sess.run(tf_variables.global_variables_initializer()) + sess.run(factor.make_covariance_update_op(0.0)) + cov = sess.run(factor.get_cov()) + + # Cov should be rank 3^3, as each spatial position donates a rank-1 + # update. + self.assertMatrixRank(width**3, cov) def testConvOutputKroneckerFactorInit(self): with tf_ops.Graph().as_default(): random_seed.set_random_seed(200) tensor = array_ops.ones((2, 3, 4, 5), name='a/b/c') factor = ff.ConvOutputKroneckerFactor((tensor,)) + factor.instantiate_cov_variables() self.assertEqual([5, 5], factor.get_cov().get_shape().as_list()) def testConvOutputKroneckerFactorInitFloat64(self): @@ -556,22 +839,17 @@ class ConvOutputKroneckerFactorTest(test.TestCase): random_seed.set_random_seed(200) tensor = array_ops.ones((2, 3, 4, 5), dtype=dtype, name='a/b/c') factor = ff.ConvOutputKroneckerFactor((tensor,)) + factor.instantiate_cov_variables() cov = factor.get_cov() self.assertEqual(cov.dtype, dtype) self.assertEqual([5, 5], cov.get_shape().as_list()) - def testConvOutputKroneckerFactorInitNotEnoughDims(self): - with tf_ops.Graph().as_default(): - random_seed.set_random_seed(200) - tensor = array_ops.ones((2, 3), name='a/b/c') - with self.assertRaises(IndexError): - ff.ConvOutputKroneckerFactor(tensor) - def testMakeCovarianceUpdateOp(self): with tf_ops.Graph().as_default(), self.test_session() as sess: random_seed.set_random_seed(200) tensor = np.arange(1, 17).reshape(2, 2, 2, 2).astype(np.float32) factor = ff.ConvOutputKroneckerFactor((array_ops.constant(tensor),)) + factor.instantiate_cov_variables() sess.run(tf_variables.global_variables_initializer()) new_cov = sess.run(factor.make_covariance_update_op(.5)) @@ -586,6 +864,7 @@ class FullyConnectedMultiKFTest(test.TestCase): tensor = array_ops.ones((2, 3), name='a/b/c') tensor_list = [tensor] factor = ff.FullyConnectedMultiKF((tensor_list,), has_bias=False) + factor.instantiate_cov_variables() self.assertEqual([3, 3], factor.get_cov().get_shape().as_list()) def testFullyConnectedMultiKFInitFloat64(self): @@ -595,6 +874,7 @@ class FullyConnectedMultiKFTest(test.TestCase): tensor = array_ops.ones((2, 3), dtype=dtype, name='a/b/c') tensor_list = [tensor] factor = ff.FullyConnectedMultiKF((tensor_list,), has_bias=False) + factor.instantiate_cov_variables() cov = factor.get_cov() self.assertEqual(cov.dtype, dtype) self.assertEqual([3, 3], cov.get_shape().as_list()) @@ -605,6 +885,7 @@ class FullyConnectedMultiKFTest(test.TestCase): tensor = array_ops.constant([[1., 2.], [3., 4.]], name='a/b/c') tensor_list = [tensor] factor = ff.FullyConnectedMultiKF((tensor_list,), has_bias=True) + factor.instantiate_cov_variables() sess.run(tf_variables.global_variables_initializer()) new_cov = sess.run(factor.make_covariance_update_op(.5)) @@ -616,6 +897,7 @@ class FullyConnectedMultiKFTest(test.TestCase): tensor = array_ops.constant([[1., 2.], [3., 4.]], name='a/b/c') tensor_list = [tensor] factor = ff.FullyConnectedMultiKF((tensor_list,)) + factor.instantiate_cov_variables() sess.run(tf_variables.global_variables_initializer()) new_cov = sess.run(factor.make_covariance_update_op(.5)) diff --git a/tensorflow/contrib/kfac/python/kernel_tests/layer_collection_test.py b/tensorflow/contrib/kfac/python/kernel_tests/layer_collection_test.py index b8ccbeadd0a9d69edb41fef50e3edb090457adf2..bae6bd7a3bd47bc50378afe95d26d57535377f6f 100644 --- a/tensorflow/contrib/kfac/python/kernel_tests/layer_collection_test.py +++ b/tensorflow/contrib/kfac/python/kernel_tests/layer_collection_test.py @@ -104,14 +104,31 @@ class LayerCollectionTest(test.TestCase): array_ops.constant(3), approx=layer_collection.APPROX_DIAGONAL_NAME) lc.register_conv2d( - array_ops.constant(4), [1, 1, 1, 1], 'SAME', - array_ops.ones((1, 1, 1, 1)), array_ops.constant(3)) + params=array_ops.ones((2, 3, 4, 5)), + strides=[1, 1, 1, 1], + padding='SAME', + inputs=array_ops.ones((1, 2, 3, 4)), + outputs=array_ops.ones((1, 1, 1, 5))) lc.register_conv2d( - array_ops.constant(4), [1, 1, 1, 1], - 'SAME', - array_ops.ones((1, 1, 1, 1)), - array_ops.constant(3), + params=array_ops.ones((2, 3, 4, 5)), + strides=[1, 1, 1, 1], + padding='SAME', + inputs=array_ops.ones((1, 2, 3, 4)), + outputs=array_ops.ones((1, 1, 1, 5)), approx=layer_collection.APPROX_DIAGONAL_NAME) + lc.register_separable_conv2d( + depthwise_params=array_ops.ones((3, 3, 1, 2)), + pointwise_params=array_ops.ones((1, 1, 2, 4)), + inputs=array_ops.ones((32, 5, 5, 1)), + depthwise_outputs=array_ops.ones((32, 5, 5, 2)), + pointwise_outputs=array_ops.ones((32, 5, 5, 4)), + strides=[1, 1, 1, 1], + padding='SAME') + lc.register_convolution( + params=array_ops.ones((3, 3, 1, 8)), + inputs=array_ops.ones((32, 5, 5, 1)), + outputs=array_ops.ones((32, 5, 5, 8)), + padding='SAME') lc.register_generic( array_ops.constant(5), 16, approx=layer_collection.APPROX_FULL_NAME) lc.register_generic( @@ -119,7 +136,7 @@ class LayerCollectionTest(test.TestCase): 16, approx=layer_collection.APPROX_DIAGONAL_NAME) - self.assertEqual(6, len(lc.get_blocks())) + self.assertEqual(9, len(lc.get_blocks())) def testRegisterBlocksMultipleRegistrations(self): with ops.Graph().as_default(): @@ -237,16 +254,16 @@ class LayerCollectionTest(test.TestCase): # Create a new loss function by name. lc.register_categorical_predictive_distribution(logits, name='loss1') - self.assertEqual(1, len(lc.losses)) + self.assertEqual(1, len(lc.towers_by_loss)) # Add logits to same loss function. lc.register_categorical_predictive_distribution( logits, name='loss1', reuse=True) - self.assertEqual(1, len(lc.losses)) + self.assertEqual(1, len(lc.towers_by_loss)) # Add another new loss function. lc.register_categorical_predictive_distribution(logits, name='loss2') - self.assertEqual(2, len(lc.losses)) + self.assertEqual(2, len(lc.towers_by_loss)) def testLossFunctionWithoutName(self): """Ensure loss functions get unique names if 'name' not specified.""" @@ -298,13 +315,9 @@ class LayerCollectionTest(test.TestCase): name='loss1', reuse=layer_collection.VARIABLE_SCOPE) - self.assertEqual(len(lc.losses), 1) - loss = lc.losses[0] - + self.assertEqual(len(lc.towers_by_loss), 1) # Three successful registrations. - self.assertEqual(loss.params.shape.as_list(), - [3 * batch_size, output_size]) - self.assertEqual(loss.targets.shape.as_list(), [3 * batch_size]) + self.assertEqual(len(lc.towers_by_loss[0]), 3) def testRegisterCategoricalPredictiveDistributionBatchSize1(self): with ops.Graph().as_default(): @@ -479,17 +492,6 @@ class LayerCollectionTest(test.TestCase): variables = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES) self.assertTrue(all([var.name.startswith(scope) for var in variables])) - def testGetUseCountMap(self): - """Ensure get_use_count_map() sums 'num_registered_minibatches'.""" - lc = layer_collection.LayerCollection() - lc.fisher_blocks = { - 'a': MockFisherBlock(), - ('a', 'c'): MockFisherBlock(), - ('b', 'c'): MockFisherBlock() - } - use_count_map = lc.get_use_count_map() - self.assertDictEqual({'a': 4, 'b': 2, 'c': 4}, use_count_map) - def testIdentifyLinkedParametersSomeRegisteredInOtherTuples(self): x = variable_scope.get_variable('x', shape=()) y = variable_scope.get_variable('y', shape=()) @@ -550,6 +552,32 @@ class LayerCollectionTest(test.TestCase): self.assertIsInstance(lc.fisher_blocks[b_0], fisher_blocks.FullFB) self.assertIsInstance(lc.fisher_blocks[b_1], fisher_blocks.NaiveDiagonalFB) + def testDefaultLayerCollection(self): + with ops.Graph().as_default(): + # Can't get default if there isn't one set. + with self.assertRaises(ValueError): + layer_collection.get_default_layer_collection() + + # Can't set default twice. + lc = layer_collection.LayerCollection() + layer_collection.set_default_layer_collection(lc) + with self.assertRaises(ValueError): + layer_collection.set_default_layer_collection(lc) + + # Same as one set. + self.assertTrue(lc is layer_collection.get_default_layer_collection()) + + # Can set to None. + layer_collection.set_default_layer_collection(None) + with self.assertRaises(ValueError): + layer_collection.get_default_layer_collection() + + # as_default() is the same as setting/clearing. + with lc.as_default(): + self.assertTrue(lc is layer_collection.get_default_layer_collection()) + with self.assertRaises(ValueError): + layer_collection.get_default_layer_collection() + if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/kfac/python/kernel_tests/loss_functions_test.py b/tensorflow/contrib/kfac/python/kernel_tests/loss_functions_test.py index ae787b6f1ac90218f2ac73d37fb270df0b822de2..c00af5593f085e3b1f3e030a24f4b821115cc869 100644 --- a/tensorflow/contrib/kfac/python/kernel_tests/loss_functions_test.py +++ b/tensorflow/contrib/kfac/python/kernel_tests/loss_functions_test.py @@ -24,7 +24,6 @@ from tensorflow.contrib.kfac.python.ops import loss_functions from tensorflow.python.framework import constant_op from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops -from tensorflow.python.ops import random_ops from tensorflow.python.platform import test @@ -97,22 +96,6 @@ class CategoricalLogitsNegativeLogProbLossTest(test.TestCase): # difficult to say if the output is correct or not... neg_log_prob = sess.run(neg_log_prob) - def testMultiMinibatchRegistration(self): - """Ensure this loss function supports registering multiple minibatches.""" - with ops.Graph().as_default(): - tower_logits = [] - loss = None - num_towers = 5 - for _ in range(num_towers): - logits = random_ops.random_uniform(shape=[2, 3]) - tower_logits.append(logits) - if loss is None: - loss = loss_functions.CategoricalLogitsNegativeLogProbLoss(logits) - else: - loss.register_additional_minibatch(logits) - self.assertListEqual(loss.input_minibatches, tower_logits) - self.assertEqual(loss.num_registered_minibatches, num_towers) - def testMultiplyFisherSingleVector(self): with ops.Graph().as_default(), self.test_session() as sess: logits = np.array([1., 2., 3.]) @@ -203,23 +186,5 @@ class OnehotCategoricalLogitsNegativeLogProbLossTest(test.TestCase): # difficult to say if the output is correct or not... neg_log_prob = sess.run(neg_log_prob) - def testMultiMinibatchRegistration(self): - """Ensure this loss function supports registering multiple minibatches.""" - with ops.Graph().as_default(): - tower_logits = [] - loss = None - num_towers = 5 - for _ in range(num_towers): - logits = random_ops.random_uniform(shape=[2, 3]) - tower_logits.append(logits) - if loss is None: - loss = loss_functions.OnehotCategoricalLogitsNegativeLogProbLoss( - logits) - else: - loss.register_additional_minibatch(logits) - self.assertListEqual(loss.input_minibatches, tower_logits) - self.assertEqual(loss.num_registered_minibatches, num_towers) - - if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/kfac/python/kernel_tests/utils_test.py b/tensorflow/contrib/kfac/python/kernel_tests/utils_test.py index 97a97adbf5577cd2694d3055acaa59258ad27964..2cee01212a11595669e9df0fc95a5657926c1038 100644 --- a/tensorflow/contrib/kfac/python/kernel_tests/utils_test.py +++ b/tensorflow/contrib/kfac/python/kernel_tests/utils_test.py @@ -29,6 +29,8 @@ from tensorflow.python.framework import random_seed from tensorflow.python.ops import array_ops from tensorflow.python.ops import linalg_ops from tensorflow.python.ops import math_ops +from tensorflow.python.ops import nn_ops +from tensorflow.python.ops import random_ops from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables from tensorflow.python.platform import test @@ -325,6 +327,84 @@ class UtilsTest(test.TestCase): ], values) + def testExtractConvolutionPatches(self): + with ops.Graph().as_default(), self.test_session() as sess: + batch_size = 10 + image_spatial_shape = [9, 10, 11] + in_channels = out_channels = 32 + kernel_spatial_shape = [5, 3, 3] + spatial_strides = [1, 2, 1] + spatial_dilation = [1, 1, 1] + padding = 'SAME' + + images = random_ops.random_uniform( + [batch_size] + image_spatial_shape + [in_channels], seed=0) + kernel_shape = kernel_spatial_shape + [in_channels, out_channels] + kernel = random_ops.random_uniform(kernel_shape, seed=1) + + # Ensure shape matches expectation. + patches = utils.extract_convolution_patches( + images, + kernel_shape, + padding, + strides=spatial_strides, + dilation_rate=spatial_dilation) + result_spatial_shape = ( + patches.shape.as_list()[1:1 + len(image_spatial_shape)]) + self.assertEqual(patches.shape.as_list(), + [batch_size] + result_spatial_shape + + kernel_spatial_shape + [in_channels]) + + # Ensure extract...patches() + matmul() and convolution() implementation + # give the same answer. + outputs = nn_ops.convolution( + images, + kernel, + padding, + strides=spatial_strides, + dilation_rate=spatial_dilation) + + patches_flat = array_ops.reshape( + patches, [-1, np.prod(kernel_spatial_shape) * in_channels]) + kernel_flat = array_ops.reshape(kernel, [-1, out_channels]) + outputs_flat = math_ops.matmul(patches_flat, kernel_flat) + + outputs_, outputs_flat_ = sess.run([outputs, outputs_flat]) + self.assertAllClose(outputs_.flatten(), outputs_flat_.flatten()) + + def testExtractPointwiseConv2dPatches(self): + with ops.Graph().as_default(), self.test_session() as sess: + batch_size = 10 + image_height = image_width = 8 + in_channels = out_channels = 3 + kernel_height = kernel_width = 1 + strides = [1, 1, 1, 1] + padding = 'VALID' + + images = random_ops.random_uniform( + [batch_size, image_height, image_width, in_channels], seed=0) + kernel_shape = [kernel_height, kernel_width, in_channels, out_channels] + kernel = random_ops.random_uniform(kernel_shape, seed=1) + + # Ensure shape matches expectation. + patches = utils.extract_pointwise_conv2d_patches(images, kernel_shape) + self.assertEqual(patches.shape.as_list(), [ + batch_size, image_height, image_width, kernel_height, kernel_width, + in_channels + ]) + + # Ensure extract...patches() + matmul() and conv2d() implementation + # give the same answer. + outputs = nn_ops.conv2d(images, kernel, strides, padding) + + patches_flat = array_ops.reshape( + patches, [-1, kernel_height * kernel_width * in_channels]) + kernel_flat = array_ops.reshape(kernel, [-1, out_channels]) + outputs_flat = math_ops.matmul(patches_flat, kernel_flat) + + outputs_, outputs_flat_ = sess.run([outputs, outputs_flat]) + self.assertAllClose(outputs_.flatten(), outputs_flat_.flatten()) + if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/kfac/python/ops/estimator.py b/tensorflow/contrib/kfac/python/ops/estimator.py index a7e268c48ae326a4d8fa5fe4a4ed15b8b83a0ed9..64755be65c4b5686397dbfd798fec1ed70ae61dc 100644 --- a/tensorflow/contrib/kfac/python/ops/estimator.py +++ b/tensorflow/contrib/kfac/python/ops/estimator.py @@ -27,6 +27,7 @@ from tensorflow.contrib.kfac.python.ops import utils from tensorflow.python.framework import ops as tf_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import gradients_impl +from tensorflow.python.ops import variable_scope from tensorflow.python.util import nest @@ -65,6 +66,13 @@ class _DeviceContextGenerator(object): yield +def _make_thunk_on_device(func, device): + def thunk(): + with tf_ops.device(device): + return func() + return thunk + + class FisherEstimator(object): """Fisher estimator class supporting various approximations of the Fisher. @@ -83,26 +91,35 @@ class FisherEstimator(object): """ def __init__(self, - damping_fn, variables, cov_ema_decay, + damping, layer_collection, + exps=(-1,), estimation_mode="gradients", colocate_gradients_with_ops=True, - cov_devices=None, - inv_devices=None): + name="FisherEstimator"): """Create a FisherEstimator object. Args: - damping_fn: Function, accepts no arguments and returns damping value. variables: A list of the variables for which to estimate the Fisher. This must match the variables registered in layer_collection (if it is not None). cov_ema_decay: The decay factor used when calculating the covariance estimate moving averages. + damping: float. The damping factor used to stabilize training due to + errors in the local approximation with the Fisher information matrix, + and to regularize the update direction by making it closer to the + gradient. (Higher damping means the update looks more like a standard + gradient update - see Tikhonov regularization.) layer_collection: The layer collection object, which holds the fisher blocks, kronecker factors, and losses associated with the graph. + exps: List of floats or ints. These represent the different matrix + powers of the approximate Fisher that the FisherEstimator will be able + to multiply vectors by. If the user asks for a matrix power other + one of these (or 1, which is always supported), there will be a + failure. (Default: (-1,)) estimation_mode: The type of estimator to use for the Fishers. Can be 'gradients', 'empirical', 'curvature_prop', or 'exact'. (Default: 'gradients'). 'gradients' is the basic estimation approach @@ -121,23 +138,17 @@ class FisherEstimator(object): equal to the output dimension, roughly speaking. colocate_gradients_with_ops: Whether we should request gradients be colocated with their respective ops. (Default: True) - cov_devices: Iterable of device strings (e.g. '/gpu:0'). Covariance - computations will be placed on these devices in a round-robin fashion. - Can be None, which means that no devices are specified. - inv_devices: Iterable of device strings (e.g. '/gpu:0'). Inversion - computations will be placed on these devices in a round-robin fashion. - Can be None, which means that no devices are specified. - + name: A string. A name given to this estimator, which is added to the + variable scope when constructing variables and ops. + (Default: "FisherEstimator") Raises: ValueError: If no losses have been registered with layer_collection. """ - self._damping_fn = damping_fn - self._cov_ema_decay = cov_ema_decay self._variables = variables + self._cov_ema_decay = cov_ema_decay + self._damping = damping self._estimation_mode = estimation_mode self._layers = layer_collection - self._layers.create_subgraph() - self._layers.check_registration(variables) self._gradient_fns = { "gradients": self._get_grads_lists_gradients, "empirical": self._get_grads_lists_empirical, @@ -146,30 +157,10 @@ class FisherEstimator(object): } self._colocate_gradients_with_ops = colocate_gradients_with_ops - # TODO(b/70674513): Factor device placement outside of this class. - self._cov_device_context_generator = _DeviceContextGenerator(cov_devices) - if inv_devices == cov_devices: - self._inv_device_context_generator = self._cov_device_context_generator - else: - self._inv_device_context_generator = _DeviceContextGenerator(inv_devices) - - self._instantiate_factors() + self._made_vars = False + self._exps = exps - self.cov_update_thunks = [ - self._create_cov_update_thunk(factor) - for factor in self._layers.get_factors() - ] - self.cov_update_ops = [thunk() for thunk in self.cov_update_thunks] - self.cov_update_op = control_flow_ops.group( - self.cov_update_ops, name="cov_update_op") - - self.inv_update_thunks = [ - self._create_inv_update_thunk(factor) - for factor in self._layers.get_factors() - ] - self.inv_update_ops = [thunk() for thunk in self.inv_update_thunks] - self.inv_update_op = control_flow_ops.group( - self.inv_update_ops, name="inv_update_op") + self._name = name @property def variables(self): @@ -177,7 +168,21 @@ class FisherEstimator(object): @property def damping(self): - return self._damping_fn() + return self._damping + + @property + def blocks(self): + """All registered FisherBlocks.""" + return self._layers.get_blocks() + + @property + def factors(self): + """All registered FisherFactors.""" + return self._layers.get_factors() + + @property + def name(self): + return self._name def _apply_transformation(self, vecs_and_vars, transform): """Applies an block-wise transformation to the corresponding vectors. @@ -212,9 +217,7 @@ class FisherEstimator(object): A list of (transformed vector, var) pairs in the same order as vecs_and_vars. """ - - return self._apply_transformation(vecs_and_vars, - lambda fb, vec: fb.multiply_inverse(vec)) + return self.multiply_matpower(-1, vecs_and_vars) def multiply(self, vecs_and_vars): """Multiplies the vectors by the corresponding (damped) blocks. @@ -226,9 +229,22 @@ class FisherEstimator(object): A list of (transformed vector, var) pairs in the same order as vecs_and_vars. """ + return self.multiply_matpower(1, vecs_and_vars) - return self._apply_transformation(vecs_and_vars, - lambda fb, vec: fb.multiply(vec)) + def multiply_matpower(self, exp, vecs_and_vars): + """Multiplies the vecs by the corresponding matrix powers of the blocks. + + Args: + exp: A float representing the power to raise the blocks by before + multiplying it by the vector. + vecs_and_vars: List of (vector, variable) pairs. + + Returns: + A list of (transformed vector, var) pairs in the same order as + vecs_and_vars. + """ + fcn = lambda fb, vec: fb.multiply_matpower(vec, exp) + return self._apply_transformation(vecs_and_vars, fcn) def _instantiate_factors(self): """Instantiates FisherFactors' variables. @@ -236,9 +252,9 @@ class FisherEstimator(object): Raises: ValueError: If estimation_mode was improperly specified at construction. """ - fisher_blocks_list = self._layers.get_blocks() + blocks = self.blocks tensors_to_compute_grads = [ - fb.tensors_to_compute_grads() for fb in fisher_blocks_list + block.tensors_to_compute_grads() for block in blocks ] try: @@ -248,45 +264,283 @@ class FisherEstimator(object): raise ValueError("Unrecognized value {} for estimation_mode.".format( self._estimation_mode)) - # TODO(b/68033310): This loop round-robins the "concat" operations which - # gather the inputs for the cov_updates. In future, we might do these - # computations locally then communicate the results, which would require a - # modification to this code. - for grads_list, fb in zip(grads_lists, fisher_blocks_list): - with self._cov_device_context_generator(): - fb.instantiate_factors(grads_list, self.damping) + for grads_list, block in zip(grads_lists, blocks): + block.instantiate_factors(grads_list, self.damping) + + def _check_vars_unmade_and_set_made_flag(self): + if self._made_vars: + raise Exception("Already made variables.") + self._made_vars = True + + def made_vars(self): + return self._made_vars + + def _register_matrix_functions(self): + for exp in self._exps: + for block in self.blocks: + block.register_matpower(exp) + + def _finalize_layer_collection(self): + self._layers.create_subgraph() + self._layers.check_registration(self.variables) + self._instantiate_factors() + self._register_matrix_functions() + + def make_ops_and_vars(self, scope=None): + """Make ops and vars with no specific device placement. + + See make_ops_and_vars_round_robin for further details. + + Args: + scope: A string or None. If None it will be set to the name of this + estimator (given by the name property). All variables will be created, + and all ops will execute, inside of a variable scope of the given + name. (Default: None) + Returns: + cov_update_ops: List of ops that compute the cov updates. Corresponds + one-to-one with the list of factors given by the "factors" property. + cov_update_op: cov_update_ops grouped into a single op. + inv_update_ops: List of ops that compute the inv updates. Corresponds + one-to-one with the list of factors given by the "factors" property. + inv_update_op: inv_update_ops grouped into a single op. + cov_update_thunks: Thunks that make the ops in cov_update_ops. + inv_update_thunks: Thunks that make the ops in inv_update_ops. + """ + return self.make_ops_and_vars_round_robin(scope=scope) + + # TODO(b/70674513): Factor device placement outside of this class. + def make_ops_and_vars_round_robin(self, scope=None, cov_devices=None, + inv_devices=None): + """Make ops and vars with a round-robin device placement strategy. + + For each factor, all of that factor's cov variables and their associated + update ops will be placed on a particular device. A new device is chosen + for each factor by cycling through list of devices in the cov_devices + argument. If cov_devices is None then no explicit device placement occurs. + + An analogous strategy is followed for inverse update ops, with the list of + devices being given by the inv_devices argument. + + Inverse variables on the other hand are not placed on any specific device + (they will just use the current the device placement context, whatever + that happens to be). The idea is that the inverse variable belong where + they will be accessed most often, which is the device that actually applies + the preconditioner to the gradient. The user will be responsible for setting + the device context for this. - def _create_cov_update_thunk(self, factor): + Args: + scope: A string or None. If None it will be set to the name of this + estimator (given by the name property). All variables will be created, + and all ops will execute, inside of a variable scope of the given + name. (Default: None) + cov_devices: Iterable of device strings (e.g. '/gpu:0'). Covariance + computations will be placed on these devices in a round-robin fashion. + Can be None, which means that no devices are specified. + inv_devices: Iterable of device strings (e.g. '/gpu:0'). Inversion + computations will be placed on these devices in a round-robin fashion. + Can be None, which means that no devices are specified. + + Returns: + cov_update_ops: List of ops that compute the cov updates. Corresponds + one-to-one with the list of factors given by the "factors" property. + cov_update_op: cov_update_ops grouped into a single op. + inv_update_ops: List of ops that compute the inv updates. Corresponds + one-to-one with the list of factors given by the "factors" property. + inv_update_op: inv_update_ops grouped into a single op. + cov_update_thunks: Thunks that make the ops in cov_update_ops. + inv_update_thunks: Thunks that make the ops in inv_update_ops. + """ + (cov_update_thunks, + inv_update_thunks) = self.make_vars_and_create_op_thunks_round_robin( + scope=scope, + cov_devices=cov_devices, + inv_devices=inv_devices) + cov_update_ops = [thunk() for thunk in cov_update_thunks] + inv_update_ops = [thunk() for thunk in inv_update_thunks] + + scope = self.name if scope is None else scope + with variable_scope.variable_scope(scope): + cov_update_op = control_flow_ops.group(cov_update_ops, + name="cov_update_op") + inv_update_op = control_flow_ops.group(inv_update_ops, + name="inv_update_op") + + return (cov_update_ops, cov_update_op, inv_update_ops, inv_update_op, + cov_update_thunks, inv_update_thunks) + + def make_vars_and_create_op_thunks_round_robin(self, + scope=None, + cov_devices=None, + inv_devices=None): + """Make vars and create op thunks w/ a round-robin device placement strat. + + For each factor, all of that factor's cov variables and their associated + update ops will be placed on a particular device. A new device is chosen + for each factor by cycling through list of devices in the cov_devices + argument. If cov_devices is None then no explicit device placement occurs. + + An analogous strategy is followed for inverse update ops, with the list of + devices being given by the inv_devices argument. + + Inverse variables on the other hand are not placed on any specific device + (they will just use the current the device placement context, whatever + that happens to be). The idea is that the inverse variable belong where + they will be accessed most often, which is the device that actually applies + the preconditioner to the gradient. The user will be responsible for setting + the device context for this. + + Args: + scope: A string or None. If None it will be set to the name of this + estimator (given by the name property). All variables will be created, + and all thunks will execute, inside of a variable scope of the given + name. (Default: None) + cov_devices: Iterable of device strings (e.g. '/gpu:0'). Covariance + computations will be placed on these devices in a round-robin fashion. + Can be None, which means that no devices are specified. + inv_devices: Iterable of device strings (e.g. '/gpu:0'). Inversion + computations will be placed on these devices in a round-robin fashion. + Can be None, which means that no devices are specified. + Returns: + cov_update_thunks: List of cov update thunks. Corresponds one-to-one with + the list of factors given by the "factors" property. + inv_update_thunks: List of inv update thunks. Corresponds one-to-one with + the list of factors given by the "factors" property. + """ + + (cov_variable_thunks_raw, cov_update_thunks_raw, inv_variable_thunks_raw, + inv_update_thunks_raw) = self.create_ops_and_vars_thunks(scope=scope) + + if cov_devices: + cov_update_thunks = [] + for cov_variable_thunk, cov_update_thunk, device in zip( + cov_variable_thunks_raw, cov_update_thunks_raw, + itertools.cycle(cov_devices)): + with tf_ops.device(device): + cov_variable_thunk() + cov_update_thunks.append(_make_thunk_on_device(cov_update_thunk, + device)) + else: + for cov_variable_thunk in cov_variable_thunks_raw: + cov_variable_thunk() + cov_update_thunks = cov_update_thunks_raw + + for inv_variable_thunk in inv_variable_thunks_raw: + inv_variable_thunk() + + if inv_devices: + inv_update_thunks = [] + for inv_update_thunk, device in zip(inv_update_thunks_raw, + itertools.cycle(inv_devices)): + inv_update_thunks.append(_make_thunk_on_device(inv_update_thunk, + device)) + else: + inv_update_thunks = inv_update_thunks_raw + + return cov_update_thunks, inv_update_thunks + + def create_ops_and_vars_thunks(self, scope=None): + """Create thunks that make the ops and vars on demand. + + This function returns 4 lists of thunks: cov_variable_thunks, + cov_update_thunks, inv_variable_thunks, and inv_update_thunks. + + The length of each list is the number of factors and the i-th element of + each list corresponds to the i-th factor (given by the "factors" property). + + Note that the execution of these thunks must happen in a certain + partial order. The i-th element of cov_variable_thunks must execute + before the i-th element of cov_update_thunks (and also the i-th element + of inv_update_thunks). Similarly, the i-th element of inv_variable_thunks + must execute before the i-th element of inv_update_thunks. + + TL;DR (oversimplified): Execute the thunks according to the order that + they are returned. + + Args: + scope: A string or None. If None it will be set to the name of this + estimator (given by the name property). All thunks will execute inside + of a variable scope of the given name. (Default: None) + Returns: + cov_variable_thunks: A list of thunks that make the cov variables. + cov_update_thunks: A list of thunks that make the cov update ops. + inv_variable_thunks: A list of thunks that make the inv variables. + inv_update_thunks: A list of thunks that make the inv update ops. + """ + self._check_vars_unmade_and_set_made_flag() + + self._finalize_layer_collection() + + scope = self.name if scope is None else scope + + cov_variable_thunks = [ + self._create_cov_variable_thunk(factor, scope) + for factor in self.factors + ] + cov_update_thunks = [ + self._create_cov_update_thunk(factor, scope) for factor in self.factors + ] + inv_variable_thunks = [ + self._create_inv_variable_thunk(factor, scope) + for factor in self.factors + ] + inv_update_thunks = [ + self._create_inv_update_thunk(factor, scope) for factor in self.factors + ] + + return (cov_variable_thunks, cov_update_thunks, + inv_variable_thunks, inv_update_thunks) + + def _create_cov_variable_thunk(self, factor, scope): + """Constructs a covariance variable thunk for a single FisherFactor.""" + + def thunk(): + with variable_scope.variable_scope(scope): + return factor.instantiate_cov_variables() + + return thunk + + def _create_cov_update_thunk(self, factor, scope): """Constructs a covariance update thunk for a single FisherFactor.""" def thunk(): - with tf_ops.name_scope( - "create_cov_update_thunk", values=[self._cov_ema_decay]): + with variable_scope.variable_scope(scope): return factor.make_covariance_update_op(self._cov_ema_decay) return thunk - def _create_inv_update_thunk(self, factor): + def _create_inv_variable_thunk(self, factor, scope): + """Constructs a inverse variable thunk for a single FisherFactor.""" + + def thunk(): + with variable_scope.variable_scope(scope): + return factor.instantiate_inv_variables() + + return thunk + + def _create_inv_update_thunk(self, factor, scope): """Constructs an inverse update thunk for a single FisherFactor.""" def thunk(): - with tf_ops.name_scope("create_inv_update_thunk"): - with self._inv_device_context_generator(): - return control_flow_ops.group(factor.make_inverse_update_ops()) + with variable_scope.variable_scope(scope): + return control_flow_ops.group(factor.make_inverse_update_ops()) return thunk def _get_grads_lists_gradients(self, tensors): + # Passing in a list of loss values is better than passing in the sum as + # the latter creates unnessesary ops on the default device grads_flat = gradients_impl.gradients( - self._layers.total_sampled_loss(), + self._layers.eval_losses_on_samples(), nest.flatten(tensors), colocate_gradients_with_ops=self._colocate_gradients_with_ops) grads_all = nest.pack_sequence_as(tensors, grads_flat) return tuple((grad,) for grad in grads_all) def _get_grads_lists_empirical(self, tensors): + # Passing in a list of loss values is better than passing in the sum as + # the latter creates unnessesary ops on the default device grads_flat = gradients_impl.gradients( - self._layers.total_loss(), + self._layers.eval_losses(), nest.flatten(tensors), colocate_gradients_with_ops=self._colocate_gradients_with_ops) grads_all = nest.pack_sequence_as(tensors, grads_flat) @@ -295,9 +549,10 @@ class FisherEstimator(object): def _get_transformed_random_signs(self): transformed_random_signs = [] for loss in self._layers.losses: - transformed_random_signs.append( - loss.multiply_fisher_factor( - utils.generate_random_signs(loss.fisher_factor_inner_shape))) + with tf_ops.colocate_with(self._layers.loss_colocation_ops[loss]): + transformed_random_signs.append( + loss.multiply_fisher_factor( + utils.generate_random_signs(loss.fisher_factor_inner_shape))) return transformed_random_signs def _get_grads_lists_curvature_prop(self, tensors): @@ -316,13 +571,14 @@ class FisherEstimator(object): # Loop over all coordinates of all losses. grads_all = [] for loss in self._layers.losses: - for index in np.ndindex(*loss.fisher_factor_inner_static_shape[1:]): - transformed_one_hot = loss.multiply_fisher_factor_replicated_one_hot( - index) - grads_flat = gradients_impl.gradients( - loss.inputs, - nest.flatten(tensors), - grad_ys=transformed_one_hot, - colocate_gradients_with_ops=self._colocate_gradients_with_ops) - grads_all.append(nest.pack_sequence_as(tensors, grads_flat)) + with tf_ops.colocate_with(self._layers.loss_colocation_ops[loss]): + for index in np.ndindex(*loss.fisher_factor_inner_static_shape[1:]): + transformed_one_hot = loss.multiply_fisher_factor_replicated_one_hot( + index) + grads_flat = gradients_impl.gradients( + loss.inputs, + nest.flatten(tensors), + grad_ys=transformed_one_hot, + colocate_gradients_with_ops=self._colocate_gradients_with_ops) + grads_all.append(nest.pack_sequence_as(tensors, grads_flat)) return zip(*grads_all) diff --git a/tensorflow/contrib/kfac/python/ops/fisher_blocks.py b/tensorflow/contrib/kfac/python/ops/fisher_blocks.py index cf38d28b43836dced8babe2ffa7853b1c4b1b369..31f4689fbfbbf13872c237913a37478f3c2debe0 100644 --- a/tensorflow/contrib/kfac/python/ops/fisher_blocks.py +++ b/tensorflow/contrib/kfac/python/ops/fisher_blocks.py @@ -40,10 +40,12 @@ from __future__ import print_function import abc import enum # pylint: disable=g-bad-import-order +import numpy as np import six from tensorflow.contrib.kfac.python.ops import fisher_factors from tensorflow.contrib.kfac.python.ops import utils +from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops @@ -121,12 +123,44 @@ def compute_pi_adjusted_damping(left_cov, right_cov, damping): return (damping, damping) +class PackagedFunc(object): + """A Python thunk with a stable ID. + + Enables stable names for lambdas. + """ + + def __init__(self, func, func_id): + """Initializes PackagedFunc. + + Args: + func: a zero-arg Python function. + func_id: a hashable, function that produces a hashable, or a list/tuple + thereof. + """ + self._func = func + func_id = func_id if isinstance(func_id, (tuple, list)) else (func_id,) + self._func_id = func_id + + def __call__(self): + return self._func() + + @property + def func_id(self): + """A hashable identifier for this function.""" + return tuple(elt() if callable(elt) else elt for elt in self._func_id) + + +def _package_func(func, func_id): + return PackagedFunc(func, func_id) + + @six.add_metaclass(abc.ABCMeta) class FisherBlock(object): """Abstract base class for objects modeling approximate Fisher matrix blocks. - Subclasses must implement multiply_inverse(), instantiate_factors(), and - tensors_to_compute_grads() methods. + Subclasses must implement register_matpower, multiply_matpower, + instantiate_factors, tensors_to_compute_grads, and num_registered_minibatches + methods. """ def __init__(self, layer_collection): @@ -145,6 +179,32 @@ class FisherBlock(object): pass @abc.abstractmethod + def register_matpower(self, exp): + """Registers a matrix power to be computed by the block. + + Args: + exp: A float representing the power to raise the block by. + """ + pass + + def register_inverse(self): + """Registers a matrix inverse to be computed by the block.""" + self.register_matpower(-1) + + @abc.abstractmethod + def multiply_matpower(self, vector, exp): + """Multiplies the vector by the (damped) matrix-power of the block. + + Args: + vector: The vector (a Tensor or tuple of Tensors) to be multiplied. + exp: A float representing the power to raise the block by before + multiplying it by the vector. + + Returns: + The vector left-multiplied by the (damped) matrix-power of the block. + """ + pass + def multiply_inverse(self, vector): """Multiplies the vector by the (damped) inverse of the block. @@ -154,9 +214,8 @@ class FisherBlock(object): Returns: The vector left-multiplied by the (damped) inverse of the block. """ - pass + return self.multiply_matpower(vector, -1) - @abc.abstractmethod def multiply(self, vector): """Multiplies the vector by the (damped) block. @@ -166,7 +225,7 @@ class FisherBlock(object): Returns: The vector left-multiplied by the (damped) block. """ - pass + return self.multiply_matpower(vector, 1) @abc.abstractmethod def tensors_to_compute_grads(self): @@ -207,21 +266,18 @@ class FullFB(FisherBlock): super(FullFB, self).__init__(layer_collection) def instantiate_factors(self, grads_list, damping): - self._damping = damping + self._damping_func = _package_func(lambda: damping, (damping,)) + self._factor = self._layer_collection.make_or_get_factor( fisher_factors.FullFactor, (grads_list, self._batch_size)) - self._factor.register_damped_inverse(damping) - def multiply_inverse(self, vector): - vector_flat = utils.tensors_to_column(vector) - out_flat = self._factor.left_multiply_inverse( - vector_flat, self._damping) - return utils.column_to_tensors(vector, out_flat) + def register_matpower(self, exp): + self._factor.register_matpower(exp, self._damping_func) - def multiply(self, vector): + def multiply_matpower(self, vector, exp): vector_flat = utils.tensors_to_column(vector) - out_flat = self._factor.left_multiply( - vector_flat, self._damping) + out_flat = self._factor.left_multiply_matpower( + vector_flat, exp, self._damping_func) return utils.column_to_tensors(vector, out_flat) def full_fisher_block(self): @@ -271,22 +327,20 @@ class NaiveDiagonalFB(FisherBlock): super(NaiveDiagonalFB, self).__init__(layer_collection) def instantiate_factors(self, grads_list, damping): - self._damping = damping + self._damping_func = _package_func(lambda: damping, (damping,)) + self._factor = self._layer_collection.make_or_get_factor( fisher_factors.NaiveDiagonalFactor, (grads_list, self._batch_size)) - def multiply_inverse(self, vector): - vector_flat = utils.tensors_to_column(vector) - print("vector_flat: %s" % vector_flat) - out_flat = self._factor.left_multiply_inverse( - vector_flat, self._damping) - print("out_flat: %s" % out_flat) - return utils.column_to_tensors(vector, out_flat) + def register_matpower(self, exp): + # Not needed for this. Matrix powers are computed on demand in the + # diagonal case + pass - def multiply(self, vector): + def multiply_matpower(self, vector, exp): vector_flat = utils.tensors_to_column(vector) - out_flat = self._factor.left_multiply( - vector_flat, self._damping) + out_flat = self._factor.left_multiply_matpower( + vector_flat, exp, self._damping_func) return utils.column_to_tensors(vector, out_flat) def full_fisher_block(self): @@ -312,7 +366,89 @@ class NaiveDiagonalFB(FisherBlock): return math_ops.reduce_sum(self._batch_sizes) -class FullyConnectedDiagonalFB(FisherBlock): +class InputOutputMultiMinibatch(object): + """Mix-in class for blocks with inputs & outputs and multiple mini-batches.""" + + def __init__(self, *args, **kwargs): + self.__inputs = [] + self.__outputs = [] + super(InputOutputMultiMinibatch, self).__init__(*args, **kwargs) + + def tensors_to_compute_grads(self): + """Tensors to compute derivative of loss with respect to.""" + return self._outputs + + def register_additional_minibatch(self, inputs, outputs): + self._inputs.append(inputs) + self._outputs.append(outputs) + + @property + def num_registered_minibatches(self): + result = len(self._inputs) + assert result == len(self._outputs) + return result + + @property + def _inputs(self): + return self.__inputs + + @property + def _outputs(self): + return self.__outputs + + def _package_minibatches(self, grads_list): + """Constructs PartitionedTensor for inputs, grads_list. + + The purpose of this method is to package up the towers/minibatch dimension + of these arrays into PartitionedTensor objects. + + Args: + grads_list: 2-D list of Tensors. First index is for source, second + index for tower. + + Returns: + inputs: PartitionedTensor. + grads_list: Tuple of PartitionedTensors, one per source. + """ + inputs = utils.PartitionedTensor(self._inputs) + grads_list = tuple(utils.PartitionedTensor(grads) for grads in grads_list) + + return inputs, grads_list + + def _package_minibatches_multi(self, grads_list): + """Constructs PartitionedTensors for inputs, grads_list. + + The purpose of this method is to package up the towers/minibatch dimension + of these arrays into PartitionedTensor objects. + + This version of this function is for use with FisherBlocks that deal with + multiple uses or time-steps. One PartitionedTensor is created for each + use/time-step. + + Args: + grads_list: 3-D tuple of Tensors. First index is for source, second + index is for tower, third is for use/time-step. + + Returns: + inputs: A tuple of PartitionedTensor's, one per use/time-step. + grads_list: 2-D tuple of PartitionedTensors. First index is for source, + second is for use/time-step. + """ + # self._inputs is a 2-D tuple. First index is tower/mini-batch, second is + # use/time-step. + inputs = self._inputs + num_uses = len(inputs[0]) + assert all(len(input_) == num_uses for input_ in inputs) + assert all(len(grad) == num_uses for grads in grads_list for grad in grads) + + inputs = tuple(utils.PartitionedTensor(input_) for input_ in zip(*inputs)) + grads_list = tuple(tuple(utils.PartitionedTensor(grad) + for grad in zip(*grads)) for grads in grads_list) + + return inputs, grads_list + + +class FullyConnectedDiagonalFB(InputOutputMultiMinibatch, FisherBlock): """FisherBlock for fully-connected (dense) layers using a diagonal approx. Estimates the Fisher Information matrix's diagonal entries for a fully @@ -344,80 +480,46 @@ class FullyConnectedDiagonalFB(FisherBlock): has_bias: Whether the component Kronecker factors have an additive bias. (Default: False) """ - self._inputs = [] - self._outputs = [] self._has_bias = has_bias super(FullyConnectedDiagonalFB, self).__init__(layer_collection) def instantiate_factors(self, grads_list, damping): - inputs = _concat_along_batch_dim(self._inputs) - grads_list = tuple(_concat_along_batch_dim(grads) for grads in grads_list) + inputs, grads_list = self._package_minibatches(grads_list) - self._damping = damping self._factor = self._layer_collection.make_or_get_factor( fisher_factors.FullyConnectedDiagonalFactor, (inputs, grads_list, self._has_bias)) - def multiply_inverse(self, vector): - """Approximate damped inverse Fisher-vector product. - - Args: - vector: Tensor or 2-tuple of Tensors. if self._has_bias, Tensor of shape - [input_size, output_size] corresponding to layer's weights. If not, a - 2-tuple of the former and a Tensor of shape [output_size] corresponding - to the layer's bias. + self._damping_func = _package_func(lambda: damping, (damping,)) - Returns: - Tensor of the same shape, corresponding to the inverse Fisher-vector - product. - """ - reshaped_vec = utils.layer_params_to_mat2d(vector) - reshaped_out = self._factor.left_multiply_inverse( - reshaped_vec, self._damping) - return utils.mat2d_to_layer_params(vector, reshaped_out) + def register_matpower(self, exp): + # Not needed for this. Matrix powers are computed on demand in the + # diagonal case + pass - def multiply(self, vector): - """Approximate damped Fisher-vector product. + def multiply_matpower(self, vector, exp): + """Multiplies the vector by the (damped) matrix-power of the block. Args: vector: Tensor or 2-tuple of Tensors. if self._has_bias, Tensor of shape [input_size, output_size] corresponding to layer's weights. If not, a 2-tuple of the former and a Tensor of shape [output_size] corresponding to the layer's bias. + exp: A scalar representing the power to raise the block before multiplying + it by the vector. Returns: - Tensor of the same shape, corresponding to the Fisher-vector product. + The vector left-multiplied by the (damped) matrix-power of the block. """ reshaped_vec = utils.layer_params_to_mat2d(vector) - reshaped_out = self._factor.left_multiply( - reshaped_vec, self._damping) + reshaped_out = self._factor.left_multiply_matpower( + reshaped_vec, exp, self._damping_func) return utils.mat2d_to_layer_params(vector, reshaped_out) - def tensors_to_compute_grads(self): - """Tensors to compute derivative of loss with respect to.""" - return self._outputs - - def register_additional_minibatch(self, inputs, outputs): - """Registers an additional minibatch to the FisherBlock. - - Args: - inputs: Tensor of shape [batch_size, input_size]. Inputs to the - matrix-multiply. - outputs: Tensor of shape [batch_size, output_size]. Layer preactivations. - """ - self._inputs.append(inputs) - self._outputs.append(outputs) - - @property - def num_registered_minibatches(self): - result = len(self._inputs) - assert result == len(self._outputs) - return result - -class ConvDiagonalFB(FisherBlock): - """FisherBlock for convolutional layers using a diagonal approx. +class ConvDiagonalFB(InputOutputMultiMinibatch, FisherBlock): + """FisherBlock for 2-D convolutional layers using a diagonal approx. Estimates the Fisher Information matrix's diagonal entries for a convolutional layer. Unlike NaiveDiagonalFB this uses the low-variance "sum of squares" @@ -441,7 +543,13 @@ class ConvDiagonalFB(FisherBlock): to the layer's parameters 'w'. """ - def __init__(self, layer_collection, params, strides, padding): + def __init__(self, + layer_collection, + params, + strides, + padding, + data_format=None, + dilations=None): """Creates a ConvDiagonalFB block. Args: @@ -453,67 +561,72 @@ class ConvDiagonalFB(FisherBlock): containing the previous and a Tensor of shape [out_channels]. strides: The stride size in this layer (1-D Tensor of length 4). padding: The padding in this layer (e.g. "SAME"). + data_format: str or None. Format of input data. + dilations: List of 4 ints or None. Rate for dilation along all dimensions. + + Raises: + ValueError: if strides is not length-4. + ValueError: if dilations is not length-4. + ValueError: if channel is not last dimension. """ - self._inputs = [] - self._outputs = [] - self._strides = tuple(strides) if isinstance(strides, list) else strides + if len(strides) != 4: + raise ValueError("strides must contain 4 numbers.") + + if dilations is None: + dilations = [1, 1, 1, 1] + + if len(dilations) != 4: + raise ValueError("dilations must contain 4 numbers.") + + if not utils.is_data_format_channel_last(data_format): + raise ValueError("data_format must be channels-last.") + + self._strides = maybe_tuple(strides) self._padding = padding + self._data_format = data_format + self._dilations = maybe_tuple(dilations) self._has_bias = isinstance(params, (tuple, list)) fltr = params[0] if self._has_bias else params self._filter_shape = tuple(fltr.shape.as_list()) + if len(self._filter_shape) != 4: + raise ValueError( + "Convolution filter must be of shape" + " [filter_height, filter_width, in_channels, out_channels].") + super(ConvDiagonalFB, self).__init__(layer_collection) def instantiate_factors(self, grads_list, damping): - # Concatenate inputs, grads_list into single Tensors. - inputs = _concat_along_batch_dim(self._inputs) - grads_list = tuple(_concat_along_batch_dim(grads) for grads in grads_list) + inputs, grads_list = self._package_minibatches(grads_list) # Infer number of locations upon which convolution is applied. - inputs_shape = tuple(inputs.shape.as_list()) - self._num_locations = ( - inputs_shape[1] * inputs_shape[2] // - (self._strides[1] * self._strides[2])) - - self._damping = (self._num_locations - * normalize_damping(damping, self._num_locations)) + self._num_locations = num_conv_locations(inputs.shape.as_list(), + self._strides) self._factor = self._layer_collection.make_or_get_factor( fisher_factors.ConvDiagonalFactor, (inputs, grads_list, self._filter_shape, self._strides, self._padding, - self._has_bias)) + self._data_format, self._dilations, self._has_bias)) - def multiply_inverse(self, vector): - reshaped_vect = utils.layer_params_to_mat2d(vector) - reshaped_out = self._factor.left_multiply_inverse( - reshaped_vect, self._damping) - return utils.mat2d_to_layer_params(vector, reshaped_out) + def damping_func(): + return self._num_locations * normalize_damping(damping, + self._num_locations) - def multiply(self, vector): - reshaped_vect = utils.layer_params_to_mat2d(vector) - reshaped_out = self._factor.left_multiply( - reshaped_vect, self._damping) - return utils.mat2d_to_layer_params(vector, reshaped_out) - - def tensors_to_compute_grads(self): - return self._outputs - - def register_additional_minibatch(self, inputs, outputs): - """Registers an additional minibatch to the FisherBlock. + damping_id = (self._num_locations, "mult", "normalize_damping", damping, + self._num_locations) + self._damping_func = _package_func(damping_func, damping_id) - Args: - inputs: Tensor of shape [batch_size, height, width, input_size]. Inputs to - the convolution. - outputs: Tensor of shape [batch_size, height, width, output_size]. Layer - preactivations. - """ - self._inputs.append(inputs) - self._outputs.append(outputs) + def register_matpower(self, exp): + # Not needed for this. Matrix powers are computed on demand in the + # diagonal case + pass - @property - def num_registered_minibatches(self): - return len(self._inputs) + def multiply_matpower(self, vector, exp): + reshaped_vect = utils.layer_params_to_mat2d(vector) + reshaped_out = self._factor.left_multiply_matpower( + reshaped_vect, exp, self._damping_func) + return utils.mat2d_to_layer_params(vector, reshaped_out) class KroneckerProductFB(FisherBlock): @@ -523,22 +636,40 @@ class KroneckerProductFB(FisherBlock): output factors. """ - def _register_damped_input_and_output_inverses(self, damping): - """Registers damped inverses for both the input and output factors. - - Sets the instance members _input_damping and _output_damping. Requires the - instance members _input_factor and _output_factor. + def __init__(self, layer_collection): + super(KroneckerProductFB, self).__init__(layer_collection) + + def _setup_damping(self, damping, normalization=None): + """Makes functions that compute the damping values for both factors.""" + def compute_damping(): + if normalization is not None: + maybe_normalized_damping = normalize_damping(damping, normalization) + else: + maybe_normalized_damping = damping + + return compute_pi_adjusted_damping(self._input_factor.get_cov(), + self._output_factor.get_cov(), + maybe_normalized_damping**0.5) + + if normalization is not None: + damping_id = ("compute_pi_adjusted_damping", + "cov", self._input_factor.name, + "cov", self._output_factor.name, + "normalize_damping", damping, normalization, "power", 0.5) + else: + damping_id = ("compute_pi_adjusted_damping", + "cov", self._input_factor.name, + "cov", self._output_factor.name, + damping, "power", 0.5) - Args: - damping: The base damping factor (float or Tensor) for the damped inverse. - """ - self._input_damping, self._output_damping = compute_pi_adjusted_damping( - self._input_factor.get_cov(), - self._output_factor.get_cov(), - damping**0.5) + self._input_damping_func = _package_func(lambda: compute_damping()[0], + damping_id + ("ref", 0)) + self._output_damping_func = _package_func(lambda: compute_damping()[1], + damping_id + ("ref", 1)) - self._input_factor.register_damped_inverse(self._input_damping) - self._output_factor.register_damped_inverse(self._output_damping) + def register_matpower(self, exp): + self._input_factor.register_matpower(exp, self._input_damping_func) + self._output_factor.register_matpower(exp, self._output_damping_func) @property def _renorm_coeff(self): @@ -552,28 +683,15 @@ class KroneckerProductFB(FisherBlock): """ return 1.0 - def multiply_inverse(self, vector): - reshaped_vector = utils.layer_params_to_mat2d(vector) - reshaped_out = self._output_factor.right_multiply_inverse( - reshaped_vector, - self._output_damping) - reshaped_out = self._input_factor.left_multiply_inverse( - reshaped_out, self._input_damping) - if self._renorm_coeff != 1.0: - reshaped_out /= math_ops.cast( - self._renorm_coeff, dtype=reshaped_out.dtype) - return utils.mat2d_to_layer_params(vector, reshaped_out) - - def multiply(self, vector): + def multiply_matpower(self, vector, exp): reshaped_vector = utils.layer_params_to_mat2d(vector) - reshaped_out = self._output_factor.right_multiply( - reshaped_vector, - self._output_damping) - reshaped_out = self._input_factor.left_multiply( - reshaped_out, self._input_damping) + reshaped_out = self._output_factor.right_multiply_matpower( + reshaped_vector, exp, self._output_damping_func) + reshaped_out = self._input_factor.left_multiply_matpower( + reshaped_out, exp, self._input_damping_func) if self._renorm_coeff != 1.0: - reshaped_out *= math_ops.cast( - self._renorm_coeff, dtype=reshaped_out.dtype) + renorm_coeff = math_ops.cast(self._renorm_coeff, dtype=reshaped_out.dtype) + reshaped_out *= math_ops.cast(renorm_coeff**exp, dtype=reshaped_out.dtype) return utils.mat2d_to_layer_params(vector, reshaped_out) def full_fisher_block(self): @@ -590,7 +708,7 @@ class KroneckerProductFB(FisherBlock): right_factor) -class EmbeddingKFACFB(KroneckerProductFB): +class EmbeddingKFACFB(InputOutputMultiMinibatch, KroneckerProductFB): """K-FAC FisherBlock for embedding layers. This FisherBlock is similar to EmbeddingKFACFB, except that its @@ -608,8 +726,6 @@ class EmbeddingKFACFB(KroneckerProductFB): Fisher information matrix to which this FisherBlock belongs. vocab_size: int. Size of vocabulary for this embedding layer. """ - self._inputs = [] - self._outputs = [] self._vocab_size = vocab_size super(EmbeddingKFACFB, self).__init__(layer_collection) @@ -624,41 +740,18 @@ class EmbeddingKFACFB(KroneckerProductFB): damping: 0-D Tensor or float. 'damping' * identity is approximately added to this FisherBlock's Fisher approximation. """ - # TODO(b/68033310): Validate which of, - # (1) summing on a single device (as below), or - # (2) on each device in isolation and aggregating - # is faster. - inputs = _concat_along_batch_dim(self._inputs) - grads_list = tuple(_concat_along_batch_dim(grads) for grads in grads_list) + inputs, grads_list = self._package_minibatches(grads_list) self._input_factor = self._layer_collection.make_or_get_factor( # fisher_factors.EmbeddingInputKroneckerFactor, # - ((inputs,), self._vocab_size)) + (inputs, self._vocab_size)) self._output_factor = self._layer_collection.make_or_get_factor( # fisher_factors.FullyConnectedKroneckerFactor, # (grads_list,)) - self._register_damped_input_and_output_inverses(damping) - - def tensors_to_compute_grads(self): - return self._outputs - - def register_additional_minibatch(self, inputs, outputs): - """Registers an additional minibatch to the FisherBlock. - - Args: - inputs: Tensor of shape [batch_size, input_size]. Inputs to the - matrix-multiply. - outputs: Tensor of shape [batch_size, output_size]. Layer preactivations. - """ - self._inputs.append(inputs) - self._outputs.append(outputs) - - @property - def num_registered_minibatches(self): - return len(self._inputs) + self._setup_damping(damping) -class FullyConnectedKFACBasicFB(KroneckerProductFB): +class FullyConnectedKFACBasicFB(InputOutputMultiMinibatch, KroneckerProductFB): """K-FAC FisherBlock for fully-connected (dense) layers. This uses the Kronecker-factorized approximation from the original @@ -674,8 +767,6 @@ class FullyConnectedKFACBasicFB(KroneckerProductFB): has_bias: Whether the component Kronecker factors have an additive bias. (Default: False) """ - self._inputs = [] - self._outputs = [] self._has_bias = has_bias super(FullyConnectedKFACBasicFB, self).__init__(layer_collection) @@ -690,12 +781,7 @@ class FullyConnectedKFACBasicFB(KroneckerProductFB): damping: 0-D Tensor or float. 'damping' * identity is approximately added to this FisherBlock's Fisher approximation. """ - # TODO(b/68033310): Validate which of, - # (1) summing on a single device (as below), or - # (2) on each device in isolation and aggregating - # is faster. - inputs = _concat_along_batch_dim(self._inputs) - grads_list = tuple(_concat_along_batch_dim(grads) for grads in grads_list) + inputs, grads_list = self._package_minibatches(grads_list) self._input_factor = self._layer_collection.make_or_get_factor( # fisher_factors.FullyConnectedKroneckerFactor, # @@ -703,29 +789,11 @@ class FullyConnectedKFACBasicFB(KroneckerProductFB): self._output_factor = self._layer_collection.make_or_get_factor( # fisher_factors.FullyConnectedKroneckerFactor, # (grads_list,)) - self._register_damped_input_and_output_inverses(damping) - - def tensors_to_compute_grads(self): - return self._outputs - - def register_additional_minibatch(self, inputs, outputs): - """Registers an additional minibatch to the FisherBlock. - - Args: - inputs: Tensor of shape [batch_size, input_size]. Inputs to the - matrix-multiply. - outputs: Tensor of shape [batch_size, output_size]. Layer preactivations. - """ - self._inputs.append(inputs) - self._outputs.append(outputs) - - @property - def num_registered_minibatches(self): - return len(self._inputs) + self._setup_damping(damping) -class ConvKFCBasicFB(KroneckerProductFB): - """FisherBlock for 2D convolutional layers using the basic KFC approx. +class ConvKFCBasicFB(InputOutputMultiMinibatch, KroneckerProductFB): + """FisherBlock for convolutional layers using the basic KFC approx. Estimates the Fisher Information matrix's blog for a convolutional layer. @@ -748,23 +816,40 @@ class ConvKFCBasicFB(KroneckerProductFB): See equation 23 in https://arxiv.org/abs/1602.01407 for details. """ - def __init__(self, layer_collection, params, strides, padding): + def __init__(self, + layer_collection, + params, + padding, + strides=None, + dilation_rate=None, + data_format=None, + extract_patches_fn=None): """Creates a ConvKFCBasicFB block. Args: layer_collection: The collection of all layers in the K-FAC approximate Fisher information matrix to which this FisherBlock belongs. params: The parameters (Tensor or tuple of Tensors) of this layer. If - kernel alone, a Tensor of shape [kernel_height, kernel_width, + kernel alone, a Tensor of shape [..spatial_filter_shape.., in_channels, out_channels]. If kernel and bias, a tuple of 2 elements containing the previous and a Tensor of shape [out_channels]. - strides: The stride size in this layer (1-D Tensor of length 4). - padding: The padding in this layer (1-D of Tensor length 4). + padding: str. Padding method. + strides: List of ints or None. Contains [..spatial_filter_strides..] if + 'extract_patches_fn' is compatible with tf.nn.convolution(), else + [1, ..spatial_filter_strides, 1]. + dilation_rate: List of ints or None. Rate for dilation along each spatial + dimension if 'extract_patches_fn' is compatible with + tf.nn.convolution(), else [1, ..spatial_dilation_rates.., 1]. + data_format: str or None. Format of input data. + extract_patches_fn: str or None. Name of function that extracts image + patches. One of "extract_convolution_patches", "extract_image_patches", + "extract_pointwise_conv2d_patches". """ - self._inputs = [] - self._outputs = [] - self._strides = tuple(strides) if isinstance(strides, list) else strides self._padding = padding + self._strides = maybe_tuple(strides) + self._dilation_rate = maybe_tuple(dilation_rate) + self._data_format = data_format + self._extract_patches_fn = extract_patches_fn self._has_bias = isinstance(params, (tuple, list)) fltr = params[0] if self._has_bias else params @@ -773,135 +858,314 @@ class ConvKFCBasicFB(KroneckerProductFB): super(ConvKFCBasicFB, self).__init__(layer_collection) def instantiate_factors(self, grads_list, damping): - # TODO(b/68033310): Validate which of, - # (1) summing on a single device (as below), or - # (2) on each device in isolation and aggregating - # is faster. - inputs = _concat_along_batch_dim(self._inputs) - grads_list = tuple(_concat_along_batch_dim(grads) for grads in grads_list) + inputs, grads_list = self._package_minibatches(grads_list) # Infer number of locations upon which convolution is applied. - self._num_locations = num_conv_locations(inputs.shape.as_list(), + self._num_locations = num_conv_locations(self._inputs[0].shape.as_list(), self._strides) self._input_factor = self._layer_collection.make_or_get_factor( fisher_factors.ConvInputKroneckerFactor, - (inputs, self._filter_shape, self._strides, self._padding, + (inputs, self._filter_shape, self._padding, self._strides, + self._dilation_rate, self._data_format, self._extract_patches_fn, self._has_bias)) self._output_factor = self._layer_collection.make_or_get_factor( fisher_factors.ConvOutputKroneckerFactor, (grads_list,)) - damping = normalize_damping(damping, self._num_locations) - self._register_damped_input_and_output_inverses(damping) - self._damping = damping + self._setup_damping(damping, normalization=self._num_locations) @property def _renorm_coeff(self): return self._num_locations - def tensors_to_compute_grads(self): - return self._outputs - def register_additional_minibatch(self, inputs, outputs): - """Registers an additional minibatch to the FisherBlock. +class DepthwiseConvDiagonalFB(ConvDiagonalFB): + """FisherBlock for depthwise_conv2d(). + + Equivalent to ConvDiagonalFB applied to each input channel in isolation. + """ + + def __init__(self, + layer_collection, + params, + strides, + padding, + rate=None, + data_format=None): + """Creates a DepthwiseConvKFCBasicFB block. Args: - inputs: Tensor of shape [batch_size, height, width, input_size]. Inputs to - the convolution. - outputs: Tensor of shape [batch_size, height, width, output_size]. Layer - preactivations. + layer_collection: The collection of all layers in the K-FAC approximate + Fisher information matrix to which this FisherBlock belongs. + params: Tensor of shape [filter_height, filter_width, in_channels, + channel_multiplier]. + strides: List of 4 ints. Strides along all dimensions. + padding: str. Padding method. + rate: List of 4 ints or None. Rate for dilation along all dimensions. + data_format: str or None. Format of input data. + + Raises: + NotImplementedError: If parameters contains bias. + ValueError: If filter is not 4-D. + ValueError: If strides is not length-4. + ValueError: If rates is not length-2. + ValueError: If channels are not last dimension. """ - self._inputs.append(inputs) - self._outputs.append(outputs) + if isinstance(params, (tuple, list)): + raise NotImplementedError("Bias not yet supported.") - @property - def num_registered_minibatches(self): - return len(self._inputs) + if params.shape.ndims != 4: + raise ValueError("Filter must be 4-D.") + + if len(strides) != 4: + raise ValueError("strides must account for 4 dimensions.") + + if rate is not None: + if len(rate) != 2: + raise ValueError("rate must only account for spatial dimensions.") + rate = [1, rate[0], rate[1], 1] # conv2d expects 4-element rate. + + if not utils.is_data_format_channel_last(data_format): + raise ValueError("data_format must be channels-last.") + + super(DepthwiseConvDiagonalFB, self).__init__( + layer_collection=layer_collection, + params=params, + strides=strides, + padding=padding, + dilations=rate, + data_format=data_format) + + # This is a hack to overwrite the same setting in ConvKFCBasicFB.__init__(). + filter_height, filter_width, in_channels, channel_multiplier = ( + params.shape.as_list()) + self._filter_shape = (filter_height, filter_width, in_channels, + in_channels * channel_multiplier) + + def multiply_matpower(self, vector, exp): + conv2d_vector = depthwise_conv2d_filter_to_conv2d_filter(vector) + conv2d_result = super(DepthwiseConvDiagonalFB, self).multiply_matpower( + conv2d_vector, exp) + return conv2d_filter_to_depthwise_conv2d_filter(conv2d_result) + + +class DepthwiseConvKFCBasicFB(ConvKFCBasicFB): + """FisherBlock for depthwise_conv2d(). + + Equivalent to ConvKFCBasicFB applied to each input channel in isolation. + """ + + def __init__(self, + layer_collection, + params, + strides, + padding, + rate=None, + data_format=None): + """Creates a DepthwiseConvKFCBasicFB block. + + Args: + layer_collection: The collection of all layers in the K-FAC approximate + Fisher information matrix to which this FisherBlock belongs. + params: Tensor of shape [filter_height, filter_width, in_channels, + channel_multiplier]. + strides: List of 4 ints. Strides along all dimensions. + padding: str. Padding method. + rate: List of 4 ints or None. Rate for dilation along all dimensions. + data_format: str or None. Format of input data. + + Raises: + NotImplementedError: If parameters contains bias. + ValueError: If filter is not 4-D. + ValueError: If strides is not length-4. + ValueError: If rates is not length-2. + ValueError: If channels are not last dimension. + """ + if isinstance(params, (tuple, list)): + raise NotImplementedError("Bias not yet supported.") + + if params.shape.ndims != 4: + raise ValueError("Filter must be 4-D.") + + if len(strides) != 4: + raise ValueError("strides must account for 4 dimensions.") + + if rate is not None: + if len(rate) != 2: + raise ValueError("rate must only account for spatial dimensions.") + rate = [1, rate[0], rate[1], 1] # conv2d expects 4-element rate. + + if not utils.is_data_format_channel_last(data_format): + raise ValueError("data_format must be channels-last.") + + super(DepthwiseConvKFCBasicFB, self).__init__( + layer_collection=layer_collection, + params=params, + padding=padding, + strides=strides, + dilation_rate=rate, + data_format=data_format, + extract_patches_fn="extract_image_patches") + + # This is a hack to overwrite the same setting in ConvKFCBasicFB.__init__(). + filter_height, filter_width, in_channels, channel_multiplier = ( + params.shape.as_list()) + self._filter_shape = (filter_height, filter_width, in_channels, + in_channels * channel_multiplier) + def multiply_matpower(self, vector, exp): + conv2d_vector = depthwise_conv2d_filter_to_conv2d_filter(vector) + conv2d_result = super(DepthwiseConvKFCBasicFB, self).multiply_matpower( + conv2d_vector, exp) + return conv2d_filter_to_depthwise_conv2d_filter(conv2d_result) -def _concat_along_batch_dim(tensor_list): - """Concatenate tensors along batch (first) dimension. + +def depthwise_conv2d_filter_to_conv2d_filter(filter, name=None): # pylint: disable=redefined-builtin + """Converts a convolution filter for use with conv2d. + + Transforms a filter for use with tf.nn.depthwise_conv2d() to one that's + compatible with tf.nn.conv2d(). Args: - tensor_list: list of Tensors or list of tuples of Tensors. + filter: Tensor of shape [height, width, in_channels, channel_multiplier]. + name: None or str. Name of Op. Returns: - Tensor or tuple of Tensors. + Tensor of shape [height, width, in_channels, out_channels]. - Raises: - ValueError: If 'tensor_list' is empty. + """ + with ops.name_scope(name, "depthwise_conv2d_filter_to_conv2d_filter", + [filter]): + filter = ops.convert_to_tensor(filter) + filter_height, filter_width, in_channels, channel_multiplier = ( + filter.shape.as_list()) + + results = [] + for i in range(in_channels): + # Slice out one in_channel's filter. Insert zeros around it to force it + # to affect that channel and that channel alone. + elements = [] + if i > 0: + elements.append( + array_ops.zeros( + [filter_height, filter_width, i, channel_multiplier])) + elements.append(filter[:, :, i:(i + 1), :]) + if i + 1 < in_channels: + elements.append( + array_ops.zeros([ + filter_height, filter_width, in_channels - (i + 1), + channel_multiplier + ])) + + # Concat along in_channel. + results.append( + array_ops.concat(elements, axis=-2, name="in_channel_%d" % i)) + + # Concat along out_channel. + return array_ops.concat(results, axis=-1, name="out_channel") + + +def conv2d_filter_to_depthwise_conv2d_filter(filter, name=None): # pylint: disable=redefined-builtin + """Converts a convolution filter for use with depthwise_conv2d. + + Transforms a filter for use with tf.nn.conv2d() to one that's + compatible with tf.nn.depthwise_conv2d(). Ignores all filters but those along + the diagonal. + + Args: + filter: Tensor of shape [height, width, in_channels, out_channels]. + name: None or str. Name of Op. + + Returns: + Tensor of shape, + [height, width, in_channels, channel_multiplier] + Raises: + ValueError: if out_channels is not evenly divisible by in_channels. """ - if not tensor_list: - raise ValueError( - "Cannot concatenate Tensors if there are no Tensors to concatenate.") - - if isinstance(tensor_list[0], (tuple, list)): - # [(tensor1a, tensor1b), - # (tensor2a, tensor2b), ...] --> (tensor_a, tensor_b) - return tuple( - array_ops.concat(tensors, axis=0) for tensors in zip(*tensor_list)) - else: - # [tensor1, tensor2] --> tensor - return array_ops.concat(tensor_list, axis=0) + with ops.name_scope(name, "conv2d_filter_to_depthwise_conv2d_filter", + [filter]): + filter = ops.convert_to_tensor(filter) + filter_height, filter_width, in_channels, out_channels = ( + filter.shape.as_list()) + + if out_channels % in_channels != 0: + raise ValueError("out_channels must be evenly divisible by in_channels.") + channel_multiplier = out_channels // in_channels + + results = [] + filter = array_ops.reshape(filter, [ + filter_height, filter_width, in_channels, in_channels, + channel_multiplier + ]) + for i in range(in_channels): + # Slice out output corresponding to the correct filter. + filter_slice = array_ops.reshape( + filter[:, :, i, i, :], + [filter_height, filter_width, 1, channel_multiplier]) + results.append(filter_slice) + + # Concat along out_channel. + return array_ops.concat(results, axis=-2, name="in_channels") + + +def maybe_tuple(obj): + if not isinstance(obj, list): + return obj + return tuple(obj) def num_conv_locations(input_shape, strides): """Returns the number of spatial locations a 2D Conv kernel is applied to. Args: - input_shape: list representing shape of inputs to the Conv layer. - strides: list representing strides for the Conv kernel. + input_shape: List of ints representing shape of inputs to + tf.nn.convolution(). + strides: List of ints representing strides along spatial dimensions as + passed in to tf.nn.convolution(). Returns: A scalar |T| denoting the number of spatial locations for the Conv layer. """ - return input_shape[1] * input_shape[2] // (strides[1] * strides[2]) + spatial_input_locations = np.prod(input_shape[1:-1]) + + if strides is None: + spatial_strides_divisor = 1 + else: + spatial_strides_divisor = np.prod(strides) + return spatial_input_locations // spatial_strides_divisor -class FullyConnectedMultiIndepFB(KroneckerProductFB): + +class FullyConnectedMultiIndepFB(InputOutputMultiMinibatch, KroneckerProductFB): """FisherBlock for fully-connected layers that share parameters. """ - def __init__(self, layer_collection, inputs, outputs, has_bias=False): + def __init__(self, layer_collection, has_bias=False): """Creates a FullyConnectedMultiIndepFB block. Args: layer_collection: LayerCollection instance. - inputs: list or tuple of Tensors. Each Tensor has shape [batch_size, - inputs_size]. - outputs: list or tuple of Tensors. Each Tensor has shape [batch_size, - outputs_size]. has_bias: bool. If True, estimates Fisher with respect to a bias parameter as well as the layer's parameters. """ - - assert len(inputs) == len(outputs) - # We need to make sure inputs and outputs are tuples and not lists so that - # they get hashed by layer_collection.make_or_get_factor properly. - self._inputs = tuple(inputs) - self._outputs = tuple(outputs) self._has_bias = has_bias - self._num_uses = len(inputs) super(FullyConnectedMultiIndepFB, self).__init__(layer_collection) - @property - def num_registered_minibatches(self): - # TODO(b/69411207): Add support for registering additional minibatches. - return 1 - def instantiate_factors(self, grads_list, damping): + self._num_uses = float(len(self._inputs[0])) + inputs, grads_list = self._package_minibatches_multi(grads_list) + self._input_factor = self._layer_collection.make_or_get_factor( fisher_factors.FullyConnectedMultiKF, - ((self._inputs,), self._has_bias)) + ((inputs,), self._has_bias)) self._output_factor = self._layer_collection.make_or_get_factor( fisher_factors.FullyConnectedMultiKF, (grads_list,)) - damping = normalize_damping(damping, self._num_uses) - self._register_damped_input_and_output_inverses(damping) + self._setup_damping(damping, normalization=self._num_uses) @property def _renorm_coeff(self): @@ -910,9 +1174,6 @@ class FullyConnectedMultiIndepFB(KroneckerProductFB): def tensors_to_compute_grads(self): return self._outputs - def num_inputs(self): - return len(self._inputs) - class SeriesFBApproximation(enum.IntEnum): """See FullyConnectedSeriesFB.__init__ for description and usage.""" @@ -920,22 +1181,20 @@ class SeriesFBApproximation(enum.IntEnum): option2 = 2 -class FullyConnectedSeriesFB(FisherBlock): +class FullyConnectedSeriesFB(InputOutputMultiMinibatch, FisherBlock): """FisherBlock for fully-connected layers that share parameters across time. See the following preprint for details: https://openreview.net/pdf?id=HyMTkQZAb See the end of the appendix of the paper for a pseudo-code of the - algorithm being implemented by multiply_inverse here. Note that we are + algorithm being implemented by multiply_matpower here. Note that we are using pre-computed versions of certain matrix-matrix products to speed things up. This is explicitly explained wherever it is done. """ def __init__(self, layer_collection, - inputs, - outputs, has_bias=False, option=SeriesFBApproximation.option2): """Constructs a new `FullyConnectedSeriesFB`. @@ -943,10 +1202,6 @@ class FullyConnectedSeriesFB(FisherBlock): Args: layer_collection: The collection of all layers in the K-FAC approximate Fisher information matrix to which this FisherBlock belongs. - inputs: List of tensors of shape [batch_size, input_size]. - Inputs to the layer. - outputs: List of tensors of shape [batch_size, input_size]. - Outputs of the layer (before activations). has_bias: Whether the layer includes a bias parameter. option: A `SeriesFBApproximation` specifying the simplifying assumption to be used in this block. `option1` approximates the cross-covariance @@ -955,48 +1210,61 @@ class FullyConnectedSeriesFB(FisherBlock): 3.5 of the paper for more details. """ - assert len(inputs) == len(outputs) - # We need to make sure inputs and outputs are tuples and not lists so that - # they get hashed by layer_collection.make_or_get_factor properly. - self._inputs = tuple(inputs) - self._outputs = tuple(outputs) self._has_bias = has_bias - self._num_timesteps = len(inputs) self._option = option super(FullyConnectedSeriesFB, self).__init__(layer_collection) - @property - def num_registered_minibatches(self): - # TODO(b/69411207): Add support for registering additional minibatches. - return 1 - def instantiate_factors(self, grads_list, damping): + self._num_timesteps = len(self._inputs[0]) + inputs, grads_list = self._package_minibatches_multi(grads_list) + self._input_factor = self._layer_collection.make_or_get_factor( - fisher_factors.FullyConnectedMultiKF, ((self._inputs,), self._has_bias)) + fisher_factors.FullyConnectedMultiKF, ((inputs,), self._has_bias)) + self._input_factor.register_cov_dt1() self._output_factor = self._layer_collection.make_or_get_factor( fisher_factors.FullyConnectedMultiKF, (grads_list,)) - - damping = normalize_damping(damping, self._num_timesteps) - self._damping_input, self._damping_output = compute_pi_adjusted_damping( - self._input_factor.get_cov(), - self._output_factor.get_cov(), - damping**0.5) + self._output_factor.register_cov_dt1() + + def compute_damping(): + normalized_damping = normalize_damping(damping, self._num_timesteps) + return compute_pi_adjusted_damping(self._input_factor.get_cov(), + self._output_factor.get_cov(), + normalized_damping**0.5) + + damping_id = ("compute_pi_adjusted_damping", + "cov", self._input_factor.name, + "cov", self._output_factor.name, + "normalize_damping", + damping, self._num_timesteps, "power", 0.5) + self._input_damping_func = _package_func(lambda: compute_damping()[0], + damping_id + ("ref", 0)) + self._output_damping_func = _package_func(lambda: compute_damping()[1], + damping_id + ("ref", 1)) + + def register_matpower(self, exp): + if exp != -1: + raise NotImplementedError("FullyConnectedSeriesFB only supports inverse" + "multiplications.") if self._option == SeriesFBApproximation.option1: - self._input_factor.register_option1quants(self._damping_input) - self._output_factor.register_option1quants(self._damping_output) + self._input_factor.register_option1quants(self._input_damping_func) + self._output_factor.register_option1quants(self._output_damping_func) elif self._option == SeriesFBApproximation.option2: - self._input_factor.register_option2quants(self._damping_input) - self._output_factor.register_option2quants(self._damping_output) + self._input_factor.register_option2quants(self._input_damping_func) + self._output_factor.register_option2quants(self._output_damping_func) else: raise ValueError( "Unrecognized FullyConnectedSeriesFB approximation: {}".format( self._option)) - def multiply_inverse(self, vector): + def multiply_matpower(self, vector, exp): + if exp != -1: + raise NotImplementedError("FullyConnectedSeriesFB only supports inverse" + "multiplications.") + # pylint: disable=invalid-name Z = utils.layer_params_to_mat2d(vector) @@ -1008,8 +1276,10 @@ class FullyConnectedSeriesFB(FisherBlock): if self._option == SeriesFBApproximation.option1: # Note that L_A = A0^(-1/2) * U_A and L_G = G0^(-1/2) * U_G. - L_A, psi_A = self._input_factor.get_option1quants(self._damping_input) - L_G, psi_G = self._output_factor.get_option1quants(self._damping_output) + L_A, psi_A = self._input_factor.get_option1quants( + self._input_damping_func) + L_G, psi_G = self._output_factor.get_option1quants( + self._output_damping_func) def gamma(x): # We are assuming that each case has the same number of time-steps. @@ -1046,9 +1316,10 @@ class FullyConnectedSeriesFB(FisherBlock): # Note that P_A = A_1^T * A_0^(-1) and P_G = G_1^T * G_0^(-1), # and K_A = A_0^(-1/2) * E_A and K_G = G_0^(-1/2) * E_G. - P_A, K_A, mu_A = self._input_factor.get_option2quants(self._damping_input) + P_A, K_A, mu_A = self._input_factor.get_option2quants( + self._input_damping_func) P_G, K_G, mu_G = self._output_factor.get_option2quants( - self._damping_output) + self._output_damping_func) # Our approach differs superficially from the pseudo-code in the paper # in order to reduce the total number of matrix-matrix multiplies. @@ -1102,11 +1373,5 @@ class FullyConnectedSeriesFB(FisherBlock): # pylint: enable=invalid-name - def multiply(self, vector): - raise NotImplementedError - def tensors_to_compute_grads(self): return self._outputs - - def num_inputs(self): - return len(self._inputs) diff --git a/tensorflow/contrib/kfac/python/ops/fisher_factors.py b/tensorflow/contrib/kfac/python/ops/fisher_factors.py index 603d8b8b210279ee6d8f1de0ce10869fde23f4d9..6fc163e2323666aca8489bf146ebc8582995cf06 100644 --- a/tensorflow/contrib/kfac/python/ops/fisher_factors.py +++ b/tensorflow/contrib/kfac/python/ops/fisher_factors.py @@ -19,7 +19,6 @@ from __future__ import division from __future__ import print_function import abc -import contextlib import numpy as np import six @@ -36,6 +35,7 @@ from tensorflow.python.ops import special_math_ops from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables from tensorflow.python.training import moving_averages +from tensorflow.python.util import nest # Whether to initialize covariance estimators at a zero matrix (or the identity # matrix). @@ -53,36 +53,16 @@ EIGENVALUE_DECOMPOSITION_THRESHOLD = 2 # matrix powers. Must be nonnegative. EIGENVALUE_CLIPPING_THRESHOLD = 0.0 -# Colocate the covariance ops and variables with the input tensors for each -# factor. -COLOCATE_COV_OPS_WITH_INPUTS = True - - -@contextlib.contextmanager -def maybe_colocate_with(op): - """Context to colocate with `op` if `COLOCATE_COV_OPS_WITH_INPUTS`.""" - if COLOCATE_COV_OPS_WITH_INPUTS: - if isinstance(op, (list, tuple)): - with tf_ops.colocate_with(op[0]): - yield - else: - with tf_ops.colocate_with(op): - yield - else: - yield - def set_global_constants(init_covariances_at_zero=None, zero_debias=None, eigenvalue_decomposition_threshold=None, - eigenvalue_clipping_threshold=None, - colocate_cov_ops_with_inputs=None): + eigenvalue_clipping_threshold=None): """Sets various global constants used by the classes in this module.""" global INIT_COVARIANCES_AT_ZERO global ZERO_DEBIAS global EIGENVALUE_DECOMPOSITION_THRESHOLD global EIGENVALUE_CLIPPING_THRESHOLD - global COLOCATE_COV_OPS_WITH_INPUTS if init_covariances_at_zero is not None: INIT_COVARIANCES_AT_ZERO = init_covariances_at_zero @@ -92,8 +72,6 @@ def set_global_constants(init_covariances_at_zero=None, EIGENVALUE_DECOMPOSITION_THRESHOLD = eigenvalue_decomposition_threshold if eigenvalue_clipping_threshold is not None: EIGENVALUE_CLIPPING_THRESHOLD = eigenvalue_clipping_threshold - if colocate_cov_ops_with_inputs is not None: - COLOCATE_COV_OPS_WITH_INPUTS = colocate_cov_ops_with_inputs def inverse_initializer(shape, dtype, partition_info=None): # pylint: disable=unused-argument @@ -181,7 +159,9 @@ def scope_string_from_params(params): name_parts = [] for param in params: - if isinstance(param, (tuple, list)): + if param is None: + name_parts.append("None") + elif isinstance(param, (tuple, list)): if all([isinstance(p, int) for p in param]): name_parts.append("-".join([str(p) for p in param])) else: @@ -190,6 +170,8 @@ def scope_string_from_params(params): name_parts.append(str(param)) elif isinstance(param, (tf_ops.Tensor, variables.Variable)): name_parts.append(scope_string_from_name(param)) + elif isinstance(param, utils.PartitionedTensor): + name_parts.append(scope_string_from_name(param.tensors)) else: raise ValueError("Encountered an unsupported param type {}".format( type(param))) @@ -207,6 +189,22 @@ def scalar_or_tensor_to_string(val): return repr(val) if np.isscalar(val) else scope_string_from_name(val) +def list_to_string(lst): + return "_".join(val if isinstance(val, six.string_types) + else scalar_or_tensor_to_string(val) for val in lst) + + +def graph_func_to_id(func): + """Returns a hashable object that represents func's computation.""" + # TODO(b/74201126): replace with Topohash of func's output + return func.func_id + + +def graph_func_to_string(func): + # TODO(b/74201126): replace with Topohash of func's output + return list_to_string(func.func_id) + + @six.add_metaclass(abc.ABCMeta) class FisherFactor(object): """Base class for objects modeling factors of approximate Fisher blocks. @@ -223,13 +221,10 @@ class FisherFactor(object): Note that for blocks that aren't based on approximations, a 'factor' can be the entire block itself, as is the case for the diagonal and full representations. - - Subclasses must implement the _compute_new_cov() method, and the _var_scope - and _cov_shape properties. """ def __init__(self): - self.instantiate_covariance() + self._cov = None @abc.abstractproperty def _var_scope(self): @@ -240,6 +235,10 @@ class FisherFactor(object): """ pass + @property + def name(self): + return self._var_scope + @abc.abstractproperty def _cov_shape(self): """The shape of the variable backing this FisherFactor.""" @@ -267,8 +266,9 @@ class FisherFactor(object): """Function for initializing covariance variable.""" return covariance_initializer - def instantiate_covariance(self): - """Instantiates the covariance Variable as the instance member _cov.""" + def instantiate_cov_variables(self): + """Makes the internal cov variable(s).""" + assert self._cov is None with variable_scope.variable_scope(self._var_scope): self._cov = variable_scope.get_variable( "cov", @@ -300,20 +300,17 @@ class FisherFactor(object): """ new_cov_contribs = tuple(self._compute_new_cov(idx) for idx in range(self._num_sources)) - # This gets the job done but we might want a better solution in the future. - # In particular, we could have a separate way of specifying where the - # the cov variables finally end up, independent of where their various - # contributions are computed. Right now these are the same thing, but in - # the future we might want to perform the cov computations on each tower, - # so that each tower will be considered a "source" (allowing us to reuse - # the existing "source" code for this). - with maybe_colocate_with(new_cov_contribs[0]): - new_cov = math_ops.add_n(new_cov_contribs) - # Synchronize value across all TPU cores. - if utils.on_tpu(): - new_cov = utils.cross_replica_mean(new_cov) - return moving_averages.assign_moving_average( - self._cov, new_cov, ema_decay, zero_debias=ZERO_DEBIAS) + new_cov = math_ops.add_n(new_cov_contribs) + # Synchronize value across all TPU cores. + if utils.on_tpu(): + new_cov = utils.cross_replica_mean(new_cov) + return moving_averages.assign_moving_average( + self._cov, new_cov, ema_decay, zero_debias=ZERO_DEBIAS) + + @abc.abstractmethod + def instantiate_inv_variables(self): + """Makes the internal "inverse" variable(s).""" + pass @abc.abstractmethod def make_inverse_update_ops(self): @@ -341,70 +338,47 @@ class FisherFactor(object): return self._cov @abc.abstractmethod - def left_multiply(self, x, damping): - """Multiplies 'x' by the damped covariance of this factor. + def left_multiply_matpower(self, x, exp, damping_func): + """Left multiplies 'x' by matrix power of this factor (w/ damping applied). - Let C be the covariance matrix this factor represents, and - D = C + damping * I be its damped variant. This method calculates - matmul(D, vec(x)). + This calculation is essentially: + (C + damping * I)**exp * x + where * is matrix-multiplication, ** is matrix power, I is the identity + matrix, and C is the matrix represented by this factor. - Args: - x: Tensor. Represents a single vector. Shape depends on implementation. - damping: 0-D Tensor. Damping to add to C's diagonal. - - Returns: - Tensor of same shape as 'x'. - """ - pass - - @abc.abstractmethod - def right_multiply(self, x, damping): - """Multiplies 'x' by the damped covariance of this factor. - - Let C be the covariance matrix this factor represents, and - D = C + damping * I be its damped variant. This method calculates - matmul(vec(x), D). + x can represent either a matrix or a vector. For some factors, 'x' might + represent a vector but actually be stored as a 2D matrix for convenience. Args: x: Tensor. Represents a single vector. Shape depends on implementation. - damping: 0-D Tensor. Damping to add to C's diagonal. + exp: float. The matrix exponent to use. + damping_func: A function that computes a 0-D Tensor or a float which will + be the damping value used. i.e. damping = damping_func(). Returns: - Tensor of same shape as 'x'. + Tensor of same shape as 'x' representing the result of the multiplication. """ pass @abc.abstractmethod - def left_multiply_inverse(self, x, damping): - """Multiplies 'x' by damped inverse of this factor. - - Let C be the covariance matrix this factor represents and - E = inv(C + damping * I) be its damped inverse. This method calculates - matmul(E, vec(x)). - - Args: - x: Tensor. Represents a single vector. Shape depends on implementation. - damping: 0-D Tensor. Damping to add to C's diagonal. - - Returns: - Tensor of same shape as 'x'. - """ - pass + def right_multiply_matpower(self, x, exp, damping_func): + """Right multiplies 'x' by matrix power of this factor (w/ damping applied). - @abc.abstractmethod - def right_multiply_inverse(self, x, damping): - """Multiplies 'x' by damped inverse of this factor. + This calculation is essentially: + x * (C + damping * I)**exp + where * is matrix-multiplication, ** is matrix power, I is the identity + matrix, and C is the matrix represented by this factor. - Let C be the covariance matrix this factor represents and - E = inv(C + damping * I) be its damped inverse. This method calculates - matmul(vec(x), E). + Unlike left_multiply_matpower, x will always be a matrix. Args: x: Tensor. Represents a single vector. Shape depends on implementation. - damping: 0-D Tensor. Damping to add to C's diagonal. + exp: float. The matrix exponent to use. + damping_func: A function that computes a 0-D Tensor or a float which will + be the damping value used. i.e. damping = damping_func(). Returns: - Tensor of same shape as 'x'. + Tensor of same shape as 'x' representing the result of the multiplication. """ pass @@ -428,47 +402,52 @@ class InverseProvidingFactor(FisherFactor): # the latter. def __init__(self): - self._inverses_by_damping = {} - self._matpower_by_exp_and_damping = {} + self._matpower_by_exp_and_damping = {} # { (float, hashable): variable } + self._matpower_registrations = set() # { (float, hashable) } self._eigendecomp = None + self._damping_funcs_by_id = {} # {hashable: lambda} super(InverseProvidingFactor, self).__init__() - def register_damped_inverse(self, damping): - """Registers a damped inverse needed by a FisherBlock. + def _register_damping(self, damping_func): + damping_id = graph_func_to_id(damping_func) + if damping_id not in self._damping_funcs_by_id: + self._damping_funcs_by_id[damping_id] = damping_func + return damping_id - This creates a variable and signals make_inverse_update_ops to make the - corresponding update op. The variable can be read via the method - get_inverse. + def register_inverse(self, damping_func): + # Just for backwards compatibility of some old code and tests + self.register_matpower(-1, damping_func) - Args: - damping: The damping value (float or Tensor) for this factor. - """ - if damping not in self._inverses_by_damping: - damping_string = scalar_or_tensor_to_string(damping) - with variable_scope.variable_scope(self._var_scope): - inv = variable_scope.get_variable( - "inv_damp{}".format(damping_string), - initializer=inverse_initializer, - shape=self._cov_shape, - trainable=False, - dtype=self._dtype) - self._inverses_by_damping[damping] = inv - - def register_matpower(self, exp, damping): - """Registers a matrix power needed by a FisherBlock. + def register_matpower(self, exp, damping_func): + """Registers a matrix power to be maintained and served on demand. This creates a variable and signals make_inverse_update_ops to make the corresponding update op. The variable can be read via the method get_matpower. Args: - exp: The exponent (float or Tensor) to raise the matrix to. - damping: The damping value (float or Tensor). + exp: float. The exponent to use in the matrix power. + damping_func: A function that computes a 0-D Tensor or a float which will + be the damping value used. i.e. damping = damping_func(). """ - if (exp, damping) not in self._matpower_by_exp_and_damping: + if exp == 1.0: + # We don't register these. The user shouldn't even be calling this + # function with exp = 1.0. + return + + damping_id = self._register_damping(damping_func) + + if (exp, damping_id) not in self._matpower_registrations: + self._matpower_registrations.add((exp, damping_id)) + + def instantiate_inv_variables(self): + """Makes the internal "inverse" variable(s).""" + + for (exp, damping_id) in self._matpower_registrations: exp_string = scalar_or_tensor_to_string(exp) - damping_string = scalar_or_tensor_to_string(damping) + damping_func = self._damping_funcs_by_id[damping_id] + damping_string = graph_func_to_string(damping_func) with variable_scope.variable_scope(self._var_scope): matpower = variable_scope.get_variable( "matpower_exp{}_damp{}".format(exp_string, damping_string), @@ -476,34 +455,35 @@ class InverseProvidingFactor(FisherFactor): shape=self._cov_shape, trainable=False, dtype=self._dtype) - self._matpower_by_exp_and_damping[(exp, damping)] = matpower + assert (exp, damping_id) not in self._matpower_by_exp_and_damping + self._matpower_by_exp_and_damping[(exp, damping_id)] = matpower def make_inverse_update_ops(self): """Create and return update ops corresponding to registered computations.""" ops = [] - # We do this to ensure that we don't reuse the eigendecomp from old calls - # to make_inverse_update_ops that may be placed on different devices. This - # can happen is the user has both a permanent and lazily constructed - # version of the inverse ops (and only uses one of them). - self.reset_eigendecomp() + num_inverses = sum(1 for (exp, _) in self._matpower_by_exp_and_damping + if exp == -1) + + num_other_matpower = len(self._matpower_by_exp_and_damping) - num_inverses + + other_matrix_power_registered = num_other_matpower >= 1 - num_inverses = len(self._inverses_by_damping) - matrix_power_registered = bool(self._matpower_by_exp_and_damping) use_eig = ( - self._eigendecomp or matrix_power_registered or + self._eigendecomp or other_matrix_power_registered or num_inverses >= EIGENVALUE_DECOMPOSITION_THRESHOLD) + # We precompute these so we don't need to evaluate them multiple times (for + # each matrix power that uses them) + damping_value_by_id = {damping_id: self._damping_funcs_by_id[damping_id]() + for damping_id in self._damping_funcs_by_id} + if use_eig: eigenvalues, eigenvectors = self.get_eigendecomp() # pylint: disable=unpacking-non-sequence - for damping, inv in self._inverses_by_damping.items(): - ops.append( - inv.assign( - math_ops.matmul(eigenvectors / (eigenvalues + damping), - array_ops.transpose(eigenvectors)))) - - for (exp, damping), matpower in self._matpower_by_exp_and_damping.items(): + for (exp, damping_id), matpower in ( + self._matpower_by_exp_and_damping.items()): + damping = damping_value_by_id[damping_id] ops.append( matpower.assign( math_ops.matmul(eigenvectors * @@ -512,28 +492,31 @@ class InverseProvidingFactor(FisherFactor): # These ops share computation and should be run on a single device. ops = [control_flow_ops.group(*ops)] else: - for damping, inv in self._inverses_by_damping.items(): - ops.append(inv.assign(utils.posdef_inv(self._cov, damping))) + for (exp, damping_id), matpower in ( + self._matpower_by_exp_and_damping.items()): + assert exp == -1 + damping = damping_value_by_id[damping_id] + ops.append(matpower.assign(utils.posdef_inv(self._cov, damping))) + self._eigendecomp = False return ops - def get_damped_inverse(self, damping): - # Note that this function returns a variable which gets updated by the - # inverse ops. It may be stale / inconsistent with the latest value of - # get_cov(). - return self._inverses_by_damping[damping] + def get_inverse(self, damping_func): + # Just for backwards compatibility of some old code and tests + damping_id = graph_func_to_id(damping_func) + return self._matpower_by_exp_and_damping[(-1, damping_id)] - def get_matpower(self, exp, damping): + def get_matpower(self, exp, damping_func): # Note that this function returns a variable which gets updated by the # inverse ops. It may be stale / inconsistent with the latest value of # get_cov(). - return self._matpower_by_exp_and_damping[(exp, damping)] + damping_id = graph_func_to_id(damping_func) + return self._matpower_by_exp_and_damping[(exp, damping_id)] def get_eigendecomp(self): """Creates or retrieves eigendecomposition of self._cov.""" - # Unlike get_inverse and get_matpower this doesn't retrieve a stored - # variable, but instead always computes a fresh version from the current - # value of get_cov(). + # Unlike get_matpower this doesn't retrieve a stored variable, but instead + # always computes a fresh version from the current value of get_cov(). if not self._eigendecomp: eigenvalues, eigenvectors = linalg_ops.self_adjoint_eig(self._cov) @@ -546,63 +529,42 @@ class InverseProvidingFactor(FisherFactor): return self._eigendecomp - def reset_eigendecomp(self): - self._eigendecomp = None - def get_cov(self): # Variable contains full covariance matrix. return self.get_cov_var() - def left_multiply(self, x, damping): - n = self.get_cov().shape[0] - damped_cov = self.get_cov() + damping * array_ops.eye(n) - + def left_multiply_matpower(self, x, exp, damping_func): if isinstance(x, tf_ops.IndexedSlices): - raise NotImplementedError( - "Left-multiply not yet supported for IndexedSlices.") + raise ValueError("Left-multiply not yet supported for IndexedSlices.") - if len(x.shape) != 2: + if x.shape.ndims != 2: raise ValueError( "InverseProvidingFactors apply to matrix-shaped vectors. Found: %s." % (x,)) - return math_ops.matmul(damped_cov, x) + if exp == 1: + return math_ops.matmul(self.get_cov(), x) + damping_func() * x - def right_multiply(self, x, damping): - n = self.get_cov().shape[0] - damped_cov = self.get_cov() + damping * array_ops.eye(n) + return math_ops.matmul(self.get_matpower(exp, damping_func), x) + def right_multiply_matpower(self, x, exp, damping_func): if isinstance(x, tf_ops.IndexedSlices): - return utils.matmul_sparse_dense(x, damped_cov) - - if len(x.shape) != 2: - raise ValueError( - "InverseProvidingFactors apply to matrix-shaped vectors. Found: %s." - % (x,)) + if exp == 1: + n = self.get_cov().shape[0] + damped_cov = self.get_cov() + damping_func() * array_ops.eye(n) + return utils.matmul_sparse_dense(x, damped_cov) - return math_ops.matmul(x, damped_cov) - - def left_multiply_inverse(self, x, damping): - if isinstance(x, tf_ops.IndexedSlices): - raise ValueError("Left-multiply not yet supported for IndexedSlices.") + return utils.matmul_sparse_dense(x, self.get_matpower(exp, damping_func)) if x.shape.ndims != 2: raise ValueError( "InverseProvidingFactors apply to matrix-shaped vectors. Found: %s." % (x,)) - return math_ops.matmul(self.get_damped_inverse(damping), x) - - def right_multiply_inverse(self, x, damping): - if isinstance(x, tf_ops.IndexedSlices): - return utils.matmul_sparse_dense(x, self.get_damped_inverse(damping)) - - if x.shape.ndims != 2: - raise ValueError( - "InverseProvidingFactors apply to matrix-shaped vectors. Found: %s." - % (x,)) + if exp == 1: + return math_ops.matmul(x, self.get_cov()) + damping_func() * x - return math_ops.matmul(x, self.get_damped_inverse(damping)) + return math_ops.matmul(x, self.get_matpower(exp, damping_func)) class FullFactor(InverseProvidingFactor): @@ -622,7 +584,7 @@ class FullFactor(InverseProvidingFactor): @property def _var_scope(self): - return "ff_full/" + scope_string_from_params( + return "ff_full_" + scope_string_from_params( [self._params_grads, self._batch_size]) @property @@ -641,11 +603,10 @@ class FullFactor(InverseProvidingFactor): def _compute_new_cov(self, idx=0): # This will be a very basic rank 1 estimate - with maybe_colocate_with(self._params_grads[idx]): - params_grads_flat = utils.tensors_to_column(self._params_grads[idx]) - return ((params_grads_flat * array_ops.transpose( - params_grads_flat)) / math_ops.cast(self._batch_size, - params_grads_flat.dtype)) + params_grads_flat = utils.tensors_to_column(self._params_grads[idx]) + return ((params_grads_flat * array_ops.transpose( + params_grads_flat)) / math_ops.cast(self._batch_size, + params_grads_flat.dtype)) class DiagonalFactor(FisherFactor): @@ -656,6 +617,7 @@ class DiagonalFactor(FisherFactor): """ def __init__(self): + self._damping_funcs_by_id = {} # { hashable: lambda } super(DiagonalFactor, self).__init__() @property @@ -665,43 +627,30 @@ class DiagonalFactor(FisherFactor): def make_inverse_update_ops(self): return [] + def instantiate_inv_variables(self): + pass + def get_cov(self): # self.get_cov() could be any shape, but it must have one entry per # parameter. Flatten it into a vector. cov_diag_vec = array_ops.reshape(self.get_cov_var(), [-1]) return array_ops.diag(cov_diag_vec) - def left_multiply(self, x, damping): - damped_cov = self.get_cov_var() + damping - if isinstance(x, tf_ops.IndexedSlices): - return utils.matmul_diag_sparse(array_ops.reshape(damped_cov, [-1]), x) - - if x.shape != damped_cov.shape: - raise ValueError("x (%s) and cov (%s) must have same shape." % - (x, damped_cov)) - - return damped_cov * x - - def right_multiply(self, x, damping): - raise NotImplementedError("Only left-multiply is currently supported.") - - def left_multiply_inverse(self, x, damping): - inverse = 1. / (self.get_cov_var() + damping) + def left_multiply_matpower(self, x, exp, damping_func): + matpower = (self.get_cov_var() + damping_func())**exp if isinstance(x, tf_ops.IndexedSlices): - return utils.matmul_diag_sparse(array_ops.reshape(inverse, [-1]), x) + return utils.matmul_diag_sparse(array_ops.reshape(matpower, [-1]), x) - if x.shape != inverse.shape: + if x.shape != matpower.shape: raise ValueError("x (%s) and cov (%s) must have same shape." % - (x, inverse)) - - return inverse * x + (x, matpower)) + return matpower * x - def right_multiply_inverse(self, x, damping): + def right_multiply_matpower(self, x, exp, damping_func): raise NotImplementedError("Only left-multiply is currently supported.") - def register_damped_inverse(self, damping): - # DiagonalFactors don't keep explicit inverses. + def register_matpower(self, exp, damping_func): pass @@ -730,7 +679,7 @@ class NaiveDiagonalFactor(DiagonalFactor): @property def _var_scope(self): - return "ff_naivediag/" + scope_string_from_params( + return "ff_naivediag_" + scope_string_from_params( [self._params_grads, self._batch_size]) @property @@ -748,10 +697,9 @@ class NaiveDiagonalFactor(DiagonalFactor): return self._params_grads[0][0].dtype def _compute_new_cov(self, idx=0): - with maybe_colocate_with(self._params_grads[idx]): - params_grads_flat = utils.tensors_to_column(self._params_grads[idx]) - return (math_ops.square(params_grads_flat) / math_ops.cast( - self._batch_size, params_grads_flat.dtype)) + params_grads_flat = utils.tensors_to_column(self._params_grads[idx]) + return (math_ops.square(params_grads_flat) / math_ops.cast( + self._batch_size, params_grads_flat.dtype)) class EmbeddingInputKroneckerFactor(DiagonalFactor): @@ -772,8 +720,8 @@ class EmbeddingInputKroneckerFactor(DiagonalFactor): """Instantiate EmbeddingInputKroneckerFactor. Args: - input_ids: Tuple of Tensors of shape [batch_size, input_size] and dtype - int32. Indices into embedding matrix. + input_ids: Tensor of shape [batch_size, input_size] and dtype int32. + Indices into embedding matrix. vocab_size: int or 0-D Tensor. Maximum value for entries in 'input_ids'. dtype: dtype for covariance statistics. Must be a floating point type. Defaults to float32. @@ -786,7 +734,7 @@ class EmbeddingInputKroneckerFactor(DiagonalFactor): @property def _var_scope(self): - return "ff_diag_embedding/" + scope_string_from_params(self._input_ids) + return "ff_diag_embedding_" + scope_string_from_params(self._input_ids) @property def _cov_shape(self): @@ -794,42 +742,45 @@ class EmbeddingInputKroneckerFactor(DiagonalFactor): @property def _num_sources(self): - return len(self._input_ids) + return 1 @property def _dtype(self): return self._cov_dtype def _compute_new_cov(self, idx=0): - with maybe_colocate_with(self._input_ids): - input_ids = self._input_ids[idx] - if len(input_ids.shape) > 2: - raise ValueError( - "Input to embeddings must have rank <= 2. Found rank %d." % len( - input_ids.shape)) + if idx != 0: + raise ValueError("EmbeddingInputKroneckerFactor only supports idx = 0") - batch_size = array_ops.shape(input_ids)[0] + input_ids = self._input_ids - # Transform indices into one-hot vectors. - # - # TODO(b/72714822): There must be a faster way to construct the diagonal - # covariance matrix! This operation is O(batch_size * vocab_size), where - # it should be O(batch_size * input_size). - flat_input_ids = array_ops.reshape(input_ids, [-1]) - one_hots = array_ops.one_hot(flat_input_ids, - self._vocab_size) # [?, vocab_size] + if len(input_ids.shape) > 2: + raise ValueError( + "Input to embeddings must have rank <= 2. Found rank %d." % len( + input_ids.shape)) + + batch_size = array_ops.shape(input_ids)[0] - # Take average across examples. Note that, because all entries have - # magnitude zero or one, there's no need to square the entries. - # - # TODO(b/72714822): Support for SparseTensor, other kinds of aggregation - # within an example such as average. - # - # TODO(b/72714822): Support for partitioned embeddings. - new_cov = math_ops.reduce_sum(one_hots, axis=0) # [vocab_size] - new_cov /= math_ops.cast(batch_size, new_cov.dtype) + # Transform indices into one-hot vectors. + # + # TODO(b/72714822): There must be a faster way to construct the diagonal + # covariance matrix! This operation is O(batch_size * vocab_size), where + # it should be O(batch_size * input_size). + flat_input_ids = array_ops.reshape(input_ids, [-1]) + one_hots = array_ops.one_hot(flat_input_ids, + self._vocab_size) # [?, vocab_size] - return new_cov + # Take average across examples. Note that, because all entries have + # magnitude zero or one, there's no need to square the entries. + # + # TODO(b/72714822): Support for SparseTensor, other kinds of aggregation + # within an example such as average. + # + # TODO(b/72714822): Support for partitioned embeddings. + new_cov = math_ops.reduce_sum(one_hots, axis=0) # [vocab_size] + new_cov /= math_ops.cast(batch_size, new_cov.dtype) + + return new_cov class FullyConnectedDiagonalFactor(DiagonalFactor): @@ -850,23 +801,23 @@ class FullyConnectedDiagonalFactor(DiagonalFactor): """Instantiate FullyConnectedDiagonalFactor. Args: - inputs: Tensor of shape [batch_size, input_size]. Inputs to fully - connected layer. - outputs_grads: List of Tensors of shape [batch_size, output_size]. - Gradient of loss with respect to layer's preactivations. + inputs: Tensor of shape [batch_size, input_size]. Inputs to this layer. + outputs_grads: List of Tensors, each of shape [batch_size, output_size], + which are the gradients of the loss with respect to the layer's + outputs. One Tensor for each "source". + has_bias: bool. If True, append '1' to each input. """ self._inputs = inputs self._has_bias = has_bias self._outputs_grads = outputs_grads - self._batch_size = array_ops.shape(inputs)[0] self._squared_inputs = None super(FullyConnectedDiagonalFactor, self).__init__() @property def _var_scope(self): - return "ff_diagfc/" + scope_string_from_params( + return "ff_diagfc_" + scope_string_from_params( (self._inputs,) + tuple(self._outputs_grads)) @property @@ -883,25 +834,30 @@ class FullyConnectedDiagonalFactor(DiagonalFactor): def _dtype(self): return self._outputs_grads[0].dtype + def make_covariance_update_op(self, ema_decay): + inputs = self._inputs + + if self._has_bias: + inputs = append_homog(inputs) + self._squared_inputs = math_ops.square(inputs) + + return super(FullyConnectedDiagonalFactor, self).make_covariance_update_op( + ema_decay) + def _compute_new_cov(self, idx=0): + batch_size = array_ops.shape(self._squared_inputs)[0] + outputs_grad = self._outputs_grads[idx] + # The well-known special formula that uses the fact that the entry-wise # square of an outer product is the outer-product of the entry-wise squares. # The gradient is the outer product of the input and the output gradients, # so we just square both and then take their outer-product. - with maybe_colocate_with(self._outputs_grads[idx]): - # We only need to compute squared_inputs once - if self._squared_inputs is None: - inputs = self._inputs - if self._has_bias: - inputs = append_homog(self._inputs) - self._squared_inputs = math_ops.square(inputs) - - new_cov = math_ops.matmul( - self._squared_inputs, - math_ops.square(self._outputs_grads[idx]), - transpose_a=True) - new_cov /= math_ops.cast(self._batch_size, new_cov.dtype) - return new_cov + new_cov = math_ops.matmul( + self._squared_inputs, + math_ops.square(outputs_grad), + transpose_a=True) + new_cov /= math_ops.cast(batch_size, new_cov.dtype) + return new_cov class ConvDiagonalFactor(DiagonalFactor): @@ -913,35 +869,64 @@ class ConvDiagonalFactor(DiagonalFactor): filter_shape, strides, padding, + data_format=None, + dilations=None, has_bias=False): """Creates a ConvDiagonalFactor object. Args: inputs: Tensor of shape [batch_size, height, width, in_channels]. Input activations to this layer. - outputs_grads: Tensor of shape [batch_size, height, width, out_channels]. - Per-example gradients to the loss with respect to the layer's output - preactivations. + outputs_grads: List of Tensors, each of shape [batch_size, + height, width, out_channels], which are the gradients of the loss + with respect to the layer's outputs. One Tensor for each "source". filter_shape: Tuple of 4 ints: (kernel_height, kernel_width, in_channels, out_channels). Represents shape of kernel used in this layer. strides: The stride size in this layer (1-D Tensor of length 4). padding: The padding in this layer (1-D of Tensor length 4). + data_format: None or str. Format of conv2d inputs. + dilations: None or tuple of 4 ints. has_bias: Python bool. If True, the layer is assumed to have a bias parameter in addition to its filter parameter. + + Raises: + ValueError: If inputs, output_grads, and filter_shape do not agree on + in_channels or out_channels. + ValueError: If strides, dilations are not length-4 lists of ints. + ValueError: If data_format does not put channel last. """ + if not utils.is_data_format_channel_last(data_format): + raise ValueError("Channel must be last.") + if inputs.shape.ndims != 4: + raise ValueError("inputs must be 4-D Tensor.") + if inputs.shape.as_list()[-1] != filter_shape[-2]: + raise ValueError("inputs and filter_shape must agree on in_channels.") + for i, outputs_grad in enumerate(outputs_grads): + if outputs_grad.shape.ndims != 4: + raise ValueError("outputs[%d] must be 4-D Tensor." % i) + if outputs_grad.shape.as_list()[-1] != filter_shape[-1]: + raise ValueError( + "outputs[%d] and filter_shape must agree on out_channels." % i) + if len(strides) != 4: + raise ValueError("strides must be length-4 list of ints.") + if dilations is not None and len(dilations) != 4: + raise ValueError("dilations must be length-4 list of ints.") + self._inputs = inputs + self._outputs_grads = outputs_grads self._filter_shape = filter_shape self._strides = strides self._padding = padding + self._data_format = data_format + self._dilations = dilations self._has_bias = has_bias - self._outputs_grads = outputs_grads self._patches = None super(ConvDiagonalFactor, self).__init__() @property def _var_scope(self): - return "ff_convdiag/" + scope_string_from_name( + return "ff_convdiag_" + scope_string_from_params( (self._inputs,) + tuple(self._outputs_grads)) @property @@ -961,38 +946,36 @@ class ConvDiagonalFactor(DiagonalFactor): return self._outputs_grads[0].dtype def make_covariance_update_op(self, ema_decay): - with maybe_colocate_with(self._inputs): - filter_height, filter_width, _, _ = self._filter_shape + filter_height, filter_width, _, _ = self._filter_shape - # TODO(b/64144716): there is potential here for a big savings in terms - # of memory use. - patches = array_ops.extract_image_patches( - self._inputs, - ksizes=[1, filter_height, filter_width, 1], - strides=self._strides, - rates=[1, 1, 1, 1], - padding=self._padding) - - if self._has_bias: - patches = append_homog(patches) + # TODO(b/64144716): there is potential here for a big savings in terms + # of memory use. + if self._dilations is None: + rates = (1, 1, 1, 1) + else: + rates = tuple(self._dilations) + patches = array_ops.extract_image_patches( + self._inputs, + ksizes=[1, filter_height, filter_width, 1], + strides=self._strides, + rates=rates, + padding=self._padding) - self._patches = patches + if self._has_bias: + patches = append_homog(patches) - op = super(ConvDiagonalFactor, self).make_covariance_update_op(ema_decay) + self._patches = patches - self._patches = None - - return op + return super(ConvDiagonalFactor, self).make_covariance_update_op(ema_decay) def _compute_new_cov(self, idx=0): - with maybe_colocate_with(self._outputs_grads[idx]): - outputs_grad = self._outputs_grads[idx] - batch_size = array_ops.shape(self._patches)[0] + batch_size = array_ops.shape(self._patches)[0] + outputs_grad = self._outputs_grads[idx] - new_cov = self._convdiag_sum_of_squares(self._patches, outputs_grad) - new_cov /= math_ops.cast(batch_size, new_cov.dtype) + new_cov = self._convdiag_sum_of_squares(self._patches, outputs_grad) + new_cov /= math_ops.cast(batch_size, new_cov.dtype) - return new_cov + return new_cov def _convdiag_sum_of_squares(self, patches, outputs_grad): # This computes the sum of the squares of the per-training-case "gradients". @@ -1013,8 +996,9 @@ class FullyConnectedKroneckerFactor(InverseProvidingFactor): """Instantiate FullyConnectedKroneckerFactor. Args: - tensors: List of Tensors of shape [batch_size, n]. Represents either a - layer's inputs or its output's gradients. + tensors: List of Tensors, each of shape [batch_size, n], one for each + source. The Tensors are typically either a layer's inputs or its + output's gradients. has_bias: bool. If True, append '1' to each row. """ # The tensor argument is either a tensor of input activations or a tensor of @@ -1025,8 +1009,8 @@ class FullyConnectedKroneckerFactor(InverseProvidingFactor): @property def _var_scope(self): - return "ff_fckron/" + scope_string_from_params( - [self._tensors, self._has_bias]) + return "ff_fckron_" + scope_string_from_params( + tuple(self._tensors) + (self._has_bias,)) @property def _cov_shape(self): @@ -1042,11 +1026,10 @@ class FullyConnectedKroneckerFactor(InverseProvidingFactor): return self._tensors[0].dtype def _compute_new_cov(self, idx=0): - with maybe_colocate_with(self._tensors[idx]): - tensor = self._tensors[idx] - if self._has_bias: - tensor = append_homog(tensor) - return compute_cov(tensor) + tensor = self._tensors[idx] + if self._has_bias: + tensor = append_homog(tensor) + return compute_cov(tensor) class ConvInputKroneckerFactor(InverseProvidingFactor): @@ -1062,39 +1045,55 @@ class ConvInputKroneckerFactor(InverseProvidingFactor): def __init__(self, inputs, filter_shape, - strides, padding, + strides=None, + dilation_rate=None, + data_format=None, + extract_patches_fn=None, has_bias=False): """Initializes ConvInputKroneckerFactor. Args: - inputs: Tensor of shape [batch_size, height, width, in_channels]. Inputs - to layer. - filter_shape: 1-D Tensor of length 4. Contains [kernel_height, - kernel_width, in_channels, out_channels]. - strides: 1-D Tensor of length 4. Contains [batch_stride, height_stride, - width_stride, in_channel_stride]. + inputs: Tensor of shape [batch_size, ..spatial_input_size.., in_channels]. + Inputs to layer. + filter_shape: List of ints. Contains [..spatial_filter_size.., + in_channels, out_channels]. Shape of convolution kernel. padding: str. Padding method for layer. "SAME" or "VALID". + strides: List of ints or None. Contains [..spatial_filter_strides..] if + 'extract_patches_fn' is compatible with tf.nn.convolution(), else + [1, ..spatial_filter_strides, 1]. + dilation_rate: List of ints or None. Rate for dilation along each spatial + dimension if 'extract_patches_fn' is compatible with + tf.nn.convolution(), else [1, ..spatial_dilation_rates.., 1]. + data_format: str or None. Format of input data. + extract_patches_fn: str or None. Name of function that extracts image + patches. One of "extract_convolution_patches", "extract_image_patches", + "extract_pointwise_conv2d_patches". has_bias: bool. If True, append 1 to in_channel. """ + self._inputs = inputs self._filter_shape = filter_shape self._strides = strides self._padding = padding + self._dilation_rate = dilation_rate + self._data_format = data_format + self._extract_patches_fn = extract_patches_fn self._has_bias = has_bias - self._inputs = inputs + super(ConvInputKroneckerFactor, self).__init__() @property def _var_scope(self): - return "ff_convinkron/" + scope_string_from_params([ + return "ff_convinkron_" + scope_string_from_params([ self._inputs, self._filter_shape, self._strides, self._padding, - self._has_bias + self._dilation_rate, self._data_format, self._has_bias ]) @property def _cov_shape(self): - filter_height, filter_width, in_channels, _ = self._filter_shape - size = filter_height * filter_width * in_channels + self._has_bias + spatial_filter_shape = self._filter_shape[0:-2] + in_channels = self._filter_shape[-2] + size = np.prod(spatial_filter_shape) * in_channels + self._has_bias return [size, size] @property @@ -1109,37 +1108,62 @@ class ConvInputKroneckerFactor(InverseProvidingFactor): if idx != 0: raise ValueError("ConvInputKroneckerFactor only supports idx = 0") - with maybe_colocate_with(self._inputs): - filter_height, filter_width, in_channels, _ = self._filter_shape - - # TODO(b/64144716): there is potential here for a big savings in terms of - # memory use. + # TODO(b/64144716): there is potential here for a big savings in terms of + # memory use. + if self._extract_patches_fn in [None, "extract_convolution_patches"]: + patches = utils.extract_convolution_patches( + self._inputs, + self._filter_shape, + padding=self._padding, + strides=self._strides, + dilation_rate=self._dilation_rate, + data_format=self._data_format) + + elif self._extract_patches_fn == "extract_image_patches": + assert self._inputs.shape.ndims == 4 + assert len(self._filter_shape) == 4 + assert len(self._strides) == 4, self._strides + if self._dilation_rate is None: + rates = [1, 1, 1, 1] + else: + rates = self._dilation_rate + assert len(rates) == 4 + assert rates[0] == rates[-1] == 1 patches = array_ops.extract_image_patches( self._inputs, - ksizes=[1, filter_height, filter_width, 1], + ksizes=[1] + list(self._filter_shape[0:-2]) + [1], strides=self._strides, - rates=[1, 1, 1, 1], + rates=rates, padding=self._padding) - flatten_size = (filter_height * filter_width * in_channels) - # patches_flat below is the matrix [[A_l]] from the KFC paper (tilde - # omitted over A for clarity). It has shape M|T| x J|Delta| (eq. 14), - # where M = minibatch size, |T| = number of spatial locations, - # |Delta| = number of spatial offsets, and J = number of input maps - # for convolutional layer l. - patches_flat = array_ops.reshape(patches, [-1, flatten_size]) - # We append a homogenous coordinate to patches_flat if the layer has - # bias parameters. This gives us [[A_l]]_H from the paper. - if self._has_bias: - patches_flat = append_homog(patches_flat) - # We call compute_cov without passing in a normalizer. compute_cov uses - # the first dimension of patches_flat i.e. M|T| as the normalizer by - # default. Hence we end up computing 1/M|T| * [[A_l]]^T [[A_l]], with - # shape J|Delta| x J|Delta|. This is related to hat{Omega}_l from - # the paper but has a different scale here for consistency with - # ConvOutputKroneckerFactor. - # (Tilde omitted over A for clarity.) - return compute_cov(patches_flat) + elif self._extract_patches_fn == "extract_pointwise_conv2d_patches": + assert self._strides in [None, [1, 1, 1, 1], (1, 1, 1, 1)] + assert self._filter_shape[0] == self._filter_shape[1] == 1 + patches = utils.extract_pointwise_conv2d_patches( + self._inputs, self._filter_shape, data_format=None) + + else: + raise NotImplementedError(self._extract_patches_fn) + + flatten_size = np.prod(self._filter_shape[0:-1]) + # patches_flat below is the matrix [[A_l]] from the KFC paper (tilde + # omitted over A for clarity). It has shape M|T| x J|Delta| (eq. 14), + # where M = minibatch size, |T| = number of spatial locations, + # |Delta| = number of spatial offsets, and J = number of input maps + # for convolutional layer l. + patches_flat = array_ops.reshape(patches, [-1, flatten_size]) + # We append a homogenous coordinate to patches_flat if the layer has + # bias parameters. This gives us [[A_l]]_H from the paper. + if self._has_bias: + patches_flat = append_homog(patches_flat) + # We call compute_cov without passing in a normalizer. compute_cov uses + # the first dimension of patches_flat i.e. M|T| as the normalizer by + # default. Hence we end up computing 1/M|T| * [[A_l]]^T [[A_l]], with + # shape J|Delta| x J|Delta|. This is related to hat{Omega}_l from + # the paper but has a different scale here for consistency with + # ConvOutputKroneckerFactor. + # (Tilde omitted over A for clarity.) + return compute_cov(patches_flat) class ConvOutputKroneckerFactor(InverseProvidingFactor): @@ -1153,20 +1177,27 @@ class ConvOutputKroneckerFactor(InverseProvidingFactor): Section 3.1 Estimating the factors. """ - def __init__(self, outputs_grads): + def __init__(self, outputs_grads, data_format=None): """Initializes ConvOutputKroneckerFactor. Args: outputs_grads: list of Tensors. Each Tensor is of shape - [batch_size, height, width, out_channels]. + [batch_size, ..spatial_input_size.., out_channels]. One Tensor per + source. + data_format: None or str. Format of outputs_grads. + + Raises: + ValueError: If channels are not final dimension. """ - self._out_channels = outputs_grads[0].shape.as_list()[3] + if not utils.is_data_format_channel_last(data_format): + raise ValueError("Channel must be last.") + self._out_channels = outputs_grads[0].shape.as_list()[-1] self._outputs_grads = outputs_grads super(ConvOutputKroneckerFactor, self).__init__() @property def _var_scope(self): - return "ff_convoutkron/" + scope_string_from_params(self._outputs_grads) + return "ff_convoutkron_" + scope_string_from_params(self._outputs_grads) @property def _cov_shape(self): @@ -1182,22 +1213,22 @@ class ConvOutputKroneckerFactor(InverseProvidingFactor): return self._outputs_grads[0].dtype def _compute_new_cov(self, idx=0): - with maybe_colocate_with(self._outputs_grads[idx]): - # reshaped_tensor below is the matrix DS_l defined in the KFC paper - # (tilde omitted over S for clarity). It has shape M|T| x I, where - # M = minibatch size, |T| = number of spatial locations, and - # I = number of output maps for convolutional layer l. - reshaped_tensor = array_ops.reshape(self._outputs_grads[idx], - [-1, self._out_channels]) - # Following the reasoning in ConvInputKroneckerFactor._compute_new_cov, - # compute_cov here returns 1/M|T| * DS_l^T DS_l = hat{Gamma}_l - # as defined in the paper, with shape I x I. - # (Tilde omitted over S for clarity.) - return compute_cov(reshaped_tensor) + outputs_grad = self._outputs_grads[idx] + + # reshaped_tensor below is the matrix DS_l defined in the KFC paper + # (tilde omitted over S for clarity). It has shape M|T| x I, where + # M = minibatch size, |T| = number of spatial locations, and + # I = number of output maps for convolutional layer l. + reshaped_tensor = array_ops.reshape(outputs_grad, [-1, self._out_channels]) + # Following the reasoning in ConvInputKroneckerFactor._compute_new_cov, + # compute_cov here returns 1/M|T| * DS_l^T DS_l = hat{Gamma}_l + # as defined in the paper, with shape I x I. + # (Tilde omitted over S for clarity.) + return compute_cov(reshaped_tensor) class FullyConnectedMultiKF(InverseProvidingFactor): - """Kronecker factor for a fully connected recurrent layer.""" + """Kronecker factor for a fully connected layer used multiple times.""" def __init__(self, tensor_lists, @@ -1205,25 +1236,32 @@ class FullyConnectedMultiKF(InverseProvidingFactor): """Constructs a new `FullyConnectedMultiKF`. Args: - tensor_lists: List of lists of Tensors of shape [batch_size, n]. + tensor_lists: 2D array (list of lists) of Tensors of shape + [batch_size, n]. Each of these tensors is usually a layer's inputs or + its output's gradients. The first dimension of the array is the source, + and the second is the use in the graph (which is sometimes a + "time-step"). has_bias: bool. If True, '1' is appended to each row. """ self._tensor_lists = tensor_lists self._has_bias = has_bias - self._batch_size = array_ops.shape(tensor_lists[0][0])[0] self._num_timesteps = len(tensor_lists[0]) self._tensors = [None] * len(tensor_lists) self._cov_dt1 = None + self._make_cov_dt1 = False self._option1quants_by_damping = {} self._option2quants_by_damping = {} + self._option1quants_registrations = set() + self._option2quants_registrations = set() super(FullyConnectedMultiKF, self).__init__() @property def _var_scope(self): - return "ff_fc_multi/" + scope_string_from_params(self._tensor_lists) + return "ff_fc_multi_" + scope_string_from_params( + tuple(nest.flatten(self._tensor_lists)) + (self._has_bias,)) @property def _num_sources(self): @@ -1240,43 +1278,40 @@ class FullyConnectedMultiKF(InverseProvidingFactor): if self._cov_dt1 is not None: new_cov_dt1_contribs = tuple(self._compute_new_cov_dt1(idx) for idx in range(self._num_sources)) + new_cov_dt1 = math_ops.add_n(new_cov_dt1_contribs) + op2 = moving_averages.assign_moving_average( + self._cov_dt1, new_cov_dt1, ema_decay, zero_debias=ZERO_DEBIAS) - with maybe_colocate_with(new_cov_dt1_contribs[0]): - new_cov_dt1 = math_ops.add_n(new_cov_dt1_contribs) - - op2 = moving_averages.assign_moving_average( - self._cov_dt1, new_cov_dt1, ema_decay, zero_debias=ZERO_DEBIAS) - - # TODO(b/69112164): - # It's important that _cov and _cov_dt1 remain consistent with each - # other while the inverse ops are happening. How can we ensure this? - # We will need to add explicit synchronization for this to - # work with asynchronous training. - op = control_flow_ops.group(op, op2) + # TODO(b/69112164): + # It's important that _cov and _cov_dt1 remain consistent with each + # other while the inverse ops are happening. How can we ensure this? + # We will need to add explicit synchronization for this to + # work with asynchronous training. + op = control_flow_ops.group(op, op2) return op def _compute_new_cov(self, idx=0): - with maybe_colocate_with(self._tensor_lists[idx]): - tensor = array_ops.concat(self._tensor_lists[idx], 0) - if self._has_bias: - tensor = append_homog(tensor) - # We save these so they can be used by _compute_new_cov_dt1 - self._tensors[idx] = tensor - return compute_cov(tensor) - - def _compute_new_cov_dt1(self, idx=0): + # Concatenate across time/replications + tensor = array_ops.concat(self._tensor_lists[idx], 0) + if self._has_bias: + tensor = append_homog(tensor) + # We save these so they can be used by _compute_new_cov_dt1 + self._tensors[idx] = tensor + return compute_cov(tensor) + + def _compute_new_cov_dt1(self, idx=0): # pylint: disable=missing-docstring tensor = self._tensors[idx] - with maybe_colocate_with(tensor): - # Is there a more elegant way to do this computation? - tensor_present = tensor[:-self._batch_size, :] - tensor_future = tensor[self._batch_size:, :] - # We specify a normalizer for this computation to ensure a PSD Fisher - # block estimate. This is equivalent to padding with zeros, as was done - # in Section B.2 of the appendix. - normalizer = self._num_timesteps * self._batch_size - return compute_cov( - tensor_future, tensor_right=tensor_present, normalizer=normalizer) + batch_size = array_ops.shape(self._tensor_lists[idx][0])[0] + # Is there a more elegant way to do this computation? + tensor_present = tensor[:-batch_size, :] + tensor_future = tensor[batch_size:, :] + # We specify a normalizer for this computation to ensure a PSD Fisher + # block estimate. This is equivalent to padding with zeros, as was done + # in Section B.2 of the appendix. + normalizer = self._num_timesteps * batch_size + return compute_cov( + tensor_future, tensor_right=tensor_present, normalizer=normalizer) @property def _cov_shape(self): @@ -1288,23 +1323,25 @@ class FullyConnectedMultiKF(InverseProvidingFactor): size = self._tensor_lists[0][0].shape[1] + self._has_bias return [size] - def get_option1quants(self, damping): - return self._option1quants_by_damping[damping] + def get_option1quants(self, damping_func): + damping_id = graph_func_to_id(damping_func) + return self._option1quants_by_damping[damping_id] - def get_option2quants(self, damping): - return self._option2quants_by_damping[damping] + def get_option2quants(self, damping_func): + damping_id = graph_func_to_id(damping_func) + return self._option2quants_by_damping[damping_id] def get_cov_dt1(self): assert self._cov_dt1 is not None return self._cov_dt1 def register_cov_dt1(self): - """Create a variable representing temporal cross-covariance. + self._make_cov_dt1 = True - (This is technically the second moment, not covariance, since it's - not mean subtracted.) - """ - if self._cov_dt1 is None: + def instantiate_cov_variables(self): + super(FullyConnectedMultiKF, self).instantiate_cov_variables() + assert self._cov_dt1 is None + if self._make_cov_dt1: with variable_scope.variable_scope(self._var_scope): self._cov_dt1 = variable_scope.get_variable( "cov_dt1", @@ -1313,15 +1350,25 @@ class FullyConnectedMultiKF(InverseProvidingFactor): trainable=False, dtype=self._dtype) - def register_option1quants(self, damping): + def register_option1quants(self, damping_func): + damping_id = self._register_damping(damping_func) + if damping_id not in self._option1quants_registrations: + self._option1quants_registrations.add(damping_id) - self.register_cov_dt1() + def register_option2quants(self, damping_func): + damping_id = self._register_damping(damping_func) + if damping_id not in self._option2quants_registrations: + self._option2quants_registrations.add(damping_id) - if damping not in self._option1quants_by_damping: + def instantiate_inv_variables(self): + super(FullyConnectedMultiKF, self).instantiate_inv_variables() + + for damping_id in self._option1quants_registrations: + damping_func = self._damping_funcs_by_id[damping_id] + damping_string = graph_func_to_string(damping_func) # It's questionable as to whether we should initialize with stuff like # this at all. Ideally these values should never be used until they are # updated at least once. - damping_string = scalar_or_tensor_to_string(damping) with variable_scope.variable_scope(self._var_scope): Lmat = variable_scope.get_variable( # pylint: disable=invalid-name "Lmat_damp{}".format(damping_string), @@ -1336,17 +1383,15 @@ class FullyConnectedMultiKF(InverseProvidingFactor): trainable=False, dtype=self._dtype) - self._option1quants_by_damping[damping] = (Lmat, psi) - - def register_option2quants(self, damping): + assert damping_id not in self._option1quants_by_damping + self._option1quants_by_damping[damping_id] = (Lmat, psi) - self.register_cov_dt1() - - if damping not in self._option2quants_by_damping: + for damping_id in self._option2quants_registrations: + damping_func = self._damping_funcs_by_id[damping_id] + damping_string = graph_func_to_string(damping_func) # It's questionable as to whether we should initialize with stuff like # this at all. Ideally these values should never be used until they are # updated at least once. - damping_string = scalar_or_tensor_to_string(damping) with variable_scope.variable_scope(self._var_scope): Pmat = variable_scope.get_variable( # pylint: disable=invalid-name "Lmat_damp{}".format(damping_string), @@ -1367,14 +1412,15 @@ class FullyConnectedMultiKF(InverseProvidingFactor): trainable=False, dtype=self._dtype) - self._option2quants_by_damping[damping] = (Pmat, Kmat, mu) + assert damping_id not in self._option2quants_by_damping + self._option2quants_by_damping[damping_id] = (Pmat, Kmat, mu) def make_inverse_update_ops(self): """Create and return update ops corresponding to registered computations.""" # TODO(b/69918258): Add correctness tests for this method. # pylint: disable=invalid-name - ops = super(FullyConnectedMultiKF, self).make_inverse_update_ops() + ops = [] if (len(self._option1quants_by_damping) + len(self._option2quants_by_damping)): @@ -1395,8 +1441,10 @@ class FullyConnectedMultiKF(InverseProvidingFactor): # consistently, or are somehow read between or during the cov updates. # Can this possibly happen? Is there a way to prevent it? - for damping, (Lmat_var, - psi_var) in self._option1quants_by_damping.items(): + for damping_id, (Lmat_var, + psi_var) in self._option1quants_by_damping.items(): + + damping = self._damping_funcs_by_id[damping_id]() invsqrtC0 = math_ops.matmul( eigen_V * (eigen_e + damping)**(-0.5), eigen_V, transpose_b=True) @@ -1421,8 +1469,10 @@ class FullyConnectedMultiKF(InverseProvidingFactor): ops.append(Lmat_var.assign(Lmat)) ops.append(psi_var.assign(psi)) - for damping, (Pmat_var, Kmat_var, - mu_var) in self._option2quants_by_damping.items(): + for damping_id, (Pmat_var, Kmat_var, + mu_var) in self._option2quants_by_damping.items(): + + damping = self._damping_funcs_by_id[damping_id]() # compute C0^(-1/2) invsqrtC0 = math_ops.matmul( @@ -1463,6 +1513,7 @@ class FullyConnectedMultiKF(InverseProvidingFactor): ops.append(Kmat_var.assign(Kmat)) ops.append(mu_var.assign(mu)) + ops += super(FullyConnectedMultiKF, self).make_inverse_update_ops() return [control_flow_ops.group(*ops)] # pylint: enable=invalid-name diff --git a/tensorflow/contrib/kfac/python/ops/layer_collection.py b/tensorflow/contrib/kfac/python/ops/layer_collection.py index ce9005b9ce99a4efa5f2821c56e199dd2086482e..4eb5e4c092b50ff4a908a22312330c40ca93cbee 100644 --- a/tensorflow/contrib/kfac/python/ops/layer_collection.py +++ b/tensorflow/contrib/kfac/python/ops/layer_collection.py @@ -26,6 +26,7 @@ from __future__ import print_function from collections import defaultdict from collections import OrderedDict +from contextlib import contextmanager from functools import partial import math @@ -75,6 +76,27 @@ _FULLY_CONNECTED_MULTI_APPROX_TO_BLOCK_TYPES = { # tf.get_variable_scope().reuse. VARIABLE_SCOPE = "VARIABLE_SCOPE" +_DEFAULT_LAYER_COLLECTION = None + + +def get_default_layer_collection(): + """Get default LayerCollection.""" + if _DEFAULT_LAYER_COLLECTION is None: + raise ValueError( + "Attempted to retrieve default LayerCollection when none is set. Use " + "LayerCollection.as_default().") + + return _DEFAULT_LAYER_COLLECTION + + +def set_default_layer_collection(layer_collection): + global _DEFAULT_LAYER_COLLECTION + + if _DEFAULT_LAYER_COLLECTION is not None and layer_collection is not None: + raise ValueError("Default LayerCollection is already set.") + + _DEFAULT_LAYER_COLLECTION = layer_collection + class LayerParametersDict(OrderedDict): """An OrderedDict where keys are Tensors or tuples of Tensors. @@ -130,6 +152,8 @@ class LayerCollection(object): fisher_factors: an OrderedDict mapping tuples to FisherFactor instances. losses: a list of LossFunction objects. The loss to be optimized is their sum. + loss_colocation_ops: ops to colocate loss function evaluations with. These + will typically be the inputs to the losses. """ def __init__(self, @@ -148,14 +172,21 @@ class LayerCollection(object): self._default_convolution_2d_approximation = APPROX_KRONECKER_NAME self._default_fully_connected_multi_approximation = ( APPROX_KRONECKER_SERIES_2_NAME) + self.loss_colocation_ops = {} + self._vars_to_uses = defaultdict(lambda: 0) with variable_scope.variable_scope(None, default_name=name) as scope: self._var_scope = scope.name @property def losses(self): - """LossFunctions registered with this LayerCollection.""" - return list(self._loss_dict.values()) + """Tuple of LossFunction objects registered with this LayerCollection.""" + return nest.flatten(self.towers_by_loss) + + @property + def towers_by_loss(self): + """Tuple across losses of LossFunction objects registered to each tower.""" + return tuple(tuple(lst) for lst in self._loss_dict.values()) @property def registered_variables(self): @@ -290,23 +321,74 @@ class LayerCollection(object): self.fisher_blocks[layer_key] = fisher_block return fisher_block - def get_use_count_map(self): - """Returns a dict of variables to their number of registrations.""" - # TODO(b/70283403): Reimplement this in the old way, where each - # registration function would be responsible for incrementing the count. - # Also, this version has a bug: it won't do the right thing for generic - # registration for parameters that are shared. i.e. it won't set the use - # count to infinity. - vars_to_uses = defaultdict(int) - for key, block in six.iteritems(self.fisher_blocks): - n = ( - block.num_inputs()*block.num_registered_minibatches if isinstance( - block, (fb.FullyConnectedSeriesFB, fb.FullyConnectedMultiIndepFB)) - else block.num_registered_minibatches) - key = utils.ensure_sequence(key) - for k in key: - vars_to_uses[k] += n - return vars_to_uses + def register_loss_function(self, + loss, + colocation_op, + base_name, + name=None, + reuse=VARIABLE_SCOPE): + """Registers a LossFunction object. + + Args: + loss: The LossFunction object. + colocation_op: The op to colocate the loss function's computations with. + base_name: The name to derive a new unique name from is the name argument + is None. + name: (OPTIONAL) str or None. Unique name for this loss function. If None, + a new name is generated. (Default: None) + reuse: (OPTIONAL) bool or str. If True, reuse an existing FisherBlock. + If False, create a new FisherBlock. If VARIABLE_SCOPE, use + tf.get_variable_scope().reuse. + + Raises: + ValueError: If reuse == True and name == None. + ValueError: If reuse == True and seed != None. + KeyError: If reuse == True and no existing LossFunction with 'name' found. + KeyError: If reuse == False and existing LossFunction with 'name' found. + """ + + name = name or self._graph.unique_name(base_name) + + if reuse == VARIABLE_SCOPE: + reuse = variable_scope.get_variable_scope().reuse + + if reuse: + if name is None: + raise ValueError( + "If reuse is enabled, loss function's name must be set.") + + loss_list = self._loss_dict.get(name, None) + + if loss_list is None: + raise KeyError( + "Unable to find loss function named {}. Register a new loss " + "function with reuse=False.".format(name)) + else: + if name in self._loss_dict: + raise KeyError( + "Loss function named {} already exists. Set reuse=True to append " + "another minibatch/tower.".format(name)) + + loss_list = [] + self._loss_dict[name] = loss_list + + loss_list.append(loss) + self.loss_colocation_ops[loss] = colocation_op + + def _get_use_count_map(self): + """Returns a dict mapping variables to their number of registrations.""" + return self._vars_to_uses + + def _add_uses(self, params, uses): + """Register additional uses by params in the graph. + + Args: + params: Variable or tuple of Variables. Parameters for a layer. + uses: int or float. Number of additional uses for these parameters. + """ + params = params if isinstance(params, (tuple, list)) else (params,) + for var in params: + self._vars_to_uses[var] += uses def check_registration(self, variables): """Checks that all variable uses have been registered properly. @@ -324,7 +406,7 @@ class LayerCollection(object): # Note that overlapping parameters (i.e. those that share variables) will # be caught by layer_collection.LayerParametersDict during registration. - reg_use_map = self.get_use_count_map() + reg_use_map = self._get_use_count_map() error_messages = [] @@ -414,12 +496,27 @@ class LayerCollection(object): inputs_to_losses = nest.flatten(tuple(loss.inputs for loss in self.losses)) self._subgraph = utils.SubGraph(inputs_to_losses) + def eval_losses(self): + """Return evaluated losses (colocated with inputs to losses).""" + evals = [] + for loss in self.losses: + with ops.colocate_with(self.loss_colocation_ops[loss]): + evals.append(loss.evaluate()) + return evals + + def eval_losses_on_samples(self): + """Return losses evaluated on samples (colocated with inputs to losses).""" + evals = [] + for loss in self.losses: + with ops.colocate_with(self.loss_colocation_ops[loss]): + evals.append(loss.evaluate_on_sample()) + return evals + def total_loss(self): - return math_ops.add_n(tuple(loss.evaluate() for loss in self.losses)) + return math_ops.add_n(self.eval_losses()) def total_sampled_loss(self): - return math_ops.add_n( - tuple(loss.evaluate_on_sample() for loss in self.losses)) + return math_ops.add_n(self.eval_losses_on_samples()) def _get_linked_approx(self, params): """If params were linked, return their specified approximation.""" @@ -469,6 +566,8 @@ class LayerCollection(object): params, fb.EmbeddingKFACFB(self, vocab_size), reuse=reuse) block.register_additional_minibatch(inputs, outputs) + self._add_uses(params, 1) + def register_fully_connected(self, params, inputs, @@ -505,30 +604,37 @@ class LayerCollection(object): block_type = _FULLY_CONNECTED_APPROX_TO_BLOCK_TYPES[approx] has_bias = isinstance(params, (tuple, list)) - block = self.register_block(params, block_type(self, has_bias), reuse=reuse) + block = self.register_block(params, block_type(self, has_bias=has_bias), + reuse=reuse) block.register_additional_minibatch(inputs, outputs) + self._add_uses(params, 1) + def register_conv2d(self, params, strides, padding, inputs, outputs, + data_format=None, + dilations=None, approx=None, reuse=VARIABLE_SCOPE): - """Registers a convolutional layer. + """Registers a call to tf.nn.conv2d(). Args: params: Tensor or 2-tuple of Tensors corresponding to weight and bias of this layer. Weight matrix should have shape [kernel_height, kernel_width, in_channels, out_channels]. Bias should have shape [out_channels]. - strides: 1-D Tensor of length 4. Strides for convolution kernel. + strides: List of 4 ints. Strides for convolution kernel. padding: string. see tf.nn.conv2d for valid values. inputs: Tensor of shape [batch_size, height, width, in_channels]. Inputs to layer. outputs: Tensor of shape [batch_size, height, width, out_channels]. Output produced by layer. + data_format: str or None. Format of data. + dilations: List of 4 ints. Dilations along each dimension. approx: str. One of "kron" or "diagonal". reuse: bool or str. If True, reuse an existing FisherBlock. If False, create a new FisherBlock. If "VARIABLE_SCOPE", use @@ -549,10 +655,206 @@ class LayerCollection(object): raise ValueError("Bad value {} for approx.".format(approx)) block_type = _CONV2D_APPROX_TO_BLOCK_TYPES[approx] + if approx == APPROX_KRONECKER_NAME: + block = self.register_block( + params, + block_type( + layer_collection=self, + params=params, + padding=padding, + strides=strides, + data_format=data_format, + dilation_rate=dilations, + extract_patches_fn="extract_image_patches"), + reuse=reuse) + elif approx == APPROX_DIAGONAL_NAME: + assert strides[0] == strides[-1] == 1 + block = self.register_block( + params, + block_type( + layer_collection=self, + params=params, + padding=padding, + strides=strides, + dilations=dilations, + data_format=data_format), + reuse=reuse) + else: + raise NotImplementedError + + block.register_additional_minibatch(inputs, outputs) + + self._add_uses(params, 1) + + def register_convolution(self, + params, + inputs, + outputs, + padding, + strides=None, + dilation_rate=None, + data_format=None, + approx=None, + reuse=VARIABLE_SCOPE): + """Register a call to tf.nn.convolution(). + + Args: + params: Tensor or 2-tuple of Tensors corresponding to weight and bias of + this layer. Weight matrix should have shape [..filter_spatial_size.., + in_channels, out_channels]. Bias should have shape [out_channels]. + inputs: Tensor of shape [batch_size, ..input_spatial_size.., in_channels]. + Inputs to layer. + outputs: Tensor of shape [batch_size, ..output_spatial_size.., + out_channels]. Output produced by layer. + padding: string. see tf.nn.conv2d for valid values. + strides: List of ints of length len(..input_spatial_size..). Strides for + convolution kernel in spatial dimensions. + dilation_rate: List of ints of length len(..input_spatial_size..). + Dilations along spatial dimension. + data_format: str or None. Format of data. + approx: str. One of "kron" or "diagonal". + reuse: bool or str. If True, reuse an existing FisherBlock. If False, + create a new FisherBlock. If "VARIABLE_SCOPE", use + tf.get_variable_scope().reuse. + + Raises: + ValueError: For improper value to 'approx'. + KeyError: If reuse == True but no FisherBlock found for 'params'. + ValueError: If reuse == True and FisherBlock found but of the wrong type. + """ + assert approx is None or approx == APPROX_KRONECKER_NAME + + block = self.register_block( + params, + fb.ConvKFCBasicFB( + layer_collection=self, + params=params, + padding=padding, + strides=strides, + dilation_rate=dilation_rate, + data_format=data_format), + reuse=reuse) + block.register_additional_minibatch(inputs, outputs) + + self._add_uses(params, 1) + + def register_depthwise_conv2d(self, + params, + inputs, + outputs, + strides, + padding, + rate=None, + data_format=None, + approx=None, + reuse=VARIABLE_SCOPE): + """Register a call to tf.nn.depthwise_conv2d(). + + Args: + params: 4-D Tensor of shape [filter_height, filter_width, + in_channels, channel_multiplier]. Convolutional filter. + inputs: Tensor of shape [batch_size, input_height, input_width, + in_channels]. Inputs to layer. + outputs: Tensor of shape [batch_size, output_height, output_width, + in_channels * channel_multiplier]. Output produced by depthwise conv2d. + strides: List of ints of length 4. Strides along all dimensions. + padding: string. see tf.nn.conv2d for valid values. + rate: None or List of ints of length 2. Dilation rates in spatial + dimensions. + data_format: str or None. Format of data. + approx: None or str. Must be "diagonal" if non-None. + reuse: bool or str. If True, reuse an existing FisherBlock. If False, + create a new FisherBlock. If "VARIABLE_SCOPE", use + tf.get_variable_scope().reuse. + + Raises: + ValueError: For improper value to 'approx'. + KeyError: If reuse == True but no FisherBlock found for 'params'. + ValueError: If reuse == True and FisherBlock found but of the wrong type. + """ + assert approx is None or approx == APPROX_DIAGONAL_NAME + assert data_format in [None, "NHWC"] + block = self.register_block( - params, block_type(self, params, strides, padding), reuse=reuse) + params, + fb.DepthwiseConvDiagonalFB( + layer_collection=self, + params=params, + strides=strides, + padding=padding, + rate=rate, + data_format=data_format), + reuse=reuse) block.register_additional_minibatch(inputs, outputs) + self._add_uses(params, 1) + + def register_separable_conv2d(self, + depthwise_params, + pointwise_params, + inputs, + depthwise_outputs, + pointwise_outputs, + strides, + padding, + rate=None, + data_format=None, + approx=None, + reuse=VARIABLE_SCOPE): + """Register a call to tf.nn.separable_conv2d(). + + Note: This requires access to intermediate outputs betwee depthwise and + pointwise convolutions. + + Args: + depthwise_params: 4-D Tensor of shape [filter_height, filter_width, + in_channels, channel_multiplier]. Filter for depthwise conv2d. + pointwise_params: 4-D Tensor of shape [1, 1, in_channels * + channel_multiplier, out_channels]. Filter for pointwise conv2d. + inputs: Tensor of shape [batch_size, input_height, input_width, + in_channels]. Inputs to layer. + depthwise_outputs: Tensor of shape [batch_size, output_height, + output_width, in_channels * channel_multiplier]. Output produced by + depthwise conv2d. + pointwise_outputs: Tensor of shape [batch_size, output_height, + output_width, out_channels]. Output produced by pointwise conv2d. + strides: List of ints of length 4. Strides for depthwise conv2d kernel in + all dimensions. + padding: string. see tf.nn.conv2d for valid values. + rate: None or List of ints of length 2. Dilation rate of depthwise conv2d + kernel in spatial dimensions. + data_format: str or None. Format of data. + approx: None or str. Must be "kron" if non-None. + reuse: bool or str. If True, reuse an existing FisherBlock. If False, + create a new FisherBlock. If "VARIABLE_SCOPE", use + tf.get_variable_scope().reuse. + + Raises: + ValueError: For improper value to 'approx'. + KeyError: If reuse == True but no FisherBlock found for 'params'. + ValueError: If reuse == True and FisherBlock found but of the wrong type. + """ + self.register_depthwise_conv2d( + params=depthwise_params, + inputs=inputs, + outputs=depthwise_outputs, + strides=strides, + padding=padding, + rate=rate, + data_format=data_format, + approx=APPROX_DIAGONAL_NAME, + reuse=reuse) + + self.register_conv2d( + params=pointwise_params, + inputs=depthwise_outputs, + outputs=pointwise_outputs, + strides=[1, 1, 1, 1], + padding="VALID", + data_format=data_format, + approx=approx, + reuse=reuse) + def register_generic(self, params, batch_size, @@ -586,8 +888,10 @@ class LayerCollection(object): block = self.register_block(params, block_type(self, params), reuse=reuse) block.register_additional_minibatch(batch_size) + self._add_uses(params, float("inf")) + def register_fully_connected_multi(self, params, inputs, outputs, - approx=None): + approx=None, reuse=VARIABLE_SCOPE): """Register fully connected layers with shared parameters. This can handle general fully-connected layers with shared parameters, but @@ -604,6 +908,9 @@ class LayerCollection(object): [batch_size, output_size]. Outputs produced by layer. In the case of RNNs, one Tensor per time step. approx: str. One of "kron_indep", "kron_series_1", or "kron_series_2". + reuse: bool or str. If True, reuse an existing FisherBlock. If False, + create a new FisherBlock. If "VARIABLE_SCOPE", use + tf.get_variable_scope().reuse. Raises: ValueError: For improper value to 'approx'. @@ -621,11 +928,14 @@ class LayerCollection(object): raise ValueError("Bad value {} for approx.".format(approx)) block_type = _FULLY_CONNECTED_MULTI_APPROX_TO_BLOCK_TYPES[approx] - # For now we don't support multiple minibatches for this type of layer, so - # we set reuse=False - self.register_block(params, - block_type(self, inputs, outputs, has_bias=has_bias), - reuse=False) + block = self.register_block(params, block_type(self, has_bias=has_bias), + reuse=reuse) + block.register_additional_minibatch(inputs, outputs) + self._add_uses(params, len(inputs)) + + # TODO(b/74108452): change the loss registration functions names to refer + # to "loss functions" instead of distributions. Following naming convention + # of the loss function classes themselves. def register_categorical_predictive_distribution(self, logits, @@ -648,50 +958,20 @@ class LayerCollection(object): reuse: (OPTIONAL) bool or str. If True, reuse an existing FisherBlock. If False, create a new FisherBlock. If VARIABLE_SCOPE, use tf.get_variable_scope().reuse. - - Raises: - ValueError: If reuse == True and name == None. - ValueError: If reuse == True and seed != None. - KeyError: If reuse == True and no existing LossFunction with 'name' found. - KeyError: If reuse == False and existing LossFunction with 'name' found. """ - name = name or self._graph.unique_name( - "register_categorical_predictive_distribution") - - if reuse == VARIABLE_SCOPE: - reuse = variable_scope.get_variable_scope().reuse - - if reuse: - if name is None: - raise ValueError( - "If reuse is enabled, loss function's name must be set.") - if seed is not None: - raise ValueError( - "Seed can only be specified at LossFunction instantiation.") - - loss = self._loss_dict.get(name, None) - - if loss is None: - raise KeyError( - "Unable to find loss function named {}. Create a new LossFunction " - "with reuse=False.".format(name)) - - loss.register_additional_minibatch(logits, targets=targets) - else: - if name in self._loss_dict: - raise KeyError( - "Loss function named {} already exists. Set reuse=True to append " - "another minibatch.".format(name)) - loss = lf.CategoricalLogitsNegativeLogProbLoss( - logits, targets=targets, seed=seed) - self._loss_dict[name] = loss + loss = lf.CategoricalLogitsNegativeLogProbLoss(logits, targets=targets, + seed=seed) + self.register_loss_function(loss, logits, + "categorical_predictive_distribution", + name=name, reuse=reuse) def register_normal_predictive_distribution(self, mean, var=0.5, seed=None, targets=None, - name=None): + name=None, + reuse=VARIABLE_SCOPE): """Registers a normal predictive distribution. Args: @@ -708,21 +988,22 @@ class LayerCollection(object): (Default: None) name: (OPTIONAL) str or None. Unique name for this loss function. If None, a new name is generated. (Default: None) + reuse: (OPTIONAL) bool or str. If True, reuse an existing FisherBlock. + If False, create a new FisherBlock. If VARIABLE_SCOPE, use + tf.get_variable_scope().reuse. """ - name = name or self._graph.unique_name( - "register_normal_predictive_distribution") - if name in self._loss_dict: - raise NotImplementedError( - "Adding logits to an existing LossFunction not yet supported.") - loss = lf.NormalMeanNegativeLogProbLoss( - mean, var, targets=targets, seed=seed) - self._loss_dict[name] = loss + loss = lf.NormalMeanNegativeLogProbLoss(mean, var, targets=targets, + seed=seed) + self.register_loss_function(loss, mean, + "normal_predictive_distribution", + name=name, reuse=reuse) def register_multi_bernoulli_predictive_distribution(self, logits, seed=None, targets=None, - name=None): + name=None, + reuse=VARIABLE_SCOPE): """Registers a multi-Bernoulli predictive distribution. Args: @@ -735,15 +1016,15 @@ class LayerCollection(object): (Default: None) name: (OPTIONAL) str or None. Unique name for this loss function. If None, a new name is generated. (Default: None) + reuse: (OPTIONAL) bool or str. If True, reuse an existing FisherBlock. + If False, create a new FisherBlock. If VARIABLE_SCOPE, use + tf.get_variable_scope().reuse. """ - name = name or self._graph.unique_name( - "register_multi_bernoulli_predictive_distribution") - if name in self._loss_dict: - raise NotImplementedError( - "Adding logits to an existing LossFunction not yet supported.") - loss = lf.MultiBernoulliNegativeLogProbLoss( - logits, targets=targets, seed=seed) - self._loss_dict[name] = loss + loss = lf.MultiBernoulliNegativeLogProbLoss(logits, targets=targets, + seed=seed) + self.register_loss_function(loss, logits, + "multi_bernoulli_predictive_distribution", + name=name, reuse=reuse) def make_or_get_factor(self, cls, args): """Insert 'cls(args)' into 'self.fisher_factors' if not already present. @@ -772,3 +1053,10 @@ class LayerCollection(object): with variable_scope.variable_scope(self._var_scope): self.fisher_factors[key] = cls(*args) return self.fisher_factors[key] + + @contextmanager + def as_default(self): + """Sets this LayerCollection as the default.""" + set_default_layer_collection(self) + yield + set_default_layer_collection(None) diff --git a/tensorflow/contrib/kfac/python/ops/layer_collection_lib.py b/tensorflow/contrib/kfac/python/ops/layer_collection_lib.py index f8aa230d9ca1f542950f56b1e6cf1ab7ccd3d05f..9f4685380705bd409dbcd7e85d0e3bb4189a6adc 100644 --- a/tensorflow/contrib/kfac/python/ops/layer_collection_lib.py +++ b/tensorflow/contrib/kfac/python/ops/layer_collection_lib.py @@ -30,6 +30,8 @@ from tensorflow.python.util.all_util import remove_undocumented # pylint: enable=unused-import,line-too-long,wildcard-import _allowed_symbols = [ + "get_default_layer_collection", + "set_default_layer_collection", "LayerParametersDict", "LayerCollection", "APPROX_KRONECKER_NAME", diff --git a/tensorflow/contrib/kfac/python/ops/loss_functions.py b/tensorflow/contrib/kfac/python/ops/loss_functions.py index cb3e698b9ceab920785adf735f88bd8e535a628f..e7d4243fc3d1c2d860693f2f62447b1c9aeeee03 100644 --- a/tensorflow/contrib/kfac/python/ops/loss_functions.py +++ b/tensorflow/contrib/kfac/python/ops/loss_functions.py @@ -57,30 +57,6 @@ class LossFunction(object): """The inputs to the loss function (excluding the targets).""" pass - @property - def input_minibatches(self): - """A `list` of inputs to the loss function, separated by minibatch. - - Typically there will be one minibatch per tower in a multi-tower setup. - Returns a list consisting of `self.inputs` by default; `LossFunction`s - supporting registering multiple minibatches should override this method. - - Returns: - A `list` of `Tensor`s representing - """ - return [self.inputs] - - @property - def num_registered_minibatches(self): - """Number of minibatches registered for this LossFunction. - - Typically equal to the number of towers in a multi-tower setup. - - Returns: - An `int` representing the number of registered minibatches. - """ - return len(self.input_minibatches) - def evaluate(self): """Evaluate the loss function on the targets.""" if self.targets is not None: @@ -474,7 +450,6 @@ class NormalMeanVarianceNegativeLogProbLoss(DistributionNegativeLogProbLoss): assert len(variance.shape) == 2, "Expect 2D variance tensor." self._mean = mean self._variance = variance - self._scale = math_ops.sqrt(variance) self._targets = targets super(NormalMeanVarianceNegativeLogProbLoss, self).__init__(seed=seed) @@ -484,7 +459,7 @@ class NormalMeanVarianceNegativeLogProbLoss(DistributionNegativeLogProbLoss): @property def dist(self): - return normal.Normal(loc=self._mean, scale=self._scale) + return normal.Normal(loc=self._mean, scale=math_ops.sqrt(self._variance)) @property def params(self): @@ -502,7 +477,7 @@ class NormalMeanVarianceNegativeLogProbLoss(DistributionNegativeLogProbLoss): @property def _fisher_mean_factor(self): - return 1. / self._scale + return 1. / math_ops.sqrt(self._variance) @property def _fisher_var(self): @@ -611,36 +586,13 @@ class CategoricalLogitsNegativeLogProbLoss(DistributionNegativeLogProbLoss, index in [0, output_size). seed: int or None. Default random seed when sampling. """ - self._logits_components = [] - self._targets_components = [] - self.register_additional_minibatch(logits, targets=targets) + self._logits = logits + self._targets = targets super(CategoricalLogitsNegativeLogProbLoss, self).__init__(seed=seed) - def register_additional_minibatch(self, logits, targets=None): - """Register an additiona minibatch's worth of parameters. - - Args: - logits: Tensor of shape [batch_size, output_size]. Parameters for - underlying distribution. - targets: None or Tensor of shape [batch_size, output_size]. Each row must - be a one-hot vector. - """ - self._logits_components.append(logits) - self._targets_components.append(targets) - - @property - def _logits(self): - return array_ops.concat(self._logits_components, axis=0) - - @property - def input_minibatches(self): - return self._logits_components - @property def targets(self): - if all(target is None for target in self._targets_components): - return None - return array_ops.concat(self._targets_components, axis=0) + return self._targets @property def dist(self): diff --git a/tensorflow/contrib/kfac/python/ops/optimizer.py b/tensorflow/contrib/kfac/python/ops/optimizer.py index 5d456bcb79ff00cedc1aaa7244cc8722d21f6e98..083da768ec97aca3e63995491bb579835bb5377f 100644 --- a/tensorflow/contrib/kfac/python/ops/optimizer.py +++ b/tensorflow/contrib/kfac/python/ops/optimizer.py @@ -18,6 +18,8 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import warnings + # pylint disable=long-line from tensorflow.contrib.kfac.python.ops import curvature_matrix_vector_products as cmvp from tensorflow.contrib.kfac.python.ops import estimator as est @@ -50,6 +52,7 @@ class KfacOptimizer(gradient_descent.GradientDescentOptimizer): name="KFAC", estimation_mode="gradients", colocate_gradients_with_ops=True, + batch_size=None, cov_devices=None, inv_devices=None): """Initializes the KFAC optimizer with the given settings. @@ -91,12 +94,16 @@ class KfacOptimizer(gradient_descent.GradientDescentOptimizer): colocate_gradients_with_ops: Whether we should request gradients we compute in the estimator be colocated with their respective ops. (Default: True) + batch_size: The size of the mini-batch. Only needed when momentum_type + == 'qmodel' or when automatic adjustment is used. (Default: None) cov_devices: Iterable of device strings (e.g. '/gpu:0'). Covariance computations will be placed on these devices in a round-robin fashion. - Can be None, which means that no devices are specified. + Can be None, which means that no devices are specified. Only used + with (soon-to-be-depcrecated "convenience" properties). inv_devices: Iterable of device strings (e.g. '/gpu:0'). Inversion computations will be placed on these devices in a round-robin fashion. - Can be None, which means that no devices are specified. + Can be None, which means that no devices are specified. Only used + with (soon-to-be-depcrecated "convenience" properties). Raises: ValueError: If the momentum type is unsupported. @@ -110,6 +117,15 @@ class KfacOptimizer(gradient_descent.GradientDescentOptimizer): if variables is None: variables = tf_variables.trainable_variables() + # Parameters to be passed to the Fisher estimator: + self._variables = variables + self._cov_ema_decay = cov_ema_decay + self._layers = layer_collection + self._estimation_mode = estimation_mode + self._colocate_gradients_with_ops = colocate_gradients_with_ops + self._cov_devices = cov_devices + self._inv_devices = inv_devices + # The below paramaters are required only if damping needs to be adapated. # These parameters can be set by calling # set_damping_adaptation_params() explicitly. @@ -130,17 +146,6 @@ class KfacOptimizer(gradient_descent.GradientDescentOptimizer): self._q_model_change = None self._update_damping_op = None - self._layers = layer_collection - self._fisher_est = est.FisherEstimator( - lambda: self.damping, - variables, - cov_ema_decay, - layer_collection, - estimation_mode=estimation_mode, - colocate_gradients_with_ops=colocate_gradients_with_ops, - cov_devices=cov_devices, - inv_devices=inv_devices) - momentum_type = momentum_type.lower() legal_momentum_types = ["regular", "adam", "qmodel"] @@ -148,20 +153,27 @@ class KfacOptimizer(gradient_descent.GradientDescentOptimizer): raise ValueError("Unsupported momentum type {}. Must be one of {}." .format(momentum_type, legal_momentum_types)) if momentum_type != "regular" and norm_constraint is not None: - raise ValueError("Update clipping is only supported with momentum" + raise ValueError("Update clipping is only supported with momentum " "type 'regular'.") if momentum_type not in ["regular", "adam"] and momentum != 0: raise ValueError("Momentum must be unspecified if using a momentum_type " "other than 'regular' or 'adam'.") + # Extra parameters of the optimizer self._momentum = momentum self._momentum_type = momentum_type self._norm_constraint = norm_constraint - - # this is a bit of a hack - # TODO(duckworthd): Handle this in a better way (e.g. pass it in?) - self._batch_size = array_ops.shape(layer_collection.losses[0].inputs)[0] - self._losses = layer_collection.losses + self._batch_size = batch_size + + with variable_scope.variable_scope(name): + self._fisher_est = est.FisherEstimator( + self._variables, + self._cov_ema_decay, + self.damping, + self._layers, + exps=(-1,), + estimation_mode=self._estimation_mode, + colocate_gradients_with_ops=self._colocate_gradients_with_ops) super(KfacOptimizer, self).__init__(learning_rate, name=name) @@ -178,6 +190,10 @@ class KfacOptimizer(gradient_descent.GradientDescentOptimizer): style rule described in Section 6.5 of "Optimizing Neural Networks with Kronecker-factored Approximate Curvature". + Note that this function creates Tensorflow variables which store a few + scalars and are accessed by the ops which update the damping (as part + of the training op returned by the minimize() method). + Args: is_chief: `Boolean`, `True` if the worker is chief. prev_train_batch: Training data used to minimize loss in the previous @@ -199,6 +215,7 @@ class KfacOptimizer(gradient_descent.GradientDescentOptimizer): """ if self._adapt_damping: raise ValueError("Damping adaptation parameters already set.") + with variable_scope.variable_scope(self.get_name()): self._adapt_damping = True self._is_chief = is_chief @@ -221,31 +238,37 @@ class KfacOptimizer(gradient_descent.GradientDescentOptimizer): @property def cov_update_thunks(self): - return self._fisher_est.cov_update_thunks + self._maybe_make_and_save_everything() + return self._cov_update_thunks @property def cov_update_ops(self): - return self._fisher_est.cov_update_ops + self._maybe_make_and_save_everything() + return self._cov_update_ops @property def cov_update_op(self): - return self._fisher_est.cov_update_op + self._maybe_make_and_save_everything() + return self._cov_update_op @property def inv_update_thunks(self): - return self._fisher_est.inv_update_thunks + self._maybe_make_and_save_everything() + return self._inv_update_thunks @property def inv_update_ops(self): - return self._fisher_est.inv_update_ops + self._maybe_make_and_save_everything() + return self._inv_update_ops @property def inv_update_op(self): - return self._fisher_est.inv_update_op + self._maybe_make_and_save_everything() + return self._inv_update_op @property def variables(self): - return self._fisher_est.variables + return self._variables @property def damping(self): @@ -258,25 +281,162 @@ class KfacOptimizer(gradient_descent.GradientDescentOptimizer): def damping_adaptation_interval(self): return self._damping_adaptation_interval + def _maybe_make_and_save_everything(self): + if not self._fisher_est.made_vars(): + warnings.warn("These convenience properties will be depcrecated soon. " + "Please use explicit op/thunk creation methods instead " + "(e.g. make_ops_and_vars_round_robin, etc).", + DeprecationWarning) + (self._cov_update_ops, self._cov_update_op, self._inv_update_ops, + self._inv_update_op, self._cov_update_thunks, + self._inv_update_thunks) = self.make_ops_and_vars_round_robin( + cov_devices=self._cov_devices, + inv_devices=self._inv_devices) + + def make_ops_and_vars(self): + """Make ops and vars with no specific device placement. + + See make_ops_and_vars_round_robin for details. + + Returns: + cov_update_ops: List of ops that compute the cov updates. Corresponds + one-to-one with the list of factors given by the "factors" property. + cov_update_op: cov_update_ops grouped into a single op. + inv_update_ops: List of ops that compute the inv updates. Corresponds + one-to-one with the list of factors given by the "factors" property. + cov_update_op: cov_update_ops grouped into a single op. + inv_update_op: inv_update_ops grouped into a single op. + """ + with variable_scope.variable_scope(self.get_name()): + return self._fisher_est.make_ops_and_vars() + + def make_ops_and_vars_round_robin(self, cov_devices=None, inv_devices=None): + """Make ops and vars with a round-robin device placement strategy. + + For each factor, all of that factor's cov variables and their associated + update ops will be placed on a particular device. A new device is chosen + for each factor by cycling through list of devices in the cov_devices + argument. If cov_devices is None then no explicit device placement occurs. + + An analogous strategy is followed for inverse update ops, with the list of + devices being given by the inv_devices argument. + + Inverse variables on the other hand are not placed on any specific device + (they will just use the current the device placement context, whatever + that happens to be). The idea is that the inverse variable belong where + they will be accessed most often, which is the device that actually applies + the preconditioner to the gradient. The user will be responsible for setting + the device context for this. + + Args: + cov_devices: Iterable of device strings (e.g. '/gpu:0'). Covariance + computations will be placed on these devices in a round-robin fashion. + Can be None, which means that no devices are specified. + inv_devices: Iterable of device strings (e.g. '/gpu:0'). Inversion + computations will be placed on these devices in a round-robin fashion. + Can be None, which means that no devices are specified. + + Returns: + cov_update_ops: List of ops that compute the cov updates. Corresponds + one-to-one with the list of factors given by the "factors" property. + cov_update_op: cov_update_ops grouped into a single op. + inv_update_ops: List of ops that compute the inv updates. Corresponds + one-to-one with the list of factors given by the "factors" property. + cov_update_op: cov_update_ops grouped into a single op. + inv_update_op: inv_update_ops grouped into a single op. + cov_update_thunks: Thunks that make the ops in cov_update_ops. + inv_update_thunks: Thunks that make the ops in inv_update_ops. + """ + with variable_scope.variable_scope(self.get_name()): + return self._fisher_est.make_ops_and_vars_round_robin( + cov_devices=cov_devices, inv_devices=inv_devices) + + def make_vars_and_create_op_thunks_round_robin(self, + cov_devices=None, + inv_devices=None): + """Make vars and create op thunks w/ a round-robin device placement strat. + + For each factor, all of that factor's cov variables and their associated + update ops will be placed on a particular device. A new device is chosen + for each factor by cycling through list of devices in the cov_devices + argument. If cov_devices is None then no explicit device placement occurs. + + An analogous strategy is followed for inverse update ops, with the list of + devices being given by the inv_devices argument. + + Inverse variables on the other hand are not placed on any specific device + (they will just use the current the device placement context, whatever + that happens to be). The idea is that the inverse variable belong where + they will be accessed most often, which is the device that actually applies + the preconditioner to the gradient. The user will be responsible for setting + the device context for this. + + Args: + cov_devices: Iterable of device strings (e.g. '/gpu:0'). Covariance + computations will be placed on these devices in a round-robin fashion. + Can be None, which means that no devices are specified. + inv_devices: Iterable of device strings (e.g. '/gpu:0'). Inversion + computations will be placed on these devices in a round-robin fashion. + Can be None, which means that no devices are specified. + Returns: + cov_update_thunks: List of cov update thunks. Corresponds one-to-one with + the list of factors given by the "factors" property. + inv_update_thunks: List of inv update thunks. Corresponds one-to-one with + the list of factors given by the "factors" property. + """ + scope = self.get_name() + "/" + self._fisher_est.name + return self._fisher_est.make_vars_and_create_op_thunks_round_robin( + scope=scope, cov_devices=cov_devices, inv_devices=inv_devices) + + def ops_and_vars_thunks(self): + """Create thunks that make the ops and vars on demand. + + This function returns 4 lists of thunks: cov_variable_thunks, + cov_update_thunks, inv_variable_thunks, and inv_update_thunks. + + The length of each list is the number of factors and the i-th element of + each list corresponds to the i-th factor (given by the "factors" property). + + Note that the execution of these thunks must happen in a certain + partial order. The i-th element of cov_variable_thunks must execute + before the i-th element of cov_update_thunks (and also the i-th element + of inv_update_thunks). Similarly, the i-th element of inv_variable_thunks + must execute before the i-th element of inv_update_thunks. + + TL;DR (oversimplified): Execute the thunks according to the order that + they are returned. + + Returns: + cov_variable_thunks: A list of thunks that make the cov variables. + cov_update_thunks: A list of thunks that make the cov update ops. + inv_variable_thunks: A list of thunks that make the inv variables. + inv_update_thunks: A list of thunks that make the inv update ops. + """ + scope = self.get_name() + "/" + self._fisher_est.name + return self._fisher_est.ops_and_vars_thunks(scope=scope) + def minimize(self, *args, **kwargs): - kwargs["var_list"] = kwargs.get("var_list") or self.variables - if set(kwargs["var_list"]) != set(self.variables): - raise ValueError("var_list doesn't match with set of Fisher-estimating " - "variables.") - if self._adapt_damping and self._is_chief: - global_step = kwargs.get("global_step", None) - if not global_step: - raise KeyError("global_step needs to be passed to optimizer.minimize " - "if damping parameter is adapted.") - update_damping_op = self._update_damping(self._prev_train_batch, - global_step) - with ops.control_dependencies([update_damping_op]): - loss = args[0] - loss_assign_op = state_ops.assign(self._prev_loss, loss) - train_op = super(KfacOptimizer, self).minimize(*args, **kwargs) - return control_flow_ops.group(loss_assign_op, train_op) - else: - return super(KfacOptimizer, self).minimize(*args, **kwargs) + # Should this variable scope encompass everything below? Or will the super- + # class make another copy of the same name scope? + with variable_scope.variable_scope(self.get_name()): + kwargs["var_list"] = kwargs.get("var_list") or self.variables + if set(kwargs["var_list"]) != set(self.variables): + raise ValueError("var_list doesn't match with set of Fisher-estimating " + "variables.") + if self._adapt_damping and self._is_chief: + global_step = kwargs.get("global_step", None) + if not global_step: + raise KeyError("global_step needs to be passed to optimizer.minimize " + "if damping parameter is adapted.") + update_damping_op = self._update_damping(self._prev_train_batch, + global_step) + with ops.control_dependencies([update_damping_op]): + loss = args[0] + loss_assign_op = state_ops.assign(self._prev_loss, loss) + train_op = super(KfacOptimizer, self).minimize(*args, **kwargs) + return control_flow_ops.group(loss_assign_op, train_op) + else: + return super(KfacOptimizer, self).minimize(*args, **kwargs) def compute_gradients(self, *args, **kwargs): # args[1] could be our var_list @@ -301,6 +461,8 @@ class KfacOptimizer(gradient_descent.GradientDescentOptimizer): Returns: An `Operation` that applies the specified gradients. """ + self._maybe_make_and_save_everything() + # In Python 3, grads_and_vars can be a zip() object which can only be # iterated over once. By converting it to a list, we ensure that it can be # iterated over more than once. @@ -450,7 +612,8 @@ class KfacOptimizer(gradient_descent.GradientDescentOptimizer): = qmodel(alpha*precon_grad + mu*prev_update) - L(theta). """ - cmvpc = cmvp.CurvatureMatrixVectorProductComputer(self._losses, variables) + cmvpc = cmvp.CurvatureMatrixVectorProductComputer(self._layers.losses, + variables) # compute the matrix-vector products with the transposed Fisher factor fft_precon_grads = cmvpc.multiply_fisher_factor_transpose(precon_grads) diff --git a/tensorflow/contrib/kfac/python/ops/utils.py b/tensorflow/contrib/kfac/python/ops/utils.py index 88e6fb20e8f97528aea2a92752d79344c27bbf24..af26f5e56bf9bb22cc9bc2b409209d027477ed89 100644 --- a/tensorflow/contrib/kfac/python/ops/utils.py +++ b/tensorflow/contrib/kfac/python/ops/utils.py @@ -24,11 +24,13 @@ from tensorflow.contrib.tpu.python.ops import tpu_ops from tensorflow.contrib.tpu.python.tpu import tpu_function from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops +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 gradients_impl from tensorflow.python.ops import linalg_ops from tensorflow.python.ops import math_ops +from tensorflow.python.ops import nn_ops from tensorflow.python.ops import random_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variables @@ -430,6 +432,127 @@ def batch_execute(global_step, thunks, batch_size, name=None): return result +def extract_convolution_patches(inputs, + filter_shape, + padding, + strides=None, + dilation_rate=None, + name=None, + data_format=None): + """Extracts inputs to each output coordinate in tf.nn.convolution. + + This is a generalization of tf.extract_image_patches() to tf.nn.convolution(), + where the number of spatial dimensions may be something other than 2. + + Assumes, + - First dimension of inputs is batch_size + - Convolution filter is applied to all input channels. + + Args: + inputs: Tensor of shape [batch_size, ..spatial_image_shape.., + ..spatial_filter_shape.., in_channels]. Inputs to tf.nn.convolution(). + filter_shape: List of ints. Shape of filter passed to tf.nn.convolution(). + padding: string. Padding method. One of "VALID", "SAME". + strides: None or list of ints. Strides along spatial dimensions. + dilation_rate: None or list of ints. Dilation along spatial dimensions. + name: None or str. Name of Op. + data_format: None or str. Format of data. + + Returns: + Tensor of shape [batch_size, ..spatial_image_shape.., + ..spatial_filter_shape.., in_channels] + + Raises: + ValueError: If data_format does not put channel last. + ValueError: If inputs and filter disagree on in_channels. + """ + if not is_data_format_channel_last(data_format): + raise ValueError("Channel must be last dimension.") + with ops.name_scope(name, "extract_convolution_patches", + [inputs, filter_shape, padding, strides, dilation_rate]): + batch_size = inputs.shape.as_list()[0] + in_channels = inputs.shape.as_list()[-1] + + # filter_shape = spatial_filter_shape + [in_channels, out_channels] + spatial_filter_shape = filter_shape[:-2] + if in_channels != filter_shape[-2]: + raise ValueError("inputs and filter_shape must agree on in_channels.") + + # Map each input feature to a location in the output. + out_channels = np.prod(spatial_filter_shape) * in_channels + filters = linalg_ops.eye(out_channels) + filters = array_ops.reshape( + filters, + list(spatial_filter_shape) + [in_channels, out_channels]) + + result = nn_ops.convolution( + inputs, + filters, + padding=padding, + strides=strides, + dilation_rate=dilation_rate) + spatial_output_shape = result.shape.as_list()[1:-1] + result = array_ops.reshape(result, + [batch_size or -1] + spatial_output_shape + + list(spatial_filter_shape) + [in_channels]) + + return result + + +def extract_pointwise_conv2d_patches(inputs, + filter_shape, + name=None, + data_format=None): + """Extract patches for a 1x1 conv2d. + + Args: + inputs: 4-D Tensor of shape [batch_size, height, width, in_channels]. + filter_shape: List of 4 ints. Shape of filter to apply with conv2d() + name: None or str. Name for Op. + data_format: None or str. Format for data. See 'data_format' in + tf.nn.conv2d() for details. + + Returns: + Tensor of shape [batch_size, ..spatial_input_shape.., + ..spatial_filter_shape.., in_channels] + + Raises: + ValueError: if inputs is not 4-D. + ValueError: if filter_shape is not [1, 1, ?, ?] + ValueError: if data_format is not channels-last. + """ + if inputs.shape.ndims != 4: + raise ValueError("inputs must have 4 dims.") + if len(filter_shape) != 4: + raise ValueError("filter_shape must have 4 dims.") + if filter_shape[0] != 1 or filter_shape[1] != 1: + raise ValueError("filter_shape must have shape 1 along spatial dimensions.") + if not is_data_format_channel_last(data_format): + raise ValueError("data_format must be channels last.") + with ops.name_scope(name, "extract_pointwise_conv2d_patches", + [inputs, filter_shape]): + ksizes = [1, 1, 1, 1] # Spatial shape is 1x1. + strides = [1, 1, 1, 1] # Operate on all pixels. + rates = [1, 1, 1, 1] # Dilation has no meaning with spatial shape = 1. + padding = "VALID" # Doesn't matter. + result = array_ops.extract_image_patches(inputs, ksizes, strides, rates, + padding) + + batch_size, input_height, input_width, in_channels = inputs.shape.as_list() + filter_height, filter_width, in_channels, _ = filter_shape + return array_ops.reshape(result, [ + batch_size, input_height, input_width, filter_height, filter_width, + in_channels + ]) + + +def is_data_format_channel_last(data_format): + """True if data_format puts channel last.""" + if data_format is None: + return True + return data_format.endswith("C") + + def matmul_sparse_dense(A, B, name=None): # pylint: disable=invalid-name """Computes matmul(A, B) where A is sparse, B is dense. @@ -482,5 +605,76 @@ def matmul_diag_sparse(A_diag, B, name=None): # pylint: disable=invalid-name a = array_ops.reshape(a, list(a.shape) + [1] * (B.values.shape.ndims - 1)) return ops.IndexedSlices(a * B.values, B.indices, dense_shape=B.dense_shape) + +class PartitionedTensor(object): + """A Tensor partitioned across its 0-th dimension.""" + + def __init__(self, tensors): + """Initializes PartitionedTensor. + + Args: + tensors: List of Tensors. All Tensors must agree on shape (excepting + batch dimension) and dtype. + + Raises: + ValueError: If 'tensors' has length zero. + ValueError: if contents of 'tensors' don't agree on shape or dtype. + """ + if not tensors: + raise ValueError("tensors must be a list of 1+ Tensors.") + + dtype = tensors[0].dtype + if not all(tensor.dtype == dtype for tensor in tensors): + raise ValueError("all tensors must have dtype = %s." % dtype) + + shape = tensors[0].shape[1:] + if not all(tensor.shape[1:] == shape for tensor in tensors): + raise ValueError("All tensors must have shape = %s (excluding batch " + "dimension)." % shape) + + self.tensors = tensors + self._concats = {} # {device: Tensor} + + @property + def shape(self): + feature_shape = self.tensors[0].shape[1:] + batch_size = sum([tensor.shape[0] for tensor in self.tensors], + tensor_shape.Dimension(0)) + return tensor_shape.TensorShape([batch_size]).concatenate(feature_shape) + + def get_shape(self): + return self.shape + + @property + def dtype(self): + return self.tensors[0].dtype + + def devices(self): + return set(tensor.device for tensor in self.tensors) + + def __str__(self): + return "PartitionedTensor([%s, ...], dtype=%s, shape=%s)" % ( + self.tensors[0].name, self.dtype.name, tuple(self.shape.as_list())) + + def __hash__(self): + return hash(tuple(self.tensors)) + + def as_tensor(self, dtype=None, name=None, as_ref=False): + with ops.name_scope(name, "PartitionedTensor.as_tensor", self.tensors): + assert not as_ref + assert dtype in [None, self.dtype] + result = array_ops.concat(self.tensors, axis=0) + + # Cache 'result' if we haven't already cached a value for this device. + if result.device not in self._concats: + self._concats[result.device] = result + return self._concats[result.device] + + +ops.register_tensor_conversion_function( + PartitionedTensor, + lambda val, dtype, name, as_ref: val.as_tensor(dtype, name, as_ref)) + + # TODO(b/69623235): Add a function for finding tensors that share gradients # to eliminate redundant fisher factor computations. diff --git a/tensorflow/contrib/kfac/python/ops/utils_lib.py b/tensorflow/contrib/kfac/python/ops/utils_lib.py index 8e424a794691484fdea7d8481677aa641c433d4c..330d222dbf70fcfa02ffd47261c0513d9dd6e0e9 100644 --- a/tensorflow/contrib/kfac/python/ops/utils_lib.py +++ b/tensorflow/contrib/kfac/python/ops/utils_lib.py @@ -40,6 +40,9 @@ _allowed_symbols = [ "fwd_gradients", "ensure_sequence", "batch_execute", + "extract_convolution_patches", + "extract_pointwise_conv2d_patches", + "is_data_format_channel_last", "matmul_sparse_dense", "matmul_diag_sparse", ] diff --git a/tensorflow/contrib/labeled_tensor/BUILD b/tensorflow/contrib/labeled_tensor/BUILD index 894e6f6946bb59810a9da2d304cc0dd43d25201d..544065dac6a10094a376c18e84521b1a26401cdd 100644 --- a/tensorflow/contrib/labeled_tensor/BUILD +++ b/tensorflow/contrib/labeled_tensor/BUILD @@ -70,6 +70,7 @@ py_test( "python/ops/core_test.py", ], srcs_version = "PY2AND3", + tags = ["no_windows"], # TODO: needs investigation on Windows deps = [ ":_typecheck", ":core", diff --git a/tensorflow/contrib/layers/BUILD b/tensorflow/contrib/layers/BUILD index 852d06e1e3cc8f8deecd15b7436cd4e4a393ad66..cc7bbabf210ded9a31eb789fa8b94e8bde62ea43 100644 --- a/tensorflow/contrib/layers/BUILD +++ b/tensorflow/contrib/layers/BUILD @@ -188,6 +188,7 @@ py_test( size = "small", srcs = ["python/layers/normalization_test.py"], srcs_version = "PY2AND3", + tags = ["no_windows"], # TODO: needs investigation on Windows deps = [ ":layers_py", "//tensorflow/contrib/framework:framework_py", @@ -353,6 +354,7 @@ py_test( size = "small", srcs = ["python/ops/sparse_ops_test.py"], srcs_version = "PY2AND3", + tags = ["no_windows"], # TODO: needs investigation on Windows deps = [ ":layers_py", "//tensorflow/python:array_ops", diff --git a/tensorflow/contrib/layers/python/layers/embedding_ops.py b/tensorflow/contrib/layers/python/layers/embedding_ops.py index b62e3050cd7003f1ba72061b133ff9b5d6b616da..ffa208540dae975cb139ad6d76dcf392678ba0ee 100644 --- a/tensorflow/contrib/layers/python/layers/embedding_ops.py +++ b/tensorflow/contrib/layers/python/layers/embedding_ops.py @@ -470,7 +470,7 @@ def embedding_lookup_unique(params, ids, name=None): ids = ops.convert_to_tensor(ids) shape = array_ops.shape(ids) ids_flat = array_ops.reshape( - ids, math_ops.reduce_prod(shape, keep_dims=True)) + ids, math_ops.reduce_prod(shape, keepdims=True)) unique_ids, idx = array_ops.unique(ids_flat) unique_embeddings = embedding_ops.embedding_lookup(params, unique_ids) embeds_flat = array_ops.gather(unique_embeddings, idx) diff --git a/tensorflow/contrib/layers/python/layers/encoders.py b/tensorflow/contrib/layers/python/layers/encoders.py index 89c9d37bd09cb6c43eebb91f3a16600eae9cb490..f42112206d0db9d2e42bd4cff19f6a6533951d46 100644 --- a/tensorflow/contrib/layers/python/layers/encoders.py +++ b/tensorflow/contrib/layers/python/layers/encoders.py @@ -125,7 +125,7 @@ def embed_sequence(ids, `reuse` is `None` or `False`. """ if not (reuse or (vocab_size and embed_dim)): - raise ValueError('Must specify vocab size and embedding dimension when not' + raise ValueError('Must specify vocab size and embedding dimension when not ' 'reusing. Got vocab_size=%s and embed_dim=%s' % ( vocab_size, embed_dim)) with variable_scope.variable_scope( diff --git a/tensorflow/contrib/layers/python/layers/layers.py b/tensorflow/contrib/layers/python/layers/layers.py index 80cbe68870808328b387e2044fe236af5a5e39f8..350bcb3bca11b4cad18ce863ab1496076477aa3c 100644 --- a/tensorflow/contrib/layers/python/layers/layers.py +++ b/tensorflow/contrib/layers/python/layers/layers.py @@ -2747,7 +2747,7 @@ def softmax(logits, scope=None): logits_2d = array_ops.reshape(logits, [-1, num_logits]) predictions = nn.softmax(logits_2d) predictions = array_ops.reshape(predictions, array_ops.shape(logits)) - if context.in_graph_mode(): + if not context.executing_eagerly(): predictions.set_shape(logits.get_shape()) return predictions diff --git a/tensorflow/contrib/learn/BUILD b/tensorflow/contrib/learn/BUILD index abf6e393bb0fbbce4e43f6d209e9b30517df36c3..b05f5eeaeee8fb927970b608f65495f33d63f764 100644 --- a/tensorflow/contrib/learn/BUILD +++ b/tensorflow/contrib/learn/BUILD @@ -5,6 +5,8 @@ licenses(["notice"]) # Apache 2.0 exports_files(["LICENSE"]) +load("//tensorflow:tensorflow.bzl", "py_test") + package(default_visibility = [ "//engedu/ml/tf_from_scratch:__pkg__", "//tensorflow:internal", @@ -115,6 +117,7 @@ py_test( size = "small", srcs = ["python/learn/learn_io/data_feeder_test.py"], srcs_version = "PY2AND3", + tags = ["no_windows"], # TODO: needs investigation on Windows deps = [ ":learn", "//tensorflow/python:client_testlib", @@ -170,6 +173,7 @@ tf_py_test( "//tensorflow/python:variables", "//tensorflow/python/estimator", ], + tags = ["no_windows"], # TODO: needs investigation on Windows ) py_test( @@ -188,6 +192,7 @@ py_test( size = "small", srcs = ["python/learn/graph_actions_test.py"], srcs_version = "PY2AND3", + tags = ["no_windows"], # TODO: needs investigation on Windows deps = [ ":learn", "//tensorflow/contrib/framework:framework_py", @@ -426,6 +431,10 @@ py_test( size = "medium", srcs = ["python/learn/estimators/kmeans_test.py"], srcs_version = "PY2AND3", + tags = [ + "noasan", # b/73741358 + "nomac", + ], deps = [ ":learn", "//tensorflow/python:array_ops", @@ -584,6 +593,7 @@ py_test( size = "small", srcs = ["python/learn/learn_io/io_test.py"], srcs_version = "PY2AND3", + tags = ["no_windows"], # TODO: needs investigation on Windows deps = [ ":learn", "//tensorflow/contrib/learn/python/learn/datasets", @@ -813,6 +823,7 @@ py_test( size = "small", srcs = ["python/learn/utils/saved_model_export_utils_test.py"], srcs_version = "PY2AND3", + tags = ["no_windows"], # TODO: needs investigation on Windows deps = [ ":learn", "//tensorflow/contrib/layers:layers_py", diff --git a/tensorflow/contrib/learn/README.md b/tensorflow/contrib/learn/README.md new file mode 100644 index 0000000000000000000000000000000000000000..d516bffc5e0327a3400068b35de5503e5a925a54 --- /dev/null +++ b/tensorflow/contrib/learn/README.md @@ -0,0 +1,143 @@ +EVERYTHING IN THIS DIRECTORY IS DEPRECATED. + +Using functions or classes will result in warnings. + +Instructions for converting to current alternatives are included in the +warnings. A high-level overview is below. + +## Canned Estimators + +Many canned estimators (subclasses of `Estimator`) have equivalents in core: +`DNNClassifier`, `DNNRegressor`, `DNNEstimator`, `LinearClassifier`, +`LinearRegressor`, `DNNLinearCombinedClassifier` and +`DNNLinearCombinedRegressor`. They are exposed under `tf.estimator`. +`DNNEstimator`, `LinearEstimator` and `DNNLinearCombinedEstimator` +are exposed under `tf.contrib.estimator`. + +To migrate to the new api, users need to take the following steps: + +* Replace `tf.contrib.learn` with `tf.estimator`. +* If you subclass any of the estimators, stop doing that. You should be able to + write a factory method that returns a canned estimator instead. If this is not + possible (if you override methods from the canned estimator), consider writing + a custom estimator instead. See `tf.estimator.Estimator`. +* Set `loss_reduction=tf.losses.Reduction.SUM_OVER_BATCH_SIZE` to preserve loss + reduction as the average over batch. +* Some optimizer-related arguments are no longer passed in the estimator + constructor. Instead, we provide methods that perform the same job by wrapping + an optimizer. Specifically: + * `gradient_clip_norm`: Use `tf.contrib.estimator.clip_gradients_by_norm` + * `embedding_lr_multipliers`: Not supported. + Other arguments: + * `input_layer_min_slice_size`: Replaced by `input_layer_partitioner` + * `enable_centered_bias`: Not supported. Dropping this argument is unlikely to + harm your model. + * `feature_engineering_fn`: Not supported. You can call your + `feature_engineering_fn` inside your input_fn: + ```python + def new_input_fn(): + features, labels = old_input_fn() + return feature_engineering_fn(features, labels) + ``` +* Use `tf.reshape` to reshape labels in your `input_fn`. `tf.estimator` + classifiers and regressors expect labels as a 2D Tensor of shape + `[batch_size, 1]`, or `[batch_size, n_labels]`. In contrast, + `tf.contrib.learn` classifiers and regressors supported labels with shape + `[batch_size]`. +* If you pass custom metrics from the `evaluate()` method call, use + `tf.contrib.estimator.add_metrics`. +* Replace your `serving_input_fn` with a `serving_input_receiver_fn`. + Note this should be entirely distinct from your training `input_fn`, so if you + previously had one `input_fn` with different "modes", you should now factor + that apart. Where the former returned either a simple `(features, labels)` + tuple or `InputFnOps`, you should now return a `ServingInputReceiver`. + If you were generating your `serving_input_fn` using the + `build_parsing_serving_input_fn` helper, you can simply drop in the + replacement `build_parsing_serving_input_receiver_fn`. + +Some remaining estimators/classes: + +* `DynamicRnnEstimator`: Consider a custom `model_fn`. +* `KMeansClustering`: Use `tf.contrib.factorization.KMeansClustering`. +* `LogisticRegressor`: Not supported. Instead, use `binary_classification_head` + with a custom `model_fn`, or with `DNNEstimator`. +* `StateSavingRnnEstimator`: Consider a custom `model_fn`. +* SVM: Consider a custom `model_fn`. +* `LinearComposableModel` and `DNNComposableModel`: Not supported. + Consider `tf.contrib.estimator.DNNEstimator`, or write a custom model_fn. +* `MetricSpec`: Deprecated. For adding custom metrics to canned Estimators, use + `tf.contrib.estimator.add_metrics`. + +## Estimator +`tf.contrib.learn.Estimator` is migrated to `tf.estimator.Estimator`. + +To migrate, users need to take the following steps: + +* Replace `tf.contrib.learn.Estimator` with `tf.estimator.Estimator`. +* If you pass a `config` argument to `Estimator`, this must be + `tf.estimator.RunConfig`. You may need to edit your code accordingly. +* Edit your `model_fn` to return `tf.estimator.EstimatorSpec`. Refer to + `EstimatorSpec` for documentation of specific fields. +* If your `model_fn` uses the `mode` argument, use `tf.estimator.ModeKeys`. + +Some related classes: +* `Evaluable`, `Trainable`: Not supported, merged into `tf.estimator.Estimator`. +* ExportStrategy: Replaced by `tf.estimator.Exporter`. + +## Head/MultiHead +These classes are now supported under `tf.contrib.estimator`, e.g. +`tf.contrib.estimator.multi_class_head` and `tf.contrib.estimator.multi_head`. + +Some differences: + +* `multi_class_head`: If you use `tf.contrib.learn.multi_class_head` with + `n_classes=2`, switch to `tf.contrib.estimator.binary_classification_head`. +* `loss_only_head`: Not supported. +* `poisson_regression_head`: Not supported (yet). +* `binary_svm_head`: Not supported (yet). +* `no_op_train_fn`: Replace it with `tf.no_op`. + +Some arguments are renamed, please refer to documentation. In addition: + +* `loss_fn`: Supported for `multi_label_head`. If you need it for other heads, + please open an issue. +* `metric_class_ids`: Not supported (yet). +* `enable_centered_bias`: Not supported. Dropping this argument is unlikely to + harm your model. +* `label_name`: Not needed in `tf.estimator`. If you don’t use `multi_head`, + drop this argument. If you use `multi_head`, refer to + `tf.contrib.estimator.multi_head` documentation. + +## Experiment Class - Distributed Training Tooling + +Switch to `tf.estimator.train_and_evaluate`. Some differences: + +* Most of the constructor arguments, like `train_input_fn`, `eval_input_fn`, + should be wrapped into `tf.estimator.TrainSpec` and `tf.estimator.EvalSpec`. +* Remove the `experiment_fn`. Instead, create the `Estimator`, + `train_spec` and `eval_spec`, then call `tf.estimator.train_and_evaluate` + directly. +* Inside `tf.estimator.EvalSpec`, the `exporter` field is the replacement + for `export_strategy`. To be precise, `tf.estimator.LatestExporter` is the + replacement for `tf.contrib.learn.make_export_strategy`. If you want to export + only at the end of training use `tf.estimator.FinalExporter`. +* If the `TF_CONFIG` environment variable is constructed manually, please read + the `train_and_evaluate` documentation for the new requirementds (in + particular, the chief node and evaluator node). + +## Others Classes and Functions + +* `tf.contrib.learn.datasets` is deprecated. We are adding ready to use datasets + to tensorflow/models. Many smaller datasets are available from other sources, + such as scikits.learn. Some Python processing may have to be written, but this + is straightforward to implement using the standard modules. +* `tf.contrib.learn.preprocessing`: Deprecated. The python-only preprocessing + functions are not a good fit for TensorFlow. Please use `tf.data`, and + consider tensorflow/transform for more complex use cases. +* `tf.contrib.learn.models`: Not supported, use canned estimators instead. +* `tf.contrib.learn.monitors`: Implement `SessionRunHook` instead. Hook + implementations are in `tf.train`. +* `tf.contrib.learn.learn_io`: Use the methods in `tf.estimator.inputs`, such as + `tf.estimator.inputs.numpy_input_fn`. Some utility functions have no + equivalent, we encourage the use of `tf.data`. + diff --git a/tensorflow/contrib/learn/__init__.py b/tensorflow/contrib/learn/__init__.py index 3698af027e38f1063ad829c26eb179734968f813..79bd73faaf1301a2fc4999b64f88d30542577980 100644 --- a/tensorflow/contrib/learn/__init__.py +++ b/tensorflow/contrib/learn/__init__.py @@ -13,8 +13,11 @@ # limitations under the License. # ============================================================================== -# TODO(ptucker,ipolosukhin): Improve descriptions. -"""High level API for learning. +"""High level API for learning (DEPRECATED). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. See the @{$python/contrib.learn} guide. diff --git a/tensorflow/contrib/learn/python/__init__.py b/tensorflow/contrib/learn/python/__init__.py index bbebd5ab9792cb937219cf937f08c4d4e6e44a92..df23aeb2c433c2b4392f706730f715246ce01cea 100644 --- a/tensorflow/contrib/learn/python/__init__.py +++ b/tensorflow/contrib/learn/python/__init__.py @@ -13,7 +13,12 @@ # limitations under the License. # ============================================================================== -"""High level API for learning with TensorFlow.""" +"""High level API for learning with TensorFlow (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/contrib/learn/python/learn/__init__.py b/tensorflow/contrib/learn/python/learn/__init__.py index cdc67c77d5fd1df61016835dc75ba44feb458cf9..76e0e8ac8f19026086959f3b197cfd1a81e65a3e 100644 --- a/tensorflow/contrib/learn/python/learn/__init__.py +++ b/tensorflow/contrib/learn/python/learn/__init__.py @@ -13,7 +13,12 @@ # limitations under the License. # ============================================================================== -"""High level API for learning with TensorFlow.""" +"""High level API for learning with TensorFlow (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/contrib/learn/python/learn/basic_session_run_hooks.py b/tensorflow/contrib/learn/python/learn/basic_session_run_hooks.py index 2284ec46e971731af74f17678fc0d1d3888419e2..fed1c44d1970bf07c808ace817aa9972d7776d88 100644 --- a/tensorflow/contrib/learn/python/learn/basic_session_run_hooks.py +++ b/tensorflow/contrib/learn/python/learn/basic_session_run_hooks.py @@ -12,20 +12,47 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Some common SessionRunHook classes.""" +"""Some common SessionRunHook classes (deprected). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.python.training import basic_session_run_hooks +from tensorflow.python.util.deprecation import deprecated_alias # pylint: disable=invalid-name -LoggingTensorHook = basic_session_run_hooks.LoggingTensorHook -StopAtStepHook = basic_session_run_hooks.StopAtStepHook -CheckpointSaverHook = basic_session_run_hooks.CheckpointSaverHook -StepCounterHook = basic_session_run_hooks.StepCounterHook -NanLossDuringTrainingError = basic_session_run_hooks.NanLossDuringTrainingError -NanTensorHook = basic_session_run_hooks.NanTensorHook -SummarySaverHook = basic_session_run_hooks.SummarySaverHook +LoggingTensorHook = deprecated_alias( + 'tf.contrib.learn.basic_session_run_hooks.LoggingTensorHook', + 'tf.train.LoggingTensorHook', + basic_session_run_hooks.LoggingTensorHook) +StopAtStepHook = deprecated_alias( + 'tf.contrib.learn.basic_session_run_hooks.StopAtStepHook', + 'tf.train.StopAtStepHook', + basic_session_run_hooks.StopAtStepHook) +CheckpointSaverHook = deprecated_alias( + 'tf.contrib.learn.basic_session_run_hooks.CheckpointSaverHook', + 'tf.train.CheckpointSaverHook', + basic_session_run_hooks.CheckpointSaverHook) +StepCounterHook = deprecated_alias( + 'tf.contrib.learn.basic_session_run_hooks.StepCounterHook', + 'tf.train.StepCounterHook', + basic_session_run_hooks.StepCounterHook) +NanLossDuringTrainingError = deprecated_alias( + 'tf.contrib.learn.basic_session_run_hooks.NanLossDuringTrainingError', + 'tf.train.NanLossDuringTrainingError', + basic_session_run_hooks.NanLossDuringTrainingError) +NanTensorHook = deprecated_alias( + 'tf.contrib.learn.basic_session_run_hooks.NanTensorHook', + 'tf.train.NanTensorHook', + basic_session_run_hooks.NanTensorHook) +SummarySaverHook = deprecated_alias( + 'tf.contrib.learn.basic_session_run_hooks.SummarySaverHook', + 'tf.train.SummarySaverHook', + basic_session_run_hooks.SummarySaverHook) # pylint: enable=invalid-name diff --git a/tensorflow/contrib/learn/python/learn/datasets/__init__.py b/tensorflow/contrib/learn/python/learn/datasets/__init__.py index 7240b0de149051afa045a8113f9e9b212840c311..3c34712ac859d32f549468345950a93d2ed2aa56 100644 --- a/tensorflow/contrib/learn/python/learn/datasets/__init__.py +++ b/tensorflow/contrib/learn/python/learn/datasets/__init__.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Dataset utilities and synthetic/reference datasets.""" +"""Dataset utilities and synthetic/reference datasets (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -27,6 +32,7 @@ from tensorflow.contrib.learn.python.learn.datasets import base from tensorflow.contrib.learn.python.learn.datasets import mnist from tensorflow.contrib.learn.python.learn.datasets import synthetic from tensorflow.contrib.learn.python.learn.datasets import text_datasets +from tensorflow.python.util.deprecation import deprecated # Export load_iris and load_boston. load_iris = base.load_iris @@ -51,6 +57,7 @@ SYNTHETIC = { } +@deprecated(None, 'Please use tf.data.') def load_dataset(name, size='small', test_with_fake_data=False): """Loads dataset by name. @@ -73,8 +80,9 @@ def load_dataset(name, size='small', test_with_fake_data=False): return DATASETS[name]() +@deprecated(None, 'Please use tf.data.') def make_dataset(name, n_samples=100, noise=None, seed=42, *args, **kwargs): - """Creates binary synthetic datasets + """Creates binary synthetic datasets. Args: name: str, name of the dataset to generate diff --git a/tensorflow/contrib/learn/python/learn/datasets/base.py b/tensorflow/contrib/learn/python/learn/datasets/base.py index ca720ae5ed26e74da12bd6c5a37231b41442f76f..3b5c9b97c08a388e1f35249967b6cab26861f100 100644 --- a/tensorflow/contrib/learn/python/learn/datasets/base.py +++ b/tensorflow/contrib/learn/python/learn/datasets/base.py @@ -12,7 +12,13 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Base utilities for loading datasets.""" + +"""Base utilities for loading datasets (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -29,11 +35,14 @@ import numpy as np from six.moves import urllib from tensorflow.python.platform import gfile +from tensorflow.python.util.deprecation import deprecated + Dataset = collections.namedtuple('Dataset', ['data', 'target']) Datasets = collections.namedtuple('Datasets', ['train', 'validation', 'test']) +@deprecated(None, 'Use tf.data instead.') def load_csv_with_header(filename, target_dtype, features_dtype, @@ -53,6 +62,7 @@ def load_csv_with_header(filename, return Dataset(data=data, target=target) +@deprecated(None, 'Use tf.data instead.') def load_csv_without_header(filename, target_dtype, features_dtype, @@ -70,6 +80,7 @@ def load_csv_without_header(filename, return Dataset(data=data, target=target) +@deprecated(None, 'Use tf.data instead.') def shrink_csv(filename, ratio): """Create a smaller dataset of only 1/ratio of original data.""" filename_small = filename.replace('.', '_small.') @@ -84,6 +95,7 @@ def shrink_csv(filename, ratio): i += 1 +@deprecated(None, 'Use scikits.learn.datasets.') def load_iris(data_path=None): """Load Iris dataset. @@ -100,6 +112,7 @@ def load_iris(data_path=None): data_path, target_dtype=np.int, features_dtype=np.float) +@deprecated(None, 'Use scikits.learn.datasets.') def load_boston(data_path=None): """Load Boston housing dataset. @@ -116,7 +129,12 @@ def load_boston(data_path=None): data_path, target_dtype=np.float, features_dtype=np.float) -def retry(initial_delay, max_delay, factor=2.0, jitter=0.25, is_retriable=None): +@deprecated(None, 'Use the retry module or similar alternatives.') +def retry(initial_delay, + max_delay, + factor=2.0, + jitter=0.25, + is_retriable=None): """Simple decorator for wrapping retriable functions. Args: @@ -152,7 +170,7 @@ def retry(initial_delay, max_delay, factor=2.0, jitter=0.25, is_retriable=None): for delay in delays(): try: return fn(*args, **kwargs) - except Exception as e: # pylint: disable=broad-except) + except Exception as e: # pylint: disable=broad-except if is_retriable is None: continue @@ -176,11 +194,13 @@ def _is_retriable(e): return isinstance(e, IOError) and e.errno in _RETRIABLE_ERRNOS +@deprecated(None, 'Please use urllib or similar directly.') @retry(initial_delay=1.0, max_delay=16.0, is_retriable=_is_retriable) def urlretrieve_with_retry(url, filename=None): return urllib.request.urlretrieve(url, filename) +@deprecated(None, 'Please write your own downloading logic.') def maybe_download(filename, work_directory, source_url): """Download the data from source url, unless it's already here. diff --git a/tensorflow/contrib/learn/python/learn/datasets/mnist.py b/tensorflow/contrib/learn/python/learn/datasets/mnist.py index 37f9175015a239f763c7721cf36ab8063c0a3e32..abbb44c2f5b701829ce16f64eadd8ebc04c84e2c 100644 --- a/tensorflow/contrib/learn/python/learn/datasets/mnist.py +++ b/tensorflow/contrib/learn/python/learn/datasets/mnist.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Functions for downloading and reading MNIST data.""" +"""Functions for downloading and reading MNIST data (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -27,6 +32,7 @@ from tensorflow.contrib.learn.python.learn.datasets import base from tensorflow.python.framework import dtypes from tensorflow.python.framework import random_seed from tensorflow.python.platform import gfile +from tensorflow.python.util.deprecation import deprecated # CVDF mirror of http://yann.lecun.com/exdb/mnist/ DEFAULT_SOURCE_URL = 'https://storage.googleapis.com/cvdf-datasets/mnist/' @@ -37,6 +43,7 @@ def _read32(bytestream): return numpy.frombuffer(bytestream.read(4), dtype=dt)[0] +@deprecated(None, 'Please use tf.data to implement this functionality.') def extract_images(f): """Extract the images into a 4D uint8 numpy array [index, y, x, depth]. @@ -65,6 +72,7 @@ def extract_images(f): return data +@deprecated(None, 'Please use tf.one_hot on tensors.') def dense_to_one_hot(labels_dense, num_classes): """Convert class labels from scalars to one-hot vectors.""" num_labels = labels_dense.shape[0] @@ -74,6 +82,7 @@ def dense_to_one_hot(labels_dense, num_classes): return labels_one_hot +@deprecated(None, 'Please use tf.data to implement this functionality.') def extract_labels(f, one_hot=False, num_classes=10): """Extract the labels into a 1D uint8 numpy array [index]. @@ -103,7 +112,15 @@ def extract_labels(f, one_hot=False, num_classes=10): class DataSet(object): + """Container class for a dataset (deprecated). + + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + """ + @deprecated(None, 'Please use alternatives such as official/mnist/dataset.py' + ' from tensorflow/models.') def __init__(self, images, labels, @@ -210,6 +227,8 @@ class DataSet(object): return self._images[start:end], self._labels[start:end] +@deprecated(None, 'Please use alternatives such as official/mnist/dataset.py' + ' from tensorflow/models.') def read_data_sets(train_dir, fake_data=False, one_hot=False, @@ -275,5 +294,7 @@ def read_data_sets(train_dir, return base.Datasets(train=train, validation=validation, test=test) +@deprecated(None, 'Please use alternatives such as official/mnist/dataset.py' + ' from tensorflow/models.') def load_mnist(train_dir='MNIST-data'): return read_data_sets(train_dir) diff --git a/tensorflow/contrib/learn/python/learn/datasets/produce_small_datasets.py b/tensorflow/contrib/learn/python/learn/datasets/produce_small_datasets.py index 6e0ba38941ce4650ede9f7210e284bde2ed8e6a9..a4848fa64a72f031ef35c0c3256e97a7326acd60 100644 --- a/tensorflow/contrib/learn/python/learn/datasets/produce_small_datasets.py +++ b/tensorflow/contrib/learn/python/learn/datasets/produce_small_datasets.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Produce DBpedia datasets of a smaller size.""" +"""Produce DBpedia datasets of a smaller size (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/contrib/learn/python/learn/datasets/synthetic.py b/tensorflow/contrib/learn/python/learn/datasets/synthetic.py index 9a843168c27d9cae3f55efe4fe4c688d86c745f3..6a0e3350b3d1052249160a2a997a76de7a5040c3 100644 --- a/tensorflow/contrib/learn/python/learn/datasets/synthetic.py +++ b/tensorflow/contrib/learn/python/learn/datasets/synthetic.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Synthetic dataset generators.""" +"""Synthetic dataset generators (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -21,8 +26,10 @@ from __future__ import print_function import numpy as np from tensorflow.contrib.learn.python.learn.datasets.base import Dataset +from tensorflow.python.util.deprecation import deprecated +@deprecated(None, 'Consider using synthetic datasets from scikits.learn.') def circles(n_samples=100, noise=None, seed=None, @@ -93,6 +100,7 @@ def circles(n_samples=100, return Dataset(data=X[indices], target=y[indices]) +@deprecated(None, 'Consider using synthetic datasets from scikits.learn.') def spirals(n_samples=100, noise=None, seed=None, diff --git a/tensorflow/contrib/learn/python/learn/datasets/text_datasets.py b/tensorflow/contrib/learn/python/learn/datasets/text_datasets.py index 2596a2ecaf1572506504831e8b08fab9b5dbc119..ce9466301728082f8e9d99c90989ba8fe623bcf0 100644 --- a/tensorflow/contrib/learn/python/learn/datasets/text_datasets.py +++ b/tensorflow/contrib/learn/python/learn/datasets/text_datasets.py @@ -13,7 +13,12 @@ # limitations under the License. # ============================================================================== -"""Text datasets.""" +"""Text datasets (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -26,10 +31,12 @@ import numpy as np from tensorflow.contrib.learn.python.learn.datasets import base from tensorflow.python.platform import gfile +from tensorflow.python.util.deprecation import deprecated DBPEDIA_URL = 'https://github.com/le-scientifique/torchDatasets/raw/master/dbpedia_csv.tar.gz' +@deprecated(None, 'See contrib/learn/README.md') def maybe_download_dbpedia(data_dir): """Download if DBpedia data is not present.""" train_path = os.path.join(data_dir, 'dbpedia_csv/train.csv') @@ -41,6 +48,7 @@ def maybe_download_dbpedia(data_dir): tfile.extractall(data_dir) +@deprecated(None, 'See contrib/learn/README.md') def load_dbpedia(size='small', test_with_fake_data=False): """Get DBpedia datasets from CSV files.""" if not test_with_fake_data: diff --git a/tensorflow/contrib/learn/python/learn/estimators/__init__.py b/tensorflow/contrib/learn/python/learn/estimators/__init__.py index 4981750c94c7ac31e23b7a3f71ca30e3c9573a20..3e64595f312bcc2a2e8dcba589fb993a249b684b 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/__init__.py +++ b/tensorflow/contrib/learn/python/learn/estimators/__init__.py @@ -13,7 +13,12 @@ # limitations under the License. # ============================================================================== -"""An estimator is a rule for calculating an estimate of a given quantity. +"""An estimator is a rule for calculating an estimate of a given quantity (deprecated). + +These classes are deprecated and replaced with `tf.estimator`. + +See [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. # Estimators diff --git a/tensorflow/contrib/learn/python/learn/estimators/_sklearn.py b/tensorflow/contrib/learn/python/learn/estimators/_sklearn.py index 15277415a1ce83dc1d4a334e60fe1933ba244df0..1f0e4663d060a3850e2002b27f809fde1db47e48 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/_sklearn.py +++ b/tensorflow/contrib/learn/python/learn/estimators/_sklearn.py @@ -13,7 +13,7 @@ # limitations under the License. # ============================================================================== -"""sklearn cross-support.""" +"""sklearn cross-support (deprecated).""" from __future__ import absolute_import from __future__ import division @@ -132,6 +132,8 @@ class _TransformerMixin(): class NotFittedError(ValueError, AttributeError): """Exception class to raise if estimator is used before fitting. + USE OF THIS EXCEPTION IS DEPRECATED. + This class inherits from both ValueError and AttributeError to help with exception handling and backward compatibility. diff --git a/tensorflow/contrib/learn/python/learn/estimators/composable_model.py b/tensorflow/contrib/learn/python/learn/estimators/composable_model.py index a02c726c74946d93b8e1726473db746220b00795..1fa58271e2b886cd143683a759145fd750791473 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/composable_model.py +++ b/tensorflow/contrib/learn/python/learn/estimators/composable_model.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""TensorFlow composable models used as building blocks for estimators.""" +"""TensorFlow composable models used as building blocks for estimators (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -34,6 +39,7 @@ from tensorflow.python.ops import nn from tensorflow.python.ops import partitioned_variables from tensorflow.python.ops import variable_scope from tensorflow.python.summary import summary +from tensorflow.python.util.deprecation import deprecated class _ComposableModel(object): @@ -46,6 +52,7 @@ class _ComposableModel(object): _ComposableModel and its subclasses are not part of the public tf.learn API. """ + @deprecated(None, "Please use model_fns in tf.estimator.") def __init__(self, num_label_columns, optimizer, @@ -141,6 +148,10 @@ class _ComposableModel(object): class LinearComposableModel(_ComposableModel): """A _ComposableModel that implements linear regression. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Instances of this class can be used to build estimators through the use of composition. """ @@ -252,6 +263,10 @@ class LinearComposableModel(_ComposableModel): class DNNComposableModel(_ComposableModel): """A _ComposableModel that implements a DNN. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Instances of this class can be used to build estimators through the use of composition. """ diff --git a/tensorflow/contrib/learn/python/learn/estimators/constants.py b/tensorflow/contrib/learn/python/learn/estimators/constants.py index fc69e810244a182b864be856e6720f8584f7aa65..d2548946bc77dea7c452d61c7e2b6e12c3d6239a 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/constants.py +++ b/tensorflow/contrib/learn/python/learn/estimators/constants.py @@ -13,9 +13,11 @@ # limitations under the License. # ============================================================================== -"""Constants regarding Estimators. +"""Constants regarding Estimators (deprecated). -This file is obsoleted in the move of Estimator to core. +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. """ from __future__ import absolute_import from __future__ import division @@ -25,6 +27,8 @@ from __future__ import print_function class ProblemType(object): """Enum-like values for the type of problem that the model solves. + THIS CLASS IS DEPRECATED. + These values are used when exporting the model to produce the appropriate signature function for serving. diff --git a/tensorflow/contrib/learn/python/learn/estimators/debug.py b/tensorflow/contrib/learn/python/learn/estimators/debug.py index 9d5f6c2bf969d7c85d251bf1b06a0307a41b2297..24b067b7e38b12df3d1d0c49f626344217218571 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/debug.py +++ b/tensorflow/contrib/learn/python/learn/estimators/debug.py @@ -12,7 +12,11 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Debug estimators. +"""Debug estimators (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. Debug estimators are bias-only estimators that can be used for debugging and as simple baselines. @@ -118,6 +122,10 @@ def debug_model_fn(features, labels, mode, params, config=None): class DebugClassifier(estimator.Estimator): """A classifier for TensorFlow Debug models. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Example: ```python @@ -237,6 +245,10 @@ class DebugClassifier(estimator.Estimator): class DebugRegressor(estimator.Estimator): """A regressor for TensorFlow Debug models. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Example: ```python diff --git a/tensorflow/contrib/learn/python/learn/estimators/dnn.py b/tensorflow/contrib/learn/python/learn/estimators/dnn.py index c17b41c0f767e19d9c3635a8f60347a49b297cfb..eabebb7e881558471c343c0573cc9a8f4a425312 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/dnn.py +++ b/tensorflow/contrib/learn/python/learn/estimators/dnn.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Deep Neural Network estimators.""" +"""Deep Neural Network estimators (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -212,6 +217,10 @@ def _dnn_model_fn(features, labels, mode, params, config=None): class DNNClassifier(estimator.Estimator): """A classifier for TensorFlow DNN models. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Example: ```python @@ -521,6 +530,10 @@ class DNNClassifier(estimator.Estimator): class DNNRegressor(estimator.Estimator): """A regressor for TensorFlow DNN models. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Example: ```python @@ -796,6 +809,10 @@ class DNNRegressor(estimator.Estimator): class DNNEstimator(estimator.Estimator): """A Estimator for TensorFlow DNN models with user specified _Head. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Example: ```python diff --git a/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.py b/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.py index 726612235050def6e7addb503cc6646a25de0e42..3d85533d92d17095bae9a69f229171e1bf61ba10 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.py +++ b/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.py @@ -13,7 +13,12 @@ # limitations under the License. # ============================================================================== -"""TensorFlow estimators for Linear and DNN joined training models.""" +"""TensorFlow estimators for Linear and DNN joined training models (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -372,6 +377,10 @@ def _dnn_linear_combined_model_fn(features, labels, mode, params, config=None): class DNNLinearCombinedEstimator(estimator.Estimator): """An estimator for TensorFlow Linear and DNN joined training models. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Note: New users must set `fix_global_step_increment_bug=True` when creating an estimator. @@ -490,6 +499,10 @@ class DNNLinearCombinedEstimator(estimator.Estimator): class DNNLinearCombinedClassifier(estimator.Estimator): """A classifier for TensorFlow Linear and DNN joined training models. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Note: New users must set `fix_global_step_increment_bug=True` when creating an estimator. @@ -832,6 +845,10 @@ class DNNLinearCombinedClassifier(estimator.Estimator): class DNNLinearCombinedRegressor(estimator.Estimator): """A regressor for TensorFlow Linear and DNN joined training models. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Note: New users must set `fix_global_step_increment_bug=True` when creating an estimator. diff --git a/tensorflow/contrib/learn/python/learn/estimators/dynamic_rnn_estimator.py b/tensorflow/contrib/learn/python/learn/estimators/dynamic_rnn_estimator.py index 69440e823ef1ed2d739f28bc14587891f2de80bb..a703dc66e922d48ceb64edc2a979061b8e45db49 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/dynamic_rnn_estimator.py +++ b/tensorflow/contrib/learn/python/learn/estimators/dynamic_rnn_estimator.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Estimator for Dynamic RNNs.""" +"""Estimator for Dynamic RNNs (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -540,6 +545,12 @@ def _get_dynamic_rnn_model_fn( class DynamicRnnEstimator(estimator.Estimator): + """Dynamically unrolled RNN (deprecated). + + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + """ def __init__(self, problem_type, diff --git a/tensorflow/contrib/learn/python/learn/estimators/estimator.py b/tensorflow/contrib/learn/python/learn/estimators/estimator.py index 4b63e08ab3372849309ee5d28d754de82e9632f4..7a026a15e4aeea0dde4ed9f7de053a757a0abb58 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/estimator.py +++ b/tensorflow/contrib/learn/python/learn/estimators/estimator.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Base Estimator class.""" +"""Base Estimator class (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -138,6 +143,7 @@ def _get_input_fn(x, y, input_fn, feed_fn, batch_size, shuffle=False, epochs=1): return df.input_builder, df.get_feed_dict_fn() +@deprecated(None, 'Please specify feature columns explicitly.') def infer_real_valued_columns_from_input_fn(input_fn): """Creates `FeatureColumn` objects for inputs defined by `input_fn`. @@ -158,6 +164,7 @@ def infer_real_valued_columns_from_input_fn(input_fn): return layers.infer_real_valued_columns(features) +@deprecated(None, 'Please specify feature columns explicitly.') def infer_real_valued_columns_from_input(x): """Creates `FeatureColumn` objects for inputs defined by input `x`. @@ -389,6 +396,10 @@ class BaseEstimator(sklearn.BaseEstimator, evaluable.Evaluable, trainable.Trainable): """Abstract BaseEstimator class to train and evaluate TensorFlow models. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Users should not instantiate or subclass this class. Instead, use an `Estimator`. """ @@ -399,6 +410,8 @@ class BaseEstimator(sklearn.BaseEstimator, evaluable.Evaluable, # TODO(wicke): Remove this once launcher takes over config functionality _Config = run_config.RunConfig # pylint: disable=invalid-name + @deprecated(None, 'Please replace uses of any Estimator from tf.contrib.learn' + ' with an Estimator from tf.estimator.*') def __init__(self, model_dir=None, config=None): """Initializes a BaseEstimator instance. @@ -457,6 +470,20 @@ class BaseEstimator(sklearn.BaseEstimator, evaluable.Evaluable, # TODO(wicke): make RunConfig immutable, and then return it without a copy. return copy.deepcopy(self._config) + @property + def model_fn(self): + """Returns the model_fn which is bound to self.params. + + Returns: + The model_fn with the following signature: + `def model_fn(features, labels, mode, metrics)` + """ + + def public_model_fn(features, labels, mode, config): + return self._call_model_fn(features, labels, mode, config=config) + + return public_model_fn + @deprecated_args(SCIKIT_DECOUPLE_DATE, SCIKIT_DECOUPLE_INSTRUCTIONS, ('x', None), ('y', None), ('batch_size', None)) def fit(self, @@ -890,8 +917,8 @@ class BaseEstimator(sklearn.BaseEstimator, evaluable.Evaluable, if feed_fn: hooks.append(basic_session_run_hooks.FeedFnHook(feed_fn)) if steps == 0: - logging.warning('evaluation steps are 0. If `input_fn` does not raise' - 'OutOfRangeError`, the evaluation will never stop.' + logging.warning('evaluation steps are 0. If `input_fn` does not raise ' + '`OutOfRangeError`, the evaluation will never stop. ' 'Use steps=None if intended.') if steps: hooks.append( @@ -1074,6 +1101,10 @@ def _identity_feature_engineering_fn(features, labels): class Estimator(BaseEstimator): """Estimator class is the basic TensorFlow model trainer/evaluator. + + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. """ def __init__(self, @@ -1162,7 +1193,7 @@ class Estimator(BaseEstimator): self._feature_engineering_fn = ( feature_engineering_fn or _identity_feature_engineering_fn) - def _call_model_fn(self, features, labels, mode, metrics=None): + def _call_model_fn(self, features, labels, mode, metrics=None, config=None): """Calls model function with support of 2, 3 or 4 arguments. Args: @@ -1170,6 +1201,7 @@ class Estimator(BaseEstimator): labels: labels dict. mode: ModeKeys metrics: Dict of metrics. + config: RunConfig. Returns: A `ModelFnOps` object. If model_fn returns a tuple, wraps them up in a @@ -1186,7 +1218,10 @@ class Estimator(BaseEstimator): if 'params' in model_fn_args: kwargs['params'] = self.params if 'config' in model_fn_args: - kwargs['config'] = self.config + if config: + kwargs['config'] = config + else: + kwargs['config'] = self.config if 'model_dir' in model_fn_args: kwargs['model_dir'] = self.model_dir model_fn_results = self._model_fn(features, labels, **kwargs) @@ -1458,8 +1493,14 @@ class Estimator(BaseEstimator): # For time of deprecation x,y from Estimator allow direct access. # pylint: disable=protected-access class SKCompat(sklearn.BaseEstimator): - """Scikit learn wrapper for TensorFlow Learn Estimator.""" + """Scikit learn wrapper for TensorFlow Learn Estimator. + + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + """ + @deprecated(None, 'Please switch to the Estimator interface.') def __init__(self, estimator): self._estimator = estimator diff --git a/tensorflow/contrib/learn/python/learn/estimators/estimator_test_utils.py b/tensorflow/contrib/learn/python/learn/estimators/estimator_test_utils.py index fd47710e3015de9ae6a453f98978b0ef8f88968c..e4c31396baf8271c49395a2b87b454dbc77177e2 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/estimator_test_utils.py +++ b/tensorflow/contrib/learn/python/learn/estimators/estimator_test_utils.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Utils for Estimator.""" +"""Utils for Estimator (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/contrib/learn/python/learn/estimators/head.py b/tensorflow/contrib/learn/python/learn/estimators/head.py index 9b124b2c19f16bbc9b2afeadb82a32006e1a0ae9..2b4b6eff39f4fc8a20a149edfc07d2f4f27a9bae 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/head.py +++ b/tensorflow/contrib/learn/python/learn/estimators/head.py @@ -12,8 +12,13 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Abstractions for the head(s) of a model. +"""Abstractions for the head(s) of a model (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. """ + from __future__ import absolute_import from __future__ import division from __future__ import print_function @@ -47,11 +52,16 @@ from tensorflow.python.summary import summary from tensorflow.python.training import training from tensorflow.python.util import tf_decorator from tensorflow.python.util import tf_inspect +from tensorflow.python.util.deprecation import deprecated class Head(object): """Interface for the head/top of a model. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Given logits (or output of a hidden layer), a Head knows how to compute predictions, loss, default metric and export signature. It is meant to, @@ -177,6 +187,7 @@ class Head(object): raise NotImplementedError("Calling an abstract method.") +@deprecated(None, "Please switch to tf.contrib.estimator.*_head.") def regression_head(label_name=None, weight_column_name=None, label_dimension=1, @@ -216,6 +227,7 @@ def regression_head(label_name=None, link_fn=(link_fn if link_fn is not None else array_ops.identity)) +@deprecated(None, "Please switch to tf.contrib.estimator.*_head.") def poisson_regression_head(label_name=None, weight_column_name=None, label_dimension=1, @@ -254,6 +266,7 @@ def poisson_regression_head(label_name=None, # TODO(zakaria): Consider adding a _RegressionHead for logistic_regression +@deprecated(None, "Please switch to tf.contrib.estimator.*_head.") def multi_class_head(n_classes, label_name=None, weight_column_name=None, @@ -335,6 +348,7 @@ def multi_class_head(n_classes, label_keys=label_keys) +@deprecated(None, "Please switch to tf.contrib.estimator.*_head.") def binary_svm_head( label_name=None, weight_column_name=None, @@ -370,6 +384,7 @@ def binary_svm_head( thresholds=thresholds) +@deprecated(None, "Please switch to tf.contrib.estimator.*_head.") def multi_label_head(n_classes, label_name=None, weight_column_name=None, @@ -430,6 +445,7 @@ def multi_label_head(n_classes, loss_fn=_wrap_custom_loss_fn(loss_fn) if loss_fn else None) +@deprecated(None, "Please switch to tf.contrib.estimator.*_head.") def loss_only_head(loss_fn, head_name=None): """Creates a Head that contains only loss terms. @@ -447,6 +463,7 @@ def loss_only_head(loss_fn, head_name=None): return _LossOnlyHead(loss_fn, head_name=head_name) +@deprecated(None, "Please switch to tf.contrib.estimator.*_head.") def multi_head(heads, loss_weights=None): """Creates a MultiHead stemming from same logits/hidden layer. @@ -479,6 +496,7 @@ def multi_head(heads, loss_weights=None): return _MultiHead(heads, loss_merger=_weighted_loss_merger) +@deprecated(None, "Use 'lambda _: tf.no_op()'.") def no_op_train_fn(loss): del loss return control_flow_ops.no_op() diff --git a/tensorflow/contrib/learn/python/learn/estimators/head_test.py b/tensorflow/contrib/learn/python/learn/estimators/head_test.py index 6d5da81b4c2087fb9c5307902e452a6220a17cd0..7c2d9bb0767cb979dae9c84b5342d129225677ed 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/head_test.py +++ b/tensorflow/contrib/learn/python/learn/estimators/head_test.py @@ -362,7 +362,7 @@ class MultiLabelHeadTest(test.TestCase): "auc_precision_recall": 0.166667, "auc_precision_recall/class0": 0, "auc_precision_recall/class1": 0., - "auc_precision_recall/class2": 0.49999, + "auc_precision_recall/class2": 1., "labels/actual_label_mean/class0": self._labels[0][0], "labels/actual_label_mean/class1": self._labels[0][1], "labels/actual_label_mean/class2": self._labels[0][2], @@ -748,7 +748,7 @@ class BinaryClassificationHeadTest(test.TestCase): "accuracy/baseline_label_mean": label_mean, "accuracy/threshold_0.500000_mean": 1. / 2, "auc": 1. / 2, - "auc_precision_recall": 0.25, + "auc_precision_recall": 0.749999, "labels/actual_label_mean": label_mean, "labels/prediction_mean": .731059, # softmax "loss": expected_loss, diff --git a/tensorflow/contrib/learn/python/learn/estimators/kmeans.py b/tensorflow/contrib/learn/python/learn/estimators/kmeans.py index 8f9d6fc318a357853bdb8e3264f6691b410006b1..66ebcfd1d81904b9afe5be6bd1a648fe325e1e0b 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/kmeans.py +++ b/tensorflow/contrib/learn/python/learn/estimators/kmeans.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Implementation of k-means clustering on top of `Estimator` API. +"""Implementation of k-means clustering on top of `Estimator` API (deprecated). This module is deprecated. Please use @{tf.contrib.factorization.KMeansClustering} instead of @@ -153,7 +153,12 @@ def _kmeans_clustering_model_fn(features, labels, mode, params, config): # TODO(agarwal,ands): support sharded input. class KMeansClustering(estimator.Estimator): - """An Estimator for K-Means clustering.""" + """An Estimator for K-Means clustering. + + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + """ SQUARED_EUCLIDEAN_DISTANCE = clustering_ops.SQUARED_EUCLIDEAN_DISTANCE COSINE_DISTANCE = clustering_ops.COSINE_DISTANCE RANDOM_INIT = clustering_ops.RANDOM_INIT diff --git a/tensorflow/contrib/learn/python/learn/estimators/linear.py b/tensorflow/contrib/learn/python/learn/estimators/linear.py index 37aa8b339622415d082933cdf66d2472a4119b48..64d7ecc68e7abb1d36a3eb098fedd8184d6e9d77 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/linear.py +++ b/tensorflow/contrib/learn/python/learn/estimators/linear.py @@ -13,7 +13,12 @@ # limitations under the License. # ============================================================================== -"""Linear Estimators.""" +"""Linear Estimators (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -305,6 +310,10 @@ class _SdcaUpdateWeightsHook(session_run_hook.SessionRunHook): class LinearClassifier(estimator.Estimator): """Linear classifier model. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Train a linear model to classify instances into one of multiple possible classes. When number of possible classes is 2, this is binary classification. @@ -625,6 +634,10 @@ class LinearClassifier(estimator.Estimator): class LinearRegressor(estimator.Estimator): """Linear regressor model. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Train a linear regression model to predict label value given observation of feature values. @@ -860,6 +873,10 @@ class LinearRegressor(estimator.Estimator): class LinearEstimator(estimator.Estimator): """Linear model with user specified head. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Train a generalized linear model to predict label value given observation of feature values. diff --git a/tensorflow/contrib/learn/python/learn/estimators/logistic_regressor.py b/tensorflow/contrib/learn/python/learn/estimators/logistic_regressor.py index fb339160d58e09d4ffd50090f2dbbcec08bebe47..3cbcc6e98de1c915c302617e4591c9baa33adeaf 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/logistic_regressor.py +++ b/tensorflow/contrib/learn/python/learn/estimators/logistic_regressor.py @@ -12,7 +12,11 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Logistic regression (aka binary classifier) class. +"""Logistic regression (aka binary classifier) class (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. This defines some useful basic metrics for using logistic regression to classify a binary event (0 vs 1). @@ -75,6 +79,10 @@ def LogisticRegressor( # pylint: disable=invalid-name feature_engineering_fn=None): """Builds a logistic regression Estimator for binary classification. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + This method provides a basic Estimator with some additional metrics for custom binary classification models, including AUC, precision/recall and accuracy. diff --git a/tensorflow/contrib/learn/python/learn/estimators/metric_key.py b/tensorflow/contrib/learn/python/learn/estimators/metric_key.py index 99388f116b345bd038f2985606c6203011597ea2..f264248e44d9aa48f26ee32e36746bd4c3145a8d 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/metric_key.py +++ b/tensorflow/contrib/learn/python/learn/estimators/metric_key.py @@ -12,14 +12,20 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Enum for metric keys.""" +"""Enum for metric keys (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division from __future__ import print_function class MetricKey(object): - """Metric key strings.""" + """Metric key strings (deprecated).""" + LOSS = "loss" AUC = "auc" AUC_PR = "auc_precision_recall" diff --git a/tensorflow/contrib/learn/python/learn/estimators/model_fn.py b/tensorflow/contrib/learn/python/learn/estimators/model_fn.py index 44e6c7c52dac524a22e9099e33e2aef82f8fe7ba..dcb161180c99ce71195c820217e8bdaf79d70901 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/model_fn.py +++ b/tensorflow/contrib/learn/python/learn/estimators/model_fn.py @@ -13,7 +13,12 @@ # limitations under the License. # ============================================================================== -"""Classes and methods related to model_fn.""" +"""Classes and methods related to model_fn (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -37,10 +42,13 @@ from tensorflow.python.ops import array_ops from tensorflow.python.platform import tf_logging as logging from tensorflow.python.saved_model import signature_constants from tensorflow.python.training import session_run_hook +from tensorflow.python.util.deprecation import deprecated class ModeKeys(object): - """Standard names for model modes. + """Standard names for model modes (deprecated). + + THIS CLASS IS DEPRECATED. The following standard keys are defined: @@ -65,8 +73,16 @@ class ModelFnOps( 'output_alternatives', 'training_chief_hooks', 'training_hooks', 'scaffold', 'mode' ])): - """Ops returned from a model_fn.""" + """Ops returned from a model_fn. + + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + """ + @deprecated(None, 'When switching to tf.estimator.Estimator, use ' + 'tf.estimator.EstimatorSpec. You can use the `estimator_spec`' + ' method to create an equivalent one.') def __new__(cls, mode, predictions=None, diff --git a/tensorflow/contrib/learn/python/learn/estimators/prediction_key.py b/tensorflow/contrib/learn/python/learn/estimators/prediction_key.py index f8d87b8914307a86eb2f46123a28ff11eb925eda..6fd2fc9d592cef4e44a640e2f27cb28b367d44d5 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/prediction_key.py +++ b/tensorflow/contrib/learn/python/learn/estimators/prediction_key.py @@ -12,7 +12,11 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Enum for model prediction keys. +"""Enum for model prediction keys (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. This file is obsoleted in the move of Estimator to core. """ @@ -22,6 +26,8 @@ from __future__ import print_function class PredictionKey(object): + """THIS CLASS IS DEPRECATED.""" + CLASSES = "classes" PROBABILITIES = "probabilities" LOGITS = "logits" diff --git a/tensorflow/contrib/learn/python/learn/estimators/rnn_common.py b/tensorflow/contrib/learn/python/learn/estimators/rnn_common.py index 2752bc2d90ee0f51b2c40ccc4d24a4eb21cff38f..215022e5d9e5d3cd5d6a96583b325b19a1719568 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/rnn_common.py +++ b/tensorflow/contrib/learn/python/learn/estimators/rnn_common.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Common operations for RNN Estimators.""" +"""Common operations for RNN Estimators (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/contrib/learn/python/learn/estimators/run_config.py b/tensorflow/contrib/learn/python/learn/estimators/run_config.py index fd90fd1cc6277e7d80287aefdbab6134dac7c0d5..1d161093de01ef838d0c75ec9a39574c7529bd57 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/run_config.py +++ b/tensorflow/contrib/learn/python/learn/estimators/run_config.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Run Config.""" +"""Run Config (deprecated, use tf.estimator.RunConfig instead). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -29,11 +34,12 @@ from tensorflow.core.protobuf import config_pb2 from tensorflow.python.estimator import run_config as core_run_config from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import server_lib +from tensorflow.python.util.deprecation import deprecated # A list of the property names in RunConfig user allows to change. They will # not affect the execution framework, so when execution framework checks the -# `uid` of the RunConfig, it should be ingored. +# `uid` of the RunConfig, it should be ignored. _DEFAULT_UID_WHITE_LIST = [ 'tf_random_seed', 'save_summary_steps', @@ -47,6 +53,7 @@ _DEFAULT_UID_WHITE_LIST = [ class Environment(object): + """DEPRECATED CLASS.""" # For running general distributed training. CLOUD = 'cloud' # For running Google-internal distributed training. @@ -56,6 +63,7 @@ class Environment(object): class TaskType(object): + """DEPRECATED CLASS.""" MASTER = 'master' PS = 'ps' WORKER = 'worker' @@ -64,6 +72,8 @@ class TaskType(object): class ClusterConfig(object): """This class specifies the configurations for a distributed run. + THIS CLASS IS DEPRECATED. Use tf.estimator.RunConfig instead. + If you're using an `Estimator`, you should probably use the subclass RunConfig instead. """ @@ -211,10 +221,13 @@ class ClusterConfig(object): class RunConfig(ClusterConfig, core_run_config.RunConfig): """This class specifies the configurations for an `Estimator` run. - This class is the implementation of @{tf.estimator.RunConfig} interface. + This class is a deprecated implementation of @{tf.estimator.RunConfig} + interface. """ _USE_DEFAULT = 0 + @deprecated(None, 'When switching to tf.estimator.Estimator, use' + ' tf.estimator.RunConfig instead.') def __init__(self, master=None, num_cores=0, diff --git a/tensorflow/contrib/learn/python/learn/estimators/state_saving_rnn_estimator.py b/tensorflow/contrib/learn/python/learn/estimators/state_saving_rnn_estimator.py index 0cea35e219a4457417a161a3ac4ac4292fd24f53..de78c72c3ae3ef14f5f7c46b1d47f82e8266c7c6 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/state_saving_rnn_estimator.py +++ b/tensorflow/contrib/learn/python/learn/estimators/state_saving_rnn_estimator.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Estimator for State Saving RNNs.""" +"""Estimator for State Saving RNNs (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -528,6 +533,12 @@ def _get_rnn_model_fn(cell_type, class StateSavingRnnEstimator(estimator.Estimator): + """RNN with static unrolling and state saving (deprecated). + + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + """ def __init__(self, problem_type, diff --git a/tensorflow/contrib/learn/python/learn/estimators/svm.py b/tensorflow/contrib/learn/python/learn/estimators/svm.py index 72920d73c0c92886e54f533ad7fe170fe27d9870..3459997baba16fc0d4045e50819ecdd0e7121657 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/svm.py +++ b/tensorflow/contrib/learn/python/learn/estimators/svm.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Support Vector Machine (SVM) Estimator.""" +"""Support Vector Machine (SVM) Estimator (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -36,6 +41,10 @@ def _as_iterable(preds, output): class SVM(estimator.Estimator): """Support Vector Machine (SVM) model for binary classification. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Currently, only linear SVMs are supported. For the underlying optimization problem, the `SDCAOptimizer` is used. For performance and convergence tuning, the num_loss_partitions parameter passed to `SDCAOptimizer` (see `__init__()` diff --git a/tensorflow/contrib/learn/python/learn/estimators/tensor_signature.py b/tensorflow/contrib/learn/python/learn/estimators/tensor_signature.py index a120bc6cc3975a3d4559d018c8aa74ff42a16d2d..71b5658dd174d2b47e33860844359f68e6768026 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/tensor_signature.py +++ b/tensorflow/contrib/learn/python/learn/estimators/tensor_signature.py @@ -13,7 +13,12 @@ # limitations under the License. # ============================================================================== -"""TensorSignature class and utilities.""" +"""TensorSignature class and utilities (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -33,6 +38,10 @@ class TensorSignature(collections.namedtuple( "TensorSignature", ["dtype", "shape", "is_sparse"])): """Signature of the `Tensor` object. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Useful to check compatibility of tensors. Example: diff --git a/tensorflow/contrib/learn/python/learn/estimators/test_data.py b/tensorflow/contrib/learn/python/learn/estimators/test_data.py index ed201bfc58f273e6587850032386c2686aea4148..e4b057b4f5a9e081c2d891bd9828ffc315e51e91 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/test_data.py +++ b/tensorflow/contrib/learn/python/learn/estimators/test_data.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Test data utilities.""" +"""Test data utilities (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/contrib/learn/python/learn/evaluable.py b/tensorflow/contrib/learn/python/learn/evaluable.py index 8f6cd39864b437f163dd7c1140dc88755ce98529..10881ca885599bc81386e15f814a2687d907f63b 100644 --- a/tensorflow/contrib/learn/python/learn/evaluable.py +++ b/tensorflow/contrib/learn/python/learn/evaluable.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""`Evaluable` interface.""" +"""`Evaluable` interface (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -23,6 +28,10 @@ import abc class Evaluable(object): """Interface for objects that are evaluatable by, e.g., `Experiment`. + + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. """ __metaclass__ = abc.ABCMeta diff --git a/tensorflow/contrib/learn/python/learn/experiment.py b/tensorflow/contrib/learn/python/learn/experiment.py index 331bc115499c8d6f4057bf1c0908bcea05f005a3..3744abd860e7f460133873eb534fd75887182f78 100644 --- a/tensorflow/contrib/learn/python/learn/experiment.py +++ b/tensorflow/contrib/learn/python/learn/experiment.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Experiment class collecting information needed for a single training run.""" +"""Experiment class collecting information for a single training run (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -25,7 +30,6 @@ import os import time from tensorflow.contrib.framework import deprecated -from tensorflow.contrib.framework import deprecated_args from tensorflow.contrib.framework.python.framework import experimental from tensorflow.contrib.learn.python.learn import evaluable from tensorflow.contrib.learn.python.learn import export_strategy @@ -118,6 +122,10 @@ class _EvalAndExportListener(basic_session_run_hooks.CheckpointSaverListener): class Experiment(object): """Experiment is a class containing all information needed to train a model. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + After an experiment is created (by passing an Estimator and inputs for training and evaluation), an Experiment instance knows how to invoke training and eval loops in a sensible fashion for distributed training. @@ -125,16 +133,8 @@ class Experiment(object): # TODO(ispir): remove delay_workers_by_global_step and make global step based # waiting as only behavior. - @deprecated_args( - "2016-10-23", - "local_eval_frequency is deprecated as local_run will be renamed to " - "train_and_evaluate. Use min_eval_frequency and call train_and_evaluate " - "instead. Note, however, that the default for min_eval_frequency is 1, " - "meaning models will be evaluated every time a new checkpoint is " - "available. In contrast, the default for local_eval_frequency is None, " - "resulting in evaluation occurring only after training has completed. " - "min_eval_frequency is ignored when calling the deprecated local_run.", - "local_eval_frequency") + @deprecated(None, "Please switch to tf.estimator.train_and_evaluate. You will" + " also have to convert to a tf.estimator.Estimator.") def __init__(self, estimator, train_input_fn, @@ -358,7 +358,7 @@ class Experiment(object): self._start_server() elif config.cluster_spec and config.master: raise ValueError( - "For distributed runtime, Experiment class only works with" + "For distributed runtime, Experiment class only works with " "tf.contrib.learn.RunConfig for now, but provided {}".format( type(config))) diff --git a/tensorflow/contrib/learn/python/learn/export_strategy.py b/tensorflow/contrib/learn/python/learn/export_strategy.py index 55a8b824312b89e0ac66513242191f4201ac212a..075cab536ecb5279e7e6f23abb0b70c75043a7ec 100644 --- a/tensorflow/contrib/learn/python/learn/export_strategy.py +++ b/tensorflow/contrib/learn/python/learn/export_strategy.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""ExportStrategy class represents different flavors of model export.""" +"""ExportStrategy class represents different flavors of model export (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -21,6 +26,7 @@ from __future__ import print_function import collections from tensorflow.python.util import tf_inspect +from tensorflow.python.util.deprecation import deprecated __all__ = ['ExportStrategy'] @@ -30,6 +36,10 @@ class ExportStrategy( ['name', 'export_fn', 'strip_default_attrs'])): """A class representing a type of model export. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Typically constructed by a utility function specific to the exporter, such as `saved_model_export_utils.make_export_strategy()`. @@ -56,6 +66,8 @@ class ExportStrategy( forward compatibility of the resulting `SavedModel`. """ + @deprecated(None, 'Please switch to tf.estimator.train_and_evaluate, and use ' + 'tf.estimator.Exporter.') def __new__(cls, name, export_fn, strip_default_attrs=None): return super(ExportStrategy, cls).__new__( cls, name, export_fn, strip_default_attrs) diff --git a/tensorflow/contrib/learn/python/learn/graph_actions.py b/tensorflow/contrib/learn/python/learn/graph_actions.py index 98365c05f663e5d2a06703457fc5663d7135f7d9..a997fab723a16dddf150aa9397863605e4e77933 100644 --- a/tensorflow/contrib/learn/python/learn/graph_actions.py +++ b/tensorflow/contrib/learn/python/learn/graph_actions.py @@ -13,7 +13,12 @@ # limitations under the License. # ============================================================================== -"""High level operations on graphs.""" +"""High level operations on graphs (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -68,6 +73,7 @@ def clear_summary_writers(): return summary_io.SummaryWriterCache.clear() +@deprecated(None, 'Use `SummaryWriterCache.get` directly.') def get_summary_writer(logdir): """Returns single SummaryWriter per logdir in current run. diff --git a/tensorflow/contrib/learn/python/learn/learn_io/__init__.py b/tensorflow/contrib/learn/python/learn/learn_io/__init__.py index 06c3782a471537cf3879450e6bd20899a35d96ac..8b133a4440d8cbc19abca64f972791fc16ade6f8 100644 --- a/tensorflow/contrib/learn/python/learn/learn_io/__init__.py +++ b/tensorflow/contrib/learn/python/learn/learn_io/__init__.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Tools to allow different io formats.""" +"""Tools to allow different io formats (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/contrib/learn/python/learn/learn_io/dask_io.py b/tensorflow/contrib/learn/python/learn/learn_io/dask_io.py index 7d666391cea3c0a52a2cb7e324c00d5f480710d5..e0a1948d95a727675dac8ff3ce9f55c35d5f8d8d 100644 --- a/tensorflow/contrib/learn/python/learn/learn_io/dask_io.py +++ b/tensorflow/contrib/learn/python/learn/learn_io/dask_io.py @@ -13,7 +13,12 @@ # limitations under the License. # ============================================================================== -"""Methods to allow dask.DataFrame.""" +"""Methods to allow dask.DataFrame (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -21,6 +26,8 @@ from __future__ import print_function import numpy as np +from tensorflow.python.util.deprecation import deprecated + try: # pylint: disable=g-import-not-at-top import dask.dataframe as dd @@ -60,6 +67,7 @@ def _construct_dask_df_with_divisions(df): return dd.Series(merge(dsk, df.dask), name, df.name, divisions) +@deprecated(None, 'Please feed input to tf.data to support dask.') def extract_dask_data(data): """Extract data from dask.Series or dask.DataFrame for predictors. @@ -81,6 +89,7 @@ def extract_dask_data(data): return data +@deprecated(None, 'Please feed input to tf.data to support dask.') def extract_dask_labels(labels): """Extract data from dask.Series or dask.DataFrame for labels. diff --git a/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py b/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py index 96be8b1bc402479d5611965f27abb197363cb939..c45b1d186471125776d6536112aebb66bb5ad558 100644 --- a/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py +++ b/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Implementations of different data feeders to provide data for TF trainer.""" +"""Implementations of different data feeders to provide data for TF trainer (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" # TODO(ipolosukhin): Replace this module with feed-dict queue runners & queues. @@ -31,6 +36,7 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import tensor_util from tensorflow.python.ops import array_ops from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.util.deprecation import deprecated # pylint: disable=g-multiple-import,g-bad-import-order from .pandas_io import HAS_PANDAS, extract_pandas_data, extract_pandas_matrix, extract_pandas_labels @@ -101,6 +107,7 @@ def _is_iterable(x): return hasattr(x, 'next') or hasattr(x, '__next__') +@deprecated(None, 'Please use tensorflow/transform or tf.data.') def setup_train_data_feeder(x, y, n_classes, @@ -188,6 +195,7 @@ def _batch_data(x, batch_size=None): yield np.matrix(chunk) +@deprecated(None, 'Please use tensorflow/transform or tf.data.') def setup_predict_data_feeder(x, batch_size=None): """Returns an iterable for feeding into predict step. @@ -219,6 +227,7 @@ def setup_predict_data_feeder(x, batch_size=None): return [x] +@deprecated(None, 'Please use tensorflow/transform or tf.data.') def setup_processor_data_feeder(x): """Sets up processor iterable. @@ -233,6 +242,7 @@ def setup_processor_data_feeder(x): return x +@deprecated(None, 'Please convert numpy dtypes explicitly.') def check_array(array, dtype): """Checks array on dtype and converts it if different. @@ -275,8 +285,14 @@ def _check_dtype(dtype): class DataFeeder(object): - """Data feeder is an example class to sample data for TF trainer.""" + """Data feeder is an example class to sample data for TF trainer. + + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + """ + @deprecated(None, 'Please use tensorflow/transform or tf.data.') def __init__(self, x, y, @@ -563,6 +579,10 @@ class DataFeeder(object): class StreamingDataFeeder(DataFeeder): """Data feeder for TF trainer that reads data from iterator. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Streaming data feeder allows to read data as it comes it from disk or somewhere else. It's custom to have this iterators rotate infinetly over the dataset, to allow control of how much to learn on the trainer side. @@ -771,11 +791,16 @@ class StreamingDataFeeder(DataFeeder): class DaskDataFeeder(object): """Data feeder for that reads data from dask.Series and dask.DataFrame. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Numpy arrays can be serialized to disk and it's possible to do random seeks into them. DaskDataFeeder will remove requirement to have full dataset in the memory and still do random seeks for sampling of batches. """ + @deprecated(None, 'Please feed input to tf.data to support dask.') def __init__(self, x, y, diff --git a/tensorflow/contrib/learn/python/learn/learn_io/generator_io.py b/tensorflow/contrib/learn/python/learn/learn_io/generator_io.py index 884faf8335e2a3ca1d27d2d93b4c817131648774..f8aaa0c9e3e5b589a6ad47678dba3dc38de7c471 100644 --- a/tensorflow/contrib/learn/python/learn/learn_io/generator_io.py +++ b/tensorflow/contrib/learn/python/learn/learn_io/generator_io.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Methods to allow generator of dict with numpy arrays.""" +"""Methods to allow generator of dict with numpy arrays (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -23,8 +28,10 @@ from types import FunctionType from types import GeneratorType from tensorflow.python.estimator.inputs.queues.feeding_functions import _enqueue_data as enqueue_data +from tensorflow.python.util.deprecation import deprecated +@deprecated(None, 'Please use tf.data.') def generator_input_fn(x, target_key=None, batch_size=128, diff --git a/tensorflow/contrib/learn/python/learn/learn_io/graph_io.py b/tensorflow/contrib/learn/python/learn/learn_io/graph_io.py index 3a46c239688017f9204d2c6182a6f81cd325a417..9e816f54b6cf8dee84c6d62406ab3db700054d06 100644 --- a/tensorflow/contrib/learn/python/learn/learn_io/graph_io.py +++ b/tensorflow/contrib/learn/python/learn/learn_io/graph_io.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Methods to read data in the graph.""" +"""Methods to read data in the graph (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -34,11 +39,13 @@ from tensorflow.python.platform import gfile from tensorflow.python.summary import summary from tensorflow.python.training import input as input_ops from tensorflow.python.training import queue_runner +from tensorflow.python.util.deprecation import deprecated # Default name for key in the feature dict. KEY_FEATURE_NAME = '__key__' +@deprecated(None, 'Use tf.data.') def read_batch_examples(file_pattern, batch_size, reader, @@ -106,6 +113,7 @@ def read_batch_examples(file_pattern, return examples +@deprecated(None, 'Use tf.data.') def read_keyed_batch_examples(file_pattern, batch_size, reader, @@ -175,6 +183,7 @@ def read_keyed_batch_examples(file_pattern, seed=seed) +@deprecated(None, 'Use tf.data.') def read_keyed_batch_examples_shared_queue(file_pattern, batch_size, reader, @@ -452,6 +461,7 @@ def _read_keyed_batch_examples_helper(file_pattern, return queued_examples_with_keys +@deprecated(None, 'Use tf.data.') def read_keyed_batch_features(file_pattern, batch_size, features, @@ -540,6 +550,7 @@ def read_keyed_batch_features(file_pattern, name=scope) +@deprecated(None, 'Use tf.data.') def read_keyed_batch_features_shared_queue(file_pattern, batch_size, features, @@ -620,6 +631,7 @@ def read_keyed_batch_features_shared_queue(file_pattern, name=scope) +@deprecated(None, 'Use tf.data.') def queue_parsed_features(parsed_features, keys=None, feature_queue_capacity=100, @@ -742,6 +754,7 @@ def queue_parsed_features(parsed_features, return dequeued_keys, dequeued_parsed_features +@deprecated(None, 'Use tf.data.') def read_batch_features(file_pattern, batch_size, features, @@ -821,6 +834,7 @@ def read_batch_features(file_pattern, return features +@deprecated(None, 'Use tf.data.') def read_batch_record_features(file_pattern, batch_size, features, diff --git a/tensorflow/contrib/learn/python/learn/learn_io/numpy_io.py b/tensorflow/contrib/learn/python/learn/learn_io/numpy_io.py index 692438807fbd7febb156d4db73b5d3deba6c987d..29552d24f1eaa0d85a99c8b09f69d007e7e4fe9f 100644 --- a/tensorflow/contrib/learn/python/learn/learn_io/numpy_io.py +++ b/tensorflow/contrib/learn/python/learn/learn_io/numpy_io.py @@ -12,15 +12,22 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Methods to allow dict of numpy arrays.""" +"""Methods to allow dict of numpy arrays (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.python.estimator.inputs.numpy_io import numpy_input_fn as core_numpy_input_fn +from tensorflow.python.util.deprecation import deprecated +@deprecated(None, 'Use tf.estimator.inputs.numpy_input_fn.') def numpy_input_fn(x, y=None, batch_size=128, diff --git a/tensorflow/contrib/learn/python/learn/learn_io/pandas_io.py b/tensorflow/contrib/learn/python/learn/learn_io/pandas_io.py index ede7558eafa9237dc63aa95a62e599c5e9755822..b4ef055f5ae484ec704ad42efcf2c00c4a7a4f56 100644 --- a/tensorflow/contrib/learn/python/learn/learn_io/pandas_io.py +++ b/tensorflow/contrib/learn/python/learn/learn_io/pandas_io.py @@ -13,13 +13,19 @@ # limitations under the License. # ============================================================================== -"""Methods to allow pandas.DataFrame.""" +"""Methods to allow pandas.DataFrame (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.python.estimator.inputs.pandas_io import pandas_input_fn as core_pandas_input_fn +from tensorflow.python.util.deprecation import deprecated try: # pylint: disable=g-import-not-at-top @@ -47,6 +53,7 @@ PANDAS_DTYPES = { } +@deprecated(None, 'Please use tf.estimator.inputs.pandas_input_fn') def pandas_input_fn(x, y=None, batch_size=128, @@ -66,6 +73,7 @@ def pandas_input_fn(x, target_column=target_column) +@deprecated(None, 'Please access pandas data directly.') def extract_pandas_data(data): """Extract data from pandas.DataFrame for predictors. @@ -96,6 +104,7 @@ def extract_pandas_data(data): 'float, or bool. Found: ' + ', '.join(error_report)) +@deprecated(None, 'Please access pandas data directly.') def extract_pandas_matrix(data): """Extracts numpy matrix from pandas DataFrame. @@ -111,6 +120,7 @@ def extract_pandas_matrix(data): return data.as_matrix() +@deprecated(None, 'Please access pandas data directly.') def extract_pandas_labels(labels): """Extract data from pandas.DataFrame for labels. diff --git a/tensorflow/contrib/learn/python/learn/learn_runner.py b/tensorflow/contrib/learn/python/learn/learn_runner.py index 2af723a0d64822e81fa0fbeb106ab812de6ab4e8..d719a3e488b9905ef7903e21d90dbaae0449735c 100644 --- a/tensorflow/contrib/learn/python/learn/learn_runner.py +++ b/tensorflow/contrib/learn/python/learn/learn_runner.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Runs an Experiment.""" +"""Runs an Experiment (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -22,6 +27,7 @@ from tensorflow.contrib.learn.python.learn.estimators import run_config as run_c from tensorflow.contrib.learn.python.learn.experiment import Experiment from tensorflow.contrib.training.python.training import hparam as hparam_lib from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.util.deprecation import deprecated # TODO(xiejw): Refactor the learn_runner to make code reusable. @@ -99,6 +105,7 @@ def _wrapped_experiment_fn_with_uid_check(experiment_fn, require_hparams=False): return wrapped_experiment_fn +@deprecated(None, 'Use tf.estimator.train_and_evaluate.') def run(experiment_fn, output_dir=None, schedule=None, run_config=None, hparams=None): """Make and run an experiment. @@ -218,6 +225,7 @@ def run(experiment_fn, output_dir=None, schedule=None, run_config=None, return _execute_schedule(experiment, schedule) +@deprecated(None, 'Use tf.estimator.train_and_evaluate.') def tune(experiment_fn, tuner): """Tune an experiment with hyper-parameters. diff --git a/tensorflow/contrib/learn/python/learn/learn_runner_lib.py b/tensorflow/contrib/learn/python/learn/learn_runner_lib.py index 7d9b1c7716f0ab1f2274ca53406175240b613027..ba2d067787c1dfd4e4820ecc916f1053e9f3cf60 100644 --- a/tensorflow/contrib/learn/python/learn/learn_runner_lib.py +++ b/tensorflow/contrib/learn/python/learn/learn_runner_lib.py @@ -12,7 +12,11 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Utilities to run and tune an Experiment. +"""Utilities to run and tune an Experiment (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. @@run @@tune diff --git a/tensorflow/contrib/learn/python/learn/metric_spec.py b/tensorflow/contrib/learn/python/learn/metric_spec.py index 6440bc204b8e339ff51311dcc87b36f556b94092..97220365d5dddb82b602369f06bea021a86d584f 100644 --- a/tensorflow/contrib/learn/python/learn/metric_spec.py +++ b/tensorflow/contrib/learn/python/learn/metric_spec.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""The metric spec class to flexibly connect models and metrics.""" +"""The metric spec class to flexibly connect models and metrics (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -22,6 +27,7 @@ import six from tensorflow.python.platform import tf_logging as logging from tensorflow.python.util import tf_inspect +from tensorflow.python.util.deprecation import deprecated def _assert_named_args(sentinel): @@ -223,6 +229,10 @@ def _adapt_metric_fn( class MetricSpec(object): """MetricSpec connects a model to metric functions. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + The MetricSpec class contains all information necessary to connect the output of a `model_fn` to the metrics (usually, streaming metrics) that are used in evaluation. @@ -284,6 +294,7 @@ class MetricSpec(object): """ + @deprecated(None, 'Use tf.estimator.EstimatorSpec.eval_metric_ops.') def __init__(self, metric_fn, prediction_key=None, diff --git a/tensorflow/contrib/learn/python/learn/models.py b/tensorflow/contrib/learn/python/learn/models.py index 4283240d018c949bb35aeb12032d2ee8b75884a5..bd4bbf9f8c9ad7e8a0fc06d8c0dc24672536c158 100644 --- a/tensorflow/contrib/learn/python/learn/models.py +++ b/tensorflow/contrib/learn/python/learn/models.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Various high level TF models.""" +"""Various high level TF models (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -28,8 +33,10 @@ from tensorflow.python.ops import array_ops as array_ops_ from tensorflow.python.ops import init_ops from tensorflow.python.ops import variable_scope as vs from tensorflow.python.summary import summary +from tensorflow.python.util.deprecation import deprecated +@deprecated(None, 'Consider using a tf.estimator.LinearRegressor') def linear_regression_zero_init(x, y): """Linear regression subgraph with zero-value initial weights and bias. @@ -43,6 +50,7 @@ def linear_regression_zero_init(x, y): return linear_regression(x, y, init_mean=0.0, init_stddev=0.0) +@deprecated(None, 'Consider using a class from tf.estimator.LinearClassifier') def logistic_regression_zero_init(x, y): """Logistic regression subgraph with zero-value initial weights and bias. @@ -56,6 +64,7 @@ def logistic_regression_zero_init(x, y): return logistic_regression(x, y, init_mean=0.0, init_stddev=0.0) +@deprecated(None, 'Consider using a class from tf.estimator.') def linear_regression(x, y, init_mean=None, init_stddev=1.0): """Creates linear regression TensorFlow subgraph. @@ -107,6 +116,7 @@ def linear_regression(x, y, init_mean=None, init_stddev=1.0): return losses_ops.mean_squared_error_regressor(x, y, weights, bias) +@deprecated(None, 'Consider using a class from tf.estimator.') def logistic_regression(x, y, class_weight=None, @@ -203,6 +213,7 @@ def _reverse_seq(input_seq, lengths): return result +@deprecated(None, 'Please consider `tf.nn.bidirectional_dynamic_rnn`.') def bidirectional_rnn(cell_fw, cell_bw, inputs, @@ -283,6 +294,7 @@ def bidirectional_rnn(cell_fw, # End of TensorFlow 0.7 +@deprecated(None, 'Please consider tensorflow/tensor2tensor.') def get_rnn_model(rnn_size, cell_type, num_layers, input_op_fn, bidirectional, target_predictor_fn, sequence_length, initial_state, attn_length, attn_size, attn_vec_size): diff --git a/tensorflow/contrib/learn/python/learn/monitored_session.py b/tensorflow/contrib/learn/python/learn/monitored_session.py index 22602e9f69d972505d83a66a6f9183b5e4d15c44..ac0433f1775feeed2ec3cf49291da01500bef01b 100644 --- a/tensorflow/contrib/learn/python/learn/monitored_session.py +++ b/tensorflow/contrib/learn/python/learn/monitored_session.py @@ -13,7 +13,11 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""A wrapper of Session API which runs hooks.""" +"""A wrapper of Session API which runs hooks (deprecated). + +These are deprecated aliases for classes and functions in `tf.train`. Please use +those directly. +""" from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/contrib/learn/python/learn/monitors.py b/tensorflow/contrib/learn/python/learn/monitors.py index 9457a73ecfb41782c888e3bba0b140db83d4d464..77f7c73d5412d40b338eaff4cf04d99fd0892723 100644 --- a/tensorflow/contrib/learn/python/learn/monitors.py +++ b/tensorflow/contrib/learn/python/learn/monitors.py @@ -12,7 +12,11 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Monitors instrument the training process. +"""Monitors instrument the training process (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. @@get_default_monitors @@BaseMonitor @@ -59,6 +63,10 @@ from tensorflow.python.util import tf_inspect class BaseMonitor(object): """Base class for Monitors. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Defines basic interfaces of Monitors. Monitors can either be run on all workers or, more commonly, restricted to run exclusively on the elected chief worker. @@ -229,6 +237,10 @@ def _extract_output(outputs, request): class EveryN(BaseMonitor): """Base class for monitors that execute callbacks every N steps. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + This class adds three new callbacks: - every_n_step_begin - every_n_step_end @@ -418,6 +430,10 @@ class StopAtStep(BaseMonitor): class PrintTensor(EveryN): """Prints given tensors every N steps. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + This is an `EveryN` monitor and has consistent semantic for `every_n` and `first_n`. @@ -455,9 +471,12 @@ class PrintTensor(EveryN): class LoggingTrainable(EveryN): """Writes trainable variable values into log every N steps. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Write the tensors in trainable variables `every_n` steps, starting with the `first_n`th step. - """ def __init__(self, scope=None, every_n=100, first_n=1): @@ -493,7 +512,12 @@ class LoggingTrainable(EveryN): class SummarySaver(EveryN): - """Saves summaries every N steps.""" + """Saves summaries every N steps. + + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + """ def __init__(self, summary_op, @@ -554,6 +578,10 @@ class SummarySaver(EveryN): class ValidationMonitor(EveryN): """Runs evaluation of a given estimator, at most every N steps. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Note that the evaluation is done based on the saved checkpoint, which will usually be older than the current step. @@ -756,6 +784,10 @@ class ValidationMonitor(EveryN): class CaptureVariable(EveryN): """Captures a variable's values into a collection. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + This monitor is useful for unit testing. You should exercise caution when using this monitor in production, since it never discards values. @@ -794,6 +826,7 @@ class CaptureVariable(EveryN): self._var_values[step] = _extract_output(outputs, self._var_name) +@deprecation.deprecated(None, "Use tf.train.MonitoredTrainingSession.") def get_default_monitors(loss_op=None, summary_op=None, save_summary_steps=100, @@ -828,6 +861,10 @@ def get_default_monitors(loss_op=None, class GraphDump(BaseMonitor): """Dumps almost all tensors in the graph at every step. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Note, this is very expensive, prefer `PrintTensor` in production. """ @@ -917,7 +954,12 @@ class GraphDump(BaseMonitor): class ExportMonitor(EveryN): - """Monitor that exports Estimator every N steps.""" + """Monitor that exports Estimator every N steps. + + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + """ @deprecation.deprecated("2017-03-25", "ExportMonitor is deprecated. Please pass an " @@ -1040,7 +1082,12 @@ class ExportMonitor(EveryN): class CheckpointSaver(BaseMonitor): - """Saves checkpoints every N steps or N seconds.""" + """Saves checkpoints every N steps or N seconds. + + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + """ def __init__(self, checkpoint_dir, @@ -1125,7 +1172,12 @@ class CheckpointSaver(BaseMonitor): class StepCounter(EveryN): - """Steps per second monitor.""" + """Steps per second monitor. + + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + """ def __init__(self, every_n_steps=100, output_dir=None, summary_writer=None): super(StepCounter, self).__init__(every_n_steps=every_n_steps) @@ -1165,6 +1217,10 @@ class NanLossDuringTrainingError(RuntimeError): class NanLoss(EveryN): """NaN Loss monitor. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Monitors loss and stops training if loss is NaN. Can either fail with exception or just stop training. """ diff --git a/tensorflow/contrib/learn/python/learn/ops/__init__.py b/tensorflow/contrib/learn/python/learn/ops/__init__.py index 33962e34cc685ce2c830a7bbfd1b5c626bcd8b31..efb1f47cf5bb2dcd0fb37b7b85cd8f170d56e4d1 100644 --- a/tensorflow/contrib/learn/python/learn/ops/__init__.py +++ b/tensorflow/contrib/learn/python/learn/ops/__init__.py @@ -13,7 +13,12 @@ # limitations under the License. # ============================================================================== -"""Various TensorFlow Ops.""" +"""Various TensorFlow Ops (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/contrib/learn/python/learn/ops/embeddings_ops.py b/tensorflow/contrib/learn/python/learn/ops/embeddings_ops.py index fa3b7323e343371e986b763d30a8a44620894549..8f9811cf251ae0af1e0055a56e1358c2771b1367 100644 --- a/tensorflow/contrib/learn/python/learn/ops/embeddings_ops.py +++ b/tensorflow/contrib/learn/python/learn/ops/embeddings_ops.py @@ -13,7 +13,11 @@ # limitations under the License. # ============================================================================== -"""TensorFlow Ops to work with embeddings. +"""TensorFlow Ops to work with embeddings (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. Note: categorical variables are handled via embeddings in many cases. For example, in case of words. @@ -57,7 +61,7 @@ def embedding_lookup(params, ids, name='embedding_lookup'): ids = ops.convert_to_tensor(ids) shape = array_ops_.shape(ids) ids_flat = array_ops_.reshape( - ids, math_ops.reduce_prod(shape, keep_dims=True)) + ids, math_ops.reduce_prod(shape, keepdims=True)) embeds_flat = nn.embedding_lookup(params, ids_flat, name) embed_shape = array_ops_.concat([shape, [-1]], 0) embeds = array_ops_.reshape(embeds_flat, embed_shape) diff --git a/tensorflow/contrib/learn/python/learn/ops/losses_ops.py b/tensorflow/contrib/learn/python/learn/ops/losses_ops.py index b040ab3bb6c516158589a8e30d56fff1f7728951..92976d1539c7ddc226b81f903beee82b798ec8db 100644 --- a/tensorflow/contrib/learn/python/learn/ops/losses_ops.py +++ b/tensorflow/contrib/learn/python/learn/ops/losses_ops.py @@ -13,7 +13,12 @@ # limitations under the License. # ============================================================================== -"""TensorFlow Ops for loss computation.""" +"""TensorFlow Ops for loss computation (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/contrib/learn/python/learn/ops/seq2seq_ops.py b/tensorflow/contrib/learn/python/learn/ops/seq2seq_ops.py index 45727faab4362abeab18f77861353eb53976023a..aa37cb4a76e2a6157bf077d327248353bd516472 100644 --- a/tensorflow/contrib/learn/python/learn/ops/seq2seq_ops.py +++ b/tensorflow/contrib/learn/python/learn/ops/seq2seq_ops.py @@ -13,7 +13,12 @@ # limitations under the License. # ============================================================================== -"""TensorFlow Ops for Sequence to Sequence models.""" +"""TensorFlow Ops for Sequence to Sequence models (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -26,8 +31,10 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn from tensorflow.python.ops import variable_scope as vs +from tensorflow.python.util.deprecation import deprecated +@deprecated(None, 'Please use tf.nn/tf.layers directly.') def sequence_classifier(decoding, labels, sampling_decoding=None, name=None): """Returns predictions and loss for sequence of predictions. @@ -57,6 +64,7 @@ def sequence_classifier(decoding, labels, sampling_decoding=None, name=None): return array_ops.stack(predictions, axis=1), loss +@deprecated(None, 'Please use tf.nn/tf.layers directly.') def seq2seq_inputs(x, y, input_length, output_length, sentinel=None, name=None): """Processes inputs for Sequence to Sequence models. @@ -87,6 +95,7 @@ def seq2seq_inputs(x, y, input_length, output_length, sentinel=None, name=None): return in_x, in_y, out_y +@deprecated(None, 'Please use tf.nn/tf.layers directly.') def rnn_decoder(decoder_inputs, initial_state, cell, scope=None): """RNN Decoder that creates training and sampling sub-graphs. @@ -123,6 +132,7 @@ def rnn_decoder(decoder_inputs, initial_state, cell, scope=None): return outputs, states, sampling_outputs, sampling_states +@deprecated(None, 'Please use tf.nn/tf.layers directly.') def rnn_seq2seq(encoder_inputs, decoder_inputs, encoder_cell, diff --git a/tensorflow/contrib/learn/python/learn/preprocessing/__init__.py b/tensorflow/contrib/learn/python/learn/preprocessing/__init__.py index 7bcc177d4ea0ab57f092d68888a72de2b2fd5edc..e8c6e1acf80f0791421bee59aff30e67bccb44b2 100644 --- a/tensorflow/contrib/learn/python/learn/preprocessing/__init__.py +++ b/tensorflow/contrib/learn/python/learn/preprocessing/__init__.py @@ -13,7 +13,12 @@ # limitations under the License. # ============================================================================== -"""Preprocessing tools useful for building models.""" +"""Preprocessing tools useful for building models (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/contrib/learn/python/learn/preprocessing/categorical.py b/tensorflow/contrib/learn/python/learn/preprocessing/categorical.py index 154739d497ec1029026eaca1e93b37cd225f1050..faba3b2025e8abb51d1989c3fafbd5e711d6559b 100644 --- a/tensorflow/contrib/learn/python/learn/preprocessing/categorical.py +++ b/tensorflow/contrib/learn/python/learn/preprocessing/categorical.py @@ -13,7 +13,12 @@ # limitations under the License. # ============================================================================== -"""Implements preprocessing transformers for categorical variables.""" +"""Implements preprocessing transformers for categorical variables (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -22,6 +27,8 @@ from __future__ import print_function import math import numpy as np +from tensorflow.python.util.deprecation import deprecated + # pylint: disable=g-bad-import-order from . import categorical_vocabulary from ..learn_io.data_feeder import setup_processor_data_feeder @@ -31,10 +38,16 @@ from ..learn_io.data_feeder import setup_processor_data_feeder class CategoricalProcessor(object): """Maps documents to sequences of word ids. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + As a common convention, Nan values are handled as unknown tokens. Both float('nan') and np.nan are accepted. """ + @deprecated(None, 'Please use tensorflow/transform or tf.data for sequence ' + 'processing.') def __init__(self, min_frequency=0, share=False, vocabularies=None): """Initializes a CategoricalProcessor instance. diff --git a/tensorflow/contrib/learn/python/learn/preprocessing/categorical_vocabulary.py b/tensorflow/contrib/learn/python/learn/preprocessing/categorical_vocabulary.py index 5709955c49fba50ca4a299a443a2902bbd9c6b23..3ac370a6ab4423846e810900514445ad5269b680 100644 --- a/tensorflow/contrib/learn/python/learn/preprocessing/categorical_vocabulary.py +++ b/tensorflow/contrib/learn/python/learn/preprocessing/categorical_vocabulary.py @@ -13,7 +13,11 @@ # limitations under the License. # ============================================================================== -"""Categorical vocabulary classes to map categories to indexes. +"""Categorical vocabulary classes to map categories to indexes (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. Can be used for categorical variables, sparse variables and words. """ @@ -25,14 +29,21 @@ from __future__ import print_function import collections import six +from tensorflow.python.util.deprecation import deprecated + class CategoricalVocabulary(object): """Categorical variables vocabulary class. + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + Accumulates and provides mapping from classes to indexes. Can be easily used for words. """ + @deprecated(None, 'Please use tensorflow/transform or tf.data.') def __init__(self, unknown_token="", support_reverse=True): self._unknown_token = unknown_token self._mapping = {unknown_token: 0} diff --git a/tensorflow/contrib/learn/python/learn/preprocessing/text.py b/tensorflow/contrib/learn/python/learn/preprocessing/text.py index 3af2074c2a46f0258c04111fff0235ba8309625e..f2b6776be7789a9433bfe41eb9354b74347059ec 100644 --- a/tensorflow/contrib/learn/python/learn/preprocessing/text.py +++ b/tensorflow/contrib/learn/python/learn/preprocessing/text.py @@ -13,7 +13,12 @@ # limitations under the License. # ============================================================================== -"""Implements a number of text preprocessing utilities.""" +"""Implements a number of text preprocessing utilities (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -24,6 +29,7 @@ import numpy as np import six from tensorflow.python.platform import gfile +from tensorflow.python.util.deprecation import deprecated from .categorical_vocabulary import CategoricalVocabulary # pylint: disable=g-bad-import-order @@ -38,6 +44,7 @@ TOKENIZER_RE = re.compile(r"[A-Z]{2,}(?![a-z])|[A-Z][a-z]+(?=[A-Z])|[\'\w\-]+", re.UNICODE) +@deprecated(None, 'Please use tensorflow/transform or tf.data.') def tokenizer(iterator): """Tokenizer generator. @@ -51,9 +58,16 @@ def tokenizer(iterator): yield TOKENIZER_RE.findall(value) +@deprecated(None, 'Please use tensorflow/transform or tf.data.') class ByteProcessor(object): - """Maps documents into sequence of ids for bytes.""" + """Maps documents into sequence of ids for bytes. + + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + """ + @deprecated(None, 'Please use tensorflow/transform or tf.data.') def __init__(self, max_document_length): self.max_document_length = max_document_length @@ -108,8 +122,14 @@ class ByteProcessor(object): class VocabularyProcessor(object): - """Maps documents to sequences of word ids.""" + """Maps documents to sequences of word ids. + + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + """ + @deprecated(None, 'Please use tensorflow/transform or tf.data.') def __init__(self, max_document_length, min_frequency=0, diff --git a/tensorflow/contrib/learn/python/learn/session_run_hook.py b/tensorflow/contrib/learn/python/learn/session_run_hook.py index a8ba2be97206f2b974d256ad2c62c21a4e3e55d8..87edc9b720bdb3edcd5f2dcd1662d14da53c51cf 100644 --- a/tensorflow/contrib/learn/python/learn/session_run_hook.py +++ b/tensorflow/contrib/learn/python/learn/session_run_hook.py @@ -12,7 +12,11 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""This file is deprecated. Use tensorflow.python.training.session_run_hook.""" +"""This file is deprecated. Use `tensorflow.python.training.session_run_hook`. + +See [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/contrib/learn/python/learn/summary_writer_cache.py b/tensorflow/contrib/learn/python/learn/summary_writer_cache.py index 919d415c302b8ec17202aad34ff0bee69bfee2c7..d663cf5fb79c428b0e70d66b0f1305f0559a05c9 100644 --- a/tensorflow/contrib/learn/python/learn/summary_writer_cache.py +++ b/tensorflow/contrib/learn/python/learn/summary_writer_cache.py @@ -12,7 +12,10 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Wrapper for a Session-like object that handles threads and recovery. +"""Wrapper for a Session-like object that handles threads and recovery (deprecated). + +These are deprecated aliases for classes and functions in `tf.train`. Please use +those directly. Based on an original design of Illia Polosukhin. """ diff --git a/tensorflow/contrib/learn/python/learn/trainable.py b/tensorflow/contrib/learn/python/learn/trainable.py index 429b6040be21d8cbe1f2bba58090366552fdfbe7..a1a3f20dcd8cb5ff7baa559ac41d5e5c40780511 100644 --- a/tensorflow/contrib/learn/python/learn/trainable.py +++ b/tensorflow/contrib/learn/python/learn/trainable.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""`Trainable` interface.""" +"""`Trainable` interface (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division @@ -23,6 +28,8 @@ import abc class Trainable(object): """Interface for objects that are trainable by, e.g., `Experiment`. + + THIS CLASS IS DEPRECATED. """ __metaclass__ = abc.ABCMeta diff --git a/tensorflow/contrib/learn/python/learn/utils/__init__.py b/tensorflow/contrib/learn/python/learn/utils/__init__.py index 48978d0ac34cec2b18e6794dcf3b260bc3b683c4..66d8dc6fd43b383919a16515bc96be492a253bf6 100644 --- a/tensorflow/contrib/learn/python/learn/utils/__init__.py +++ b/tensorflow/contrib/learn/python/learn/utils/__init__.py @@ -13,7 +13,12 @@ # limitations under the License. # ============================================================================== -"""TensorFlow Learn Utils.""" +"""TensorFlow Learn Utils (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/contrib/learn/python/learn/utils/export.py b/tensorflow/contrib/learn/python/learn/utils/export.py index cb34cb1d26b6812c7f3f39e9f965615de5a8ef07..3eacac7a3d3dcff4d39025fdee88e16e385b1b84 100644 --- a/tensorflow/contrib/learn/python/learn/utils/export.py +++ b/tensorflow/contrib/learn/python/learn/utils/export.py @@ -13,14 +13,18 @@ # limitations under the License. # ============================================================================== -"""Export utilities.""" +"""Export utilities (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. +""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.contrib.framework import deprecated -from tensorflow.python.training import training_util from tensorflow.contrib.session_bundle import exporter from tensorflow.contrib.session_bundle import gc from tensorflow.python.client import session as tf_session @@ -32,6 +36,7 @@ from tensorflow.python.ops import lookup_ops from tensorflow.python.ops import variables from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import saver as tf_saver +from tensorflow.python.training import training_util @deprecated('2017-03-25', 'Please use Estimator.export_savedmodel() instead.') diff --git a/tensorflow/contrib/learn/python/learn/utils/gc.py b/tensorflow/contrib/learn/python/learn/utils/gc.py index 226915987a4934626066b12810f579ae675107b2..916aecbea88b10bbef316ffb89d4c4d89667cb29 100644 --- a/tensorflow/contrib/learn/python/learn/utils/gc.py +++ b/tensorflow/contrib/learn/python/learn/utils/gc.py @@ -13,7 +13,11 @@ # limitations under the License. # ============================================================================== -r"""System for specifying garbage collection (GC) of path based data. +r"""System for specifying garbage collection (GC) of path based data (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. This framework allows for GC of data specified by path names, for example files on disk. gc.Path objects each represent a single item stored at a path and may @@ -73,10 +77,12 @@ import os from tensorflow.python.platform import gfile from tensorflow.python.util import compat +from tensorflow.python.util.deprecation import deprecated Path = collections.namedtuple('Path', 'path export_version') +@deprecated(None, 'Please implement your own file management or use Saver.') def largest_export_versions(n): """Creates a filter that keeps the largest n export versions. @@ -97,6 +103,7 @@ def largest_export_versions(n): return keep +@deprecated(None, 'Please implement your own file management or use Saver.') def one_of_every_n_export_versions(n): """Creates a filter that keeps one of every n export versions. @@ -128,6 +135,7 @@ def one_of_every_n_export_versions(n): return keep +@deprecated(None, 'Please implement your own file management or use Saver.') def mod_export_version(n): """Creates a filter that keeps every export that is a multiple of n. @@ -146,6 +154,7 @@ def mod_export_version(n): return keep +@deprecated(None, 'Please implement your own file management or use Saver.') def union(lf, rf): """Creates a filter that keeps the union of two filters. @@ -163,6 +172,7 @@ def union(lf, rf): return keep +@deprecated(None, 'Please implement your own file management or use Saver.') def negation(f): """Negate a filter. @@ -179,6 +189,7 @@ def negation(f): return keep +@deprecated(None, 'Please implement your own file name management.') def get_paths(base_dir, parser): """Gets a list of Paths in a given directory. diff --git a/tensorflow/contrib/learn/python/learn/utils/input_fn_utils.py b/tensorflow/contrib/learn/python/learn/utils/input_fn_utils.py index b2521933e524e7ec24d73d4b5171f33e507dd88c..b92eb9fea8b7ccea56c781df74dcfa1cc5508e48 100644 --- a/tensorflow/contrib/learn/python/learn/utils/input_fn_utils.py +++ b/tensorflow/contrib/learn/python/learn/utils/input_fn_utils.py @@ -12,7 +12,11 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Utilities for creating input_fns. +"""Utilities for creating input_fns (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. Contents of this file are moved to tensorflow/python/estimator/export.py. InputFnOps is renamed to ServingInputReceiver. @@ -32,13 +36,17 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import array_ops from tensorflow.python.ops import parsing_ops +from tensorflow.python.util.deprecation import deprecated class InputFnOps(collections.namedtuple('InputFnOps', ['features', 'labels', 'default_inputs'])): - """A return type for an input_fn. + """A return type for an input_fn (deprecated). + + THIS CLASS IS DEPRECATED. Please use tf.estimator.export.ServingInputReceiver + instead. This return type is currently only supported for serving input_fn. Training and eval input_fn should return a `(features, labels)` tuple. @@ -56,6 +64,8 @@ class InputFnOps(collections.namedtuple('InputFnOps', """ +@deprecated(None, 'Please use ' + 'tf.estimator.export.build_parsing_serving_input_receiver_fn.') def build_parsing_serving_input_fn(feature_spec, default_batch_size=None): """Build an input_fn appropriate for serving, expecting fed tf.Examples. @@ -84,6 +94,8 @@ def build_parsing_serving_input_fn(feature_spec, default_batch_size=None): return input_fn +@deprecated(None, 'Please use ' + 'tf.estimator.export.build_raw_serving_input_receiver_fn.') def build_default_serving_input_fn(features, default_batch_size=None): """Build an input_fn appropriate for serving, expecting feature Tensors. diff --git a/tensorflow/contrib/learn/python/learn/utils/inspect_checkpoint.py b/tensorflow/contrib/learn/python/learn/utils/inspect_checkpoint.py index 6a63fb545a56e6040b0b0c3bbb6a17cd96925895..6dbaa15f8391b0044be8e30ca191753beb88db93 100644 --- a/tensorflow/contrib/learn/python/learn/utils/inspect_checkpoint.py +++ b/tensorflow/contrib/learn/python/learn/utils/inspect_checkpoint.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""A simple script for inspect checkpoint files.""" +"""A simple script for inspect checkpoint files (deprecated).""" from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils.py b/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils.py index 1593380007b2799fb1d17e92408ab19a7b47fe1e..c7cdb4131215c388412407a008113de13bdd0934 100644 --- a/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils.py +++ b/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils.py @@ -12,7 +12,11 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Utilities supporting export to SavedModel. +"""Utilities supporting export to SavedModel (deprecated). + +This module and all its submodules are deprecated. See +[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) +for migration instructions. Some contents of this file are moved to tensorflow/python/estimator/export.py: @@ -52,8 +56,9 @@ from tensorflow.python.saved_model import signature_constants from tensorflow.python.saved_model import signature_def_utils from tensorflow.python.summary import summary_iterator from tensorflow.python.training import saver - from tensorflow.python.util import compat +from tensorflow.python.util.deprecation import deprecated + # A key for use in the input_alternatives dict indicating the default input. # This is the input that will be expected when a serving request does not @@ -77,6 +82,7 @@ FEATURES_INPUT_ALTERNATIVE_KEY = 'features_input_alternative' _FALLBACK_DEFAULT_OUTPUT_ALTERNATIVE_KEY = 'default_output_alternative' +@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.') def build_standardized_signature_def(input_tensors, output_tensors, problem_type): """Build a SignatureDef using problem type and input and output Tensors. @@ -156,6 +162,7 @@ def _is_regression_problem(problem_type, input_tensors, output_tensors): len(input_tensors) == 1 and len(output_tensors) == 1) +@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.') def get_input_alternatives(input_ops): """Obtain all input alternatives using the input_fn output and heuristics.""" input_alternatives = {} @@ -181,6 +188,7 @@ def get_input_alternatives(input_ops): return input_alternatives, features +@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.') def get_output_alternatives(model_fn_ops, default_output_alternative_key=None): """Obtain all output alternatives using the model_fn output and heuristics. @@ -246,6 +254,7 @@ def get_output_alternatives(model_fn_ops, default_output_alternative_key=None): sorted(output_alternatives.keys()))) +@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.') def build_all_signature_defs(input_alternatives, output_alternatives, actual_default_output_alternative_key): """Build `SignatureDef`s from all pairs of input and output alternatives.""" @@ -279,6 +288,7 @@ def build_all_signature_defs(input_alternatives, output_alternatives, MAX_DIRECTORY_CREATION_ATTEMPTS = 10 +@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.') def get_timestamped_export_dir(export_dir_base): """Builds a path to a new subdirectory within the base directory. @@ -317,6 +327,7 @@ def get_timestamped_export_dir(export_dir_base): '{} attempts.'.format(MAX_DIRECTORY_CREATION_ATTEMPTS)) +@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.') def get_temp_export_dir(timestamped_export_dir): """Builds a directory name based on the argument but starting with 'temp-'. @@ -344,6 +355,7 @@ def _export_version_parser(path): return path._replace(export_version=int(filename)) +@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.') def get_most_recent_export(export_dir_base): """Locate the most recent SavedModel export in a directory of many exports. @@ -363,6 +375,7 @@ def get_most_recent_export(export_dir_base): return next(iter(results or []), None) +@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.') def garbage_collect_exports(export_dir_base, exports_to_keep): """Deletes older exports, retaining only a given number of the most recent. @@ -387,6 +400,7 @@ def garbage_collect_exports(export_dir_base, exports_to_keep): logging.warn('Can not delete %s recursively: %s', p.path, e) +@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.') def make_export_strategy(serving_input_fn, default_output_alternative_key=None, assets_extra=None, @@ -400,7 +414,7 @@ def make_export_strategy(serving_input_fn, `InputFnOps`. default_output_alternative_key: the name of the head to serve when an incoming serving request does not explicitly request a specific head. - Must be `None` if the estimator inherits from ${tf.estimator.Estimator} + Must be `None` if the estimator inherits from @{tf.estimator.Estimator} or for single-headed models. assets_extra: A dict specifying how to populate the assets.extra directory within the exported SavedModel. Each key should give the destination @@ -438,7 +452,7 @@ def make_export_strategy(serving_input_fn, The string path to the exported directory. Raises: - ValueError: If `estimator` is a ${tf.estimator.Estimator} instance + ValueError: If `estimator` is a @{tf.estimator.Estimator} instance and `default_output_alternative_key` was specified. """ if isinstance(estimator, core_estimator.Estimator): @@ -469,6 +483,8 @@ def make_export_strategy(serving_input_fn, return export_strategy.ExportStrategy('Servo', export_fn, strip_default_attrs) +@deprecated(None, + 'Use tf.estimator.export.build_parsing_serving_input_receiver_fn') def make_parsing_export_strategy(feature_columns, default_output_alternative_key=None, assets_extra=None, @@ -487,7 +503,7 @@ def make_parsing_export_strategy(feature_columns, that must be provided at serving time (excluding labels!). default_output_alternative_key: the name of the head to serve when an incoming serving request does not explicitly request a specific head. - Must be `None` if the estimator inherits from ${tf.estimator.Estimator} + Must be `None` if the estimator inherits from @{tf.estimator.Estimator} or for single-headed models. assets_extra: A dict specifying how to populate the assets.extra directory within the exported SavedModel. Each key should give the destination @@ -555,8 +571,14 @@ def _default_compare_fn(curr_best_eval_result, cand_eval_result): class BestModelSelector(object): - """A helper that keeps track of export selection candidates.""" + """A helper that keeps track of export selection candidates. + + THIS CLASS IS DEPRECATED. See + [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) + for general migration instructions. + """ + @deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.') def __init__(self, event_file_pattern=None, compare_fn=None): """Constructor of this class. @@ -622,6 +644,7 @@ class BestModelSelector(object): return best_eval_result +@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.') def make_best_model_export_strategy( serving_input_fn, exports_to_keep=1, @@ -707,6 +730,7 @@ def make_best_model_export_strategy( # TODO(b/67013778): Revisit this approach when corresponding changes to # TF Core are finalized. +@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.') def extend_export_strategy(base_export_strategy, post_export_fn, post_export_name=None): @@ -741,7 +765,7 @@ def extend_export_strategy(base_export_strategy, The string path to the SavedModel indicated by post_export_fn. Raises: - ValueError: If `estimator` is a ${tf.estimator.Estimator} instance + ValueError: If `estimator` is a @{tf.estimator.Estimator} instance and `default_output_alternative_key` was specified or if post_export_fn does not return a valid directory. RuntimeError: If unable to create temporary or final export directory. diff --git a/tensorflow/contrib/linalg/BUILD b/tensorflow/contrib/linalg/BUILD index 208e7bc69be76680868c766bc99429eea5870c80..d4f2e7063184d962f4654cf8df4ab966c1941139 100644 --- a/tensorflow/contrib/linalg/BUILD +++ b/tensorflow/contrib/linalg/BUILD @@ -43,6 +43,23 @@ cuda_py_test( ], ) +cuda_py_test( + name = "linear_operator_block_diag_test", + size = "medium", + srcs = ["python/kernel_tests/linear_operator_block_diag_test.py"], + additional_deps = [ + ":linalg_py", + "//third_party/py/numpy", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework", + "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:framework_test_lib", + "//tensorflow/python:math_ops", + "//tensorflow/python:platform_test", + ], +) + filegroup( name = "all_files", srcs = glob( diff --git a/tensorflow/contrib/linalg/__init__.py b/tensorflow/contrib/linalg/__init__.py index 4720692c3384ba1bede1f486c1b1e0e69d10a63a..14cc3b2b4971de1a31960ee33c2f304154b1f411 100644 --- a/tensorflow/contrib/linalg/__init__.py +++ b/tensorflow/contrib/linalg/__init__.py @@ -17,6 +17,7 @@ See the @{$python/contrib.linalg} guide. @@LinearOperator +@@LinearOperatorBlockDiag @@LinearOperatorDiag @@LinearOperatorIdentity @@LinearOperatorScaledIdentity @@ -34,6 +35,7 @@ from __future__ import print_function # pylint: disable=unused-import,wildcard-import,line-too-long,g-importing-member from tensorflow.contrib.linalg.python.ops.linear_operator_addition import * +from tensorflow.contrib.linalg.python.ops.linear_operator_block_diag import * from tensorflow.python.ops.linalg.linear_operator import * from tensorflow.python.ops.linalg.linear_operator_composition import * from tensorflow.python.ops.linalg.linear_operator_diag import * @@ -45,4 +47,5 @@ from tensorflow.python.ops.linalg.linear_operator_lower_triangular import * # pylint: enable=unused-import,wildcard-import,line-too-long,g-importing-member from tensorflow.python.util.all_util import remove_undocumented + remove_undocumented(__name__) diff --git a/tensorflow/contrib/linalg/python/kernel_tests/linear_operator_block_diag_test.py b/tensorflow/contrib/linalg/python/kernel_tests/linear_operator_block_diag_test.py new file mode 100644 index 0000000000000000000000000000000000000000..cc1a047d6a2b6029080fad3f240aa00f50504f07 --- /dev/null +++ b/tensorflow/contrib/linalg/python/kernel_tests/linear_operator_block_diag_test.py @@ -0,0 +1,253 @@ +# 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. +# ============================================================================== + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.linalg.python.ops import linear_operator_block_diag as block_diag +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import random_seed +from tensorflow.python.ops import array_ops +from tensorflow.python.ops.linalg import linalg as linalg_lib +from tensorflow.python.ops.linalg import linear_operator_test_util +from tensorflow.python.ops.linalg import linear_operator_util +from tensorflow.python.platform import test + +linalg = linalg_lib +random_seed.set_random_seed(23) +rng = np.random.RandomState(0) + + +def _block_diag_dense(expected_shape, blocks): + """Convert a list of blocks, into a dense block diagonal matrix.""" + rows = [] + num_cols = 0 + for block in blocks: + # Get the batch shape for the block. + batch_row_shape = array_ops.shape(block)[:-1] + + zeros_to_pad_before_shape = array_ops.concat( + [batch_row_shape, [num_cols]], axis=-1) + zeros_to_pad_before = array_ops.zeros( + shape=zeros_to_pad_before_shape, dtype=block.dtype) + num_cols += array_ops.shape(block)[-1] + zeros_to_pad_after_shape = array_ops.concat( + [batch_row_shape, [expected_shape[-2] - num_cols]], axis=-1) + zeros_to_pad_after = array_ops.zeros( + zeros_to_pad_after_shape, dtype=block.dtype) + + rows.append(array_ops.concat( + [zeros_to_pad_before, block, zeros_to_pad_after], axis=-1)) + + return array_ops.concat(rows, axis=-2) + + +class SquareLinearOperatorBlockDiagTest( + linear_operator_test_util.SquareLinearOperatorDerivedClassTest): + """Most tests done in the base class LinearOperatorDerivedClassTest.""" + + def setUp(self): + # Increase from 1e-6 to 1e-4 + self._atol[dtypes.float32] = 1e-4 + self._atol[dtypes.complex64] = 1e-4 + self._rtol[dtypes.float32] = 1e-4 + self._rtol[dtypes.complex64] = 1e-4 + + @property + def _operator_build_infos(self): + build_info = linear_operator_test_util.OperatorBuildInfo + return [ + build_info((0, 0)), + build_info((1, 1)), + build_info((1, 3, 3)), + build_info((5, 5), blocks=[(2, 2), (3, 3)]), + ] + + def _operator_and_mat_and_feed_dict(self, build_info, dtype, use_placeholder): + shape = list(build_info.shape) + expected_blocks = ( + build_info.__dict__["blocks"] if "blocks" in build_info.__dict__ + else [shape]) + matrices = [ + linear_operator_test_util.random_positive_definite_matrix( + block_shape, dtype, force_well_conditioned=True) + for block_shape in expected_blocks + ] + + if use_placeholder: + matrices_ph = [ + array_ops.placeholder(dtype=dtype) for _ in expected_blocks + ] + # Evaluate here because (i) you cannot feed a tensor, and (ii) + # values are random and we want the same value used for both mat and + # feed_dict. + matrices = self.evaluate(matrices) + operator = block_diag.LinearOperatorBlockDiag( + [linalg.LinearOperatorFullMatrix( + m_ph, is_square=True) for m_ph in matrices_ph], + is_square=True) + feed_dict = {m_ph: m for (m_ph, m) in zip(matrices_ph, matrices)} + else: + operator = block_diag.LinearOperatorBlockDiag( + [linalg.LinearOperatorFullMatrix( + m, is_square=True) for m in matrices]) + feed_dict = None + # Should be auto-set. + self.assertTrue(operator.is_square) + + # Broadcast the shapes. + expected_shape = list(build_info.shape) + + matrices = linear_operator_util.broadcast_matrix_batch_dims(matrices) + + block_diag_dense = _block_diag_dense(expected_shape, matrices) + + if not use_placeholder: + block_diag_dense.set_shape( + expected_shape[:-2] + [expected_shape[-1], expected_shape[-1]]) + + return operator, block_diag_dense, feed_dict + + def test_is_x_flags(self): + # Matrix with two positive eigenvalues, 1, and 1. + # The matrix values do not effect auto-setting of the flags. + matrix = [[1., 0.], [1., 1.]] + operator = block_diag.LinearOperatorBlockDiag( + [linalg.LinearOperatorFullMatrix(matrix)], + is_positive_definite=True, + is_non_singular=True, + is_self_adjoint=False) + self.assertTrue(operator.is_positive_definite) + self.assertTrue(operator.is_non_singular) + self.assertFalse(operator.is_self_adjoint) + + def test_is_non_singular_auto_set(self): + # Matrix with two positive eigenvalues, 11 and 8. + # The matrix values do not effect auto-setting of the flags. + matrix = [[11., 0.], [1., 8.]] + operator_1 = linalg.LinearOperatorFullMatrix(matrix, is_non_singular=True) + operator_2 = linalg.LinearOperatorFullMatrix(matrix, is_non_singular=True) + + operator = block_diag.LinearOperatorBlockDiag( + [operator_1, operator_2], + is_positive_definite=False, # No reason it HAS to be False... + is_non_singular=None) + self.assertFalse(operator.is_positive_definite) + self.assertTrue(operator.is_non_singular) + + with self.assertRaisesRegexp(ValueError, "always non-singular"): + block_diag.LinearOperatorBlockDiag( + [operator_1, operator_2], is_non_singular=False) + + def test_name(self): + matrix = [[11., 0.], [1., 8.]] + operator_1 = linalg.LinearOperatorFullMatrix(matrix, name="left") + operator_2 = linalg.LinearOperatorFullMatrix(matrix, name="right") + + operator = block_diag.LinearOperatorBlockDiag([operator_1, operator_2]) + + self.assertEqual("left_ds_right", operator.name) + + def test_different_dtypes_raises(self): + operators = [ + linalg.LinearOperatorFullMatrix(rng.rand(2, 3, 3)), + linalg.LinearOperatorFullMatrix(rng.rand(2, 3, 3).astype(np.float32)) + ] + with self.assertRaisesRegexp(TypeError, "same dtype"): + block_diag.LinearOperatorBlockDiag(operators) + + def test_non_square_operator_raises(self): + operators = [ + linalg.LinearOperatorFullMatrix(rng.rand(3, 4), is_square=False), + linalg.LinearOperatorFullMatrix(rng.rand(3, 3)) + ] + with self.assertRaisesRegexp(ValueError, "square matrices"): + block_diag.LinearOperatorBlockDiag(operators) + + def test_empty_operators_raises(self): + with self.assertRaisesRegexp(ValueError, "non-empty"): + block_diag.LinearOperatorBlockDiag([]) + + +# This test is for blocks with different batch dimensions. +# LinearOperatorFullMatrix doesn't broadcast matmul/solve. +class SquareDiagLinearOperatorBlockDiagTest( + linear_operator_test_util.SquareLinearOperatorDerivedClassTest): + """Most tests done in the base class LinearOperatorDerivedClassTest.""" + + def setUp(self): + # Increase from 1e-6 to 1e-4 + self._atol[dtypes.float32] = 1e-4 + self._atol[dtypes.complex64] = 1e-4 + self._rtol[dtypes.float32] = 1e-4 + self._rtol[dtypes.complex64] = 1e-4 + + @property + def _operator_build_infos(self): + build_info = linear_operator_test_util.OperatorBuildInfo + return [ + build_info((3, 7, 7), blocks=[(1, 2, 2), (3, 2, 2), (1, 3, 3)]), + build_info((2, 1, 6, 6), blocks=[(2, 1, 2, 2), (1, 1, 4, 4)]), + ] + + def _operator_and_mat_and_feed_dict(self, build_info, dtype, use_placeholder): + shape = list(build_info.shape) + expected_blocks = ( + build_info.__dict__["blocks"] if "blocks" in build_info.__dict__ + else [shape]) + diag_matrices = [ + linear_operator_test_util.random_uniform( + shape=block_shape[:-1], minval=1., maxval=20., dtype=dtype) + for block_shape in expected_blocks + ] + + if use_placeholder: + diag_matrices_ph = [ + array_ops.placeholder(dtype=dtype) for _ in expected_blocks + ] + diag_matrices = self.evaluate(diag_matrices) + # Evaluate here because (i) you cannot feed a tensor, and (ii) + # values are random and we want the same value used for both mat and + # feed_dict. + operator = block_diag.LinearOperatorBlockDiag( + [linalg.LinearOperatorDiag(m_ph) for m_ph in diag_matrices_ph]) + feed_dict = {m_ph: m for (m_ph, m) in zip( + diag_matrices_ph, diag_matrices)} + else: + operator = block_diag.LinearOperatorBlockDiag( + [linalg.LinearOperatorDiag(m) for m in diag_matrices]) + feed_dict = None + # Should be auto-set. + self.assertTrue(operator.is_square) + + # Broadcast the shapes. + expected_shape = list(build_info.shape) + + matrices = linear_operator_util.broadcast_matrix_batch_dims( + [array_ops.matrix_diag(diag_block) for diag_block in diag_matrices]) + + block_diag_dense = _block_diag_dense(expected_shape, matrices) + if not use_placeholder: + block_diag_dense.set_shape( + expected_shape[:-2] + [expected_shape[-1], expected_shape[-1]]) + + return operator, block_diag_dense, feed_dict + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/linalg/python/ops/linear_operator_block_diag.py b/tensorflow/contrib/linalg/python/ops/linear_operator_block_diag.py new file mode 100644 index 0000000000000000000000000000000000000000..5d7a99664d38eca035bd5a86710050bce4b22c1e --- /dev/null +++ b/tensorflow/contrib/linalg/python/ops/linear_operator_block_diag.py @@ -0,0 +1,358 @@ +# 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. +# ============================================================================== +"""Create a Block Diagonal operator from one or more `LinearOperators`.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.framework import common_shapes +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.framework import tensor_shape +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import check_ops +from tensorflow.python.ops.linalg import linear_operator +from tensorflow.python.ops.linalg import linear_operator_util + + +class LinearOperatorBlockDiag(linear_operator.LinearOperator): + """Combines one or more `LinearOperators` in to a Block Diagonal matrix. + + This operator combines one or more linear operators `[op1,...,opJ]`, + building a new `LinearOperator`, whose underlying matrix representation is + square and has each operator `opi` on the main diagonal, and zero's elsewhere. + + #### Shape compatibility + + If `opj` acts like a [batch] square matrix `Aj`, then `op_combined` acts like + the [batch] square matrix formed by having each matrix `Aj` on the main + diagonal. + + + Each `opj` is required to represent a square matrix, and hence will have + shape `batch_shape_j + [M_j, M_j]`. + + If `opj` has shape `batch_shape_j + [M_j, M_j]`, then the combined operator + has shape `broadcast_batch_shape + [sum M_j, sum M_j]`, where + `broadcast_batch_shape` is the mutual broadcast of `batch_shape_j`, + `j = 1,...,J`, assuming the intermediate batch shapes broadcast. + Even if the combined shape is well defined, the combined operator's + methods may fail due to lack of broadcasting ability in the defining + operators' methods. + + ```python + # Create a 4 x 4 linear operator combined of two 2 x 2 operators. + operator_1 = LinearOperatorFullMatrix([[1., 2.], [3., 4.]]) + operator_2 = LinearOperatorFullMatrix([[1., 0.], [0., 1.]]) + operator = LinearOperatorBlockDiag([operator_1, operator_2]) + + operator.to_dense() + ==> [[1., 2., 0., 0.], + [3., 4., 0., 0.], + [0., 0., 1., 0.], + [0., 0., 0., 1.]] + + operator.shape + ==> [4, 4] + + operator.log_abs_determinant() + ==> scalar Tensor + + x1 = ... # Shape [2, 2] Tensor + x2 = ... # Shape [2, 2] Tensor + x = tf.concat([x1, x2], 0) # Shape [2, 4] Tensor + operator.matmul(x) + ==> tf.concat([operator_1.matmul(x1), operator_2.matmul(x2)]) + + # Create a [2, 3] batch of 4 x 4 linear operators. + matrix_44 = tf.random_normal(shape=[2, 3, 4, 4]) + operator_44 = LinearOperatorFullMatrix(matrix) + + # Create a [1, 3] batch of 5 x 5 linear operators. + matrix_55 = tf.random_normal(shape=[1, 3, 5, 5]) + operator_55 = LinearOperatorFullMatrix(matrix_55) + + # Combine to create a [2, 3] batch of 9 x 9 operators. + operator_99 = LinearOperatorBlockDiag([operator_44, operator_55]) + + # Create a shape [2, 3, 9] vector. + x = tf.random_normal(shape=[2, 3, 9]) + operator_99.matmul(x) + ==> Shape [2, 3, 9] Tensor + ``` + + #### Performance + + The performance of `LinearOperatorBlockDiag` on any operation is equal to + the sum of the individual operators' operations. + + + #### Matrix property hints + + This `LinearOperator` is initialized with boolean flags of the form `is_X`, + for `X = non_singular, self_adjoint, positive_definite, square`. + These have the following meaning: + + * If `is_X == True`, callers should expect the operator to have the + property `X`. This is a promise that should be fulfilled, but is *not* a + runtime assert. For example, finite floating point precision may result + in these promises being violated. + * If `is_X == False`, callers should expect the operator to not have `X`. + * If `is_X == None` (the default), callers should have no expectation either + way. + """ + + def __init__(self, + operators, + is_non_singular=None, + is_self_adjoint=None, + is_positive_definite=None, + is_square=True, + name=None): + r"""Initialize a `LinearOperatorBlockDiag`. + + `LinearOperatorBlockDiag` is initialized with a list of operators + `[op_1,...,op_J]`. + + Args: + operators: Iterable of `LinearOperator` objects, each with + the same `dtype` and composable shape. + is_non_singular: Expect that this operator is non-singular. + is_self_adjoint: Expect that this operator is equal to its hermitian + transpose. + is_positive_definite: Expect that this operator is positive definite, + meaning the quadratic form `x^H A x` has positive real part for all + nonzero `x`. Note that we do not require the operator to be + self-adjoint to be positive-definite. See: + https://en.wikipedia.org/wiki/Positive-definite_matrix\ + #Extension_for_non_symmetric_matrices + is_square: Expect that this operator acts like square [batch] matrices. + This is true by default, and will raise a `ValueError` otherwise. + name: A name for this `LinearOperator`. Default is the individual + operators names joined with `_o_`. + + Raises: + TypeError: If all operators do not have the same `dtype`. + ValueError: If `operators` is empty or are non-square. + """ + # Validate operators. + check_ops.assert_proper_iterable(operators) + operators = list(operators) + if not operators: + raise ValueError( + "Expected a non-empty list of operators. Found: %s" % operators) + self._operators = operators + + # Validate dtype. + dtype = operators[0].dtype + for operator in operators: + if operator.dtype != dtype: + name_type = (str((o.name, o.dtype)) for o in operators) + raise TypeError( + "Expected all operators to have the same dtype. Found %s" + % " ".join(name_type)) + + # Auto-set and check hints. + if all(operator.is_non_singular for operator in operators): + if is_non_singular is False: + raise ValueError( + "The direct sum of non-singular operators is always non-singular.") + is_non_singular = True + + if all(operator.is_self_adjoint for operator in operators): + if is_self_adjoint is False: + raise ValueError( + "The direct sum of self-adjoint operators is always self-adjoint.") + is_self_adjoint = True + + if all(operator.is_positive_definite for operator in operators): + if is_positive_definite is False: + raise ValueError( + "The direct sum of positive definite operators is always " + "positive definite.") + is_positive_definite = True + + if not (is_square and all(operator.is_square for operator in operators)): + raise ValueError( + "Can only represent a block diagonal of square matrices.") + + # Initialization. + graph_parents = [] + for operator in operators: + graph_parents.extend(operator.graph_parents) + + if name is None: + # Using ds to mean direct sum. + name = "_ds_".join(operator.name for operator in operators) + with ops.name_scope(name, values=graph_parents): + super(LinearOperatorBlockDiag, self).__init__( + dtype=dtype, + graph_parents=graph_parents, + is_non_singular=is_non_singular, + is_self_adjoint=is_self_adjoint, + is_positive_definite=is_positive_definite, + is_square=True, + name=name) + + @property + def operators(self): + return self._operators + + def _shape(self): + # Get final matrix shape. + domain_dimension = self.operators[0].domain_dimension + range_dimension = self.operators[0].range_dimension + for operator in self.operators[1:]: + domain_dimension += operator.domain_dimension + range_dimension += operator.range_dimension + + matrix_shape = tensor_shape.TensorShape([domain_dimension, range_dimension]) + + # Get broadcast batch shape. + # broadcast_shape checks for compatibility. + batch_shape = self.operators[0].batch_shape + for operator in self.operators[1:]: + batch_shape = common_shapes.broadcast_shape( + batch_shape, operator.batch_shape) + + return batch_shape.concatenate(matrix_shape) + + def _shape_tensor(self): + # Avoid messy broadcasting if possible. + if self.shape.is_fully_defined(): + return ops.convert_to_tensor( + self.shape.as_list(), dtype=dtypes.int32, name="shape") + + domain_dimension = self.operators[0].domain_dimension_tensor() + range_dimension = self.operators[0].range_dimension_tensor() + for operator in self.operators[1:]: + domain_dimension += operator.domain_dimension_tensor() + range_dimension += operator.range_dimension_tensor() + + matrix_shape = array_ops.stack([domain_dimension, range_dimension]) + + # Dummy Tensor of zeros. Will never be materialized. + zeros = array_ops.zeros(shape=self.operators[0].batch_shape_tensor()) + for operator in self.operators[1:]: + zeros += array_ops.zeros(shape=operator.batch_shape_tensor()) + batch_shape = array_ops.shape(zeros) + + return array_ops.concat((batch_shape, matrix_shape), 0) + + def _matmul(self, x, adjoint=False, adjoint_arg=False): + split_dim = -1 if adjoint_arg else -2 + # Split input by rows normally, and otherwise columns. + split_x = self._split_input_into_blocks(x, axis=split_dim) + + result_list = [] + for index, operator in enumerate(self.operators): + result_list += [operator.matmul( + split_x[index], adjoint=adjoint, adjoint_arg=adjoint_arg)] + result_list = linear_operator_util.broadcast_matrix_batch_dims( + result_list) + return array_ops.concat(result_list, axis=-2) + + def _determinant(self): + result = self.operators[0].determinant() + for operator in self.operators[1:]: + result *= operator.determinant() + return result + + def _log_abs_determinant(self): + result = self.operators[0].log_abs_determinant() + for operator in self.operators[1:]: + result += operator.log_abs_determinant() + return result + + def _solve(self, rhs, adjoint=False, adjoint_arg=False): + split_dim = -1 if adjoint_arg else -2 + # Split input by rows normally, and otherwise columns. + split_rhs = self._split_input_into_blocks(rhs, axis=split_dim) + + solution_list = [] + for index, operator in enumerate(self.operators): + solution_list += [operator.solve( + split_rhs[index], adjoint=adjoint, adjoint_arg=adjoint_arg)] + + solution_list = linear_operator_util.broadcast_matrix_batch_dims( + solution_list) + return array_ops.concat(solution_list, axis=-2) + + def _diag_part(self): + diag_list = [] + for operator in self.operators: + # Extend the axis for broadcasting. + diag_list += [operator.diag_part()[..., array_ops.newaxis]] + diag_list = linear_operator_util.broadcast_matrix_batch_dims(diag_list) + diagonal = array_ops.concat(diag_list, axis=-2) + return array_ops.squeeze(diagonal, axis=-1) + + def _trace(self): + result = self.operators[0].trace() + for operator in self.operators[1:]: + result += operator.trace() + return result + + def _to_dense(self): + num_cols = 0 + rows = [] + broadcasted_blocks = [operator.to_dense() for operator in self.operators] + broadcasted_blocks = linear_operator_util.broadcast_matrix_batch_dims( + broadcasted_blocks) + for block in broadcasted_blocks: + batch_row_shape = array_ops.shape(block)[:-1] + + zeros_to_pad_before_shape = array_ops.concat( + [batch_row_shape, [num_cols]], axis=-1) + zeros_to_pad_before = array_ops.zeros( + shape=zeros_to_pad_before_shape, dtype=block.dtype) + num_cols += array_ops.shape(block)[-1] + zeros_to_pad_after_shape = array_ops.concat( + [batch_row_shape, + [self.domain_dimension_tensor() - num_cols]], axis=-1) + zeros_to_pad_after = array_ops.zeros( + shape=zeros_to_pad_after_shape, dtype=block.dtype) + + rows.append(array_ops.concat( + [zeros_to_pad_before, block, zeros_to_pad_after], axis=-1)) + + mat = array_ops.concat(rows, axis=-2) + mat.set_shape(self.shape) + return mat + + def _split_input_into_blocks(self, x, axis=-1): + """Split `x` into blocks matching `operators`'s `domain_dimension`. + + Specifically, if we have a block diagonal matrix, with block sizes + `[M_j, M_j] j = 1..J`, this method splits `x` on `axis` into `J` + tensors, whose shape at `axis` is `M_j`. + + Args: + x: `Tensor`. `x` is split into `J` tensors. + axis: Python `Integer` representing the axis to split `x` on. + + Returns: + A list of `Tensor`s. + """ + block_sizes = [] + if self.shape.is_fully_defined(): + for operator in self.operators: + block_sizes += [operator.domain_dimension.value] + else: + for operator in self.operators: + block_sizes += [operator.domain_dimension_tensor()] + + return array_ops.split(x, block_sizes, axis=axis) 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 70f777f08bd5b8157e601f19019075d3e7543811..cfe62fac43b35d863eb559b95057ae62a41bed49 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 @@ -270,14 +270,14 @@ class SdcaWithLogisticLossTest(SdcaModelTest): train_op = lr.minimize() - def Minimize(): + def minimize(): with self._single_threaded_test_session(): for _ in range(_MAX_ITERATIONS): - train_op.run() + train_op.run() # pylint: disable=cell-var-from-loop threads = [] for _ in range(num_loss_partitions): - threads.append(threading.Thread(target=Minimize)) + threads.append(threading.Thread(target=minimize)) threads[-1].start() for t in threads: @@ -395,7 +395,7 @@ class SdcaWithLogisticLossTest(SdcaModelTest): predicted_labels = get_binary_predictions_for_logistic(predictions) self.assertAllClose([0, 1, 1, 1], predicted_labels.eval()) self.assertAllClose( - 0.01, lr.approximate_duality_gap().eval(), rtol=1e-2, atol=1e-2) + 0.0, lr.approximate_duality_gap().eval(), rtol=1e-2, atol=1e-2) def testFractionalExampleLabel(self): # Setup test data with 1 positive, and 1 mostly-negative example. @@ -407,7 +407,7 @@ class SdcaWithLogisticLossTest(SdcaModelTest): make_example_proto({ 'age': [1], 'gender': [1] - }, 1), + }, 0.9), ] example_weights = [1.0, 1.0] for num_shards in _SHARD_NUMBERS: diff --git a/tensorflow/contrib/lite/BUILD b/tensorflow/contrib/lite/BUILD index 44c4a7e2ca8d019ca602c7f2b492cd1e70b17561..5cfbb544b73991195a7bba9528ee9550104f3d78 100644 --- a/tensorflow/contrib/lite/BUILD +++ b/tensorflow/contrib/lite/BUILD @@ -132,6 +132,7 @@ cc_library( ":memory_planner", ":schema_fbs_version", ":simple_memory_arena", + ":util", "//tensorflow/contrib/lite/kernels:gemm_support", "//tensorflow/contrib/lite/nnapi:nnapi_lib", "//tensorflow/contrib/lite/schema:schema_fbs", @@ -232,6 +233,27 @@ cc_test( ], ) +cc_library( + name = "util", + srcs = ["util.cc"], + hdrs = ["util.h"], + deps = [ + ":context", + ], +) + +cc_test( + name = "util_test", + size = "small", + srcs = ["util_test.cc"], + deps = [ + ":context", + ":util", + "//tensorflow/contrib/lite/testing:util", + "@com_google_googletest//:gtest", + ], +) + # Test the serialization of a model with optional tensors. # Model tests diff --git a/tensorflow/contrib/lite/Makefile b/tensorflow/contrib/lite/Makefile index 7f316292724ea0baaf034d4e914773ad97a957d4..b4504f246a0f806d35d8c3d659717a86d2f2a4f5 100644 --- a/tensorflow/contrib/lite/Makefile +++ b/tensorflow/contrib/lite/Makefile @@ -27,10 +27,10 @@ LIBDIR := $(MAKEFILE_DIR)/gen/lib/ GENDIR := $(MAKEFILE_DIR)/gen/obj/ # Settings for the host compiler. -CXX := $(CC_PREFIX) gcc +CXX := $(CC_PREFIX)gcc CXXFLAGS := --std=c++11 -O3 -DNDEBUG -CC := $(CC_PREFIX) gcc -CFLAGS := +CC := $(CC_PREFIX)gcc +CFLAGS := -O3 -DNDEBUG LDOPTS := LDOPTS += -L/usr/local/lib ARFLAGS := -r @@ -57,10 +57,11 @@ LIBS := \ # If we're on Linux, also link in the dl library. ifeq ($(HOST_OS),LINUX) - LIBS += -ldl -lpthread + LIBS += -ldl endif include $(MAKEFILE_DIR)/ios_makefile.inc +include $(MAKEFILE_DIR)/rpi_makefile.inc # This library is the main target for this makefile. It will contain a minimal # runtime that can be linked in to other programs. diff --git a/tensorflow/contrib/lite/README.md b/tensorflow/contrib/lite/README.md index 00e93d2c4f3ab27057b855fba6fccf2ec8d7a1c1..5194f015b5b84189e3a8caf5fb0bc0204deb7bb2 100644 --- a/tensorflow/contrib/lite/README.md +++ b/tensorflow/contrib/lite/README.md @@ -91,7 +91,7 @@ Currently, we only support building the Android demo app within a Python 2 environment (due to a Bazel bug). ### More about the demo -The demo is resizing each camera image frame to (224 width * 224 height) to match the quantized Mobilenet model being used (229 * 229 for Inception-v3). The resized image is converted into a ByteBuffer row by row of size 1 * 224 * 224 * 3 bytes, where 1 is the number of images in a batch. 224 * 224 (299 * 299) is the width and height of the image. 3 bytes represents three colors of a pixel. This demo uses the TensorFlow Lite Java inference API for models which take a single input and provide a single output. This outputs a two-dimensional array, with the first dimension being the category index and the second dimension being the confidence of classification. Both models have 1001 unique categories and the app sorts the probabilities of all the categories and displays the top three. The model file must be downloaded and bundled within the assets directory of the app. +The demo is resizing each camera image frame to (224 width * 224 height) to match the quantized Mobilenet model being used (299 * 299 for Inception-v3). The resized image is converted into a ByteBuffer row by row of size 1 * 224 * 224 * 3 bytes, where 1 is the number of images in a batch. 224 * 224 (299 * 299) is the width and height of the image. 3 bytes represents three colors of a pixel. This demo uses the TensorFlow Lite Java inference API for models which take a single input and provide a single output. This outputs a two-dimensional array, with the first dimension being the category index and the second dimension being the confidence of classification. Both models have 1001 unique categories and the app sorts the probabilities of all the categories and displays the top three. The model file must be downloaded and bundled within the assets directory of the app. # iOS Demo App @@ -99,7 +99,7 @@ Similar to the Android demo app, there's an iOS camera app that uses exactly the This demo app requires a camera so it doesn't work with simulators. It need to be executed on a real iOS device. Follow the instructions to build and run the demo app: -1. Run `third_party/tensorflow/contrib/lite/examples/ios/download_models.sh` to download the model files used by the demo app. +1. Run `tensorflow/contrib/lite/examples/ios/download_models.sh` to download the model files used by the demo app. 1. Install [CocoaPods](https://cocoapods.org/) if it wasn't installed yet: `sudo gem install cocoapods`. 1. Run `pod install` in `tensorflow/contrib/lite/examples/ios/camera` to generate the workspace file. 1. Open the project by running `open tflite_camera_example.xcworkspace`, and build the app in XCode. diff --git a/tensorflow/contrib/lite/arena_planner.h b/tensorflow/contrib/lite/arena_planner.h index 58bc164619c2c053b9492e9a0e5de2da30e199af..f84b3dad9550e789237c8e45971002c7d336b9d3 100644 --- a/tensorflow/contrib/lite/arena_planner.h +++ b/tensorflow/contrib/lite/arena_planner.h @@ -33,7 +33,7 @@ class AllocationInfo; // each tensor needs to be allocated and deallocated, and preallocates all the // necessary memory (the PlanAllocations phase). It then assigns portions of // this memory buffer to each tensor (the ExecuteAllocations phase). Tensors may -// share some of the bufer if a tensor B is to be allocated after another tensor +// share some of the buffer if a tensor B is to be allocated after another tensor // A has been deallocated. // // If dynamic tensors are used the planning steps can be repeated during model diff --git a/tensorflow/contrib/lite/build_def.bzl b/tensorflow/contrib/lite/build_def.bzl index 19829e4991651111e13fc1805f97daef8bc016a7..2813d1c347163e67c70983d3dd49773f4a4b4544 100644 --- a/tensorflow/contrib/lite/build_def.bzl +++ b/tensorflow/contrib/lite/build_def.bzl @@ -104,7 +104,7 @@ def tflite_jni_binary(name, """Builds a jni binary for TFLite.""" linkopts = linkopts + [ "-Wl,--version-script", # Export only jni functions & classes. - linkscript, + "$(location {})".format(linkscript), ] native.cc_binary( name=name, diff --git a/tensorflow/tools/integration_tests/gcs_smoke_test/setup.sh b/tensorflow/contrib/lite/build_rpi_lib.sh similarity index 69% rename from tensorflow/tools/integration_tests/gcs_smoke_test/setup.sh rename to tensorflow/contrib/lite/build_rpi_lib.sh index 6553ba5e3093c26d3c95f40216cd3922a1fb9e4e..3824b16412ed26a6cab79df3242da6017c3322b0 100755 --- a/tensorflow/tools/integration_tests/gcs_smoke_test/setup.sh +++ b/tensorflow/contrib/lite/build_rpi_lib.sh @@ -1,5 +1,5 @@ -#!/bin/bash -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +#!/bin/bash -x +# 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. @@ -13,8 +13,10 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -GCS_NUMBER=$(cat /dev/urandom | tr -dc 'A-F0-9' | fold -w 8 | head -n 1) -GCS_PATH="$1"/"$GCS_NUMBER".tfrecord -echo "gcs_path=$GCS_PATH" > "$_SETUP_OUTPUT" -touch "$_SETUP_DONE" +set -e + +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +cd "$SCRIPT_DIR/../../.." + +CC_PREFIX=arm-linux-gnueabihf- make -j 3 -f tensorflow/contrib/lite/Makefile TARGET=RPI TARGET_ARCH=armv7 diff --git a/tensorflow/contrib/lite/builtin_ops.h b/tensorflow/contrib/lite/builtin_ops.h index 88cdf1d46312f1e610825f23f3d8d357b0762bac..ea3ae3489ecf07b22a02829c5235ad59264496af 100644 --- a/tensorflow/contrib/lite/builtin_ops.h +++ b/tensorflow/contrib/lite/builtin_ops.h @@ -24,7 +24,7 @@ extern "C" { #endif // __cplusplus // The enum for builtin operators. -// Note: CUSTOM and DELEGATE are 2 special ops which are not real biultin +// Note: CUSTOM and DELEGATE are 2 special ops which are not real builtin // ops. typedef enum { kTfLiteBuiltinAdd = 0, @@ -32,6 +32,7 @@ typedef enum { kTfLiteBuiltinConcatenation = 2, kTfLiteBuiltinConv2d = 3, kTfLiteBuiltinDepthwiseConv2d = 4, + kTfLiteBuiltinDequantize = 6, kTfLiteBuiltinEmbeddingLookup = 7, kTfLiteBuiltinFullyConnected = 9, kTfLiteBuiltinHashtableLookup = 10, @@ -77,6 +78,7 @@ typedef enum { kTfLiteBuiltinLogSoftmax = 50, kTfLiteBuiltinDelegate = 51, kTfLiteBuiltinBidirectionalSequenceLstm = 52, + kTfLiteBuiltinCast = 53, } TfLiteBuiltinOperator; #ifdef __cplusplus diff --git a/tensorflow/contrib/lite/context.c b/tensorflow/contrib/lite/context.c index c09e838c5c2e50e0f4a38eaf66e55246fd9a6f7f..5c6f5e72a47180cd98be46f60cfa8eaf28197806 100644 --- a/tensorflow/contrib/lite/context.c +++ b/tensorflow/contrib/lite/context.c @@ -17,9 +17,14 @@ limitations under the License. #include #include +int TfLiteIntArrayGetSizeInBytes(int size) { + static TfLiteIntArray dummy; + return sizeof(dummy) + sizeof(dummy.data[0]) * size; +} + TfLiteIntArray* TfLiteIntArrayCreate(int size) { TfLiteIntArray* ret = - (TfLiteIntArray*)malloc(sizeof(*ret) + sizeof(ret->data[0]) * size); + (TfLiteIntArray*)malloc(TfLiteIntArrayGetSizeInBytes(size)); ret->size = size; return ret; } @@ -55,12 +60,16 @@ TfLiteIntArray* TfLiteIntArrayCopy(TfLiteIntArray* src) { void TfLiteIntArrayFree(TfLiteIntArray* a) { free(a); } -void TfLiteTensorFree(TfLiteTensor* t) { +void TfLiteTensorDataFree(TfLiteTensor* t) { if (t->allocation_type == kTfLiteDynamic && t->data.raw) { free(t->data.raw); } - if (t->dims) TfLiteIntArrayFree(t->dims); t->data.raw = NULL; +} + +void TfLiteTensorFree(TfLiteTensor* t) { + TfLiteTensorDataFree(t); + if (t->dims) TfLiteIntArrayFree(t->dims); t->dims = NULL; } diff --git a/tensorflow/contrib/lite/context.h b/tensorflow/contrib/lite/context.h index ed7f4515fa4437d61a37be93616c28a046295c5a..45184b05ecefb504c75815ae900f3b605359a443 100644 --- a/tensorflow/contrib/lite/context.h +++ b/tensorflow/contrib/lite/context.h @@ -29,6 +29,7 @@ limitations under the License. #ifndef TENSORFLOW_CONTRIB_LITE_CONTEXT_H_ #define TENSORFLOW_CONTRIB_LITE_CONTEXT_H_ +#include #include #include @@ -40,6 +41,7 @@ typedef enum { kTfLiteOk = 0, kTfLiteError = 1 } TfLiteStatus; // Forward declare so GetNode can use this is in Context. typedef struct _TfLiteRegistration TfLiteRegistration; +typedef struct _TfLiteDelegate TfLiteDelegate; #define kOptionalTensor (-1) @@ -57,6 +59,10 @@ typedef struct { #endif } TfLiteIntArray; +// Given the size (number of elements) in a TfLiteIntArray, calculate its size +// in bytes. +int TfLiteIntArrayGetSizeInBytes(int size); + // Create a array of a given `size` (uninitialized entries). // This returns a pointer, that you must free using TfLiteIntArrayFree(). TfLiteIntArray* TfLiteIntArrayCreate(int size); @@ -162,6 +168,11 @@ typedef enum { kTfLiteDynamic, } TfLiteAllocationType; +// The delegates should use zero or positive integers to represent handles. +// -1 is reserved from unallocated status. +typedef int TfLiteBufferHandle; +const TfLiteBufferHandle kTfLiteNullBufferHandle = -1; + // An tensor in the interpreter system which is a wrapper around a buffer of // data including a dimensionality (or NULL if not currently defined). typedef struct { @@ -194,8 +205,27 @@ typedef struct { // Null-terminated name of this tensor. const char* name; + + // The delegate which knows how to handle `buffer_handle`. + // WARNING: This is an experimental interface that is subject to change. + TfLiteDelegate* delegate; + + // An integer buffer handle that can be handled by `delegate`. + // The value is valid only when delegate is not null. + // WARNING: This is an experimental interface that is subject to change. + TfLiteBufferHandle buffer_handle; + + // If the delegate uses its own buffer (e.g. GPU memory), the delegate is + // responsible to set data_is_stale to true. + // `delegate->CopyFromBufferHandle` can be called to copy the data from + // delegate buffer. + // WARNING: This is an // experimental interface that is subject to change. + bool data_is_stale; } TfLiteTensor; +// Free data memory of tensor `t`; +void TfLiteTensorDataFree(TfLiteTensor* t); + // Free memory of tensor `t`; void TfLiteTensorFree(TfLiteTensor* t); @@ -234,6 +264,11 @@ typedef struct { // WARNING: This is an experimental interface that is subject to change. const void* custom_initial_data; int custom_initial_data_size; + + // The pointer to the delegate. This is non-null only when the node is + // created by calling `interpreter.ModifyGraphWithDelegate`. + // WARNING: This is an experimental interface that is subject to change. + TfLiteDelegate* delegate; } TfLiteNode; typedef struct TfLiteContext { @@ -287,11 +322,16 @@ typedef struct TfLiteContext { // does not take ownership of `nodes_to_replace`. TfLiteStatus (*ReplaceSubgraphsWithDelegateKernels)( struct TfLiteContext*, TfLiteRegistration registration, - const TfLiteIntArray* nodes_to_replace); + const TfLiteIntArray* nodes_to_replace, TfLiteDelegate* delegate); + + // Number of threads that are recommended to subsystems like gemmlowp and + // eigen. + int recommended_num_threads; // TODO(ahentz): we should create a more general mechanism for this sort of // library-global objects. void* gemm_context; + void* eigen_context; } TfLiteContext; typedef struct _TfLiteRegistration { @@ -338,19 +378,47 @@ typedef struct _TfLiteRegistration { } TfLiteRegistration; // WARNING: This is an experimental interface that is subject to change. -typedef struct { +typedef struct _TfLiteDelegate { // Data that delegate needs to identify itself. This data is owned by the // delegate. The delegate is owned in the user code, so the delegate is // responsible for doing this when it is destroyed. void* data_; + // Invoked by ModifyGraphWithDelegate. This prepare is called, giving the // delegate a view of the current graph through TfLiteContext*. It typically // will look at the nodes and call ReplaceSubgraphsWithDelegateKernels() // to ask the TensorFlow lite runtime to create macro-nodes to represent // delegated subgraphs of the original graph. - TfLiteStatus (*Prepare)(TfLiteContext* context, void* data); + TfLiteStatus (*Prepare)(TfLiteContext* context, TfLiteDelegate* delegate); + + // Copy the data from delegate buffer handle to raw memory. + // This can be null if the delegate doesn't use its own buffer. + TfLiteStatus (*CopyFromBufferHandle)(TfLiteDelegate* delegate, + TfLiteBufferHandle buffer_handle, + void* data, int size); + + // Copy the data from raw memory to delegate buffer handle. + // This can be null if the delegate doesn't use its own buffer. + TfLiteStatus (*CopyToBufferHandle)(TfLiteDelegate* delegate, + TfLiteBufferHandle buffer_handle, + void* data, int size); + + // Free the Delegate Buffer Handle. Note: This only frees the handle, but + // this doesn't release the underlying resource (e.g. textures). The + // resources are either owned by application layer or the delegate. + // This can be null if the delegate doesn't use its own buffer. + void (*FreeBufferHandle)(TfLiteDelegate* delegate, + TfLiteBufferHandle* handle); } TfLiteDelegate; +// WARNING: This is an experimental interface that is subject to change. +typedef struct { + TfLiteDelegate* delegate; + TfLiteIntArray* nodes_to_replace; + TfLiteIntArray* input_tensors; + TfLiteIntArray* output_tensors; +} TfLiteDelegateParams; + #ifdef __cplusplus } // extern "C" #endif // __cplusplus diff --git a/tensorflow/contrib/lite/error_reporter.h b/tensorflow/contrib/lite/error_reporter.h index da193d2586e9123341b9a41be049ee2a4382017a..3c5f805f12f6a1fb7185c140604f692ac282a143 100644 --- a/tensorflow/contrib/lite/error_reporter.h +++ b/tensorflow/contrib/lite/error_reporter.h @@ -30,7 +30,7 @@ namespace tflite { // va_list args; // foo.Report("test %d", args); // where args is va_list // -// Sublclass ErrorReporter to provide another reporting destination. +// Subclass ErrorReporter to provide another reporting destination. // For example, if you have a GUI program, you might redirect to a buffer // that drives a GUI error log box. class ErrorReporter { diff --git a/tensorflow/contrib/lite/g3doc/ios.md b/tensorflow/contrib/lite/g3doc/ios.md index a359b8d4b481dbc15cc86db14eabda5433722b8b..e0358a444d6dffc377bf13ee72ba5477359d6e07 100644 --- a/tensorflow/contrib/lite/g3doc/ios.md +++ b/tensorflow/contrib/lite/g3doc/ios.md @@ -22,6 +22,15 @@ Then install brew install automake brew install libtool ``` +If you get an error where either automake or libtool install but do not link correctly, you'll first need to: +```bash +sudo chown -R $(whoami) /usr/local/* +``` +Then follow the instructions to perform the linking: +```bash +brew link automake +brew link libtool +``` Then you need to run a shell script to download the dependencies you need: diff --git a/tensorflow/contrib/lite/g3doc/rpi.md b/tensorflow/contrib/lite/g3doc/rpi.md new file mode 100644 index 0000000000000000000000000000000000000000..7a3a231626d0e1c71e474ff4ff16789ebe2901db --- /dev/null +++ b/tensorflow/contrib/lite/g3doc/rpi.md @@ -0,0 +1,50 @@ +# TensorFlow Lite for Raspberry Pi + +## Cross compiling +### Installing toolchian +This has been tested on Ubuntu 16.04.3 64bit and Tensorflow devel docker image [tensorflow/tensorflow:nightly-devel](https://hub.docker.com/r/tensorflow/tensorflow/tags/). + +To cross compiling TensorFlow Lite. First you should install the toolchain and libs. +```bash +sudo apt-get update +sudo apt-get install crossbuild-essential-armhf +``` +> If you are using docker, you may not use `sudo` + +### Building +Clone this Tensorflow repository, Run this script at the root of the repository to download all the dependencies: +> The Tensorflow repository is in `/tensorflow` if you are using `tensorflow/tensorflow:nightly-devel` docker image, just try it. +```bash +./tensorflow/contrib/lite/download_dependencies.sh +``` +Note than you only need to to this once. + +You should then be able to compile: +```bash +./tensorflow/contrib/lite/build_rpi_lib.sh +``` + +This should compile a static library in: +`tensorflow/contrib/lite/gen/lib/rpi_armv7/libtensorflow-lite.a`. + +## Native compiling +This has been tested on Raspberry Pi 3b, Raspbian GNU/Linux 9.1 (stretch), gcc version 6.3.0 20170516 (Raspbian 6.3.0-18+rpi1). + +Log in to you RPI, install the toolchain. +```bash +sudo apt-get instal build-essential +``` + +First, clone this TensorFlow repository. Run this at the root of the repository: +```bash +./tensorflow/contrib/lite/download_dependencies.sh +``` +Note than you only need to to this once. + +You should then be able to compile: +```bash +./tensorflow/contrib/lite/build_rpi_lib.sh +``` + +This should compile a static library in: +`tensorflow/contrib/lite/gen/lib/rpi_armv7/libtensorflow-lite.a`. diff --git a/tensorflow/contrib/lite/interpreter.cc b/tensorflow/contrib/lite/interpreter.cc index 370e4955271679975072d458e0ad9837a69d9556..cee57bba5ec8a35b0935c171774eb2ba62946afe 100644 --- a/tensorflow/contrib/lite/interpreter.cc +++ b/tensorflow/contrib/lite/interpreter.cc @@ -26,15 +26,30 @@ limitations under the License. #include "tensorflow/contrib/lite/memory_planner.h" #include "tensorflow/contrib/lite/nnapi_delegate.h" #include "tensorflow/contrib/lite/schema/schema_generated.h" +#include "tensorflow/contrib/lite/util.h" + +namespace tflite { namespace { -// std::vector preallocation tuning. -constexpr const int kSlotsToReserve = 128; +// Stub method which returns kTfLiteError when the function is forbidden. +// We're registrating this function to several different function to save +// compiled binary size. Please note the restrictions: +// * The type of first parameter have to be `TfLiteContext*`. +// * All paramteters must be trivailly destructible. (E.g. No C++ class) +TfLiteStatus ForbiddenContextFunction(TfLiteContext* context, ...) { + context->ReportError(context, + "The function is forbidden if not calling in delegate."); + return kTfLiteError; +} -} // namespace +// Set the ForbiddenContextFunction to a compatible function pointer. +template +void SetForbiddenContextFunction(FunctionType* func) { + *func = reinterpret_cast(ForbiddenContextFunction); +} -namespace tflite { +} // namespace // A trivial implementation of GraphInfo around the Interpreter. // NOTE: this interpreter info represents the subset of the @@ -77,16 +92,18 @@ Interpreter::Interpreter(ErrorReporter* error_reporter) context_.AddTensors = AddTensors; context_.tensors = nullptr; context_.tensors_size = 0; + context_.eigen_context = nullptr; context_.gemm_context = nullptr; + context_.recommended_num_threads = -1; // Invalid to call these these except from TfLiteDelegate - context_.GetNodeAndRegistration = nullptr; - context_.ReplaceSubgraphsWithDelegateKernels = nullptr; - context_.GetExecutionPlan = nullptr; + SetForbiddenContextFunction(&context_.GetNodeAndRegistration); + SetForbiddenContextFunction(&context_.ReplaceSubgraphsWithDelegateKernels); + SetForbiddenContextFunction(&context_.GetExecutionPlan); // Reserve some space for the tensors to avoid excessive resizing. - tensors_.reserve(kSlotsToReserve); - nodes_and_registration_.reserve(kSlotsToReserve); + tensors_.reserve(kTensorsReservedCapacity); + nodes_and_registration_.reserve(kTensorsReservedCapacity); next_execution_plan_index_to_prepare_ = 0; UseNNAPI(false); } @@ -103,19 +120,99 @@ Interpreter::~Interpreter() { } for (int i = 0; i < context_.tensors_size; i++) { - TfLiteTensorFree(&context_.tensors[i]); + TfLiteTensor* tensor = &context_.tensors[i]; + if (tensor->buffer_handle != kTfLiteNullBufferHandle) { + tensor->delegate->FreeBufferHandle(tensor->delegate, + &tensor->buffer_handle); + } + TfLiteTensorFree(tensor); } } TfLiteStatus Interpreter::ReplaceSubgraphsWithDelegateKernels( TfLiteContext* context, TfLiteRegistration registration, - const TfLiteIntArray* nodes_to_replace) { + const TfLiteIntArray* nodes_to_replace, TfLiteDelegate* delegate) { return static_cast(context->impl_) - ->ReplaceSubgraphsWithDelegateKernels(registration, nodes_to_replace); + ->ReplaceSubgraphsWithDelegateKernels(registration, nodes_to_replace, + delegate); +} + +namespace { + +// Copy a std::vector to an existing TfLiteIntArray. +// This is a low-level data manipulation function, and it's caller's +// responsibility to ensure TfLiteIntArray has enough size. +void CopyVectorToTfLiteIntArray(const std::vector& vec, + TfLiteIntArray* arr) { + arr->size = vec.size(); + memcpy(arr->data, vec.data(), sizeof(int) * arr->size); +} + +// This function allocates a continuous memory space that contains a +// TfLiteDelegateParams followed by a several TfLiteIntArray. +// When calling `free` at TfLiteDelegateParams*, all the allocated space +// will be freed together. +// +// +-----------------------------------+ +// | TfLiteDelegateParams | +// | TfLiteDelegate* delegate; | +// | TfLiteIntArray* nodes_to_replace; |--\ +// | TfLiteIntArray* input_tensors; |--+--\ +// | TfLiteIntArray* output_tensors; |--+--+--\ +// +-----------------------------------+ | | | +// | TfLiteIntArray (variable size) |<-/ | | +// +-----------------------------------+ | | +// | TfLiteIntArray (variable size) |<----/ | +// +-----------------------------------+ | +// | TfLiteIntArray (variable size) |<-------/ +// +-----------------------------------+ +TfLiteDelegateParams* CreateDelegateParams(TfLiteDelegate* delegate, + const Subgraph& subgraph) { + // Step 1: Calculate the allocation size. + int allocation_size = sizeof(TfLiteDelegateParams); + + int nodes_to_replace_size = + TfLiteIntArrayGetSizeInBytes(subgraph.nodes.size()); + allocation_size += nodes_to_replace_size; + + int input_tensors_size = + TfLiteIntArrayGetSizeInBytes(subgraph.input_tensors.size()); + allocation_size += input_tensors_size; + + int output_tensors_size = + TfLiteIntArrayGetSizeInBytes(subgraph.output_tensors.size()); + allocation_size += output_tensors_size; + + // Step 2: Allocate the memory. + // Use `char*` for conveniently step through the allocated space by bytes. + char* allocation = reinterpret_cast(malloc(allocation_size)); + + // Step 3: Fill all data structures structures. + TfLiteDelegateParams* params = + reinterpret_cast(allocation); + params->delegate = delegate; + allocation += sizeof(TfLiteDelegateParams); + + params->nodes_to_replace = reinterpret_cast(allocation); + CopyVectorToTfLiteIntArray(subgraph.nodes, params->nodes_to_replace); + allocation += nodes_to_replace_size; + + params->input_tensors = reinterpret_cast(allocation); + CopyVectorToTfLiteIntArray(subgraph.input_tensors, params->input_tensors); + allocation += input_tensors_size; + + params->output_tensors = reinterpret_cast(allocation); + CopyVectorToTfLiteIntArray(subgraph.output_tensors, params->output_tensors); + allocation += output_tensors_size; + + return params; } +} // namespace + TfLiteStatus Interpreter::ReplaceSubgraphsWithDelegateKernels( - TfLiteRegistration registration, const TfLiteIntArray* nodes_to_replace) { + TfLiteRegistration registration, const TfLiteIntArray* nodes_to_replace, + TfLiteDelegate* delegate) { // Annotate the registration as DELEGATE op. registration.builtin_code = BuiltinOperator_DELEGATE; @@ -127,30 +224,37 @@ TfLiteStatus Interpreter::ReplaceSubgraphsWithDelegateKernels( execution_plan_.clear(); for (auto& subgraph : subgraphs) { - // Turn subgraph.nodes into a TfLiteIntArray compatible data structure. - // TODO(aselle): Avoid this copy by constructing subgraph.nodes that way - // in the first place - subgraph.nodes.insert(subgraph.nodes.begin(), - static_cast(subgraph.nodes.size())); // Subgraphs calimed by the delegate should have a "macro" op created, the // other subgraphs (kTfNonPartition) just have their nodes added back to // the execution plan. switch (subgraph.type) { case Subgraph::kTfNonPartition: - for (auto it = subgraph.nodes.begin() + 1; it != subgraph.nodes.end(); + for (auto it = subgraph.nodes.begin(); it != subgraph.nodes.end(); ++it) { execution_plan_.push_back(*it); } break; case Subgraph::kTfPartition: { - void* builtin_data = nullptr; int node_index; - // Create a node that represents computation of this subgraph. - AddNodeWithParameters( - subgraph.input_tensors, subgraph.output_tensors, - reinterpret_cast(subgraph.nodes.data()), - subgraph.nodes.size() * sizeof(subgraph.nodes[0]), builtin_data, - ®istration, &node_index); + + TfLiteDelegateParams* params = CreateDelegateParams(delegate, subgraph); + AddNodeWithParameters(subgraph.input_tensors, subgraph.output_tensors, + nullptr, 0, params, ®istration, &node_index); + + // Initialize the output tensors's delegate-related fields. + for (int tensor_index : subgraph.output_tensors) { + TfLiteTensor* tensor = &tensors_[tensor_index]; + TF_LITE_ENSURE_EQ(&context_, tensor->delegate, nullptr); + TF_LITE_ENSURE_EQ(&context_, tensor->buffer_handle, + kTfLiteNullBufferHandle); + // buffer_handle will be filled in delegate's `Prepare` + // function. + tensor->delegate = delegate; + } + + // Associate the node with the delegate. + TfLiteNode* node = &nodes_and_registration_[node_index].first; + node->delegate = delegate; } break; case Subgraph::kTfUnexplored: return kTfLiteError; @@ -169,8 +273,8 @@ TfLiteStatus Interpreter::GetExecutionPlan(TfLiteIntArray** execution_plan) { *execution_plan = plan_cache_.get(); static_assert(sizeof(plan_cache_->data[0]) == sizeof(execution_plan_[0]), "TfLiteIntArray and execution_plan do not contain same type."); - memcpy(plan_cache_->data, execution_plan_.data(), - sizeof(plan_cache_->data[0]) * execution_plan_.size()); + std::memcpy(plan_cache_->data, execution_plan_.data(), + sizeof(plan_cache_->data[0]) * execution_plan_.size()); return kTfLiteOk; } @@ -240,14 +344,6 @@ TfLiteStatus Interpreter::BytesRequired(TfLiteType type, const int* dims, return kTfLiteOk; } -namespace { -TfLiteIntArray* convertVectorToTfLiteIntArray(const std::vector& x) { - TfLiteIntArray* lite = TfLiteIntArrayCreate(x.size()); - for (size_t i = 0; i < x.size(); i++) lite->data[i] = x[i]; - return lite; -} -} // namespace - TfLiteStatus Interpreter::AllocateTensors() { next_execution_plan_index_to_prepare_ = 0; if (memory_planner_) { @@ -282,7 +378,6 @@ TfLiteStatus Interpreter::AddNodeWithParameters( int new_node_index = nodes_and_registration_.size(); if (node_index) *node_index = new_node_index; nodes_and_registration_.resize(nodes_and_registration_.size() + 1); - auto& node_and_reg = nodes_and_registration_.back(); TfLiteNode& node = node_and_reg.first; if (node.inputs) TfLiteIntArrayFree(node.inputs); @@ -292,8 +387,8 @@ TfLiteStatus Interpreter::AddNodeWithParameters( // NOTE, here we are not using move semantics yet, since our internal // representation isn't std::vector, but in the future we would like to avoid // copies, so we want the interface to take r-value references now. - node.inputs = convertVectorToTfLiteIntArray(inputs); - node.outputs = convertVectorToTfLiteIntArray(outputs); + node.inputs = ConvertVectorToTfLiteIntArray(inputs); + node.outputs = ConvertVectorToTfLiteIntArray(outputs); node.temporaries = TfLiteIntArrayCreate(0); if (init_data) { node.user_data = OpInit(*registration, init_data, init_data_size); @@ -306,6 +401,7 @@ TfLiteStatus Interpreter::AddNodeWithParameters( node.builtin_data = builtin_data_deleter.release(); // TODO(ycling): Filling `custom_initial_data` and `custom_initial_data_size` // properly for nodes generated by ReplaceSubgraphsWithDelegateKernels. + if (registration->builtin_code == BuiltinOperator_CUSTOM) { // When it's a CUSTOM op, the `custom_options` field in the Flatbuffer // `Operator` table is passed in. @@ -316,6 +412,7 @@ TfLiteStatus Interpreter::AddNodeWithParameters( node.custom_initial_data_size = 0; } + node.delegate = nullptr; node_and_reg.second = *registration; execution_plan_.push_back(new_node_index); return kTfLiteOk; @@ -329,7 +426,7 @@ TfLiteStatus Interpreter::ResizeInputTensor(int tensor_index, TF_LITE_ENSURE(&context_, tensor_index < context_.tensors_size && tensor_index >= 0); invokable_ = false; - TfLiteIntArray* dims_lite = convertVectorToTfLiteIntArray(dims); + TfLiteIntArray* dims_lite = ConvertVectorToTfLiteIntArray(dims); return ResizeTensorImpl(&context_.tensors[tensor_index], dims_lite); } @@ -353,6 +450,7 @@ TfLiteStatus Interpreter::PrepareOpsStartingAt( TfLiteNode& node = nodes_and_registration_[node_index].first; const TfLiteRegistration& registration = nodes_and_registration_[node_index].second; + EnsureTensorsVectorCapacity(); if (OpPrepare(registration, &node) == kTfLiteError) { return kTfLiteError; } @@ -430,10 +528,29 @@ TfLiteStatus Interpreter::Invoke() { TfLiteNode& node = nodes_and_registration_[node_index].first; const TfLiteRegistration& registration = nodes_and_registration_[node_index].second; + + // TODO(ycling): This is an extra loop through inputs to check if the data + // need to be copied from Delegate buffer to raw memory, which is often not + // needed. We may want to cache this in prepare to know if this needs to be + // done for a node or not. + for (int i = 0; i < node.inputs->size; ++i) { + int tensor_index = node.inputs->data[i]; + if (tensor_index == kOptionalTensor) { + continue; + } + TfLiteTensor* tensor = &tensors_[tensor_index]; + if (tensor->delegate && tensor->delegate != node.delegate && + tensor->data_is_stale) { + EnsureTensorDataIsReadable(tensor_index); + } + } + + EnsureTensorsVectorCapacity(); if (OpInvoke(registration, &node) == kTfLiteError) { status = kTfLiteError; } } + return status; } @@ -469,6 +586,7 @@ TfLiteStatus Interpreter::AddTensors(int tensors_to_add, tensors_.resize(tensors_.size() + tensors_to_add); for (int i = base_index; i < tensors_.size(); i++) { memset(&tensors_[i], 0, sizeof(tensors_[i])); + tensors_[i].buffer_handle = kTfLiteNullBufferHandle; } context_.tensors = tensors_.data(); context_.tensors_size = tensors_.size(); @@ -501,9 +619,9 @@ TfLiteStatus Interpreter::GetNodeAndRegistration( } TfLiteStatus Interpreter::SetTensorParametersReadOnly( - int tensor_index, TfLiteType type, const char* name, - const std::vector& dims, TfLiteQuantizationParams quantization, - const char* buffer, size_t bytes, const Allocation* allocation) { + int tensor_index, TfLiteType type, const char* name, const int rank, + const int* dims, TfLiteQuantizationParams quantization, const char* buffer, + size_t bytes, const Allocation* allocation) { TF_LITE_ENSURE(&context_, tensor_index < context_.tensors_size && tensor_index >= 0); // For most tensors we know exactly how much memory is necessary so we can @@ -511,14 +629,27 @@ TfLiteStatus Interpreter::SetTensorParametersReadOnly( // because their sizes change with the contents of the individual strings. if (type != kTfLiteString) { size_t required_bytes; - TF_LITE_ENSURE_OK(&context_, BytesRequired(type, dims.data(), dims.size(), - &required_bytes)); + TF_LITE_ENSURE_OK(&context_, + BytesRequired(type, dims, rank, &required_bytes)); TF_LITE_ENSURE_EQ(&context_, required_bytes, bytes); } - invokable_ = false; - TfLiteTensorReset(type, name, convertVectorToTfLiteIntArray(dims), - quantization, const_cast(buffer), bytes, - kTfLiteMmapRo, allocation, &context_.tensors[tensor_index]); + + TfLiteTensor& tensor = context_.tensors[tensor_index]; + if (type == tensor.type && + EqualArrayAndTfLiteIntArray(tensor.dims, rank, dims)) { + // Fast path which does not invalidate the invokable property. + TfLiteTensorDataFree(&tensor); + tensor.data.raw = const_cast(buffer); + if (!tensor.dims) tensor.dims = ConvertArrayToTfLiteIntArray(rank, dims); + tensor.params = quantization; + tensor.allocation_type = kTfLiteMmapRo; + tensor.allocation = allocation; + } else { + invokable_ = false; + TfLiteTensorReset(type, name, ConvertArrayToTfLiteIntArray(rank, dims), + quantization, const_cast(buffer), bytes, + kTfLiteMmapRo, allocation, &tensor); + } return kTfLiteOk; } @@ -527,8 +658,8 @@ TfLiteStatus Interpreter::SetTensorParametersReadOnly( // bytes. The lifetime of buffer must be ensured to be greater or equal // to Interpreter. TfLiteStatus Interpreter::SetTensorParametersReadWrite( - int tensor_index, TfLiteType type, const char* name, - const std::vector& dims, TfLiteQuantizationParams quantization) { + int tensor_index, TfLiteType type, const char* name, const int rank, + const int* dims, TfLiteQuantizationParams quantization) { invokable_ = false; TF_LITE_ENSURE(&context_, tensor_index < context_.tensors_size && tensor_index >= 0); @@ -538,10 +669,10 @@ TfLiteStatus Interpreter::SetTensorParametersReadWrite( // many bytes we will need based on the dimensions. String tensors are // allocated dynamically and we can't know ahead of time how much space // they will require. - TF_LITE_ENSURE_OK(&context_, BytesRequired(type, dims.data(), dims.size(), - &required_bytes)); + TF_LITE_ENSURE_OK(&context_, + BytesRequired(type, dims, rank, &required_bytes)); } - TfLiteTensorReset(type, name, convertVectorToTfLiteIntArray(dims), + TfLiteTensorReset(type, name, ConvertArrayToTfLiteIntArray(rank, dims), quantization, /*buffer=*/nullptr, required_bytes, type == kTfLiteString ? kTfLiteDynamic : kTfLiteArenaRw, @@ -604,10 +735,7 @@ void Interpreter::UseNNAPI(bool enable) { } void Interpreter::SetNumThreads(int num_threads) { - // TODO(ahentz): this forces us to link against gemmlowp even when the ops - // don't use it. We should implement some dynamic mechanism for this sort of - // library-specific initialization. - tflite::gemm_support::SetMaxNumThreads(&context_, num_threads); + context_.recommended_num_threads = num_threads; } TfLiteStatus Interpreter::ModifyGraphWithDelegate(TfLiteDelegate* delegate) { @@ -618,12 +746,43 @@ TfLiteStatus Interpreter::ModifyGraphWithDelegate(TfLiteDelegate* delegate) { ReplaceSubgraphsWithDelegateKernels; context_.GetExecutionPlan = GetExecutionPlan; - TfLiteStatus status = delegate->Prepare(&context_, delegate->data_); + TfLiteStatus status = delegate->Prepare(&context_, delegate); // Remove additional context info. - context_.GetNodeAndRegistration = nullptr; - context_.ReplaceSubgraphsWithDelegateKernels = nullptr; - context_.GetExecutionPlan = nullptr; + SetForbiddenContextFunction(&context_.GetNodeAndRegistration); + SetForbiddenContextFunction(&context_.ReplaceSubgraphsWithDelegateKernels); + SetForbiddenContextFunction(&context_.GetExecutionPlan); return status; } +TfLiteStatus Interpreter::SetBufferHandle(int tensor_index, + TfLiteBufferHandle buffer_handle, + TfLiteDelegate* delegate) { + TF_LITE_ENSURE(&context_, tensor_index < tensors_size()); + TfLiteTensor* tensor = &tensors_[tensor_index]; + + TF_LITE_ENSURE(&context_, + tensor->delegate == nullptr || tensor->delegate == delegate); + tensor->delegate = delegate; + if (tensor->buffer_handle != kTfLiteNullBufferHandle) { + TF_LITE_ENSURE(&context_, tensor->delegate->FreeBufferHandle != nullptr); + tensor->delegate->FreeBufferHandle(tensor->delegate, + &tensor->buffer_handle); + } + tensor->buffer_handle = buffer_handle; + + return kTfLiteOk; +} + +TfLiteStatus Interpreter::GetBufferHandle(int tensor_index, + TfLiteBufferHandle* buffer_handle, + TfLiteDelegate** delegate) { + TF_LITE_ENSURE(&context_, tensor_index < tensors_size()); + TfLiteTensor* tensor = &tensors_[tensor_index]; + + *delegate = tensor->delegate; + *buffer_handle = tensor->buffer_handle; + + return kTfLiteOk; +} + } // namespace tflite diff --git a/tensorflow/contrib/lite/interpreter.h b/tensorflow/contrib/lite/interpreter.h index a9df2627e02486f71e9b5b7b0d1bfd89c7ec70c0..b481ee0891ff8e69d186b995c2b75417d1e38c43 100644 --- a/tensorflow/contrib/lite/interpreter.h +++ b/tensorflow/contrib/lite/interpreter.h @@ -134,18 +134,34 @@ class Interpreter { // This variant assumes an external buffer has been allocated of size // bytes. The lifetime of buffer must be ensured to be greater or equal // to Interpreter. - TfLiteStatus SetTensorParametersReadOnly( + inline TfLiteStatus SetTensorParametersReadOnly( int tensor_index, TfLiteType type, const char* name, const std::vector& dims, TfLiteQuantizationParams quantization, + const char* buffer, size_t bytes, + const Allocation* allocation = nullptr) { + return SetTensorParametersReadOnly(tensor_index, type, name, dims.size(), + dims.data(), quantization, buffer, bytes, + allocation); + }; + + TfLiteStatus SetTensorParametersReadOnly( + int tensor_index, TfLiteType type, const char* name, const int rank, + const int* dims, TfLiteQuantizationParams quantization, const char* buffer, size_t bytes, const Allocation* allocation = nullptr); // Set description of inputs/outputs/data/fptrs for node `node_index`. // This variant assumes an external buffer has been allocated of size // bytes. The lifetime of buffer must be ensured to be greater or equal // to Interpreter. - TfLiteStatus SetTensorParametersReadWrite( + inline TfLiteStatus SetTensorParametersReadWrite( int tensor_index, TfLiteType type, const char* name, - const std::vector& dims, TfLiteQuantizationParams quantization); + const std::vector& dims, TfLiteQuantizationParams quantization) { + return SetTensorParametersReadWrite(tensor_index, type, name, dims.size(), + dims.data(), quantization); + } + TfLiteStatus SetTensorParametersReadWrite( + int tensor_index, TfLiteType type, const char* name, const int rank, + const int* dims, TfLiteQuantizationParams quantization); // Functions to access tensor data @@ -265,6 +281,54 @@ class Interpreter { void set_model(const Model* model) { model_ = const_cast(model); } Model* model() const { return model_; } + // Ensure the data in `tensor.data` is readable. In case delegate is used, + // it might require to copy the data from delegate buffer to raw memory. + TfLiteStatus EnsureTensorDataIsReadable(int tensor_index) { + TF_LITE_ENSURE(&context_, tensor_index < tensors_size()); + TfLiteTensor* tensor = &tensors_[tensor_index]; + if (tensor->data_is_stale) { + TF_LITE_ENSURE(&context_, tensor->delegate != nullptr); + TF_LITE_ENSURE(&context_, + tensor->buffer_handle != kTfLiteNullBufferHandle); + // This can be null if the delegate doesn't use its own buffer. + TF_LITE_ENSURE(&context_, + tensor->delegate->CopyFromBufferHandle != nullptr); + tensor->delegate->CopyFromBufferHandle(tensor->delegate, + tensor->buffer_handle, + tensor->data.raw, tensor->bytes); + tensor->data_is_stale = false; + } + return kTfLiteOk; + } + + // Set the delegate buffer handle to a tensor. It can be called in the + // following cases: + // 1. Set the buffer handle to a tensor that's not being written by a + // delegate. For example, feeding an OpenGL texture as the input of the + // inference graph. + // 2. Set the buffer handle to a tensor that uses the same delegate. + // For example, set an OpenGL texture as the output of inference, while + // the node which produces output is an OpenGL delegate node. + // WARNING: This is an experimental API and subject to change. + TfLiteStatus SetBufferHandle(int tensor_index, + TfLiteBufferHandle buffer_handle, + TfLiteDelegate* delegate); + + // Get the delegate buffer handle, and the delegate which can process the + // buffer handle. + // WARNING: This is an experimental API and subject to change. + TfLiteStatus GetBufferHandle(int tensor_index, + TfLiteBufferHandle* buffer_handle, + TfLiteDelegate** delegate); + + // The default capacity of `tensors_` vector. + static constexpr int kTensorsReservedCapacity = 128; + // The capacity headroom of `tensors_` vector before calling ops' + // `prepare` and `invoke` function. In these functions, it's guaranteed + // allocating up to `kTensorsCapacityHeadroom` more tensors won't invalidate + // pointers to existing tensors. + static constexpr int kTensorsCapacityHeadroom = 16; + private: // Give 'op_reg' a chance to initialize itself using the contents of // 'buffer'. @@ -347,14 +411,15 @@ class Interpreter { // Entry point for C API ReplaceSubgraphsWithDelegateKernels static TfLiteStatus ReplaceSubgraphsWithDelegateKernels( TfLiteContext* context, TfLiteRegistration registration, - const TfLiteIntArray* nodes_to_replace); + const TfLiteIntArray* nodes_to_replace, TfLiteDelegate* delegate); // Update the execution graph to replace some of the nodes with stub // nodes. Specifically any node index that has `nodes[index]==1` will be // slated for replacement with a delegate kernel specified by registration. // WARNING: This is an experimental interface that is subject to change. TfLiteStatus ReplaceSubgraphsWithDelegateKernels( - TfLiteRegistration registration, const TfLiteIntArray* nodes_to_replace); + TfLiteRegistration registration, const TfLiteIntArray* nodes_to_replace, + TfLiteDelegate* delegate); // WARNING: This is an experimental interface that is subject to change. // Gets the internal pointer to a TensorFlow lite node by node_index. @@ -377,6 +442,18 @@ class Interpreter { static TfLiteStatus GetExecutionPlan(struct TfLiteContext* context, TfLiteIntArray** execution_plan); + // Ensures that `tensors_` has at least `kTensorsCapacityHeadroom` extra + // capacity. Calling this function may invalidate existing pointers to + // tensors. After calling this function, adding `kTensorsCapacityHeadroom` + // more tensors won't invalidate the pointer to existing tensors. + void EnsureTensorsVectorCapacity() { + const int required_capacity = tensors_size() + kTensorsCapacityHeadroom; + if (required_capacity > tensors_.capacity()) { + tensors_.reserve(required_capacity); + context_.tensors = tensors_.data(); + } + } + // A pure C data structure used to communicate with the pure C plugin // interface. To avoid copying tensor metadata, this is also the definitive // structure to store tensors. @@ -411,7 +488,7 @@ class Interpreter { // During Invoke(), Interpreter will allocate input tensors first, which are // known to be fixed size. Then it will allocate outputs from nodes as many // as possible. When there is a node that produces dynamic sized tensor. - // Intepreter will stop allocating tensors, set the value of next allocate + // Interpreter will stop allocating tensors, set the value of next allocate // node id, and execute the node to generate the output tensor before continue // to allocate successors. This process repeats until all nodes are executed. // NOTE: this relies on the order of nodes that is in topological order. diff --git a/tensorflow/contrib/lite/interpreter_test.cc b/tensorflow/contrib/lite/interpreter_test.cc index 28c96e5dde6ffa62bb073db9716a00f91c6e0bdf..72d4acedbe72beea276c7c8fc1f7ddce01796d41 100644 --- a/tensorflow/contrib/lite/interpreter_test.cc +++ b/tensorflow/contrib/lite/interpreter_test.cc @@ -40,7 +40,7 @@ TEST(BasicInterpreter, InvokeInvalidModel) { ASSERT_EQ(interpreter.Invoke(), kTfLiteOk); } -// Test size accesser functions. +// Test size accessor functions. TEST(BasicInterpreter, TestSizeFunctions) { Interpreter interpreter; int base_index; @@ -561,6 +561,86 @@ TEST(BasicInterpreter, TestCustomErrorReporter) { ASSERT_EQ(reporter.calls, 1); } +TEST(BasicInterpreter, TestUnsupportedDelegateFunctions) { + Interpreter interpreter; + ASSERT_EQ(interpreter.AddTensors(2), kTfLiteOk); + TfLiteRegistration registration = { + .init = nullptr, .free = nullptr, .prepare = nullptr, .invoke = nullptr}; + // These functions are only supported inside Delegate's Prepare function. + // The test verifies that these functions returns `kTfLiteError`, but not + // `kTfLiteOk` or just crashes. + registration.prepare = [](TfLiteContext* context, TfLiteNode* node) { + { + TfLiteIntArray* execution_plan; + EXPECT_EQ(context->GetExecutionPlan(context, &execution_plan), + kTfLiteError); + } + { + TfLiteNode* node; + TfLiteRegistration* registration; + EXPECT_EQ( + context->GetNodeAndRegistration(context, 0, &node, ®istration), + kTfLiteError); + } + { + TfLiteRegistration delegate_registration = {nullptr, nullptr, nullptr, + nullptr}; + TfLiteIntArray nodes_to_replace; + nodes_to_replace.size = 0; + EXPECT_EQ(context->ReplaceSubgraphsWithDelegateKernels( + context, delegate_registration, &nodes_to_replace, nullptr), + kTfLiteError); + } + return kTfLiteError; + }; + ASSERT_EQ(interpreter.SetInputs({0}), kTfLiteOk); + ASSERT_EQ(interpreter.SetOutputs({0}), kTfLiteOk); + ASSERT_EQ(interpreter.AddNodeWithParameters({0}, {1}, nullptr, 0, nullptr, + ®istration), + kTfLiteOk); + EXPECT_EQ(interpreter.AllocateTensors(), kTfLiteError); +} + +TEST(InterpreterTensorsCapacityTest, TestWithinHeadroom) { + Interpreter interpreter; + ASSERT_EQ(interpreter.AddTensors(Interpreter::kTensorsReservedCapacity), + kTfLiteOk); + TfLiteRegistration registration = {nullptr, nullptr, nullptr, nullptr}; + registration.prepare = [](TfLiteContext* context, TfLiteNode* node) { + TfLiteTensor* first_tensor = context->tensors; + + int new_tensor_index; + context->AddTensors(context, Interpreter::kTensorsCapacityHeadroom, + &new_tensor_index); + EXPECT_EQ(first_tensor, context->tensors); + return kTfLiteOk; + }; + ASSERT_EQ(interpreter.AddNodeWithParameters({0}, {1}, nullptr, 0, nullptr, + ®istration), + kTfLiteOk); + ASSERT_EQ(interpreter.AllocateTensors(), kTfLiteOk); +} + +TEST(InterpreterTensorsCapacityTest, TestExceedHeadroom) { + Interpreter interpreter; + ASSERT_EQ(interpreter.AddTensors(Interpreter::kTensorsReservedCapacity), + kTfLiteOk); + TfLiteRegistration registration = {nullptr, nullptr, nullptr, nullptr}; + registration.prepare = [](TfLiteContext* context, TfLiteNode* node) { + TfLiteTensor* first_tensor = context->tensors; + + int new_tensor_index; + context->AddTensors(context, Interpreter::kTensorsCapacityHeadroom + 1, + &new_tensor_index); + EXPECT_NE(first_tensor, context->tensors); + return kTfLiteOk; + }; + ASSERT_EQ(interpreter.AddNodeWithParameters({0}, {1}, nullptr, 0, nullptr, + ®istration), + kTfLiteOk); + ASSERT_EQ(interpreter.AllocateTensors(), kTfLiteOk); +} + // Test fixture that allows playing with execution plans. It creates a two // node graph that can be executed in either [0,1] order or [1,0] order. // The CopyOp records when it is invoked in the class member run_order_ @@ -723,26 +803,38 @@ TfLiteRegistration AddOpRegistration() { } class TestDelegate : public ::testing::Test { - public: - TestDelegate() { - interpreter_.AddTensors(5); - interpreter_.SetInputs({0, 1}); - interpreter_.SetOutputs({3, 4}); + protected: + void SetUp() override { + interpreter_.reset(new Interpreter); + interpreter_->AddTensors(5); + interpreter_->SetInputs({0, 1}); + interpreter_->SetOutputs({3, 4}); TfLiteQuantizationParams quant; - interpreter_.SetTensorParametersReadWrite(0, kTfLiteFloat32, "", {3}, - quant); - interpreter_.SetTensorParametersReadWrite(1, kTfLiteFloat32, "", {3}, - quant); - interpreter_.SetTensorParametersReadWrite(2, kTfLiteFloat32, "", {3}, - quant); - interpreter_.SetTensorParametersReadWrite(3, kTfLiteFloat32, "", {3}, - quant); + interpreter_->SetTensorParametersReadWrite(0, kTfLiteFloat32, "", {3}, + quant); + interpreter_->SetTensorParametersReadWrite(1, kTfLiteFloat32, "", {3}, + quant); + interpreter_->SetTensorParametersReadWrite(2, kTfLiteFloat32, "", {3}, + quant); + interpreter_->SetTensorParametersReadWrite(3, kTfLiteFloat32, "", {3}, + quant); TfLiteRegistration reg = AddOpRegistration(); - interpreter_.AddNodeWithParameters({0, 0}, {2}, nullptr, 0, nullptr, ®); - interpreter_.AddNodeWithParameters({1, 1}, {3}, nullptr, 0, nullptr, ®); - interpreter_.AddNodeWithParameters({2, 1}, {4}, nullptr, 0, nullptr, ®); + interpreter_->AddNodeWithParameters({0, 0}, {2}, nullptr, 0, nullptr, ®); + interpreter_->AddNodeWithParameters({1, 1}, {3}, nullptr, 0, nullptr, ®); + interpreter_->AddNodeWithParameters({2, 1}, {4}, nullptr, 0, nullptr, ®); + } + + void TearDown() override { + // Interpreter relies on delegate_ to free the resources properly. Thus + // the life cycle of delegate must be longer than interpreter. + interpreter_.reset(); + delegate_.reset(); } + TfLiteBufferHandle last_allocated_handle_ = kTfLiteNullBufferHandle; + + TfLiteBufferHandle AllocateBufferHandle() { return ++last_allocated_handle_; } + protected: class SimpleDelegate { public: @@ -751,8 +843,8 @@ class TestDelegate : public ::testing::Test { // value-copyable and compatible with TfLite. explicit SimpleDelegate(const std::vector& nodes) : nodes_(nodes) { delegate_.Prepare = [](TfLiteContext* context, - void* data) -> TfLiteStatus { - auto* simple = reinterpret_cast(data); + TfLiteDelegate* delegate) -> TfLiteStatus { + auto* simple = reinterpret_cast(delegate->data_); TfLiteIntArray* nodes_to_separate = TfLiteIntArrayCreate(simple->nodes_.size()); // Mark nodes that we want in TfLiteIntArray* structure. @@ -783,10 +875,26 @@ class TestDelegate : public ::testing::Test { } context->ReplaceSubgraphsWithDelegateKernels( - context, FakeFusedRegistration(), nodes_to_separate); + context, FakeFusedRegistration(), nodes_to_separate, delegate); TfLiteIntArrayFree(nodes_to_separate); return kTfLiteOk; }; + delegate_.CopyToBufferHandle = [](TfLiteDelegate* delegate, + TfLiteBufferHandle buffer_handle, + void* data, int size) -> TfLiteStatus { + // TODO(ycling): Implement tests to test buffer copying logic. + return kTfLiteOk; + }; + delegate_.CopyFromBufferHandle = + [](TfLiteDelegate* delegate, TfLiteBufferHandle buffer_handle, + void* data, int size) -> TfLiteStatus { + // TODO(ycling): Implement tests to test buffer copying logic. + return kTfLiteOk; + }; + delegate_.FreeBufferHandle = [](TfLiteDelegate* delegate, + TfLiteBufferHandle* handle) { + *handle = kTfLiteNullBufferHandle; + }; // Store type-punned data SimpleDelegate structure. delegate_.data_ = reinterpret_cast(this); } @@ -803,36 +911,118 @@ class TestDelegate : public ::testing::Test { std::vector nodes_; TfLiteDelegate delegate_; }; - Interpreter interpreter_; + std::unique_ptr interpreter_; + std::unique_ptr delegate_; }; TEST_F(TestDelegate, BasicDelegate) { - interpreter_.Invoke(); - SimpleDelegate simple({0, 1, 2}); - interpreter_.ModifyGraphWithDelegate(simple.get_tf_lite_delegate()); - - ASSERT_EQ(interpreter_.execution_plan().size(), 1); - int node = interpreter_.execution_plan()[0]; - const auto* node_and_reg = interpreter_.node_and_registration(node); - ASSERT_EQ(node_and_reg->second.custom_name, + interpreter_->Invoke(); + delegate_ = std::unique_ptr(new SimpleDelegate({0, 1, 2})); + interpreter_->ModifyGraphWithDelegate(delegate_->get_tf_lite_delegate()); + + ASSERT_EQ(interpreter_->execution_plan().size(), 1); + int node = interpreter_->execution_plan()[0]; + const auto* node_and_reg = interpreter_->node_and_registration(node); + EXPECT_EQ(node_and_reg->second.custom_name, SimpleDelegate::FakeFusedRegistration().custom_name); + + const TfLiteDelegateParams* params = + reinterpret_cast( + node_and_reg->first.builtin_data); + ASSERT_EQ(params->nodes_to_replace->size, 3); + EXPECT_EQ(params->nodes_to_replace->data[0], 0); + EXPECT_EQ(params->nodes_to_replace->data[1], 1); + EXPECT_EQ(params->nodes_to_replace->data[2], 2); + + ASSERT_EQ(params->input_tensors->size, 2); + EXPECT_EQ(params->input_tensors->data[0], 0); + EXPECT_EQ(params->input_tensors->data[1], 1); + + ASSERT_EQ(params->output_tensors->size, 2); + EXPECT_EQ(params->output_tensors->data[0], 3); + EXPECT_EQ(params->output_tensors->data[1], 4); } TEST_F(TestDelegate, ComplexDeligate) { - interpreter_.Invoke(); - SimpleDelegate simple({1, 2}); - interpreter_.ModifyGraphWithDelegate(simple.get_tf_lite_delegate()); + interpreter_->Invoke(); + delegate_ = std::unique_ptr(new SimpleDelegate({1, 2})); + interpreter_->ModifyGraphWithDelegate(delegate_->get_tf_lite_delegate()); - ASSERT_EQ(interpreter_.execution_plan().size(), 2); + ASSERT_EQ(interpreter_->execution_plan().size(), 2); // 0th should be a non-delegated original op - ASSERT_EQ(interpreter_.execution_plan()[0], 0); + ASSERT_EQ(interpreter_->execution_plan()[0], 0); // 1st should be a new macro op (3) which didn't exist) - ASSERT_EQ(interpreter_.execution_plan()[1], 3); - const auto* node_and_reg = interpreter_.node_and_registration(3); + ASSERT_EQ(interpreter_->execution_plan()[1], 3); + const auto* node_and_reg = interpreter_->node_and_registration(3); ASSERT_EQ(node_and_reg->second.custom_name, SimpleDelegate::FakeFusedRegistration().custom_name); } +TEST_F(TestDelegate, SetBufferHandleToInput) { + interpreter_->Invoke(); + delegate_ = std::unique_ptr(new SimpleDelegate({0, 1, 2})); + TfLiteDelegate* delegate = delegate_->get_tf_lite_delegate(); + interpreter_->ModifyGraphWithDelegate(delegate); + + constexpr int kOutputTensorIndex = 0; + TfLiteTensor* tensor = interpreter_->tensor(kOutputTensorIndex); + ASSERT_EQ(tensor->delegate, nullptr); + ASSERT_EQ(tensor->buffer_handle, kTfLiteNullBufferHandle); + + TfLiteBufferHandle handle = AllocateBufferHandle(); + TfLiteStatus status = + interpreter_->SetBufferHandle(kOutputTensorIndex, handle, delegate); + ASSERT_EQ(status, kTfLiteOk); + EXPECT_EQ(tensor->delegate, delegate); + EXPECT_EQ(tensor->buffer_handle, handle); +} + +TEST_F(TestDelegate, SetBufferHandleToOutput) { + interpreter_->Invoke(); + delegate_ = std::unique_ptr(new SimpleDelegate({0, 1, 2})); + TfLiteDelegate* delegate = delegate_->get_tf_lite_delegate(); + interpreter_->ModifyGraphWithDelegate(delegate); + + constexpr int kOutputTensorIndex = 3; + TfLiteTensor* tensor = interpreter_->tensor(kOutputTensorIndex); + // Before setting the buffer handle, the tensor's `delegate` is already set + // because it will be written by the delegate. + ASSERT_EQ(tensor->delegate, delegate); + ASSERT_EQ(tensor->buffer_handle, kTfLiteNullBufferHandle); + + TfLiteBufferHandle handle = AllocateBufferHandle(); + TfLiteStatus status = + interpreter_->SetBufferHandle(kOutputTensorIndex, handle, delegate); + ASSERT_EQ(status, kTfLiteOk); + EXPECT_EQ(tensor->delegate, delegate); + EXPECT_EQ(tensor->buffer_handle, handle); +} + +TEST_F(TestDelegate, SetInvalidHandleToTensor) { + interpreter_->Invoke(); + delegate_ = std::unique_ptr(new SimpleDelegate({0, 1, 2})); + TfLiteDelegate* delegate = delegate_->get_tf_lite_delegate(); + interpreter_->ModifyGraphWithDelegate(delegate); + + SimpleDelegate another_simple_delegate({0, 1, 2}); + + constexpr int kOutputTensorIndex = 3; + TfLiteTensor* tensor = interpreter_->tensor(kOutputTensorIndex); + // Before setting the buffer handle, the tensor's `delegate` is already set + // because it will be written by the delegate. + ASSERT_EQ(tensor->delegate, delegate); + ASSERT_EQ(tensor->buffer_handle, kTfLiteNullBufferHandle); + + TfLiteBufferHandle handle = AllocateBufferHandle(); + TfLiteStatus status = interpreter_->SetBufferHandle( + kOutputTensorIndex, handle, + another_simple_delegate.get_tf_lite_delegate()); + // Setting a buffer handle to a tensor with another delegate will fail. + ASSERT_EQ(status, kTfLiteError); + EXPECT_EQ(tensor->delegate, delegate); + EXPECT_EQ(tensor->buffer_handle, kTfLiteNullBufferHandle); +} + } // namespace } // namespace tflite diff --git a/tensorflow/contrib/lite/java/BUILD b/tensorflow/contrib/lite/java/BUILD index 35aacb70002d1d454f675484e4398bcdffc4acf1..f52d6ba6c5390e631d29e75f833aa4dd5bba1a68 100644 --- a/tensorflow/contrib/lite/java/BUILD +++ b/tensorflow/contrib/lite/java/BUILD @@ -29,7 +29,7 @@ android_library( visibility = ["//visibility:public"], deps = [ ":tflite_runtime", - "@javax_validation", + "@org_checkerframework_qual", ], ) @@ -42,7 +42,7 @@ android_library( ), visibility = ["//visibility:public"], deps = [ - "@javax_validation", + "@org_checkerframework_qual", ], ) @@ -58,7 +58,7 @@ java_library( deps = [ ":libtensorflowlite_jni.so", "//tensorflow/contrib/lite/java/src/main/native", - "@javax_validation", + "@org_checkerframework_qual", ], ) diff --git a/tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifierQuantizedMobileNet.java b/tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifierQuantizedMobileNet.java index 156c895146940adfe71f111be6e354e02b75ea48..c533de7927050d3844089e4d74820611a340e573 100644 --- a/tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifierQuantizedMobileNet.java +++ b/tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifierQuantizedMobileNet.java @@ -49,7 +49,7 @@ public class ImageClassifierQuantizedMobileNet extends ImageClassifier { @Override protected String getLabelPath() { - return "labels_mobilenet_quant_v1_224.txt"; + return "labels.txt"; } @Override diff --git a/tensorflow/contrib/lite/java/proguard.flags b/tensorflow/contrib/lite/java/proguard.flags new file mode 100644 index 0000000000000000000000000000000000000000..8ee3d7e7ae728b27789336ac56208acdf13ee424 --- /dev/null +++ b/tensorflow/contrib/lite/java/proguard.flags @@ -0,0 +1,3 @@ +-keepclassmembers class org.tensorflow.lite.NativeInterpreterWrapper { + private long inferenceDurationNanoseconds; +} \ No newline at end of file diff --git a/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/DataType.java b/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/DataType.java index d63c299589d2e8ce1051a52d29b533ed126bbcf7..fc16488a6459eb227fde712055d3e8ccfcce0070 100644 --- a/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/DataType.java +++ b/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/DataType.java @@ -71,6 +71,23 @@ enum DataType { throw new IllegalArgumentException("DataType " + this + " is not supported yet"); } + /** Gets string names of the data type. */ + String toStringName() { + switch (this) { + case FLOAT32: + return "float"; + case INT32: + return "int"; + case UINT8: + return "byte"; + case INT64: + return "long"; + case BYTEBUFFER: + return "ByteBuffer"; + } + throw new IllegalArgumentException("DataType " + this + " is not supported yet"); + } + // Cached to avoid copying it private static final DataType[] values = values(); } diff --git a/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/Interpreter.java b/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/Interpreter.java index dd883d69d2065236ee29012b9bde99972aefbcf7..cc17b491f2a1a42d704c0262095ce817f3828764 100644 --- a/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/Interpreter.java +++ b/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/Interpreter.java @@ -19,7 +19,7 @@ import java.io.File; import java.nio.MappedByteBuffer; import java.util.HashMap; import java.util.Map; -import javax.validation.constraints.NotNull; +import org.checkerframework.checker.nullness.qual.NonNull; /** * Driver class to drive model inference with TensorFlow Lite. @@ -60,7 +60,7 @@ public final class Interpreter implements AutoCloseable { * * @param modelFile: a File of a pre-trained TF Lite model. */ - public Interpreter(@NotNull File modelFile) { + public Interpreter(@NonNull File modelFile) { if (modelFile == null) { return; } @@ -73,20 +73,23 @@ public final class Interpreter implements AutoCloseable { *

The {@code MappedByteBuffer} should remain unchanged after the construction of a {@code * Interpreter}. */ - public Interpreter(@NotNull MappedByteBuffer mappedByteBuffer) { + public Interpreter(@NonNull MappedByteBuffer mappedByteBuffer) { wrapper = new NativeInterpreterWrapper(mappedByteBuffer); } /** * 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. + * * @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. * @param output a multidimensional array of output data. */ - public void run(@NotNull Object input, @NotNull Object output) { + public void run(@NonNull Object input, @NonNull Object output) { Object[] inputs = {input}; Map outputs = new HashMap<>(); outputs.put(0, output); @@ -96,6 +99,9 @@ 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. + * * @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 @@ -105,7 +111,7 @@ public final class Interpreter implements AutoCloseable { * needs to keep entries for the outputs to be used. */ public void runForMultipleInputsOutputs( - @NotNull Object[] inputs, @NotNull Map outputs) { + @NonNull Object[] inputs, @NonNull Map outputs) { if (wrapper == null) { throw new IllegalStateException("The Interpreter has already been closed."); } @@ -128,7 +134,7 @@ public final class Interpreter implements AutoCloseable { * *

IllegalArgumentException will be thrown if it fails to resize. */ - public void resizeInput(int idx, @NotNull int[] dims) { + public void resizeInput(int idx, @NonNull int[] dims) { if (wrapper == null) { throw new IllegalStateException("The Interpreter has already been closed."); } @@ -161,6 +167,27 @@ public final class Interpreter implements AutoCloseable { return wrapper.getOutputIndex(opName); } + /** + * Returns native inference timing. + *

IllegalArgumentException will be thrown if the model is not initialized by the + * {@link Interpreter}. + */ + public Long getLastNativeInferenceDurationNanoseconds() { + if (wrapper == null) { + throw new IllegalStateException("The interpreter has already been closed."); + } + return wrapper.getLastNativeInferenceDurationNanoseconds(); + } + + /** Turns on/off Android NNAPI for hardware acceleration when it is available. */ + public void setUseNNAPI(boolean useNNAPI) { + if (wrapper != null) { + wrapper.setUseNNAPI(useNNAPI); + } else { + throw new IllegalStateException("NativeInterpreterWrapper has already been closed."); + } + } + /** Release resources associated with the {@code Interpreter}. */ @Override public void close() { diff --git a/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/NativeInterpreterWrapper.java b/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/NativeInterpreterWrapper.java index 5ee594dec492ad2fee22e603a6de311b3fed4cac..014636ffff3afd3a9579081690cde792647118a3 100644 --- a/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/NativeInterpreterWrapper.java +++ b/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/NativeInterpreterWrapper.java @@ -35,6 +35,7 @@ final class NativeInterpreterWrapper implements AutoCloseable { errorHandle = createErrorReporter(ERROR_BUFFER_SIZE); modelHandle = createModel(modelPath, errorHandle); interpreterHandle = createInterpreter(modelHandle, errorHandle); + isMemoryAllocated = true; } /** @@ -47,6 +48,7 @@ final class NativeInterpreterWrapper implements AutoCloseable { errorHandle = createErrorReporter(ERROR_BUFFER_SIZE); modelHandle = createModelWithBuffer(modelByteBuffer, errorHandle); interpreterHandle = createInterpreter(modelHandle, errorHandle); + isMemoryAllocated = true; } /** Releases resources associated with this {@code NativeInterpreterWrapper}. */ @@ -59,6 +61,7 @@ final class NativeInterpreterWrapper implements AutoCloseable { modelByteBuffer = null; inputsIndexes = null; outputsIndexes = null; + isMemoryAllocated = false; } /** Sets inputs, runs model inference and returns outputs. */ @@ -91,11 +94,21 @@ final class NativeInterpreterWrapper implements AutoCloseable { i, inputs.length)); } } + inferenceDurationNanoseconds = -1; long[] outputsHandles = - run(interpreterHandle, errorHandle, sizes, dataTypes, numsOfBytes, inputs); + run( + interpreterHandle, + errorHandle, + sizes, + dataTypes, + numsOfBytes, + inputs, + this, + isMemoryAllocated); if (outputsHandles == null || outputsHandles.length == 0) { throw new IllegalStateException("Interpreter has no outputs."); } + isMemoryAllocated = true; Tensor[] outputs = new Tensor[outputsHandles.length]; for (int i = 0; i < outputsHandles.length; ++i) { outputs[i] = Tensor.fromHandle(outputsHandles[i]); @@ -109,14 +122,18 @@ final class NativeInterpreterWrapper implements AutoCloseable { Object[] sizes, int[] dtypes, int[] numsOfBytes, - Object[] values); + Object[] values, + NativeInterpreterWrapper wrapper, + boolean memoryAllocated); /** Resizes dimensions of a specific input. */ void resizeInput(int idx, int[] dims) { - resizeInput(interpreterHandle, errorHandle, idx, dims); + if (resizeInput(interpreterHandle, errorHandle, idx, dims)) { + isMemoryAllocated = false; + } } - private static native void resizeInput( + private static native boolean resizeInput( long interpreterHandle, long errorHandle, int inputIdx, int[] dims); void setUseNNAPI(boolean useNNAPI) { @@ -236,6 +253,35 @@ final class NativeInterpreterWrapper implements AutoCloseable { } } + /** + * Gets the last inference duration in nanoseconds. It returns null if there is no previous + * inference run or the last inference run failed. + */ + Long getLastNativeInferenceDurationNanoseconds() { + return (inferenceDurationNanoseconds < 0) ? null : inferenceDurationNanoseconds; + } + + /** + * Gets the dimensions of an input. It throws IllegalArgumentException if input index is invalid. + */ + int[] getInputDims(int index) { + return getInputDims(interpreterHandle, index, -1); + } + + /** + * Gets the dimensions of an input. If numBytes >= 0, it will check whether num of bytes match the + * input. + */ + private static native int[] getInputDims(long interpreterHandle, int inputIdx, int numBytes); + + /** Gets the type of an output. It throws IllegalArgumentException if output index is invalid. */ + String getOutputDataType(int index) { + int type = getOutputDataType(interpreterHandle, index); + return DataType.fromNumber(type).toStringName(); + } + + private static native int getOutputDataType(long interpreterHandle, int outputIdx); + private static final int ERROR_BUFFER_SIZE = 512; private long errorHandle; @@ -246,12 +292,16 @@ final class NativeInterpreterWrapper implements AutoCloseable { private int inputSize; + private long inferenceDurationNanoseconds = -1; + private MappedByteBuffer modelByteBuffer; private Map inputsIndexes; private Map outputsIndexes; + private boolean isMemoryAllocated = false; + private static native String[] getInputNames(long interpreterHandle); private static native String[] getOutputNames(long interpreterHandle); @@ -268,8 +318,6 @@ final class NativeInterpreterWrapper implements AutoCloseable { private static native void delete(long errorHandle, long modelHandle, long interpreterHandle); - private static native int[] getInputDims(long interpreterHandle, int inputIdx, int numBytes); - static { TensorFlowLite.init(); } diff --git a/tensorflow/contrib/lite/java/src/main/native/BUILD b/tensorflow/contrib/lite/java/src/main/native/BUILD index 15806d57c8ed7a45d2db9b80e2aab8e22349ee3e..3571182ca92e959d54935cfdc76679ab69a8cfa9 100644 --- a/tensorflow/contrib/lite/java/src/main/native/BUILD +++ b/tensorflow/contrib/lite/java/src/main/native/BUILD @@ -11,6 +11,7 @@ licenses(["notice"]) # Apache 2.0 cc_library( name = "native_framework_only", srcs = [ + "duration_utils_jni.cc", "exception_jni.cc", "nativeinterpreterwrapper_jni.cc", "tensor_jni.cc", diff --git a/tensorflow/contrib/lite/java/src/main/native/duration_utils_jni.cc b/tensorflow/contrib/lite/java/src/main/native/duration_utils_jni.cc new file mode 100644 index 0000000000000000000000000000000000000000..0e08a04370592f6e3c92b5811fa7e163f808e03c --- /dev/null +++ b/tensorflow/contrib/lite/java/src/main/native/duration_utils_jni.cc @@ -0,0 +1,38 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include +#include + +namespace tflite { + +// Gets the elapsed wall-clock timespec. +timespec getCurrentTime() { + timespec time; + clock_gettime(CLOCK_MONOTONIC, &time); + return time; +} + +// Computes the time diff from two timespecs. Returns '-1' if 'stop' is earlier +// than 'start'. +jlong timespec_diff_nanoseconds(struct timespec* start, struct timespec* stop) { + jlong result = stop->tv_sec - start->tv_sec; + if (result < 0) return -1; + result = 1000000000 * result + (stop->tv_nsec - start->tv_nsec); + if (result < 0) return -1; + return result; +} + +} // namespace tflite diff --git a/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.cc b/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.cc index c346f9f92e360c0722ebac440d790da6441ceecf..2870ffe8eb68610adf3bdd4d26218678e7404d2e 100644 --- a/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.cc +++ b/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.cc @@ -79,6 +79,21 @@ TfLiteType resolveDataType(jint data_type) { } } +int getDataType(TfLiteType data_type) { + switch (data_type) { + case kTfLiteFloat32: + return 1; + case kTfLiteInt32: + return 2; + case kTfLiteUInt8: + return 3; + case kTfLiteInt64: + return 4; + default: + return -1; + } +} + void printDims(char* buffer, int max_size, int* dims, int num_dims) { if (max_size <= 0) return; buffer[0] = '?'; @@ -149,6 +164,45 @@ TfLiteStatus checkInputs(JNIEnv* env, tflite::Interpreter* interpreter, return kTfLiteOk; } +// Checks whether there is any difference between dimensions of a tensor and a +// given dimensions. Returns true if there is difference, else false. +bool areDimsDifferent(JNIEnv* env, TfLiteTensor* tensor, jintArray dims) { + int num_dims = static_cast(env->GetArrayLength(dims)); + jint* ptr = env->GetIntArrayElements(dims, nullptr); + if (ptr == nullptr) { + throwException(env, kIllegalArgumentException, + "Empty dimensions of input array."); + return true; + } + if (tensor->dims->size != num_dims) { + return true; + } + for (int i = 0; i < num_dims; ++i) { + if (ptr[i] != tensor->dims->data[i]) { + return true; + } + } + env->ReleaseIntArrayElements(dims, ptr, JNI_ABORT); + return false; +} + +bool areInputDimensionsTheSame(JNIEnv* env, tflite::Interpreter* interpreter, + int input_size, jobjectArray sizes) { + if (interpreter->inputs().size() != input_size) { + return false; + } + for (int i = 0; i < input_size; ++i) { + int input_idx = interpreter->inputs()[i]; + jintArray dims = + static_cast(env->GetObjectArrayElement(sizes, i)); + TfLiteTensor* target = interpreter->tensor(input_idx); + if (areDimsDifferent(env, target, dims)) return false; + env->DeleteLocalRef(dims); + if (env->ExceptionCheck()) return false; + } + return true; +} + TfLiteStatus resizeInputs(JNIEnv* env, tflite::Interpreter* interpreter, int input_size, jobjectArray sizes) { for (int i = 0; i < input_size; ++i) { @@ -344,6 +398,15 @@ Java_org_tensorflow_lite_NativeInterpreterWrapper_createInterpreter( throwException(env, kIllegalArgumentException, "Cannot create interpreter: %s", error_reporter->CachedErrorMessage()); + return 0; + } + // allocates memory + status = interpreter->AllocateTensors(); + if (status != kTfLiteOk) { + throwException(env, kNullPointerException, + "Can not allocate memory for the interpreter", + error_reporter->CachedErrorMessage()); + return 0; } return reinterpret_cast(interpreter.release()); } @@ -353,7 +416,7 @@ JNIEXPORT jlongArray JNICALL Java_org_tensorflow_lite_NativeInterpreterWrapper_run( JNIEnv* env, jclass clazz, jlong interpreter_handle, jlong error_handle, jobjectArray sizes, jintArray data_types, jintArray nums_of_bytes, - jobjectArray values) { + jobjectArray values, jobject wrapper, jboolean memory_allocated) { tflite::Interpreter* interpreter = convertLongToInterpreter(env, interpreter_handle); if (interpreter == nullptr) return nullptr; @@ -365,25 +428,29 @@ Java_org_tensorflow_lite_NativeInterpreterWrapper_run( TfLiteStatus status = checkInputs(env, interpreter, input_size, data_types, nums_of_bytes, values, sizes); if (status != kTfLiteOk) return nullptr; - // resizes inputs - status = resizeInputs(env, interpreter, input_size, sizes); - if (status != kTfLiteOk) { - throwException(env, kNullPointerException, "Can not resize the input: %s", - error_reporter->CachedErrorMessage()); - return nullptr; - } - // allocates memory - status = interpreter->AllocateTensors(); - if (status != kTfLiteOk) { - throwException(env, kNullPointerException, - "Can not allocate memory for the given inputs: %s", - error_reporter->CachedErrorMessage()); - return nullptr; + if (!memory_allocated || + !areInputDimensionsTheSame(env, interpreter, input_size, sizes)) { + // resizes inputs + status = resizeInputs(env, interpreter, input_size, sizes); + if (status != kTfLiteOk) { + throwException(env, kNullPointerException, "Can not resize the input: %s", + error_reporter->CachedErrorMessage()); + return nullptr; + } + // allocates memory + status = interpreter->AllocateTensors(); + if (status != kTfLiteOk) { + throwException(env, kNullPointerException, + "Can not allocate memory for the given inputs: %s", + error_reporter->CachedErrorMessage()); + return nullptr; + } } // sets inputs status = setInputs(env, interpreter, input_size, data_types, nums_of_bytes, values); if (status != kTfLiteOk) return nullptr; + timespec beforeInference = ::tflite::getCurrentTime(); // runs inference if (interpreter->Invoke() != kTfLiteOk) { throwException(env, kIllegalArgumentException, @@ -391,6 +458,17 @@ Java_org_tensorflow_lite_NativeInterpreterWrapper_run( error_reporter->CachedErrorMessage()); return nullptr; } + timespec afterInference = ::tflite::getCurrentTime(); + jclass wrapper_clazz = env->GetObjectClass(wrapper); + jfieldID fid = + env->GetFieldID(wrapper_clazz, "inferenceDurationNanoseconds", "J"); + if (env->ExceptionCheck()) { + env->ExceptionClear(); + } else if (fid != nullptr) { + env->SetLongField( + wrapper, fid, + ::tflite::timespec_diff_nanoseconds(&beforeInference, &afterInference)); + } // returns outputs const std::vector& results = interpreter->outputs(); if (results.empty()) { @@ -414,7 +492,7 @@ Java_org_tensorflow_lite_NativeInterpreterWrapper_getInputDims( tflite::Interpreter* interpreter = convertLongToInterpreter(env, handle); if (interpreter == nullptr) return nullptr; const int idx = static_cast(input_idx); - if (input_idx >= interpreter->inputs().size()) { + if (input_idx < 0 || input_idx >= interpreter->inputs().size()) { throwException(env, kIllegalArgumentException, "Out of range: Failed to get %d-th input out of %d inputs", input_idx, interpreter->inputs().size()); @@ -422,45 +500,72 @@ Java_org_tensorflow_lite_NativeInterpreterWrapper_getInputDims( } TfLiteTensor* target = interpreter->tensor(interpreter->inputs()[idx]); int size = target->dims->size; - int expected_num_bytes = elementByteSize(target->type); - for (int i = 0; i < size; ++i) { - expected_num_bytes *= target->dims->data[i]; - } - if (num_bytes != expected_num_bytes) { - throwException(env, kIllegalArgumentException, - "Failed to get input dimensions. %d-th input should have" - " %d bytes, but found %d bytes.", - idx, expected_num_bytes, num_bytes); - return nullptr; + if (num_bytes >= 0) { // verifies num of bytes matches if num_bytes if valid. + int expected_num_bytes = elementByteSize(target->type); + for (int i = 0; i < size; ++i) { + expected_num_bytes *= target->dims->data[i]; + } + if (num_bytes != expected_num_bytes) { + throwException(env, kIllegalArgumentException, + "Failed to get input dimensions. %d-th input should have" + " %d bytes, but found %d bytes.", + idx, expected_num_bytes, num_bytes); + return nullptr; + } } jintArray outputs = env->NewIntArray(size); env->SetIntArrayRegion(outputs, 0, size, &(target->dims->data[0])); return outputs; } -JNIEXPORT void JNICALL +JNIEXPORT jint JNICALL +Java_org_tensorflow_lite_NativeInterpreterWrapper_getOutputDataType( + JNIEnv* env, jclass clazz, jlong handle, jint output_idx) { + tflite::Interpreter* interpreter = convertLongToInterpreter(env, handle); + if (interpreter == nullptr) return -1; + const int idx = static_cast(output_idx); + if (output_idx < 0 || output_idx >= interpreter->outputs().size()) { + throwException(env, kIllegalArgumentException, + "Out of range: Failed to get %d-th output out of %d outputs", + output_idx, interpreter->outputs().size()); + return -1; + } + TfLiteTensor* target = interpreter->tensor(interpreter->outputs()[idx]); + int type = getDataType(target->type); + return static_cast(type); +} + +JNIEXPORT jboolean JNICALL Java_org_tensorflow_lite_NativeInterpreterWrapper_resizeInput( JNIEnv* env, jclass clazz, jlong interpreter_handle, jlong error_handle, jint input_idx, jintArray dims) { BufferErrorReporter* error_reporter = convertLongToErrorReporter(env, error_handle); - if (error_reporter == nullptr) return; + if (error_reporter == nullptr) return JNI_FALSE; tflite::Interpreter* interpreter = convertLongToInterpreter(env, interpreter_handle); - if (interpreter == nullptr) return; + if (interpreter == nullptr) return JNI_FALSE; const int idx = static_cast(input_idx); if (idx < 0 || idx >= interpreter->inputs().size()) { throwException(env, kIllegalArgumentException, "Can not resize %d-th input for a model having %d inputs.", idx, interpreter->inputs().size()); + return JNI_FALSE; } - TfLiteStatus status = interpreter->ResizeInputTensor( - interpreter->inputs()[idx], convertJIntArrayToVector(env, dims)); - if (status != kTfLiteOk) { - throwException(env, kIllegalArgumentException, - "Failed to resize %d-th input: %s", idx, - error_reporter->CachedErrorMessage()); + // check whether it is resizing with the same dimensions. + TfLiteTensor* target = interpreter->tensor(input_idx); + bool is_changed = areDimsDifferent(env, target, dims); + if (is_changed) { + TfLiteStatus status = interpreter->ResizeInputTensor( + interpreter->inputs()[idx], convertJIntArrayToVector(env, dims)); + if (status != kTfLiteOk) { + throwException(env, kIllegalArgumentException, + "Failed to resize %d-th input: %s", idx, + error_reporter->CachedErrorMessage()); + return JNI_FALSE; + } } + return is_changed ? JNI_TRUE : JNI_FALSE; } JNIEXPORT void JNICALL Java_org_tensorflow_lite_NativeInterpreterWrapper_delete( diff --git a/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.h b/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.h index c52a7e4e439936344be26d5761fb5747db64794a..d611ec7f38669341761068f571be6311c2f3cb6a 100644 --- a/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.h +++ b/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.h @@ -18,6 +18,7 @@ limitations under the License. #include #include +#include #include #include "tensorflow/contrib/lite/context.h" #include "tensorflow/contrib/lite/interpreter.h" @@ -28,6 +29,9 @@ limitations under the License. namespace tflite { // This is to be provided at link-time by a library. extern std::unique_ptr CreateOpResolver(); +extern timespec getCurrentTime(); +extern jlong timespec_diff_nanoseconds(struct timespec* start, + struct timespec* stop); } // namespace tflite #ifdef __cplusplus @@ -104,21 +108,23 @@ Java_org_tensorflow_lite_NativeInterpreterWrapper_createInterpreter( /* * Class: org_tensorflow_lite_NativeInterpreterWrapper * Method: - * Signature: (JJ[Ljava/lang/Object;[I[I[Ljava/lang/Object;)[J + * Signature: + * (JJ[Ljava/lang/Object;[I[I[Ljava/lang/Object;Ljava/lang/Object;Z)[J */ JNIEXPORT jlongArray JNICALL Java_org_tensorflow_lite_NativeInterpreterWrapper_run( JNIEnv* env, jclass clazz, jlong interpreter_handle, jlong error_handle, jobjectArray sizes, jintArray data_types, jintArray nums_of_bytes, - jobjectArray values); + jobjectArray values, jobject wrapper, jboolean memory_allocated); /* * Class: org_tensorflow_lite_NativeInterpreterWrapper * Method: * Signature: (JII)[I * - * It gets input dimensions if num_bytes matches number of bytes required by - * the input, else returns null and throws IllegalArgumentException. + * Gets input dimensions. If num_bytes is non-negative, it will check whether + * num_bytes matches num of bytes required by the input, and return null and + * throw IllegalArgumentException if not. */ JNIEXPORT jintArray JNICALL Java_org_tensorflow_lite_NativeInterpreterWrapper_getInputDims( @@ -127,11 +133,23 @@ Java_org_tensorflow_lite_NativeInterpreterWrapper_getInputDims( /* * Class: org_tensorflow_lite_NativeInterpreterWrapper * Method: - * Signature: (JJI[I) + * Signature: (JI)I * - * It resizes dimensions of a input. + * Gets output dimensions. */ -JNIEXPORT void JNICALL +JNIEXPORT jint JNICALL +Java_org_tensorflow_lite_NativeInterpreterWrapper_getOutputDataType( + JNIEnv* env, jclass clazz, jlong handle, jint output_idx); + +/* + * Class: org_tensorflow_lite_NativeInterpreterWrapper + * Method: + * Signature: (JJI[I)Z + * + * It returns true if resizing input tensor to different dimensions, else return + * false. + */ +JNIEXPORT jboolean JNICALL Java_org_tensorflow_lite_NativeInterpreterWrapper_resizeInput( JNIEnv* env, jclass clazz, jlong interpreter_handle, jlong error_handle, jint input_idx, jintArray dims); diff --git a/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/InterpreterTest.java b/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/InterpreterTest.java index 424b3de6c97672e310c54230a7ac1204f46d9ac8..61d6c35ec86beebf78dd81e17e145863516802fa 100644 --- a/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/InterpreterTest.java +++ b/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/InterpreterTest.java @@ -218,4 +218,52 @@ public final class InterpreterTest { int index = interpreter.getOutputIndex("MobilenetV1/Predictions/Softmax"); assertThat(index).isEqualTo(0); } + + @Test + public void testTurnOffNNAPI() throws Exception { + Path path = MODEL_FILE.toPath(); + FileChannel fileChannel = + (FileChannel) Files.newByteChannel(path, EnumSet.of(StandardOpenOption.READ)); + MappedByteBuffer mappedByteBuffer = + fileChannel.map(FileChannel.MapMode.READ_ONLY, 0, fileChannel.size()); + Interpreter interpreter = new Interpreter(mappedByteBuffer); + interpreter.setUseNNAPI(true); + float[] oneD = {1.23f, 6.54f, 7.81f}; + float[][] twoD = {oneD, oneD, oneD, oneD, oneD, oneD, oneD, oneD}; + float[][][] threeD = {twoD, twoD, twoD, twoD, twoD, twoD, twoD, twoD}; + float[][][][] fourD = {threeD, threeD}; + float[][][][] parsedOutputs = new float[2][8][8][3]; + interpreter.run(fourD, parsedOutputs); + float[] outputOneD = parsedOutputs[0][0][0]; + float[] expected = {3.69f, 19.62f, 23.43f}; + assertThat(outputOneD).usingTolerance(0.1f).containsExactly(expected).inOrder(); + interpreter.setUseNNAPI(false); + interpreter.run(fourD, parsedOutputs); + outputOneD = parsedOutputs[0][0][0]; + assertThat(outputOneD).usingTolerance(0.1f).containsExactly(expected).inOrder(); + interpreter.close(); + fileChannel.close(); + } + + @Test + public void testTurnOnNNAPI() throws Exception { + Path path = MODEL_FILE.toPath(); + FileChannel fileChannel = + (FileChannel) Files.newByteChannel(path, EnumSet.of(StandardOpenOption.READ)); + MappedByteBuffer mappedByteBuffer = + fileChannel.map(FileChannel.MapMode.READ_ONLY, 0, fileChannel.size()); + Interpreter interpreter = new Interpreter(mappedByteBuffer); + interpreter.setUseNNAPI(true); + float[] oneD = {1.23f, 6.54f, 7.81f}; + float[][] twoD = {oneD, oneD, oneD, oneD, oneD, oneD, oneD, oneD}; + float[][][] threeD = {twoD, twoD, twoD, twoD, twoD, twoD, twoD, twoD}; + float[][][][] fourD = {threeD, threeD}; + float[][][][] parsedOutputs = new float[2][8][8][3]; + interpreter.run(fourD, parsedOutputs); + float[] outputOneD = parsedOutputs[0][0][0]; + float[] expected = {3.69f, 19.62f, 23.43f}; + assertThat(outputOneD).usingTolerance(0.1f).containsExactly(expected).inOrder(); + interpreter.close(); + fileChannel.close(); + } } diff --git a/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/NativeInterpreterWrapperTest.java b/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/NativeInterpreterWrapperTest.java index 90323555d88419d837a76bca7de6d9998e388fca..d6b4e9f438ab19a1c51b8fcc6047f75075a34693 100644 --- a/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/NativeInterpreterWrapperTest.java +++ b/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/NativeInterpreterWrapperTest.java @@ -94,6 +94,30 @@ public final class NativeInterpreterWrapperTest { wrapper.close(); } + @Test + public void testRunWithInputsOfSameDims() { + NativeInterpreterWrapper wrapper = new NativeInterpreterWrapper(FLOAT_MODEL_PATH); + float[] oneD = {1.23f, -6.54f, 7.81f}; + float[][] twoD = {oneD, oneD, oneD, oneD, oneD, oneD, oneD, oneD}; + float[][][] threeD = {twoD, twoD, twoD, twoD, twoD, twoD, twoD, twoD}; + float[][][][] fourD = {threeD, threeD}; + Object[] inputs = {fourD}; + Tensor[] outputs = wrapper.run(inputs); + assertThat(outputs.length).isEqualTo(1); + float[][][][] parsedOutputs = new float[2][8][8][3]; + outputs[0].copyTo(parsedOutputs); + float[] outputOneD = parsedOutputs[0][0][0]; + float[] expected = {3.69f, -19.62f, 23.43f}; + assertThat(outputOneD).usingTolerance(0.1f).containsExactly(expected).inOrder(); + outputs = wrapper.run(inputs); + assertThat(outputs.length).isEqualTo(1); + parsedOutputs = new float[2][8][8][3]; + outputs[0].copyTo(parsedOutputs); + outputOneD = parsedOutputs[0][0][0]; + assertThat(outputOneD).usingTolerance(0.1f).containsExactly(expected).inOrder(); + wrapper.close(); + } + @Test public void testRunWithInt() { NativeInterpreterWrapper wrapper = new NativeInterpreterWrapper(INT_MODEL_PATH); @@ -417,4 +441,87 @@ public final class NativeInterpreterWrapperTest { assertThat(shape[1]).isEqualTo(3); assertThat(shape[2]).isEqualTo(1); } + + @Test + public void testGetInferenceLatency() { + NativeInterpreterWrapper wrapper = new NativeInterpreterWrapper(FLOAT_MODEL_PATH); + float[] oneD = {1.23f, 6.54f, 7.81f}; + float[][] twoD = {oneD, oneD, oneD, oneD, oneD, oneD, oneD, oneD}; + float[][][] threeD = {twoD, twoD, twoD, twoD, twoD, twoD, twoD, twoD}; + float[][][][] fourD = {threeD, threeD}; + Object[] inputs = {fourD}; + Tensor[] outputs = wrapper.run(inputs); + assertThat(outputs.length).isEqualTo(1); + assertThat(wrapper.getLastNativeInferenceDurationNanoseconds()).isGreaterThan(0L); + wrapper.close(); + } + + @Test + public void testGetInferenceLatencyWithNewWrapper() { + NativeInterpreterWrapper wrapper = new NativeInterpreterWrapper(FLOAT_MODEL_PATH); + assertThat(wrapper.getLastNativeInferenceDurationNanoseconds()).isNull(); + wrapper.close(); + } + + @Test + public void testGetLatencyAfterFailedInference() { + NativeInterpreterWrapper wrapper = new NativeInterpreterWrapper(FLOAT_MODEL_PATH); + float[] oneD = {1.23f, 6.54f, 7.81f}; + float[][] twoD = {oneD, oneD, oneD, oneD, oneD, oneD, oneD}; + float[][][] threeD = {twoD, twoD, twoD, twoD, twoD, twoD, twoD, twoD}; + float[][][][] fourD = {threeD, threeD}; + Object[] inputs = {fourD}; + try { + wrapper.run(inputs); + fail(); + } catch (IllegalArgumentException e) { + assertThat(e) + .hasMessageThat() + .contains("0-th input dimension should be [?,8,8,3], but found [?,8,7,3]"); + } + assertThat(wrapper.getLastNativeInferenceDurationNanoseconds()).isNull(); + wrapper.close(); + } + + @Test + public void testGetInputDims() { + NativeInterpreterWrapper wrapper = new NativeInterpreterWrapper(FLOAT_MODEL_PATH); + int[] expectedDims = {1, 8, 8, 3}; + assertThat(wrapper.getInputDims(0)).isEqualTo(expectedDims); + wrapper.close(); + } + + @Test + public void testGetInputDimsOutOfRange() { + NativeInterpreterWrapper wrapper = new NativeInterpreterWrapper(FLOAT_MODEL_PATH); + try { + wrapper.getInputDims(-1); + fail(); + } catch (IllegalArgumentException e) { + assertThat(e).hasMessageThat().contains("Out of range"); + } + try { + wrapper.getInputDims(1); + fail(); + } catch (IllegalArgumentException e) { + assertThat(e).hasMessageThat().contains("Out of range"); + } + wrapper.close(); + } + + @Test + public void testGetOutputDataType() { + NativeInterpreterWrapper wrapper = new NativeInterpreterWrapper(FLOAT_MODEL_PATH); + assertThat(wrapper.getOutputDataType(0)).contains("float"); + wrapper.close(); + wrapper = new NativeInterpreterWrapper(LONG_MODEL_PATH); + assertThat(wrapper.getOutputDataType(0)).contains("long"); + wrapper.close(); + wrapper = new NativeInterpreterWrapper(INT_MODEL_PATH); + assertThat(wrapper.getOutputDataType(0)).contains("int"); + wrapper.close(); + wrapper = new NativeInterpreterWrapper(BYTE_MODEL_PATH); + assertThat(wrapper.getOutputDataType(0)).contains("byte"); + wrapper.close(); + } } diff --git a/tensorflow/contrib/lite/java/src/testhelper/java/org/tensorflow/lite/TestHelper.java b/tensorflow/contrib/lite/java/src/testhelper/java/org/tensorflow/lite/TestHelper.java index 8660cabf709e6531a5667a16e5cf43a93c7135bd..3aef0c3bb6cc4748de0e55d31f0215a77320ae69 100644 --- a/tensorflow/contrib/lite/java/src/testhelper/java/org/tensorflow/lite/TestHelper.java +++ b/tensorflow/contrib/lite/java/src/testhelper/java/org/tensorflow/lite/TestHelper.java @@ -32,4 +32,55 @@ public class TestHelper { throw new IllegalArgumentException("Interpreter has not initialized; Failed to setUseNNAPI."); } } + + /** + * Gets the last inference duration in nanoseconds. It returns null if there is no previous + * inference run or the last inference run failed. + * + * @param interpreter an instance of {@code Interpreter}. If it is not initialized, an {@code + * IllegalArgumentException} will be thrown. + */ + public static Long getLastNativeInferenceDurationNanoseconds(Interpreter interpreter) { + if (interpreter != null && interpreter.wrapper != null) { + return interpreter.wrapper.getLastNativeInferenceDurationNanoseconds(); + } else { + throw new IllegalArgumentException("Interpreter has not initialized; Failed to get latency."); + } + } + + /** + * Gets the dimensions of an input. + * + * @param interpreter an instance of {@code Interpreter}. If it is not initialized, an {@code + * IllegalArgumentException} will be thrown. + * @param index an integer index of the input. If it is invalid, an {@code + * IllegalArgumentException} will be thrown. + */ + public static int[] getInputDims(Interpreter interpreter, int index) { + if (interpreter != null && interpreter.wrapper != null) { + return interpreter.wrapper.getInputDims(index); + } else { + throw new IllegalArgumentException( + "Interpreter has not initialized;" + " Failed to get input dimensions."); + } + } + + /** + * Gets the string name of the data type of an output. + * + * @param interpreter an instance of {@code Interpreter}. If it is not initialized, an {@code + * IllegalArgumentException} will be thrown. + * @param index an integer index of the output. If it is invalid, an {@code + * IllegalArgumentException} will be thrown. + * @return string name of the data type. Possible values include "float", "int", "byte", and + * "long". + */ + public static String getOutputDataType(Interpreter interpreter, int index) { + if (interpreter != null && interpreter.wrapper != null) { + return interpreter.wrapper.getOutputDataType(index); + } else { + throw new IllegalArgumentException( + "Interpreter has not initialized;" + " Failed to get output data type."); + } + } } diff --git a/tensorflow/contrib/lite/kernels/BUILD b/tensorflow/contrib/lite/kernels/BUILD index 956bd35fe67b3a487f5eb545a827908e12127455..b8ab6d96a08679e0ee8ba8938f0fedb5a9e6c6fe 100644 --- a/tensorflow/contrib/lite/kernels/BUILD +++ b/tensorflow/contrib/lite/kernels/BUILD @@ -5,15 +5,17 @@ package(default_visibility = [ licenses(["notice"]) # Apache 2.0 load("//tensorflow/contrib/lite:build_def.bzl", "tflite_copts") -load( - "//tensorflow:tensorflow.bzl", - "tf_cc_test", -) +load("//tensorflow/contrib/lite:special_rules.bzl", "tflite_portable_test_suite") +load("//tensorflow:tensorflow.bzl", "tf_cc_test") tf_cc_test( name = "optional_tensor_test", size = "small", srcs = ["optional_tensor_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -33,11 +35,27 @@ cc_library( "//tensorflow/contrib/lite:schema_fbs_version", "//tensorflow/contrib/lite:string_util", "//tensorflow/contrib/lite/testing:util", - "//tensorflow/core:lib", + "//tensorflow/core:tflite_portable_logging", "@com_google_googletest//:gtest", ], ) +cc_library( + name = "eigen_support", + srcs = [ + "eigen_support.cc", + ], + hdrs = [ + "eigen_support.h", + ], + copts = tflite_copts(), + deps = [ + ":op_macros", + "//tensorflow/contrib/lite:context", + "//third_party/eigen3", + ], +) + cc_library( name = "gemm_support", srcs = [ @@ -90,6 +108,10 @@ tf_cc_test( name = "kernel_util_test", size = "small", srcs = ["kernel_util_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":kernel_util", "//tensorflow/contrib/lite/testing:util", @@ -97,6 +119,17 @@ tf_cc_test( ], ) +tf_cc_test( + name = "test_util_test", + size = "small", + srcs = ["test_util_test.cc"], + deps = [ + ":test_util", + "//tensorflow/contrib/lite/testing:util", + "@com_google_googletest//:gtest", + ], +) + cc_library( name = "builtin_ops", srcs = [ @@ -106,9 +139,11 @@ cc_library( "batch_to_space_nd.cc", "bidirectional_sequence_lstm.cc", "bidirectional_sequence_rnn.cc", + "cast.cc", "concatenation.cc", "conv.cc", "depthwise_conv.cc", + "dequantize.cc", "div.cc", "embedding_lookup.cc", "embedding_lookup_sparse.cc", @@ -154,6 +189,7 @@ cc_library( }), deps = [ ":activation_functor", + ":eigen_support", ":kernel_util", ":op_macros", "//tensorflow/contrib/lite:builtin_op_data", @@ -176,6 +212,10 @@ tf_cc_test( name = "activations_test", size = "small", srcs = ["activations_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -188,6 +228,10 @@ tf_cc_test( name = "add_test", size = "small", srcs = ["add_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -200,6 +244,10 @@ tf_cc_test( name = "transpose_test", size = "small", srcs = ["transpose_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -214,6 +262,10 @@ tf_cc_test( name = "space_to_batch_nd_test", size = "small", srcs = ["space_to_batch_nd_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -226,6 +278,22 @@ tf_cc_test( name = "batch_to_space_nd_test", size = "small", srcs = ["batch_to_space_nd_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], + deps = [ + ":builtin_ops", + "//tensorflow/contrib/lite:framework", + "//tensorflow/contrib/lite/kernels:test_util", + "@com_google_googletest//:gtest", + ], +) + +tf_cc_test( + name = "cast_test", + size = "small", + srcs = ["cast_test.cc"], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -238,6 +306,10 @@ tf_cc_test( name = "concatenation_test", size = "small", srcs = ["concatenation_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -250,6 +322,10 @@ tf_cc_test( name = "conv_test", size = "small", srcs = ["conv_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -263,6 +339,10 @@ tf_cc_test( name = "depthwise_conv_test", size = "small", srcs = ["depthwise_conv_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -271,10 +351,27 @@ tf_cc_test( ], ) +tf_cc_test( + name = "dequantize_test", + size = "small", + srcs = ["dequantize_test.cc"], + deps = [ + ":builtin_ops", + "//tensorflow/contrib/lite:framework", + "//tensorflow/contrib/lite/kernels:test_util", + "@com_google_absl//absl/memory", + "@com_google_googletest//:gtest", + ], +) + tf_cc_test( name = "basic_rnn_test", size = "small", srcs = ["basic_rnn_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -287,6 +384,10 @@ tf_cc_test( name = "bidirectional_sequence_lstm_test", size = "small", srcs = ["bidirectional_sequence_lstm_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -299,6 +400,10 @@ tf_cc_test( name = "unidirectional_sequence_lstm_test", size = "small", srcs = ["unidirectional_sequence_lstm_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -311,6 +416,9 @@ tf_cc_test( name = "bidirectional_sequence_rnn_test", size = "small", srcs = ["bidirectional_sequence_rnn_test.cc"], + tags = [ + "tflite_not_portable", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -323,6 +431,10 @@ tf_cc_test( name = "unidirectional_sequence_rnn_test", size = "small", srcs = ["unidirectional_sequence_rnn_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -335,6 +447,10 @@ tf_cc_test( name = "l2norm_test", size = "small", srcs = ["l2norm_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -347,6 +463,10 @@ tf_cc_test( name = "exp_test", size = "small", srcs = ["exp_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -359,6 +479,10 @@ tf_cc_test( name = "mean_test", size = "small", srcs = ["mean_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -371,6 +495,10 @@ tf_cc_test( name = "mul_test", size = "small", srcs = ["mul_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -383,6 +511,10 @@ tf_cc_test( name = "pad_test", size = "small", srcs = ["pad_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -395,6 +527,10 @@ tf_cc_test( name = "reshape_test", size = "small", srcs = ["reshape_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -407,6 +543,10 @@ tf_cc_test( name = "gather_test", size = "small", srcs = ["gather_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:builtin_op_data", @@ -420,6 +560,10 @@ tf_cc_test( name = "topk_v2_test", size = "small", srcs = ["topk_v2_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:builtin_op_data", @@ -433,6 +577,10 @@ tf_cc_test( name = "resize_bilinear_test", size = "small", srcs = ["resize_bilinear_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -445,6 +593,10 @@ tf_cc_test( name = "svdf_test", size = "small", srcs = ["svdf_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -457,6 +609,10 @@ tf_cc_test( name = "embedding_lookup_test", size = "small", srcs = ["embedding_lookup_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -469,6 +625,10 @@ tf_cc_test( name = "embedding_lookup_sparse_test", size = "small", srcs = ["embedding_lookup_sparse_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -481,6 +641,10 @@ tf_cc_test( name = "fully_connected_test", size = "small", srcs = ["fully_connected_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -493,6 +657,10 @@ tf_cc_test( name = "local_response_norm_test", size = "small", srcs = ["local_response_norm_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -505,6 +673,10 @@ tf_cc_test( name = "pooling_test", size = "small", srcs = ["pooling_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -517,6 +689,10 @@ tf_cc_test( name = "softmax_test", size = "small", srcs = ["softmax_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -530,6 +706,10 @@ tf_cc_test( name = "log_softmax_test", size = "small", srcs = ["log_softmax_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -543,6 +723,10 @@ tf_cc_test( name = "lsh_projection_test", size = "small", srcs = ["lsh_projection_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -555,6 +739,10 @@ tf_cc_test( name = "hashtable_lookup_test", size = "small", srcs = ["hashtable_lookup_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -568,6 +756,10 @@ tf_cc_test( name = "lstm_test", size = "small", srcs = ["lstm_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -580,6 +772,10 @@ tf_cc_test( name = "skip_gram_test", size = "small", srcs = ["skip_gram_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -593,6 +789,10 @@ tf_cc_test( name = "space_to_depth_test", size = "small", srcs = ["space_to_depth_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -605,6 +805,10 @@ tf_cc_test( name = "split_test", size = "small", srcs = ["split_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -617,6 +821,10 @@ tf_cc_test( name = "squeeze_test", size = "small", srcs = ["squeeze_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -629,6 +837,10 @@ tf_cc_test( name = "strided_slice_test", size = "small", srcs = ["strided_slice_test.cc"], + tags = [ + "tflite_not_portable_ios_arm64", + "tflite_not_portable_ios_x86_64", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -648,3 +860,5 @@ filegroup( ), visibility = ["//tensorflow:__subpackages__"], ) + +tflite_portable_test_suite() diff --git a/tensorflow/contrib/lite/kernels/activations.cc b/tensorflow/contrib/lite/kernels/activations.cc index 6acded3091cb820ba641bac2498799d295d7dc7f..093761c43c1cb41ddb2245da13c963014b51271c 100644 --- a/tensorflow/contrib/lite/kernels/activations.cc +++ b/tensorflow/contrib/lite/kernels/activations.cc @@ -63,6 +63,33 @@ TfLiteStatus GenericPrepare(TfLiteContext* context, TfLiteNode* node) { TfLiteIntArrayCopy(input->dims)); } +TfLiteStatus TanhPrepare(TfLiteContext* context, TfLiteNode* node) { + OpData* data = reinterpret_cast(node->user_data); + + TF_LITE_ENSURE_EQ(context, NumInputs(node), 1); + TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); + TfLiteTensor* input = GetInput(context, node, 0); + TfLiteTensor* output = GetOutput(context, node, 0); + TF_LITE_ENSURE_EQ(context, input->type, output->type); + + if (input->type == kTfLiteUInt8) { + static constexpr int kInputIntegerBits = 4; + + const double input_real_multiplier = + input->params.scale * + static_cast(1 << (31 - kInputIntegerBits)); + + QuantizeMultiplierGreaterThanOne(input_real_multiplier, + &data->input_multiplier, + &data->input_left_shift); + data->input_range_radius = + CalculateInputRadius(kInputIntegerBits, data->input_left_shift); + } + + return context->ResizeTensor(context, output, + TfLiteIntArrayCopy(input->dims)); +} + TfLiteStatus SigmoidPrepare(TfLiteContext* context, TfLiteNode* node) { OpData* data = reinterpret_cast(node->user_data); @@ -180,6 +207,7 @@ TfLiteStatus Relu6Eval(TfLiteContext* context, TfLiteNode* node) { } TfLiteStatus TanhEval(TfLiteContext* context, TfLiteNode* node) { + OpData* data = reinterpret_cast(node->user_data); TfLiteTensor* input = GetInput(context, node, 0); TfLiteTensor* output = GetOutput(context, node, 0); switch (input->type) { @@ -191,6 +219,14 @@ TfLiteStatus TanhEval(TfLiteContext* context, TfLiteNode* node) { for (; in < in_end; in++, out++) *out = std::tanh(*in); return kTfLiteOk; } break; + case kTfLiteUInt8: { + optimized_ops::Tanh(GetTensorData(input), GetTensorDims(input), + input->params.zero_point, data->input_range_radius, + data->input_multiplier, data->input_left_shift, + GetTensorData(output), + GetTensorDims(output)); + return kTfLiteOk; + } break; default: context->ReportError(context, "Only float32 supported currently."); return kTfLiteError; @@ -376,8 +412,8 @@ TfLiteRegistration* Register_RELU6() { } TfLiteRegistration* Register_TANH() { - static TfLiteRegistration r = {/*init=*/nullptr, /*free=*/nullptr, - activations::GenericPrepare, + static TfLiteRegistration r = {activations::Init, activations::Free, + activations::TanhPrepare, activations::TanhEval}; return &r; } diff --git a/tensorflow/contrib/lite/kernels/activations_test.cc b/tensorflow/contrib/lite/kernels/activations_test.cc index 302e52b96db0206f77eb4c8fcffd565b1db0cd3e..b9a96e3f79677c5a94ade86a6b334abb4c265fa1 100644 --- a/tensorflow/contrib/lite/kernels/activations_test.cc +++ b/tensorflow/contrib/lite/kernels/activations_test.cc @@ -52,6 +52,14 @@ class BaseActivationsOpModel : public SingleOpModel { BuildInterpreter({GetShape(input_)}); } + BaseActivationsOpModel(BuiltinOperator type, const TensorData &input, + const TensorData &output) { + input_ = AddInput(input); + output_ = AddOutput(output); + SetBuiltinOp(type, BuiltinOptions_NONE, 0); + BuildInterpreter({GetShape(input_)}); + } + protected: int input_; int output_; @@ -143,6 +151,27 @@ TEST(FloatActivationsOpTest, Tanh) { }))); } +TEST(QuantizedActivationsOpTest, Tanh) { + QuantizedActivationsOpModel m( + BuiltinOperator_TANH, + /*input=*/{TensorType_UINT8, {1, 2, 4, 1}, -8, 8}, + /*output=*/{TensorType_UINT8, {1, 2, 4, 1}, -1, 1}); + m.SetInput({ + 0, -6, 2, 4, // + -4, -2, 8, 1, // + }); + m.Invoke(); + EXPECT_THAT(m.GetDequantizedOutput(), + ElementsAreArray(ArrayFloatNear( + { + 0.0, -0.999987, 0.964027, 0.999329, // + -0.996078, -0.96402, 0.99999, 0.76159, // + }, + 4 * (1. / 256)))); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray({128, 0, 251, 255, 0, 5, 255, 226})); +} + TEST(FloatActivationsOpTest, Sigmoid) { FloatActivationsOpModel m(BuiltinOperator_LOGISTIC, /*input=*/{TensorType_FLOAT32, {1, 2, 4, 1}}); diff --git a/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc b/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc index 8d70df5e21fab110be238a6f72abe9aac8a75622..a64ac42bc43336db928d2682e290f5263f3db0f4 100644 --- a/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc +++ b/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc @@ -24,6 +24,7 @@ limitations under the License. #include "tensorflow/contrib/lite/builtin_op_data.h" #include "tensorflow/contrib/lite/context.h" #include "tensorflow/contrib/lite/kernels/activation_functor.h" +#include "tensorflow/contrib/lite/kernels/internal/kernel_utils.h" #include "tensorflow/contrib/lite/kernels/internal/tensor_utils.h" #include "tensorflow/contrib/lite/kernels/kernel_util.h" #include "tensorflow/contrib/lite/kernels/op_macros.h" @@ -443,166 +444,6 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { return kTfLiteOk; } -// Performs an LSTM batch inference step for input specified by input_ptr_batch. -// The LSTM cell is specified by the pointers to its weights (*_weights_ptr) and -// biases (*_bias_ptr), and buffers (*_scratch), along with additional -// parameters: -// - params: various LSTM params including activation, clipping, etc., -// - use_cifg: use coupled input forget gates, -// - use_peephole: whether to use peephole connection or not, -// - n_batch: size of batch, -// - n_cell: number of cells (or units), -// - n_input: the input size, -// - n_output: the output size. -// -// The pointers to the hidden state and the output are updated as a result. -// -// The pointers with the suffix "_batch" point to data aligned in batch_major -// order, and each step processes batch_size many inputs from input_ptr_batch, -// and updates batch_size many outputs and hidden states. -void LstmBatchStep( - const float* input_ptr_batch, const float* input_to_input_weights_ptr, - const float* input_to_forget_weights_ptr, - const float* input_to_cell_weights_ptr, - const float* input_to_output_weights_ptr, - const float* recurrent_to_input_weights_ptr, - const float* recurrent_to_forget_weights_ptr, - const float* recurrent_to_cell_weights_ptr, - const float* recurrent_to_output_weights_ptr, - const float* cell_to_input_weights_ptr, - const float* cell_to_forget_weights_ptr, - const float* cell_to_output_weights_ptr, const float* input_gate_bias_ptr, - const float* forget_gate_bias_ptr, const float* cell_bias_ptr, - const float* output_gate_bias_ptr, const float* projection_weights_ptr, - const float* projection_bias_ptr, const TfLiteLSTMParams* params, - bool use_cifg, bool use_peephole, int n_batch, int n_cell, int n_input, - int n_output, float* output_state_ptr, float* cell_state_ptr, - float* input_gate_scratch, float* forget_gate_scratch, float* cell_scratch, - float* output_gate_scratch, float* output_ptr_time) { - // Initialize scratch buffers with bias. - if (!use_cifg) { - tensor_utils::VectorBatchVectorAssign(input_gate_bias_ptr, n_cell, n_batch, - input_gate_scratch); - } - tensor_utils::VectorBatchVectorAssign(forget_gate_bias_ptr, n_cell, n_batch, - forget_gate_scratch); - tensor_utils::VectorBatchVectorAssign(cell_bias_ptr, n_cell, n_batch, - cell_scratch); - tensor_utils::VectorBatchVectorAssign(output_gate_bias_ptr, n_cell, n_batch, - output_gate_scratch); - - // For each batch and cell: compute input_weight * input. - if (!use_cifg) { - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - input_to_input_weights_ptr, n_cell, n_input, input_ptr_batch, n_batch, - input_gate_scratch, /*result_stride=*/1); - } - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - input_to_forget_weights_ptr, n_cell, n_input, input_ptr_batch, n_batch, - forget_gate_scratch, /*result_stride=*/1); - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - input_to_cell_weights_ptr, n_cell, n_input, input_ptr_batch, n_batch, - cell_scratch, /*result_stride=*/1); - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - input_to_output_weights_ptr, n_cell, n_input, input_ptr_batch, n_batch, - output_gate_scratch, /*result_stride=*/1); - - // For each batch and cell: compute recurrent_weight * output_state. - if (!use_cifg) { - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - recurrent_to_input_weights_ptr, n_cell, n_output, output_state_ptr, - n_batch, input_gate_scratch, - /*result_stride=*/1); - } - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - recurrent_to_forget_weights_ptr, n_cell, n_output, output_state_ptr, - n_batch, forget_gate_scratch, - /*result_stride=*/1); - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - recurrent_to_cell_weights_ptr, n_cell, n_output, output_state_ptr, - n_batch, cell_scratch, /*result_stride=*/1); - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - recurrent_to_output_weights_ptr, n_cell, n_output, output_state_ptr, - n_batch, output_gate_scratch, - /*result_stride=*/1); - - // For each batch and cell: update input gate. - if (!use_cifg) { - if (use_peephole) { - tensor_utils::VectorBatchVectorCwiseProductAccumulate( - cell_to_input_weights_ptr, n_cell, cell_state_ptr, n_batch, - input_gate_scratch); - } - tensor_utils::ApplySigmoidToVector(input_gate_scratch, n_cell * n_batch, - input_gate_scratch); - } - - // For each batch and cell: update forget gate. - if (use_peephole) { - tensor_utils::VectorBatchVectorCwiseProductAccumulate( - cell_to_forget_weights_ptr, n_cell, cell_state_ptr, n_batch, - forget_gate_scratch); - } - tensor_utils::ApplySigmoidToVector(forget_gate_scratch, n_cell * n_batch, - forget_gate_scratch); - - // For each batch and cell: update the cell. - tensor_utils::VectorVectorCwiseProduct(forget_gate_scratch, cell_state_ptr, - n_batch * n_cell, cell_state_ptr); - tensor_utils::ApplyActivationToVector(cell_scratch, n_batch * n_cell, - params->activation, cell_scratch); - if (use_cifg) { - tensor_utils::Sub1Vector(forget_gate_scratch, n_batch * n_cell, - forget_gate_scratch); - tensor_utils::VectorVectorCwiseProductAccumulate( - cell_scratch, forget_gate_scratch, n_batch * n_cell, cell_state_ptr); - } else { - tensor_utils::VectorVectorCwiseProductAccumulate( - cell_scratch, input_gate_scratch, n_batch * n_cell, cell_state_ptr); - } - if (params->cell_clip > 0.0) { - tensor_utils::ClipVector(cell_state_ptr, n_batch * n_cell, - params->cell_clip, cell_state_ptr); - } - - // For each batch and cell: update the output gate. - if (use_peephole) { - tensor_utils::VectorBatchVectorCwiseProductAccumulate( - cell_to_output_weights_ptr, n_cell, cell_state_ptr, n_batch, - output_gate_scratch); - } - tensor_utils::ApplySigmoidToVector(output_gate_scratch, n_batch * n_cell, - output_gate_scratch); - tensor_utils::ApplyActivationToVector(cell_state_ptr, n_batch * n_cell, - params->activation, cell_scratch); - tensor_utils::VectorVectorCwiseProduct(output_gate_scratch, cell_scratch, - n_batch * n_cell, output_gate_scratch); - - // For each batch: update the projection and output_state. - const bool use_projection_weight = (projection_weights_ptr != nullptr); - const bool use_projection_bias = (projection_bias_ptr != nullptr); - if (use_projection_weight) { - if (use_projection_bias) { - tensor_utils::VectorBatchVectorAssign(projection_bias_ptr, n_output, - n_batch, output_ptr_time); - } else { - tensor_utils::ZeroVector(output_ptr_time, n_batch * n_output); - } - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - projection_weights_ptr, n_output, n_cell, output_gate_scratch, n_batch, - output_ptr_time, /*result_stride=*/1); - if (params->proj_clip > 0.0) { - tensor_utils::ClipVector(output_ptr_time, n_batch * n_output, - params->proj_clip, output_ptr_time); - } - } else { - tensor_utils::CopyVector(output_gate_scratch, n_batch * n_output, - output_ptr_time); - } - tensor_utils::CopyVector(output_ptr_time, n_batch * n_output, - output_state_ptr); -} - // The LSTM Op engine. TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { auto* params = reinterpret_cast(node->builtin_data); @@ -756,7 +597,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { const float* input_ptr_batch = input->data.f + t * n_batch * n_input; float* output_ptr_time = fw_output->data.f + t * n_batch * n_fw_output; - LstmBatchStep( + kernel_utils::LstmStep( input_ptr_batch, fw_input_to_input_weights_ptr, fw_input_to_forget_weights->data.f, fw_input_to_cell_weights->data.f, fw_input_to_output_weights->data.f, fw_recurrent_to_input_weights_ptr, @@ -766,11 +607,10 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { fw_cell_to_forget_weights_ptr, fw_cell_to_output_weights_ptr, fw_input_gate_bias_ptr, fw_forget_gate_bias->data.f, fw_cell_bias->data.f, fw_output_gate_bias->data.f, - fw_projection_weights_ptr, fw_projection_bias_ptr, params, fw_use_cifg, - fw_use_peephole, n_batch, n_fw_cell, n_input, n_fw_output, - fw_output_state->data.f, fw_cell_state->data.f, fw_input_gate_scratch, - fw_forget_gate_scratch, fw_cell_scratch, fw_output_gate_scratch, - output_ptr_time); + fw_projection_weights_ptr, fw_projection_bias_ptr, params, n_batch, + n_fw_cell, n_input, n_fw_output, fw_output_state->data.f, + fw_cell_state->data.f, fw_input_gate_scratch, fw_forget_gate_scratch, + fw_cell_scratch, fw_output_gate_scratch, output_ptr_time); } // n_cell and n_output will be the same size when there is no projection. @@ -828,7 +668,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { const float* input_ptr_batch = input->data.f + t * n_batch * n_input; float* output_ptr_time = bw_output->data.f + t * n_batch * n_bw_output; - LstmBatchStep( + kernel_utils::LstmStep( input_ptr_batch, bw_input_to_input_weights_ptr, bw_input_to_forget_weights->data.f, bw_input_to_cell_weights->data.f, bw_input_to_output_weights->data.f, bw_recurrent_to_input_weights_ptr, @@ -838,11 +678,10 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { bw_cell_to_forget_weights_ptr, bw_cell_to_output_weights_ptr, bw_input_gate_bias_ptr, bw_forget_gate_bias->data.f, bw_cell_bias->data.f, bw_output_gate_bias->data.f, - bw_projection_weights_ptr, bw_projection_bias_ptr, params, bw_use_cifg, - bw_use_peephole, n_batch, n_bw_cell, n_input, n_bw_output, - bw_output_state->data.f, bw_cell_state->data.f, bw_input_gate_scratch, - bw_forget_gate_scratch, bw_cell_scratch, bw_output_gate_scratch, - output_ptr_time); + bw_projection_weights_ptr, bw_projection_bias_ptr, params, n_batch, + n_bw_cell, n_input, n_bw_output, bw_output_state->data.f, + bw_cell_state->data.f, bw_input_gate_scratch, bw_forget_gate_scratch, + bw_cell_scratch, bw_output_gate_scratch, output_ptr_time); } // Backward step. diff --git a/tensorflow/contrib/lite/kernels/cast.cc b/tensorflow/contrib/lite/kernels/cast.cc new file mode 100644 index 0000000000000000000000000000000000000000..19942de7bc0c083f192a4b337b224b778d991140 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/cast.cc @@ -0,0 +1,99 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include +#include "tensorflow/contrib/lite/builtin_op_data.h" +#include "tensorflow/contrib/lite/context.h" +#include "tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h" +#include "tensorflow/contrib/lite/kernels/internal/tensor.h" +#include "tensorflow/contrib/lite/kernels/kernel_util.h" +#include "tensorflow/contrib/lite/kernels/op_macros.h" +#include "tensorflow/contrib/lite/string_util.h" + +namespace tflite { +namespace ops { +namespace builtin { +namespace cast { +constexpr int kInputTensor = 0; +constexpr int kOutputTensor = 0; + +TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { + TF_LITE_ENSURE_EQ(context, NumInputs(node), 1); + TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); + TfLiteTensor* input = GetInput(context, node, kInputTensor); + TfLiteTensor* output = GetOutput(context, node, kOutputTensor); + return context->ResizeTensor(context, output, + TfLiteIntArrayCopy(input->dims)); +} + +template +void copyCast(const FromT* in, ToT* out, int num_elements) { + std::transform(in, in + num_elements, out, + [](FromT a) { return static_cast(a); }); +} + +template +TfLiteStatus copyToTensor(const FromT* in, TfLiteTensor* out, + int num_elements) { + switch (out->type) { + case kTfLiteInt64: + copyCast(in, out->data.i64, num_elements); + break; + case kTfLiteInt32: + copyCast(in, out->data.i32, num_elements); + break; + case kTfLiteUInt8: + copyCast(in, out->data.uint8, num_elements); + break; + case kTfLiteFloat32: + copyCast(in, out->data.f, num_elements); + break; + default: + // Unsupported type. + return kTfLiteError; + } + return kTfLiteOk; +} + +TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { + TfLiteTensor* input = GetInput(context, node, kInputTensor); + TfLiteTensor* output = GetOutput(context, node, kOutputTensor); + const int num_elements = NumElements(input); + TF_LITE_ENSURE_EQ(context, num_elements, NumElements(output)); + switch (input->type) { + case kTfLiteInt64: + return copyToTensor(input->data.i64, output, num_elements); + case kTfLiteInt32: + return copyToTensor(input->data.i32, output, num_elements); + case kTfLiteUInt8: + return copyToTensor(input->data.uint8, output, num_elements); + case kTfLiteFloat32: + return copyToTensor(input->data.f, output, num_elements); + default: + // Unsupported type. + return kTfLiteError; + } + return kTfLiteOk; +} +} // namespace cast + +TfLiteRegistration* Register_CAST() { + static TfLiteRegistration r = {nullptr, nullptr, cast::Prepare, cast::Eval}; + return &r; +} + +} // namespace builtin +} // namespace ops +} // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/cast_test.cc b/tensorflow/contrib/lite/kernels/cast_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..4e56482a371550b6275a6380e2beebe3cef958ff --- /dev/null +++ b/tensorflow/contrib/lite/kernels/cast_test.cc @@ -0,0 +1,66 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include "tensorflow/contrib/lite/interpreter.h" +#include "tensorflow/contrib/lite/kernels/register.h" +#include "tensorflow/contrib/lite/kernels/test_util.h" +#include "tensorflow/contrib/lite/model.h" + +namespace tflite { +namespace { + +using ::testing::ElementsAreArray; + +class CastOpModel : public SingleOpModel { + public: + CastOpModel(const TensorData& input, const TensorData& output) { + input_ = AddInput(input); + output_ = AddOutput(output); + SetBuiltinOp(BuiltinOperator_CAST, BuiltinOptions_CastOptions, + CreateCastOptions(builder_).Union()); + BuildInterpreter({GetShape(input_)}); + } + + int input() const { return input_; } + int output() const { return output_; } + + protected: + int input_; + int output_; +}; + +TEST(CastOpModel, CastIntToFloat) { + CastOpModel m({TensorType_INT64, {2, 3}}, {TensorType_FLOAT32, {2, 3}}); + m.PopulateTensor(m.input(), {100, 200, 300, 400, 500, 600}); + m.Invoke(); + EXPECT_THAT(m.ExtractVector(m.output()), + ElementsAreArray({100.f, 200.f, 300.f, 400.f, 500.f, 600.f})); +} + +TEST(CastOpModel, CastFloatToInt) { + CastOpModel m({TensorType_FLOAT32, {3, 2}}, {TensorType_INT32, {3, 2}}); + m.PopulateTensor(m.input(), {100.f, 20.f, 3.f, 0.4f, 0.999f, 1.1f}); + m.Invoke(); + EXPECT_THAT(m.ExtractVector(m.output()), + ElementsAreArray({100, 20, 3, 0, 0, 1})); +} + +} // namespace +} // namespace tflite +int main(int argc, char** argv) { + ::tflite::LogToStderr(); + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} diff --git a/tensorflow/contrib/lite/kernels/conv.cc b/tensorflow/contrib/lite/kernels/conv.cc index b93a416351cae34b2df8791e382a8a2cd38dcffb..e0cd12f1b4042c3d8b28159e288166bf1437e6ef 100644 --- a/tensorflow/contrib/lite/kernels/conv.cc +++ b/tensorflow/contrib/lite/kernels/conv.cc @@ -23,6 +23,7 @@ limitations under the License. #include "tensorflow/contrib/lite/builtin_op_data.h" #include "tensorflow/contrib/lite/context.h" +#include "tensorflow/contrib/lite/kernels/eigen_support.h" #include "tensorflow/contrib/lite/kernels/gemm_support.h" #include "tensorflow/contrib/lite/kernels/internal/optimized/cblas_conv.h" #include "tensorflow/contrib/lite/kernels/internal/optimized/multithreaded_conv.h" @@ -43,6 +44,8 @@ namespace conv { enum KernelType { kReference, kGenericOptimized, // Neon-free + // kMultithreadOptimized is a mixture of an Eigen-based kernel when threads + // are available and kGenericOptimized when we must use only one thread. kMultithreadOptimized, // The kernel uses use CBLAS interface for matrix multiplication. // It's fast when an optimized CBLAS implementation is available (e.g. Apple @@ -61,7 +64,7 @@ struct OpData { TfLitePaddingValues padding; // The scaling factor from input to output (aka the 'real multiplier') can - // be represented as a fixed point multipler plus a left shift. + // be represented as a fixed point multiplier plus a left shift. int32_t output_multiplier; int output_shift; // The range of the fused activation layer. For example for kNone and @@ -75,6 +78,8 @@ struct OpData { bool need_hwcn_weights; bool have_weights_been_transposed; bool need_im2col; + + bool run_multithreaded_kernel; }; void* Init(TfLiteContext* context, const char* buffer, size_t length) { @@ -83,10 +88,15 @@ void* Init(TfLiteContext* context, const char* buffer, size_t length) { // to carry information from Prepare() to Eval(). auto* data = new OpData; gemm_support::IncrementUsageCounter(context); + eigen_support::IncrementUsageCounter(context); + + data->run_multithreaded_kernel = context->recommended_num_threads != 1; + return data; } void Free(TfLiteContext* context, void* buffer) { + eigen_support::DecrementUsageCounter(context); gemm_support::DecrementUsageCounter(context); delete reinterpret_cast(buffer); } @@ -137,7 +147,8 @@ static TfLiteStatus AllocateTemporaryTensorsIfRequired(TfLiteContext* context, // buffer to store the results. // This path is only used for float processing, so only create the buffer if // we're running with that data type. - data->need_hwcn_weights = (input->type == kTfLiteFloat32); + data->need_hwcn_weights = + (input->type == kTfLiteFloat32 && data->run_multithreaded_kernel); int temporaries_count = 0; if (data->need_im2col) { @@ -449,8 +460,13 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { // separate ops to avoid dispatch overhead here. switch (input->type) { // Already know in/outtypes are same. case kTfLiteFloat32: - EvalFloat(context, node, params, data, input, filter, bias, - im2col, hwcn_weights, output); + if (data->run_multithreaded_kernel) { + EvalFloat(context, node, params, data, input, filter, bias, + im2col, hwcn_weights, output); + } else { + EvalFloat(context, node, params, data, input, filter, + bias, im2col, hwcn_weights, output); + } break; case kTfLiteUInt8: EvalQuantized(context, node, params, data, input, filter, diff --git a/tensorflow/contrib/lite/kernels/depthwise_conv.cc b/tensorflow/contrib/lite/kernels/depthwise_conv.cc index 15dbfe08c82befcf001b9ed9a053528b5606053e..cad9ce114c8387047af2b63bee704035fd329330 100644 --- a/tensorflow/contrib/lite/kernels/depthwise_conv.cc +++ b/tensorflow/contrib/lite/kernels/depthwise_conv.cc @@ -52,7 +52,7 @@ enum KernelType { struct OpData { TfLitePaddingValues padding; // The scaling factor from input to output (aka the 'real multiplier') can - // be represented as a fixed point multipler plus a left shift. + // be represented as a fixed point multiplier plus a left shift. int32_t output_multiplier; int output_shift; // The range of the fused activation layer. For example for kNone and diff --git a/tensorflow/contrib/lite/kernels/dequantize.cc b/tensorflow/contrib/lite/kernels/dequantize.cc new file mode 100644 index 0000000000000000000000000000000000000000..e685f2465f627cf30e02564e6f16e1ec69e208e2 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/dequantize.cc @@ -0,0 +1,77 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include + +#include "tensorflow/contrib/lite/builtin_op_data.h" +#include "tensorflow/contrib/lite/context.h" +#include "tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h" +#include "tensorflow/contrib/lite/kernels/internal/tensor.h" +#include "tensorflow/contrib/lite/kernels/kernel_util.h" +#include "tensorflow/contrib/lite/kernels/op_macros.h" + +namespace tflite { +namespace ops { +namespace builtin { +namespace dequantize { + +struct OpContext { + OpContext(TfLiteContext* context, TfLiteNode* node) { + input = GetInput(context, node, 0); + output = GetOutput(context, node, 0); + } + TfLiteTensor* input; + TfLiteTensor* output; +}; + +TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { + TF_LITE_ENSURE_EQ(context, NumInputs(node), 1); + TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); + + OpContext op_context(context, node); + + TF_LITE_ENSURE(context, op_context.input->type == kTfLiteUInt8); + + op_context.output->type = kTfLiteFloat32; + return context->ResizeTensor(context, op_context.output, + TfLiteIntArrayCopy(op_context.input->dims)); +} + +TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { + OpContext op_context(context, node); + + auto zero_point = op_context.input->params.zero_point; + auto scale = op_context.input->params.scale; + + optimized_ops::Dequantize(GetTensorData(op_context.input), + GetTensorDims(op_context.input), zero_point, scale, + GetTensorData(op_context.output), + GetTensorDims(op_context.output)); + return kTfLiteOk; +} + +} // namespace dequantize + +TfLiteRegistration* Register_DEQUANTIZE_OPT() { + static TfLiteRegistration r = {nullptr, nullptr, dequantize::Prepare, + dequantize::Eval}; + return &r; +} + +TfLiteRegistration* Register_DEQUANTIZE() { return Register_DEQUANTIZE_OPT(); } + +} // namespace builtin +} // namespace ops +} // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/dequantize_test.cc b/tensorflow/contrib/lite/kernels/dequantize_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..fcd74206177a0a97db168338e3619d4b95c052a9 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/dequantize_test.cc @@ -0,0 +1,65 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include "tensorflow/contrib/lite/interpreter.h" +#include "tensorflow/contrib/lite/kernels/register.h" +#include "tensorflow/contrib/lite/kernels/test_util.h" +#include "tensorflow/contrib/lite/model.h" + +namespace tflite { +namespace { + +using ::testing::ElementsAreArray; + +class DequantizeOpModel : public SingleOpModel { + public: + DequantizeOpModel(std::initializer_list shape, float min, float max) { + input_ = AddInput({TensorType_UINT8, shape, min, max}); + output_ = AddOutput({TensorType_FLOAT32, shape}); + SetBuiltinOp(BuiltinOperator_DEQUANTIZE, BuiltinOptions_DequantizeOptions, + CreateDequantizeOptions(builder_).Union()); + + BuildInterpreter({GetShape(input_)}); + } + + void SetInput(std::initializer_list data) { + PopulateTensor(input_, data); + } + + std::vector GetOutput() { return ExtractVector(output_); } + + private: + int input_; + int output_; +}; + +TEST(SplitOpTest, FourDimensional) { + DequantizeOpModel m({2, 5}, -63.5, 64); + + 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}))); +} + +} // namespace +} // namespace tflite + +int main(int argc, char** argv) { + ::tflite::LogToStderr(); + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} diff --git a/tensorflow/contrib/lite/kernels/eigen_support.cc b/tensorflow/contrib/lite/kernels/eigen_support.cc new file mode 100644 index 0000000000000000000000000000000000000000..213e46555210102b8faeb2e4d9900f924a023366 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/eigen_support.cc @@ -0,0 +1,53 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include "tensorflow/contrib/lite/kernels/eigen_support.h" + +#include "third_party/eigen3/Eigen/Core" +#include "tensorflow/contrib/lite/kernels/op_macros.h" + +namespace tflite { +namespace eigen_support { + +struct RefCountedEigenContext { + int num_references = 0; +}; + +void IncrementUsageCounter(TfLiteContext* context) { + auto* ptr = reinterpret_cast(context->eigen_context); + if (ptr == nullptr) { + if (context->recommended_num_threads != -1) { + Eigen::setNbThreads(context->recommended_num_threads); + } + ptr = new RefCountedEigenContext; + ptr->num_references = 0; + context->eigen_context = ptr; + } + ptr->num_references++; +} + +void DecrementUsageCounter(TfLiteContext* context) { + auto* ptr = reinterpret_cast(context->eigen_context); + if (ptr == nullptr) { + TF_LITE_FATAL( + "Call to DecrementUsageCounter() not preceded by " + "IncrementUsageCounter()"); + } + if (--ptr->num_references == 0) { + delete ptr; + } +} + +} // namespace eigen_support +} // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/eigen_support.h b/tensorflow/contrib/lite/kernels/eigen_support.h new file mode 100644 index 0000000000000000000000000000000000000000..d47e691123282a8a8cc53c29be1d95af037e3939 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/eigen_support.h @@ -0,0 +1,34 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_CONTRIB_LITE_KERNELS_EIGEN_SUPPORT_H_ +#define TENSORFLOW_CONTRIB_LITE_KERNELS_EIGEN_SUPPORT_H_ + +#include "tensorflow/contrib/lite/context.h" + +namespace tflite { +namespace eigen_support { + +// Let the framework know that the op will be using Eigen. If necessary a set of +// temporary Eigen objects might be created and placed in 'context'. +void IncrementUsageCounter(TfLiteContext* context); + +// Let the framework know that the op stopped using Eigen. If there are no more +// usages all temporary Eigen objects will be deleted. +void DecrementUsageCounter(TfLiteContext* context); + +} // namespace eigen_support +} // namespace tflite + +#endif // TENSORFLOW_CONTRIB_LITE_KERNELS_EIGEN_SUPPORT_H_ diff --git a/tensorflow/contrib/lite/kernels/fully_connected.cc b/tensorflow/contrib/lite/kernels/fully_connected.cc index a77fe94e499078bc2f0660e8e49fd557ed0f625d..888e67966c0a408257e763a405bf6e928310f4d9 100644 --- a/tensorflow/contrib/lite/kernels/fully_connected.cc +++ b/tensorflow/contrib/lite/kernels/fully_connected.cc @@ -48,7 +48,7 @@ enum KernelType { struct OpData { // The scaling factor from input to output (aka the 'real multiplier') can - // be represented as a fixed point multipler plus a left shift. + // be represented as a fixed point multiplier plus a left shift. int32_t output_multiplier; int output_shift; // The range of the fused activation layer. For example for kNone and diff --git a/tensorflow/contrib/lite/kernels/gemm_support.cc b/tensorflow/contrib/lite/kernels/gemm_support.cc index eb2b0aacf7ecc3ed5dbde5ccce7a46dcda0a93b3..76a5165d148c6c1829580a47456cebce321d7c5a 100644 --- a/tensorflow/contrib/lite/kernels/gemm_support.cc +++ b/tensorflow/contrib/lite/kernels/gemm_support.cc @@ -29,6 +29,9 @@ void IncrementUsageCounter(TfLiteContext* context) { if (ptr == nullptr) { ptr = new RefCountedGemmContext; ptr->gemm_context_ = new gemmlowp::GemmContext(); + if (context->recommended_num_threads != -1) { + ptr->gemm_context_->set_max_num_threads(context->recommended_num_threads); + } ptr->num_references_ = 0; context->gemm_context = ptr; } @@ -58,11 +61,5 @@ gemmlowp::GemmContext* GetFromContext(TfLiteContext* context) { return ptr->gemm_context_; } -void SetMaxNumThreads(TfLiteContext* context, int num_threads) { - IncrementUsageCounter(context); - GetFromContext(context)->set_max_num_threads(num_threads); - DecrementUsageCounter(context); -} - } // namespace gemm_support } // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/gemm_support.h b/tensorflow/contrib/lite/kernels/gemm_support.h index 466781cbcecc7fb851d9078c450cc6c12364d2bb..37af772c6846f2f8124faabf1a0f0987e2e9393d 100644 --- a/tensorflow/contrib/lite/kernels/gemm_support.h +++ b/tensorflow/contrib/lite/kernels/gemm_support.h @@ -45,9 +45,6 @@ void IncrementUsageCounter(TfLiteContext* context); // 'context'. If there are no more usages the GemmContext will be deleted. void DecrementUsageCounter(TfLiteContext* context); -// Set the maximum number threads available for gemmlowp operations. -void SetMaxNumThreads(TfLiteContext* context, int num_threads); - } // namespace gemm_support } // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/internal/BUILD b/tensorflow/contrib/lite/kernels/internal/BUILD index f47fb04cbaa688b75e763ff9d3cb7df44ac3f166..aa3957bee133c8b51a82e9c62884ce365e086d2e 100644 --- a/tensorflow/contrib/lite/kernels/internal/BUILD +++ b/tensorflow/contrib/lite/kernels/internal/BUILD @@ -10,21 +10,25 @@ tflite_deps_intel = [ "@arm_neon_2_x86_sse", ] +HARD_FP_FLAGS_IF_APPLICABLE = select({ + "//tensorflow:android_arm": ["-mfloat-abi=softfp"], + "//tensorflow:android_arm64": ["-mfloat-abi=softfp"], + "//tensorflow:android_armeabi": ["-mfloat-abi=softfp"], + "//conditions:default": [], +}) + NEON_FLAGS_IF_APPLICABLE = select({ ":arm": [ "-O3", "-mfpu=neon", - "-mfloat-abi=softfp", ], ":armeabi-v7a": [ "-O3", "-mfpu=neon", - "-mfloat-abi=softfp", ], ":armv7a": [ "-O3", "-mfpu=neon", - "-mfloat-abi=softfp", ], "//conditions:default": [ "-O3", @@ -145,6 +149,7 @@ cc_library( "common.h", "optimized/depthwiseconv_float.h", "optimized/depthwiseconv_uint8.h", + "optimized/depthwiseconv_uint8_3x3_filter.h", "optimized/optimized_ops.h", ], copts = tflite_copts(), @@ -208,7 +213,10 @@ cc_library( "compatibility.h", "quantization_util.h", ], - deps = [":round"], + deps = [ + ":round", + ":types", + ], ) cc_test( @@ -283,7 +291,7 @@ cc_library( "optimized/neon_tensor_utils.h", "optimized/tensor_utils_impl.h", ], - copts = NEON_FLAGS_IF_APPLICABLE, + copts = NEON_FLAGS_IF_APPLICABLE + HARD_FP_FLAGS_IF_APPLICABLE, deps = [ ":cpu_check", ":portable_tensor_utils", @@ -305,6 +313,27 @@ cc_library( ], ) +# Audio support classes imported directly from TensorFlow. +cc_library( + name = "audio_utils", + srcs = [ + "mfcc.cc", + "mfcc_dct.cc", + "mfcc_mel_filterbank.cc", + "spectrogram.cc", + ], + hdrs = [ + "mfcc.h", + "mfcc_dct.h", + "mfcc_mel_filterbank.h", + "spectrogram.h", + ], + deps = [ + "//third_party/fft2d:fft2d_headers", + "@fft2d", + ], +) + cc_library( name = "tensor_utils", srcs = [ diff --git a/tensorflow/contrib/lite/kernels/internal/kernel_utils.cc b/tensorflow/contrib/lite/kernels/internal/kernel_utils.cc index 510395126ce3785b1d44fec1e0eb994c29ff0db7..f142374269606bdd3d4184af013749102666ab89 100644 --- a/tensorflow/contrib/lite/kernels/internal/kernel_utils.cc +++ b/tensorflow/contrib/lite/kernels/internal/kernel_utils.cc @@ -40,5 +40,152 @@ void RnnBatchStep(const float* input_ptr_batch, const float* input_weights_ptr, hidden_state_ptr_batch); } +void LstmStep( + const float* input_ptr_batch, const float* input_to_input_weights_ptr, + const float* input_to_forget_weights_ptr, + const float* input_to_cell_weights_ptr, + const float* input_to_output_weights_ptr, + const float* recurrent_to_input_weights_ptr, + const float* recurrent_to_forget_weights_ptr, + const float* recurrent_to_cell_weights_ptr, + const float* recurrent_to_output_weights_ptr, + const float* cell_to_input_weights_ptr, + const float* cell_to_forget_weights_ptr, + const float* cell_to_output_weights_ptr, const float* input_gate_bias_ptr, + const float* forget_gate_bias_ptr, const float* cell_bias_ptr, + const float* output_gate_bias_ptr, const float* projection_weights_ptr, + const float* projection_bias_ptr, const TfLiteLSTMParams* params, + int n_batch, int n_cell, int n_input, int n_output, float* output_state_ptr, + float* cell_state_ptr, float* input_gate_scratch, + float* forget_gate_scratch, float* cell_scratch, float* output_gate_scratch, + float* output_ptr_batch) { + // Since we have already checked that weights are all there or none, we can + // check the existense of only one to the get the condition. + const bool use_cifg = (input_to_input_weights_ptr == nullptr); + const bool use_peephole = (cell_to_output_weights_ptr != nullptr); + // Initialize scratch buffers with bias. + if (!use_cifg) { + tensor_utils::VectorBatchVectorAssign(input_gate_bias_ptr, n_cell, n_batch, + input_gate_scratch); + } + tensor_utils::VectorBatchVectorAssign(forget_gate_bias_ptr, n_cell, n_batch, + forget_gate_scratch); + tensor_utils::VectorBatchVectorAssign(cell_bias_ptr, n_cell, n_batch, + cell_scratch); + tensor_utils::VectorBatchVectorAssign(output_gate_bias_ptr, n_cell, n_batch, + output_gate_scratch); + + // For each batch and cell: compute input_weight * input. + if (!use_cifg) { + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + input_to_input_weights_ptr, n_cell, n_input, input_ptr_batch, n_batch, + input_gate_scratch, /*result_stride=*/1); + } + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + input_to_forget_weights_ptr, n_cell, n_input, input_ptr_batch, n_batch, + forget_gate_scratch, /*result_stride=*/1); + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + input_to_cell_weights_ptr, n_cell, n_input, input_ptr_batch, n_batch, + cell_scratch, /*result_stride=*/1); + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + input_to_output_weights_ptr, n_cell, n_input, input_ptr_batch, n_batch, + output_gate_scratch, /*result_stride=*/1); + + // For each batch and cell: compute recurrent_weight * output_state. + if (!use_cifg) { + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + recurrent_to_input_weights_ptr, n_cell, n_output, output_state_ptr, + n_batch, input_gate_scratch, + /*result_stride=*/1); + } + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + recurrent_to_forget_weights_ptr, n_cell, n_output, output_state_ptr, + n_batch, forget_gate_scratch, + /*result_stride=*/1); + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + recurrent_to_cell_weights_ptr, n_cell, n_output, output_state_ptr, + n_batch, cell_scratch, /*result_stride=*/1); + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + recurrent_to_output_weights_ptr, n_cell, n_output, output_state_ptr, + n_batch, output_gate_scratch, + /*result_stride=*/1); + + // For each batch and cell: update input gate. + if (!use_cifg) { + if (use_peephole) { + tensor_utils::VectorBatchVectorCwiseProductAccumulate( + cell_to_input_weights_ptr, n_cell, cell_state_ptr, n_batch, + input_gate_scratch); + } + tensor_utils::ApplySigmoidToVector(input_gate_scratch, n_cell * n_batch, + input_gate_scratch); + } + + // For each batch and cell: update forget gate. + if (use_peephole) { + tensor_utils::VectorBatchVectorCwiseProductAccumulate( + cell_to_forget_weights_ptr, n_cell, cell_state_ptr, n_batch, + forget_gate_scratch); + } + tensor_utils::ApplySigmoidToVector(forget_gate_scratch, n_cell * n_batch, + forget_gate_scratch); + + // For each batch and cell: update the cell. + tensor_utils::VectorVectorCwiseProduct(forget_gate_scratch, cell_state_ptr, + n_batch * n_cell, cell_state_ptr); + tensor_utils::ApplyActivationToVector(cell_scratch, n_batch * n_cell, + params->activation, cell_scratch); + if (use_cifg) { + tensor_utils::Sub1Vector(forget_gate_scratch, n_batch * n_cell, + forget_gate_scratch); + tensor_utils::VectorVectorCwiseProductAccumulate( + cell_scratch, forget_gate_scratch, n_batch * n_cell, cell_state_ptr); + } else { + tensor_utils::VectorVectorCwiseProductAccumulate( + cell_scratch, input_gate_scratch, n_batch * n_cell, cell_state_ptr); + } + if (params->cell_clip > 0.0) { + tensor_utils::ClipVector(cell_state_ptr, n_batch * n_cell, + params->cell_clip, cell_state_ptr); + } + + // For each batch and cell: update the output gate. + if (use_peephole) { + tensor_utils::VectorBatchVectorCwiseProductAccumulate( + cell_to_output_weights_ptr, n_cell, cell_state_ptr, n_batch, + output_gate_scratch); + } + tensor_utils::ApplySigmoidToVector(output_gate_scratch, n_batch * n_cell, + output_gate_scratch); + tensor_utils::ApplyActivationToVector(cell_state_ptr, n_batch * n_cell, + params->activation, cell_scratch); + tensor_utils::VectorVectorCwiseProduct(output_gate_scratch, cell_scratch, + n_batch * n_cell, output_gate_scratch); + + // For each batch: update the projection and output_state. + const bool use_projection_weight = (projection_weights_ptr != nullptr); + const bool use_projection_bias = (projection_bias_ptr != nullptr); + if (use_projection_weight) { + if (use_projection_bias) { + tensor_utils::VectorBatchVectorAssign(projection_bias_ptr, n_output, + n_batch, output_ptr_batch); + } else { + tensor_utils::ZeroVector(output_ptr_batch, n_batch * n_output); + } + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + projection_weights_ptr, n_output, n_cell, output_gate_scratch, n_batch, + output_ptr_batch, /*result_stride=*/1); + if (params->proj_clip > 0.0) { + tensor_utils::ClipVector(output_ptr_batch, n_batch * n_output, + params->proj_clip, output_ptr_batch); + } + } else { + tensor_utils::CopyVector(output_gate_scratch, n_batch * n_output, + output_ptr_batch); + } + tensor_utils::CopyVector(output_ptr_batch, n_batch * n_output, + output_state_ptr); +} + } // namespace kernel_utils } // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/internal/kernel_utils.h b/tensorflow/contrib/lite/kernels/internal/kernel_utils.h index 9872d4500b862388ed4b96c97e3755f548e35d35..3ec60ee57a87833959a34ba95d32df15bea188a4 100644 --- a/tensorflow/contrib/lite/kernels/internal/kernel_utils.h +++ b/tensorflow/contrib/lite/kernels/internal/kernel_utils.h @@ -35,6 +35,42 @@ void RnnBatchStep(const float* input_ptr_batch, const float* input_weights_ptr, TfLiteFusedActivation activation, float* hidden_state_ptr_batch, float* output_ptr_batch); +// Performs an LSTM batch inference step for input specified by input_ptr_batch. +// The LSTM cell is specified by the pointers to its weights (*_weights_ptr) and +// biases (*_bias_ptr), and buffers (*_scratch), along with additional +// parameters: +// - params: various LSTM params including activation, clipping, etc., +// - n_batch: size of batch, +// - n_cell: number of cells (or units), +// - n_input: the input size, +// - n_output: the output size. +// +// The pointers to the cell and output state and the output are updated. Unless +// projection is specified output and output state contain the same data. +// +// The pointers with the suffix "_batch" point to data aligned in batch_major +// order, and each step processes batch_size many inputs from input_ptr_batch, +// and updates batch_size many cell and output states. +void LstmStep( + const float* input_ptr_batch, const float* input_to_input_weights_ptr, + const float* input_to_forget_weights_ptr, + const float* input_to_cell_weights_ptr, + const float* input_to_output_weights_ptr, + const float* recurrent_to_input_weights_ptr, + const float* recurrent_to_forget_weights_ptr, + const float* recurrent_to_cell_weights_ptr, + const float* recurrent_to_output_weights_ptr, + const float* cell_to_input_weights_ptr, + const float* cell_to_forget_weights_ptr, + const float* cell_to_output_weights_ptr, const float* input_gate_bias_ptr, + const float* forget_gate_bias_ptr, const float* cell_bias_ptr, + const float* output_gate_bias_ptr, const float* projection_weights_ptr, + const float* projection_bias_ptr, const TfLiteLSTMParams* params, + int n_batch, int n_cell, int n_input, int n_output, float* output_state_ptr, + float* cell_state_ptr, float* input_gate_scratch, + float* forget_gate_scratch, float* cell_scratch, float* output_gate_scratch, + float* output_ptr_batch); + } // namespace kernel_utils } // namespace tflite #endif // TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_KERNEL_UTILS_H_ diff --git a/tensorflow/contrib/lite/kernels/internal/mfcc.cc b/tensorflow/contrib/lite/kernels/internal/mfcc.cc new file mode 100644 index 0000000000000000000000000000000000000000..eafe0c7afee6fabd5a4a258aa5176e23f5e8d62a --- /dev/null +++ b/tensorflow/contrib/lite/kernels/internal/mfcc.cc @@ -0,0 +1,65 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include + +#include "tensorflow/contrib/lite/kernels/internal/mfcc.h" + +namespace tflite { +namespace internal { + +const double kDefaultUpperFrequencyLimit = 4000; +const double kDefaultLowerFrequencyLimit = 20; +const double kFilterbankFloor = 1e-12; +const int kDefaultFilterbankChannelCount = 40; +const int kDefaultDCTCoefficientCount = 13; + +Mfcc::Mfcc() + : initialized_(false), + lower_frequency_limit_(kDefaultLowerFrequencyLimit), + upper_frequency_limit_(kDefaultUpperFrequencyLimit), + filterbank_channel_count_(kDefaultFilterbankChannelCount), + dct_coefficient_count_(kDefaultDCTCoefficientCount) {} + +bool Mfcc::Initialize(int input_length, double input_sample_rate) { + bool initialized = mel_filterbank_.Initialize( + input_length, input_sample_rate, filterbank_channel_count_, + lower_frequency_limit_, upper_frequency_limit_); + initialized &= + dct_.Initialize(filterbank_channel_count_, dct_coefficient_count_); + initialized_ = initialized; + return initialized; +} + +void Mfcc::Compute(const std::vector& spectrogram_frame, + std::vector* output) const { + if (!initialized_) { + // LOG(ERROR) << "Mfcc not initialized."; + return; + } + std::vector working; + mel_filterbank_.Compute(spectrogram_frame, &working); + for (int i = 0; i < working.size(); ++i) { + double val = working[i]; + if (val < kFilterbankFloor) { + val = kFilterbankFloor; + } + working[i] = log(val); + } + dct_.Compute(working, output); +} + +} // namespace internal +} // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/internal/mfcc.h b/tensorflow/contrib/lite/kernels/internal/mfcc.h new file mode 100644 index 0000000000000000000000000000000000000000..d8500ecdcf38e5dcfe9eb89915501678455b3dd9 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/internal/mfcc.h @@ -0,0 +1,78 @@ +/* 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. +==============================================================================*/ + +// Basic class for computing MFCCs from spectrogram slices. + +#ifndef TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_MFCC_H_ +#define TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_MFCC_H_ + +#include + +#include "tensorflow/contrib/lite/kernels/internal/mfcc_dct.h" +#include "tensorflow/contrib/lite/kernels/internal/mfcc_mel_filterbank.h" + +namespace tflite { +namespace internal { + +class Mfcc { + public: + Mfcc(); + bool Initialize(int input_length, double input_sample_rate); + + // Input is a single squared-magnitude spectrogram frame. The input spectrum + // is converted to linear magnitude and weighted into bands using a + // triangular mel filterbank, and a discrete cosine transform (DCT) of the + // values is taken. Output is populated with the lowest dct_coefficient_count + // of these values. + void Compute(const std::vector& spectrogram_frame, + std::vector* output) const; + + void set_upper_frequency_limit(double upper_frequency_limit) { + // CHECK(!initialized_) << "Set frequency limits before calling + // Initialize."; + upper_frequency_limit_ = upper_frequency_limit; + } + + void set_lower_frequency_limit(double lower_frequency_limit) { + // CHECK(!initialized_) << "Set frequency limits before calling + // Initialize."; + lower_frequency_limit_ = lower_frequency_limit; + } + + void set_filterbank_channel_count(int filterbank_channel_count) { + /// CHECK(!initialized_) << "Set channel count before calling Initialize."; + filterbank_channel_count_ = filterbank_channel_count; + } + + void set_dct_coefficient_count(int dct_coefficient_count) { + // CHECK(!initialized_) << "Set coefficient count before calling + // Initialize."; + dct_coefficient_count_ = dct_coefficient_count; + } + + private: + MfccMelFilterbank mel_filterbank_; + MfccDct dct_; + bool initialized_; + double lower_frequency_limit_; + double upper_frequency_limit_; + int filterbank_channel_count_; + int dct_coefficient_count_; +}; + +} // namespace internal +} // namespace tflite + +#endif // TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_MFCC_H_ diff --git a/tensorflow/contrib/lite/kernels/internal/mfcc_dct.cc b/tensorflow/contrib/lite/kernels/internal/mfcc_dct.cc new file mode 100644 index 0000000000000000000000000000000000000000..b0b7d181bdcf01688a387f33a3e64fc904324b50 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/internal/mfcc_dct.cc @@ -0,0 +1,78 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/lite/kernels/internal/mfcc_dct.h" + +#include + +namespace tflite { +namespace internal { + +MfccDct::MfccDct() : initialized_(false) {} + +bool MfccDct::Initialize(int input_length, int coefficient_count) { + coefficient_count_ = coefficient_count; + input_length_ = input_length; + + if (coefficient_count_ < 1) { + return false; + } + + if (input_length < 1) { + return false; + } + + if (coefficient_count_ > input_length_) { + return false; + } + + cosines_.resize(coefficient_count_); + double fnorm = sqrt(2.0 / input_length_); + // Some platforms don't have M_PI, so define a local constant here. + const double pi = atan(1) * 4; + double arg = pi / input_length_; + for (int i = 0; i < coefficient_count_; ++i) { + cosines_[i].resize(input_length_); + for (int j = 0; j < input_length_; ++j) { + cosines_[i][j] = fnorm * cos(i * arg * (j + 0.5)); + } + } + initialized_ = true; + return true; +} + +void MfccDct::Compute(const std::vector &input, + std::vector *output) const { + if (!initialized_) { + return; + } + + output->resize(coefficient_count_); + int length = input.size(); + if (length > input_length_) { + length = input_length_; + } + + for (int i = 0; i < coefficient_count_; ++i) { + double sum = 0.0; + for (int j = 0; j < length; ++j) { + sum += cosines_[i][j] * input[j]; + } + (*output)[i] = sum; + } +} + +} // namespace internal +} // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/internal/mfcc_dct.h b/tensorflow/contrib/lite/kernels/internal/mfcc_dct.h new file mode 100644 index 0000000000000000000000000000000000000000..a53f5cbd9bb70c7c9dd49672681140bb9cbd2f4f --- /dev/null +++ b/tensorflow/contrib/lite/kernels/internal/mfcc_dct.h @@ -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. +==============================================================================*/ + +// Basic minimal DCT class for MFCC speech processing. + +#ifndef TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_MFCC_DCT_H_ +#define TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_MFCC_DCT_H_ + +#include + +namespace tflite { +namespace internal { + +class MfccDct { + public: + MfccDct(); + bool Initialize(int input_length, int coefficient_count); + void Compute(const std::vector& input, + std::vector* output) const; + + private: + bool initialized_; + int coefficient_count_; + int input_length_; + std::vector > cosines_; +}; + +} // namespace internal +} // namespace tflite + +#endif // TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_MFCC_DCT_H_ diff --git a/tensorflow/contrib/lite/kernels/internal/mfcc_mel_filterbank.cc b/tensorflow/contrib/lite/kernels/internal/mfcc_mel_filterbank.cc new file mode 100644 index 0000000000000000000000000000000000000000..c3deb33d91a47bfe54b7c84d2a615df2422f90cc --- /dev/null +++ b/tensorflow/contrib/lite/kernels/internal/mfcc_mel_filterbank.cc @@ -0,0 +1,204 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +// This code resamples the FFT bins, and smooths then with triangle-shaped +// weights to create a mel-frequency filter bank. For filter i centered at f_i, +// there is a triangular weighting of the FFT bins that extends from +// filter f_i-1 (with a value of zero at the left edge of the triangle) to f_i +// (where the filter value is 1) to f_i+1 (where the filter values returns to +// zero). + +// Note: this code fails if you ask for too many channels. The algorithm used +// here assumes that each FFT bin contributes to at most two channels: the +// right side of a triangle for channel i, and the left side of the triangle +// for channel i+1. If you ask for so many channels that some of the +// resulting mel triangle filters are smaller than a single FFT bin, these +// channels may end up with no contributing FFT bins. The resulting mel +// spectrum output will have some channels that are always zero. + +#include "tensorflow/contrib/lite/kernels/internal/mfcc_mel_filterbank.h" + +#include + +namespace tflite { +namespace internal { + +MfccMelFilterbank::MfccMelFilterbank() : initialized_(false) {} + +bool MfccMelFilterbank::Initialize(int input_length, double input_sample_rate, + int output_channel_count, + double lower_frequency_limit, + double upper_frequency_limit) { + num_channels_ = output_channel_count; + sample_rate_ = input_sample_rate; + input_length_ = input_length; + + if (num_channels_ < 1) { + // LOG(ERROR) << "Number of filterbank channels must be positive."; + return false; + } + + if (sample_rate_ <= 0) { + // LOG(ERROR) << "Sample rate must be positive."; + return false; + } + + if (input_length < 2) { + // LOG(ERROR) << "Input length must greater than 1."; + return false; + } + + if (lower_frequency_limit < 0) { + // LOG(ERROR) << "Lower frequency limit must be nonnegative."; + return false; + } + + if (upper_frequency_limit <= lower_frequency_limit) { + /// LOG(ERROR) << "Upper frequency limit must be greater than " + // << "lower frequency limit."; + return false; + } + + // An extra center frequency is computed at the top to get the upper + // limit on the high side of the final triangular filter. + center_frequencies_.resize(num_channels_ + 1); + const double mel_low = FreqToMel(lower_frequency_limit); + const double mel_hi = FreqToMel(upper_frequency_limit); + const double mel_span = mel_hi - mel_low; + const double mel_spacing = mel_span / static_cast(num_channels_ + 1); + for (int i = 0; i < num_channels_ + 1; ++i) { + center_frequencies_[i] = mel_low + (mel_spacing * (i + 1)); + } + + // Always exclude DC; emulate HTK. + const double hz_per_sbin = + 0.5 * sample_rate_ / static_cast(input_length_ - 1); + start_index_ = static_cast(1.5 + (lower_frequency_limit / hz_per_sbin)); + end_index_ = static_cast(upper_frequency_limit / hz_per_sbin); + + // Maps the input spectrum bin indices to filter bank channels/indices. For + // each FFT bin, band_mapper tells us which channel this bin contributes to + // on the right side of the triangle. Thus this bin also contributes to the + // left side of the next channel's triangle response. + band_mapper_.resize(input_length_); + int channel = 0; + for (int i = 0; i < input_length_; ++i) { + double melf = FreqToMel(i * hz_per_sbin); + if ((i < start_index_) || (i > end_index_)) { + band_mapper_[i] = -2; // Indicate an unused Fourier coefficient. + } else { + while ((center_frequencies_[channel] < melf) && + (channel < num_channels_)) { + ++channel; + } + band_mapper_[i] = channel - 1; // Can be == -1 + } + } + + // Create the weighting functions to taper the band edges. The contribution + // of any one FFT bin is based on its distance along the continuum between two + // mel-channel center frequencies. This bin contributes weights_[i] to the + // current channel and 1-weights_[i] to the next channel. + weights_.resize(input_length_); + for (int i = 0; i < input_length_; ++i) { + channel = band_mapper_[i]; + if ((i < start_index_) || (i > end_index_)) { + weights_[i] = 0.0; + } else { + if (channel >= 0) { + weights_[i] = + (center_frequencies_[channel + 1] - FreqToMel(i * hz_per_sbin)) / + (center_frequencies_[channel + 1] - center_frequencies_[channel]); + } else { + weights_[i] = (center_frequencies_[0] - FreqToMel(i * hz_per_sbin)) / + (center_frequencies_[0] - mel_low); + } + } + } + // Check the sum of FFT bin weights for every mel band to identify + // situations where the mel bands are so narrow that they don't get + // significant weight on enough (or any) FFT bins -- i.e., too many + // mel bands have been requested for the given FFT size. + std::vector bad_channels; + for (int c = 0; c < num_channels_; ++c) { + float band_weights_sum = 0.0; + for (int i = 0; i < input_length_; ++i) { + if (band_mapper_[i] == c - 1) { + band_weights_sum += (1.0 - weights_[i]); + } else if (band_mapper_[i] == c) { + band_weights_sum += weights_[i]; + } + } + // The lowest mel channels have the fewest FFT bins and the lowest + // weights sum. But given that the target gain at the center frequency + // is 1.0, if the total sum of weights is 0.5, we're in bad shape. + if (band_weights_sum < 0.5) { + bad_channels.push_back(c); + } + } + if (!bad_channels.empty()) { + /* + LOG(ERROR) << "Missing " << bad_channels.size() << " bands " + << " starting at " << bad_channels[0] + << " in mel-frequency design. " + << "Perhaps too many channels or " + << "not enough frequency resolution in spectrum. (" + << "input_length: " << input_length + << " input_sample_rate: " << input_sample_rate + << " output_channel_count: " << output_channel_count + << " lower_frequency_limit: " << lower_frequency_limit + << " upper_frequency_limit: " << upper_frequency_limit; + */ + } + initialized_ = true; + return true; +} + +// Compute the mel spectrum from the squared-magnitude FFT input by taking the +// square root, then summing FFT magnitudes under triangular integration windows +// whose widths increase with frequency. +void MfccMelFilterbank::Compute(const std::vector &input, + std::vector *output) const { + if (!initialized_) { + // LOG(ERROR) << "Mel Filterbank not initialized."; + return; + } + + if (input.size() <= end_index_) { + // LOG(ERROR) << "Input too short to compute filterbank"; + return; + } + + // Ensure output is right length and reset all values. + output->assign(num_channels_, 0.0); + + for (int i = start_index_; i <= end_index_; i++) { // For each FFT bin + double spec_val = sqrt(input[i]); + double weighted = spec_val * weights_[i]; + int channel = band_mapper_[i]; + if (channel >= 0) + (*output)[channel] += weighted; // Right side of triangle, downward slope + channel++; + if (channel < num_channels_) + (*output)[channel] += spec_val - weighted; // Left side of triangle + } +} + +double MfccMelFilterbank::FreqToMel(double freq) const { + return 1127.0 * log(1.0 + (freq / 700.0)); +} + +} // namespace internal +} // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/internal/mfcc_mel_filterbank.h b/tensorflow/contrib/lite/kernels/internal/mfcc_mel_filterbank.h new file mode 100644 index 0000000000000000000000000000000000000000..c1db28243eea39a694b7613ac7144dce9b294897 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/internal/mfcc_mel_filterbank.h @@ -0,0 +1,63 @@ +/* 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. +==============================================================================*/ + +// Basic class for applying a mel-scale mapping to a power spectrum. + +#ifndef TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_MFCC_MEL_FILTERBANK_H_ +#define TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_MFCC_MEL_FILTERBANK_H_ + +#include + +namespace tflite { +namespace internal { + +class MfccMelFilterbank { + public: + MfccMelFilterbank(); + bool Initialize(int input_length, // Number of unique FFT bins fftsize/2+1. + double input_sample_rate, int output_channel_count, + double lower_frequency_limit, double upper_frequency_limit); + + // Takes a squared-magnitude spectrogram slice as input, computes a + // triangular-mel-weighted linear-magnitude filterbank, and places the result + // in output. + void Compute(const std::vector& input, + std::vector* output) const; + + private: + double FreqToMel(double freq) const; + bool initialized_; + int num_channels_; + double sample_rate_; + int input_length_; + std::vector center_frequencies_; // In mel, for each mel channel. + + // Each FFT bin b contributes to two triangular mel channels, with + // proportion weights_[b] going into mel channel band_mapper_[b], and + // proportion (1 - weights_[b]) going into channel band_mapper_[b] + 1. + // Thus, weights_ contains the weighting applied to each FFT bin for the + // upper-half of the triangular band. + std::vector weights_; // Right-side weight for this fft bin. + + // FFT bin i contributes to the upper side of mel channel band_mapper_[i] + std::vector band_mapper_; + int start_index_; // Lowest FFT bin used to calculate mel spectrum. + int end_index_; // Highest FFT bin used to calculate mel spectrum. +}; + +} // namespace internal +} // namespace tflite + +#endif // TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_MFCC_MEL_FILTERBANK_H_ diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_uint8.h b/tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_uint8.h index dbc4f0d6fdca8279072d6ea225334722d6a89eb2..08674a6c59192a71bae48eac467fecc7b7dff7b7 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_uint8.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_uint8.h @@ -18,6 +18,7 @@ limitations under the License. #include "fixedpoint/fixedpoint.h" #include "public/gemmlowp.h" #include "tensorflow/contrib/lite/kernels/internal/common.h" +#include "tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_uint8_3x3_filter.h" #include "tensorflow/contrib/lite/kernels/internal/types.h" namespace tflite { @@ -1692,6 +1693,22 @@ inline void DepthwiseConv(const uint8* input_data, const Dims<4>& input_dims, const int output_width = ArraySize(output_dims, 1); TFLITE_DCHECK(output_depth == input_depth * depth_multiplier); +#ifdef __aarch64__ + // Call kernel optimized for depthwise convolutions using 3x3 filters, + // stride = 1, no padding, depth_multiplier = 1 and depth a multiple of 16. + if (filter_width == 3 && filter_height == 3 && depth_multiplier == 1 && + stride_width == 1 && stride_height == 1 && pad_width == 0 && + pad_height == 0 && (input_depth % 16) == 0) { + DepthwiseConv3by3FilterDepth16( + input_data, input_dims, input_offset, filter_data, filter_dims, + filter_offset, bias_data, bias_dims, stride_width, stride_height, + pad_width, pad_height, depth_multiplier, output_offset, + output_multiplier, output_shift, output_activation_min, + output_activation_max, output_data, output_dims); + return; + } +#endif + static const int kAccBufferMaxSize = 2048; int32 acc_buffer[kAccBufferMaxSize]; TFLITE_DCHECK_GE(kAccBufferMaxSize, output_depth); diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_uint8_3x3_filter.h b/tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_uint8_3x3_filter.h new file mode 100644 index 0000000000000000000000000000000000000000..e0335b2c74cbf6d9d4f17d816f5802286628964f --- /dev/null +++ b/tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_uint8_3x3_filter.h @@ -0,0 +1,653 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_OPTIMIZED_DEPTHWISECONV_UINT8_3X3_FILTER_H_ +#define TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_OPTIMIZED_DEPTHWISECONV_UINT8_3X3_FILTER_H_ + +#include "fixedpoint/fixedpoint.h" +#include "public/gemmlowp.h" +#include "tensorflow/contrib/lite/kernels/internal/common.h" +#include "tensorflow/contrib/lite/kernels/internal/types.h" + +namespace tflite { +namespace optimized_ops { + +#ifdef __aarch64__ + +inline void preload_l1_keep(const uint8* ptr) { +#ifdef GEMMLOWP_ARM_64 + asm volatile("prfm pldl1keep, [%[ptr]]\n" ::[ptr] "r"(ptr) :); +#else + gemmlowp::Prefetch(ptr); +#endif +} + +// Implementation of quantized DepthwiseConv for 3x3 filters. + +// Below are helper structs to remove the use of arrays. +// There is an llvm bug that causes significant slowdown when using arrays for +// NEON intrinsics vector data types. +// See: https://bugs.llvm.org/show_bug.cgi?id=34945 + +struct Int32x16 { + int32x4_t v0, v1, v2, v3; +}; + +struct Int16x16 { + int16x8_t low, high; +}; + +struct Int16x16x3 { + Int16x16 v0, v1, v2; +}; + +struct Filter3x3x16 { + Int16x16x3 r0, r1, r2; +}; + +// Loads 3x3 filter of depth 16 and adds filter offsets. +inline Filter3x3x16 LoadFilterDepth16(const uint8* filter_ptr, + int32 filter_offset, int output_depth) { + Filter3x3x16 filter; + + uint8x8_t temp_u8_0, temp_u8_1, temp_u8_2, temp_u8_3, temp_u8_4, temp_u8_5, + temp_u8_6, temp_u8_7, temp_u8_8, temp_u8_9, temp_u8_10, temp_u8_11, + temp_u8_12, temp_u8_13, temp_u8_14, temp_u8_15, temp_u8_16, temp_u8_17; + int16x8_t filter_offset_vec = vdupq_n_s16(filter_offset); + + temp_u8_0 = vld1_u8(filter_ptr + 0 * output_depth); + temp_u8_1 = vld1_u8(filter_ptr + 0 * output_depth + 8); + temp_u8_2 = vld1_u8(filter_ptr + 1 * output_depth); + temp_u8_3 = vld1_u8(filter_ptr + 1 * output_depth + 8); + temp_u8_4 = vld1_u8(filter_ptr + 2 * output_depth); + temp_u8_5 = vld1_u8(filter_ptr + 2 * output_depth + 8); + + temp_u8_6 = vld1_u8(filter_ptr + 3 * output_depth); + temp_u8_7 = vld1_u8(filter_ptr + 3 * output_depth + 8); + temp_u8_8 = vld1_u8(filter_ptr + 4 * output_depth); + temp_u8_9 = vld1_u8(filter_ptr + 4 * output_depth + 8); + temp_u8_10 = vld1_u8(filter_ptr + 5 * output_depth); + temp_u8_11 = vld1_u8(filter_ptr + 5 * output_depth + 8); + + temp_u8_12 = vld1_u8(filter_ptr + 6 * output_depth); + temp_u8_13 = vld1_u8(filter_ptr + 6 * output_depth + 8); + temp_u8_14 = vld1_u8(filter_ptr + 7 * output_depth); + temp_u8_15 = vld1_u8(filter_ptr + 7 * output_depth + 8); + temp_u8_16 = vld1_u8(filter_ptr + 8 * output_depth); + temp_u8_17 = vld1_u8(filter_ptr + 8 * output_depth + 8); + + filter.r0.v0.low = vreinterpretq_s16_u16(vmovl_u8(temp_u8_0)); + filter.r0.v0.high = vreinterpretq_s16_u16(vmovl_u8(temp_u8_1)); + filter.r0.v1.low = vreinterpretq_s16_u16(vmovl_u8(temp_u8_2)); + filter.r0.v1.high = vreinterpretq_s16_u16(vmovl_u8(temp_u8_3)); + filter.r0.v2.low = vreinterpretq_s16_u16(vmovl_u8(temp_u8_4)); + filter.r0.v2.high = vreinterpretq_s16_u16(vmovl_u8(temp_u8_5)); + + filter.r1.v0.low = vreinterpretq_s16_u16(vmovl_u8(temp_u8_6)); + filter.r1.v0.high = vreinterpretq_s16_u16(vmovl_u8(temp_u8_7)); + filter.r1.v1.low = vreinterpretq_s16_u16(vmovl_u8(temp_u8_8)); + filter.r1.v1.high = vreinterpretq_s16_u16(vmovl_u8(temp_u8_9)); + filter.r1.v2.low = vreinterpretq_s16_u16(vmovl_u8(temp_u8_10)); + filter.r1.v2.high = vreinterpretq_s16_u16(vmovl_u8(temp_u8_11)); + + filter.r2.v0.low = vreinterpretq_s16_u16(vmovl_u8(temp_u8_12)); + filter.r2.v0.high = vreinterpretq_s16_u16(vmovl_u8(temp_u8_13)); + filter.r2.v1.low = vreinterpretq_s16_u16(vmovl_u8(temp_u8_14)); + filter.r2.v1.high = vreinterpretq_s16_u16(vmovl_u8(temp_u8_15)); + filter.r2.v2.low = vreinterpretq_s16_u16(vmovl_u8(temp_u8_16)); + filter.r2.v2.high = vreinterpretq_s16_u16(vmovl_u8(temp_u8_17)); + + filter.r0.v0.low = vaddq_s16(filter.r0.v0.low, filter_offset_vec); + filter.r0.v0.high = vaddq_s16(filter.r0.v0.high, filter_offset_vec); + filter.r0.v1.low = vaddq_s16(filter.r0.v1.low, filter_offset_vec); + filter.r0.v1.high = vaddq_s16(filter.r0.v1.high, filter_offset_vec); + filter.r0.v2.low = vaddq_s16(filter.r0.v2.low, filter_offset_vec); + filter.r0.v2.high = vaddq_s16(filter.r0.v2.high, filter_offset_vec); + + filter.r1.v0.low = vaddq_s16(filter.r1.v0.low, filter_offset_vec); + filter.r1.v0.high = vaddq_s16(filter.r1.v0.high, filter_offset_vec); + filter.r1.v1.low = vaddq_s16(filter.r1.v1.low, filter_offset_vec); + filter.r1.v1.high = vaddq_s16(filter.r1.v1.high, filter_offset_vec); + filter.r1.v2.low = vaddq_s16(filter.r1.v2.low, filter_offset_vec); + filter.r1.v2.high = vaddq_s16(filter.r1.v2.high, filter_offset_vec); + + filter.r2.v0.low = vaddq_s16(filter.r2.v0.low, filter_offset_vec); + filter.r2.v0.high = vaddq_s16(filter.r2.v0.high, filter_offset_vec); + filter.r2.v1.low = vaddq_s16(filter.r2.v1.low, filter_offset_vec); + filter.r2.v1.high = vaddq_s16(filter.r2.v1.high, filter_offset_vec); + filter.r2.v2.low = vaddq_s16(filter.r2.v2.low, filter_offset_vec); + filter.r2.v2.high = vaddq_s16(filter.r2.v2.high, filter_offset_vec); + + return filter; +} + +// Loads 3 input cells of depth 16 and adds input offsets. +inline Int16x16x3 LoadInputRowDepth16(const uint8* ptr, int input_depth, + int32 input_offset, + Int16x16x3 input_row) { + uint8x8_t temp_0, temp_1; + int16x8_t offset_vec = vdupq_n_s16(input_offset); + + temp_0 = vld1_u8(ptr + 0 * input_depth); + temp_1 = vld1_u8(ptr + 0 * input_depth + 8); + input_row.v0.low = vreinterpretq_s16_u16(vmovl_u8(temp_0)); + input_row.v0.high = vreinterpretq_s16_u16(vmovl_u8(temp_1)); + input_row.v0.low = vaddq_s16(input_row.v0.low, offset_vec); + input_row.v0.high = vaddq_s16(input_row.v0.high, offset_vec); + + temp_0 = vld1_u8(ptr + 1 * input_depth); + temp_1 = vld1_u8(ptr + 1 * input_depth + 8); + input_row.v1.low = vreinterpretq_s16_u16(vmovl_u8(temp_0)); + input_row.v1.high = vreinterpretq_s16_u16(vmovl_u8(temp_1)); + input_row.v1.low = vaddq_s16(input_row.v1.low, offset_vec); + input_row.v1.high = vaddq_s16(input_row.v1.high, offset_vec); + + temp_0 = vld1_u8(ptr + 2 * input_depth); + temp_1 = vld1_u8(ptr + 2 * input_depth + 8); + input_row.v2.low = vreinterpretq_s16_u16(vmovl_u8(temp_0)); + input_row.v2.high = vreinterpretq_s16_u16(vmovl_u8(temp_1)); + input_row.v2.low = vaddq_s16(input_row.v2.low, offset_vec); + input_row.v2.high = vaddq_s16(input_row.v2.high, offset_vec); + + return input_row; +} + +// Performs multiply accumulate on 3 inputs of depth 16. +inline Int32x16 MultiplyAccumulateRowDepth16(Int32x16 output, + const Int16x16x3& filter_row, + const Int16x16x3& input_row) { + output.v0 = vmlal_s16(output.v0, vget_low_s16(filter_row.v0.low), + vget_low_s16(input_row.v0.low)); + output.v1 = vmlal_s16(output.v1, vget_high_s16(filter_row.v0.low), + vget_high_s16(input_row.v0.low)); + output.v2 = vmlal_s16(output.v2, vget_low_s16(filter_row.v0.high), + vget_low_s16(input_row.v0.high)); + output.v3 = vmlal_s16(output.v3, vget_high_s16(filter_row.v0.high), + vget_high_s16(input_row.v0.high)); + + output.v0 = vmlal_s16(output.v0, vget_low_s16(filter_row.v1.low), + vget_low_s16(input_row.v1.low)); + output.v1 = vmlal_s16(output.v1, vget_high_s16(filter_row.v1.low), + vget_high_s16(input_row.v1.low)); + output.v2 = vmlal_s16(output.v2, vget_low_s16(filter_row.v1.high), + vget_low_s16(input_row.v1.high)); + output.v3 = vmlal_s16(output.v3, vget_high_s16(filter_row.v1.high), + vget_high_s16(input_row.v1.high)); + + output.v0 = vmlal_s16(output.v0, vget_low_s16(filter_row.v2.low), + vget_low_s16(input_row.v2.low)); + output.v1 = vmlal_s16(output.v1, vget_high_s16(filter_row.v2.low), + vget_high_s16(input_row.v2.low)); + output.v2 = vmlal_s16(output.v2, vget_low_s16(filter_row.v2.high), + vget_low_s16(input_row.v2.high)); + output.v3 = vmlal_s16(output.v3, vget_high_s16(filter_row.v2.high), + vget_high_s16(input_row.v2.high)); + + return output; +} + +// Applies activation, offset and downquantize on a set of accumulator +// registers of depth 16. Stores results to output. +inline void DownquantizeAndStoreDepth16(Int32x16 acc, int32 output_multiplier, + int output_shift, + int32x4_t output_offset_vec, + int32x4_t output_activation_min_vec, + int32x4_t output_activation_max_vec, + uint8* output_ptr) { + // Fixed-point multiplication. + acc.v0 = vqrdmulhq_n_s32(acc.v0, output_multiplier); + acc.v1 = vqrdmulhq_n_s32(acc.v1, output_multiplier); + acc.v2 = vqrdmulhq_n_s32(acc.v2, output_multiplier); + acc.v3 = vqrdmulhq_n_s32(acc.v3, output_multiplier); + + using gemmlowp::RoundingDivideByPOT; + acc.v0 = RoundingDivideByPOT(acc.v0, output_shift); + acc.v1 = RoundingDivideByPOT(acc.v1, output_shift); + acc.v2 = RoundingDivideByPOT(acc.v2, output_shift); + acc.v3 = RoundingDivideByPOT(acc.v3, output_shift); + + // Add the output offset. + acc.v0 = vaddq_s32(acc.v0, output_offset_vec); + acc.v1 = vaddq_s32(acc.v1, output_offset_vec); + acc.v2 = vaddq_s32(acc.v2, output_offset_vec); + acc.v3 = vaddq_s32(acc.v3, output_offset_vec); + + // Apply the activation function. + acc.v0 = vmaxq_s32(acc.v0, output_activation_min_vec); + acc.v1 = vmaxq_s32(acc.v1, output_activation_min_vec); + acc.v2 = vmaxq_s32(acc.v2, output_activation_min_vec); + acc.v3 = vmaxq_s32(acc.v3, output_activation_min_vec); + + acc.v0 = vminq_s32(acc.v0, output_activation_max_vec); + acc.v1 = vminq_s32(acc.v1, output_activation_max_vec); + acc.v2 = vminq_s32(acc.v2, output_activation_max_vec); + acc.v3 = vminq_s32(acc.v3, output_activation_max_vec); + + // Saturating cast to uint8 and store to destination. + int16x4_t acc_tlla_s16 = vqmovn_s32(acc.v0); + int16x4_t acc_tllb_s16 = vqmovn_s32(acc.v1); + int16x4_t acc_tlha_s16 = vqmovn_s32(acc.v2); + int16x4_t acc_tlhb_s16 = vqmovn_s32(acc.v3); + + int16x8_t res_s16_0 = vcombine_s16(acc_tlla_s16, acc_tllb_s16); + int16x8_t res_s16_1 = vcombine_s16(acc_tlha_s16, acc_tlhb_s16); + uint8x8_t res_u8_0 = vqmovun_s16(res_s16_0); + uint8x8_t res_u8_1 = vqmovun_s16(res_s16_1); + vst1q_u8(output_ptr, vcombine_u8(res_u8_0, res_u8_1)); +} + +// A kernel that is optimized on the number of output cells in the x and y +// direction, and the stride. Assumes 3x3 filters of 16 depth. +template +struct ConvKernel3x3FilterDepth16 {}; + +template <> +struct ConvKernel3x3FilterDepth16<1, 2, 1> { + static void Run(const Filter3x3x16& filter, const uint8* input_ptr, + int input_depth, int32 input_offset, int input_row_width, + const int32* bias_ptr, int32 output_offset, + int32 output_multiplier, int output_shift, + int32 output_activation_min, int32 output_activation_max, + uint8* output_ptr, int output_depth, int output_width) { + // 16 depth accumulators for the 2 outputs. + Int32x16 acc0, acc1; + + // Accumulators for top filter. + acc0.v0 = vld1q_s32(bias_ptr); + acc0.v1 = vld1q_s32(bias_ptr + 4); + acc0.v2 = vld1q_s32(bias_ptr + 8); + acc0.v3 = vld1q_s32(bias_ptr + 12); + // Accumulators for bottom filter. + acc1.v0 = vld1q_s32(bias_ptr); + acc1.v1 = vld1q_s32(bias_ptr + 4); + acc1.v2 = vld1q_s32(bias_ptr + 8); + acc1.v3 = vld1q_s32(bias_ptr + 12); + + // Main multiply accumulate work. + { + // Load inputs for one filter row at a time. + Int16x16x3 input; + + // Do first row of top filter. + input = LoadInputRowDepth16(input_ptr, input_depth, input_offset, input); + acc0 = MultiplyAccumulateRowDepth16(acc0, filter.r0, input); + + // Do second row of top filter. + input = LoadInputRowDepth16(input_ptr + input_row_width, input_depth, + input_offset, input); + acc0 = MultiplyAccumulateRowDepth16(acc0, filter.r1, input); + + // The inputs to second row of the top filter are also the inputs to the + // first row of the bottom filter. + acc1 = MultiplyAccumulateRowDepth16(acc1, filter.r0, input); + + // Do third row of top filter. + input = LoadInputRowDepth16(input_ptr + 2 * input_row_width, input_depth, + input_offset, input); + acc0 = MultiplyAccumulateRowDepth16(acc0, filter.r2, input); + + // The inputs to third row of the top filter are also the inputs to the + // second row of the bottom filter. + acc1 = MultiplyAccumulateRowDepth16(acc1, filter.r1, input); + + // Do third row of bottom filter. + input = LoadInputRowDepth16(input_ptr + 3 * input_row_width, input_depth, + input_offset, input); + acc1 = MultiplyAccumulateRowDepth16(acc1, filter.r2, input); + } + + // Apply activation, downquantize and store. + int32x4_t output_offset_vec = vdupq_n_s32(output_offset); + int32x4_t output_activation_min_vec = vdupq_n_s32(output_activation_min); + int32x4_t output_activation_max_vec = vdupq_n_s32(output_activation_max); + + DownquantizeAndStoreDepth16(acc0, output_multiplier, output_shift, + output_offset_vec, output_activation_min_vec, + output_activation_max_vec, output_ptr); + + DownquantizeAndStoreDepth16(acc1, output_multiplier, output_shift, + output_offset_vec, output_activation_min_vec, + output_activation_max_vec, + output_ptr + output_depth * output_width); + } +}; + +template <> +struct ConvKernel3x3FilterDepth16<1, 2, 2> { + static void Run(const Filter3x3x16& filter, const uint8* input_ptr, + int input_depth, int32 input_offset, int input_row_width, + const int32* bias_ptr, int32 output_offset, + int32 output_multiplier, int output_shift, + int32 output_activation_min, int32 output_activation_max, + uint8* output_ptr, int output_depth, int output_width) { + // 16 depth accumulators for the 2 outputs. + Int32x16 acc0, acc1; + + // Accumulators for top filter. + acc0.v0 = vld1q_s32(bias_ptr); + acc0.v1 = vld1q_s32(bias_ptr + 4); + acc0.v2 = vld1q_s32(bias_ptr + 8); + acc0.v3 = vld1q_s32(bias_ptr + 12); + // Accumulators for bottom filter. + acc1.v0 = vld1q_s32(bias_ptr); + acc1.v1 = vld1q_s32(bias_ptr + 4); + acc1.v2 = vld1q_s32(bias_ptr + 8); + acc1.v3 = vld1q_s32(bias_ptr + 12); + + // Main multiply accumulate work. + { + // Load inputs for one filter row at a time. + Int16x16x3 input; + + // Do first row of top filter. + input = LoadInputRowDepth16(input_ptr, input_depth, input_offset, input); + acc0 = MultiplyAccumulateRowDepth16(acc0, filter.r0, input); + + // Do second row of top filter. + input = LoadInputRowDepth16(input_ptr + input_row_width, input_depth, + input_offset, input); + acc0 = MultiplyAccumulateRowDepth16(acc0, filter.r1, input); + + // Do third row of top filter. + input = LoadInputRowDepth16(input_ptr + 2 * input_row_width, input_depth, + input_offset, input); + acc0 = MultiplyAccumulateRowDepth16(acc0, filter.r2, input); + + // The inputs to third row of the top filter are also the inputs + // to first row of the bottom filter. + acc1 = MultiplyAccumulateRowDepth16(acc1, filter.r0, input); + + // Do second row of bottom filter. + input = LoadInputRowDepth16(input_ptr + 3 * input_row_width, input_depth, + input_offset, input); + acc1 = MultiplyAccumulateRowDepth16(acc1, filter.r1, input); + + // Do third row of bottom filter. + input = LoadInputRowDepth16(input_ptr + 4 * input_row_width, input_depth, + input_offset, input); + acc1 = MultiplyAccumulateRowDepth16(acc1, filter.r2, input); + } + + // Apply activation, downquantize and store. + int32x4_t output_offset_vec = vdupq_n_s32(output_offset); + int32x4_t output_activation_min_vec = vdupq_n_s32(output_activation_min); + int32x4_t output_activation_max_vec = vdupq_n_s32(output_activation_max); + + DownquantizeAndStoreDepth16(acc0, output_multiplier, output_shift, + output_offset_vec, output_activation_min_vec, + output_activation_max_vec, output_ptr); + + DownquantizeAndStoreDepth16(acc1, output_multiplier, output_shift, + output_offset_vec, output_activation_min_vec, + output_activation_max_vec, + output_ptr + output_depth * output_width); + } +}; + +template <> +struct ConvKernel3x3FilterDepth16<1, 1> { + static void Run(const Filter3x3x16& filter, const uint8* input_ptr, + int input_depth, int32 input_offset, int input_row_width, + const int32* bias_ptr, int32 output_offset, + int32 output_multiplier, int output_shift, + int32 output_activation_min, int32 output_activation_max, + uint8* output_ptr, int output_depth, int output_width) { + Int32x16 acc; + acc.v0 = vld1q_s32(bias_ptr); + acc.v1 = vld1q_s32(bias_ptr + 4); + acc.v2 = vld1q_s32(bias_ptr + 8); + acc.v3 = vld1q_s32(bias_ptr + 12); + + // Main multiply accumulate work. + { + // Load inputs for one filter row at a time. + Int16x16x3 input; + + // Do first row. + input = LoadInputRowDepth16(input_ptr, input_depth, input_offset, input); + acc = MultiplyAccumulateRowDepth16(acc, filter.r0, input); + + // Do second row. + input = LoadInputRowDepth16(input_ptr + input_row_width, input_depth, + input_offset, input); + acc = MultiplyAccumulateRowDepth16(acc, filter.r1, input); + + // Do third row. + input = LoadInputRowDepth16(input_ptr + 2 * input_row_width, input_depth, + input_offset, input); + acc = MultiplyAccumulateRowDepth16(acc, filter.r2, input); + } + + // Apply activation, downquantize and store. + int32x4_t output_offset_vec = vdupq_n_s32(output_offset); + int32x4_t output_activation_min_vec = vdupq_n_s32(output_activation_min); + int32x4_t output_activation_max_vec = vdupq_n_s32(output_activation_max); + + DownquantizeAndStoreDepth16(acc, output_multiplier, output_shift, + output_offset_vec, output_activation_min_vec, + output_activation_max_vec, output_ptr); + } +}; + +inline void DepthwiseConv3by3FilterDepth16( + const uint8* input_data, const Dims<4>& input_dims, int32 input_offset, + const uint8* filter_data, const Dims<4>& filter_dims, int32 filter_offset, + const int32* bias_data, const Dims<4>& bias_dims, int stride_width, + int stride_height, int pad_width, int pad_height, int depth_multiplier, + int32 output_offset, int32 output_multiplier, int output_shift, + int32 output_activation_min, int32 output_activation_max, + uint8* output_data, const Dims<4>& output_dims) { + const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); + const int output_depth = MatchingArraySize(filter_dims, 0, output_dims, 0); + const int input_height = ArraySize(input_dims, 2); + const int input_width = ArraySize(input_dims, 1); + const int input_depth = ArraySize(input_dims, 0); + const int filter_height = ArraySize(filter_dims, 2); + const int filter_width = ArraySize(filter_dims, 1); + const int output_height = ArraySize(output_dims, 2); + const int output_width = ArraySize(output_dims, 1); + + // Algorithm assumes below constraints. It is optimized for depth multiplier + // of 1, 3x3 filter, no padding, strides 1 and 2. + TFLITE_DCHECK(output_depth == input_depth * depth_multiplier); + TFLITE_DCHECK(depth_multiplier == 1); + TFLITE_DCHECK(filter_height == 3); + TFLITE_DCHECK(filter_width == 3); + TFLITE_DCHECK(pad_height == 0); + TFLITE_DCHECK(pad_width == 0); + TFLITE_DCHECK(stride_width == 1); + TFLITE_DCHECK(stride_height == 1); + + // The number of outputs to process in the main loop. + const int num_x_outputs = 1; + const int num_y_outputs = 2; + + const int input_row_width = output_depth * (input_width + 2 * pad_width); + const int input_batch_size = + input_row_width * (input_height + 2 * pad_height); + const int output_batch_size = output_depth * output_width * output_height; + const int input_ptr_x_increment = input_depth * stride_width; + + // Calculate extents of non-boundary loop. + int out_x_start = 0; + for (; out_x_start < input_width; out_x_start++) { + int in_x = (out_x_start * stride_width) - pad_width; + if (in_x >= 0) { + break; + } + } + int out_x_end = output_width - 1; + for (; out_x_end >= 0; out_x_end--) { + int in_x = (out_x_end * stride_width) - pad_width; + int in_x_end = in_x + filter_width + (num_x_outputs - 1) * stride_width; + if (in_x_end <= input_width) { + out_x_end++; + break; + } + } + int out_y_start = 0; + for (; out_y_start < input_height; out_y_start++) { + int in_y = (out_y_start * stride_height) - pad_height; + if (in_y >= 0) { + break; + } + } + int out_y_end = output_height - 1; + for (; out_y_end >= 0; out_y_end--) { + int in_y = (out_y_end * stride_height) - pad_height; + int in_y_end = in_y + filter_height + (num_y_outputs - 1) * stride_height; + if (in_y_end <= input_height) { + out_y_end++; + break; + } + } + + // Offsets for preloading inputs. + const int i0 = 0; + const int i1 = input_depth; + const int i2 = 2 * input_depth; + const int i3 = input_row_width; + const int i4 = input_row_width + input_depth; + const int i5 = input_row_width + 2 * input_depth; + const int i6 = 2 * input_row_width; + const int i7 = 2 * input_row_width + input_depth; + const int i8 = 2 * input_row_width + 2 * input_depth; + const int i9 = 3 * input_row_width; + const int i10 = 3 * input_row_width + input_depth; + const int i11 = 3 * input_row_width + 2 * input_depth; + + for (int b = 0; b < batches; ++b) { + const int32* bias_ptr = bias_data; + const uint8* filter_ptr = filter_data; + + const int in_batch_offset = b * input_batch_size; + const int out_batch_offset = b * output_batch_size; + + int depth = 0; + for (; depth <= output_depth - 16; depth += 16) { + Filter3x3x16 filter = + LoadFilterDepth16(filter_ptr, filter_offset, output_depth); + + // Handle 1x2 outputs. + int out_y = out_y_start; + for (; out_y < out_y_end; out_y += num_y_outputs) { + int out_x = out_x_start; + + int in_y_offset = + stride_height * input_row_width * (out_y + pad_height); + int in_x_offset = stride_width * input_depth * (out_x + pad_width); + + const uint8* input_ptr = + input_data + depth + in_x_offset + in_y_offset + in_batch_offset; + + uint8* output_ptr = output_data + depth + (out_x * output_depth) + + (output_depth * output_width * out_y) + + out_batch_offset; + + // Preload inputs. If input depth is large, preload every value of the + // input for this depth range. Otherwise, preload only the first values + // of each row. + if (input_depth >= 32) { + preload_l1_keep(input_ptr + i0); + preload_l1_keep(input_ptr + i1); + preload_l1_keep(input_ptr + i2); + preload_l1_keep(input_ptr + i3); + preload_l1_keep(input_ptr + i4); + preload_l1_keep(input_ptr + i5); + preload_l1_keep(input_ptr + i6); + preload_l1_keep(input_ptr + i7); + preload_l1_keep(input_ptr + i8); + preload_l1_keep(input_ptr + i9); + preload_l1_keep(input_ptr + i10); + preload_l1_keep(input_ptr + i11); + } else { + preload_l1_keep(input_ptr + i0); + preload_l1_keep(input_ptr + i3); + preload_l1_keep(input_ptr + i6); + preload_l1_keep(input_ptr + i9); + } + + for (; out_x < out_x_end; out_x += num_x_outputs) { + ConvKernel3x3FilterDepth16<1, 2, 1>::Run( + filter, input_ptr, input_depth, input_offset, input_row_width, + bias_ptr, output_offset, output_multiplier, output_shift, + output_activation_min, output_activation_max, output_ptr, + output_depth, output_width); + + input_ptr += input_ptr_x_increment * num_x_outputs; + output_ptr += output_depth * num_x_outputs; + + // Preload the next inputs depending on stride. + if (stride_width == 1) { + preload_l1_keep(input_ptr + i2); + preload_l1_keep(input_ptr + i5); + preload_l1_keep(input_ptr + i8); + preload_l1_keep(input_ptr + i11); + } else if (stride_width == 2) { + preload_l1_keep(input_ptr + i1); + preload_l1_keep(input_ptr + i2); + preload_l1_keep(input_ptr + i4); + preload_l1_keep(input_ptr + i5); + preload_l1_keep(input_ptr + i7); + preload_l1_keep(input_ptr + i8); + preload_l1_keep(input_ptr + i10); + preload_l1_keep(input_ptr + i11); + } + } + + // Handle the rest of the right side. + for (; out_x < output_width; out_x++) { + // This code path can only be reached if we're handling >1 x outputs + // at a time or support padding. + } + } + + // Handle the rest of the bottom side. + for (; out_y < output_height; out_y++) { + int out_x = out_x_start; + + int in_y_offset = + stride_height * input_row_width * (out_y + pad_height); + int in_x_offset = stride_width * input_depth * (out_x + pad_width); + + const uint8* input_ptr = + input_data + depth + in_x_offset + in_y_offset + in_batch_offset; + + uint8* output_ptr = output_data + depth + (out_x * output_depth) + + (output_depth * output_width * out_y) + + out_batch_offset; + + for (; out_x < output_width; out_x++) { + ConvKernel3x3FilterDepth16<1, 1>::Run( + filter, input_ptr, input_depth, input_offset, input_row_width, + bias_ptr, output_offset, output_multiplier, output_shift, + output_activation_min, output_activation_max, output_ptr, + output_depth, output_width); + + input_ptr += input_ptr_x_increment; + output_ptr += output_depth; + } + } + filter_ptr += 16; + bias_ptr += 16; + } + } +} + +#endif // __aarch64__ + +} // namespace optimized_ops +} // namespace tflite + +#endif // TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_OPTIMIZED_DEPTHWISECONV_UINT8_3X3_FILTER_H_ diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h index 3866f86d38a6f200e091497cab2972ed92e25c6b..6bbc213cc67f1421a4782636a0f5142c9a4664f3 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h @@ -610,6 +610,58 @@ inline void FullyConnected(const uint8* input_data, const Dims<4>& input_dims, input_offset, output_pipeline); } +inline void FullyConnected(const uint8* input_data, const Dims<4>& input_dims, + int32 input_offset, const uint8* filter_data, + const Dims<4>& filter_dims, int32 filter_offset, + const int32* bias_data, const Dims<4>& bias_dims, + int32 output_offset, int32 output_multiplier, + int output_shift, int32 output_activation_min, + int32 output_activation_max, int16* output_data, + const Dims<4>& output_dims, + gemmlowp::GemmContext* gemm_context) { + gemmlowp::ScopedProfilingLabel label("FullyConnected/Uint8Int16"); + // This is a copy of the reference implementation. We do not currently have a + // properly optimized version. + (void)gemm_context; // only used in properly optimized code. + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + TFLITE_DCHECK_EQ(output_offset, 0); + // TODO(benoitjacob): This really should be: + // const int batches = ArraySize(output_dims, 1); + // but the current --variable_batch hack consists in overwriting the 3rd + // dimension with the runtime batch size, as we don't keep track for each + // array of which dimension is the batch dimension in it. + const int batches = ArraySize(output_dims, 1) * ArraySize(output_dims, 2) * + ArraySize(output_dims, 3); + const int output_depth = MatchingArraySize(filter_dims, 1, output_dims, 0); + const int accum_depth = ArraySize(filter_dims, 0); + TFLITE_DCHECK(IsPackedWithoutStrides(input_dims)); + TFLITE_DCHECK(IsPackedWithoutStrides(filter_dims)); + for (int b = 0; b < batches; ++b) { + for (int out_c = 0; out_c < output_depth; ++out_c) { + // Internal accumulation. + // Initialize accumulator with the bias-value. + int32 accum = bias_data[out_c]; + // Accumulation loop. + for (int d = 0; d < accum_depth; ++d) { + int16 input_val = input_data[b * accum_depth + d] + input_offset; + int16 filter_val = filter_data[out_c * accum_depth + d] + filter_offset; + accum += filter_val * input_val; + } + // Down-scale the final int32 accumulator to the scale used by our + // (16-bit, typically 3 integer bits) fixed-point format. The quantized + // multiplier and shift here have been pre-computed offline + // (e.g. by toco). + accum = MultiplyByQuantizedMultiplier(accum, output_multiplier, + -output_shift); + // Saturate, cast to int16, and store to output array. + accum = std::max(accum, output_activation_min - output_offset); + accum = std::min(accum, output_activation_max - output_offset); + accum += output_offset; + output_data[out_c + output_depth * b] = accum; + } + } +} + // legacy, for compatibility with old checked-in code template void FullyConnected(const uint8* input_data, const Dims<4>& input_dims, @@ -768,6 +820,7 @@ inline void DilatedConv(const float* input_data, const Dims<4>& input_dims, float output_activation_max, float* output_data, const Dims<4>& output_dims, float* im2col_data, const Dims<4>& im2col_dims) { + gemmlowp::ScopedProfilingLabel label("DilatedConv"); // This is a copy of the reference Conv implementation. We do not currently // have an optimized path for dilation. (void)im2col_data; // only used in optimized code. @@ -1598,6 +1651,39 @@ inline void Add(int left_shift, const uint8* input1_data, } } +template +inline void Add(const int16* input1_data, const Dims<4>& input1_dims, + int input1_shift, const int16* input2_data, + const Dims<4>& input2_dims, int input2_shift, + int16* output_data, const Dims<4>& output_dims) { + gemmlowp::ScopedProfilingLabel label("Add/Int16"); + // This is a copy of the reference implementation. We do not currently have a + // properly optimized version. + static_assert(Ac == FusedActivationFunctionType::kNone, ""); + + const int flat_size = RequiredBufferSizeForDims(output_dims); + TFLITE_DCHECK_EQ(RequiredBufferSizeForDims(input1_dims), flat_size); + TFLITE_DCHECK_EQ(RequiredBufferSizeForDims(input2_dims), flat_size); + + TFLITE_DCHECK(input1_shift == 0 || input2_shift == 0); + TFLITE_DCHECK_GE(input1_shift, 0); + TFLITE_DCHECK_GE(input2_shift, 0); + const int16* not_shift_input = input1_shift == 0 ? input1_data : input2_data; + const int16* shift_input = input1_shift == 0 ? input2_data : input1_data; + const int input_shift = input1_shift == 0 ? input2_shift : input1_shift; + + for (int i = 0; i < flat_size; i++) { + // F0 uses 0 integer bits, range [-1, 1]. + using F0 = gemmlowp::FixedPoint; + + F0 input_ready_scaled = F0::FromRaw(not_shift_input[i]); + F0 scaled_input = + F0::FromRaw(gemmlowp::RoundingDivideByPOT(shift_input[i], input_shift)); + F0 result = gemmlowp::SaturatingAdd(scaled_input, input_ready_scaled); + output_data[i] = result.raw(); + } +} + template void Add(const int32* input1_data, const Dims<4>& input1_dims, const int32* input2_data, const Dims<4>& input2_dims, @@ -1872,6 +1958,57 @@ void Mul(const int32* input1_data, const Dims<4>& input1_dims, } } +inline void Mul(const int16* input1_data, const Dims<4>& input1_dims, + const int16* input2_data, const Dims<4>& input2_dims, + int16* output_data, const Dims<4>& output_dims) { + gemmlowp::ScopedProfilingLabel label("Mul/Int16"); + // This is a copy of the reference implementation. We do not currently have a + // properly optimized version. + + const int flat_size = RequiredBufferSizeForDims(output_dims); + TFLITE_DCHECK_EQ(RequiredBufferSizeForDims(input1_dims), flat_size); + TFLITE_DCHECK_EQ(RequiredBufferSizeForDims(input2_dims), flat_size); + + for (int i = 0; i < flat_size; i++) { + // F0 uses 0 integer bits, range [-1, 1]. + using F0 = gemmlowp::FixedPoint; + + F0 unclamped_result = + F0::FromRaw(input1_data[i]) * F0::FromRaw(input2_data[i]); + output_data[i] = unclamped_result.raw(); + } +} + +inline void Mul(const int16* input1_data, const Dims<4>& input1_dims, + const int16* input2_data, const Dims<4>& input2_dims, + int32 output_offset, int32 output_activation_min, + int32 output_activation_max, uint8* output_data, + const Dims<4>& output_dims) { + gemmlowp::ScopedProfilingLabel label("Mul/Int16Uint8"); + // This is a copy of the reference implementation. We do not currently have a + // properly optimized version. + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + + const int flat_size = RequiredBufferSizeForDims(output_dims); + TFLITE_DCHECK_EQ(RequiredBufferSizeForDims(input1_dims), flat_size); + TFLITE_DCHECK_EQ(RequiredBufferSizeForDims(input2_dims), flat_size); + + for (int i = 0; i < flat_size; i++) { + // F0 uses 0 integer bits, range [-1, 1]. + using F0 = gemmlowp::FixedPoint; + + F0 unclamped_result = + F0::FromRaw(input1_data[i]) * F0::FromRaw(input2_data[i]); + int16 rescaled_result = + gemmlowp::RoundingDivideByPOT(unclamped_result.raw(), 8); + int16 clamped_result = + std::min(output_activation_max - output_offset, rescaled_result); + clamped_result = + std::max(output_activation_min - output_offset, clamped_result); + output_data[i] = output_offset + clamped_result; + } +} + // TODO(jiawen): We can implement BroadcastMul on buffers of arbitrary // dimensionality if the runtime code does a single loop over one dimension // that handles broadcasting as the base case. The code generator would then @@ -3631,6 +3768,28 @@ inline void Logistic(const uint8* input_data, const Dims<4>& input_dims, } } +inline void Logistic(const int16* input_data, const Dims<4>& input_dims, + int16* output_data, const Dims<4>& output_dims) { + gemmlowp::ScopedProfilingLabel label("Logistic/Int16"); + // This is a copy of the reference implementation. We do not currently have a + // properly optimized version. + const int flat_size = RequiredBufferSizeForDims(output_dims); + TFLITE_DCHECK_EQ(RequiredBufferSizeForDims(input_dims), flat_size); + + for (int i = 0; i < flat_size; i++) { + // F0 uses 0 integer bits, range [-1, 1]. + // This is the return type of math functions such as tanh, logistic, + // whose range is in [-1, 1]. + using F0 = gemmlowp::FixedPoint; + // F3 uses 3 integer bits, range [-8, 8], the input range expected here. + using F3 = gemmlowp::FixedPoint; + + const F3 input = F3::FromRaw(input_data[i]); + F0 output = gemmlowp::logistic(input); + output_data[i] = output.raw(); + } +} + inline void Tanh(const float* input_data, const Dims<4>& input_dims, float* output_data, const Dims<4>& output_dims) { gemmlowp::ScopedProfilingLabel label("Tanh"); @@ -3789,6 +3948,45 @@ inline void Tanh(const uint8* input_data, const Dims<4>& input_dims, output_data[c] = output_val; } } + +inline void Tanh(const int16* input_data, const Dims<4>& input_dims, + int input_left_shift, int16* output_data, + const Dims<4>& output_dims) { + gemmlowp::ScopedProfilingLabel label("Tanh/Int16"); + // This is a copy of the reference implementation. We do not currently have a + // properly optimized version. + + // Support for shifts is limited until we have a parameterized version of + // SaturatingRoundingMultiplyByPOT(). + TFLITE_DCHECK_GE(input_left_shift, 0); + TFLITE_DCHECK_LE(input_left_shift, 1); + + const int flat_size = RequiredBufferSizeForDims(output_dims); + TFLITE_DCHECK_EQ(RequiredBufferSizeForDims(input_dims), flat_size); + + // F0 uses 0 integer bits, range [-1, 1]. + // This is the return type of math functions such as tanh, logistic, + // whose range is in [-1, 1]. + using F0 = gemmlowp::FixedPoint; + // F3 uses 3 integer bits, range [-8, 8], the input range expected here. + using F3 = gemmlowp::FixedPoint; + + if (input_left_shift == 0) { + for (int i = 0; i < flat_size; i++) { + F3 input = F3::FromRaw(input_data[i]); + F0 output = gemmlowp::tanh(input); + output_data[i] = output.raw(); + } + } else { + for (int i = 0; i < flat_size; i++) { + F3 input = F3::FromRaw( + gemmlowp::SaturatingRoundingMultiplyByPOT<1>(input_data[i])); + F0 output = gemmlowp::tanh(input); + output_data[i] = output.raw(); + } + } +} + inline void Dequantize(const uint8* input_data, const Dims<4>& input_dims, int32 zero_point, double scale, float* output_data, const Dims<4>& output_dims) { @@ -4725,6 +4923,78 @@ void Transpose(const T* input, const Dims<4>& input_dims, T* output, } } +inline void TransposeConv(const float* input_data, const Dims<4>& input_dims, + const float* filter_data, const Dims<4>& filter_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, float* output_data, + const Dims<4>& output_dims) { + gemmlowp::ScopedProfilingLabel label("TransposeConv"); + // THIS FUNCTION IS A COPY FROM reference_ops.h. + // To optimize, start by using the conv code with transposed weights for the + // case of stride_height = stride_width = 1. + const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); + const int input_depth = MatchingArraySize(input_dims, 0, filter_dims, 3); + const int output_depth = MatchingArraySize(filter_dims, 0, output_dims, 0); + const int input_height = ArraySize(input_dims, 2); + const int input_width = ArraySize(input_dims, 1); + const int filter_height = ArraySize(filter_dims, 2); + const int filter_width = ArraySize(filter_dims, 1); + const int output_height = ArraySize(output_dims, 2); + const int output_width = ArraySize(output_dims, 1); + + // Although transpose convolution simplifies to convolution with transposed + // weights for strides of 1, non-unitary striding complicates matters. To + // keep this reference implementation as clear as possible, we use a "scatter" + // access pattern, where we loop through all the input elements, computing + // their influence on the output, rather than looping through the output + // elements in the typical "gather" access pattern of a conv. We therefore + // must initialize the output array to zero. + for (int batch = 0; batch < batches; ++batch) { + for (int out_y = 0; out_y < output_height; ++out_y) { + for (int out_x = 0; out_x < output_width; ++out_x) { + for (int out_channel = 0; out_channel < output_depth; ++out_channel) { + output_data[Offset(output_dims, out_channel, out_x, out_y, batch)] = + 0.0f; + } + } + } + } + + // Loop through input elements one at a time. + for (int batch = 0; batch < batches; ++batch) { + for (int in_y = 0; in_y < input_height; ++in_y) { + for (int in_x = 0; in_x < input_width; ++in_x) { + for (int in_channel = 0; in_channel < input_depth; ++in_channel) { + // Loop through the output elements it will influence + const int out_x_origin = (in_x * stride_width) - pad_width; + const int out_y_origin = (in_y * stride_height) - pad_height; + for (int filter_y = 0; filter_y < filter_height; ++filter_y) { + for (int filter_x = 0; filter_x < filter_width; ++filter_x) { + for (int out_channel = 0; out_channel < input_depth; + ++out_channel) { + // Compute output element location + const int out_x = out_x_origin + filter_x; + const int out_y = out_y_origin + filter_y; + // We cannot accumulate out of bounds + if ((out_x >= 0) && (out_x < output_width) && (out_y >= 0) && + (out_y < output_height)) { + float input_value = input_data[Offset(input_dims, in_channel, + in_x, in_y, batch)]; + float filter_value = + filter_data[Offset(filter_dims, out_channel, filter_x, + filter_y, in_channel)]; + output_data[Offset(output_dims, out_channel, out_x, out_y, + batch)] += input_value * filter_value; + } + } + } + } + } + } + } + } +} + } // namespace optimized_ops } // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/internal/quantization_util.h b/tensorflow/contrib/lite/kernels/internal/quantization_util.h index ba06bc0975b6847b24592daa60efe99983d03707..f7706c793883f012d2b66cf3d3167a59afe31f91 100644 --- a/tensorflow/contrib/lite/kernels/internal/quantization_util.h +++ b/tensorflow/contrib/lite/kernels/internal/quantization_util.h @@ -12,13 +12,91 @@ 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 PHOTOS_VISION_LEARNING_TENSORFLOW_MINI_QUANTIZATION_UTIL_H_ -#define PHOTOS_VISION_LEARNING_TENSORFLOW_MINI_QUANTIZATION_UTIL_H_ +#ifndef TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_QUANTIZATION_UTIL_H_ +#define TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_QUANTIZATION_UTIL_H_ +#include #include +#include + +#include "tensorflow/contrib/lite/kernels/internal/compatibility.h" +#include "tensorflow/contrib/lite/kernels/internal/round.h" +#include "tensorflow/contrib/lite/kernels/internal/types.h" namespace tflite { +// Given the min and max values of a float array, return +// reasonable quantization parameters to use for this array. +template +QuantizationParams ChooseQuantizationParams(double rmin, double rmax) { + const T qmin = std::numeric_limits::min(); + const T qmax = std::numeric_limits::max(); + const double qmin_double = qmin; + const double qmax_double = qmax; + // 0 should always be a representable value. Let's assume that the initial + // min,max range contains 0. + TFLITE_CHECK_LE(rmin, 0.); + TFLITE_CHECK_GE(rmax, 0.); + if (rmin == rmax) { + // Special case where the min,max range is a point. Should be {0}. + TFLITE_CHECK_EQ(rmin, 0.); + TFLITE_CHECK_EQ(rmax, 0.); + QuantizationParams quantization_params; + quantization_params.zero_point = 0; + quantization_params.scale = 0.; + return quantization_params; + } + + // General case. + // + // First determine the scale. + const double scale = (rmax - rmin) / (qmax_double - qmin_double); + + // Zero-point computation. + // First the initial floating-point computation. The zero-point can be + // determined from solving an affine equation for any known pair + // (real value, corresponding quantized value). + // We know two such pairs: (rmin, qmin) and (rmax, qmax). + // The arithmetic error on the zero point computed from either pair + // will be roughly machine_epsilon * (sum of absolute values of terms) + // so we want to use the variant that adds the smaller terms. + const double zero_point_from_min = qmin_double - rmin / scale; + const double zero_point_from_max = qmax_double - rmax / scale; + const double zero_point_from_min_error = + std::abs(qmin_double) + std::abs(rmin / scale); + const double zero_point_from_max_error = + std::abs(qmax_double) + std::abs(rmax / scale); + + const double zero_point_double = + zero_point_from_min_error < zero_point_from_max_error + ? zero_point_from_min + : zero_point_from_max; + + // Now we need to nudge the zero point to be an integer + // (our zero points are integer, and this is motivated by the requirement + // to be able to represent the real value "0" exactly as a quantized value, + // which is required in multiple places, for example in Im2col with SAME + // padding). + T nudged_zero_point = 0; + if (zero_point_double < qmin_double) { + nudged_zero_point = qmin; + } else if (zero_point_double > qmax_double) { + nudged_zero_point = qmax; + } else { + nudged_zero_point = static_cast(round(zero_point_double)); + } + // The zero point should always be in the range of quantized value, + // [qmin, qmax]. + TFLITE_CHECK_GE(nudged_zero_point, qmin); + TFLITE_CHECK_LE(nudged_zero_point, qmax); + + // Finally, store the result nudged quantization params. + QuantizationParams quantization_params; + quantization_params.zero_point = nudged_zero_point; + quantization_params.scale = scale; + return quantization_params; +} + // Decompose a double multiplier into a Q0.31 int32 representation of its // significand, and shift representation of NEGATIVE its exponent --- // this is intended as a RIGHT-shift. @@ -63,4 +141,4 @@ int CalculateInputRadius(int input_integer_bits, int input_left_shift); } // namespace tflite -#endif // PHOTOS_VISION_LEARNING_TENSORFLOW_MINI_QUANTIZATION_UTIL_H_ +#endif // TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_QUANTIZATION_UTIL_H_ diff --git a/tensorflow/contrib/lite/kernels/internal/quantization_util_test.cc b/tensorflow/contrib/lite/kernels/internal/quantization_util_test.cc index 19b1b408ec74b0939065b0ad10b91ecfc2cd4765..4ae2085c30ed08790eaff27c7921909d47687707 100644 --- a/tensorflow/contrib/lite/kernels/internal/quantization_util_test.cc +++ b/tensorflow/contrib/lite/kernels/internal/quantization_util_test.cc @@ -22,6 +22,51 @@ namespace { using ::testing::Pair; +// Example taken from http://www.tensorflow.org/performance/quantization +// +// Quantized | Float +// --------- | ----- +// 0 | -10.0 +// 255 | 30.0 +// 128 | 10.0 +TEST(QuantizationUtilTest, ChooseQuantizationParams) { + QuantizationParams qp = ChooseQuantizationParams(-10.0, 30.0); + EXPECT_NEAR(qp.scale, 0.156863, 1e-5); + EXPECT_EQ(qp.zero_point, 64); +} + +TEST(QuantizationUtilTest, ChooseQuantizationParamsZeroPointOnMinBoundary) { + QuantizationParams qp = ChooseQuantizationParams(0.0, 30.0); + EXPECT_NEAR(qp.scale, 0.117647, 1e-5); + EXPECT_EQ(qp.zero_point, 0); +} + +TEST(QuantizationUtilTest, ChooseQuantizationParamsZeroNotInRange) { + // Assumption is that zero is within the range. + EXPECT_DEATH(ChooseQuantizationParams(10.0, 30.0), ""); +} + +TEST(QuantizationUtilTest, ChooseQuantizationParamsEmptyRangePositive) { + // Assumption is that zero is within the range. + EXPECT_DEATH(ChooseQuantizationParams(30.0, 30.0), ""); +} + +TEST(QuantizationUtilTest, ChooseQuantizationParamsEmptyRangeZero) { + QuantizationParams qp = ChooseQuantizationParams(0.0, 0.0); + EXPECT_NEAR(qp.scale, 0.0, 1e-5); + EXPECT_EQ(qp.zero_point, 0); +} + +TEST(QuantizationUtilTest, ChooseQuantizationParamsZeroPointOnMaxBoundary) { + QuantizationParams qp = ChooseQuantizationParams(-10.0, 0.0); + EXPECT_NEAR(qp.scale, 0.039216, 1e-5); + EXPECT_EQ(qp.zero_point, 255); +} + +TEST(QuantizationUtilTest, ChooseQuantizationParamsInvalidRange) { + EXPECT_DEATH(ChooseQuantizationParams(10.0, -30.0), ""); +} + TEST(QuantizationUtilTest, QuantizeMultiplierSmallerThanOne) { auto quantize = [](double d) { int32_t q; diff --git a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h index f5290a14d36edfaf7e570b408d79ce8c833c806d..d3d15edf4c0c904697817cc7c191f723bd52b7e2 100644 --- a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h @@ -551,6 +551,55 @@ inline void FullyConnected(const uint8* input_data, const Dims<4>& input_dims, } } +inline void FullyConnected(const uint8* input_data, const Dims<4>& input_dims, + int32 input_offset, const uint8* filter_data, + const Dims<4>& filter_dims, int32 filter_offset, + const int32* bias_data, const Dims<4>& bias_dims, + int32 output_offset, int32 output_multiplier, + int output_shift, int32 output_activation_min, + int32 output_activation_max, int16* output_data, + const Dims<4>& output_dims, + gemmlowp::GemmContext* gemm_context) { + (void)gemm_context; // only used in optimized code. + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + TFLITE_DCHECK_EQ(output_offset, 0); + // TODO(benoitjacob): This really should be: + // const int batches = ArraySize(output_dims, 1); + // but the current --variable_batch hack consists in overwriting the 3rd + // dimension with the runtime batch size, as we don't keep track for each + // array of which dimension is the batch dimension in it. + const int batches = ArraySize(output_dims, 1) * ArraySize(output_dims, 2) * + ArraySize(output_dims, 3); + const int output_depth = MatchingArraySize(filter_dims, 1, output_dims, 0); + const int accum_depth = ArraySize(filter_dims, 0); + TFLITE_DCHECK(IsPackedWithoutStrides(input_dims)); + TFLITE_DCHECK(IsPackedWithoutStrides(filter_dims)); + for (int b = 0; b < batches; ++b) { + for (int out_c = 0; out_c < output_depth; ++out_c) { + // Internal accumulation. + // Initialize accumulator with the bias-value. + int32 accum = bias_data[out_c]; + // Accumulation loop. + for (int d = 0; d < accum_depth; ++d) { + int16 input_val = input_data[b * accum_depth + d] + input_offset; + int16 filter_val = filter_data[out_c * accum_depth + d] + filter_offset; + accum += filter_val * input_val; + } + // Down-scale the final int32 accumulator to the scale used by our + // (16-bit, typically 3 integer bits) fixed-point format. The quantized + // multiplier and shift here have been pre-computed offline + // (e.g. by toco). + accum = MultiplyByQuantizedMultiplier(accum, output_multiplier, + -output_shift); + // Saturate, cast to int16, and store to output array. + accum = std::max(accum, output_activation_min - output_offset); + accum = std::min(accum, output_activation_max - output_offset); + accum += output_offset; + output_data[out_c + output_depth * b] = accum; + } + } +} + // legacy, for compatibility with old checked-in code template void FullyConnected(const uint8* input_data, const Dims<4>& input_dims, @@ -903,6 +952,36 @@ inline void Add(int left_shift, const uint8* input1_data, } } +template +inline void Add(const int16* input1_data, const Dims<4>& input1_dims, + int input1_shift, const int16* input2_data, + const Dims<4>& input2_dims, int input2_shift, + int16* output_data, const Dims<4>& output_dims) { + static_assert(Ac == FusedActivationFunctionType::kNone, ""); + + const int flat_size = RequiredBufferSizeForDims(output_dims); + TFLITE_DCHECK_EQ(RequiredBufferSizeForDims(input1_dims), flat_size); + TFLITE_DCHECK_EQ(RequiredBufferSizeForDims(input2_dims), flat_size); + + TFLITE_DCHECK(input1_shift == 0 || input2_shift == 0); + TFLITE_DCHECK_GE(input1_shift, 0); + TFLITE_DCHECK_GE(input2_shift, 0); + const int16* not_shift_input = input1_shift == 0 ? input1_data : input2_data; + const int16* shift_input = input1_shift == 0 ? input2_data : input1_data; + const int input_shift = input1_shift == 0 ? input2_shift : input1_shift; + + for (int i = 0; i < flat_size; i++) { + // F0 uses 0 integer bits, range [-1, 1]. + using F0 = gemmlowp::FixedPoint; + + F0 input_ready_scaled = F0::FromRaw(not_shift_input[i]); + F0 scaled_input = + F0::FromRaw(gemmlowp::RoundingDivideByPOT(shift_input[i], input_shift)); + F0 result = gemmlowp::SaturatingAdd(scaled_input, input_ready_scaled); + output_data[i] = result.raw(); + } +} + // TODO(jiawen): We can implement BroadcastAdd on buffers of arbitrary // dimensionality if the runtime code does a single loop over one dimension // that handles broadcasting as the base case. The code generator would then @@ -1184,6 +1263,53 @@ inline void BroadcastMul(const uint8* input1_data, const Dims<4>& input1_dims, } } +inline void Mul(const int16* input1_data, const Dims<4>& input1_dims, + const int16* input2_data, const Dims<4>& input2_dims, + int16* output_data, const Dims<4>& output_dims) { + gemmlowp::ScopedProfilingLabel label("Mul/Int16"); + + const int flat_size = RequiredBufferSizeForDims(output_dims); + TFLITE_DCHECK_EQ(RequiredBufferSizeForDims(input1_dims), flat_size); + TFLITE_DCHECK_EQ(RequiredBufferSizeForDims(input2_dims), flat_size); + + for (int i = 0; i < flat_size; i++) { + // F0 uses 0 integer bits, range [-1, 1]. + using F0 = gemmlowp::FixedPoint; + + F0 unclamped_result = + F0::FromRaw(input1_data[i]) * F0::FromRaw(input2_data[i]); + output_data[i] = unclamped_result.raw(); + } +} + +inline void Mul(const int16* input1_data, const Dims<4>& input1_dims, + const int16* input2_data, const Dims<4>& input2_dims, + int32 output_offset, int32 output_activation_min, + int32 output_activation_max, uint8* output_data, + const Dims<4>& output_dims) { + gemmlowp::ScopedProfilingLabel label("Mul/Int16Uint8"); + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + + const int flat_size = RequiredBufferSizeForDims(output_dims); + TFLITE_DCHECK_EQ(RequiredBufferSizeForDims(input1_dims), flat_size); + TFLITE_DCHECK_EQ(RequiredBufferSizeForDims(input2_dims), flat_size); + + for (int i = 0; i < flat_size; i++) { + // F0 uses 0 integer bits, range [-1, 1]. + using F0 = gemmlowp::FixedPoint; + + F0 unclamped_result = + F0::FromRaw(input1_data[i]) * F0::FromRaw(input2_data[i]); + int16 rescaled_result = + gemmlowp::RoundingDivideByPOT(unclamped_result.raw(), 8); + int16 clamped_result = + std::min(output_activation_max - output_offset, rescaled_result); + clamped_result = + std::max(output_activation_min - output_offset, clamped_result); + output_data[i] = output_offset + clamped_result; + } +} + // legacy, for compatibility with old checked-in code template inline void BroadcastMul(const uint8* input1_data, const Dims<4>& input1_dims, @@ -2317,11 +2443,13 @@ inline void Logistic(const uint8* input_data, const Dims<4>& input_dims, const FixedPoint4 input_val_f4 = FixedPoint4::FromRaw(input_val_rescaled); const FixedPoint0 output_val_f0 = gemmlowp::logistic(input_val_f4); + // Convert from Q0.31 to Q23.8. using gemmlowp::RoundingDivideByPOT; int32 output_val_s32 = RoundingDivideByPOT(output_val_f0.raw(), 23); if (output_val_s32 == 256) { output_val_s32 = 255; } + // Reinterpret as U0.8. TFLITE_DCHECK_GE(output_val_s32, 0); TFLITE_DCHECK_LE(output_val_s32, 255); output_val = static_cast(output_val_s32); @@ -2333,6 +2461,25 @@ inline void Logistic(const uint8* input_data, const Dims<4>& input_dims, } } +inline void Logistic(const int16* input_data, const Dims<4>& input_dims, + int16* output_data, const Dims<4>& output_dims) { + const int flat_size = RequiredBufferSizeForDims(output_dims); + TFLITE_DCHECK_EQ(RequiredBufferSizeForDims(input_dims), flat_size); + + for (int i = 0; i < flat_size; i++) { + // F0 uses 0 integer bits, range [-1, 1]. + // This is the return type of math functions such as tanh, logistic, + // whose range is in [-1, 1]. + using F0 = gemmlowp::FixedPoint; + // F3 uses 3 integer bits, range [-8, 8], the input range expected here. + using F3 = gemmlowp::FixedPoint; + + const F3 input = F3::FromRaw(input_data[i]); + F0 output = gemmlowp::logistic(input); + output_data[i] = output.raw(); + } +} + inline void Tanh(const float* input_data, const Dims<4>& input_dims, float* output_data, const Dims<4>& output_dims) { const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); @@ -2382,13 +2529,14 @@ inline void Tanh(const uint8* input_data, const Dims<4>& input_dims, const FixedPoint4 input_val_f4 = FixedPoint4::FromRaw(input_val_rescaled); const FixedPoint0 output_val_f0 = gemmlowp::tanh(input_val_f4); - + // Convert from Q0.31 to Q24.7. using gemmlowp::RoundingDivideByPOT; int32 output_val_s32 = RoundingDivideByPOT(output_val_f0.raw(), 24); output_val_s32 += output_zero_point; if (output_val_s32 == 256) { output_val_s32 = 255; } + // Reinterpret as Q0.7, encoded in uint8. TFLITE_DCHECK_GE(output_val_s32, 0); TFLITE_DCHECK_LE(output_val_s32, 255); output_val = static_cast(output_val_s32); @@ -2400,6 +2548,40 @@ inline void Tanh(const uint8* input_data, const Dims<4>& input_dims, } } +inline void Tanh(const int16* input_data, const Dims<4>& input_dims, + int input_left_shift, int16* output_data, + const Dims<4>& output_dims) { + // Support for shifts is limited until we have a parameterized version of + // SaturatingRoundingMultiplyByPOT(). + TFLITE_DCHECK_GE(input_left_shift, 0); + TFLITE_DCHECK_LE(input_left_shift, 1); + + const int flat_size = RequiredBufferSizeForDims(output_dims); + TFLITE_DCHECK_EQ(RequiredBufferSizeForDims(input_dims), flat_size); + + // F0 uses 0 integer bits, range [-1, 1]. + // This is the return type of math functions such as tanh, logistic, + // whose range is in [-1, 1]. + using F0 = gemmlowp::FixedPoint; + // F3 uses 3 integer bits, range [-8, 8], the input range expected here. + using F3 = gemmlowp::FixedPoint; + + if (input_left_shift == 0) { + for (int i = 0; i < flat_size; i++) { + F3 input = F3::FromRaw(input_data[i]); + F0 output = gemmlowp::tanh(input); + output_data[i] = output.raw(); + } + } else { + for (int i = 0; i < flat_size; i++) { + F3 input = F3::FromRaw( + gemmlowp::SaturatingRoundingMultiplyByPOT<1>(input_data[i])); + F0 output = gemmlowp::tanh(input); + output_data[i] = output.raw(); + } + } +} + inline void Dequantize(const uint8* input_data, const Dims<4>& input_dims, int32 zero_point, double scale, float* output_data, const Dims<4>& output_dims) { @@ -2899,9 +3081,11 @@ inline void Mean(T* input_data, const int* input_dims, const int input_num_dims, for (int idx = 0; idx < num_resolved_axis; ++idx) { num_elements_in_axis *= static_cast(input_dims[resolved_axis[idx]]); } - for (size_t idx = 0; idx < num_outputs; ++idx) { - output_data[idx] = static_cast(static_cast(output_data[idx]) / - num_elements_in_axis); + if (num_elements_in_axis > 0) { + for (size_t idx = 0; idx < num_outputs; ++idx) { + output_data[idx] = static_cast(static_cast(output_data[idx]) / + num_elements_in_axis); + } } } @@ -3082,6 +3266,67 @@ void Transpose(const T* input, const Dims<4>& input_dims, T* output, } } +inline void TransposeConv(const float* input_data, const Dims<4>& input_dims, + const float* filter_data, const Dims<4>& filter_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, float* output_data, + const Dims<4>& output_dims) { + const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); + const int input_depth = MatchingArraySize(input_dims, 0, filter_dims, 3); + const int output_depth = MatchingArraySize(filter_dims, 0, output_dims, 0); + const int input_height = ArraySize(input_dims, 2); + const int input_width = ArraySize(input_dims, 1); + const int filter_height = ArraySize(filter_dims, 2); + const int filter_width = ArraySize(filter_dims, 1); + const int output_height = ArraySize(output_dims, 2); + const int output_width = ArraySize(output_dims, 1); + + // Although transpose convolution simplifies to convolution with transposed + // weights for strides of 1, non-unitary striding complicates matters. To + // keep this reference implementation as clear as possible, we use a "scatter" + // access pattern, where we loop through all the input elements, computing + // their influence on the output, rather than looping through the output + // elements in the typical "gather" access pattern of a conv. We therefore + // must initialize the output array to zero. + for (int i = 0; i < RequiredBufferSizeForDims(output_dims); i++) { + output_data[i] = 0.0f; + } + + // Loop through input elements one at a time. + for (int batch = 0; batch < batches; ++batch) { + for (int in_y = 0; in_y < input_height; ++in_y) { + for (int in_x = 0; in_x < input_width; ++in_x) { + for (int in_channel = 0; in_channel < input_depth; ++in_channel) { + // Loop through the output elements it will influence + const int out_x_origin = (in_x * stride_width) - pad_width; + const int out_y_origin = (in_y * stride_height) - pad_height; + for (int filter_y = 0; filter_y < filter_height; ++filter_y) { + for (int filter_x = 0; filter_x < filter_width; ++filter_x) { + for (int out_channel = 0; out_channel < output_depth; + ++out_channel) { + // Compute output element location + const int out_x = out_x_origin + filter_x; + const int out_y = out_y_origin + filter_y; + // We cannot accumulate out of bounds + if ((out_x >= 0) && (out_x < output_width) && (out_y >= 0) && + (out_y < output_height)) { + float input_value = input_data[Offset(input_dims, in_channel, + in_x, in_y, batch)]; + float filter_value = + filter_data[Offset(filter_dims, out_channel, filter_x, + filter_y, in_channel)]; + output_data[Offset(output_dims, out_channel, out_x, out_y, + batch)] += input_value * filter_value; + } + } + } + } + } + } + } + } +} + } // namespace reference_ops } // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/internal/spectrogram.cc b/tensorflow/contrib/lite/kernels/internal/spectrogram.cc new file mode 100644 index 0000000000000000000000000000000000000000..b366bdc9aebf3784248a39225a442475a4f0d550 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/internal/spectrogram.cc @@ -0,0 +1,245 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/lite/kernels/internal/spectrogram.h" + +#include +#include + +#include "third_party/fft2d/fft.h" + +namespace tflite { +namespace internal { + +using std::complex; + +namespace { +// Returns the default Hann window function for the spectrogram. +void GetPeriodicHann(int window_length, std::vector* window) { + // Some platforms don't have M_PI, so define a local constant here. + const double pi = std::atan(1) * 4; + window->resize(window_length); + for (int i = 0; i < window_length; ++i) { + (*window)[i] = 0.5 - 0.5 * cos((2 * pi * i) / window_length); + } +} +} // namespace + +bool Spectrogram::Initialize(int window_length, int step_length) { + std::vector window; + GetPeriodicHann(window_length, &window); + return Initialize(window, step_length); +} + +inline int Log2Floor(uint n) { + if (n == 0) return -1; + int log = 0; + uint value = n; + for (int i = 4; i >= 0; --i) { + int shift = (1 << i); + uint x = value >> shift; + if (x != 0) { + value = x; + log += shift; + } + } + assert(value == 1); + return log; +} + +inline int Log2Ceiling(uint n) { + int floor = Log2Floor(n); + if (n == (n & ~(n - 1))) // zero or a power of two + return floor; + else + return floor + 1; +} + +inline uint NextPowerOfTwo(uint value) { + int exponent = Log2Ceiling(value); + // DCHECK_LT(exponent, std::numeric_limits::digits); + return 1 << exponent; +} + +bool Spectrogram::Initialize(const std::vector& window, + int step_length) { + window_length_ = window.size(); + window_ = window; // Copy window. + if (window_length_ < 2) { + // LOG(ERROR) << "Window length too short."; + initialized_ = false; + return false; + } + + step_length_ = step_length; + if (step_length_ < 1) { + // LOG(ERROR) << "Step length must be positive."; + initialized_ = false; + return false; + } + + fft_length_ = NextPowerOfTwo(window_length_); + // CHECK(fft_length_ >= window_length_); + output_frequency_channels_ = 1 + fft_length_ / 2; + + // Allocate 2 more than what rdft needs, so we can rationalize the layout. + fft_input_output_.assign(fft_length_ + 2, 0.0); + + int half_fft_length = fft_length_ / 2; + fft_double_working_area_.assign(half_fft_length, 0.0); + fft_integer_working_area_.assign(2 + static_cast(sqrt(half_fft_length)), + 0); + // Set flag element to ensure that the working areas are initialized + // on the first call to cdft. It's redundant given the assign above, + // but keep it as a reminder. + fft_integer_working_area_[0] = 0; + input_queue_.clear(); + samples_to_next_step_ = window_length_; + initialized_ = true; + return true; +} + +template +bool Spectrogram::ComputeComplexSpectrogram( + const std::vector& input, + std::vector>>* output) { + if (!initialized_) { + // LOG(ERROR) << "ComputeComplexSpectrogram() called before successful call + // " + // << "to Initialize()."; + return false; + } + // CHECK(output); + output->clear(); + int input_start = 0; + while (GetNextWindowOfSamples(input, &input_start)) { + // DCHECK_EQ(input_queue_.size(), window_length_); + ProcessCoreFFT(); // Processes input_queue_ to fft_input_output_. + // Add a new slice vector onto the output, to save new result to. + output->resize(output->size() + 1); + // Get a reference to the newly added slice to fill in. + auto& spectrogram_slice = output->back(); + spectrogram_slice.resize(output_frequency_channels_); + for (int i = 0; i < output_frequency_channels_; ++i) { + // This will convert double to float if it needs to. + spectrogram_slice[i] = complex( + fft_input_output_[2 * i], fft_input_output_[2 * i + 1]); + } + } + return true; +} +// Instantiate it four ways: +template bool Spectrogram::ComputeComplexSpectrogram( + const std::vector& input, std::vector>>*); +template bool Spectrogram::ComputeComplexSpectrogram( + const std::vector& input, + std::vector>>*); +template bool Spectrogram::ComputeComplexSpectrogram( + const std::vector& input, + std::vector>>*); +template bool Spectrogram::ComputeComplexSpectrogram( + const std::vector& input, + std::vector>>*); + +template +bool Spectrogram::ComputeSquaredMagnitudeSpectrogram( + const std::vector& input, + std::vector>* output) { + if (!initialized_) { + // LOG(ERROR) << "ComputeSquaredMagnitudeSpectrogram() called before " + // << "successful call to Initialize()."; + return false; + } + // CHECK(output); + output->clear(); + int input_start = 0; + while (GetNextWindowOfSamples(input, &input_start)) { + // DCHECK_EQ(input_queue_.size(), window_length_); + ProcessCoreFFT(); // Processes input_queue_ to fft_input_output_. + // Add a new slice vector onto the output, to save new result to. + output->resize(output->size() + 1); + // Get a reference to the newly added slice to fill in. + auto& spectrogram_slice = output->back(); + spectrogram_slice.resize(output_frequency_channels_); + for (int i = 0; i < output_frequency_channels_; ++i) { + // Similar to the Complex case, except storing the norm. + // But the norm function is known to be a performance killer, + // so do it this way with explicit real and imagninary temps. + const double re = fft_input_output_[2 * i]; + const double im = fft_input_output_[2 * i + 1]; + // Which finally converts double to float if it needs to. + spectrogram_slice[i] = re * re + im * im; + } + } + return true; +} +// Instantiate it four ways: +template bool Spectrogram::ComputeSquaredMagnitudeSpectrogram( + const std::vector& input, std::vector>*); +template bool Spectrogram::ComputeSquaredMagnitudeSpectrogram( + const std::vector& input, std::vector>*); +template bool Spectrogram::ComputeSquaredMagnitudeSpectrogram( + const std::vector& input, std::vector>*); +template bool Spectrogram::ComputeSquaredMagnitudeSpectrogram( + const std::vector& input, std::vector>*); + +// Return true if a full window of samples is prepared; manage the queue. +template +bool Spectrogram::GetNextWindowOfSamples(const std::vector& input, + int* input_start) { + auto input_it = input.begin() + *input_start; + int input_remaining = input.end() - input_it; + if (samples_to_next_step_ > input_remaining) { + // Copy in as many samples are left and return false, no full window. + input_queue_.insert(input_queue_.end(), input_it, input.end()); + *input_start += input_remaining; // Increases it to input.size(). + samples_to_next_step_ -= input_remaining; + return false; // Not enough for a full window. + } else { + // Copy just enough into queue to make a new window, then trim the + // front off the queue to make it window-sized. + input_queue_.insert(input_queue_.end(), input_it, + input_it + samples_to_next_step_); + *input_start += samples_to_next_step_; + input_queue_.erase( + input_queue_.begin(), + input_queue_.begin() + input_queue_.size() - window_length_); + // DCHECK_EQ(window_length_, input_queue_.size()); + samples_to_next_step_ = step_length_; // Be ready for next time. + return true; // Yes, input_queue_ now contains exactly a window-full. + } +} + +void Spectrogram::ProcessCoreFFT() { + for (int j = 0; j < window_length_; ++j) { + fft_input_output_[j] = input_queue_[j] * window_[j]; + } + // Zero-pad the rest of the input buffer. + for (int j = window_length_; j < fft_length_; ++j) { + fft_input_output_[j] = 0.0; + } + const int kForwardFFT = 1; // 1 means forward; -1 reverse. + // This real FFT is a fair amount faster than using cdft here. + rdft(fft_length_, kForwardFFT, &fft_input_output_[0], + &fft_integer_working_area_[0], &fft_double_working_area_[0]); + // Make rdft result look like cdft result; + // unpack the last real value from the first position's imag slot. + fft_input_output_[fft_length_] = fft_input_output_[1]; + fft_input_output_[fft_length_ + 1] = 0; + fft_input_output_[1] = 0; +} + +} // namespace internal +} // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/internal/spectrogram.h b/tensorflow/contrib/lite/kernels/internal/spectrogram.h new file mode 100644 index 0000000000000000000000000000000000000000..b77a68f7dfe6edb07ec4e5db540c673b2d6f6d6e --- /dev/null +++ b/tensorflow/contrib/lite/kernels/internal/spectrogram.h @@ -0,0 +1,110 @@ +/* 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. +==============================================================================*/ + +// Class for generating spectrogram slices from a waveform. +// Initialize() should be called before calls to other functions. Once +// Initialize() has been called and returned true, The Compute*() functions can +// be called repeatedly with sequential input data (ie. the first element of the +// next input vector directly follows the last element of the previous input +// vector). Whenever enough audio samples are buffered to produce a +// new frame, it will be placed in output. Output is cleared on each +// call to Compute*(). This class is thread-unsafe, and should only be +// called from one thread at a time. +// With the default parameters, the output of this class should be very +// close to the results of the following MATLAB code: +// overlap_samples = window_length_samples - step_samples; +// window = hann(window_length_samples, 'periodic'); +// S = abs(spectrogram(audio, window, overlap_samples)).^2; + +#ifndef TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_SPECTROGRAM_H_ +#define TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_SPECTROGRAM_H_ + +#include +#include +#include + +#include "third_party/fft2d/fft.h" + +namespace tflite { +namespace internal { + +class Spectrogram { + public: + Spectrogram() : initialized_(false) {} + ~Spectrogram() {} + + // Initializes the class with a given window length and step length + // (both in samples). Internally a Hann window is used as the window + // function. Returns true on success, after which calls to Process() + // are possible. window_length must be greater than 1 and step + // length must be greater than 0. + bool Initialize(int window_length, int step_length); + + // Initialize with an explicit window instead of a length. + bool Initialize(const std::vector& window, int step_length); + + // Processes an arbitrary amount of audio data (contained in input) + // to yield complex spectrogram frames. After a successful call to + // Initialize(), Process() may be called repeatedly with new input data + // each time. The audio input is buffered internally, and the output + // vector is populated with as many temporally-ordered spectral slices + // as it is possible to generate from the input. The output is cleared + // on each call before the new frames (if any) are added. + // + // The template parameters can be float or double. + template + bool ComputeComplexSpectrogram( + const std::vector& input, + std::vector>>* output); + + // This function works as the one above, but returns the power + // (the L2 norm, or the squared magnitude) of each complex value. + template + bool ComputeSquaredMagnitudeSpectrogram( + const std::vector& input, + std::vector>* output); + + // Return reference to the window function used internally. + const std::vector& GetWindow() const { return window_; } + + // Return the number of frequency channels in the spectrogram. + int output_frequency_channels() const { return output_frequency_channels_; } + + private: + template + bool GetNextWindowOfSamples(const std::vector& input, + int* input_start); + void ProcessCoreFFT(); + + int fft_length_; + int output_frequency_channels_; + int window_length_; + int step_length_; + bool initialized_; + int samples_to_next_step_; + + std::vector window_; + std::vector fft_input_output_; + std::deque input_queue_; + + // Working data areas for the FFT routines. + std::vector fft_integer_working_area_; + std::vector fft_double_working_area_; +}; + +} // namespace internal +} // namespace tflite + +#endif // TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_SPECTROGRAM_H_ diff --git a/tensorflow/contrib/lite/kernels/internal/types.h b/tensorflow/contrib/lite/kernels/internal/types.h index afe131b06ec41201395e80aa5415fd7db990f8d4..293538fcbb6406d6065d8efd25adb3b163638c92 100644 --- a/tensorflow/contrib/lite/kernels/internal/types.h +++ b/tensorflow/contrib/lite/kernels/internal/types.h @@ -21,6 +21,22 @@ namespace tflite { enum class FusedActivationFunctionType : uint8 { kNone, kRelu6, kRelu1, kRelu }; +// Quantization parameters, determining the mapping of quantized values +// to real values (i.e. determining how quantized values are mathematically +// interpreted). +// +// The correspondence is as follows: +// +// real_value = scale * (quantized_value - zero_point); +// +// In other words, zero_point designates which quantized value corresponds to +// the real 0 value, and scale designates the difference between the real values +// corresponding to consecutive quantized values differing by 1. +struct QuantizationParams { + int32 zero_point = 0; + double scale = 0.0; +}; + template struct Dims { int sizes[N]; diff --git a/tensorflow/contrib/lite/kernels/kernel_util.h b/tensorflow/contrib/lite/kernels/kernel_util.h index 28f53b9fbbc5620f2fab5c73e40bed8af4af5f1e..21da1daff7783bc98bc36814f1f9605a83eb65cf 100644 --- a/tensorflow/contrib/lite/kernels/kernel_util.h +++ b/tensorflow/contrib/lite/kernels/kernel_util.h @@ -58,7 +58,7 @@ inline bool IsConstantTensor(TfLiteTensor* tensor) { } // Determines whether tensor is dynamic. Note that a tensor can be non-const and -// not dynamic. This function specificially checks for a dynamic tensor. +// not dynamic. This function specifically checks for a dynamic tensor. inline bool IsDynamicTensor(TfLiteTensor* tensor) { return tensor->allocation_type == kTfLiteDynamic; } diff --git a/tensorflow/contrib/lite/kernels/lsh_projection.cc b/tensorflow/contrib/lite/kernels/lsh_projection.cc index 5f73b56ed9790b216adc788490faebaabd2bc756..0ee35775d50b8750455572f789d7b92481655a95 100644 --- a/tensorflow/contrib/lite/kernels/lsh_projection.cc +++ b/tensorflow/contrib/lite/kernels/lsh_projection.cc @@ -13,7 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -// LSH Projection projects an input to a bit vector via locality senstive +// LSH Projection projects an input to a bit vector via locality sensitive // hashing. // // Options: diff --git a/tensorflow/contrib/lite/kernels/lstm.cc b/tensorflow/contrib/lite/kernels/lstm.cc index 6c06264d845c24e71647b6fd2374734be32383ef..8cf1165135bdb0d4669bb97fd2d98e3dc044b4d9 100644 --- a/tensorflow/contrib/lite/kernels/lstm.cc +++ b/tensorflow/contrib/lite/kernels/lstm.cc @@ -24,6 +24,7 @@ limitations under the License. #include "tensorflow/contrib/lite/builtin_op_data.h" #include "tensorflow/contrib/lite/context.h" #include "tensorflow/contrib/lite/kernels/activation_functor.h" +#include "tensorflow/contrib/lite/kernels/internal/kernel_utils.h" #include "tensorflow/contrib/lite/kernels/internal/tensor_utils.h" #include "tensorflow/contrib/lite/kernels/kernel_util.h" #include "tensorflow/contrib/lite/kernels/op_macros.h" @@ -212,9 +213,9 @@ TfLiteStatus CheckInputTensorDimensions(TfLiteContext* context, // present. // 2) If projection weight is present, then projection bias is optional. // TODO(ghodrat): make sure this is correct. - const bool projecton_tensors_consistent = + const bool projection_tensors_consistent = ((projection_weights != nullptr) || (projection_bias == nullptr)); - TF_LITE_ENSURE(context, projecton_tensors_consistent == true); + TF_LITE_ENSURE(context, projection_tensors_consistent == true); return kTfLiteOk; } @@ -356,7 +357,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { const int n_output = recurrent_to_output_weights->dims->data[1]; // Since we have already checked that weights are all there or none, we can - // check the existense of only one to the get the condition. + // check the existence of only one to get the condition. const bool use_cifg = (input_to_input_weights == nullptr); const bool use_peephole = (cell_to_output_weights != nullptr); @@ -377,127 +378,54 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { output_gate_scratch = scratch_buffer->data.f + 3 * n_cell * n_batch; } - // Initialize scratch buffers with bias. - if (!use_cifg) { - tensor_utils::VectorBatchVectorAssign(input_gate_bias->data.f, n_cell, - n_batch, input_gate_scratch); - } - tensor_utils::VectorBatchVectorAssign(forget_gate_bias->data.f, n_cell, - n_batch, forget_gate_scratch); - tensor_utils::VectorBatchVectorAssign(cell_bias->data.f, n_cell, n_batch, - cell_scratch); - tensor_utils::VectorBatchVectorAssign(output_gate_bias->data.f, n_cell, - n_batch, output_gate_scratch); - - // For each batch and cell: compute input_weight * input. - if (!use_cifg) { - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - input_to_input_weights->data.f, n_cell, n_input, input->data.f, n_batch, - input_gate_scratch, /*result_stride=*/1); - } - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - input_to_forget_weights->data.f, n_cell, n_input, input->data.f, n_batch, - forget_gate_scratch, /*result_stride=*/1); - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - input_to_cell_weights->data.f, n_cell, n_input, input->data.f, n_batch, - cell_scratch, /*result_stride=*/1); - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - input_to_output_weights->data.f, n_cell, n_input, input->data.f, n_batch, - output_gate_scratch, /*result_stride=*/1); - - // For each batch and cell: compute recurrent_weight * output_state. - if (!use_cifg) { - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - recurrent_to_input_weights->data.f, n_cell, n_output, - output_state->data.f, n_batch, input_gate_scratch, /*result_stride=*/1); - } - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - recurrent_to_forget_weights->data.f, n_cell, n_output, - output_state->data.f, n_batch, forget_gate_scratch, /*result_stride=*/1); - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - recurrent_to_cell_weights->data.f, n_cell, n_output, output_state->data.f, - n_batch, cell_scratch, /*result_stride=*/1); - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - recurrent_to_output_weights->data.f, n_cell, n_output, - output_state->data.f, n_batch, output_gate_scratch, /*result_stride=*/1); - - // For each batch and cell: update input gate. - if (!use_cifg) { - if (use_peephole) { - tensor_utils::VectorBatchVectorCwiseProductAccumulate( - cell_to_input_weights->data.f, n_cell, cell_state->data.f, n_batch, - input_gate_scratch); - } - tensor_utils::ApplySigmoidToVector(input_gate_scratch, n_cell * n_batch, - input_gate_scratch); - } - - // For each batch and cell: update forget gate. - if (use_peephole) { - tensor_utils::VectorBatchVectorCwiseProductAccumulate( - cell_to_forget_weights->data.f, n_cell, cell_state->data.f, n_batch, - forget_gate_scratch); - } - tensor_utils::ApplySigmoidToVector(forget_gate_scratch, n_cell * n_batch, - forget_gate_scratch); - - // For each batch and cell: update the cell. - tensor_utils::VectorVectorCwiseProduct(forget_gate_scratch, - cell_state->data.f, n_batch * n_cell, - cell_state->data.f); - tensor_utils::ApplyActivationToVector(cell_scratch, n_batch * n_cell, - params->activation, cell_scratch); - if (use_cifg) { - tensor_utils::Sub1Vector(forget_gate_scratch, n_batch * n_cell, - forget_gate_scratch); - tensor_utils::VectorVectorCwiseProductAccumulate( - cell_scratch, forget_gate_scratch, n_batch * n_cell, - cell_state->data.f); - } else { - tensor_utils::VectorVectorCwiseProductAccumulate( - cell_scratch, input_gate_scratch, n_batch * n_cell, cell_state->data.f); - } - if (params->cell_clip > 0.0) { - tensor_utils::ClipVector(cell_state->data.f, n_batch * n_cell, - params->cell_clip, cell_state->data.f); - } - - // For each batch and cell: update the output gate. - if (use_peephole) { - tensor_utils::VectorBatchVectorCwiseProductAccumulate( - cell_to_output_weights->data.f, n_cell, cell_state->data.f, n_batch, - output_gate_scratch); - } - tensor_utils::ApplySigmoidToVector(output_gate_scratch, n_batch * n_cell, - output_gate_scratch); - tensor_utils::ApplyActivationToVector(cell_state->data.f, n_batch * n_cell, - params->activation, cell_scratch); - tensor_utils::VectorVectorCwiseProduct(output_gate_scratch, cell_scratch, - n_batch * n_cell, output_gate_scratch); - - // For each batch: update the projection and output_state. - const bool use_projection_weight = (projection_weights != nullptr); - const bool use_projection_bias = (projection_bias != nullptr); - if (use_projection_weight) { - if (use_projection_bias) { - tensor_utils::VectorBatchVectorAssign(projection_bias->data.f, n_output, - n_batch, output->data.f); - } else { - tensor_utils::ZeroVector(output->data.f, n_batch * n_output); - } - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - projection_weights->data.f, n_output, n_cell, output_gate_scratch, - n_batch, output->data.f, /*result_stride=*/1); - if (params->proj_clip > 0.0) { - tensor_utils::ClipVector(output->data.f, n_batch * n_output, - params->proj_clip, output->data.f); - } - } else { - tensor_utils::CopyVector(output_gate_scratch, n_batch * n_output, - output->data.f); - } - tensor_utils::CopyVector(output->data.f, n_batch * n_output, - output_state->data.f); + // Check optional tensors, the respective pointers can be null. + const float* input_to_input_weights_ptr = + (use_cifg) ? nullptr : input_to_input_weights->data.f; + const float* recurrent_to_input_weights_ptr = + (use_cifg) ? nullptr : recurrent_to_input_weights->data.f; + const float* input_gate_bias_ptr = + (use_cifg) ? nullptr : input_gate_bias->data.f; + const float* cell_to_input_weights_ptr = + (use_peephole && !use_cifg) ? cell_to_input_weights->data.f : nullptr; + const float* cell_to_forget_weights_ptr = + (use_peephole) ? cell_to_forget_weights->data.f : nullptr; + const float* cell_to_output_weights_ptr = + (use_peephole) ? cell_to_output_weights->data.f : nullptr; + const float* projection_weights_ptr = + (projection_weights == nullptr) ? nullptr : projection_weights->data.f; + const float* projection_bias_ptr = + (projection_bias == nullptr) ? nullptr : projection_bias->data.f; + + // Required tensors, pointers are non-null. + const float* input_ptr_batch = input->data.f; + const float* input_to_forget_weights_ptr = input_to_forget_weights->data.f; + const float* input_to_cell_weights_ptr = input_to_cell_weights->data.f; + const float* input_to_output_weights_ptr = input_to_output_weights->data.f; + const float* recurrent_to_forget_weights_ptr = + recurrent_to_forget_weights->data.f; + const float* recurrent_to_cell_weights_ptr = + recurrent_to_cell_weights->data.f; + const float* recurrent_to_output_weights_ptr = + recurrent_to_output_weights->data.f; + const float* forget_gate_bias_ptr = forget_gate_bias->data.f; + const float* cell_bias_ptr = cell_bias->data.f; + const float* output_gate_bias_ptr = output_gate_bias->data.f; + + float* output_state_ptr = output_state->data.f; + float* cell_state_ptr = cell_state->data.f; + float* output_ptr_batch = output->data.f; + + kernel_utils::LstmStep( + input_ptr_batch, input_to_input_weights_ptr, input_to_forget_weights_ptr, + input_to_cell_weights_ptr, input_to_output_weights_ptr, + recurrent_to_input_weights_ptr, recurrent_to_forget_weights_ptr, + recurrent_to_cell_weights_ptr, recurrent_to_output_weights_ptr, + cell_to_input_weights_ptr, cell_to_forget_weights_ptr, + cell_to_output_weights_ptr, input_gate_bias_ptr, forget_gate_bias_ptr, + cell_bias_ptr, output_gate_bias_ptr, projection_weights_ptr, + projection_bias_ptr, params, n_batch, n_cell, n_input, n_output, + output_state_ptr, cell_state_ptr, input_gate_scratch, forget_gate_scratch, + cell_scratch, output_gate_scratch, output_ptr_batch); return kTfLiteOk; } diff --git a/tensorflow/contrib/lite/kernels/mean_test.cc b/tensorflow/contrib/lite/kernels/mean_test.cc index c4c53c2ded351849e7c458fc754c36395a25ebd0..2d6d4bc2da4b75289ee27c3f2a12787216716d44 100644 --- a/tensorflow/contrib/lite/kernels/mean_test.cc +++ b/tensorflow/contrib/lite/kernels/mean_test.cc @@ -74,7 +74,7 @@ class MeanOpDynamicModel : public BaseMeanOpModel { } }; -TEST(ConstMeanOpTest, NotKeepDims) { +TEST(ConstFloatMeanOpTest, NotKeepDims) { std::initializer_list data = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; @@ -86,7 +86,7 @@ TEST(ConstMeanOpTest, NotKeepDims) { EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({12, 13}))); } -TEST(ConstMeanOpTest, KeepDims) { +TEST(ConstFloatMeanOpTest, KeepDims) { std::initializer_list data = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; @@ -99,7 +99,7 @@ TEST(ConstMeanOpTest, KeepDims) { ElementsAreArray(ArrayFloatNear({10.5, 12.5, 14.5}))); } -TEST(DynamicMeanOpTest, NotKeepDims) { +TEST(DynamicFloatMeanOpTest, NotKeepDims) { std::initializer_list data = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; @@ -114,7 +114,7 @@ TEST(DynamicMeanOpTest, NotKeepDims) { EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({12, 13}))); } -TEST(DynamicMeanOpTest, KeepDims) { +TEST(DynamicFloatMeanOpTest, KeepDims) { std::initializer_list data = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; @@ -130,6 +130,70 @@ TEST(DynamicMeanOpTest, KeepDims) { ElementsAreArray(ArrayFloatNear({10.5, 12.5, 14.5}))); } +TEST(DynamicFloatMeanOpTest, Scale) { + std::initializer_list data = {9.527}; + MeanOpDynamicModel m({TensorType_FLOAT32, {1}}, {TensorType_FLOAT32, {1}}, + {TensorType_INT32, {1}}, true); + std::initializer_list axis = {0}; + m.SetAxis(axis); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1})); + EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({9.527}))); +} + +TEST(ConstUint8MeanOpTest, NotKeepDims) { + std::initializer_list data = {1, 2, 3, 4, 5, 6, 7, 8, + 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 22, 23, 24}; + MeanOpConstModel m({TensorType_UINT8, {4, 3, 2}}, {TensorType_UINT8, {2}}, + {4}, {1, 0, -3, -3}, false); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2})); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({12, 13})); +} + +TEST(ConstUint8MeanOpTest, KeepDims) { + std::initializer_list data = {1, 2, 3, 4, 5, 6, 7, 8, + 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 22, 23, 24}; + MeanOpConstModel m({TensorType_UINT8, {4, 3, 2}}, {TensorType_UINT8, {3}}, + {2}, {0, 2}, true); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 3, 1})); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({10, 12, 14})); +} + +TEST(DynamicUint8MeanOpTest, NotKeepDims) { + std::initializer_list data = {1, 2, 3, 4, 5, 6, 7, 8, + 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 22, 23, 24}; + MeanOpDynamicModel m({TensorType_UINT8, {4, 3, 2}}, {TensorType_UINT8, {2}}, + {TensorType_INT32, {4}}, false); + std::initializer_list axis = {1, 0, -3, -3}; + m.SetAxis(axis); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2})); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({12, 13})); +} + +TEST(DynamicUint8MeanOpTest, KeepDims) { + std::initializer_list data = {1, 2, 3, 4, 5, 6, 7, 8, + 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 22, 23, 24}; + MeanOpDynamicModel m({TensorType_UINT8, {4, 3, 2}}, {TensorType_UINT8, {3}}, + {TensorType_INT32, {2}}, true); + std::initializer_list axis = {0, 2}; + m.SetAxis(axis); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 3, 1})); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({10, 12, 14})); +} + } // namespace } // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/register.cc b/tensorflow/contrib/lite/kernels/register.cc index aea6f8d9d34420363cc1045425f3d27b12af449e..9537b79a9ae4cb1eebaecb3fb2cb446726eb2baa 100644 --- a/tensorflow/contrib/lite/kernels/register.cc +++ b/tensorflow/contrib/lite/kernels/register.cc @@ -65,6 +65,8 @@ TfLiteRegistration* Register_STRIDED_SLICE(); TfLiteRegistration* Register_EXP(); TfLiteRegistration* Register_TOPK_V2(); TfLiteRegistration* Register_LOG_SOFTMAX(); +TfLiteRegistration* Register_CAST(); +TfLiteRegistration* Register_DEQUANTIZE(); BuiltinOpResolver::BuiltinOpResolver() { AddBuiltin(BuiltinOperator_RELU, Register_RELU()); @@ -119,6 +121,8 @@ BuiltinOpResolver::BuiltinOpResolver() { AddBuiltin(BuiltinOperator_EXP, Register_EXP()); AddBuiltin(BuiltinOperator_TOPK_V2, Register_TOPK_V2()); AddBuiltin(BuiltinOperator_LOG_SOFTMAX, Register_LOG_SOFTMAX()); + AddBuiltin(BuiltinOperator_CAST, Register_CAST()); + AddBuiltin(BuiltinOperator_DEQUANTIZE, Register_DEQUANTIZE()); } TfLiteRegistration* BuiltinOpResolver::FindOp( diff --git a/tensorflow/contrib/lite/kernels/reshape.cc b/tensorflow/contrib/lite/kernels/reshape.cc index f3e6ddc9f480e3863cac52157ae28b7329ee2088..438f70d3115130efe477a3ceeccd2e77108c979a 100644 --- a/tensorflow/contrib/lite/kernels/reshape.cc +++ b/tensorflow/contrib/lite/kernels/reshape.cc @@ -49,20 +49,20 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { TfLiteIntArray* output_size = TfLiteIntArrayCreate(params->num_dimensions); int num_output_elements = 1; - int strech_dim = -1; + int stretch_dim = -1; for (int i = 0; i < params->num_dimensions; ++i) { int value = params->shape[i]; if (value == -1) { - TF_LITE_ENSURE_EQ(context, strech_dim, -1); - strech_dim = i; + TF_LITE_ENSURE_EQ(context, stretch_dim, -1); + stretch_dim = i; } else { num_output_elements *= value; output_size->data[i] = value; } } - if (strech_dim != -1) { - output_size->data[strech_dim] = num_input_elements / num_output_elements; - num_output_elements *= output_size->data[strech_dim]; + if (stretch_dim != -1) { + output_size->data[stretch_dim] = num_input_elements / num_output_elements; + num_output_elements *= output_size->data[stretch_dim]; } TF_LITE_ENSURE_EQ(context, num_input_elements, num_output_elements); diff --git a/tensorflow/contrib/lite/kernels/reshape_test.cc b/tensorflow/contrib/lite/kernels/reshape_test.cc index 0fbcf6e6aa311d2cac491336ee54ccf58bbda8fd..aecbd0399f7454045e8189072f45b695b0525204 100644 --- a/tensorflow/contrib/lite/kernels/reshape_test.cc +++ b/tensorflow/contrib/lite/kernels/reshape_test.cc @@ -60,7 +60,7 @@ TEST(ReshapeOpTest, TooManyDimensions) { TEST(ReshapeOpTest, TooManySpecialDimensions) { EXPECT_DEATH(ReshapeOpModel({1, 2, 4, 1}, {-1, -1, 2, 4}), - "strech_dim != -1"); + "stretch_dim != -1"); } TEST(ReshapeOpTest, SimpleTest) { diff --git a/tensorflow/contrib/lite/kernels/strided_slice.cc b/tensorflow/contrib/lite/kernels/strided_slice.cc index fb1e11e0ca00abb36d7f29d562711a7bbcbeca1c..eb374d903182f46b40f5c80bfd769a19a5594742 100644 --- a/tensorflow/contrib/lite/kernels/strided_slice.cc +++ b/tensorflow/contrib/lite/kernels/strided_slice.cc @@ -48,7 +48,7 @@ struct StridedSliceContext { output = GetOutput(context, node, kOutputTensor); dims = NumDimensions(input); } - TfLiteStridedSliceParams* params; + const TfLiteStridedSliceParams* params; TfLiteTensor* input; TfLiteTensor* begin; TfLiteTensor* end; @@ -199,19 +199,17 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { strides.emplace_back(1); } - op_context.params->begin_mask = + int begin_mask = ReverseMaskBits(op_context.params->begin_mask, op_context.dims); - op_context.params->end_mask = - ReverseMaskBits(op_context.params->end_mask, op_context.dims); - op_context.params->shrink_axis_mask = + int end_mask = ReverseMaskBits(op_context.params->end_mask, op_context.dims); + int shrink_axis_mask = ReverseMaskBits(op_context.params->shrink_axis_mask, op_context.dims); -#define TF_LITE_STRIDED_SLICE(kernel_type, data_type) \ - kernel_type::StridedSlice( \ - GetTensorData(op_context.input), \ - GetTensorDims(op_context.input), op_context.params->begin_mask, \ - op_context.params->end_mask, op_context.params->shrink_axis_mask, \ - starts, stops, strides, GetTensorData(op_context.output), \ +#define TF_LITE_STRIDED_SLICE(kernel_type, data_type) \ + kernel_type::StridedSlice( \ + GetTensorData(op_context.input), \ + GetTensorDims(op_context.input), begin_mask, end_mask, shrink_axis_mask, \ + starts, stops, strides, GetTensorData(op_context.output), \ GetTensorDims(op_context.output)) switch (op_context.input->type) { diff --git a/tensorflow/contrib/lite/kernels/strided_slice_test.cc b/tensorflow/contrib/lite/kernels/strided_slice_test.cc index 5cac04b38364958c5b0794c21742e8b592372ae9..5c98c5f43181fe75f35716dae5682113bde883ec 100644 --- a/tensorflow/contrib/lite/kernels/strided_slice_test.cc +++ b/tensorflow/contrib/lite/kernels/strided_slice_test.cc @@ -522,6 +522,28 @@ TEST(StridedSliceOpTest, In3D_IdentityShrinkAxis7) { EXPECT_TRUE(m.GetOutputShape().empty()); EXPECT_THAT(m.GetOutput(), ElementsAreArray({1})); } + +// This tests catches a very subtle bug that was fixed by cl/188403234. +TEST(StridedSliceOpTest, RunTwice) { + StridedSliceOpModel m({2, 3}, {2}, {2}, {2}, 1, 0, 0, 0, 0); + + auto setup_inputs = [&m]() { + m.SetInput({1, 2, 3, 4, 5, 6}); + m.SetBegin({1, 0}); + m.SetEnd({2, 2}); + m.SetStrides({1, 1}); + }; + + setup_inputs(); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({1, 2, 4, 5})); + + setup_inputs(); + m.Invoke(); + // Prior to cl/188403234 this was {4, 5}. + EXPECT_THAT(m.GetOutput(), ElementsAreArray({1, 2, 4, 5})); +} + } // namespace } // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/test_util.cc b/tensorflow/contrib/lite/kernels/test_util.cc index 373310bd87370a670a847cf5328633956028a850..0bb28b50b2a5e5a9fd803ecf1b0928026f63881e 100644 --- a/tensorflow/contrib/lite/kernels/test_util.cc +++ b/tensorflow/contrib/lite/kernels/test_util.cc @@ -141,8 +141,8 @@ void SingleOpModel::SetBuiltinOp(BuiltinOperator type, void SingleOpModel::SetCustomOp( const string& name, const std::vector& custom_option, - const std::function& registeration) { - custom_registrations_[name] = registeration; + const std::function& registration) { + custom_registrations_[name] = registration; opcodes_.push_back( CreateOperatorCodeDirect(builder_, BuiltinOperator_CUSTOM, name.data())); operators_.push_back(CreateOperator( diff --git a/tensorflow/contrib/lite/kernels/test_util.h b/tensorflow/contrib/lite/kernels/test_util.h index 7d476ba1eaffbb24fb77390c0e71c32d60b6411e..a9064d54e7704d52eefa34f6bf446ec1cfe68fe1 100644 --- a/tensorflow/contrib/lite/kernels/test_util.h +++ b/tensorflow/contrib/lite/kernels/test_util.h @@ -39,10 +39,10 @@ inline std::vector Quantize(const std::vector& data, float scale, int32_t zero_point) { std::vector q; for (float f : data) { - q.push_back(std::max( + q.push_back(static_cast(std::max( std::numeric_limits::min(), - std::min(std::numeric_limits::max(), - static_cast(std::round(zero_point + (f / scale)))))); + std::min(std::numeric_limits::max(), + std::round(zero_point + (f / scale)))))); } return q; } diff --git a/tensorflow/contrib/lite/kernels/test_util_test.cc b/tensorflow/contrib/lite/kernels/test_util_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..1e10e89061213b6fcabd404310893dd97a51d83f --- /dev/null +++ b/tensorflow/contrib/lite/kernels/test_util_test.cc @@ -0,0 +1,51 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include "tensorflow/contrib/lite/kernels/test_util.h" +#include + +namespace tflite { +namespace { + +using ::testing::ElementsAreArray; + +TEST(TestUtilTest, QuantizeVector) { + std::vector data = {-1.0, -0.5, 0.0, 0.5, 1.0, 1000.0}; + auto q_data = Quantize(data, /*scale=*/1.0, /*zero_point=*/0); + std::vector expected = {0, 0, 0, 1, 1, 255}; + EXPECT_THAT(q_data, ElementsAreArray(expected)); +} + +TEST(TestUtilTest, QuantizeVectorScalingDown) { + std::vector data = {-1.0, -0.5, 0.0, 0.5, 1.0, 1000.0}; + auto q_data = Quantize(data, /*scale=*/10.0, /*zero_point=*/0); + std::vector expected = {0, 0, 0, 0, 0, 100}; + EXPECT_THAT(q_data, ElementsAreArray(expected)); +} + +TEST(TestUtilTest, QuantizeVectorScalingUp) { + std::vector data = {-1.0, -0.5, 0.0, 0.5, 1.0, 1000.0}; + auto q_data = Quantize(data, /*scale=*/0.1, /*zero_point=*/0); + std::vector expected = {0, 0, 0, 5, 10, 255}; + EXPECT_THAT(q_data, ElementsAreArray(expected)); +} + +} // namespace +} // namespace tflite + +int main(int argc, char** argv) { + ::tflite::LogToStderr(); + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} diff --git a/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc b/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc index 9cdb58714edb5fee771fc45f3c53a570f8fb28d1..42941a97db70adb37c20500c8f9438adfea25389 100644 --- a/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc +++ b/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc @@ -24,6 +24,7 @@ limitations under the License. #include "tensorflow/contrib/lite/builtin_op_data.h" #include "tensorflow/contrib/lite/context.h" #include "tensorflow/contrib/lite/kernels/activation_functor.h" +#include "tensorflow/contrib/lite/kernels/internal/kernel_utils.h" #include "tensorflow/contrib/lite/kernels/internal/tensor_utils.h" #include "tensorflow/contrib/lite/kernels/kernel_util.h" #include "tensorflow/contrib/lite/kernels/op_macros.h" @@ -359,7 +360,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { const int n_output = recurrent_to_output_weights->dims->data[1]; // Since we have already checked that weights are all there or none, we can - // check the existense of only one to the get the condition. + // check the existence of only one to get the condition. const bool use_cifg = (input_to_input_weights == nullptr); const bool use_peephole = (cell_to_output_weights != nullptr); @@ -380,135 +381,57 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { output_gate_scratch = scratch_buffer->data.f + 3 * n_cell * n_batch; } + // Check optional tensors, the respective pointers can be null. + const float* input_to_input_weights_ptr = + (use_cifg) ? nullptr : input_to_input_weights->data.f; + const float* recurrent_to_input_weights_ptr = + (use_cifg) ? nullptr : recurrent_to_input_weights->data.f; + const float* input_gate_bias_ptr = + (use_cifg) ? nullptr : input_gate_bias->data.f; + const float* cell_to_input_weights_ptr = + (use_peephole && !use_cifg) ? cell_to_input_weights->data.f : nullptr; + const float* cell_to_forget_weights_ptr = + (use_peephole) ? cell_to_forget_weights->data.f : nullptr; + const float* cell_to_output_weights_ptr = + (use_peephole) ? cell_to_output_weights->data.f : nullptr; + const float* projection_weights_ptr = + (projection_weights == nullptr) ? nullptr : projection_weights->data.f; + const float* projection_bias_ptr = + (projection_bias == nullptr) ? nullptr : projection_bias->data.f; + + // Required tensors, pointers are non-null. + const float* input_to_forget_weights_ptr = input_to_forget_weights->data.f; + const float* input_to_cell_weights_ptr = input_to_cell_weights->data.f; + const float* input_to_output_weights_ptr = input_to_output_weights->data.f; + const float* recurrent_to_forget_weights_ptr = + recurrent_to_forget_weights->data.f; + const float* recurrent_to_cell_weights_ptr = + recurrent_to_cell_weights->data.f; + const float* recurrent_to_output_weights_ptr = + recurrent_to_output_weights->data.f; + const float* forget_gate_bias_ptr = forget_gate_bias->data.f; + const float* cell_bias_ptr = cell_bias->data.f; + const float* output_gate_bias_ptr = output_gate_bias->data.f; + + float* output_state_ptr = output_state->data.f; + float* cell_state_ptr = cell_state->data.f; + for (int t = 0; t < max_time; t++) { - const float* input_ptr_time = input->data.f + t * n_batch * n_input; - // Initialize scratch buffers with bias. - if (!use_cifg) { - tensor_utils::VectorBatchVectorAssign(input_gate_bias->data.f, n_cell, - n_batch, input_gate_scratch); - } - tensor_utils::VectorBatchVectorAssign(forget_gate_bias->data.f, n_cell, - n_batch, forget_gate_scratch); - tensor_utils::VectorBatchVectorAssign(cell_bias->data.f, n_cell, n_batch, - cell_scratch); - tensor_utils::VectorBatchVectorAssign(output_gate_bias->data.f, n_cell, - n_batch, output_gate_scratch); - - // For each batch and cell: compute input_weight * input. - if (!use_cifg) { - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - input_to_input_weights->data.f, n_cell, n_input, input_ptr_time, - n_batch, input_gate_scratch, /*result_stride=*/1); - } - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - input_to_forget_weights->data.f, n_cell, n_input, input_ptr_time, - n_batch, forget_gate_scratch, /*result_stride=*/1); - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - input_to_cell_weights->data.f, n_cell, n_input, input_ptr_time, n_batch, - cell_scratch, /*result_stride=*/1); - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - input_to_output_weights->data.f, n_cell, n_input, input_ptr_time, - n_batch, output_gate_scratch, /*result_stride=*/1); - - // For each batch and cell: compute recurrent_weight * output_state. - if (!use_cifg) { - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - recurrent_to_input_weights->data.f, n_cell, n_output, - output_state->data.f, n_batch, input_gate_scratch, - /*result_stride=*/1); - } - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - recurrent_to_forget_weights->data.f, n_cell, n_output, - output_state->data.f, n_batch, forget_gate_scratch, - /*result_stride=*/1); - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - recurrent_to_cell_weights->data.f, n_cell, n_output, - output_state->data.f, n_batch, cell_scratch, /*result_stride=*/1); - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - recurrent_to_output_weights->data.f, n_cell, n_output, - output_state->data.f, n_batch, output_gate_scratch, - /*result_stride=*/1); - - // For each batch and cell: update input gate. - if (!use_cifg) { - if (use_peephole) { - tensor_utils::VectorBatchVectorCwiseProductAccumulate( - cell_to_input_weights->data.f, n_cell, cell_state->data.f, n_batch, - input_gate_scratch); - } - tensor_utils::ApplySigmoidToVector(input_gate_scratch, n_cell * n_batch, - input_gate_scratch); - } - - // For each batch and cell: update forget gate. - if (use_peephole) { - tensor_utils::VectorBatchVectorCwiseProductAccumulate( - cell_to_forget_weights->data.f, n_cell, cell_state->data.f, n_batch, - forget_gate_scratch); - } - tensor_utils::ApplySigmoidToVector(forget_gate_scratch, n_cell * n_batch, - forget_gate_scratch); - - // For each batch and cell: update the cell. - tensor_utils::VectorVectorCwiseProduct(forget_gate_scratch, - cell_state->data.f, n_batch * n_cell, - cell_state->data.f); - tensor_utils::ApplyActivationToVector(cell_scratch, n_batch * n_cell, - params->activation, cell_scratch); - if (use_cifg) { - tensor_utils::Sub1Vector(forget_gate_scratch, n_batch * n_cell, - forget_gate_scratch); - tensor_utils::VectorVectorCwiseProductAccumulate( - cell_scratch, forget_gate_scratch, n_batch * n_cell, - cell_state->data.f); - } else { - tensor_utils::VectorVectorCwiseProductAccumulate( - cell_scratch, input_gate_scratch, n_batch * n_cell, - cell_state->data.f); - } - if (params->cell_clip > 0.0) { - tensor_utils::ClipVector(cell_state->data.f, n_batch * n_cell, - params->cell_clip, cell_state->data.f); - } - - // For each batch and cell: update the output gate. - if (use_peephole) { - tensor_utils::VectorBatchVectorCwiseProductAccumulate( - cell_to_output_weights->data.f, n_cell, cell_state->data.f, n_batch, - output_gate_scratch); - } - tensor_utils::ApplySigmoidToVector(output_gate_scratch, n_batch * n_cell, - output_gate_scratch); - tensor_utils::ApplyActivationToVector(cell_state->data.f, n_batch * n_cell, - params->activation, cell_scratch); - tensor_utils::VectorVectorCwiseProduct(output_gate_scratch, cell_scratch, - n_batch * n_cell, - output_gate_scratch); - - // For each batch: update the projection and output_state. - const bool use_projection_weight = (projection_weights != nullptr); - const bool use_projection_bias = (projection_bias != nullptr); - float* output_ptr_time = output->data.f + t * n_batch * n_output; - if (use_projection_weight) { - if (use_projection_bias) { - tensor_utils::VectorBatchVectorAssign(projection_bias->data.f, n_output, - n_batch, output_ptr_time); - } else { - tensor_utils::ZeroVector(output_ptr_time, n_batch * n_output); - } - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - projection_weights->data.f, n_output, n_cell, output_gate_scratch, - n_batch, output_ptr_time, /*result_stride=*/1); - if (params->proj_clip > 0.0) { - tensor_utils::ClipVector(output_ptr_time, n_batch * n_output, - params->proj_clip, output_ptr_time); - } - } else { - tensor_utils::CopyVector(output_gate_scratch, n_batch * n_output, - output_ptr_time); - } - tensor_utils::CopyVector(output_ptr_time, n_batch * n_output, - output_state->data.f); + const float* input_ptr_batch = input->data.f + t * n_batch * n_input; + float* output_ptr_batch = output->data.f + t * n_batch * n_output; + + kernel_utils::LstmStep( + input_ptr_batch, input_to_input_weights_ptr, + input_to_forget_weights_ptr, input_to_cell_weights_ptr, + input_to_output_weights_ptr, recurrent_to_input_weights_ptr, + recurrent_to_forget_weights_ptr, recurrent_to_cell_weights_ptr, + recurrent_to_output_weights_ptr, cell_to_input_weights_ptr, + cell_to_forget_weights_ptr, cell_to_output_weights_ptr, + input_gate_bias_ptr, forget_gate_bias_ptr, cell_bias_ptr, + output_gate_bias_ptr, projection_weights_ptr, projection_bias_ptr, + params, n_batch, n_cell, n_input, n_output, output_state_ptr, + cell_state_ptr, input_gate_scratch, forget_gate_scratch, cell_scratch, + output_gate_scratch, output_ptr_batch); } return kTfLiteOk; } diff --git a/tensorflow/contrib/lite/memory_planner.h b/tensorflow/contrib/lite/memory_planner.h index 5cd6c208500f3ea84ab8146f7f136e8b7851ff03..0294ec815c4820d41361b8cd4a814b74c3c1d770 100644 --- a/tensorflow/contrib/lite/memory_planner.h +++ b/tensorflow/contrib/lite/memory_planner.h @@ -34,8 +34,8 @@ class MemoryPlanner { // [first_node, last_node]. virtual TfLiteStatus ExecuteAllocations(int first_node, int last_node) = 0; - // Invalidates allocations made earliers. This is called when tensors sizes - // have change. All planned allocations remain, but can't be used until + // Invalidates allocations made earlier. This is called when tensors sizes + // have changed. All planned allocations remain, but can't be used until // ExecuteAllocations() is called. virtual TfLiteStatus ResetAllocations() = 0; }; diff --git a/tensorflow/contrib/lite/model.cc b/tensorflow/contrib/lite/model.cc index 725f2838c574fcc2ba389401f92575279ebc144c..8c456e70da2842c9972be723ad764f1658bd310d 100644 --- a/tensorflow/contrib/lite/model.cc +++ b/tensorflow/contrib/lite/model.cc @@ -287,6 +287,8 @@ void* ParseOpData(const Operator* op, BuiltinOperator op_type, case BuiltinOperator_EXP: case BuiltinOperator_TOPK_V2: case BuiltinOperator_LOG_SOFTMAX: + case BuiltinOperator_CAST: + case BuiltinOperator_DEQUANTIZE: break; case BuiltinOperator_LSH_PROJECTION: { TfLiteLSHProjectionParams* params = diff --git a/tensorflow/contrib/lite/model.h b/tensorflow/contrib/lite/model.h index a467df5bb4eee3f6ce814512cb8b74bf09a6a4e7..8dc1c794dce6f9df4b5d0c686c177ac94a086e2f 100644 --- a/tensorflow/contrib/lite/model.h +++ b/tensorflow/contrib/lite/model.h @@ -64,7 +64,7 @@ class FlatBufferModel { const tflite::Model* model_spec, ErrorReporter* error_reporter = DefaultErrorReporter()); - // Releases memory or unmaps mmaped meory. + // Releases memory or unmaps mmaped memory. ~FlatBufferModel(); // Copying or assignment is disallowed to simplify ownership semantics. diff --git a/tensorflow/contrib/lite/nnapi/NeuralNetworksShim.h b/tensorflow/contrib/lite/nnapi/NeuralNetworksShim.h index 76032771af2c8e099aed498b2071816646f3b606..bd49d327c995ef53dc6cf9f8301ab749c925b2c7 100644 --- a/tensorflow/contrib/lite/nnapi/NeuralNetworksShim.h +++ b/tensorflow/contrib/lite/nnapi/NeuralNetworksShim.h @@ -569,7 +569,7 @@ enum { ANEURALNETWORKS_LOGISTIC = 14, /** - * Projects an input to a bit vector via locality senstive hashing. + * Projects an input to a bit vector via locality sensitive hashing. * * Inputs: * * 0: Hash functions. Dim.size == 2, DataType: Float. diff --git a/tensorflow/contrib/lite/nnapi_delegate.cc b/tensorflow/contrib/lite/nnapi_delegate.cc index e631ffd845d3b31232070b935c12aa8a2e8ce05e..9d00d965d397d3af0dd454609901685ba965ded6 100644 --- a/tensorflow/contrib/lite/nnapi_delegate.cc +++ b/tensorflow/contrib/lite/nnapi_delegate.cc @@ -346,7 +346,9 @@ void AddOpsAndParams(tflite::Interpreter* interpreter, case tflite::BuiltinOperator_STRIDED_SLICE: case tflite::BuiltinOperator_EXP: case tflite::BuiltinOperator_LOG_SOFTMAX: + case tflite::BuiltinOperator_DEQUANTIZE: case tflite::BuiltinOperator_DELEGATE: + case tflite::BuiltinOperator_CAST: FATAL("Op code %d is currently not delegated to NNAPI", builtin); nn_op_type = -1; // set to invalid break; diff --git a/tensorflow/contrib/lite/python/BUILD b/tensorflow/contrib/lite/python/BUILD index 82feae0f0041997949212613c654a5695f468d56..76607af07986f93abd8f7bf22f222d0153a3087c 100644 --- a/tensorflow/contrib/lite/python/BUILD +++ b/tensorflow/contrib/lite/python/BUILD @@ -4,6 +4,38 @@ package(default_visibility = ["//tensorflow:internal"]) load("//tensorflow:tensorflow.bzl", "py_test") +filegroup( + name = "interpreter_test_data", + srcs = glob(["**/testdata/*"]), + visibility = ["//tensorflow:__subpackages__"], +) + +py_library( + name = "interpreter", + srcs = [ + "interpreter.py", + ], + srcs_version = "PY2AND3", + visibility = ["//visibility:public"], + deps = [ + "//tensorflow/contrib/lite/python/interpreter_wrapper:tensorflow_wrap_interpreter_wrapper", + ], +) + +py_test( + name = "interpreter_test", + srcs = ["interpreter_test.py"], + data = [":interpreter_test_data"], + srcs_version = "PY2AND3", + tags = ["no_oss"], + deps = [ + ":interpreter", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:platform_test", + ], +) + py_library( name = "lite", srcs = ["lite.py"], diff --git a/tensorflow/contrib/lite/python/interpreter.py b/tensorflow/contrib/lite/python/interpreter.py new file mode 100644 index 0000000000000000000000000000000000000000..5b5a7c3199a480d943aa4a8ce34710a703017d28 --- /dev/null +++ b/tensorflow/contrib/lite/python/interpreter.py @@ -0,0 +1,135 @@ +# 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. +# ============================================================================== +"""Python TF-Lite interpreter.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.lite.python.interpreter_wrapper import tensorflow_wrap_interpreter_wrapper as interpreter_wrapper + + +class Interpreter(object): + """Interpreter inferace for TF-Lite Models.""" + + def __init__(self, model_path): + """Constructor. + + Args: + model_path: Path to TF-Lite Flatbuffer file. + + Raises: + ValueError: If the interpreter was unable to open the model. + """ + self._interpreter = ( + interpreter_wrapper.InterpreterWrapper_CreateWrapperCPP(model_path)) + if not self._interpreter: + raise ValueError('Failed to open {}'.format(model_path)) + + def allocate_tensors(self): + if not self._interpreter.AllocateTensors(): + raise ValueError('Failed to allocate tensors') + + def _get_tensor_details(self, tensor_index): + """Gets tensor details. + + Args: + tensor_index: Tensor index of tensor to query. + + Returns: + a dictionary containing the name, index, shape and type of the tensor. + + Raises: + ValueError: If tensor_index is invalid. + """ + tensor_index = int(tensor_index) + tensor_name = self._interpreter.TensorName(tensor_index) + tensor_size = self._interpreter.TensorSize(tensor_index) + tensor_type = self._interpreter.TensorType(tensor_index) + + if not tensor_name or not tensor_type: + raise ValueError('Could not get tensor details') + + details = { + 'name': tensor_name, + 'index': tensor_index, + 'shape': tensor_size, + 'dtype': tensor_type, + } + + return details + + def get_input_details(self): + """Gets model input details. + + Returns: + A list of input details. + """ + return [ + self._get_tensor_details(i) for i in self._interpreter.InputIndices() + ] + + def set_tensor(self, tensor_index, value): + """Sets the value of the input. + + Args: + tensor_index: Tensor index of tensor to set. This value can be gotten from + the 'index' field in get_input_details. + value: Value of tensor to set. + + Raises: + ValueError: If the interpreter could not set the tensor. + """ + if not self._interpreter.SetTensor(tensor_index, value): + raise ValueError('Failed to set tensor') + + def resize_tensor_input(self, input_index, tensor_size): + """Resizes an input tensor. + + Args: + input_index: Tensor index of input to set. This value can be gotten from + the 'index' field in get_input_details. + tensor_size: The tensor_shape to resize the input to. + + Raises: + ValueError: If the interpreter could not resize the input tensor. + """ + if not self.ResizeInputTensor.SetTensor(input_index, tensor_size): + raise ValueError('Failed to set input') + + def get_output_details(self): + """Gets model output details. + + Returns: + A list of output details. + """ + return [ + self._get_tensor_details(i) for i in self._interpreter.OutputIndices() + ] + + def get_tensor(self, tensor_index): + """Sets the value of the input. + + Args: + tensor_index: Tensor index of tensor to get. This value can be gotten from + the 'index' field in get_output_details. + + Returns: + a numpy array. + """ + return self._interpreter.GetTensor(tensor_index) + + def invoke(self): + if not self._interpreter.Invoke(): + raise ValueError('Failed to invoke TFLite model') diff --git a/tensorflow/contrib/lite/python/interpreter_test.py b/tensorflow/contrib/lite/python/interpreter_test.py new file mode 100644 index 0000000000000000000000000000000000000000..e0215b721c1a7627ced1080def0fbd426c94c2f4 --- /dev/null +++ b/tensorflow/contrib/lite/python/interpreter_test.py @@ -0,0 +1,82 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""TensorFlow Lite Python Interface: Sanity check.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.lite.python import interpreter as interpreter_wrapper +from tensorflow.python.framework import test_util +from tensorflow.python.platform import resource_loader +from tensorflow.python.platform import test + + +class InterpreterTest(test_util.TensorFlowTestCase): + + def testFloat(self): + interpreter = interpreter_wrapper.Interpreter( + resource_loader.get_path_to_datafile('testdata/permute_float.tflite')) + interpreter.allocate_tensors() + + input_details = interpreter.get_input_details() + self.assertEqual(1, len(input_details)) + self.assertEqual('input', input_details[0]['name']) + self.assertEqual(np.float32, input_details[0]['dtype']) + self.assertTrue(([1, 4] == input_details[0]['shape']).all()) + + output_details = interpreter.get_output_details() + self.assertEqual(1, len(output_details)) + self.assertEqual('output', output_details[0]['name']) + self.assertEqual(np.float32, output_details[0]['dtype']) + self.assertTrue(([1, 4] == output_details[0]['shape']).all()) + + test_input = np.array([[1.0, 2.0, 3.0, 4.0]], dtype=np.float32) + expected_output = np.array([[4.0, 3.0, 2.0, 1.0]], dtype=np.float32) + interpreter.set_tensor(input_details[0]['index'], test_input) + interpreter.invoke() + + output_data = interpreter.get_tensor(output_details[0]['index']) + self.assertTrue((expected_output == output_data).all()) + + def testUint8(self): + interpreter = interpreter_wrapper.Interpreter( + resource_loader.get_path_to_datafile('testdata/permute_uint8.tflite')) + interpreter.allocate_tensors() + + input_details = interpreter.get_input_details() + self.assertEqual(1, len(input_details)) + self.assertEqual('input', input_details[0]['name']) + self.assertEqual(np.uint8, input_details[0]['dtype']) + self.assertTrue(([1, 4] == input_details[0]['shape']).all()) + + output_details = interpreter.get_output_details() + self.assertEqual(1, len(output_details)) + self.assertEqual('output', output_details[0]['name']) + self.assertEqual(np.uint8, output_details[0]['dtype']) + self.assertTrue(([1, 4] == output_details[0]['shape']).all()) + + test_input = np.array([[1, 2, 3, 4]], dtype=np.uint8) + expected_output = np.array([[4, 3, 2, 1]], dtype=np.uint8) + interpreter.set_tensor(input_details[0]['index'], test_input) + interpreter.invoke() + + output_data = interpreter.get_tensor(output_details[0]['index']) + self.assertTrue((expected_output == output_data).all()) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/lite/python/interpreter_wrapper/BUILD b/tensorflow/contrib/lite/python/interpreter_wrapper/BUILD new file mode 100644 index 0000000000000000000000000000000000000000..453eda6e7345762666917fd501b69c7181c349e8 --- /dev/null +++ b/tensorflow/contrib/lite/python/interpreter_wrapper/BUILD @@ -0,0 +1,32 @@ +package( + default_visibility = ["//visibility:public"], +) + +licenses(["notice"]) # Apache 2.0 + +load("//tensorflow:tensorflow.bzl", "tf_py_wrap_cc") + +cc_library( + name = "interpreter_wrapper_lib", + srcs = ["interpreter_wrapper.cc"], + hdrs = ["interpreter_wrapper.h"], + deps = [ + "//tensorflow/contrib/lite:framework", + "//tensorflow/contrib/lite/kernels:builtin_ops", + "//tensorflow/core:lib", + "//tensorflow/python:numpy_lib", + "//util/python:python_headers", + "@com_google_absl//absl/memory", + ], +) + +tf_py_wrap_cc( + name = "tensorflow_wrap_interpreter_wrapper", + srcs = [ + "interpreter_wrapper.i", + ], + deps = [ + ":interpreter_wrapper_lib", + "//util/python:python_headers", + ], +) diff --git a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc new file mode 100644 index 0000000000000000000000000000000000000000..f30067de94cf631260d42ca1f0179ee4f65582c8 --- /dev/null +++ b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc @@ -0,0 +1,313 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include "tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h" + +#include + +#include "absl/memory/memory.h" +#include "tensorflow/contrib/lite/interpreter.h" +#include "tensorflow/contrib/lite/kernels/register.h" +#include "tensorflow/contrib/lite/model.h" +#include "tensorflow/core/platform/logging.h" +#include "tensorflow/python/lib/core/numpy.h" + +#if PY_MAJOR_VERSION >= 3 +#define PY_TO_CPPSTRING PyBytes_AsStringAndSize +#define CPP_TO_PYSTRING PyBytes_FromStringAndSize +#else +#define PY_TO_CPPSTRING PyString_AsStringAndSize +#define CPP_TO_PYSTRING PyString_FromStringAndSize +#endif + +namespace tflite { +namespace interpreter_wrapper { + +namespace { +std::unique_ptr CreateInterpreter( + const tflite::FlatBufferModel* model, + const tflite::ops::builtin::BuiltinOpResolver& resolver) { + if (!model) { + return nullptr; + } + + std::unique_ptr interpreter; + tflite::InterpreterBuilder(*model, resolver)(&interpreter); + if (interpreter) { + for (const int input_index : interpreter->inputs()) { + const TfLiteTensor* tensor = interpreter->tensor(input_index); + CHECK(tensor); + const TfLiteIntArray* dims = tensor->dims; + if (!dims) { + continue; + } + + std::vector input_dims(dims->data, dims->data + dims->size); + interpreter->ResizeInputTensor(input_index, input_dims); + } + } + return interpreter; +} + +int TfLiteTypeToPyArrayType(TfLiteType tf_lite_type) { + switch (tf_lite_type) { + case kTfLiteFloat32: + return NPY_FLOAT32; + case kTfLiteInt32: + return NPY_INT32; + case kTfLiteUInt8: + return NPY_UINT8; + case kTfLiteInt64: + return NPY_INT64; + case kTfLiteString: + return NPY_OBJECT; + case kTfLiteNoType: + return -1; + } + LOG(ERROR) << "Unknown TfLiteType " << tf_lite_type; + return -1; +} + +TfLiteType TfLiteTypeFromPyArray(PyArrayObject* array) { + int pyarray_type = PyArray_TYPE(array); + switch (pyarray_type) { + case NPY_FLOAT32: + return kTfLiteFloat32; + case NPY_INT32: + return kTfLiteInt32; + case NPY_UINT8: + return kTfLiteUInt8; + case NPY_INT64: + return kTfLiteInt64; + case NPY_OBJECT: + case NPY_STRING: + case NPY_UNICODE: + return kTfLiteString; + } + LOG(ERROR) << "Unknown PyArray dtype " << pyarray_type; + return kTfLiteNoType; +} + +struct PyDecrefDeleter { + void operator()(PyObject* p) const { Py_DECREF(p); } +}; + +PyObject* PyArrayFromIntVector(const int* data, npy_intp size) { + void* pydata = malloc(size * sizeof(int)); + memcpy(pydata, data, size * sizeof(int)); + return PyArray_SimpleNewFromData(1, &size, NPY_INT32, pydata); +} + +} // namespace + +InterpreterWrapper::InterpreterWrapper( + std::unique_ptr model) + : model_(std::move(model)), + resolver_(absl::make_unique()), + interpreter_(CreateInterpreter(model_.get(), *resolver_)) {} + +InterpreterWrapper::~InterpreterWrapper() {} + +bool InterpreterWrapper::AllocateTensors() { + if (!interpreter_) { + LOG(ERROR) << "Cannot allocate tensors: invalid interpreter."; + return false; + } + + if (interpreter_->AllocateTensors() != kTfLiteOk) { + LOG(ERROR) << "Unable to allocate tensors."; + return false; + } + + return true; +} + +bool InterpreterWrapper::Invoke() { + return interpreter_ ? (interpreter_->Invoke() == kTfLiteOk) : false; +} + +PyObject* InterpreterWrapper::InputIndices() const { + PyObject* np_array = PyArrayFromIntVector(interpreter_->inputs().data(), + interpreter_->inputs().size()); + + return PyArray_Return(reinterpret_cast(np_array)); +} + +PyObject* InterpreterWrapper::OutputIndices() const { + PyObject* np_array = PyArrayFromIntVector(interpreter_->outputs().data(), + interpreter_->outputs().size()); + + return PyArray_Return(reinterpret_cast(np_array)); +} + +bool InterpreterWrapper::ResizeInputTensor(int i, PyObject* value) { + if (!interpreter_) { + LOG(ERROR) << "Invalid interpreter."; + return false; + } + + std::unique_ptr array_safe( + PyArray_FromAny(value, nullptr, 0, 0, NPY_ARRAY_CARRAY, nullptr)); + if (!array_safe) { + LOG(ERROR) << "Failed to convert value into readable tensor."; + return false; + } + + PyArrayObject* array = reinterpret_cast(array_safe.get()); + + if (PyArray_NDIM(array) != 1) { + LOG(ERROR) << "Expected 1-D defining input shape."; + return false; + } + + if (PyArray_TYPE(array) != NPY_INT32) { + LOG(ERROR) << "Shape must be an int32 array"; + return false; + } + + std::vector dims(PyArray_SHAPE(array)[0]); + memcpy(dims.data(), PyArray_BYTES(array), dims.size() * sizeof(int)); + + return interpreter_->ResizeInputTensor(i, dims); +} + +std::string InterpreterWrapper::TensorName(int i) const { + if (!interpreter_ || i >= interpreter_->tensors_size() || i < 0) { + return ""; + } + + const TfLiteTensor* tensor = interpreter_->tensor(i); + return tensor->name; +} + +PyObject* InterpreterWrapper::TensorType(int i) const { + if (!interpreter_ || i >= interpreter_->tensors_size() || i < 0) { + return nullptr; + } + + const TfLiteTensor* tensor = interpreter_->tensor(i); + int typenum = TfLiteTypeToPyArrayType(tensor->type); + return PyArray_TypeObjectFromType(typenum); +} + +PyObject* InterpreterWrapper::TensorSize(int i) const { + if (!interpreter_ || i >= interpreter_->tensors_size() || i < 0) { + Py_INCREF(Py_None); + return Py_None; + } + + const TfLiteTensor* tensor = interpreter_->tensor(i); + PyObject* np_array = + PyArrayFromIntVector(tensor->dims->data, tensor->dims->size); + + return PyArray_Return(reinterpret_cast(np_array)); +} + +bool InterpreterWrapper::SetTensor(int i, PyObject* value) { + if (!interpreter_) { + LOG(ERROR) << "Invalid interpreter."; + return false; + } + + if (i >= interpreter_->tensors_size()) { + LOG(ERROR) << "Invalid tensor index: " << i << " exceeds max tensor index " + << interpreter_->tensors_size(); + return false; + } + + std::unique_ptr array_safe( + PyArray_FromAny(value, nullptr, 0, 0, NPY_ARRAY_CARRAY, nullptr)); + if (!array_safe) { + LOG(ERROR) << "Failed to convert value into readable tensor."; + return false; + } + + PyArrayObject* array = reinterpret_cast(array_safe.get()); + const TfLiteTensor* tensor = interpreter_->tensor(i); + + if (TfLiteTypeFromPyArray(array) != tensor->type) { + LOG(ERROR) << "Cannot set tensor:" + << " Got tensor of type " << TfLiteTypeFromPyArray(array) + << " but expected type " << tensor->type << " for input " << i; + return false; + } + + if (PyArray_NDIM(array) != tensor->dims->size) { + LOG(ERROR) << "Cannot set tensor: Dimension mismatch"; + return false; + } + + for (int j = 0; j < PyArray_NDIM(array); j++) { + if (tensor->dims->data[j] != PyArray_SHAPE(array)[j]) { + LOG(ERROR) << "Cannot set tensor: Dimension mismatch"; + return false; + } + } + + size_t size = PyArray_NBYTES(array); + DCHECK_EQ(size, tensor->bytes); + memcpy(tensor->data.raw, PyArray_DATA(array), size); + return true; +} + +PyObject* InterpreterWrapper::GetTensor(int i) const { + if (!interpreter_) { + LOG(ERROR) << "Invalid interpreter."; + Py_INCREF(Py_None); + return Py_None; + } + + if (i >= interpreter_->tensors_size()) { + LOG(ERROR) << "Invalid tensor index: " << i << " exceeds max tensor index " + << interpreter_->inputs().size(); + Py_INCREF(Py_None); + return Py_None; + } + + const TfLiteTensor* output_tensor = interpreter_->tensor(i); + const int tensor_size = output_tensor->bytes; + if (tensor_size <= 0) { + LOG(ERROR) << "Invalid tensor size"; + Py_INCREF(Py_None); + return Py_None; + } + + int type_num = TfLiteTypeToPyArrayType(output_tensor->type); + if (type_num == -1) { + LOG(ERROR) << "Unknown tensor type " << output_tensor->type; + Py_INCREF(Py_None); + return Py_None; + } + + void* data = malloc(tensor_size); + memcpy(data, output_tensor->data.raw, tensor_size); + + const TfLiteIntArray* output_dims = output_tensor->dims; + std::vector dims(output_dims->data, + output_dims->data + output_dims->size); + PyObject* np_array = + PyArray_SimpleNewFromData(dims.size(), dims.data(), type_num, data); + + return PyArray_Return(reinterpret_cast(np_array)); +} + +InterpreterWrapper* InterpreterWrapper::CreateWrapperCPP( + const char* model_path) { + std::unique_ptr model = + tflite::FlatBufferModel::BuildFromFile(model_path); + return model ? new InterpreterWrapper(std::move(model)) : nullptr; +} + +} // namespace interpreter_wrapper +} // namespace tflite diff --git a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h new file mode 100644 index 0000000000000000000000000000000000000000..dea71ca8794da3aea4a4266721df0a9ebbe6bb58 --- /dev/null +++ b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h @@ -0,0 +1,72 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_CONTRIB_LITE_PYTHON_INTERPRETER_WRAPPER_INTERPRETER_WRAPPER_H_ +#define TENSORFLOW_CONTRIB_LITE_PYTHON_INTERPRETER_WRAPPER_INTERPRETER_WRAPPER_H_ + +#include +#include +#include + +#include + +// We forward declare TFLite classes here to avoid exposing them to SWIG. +namespace tflite { +namespace ops { +namespace builtin { +class BuiltinOpResolver; +} // namespace builtin +} // namespace ops + +class FlatBufferModel; +class Interpreter; + +namespace interpreter_wrapper { + +class InterpreterWrapper { + public: + // SWIG caller takes ownership of pointer. + static InterpreterWrapper* CreateWrapperCPP(const char* model_path); + + ~InterpreterWrapper(); + bool AllocateTensors(); + bool Invoke(); + + PyObject* InputIndices() const; + PyObject* OutputIndices() const; + bool ResizeInputTensor(int i, PyObject* value); + + std::string TensorName(int i) const; + PyObject* TensorType(int i) const; + PyObject* TensorSize(int i) const; + bool SetTensor(int i, PyObject* value); + PyObject* GetTensor(int i) const; + + private: + InterpreterWrapper(std::unique_ptr model); + + // InterpreterWrapper is not copyable or assignable. We avoid the use of + // InterpreterWrapper() = delete here for SWIG compatibility. + InterpreterWrapper(); + InterpreterWrapper(const InterpreterWrapper& rhs); + + const std::unique_ptr model_; + const std::unique_ptr resolver_; + const std::unique_ptr interpreter_; +}; + +} // namespace interpreter_wrapper +} // namespace tflite + +#endif // TENSORFLOW_CONTRIB_LITE_PYTHON_INTERPRETER_WRAPPER_INTERPRETER_WRAPPER_H_ diff --git a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.i b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.i new file mode 100644 index 0000000000000000000000000000000000000000..7f51f9f00d1b2fe057052f7b7bd52bcb65231164 --- /dev/null +++ b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.i @@ -0,0 +1,25 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +%include "std_string.i" + + +%{ +#define SWIG_FILE_WITH_INIT +#include "tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h" +%} + + +%include "tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h" diff --git a/tensorflow/contrib/lite/python/lite.py b/tensorflow/contrib/lite/python/lite.py index 5d2f21653762a405a57288a7ba38323e5e42b3e1..35d224924ee4d8cd94543e10c082afee25b7630e 100644 --- a/tensorflow/contrib/lite/python/lite.py +++ b/tensorflow/contrib/lite/python/lite.py @@ -202,11 +202,12 @@ def toco_convert(input_data, input_array.name = _tensor_name(input_tensor) input_array.shape.dims.extend(map(int, input_tensor.get_shape())) - toco.inference_input_type = tflite_input_type for output_tensor in output_tensors: model.output_arrays.append(_tensor_name(output_tensor)) + # TODO(aselle): Consider handling the case of allowing quantized + # inputs to be converted to float (via the toco.inference_input_type field). data = toco_convert_protos(model.SerializeToString(), toco.SerializeToString(), input_data.SerializeToString()) diff --git a/tensorflow/contrib/lite/python/op_hint.py b/tensorflow/contrib/lite/python/op_hint.py index 9a3971228a683211e84b4c55d3a3e8d574b5ed94..7908689ce4a719ab15bd49a368a87f9cad7c6d61 100644 --- a/tensorflow/contrib/lite/python/op_hint.py +++ b/tensorflow/contrib/lite/python/op_hint.py @@ -119,8 +119,10 @@ class OpHint(object): def _setattr(self, dest_op, name, value): tensor_value = _ops.convert_to_tensor(value) - dest_op.op.node_def.attr[name].tensor.CopyFrom( - tensor_value.op.node_def.attr["value"].tensor) + # pylint: disable=protected-access + dest_op.op._set_attr(name, _attr_value_pb2.AttrValue( + tensor=tensor_value.op.node_def.attr["value"].tensor)) + # pylint: enable=protected-access def add_inputs(self, *args): """Add a sequence of inputs to the function invocation. diff --git a/tensorflow/contrib/lite/rpi_makefile.inc b/tensorflow/contrib/lite/rpi_makefile.inc new file mode 100644 index 0000000000000000000000000000000000000000..832ef5824bea86a368184bd7e3d17915739e9d46 --- /dev/null +++ b/tensorflow/contrib/lite/rpi_makefile.inc @@ -0,0 +1,33 @@ +# Settings for Raspberry Pi. +ifeq ($(TARGET), RPI) + ifeq ($(TARGET_ARCH), armv7) + CXXFLAGS += \ + -march=armv7-a \ + -mfpu=neon-vfpv4 \ + -funsafe-math-optimizations \ + -ftree-vectorize + + CCFLAGS += \ + -march=armv7-a \ + -mfpu=neon-vfpv4 \ + -funsafe-math-optimizations \ + -ftree-vectorize + + LDFLAGS := \ + -Wl,--no-export-dynamic \ + -Wl,--exclude-libs,ALL \ + -Wl,--gc-sections \ + -Wl,--as-needed + endif + + LIBS := \ + -lstdc++ \ + -lpthread \ + -lm \ + -ldl + + OBJDIR := $(OBJDIR)rpi_$(TARGET_ARCH)/ + LIBDIR := $(LIBDIR)rpi_$(TARGET_ARCH)/ + BINDIR := $(BINDIR)rpi_$(TARGET_ARCH)/ + DEPDIR := $(DEPDIR)rpi_$(TARGET_ARCH)/ +endif diff --git a/tensorflow/contrib/lite/schema/BUILD b/tensorflow/contrib/lite/schema/BUILD index 54167ddd9a5a003d0ff21e6627a1dbe94afa3e87..a758c5e7e1434447e83c993034d6496bff8b2a48 100644 --- a/tensorflow/contrib/lite/schema/BUILD +++ b/tensorflow/contrib/lite/schema/BUILD @@ -1,10 +1,13 @@ -package(default_visibility = [ - "//visibility:public", -]) +package( + default_visibility = [ + "//visibility:public", + ], +) licenses(["notice"]) # Apache 2.0 load("//tensorflow:tensorflow.bzl", "py_test") +load("//tensorflow/contrib/lite:special_rules.bzl", "tflite_portable_test_suite") py_binary( name = "upgrade_schema", @@ -80,3 +83,5 @@ filegroup( ), visibility = ["//tensorflow:__subpackages__"], ) + +tflite_portable_test_suite() diff --git a/tensorflow/contrib/lite/schema/schema.fbs b/tensorflow/contrib/lite/schema/schema.fbs index 98ac0469d1b885aa8047d35c8d814da4b61eff0c..04387fed33fb8d99cd862a5c5fe0c8465f84337e 100644 --- a/tensorflow/contrib/lite/schema/schema.fbs +++ b/tensorflow/contrib/lite/schema/schema.fbs @@ -75,7 +75,7 @@ enum BuiltinOperator : byte { CONV_2D = 3, DEPTHWISE_CONV_2D = 4, // DEPTH_TO_SPACE = 5, - // DEQUANTIZE = 6, + DEQUANTIZE = 6, EMBEDDING_LOOKUP = 7, // FLOOR = 8, FULLY_CONNECTED = 9, @@ -129,6 +129,7 @@ enum BuiltinOperator : byte { // WARNING: Experimental interface, subject to change DELEGATE = 51, BIDIRECTIONAL_SEQUENCE_LSTM = 52, + CAST = 53, } // Options for the builtin operators. @@ -169,6 +170,8 @@ union BuiltinOptions { TopKV2Options, SplitOptions, LogSoftmaxOptions, + CastOptions, + DequantizeOptions, } enum Padding : byte { SAME, VALID } @@ -374,6 +377,12 @@ table StridedSliceOptions { table LogSoftmaxOptions { } +table CastOptions { +} + +table DequantizeOptions { +} + // An OperatorCode can be an enum value (BuiltinOperator) if the operator is a // builtin, or a string if the operator is custom. table OperatorCode { diff --git a/tensorflow/contrib/lite/schema/schema_generated.h b/tensorflow/contrib/lite/schema/schema_generated.h index 99e1accaa71ffc92514595a745fcb60115ef61a0..b922de20816bafc6e01c4bb66ddeef839be2bbd8 100755 --- a/tensorflow/contrib/lite/schema/schema_generated.h +++ b/tensorflow/contrib/lite/schema/schema_generated.h @@ -139,6 +139,12 @@ struct StridedSliceOptionsT; struct LogSoftmaxOptions; struct LogSoftmaxOptionsT; +struct CastOptions; +struct CastOptionsT; + +struct DequantizeOptions; +struct DequantizeOptionsT; + struct OperatorCode; struct OperatorCodeT; @@ -201,6 +207,7 @@ enum BuiltinOperator { BuiltinOperator_CONCATENATION = 2, BuiltinOperator_CONV_2D = 3, BuiltinOperator_DEPTHWISE_CONV_2D = 4, + BuiltinOperator_DEQUANTIZE = 6, BuiltinOperator_EMBEDDING_LOOKUP = 7, BuiltinOperator_FULLY_CONNECTED = 9, BuiltinOperator_HASHTABLE_LOOKUP = 10, @@ -246,17 +253,19 @@ enum BuiltinOperator { BuiltinOperator_LOG_SOFTMAX = 50, BuiltinOperator_DELEGATE = 51, BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM = 52, + BuiltinOperator_CAST = 53, BuiltinOperator_MIN = BuiltinOperator_ADD, - BuiltinOperator_MAX = BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM + BuiltinOperator_MAX = BuiltinOperator_CAST }; -inline BuiltinOperator (&EnumValuesBuiltinOperator())[50] { +inline BuiltinOperator (&EnumValuesBuiltinOperator())[52] { static BuiltinOperator values[] = { BuiltinOperator_ADD, BuiltinOperator_AVERAGE_POOL_2D, BuiltinOperator_CONCATENATION, BuiltinOperator_CONV_2D, BuiltinOperator_DEPTHWISE_CONV_2D, + BuiltinOperator_DEQUANTIZE, BuiltinOperator_EMBEDDING_LOOKUP, BuiltinOperator_FULLY_CONNECTED, BuiltinOperator_HASHTABLE_LOOKUP, @@ -301,7 +310,8 @@ inline BuiltinOperator (&EnumValuesBuiltinOperator())[50] { BuiltinOperator_SPLIT, BuiltinOperator_LOG_SOFTMAX, BuiltinOperator_DELEGATE, - BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM + BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM, + BuiltinOperator_CAST }; return values; } @@ -314,7 +324,7 @@ inline const char **EnumNamesBuiltinOperator() { "CONV_2D", "DEPTHWISE_CONV_2D", "", - "", + "DEQUANTIZE", "EMBEDDING_LOOKUP", "", "FULLY_CONNECTED", @@ -361,6 +371,7 @@ inline const char **EnumNamesBuiltinOperator() { "LOG_SOFTMAX", "DELEGATE", "BIDIRECTIONAL_SEQUENCE_LSTM", + "CAST", nullptr }; return names; @@ -409,11 +420,13 @@ enum BuiltinOptions { BuiltinOptions_TopKV2Options = 34, BuiltinOptions_SplitOptions = 35, BuiltinOptions_LogSoftmaxOptions = 36, + BuiltinOptions_CastOptions = 37, + BuiltinOptions_DequantizeOptions = 38, BuiltinOptions_MIN = BuiltinOptions_NONE, - BuiltinOptions_MAX = BuiltinOptions_LogSoftmaxOptions + BuiltinOptions_MAX = BuiltinOptions_DequantizeOptions }; -inline BuiltinOptions (&EnumValuesBuiltinOptions())[37] { +inline BuiltinOptions (&EnumValuesBuiltinOptions())[39] { static BuiltinOptions values[] = { BuiltinOptions_NONE, BuiltinOptions_Conv2DOptions, @@ -451,7 +464,9 @@ inline BuiltinOptions (&EnumValuesBuiltinOptions())[37] { BuiltinOptions_ExpOptions, BuiltinOptions_TopKV2Options, BuiltinOptions_SplitOptions, - BuiltinOptions_LogSoftmaxOptions + BuiltinOptions_LogSoftmaxOptions, + BuiltinOptions_CastOptions, + BuiltinOptions_DequantizeOptions }; return values; } @@ -495,6 +510,8 @@ inline const char **EnumNamesBuiltinOptions() { "TopKV2Options", "SplitOptions", "LogSoftmaxOptions", + "CastOptions", + "DequantizeOptions", nullptr }; return names; @@ -653,6 +670,14 @@ template<> struct BuiltinOptionsTraits { static const BuiltinOptions enum_value = BuiltinOptions_LogSoftmaxOptions; }; +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_CastOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_DequantizeOptions; +}; + struct BuiltinOptionsUnion { BuiltinOptions type; void *value; @@ -972,6 +997,22 @@ struct BuiltinOptionsUnion { return type == BuiltinOptions_LogSoftmaxOptions ? reinterpret_cast(value) : nullptr; } + CastOptionsT *AsCastOptions() { + return type == BuiltinOptions_CastOptions ? + reinterpret_cast(value) : nullptr; + } + const CastOptionsT *AsCastOptions() const { + return type == BuiltinOptions_CastOptions ? + reinterpret_cast(value) : nullptr; + } + DequantizeOptionsT *AsDequantizeOptions() { + return type == BuiltinOptions_DequantizeOptions ? + reinterpret_cast(value) : nullptr; + } + const DequantizeOptionsT *AsDequantizeOptions() const { + return type == BuiltinOptions_DequantizeOptions ? + reinterpret_cast(value) : nullptr; + } }; bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *obj, BuiltinOptions type); @@ -3635,6 +3676,86 @@ inline flatbuffers::Offset CreateLogSoftmaxOptions( flatbuffers::Offset CreateLogSoftmaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogSoftmaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +struct CastOptionsT : public flatbuffers::NativeTable { + typedef CastOptions TableType; + CastOptionsT() { + } +}; + +struct CastOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef CastOptionsT NativeTableType; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + CastOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(CastOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const CastOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct CastOptionsBuilder { + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit CastOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + CastOptionsBuilder &operator=(const CastOptionsBuilder &); + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateCastOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + CastOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateCastOptions(flatbuffers::FlatBufferBuilder &_fbb, const CastOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct DequantizeOptionsT : public flatbuffers::NativeTable { + typedef DequantizeOptions TableType; + DequantizeOptionsT() { + } +}; + +struct DequantizeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef DequantizeOptionsT NativeTableType; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + DequantizeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(DequantizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const DequantizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct DequantizeOptionsBuilder { + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit DequantizeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + DequantizeOptionsBuilder &operator=(const DequantizeOptionsBuilder &); + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateDequantizeOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + DequantizeOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateDequantizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const DequantizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + struct OperatorCodeT : public flatbuffers::NativeTable { typedef OperatorCode TableType; BuiltinOperator builtin_code; @@ -3860,6 +3981,12 @@ struct Operator FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { const LogSoftmaxOptions *builtin_options_as_LogSoftmaxOptions() const { return builtin_options_type() == BuiltinOptions_LogSoftmaxOptions ? static_cast(builtin_options()) : nullptr; } + const CastOptions *builtin_options_as_CastOptions() const { + return builtin_options_type() == BuiltinOptions_CastOptions ? static_cast(builtin_options()) : nullptr; + } + const DequantizeOptions *builtin_options_as_DequantizeOptions() const { + return builtin_options_type() == BuiltinOptions_DequantizeOptions ? static_cast(builtin_options()) : nullptr; + } const flatbuffers::Vector *custom_options() const { return GetPointer *>(VT_CUSTOM_OPTIONS); } @@ -4030,6 +4157,14 @@ template<> inline const LogSoftmaxOptions *Operator::builtin_options_as inline const CastOptions *Operator::builtin_options_as() const { + return builtin_options_as_CastOptions(); +} + +template<> inline const DequantizeOptions *Operator::builtin_options_as() const { + return builtin_options_as_DequantizeOptions(); +} + struct OperatorBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; @@ -5512,6 +5647,52 @@ inline flatbuffers::Offset CreateLogSoftmaxOptions(flatbuffer _fbb); } +inline CastOptionsT *CastOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = new CastOptionsT(); + UnPackTo(_o, _resolver); + return _o; +} + +inline void CastOptions::UnPackTo(CastOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset CastOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const CastOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateCastOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateCastOptions(flatbuffers::FlatBufferBuilder &_fbb, const CastOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const CastOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateCastOptions( + _fbb); +} + +inline DequantizeOptionsT *DequantizeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = new DequantizeOptionsT(); + UnPackTo(_o, _resolver); + return _o; +} + +inline void DequantizeOptions::UnPackTo(DequantizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset DequantizeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DequantizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateDequantizeOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateDequantizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const DequantizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DequantizeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateDequantizeOptions( + _fbb); +} + inline OperatorCodeT *OperatorCode::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new OperatorCodeT(); UnPackTo(_o, _resolver); @@ -5836,6 +6017,14 @@ inline bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *ob auto ptr = reinterpret_cast(obj); return verifier.VerifyTable(ptr); } + case BuiltinOptions_CastOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_DequantizeOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } default: return false; } } @@ -5998,6 +6187,14 @@ inline void *BuiltinOptionsUnion::UnPack(const void *obj, BuiltinOptions type, c auto ptr = reinterpret_cast(obj); return ptr->UnPack(resolver); } + case BuiltinOptions_CastOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_DequantizeOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } default: return nullptr; } } @@ -6148,6 +6345,14 @@ inline flatbuffers::Offset BuiltinOptionsUnion::Pack(flatbuffers::FlatBuff auto ptr = reinterpret_cast(value); return CreateLogSoftmaxOptions(_fbb, ptr, _rehasher).Union(); } + case BuiltinOptions_CastOptions: { + auto ptr = reinterpret_cast(value); + return CreateCastOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_DequantizeOptions: { + auto ptr = reinterpret_cast(value); + return CreateDequantizeOptions(_fbb, ptr, _rehasher).Union(); + } default: return 0; } } @@ -6298,6 +6503,14 @@ inline BuiltinOptionsUnion::BuiltinOptionsUnion(const BuiltinOptionsUnion &u) FL value = new LogSoftmaxOptionsT(*reinterpret_cast(u.value)); break; } + case BuiltinOptions_CastOptions: { + value = new CastOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_DequantizeOptions: { + value = new DequantizeOptionsT(*reinterpret_cast(u.value)); + break; + } default: break; } @@ -6485,6 +6698,16 @@ inline void BuiltinOptionsUnion::Reset() { delete ptr; break; } + case BuiltinOptions_CastOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_DequantizeOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } default: break; } value = nullptr; diff --git a/tensorflow/contrib/lite/schema/upgrade_schema.py b/tensorflow/contrib/lite/schema/upgrade_schema.py index 94f5730be5d991ae13fb019e4d035e23f76fe441..e0b36d3d3ee94b00cccd3968d14c63fe19c3c27c 100644 --- a/tensorflow/contrib/lite/schema/upgrade_schema.py +++ b/tensorflow/contrib/lite/schema/upgrade_schema.py @@ -39,8 +39,8 @@ import tensorflow as tf from tensorflow.python.platform import resource_loader parser = argparse.ArgumentParser( - description="Script to move TFLite models from pre-release schema to" - " new schema.") + description="Script to move TFLite models from pre-release schema to " + "new schema.") parser.add_argument( "input", type=str, @@ -48,7 +48,7 @@ parser.add_argument( parser.add_argument( "output", type=str, - help="Output json or bin TensorFlow lite model compliant with" + help="Output json or bin TensorFlow lite model compliant with " "the new schema. Extension must be `.json`, `.bin` or `.tflite`.") @@ -258,7 +258,7 @@ class Converter(object): # Check if builtin_code is the appropriate string type # use type("") instead of str or unicode. for py2and3 if not isinstance(operator_code["builtin_code"], type(u"")): - raise ValueError("builtin_code %r is non-string. this usually means" + raise ValueError("builtin_code %r is non-string. this usually means " "your model has consistency problems." % (operator_code["builtin_code"])) operator_code["builtin_code"] = (RemapOperator( diff --git a/tensorflow/contrib/lite/simple_memory_arena.cc b/tensorflow/contrib/lite/simple_memory_arena.cc index 4aab244989ca5300fbe74162e03deaac89af60ad..2f2004f56bcad5b56f9dd6d4bc824ec14d79e795 100644 --- a/tensorflow/contrib/lite/simple_memory_arena.cc +++ b/tensorflow/contrib/lite/simple_memory_arena.cc @@ -113,21 +113,21 @@ TfLiteStatus SimpleMemoryArena::Commit(TfLiteContext* context) { underlying_buffer_size_ = required_size; underlying_buffer_aligned_ptr_ = new_underlying_buffer_aligned_ptr; } - commited_ = true; + committed_ = true; return underlying_buffer_ != nullptr ? kTfLiteOk : kTfLiteError; } TfLiteStatus SimpleMemoryArena::ResolveAlloc(TfLiteContext* context, const ArenaAlloc& alloc, char** output_ptr) { - TF_LITE_ENSURE(context, commited_); + TF_LITE_ENSURE(context, committed_); TF_LITE_ENSURE(context, output_ptr != nullptr); *output_ptr = underlying_buffer_aligned_ptr_ + alloc.offset; return kTfLiteOk; } TfLiteStatus SimpleMemoryArena::Clear() { - commited_ = false; + committed_ = false; high_water_mark_ = 0; allocs_.clear(); return kTfLiteOk; diff --git a/tensorflow/contrib/lite/simple_memory_arena.h b/tensorflow/contrib/lite/simple_memory_arena.h index 0535522374c63459d029c252ebe94628cf3122d5..5faf78b59e3755d22e4e866d433e622baa6c66c1 100644 --- a/tensorflow/contrib/lite/simple_memory_arena.h +++ b/tensorflow/contrib/lite/simple_memory_arena.h @@ -22,7 +22,7 @@ limitations under the License. namespace tflite { // This little structure holds the offset and the size for a dynamic memory -// allocation in the memory arena. When the arena is commited and the +// allocation in the memory arena. When the arena is committed and the // underlying buffer is set, the alloc can be resolved into an actual memory // pointer. struct ArenaAlloc { @@ -43,7 +43,7 @@ struct ArenaAlloc { class SimpleMemoryArena { public: explicit SimpleMemoryArena(size_t arena_alignment) - : commited_(false), + : committed_(false), arena_alignment_(arena_alignment), high_water_mark_(0), underlying_buffer_size_(0), @@ -73,7 +73,7 @@ class SimpleMemoryArena { } private: - bool commited_; + bool committed_; size_t arena_alignment_; size_t high_water_mark_; std::unique_ptr underlying_buffer_; diff --git a/tensorflow/contrib/lite/special_rules.bzl b/tensorflow/contrib/lite/special_rules.bzl new file mode 100644 index 0000000000000000000000000000000000000000..54083c49182c707620cbd231b957405cfe24be92 --- /dev/null +++ b/tensorflow/contrib/lite/special_rules.bzl @@ -0,0 +1,6 @@ +"""External versions of build rules that differ outside of Google.""" + +def tflite_portable_test_suite(**kwargs): + """This is a no-op outside of Google.""" + _ignore = [kwargs] + pass diff --git a/tensorflow/contrib/lite/testing/BUILD b/tensorflow/contrib/lite/testing/BUILD index b5960d6f8d97a440323f01bda1c4976fe5584ac5..631601656dbad59b7cf0b9b81276d42ea9322cb6 100644 --- a/tensorflow/contrib/lite/testing/BUILD +++ b/tensorflow/contrib/lite/testing/BUILD @@ -8,6 +8,7 @@ load( "//tensorflow/contrib/lite:build_def.bzl", "gen_zipped_test_files", ) +load("//tensorflow/contrib/lite:special_rules.bzl", "tflite_portable_test_suite") load( "//tensorflow:tensorflow.bzl", "tf_cc_test", @@ -236,6 +237,9 @@ cc_test( size = "small", srcs = ["tf_driver_test.cc"], data = ["//tensorflow/contrib/lite:testdata/multi_add.pb"], + tags = [ + "tflite_not_portable", + ], deps = [ ":tf_driver", "@com_google_googletest//:gtest_main", @@ -259,6 +263,9 @@ cc_test( name = "generate_testspec_test", size = "small", srcs = ["generate_testspec_test.cc"], + tags = [ + "tflite_not_portable", + ], deps = [ ":generate_testspec", "@com_google_googletest//:gtest_main", @@ -317,7 +324,11 @@ tf_cc_test( "//tensorflow/contrib/lite:testdata/multi_add.bin", "//tensorflow/contrib/lite:testdata/multi_add.pb", ], - tags = ["no_oss"], + tags = [ + "no_cuda_on_cpu_tap", + "no_oss", + "tflite_not_portable", + ], deps = [ ":tflite_diff_flags", ":tflite_diff_util", @@ -336,7 +347,10 @@ tf_cc_test( ], data = [":optest"], shard_count = 20, - tags = ["no_oss"], + tags = [ + "no_oss", + "tflite_not_portable", + ], deps = [ ":parse_testdata_lib", ":tflite_driver", @@ -370,3 +384,5 @@ filegroup( ), visibility = ["//tensorflow:__subpackages__"], ) + +tflite_portable_test_suite() diff --git a/tensorflow/contrib/lite/testing/generate_examples.py b/tensorflow/contrib/lite/testing/generate_examples.py index 5488b71fcf644070710acc4b2b2886e9a96facb6..420bdb41f140d2ef35c66f47f386b016eb9e60c0 100644 --- a/tensorflow/contrib/lite/testing/generate_examples.py +++ b/tensorflow/contrib/lite/testing/generate_examples.py @@ -1571,7 +1571,7 @@ def make_transpose_tests(zip_path): }, { "dtype": [tf.float32], "input_shape": [[1, 2, 3, 4, 5]], - "perm": [[0, 1, 2, 3, 4]], + "perm": [[4, 3, 2, 1, 0]], "constant_perm": [True, False], }] diff --git a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc index 976363fd444f8ff9234a56e76f6c30d9f3fb485a..86606d12393b94567fbe1fceb6d708b266efe4a8 100644 --- a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc +++ b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc @@ -92,6 +92,9 @@ std::map kBrokenTests = { // Transpose only supports 1D-4D input tensors. {R"(^\/transpose.*input_shape=\[.,.,.,.,.\])", "71545879"}, + + // Lstm kernel gets different results on tsan, asan, msan. + {R"(^\/lstmdtype=tf.float32.*)", "73830845"}, }; // Allows test data to be unzipped into a temporary directory and makes diff --git a/tensorflow/contrib/lite/toco/BUILD b/tensorflow/contrib/lite/toco/BUILD index 17407f3db27ead984d1cfffc3f0085ac86f5318f..395abc532607d3cab49be6e995648f2839c1555c 100644 --- a/tensorflow/contrib/lite/toco/BUILD +++ b/tensorflow/contrib/lite/toco/BUILD @@ -173,6 +173,7 @@ cc_library( "graph_transformations/convert_expanddims_to_reshape.cc", "graph_transformations/convert_pure_conv_to_depthwise.cc", "graph_transformations/convert_reorder_axes.cc", + "graph_transformations/convert_squeeze_to_reshape.cc", "graph_transformations/convert_trivial_addn_to_add.cc", "graph_transformations/convert_trivial_stack_to_reshape.cc", "graph_transformations/convert_trivial_transpose_to_reshape.cc", @@ -195,6 +196,7 @@ cc_library( "graph_transformations/identify_relu1.cc", "graph_transformations/lstm_utils.cc", "graph_transformations/make_initial_dequantize_operator.cc", + "graph_transformations/propagate_activation_function_into_constants.cc", "graph_transformations/propagate_array_data_types.cc", "graph_transformations/propagate_fixed_sizes.cc", "graph_transformations/quantize.cc", @@ -218,6 +220,7 @@ cc_library( "graph_transformations/resolve_constant_concatenation.cc", "graph_transformations/resolve_constant_fake_quant.cc", "graph_transformations/resolve_constant_fill.cc", + "graph_transformations/resolve_constant_gather.cc", "graph_transformations/resolve_constant_range.cc", "graph_transformations/resolve_constant_shape_or_rank.cc", "graph_transformations/resolve_constant_stack.cc", @@ -240,6 +243,7 @@ cc_library( "graph_transformations/resolve_tensorflow_tile.cc", "graph_transformations/resolve_transpose_attributes.cc", "graph_transformations/unfuse_activation_functions.cc", + "graph_transformations/unpartition_embedding_lookup.cc", "graph_transformations/unroll_batch_matmul.cc", ], hdrs = [ @@ -328,6 +332,7 @@ cc_library( ":toco_graphviz_dump_options", ":toco_port", ":types_proto_cc", + "//tensorflow/contrib/lite/kernels/internal:quantization_util", "//tensorflow/core:lib", "@com_google_absl//absl/strings", "@protobuf_archive//:protobuf_headers", diff --git a/tensorflow/contrib/lite/toco/export_tensorflow.cc b/tensorflow/contrib/lite/toco/export_tensorflow.cc index 6900468ec6484d5c1896752286a2fa72f4d38c07..22a23357b36c16ea937e726f1e49aa95d7f964e3 100644 --- a/tensorflow/contrib/lite/toco/export_tensorflow.cc +++ b/tensorflow/contrib/lite/toco/export_tensorflow.cc @@ -548,6 +548,38 @@ void ConvertDepthwiseConvOperator(const Model& model, } } +void ConvertTransposeConvOperator(const Model& model, + const TransposeConvOperator& src_op, + GraphDef* tensorflow_graph) { + auto* conv2d_op = tensorflow_graph->add_node(); + conv2d_op->set_op("Conv2DBackpropInput"); + conv2d_op->set_name(src_op.outputs[0]); + *conv2d_op->add_input() = src_op.inputs[0]; + *conv2d_op->add_input() = src_op.inputs[1]; + *conv2d_op->add_input() = src_op.inputs[2]; + (*conv2d_op->mutable_attr())["T"].set_type(DT_FLOAT); + const string& weights_array_name = WalkUpToConstantArray( + model, src_op.inputs[TransposeConvOperator::WEIGHTS]); + const auto& weights_array = model.GetArray(weights_array_name); + CHECK(weights_array.buffer->type == ArrayDataType::kFloat); + ConvertFloatTensorConst(model, weights_array_name, AxesOrder::kOHWI, + AxesOrder::kHWIO, tensorflow_graph); + auto& strides = (*conv2d_op->mutable_attr())["strides"]; + strides.mutable_list()->add_i(1); + strides.mutable_list()->add_i(src_op.stride_height); + strides.mutable_list()->add_i(src_op.stride_width); + strides.mutable_list()->add_i(1); + string padding; + if (src_op.padding.type == PaddingType::kSame) { + padding = "SAME"; + } else if (src_op.padding.type == PaddingType::kValid) { + padding = "VALID"; + } else { + LOG(FATAL) << "Bad padding (only SAME and VALID are supported)"; + } + (*conv2d_op->mutable_attr())["padding"].set_s(padding); +} + void ConvertDepthToSpaceOperator(const Model& model, const DepthToSpaceOperator& src_op, GraphDef* tensorflow_graph) { @@ -1622,9 +1654,11 @@ void ConvertSqueezeOperator(const Model& model, const SqueezeOperator& src_op, const auto params_type = GetTensorFlowDataType(model, src_op.inputs[0]); (*new_op->mutable_attr())["T"].set_type(params_type); - auto& squeeze_dims = (*new_op->mutable_attr())["squeeze_dims"]; - for (int i : src_op.squeeze_dims) { - squeeze_dims.mutable_list()->add_i(i); + if (!src_op.squeeze_dims.empty()) { + auto& squeeze_dims = (*new_op->mutable_attr())["squeeze_dims"]; + for (int i : src_op.squeeze_dims) { + squeeze_dims.mutable_list()->add_i(i); + } } } @@ -1859,6 +1893,10 @@ void ConvertOperator(const Model& model, const Operator& src_op, ConvertExpandDimsOperator(model, static_cast(src_op), tensorflow_graph); + } else if (src_op.type == OperatorType::kTransposeConv) { + ConvertTransposeConvOperator( + model, static_cast(src_op), + tensorflow_graph); } else { LOG(FATAL) << "Unhandled operator type " << OperatorTypeName(src_op.type); } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/convert_squeeze_to_reshape.cc b/tensorflow/contrib/lite/toco/graph_transformations/convert_squeeze_to_reshape.cc new file mode 100644 index 0000000000000000000000000000000000000000..81cedb5dad751aacbbb32326db73de386aba282d --- /dev/null +++ b/tensorflow/contrib/lite/toco/graph_transformations/convert_squeeze_to_reshape.cc @@ -0,0 +1,85 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include +#include +#include + +#include "absl/strings/str_cat.h" +#include "tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h" +#include "tensorflow/contrib/lite/toco/model.h" +#include "tensorflow/contrib/lite/toco/tooling_util.h" +#include "tensorflow/core/platform/logging.h" + +namespace toco { + +// Replaces a tf.squeeze operator with a reshape. +// Squeeze removes dimensions == 1 (if in the list of squeeze_dims). This +// means that the data layout will never change with this op, just the shape. +// By converting these to reshapes once we have run shape propagation we allow +// standard reshape optimization transforms to do their magic. +bool ConvertSqueezeToReshape::Run(Model* model, std::size_t op_index) { + auto squeeze_it = model->operators.begin() + op_index; + if (squeeze_it->get()->type != OperatorType::kSqueeze) { + return false; + } + auto squeeze_op = static_cast(squeeze_it->get()); + CHECK_EQ(squeeze_op->inputs.size(), 1); + CHECK_EQ(squeeze_op->outputs.size(), 1); + + const auto& input_array = model->GetArray(squeeze_op->inputs[0]); + if (!input_array.has_shape()) { + // Yield until input dims have been resolved. + return false; + } + if (input_array.shape().dimensions_count() == 0) { + // Input array cannot be 0-D. + return false; + } + if (!model->HasArray(squeeze_op->outputs[0]) || + !model->GetArray(squeeze_op->outputs[0]).has_shape()) { + // Yield until shape propagation has set the output shape for us. + return false; + } + + // We use the output shape that has been calculated by shape propagation. + const auto& output_shape = model->GetArray(squeeze_op->outputs[0]).shape(); + + // Empty shapes will not work as empty data arrays. + if (output_shape.dimensions_count() == 0) { + return false; + } + + auto* reshape_op = new TensorFlowReshapeOperator; + reshape_op->inputs = { + squeeze_op->inputs[0], + CreateInt32Array(model, squeeze_op->outputs[0] + "_shape", + output_shape.dims()), + }; + reshape_op->outputs = squeeze_op->outputs; + + AddMessageF("Replacing %s with %s", LogName(*squeeze_op), + LogName(*reshape_op)); + + // Replace the operator in the graph. + const auto reshape_it = model->operators.emplace(squeeze_it, reshape_op); + squeeze_it = reshape_it + 1; + CHECK_EQ(squeeze_it->get(), squeeze_op); + model->operators.erase(squeeze_it); + + return true; +} + +} // namespace toco diff --git a/tensorflow/contrib/lite/toco/graph_transformations/convert_trivial_transpose_to_reshape.cc b/tensorflow/contrib/lite/toco/graph_transformations/convert_trivial_transpose_to_reshape.cc index c2b166033c33b777bad88cb712adf8517be1762a..5a36a90b3841504d6f018832777e50bac95218d7 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/convert_trivial_transpose_to_reshape.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/convert_trivial_transpose_to_reshape.cc @@ -21,6 +21,33 @@ limitations under the License. namespace toco { +namespace { + +bool TransposeAffectsMemoryOrder(std::vector perm, + std::vector in_shape) { + CHECK_EQ(perm.size(), in_shape.size()); + // See what the ordering of the non-unary columns are before and after + // transpose permutation. If the major indices stay in the same order (not + // just the shape) then the flat buffer representation shouldn't change. + std::vector old_major_index_ordering; + std::vector new_major_index_ordering; + for (int i = 0; i < in_shape.size(); i++) { + if (in_shape[i] != 1) { + old_major_index_ordering.push_back(i); + } + + if (in_shape[perm[i]] != 1) { + new_major_index_ordering.push_back(perm[i]); + } + } + + CHECK_EQ(new_major_index_ordering.size(), old_major_index_ordering.size()); + + return old_major_index_ordering != new_major_index_ordering; +} + +} // namespace + bool ConvertTrivialTransposeToReshape::Run(Model* model, std::size_t op_index) { auto transpose_it = model->operators.begin() + op_index; if (transpose_it->get()->type != OperatorType::kTranspose) { @@ -29,23 +56,26 @@ bool ConvertTrivialTransposeToReshape::Run(Model* model, std::size_t op_index) { TransposeOperator* transpose_op = static_cast(transpose_it->get()); + const auto& input_array = model->GetArray(transpose_op->inputs[0]); const auto& output_array = model->GetArray(transpose_op->outputs[0]); - if (!output_array.has_shape()) { + if (!input_array.has_shape() || !output_array.has_shape()) { // Yield until PropagateFixedSizes has been run on this op. return false; } // Note: We can assume we have error checked inputs in PropagateFixedSizes. - // This transpose is trivial if we only have one non-unitary dimension. - std::vector const& dims = output_array.shape().dims(); - unsigned non_unitary_axis_count = 0; - for (int i = 0; i < dims.size(); i++) { - if (dims[i] != 1) { - non_unitary_axis_count++; - } + // Check that the permutation has propogated. + std::vector const& perm = transpose_op->perm; + if (perm.empty()) { + return false; } - if (non_unitary_axis_count > 1) { - // Transpose is not trivial + + // This transpose is trivial if non-unitary dimensions remain in the same + // order. + std::vector const& input_dims = input_array.shape().dims(); + std::vector const& output_dims = output_array.shape().dims(); + + if (TransposeAffectsMemoryOrder(perm, input_dims)) { return false; } @@ -61,11 +91,11 @@ bool ConvertTrivialTransposeToReshape::Run(Model* model, std::size_t op_index) { string shape_array_name = toco::AvailableArrayName(*model, perm_array_name); Array& shape_array = model->GetOrCreateArray(shape_array_name); *(shape_array.mutable_shape()->mutable_dims()) = { - 1, static_cast(dims.size())}; + 1, static_cast(output_dims.size())}; reshape_op->inputs.push_back(shape_array_name); shape_array.data_type = ArrayDataType::kInt32; auto& shape_buffer = shape_array.GetMutableBuffer(); - shape_buffer.data = dims; + shape_buffer.data = output_dims; // Delete perm array if unused if (IsDiscardableArray(*model, perm_array_name) && diff --git a/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h b/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h index f2c81ebc81c2928ae60d66bfcd7f643c5412f196..11e5e19f508ecfb2131b391af9b4e51bb9cf0fb4 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h +++ b/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h @@ -114,6 +114,7 @@ void RunGraphTransformations(Model* model, const string& message, // List of all graph transformations DECLARE_GRAPH_TRANSFORMATION(ConvertExpandDimsToReshape) DECLARE_GRAPH_TRANSFORMATION(ConvertPureConvToDepthwise) +DECLARE_GRAPH_TRANSFORMATION(ConvertSqueezeToReshape) DECLARE_GRAPH_TRANSFORMATION(ConvertTrivialAddNToAdd) DECLARE_GRAPH_TRANSFORMATION(ConvertTrivialStackToReshape) DECLARE_GRAPH_TRANSFORMATION(ConvertTrivialTransposeToReshape) @@ -130,6 +131,7 @@ DECLARE_GRAPH_TRANSFORMATION(MergeLstmCellInputs) DECLARE_GRAPH_TRANSFORMATION(IdentifyRelu1) DECLARE_GRAPH_TRANSFORMATION(IdentifyDilatedConv) DECLARE_GRAPH_TRANSFORMATION(MakeInitialDequantizeOperator) +DECLARE_GRAPH_TRANSFORMATION(PropagateActivationFunctionIntoConstants) DECLARE_GRAPH_TRANSFORMATION(PropagateArrayDataTypes) DECLARE_GRAPH_TRANSFORMATION(PropagateFixedSizes) DECLARE_GRAPH_TRANSFORMATION(HardcodeMinMax) @@ -175,8 +177,10 @@ DECLARE_GRAPH_TRANSFORMATION(ResolveConstantShapeOrRank) DECLARE_GRAPH_TRANSFORMATION(ResolveConstantStack) DECLARE_GRAPH_TRANSFORMATION(ResolveConstantStridedSlice) DECLARE_GRAPH_TRANSFORMATION(ResolveConstantFill) +DECLARE_GRAPH_TRANSFORMATION(ResolveConstantGather) DECLARE_GRAPH_TRANSFORMATION(ResolveMultiplyByZero) DECLARE_GRAPH_TRANSFORMATION(Dequantize) +DECLARE_GRAPH_TRANSFORMATION(UnpartitionEmbeddingLookup) class ResolveReshapeAttributes : public GraphTransformation { public: diff --git a/tensorflow/contrib/lite/toco/graph_transformations/hardcode_min_max.cc b/tensorflow/contrib/lite/toco/graph_transformations/hardcode_min_max.cc index 938d76386d6f315abfe6fe55b133cb4d19014f01..5cc82da5d544846cc095046ceccf0664525aae41 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/hardcode_min_max.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/hardcode_min_max.cc @@ -326,9 +326,12 @@ bool HardcodeMinMax::Run(Model* model, std::size_t op_index) { changed = HardcodeMinMaxForAverageOrMaxPool(model, op); break; + case OperatorType::kStridedSlice: case OperatorType::kSqueeze: case OperatorType::kTensorFlowReshape: case OperatorType::kPad: + case OperatorType::kGather: + case OperatorType::kTranspose: changed = HardcodeMinMaxFromFirstInput(model, op); break; diff --git a/tensorflow/contrib/lite/toco/graph_transformations/make_initial_dequantize_operator.cc b/tensorflow/contrib/lite/toco/graph_transformations/make_initial_dequantize_operator.cc index d83603e9a2c59ae74a5e5fda5b11178740336bfb..935da9f966ca63095faa17476be3a559d1a0193a 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/make_initial_dequantize_operator.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/make_initial_dequantize_operator.cc @@ -85,8 +85,8 @@ bool AddDequantizeOperatorToInput(const string& input_name, const Operator* op, auto& dequantized_input_minmax = dequantized_input_array.GetOrCreateMinMax(); dequantized_input_minmax = input_minmax; auto& input_qparams = input_array.GetOrCreateQuantizationParams(); - GetQuantizationParamsFromMinMax( - model->flags, input_minmax, &input_qparams); + GetQuantizationParamsFromMinMax(input_minmax, + &input_qparams); transformation->AddMessageF( "Created %s" diff --git a/tensorflow/contrib/lite/toco/graph_transformations/propagate_activation_function_into_constants.cc b/tensorflow/contrib/lite/toco/graph_transformations/propagate_activation_function_into_constants.cc new file mode 100644 index 0000000000000000000000000000000000000000..cf17c49b1098d02468935aa72d1d1e73b4addbe1 --- /dev/null +++ b/tensorflow/contrib/lite/toco/graph_transformations/propagate_activation_function_into_constants.cc @@ -0,0 +1,121 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include +#include +#include + +#include "tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h" +#include "tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_passthrough.h" +#include "tensorflow/contrib/lite/toco/model.h" +#include "tensorflow/contrib/lite/toco/runtime/types.h" +#include "tensorflow/contrib/lite/toco/tooling_util.h" +#include "tensorflow/core/platform/logging.h" + +namespace toco { + +bool PropagateActivationFunctionIntoConstants::Run(Model* model, + std::size_t op_index) { + const auto ac_it = model->operators.begin() + op_index; + const auto* ac_op = ac_it->get(); + if (ac_op->type != OperatorType::kRelu6 && + ac_op->type != OperatorType::kRelu1 && + ac_op->type != OperatorType::kRelu) { + return false; + } + + // Find the op producing the array passed to this activation function. + auto* src_op = GetOpWithOutput(*model, ac_op->inputs[0]); + if (!src_op) { + return false; + } + + // Ensure the src_op is not used without the activation function applied. + if (CountTrueOutputs(*model, *src_op) > 1) { + AddMessageF( + "Not propagating activation function %s into %s because it has more " + "than one consumed output", + LogName(*ac_op), LogName(*src_op)); + } + + // Filter to the list of supported ops. + string src_op_input; + switch (src_op->type) { + case OperatorType::kGather: + src_op_input = src_op->inputs[0]; + break; + default: + return false; + } + CHECK_EQ(src_op->outputs[0], ac_op->inputs[0]); + + // Ensure the input is constant as otherwise this needs to happen at runtime. + // If we bail here, it's still possible that FuseActivationFunctions will fuse + // the activation if it's supported by the op. + if (!IsConstantParameterArray(*model, src_op_input)) { + AddMessageF( + "Not propagating activation function %s into %s:%s because it is not " + "constant", + LogName(*ac_op), LogName(*src_op), src_op_input); + return false; + } + + // Get the array we'll be working with and ensure it's a compatible type. + auto& const_array = model->GetArray(src_op_input); + if (const_array.data_type != ArrayDataType::kFloat) { + AddMessageF( + "Not propagating activation function %s into %s:%s because it is " + "non-float data", + LogName(*ac_op), LogName(*src_op), src_op_input); + return false; + } + auto& const_array_data = + const_array.GetMutableBuffer().data; + + // Perform the activation function directly into the constant data array. + for (size_t i = 0; i < const_array_data.size(); ++i) { + const float value = const_array_data[i]; + float new_value = value; + switch (ac_op->type) { + case OperatorType::kRelu: { + static constexpr float kLower = 0; + new_value = value < kLower ? kLower : value; + break; + } + case OperatorType::kRelu1: { + static constexpr float kUpper = 1; + static constexpr float kLower = -1; + new_value = value > kUpper ? kUpper : value < kLower ? kLower : value; + break; + } + case OperatorType::kRelu6: { + static constexpr float kUpper = 6; + static constexpr float kLower = 0; + new_value = value > kUpper ? kUpper : value < kLower ? kLower : value; + break; + } + default: + LOG(FATAL) << "Unsupported activation function " << LogName(*ac_op); + return false; + } + const_array_data[i] = new_value; + } + + AddMessageF("Propagated activation function %s into %s:%s", LogName(*ac_op), + LogName(*src_op), src_op_input); + return RemoveTrivialPassthroughOp(this, model, op_index); +} + +} // namespace toco diff --git a/tensorflow/contrib/lite/toco/graph_transformations/propagate_array_data_types.cc b/tensorflow/contrib/lite/toco/graph_transformations/propagate_array_data_types.cc index f0d107232b4517115aa3f64b39b825dbaffb83ce..778da39bf13563cbbdbe54f1140595b057253ae3 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/propagate_array_data_types.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/propagate_array_data_types.cc @@ -71,6 +71,11 @@ bool PropagateArrayDataTypes::Run(Model* model, std::size_t op_index) { CHECK_GE(op->inputs.size(), 2); const ArrayDataType data_type = model->GetArray(op->inputs[1]).data_type; SetDataTypeForAllOutputs(model, op, data_type); + } else if (op->type == OperatorType::kTransposeConv) { + // These operators produce an output with the same type as their 3rd input + CHECK_GE(op->inputs.size(), 3); + const ArrayDataType data_type = model->GetArray(op->inputs[2]).data_type; + SetDataTypeForAllOutputs(model, op, data_type); } else if (op->type == OperatorType::kCast) { // Data type of the Cast op is specified. CHECK_EQ(op->outputs.size(), 1); @@ -97,10 +102,13 @@ bool PropagateArrayDataTypes::Run(Model* model, std::size_t op_index) { SetDataTypeForAllOutputs(model, op, data_type); } else if (op->type == OperatorType::kTensorFlowUnsupported) { auto* unsupported_op = static_cast(op); - if (unsupported_op->output_data_types.size() != op->outputs.size()) { + // Some output tensors from the op could be eliminated by optimization. + // This can make unsupported_op->output_data_types have more elements than + // op->outputs. + if (unsupported_op->output_data_types.size() < op->outputs.size()) { return false; } - for (int i = 0; i < unsupported_op->output_data_types.size(); ++i) { + for (int i = 0; i < op->outputs.size(); ++i) { auto output = op->outputs[i]; auto data_type = unsupported_op->output_data_types[i]; model->GetArray(output).data_type = data_type; diff --git a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc index 0e2e5ecf30053103492337685d85a2aacf832caf..375848a7d408b58eab8692a15376b5b2e04b34af 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc @@ -190,6 +190,116 @@ void ProcessConvOperator(Model* model, ConvOperator* op) { } } +void ProcessTransposeConvOperator(Model* model, TransposeConvOperator* op) { + // TransposeConv is unique in that it is specifically given the output shape + // as a 1D array on it's 1st input. Theoretically then, resolving the output + // shape is as easy as waiting for this input to be resolved. However, we also + // have to calculate the padding which requires the weights shape. So, we + // might as well calculate the output shape and ensure it matches the + // specified one + + // Check if we have already run. + auto& output_array = model->GetArray(op->outputs[0]); + if (output_array.has_shape()) { + return; + } + + // SPECIFIED OUTPUT SHAPE + // The below is the specified, or prescribed output shape, _given_ to the + // operator as an input. + auto& specified_output_shape_array = + model->GetArray(op->inputs[TransposeConvOperator::OUTPUT_SHAPE]); + if (!specified_output_shape_array.has_shape() || + !specified_output_shape_array.buffer) { + // Yield until the specified output shape is resolved as a constant + return; + } + + CHECK(specified_output_shape_array.data_type == ArrayDataType::kInt32) + << "TransposeConv input_dims must be int32"; + + CHECK(specified_output_shape_array.shape().dimensions_count() == 1 && + specified_output_shape_array.shape().dims(0) == 4) + << "TransposeConv requires a 1D, 4 element array on it's 0th input " + "specifying the output shape. \"" + << op->inputs[TransposeConvOperator::OUTPUT_SHAPE] << "\" had shape " + << toco::ShapeToString(specified_output_shape_array.shape()); + + // COMPUTE PADDING + // We require the weights shape to calculate padding. + const auto& weights_array = + model->GetArray(op->inputs[TransposeConvOperator::WEIGHTS]); + if (!weights_array.has_shape()) { + // Yield until weights dims have been resolved. + return; + } + const auto& weights_shape = weights_array.shape(); + CHECK_EQ(weights_shape.dimensions_count(), 4) + << "TransposeConv weights must have 4 input dimensions. Input weights \"" + << op->inputs[TransposeConvOperator::WEIGHTS] << "\" had shape " + << toco::ShapeToString(weights_shape) << "."; + + CHECK(weights_shape.dims(0) == 1 && weights_shape.dims(3) == 1) + << "TransposeConv weights dimensions must begin and end with 1. Input " + "weights \"" + << op->inputs[TransposeConvOperator::WEIGHTS] << "\" had shape " + << toco::ShapeToString(weights_shape) << "."; + + // Compute padding + const int kheight = weights_shape.dims(1); + const int kwidth = weights_shape.dims(2); + op->padding.GetOrCreateFixedPadding(); + if (op->padding.type == PaddingType::kValid) { + op->padding.fixed->height = 0; + op->padding.fixed->width = 0; + } else if (op->padding.type == PaddingType::kSame) { + op->padding.fixed->height = (kheight - 1) / 2; + op->padding.fixed->width = (kwidth - 1) / 2; + } else { + LOG(FATAL) << "TransposeConv only supports SAME or VALID padding"; + } + + // VALIDATE OUTPUT SHAPE + // Compute the output shape from the input and weights shapes to verify it + // agrees with the specified output shape. + const auto& input_array = + model->GetArray(op->inputs[TransposeConvOperator::DATA_INPUT]); + if (!input_array.has_shape()) { + // Yield until input dims have been resolved. + return; + } + const auto& input_shape = input_array.shape(); + CHECK_EQ(input_shape.dimensions_count(), 4) + << "TransposeConv input shape must have 4 dimensions. Input \"" + << op->inputs[TransposeConvOperator::WEIGHTS] << "\" had shape " + << toco::ShapeToString(weights_shape) << "."; + + // Compute output shape + const int input_width = input_shape.dims(2); + const int input_height = input_shape.dims(1); + int output_height = op->stride_height * (input_height - 1); + int output_width = op->stride_width * (input_width - 1); + if (op->padding.type == PaddingType::kValid) { + output_height += kheight; + output_width += kwidth; + } else if (op->padding.type == PaddingType::kSame) { + output_height += 1; + output_width += 1; + } + + CHECK(specified_output_shape_array.GetBuffer().data == + std::vector({input_shape.dims(0), output_height, output_width, + weights_shape.dims(3)})) + << "Specified output shape: " << ShapeToString(output_array.shape()) + << ", does not agree with shape computed from input data and weights: [" + << input_shape.dims(0) << ", " << output_height << ", " << output_width + << ", " << weights_shape.dims(3) << "]."; + + // SUCCESS: Set the op's output shape according to the specified output shape. + *(output_array.mutable_shape()->mutable_dims()) = + specified_output_shape_array.GetBuffer().data; +} + void ProcessDepthwiseConvOperator(Model* model, DepthwiseConvOperator* op) { if (!EnsureBiasVectorShape(model, op)) { return; @@ -1300,7 +1410,7 @@ void ProcessTransposeOperator(Model* model, TransposeOperator* op) { std::vector const& perm = perm_array.GetBuffer().data; CHECK_EQ(perm.size(), input_shape.dimensions_count()) - << "Transpose permutation input " << op->inputs[0] + << "Transpose permutation input " << op->inputs[1] << " must be same length as input dimensions"; std::vector* output_dims = output_array.mutable_shape()->mutable_dims(); for (int i = 0; i < perm.size(); i++) { @@ -1402,8 +1512,8 @@ bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) { ProcessConvOperator(model, static_cast(op)); break; case OperatorType::kTransposeConv: - // Unimplemented, hopefully another graph transformation will drop it or - // rewrite it. + ProcessTransposeConvOperator(model, + static_cast(op)); break; case OperatorType::kDepthwiseConv: ProcessDepthwiseConvOperator(model, @@ -1542,6 +1652,12 @@ bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) { case OperatorType::kTranspose: ProcessTransposeOperator(model, static_cast(op)); break; + case OperatorType::kDynamicPartition: + case OperatorType::kDynamicStitch: + // DynamicPartition/DynamicStitch are currently only supported for + // transforms that remove them, so we avoid propagating shapes through + // them and let things settle once they've been removed. + break; default: // Unimplemented, another graph transformation should drop it. LOG(FATAL) << "Unhandled operator type " << OperatorTypeName(op->type); diff --git a/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc b/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc index 77316751bc2642a0c974d16f694aeebe1cd53a9f..ad3f05274b59e3019726f1b7a7080e74d1934c89 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc @@ -49,7 +49,10 @@ bool SupportsQuantization(const Operator& op) { type == OperatorType::kTensorFlowReshape || type == OperatorType::kTanh || type == OperatorType::kMul || type == OperatorType::kSpaceToDepth || - type == OperatorType::kDepthToSpace || type == OperatorType::kLstmCell; + type == OperatorType::kStridedSlice || + type == OperatorType::kDepthToSpace || + type == OperatorType::kLstmCell || type == OperatorType::kGather || + type == OperatorType::kTranspose; } template @@ -222,7 +225,49 @@ ArrayDataType GetQuantizedDataType(const Array& array, default: LOG(FATAL) << "Unhandled final quantization type " << static_cast(array.final_data_type); - return default_type; + } +} + +void GetQuantizationParams(ArrayDataType data_type, const MinMax& minmax, + QuantizationParams* quantization_params) { + switch (data_type) { + case ArrayDataType::kInt8: + GetQuantizationParamsFromMinMax( + minmax, quantization_params); + break; + case ArrayDataType::kUint8: + GetQuantizationParamsFromMinMax( + minmax, quantization_params); + break; + case ArrayDataType::kInt16: + GetQuantizationParamsFromMinMax( + minmax, quantization_params); + break; + case ArrayDataType::kUint16: + GetQuantizationParamsFromMinMax( + minmax, quantization_params); + break; + case ArrayDataType::kInt32: + GetQuantizationParamsFromMinMax( + minmax, quantization_params); + break; + case ArrayDataType::kUint32: + GetQuantizationParamsFromMinMax( + minmax, quantization_params); + break; + case ArrayDataType::kInt64: + GetQuantizationParamsFromMinMax( + minmax, quantization_params); + break; + case ArrayDataType::kUint64: + GetQuantizationParamsFromMinMax( + minmax, quantization_params); + break; + case ArrayDataType::kFloat: + case ArrayDataType::kNone: + default: + LOG(FATAL) << "Unhandled final quantization type " + << static_cast(data_type); } } @@ -284,16 +329,14 @@ bool ChooseQuantizationForOperatorInput( if (op.type == OperatorType::kLstmCell) { if (input_index == LstmCellOperator::PREV_STATE_INPUT) { - GetQuantizationParamsFromMinMax( - model->flags, minmax, quantization_params); *quantized_data_type = ArrayDataType::kInt16; + GetQuantizationParams(*quantized_data_type, minmax, quantization_params); return true; } } - GetQuantizationParamsFromMinMax(model->flags, minmax, - quantization_params); *quantized_data_type = GetQuantizedDataType(array, ArrayDataType::kUint8); + GetQuantizationParams(*quantized_data_type, minmax, quantization_params); transformation->AddMessageF( "For input array %s with min=%g" ", max=%g" @@ -416,15 +459,13 @@ bool ChooseQuantizationForOperatorOutput( if (op.type == OperatorType::kLstmCell) { if (output_index == LstmCellOperator::STATE_OUTPUT || output_index == LstmCellOperator::ACTIV_TEMP) { - GetQuantizationParamsFromMinMax( - model->flags, minmax, quantization_params); *quantized_data_type = ArrayDataType::kInt16; + GetQuantizationParams(*quantized_data_type, minmax, quantization_params); return true; } } - GetQuantizationParamsFromMinMax(model->flags, minmax, - quantization_params); *quantized_data_type = GetQuantizedDataType(array, ArrayDataType::kUint8); + GetQuantizationParams(*quantized_data_type, minmax, quantization_params); transformation->AddMessageF( "For output array %s with min=%g, max=%g" ", chose to quantize as %s with zero_point=%d" @@ -472,9 +513,11 @@ bool Quantize::Run(Model* model, std::size_t op_index) { // // Let us just guard this assumption by the following assertion: for (const auto& input : op.inputs) { - if (IsInputArray(*model, input)) { - const auto& input_array = model->GetArray(input); - CHECK(input_array.quantization_params); + const auto& input_array = model->GetArray(input); + if (IsInputArray(*model, input) && + input_array.data_type == ArrayDataType::kFloat) { + CHECK(input_array.quantization_params) + << "Input array " << input << " is missing quantization_params"; } } if (!SupportsQuantization(op)) { diff --git a/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_passthrough.cc b/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_passthrough.cc index 587f171bbf823408a45083c36d52f1d38c300123..aa93ace03af300f9cbd3f9c6620a6a58b9329aa4 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_passthrough.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_passthrough.cc @@ -60,7 +60,9 @@ bool RemoveTrivialPassthroughOp(GraphTransformation* transformation, for (int i = 0; i < passthru_op->inputs.size(); i++) { if (!model->GetArray(passthru_op->inputs[i]).buffer) { count_nonconstant_input_arrays++; - main_input_array_index = i; + if (count_nonconstant_input_arrays == 1) { + main_input_array_index = i; + } } } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_quantized_activation_func.cc b/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_quantized_activation_func.cc index 28f76c9d36d6f68c8997fa0cf620c8aec4273619..9b65feaa6443cd32ac1bef961600ff225d52d4b2 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_quantized_activation_func.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_quantized_activation_func.cc @@ -12,6 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ +#include #include #include #include @@ -30,6 +31,7 @@ bool RemoveTrivialQuantizedActivationFunc::Run(Model* model, const auto it = model->operators.begin() + op_index; auto* op = it->get(); if (op->fused_activation_function != FusedActivationFunctionType::kRelu && + op->fused_activation_function != FusedActivationFunctionType::kRelu1 && op->fused_activation_function != FusedActivationFunctionType::kRelu6) { return false; } @@ -42,33 +44,49 @@ bool RemoveTrivialQuantizedActivationFunc::Run(Model* model, } const auto& quantization_params = output_array.GetQuantizationParams(); + double clamp_min; + double clamp_max; + switch (op->fused_activation_function) { + case FusedActivationFunctionType::kRelu: + clamp_min = 0.0; + clamp_max = std::numeric_limits::infinity(); + break; + case FusedActivationFunctionType::kRelu1: + clamp_min = -1.0; + clamp_max = 1.0; + break; + case FusedActivationFunctionType::kRelu6: + clamp_min = 0.0; + clamp_max = 6.0; + break; + default: + LOG(FATAL) << "Unsupported fused activation type: " + << static_cast(op->fused_activation_function); + return false; + } + bool has_nontrivial_min_bound = false; bool has_nontrivial_max_bound = false; - if (op->fused_activation_function == FusedActivationFunctionType::kRelu || - op->fused_activation_function == FusedActivationFunctionType::kRelu6) { - double lowest_representable_output = - (0. - quantization_params.zero_point) * quantization_params.scale; - if (lowest_representable_output < 0.) { - has_nontrivial_min_bound = true; - AddMessageF( - "Quantized activation function is not trivial: " - "the lowest representable output value %g" - " less than the clamp min bound.", - lowest_representable_output); - } + double lowest_representable_output = + (0. - quantization_params.zero_point) * quantization_params.scale; + if (lowest_representable_output < clamp_min) { + has_nontrivial_min_bound = true; + AddMessageF( + "Quantized activation function is not trivial: " + "the lowest representable output value %g" + " less than the clamp min bound %g.", + lowest_representable_output, clamp_min); } - if (op->fused_activation_function == FusedActivationFunctionType::kRelu6) { - double highest_representable_output = - (255. - quantization_params.zero_point) * quantization_params.scale; - if (highest_representable_output > 6.) { - has_nontrivial_max_bound = true; - AddMessageF( - "Quantized activation function is not trivial: " - "the highest representable output value %g" - " is greater than the clamp max bound.", - highest_representable_output); - } + double highest_representable_output = + (255. - quantization_params.zero_point) * quantization_params.scale; + if (highest_representable_output > clamp_max) { + has_nontrivial_max_bound = true; + AddMessageF( + "Quantized activation function is not trivial: " + "the highest representable output value %g" + " is greater than the clamp max bound %g.", + highest_representable_output, clamp_max); } if (has_nontrivial_min_bound || has_nontrivial_max_bound) { diff --git a/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_reshape.cc b/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_reshape.cc index 90f9381ec154f145cda826ff9730ff332cd96701..61477d59aea2f11c6347b84d8863763a86c43558 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_reshape.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_reshape.cc @@ -61,8 +61,8 @@ bool IsReshapeTrivial(const Model& model, const Operator& op, if (next_op->type == OperatorType::kTensorFlowReshape) { transformation->AddMessageF( "%s is trivial because its output is only consumed by another " - "Reshape op", - LogName(op)); + "Reshape op %s", + LogName(op), LogName(*next_op)); return true; } } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/reorder_activation_functions.cc b/tensorflow/contrib/lite/toco/graph_transformations/reorder_activation_functions.cc index 30a005c789bb12e880e8e4534088d99ebacba84a..9852c86c21b9a0714bc728e60b5d9dfe61ff52d1 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/reorder_activation_functions.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/reorder_activation_functions.cc @@ -42,14 +42,22 @@ bool ReorderActivationFunctions::Run(Model* model, std::size_t op_index) { std::unique_ptr& exchange_op = *exchange_it; DCHECK(exchange_op); - if (exchange_op->type != OperatorType::kTensorFlowReshape) { - return false; + // Allow activation functions to move up over any operator that does not + // change the values. + switch (exchange_op->type) { + case OperatorType::kExpandDims: + case OperatorType::kSqueeze: + case OperatorType::kTensorFlowReshape: + case OperatorType::kTranspose: + break; + default: + return false; } DCHECK_EQ(exchange_op->outputs[0], ac_op->inputs[0]); - const auto& exchange_op_input = exchange_op->inputs[0]; - const auto& intermediate_array = exchange_op->outputs[0]; - const auto& ac_op_output = ac_op->outputs[0]; + const auto exchange_op_input = exchange_op->inputs[0]; + const auto intermediate_array = exchange_op->outputs[0]; + const auto ac_op_output = ac_op->outputs[0]; int count_ops_consuming_output = CountOpsWithInput(*model, intermediate_array); @@ -62,32 +70,58 @@ bool ReorderActivationFunctions::Run(Model* model, std::size_t op_index) { return false; } - // If the ac_op was originally producing an output_array we can't reorder as - // otherwise the output array would change. It'd be nice to still be able to - // reorder but if code is relying on the fetch names instead of array indices - // this won't work. - for (int i = 0; i < model->flags.output_arrays_size(); ++i) { - if (model->flags.output_arrays(i) == ac_op->outputs[0]) { - AddMessageF( - "Not exchanging activation function with %s to preserve output array " - "name %s", - LogName(*exchange_op), ac_op->outputs[0]); - return false; - } - } - - // Rewire by changing inputs, including all consumers. - Operator* consumer = GetFirstOpWithInput(*model, ac_op_output); - while (consumer) { - for (int i = 0; i < consumer->inputs.size(); ++i) { - if (consumer->inputs[i] == ac_op_output) { - consumer->inputs[i] = intermediate_array; + // If the ac_op was originally producing an output_array we can't trivially + // reorder as otherwise the output array name would change and break + // downstream assumptions. To work around that we perform some renaming below + // in that case at the cost of a bit more confusing array names in this rare + // case. + bool is_ac_op_output = + std::find(model->flags.output_arrays().begin(), + model->flags.output_arrays().end(), + ac_op_output) != model->flags.output_arrays().end(); + if (is_ac_op_output) { + // To preserve the output array name of the activation function we need to + // create a temporary to use to pass between ac->ex. + // + // Original: + // (a) -> EX -> (b) -> AC -> (c) + // Now: + // (a) -> AC -> (c') -> EX -> (c) + AddMessageF( + "Exchanging activation function %s with %s but renaming to preserve " + "output array %s", + LogName(*ac_op), LogName(*exchange_op), ac_op->outputs[0]); + + auto renamed_ac_op_output = + AvailableArrayName(*model, ac_op_output + "_exchange"); + ac_op->inputs[0] = exchange_op_input; + ac_op->outputs[0] = renamed_ac_op_output; + model->EraseArray(exchange_op->outputs[0]); + exchange_op->inputs[0] = renamed_ac_op_output; + exchange_op->outputs[0] = ac_op_output; + } else { + // Simply swap the order and update consumers to use the exchange_op output + // array (b). + // + // Original: + // (a) -> EX -> (b) -> AC -> (c) + // Now: + // (a) -> AC -> (c) -> EX -> (b) + AddMessageF("Exchanging activation function %s with %s", LogName(*ac_op), + LogName(*exchange_op)); + + Operator* consumer = GetFirstOpWithInput(*model, ac_op_output); + while (consumer) { + for (int i = 0; i < consumer->inputs.size(); ++i) { + if (consumer->inputs[i] == ac_op_output) { + consumer->inputs[i] = intermediate_array; + } } + consumer = GetFirstOpWithInput(*model, ac_op_output); } - consumer = GetFirstOpWithInput(*model, ac_op_output); + ac_op->inputs[0] = exchange_op_input; + exchange_op->inputs[0] = ac_op_output; } - ac_op->inputs[0] = exchange_op_input; - exchange_op->inputs[0] = ac_op_output; // Clear shapes; this will allow shape propagation to fix the sizes for us. model->GetOrCreateArray(ac_op->outputs[0]).clear_shape(); diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_fake_quant.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_fake_quant.cc index 944901ece77430708013ea4ca340a30511ba0174..625d90205a801ad7c3fc1026c9cedc9b509f920d 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_fake_quant.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_fake_quant.cc @@ -55,8 +55,8 @@ bool ResolveConstantFakeQuant::Run(Model* model, std::size_t op_index) { const int size = input_buffer.data.size(); output_buffer.data.resize(size); QuantizationParams qparams; - GetQuantizationParamsFromMinMax( - model->flags, *fakequant_op->minmax, &qparams); + GetQuantizationParamsFromMinMax(*fakequant_op->minmax, + &qparams); for (int i = 0; i < size; i++) { const double src_val = input_buffer.data[i]; const double unclamped_quantized_val = diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_gather.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_gather.cc new file mode 100644 index 0000000000000000000000000000000000000000..d999c2df9483e096f333c6af83e1d9fee873d4d6 --- /dev/null +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_gather.cc @@ -0,0 +1,134 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include + +#include "tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h" +#include "tensorflow/contrib/lite/toco/model.h" +#include "tensorflow/contrib/lite/toco/tooling_util.h" +#include "tensorflow/core/platform/logging.h" + +namespace toco { + +namespace { + +// Gathers data from axis 0. +template +inline void Gather(const Array& input_array, int input_rank, + const Array& coords_array, Array* output_array) { + const Shape& input_shape = input_array.shape(); + const std::vector>& input_data = + input_array.GetBuffer().data; + const Shape& coords_shape = coords_array.shape(); + const std::vector& coords_data = + coords_array.GetBuffer().data; + + const Shape& output_shape = output_array->shape(); + std::vector>& output_data = + output_array->GetMutableBuffer().data; + output_data.resize(RequiredBufferSizeForShape(output_shape)); + + int rev_input_rank = input_shape.dimensions_count() - 1 - (input_rank - 1); + CHECK_EQ(coords_shape.dims(0), output_array->shape().dims(rev_input_rank)); + + int stride = 1; + for (int i = input_shape.dimensions_count() - 1; i >= input_rank - 1; --i) { + stride *= input_shape.dims(i); + } + + for (int i = 0; i < coords_shape.dims(0); ++i) { + DCHECK_GE(coords_data[i], 0); + DCHECK_LT(coords_data[i], input_shape.dims(rev_input_rank)); + DataType* out = output_data.data() + i * stride; + const DataType* in = input_data.data() + coords_data[i] * stride; + memcpy(out, in, sizeof(DataType) * stride); + } +} + +} // namespace + +// Resolves a constant Gather operation. +// This simply performs the gather and produces the output array with the +// appropriate values. +bool ResolveConstantGather::Run(Model* model, std::size_t op_index) { + auto it = model->operators.begin() + op_index; + const auto* base_op = it->get(); + if (base_op->type != OperatorType::kGather) { + return false; + } + const auto* op = static_cast(base_op); + + CHECK_EQ(op->inputs.size(), 2); + CHECK_EQ(op->outputs.size(), 1); + auto& output_array = model->GetArray(op->outputs[0]); + if (output_array.data_type == ArrayDataType::kNone) { + // Yield until the output type has been set by PropagateArrayDataTypes. + return false; + } + if (!output_array.has_shape()) { + // Yield until the output shape has been set by PropagateFixedShapes. + return false; + } + + // Only handling axis=0 for now. + if (op->axis != 0) { + AddMessageF("%s has axis %d; only axis=0 is supported", LogName(*op), + op->axis); + return false; + } + + // We require constant inputs. + if (!IsConstantParameterArray(*model, op->inputs[0]) || + !IsConstantParameterArray(*model, op->inputs[1])) { + return false; + } + const Array& input_array = model->GetArray(op->inputs[0]); + const Array& coords_array = model->GetArray(op->inputs[1]); + CHECK(coords_array.data_type == ArrayDataType::kInt32) + << "Only int32 indices are supported"; + + CHECK(!output_array.buffer); + switch (output_array.data_type) { + case ArrayDataType::kFloat: + Gather(input_array, op->input_rank, coords_array, + &output_array); + break; + case ArrayDataType::kUint8: + Gather(input_array, op->input_rank, coords_array, + &output_array); + break; + case ArrayDataType::kInt32: + Gather(input_array, op->input_rank, coords_array, + &output_array); + break; + case ArrayDataType::kInt64: + Gather(input_array, op->input_rank, coords_array, + &output_array); + break; + default: + LOG(FATAL) << "Unsupported data type given to Gather op with output \"" + << op->outputs[0] << "\""; + break; + } + + // Erase input arrays if no longer used after we remove the op. + DeleteArrayIfUsedOnce(op->inputs[0], model); + DeleteArrayIfUsedOnce(op->inputs[1], model); + + // Erase the operator. + model->operators.erase(it); + return true; +} + +} // namespace toco diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_unary.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_unary.cc index f227554bc505efe6a758fdd9894fee43f2500641..6d5636d744bed193c212227e69f87ae2caf84273 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_unary.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_unary.cc @@ -40,7 +40,10 @@ bool ResolveConstantUnaryOperator::Run(Model* model, std::size_t op_index) { unary_op->type != OperatorType::kTensorFlowSum && unary_op->type != OperatorType::kTensorFlowMin && unary_op->type != OperatorType::kTensorFlowMax && - unary_op->type != OperatorType::kTensorFlowReshape) { + unary_op->type != OperatorType::kTensorFlowReshape && + unary_op->type != OperatorType::kRelu6 && + unary_op->type != OperatorType::kRelu1 && + unary_op->type != OperatorType::kRelu) { return false; } // Check if the input is a constant parameter. @@ -138,12 +141,32 @@ bool ResolveConstantUnaryOperator::Run(Model* model, std::size_t op_index) { memcpy(output_float_data.data(), (*input_float_data).data(), output_buffer_size * sizeof(output_float_data[0])); } else if (unary_op->type == OperatorType::kTensorFlowSum) { - // At the moment only full reduction across all dimensions is supported. - float sum = 0.f; - for (int i = 0; i < input_buffer_size; i++) { - sum += (*input_float_data)[i]; + CHECK_EQ(unary_op->inputs.size(), 2) << "Sum needs 2 inputs"; + if (!IsConstantParameterArray(*model, unary_op->inputs[1])) { + AddMessageF("Axis input is non-constant"); + return false; } - for (int i = 0; i < output_buffer_size; ++i) { + auto& axis_array = model->GetArray(unary_op->inputs[1]); + CHECK(axis_array.data_type == ArrayDataType::kInt32); + int axis = axis_array.GetBuffer().data[0]; + CHECK_LT(axis, input_shape.dimensions_count()) << "Axis out of bounds"; + + // We currently only handle reduction on axis 0. + CHECK_EQ(axis, 0) << "Only reduction along axis 0 is supported"; + // We currently only handle 1-D and 2-D input tensors. + CHECK_LE(input_shape.dimensions_count(), 2) << "Rank >2 not yet supported"; + // We only support keep_dims=true; shape prop will need to change otherwise. + auto sum_op = static_cast(unary_op); + CHECK(sum_op->keep_dims) << "Only keep_dims=true is supported"; + + std::vector indices(input_shape.dimensions_count()); + for (int i = 0; i < input_shape.dims(1); ++i) { + indices[1] = i; + float sum = 0.f; + for (int j = 0; j < input_shape.dims(0); ++j) { + indices[0] = j; + sum += (*input_float_data)[Offset(input_shape, indices)]; + } output_float_data[i] = sum; } } else if (unary_op->type == OperatorType::kTensorFlowMin) { @@ -193,6 +216,37 @@ bool ResolveConstantUnaryOperator::Run(Model* model, std::size_t op_index) { } output_float_data[i] = outval; } + } else if (unary_op->type == OperatorType::kRelu6 && + unary_op->type == OperatorType::kRelu1 && + unary_op->type == OperatorType::kRelu) { + for (size_t i = 0; i < output_buffer_size; ++i) { + const float value = (*input_float_data)[i]; + float new_value = 0.0f; + switch (unary_op->type) { + case OperatorType::kRelu: { + static constexpr float kLower = 0; + new_value = value < kLower ? kLower : value; + break; + } + case OperatorType::kRelu1: { + static constexpr float kUpper = 1; + static constexpr float kLower = -1; + new_value = value > kUpper ? kUpper : value < kLower ? kLower : value; + break; + } + case OperatorType::kRelu6: { + static constexpr float kUpper = 6; + static constexpr float kLower = 0; + new_value = value > kUpper ? kUpper : value < kLower ? kLower : value; + break; + } + default: + LOG(FATAL) << "Unsupported activation function " + << LogName(*unary_op); + return false; + } + output_float_data[i] = new_value; + } } else { LOG(FATAL) << "should not get here."; } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/unpartition_embedding_lookup.cc b/tensorflow/contrib/lite/toco/graph_transformations/unpartition_embedding_lookup.cc new file mode 100644 index 0000000000000000000000000000000000000000..48c326651f3201b4f7a31ac2440b171841e8ed7b --- /dev/null +++ b/tensorflow/contrib/lite/toco/graph_transformations/unpartition_embedding_lookup.cc @@ -0,0 +1,240 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include +#include + +#include "tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h" +#include "tensorflow/contrib/lite/toco/model.h" +#include "tensorflow/contrib/lite/toco/tooling_util.h" + +namespace toco { + +bool UnpartitionEmbeddingLookup::Run(Model* model, std::size_t op_index) { + // Collapses a partitioned tf.nn.embedding_lookup back into a single Gather. + // https://www.tensorflow.org/api_docs/python/tf/nn/embedding_lookup + // This transform attempts to identify the len(params) > 1 case and collapse + // it to the len(params) = 1 case by concatenating the original params and + // reversing the partitioning. + // + // If len(params) to the tf.nn.embedding_lookup == 1, the whole op becomes + // simply a gather: + // https://github.com/tensorflow/tensorflow/blob/r1.5/tensorflow/python/ops/embedding_ops.py#L150 + // + // Notes on this implementation: + // - only supports partition_strategy='mod' + // + // A rough graph of a partitioned embedding_lookup looks like: + // (ids)--+-->FloorDiv--+-->DynamicPartition-->[[Gather]]--\ + // \-->FloorMod--/ | + // V | + // Range-->DynamicPartition-------->DynamicStitch<---------/ + // (const) V + // (embeddings) + + // First look for the final DynamicStitch. + auto op_it = model->operators.begin() + op_index; + if (op_it->get()->type != OperatorType::kDynamicStitch) { + return false; + } + auto* stitch_op = static_cast(op_it->get()); + + // Split up the DynamicStitch inputs into the indices and data. + std::vector stitch_indices_inputs; + std::vector stitch_data_inputs; + for (size_t i = 0; i < stitch_op->num_partitions; ++i) { + stitch_indices_inputs.push_back(stitch_op->inputs[i]); + } + for (size_t i = stitch_op->num_partitions; i < stitch_op->num_partitions * 2; + ++i) { + stitch_data_inputs.push_back(stitch_op->inputs[i]); + } + + // Validate all indices come from the same DynamicPartition. + DynamicPartitionOperator* indices_partition_op = nullptr; + for (const string& indices_partition_output_name : stitch_indices_inputs) { + auto* op = GetOpWithOutput(*model, indices_partition_output_name); + CHECK(op) << "Source of " << indices_partition_output_name << " not found"; + if (op->type != OperatorType::kDynamicPartition) { + AddMessageF( + "Skipping because indices input %s into " + "%s is unexpected", + LogName(*op), LogName(*stitch_op)); + return false; + } + if (!indices_partition_op) { + indices_partition_op = static_cast(op); + } else { + // Ensure this is the same op as previous ones. + if (op != indices_partition_op) { + AddMessageF( + "Skipping because indices input %s into " + "%s is from a different source op than others", + LogName(*op), LogName(*stitch_op)); + return false; + } + } + } + CHECK(indices_partition_op) << "No indices inputs"; + + // The data for the indices must be a constant range of the array shape. + if (!IsConstantParameterArray(*model, indices_partition_op->inputs[0])) { + AddMessageF("Skipping because indices partition data is non-constant"); + return false; + } + auto& indices_data_array = model->GetArray(indices_partition_op->inputs[0]); + if (indices_data_array.data_type == ArrayDataType::kNone) { + // Yield until data types are propagated. + return false; + } + CHECK(indices_data_array.data_type == ArrayDataType::kInt32) + << "Indices partition inputs must be int32"; + const auto& indices_data_buffer = + indices_data_array.GetBuffer().data; + for (size_t i = 0; i < indices_data_buffer.size(); ++i) { + CHECK_EQ(indices_data_buffer[i], i) << "Indices range must be identity"; + } + + // Find all of the gathers used for the data inputs. + std::vector gather_ops; + for (const string& gather_output_name : stitch_data_inputs) { + auto* op = GetOpWithOutput(*model, gather_output_name); + CHECK(op) << "Source of " << gather_output_name << " not found"; + if (op->type != OperatorType::kGather) { + AddMessageF( + "Skipping because data input %s into %s " + "is unexpected", + LogName(*op), LogName(*stitch_op)); + return false; + } + gather_ops.push_back(static_cast(op)); + } + + // Validate all gathers come from the same DynamicPartition. + DynamicPartitionOperator* data_partition_op = nullptr; + for (auto* gather_op : gather_ops) { + auto* op = GetOpWithOutput(*model, gather_op->inputs[1]); + CHECK(op) << "Source of " << gather_op->inputs[1] << " not found"; + if (op->type != OperatorType::kDynamicPartition) { + AddMessageF( + "Skipping because data input %s into " + "%s is unexpected", + LogName(*op), LogName(*gather_op)); + return false; + } + if (!data_partition_op) { + data_partition_op = static_cast(op); + } else { + // Ensure this is the same op as previous ones. + if (op != data_partition_op) { + AddMessageF( + "Skipping because data input %s into " + "%s is from a different source op than others", + LogName(*op), LogName(*gather_op)); + return false; + } + } + } + CHECK(data_partition_op) << "No data inputs"; + + // Validate the partition ops have the same sizes. + CHECK_EQ(indices_partition_op->num_partitions, + data_partition_op->num_partitions) + << "Indices and data partition ops have differing dimensions"; + int num_partitions = indices_partition_op->num_partitions; + + // Partition strategy of 'mod' gives us a FloorMod and FloorDiv. + // The gather partition uses the FloorDiv as the data and FloorMod as the + // partitions and the indices use the FloorMod as their partitions. + Operator* div_op = GetOpWithOutput(*model, data_partition_op->inputs[0]); + Operator* mod_op = GetOpWithOutput(*model, data_partition_op->inputs[1]); + CHECK(div_op && div_op->type == OperatorType::kFloorDiv) + << "Unsupported partition strategy"; + CHECK(mod_op && mod_op->type == OperatorType::kFloorMod) + << "Unsupported partition strategy"; + CHECK_EQ(mod_op, GetOpWithOutput(*model, indices_partition_op->inputs[1])) + << "Indices and data parition ops require the same partition strategy " + "and inputs"; + + // Glob together all of the gather data. This is not yet in the correct order. + auto* gather_params_concat_op = new ConcatenationOperator; + for (const auto& gather_op : gather_ops) { + gather_params_concat_op->inputs.push_back(gather_op->inputs[0]); + } + gather_params_concat_op->outputs.push_back( + AvailableArrayName(*model, gather_ops[0]->inputs[0] + "_unpartitioned")); + op_it = model->operators.emplace(op_it, gather_params_concat_op) + 1; + model->GetOrCreateArray(gather_params_concat_op->outputs[0]); + + // Permute the gather params to undo the partitioning that was originally + // done. + auto* gather_params_permute_op = new GatherOperator; + gather_params_permute_op->inputs.push_back( + gather_params_concat_op->outputs[0]); + gather_params_permute_op->inputs.push_back( + AvailableArrayName(*model, gather_ops[0]->inputs[0] + "_permuted/perm")); + gather_params_permute_op->outputs.push_back( + AvailableArrayName(*model, gather_ops[0]->inputs[0] + "_permuted")); + op_it = model->operators.emplace(op_it, gather_params_permute_op) + 1; + model->GetOrCreateArray(gather_params_permute_op->outputs[0]); + const auto& partition_array = model->GetArray(gather_ops[0]->inputs[0]); + const auto& partition_array_dims = partition_array.shape().dims(); + gather_params_permute_op->input_rank = + partition_array.shape().dimensions_count(); + auto& perm_array = + model->GetOrCreateArray(gather_params_permute_op->inputs[1]); + perm_array.data_type = ArrayDataType::kInt32; + perm_array.mutable_shape()->ReplaceDims( + {num_partitions * partition_array_dims[0]}); + auto& perm_data = perm_array.GetMutableBuffer().data; + perm_data.resize(RequiredBufferSizeForShape(perm_array.shape())); + // NOTE: this is what relies on the partition_strategy. + for (int i = 0; i < num_partitions * partition_array_dims[0]; ++i) { + int p = i % num_partitions; + perm_data[i] = p * partition_array_dims[0] + i / num_partitions; + } + + // Insert the new unpartitioned gather op. + auto* merged_gather_op = new GatherOperator; + merged_gather_op->inputs = {gather_params_permute_op->outputs[0], + mod_op->inputs[0]}; + merged_gather_op->outputs = {stitch_op->outputs[0]}; + merged_gather_op->input_rank = partition_array.shape().dimensions_count(); + model->operators.emplace(op_it, merged_gather_op); + + AddMessageF( + "Replacing suspected partitioned tf.nn.embedding_lookup (starting at %s " + "+ %s and ending at %s) with a single unpartitioned gather %s", + LogName(*div_op), LogName(*mod_op), LogName(*stitch_op), + LogName(*merged_gather_op)); + + // Ensure the stitch output array is dead, as we don't want whatever was in it + // previously now that we've redefined it. It'll be recreated when needed. + model->EraseArray(stitch_op->outputs[0]); + model->GetOrCreateArray(merged_gather_op->outputs[0]); + + // Erase all the original ops. + DeleteOpAndArraysIfUnused(model, div_op); + DeleteOpAndArraysIfUnused(model, mod_op); + for (auto* gather_op : gather_ops) { + DeleteOpAndArraysIfUnused(model, gather_op); + } + DeleteOpAndArraysIfUnused(model, indices_partition_op); + DeleteOpAndArraysIfUnused(model, data_partition_op); + DeleteOpAndArraysIfUnused(model, stitch_op); + return true; +} + +} // namespace toco diff --git a/tensorflow/contrib/lite/toco/import_tensorflow.cc b/tensorflow/contrib/lite/toco/import_tensorflow.cc index 27d2f33a8d278156262753e6572c10ff967bda4c..a7a50e6fc9326338d69cd0334c83b60e2fa50402 100644 --- a/tensorflow/contrib/lite/toco/import_tensorflow.cc +++ b/tensorflow/contrib/lite/toco/import_tensorflow.cc @@ -272,6 +272,39 @@ void ImportInt64Array(const TensorProto& input_tensor, Array* output_array) { } } +void ImportBoolArray(const TensorProto& input_tensor, Array* output_array) { + CHECK_EQ(input_tensor.dtype(), DT_BOOL); + const auto& input_shape = input_tensor.tensor_shape(); + CHECK_LE(input_shape.dim_size(), 4); + ImportShape(input_shape.dim(), output_array->mutable_shape()); + int input_flat_size = 1; + for (int k = 0; k < input_shape.dim_size(); k++) { + input_flat_size *= input_shape.dim(k).size(); + } + auto& output_bool_data = + output_array->GetMutableBuffer().data; + output_bool_data.resize(RequiredBufferSizeForShape(output_array->shape()), + false); + if (input_tensor.bool_val_size()) { + for (int i = 0; i < input_tensor.bool_val_size(); i++) { + output_bool_data[i] = input_tensor.bool_val(i); + } + } else if (input_tensor.tensor_content().size() == input_flat_size) { + std::vector buf(input_tensor.tensor_content().size()); + toco::port::CopyToBuffer(input_tensor.tensor_content(), buf.data()); + for (int i = 0; i < input_tensor.tensor_content().size(); i++) { + output_bool_data[i] = static_cast(buf[i]); + } + } else { + // Some graphs have bool const nodes without actual value... + // assuming that 'false' is implied. + // So far only encountered that in an array with 1 entry, let's + // require that until we encounter a graph where that's not the case. + CHECK_EQ(output_bool_data.size(), 1); + output_bool_data[0] = false; + } +} + void ImportStringArray(const TensorProto& input_tensor, Array* output_array) { CHECK_EQ(input_tensor.dtype(), DT_STRING); const auto& input_shape = input_tensor.tensor_shape(); @@ -318,6 +351,18 @@ void CheckInputsCount(const NodeDef& node, << " input(s) other than control dependencies: " << node.DebugString(); } +template +string CreateConstArray(Model* model, string const& name, + std::vector > const& data) { + // Utility function to create a const 1D array, useful for input parameters. + string array_name = toco::AvailableArrayName(*model, name); + auto& array = model->GetOrCreateArray(array_name); + array.data_type = T; + array.mutable_shape()->mutable_dims()->emplace_back(data.size()); + array.GetMutableBuffer().data = data; + return array_name; +} + void ConvertConstOperator(const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { @@ -347,6 +392,10 @@ void ConvertConstOperator(const NodeDef& node, array.data_type = ArrayDataType::kString; ImportStringArray(tensor, &array); break; + case DT_BOOL: + array.data_type = ArrayDataType::kBool; + ImportBoolArray(tensor, &array); + break; default: array.data_type = ArrayDataType::kNone; // do nothing, silently ignore the Const data. @@ -678,9 +727,12 @@ void ConvertSqueezeOperator(const NodeDef& node, op->inputs.push_back(node.input(0)); op->outputs.push_back(node.name()); - const auto& squeeze_dims = GetListAttr(node, "squeeze_dims"); - for (int i = 0; i < squeeze_dims.i_size(); ++i) { - op->squeeze_dims.push_back(squeeze_dims.i(i)); + // When omitted we are to squeeze all dimensions == 1. + if (HasAttr(node, "squeeze_dims")) { + const auto& squeeze_dims = GetListAttr(node, "squeeze_dims"); + for (int i = 0; i < squeeze_dims.i_size(); ++i) { + op->squeeze_dims.push_back(squeeze_dims.i(i)); + } } model->operators.emplace_back(op); @@ -1399,12 +1451,8 @@ void ConvertFusedBatchNormOperator(const NodeDef& node, const string& moving_variance_input = node.input(4); // Create an array holding the epsilon value (typically, 0.001). - const string epsilon_array_name = node.name() + "_epsilon_array"; - auto& epsilon_array = model->GetOrCreateArray(epsilon_array_name); - epsilon_array.data_type = ArrayDataType::kFloat; - *epsilon_array.mutable_shape()->mutable_dims() = {1}; - epsilon_array.GetMutableBuffer().data.push_back( - GetFloatAttr(node, "epsilon")); + const string epsilon_array_name = CreateConstArray( + model, node.name() + "_epsilon_array", {GetFloatAttr(node, "epsilon")}); // Add epsilon to the moving variance. const string epsilon_add_op_name = node.name() + "_epsilon"; @@ -1532,16 +1580,56 @@ void ConvertTransposeConvOperator(const NodeDef& node, CHECK_EQ(node.op(), "Conv2DBackpropInput"); CheckInputsCount(node, tf_import_flags, 3); auto* op = new TransposeConvOperator; - op->inputs.push_back(node.input(2)); - op->inputs.push_back(node.input(1)); op->inputs.push_back(node.input(0)); + op->inputs.push_back(node.input(1)); + op->inputs.push_back(node.input(2)); op->outputs.push_back(node.name()); const auto& strides = GetListAttr(node, "strides"); - CHECK_EQ(strides.i_size(), 4); - CHECK_EQ(strides.i(0), 1); op->stride_height = strides.i(1); op->stride_width = strides.i(2); - CHECK_EQ(strides.i(3), 1); + CHECK_EQ(strides.i_size(), 4) + << "Can only import TransposeConv ops with 4D strides. TensorFlow op \"" + << node.name() << "\" has " << strides.i_size() << "D strides."; + CHECK((strides.i(0) == 1) && (strides.i(3) == 1)) + << "Can only import TransposeConv ops with striding along the height " + "(1st) or width (2nd) axis. TensorFlow op \"" + << node.name() << "\" had strides:[ " << strides.i(0) << ", " + << strides.i(1) << ", " << strides.i(2) << ", " << strides.i(3) << "]."; + op->stride_height = strides.i(1); + op->stride_width = strides.i(2); + if (HasAttr(node, "dilations")) { + const auto& dilations = GetListAttr(node, "dilations"); + CHECK_EQ(dilations.i_size(), 4) + << "Dilation unsupported in TransposeConv. TensorFlow op \"" + << node.name() << "\" had dilations"; + CHECK((dilations.i(0) == 1) && (dilations.i(1) == 1) && + (dilations.i(1) == 1) && (dilations.i(3) == 1)) + << "Dilation unsupported in TransposeConv. TensorFlow op \"" + << node.name() << "\" had dilations:[ " << dilations.i(0) << ", " + << dilations.i(1) << ", " << dilations.i(2) << ", " << dilations.i(3) + << "]."; + } + + const string& weights_name = node.input(TransposeConvOperator::WEIGHTS); + const string& transposed_weights_name = weights_name + "_transposed"; + // Check if a TransposeOperator was already created for these weights + // (can happen when multiple layers share the same weights). + const Operator* existing_transpose = + GetOpWithOutput(*model, transposed_weights_name); + if (existing_transpose) { + CHECK(existing_transpose->type == OperatorType::kTranspose); + } else { + // Transpose weights from HWIO order to OHWI order, which is more efficient + // for computation + TransposeOperator* transpose = new TransposeOperator; + string perm_array = CreateConstArray( + model, node.name() + "_transpose_perm", {3, 0, 1, 2}); + transpose->inputs = {weights_name, perm_array}; + transpose->outputs = {transposed_weights_name}; + model->operators.emplace_back(transpose); + } + op->inputs[1] = transposed_weights_name; + auto const& padding = GetStringAttr(node, "padding"); if (padding == "SAME") { op->padding.type = PaddingType::kSame; @@ -1837,19 +1925,9 @@ void ConvertTopKV2Operator(const NodeDef& node, op->inputs.push_back(node.input(0)); // K can be encoded as attr (TopK) convert it to a const. if (HasAttr(node, "k")) { - // Convert attribute into const tensor. - const string array_name = node.name() + "k"; - auto& array = model->GetOrCreateArray(array_name); - array.data_type = ArrayDataType::kInt32; - // Size of array is always 1. - array.mutable_shape()->mutable_dims()->emplace_back(1); - - auto& output_int_data = - array.GetMutableBuffer().data; - output_int_data.resize(1); - output_int_data[0] = GetIntAttr(node, "k"); - op->inputs.push_back(array_name); - + string k_array = CreateConstArray( + model, node.name() + "k", {GetIntAttr(node, "k")}); + op->inputs.push_back(k_array); } else { CheckInputsCount(node, tf_import_flags, 2); op->inputs.push_back(node.input(1)); @@ -1859,6 +1937,42 @@ void ConvertTopKV2Operator(const NodeDef& node, op->outputs.push_back(node.name() + ":1"); model->operators.emplace_back(op.release()); } + +void ConvertDynamicPartitionOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { + auto op = absl::make_unique(); + CHECK(HasAttr(node, "num_partitions")); + op->num_partitions = GetIntAttr(node, "num_partitions"); + CheckInputsCount(node, tf_import_flags, 2); + op->inputs.push_back(node.input(0)); + op->inputs.push_back(node.input(1)); + CHECK_GT(op->num_partitions, 1); + op->outputs.push_back(node.name()); // Implicit :0. + for (int i = 1; i < op->num_partitions; ++i) { + op->outputs.push_back(node.name() + ":" + std::to_string(i)); + } + model->operators.emplace_back(op.release()); +} + +void ConvertDynamicStitchOperator(const NodeDef& node, + const TensorFlowImportFlags& tf_import_flags, + Model* model) { + // The parallel and non-parallel variants are the same besides whether they + // have a parallel loop; there are no behavioral differences. + CHECK(node.op() == "DynamicStitch" || node.op() == "ParallelDynamicStitch"); + auto op = absl::make_unique(); + CHECK(HasAttr(node, "N")); + op->num_partitions = GetIntAttr(node, "N"); + // Expect all ID partitions + all value partitions. + CheckInputsCount(node, tf_import_flags, op->num_partitions * 2); + for (int i = 0; i < op->num_partitions * 2; ++i) { + op->inputs.push_back(node.input(i)); + } + op->outputs.push_back(node.name()); + model->operators.emplace_back(op.release()); +} + } // namespace std::unique_ptr ImportTensorFlowGraphDef( @@ -2044,6 +2158,11 @@ std::unique_ptr ImportTensorFlowGraphDef( ConvertExpOperator(node, tf_import_flags, model); } else if (node.op() == "TopK" || node.op() == "TopKV2") { ConvertTopKV2Operator(node, tf_import_flags, model); + } else if (node.op() == "DynamicPartition") { + ConvertDynamicPartitionOperator(node, tf_import_flags, model); + } else if (node.op() == "DynamicStitch" || + node.op() == "ParallelDynamicStitch") { + ConvertDynamicStitchOperator(node, tf_import_flags, model); } else { ConvertUnsupportedOperator(node, tf_import_flags, model); } diff --git a/tensorflow/contrib/lite/toco/model.h b/tensorflow/contrib/lite/toco/model.h index 346859ab392d257355b21411a1b3691c8dda5421..3fa0089cba15fbfed869b5c33883741c35493cda 100644 --- a/tensorflow/contrib/lite/toco/model.h +++ b/tensorflow/contrib/lite/toco/model.h @@ -29,6 +29,8 @@ limitations under the License. namespace toco { +using tflite::QuantizationParams; + enum class OperatorType { kNone, // General-purpose neural network operators. @@ -115,6 +117,8 @@ enum class OperatorType { kTensorFlowTile, kTranspose, kTopK_V2, + kDynamicPartition, + kDynamicStitch, // An unsupported TF operation. It's only needed to be able to represent TF // graph internally and is expected to be dropped by graph transformations. kTensorFlowUnsupported, @@ -244,6 +248,8 @@ struct GenericBuffer { // in containers and have the containers call the right subclass destructor. virtual ~GenericBuffer() {} + virtual int Length() const = 0; + const ArrayDataType type; protected: @@ -256,6 +262,8 @@ template struct Buffer : GenericBuffer { Buffer() : GenericBuffer(A) {} + int Length() const override { return data.size(); } + std::vector> data; }; @@ -840,19 +848,29 @@ struct SqueezeOperator : Operator { }; // Inputs: -// inputs[0]: required: the input activations array -// inputs[1]: required: the Conv weights -// channel. +// inputs[0]: required: the output shape +// inputs[1]: required: the weights +// inputs[2]: required: the input activations array +// NOTE: The input activations is NOT the first input. +// // // Outputs: // outputs[0]: required: the output activations array // // TensorFlow equivalent: Conv2DBackpropInput struct TransposeConvOperator : Operator { + enum Inputs { + OUTPUT_SHAPE = 0, + WEIGHTS = 1, + DATA_INPUT = 2, + }; + TransposeConvOperator() : Operator(OperatorType::kTransposeConv) {} Padding padding; int stride_width = 0; int stride_height = 0; + // Dilation is possible with transpose convolution, but Tensorflow does not + // currently support it, so we omit it. }; // Given a tensor input, this operation calculates element-wise exponential @@ -1410,6 +1428,30 @@ struct TopKV2Operator : Operator { TopKV2Operator() : Operator(OperatorType::kTopK_V2) {} }; +// DynamicPartition operator: +// +// Inputs: +// inputs[0]: required: data. +// inputs[1]: required: partitions. +// +// TensorFlow equivalent: DynamicPartition +struct DynamicPartitionOperator : Operator { + DynamicPartitionOperator() : Operator(OperatorType::kDynamicPartition) {} + int num_partitions; +}; + +// DynamicStitch operator: +// +// Inputs: +// inputs[0,N): required: indices. +// inputs[N,2N): required: data. +// +// TensorFlow equivalent: DynamicStitch/ParallelDynamicStitch +struct DynamicStitchOperator : Operator { + DynamicStitchOperator() : Operator(OperatorType::kDynamicStitch) {} + int num_partitions; +}; + // Alloc's are used for transient arrays only. An Alloc specifies which interval // of the "transient_data" workspace buffer passed to inference functions, is to // be used for the transient array at hand. The 'start' and 'end' values are @@ -1423,22 +1465,6 @@ inline bool operator<(const Alloc& a, const Alloc& b) { return a.start < b.start; } -// Quantization parameters, determining the mapping of quantized values -// to real values (i.e. determining how quantized values are mathematically -// interpreted). -// -// The correspondence is as follows: -// -// real_value = scale * (quantized_value - zero_point); -// -// In other words, zero_point designates which quantized value corresponds to -// the real 0 value, and scale designates the difference between the real values -// corresponding to consecutive quantized values differing by 1. -struct QuantizationParams { - int32 zero_point = 0; - double scale = 0.; -}; - class Shape { public: // For Shape, we stick to half-way encapsulation for now: diff --git a/tensorflow/contrib/lite/toco/tensorflow_graph_matching/resolve_cluster.cc b/tensorflow/contrib/lite/toco/tensorflow_graph_matching/resolve_cluster.cc index fddf6cc83686632033f31496ec42b33e2ea15f20..5e421ba944cccd9746c66bc33e986b4406dd3bf5 100644 --- a/tensorflow/contrib/lite/toco/tensorflow_graph_matching/resolve_cluster.cc +++ b/tensorflow/contrib/lite/toco/tensorflow_graph_matching/resolve_cluster.cc @@ -144,7 +144,9 @@ std::unique_ptr MaybeReplaceCompositeSubgraph( MaybeResolveClusters(tf_graph, cluster_factories); // Copy function definitions - *(pruned_graph->mutable_library()) = tf_graph.library(); + if (pruned_graph) { + *(pruned_graph->mutable_library()) = tf_graph.library(); + } return pruned_graph; } diff --git a/tensorflow/contrib/lite/toco/tflite/import.cc b/tensorflow/contrib/lite/toco/tflite/import.cc index 5b1ab514b23248cd98e66847185d0e8b9fe2d6aa..e16784fd21dd5e6eeac5f03ba6250341dcdfbdb4 100644 --- a/tensorflow/contrib/lite/toco/tflite/import.cc +++ b/tensorflow/contrib/lite/toco/tflite/import.cc @@ -64,6 +64,9 @@ void ImportTensors(const ::tflite::Model& input_model, Model* model) { auto shape = input_tensor->shape(); if (shape) { + // If the shape is 0-dimensional, make sure to record it as such, + // as oppose to leaving the array without a shape. + array.mutable_shape()->mutable_dims()->clear(); for (int i = 0; i < shape->Length(); ++i) { auto d = shape->Get(i); array.mutable_shape()->mutable_dims()->push_back(d); @@ -167,8 +170,8 @@ std::unique_ptr Import(const ModelFlags& model_flags, // Full list of all known operators. const auto ops_by_name = BuildOperatorByNameMap(); - if (input_model->subgraphs()->size() != 1) { - LOG(FATAL) << "# of subgraphs in tflite should be exactly 1 for now."; + if (!input_model->subgraphs() || input_model->subgraphs()->size() != 1) { + LOG(FATAL) << "Number of subgraphs in tflite should be exactly 1."; } std::unique_ptr model; model.reset(new Model); diff --git a/tensorflow/contrib/lite/toco/tflite/import_test.cc b/tensorflow/contrib/lite/toco/tflite/import_test.cc index aad6e780d5eb5c3dbc880906df5053ad231ffd54..f25b17087639d1b04de4aaf371726fa594427ff1 100644 --- a/tensorflow/contrib/lite/toco/tflite/import_test.cc +++ b/tensorflow/contrib/lite/toco/tflite/import_test.cc @@ -27,33 +27,32 @@ namespace { using ::testing::ElementsAre; +using flatbuffers::Offset; +using flatbuffers::Vector; class ImportTest : public ::testing::Test { protected: template - flatbuffers::Offset> CreateDataVector( - const std::vector& data) { + Offset> CreateDataVector(const std::vector& data) { return builder_.CreateVector(reinterpret_cast(data.data()), sizeof(T) * data.size()); } - // This is a very simplistic model. We are not interested in testing all the - // details here, since tf.mini's testing framework will be exercising all the - // conversions multiple times, and the conversion of operators is tested by - // separate unittests. - void BuildTestModel() { - // The tensors + Offset>> BuildBuffers() { + auto buf0 = ::tflite::CreateBuffer(builder_, CreateDataVector({})); + auto buf1 = + ::tflite::CreateBuffer(builder_, CreateDataVector({1.0f, 2.0f})); + auto buf2 = + ::tflite::CreateBuffer(builder_, CreateDataVector({3.0f})); + return builder_.CreateVector( + std::vector>({buf0, buf1, buf2})); + } + + Offset>> BuildTensors() { auto q = ::tflite::CreateQuantizationParameters( builder_, /*min=*/builder_.CreateVector({0.1f}), /*max=*/builder_.CreateVector({0.2f}), /*scale=*/builder_.CreateVector({0.3f}), /*zero_point=*/builder_.CreateVector({100ll})); - auto buf0 = ::tflite::CreateBuffer(builder_, CreateDataVector({})); - auto buf1 = - ::tflite::CreateBuffer(builder_, CreateDataVector({1.0f, 2.0f})); - auto buf2 = - ::tflite::CreateBuffer(builder_, CreateDataVector({3.0f})); - auto buffers = builder_.CreateVector( - std::vector>({buf0, buf1, buf2})); auto t1 = ::tflite::CreateTensor(builder_, builder_.CreateVector({1, 2, 3, 4}), ::tflite::TensorType_FLOAT32, 1, @@ -62,17 +61,28 @@ class ImportTest : public ::testing::Test { ::tflite::CreateTensor(builder_, builder_.CreateVector({2, 1}), ::tflite::TensorType_FLOAT32, 2, builder_.CreateString("tensor_two"), q); - auto tensors = builder_.CreateVector( - std::vector>({t1, t2})); + return builder_.CreateVector( + std::vector>({t1, t2})); + } - // The operator codes. + Offset>> BuildOpCodes() { auto c1 = ::tflite::CreateOperatorCode(builder_, ::tflite::BuiltinOperator_CUSTOM, builder_.CreateString("custom_op_one")); auto c2 = ::tflite::CreateOperatorCode( builder_, ::tflite::BuiltinOperator_CONV_2D, 0); - auto opcodes = builder_.CreateVector( - std::vector>({c1, c2})); + return builder_.CreateVector( + std::vector>({c1, c2})); + } + + // This is a very simplistic model. We are not interested in testing all the + // details here, since tf.mini's testing framework will be exercising all the + // conversions multiple times, and the conversion of operators is tested by + // separate unittests. + void BuildTestModel() { + auto buffers = BuildBuffers(); + auto tensors = BuildTensors(); + auto opcodes = BuildOpCodes(); auto subgraph = ::tflite::CreateSubGraph(builder_, tensors, 0, 0, 0); std::vector> subgraph_vector( @@ -133,6 +143,19 @@ TEST_F(ImportTest, Tensors) { EXPECT_EQ(100, q->zero_point); } +TEST_F(ImportTest, NoSubGraphs) { + auto buffers = BuildBuffers(); + auto opcodes = BuildOpCodes(); + auto subgraphs = 0; // no subgraphs in this model + auto comment = builder_.CreateString(""); + builder_.Finish(::tflite::CreateModel(builder_, TFLITE_SCHEMA_VERSION, + opcodes, subgraphs, comment, buffers)); + input_model_ = ::tflite::GetModel(builder_.GetBufferPointer()); + + EXPECT_DEATH(Import(ModelFlags(), InputModelAsString()), + "Number of subgraphs in tflite should be exactly 1."); +} + // TODO(ahentz): still need tests for Operators and IOTensors. } // namespace diff --git a/tensorflow/contrib/lite/toco/tflite/types.cc b/tensorflow/contrib/lite/toco/tflite/types.cc index b4c2851502a40a1ca36965d4ddd2c8a15b8fe60f..0afd2f3df57caf3214dd198bfa2ee75fa7a8fd7b 100644 --- a/tensorflow/contrib/lite/toco/tflite/types.cc +++ b/tensorflow/contrib/lite/toco/tflite/types.cc @@ -90,6 +90,8 @@ flatbuffers::Offset> DataBuffer::Serialize( return CopyBuffer(array, builder); case ArrayDataType::kInt32: return CopyBuffer(array, builder); + case ArrayDataType::kInt64: + return CopyBuffer(array, builder); case ArrayDataType::kString: return CopyBuffer(array, builder); case ArrayDataType::kUint8: diff --git a/tensorflow/contrib/lite/toco/toco_tooling.cc b/tensorflow/contrib/lite/toco/toco_tooling.cc index a09a3c4ef56edc6ba7fd19eb1ff45a2e41cf3dd2..024335b5e4ca4547eba24b0944dc539eddc6bdf7 100644 --- a/tensorflow/contrib/lite/toco/toco_tooling.cc +++ b/tensorflow/contrib/lite/toco/toco_tooling.cc @@ -52,12 +52,14 @@ void MakeGeneralGraphTransformationsSet( GraphTransformationsSet* transformations) { CHECK(transformations->empty()); transformations->Add(new ConvertExpandDimsToReshape); + transformations->Add(new ConvertSqueezeToReshape); transformations->Add(new ConvertTrivialAddNToAdd); transformations->Add(new ConvertTrivialStackToReshape); transformations->Add(new ConvertTrivialTransposeToReshape); transformations->Add(new ConvertReorderAxes); transformations->Add(new ResolveReshapeAttributes); transformations->Add(new ResolveTransposeAttributes); + transformations->Add(new PropagateActivationFunctionIntoConstants); transformations->Add(new PropagateArrayDataTypes); transformations->Add(new PropagateFixedSizes); transformations->Add(new RemoveTensorFlowAssert); @@ -76,6 +78,7 @@ void MakeGeneralGraphTransformationsSet( transformations->Add(new ResolveBatchNormalization); transformations->Add(new ResolveConstantBinaryOperator); transformations->Add(new ResolveConstantFill); + transformations->Add(new ResolveConstantGather); transformations->Add(new ResolveConstantRange); transformations->Add(new ResolveConstantStack); transformations->Add(new ResolveConstantStridedSlice); @@ -102,6 +105,7 @@ void MakeGeneralGraphTransformationsSet( transformations->Add(new ResolveConstantShapeOrRank); transformations->Add(new MakeInitialDequantizeOperator); transformations->Add(new ResolveConstantFakeQuant); + transformations->Add(new UnpartitionEmbeddingLookup); } bool SupportsQuantization(FileFormat format) { diff --git a/tensorflow/contrib/lite/toco/tooling_util.cc b/tensorflow/contrib/lite/toco/tooling_util.cc index 9e725822383b06985bbb5cffdc19a759bc6d5cf3..e70291ad0ef4bb4c1969cfc3c87bf0f8e50ecef5 100644 --- a/tensorflow/contrib/lite/toco/tooling_util.cc +++ b/tensorflow/contrib/lite/toco/tooling_util.cc @@ -84,6 +84,8 @@ string ArrayDataTypeName(ArrayDataType data_type) { return "Uint64"; case ArrayDataType::kString: return "String"; + case ArrayDataType::kBool: + return "Bool"; case ArrayDataType::kNone: return "None"; default: @@ -157,6 +159,15 @@ bool DeleteArrayIfUsedOnce(const string& array_name, Model* model) { return false; } +void DeleteOpAndArraysIfUnused(Model* model, Operator* op) { + for (const string& array_name : op->inputs) { + DeleteArrayIfUsedOnce(array_name, model); + } + auto op_it = FindOp(*model, op); + CHECK(op_it != model->operators.end()); + model->operators.erase(op_it); +} + std::vector>::const_iterator FindOpWithOutput( const Model& model, const string& array_name) { for (auto it = model.operators.begin(); it != model.operators.end(); ++it) { @@ -345,6 +356,8 @@ const char* OperatorTypeName(OperatorType type) { HANDLE_OPERATORTYPENAME_CASE(TopK_V2) HANDLE_OPERATORTYPENAME_CASE(TensorFlowUnsupported) HANDLE_OPERATORTYPENAME_CASE(Exp) + HANDLE_OPERATORTYPENAME_CASE(DynamicPartition) + HANDLE_OPERATORTYPENAME_CASE(DynamicStitch) default: LOG(FATAL) << "Unhandled op type"; #undef HANDLE_OPERATORTYPENAME_CASE @@ -809,9 +822,15 @@ void CheckEachArray(const Model& model) { // It's OK to have a buffer or an alloc, but not both. // (Since allocs are for transient arrays without a buffer). CHECK(!array->buffer || !array->alloc); - // If there is a buffer, its type should be consistent with data_type. if (array->buffer) { + // If there is a buffer, its type should be consistent with data_type. CHECK(array->buffer->type == array->data_type); + // The presence of a fixed buffer should imply the presence of a fixed + // shape. + CHECK(array->has_shape()); + // The shape flat-size should agree with the buffer length. + CHECK_EQ(array->buffer->Length(), + RequiredBufferSizeForShape(array->shape())); } // Check name. Either "name_with_suffix_8", "name_with_port:3", but not @@ -1191,7 +1210,7 @@ void ResolveModelFlags(const ModelFlags& model_flags, Model* model) { << "This model does not define output arrays, so a " "--output_arrays flag must be given on the command-line."; - for (const auto& input_array_proto : model->flags.input_arrays()) { + for (auto& input_array_proto : *model->flags.mutable_input_arrays()) { auto& input_array = model->GetOrCreateArray(input_array_proto.name()); if (input_array_proto.has_data_type()) { const ArrayDataType specified_type = @@ -1235,6 +1254,11 @@ void ResolveModelFlags(const ModelFlags& model_flags, Model* model) { for (int i = 0; i < input_array_dims.size(); i++) { CHECK_EQ(input_array_dims[i], input_array_proto.shape().dims(i)); } + } else { + for (int i = 0; i < input_array.shape().dimensions_count(); i++) { + input_array_proto.mutable_shape()->add_dims( + input_array.shape().dims(i)); + } } } @@ -1330,6 +1354,8 @@ void UseDefaultMinMaxRangeValues(Model* model, double default_ranges_min, int ElementSize(ArrayDataType data_type) { switch (data_type) { + case ArrayDataType::kBool: + return sizeof(bool); case ArrayDataType::kFloat: return 4; case ArrayDataType::kInt8: @@ -1355,7 +1381,7 @@ int ElementSize(ArrayDataType data_type) { LOG(FATAL) << "Transient arrays with strings are not supported yet"; return 0; default: - LOG(FATAL) << "Should not get here."; + LOG(FATAL) << "Unknown data_type = " << static_cast(data_type); return 0; } } @@ -1785,7 +1811,10 @@ bool IsDiscardableArray(const Model& model, const string& array_name) { void CheckFinalDataTypesSatisfied(const Model& model) { for (const auto& array_entry : model.GetArrayMap()) { const auto& array = *array_entry.second; - if (array.final_data_type != ArrayDataType::kNone) { + // If the final data type is int16, the data type may be float, for example + // after dequantization. + if (array.final_data_type != ArrayDataType::kNone && + array.final_data_type != ArrayDataType::kInt16) { CHECK(array.final_data_type == array.data_type) << "Array \"" << array_entry.first << "\" has mis-matching actual and final data types (" diff --git a/tensorflow/contrib/lite/toco/tooling_util.h b/tensorflow/contrib/lite/toco/tooling_util.h index 11208ed667212d56f9ef45e4f394e0bbf5000cbc..05360e3b0a8510f57724d98dab0e2097b1062ced 100644 --- a/tensorflow/contrib/lite/toco/tooling_util.h +++ b/tensorflow/contrib/lite/toco/tooling_util.h @@ -28,6 +28,7 @@ limitations under the License. #if TOCO_SUPPORT_PORTABLE_PROTOS #include "third_party/protobuf/src/google/protobuf/text_format.h" #endif // TOCO_SUPPORT_PORTABLE_PROTOS +#include "tensorflow/contrib/lite/kernels/internal/quantization_util.h" #include "tensorflow/contrib/lite/toco/model.h" #include "tensorflow/contrib/lite/toco/model_flags.pb.h" #include "tensorflow/contrib/lite/toco/runtime/types.h" @@ -64,6 +65,10 @@ int CountOpsWithInput(const Model& model, const string& array_name); bool DeleteArrayIfUnused(const string& array_name, Model* model); bool DeleteArrayIfUsedOnce(const string& array_name, Model* model); +// Deletes the op and any of its input and output arrays if they are unused +// after the op has been deleted. +void DeleteOpAndArraysIfUnused(Model* model, Operator* op); + std::vector>::const_iterator FindOpWithOutput( const Model& model, const string& array_name); Operator* GetOpWithOutput(const Model& model, const string& array_name); @@ -71,8 +76,6 @@ Operator* GetOpWithOutput(const Model& model, const string& array_name); std::vector>::iterator FindOpWithOutput( Model& model, const string& array_name); -Operator* GetOpWithOutput(const Model& model, const string& array_name); - std::vector>::const_iterator FindOpWithInput( const Model& model, const string& array_name); @@ -144,75 +147,14 @@ void FixNoOrphanedArray(Model* model); void ResolveModelFlags(const ModelFlags& model_flags, Model* model); template -void GetQuantizationParamsFromMinMax(const ModelFlags& model_flags, - const MinMax& minmax, +void GetQuantizationParamsFromMinMax(const MinMax& minmax, QuantizationParams* quantization_params) { using Integer = DataType; - const Integer qmin = std::numeric_limits::min(); - const Integer qmax = std::numeric_limits::max(); - const double qmin_double = qmin; - const double qmax_double = qmax; const double rmin = minmax.min; const double rmax = minmax.max; - // 0 should always be a representable value. Let's assume that the initial - // min,max range contains 0. - CHECK_LE(rmin, 0.); - CHECK_GE(rmax, 0.); - if (rmin == rmax) { - // Special case where the min,max range is a point. Should be {0}. - CHECK_EQ(rmin, 0.); - CHECK_EQ(rmax, 0.); - quantization_params->zero_point = 0; - quantization_params->scale = 0.; - return; - } - // General case. - // - // First determine the scale. - const double scale = (rmax - rmin) / (qmax_double - qmin_double); - - // Zero-point computation. - // First the initial floating-point computation. The zero-point can be - // determined from solving an affine equation for any known pair - // (real value, corresponding quantized value). - // We know two such pairs: (rmin, qmin) and (rmax, qmax). - // The arithmetic error on the zero point computed from either pair - // will be roughly machine_epsilon * (sum of absolute values of terms) - // so we want to use the variant that adds the smaller terms. - const double zero_point_from_min = qmin_double - rmin / scale; - const double zero_point_from_max = qmax_double - rmax / scale; - const double zero_point_from_min_error = - std::abs(qmin_double) + std::abs(rmin / scale); - const double zero_point_from_max_error = - std::abs(qmax_double) + std::abs(rmax / scale); - - const double zero_point_double = - zero_point_from_min_error < zero_point_from_max_error - ? zero_point_from_min - : zero_point_from_max; - - // Now we need to nudge the zero point to be an integer - // (our zero points are integer, and this is motivated by the requirement - // to be able to represent the real value "0" exactly as a quantized value, - // which is required in multiple places, for example in Im2col with SAME - // padding). - Integer nudged_zero_point = 0; - if (zero_point_double < qmin_double) { - nudged_zero_point = qmin; - } else if (zero_point_double > qmax_double) { - nudged_zero_point = qmax; - } else { - nudged_zero_point = static_cast(std::round(zero_point_double)); - } - // The zero point should always be in the range of quantized value, - // [qmin, qmax]. - CHECK_GE(nudged_zero_point, qmin); - CHECK_LE(nudged_zero_point, qmax); - - // Finally, store the result nudged quantization params. - quantization_params->zero_point = nudged_zero_point; - quantization_params->scale = scale; + *quantization_params = + ::tflite::ChooseQuantizationParams(rmin, rmax); } void CheckIsReadyForQuantization(const Model& model); diff --git a/tensorflow/contrib/lite/tools/BUILD b/tensorflow/contrib/lite/tools/BUILD index 999ccf2ebc009b6b7c50a9a2d1667d69a3f690e7..b5abbc0712599814e078d19bc015bc7bf1812f95 100644 --- a/tensorflow/contrib/lite/tools/BUILD +++ b/tensorflow/contrib/lite/tools/BUILD @@ -4,6 +4,7 @@ package(default_visibility = [ licenses(["notice"]) # Apache 2.0 +load("//tensorflow/contrib/lite:special_rules.bzl", "tflite_portable_test_suite") load("//tensorflow:tensorflow.bzl", "tf_cc_binary") py_binary( @@ -45,7 +46,15 @@ tf_cc_binary( "//tensorflow/contrib/lite:framework", "//tensorflow/contrib/lite:string_util", "//tensorflow/contrib/lite/kernels:builtin_ops", - ], + ] + select({ + "//tensorflow:android": [ + "//tensorflow/core:android_tensorflow_lib", + ], + "//conditions:default": [ + "//tensorflow/core:framework_internal", + "//tensorflow/core:lib", + ], + }), ) cc_library( @@ -111,6 +120,9 @@ cc_test( name = "verifier_test", size = "small", srcs = ["verifier_test.cc"], + tags = [ + "tflite_not_portable", + ], deps = [ ":mutable_op_resolver", ":verifier", @@ -124,3 +136,5 @@ cc_test( "@flatbuffers", ], ) + +tflite_portable_test_suite() diff --git a/tensorflow/contrib/lite/tools/benchmark_model.cc b/tensorflow/contrib/lite/tools/benchmark_model.cc index 6ae3ab57294a92162b15f326630ac202a9ba2a82..93c80e0f5e021f76bff6858b0ea3370724393d6d 100644 --- a/tensorflow/contrib/lite/tools/benchmark_model.cc +++ b/tensorflow/contrib/lite/tools/benchmark_model.cc @@ -1,4 +1,4 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. @@ -25,36 +25,89 @@ limitations under the License. #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/string_util.h" #include "tensorflow/contrib/lite/tools/mutable_op_resolver.h" +#include "tensorflow/core/lib/strings/str_util.h" +#include "tensorflow/core/platform/env.h" +#include "tensorflow/core/platform/init_main.h" +#include "tensorflow/core/platform/logging.h" +#include "tensorflow/core/util/command_line_flags.h" #ifdef TFLITE_CUSTOM_OPS_HEADER void RegisterSelectedOps(::tflite::MutableOpResolver* resolver); #endif -#define LOG(x) std::cerr +namespace tflite { -#define CHECK(x) \ - if (!(x)) { \ - LOG(ERROR) << #x << "failed"; \ - exit(1); \ +using ::tensorflow::Env; +using ::tensorflow::str_util::Split; +using ::tensorflow::str_util::SplitAndParseAsFloats; +using ::tensorflow::str_util::SplitAndParseAsInts; + +struct InputLayerInfo { + string name; + TfLiteType data_type; + std::vector shape; + // Note that initialization_values is currently unused. + std::vector initialization_values; +}; + +template +void FillRandomValue(T* ptr, const std::vector& sizes, + const std::function& random_func) { + int num_elements = 1; + for (int dim : sizes) { + num_elements *= dim; + } + for (int i = 0; i < num_elements; ++i) { + *ptr++ = random_func(); } +} -namespace tensorflow { -namespace benchmark_tflite_model { +void FillRandomString(tflite::DynamicBuffer* buffer, + const std::vector& sizes, + const std::function& random_func) { + int num_elements = 1; + for (int dim : sizes) { + num_elements *= dim; + } + for (int i = 0; i < num_elements; ++i) { + auto str = random_func(); + buffer->AddString(str.data(), str.length()); + } +} -std::unique_ptr model; -std::unique_ptr interpreter; +TfLiteType TfLiteTypeFromString(const string& input_layer_type) { + if (input_layer_type == "string") + return kTfLiteString; + else if (input_layer_type == "float") + return kTfLiteFloat32; + else if (input_layer_type == "uint8") + return kTfLiteUInt8; + else if (input_layer_type == "int32") + return kTfLiteInt32; + else if (input_layer_type == "int64") + return kTfLiteInt64; + else + return kTfLiteNoType; +} -void InitImpl(const std::string& graph, const std::vector& sizes, - const std::string& input_layer_type, int num_threads) { - CHECK(graph.c_str()); +std::vector ShapeFromTfLiteTensor(TfLiteTensor* t) { + std::vector result; + result.reserve(t->dims->size); + for (int i = 0; i < t->dims->size; ++i) { + result.push_back(t->dims->data[i]); + } + CHECK(!result.empty()) << "Found no shapes in model"; + return result; +} - model = tflite::FlatBufferModel::BuildFromFile(graph.c_str()); +bool CreateInterpreter(const string& graph, + std::unique_ptr* model, + std::unique_ptr* interpreter) { + *model = tflite::FlatBufferModel::BuildFromFile(graph.c_str()); if (!model) { - LOG(FATAL) << "Failed to mmap model " << graph; + std::cerr << "Failed to load model " << graph << std::endl; + return false; } - LOG(INFO) << "Loaded model " << graph; - model->error_reporter(); - LOG(INFO) << "resolved reporter"; #ifdef TFLITE_CUSTOM_OPS_HEADER tflite::MutableOpResolver resolver; @@ -63,34 +116,360 @@ void InitImpl(const std::string& graph, const std::vector& sizes, tflite::ops::builtin::BuiltinOpResolver resolver; #endif - tflite::InterpreterBuilder(*model, resolver)(&interpreter); - if (!interpreter) { - LOG(FATAL) << "Failed to construct interpreter"; + tflite::InterpreterBuilder(*(model->get()), resolver)(interpreter); + if (!(*interpreter)) { + std::cerr << "Failed to construct interpreter" << std::endl; + return false; } + return true; +} + +bool PrepareInterpreter(const std::vector inputs, + int num_threads, bool use_nnapi, + Interpreter* interpreter) { if (num_threads != -1) { interpreter->SetNumThreads(num_threads); } - int input = interpreter->inputs()[0]; + interpreter->UseNNAPI(use_nnapi); - if (input_layer_type != "string") { - interpreter->ResizeInputTensor(input, sizes); + // Check that all names and types match + for (const InputLayerInfo& input : inputs) { + for (int i : interpreter->inputs()) { + TfLiteTensor* t = interpreter->tensor(i); + CHECK_EQ(t->name, input.name) + << "Tensor # " << i << " is named " << t->name + << " but flags call it " << input.name; + CHECK_EQ(t->type, input.data_type) + << "Could not match the type of input tensor " << t->name; + } + } + + // Resize all non-string tensors. + for (const InputLayerInfo& input : inputs) { + for (int i : interpreter->inputs()) { + TfLiteTensor* t = interpreter->tensor(i); + if (t->type != kTfLiteString) { + interpreter->ResizeInputTensor(i, input.shape); + } + } } if (interpreter->AllocateTensors() != kTfLiteOk) { - LOG(FATAL) << "Failed to allocate tensors!"; + std::cerr << "Failed to allocate tensors!" << std::endl; + return false; + } + + // Set the values of the input tensors. + for (int i : interpreter->inputs()) { + TfLiteTensor* t = interpreter->tensor(i); + std::vector sizes = ShapeFromTfLiteTensor(t); + + // TODO(ahentz): below we ignore the O-th dimension (number of batches). + if (t->type == kTfLiteFloat32) { + FillRandomValue( + interpreter->typed_tensor(i), + std::vector(sizes.begin() + 1, sizes.end()), + []() { return static_cast(rand()) / RAND_MAX - 0.5f; }); + } else if (t->type == kTfLiteUInt8) { + FillRandomValue( + interpreter->typed_tensor(i), + std::vector(sizes.begin() + 1, sizes.end()), + []() { return static_cast(rand()) % 255; }); + } else if (t->type == kTfLiteString) { + tflite::DynamicBuffer buffer; + FillRandomString(&buffer, sizes, []() { + return "we're have some friends over saturday to hang out in the yard"; + }); + buffer.WriteToTensor(interpreter->tensor(i)); + } else { + std::cerr << "Don't know how to populate tensor " << t->name + << " of type " << t->type << std::endl; + return false; + } + } + return true; +} + +bool PopulateInputLayerInfo(const string& names_string, + const string& shapes_string, + const string& types_string, + const string& values_string, + std::vector* info) { + std::vector names = Split(names_string, ','); + std::vector shapes = Split(shapes_string, ':'); + std::vector types = Split(types_string, ','); + std::vector values = Split(values_string, ':'); + + if (names.size() != shapes.size()) { + LOG(ERROR) << "The number of items in" + << " --input_layer_shape (" << shapes_string << ", with " + << shapes.size() << " items)" + << " must match the number of items in" + << " --input_layer (" << names_string << ", with " + << names.size() << " items)." + << " For example --input_layer=input1,input2" + << " --input_layer_shape=1,224,224,4:1,20"; + return false; + } + if (names.size() != types.size()) { + LOG(ERROR) << "The number of items in" + << " --input_layer_type (" << types_string << ", with " + << types.size() << " items)" + << " must match the number of items in" + << " --input_layer (" << names_string << ", with " + << names.size() << " items)." + << " For example --input_layer=input1,input2" + << " --input_layer_type=float,int"; + return false; + } + + for (int i = 0; i < names.size(); ++i) { + info->push_back(InputLayerInfo()); + InputLayerInfo& input = info->back(); + + input.name = names[i]; + + input.data_type = TfLiteTypeFromString(types[i]); + CHECK(input.data_type != kTfLiteNoType) + << types[i] << " was an invalid type"; + + CHECK(SplitAndParseAsInts(shapes[i], ',', &input.shape)) + << "Incorrect size string specified: " << shapes[i]; + for (int dim : input.shape) { + if (dim == -1) { + LOG(ERROR) << "Any unknown sizes in the shapes (-1's) must be replaced" + << " with the size you want to benchmark with."; + return false; + } + } + + if (i < values.size()) { + CHECK(SplitAndParseAsFloats(values[i], ',', &input.initialization_values)) + << "Incorrect initialization values string specified: " << values[i]; + } + } + + return true; +} + +bool RunBenchmark(Interpreter* interpreter, int64_t* inference_time_us) { + const int64_t start_time = Env::Default()->NowMicros(); + + if (interpreter->Invoke() != kTfLiteOk) { + std::cerr << "Failed to invoke!"; + return false; } + + const int64_t end_time = Env::Default()->NowMicros(); + *inference_time_us = end_time - start_time; + return true; +} + +class Latencies { + public: + void AddMeasurement(int64_t time_us) { + max_ = std::max(time_us, max_); + min_ = std::min(time_us, min_); + ++count_; + sum_ += time_us; + squared_sum_ += static_cast(time_us) * time_us; + } + + double avg() const { + if (count_ == 0) return std::numeric_limits::quiet_NaN(); + return static_cast(sum_) / count_; + } + + int64_t std_deviation() const { + if (count_ == 0 || min_ == max_) return 0; + return sqrt(squared_sum_ / count_ - avg() * avg()); + } + + void OutputToStream(std::ostream* stream) const { + *stream << "count=" << count_; + if (count_ == 0) return; + *stream << " min=" << min_ << " max=" << max_; + *stream << " avg=" << avg() << " std=" << std_deviation(); + } + + private: + int64_t count_ = 0; + int64_t min_ = std::numeric_limits::max(); + int64_t max_ = std::numeric_limits::min(); + int64_t sum_ = 0; + double squared_sum_ = 0; +}; + +bool TimeMultipleRuns(Interpreter* interpreter, double sleep_seconds, + int num_runs, int64* total_time_us) { + // Convert the run_delay string into a timespec. + timespec req; + req.tv_sec = static_cast(sleep_seconds); + req.tv_nsec = (sleep_seconds - req.tv_sec) * 1000000000; + + *total_time_us = 0; + + std::cout << "Running benchmark for " << num_runs + << " iterations: " << std::endl; + + Latencies latencies; + for (int i = 0; i < num_runs; ++i) { + int64_t time_us; + bool run_status = RunBenchmark(interpreter, &time_us); + latencies.AddMeasurement(time_us); + *total_time_us += time_us; + if (!run_status) { + std::cout << "Failed on run " << i << std::endl; + return false; + } + + // If requested, sleep between runs for an arbitrary amount of time. + // This can be helpful to determine the effect of mobile processor + // scaling and thermal throttling. + if (sleep_seconds > 0.0) { +#ifdef PLATFORM_WINDOWS + Sleep(sleep_seconds * 1000); +#else + nanosleep(&req, nullptr); +#endif + } + } + latencies.OutputToStream(&std::cout); + std::cout << std::endl; + + return true; } int Main(int argc, char** argv) { - InitImpl("", {}, "", 1); + using tensorflow::Flag; + using tensorflow::Flags; + + string graph; // e.g.: /data/local/tmp/tfl_inception-v1_model.fb + string input_layer_string; // e.g.: input + string input_layer_shape_string; // e.g.: 1,224,224,3 + string input_layer_type_string; // e.g.: float + string input_layer_values_string; + string output_layer_string; // e.g.: output + int num_runs = 50; + string run_delay = "-1.0"; + int num_threads = -1; + string benchmark_name = ""; + string output_prefix = ""; + int warmup_runs = 1; + bool use_nnapi = false; + + std::vector flag_list = { + Flag("graph", &graph, "graph file name"), + // All the following flags are optional, but can be used in order + // to benchmark different input shapes. + Flag("input_layer", &input_layer_string, "input layer names"), + Flag("input_layer_shape", &input_layer_shape_string, "input layer shape"), + Flag("input_layer_type", &input_layer_type_string, "input layer type"), + Flag("input_layer_values", &input_layer_values_string, + "values to initialize the inputs with"), + Flag("output_layer", &output_layer_string, "output layer name"), + Flag("num_runs", &num_runs, "number of runs"), + Flag("run_delay", &run_delay, "delay between runs in seconds"), + Flag("num_threads", &num_threads, "number of threads"), + Flag("benchmark_name", &benchmark_name, "benchmark name"), + Flag("output_prefix", &output_prefix, "benchmark output prefix"), + Flag("warmup_runs", &warmup_runs, "how many runs to initialize model"), + Flag("use_nnapi", &use_nnapi, "use nnapi api"), + }; + string usage = Flags::Usage(argv[0], flag_list); + const bool parse_result = Flags::Parse(&argc, argv, flag_list); + tensorflow::port::InitMain(argv[0], &argc, &argv); + + if (!parse_result) { + std::cerr << usage << std::endl; + return -1; + } + + std::cout << "Graph: [" << graph << "]" << std::endl; + if (!input_layer_string.empty()) { + std::cout << "Input layers: [" << input_layer_string << "]" << std::endl; + std::cout << "Input shapes: [" << input_layer_shape_string << "]" + << std::endl; + std::cout << "Input types: [" << input_layer_type_string << "]" + << std::endl; + } + if (!output_layer_string.empty()) { + std::cout << "Output layers: [" << output_layer_string << "]" << std::endl; + } + std::cout << "Num runs: [" << num_runs << "]" << std::endl; + std::cout << "Inter-run delay (seconds): [" << run_delay << "]" << std::endl; + std::cout << "Num threads: [" << num_threads << "]" << std::endl; + if (!benchmark_name.empty()) { + std::cout << "Benchmark name: [" << benchmark_name << "]" << std::endl; + std::cout << "Output prefix: [" << output_prefix << "]" << std::endl; + } + std::cout << "Warmup runs: [" << warmup_runs << "]" << std::endl; + std::cout << "Use nnapi : [" << use_nnapi << "]" << std::endl; + + if (graph.empty()) { + std::cout + << "Please specify the name of your TF Lite input file with --graph" + << std::endl; + return -1; + } + + std::vector inputs; + if (!PopulateInputLayerInfo(input_layer_string, input_layer_shape_string, + input_layer_type_string, + input_layer_values_string, &inputs)) { + return -1; + } + + int64 initialization_start_us = Env::Default()->NowMicros(); + + std::unique_ptr model; + std::unique_ptr interpreter; + if (!CreateInterpreter(graph, &model, &interpreter)) { + return -1; + } + if (!PrepareInterpreter(inputs, num_threads, use_nnapi, interpreter.get())) { + return -1; + } + + int64 initialization_end_us = Env::Default()->NowMicros(); + + const double initialization_time_s = + (initialization_end_us - initialization_start_us) / 1000000.0f; + std::cout << "Initialized session in " << initialization_time_s << "s" + << std::endl; + + const double sleep_seconds = std::strtod(run_delay.c_str(), nullptr); + + // If requested, run through the graph first to preinitialize everything + // before the benchmarking runs. + int64 warmup_time_us = 0; + if (warmup_runs > 0) { + if (!TimeMultipleRuns(interpreter.get(), sleep_seconds, warmup_runs, + &warmup_time_us)) { + std::cerr << "Warmup failed" << std::endl; + return -1; + } + } + + // Capture overall inference time without stat logging overhead. This is the + // timing data that can be compared to other libaries. + int64 no_stat_time_us = 0; + if (!TimeMultipleRuns(interpreter.get(), sleep_seconds, num_runs, + &no_stat_time_us)) { + std::cerr << "Timing failed." << std::endl; + return -1; + } + + std::cout << "Average inference timings in us: " << no_stat_time_us / num_runs + << " , Warmup: " + << (warmup_runs > 0 ? warmup_time_us / warmup_runs : 0) << ", " + << std::endl; + return 0; } -} // namespace benchmark_tflite_model -} // namespace tensorflow +} // namespace tflite -int main(int argc, char** argv) { - return tensorflow::benchmark_tflite_model::Main(argc, argv); -} +int main(int argc, char** argv) { return ::tflite::Main(argc, argv); } diff --git a/tensorflow/contrib/lite/util.cc b/tensorflow/contrib/lite/util.cc new file mode 100644 index 0000000000000000000000000000000000000000..fb4af07d060cac3a6a4e01c7d625b6db5241f10d --- /dev/null +++ b/tensorflow/contrib/lite/util.cc @@ -0,0 +1,41 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include "tensorflow/contrib/lite/util.h" + +namespace tflite { + +TfLiteIntArray* ConvertVectorToTfLiteIntArray(const std::vector& input) { + return ConvertArrayToTfLiteIntArray(input.size(), input.data()); +} + +TfLiteIntArray* ConvertArrayToTfLiteIntArray(const int rank, const int* dims) { + TfLiteIntArray* output = TfLiteIntArrayCreate(rank); + for (size_t i = 0; i < rank; i++) { + output->data[i] = dims[i]; + } + return output; +} + +bool EqualArrayAndTfLiteIntArray(const TfLiteIntArray* a, const int b_size, + const int* b) { + if (!a) return false; + if (a->size != b_size) return false; + for (int i = 0; i < a->size; ++i) { + if (a->data[i] != b[i]) return false; + } + return true; +} + +} // namespace tflite diff --git a/tensorflow/contrib/lite/util.h b/tensorflow/contrib/lite/util.h new file mode 100644 index 0000000000000000000000000000000000000000..a34db35823104414cce028b9119397da085d05b1 --- /dev/null +++ b/tensorflow/contrib/lite/util.h @@ -0,0 +1,40 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +// This file provides general C++ utility functions in TFLite. +// For example: Converting between `TfLiteIntArray`, `std::vector` and +// Flatbuffer vectors. These functions can't live in `context.h` since it's pure +// C. + +#ifndef TENSORFLOW_CONTRIB_LITE_UTIL_H_ +#define TENSORFLOW_CONTRIB_LITE_UTIL_H_ + +#include +#include "tensorflow/contrib/lite/context.h" + +namespace tflite { + +// Converts a `std::vector` to a `TfLiteIntArray`. +TfLiteIntArray* ConvertVectorToTfLiteIntArray(const std::vector& input); + +TfLiteIntArray* ConvertArrayToTfLiteIntArray(const int rank, const int* dims); + +// Checks whether a `TfLiteIntArray` and an int array have matching elements. +bool EqualArrayAndTfLiteIntArray(const TfLiteIntArray* a, const int b_size, + const int* b); + +} // namespace tflite + +#endif // TENSORFLOW_CONTRIB_LITE_UTIL_H_ diff --git a/tensorflow/contrib/lite/util_test.cc b/tensorflow/contrib/lite/util_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..04579c53aa4835c47d812c89a1554a0d2f2f30b8 --- /dev/null +++ b/tensorflow/contrib/lite/util_test.cc @@ -0,0 +1,50 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include +#include +#include + +#include "tensorflow/contrib/lite/context.h" +#include "tensorflow/contrib/lite/util.h" + +namespace tflite { +namespace { + +TEST(ConvertVectorToTfLiteIntArray, TestWithVector) { + std::vector input = {1, 2}; + TfLiteIntArray* output = ConvertVectorToTfLiteIntArray(input); + ASSERT_NE(output, nullptr); + EXPECT_EQ(output->size, 2); + EXPECT_EQ(output->data[0], 1); + EXPECT_EQ(output->data[1], 2); + TfLiteIntArrayFree(output); +} + +TEST(ConvertVectorToTfLiteIntArray, TestWithEmptyVector) { + std::vector input; + TfLiteIntArray* output = ConvertVectorToTfLiteIntArray(input); + ASSERT_NE(output, nullptr); + EXPECT_EQ(output->size, 0); + TfLiteIntArrayFree(output); +} + +} // namespace +} // namespace tflite + +int main(int argc, char** argv) { + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} diff --git a/tensorflow/contrib/lookup/BUILD b/tensorflow/contrib/lookup/BUILD index 8ca03f4193f260ce32f942ccaf76a8260b282156..0a6edc33c57435e9466b27cb1d33570d7a98cb12 100644 --- a/tensorflow/contrib/lookup/BUILD +++ b/tensorflow/contrib/lookup/BUILD @@ -46,6 +46,7 @@ tf_py_test( "//tensorflow/python:variables", ], grpc_enabled = True, + tags = ["no_windows"], # TODO: needs investigation on Windows ) filegroup( diff --git a/tensorflow/contrib/lookup/lookup_ops.py b/tensorflow/contrib/lookup/lookup_ops.py index a430dac4ec43ce31f0b5aaae5e7b0b51d25c9632..62f1c810fc72ba7e27c4553006c947f8fa0ef629 100644 --- a/tensorflow/contrib/lookup/lookup_ops.py +++ b/tensorflow/contrib/lookup/lookup_ops.py @@ -341,23 +341,21 @@ class MutableHashTable(LookupInterface): # training to work correctly. Use the node name if no shared_name has been # explicitly specified. use_node_name_sharing = checkpoint and shared_name is None - # pylint: disable=protected-access if self._default_value.get_shape().ndims == 0: - self._table_ref = gen_lookup_ops._mutable_hash_table_v2( + self._table_ref = gen_lookup_ops.mutable_hash_table_v2( shared_name=shared_name, use_node_name_sharing=use_node_name_sharing, key_dtype=key_dtype, value_dtype=value_dtype, name=name) else: - self._table_ref = gen_lookup_ops._mutable_hash_table_of_tensors_v2( + self._table_ref = gen_lookup_ops.mutable_hash_table_of_tensors_v2( shared_name=shared_name, use_node_name_sharing=use_node_name_sharing, key_dtype=key_dtype, value_dtype=value_dtype, value_shape=self._default_value.get_shape(), name=name) - # pylint: enable=protected-access super(MutableHashTable, self).__init__(key_dtype, value_dtype, self._table_ref.op.name.split( "/")[-1]) @@ -378,9 +376,7 @@ class MutableHashTable(LookupInterface): with ops.name_scope(name, "%s_Size" % self._name, [self._table_ref]) as name: with ops.colocate_with(self._table_ref): - - # pylint: disable=protected-access - return gen_lookup_ops._lookup_table_size_v2(self._table_ref, name=name) + return gen_lookup_ops.lookup_table_size_v2(self._table_ref, name=name) def lookup(self, keys, name=None): """Looks up `keys` in a table, outputs the corresponding values. @@ -406,8 +402,7 @@ class MutableHashTable(LookupInterface): with ops.name_scope(name, "%s_lookup_table_find" % self._name, (self._table_ref, keys, self._default_value)) as name: with ops.colocate_with(self._table_ref): - # pylint: disable=protected-access - values = gen_lookup_ops._lookup_table_find_v2( + values = gen_lookup_ops.lookup_table_find_v2( self._table_ref, keys, self._default_value, name=name) values.set_shape(keys.get_shape().concatenate(self._value_shape)) @@ -437,7 +432,7 @@ class MutableHashTable(LookupInterface): [self._table_ref, keys, values]) as name: with ops.colocate_with(self._table_ref): # pylint: disable=protected-access - op = gen_lookup_ops._lookup_table_insert_v2( + op = gen_lookup_ops.lookup_table_insert_v2( self._table_ref, keys, values, name=name) return op @@ -454,8 +449,7 @@ class MutableHashTable(LookupInterface): with ops.name_scope(name, "%s_lookup_table_export_values" % self._name, [self._table_ref]) as name: with ops.colocate_with(self._table_ref): - # pylint: disable=protected-access - exported_keys, exported_values = gen_lookup_ops._lookup_table_export_v2( + exported_keys, exported_values = gen_lookup_ops.lookup_table_export_v2( self._table_ref, self._key_dtype, self._value_dtype, name=name) exported_values.set_shape(exported_keys.get_shape().concatenate( @@ -477,7 +471,7 @@ class MutableHashTable(LookupInterface): def restore(self, restored_tensors, unused_restored_shapes): # pylint: disable=protected-access with ops.colocate_with(self.op._table_ref): - return gen_lookup_ops._lookup_table_import_v2( + return gen_lookup_ops.lookup_table_import_v2( self.op._table_ref, restored_tensors[0], restored_tensors[1]) @@ -551,8 +545,7 @@ class MutableDenseHashTable(LookupInterface): # explicitly specified. use_node_name_sharing = checkpoint and shared_name is None empty_key = ops.convert_to_tensor(empty_key, dtype=key_dtype) - # pylint: disable=protected-access - self._table_ref = gen_lookup_ops._mutable_dense_hash_table_v2( + self._table_ref = gen_lookup_ops.mutable_dense_hash_table_v2( empty_key=empty_key, shared_name=shared_name, use_node_name_sharing=use_node_name_sharing, @@ -560,7 +553,6 @@ class MutableDenseHashTable(LookupInterface): value_shape=self._value_shape, initial_num_buckets=initial_num_buckets, name=name) - # pylint: enable=protected-access super(MutableDenseHashTable, self).__init__( key_dtype, value_dtype, self._table_ref.op.name.split("/")[-1]) @@ -580,8 +572,7 @@ class MutableDenseHashTable(LookupInterface): with ops.name_scope(name, "%s_Size" % self._name, [self._table_ref]) as name: with ops.colocate_with(self._table_ref): - # pylint: disable=protected-access - return gen_lookup_ops._lookup_table_size_v2(self._table_ref, name=name) + return gen_lookup_ops.lookup_table_size_v2(self._table_ref, name=name) def lookup(self, keys, name=None): """Looks up `keys` in a table, outputs the corresponding values. @@ -607,8 +598,7 @@ class MutableDenseHashTable(LookupInterface): with ops.name_scope(name, "%s_lookup_table_find" % self._name, [self._table_ref, keys]) as name: with ops.colocate_with(self._table_ref): - # pylint: disable=protected-access - values = gen_lookup_ops._lookup_table_find_v2( + values = gen_lookup_ops.lookup_table_find_v2( self._table_ref, keys, self._default_value, name=name) if keys.get_shape().ndims is not None and keys.get_shape().ndims > 0: @@ -640,8 +630,7 @@ class MutableDenseHashTable(LookupInterface): with ops.name_scope(name, "%s_lookup_table_insert" % self._name, [self._table_ref, keys, values]) as name: with ops.colocate_with(self._table_ref): - # pylint: disable=protected-access - op = gen_lookup_ops._lookup_table_insert_v2( + op = gen_lookup_ops.lookup_table_insert_v2( self._table_ref, keys, values, name=name) return op @@ -658,8 +647,7 @@ class MutableDenseHashTable(LookupInterface): with ops.name_scope(name, "%s_lookup_table_export_values" % self._name, [self._table_ref]) as name: with ops.colocate_with(self._table_ref): - # pylint: disable=protected-access - exported_keys, exported_values = gen_lookup_ops._lookup_table_export_v2( + exported_keys, exported_values = gen_lookup_ops.lookup_table_export_v2( self._table_ref, self._key_dtype, self._value_dtype, name=name) exported_values.set_shape(exported_keys.get_shape().concatenate( @@ -681,5 +669,5 @@ class MutableDenseHashTable(LookupInterface): def restore(self, restored_tensors, unused_restored_shapes): # pylint: disable=protected-access with ops.colocate_with(self.op._table_ref): - return gen_lookup_ops._lookup_table_import_v2( + return gen_lookup_ops.lookup_table_import_v2( self.op._table_ref, restored_tensors[0], restored_tensors[1]) diff --git a/tensorflow/contrib/makefile/Makefile b/tensorflow/contrib/makefile/Makefile index 81327407d44b4317b7aecb964a689a35aa35c163..05e8d9064bea748c935859f5f9b4c7e646f504cf 100644 --- a/tensorflow/contrib/makefile/Makefile +++ b/tensorflow/contrib/makefile/Makefile @@ -677,6 +677,7 @@ endif # TEGRA TF_CC_SRCS := $(filter-out $(CORE_CC_EXCLUDE_SRCS), $(CORE_CC_ALL_SRCS)) # Add in any extra files that don't fit the patterns easily TF_CC_SRCS += tensorflow/contrib/makefile/downloads/fft2d/fftsg.c +TF_CC_SRCS += tensorflow/core/common_runtime/gpu/gpu_id_manager.cc # Also include the op and kernel definitions. TF_CC_SRCS += $(shell cat $(MAKEFILE_DIR)/tf_op_files.txt) PBT_CC_SRCS := $(shell cat $(MAKEFILE_DIR)/tf_pb_text_files.txt) diff --git a/tensorflow/contrib/makefile/README.md b/tensorflow/contrib/makefile/README.md index 995230dfa848532dc2a50b85f58d19ba264f293e..6c3b02e12b3082be8bfcc316c4c6122931eb5f76 100644 --- a/tensorflow/contrib/makefile/README.md +++ b/tensorflow/contrib/makefile/README.md @@ -194,6 +194,8 @@ with: srcs = glob(["libs/arm64-v8a/*.so"]), ``` +If you are building for Android TV (Shield TV devices), replace "portrait" with "landscape" for android:screenOrientation in all four activities in tensorflow/examples/android/AndroidManifest.xml + Then run: ```bash # Create dir for native libs diff --git a/tensorflow/contrib/makefile/build_all_ios.sh b/tensorflow/contrib/makefile/build_all_ios.sh index 2d9979183975e6a17527b40ef5ee1795ced44a7b..0a458a27b3ac9b1a24b0f42de2f0166d515e8cd9 100755 --- a/tensorflow/contrib/makefile/build_all_ios.sh +++ b/tensorflow/contrib/makefile/build_all_ios.sh @@ -80,10 +80,9 @@ if [[ ! -z "${OPTIMIZE_FOR_GRAPH}" ]]; then fi else echo "${PRNT_SLCTV_BIN} found. Using it" - ${PRNT_SLCTV_BIN} --graphs=${OPTIMIZE_FOR_GRAPH} > ${TOP_SRCDIR}/tensorflow/core/framework/ops_to_register.h - fi + ${PRNT_SLCTV_BIN} --graphs=${OPTIMIZE_FOR_GRAPH} > ${TOP_SRCDIR}/tensorflow/core/framework/ops_to_register.h fi if [[ "${ONLY_MAKE_TENSORFLOW}" != "true" ]]; then @@ -111,7 +110,7 @@ if [[ -z "${BUILD_ARCH}" ]]; then TARGET_NSYNC_LIB=`tensorflow/contrib/makefile/compile_nsync.sh -t ios` else # arch specified so build just that - TARGET_NSYNC_LIB=`tensorflow/contrib/makefile/compile_nsync.sh -t ios -a ${BUILD_ARCH}` + TARGET_NSYNC_LIB=`tensorflow/contrib/makefile/compile_nsync.sh -t ios -a "${BUILD_ARCH}"` fi export HOST_NSYNC_LIB TARGET_NSYNC_LIB diff --git a/tensorflow/contrib/metrics/python/ops/metric_ops.py b/tensorflow/contrib/metrics/python/ops/metric_ops.py index 31e274c5fd7c670458b1b40a4f58c668a23776c7..81f05e7ce587ed1da67a17efbbeb809dbe7fc0b3 100644 --- a/tensorflow/contrib/metrics/python/ops/metric_ops.py +++ b/tensorflow/contrib/metrics/python/ops/metric_ops.py @@ -1263,7 +1263,7 @@ def _compute_placement_auc(labels, predictions, weights, alpha, weights_for_true = ordered_weights * float_labels_for_true weights_for_false = ordered_weights * float_labels_for_false - # For each set of weights with the same segmented indices, we add up the + # For each set of weights with the same segmented indices, we add up the # weight values. Note that for each label, we deliberately rely on weights # for the opposite label. weight_totals_for_true = math_ops.segment_sum(weights_for_false, @@ -3646,8 +3646,8 @@ def cohen_kappa(labels, `updates_collections` are not a list or tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): - raise RuntimeError('tf.contrib.metrics.cohen_kappa is not supported' + if context.executing_eagerly(): + raise RuntimeError('tf.contrib.metrics.cohen_kappa is not supported ' 'when eager execution is enabled.') if num_classes < 2: raise ValueError('`num_classes` must be >= 2.' diff --git a/tensorflow/contrib/metrics/python/ops/metric_ops_test.py b/tensorflow/contrib/metrics/python/ops/metric_ops_test.py index b387f26c0195432fb972dac450d2919bdaa702a1..33eb655fb660f0ecdfe1c5ab870d7f17690ae3ff 100644 --- a/tensorflow/contrib/metrics/python/ops/metric_ops_test.py +++ b/tensorflow/contrib/metrics/python/ops/metric_ops_test.py @@ -1802,9 +1802,9 @@ class StreamingAUCTest(test.TestCase): auc, update_op = metrics.streaming_auc(predictions, labels, curve='PR') sess.run(variables.local_variables_initializer()) - self.assertAlmostEqual(0.54166603, sess.run(update_op), delta=1e-3) + self.assertAlmostEqual(0.79166, sess.run(update_op), delta=1e-3) - self.assertAlmostEqual(0.54166603, auc.eval(), delta=1e-3) + self.assertAlmostEqual(0.79166, auc.eval(), delta=1e-3) def testAnotherAUCPRSpecialCase(self): with self.test_session() as sess: @@ -1816,9 +1816,9 @@ class StreamingAUCTest(test.TestCase): auc, update_op = metrics.streaming_auc(predictions, labels, curve='PR') sess.run(variables.local_variables_initializer()) - self.assertAlmostEqual(0.44365042, sess.run(update_op), delta=1e-3) + self.assertAlmostEqual(0.610317, sess.run(update_op), delta=1e-3) - self.assertAlmostEqual(0.44365042, auc.eval(), delta=1e-3) + self.assertAlmostEqual(0.610317, auc.eval(), delta=1e-3) def testThirdAUCPRSpecialCase(self): with self.test_session() as sess: @@ -1830,9 +1830,9 @@ class StreamingAUCTest(test.TestCase): auc, update_op = metrics.streaming_auc(predictions, labels, curve='PR') sess.run(variables.local_variables_initializer()) - self.assertAlmostEqual(0.73611039, sess.run(update_op), delta=1e-3) + self.assertAlmostEqual(0.90277, sess.run(update_op), delta=1e-3) - self.assertAlmostEqual(0.73611039, auc.eval(), delta=1e-3) + self.assertAlmostEqual(0.90277, auc.eval(), delta=1e-3) def testAllIncorrect(self): inputs = np.random.randint(0, 2, size=(100, 1)) @@ -1865,9 +1865,9 @@ class StreamingAUCTest(test.TestCase): auc, update_op = metrics.streaming_auc(predictions, labels, curve='PR') sess.run(variables.local_variables_initializer()) - self.assertAlmostEqual(0.49999976, sess.run(update_op), 6) + self.assertAlmostEqual(1, sess.run(update_op), 6) - self.assertAlmostEqual(0.49999976, auc.eval(), 6) + self.assertAlmostEqual(1, auc.eval(), 6) def testWithMultipleUpdates(self): num_samples = 1000 @@ -6888,8 +6888,7 @@ class CohenKappaTest(test.TestCase): # [[0, 25, 0], # [0, 0, 25], # [25, 0, 0]] - # Calculated by v0.19: sklearn.metrics.cohen_kappa_score( - # labels, predictions) + # Calculated by v0.19: sklearn.metrics.cohen_kappa_score(labels, predictions) expect = -0.333333333333 with self.test_session() as sess: @@ -6948,8 +6947,7 @@ class CohenKappaTest(test.TestCase): weights_t: weights[batch_start:batch_end] }) # Calculated by v0.19: sklearn.metrics.cohen_kappa_score( - # labels_np, predictions_np, - # sample_weight=weights_np) + # labels_np, predictions_np, sample_weight=weights_np) expect = 0.289965397924 self.assertAlmostEqual(expect, kappa.eval(), 5) diff --git a/tensorflow/contrib/model_pruning/README.md b/tensorflow/contrib/model_pruning/README.md index d286750c257e9a78a82c95c1fc872b3ca6972203..52b659c69fdfc507e6259e928d79c65471f2f025 100644 --- a/tensorflow/contrib/model_pruning/README.md +++ b/tensorflow/contrib/model_pruning/README.md @@ -134,7 +134,7 @@ $ bazel-bin/$examples_dir/cifar10/cifar10_eval --run_once ### Block Sparsity -For some hardware architectures, it may be beneficial to induce spatially correlated sparsity. To train models in which the weight tensors have block sparse structure, set *block_height* and *block_width* hyperparameters to the desired block configuration (2x2, 4x4, 4x1, 1x8, etc). Currently, block sparsity is supported for weight tensors with rank 2 only. The matrix is partitioned into non-overlapping blocks of size *[block_height, block_dim]* and the either the average or max absolute value in this block is taken as a proxy for the entire block (set by *block_pooling_function* hyperparameter). +For some hardware architectures, it may be beneficial to induce spatially correlated sparsity. To train models in which the weight tensors have block sparse structure, set *block_height* and *block_width* hyperparameters to the desired block configuration (2x2, 4x4, 4x1, 1x8, etc). Currently, block sparsity is only supported for weight tensors which can be squeezed to rank 2. The matrix is partitioned into non-overlapping blocks of size *[block_height, block_dim]* and the either the average or max absolute value in this block is taken as a proxy for the entire block (set by *block_pooling_function* hyperparameter). The convolution layer tensors are always pruned used block dimensions of [1,1]. ## References diff --git a/tensorflow/contrib/model_pruning/python/pruning.py b/tensorflow/contrib/model_pruning/python/pruning.py index d16af9da19816211ee22f6ea48a347f0b9a4e612..86963be4b8aee396704752bab87e0a6b49ab1a49 100644 --- a/tensorflow/contrib/model_pruning/python/pruning.py +++ b/tensorflow/contrib/model_pruning/python/pruning.py @@ -523,7 +523,8 @@ class Pruning(object): """Performs block-granular masking of the weights. Block pruning occurs only if the block_height or block_width is > 1 and - if the weight tensor has ndims = 2. Otherwise, elementwise pruning occurs. + if the weight tensor, when squeezed, has ndims = 2. Otherwise, elementwise + pruning occurs. Args: weights: The weight tensor that needs to be masked. threshold: The current threshold value. The function will compute a new @@ -540,7 +541,8 @@ class Pruning(object): Raises: ValueError: if block pooling function is not AVG or MAX """ - if weights.get_shape().ndims != 2 or self._block_dim == [1, 1]: + squeezed_weights = array_ops.squeeze(weights) + if squeezed_weights.get_shape().ndims != 2 or self._block_dim == [1, 1]: return self._update_mask(weights, threshold) if self._block_pooling_function not in ['AVG', 'MAX']: @@ -549,9 +551,11 @@ class Pruning(object): with ops.name_scope(weights.op.name + '_pruning_ops'): abs_weights = math_ops.abs( - array_ops.reshape( - weights, [1, weights.get_shape()[0], - weights.get_shape()[1], 1])) + array_ops.reshape(weights, [ + 1, + squeezed_weights.get_shape()[0], + squeezed_weights.get_shape()[1], 1 + ])) pool_window = [self._block_dim[0], self._block_dim[1]] pooled_weights = nn_ops.pool( abs_weights, @@ -572,9 +576,10 @@ class Pruning(object): array_ops.ones(self._block_dim)) sliced_mask = array_ops.slice( updated_mask, [0, 0], - [weights.get_shape()[0], - weights.get_shape()[1]]) - return smoothed_threshold, sliced_mask + [squeezed_weights.get_shape()[0], + squeezed_weights.get_shape()[1]]) + return smoothed_threshold, array_ops.reshape(sliced_mask, + array_ops.shape(weights)) def _get_mask_assign_ops(self): # Make sure the assignment ops have not already been added to the list diff --git a/tensorflow/contrib/model_pruning/python/pruning_test.py b/tensorflow/contrib/model_pruning/python/pruning_test.py index 1767b4bb94a9bb56bc6a4933423ad27d8cf3ed35..89e65713197afc6ed37346cb67a6e9be3fa9290f 100644 --- a/tensorflow/contrib/model_pruning/python/pruning_test.py +++ b/tensorflow/contrib/model_pruning/python/pruning_test.py @@ -140,6 +140,23 @@ class PruningTest(test.TestCase): [0.0, -0.3, 0.0, -0.4]]) expected_mask = [[0, 0, 0, 0], [0, 0, 0, 0], [1, 1, 1, 1], [1, 1, 1, 1]] + self._blockMasking(param_list + ["block_pooling_function=MAX"], weights_max, + expected_mask) + self._blockMasking(param_list + ["block_pooling_function=AVG"], weights_avg, + expected_mask) + + def testBlockMaskingWithHigherDimensions(self): + param_list = ["block_height=2", "block_width=2", "threshold_decay=0"] + + # Weights as in testBlockMasking, but with one extra dimension. + weights_avg = constant_op.constant( + [[[0.1, 0.1, 0.2, 0.2], [0.1, 0.1, 0.2, 0.2], [0.3, 0.3, 0.4, 0.4], + [0.3, 0.3, 0.4, 0.4]]]) + weights_max = constant_op.constant( + [[[0.1, 0.0, 0.2, 0.0], [0.0, -0.1, 0.0, -0.2], [0.3, 0.0, 0.4, 0.0], + [0.0, -0.3, 0.0, -0.4]]]) + expected_mask = [[[0, 0, 0, 0], [0, 0, 0, 0], [1, 1, 1, 1], [1, 1, 1, 1]]] + self._blockMasking(param_list + ["block_pooling_function=MAX"], weights_max, expected_mask) self._blockMasking(param_list + ["block_pooling_function=AVG"], diff --git a/tensorflow/contrib/mpi/mpi_utils.h b/tensorflow/contrib/mpi/mpi_utils.h index fa297c28cb47d43ba927ab941854bd472d90b465..df055ff56731140b3bd09704c70e65f81362f763 100644 --- a/tensorflow/contrib/mpi/mpi_utils.h +++ b/tensorflow/contrib/mpi/mpi_utils.h @@ -24,6 +24,8 @@ limitations under the License. #include "tensorflow/core/lib/strings/str_util.h" +// Skip MPI C++ bindings support, this matches the usage in other places +#define OMPI_SKIP_MPICXX #include "third_party/mpi/mpi.h" #define MPI_CHECK(cmd) \ do { \ diff --git a/tensorflow/contrib/nccl/BUILD b/tensorflow/contrib/nccl/BUILD index 5ac96007df7ee08b1e32aacd28f83768859810a9..94d01efee1546feca89a7e88acedf915b1dfb3a4 100644 --- a/tensorflow/contrib/nccl/BUILD +++ b/tensorflow/contrib/nccl/BUILD @@ -52,6 +52,7 @@ tf_cuda_cc_test( "manual", "multi_gpu", "no_oss", + "noguitar", "notap", ], deps = @@ -136,6 +137,7 @@ cuda_py_test( "manual", "multi_gpu", "no_oss", + "noguitar", "notap", ], ) diff --git a/tensorflow/contrib/nccl/python/ops/nccl_ops.py b/tensorflow/contrib/nccl/python/ops/nccl_ops.py index 8dc038b9ac992de7db8b762e3697c6693099e192..794372a1f4b0dcc41bcf0da611f5bc2ec9301973 100644 --- a/tensorflow/contrib/nccl/python/ops/nccl_ops.py +++ b/tensorflow/contrib/nccl/python/ops/nccl_ops.py @@ -267,5 +267,5 @@ def _check_device(tensor, expected=None): def _check_graph_mode(): - if context.in_eager_mode(): + if context.executing_eagerly(): raise ValueError('Nccl ops are not supported in eager mode') diff --git a/tensorflow/contrib/opt/BUILD b/tensorflow/contrib/opt/BUILD index bc374d66c31922aa54542825ea4e04a444d96c5d..827279bd476f9666a972f43ad557fde6d0b6c59a 100644 --- a/tensorflow/contrib/opt/BUILD +++ b/tensorflow/contrib/opt/BUILD @@ -70,9 +70,6 @@ py_test( srcs = ["python/training/moving_average_optimizer_test.py"], srcs_version = "PY2AND3", tags = [ - "manual", - "no_oss", # b/73507407 - "notap", "notsan", # b/31055119 ], deps = [ diff --git a/tensorflow/contrib/opt/python/training/addsign_test.py b/tensorflow/contrib/opt/python/training/addsign_test.py index bd19ee3e7ac514448c6d79272abb86a154f55e9a..08d45ed73f3ae4b580d7078272e79fef22ef67c5 100644 --- a/tensorflow/contrib/opt/python/training/addsign_test.py +++ b/tensorflow/contrib/opt/python/training/addsign_test.py @@ -97,7 +97,7 @@ class AddSignTest(test.TestCase): global_step=global_step) neg_update = opt.apply_gradients(zip([-grads0, -grads1], [var0, var1]), global_step=global_step) - if context.in_graph_mode(): + if not context.executing_eagerly(): self.evaluate(variables.global_variables_initializer()) # Fetch params to validate initial values self.assertAllClose([1.0, 2.0], self.evaluate(var0)) @@ -108,13 +108,13 @@ class AddSignTest(test.TestCase): # last 3 steps with negative gradient (sign(gm) should be -1) for t in range(1, 8): if t < 5: - if context.in_graph_mode(): + if not context.executing_eagerly(): self.evaluate(update) elif t > 1: opt.apply_gradients(zip([grads0, grads1], [var0, var1]), global_step=global_step) else: - if context.in_graph_mode(): + if not context.executing_eagerly(): self.evaluate(neg_update) elif t > 1: opt.apply_gradients(zip([-grads0, -grads1], [var0, var1]), diff --git a/tensorflow/contrib/opt/python/training/powersign_test.py b/tensorflow/contrib/opt/python/training/powersign_test.py index ff7b1a72d47d8ef54980905323bcaf358c988a82..5214082dd66f00eadadad71d50f7e00b178b8c10 100644 --- a/tensorflow/contrib/opt/python/training/powersign_test.py +++ b/tensorflow/contrib/opt/python/training/powersign_test.py @@ -99,7 +99,7 @@ class PowerSignTest(test.TestCase): neg_update = opt.apply_gradients(zip([-grads0, -grads1], [var0, var1]), global_step=global_step) - if context.in_graph_mode(): + if not context.executing_eagerly(): self.evaluate(variables.global_variables_initializer()) # Fetch params to validate initial values self.assertAllClose([1.0, 2.0], self.evaluate(var0)) @@ -110,13 +110,13 @@ class PowerSignTest(test.TestCase): # last 3 steps with negative gradient (sign(gm) should be -1) for t in range(1, 8): if t < 5: - if context.in_graph_mode(): + if not context.executing_eagerly(): self.evaluate(update) elif t > 1: opt.apply_gradients(zip([grads0, grads1], [var0, var1]), global_step=global_step) else: - if context.in_graph_mode(): + if not context.executing_eagerly(): self.evaluate(neg_update) elif t > 1: opt.apply_gradients(zip([-grads0, -grads1], [var0, var1]), diff --git a/tensorflow/contrib/predictor/predictor_factories.py b/tensorflow/contrib/predictor/predictor_factories.py index 04b5d5bdf158dc6a478d7a24b538c75d1dca8d45..6e77e934fe19851eea9ed0b74eb7aecc76f6237a 100644 --- a/tensorflow/contrib/predictor/predictor_factories.py +++ b/tensorflow/contrib/predictor/predictor_factories.py @@ -53,7 +53,7 @@ def from_contrib_estimator(estimator, `Estimator`. """ if isinstance(estimator, core_estimator.Estimator): - raise TypeError('Espected estimator to be of type ' + raise TypeError('Expected estimator to be of type ' 'tf.contrib.learn.Estimator, but got type ' 'tf.python.estimator.Estimator. You likely want to call ' 'from_estimator.') @@ -88,7 +88,7 @@ def from_estimator(estimator, `Estimator`. """ if isinstance(estimator, contrib_estimator.Estimator): - raise TypeError('Espected estimator to be of type ' + raise TypeError('Expected estimator to be of type ' 'tf.python.estimator.Estimator, but got type ' 'tf.contrib.learn.Estimator. You likely want to call ' 'from_contrib_estimator.') diff --git a/tensorflow/contrib/py2tf/__init__.py b/tensorflow/contrib/py2tf/__init__.py index 379fa7fd5c2a22b5b16a21cca8c2ea8afdcaeefa..6531183cb59af774299eb767cce111d2ec6f32b4 100644 --- a/tensorflow/contrib/py2tf/__init__.py +++ b/tensorflow/contrib/py2tf/__init__.py @@ -23,6 +23,7 @@ from __future__ import print_function from tensorflow.contrib.py2tf import utils from tensorflow.contrib.py2tf.impl.api import convert +from tensorflow.contrib.py2tf.impl.api import converted_call from tensorflow.contrib.py2tf.impl.api import graph_ready from tensorflow.contrib.py2tf.impl.api import to_code from tensorflow.contrib.py2tf.impl.api import to_graph @@ -30,7 +31,8 @@ from tensorflow.contrib.py2tf.pyct.transformer import PyFlowParseError from tensorflow.python.util.all_util import remove_undocumented _allowed_symbols = [ - 'to_graph', 'to_code', 'convert', 'graph_ready', 'utils', 'PyFlowParseError' + 'to_graph', 'to_code', 'convert', 'graph_ready', 'converted_call', 'utils', + 'PyFlowParseError' ] remove_undocumented(__name__, _allowed_symbols) diff --git a/tensorflow/contrib/py2tf/converters/BUILD b/tensorflow/contrib/py2tf/converters/BUILD index 42baaaaba72c6c3dbc896e87b6e0f3c62b7f06fc..4bb6f76019739fc3b5bf4bf52e302a698693db5a 100644 --- a/tensorflow/contrib/py2tf/converters/BUILD +++ b/tensorflow/contrib/py2tf/converters/BUILD @@ -25,10 +25,13 @@ py_library( "control_flow.py", "decorators.py", "for_loops.py", + "ifexp.py", "list_comprehension.py", + "lists.py", "logical_expressions.py", "name_scopes.py", "side_effect_guards.py", + "single_return.py", ], srcs_version = "PY2AND3", visibility = ["//tensorflow:__subpackages__"], @@ -46,6 +49,7 @@ py_library( visibility = ["//tensorflow:__subpackages__"], deps = [ ":converters", + "//tensorflow/contrib/py2tf/pyct", "//tensorflow/contrib/py2tf/pyct/static_analysis", "//tensorflow/contrib/py2tf/utils", "@gast_archive//:gast", @@ -59,7 +63,6 @@ py_test( srcs_version = "PY2AND3", deps = [ ":test_lib", - "//tensorflow/contrib/py2tf/pyct", "//tensorflow/python:client_testlib", ], ) @@ -70,7 +73,6 @@ py_test( srcs_version = "PY2AND3", deps = [ ":test_lib", - "//tensorflow/contrib/py2tf/pyct", "//tensorflow/python:client_testlib", ], ) @@ -79,9 +81,9 @@ py_test( name = "builtin_functions_test", srcs = ["builtin_functions_test.py"], srcs_version = "PY2AND3", + tags = ["no_windows"], # TODO: needs investigation on Windows deps = [ ":test_lib", - "//tensorflow/contrib/py2tf/pyct", "//tensorflow/python:client_testlib", ], ) @@ -90,9 +92,10 @@ py_test( name = "call_trees_test", srcs = ["call_trees_test.py"], srcs_version = "PY2AND3", + tags = ["no_windows"], # TODO: needs investigation on Windows deps = [ ":test_lib", - "//tensorflow/contrib/py2tf/pyct", + "//tensorflow/contrib/py2tf/impl", "//tensorflow/python:client_testlib", ], ) @@ -103,7 +106,6 @@ py_test( srcs_version = "PY2AND3", deps = [ ":test_lib", - "//tensorflow/contrib/py2tf/pyct", "//tensorflow/python:client_testlib", ], ) @@ -114,7 +116,6 @@ py_test( srcs_version = "PY2AND3", deps = [ ":test_lib", - "//tensorflow/contrib/py2tf/pyct", "//tensorflow/python:client_testlib", ], ) @@ -125,7 +126,6 @@ py_test( srcs_version = "PY2AND3", deps = [ ":test_lib", - "//tensorflow/contrib/py2tf/pyct", "//tensorflow/python:client_testlib", ], ) @@ -136,7 +136,6 @@ py_test( srcs_version = "PY2AND3", deps = [ ":test_lib", - "//tensorflow/contrib/py2tf/pyct", "//tensorflow/python:client_testlib", ], ) @@ -157,7 +156,16 @@ py_test( srcs_version = "PY2AND3", deps = [ ":test_lib", - "//tensorflow/contrib/py2tf/pyct", + "//tensorflow/python:client_testlib", + ], +) + +py_test( + name = "lists_test", + srcs = ["lists_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":test_lib", "//tensorflow/python:client_testlib", ], ) @@ -168,7 +176,6 @@ py_test( srcs_version = "PY2AND3", deps = [ ":test_lib", - "//tensorflow/contrib/py2tf/pyct", "//tensorflow/python:client_testlib", ], ) @@ -182,6 +189,27 @@ py_test( "flaky", "notap", ], + deps = [ + ":test_lib", + "//tensorflow/python:client_testlib", + ], +) + +py_test( + name = "single_return_test", + srcs = ["single_return_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":test_lib", + "//tensorflow/contrib/py2tf/pyct", + "//tensorflow/python:client_testlib", + ], +) + +py_test( + name = "ifexp_test", + srcs = ["ifexp_test.py"], + srcs_version = "PY2AND3", deps = [ ":test_lib", "//tensorflow/contrib/py2tf/pyct", diff --git a/tensorflow/contrib/py2tf/converters/builtin_functions.py b/tensorflow/contrib/py2tf/converters/builtin_functions.py index e69038acedd6b7a251c3328a14f36ed107bde746..f1129ef153e6be6cbcbbf4bab63c4fe32ec77147 100644 --- a/tensorflow/contrib/py2tf/converters/builtin_functions.py +++ b/tensorflow/contrib/py2tf/converters/builtin_functions.py @@ -36,23 +36,24 @@ class BuiltinFunctionTransformer(transformer.Base): # pylint:disable=invalid-name - def _convert_len(self, node): + def _convert_builtin(self, node): template = """ - py2tf_utils.dynamic_len(args) + py2tf_utils.dynamic_builtin(func, args) """ - return templates.replace(template, args=node.args)[0].value + return templates.replace(template, func=node.func, args=node.args)[0].value def _convert_print(self, node): template = """ - py2tf_utils.call_print(args) + py2tf_utils.dynamic_print(args) """ return templates.replace(template, args=node.args)[0].value def visit_Call(self, node): self.generic_visit(node) # TODO(mdan): This won't work if the function was hidden. - if isinstance(node.func, gast.Name) and node.func.id == 'len': - return self._convert_len(node) + if isinstance(node.func, gast.Name) and node.func.id in ('len', 'range'): + return self._convert_builtin(node) + # Print needs to be handled separately because it can be read as statement. if isinstance(node.func, gast.Name) and node.func.id == 'print': return self._convert_print(node) return node diff --git a/tensorflow/contrib/py2tf/converters/call_trees.py b/tensorflow/contrib/py2tf/converters/call_trees.py index 1050ba654c63bb52c1c5e71c981a6a0baa3fc987..ca8726f9160d106ebd82e01e399e65fb77b02aab 100644 --- a/tensorflow/contrib/py2tf/converters/call_trees.py +++ b/tensorflow/contrib/py2tf/converters/call_trees.py @@ -27,6 +27,7 @@ import types import gast from tensorflow.contrib.py2tf.pyct import anno +from tensorflow.contrib.py2tf.pyct import inspect_utils from tensorflow.contrib.py2tf.pyct import parser from tensorflow.contrib.py2tf.pyct import templates from tensorflow.contrib.py2tf.pyct import transformer @@ -72,9 +73,8 @@ class CallTreeTransformer(transformer.Base): self.uncompiled_modules = uncompiled_modules self.nocompile_decorators = nocompile_decorators - # pylint:disable=invalid-name - def _resolve_name(self, node): + """Used to resolve decorator info.""" if isinstance(node, gast.Call): return self._resolve_name(node.func) if isinstance(node, gast.Name): @@ -99,7 +99,13 @@ class CallTreeTransformer(transformer.Base): (owner_type, node.attr)) return None + def _function_is_compilable(self, target_entity): + """Determines whether an entity can be compiled at all.""" + # TODO(mdan): This is just a placeholder. Implement. + return not isinstance(target_entity, types.BuiltinFunctionType) + def _should_compile(self, node, fqn): + """Determines whether an entity should be compiled in the context.""" for i in range(1, len(fqn)): if fqn[:i] in self.uncompiled_modules: return False @@ -141,33 +147,6 @@ class CallTreeTransformer(transformer.Base): return True - def _determine_function_owner(self, m): - # TODO(mdan): The parent type should be known at analysis. Use that instead. - if hasattr(m, 'im_class'): # Python 2 - return m.im_class - if hasattr(m, '__qualname__'): # Python 3 - # Object attributes: should be bound to "self". - if hasattr(m, '__self__'): - return type(m.__self__) - - # Class attributes: should have the owner name in their namespace. - qn = m.__qualname__.split('.') - if len(qn) < 2: - return None - owner_name, func_name = qn[-2:] - if func_name != m.__name__: - raise ValueError('Inconsistent names detected ' - '(__qualname__[1] = "%s", __name__ = "%s") for %s.' % - (func_name, m.__name__, m)) - if owner_name == '': - return None - if owner_name not in self.context.namespace: - raise ValueError( - 'Could not resolve name "%s" while analyzing %s. Namespace:\n%s' % - (owner_name, m, self.context.namespace)) - return self.context.namespace[owner_name] - return None - def _rename_compilable_function(self, node): assert anno.hasanno(node.func, 'live_val') assert anno.hasanno(node.func, 'fqn') @@ -182,7 +161,11 @@ class CallTreeTransformer(transformer.Base): target_fqn, live_entity=target_entity) do_rename = True else: - owner_type = self._determine_function_owner(target_entity) + if anno.hasanno(node.func, 'parent_type'): + owner_type = anno.getanno(node.func, 'parent_type') + else: + # Fallback - not reliable. + owner_type = inspect_utils.getmethodclass(target_entity) new_name, do_rename = self.context.namer.compiled_function_name( target_fqn, live_entity=target_entity, owner_type=owner_type) @@ -202,9 +185,35 @@ class CallTreeTransformer(transformer.Base): """ return templates.replace(template, func=node.func, original_args=node.args) - def _function_is_compilable(self, target_entity): - # TODO(mdan): This is just a placeholder. Implement. - return not isinstance(target_entity, types.BuiltinFunctionType) + def _insert_dynamic_conversion(self, node): + """Inlines a dynamic conversion for a dynamic function.""" + # TODO(mdan): Pass information on the statically compiled functions. + # Having access to the statically compiled functions can help avoid + # unnecessary compilation. + # For example, this would lead to function `a` being compiled twice: + # + # def a(): + # v = b + # b() + # def b(): + # a() + # + # This is really a problem with recursive calls, which currently can + # only be gated by a static condition, and should be rare. + # TODO(mdan): It probably makes sense to use dynamic conversion every time. + # Before we could convert all the time though, we'd need a reasonable + # caching mechanism. + template = """ + py2tf_api.converted_call(func, True, False, {}, original_args) + """ + call_expr = templates.replace( + template, func=node.func, original_args=node.args) + new_call = call_expr[0].value + # TODO(mdan): Improve the template mechanism to better support this. + new_call.keywords = node.keywords + return new_call + + # pylint:disable=invalid-name def visit_Expr(self, node): if isinstance(node.value, gast.Call): @@ -245,9 +254,9 @@ class CallTreeTransformer(transformer.Base): raise NotImplementedError('py_func with return values') else: if self.context.recursive: - raise NotImplementedError('Could not resolve target function.') + node = self._insert_dynamic_conversion(node) else: - # TODO(mdan): Double check. Is this reachable code? + # Unresolved functions are allowed in non-recursive mode. pass return node diff --git a/tensorflow/contrib/py2tf/converters/call_trees_test.py b/tensorflow/contrib/py2tf/converters/call_trees_test.py index 777648dc0b31863227262fbf931aba680bb4ed98..d482a9ef7897388839bbf8f9e4bfc5839d42b2d7 100644 --- a/tensorflow/contrib/py2tf/converters/call_trees_test.py +++ b/tensorflow/contrib/py2tf/converters/call_trees_test.py @@ -47,6 +47,21 @@ class CallTreesTest(converter_test_base.TestCase): result.renamed_test_fn_1 = renamed_test_fn_1 self.assertEquals(3, result.test_fn_2(1)) + def test_dynamic_function(self): + + def test_fn_1(): + raise ValueError('This should be masked by the mock.') + + def test_fn_2(f): + return f() + 3 + + node = self.parse_and_analyze(test_fn_2, {}) + node = call_trees.transform(node, self.ctx, (), ()) + + with self.compiled(node) as result: + # 10 = 7 (from the mock) + 3 (from test_fn_2) + self.assertEquals(10, result.test_fn_2(test_fn_1)) + def test_simple_methods(self): class TestClass(object): @@ -59,6 +74,7 @@ class CallTreesTest(converter_test_base.TestCase): node = self.parse_and_analyze( TestClass.test_fn_2, {'TestClass': TestClass}, + namer=converter_test_base.FakeNoRenameNamer(), arg_types={'self': (TestClass.__name__, TestClass)}) node = call_trees.transform(node, self.ctx, (), ()) diff --git a/tensorflow/contrib/py2tf/converters/control_flow.py b/tensorflow/contrib/py2tf/converters/control_flow.py index d53e3e4fd6d87004cbe55bd430346ad263e898ea..762c26f0c77e13c077761ceec41cb29db9149a35 100644 --- a/tensorflow/contrib/py2tf/converters/control_flow.py +++ b/tensorflow/contrib/py2tf/converters/control_flow.py @@ -171,6 +171,14 @@ class ControlFlowTransformer(transformer.Base): all_referenced = body_scope.referenced state = list(body_closure) + if not state: + # TODO(mdan): Implement this properly. + # To complete this statement, 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 while loop: no outputs') + state_ssf = [ self.context.namer.new_symbol(s.ssf(), all_referenced) for s in state ] diff --git a/tensorflow/contrib/py2tf/converters/converter_test_base.py b/tensorflow/contrib/py2tf/converters/converter_test_base.py index afa5c2f96fb55302e67e5ecac3532cb87827871a..8c08c5492a4b10d4abb0ec3b19b39d5b17e41a0a 100644 --- a/tensorflow/contrib/py2tf/converters/converter_test_base.py +++ b/tensorflow/contrib/py2tf/converters/converter_test_base.py @@ -25,6 +25,7 @@ from tensorflow.contrib.py2tf import utils from tensorflow.contrib.py2tf.pyct import compiler from tensorflow.contrib.py2tf.pyct import context from tensorflow.contrib.py2tf.pyct import parser +from tensorflow.contrib.py2tf.pyct import pretty_printer from tensorflow.contrib.py2tf.pyct import qual_names from tensorflow.contrib.py2tf.pyct.static_analysis import activity from tensorflow.contrib.py2tf.pyct.static_analysis import live_values @@ -52,26 +53,49 @@ class FakeNamer(object): return ('renamed_%s' % '_'.join(original_fqn)), True +class FakeNoRenameNamer(FakeNamer): + + def compiled_function_name(self, original_fqn, **_): + return str(original_fqn), False + + class TestCase(test.TestCase): """Base class for unit tests in this module. Contains relevant utilities.""" @contextlib.contextmanager def compiled(self, node, *symbols): - source = '' + source = None + + self.dynamic_calls = [] + def converted_call(*args): + """Mock version of api.converted_call.""" + self.dynamic_calls.append(args) + return 7 + try: result, source = compiler.ast_to_object(node) - result.tf = self.make_fake_tf(*symbols) + result.tf = self.make_fake_mod('fake_tf', *symbols) result.py2tf_utils = utils + result.py2tf_api = self.make_fake_mod('fake_api', converted_call) yield result except Exception: # pylint:disable=broad-except - print('Offending compiled code:\n%s' % source) + if source is None: + print('Offending AST:\n%s' % pretty_printer.fmt(node, color=False)) + else: + print('Offending compiled code:\n%s' % source) raise - def make_fake_tf(self, *symbols): - fake_tf = imp.new_module('fake_tf') + def make_fake_mod(self, name, *symbols): + fake_mod = imp.new_module(name) for s in symbols: - setattr(fake_tf, s.__name__, s) - return fake_tf + if hasattr(s, '__name__'): + setattr(fake_mod, s.__name__, s) + elif hasattr(s, 'name'): + # This is a bit of a hack, but works for things like tf.int32 + setattr(fake_mod, s.name, s) + else: + raise ValueError('can not attach %s - what should be its name?' % s) + return fake_mod def attach_namespace(self, module, **ns): for k, v in ns.items(): @@ -94,7 +118,8 @@ class TestCase(test.TestCase): arg_values=None, arg_types=arg_types, owner_type=owner_type, - recursive=recursive) + recursive=recursive, + type_annotation_func=utils.set_element_type) node = qual_names.resolve(node) node = activity.resolve(node, ctx) node = live_values.resolve(node, ctx, {}) diff --git a/tensorflow/contrib/py2tf/converters/for_loops.py b/tensorflow/contrib/py2tf/converters/for_loops.py index 935dade0ed30975dd29c8ffe5be875993936d241..4297c1cf2a3632e097973280cc985fc48da64475 100644 --- a/tensorflow/contrib/py2tf/converters/for_loops.py +++ b/tensorflow/contrib/py2tf/converters/for_loops.py @@ -37,14 +37,18 @@ class ForLoopCanonicalizationTransformer(transformer.Base): def visit_For(self, node): self.generic_visit(node) body_scope = anno.getanno(node, NodeAnno.BODY_SCOPE) - + i_var = self.context.namer.new_symbol('i', body_scope.referenced) + n_var = self.context.namer.new_symbol('n', body_scope.referenced) + iterated_var = self.context.namer.new_symbol('iterated', + body_scope.referenced) + # TODO(mdan): Use TensorListFromTensor(loop_iter) here. if anno.hasanno(node, 'extra_cond'): template = """ i = 0 - n = len(loop_iter) + iterated = loop_iter + n = len(iterated) while i < n and extra_cond: - # TODO(mdan): Use TensorListFromTensor(loop_iter) here. - target = loop_iter[i] + target = iterated[i] body i += 1 """ @@ -53,17 +57,18 @@ class ForLoopCanonicalizationTransformer(transformer.Base): loop_iter=node.iter, target=node.target, body=node.body, - i=self.context.namer.new_symbol('i', body_scope.referenced), - n=self.context.namer.new_symbol('n', body_scope.referenced), + i=i_var, + n=n_var, + iterated=iterated_var, extra_cond=anno.getanno(node, 'extra_cond')) else: template = """ i = 0 - n = len(loop_iter) + iterated = loop_iter + n = len(iterated) while i < n: - # TODO(mdan): Use TensorListFromTensor(loop_iter) here. - target = loop_iter[i] - body # pylint:disable=pointless-statement + target = iterated[i] + body i += 1 """ repl = templates.replace( @@ -71,8 +76,9 @@ class ForLoopCanonicalizationTransformer(transformer.Base): loop_iter=node.iter, target=node.target, body=node.body, - i=self.context.namer.new_symbol('i', body_scope.referenced), - n=self.context.namer.new_symbol('n', body_scope.referenced)) + i=i_var, + n=n_var, + iterated=iterated_var) return repl def visit_Continue(self, node): diff --git a/tensorflow/contrib/py2tf/converters/for_loops_test.py b/tensorflow/contrib/py2tf/converters/for_loops_test.py index 70a367d3b517e528b67f260d607431d324d2ab7d..b6e3e8c8d8d4960977e2b72b56a3fab8329ad2a7 100644 --- a/tensorflow/contrib/py2tf/converters/for_loops_test.py +++ b/tensorflow/contrib/py2tf/converters/for_loops_test.py @@ -42,6 +42,29 @@ class ControlFlowTest(converter_test_base.TestCase): l = [] self.assertEqual(test_fn(l), result.test_fn(l)) + def test_for_with_iterated_expression(self): + + eval_count = [0] + + def count_evals(x): + eval_count[0] += 1 + return x + + def test_fn(n): + s = 0 + for e in count_evals(range(n)): + s += e + return s + + node = self.parse_and_analyze(test_fn, {'count_evals': count_evals}) + node = for_loops.transform(node, self.ctx) + + with self.compiled(node) as result: + result.count_evals = count_evals + self.assertEqual(test_fn(5), result.test_fn(5)) + # count_evals ran twice, once for test_fn and another for result.test_fn + self.assertEqual(eval_count[0], 2) + if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/py2tf/utils/printing.py b/tensorflow/contrib/py2tf/converters/ifexp.py similarity index 51% rename from tensorflow/contrib/py2tf/utils/printing.py rename to tensorflow/contrib/py2tf/converters/ifexp.py index 95a62bd80b5f4854e6a062df18d882f7bd495555..5fd6f348af0df81a6ff35745da603bd431130e20 100644 --- a/tensorflow/contrib/py2tf/utils/printing.py +++ b/tensorflow/contrib/py2tf/converters/ifexp.py @@ -12,36 +12,38 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""TensorFlow printing support utilities.""" +"""Canonicalizes the ternary conditional operator.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.py2tf.utils import py_func -from tensorflow.python.ops import logging_ops +from tensorflow.contrib.py2tf.pyct import templates +from tensorflow.contrib.py2tf.pyct import transformer -def is_tf_print_compatible(value): - # TODO(mdan): Enable once we can reliably test this. - # This is currently disabled because we can't capture the output of - # op kernels from Python. - del value - return False +class IfExp(transformer.Base): + """Canonicalizes all IfExp nodes into plain conditionals.""" + def visit_IfExp(self, node): + template = """ + py2tf_utils.run_cond(test, lambda: body, lambda: orelse) + """ + desugared_ifexp = templates.replace_as_expression( + template, test=node.test, body=node.body, orelse=node.orelse) + return desugared_ifexp -def call_print(*values): - """Compiled counterpart of the print builtin. - The function attempts to use tf.Print if all the values are compatible. - Otherwise, it will fall back to py_func. +def transform(node, context): + """Desugar IfExp nodes into plain conditionals. Args: - *values: values to print + node: an AST node to transform + context: a context object + Returns: - A dummy value indicating the print completed. If tf. + new_node: an AST with no IfExp nodes, only conditionals. """ - if all(map(is_tf_print_compatible, values)): - return logging_ops.Print(1, values) - return py_func.wrap_py_func(print, None, values, use_dummy_return=True) + node = IfExp(context).visit(node) + return node diff --git a/tensorflow/contrib/py2tf/converters/ifexp_test.py b/tensorflow/contrib/py2tf/converters/ifexp_test.py new file mode 100644 index 0000000000000000000000000000000000000000..9c357ef35b550833bcb79d39f0bdbc6d758d31a5 --- /dev/null +++ b/tensorflow/contrib/py2tf/converters/ifexp_test.py @@ -0,0 +1,106 @@ +# 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 ifexp module.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.py2tf import utils +from tensorflow.contrib.py2tf.converters import converter_test_base +from tensorflow.contrib.py2tf.converters import ifexp +from tensorflow.python.platform import test + + +class IfExpTest(converter_test_base.TestCase): + + def compiled_fn(self, test_fn, *args): + node = self.parse_and_analyze(test_fn, {}) + node = ifexp.transform(node, self.ctx) + module = self.compiled(node, *args) + return module + + def test_simple(self): + + def test_fn(x): + return 1 if x else 0 + + with self.compiled_fn(test_fn) as result: + result.py2tf_util = utils + for x in [0, 1]: + self.assertEqual(test_fn(x), result.test_fn(x)) + + def test_fn(self): + + def f(x): + return 3 * x + + def test_fn(x): + y = f(x * x if x > 0 else x) + return y + + with self.compiled_fn(test_fn) as result: + result.py2tf_util = utils + result.f = f + for x in [-2, 2]: + self.assertEqual(test_fn(x), result.test_fn(x)) + + def test_exp(self): + + def test_fn(x): + return x * x if x > 0 else x + + with self.compiled_fn(test_fn) as result: + result.py2tf_util = utils + for x in [-2, 2]: + self.assertEqual(test_fn(x), result.test_fn(x)) + + def test_nested(self): + + def test_fn(x): + return x * x if x > 0 else x if x else 1 + + with self.compiled_fn(test_fn) as result: + result.py2tf_util = utils + for x in [-2, 0, 2]: + self.assertEqual(test_fn(x), result.test_fn(x)) + + def test_in_cond(self): + + def test_fn(x): + if x > 0: + return x * x if x < 5 else x * x * x + return -x + + with self.compiled_fn(test_fn) as result: + result.py2tf_util = utils + for x in [-2, 2, 5]: + self.assertEqual(test_fn(x), result.test_fn(x)) + + def test_assign_in_cond(self): + + def test_fn(x): + if x > 0: + x = -x if x < 5 else x + return x + + with self.compiled_fn(test_fn) as result: + result.py2tf_util = utils + for x in [-2, 2, 5]: + self.assertEqual(test_fn(x), result.test_fn(x)) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/py2tf/converters/lists.py b/tensorflow/contrib/py2tf/converters/lists.py new file mode 100644 index 0000000000000000000000000000000000000000..06e1dad8f4d652da78ed39309f5b40598e368ea6 --- /dev/null +++ b/tensorflow/contrib/py2tf/converters/lists.py @@ -0,0 +1,103 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Converter for list operations. + +This includes converting Python lists to TensorArray/TensorList. +""" + +# TODO(mdan): Elaborate the logic here. +# TODO(mdan): Does it even make sense to attempt to try to use TAs? +# The current rule (always convert to TensorArray) is naive and insufficient. +# In general, a better mechanism could look like: +# * convert to TensorList by default +# * leave as Python list if the user explicitly forbids it +# * convert to TensorArray only when complete write once behavior can be +# guaranteed (e.g. list comprehensions) + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import gast + +from tensorflow.contrib.py2tf.pyct import anno +from tensorflow.contrib.py2tf.pyct import templates +from tensorflow.contrib.py2tf.pyct import transformer +from tensorflow.python.framework import dtypes + + +class ListTransformer(transformer.Base): + """Converts lists and related operations to their TF counterpart.""" + + def _empty_list(self, node): + if not anno.hasanno(node, 'element_type'): + raise NotImplementedError( + 'type inference for empty lists is not yet supported; ' + 'use utils.set_element_type(, ) to continue') + dtype = anno.getanno(node, 'element_type') + if not isinstance(dtype, dtypes.DType): + # TODO(mdan): Allow non-TF dtypes? + # That would be consistent with the dynamic dispatch pattern, but + # we must make sure that doesn't become confusing. + raise NotImplementedError('element type "%s" not yet supported' % dtype) + + dtype_name = dtype.name + # TODO(mdan): Does it ever make sense not to use tensor lists? + template = """ + tf.TensorArray(tf.dtype_name, size=0, dynamic_size=True) + """ + return templates.replace_as_expression(template, dtype_name=dtype_name) + + def _pre_populated_list(self, node): + raise NotImplementedError() + + def visit_Expr(self, node): + node = self.generic_visit(node) + if isinstance(node.value, gast.Call): + call_node = node.value + qn = anno.getanno(call_node.func, anno.Basic.QN) + + if qn.qn[-1] == 'append' and (len(call_node.args) == 1): + template = """ + target = py2tf_utils.dynamic_list_append(target, element) + """ + node = templates.replace( + template, + target=qn.parent.ast(), + element=call_node.args[0]) + return node + + def visit_Assign(self, node): + node = self.generic_visit(node) + + # Only convert lists when they are assigned to a variable, e.g.: + # l = [] + # TODO(mdan): This rule should be improved. + if len(node.targets) != 1: + return node + if not isinstance(node.value, gast.List): + return node + if not isinstance(node.value.ctx, gast.Load): + return node + + if node.value.elts: + node.value = self._pre_populated_list(node.value) + else: + node.value = self._empty_list(node.value) + return node + + +def transform(node, context): + return ListTransformer(context).visit(node) diff --git a/tensorflow/contrib/py2tf/utils/printing_test.py b/tensorflow/contrib/py2tf/converters/lists_test.py similarity index 50% rename from tensorflow/contrib/py2tf/utils/printing_test.py rename to tensorflow/contrib/py2tf/converters/lists_test.py index 2070deb304d8df2433fb9a95ae36d48415578482..671a1cc7b1225061a00731596c536c4403e0bdff 100644 --- a/tensorflow/contrib/py2tf/utils/printing_test.py +++ b/tensorflow/contrib/py2tf/converters/lists_test.py @@ -12,41 +12,40 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Tests for printing module.""" +"""Tests for lists module.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function -import sys +from tensorflow.contrib.py2tf import utils +from tensorflow.contrib.py2tf.converters import converter_test_base +from tensorflow.contrib.py2tf.converters import lists +from tensorflow.python.framework import dtypes +from tensorflow.python.ops import tensor_array_ops +from tensorflow.python.platform import test -import six -from tensorflow.contrib.py2tf.utils import printing -from tensorflow.python.platform import test +class ListTest(converter_test_base.TestCase): + def test_empty_annotated_list(self): -class ContextManagersTest(test.TestCase): + def test_fn(): + l = [] + utils.set_element_type(l, dtypes.int32) + l.append(1) + return l - def test_call_print_tf(self): - try: - out_capturer = six.StringIO() - sys.stdout = out_capturer - with self.test_session() as sess: - sess.run(printing.call_print('test message', 1)) - self.assertEqual(out_capturer.getvalue(), 'test message 1\n') - finally: - sys.stdout = sys.__stdout__ - - def test_call_print_py_func(self): - try: - out_capturer = six.StringIO() - sys.stdout = out_capturer + node = self.parse_and_analyze(test_fn, {'dtypes': dtypes, 'utils': utils}) + node = lists.transform(node, self.ctx) + + with self.compiled(node, tensor_array_ops.TensorArray, + dtypes.int32) as result: + # TODO(mdan): Attach these additional modules automatically. + result.utils = utils + result.dtypes = dtypes with self.test_session() as sess: - sess.run(printing.call_print('test message', [1, 2])) - self.assertEqual(out_capturer.getvalue(), 'test message [1, 2]\n') - finally: - sys.stdout = sys.__stdout__ + self.assertEqual(test_fn(), sess.run(result.test_fn().stack())) if __name__ == '__main__': diff --git a/tensorflow/contrib/py2tf/converters/logical_expressions.py b/tensorflow/contrib/py2tf/converters/logical_expressions.py index df980d41c9c57e325bee9a1fa870d9c95f46ea41..10192e6a036c4a44aa1e6f1b4a390579bd703373 100644 --- a/tensorflow/contrib/py2tf/converters/logical_expressions.py +++ b/tensorflow/contrib/py2tf/converters/logical_expressions.py @@ -23,52 +23,116 @@ from __future__ import print_function import gast -from tensorflow.contrib.py2tf.pyct import parser +from tensorflow.contrib.py2tf.pyct import anno +from tensorflow.contrib.py2tf.pyct import templates +from tensorflow.contrib.py2tf.pyct import transformer -class LogicalExpressionTransformer(gast.NodeTransformer): +# TODO(mdan): Properly extrack boolean ops according to lazy eval rules. +# Note that this isn't completely safe either, because tensors may have control +# dependencies. +# Note that for loops that should be done after the loop was converted to +# tf.while_loop so that the expanded conditionals are properly scoped. + +# Used to signal that an operand is safe for non-lazy evaluation. +SAFE_BOOLEAN_OPERAND = 'SAFE_BOOLEAN_OPERAND' + + +class LogicalExpressionTransformer(transformer.Base): """Converts logical expressions to corresponding TF calls.""" - def __init__(self): + def __init__(self, context): + super(LogicalExpressionTransformer, self).__init__(context) # TODO(mdan): Look into replacing with bitwise operators instead. self.op_mapping = { - gast.And: 'tf.logical_and', - gast.Or: 'tf.logical_or', - gast.Not: 'tf.logical_not', - gast.Eq: 'tf.equal', + gast.And: 'logical_and', + gast.Eq: 'equal', + gast.Gt: 'greater', + gast.GtE: 'greater_equal', + gast.Lt: 'less', + gast.LtE: 'less_equal', + gast.Not: 'logical_not', + gast.NotEq: 'not_equal', + gast.Or: 'logical_or', + gast.USub: 'negative', + gast.Is: 'py2tf_utils.dynamic_is', + gast.IsNot: 'py2tf_utils.dynamic_is_not' } + def _expect_simple_symbol(self, operand): + if isinstance(operand, gast.Name): + return + if anno.hasanno(operand, SAFE_BOOLEAN_OPERAND): + return + raise NotImplementedError( + 'only simple local variables are supported in logical and compound ' + 'comparison expressions; for example, we support "a or b" but not ' + '"a.x or b"; for a workaround, assign the expression to a local ' + 'variable and use that instead, for example "tmp = a.x", "tmp or b"') + + def _matching_tf_op(self, operator): + op_type = type(operator) + mapped_op = self.op_mapping.get(op_type) + if not mapped_op: + raise NotImplementedError('operator %s is not yet supported' % op_type) + return mapped_op + + def _inline_tf_op(self, op_name, args): + if 'py2tf_utils' in op_name: + # TODO(alexbw): explicitly spelling out the attribute function name + # until fix for issue highlighted in cl/188931581 lands. + template = """ + py2tf_utils.op_name(args) + """ + op_name = op_name.replace('py2tf_utils.', '') + else: + template = """ + tf.op_name(args) + """ + replacement = templates.replace_as_expression( + template, op_name=op_name, args=args) + anno.setanno(replacement, SAFE_BOOLEAN_OPERAND, True) + return replacement + def visit_Compare(self, node): node = self.generic_visit(node) - if len(node.ops) > 1: - raise NotImplementedError() - cmp_type = type(node.ops[0]) - if cmp_type in self.op_mapping: - tf_function = parser.parse_str(self.op_mapping[cmp_type]).body[0].value - return gast.Call( - func=tf_function, args=[node.left, node.comparators[0]], keywords=[]) - return node + ops_and_comps = list(zip(node.ops, node.comparators)) + left = node.left + op_tree = None + + # Repeated comparisons are converted to conjunctions: + # a < b < c -> a < b and b < c + while ops_and_comps: + op, right = ops_and_comps.pop(0) + binary_comparison = self._inline_tf_op(self._matching_tf_op(op), + (left, right)) + if isinstance(left, gast.Name) and isinstance(right, gast.Name): + anno.setanno(binary_comparison, SAFE_BOOLEAN_OPERAND, True) + if op_tree: + self._expect_simple_symbol(right) + op_tree = self._inline_tf_op('logical_and', + (binary_comparison, op_tree)) + else: + op_tree = binary_comparison + left = right + assert op_tree is not None + return op_tree def visit_UnaryOp(self, node): node = self.generic_visit(node) - if isinstance(node.op, gast.Not): - tf_function = parser.parse_str(self.op_mapping[type( - node.op)]).body[0].value - node = gast.Call(func=tf_function, args=[node.operand], keywords=[]) - return node + return self._inline_tf_op(self._matching_tf_op(node.op), node.operand) def visit_BoolOp(self, node): - # TODO(mdan): A normalizer may be useful here. Use ANF? node = self.generic_visit(node) - tf_function = parser.parse_str(self.op_mapping[type(node.op)]).body[0].value - left = node.values[0] - for i in range(1, len(node.values)): - left = gast.Call( - func=tf_function, args=[left, node.values[i]], keywords=[]) - return left - - -def transform(node): - transformer = LogicalExpressionTransformer() - node = transformer.visit(node) - return node + node_values = node.values + right = node.values.pop() + self._expect_simple_symbol(right) + while node_values: + left = node_values.pop() + self._expect_simple_symbol(left) + right = self._inline_tf_op(self._matching_tf_op(node.op), (left, right)) + return right + + +def transform(node, context): + return LogicalExpressionTransformer(context).visit(node) diff --git a/tensorflow/contrib/py2tf/converters/logical_expressions_test.py b/tensorflow/contrib/py2tf/converters/logical_expressions_test.py index a28326c517d468230f35e45f0fbfe5257d769895..eb28c309a429f2267cc1ae1f6f65a8cde0ad91b8 100644 --- a/tensorflow/contrib/py2tf/converters/logical_expressions_test.py +++ b/tensorflow/contrib/py2tf/converters/logical_expressions_test.py @@ -32,7 +32,7 @@ class GradientsFunctionTest(converter_test_base.TestCase): return a == b node = self.parse_and_analyze(test_fn, {}) - node = logical_expressions.transform(node) + node = logical_expressions.transform(node, self.ctx) with self.compiled(node, math_ops.equal) as result: with self.test_session() as sess: @@ -45,7 +45,7 @@ class GradientsFunctionTest(converter_test_base.TestCase): return (a or b) and (a or b or c) node = self.parse_and_analyze(test_fn, {}) - node = logical_expressions.transform(node) + node = logical_expressions.transform(node, self.ctx) with self.compiled(node, math_ops.logical_or, math_ops.logical_and) as result: diff --git a/tensorflow/contrib/py2tf/converters/single_return.py b/tensorflow/contrib/py2tf/converters/single_return.py new file mode 100644 index 0000000000000000000000000000000000000000..1194b98f5ebeffa79a41fc3b32aa79ffd8cc407b --- /dev/null +++ b/tensorflow/contrib/py2tf/converters/single_return.py @@ -0,0 +1,317 @@ +# 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. +# ============================================================================== +"""Canonicalizes functions with multiple returns to use just one.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import gast + +from tensorflow.contrib.py2tf.pyct import anno +from tensorflow.contrib.py2tf.pyct import ast_util +from tensorflow.contrib.py2tf.pyct import templates +from tensorflow.contrib.py2tf.pyct import transformer +from tensorflow.contrib.py2tf.pyct.static_analysis.annos import NodeAnno + + +# TODO(mdan): Move this logic into transformer_base. +class BodyVisitor(transformer.Base): + """Walks breadth- or depth-first the list-of-nodes bodies of AST nodes.""" + + def __init__(self, context, depth_first=False): + self.depth_first = depth_first + self.changes_made = False + super(BodyVisitor, self).__init__(context) + + def visit_nodelist(self, nodelist): + for node in nodelist: + if isinstance(node, list): + node = self.visit_nodelist(node) + else: + node = self.generic_visit(node) + return nodelist + + def visit_If(self, node): + if self.depth_first: + node = self.generic_visit(node) + node.body = self.visit_nodelist(node.body) + node.orelse = self.visit_nodelist(node.orelse) + if not self.depth_first: + node = self.generic_visit(node) + return node + + def visit_For(self, node): + if self.depth_first: + node = self.generic_visit(node) + node.body = self.visit_nodelist(node.body) + node.orelse = self.visit_nodelist(node.orelse) + if not self.depth_first: + node = self.generic_visit(node) + return node + + def visit_While(self, node): + if self.depth_first: + node = self.generic_visit(node) + node.body = self.visit_nodelist(node.body) + node.orelse = self.visit_nodelist(node.orelse) + if not self.depth_first: + node = self.generic_visit(node) + return node + + def visit_Try(self, node): + if self.depth_first: + node = self.generic_visit(node) + node.body = self.visit_nodelist(node.body) + node.orelse = self.visit_nodelist(node.orelse) + node.finalbody = self.visit_nodelist(node.finalbody) + for i in range(len(node.handlers)): + node.handlers[i].body = self.visit_nodelist(node.handlers[i].body) + if not self.depth_first: + node = self.generic_visit(node) + return node + + def visit_With(self, node): + if self.depth_first: + node = self.generic_visit(node) + node.body = self.visit_nodelist(node.body) + if not self.depth_first: + node = self.generic_visit(node) + return node + + def visit_FunctionDef(self, node): + if self.depth_first: + node = self.generic_visit(node) + node.body = self.visit_nodelist(node.body) + self.generic_visit(node) + if not self.depth_first: + node = self.generic_visit(node) + return node + + +class FoldElse(BodyVisitor): + + def visit_nodelist(self, nodelist): + for i in range(len(nodelist)): + node = nodelist[i] + if isinstance(node, gast.If): + true_branch_returns = isinstance(node.body[-1], gast.Return) + false_branch_returns = len(node.orelse) and isinstance( + node.orelse[-1], gast.Return) + # If the last node in the if body is a return, + # then every line after this if statement effectively + # belongs in the else. + if true_branch_returns and not false_branch_returns: + for j in range(i + 1, len(nodelist)): + nodelist[i].orelse.append(ast_util.copy_clean(nodelist[j])) + if nodelist[i + 1:]: + self.changes_made = True + return nodelist[:i + 1] + elif not true_branch_returns and false_branch_returns: + for j in range(i + 1, len(nodelist)): + nodelist[i].body.append(ast_util.copy_clean(nodelist[j])) + if nodelist[i + 1:]: + self.changes_made = True + return nodelist[:i + 1] + elif true_branch_returns and false_branch_returns: + if nodelist[i + 1:]: + raise ValueError( + 'Unreachable code after conditional where both branches return.' + ) + return nodelist + elif isinstance(node, gast.Return) and nodelist[i + 1:]: + raise ValueError( + 'Cannot have statements after a return in the same basic block') + return nodelist + + +def contains_return(node): + for n in gast.walk(node): + if isinstance(n, gast.Return): + return True + return False + + +class LiftReturn(transformer.Base): + """Move return statements out of If and With blocks.""" + + def __init__(self, context): + self.changes_made = False + self.common_return_name = None + super(LiftReturn, self).__init__(context) + + def visit_If(self, node): + # Depth-first traversal of if statements + node = self.generic_visit(node) + + # We check if both branches return, and if so, lift the return out of the + # conditional. We don't enforce that the true and false branches either + # both return or both do not, because FoldElse might move a return + # into a branch after this transform completes. FoldElse and LiftReturn + # are alternately run until the code reaches a fixed point. + true_branch_returns = isinstance(node.body[-1], gast.Return) + false_branch_returns = len(node.orelse) and isinstance( + node.orelse[-1], gast.Return) + if true_branch_returns and false_branch_returns: + node.body[-1] = templates.replace( + 'a = b', a=self.common_return_name, b=node.body[-1].value)[0] + node.orelse[-1] = templates.replace( + 'a = b', a=self.common_return_name, b=node.orelse[-1].value)[0] + return_node = templates.replace('return a', a=self.common_return_name)[0] + self.changes_made = True + return [node, return_node] + else: + return node + + def visit_With(self, node): + # Depth-first traversal of syntax + node = self.generic_visit(node) + + # If the with statement returns, lift the return + if isinstance(node.body[-1], gast.Return): + node.body[-1] = templates.replace( + 'a = b', a=self.common_return_name, b=node.body[-1].value)[0] + return_node = templates.replace('return a', a=self.common_return_name)[0] + node = self.generic_visit(node) + self.changes_made = True + return [node, return_node] + else: + return node + + def visit_FunctionDef(self, node): + # Ensure we're doing depth-first traversal + last_return_name = self.common_return_name + body_scope = anno.getanno(node, NodeAnno.BODY_SCOPE) + referenced_names = body_scope.referenced + self.common_return_name = self.context.namer.new_symbol( + 'return_', referenced_names) + node = self.generic_visit(node) + self.common_return_name = last_return_name + return node + + +class DetectReturnInUnsupportedControlFlow(gast.NodeVisitor): + """Throws an error if code returns inside loops or try/except.""" + + # First, throw an error if we detect a return statement in a loop. + # TODO(alexbw): we need to learn to handle returns inside a loop, + # but don't currently have the TF constructs to do so (need something + # that looks vaguely like a goto). + + def __init__(self): + self.cant_return = False + super(DetectReturnInUnsupportedControlFlow, self).__init__() + + def visit_While(self, node): + self.cant_return = True + self.generic_visit(node) + self.cant_return = False + + def visit_For(self, node): + self.cant_return = True + self.generic_visit(node) + self.cant_return = False + + def visit_Try(self, node): + self.cant_return = True + self.generic_visit(node) + self.cant_return = False + + def visit_Return(self, node): + if self.cant_return: + raise ValueError( + 'Pyflow currently does not support `return` statements in loops. ' + 'Try assigning to a variable in the while loop, and returning ' + 'outside of the loop') + + +class DetectReturnInConditional(gast.NodeVisitor): + """Assert that no return statements are present in conditionals.""" + + def __init__(self): + self.cant_return = False + super(DetectReturnInConditional, self).__init__() + + def visit_If(self, node): + self.cant_return = True + self.generic_visit(node) + self.cant_return = False + + def visit_Return(self, node): + if self.cant_return: + raise ValueError( + 'After transforms, a conditional contained a `return `statement, ' + 'which is not allowed. This is a bug, and should not happen.') + + +class DetectReturnInFunctionDef(gast.NodeVisitor): + + def visit_FunctionDef(self, node): + self.generic_visit(node) + if not contains_return(node): + raise ValueError( + 'Each function definition should contain at least one return.') + + +def transform(node, context): + """Ensure a function has only a single return. + + This transforms an AST node with multiple returns successively into containing + only a single return node. + There are a few restrictions on what we can handle: + - An AST being transformed must contain at least one return. + - No returns allowed in loops. We have to know the type of the return value, + and we currently don't have either a type inference system to discover it, + nor do we have a mechanism for late type binding in TensorFlow. + - After all transformations are finished, a Return node is not allowed inside + control flow. If we were unable to move a return outside of control flow, + this is an error. + + Args: + node: an AST node to transform + context: a context object + + Returns: + new_node: an AST with a single return value + + Raises: + ValueError: if the AST is structured so that we can't perform the + transform. + """ + # Make sure that the function has at least one return statement + # TODO(alexbw): turning off this assertion for now -- + # we need to not require this in e.g. class constructors. + # DetectReturnInFunctionDef().visit(node) + + # Make sure there's no returns in unsupported locations (loops, try/except) + DetectReturnInUnsupportedControlFlow().visit(node) + + while True: + + # Try to lift all returns out of if statements and with blocks + lr = LiftReturn(context) + node = lr.visit(node) + changes_made = lr.changes_made + fe = FoldElse(context) + node = fe.visit(node) + changes_made = changes_made or fe.changes_made + + if not changes_made: + break + + # Make sure we've scrubbed all returns from conditionals + DetectReturnInConditional().visit(node) + + return node diff --git a/tensorflow/contrib/py2tf/converters/single_return_test.py b/tensorflow/contrib/py2tf/converters/single_return_test.py new file mode 100644 index 0000000000000000000000000000000000000000..2ea7a9d6d3e25c8dafd8f211994c8fe99bd0e781 --- /dev/null +++ b/tensorflow/contrib/py2tf/converters/single_return_test.py @@ -0,0 +1,189 @@ +# 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 single_return module.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.py2tf.converters import converter_test_base +from tensorflow.contrib.py2tf.converters import single_return +from tensorflow.python.framework.ops import name_scope +from tensorflow.python.platform import test + + +class SingleReturnTest(converter_test_base.TestCase): + + def compiled_fn(self, test_fn, *args): + node = self.parse_and_analyze(test_fn, {}) + node = single_return.transform(node, self.ctx) + module = self.compiled(node, *args) + return module + + def test_noop(self): + # Noop + def test_fn(x): + return x + + with self.compiled_fn(test_fn) as result: + self.assertEqual(test_fn(2.0), result.test_fn(2.0)) + + def test_return_expression(self): + # ANF + def test_fn(x): + return x * x + + with self.compiled_fn(test_fn) as result: + x = 2 + self.assertEqual(test_fn(x), result.test_fn(x)) + + def test_merge(self): + # Simple merge + def test_fn(x): + if x > 0: + return x + else: + return x * x + + with self.compiled_fn(test_fn) as result: + for x in [-2, 2]: + self.assertEqual(test_fn(x), result.test_fn(x)) + + def test_orphan_branch(self): + + def test_fn(x): + if x > 0: + return x + + with self.assertRaises(ValueError): + self.compiled_fn(test_fn) + + def test_lift_body_into_false_branch(self): + + def test_fn(x): + if x > 0: + return x + return x * x + + with self.compiled_fn(test_fn) as result: + for x in [-2, 2]: + self.assertEqual(test_fn(x), result.test_fn(x)) + + def test_lift_body_into_true_branch(self): + + def test_fn(x): + if x < 0: + x *= x + else: + # TODO(alexbw): linter bug here that requires us suppress this warning. + return x # pylint: disable=undefined-loop-variable + return x + + with self.compiled_fn(test_fn) as result: + for x in [-2, 2]: + self.assertEqual(test_fn(x), result.test_fn(x)) + + def test_nested_if(self): + + def test_fn(x): + if x > 0: + if x < 5: + return x + else: + return x * x + else: + return x * x * x + + with self.compiled_fn(test_fn) as result: + for x in [-2, 2, 5]: + self.assertEqual(test_fn(x), result.test_fn(x)) + + def test_context_manager(self): + + def test_fn(x): + + with name_scope(''): + return x * x + + with self.compiled_fn(test_fn) as result: + result.name_scope = name_scope + for x in [-2, 2]: + self.assertEqual(test_fn(x), result.test_fn(x)) + + def test_context_manager_in_conditional(self): + + def test_fn(x): + if x > 0: + with name_scope(''): + return x * x + else: + return x + + with self.compiled_fn(test_fn, name_scope) as result: + result.name_scope = name_scope + for x in [-2, 2]: + self.assertEqual(test_fn(x), result.test_fn(x)) + + def text_conditional_in_context_manager(self): + + def test_fn(x): + with name_scope(''): + if x > 0: + return x * x + else: + return x + + with self.compiled_fn(test_fn) as result: + result.name_scope = name_scope + for x in [-2, 2]: + self.assertEqual(test_fn(x), result.test_fn(x)) + + def test_no_return(self): + + def test_fn(x): + x *= x + + with self.compiled_fn(test_fn) as result: + self.assertEqual(test_fn(2), result.test_fn(2)) + + def test_nested_functiondefs(self): + + def test_fn(x): + + def inner_fn(y): + if y > 0: + return y * y + else: + return y + + return inner_fn(x) + + with self.compiled_fn(test_fn) as result: + for x in [-2, 2]: + self.assertEqual(test_fn(x), result.test_fn(x)) + + def test_loop(self): + + def test_fn(x): + for _ in range(10): + return x + return x + + with self.assertRaises(ValueError): + self.compiled_fn(test_fn) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/py2tf/impl/api.py b/tensorflow/contrib/py2tf/impl/api.py index 29d2e038a73c8cac89c121ec65c32f0d4f68aff6..883b304089024363f41cabde2cb74c49f01ae836 100644 --- a/tensorflow/contrib/py2tf/impl/api.py +++ b/tensorflow/contrib/py2tf/impl/api.py @@ -26,7 +26,9 @@ import six from tensorflow.contrib.py2tf.impl import config from tensorflow.contrib.py2tf.impl import conversion from tensorflow.contrib.py2tf.pyct import compiler +from tensorflow.contrib.py2tf.pyct import inspect_utils from tensorflow.contrib.py2tf.pyct import parser +from tensorflow.contrib.py2tf.utils import builtins from tensorflow.python.platform import tf_logging as logging from tensorflow.python.util import tf_inspect @@ -110,28 +112,7 @@ def convert(recursive=False, verbose=False, arg_types=None): @wraps(f) def wrapper(*args, **kwargs): - """Wrapper that calls the compiled version of the wrapped function.""" - partial_types = () - arg_values = {} - arg_names = tf_inspect.getargspec(f)[0] - for name, arg in zip(arg_names, args): - arg_values[name] = arg - arg_class = arg.__class__ - # If arg_value_hints specifies any name, use that instead. - if name not in arg_types: - arg_types[name] = (arg_class.__name__, arg_class) - if name == 'self' and tf_inspect.isclass(arg_class): - # Annotated methods need to specify that their owner type is partial, - # otherwise other members they call will not be converted. - partial_types = (arg_class,) - wrapped = to_graph( - f, - recursive=recursive, - verbose=verbose, - arg_values=arg_values, - arg_types=arg_types, - partial_types=partial_types) - return wrapped(*args, **kwargs) + return converted_call(f, recursive, verbose, arg_types, *args, **kwargs) # Sometimes the decorator is just desugared, making it impossible to detect. # This attribute makes detection easier. @@ -141,6 +122,78 @@ def convert(recursive=False, verbose=False, arg_types=None): return decorator +def converted_call(f, recursive, verbose, arg_types, *args, **kwargs): + """Compiles a function call inline.""" + # TODO(mdan): This needs cleanup. + # In particular, we may want to avoid renaming functions altogether. + + if conversion.is_whitelisted_for_graph(f): + return f(*args, **kwargs) + + unknown_arg_value = object() # Sentinel for arguments of unknown value + + if tf_inspect.isbuiltin(f): + return builtins.dynamic_builtin(f, *args, **kwargs) + + if tf_inspect.isfunction(f) or tf_inspect.ismethod(f): + # Regular functions + target_entity = f + arg_map_target = f + effective_args = args + f_class = inspect_utils.getmethodclass(f) + + if f_class is not None: + partial_types = (f_class,) + else: + partial_types = () + + elif tf_inspect.isclass(f): + # Constructors + target_entity = f + arg_map_target = f.__init__ + effective_args = (unknown_arg_value,) + args + partial_types = () + + elif hasattr(f, '__call__') and hasattr(f, '__class__'): + # Callable objects + target_entity = f.__call__ + arg_map_target = f.__call__ + effective_args = (f,) + args + partial_types = (f.__class__,) + + else: + NotImplementedError('unknown callable type "%s"' % type(f)) + + arg_values = tf_inspect.getcallargs(arg_map_target, *args, **kwargs) + for name, arg in arg_values.items(): + if arg is unknown_arg_value: + continue + arg_class = arg.__class__ + # If arg_value_hints specifies any name, use that instead. + if name not in arg_types: + arg_types[name] = (arg_class.__name__, arg_class) + + # When called from within a decorator, this is the only indication that + # the function is a method - it appears that the decorator is applied + # before the method is bound. + if not partial_types: + if 'self' in arg_values: + if tf_inspect.isclass(arg_values['self'].__class__): + partial_types = (arg_values['self'].__class__,) + elif 'cls' in arg_values: + if tf_inspect.isclass(arg_values['cls']): + partial_types = (arg_values['cls'],) + + converted_f = to_graph( + target_entity, + recursive=recursive, + verbose=verbose, + arg_values=arg_values, + arg_types=arg_types, + partial_types=partial_types) + return converted_f(*effective_args, **kwargs) + + def to_graph(e, recursive=True, verbose=False, @@ -181,7 +234,7 @@ def to_graph(e, module = gast.Module([]) for import_line in config.COMPILED_IMPORT_STATEMENTS: - module.body.append(parser.parse_str(import_line)) + module.body.extend(parser.parse_str(import_line).body) for dep in conversion_map.dependency_cache.values(): module.body.append(dep) compiled_node, compiled_src = compiler.ast_to_object(module) @@ -189,7 +242,7 @@ def to_graph(e, # The compiled code should see everything the entry function saw. # TODO(mdan): This might not work well if the call tree spans modules? if tf_inspect.isfunction(e): - compiled_node.__dict__.update(six.get_function_globals(e)) + compiled_node.__dict__.update(inspect_utils.getnamespace(e)) compiled_fn = getattr(compiled_node, name) if verbose: diff --git a/tensorflow/contrib/py2tf/impl/api_test.py b/tensorflow/contrib/py2tf/impl/api_test.py index 51e99864adeba9c928b6e74eb759054ef1d1d78c..13f8e66018920a5b13f8bd3f00c67d3bbdd519aa 100644 --- a/tensorflow/contrib/py2tf/impl/api_test.py +++ b/tensorflow/contrib/py2tf/impl/api_test.py @@ -18,23 +18,26 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.contrib.py2tf import utils from tensorflow.contrib.py2tf.impl import api from tensorflow.contrib.py2tf.impl import config from tensorflow.contrib.py2tf.pyct import parser from tensorflow.python.framework import constant_op -from tensorflow.python.ops import math_ops from tensorflow.python.platform import test +tf = utils.fake_tf() + + class ApiTest(test.TestCase): def setUp(self): - config.DEFAULT_UNCOMPILED_MODULES.add((math_ops.__name__,)) config.COMPILED_IMPORT_STATEMENTS = ( - 'from tensorflow.python.framework ' - 'import ops as tf', + 'from __future__ import print_function', 'from tensorflow.contrib.py2tf import utils as ' - 'py2tf_utils') + 'py2tf_utils', + 'tf = py2tf_utils.fake_tf()' + ) def test_decorator_recurses(self): @@ -47,7 +50,7 @@ class ApiTest(test.TestCase): @api.convert(recursive=True) def test_method(self, x, s, a): - while math_ops.reduce_sum(x) > s: + while tf.reduce_sum(x) > s: x //= self.called_member(a) return x @@ -63,11 +66,11 @@ class ApiTest(test.TestCase): class TestClass(object): def called_member(self, a): - return math_ops.negative(a) + return tf.negative(a) @api.convert(recursive=False) def test_method(self, x, s, a): - while math_ops.reduce_sum(x) > s: + while tf.reduce_sum(x) > s: x //= self.called_member(a) return x @@ -84,11 +87,11 @@ class ApiTest(test.TestCase): @api.graph_ready def called_member(self, a): - return math_ops.negative(a) + return tf.negative(a) @api.convert(recursive=True) def test_method(self, x, s, a): - while math_ops.reduce_sum(x) > s: + while tf.reduce_sum(x) > s: x //= self.called_member(a) return x @@ -111,7 +114,7 @@ class ApiTest(test.TestCase): @api.convert(recursive=True) def test_method(self, x, s, a): - while math_ops.reduce_sum(x) > s: + while tf.reduce_sum(x) > s: x //= self.called_member(a) return x @@ -133,7 +136,7 @@ class ApiTest(test.TestCase): @api.convert(recursive=True) def test_method(self, x, s, a): - while math_ops.reduce_sum(x) > s: + while tf.reduce_sum(x) > s: x //= api.convert_inline(self.called_member, a) return x @@ -149,11 +152,11 @@ class ApiTest(test.TestCase): class TestClass(object): def called_member(self, a): - return math_ops.negative(a) + return tf.negative(a) @api.convert(recursive=True) def test_method(self, x, s, a): - while math_ops.reduce_sum(x) > s: + while tf.reduce_sum(x) > s: x //= api.graph_ready(self.called_member(a)) return x @@ -166,7 +169,7 @@ class ApiTest(test.TestCase): def test_to_graph_basic(self): def test_fn(x, s): - while math_ops.reduce_sum(x) > s: + while tf.reduce_sum(x) > s: x //= 2 return x @@ -178,7 +181,7 @@ class ApiTest(test.TestCase): def test_to_code_basic(self): def test_fn(x, s): - while math_ops.reduce_sum(x) > s: + while tf.reduce_sum(x) > s: x /= 2 return x diff --git a/tensorflow/contrib/py2tf/impl/config.py b/tensorflow/contrib/py2tf/impl/config.py index c90e85c96b690b7781358b173e5d83fe60e29c00..bdbc6663dd65ed66c55ad2d2e52428084bbea219 100644 --- a/tensorflow/contrib/py2tf/impl/config.py +++ b/tensorflow/contrib/py2tf/impl/config.py @@ -31,12 +31,16 @@ PYTHON_LITERALS = { DEFAULT_UNCOMPILED_MODULES = set(( ('tensorflow',), (utils.__name__,), + + # All of tensorflow's subpackages. Unlike the root tf module, they don't + # have well-known names. Not refering to the module directly to avoid + # circular imports. + (utils.__name__[:-len('.contrib.py2tf.utils')],), )) NO_SIDE_EFFECT_CONSTRUCTORS = set(('tensorflow',)) # TODO(mdan): Also allow controlling the generated names (for testability). -# TODO(mdan): Make sure copybara renames the reference below. COMPILED_IMPORT_STATEMENTS = ( 'from __future__ import print_function', 'import tensorflow as tf', diff --git a/tensorflow/contrib/py2tf/impl/conversion.py b/tensorflow/contrib/py2tf/impl/conversion.py index 4bf698f2072256f9b87d4b36159fd372536ec7a1..37b24ab55fdd1b03e12e9afe06530e3c26218b61 100644 --- a/tensorflow/contrib/py2tf/impl/conversion.py +++ b/tensorflow/contrib/py2tf/impl/conversion.py @@ -19,7 +19,6 @@ from __future__ import division from __future__ import print_function import gast -import six from tensorflow.contrib.py2tf import utils from tensorflow.contrib.py2tf.converters import asserts @@ -30,17 +29,22 @@ from tensorflow.contrib.py2tf.converters import continue_statements from tensorflow.contrib.py2tf.converters import control_flow from tensorflow.contrib.py2tf.converters import decorators from tensorflow.contrib.py2tf.converters import for_loops +from tensorflow.contrib.py2tf.converters import ifexp +from tensorflow.contrib.py2tf.converters import lists from tensorflow.contrib.py2tf.converters import logical_expressions from tensorflow.contrib.py2tf.converters import name_scopes from tensorflow.contrib.py2tf.converters import side_effect_guards +from tensorflow.contrib.py2tf.converters import single_return from tensorflow.contrib.py2tf.impl import config from tensorflow.contrib.py2tf.impl import naming from tensorflow.contrib.py2tf.pyct import context +from tensorflow.contrib.py2tf.pyct import inspect_utils from tensorflow.contrib.py2tf.pyct import parser from tensorflow.contrib.py2tf.pyct import qual_names from tensorflow.contrib.py2tf.pyct.static_analysis import activity from tensorflow.contrib.py2tf.pyct.static_analysis import live_values from tensorflow.contrib.py2tf.pyct.static_analysis import type_info +from tensorflow.contrib.py2tf.utils import type_hints from tensorflow.python.util import tf_inspect @@ -48,7 +52,9 @@ from tensorflow.python.util import tf_inspect class ConversionMap(object): - """ConversionMaps keep track of converting function hierarchies. + """ConversionMap keeps track of converting function hierarchies. + + This object is mutable, and is updated as functions are converted. Attributes: recursive: Whether to recusrively convert any functions that the decorator @@ -97,6 +103,24 @@ class ConversionMap(object): self.dependency_cache[original_entity] = converted_ast +def is_whitelisted_for_graph(o): + """Check whether an entity is whitelisted for use in graph mode. + + Examples of whitelisted entities include all members of the tensorflow + package. + + Args: + o: A Python entity. + Returns: + Boolean + """ + m = tf_inspect.getmodule(o) + for prefix, in config.DEFAULT_UNCOMPILED_MODULES: + if m.__name__.startswith(prefix): + return True + return False + + def entity_to_graph(o, conversion_map, arg_values, arg_types): """Compile a Python entity into equivalent TensorFlow. @@ -136,14 +160,20 @@ def entity_to_graph(o, conversion_map, arg_values, arg_types): conversion_map.add_to_cache(o, node) if conversion_map.recursive: - for obj in conversion_map.name_map.keys(): - if obj not in conversion_map.dependency_cache: - if (hasattr(obj, 'im_class') and - getattr(obj, 'im_class') not in conversion_map.partial_types): - # Class members are converted with their objects, unless they're - # only converted partially. - continue - entity_to_graph(obj, conversion_map, {}, {}) + while True: + candidate = None + for obj in conversion_map.name_map.keys(): + if obj not in conversion_map.dependency_cache: + candidate = obj + break + if candidate is None: + break + if (hasattr(candidate, 'im_class') and + getattr(candidate, 'im_class') not in conversion_map.partial_types): + # Class members are converted with their objects, unless they're + # only converted partially. + continue + entity_to_graph(candidate, conversion_map, {}, {}) return node, new_name @@ -151,11 +181,12 @@ def entity_to_graph(o, conversion_map, arg_values, arg_types): def class_to_graph(c, conversion_map): """Specialization of `entity_to_graph` for classes.""" converted_members = {} - members = tf_inspect.getmembers(c, predicate=tf_inspect.ismethod) + method_filter = lambda m: tf_inspect.isfunction(m) or tf_inspect.ismethod(m) + members = tf_inspect.getmembers(c, predicate=method_filter) if not members: - raise ValueError('Cannot convert %s: it has no member methods.') + raise ValueError('Cannot convert %s: it has no member methods.' % c) - class_globals = None + class_namespace = None for _, m in members: node, _ = function_to_graph( m, @@ -164,16 +195,16 @@ def class_to_graph(c, conversion_map): arg_types={'self': (c.__name__, c)}, owner_type=c) # TODO(mdan): Do not assume all members have the same view of globals. - if class_globals is None: - class_globals = six.get_function_globals(m) + if class_namespace is None: + class_namespace = inspect_utils.getnamespace(m) converted_members[m] = node - namer = conversion_map.new_namer(class_globals) + namer = conversion_map.new_namer(class_namespace) class_name = namer.compiled_class_name(c.__name__, c) node = gast.ClassDef( class_name, bases=[], keywords=[], - body=converted_members.values(), + body=list(converted_members.values()), decorator_list=[]) return node, class_name @@ -202,19 +233,11 @@ def function_to_graph(f, conversion_map, arg_values, arg_types, """Specialization of `entity_to_graph` for callable functions.""" node, source = parser.parse_entity(f) node = node.body[0] - namespace = six.get_function_globals(f) - - # This is needed for non-global functions. - closure = six.get_function_closure(f) - if closure: - for e in closure: - if callable(e.cell_contents): - fn = e.cell_contents - namespace[fn.__name__] = fn + namespace = inspect_utils.getnamespace(f) _add_self_references(namespace, conversion_map.api_module) - namer = conversion_map.new_namer(namespace) + ctx = context.EntityContext( namer=namer, source_code=source, @@ -223,7 +246,8 @@ def function_to_graph(f, conversion_map, arg_values, arg_types, arg_values=arg_values, arg_types=arg_types, owner_type=owner_type, - recursive=conversion_map.recursive) + recursive=conversion_map.recursive, + type_annotation_func=type_hints.set_element_type) node, deps = node_to_graph(node, ctx, conversion_map.nocompile_decorators) # TODO(mdan): This somewhat duplicates the call rename logic in call_treest.py @@ -276,10 +300,15 @@ def node_to_graph(node, ctx, nocompile_decorators): # to re-run the analysis. node = _static_analysis_pass(node, ctx) + + # TODO(mdan): Clean this up. + # Some intermediate analyses are not required, and some comments got orphaned. + # Past this point, line numbers are no longer accurate so we ignore the # source. # TODO(mdan): Is it feasible to reconstruct intermediate source code? ctx.source_code = None + node = ifexp.transform(node, ctx) node, deps = decorators.transform(node, nocompile_decorators) node = break_statements.transform(node, ctx) node = asserts.transform(node, ctx) @@ -291,6 +320,10 @@ def node_to_graph(node, ctx, nocompile_decorators): ctx.namespace['len'] = len node = _static_analysis_pass(node, ctx) + node = single_return.transform(node, ctx) + + node = _static_analysis_pass(node, ctx) + node = lists.transform(node, ctx) node = for_loops.transform(node, ctx) # for_loops may insert new global references. node = builtin_functions.transform(node, ctx) @@ -302,7 +335,7 @@ def node_to_graph(node, ctx, nocompile_decorators): # control_flow may create new symbols and change scopes. node = _static_analysis_pass(node, ctx) - node = logical_expressions.transform(node) + node = logical_expressions.transform(node, ctx) node = side_effect_guards.transform(node, ctx) node = name_scopes.transform(node, ctx) diff --git a/tensorflow/contrib/py2tf/impl/conversion_test.py b/tensorflow/contrib/py2tf/impl/conversion_test.py index 7816f958575d58236007fc7f0f1f3d1f3a99c4cf..9ff256aace7a0e7ac5e7ac07e580b8bed7d8df6f 100644 --- a/tensorflow/contrib/py2tf/impl/conversion_test.py +++ b/tensorflow/contrib/py2tf/impl/conversion_test.py @@ -20,12 +20,23 @@ from __future__ import print_function import gast +from tensorflow.contrib.py2tf import utils from tensorflow.contrib.py2tf.impl import conversion +from tensorflow.python.framework import constant_op from tensorflow.python.platform import test class ConversionTest(test.TestCase): + def test_is_whitelisted_for_graph(self): + + def test_fn(): + return constant_op.constant(1) + + self.assertFalse(conversion.is_whitelisted_for_graph(test_fn)) + self.assertTrue(conversion.is_whitelisted_for_graph(utils)) + self.assertTrue(conversion.is_whitelisted_for_graph(constant_op.constant)) + def test_entity_to_graph_unsupported_types(self): with self.assertRaises(ValueError): conversion_map = conversion.ConversionMap(True, (), (), None) diff --git a/tensorflow/contrib/py2tf/pyct/anno.py b/tensorflow/contrib/py2tf/pyct/anno.py index 7a0528b6d0b65b6604930b7a13d8493af9d61f02..cc4a7edf02ed7556c9a552d8730e4c7875038c83 100644 --- a/tensorflow/contrib/py2tf/pyct/anno.py +++ b/tensorflow/contrib/py2tf/pyct/anno.py @@ -70,3 +70,8 @@ def delanno(node, key, field_name='___pyct_anno'): if not annotations: delattr(node, field_name) node._fields = tuple(f for f in node._fields if f != field_name) + + +def copyanno(from_node, to_node, key, field_name='___pyct_anno'): + if hasanno(from_node, key, field_name): + setanno(to_node, key, getanno(from_node, key, field_name), field_name) diff --git a/tensorflow/contrib/py2tf/pyct/anno_test.py b/tensorflow/contrib/py2tf/pyct/anno_test.py index ff40bfe1f50ae731648afdf509c26c3a70d3f6cb..6c29918fdfaaa0224f20a2c3cb2ea8088f3eb52b 100644 --- a/tensorflow/contrib/py2tf/pyct/anno_test.py +++ b/tensorflow/contrib/py2tf/pyct/anno_test.py @@ -24,6 +24,9 @@ from tensorflow.contrib.py2tf.pyct import anno from tensorflow.python.platform import test +# TODO(mdan): Consider strong types instead of primitives. + + class AnnoTest(test.TestCase): def test_basic(self): @@ -42,6 +45,17 @@ class AnnoTest(test.TestCase): with self.assertRaises(AttributeError): anno.getanno(node, 'foo') + def test_copyanno(self): + node_1 = ast.Name() + anno.setanno(node_1, 'foo', 3) + + node_2 = ast.Name() + anno.copyanno(node_1, node_2, 'foo') + anno.copyanno(node_1, node_2, 'bar') + + self.assertTrue(anno.hasanno(node_2, 'foo')) + self.assertFalse(anno.hasanno(node_2, 'bar')) + if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/py2tf/pyct/ast_util_test.py b/tensorflow/contrib/py2tf/pyct/ast_util_test.py index e0b00c178168f96e656c57cc75a76e6da8af1d8a..a871ccad6fc7ea1487e41fd6da3ce6120bdcbcbd 100644 --- a/tensorflow/contrib/py2tf/pyct/ast_util_test.py +++ b/tensorflow/contrib/py2tf/pyct/ast_util_test.py @@ -33,15 +33,15 @@ class AstUtilTest(test.TestCase): ast.Name('b', ast.Load()), ast.Attribute(ast.Name('b', None), 'c', ast.Store()), ast.Attribute( - ast.Attribute(ast.Name('b', None), 'c', ast.Load()), 'd', - None) + ast.Attribute(ast.Name('b', None), 'c', ast.Load()), 'd', None) ], None) node = qual_names.resolve(node) node = ast_util.rename_symbols( - node, - { - qual_names.QN('a'): qual_names.QN('renamed_a'), - qual_names.QN('b.c'): qual_names.QN('renamed_b_c'), + node, { + qual_names.QN('a'): + qual_names.QN('renamed_a'), + qual_names.QN(qual_names.QN('b'), attr='c'): + qual_names.QN('renamed_b_c'), }) self.assertEqual(node.elts[0].id, 'renamed_a') diff --git a/tensorflow/contrib/py2tf/pyct/compiler.py b/tensorflow/contrib/py2tf/pyct/compiler.py index 51cf6930e8bcb3728ee55bf5d4781f01a5ef73bd..507dbc7ed3de9c0b8874164e97a3d1d149e42423 100644 --- a/tensorflow/contrib/py2tf/pyct/compiler.py +++ b/tensorflow/contrib/py2tf/pyct/compiler.py @@ -39,7 +39,10 @@ def ast_to_source(node, indentation): astor.string_repr.pretty_string) generator.visit(node) generator.result.append('\n') - return astor.source_repr.pretty_source(generator.result).lstrip() + # In some versions of Python, literals may appear as actual values. This + # ensures everything is string. + code = map(str, generator.result) + return astor.source_repr.pretty_source(code).lstrip() def ast_to_object( diff --git a/tensorflow/contrib/py2tf/pyct/compiler_test.py b/tensorflow/contrib/py2tf/pyct/compiler_test.py index c1f84238efa7dd6fc0748748a2cb4f074572b4c6..243f4c81538f5853a01ff444f2ff16ccf7cd5d62 100644 --- a/tensorflow/contrib/py2tf/pyct/compiler_test.py +++ b/tensorflow/contrib/py2tf/pyct/compiler_test.py @@ -23,11 +23,28 @@ import textwrap import gast from tensorflow.contrib.py2tf.pyct import compiler +from tensorflow.contrib.py2tf.pyct import parser from tensorflow.python.platform import test +from tensorflow.python.util import tf_inspect class CompilerTest(test.TestCase): + def test_parser_compile_idempotent(self): + + def test_fn(x): + a = True + b = '' + if a: + b = x + 1 + return b + + self.assertEqual( + textwrap.dedent(tf_inspect.getsource(test_fn)), + tf_inspect.getsource( + compiler.ast_to_object( + parser.parse_entity(test_fn)[0].body[0])[0].test_fn)) + def test_ast_to_source(self): node = gast.If( test=gast.Num(1), diff --git a/tensorflow/contrib/py2tf/pyct/context.py b/tensorflow/contrib/py2tf/pyct/context.py index 4fcf2a687d58af951adfc0dcf52ff7303d2b17f5..b34015cfd2888f0dbeb6492b9e7335d561bf4763 100644 --- a/tensorflow/contrib/py2tf/pyct/context.py +++ b/tensorflow/contrib/py2tf/pyct/context.py @@ -22,6 +22,8 @@ from __future__ import print_function class EntityContext(object): """Contains information about an entity, like source code. + In general, objects of this class should be considered immutable. + Attributes: namer: Namer that matches the contract of all converters. source_code: The entity's source code. @@ -33,8 +35,9 @@ class EntityContext(object): owner_type: The surrounding class type of the function, if present. """ + # TODO(mdan): Remove the default and update tests. def __init__(self, namer, source_code, source_file, namespace, arg_values, - arg_types, owner_type, recursive): + arg_types, owner_type, recursive, type_annotation_func=None): self.namer = namer self.source_code = source_code self.source_file = source_file @@ -43,3 +46,4 @@ class EntityContext(object): self.arg_types = {} if arg_types is None else arg_types self.owner_type = owner_type self.recursive = recursive + self.type_annotation_func = type_annotation_func diff --git a/tensorflow/contrib/py2tf/pyct/inspect_utils.py b/tensorflow/contrib/py2tf/pyct/inspect_utils.py index 86cf52afd59f995284d036080e65eb749dfbca04..d19c6ed75e0f0651781d6e1ed80f7be11fb8a5a4 100644 --- a/tensorflow/contrib/py2tf/pyct/inspect_utils.py +++ b/tensorflow/contrib/py2tf/pyct/inspect_utils.py @@ -21,33 +21,58 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import itertools + import six from tensorflow.python.util import tf_inspect -def getcallargs(c, *args, **kwargs): - """Extension of getcallargs to non-function callables.""" - if tf_inspect.isfunction(c): - # The traditional getcallargs - return tf_inspect.getcallargs(c, *args, **kwargs) +def getnamespace(f): + """Returns the complete namespace of a function. + + Namespace is defined here as the mapping of all non-local variables to values. + This includes the globals and the closure variables. Note that this captures + the entire globals collection of the function, and may contain extra symbols + that it does not actually use. + + Args: + f: User defined function. + Returns: + A dict mapping symbol names to values. + """ + namespace = dict(six.get_function_globals(f)) + closure = six.get_function_closure(f) + freevars = six.get_function_code(f).co_freevars + if freevars and closure: + for name, cell in zip(freevars, closure): + namespace[name] = cell.cell_contents + return namespace + + +def getmethodclass(m): + """Resolves a function's owner, e.g. a method's class. + + Note that this returns the object that the function was retrieved from, not + necessarily the class where it was defined. - if tf_inspect.isclass(c): - # Constructors: pass a fake None for self, then remove it. - arg_map = tf_inspect.getcallargs(c.__init__, None, *args, **kwargs) - assert 'self' in arg_map, 'no "self" argument, is this not a constructor?' - del arg_map['self'] - return arg_map + This function relies on Python stack frame support in the interpreter, and + has the same limitations that inspect.currentframe. - if hasattr(c, '__call__'): - # Callable objects: map self to the object itself - return tf_inspect.getcallargs(c.__call__, *args, **kwargs) + Limitations. This function will only work correctly if the owned class is + visible in the caller's global or local variables. - raise NotImplementedError('unknown callable "%s"' % type(c)) + Args: + m: A user defined function + Returns: + The class that this function was retrieved from, or None if the function + is not an object or class method, or the class that owns the object or + method is not visible to m. -def getmethodclass(m, namespace): - """Resolves a function's owner, e.g. a method's class.""" + Raises: + ValueError: if the class could not be resolved for any unexpected reason. + """ # Instance method and class methods: should be bound to a non-null "self". # If self is a class, then it's a class method. @@ -57,34 +82,38 @@ def getmethodclass(m, namespace): return m.__self__ return type(m.__self__) - # Class and static methods: platform specific. - if hasattr(m, 'im_class'): # Python 2 - return m.im_class - - if hasattr(m, '__qualname__'): # Python 3 - qn = m.__qualname__.split('.') - if len(qn) < 2: - return None - owner_name, func_name = qn[-2:] - assert func_name == m.__name__, ( - 'inconsistent names detected ' - '(__qualname__[1] = "%s", __name__ = "%s") for %s.' % (func_name, - m.__name__, m)) - if owner_name == '': - return None - if owner_name not in namespace: - raise ValueError( - 'Could not resolve name "%s" while analyzing %s. Namespace:\n%s' % - (owner_name, m, namespace)) - return namespace[owner_name] - - if six.PY2: - # In Python 2 it's impossible, to our knowledge, to detect the class of a - # static function. So we're forced to walk all the objects in the - # namespace and see if they own it. If any reader finds a better solution, - # please let us know. - for _, v in namespace.items(): - if hasattr(v, m.__name__) and getattr(v, m.__name__) is m: - return v + # Class, static and unbound methods: search all defined classes in any + # namespace. This is inefficient but more robust method. + owners = [] + caller_frame = tf_inspect.currentframe().f_back + try: + # TODO(mdan): This doesn't consider cell variables. + # TODO(mdan): This won't work if the owner is hidden inside a container. + # Cell variables may be pulled using co_freevars and the closure. + for v in itertools.chain(caller_frame.f_locals.values(), + caller_frame.f_globals.values()): + if hasattr(v, m.__name__): + candidate = getattr(v, m.__name__) + # Py2 methods may be bound or unbound, extract im_func to get the + # underlying function. + if hasattr(candidate, 'im_func'): + candidate = candidate.im_func + if hasattr(m, 'im_func'): + m = m.im_func + if candidate is m: + owners.append(v) + finally: + del caller_frame + + if owners: + if len(owners) == 1: + return owners[0] + + # If multiple owners are found, and are not subclasses, raise an error. + owner_types = tuple(o if tf_inspect.isclass(o) else type(o) for o in owners) + for o in owner_types: + if tf_inspect.isclass(o) and issubclass(o, tuple(owner_types)): + return o + raise ValueError('Found too many owners of %s: %s' % (m, owners)) return None diff --git a/tensorflow/contrib/py2tf/pyct/inspect_utils_test.py b/tensorflow/contrib/py2tf/pyct/inspect_utils_test.py index 5d92e75b1899e99c983325c9a474df43feb17d55..5528ac851f74bd7b7dacdbe7b930945afa8c9783 100644 --- a/tensorflow/contrib/py2tf/pyct/inspect_utils_test.py +++ b/tensorflow/contrib/py2tf/pyct/inspect_utils_test.py @@ -20,6 +20,8 @@ from __future__ import print_function from functools import wraps +import six + from tensorflow.contrib.py2tf.pyct import inspect_utils from tensorflow.python.platform import test @@ -76,6 +78,10 @@ def free_function(): pass +def factory(): + return free_function + + def free_factory(): def local_function(): pass @@ -84,87 +90,87 @@ def free_factory(): class InspectUtilsTest(test.TestCase): - def test_getcallargs_constructor(self): - - class TestSuperclass(object): + def test_getnamespace_globals(self): + ns = inspect_utils.getnamespace(factory) + self.assertEqual(ns['free_function'], free_function) - def __init__(self, x): - pass - - class TestCallable(TestSuperclass): - pass + def test_getnamespace_hermetic(self): - self.assertDictEqual({ - 'x': 1 - }, inspect_utils.getcallargs(TestCallable, 1)) + # Intentionally hiding the global function to make sure we don't overwrite + # it in the global namespace. + free_function = object() # pylint:disable=redefined-outer-name - def test_getcallargs_object(self): + def test_fn(): + return free_function - class TestCallable(object): + ns = inspect_utils.getnamespace(test_fn) + globs = six.get_function_globals(test_fn) + self.assertTrue(ns['free_function'] is free_function) + self.assertFalse(globs['free_function'] is free_function) - def __call__(self, x): - pass + def test_getnamespace_locals(self): - obj = TestCallable() - self.assertDictEqual({ - 'self': obj, - 'x': 1 - }, inspect_utils.getcallargs(obj, 1)) + def called_fn(): + return 0 - def test_getcallargs_function(self): + closed_over_list = [] + closed_over_primitive = 1 - def test_fn(x): - return x + 1 + def local_fn(): + closed_over_list.append(1) + local_var = 1 + return called_fn() + local_var + closed_over_primitive - self.assertDictEqual({ - 'x': 1 - }, inspect_utils.getcallargs(test_fn, 1)) + ns = inspect_utils.getnamespace(local_fn) + self.assertEqual(ns['called_fn'], called_fn) + self.assertEqual(ns['closed_over_list'], closed_over_list) + self.assertEqual(ns['closed_over_primitive'], closed_over_primitive) + self.assertTrue('local_var' not in ns) def test_getmethodclass(self): self.assertEqual( - inspect_utils.getmethodclass(free_function, {}), None) + inspect_utils.getmethodclass(free_function), None) self.assertEqual( - inspect_utils.getmethodclass(free_factory(), {}), None) + inspect_utils.getmethodclass(free_factory()), None) - ns = {'TestClass': TestClass} self.assertEqual( - inspect_utils.getmethodclass(TestClass.member_function, ns), + inspect_utils.getmethodclass(TestClass.member_function), TestClass) self.assertEqual( - inspect_utils.getmethodclass(TestClass.decorated_member, ns), + inspect_utils.getmethodclass(TestClass.decorated_member), TestClass) self.assertEqual( - inspect_utils.getmethodclass(TestClass.fn_decorated_member, ns), + inspect_utils.getmethodclass(TestClass.fn_decorated_member), TestClass) self.assertEqual( - inspect_utils.getmethodclass(TestClass.wrap_decorated_member, ns), + inspect_utils.getmethodclass(TestClass.wrap_decorated_member), TestClass) self.assertEqual( - inspect_utils.getmethodclass(TestClass.static_method, ns), + inspect_utils.getmethodclass(TestClass.static_method), TestClass) self.assertEqual( - inspect_utils.getmethodclass(TestClass.class_method, ns), + inspect_utils.getmethodclass(TestClass.class_method), TestClass) test_obj = TestClass() self.assertEqual( - inspect_utils.getmethodclass(test_obj.member_function, ns), + inspect_utils.getmethodclass(test_obj.member_function), TestClass) self.assertEqual( - inspect_utils.getmethodclass(test_obj.decorated_member, ns), + inspect_utils.getmethodclass(test_obj.decorated_member), TestClass) self.assertEqual( - inspect_utils.getmethodclass(test_obj.fn_decorated_member, ns), + inspect_utils.getmethodclass(test_obj.fn_decorated_member), TestClass) self.assertEqual( - inspect_utils.getmethodclass(test_obj.wrap_decorated_member, ns), + inspect_utils.getmethodclass(test_obj.wrap_decorated_member), TestClass) self.assertEqual( - inspect_utils.getmethodclass(test_obj.static_method, ns), + inspect_utils.getmethodclass(test_obj.static_method), TestClass) self.assertEqual( - inspect_utils.getmethodclass(test_obj.class_method, ns), + inspect_utils.getmethodclass(test_obj.class_method), TestClass) def test_getmethodclass_locals(self): @@ -190,34 +196,33 @@ class InspectUtilsTest(test.TestCase): pass self.assertEqual( - inspect_utils.getmethodclass(local_function, {}), None) + inspect_utils.getmethodclass(local_function), None) - ns = {'LocalClass': LocalClass} self.assertEqual( - inspect_utils.getmethodclass(LocalClass.member_function, ns), + inspect_utils.getmethodclass(LocalClass.member_function), LocalClass) self.assertEqual( - inspect_utils.getmethodclass(LocalClass.decorated_member, ns), + inspect_utils.getmethodclass(LocalClass.decorated_member), LocalClass) self.assertEqual( - inspect_utils.getmethodclass(LocalClass.fn_decorated_member, ns), + inspect_utils.getmethodclass(LocalClass.fn_decorated_member), LocalClass) self.assertEqual( - inspect_utils.getmethodclass(LocalClass.wrap_decorated_member, ns), + inspect_utils.getmethodclass(LocalClass.wrap_decorated_member), LocalClass) test_obj = LocalClass() self.assertEqual( - inspect_utils.getmethodclass(test_obj.member_function, ns), + inspect_utils.getmethodclass(test_obj.member_function), LocalClass) self.assertEqual( - inspect_utils.getmethodclass(test_obj.decorated_member, ns), + inspect_utils.getmethodclass(test_obj.decorated_member), LocalClass) self.assertEqual( - inspect_utils.getmethodclass(test_obj.fn_decorated_member, ns), + inspect_utils.getmethodclass(test_obj.fn_decorated_member), LocalClass) self.assertEqual( - inspect_utils.getmethodclass(test_obj.wrap_decorated_member, ns), + inspect_utils.getmethodclass(test_obj.wrap_decorated_member), LocalClass) diff --git a/tensorflow/contrib/py2tf/pyct/qual_names.py b/tensorflow/contrib/py2tf/pyct/qual_names.py index 8717ee6cff198ff31f6cbdb7213e5a8dd3df1149..6bcbaeb2aeb3043919e84bc6599edf5aee583c6d 100644 --- a/tensorflow/contrib/py2tf/pyct/qual_names.py +++ b/tensorflow/contrib/py2tf/pyct/qual_names.py @@ -25,34 +25,87 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import collections + import gast from tensorflow.contrib.py2tf.pyct import anno +class Symbol(collections.namedtuple('Symbol', ['name'])): + """Represents a Python symbol.""" + + +class StringLiteral(collections.namedtuple('StringLiteral', ['value'])): + """Represents a Python string literal.""" + + def __str__(self): + return '\'%s\'' % self.value + + def __repr__(self): + return str(self) + + +class NumberLiteral(collections.namedtuple('NumberLiteral', ['value'])): + """Represents a Python numeric literal.""" + + def __str__(self): + return '%s' % self.value + + def __repr__(self): + return str(self) + + +# TODO(mdan): Use subclasses to remove the has_attr has_subscript booleans. class QN(object): """Represents a qualified name.""" - def __init__(self, base, attr=None): - if attr: + def __init__(self, base, attr=None, subscript=None): + if attr is not None and subscript is not None: + raise ValueError('A QN can only be either an attr or a subscript, not ' + 'both: attr={}, subscript={}.'.format(attr, subscript)) + self._has_attr = False + self._has_subscript = False + + if attr is not None: if not isinstance(base, QN): - raise ValueError('For attribute QNs, base must be a QN.') + raise ValueError( + 'for attribute QNs, base must be a QN; got instead "%s"' % base) + if not isinstance(attr, str): + raise ValueError('attr may only be a string; got instead "%s"' % attr) self._parent = base - self.qn = base.qn + (attr,) + # TODO(mdan): Get rid of the tuple - it can only have 1 or 2 elements now. + self.qn = (base, attr) + self._has_attr = True + + elif subscript is not None: + if not isinstance(base, QN): + raise ValueError('For subscript QNs, base must be a QN.') + self._parent = base + self.qn = (base, subscript) + self._has_subscript = True + else: - if isinstance(base, QN): - if base.is_composite(): - self._parent = base.parent - else: - self._parent = None - self.qn = base.qn - else: - self._parent = None - self.qn = tuple(base.split('.')) + if not isinstance(base, (str, StringLiteral, NumberLiteral)): + # TODO(mdan): Require Symbol instead of string. + raise ValueError( + 'For simple QNs, base must be a string or a Literal object.') + assert '.' not in base and '[' not in base and ']' not in base + self._parent = None + self.qn = (base,) + + def is_symbol(self): + return isinstance(self.qn[0], str) def is_composite(self): return len(self.qn) > 1 + def has_subscript(self): + return self._has_subscript + + def has_attr(self): + return self._has_attr + @property def parent(self): if self._parent is None: @@ -60,26 +113,54 @@ class QN(object): return self._parent def __hash__(self): - return hash(self.qn) + return hash(self.qn + (self._has_attr, self._has_subscript)) def __eq__(self, other): - return self.qn == other.qn + return (isinstance(other, QN) and self.qn == other.qn and + self.has_subscript() == other.has_subscript() and + self.has_attr() == other.has_attr()) def __str__(self): - return '.'.join(self.qn) + if self.has_subscript(): + return str(self.qn[0]) + '[' + str(self.qn[1]) + ']' + if self.has_attr(): + return '.'.join(map(str, self.qn)) + else: + return str(self.qn[0]) def __repr__(self): return str(self) def ssf(self): """Simple symbol form.""" - return '_'.join(self.qn) + ssfs = [n.ssf() if isinstance(n, QN) else n for n in self.qn] + ssf_string = '' + for i in range(0, len(self.qn) - 1): + if self.has_subscript(): + delimiter = '_sub_' + else: + delimiter = '_' + ssf_string += ssfs[i] + delimiter + return ssf_string + ssfs[-1] def ast(self): # The caller must adjust the context appropriately. - if self.is_composite(): + if self.has_subscript(): + return gast.Subscript(self.parent.ast(), gast.Index(self.qn[-1].ast()), + None) + if self.has_attr(): return gast.Attribute(self.parent.ast(), self.qn[-1], None) - return gast.Name(self.qn[0], None, None) + + base = self.qn[0] + if isinstance(base, str): + return gast.Name(base, None, None) + elif isinstance(base, StringLiteral): + return gast.Str(base.value) + elif isinstance(base, NumberLiteral): + return gast.Num(base.value) + else: + assert False, ('the constructor should prevent types other than ' + 'str, StringLiteral and NumberLiteral') class QnResolver(gast.NodeTransformer): @@ -88,15 +169,41 @@ class QnResolver(gast.NodeTransformer): Note: Not using NodeAnnos to avoid circular dependencies. """ + def visit_Call(self, node): + node = self.generic_visit(node) + # This helps treat the following cases uniformly: + # a = b[i] + # a = b()[i] + anno.copyanno(node.func, node, anno.Basic.QN) + return node + def visit_Name(self, node): - self.generic_visit(node) + node = self.generic_visit(node) anno.setanno(node, anno.Basic.QN, QN(node.id)) return node def visit_Attribute(self, node): - self.generic_visit(node) + node = self.generic_visit(node) + anno.setanno(node, anno.Basic.QN, + QN(anno.getanno(node.value, anno.Basic.QN), attr=node.attr)) + return node + + def visit_Subscript(self, node): + node = self.generic_visit(node) + s = node.slice + if not isinstance(s, gast.Index): + # TODO(mdan): Support range and multi-dimensional indices. + # Continuing silently because some demos use these. + return node + if isinstance(s.value, gast.Num): + subscript = QN(NumberLiteral(s.value.n)) + elif isinstance(s.value, gast.Str): + subscript = QN(StringLiteral(s.value.s)) + else: + subscript = anno.getanno(node.slice.value, anno.Basic.QN) anno.setanno(node, anno.Basic.QN, - QN(anno.getanno(node.value, anno.Basic.QN), node.attr)) + QN(anno.getanno(node.value, anno.Basic.QN), + subscript=subscript)) return node diff --git a/tensorflow/contrib/py2tf/pyct/qual_names_test.py b/tensorflow/contrib/py2tf/pyct/qual_names_test.py index 1b1eee2deca18bb0540c17d6ee85d421602aa2b7..f2cd8e98f02213c9035fdb5b20e0862f0a8fd3f6 100644 --- a/tensorflow/contrib/py2tf/pyct/qual_names_test.py +++ b/tensorflow/contrib/py2tf/pyct/qual_names_test.py @@ -23,13 +23,15 @@ import textwrap from tensorflow.contrib.py2tf.pyct import anno from tensorflow.contrib.py2tf.pyct import parser from tensorflow.contrib.py2tf.pyct import qual_names +from tensorflow.contrib.py2tf.pyct.qual_names import QN +from tensorflow.contrib.py2tf.pyct.qual_names import resolve from tensorflow.python.platform import test class QNTest(test.TestCase): def test_basic(self): - a = qual_names.QN('a') + a = QN('a') self.assertEqual(a.qn, ('a',)) self.assertEqual(str(a), 'a') self.assertEqual(a.ssf(), 'a') @@ -38,8 +40,8 @@ class QNTest(test.TestCase): with self.assertRaises(ValueError): _ = a.parent - a_b = qual_names.QN(a, 'b') - self.assertEqual(a_b.qn, ('a', 'b')) + a_b = QN(a, attr='b') + self.assertEqual(a_b.qn, (a, 'b')) self.assertEqual(str(a_b), 'a.b') self.assertEqual(a_b.ssf(), 'a_b') self.assertEqual(a_b.ast().value.id, 'a') @@ -47,13 +49,48 @@ class QNTest(test.TestCase): self.assertTrue(a_b.is_composite()) self.assertEqual(a_b.parent.qn, ('a',)) - a2 = qual_names.QN(a) + def test_subscripts(self): + a = QN('a') + b = QN('b') + a_sub_b = QN(a, subscript=b) + self.assertEqual(a_sub_b.qn, (a, b)) + self.assertEqual(str(a_sub_b), 'a[b]') + self.assertEqual(a_sub_b.ssf(), 'a_sub_b') + self.assertEqual(a_sub_b.ast().value.id, 'a') + self.assertEqual(a_sub_b.ast().slice.value.id, 'b') + self.assertTrue(a_sub_b.is_composite()) + self.assertTrue(a_sub_b.has_subscript()) + self.assertEqual(a_sub_b.parent.qn, ('a',)) + + c = QN('c') + b_sub_c = QN(b, subscript=c) + a_sub_b_sub_c = QN(a, subscript=b_sub_c) + self.assertEqual(a_sub_b_sub_c.qn, (a, b_sub_c)) + self.assertTrue(a_sub_b.is_composite()) + self.assertTrue(a_sub_b_sub_c.is_composite()) + self.assertTrue(a_sub_b.has_subscript()) + self.assertTrue(a_sub_b_sub_c.has_subscript()) + self.assertEqual(b_sub_c.qn, (b, c)) + self.assertEqual(str(a_sub_b_sub_c), 'a[b[c]]') + self.assertEqual(a_sub_b_sub_c.ssf(), 'a_sub_b_sub_c') + self.assertEqual(a_sub_b_sub_c.ast().value.id, 'a') + self.assertEqual(a_sub_b_sub_c.ast().slice.value.value.id, 'b') + self.assertEqual(a_sub_b_sub_c.ast().slice.value.slice.value.id, 'c') + self.assertEqual(b_sub_c.ast().slice.value.id, 'c') + self.assertEqual(a_sub_b_sub_c.parent.qn, ('a',)) + with self.assertRaises(ValueError): + QN('a', 'b') + + def test_equality(self): + a = QN('a') + a2 = QN('a') + a_b = QN(a, attr='b') self.assertEqual(a2.qn, ('a',)) with self.assertRaises(ValueError): _ = a.parent - a_b2 = qual_names.QN(a_b) - self.assertEqual(a_b2.qn, ('a', 'b')) + a_b2 = QN(a, attr='b') + self.assertEqual(a_b2.qn, (a, 'b')) self.assertEqual(a_b2.parent.qn, ('a',)) self.assertTrue(a2 == a) @@ -65,16 +102,57 @@ class QNTest(test.TestCase): self.assertTrue(a_b2 == a_b) self.assertFalse(a_b2 is a_b) self.assertFalse(a_b2 == a) + a_sub_b = QN(a, subscript='b') + a_sub_b2 = QN(a, subscript='b') + self.assertTrue(a_sub_b == a_sub_b2) + self.assertFalse(a_sub_b == a_b) - with self.assertRaises(ValueError): - qual_names.QN('a', 'b') + def test_nested_attrs_subscripts(self): + a = QN('a') + b = QN('b') + c = QN('c') + b_sub_c = QN(b, subscript=c) + a_sub_b_sub_c = QN(a, subscript=b_sub_c) + + b_dot_c = QN(b, attr='c') + a_sub__b_dot_c = QN(a, subscript=b_dot_c) + + a_sub_b = QN(a, subscript=b) + a_sub_b__dot_c = QN(a_sub_b, attr='c') + + a_dot_b = QN(a, attr='b') + a_dot_b_sub_c = QN(a_dot_b, subscript=c) + + self.assertEqual(str(a_sub_b_sub_c), 'a[b[c]]') + self.assertEqual(str(a_sub__b_dot_c), 'a[b.c]') + self.assertEqual(str(a_sub_b__dot_c), 'a[b].c') + self.assertEqual(str(a_dot_b_sub_c), 'a.b[c]') + + self.assertNotEqual(a_sub_b_sub_c, a_sub__b_dot_c) + self.assertNotEqual(a_sub_b_sub_c, a_sub_b__dot_c) + self.assertNotEqual(a_sub_b_sub_c, a_dot_b_sub_c) + + self.assertNotEqual(a_sub__b_dot_c, a_sub_b__dot_c) + self.assertNotEqual(a_sub__b_dot_c, a_dot_b_sub_c) + + self.assertNotEqual(a_sub_b__dot_c, a_dot_b_sub_c) def test_hashable(self): - d = {qual_names.QN('a'): 'a', qual_names.QN('b'): 'b'} + d = {QN('a'): 'a', QN('b'): 'b'} + self.assertEqual(d[QN('a')], 'a') + self.assertEqual(d[QN('b')], 'b') + self.assertTrue(QN('c') not in d) + + def test_literals(self): + a = QN('a') + a_sub_str_b = QN(a, subscript=QN(qual_names.StringLiteral('b'))) + a_sub_b = QN(a, subscript=QN('b')) - self.assertEqual(d[qual_names.QN('a')], 'a') - self.assertEqual(d[qual_names.QN('b')], 'b') - self.assertTrue(qual_names.QN('c') not in d) + self.assertNotEqual(a_sub_str_b, a_sub_b) + self.assertNotEqual(hash(a_sub_str_b), hash(a_sub_b)) + + a_sub_three = QN(a, subscript=QN(qual_names.NumberLiteral(3))) + self.assertEqual(a_sub_three.ast().slice.value.n, 3) class QNResolverTest(test.TestCase): @@ -90,7 +168,7 @@ class QNResolverTest(test.TestCase): [f, (g.h.i)] j(k, l) """ - nodes = qual_names.resolve(parser.parse_str(textwrap.dedent(samples))) + nodes = resolve(parser.parse_str(textwrap.dedent(samples))) nodes = tuple(n.value for n in nodes.body) self.assertQNStringIs(nodes[0], 'a') @@ -103,6 +181,33 @@ class QNResolverTest(test.TestCase): self.assertQNStringIs(nodes[4].args[0], 'k') self.assertQNStringIs(nodes[4].args[1], 'l') + def test_subscript_resolve(self): + samples = """ + x[i] + x[i.b] + a.b[c] + a.b[x.y] + a[z[c]] + a[b[c[d]]] + a[b].c + a.b.c[d].e.f + a.b[c[d]].e.f + a.b[c[d.e.f].g].h + """ + nodes = resolve(parser.parse_str(textwrap.dedent(samples))) + nodes = tuple(n.value for n in nodes.body) + + self.assertQNStringIs(nodes[0], 'x[i]') + self.assertQNStringIs(nodes[1], 'x[i.b]') + self.assertQNStringIs(nodes[2], 'a.b[c]') + self.assertQNStringIs(nodes[3], 'a.b[x.y]') + self.assertQNStringIs(nodes[4], 'a[z[c]]') + self.assertQNStringIs(nodes[5], 'a[b[c[d]]]') + self.assertQNStringIs(nodes[6], 'a[b].c') + self.assertQNStringIs(nodes[7], 'a.b.c[d].e.f') + self.assertQNStringIs(nodes[8], 'a.b[c[d]].e.f') + self.assertQNStringIs(nodes[9], 'a.b[c[d.e.f].g].h') + if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/py2tf/pyct/static_analysis/BUILD b/tensorflow/contrib/py2tf/pyct/static_analysis/BUILD index fbfce18c60cca4b105e7de3c3ea7b9c3438f6b2a..2799b56a0042e99b8f8b38100d07c5afaef9f424 100644 --- a/tensorflow/contrib/py2tf/pyct/static_analysis/BUILD +++ b/tensorflow/contrib/py2tf/pyct/static_analysis/BUILD @@ -60,6 +60,7 @@ py_test( deps = [ ":static_analysis", "//tensorflow/contrib/py2tf/pyct", + "//tensorflow/contrib/py2tf/utils", "//tensorflow/python:client_testlib", ], ) diff --git a/tensorflow/contrib/py2tf/pyct/static_analysis/activity.py b/tensorflow/contrib/py2tf/pyct/static_analysis/activity.py index 02ea6fdeaf78152b6bc48983f79b36f43d4f665d..87fc8c979c4e3310fb3aa82b0f23d909b0170cda 100644 --- a/tensorflow/contrib/py2tf/pyct/static_analysis/activity.py +++ b/tensorflow/contrib/py2tf/pyct/static_analysis/activity.py @@ -71,13 +71,33 @@ class Scope(object): tuple(self.modified)) def copy_from(self, other): + """Recursively copies the contents of this scope from another scope.""" + if (self.parent is None) != (other.parent is None): + raise ValueError('cannot copy scopes of different structures') + if other.parent is not None: + self.parent.copy_from(other.parent) + self.isolated = other.isolated self.modified = copy.copy(other.modified) self.created = copy.copy(other.created) self.used = copy.copy(other.used) self.params = copy.copy(other.params) self.returned = copy.copy(other.returned) + @classmethod + def copy_of(cls, other): + if other.parent is not None: + parent = cls.copy_of(other.parent) + else: + parent = None + new_copy = cls(parent) + new_copy.copy_from(other) + return new_copy + def merge_from(self, other): + if (self.parent is None) != (other.parent is None): + raise ValueError('cannot merge scopes of different structures') + if other.parent is not None: + self.parent.merge_from(other.parent) self.modified |= other.modified self.created |= other.created self.used |= other.used @@ -225,14 +245,12 @@ class ActivityAnalizer(transformer.Base): # modifies the parent state causing the other child blocks to be # processed incorrectly. So we need to checkpoint the parent scope so that # each child sees the same context. - before_parent = Scope(None) - before_parent.copy_from(self.scope) + before_parent = Scope.copy_of(self.scope) after_children = [] for child, scope_name in children: self.scope.copy_from(before_parent) parent = self._process_block_node(parent, child, scope_name) - after_child = Scope(None) - after_child.copy_from(self.scope) + after_child = Scope.copy_of(self.scope) after_children.append(after_child) for after_child in after_children: self.scope.merge_from(after_child) @@ -250,6 +268,15 @@ class ActivityAnalizer(transformer.Base): self.scope = current_scope return node + def visit_With(self, node): + current_scope = self.scope + with_scope = Scope(current_scope, isolated=False) + self.scope = with_scope + self.generic_visit(node) + anno.setanno(node, NodeAnno.BODY_SCOPE, with_scope) + self.scope = current_scope + return node + def visit_If(self, node): self.visit(node.test) node = self._process_parallel_blocks(node, diff --git a/tensorflow/contrib/py2tf/pyct/static_analysis/activity_test.py b/tensorflow/contrib/py2tf/pyct/static_analysis/activity_test.py index 69f5f4fc582f159e46c8b8929a90ca95b724794d..b16d15b39d8eb4c444cbc50ae62baa3a8fcc7841 100644 --- a/tensorflow/contrib/py2tf/pyct/static_analysis/activity_test.py +++ b/tensorflow/contrib/py2tf/pyct/static_analysis/activity_test.py @@ -45,7 +45,7 @@ class ScopeTest(test.TestCase): scope.mark_read(QN('bar')) self.assertFalse(scope.has(QN('bar'))) - def test_copy(self): + def test_copy_from(self): scope = activity.Scope(None) scope.mark_write(QN('foo')) @@ -65,6 +65,17 @@ class ScopeTest(test.TestCase): self.assertTrue(QN('bar') in scope.created) self.assertFalse(QN('bar') in other.created) + def test_copy_of(self): + scope = activity.Scope(None) + scope.mark_read(QN('foo')) + + self.assertTrue(QN('foo') in activity.Scope.copy_of(scope).used) + + child_scope = activity.Scope(scope) + child_scope.mark_read(QN('bar')) + + self.assertTrue(QN('bar') in activity.Scope.copy_of(child_scope).used) + def test_nesting(self): scope = activity.Scope(None) scope.mark_write(QN('foo')) @@ -133,7 +144,7 @@ class ActivityAnalizerTest(test.TestCase): anno.getanno(node.body[0].body[2].value, NodeAnno.IS_LOCAL)) # b in return b - def assertScopeIs(self, scope, used, modified, created): + def assertScopeIsRmc(self, scope, used, modified, created): self.assertItemsEqual(used, tuple(str(s) for s in scope.used)) self.assertItemsEqual(modified, tuple(str(s) for s in scope.modified)) self.assertItemsEqual(created, tuple(str(s) for s in scope.created)) @@ -159,7 +170,7 @@ class ActivityAnalizerTest(test.TestCase): print_args_scope = anno.getanno(print_node, NodeAnno.ARGS_SCOPE) # We basically need to detect which variables are captured by the call # arguments. - self.assertScopeIs(print_args_scope, ('a', 'b'), (), ()) + self.assertScopeIsRmc(print_args_scope, ('a', 'b'), (), ()) def test_call(self): @@ -173,7 +184,7 @@ class ActivityAnalizerTest(test.TestCase): call_node = node.body[0].body[2].value # We basically need to detect which variables are captured by the call # arguments. - self.assertScopeIs( + self.assertScopeIsRmc( anno.getanno(call_node, NodeAnno.ARGS_SCOPE), ('a', 'b'), (), ()) def test_while(self): @@ -187,10 +198,10 @@ class ActivityAnalizerTest(test.TestCase): node = self._parse_and_analyze(test_fn) while_node = node.body[0].body[1] - self.assertScopeIs( + self.assertScopeIsRmc( anno.getanno(while_node, NodeAnno.BODY_SCOPE), ('b',), ('b', 'c'), ('c',)) - self.assertScopeIs( + self.assertScopeIsRmc( anno.getanno(while_node, NodeAnno.BODY_SCOPE).parent, ('a', 'b', 'c'), ('b', 'c'), ('a', 'b', 'c')) @@ -205,9 +216,9 @@ class ActivityAnalizerTest(test.TestCase): node = self._parse_and_analyze(test_fn) for_node = node.body[0].body[1] - self.assertScopeIs( + self.assertScopeIsRmc( anno.getanno(for_node, NodeAnno.BODY_SCOPE), ('b',), ('b', 'c'), ('c',)) - self.assertScopeIs( + self.assertScopeIsRmc( anno.getanno(for_node, NodeAnno.BODY_SCOPE).parent, ('a', 'b', 'c'), ('b', 'c', '_'), ('a', 'b', 'c', '_')) @@ -226,21 +237,40 @@ class ActivityAnalizerTest(test.TestCase): node = self._parse_and_analyze(test_fn) if_node = node.body[0].body[0] - self.assertScopeIs( + self.assertScopeIsRmc( anno.getanno(if_node, NodeAnno.BODY_SCOPE), ('x', 'y'), ('x', 'y', 'z'), ('y', 'z')) # TODO(mdan): Double check: is it ok to not mark a local symbol as not read? - self.assertScopeIs( + self.assertScopeIsRmc( anno.getanno(if_node, NodeAnno.BODY_SCOPE).parent, ('x', 'z', 'u'), ('x', 'y', 'z', 'u'), ('x', 'y', 'z', 'u')) - self.assertScopeIs( + self.assertScopeIsRmc( anno.getanno(if_node, NodeAnno.ORELSE_SCOPE), ('x', 'y'), ('x', 'y', 'u'), ('y', 'u')) - self.assertScopeIs( + self.assertScopeIsRmc( anno.getanno(if_node, NodeAnno.ORELSE_SCOPE).parent, ('x', 'z', 'u'), ('x', 'y', 'z', 'u'), ('x', 'y', 'z', 'u')) - def test_functiondef(self): + def test_nested_if_else_creation(self): + + def test_fn(b): + if b > 0: + if b < 5: + a = b + else: + a = b * b + return a + + node = self._parse_and_analyze(test_fn) + inner_if_node = node.body[0].body[0].body[0] + self.assertScopeIsRmc( + anno.getanno(inner_if_node, NodeAnno.BODY_SCOPE), ('b',), ('a',), + ('a',)) + self.assertScopeIsRmc( + anno.getanno(inner_if_node, NodeAnno.ORELSE_SCOPE), ('b',), ('a',), + ('a',)) + + def test_function_def(self): def test_fn(a): @@ -257,11 +287,11 @@ class ActivityAnalizerTest(test.TestCase): node = self._parse_and_analyze(test_fn) fndef_node = node.body[0].body[0] - self.assertScopeIs( + self.assertScopeIsRmc( anno.getanno(fndef_node, NodeAnno.BODY_SCOPE).parent, ('b', 'i', 'f', 'c', 'a'), ('f', 'b', 'c', 'i'), ('f', 'a', 'b', 'c', 'i')) - self.assertScopeIs( + self.assertScopeIsRmc( anno.getanno(fndef_node, NodeAnno.BODY_SCOPE), ('x', 'y'), ('y',), ( 'x', 'y', @@ -284,13 +314,13 @@ class ActivityAnalizerTest(test.TestCase): node = self._parse_and_analyze(test_fn) call_node = node.body[0].body[0].value - self.assertScopeIs( + self.assertScopeIsRmc( anno.getanno(call_node, NodeAnno.ARGS_SCOPE), ('a', 'a.b', 'a.c'), (), ()) if_node = node.body[0].body[1] - self.assertScopeIs( + self.assertScopeIsRmc( anno.getanno(if_node, NodeAnno.BODY_SCOPE), ('a',), ('a.b',), ()) - self.assertScopeIs( + self.assertScopeIsRmc( anno.getanno(if_node, NodeAnno.ORELSE_SCOPE), ('a', 'a.c', 'd', 'd.e', 'f'), ('a.c', 'd', 'd.e', 'f'), ('d', 'f')) diff --git a/tensorflow/contrib/py2tf/pyct/static_analysis/annos.py b/tensorflow/contrib/py2tf/pyct/static_analysis/annos.py index 2d8e49442364fdd4a4752c8a83a5f3b76117fe57..5254b83ca7c775867fc2ad5ef0a0ad93ac483ba0 100644 --- a/tensorflow/contrib/py2tf/pyct/static_analysis/annos.py +++ b/tensorflow/contrib/py2tf/pyct/static_analysis/annos.py @@ -34,13 +34,14 @@ class NodeAnno(NoValue): """ # Symbols - + # These flags are boolean. IS_LOCAL = 'Symbol is local to the function scope being analized.' IS_PARAM = 'Symbol is a parameter to the function being analized.' IS_MODIFIED_SINCE_ENTRY = ( 'Symbol has been explicitly replaced in the current function scope.') # Scopes + # Scopes are represented by objects of type activity.Scope. ARGS_SCOPE = 'The scope for the argument list of a function call.' BODY_SCOPE = ( 'The scope for the main body of a statement (True branch for if ' @@ -48,3 +49,10 @@ class NodeAnno(NoValue): ORELSE_SCOPE = ( 'The scope for the orelse body of a statement (False branch for if ' 'statements, orelse body for loops).') + + # Type and Value annotations + # Type annotations are represented by objects of type type_info.Type. + STATIC_INFO = ( + 'The type or value information that should be asserted about the entity ' + 'referenced by the symbol holding this annotation, irrespective of the ' + 'execution context.') diff --git a/tensorflow/contrib/py2tf/pyct/static_analysis/live_values.py b/tensorflow/contrib/py2tf/pyct/static_analysis/live_values.py index 9c0a9a9e74eccb3d22840032e8f0c2b81e051e7e..0388be5d252389f2f3516c8b27828905d6475589 100644 --- a/tensorflow/contrib/py2tf/pyct/static_analysis/live_values.py +++ b/tensorflow/contrib/py2tf/pyct/static_analysis/live_values.py @@ -86,6 +86,7 @@ class LiveValueResolver(transformer.Base): if not hasattr(parent_object, node.attr): raise AttributeError('%s has no attribute %s' % (parent_object, node.attr)) + anno.setanno(node, 'parent_type', type(parent_object)) anno.setanno(node, 'live_val', getattr(parent_object, node.attr)) anno.setanno(node, 'fqn', anno.getanno(node.value, 'fqn') + (node.attr,)) # TODO(mdan): Investigate the role built-in annotations can play here. @@ -96,6 +97,7 @@ class LiveValueResolver(transformer.Base): # This would not hold for dynamic members like function attributes. # For the dynamic case, we simply leave the node without an annotation, # and let downstream consumers figure out what to do. + anno.setanno(node, 'parent_type', parent_type) anno.setanno(node, 'live_val', getattr(parent_type, node.attr)) anno.setanno(node, 'fqn', anno.getanno(node.value, 'type_fqn') + (node.attr,)) diff --git a/tensorflow/contrib/py2tf/pyct/static_analysis/live_values_test.py b/tensorflow/contrib/py2tf/pyct/static_analysis/live_values_test.py index 1e81bc70a85b9e68b955d23554c09004284449ea..c133a455b3dd328689102634c6076f366212ac25 100644 --- a/tensorflow/contrib/py2tf/pyct/static_analysis/live_values_test.py +++ b/tensorflow/contrib/py2tf/pyct/static_analysis/live_values_test.py @@ -103,6 +103,7 @@ class LiveValuesResolverTest(test.TestCase): arg_types={'self': (TestClass.__name__, TestClass)}) func_node = node.body[0].body[0].value.func self.assertEquals(TestClass.member, anno.getanno(func_node, 'live_val')) + self.assertEquals(TestClass, anno.getanno(func_node, 'parent_type')) self.assertEquals(('TestClass', 'member'), anno.getanno(func_node, 'fqn')) diff --git a/tensorflow/contrib/py2tf/pyct/static_analysis/type_info.py b/tensorflow/contrib/py2tf/pyct/static_analysis/type_info.py index 8203bda0f9a792a5b24b9abb25d8f39b61625748..5556a58c025da695bcef10352c597c7c8dd612d9 100644 --- a/tensorflow/contrib/py2tf/pyct/static_analysis/type_info.py +++ b/tensorflow/contrib/py2tf/pyct/static_analysis/type_info.py @@ -14,9 +14,29 @@ # ============================================================================== """Type resolution. +This analyzer uses known live values to further infer object types. This +may include for instance constructed objects and object member functions. + +In addition, the analyzer will also process annotations for TF (staged) type +annotations. + Requires annotations generated by LiveValuesResolver. """ +# TODO(mdan): This would be more robust with a CFG. +# Situations with multiple reaching modifications (e.g. modified inside and +# outside a control flow statement) should be more robustly detected and +# analyzed. + +# TODO(mdan): Look into using Python AST's type annotation fields instead. +# It would be desirable to use that mechanism if we can. +# Some caveats to consider: We may need to annotate other nodes like +# Attribute. It may also not be feasible for us to faithfully to replicate +# PY3's type annotations where it isn't available. It would also require us +# to design rigorous type definitions that can accommodate Python types +# as well as TensorFLow dtypes and shapes. + + from __future__ import absolute_import from __future__ import division from __future__ import print_function @@ -29,7 +49,7 @@ from tensorflow.python.util import tf_inspect class Scope(object): - """Encloses symbol value references. + """Tracks symbol value references. Attributes: values: A dict mapping string to gast.Node, containing the value that was @@ -138,11 +158,14 @@ class TypeInfoResolver(transformer.Base): elif isinstance(node.ctx, gast.Load) and self.scope.hasval(qn): # E.g. if we had # a = b - # then for future references to `a` we should have traced_source = `b` - traced_source = self.scope.getval(qn) - if anno.hasanno(traced_source, 'type'): - anno.setanno(node, 'type', anno.getanno(traced_source, 'type')) - anno.setanno(node, 'type_fqn', anno.getanno(traced_source, 'type_fqn')) + # then for future references to `a` we should have definition = `b` + definition = self.scope.getval(qn) + if anno.hasanno(definition, 'type'): + anno.setanno(node, 'type', anno.getanno(definition, 'type')) + anno.setanno(node, 'type_fqn', anno.getanno(definition, 'type_fqn')) + if anno.hasanno(definition, 'element_type'): + anno.setanno(node, 'element_type', + anno.getanno(definition, 'element_type')) return node def _process_variable_assignment(self, source, targets): @@ -181,6 +204,34 @@ class TypeInfoResolver(transformer.Base): self._process_variable_assignment(node.value, node.targets) return node + def visit_Call(self, node): + if anno.hasanno(node.func, 'live_val'): + # Symbols targeted by the "set_type" marker function are assigned the data + # type that it specified. + if (anno.getanno(node.func, 'live_val') is + self.context.type_annotation_func): + # Expecting the actual type to be the second argument. + if len(node.args) != 2: + raise ValueError('"%s" must have exactly two parameters' + % self.context.type_annotation_func) + if not anno.hasanno(node.args[0], anno.Basic.QN): + raise ValueError('the first argument of "%s" must by a symbol' + % self.context.type_annotation_func) + if not anno.hasanno(node.args[1], 'live_val'): + raise ValueError( + 'the second argument of "%s" must be statically resolvable' % + self.context.type_annotation_func) + target_symbol = anno.getanno(node.args[0], anno.Basic.QN) + element_type = anno.getanno(node.args[1], 'live_val') + # Find the definition of this symbol and annotate it with the given + # data type. That in turn will cause future uses of the symbol + # to receive the same type annotation. + definition = self.scope.getval(target_symbol) + anno.setanno(node, 'element_type', element_type) + anno.setanno(definition, 'element_type', element_type) + # TODO(mdan): Should we update references between definition and here? + return self.generic_visit(node) + def resolve(node, context): return TypeInfoResolver(context).visit(node) diff --git a/tensorflow/contrib/py2tf/pyct/static_analysis/type_info_test.py b/tensorflow/contrib/py2tf/pyct/static_analysis/type_info_test.py index a3e78202c80e45552c038a6a1da763eb30aff52f..0d9d5a85f055b170ea6e493e8ac185f1298ebf3c 100644 --- a/tensorflow/contrib/py2tf/pyct/static_analysis/type_info_test.py +++ b/tensorflow/contrib/py2tf/pyct/static_analysis/type_info_test.py @@ -18,6 +18,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.contrib.py2tf import utils from tensorflow.contrib.py2tf.pyct import anno from tensorflow.contrib.py2tf.pyct import context from tensorflow.contrib.py2tf.pyct import parser @@ -56,7 +57,10 @@ class ScopeTest(test.TestCase): class TypeInfoResolverTest(test.TestCase): - def _parse_and_analyze(self, test_fn, namespace, arg_types=None): + def _parse_and_analyze(self, + test_fn, + namespace, + arg_types=None): node, source = parser.parse_entity(test_fn) ctx = context.EntityContext( namer=None, @@ -66,7 +70,8 @@ class TypeInfoResolverTest(test.TestCase): arg_values=None, arg_types=arg_types, owner_type=None, - recursive=True) + recursive=True, + type_annotation_func=utils.set_element_type) node = qual_names.resolve(node) node = activity.resolve(node, ctx) node = live_values.resolve(node, ctx, {}) @@ -175,6 +180,22 @@ class TypeInfoResolverTest(test.TestCase): method_call = node.body[0].body[1].value.func self.assertFalse(anno.hasanno(method_call, 'live_val')) + def test_type_annotation(self): + + class Foo(object): + pass + + def test_fn(): + f = [] + f = utils.set_element_type(f, Foo) + return f + + node = self._parse_and_analyze(test_fn, {'Foo': Foo, 'utils': utils}) + f_def = node.body[0].body[0].value + self.assertEqual(anno.getanno(f_def, 'element_type'), Foo) + f_ref = node.body[0].body[1].value + self.assertEqual(anno.getanno(f_ref, 'element_type'), Foo) + if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/py2tf/pyct/templates.py b/tensorflow/contrib/py2tf/pyct/templates.py index 6ee6c0c5ceb70d87779ee313670135cadc5214b5..cdd71dc56de33cde46d6115085350a321093d792 100644 --- a/tensorflow/contrib/py2tf/pyct/templates.py +++ b/tensorflow/contrib/py2tf/pyct/templates.py @@ -79,6 +79,17 @@ class ReplaceTransformer(gast.NodeTransformer): else: raise ValueError('unexpected node type "%s"' % node) + def visit_Attribute(self, node): + node = self.generic_visit(node) + if node.attr not in self.replacements: + return node + repl = self.replacements[node.attr] + if not isinstance(repl, gast.Name): + raise ValueError( + 'An attribute can only be replaced by a Name node. Found: %s' % repl) + node.attr = repl.id + return node + def visit_Name(self, node): if node.id not in self.replacements: return node @@ -154,3 +165,17 @@ def replace(template, **replacements): if isinstance(results, list): return [qual_names.resolve(r) for r in results] return qual_names.resolve(results) + + +def replace_as_expression(template, **replacements): + """Variant of replace that generates expressions, instead of code blocks.""" + replacement = replace(template, **replacements) + if len(replacement) != 1: + raise ValueError( + 'single expression expected; for more general templates use replace') + node = replacement[0] + if not isinstance(node, gast.Expr): + raise ValueError( + 'the template is expected to generate an expression node; instead ' + 'found %s' % node) + return node.value diff --git a/tensorflow/contrib/py2tf/pyct/templates_test.py b/tensorflow/contrib/py2tf/pyct/templates_test.py index 8ccfde8573724741b0bbe4eacb3c54beb381ee7e..d7835b80a7f53c3ba012d01cac34b68c57bfe348 100644 --- a/tensorflow/contrib/py2tf/pyct/templates_test.py +++ b/tensorflow/contrib/py2tf/pyct/templates_test.py @@ -18,6 +18,8 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import imp + import gast from tensorflow.contrib.py2tf.pyct import compiler @@ -62,7 +64,7 @@ class TemplatesTest(test.TestCase): result, _ = compiler.ast_to_object(node) self.assertEquals(7, result.test_fn(2)) - def test_code_block(self): + def test_replace_code_block(self): template = """ def test_fn(a): block @@ -79,6 +81,43 @@ class TemplatesTest(test.TestCase): result, _ = compiler.ast_to_object(node) self.assertEquals(3, result.test_fn(1)) + def test_replace_attribute(self): + template = """ + def test_fn(a): + return a.foo + """ + + node = templates.replace(template, foo='b')[0] + result, _ = compiler.ast_to_object(node) + mod = imp.new_module('test') + mod.b = 3 + self.assertEquals(3, result.test_fn(mod)) + + with self.assertRaises(ValueError): + templates.replace(template, foo=1) + + def replace_as_expression(self): + template = """ + foo(a) + """ + + node = templates.replace(template, foo='bar', a='baz') + self.assertTrue(node is gast.Call) + self.assertEqual(node.func.id, 'bar') + self.assertEqual(node.func.args[0].id, 'baz') + + def replace_as_expression_restrictions(self): + template = """ + foo(a) + bar(b) + """ + with self.assertRaises(ValueError): + templates.replace_as_expression(template) + with self.assertRaises(ValueError): + templates.replace('') + with self.assertRaises(ValueError): + templates.replace('a = b') + if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/py2tf/pyct/transformer.py b/tensorflow/contrib/py2tf/pyct/transformer.py index 877d52af016af720424c8a56257fec9ab64611cb..57016bb4ce84776dfc8dfbe380322a03eb4b37b8 100644 --- a/tensorflow/contrib/py2tf/pyct/transformer.py +++ b/tensorflow/contrib/py2tf/pyct/transformer.py @@ -44,6 +44,12 @@ class Base(gast.NodeTransformer): self._col_offset = 0 self.context = context + def debug_print(self, node): + """Helper method useful for debugging.""" + if __debug__: + print(pretty_printer.fmt(node)) + return node + def visit(self, node): source_code = self.context.source_code source_file = self.context.source_file diff --git a/tensorflow/contrib/py2tf/utils/BUILD b/tensorflow/contrib/py2tf/utils/BUILD index c2fdd40707775783140390e4b5c0186c9c3e562e..8bc338e801aa283967f4f6e6a659df9683cbc154 100644 --- a/tensorflow/contrib/py2tf/utils/BUILD +++ b/tensorflow/contrib/py2tf/utils/BUILD @@ -20,25 +20,28 @@ py_library( name = "utils", srcs = [ "__init__.py", + "builtins.py", "context_managers.py", "misc.py", "multiple_dispatch.py", - "printing.py", "py_func.py", "tensor_list.py", + "testing.py", "type_check.py", + "type_hints.py", ], srcs_version = "PY2AND3", visibility = ["//tensorflow:__subpackages__"], deps = [ + "//tensorflow/python:list_ops", "//tensorflow/python:script_ops", "@six_archive//:six", ], ) py_test( - name = "context_managers_test", - srcs = ["context_managers_test.py"], + name = "builtins_test", + srcs = ["builtins_test.py"], srcs_version = "PY2AND3", deps = [ ":utils", @@ -47,8 +50,8 @@ py_test( ) py_test( - name = "misc_test", - srcs = ["misc_test.py"], + name = "context_managers_test", + srcs = ["context_managers_test.py"], srcs_version = "PY2AND3", deps = [ ":utils", @@ -57,8 +60,8 @@ py_test( ) py_test( - name = "multiple_dispatch_test", - srcs = ["multiple_dispatch_test.py"], + name = "misc_test", + srcs = ["misc_test.py"], srcs_version = "PY2AND3", deps = [ ":utils", @@ -67,8 +70,8 @@ py_test( ) py_test( - name = "py_func_test", - srcs = ["py_func_test.py"], + name = "multiple_dispatch_test", + srcs = ["multiple_dispatch_test.py"], srcs_version = "PY2AND3", deps = [ ":utils", @@ -77,9 +80,10 @@ py_test( ) py_test( - name = "printing_test", - srcs = ["printing_test.py"], + name = "py_func_test", + srcs = ["py_func_test.py"], srcs_version = "PY2AND3", + tags = ["no_windows"], # TODO: needs investigation on Windows deps = [ ":utils", "//tensorflow/python:client_testlib", diff --git a/tensorflow/contrib/py2tf/utils/__init__.py b/tensorflow/contrib/py2tf/utils/__init__.py index d931322bf34cc36b614e587bbf5a36f5c1a4e38c..d9d8e3468966bc9da31c3fc756a9660f5ff7d115 100644 --- a/tensorflow/contrib/py2tf/utils/__init__.py +++ b/tensorflow/contrib/py2tf/utils/__init__.py @@ -18,11 +18,17 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.contrib.py2tf.utils.builtins import dynamic_builtin +from tensorflow.contrib.py2tf.utils.builtins import dynamic_print +from tensorflow.contrib.py2tf.utils.builtins import dynamic_range from tensorflow.contrib.py2tf.utils.context_managers import control_dependency_on_returns from tensorflow.contrib.py2tf.utils.misc import alias_tensors -from tensorflow.contrib.py2tf.utils.misc import dynamic_len +from tensorflow.contrib.py2tf.utils.multiple_dispatch import dynamic_is +from tensorflow.contrib.py2tf.utils.multiple_dispatch import dynamic_is_not from tensorflow.contrib.py2tf.utils.multiple_dispatch import run_cond from tensorflow.contrib.py2tf.utils.multiple_dispatch import run_while -from tensorflow.contrib.py2tf.utils.printing import call_print from tensorflow.contrib.py2tf.utils.py_func import wrap_py_func +from tensorflow.contrib.py2tf.utils.tensor_list import dynamic_list_append +from tensorflow.contrib.py2tf.utils.testing import fake_tf from tensorflow.contrib.py2tf.utils.type_check import is_tensor +from tensorflow.contrib.py2tf.utils.type_hints import set_element_type diff --git a/tensorflow/contrib/py2tf/utils/builtins.py b/tensorflow/contrib/py2tf/utils/builtins.py new file mode 100644 index 0000000000000000000000000000000000000000..3cb62b55d4d23545af4d641ecab1663ee7f7b876 --- /dev/null +++ b/tensorflow/contrib/py2tf/utils/builtins.py @@ -0,0 +1,99 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Builtin conversion utilities.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import six + +from tensorflow.contrib.py2tf.utils import py_func +from tensorflow.contrib.py2tf.utils import type_check +from tensorflow.python.framework import tensor_util +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import logging_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.util import tf_inspect + + +def dynamic_builtin(f, *args, **kwargs): + """Converts a builtin function call inline.""" + # Some built-ins may be objects. + if not tf_inspect.isbuiltin(f) and f not in (range,): + return f(*args, **kwargs) + + if f is len: + return dynamic_len(*args, **kwargs) + if six.PY2 and f is xrange: + return dynamic_range(*args, **kwargs) + if f is range: + return dynamic_range(*args, **kwargs) + + raise NotImplementedError( + 'The "%s" builtin is not yet supported.' % f.__name__) + + +def dynamic_len(list_or_tensor): + """Implementation of len using dynamic dispatch.""" + if tensor_util.is_tensor(list_or_tensor): + shape = list_or_tensor.shape + if not shape: + raise ValueError( + 'len requires non-zero rank for tensor "%s"' % list_or_tensor) + return array_ops.shape(list_or_tensor)[0] + + return len(list_or_tensor) + + +def dynamic_range(start_or_stop, stop=None, step=None): + """Implementation of range using dynamic dispatch.""" + if type_check.is_tensor(start_or_stop, stop, step): + if step is not None: + return math_ops.range(start_or_stop, stop, step) + if stop is not None: + return math_ops.range(start_or_stop, stop) + return math_ops.range(start_or_stop) + + if step is not None: + return range(start_or_stop, stop, step) + elif stop is not None: + return range(start_or_stop, stop) + return range(start_or_stop) + + +def is_tf_print_compatible(value): + # TODO(mdan): Enable once we can reliably test this. + # This is currently disabled because we can't capture the output of + # op kernels from Python. + del value + return False + + +def dynamic_print(*values): + """Implementartion of print using dynamic dispatch. + + The function attempts to use tf.Print if all the values are compatible. + Otherwise, it will fall back to py_func. + + Args: + *values: values to print + Returns: + A dummy value indicating the print completed. If tf. + """ + + if all(map(is_tf_print_compatible, values)): + return logging_ops.Print(1, values) + return py_func.wrap_py_func(print, None, values, use_dummy_return=True) diff --git a/tensorflow/contrib/py2tf/utils/builtins_test.py b/tensorflow/contrib/py2tf/utils/builtins_test.py new file mode 100644 index 0000000000000000000000000000000000000000..59b3573d38c5bd98f416c7b77d1bc772cb8069dd --- /dev/null +++ b/tensorflow/contrib/py2tf/utils/builtins_test.py @@ -0,0 +1,111 @@ +# 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 builtins module.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import sys + +import six + +from tensorflow.contrib.py2tf.utils import builtins +from tensorflow.python.framework import constant_op +from tensorflow.python.platform import test + + +class BuiltinsTest(test.TestCase): + + def test_dynamic_len_tf_scalar(self): + a = constant_op.constant(1) + + with self.assertRaises(ValueError): + with self.test_session() as sess: + sess.run(builtins.dynamic_builtin(len, a)) + + def test_dynamic_len_tf_array(self): + a = constant_op.constant([1, 2, 3]) + + with self.test_session() as sess: + self.assertEqual(3, sess.run(builtins.dynamic_builtin(len, a))) + + def test_dynamic_len_tf_matrix(self): + a = constant_op.constant([[1, 2], [3, 4]]) + + with self.test_session() as sess: + self.assertEqual(2, sess.run(builtins.dynamic_builtin(len, a))) + + def test_dynamic_len_py_list(self): + a = [3] * 5 + + self.assertEqual(5, builtins.dynamic_builtin(len, a)) + + def test_dynamic_range_all_python(self): + self.assertListEqual(list(builtins.dynamic_builtin(range, 3)), [0, 1, 2]) + self.assertListEqual(list(builtins.dynamic_builtin(range, 1, 3)), [1, 2]) + self.assertListEqual( + list(builtins.dynamic_builtin(range, 2, 0, -1)), [2, 1]) + + def test_dynamic_range_tf(self): + with self.test_session() as sess: + self.assertAllEqual( + sess.run(builtins.dynamic_builtin(range, constant_op.constant(3))), + [0, 1, 2]) + self.assertAllEqual( + sess.run(builtins.dynamic_builtin(range, 1, constant_op.constant(3))), + [1, 2]) + self.assertAllEqual( + sess.run( + builtins.dynamic_builtin(range, 2, 0, constant_op.constant(-1))), + [2, 1]) + + def test_dynamic_range_detection(self): + def range(x): # pylint:disable=redefined-builtin + return x + + # Functions that just have the names of builtins are ignored. + self.assertEqual(builtins.dynamic_builtin(range, 1), 1) + if six.PY2: + self.assertListEqual( + list(builtins.dynamic_builtin(xrange, 3)), [0, 1, 2]) + self.assertListEqual( + list(builtins.dynamic_builtin(six.moves.range, 3)), [0, 1, 2]) + self.assertListEqual( + list(builtins.dynamic_builtin(six.moves.xrange, 3)), [0, 1, 2]) + + def test_dynamic_print_tf(self): + try: + out_capturer = six.StringIO() + sys.stdout = out_capturer + with self.test_session() as sess: + sess.run(builtins.dynamic_print('test message', 1)) + self.assertEqual(out_capturer.getvalue(), 'test message 1\n') + finally: + sys.stdout = sys.__stdout__ + + def test_dynamic_print_complex(self): + try: + out_capturer = six.StringIO() + sys.stdout = out_capturer + with self.test_session() as sess: + sess.run(builtins.dynamic_print('test message', [1, 2])) + self.assertEqual(out_capturer.getvalue(), 'test message [1, 2]\n') + finally: + sys.stdout = sys.__stdout__ + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/py2tf/utils/context_managers.py b/tensorflow/contrib/py2tf/utils/context_managers.py index 38d9e11fe9069722b9023fee848bf53e1f72de6a..3d150a95817b83c4d7aaa78dc250092dcc4c5a9b 100644 --- a/tensorflow/contrib/py2tf/utils/context_managers.py +++ b/tensorflow/contrib/py2tf/utils/context_managers.py @@ -21,6 +21,7 @@ from __future__ import print_function import contextlib from tensorflow.python.framework import ops +from tensorflow.python.ops import tensor_array_ops def control_dependency_on_returns(return_value): @@ -34,9 +35,15 @@ def control_dependency_on_returns(return_value): Returns: A context manager. """ + def control_dependency_handle(t): + if isinstance(t, tensor_array_ops.TensorArray): + return t.flow + return t + if return_value is None: return contextlib.contextmanager(lambda: (yield))() # TODO(mdan): Filter to tensor objects. if not isinstance(return_value, (list, tuple)): return_value = (return_value,) + return_value = tuple(control_dependency_handle(t) for t in return_value) return ops.control_dependencies(return_value) diff --git a/tensorflow/contrib/py2tf/utils/context_managers_test.py b/tensorflow/contrib/py2tf/utils/context_managers_test.py index 633ba93540e696889a6b2b71b40b999da39d48ff..404f6e44e59d8bd6131367e3234843f03b351910 100644 --- a/tensorflow/contrib/py2tf/utils/context_managers_test.py +++ b/tensorflow/contrib/py2tf/utils/context_managers_test.py @@ -20,6 +20,8 @@ from __future__ import print_function from tensorflow.contrib.py2tf.utils import context_managers from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.ops import tensor_array_ops from tensorflow.python.platform import test @@ -32,6 +34,9 @@ class ContextManagersTest(test.TestCase): with context_managers.control_dependency_on_returns( constant_op.constant(1)): pass + with context_managers.control_dependency_on_returns( + tensor_array_ops.TensorArray(dtypes.int32, size=1)): + pass with context_managers.control_dependency_on_returns( [constant_op.constant(1), constant_op.constant(2)]): diff --git a/tensorflow/contrib/py2tf/utils/misc.py b/tensorflow/contrib/py2tf/utils/misc.py index 7548048388766d0f12a55eecd77fca2706f9734b..1b06caf0bdeb6f4a079e33f2e887d2dca017adc2 100644 --- a/tensorflow/contrib/py2tf/utils/misc.py +++ b/tensorflow/contrib/py2tf/utils/misc.py @@ -19,22 +19,9 @@ from __future__ import division from __future__ import print_function from tensorflow.python.framework import ops -from tensorflow.python.framework import tensor_util from tensorflow.python.ops import array_ops -def dynamic_len(list_or_tensor): - """Implementation of len using dynamic dispatch.""" - if tensor_util.is_tensor(list_or_tensor): - shape = list_or_tensor.shape - if not shape: - raise ValueError( - 'len requires non-zero rank for tensor "%s"' % list_or_tensor) - return array_ops.shape(list_or_tensor)[0] - - return len(list_or_tensor) - - def alias_tensors(*args): """Wrap any Tensor arguments with an identity op. diff --git a/tensorflow/contrib/py2tf/utils/misc_test.py b/tensorflow/contrib/py2tf/utils/misc_test.py index ec88e7cb74bd40b851fb3d2fe246d37d8c668d82..8aedd4cd64798660cc07364c45487399986c9be6 100644 --- a/tensorflow/contrib/py2tf/utils/misc_test.py +++ b/tensorflow/contrib/py2tf/utils/misc_test.py @@ -19,37 +19,12 @@ from __future__ import division from __future__ import print_function from tensorflow.contrib.py2tf.utils.misc import alias_tensors -from tensorflow.contrib.py2tf.utils.misc import dynamic_len from tensorflow.python.framework.constant_op import constant from tensorflow.python.ops.variables import Variable from tensorflow.python.platform import test -class ContextManagersTest(test.TestCase): - - def test_dynamic_len_tf_scalar(self): - a = constant(1) - - with self.assertRaises(ValueError): - with self.test_session() as sess: - sess.run(dynamic_len(a)) - - def test_dynamic_len_tf_array(self): - a = constant([1, 2, 3]) - - with self.test_session() as sess: - self.assertEqual(3, sess.run(dynamic_len(a))) - - def test_dynamic_len_tf_matrix(self): - a = constant([[1, 2], [3, 4]]) - - with self.test_session() as sess: - self.assertEqual(2, sess.run(dynamic_len(a))) - - def test_dynamic_len_py_list(self): - a = [3] * 5 - - self.assertEqual(5, dynamic_len(a)) +class MiscTest(test.TestCase): def test_alias_single_tensor(self): a = constant(1) diff --git a/tensorflow/contrib/py2tf/utils/multiple_dispatch.py b/tensorflow/contrib/py2tf/utils/multiple_dispatch.py index a855fdc075941915035d1e3380846ff912803494..da7a942703d83b55edbd1607cb49ad4137daeb04 100644 --- a/tensorflow/contrib/py2tf/utils/multiple_dispatch.py +++ b/tensorflow/contrib/py2tf/utils/multiple_dispatch.py @@ -22,6 +22,21 @@ import six from tensorflow.contrib.py2tf.utils.type_check import is_tensor from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import math_ops + + +def dynamic_is(left, right): + if is_tensor(left, right): + return math_ops.equal(left.name, right.name) + else: + return left is right + + +def dynamic_is_not(left, right): + if is_tensor(left, right): + return math_ops.not_equal(left.name, right.name) + else: + return left is not right def run_cond(condition, true_fn, false_fn): diff --git a/tensorflow/contrib/py2tf/utils/multiple_dispatch_test.py b/tensorflow/contrib/py2tf/utils/multiple_dispatch_test.py index 5bb4d4086b002211eebb86783bb7212c707a1418..8d89b6898a366fe90ee1d43a55d0a7f10690224b 100644 --- a/tensorflow/contrib/py2tf/utils/multiple_dispatch_test.py +++ b/tensorflow/contrib/py2tf/utils/multiple_dispatch_test.py @@ -17,6 +17,9 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function + +import numpy as np + from tensorflow.contrib.py2tf.utils import multiple_dispatch from tensorflow.python.client.session import Session from tensorflow.python.framework.constant_op import constant @@ -25,6 +28,33 @@ from tensorflow.python.platform import test class MultipleDispatchTest(test.TestCase): + def test_dynamic_is_python(self): + a = np.eye(3) + also_a = a + not_actually_a = np.eye(3) + should_be_true1 = multiple_dispatch.dynamic_is(a, also_a) + should_be_false1 = multiple_dispatch.dynamic_is_not(a, also_a) + should_be_true2 = multiple_dispatch.dynamic_is_not(a, not_actually_a) + should_be_false2 = multiple_dispatch.dynamic_is(a, not_actually_a) + self.assertTrue(should_be_true1) + self.assertTrue(should_be_true2) + self.assertFalse(should_be_false1) + self.assertFalse(should_be_false2) + + def test_dynamic_is_tf(self): + with Session().as_default(): + a = constant([2.0]) + also_a = a + not_actually_a = constant([2.0]) + should_be_true1 = multiple_dispatch.dynamic_is(a, also_a) + should_be_false1 = multiple_dispatch.dynamic_is_not(a, also_a) + should_be_true2 = multiple_dispatch.dynamic_is_not(a, not_actually_a) + should_be_false2 = multiple_dispatch.dynamic_is(a, not_actually_a) + self.assertTrue(should_be_true1.eval()) + self.assertTrue(should_be_true2.eval()) + self.assertFalse(should_be_false1.eval()) + self.assertFalse(should_be_false2.eval()) + def test_run_cond_python(self): true_fn = lambda: 2.0 false_fn = lambda: 3.0 diff --git a/tensorflow/contrib/py2tf/utils/tensor_list.py b/tensorflow/contrib/py2tf/utils/tensor_list.py index b6ff49e2a0eff384f10903e12212ab929e267804..2556f412891b4f0b954af5a6f0193341a6a5020a 100644 --- a/tensorflow/contrib/py2tf/utils/tensor_list.py +++ b/tensorflow/contrib/py2tf/utils/tensor_list.py @@ -18,7 +18,26 @@ 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 list_ops +from tensorflow.python.ops import tensor_array_ops + + +def dynamic_list_append(target, element): + """Converts a list append call inline.""" + if isinstance(target, tensor_array_ops.TensorArray): + return target.write(target.size(), element) + # TODO(mdan): What's the right way to check this? + # TODO(mdan): We may not need this branch. + # It may be possible to use TensorList alone if the loop body will not + # require wrapping it, although we'd have to think about an autoboxing + # mechanism for lists received as parameter. + if isinstance(target, ops.Tensor): + return list_ops.tensor_list_push_back(target, element) + + # Python targets (including TensorList): fallback to their original append. + target.append(element) + return target class TensorList(object): diff --git a/tensorflow/contrib/py2tf/utils/tensor_list_test.py b/tensorflow/contrib/py2tf/utils/tensor_list_test.py index b5e554a162674e08da21785dcbe193c54647f128..110e4d105e934d9d752afc2ccc0c53c99b70d41d 100644 --- a/tensorflow/contrib/py2tf/utils/tensor_list_test.py +++ b/tensorflow/contrib/py2tf/utils/tensor_list_test.py @@ -21,13 +21,41 @@ from __future__ import print_function from tensorflow.contrib.py2tf.utils import tensor_list as tl from tensorflow.python.client.session import Session from tensorflow.python.eager import context +from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework.constant_op import constant +from tensorflow.python.ops import list_ops +from tensorflow.python.ops import tensor_array_ops from tensorflow.python.platform import test class TensorListTest(test.TestCase): + def _shape(self, shape_tuple): + return constant(shape_tuple, dtypes.int32) + + def test_dynamic_list_append(self): + l = [] + l = tl.dynamic_list_append(l, 1) + self.assertListEqual(l, [1]) + + l = list_ops.empty_tensor_list(self._shape(()), dtypes.int32) + l = tl.dynamic_list_append(l, 1) + s = list_ops.tensor_list_stack(l, element_dtype=dtypes.int32) + with self.test_session() as sess: + self.assertAllEqual(sess.run(s), [1]) + + l = tensor_array_ops.TensorArray(dtypes.int32, size=0, dynamic_size=True) + l = tl.dynamic_list_append(l, 1) + s = l.stack() + with self.test_session() as sess: + self.assertAllEqual(sess.run(s), [1]) + + l = tl.TensorList(self._shape(()), dtypes.int32) + l = tl.dynamic_list_append(l, 1) + with self.test_session() as sess: + self.assertAllEqual(sess.run(l[0]), 1) + def test_list_append_python(self): with context.eager_mode(): a = constant(3.0) diff --git a/tensorflow/contrib/bayesflow/python/ops/custom_grad.py b/tensorflow/contrib/py2tf/utils/testing.py similarity index 65% rename from tensorflow/contrib/bayesflow/python/ops/custom_grad.py rename to tensorflow/contrib/py2tf/utils/testing.py index ca1ecb9c40204c3c723fa3423cfe148e823adc28..cb4785d0dc0f4674b3560418daeb6733364b21e7 100644 --- a/tensorflow/contrib/bayesflow/python/ops/custom_grad.py +++ b/tensorflow/contrib/py2tf/utils/testing.py @@ -12,23 +12,24 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Functions for specifying custom gradients. - -See ${python/contrib.bayesflow.custom_gradient}. -""" +"""Testing utilities.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function -# go/tf-wildcard-import -# pylint: disable=wildcard-import -from tensorflow.contrib.bayesflow.python.ops.custom_grad_impl import * -# pylint: enable=wildcard-import -from tensorflow.python.util.all_util import remove_undocumented +import imp + +from tensorflow.python.framework import ops +from tensorflow.python.ops import math_ops -_allowed_symbols = [ - 'custom_gradient', -] -remove_undocumented(__name__, _allowed_symbols) +def fake_tf(): + """Creates a fake module that looks like TensorFlow, for testing.""" + mod = imp.new_module('tensorflow') + mod_contents = dict() + mod_contents.update(math_ops.__dict__) + mod_contents.update(ops.__dict__) + mod_contents.update(mod.__dict__) + mod.__dict__.update(mod_contents) + return mod diff --git a/tensorflow/contrib/py2tf/utils/type_check.py b/tensorflow/contrib/py2tf/utils/type_check.py index 9ca2dec872c8a9ca7bedaa8603f70e3214a3e24a..b9b2b451a4e22684a19f0d10fbf5e4fae5d6152b 100644 --- a/tensorflow/contrib/py2tf/utils/type_check.py +++ b/tensorflow/contrib/py2tf/utils/type_check.py @@ -22,12 +22,12 @@ from tensorflow.python.framework import tensor_util def is_tensor(*args): - """Check if all arguments are tensors. + """Check if any arguments are tensors. Args: *args: Python objects that may or may not be tensors. Returns: - True if all *args are TensorFlow types, False if one or more are not. + True if any *args are TensorFlow types, False if none are. """ return any([tensor_util.is_tensor(a) for a in args]) diff --git a/tensorflow/contrib/bayesflow/python/ops/halton_sequence.py b/tensorflow/contrib/py2tf/utils/type_hints.py similarity index 54% rename from tensorflow/contrib/bayesflow/python/ops/halton_sequence.py rename to tensorflow/contrib/py2tf/utils/type_hints.py index 49d747d538f5a4aa3134d28ba00a651cb509fa41..aeb9e545610460afbe364dfcfc7a54b9aede29fe 100644 --- a/tensorflow/contrib/bayesflow/python/ops/halton_sequence.py +++ b/tensorflow/contrib/py2tf/utils/type_hints.py @@ -12,22 +12,30 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Support for low discrepancy Halton sequences. +"""No-op utilities that provide static type hints. +These are used when the data type is not known at creation, for instance in the +case of empty lists. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function -# go/tf-wildcard-import -# pylint: disable=wildcard-import -from tensorflow.contrib.bayesflow.python.ops.halton_sequence_impl import * -# pylint: enable=wildcard-import -from tensorflow.python.util.all_util import remove_undocumented -_allowed_symbols = [ - 'sample', -] +def set_element_type(entity, dtype, shape=None): + """Indicates that the entity is expected hold items of specified type. -remove_undocumented(__name__, _allowed_symbols) + This function is a no-op. Its presence merely marks the data type of its + argument. The staged TensorFlow ops will reflect and assert this data type. + + Args: + entity: A Tensor or TensorArray. + dtype: TensorFlow dtype value to assert for entity. + shape: Optional shape to assert for entity. + Returns: + The value of entity, unchanged. + """ + del dtype + del shape + return entity diff --git a/tensorflow/contrib/quantize/BUILD b/tensorflow/contrib/quantize/BUILD index aec9f47ccb20349c08bbe2fd813ee24a807f9fe3..0b7629620418340d803753be0df1f04c342dc490 100644 --- a/tensorflow/contrib/quantize/BUILD +++ b/tensorflow/contrib/quantize/BUILD @@ -24,6 +24,7 @@ py_test( "//tensorflow/python:framework_test_lib", "//tensorflow/python:platform_test", "//tensorflow/python:session", + "//tensorflow/python:variable_scope", ], ) diff --git a/tensorflow/contrib/quantize/README.md b/tensorflow/contrib/quantize/README.md index 8b0e7bb68f5a11f5d1942f7cf048e96768da259e..348c824a4072c3329ac4a3441c19c71598bc9c03 100644 --- a/tensorflow/contrib/quantize/README.md +++ b/tensorflow/contrib/quantize/README.md @@ -3,8 +3,7 @@ tf.contrib.quantize provides tools for transforming graphs to include ops to model quantization of weights, biases and activations during both training and inference. This is done using the -[fake quantization op] -(https://www.tensorflow.org/versions/r0.12/api_docs/python/array_ops/fake_quantization). +[fake quantization op](https://www.tensorflow.org/versions/r0.12/api_docs/python/array_ops/fake_quantization). Recent literature has shown that fixed point networks provide comparable performance to floating point networks [1]. This is achieved by modeling the diff --git a/tensorflow/contrib/quantize/python/common.py b/tensorflow/contrib/quantize/python/common.py index 3a1fa61e43986af1a1315d5a9e6f010e802ea157..3138149468c87b649f120a81b28c60947dcf2c4e 100644 --- a/tensorflow/contrib/quantize/python/common.py +++ b/tensorflow/contrib/quantize/python/common.py @@ -23,6 +23,7 @@ import re from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import state_ops from tensorflow.python.ops import variable_scope @@ -101,7 +102,7 @@ def CreateOrGetQuantizationStep(): Quantization step Tensor. """ quantization_step_name = 'fake_quantization_step' - quantization_step_tensor_name = quantization_step_name + '/AssignAdd:0' + quantization_step_tensor_name = quantization_step_name + '/Identity:0' g = ops.get_default_graph() try: return g.get_tensor_by_name(quantization_step_tensor_name) @@ -118,5 +119,7 @@ def CreateOrGetQuantizationStep(): with g.name_scope(quantization_step_tensor.op.name + '/'): # We return the incremented variable tensor. Since this is used in conds # for quant_delay and freeze_bn_delay, it will run once per graph - # execution. - return state_ops.assign_add(quantization_step_tensor, 1) + # execution. We return an identity to force resource variables and + # normal variables to return a tensor of the same name. + return array_ops.identity( + state_ops.assign_add(quantization_step_tensor, 1)) diff --git a/tensorflow/contrib/quantize/python/common_test.py b/tensorflow/contrib/quantize/python/common_test.py index d6237fe5e38d905bf262d7be3746b9ee6046da47..06c62f2d265503bf42d46fb682a398ce1f4d15fb 100644 --- a/tensorflow/contrib/quantize/python/common_test.py +++ b/tensorflow/contrib/quantize/python/common_test.py @@ -22,6 +22,7 @@ from tensorflow.contrib.quantize.python import common from tensorflow.python.client import session from tensorflow.python.framework import ops from tensorflow.python.framework import test_util +from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables from tensorflow.python.platform import googletest @@ -29,8 +30,15 @@ from tensorflow.python.platform import googletest class CommonTest(test_util.TensorFlowTestCase): def testCreateOrGetQuantizationStep(self): + self._TestCreateOrGetQuantizationStep(False) + + def testCreateOrGetQuantizationStepResourceVar(self): + self._TestCreateOrGetQuantizationStep(True) + + def _TestCreateOrGetQuantizationStep(self, use_resource): g = ops.Graph() with session.Session(graph=g) as sess: + variable_scope.get_variable_scope().set_use_resource(use_resource) quantization_step_tensor = common.CreateOrGetQuantizationStep() # Check that operations are added to the graph. diff --git a/tensorflow/contrib/quantize/python/fold_batch_norms.py b/tensorflow/contrib/quantize/python/fold_batch_norms.py index 75d9eb0e58d96e4bb2946684febd250e2e1a6b4a..1afcbb850496fa1f654b7340946e087d403d3549 100644 --- a/tensorflow/contrib/quantize/python/fold_batch_norms.py +++ b/tensorflow/contrib/quantize/python/fold_batch_norms.py @@ -31,6 +31,7 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn from tensorflow.python.ops import nn_ops +from tensorflow.python.ops import variable_scope from tensorflow.python.util import compat @@ -194,7 +195,7 @@ def _FindFusedBatchNorms(graph): layer_op = match_result.get_op(layer_pattern) layer_tensor = match_result.get_tensor(layer_pattern) bn_op = match_result.get_op(batch_norm_pattern) - batch_epsilon_tensor = bn_op.get_attr('epsilon') + batch_epsilon = bn_op.get_attr('epsilon') # In the MatMul case, the output of batch norm is reshaped back into a # 2D tensor, so the output_tensor is the output of the Reshape op. @@ -207,6 +208,11 @@ def _FindFusedBatchNorms(graph): continue output_tensor = output_reshape_op.outputs[0] + # Ensure that the output tensor has consumers, otherwise this is a dangling + # node and not a match. + if not output_tensor.consumers(): + continue + input_tensor = match_result.get_tensor(input_pattern) weight_tensor = match_result.get_tensor(weight_pattern) gamma_tensor = match_result.get_tensor(gamma_pattern) @@ -231,7 +237,7 @@ def _FindFusedBatchNorms(graph): # The batch variance used during forward and backward prop is biased, # i.e it is calculated as: V=sum(x(k)-mu)^2/N. For the moving average # calculation, the variance is corrected by the term N/N-1 (Bessel's - # correction). The variance tensor read from FuseBatchNorm has bessel's + # correction). The variance tensor read from FuseBatchNorm has Bessel's # correction applied, so we undo it here. scope, sep, _ = bn_op.name.rpartition('/') g = ops.get_default_graph() @@ -270,7 +276,7 @@ def _FindFusedBatchNorms(graph): moving_variance_tensor=moving_variance_tensor, bn_decay_mean_tensor=bn_decay_mean_tensor, bn_decay_var_tensor=bn_decay_var_tensor, - batch_epsilon_tensor=batch_epsilon_tensor) + batch_epsilon=batch_epsilon) def _ComputeBatchNormCorrections(context, match, freeze_batch_norm_delay, @@ -300,7 +306,7 @@ def _ComputeBatchNormCorrections(context, match, freeze_batch_norm_delay, Args: context: The scope under which we look for batch norm params - match: Object containg required batch norm tensors for correction + match: Object containing required batch norm tensors for correction computation. freeze_batch_norm_delay: Delay in steps at which computation switches from regular batch norm to frozen mean and variance. @@ -313,9 +319,8 @@ def _ComputeBatchNormCorrections(context, match, freeze_batch_norm_delay, g = ops.get_default_graph() with g.name_scope(context + '/batch_norm_correction'): recip_sigma_mv = math_ops.rsqrt( - match.moving_variance_tensor + match.batch_epsilon_tensor) - recip_sigma = math_ops.rsqrt( - match.variance_tensor + match.batch_epsilon_tensor) + match.moving_variance_tensor + match.batch_epsilon) + recip_sigma = math_ops.rsqrt(match.variance_tensor + match.batch_epsilon) correction_scale = math_ops.divide( recip_sigma_mv, recip_sigma, name='scale_compute') correction_scale = array_ops.identity( @@ -434,6 +439,9 @@ def _FoldUnfusedBatchNorms(graph, is_training, freeze_batch_norm_delay): for bn in common.BatchNormGroups(graph): has_scaling = _HasScaling(graph, input_to_ops_map, bn) + if not _IsValidUnfusedBatchNorm(graph, bn): + continue + # The mangling code intimately depends on BatchNorm node's internals. original_op, folded_op = _CreateFoldedOp( graph, @@ -462,6 +470,15 @@ def _FoldUnfusedBatchNorms(graph, is_training, freeze_batch_norm_delay): raise ValueError('Unexpected inputs to op: %s' % add_bypass.name) +def _IsValidUnfusedBatchNorm(graph, context): + """Checks that the output of the unfused batch norm has consumers.""" + add_shift = graph.get_operation_by_name( + context + '/BatchNorm/batchnorm/add_1') + # Ensure that the output tensor of batch norm has consumers, otherwise this + # is a dangling node and not a match. + return bool(add_shift.outputs[0].consumers()) + + def _GetBatchNormParams(graph, context, has_scaling): """Extracts relevant tensors for folding batch norms. @@ -478,7 +495,7 @@ def _GetBatchNormParams(graph, context, has_scaling): batch_variance_tensor = None moving_mean_tensor = None moving_variance_tensor = None - batch_epsilon_tensor = None + batch_epsilon = None bn_decay_mean_tensor = None bn_decay_var_tensor = None @@ -486,15 +503,23 @@ def _GetBatchNormParams(graph, context, has_scaling): base_context = split_context[-1] oplist = graph.get_operations() - op_suffix_gamma = base_context + '/BatchNorm/gamma' op_suffix_mean = base_context + '/BatchNorm/moments/Squeeze' op_suffix_variance = base_context + '/BatchNorm/moments/Squeeze_1' - op_suffix_moving_variance = base_context + '/BatchNorm/moving_variance/read' - op_suffix_moving_mean = base_context + '/BatchNorm/moving_mean/read' op_suffix_epsilon = base_context + '/BatchNorm/batchnorm/add/y' op_suffix_bn_decay_mean = base_context + '/BatchNorm/AssignMovingAvg/decay' op_suffix_bn_decay_var = base_context + '/BatchNorm/AssignMovingAvg_1/decay' + if variable_scope.get_variable_scope().use_resource: + op_suffix_gamma = base_context + '/BatchNorm/gamma/Read/ReadVariableOp' + op_suffix_moving_variance = ( + base_context + '/BatchNorm/moving_variance/Read/ReadVariableOp') + op_suffix_moving_mean = ( + base_context + '/BatchNorm/moving_mean/Read/ReadVariableOp') + else: + op_suffix_gamma = base_context + '/BatchNorm/gamma' + op_suffix_moving_variance = base_context + '/BatchNorm/moving_variance/read' + op_suffix_moving_mean = base_context + '/BatchNorm/moving_mean/read' + # Parse through list of ops to find relevant ops for op in oplist: if op.name.endswith(op_suffix_mean): @@ -509,7 +534,7 @@ def _GetBatchNormParams(graph, context, has_scaling): if op.name.endswith(op_suffix_moving_variance): moving_variance_tensor = graph.get_tensor_by_name(op.name + ':0') if op.name.endswith(op_suffix_epsilon): - batch_epsilon_tensor = graph.get_tensor_by_name(op.name + ':0') + batch_epsilon = graph.get_tensor_by_name(op.name + ':0') if op.name.endswith(op_suffix_bn_decay_mean): bn_decay_mean_tensor = graph.get_tensor_by_name(op.name + ':0') if op.name.endswith(op_suffix_bn_decay_var): @@ -535,7 +560,7 @@ def _GetBatchNormParams(graph, context, has_scaling): moving_variance_tensor=moving_variance_tensor, bn_decay_mean_tensor=bn_decay_mean_tensor, bn_decay_var_tensor=bn_decay_var_tensor, - batch_epsilon_tensor=batch_epsilon_tensor) + batch_epsilon=batch_epsilon) def _CreateFoldedOp(graph, context, has_scaling, freeze_batch_norm_delay, @@ -816,7 +841,7 @@ class _BatchNormMatch(object): def __init__(self, layer_op, bn_op, output_tensor, input_tensor, weight_tensor, gamma_tensor, beta_tensor, mean_tensor, variance_tensor, moving_mean_tensor, moving_variance_tensor, - bn_decay_mean_tensor, bn_decay_var_tensor, batch_epsilon_tensor): + bn_decay_mean_tensor, bn_decay_var_tensor, batch_epsilon): self._layer_op = layer_op self._bn_op = bn_op self._output_tensor = output_tensor @@ -830,7 +855,7 @@ class _BatchNormMatch(object): self._moving_variance_tensor = moving_variance_tensor self._bn_decay_mean_tensor = bn_decay_mean_tensor self._bn_decay_var_tensor = bn_decay_var_tensor - self._batch_epsilon_tensor = batch_epsilon_tensor + self._batch_epsilon = batch_epsilon @property def layer_op(self): @@ -877,8 +902,8 @@ class _BatchNormMatch(object): return self._moving_variance_tensor @property - def batch_epsilon_tensor(self): - return self._batch_epsilon_tensor + def batch_epsilon(self): + return self._batch_epsilon @property def bn_decay_mean_tensor(self): diff --git a/tensorflow/contrib/quantize/python/quant_ops.py b/tensorflow/contrib/quantize/python/quant_ops.py index 0a8e35080cb08f71dc28e33c6138a12656e5a5ea..a4f7b1b22139588be29171126d43b872d6658168 100644 --- a/tensorflow/contrib/quantize/python/quant_ops.py +++ b/tensorflow/contrib/quantize/python/quant_ops.py @@ -282,8 +282,8 @@ def _FakeQuantWithMinMaxVars(inputs, min_var, max_var, per_channel, num_bits, Args: inputs: a tensor containing values to be quantized. min_var: a variable containing quantization range lower end(s). - max_var: a variable containing quantization range lupper end(s). - per_channel: a boolean specifying whether to use per-channel quantizatioh. + max_var: a variable containing quantization range upper end(s). + per_channel: a boolean specifying whether to use per-channel quantization. num_bits: Number of bits to use for quantization, must be between 2 and 8. narrow_range: Whether to use the narrow quantization range [1; 2^num_bits - 1] or wide range [0; 2^num_bits - 1]. diff --git a/tensorflow/contrib/quantize/python/quantize.py b/tensorflow/contrib/quantize/python/quantize.py index 5fd806d195dce671d079386ea4b6c89042e26cf6..ec721afbc8d1bedd9214ceeb38286af62aad5d65 100644 --- a/tensorflow/contrib/quantize/python/quantize.py +++ b/tensorflow/contrib/quantize/python/quantize.py @@ -35,8 +35,7 @@ _QUANTIZABLE_TYPES = {'Conv2D', 'MatMul', 'DepthwiseConv2dNative'} _ACTIVATION_TYPES = {'Relu', 'Relu6', 'Identity'} # Weight types that are supported by the quantization rewrite. -# TODO(suharshs): Add support for ResourceVariable. -_WEIGHT_TYPES = {'Variable', 'VariableV2'} +_WEIGHT_TYPES = {'Variable', 'VariableV2', 'VarHandleOp'} def Quantize(graph, @@ -137,7 +136,7 @@ def _FindLayersToQuantize(graph): input_pattern = graph_matcher.OpTypePattern('*') weight_var_pattern = graph_matcher.OpTypePattern('|'.join(_WEIGHT_TYPES)) weight_pattern = graph_matcher.OpTypePattern( - 'Identity', inputs=[weight_var_pattern]) + 'Identity|ReadVariableOp', inputs=[weight_var_pattern]) folded_weight_pattern = graph_matcher.OpTypePattern('Mul') @@ -268,7 +267,7 @@ def _InsertQuantOp(context, """Inserts a quant op between a producer op and (multiple) consumer ops. Args: - context: Context w,here producer and consumer operations are nested. + context: Context where producer and consumer operations are nested. name: Name for the new quantization op within the context. producer: Producer operation of the pairs where quantization will be inserted. diff --git a/tensorflow/contrib/quantize/python/quantize_graph.py b/tensorflow/contrib/quantize/python/quantize_graph.py index 5a3a74cec4864ad3808d485849334c81f569d300..5abdcd2475ce481f5394e9d6e664f0aa2a928d4f 100644 --- a/tensorflow/contrib/quantize/python/quantize_graph.py +++ b/tensorflow/contrib/quantize/python/quantize_graph.py @@ -158,7 +158,7 @@ def experimental_create_training_graph(input_graph=None, often fail. Args: - input_graph: The tf.Graph to be transformed,if None then defaults to the + input_graph: The tf.Graph to be transformed, if None then defaults to the default graph. weight_bits: Number of bits to use for quantizing weights. activation_bits: Number of bits to use for quantizing activations. diff --git a/tensorflow/contrib/quantize/python/quantize_parameterized_test.py b/tensorflow/contrib/quantize/python/quantize_parameterized_test.py index 639a7454a92aebd7289c59498cebff82cc003f75..db745aa56212af6a9c20e06ee9e4e5d6e27cf3c3 100644 --- a/tensorflow/contrib/quantize/python/quantize_parameterized_test.py +++ b/tensorflow/contrib/quantize/python/quantize_parameterized_test.py @@ -28,6 +28,7 @@ from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn_ops +from tensorflow.python.ops import variable_scope from tensorflow.python.platform import googletest batch_norm = layers.batch_norm @@ -56,52 +57,46 @@ class QuantizeTest(test_util.TensorFlowTestCase): (array_ops.identity, 'Identity', True, 5000), ] for params in parameters_list: - test_fn(params[0], params[1], params[2], params[3]) + # Test everything with resource variables and normal variables. + test_fn(params[0], params[1], params[2], params[3], False) + test_fn(params[0], params[1], params[2], params[3], True) - def _TestQuantize_Conv2dWithoutBatchNorm(self, activation, activation_op_name, - with_bypass, delay): - """Tests quantization: inputs -> Conv2d no batch norm -> Activation. - - Args: - activation: Callable that returns an Operation, a factory method for the - Activation. - activation_op_name: String, name of the Activation operation. - with_bypass: Bool, when true there is an extra connection added from - inputs to just before Activation. - delay: Int (optional), delay in number of steps until quantization starts. - """ - graph = ops.Graph() - with graph.as_default(): - batch_size, height, width, depth = 5, 128, 128, 3 - inputs = array_ops.zeros((batch_size, height, width, depth)) - stride = 1 if with_bypass else 2 - out_depth = 3 if with_bypass else 32 - activation_fn = None if with_bypass else activation - scope = 'test/test2' if with_bypass else 'test' - node = conv2d(inputs, out_depth, [5, 5], stride=stride, padding='SAME', - weights_initializer=self._WeightInit(0.09), - activation_fn=activation_fn, scope=scope) - if with_bypass: - node = math_ops.add(inputs, node, name='test/Add') - node = activation(node, name='test/' + activation_op_name) - update_barrier = control_flow_ops.no_op(name='update_barrier') - with ops.control_dependencies([update_barrier]): - array_ops.identity(node, name='control_dependency') - - quantize.Quantize(graph, True, quant_delay=delay) + def _AssertCorrectQuantizedGraphWithoutBatchNorm( + self, graph, scope, layer, activation_op_name, with_bypass, delay, + use_resource): quantization_node_name = 'FakeQuantWithMinMaxVars' weights_quant = graph.get_operation_by_name(scope + '/weights_quant/' + quantization_node_name) self.assertEqual(weights_quant.type, quantization_node_name) - expected_inputs = [ - scope + '/weights_quant/AssignMinLast', - scope + '/weights_quant/AssignMaxLast', scope + '/weights/read' - ] + + # Assemble the expected inputs. + if use_resource: + expected_inputs = [ + scope + '/weights_quant/FakeQuantWithMinMaxVars/ReadVariableOp', + scope + '/weights_quant/FakeQuantWithMinMaxVars/ReadVariableOp_1', + ] + if layer == 'DepthwiseConv2dNative': + expected_inputs.append(scope + '/depthwise/ReadVariableOp') + else: + expected_inputs.append(scope + '/' + layer + '/ReadVariableOp') + else: + expected_inputs = [ + scope + '/weights_quant/AssignMinLast', + scope + '/weights_quant/AssignMaxLast', + ] + if layer == 'DepthwiseConv2dNative': + expected_inputs.append(scope + '/depthwise_weights/read') + else: + expected_inputs.append(scope + '/weights/read') + self._AssertInputOpsAre(weights_quant, expected_inputs) if delay and delay > 0: output_op_name = scope + '/weights_quant/delayed_quant/Switch_1' else: - output_op_name = scope + '/Conv2D' + if layer == 'DepthwiseConv2dNative': + output_op_name = scope + '/depthwise' + else: + output_op_name = scope + '/' + layer self._AssertOutputGoesToOps(weights_quant, graph, [output_op_name]) @@ -109,10 +104,17 @@ class QuantizeTest(test_util.TensorFlowTestCase): conv_quant = graph.get_operation_by_name(scope + '/conv_quant/' + quantization_node_name) self.assertEqual(conv_quant.type, quantization_node_name) - expected_inputs = [ - scope + '/conv_quant/AssignMinEma', - scope + '/conv_quant/AssignMaxEma', scope + '/BiasAdd' - ] + if use_resource: + expected_inputs = [ + scope + '/conv_quant/FakeQuantWithMinMaxVars/ReadVariableOp', + scope + '/conv_quant/FakeQuantWithMinMaxVars/ReadVariableOp_1', + scope + '/BiasAdd', + ] + else: + expected_inputs = [ + scope + '/conv_quant/AssignMinEma', + scope + '/conv_quant/AssignMaxEma', scope + '/BiasAdd' + ] self._AssertInputOpsAre(conv_quant, expected_inputs) output_op_name = (scope + '/conv_quant/delayed_quant/Switch_1' if delay else 'test/Add') @@ -121,22 +123,76 @@ class QuantizeTest(test_util.TensorFlowTestCase): act_quant = graph.get_operation_by_name('test/act_quant/' + quantization_node_name) self.assertEqual(act_quant.type, quantization_node_name) - - expected_inputs = [ - 'test/act_quant/AssignMinEma', 'test/act_quant/AssignMaxEma', - 'test/' + activation_op_name - ] + if use_resource: + expected_inputs = [ + 'test/act_quant/FakeQuantWithMinMaxVars/ReadVariableOp', + 'test/act_quant/FakeQuantWithMinMaxVars/ReadVariableOp_1', + 'test/' + activation_op_name, + ] + else: + expected_inputs = [ + 'test/act_quant/AssignMinEma', 'test/act_quant/AssignMaxEma', + 'test/' + activation_op_name + ] self._AssertInputOpsAre(act_quant, expected_inputs) output_op_name = ('test/act_quant/delayed_quant/Switch_1' if delay else 'control_dependency') self._AssertOutputGoesToOps(act_quant, graph, [output_op_name]) + self._AssertIdempotent(graph) def testQuantize_Conv2dWithoutBatchNorm(self): self._RunWithoutBatchNormTestOverParameters( self._TestQuantize_Conv2dWithoutBatchNorm) + def _TestQuantize_Conv2dWithoutBatchNorm(self, activation, activation_op_name, + with_bypass, delay, use_resource): + """Tests quantization: inputs -> Conv2d no batch norm -> Activation. + + Args: + activation: Callable that returns an Operation, a factory method for the + Activation. + activation_op_name: String, name of the Activation operation. + with_bypass: Bool, when true there is an extra connection added from + inputs to just before Activation. + delay: Int (optional), delay in number of steps until quantization starts. + use_resource: Bool, when true uses resource variables. + """ + graph = ops.Graph() + with graph.as_default(): + variable_scope.get_variable_scope().set_use_resource(use_resource) + batch_size, height, width, depth = 5, 128, 128, 3 + inputs = array_ops.zeros((batch_size, height, width, depth)) + stride = 1 if with_bypass else 2 + out_depth = 3 if with_bypass else 32 + activation_fn = None if with_bypass else activation + scope = 'test/test2' if with_bypass else 'test' + node = conv2d( + inputs, + out_depth, [5, 5], + stride=stride, + padding='SAME', + weights_initializer=self._WeightInit(0.09), + activation_fn=activation_fn, + scope=scope) + if with_bypass: + node = math_ops.add(inputs, node, name='test/Add') + node = activation(node, name='test/' + activation_op_name) + update_barrier = control_flow_ops.no_op(name='update_barrier') + with ops.control_dependencies([update_barrier]): + array_ops.identity(node, name='control_dependency') + + quantize.Quantize(graph, True, quant_delay=delay) + + self._AssertCorrectQuantizedGraphWithoutBatchNorm( + graph, scope, 'Conv2D', activation_op_name, with_bypass, delay, + use_resource) + + def testQuantize_FCWithoutBatchNorm(self): + self._RunWithoutBatchNormTestOverParameters( + self._TestQuantize_FCWithoutBatchNorm) + def _TestQuantize_FCWithoutBatchNorm(self, activation, activation_op_name, - with_bypass, delay): + with_bypass, delay, use_resource): """Tests quantization: inputs -> FC no batch norm -> Activation. Args: @@ -146,72 +202,40 @@ class QuantizeTest(test_util.TensorFlowTestCase): with_bypass: Bool, when true there is an extra connection added from inputs to just before Activation. delay: Int (optional), delay in number of steps until quantization starts. + use_resource: Bool, when true uses resource variables. """ graph = ops.Graph() with graph.as_default(): + variable_scope.get_variable_scope().set_use_resource(use_resource) batch_size, depth = 5, 256 inputs = array_ops.zeros((batch_size, depth)) out_depth = 256 if with_bypass else 128 activation_fn = None if with_bypass else activation scope = 'test/test2' if with_bypass else 'test' - node = fully_connected(inputs, out_depth, - weights_initializer=self._WeightInit(0.03), - activation_fn=activation_fn, scope=scope) + node = fully_connected( + inputs, + out_depth, + weights_initializer=self._WeightInit(0.03), + activation_fn=activation_fn, + scope=scope) if with_bypass: node = math_ops.add(inputs, node, name='test/Add') node = activation(node, name='test/' + activation_op_name) update_barrier = control_flow_ops.no_op(name='update_barrier') with ops.control_dependencies([update_barrier]): array_ops.identity(node, name='control_dependency') - quantize.Quantize(graph, True, quant_delay=delay) - quantization_node_name = 'FakeQuantWithMinMaxVars' - weights_quant = graph.get_operation_by_name(scope + '/weights_quant/' + - quantization_node_name) - self.assertEqual(weights_quant.type, quantization_node_name) - expected_inputs = [ - scope + '/weights_quant/AssignMinLast', - scope + '/weights_quant/AssignMaxLast', scope + '/weights/read' - ] - self._AssertInputOpsAre(weights_quant, expected_inputs) - if delay and delay > 0: - output_op_name = scope + '/weights_quant/delayed_quant/Switch_1' - else: - output_op_name = scope + '/MatMul' - self._AssertOutputGoesToOps(weights_quant, graph, [output_op_name]) - - if with_bypass: - conv_quant = graph.get_operation_by_name(scope + '/conv_quant/' + - quantization_node_name) - self.assertEqual(conv_quant.type, quantization_node_name) - expected_inputs = [ - scope + '/conv_quant/AssignMinEma', - scope + '/conv_quant/AssignMaxEma', scope + '/BiasAdd' - ] - self._AssertInputOpsAre(conv_quant, expected_inputs) - output_op_name = (scope + '/conv_quant/delayed_quant/Switch_1' - if delay else 'test/Add') - self._AssertOutputGoesToOps(conv_quant, graph, [output_op_name]) - - act_quant = graph.get_operation_by_name('test/act_quant/' + - quantization_node_name) - self.assertEqual(act_quant.type, quantization_node_name) - expected_inputs = [ - 'test/act_quant/AssignMinEma', 'test/act_quant/AssignMaxEma', - 'test/' + activation_op_name - ] - self._AssertInputOpsAre(act_quant, expected_inputs) - output_op_name = ('test/act_quant/delayed_quant/Switch_1' - if delay else 'control_dependency') - self._AssertOutputGoesToOps(act_quant, graph, [output_op_name]) + self._AssertCorrectQuantizedGraphWithoutBatchNorm( + graph, scope, 'MatMul', activation_op_name, with_bypass, delay, + use_resource) - def testQuantize_FCWithoutBatchNorm(self): + def testQuantize_DepthwiseConv2dWithoutBatchNorm(self): self._RunWithoutBatchNormTestOverParameters( - self._TestQuantize_FCWithoutBatchNorm) + self._TestQuantize_DepthwiseConv2dWithoutBatchNorm) def _TestQuantize_DepthwiseConv2dWithoutBatchNorm( - self, activation, activation_op_name, with_bypass, delay): + self, activation, activation_op_name, with_bypass, delay, use_resource): """Tests quantization: inputs -> DWConv2d no batch norm -> Activation. Args: @@ -221,71 +245,36 @@ class QuantizeTest(test_util.TensorFlowTestCase): with_bypass: Bool, when true there is an extra connection added from inputs to just before Activation. delay: Int (optional), delay in number of steps until quantization starts. + use_resource: Bool, when true uses resource variables. """ graph = ops.Graph() with graph.as_default(): + variable_scope.get_variable_scope().set_use_resource(use_resource) batch_size, height, width, depth = 5, 128, 128, 3 inputs = array_ops.zeros((batch_size, height, width, depth)) stride = 1 if with_bypass else 2 activation_fn = None if with_bypass else activation scope = 'test/test2' if with_bypass else 'test' - node = separable_conv2d(inputs, None, [5, 5], stride=stride, - depth_multiplier=1.0, padding='SAME', - weights_initializer=self._WeightInit(0.09), - activation_fn=activation_fn, scope=scope) + node = separable_conv2d( + inputs, + None, [5, 5], + stride=stride, + depth_multiplier=1.0, + padding='SAME', + weights_initializer=self._WeightInit(0.09), + activation_fn=activation_fn, + scope=scope) if with_bypass: node = math_ops.add(inputs, node, name='test/Add') node = activation(node, name='test/' + activation_op_name) update_barrier = control_flow_ops.no_op(name='update_barrier') with ops.control_dependencies([update_barrier]): array_ops.identity(node, name='control_dependency') - quantize.Quantize(graph, True, quant_delay=delay) - quantization_node_name = 'FakeQuantWithMinMaxVars' - weights_quant = graph.get_operation_by_name(scope + '/weights_quant/' + - quantization_node_name) - self.assertEqual(weights_quant.type, quantization_node_name) - expected_inputs = [ - scope + '/weights_quant/AssignMinLast', - scope + '/weights_quant/AssignMaxLast', - scope + '/depthwise_weights/read' - ] - self._AssertInputOpsAre(weights_quant, expected_inputs) - if delay and delay > 0: - output_op_name = scope + '/weights_quant/delayed_quant/Switch_1' - else: - output_op_name = scope + '/depthwise' - self._AssertOutputGoesToOps(weights_quant, graph, [output_op_name]) - - if with_bypass: - conv_quant = graph.get_operation_by_name(scope + '/conv_quant/' + - quantization_node_name) - self.assertEqual(conv_quant.type, quantization_node_name) - expected_inputs = [ - scope + '/conv_quant/AssignMinEma', - scope + '/conv_quant/AssignMaxEma', scope + '/BiasAdd' - ] - self._AssertInputOpsAre(conv_quant, expected_inputs) - output_op_name = (scope + '/conv_quant/delayed_quant/Switch_1' - if delay else 'test/Add') - self._AssertOutputGoesToOps(conv_quant, graph, [output_op_name]) - - act_quant = graph.get_operation_by_name('test/act_quant/' + - quantization_node_name) - self.assertEqual(act_quant.type, quantization_node_name) - expected_inputs = [ - 'test/act_quant/AssignMinEma', 'test/act_quant/AssignMaxEma', - 'test/' + activation_op_name - ] - self._AssertInputOpsAre(act_quant, expected_inputs) - output_op_name = ('test/act_quant/delayed_quant/Switch_1' - if delay else 'control_dependency') - self._AssertOutputGoesToOps(act_quant, graph, [output_op_name]) - - def testQuantize_DepthwiseConv2dWithoutBatchNorm(self): - self._RunWithoutBatchNormTestOverParameters( - self._TestQuantize_DepthwiseConv2dWithoutBatchNorm) + self._AssertCorrectQuantizedGraphWithoutBatchNorm( + graph, scope, 'DepthwiseConv2dNative', activation_op_name, with_bypass, + delay, use_resource) def _RunBatchNormTestOverParameters(self, test_fn): # TODO(suharshs): Use parameterized test once OSS TF supports it. @@ -317,13 +306,88 @@ class QuantizeTest(test_util.TensorFlowTestCase): (array_ops.identity, 'Identity', True, 5000, True) ] for params in parameters_list: - test_fn(params[0], params[1], params[2], params[3], params[4]) + # Test everything with resource variables and normal variables. + test_fn(params[0], params[1], params[2], params[3], params[4], False) + test_fn(params[0], params[1], params[2], params[3], params[4], True) + + def _AssertCorrectQuantizedGraphWithBatchNorm(self, graph, scope, layer, + activation_op_name, with_bypass, + delay, use_resource): + quantization_node_name = 'FakeQuantWithMinMaxVars' + weights_quant = graph.get_operation_by_name( + scope + '/weights_quant/' + quantization_node_name) + self.assertEqual(weights_quant.type, quantization_node_name) + if use_resource: + expected_inputs = [ + scope + '/weights_quant/FakeQuantWithMinMaxVars/ReadVariableOp', + scope + '/weights_quant/FakeQuantWithMinMaxVars/ReadVariableOp_1', + ] + else: + expected_inputs = [ + scope + '/weights_quant/' + 'AssignMinLast', + scope + '/weights_quant/' + 'AssignMaxLast' + ] + expected_inputs.append(scope + '/mul_fold') + + self._AssertInputOpsAre(weights_quant, expected_inputs) + if layer == 'DepthwiseConv2dNative': + output_op_name = scope + ('/weights_quant/delayed_quant/Switch_1' + if delay else '/depthwise_Fold') + else: + output_op_name = scope + ('/weights_quant/delayed_quant/Switch_1' + if delay else '/' + layer + '_Fold') + self._AssertOutputGoesToOps(weights_quant, graph, [output_op_name]) + + if with_bypass: + conv_quant = graph.get_operation_by_name( + scope + '/conv_quant/' + quantization_node_name) + self.assertEqual(conv_quant.type, quantization_node_name) + + if use_resource: + expected_inputs = [ + scope + '/conv_quant/FakeQuantWithMinMaxVars/ReadVariableOp', + scope + '/conv_quant/FakeQuantWithMinMaxVars/ReadVariableOp_1', + ] + else: + expected_inputs = [ + scope + '/conv_quant/AssignMinEma', + scope + '/conv_quant/AssignMaxEma', + ] + expected_inputs.append(scope + '/add_fold') + + self._AssertInputOpsAre(conv_quant, expected_inputs) + output_op_name = ( + scope + '/conv_quant/delayed_quant/Switch_1' if delay else 'test/Add') + self._AssertOutputGoesToOps(conv_quant, graph, [output_op_name]) + + act_quant = graph.get_operation_by_name( + 'test/act_quant/' + quantization_node_name) + self.assertEqual(act_quant.type, quantization_node_name) + + if use_resource: + expected_inputs = [ + 'test/act_quant/FakeQuantWithMinMaxVars/ReadVariableOp', + 'test/act_quant/FakeQuantWithMinMaxVars/ReadVariableOp_1', + ] + else: + expected_inputs = [ + 'test/act_quant/AssignMinEma', + 'test/act_quant/AssignMaxEma', + ] + expected_inputs.append('test/' + activation_op_name) + + self._AssertInputOpsAre(act_quant, expected_inputs) + output_op_name = ('test/act_quant/delayed_quant/Switch_1' + if delay else 'control_dependency') + self._AssertOutputGoesToOps(act_quant, graph, [output_op_name]) + self._AssertIdempotent(graph) def testQuantize_Conv2dWithBatchNorm(self): self._RunBatchNormTestOverParameters(self._TestQuantize_Conv2dWithBatchNorm) def _TestQuantize_Conv2dWithBatchNorm(self, activation, activation_op_name, - with_bypass, delay, fused_batch_norm): + with_bypass, delay, fused_batch_norm, + use_resource): """Tests quantization: inputs -> Conv2d with batch norm -> Activation. Args: @@ -334,9 +398,11 @@ class QuantizeTest(test_util.TensorFlowTestCase): inputs to just before Activation. delay: Int (optional), delay in number of steps until quantization starts. fused_batch_norm: Bool, when true use FusedBatchNorm. + use_resource: Bool, when true uses resource variables. """ graph = ops.Graph() with graph.as_default(): + variable_scope.get_variable_scope().set_use_resource(use_resource) batch_size, height, width, depth = 5, 128, 128, 3 inputs = array_ops.zeros((batch_size, height, width, depth)) stride = 1 if with_bypass else 2 @@ -353,7 +419,7 @@ class QuantizeTest(test_util.TensorFlowTestCase): normalizer_params=self._BatchNormParams(fused_batch_norm), scope=scope) - # Manually add a bypass (optionaly) and an activation. + # Manually add a bypass (optional) and an activation. if with_bypass: node = math_ops.add(inputs, node, name='test/Add') @@ -364,52 +430,18 @@ class QuantizeTest(test_util.TensorFlowTestCase): array_ops.identity(node, name='control_dependency') fold_batch_norms.FoldBatchNorms(graph, is_training=True) - quantize.Quantize(graph, True, quant_delay=delay) - quantization_node_name = 'FakeQuantWithMinMaxVars' - weights_quant = graph.get_operation_by_name(scope + '/weights_quant/' + - quantization_node_name) - self.assertEqual(weights_quant.type, quantization_node_name) - expected_inputs = [ - scope + '/weights_quant/' + 'AssignMinLast', - scope + '/weights_quant/' + 'AssignMaxLast', scope + '/mul_fold' - ] - self._AssertInputOpsAre(weights_quant, expected_inputs) - output_op_name = scope + ('/weights_quant/delayed_quant/Switch_1' - if delay else '/Conv2D_Fold') - self._AssertOutputGoesToOps(weights_quant, graph, [output_op_name]) - - if with_bypass: - conv_quant = graph.get_operation_by_name(scope + '/conv_quant/' + - quantization_node_name) - self.assertEqual(conv_quant.type, quantization_node_name) - expected_inputs = [ - scope + '/conv_quant/AssignMinEma', - scope + '/conv_quant/AssignMaxEma', scope + '/add_fold' - ] - self._AssertInputOpsAre(conv_quant, expected_inputs) - output_op_name = (scope + '/conv_quant/delayed_quant/Switch_1' - if delay else 'test/Add') - self._AssertOutputGoesToOps(conv_quant, graph, [output_op_name]) - - act_quant = graph.get_operation_by_name('test/act_quant/' + - quantization_node_name) - self.assertEqual(act_quant.type, quantization_node_name) - expected_inputs = [ - 'test/act_quant/AssignMinEma', 'test/act_quant/AssignMaxEma', - 'test/' + activation_op_name - ] - self._AssertInputOpsAre(act_quant, expected_inputs) - output_op_name = ('test/act_quant/delayed_quant/Switch_1' - if delay else 'control_dependency') - self._AssertOutputGoesToOps(act_quant, graph, [output_op_name]) + self._AssertCorrectQuantizedGraphWithBatchNorm( + graph, scope, 'Conv2D', activation_op_name, with_bypass, delay, + use_resource) def testQuantize_FCWithBatchNorm(self): self._RunBatchNormTestOverParameters(self._TestQuantize_FCWithBatchNorm) def _TestQuantize_FCWithBatchNorm(self, activation, activation_op_name, - with_bypass, delay, fused_batch_norm): + with_bypass, delay, fused_batch_norm, + use_resource): """Tests quantization: inputs -> FC with batch norm -> Activation. Args: @@ -420,9 +452,11 @@ class QuantizeTest(test_util.TensorFlowTestCase): inputs to just before Activation. delay: Int (optional), delay in number of steps until quantization starts. fused_batch_norm: Bool, when true use FusedBatchNorm. + use_resource: Bool, when true uses resource variables. """ graph = ops.Graph() with graph.as_default(): + variable_scope.get_variable_scope().set_use_resource(use_resource) batch_size, depth = 5, 256 inputs = array_ops.zeros((batch_size, depth)) out_depth = 256 if with_bypass else 128 @@ -436,7 +470,7 @@ class QuantizeTest(test_util.TensorFlowTestCase): normalizer_params=self._BatchNormParams(fused_batch_norm), scope=scope) - # Manually add a bypass (optionaly) and an activation. + # Manually add a bypass (optional) and an activation. if with_bypass: node = math_ops.add(inputs, node, name='test/Add') @@ -450,43 +484,9 @@ class QuantizeTest(test_util.TensorFlowTestCase): quantize.Quantize(graph, True, quant_delay=delay) - quantization_node_name = 'FakeQuantWithMinMaxVars' - weights_quant = graph.get_operation_by_name(scope + '/weights_quant/' + - quantization_node_name) - self.assertEqual(weights_quant.type, quantization_node_name) - expected_inputs = [ - scope + '/weights_quant/' + 'AssignMinLast', - scope + '/weights_quant/' + 'AssignMaxLast', scope + '/mul_fold' - ] - self._AssertInputOpsAre(weights_quant, expected_inputs) - output_op_name = scope + ('/weights_quant/delayed_quant/Switch_1' - if delay else '/MatMul_Fold') - self._AssertOutputGoesToOps(weights_quant, graph, [output_op_name]) - - if with_bypass: - conv_quant = graph.get_operation_by_name(scope + '/conv_quant/' + - quantization_node_name) - self.assertEqual(conv_quant.type, quantization_node_name) - expected_inputs = [ - scope + '/conv_quant/AssignMinEma', - scope + '/conv_quant/AssignMaxEma', scope + '/add_fold' - ] - self._AssertInputOpsAre(conv_quant, expected_inputs) - output_op_name = (scope + '/conv_quant/delayed_quant/Switch_1' - if delay else 'test/Add') - self._AssertOutputGoesToOps(conv_quant, graph, [output_op_name]) - - act_quant = graph.get_operation_by_name('test/act_quant/' + - quantization_node_name) - self.assertEqual(act_quant.type, quantization_node_name) - expected_inputs = [ - 'test/act_quant/AssignMinEma', 'test/act_quant/AssignMaxEma', - 'test/' + activation_op_name - ] - self._AssertInputOpsAre(act_quant, expected_inputs) - output_op_name = ('test/act_quant/delayed_quant/Switch_1' - if delay else 'control_dependency') - self._AssertOutputGoesToOps(act_quant, graph, [output_op_name]) + self._AssertCorrectQuantizedGraphWithBatchNorm( + graph, scope, 'MatMul', activation_op_name, with_bypass, delay, + use_resource) def testQuantize_DepthwiseConv2dWithBatchNorm(self): self._RunBatchNormTestOverParameters( @@ -494,7 +494,7 @@ class QuantizeTest(test_util.TensorFlowTestCase): def _TestQuantize_DepthwiseConv2dWithBatchNorm( self, activation, activation_op_name, with_bypass, delay, - fused_batch_norm): + fused_batch_norm, use_resource): """Tests quantization: inputs -> DWConv2d with batch norm -> Activation. Args: @@ -505,9 +505,11 @@ class QuantizeTest(test_util.TensorFlowTestCase): inputs to just before Activation. delay: Int (optional), delay in number of steps until quantization starts. fused_batch_norm: Bool, when true use FusedBatchNorm. + use_resource: Bool, when true uses resource variables. """ graph = ops.Graph() with graph.as_default(): + variable_scope.get_variable_scope().set_use_resource(use_resource) batch_size, height, width, depth = 5, 128, 128, 3 inputs = array_ops.zeros((batch_size, height, width, depth)) stride = 1 if with_bypass else 2 @@ -524,7 +526,7 @@ class QuantizeTest(test_util.TensorFlowTestCase): normalizer_params=self._BatchNormParams(fused_batch_norm), scope=scope) - # Manually add a bypass (optionaly) and an activation. + # Manually add a bypass (optional) and an activation. if with_bypass: node = math_ops.add(inputs, node, name='test/Add') @@ -535,45 +537,21 @@ class QuantizeTest(test_util.TensorFlowTestCase): array_ops.identity(node, name='control_dependency') fold_batch_norms.FoldBatchNorms(graph, is_training=True) - quantize.Quantize(graph, True, quant_delay=delay) - quantization_node_name = 'FakeQuantWithMinMaxVars' - weights_quant = graph.get_operation_by_name(scope + '/weights_quant/' + - quantization_node_name) - self.assertEqual(weights_quant.type, quantization_node_name) - expected_inputs = [ - scope + '/weights_quant/' + 'AssignMinLast', - scope + '/weights_quant/' + 'AssignMaxLast', scope + '/mul_fold' - ] - self._AssertInputOpsAre(weights_quant, expected_inputs) - output_op_name = scope + ('/weights_quant/delayed_quant/Switch_1' - if delay else '/depthwise_Fold') - self._AssertOutputGoesToOps(weights_quant, graph, [output_op_name]) - if with_bypass: - conv_quant = graph.get_operation_by_name(scope + '/conv_quant/' + - quantization_node_name) - self.assertEqual(conv_quant.type, quantization_node_name) - expected_inputs = [ - scope + '/conv_quant/AssignMinEma', - scope + '/conv_quant/AssignMaxEma', scope + '/add_fold' - ] - self._AssertInputOpsAre(conv_quant, expected_inputs) - output_op_name = (scope + '/conv_quant/delayed_quant/Switch_1' - if delay else 'test/Add') - self._AssertOutputGoesToOps(conv_quant, graph, [output_op_name]) + self._AssertCorrectQuantizedGraphWithBatchNorm( + graph, scope, 'DepthwiseConv2dNative', activation_op_name, + with_bypass, delay, use_resource) - act_quant = graph.get_operation_by_name('test/act_quant/' + - quantization_node_name) - self.assertEqual(act_quant.type, quantization_node_name) - expected_inputs = [ - 'test/act_quant/AssignMinEma', 'test/act_quant/AssignMaxEma', - 'test/' + activation_op_name - ] - self._AssertInputOpsAre(act_quant, expected_inputs) - output_op_name = ('test/act_quant/delayed_quant/Switch_1' - if delay else 'control_dependency') - self._AssertOutputGoesToOps(act_quant, graph, [output_op_name]) + def _AssertIdempotent(self, graph): + # Ensure that calling the rewrite again doesn't change the graph. + graph_def_before = str(graph.as_graph_def()) + with graph.as_default(): + # Ensuring that calling the rewrite again doesn't add more nodes. + fold_batch_norms.FoldBatchNorms(graph, is_training=True) + quantize.Quantize(graph, True) + graph_def_after = str(graph.as_graph_def()) + self.assertEqual(graph_def_before, graph_def_after) def _BatchNormParams(self, fused=False): return {'center': True, 'scale': True, 'decay': 1.0 - 0.003, 'fused': fused} @@ -587,7 +565,7 @@ class QuantizeTest(test_util.TensorFlowTestCase): stddev: Standard deviation of normal variable. Returns: - An initialized that initialzes with a truncated normal variable. + An initialized that initializes with a truncated normal variable. """ return init_ops.truncated_normal_initializer(stddev=stddev) diff --git a/tensorflow/contrib/quantize/python/quantize_test.py b/tensorflow/contrib/quantize/python/quantize_test.py index ef59475167137e203db2f6ca7f43c7b8f1938060..b2e5707a6d5bccd90ae9663ad7e8bf1a0482a297 100644 --- a/tensorflow/contrib/quantize/python/quantize_test.py +++ b/tensorflow/contrib/quantize/python/quantize_test.py @@ -144,7 +144,7 @@ class QuantizeTest(test_util.TensorFlowTestCase): stddev: Standard deviation of normal variable. Returns: - An initialized that initialzes with a truncated normal variable. + An initialized that initializes with a truncated normal variable. """ return init_ops.truncated_normal_initializer(stddev=stddev) diff --git a/tensorflow/contrib/remote_fused_graph/pylib/BUILD b/tensorflow/contrib/remote_fused_graph/pylib/BUILD index 27f0a7f58f78135f1d73ae04bd1e76ef496fa549..54c66271cd43c85bc7be139dbdf70b9178ad1622 100644 --- a/tensorflow/contrib/remote_fused_graph/pylib/BUILD +++ b/tensorflow/contrib/remote_fused_graph/pylib/BUILD @@ -38,7 +38,6 @@ py_test( size = "small", srcs = ["python/ops/remote_fused_graph_ops_test.py"], srcs_version = "PY2AND3", - tags = ["no_windows"], deps = [ ":remote_fused_graph_ops_py", "//tensorflow/core:protos_all_py", diff --git a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py index 0e62b315b61cb3ceeb5cfd33bf5102a71abef83b..d41fc0b3ac1cee4eacc88cb0f41df1f9ee59e7c3 100644 --- a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py +++ b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py @@ -187,6 +187,8 @@ class RNNCellTest(test.TestCase): ], state_is_tuple=False) self.assertEqual(cell.dtype, None) + self.assertEqual("cell-0", cell._checkpoint_dependencies[0].name) + self.assertEqual("cell-1", cell._checkpoint_dependencies[1].name) g, out_m = cell(x, m) # Layer infers the input type. self.assertEqual(cell.dtype, dtype.name) diff --git a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py index 57521c6a9ba0b2d66639017b09c541e270276323..de5df912921932056526e1e6dc5dbb905735f775 100644 --- a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py +++ b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py @@ -869,7 +869,7 @@ class LSTMTest(test.TestCase): num_proj = 4 max_length = 8 sequence_length = [4, 6] - in_graph_mode = context.in_graph_mode() + in_graph_mode = not context.executing_eagerly() with self.test_session(graph=ops_lib.Graph()) as sess: initializer = init_ops.random_uniform_initializer( -0.01, 0.01, seed=self._seed) @@ -934,8 +934,7 @@ class LSTMTest(test.TestCase): if in_graph_mode: self.assertAllEqual(outputs_static, outputs_dynamic) else: - self.assertAllEqual( - array_ops.stack(outputs_static).numpy(), outputs_dynamic.numpy()) + self.assertAllEqual(array_ops.stack(outputs_static), outputs_dynamic) self.assertAllEqual(np.hstack(state_static), np.hstack(state_dynamic)) @test_util.run_in_graph_and_eager_modes() @@ -946,7 +945,7 @@ class LSTMTest(test.TestCase): num_proj = 4 max_length = 8 sequence_length = [4, 6] - in_graph_mode = context.in_graph_mode() + in_graph_mode = not context.executing_eagerly() with self.test_session(graph=ops_lib.Graph()) as sess: initializer = init_ops.random_uniform_initializer( -0.01, 0.01, seed=self._seed) @@ -1022,10 +1021,9 @@ class LSTMTest(test.TestCase): if in_graph_mode: self.assertAllEqual(outputs_static, outputs_dynamic) else: - self.assertAllEqual( - array_ops.stack(outputs_static).numpy(), outputs_dynamic.numpy()) - state_static = [s.numpy() for s in nest.flatten(state_static)] - state_dynamic = [s.numpy() for s in nest.flatten(state_dynamic)] + self.assertAllEqual(array_ops.stack(outputs_static), outputs_dynamic) + state_static = nest.flatten(state_static) + state_dynamic = nest.flatten(state_dynamic) self.assertAllEqual(np.hstack(state_static), np.hstack(state_dynamic)) def _testDynamicEquivalentToStaticRNN(self, use_sequence_length): @@ -1043,7 +1041,7 @@ class LSTMTest(test.TestCase): else: sequence_length = None - in_graph_mode = context.in_graph_mode() + in_graph_mode = not context.executing_eagerly() # TODO(b/68017812): Eager ignores operation seeds, so we need to create a # single cell and reuse it across the static and dynamic RNNs. Remove this diff --git a/tensorflow/contrib/rnn/python/kernel_tests/rnn_cell_test.py b/tensorflow/contrib/rnn/python/kernel_tests/rnn_cell_test.py index 7b883ebc5d7756f1bdf445f900500a4b89e6cffd..63fdd91d368d97007280871f3886e5649e6b2e86 100644 --- a/tensorflow/contrib/rnn/python/kernel_tests/rnn_cell_test.py +++ b/tensorflow/contrib/rnn/python/kernel_tests/rnn_cell_test.py @@ -455,8 +455,8 @@ class RNNCellTest(test.TestCase): self.assertAllClose(np.concatenate(res[1], axis=1), expected_state) def testAttentionCellWrapperFailures(self): - with self.assertRaisesRegexp(TypeError, - "The parameter cell is not RNNCell."): + with self.assertRaisesRegexp( + TypeError, rnn_cell_impl.ASSERT_LIKE_RNNCELL_ERROR_REGEXP): contrib_rnn_cell.AttentionCellWrapper(None, 0) num_units = 8 @@ -878,7 +878,6 @@ class RNNCellTest(test.TestCase): shape = [2, 1] filter_size = [3] num_features = 1 - batch_size = 2 expected_state_c = np.array( [[[1.4375670191], [1.4375670191]], [[2.7542609292], [2.7542609292]]], dtype=np.float32) @@ -912,7 +911,6 @@ class RNNCellTest(test.TestCase): shape = [2, 2, 1] filter_size = [3, 3] num_features = 1 - batch_size = 2 expected_state_c = np.array( [[[[1.4375670191], [1.4375670191]], [[1.4375670191], [1.4375670191]]], [[[2.7542609292], [2.7542609292]], [[2.7542609292], [2.7542609292]] @@ -954,7 +952,6 @@ class RNNCellTest(test.TestCase): shape = [2, 2, 2, 1] filter_size = [3, 3, 3] num_features = 1 - batch_size = 2 expected_state_c = np.array( [[[[[1.4375670191], [1.4375670191]], [[1.4375670191], [1.4375670191]] ], [[[1.4375670191], [1.4375670191]], [[1.4375670191], @@ -1031,57 +1028,92 @@ class RNNCellTest(test.TestCase): num_units = 4 number_of_groups = 1 - with self.test_session() as sess: - with variable_scope.variable_scope( - "root1", initializer=init_ops.constant_initializer(0.5)): - x = array_ops.ones([batch_size, num_units]) - # When number_of_groups = 1, G-LSTM is equivalent to regular LSTM - gcell = contrib_rnn_cell.GLSTMCell( - num_units=num_units, number_of_groups=number_of_groups) - cell = rnn_cell.LSTMCell(num_units=num_units) - self.assertTrue(isinstance(gcell.state_size, tuple)) - zero_state = gcell.zero_state( - batch_size=batch_size, dtype=dtypes.float32) - gh, gs = gcell(x, zero_state) - h, g = cell(x, zero_state) + # Try with input dimension equal to num_units or not. + for num_inputs in [num_units, num_units + number_of_groups]: + with self.test_session() as sess: + with variable_scope.variable_scope( + "root1_%d" % num_inputs, + initializer=init_ops.constant_initializer(0.5)): + x = array_ops.ones([batch_size, num_inputs]) + # When number_of_groups = 1, G-LSTM is equivalent to regular LSTM + gcell = contrib_rnn_cell.GLSTMCell( + num_units=num_units, number_of_groups=number_of_groups) + cell = rnn_cell.LSTMCell(num_units=num_units) + self.assertTrue(isinstance(gcell.state_size, tuple)) + zero_state = gcell.zero_state( + batch_size=batch_size, dtype=dtypes.float32) + gh, gs = gcell(x, zero_state) + h, g = cell(x, zero_state) - sess.run([variables.global_variables_initializer()]) - glstm_result = sess.run([gh, gs]) - lstm_result = sess.run([h, g]) + sess.run([variables.global_variables_initializer()]) + glstm_result = sess.run([gh, gs]) + lstm_result = sess.run([h, g]) - self.assertAllClose(glstm_result[0], lstm_result[0], 1e-5) - self.assertAllClose(glstm_result[1], lstm_result[1], 1e-5) + self.assertAllClose(glstm_result[0], lstm_result[0], 1e-5) + self.assertAllClose(glstm_result[1], lstm_result[1], 1e-5) # Test that G-LSTM subgroup act like corresponding sub-LSTMs batch_size = 2 num_units = 4 number_of_groups = 2 - with self.test_session() as sess: + # Try with num_inputs equal to or not equal to num_units. + for num_inputs in [num_units, num_units + number_of_groups]: + with self.test_session() as sess: + with variable_scope.variable_scope( + "root2_%d" % num_inputs, + initializer=init_ops.constant_initializer(0.5)): + # input for G-LSTM with 2 groups + glstm_input = array_ops.ones([batch_size, num_inputs]) + gcell = contrib_rnn_cell.GLSTMCell( + num_units=num_units, number_of_groups=number_of_groups) + gcell_zero_state = gcell.zero_state( + batch_size=batch_size, dtype=dtypes.float32) + gh, gs = gcell(glstm_input, gcell_zero_state) + + # input for LSTM cell simulating single G-LSTM group + lstm_input = array_ops.ones( + [batch_size, num_inputs / number_of_groups]) + # note division by number_of_groups. This cell one simulates G-LSTM + # group + cell = rnn_cell.LSTMCell(num_units=int(num_units / number_of_groups)) + cell_zero_state = cell.zero_state( + batch_size=batch_size, dtype=dtypes.float32) + h, g = cell(lstm_input, cell_zero_state) + + sess.run([variables.global_variables_initializer()]) + [gh_res, h_res] = sess.run([gh, h]) + self.assertAllClose(gh_res[:, 0:int(num_units / number_of_groups)], + h_res, 1e-5) + self.assertAllClose(gh_res[:, int(num_units / number_of_groups):], + h_res, 1e-5) + + def testGLSTMCellFailure(self): + batch_size = 2 + num_units = 4 + number_of_groups = 2 + with self.test_session(): with variable_scope.variable_scope( - "root2", initializer=init_ops.constant_initializer(0.5)): - # input for G-LSTM with 2 groups - glstm_input = array_ops.ones([batch_size, num_units]) + "glstm_failure", initializer=init_ops.constant_initializer(0.5)): gcell = contrib_rnn_cell.GLSTMCell( num_units=num_units, number_of_groups=number_of_groups) gcell_zero_state = gcell.zero_state( batch_size=batch_size, dtype=dtypes.float32) - gh, gs = gcell(glstm_input, gcell_zero_state) - # input for LSTM cell simulating single G-LSTM group - lstm_input = array_ops.ones([batch_size, num_units / number_of_groups]) - # note division by number_of_groups. This cell one simulates G-LSTM group - cell = rnn_cell.LSTMCell(num_units=int(num_units / number_of_groups)) - cell_zero_state = cell.zero_state( - batch_size=batch_size, dtype=dtypes.float32) - h, g = cell(lstm_input, cell_zero_state) + # Try an input with statically-unknown innermost dimension. + glstm_input = array_ops.placeholder( + dtypes.float32, shape=[batch_size, None]) + with self.assertRaisesRegexp(ValueError, + "input size must be statically known"): + gcell(glstm_input, gcell_zero_state) - sess.run([variables.global_variables_initializer()]) - [gh_res, h_res] = sess.run([gh, h]) - self.assertAllClose(gh_res[:, 0:int(num_units / number_of_groups)], - h_res, 1e-5) - self.assertAllClose(gh_res[:, int(num_units / number_of_groups):], - h_res, 1e-5) + # Try an input whose innermost dimension isn't divisible into groups. + glstm_input = array_ops.placeholder( + dtypes.float32, shape=[batch_size, 3]) + with self.assertRaisesRegexp( + ValueError, + r"input size \(3\) must be divisible by number_of_groups \(2\)"): + gcell(glstm_input, gcell_zero_state) class LayerNormBasicLSTMCellTest(test.TestCase): @@ -1168,7 +1200,7 @@ class LayerNormBasicLSTMCellTest(test.TestCase): h1 = array_ops.zeros([1, 2]) state1 = rnn_cell.LSTMStateTuple(c1, h1) state = (state0, state1) - single_cell = lambda: contrib_rnn_cell.LayerNormBasicLSTMCell(2, layer_norm=False) + single_cell = lambda: contrib_rnn_cell.LayerNormBasicLSTMCell(2, layer_norm=False) # pylint: disable=line-too-long cell = rnn_cell.MultiRNNCell([single_cell() for _ in range(2)]) g, out_m = cell(x, state) sess.run([variables.global_variables_initializer()]) @@ -1200,7 +1232,7 @@ class LayerNormBasicLSTMCellTest(test.TestCase): self.assertAllClose(expected_state1_h, actual_state1_h, 1e-5) with variable_scope.variable_scope( - "other", initializer=init_ops.constant_initializer(0.5)) as vs: + "other", initializer=init_ops.constant_initializer(0.5)): x = array_ops.zeros( [1, 3]) # Test BasicLSTMCell with input_size != num_units. c = array_ops.zeros([1, 2]) @@ -1549,7 +1581,7 @@ class WeightNormLSTMCellTest(test.TestCase): """Compared cell output with pre-calculated values.""" def _cell_output(self, cell): - """Calculate cell output""" + """Calculates cell output.""" with self.test_session() as sess: init = init_ops.constant_initializer(0.5) @@ -1576,7 +1608,7 @@ class WeightNormLSTMCellTest(test.TestCase): return actual_state_c, actual_state_h def testBasicCell(self): - """Tests cell w/o peepholes and w/o normalisation""" + """Tests cell w/o peepholes and w/o normalisation.""" def cell(): return contrib_rnn_cell.WeightNormLSTMCell(2, @@ -1592,7 +1624,7 @@ class WeightNormLSTMCellTest(test.TestCase): self.assertAllClose(expected_h, actual_h, 1e-5) def testNonbasicCell(self): - """Tests cell with peepholes and w/o normalisation""" + """Tests cell with peepholes and w/o normalisation.""" def cell(): return contrib_rnn_cell.WeightNormLSTMCell(2, @@ -1607,9 +1639,8 @@ class WeightNormLSTMCellTest(test.TestCase): self.assertAllClose(expected_c, actual_c, 1e-5) self.assertAllClose(expected_h, actual_h, 1e-5) - def testBasicCellWithNorm(self): - """Tests cell w/o peepholes and with normalisation""" + """Tests cell w/o peepholes and with normalisation.""" def cell(): return contrib_rnn_cell.WeightNormLSTMCell(2, @@ -1625,7 +1656,7 @@ class WeightNormLSTMCellTest(test.TestCase): self.assertAllClose(expected_h, actual_h, 1e-5) def testNonBasicCellWithNorm(self): - """Tests cell with peepholes and with normalisation""" + """Tests cell with peepholes and with normalisation.""" def cell(): return contrib_rnn_cell.WeightNormLSTMCell(2, diff --git a/tensorflow/contrib/rnn/python/ops/core_rnn_cell.py b/tensorflow/contrib/rnn/python/ops/core_rnn_cell.py index 8109ebc718353300f94536c5d7ae3332da584a1d..645f82624bf67b96ffc8520289b293b45f0e69e2 100644 --- a/tensorflow/contrib/rnn/python/ops/core_rnn_cell.py +++ b/tensorflow/contrib/rnn/python/ops/core_rnn_cell.py @@ -40,7 +40,6 @@ from tensorflow.python.util import nest # pylint: disable=protected-access,invalid-name RNNCell = rnn_cell_impl.RNNCell -_like_rnncell = rnn_cell_impl._like_rnncell _WEIGHTS_VARIABLE_NAME = rnn_cell_impl._WEIGHTS_VARIABLE_NAME _BIAS_VARIABLE_NAME = rnn_cell_impl._BIAS_VARIABLE_NAME # pylint: enable=protected-access,invalid-name @@ -221,8 +220,7 @@ class EmbeddingWrapper(RNNCell): ValueError: if embedding_classes is not positive. """ super(EmbeddingWrapper, self).__init__(_reuse=reuse) - if not _like_rnncell(cell): - raise TypeError("The parameter cell is not RNNCell.") + rnn_cell_impl.assert_like_rnncell("cell", cell) if embedding_classes <= 0 or embedding_size <= 0: raise ValueError("Both embedding_classes and embedding_size must be > 0: " "%d, %d." % (embedding_classes, embedding_size)) @@ -301,8 +299,7 @@ class InputProjectionWrapper(RNNCell): super(InputProjectionWrapper, self).__init__(_reuse=reuse) if input_size is not None: logging.warn("%s: The input_size parameter is deprecated.", self) - if not _like_rnncell(cell): - raise TypeError("The parameter cell is not RNNCell.") + rnn_cell_impl.assert_like_rnncell("cell", cell) self._cell = cell self._num_proj = num_proj self._activation = activation @@ -356,8 +353,7 @@ class OutputProjectionWrapper(RNNCell): ValueError: if output_size is not positive. """ super(OutputProjectionWrapper, self).__init__(_reuse=reuse) - if not _like_rnncell(cell): - raise TypeError("The parameter cell is not RNNCell.") + rnn_cell_impl.assert_like_rnncell("cell", cell) if output_size < 1: raise ValueError("Parameter output_size must be > 0: %d." % output_size) self._cell = cell diff --git a/tensorflow/contrib/rnn/python/ops/lstm_ops.py b/tensorflow/contrib/rnn/python/ops/lstm_ops.py index 4eb4fbcd92f0d7cb3bee712862c8950a1971b632..9e61fc54d10c1b75786450060e428c73974760a7 100644 --- a/tensorflow/contrib/rnn/python/ops/lstm_ops.py +++ b/tensorflow/contrib/rnn/python/ops/lstm_ops.py @@ -480,8 +480,7 @@ class LSTMBlockWrapper(base_layer.Layer): """Run this LSTM on inputs, starting from the given state. Args: - inputs: `3-D` tensor with shape `[time_len, batch_size, input_size]` - or a list of `time_len` tensors of shape `[batch_size, input_size]`. + inputs: `3-D` tensor with shape `[time_len, batch_size, input_size]`. initial_state: a tuple `(initial_cell_state, initial_output)` with tensors of shape `[batch_size, self._num_units]`. If this is not provided, the cell is expected to create a zero initial state of type `dtype`. diff --git a/tensorflow/contrib/rnn/python/ops/rnn_cell.py b/tensorflow/contrib/rnn/python/ops/rnn_cell.py index a6c2d9cdbb2b6f61d59960f708000e945c6115e9..358b2eb02be81def1cc76c89e564700bbae15833 100644 --- a/tensorflow/contrib/rnn/python/ops/rnn_cell.py +++ b/tensorflow/contrib/rnn/python/ops/rnn_cell.py @@ -1143,8 +1143,7 @@ class AttentionCellWrapper(rnn_cell_impl.RNNCell): `state_is_tuple` is `False` or if attn_length is zero or less. """ super(AttentionCellWrapper, self).__init__(_reuse=reuse) - if not rnn_cell_impl._like_rnncell(cell): # pylint: disable=protected-access - raise TypeError("The parameter cell is not RNNCell.") + rnn_cell_impl.assert_like_rnncell("cell", cell) if nest.is_sequence(cell.state_size) and not state_is_tuple: raise ValueError( "Cell returns tuple of states, but the flag " @@ -2059,16 +2058,19 @@ class ConvLSTMCell(rnn_cell_impl.RNNCell): initializers=None, name="conv_lstm_cell"): """Construct ConvLSTMCell. + Args: conv_ndims: Convolution dimensionality (1, 2 or 3). input_shape: Shape of the input as int tuple, excluding the batch size. output_channels: int, number of output channels of the conv LSTM. kernel_shape: Shape of kernel as in tuple (of size 1,2 or 3). - use_bias: Use bias in convolutions. + use_bias: (bool) Use bias in convolutions. skip_connection: If set to `True`, concatenate the input to the - output of the conv LSTM. Default: `False`. + output of the conv LSTM. Default: `False`. forget_bias: Forget bias. + initializers: Unused. name: Name of the module. + Raises: ValueError: If `skip_connection` is `True` and stride is different from 1 or if `input_shape` is incompatible with `conv_ndims`. @@ -2131,7 +2133,7 @@ class Conv1DLSTMCell(ConvLSTMCell): def __init__(self, name="conv_1d_lstm_cell", **kwargs): """Construct Conv1DLSTM. See `ConvLSTMCell` for more details.""" - super(Conv1DLSTMCell, self).__init__(conv_ndims=1, **kwargs) + super(Conv1DLSTMCell, self).__init__(conv_ndims=1, name=name, **kwargs) class Conv2DLSTMCell(ConvLSTMCell): @@ -2142,7 +2144,7 @@ class Conv2DLSTMCell(ConvLSTMCell): def __init__(self, name="conv_2d_lstm_cell", **kwargs): """Construct Conv2DLSTM. See `ConvLSTMCell` for more details.""" - super(Conv2DLSTMCell, self).__init__(conv_ndims=2, **kwargs) + super(Conv2DLSTMCell, self).__init__(conv_ndims=2, name=name, **kwargs) class Conv3DLSTMCell(ConvLSTMCell): @@ -2153,19 +2155,23 @@ class Conv3DLSTMCell(ConvLSTMCell): def __init__(self, name="conv_3d_lstm_cell", **kwargs): """Construct Conv3DLSTM. See `ConvLSTMCell` for more details.""" - super(Conv3DLSTMCell, self).__init__(conv_ndims=3, **kwargs) + super(Conv3DLSTMCell, self).__init__(conv_ndims=3, name=name, **kwargs) def _conv(args, filter_size, num_features, bias, bias_start=0.0): - """convolution: + """Convolution. + Args: args: a Tensor or a list of Tensors of dimension 3D, 4D or 5D, batch x n, Tensors. filter_size: int tuple of filter height and width. num_features: int, number of features. + bias: Whether to use biases in the convolution layer. bias_start: starting value to initialize the bias; 0 by default. + Returns: A 3D, 4D, or 5D Tensor with shape [batch ... num_features] + Raises: ValueError: if some of the arguments has unspecified or wrong shape. """ @@ -2225,6 +2231,13 @@ class GLSTMCell(rnn_cell_impl.RNNCell): O. Kuchaiev and B. Ginsburg "Factorization Tricks for LSTM Networks", ICLR 2017 workshop. + + In brief, a G-LSTM cell consists of one LSTM sub-cell per group, where each + sub-cell operates on an evenly-sized sub-vector of the input and produces an + evenly-sized sub-vector of the output. For example, a G-LSTM cell with 128 + units and 4 groups consists of 4 LSTMs sub-cells with 32 units each. If that + G-LSTM cell is fed a 200-dim input, then each sub-cell receives a 50-dim part + of the input and produces a 32-dim part of the output. """ def __init__(self, @@ -2298,7 +2311,7 @@ class GLSTMCell(rnn_cell_impl.RNNCell): return self._output_size def _get_input_for_group(self, inputs, group_id, group_size): - """Slices inputs into groups to prepare for processing by cell's groups + """Slices inputs into groups to prepare for processing by cell's groups. Args: inputs: cell input or it's previous state, @@ -2320,9 +2333,12 @@ class GLSTMCell(rnn_cell_impl.RNNCell): """Run one step of G-LSTM. Args: - inputs: input Tensor, 2D, [batch x num_units]. - state: this must be a tuple of state Tensors, both `2-D`, - with column sizes `c_state` and `m_state`. + inputs: input Tensor, 2D, [batch x num_inputs]. num_inputs must be + statically-known and evenly divisible into groups. The innermost + vectors of the inputs are split into evenly-sized sub-vectors and fed + into the per-group LSTM sub-cells. + state: this must be a tuple of state Tensors, both `2-D`, with column + sizes `c_state` and `m_state`. Returns: A tuple containing: @@ -2337,11 +2353,24 @@ class GLSTMCell(rnn_cell_impl.RNNCell): Raises: ValueError: If input size cannot be inferred from inputs via - static shape inference. + static shape inference, or if the input shape is incompatible + with the number of groups. """ (c_prev, m_prev) = state self._batch_size = inputs.shape[0].value or array_ops.shape(inputs)[0] + + # If the input size is statically-known, calculate and validate its group + # size. Otherwise, use the output group size. + input_size = inputs.shape[1].value + if input_size is None: + raise ValueError("input size must be statically known") + if input_size % self._number_of_groups != 0: + raise ValueError( + "input size (%d) must be divisible by number_of_groups (%d)" % + (input_size, self._number_of_groups)) + input_group_size = int(input_size / self._number_of_groups) + dtype = inputs.dtype scope = vs.get_variable_scope() with vs.variable_scope(scope, initializer=self._initializer): @@ -2354,8 +2383,7 @@ class GLSTMCell(rnn_cell_impl.RNNCell): with vs.variable_scope("group%d" % group_id): x_g_id = array_ops.concat( [ - self._get_input_for_group(inputs, group_id, - self._group_shape[0]), + self._get_input_for_group(inputs, group_id, input_group_size), self._get_input_for_group(m_prev, group_id, self._group_shape[0]) ], @@ -2684,7 +2712,7 @@ class LayerNormLSTMCell(rnn_cell_impl.RNNCell): class SRUCell(rnn_cell_impl.LayerRNNCell): - """SRU, Simple Recurrent Unit + """SRU, Simple Recurrent Unit. Implementation based on Training RNNs as Fast as CNNs (cf. https://arxiv.org/abs/1709.02755). @@ -2732,12 +2760,13 @@ class SRUCell(rnn_cell_impl.LayerRNNCell): input_depth = inputs_shape[1].value + # pylint: disable=protected-access self._kernel = self.add_variable( rnn_cell_impl._WEIGHTS_VARIABLE_NAME, shape=[input_depth, 4 * self._num_units]) - + # pylint: enable=protected-access self._bias = self.add_variable( - rnn_cell_impl._BIAS_VARIABLE_NAME, + rnn_cell_impl._BIAS_VARIABLE_NAME, # pylint: disable=protected-access shape=[2 * self._num_units], initializer=init_ops.constant_initializer(0.0, dtype=self.dtype)) @@ -2746,7 +2775,7 @@ class SRUCell(rnn_cell_impl.LayerRNNCell): def call(self, inputs, state): """Simple recurrent unit (SRU) with num_units cells.""" - U = math_ops.matmul(inputs, self._kernel) + U = math_ops.matmul(inputs, self._kernel) # pylint: disable=invalid-name x_bar, f_intermediate, r_intermediate, x_tx = array_ops.split( value=U, num_or_size_splits=4, axis=1) @@ -2876,6 +2905,7 @@ class WeightNormLSTMCell(rnn_cell_impl.RNNCell): Args: args: a 2D Tensor or a list of 2D, batch x n, Tensors. output_size: int, second dimension of W[i]. + norm: bool, whether to normalize the weights. bias: boolean, whether to add a bias term or not. bias_initializer: starting value to initialize the bias (default is all zeros). diff --git a/tensorflow/contrib/saved_model/BUILD b/tensorflow/contrib/saved_model/BUILD index 245fe07f2bcdaddb2bc47c0e1234dc1f19bd85e3..b10757df47a4c38b98692c3d1995b3f040f0b478 100644 --- a/tensorflow/contrib/saved_model/BUILD +++ b/tensorflow/contrib/saved_model/BUILD @@ -53,6 +53,7 @@ py_test( size = "small", srcs = ["python/saved_model/reader_test.py"], srcs_version = "PY2AND3", + tags = ["no_windows"], # TODO: needs investigation on Windows visibility = ["//visibility:private"], deps = [ ":saved_model_py", diff --git a/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py b/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py index 0a53fd66dbe4d28ea102773b9c5bae50b9d18e9c..f8da5a3e1729a300a7bef3547d1819d7363a7194 100644 --- a/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py +++ b/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py @@ -1152,9 +1152,7 @@ class AttentionWrapper(rnn_cell_impl.RNNCell): is a list, and its length does not match that of `attention_layer_size`. """ super(AttentionWrapper, self).__init__(name=name) - if not rnn_cell_impl._like_rnncell(cell): # pylint: disable=protected-access - raise TypeError( - "cell must be an RNNCell, saw type: %s" % type(cell).__name__) + rnn_cell_impl.assert_like_rnncell("cell", cell) if isinstance(attention_mechanism, (list, tuple)): self._is_multi = True attention_mechanisms = attention_mechanism diff --git a/tensorflow/contrib/seq2seq/python/ops/basic_decoder.py b/tensorflow/contrib/seq2seq/python/ops/basic_decoder.py index ed226239b860e2250072a28a5538b816642ec54b..7eb95e5a70de985dca0d4b565ba03bdf454b6161 100644 --- a/tensorflow/contrib/seq2seq/python/ops/basic_decoder.py +++ b/tensorflow/contrib/seq2seq/python/ops/basic_decoder.py @@ -59,8 +59,7 @@ class BasicDecoder(decoder.Decoder): Raises: TypeError: if `cell`, `helper` or `output_layer` have an incorrect type. """ - if not rnn_cell_impl._like_rnncell(cell): # pylint: disable=protected-access - raise TypeError("cell must be an RNNCell, received: %s" % type(cell)) + rnn_cell_impl.assert_like_rnncell("cell", cell) if not isinstance(helper, helper_py.Helper): raise TypeError("helper must be a Helper, received: %s" % type(helper)) if (output_layer is not None diff --git a/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py b/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py index 554eb24e5260724a905b099091bf8aea461554cf..6adbb8be40fb183fadd15c3155aca287ac6563b7 100644 --- a/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py +++ b/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py @@ -195,8 +195,7 @@ class BeamSearchDecoder(decoder.Decoder): ValueError: If `start_tokens` is not a vector or `end_token` is not a scalar. """ - if not rnn_cell_impl._like_rnncell(cell): # pylint: disable=protected-access - raise TypeError("cell must be an RNNCell, received: %s" % type(cell)) + rnn_cell_impl.assert_like_rnncell("cell", cell) # pylint: disable=protected-access if (output_layer is not None and not isinstance(output_layer, layers_base.Layer)): raise TypeError( @@ -300,12 +299,13 @@ class BeamSearchDecoder(decoder.Decoder): """ finished, start_inputs = self._finished, self._start_inputs + dtype = nest.flatten(self._initial_cell_state)[0].dtype log_probs = array_ops.one_hot( # shape(batch_sz, beam_sz) array_ops.zeros([self._batch_size], dtype=dtypes.int32), depth=self._beam_width, - on_value=0.0, - off_value=-np.Inf, - dtype=nest.flatten(self._initial_cell_state)[0].dtype) + on_value=ops.convert_to_tensor(0.0, dtype=dtype), + off_value=ops.convert_to_tensor(-np.Inf, dtype=dtype), + dtype=dtype) initial_state = BeamSearchDecoderState( cell_state=self._initial_cell_state, @@ -570,7 +570,6 @@ def _beam_search_step(time, logits, next_cell_state, beam_state, batch_size, time = ops.convert_to_tensor(time, name="time") # During the first time step we only consider the initial beam - scores_shape = array_ops.shape(scores) scores_flat = array_ops.reshape(scores, [batch_size, -1]) # Pick the next beams according to the specified successors function diff --git a/tensorflow/contrib/session_bundle/BUILD b/tensorflow/contrib/session_bundle/BUILD index 67011c8fef6c4f54db2626ffe7ae1299bddbb352..3ad88a8a22966a072e0c43d6c41369333aba5cbe 100644 --- a/tensorflow/contrib/session_bundle/BUILD +++ b/tensorflow/contrib/session_bundle/BUILD @@ -165,6 +165,7 @@ py_test( name = "gc_test", srcs = ["gc_test.py"], srcs_version = "PY2AND3", + tags = ["no_windows"], # TODO: needs investigation on Windows visibility = ["//visibility:private"], deps = [ ":gc", diff --git a/tensorflow/contrib/slim/README.md b/tensorflow/contrib/slim/README.md index 2d9df8f27ee98431f51fd39c168325b8f625dce9..40f484fd78302163ba36142dec057478fe899189 100644 --- a/tensorflow/contrib/slim/README.md +++ b/tensorflow/contrib/slim/README.md @@ -94,7 +94,7 @@ of thin wrapper functions in [variables.py](https://www.tensorflow.org/code/tensorflow/contrib/framework/python/ops/variables.py) which allow callers to easily define variables. -For example, to create a `weight` variable, initialize it using a truncated +For example, to create a `weights` variable, initialize it using a truncated normal distribution, regularize it with an `l2_loss` and place it on the `CPU`, one need only declare the following: diff --git a/tensorflow/contrib/slim/python/slim/data/BUILD b/tensorflow/contrib/slim/python/slim/data/BUILD index 5daabbd62e7e63608a7a86a8b7fb0bc0d570b28b..7aa1684839184900547e3304358315246ac2b140 100644 --- a/tensorflow/contrib/slim/python/slim/data/BUILD +++ b/tensorflow/contrib/slim/python/slim/data/BUILD @@ -61,6 +61,7 @@ py_test( name = "dataset_data_provider_test", srcs = ["dataset_data_provider_test.py"], srcs_version = "PY2AND3", + tags = ["no_windows"], # TODO: needs investigation on Windows deps = [ ":dataset", ":dataset_data_provider", diff --git a/tensorflow/contrib/summary/summary_ops.py b/tensorflow/contrib/summary/summary_ops.py index b6249fc92f712b21197c2167fb5d1c4af1f48ca5..c1724c6e43c0365ceac18bb1d9dfea7351cca060 100644 --- a/tensorflow/contrib/summary/summary_ops.py +++ b/tensorflow/contrib/summary/summary_ops.py @@ -110,7 +110,7 @@ class SummaryWriter(object): def __init__(self, resource): self._resource = resource - if context.in_eager_mode() and self._resource is not None: + if context.executing_eagerly() and self._resource is not None: self._resource_deleter = resource_variable_ops.EagerResourceDeleter( handle=self._resource, handle_device="cpu:0") @@ -158,7 +158,7 @@ def initialize( @{tf.contrib.summary.SummaryWriter}. ValueError: If session wasn't passed and no default session. """ - if context.in_eager_mode(): + if context.executing_eagerly(): return if context.context().summary_writer_resource is None: raise RuntimeError("No default tf.contrib.summary.SummaryWriter found") @@ -269,7 +269,7 @@ def _make_summary_writer(name, factory, **kwargs): resource = gen_summary_ops.summary_writer(shared_name=name) # TODO(apassos): Consider doing this instead. # node = factory(resource, **kwargs) - # if not context.in_eager_mode(): + # if not context.executing_eagerly(): # ops.get_default_session().run(node) ops.add_to_collection(_SUMMARY_WRITER_INIT_COLLECTION_NAME, factory(resource, **kwargs)) @@ -295,7 +295,7 @@ def all_summary_ops(): Returns: The summary ops. """ - if context.in_eager_mode(): + if context.executing_eagerly(): return None return ops.get_collection(ops.GraphKeys._SUMMARY_COLLECTION) # pylint: disable=protected-access @@ -309,7 +309,7 @@ def summary_writer_initializer_op(): Raises: RuntimeError: If in Eager mode. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError( "tf.contrib.summary.summary_writer_initializer_op is only " "supported in graph mode.") @@ -328,8 +328,12 @@ def summary_writer_function(name, tensor, function, family=None): Returns: The result of writing the summary. """ + name_scope = ops.get_name_scope() + if name_scope: + # Add a slash to allow reentering the name scope. + name_scope += "/" def record(): - with summary_op_util.summary_scope( + with ops.name_scope(name_scope), summary_op_util.summary_scope( name, family, values=[tensor]) as (tag, scope): with ops.control_dependencies([function(tag, scope)]): return constant_op.constant(True) @@ -477,7 +481,7 @@ def graph(param, step=None, name=None): Raises: TypeError: If `param` isn't already a @{tf.Tensor} in graph mode. """ - if not context.in_eager_mode() and not isinstance(param, ops.Tensor): + if not context.executing_eagerly() and not isinstance(param, ops.Tensor): raise TypeError("graph() needs a tf.Tensor (e.g. tf.placeholder) in graph " "mode, but was: %s" % type(param)) writer = context.context().summary_writer_resource diff --git a/tensorflow/contrib/summary/summary_ops_graph_test.py b/tensorflow/contrib/summary/summary_ops_graph_test.py index 2b7806f80d020e0064b0f5cf32fd765a9ee993d1..3aba04540eba12092d884cca10e23546eb91c91d 100644 --- a/tensorflow/contrib/summary/summary_ops_graph_test.py +++ b/tensorflow/contrib/summary/summary_ops_graph_test.py @@ -85,6 +85,38 @@ class DbTest(summary_test_util.SummaryDbTest): self.assertEqual(len(events), 2) self.assertEqual(events[1].summary.value[0].tag, 'my_scalar') + def testScalarSummaryNameScope(self): + """Test record_summaries_every_n_global_steps and all_summaries().""" + with ops.Graph().as_default(), self.test_session() as sess: + global_step = training_util.get_or_create_global_step() + global_step.initializer.run() + with ops.device('/cpu:0'): + step_increment = state_ops.assign_add(global_step, 1) + sess.run(step_increment) # Increment global step from 0 to 1 + + logdir = tempfile.mkdtemp() + with summary_ops.create_file_writer(logdir, max_queue=0, + name='t2').as_default(): + with summary_ops.record_summaries_every_n_global_steps(2): + summary_ops.initialize() + with ops.name_scope('scope'): + summary_op = summary_ops.scalar('my_scalar', 2.0) + + # Neither of these should produce a summary because + # global_step is 1 and "1 % 2 != 0" + sess.run(summary_ops.all_summary_ops()) + sess.run(summary_op) + events = summary_test_util.events_from_logdir(logdir) + self.assertEqual(len(events), 1) + + # Increment global step from 1 to 2 and check that the summary + # is now written + sess.run(step_increment) + sess.run(summary_ops.all_summary_ops()) + events = summary_test_util.events_from_logdir(logdir) + self.assertEqual(len(events), 2) + self.assertEqual(events[1].summary.value[0].tag, 'scope/my_scalar') + def testSummaryGraphModeCond(self): with ops.Graph().as_default(), self.test_session(): training_util.get_or_create_global_step() diff --git a/tensorflow/contrib/summary/summary_ops_test.py b/tensorflow/contrib/summary/summary_ops_test.py index bb7215f879411e91a1c47b87f5caede63fffea74..c756f8b27055f9cf86a311e485d97745a3c7a95b 100644 --- a/tensorflow/contrib/summary/summary_ops_test.py +++ b/tensorflow/contrib/summary/summary_ops_test.py @@ -29,6 +29,7 @@ from tensorflow.core.framework import types_pb2 from tensorflow.python.eager import function from tensorflow.python.eager import test 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 state_ops @@ -107,6 +108,20 @@ class TargetTest(test_util.TensorFlowTestCase): self.assertEqual(len(events), 2) self.assertEqual(events[1].summary.value[0].tag, 'scalar') + def testSummaryNameScope(self): + training_util.get_or_create_global_step() + logdir = tempfile.mkdtemp() + with summary_ops.create_file_writer( + logdir, max_queue=0, + name='t2').as_default(), summary_ops.always_record_summaries(): + + with ops.name_scope('scope'): + summary_ops.scalar('scalar', 2.0) + + events = summary_test_util.events_from_logdir(logdir) + self.assertEqual(len(events), 2) + self.assertEqual(events[1].summary.value[0].tag, 'scope/scalar') + def testSummaryGlobalStep(self): step = training_util.get_or_create_global_step() logdir = tempfile.mkdtemp() diff --git a/tensorflow/contrib/tensor_forest/BUILD b/tensorflow/contrib/tensor_forest/BUILD index 1e4cc3f0952ef74a1c89b7ed2d8c357fa8847ad5..07b6b1f142ba4d113c0d45db5a8a2ce3f66f4154 100644 --- a/tensorflow/contrib/tensor_forest/BUILD +++ b/tensorflow/contrib/tensor_forest/BUILD @@ -553,7 +553,6 @@ py_test( srcs = ["client/random_forest_test.py"], srcs_version = "PY2AND3", tags = [ - "no_windows", "nomac", # b/63258195 "notsan", ], diff --git a/tensorflow/contrib/tensorboard/BUILD b/tensorflow/contrib/tensorboard/BUILD index 2e0a46ffe432341a423ac159deb7745d9ef15374..db2e000ef875c354edcdf0fa50fd3ac6d4907c17 100644 --- a/tensorflow/contrib/tensorboard/BUILD +++ b/tensorflow/contrib/tensorboard/BUILD @@ -9,11 +9,11 @@ exports_files(["LICENSE"]) # For platform specific build config load("//tensorflow/core:platform/default/build_config.bzl", "tf_proto_library") +load("//tensorflow:tensorflow.bzl", "py_test") tf_proto_library( name = "protos_all", srcs = glob(["**/*.proto"]), - go_api_version = 2, visibility = ["//visibility:public"], ) diff --git a/tensorflow/contrib/tensorrt/BUILD b/tensorflow/contrib/tensorrt/BUILD index 65a0e903a74d066dcec6f2fdb70a22a0872b802f..906cc3f0344e7cb641589bd522e33d658150d3b5 100644 --- a/tensorflow/contrib/tensorrt/BUILD +++ b/tensorflow/contrib/tensorrt/BUILD @@ -47,7 +47,10 @@ tf_cuda_cc_test( tf_custom_op_library( name = "python/ops/_trt_engine_op.so", - srcs = ["ops/trt_engine_op.cc"], + srcs = [ + "ops/trt_calib_op.cc", + "ops/trt_engine_op.cc", + ], deps = [ ":trt_engine_op_kernel", ":trt_shape_function", @@ -71,11 +74,19 @@ tf_cuda_library( cc_library( name = "trt_engine_op_kernel", - srcs = ["kernels/trt_engine_op.cc"], - hdrs = ["kernels/trt_engine_op.h"], + srcs = [ + "kernels/trt_calib_op.cc", + "kernels/trt_engine_op.cc", + ], + hdrs = [ + "kernels/trt_calib_op.h", + "kernels/trt_engine_op.h", + ], copts = tf_copts(), + visibility = ["//visibility:public"], deps = [ ":trt_logging", + ":trt_resources", "//tensorflow/core:gpu_headers_lib", "//tensorflow/core:lib_proto_parsing", "//tensorflow/core:stream_executor_headers_lib", @@ -87,7 +98,10 @@ cc_library( ) tf_gen_op_libs( - op_lib_names = ["trt_engine_op"], + op_lib_names = [ + "trt_engine_op", + "trt_calib_op", + ], deps = if_tensorrt([ "@local_config_tensorrt//:nv_infer", ]), @@ -107,7 +121,9 @@ tf_cuda_library( tf_gen_op_wrapper_py( name = "trt_engine_op", + gen_locally = True, deps = [ + ":trt_calib_op_op_lib", ":trt_engine_op_op_lib", ":trt_logging", ":trt_shape_function", @@ -139,6 +155,7 @@ py_library( deps = [ ":trt_convert_py", ":trt_ops_py", + "//tensorflow/python:errors", ], ) @@ -171,6 +188,27 @@ tf_py_wrap_cc( ], ) +tf_cuda_library( + name = "trt_resources", + srcs = [ + "resources/trt_int8_calibrator.cc", + "resources/trt_resource_manager.cc", + ], + hdrs = [ + "resources/trt_int8_calibrator.h", + "resources/trt_resource_manager.h", + "resources/trt_resources.h", + ], + deps = [ + ":trt_logging", + "//tensorflow/core:framework_headers_lib", + "//tensorflow/core:framework_lite", + "//tensorflow/core:lib_proto_parsing", + ] + if_tensorrt([ + "@local_config_tensorrt//:nv_infer", + ]), +) + # Library for the node-level conversion portion of TensorRT operation creation tf_cuda_library( name = "trt_conversion", @@ -185,6 +223,7 @@ tf_cuda_library( deps = [ ":segment", ":trt_logging", + ":trt_resources", "//tensorflow/core/grappler:grappler_item", "//tensorflow/core/grappler:utils", "//tensorflow/core:framework", diff --git a/tensorflow/contrib/tensorrt/README.md b/tensorflow/contrib/tensorrt/README.md index dfcce0fd00eedf3341850bbc23927dc3b2e2d2aa..461e627e99c38fe492dc8a8a1c9fb06e6cfda3a2 100644 --- a/tensorflow/contrib/tensorrt/README.md +++ b/tensorflow/contrib/tensorrt/README.md @@ -2,7 +2,8 @@ Using TensorRT in TensorFlow ============================ This module provides necessary bindings and introduces TRT_engine_op -operator that wraps a subgraph in TensorRT. +operator that wraps a subgraph in TensorRT. This is still a work in progress +but should be useable with most common graphs. Compilation ----------- @@ -15,26 +16,10 @@ configure script should find the necessary components from the system automatically. If installed from tar packages, user has to set path to location where the library is installed during configuration. - -``` +```shell 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 is shown below. - -```python -import tensorflow as tf -import tensorflow.contrib.tensorrt as trt -#... create and train or load model -gdef = sess.graph.as_graph_def() -trt_gdef = trt.create_inference_graph( - gdef, #original graph_def - ["output"], #name of output node(s) - max_batch_size, #maximum batch size to run the inference - max_workspace_size_bytes) # max memory for TensorRT to use -tf.reset_default_graph() -tf.import_graph_def(graph_def=trt_gdef) -#...... run inference -``` +will be available. An example use can be found in test/test_tftrt.py directory diff --git a/tensorflow/contrib/tensorrt/__init__.py b/tensorflow/contrib/tensorrt/__init__.py index fd551d70b4385b14b84b7b98a6d16b0c03733d38..140ad4828208ae4844a49bf664955b50cd9e51cd 100644 --- a/tensorflow/contrib/tensorrt/__init__.py +++ b/tensorflow/contrib/tensorrt/__init__.py @@ -18,6 +18,18 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -# pylint: disable=unused-import,wildcard-import -from tensorflow.contrib.tensorrt.python import * -# pylint: enable=unused-import,wildcard-import +from tensorflow.python.framework import errors + +# pylint: disable=unused-import,wildcard-import,g-import-not-at-top +try: + from tensorflow.contrib.tensorrt.python import * +except errors.NotFoundError as e: + no_trt_message = ( + '**** Failed to initialize TensorRT. This is either because the TensorRT' + ' installation path is not in LD_LIBRARY_PATH, or because you do not have' + ' it installed. If not installed, please go to' + ' https://developer.nvidia.com/tensorrt to download and install' + ' TensorRT ****') + print(no_trt_message) + raise e +# pylint: enable=unused-import,wildcard-import,g-import-not-at-top diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.cc b/tensorflow/contrib/tensorrt/convert/convert_graph.cc index 970f8104736d95d09ea3ffabb07f84d8591a8f9c..eea8c8efa28c1b7f9a1979311e920ca5c89f830a 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_graph.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_graph.cc @@ -15,6 +15,7 @@ limitations under the License. #include "tensorflow/contrib/tensorrt/convert/convert_graph.h" +#include #include #include #include @@ -48,13 +49,29 @@ namespace tensorrt { namespace convert { namespace { -static bool IsTensorRTCandidate(const tensorflow::NodeDef& node_def) { +bool IsTensorRTCandidate(const tensorflow::NodeDef& node_def) { // LINT.IfChange // TODO(jie): Segmentation shouldn't associated with op name. // Split it into a registration for each kernel. static const std::set candidate_ops = { - "Identity", "Const", "Conv2D", "MaxPool", "BiasAdd", "Relu", - "Add", "Mul", "Sub", "Rsqrt", "Pad" // "Placeholder" ,"Mean" + "Identity", + "Const", + "Conv2D", + "MaxPool", + "BiasAdd", + "Relu", + "Add", + "Mul", + "Sub", + "Rsqrt", + "Pad", + "Mean", + "AvgPool", + "ConcatV2", + "DepthwiseConv2dNative", + "FusedBatchNorm", + "FusedBatchNormV2", + // TODO(ben,jie): ... }; // LINT.ThenChange(//tensorflow/contrib/tensorrt/convert/convert_nodes.h) return candidate_ops.count(node_def.op()); @@ -69,6 +86,8 @@ void GetSubGraphIncomingEdges(const tensorflow::Graph& graph, if (!subgraph_node_ids.count(edge->src()->id()) && !edge->src()->IsSource()) { incoming_edges->insert(edge); + } else { + VLOG(2) << edge->src()->name() << " N, "; } } } @@ -82,7 +101,10 @@ void GetSubGraphOutgoingEdges(const tensorflow::Graph& graph, for (const tensorflow::Edge* edge : node->out_edges()) { if (!subgraph_node_ids.count(edge->dst()->id()) && !edge->dst()->IsSink()) { + VLOG(2) << edge->dst()->name() << " Y, "; outgoing_edges->insert(edge); + } else { + VLOG(2) << edge->dst()->name() << " N, "; } } } @@ -109,74 +131,150 @@ std::unordered_map> BuildTensorNameMap( } return result; } - -tensorflow::Status ConvertSubGraphToTensorRT( - const std::vector& output_names, - const std::set& subgraph_node_ids, - size_t max_batch_size, // Max batch size that engine will be created for - // Max amount of memory that engine will be allowed to consume, in bytes - size_t max_workspace_size_bytes, - const tensorflow::grappler::GraphProperties& graph_properties, - tensorflow::Graph* graph) { - tensorflow::EdgeSet subgraph_incoming_edges; - GetSubGraphIncomingEdges(*graph, subgraph_node_ids, &subgraph_incoming_edges); - +// TODO(sami): convert references to pointers +struct ConvertGraphParams { + ConvertGraphParams( + tensorflow::Graph& inp_graph, + const std::vector& output_node_names, + const std::set& subgraph_node_id_numbers, + size_t max_supported_batch_size, size_t max_consumed_workspace_size_bytes, + const tensorflow::grappler::GraphProperties& current_graph_properties, + std::unordered_map>* output_edges, + int engine_precision_mode) + : graph(inp_graph), + output_names(output_node_names), + subgraph_node_ids(subgraph_node_id_numbers), + max_batch_size(max_supported_batch_size), + max_workspace_size_bytes(max_consumed_workspace_size_bytes), + graph_properties(current_graph_properties), + output_edge_map(output_edges), + precision_mode(engine_precision_mode) {} + tensorflow::Graph& graph; + const std::vector& output_names; + const std::set& subgraph_node_ids; + size_t max_batch_size; + size_t max_workspace_size_bytes; + const tensorflow::grappler::GraphProperties& graph_properties; + std::unordered_map>* output_edge_map; + int precision_mode; std::vector> subgraph_inputs; + std::vector> subgraph_outputs; + tensorflow::EdgeSet subgraph_incoming_edges; + tensorflow::EdgeSet subgraph_outgoing_edges; +}; - // Collect inputs by looking for incoming edges - for (const tensorflow::Edge* edge : subgraph_incoming_edges) { - subgraph_inputs.push_back({edge->src()->id(), edge->src_output()}); +static tensorflow::Status FillSubGraphEdgeSets(ConvertGraphParams* p) { + GetSubGraphIncomingEdges(p->graph, p->subgraph_node_ids, + &p->subgraph_incoming_edges); + for (const tensorflow::Edge* edge : p->subgraph_incoming_edges) { + p->subgraph_inputs.push_back({edge->src()->id(), edge->src_output()}); } + auto output_name_to_index_map = BuildTensorNameMap(p->output_names); std::set> subgraph_outputs_set; // Collect outputs referenced from output_names - auto output_name_to_index_map = BuildTensorNameMap(output_names); - for (int node_id : subgraph_node_ids) { - tensorflow::Node* node = graph->FindNodeId(node_id); + for (int node_id : p->subgraph_node_ids) { + tensorflow::Node* node = p->graph.FindNodeId(node_id); if (output_name_to_index_map.count(node->name())) { for (int index : output_name_to_index_map.at(node->name())) { subgraph_outputs_set.insert({node_id, index}); } } } - // Collect outputs referenced from outgoing edges - tensorflow::EdgeSet subgraph_outgoing_edges; - GetSubGraphOutgoingEdges(*graph, subgraph_node_ids, &subgraph_outgoing_edges); - for (const tensorflow::Edge* edge : subgraph_outgoing_edges) { + GetSubGraphOutgoingEdges(p->graph, p->subgraph_node_ids, + &p->subgraph_outgoing_edges); + for (const tensorflow::Edge* edge : p->subgraph_outgoing_edges) { subgraph_outputs_set.insert({edge->src()->id(), edge->src_output()}); } - // Impose an ordering on the outputs - std::vector> subgraph_outputs( - subgraph_outputs_set.begin(), subgraph_outputs_set.end()); - // Build TensorRT node and add it to the graph + p->subgraph_outputs.reserve(subgraph_outputs_set.size()); + p->subgraph_outputs.insert(p->subgraph_outputs.begin(), + subgraph_outputs_set.begin(), + subgraph_outputs_set.end()); + return tensorflow::Status::OK(); +}; + +tensorflow::Status GetCalibNode(ConvertGraphParams* params) { + TF_RETURN_IF_ERROR(FillSubGraphEdgeSets(params)); tensorflow::NodeDef trt_node_def; - TF_RETURN_IF_ERROR(ConvertSubGraphToTensorRTNodeDef( - *graph, subgraph_node_ids, subgraph_inputs, subgraph_outputs, - max_batch_size, max_workspace_size_bytes, graph_properties, - &trt_node_def)); + SubGraphParams s(params->graph, params->subgraph_node_ids, + params->subgraph_inputs, params->subgraph_outputs, + params->max_batch_size, params->max_workspace_size_bytes, + params->graph_properties, params->output_edge_map, + &trt_node_def, params->precision_mode); + TF_RETURN_IF_ERROR(InjectCalibrationNode(s)); tensorflow::Status status; - tensorflow::Node* trt_node = graph->AddNode(trt_node_def, &status); + tensorflow::Node* trt_node = params->graph.AddNode(trt_node_def, &status); + + TF_RETURN_IF_ERROR(status); + + for (auto in_edge : + params->subgraph_incoming_edges) { // loop over incoming edges and + // attach them to calib node + // tensorflow::Node* src_node = in_edge->src(); + auto src_output = in_edge->src_output(); + auto dst_node = in_edge->dst(); + auto dst_input = in_edge->dst_input(); + VLOG(1) << " update edge " << trt_node->name() << ":" << src_output + << " -> " << dst_node->name() << ":" << dst_input; + TF_RETURN_IF_ERROR( + params->graph.UpdateEdge(trt_node, src_output, dst_node, dst_input)); + } + return tensorflow::Status::OK(); +} + +tensorflow::Status ConvertSubGraphToTensorRT(ConvertGraphParams* params) { + TF_RETURN_IF_ERROR(FillSubGraphEdgeSets(params)); + tensorflow::NodeDef trt_node_def; + + SubGraphParams s(params->graph, params->subgraph_node_ids, + params->subgraph_inputs, params->subgraph_outputs, + params->max_batch_size, params->max_workspace_size_bytes, + params->graph_properties, params->output_edge_map, + &trt_node_def, params->precision_mode); + TF_RETURN_IF_ERROR(ConvertSubGraphToTensorRTNodeDef(s)); + tensorflow::Status status; + tensorflow::Node* trt_node = params->graph.AddNode(trt_node_def, &status); + + // AddNode does not wire edges. + // Re-map incoming edges to use the new TRT node instead of the orig subgraph + std::map, int> subgraph_edge_to_input_map; + for (size_t i = 0; i < params->subgraph_inputs.size(); ++i) { + subgraph_edge_to_input_map.insert({params->subgraph_inputs.at(i), i}); + } + for (const tensorflow::Edge* edge : params->subgraph_incoming_edges) { + std::pair old_src = {edge->src()->id(), edge->src_output()}; + int new_src_output = subgraph_edge_to_input_map.at(old_src); + params->graph.AddEdge(edge->src(), edge->src_output(), trt_node, + new_src_output); + params->graph.RemoveEdge(edge); + } + + VLOG(2) << "new wiring edges: " << trt_node->in_edges().size(); + for (const tensorflow::Edge* edge : trt_node->in_edges()) { + VLOG(2) << edge->src()->name() << " port: " << edge->src_output(); + } + TF_RETURN_IF_ERROR(status); // Re-map outgoing edges to use the new TRT node instead of the orig subgraph std::map, int> subgraph_edge_to_output_map; - for (size_t i = 0; i < subgraph_outputs.size(); ++i) { - subgraph_edge_to_output_map.insert({subgraph_outputs.at(i), i}); + for (size_t i = 0; i < params->subgraph_outputs.size(); ++i) { + subgraph_edge_to_output_map.insert({params->subgraph_outputs.at(i), i}); } TF_RETURN_IF_ERROR(status); - for (const tensorflow::Edge* edge : subgraph_outgoing_edges) { + for (const tensorflow::Edge* edge : params->subgraph_outgoing_edges) { std::pair old_src = {edge->src()->id(), edge->src_output()}; int new_src_output = subgraph_edge_to_output_map.at(old_src); - TF_RETURN_IF_ERROR(graph->UpdateEdge(trt_node, new_src_output, edge->dst(), - edge->dst_input())); + TF_RETURN_IF_ERROR(params->graph.UpdateEdge( + trt_node, new_src_output, edge->dst(), edge->dst_input())); } // Remove the original subgraph - for (int node_id : subgraph_node_ids) { - tensorflow::Node* node = graph->FindNodeId(node_id); + for (int node_id : params->subgraph_node_ids) { + tensorflow::Node* node = params->graph.FindNodeId(node_id); // Don't remove the input placeholders if (node->type_string() == "Placeholder") { continue; } - graph->RemoveNode(node); + params->graph.RemoveNode(node); } return tensorflow::Status::OK(); } @@ -194,12 +292,39 @@ tensorflow::Status BuildNodeMap( } } // namespace +tensorflow::Status ConvertCalibGraphToInferGraph( + const tensorflow::GraphDef& graph_def, tensorflow::GraphDef* infer_graph) { + VLOG(0) << "Starting Calib Conversion"; + tensorflow::Graph graph(tensorflow::OpRegistry::Global()); + TF_RETURN_IF_ERROR(tensorflow::ConvertGraphDefToGraph( + tensorflow::GraphConstructorOptions(), graph_def, &graph)); + // get calib nodes + std::vector calib_nodes; + for (auto node : graph.op_nodes()) { + if (node->type_string() == "TRTCalibOp") { + VLOG(1) << "Found Calib Node"; + calib_nodes.push_back(node); + } + } + VLOG(0) << "Num Calib nodes in graph= " << calib_nodes.size(); + if (calib_nodes.size() == 0) + return tensorflow::errors::FailedPrecondition( + "Graph doesn't contain any calibration nodes!." + " Please generate calibration graph and run calibration first"); + for (auto n : calib_nodes) { + TF_RETURN_IF_ERROR( + tensorrt::convert::ConvertCalibrationNodeToEngineNode(graph, n)); + } + graph.ToGraphDef(infer_graph); + return tensorflow::Status::OK(); +} tensorflow::Status ConvertGraphDefToTensorRT( const tensorflow::GraphDef& graph_def, const std::vector& output_names, size_t max_batch_size, - size_t max_workspace_size_bytes, tensorflow::GraphDef* new_graph_def) { - // Optimization pass + size_t max_workspace_size_bytes, tensorflow::GraphDef* new_graph_def, + int precision_mode = FP32MODE, int minimum_segment_size = 3) { + // optimization pass tensorflow::grappler::GrapplerItem item; item.fetch = output_names; tensorflow::GraphDef gdef; @@ -209,16 +334,23 @@ tensorflow::Status ConvertGraphDefToTensorRT( tensorflow::grappler::LayoutOptimizer optimizer; tensorflow::grappler::Cluster* cluster; - // Virtual cluster + // virtual cluster tensorflow::DeviceProperties device_properties; + device_properties.set_type("GPU"); device_properties.mutable_environment()->insert({"architecture", "6"}); cluster = new tensorflow::grappler::VirtualCluster({{"/GPU:0", device_properties}}); + // single machine + int num_cpu_cores = tensorflow::grappler::GetNumAvailableLogicalCPUCores(); + int num_gpus = tensorflow::grappler::GetNumAvailableGPUs(); + VLOG(2) << "cpu_cores: " << num_cpu_cores; + VLOG(2) << "gpus: " << num_gpus; + TF_RETURN_IF_ERROR(optimizer.Optimize(cluster, item, &gdef)); - // Constant folding + // constant folding item.graph = gdef; tensorflow::grappler::ConstantFolding fold(nullptr); TF_RETURN_IF_ERROR(fold.Optimize(nullptr, item, &gdef)); @@ -226,7 +358,6 @@ tensorflow::Status ConvertGraphDefToTensorRT( // AJ refactoring shape inference through grappler/GraphProperties. tensorflow::grappler::GraphProperties static_graph_properties(item); TF_RETURN_IF_ERROR(static_graph_properties.InferStatically(false)); - // Build full graph tensorflow::FunctionLibraryDefinition flib(tensorflow::OpRegistry::Global(), gdef.library()); @@ -243,7 +374,7 @@ tensorflow::Status ConvertGraphDefToTensorRT( } // TODO(sami): this should be passed as a knob!!!! - segment_options.minimum_segment_size = 2; + segment_options.minimum_segment_size = minimum_segment_size; tensorflow::tensorrt::segment::SegmentNodesVector segments; TF_RETURN_IF_ERROR(tensorrt::segment::SegmentGraph( gdef, IsTensorRTCandidate, segment_options, &segments)); @@ -252,14 +383,37 @@ tensorflow::Status ConvertGraphDefToTensorRT( } std::unordered_map node_map; TF_RETURN_IF_ERROR(BuildNodeMap(graph, &node_map)); + std::unordered_map> output_edge_map; + int count = 0; + float total_num_nodes_in_segments = 0.; + for (auto s : segments) { + total_num_nodes_in_segments += s.size(); + } for (const std::set& subgraph_node_names : segments) { std::set subgraph_node_ids; + size_t max_mem_per_engine = + max_workspace_size_bytes * + ((float)subgraph_node_names.size() / total_num_nodes_in_segments); + std::stringstream oss; for (const string& node_name : subgraph_node_names) { + oss << " " << node_name; subgraph_node_ids.insert(node_map.at(node_name)->id()); } - TF_RETURN_IF_ERROR(ConvertSubGraphToTensorRT( - output_names, subgraph_node_ids, max_batch_size, - max_workspace_size_bytes, static_graph_properties, &graph)); + VLOG(2) << "Subgraph nodes" << oss.str(); + ConvertGraphParams p(graph, output_names, subgraph_node_ids, max_batch_size, + max_mem_per_engine, static_graph_properties, + &output_edge_map, precision_mode); + if (precision_mode == INT8MODE) { + TF_RETURN_IF_ERROR(GetCalibNode(&p)); + } else { + tensorflow::Status status = ConvertSubGraphToTensorRT(&p); + if (status != tensorflow::Status::OK()) { + LOG(WARNING) << "subgraph conversion error for subgraph_index:" << count + << " due to: \n" + << status.ToString() << " SKIPPING......"; + } + count++; + } } graph.ToGraphDef(new_graph_def); return tensorflow::Status::OK(); diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.h b/tensorflow/contrib/tensorrt/convert/convert_graph.h index 154ad3f2e8fb0ae702448097fbdece510df30223..e1596e89e229813245b35e568cd349b738c2d879 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_graph.h +++ b/tensorflow/contrib/tensorrt/convert/convert_graph.h @@ -28,6 +28,11 @@ namespace tensorflow { namespace tensorrt { namespace convert { +// This method converts an already generated calibration graph which was used in +// calibration runs to an inference graph +tensorflow::Status ConvertCalibGraphToInferGraph( + const tensorflow::GraphDef& graph_def, tensorflow::GraphDef* new_graph_def); + // max_batch_size: maximum batch size which can be used for inference for // optimization targets inference run with max batch size. // max_workspace_size_bytes: The upper bound of memory allowence for @@ -35,7 +40,8 @@ namespace convert { tensorflow::Status ConvertGraphDefToTensorRT( const tensorflow::GraphDef& graph_def, const std::vector& output_names, size_t max_batch_size, - size_t max_workspace_size_bytes, tensorflow::GraphDef* new_graph_def); + size_t max_workspace_size_bytes, tensorflow::GraphDef* new_graph_def, + int precision_mode, int minimum_segment_size); } // namespace convert } // namespace tensorrt diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index 9ee717dd7fb1eff4a11fb104cf5806ec8ab853d2..75a3c3d034dff3e9d044d31c1693395deb951582 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -24,6 +24,10 @@ limitations under the License. #include #include +#include "tensorflow/contrib/tensorrt/log/trt_logger.h" +#include "tensorflow/contrib/tensorrt/resources/trt_resource_manager.h" +#include "tensorflow/contrib/tensorrt/resources/trt_resources.h" +#include "tensorflow/core/framework/node_def.pb.h" // NOLINT #include "tensorflow/core/framework/node_def_builder.h" #include "tensorflow/core/framework/tensor_shape.pb.h" // NOLINT #include "tensorflow/core/framework/types.h" @@ -32,6 +36,7 @@ limitations under the License. #include "tensorflow/core/graph/graph_constructor.h" #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/lib/core/status.h" +#include "tensorflow/core/lib/strings/str_util.h" #include "tensorflow/core/lib/strings/strcat.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/tensor_coding.h" @@ -39,7 +44,6 @@ limitations under the License. #if GOOGLE_CUDA #if GOOGLE_TENSORRT -#include "tensorflow/contrib/tensorrt/log/trt_logger.h" #include "tensorrt/include/NvInfer.h" // Check if the types are equal. Cast to int first so that failure log message @@ -49,6 +53,7 @@ limitations under the License. namespace tensorflow { namespace tensorrt { namespace convert { +using ::tensorflow::strings::StrCat; namespace { @@ -65,7 +70,8 @@ inline tensorflow::Status ConvertDType(tensorflow::DataType tf_dtype, *trt_dtype = nvinfer1::DataType::kHALF; break; default: - return tensorflow::errors::InvalidArgument("Unsupported data type"); + return tensorflow::errors::InvalidArgument( + "Unsupported data type " + tensorflow::DataTypeString(tf_dtype)); } return tensorflow::Status::OK(); } @@ -112,6 +118,18 @@ static std::vector> CreateSamePadding( return padding; } +string GetCommonNameScope(const string& op_name_a, const string& op_name_b) { + size_t last_scope_separator = 0; + for (size_t i = 0; i < std::min(op_name_a.size(), op_name_b.size()); ++i) { + if (op_name_a[i] != op_name_b[i]) { + break; + } else if (op_name_a[i] == '/') { + last_scope_separator = i + 1; + } + } + return op_name_a.substr(0, last_scope_separator); +} + class TRT_ShapedWeights { public: TRT_ShapedWeights(tensorflow::DataType type, const void* values, @@ -244,6 +262,11 @@ std::vector TFAttrs::get>(string key) const { return std::vector(attr.begin(), attr.end()); } +template <> +std::vector TFAttrs::get>(string key) const { + auto attr = this->at(key)->list().s(); + return std::vector(attr.begin(), attr.end()); +} template <> nvinfer1::Dims TFAttrs::get(string key) const { auto values = this->get>(key); @@ -266,6 +289,17 @@ tensorflow::DataType TFAttrs::get(string key) const { return this->at(key)->type(); } +template <> +float TFAttrs::get(string key) const { + return this->at(key)->f(); +} + +template <> +bool TFAttrs::get(string key) const { + return this->at(key)->b(); +} + +// TODO(jie): reorder4 & reorder2 should be merged? template void Reorder4(nvinfer1::DimsNCHW shape, const T* idata, nvinfer1::DimsNCHW istrides, T* odata, @@ -283,29 +317,87 @@ void Reorder4(nvinfer1::DimsNCHW shape, const T* idata, } } +template +void Reorder2(nvinfer1::DimsHW shape, const T* idata, nvinfer1::DimsHW istrides, + T* odata, nvinfer1::DimsHW ostrides) { + for (int h = 0; h < shape.h(); ++h) { + for (int w = 0; w < shape.w(); ++w) { + odata[h * ostrides.h() + w * ostrides.w()] = + idata[h * ostrides.h() + w * ostrides.w()]; + } + } +} + +// TODO(jie): fallback to tensorflow!! +void ReorderCKtoKC(const TRT_ShapedWeights& iweights, + TRT_ShapedWeights* oweights) { + int c = iweights.shape_.d[0]; + int k = iweights.shape_.d[1]; + oweights->shape_.d[0] = k; + oweights->shape_.d[1] = c; + nvinfer1::DimsHW istrides = {1, k}; + nvinfer1::DimsHW ostrides = {c, 1}; + switch (iweights.type_) { + case tensorflow::DataType::DT_FLOAT: { + Reorder2({k, c}, static_cast(iweights.GetValues()), + istrides, + static_cast(const_cast(oweights->GetValues())), + ostrides); + break; + } + case tensorflow::DataType::DT_HALF: { + Reorder2( + {k, c}, static_cast(iweights.GetValues()), + istrides, + static_cast(const_cast(oweights->GetValues())), + ostrides); + break; + } + default: + LOG(FATAL) << "Unsupported type in reorder expected fp32 or fp16 but got " + << DataTypeString(iweights.type_); + } +} + void ReorderRSCKToKCRS(const TRT_ShapedWeights& iweights, - TRT_ShapedWeights* oweights) { + TRT_ShapedWeights* oweights, int num_groups) { CHECK_EQ(iweights.type_, oweights->type_); CHECK_EQ(iweights.size_bytes(), oweights->size_bytes()); int r = iweights.shape_.d[0]; int s = iweights.shape_.d[1]; - int c = iweights.shape_.d[2]; - int k = iweights.shape_.d[3]; - oweights->shape_.d[0] = k; - oweights->shape_.d[1] = c; + // TRT requires GKcRS, while TF depthwise has RSCK + // where c=1, C=G + VLOG(2) << "num_groups: " << num_groups; + int c = iweights.shape_.d[2] / num_groups; + VLOG(2) << "c" << iweights.shape_.d[2] << " then " << c; + int k = iweights.shape_.d[3] * num_groups; + VLOG(2) << "k" << iweights.shape_.d[3] << " then " << k; + oweights->shape_.d[0] = k / num_groups; + oweights->shape_.d[1] = c * num_groups; oweights->shape_.d[2] = r; oweights->shape_.d[3] = s; nvinfer1::DimsNCHW istrides = {1, k, s * k * c, c * k}; nvinfer1::DimsNCHW ostrides = {c * r * s, r * s, s, 1}; switch (iweights.type_) { - case tensorflow::DataType::DT_FLOAT: + case tensorflow::DataType::DT_FLOAT: { Reorder4({k, c, r, s}, static_cast(iweights.GetValues()), istrides, static_cast(const_cast(oweights->GetValues())), ostrides); break; + } + case tensorflow::DataType::DT_HALF: { + Reorder4( + {k, c, r, s}, static_cast(iweights.GetValues()), + istrides, + static_cast(const_cast(oweights->GetValues())), + ostrides); + break; + } + default: - LOG(FATAL) << "!!!!!!!!!!!!!!!!!!!!!!!!broke!!!!!!!!!!!!"; + LOG(FATAL) << "Unsupported type, expected fp32 or fp16 but got " + << DataTypeString(iweights.type_); } } @@ -323,12 +415,11 @@ inline std::shared_ptr infer_object(T* obj) { return std::shared_ptr(obj, InferDeleter()); } -// Logger for GIE info/warning/errors class Converter; using OpConverter = std::function const&, + const std::vector&, std::vector*)>; class Converter { @@ -336,34 +427,57 @@ class Converter { std::unordered_map op_registry_; nvinfer1::INetworkDefinition* trt_network_; std::list> temp_bufs_; - + tensorflow::tensorrt::TRTWeightStore* weight_store_; + bool fp16_; void register_op_converters(); - std::vector get_inputs( const tensorflow::NodeDef& node_def) { std::vector inputs; - for (const auto& input_name : node_def.input()) { - VLOG(2) << "Retrieve input: " << input_name; - inputs.push_back(trt_tensors_.at(input_name)); + for (auto const& input_name : node_def.input()) { + /************************************************************************* + * TODO(jie) handle case 1) here + * Normalizes the inputs and extracts associated metadata: + * 1) Inputs can contain a colon followed by a suffix of characters. + * That suffix may be a single number (e.g. inputName:1) or several + * word characters separated from a number by a colon + * (e.g. inputName:foo:1). The + * latter case is used to denote inputs and outputs of functions. + * 2) Control dependency inputs contain caret at the beginning and we + * remove this and annotate the edge as a control dependency. + ************************************************************************/ + string name = input_name[0] == '^' ? input_name.substr(1) : input_name; + auto first = name.find_first_of(':'); + if (first != string::npos && first + 2 == name.size() && + name[first + 1] == '0') + name.erase(first); + + VLOG(2) << "retrieve input: " << name; + if (trt_tensors_.count(name)) { + inputs.push_back(trt_tensors_.at(name)); + } else { + LOG(FATAL) << "input: " << name << " not availabled for node at, " + << node_def.name(); + } } return inputs; } public: - explicit Converter(nvinfer1::INetworkDefinition* trt_network) - : trt_network_(trt_network) { + explicit Converter(nvinfer1::INetworkDefinition* trt_network, + tensorflow::tensorrt::TRTWeightStore* ws, bool fp16) + : trt_network_(trt_network), weight_store_(ws), fp16_(fp16) { this->register_op_converters(); } - + tensorflow::tensorrt::TRTWeightStore* weight_store() { return weight_store_; } TRT_ShapedWeights get_temp_weights(tensorflow::DataType type, nvinfer1::Dims shape) { TRT_ShapedWeights weights(type, nullptr, shape); // TODO(jie): check weights size_bytes. 0 means type error - temp_bufs_.push_back(std::vector(weights.size_bytes())); - weights.SetValues(temp_bufs_.back().data()); + weight_store_->store_.push_back(std::vector(weights.size_bytes())); + weights.SetValues(weight_store_->store_.back().data()); return weights; } - + bool isFP16() { return fp16_; }; TRT_ShapedWeights get_temp_weights_like(const TRT_ShapedWeights& weights) { return this->get_temp_weights(weights.type_, weights.shape_); } @@ -382,7 +496,7 @@ class Converter { TRT_TensorOrWeights output = outputs.at(i); // TODO(jie): tf protobuf seems to be omitting the :0 suffix string output_name = node_def.name(); - if (i != 0) output_name = output_name + ":" + std::to_string(i); + if (i != 0) output_name = StrCat(output_name, ":", i); if (output.is_tensor()) { output.tensor()->setName(output_name.c_str()); } @@ -448,7 +562,7 @@ struct LambdaFactory { switch (op) { case OP_CATEGORY::RSQRT: { VLOG(2) << "RSQRT GETS DONE"; - return [](T t) -> T { return 1.0 / std::sqrt(t); }; + return [](T t) -> T { return 1.0 / sqrt(t); }; } case OP_CATEGORY::NEG: return [](T t) -> T { return -t; }; @@ -534,6 +648,22 @@ struct LambdaFactory { } }; +template <> +std::function LambdaFactory::unary() { + switch (op) { + case OP_CATEGORY::RSQRT: { + VLOG(2) << "RSQRT GETS DONE"; + return [](Eigen::half t) -> Eigen::half { + return Eigen::half(1.0 / sqrt(float(t))); + }; + } + case OP_CATEGORY::NEG: + return [](Eigen::half t) -> Eigen::half { return -t; }; + default: + VLOG(2) << "Not supported op for unary: " << static_cast(op); + return nullptr; + } +} tensorflow::Status UnaryCompute(const TRT_ShapedWeights& iweights, TRT_ShapedWeights* oweights, LambdaFactory unary_op) { @@ -545,6 +675,14 @@ tensorflow::Status UnaryCompute(const TRT_ShapedWeights& iweights, std::transform(inp, inp + iweights.count(), oup, unary_op.unary()); break; } + case tensorflow::DataType::DT_HALF: { + auto inp = static_cast(iweights.GetValues()); + auto oup = + static_cast(const_cast(oweights->GetValues())); + std::transform(inp, inp + iweights.count(), oup, + unary_op.unary()); + break; + } default: return tensorflow::errors::Unimplemented( "Data type not supported: " + @@ -588,6 +726,32 @@ tensorflow::Status BinaryCompute(const TRT_ShapedWeights& iweights_l, } break; } + case tensorflow::DataType::DT_HALF: { + auto inp_l = static_cast(iweights_l.GetValues()); + auto inp_r = static_cast(iweights_r.GetValues()); + auto oup = + static_cast(const_cast(oweights->GetValues())); + + if (iweights_l.count() != iweights_r.count()) { + // We only supports broadcast of RankZero + if (iweights_l.count() == 1) { + VLOG(2) << "I bet it is not working!" << (*inp_l); + std::transform(inp_r, inp_r + iweights_r.count(), oup, + binary_op.broadcast_l(*inp_l)); + } else if (iweights_r.count() == 1) { + VLOG(2) << "I bet it is not working!" << (*inp_r); + std::transform(inp_l, inp_l + iweights_l.count(), oup, + binary_op.broadcast_r(*inp_r)); + } else { + return tensorflow::errors::Unimplemented( + "Binary op with non-rankZero broadcast not supported"); + } + } else { + std::transform(inp_l, inp_l + iweights_l.count(), inp_r, oup, + binary_op.binary()); + } + break; + } default: return tensorflow::errors::Unimplemented( "Data type not supported: " + @@ -599,7 +763,7 @@ tensorflow::Status BinaryCompute(const TRT_ShapedWeights& iweights_l, tensorflow::Status ConstantFoldUnary( Converter& ctx, const tensorflow::NodeDef& node_def, - std::vector const& inputs, + const std::vector& inputs, std::vector* outputs) { TRT_ShapedWeights weights_input = inputs.at(0).weights(); @@ -613,13 +777,12 @@ tensorflow::Status ConstantFoldUnary( CHECK_EQ(weights_input.type_, TFAttrs(node_def).get("T")); - // Maybe I should do a switch LambdaFactory unary_op; if (node_def.op() == "Rsqrt") { // Compute rsqrt unary_op.op = LambdaFactory::OP_CATEGORY::RSQRT; auto ret = UnaryCompute(weights_input, &weights_output, unary_op); - // PAss the output + // Pass the output if (ret == tensorflow::Status::OK()) { outputs->push_back(TRT_TensorOrWeights(weights_output)); } @@ -631,11 +794,11 @@ tensorflow::Status ConstantFoldUnary( } // TODO(jie,ben) broadcast is needed yet not implemented -// Let's get the simple stuff working first. Maybe we should fall bakc to TF +// Let's get the simple stuff working first. Maybe we should fall back to TF // approach for constant folding tensorflow::Status ConstantFoldBinary( Converter& ctx, const tensorflow::NodeDef& node_def, - std::vector const& inputs, + const std::vector& inputs, std::vector* outputs) { TRT_ShapedWeights weights_input_l = inputs.at(0).weights(); TRT_ShapedWeights weights_input_r = inputs.at(1).weights(); @@ -648,12 +811,12 @@ tensorflow::Status ConstantFoldBinary( "Binary op implicit broadcast not supported: " + node_def.op()); // TODO(jie): constant fold should really fall back to TF. - int nb_dims = weights_input_l.shape_.nbDims; + int num_dims = weights_input_l.shape_.nbDims; nvinfer1::Dims output_shape; - output_shape.nbDims = nb_dims; - VLOG(2) << "nb_dims: " << nb_dims + output_shape.nbDims = num_dims; + VLOG(2) << "nb_dims: " << num_dims << ", the other: " << weights_input_r.shape_.nbDims; - for (int i = 0; i < nb_dims; i++) { + for (int i = 0; i < num_dims; i++) { if (weights_input_l.shape_.d[i] == weights_input_r.shape_.d[i]) { output_shape.d[i] = weights_input_l.shape_.d[i]; } else if (weights_input_l.shape_.d[i] == 1 || @@ -678,7 +841,6 @@ tensorflow::Status ConstantFoldBinary( // Allocate output weights TRT_ShapedWeights weights_output = ctx.get_temp_weights(dtype, output_shape); - // Maybe I should do a switch LambdaFactory binary_op; if (node_def.op() == "Sub") { binary_op.op = LambdaFactory::OP_CATEGORY::SUB; @@ -712,48 +874,90 @@ tensorflow::Status BinaryTensorOpWeight( // Maybe this part has to be moved into the block of rsqrt later // Check type consistency - auto dtype = TFAttrs(node_def).get("T"); - CHECK_EQ_TYPE(tensor->getType(), dtype); // Cast to int for error messages nvinfer1::DataType ttype; TF_CHECK_OK(ConvertDType(weights.type_, &ttype)); - CHECK_EQ_TYPE(ttype, dtype); // Cast to int for error message // Check scale mode auto dims_w = weights.shape_; auto dims_t = tensor->getDimensions(); - // Default to channel-wise + // default to element-wise auto scale_mode = nvinfer1::ScaleMode::kELEMENTWISE; + // TODO(jie): maybe use a permuatation instead to support more cases; + bool permutation_flag = false; + if (weights.count() == 1) { VLOG(2) << "UNIFORM"; scale_mode = nvinfer1::ScaleMode::kUNIFORM; } else { - // No broadcasting on Batch dimension; - assert(dims_w.d[0] == 1); - - // Broadcasting on Channel dimension only allowed in kUNIFORM - assert(dims_w.d[1] == dims_t.d[0]); - assert(dims_w.nbDims == dims_t.nbDims); - - // Default is element; - for (int i = 2; i < dims_w.nbDims; i++) { - if (dims_w.d[i] != dims_t.d[i - 1]) { - scale_mode = nvinfer1::ScaleMode::kCHANNEL; - break; + // no broadcasting on Batch dimension; + VLOG(2) << "WEIGHTS DIM: " << dims_w.nbDims + << " tensor DIM: " << dims_t.nbDims; + if (dims_w.nbDims == dims_t.nbDims + 1) { + if (dims_w.d[0] == 1) { + for (int i = 1; i < dims_w.nbDims; i++) { + dims_w.d[i - 1] = dims_w.d[i]; + } + dims_w.nbDims--; + } else { + return tensorflow::errors::InvalidArgument( + "Binary op cannot operate on batch, " + node_def.name()); } } - if (scale_mode == nvinfer1::ScaleMode::kELEMENTWISE) { + + if (dims_w.nbDims == dims_t.nbDims && dims_w.d[0] == dims_t.d[0]) { scale_mode = nvinfer1::ScaleMode::kELEMENTWISE; - for (int i = 2; i < dims_w.nbDims; i++) { - if (dims_w.d[i] != 1) - return tensorflow::errors::InvalidArgument( - "Weight shape not compatible at, " + node_def.name()); + // default is element; + for (int i = 1; i < dims_w.nbDims; i++) { + if (dims_w.d[i] != dims_t.d[i]) { + // if dimension does not match, switch back to channel; + VLOG(2) << "channel"; + scale_mode = nvinfer1::ScaleMode::kCHANNEL; + break; + } + } + // if channel as candidate, validate it + if (scale_mode == nvinfer1::ScaleMode::kCHANNEL) { + for (int i = 1; i < dims_w.nbDims; i++) { + if (dims_w.d[i] != 1) + return tensorflow::errors::InvalidArgument( + "Weight shape not compatible at, " + node_def.name()); + } + } else { + VLOG(2) << "elementwise"; + } + } else if (dims_w.nbDims == 1 && + dims_w.d[0] == dims_t.d[dims_t.nbDims - 1]) { + // channel wise and broadcast required; + permutation_flag = true; + scale_mode = nvinfer1::ScaleMode::kCHANNEL; + } else { + return tensorflow::errors::InvalidArgument( + "Weight shape not compatible at, " + node_def.name()); + } + } + + // transpose last dimension + std::vector permutation(dims_t.nbDims + 1); + if (permutation_flag) { + if (scale_mode == nvinfer1::ScaleMode::kCHANNEL && dims_t.nbDims > 1) { + // we swap the last dimension into channel for trt. + // because of tensorflow default broadcasting rules. + for (int i = 0; i < static_cast(permutation.size()); i++) { + permutation[i] = i; } + permutation[1] = dims_t.nbDims; + permutation[dims_t.nbDims] = 1; + tensor = ctx.TransposeTensor(const_cast(tensor), + permutation); + } else { + return tensorflow::errors::InvalidArgument( + "Transpose cannot be applied, " + node_def.name()); } } - // Prepare weights + // prepare weights TRT_ShapedWeights shift_weights(weights.type_); TRT_ShapedWeights scale_weights(weights.type_); TRT_ShapedWeights power_weights(weights.type_); @@ -779,88 +983,26 @@ tensorflow::Status BinaryTensorOpWeight( scale_weights, power_weights); nvinfer1::ITensor* output_tensor = layer->getOutput(0); + // transpose back dimension + if (permutation_flag) { + output_tensor = ctx.TransposeTensor(output_tensor, permutation); + } // Pass the output outputs->push_back(TRT_TensorOrWeights(output_tensor)); return tensorflow::Status::OK(); } -tensorflow::Status BinaryTensorOpTensor( - Converter& ctx, const tensorflow::NodeDef& node_def, - const nvinfer1::ITensor* tensor_l, const nvinfer1::ITensor* tensor_r, - std::vector* outputs) { - static const std::unordered_map ops{ - {"Add", nvinfer1::ElementWiseOperation::kSUM}, - {"Mul", nvinfer1::ElementWiseOperation::kPROD}, - // {"max", nvinfer1::ElementWiseOperation::kMAX}, - // {"min", nvinfer1::ElementWiseOperation::kMIN}, - {"Sub", nvinfer1::ElementWiseOperation::kSUB}, - {"Div", nvinfer1::ElementWiseOperation::kDIV}, - }; - - // FIXME assume type matches input weights - // Get trt type & shape - TFAttrs attrs(node_def); - // Maybe this part has to be moved into the block of rsqrt later - nvinfer1::DataType dtype = attrs.get("T"); - - // Check type consistency - CHECK_EQ_TYPE(tensor_l->getType(), dtype); - CHECK_EQ_TYPE(tensor_r->getType(), dtype); - auto op_pair = ops.find(node_def.op()); - if (op_pair == ops.end()) - return tensorflow::errors::Unimplemented("binary op: " + node_def.op() + - " not supported at: " + - node_def.name()); - - nvinfer1::IElementWiseLayer* layer = ctx.network()->addElementWise( - *const_cast(tensor_l), - *const_cast(tensor_r), op_pair->second); - - nvinfer1::ITensor* output_tensor = layer->getOutput(0); - - // Pass the output - outputs->push_back(TRT_TensorOrWeights(output_tensor)); - return tensorflow::Status::OK(); -} +enum class ConvolutionType { DEFAULT, DEPTHWISE_CONV }; -tensorflow::Status ConvertPlaceholder( +tensorflow::Status ConvertConv2DHelper( Converter& ctx, const tensorflow::NodeDef& node_def, - std::vector const& inputs, - std::vector* outputs) { - VLOG(2) << "Placeholder should have been replace already"; - return tensorflow::errors::Unimplemented(", cannot convert Placeholder op"); - // OK this make sense since we are supposed to replace it with input - TFAttrs attrs(node_def); - nvinfer1::DataType dtype = attrs.get("dtype"); - nvinfer1::Dims dims = attrs.get("shape"); - - dims.nbDims--; - for (int i = 0; i < dims.nbDims; i++) dims.d[i] = dims.d[i + 1]; - - nvinfer1::ITensor* output = - ctx.network()->addInput(node_def.name().c_str(), dtype, dims); - if (!output) { - return tensorflow::errors::InvalidArgument("Failed to create Input layer"); - } - outputs->push_back(TRT_TensorOrWeights(output)); - return tensorflow::Status::OK(); -} + const std::vector& inputs, + std::vector* outputs, + int group // group ==0 specifies depthwise conv +) { + const nvinfer1::ITensor* tensor = inputs.at(0).tensor(); -tensorflow::Status ConvertConv2D(Converter& ctx, - const tensorflow::NodeDef& node_def, - const std::vector& inputs, - std::vector* outputs) { - nvinfer1::ITensor const* tensor = inputs.at(0).tensor(); - // TODO(jie): handle NHWC/NCHW transpose; - TRT_ShapedWeights weights_rsck = inputs.at(1).weights(); - TRT_ShapedWeights weights = ctx.get_temp_weights_like(weights_rsck); - ReorderRSCKToKCRS(weights_rsck, &weights); - TRT_ShapedWeights biases(weights.type_); - int noutput = weights.shape_.d[0]; - nvinfer1::DimsHW kernel_size; - kernel_size.h() = weights.shape_.d[2]; - kernel_size.w() = weights.shape_.d[3]; TFAttrs attrs(node_def); int h_index = 2; @@ -874,11 +1016,31 @@ tensorflow::Status ConvertConv2D(Converter& ctx, // TODO(jie): transpose it } + // tensor after transpose (NCHW) + auto tensor_dim = tensor->getDimensions(); + + int num_groups = group; + if (num_groups == 0) // depthwise convolution + num_groups = tensor_dim.d[0]; + VLOG(2) << "groups count: " << num_groups; + + TRT_ShapedWeights weights_rsck = inputs.at(1).weights(); + TRT_ShapedWeights weights = ctx.get_temp_weights_like(weights_rsck); + ReorderRSCKToKCRS(weights_rsck, &weights, num_groups); + TRT_ShapedWeights biases(weights.type_); + int noutput = weights.shape_.d[0] * num_groups; + nvinfer1::DimsHW kernel_size; + kernel_size.h() = weights.shape_.d[2]; + kernel_size.w() = weights.shape_.d[3]; + VLOG(2) << "kernel size: " << kernel_size.h() << ", " << kernel_size.w(); + // TODO(jie): stride. (NHWC/NCHW) auto tf_stride = attrs.get>("strides"); + VLOG(2) << "h_INDEX" << h_index << ", w_index " << w_index; + VLOG(2) << "stride!!!: " << tf_stride[0] << tf_stride[1] << tf_stride[2] + << tf_stride[3]; nvinfer1::DimsHW stride(tf_stride[h_index], tf_stride[w_index]); - auto tensor_dim = tensor->getDimensions(); std::vector> padding; // TODO(jie): padding. if (attrs.get("padding") == "SAME") { @@ -919,10 +1081,11 @@ tensorflow::Status ConvertConv2D(Converter& ctx, layer->setStride(stride); layer->setPadding({padding[0].first, padding[1].first}); layer->setName(node_def.name().c_str()); + layer->setNbGroups(num_groups); nvinfer1::ITensor* output_tensor = layer->getOutput(0); auto dim_after = output_tensor->getDimensions(); - VLOG(2) << "TENSOR out: " << dim_after.d[0] << ", " << dim_after.d[1] + VLOG(2) << "TENSOR out: " << dim_after.d[0] << ", " << dim_after.d[1] << ", " << dim_after.d[2] << ", " << dim_after.d[3]; if (data_format == "NHWC") { @@ -935,11 +1098,101 @@ tensorflow::Status ConvertConv2D(Converter& ctx, return tensorflow::Status::OK(); } +tensorflow::Status ConvertConv2DHelper( + Converter& ctx, const tensorflow::NodeDef& node_def, + const std::vector& inputs, + std::vector* outputs, ConvolutionType type) { + switch (type) { + case ConvolutionType::DEFAULT: + return ConvertConv2DHelper(ctx, node_def, inputs, outputs, 1); + case ConvolutionType::DEPTHWISE_CONV: + return ConvertConv2DHelper(ctx, node_def, inputs, outputs, 0); + } + return tensorflow::errors::Unimplemented("unsupported convolution type at, " + + node_def.name()); +} + +tensorflow::Status BinaryTensorOpTensor( + Converter& ctx, const tensorflow::NodeDef& node_def, + const nvinfer1::ITensor* tensor_l, const nvinfer1::ITensor* tensor_r, + std::vector* outputs) { + static const std::unordered_map ops{ + {"Add", nvinfer1::ElementWiseOperation::kSUM}, + {"Mul", nvinfer1::ElementWiseOperation::kPROD}, + {"Sub", nvinfer1::ElementWiseOperation::kSUB}, + {"Div", nvinfer1::ElementWiseOperation::kDIV}, + }; + + // FIXME assume type matches input weights + // get trt type & shape + TFAttrs attrs(node_def); + // maybe this part has to be moved into the block of rsqrt later + nvinfer1::DataType dtype = attrs.get("T"); + + // check type consistency + CHECK_EQ_TYPE(tensor_l->getType(), dtype); + CHECK_EQ_TYPE(tensor_r->getType(), dtype); + auto op_pair = ops.find(node_def.op()); + if (op_pair == ops.end()) + return tensorflow::errors::Unimplemented("binary op: " + node_def.op() + + " not supported at: " + + node_def.name()); + + nvinfer1::IElementWiseLayer* layer = ctx.network()->addElementWise( + *const_cast(tensor_l), + *const_cast(tensor_r), op_pair->second); + + nvinfer1::ITensor* output_tensor = layer->getOutput(0); + + // pass the output + outputs->push_back(TRT_TensorOrWeights(output_tensor)); + return tensorflow::Status::OK(); +} + +tensorflow::Status ConvertPlaceholder( + Converter& ctx, const tensorflow::NodeDef& node_def, + const std::vector& inputs, + std::vector* outputs) { + VLOG(2) << "Placeholder should have been replace already"; + return tensorflow::errors::Unimplemented("cannot convert Placeholder op"); + // OK this make sense since we are supposed to replace it with input + TFAttrs attrs(node_def); + nvinfer1::DataType dtype = attrs.get("dtype"); + nvinfer1::Dims dims = attrs.get("shape"); + + dims.nbDims--; + for (int i = 0; i < dims.nbDims; i++) dims.d[i] = dims.d[i + 1]; + + nvinfer1::ITensor* output = + ctx.network()->addInput(node_def.name().c_str(), dtype, dims); + if (!output) { + return tensorflow::errors::InvalidArgument("Failed to create Input layer"); + } + outputs->push_back(TRT_TensorOrWeights(output)); + return tensorflow::Status::OK(); +} + +tensorflow::Status ConvertConv2D(Converter& ctx, + const tensorflow::NodeDef& node_def, + const std::vector& inputs, + std::vector* outputs) { + return ConvertConv2DHelper(ctx, node_def, inputs, outputs, + ConvolutionType::DEFAULT); +} + +tensorflow::Status ConvertConv2DDepthwise( + Converter& ctx, const tensorflow::NodeDef& node_def, + const std::vector& inputs, + std::vector* outputs) { + return ConvertConv2DHelper(ctx, node_def, inputs, outputs, + ConvolutionType::DEPTHWISE_CONV); +} + tensorflow::Status ConvertPool(Converter& ctx, const tensorflow::NodeDef& node_def, - std::vector const& inputs, + const std::vector& inputs, std::vector* outputs) { - nvinfer1::ITensor const* tensor = inputs.at(0).tensor(); + const nvinfer1::ITensor* tensor = inputs.at(0).tensor(); TFAttrs attrs(node_def); int h_index = 2; @@ -957,6 +1210,8 @@ tensorflow::Status ConvertPool(Converter& ctx, // TODO(jie): support other pooling 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("Only supports Max pool"); @@ -1019,9 +1274,9 @@ tensorflow::Status ConvertPool(Converter& ctx, tensorflow::Status ConvertActivation( Converter& ctx, const tensorflow::NodeDef& node_def, - std::vector const& inputs, + const std::vector& inputs, std::vector* outputs) { - nvinfer1::ITensor const* tensor = inputs.at(0).tensor(); + const nvinfer1::ITensor* tensor = inputs.at(0).tensor(); nvinfer1::IActivationLayer* layer = ctx.network()->addActivation( *const_cast(tensor), nvinfer1::ActivationType::kRELU); nvinfer1::ITensor* output_tensor = layer->getOutput(0); @@ -1031,14 +1286,14 @@ tensorflow::Status ConvertActivation( tensorflow::Status ConvertScale(Converter& ctx, const tensorflow::NodeDef& node_def, - std::vector const& inputs, + const std::vector& inputs, std::vector* outputs) { if (inputs.size() != 2 || !inputs.at(0).is_tensor() || !inputs.at(1).is_weights()) return tensorflow::errors::Unimplemented( "Only supports tensor op weight for now, at " + node_def.name()); // Implement tensor binaryOp weight [channel wise] for now; - nvinfer1::ITensor const* tensor = inputs.at(0).tensor(); + const nvinfer1::ITensor* tensor = inputs.at(0).tensor(); // TODO(jie): handle NHWC/NCHW transpose; TRT_ShapedWeights weights = inputs.at(1).weights(); @@ -1055,16 +1310,33 @@ tensorflow::Status ConvertScale(Converter& ctx, } else { VLOG(2) << "NCHW !!!!"; } - nvinfer1::IScaleLayer* layer = ctx.network()->addScale( - *const_cast(tensor), nvinfer1::ScaleMode::kCHANNEL, - weights, empty_weights, empty_weights); - nvinfer1::ITensor* output_tensor = layer->getOutput(0); - if (data_format == "NHWC") { - // TODO(jie): transpose it back! - output_tensor = ctx.TransposeTensor(output_tensor, {0, 2, 3, 1}); - } else { - VLOG(2) << "NCHW !!!!"; + auto dims = tensor->getDimensions(); + VLOG(2) << "tensor dimensions: " << dims.nbDims; + for (int i = 0; i < dims.nbDims; i++) { + VLOG(2) << "i: " << dims.d[i]; + } + dims = weights.shape_; + VLOG(2) << "tensor dimensions: " << dims.nbDims; + for (int i = 0; i < dims.nbDims; i++) { + VLOG(2) << "i: " << dims.d[i]; + } + + nvinfer1::ScaleMode mode = nvinfer1::ScaleMode::kCHANNEL; + if (weights.shape_.d[0] == 1) { + mode = nvinfer1::ScaleMode::kUNIFORM; + } + + nvinfer1::IScaleLayer* layer = + ctx.network()->addScale(*const_cast(tensor), mode, + weights, empty_weights, empty_weights); + + nvinfer1::ITensor* output_tensor = layer->getOutput(0); + if (data_format == "NHWC") { + // TODO(jie): transpose it back! + output_tensor = ctx.TransposeTensor(output_tensor, {0, 2, 3, 1}); + } else { + VLOG(2) << "NCHW !!!!"; } outputs->push_back(TRT_TensorOrWeights(output_tensor)); return tensorflow::Status::OK(); @@ -1072,7 +1344,7 @@ tensorflow::Status ConvertScale(Converter& ctx, tensorflow::Status ConvertConst(Converter& ctx, const tensorflow::NodeDef& node_def, - std::vector const& inputs, + const std::vector& inputs, std::vector* outputs) { const auto& weights_tensor = node_def.attr().at("value").tensor(); @@ -1091,20 +1363,144 @@ tensorflow::Status ConvertConst(Converter& ctx, VLOG(2) << "SCALAR!!!" << node_def.name(); nvinfer1::Dims scalar_shape; if (tensor.dims() > 0) { - VLOG(2) << "Dimensions: " << tensor.dims(); - weights = TRT_ShapedWeights(dtype, weights_tensor.float_val().data(), - GetTensorShape(tensor)); + VLOG(2) << "dimensions: " << tensor.dims(); + VLOG(2) << "size: " << weights_tensor.float_val_size(); + scalar_shape = GetTensorShape(tensor); + for (int i = 0; i < scalar_shape.nbDims; i++) + VLOG(2) << scalar_shape.d[i]; + if (GetShapeSize(scalar_shape) != weights_tensor.float_val_size()) { + if (weights_tensor.float_val_size() == 1 || + scalar_shape.d[0] == weights_tensor.float_val_size()) { + scalar_shape.nbDims = 1; + // no dimension provided. flatten it + scalar_shape.d[0] = weights_tensor.float_val_size(); + scalar_shape.type[0] = nvinfer1::DimensionType::kSPATIAL; + } else { + LOG(FATAL) << "Broadcast on weights only supports kCHANNEL and" + << " kUNIFORM, at: " << node_def.name(); + } + } } else { VLOG(2) << "Dimensions: " << tensor.dims(); scalar_shape.nbDims = 1; - scalar_shape.d[0] = 1; + // no dimension provided. flatten it + scalar_shape.d[0] = weights_tensor.float_val_size(); scalar_shape.type[0] = nvinfer1::DimensionType::kSPATIAL; for (int i = 1; i < nvinfer1::Dims::MAX_DIMS; i++) { scalar_shape.d[i] = 0; scalar_shape.type[i] = nvinfer1::DimensionType::kSPATIAL; } - weights = TRT_ShapedWeights(dtype, weights_tensor.float_val().data(), - scalar_shape); + } + if (ctx.isFP16()) { + auto dtype_new = tensorflow::DataType::DT_HALF; + size_t len_data = tensorflow::DataTypeSize(dtype_new); + for (int i = 0; i < scalar_shape.nbDims; i++) + len_data *= scalar_shape.d[i]; + ctx.weight_store()->store_.push_back(std::vector(len_data)); + void* dst = static_cast(&(ctx.weight_store()->store_.back()[0])); + tensorflow::Tensor temp_tensor(tensorflow::DT_HALF, tensor.shape()); + auto half_tensor = temp_tensor.flat(); + Eigen::DefaultDevice defd; + half_tensor.device(defd) = + tensor.flat().template cast(); + memcpy(dst, half_tensor.data(), len_data); // store into weight store + weights = TRT_ShapedWeights(dtype_new, dst, scalar_shape); + } else { + size_t len_data = tensorflow::DataTypeSize(dtype); + for (int i = 0; i < scalar_shape.nbDims; i++) + len_data *= scalar_shape.d[i]; + ctx.weight_store()->store_.push_back(std::vector(len_data)); + void* dst = static_cast(&(ctx.weight_store()->store_.back()[0])); + std::vector tensor_data( + weights_tensor.float_val().begin(), + weights_tensor.float_val() + .end()); // make a local copy first to flatten + memcpy(dst, tensor_data.data(), len_data); // store into weight store + weights = TRT_ShapedWeights(dtype, dst, scalar_shape); + } + } else if (!weights_tensor.int_val().empty()) { + VLOG(2) << "int!!!" << node_def.name(); + nvinfer1::Dims scalar_shape; + if (tensor.dims() > 0) { + VLOG(2) << "dimensions: " << tensor.dims(); + scalar_shape = GetTensorShape(tensor); + if (GetShapeSize(scalar_shape) != weights_tensor.int_val_size()) { + if (weights_tensor.int_val_size() == 1 || + scalar_shape.d[0] == weights_tensor.int_val_size()) { + scalar_shape.nbDims = 1; + // no dimension provided. flatten it + scalar_shape.d[0] = weights_tensor.int_val_size(); + scalar_shape.type[0] = nvinfer1::DimensionType::kSPATIAL; + } else { + LOG(FATAL) << "Broadcast on weights only supports kCHANNEL and" + << " kUNIFORM, at: " << node_def.name(); + } + } + } else { + VLOG(2) << "dimensions: " << tensor.dims(); + scalar_shape.nbDims = 1; + // no dimension provided. flatten it + scalar_shape.d[0] = weights_tensor.int_val_size(); + scalar_shape.type[0] = nvinfer1::DimensionType::kSPATIAL; + for (int i = 1; i < nvinfer1::Dims::MAX_DIMS; i++) { + scalar_shape.d[i] = 0; + scalar_shape.type[i] = nvinfer1::DimensionType::kSPATIAL; + } + } + if (ctx.isFP16()) { + auto dtype_new = tensorflow::DataType::DT_HALF; + size_t len_data = tensorflow::DataTypeSize(dtype_new); + for (int i = 0; i < scalar_shape.nbDims; i++) + len_data *= scalar_shape.d[i]; + ctx.weight_store()->store_.push_back(std::vector(len_data)); + void* dst = static_cast(&(ctx.weight_store()->store_.back()[0])); + tensorflow::Tensor temp_tensor(tensorflow::DT_HALF, tensor.shape()); + TTypes::Flat half_tensor = temp_tensor.flat(); + Eigen::DefaultDevice defd; + switch (dtype) { + case (tensorflow::DT_INT32): { + half_tensor.device(defd) = + tensor.flat().template cast(); + break; + } + case (tensorflow::DT_INT16): { + half_tensor.device(defd) = + tensor.flat().template cast(); + break; + } + case (tensorflow::DT_INT8): { + half_tensor.device(defd) = + tensor.flat().template cast(); + break; + } + case (tensorflow::DT_UINT8): { + half_tensor.device(defd) = + tensor.flat().template cast(); + break; + } + default: + return tensorflow::errors::InvalidArgument( + "Datatype " + tensorflow::DataTypeString(dtype) + + " for FP16 conversion"); + break; + }; + memcpy(dst, half_tensor.data(), len_data); // store into weight store + weights = TRT_ShapedWeights(dtype_new, dst, scalar_shape); + } else { + size_t len_data = tensorflow::DataTypeSize(dtype); + for (int i = 0; i < scalar_shape.nbDims; i++) + len_data *= scalar_shape.d[i]; + size_t len_tensor = weights_tensor.int_val_size() * sizeof(int32); + len_data = std::max(len_data, len_tensor); + ctx.weight_store()->store_.push_back(std::vector(len_data)); + void* dst = static_cast(&(ctx.weight_store()->store_.back()[0])); + std::vector tensor_data( + weights_tensor.int_val().begin(), + weights_tensor.int_val() + .end()); // make a local copy first to flatten + // doesn't have to be contigous + memcpy(dst, tensor_data.data(), len_tensor); // store into weight store + weights = TRT_ShapedWeights(dtype, dst, scalar_shape); } } else if (!weights_tensor.tensor_content().empty()) { VLOG(2) << "TENSOR!!!" << node_def.name(); @@ -1130,7 +1526,7 @@ tensorflow::Status ConvertConst(Converter& ctx, tensorflow::Status ConvertIdentity( Converter& ctx, const tensorflow::NodeDef& node_def, - std::vector const& inputs, + const std::vector& inputs, std::vector* outputs) { outputs->push_back(inputs.at(0)); return tensorflow::Status::OK(); @@ -1138,7 +1534,7 @@ tensorflow::Status ConvertIdentity( tensorflow::Status ConvertBinary(Converter& ctx, const tensorflow::NodeDef& node_def, - std::vector const& inputs, + const std::vector& inputs, std::vector* outputs) { if (inputs.size() != 2) return tensorflow::errors::FailedPrecondition( @@ -1165,7 +1561,7 @@ tensorflow::Status ConvertBinary(Converter& ctx, tensorflow::Status ConvertUnary(Converter& ctx, const tensorflow::NodeDef& node_def, - std::vector const& inputs, + const std::vector& inputs, std::vector* outputs) { if (inputs.size() != 1) return tensorflow::errors::FailedPrecondition( @@ -1183,7 +1579,7 @@ tensorflow::Status ConvertUnary(Converter& ctx, tensorflow::Status ConvertReduce(Converter& ctx, const tensorflow::NodeDef& node_def, - std::vector const& inputs, + const std::vector& inputs, std::vector* outputs) { if (inputs.size() != 2 || !inputs.at(0).is_tensor() || !inputs.at(1).is_weights()) @@ -1191,7 +1587,7 @@ tensorflow::Status ConvertReduce(Converter& ctx, "Input expects tensor and weights, at" + node_def.name()); // Implement tensor binaryOp weight [channel wise] for now; - nvinfer1::ITensor const* tensor = inputs.at(0).tensor(); + const nvinfer1::ITensor* tensor = inputs.at(0).tensor(); auto dims = tensor->getDimensions(); // Restore implicit batch dimension int nb_dims = dims.nbDims + 1; @@ -1229,6 +1625,7 @@ tensorflow::Status ConvertReduce(Converter& ctx, return tensorflow::errors::InvalidArgument("TRT cannot reduce at 0, at" + node_def.name()); if (index_list_data[i] == 1) permuted_index = 1; + idx_set.emplace(index_list_data[i]); } @@ -1236,7 +1633,7 @@ tensorflow::Status ConvertReduce(Converter& ctx, nvinfer1::DimsHW pool_kernel; if (permuted_index == 1) { for (int i = 2; i < nb_dims; i++) { - if (idx_set.count(i)) { + if (idx_set.count(i) == 0) { permuted_index = i; break; } @@ -1271,12 +1668,13 @@ tensorflow::Status ConvertReduce(Converter& ctx, output_tensor = ctx.TransposeTensor( const_cast(output_tensor), permutation_order); } + outputs->push_back(TRT_TensorOrWeights(output_tensor)); return tensorflow::Status::OK(); } tensorflow::Status ConvertPad(Converter& ctx, const tensorflow::NodeDef& node_def, - std::vector const& inputs, + const std::vector& inputs, std::vector* outputs) { if (inputs.size() != 2 || !inputs.at(0).is_tensor() || !inputs.at(1).is_weights()) @@ -1284,7 +1682,7 @@ tensorflow::Status ConvertPad(Converter& ctx, "Input expects tensor and weights, at" + node_def.name()); // Implement tensor binaryOp weight [channel wise] for now; - nvinfer1::ITensor const* tensor = inputs.at(0).tensor(); + const nvinfer1::ITensor* tensor = inputs.at(0).tensor(); auto dims = tensor->getDimensions(); // Restore implicit batch dimension int nb_dims = dims.nbDims + 1; @@ -1371,19 +1769,287 @@ tensorflow::Status ConvertPad(Converter& ctx, return tensorflow::Status::OK(); } +tensorflow::Status ConvertConcat(Converter& ctx, + const tensorflow::NodeDef& node_def, + const std::vector& inputs, + std::vector* outputs) { + // not including the last input (axis) here + int input_size = static_cast(inputs.size()) - 1; + + if (!inputs.at(0).is_tensor()) + return tensorflow::errors::InvalidArgument( + "Concat in TRT support only Tensor input, at " + node_def.name()); + + // We are retrieving the axis + TRT_ShapedWeights axis = inputs.at(input_size).weights(); + + TFAttrs attrs(node_def); + // auto attr_size = attrs.at("N")->i(); + // auto data_type = attrs.get("T"); + auto index_type = attrs.get("Tidx"); + + // TODO(jie): handle data type + // Only expect to handle INT32 as index attributes for now + if (index_type != tensorflow::DataType::DT_INT32) + return tensorflow::errors::Unimplemented( + "Tidx supports only DT_INT32, at " + node_def.name()); + + int index = *(static_cast(const_cast(axis.GetValues()))); + + // TODO(jie): early termination with no-op (attr_size==1) + + auto dim = inputs.at(0).tensor()->getDimensions(); + // dimension check + if (index > dim.nbDims + 1) + return tensorflow::errors::InvalidArgument( + "Concatenate on axis out of dimension range, at " + node_def.name()); + + if (index == 0) + return tensorflow::errors::InvalidArgument( + "Concatenate on batch dimension not supported, at " + node_def.name()); + + // incase we need permutation; + std::vector permutation_order(dim.nbDims + 1); + + for (int i = 0; i < dim.nbDims + 1; i++) permutation_order[i] = i; + + if (index != 1) { + permutation_order[1] = index - 1; + permutation_order[index - 1] = 1; + } + + std::vector inputs_vec; + // Shap chack (all input tensor should have same shape) + // starting from 0 since we are probably also doing transpose here; + for (int i = 0; i < input_size; i++) { + auto tensor_i = inputs.at(i).tensor(); + auto dim_i = tensor_i->getDimensions(); + if (dim_i.nbDims != dim.nbDims) + return tensorflow::errors::InvalidArgument( + "Concatenate receives inputs with inconsistent dimensions, at " + + node_def.name()); + + for (int j = 0; j < dim.nbDims; j++) { + // check dimension consistency on non-concatenate axis + if (j != index - 1 && dim_i.d[j] != dim.d[j]) + return tensorflow::errors::InvalidArgument( + "Concatenate receives inputs with inconsistent shape, at" + + node_def.name()); + } + + // TRT does concatenation only on channel! + if (index != 1) + tensor_i = ctx.TransposeTensor(const_cast(tensor_i), + permutation_order); + + inputs_vec.push_back(tensor_i); + } + + // nvinfer1::ITensor const* tensor = inputs.at(0).tensor(); + nvinfer1::IConcatenationLayer* layer = ctx.network()->addConcatenation( + const_cast(inputs_vec.data()), + inputs_vec.size()); + nvinfer1::ITensor* output_tensor = layer->getOutput(0); + + if (index != 1) { + output_tensor = ctx.TransposeTensor(output_tensor, permutation_order); + } + outputs->push_back(TRT_TensorOrWeights(output_tensor)); + return tensorflow::Status::OK(); +} + +tensorflow::Status ConvertFusedBatchNorm( + Converter& ctx, const tensorflow::NodeDef& node_def, + const std::vector& inputs, + std::vector* outputs) { + TFAttrs attrs(node_def); + float epsilon = attrs.get("epsilon"); + 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()); + } + bool is_training = attrs.get("is_training"); + if (is_training) { + return tensorflow::errors::Unimplemented( + "only is_training=false is supported, at " + node_def.name()); + } + nvinfer1::ITensor const* tensor = inputs.at(0).tensor(); + TRT_ShapedWeights scale_weights = inputs.at(1).weights(); + TRT_ShapedWeights offset_weights = inputs.at(2).weights(); + TRT_ShapedWeights mean_weights = inputs.at(3).weights(); + TRT_ShapedWeights variance_weights = inputs.at(4).weights(); + TRT_ShapedWeights dummy_power_weights(scale_weights.type_); + TRT_ShapedWeights combined_scale_weights = + ctx.get_temp_weights_like(scale_weights); + TRT_ShapedWeights combined_offset_weights = + ctx.get_temp_weights_like(offset_weights); + size_t nweight = scale_weights.count(); + if ((scale_weights.type_ == offset_weights.type_) && + (mean_weights.type_ == variance_weights.type_) && + (scale_weights.type_ == variance_weights.type_)) { + if ((scale_weights.type_ != tensorflow::DataType::DT_FLOAT) && + (scale_weights.type_ != tensorflow::DataType::DT_HALF)) { + return tensorflow::errors::Unimplemented( + "only float32 or float16 weight data type is supported, for node " + + node_def.name() + " got " + + tensorflow::DataTypeString(scale_weights.type_)); + } + if (scale_weights.type_ == tensorflow::DT_FLOAT) { + for (size_t i = 0; i < nweight; ++i) { + float scale = (static_cast(scale_weights.GetValues()))[i]; + float offset = + (static_cast(offset_weights.GetValues()))[i]; + float mean = (static_cast(mean_weights.GetValues()))[i]; + float variance = + (static_cast(variance_weights.GetValues()))[i]; + float& combined_scale_ref = const_cast( + static_cast(combined_scale_weights.GetValues()))[i]; + float& combined_offset_ref = const_cast( + static_cast(combined_offset_weights.GetValues()))[i]; + combined_scale_ref = scale / sqrtf(variance + epsilon); + combined_offset_ref = offset - mean * combined_scale_ref; + } + } else { + const Eigen::half* scale_vals = + (static_cast(scale_weights.GetValues())); + const Eigen::half* off_vals = + (static_cast(offset_weights.GetValues())); + const Eigen::half* mean_vals = + (static_cast(mean_weights.GetValues())); + const Eigen::half* variance_vals = + (static_cast(variance_weights.GetValues())); + Eigen::half* comb_scale_vals = const_cast( + static_cast(combined_scale_weights.GetValues())); + Eigen::half* comb_off_vals = const_cast( + static_cast(combined_offset_weights.GetValues())); + for (size_t i = 0; i < nweight; ++i) { + float scale(scale_vals[i]); + float offset(off_vals[i]); + float mean(mean_vals[i]); + float variance(variance_vals[i]); + float combined_scale_ref = scale / sqrtf(variance + epsilon); + comb_scale_vals[i] = Eigen::half(combined_scale_ref); + float combined_offset_ref = offset - mean * combined_scale_ref; + comb_off_vals[i] = Eigen::half(combined_offset_ref); + } + } + } + nvinfer1::IScaleLayer* layer = ctx.network()->addScale( + *const_cast(tensor), nvinfer1::ScaleMode::kCHANNEL, + combined_offset_weights.GetWeightsForTRT(), + combined_scale_weights.GetWeightsForTRT(), + dummy_power_weights.GetWeightsForTRT()); + nvinfer1::ITensor* output_tensor = layer->getOutput(0); + outputs->push_back(TRT_TensorOrWeights(output_tensor)); + return tensorflow::Status::OK(); +} + +tensorflow::Status ConvertMatMul(Converter& ctx, + const tensorflow::NodeDef& node_def, + const std::vector& inputs, + std::vector* outputs) { + const nvinfer1::ITensor* tensor = inputs.at(0).tensor(); + + // TODO(jie): transpose! + TFAttrs attrs(node_def); + + TRT_ShapedWeights weights_ck = inputs.at(1).weights(); + TRT_ShapedWeights weights = ctx.get_temp_weights_like(weights_ck); + ReorderCKtoKC(weights_ck, &weights); + TRT_ShapedWeights biases(weights.type_); + + int noutput = weights.shape_.d[0]; + + nvinfer1::IFullyConnectedLayer* layer = ctx.network()->addFullyConnected( + *const_cast(tensor), noutput, weights, biases); + + nvinfer1::ITensor* output_tensor = layer->getOutput(0); + outputs->push_back(TRT_TensorOrWeights(output_tensor)); + return tensorflow::Status::OK(); +} + +tensorflow::Status ConvertReshape( + Converter& ctx, const tensorflow::NodeDef& node_def, + const std::vector& inputs, + std::vector* outputs) { + if (inputs.size() != 2 || !inputs.at(0).is_tensor() || + !inputs.at(1).is_weights()) + return tensorflow::errors::InvalidArgument( + "Input expects tensor and weights, at" + node_def.name()); + + // implement tensor binaryOp weight [channel wise] for now; + const nvinfer1::ITensor* tensor = inputs.at(0).tensor(); + auto dims = tensor->getDimensions(); + // restore implicit batch dimension + + TRT_ShapedWeights shape = inputs.at(1).weights(); + + TFAttrs attrs(node_def); + + auto padding_type = attrs.get("Tshape"); + + if (shape.shape_.nbDims != 1) + return tensorflow::errors::InvalidArgument( + "reshape new shape is not 1 dimensional, at " + node_def.name()); + + // Only expect to handle INT32 as attributes for now + if (padding_type != tensorflow::DataType::DT_INT32) + return tensorflow::errors::Unimplemented( + "reshape new shape supports only DT_INT32, at " + node_def.name()); + + auto shape_data = static_cast(const_cast(shape.GetValues())); + + if (shape_data[0] != -1) + return tensorflow::errors::InvalidArgument( + "reshape new shape first dimension is not -1, at " + node_def.name()); + + auto shape_num_dims = shape.shape_.d[0]; + VLOG(2) << "shape dimensions: " << shape_num_dims; + int volume_w = 1; + for (int i = 1; i < shape.shape_.d[0]; i++) volume_w *= shape_data[i]; + + int volume_t = 1; + for (int i = 0; i < dims.nbDims; i++) volume_t *= dims.d[i]; + + VLOG(2) << "volume: " << volume_t << " volume weights: " << volume_w; + if (volume_w != volume_t) + return tensorflow::errors::InvalidArgument( + "volume does not agree between tensor and new shape, at " + + node_def.name()); + + nvinfer1::IShuffleLayer* layer = + ctx.network()->addShuffle(*const_cast(tensor)); + + nvinfer1::Dims reshape_dims; + VLOG(2) << "new dimension: " << shape_num_dims - 1; + reshape_dims.nbDims = shape_num_dims - 1; + for (int32_t i = 0; i < reshape_dims.nbDims; ++i) { + reshape_dims.d[i] = shape_data[i + 1]; + } + layer->setReshapeDimensions(reshape_dims); + VLOG(2) << "new dimension: " << shape_num_dims - 1; + + nvinfer1::ITensor* output_tensor = layer->getOutput(0); + auto dims_output = output_tensor->getDimensions(); + VLOG(2) << "output tensor dimension:" << dims_output.nbDims; + outputs->push_back(TRT_TensorOrWeights(output_tensor)); + return tensorflow::Status::OK(); +} + void Converter::register_op_converters() { // vgg_16 slim implementation op_registry_["Placeholder"] = ConvertPlaceholder; op_registry_["Conv2D"] = ConvertConv2D; + op_registry_["DepthwiseConv2dNative"] = ConvertConv2DDepthwise; op_registry_["Relu"] = ConvertActivation; op_registry_["MaxPool"] = ConvertPool; + op_registry_["AvgPool"] = ConvertPool; // This could be really handled as ConvertBinary op_registry_["BiasAdd"] = ConvertScale; op_registry_["Const"] = ConvertConst; - // op_registry_["MatMul"] = ConvertFullyConnected; // Not used in vgg // TODO(ben,jie): this is a temp hack. op_registry_["Identity"] = ConvertIdentity; // Identity should be removed - // op_registry_["AvgPool"] = ConvertPool; // resnet_50_v1 slim implementation op_registry_["Add"] = ConvertBinary; @@ -1393,26 +2059,364 @@ void Converter::register_op_converters() { op_registry_["Mean"] = ConvertReduce; op_registry_["Pad"] = ConvertPad; // TODO(ben,jie): Add more ops + + op_registry_["ConcatV2"] = ConvertConcat; + op_registry_["MatMul"] = ConvertMatMul; + op_registry_["Reshape"] = ConvertReshape; + op_registry_["FusedBatchNorm"] = ConvertFusedBatchNorm; + op_registry_["FusedBatchNormV2"] = ConvertFusedBatchNorm; } } // namespace +tensorflow::Status GetTensorRTGraph(tensorrt::convert::SubGraphParams& s) { + return tensorflow::errors::Unimplemented("Not implemented yet"); +} +tensorflow::Status ConvertCalibrationNodeToEngineNode( + tensorflow::Graph& graph, tensorflow::Node* c_node) { + const auto ndef = c_node->def(); + + TFAttrs attrs(ndef); + std::vector segment_nodes( + attrs.get>("segment_nodes")); + std::vector output_nodes( + attrs.get>("segment_output_names")); + std::vector input_names( + attrs.get>("input_names")); + string res_name = attrs.get("resource_name"); + VLOG(1) << "Node name " << c_node->name() << " res_name " << res_name; + string engine_name = "my_trt_op"; + { + const auto node_id = tensorflow::str_util::Split(res_name, "_"); + engine_name += node_id.back(); + } + std::map node_maps; + + for (auto n : graph.op_nodes()) { + node_maps.insert({n->name(), n}); + } + VLOG(1) << "Output Nodes:"; + std::vector out_types; + std::vector out_edges; + for (auto& i : output_nodes) { + auto node_port = tensorflow::str_util::Split(i, ":"); + VLOG(1) << " " << i << " in graph " << node_maps.count(i); + auto out_node_name = node_port.at(0); + if (node_port.size() > 1) { + VLOG(1) << "Multi port output" << node_port.at(0) << " " + << node_port.at(1) << " size=" << node_port.size(); + } + auto node_it = node_maps.find(out_node_name); + if (node_it != node_maps.end()) { + tensorflow::Node* out_node = node_it->second; + int port = 0; + if (node_port.size() == 2) { + port = std::strtoul(node_port.at(1).c_str(), nullptr, 10); + out_types.push_back(out_node->output_type(port)); + } else { + out_types.push_back(out_node->output_type(0)); + } + for (auto out_edge : out_node->out_edges()) { + if (out_edge->src_output() == port) { + out_edges.push_back(out_edge); + break; + } + } + } else { + LOG(WARNING) << " couldn't find output node " << out_node_name; + } + } + VLOG(1) << "Input Nodes:"; + for (auto& i : input_names) { + VLOG(1) << " " << i << " in graph " << node_maps.count(i); + } + auto trt_rm = tensorflow::tensorrt::TRTResourceManager::instance(); + auto resmgr = trt_rm->getManager("TRTCalibOps"); + tensorflow::tensorrt::TRTCalibrationResource* calib_res = nullptr; + auto status = resmgr->Lookup(res_name, res_name, &calib_res); + if (!status.ok() || !calib_res->calibrator_) { + return tensorflow::errors::FailedPrecondition( + "You must run calibration" + " and inference conversion in the same proces"); + } + + calib_res->calibrator_->setDone(); + calib_res->thr_->join(); + delete calib_res->thr_; + if (!calib_res->engine_) { + LOG(FATAL) << "Calibration failed!, engine is nullptr. Did you run " + "calibration graph?"; + } + auto weight_rmgr = trt_rm->getManager("WeightStore"); + TF_CHECK_OK(weight_rmgr->Delete( + res_name, res_name)); + auto engine_plan = calib_res->engine_->serialize(); + calib_res->engine_->destroy(); + calib_res->network_->destroy(); + calib_res->builder_->destroy(); + calib_res->thr_ = nullptr; + calib_res->engine_ = nullptr; + calib_res->builder_ = nullptr; + tensorflow::NodeDefBuilder op_builder(engine_name, "TRTEngineOp"); + std::vector income_edges; + for (const auto in_edge : c_node->in_edges()) { + auto src = in_edge->src(); + int dest_port = in_edge->dst_input(); + income_edges.emplace_back(src->name(), in_edge->src_output(), + c_node->input_type(dest_port)); + } + tensorflow::gtl::ArraySlice input_list( + income_edges); + op_builder.Input(input_list); + tensorflow::NodeDef engine_node; + const char* engine_plan_data = static_cast(engine_plan->data()); + string engine_plan_string(engine_plan_data, + engine_plan_data + engine_plan->size()); + status = op_builder.Attr("serialized_engine", engine_plan_string) + .Attr("input_nodes", input_names) + .Attr("output_nodes", output_nodes) + .Attr("OutT", out_types) + .Finalize(&engine_node); + if (!status.ok()) { + LOG(ERROR) << "Engine Node creation failed"; + return status; + } + auto trt_engine_node = graph.AddNode(engine_node, &status); + TF_CHECK_OK(status); + for (size_t i = 0; i < out_edges.size(); i++) { + VLOG(1) << "Connecting trt_engine_node output " << i << " with " + << out_edges.at(i)->dst()->name() << " port " + << out_edges.at(i)->dst_input(); + TF_RETURN_IF_ERROR(graph.UpdateEdge(trt_engine_node, i, + out_edges.at(i)->dst(), + out_edges.at(i)->dst_input())); + } + VLOG(1) << "Segment nodes:"; + for (auto& i : segment_nodes) { + VLOG(1) << " " << i << " in graph " << node_maps.count(i); + auto it = node_maps.find(i); + if (it != node_maps.end()) { + graph.RemoveNode(it->second); + } + } + graph.RemoveNode(c_node); + return tensorflow::Status::OK(); +} + +tensorflow::Status InjectCalibrationNode(tensorrt::convert::SubGraphParams& s) { + // Visit nodes in reverse topological order and construct the TRT network. + + // Toposort + std::vector order_vec; + tensorflow::GetPostOrder(s.graph, &order_vec); + // Select just the subgraph + std::list order; + for (tensorflow::Node* node : order_vec) { + if (s.subgraph_node_ids.count(node->id())) { + order.push_front(node); // we want topological order to contstruct the + // network layer by layer + } + } + // topological order is needed to build TRT network + static int static_id = 0; + string subgraph_name_scope; + if (!order.empty()) { + subgraph_name_scope = order.front()->name(); + } + for (const tensorflow::Node* node : order) { + subgraph_name_scope = GetCommonNameScope(subgraph_name_scope, node->name()); + } + // TODO(sami,ben,jie): proper naming! + string calib_op_name = + StrCat(subgraph_name_scope, "my_trt_calib_op_", static_id); + string engine_name = StrCat(subgraph_name_scope, "my_trt_op", static_id); + static_id++; + auto trt_rmgr = tensorflow::tensorrt::TRTResourceManager::instance(); + auto op_rmgr = trt_rmgr->getManager("TRTCalibOps"); + auto op_res = new tensorflow::tensorrt::TRTCalibrationResource(); + TF_CHECK_OK(op_rmgr->Create(calib_op_name, calib_op_name, op_res)); + op_res->logger_ = new tensorflow::tensorrt::Logger(); + op_res->builder_ = nvinfer1::createInferBuilder(*(op_res->logger_)); + + if (!op_res->builder_) { + return tensorflow::errors::Internal( + "failed to create TensorRT builder object"); + } + + op_res->network_ = op_res->builder_->createNetwork(); + if (!op_res->network_) { + return tensorflow::errors::Internal( + "failed to create TensorRT network object"); + } + + // Build the network + auto weight_rmgr = trt_rmgr->getManager("WeightStore"); + auto ws = new tensorflow::tensorrt::TRTWeightStore(); + TF_CHECK_OK(weight_rmgr->Create(calib_op_name, calib_op_name, ws)); + Converter converter(op_res->network_, ws, s.precision_mode == FP16MODE); + std::vector input_names; + std::vector input_dtypes; + for (const std::pair& input : s.input_inds) { + VLOG(2) << "parsing input. Node id= " << input.first; + int node_id = input.first; + int output_idx = input.second; + tensorflow::Node* node = s.graph.FindNodeId(node_id); + auto node_name = node->name(); + input_names.push_back(node_name); // insert original node name without port + // TODO(jie): alternative :) + if (!s.graph_properties.HasOutputProperties(node_name)) + return tensorflow::errors::Internal("failed to find input node: " + + node_name); + + auto op_info_vec = s.graph_properties.GetOutputProperties(node_name); + if (static_cast(op_info_vec.size()) < output_idx) + return tensorflow::errors::Internal( + "accessing output index of: ", output_idx, ", at node: ", node_name, + "with output entry from shape_map: ", op_info_vec.size()); + + auto op_info = op_info_vec.at(output_idx); + + tensorflow::DataType tf_dtype = op_info.dtype(); + input_dtypes.push_back(tf_dtype); + + nvinfer1::DataType dtype(nvinfer1::DataType::kFLOAT); + TF_CHECK_OK(ConvertDType(tf_dtype, &dtype)); + + VLOG(2) << "accessing output index of: " << output_idx + << ", at node: " << node_name + << "with output entry from shape_map: " << op_info_vec.size(); + + // TODO(ben,jie): update TRT input format/dimension + nvinfer1::DimsCHW input_dim_psuedo_chw; + for (int i = 0; i < 3; i++) input_dim_psuedo_chw.d[i] = 1; + + for (int i = 1; i < op_info.shape().dim_size(); i++) { + VLOG(2) << "dimension: " << i + << " , size: " << op_info.shape().dim(i).size(); + input_dim_psuedo_chw.d[i - 1] = op_info.shape().dim(i).size(); + } + + // TODO(ben,jie): proper way to restore input tensor name? + auto input_tensor_name = node_name; + if (output_idx != 0) input_tensor_name = StrCat(node_name, ":", output_idx); + + nvinfer1::ITensor* input_tensor = converter.network()->addInput( + input_tensor_name.c_str(), dtype, input_dim_psuedo_chw); + + if (!input_tensor) + return tensorflow::errors::InvalidArgument( + "Failed to create Input layer"); + VLOG(2) << "input tensor name :" << input_tensor_name; + + if (!converter.insert_input_tensor(input_tensor_name, input_tensor)) + return tensorflow::errors::AlreadyExists( + "output tensor already exists for op: " + input_tensor_name); + } + + VLOG(2) << "finished sorting"; + + for (const tensorflow::Node* node : order) { + const tensorflow::NodeDef& node_def = node->def(); + VLOG(2) << "converting node: " << node_def.name() << " , " << node_def.op(); + TF_RETURN_IF_ERROR(converter.convert_node(node_def)); + } + + VLOG(2) << "finished conversion"; + + // Gather output metadata + std::vector output_names; + std::vector output_dtypes; + int trt_engine_op_output_idx = 0; + for (const std::pair& output : s.output_inds) { + int node_id = output.first; + int output_idx = output.second; + tensorflow::Node* node = s.graph.FindNodeId(node_id); + string op_name = node->name(); + string tensor_name = op_name; + + s.output_edge_map->insert( + {trt_engine_op_output_idx == 0 + ? engine_name + : StrCat(engine_name, ":", trt_engine_op_output_idx), + {output_idx, tensor_name}}); + trt_engine_op_output_idx++; + if (output_idx != 0) { + tensor_name = StrCat(tensor_name, ":", output_idx); + } + VLOG(1) << "output tensor name: " << tensor_name; + output_names.push_back(tensor_name); + auto tensor_or_weights = converter.get_tensor(tensor_name); + if (!tensor_or_weights.is_tensor()) { + return tensorflow::errors::InvalidArgument( + "Output node is weights not tensor"); + } + nvinfer1::ITensor* tensor = tensor_or_weights.tensor(); + if (!tensor) { + return tensorflow::errors::NotFound("Output tensor not found: " + + tensor_name); + } + converter.network()->markOutput(*tensor); + tensorflow::DataType tf_dtype = node->output_type(output_idx); + output_dtypes.push_back(tf_dtype); + nvinfer1::DataType trt_dtype = nvinfer1::DataType::kFLOAT; + TF_RETURN_IF_ERROR(ConvertDType(tf_dtype, &trt_dtype)); + tensor->setType(trt_dtype); + } + + VLOG(2) << "finished output"; + + // Build the engine + op_res->builder_->setMaxBatchSize(s.max_batch_size); + op_res->builder_->setMaxWorkspaceSize(s.max_workspace_size_bytes); + + // Build the TRT op + // TODO(sami,ben,jie): proper naming! + tensorflow::NodeDefBuilder op_builder(calib_op_name, "TRTCalibOp"); + std::vector income_edges; + for (size_t i = 0; i < input_names.size(); ++i) { + int output_idx = s.input_inds.at(i).second; + // we wired up the input here already, it is redundant to do it again in + // ConvertSubGraphToTensorRT(convert_graph.cc) + auto incoming_edge = tensorflow::NodeDefBuilder::NodeOut( + input_names.at(i), output_idx, input_dtypes.at(i)); + VLOG(1) << calib_op_name << " input " << i << " = " << input_names.at(i) + << ":" << output_idx + << " dType= " << tensorflow::DataTypeString(input_dtypes.at(i)); + income_edges.push_back(incoming_edge); + } + tensorflow::gtl::ArraySlice input_list( + income_edges); + op_builder.Input(input_list); + std::vector segment_names; + segment_names.reserve(s.subgraph_node_ids.size()); + for (int i : s.subgraph_node_ids) { + auto node = s.graph.FindNodeId(i); + segment_names.push_back(node->name()); + } + LOG(INFO) << "finished op preparation"; + + auto status = op_builder.Attr("segment_nodes", segment_names) + .Attr("input_names", input_names) + .Attr("segment_output_names", output_names) + .Attr("resource_name", calib_op_name) + .Finalize(s.trt_node); + + LOG(INFO) << status.ToString(); + LOG(INFO) << "finished op building"; + + return tensorflow::Status::OK(); +} tensorflow::Status ConvertSubGraphToTensorRTNodeDef( - const tensorflow::Graph& graph, const std::set& subgraph_node_ids, - const std::vector>& input_inds, - const std::vector>& output_inds, size_t max_batch_size, - size_t max_workspace_size_bytes, - const tensorflow::grappler::GraphProperties& graph_properties, - tensorflow::NodeDef* trt_node) { + tensorrt::convert::SubGraphParams& s) { // Visit nodes in reverse topological order and construct the TRT network. // Toposort std::vector order_vec; - tensorflow::GetPostOrder(graph, &order_vec); + tensorflow::GetPostOrder(s.graph, &order_vec); // Select just the subgraph std::list order; for (tensorflow::Node* node : order_vec) { - if (subgraph_node_ids.count(node->id())) { + if (s.subgraph_node_ids.count(node->id())) { // We want topological order to contstruct the // network layer by layer order.push_front(node); @@ -1434,46 +2438,86 @@ tensorflow::Status ConvertSubGraphToTensorRTNodeDef( "Failed to create TensorRT network object"); } + string subgraph_name_scope; + if (!order.empty()) { + subgraph_name_scope = order.front()->name(); + } + for (const tensorflow::Node* node : order) { + subgraph_name_scope = GetCommonNameScope(subgraph_name_scope, node->name()); + } + static int static_id = 0; + // TODO(sami,ben,jie): proper naming! + string engine_name = StrCat(subgraph_name_scope, "my_trt_op"); + engine_name = StrCat(engine_name, static_id++); + auto trt_rmgr = tensorflow::tensorrt::TRTResourceManager::instance(); + auto weight_rmgr = trt_rmgr->getManager("WeightStore"); + auto ws = new tensorflow::tensorrt::TRTWeightStore(); + TF_CHECK_OK(weight_rmgr->Create(engine_name, engine_name, ws)); + // Build the network - Converter converter(trt_network.get()); + Converter converter(trt_network.get(), ws, s.precision_mode == FP16MODE); std::vector input_names; std::vector input_dtypes; - for (std::pair const& input : input_inds) { + for (const std::pair& input : s.input_inds) { + VLOG(2) << "parsing input!!!!!"; int node_id = input.first; int output_idx = input.second; - tensorflow::Node* node = graph.FindNodeId(node_id); + tensorflow::Node* node = s.graph.FindNodeId(node_id); auto node_name = node->name(); - input_names.push_back(node_name); // Insert original node name without port - // TODO(jie): alternative :) - if (!graph_properties.HasOutputProperties(node_name)) - return tensorflow::errors::Internal("Failed to find input node: " + - node_name); + // input_names should use the node name in the graph + // here it should be the input tensor name -> matching the binding + // insert original node name without port + auto tensor_name = node_name; + if (output_idx != 0) { + tensor_name = StrCat(tensor_name, ":", output_idx); + } - auto op_info_vec = graph_properties.GetOutputProperties(node_name); - if (static_cast(op_info_vec.size()) < output_idx) - return tensorflow::errors::Internal( - "Accessing output index of: " + std::to_string(output_idx) + - ", at node: " + node_name + " with output entry from shape_map: " + - std::to_string(op_info_vec.size())); + VLOG(2) << "input name: " << node_name << " tensor_name: " << tensor_name + << " idx: " << output_idx; - auto op_info = op_info_vec.at(output_idx); + auto shape_inference_node_name = node_name; + auto shape_inference_output_idx = output_idx; + // rewire the shape inference to original node in the graph + if (s.output_edge_map->count(tensor_name)) { + shape_inference_node_name = s.output_edge_map->at(tensor_name).second; + shape_inference_output_idx = s.output_edge_map->at(tensor_name).first; + } + if (shape_inference_output_idx < 0) continue; + VLOG(2) << "shapeinference name: " << shape_inference_node_name + << " idx: " << shape_inference_output_idx; + + if (!s.graph_properties.HasOutputProperties(shape_inference_node_name)) + return tensorflow::errors::Internal("failed to find input node: " + + shape_inference_node_name); + + auto op_info_vec = + s.graph_properties.GetOutputProperties(shape_inference_node_name); + if (static_cast(op_info_vec.size()) <= shape_inference_output_idx) + return tensorflow::errors::Internal( + "accessing output index of: ", shape_inference_output_idx, + ", at node: ", shape_inference_node_name, + " with output entry from shape_map: ", op_info_vec.size()); + auto op_info = op_info_vec.at(shape_inference_output_idx); tensorflow::DataType tf_dtype = op_info.dtype(); input_dtypes.push_back(tf_dtype); nvinfer1::DataType dtype(nvinfer1::DataType::kFLOAT); TF_CHECK_OK(ConvertDType(tf_dtype, &dtype)); - VLOG(2) << "Accessing output index of: " << std::to_string(output_idx) + VLOG(2) << "Accessing output index of: " << output_idx << ", at node: " << node_name - << " with output entry from shape_map: " - << std::to_string(op_info_vec.size()); - + << " with output entry from shape_map: " << op_info_vec.size(); // TODO(ben,jie): update TRT input format/dimension nvinfer1::DimsCHW input_dim_psuedo_chw; for (int i = 0; i < 3; i++) input_dim_psuedo_chw.d[i] = 1; + // TODO(jie): TRT 3.x only support 4 dimensional input tensor. + // update the code once TRT 4.0 comes out. + if (op_info.shape().dim_size() != 4) + return tensorflow::errors::Unimplemented("require 4 dimensional input"); + for (int i = 1; i < op_info.shape().dim_size(); i++) { VLOG(2) << "dimension: " << i << " , size: " << op_info.shape().dim(i).size(); @@ -1482,9 +2526,11 @@ tensorflow::Status ConvertSubGraphToTensorRTNodeDef( // TODO(ben,jie): proper way to restore input tensor name? auto input_tensor_name = node_name; - if (output_idx != 0) - input_tensor_name = node_name + ":" + std::to_string(output_idx); + if (output_idx != 0) { + input_tensor_name = StrCat(node_name, ":", output_idx); + } + input_names.push_back(input_tensor_name); nvinfer1::ITensor* input_tensor = converter.network()->addInput( input_tensor_name.c_str(), dtype, input_dim_psuedo_chw); @@ -1511,14 +2557,22 @@ tensorflow::Status ConvertSubGraphToTensorRTNodeDef( // Gather output metadata std::vector output_names; std::vector output_dtypes; - for (std::pair const& output : output_inds) { + int trt_engine_op_output_idx = 0; + for (const std::pair& output : s.output_inds) { int node_id = output.first; int output_idx = output.second; - tensorflow::Node* node = graph.FindNodeId(node_id); + tensorflow::Node* node = s.graph.FindNodeId(node_id); string op_name = node->name(); string tensor_name = op_name; + + s.output_edge_map->insert( + {trt_engine_op_output_idx == 0 + ? engine_name + : StrCat(engine_name, ":", trt_engine_op_output_idx), + {output_idx, tensor_name}}); + trt_engine_op_output_idx++; if (output_idx != 0) - tensor_name = tensor_name + ":" + std::to_string(output_idx); + tensorflow::strings::StrAppend(&tensor_name, ":", output_idx); VLOG(2) << "Output tensor name: " << tensor_name; output_names.push_back(tensor_name); auto tensor_or_weights = converter.get_tensor(tensor_name); @@ -1540,19 +2594,25 @@ tensorflow::Status ConvertSubGraphToTensorRTNodeDef( } VLOG(2) << "Finished output"; - // TODO(jie): static_id is not thread safe. - static int static_id = 0; // Build the engine - trt_builder->setMaxBatchSize(max_batch_size); - trt_builder->setMaxWorkspaceSize(max_workspace_size_bytes); - VLOG(0) << "Starting build engine " << static_id; - // TODO(ben,jie): half2 and int8 mode support + trt_builder->setMaxBatchSize(s.max_batch_size); + trt_builder->setMaxWorkspaceSize(s.max_workspace_size_bytes); + VLOG(0) << "Max batch size= " << s.max_batch_size + << " max workspace size= " << s.max_workspace_size_bytes; + if (s.precision_mode == FP16MODE) { + trt_builder->setHalf2Mode(true); + VLOG(0) << "Using FP16 precision mode"; + } + LOG(INFO) << "starting build engine"; string engine_plan_string; { auto trt_engine = infer_object(trt_builder->buildCudaEngine(*converter.network())); VLOG(0) << "Built network"; + if (trt_engine.get() == nullptr) { + return tensorflow::errors::Internal("Engine building failure"); + } auto engine_plan = infer_object(trt_engine->serialize()); VLOG(0) << "Serialized engine"; const char* engine_plan_data = @@ -1560,18 +2620,19 @@ tensorflow::Status ConvertSubGraphToTensorRTNodeDef( engine_plan_string = string(engine_plan_data, engine_plan_data + engine_plan->size()); } - - VLOG(0) << "Finished engine"; + TF_RETURN_IF_ERROR(weight_rmgr->Delete( + engine_name, engine_name)); + LOG(INFO) << "finished engine " << engine_name; // Build the TRT op - // TODO(sami,ben,jie): proper naming! - tensorflow::NodeDefBuilder op_builder( - tensorflow::strings::StrCat("my_trt_op", static_id++), "TRTEngineOp"); + tensorflow::NodeDefBuilder op_builder(engine_name, "TRTEngineOp"); std::vector income_edges; + VLOG(2) << "input edge size: " << input_names.size(); for (size_t i = 0; i < input_names.size(); ++i) { - int output_idx = input_inds.at(i).second; - // We wired up the input here already, it is redundant to do it again in - // ConvertSubGraphToTensorRT(convert_graph.cc) + VLOG(2) << "input edges: " << i << " " << input_names.at(i); + int output_idx = s.input_inds.at(i).second; + // we wired up the input here already, it is redundant to do it again in + // ConvertSubGraphToTensorRT(convert_graph.cc) auto incoming_edge = tensorflow::NodeDefBuilder::NodeOut( input_names.at(i), output_idx, input_dtypes.at(i)); income_edges.push_back(incoming_edge); @@ -1586,7 +2647,7 @@ tensorflow::Status ConvertSubGraphToTensorRTNodeDef( .Attr("input_nodes", input_names) .Attr("output_nodes", output_names) .Attr("OutT", output_dtypes) - .Finalize(trt_node); + .Finalize(s.trt_node); VLOG(0) << status.ToString() << " finished op building"; diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.h b/tensorflow/contrib/tensorrt/convert/convert_nodes.h index 2e7fd19566e1ed3719b932c7443a9c3f652b2d3e..954a1e72f8604371fc00e088a67b4d411314dda6 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.h +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.h @@ -17,6 +17,8 @@ limitations under the License. #define TENSORFLOW_CONTRIB_TENSORRT_CONVERT_CONVERT_NODES_H_ #include +#include +#include #include #include @@ -32,16 +34,49 @@ namespace tensorflow { namespace tensorrt { namespace convert { -tensorflow::Status ConvertSubGraphToTensorRTNodeDef( - const tensorflow::Graph& graph, const std::set& subgraph_node_ids, - const std::vector>& - input_inds, // {node_id, output_idx} - const std::vector>& - output_inds, // {node_id, output_idx} - size_t max_batch_size, size_t max_workspace_size_bytes, - const tensorflow::grappler::GraphProperties& graph_prop, - tensorflow::NodeDef* trt_node); +const int FP32MODE = 0; +const int FP16MODE = 1; +const int INT8MODE = 2; +struct SubGraphParams { + SubGraphParams( + tensorflow::Graph& inp_graph, + const std::set& subgraph_node_id_numbers, + const std::vector>& input_indices, + const std::vector>& output_indices, + size_t max_supported_batch_size, size_t max_consumed_workspace_size_bytes, + const tensorflow::grappler::GraphProperties& current_graph_properties, + std::unordered_map>* output_edges, + tensorflow::NodeDef* constructed_trt_node, + int engine_precision_mode = FP32MODE) + : graph(inp_graph), + subgraph_node_ids(subgraph_node_id_numbers), + input_inds(input_indices), + output_inds(output_indices), + max_batch_size(max_supported_batch_size), + max_workspace_size_bytes(max_consumed_workspace_size_bytes), + graph_properties(current_graph_properties), + output_edge_map(output_edges), + trt_node(constructed_trt_node), + precision_mode(engine_precision_mode) {} + + tensorflow::Graph& graph; + const std::set& subgraph_node_ids; + const std::vector>& input_inds; // {node_id, output_idx} + const std::vector>& output_inds; // {node_id, output_idx} + size_t max_batch_size; + size_t max_workspace_size_bytes; + const tensorflow::grappler::GraphProperties& graph_properties; + std::unordered_map>* output_edge_map; + tensorflow::NodeDef* trt_node; + const int precision_mode; +}; + +// TODO(sami): Replace references with const reference or pointers +tensorflow::Status ConvertSubGraphToTensorRTNodeDef(SubGraphParams& params); +tensorflow::Status InjectCalibrationNode(SubGraphParams& params); +tensorflow::Status ConvertCalibrationNodeToEngineNode(tensorflow::Graph& graph, + tensorflow::Node* c_node); } // namespace convert } // namespace tensorrt } // namespace tensorflow diff --git a/tensorflow/contrib/tensorrt/kernels/trt_calib_op.cc b/tensorflow/contrib/tensorrt/kernels/trt_calib_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..aea44fd8a2fcc4c359a6cb0c98ae34711708326e --- /dev/null +++ b/tensorflow/contrib/tensorrt/kernels/trt_calib_op.cc @@ -0,0 +1,136 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/tensorrt/kernels/trt_calib_op.h" +#include "tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.h" +#include "tensorflow/contrib/tensorrt/resources/trt_resource_manager.h" +#include "tensorflow/contrib/tensorrt/resources/trt_resources.h" +#include "tensorflow/core/framework/tensor.h" +#include "tensorflow/core/framework/tensor_shape.h" +#include "tensorflow/core/framework/tensor_types.h" +#include "tensorflow/core/framework/types.h" +#include "tensorflow/core/platform/stream_executor.h" + +#if GOOGLE_CUDA +#if GOOGLE_TENSORRT +#include "cuda/include/cuda_runtime_api.h" +#include "tensorrt/include/NvInfer.h" + +namespace tensorflow { +namespace tensorrt { + +TRTCalibOp::TRTCalibOp(OpKernelConstruction* context) : OpKernel(context) { + OP_REQUIRES_OK(context, context->GetAttr("segment_nodes", &segment_nodes_)); + OP_REQUIRES_OK(context, context->GetAttr("input_names", &input_names_)); + OP_REQUIRES_OK(context, context->GetAttr("resource_name", &resource_name_)); +}; + +#define TYPECASE(dt, X, Y) \ + case dt: { \ + return (void*)X->flat::Type>().data(); \ + } + +void* GetTensorAddress(const Tensor* tensor_ptr) { + auto tensor_type = tensor_ptr->dtype(); + switch (tensor_type) { + TYPECASE(tensorflow::DT_FLOAT, tensor_ptr, dest_ptr); + TYPECASE(tensorflow::DT_HALF, tensor_ptr, dest_ptr); + TYPECASE(tensorflow::DT_INT8, tensor_ptr, dest_ptr); + default: { + LOG(FATAL) << "Unsupported Data type " + << tensorflow::DataTypeString(tensor_type); + return nullptr; + } + } +} + +void TRTCalibOp::Compute(tensorflow::OpKernelContext* ctx) { + // TODO(aaroey): make sure ctx->resource_mgr() is used in future PR. + auto trt_rm = tensorflow::tensorrt::TRTResourceManager::instance(); + auto res_mgr = trt_rm->getManager("TRTCalibOps"); + tensorflow::tensorrt::TRTCalibrationResource* calib_res = nullptr; + auto status = res_mgr->Lookup(resource_name_, resource_name_, &calib_res); + + if (!status.ok()) { + ctx->SetStatus(status); + return; + } + int num_inputs = ctx->num_inputs(); + // first run instantiate calibrator + if (calib_res->calibrator_ == nullptr) { + dev_tensors_.resize(num_inputs); + int batch_size = ctx->input(0).dim_size(0); + VLOG(1) << " Constructing calibrator"; + for (int i = 0; i < num_inputs; i++) { + // allocate workspace on device for inputs + const tensorflow::Tensor& t = ctx->input(i); + OP_REQUIRES_OK(ctx, + ctx->allocate_persistent(t.dtype(), t.shape(), + &dev_tensors_.at(i), nullptr)); + const auto device_tensor = dev_tensors_.at(i).AccessTensor(ctx); + CHECK_EQ(t.TotalBytes(), device_tensor->TotalBytes()); + void* device_address = GetTensorAddress(device_tensor); + device_buffers_.emplace(input_names_.at(i), + std::pair( + device_address, device_tensor->TotalBytes())); + } + + calib_res->calibrator_ = + new TRTInt8Calibrator(device_buffers_, batch_size, resource_name_); + string label(resource_name_); + calib_res->thr_ = new std::thread([calib_res, label]() { + VLOG(1) << "Starting calibration thread, Calibration Resource @ " + << calib_res; + calib_res->builder_->setInt8Calibrator(calib_res->calibrator_); + calib_res->builder_->setInt8Mode(true); + calib_res->engine_ = calib_res->builder_->buildCudaEngine( + *calib_res->network_); // will loop until we terminate calibrator + VLOG(1) << "Calibration loop terminated " << label; + }); + VLOG(1) << "initialized calibrator resource"; + } // calibrator initialized + + // Pass input data to calibrator + std::unordered_map input_data; + for (int i = 0; i < num_inputs; i++) { + const Tensor& t = ctx->input(i); + void* data_address = GetTensorAddress(&t); + const auto device_tensor = dev_tensors_.at(i).AccessTensor(ctx); + CHECK_EQ(t.TotalBytes(), + device_tensor->TotalBytes()); // use the tensor so FW keeps it + input_data.emplace(input_names_.at(i), data_address); + ctx->set_output(i, t); + } + VLOG(2) << "Filled map for sending"; + // copied from cuda_kernel_helper since it seems only valid in *.cu.cc files + const cudaStream_t* stream = CHECK_NOTNULL( + reinterpret_cast(ctx->op_device_context() + ->stream() + ->implementation() + ->CudaStreamMemberHack())); + calib_res->calibrator_->setBatch(input_data, *stream); + VLOG(2) << "Passed calibration data"; + // TODO(aaroey): make sure we wait for the completion of calibration on the + // last batch in future PR. +}; + +#undef TYPECASE + +REGISTER_KERNEL_BUILDER(Name("TRTCalibOp").Device(DEVICE_GPU), TRTCalibOp); + +} // namespace tensorrt +} // namespace tensorflow +#endif +#endif diff --git a/tensorflow/contrib/tensorrt/kernels/trt_calib_op.h b/tensorflow/contrib/tensorrt/kernels/trt_calib_op.h new file mode 100644 index 0000000000000000000000000000000000000000..23df9db32f077a080eaff7479fcbe90d6a504c42 --- /dev/null +++ b/tensorflow/contrib/tensorrt/kernels/trt_calib_op.h @@ -0,0 +1,52 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_CONTRIB_TENSORRT_KERNELS_TRT_CALIB_OP_H +#define TENSORFLOW_CONTRIB_TENSORRT_KERNELS_TRT_CALIB_OP_H + +#include +#include +#include +#include +#include +#include "tensorflow/core/framework/op.h" +#include "tensorflow/core/framework/op_kernel.h" +#include "tensorflow/core/framework/tensor_shape.h" +#include "tensorflow/core/platform/types.h" + +#if GOOGLE_CUDA +#if GOOGLE_TENSORRT +namespace tensorflow { +namespace tensorrt { +// TODO(sami): Convert this to async kernel! +class TRTCalibOp : public OpKernel { + public: + explicit TRTCalibOp(OpKernelConstruction* context); + + void Compute(OpKernelContext* context) override; + + private: + string resource_name_; + std::vector segment_nodes_; + std::vector input_names_; + std::vector shapes_; + std::unordered_map> device_buffers_; + std::vector dev_tensors_; +}; +} // namespace tensorrt +} // namespace tensorflow +#endif +#endif +#endif // TENSORFLOW_CONTRIB_TENSORRT_KERNELS_TRT_CALIB_OP_H diff --git a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc index 8efdf63ebebc4d7a199c60635ca64348d2b30505..b32371b642f38b0851955a4a3beab97b86e1f6a0 100644 --- a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc +++ b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc @@ -24,8 +24,12 @@ limitations under the License. #include "cuda/include/cuda_runtime_api.h" namespace tensorflow { -namespace tensorrt { static ::tensorflow::tensorrt::Logger logger; +namespace gpu = ::perftools::gputools; +using IRuntime = nvinfer1::IRuntime; +using Dims = nvinfer1::Dims; + +namespace tensorrt { TRTEngineOp::TRTEngineOp(OpKernelConstruction* context) : OpKernel(context) { // read serialized_engine @@ -40,10 +44,21 @@ TRTEngineOp::TRTEngineOp(OpKernelConstruction* context) : OpKernel(context) { // TODO(samikama) runtime should be taken from a resourcemanager as well. // Only engine should be in the op and context and runtime should be taken // from resourcemanager - nvinfer1::IRuntime* infer = nvinfer1::createInferRuntime(logger); + // TODO(jie): cudaSetDevice make sure trt engine is allocated on the same + // gpu where the input/output is also located. + int gpu_id = context->device()->tensorflow_gpu_device_info()->gpu_id; + cudaSetDevice(gpu_id); + int device; + cudaGetDevice(&device); + if (gpu_id != device) LOG(FATAL) << "set device failed!"; + + // TODO(samikama) runtime should be taken from a resourcemanager as well. + // Only engine should be in the op and context and runtime should be taken + // from resourcemanager + + IRuntime* infer = nvinfer1::createInferRuntime(logger); trt_engine_ptr_.reset(infer->deserializeCudaEngine( serialized_engine.c_str(), serialized_engine.size(), nullptr)); - trt_execution_context_ptr_.reset(trt_engine_ptr_->createExecutionContext()); // Runtime is safe to delete after engine creation infer->destroy(); @@ -55,7 +70,6 @@ void TRTEngineOp::Compute(OpKernelContext* context) { size_t binding_index; int num_batch = 0; - bool valid = true; for (int i = 0; i < context->num_inputs(); i++) { // Grab the input tensor binding_index = trt_engine_ptr_->getBindingIndex(input_nodes_[i].c_str()); @@ -64,8 +78,12 @@ void TRTEngineOp::Compute(OpKernelContext* context) { const TensorShape& input_shape = input_tensor.shape(); if (i == 0) { num_batch = input_shape.dim_size(0); + if (num_batch > trt_engine_ptr_->getMaxBatchSize()) { + LOG(FATAL) << "input tensor batch larger than max_batch_size: " + << trt_engine_ptr_->getMaxBatchSize(); + } } else if (num_batch != input_shape.dim_size(0)) { - valid = false; + LOG(FATAL) << "input data inconsistent batch size"; break; } switch (trt_engine_ptr_->getBindingDataType(binding_index)) { @@ -81,9 +99,6 @@ void TRTEngineOp::Compute(OpKernelContext* context) { } } - // Might want a different way to inform the user of batch size inconsistency - if (!valid) LOG(WARNING) << "input data inconsistent batch size"; - for (int i = 0; i < static_cast(output_nodes_.size()); i++) { // This is bad that we have to reallocate output buffer every run. // Create an output tensor @@ -126,9 +141,11 @@ void TRTEngineOp::Compute(OpKernelContext* context) { ->implementation() ->CudaStreamMemberHack())); - // execution handled by TF since we are getting stream from TF. - // it is safe for CPU pointer array (buffers) to go out of scope after enqueue - trt_execution_context_ptr_->enqueue(num_batch, &buffers[0], *stream, nullptr); + // TODO(jie): trt enqueue does not return error + auto ret = trt_execution_context_ptr_->enqueue(num_batch, &buffers[0], + *stream, nullptr); + VLOG(2) << "enqueue returns: " << ret; + // sync should be done by TF. } REGISTER_KERNEL_BUILDER(Name("TRTEngineOp").Device(DEVICE_GPU), TRTEngineOp); diff --git a/tensorflow/contrib/tensorrt/log/trt_logger.cc b/tensorflow/contrib/tensorrt/log/trt_logger.cc index 7add8cb8b3d2a04206ee4174e79a1a4b86e05f30..dda0dc9e712eb726800abfb6084f4f708d04825b 100644 --- a/tensorflow/contrib/tensorrt/log/trt_logger.cc +++ b/tensorflow/contrib/tensorrt/log/trt_logger.cc @@ -27,19 +27,19 @@ void Logger::log(Severity severity, const char* msg) { // Suppress info-level messages switch (severity) { case Severity::kINFO: { // Mark TRT info messages as debug! - VLOG(2) << msg; + VLOG(2) << name_ << " " << msg; break; } case Severity::kWARNING: { - LOG(WARNING) << msg; + LOG(WARNING) << name_ << " " << msg; break; } case Severity::kERROR: { - LOG(ERROR) << msg; + LOG(ERROR) << name_ << " " << msg; break; } case Severity::kINTERNAL_ERROR: { - LOG(FATAL) << msg; + LOG(FATAL) << name_ << " " << msg; break; } // This is useless for now. But would catch it in future if enum changes. It diff --git a/tensorflow/contrib/tensorrt/log/trt_logger.h b/tensorflow/contrib/tensorrt/log/trt_logger.h index d71f66b933a8068a6276a7e070755e0075543bb5..7f3544f8cfda8dce13881e1f8f4388b640e315f4 100644 --- a/tensorflow/contrib/tensorrt/log/trt_logger.h +++ b/tensorflow/contrib/tensorrt/log/trt_logger.h @@ -27,9 +27,11 @@ namespace tensorrt { // Logger for GIE info/warning/errors class Logger : public nvinfer1::ILogger { - private: + public: + Logger(string name = "DefaultLogger") : name_(name){}; void log(nvinfer1::ILogger::Severity severity, const char* msg) override; + private: string name_; }; diff --git a/tensorflow/contrib/tensorrt/ops/trt_calib_op.cc b/tensorflow/contrib/tensorrt/ops/trt_calib_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..4835e5065068ec7a59995eb7f6126b31aecf6704 --- /dev/null +++ b/tensorflow/contrib/tensorrt/ops/trt_calib_op.cc @@ -0,0 +1,37 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/core/framework/op.h" +#include "tensorflow/core/framework/shape_inference.h" +namespace tensorflow { + +REGISTER_OP("TRTCalibOp") + .Attr("segment_nodes: list(string)") // names of the ops in segment + .Attr("segment_output_names: list(string)") // names of the output ops in + // segment + .Attr("input_names: list(string)") // names of the inputs for + // passing into tensorrt + .Attr("resource_name: string") + .Attr("InT: list({int8, float16, float32})") + .Input("in_tensor: InT") + .Output("out_tensor: InT") + .SetShapeFn([](tensorflow::shape_inference::InferenceContext* c) { + for (int i = 0; i < c->num_inputs(); i++) { + c->set_output(i, c->input(i)); + } + return Status::OK(); + }); + +} // namespace tensorflow diff --git a/tensorflow/contrib/tensorrt/python/__init__.py b/tensorflow/contrib/tensorrt/python/__init__.py index 7e050a768ce97af1fc1d2c85cb52640b4c6a6a97..0b2321b5fc7bcbd53c01d1c97cafcfcb229a83ef 100644 --- a/tensorflow/contrib/tensorrt/python/__init__.py +++ b/tensorflow/contrib/tensorrt/python/__init__.py @@ -20,5 +20,6 @@ from __future__ import print_function # pylint: disable=unused-import,line-too-long from tensorflow.contrib.tensorrt.python.ops import trt_engine_op +from tensorflow.contrib.tensorrt.python.trt_convert import calib_graph_to_infer_graph from tensorflow.contrib.tensorrt.python.trt_convert import create_inference_graph # pylint: enable=unused-import,line-too-long diff --git a/tensorflow/contrib/tensorrt/python/trt_convert.py b/tensorflow/contrib/tensorrt/python/trt_convert.py index 9454862f857ab743712ce409ff007de55e72a68e..666220d78c76960b2a3217486b308c17b912b467 100644 --- a/tensorflow/contrib/tensorrt/python/trt_convert.py +++ b/tensorflow/contrib/tensorrt/python/trt_convert.py @@ -20,11 +20,17 @@ from __future__ import print_function # pylint: disable=unused-import,line-too-long import six as _six +from tensorflow.contrib.tensorrt.wrap_conversion import calib_convert from tensorflow.contrib.tensorrt.wrap_conversion import trt_convert from tensorflow.core.framework import graph_pb2 +from tensorflow.core.protobuf import rewriter_config_pb2 from tensorflow.python.framework import errors from tensorflow.python.framework import errors_impl as _impl +from tensorflow.python.framework import meta_graph from tensorflow.python.framework import ops +from tensorflow.python.grappler import tf_optimizer +from tensorflow.python.util import compat +# pylint: enable=unused-import,line-too-long # TODO(skama): get outputs from session when implemented as c++ @@ -32,22 +38,33 @@ from tensorflow.python.framework import ops def create_inference_graph(input_graph_def, outputs, max_batch_size=1, - max_workspace_size_bytes=2 << 20): + max_workspace_size_bytes=2 << 20, + precision_mode="FP32", + minimum_segment_size=3): """Python wrapper for the TRT transormation. - Args: input_graph_def: GraphDef object containing a model to be transformed. - outputs: List of tensors or node names for the model outputs. + outputs: list of tensors or node names for the model outputs. max_batch_size: max size for the input batch max_workspace_size_bytes: parameter to control memory allocation (in Bytes) + precision_mode: one of 'FP32', 'FP16' and 'INT8' + minimum_segment_size: the minimum number of nodes required for a subgraph to + be replaced by TRTEngineOp. Returns: New GraphDef with TRTEngineOps placed in graph replacing subgraphs. Raises: + ValueError: if the provided precision mode is invalid. RuntimeError: if the returned status message is malformed. """ + supported_precision_modes = {"FP32": 0, "FP16": 1, "INT8": 2} + if precision_mode.upper() not in supported_precision_modes: + raise ValueError(("precision mode '{}' is not supported." + "It should be one of {}").format( + precision_mode, "{'FP32', 'FP16', 'INT8'}")) + mode = supported_precision_modes[precision_mode.upper()] def py2bytes(inp): return inp @@ -83,7 +100,7 @@ def create_inference_graph(input_graph_def, # pair or strings where first one is encoded status and the second # one is the transformed graphs protobuf string. out = trt_convert(input_graph_def_str, out_names, max_batch_size, - max_workspace_size_bytes) + max_workspace_size_bytes, mode, minimum_segment_size) status = to_string(out[0]) output_graph_def_string = out[1] del input_graph_def_str # Save some memory @@ -101,3 +118,46 @@ def create_inference_graph(input_graph_def, output_graph_def.ParseFromString(output_graph_def_string) del output_graph_def_string # Save some memory return output_graph_def + + +def calib_graph_to_infer_graph(calibration_graph_def): + """Convert an existing calibration graph to inference graph. + + Args: + calibration_graph_def: the calibration GraphDef object with calibration data + Returns: + New GraphDef with TRTEngineOps placed in graph replacing calibration nodes. + Raises: + RuntimeError: if the returned status message is malformed. + """ + + def py2string(inp): + return inp + + def py3string(inp): + return inp.decode("utf-8") + + if _six.PY2: + to_string = py2string + else: + to_string = py3string + + graph_str = calibration_graph_def.SerializeToString() + out = calib_convert(graph_str) + status = to_string(out[0]) + output_graph_def_string = out[1] + del graph_str # Save some memory + if len(status) < 2: + raise _impl.UnknownError(None, None, status) + if status[:2] != "OK": + msg = status.split(";") + if len(msg) == 1: + raise RuntimeError("Status message is malformed {}".format(status)) + # pylint: disable=protected-access + raise _impl._make_specific_exception(None, None, ";".join(msg[1:]), + int(msg[0])) + # pylint: enable=protected-access + output_graph_def = graph_pb2.GraphDef() + output_graph_def.ParseFromString(output_graph_def_string) + del output_graph_def_string # Save some memory + return output_graph_def diff --git a/tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.cc b/tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.cc new file mode 100644 index 0000000000000000000000000000000000000000..74df75902ed4ba12d34ed89c04666c38411d6d7d --- /dev/null +++ b/tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.cc @@ -0,0 +1,129 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.h" + +#include +#include +#include + +#include "tensorflow/core/platform/logging.h" + +#if GOOGLE_CUDA +#if GOOGLE_TENSORRT +#include "cuda/include/cuda_runtime_api.h" + +namespace tensorflow { +namespace tensorrt { + +// set the batch size before constructing the thread to execute engine +int TRTInt8Calibrator::getBatchSize() const { return batch_size_; } + +TRTInt8Calibrator::TRTInt8Calibrator( + const std::unordered_map>& dev_buffers, + int batch_size, string engine_name) + : batch_size_(batch_size), + done_(false), + dev_buffers_(dev_buffers), + calib_running_(false), + batch_is_set_(false), + engine_name_(engine_name) {} + +bool TRTInt8Calibrator::setBatch(const std::unordered_map& data, + const cudaStream_t stream) { + tensorflow::mutex_lock lock(cond_mtx_); + while ((calib_running_ || batch_is_set_) && + !done_) { // wait while calibration is running + cond_.wait(lock); + } + if (done_) return false; + CHECK(!calib_running_ && !batch_is_set_); + VLOG(1) << "Set Batch Waiting finished"; + for (const auto it : data) { + auto devptr = dev_buffers_.find(it.first); + if (devptr == dev_buffers_.end()) { + LOG(FATAL) << "FATAL " << engine_name_ << " input name '" << it.first + << "' does not match with the buffer names"; + } + const auto& d = devptr->second; + + // TODO(aaroey): we should not use sync copy on default stream. Make sure + // stream->ThenMemcpy() is used in future PRs. + // TODO(sami,aaroey): Need to figureout a way to ensure synchronization + // between stream, perhaps using a tensor? + auto status = cudaMemcpyAsync(d.first, it.second, d.second, + cudaMemcpyDeviceToDevice, stream); + if (status != cudaSuccess) { + LOG(FATAL) << "cudaMemcpy " << engine_name_ << " for '" << it.first + << "' failed with " << status; + } + } + + // TODO(Sami, aaorey): Find an alternative way! + cudaStreamSynchronize( + stream); // we have to wait for the stream before returning! + batch_is_set_ = true; + cond_.notify_all(); + return true; +} + +bool TRTInt8Calibrator::getBatch(void** bindings, const char** names, + int num_bindings) { + tensorflow::mutex_lock lock(cond_mtx_); + calib_running_ = false; + cond_.notify_all(); + while ((!batch_is_set_ && !done_)) { // wait until new batch arrives + cond_.wait(lock); + + } + if (done_) { + return false; + } + + for (int i = 0; i < num_bindings; i++) { + auto it = dev_buffers_.find(names[i]); + if (it == dev_buffers_.end()) { + LOG(FATAL) << "Calibration engine asked for unknown tensor name '" + << names[i] << "' at position " << i; + } + + bindings[i] = it->second.first; + } + batch_is_set_ = false; + calib_running_ = true; + return true; +} + +const void* TRTInt8Calibrator::readCalibrationCache(std::size_t& length) { + return nullptr; +} + +void TRTInt8Calibrator::setDone() { + tensorflow::mutex_lock lock(cond_mtx_); + done_ = true; + cond_.notify_all(); +} + +void TRTInt8Calibrator::writeCalibrationCache(const void* ptr, + std::size_t length) {} +TRTInt8Calibrator::~TRTInt8Calibrator() { + VLOG(1) << "Destroying calibrator for " << engine_name_; +} + +} // namespace tensorrt +} // namespace tensorflow + +#endif +#endif diff --git a/tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.h b/tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.h new file mode 100644 index 0000000000000000000000000000000000000000..d77aa2c5ab184756adaee38f88180b3c128ebe03 --- /dev/null +++ b/tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.h @@ -0,0 +1,72 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_CONTRIB_TENSORRT_RESOURCES_TRT_INT8_CALIBRATOR_H_ +#define TENSORFLOW_CONTRIB_TENSORRT_RESOURCES_TRT_INT8_CALIBRATOR_H_ + +#include +#include +#include +#include +#include "tensorflow/core/platform/mutex.h" + +#if GOOGLE_CUDA +#if GOOGLE_TENSORRT + +#include "cuda/include/cuda_runtime_api.h" +#include "tensorrt/include/NvInfer.h" + +namespace tensorflow { +namespace tensorrt { +// This class provides a 1 element queue to match TFs push model to +// TRTs pull model for calibration. When TRT implements a means for +// a push calibration This class should be updated accordingly + +struct TRTInt8Calibrator : public nvinfer1::IInt8EntropyCalibrator { + public: + TRTInt8Calibrator( + const std::unordered_map>& dev_buffers, + int batch_size, string engine_name); + int getBatchSize() const override; + bool getBatch(void* bindings[], const char* names[], + int num_bindings) override; + bool setBatch(const std::unordered_map& data, + const cudaStream_t stream); + void setDone(); + const void* readCalibrationCache(std::size_t& length) override; + void writeCalibrationCache(const void* ptr, std::size_t length) override; + ~TRTInt8Calibrator(); + + private: + const int batch_size_; + tensorflow::mutex cond_mtx_; // mutex for condition_variable + tensorflow::condition_variable cond_; // condition variable to implement + // producer-consumer queue for + // calibration + bool done_; + const std::unordered_map> + dev_buffers_; // map to keep tensorrt input buffers and sizes keyed with + // buffer names + bool calib_running_; + bool batch_is_set_; + string engine_name_; +}; + +} // namespace tensorrt +} // namespace tensorflow + +#endif +#endif +#endif // TENSORFLOW_CONTRIB_TENSORRT_RESOURCES_TRT_INT8_CALIBRATOR_H_ diff --git a/tensorflow/contrib/tensorrt/resources/trt_resource_manager.cc b/tensorflow/contrib/tensorrt/resources/trt_resource_manager.cc new file mode 100644 index 0000000000000000000000000000000000000000..e663eed4dd6704e2f41bde1dfabd411e86669ecd --- /dev/null +++ b/tensorflow/contrib/tensorrt/resources/trt_resource_manager.cc @@ -0,0 +1,39 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/tensorrt/resources/trt_resource_manager.h" +#include "tensorflow/core/platform/logging.h" + +namespace tensorflow { +namespace tensorrt { + +std::shared_ptr +tensorflow::tensorrt::TRTResourceManager::getManager(const string& op_name) { + // mutex is held for lookup only. Most instantiations where mutex will be held + // longer will be during op creation and should be ok. + tensorflow::mutex_lock lock(map_mutex_); + auto s = managers_.find(op_name); + if (s == managers_.end()) { + auto it = managers_.emplace( + op_name, std::make_shared(op_name)); + VLOG(1) << "Returning a new manager " << op_name; + return it.first->second; + } + VLOG(1) << "Returning old manager " << op_name; + return s->second; +} + +} // namespace tensorrt +} // namespace tensorflow diff --git a/tensorflow/contrib/tensorrt/resources/trt_resource_manager.h b/tensorflow/contrib/tensorrt/resources/trt_resource_manager.h new file mode 100644 index 0000000000000000000000000000000000000000..5f8ad491d3c13e8911b0b95c3e95e19afe4d59c0 --- /dev/null +++ b/tensorflow/contrib/tensorrt/resources/trt_resource_manager.h @@ -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. +==============================================================================*/ + +#ifndef TENSORFLOW_CONTRIB_TENSORRT_RESOURCES_TRT_RESOURCE_MANAGER_H_ +#define TENSORFLOW_CONTRIB_TENSORRT_RESOURCES_TRT_RESOURCE_MANAGER_H_ +#include + +#include +#include +#include "tensorflow/core/framework/resource_mgr.h" +#include "tensorflow/core/platform/mutex.h" + +namespace tensorflow { +namespace tensorrt { + +class TRTResourceManager { + TRTResourceManager() = default; + + public: + static std::shared_ptr instance() { + static std::shared_ptr instance_( + new TRTResourceManager); + return instance_; + } + // returns a manager for given op, if it doesn't exists it creates one + std::shared_ptr getManager(const string& op_name); + + private: + std::unordered_map> + managers_; + tensorflow::mutex map_mutex_; +}; + +} // namespace tensorrt +} // namespace tensorflow + +#endif // TENSORFLOW_CONTRIB_TENSORRT_RESOURCE_TRT_RESOURCE_MANAGER_H_ diff --git a/tensorflow/contrib/tensorrt/resources/trt_resources.h b/tensorflow/contrib/tensorrt/resources/trt_resources.h new file mode 100644 index 0000000000000000000000000000000000000000..3c85968ae7acf5c5fc567be6805a5d226b1094c7 --- /dev/null +++ b/tensorflow/contrib/tensorrt/resources/trt_resources.h @@ -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. +==============================================================================*/ + +#ifndef TENSORFLOW_CONTRIB_TENSORRT_RESOURCES_TRTRESOURCES_H_ +#define TENSORFLOW_CONTRIB_TENSORRT_RESOURCES_TRTRESOURCES_H_ + +#include +#include +#include +#include +#include +#include "tensorflow/contrib/tensorrt/log/trt_logger.h" +#include "tensorflow/core/framework/resource_mgr.h" + +#if GOOGLE_CUDA +#if GOOGLE_TENSORRT +#include "tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.h" +#include "tensorrt/include/NvInfer.h" + +namespace tensorflow { +namespace tensorrt { +class TRTCalibrationResource : public tensorflow::ResourceBase { + public: + TRTCalibrationResource() + : calibrator_(nullptr), + builder_(nullptr), + network_(nullptr), + engine_(nullptr), + logger_(nullptr), + thr_(nullptr) {} + string DebugString() override { + std::stringstream oss; + oss << " Calibrator = " << std::hex << calibrator_ << std::dec << std::endl + << " Builder = " << std::hex << builder_ << std::dec << std::endl + << " Network = " << std::hex << network_ << std::dec << std::endl + << " Engine = " << std::hex << engine_ << std::dec << std::endl + << " Logger = " << std::hex << logger_ << std::dec << std::endl + << " Thread = " << std::hex << thr_ << std::dec << std::endl; + return oss.str(); + } + ~TRTCalibrationResource() { + VLOG(0) << "Destroying Calibration Resource " << std::endl << DebugString(); + } + TRTInt8Calibrator* calibrator_; + nvinfer1::IBuilder* builder_; + nvinfer1::INetworkDefinition* network_; + nvinfer1::ICudaEngine* engine_; + tensorflow::tensorrt::Logger* logger_; + // TODO(sami): Use threadpool threads! + std::thread* thr_; +}; + +class TRTWeightStore : public tensorflow::ResourceBase { + public: + TRTWeightStore() {} + std::list> store_; + string DebugString() override { + std::stringstream oss; + size_t lenBytes = 0; + for (const auto& v : store_) { + lenBytes += v.size() * sizeof(uint8_t); + } + oss << " Number of entries = " << store_.size() << std::endl + << " Total number of bytes = " + << store_.size() * sizeof(std::vector) + lenBytes << std::endl; + return oss.str(); + } + virtual ~TRTWeightStore() { VLOG(1) << "Destroying store" << DebugString(); } +}; + +class TRTEngineResource : public tensorflow::ResourceBase { + public: + TRTEngineResource() : runtime_(nullptr), ctx_(nullptr){}; + string DebugString() override { return string(""); } + nvinfer1::IRuntime* runtime_; + nvinfer1::IExecutionContext* ctx_; +}; + +} // namespace tensorrt +} // namespace tensorflow +#endif // TENSORFLOW_CONTRIB_TENSORRT_RESOURCEMGR_TRTRESOURCES_H_ +#endif +#endif diff --git a/tensorflow/contrib/tensorrt/test/test_tftrt.py b/tensorflow/contrib/tensorrt/test/test_tftrt.py index c78f6f222457a875525e768eacc9a4ebf28ad504..0b661bd536c7c7f4566c89176420c3b40007bb74 100644 --- a/tensorflow/contrib/tensorrt/test/test_tftrt.py +++ b/tensorflow/contrib/tensorrt/test/test_tftrt.py @@ -60,6 +60,7 @@ def get_simple_graph_def(): def run_graph(gdef, dumm_inp): + """Run given graphdef once.""" gpu_options = cpb2.GPUOptions(per_process_gpu_memory_fraction=0.50) ops.reset_default_graph() g = ops.Graph() @@ -74,15 +75,65 @@ def run_graph(gdef, dumm_inp): return val +# Use real data that is representatitive of the inference dataset +# for calibration. For this test script it is random data. +def run_calibration(gdef, dumm_inp): + """Run given calibration graph multiple times.""" + gpu_options = cpb2.GPUOptions(per_process_gpu_memory_fraction=0.50) + ops.reset_default_graph() + g = ops.Graph() + with g.as_default(): + inp, out = importer.import_graph_def( + graph_def=gdef, return_elements=["input", "output"]) + inp = inp.outputs[0] + out = out.outputs[0] + with csess.Session( + config=cpb2.ConfigProto(gpu_options=gpu_options), graph=g) as sess: + # run over real calibration data here, we are mimicking a calibration set of + # 30 different batches. Use as much calibration data as you want + for _ in range(30): + val = sess.run(out, {inp: dumm_inp}) + return val + + if "__main__" in __name__: inp_dims = (100, 24, 24, 2) dummy_input = np.random.random_sample(inp_dims) - gdef = get_simple_graph_def() + orig_graph = get_simple_graph_def() # use a frozen graph for inference # Get optimized graph - trt_graph = trt.create_inference_graph(gdef, ["output"], inp_dims[0]) - o1 = run_graph(gdef, dummy_input) + trt_graph = trt.create_inference_graph( + input_graph_def=orig_graph, + outputs=["output"], + max_batch_size=inp_dims[0], + max_workspace_size_bytes=1 << 25, + precision_mode="FP32", # TRT Engine precision "FP32","FP16" or "INT8" + minimum_segment_size=2 # minimum number of nodes in an engine + ) + o1 = run_graph(orig_graph, dummy_input) o2 = run_graph(trt_graph, dummy_input) o3 = run_graph(trt_graph, dummy_input) assert np.array_equal(o1, o2) assert np.array_equal(o3, o2) # sanity check + fp16_graph = trt.create_inference_graph( + input_graph_def=orig_graph, + outputs=["output"], + max_batch_size=inp_dims[0], + max_workspace_size_bytes=1 << 25, + precision_mode="FP16", # TRT Engine precision "FP32","FP16" or "INT8" + minimum_segment_size=2 # minimum number of nodes in an engine + ) + int8_calib_gdef = trt.create_inference_graph( + input_graph_def=orig_graph, + outputs=["output"], + max_batch_size=inp_dims[0], + max_workspace_size_bytes=1 << 25, + precision_mode="INT8", # TRT Engine precision "FP32","FP16" or "INT8" + minimum_segment_size=2 # minimum number of nodes in an engine + ) + o4 = run_graph(fp16_graph, dummy_input) + _ = run_calibration(int8_calib_gdef, dummy_input) + int8_graph = trt.calib_graph_to_infer_graph(int8_calib_gdef) + o5 = run_graph(int8_graph, dummy_input) + assert np.allclose(o1, o4) + assert np.allclose(o1, o5) print("Pass") diff --git a/tensorflow/contrib/tensorrt/trt_conversion.i b/tensorflow/contrib/tensorrt/trt_conversion.i index d679945d569c1784448b6cb09c2f431b9cda56d7..46480e99a113afb34702b0ecd71468d4bdc83f98 100644 --- a/tensorflow/contrib/tensorrt/trt_conversion.i +++ b/tensorflow/contrib/tensorrt/trt_conversion.i @@ -64,13 +64,17 @@ PyObject* pair_helper(std::pair* in) { %ignoreall %unignore tensorflow; %unignore trt_convert; +%unignore calib_convert; %{ + std::pair trt_convert( string graph_def_string, // The serialized GraphDef string. std::vector output_names, size_t max_batch_size, - size_t max_workspace_size_bytes + size_t max_workspace_size_bytes, + int precision_mode, + int minimum_segment_size // Unfortunately we can't use TF_Status here since it // is in c/c_api and brings in a lot of other libraries // which in turn declare ops. These ops are included @@ -90,16 +94,64 @@ std::pair trt_convert( return std::pair{out_status, ""}; } + if(precision_mode < 0 || precision_mode > 2){ + out_status = "InvalidArgument;Invalid precision_mode"; + return std::pair{out_status, ""}; + } if (!output_names.size()) { out_status = "InvalidArgument;Size of the output_names vector is 0"; return std::pair{out_status, ""}; - // return ""; } tensorflow::GraphDef outGraph; tensorflow::Status conversion_status = tensorflow::tensorrt::convert::ConvertGraphDefToTensorRT( graph_def, output_names, max_batch_size, max_workspace_size_bytes, - &outGraph); + &outGraph, precision_mode, minimum_segment_size); + if (!conversion_status.ok()) { + auto retCode = (int)conversion_status.code(); + char buff[2000]; + snprintf(buff, 2000, "%d;%s", retCode, + conversion_status.error_message().c_str()); + out_status = buff; + return std::pair{out_status, ""}; + } + string result; + if (!outGraph.SerializeToString(&result)) { + out_status = "InvalidArgument;Couldn't serialize output as a GraphDef"; + return std::pair{out_status, ""}; + } + out_status = "OK;All good!"; + return std::pair{out_status, result}; +#else + // Returns FAILED_PRECONDITION. + return std::pair{"9;TensorRT is not enabled!", ""}; +#endif // GOOGLE_CUDA && GOOGLE_TENSORRT +} + +std::pair calib_convert(string graph_def_string // const tensorflow::GraphDef& + // unfortunately we can't use TF_Status here since it + // is in c/c_api and brings in a lot of other libraries + // which in turn declare ops. These ops are included + // statically in our library and cause an abort when + // module is loaded due to double registration + // until Tensorflow properly exposes these headers + // we have to work around this by returning a string + // and converting it to exception on python side. + //,TF_Status* out_status) { +) { +#if GOOGLE_CUDA && GOOGLE_TENSORRT + string out_status; + + tensorflow::GraphDef graph_def; + if (!graph_def.ParseFromString(graph_def_string)) { + out_status = "InvalidArgument;Couldn't interpret input as a GraphDef"; + return std::pair{out_status, ""}; + } + + tensorflow::GraphDef outGraph; + tensorflow::Status conversion_status = + tensorflow::tensorrt::convert::ConvertCalibGraphToInferGraph(graph_def, + &outGraph); if (!conversion_status.ok()) { auto retCode = (int)conversion_status.code(); char buff[2000]; @@ -122,10 +174,13 @@ std::pair trt_convert( } %} +std::pair calib_convert(string graph_def_string); + std::pair trt_convert(string graph_def_string, std::vector output_names, size_t max_batch_size, - size_t max_workspace_size_bytes); + size_t max_workspace_size_bytes, + int precision_mode, int minimum_segment_size); %unignoreall diff --git a/tensorflow/contrib/testing/python/framework/fake_summary_writer.py b/tensorflow/contrib/testing/python/framework/fake_summary_writer.py index f2065c666255984c8ab770fc10f682b1eabad095..15a415df303df5be44e89c00005cb253ae2af286 100644 --- a/tensorflow/contrib/testing/python/framework/fake_summary_writer.py +++ b/tensorflow/contrib/testing/python/framework/fake_summary_writer.py @@ -18,6 +18,7 @@ from __future__ import division from __future__ import print_function from tensorflow.core.framework import summary_pb2 +from tensorflow.python.framework import test_util from tensorflow.python.summary.writer import writer from tensorflow.python.summary.writer import writer_cache @@ -85,7 +86,11 @@ class FakeSummaryWriter(object): if expected_added_graphs is not None: test_case.assertEqual(expected_added_graphs, self._added_graphs) if expected_added_meta_graphs is not None: - test_case.assertEqual(expected_added_meta_graphs, self._added_meta_graphs) + test_case.assertEqual(len(expected_added_meta_graphs), + len(self._added_meta_graphs)) + for expected, actual in zip(expected_added_meta_graphs, + self._added_meta_graphs): + test_util.assert_meta_graph_protos_equal(test_case, expected, actual) if expected_session_logs is not None: test_case.assertEqual(expected_session_logs, self._added_session_logs) diff --git a/tensorflow/contrib/timeseries/examples/BUILD b/tensorflow/contrib/timeseries/examples/BUILD index bb86ecb2209f9bed3ad6c37f4b23bc7b361e1bd6..70bf67c7793ba96a8be0ced58421f778a5776d6c 100644 --- a/tensorflow/contrib/timeseries/examples/BUILD +++ b/tensorflow/contrib/timeseries/examples/BUILD @@ -25,7 +25,10 @@ py_test( srcs = ["predict_test.py"], data = ["data/period_trend.csv"], srcs_version = "PY2AND3", - tags = ["notsan"], # b/67513579 + tags = [ + "no_windows", # TODO: needs investigation on Windows + "notsan", # b/67513579 + ], deps = [ ":predict", "//tensorflow/python:client_testlib", diff --git a/tensorflow/contrib/timeseries/examples/data/multivariate_periods.csv b/tensorflow/contrib/timeseries/examples/data/multivariate_periods.csv index b49a0662c29b1d810f4be31ca1f318f0571f533e..9b15b4f0b26f11ac3281ca4206654872984628b6 100644 --- a/tensorflow/contrib/timeseries/examples/data/multivariate_periods.csv +++ b/tensorflow/contrib/timeseries/examples/data/multivariate_periods.csv @@ -1,100 +1,100 @@ -0,0.926906299771,1.99107237682,2.56546245685,3.07914768197,4.04839057867,1.,0. -1,0.108010001864,1.41645361423,2.1686839775,2.94963962176,4.1263503303,1.,0. -2,-0.800567600028,1.0172132907,1.96434754116,2.99885333086,4.04300485864,1.,0. -3,0.0607042871898,0.719540073421,1.9765012584,2.89265588817,4.0951014426,1.,0. -4,0.933712200629,0.28052120776,1.41018552514,2.69232603996,4.06481164223,1.,0. 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a/tensorflow/contrib/timeseries/examples/known_anomaly.py b/tensorflow/contrib/timeseries/examples/known_anomaly.py index 7659dd308a7ee1b70d6688b85e4f6157ddee0540..c08c0b0acb917f527d7efa91874d6405b9220083 100644 --- a/tensorflow/contrib/timeseries/examples/known_anomaly.py +++ b/tensorflow/contrib/timeseries/examples/known_anomaly.py @@ -46,12 +46,12 @@ def train_and_evaluate_exogenous(csv_file_name=_DATA_FILE, train_steps=300): # Indicate the format of our exogenous feature, in this case a string # representing a boolean value. - string_feature = tf.contrib.layers.sparse_column_with_keys( - column_name="is_changepoint", keys=["no", "yes"]) + string_feature = tf.feature_column.categorical_column_with_vocabulary_list( + key="is_changepoint", vocabulary_list=["no", "yes"]) # Specify the way this feature is presented to the model, here using a one-hot # encoding. - one_hot_feature = tf.contrib.layers.one_hot_column( - sparse_id_column=string_feature) + one_hot_feature = tf.feature_column.indicator_column( + categorical_column=string_feature) estimator = tf.contrib.timeseries.StructuralEnsembleRegressor( periodicities=12, diff --git a/tensorflow/contrib/timeseries/examples/lstm.py b/tensorflow/contrib/timeseries/examples/lstm.py index f37cafcc502dc9415db0829b9b067b862f87dca7..2eee878196bb64b523c491ca808ca8d6ff5dd36c 100644 --- a/tensorflow/contrib/timeseries/examples/lstm.py +++ b/tensorflow/contrib/timeseries/examples/lstm.py @@ -59,10 +59,10 @@ class _LSTMModel(ts_model.SequentialTimeSeriesModel): num_units: The number of units in the model's LSTMCell. num_features: The dimensionality of the time series (features per timestep). - exogenous_feature_columns: A list of tf.contrib.layers.FeatureColumn - objects representing features which are inputs to the model but are - not predicted by it. These must then be present for training, - evaluation, and prediction. + exogenous_feature_columns: A list of `tf.feature_column`s representing + features which are inputs to the model but are not predicted by + it. These must then be present for training, evaluation, and + prediction. dtype: The floating point data type to use. """ super(_LSTMModel, self).__init__( @@ -189,12 +189,16 @@ def train_and_predict( export_directory=None): """Train and predict using a custom time series model.""" # Construct an Estimator from our LSTM model. + categorical_column = tf.feature_column.categorical_column_with_hash_bucket( + key="categorical_exogenous_feature", hash_bucket_size=16) exogenous_feature_columns = [ # Exogenous features are not part of the loss, but can inform # predictions. In this example the features have no extra information, but # are included as an API example. - tf.contrib.layers.real_valued_column( - "2d_exogenous_feature", dimension=2)] + tf.feature_column.numeric_column( + "2d_exogenous_feature", shape=(2,)), + tf.feature_column.embedding_column( + categorical_column=categorical_column, dimension=10)] estimator = ts_estimators.TimeSeriesRegressor( model=_LSTMModel(num_features=5, num_units=128, exogenous_feature_columns=exogenous_feature_columns), @@ -205,7 +209,11 @@ def train_and_predict( csv_file_name, column_names=((tf.contrib.timeseries.TrainEvalFeatures.TIMES,) + (tf.contrib.timeseries.TrainEvalFeatures.VALUES,) * 5 - + ("2d_exogenous_feature",) * 2)) + + ("2d_exogenous_feature",) * 2 + + ("categorical_exogenous_feature",)), + # Data types other than for `times` need to be specified if they aren't + # float32. In this case one of our exogenous features has string dtype. + column_dtypes=((tf.int64,) + (tf.float32,) * 7 + (tf.string,))) train_input_fn = tf.contrib.timeseries.RandomWindowInputFn( reader, batch_size=4, window_size=32) estimator.train(input_fn=train_input_fn, steps=training_steps) @@ -215,7 +223,9 @@ def train_and_predict( predict_exogenous_features = { "2d_exogenous_feature": numpy.concatenate( [numpy.ones([1, 100, 1]), numpy.zeros([1, 100, 1])], - axis=-1)} + axis=-1), + "categorical_exogenous_feature": numpy.array( + ["strkey"] * 100)[None, :, None]} (predictions,) = tuple(estimator.predict( input_fn=tf.contrib.timeseries.predict_continuation_input_fn( evaluation, steps=100, diff --git a/tensorflow/contrib/timeseries/python/timeseries/BUILD b/tensorflow/contrib/timeseries/python/timeseries/BUILD index ed3ed4c0e1731df62e9197aa7471fd6a31e9858e..64f5cd83575e1eeaa35360c4dc8c5a0d1f384066 100644 --- a/tensorflow/contrib/timeseries/python/timeseries/BUILD +++ b/tensorflow/contrib/timeseries/python/timeseries/BUILD @@ -156,9 +156,7 @@ py_test( "head_test.py", ], srcs_version = "PY2AND3", - tags = [ - "no_pip_gpu", # b/63391119 - ], + tags = ["no_pip_gpu"], # b/63391119 deps = [ ":feature_keys", ":head", @@ -427,6 +425,7 @@ py_test( srcs_version = "PY2AND3", tags = [ "no_pip_gpu", # b/63391119 + "no_windows", # TODO: needs investigation on Windows ], deps = [ ":feature_keys", diff --git a/tensorflow/contrib/timeseries/python/timeseries/estimators.py b/tensorflow/contrib/timeseries/python/timeseries/estimators.py index f8355f366fe8e191ab570fd271bbe4a8bf71c73d..8d13343e82340dae11b0be54e3bc3152060dca36 100644 --- a/tensorflow/contrib/timeseries/python/timeseries/estimators.py +++ b/tensorflow/contrib/timeseries/python/timeseries/estimators.py @@ -18,8 +18,6 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.layers.python.layers import feature_column - from tensorflow.contrib.timeseries.python.timeseries import ar_model from tensorflow.contrib.timeseries.python.timeseries import feature_keys from tensorflow.contrib.timeseries.python.timeseries import head as ts_head_lib @@ -31,10 +29,12 @@ from tensorflow.contrib.timeseries.python.timeseries.state_space_models.filterin from tensorflow.python.estimator import estimator_lib from tensorflow.python.estimator.export import export_lib +from tensorflow.python.feature_column import feature_column from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import array_ops +from tensorflow.python.ops import parsing_ops from tensorflow.python.training import training as train @@ -117,22 +117,29 @@ class TimeSeriesRegressor(estimator_lib.Estimator): dtype=self._model.dtype), shape=(default_batch_size, default_series_length, self._model.num_features))) - with ops.Graph().as_default(): - # Default placeholders have only an unknown batch dimension. Make them - # in a separate graph, then splice in the series length to the shapes - # and re-create them in the outer graph. - exogenous_feature_shapes = { - key: (value.get_shape(), value.dtype) for key, value - in feature_column.make_place_holder_tensors_for_base_features( - self._model.exogenous_feature_columns).items()} - for feature_key, (batch_only_feature_shape, value_dtype) in ( - exogenous_feature_shapes.items()): - batch_only_feature_shape = batch_only_feature_shape.with_rank_at_least( - 1).as_list() - feature_shape = ([default_batch_size, default_series_length] - + batch_only_feature_shape[1:]) - placeholders[feature_key] = array_ops.placeholder( - dtype=value_dtype, name=feature_key, shape=feature_shape) + if self._model.exogenous_feature_columns: + with ops.Graph().as_default(): + # Default placeholders have only an unknown batch dimension. Make them + # in a separate graph, then splice in the series length to the shapes + # and re-create them in the outer graph. + parsed_features = ( + feature_column.make_parse_example_spec( + self._model.exogenous_feature_columns)) + placeholder_features = parsing_ops.parse_example( + serialized=array_ops.placeholder( + shape=[None], dtype=dtypes.string), + features=parsed_features) + exogenous_feature_shapes = { + key: (value.get_shape(), value.dtype) for key, value + in placeholder_features.items()} + for feature_key, (batch_only_feature_shape, value_dtype) in ( + exogenous_feature_shapes.items()): + batch_only_feature_shape = ( + batch_only_feature_shape.with_rank_at_least(1).as_list()) + feature_shape = ([default_batch_size, default_series_length] + + batch_only_feature_shape[1:]) + placeholders[feature_key] = array_ops.placeholder( + dtype=value_dtype, name=feature_key, shape=feature_shape) # Models may not know the shape of their state without creating some # variables/ops. Avoid polluting the default graph by making a new one. We # use only static metadata from the returned Tensors. @@ -333,11 +340,11 @@ class StructuralEnsembleRegressor(StateSpaceRegressor): determine the model size. Learning autoregressive coefficients typically requires more steps and a smaller step size than other components. - exogenous_feature_columns: A list of tf.contrib.layers.FeatureColumn - objects (for example tf.contrib.layers.embedding_column) corresponding - to exogenous features which provide extra information to the model but - are not part of the series to be predicted. Passed to - tf.contrib.layers.input_from_feature_columns. + exogenous_feature_columns: A list of `tf.feature_column`s (for example + `tf.feature_column.embedding_column`) corresponding to exogenous + features which provide extra information to the model but are not part + of the series to be predicted. Passed to + `tf.feature_column.input_layer`. exogenous_update_condition: A function taking two Tensor arguments, `times` (shape [batch size]) and `features` (a dictionary mapping exogenous feature keys to Tensors with shapes [batch size, ...]), and diff --git a/tensorflow/contrib/timeseries/python/timeseries/head.py b/tensorflow/contrib/timeseries/python/timeseries/head.py index 5c49e903abde6d7487d1ffdb83ff902ff6b63585..f4d9351432ef32f22334667766528907eac77f19 100644 --- a/tensorflow/contrib/timeseries/python/timeseries/head.py +++ b/tensorflow/contrib/timeseries/python/timeseries/head.py @@ -96,8 +96,12 @@ class _TimeSeriesRegressionHead(head_lib._Head): # pylint:disable=protected-acc def _train_ops(self, features): """Add training ops to the graph.""" mode = estimator_lib.ModeKeys.TRAIN - with variable_scope.variable_scope("model"): + with variable_scope.variable_scope( + "model", + # Use ResourceVariables to avoid race conditions. + use_resource=True): model_outputs = self.create_loss(features, mode) + train_op = optimizers.optimize_loss( model_outputs.loss, global_step=training_util.get_global_step(), @@ -112,7 +116,7 @@ class _TimeSeriesRegressionHead(head_lib._Head): # pylint:disable=protected-acc def _evaluate_ops(self, features): """Add ops for evaluation (aka filtering) to the graph.""" mode = estimator_lib.ModeKeys.EVAL - with variable_scope.variable_scope("model"): + with variable_scope.variable_scope("model", use_resource=True): model_outputs = self.create_loss(features, mode) metrics = {} # Just output in-sample predictions for the last chunk seen @@ -132,7 +136,7 @@ class _TimeSeriesRegressionHead(head_lib._Head): # pylint:disable=protected-acc def _predict_ops(self, features): """Add ops for prediction to the graph.""" - with variable_scope.variable_scope("model"): + with variable_scope.variable_scope("model", use_resource=True): prediction = self.model.predict(features=features) prediction[feature_keys.PredictionResults.TIMES] = features[ feature_keys.PredictionFeatures.TIMES] @@ -141,7 +145,7 @@ class _TimeSeriesRegressionHead(head_lib._Head): # pylint:disable=protected-acc def _serving_ops(self, features): """Add ops for serving to the graph.""" - with variable_scope.variable_scope("model"): + with variable_scope.variable_scope("model", use_resource=True): prediction_outputs = self.model.predict(features=features) with variable_scope.variable_scope("model", reuse=True): filtering_outputs = self.create_loss( diff --git a/tensorflow/contrib/timeseries/python/timeseries/model.py b/tensorflow/contrib/timeseries/python/timeseries/model.py index bac7d1ebf59b28d4688a3d1a69ecdc1fc12248e0..7644764a7459db3951fe9a2790389713dd412a8f 100644 --- a/tensorflow/contrib/timeseries/python/timeseries/model.py +++ b/tensorflow/contrib/timeseries/python/timeseries/model.py @@ -21,18 +21,17 @@ from __future__ import print_function import abc import collections -from tensorflow.contrib import layers -from tensorflow.contrib.layers import feature_column - from tensorflow.contrib.timeseries.python.timeseries import math_utils from tensorflow.contrib.timeseries.python.timeseries.feature_keys import PredictionFeatures from tensorflow.contrib.timeseries.python.timeseries.feature_keys import TrainEvalFeatures +from tensorflow.python.feature_column import feature_column from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops +from tensorflow.python.ops import parsing_ops from tensorflow.python.ops import tensor_array_ops from tensorflow.python.ops import variable_scope @@ -66,11 +65,11 @@ class TimeSeriesModel(object): Args: num_features: Number of features for the time series - exogenous_feature_columns: A list of tf.contrib.layers.FeatureColumn - objects (for example tf.contrib.layers.embedding_column) corresponding - to exogenous features which provide extra information to the model but - are not part of the series to be predicted. Passed to - tf.contrib.layers.input_from_feature_columns. + exogenous_feature_columns: A list of `tf.feature_column`s (for example + `tf.feature_column.embedding_column`) corresponding to exogenous + features which provide extra information to the model but are not + part of the series to be predicted. Passed to + `tf.feature_column.input_layer`. dtype: The floating point datatype to use. """ if exogenous_feature_columns: @@ -86,7 +85,7 @@ class TimeSeriesModel(object): @property def exogenous_feature_columns(self): - """`FeatureColumn` objects for features which are not predicted.""" + """`tf.feature_colum`s for features which are not predicted.""" return self._exogenous_feature_columns # TODO(allenl): Move more of the generic machinery for generating and @@ -265,11 +264,14 @@ class TimeSeriesModel(object): if not self._exogenous_feature_columns: return (0,) with ops.Graph().as_default(): - placeholder_features = ( - feature_column.make_place_holder_tensors_for_base_features( + parsed_features = ( + feature_column.make_parse_example_spec( self._exogenous_feature_columns)) - embedded = layers.input_from_feature_columns( - columns_to_tensors=placeholder_features, + placeholder_features = parsing_ops.parse_example( + serialized=array_ops.placeholder(shape=[None], dtype=dtypes.string), + features=parsed_features) + embedded = feature_column.input_layer( + features=placeholder_features, feature_columns=self._exogenous_feature_columns) return embedded.get_shape().as_list()[1:] @@ -308,13 +310,13 @@ class TimeSeriesModel(object): # Avoid shape warnings when embedding "scalar" exogenous features (those # with only batch and window dimensions); input_from_feature_columns # expects input ranks to match the embedded rank. - if tensor.get_shape().ndims == 1: + if tensor.get_shape().ndims == 1 and tensor.dtype != dtypes.string: exogenous_features_single_batch_dimension[name] = tensor[:, None] else: exogenous_features_single_batch_dimension[name] = tensor embedded_exogenous_features_single_batch_dimension = ( - layers.input_from_feature_columns( - columns_to_tensors=exogenous_features_single_batch_dimension, + feature_column.input_layer( + features=exogenous_features_single_batch_dimension, feature_columns=self._exogenous_feature_columns, trainable=True)) exogenous_regressors = array_ops.reshape( @@ -381,8 +383,8 @@ class SequentialTimeSeriesModel(TimeSeriesModel): may use _scale_back_data or _scale_back_variance to return predictions to the input scale. dtype: The floating point datatype to use. - exogenous_feature_columns: A list of tf.contrib.layers.FeatureColumn - objects. See `TimeSeriesModel`. + exogenous_feature_columns: A list of `tf.feature_column`s objects. See + `TimeSeriesModel`. exogenous_update_condition: A function taking two Tensor arguments `times` (shape [batch size]) and `features` (a dictionary mapping exogenous feature keys to Tensors with shapes [batch size, ...]) and returning a diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/BUILD b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/BUILD index c86d06e9236962cbabbc56afa1cfe213e0c78bc0..07df7bc9a5cd45e0b54e78aaa6ea02fedc584cc3 100644 --- a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/BUILD +++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/BUILD @@ -40,6 +40,7 @@ py_test( timeout = "long", # Moderate but for asan srcs = ["state_space_model_test.py"], srcs_version = "PY2AND3", + tags = ["no_windows"], # TODO: needs investigation on Windows deps = [ ":state_space_model", "//tensorflow/contrib/layers:layers_py", diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model.py b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model.py index 6257002647ed53bbde3ace11a6b45e4e2cdeb57d..951c6546d5fed77e0cfa98a4e774b804639d7dad 100644 --- a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model.py +++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model.py @@ -112,11 +112,11 @@ class StateSpaceModelConfiguration( exogenous_noise_decreases: If True, exogenous regressors can "set" model state, decreasing uncertainty. If both this parameter and exogenous_noise_increases are False, exogenous regressors are ignored. - exogenous_feature_columns: A list of tf.contrib.layers.FeatureColumn - objects (for example tf.contrib.layers.embedding_column) corresponding - to exogenous features which provide extra information to the model but - are not part of the series to be predicted. Passed to - tf.contrib.layers.input_from_feature_columns. + exogenous_feature_columns: A list of `tf.feature_column`s (for example + `tf.feature_column.embedding_column`) corresponding to exogenous + features which provide extra information to the model but are not part + of the series to be predicted. Passed to + `tf.feature_column.input_layer`. exogenous_update_condition: A function taking two Tensor arguments `times` (shape [batch size]) and `features` (a dictionary mapping exogenous feature keys to Tensors with shapes [batch size, ...]) and returning a diff --git a/tensorflow/contrib/tpu/BUILD b/tensorflow/contrib/tpu/BUILD index c48e84ddfaac8ac9c07e061847315eab3fd72152..f9d433a45b12630ef46048dae4be4c922e84053e 100644 --- a/tensorflow/contrib/tpu/BUILD +++ b/tensorflow/contrib/tpu/BUILD @@ -24,6 +24,7 @@ cc_library( name = "all_ops", deps = [ ":cross_replica_ops_op_lib", + ":host_compute_ops_op_lib", ":infeed_ops_op_lib", ":outfeed_ops_op_lib", ":replication_ops_op_lib", @@ -69,6 +70,7 @@ py_library( tf_gen_op_libs( op_lib_names = [ "cross_replica_ops", + "host_compute_ops", "infeed_ops", "outfeed_ops", "replication_ops", @@ -78,6 +80,7 @@ tf_gen_op_libs( deps = [ "//tensorflow/contrib/tpu/proto:tpu_embedding_config_proto_cc", "//tensorflow/core:lib_proto_parsing", + "//tensorflow/core:protos_all_cc", ], ) @@ -85,6 +88,7 @@ tf_custom_op_library( name = "python/ops/_tpu_ops.so", srcs = [ "ops/cross_replica_ops.cc", + "ops/host_compute_ops.cc", "ops/infeed_ops.cc", "ops/outfeed_ops.cc", "ops/replication_ops.cc", @@ -101,6 +105,7 @@ tf_gen_op_wrapper_py( name = "tpu_ops", deps = [ ":cross_replica_ops_op_lib", + ":host_compute_ops_op_lib", ":infeed_ops_op_lib", ":outfeed_ops_op_lib", ":replication_ops_op_lib", @@ -163,6 +168,7 @@ py_library( ], srcs_version = "PY2AND3", deps = [ + ":datasets", ":profiler", ":tpu_py", "//tensorflow/contrib/tpu/proto:topology_proto_py", @@ -181,6 +187,33 @@ py_library( ], ) +py_library( + name = "datasets", + srcs = [ + "python/tpu/datasets.py", + ], + srcs_version = "PY2AND3", + deps = [ + "//tensorflow/contrib/data/python/ops:transformation_ops", + "//tensorflow/python:dtypes", + "//tensorflow/python:function", + "//tensorflow/python:functional_ops", + "//tensorflow/python/data/ops:dataset_ops", + "//tensorflow/python/data/ops:iterator_ops", + "//tensorflow/python/data/ops:readers", + ], +) + +tf_py_test( + name = "datasets_test", + srcs = ["python/tpu/datasets_test.py"], + additional_deps = [ + "//tensorflow/python:client_testlib", + ":datasets", + ], + grpc_enabled = True, +) + tf_py_test( name = "tpu_test", size = "small", @@ -192,6 +225,7 @@ tf_py_test( "//tensorflow/python:framework", "//tensorflow/python:layers", ], + tags = ["no_windows"], # TODO: needs investigation on Windows ) tf_py_test( diff --git a/tensorflow/contrib/tpu/ops/host_compute_ops.cc b/tensorflow/contrib/tpu/ops/host_compute_ops.cc new file mode 100644 index 0000000000000000000000000000000000000000..48aeb81ac1311d3acd4972810f0a27a382f8b136 --- /dev/null +++ b/tensorflow/contrib/tpu/ops/host_compute_ops.cc @@ -0,0 +1,64 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/core/framework/common_shape_fns.h" +#include "tensorflow/core/framework/op.h" +#include "tensorflow/core/framework/shape_inference.h" + +namespace tensorflow { + +REGISTER_OP("_XlaSendFromHost") + .Input("inputs: Tinputs") + .Input("dynamic_key: string") + .Attr("Tinputs: list(type) >= 0") + .Attr("key: string") + .Attr("device_ordinal: int") + .SetIsStateful() + .SetShapeFn(::tensorflow::shape_inference::NoOutputs) + .Doc(R"doc( +A placeholder op for multiple values that will be sent from TensorFlow to a +running XLA computation. + +inputs: A list of tensors that will be sent to the XLA computation. +dynamic_key: The key sent at runtime by the compile node to identify which +execution the transfer corresponds to. +Tinputs: The element types of each element in `inputs`. +key: A key that is unique in the computation and associates the send with the consumer in +the XLA computation. +device_ordinal: The device to use. +)doc"); + +REGISTER_OP("_XlaRecvAtHost") + .Input("dynamic_key: string") + .Output("outputs: Toutputs") + .Attr("Toutputs: list(type) >= 0") + .Attr("key: string") + .Attr("device_ordinal: int") + .SetIsStateful() + .SetShapeFn(::tensorflow::shape_inference::UnknownShape) + .Doc(R"doc( +A placeholder op for multiple values that will be sent to TensorFlow from a +running XLA computation. + +dynamic_key: The key sent at runtime by the compile node to identify which +execution the transfer corresponds to. +outputs: A list of tensors that will be received from the XLA computation. +Toutputs: The element types of each element in `outputs`. +key: A key that is unique in the computation and associates the send with the consumer in +the XLA computation. +device_ordinal: The device to use. +)doc"); + +} // namespace tensorflow diff --git a/tensorflow/contrib/tpu/ops/tpu_configuration_ops.cc b/tensorflow/contrib/tpu/ops/tpu_configuration_ops.cc index f8de8baa65339383c7f92284ee274a434f12f8c2..7bf5c21d0b526ee5e32448f75d39eca8add6d877 100644 --- a/tensorflow/contrib/tpu/ops/tpu_configuration_ops.cc +++ b/tensorflow/contrib/tpu/ops/tpu_configuration_ops.cc @@ -191,6 +191,7 @@ REGISTER_OP("ConfigureDistributedTPU") .Output("topology: string") .Attr("embedding_config: string = ''") .Attr("tpu_embedding_config: string = ''") + .Attr("is_global_init: bool = false") .SetIsStateful() .SetShapeFn(shape_inference::UnknownShape) .Doc(R"doc( @@ -202,6 +203,7 @@ topology. tpu_embedding_config: Serialized tensorflow.tpu.TPUEmbeddingConfiguration that describes the embedding lookups of the program. embedding_config: Reserved. Do not use. +is_global_init: Reserved. Do not use. )doc"); REGISTER_OP("ShutdownDistributedTPU") diff --git a/tensorflow/contrib/tpu/ops/tpu_embedding_ops.cc b/tensorflow/contrib/tpu/ops/tpu_embedding_ops.cc index cc32a265286951a1e4d59228da6b3ac83a75c5e9..72d37f774cc518c559b5953561957a799a7da568 100644 --- a/tensorflow/contrib/tpu/ops/tpu_embedding_ops.cc +++ b/tensorflow/contrib/tpu/ops/tpu_embedding_ops.cc @@ -50,7 +50,7 @@ namespace tensorflow { // TPU Embeddings use dedicated ops to enforce Host/TPU consistency in the // state of embedding table variables. Before beginning training or inference, // the model must Load the optimizer parameters into the TPU memories. Before -// saving a checkpoint, the model must Retreieve the parameters back into the +// saving a checkpoint, the model must Retrieve the parameters back into the // host CPU memory. REGISTER_OP("TPUEmbeddingLoadGradientDescentParameters") @@ -263,7 +263,7 @@ REGISTER_OP("TPUEmbeddingReceiveActivations") .SetIsStateful() .SetShapeFn(tpu_embedding_config_util::ActivationShapes) .Doc(R"doc( -An op that receives embeddng activations on the TPU. +An op that receives embedding activations on the TPU. The TPU system performs the embedding lookups and aggregations specified by the arguments to TPUEmbeddingEnqueueSparseBatch. The results of these @@ -293,7 +293,7 @@ REGISTER_OP("TPUEmbeddingActivations") An op enabling differentiation of TPU Embeddings. This op simply returns its first input, which is assumed to have been sliced -from the Tensors returnd by TPUEmbeddingDequeueActivations. The presence of this +from the Tensors returned by TPUEmbeddingDequeueActivations. The presence of this op, and its first argument being a trainable Variable, enables automatic differentiation of graphs containing embeddings via the TPU Embedding Python libraries. diff --git a/tensorflow/contrib/tpu/profiler/tf_op_stats.proto b/tensorflow/contrib/tpu/profiler/tf_op_stats.proto index 2094294baad63ae73712c8648b588accd4551ef8..e5c798aa2f463a1a2d7cb041ba9b51569958f4fd 100644 --- a/tensorflow/contrib/tpu/profiler/tf_op_stats.proto +++ b/tensorflow/contrib/tpu/profiler/tf_op_stats.proto @@ -77,6 +77,8 @@ message StepInfoResult { // The infeed duration in picoseconds. // Can turn into a map if we want a variable number of ops. optional uint64 infeed_duration_ps = 3; + // The start time of this step in picoseconds. + optional uint64 begin_ps = 4; } // Result proto for a sequence of steps. @@ -155,6 +157,54 @@ message RunEnvironmentResult { repeated HostDependentJobInfoResult host_dependent_job_info = 6; } +// The types of host operations that are tracked. +enum HostOp { + // Invalid host op. + kINVALIDHostOp = 0; + // Each of host op type has two parts: + // (1) the stage where the op happens and (2) the op name. + // stage = Input Data Producer, op = Get Next Batch. + kInputDataProducerGetNextBatch = 1; + // stage = Input Data Producer, op = Session Run. + kInputDataProducerSessionRun = 2; + // stage = Input Data Producer, op = Forward Batch. + kInputDataProducerForwardBatch = 3; + // stage = Infeed Thread, op = Get Next Batch. + kInfeedThreadGetNextBatch = 4; + // stage = Infeed Thread, op = Session Run. + kInfeedThreadSessionRun = 5; + // stage = Infeed Thread, op = Forward Batch. + kInfeedThreadForwardBatch = 6; + // stage = Outfeed Thread, op = Get Next Batch. + kOutfeedThreadGetNextBatch = 7; + // stage = Outfeed Thread, op = Session Run. + kOutfeedThreadSessionRun = 8; + // stage = Outfeed Thread, op = Forward Batch. + kOutfeedThreadForwardBatch = 9; +} + +// Result proto for the host ops per TPU step. +message HostOpsPerTpuStep { + // Whether the data in this message is valid. + optional bool valid = 1 [default = false]; + // The current TPU step number. + optional uint32 tpu_step_num = 2; + // The beginning time of the current TPU step on the device in picoseconds. + optional uint64 tpu_step_begin_ps = 3; + // The ending time of the current TPU step on the device in picoseconds. + optional uint64 tpu_step_end_ps = 4; + // For each possible host operation, maps to the difference between the TPU + // step number that the host op targets and the current TPU step number. + // The key is HostOp, value is the step difference. + map step_diffs = 5; +} + +// Result proto for the host ops for all TPU steps. +message HostOpsResult { + // A sequence of HostOpsPerTpuStep (one for each TPU step) + repeated HostOpsPerTpuStep host_op_sequence = 1; +} + // Result proto for TfStatsHelper. message TfOpStats { // The result for the TF-metric database. @@ -171,4 +221,6 @@ message TfOpStats { optional double matrix_unit_utilization_percent = 6; // The run environment of this profiling session. optional RunEnvironmentResult run_environment = 7; + // The result for the host operations. + optional HostOpsResult host_ops = 8; } diff --git a/tensorflow/contrib/tpu/python/tpu/datasets.py b/tensorflow/contrib/tpu/python/tpu/datasets.py new file mode 100644 index 0000000000000000000000000000000000000000..465c668fd8b42f150892f8e4b52de76c6fe13fa9 --- /dev/null +++ b/tensorflow/contrib/tpu/python/tpu/datasets.py @@ -0,0 +1,184 @@ +# 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. +# ====================================== +"""Library of Cloud TPU helper functions for data loading.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.data.python.ops import batching +from tensorflow.contrib.data.python.ops import interleave_ops +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.data.ops import iterator_ops +from tensorflow.python.data.ops import readers +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import function +from tensorflow.python.framework import ops +from tensorflow.python.ops import functional_ops + + +def _TextLineDataset(filename): + buffer_size = 8 * 1024 * 1024 # 8 MiB per file + dataset = readers.TextLineDataset(filename, buffer_size=buffer_size) + return dataset + + +def _TFRecordDataset(filename): + buffer_size = 8 * 1024 * 1024 # 8 MiB per file + dataset = readers.TFRecordDataset(filename, buffer_size=buffer_size) + return dataset + + +_FILETYPE_MAP = { + 'tfrecord': _TFRecordDataset, + 'textline': _TextLineDataset, + 'text': _TextLineDataset, +} + + +def StreamingFilesDataset(files, + filetype=None, + file_reader_job=None, + worker_job=None, + num_epochs=None, + filename_shuffle_buffer_size=None, + num_parallel_reads=None, + batch_transfer_size=None, + sloppy=None): + """StreamingFilesDataset constructs a dataset to stream from workers (GCE VM). + + Because Cloud TPUs are allocated over the network, a Cloud TPU cannot read + files local to your GCE VM. In order to train using files stored on your local + VM (e.g. on local SSD for extreme performance), use the StreamingFilesDataset + helper to generate a dataset to feed your Cloud TPU with files from your GCE + VM. + + The resulting dataset may return an OutOfRangeError if there are no files + found as a result of the fileglob expansion. + + Note: StreamingFilesDataset assumes that the session is using a + TPUClusterResolver and has therefore a worker and a coordinator job. File + loading will be done on the coordinator job. + + Args: + files: A string glob to match files, or a `tf.data.Dataset` generating file + names. + filetype: A string (one of 'tfrecord', or 'textline') or a single-argument + TensorFlow function that when given a filename returns a dataset. + file_reader_job: An optional string that corresponds to the job that should + perform the file reads. + worker_job: An optional string that corresponds to the job that should + process the tensors (i.e. your GPU or TPU worker). + num_epochs: The number of epochs through the training set that should be + generated. By default, it will repeat infinitely. + filename_shuffle_buffer_size: An optional integer whose value controls the + shuffling of the file names. If you would like to read from the files in + the same order, set to 0 or False. + num_parallel_reads: An optional integer controlling the number of files to + read from concurrently. (Set to 1 for no parallelism.) + batch_transfer_size: An optional integer controlling the batching used to + amortize the remote function invocation overhead. Set to a very large + number to increase throughput. Set to a very small number to reduce memory + consumption. Set to False to skip batching. + sloppy: (Optional.) If `False`, read input data while maintaining a + deterministic order. (This may have significant performance impacts.) + sloppy defaults to: True. + Returns: + A `tf.data.Dataset` with an infinite stream of elements generated by a + parallel interleaving of the set of files matched (or generated) by `files` + with a type is the output of the dataset specified by `filetype`. + + Raises: + ValueError: if any argument is not of the expected type. + """ + if filetype is None: + filetype = 'tfrecord' + + if isinstance(filetype, str): + if filetype not in _FILETYPE_MAP: + raise ValueError('Unexpected filetype: %s' % filetype) + reader_fn = _FILETYPE_MAP[filetype] + elif callable(filetype): + reader_fn = filetype + else: + raise ValueError('filetype should be a string or a callable') + + file_reader_job = file_reader_job or 'coordinator' + + worker_job = worker_job or 'worker' + + if filename_shuffle_buffer_size is None: + filename_shuffle_buffer_size = 4096 + + num_parallel_reads = num_parallel_reads or 8 + + if batch_transfer_size is None: + batch_transfer_size = 256 + + if sloppy is None: + sloppy = True + + with ops.device('/job:%s' % file_reader_job): + if isinstance(files, str): + source_dataset = dataset_ops.Dataset.list_files(files) + elif isinstance(files, dataset_ops.Dataset): + source_dataset = files + else: + raise ValueError('files was not a string or a dataset: %s' % files) + + if filename_shuffle_buffer_size: + source_dataset = source_dataset.shuffle( + buffer_size=filename_shuffle_buffer_size) + + # NOTE: We perform the `repeat` on the source dataset, because the output + # dataset does not currently have enough information to recreate an iterator + # over the source dataset when it reaches the end. + source_dataset = source_dataset.repeat(num_epochs) + + source_dataset = source_dataset.apply( + interleave_ops.parallel_interleave( + reader_fn, cycle_length=num_parallel_reads, sloppy=sloppy)) + + if batch_transfer_size: + source_dataset = source_dataset.batch(batch_transfer_size) + + source_dataset = source_dataset.prefetch(1) + + source_iterator = source_dataset.make_one_shot_iterator() + source_handle = source_iterator.string_handle() + + @function.Defun(dtypes.string) + def LoadingFunc(h): + remote_iterator = iterator_ops.Iterator.from_string_handle( + h, source_dataset.output_types, source_dataset.output_shapes) + return remote_iterator.get_next() + + def MapFn(unused_input): + return functional_ops.remote_call( + args=[source_handle], + Tout=[dtypes.string], + f=LoadingFunc, + target='/job:%s/replica:0/task:0/cpu:0' % file_reader_job) + + with ops.device('/job:%s' % worker_job): + output_dataset = dataset_ops.Dataset.range(2).repeat().map( + MapFn, num_parallel_calls=4 if sloppy else None) + output_dataset = output_dataset.prefetch(1) + + if batch_transfer_size: + # Undo the batching used during the transfer. + output_dataset = output_dataset.apply(batching.unbatch()).prefetch(1) + + return output_dataset diff --git a/tensorflow/contrib/tpu/python/tpu/datasets_test.py b/tensorflow/contrib/tpu/python/tpu/datasets_test.py new file mode 100644 index 0000000000000000000000000000000000000000..918cf0ed8e513de0d4207f7d2aac61ad886c8288 --- /dev/null +++ b/tensorflow/contrib/tpu/python/tpu/datasets_test.py @@ -0,0 +1,181 @@ +# 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. +# ============================================================================== +"""TPU datasets tests.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os + +from tensorflow.contrib.tpu.python.tpu import datasets +from tensorflow.core.protobuf import cluster_pb2 +from tensorflow.core.protobuf import config_pb2 +from tensorflow.python.client import session +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.data.ops import readers +from tensorflow.python.lib.io import python_io +from tensorflow.python.platform import test +from tensorflow.python.training import server_lib +from tensorflow.python.util import compat + +_NUM_FILES = 10 +_NUM_ENTRIES = 20 + + +class DatasetsTest(test.TestCase): + + def setUp(self): + super(DatasetsTest, self).setUp() + self._coord = server_lib.Server.create_local_server() + self._worker = server_lib.Server.create_local_server() + + self._cluster_def = cluster_pb2.ClusterDef() + worker_job = self._cluster_def.job.add() + worker_job.name = 'worker' + worker_job.tasks[0] = self._worker.target[len('grpc://'):] + coord_job = self._cluster_def.job.add() + coord_job.name = 'coordinator' + coord_job.tasks[0] = self._coord.target[len('grpc://'):] + + session_config = config_pb2.ConfigProto(cluster_def=self._cluster_def) + + self._sess = session.Session(self._worker.target, config=session_config) + + def testTextLineDataset(self): + all_contents = [] + for i in range(_NUM_FILES): + filename = os.path.join(self.get_temp_dir(), 'text_line.%d.txt' % i) + contents = [] + for j in range(_NUM_ENTRIES): + contents.append(compat.as_bytes('%d: %d' % (i, j))) + with open(filename, 'wb') as f: + f.write(b'\n'.join(contents)) + all_contents.extend(contents) + + dataset = datasets.StreamingFilesDataset( + os.path.join(self.get_temp_dir(), 'text_line.*.txt'), filetype='text') + + iterator = dataset.make_initializable_iterator() + self._sess.run(iterator.initializer) + get_next = iterator.get_next() + + retrieved_values = [] + for _ in range(4 * len(all_contents)): + retrieved_values.append(compat.as_bytes(self._sess.run(get_next))) + + self.assertEqual(set(all_contents), set(retrieved_values)) + + def testTFRecordDataset(self): + all_contents = [] + for i in range(_NUM_FILES): + filename = os.path.join(self.get_temp_dir(), 'tf_record.%d' % i) + writer = python_io.TFRecordWriter(filename) + for j in range(_NUM_ENTRIES): + record = compat.as_bytes('Record %d of file %d' % (j, i)) + writer.write(record) + all_contents.append(record) + writer.close() + + dataset = datasets.StreamingFilesDataset( + os.path.join(self.get_temp_dir(), 'tf_record*'), filetype='tfrecord') + + iterator = dataset.make_initializable_iterator() + self._sess.run(iterator.initializer) + get_next = iterator.get_next() + + retrieved_values = [] + for _ in range(4 * len(all_contents)): + retrieved_values.append(compat.as_bytes(self._sess.run(get_next))) + + self.assertEqual(set(all_contents), set(retrieved_values)) + + def testTFRecordDatasetFromDataset(self): + filenames = [] + all_contents = [] + for i in range(_NUM_FILES): + filename = os.path.join(self.get_temp_dir(), 'tf_record.%d' % i) + filenames.append(filename) + writer = python_io.TFRecordWriter(filename) + for j in range(_NUM_ENTRIES): + record = compat.as_bytes('Record %d of file %d' % (j, i)) + writer.write(record) + all_contents.append(record) + writer.close() + + filenames = dataset_ops.Dataset.from_tensor_slices(filenames) + + dataset = datasets.StreamingFilesDataset(filenames, filetype='tfrecord') + + iterator = dataset.make_initializable_iterator() + self._sess.run(iterator.initializer) + get_next = iterator.get_next() + + retrieved_values = [] + for _ in range(4 * len(all_contents)): + retrieved_values.append(compat.as_bytes(self._sess.run(get_next))) + + self.assertEqual(set(all_contents), set(retrieved_values)) + + def testArbitraryReaderFunc(self): + + def MakeRecord(i, j): + return compat.as_bytes('%04d-%04d' % (i, j)) + + record_bytes = len(MakeRecord(10, 200)) + + all_contents = [] + for i in range(_NUM_FILES): + filename = os.path.join(self.get_temp_dir(), 'fixed_length.%d' % i) + with open(filename, 'wb') as f: + for j in range(_NUM_ENTRIES): + record = MakeRecord(i, j) + f.write(record) + all_contents.append(record) + + def FixedLengthFile(filename): + return readers.FixedLengthRecordDataset(filename, record_bytes) + + dataset = datasets.StreamingFilesDataset( + os.path.join(self.get_temp_dir(), 'fixed_length*'), + filetype=FixedLengthFile) + + iterator = dataset.make_initializable_iterator() + self._sess.run(iterator.initializer) + get_next = iterator.get_next() + + retrieved_values = [] + for _ in range(4 * len(all_contents)): + retrieved_values.append(compat.as_bytes(self._sess.run(get_next))) + + self.assertEqual(set(all_contents), set(retrieved_values)) + + def testUnexpectedFiletypeString(self): + with self.assertRaises(ValueError): + datasets.StreamingFilesDataset( + os.path.join(self.get_temp_dir(), '*'), filetype='foo') + + def testUnexpectedFiletypeType(self): + with self.assertRaises(ValueError): + datasets.StreamingFilesDataset( + os.path.join(self.get_temp_dir(), '*'), filetype=3) + + def testUnexpectedFilesType(self): + with self.assertRaises(ValueError): + datasets.StreamingFilesDataset(123, filetype='tfrecord') + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/tpu/python/tpu/device_assignment.py b/tensorflow/contrib/tpu/python/tpu/device_assignment.py index bdd9b88af55fa4fb483ddbdbe5c51d7076cce675..726b2d248e3086e1882004827076ed3e563d960d 100644 --- a/tensorflow/contrib/tpu/python/tpu/device_assignment.py +++ b/tensorflow/contrib/tpu/python/tpu/device_assignment.py @@ -191,9 +191,9 @@ class DeviceAssignment(object): logical_core: A tuple of three integers which represents a logical core. Returns: A sorted list of the replicas that are attached to that task and - loical_core. + logical_core. Raises: - ValueError: If no replica exisis in the task which contains the logical + ValueError: If no replica exists in the task which contains the logical core. """ try: diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_config.py b/tensorflow/contrib/tpu/python/tpu/tpu_config.py index 644070218214643923b9ca3ee138615ec568e8b5..38b5ea23103730630ae8e1cdd7b9180a501013c5 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_config.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_config.py @@ -26,6 +26,7 @@ import os import numpy as np from tensorflow.contrib.tpu.python.tpu import util as util_lib +from tensorflow.core.protobuf import config_pb2 from tensorflow.python.estimator import run_config as run_config_lib from tensorflow.python.platform import tf_logging as logging @@ -65,7 +66,7 @@ class TPUConfig( cores. This is required by model-parallelism which enables partitioning the model to multiple cores. For example, [2, 2, 1] means the model is partitioned across 4 cores which span two cores in both x and y - coordinates. Please refer to ${tf.contrib.tpu.TopologyProto} for the + coordinates. Please refer to @{tf.contrib.tpu.Topology} for the geometry of a TPU mesh. per_host_input_for_training: If `True`, `input_fn` is invoked Per-Host rather than Per-Core. With Per-Host input pipeline deployment, `input_fn` @@ -140,6 +141,7 @@ class RunConfig(run_config_lib.RunConfig): tpu_config=None, evaluation_master=None, master=None, + cluster=None, **kwargs): """Constructs a RunConfig. @@ -148,15 +150,26 @@ class RunConfig(run_config_lib.RunConfig): evaluation_master: a string. The address of the master to use for eval. Defaults to master if not set. master: a string. The address of the master to use for training. + cluster: a ClusterResolver **kwargs: keyword config parameters. + + Raises: + ValueError: if cluster is not None and the provided session_config has a + cluster_def already. """ super(RunConfig, self).__init__(**kwargs) self._tpu_config = tpu_config or TPUConfig() + self._cluster = cluster - # If user sets master and/or evaluation_master explicilty, including empty + # If user sets master and/or evaluation_master explicitly, including empty # string '', take it. Otherwise, take the values set by parent class. if master is not None: + if cluster is not None: + raise ValueError('Both master and cluster are set.') self._master = master + else: + if cluster: + self._master = cluster.master() if evaluation_master is not None: self._evaluation_master = evaluation_master @@ -170,6 +183,20 @@ class RunConfig(run_config_lib.RunConfig): # evaluation_master to master, unless user overwrites it. self._evaluation_master = self._master + # Set the ClusterSpec to use + if cluster: + self._cluster_spec = cluster.cluster_spec() + + # Merge the cluster_def into the ConfigProto. + if self._session_config is None: # pylint: disable=access-member-before-definition + self._session_config = config_pb2.ConfigProto(allow_soft_placement=True) + if self._session_config.HasField('cluster_def'): + raise ValueError( + 'You cannot provide a ClusterResolver and ' + 'session_config.cluster_def.') + self._session_config.cluster_def.CopyFrom( + self._cluster_spec.as_cluster_def()) + @property def evaluation_master(self): return self._evaluation_master @@ -182,6 +209,10 @@ class RunConfig(run_config_lib.RunConfig): def tpu_config(self): return self._tpu_config + @property + def cluster(self): + return self._cluster + def replace(self, **kwargs): if 'tpu_config' not in kwargs: return super(RunConfig, self).replace(**kwargs) diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_context.py b/tensorflow/contrib/tpu/python/tpu/tpu_context.py index c5c46ea741ea64ca37089431f8ed66cad7bc31fb..3bac2db77e95520a6c9c4c17658267a9a6588d94 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_context.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_context.py @@ -39,7 +39,7 @@ class _TPUContext(object): This immutable object holds TPUEstimator config, train/eval batch size, and `TPUEstimator.use_tpu`, which is expected to be passed around. It also - provides utility functions, basded on the current state, to determine other + provides utility functions, based on the current state, to determine other information commonly required by TPU computation, such as TPU device names, TPU hosts, shard batch size, etc. @@ -218,7 +218,7 @@ class _TPUContext(object): model, when mode == PREDICT. Only with this bool, we could tell whether user is calling the Estimator.predict or Estimator.export_savedmodel, which are running on TPU and CPU - respectively. Parent class Estimator does not distingush these two. + respectively. Parent class Estimator does not distinguish these two. Returns: bool, whether current input_fn or model_fn should be running on CPU. diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py index 1b2eda1caa0fa2779834d65b5a49121d9cc0af56..52a3ee8283dc374ab14b07e0265f5a7c15a5589f 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py @@ -133,7 +133,7 @@ def _increase_eval_step_op(iterations_per_loop): """Returns an op to increase the eval step for TPU evaluation. Args: - iterations_per_loop: Tensor. The number of eval steps runnining in TPU + iterations_per_loop: Tensor. The number of eval steps running in TPU system before returning to CPU host for each `Session.run`. Returns: @@ -605,17 +605,17 @@ class _StoppingPredictHook(session_run_hook.SessionRunHook): # batch. And we append one more batch to signal the system it should stop. # The data flow might look like # - # batch 0: images, labels, stop = 0 (user provideded) - # batch 1: images, labels, stop = 0 (user provideded) + # batch 0: images, labels, stop = 0 (user provided) + # batch 1: images, labels, stop = 0 (user provided) # ... - # batch 99: images, labels, stop = 0 (user provideded) + # batch 99: images, labels, stop = 0 (user provided) # batch 100: images, labels, stop = 1 (TPUEstimator appended) # # where the final batch (id = 100) is appended by TPUEstimator, so we # should drop it before returning the predictions to user. # To achieve that, we throw the OutOfRangeError in after_run. Once # Monitored Session sees this error in SessionRunHook.after_run, the - # "current" prediciton, i.e., batch with id=100, will be discarded + # "current" prediction, i.e., batch with id=100, will be discarded # immediately raise errors.OutOfRangeError(None, None, 'Stopped by stopping signal.') @@ -751,7 +751,7 @@ class _InputPipeline(object): 2. (features, labels) Internally, form 1 is reformed to `(features, None)` as features and labels - are passed separatedly to underlying methods. For TPU training, TPUEstimator + are passed separately to underlying methods. For TPU training, TPUEstimator may expect multiple `features` and `labels` tuples one for each core. TPUEstimator allows various different structures for inputs (namely `features` @@ -784,7 +784,8 @@ class _InputPipeline(object): def _extract_key_names(tensor_or_dict): if tensor_or_dict is None: return [] - return tensor_or_dict.keys() if isinstance(tensor_or_dict, dict) else [] + return sorted(tensor_or_dict.keys()) if isinstance( + tensor_or_dict, dict) else [] # Extract structure. has_labels = labels is not None @@ -1516,14 +1517,20 @@ class TPUEstimator(estimator_lib.Estimator): size when calling the `input_fn` and `model_fn`. Users should specify global batch size in constructor, and then get the batch size for each shard in `input_fn` and `model_fn` by `params['batch_size']`. - For training, `model_fn` gets per-core batch size; `input_fn` may get - per-core or per-host batch size depending on - `per_host_input_for_training` in `TPUConfig`. - For evaluation, `model_fn` gets per-core batch size and `input_fn` get - per-host batch size. + + - For training, `model_fn` gets per-core batch size; `input_fn` may get + per-core or per-host batch size depending on `per_host_input_for_training` + in `TPUConfig` (See docstring for TPUConfig for details). + + - For evaluation and prediction, `model_fn` gets per-core batch size and + `input_fn` get per-host batch size. + + Evaluation + ========== `model_fn` should return `TPUEstimatorSpec`, which expects the `eval_metrics` for TPU evaluation. + `TPUEstimatorSpec.eval_metrics` is a tuple of `metric_fn` and `tensors`, where `tensors` could be a list of `Tensor`s or dict of names to `Tensor`s. (See `TPUEstimatorSpec` for details). `metric_fn` takes the `tensors` and returns @@ -1535,12 +1542,17 @@ class TPUEstimator(estimator_lib.Estimator): `train_batch_size` or `eval_batch_size` unmodified as `params['batch_size']`. Current limitations: + -------------------- + + 1. TPU evaluation only works on a single host (one TPU worker). - 1. TPU evaluation only works on single host. - 2. `input_fn` for evaluation should not throw OutOfRange error for all - evaluation steps and all batches should have the same size. + 2. `input_fn` for evaluation should **NOT** raise an end-of-input exception + (`OutOfRangeError` or `StopIteration`). And all evaluation steps and all + batches should have the same size. Example (MNIST): + ---------------- + ``` # The metric Fn which runs on CPU. def metric_fn(labels, logits): @@ -1576,8 +1588,120 @@ class TPUEstimator(estimator_lib.Estimator): })) ``` - Predict support on TPU is not yet implemented. So, `predict` and - `export_savedmodel` are executed on CPU, even if `use_tpu` is true. + Prediction + ========== + + Prediction on TPU is an experimental feature to support large batch inference. + It is not designed for latency-critical system. In addition, due to some + usability issues, for prediction with small dataset, CPU `.predict`, i.e., + creating a new `TPUEstimator` instance with `use_tpu=False`, might be more + convenient. + + Note: In contrast to TPU training/evaluation, the `input_fn` for prediction + *should* raise an end-of-input exception (`OutOfRangeError` or + `StopIteration`), which serves as the stopping signal to `TPUEstimator`. To be + precise, the ops created by `input_fn` produce one batch of the data. + The `predict()` API processes one batch at a time. When reaching the end of + the data source, an end-of-input exception should be raised by one of these + operations. The user usually does not need to do this manually. As long as the + dataset is not repeated forever, the `tf.data` API will raise an end-of-input + exception automatically after the last batch has been produced. + + Note: Estimator.predict returns a Python generator. Please consume all the + data from the generator so that TPUEstimator can shutdown the TPU system + properly for user. + + Current limitations: + -------------------- + 1. TPU prediction only works on a single host (one TPU worker). + + 2. `input_fn` must return a `Dataset` instance rather than `features`. In + fact, .train() and .evaluate() also support Dataset as return value. + + 3. Each batch returned by `Dataset`'s iterator must have the *same static* + shape. This means two things: + - batch_size cannot be `None` + - the final batch must be padded by user to a full batch. + + Example (MNIST): + ---------------- + ``` + height = 32 + width = 32 + total_examples = 100 + + def predict_input_fn(params): + batch_size = params['batch_size'] + + images = tf.random_uniform( + [total_examples, height, width, 3], minval=-1, maxval=1) + + dataset = tf.data.Dataset.from_tensor_slices(images) + dataset = dataset.batch(batch_size) + dataset = dataset.map(lambda images: {'image': images}) + + def pad(tensor, missing_count): + # Pads out the batch dimension to the complete batch_size. + rank = len(tensor.shape) + assert rank > 0 + padding = tf.stack([[0, missing_count]] + [[0, 0]] * (rank - 1)) + padded_shape = (batch_size,) + tuple(tensor.shape[1:]) + padded_tensor = tf.pad(tensor, padding) + padded_tensor.set_shape(padded_shape) + return padded_tensor + + def pad_batch_if_incomplete(batch_features): + # Pads out the batch dimension for all features. + real_batch_size = tf.shape(batch_features["image"])[0] + + missing_count = tf.constant(batch_size, tf.int32) - real_batch_size + + padded_features = { + key: pad(tensor, missing_count) + for key, tensor in batch_features.iteritems() + } + padding_mask = tf.concat( + [ + tf.zeros((real_batch_size, 1), dtype=tf.int32), + tf.ones((missing_count, 1), dtype=tf.int32) + ], + axis=0) + padding_mask.set_shape((batch_size, 1)) + padded_features["is_padding"] = padding_mask + return padded_features + + dataset = dataset.map(pad_batch_if_incomplete) + + return dataset + + def model_fn(features, labels, params, mode): + # Generate predictions, called 'output', from features['image'] + + if mode == tf.estimator.ModeKeys.PREDICT: + return tf.contrib.tpu.TPUEstimatorSpec( + mode=mode, + predictions={ + 'predictions': output, + 'is_padding': features['is_padding'] + }) + + tpu_est = TPUEstimator( + model_fn=model_fn, + ..., + predict_batch_size=16) + + # Fully consume the generator so that TPUEstimator can shutdown the TPU + # system. + for item in tpu_est.predict(input_fn=input_fn): + # Filter out item if the `is_padding` is 1. + # Process the 'predictions' + ``` + + Exporting + ========= + + Exporting `SavedModel` support on TPU is not yet implemented. So, + `export_savedmodel` is executed on CPU, even if `use_tpu` is true. """ def __init__(self, @@ -1794,6 +1918,17 @@ class TPUEstimator(estimator_lib.Estimator): return _input_fn + def _validate_features_in_predict_input(self, result): + """Skip the validation. + + For TPUEstimator, we do not need to check the result type. `_InputPipeline` + has stronger check. Parent class's check generates confusing warning msg. + + Args: + result: `features` returned by input_fn. + """ + pass + def _augment_model_fn(self, model_fn, batch_axis): """Returns a new model_fn, which wraps the TPU support.""" @@ -1961,6 +2096,13 @@ class TPUEstimator(estimator_lib.Estimator): host_ops), ] + input_hooks + # TODO(b/73813593): Delete this logging once the bug is resolved. + logging.info( + 'If the Tensors in TPUEstimatorSpec.predictions dict are large, ' + 'you might observe the TPU program getting stuck (b/73813593). ' + 'Consider using small Tensors in the predictions dict to verify ' + 'the issue and report on the bug.') + return model_fn_lib.EstimatorSpec( mode, prediction_hooks=hooks, @@ -2308,6 +2450,11 @@ class _InputsWithStoppingSignals(_Inputs): """ def _map_fn(*args): + """The map fn to insert signals.""" + if len(args) == 1: + # Unpack the single Tensor/dict argument as features. This is required + # for the input_fn returns no labels. + args = args[0] features, labels = _Inputs._parse_inputs(args) new_input_dict = {} new_input_dict['features'] = features diff --git a/tensorflow/contrib/tpu/python/tpu/training_loop.py b/tensorflow/contrib/tpu/python/tpu/training_loop.py index 3d7896127a99653167f164873331a2cc95f656e8..82a75d02552b7b013452945a76b16c2c2fb9fa82 100644 --- a/tensorflow/contrib/tpu/python/tpu/training_loop.py +++ b/tensorflow/contrib/tpu/python/tpu/training_loop.py @@ -170,7 +170,7 @@ def while_loop(condition, body, inputs=None, infeed_queue=None, name=None): def repeat(n, body, inputs=None, infeed_queue=None, name=None): - """Builds a training loop that executes a fixed number of interations. + """Builds a training loop that executes a fixed number of iterations. The set of loop-carried tensors correspond to `inputs`. `body` must be a function that takes and returns the values of the diff --git a/tensorflow/contrib/training/BUILD b/tensorflow/contrib/training/BUILD index 6db373d2d5e20ea7da449530b2730403c3bb64cc..6ae2f382528c37ae647b73ea01a7f88c07580c78 100644 --- a/tensorflow/contrib/training/BUILD +++ b/tensorflow/contrib/training/BUILD @@ -324,7 +324,6 @@ tf_proto_library( name = "protos_all", srcs = glob(["**/*.proto"]), cc_api_version = 2, - go_api_version = 2, java_api_version = 2, visibility = ["//visibility:public"], ) diff --git a/tensorflow/contrib/util/loader.py b/tensorflow/contrib/util/loader.py index f4283cd9ed6eb185ec56bdfef45f6ddc5253d128..dca01d26f4c6291074d855322c8215027cfd2ace 100644 --- a/tensorflow/contrib/util/loader.py +++ b/tensorflow/contrib/util/loader.py @@ -42,9 +42,10 @@ def load_op_library(path): plugin. """ if os.name == 'nt': - # To avoid makeing every user_ops aware of windows, re-write - # the file extension from .so to .dll. - path = re.sub(r'\.so$', '.dll', path) + # To avoid making every user_ops aware of windows, re-write + # the file extension from .so to .dll if .so file doesn't exist. + if not os.path.exists(path): + path = re.sub(r'\.so$', '.dll', path) # Currently we have only some user_ops as dlls on windows - don't try # to load them if the dll is not found. diff --git a/tensorflow/contrib/verbs/README.md b/tensorflow/contrib/verbs/README.md index 58fed4e5cb4c24b0f21dfe9b99cf4c665d2591c7..4b6104a8b4d542b1d8a9cb3e48eeed4950d791cd 100644 --- a/tensorflow/contrib/verbs/README.md +++ b/tensorflow/contrib/verbs/README.md @@ -93,7 +93,7 @@ When the receiver receives the RDMA write, it will locate the relevant **RdmaTen 1. When the sender receives a tensor request, the source tensor may or may not be ready yet. The situation is handled through a process of tag matching: * If the request arrives before the tensor is ready, then a callback is put in a local table, and will be invoked once the tensor arrives. - * If the tensor is ready before the request arives, than the tensor is put in a local table. When the request arrives, it will invoke the callback immediately. + * If the tensor is ready before the request arrives, than the tensor is put in a local table. When the request arrives, it will invoke the callback immediately. In code it is done by calling **RecvLocalAsync()**, which receives the tensor's key, step-id, and the callback. 2. When the callback is invoked, the relevant tensor is removed from the tag matching table. In the case where we need to send the tensor's meta-data, the **RdmaTensorResponse** will store a copy of the tensor until the re-request arrives. 3. The sending of protocol messages (**RDMA_MESSAGE_TENSOR_REQUEST**, **RDMA_MESSAGE_META_DATA_RESPONSE** and **RDMA_MESSAGE_TENSOR_RE_REQUEST**) is done by the class **RdmaMessageBuffer**. All messages are sent using RDMA writes from/to fixed messages buffers. This implies that we cannot send on a specific channel more than one message at a time. In order to synchronize the messages, the **RdmaMessageBuffer** holds the a local and remote buffer statuses which can be either busy or idle. When a write is issued, both statuses will be changed to busy. When the write-complete event is received, the local status is changed to idle. When the write is received on the remote side, the remote side will parse the message, and return an ACK back to the sending side on which the sending side will update the remote status to idle. When both the local and remote statuses are idle, the next message can be sent. diff --git a/tensorflow/contrib/verbs/patch_notes_verbs_with_0_copies.md b/tensorflow/contrib/verbs/patch_notes_verbs_with_0_copies.md index 956b8f2147cf8154b6f1ade006d7bff194864c9b..da6fdd48e19e9d1503d1537926b1c464a0e77589 100644 --- a/tensorflow/contrib/verbs/patch_notes_verbs_with_0_copies.md +++ b/tensorflow/contrib/verbs/patch_notes_verbs_with_0_copies.md @@ -64,7 +64,7 @@ The protocol messages themselves will remain mostly unchanged at the first stage * type - The message type. * request_index - Request index. * is_dead/data_type/tensor_shape/tensor_bytes - The up-to-date meta-data. -* **RDMA_MESSAGE_BUFFER_RESPONSE** - (receiver ==> sender) Tensor re-requset after meta-data update and reallocation of result/proxy tensors. +* **RDMA_MESSAGE_BUFFER_RESPONSE** - (receiver ==> sender) Tensor re-request after meta-data update and reallocation of result/proxy tensors. * type - The message type. * name (name_size) - Name of the requested tensor. * step_id - Step ID. diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index 1893967cdd0034bcff52c84f4db0bf1e2e3334f4..c92893d997bfc7709529c5cf3a229f857be6e741 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -220,7 +220,6 @@ tf_proto_library( srcs = CORE_PROTO_SRCS + ADDITIONAL_CORE_PROTO_SRCS, cc_api_version = 2, default_header = True, - go_api_version = 2, j2objc_api_version = 1, java_api_version = 2, js_api_version = 2, @@ -339,6 +338,7 @@ cc_library( "lib/strings/strcat.h", "lib/strings/stringprintf.h", "platform/abi.h", + "platform/context.h", "platform/cpu_feature_guard.h", "platform/cpu_info.h", "platform/dynamic_annotations.h", @@ -1518,6 +1518,7 @@ LIB_INTERNAL_PUBLIC_HEADERS = tf_additional_lib_hdrs() + [ "lib/strings/base64.h", "lib/strings/ordered_code.h", "lib/strings/proto_text_util.h", + "lib/strings/proto_serialization.h", "lib/strings/scanner.h", "lib/wav/wav_io.h", "platform/demangle.h", @@ -1663,6 +1664,25 @@ cc_library( ], ) +cc_library( + name = "tflite_portable_logging", + srcs = [], + hdrs = [ + "lib/bfloat16/bfloat16.h", + "platform/default/integral_types.h", + "platform/default/logging.h", + "platform/logging.h", + "platform/macros.h", + "platform/platform.h", + "platform/types.h", + ], + copts = tf_copts(), + linkopts = ["-ldl"], + deps = [ + "//tensorflow/core/platform/default/build_config:logging", + ], +) + cc_library( name = "android_jpeg_internal", srcs = if_android([ @@ -1854,6 +1874,13 @@ cc_header_only_library( ], ) +cc_header_only_library( + name = "core_cpu_headers_lib", + deps = [ + ":core_cpu_lib", + ], +) + tf_cuda_library( name = "framework_internal_impl", srcs = FRAMEWORK_INTERNAL_PRIVATE_HEADERS + [ @@ -1919,7 +1946,7 @@ tf_cuda_library( ) + if_mkl( [ "//third_party/mkl:intel_binary_blob", - "@mkl_dnn//:mkl_dnn", + "@mkl_dnn", ], ), alwayslink = 1, @@ -2038,6 +2065,7 @@ tf_cuda_library( CORE_CPU_BASE_HDRS = GRAPH_HDRS + [ "common_runtime/device.h", + "common_runtime/eval_const_tensor.h", "common_runtime/graph_runner.h", "common_runtime/shape_refiner.h", "framework/versions.h", @@ -2046,6 +2074,7 @@ CORE_CPU_BASE_HDRS = GRAPH_HDRS + [ tf_cuda_library( name = "core_cpu_base", srcs = [ + "common_runtime/eval_const_tensor.cc", "common_runtime/shape_refiner.cc", "common_runtime/shape_refiner.h", "framework/versions.h", @@ -2104,6 +2133,8 @@ CORE_CPU_LIB_HEADERS = CORE_CPU_BASE_HDRS + [ "common_runtime/renamed_device.h", "common_runtime/rendezvous_mgr.h", "common_runtime/rendezvous_util.h", + "common_runtime/scoped_allocator.h", + "common_runtime/scoped_allocator_mgr.h", "common_runtime/session_factory.h", "common_runtime/placer.h", "common_runtime/stats_publisher_interface.h", @@ -2134,6 +2165,7 @@ tf_cuda_library( "common_runtime/graph_runner.cc", "common_runtime/local_device.cc", "common_runtime/memory_types.cc", + "common_runtime/mkl_cpu_allocator.cc", "common_runtime/optimization_registry.cc", "common_runtime/parallel_concat_optimizer.cc", "common_runtime/placer.cc", @@ -2142,6 +2174,8 @@ tf_cuda_library( "common_runtime/renamed_device.cc", "common_runtime/rendezvous_mgr.cc", "common_runtime/rendezvous_util.cc", + "common_runtime/scoped_allocator.cc", + "common_runtime/scoped_allocator_mgr.cc", "common_runtime/session.cc", "common_runtime/session_factory.cc", "common_runtime/session_options.cc", @@ -2173,6 +2207,7 @@ tf_cuda_library( ] + if_mkl( [ "//third_party/mkl:intel_binary_blob", + "@mkl_dnn", ], ), alwayslink = 1, @@ -2217,14 +2252,12 @@ tf_cuda_library( ] + if_mkl( [ "//third_party/mkl:intel_binary_blob", - "@mkl_dnn//:mkl_dnn", + "@mkl_dnn", ], ) + tf_additional_core_deps() + if_static([":core_cpu_impl"]), alwayslink = 1, ) -# This library is deprecated and no longer publicly available. -# Do not add more uses of it. cc_library( name = "regexp_internal", hdrs = [ @@ -3127,6 +3160,7 @@ tf_cc_test( ":core_cpu", ":core_cpu_internal", ":framework", + ":lib", ":test", ":test_main", ":testlib", @@ -3175,6 +3209,7 @@ tf_cc_test( "//tensorflow/core/kernels:dense_update_ops", "//tensorflow/core/kernels:fifo_queue_op", "//tensorflow/core/kernels:function_ops", + "//tensorflow/core/kernels:identity_n_op", "//tensorflow/core/kernels:identity_op", "//tensorflow/core/kernels:matmul_op", "//tensorflow/core/kernels:ops_util", @@ -3217,6 +3252,7 @@ tf_cc_test( "//tensorflow/core/kernels:fifo_queue_op", "//tensorflow/core/kernels:function_ops", "//tensorflow/core/kernels:identity_op", + "//tensorflow/core/kernels:identity_n_op", "//tensorflow/core/kernels:matmul_op", "//tensorflow/core/kernels:ops_util", "//tensorflow/core/kernels:queue_ops", @@ -3291,6 +3327,43 @@ tf_cc_test( size = "small", srcs = ["common_runtime/function_test.cc"], linkstatic = tf_kernel_tests_linkstatic(), + tags = [ + "manual", + "no_oss", + ], + deps = [ + ":core", + ":core_cpu", + ":core_cpu_internal", + ":direct_session_internal", + ":framework", + ":framework_internal", + ":lib", + ":lib_internal", + ":ops", + ":protos_all_cc", + ":test", + ":test_main", + ":testlib", + "//tensorflow/cc:cc_ops", + "//tensorflow/cc:cc_ops_internal", + "//tensorflow/cc:function_ops", + "//tensorflow/cc:functional_ops", + "//tensorflow/core/kernels:cast_op", + "//tensorflow/core/kernels:cwise_op", + "//tensorflow/core/kernels:function_ops", + "//tensorflow/core/kernels:matmul_op", + "//tensorflow/core/kernels:random_ops", + "//tensorflow/core/kernels:shape_ops", + "//third_party/eigen3", + ], +) + +tf_cc_test( + name = "common_runtime_function_threadpool_test", + size = "small", + srcs = ["common_runtime/function_threadpool_test.cc"], + linkstatic = tf_kernel_tests_linkstatic(), deps = [ ":core", ":core_cpu", @@ -3319,6 +3392,21 @@ tf_cc_test( ], ) +tf_cc_test( + name = "common_runtime_scoped_allocator_mgr_test", + size = "small", + srcs = ["common_runtime/scoped_allocator_mgr_test.cc"], + linkstatic = tf_kernel_tests_linkstatic(), + deps = [ + ":core_cpu", + ":core_cpu_internal", + ":framework", + ":lib", + ":test", + ":test_main", + ], +) + tf_cc_test_gpu( name = "gpu_allocator_retry_test", size = "medium", @@ -3623,6 +3711,13 @@ filegroup( "lib/gif/testdata/optimized.gif", # BMP data "lib/bmp/testdata/lena.bmp", + # SSIM, PSNR data + "lib/ssim/testdata/checkerboard1.png", + "lib/ssim/testdata/checkerboard2.png", + "lib/ssim/testdata/checkerboard3.png", + "lib/psnr/testdata/cat_q20.jpg", + "lib/psnr/testdata/cat_q72.jpg", + "lib/psnr/testdata/cat_q95.jpg", ], visibility = ["//visibility:public"], ) diff --git a/tensorflow/core/api_def/base_api/api_def_RegexReplace.pbtxt b/tensorflow/core/api_def/base_api/api_def_RegexReplace.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..70ad5219267fcc84368f072a6f5a122b6cc11a89 --- /dev/null +++ b/tensorflow/core/api_def/base_api/api_def_RegexReplace.pbtxt @@ -0,0 +1,25 @@ +op { + graph_op_name: "RegexReplace" + in_arg { + name: "input" + description: "The text to be processed." + } + in_arg { + name: "pattern" + description: "The regular expression to match the input." + } + in_arg { + name: "rewrite" + description: "The rewrite to be applied to the matched expresion." + } + out_arg { + name: "output" + description: "The text after applying pattern and rewrite." + } + attr { + name: "replace_global" + description: "If True, the replacement is global, otherwise the replacement\nis done only on the first match." + } + summary: "Replaces the match of pattern in input with rewrite." + description: "It follows the re2 syntax (https://github.com/google/re2/wiki/Syntax)" +} diff --git a/tensorflow/core/api_def/base_api/api_def_SdcaOptimizer.pbtxt b/tensorflow/core/api_def/base_api/api_def_SdcaOptimizer.pbtxt index b0b58ac00e6709922ed517ad2c9efebbedf450a3..9da0e124ebe02f1cfb6450b96471d7d9d146bd20 100644 --- a/tensorflow/core/api_def/base_api/api_def_SdcaOptimizer.pbtxt +++ b/tensorflow/core/api_def/base_api/api_def_SdcaOptimizer.pbtxt @@ -97,8 +97,11 @@ END } attr { name: "adaptative" + default_value { + b: True + } description: <

javac -cp libtensorflow-1.6.0-rc1.jar HelloTF.java
+
javac -cp libtensorflow-1.6.0.jar HelloTF.java
### Running @@ -239,11 +239,11 @@ two files are available to the JVM: For example, the following command line executes the `HelloTF` program on Linux and macOS X: -
java -cp libtensorflow-1.6.0-rc1.jar:. -Djava.library.path=./jni HelloTF
+
java -cp libtensorflow-1.6.0.jar:. -Djava.library.path=./jni HelloTF
And the following command line executes the `HelloTF` program on Windows: -
java -cp libtensorflow-1.6.0-rc1.jar;. -Djava.library.path=jni HelloTF
d +
java -cp libtensorflow-1.6.0.jar;. -Djava.library.path=jni HelloTF
If the program prints Hello from version, you've successfully installed TensorFlow for Java and are ready to use the API. If the program diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md index e3e115d9f618265864363810acf96033882ad89d..3e8744bf9d1e01d00c703d9153db8ac48bce1d50 100644 --- a/tensorflow/docs_src/install/install_linux.md +++ b/tensorflow/docs_src/install/install_linux.md @@ -41,7 +41,8 @@ must be installed on your system: [NVIDIA's documentation](https://developer.nvidia.com/cudnn). Ensure that you create the `CUDA_HOME` environment variable as described in the NVIDIA documentation. - * GPU card with CUDA Compute Capability 3.0 or higher. See + * GPU card with CUDA Compute Capability 3.0 or higher for building + from source and 3.5 or higher for our binaries. See [NVIDIA documentation](https://developer.nvidia.com/cuda-gpus) for a list of supported GPU cards. * The libcupti-dev library, which is the NVIDIA CUDA Profile Tools Interface. @@ -188,7 +189,7 @@ Take the following steps to install TensorFlow with Virtualenv: Virtualenv environment: 
(tensorflow)$ pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc1-cp34-cp34m-linux_x86_64.whl
+ https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0-cp34-cp34m-linux_x86_64.whl If you encounter installation problems, see [Common Installation Problems](#common_installation_problems). @@ -293,7 +294,7 @@ take the following steps: 
      $ sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc1-cp34-cp34m-linux_x86_64.whl
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0-cp34-cp34m-linux_x86_64.whl
If this step fails, see @@ -356,24 +357,23 @@ where: to 6006. * TensorFlowCPUImage is required. It identifies the Docker container. Specify one of the following values: - * gcr.io/tensorflow/tensorflow, which is the TensorFlow CPU binary image. - * gcr.io/tensorflow/tensorflow:latest-devel, which is the latest + * tensorflow/tensorflow, which is the TensorFlow CPU binary image. + * tensorflow/tensorflow:latest-devel, which is the latest TensorFlow CPU Binary image plus source code. - * gcr.io/tensorflow/tensorflow:version, which is the + * tensorflow/tensorflow:version, which is the specified version (for example, 1.1.0rc1) of TensorFlow CPU binary image. - * gcr.io/tensorflow/tensorflow:version-devel, which is + * tensorflow/tensorflow:version-devel, which is the specified version (for example, 1.1.0rc1) of the TensorFlow GPU binary image plus source code. - gcr.io is the Google Container Registry. Note that some - TensorFlow images are also available at + TensorFlow images are available at [dockerhub](https://hub.docker.com/r/tensorflow/tensorflow/). For example, the following command launches the latest TensorFlow CPU binary image in a Docker container from which you can run TensorFlow programs in a shell: 
-$ docker run -it gcr.io/tensorflow/tensorflow bash
+$ docker run -it tensorflow/tensorflow bash
The following command also launches the latest TensorFlow CPU binary image in a @@ -381,7 +381,7 @@ Docker container. However, in this Docker container, you can run TensorFlow programs in a Jupyter notebook: 
-$ docker run -it -p 8888:8888 gcr.io/tensorflow/tensorflow
+$ docker run -it -p 8888:8888 tensorflow/tensorflow
Docker will download the TensorFlow binary image the first time you launch it. @@ -405,14 +405,14 @@ where: hostPort and containerPort to `8888`. * TensorFlowGPUImage specifies the Docker container. You must specify one of the following values: - * gcr.io/tensorflow/tensorflow:latest-gpu, which is the latest + * tensorflow/tensorflow:latest-gpu, which is the latest TensorFlow GPU binary image. - * gcr.io/tensorflow/tensorflow:latest-devel-gpu, which is + * tensorflow/tensorflow:latest-devel-gpu, which is the latest TensorFlow GPU Binary image plus source code. - * gcr.io/tensorflow/tensorflow:version-gpu, which is the + * tensorflow/tensorflow:version-gpu, which is the specified version (for example, 0.12.1) of the TensorFlow GPU binary image. - * gcr.io/tensorflow/tensorflow:version-devel-gpu, which is + * tensorflow/tensorflow:version-devel-gpu, which is the specified version (for example, 0.12.1) of the TensorFlow GPU binary image plus source code. @@ -421,7 +421,7 @@ following command launches the latest TensorFlow GPU binary image in a Docker container from which you can run TensorFlow programs in a shell: 
-$ nvidia-docker run -it gcr.io/tensorflow/tensorflow:latest-gpu bash
+$ nvidia-docker run -it tensorflow/tensorflow:latest-gpu bash
The following command also launches the latest TensorFlow GPU binary image @@ -429,13 +429,13 @@ in a Docker container. In this Docker container, you can run TensorFlow programs in a Jupyter notebook: 
-$ nvidia-docker run -it -p 8888:8888 gcr.io/tensorflow/tensorflow:latest-gpu
+$ nvidia-docker run -it -p 8888:8888 tensorflow/tensorflow:latest-gpu
The following command installs an older TensorFlow version (0.12.1): 
-$ nvidia-docker run -it -p 8888:8888 gcr.io/tensorflow/tensorflow:0.12.1-gpu
+$ nvidia-docker run -it -p 8888:8888 tensorflow/tensorflow:0.12.1-gpu
Docker will download the TensorFlow binary image the first time you launch it. @@ -480,7 +480,7 @@ Take the following steps to install TensorFlow in an Anaconda environment: 
      (tensorflow)$ pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc1-cp34-cp34m-linux_x86_64.whl
+ https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0-cp34-cp34m-linux_x86_64.whl ## Validate your installation @@ -505,7 +505,7 @@ If you installed through Docker, start a Docker container from which you can run bash. For example: 
-$ docker run -it gcr.io/tensorflow/tensorflow bash
+$ docker run -it tensorflow/tensorflow bash
@@ -647,14 +647,14 @@ This section documents the relevant values for Linux installations. CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc1-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0-cp27-none-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0rc1-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0-cp27-none-linux_x86_64.whl
Note that GPU support requires the NVIDIA hardware and software described in @@ -666,14 +666,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc1-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0-cp34-cp34m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0rc1-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0-cp34-cp34m-linux_x86_64.whl
Note that GPU support requires the NVIDIA hardware and software described in @@ -685,14 +685,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc1-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0-cp35-cp35m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0rc1-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0-cp35-cp35m-linux_x86_64.whl
@@ -704,14 +704,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc1-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0-cp36-cp36m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0rc1-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0-cp36-cp36m-linux_x86_64.whl
diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md index 5be38ae1ef9fe994e5da18449fd0b3dc7335146b..205db8e6bdd438ee5502f6cd1d92c30ea9e33152 100644 --- a/tensorflow/docs_src/install/install_mac.md +++ b/tensorflow/docs_src/install/install_mac.md @@ -118,8 +118,8 @@ Take the following steps to install TensorFlow with Virtualenv: Python 2.7, the command to install TensorFlow in the active Virtualenv is as follows: - 
 $ pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc1-py3-none-any.whl
+ 
 $ pip install --upgrade \
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0-py2-none-any.whl
If you encounter installation problems, see [Common Installation Problems](#common-installation-problems). @@ -241,8 +241,8 @@ take the following steps: you are installing TensorFlow for Mac OS and Python 2.7 issue the following command: - 
 $ sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc1-py3-none-any.whl
+ 
 $ sudo pip install --upgrade \
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0-py2-none-any.whl
If the preceding command fails, see [installation problems](#common-installation-problems). @@ -292,24 +292,23 @@ where: to 6006. * TensorFlowImage is required. It identifies the Docker container. You must specify one of the following values: - * gcr.io/tensorflow/tensorflow: TensorFlow binary image. - * gcr.io/tensorflow/tensorflow:latest-devel: TensorFlow + * tensorflow/tensorflow: TensorFlow binary image. + * tensorflow/tensorflow:latest-devel: TensorFlow Binary image plus source code. -gcr.io is the Google Container Registry. Note that some -TensorFlow images are also available at +The TensorFlow images are available at [dockerhub](https://hub.docker.com/r/tensorflow/tensorflow/). For example, the following command launches a TensorFlow CPU binary image in a Docker container from which you can run TensorFlow programs in a shell: -
$ docker run -it gcr.io/tensorflow/tensorflow bash
+
$ docker run -it tensorflow/tensorflow bash
The following command also launches a TensorFlow CPU binary image in a Docker container. However, in this Docker container, you can run TensorFlow programs in a Jupyter notebook: -
$ docker run -it -p 8888:8888 gcr.io/tensorflow/tensorflow
+
$ docker run -it -p 8888:8888 tensorflow/tensorflow
Docker will download the TensorFlow binary image the first time you launch it. @@ -351,7 +350,7 @@ Take the following steps to install TensorFlow in an Anaconda environment: TensorFlow for Python 2.7: 
 (targetDirectory)$ pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc1-py2-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0-py2-none-any.whl @@ -376,7 +375,7 @@ do the following: If you installed through Docker, start a Docker container that runs bash. For example: -
$ docker run -it gcr.io/tensorflow/tensorflow bash
+
$ docker run -it tensorflow/tensorflow bash
@@ -524,7 +523,7 @@ This section documents the relevant values for Mac OS installations. 
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc1-py2-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0-py2-none-any.whl
@@ -532,5 +531,5 @@ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc1-py2-none-a 
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc1-py3-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0-py3-none-any.whl
diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md index 8d83e9f1190ed307ca99d81168df7dfab51e4507..c09c9c2c0c6eaabe9066a2630a3aec39f05fcfec 100644 --- a/tensorflow/docs_src/install/install_sources.md +++ b/tensorflow/docs_src/install/install_sources.md @@ -359,10 +359,10 @@ Invoke `pip install` to install that pip package. The filename of the `.whl` file depends on your platform. For example, the following command will install the pip package -for TensorFlow 1.6.0rc1 on Linux: +for TensorFlow 1.6.0 on Linux: 
-$ sudo pip install /tmp/tensorflow_pkg/tensorflow-1.6.0rc1-py2-none-any.whl
+$ sudo pip install /tmp/tensorflow_pkg/tensorflow-1.6.0-py2-none-any.whl
## Validate your installation @@ -393,8 +393,7 @@ TensorFlow programs: 
Hello, TensorFlow!
-If you are new to TensorFlow, see @{$get_started/premade_estimators$Getting Started with -TensorFlow}. +If you are new to TensorFlow, see @{$get_started/premade_estimators$Getting Started with TensorFlow}. If the system outputs an error message instead of a greeting, see [Common installation problems](#common_installation_problems). diff --git a/tensorflow/docs_src/install/install_windows.md b/tensorflow/docs_src/install/install_windows.md index dedf485f93d6fd6a8ce7b4465548cc998d307daa..2413bc9cfbbfd577ebd583be4da82994e8551c9e 100644 --- a/tensorflow/docs_src/install/install_windows.md +++ b/tensorflow/docs_src/install/install_windows.md @@ -17,7 +17,7 @@ You must choose one of the following types of TensorFlow to install: NVIDIA® GPU, you must install this version. Note that this version of TensorFlow is typically much easier to install (typically, in 5 or 10 minutes), so even if you have an NVIDIA GPU, we recommend - installing this version first. + installing this version first. Prebuilt binaries will use AVX instructions. * **TensorFlow with GPU support**. TensorFlow programs typically run significantly faster on a GPU than on a CPU. Therefore, if your system has a NVIDIA® GPU meeting the prerequisites shown below @@ -41,7 +41,8 @@ installed on your system: Note that cuDNN is typically installed in a different location from the other CUDA DLLs. Ensure that you add the directory where you installed the cuDNN DLL to your `%PATH%` environment variable. - * GPU card with CUDA Compute Capability 3.0 or higher. See + * GPU card with CUDA Compute Capability 3.0 or higher for building + from source and 3.5 or higher for our binaries. See [NVIDIA documentation](https://developer.nvidia.com/cuda-gpus) for a list of supported GPU cards. @@ -153,8 +154,7 @@ TensorFlow programs: 
Hello, TensorFlow!
-If you are new to TensorFlow, see @{$get_started/premade_estimators$Getting Started with -TensorFlow}. +If you are new to TensorFlow, see @{$get_started/premade_estimators$Getting Started with TensorFlow}. If the system outputs an error message instead of a greeting, see [Common installation problems](#common_installation_problems). diff --git a/tensorflow/docs_src/mobile/android_build.md b/tensorflow/docs_src/mobile/android_build.md index b5a1d5d7d1bf3b456ab24165e273969bdbd7bfca..08a5fbe41c87c88399682208c38bf7a892d8fc1a 100644 --- a/tensorflow/docs_src/mobile/android_build.md +++ b/tensorflow/docs_src/mobile/android_build.md @@ -90,8 +90,8 @@ using [ADB](https://developer.android.com/studio/command-line/adb.html). This requires some knowledge of build systems and Android developer tools, but we'll guide you through the basics here. -- First, follow our instructions for @{$install/install_sources$installing from - sources}. This will also guide you through installing Bazel and cloning the +- First, follow our instructions for @{$install/install_sources$installing from sources}. + This will also guide you through installing Bazel and cloning the TensorFlow code. - Download the Android [SDK](https://developer.android.com/studio/index.html) diff --git a/tensorflow/docs_src/mobile/leftnav_files b/tensorflow/docs_src/mobile/leftnav_files index ac50f528ba468d8a830c059539d3399f413f39c8..4cf134cc3c2c323405d769a5ced5d5a68f188203 100644 --- a/tensorflow/docs_src/mobile/leftnav_files +++ b/tensorflow/docs_src/mobile/leftnav_files @@ -2,6 +2,7 @@ index.md ### TensorFlow Lite tflite/index.md tflite/demo_android.md +tflite/demo_ios.md >>> ### TensorFlow Mobile mobile_intro.md diff --git a/tensorflow/docs_src/mobile/optimizing.md b/tensorflow/docs_src/mobile/optimizing.md index 44cacff5dbbcb0685044c342184464b47a8ed090..ca9cb043e9282702044b0da26c27f5bf29141cae 100644 --- a/tensorflow/docs_src/mobile/optimizing.md +++ b/tensorflow/docs_src/mobile/optimizing.md @@ -290,8 +290,8 @@ run it on a 64-bit ARM device: You can interpret the results in exactly the same way as the desktop version above. If you have any trouble figuring out what the right input and output -names and types are, take a look at the @{$mobile/prepare_models$Preparing -models} page for details about detecting these for your model, and look at the +names and types are, take a look at the @{$mobile/prepare_models$Preparing models} +page for details about detecting these for your model, and look at the `summarize_graph` tool which may give you helpful information. diff --git a/tensorflow/docs_src/mobile/tflite/demo_android.md b/tensorflow/docs_src/mobile/tflite/demo_android.md index 79b567897cb8a38ed2e27e73aa7e8fee95f718b8..c94b5597a673a7e68aed517b325b9719b3b73bbd 100644 --- a/tensorflow/docs_src/mobile/tflite/demo_android.md +++ b/tensorflow/docs_src/mobile/tflite/demo_android.md @@ -8,6 +8,9 @@ You'll need an Android device running Android 5.0 or higher to run the demo. To get you started working with TensorFlow Lite on Android, we'll walk you through building and deploying our TensorFlow demo app in Android Studio. +Note: For a more detailed guide see the +[TFLite Codelab](https://codelabs.developers.google.com/codelabs/tensorflow-for-poets-2-tflite/index.html#0) + It's also possible to build the demo app with Bazel, but we only recommend this for advanced users who are very familiar with the Bazel build environment. For more information on that, see our page [on Github](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite#building-tensorflow-lite-and-the-demo-app-from-source). diff --git a/tensorflow/docs_src/mobile/tflite/demo_ios.md b/tensorflow/docs_src/mobile/tflite/demo_ios.md new file mode 100644 index 0000000000000000000000000000000000000000..3ee9b1cbca6cfef98616bd33bbf91b756b4efa15 --- /dev/null +++ b/tensorflow/docs_src/mobile/tflite/demo_ios.md @@ -0,0 +1,68 @@ +# TensorFlow Lite Demo for iOS + +The TensorFlow Lite demo is a camera app that continuously classifies whatever +it sees from your device's back camera, using a quantized MobileNet model. These +instructions walk you through building and running the demo on an iOS device. + +## Prerequisites + +* You must have [Xcode](https://developer.apple.com/xcode/) installed and have a + valid Apple Developer ID, and have an iOS device set up and linked to your + developer account with all of the appropriate certificates. For these + instructions, we assume that you have already been able to build and deploy an + app to an iOS device with your current developer environment. + +* The demo app requires a camera and must be executed on a real iOS device. You + can build it and run with the iPhone Simulator but it won't have any camera + information to classify. + +* You don't need to build the entire TensorFlow library to run the demo, but you + will need to clone the TensorFlow repository if you haven't already: + + git clone https://github.com/tensorflow/tensorflow + +* You'll also need the Xcode command-line tools: + + xcode-select --install + + If this is a new install, you will need to run the Xcode application once to + agree to the license before continuing. + +## Building the iOS Demo App + +1. Install CocoaPods if you don't have it: + + sudo gem install cocoapods + +2. Download the model files used by the demo app (this is done from inside the + cloned directory): + + sh tensorflow/contrib/lite/examples/ios/download_models.sh + +3. Install the pod to generate the workspace file: + + cd tensorflow/contrib/lite/examples/ios/camera + pod install + + If you have installed this pod before and that command doesn't work, try + + pod update + + At the end of this step you should have a file called + `tflite_camera_example.xcworkspace`. + +4. Open the project in Xcode by typing this on the command line: + + open tflite_camera_example.xcworkspace + + This launches Xcode if it isn't open already and opens the + `tflite_camera_example` project. + +5. Build and run the app in Xcode. + + Note that as mentioned earlier, you must already have a device set up and + linked to your Apple Developer account in order to deploy the app on a + device. + +You'll have to grant permissions for the app to use the device's camera. Point +the camera at various objects and enjoy seeing how the model classifies things! diff --git a/tensorflow/docs_src/performance/leftnav_files b/tensorflow/docs_src/performance/leftnav_files index 316f023f43dcfe781c7819d1681335267ddd5f76..d11a7e5d07c3e6cfa092e7ac11189ce6c272c1ad 100644 --- a/tensorflow/docs_src/performance/leftnav_files +++ b/tensorflow/docs_src/performance/leftnav_files @@ -2,6 +2,7 @@ performance_guide.md datasets_performance.md performance_models.md benchmarks.md +quantization.md ### XLA xla/index.md @@ -11,6 +12,3 @@ xla/jit.md xla/operation_semantics.md xla/shapes.md xla/tfcompile.md - -### Quantization -quantization.md diff --git a/tensorflow/docs_src/performance/performance_guide.md b/tensorflow/docs_src/performance/performance_guide.md index cd47fc2803bc1429d28bd0ae4c2ad68e632a6f03..580a899ac4e4f5c3d97ce023f25083168fe00d01 100644 --- a/tensorflow/docs_src/performance/performance_guide.md +++ b/tensorflow/docs_src/performance/performance_guide.md @@ -78,7 +78,7 @@ training CIFAR-10 illustrates the use of the `tf.data` API along with The `tf.data` API utilizes C++ multi-threading and has a much lower overhead than the Python-based `queue_runner` that is limited by Python's multi-threading performance. A detailed performance guide for the `tf.data` API can be found -[here](#datasets_performance). +[here](@{$datasets_performance}). While feeding data using a `feed_dict` offers a high level of flexibility, in general `feed_dict` does not provide a scalable solution. If only a single GPU diff --git a/tensorflow/docs_src/performance/quantization.md b/tensorflow/docs_src/performance/quantization.md index 544274cab68934419e8601a4d9714d80335fca28..411889cb1c616130f809e6228cc692ba3f951d48 100644 --- a/tensorflow/docs_src/performance/quantization.md +++ b/tensorflow/docs_src/performance/quantization.md @@ -1,226 +1,253 @@ -# How to Quantize Neural Networks with TensorFlow - -When modern neural networks were being developed, the biggest challenge was -getting them to work at all! That meant that accuracy and speed during training -were the top priorities. Using floating point arithmetic was the easiest way to -preserve accuracy, and GPUs were well-equipped to accelerate those calculations, -so it's natural that not much attention was paid to other numerical formats. - -These days, we actually have a lot of models being deployed in commercial -applications. The computation demands of training grow with the number of -researchers, but the cycles needed for inference expand in proportion to users. -That means pure inference efficiency has become a burning issue for a lot of -teams. - -That is where quantization comes in. It's an umbrella term that covers a lot of -different techniques to store numbers and perform calculations on them in more -compact formats than 32-bit floating point. I am going to focus on eight-bit -fixed point, for reasons I'll go into more detail on later. - -[TOC] - -## Why does Quantization Work? - -Training neural networks is done by applying many tiny nudges to the weights, -and these small increments typically need floating point precision to work -(though there are research efforts to use quantized representations here too). - -Taking a pre-trained model and running inference is very different. One of the -magical qualities of deep networks is that they tend to cope very well with high -levels of noise in their inputs. If you think about recognizing an object in a -photo you've just taken, the network has to ignore all the CCD noise, lighting -changes, and other non-essential differences between it and the training -examples it's seen before, and focus on the important similarities instead. This -ability means that they seem to treat low-precision calculations as just another -source of noise, and still produce accurate results even with numerical formats -that hold less information. - -## Why Quantize? - -Neural network models can take up a lot of space on disk, with the original -AlexNet being over 200 MB in float format for example. Almost all of that size -is taken up with the weights for the neural connections, since there are often -many millions of these in a single model. Because they're all slightly different -floating point numbers, simple compression formats like zip don't compress them -well. They are arranged in large layers though, and within each layer the -weights tend to be normally distributed within a certain range, for example -3.0 -to 6.0. - -The simplest motivation for quantization is to shrink file sizes by storing the -min and max for each layer, and then compressing each float value to an -eight-bit integer representing the closest real number in a linear set of 256 -within the range. For example with the -3.0 to 6.0 range, a 0 byte would -represent -3.0, a 255 would stand for 6.0, and 128 would represent about 1.5. -I'll go into the exact calculations later, since there's some subtleties, but -this means you can get the benefit of a file on disk that's shrunk by 75%, and -then convert back to float after loading so that your existing floating-point -code can work without any changes. - -Another reason to quantize is to reduce the computational resources you need to -do the inference calculations, by running them entirely with eight-bit inputs -and outputs. This is a lot more difficult since it requires changes everywhere -you do calculations, but offers a lot of potential rewards. Fetching eight-bit -values only requires 25% of the memory bandwidth of floats, so you'll make much -better use of caches and avoid bottlenecking on RAM access. You can also -typically use SIMD operations that do many more operations per clock cycle. In -some case you'll have a DSP chip available that can accelerate eight-bit -calculations too, which can offer a lot of advantages. - -Moving calculations over to eight bit will help you run your models faster, and -use less power (which is especially important on mobile devices). It also opens -the door to a lot of embedded systems that can't run floating point code -efficiently, so it can enable a lot of applications in the IoT world. - -## Why Not Train in Lower Precision Directly? - -There have been some experiments training at lower bit depths, but the results -seem to indicate that you need higher than eight bit to handle the back -propagation and gradients. That makes implementing the training more -complicated, and so starting with inference made sense. We also already have a -lot of float models already that we use and know well, so being able to convert -them directly is very convenient. - -## How Can You Quantize Your Models? - -TensorFlow has production-grade support for eight-bit calculations built in. It -also has a process for converting many models trained in floating-point over to -equivalent graphs using quantized calculations for inference. For example, -here's how you can translate the latest GoogLeNet model into a version that uses -eight-bit computations: - -```sh -curl -L "https://storage.googleapis.com/download.tensorflow.org/models/inception_v3_2016_08_28_frozen.pb.tar.gz" | - tar -C tensorflow/examples/label_image/data -xz -bazel build tensorflow/tools/graph_transforms:transform_graph -bazel-bin/tensorflow/tools/graph_transforms/transform_graph \ - --in_graph=tensorflow/examples/label_image/data/inception_v3_2016_08_28_frozen.pb \ - --out_graph=/tmp/quantized_graph.pb \ - --inputs=input \ - --outputs=InceptionV3/Predictions/Reshape_1 \ - --transforms='add_default_attributes strip_unused_nodes(type=float, shape="1,299,299,3") - remove_nodes(op=Identity, op=CheckNumerics) fold_constants(ignore_errors=true) - fold_batch_norms fold_old_batch_norms quantize_weights quantize_nodes - strip_unused_nodes sort_by_execution_order' +# Fixed Point Quantization + +Quantization techniques store and calculate numbers in more compact formats. +[TensorFlow Lite](/mobile/tflite/) adds quantization that uses an 8-bit fixed +point representation. + +Since a challenge for modern neural networks is optimizing for high accuracy, the +priority has been improving accuracy and speed during training. Using floating +point arithmetic is an easy way to preserve accuracy and GPUs are designed to +accelerate these calculations. + +However, as more machine learning models are deployed to mobile devices, +inference efficiency has become a critical issue. Where the computational demand +for *training* grows with the amount of models trained on different +architectures, the computational demand for *inference* grows in proportion to +the amount of users. + +## Quantization benefits + + +Using 8-bit calculations help your models run faster and use less power. This is +especially important for mobile devices and embedded applications that can't run +floating point code efficiently, for example, Internet of Things (IoT) and +robotics devices. There are additional opportunities to extend this support to +more backends and research lower precision networks. + +### Smaller file sizes {: .hide-from-toc} + +Neural network models require a lot of space on disk. For example, the original +AlexNet requires over 200 MB for the float format—almost all of that for the +model's millions of weights. Because the weights are slightly different +floating point numbers, simple compression formats perform poorly (like zip). + +Weights fall in large layers of numerical values. For each layer, weights tend to +be normally distributed within a range. Quantization can shrink file sizes by +storing the minimum and maximum weight for each layer, then compress each +weight's float value to an 8-bit integer representing the closest real number in +a linear set of 256 within the range. + +### Faster inference {: .hide-from-toc} + +Since calculations are run entirely on 8-bit inputs and outputs, quantization +reduces the computational resources needed for inference calculations. This is +more involved, requiring changes to all floating point calculations, but results +in a large speed-up for inference time. + +### Memory efficiency {: .hide-from-toc} + +Since fetching 8-bit values only requires 25% of the memory bandwidth of floats, +more efficient caches avoid bottlenecks for RAM access. In many cases, the power +consumption for running a neural network is dominated by memory access. The +savings from using fixed-point 8-bit weights and activations are significant. + +Typically, SIMD operations are available that run more operations per clock +cycle. In some cases, a DSP chip is available that accelerates 8-bit calculations +resulting in a massive speedup. + +## Fixed point quantization techniques + +The goal is to use the same precision for weights and activations during both +training and inference. But an important difference is that training consists of +a forward pass and a backward pass, while inference only uses a forward pass. +When we train the model with quantization in the loop, we ensure that the forward +pass matches precision for both training and inference. + +To minimize the loss in accuracy for fully fixed point models (weights and +activations), train the model with quantization in the loop. This simulates +quantization in the forward pass of a model so weights tend towards values that +perform better during quantized inference. The backward pass uses quantized +weights and activations and models quantization as a straight through estimator. +(See Bengio et al., [2013](https://arxiv.org/abs/1308.3432)) + +Additionally, the minimum and maximum values for activations are determined +during training. This allows a model trained with quantization in the loop to be +converted to a fixed point inference model with little effort, eliminating the +need for a separate calibration step. + +## Quantization training with TensorFlow + +TensorFlow can train models with quantization in the loop. Because training +requires small gradient adjustments, floating point values are still used. To +keep models as floating point while adding the quantization error in the training +loop, @{$array_ops#Fake_quantization$fake quantization} nodes simulate the +effect of quantization in the forward and backward passes. + +Since it's difficult to add these fake quantization operations to all the +required locations in the model, there's a function available that rewrites the +training graph. To create a fake quantized training graph: + +``` +# Build forward pass of model. +loss = tf.losses.get_total_loss() + +# Call the training rewrite which rewrites the graph in-place with +# FakeQuantization nodes and folds batchnorm for training. It is +# often needed to fine tune a floating point model for quantization +# with this training tool. When training from scratch, quant_delay +# can be used to activate quantization after training to converge +# with the float graph, effectively fine-tuning the model. +tf.contrib.quantize.create_training_graph(quant_delay=2000000) + +# Call backward pass optimizer as usual. +optimizer = tf.train.GradientDescentOptimizer(learning_rate) +optimizer.minimize(loss) ``` -This will produce a new model that runs the same operations as the original, but -with eight bit calculations internally, and all weights quantized as well. If -you look at the file size, you'll see it's about a quarter of the original (23MB -versus 91MB). You can still run this model using exactly the same inputs and -outputs though, and you should get equivalent results. Here's an example: +The rewritten *eval graph* is non-trivially different from the *training graph* +since the quantization ops affect the batch normalization step. Because of this, +we've added a separate rewrite for the *eval graph*: -```sh -bazel build tensorflow/examples/label_image:label_image -bazel-bin/tensorflow/examples/label_image/label_image \ ---graph=/tmp/quantized_graph.pb \ +``` +# Build eval model +logits = tf.nn.softmax_cross_entropy_with_logits(...) + +# Call the eval rewrite which rewrites the graph in-place with +# FakeQuantization nodes and fold batchnorm for eval. +tf.contrib.quantize.create_eval_graph() + +# Save the checkpoint and eval graph proto to disk for freezing +# and providing to TFLite. +with open(eval_graph_file, ‘w’) as f: + f.write(str(g.as_graph_def())) +saver = tf.train.Saver() +saver.save(sess, checkpoint_name) +``` + +Methods to rewrite the training and eval graphs are an active area of research +and experimentation. Although rewrites and quantized training might not work or +improve performance for all models, we are working to generalize these +techniques. + +## Generating fully quantized models + +The previously demonstrated after-rewrite eval graph only *simulates* +quantization. To generate real fixed point computations from a trained +quantization model, convert it to a fixed point kernel. Tensorflow Lite supports +this conversion from the graph resulting from `create_eval_graph`. + +First, create a frozen graph that will be the input for the TensorFlow Lite +toolchain: + +``` +bazel build tensorflow/python/tools:freeze_graph && \ + bazel-bin/tensorflow/python/tools/freeze_graph \ + --input_graph=eval_graph_def.pb \ + --input_checkpoint=checkpoint \ + --output_graph=frozen_eval_graph.pb --output_node_names=outputs ``` -You'll see that this runs the newly-quantized graph, and outputs a very similar -answer to the original. - -You can run the same process on your own models saved out as GraphDefs, with the -input and output names adapted to those your network requires. I recommend that -you run them through the freeze_graph script first, to convert checkpoints into -constants stored in the file. - -## How Does the Quantization Process Work? - -We've implemented quantization by writing equivalent eight-bit versions of -operations that are commonly used during inference. These include convolution, -matrix multiplication, activation functions, pooling operations and -concatenation. The conversion script first replaces all the individual ops it -knows about with quantized equivalents. These are small sub-graphs that have -conversion functions before and after to move the data between float and -eight-bit. Below is an example of what they look like. First here's the original -Relu operation, with float inputs and outputs: - -![Relu Diagram](https://www.tensorflow.org/images/quantization0.png) - -Then, this is the equivalent converted subgraph, still with float inputs and -outputs, but with internal conversions so the calculations are done in eight -bit. - -![Converted Diagram](https://www.tensorflow.org/images/quantization1.png) - -The min and max operations actually look at the values in the input float -tensor, and then feeds them into the Dequantize operation that converts the -tensor into eight-bits. There are more details on how the quantized representation -works later on. - -Once the individual operations have been converted, the next stage is to remove -unnecessary conversions to and from float. If there are consecutive sequences of -operations that all have float equivalents, then there will be a lot of adjacent -Dequantize/Quantize ops. This stage spots that pattern, recognizes that they -cancel each other out, and removes them, like this: - -![Stripping Diagram](https://www.tensorflow.org/images/quantization2.png) - -Applied on a large scale to models where all of the operations have quantized -equivalents, this gives a graph where all of the tensor calculations are done in -eight bit, without having to convert to float. - -## What Representation is Used for Quantized Tensors? - -We approach converting floating-point arrays of numbers into eight-bit -representations as a compression problem. We know that the weights and -activation tensors in trained neural network models tend to have values that are -distributed across comparatively small ranges (for example you might have -15 to -+15 for weights, -500 to 1000 for activations on an image model, though the -exact numbers will vary). We also know from experiment that neural nets tend to -be very robust in the face of noise, and so the noise-like error produced by -quantizing down to a small set of values will not hurt the precision of the -overall results very much. We also want to pick a representation that's easy to -perform calculations on, especially the large matrix multiplications that form -the bulk of the work that's needed to run a model. - -These led us to pick a representation that has two floats to store the overall -minimum and maximum values that are represented by the lowest and highest -quantized value. Each entry in the quantized array represents a float value in -that range, distributed linearly between the minimum and maximum. For example, -if we have minimum = -10.0, and maximum = 30.0f, and an eight-bit array, here's -what the quantized values represent: +Provide this to the TensorFlow Lite Optimizing Converter (TOCO) to get a fully +quantized TensorFLow Lite model: ``` -Quantized | Float ---------- | ----- -0 | -10.0 -255 | 30.0 -128 | 10.0 +bazel build tensorflow/contrib/lite/toco:toco && \ + ./bazel-bin/third_party/tensorflow/contrib/lite/toco/toco \ + --input_file=frozen_eval_graph.pb \ + --output_file=tflite_model.tflite \ + --input_format=TENSORFLOW_GRAPHDEF --output_format=TFLITE \ + --inference_type=QUANTIZED_UINT8 \ + --input_shape="1,224, 224,3" \ + --input_array=input \ + --output_array=outputs \ + --std_value=127.5 --mean_value=127.5 ``` -The advantages of this format are that it can represent arbitrary magnitudes of -ranges, they don't have to be symmetrical, it can represent signed and unsigned -values, and the linear spread makes doing multiplications straightforward. There -are alternatives like [Song Han's code books](http://arxiv.org/pdf/1510.00149.pdf) -that can use lower bit depths by non-linearly distributing the float values -across the representation, but these tend to be more expensive to calculate on. - -The advantage of having a strong and clear definition of the quantized format is -that it's always possible to convert back and forth from float for operations -that aren't quantization-ready, or to inspect the tensors for debugging -purposes. One implementation detail in TensorFlow that we're hoping to improve -in the future is that the minimum and maximum float values need to be passed as -separate tensors to the one holding the quantized values, so graphs can get a -bit dense! - -The nice thing about the minimum and maximum ranges is that they can often be -pre-calculated. Weight parameters are constants known at load time, so their -ranges can also be stored as constants. We often know the ranges for inputs (for -examples images are usually RGB values in the range 0.0 to 255.0), and many -activation functions have known ranges too. This can avoid having to analyze the -outputs of an operation to determine the range, which we need to do for math ops -like convolution or matrix multiplication which produce 32-bit accumulated -results from 8-bit inputs. - -## What's Next? - -We've found that we can get extremely good performance on mobile and embedded -devices by using eight-bit arithmetic rather than floating-point. You can see -the framework we use to optimize matrix multiplications at -[gemmlowp](https://github.com/google/gemmlowp). We still need to apply all the -lessons we've learned to the TensorFlow ops to get maximum performance on -mobile, but we're actively working on that. Right now, this quantized -implementation is a reasonably fast and accurate reference implementation that -we're hoping will enable wider support for our eight-bit models on a wider -variety of devices. We also hope that this demonstration will encourage the -community to explore what's possible with low-precision neural networks. +See the documentation for @{tf.contrib.quantize} and +[TensorFlow Lite](/mobile/tflite/). + +## Quantized accuracy + +Fixed point [MobileNet](https://arxiv.org/abs/1704.0486) models are released with +8-bit weights and activations. Using the rewriters, these models achieve the +Top-1 accuracies listed in Table 1. For comparison, the floating point accuracies +are listed for the same models. The code used to generate these models +[is available](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md) +along with links to all of the pretrained mobilenet_v1 models. + +
+ + + + + + + + + + + + + + + + + + + + + + + +
Image SizeDepthTop-1 Accuracy:
Floating point		Top-1 Accuracy:
Fixed point: 8 bit weights and activations
1280.250.4150.399
1280.50.5630.549
1280.750.6210.598
12810.6520.64
1600.250.4550.435
1600.50.5910.577
1600.750.6530.639
16010.680.673
1920.250.4770.458
1920.50.6170.604
1920.750.6720.662
19210.70.69
2240.250.4980.482
2240.50.6330.622
2240.750.6840.679
22410.7090.697
+
+ Table 1: MobileNet Top-1 accuracy on Imagenet Validation dataset. +
+
+ +## Representation for quantized tensors + +TensorFlow approaches the conversion of floating-point arrays of numbers into +8-bit representations as a compression problem. Since the weights and activation +tensors in trained neural network models tend to have values that are distributed +across comparatively small ranges (for example, -15 to +15 for weights or -500 to +1000 for image model activations). And since neural nets tend to be robust +handling noise, the error introduced by quantizing to a small set of values +maintains the precision of the overall results within an acceptable threshold. A +chosen representation must perform fast calculations, especially the large matrix +multiplications that comprise the bulk of the computations while running a model. + +This is represented with two floats that store the overall minimum and maximum +values corresponding to the lowest and highest quantized value. Each entry in the +quantized array represents a float value in that range, distributed linearly +between the minimum and maximum. For example, with a minimum of -10.0 and maximum +of 30.0f, and an 8-bit array, the quantized values represent the following: + +
+ + + + + +
QuantizedFloat
0-10.0
25530.0
12810.0
+
+ Table 2: Example quantized value range +
+
+ +The advantages of this representation format are: + +* It efficiently represents an arbitrary magnitude of ranges. +* The values don't have to be symmetrical. +* The format represents both signed and unsigned values. +* The linear spread makes multiplications straightforward. + +Alternative techniques use lower bit depths by non-linearly distributing the +float values across the representation, but currently are more expensive in terms +of computation time. (See Han et al., +[2016](https://arxiv.org/abs/1510.00149).) + +The advantage of having a clear definition of the quantized format is that it's +always possible to convert back and forth from fixed-point to floating-point for +operations that aren't quantization-ready, or to inspect the tensors for +debugging. diff --git a/tensorflow/docs_src/performance/xla/jit.md b/tensorflow/docs_src/performance/xla/jit.md index d4dc3e57c8fb5ec2a979b6ba7ebe2a3b6c3a5f94..d9a979ccbd31773b9d227ff946486706844a8f81 100644 --- a/tensorflow/docs_src/performance/xla/jit.md +++ b/tensorflow/docs_src/performance/xla/jit.md @@ -157,7 +157,7 @@ to fuse Ops is visible by starting at `hlo_graph_0.dot` and viewing each diagram in succession. To Render the .dot file into a png, install -[GraphViz](http://www.graphviz.org/Download..php) and run: +[GraphViz](https://www.graphviz.org/download/) and run: ```shell dot -Tpng hlo_graph_80.dot -o hlo_graph_80.png diff --git a/tensorflow/docs_src/performance/xla/operation_semantics.md b/tensorflow/docs_src/performance/xla/operation_semantics.md index b0abf5fdd2e0d8c3c20ae4bcd8f185124028df04..5e39e710a0dba74dfd68a04367ce402362520590 100644 --- a/tensorflow/docs_src/performance/xla/operation_semantics.md +++ b/tensorflow/docs_src/performance/xla/operation_semantics.md @@ -45,27 +45,30 @@ feature dimension in `operand`), the operation calculates the gradients with respect to `operand`, `offset` and `scale` across all the other dimensions. The `feature_index` must be a valid index for the feature dimension in `operand`. -The three gradients are defined by the following formulas: +The three gradients are defined by the following formulas (Assuming a +4-dimensional tensor as `operand` and (l) is the index for feature dimension): -\\( \nabla x = \nabla y * \gamma * \sqrt{\sigma^2+\epsilon} \\) +\\( coef_l = \frac{1}{mwh}\sum_{i=1}^m\sum_{j=1}^w\sum_{k=1}^h (\nabla y_{ijkl} * (x_{ijkl} - \mu_l) / (\sigma^2_{l}+\epsilon)) \\) -\\( \nabla \gamma = sum(\nabla y * (x - \mu) * \sqrt{\sigma^2 + \epsilon}) \\) +\\( \nabla x_{ijkl} = \gamma_{l} * (1/\sqrt{\sigma^2_{l}+\epsilon}) * [\nabla y_{ijkl} - mean(\nabla y) - (x_{ijkl} - \mu_{l}) * coef_l] \\) -\\( \nabla \beta = sum(\nabla y) \\) +\\( \nabla \beta_l = \sum_{i=1}^m\sum_{j=1}^w\sum_{k=1}^h \nabla y_{ijkl} \\) + +\\( \nabla \gamma_l = \sum_{i=1}^m\sum_{j=1}^w\sum_{k=1}^h \nabla y_{ijkl} * ((x_{ijkl} - \mu_l) / \sqrt{\sigma^2_{l}+\epsilon}) \\) The inputs `mean` and `variance` represents moments value across batch and spatial dimensions. The output type is a tuple of three handles: -|Outputs | Type | Semantics | -|------------- | ----------------------- | ------------------------------------| -|`grad_operand`| `ComputationDataHandle` | gradient with respect to input | -: : : `operand` : -|`grad_scale` | `ComputationDataHandle` | gradient with respect to input | -: : : `scale` : -|`grad_offset` | `ComputationDataHandle` | gradient with respect to input | -: : : `offset` : +|Outputs | Type | Semantics | +|------------- | ----------------------- | ------------------------------------ | +|`grad_operand`| `ComputationDataHandle` | gradient with respect to input | +: : : `operand` (\\( \nabla x\\)) : +|`grad_scale` | `ComputationDataHandle` | gradient with respect to input | +: : : `scale` (\\( \nabla \gamma\\)) : +|`grad_offset` | `ComputationDataHandle` | gradient with respect to input | +: : : `offset`(\\( \nabla \beta\\)) : ## BatchNormInference @@ -119,7 +122,7 @@ Normalizes an array across batch and spatial dimensions. | Arguments | Type | Semantics | | --------------- | ----------------------- | -------------------------------- | | `operand` | `ComputationDataHandle` | n dimensional array to be | -: : : normalized : +: : : normalized (x) : | `scale` | `ComputationDataHandle` | 1 dimensional array | : : : (\\(\gamma\\)) : | `offset` | `ComputationDataHandle` | 1 dimensional array | @@ -254,7 +257,7 @@ the range between the minimum and maximum, else returns the minimum value if the operand is below this range or the maximum value if the operand is above this range. That is, `clamp(a, x, b) = min(max(a, x), b)`. -All three arrays must be the same shape. Alternately, as a restricted form of +All three arrays must be the same shape. Alternatively, as a restricted form of [broadcasting](broadcasting.md), `min` and/or `max` can be a scalar of type `T`. Example with scalar `min` and `max`: @@ -1050,6 +1053,9 @@ For a more intuitive description, see the "Informal Description" section below. : : : indices of the slices we're : : : : we're stitching together into : : : : the output tensor. : +|`index_vector_dim` | `int64` | The dimension in | +: : : `gather_indices` that contains : +: : : the starting indices. : |`output_window_dims` | `ArraySlice` | The set of dimensions in the | : : : output shape that are _window : : : : dimensions_ (defined below). : @@ -1066,22 +1072,20 @@ For a more intuitive description, see the "Informal Description" section below. : : : `output_window_dims`) and the window : : : : dimensions that are elided (via : : : : `elided_window_dims`). : -|`gather_dims_to_operand_dims` | `ArraySlice` | A dimension map (the | +|`gather_dims_to_operand_dims` | `ArraySlice` | A dimension map (the | : : : array is interpreted as mapping `i` to : : : : `gather_dims_to_operand_dims[i]`) from : : : : the gather indices in `gather_indices` to : : : : the operand index space. It has to be : : : : one-to-one and total. : -If `gather_indices` is a vector with `N` elements then we implicitly reshape it -to a tensor of shape `[N,1]` before proceeding. - For every index `Out` in the output tensor, we compute two things (more precisely described later): - - An index into the first `gather_indices.rank` - `1` dimensions of - `gather_indices`, which gives us a starting index of a slice, _operand - slice_, in the operand tensor. + - An index into `gather_indices.rank` - `1` dimensions of `gather_indices`, + which gives us a starting index of a slice, _operand slice_, in the operand + tensor. These `gather_indices.rank` - `1` dimensions are all the dimensions + in `gather_indices` except `index_vector_dim`. - A _window index_ that has the same rank as the operand. This index is composed of the values in `Out` at dimensions `output_window_dims`, embedded @@ -1093,29 +1097,42 @@ should be present in the output at index `Out`. The output is a tensor of rank `output_window_dims.size` + `gather_indices.rank` - `1`. Additionally, as a shorthand, we define `output_gather_dims` of type `ArraySlice` as the set of dimensions in the output shape but not in -`output_window_dims`, in ascending order. E.g. if the output tensor has rank 5, -`output_window_dims` is {`2`, `4`} then `output_gather_dims` is {`0`, `1`, `3`} +`output_window_dims`, in ascending order. E.g. if the output tensor has rank +`5`, `output_window_dims` is {`2`, `4`} then `output_gather_dims` is {`0`, `1`, +`3`} + +If `index_vector_dim` is equal to `gather_indices.rank` we implicitly +consider `gather_indices` to have a trailing `1` dimension (i.e. if +`gather_indices` was of shape `[6,7]` and `index_vector_dim` is `2` then +we implicitly consider the shape of `gather_indices` to be `[6,7,1]`). The bounds for the output tensor along dimension `i` is computed as follows: 1. If `i` is present in `output_gather_dims` (i.e. is equal to - `output_gather_dims[k]` for some `k`) then we pick the corresponding - dimension bounds out of `gather_indices.shape` (i.e. pick - `gather_indices.shape.dims[k]`). + `output_gather_dims[k]` for some `k`) then we pick the corresponding + dimension bounds out of `gather_indices.shape`, skipping + `index_vector_dim` (i.e. pick `gather_indices.shape.dims`[`k`] if `k` + < `index_vector_dim` and `gather_indices.shape.dims`[`k`+`1`] + otherwise). 2. If `i` is present in `output_window_dims` (i.e. equal to - `output_window_dims[k]` for some `k`) then we pick the corresponding bound - out of `window_bounds` after accounting for `elided_window_dims` (i.e. we - pick `adjusted_window_bounds[k]` where `adjusted_window_bounds` is - `window_bounds` with the bounds at indices `elided_window_dims` removed). + `output_window_dims`[`k`] for some `k`) then we pick the corresponding + bound out of `window_bounds` after accounting for `elided_window_dims` + (i.e. we pick `adjusted_window_bounds`[`k`] where `adjusted_window_bounds` + is `window_bounds` with the bounds at indices `elided_window_dims` + removed). The operand index `In` corresponding to an output index `Out` is computed as follows: 1. Let `G` = { `Out`[`k`] for `k` in `output_gather_dims` }. Use `G` to slice - out vector `S` such that `S`[`i`] = `gather_indices`[`G`, `i`]. - 2. Create an index, `S``in`, into `operand` using `S` by scattering - `S` using the `gather_dims_to_operand_dims` map (`S``in` is the - starting indices for _operand slice_ mentioned above.). More precisely: + out vector `S` such that `S`[`i`] = `gather_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` = `gather_indices`. + 2. Create an index, `S``in`, into `operand` using `S` by + scattering `S` using the `gather_dims_to_operand_dims` map + (`S``in` is the starting indices for _operand slice_ mentioned + above). More precisely: 1. `S``in`[`gather_dims_to_operand_dims`[`k`]] = `S`[`k`] if `k` < `gather_dims_to_operand_dims.size`. 2. `S``in`[`_`] = `0` otherwise. @@ -1136,7 +1153,12 @@ follows: `operand.rank` is `6` and `elided_window_dims` is {`0`, `2`} then `window_dims_to_operand_dims` is {`0`→`1`, `1`→`3`, `2`→`4`, `3`→`5`}. -### Informal Description +### Informal Description and Examples + +`index_vector_dim` is set to `gather_indices.rank` - `1` in all of the +examples that follow. More interesting values for `index_vector_dim` +does not change the operation fundamentally, but makes the visual representation +more cumbersome. To get an intuition on how all of the above fits together, let's look at an example that gathers 5 slices of shape `[8,6]` from a `[16,11]` tensor. The diff --git a/tensorflow/docs_src/programmers_guide/datasets.md b/tensorflow/docs_src/programmers_guide/datasets.md index d19200e80cdfe6620789ddd273647660c10b2a60..9ccdbde627e6b2415835f7c0771eca1afa04f7f8 100644 --- a/tensorflow/docs_src/programmers_guide/datasets.md +++ b/tensorflow/docs_src/programmers_guide/datasets.md @@ -18,11 +18,11 @@ The `tf.data` API introduces two new abstractions to TensorFlow: tensors representing the image data and a label. There are two distinct ways to create a dataset: - * Creating a **source** (e.g. `Dataset.from_tensor_slices()`) constructs a + * Creating a **source** (e.g. `Dataset.from_tensor_slices()`) constructs a dataset from one or more `tf.Tensor` objects. - * Applying a **transformation** (e.g. `Dataset.batch()`) constructs a dataset + * Applying a **transformation** (e.g. `Dataset.batch()`) constructs a dataset from one or more `tf.data.Dataset` objects. * A `tf.data.Iterator` provides the main way to extract elements from a @@ -327,6 +327,35 @@ same op/node (created by `Iterator.get_next()`). Therefore, evaluating *any* of these tensors will advance the iterator for all components. A typical consumer of an iterator will include all components in a single expression. +### Saving iterator state + +The @{tf.contrib.data.make_saveable_from_iterator} function creates a +`SaveableObject` from an iterator, which can be used to save and +restore the current state of the iterator (and, effectively, the whole input +pipeline). A saveable object thus created can be added to @{tf.train.Saver} +variables list or the `tf.GraphKeys.SAVEABLE_OBJECTS` collection for saving and +restoring in the same manner as a @{tf.Variable}. Refer to +@{$saved_model$Saving and Restoring} for details on how to save and restore +variables. + +```python +# Create saveable object from iterator. +saveable = tf.contrib.data.make_saveable_from_iterator(iterator) + +# Save the iterator state by adding it to the saveable objects collection. +tf.add_to_collection(tf.GraphKeys.SAVEABLE_OBJECTS, saveable) +saver = tf.train.Saver() + +with tf.Session() as sess: + + if should_checkpoint: + saver.save(path_to_checkpoint) + +# Restore the iterator state. +with tf.Session() as sess: + saver.restore(sess, path_to_checkpoint) +``` + ## Reading input data ### Consuming NumPy arrays diff --git a/tensorflow/docs_src/programmers_guide/debugger.md b/tensorflow/docs_src/programmers_guide/debugger.md index c8fdae6f60c33776b6d9a8c1a33666ce4ddb1cb2..d1399814ee862f5f7ecc3f448d51fb3724fa3447 100644 --- a/tensorflow/docs_src/programmers_guide/debugger.md +++ b/tensorflow/docs_src/programmers_guide/debugger.md @@ -23,8 +23,13 @@ debuggers such as Python's `pdb` due to TensorFlow's computation-graph paradigm. > installed using `pip install .whl`, however curses on Windows > may not work as reliably as curses on Linux or Mac. -This tutorial demonstrates how to use the **tfdbg** command-line interface -(CLI) to debug the appearance of [`nan`s](https://en.wikipedia.org/wiki/NaN) +> NOTE: This guide focuses on the command-line interface (CLI) of tfdbg. For +> guide on how to use the graphical user interface (GUI) of tfdbg, i.e., the +> **TensorBoard Debugger Plugin**, please visit +> [its README](https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/debugger/README.md). + +This tutorial demonstrates how to use the **tfdbg** CLI to debug the appearance +of [`nan`s](https://en.wikipedia.org/wiki/NaN) and [`inf`s](https://en.wikipedia.org/wiki/Infinity), a frequently-encountered type of bug in TensorFlow model development. The following example is for users who use the low-level @@ -454,7 +459,7 @@ accuracy_score = classifier.evaluate(x=test_set.data, [debug_tflearn_iris.py](https://www.tensorflow.org/code/tensorflow/python/debug/examples/debug_tflearn_iris.py), -based on {$tflearn$tf-learn's iris tutorial}, contains a full example of how to +based on [tf-learn's iris tutorial](https://www.tensorflow.org/versions/r1.2/get_started/tflearn), contains a full example of how to use the tfdbg with `Estimator`s. To run this example, do: ```none @@ -748,6 +753,7 @@ There are three possible workarounds or solutions: # For LocalCLIDebugHook hooks = [tf_debug.LocalCLIDebugHook(dump_root="/with/lots/of/space")] ``` + Make sure that the directory pointed to by dump_root is empty or nonexistent. tfdbg cleans up the dump directories before exiting. * Reduce the batch size used during the runs. @@ -806,3 +812,13 @@ sess.run(b) the constant-folding would not occur and `tfdbg` should show the intermediate tensor dumps. + +**Q**: Is there a GUI for tfdbg? + +**A**: Yes, the **TensorBoard Debugger Plugin** is the GUI of tfdbg. + It offers features such as inspection of the computation graph, + real-time visualization of tensor values, continuation to tensor + and conditional breakpoints, and tying tensors to their + graph-construction source code, all in the browser environment. + To get started, please visit + [its README](https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/debugger/README.md). diff --git a/tensorflow/docs_src/programmers_guide/faq.md b/tensorflow/docs_src/programmers_guide/faq.md index 70931f2862de98cb1e934f85919d558a3b36304a..392ac6f7f12532c3efce5bec1917691f55c7bee5 100644 --- a/tensorflow/docs_src/programmers_guide/faq.md +++ b/tensorflow/docs_src/programmers_guide/faq.md @@ -159,8 +159,7 @@ available. These operations allow you to build sophisticated @{$reading_data$input pipelines}, at the cost of making the TensorFlow computation somewhat more complicated. See the how-to documentation for -@{$reading_data#creating-threads-to-prefetch-using-queuerunner-objects$using -`QueueRunner` objects to drive queues and readers} +@{$reading_data#creating_threads_to_prefetch_using_queuerunner_objects$using `QueueRunner` objects to drive queues and readers} for more information on how to use them. ## Variables @@ -273,7 +272,7 @@ Prefer predefined TensorFlow operations such as @{tf.decode_raw}, If your data is not easily parsable with the built-in TensorFlow operations, consider converting it, offline, to a format that is easily parsable, such -as ${tf.python_io.TFRecordWriter$`TFRecord`} format. +as @{tf.python_io.TFRecordWriter$`TFRecord`} format. The more efficient method to customize the parsing behavior is to @{$adding_an_op$add a new op written in C++} that parses your diff --git a/tensorflow/docs_src/programmers_guide/graphs.md b/tensorflow/docs_src/programmers_guide/graphs.md index 9049a5a9f3d44e255188c6c41cdb12a619464379..e69b717432e6a8fab0085eb419dcbc0991cd9d28 100644 --- a/tensorflow/docs_src/programmers_guide/graphs.md +++ b/tensorflow/docs_src/programmers_guide/graphs.md @@ -210,9 +210,8 @@ with tf.device("/device:GPU:0"): # Operations created in this context will be pinned to the GPU. result = tf.matmul(weights, img) ``` - -If you are deploying TensorFlow in a @{$deploy/distributed$typical distributed -configuration}, you might specify the job name and task ID to place variables on +If you are deploying TensorFlow in a @{$deploy/distributed$typical distributed configuration}, +you might specify the job name and task ID to place variables on a task in the parameter server job (`"/job:ps"`), and the other operations on task in the worker job (`"/job:worker"`): @@ -336,20 +335,20 @@ described below. controls the behavior of the session. For example, some of the configuration options include: - * `allow_soft_placement`. Set this to `True` to enable a "soft" device + * `allow_soft_placement`. Set this to `True` to enable a "soft" device placement algorithm, which ignores @{tf.device} annotations that attempt to place CPU-only operations on a GPU device, and places them on the CPU instead. - * `cluster_def`. When using distributed TensorFlow, this option allows you + * `cluster_def`. When using distributed TensorFlow, this option allows you to specify what machines to use in the computation, and provide a mapping between job names, task indices, and network addresses. See @{tf.train.ClusterSpec.as_cluster_def} for details. - * `graph_options.optimizer_options`. Provides control over the optimizations + * `graph_options.optimizer_options`. Provides control over the optimizations that TensorFlow performs on your graph before executing it. - * `gpu_options.allow_growth`. Set this to `True` to change the GPU memory + * `gpu_options.allow_growth`. Set this to `True` to change the GPU memory allocator so that it gradually increases the amount of memory allocated, rather than allocating most of the memory at startup. diff --git a/tensorflow/docs_src/programmers_guide/index.md b/tensorflow/docs_src/programmers_guide/index.md index 7a5e90081d9145ca934929f0af11f2a40cb2dcae..e8c2fa6990c8ecfca1cfe76b3f813b4ae6917742 100644 --- a/tensorflow/docs_src/programmers_guide/index.md +++ b/tensorflow/docs_src/programmers_guide/index.md @@ -30,8 +30,12 @@ works. The units are as follows: can still be helpful. * @{$programmers_guide/saved_model}, which explains how to save and restore variables and models. + +## Accelerators + * @{$using_gpu} explains how TensorFlow assigns operations to devices and how you can change the arrangement manually. + * @{$using_tpu} explains how to modify `Estimator` programs to run on a TPU. ## ML Concepts diff --git a/tensorflow/docs_src/programmers_guide/saved_model.md b/tensorflow/docs_src/programmers_guide/saved_model.md index f18d50b282400810b4869f78ba7f536ad5ea4798..d01d187e865ba67db9665565cdcab02f9ad16fe6 100644 --- a/tensorflow/docs_src/programmers_guide/saved_model.md +++ b/tensorflow/docs_src/programmers_guide/saved_model.md @@ -1,38 +1,33 @@ -# Saving and Restoring +# Save and Restore -This document explains how to save and restore -@{$variables$variables} and models. +The @{tf.train.Saver} class provides methods to save and restore models. The +@{tf.saved_model.simple_save} function is an easy way to build a +@{tf.saved_model$saved model} suitable for serving. +[Estimators](/programmers_guide/estimators) automatically save and restore +variables in the `model_dir`. -Important: TensorFlow model files are code. Be careful with untrusted code. -See [Using TensorFlow Securely](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/SECURITY.md) -for details. - -## Saving and restoring variables - -A TensorFlow variable provides the best way to represent shared, persistent -state manipulated by your program. (See @{$variables$Variables} for details.) -This section explains how to save and restore variables. -Note that Estimators automatically saves and restores variables -(in the `model_dir`). +## Save and restore variables -The `tf.train.Saver` class provides methods for saving and restoring models. -The `tf.train.Saver` constructor adds `save` and `restore` ops to the graph -for all, or a specified list, of the variables in the graph. The `Saver` -object provides methods to run these ops, specifying paths for the checkpoint -files to write to or read from. +TensorFlow @{$variables} are the best way to represent shared, persistent state +manipulated by your program. The `tf.train.Saver` constructor adds `save` and +`restore` ops to the graph for all, or a specified list, of the variables in the +graph. The `Saver` object provides methods to run these ops, specifying paths +for the checkpoint files to write to or read from. -The saver will restore all variables already defined in your model. If you're +`Saver` restores all variables already defined in your model. If you're loading a model without knowing how to build its graph (for example, if you're writing a generic program to load models), then read the [Overview of saving and restoring models](#models) section later in this document. -TensorFlow saves variables in binary **checkpoint files** that, -roughly speaking, map variable names to tensor values. - +TensorFlow saves variables in binary *checkpoint files* that map variable +names to tensor values. +Caution: TensorFlow model files are code. Be careful with untrusted code. +See [Using TensorFlow Securely](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) +for details. -### Saving variables +### Save variables Create a `Saver` with `tf.train.Saver()` to manage all variables in the model. For example, the following snippet demonstrates how to call the @@ -64,9 +59,7 @@ with tf.Session() as sess: print("Model saved in path: %s" % save_path) ``` - - -### Restoring variables +### Restore variables The `tf.train.Saver` object not only saves variables to checkpoint files, it also restores variables. Note that when you restore variables you do not have @@ -95,14 +88,11 @@ with tf.Session() as sess: print("v2 : %s" % v2.eval()) ``` -Notes: - -* There is not a physical file called "/tmp/model.ckpt". It is the **prefix** - of filenames created for the checkpoint. Users only interact with the - prefix instead of physical checkpoint files. +Note: There is not a physical file called `/tmp/model.ckpt`. It is the *prefix* of +filenames created for the checkpoint. Users only interact with the prefix +instead of physical checkpoint files. - -### Choosing which variables to save and restore +### Choose variables to save and restore If you do not pass any arguments to `tf.train.Saver()`, the saver handles all variables in the graph. Each variable is saved under the name that was passed @@ -201,29 +191,42 @@ chkp.print_tensors_in_checkpoint_file("/tmp/model.ckpt", tensor_name='v2', all_t -## Overview of saving and restoring models +## Save and restore models -When you want to save and load variables, the graph, and the -graph's metadata--basically, when you want to save or restore -your model--we recommend using SavedModel. -**SavedModel** is a language-neutral, recoverable, hermetic -serialization format. SavedModel enables higher-level systems -and tools to produce, consume, and transform TensorFlow models. -TensorFlow provides several mechanisms for interacting with -SavedModel, including tf.saved_model APIs, Estimator APIs and a CLI. +Use `SavedModel` to save and load your model—variables, the graph, and the +graph's metadata. This is a language-neutral, recoverable, hermetic +serialization format that enables higher-level systems and tools to produce, +consume, and transform TensorFlow models. TensorFlow provides several ways to +interact with `SavedModel`, including the @{tf.saved_model} APIs, +@{tf.estimator.Estimator}, and a command-line interface. -## APIs to build and load a SavedModel +## Build and load a SavedModel -This section focuses on the APIs for building and loading a SavedModel, -particularly when using lower-level TensorFlow APIs. +### Simple save +The easiest way to create a `SavedModel` is to use the @{tf.saved_model.simple_save} +function: -### Building a SavedModel +```python +simple_save(session, + export_dir, + inputs={"x": x, "y": y}, + outputs={"z": z}) +``` -We provide a Python implementation of the SavedModel -@{tf.saved_model.builder$builder}. -The `SavedModelBuilder` class provides functionality to +This configures the `SavedModel` so it can be loaded by +[TensorFlow serving](/serving/serving_basic) and supports the +[Predict API](https://github.com/tensorflow/serving/blob/master/tensorflow_serving/apis/predict.proto). +To access the classify, regress, or multi-inference APIs, use the manual +`SavedModel` builder APIs or an @{tf.estimator.Estimator}. + +### Manually build a SavedModel + +If your use case isn't covered by @{tf.saved_model.simple_save}, use the manual +@{tf.saved_model.builder$builder APIs} to create a `SavedModel`. + +The @{tf.saved_model.builder.SavedModelBuilder} class provides functionality to save multiple `MetaGraphDef`s. A **MetaGraph** is a dataflow graph, plus its associated variables, assets, and signatures. A **`MetaGraphDef`** is the protocol buffer representation of a MetaGraph. A **signature** is @@ -253,16 +256,51 @@ with tf.Session(graph=tf.Graph()) as sess: builder.add_meta_graph_and_variables(sess, [tag_constants.TRAINING], signature_def_map=foo_signatures, - assets_collection=foo_assets) + assets_collection=foo_assets, + strip_default_attrs=True) ... # Add a second MetaGraphDef for inference. with tf.Session(graph=tf.Graph()) as sess: ... - builder.add_meta_graph([tag_constants.SERVING]) + builder.add_meta_graph([tag_constants.SERVING], strip_default_attrs=True) ... builder.save() ``` + +#### Forward compatibility via `strip_default_attrs=True` + +Following the guidance below gives you forward compatibility only if the set of +Ops has not changed. + +The @{tf.saved_model.builder.SavedModelBuilder$`SavedModelBuilder`} class allows +users to control whether default-valued attributes must be stripped from the +@{$extend/tool_developers#nodes$`NodeDefs`} +while adding a meta graph to the SavedModel bundle. Both +@{tf.saved_model.builder.SavedModelBuilder.add_meta_graph_and_variables$`SavedModelBuilder.add_meta_graph_and_variables`} +and @{tf.saved_model.builder.SavedModelBuilder.add_meta_graph$`SavedModelBuilder.add_meta_graph`} +methods accept a Boolean flag `strip_default_attrs` that controls this behavior. + +If `strip_default_attrs` is `False`, the exported @{tf.MetaGraphDef} will have +the default valued attributes in all its @{tf.NodeDef} instances. +This can break forward compatibility with a sequence of events such as the +following: + +* An existing Op (`Foo`) is updated to include a new attribute (`T`) with a + default (`bool`) at version 101. +* A model producer such as a "trainer binary" picks up this change (version 101) + to the `OpDef` and re-exports an existing model that uses Op `Foo`. +* A model consumer (such as [Tensorflow Serving](/serving)) running an older + binary (version 100) doesn't have attribute `T` for Op `Foo`, but tries to + import this model. The model consumer doesn't recognize attribute `T` in a + `NodeDef` that uses Op `Foo` and therefore fails to load the model. +* By setting `strip_default_attrs` to True, the model producers can strip away + any default valued attributes in the `NodeDefs`. This helps ensure that newly + added attributes with defaults don't cause older model consumers to fail + loading models regenerated with newer training binaries. + +See [compatibility guidance](https://www.tensorflow.org/programmers_guide/version_compat) +for more information. ### Loading a SavedModel in Python @@ -288,7 +326,7 @@ with tf.Session(graph=tf.Graph()) as sess: ``` -### Loading a SavedModel in C++ +### Load a SavedModel in C++ The C++ version of the SavedModel [loader](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/cc/saved_model/loader.h) @@ -306,7 +344,7 @@ LoadSavedModel(session_options, run_options, export_dir, {kSavedModelTagTrain}, &bundle); ``` -### Loading and Serving a SavedModel in TensorFlow Serving +### Load and serve a SavedModel in TensorFlow serving You can easily load and serve a SavedModel with the TensorFlow Serving Model Server binary. See [instructions](https://www.tensorflow.org/serving/setup#installing_using_apt-get) @@ -374,7 +412,7 @@ SavedModel format. This section explains how to: * Serve the model from a local server and request predictions. -### Preparing serving inputs +### Prepare serving inputs During training, an @{$premade_estimators#input_fn$`input_fn()`} ingests data and prepares it for use by the model. At serving time, similarly, a @@ -448,14 +486,15 @@ to expect and how to map them to your model's expected inputs. By contrast, the *output* portion of the signature is determined by the model. -### Performing the export +### Perform the export To export your trained Estimator, call @{tf.estimator.Estimator.export_savedmodel} with the export base path and the `serving_input_receiver_fn`. ```py -estimator.export_savedmodel(export_dir_base, serving_input_receiver_fn) +estimator.export_savedmodel(export_dir_base, serving_input_receiver_fn, + strip_default_attrs=True) ``` This method builds a new graph by first calling the @@ -471,7 +510,7 @@ Session. > Note: It is your responsibility to garbage-collect old exports. > Otherwise, successive exports will accumulate under `export_dir_base`. -### Specifying the outputs of a custom model +### Specify the outputs of a custom model When writing a custom `model_fn`, you must populate the `export_outputs` element of the @{tf.estimator.EstimatorSpec} return value. This is a dict of @@ -503,7 +542,7 @@ indicating which `SignatureDef` will be served when an inference request does not specify one. -### Serving the exported model locally +### Serve the exported model locally For local deployment, you can serve your model using [TensorFlow Serving](https://github.com/tensorflow/serving), an open-source project that loads a @@ -522,7 +561,7 @@ bazel-bin/tensorflow_serving/model_servers/tensorflow_model_server --port=9000 - Now you have a server listening for inference requests via gRPC on port 9000! -### Requesting predictions from a local server +### Request predictions from a local server The server responds to gRPC requests according to the [PredictionService](https://github.com/tensorflow/serving/blob/master/tensorflow_serving/apis/prediction_service.proto#L15) @@ -615,7 +654,7 @@ passing in sample inputs in various formats (for example, Python expressions) and then fetching the output. -### Installing the SavedModel CLI +### Install the SavedModel CLI Broadly speaking, you can install TensorFlow in either of the following two ways: @@ -697,15 +736,15 @@ executing the computation graph later. For example: $ saved_model_cli show --dir \ /tmp/saved_model_dir --tag_set serve --signature_def serving_default The given SavedModel SignatureDef contains the following input(s): -inputs['x'] tensor_info: - dtype: DT_FLOAT - shape: (-1, 1) - name: x:0 + inputs['x'] tensor_info: + dtype: DT_FLOAT + shape: (-1, 1) + name: x:0 The given SavedModel SignatureDef contains the following output(s): -outputs['y'] tensor_info: - dtype: DT_FLOAT - shape: (-1, 1) - name: y:0 + outputs['y'] tensor_info: + dtype: DT_FLOAT + shape: (-1, 1) + name: y:0 Method name is: tensorflow/serving/predict ``` @@ -717,32 +756,32 @@ $ saved_model_cli show --dir /tmp/saved_model_dir --all MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs: signature_def['classify_x2_to_y3']: -The given SavedModel SignatureDef contains the following input(s): -inputs['inputs'] tensor_info: - dtype: DT_FLOAT - shape: (-1, 1) - name: x2:0 -The given SavedModel SignatureDef contains the following output(s): -outputs['scores'] tensor_info: - dtype: DT_FLOAT - shape: (-1, 1) - name: y3:0 -Method name is: tensorflow/serving/classify + The given SavedModel SignatureDef contains the following input(s): + inputs['inputs'] tensor_info: + dtype: DT_FLOAT + shape: (-1, 1) + name: x2:0 + The given SavedModel SignatureDef contains the following output(s): + outputs['scores'] tensor_info: + dtype: DT_FLOAT + shape: (-1, 1) + name: y3:0 + Method name is: tensorflow/serving/classify ... signature_def['serving_default']: -The given SavedModel SignatureDef contains the following input(s): -inputs['x'] tensor_info: - dtype: DT_FLOAT - shape: (-1, 1) - name: x:0 -The given SavedModel SignatureDef contains the following output(s): -outputs['y'] tensor_info: - dtype: DT_FLOAT - shape: (-1, 1) - name: y:0 -Method name is: tensorflow/serving/predict + The given SavedModel SignatureDef contains the following input(s): + inputs['x'] tensor_info: + dtype: DT_FLOAT + shape: (-1, 1) + name: x:0 + The given SavedModel SignatureDef contains the following output(s): + outputs['y'] tensor_info: + dtype: DT_FLOAT + shape: (-1, 1) + name: y:0 + Method name is: tensorflow/serving/predict ``` @@ -842,7 +881,7 @@ For example: `=[{"age":[22,24],"education":["BS","MS"]}]` ``` -#### Save Output +#### Save output By default, the SavedModel CLI writes output to stdout. If a directory is passed to `--outdir` option, the outputs will be saved as npy files named after @@ -851,7 +890,7 @@ output tensor keys under the given directory. Use `--overwrite` to overwrite existing output files. -#### TensorFlow Debugger (tfdbg) Integration +#### TensorFlow debugger (tfdbg) integration If `--tf_debug` option is set, the SavedModel CLI will use the TensorFlow Debugger (tfdbg) to watch the intermediate Tensors and runtime @@ -958,6 +997,3 @@ of checkpoints and assets: Each graph is associated with a specific set of tags, which enables identification during a load or restore operation. - - - diff --git a/tensorflow/docs_src/programmers_guide/summaries_and_tensorboard.md b/tensorflow/docs_src/programmers_guide/summaries_and_tensorboard.md index 05dfdfdc4d2257fc680e7fa99b666ef86e3bef09..fadfa03e78349801d69e0045991a8fa9a0a59df9 100644 --- a/tensorflow/docs_src/programmers_guide/summaries_and_tensorboard.md +++ b/tensorflow/docs_src/programmers_guide/summaries_and_tensorboard.md @@ -16,10 +16,17 @@ TensorBoard is fully configured, it looks like this: -This tutorial is intended to get you started with simple TensorBoard usage. -There are other resources available as well! The [TensorBoard's GitHub](https://github.com/tensorflow/tensorboard) -has a lot more information on TensorBoard usage, including tips & tricks, and -debugging information. +This 30-minute tutorial is intended to get you started with simple TensorBoard +usage. It assumes a basic understanding of TensorFlow. + +There are other resources available as well! The [TensorBoard GitHub](https://github.com/tensorflow/tensorboard) +has a lot more information on using individual dashboards within TensorBoard +including tips & tricks and debugging information. + +## Setup + +[Install TensorFlow](https://www.tensorflow.org/install/). Installing TensorFlow +via pip should also automatically install TensorBoard. ## Serializing the data @@ -76,7 +83,7 @@ data than you need, though. Instead, consider running the merged summary op every `n` steps. The code example below is a modification of the -@{$layers$simple MNIST tutorial}, +[simple MNIST tutorial](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/mnist/mnist.py), in which we have added some summary ops, and run them every ten steps. If you run this and then launch `tensorboard --logdir=/tmp/tensorflow/mnist`, you'll be able to visualize statistics, such as how the weights or accuracy varied during @@ -214,4 +221,5 @@ corner. Each tab represents a set of serialized data that can be visualized. For in depth information on how to use the *graph* tab to visualize your graph, see @{$graph_viz$TensorBoard: Graph Visualization}. -For more usage information on TensorBoard in general, see the [TensorBoard's GitHub](https://github.com/tensorflow/tensorboard). +For more usage information on TensorBoard in general, see the +[TensorBoard GitHub](https://github.com/tensorflow/tensorboard). diff --git a/tensorflow/docs_src/programmers_guide/using_tpu.md b/tensorflow/docs_src/programmers_guide/using_tpu.md index d74d7f3181c9cf44e6c97e13742db682858f4694..a9c2cb3e33d4817b9a35400dcce9227ddd635ff4 100644 --- a/tensorflow/docs_src/programmers_guide/using_tpu.md +++ b/tensorflow/docs_src/programmers_guide/using_tpu.md @@ -129,10 +129,9 @@ my_tpu_estimator = tf.contrib.tpu.TPUEstimator( Typically the `FLAGS` would be set by command line arguments. To switch from training locally to training on a cloud TPU you would need to: - 1) Set `FLAGS.use_tpu` to `True` - 1) Set `FLAGS.tpu_name` so the - `tf.contrib.cluster_resolver.TPUClusterResolver` can find it - 1) Set `FLAGS.model_dir` to a Google Cloud Storage bucket url (`gs://`). +* Set `FLAGS.use_tpu` to `True` +* Set `FLAGS.tpu_name` so the `tf.contrib.cluster_resolver.TPUClusterResolver` can find it +* Set `FLAGS.model_dir` to a Google Cloud Storage bucket url (`gs://`). ## Optimizer diff --git a/tensorflow/docs_src/programmers_guide/version_compat.md b/tensorflow/docs_src/programmers_guide/version_compat.md index e6613cc69f8aedf344fa25b6564889e34cd9bf53..72e427c5f8f0f6581d528f4ead18699736eafd04 100644 --- a/tensorflow/docs_src/programmers_guide/version_compat.md +++ b/tensorflow/docs_src/programmers_guide/version_compat.md @@ -183,7 +183,7 @@ Our versioning scheme has three requirements: * **Forward compatibility** to support scenarios where the producer of a graph or checkpoint is upgraded to a newer version of TensorFlow before the consumer. -* Enable evolving TensorFlow in incompatible ways. For example, removing Ops, +* Enable evolving TensorFlow in incompatible ways. For example, removing ops, adding attributes, and removing attributes. Note that while the `GraphDef` version mechanism is separate from the TensorFlow @@ -245,32 +245,51 @@ contains a main data version which is treated as either `producer` or `TF_CHECKPOINT_VERSION_MIN_CONSUMER`, and `TF_CHECKPOINT_VERSION_MIN_PRODUCER`. +### Add a new attribute with default to an existing op + +Following the guidance below gives you forward compatibility only if the set of +ops has not changed: + +1. If forward compatibility is desired, set `strip_default_attrs` to `True` + while exporting the model using either the + @{tf.saved_model.builder.SavedModelBuilder.add_meta_graph_and_variables$`add_meta_graph_and_variables`} + and @{tf.saved_model.builder.SavedModelBuilder.add_meta_graph$`add_meta_graph`} + methods of the `SavedModelBuilder` class, or + @{tf.estimator.Estimator.export_savedmodel$`Estimator.export_savedmodel`} +2. This strips off the default valued attributes at the time of + producing/exporting the models. This makes sure that the exported + @{tf.MetaGraphDef} does not contain the new op-attribute when the default + value is used. +3. Having this control could allow out-of-date consumers (for example, serving + binaries that lag behind training binaries) to continue loading the models + and prevent interruptions in model serving. + ### Evolving GraphDef versions This section explains how to use this versioning mechanism to make different types of changes to the `GraphDef` format. -#### Add an Op +#### Add an op -Add the new Op to both consumers and producers at the same time, and do not +Add the new op to both consumers and producers at the same time, and do not change any `GraphDef` versions. This type of change is automatically backward compatible, and does not impact forward compatibility plan since existing producer scripts will not suddenly use the new functionality. -#### Add an Op and switch existing Python wrappers to use it +#### Add an op and switch existing Python wrappers to use it 1. Implement new consumer functionality and increment the `GraphDef` version. 2. If it is possible to make the wrappers use the new functionality only in cases that did not work before, the wrappers can be updated now. 3. Change Python wrappers to use the new functionality. Do not increment - `min_consumer`, since models that do not use this Op should not break. + `min_consumer`, since models that do not use this op should not break. -#### Remove or restrict an Op's functionality +#### Remove or restrict an op's functionality -1. Fix all producer scripts (not TensorFlow itself) to not use the banned Op or +1. Fix all producer scripts (not TensorFlow itself) to not use the banned op or functionality. 2. Increment the `GraphDef` version and implement new consumer functionality - that bans the removed Op or functionality for GraphDefs at the new version + that bans the removed op or functionality for GraphDefs at the new version and above. If possible, make TensorFlow stop producing `GraphDefs` with the banned functionality. To do so, add the [`REGISTER_OP(...).Deprecated(deprecated_at_version, @@ -279,15 +298,15 @@ existing producer scripts will not suddenly use the new functionality. 4. Increase `min_producer` to the GraphDef version from (2) and remove the functionality entirely. -#### Change an Op's functionality +#### Change an op's functionality -1. Add a new similar Op named `SomethingV2` or similar and go through the +1. Add a new similar op named `SomethingV2` or similar and go through the process of adding it and switching existing Python wrappers to use it, which may take three weeks if forward compatibility is desired. -2. Remove the old Op (Can only take place with a major version change due to +2. Remove the old op (Can only take place with a major version change due to backward compatibility). -3. Increase `min_consumer` to rule out consumers with the old Op, add back the - old Op as an alias for `SomethingV2`, and go through the process to switch +3. Increase `min_consumer` to rule out consumers with the old op, add back the + old op as an alias for `SomethingV2`, and go through the process to switch existing Python wrappers to use it. 4. Go through the process to remove `SomethingV2`. @@ -295,6 +314,6 @@ existing producer scripts will not suddenly use the new functionality. 1. Bump the `GraphDef` version and add the bad version to `bad_consumers` for all new GraphDefs. If possible, add to `bad_consumers` only for GraphDefs - which contain a certain Op or similar. + which contain a certain op or similar. 2. If existing consumers have the bad version, push them out as soon as possible. diff --git a/tensorflow/docs_src/tutorials/image_retraining.md b/tensorflow/docs_src/tutorials/image_retraining.md index df15bc0a9c3763aa51c2fc8cf36ce9fc3544ae68..93d7c86e42aa90d145d27b56edc0abfec7034686 100644 --- a/tensorflow/docs_src/tutorials/image_retraining.md +++ b/tensorflow/docs_src/tutorials/image_retraining.md @@ -115,7 +115,7 @@ process is progressing. The training's objective is to make the loss as small as possible, so you can tell if the learning is working by keeping an eye on whether the loss keeps trending downwards, ignoring the short-term noise. -By default this script will run 4,000 training steps. Each step chooses ten +By default this script will run 4,000 training steps. Each step chooses 100 images at random from the training set, finds their bottlenecks from the cache, and feeds them into the final layer to get predictions. Those predictions are then compared against the actual labels to update the final layer's weights @@ -349,31 +349,32 @@ results, but if you intend to deploy your model on mobile devices or other resource-constrained environments you may want to trade off a little accuracy for much smaller file sizes or faster speeds. To help with that, the [retrain.py script](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/image_retraining/retrain.py) -supports 32 different variations on the [Mobilenet architecture](https://research.googleblog.com/2017/06/mobilenets-open-source-models-for.html). +supports different variations on the [Mobilenet architecture](https://research.googleblog.com/2017/06/mobilenets-open-source-models-for.html). These are a little less precise than Inception v3, but can result in far -smaller file sizes (down to less than a megabyte) and can be many times faster +smaller file sizes (a few megabytes) and can be many times faster to run. To train with one of these models, pass in the `--architecture` flag, for example: ``` python tensorflow/examples/image_retraining/retrain.py \ - --image_dir ~/flower_photos --architecture mobilenet_0.25_128_quantized + --image_dir ~/flower_photos --architecture mobilenet_0.25_128 ``` -This will create a 941KB model file in `/tmp/output_graph.pb`, with 25% of the -parameters of the full Mobilenet, taking 128x128 sized input images, and with -its weights quantized down to eight bits on disk. You can choose '1.0', '0.75', -'0.50', or '0.25' to control the number of weight parameters, and so the file -size (and to some extent the speed), '224', '192', '160', or '128' for the input -image size, with smaller sizes giving faster speeds, and an optional -'_quantized' at the end to indicate whether the file should contain 8-bit or -32-bit float weights. +This will create a 1.9MB model file in `/tmp/output_graph.pb`, with only 25% of +the number of neurons of the full Mobilenet, and trained to take 128x128 sized +input images. + +You can choose '1.0', '0.75', '0.50', or '0.25' to control the number of +neurons (activations of hidden layers); the number of weights (and hence to +some extent the file size and speed) shrinks like the square of that fraction. +You can choose '224', '192', '160', or '128' for the input image size, +with smaller sizes giving faster speeds. The speed and size advantages come at a loss to accuracy of course, but for many purposes this isn't critical. They can also be somewhat offset with improved training data. For example, training with distortions allows me to get above 80% -accuracy on the flower data set even with the 0.25/128/quantized graph above. +accuracy on the flower data set even with the 0.25/128 graph above. If you're going to be using the Mobilenet models in label_image or your own programs, you'll need to feed in an image of the specified size converted to a @@ -395,3 +396,9 @@ python tensorflow/examples/label_image/label_image.py \ --input_mean=128 --input_std=128 \ --image=$HOME/flower_photos/daisy/21652746_cc379e0eea_m.jpg ``` + +For more information on deploying the retrained model to a mobile device, see +the [codelab version](https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/#0) +of this tutorial, especially [part 2](https://codelabs.developers.google.com/codelabs/tensorflow-for-poets-2-tflite/#0), which describes +[TensorFlow Lite](/mobile/tflite/) and the additional optimizations it offers +(including quantization of model weights). diff --git a/tensorflow/docs_src/tutorials/index.md b/tensorflow/docs_src/tutorials/index.md index 8c697e48e550c4e425db33bab7257532d209ac7a..af01d3eaa12157f82c981de005708509f6652cca 100644 --- a/tensorflow/docs_src/tutorials/index.md +++ b/tensorflow/docs_src/tutorials/index.md @@ -10,7 +10,7 @@ these tutorials. These tutorials cover different aspects of image recognition: - * @{$layers}, which introduces convolutional neural networks (CNNs) and + * @{$layers$MNIST}, which introduces convolutional neural networks (CNNs) and demonstrates how to build a CNN in TensorFlow. * @{$image_recognition}, which introduces the field of image recognition and uses a pre-trained model (Inception) for recognizing images. diff --git a/tensorflow/docs_src/tutorials/kernel_methods.md b/tensorflow/docs_src/tutorials/kernel_methods.md index 63f408c2ca304d6345ffff459b799b011f8d8035..b1f06ce0a3b35a79b60f18dfe8a40dda0c732f07 100644 --- a/tensorflow/docs_src/tutorials/kernel_methods.md +++ b/tensorflow/docs_src/tutorials/kernel_methods.md @@ -1,9 +1,9 @@ # Improving Linear Models Using Explicit Kernel Methods -Note: This document uses a deprecated version of ${tf.estimator}, -which has a ${tf.contrib.learn.estimator$different interface}. +Note: This document uses a deprecated version of @{tf.estimator}, +which has a different interface (see `tf.contrib.learn Estimator`). It also uses other `contrib` methods whose -${$version_compat#not_covered$API may not be stable}. +@{$version_compat#not_covered$API may not be stable}. In this tutorial, we demonstrate how combining (explicit) kernel methods with linear models can drastically increase the latters' quality of predictions diff --git a/tensorflow/docs_src/tutorials/layers.md b/tensorflow/docs_src/tutorials/layers.md index 5111b16247e2b5c3410e69dcdf08318a35b18c2f..9b17d0d4d52e6c5aa9b00b2e50840fa8cd4e8cb6 100644 --- a/tensorflow/docs_src/tutorials/layers.md +++ b/tensorflow/docs_src/tutorials/layers.md @@ -193,7 +193,7 @@ to calculate loss, configure the training op, and generate predictions. If you're already experienced with CNNs and @{$get_started/custom_estimators$TensorFlow `Estimator`s}, and find the above code intuitive, you may want to skim these sections or just skip ahead to ["Training and Evaluating the CNN MNIST -Classifier"](#training-and-evaluating-the-cnn-mnist-classifier). +Classifier"](#training_and_evaluating_the_cnn_mnist_classifier). ### Input Layer @@ -446,7 +446,7 @@ tf.nn.softmax(logits, name="softmax_tensor") > Note: We use the `name` argument to explicitly name this operation > `softmax_tensor`, so we can reference it later. (We'll set up logging for the -> softmax values in ["Set Up a Logging Hook"](#set-up-a-logging-hook). +> softmax values in ["Set Up a Logging Hook"](#set-up-a-logging-hook)). We compile our predictions in a dict, and return an `EstimatorSpec` object: @@ -534,9 +534,8 @@ if mode == tf.estimator.ModeKeys.TRAIN: ``` > Note: For a more in-depth look at configuring training ops for Estimator model -> functions, see @{$get_started/custom_estimators#defining-the-training-op-for-the-model$"Defining -> the training op for the model"} in the @{$get_started/custom_estimators$"Creating Estimations in -> tf.estimator"} tutorial. +> functions, see @{$get_started/custom_estimators#defining_the_training_op_for_the_model$"Defining the training op for the model"} +> in the @{$get_started/custom_estimators$"Creating Estimators in tf.estimator."} tutorial. ### Add evaluation metrics @@ -625,8 +624,8 @@ operation earlier when we generated the probabilities in `cnn_model_fn`. > Note: If you don't explicitly assign a name to an operation via the `name` > argument, TensorFlow will assign a default name. A couple easy ways to > discover the names applied to operations are to visualize your graph on -> @{$graph_viz$TensorBoard}) or to enable the @{$debugger$TensorFlow Debugger -> (tfdbg)}. +> @{$graph_viz$TensorBoard}) or to enable the +> @{$programmers_guide/debugger$TensorFlow Debugger (tfdbg)}. Next, we create the `LoggingTensorHook`, passing `tensors_to_log` to the `tensors` argument. We set `every_n_iter=50`, which specifies that probabilities diff --git a/tensorflow/docs_src/tutorials/recurrent_quickdraw.md b/tensorflow/docs_src/tutorials/recurrent_quickdraw.md index e22536adb6f0b893602ff79612cfb01e10586a18..7584a76ba5d3a044377e7050993b6ad778633433 100644 --- a/tensorflow/docs_src/tutorials/recurrent_quickdraw.md +++ b/tensorflow/docs_src/tutorials/recurrent_quickdraw.md @@ -109,7 +109,8 @@ This download will take a while and download a bit more than 23GB of data. To convert the `ndjson` files to @{$python/python_io#tfrecords_format_details$TFRecord} files containing -${tf.train.Example} protos run the following command. +[`tf.train.Example`](https://www.tensorflow.org/code/tensorflow/core/example/example.proto) +protos run the following command. ```shell python create_dataset.py --ndjson_path rnn_tutorial_data \ diff --git a/tensorflow/docs_src/tutorials/wide.md b/tensorflow/docs_src/tutorials/wide.md index 005dc020f94f666da295f4ff0342fae858121012..27ce75a30dd2acd5925702611042270e767b0c73 100644 --- a/tensorflow/docs_src/tutorials/wide.md +++ b/tensorflow/docs_src/tutorials/wide.md @@ -74,8 +74,8 @@ Here's a list of columns available in the Census Income dataset: | relationship | Categorical | Wife, Own-child, Husband, | : : : Not-in-family, Other-relative, : : : : Unmarried. : -| race | Categorical | White, Asian-Pac-Islander, | -: : : Amer-Indian-Eskimo, Other, Black. : +| race | Categorical | Amer-Indian-Eskimo, Asian-Pac- | +: : : Islander, Black, White, Other. : | gender | Categorical | Female, Male. | | capital_gain | Continuous | Capital gains recorded. | | capital_loss | Continuous | Capital Losses recorded. | @@ -247,7 +247,7 @@ hours_per_week = tf.feature_column.numeric_column('hours_per_week') ### Making Continuous Features Categorical through Bucketization Sometimes the relationship between a continuous feature and the label is not -linear. As an hypothetical example, a person's income may grow with age in the +linear. As a hypothetical example, a person's income may grow with age in the early stage of one's career, then the growth may slow at some point, and finally the income decreases after retirement. In this scenario, using the raw `age` as a real-valued feature column might not be a good choice because the model can @@ -361,6 +361,16 @@ The first line of the final output should be something like `accuracy: 0.83557522`, which means the accuracy is 83.6%. Feel free to try more features and transformations and see if you can do even better! +After the model is evaluated, we can use the model to predict whether an individual has an annual income of over +50,000 dollars given an individual's information input. +```python + pred_iter = model.predict(input_fn=lambda: input_fn(FLAGS.test_data, 1, False, 1)) + for pred in pred_iter: + print(pred['classes']) +``` + +The model prediction output would be like `[b'1']` or `[b'0']` which means whether corresponding individual has an annual income of over 50,000 dollars or not. + If you'd like to see a working end-to-end example, you can download our [example code](https://github.com/tensorflow/models/tree/master/official/wide_deep/wide_deep.py) and set the `model_type` flag to `wide`. diff --git a/tensorflow/examples/android/AndroidManifest.xml b/tensorflow/examples/android/AndroidManifest.xml index bb75431a1f8bab2951299520903aa6e043f8415e..5c47ce6b673e4c9d635b867c1ccdc679f67c6ae5 100644 --- a/tensorflow/examples/android/AndroidManifest.xml +++ b/tensorflow/examples/android/AndroidManifest.xml @@ -40,6 +40,7 @@ + @@ -49,6 +50,7 @@ + @@ -58,6 +60,7 @@ + @@ -67,6 +70,7 @@ + diff --git a/tensorflow/examples/android/src/org/tensorflow/demo/CameraActivity.java b/tensorflow/examples/android/src/org/tensorflow/demo/CameraActivity.java index 8bd4abb154a8f8c74f2195d4acbb99d3d5d498ea..429138abe5338e63d602ef6005e7607d21e1e357 100644 --- a/tensorflow/examples/android/src/org/tensorflow/demo/CameraActivity.java +++ b/tensorflow/examples/android/src/org/tensorflow/demo/CameraActivity.java @@ -351,6 +351,10 @@ public abstract class CameraActivity extends Activity protected void setFragment() { String cameraId = chooseCamera(); + if (cameraId == null) { + Toast.makeText(this, "No Camera Detected", Toast.LENGTH_SHORT).show(); + finish(); + } Fragment fragment; if (useCamera2API) { @@ -416,7 +420,8 @@ public abstract class CameraActivity extends Activity @Override public boolean onKeyDown(final int keyCode, final KeyEvent event) { - if (keyCode == KeyEvent.KEYCODE_VOLUME_DOWN || keyCode == KeyEvent.KEYCODE_VOLUME_UP) { + if (keyCode == KeyEvent.KEYCODE_VOLUME_DOWN || keyCode == KeyEvent.KEYCODE_VOLUME_UP + || keyCode == KeyEvent.KEYCODE_BUTTON_L1 || keyCode == KeyEvent.KEYCODE_DPAD_CENTER) { debug = !debug; requestRender(); onSetDebug(debug); diff --git a/tensorflow/examples/android/src/org/tensorflow/demo/StylizeActivity.java b/tensorflow/examples/android/src/org/tensorflow/demo/StylizeActivity.java index 6a66ec3927be62f1f996eb18bb6c04ea66f43152..33ec65e9f73a1d04bcafdc09d1618b32e03b1dc0 100644 --- a/tensorflow/examples/android/src/org/tensorflow/demo/StylizeActivity.java +++ b/tensorflow/examples/android/src/org/tensorflow/demo/StylizeActivity.java @@ -16,8 +16,10 @@ package org.tensorflow.demo; +import android.app.UiModeManager; import android.content.Context; import android.content.res.AssetManager; +import android.content.res.Configuration; import android.graphics.Bitmap; import android.graphics.Bitmap.Config; import android.graphics.BitmapFactory; @@ -31,9 +33,11 @@ import android.graphics.Typeface; import android.media.ImageReader.OnImageAvailableListener; import android.os.Bundle; import android.os.SystemClock; +import android.util.DisplayMetrics; import android.util.Size; import android.util.TypedValue; import android.view.Display; +import android.view.KeyEvent; import android.view.MotionEvent; import android.view.View; import android.view.View.OnClickListener; @@ -43,6 +47,7 @@ import android.widget.BaseAdapter; import android.widget.Button; import android.widget.GridView; import android.widget.ImageView; +import android.widget.RelativeLayout; import android.widget.Toast; import java.io.IOException; import java.io.InputStream; @@ -381,6 +386,27 @@ public class StylizeActivity extends CameraActivity implements OnImageAvailableL grid = (GridView) findViewById(R.id.grid_layout); grid.setAdapter(adapter); grid.setOnTouchListener(gridTouchAdapter); + + // Change UI on Android TV + UiModeManager uiModeManager = (UiModeManager) getSystemService(UI_MODE_SERVICE); + if (uiModeManager.getCurrentModeType() == Configuration.UI_MODE_TYPE_TELEVISION) { + DisplayMetrics displayMetrics = new DisplayMetrics(); + getWindowManager().getDefaultDisplay().getMetrics(displayMetrics); + int styleSelectorHeight = displayMetrics.heightPixels; + int styleSelectorWidth = displayMetrics.widthPixels - styleSelectorHeight; + RelativeLayout.LayoutParams layoutParams = new RelativeLayout.LayoutParams(styleSelectorWidth, ViewGroup.LayoutParams.MATCH_PARENT); + + // Calculate number of style in a row, so all the style can show up without scrolling + int numOfStylePerRow = 3; + while (styleSelectorWidth / numOfStylePerRow * Math.ceil((float) (adapter.getCount() - 2) / numOfStylePerRow) > styleSelectorHeight) { + numOfStylePerRow++; + } + grid.setNumColumns(numOfStylePerRow); + layoutParams.addRule(RelativeLayout.ALIGN_PARENT_RIGHT); + grid.setLayoutParams(layoutParams); + adapter.buttons.clear(); + } + setStyle(adapter.items[0], 1.0f); } @@ -602,4 +628,38 @@ public class StylizeActivity extends CameraActivity implements OnImageAvailableL borderedText.drawLines(canvas, 10, canvas.getHeight() - 10, lines); } + + @Override + public boolean onKeyDown(int keyCode, KeyEvent event) { + int moveOffset = 0; + switch (keyCode) { + case KeyEvent.KEYCODE_DPAD_LEFT: + moveOffset = -1; + break; + case KeyEvent.KEYCODE_DPAD_RIGHT: + moveOffset = 1; + break; + case KeyEvent.KEYCODE_DPAD_UP: + moveOffset = -1 * grid.getNumColumns(); + break; + case KeyEvent.KEYCODE_DPAD_DOWN: + moveOffset = grid.getNumColumns(); + break; + default: + return super.onKeyDown(keyCode, event); + } + + // get the highest selected style + int currentSelect = 0; + float highestValue = 0; + for (int i = 0; i < adapter.getCount(); i++) { + if (adapter.items[i].value > highestValue) { + currentSelect = i; + highestValue = adapter.items[i].value; + } + } + setStyle(adapter.items[(currentSelect + moveOffset + adapter.getCount()) % adapter.getCount()], 1); + + return true; + } } diff --git a/tensorflow/examples/image_retraining/retrain.py b/tensorflow/examples/image_retraining/retrain.py index 25e09fecbfd093e97899807b82a03f1116dbe5ff..99a71206acbd533ec8bc5a9644435eacad564cd4 100644 --- a/tensorflow/examples/image_retraining/retrain.py +++ b/tensorflow/examples/image_retraining/retrain.py @@ -75,13 +75,16 @@ python tensorflow/examples/image_retraining/retrain.py \ --image_dir ~/flower_photos --architecture mobilenet_1.0_224 ``` -Run quantized version of mobilenet: +Run mobilenet, instrumented for quantization: ```bash python tensorflow/examples/image_retraining/retrain.py \ - --image_dir ~/flower_photos/ --architecture mobilenet_1.0_224_quantized + --image_dir ~/flower_photos/ --architecture mobilenet_1.0_224_quant ``` +These instrumented models can be converted to fully quantized mobile models via +TensorFlow Lite. + There are 32 different Mobilenet models to choose from, with a variety of file size and latency options. The first number can be '1.0', '0.75', '0.50', or '0.25' to control the size, and the second controls the input image size, either @@ -121,7 +124,6 @@ import numpy as np from six.moves import urllib import tensorflow as tf -from tensorflow.contrib.quantize.python import quant_ops from tensorflow.python.framework import graph_util from tensorflow.python.framework import tensor_shape from tensorflow.python.platform import gfile @@ -135,6 +137,9 @@ FLAGS = None # need to update these to reflect the values in the network you're using. MAX_NUM_IMAGES_PER_CLASS = 2 ** 27 - 1 # ~134M +# The location where variable checkpoints will be stored. +CHECKPOINT_NAME = '/tmp/_retrain_checkpoint' + def create_image_lists(image_dir, testing_percentage, validation_percentage): """Builds a list of training images from the file system. @@ -745,9 +750,9 @@ def variable_summaries(var): tf.summary.histogram('histogram', var) -def add_final_training_ops(class_count, final_tensor_name, bottleneck_tensor, - bottleneck_tensor_size, quantize_layer): - """Adds a new softmax and fully-connected layer for training. +def add_final_retrain_ops(class_count, final_tensor_name, bottleneck_tensor, + bottleneck_tensor_size, quantize_layer, is_training): + """Adds a new softmax and fully-connected layer for training and eval. We need to retrain the top layer to identify our new classes, so this function adds the right operations to the graph, along with some variables to hold the @@ -763,7 +768,9 @@ def add_final_training_ops(class_count, final_tensor_name, bottleneck_tensor, bottleneck_tensor: The output of the main CNN graph. bottleneck_tensor_size: How many entries in the bottleneck vector. quantize_layer: Boolean, specifying whether the newly added layer should be - quantized. + instrumented for quantized. + is_training: Boolean, specifying whether the newly add layer is for training + or eval. Returns: The tensors for the training and cross entropy results, and tensors for the @@ -778,50 +785,41 @@ def add_final_training_ops(class_count, final_tensor_name, bottleneck_tensor, ground_truth_input = tf.placeholder( tf.int64, [None], name='GroundTruthInput') - # Organizing the following ops as `final_training_ops` so they're easier - # to see in TensorBoard - layer_name = 'final_training_ops' + # Organizing the following ops so they are easier to see in TensorBoard. + layer_name = 'final_retrain_ops' with tf.name_scope(layer_name): with tf.name_scope('weights'): initial_value = tf.truncated_normal( [bottleneck_tensor_size, class_count], stddev=0.001) layer_weights = tf.Variable(initial_value, name='final_weights') - if quantize_layer: - quantized_layer_weights = quant_ops.MovingAvgQuantize( - layer_weights, is_training=True) - variable_summaries(quantized_layer_weights) - variable_summaries(layer_weights) + with tf.name_scope('biases'): layer_biases = tf.Variable(tf.zeros([class_count]), name='final_biases') - if quantize_layer: - quantized_layer_biases = quant_ops.MovingAvgQuantize( - layer_biases, is_training=True) - variable_summaries(quantized_layer_biases) - variable_summaries(layer_biases) with tf.name_scope('Wx_plus_b'): - if quantize_layer: - logits = tf.matmul(bottleneck_input, - quantized_layer_weights) + quantized_layer_biases - logits = quant_ops.MovingAvgQuantize( - logits, - init_min=-32.0, - init_max=32.0, - is_training=True, - num_bits=8, - narrow_range=False, - ema_decay=0.5) - tf.summary.histogram('pre_activations', logits) - else: - logits = tf.matmul(bottleneck_input, layer_weights) + layer_biases - tf.summary.histogram('pre_activations', logits) + logits = tf.matmul(bottleneck_input, layer_weights) + layer_biases + tf.summary.histogram('pre_activations', logits) final_tensor = tf.nn.softmax(logits, name=final_tensor_name) + # The tf.contrib.quantize functions rewrite the graph in place for + # quantization. The imported model graph has already been rewritten, so upon + # calling these rewrites, only the newly added final layer will be + # transformed. + if quantize_layer: + if is_training: + tf.contrib.quantize.create_training_graph() + else: + tf.contrib.quantize.create_eval_graph() + tf.summary.histogram('activations', final_tensor) + # If this is an eval graph, we don't need to add loss ops or an optimizer. + if not is_training: + return None, None, bottleneck_input, ground_truth_input, final_tensor + with tf.name_scope('cross_entropy'): cross_entropy_mean = tf.losses.sparse_softmax_cross_entropy( labels=ground_truth_input, logits=logits) @@ -857,13 +855,91 @@ def add_evaluation_step(result_tensor, ground_truth_tensor): return evaluation_step, prediction -def save_graph_to_file(sess, graph, graph_file_name): +def run_final_eval(sess, model_info, class_count, image_lists, jpeg_data_tensor, + decoded_image_tensor, resized_image_tensor, + bottleneck_tensor): + """Runs a final evaluation on an eval graph using the test data set. + + Args: + sess: Session for the train graph. + model_info: Model info dictionary from create_model_info() + class_count: Number of classes + image_lists: Dictionary of training images for each label. + jpeg_data_tensor: The layer to feed jpeg image data into. + decoded_image_tensor: The output of decoding and resizing the image. + resized_image_tensor: The input node of the recognition graph. + bottleneck_tensor: The bottleneck output layer of the CNN graph. + """ + (sess, bottleneck_input, ground_truth_input, evaluation_step, + prediction) = build_eval_session(model_info, class_count) + + test_bottlenecks, test_ground_truth, test_filenames = ( + get_random_cached_bottlenecks(sess, image_lists, FLAGS.test_batch_size, + 'testing', FLAGS.bottleneck_dir, + FLAGS.image_dir, jpeg_data_tensor, + decoded_image_tensor, resized_image_tensor, + bottleneck_tensor, FLAGS.architecture)) + test_accuracy, predictions = sess.run( + [evaluation_step, prediction], + feed_dict={ + bottleneck_input: test_bottlenecks, + ground_truth_input: test_ground_truth + }) + tf.logging.info('Final test accuracy = %.1f%% (N=%d)' % + (test_accuracy * 100, len(test_bottlenecks))) + + if FLAGS.print_misclassified_test_images: + tf.logging.info('=== MISCLASSIFIED TEST IMAGES ===') + for i, test_filename in enumerate(test_filenames): + if predictions[i] != test_ground_truth[i]: + tf.logging.info('%70s %s' % (test_filename, + list(image_lists.keys())[predictions[i]])) + + +def build_eval_session(model_info, class_count): + """Builds an restored eval session without train operations for exporting. + + Args: + model_info: Model info dictionary from create_model_info() + class_count: Number of classes + + Returns: + Eval session containing the restored eval graph. + The bottleneck input, ground truth, eval step, and prediction tensors. + """ + # If quantized, we need to create the correct eval graph for exporting. + eval_graph, bottleneck_tensor, _ = create_model_graph(model_info) + + eval_sess = tf.Session(graph=eval_graph) + with eval_graph.as_default(): + # Add the new layer for exporting. + (_, _, bottleneck_input, + ground_truth_input, final_tensor) = add_final_retrain_ops( + class_count, FLAGS.final_tensor_name, bottleneck_tensor, + model_info['bottleneck_tensor_size'], model_info['quantize_layer'], + False) + + # Now we need to restore the values from the training graph to the eval + # graph. + tf.train.Saver().restore(eval_sess, CHECKPOINT_NAME) + + evaluation_step, prediction = add_evaluation_step(final_tensor, + ground_truth_input) + + return (eval_sess, bottleneck_input, ground_truth_input, evaluation_step, + prediction) + + +def save_graph_to_file(graph, graph_file_name, model_info, class_count): + """Saves an graph to file, creating a valid quantized one if necessary.""" + sess, _, _, _, _ = build_eval_session(model_info, class_count) + graph = sess.graph + output_graph_def = graph_util.convert_variables_to_constants( sess, graph.as_graph_def(), [FLAGS.final_tensor_name]) with gfile.FastGFile(graph_file_name, 'wb') as f: f.write(output_graph_def.SerializeToString()) - return def prepare_file_system(): @@ -916,11 +992,10 @@ def create_model_info(architecture): return None version_string = parts[1] if (version_string != '1.0' and version_string != '0.75' and - version_string != '0.50' and version_string != '0.25'): + version_string != '0.5' and version_string != '0.25'): tf.logging.error( - """"The Mobilenet version should be '1.0', '0.75', '0.50', or '0.25', - but found '%s' for architecture '%s'""", - version_string, architecture) + """"The Mobilenet version should be '1.0', '0.75', '0.5', or '0.25', + but found '%s' for architecture '%s'""", version_string, architecture) return None size_string = parts[2] if (size_string != '224' and size_string != '192' and @@ -933,35 +1008,26 @@ def create_model_info(architecture): if len(parts) == 3: is_quantized = False else: - if parts[3] != 'quantized': + if parts[3] != 'quant': tf.logging.error( "Couldn't understand architecture suffix '%s' for '%s'", parts[3], architecture) return None is_quantized = True + data_url = 'http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/' + model_name = 'mobilenet_v1_' + version_string + '_' + size_string if is_quantized: - data_url = 'http://download.tensorflow.org/models/mobilenet_v1_' - data_url += version_string + '_' + size_string + '_quantized_frozen.tgz' - bottleneck_tensor_name = 'MobilenetV1/Predictions/Reshape:0' - resized_input_tensor_name = 'Placeholder:0' - model_dir_name = ('mobilenet_v1_' + version_string + '_' + size_string + - '_quantized_frozen') - model_base_name = 'quantized_frozen_graph.pb' - - else: - data_url = 'http://download.tensorflow.org/models/mobilenet_v1_' - data_url += version_string + '_' + size_string + '_frozen.tgz' - bottleneck_tensor_name = 'MobilenetV1/Predictions/Reshape:0' - resized_input_tensor_name = 'input:0' - model_dir_name = 'mobilenet_v1_' + version_string + '_' + size_string - model_base_name = 'frozen_graph.pb' + model_name += '_quant' + data_url += model_name + '.tgz' + bottleneck_tensor_name = 'MobilenetV1/Predictions/Reshape:0' + resized_input_tensor_name = 'input:0' + model_file_name = model_name + '_frozen.pb' bottleneck_tensor_size = 1001 input_width = int(size_string) input_height = int(size_string) input_depth = 3 - model_file_name = os.path.join(model_dir_name, model_base_name) input_mean = 127.5 input_std = 127.5 else: @@ -1011,43 +1077,45 @@ def add_jpeg_decoding(input_width, input_height, input_depth, input_mean, return jpeg_data, mul_image -def export_model(sess, architecture, saved_model_dir): +def export_model(model_info, class_count, saved_model_dir): """Exports model for serving. Args: - sess: Current active TensorFlow Session. - architecture: Model architecture. + model_info: The modelinfo for the current model. + class_count: The number of classes. saved_model_dir: Directory in which to save exported model and variables. """ - if architecture == 'inception_v3': - input_tensor = 'DecodeJpeg/contents:0' - elif architecture.startswith('mobilenet_'): - input_tensor = 'input:0' - else: - raise ValueError('Unknown architecture', architecture) - in_image = sess.graph.get_tensor_by_name(input_tensor) - inputs = {'image': tf.saved_model.utils.build_tensor_info(in_image)} - - out_classes = sess.graph.get_tensor_by_name('final_result:0') - outputs = {'prediction': tf.saved_model.utils.build_tensor_info(out_classes)} + # The SavedModel should hold the eval graph. + sess, _, _, _, _ = build_eval_session(model_info, class_count) + graph = sess.graph + with graph.as_default(): + input_tensor = model_info['resized_input_tensor_name'] + in_image = sess.graph.get_tensor_by_name(input_tensor) + inputs = {'image': tf.saved_model.utils.build_tensor_info(in_image)} + + out_classes = sess.graph.get_tensor_by_name('final_result:0') + outputs = { + 'prediction': tf.saved_model.utils.build_tensor_info(out_classes) + } - signature = tf.saved_model.signature_def_utils.build_signature_def( - inputs=inputs, - outputs=outputs, - method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME) + signature = tf.saved_model.signature_def_utils.build_signature_def( + inputs=inputs, + outputs=outputs, + method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME) - legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op') + legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op') - # Save out the SavedModel. - builder = tf.saved_model.builder.SavedModelBuilder(saved_model_dir) - builder.add_meta_graph_and_variables( - sess, [tf.saved_model.tag_constants.SERVING], - signature_def_map={ - tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: - signature - }, - legacy_init_op=legacy_init_op) - builder.save() + # Save out the SavedModel. + builder = tf.saved_model.builder.SavedModelBuilder(saved_model_dir) + builder.add_meta_graph_and_variables( + sess, [tf.saved_model.tag_constants.SERVING], + signature_def_map={ + tf.saved_model.signature_constants. + DEFAULT_SERVING_SIGNATURE_DEF_KEY: + signature + }, + legacy_init_op=legacy_init_op) + builder.save() def main(_): @@ -1064,11 +1132,6 @@ def main(_): tf.logging.error('Did not recognize architecture flag') return -1 - # Set up the pre-trained graph. - maybe_download_and_extract(model_info['data_url']) - graph, bottleneck_tensor, resized_image_tensor = ( - create_model_graph(model_info)) - # Look at the folder structure, and create lists of all the images. image_lists = create_image_lists(FLAGS.image_dir, FLAGS.testing_percentage, FLAGS.validation_percentage) @@ -1087,6 +1150,19 @@ def main(_): FLAGS.flip_left_right, FLAGS.random_crop, FLAGS.random_scale, FLAGS.random_brightness) + # Set up the pre-trained graph. + maybe_download_and_extract(model_info['data_url']) + graph, bottleneck_tensor, resized_image_tensor = ( + create_model_graph(model_info)) + + # Add the new layer that we'll be training. + with graph.as_default(): + (train_step, cross_entropy, bottleneck_input, + ground_truth_input, final_tensor) = add_final_retrain_ops( + class_count, FLAGS.final_tensor_name, bottleneck_tensor, + model_info['bottleneck_tensor_size'], model_info['quantize_layer'], + True) + with tf.Session(graph=graph) as sess: # Set up the image decoding sub-graph. jpeg_data_tensor, decoded_image_tensor = add_jpeg_decoding( @@ -1110,15 +1186,8 @@ def main(_): decoded_image_tensor, resized_image_tensor, bottleneck_tensor, FLAGS.architecture) - # Add the new layer that we'll be training. - (train_step, cross_entropy, bottleneck_input, ground_truth_input, - final_tensor) = add_final_training_ops( - len(image_lists.keys()), FLAGS.final_tensor_name, bottleneck_tensor, - model_info['bottleneck_tensor_size'], model_info['quantize_layer']) - # Create the operations we need to evaluate the accuracy of our new layer. - evaluation_step, prediction = add_evaluation_step( - final_tensor, ground_truth_input) + evaluation_step, _ = add_evaluation_step(final_tensor, ground_truth_input) # Merge all the summaries and write them out to the summaries_dir merged = tf.summary.merge_all() @@ -1128,6 +1197,10 @@ def main(_): validation_writer = tf.summary.FileWriter( FLAGS.summaries_dir + '/validation') + # Create a train saver that is used to restore values into an eval graph + # when exporting models. + train_saver = tf.train.Saver() + # Set up all our weights to their initial default values. init = tf.global_variables_initializer() sess.run(init) @@ -1168,6 +1241,9 @@ def main(_): (datetime.now(), i, train_accuracy * 100)) tf.logging.info('%s: Step %d: Cross entropy = %f' % (datetime.now(), i, cross_entropy_value)) + # TODO(suharshs): Make this use an eval graph, to avoid quantization + # moving averages being updated by the validation set, though in + # practice this makes a negligable difference. validation_bottlenecks, validation_ground_truth, _ = ( get_random_cached_bottlenecks( sess, image_lists, FLAGS.validation_batch_size, 'validation', @@ -1190,42 +1266,32 @@ def main(_): if (intermediate_frequency > 0 and (i % intermediate_frequency == 0) and i > 0): + # If we want to do an intermediate save, save a checkpoint of the train + # graph, to restore into the eval graph. + train_saver.save(sess, CHECKPOINT_NAME) intermediate_file_name = (FLAGS.intermediate_output_graphs_dir + 'intermediate_' + str(i) + '.pb') tf.logging.info('Save intermediate result to : ' + intermediate_file_name) - save_graph_to_file(sess, graph, intermediate_file_name) + save_graph_to_file(graph, intermediate_file_name, model_info, + class_count) + + # After training is complete, force one last save of the train checkpoint. + train_saver.save(sess, CHECKPOINT_NAME) # We've completed all our training, so run a final test evaluation on # some new images we haven't used before. - test_bottlenecks, test_ground_truth, test_filenames = ( - get_random_cached_bottlenecks( - sess, image_lists, FLAGS.test_batch_size, 'testing', - FLAGS.bottleneck_dir, FLAGS.image_dir, jpeg_data_tensor, - decoded_image_tensor, resized_image_tensor, bottleneck_tensor, - FLAGS.architecture)) - test_accuracy, predictions = sess.run( - [evaluation_step, prediction], - feed_dict={bottleneck_input: test_bottlenecks, - ground_truth_input: test_ground_truth}) - tf.logging.info('Final test accuracy = %.1f%% (N=%d)' % - (test_accuracy * 100, len(test_bottlenecks))) - - if FLAGS.print_misclassified_test_images: - tf.logging.info('=== MISCLASSIFIED TEST IMAGES ===') - for i, test_filename in enumerate(test_filenames): - if predictions[i] != test_ground_truth[i]: - tf.logging.info('%70s %s' % - (test_filename, - list(image_lists.keys())[predictions[i]])) + run_final_eval(sess, model_info, class_count, image_lists, jpeg_data_tensor, + decoded_image_tensor, resized_image_tensor, + bottleneck_tensor) # Write out the trained graph and labels with the weights stored as # constants. - save_graph_to_file(sess, graph, FLAGS.output_graph) + save_graph_to_file(graph, FLAGS.output_graph, model_info, class_count) with gfile.FastGFile(FLAGS.output_labels, 'w') as f: f.write('\n'.join(image_lists.keys()) + '\n') - export_model(sess, FLAGS.architecture, FLAGS.saved_model_dir) + export_model(model_info, class_count, FLAGS.saved_model_dir) if __name__ == '__main__': @@ -1406,8 +1472,9 @@ if __name__ == '__main__': form 'mobilenet__[_quantized]'. For example, 'mobilenet_1.0_224' will pick a model that is 17 MB in size and takes 224 pixel input images, while 'mobilenet_0.25_128_quantized' will choose a much - less accurate, but smaller and faster network that's 920 KB on disk and - takes 128x128 images. See https://research.googleblog.com/2017/06/mobilenets-open-source-models-for.html + smaller and less accurate model, taking 128x128 images, and instrumented + for eventual quantization via TensorFlow Lite. + See https://research.googleblog.com/2017/06/mobilenets-open-source-models-for.html for more information on Mobilenet.\ """) parser.add_argument( diff --git a/tensorflow/examples/image_retraining/retrain_test.py b/tensorflow/examples/image_retraining/retrain_test.py index 8b8dd45fd72e3d29bdb7f6291cc53b912adf3644..fb7324c58ac1be60baad840207f31a61ec6182be 100644 --- a/tensorflow/examples/image_retraining/retrain_test.py +++ b/tensorflow/examples/image_retraining/retrain_test.py @@ -67,22 +67,52 @@ class ImageRetrainingTest(test_util.TensorFlowTestCase): self.assertIsNotNone(sess.graph.get_tensor_by_name('DistortResult:0')) @tf.test.mock.patch.object(retrain, 'FLAGS', learning_rate=0.01) - def testAddFinalTrainingOps(self, flags_mock): + def testAddFinalRetrainOps(self, flags_mock): with tf.Graph().as_default(): with tf.Session() as sess: bottleneck = tf.placeholder(tf.float32, [1, 1024], name='bottleneck') - # Test creating final training op with quantization - retrain.add_final_training_ops(5, 'final', bottleneck, 1024, False) + # Test creating final training op with quantization. + retrain.add_final_retrain_ops(5, 'final', bottleneck, 1024, False, + False) self.assertIsNotNone(sess.graph.get_tensor_by_name('final:0')) @tf.test.mock.patch.object(retrain, 'FLAGS', learning_rate=0.01) - def testAddFinalTrainingOpsQuantized(self, flags_mock): - with tf.Graph().as_default(): + def testAddFinalRetrainOpsQuantized(self, flags_mock): + # Ensure that the training and eval graph for quantized models are correctly + # created. + with tf.Graph().as_default() as g: + with tf.Session() as sess: + bottleneck = tf.placeholder(tf.float32, [1, 1024], name='bottleneck') + # Test creating final training op with quantization, set is_training to + # true. + retrain.add_final_retrain_ops(5, 'final', bottleneck, 1024, True, True) + self.assertIsNotNone(sess.graph.get_tensor_by_name('final:0')) + found_fake_quant = 0 + for op in g.get_operations(): + if op.type == 'FakeQuantWithMinMaxVars': + found_fake_quant += 1 + # Ensure that the inputs of each FakeQuant operations has 2 Assign + # operations in the training graph (Assign[Min,Max]Last, + # Assign[Min,Max]Ema) + self.assertEqual(2, + len([i for i in op.inputs if 'Assign' in i.name])) + self.assertEqual(found_fake_quant, 2) + with tf.Graph().as_default() as g: with tf.Session() as sess: bottleneck = tf.placeholder(tf.float32, [1, 1024], name='bottleneck') - # Test creating final training op with quantization - retrain.add_final_training_ops(5, 'final', bottleneck, 1024, True) + # Test creating final training op with quantization, set is_training to + # false. + retrain.add_final_retrain_ops(5, 'final', bottleneck, 1024, True, False) self.assertIsNotNone(sess.graph.get_tensor_by_name('final:0')) + found_fake_quant = 0 + for op in g.get_operations(): + if op.type == 'FakeQuantWithMinMaxVars': + found_fake_quant += 1 + for i in op.inputs: + # Ensure that no operations are Assign operation since this is the + # evaluation graph. + self.assertTrue('Assign' not in i.name) + self.assertEqual(found_fake_quant, 2) def testAddEvaluationStep(self): with tf.Graph().as_default(): diff --git a/tensorflow/contrib/bayesflow/python/ops/optimizers.py b/tensorflow/experimental_api.py similarity index 54% rename from tensorflow/contrib/bayesflow/python/ops/optimizers.py rename to tensorflow/experimental_api.py index fb70628d1083836281e9327e83e109493276c64f..63a8aa9cb1dc130a7999c3b248815633998c4cd0 100644 --- a/tensorflow/contrib/bayesflow/python/ops/optimizers.py +++ b/tensorflow/experimental_api.py @@ -1,4 +1,4 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# 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. @@ -12,25 +12,27 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Probabilistic optimizer modules. -See ${python/contrib.bayesflow.optimizers}. -""" +# Bring in all of the public TensorFlow interface into this +# module. from __future__ import absolute_import from __future__ import division from __future__ import print_function -# go/tf-wildcard-import +# pylint: disable=g-bad-import-order +from tensorflow.python import pywrap_tensorflow # pylint: disable=unused-import # pylint: disable=wildcard-import -from tensorflow.contrib.bayesflow.python.ops.sgld_optimizer import * -from tensorflow.contrib.bayesflow.python.ops.variational_sgd_optimizer import * +from tensorflow.tools.api.generator.api import * # pylint: disable=redefined-builtin # pylint: enable=wildcard-import -from tensorflow.python.util.all_util import remove_undocumented -_allowed_symbols = [ - 'SGLDOptimizer', - 'VariationalSGDOptimizer', -] +from tensorflow.python.util.lazy_loader import LazyLoader +contrib = LazyLoader('contrib', globals(), 'tensorflow.contrib') +del LazyLoader -remove_undocumented(__name__, _allowed_symbols) +from tensorflow.python.platform import flags # pylint: disable=g-import-not-at-top +app.flags = flags # pylint: disable=undefined-variable + +del absolute_import +del division +del print_function diff --git a/tensorflow/go/genop/internal/api_def_map.go b/tensorflow/go/genop/internal/api_def_map.go index 07b689dbba23a3aa991983f3b373fa8445c673e1..8600452b476dee49292cbffe630026cf6077e22b 100644 --- a/tensorflow/go/genop/internal/api_def_map.go +++ b/tensorflow/go/genop/internal/api_def_map.go @@ -31,7 +31,7 @@ import ( "unsafe" "github.com/golang/protobuf/proto" - pb "github.com/tensorflow/tensorflow/tensorflow/go/genop/internal/proto/tensorflow/core/framework" + pb "github.com/tensorflow/tensorflow/tensorflow/go/genop/internal/proto/tensorflow/core/framework_go_proto" ) // Encapsulates a collection of API definitions. diff --git a/tensorflow/go/genop/internal/genop.go b/tensorflow/go/genop/internal/genop.go index 82f7510f2ed947e0a87e4d88cfce1ecaaa6362f8..fb8163121850cee36e1fcc652ca258b1fe2d42ff 100644 --- a/tensorflow/go/genop/internal/genop.go +++ b/tensorflow/go/genop/internal/genop.go @@ -47,7 +47,7 @@ import ( "unsafe" "github.com/golang/protobuf/proto" - pb "github.com/tensorflow/tensorflow/tensorflow/go/genop/internal/proto/tensorflow/core/framework" + pb "github.com/tensorflow/tensorflow/tensorflow/go/genop/internal/proto/tensorflow/core/framework_go_proto" ) // GenerateFunctionsForRegisteredOps writes a Go source code file to w @@ -359,13 +359,13 @@ type attrWrapper struct { api *pb.ApiDef_Attr } -func (a *attrWrapper) Name() string { return a.api.Name } -func (a *attrWrapper) RenameTo() string { return a.api.RenameTo } -func (a *attrWrapper) Description() string { return a.api.Description } -func (a *attrWrapper) Type() string { return a.op.Type } -func (a *attrWrapper) IsListAttr() bool { return isListAttr(a.op) } -func (a *attrWrapper) HasMinimum() bool { return a.op.HasMinimum } -func (a *attrWrapper) Minimum() int64 { return a.op.Minimum } +func (a *attrWrapper) Name() string { return a.api.Name } +func (a *attrWrapper) RenameTo() string { return a.api.RenameTo } +func (a *attrWrapper) Description() string { return a.api.Description } +func (a *attrWrapper) Type() string { return a.op.Type } +func (a *attrWrapper) IsListAttr() bool { return isListAttr(a.op) } +func (a *attrWrapper) HasMinimum() bool { return a.op.HasMinimum } +func (a *attrWrapper) Minimum() int64 { return a.op.Minimum } func (a *attrWrapper) DefaultValue() interface{} { return a.api.DefaultValue } type argWrapper struct { diff --git a/tensorflow/go/genop/internal/genop_test.go b/tensorflow/go/genop/internal/genop_test.go index b3a23dff102a690b1f7f08b675219929355f139f..d20d22e0c1502f92ade7ef5aa40985dce73b7552 100644 --- a/tensorflow/go/genop/internal/genop_test.go +++ b/tensorflow/go/genop/internal/genop_test.go @@ -22,7 +22,7 @@ import ( "testing" "github.com/golang/protobuf/proto" - pb "github.com/tensorflow/tensorflow/tensorflow/go/genop/internal/proto/tensorflow/core/framework" + pb "github.com/tensorflow/tensorflow/tensorflow/go/genop/internal/proto/tensorflow/core/framework_go_proto" ) // Creates an ApiDef based on opdef and applies overrides diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go index d9e684a661f2690c9352baec0649fbf42fc79255..469d1e9adb73849e2b3cf963ac9b8fb2a0861c35 100644 --- a/tensorflow/go/op/wrappers.go +++ b/tensorflow/go/op/wrappers.go @@ -384,230 +384,199 @@ func FakeQuantWithMinMaxVarsGradient(scope *Scope, gradients tf.Output, inputs t return op.Output(0), op.Output(1), op.Output(2) } -// MutableHashTableOfTensorsV2Attr is an optional argument to MutableHashTableOfTensorsV2. -type MutableHashTableOfTensorsV2Attr func(optionalAttr) +// FakeQuantWithMinMaxArgsGradientAttr is an optional argument to FakeQuantWithMinMaxArgsGradient. +type FakeQuantWithMinMaxArgsGradientAttr func(optionalAttr) -// MutableHashTableOfTensorsV2Container sets the optional container attribute to value. -// -// value: If non-empty, this table is placed in the given container. -// Otherwise, a default container is used. -// If not specified, defaults to "" -func MutableHashTableOfTensorsV2Container(value string) MutableHashTableOfTensorsV2Attr { +// FakeQuantWithMinMaxArgsGradientMin sets the optional min attribute to value. +// If not specified, defaults to -6 +func FakeQuantWithMinMaxArgsGradientMin(value float32) FakeQuantWithMinMaxArgsGradientAttr { return func(m optionalAttr) { - m["container"] = value + m["min"] = value } } -// MutableHashTableOfTensorsV2SharedName sets the optional shared_name attribute to value. -// -// value: If non-empty, this table is shared under the given name across -// multiple sessions. -// If not specified, defaults to "" -func MutableHashTableOfTensorsV2SharedName(value string) MutableHashTableOfTensorsV2Attr { +// FakeQuantWithMinMaxArgsGradientMax sets the optional max attribute to value. +// If not specified, defaults to 6 +func FakeQuantWithMinMaxArgsGradientMax(value float32) FakeQuantWithMinMaxArgsGradientAttr { return func(m optionalAttr) { - m["shared_name"] = value + m["max"] = value } } -// MutableHashTableOfTensorsV2UseNodeNameSharing sets the optional use_node_name_sharing attribute to value. -// If not specified, defaults to false -func MutableHashTableOfTensorsV2UseNodeNameSharing(value bool) MutableHashTableOfTensorsV2Attr { +// FakeQuantWithMinMaxArgsGradientNumBits sets the optional num_bits attribute to value. +// If not specified, defaults to 8 +func FakeQuantWithMinMaxArgsGradientNumBits(value int64) FakeQuantWithMinMaxArgsGradientAttr { return func(m optionalAttr) { - m["use_node_name_sharing"] = value + m["num_bits"] = value } } -// MutableHashTableOfTensorsV2ValueShape sets the optional value_shape attribute to value. -// If not specified, defaults to <> -func MutableHashTableOfTensorsV2ValueShape(value tf.Shape) MutableHashTableOfTensorsV2Attr { +// FakeQuantWithMinMaxArgsGradientNarrowRange sets the optional narrow_range attribute to value. +// If not specified, defaults to false +func FakeQuantWithMinMaxArgsGradientNarrowRange(value bool) FakeQuantWithMinMaxArgsGradientAttr { return func(m optionalAttr) { - m["value_shape"] = value + m["narrow_range"] = value } } -// Creates an empty hash table. -// -// This op creates a mutable hash table, specifying the type of its keys and -// values. Each value must be a vector. Data can be inserted into the table using -// the insert operations. It does not support the initialization operation. +// Compute gradients for a FakeQuantWithMinMaxArgs operation. // // Arguments: -// key_dtype: Type of the table keys. -// value_dtype: Type of the table values. +// gradients: Backpropagated gradients above the FakeQuantWithMinMaxArgs operation. +// inputs: Values passed as inputs to the FakeQuantWithMinMaxArgs operation. // -// Returns Handle to a table. -func MutableHashTableOfTensorsV2(scope *Scope, key_dtype tf.DataType, value_dtype tf.DataType, optional ...MutableHashTableOfTensorsV2Attr) (table_handle tf.Output) { +// Returns Backpropagated gradients below the FakeQuantWithMinMaxArgs operation: +// `gradients * (inputs >= min && inputs <= max)`. +func FakeQuantWithMinMaxArgsGradient(scope *Scope, gradients tf.Output, inputs tf.Output, optional ...FakeQuantWithMinMaxArgsGradientAttr) (backprops tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"key_dtype": key_dtype, "value_dtype": value_dtype} + attrs := map[string]interface{}{} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "MutableHashTableOfTensorsV2", - + Type: "FakeQuantWithMinMaxArgsGradient", + Input: []tf.Input{ + gradients, inputs, + }, Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// ResourceApplyProximalAdagradAttr is an optional argument to ResourceApplyProximalAdagrad. -type ResourceApplyProximalAdagradAttr func(optionalAttr) +// FakeQuantWithMinMaxArgsAttr is an optional argument to FakeQuantWithMinMaxArgs. +type FakeQuantWithMinMaxArgsAttr func(optionalAttr) -// ResourceApplyProximalAdagradUseLocking sets the optional use_locking attribute to value. -// -// value: If True, updating of the var and accum tensors will be protected by -// a lock; otherwise the behavior is undefined, but may exhibit less contention. -// If not specified, defaults to false -func ResourceApplyProximalAdagradUseLocking(value bool) ResourceApplyProximalAdagradAttr { +// FakeQuantWithMinMaxArgsMin sets the optional min attribute to value. +// If not specified, defaults to -6 +func FakeQuantWithMinMaxArgsMin(value float32) FakeQuantWithMinMaxArgsAttr { return func(m optionalAttr) { - m["use_locking"] = value - } -} - -// Update '*var' and '*accum' according to FOBOS with Adagrad learning rate. -// -// accum += grad * grad -// prox_v = var - lr * grad * (1 / sqrt(accum)) -// var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0} -// -// Arguments: -// var_: Should be from a Variable(). -// accum: Should be from a Variable(). -// lr: Scaling factor. Must be a scalar. -// l1: L1 regularization. Must be a scalar. -// l2: L2 regularization. Must be a scalar. -// grad: The gradient. -// -// Returns the created operation. -func ResourceApplyProximalAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, grad tf.Output, optional ...ResourceApplyProximalAdagradAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ResourceApplyProximalAdagrad", - Input: []tf.Input{ - var_, accum, lr, l1, l2, grad, - }, - Attrs: attrs, + m["min"] = value } - return scope.AddOperation(opspec) } -// MutableHashTableV2Attr is an optional argument to MutableHashTableV2. -type MutableHashTableV2Attr func(optionalAttr) - -// MutableHashTableV2Container sets the optional container attribute to value. -// -// value: If non-empty, this table is placed in the given container. -// Otherwise, a default container is used. -// If not specified, defaults to "" -func MutableHashTableV2Container(value string) MutableHashTableV2Attr { +// FakeQuantWithMinMaxArgsMax sets the optional max attribute to value. +// If not specified, defaults to 6 +func FakeQuantWithMinMaxArgsMax(value float32) FakeQuantWithMinMaxArgsAttr { return func(m optionalAttr) { - m["container"] = value + m["max"] = value } } -// MutableHashTableV2SharedName sets the optional shared_name attribute to value. -// -// value: If non-empty, this table is shared under the given name across -// multiple sessions. -// If not specified, defaults to "" -func MutableHashTableV2SharedName(value string) MutableHashTableV2Attr { +// FakeQuantWithMinMaxArgsNumBits sets the optional num_bits attribute to value. +// If not specified, defaults to 8 +func FakeQuantWithMinMaxArgsNumBits(value int64) FakeQuantWithMinMaxArgsAttr { return func(m optionalAttr) { - m["shared_name"] = value + m["num_bits"] = value } } -// MutableHashTableV2UseNodeNameSharing sets the optional use_node_name_sharing attribute to value. -// -// value: If true and shared_name is empty, the table is shared -// using the node name. +// FakeQuantWithMinMaxArgsNarrowRange sets the optional narrow_range attribute to value. // If not specified, defaults to false -func MutableHashTableV2UseNodeNameSharing(value bool) MutableHashTableV2Attr { +func FakeQuantWithMinMaxArgsNarrowRange(value bool) FakeQuantWithMinMaxArgsAttr { return func(m optionalAttr) { - m["use_node_name_sharing"] = value + m["narrow_range"] = value } } -// Creates an empty hash table. -// -// This op creates a mutable hash table, specifying the type of its keys and -// values. Each value must be a scalar. Data can be inserted into the table using -// the insert operations. It does not support the initialization operation. +// Fake-quantize the 'inputs' tensor, type float to 'outputs' tensor of same type. // -// Arguments: -// key_dtype: Type of the table keys. -// value_dtype: Type of the table values. +// Attributes `[min; max]` define the clamping range for the `inputs` data. +// `inputs` values are quantized into the quantization range (`[0; 2^num_bits - 1]` +// when `narrow_range` is false and `[1; 2^num_bits - 1]` when it is true) and +// then de-quantized and output as floats in `[min; max]` interval. +// `num_bits` is the bitwidth of the quantization; between 2 and 8, inclusive. // -// Returns Handle to a table. -func MutableHashTableV2(scope *Scope, key_dtype tf.DataType, value_dtype tf.DataType, optional ...MutableHashTableV2Attr) (table_handle tf.Output) { +// Quantization is called fake since the output is still in floating point. +func FakeQuantWithMinMaxArgs(scope *Scope, inputs tf.Output, optional ...FakeQuantWithMinMaxArgsAttr) (outputs tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"key_dtype": key_dtype, "value_dtype": value_dtype} + attrs := map[string]interface{}{} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "MutableHashTableV2", - + Type: "FakeQuantWithMinMaxArgs", + Input: []tf.Input{ + inputs, + }, Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// MapUnstageNoKeyAttr is an optional argument to MapUnstageNoKey. -type MapUnstageNoKeyAttr func(optionalAttr) +// Replaces the contents of the table with the specified keys and values. +// +// The tensor `keys` must be of the same type as the keys of the table. +// The tensor `values` must be of the type of the table values. +// +// Arguments: +// table_handle: Handle to the table. +// keys: Any shape. Keys to look up. +// values: Values to associate with keys. +// +// Returns the created operation. +func LookupTableImportV2(scope *Scope, table_handle tf.Output, keys tf.Output, values tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "LookupTableImportV2", + Input: []tf.Input{ + table_handle, keys, values, + }, + } + return scope.AddOperation(opspec) +} -// MapUnstageNoKeyCapacity sets the optional capacity attribute to value. +// MapPeekAttr is an optional argument to MapPeek. +type MapPeekAttr func(optionalAttr) + +// MapPeekCapacity sets the optional capacity attribute to value. // If not specified, defaults to 0 // // REQUIRES: value >= 0 -func MapUnstageNoKeyCapacity(value int64) MapUnstageNoKeyAttr { +func MapPeekCapacity(value int64) MapPeekAttr { return func(m optionalAttr) { m["capacity"] = value } } -// MapUnstageNoKeyMemoryLimit sets the optional memory_limit attribute to value. +// MapPeekMemoryLimit sets the optional memory_limit attribute to value. // If not specified, defaults to 0 // // REQUIRES: value >= 0 -func MapUnstageNoKeyMemoryLimit(value int64) MapUnstageNoKeyAttr { +func MapPeekMemoryLimit(value int64) MapPeekAttr { return func(m optionalAttr) { m["memory_limit"] = value } } -// MapUnstageNoKeyContainer sets the optional container attribute to value. +// MapPeekContainer sets the optional container attribute to value. // If not specified, defaults to "" -func MapUnstageNoKeyContainer(value string) MapUnstageNoKeyAttr { +func MapPeekContainer(value string) MapPeekAttr { return func(m optionalAttr) { m["container"] = value } } -// MapUnstageNoKeySharedName sets the optional shared_name attribute to value. +// MapPeekSharedName sets the optional shared_name attribute to value. // If not specified, defaults to "" -func MapUnstageNoKeySharedName(value string) MapUnstageNoKeyAttr { +func MapPeekSharedName(value string) MapPeekAttr { return func(m optionalAttr) { m["shared_name"] = value } } -// Op removes and returns a random (key, value) +// Op peeks at the values at the specified key. If the // -// from the underlying container. If the underlying container -// does not contain elements, the op will block until it does. -func MapUnstageNoKey(scope *Scope, indices tf.Output, dtypes []tf.DataType, optional ...MapUnstageNoKeyAttr) (key tf.Output, values []tf.Output) { +// underlying container does not contain this key +// this op will block until it does. +func MapPeek(scope *Scope, key tf.Output, indices tf.Output, dtypes []tf.DataType, optional ...MapPeekAttr) (values []tf.Output) { if scope.Err() != nil { return } @@ -616,9 +585,9 @@ func MapUnstageNoKey(scope *Scope, indices tf.Output, dtypes []tf.DataType, opti a(attrs) } opspec := tf.OpSpec{ - Type: "MapUnstageNoKey", + Type: "MapPeek", Input: []tf.Input{ - indices, + key, indices, }, Attrs: attrs, } @@ -628,180 +597,20 @@ func MapUnstageNoKey(scope *Scope, indices tf.Output, dtypes []tf.DataType, opti } var idx int var err error - key = op.Output(idx) if values, idx, err = makeOutputList(op, idx, "values"); err != nil { - scope.UpdateErr("MapUnstageNoKey", err) + scope.UpdateErr("MapPeek", err) return } - return key, values + return values } -// HashTableV2Attr is an optional argument to HashTableV2. -type HashTableV2Attr func(optionalAttr) - -// HashTableV2Container sets the optional container attribute to value. +// Returns (x - y)(x - y) element-wise. // -// value: If non-empty, this table is placed in the given container. -// Otherwise, a default container is used. -// If not specified, defaults to "" -func HashTableV2Container(value string) HashTableV2Attr { - return func(m optionalAttr) { - m["container"] = value - } -} - -// HashTableV2SharedName sets the optional shared_name attribute to value. -// -// value: If non-empty, this table is shared under the given name across -// multiple sessions. -// If not specified, defaults to "" -func HashTableV2SharedName(value string) HashTableV2Attr { - return func(m optionalAttr) { - m["shared_name"] = value - } -} - -// HashTableV2UseNodeNameSharing sets the optional use_node_name_sharing attribute to value. -// -// value: If true and shared_name is empty, the table is shared -// using the node name. -// If not specified, defaults to false -func HashTableV2UseNodeNameSharing(value bool) HashTableV2Attr { - return func(m optionalAttr) { - m["use_node_name_sharing"] = value - } -} - -// Creates a non-initialized hash table. -// -// This op creates a hash table, specifying the type of its keys and values. -// Before using the table you will have to initialize it. After initialization the -// table will be immutable. -// -// Arguments: -// key_dtype: Type of the table keys. -// value_dtype: Type of the table values. -// -// Returns Handle to a table. -func HashTableV2(scope *Scope, key_dtype tf.DataType, value_dtype tf.DataType, optional ...HashTableV2Attr) (table_handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"key_dtype": key_dtype, "value_dtype": value_dtype} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "HashTableV2", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Replaces the contents of the table with the specified keys and values. -// -// The tensor `keys` must be of the same type as the keys of the table. -// The tensor `values` must be of the type of the table values. -// -// Arguments: -// table_handle: Handle to the table. -// keys: Any shape. Keys to look up. -// values: Values to associate with keys. -// -// Returns the created operation. -func LookupTableImportV2(scope *Scope, table_handle tf.Output, keys tf.Output, values tf.Output) (o *tf.Operation) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "LookupTableImportV2", - Input: []tf.Input{ - table_handle, keys, values, - }, - } - return scope.AddOperation(opspec) -} - -// MapPeekAttr is an optional argument to MapPeek. -type MapPeekAttr func(optionalAttr) - -// MapPeekCapacity sets the optional capacity attribute to value. -// If not specified, defaults to 0 -// -// REQUIRES: value >= 0 -func MapPeekCapacity(value int64) MapPeekAttr { - return func(m optionalAttr) { - m["capacity"] = value - } -} - -// MapPeekMemoryLimit sets the optional memory_limit attribute to value. -// If not specified, defaults to 0 -// -// REQUIRES: value >= 0 -func MapPeekMemoryLimit(value int64) MapPeekAttr { - return func(m optionalAttr) { - m["memory_limit"] = value - } -} - -// MapPeekContainer sets the optional container attribute to value. -// If not specified, defaults to "" -func MapPeekContainer(value string) MapPeekAttr { - return func(m optionalAttr) { - m["container"] = value - } -} - -// MapPeekSharedName sets the optional shared_name attribute to value. -// If not specified, defaults to "" -func MapPeekSharedName(value string) MapPeekAttr { - return func(m optionalAttr) { - m["shared_name"] = value - } -} - -// Op peeks at the values at the specified key. If the -// -// underlying container does not contain this key -// this op will block until it does. -func MapPeek(scope *Scope, key tf.Output, indices tf.Output, dtypes []tf.DataType, optional ...MapPeekAttr) (values []tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"dtypes": dtypes} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "MapPeek", - Input: []tf.Input{ - key, indices, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - if values, idx, err = makeOutputList(op, idx, "values"); err != nil { - scope.UpdateErr("MapPeek", err) - return - } - return values -} - -// Returns (x - y)(x - y) element-wise. -// -// *NOTE*: `SquaredDifference` supports broadcasting. More about broadcasting -// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) -func SquaredDifference(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { - if scope.Err() != nil { - return +// *NOTE*: `SquaredDifference` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func SquaredDifference(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return } opspec := tf.OpSpec{ Type: "SquaredDifference", @@ -1398,6 +1207,21 @@ func Sinh(scope *Scope, x tf.Output) (y tf.Output) { return op.Output(0) } +// Computes rectified linear 6: `min(max(features, 0), 6)`. +func Relu6(scope *Scope, features tf.Output) (activations tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Relu6", + Input: []tf.Input{ + features, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Computes the sum along segments of a tensor. // // Read @{$math_ops#segmentation$the section on segmentation} for an explanation of @@ -4113,21 +3937,6 @@ func TakeDataset(scope *Scope, input_dataset tf.Output, count tf.Output, output_ return op.Output(0) } -// Computes rectified linear 6: `min(max(features, 0), 6)`. -func Relu6(scope *Scope, features tf.Output) (activations tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Relu6", - Input: []tf.Input{ - features, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // Computes rectified linear gradients for a Relu operation. // // Arguments: @@ -4531,68 +4340,6 @@ func MaxPoolGradWithArgmax(scope *Scope, input tf.Output, grad tf.Output, argmax return op.Output(0) } -// FakeQuantWithMinMaxArgsGradientAttr is an optional argument to FakeQuantWithMinMaxArgsGradient. -type FakeQuantWithMinMaxArgsGradientAttr func(optionalAttr) - -// FakeQuantWithMinMaxArgsGradientMin sets the optional min attribute to value. -// If not specified, defaults to -6 -func FakeQuantWithMinMaxArgsGradientMin(value float32) FakeQuantWithMinMaxArgsGradientAttr { - return func(m optionalAttr) { - m["min"] = value - } -} - -// FakeQuantWithMinMaxArgsGradientMax sets the optional max attribute to value. -// If not specified, defaults to 6 -func FakeQuantWithMinMaxArgsGradientMax(value float32) FakeQuantWithMinMaxArgsGradientAttr { - return func(m optionalAttr) { - m["max"] = value - } -} - -// FakeQuantWithMinMaxArgsGradientNumBits sets the optional num_bits attribute to value. -// If not specified, defaults to 8 -func FakeQuantWithMinMaxArgsGradientNumBits(value int64) FakeQuantWithMinMaxArgsGradientAttr { - return func(m optionalAttr) { - m["num_bits"] = value - } -} - -// FakeQuantWithMinMaxArgsGradientNarrowRange sets the optional narrow_range attribute to value. -// If not specified, defaults to false -func FakeQuantWithMinMaxArgsGradientNarrowRange(value bool) FakeQuantWithMinMaxArgsGradientAttr { - return func(m optionalAttr) { - m["narrow_range"] = value - } -} - -// Compute gradients for a FakeQuantWithMinMaxArgs operation. -// -// Arguments: -// gradients: Backpropagated gradients above the FakeQuantWithMinMaxArgs operation. -// inputs: Values passed as inputs to the FakeQuantWithMinMaxArgs operation. -// -// Returns Backpropagated gradients below the FakeQuantWithMinMaxArgs operation: -// `gradients * (inputs >= min && inputs <= max)`. -func FakeQuantWithMinMaxArgsGradient(scope *Scope, gradients tf.Output, inputs tf.Output, optional ...FakeQuantWithMinMaxArgsGradientAttr) (backprops tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "FakeQuantWithMinMaxArgsGradient", - Input: []tf.Input{ - gradients, inputs, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // AvgPool3DAttr is an optional argument to AvgPool3D. type AvgPool3DAttr func(optionalAttr) @@ -5642,119 +5389,14 @@ func QuantizedReluX(scope *Scope, features tf.Output, max_value tf.Output, min_f return op.Output(0), op.Output(1), op.Output(2) } -// SummaryWriterAttr is an optional argument to SummaryWriter. -type SummaryWriterAttr func(optionalAttr) +// RandomPoissonAttr is an optional argument to RandomPoisson. +type RandomPoissonAttr func(optionalAttr) -// SummaryWriterSharedName sets the optional shared_name attribute to value. -// If not specified, defaults to "" -func SummaryWriterSharedName(value string) SummaryWriterAttr { +// RandomPoissonSeed sets the optional seed attribute to value. +// If not specified, defaults to 0 +func RandomPoissonSeed(value int64) RandomPoissonAttr { return func(m optionalAttr) { - m["shared_name"] = value - } -} - -// SummaryWriterContainer sets the optional container attribute to value. -// If not specified, defaults to "" -func SummaryWriterContainer(value string) SummaryWriterAttr { - return func(m optionalAttr) { - m["container"] = value - } -} - -// Returns a handle to be used to access a summary writer. -// -// The summary writer is an in-graph resource which can be used by ops to write -// summaries to event files. -// -// Returns the summary writer resource. Scalar handle. -func SummaryWriter(scope *Scope, optional ...SummaryWriterAttr) (writer tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "SummaryWriter", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Computes gradients for SparseSegmentMean. -// -// Returns tensor "output" with same shape as grad, except for dimension 0 whose -// value is output_dim0. -// -// Arguments: -// grad: gradient propagated to the SparseSegmentMean op. -// indices: indices passed to the corresponding SparseSegmentMean op. -// segment_ids: segment_ids passed to the corresponding SparseSegmentMean op. -// output_dim0: dimension 0 of "data" passed to SparseSegmentMean op. -func SparseSegmentMeanGrad(scope *Scope, grad tf.Output, indices tf.Output, segment_ids tf.Output, output_dim0 tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SparseSegmentMeanGrad", - Input: []tf.Input{ - grad, indices, segment_ids, output_dim0, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Applies softmax to a batched N-D `SparseTensor`. -// -// The inputs represent an N-D SparseTensor with logical shape `[..., B, C]` -// (where `N >= 2`), and with indices sorted in the canonical lexicographic order. -// -// This op is equivalent to applying the normal `tf.nn.softmax()` to each innermost -// logical submatrix with shape `[B, C]`, but with the catch that *the implicitly -// zero elements do not participate*. Specifically, the algorithm is equivalent -// to the following: -// -// (1) Applies `tf.nn.softmax()` to a densified view of each innermost submatrix -// with shape `[B, C]`, along the size-C dimension; -// (2) Masks out the original implicitly-zero locations; -// (3) Renormalizes the remaining elements. -// -// Hence, the `SparseTensor` result has exactly the same non-zero indices and -// shape. -// -// Arguments: -// sp_indices: 2-D. `NNZ x R` matrix with the indices of non-empty values in a -// SparseTensor, in canonical ordering. -// sp_values: 1-D. `NNZ` non-empty values corresponding to `sp_indices`. -// sp_shape: 1-D. Shape of the input SparseTensor. -// -// Returns 1-D. The `NNZ` values for the result `SparseTensor`. -func SparseSoftmax(scope *Scope, sp_indices tf.Output, sp_values tf.Output, sp_shape tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SparseSoftmax", - Input: []tf.Input{ - sp_indices, sp_values, sp_shape, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// RandomPoissonAttr is an optional argument to RandomPoisson. -type RandomPoissonAttr func(optionalAttr) - -// RandomPoissonSeed sets the optional seed attribute to value. -// If not specified, defaults to 0 -func RandomPoissonSeed(value int64) RandomPoissonAttr { - return func(m optionalAttr) { - m["seed"] = value + m["seed"] = value } } @@ -7025,67 +6667,32 @@ func DestroyResourceOp(scope *Scope, resource tf.Output, optional ...DestroyReso return scope.AddOperation(opspec) } -// CumprodAttr is an optional argument to Cumprod. -type CumprodAttr func(optionalAttr) +// SummaryWriterAttr is an optional argument to SummaryWriter. +type SummaryWriterAttr func(optionalAttr) -// CumprodExclusive sets the optional exclusive attribute to value. -// -// value: If `True`, perform exclusive cumprod. -// If not specified, defaults to false -func CumprodExclusive(value bool) CumprodAttr { +// SummaryWriterSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func SummaryWriterSharedName(value string) SummaryWriterAttr { return func(m optionalAttr) { - m["exclusive"] = value + m["shared_name"] = value } } -// CumprodReverse sets the optional reverse attribute to value. -// -// value: A `bool` (default: False). -// If not specified, defaults to false -func CumprodReverse(value bool) CumprodAttr { +// SummaryWriterContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func SummaryWriterContainer(value string) SummaryWriterAttr { return func(m optionalAttr) { - m["reverse"] = value + m["container"] = value } } -// Compute the cumulative product of the tensor `x` along `axis`. -// -// By default, this op performs an inclusive cumprod, which means that the first -// element of the input is identical to the first element of the output: -// -// ```python -// tf.cumprod([a, b, c]) # => [a, a * b, a * b * c] -// ``` -// -// By setting the `exclusive` kwarg to `True`, an exclusive cumprod is -// performed instead: -// -// ```python -// tf.cumprod([a, b, c], exclusive=True) # => [1, a, a * b] -// ``` -// -// By setting the `reverse` kwarg to `True`, the cumprod is performed in the -// opposite direction: -// -// ```python -// tf.cumprod([a, b, c], reverse=True) # => [a * b * c, b * c, c] -// ``` -// -// This is more efficient than using separate `tf.reverse` ops. -// -// The `reverse` and `exclusive` kwargs can also be combined: +// Returns a handle to be used to access a summary writer. // -// ```python -// tf.cumprod([a, b, c], exclusive=True, reverse=True) # => [b * c, c, 1] -// ``` +// The summary writer is an in-graph resource which can be used by ops to write +// summaries to event files. // -// Arguments: -// x: A `Tensor`. Must be one of the following types: `float32`, `float64`, -// `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`, -// `complex128`, `qint8`, `quint8`, `qint32`, `half`. -// axis: A `Tensor` of type `int32` (default: 0). Must be in the range -// `[-rank(x), rank(x))`. -func Cumprod(scope *Scope, x tf.Output, axis tf.Output, optional ...CumprodAttr) (out tf.Output) { +// Returns the summary writer resource. Scalar handle. +func SummaryWriter(scope *Scope, optional ...SummaryWriterAttr) (writer tf.Output) { if scope.Err() != nil { return } @@ -7094,69 +6701,323 @@ func Cumprod(scope *Scope, x tf.Output, axis tf.Output, optional ...CumprodAttr) a(attrs) } opspec := tf.OpSpec{ - Type: "Cumprod", + Type: "SummaryWriter", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes gradients for SparseSegmentMean. +// +// Returns tensor "output" with same shape as grad, except for dimension 0 whose +// value is output_dim0. +// +// Arguments: +// grad: gradient propagated to the SparseSegmentMean op. +// indices: indices passed to the corresponding SparseSegmentMean op. +// segment_ids: segment_ids passed to the corresponding SparseSegmentMean op. +// output_dim0: dimension 0 of "data" passed to SparseSegmentMean op. +func SparseSegmentMeanGrad(scope *Scope, grad tf.Output, indices tf.Output, segment_ids tf.Output, output_dim0 tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseSegmentMeanGrad", Input: []tf.Input{ - x, axis, + grad, indices, segment_ids, output_dim0, }, - Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// Computes the mean along segments of a tensor. +// Applies softmax to a batched N-D `SparseTensor`. // -// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of -// segments. +// The inputs represent an N-D SparseTensor with logical shape `[..., B, C]` +// (where `N >= 2`), and with indices sorted in the canonical lexicographic order. // -// Computes a tensor such that -// \\(output_i = \frac{\sum_j data_j}{N}\\) where `mean` is -// over `j` such that `segment_ids[j] == i` and `N` is the total number of -// values summed. +// This op is equivalent to applying the normal `tf.nn.softmax()` to each innermost +// logical submatrix with shape `[B, C]`, but with the catch that *the implicitly +// zero elements do not participate*. Specifically, the algorithm is equivalent +// to the following: // -// If the mean is empty for a given segment ID `i`, `output[i] = 0`. +// (1) Applies `tf.nn.softmax()` to a densified view of each innermost submatrix +// with shape `[B, C]`, along the size-C dimension; +// (2) Masks out the original implicitly-zero locations; +// (3) Renormalizes the remaining elements. // -//
-// -//
+// Hence, the `SparseTensor` result has exactly the same non-zero indices and +// shape. // // Arguments: +// sp_indices: 2-D. `NNZ x R` matrix with the indices of non-empty values in a +// SparseTensor, in canonical ordering. +// sp_values: 1-D. `NNZ` non-empty values corresponding to `sp_indices`. +// sp_shape: 1-D. Shape of the input SparseTensor. // -// segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s -// first dimension. Values should be sorted and can be repeated. -// -// Returns Has same shape as data, except for dimension 0 which -// has size `k`, the number of segments. -func SegmentMean(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) { +// Returns 1-D. The `NNZ` values for the result `SparseTensor`. +func SparseSoftmax(scope *Scope, sp_indices tf.Output, sp_values tf.Output, sp_shape tf.Output) (output tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "SegmentMean", + Type: "SparseSoftmax", Input: []tf.Input{ - data, segment_ids, + sp_indices, sp_values, sp_shape, }, } op := scope.AddOperation(opspec) return op.Output(0) } -// ResourceSparseApplyCenteredRMSPropAttr is an optional argument to ResourceSparseApplyCenteredRMSProp. -type ResourceSparseApplyCenteredRMSPropAttr func(optionalAttr) - -// ResourceSparseApplyCenteredRMSPropUseLocking sets the optional use_locking attribute to value. +// Partitions `data` into `num_partitions` tensors using indices from `partitions`. // -// value: If `True`, updating of the var, mg, ms, and mom tensors is -// protected by a lock; otherwise the behavior is undefined, but may exhibit less -// contention. -// If not specified, defaults to false -func ResourceSparseApplyCenteredRMSPropUseLocking(value bool) ResourceSparseApplyCenteredRMSPropAttr { - return func(m optionalAttr) { - m["use_locking"] = value - } -} - -// Update '*var' according to the centered RMSProp algorithm. +// For each index tuple `js` of size `partitions.ndim`, the slice `data[js, ...]` +// becomes part of `outputs[partitions[js]]`. The slices with `partitions[js] = i` +// are placed in `outputs[i]` in lexicographic order of `js`, and the first +// dimension of `outputs[i]` is the number of entries in `partitions` equal to `i`. +// In detail, +// +// ```python +// outputs[i].shape = [sum(partitions == i)] + data.shape[partitions.ndim:] +// +// outputs[i] = pack([data[js, ...] for js if partitions[js] == i]) +// ``` +// +// `data.shape` must start with `partitions.shape`. +// +// For example: +// +// ```python +// # Scalar partitions. +// partitions = 1 +// num_partitions = 2 +// data = [10, 20] +// outputs[0] = [] # Empty with shape [0, 2] +// outputs[1] = [[10, 20]] +// +// # Vector partitions. +// partitions = [0, 0, 1, 1, 0] +// num_partitions = 2 +// data = [10, 20, 30, 40, 50] +// outputs[0] = [10, 20, 50] +// outputs[1] = [30, 40] +// ``` +// +// See `dynamic_stitch` for an example on how to merge partitions back. +// +//
+// +//
+// +// Arguments: +// +// partitions: Any shape. Indices in the range `[0, num_partitions)`. +// num_partitions: The number of partitions to output. +func DynamicPartition(scope *Scope, data tf.Output, partitions tf.Output, num_partitions int64) (outputs []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_partitions": num_partitions} + opspec := tf.OpSpec{ + Type: "DynamicPartition", + Input: []tf.Input{ + data, partitions, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil { + scope.UpdateErr("DynamicPartition", err) + return + } + return outputs +} + +// ResourceApplyAdagradAttr is an optional argument to ResourceApplyAdagrad. +type ResourceApplyAdagradAttr func(optionalAttr) + +// ResourceApplyAdagradUseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var and accum tensors will be protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceApplyAdagradUseLocking(value bool) ResourceApplyAdagradAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Update '*var' according to the adagrad scheme. +// +// accum += grad * grad +// var -= lr * grad * (1 / sqrt(accum)) +// +// Arguments: +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// lr: Scaling factor. Must be a scalar. +// grad: The gradient. +// +// Returns the created operation. +func ResourceApplyAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, optional ...ResourceApplyAdagradAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceApplyAdagrad", + Input: []tf.Input{ + var_, accum, lr, grad, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// CumprodAttr is an optional argument to Cumprod. +type CumprodAttr func(optionalAttr) + +// CumprodExclusive sets the optional exclusive attribute to value. +// +// value: If `True`, perform exclusive cumprod. +// If not specified, defaults to false +func CumprodExclusive(value bool) CumprodAttr { + return func(m optionalAttr) { + m["exclusive"] = value + } +} + +// CumprodReverse sets the optional reverse attribute to value. +// +// value: A `bool` (default: False). +// If not specified, defaults to false +func CumprodReverse(value bool) CumprodAttr { + return func(m optionalAttr) { + m["reverse"] = value + } +} + +// Compute the cumulative product of the tensor `x` along `axis`. +// +// By default, this op performs an inclusive cumprod, which means that the first +// element of the input is identical to the first element of the output: +// +// ```python +// tf.cumprod([a, b, c]) # => [a, a * b, a * b * c] +// ``` +// +// By setting the `exclusive` kwarg to `True`, an exclusive cumprod is +// performed instead: +// +// ```python +// tf.cumprod([a, b, c], exclusive=True) # => [1, a, a * b] +// ``` +// +// By setting the `reverse` kwarg to `True`, the cumprod is performed in the +// opposite direction: +// +// ```python +// tf.cumprod([a, b, c], reverse=True) # => [a * b * c, b * c, c] +// ``` +// +// This is more efficient than using separate `tf.reverse` ops. +// +// The `reverse` and `exclusive` kwargs can also be combined: +// +// ```python +// tf.cumprod([a, b, c], exclusive=True, reverse=True) # => [b * c, c, 1] +// ``` +// +// Arguments: +// x: A `Tensor`. Must be one of the following types: `float32`, `float64`, +// `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`, +// `complex128`, `qint8`, `quint8`, `qint32`, `half`. +// axis: A `Tensor` of type `int32` (default: 0). Must be in the range +// `[-rank(x), rank(x))`. +func Cumprod(scope *Scope, x tf.Output, axis tf.Output, optional ...CumprodAttr) (out tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Cumprod", + Input: []tf.Input{ + x, axis, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the mean along segments of a tensor. +// +// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of +// segments. +// +// Computes a tensor such that +// \\(output_i = \frac{\sum_j data_j}{N}\\) where `mean` is +// over `j` such that `segment_ids[j] == i` and `N` is the total number of +// values summed. +// +// If the mean is empty for a given segment ID `i`, `output[i] = 0`. +// +//
+// +//
+// +// Arguments: +// +// segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s +// first dimension. Values should be sorted and can be repeated. +// +// Returns Has same shape as data, except for dimension 0 which +// has size `k`, the number of segments. +func SegmentMean(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SegmentMean", + Input: []tf.Input{ + data, segment_ids, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResourceSparseApplyCenteredRMSPropAttr is an optional argument to ResourceSparseApplyCenteredRMSProp. +type ResourceSparseApplyCenteredRMSPropAttr func(optionalAttr) + +// ResourceSparseApplyCenteredRMSPropUseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var, mg, ms, and mom tensors is +// protected by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceSparseApplyCenteredRMSPropUseLocking(value bool) ResourceSparseApplyCenteredRMSPropAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Update '*var' according to the centered RMSProp algorithm. // // The centered RMSProp algorithm uses an estimate of the centered second moment // (i.e., the variance) for normalization, as opposed to regular RMSProp, which @@ -7909,74 +7770,17 @@ func ResourceScatterNdUpdate(scope *Scope, ref tf.Output, indices tf.Output, upd return scope.AddOperation(opspec) } -// StageSizeAttr is an optional argument to StageSize. -type StageSizeAttr func(optionalAttr) +// NonMaxSuppressionAttr is an optional argument to NonMaxSuppression. +type NonMaxSuppressionAttr func(optionalAttr) -// StageSizeCapacity sets the optional capacity attribute to value. -// If not specified, defaults to 0 +// NonMaxSuppressionIouThreshold sets the optional iou_threshold attribute to value. // -// REQUIRES: value >= 0 -func StageSizeCapacity(value int64) StageSizeAttr { +// value: A float representing the threshold for deciding whether boxes +// overlap too much with respect to IOU. +// If not specified, defaults to 0.5 +func NonMaxSuppressionIouThreshold(value float32) NonMaxSuppressionAttr { return func(m optionalAttr) { - m["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) -} - -// NonMaxSuppressionAttr is an optional argument to NonMaxSuppression. -type NonMaxSuppressionAttr func(optionalAttr) - -// NonMaxSuppressionIouThreshold sets the optional iou_threshold attribute to value. -// -// value: A float representing the threshold for deciding whether boxes -// overlap too much with respect to IOU. -// If not specified, defaults to 0.5 -func NonMaxSuppressionIouThreshold(value float32) NonMaxSuppressionAttr { - return func(m optionalAttr) { - m["iou_threshold"] = value + m["iou_threshold"] = value } } @@ -8705,120 +8509,6 @@ func SparseReduceSum(scope *Scope, input_indices tf.Output, input_values tf.Outp return op.Output(0) } -// Partitions `data` into `num_partitions` tensors using indices from `partitions`. -// -// For each index tuple `js` of size `partitions.ndim`, the slice `data[js, ...]` -// becomes part of `outputs[partitions[js]]`. The slices with `partitions[js] = i` -// are placed in `outputs[i]` in lexicographic order of `js`, and the first -// dimension of `outputs[i]` is the number of entries in `partitions` equal to `i`. -// In detail, -// -// ```python -// outputs[i].shape = [sum(partitions == i)] + data.shape[partitions.ndim:] -// -// outputs[i] = pack([data[js, ...] for js if partitions[js] == i]) -// ``` -// -// `data.shape` must start with `partitions.shape`. -// -// For example: -// -// ```python -// # Scalar partitions. -// partitions = 1 -// num_partitions = 2 -// data = [10, 20] -// outputs[0] = [] # Empty with shape [0, 2] -// outputs[1] = [[10, 20]] -// -// # Vector partitions. -// partitions = [0, 0, 1, 1, 0] -// num_partitions = 2 -// data = [10, 20, 30, 40, 50] -// outputs[0] = [10, 20, 50] -// outputs[1] = [30, 40] -// ``` -// -// See `dynamic_stitch` for an example on how to merge partitions back. -// -//
-// -//
-// -// Arguments: -// -// partitions: Any shape. Indices in the range `[0, num_partitions)`. -// num_partitions: The number of partitions to output. -func DynamicPartition(scope *Scope, data tf.Output, partitions tf.Output, num_partitions int64) (outputs []tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_partitions": num_partitions} - opspec := tf.OpSpec{ - Type: "DynamicPartition", - Input: []tf.Input{ - data, partitions, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil { - scope.UpdateErr("DynamicPartition", err) - return - } - return outputs -} - -// ResourceApplyAdagradAttr is an optional argument to ResourceApplyAdagrad. -type ResourceApplyAdagradAttr func(optionalAttr) - -// ResourceApplyAdagradUseLocking sets the optional use_locking attribute to value. -// -// value: If `True`, updating of the var and accum tensors will be protected -// by a lock; otherwise the behavior is undefined, but may exhibit less -// contention. -// If not specified, defaults to false -func ResourceApplyAdagradUseLocking(value bool) ResourceApplyAdagradAttr { - return func(m optionalAttr) { - m["use_locking"] = value - } -} - -// Update '*var' according to the adagrad scheme. -// -// accum += grad * grad -// var -= lr * grad * (1 / sqrt(accum)) -// -// Arguments: -// var_: Should be from a Variable(). -// accum: Should be from a Variable(). -// lr: Scaling factor. Must be a scalar. -// grad: The gradient. -// -// Returns the created operation. -func ResourceApplyAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, optional ...ResourceApplyAdagradAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ResourceApplyAdagrad", - Input: []tf.Input{ - var_, accum, lr, grad, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - // Returns element-wise remainder of division. This emulates C semantics in that // // the result here is consistent with a truncating divide. E.g. `truncate(x / y) * @@ -9405,160 +9095,412 @@ func TensorArrayV3ElementShape(value tf.Shape) TensorArrayV3Attr { return func(m optionalAttr) { m["element_shape"] = value } -} +} + +// TensorArrayV3DynamicSize sets the optional dynamic_size attribute to value. +// +// value: A boolean that determines whether writes to the TensorArray +// are allowed to grow the size. By default, this is not allowed. +// If not specified, defaults to false +func TensorArrayV3DynamicSize(value bool) TensorArrayV3Attr { + return func(m optionalAttr) { + m["dynamic_size"] = value + } +} + +// TensorArrayV3ClearAfterRead sets the optional clear_after_read attribute to value. +// +// value: If true (default), Tensors in the TensorArray are cleared +// after being read. This disables multiple read semantics but allows early +// release of memory. +// If not specified, defaults to true +func TensorArrayV3ClearAfterRead(value bool) TensorArrayV3Attr { + return func(m optionalAttr) { + m["clear_after_read"] = value + } +} + +// TensorArrayV3IdenticalElementShapes sets the optional identical_element_shapes attribute to value. +// +// value: If true (default is false), then all +// elements in the TensorArray will be expected to have have identical shapes. +// This allows certain behaviors, like dynamically checking for +// consistent shapes on write, and being able to fill in properly +// shaped zero tensors on stack -- even if the element_shape attribute +// is not fully defined. +// If not specified, defaults to false +func TensorArrayV3IdenticalElementShapes(value bool) TensorArrayV3Attr { + return func(m optionalAttr) { + m["identical_element_shapes"] = value + } +} + +// TensorArrayV3TensorArrayName sets the optional tensor_array_name attribute to value. +// +// value: Overrides the name used for the temporary tensor_array +// resource. Default value is the name of the 'TensorArray' op (which +// is guaranteed unique). +// If not specified, defaults to "" +func TensorArrayV3TensorArrayName(value string) TensorArrayV3Attr { + return func(m optionalAttr) { + m["tensor_array_name"] = value + } +} + +// An array of Tensors of given size. +// +// Write data via Write and read via Read or Pack. +// +// Arguments: +// size: The size of the array. +// dtype: The type of the elements on the tensor_array. +// +// Returns The handle to the TensorArray.A scalar used to control gradient flow. +func TensorArrayV3(scope *Scope, size tf.Output, dtype tf.DataType, optional ...TensorArrayV3Attr) (handle tf.Output, flow tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "TensorArrayV3", + Input: []tf.Input{ + size, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// MaxPool3DAttr is an optional argument to MaxPool3D. +type MaxPool3DAttr func(optionalAttr) + +// MaxPool3DDataFormat sets the optional data_format attribute to value. +// +// value: The data format of the input and output data. With the +// default format "NDHWC", the data is stored in the order of: +// [batch, in_depth, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCDHW", the data storage order is: +// [batch, in_channels, in_depth, in_height, in_width]. +// If not specified, defaults to "NDHWC" +func MaxPool3DDataFormat(value string) MaxPool3DAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Performs 3D max pooling on the input. +// +// Arguments: +// input: Shape `[batch, depth, rows, cols, channels]` tensor to pool over. +// ksize: 1-D tensor of length 5. The size of the window for each dimension of +// the input tensor. Must have `ksize[0] = ksize[4] = 1`. +// strides: 1-D tensor of length 5. The stride of the sliding window for each +// dimension of `input`. Must have `strides[0] = strides[4] = 1`. +// padding: The type of padding algorithm to use. +// +// Returns The max pooled output tensor. +func MaxPool3D(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPool3DAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MaxPool3D", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the gradients of 3-D convolution with respect to the input. +// +// DEPRECATED at GraphDef version 10: Use Conv3DBackpropInputV2 +// +// Arguments: +// input: Shape `[batch, depth, rows, cols, in_channels]`. +// filter: Shape `[depth, rows, cols, in_channels, out_channels]`. +// `in_channels` must match between `input` and `filter`. +// out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols, +// out_channels]`. +// strides: 1-D tensor of length 5. The stride of the sliding window for each +// dimension of `input`. Must have `strides[0] = strides[4] = 1`. +// padding: The type of padding algorithm to use. +func Conv3DBackpropInput(scope *Scope, input tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, padding string) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"strides": strides, "padding": padding} + opspec := tf.OpSpec{ + Type: "Conv3DBackpropInput", + Input: []tf.Input{ + input, filter, out_backprop, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResourceApplyProximalAdagradAttr is an optional argument to ResourceApplyProximalAdagrad. +type ResourceApplyProximalAdagradAttr func(optionalAttr) + +// ResourceApplyProximalAdagradUseLocking sets the optional use_locking attribute to value. +// +// value: If True, updating of the var and accum tensors will be protected by +// a lock; otherwise the behavior is undefined, but may exhibit less contention. +// If not specified, defaults to false +func ResourceApplyProximalAdagradUseLocking(value bool) ResourceApplyProximalAdagradAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Update '*var' and '*accum' according to FOBOS with Adagrad learning rate. +// +// accum += grad * grad +// prox_v = var - lr * grad * (1 / sqrt(accum)) +// var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0} +// +// Arguments: +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// lr: Scaling factor. Must be a scalar. +// l1: L1 regularization. Must be a scalar. +// l2: L2 regularization. Must be a scalar. +// grad: The gradient. +// +// Returns the created operation. +func ResourceApplyProximalAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, grad tf.Output, optional ...ResourceApplyProximalAdagradAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceApplyProximalAdagrad", + Input: []tf.Input{ + var_, accum, lr, l1, l2, grad, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// MutableHashTableOfTensorsV2Attr is an optional argument to MutableHashTableOfTensorsV2. +type MutableHashTableOfTensorsV2Attr func(optionalAttr) + +// MutableHashTableOfTensorsV2Container sets the optional container attribute to value. +// +// value: If non-empty, this table is placed in the given container. +// Otherwise, a default container is used. +// If not specified, defaults to "" +func MutableHashTableOfTensorsV2Container(value string) MutableHashTableOfTensorsV2Attr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// MutableHashTableOfTensorsV2SharedName sets the optional shared_name attribute to value. +// +// value: If non-empty, this table is shared under the given name across +// multiple sessions. +// If not specified, defaults to "" +func MutableHashTableOfTensorsV2SharedName(value string) MutableHashTableOfTensorsV2Attr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// MutableHashTableOfTensorsV2UseNodeNameSharing sets the optional use_node_name_sharing attribute to value. +// If not specified, defaults to false +func MutableHashTableOfTensorsV2UseNodeNameSharing(value bool) MutableHashTableOfTensorsV2Attr { + return func(m optionalAttr) { + m["use_node_name_sharing"] = value + } +} + +// MutableHashTableOfTensorsV2ValueShape sets the optional value_shape attribute to value. +// If not specified, defaults to <> +func MutableHashTableOfTensorsV2ValueShape(value tf.Shape) MutableHashTableOfTensorsV2Attr { + return func(m optionalAttr) { + m["value_shape"] = value + } +} + +// Creates an empty hash table. +// +// This op creates a mutable hash table, specifying the type of its keys and +// values. Each value must be a vector. Data can be inserted into the table using +// the insert operations. It does not support the initialization operation. +// +// Arguments: +// key_dtype: Type of the table keys. +// value_dtype: Type of the table values. +// +// Returns Handle to a table. +func MutableHashTableOfTensorsV2(scope *Scope, key_dtype tf.DataType, value_dtype tf.DataType, optional ...MutableHashTableOfTensorsV2Attr) (table_handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"key_dtype": key_dtype, "value_dtype": value_dtype} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MutableHashTableOfTensorsV2", -// TensorArrayV3DynamicSize sets the optional dynamic_size attribute to value. -// -// value: A boolean that determines whether writes to the TensorArray -// are allowed to grow the size. By default, this is not allowed. -// If not specified, defaults to false -func TensorArrayV3DynamicSize(value bool) TensorArrayV3Attr { - return func(m optionalAttr) { - m["dynamic_size"] = value + Attrs: attrs, } + op := scope.AddOperation(opspec) + return op.Output(0) } -// TensorArrayV3ClearAfterRead sets the optional clear_after_read attribute to value. +// HashTableV2Attr is an optional argument to HashTableV2. +type HashTableV2Attr func(optionalAttr) + +// HashTableV2Container sets the optional container attribute to value. // -// value: If true (default), Tensors in the TensorArray are cleared -// after being read. This disables multiple read semantics but allows early -// release of memory. -// If not specified, defaults to true -func TensorArrayV3ClearAfterRead(value bool) TensorArrayV3Attr { +// value: If non-empty, this table is placed in the given container. +// Otherwise, a default container is used. +// If not specified, defaults to "" +func HashTableV2Container(value string) HashTableV2Attr { return func(m optionalAttr) { - m["clear_after_read"] = value + m["container"] = value } } -// TensorArrayV3IdenticalElementShapes sets the optional identical_element_shapes attribute to value. +// HashTableV2SharedName sets the optional shared_name attribute to value. // -// value: If true (default is false), then all -// elements in the TensorArray will be expected to have have identical shapes. -// This allows certain behaviors, like dynamically checking for -// consistent shapes on write, and being able to fill in properly -// shaped zero tensors on stack -- even if the element_shape attribute -// is not fully defined. -// If not specified, defaults to false -func TensorArrayV3IdenticalElementShapes(value bool) TensorArrayV3Attr { +// value: If non-empty, this table is shared under the given name across +// multiple sessions. +// If not specified, defaults to "" +func HashTableV2SharedName(value string) HashTableV2Attr { return func(m optionalAttr) { - m["identical_element_shapes"] = value + m["shared_name"] = value } } -// TensorArrayV3TensorArrayName sets the optional tensor_array_name attribute to value. +// HashTableV2UseNodeNameSharing sets the optional use_node_name_sharing attribute to value. // -// value: Overrides the name used for the temporary tensor_array -// resource. Default value is the name of the 'TensorArray' op (which -// is guaranteed unique). -// If not specified, defaults to "" -func TensorArrayV3TensorArrayName(value string) TensorArrayV3Attr { +// value: If true and shared_name is empty, the table is shared +// using the node name. +// If not specified, defaults to false +func HashTableV2UseNodeNameSharing(value bool) HashTableV2Attr { return func(m optionalAttr) { - m["tensor_array_name"] = value + m["use_node_name_sharing"] = value } } -// An array of Tensors of given size. +// Creates a non-initialized hash table. // -// Write data via Write and read via Read or Pack. +// This op creates a hash table, specifying the type of its keys and values. +// Before using the table you will have to initialize it. After initialization the +// table will be immutable. // // Arguments: -// size: The size of the array. -// dtype: The type of the elements on the tensor_array. +// key_dtype: Type of the table keys. +// value_dtype: Type of the table values. // -// Returns The handle to the TensorArray.A scalar used to control gradient flow. -func TensorArrayV3(scope *Scope, size tf.Output, dtype tf.DataType, optional ...TensorArrayV3Attr) (handle tf.Output, flow tf.Output) { +// Returns Handle to a table. +func HashTableV2(scope *Scope, key_dtype tf.DataType, value_dtype tf.DataType, optional ...HashTableV2Attr) (table_handle tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"dtype": dtype} + attrs := map[string]interface{}{"key_dtype": key_dtype, "value_dtype": value_dtype} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "TensorArrayV3", - Input: []tf.Input{ - size, - }, + Type: "HashTableV2", + Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) + return op.Output(0) } -// MaxPool3DAttr is an optional argument to MaxPool3D. -type MaxPool3DAttr func(optionalAttr) +// MapUnstageNoKeyAttr is an optional argument to MapUnstageNoKey. +type MapUnstageNoKeyAttr func(optionalAttr) -// MaxPool3DDataFormat sets the optional data_format attribute to value. +// MapUnstageNoKeyCapacity sets the optional capacity attribute to value. +// If not specified, defaults to 0 // -// value: The data format of the input and output data. With the -// default format "NDHWC", the data is stored in the order of: -// [batch, in_depth, in_height, in_width, in_channels]. -// Alternatively, the format could be "NCDHW", the data storage order is: -// [batch, in_channels, in_depth, in_height, in_width]. -// If not specified, defaults to "NDHWC" -func MaxPool3DDataFormat(value string) MaxPool3DAttr { +// REQUIRES: value >= 0 +func MapUnstageNoKeyCapacity(value int64) MapUnstageNoKeyAttr { return func(m optionalAttr) { - m["data_format"] = value + m["capacity"] = value } } -// Performs 3D max pooling on the input. +// MapUnstageNoKeyMemoryLimit sets the optional memory_limit attribute to value. +// If not specified, defaults to 0 // -// Arguments: -// input: Shape `[batch, depth, rows, cols, channels]` tensor to pool over. -// ksize: 1-D tensor of length 5. The size of the window for each dimension of -// the input tensor. Must have `ksize[0] = ksize[4] = 1`. -// strides: 1-D tensor of length 5. The stride of the sliding window for each -// dimension of `input`. Must have `strides[0] = strides[4] = 1`. -// padding: The type of padding algorithm to use. +// REQUIRES: value >= 0 +func MapUnstageNoKeyMemoryLimit(value int64) MapUnstageNoKeyAttr { + return func(m optionalAttr) { + m["memory_limit"] = value + } +} + +// MapUnstageNoKeyContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func MapUnstageNoKeyContainer(value string) MapUnstageNoKeyAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// MapUnstageNoKeySharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func MapUnstageNoKeySharedName(value string) MapUnstageNoKeyAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Op removes and returns a random (key, value) // -// Returns The max pooled output tensor. -func MaxPool3D(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPool3DAttr) (output tf.Output) { +// from the underlying container. If the underlying container +// does not contain elements, the op will block until it does. +func MapUnstageNoKey(scope *Scope, indices tf.Output, dtypes []tf.DataType, optional ...MapUnstageNoKeyAttr) (key tf.Output, values []tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + attrs := map[string]interface{}{"dtypes": dtypes} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "MaxPool3D", + Type: "MapUnstageNoKey", Input: []tf.Input{ - input, + indices, }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Computes the gradients of 3-D convolution with respect to the input. -// -// DEPRECATED at GraphDef version 10: Use Conv3DBackpropInputV2 -// -// Arguments: -// input: Shape `[batch, depth, rows, cols, in_channels]`. -// filter: Shape `[depth, rows, cols, in_channels, out_channels]`. -// `in_channels` must match between `input` and `filter`. -// out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols, -// out_channels]`. -// strides: 1-D tensor of length 5. The stride of the sliding window for each -// dimension of `input`. Must have `strides[0] = strides[4] = 1`. -// padding: The type of padding algorithm to use. -func Conv3DBackpropInput(scope *Scope, input tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, padding string) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"strides": strides, "padding": padding} - opspec := tf.OpSpec{ - Type: "Conv3DBackpropInput", - Input: []tf.Input{ - input, filter, out_backprop, - }, - Attrs: attrs, + var idx int + var err error + key = op.Output(idx) + if values, idx, err = makeOutputList(op, idx, "values"); err != nil { + scope.UpdateErr("MapUnstageNoKey", err) + return } - op := scope.AddOperation(opspec) - return op.Output(0) + return key, values } // Inverse 2D fast Fourier transform. @@ -12219,38 +12161,95 @@ func MutableDenseHashTableV2InitialNumBuckets(value int64) MutableDenseHashTable // If not specified, defaults to 0.8 func MutableDenseHashTableV2MaxLoadFactor(value float32) MutableDenseHashTableV2Attr { return func(m optionalAttr) { - m["max_load_factor"] = value + m["max_load_factor"] = value + } +} + +// Creates an empty hash table that uses tensors as the backing store. +// +// It uses "open addressing" with quadratic reprobing to resolve +// collisions. +// +// This op creates a mutable hash table, specifying the type of its keys and +// values. Each value must be a scalar. Data can be inserted into the table using +// the insert operations. It does not support the initialization operation. +// +// Arguments: +// empty_key: The key used to represent empty key buckets internally. Must not +// be used in insert or lookup operations. +// value_dtype: Type of the table values. +// +// Returns Handle to a table. +func MutableDenseHashTableV2(scope *Scope, empty_key tf.Output, value_dtype tf.DataType, optional ...MutableDenseHashTableV2Attr) (table_handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"value_dtype": value_dtype} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MutableDenseHashTableV2", + Input: []tf.Input{ + empty_key, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + 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 } } -// Creates an empty hash table that uses tensors as the backing store. -// -// It uses "open addressing" with quadratic reprobing to resolve -// collisions. -// -// This op creates a mutable hash table, specifying the type of its keys and -// values. Each value must be a scalar. Data can be inserted into the table using -// the insert operations. It does not support the initialization operation. -// -// Arguments: -// empty_key: The key used to represent empty key buckets internally. Must not -// be used in insert or lookup operations. -// value_dtype: Type of the table values. -// -// Returns Handle to a table. -func MutableDenseHashTableV2(scope *Scope, empty_key tf.Output, value_dtype tf.DataType, optional ...MutableDenseHashTableV2Attr) (table_handle tf.Output) { +// 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{}{"value_dtype": value_dtype} + attrs := map[string]interface{}{"dtypes": dtypes} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "MutableDenseHashTableV2", - Input: []tf.Input{ - empty_key, - }, + Type: "StageSize", + Attrs: attrs, } op := scope.AddOperation(opspec) @@ -12999,6 +12998,56 @@ func Neg(scope *Scope, x tf.Output) (y tf.Output) { return op.Output(0) } +// FakeQuantWithMinMaxVarsAttr is an optional argument to FakeQuantWithMinMaxVars. +type FakeQuantWithMinMaxVarsAttr func(optionalAttr) + +// FakeQuantWithMinMaxVarsNumBits sets the optional num_bits attribute to value. +// If not specified, defaults to 8 +func FakeQuantWithMinMaxVarsNumBits(value int64) FakeQuantWithMinMaxVarsAttr { + return func(m optionalAttr) { + m["num_bits"] = value + } +} + +// FakeQuantWithMinMaxVarsNarrowRange sets the optional narrow_range attribute to value. +// If not specified, defaults to false +func FakeQuantWithMinMaxVarsNarrowRange(value bool) FakeQuantWithMinMaxVarsAttr { + return func(m optionalAttr) { + m["narrow_range"] = value + } +} + +// Fake-quantize the 'inputs' tensor of type float via global float scalars `min` +// +// and `max` to 'outputs' tensor of same shape as `inputs`. +// +// `[min; max]` define the clamping range for the `inputs` data. +// `inputs` values are quantized into the quantization range (`[0; 2^num_bits - 1]` +// when `narrow_range` is false and `[1; 2^num_bits - 1]` when it is true) and +// then de-quantized and output as floats in `[min; max]` interval. +// `num_bits` is the bitwidth of the quantization; between 2 and 8, inclusive. +// +// This operation has a gradient and thus allows for training `min` and `max` +// values. +func FakeQuantWithMinMaxVars(scope *Scope, inputs tf.Output, min tf.Output, max tf.Output, optional ...FakeQuantWithMinMaxVarsAttr) (outputs tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "FakeQuantWithMinMaxVars", + Input: []tf.Input{ + inputs, min, max, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Writes a `Summary` protocol buffer with a histogram. // // The generated @@ -16814,6 +16863,70 @@ func ResourceApplyPowerSign(scope *Scope, var_ tf.Output, m tf.Output, lr tf.Out return scope.AddOperation(opspec) } +// MutableHashTableV2Attr is an optional argument to MutableHashTableV2. +type MutableHashTableV2Attr func(optionalAttr) + +// MutableHashTableV2Container sets the optional container attribute to value. +// +// value: If non-empty, this table is placed in the given container. +// Otherwise, a default container is used. +// If not specified, defaults to "" +func MutableHashTableV2Container(value string) MutableHashTableV2Attr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// MutableHashTableV2SharedName sets the optional shared_name attribute to value. +// +// value: If non-empty, this table is shared under the given name across +// multiple sessions. +// If not specified, defaults to "" +func MutableHashTableV2SharedName(value string) MutableHashTableV2Attr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// MutableHashTableV2UseNodeNameSharing sets the optional use_node_name_sharing attribute to value. +// +// value: If true and shared_name is empty, the table is shared +// using the node name. +// If not specified, defaults to false +func MutableHashTableV2UseNodeNameSharing(value bool) MutableHashTableV2Attr { + return func(m optionalAttr) { + m["use_node_name_sharing"] = value + } +} + +// Creates an empty hash table. +// +// This op creates a mutable hash table, specifying the type of its keys and +// values. Each value must be a scalar. Data can be inserted into the table using +// the insert operations. It does not support the initialization operation. +// +// Arguments: +// key_dtype: Type of the table keys. +// value_dtype: Type of the table values. +// +// Returns Handle to a table. +func MutableHashTableV2(scope *Scope, key_dtype tf.DataType, value_dtype tf.DataType, optional ...MutableHashTableV2Attr) (table_handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"key_dtype": key_dtype, "value_dtype": value_dtype} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MutableHashTableV2", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Deprecated. Disallowed in GraphDef version >= 2. // // DEPRECATED at GraphDef version 2: Use AdjustContrastv2 instead @@ -23851,69 +23964,6 @@ func Conv2D(scope *Scope, input tf.Output, filter tf.Output, strides []int64, pa return op.Output(0) } -// FakeQuantWithMinMaxArgsAttr is an optional argument to FakeQuantWithMinMaxArgs. -type FakeQuantWithMinMaxArgsAttr func(optionalAttr) - -// FakeQuantWithMinMaxArgsMin sets the optional min attribute to value. -// If not specified, defaults to -6 -func FakeQuantWithMinMaxArgsMin(value float32) FakeQuantWithMinMaxArgsAttr { - return func(m optionalAttr) { - m["min"] = value - } -} - -// FakeQuantWithMinMaxArgsMax sets the optional max attribute to value. -// If not specified, defaults to 6 -func FakeQuantWithMinMaxArgsMax(value float32) FakeQuantWithMinMaxArgsAttr { - return func(m optionalAttr) { - m["max"] = value - } -} - -// FakeQuantWithMinMaxArgsNumBits sets the optional num_bits attribute to value. -// If not specified, defaults to 8 -func FakeQuantWithMinMaxArgsNumBits(value int64) FakeQuantWithMinMaxArgsAttr { - return func(m optionalAttr) { - m["num_bits"] = value - } -} - -// FakeQuantWithMinMaxArgsNarrowRange sets the optional narrow_range attribute to value. -// If not specified, defaults to false -func FakeQuantWithMinMaxArgsNarrowRange(value bool) FakeQuantWithMinMaxArgsAttr { - return func(m optionalAttr) { - m["narrow_range"] = value - } -} - -// Fake-quantize the 'inputs' tensor, type float to 'outputs' tensor of same type. -// -// Attributes `[min; max]` define the clamping range for the `inputs` data. -// `inputs` values are quantized into the quantization range (`[0; 2^num_bits - 1]` -// when `narrow_range` is false and `[1; 2^num_bits - 1]` when it is true) and -// then de-quantized and output as floats in `[min; max]` interval. -// `num_bits` is the bitwidth of the quantization; between 2 and 8, inclusive. -// -// Quantization is called fake since the output is still in floating point. -func FakeQuantWithMinMaxArgs(scope *Scope, inputs tf.Output, optional ...FakeQuantWithMinMaxArgsAttr) (outputs tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "FakeQuantWithMinMaxArgs", - Input: []tf.Input{ - inputs, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // StageAttr is an optional argument to Stage. type StageAttr func(optionalAttr) @@ -28230,53 +28280,3 @@ func QuantizedInstanceNorm(scope *Scope, x tf.Output, x_min tf.Output, x_max tf. op := scope.AddOperation(opspec) return op.Output(0), op.Output(1), op.Output(2) } - -// FakeQuantWithMinMaxVarsAttr is an optional argument to FakeQuantWithMinMaxVars. -type FakeQuantWithMinMaxVarsAttr func(optionalAttr) - -// FakeQuantWithMinMaxVarsNumBits sets the optional num_bits attribute to value. -// If not specified, defaults to 8 -func FakeQuantWithMinMaxVarsNumBits(value int64) FakeQuantWithMinMaxVarsAttr { - return func(m optionalAttr) { - m["num_bits"] = value - } -} - -// FakeQuantWithMinMaxVarsNarrowRange sets the optional narrow_range attribute to value. -// If not specified, defaults to false -func FakeQuantWithMinMaxVarsNarrowRange(value bool) FakeQuantWithMinMaxVarsAttr { - return func(m optionalAttr) { - m["narrow_range"] = value - } -} - -// Fake-quantize the 'inputs' tensor of type float via global float scalars `min` -// -// and `max` to 'outputs' tensor of same shape as `inputs`. -// -// `[min; max]` define the clamping range for the `inputs` data. -// `inputs` values are quantized into the quantization range (`[0; 2^num_bits - 1]` -// when `narrow_range` is false and `[1; 2^num_bits - 1]` when it is true) and -// then de-quantized and output as floats in `[min; max]` interval. -// `num_bits` is the bitwidth of the quantization; between 2 and 8, inclusive. -// -// This operation has a gradient and thus allows for training `min` and `max` -// values. -func FakeQuantWithMinMaxVars(scope *Scope, inputs tf.Output, min tf.Output, max tf.Output, optional ...FakeQuantWithMinMaxVarsAttr) (outputs tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "FakeQuantWithMinMaxVars", - Input: []tf.Input{ - inputs, min, max, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} diff --git a/tensorflow/java/maven/libtensorflow/pom.xml b/tensorflow/java/maven/libtensorflow/pom.xml index d35bb4111271c11839a160517dc9695ead5b46e9..7f3a83b195308b3261049c2845d018280dd61601 100644 --- a/tensorflow/java/maven/libtensorflow/pom.xml +++ b/tensorflow/java/maven/libtensorflow/pom.xml @@ -6,7 +6,7 @@ org.tensorflow parentpom - 1.6.0-rc1 + 1.7.0-rc0 ../ libtensorflow diff --git a/tensorflow/java/maven/libtensorflow_jni/pom.xml b/tensorflow/java/maven/libtensorflow_jni/pom.xml index d9ba1bbbfb91170257f64a56f47c6c980e8a9570..cc436ff8409a5a17d69dbe0fa0ce9c6333349987 100644 --- a/tensorflow/java/maven/libtensorflow_jni/pom.xml +++ b/tensorflow/java/maven/libtensorflow_jni/pom.xml @@ -6,7 +6,7 @@ org.tensorflow parentpom - 1.6.0-rc1 + 1.7.0-rc0 ../ libtensorflow_jni diff --git a/tensorflow/java/maven/libtensorflow_jni_gpu/pom.xml b/tensorflow/java/maven/libtensorflow_jni_gpu/pom.xml index f6f532c2c10d0a4dad9fc2d7750ea708652000b1..47f678382aad910559395cd0b304321c30c92ede 100644 --- a/tensorflow/java/maven/libtensorflow_jni_gpu/pom.xml +++ b/tensorflow/java/maven/libtensorflow_jni_gpu/pom.xml @@ -6,7 +6,7 @@ org.tensorflow parentpom - 1.6.0-rc1 + 1.7.0-rc0 ../ libtensorflow_jni_gpu diff --git a/tensorflow/java/maven/pom.xml b/tensorflow/java/maven/pom.xml index 0a6b3d23d7d37515cf275e6a46842e32ada4fee1..42d32810a263df7ba1044e1ff3e7c4e8124d478e 100644 --- a/tensorflow/java/maven/pom.xml +++ b/tensorflow/java/maven/pom.xml @@ -6,7 +6,7 @@ 4.0.0 org.tensorflow parentpom - 1.6.0-rc1 + 1.7.0-rc0 pom https://www.tensorflow.org diff --git a/tensorflow/java/maven/proto/pom.xml b/tensorflow/java/maven/proto/pom.xml index 1d8e8723731f959c8142f0648fc805593d7beac8..463893ce62dc7e72f4cd1c909ae56d097d060035 100644 --- a/tensorflow/java/maven/proto/pom.xml +++ b/tensorflow/java/maven/proto/pom.xml @@ -6,7 +6,7 @@ org.tensorflow parentpom - 1.6.0-rc1 + 1.7.0-rc0 ../ proto diff --git a/tensorflow/java/maven/tensorflow-android/pom-android.xml.template b/tensorflow/java/maven/tensorflow-android/pom-android.xml.template index 5cbd0c898dc52ec5dfb72f0a2ac893d492a7d4be..37d2372d7b09f6f144e7abb145cb75bf98356615 100644 --- a/tensorflow/java/maven/tensorflow-android/pom-android.xml.template +++ b/tensorflow/java/maven/tensorflow-android/pom-android.xml.template @@ -20,10 +20,8 @@ UTF-8 - ${build_number} ${build_commit_id} ${build_type} - ${build_url} diff --git a/tensorflow/java/maven/tensorflow-android/update.py b/tensorflow/java/maven/tensorflow-android/update.py index 4ae666e4e5351f1bdaf79d1b5cfdb63b0f811e2b..2206d800ca1fe82c5596ff39e56518bc5aea6211 100644 --- a/tensorflow/java/maven/tensorflow-android/update.py +++ b/tensorflow/java/maven/tensorflow-android/update.py @@ -45,6 +45,9 @@ def get_json(url): def get_commit_id(build_info): """Fetch the git commit id from the build info json object.""" + release_commit_id = build_info.get('build_commit_id') + if release_commit_id: + return release_commit_id actions = build_info.get('actions') build_data = next( a for a in actions @@ -95,20 +98,12 @@ def main(): release_prefix = 'https://storage.googleapis.com/tensorflow/libtensorflow' info_url = '%s/android_buildinfo-%s.json' % (release_prefix, args.version) aar_url = '%s/tensorflow-%s.aar' % (release_prefix, args.version) - build_type = 'release-matrix-android2' + build_type = 'release-android' # Retrieve build information build_info = get_json(info_url) # Check all required build info is present - if build_info.get('result') != 'SUCCESS': - raise ValueError('Invalid json: %s' % build_info) - build_url = build_info.get('url') - if not build_url: - raise ValueError('Missing url: %s' % build_info) - build_number = build_info.get('number') - if not build_number: - raise ValueError('Missing build number: %s' % build_info) build_commit_id = get_commit_id(build_info) if not build_commit_id: raise ValueError('Missing commit id: %s' % build_info) @@ -119,9 +114,7 @@ def main(): f.write( template.substitute({ 'build_commit_id': build_commit_id, - 'build_number': build_number, 'build_type': build_type, - 'build_url': build_url, 'version': args.version })) diff --git a/tensorflow/java/maven/tensorflow/pom.xml b/tensorflow/java/maven/tensorflow/pom.xml index 5c1b55085c5df1ec473a3f4e0bf750b236cfc264..60e7f3c199f1451a054f6a779a63a98ff428bd87 100644 --- a/tensorflow/java/maven/tensorflow/pom.xml +++ b/tensorflow/java/maven/tensorflow/pom.xml @@ -6,7 +6,7 @@ org.tensorflow parentpom - 1.6.0-rc1 + 1.7.0-rc0 ../ tensorflow diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD index 4c8c73548cac261131c2950d725ac41c6af3dab0..3cbeb34c5471ccb6ff8f36f2de2de57d67f974f8 100644 --- a/tensorflow/python/BUILD +++ b/tensorflow/python/BUILD @@ -28,6 +28,7 @@ load("//tensorflow:tensorflow.bzl", "py_tests") load("//tensorflow:tensorflow.bzl", "tf_py_build_info_genrule") load("//tensorflow:tensorflow.bzl", "tf_py_wrap_cc") load("//tensorflow:tensorflow.bzl", "tf_cc_shared_object") +load("//tensorflow:tensorflow.bzl", "tf_custom_op_library_additional_deps_impl") load("//tensorflow:tensorflow.bzl", "cuda_py_test") load("//tensorflow:tensorflow.bzl", "cuda_py_tests") load("//tensorflow/core:platform/default/build_config.bzl", "pyx_library") @@ -86,39 +87,39 @@ py_library( ":ops", ":platform", ":pywrap_tensorflow", + ":saver_test_utils", ":script_ops", ":session_ops", ":sets", ":sparse_ops", ":spectral_ops", + ":spectral_ops_test_util", ":standard_ops", ":state_ops", ":string_ops", + ":subscribe", ":summary", ":tensor_array_ops", - ":training", - ":saver_test_utils", - ":subscribe", ":test_ops", # TODO: Break testing code out into separate rule. - ":tf_item", ":tf_cluster", + ":tf_item", ":tf_optimizer", + ":training", ":util", ":weights_broadcast_ops", - "//third_party/py/numpy", + "//tensorflow/contrib:contrib_py", "//tensorflow/core:protos_all_py", "//tensorflow/python/data", "//tensorflow/python/estimator:estimator_py", "//tensorflow/python/feature_column:feature_column_py", "//tensorflow/python/keras", - "//tensorflow/python/ops/losses", "//tensorflow/python/ops/distributions", "//tensorflow/python/ops/linalg", + "//tensorflow/python/ops/losses", "//tensorflow/python/profiler", "//tensorflow/python/saved_model", - ] + if_not_windows([ - "//tensorflow/contrib:contrib_py", - ]), + "//third_party/py/numpy", + ], ) tf_py_build_info_genrule() @@ -765,6 +766,31 @@ py_library( ], ) +py_library( + name = "smart_cond", + srcs = ["framework/smart_cond.py"], + srcs_version = "PY2AND3", + deps = [ + ":control_flow_ops", + ":tensor_util", + ], +) + +py_test( + name = "smart_cond_test", + size = "small", + srcs = ["framework/smart_cond_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":client_testlib", + ":constant_op", + ":framework_ops", + ":math_ops", + ":session", + ":smart_cond", + ], +) + py_library( name = "sparse_tensor", srcs = ["framework/sparse_tensor.py"], @@ -921,7 +947,6 @@ py_test( srcs = ["framework/contrib_test.py"], main = "framework/contrib_test.py", srcs_version = "PY2AND3", - tags = ["no_windows"], deps = [ "//tensorflow:tensorflow_py", "//tensorflow/python:client_testlib", @@ -1286,7 +1311,6 @@ py_test( srcs = ["framework/dtypes_test.py"], main = "framework/dtypes_test.py", srcs_version = "PY2AND3", - tags = ["no_windows"], deps = [ ":framework_for_generated_wrappers", ":framework_test_lib", @@ -1628,7 +1652,6 @@ py_test( size = "small", srcs = ["ops/clip_ops_test.py"], srcs_version = "PY2AND3", - tags = ["no_windows"], deps = [ ":client_testlib", ":clip_ops", @@ -1750,6 +1773,7 @@ py_library( py_library( name = "gradients", srcs = [ + "ops/custom_gradient.py", "ops/gradients.py", "ops/gradients_impl.py", ], @@ -1763,6 +1787,7 @@ py_library( ":control_flow_util", ":framework", ":framework_for_generated_wrappers", + ":framework_ops", ":functional_ops", ":image_grad", ":linalg_grad", @@ -1775,6 +1800,8 @@ py_library( ":platform", ":spectral_grad", ":util", + "//tensorflow/python/eager:context", + "//tensorflow/python/eager:tape", "//third_party/py/numpy", "@six_archive//:six", ], @@ -1817,13 +1844,16 @@ py_library( ":control_flow_ops", ":framework", ":framework_for_generated_wrappers", + ":gradients", ":image_ops_gen", ":math_ops", + ":nn", ":nn_ops_gen", ":random_ops", ":string_ops", ":util", ":variables", + "//third_party/py/numpy", ], ) @@ -2688,7 +2718,6 @@ cuda_py_test( ], data = ["//tensorflow/core:image_testdata"], shard_count = 5, - tags = ["no_windows"], ) cuda_py_test( @@ -2857,6 +2886,7 @@ py_library( srcs = ["training/checkpointable.py"], srcs_version = "PY2AND3", deps = [ + ":array_ops", ":dtypes", ":io_ops_gen", ":ops", @@ -3050,7 +3080,6 @@ tf_proto_library( "framework/cpp_shape_inference.proto", ], ), - go_api_version = 2, ) tf_proto_library_py( @@ -3225,6 +3254,10 @@ tf_py_wrap_cc( "util/transform_graph.i", "util/util.i", ], + win_def_file = select({ + "//tensorflow:windows": ":pywrap_tensorflow_filtered_def_file", + "//conditions:default": None, + }), deps = [ ":bfloat16_lib", ":cost_analyzer_lib", @@ -3268,6 +3301,65 @@ tf_py_wrap_cc( tf_additional_gdr_deps()), ) +# ** Targets for Windows build (start) ** +# We need the following targets to expose symbols from _pywrap_tensorflow.dll + +# Build a cc_binary from tf_custom_op_library_additional_deps_impl, +# it contains all object code from its dependencies. +cc_binary( + name = "tf_custom_op_library_additional_deps.so", + linkshared = 1, + linkstatic = 1, + deps = tf_custom_op_library_additional_deps_impl(), +) + +# Get a DEF file generated by parsing all object files +# of tf_custom_op_library_additional_deps.so +filegroup( + name = "pywrap_tensorflow_def_file", + srcs = [":tf_custom_op_library_additional_deps.so"], + output_group = "def_file", +) + +# Filter the DEF file to reduce the number of symbols to 64K or less. +# Note that we also write the name of the pyd file into DEF file so that +# the dynamic libraries of custom ops can find it at runtime. +genrule( + name = "pywrap_tensorflow_filtered_def_file", + srcs = [":pywrap_tensorflow_def_file"], + outs = ["pywrap_tensorflow_filtered_def_file.def"], + cmd = select({ + "//tensorflow:windows": """ + $(location @local_config_def_file_filter//:def_file_filter) \\ + --input $(location :pywrap_tensorflow_def_file) \\ + --output $@ \\ + --target _pywrap_tensorflow_internal.pyd + """, + "//conditions:default": "touch $@", # Just a placeholder for Unix platforms + }), + tools = ["@local_config_def_file_filter//:def_file_filter"], +) + +# Get the import library of _pywrap_tensorflow_internal.dll +filegroup( + name = "pywrap_tensorflow_import_lib_file", + srcs = [":_pywrap_tensorflow_internal.so"], + output_group = "interface_library", +) + +# Create a cc_import rule for the import library of _pywrap_tensorflow_internal.dll +# so that custom ops' dynamic libraries can link against it. +cc_import( + name = "pywrap_tensorflow_import_lib", + interface_library = select({ + "//tensorflow:windows": ":pywrap_tensorflow_import_lib_file", + "//conditions:default": "not_exsiting_on_unix.lib", # Just a placeholder for Unix platforms + }), + system_provided = 1, +) + +# ** Targets for Windows build (end) ** + py_library( name = "lib", srcs = [ @@ -3629,6 +3721,7 @@ py_test( ":framework_for_generated_wrappers", ":math_ops", ":state_ops_gen", + ":variable_scope", ":variables", "//tensorflow/core:protos_all_py", ], @@ -3639,7 +3732,6 @@ py_test( size = "small", srcs = ["lib/core/bfloat16_test.py"], srcs_version = "PY2AND3", - tags = ["no_windows"], deps = [ ":client_testlib", ":lib", @@ -3919,7 +4011,13 @@ py_test( size = "small", srcs = ["training/checkpoint_utils_test.py"], srcs_version = "PY2AND3", - tags = ["no_windows"], + tags = [ + "manual", + "no_cuda_on_cpu_tap", + "no_oss", + "no_windows", + "notap", + ], deps = [ ":client", ":client_testlib", @@ -3928,6 +4026,7 @@ py_test( ":partitioned_variables", ":platform", ":pywrap_tensorflow", + ":resource_variable_ops", ":state_ops", ":training", ":variable_scope", @@ -3940,7 +4039,6 @@ py_test( size = "small", srcs = ["training/checkpoint_ops_test.py"], srcs_version = "PY2AND3", - tags = ["no_windows"], deps = [ ":checkpoint_ops_gen", ":client", @@ -3957,15 +4055,31 @@ py_test( ], ) +py_test( + name = "warm_starting_util_test", + size = "small", + srcs = ["training/warm_starting_util_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":array_ops", + ":client_testlib", + ":dtypes", + ":framework_ops", + ":init_ops", + ":training", + ":variable_scope", + ":variables", + "//tensorflow/python/feature_column", + "//third_party/py/numpy", + ], +) + py_test( name = "monitored_session_test", size = "medium", srcs = ["training/monitored_session_test.py"], srcs_version = "PY2AND3", - tags = [ - "no_windows", - "notsan", # b/67945581 - ], + tags = ["notsan"], # b/67945581 deps = [ ":array_ops", ":client_testlib", @@ -4091,6 +4205,7 @@ py_library( ":control_flow_ops", ":framework_for_generated_wrappers", ":platform", + ":smart_cond", ":tensor_util", ":util", ":variable_scope", @@ -4711,6 +4826,7 @@ py_test( srcs_version = "PY2AND3", tags = [ "grappler", + "no_cuda_on_cpu_tap", "no_pip", ], deps = [ diff --git a/tensorflow/python/__init__.py b/tensorflow/python/__init__.py index 02ed5517ca895ab070a89f8810f77dadcff9212b..5a9cd7531db6482313005feebaadf953c6da3bb4 100644 --- a/tensorflow/python/__init__.py +++ b/tensorflow/python/__init__.py @@ -139,6 +139,10 @@ from tensorflow.python.ops import state_ops from tensorflow.python.ops import string_ops from tensorflow.python.ops import tensor_array_ops +# Eager execution +from tensorflow.python.eager.context import executing_eagerly +from tensorflow.python.framework.ops import enable_eager_execution + # Symbols whitelisted for export without documentation. # TODO(cwhipkey): review these and move to contrib, expose through # documentation, or remove. @@ -198,13 +202,9 @@ tf_export('TensorInfo')(TensorInfo) _allowed_symbols.extend([ 'arg_max', 'arg_min', - 'mul', # use tf.multiply instead. - 'neg', # use tf.negative instead. - 'sub', # use tf.subtract instead. 'create_partitioned_variables', 'deserialize_many_sparse', 'lin_space', - 'list_diff', # Use tf.listdiff instead. 'listdiff', # Use tf.listdiff instead. 'parse_single_sequence_example', 'serialize_many_sparse', @@ -294,6 +294,12 @@ _allowed_symbols.extend([ 'MONOLITHIC_BUILD', ]) +# Eager execution +_allowed_symbols.extend([ + 'enable_eager_execution', + 'executing_eagerly', +]) + # Remove all extra symbols that don't have a docstring or are not explicitly # referenced in the whitelist. remove_undocumented(__name__, _allowed_symbols, [ diff --git a/tensorflow/python/client/session.py b/tensorflow/python/client/session.py index f3c4fecdc0fde0436bea76cc774edaabe1bc07dd..924d62992a6f7c6e39b73752530eab70c6b5ce91 100644 --- a/tensorflow/python/client/session.py +++ b/tensorflow/python/client/session.py @@ -1085,7 +1085,10 @@ class BaseSession(SessionInterface): if isinstance(subfeed_val, ops.Tensor): raise TypeError('The value of a feed cannot be a tf.Tensor object. ' 'Acceptable feed values include Python scalars, ' - 'strings, lists, numpy ndarrays, or TensorHandles.') + 'strings, lists, numpy ndarrays, or TensorHandles.' + 'For reference, the tensor object was ' + + str(feed_val) + ' which was passed to the ' + 'feed with key ' + str(feed) + '.') subfeed_dtype = subfeed_t.dtype.as_numpy_dtype if isinstance(subfeed_val, int) and _convert_to_numpy_obj( @@ -1217,19 +1220,12 @@ class BaseSession(SessionInterface): compat.as_bytes(options.SerializeToString())) if options else None run_metadata_ptr = tf_session.TF_NewBuffer() if run_metadata else None try: - with errors.raise_exception_on_not_ok_status() as status: - if self._created_with_new_api: - results = tf_session.TF_SessionRun_wrapper( - self._session, options_ptr, {}, fetch_list, target_list, - run_metadata_ptr, status) - else: - results = tf_session.TF_Run(self._session, options_ptr, {}, - fetch_list, target_list, status, - run_metadata_ptr) - if fetch_handler: - results = fetch_handler.build_results(self, results) - else: - results = results[0] if results else None + results = self._call_tf_sessionrun( + options_ptr, {}, fetch_list, target_list, run_metadata_ptr) + if fetch_handler: + results = fetch_handler.build_results(self, results) + else: + results = results[0] if results else None if run_metadata: proto_data = tf_session.TF_GetBuffer(run_metadata_ptr) run_metadata.ParseFromString(compat.as_bytes(proto_data)) @@ -1250,13 +1246,7 @@ class BaseSession(SessionInterface): assert len(target_list) == 1 def _single_operation_run(): - with errors.raise_exception_on_not_ok_status() as status: - if self._created_with_new_api: - tf_session.TF_SessionRun_wrapper(self._session, None, {}, [], - target_list, None, status) - else: - tf_session.TF_Run(self._session, None, {}, [], target_list, status, - None) + self._call_tf_sessionrun(None, {}, [], target_list, None) return _single_operation_run elif isinstance(fetches, ops.Tensor): @@ -1266,13 +1256,7 @@ class BaseSession(SessionInterface): assert not target_list def _single_tensor_run(): - with errors.raise_exception_on_not_ok_status() as status: - if self._created_with_new_api: - results = tf_session.TF_SessionRun_wrapper( - self._session, None, {}, fetch_list, [], None, status) - else: - results = tf_session.TF_Run(self._session, None, {}, fetch_list, [], - status, None) + results = self._call_tf_sessionrun(None, {}, fetch_list, [], None) return results[0] return _single_tensor_run @@ -1280,13 +1264,8 @@ class BaseSession(SessionInterface): # In all other cases, we must use `fetch_handler` to build the # results for us. def _fetch_handler_run(): - with errors.raise_exception_on_not_ok_status() as status: - if self._created_with_new_api: - results = tf_session.TF_SessionRun_wrapper( - self._session, None, {}, fetch_list, target_list, None, status) - else: - results = tf_session.TF_Run(self._session, None, {}, fetch_list, - target_list, status, None) + results = self._call_tf_sessionrun( + None, {}, fetch_list, target_list, None) return fetch_handler.build_results(self, results) return _fetch_handler_run @@ -1326,35 +1305,22 @@ class BaseSession(SessionInterface): fetches = _name_list(fetch_list) targets = _name_list(target_list) - def _run_fn(session, feed_dict, fetch_list, target_list, options, - run_metadata): + def _run_fn(feed_dict, fetch_list, target_list, options, run_metadata): # Ensure any changes to the graph are reflected in the runtime. self._extend_graph() - with errors.raise_exception_on_not_ok_status() as status: - if self._created_with_new_api: - return tf_session.TF_SessionRun_wrapper(session, options, feed_dict, - fetch_list, target_list, - run_metadata, status) - else: - return tf_session.TF_Run(session, options, feed_dict, fetch_list, - target_list, status, run_metadata) + return self._call_tf_sessionrun( + options, feed_dict, fetch_list, target_list, run_metadata) - def _prun_fn(session, handle, feed_dict, fetch_list): + def _prun_fn(handle, feed_dict, fetch_list): if target_list: raise RuntimeError('partial_run() requires empty target_list.') - with errors.raise_exception_on_not_ok_status() as status: - if self._created_with_new_api: - return tf_session.TF_SessionPRun_wrapper(session, handle, feed_dict, - fetch_list, status) - else: - return tf_session.TF_PRun(session, handle, feed_dict, fetch_list, - status) + return self._call_tf_sessionprun(handle, feed_dict, fetch_list) if handle is None: - return self._do_call(_run_fn, self._session, feeds, fetches, targets, - options, run_metadata) + return self._do_call(_run_fn, feeds, fetches, targets, options, + run_metadata) else: - return self._do_call(_prun_fn, self._session, handle, feeds, fetches) + return self._do_call(_prun_fn, handle, feeds, fetches) def _do_call(self, fn, *args): try: @@ -1374,23 +1340,23 @@ class BaseSession(SessionInterface): raise type(e)(node_def, op, message) def _extend_graph(self): - # Nothing to do if we're using the new session interface - # TODO(skyewm): remove this function altogether eventually if self._created_with_new_api: - return - - # Ensure any changes to the graph are reflected in the runtime. - with self._extend_lock: - if self._graph.version > self._current_version: - # pylint: disable=protected-access - graph_def, self._current_version = self._graph._as_graph_def( - from_version=self._current_version, add_shapes=self._add_shapes) - # pylint: enable=protected-access - + with self._graph._lock: # pylint: disable=protected-access with errors.raise_exception_on_not_ok_status() as status: - tf_session.TF_ExtendGraph(self._session, - graph_def.SerializeToString(), status) - self._opened = True + tf_session.ExtendSession(self._session, status) + else: + # Ensure any changes to the graph are reflected in the runtime. + with self._extend_lock: + if self._graph.version > self._current_version: + # pylint: disable=protected-access + graph_def, self._current_version = self._graph._as_graph_def( + from_version=self._current_version, add_shapes=self._add_shapes) + # pylint: enable=protected-access + + with errors.raise_exception_on_not_ok_status() as status: + tf_session.TF_ExtendGraph(self._session, + graph_def.SerializeToString(), status) + self._opened = True # The threshold to run garbage collection to delete dead tensors. _DEAD_HANDLES_THRESHOLD = 10 @@ -1441,6 +1407,27 @@ class BaseSession(SessionInterface): feed_dict[feed_tensor] = np_val return handles + def _call_tf_sessionrun(self, options, feed_dict, fetch_list, target_list, + run_metadata): + with errors.raise_exception_on_not_ok_status() as status: + if self._created_with_new_api: + return tf_session.TF_SessionRun_wrapper( + self._session, options, feed_dict, fetch_list, target_list, + run_metadata, status) + else: + return tf_session.TF_Run( + self._session, options, feed_dict, fetch_list, target_list, + status, run_metadata) + + def _call_tf_sessionprun(self, handle, feed_dict, fetch_list): + with errors.raise_exception_on_not_ok_status() as status: + if self._created_with_new_api: + return tf_session.TF_SessionPRun_wrapper( + self._session, handle, feed_dict, fetch_list, status) + else: + return tf_session.TF_PRun( + self._session, handle, feed_dict, fetch_list, status) + @tf_export('Session') class Session(BaseSession): diff --git a/tensorflow/python/client/session_test.py b/tensorflow/python/client/session_test.py index 490572254b0be6a110ef06cea15d20d780f732cf..ccd7a5117ae05fb51d6c5b3e3e81c9aba5a26d50 100644 --- a/tensorflow/python/client/session_test.py +++ b/tensorflow/python/client/session_test.py @@ -37,6 +37,7 @@ from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import function +from tensorflow.python.framework import importer from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor from tensorflow.python.framework import tensor_util @@ -46,6 +47,7 @@ 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 gen_control_flow_ops +from tensorflow.python.ops import gradients_impl from tensorflow.python.ops import math_ops # Import resource_variable_ops for the variables-to-tensor implicit conversion. from tensorflow.python.ops import resource_variable_ops # pylint: disable=unused-import @@ -60,7 +62,8 @@ from tensorflow.python.util import compat ops.RegisterShape('ConstructionFails')(common_shapes.unknown_shape) -@test_util.with_c_api +# TODO(skyewm): reenable when this works with _USE_C_SHAPES=False +# @test_util.with_c_api class SessionTest(test_util.TensorFlowTestCase): def testUseExistingGraph(self): @@ -1052,6 +1055,43 @@ class SessionTest(test_util.TensorFlowTestCase): for t in threads: t.join() + def testParallelRunAndBuild(self): + with session.Session() as sess: + c = constant_op.constant(5.0) + stop = threading.Event() + + def run_loop(): + while not stop.is_set(): + self.assertEqual(sess.run(c), 5.0) + + threads = [self.checkedThread(target=run_loop) for _ in range(100)] + for t in threads: + t.start() + + # Do some graph construction. Try to exercise non-trivial paths. + graph = ops.get_default_graph() + gdef = None + for _ in range(10): + x = array_ops.placeholder(dtype=dtypes.float32) + with ops.colocate_with(x): + y = array_ops.placeholder(dtype=dtypes.float32) + with ops.device('/cpu:0'): + z = control_flow_ops.while_loop( + lambda x, y: x < 10, lambda x, y: (x + 1, x * y), [x, y]) + with graph._attr_scope({'_a': attr_value_pb2.AttrValue(b=False)}): + gradients_impl.gradients(z, [x, y]) + if gdef is None: + gdef = graph.as_graph_def() + else: + # NOTE(skyewm): import_graph_def breaks the running threads without + # the C API enabled. This is not a regression so I didn't fix it. + if ops._USE_C_API: + importer.import_graph_def(gdef, name='import') + + stop.set() + for t in threads: + t.join() + def testRunFeedDict(self): with session.Session() as s: x = array_ops.zeros([2]) diff --git a/tensorflow/python/client/tf_session.i b/tensorflow/python/client/tf_session.i index f305cd271f98bea697ea8ff15be799d3e80db0bf..e88fc0c01a8bb7534f47e2a0389965c102bbad7b 100644 --- a/tensorflow/python/client/tf_session.i +++ b/tensorflow/python/client/tf_session.i @@ -720,6 +720,9 @@ def TF_Reset(target, containers=None, config=None): } %unignore SetRequireShapeInferenceFns; +%unignore TF_TryEvaluateConstant_wrapper; +%noexception TF_TryEvaluateConstant_wrapper; +%unignore ExtendSession; %include "tensorflow/python/client/tf_session_helper.h" diff --git a/tensorflow/python/client/tf_session_helper.cc b/tensorflow/python/client/tf_session_helper.cc index 361dbc22b097a9bc82f656d7416b88c4a3a1ec2d..a8ab91749a86749a1eef25e2674634334682d0f3 100644 --- a/tensorflow/python/client/tf_session_helper.cc +++ b/tensorflow/python/client/tf_session_helper.cc @@ -493,4 +493,19 @@ std::vector TF_ImportGraphDefResultsMissingUnusedInputMappings_wrapper( return input_strs; } +PyObject* TF_TryEvaluateConstant_wrapper(TF_Graph* graph, TF_Output output, + TF_Status* status) { + TF_Tensor* result_tensor; + bool evaluated = + TF_TryEvaluateConstant(graph, output, &result_tensor, status); + if (!evaluated || TF_GetCode(status) != TF_OK) Py_RETURN_NONE; + + Safe_TF_TensorPtr safe_result_tensor(result_tensor); + PyObject* out; + Status s = TF_TensorToPyArray(std::move(safe_result_tensor), &out); + Set_TF_Status_from_Status(status, s); + if (!s.ok()) Py_RETURN_NONE; + return out; +} + } // namespace tensorflow diff --git a/tensorflow/python/client/tf_session_helper.h b/tensorflow/python/client/tf_session_helper.h index 29d5b28f40a7c07c199eec8c8cd85de626f6b068..83318dc178f6da3828a8dc41e81b7fc3e2e19e22 100644 --- a/tensorflow/python/client/tf_session_helper.h +++ b/tensorflow/python/client/tf_session_helper.h @@ -213,6 +213,11 @@ std::vector TF_GraphGetTensorShape_wrapper(TF_Graph* graph, std::vector TF_ImportGraphDefResultsMissingUnusedInputMappings_wrapper( TF_ImportGraphDefResults* results); +// If evaluation was possible, returns the numpy ndarray of the evaluated +// result. Otherwise returns None. +PyObject* TF_TryEvaluateConstant_wrapper(TF_Graph* graph, TF_Output output, + TF_Status* status); + } // namespace tensorflow #endif // TENSORFLOW_PYTHON_CLIENT_TF_SESSION_HELPER_H_ diff --git a/tensorflow/python/data/kernel_tests/dataset_constructor_op_test.py b/tensorflow/python/data/kernel_tests/dataset_constructor_op_test.py index 14627810b57f68fd96e3e3cc7b51b4fbf7365299..ea5b41e5d819743ad03f3148d654329aea51dab7 100644 --- a/tensorflow/python/data/kernel_tests/dataset_constructor_op_test.py +++ b/tensorflow/python/data/kernel_tests/dataset_constructor_op_test.py @@ -263,7 +263,7 @@ class DatasetConstructorTest(test.TestCase): for i in range(3): results = sess.run(get_next) for component, result_component in zip( - (zip(*components[:3])[i] + expected[i]), results): + (list(zip(*components[:3]))[i] + expected[i]), results): if sparse_tensor.is_sparse(component): self.assertSparseValuesEqual(component, result_component) else: diff --git a/tensorflow/python/data/kernel_tests/filter_dataset_op_test.py b/tensorflow/python/data/kernel_tests/filter_dataset_op_test.py index b9258b720edd4ecd620c61eed18f6f975cb7f439..4f2216f0a340acb582c2d09523b0c78af99bdd90 100644 --- a/tensorflow/python/data/kernel_tests/filter_dataset_op_test.py +++ b/tensorflow/python/data/kernel_tests/filter_dataset_op_test.py @@ -17,11 +17,15 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import time + import numpy as np +from tensorflow.python.client import session from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors +from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor from tensorflow.python.ops import array_ops from tensorflow.python.ops import functional_ops @@ -156,6 +160,65 @@ class FilterDatasetTest(test.TestCase): with self.assertRaises(errors.OutOfRangeError): sess.run(get_next) + def testReturnComponent(self): + iterator = ( + dataset_ops.Dataset.zip( + (dataset_ops.Dataset.range(10), + dataset_ops.Dataset.from_tensors(True).repeat(None))) + .filter(lambda x, y: y).make_initializable_iterator()) + init_op = iterator.initializer + get_next = iterator.get_next() + + with self.test_session() as sess: + sess.run(init_op) + for i in range(10): + self.assertEqual((i, True), sess.run(get_next)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + + def testParallelFilters(self): + dataset = dataset_ops.Dataset.range(10).filter( + lambda x: math_ops.equal(x % 2, 0)) + iterators = [dataset.make_one_shot_iterator() for _ in range(10)] + next_elements = [iterator.get_next() for iterator in iterators] + with self.test_session() as sess: + self.assertEqual([0 for _ in range(10)], sess.run(next_elements)) + + +class FilterDatasetBenchmark(test.Benchmark): + + def _benchmark(self, predicate, name): + with ops.Graph().as_default(): + dataset = ( + dataset_ops.Dataset.from_tensors(True).repeat(None).filter(predicate)) + iterator = dataset.make_one_shot_iterator() + next_element = iterator.get_next() + + with session.Session() as sess: + for _ in range(5): + sess.run(next_element.op) + deltas = [] + for _ in range(100): + start = time.time() + for _ in range(100): + sess.run(next_element.op) + end = time.time() + deltas.append(end - start) + + median_wall_time = np.median(deltas) / 100 + print("Filter dataset using %s. Median wall time: %f" % + (name, median_wall_time)) + self.report_benchmark( + iters=100, + wall_time=median_wall_time, + name="benchmark_filter_dataset_%s" % name) + + def benchmarkSimpleFunction(self): + self._benchmark(array_ops.identity, "simple_function") + + def benchmarkReturnComponentOptimization(self): + self._benchmark(lambda x: x, "return_component") + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/data/kernel_tests/iterator_ops_test.py b/tensorflow/python/data/kernel_tests/iterator_ops_test.py index 23c6d7385f8d4a12019fa514f349f2598d9629de..4a14a915bdb33f1ac6e8fc1839b32bc81fa8de05 100644 --- a/tensorflow/python/data/kernel_tests/iterator_ops_test.py +++ b/tensorflow/python/data/kernel_tests/iterator_ops_test.py @@ -22,6 +22,7 @@ import warnings import numpy as np +from tensorflow.core.protobuf import cluster_pb2 from tensorflow.core.protobuf import config_pb2 from tensorflow.python.client import session from tensorflow.python.data.ops import dataset_ops @@ -44,6 +45,7 @@ from tensorflow.python.ops import script_ops from tensorflow.python.ops import variables from tensorflow.python.platform import test from tensorflow.python.training import server_lib +from tensorflow.python.util import compat class IteratorTest(test.TestCase): @@ -63,8 +65,9 @@ class IteratorTest(test.TestCase): def testCapturingStateInOneShotRaisesException(self): var = variables.Variable(37.0, name="myvar") - dataset = (dataset_ops.Dataset.from_tensor_slices([0.0, 1.0, 2.0]) - .map(lambda x: x + var)) + dataset = ( + dataset_ops.Dataset.from_tensor_slices([0.0, 1.0, 2.0]) + .map(lambda x: x + var)) with self.assertRaisesRegexp( ValueError, r"`Dataset.make_one_shot_iterator\(\)` does not support " "datasets that capture stateful objects.+myvar"): @@ -78,8 +81,9 @@ class IteratorTest(test.TestCase): def _map_fn(x, y, z): return math_ops.square(x), math_ops.square(y), math_ops.square(z) - iterator = (dataset_ops.Dataset.from_tensor_slices(components).map(_map_fn) - .repeat(14).make_one_shot_iterator()) + iterator = ( + dataset_ops.Dataset.from_tensor_slices(components).map(_map_fn) + .repeat(14).make_one_shot_iterator()) get_next = iterator.get_next() self.assertEqual([c.shape[1:] for c in components], @@ -103,8 +107,9 @@ class IteratorTest(test.TestCase): def _map_fn(x, y, z): return math_ops.square(x), math_ops.square(y), math_ops.square(z) - iterator = (dataset_ops.Dataset.from_tensor_slices(tensor_components) - .map(_map_fn).repeat(14).make_one_shot_iterator()) + iterator = ( + dataset_ops.Dataset.from_tensor_slices(tensor_components) + .map(_map_fn).repeat(14).make_one_shot_iterator()) get_next = iterator.get_next() self.assertEqual([c.shape[1:] for c in components], @@ -125,10 +130,13 @@ class IteratorTest(test.TestCase): np.array(37.0) * np.arange(7)) def within_container(): + def _map_fn(x, y, z): return math_ops.square(x), math_ops.square(y), math_ops.square(z) - iterator = (dataset_ops.Dataset.from_tensor_slices(components) - .map(_map_fn).repeat(14).make_one_shot_iterator()) + + iterator = ( + dataset_ops.Dataset.from_tensor_slices(components) + .map(_map_fn).repeat(14).make_one_shot_iterator()) return iterator.get_next() server = server_lib.Server.create_local_server() @@ -159,8 +167,8 @@ class IteratorTest(test.TestCase): # Create a session with a single thread to ensure that the # one-shot iterator initializer does not deadlock. - config = config_pb2.ConfigProto(inter_op_parallelism_threads=1, - use_per_session_threads=True) + config = config_pb2.ConfigProto( + inter_op_parallelism_threads=1, use_per_session_threads=True) with session.Session(config=config) as sess: self.assertAllEqual([1, 4, 9], sess.run(next_element)) with self.assertRaises(errors.OutOfRangeError): @@ -169,6 +177,7 @@ class IteratorTest(test.TestCase): # Test with multiple threads invoking the one-shot iterator concurrently. with session.Session(config=config) as sess: results = [] + def consumer_thread(): try: results.append(sess.run(next_element)) @@ -177,7 +186,8 @@ class IteratorTest(test.TestCase): num_threads = 8 threads = [ - self.checkedThread(consumer_thread) for _ in range(num_threads)] + self.checkedThread(consumer_thread) for _ in range(num_threads) + ] for t in threads: t.start() for t in threads: @@ -205,24 +215,24 @@ class IteratorTest(test.TestCase): sess.run(next_element) with self.test_session() as sess: + def consumer_thread(): with self.assertRaisesRegexp(errors.InvalidArgumentError, "oops"): sess.run(next_element) num_threads = 8 threads = [ - self.checkedThread(consumer_thread) for _ in range(num_threads)] + self.checkedThread(consumer_thread) for _ in range(num_threads) + ] for t in threads: t.start() for t in threads: t.join() def testSimpleSharedResource(self): - components = ( - np.array(1, dtype=np.int64), - np.array([1, 2, 3], dtype=np.int64), - np.array(37.0, dtype=np.float64) - ) + components = (np.array(1, dtype=np.int64), + np.array([1, 2, 3], dtype=np.int64), + np.array(37.0, dtype=np.float64)) server = server_lib.Server.create_local_server() @@ -231,9 +241,10 @@ class IteratorTest(test.TestCase): # first session (initializing the iterator) is visible in the # second session. with ops.Graph().as_default(): - iterator = (dataset_ops.Dataset.from_tensors(components) - .map(lambda x, y, z: (x, y, z)).make_initializable_iterator( - shared_name="shared_iterator")) + iterator = ( + dataset_ops.Dataset.from_tensors(components) + .map(lambda x, y, z: (x, y, z)).make_initializable_iterator( + shared_name="shared_iterator")) init_op = iterator.initializer get_next = iterator.get_next() @@ -269,8 +280,9 @@ class IteratorTest(test.TestCase): def testNotInitializedError(self): components = (np.array(1), np.array([1, 2, 3]), np.array(37.0)) - iterator = (dataset_ops.Dataset.from_tensors(components) - .make_initializable_iterator()) + iterator = ( + dataset_ops.Dataset.from_tensors(components) + .make_initializable_iterator()) get_next = iterator.get_next() with self.test_session() as sess: @@ -320,8 +332,8 @@ class IteratorTest(test.TestCase): def testReinitializableIteratorStaticErrors(self): # Non-matching structure for types and shapes. with self.assertRaises(TypeError): - iterator = iterator_ops.Iterator.from_structure((dtypes.int64, - dtypes.float64), [None]) + iterator = iterator_ops.Iterator.from_structure( + (dtypes.int64, dtypes.float64), [None]) # Test validation of dataset argument. iterator = iterator_ops.Iterator.from_structure((dtypes.int64, @@ -337,18 +349,18 @@ class IteratorTest(test.TestCase): # Incompatible types. with self.assertRaises(TypeError): iterator.make_initializer( - dataset_ops.Dataset.from_tensors((constant_op.constant( - [1, 2, 3], dtype=dtypes.int32), constant_op.constant( - [4., 5., 6., 7.], dtype=dtypes.float32)))) + dataset_ops.Dataset.from_tensors( + (constant_op.constant([1, 2, 3], dtype=dtypes.int32), + constant_op.constant([4., 5., 6., 7.], dtype=dtypes.float32)))) # Incompatible shapes. iterator = iterator_ops.Iterator.from_structure( (dtypes.int64, dtypes.float64), ([None], [])) with self.assertRaises(TypeError): iterator.make_initializer( - dataset_ops.Dataset.from_tensors((constant_op.constant( - [1, 2, 3], dtype=dtypes.int64), constant_op.constant( - [4., 5., 6., 7.], dtype=dtypes.float64)))) + dataset_ops.Dataset.from_tensors( + (constant_op.constant([1, 2, 3], dtype=dtypes.int64), + constant_op.constant([4., 5., 6., 7.], dtype=dtypes.float64)))) def testIteratorStringHandle(self): dataset_3 = dataset_ops.Dataset.from_tensor_slices([1, 2, 3]) @@ -370,33 +382,40 @@ class IteratorTest(test.TestCase): iterator_3_handle = sess.run(iterator_3.string_handle()) iterator_4_handle = sess.run(iterator_4.string_handle()) - self.assertEqual( - 10, sess.run(next_element, - feed_dict={handle_placeholder: iterator_4_handle})) - self.assertEqual( - 1, sess.run(next_element, - feed_dict={handle_placeholder: iterator_3_handle})) - self.assertEqual( - 20, sess.run(next_element, - feed_dict={handle_placeholder: iterator_4_handle})) - self.assertEqual( - 2, sess.run(next_element, - feed_dict={handle_placeholder: iterator_3_handle})) - self.assertEqual( - 30, sess.run(next_element, - feed_dict={handle_placeholder: iterator_4_handle})) - self.assertEqual( - 3, sess.run(next_element, - feed_dict={handle_placeholder: iterator_3_handle})) - self.assertEqual( - 40, sess.run(next_element, - feed_dict={handle_placeholder: iterator_4_handle})) + self.assertEqual(10, + sess.run( + next_element, + feed_dict={handle_placeholder: iterator_4_handle})) + self.assertEqual(1, + sess.run( + next_element, + feed_dict={handle_placeholder: iterator_3_handle})) + self.assertEqual(20, + sess.run( + next_element, + feed_dict={handle_placeholder: iterator_4_handle})) + self.assertEqual(2, + sess.run( + next_element, + feed_dict={handle_placeholder: iterator_3_handle})) + self.assertEqual(30, + sess.run( + next_element, + feed_dict={handle_placeholder: iterator_4_handle})) + self.assertEqual(3, + sess.run( + next_element, + feed_dict={handle_placeholder: iterator_3_handle})) + self.assertEqual(40, + sess.run( + next_element, + feed_dict={handle_placeholder: iterator_4_handle})) with self.assertRaises(errors.OutOfRangeError): - sess.run(next_element, - feed_dict={handle_placeholder: iterator_3_handle}) + sess.run( + next_element, feed_dict={handle_placeholder: iterator_3_handle}) with self.assertRaises(errors.OutOfRangeError): - sess.run(next_element, - feed_dict={handle_placeholder: iterator_4_handle}) + sess.run( + next_element, feed_dict={handle_placeholder: iterator_4_handle}) def testIteratorStringHandleReuseTensorObject(self): dataset = dataset_ops.Dataset.from_tensor_slices([1, 2, 3]) @@ -427,8 +446,8 @@ class IteratorTest(test.TestCase): self.assertIsNot(handle_with_name, handle_with_same_name) def testIteratorStringHandleError(self): - dataset_int_scalar = (dataset_ops.Dataset.from_tensor_slices([1, 2, - 3]).repeat()) + dataset_int_scalar = ( + dataset_ops.Dataset.from_tensor_slices([1, 2, 3]).repeat()) dataset_float_vector = (dataset_ops.Dataset.from_tensors([1.0, 2.0, 3.0])) handle_placeholder = array_ops.placeholder(dtypes.string, shape=[]) @@ -522,6 +541,58 @@ class IteratorTest(test.TestCase): target_placeholder: "/job:localhost/replica:0/task:0/cpu:1" }) + def testRemoteIteratorUsingRemoteCallOpMultiWorkers(self): + s1 = server_lib.Server.create_local_server() + s2 = server_lib.Server.create_local_server() + s3 = server_lib.Server.create_local_server() + + cluster_def = cluster_pb2.ClusterDef() + workers = cluster_def.job.add() + workers.name = "worker" + workers.tasks[0] = s1.target[len("grpc://"):] + workers.tasks[1] = s2.target[len("grpc://"):] + client = cluster_def.job.add() + client.name = "client" + client.tasks[0] = s3.target[len("grpc://"):] + config = config_pb2.ConfigProto(cluster_def=cluster_def) + + worker_devices = [ + "/job:worker/replica:0/task:%d/cpu:0" % i for i in range(2) + ] + itr_handles = [] + for device in worker_devices: + with ops.device(device): + src = dataset_ops.Dataset.from_tensor_slices([device]) + itr = src.make_one_shot_iterator() + itr_handles.append(itr.string_handle()) + + targets = dataset_ops.Dataset.from_tensor_slices(worker_devices) + handles = dataset_ops.Dataset.from_tensor_slices(itr_handles) + + @function.Defun(dtypes.string) + def loading_func(h): + remote_itr = iterator_ops.Iterator.from_string_handle( + h, itr.output_types, itr.output_shapes) + return remote_itr.get_next() + + def map_fn(target, handle): + return functional_ops.remote_call( + args=[handle], Tout=[dtypes.string], f=loading_func, target=target) + + with ops.device("/job:client"): + client_dataset = dataset_ops.Dataset.zip((targets, handles)).map(map_fn) + itr = client_dataset.make_initializable_iterator() + n = itr.get_next() + + with session.Session(s3.target, config=config) as sess: + sess.run(itr.initializer) + expected_values = worker_devices + for expected in expected_values: + self.assertEqual((compat.as_bytes(expected),), sess.run(n)) + + with self.assertRaises(errors.OutOfRangeError): + sess.run(n) + def testRemoteIteratorUsingRemoteCallOpDirectSessionGPUCPU(self): if not test_util.is_gpu_available(): self.skipTest("No GPU available") @@ -641,8 +712,7 @@ class IteratorTest(test.TestCase): with warnings.catch_warnings(record=True) as w: for _ in range(100): iterator.get_next() - self.assertEqual(100 - iterator_ops.GET_NEXT_CALL_WARNING_THRESHOLD, - len(w)) + self.assertEqual(100 - iterator_ops.GET_NEXT_CALL_WARNING_THRESHOLD, len(w)) for warning in w: self.assertTrue( iterator_ops.GET_NEXT_CALL_WARNING_MESSAGE in str(warning.message)) diff --git a/tensorflow/python/data/kernel_tests/list_files_dataset_op_test.py b/tensorflow/python/data/kernel_tests/list_files_dataset_op_test.py index 4e7691ee8144a19a62476281d86fb5df46dd3e4b..6442eb9ff554e61829796fb904342072d1846a32 100644 --- a/tensorflow/python/data/kernel_tests/list_files_dataset_op_test.py +++ b/tensorflow/python/data/kernel_tests/list_files_dataset_op_test.py @@ -46,8 +46,9 @@ class ListFilesDatasetOpTest(test.TestCase): dataset = dataset_ops.Dataset.list_files(path.join(self.tmp_dir, '*')) with self.test_session() as sess: itr = dataset.make_one_shot_iterator() + next_element = itr.get_next() with self.assertRaises(errors.OutOfRangeError): - sess.run(itr.get_next()) + sess.run(next_element) def testSimpleDirectory(self): filenames = ['a', 'b', 'c'] @@ -56,13 +57,14 @@ class ListFilesDatasetOpTest(test.TestCase): dataset = dataset_ops.Dataset.list_files(path.join(self.tmp_dir, '*')) with self.test_session() as sess: itr = dataset.make_one_shot_iterator() + next_element = itr.get_next() full_filenames = [] produced_filenames = [] for filename in filenames: full_filenames.append( compat.as_bytes(path.join(self.tmp_dir, filename))) - produced_filenames.append(compat.as_bytes(sess.run(itr.get_next()))) + produced_filenames.append(compat.as_bytes(sess.run(next_element))) self.assertItemsEqual(full_filenames, produced_filenames) with self.assertRaises(errors.OutOfRangeError): sess.run(itr.get_next()) @@ -73,12 +75,13 @@ class ListFilesDatasetOpTest(test.TestCase): with self.test_session() as sess: itr = dataset.make_initializable_iterator() + next_element = itr.get_next() sess.run( itr.initializer, feed_dict={filename_placeholder: path.join(self.tmp_dir, '*')}) with self.assertRaises(errors.OutOfRangeError): - sess.run(itr.get_next()) + sess.run(next_element) def testSimpleDirectoryInitializer(self): filenames = ['a', 'b', 'c'] @@ -89,6 +92,7 @@ class ListFilesDatasetOpTest(test.TestCase): with self.test_session() as sess: itr = dataset.make_initializable_iterator() + next_element = itr.get_next() sess.run( itr.initializer, feed_dict={filename_placeholder: path.join(self.tmp_dir, '*')}) @@ -98,7 +102,7 @@ class ListFilesDatasetOpTest(test.TestCase): for filename in filenames: full_filenames.append( compat.as_bytes(path.join(self.tmp_dir, filename))) - produced_filenames.append(compat.as_bytes(sess.run(itr.get_next()))) + produced_filenames.append(compat.as_bytes(sess.run(next_element))) self.assertItemsEqual(full_filenames, produced_filenames) @@ -114,6 +118,7 @@ class ListFilesDatasetOpTest(test.TestCase): with self.test_session() as sess: itr = dataset.make_initializable_iterator() + next_element = itr.get_next() sess.run( itr.initializer, feed_dict={filename_placeholder: path.join(self.tmp_dir, '*.py')}) @@ -123,7 +128,7 @@ class ListFilesDatasetOpTest(test.TestCase): for filename in filenames[1:-1]: full_filenames.append( compat.as_bytes(path.join(self.tmp_dir, filename))) - produced_filenames.append(compat.as_bytes(sess.run(itr.get_next()))) + produced_filenames.append(compat.as_bytes(sess.run(next_element))) self.assertItemsEqual(full_filenames, produced_filenames) with self.assertRaises(errors.OutOfRangeError): @@ -138,6 +143,7 @@ class ListFilesDatasetOpTest(test.TestCase): with self.test_session() as sess: itr = dataset.make_initializable_iterator() + next_element = itr.get_next() sess.run( itr.initializer, feed_dict={filename_placeholder: path.join(self.tmp_dir, '*.py*')}) @@ -147,13 +153,44 @@ class ListFilesDatasetOpTest(test.TestCase): for filename in filenames[1:]: full_filenames.append( compat.as_bytes(path.join(self.tmp_dir, filename))) - produced_filenames.append(compat.as_bytes(sess.run(itr.get_next()))) + produced_filenames.append(compat.as_bytes(sess.run(next_element))) self.assertItemsEqual(full_filenames, produced_filenames) with self.assertRaises(errors.OutOfRangeError): sess.run(itr.get_next()) + def testNoShuffle(self): + filenames = ['a', 'b', 'c'] + self._touchTempFiles(filenames) + + # Repeat the list twice and ensure that the order is the same each time. + # NOTE(mrry): This depends on an implementation detail of `list_files()`, + # which is that the list of files is captured when the iterator is + # initialized. Otherwise, or if e.g. the iterator were initialized more than + # once, it's possible that the non-determinism of `tf.matching_files()` + # would cause this test to fail. However, it serves as a useful confirmation + # that the `shuffle=False` argument is working as intended. + # TODO(b/73959787): Provide some ordering guarantees so that this test is + # more meaningful. + dataset = dataset_ops.Dataset.list_files( + path.join(self.tmp_dir, '*'), shuffle=False).repeat(2) + with self.test_session() as sess: + itr = dataset.make_one_shot_iterator() + next_element = itr.get_next() + + full_filenames = [] + produced_filenames = [] + for filename in filenames * 2: + full_filenames.append( + compat.as_bytes(path.join(self.tmp_dir, filename))) + produced_filenames.append(compat.as_bytes(sess.run(next_element))) + with self.assertRaises(errors.OutOfRangeError): + sess.run(itr.get_next()) + self.assertItemsEqual(full_filenames, produced_filenames) + self.assertEqual(produced_filenames[:len(filenames)], + produced_filenames[len(filenames):]) + if __name__ == '__main__': test.main() diff --git a/tensorflow/python/data/kernel_tests/reader_dataset_ops_test.py b/tensorflow/python/data/kernel_tests/reader_dataset_ops_test.py index d7140088c310767d40bd2cf3413c899375acab15..1ddedfda4e1c9d6b6949f796be1870f167435763 100644 --- a/tensorflow/python/data/kernel_tests/reader_dataset_ops_test.py +++ b/tensorflow/python/data/kernel_tests/reader_dataset_ops_test.py @@ -21,6 +21,7 @@ import gzip import os import zlib +from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.ops import iterator_ops from tensorflow.python.data.ops import readers from tensorflow.python.framework import constant_op @@ -736,12 +737,43 @@ class TFRecordDatasetTest(test.TestCase): one_mebibyte = 2**20 d = readers.TFRecordDataset(self.test_filenames, buffer_size=one_mebibyte) iterator = d.make_one_shot_iterator() + next_element = iterator.get_next() with self.test_session() as sess: for j in range(self._num_files): for i in range(self._num_records): - self.assertAllEqual(self._record(j, i), sess.run(iterator.get_next())) + self.assertAllEqual(self._record(j, i), sess.run(next_element)) with self.assertRaises(errors.OutOfRangeError): - sess.run(iterator.get_next()) + sess.run(next_element) + + def testReadFromDatasetOfFiles(self): + files = dataset_ops.Dataset.from_tensor_slices(self.test_filenames) + d = readers.TFRecordDataset(files) + iterator = d.make_one_shot_iterator() + next_element = iterator.get_next() + with self.test_session() as sess: + for j in range(self._num_files): + for i in range(self._num_records): + self.assertAllEqual(self._record(j, i), sess.run(next_element)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(next_element) + + def testReadTenEpochsFromDatasetOfFilesInParallel(self): + files = dataset_ops.Dataset.from_tensor_slices( + self.test_filenames).repeat(10) + d = readers.TFRecordDataset(files, num_parallel_reads=4) + iterator = d.make_one_shot_iterator() + next_element = iterator.get_next() + expected = [] + actual = [] + with self.test_session() as sess: + for _ in range(10): + for j in range(self._num_files): + for i in range(self._num_records): + expected.append(self._record(j, i)) + actual.append(sess.run(next_element)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(next_element) + self.assertEqual(sorted(expected), sorted(actual)) if __name__ == "__main__": diff --git a/tensorflow/python/data/kernel_tests/shuffle_dataset_op_test.py b/tensorflow/python/data/kernel_tests/shuffle_dataset_op_test.py index c089fb08c1082c1cf74d492796550980d6755591..5fcc48831f3ca744e015c92760f12ea4dbef2ff7 100644 --- a/tensorflow/python/data/kernel_tests/shuffle_dataset_op_test.py +++ b/tensorflow/python/data/kernel_tests/shuffle_dataset_op_test.py @@ -132,6 +132,33 @@ class ShuffleDatasetTest(test.TestCase): with self.assertRaises(errors.OutOfRangeError): sess.run(get_next) + def testSeedZero(self): + """Test for same behavior when the seed is a Python or Tensor zero.""" + iterator = ( + dataset_ops.Dataset.range(10).shuffle(10, seed=0) + .make_one_shot_iterator()) + get_next = iterator.get_next() + + elems = [] + with self.test_session() as sess: + for _ in range(10): + elems.append(sess.run(get_next)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + + seed_placeholder = array_ops.placeholder(dtypes.int64, shape=[]) + iterator = ( + dataset_ops.Dataset.range(10).shuffle(10, seed=seed_placeholder) + .make_initializable_iterator()) + get_next = iterator.get_next() + + with self.test_session() as sess: + sess.run(iterator.initializer, feed_dict={seed_placeholder: 0}) + for elem in elems: + self.assertEqual(elem, sess.run(get_next)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + def testDefaultArguments(self): components = [0, 1, 2, 3, 4] iterator = (dataset_ops.Dataset.from_tensor_slices(components).shuffle(5) diff --git a/tensorflow/python/data/ops/BUILD b/tensorflow/python/data/ops/BUILD index f12b358a7dc35c18338171e489fa88ba1a82d11b..3119ab003794cb9bc0c748dfeb47597e0877f5fd 100644 --- a/tensorflow/python/data/ops/BUILD +++ b/tensorflow/python/data/ops/BUILD @@ -23,6 +23,7 @@ py_library( "//tensorflow/python:tensor_util", "//tensorflow/python:util", "//tensorflow/python/data/util:nest", + "//tensorflow/python/data/util:random_seed", "//tensorflow/python/data/util:sparse", "//third_party/py/numpy", ], @@ -34,6 +35,7 @@ py_library( srcs_version = "PY2AND3", deps = [ ":dataset_ops", + "//tensorflow/python:array_ops", "//tensorflow/python:dataset_ops_gen", "//tensorflow/python:dtypes", "//tensorflow/python:framework_ops", @@ -50,9 +52,11 @@ py_library( "//tensorflow/python:dataset_ops_gen", "//tensorflow/python:dtypes", "//tensorflow/python:framework_ops", + "//tensorflow/python:resource_variable_ops", "//tensorflow/python:tensor_shape", "//tensorflow/python/data/util:nest", "//tensorflow/python/data/util:sparse", + "//tensorflow/python/eager:context", ], ) diff --git a/tensorflow/python/data/ops/dataset_ops.py b/tensorflow/python/data/ops/dataset_ops.py index 3fb1f8d5479fc461a8d1f509c5eec2d0ed4a44c9..390ce852b1337bf3ae387660e4b83b0b678f737f 100644 --- a/tensorflow/python/data/ops/dataset_ops.py +++ b/tensorflow/python/data/ops/dataset_ops.py @@ -26,16 +26,17 @@ import six from tensorflow.python.data.ops import iterator_ops from tensorflow.python.data.util import nest +from tensorflow.python.data.util import random_seed from tensorflow.python.data.util import sparse from tensorflow.python.eager import context from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import function from tensorflow.python.framework import ops -from tensorflow.python.framework import random_seed 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 array_ops from tensorflow.python.ops import gen_dataset_ops from tensorflow.python.ops import gen_io_ops from tensorflow.python.ops import math_ops @@ -90,7 +91,7 @@ class Dataset(object): Raises: RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError( "dataset.make_initializable_iterator is not supported when eager " "execution is enabled.") @@ -110,11 +111,11 @@ class Dataset(object): self.output_types, self.output_shapes, self.output_classes) - def make_one_shot_iterator(self): + def __iter__(self): """Creates an `Iterator` for enumerating the elements of this dataset. - Note: The returned iterator will be initialized automatically. - A "one-shot" iterator does not currently support re-initialization. + The returned iterator implements the Python iterator protocol and therefore + can only be used in eager mode. Returns: An `Iterator` over the elements of this dataset. @@ -122,10 +123,23 @@ class Dataset(object): Raises: RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): - raise RuntimeError( - "dataset.make_one_shot_iterator is not supported when eager " - "execution is enabled.") + if context.executing_eagerly(): + return iterator_ops.EagerIterator(self) + else: + raise RuntimeError("dataset.__iter__() is only supported when eager " + "execution is enabled.") + + def make_one_shot_iterator(self): + """Creates an `Iterator` for enumerating the elements of this dataset. + + Note: The returned iterator will be initialized automatically. + A "one-shot" iterator does not currently support re-initialization. + + Returns: + An `Iterator` over the elements of this dataset. + """ + if context.executing_eagerly(): + return iterator_ops.EagerIterator(self) # NOTE(mrry): We capture by value here to ensure that `_make_dataset()` is # a 0-argument function. @function.Defun(capture_by_value=True) @@ -557,7 +571,7 @@ class Dataset(object): return PrefetchDataset(self, buffer_size) @staticmethod - def list_files(file_pattern): + def list_files(file_pattern, shuffle=None): """A dataset of all files matching a pattern. Example: @@ -570,16 +584,31 @@ class Dataset(object): - /path/to/dir/b.py - /path/to/dir/c.py - NOTE: The order of the file names returned can be non-deterministic. + NOTE: The order of the file names returned can be non-deterministic even + when `shuffle` is `False`. Args: file_pattern: A string or scalar string `tf.Tensor`, representing the filename pattern that will be matched. + shuffle: (Optional.) If `True`, the file names will be shuffled randomly. + Defaults to `True`. Returns: Dataset: A `Dataset` of strings corresponding to file names. """ - return Dataset.from_tensor_slices(gen_io_ops.matching_files(file_pattern)) + # TODO(b/73959787): Add a `seed` argument and make the `shuffle=False` + # behavior deterministic (e.g. by sorting the filenames). + if shuffle is None: + shuffle = True + matching_files = gen_io_ops.matching_files(file_pattern) + dataset = Dataset.from_tensor_slices(matching_files) + if shuffle: + # NOTE(mrry): The shuffle buffer size must be greater than zero, but the + # list of files might be empty. + buffer_size = math_ops.maximum( + array_ops.shape(matching_files, out_type=dtypes.int64)[0], 1) + dataset = dataset.shuffle(buffer_size) + return dataset def repeat(self, count=None): """Repeats this dataset `count` times. @@ -758,11 +787,31 @@ class Dataset(object): def padded_batch(self, batch_size, padded_shapes, padding_values=None): """Combines consecutive elements of this dataset into padded batches. - Like `Dataset.dense_to_sparse_batch()`, this method combines - multiple consecutive elements of this dataset, which might have - different shapes, into a single element. The tensors in the - resulting element have an additional outer dimension, and are - padded to the respective shape in `padded_shapes`. + This transformation combines multiple consecutive elements of the input + dataset into a single element. Like @{tf.data.Dataset.batch}, the tensors + in the resulting element have an additional outer dimension, which will be + `batch_size` for all but the last element, and `N % batch_size` for the + last element (where `N` is the number of elements in this dataset). Unlike + @{tf.data.Dataset.batch}, the elements may have different shapes for some + of their components, and this transformation will pad each component to + the respective shape in `padding_shapes`. The `padding_shapes` argument + determines the resulting shape for each dimension of each component in an + output element: + + * If the dimension is a constant (e.g. `tf.Dimension(37)`), the component + will be padded out to that length in that dimension. + * If the dimension is unknown (e.g. `tf.Dimension(None)`), the component + will be padded out to the maximum length of all elements in that + dimension. + + NOTE: If the number of elements (`N`) in this dataset is not an exact + multiple of `batch_size`, the final batch contain smaller tensors with + shape `N % batch_size` in the batch dimension. If your program depends on + the batches having the same shape, consider using the + @{tf.contrib.data.padded_batch_and_drop_remainder} transformation instead. + + See also @{tf.contrib.data.dense_to_sparse_batch}, which combines elements + that may have different shapes into a @{tf.SparseTensor}. Args: batch_size: A `tf.int64` scalar `tf.Tensor`, representing the number of @@ -1484,16 +1533,7 @@ class ShuffleDataset(Dataset): self._input_dataset = input_dataset self._buffer_size = ops.convert_to_tensor( buffer_size, dtype=dtypes.int64, name="buffer_size") - seed, seed2 = random_seed.get_seed(seed) - if seed is None: - self._seed = constant_op.constant(0, dtype=dtypes.int64, name="seed") - else: - self._seed = ops.convert_to_tensor(seed, dtype=dtypes.int64, name="seed") - if seed2 is None: - self._seed2 = constant_op.constant(0, dtype=dtypes.int64, name="seed2") - else: - self._seed2 = ops.convert_to_tensor( - seed2, dtype=dtypes.int64, name="seed2") + self._seed, self._seed2 = random_seed.get_seed(seed) if reshuffle_each_iteration is None: self._reshuffle_each_iteration = True else: diff --git a/tensorflow/python/data/ops/iterator_ops.py b/tensorflow/python/data/ops/iterator_ops.py index 4756ec74820bace5bea4e1f41ebe214420fe5c3d..d79b9d6011b6ebd00a47d572165cdbba8a31bd32 100644 --- a/tensorflow/python/data/ops/iterator_ops.py +++ b/tensorflow/python/data/ops/iterator_ops.py @@ -17,14 +17,18 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import threading import warnings from tensorflow.python.data.util import nest from tensorflow.python.data.util import sparse +from tensorflow.python.eager import context from tensorflow.python.framework import dtypes +from tensorflow.python.framework import errors from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import gen_dataset_ops +from tensorflow.python.ops import resource_variable_ops from tensorflow.python.util.tf_export import tf_export @@ -412,3 +416,147 @@ class Iterator(object): of an element of this dataset. """ return self._output_types + + +_uid_counter = 0 +_uid_lock = threading.Lock() + + +def _generate_shared_name(prefix): + with _uid_lock: + global _uid_counter + uid = _uid_counter + _uid_counter += 1 + return "{}{}".format(prefix, uid) + + +class EagerIterator(object): + """An iterator producing tf.Tensor objects from a tf.data.Dataset.""" + + def __init__(self, dataset): + """Creates a new iterator over the given dataset. + + For example: + ```python + dataset = tf.data.Dataset.range(4) + for x in Iterator(dataset): + print(x) + ``` + + Tensors produced will be placed on the device on which this iterator object + was created. + + Args: + dataset: A `tf.data.Dataset` object. + + Raises: + RuntimeError: When invoked without eager execution enabled. + """ + + if not context.executing_eagerly(): + raise RuntimeError( + "{} objects can only be used when eager execution is enabled, use " + "tf.data.Dataset.make_initializable_iterator or " + "tf.data.Dataset.make_one_shot_iterator for graph construction". + format(type(self))) + with ops.device("/device:CPU:0"): + ds_variant = dataset._as_variant_tensor() # pylint: disable=protected-access + self._output_classes = dataset.output_classes + self._output_types = dataset.output_types + self._output_shapes = dataset.output_shapes + self._flat_output_types = nest.flatten( + sparse.as_dense_types(self._output_types, self._output_classes)) + self._flat_output_shapes = nest.flatten( + sparse.as_dense_shapes(self._output_shapes, self._output_classes)) + self._resource = gen_dataset_ops.iterator( + shared_name="", + container=_generate_shared_name("eageriterator"), + output_types=self._flat_output_types, + output_shapes=self._flat_output_shapes) + gen_dataset_ops.make_iterator(ds_variant, self._resource) + # Delete the resource when this object is deleted + self._resource_deleter = resource_variable_ops.EagerResourceDeleter( + handle=self._resource, handle_device="/device:CPU:0") + self._device = context.context().device_name + + def __iter__(self): + return self + + def __next__(self): # For Python 3 compatibility + return self.next() + + def _next_internal(self): + """Returns a nested structure of `tf.Tensor`s containing the next element. + """ + with ops.device(self._device): + # TODO(ashankar): Consider removing this ops.device() contextmanager + # and instead mimic ops placement in graphs: Operations on resource + # handles execute on the same device as where the resource is placed. + # NOTE(mrry): Here we use the "_sync" variant of `iterator_get_next` + # because in eager mode this code will run synchronously on the calling + # thread. Therefore we do not need to make a defensive context switch + # to a background thread, and can achieve a small constant performance + # boost by invoking the iterator synchronously. + ret = gen_dataset_ops.iterator_get_next_sync( + self._resource, + output_types=self._flat_output_types, + output_shapes=self._flat_output_shapes) + + return sparse.deserialize_sparse_tensors( + nest.pack_sequence_as(self._output_types, ret), self._output_types, + self._output_shapes, self._output_classes) + + def next(self): + """Returns a nested structure of `tf.Tensor`s containing the next element. + """ + try: + return self._next_internal() + except errors.OutOfRangeError: + raise StopIteration + + @property + def output_classes(self): + """Returns the class of each component of an element of this iterator. + + The expected values are `tf.Tensor` and `tf.SparseTensor`. + + Returns: + A nested structure of Python `type` objects corresponding to each + component of an element of this dataset. + """ + return self._output_classes + + @property + def output_shapes(self): + """Returns the shape of each component of an element of this iterator. + + Returns: + A nested structure of `tf.TensorShape` objects corresponding to each + component of an element of this dataset. + """ + return self._output_shapes + + @property + def output_types(self): + """Returns the type of each component of an element of this iterator. + + Returns: + A nested structure of `tf.DType` objects corresponding to each component + of an element of this dataset. + """ + return self._output_types + + def get_next(self, name=None): + """Returns a nested structure of `tf.Tensor`s containing the next element. + + Args: + name: (Optional.) A name for the created operation. Currently unused. + + Returns: + A nested structure of `tf.Tensor` objects. + + Raises: + `tf.errors.OutOfRangeError`: If the end of the dataset has been reached. + """ + del name + return self._next_internal() diff --git a/tensorflow/python/data/ops/readers.py b/tensorflow/python/data/ops/readers.py index fa7601741b11f018e9b53ed3b77a7561be50d3f4..6c493d8163b051b2e724335923d7b4c721523083 100644 --- a/tensorflow/python/data/ops/readers.py +++ b/tensorflow/python/data/ops/readers.py @@ -17,11 +17,15 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.python.data.ops.dataset_ops import Dataset +from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.util import convert +from tensorflow.python.data.util import nest +from tensorflow.python.data.util import sparse from tensorflow.python.framework import dtypes +from tensorflow.python.framework import function from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape +from tensorflow.python.ops import array_ops from tensorflow.python.ops import gen_dataset_ops from tensorflow.python.util.tf_export import tf_export @@ -31,7 +35,7 @@ _DEFAULT_READER_BUFFER_SIZE_BYTES = 256 * 1024 # 256 KB @tf_export("data.TextLineDataset") -class TextLineDataset(Dataset): +class TextLineDataset(dataset_ops.Dataset): """A `Dataset` comprising lines from one or more text files.""" def __init__(self, filenames, compression_type=None, buffer_size=None): @@ -73,8 +77,7 @@ class TextLineDataset(Dataset): return dtypes.string -@tf_export("data.TFRecordDataset") -class TFRecordDataset(Dataset): +class _TFRecordDataset(dataset_ops.Dataset): """A `Dataset` comprising records from one or more TFRecord files.""" def __init__(self, filenames, compression_type=None, buffer_size=None): @@ -87,7 +90,7 @@ class TFRecordDataset(Dataset): buffer_size: (Optional.) A `tf.int64` scalar representing the number of bytes in the read buffer. 0 means no buffering. """ - super(TFRecordDataset, self).__init__() + super(_TFRecordDataset, self).__init__() # Force the type to string even if filenames is an empty list. self._filenames = ops.convert_to_tensor( filenames, dtypes.string, name="filenames") @@ -118,8 +121,159 @@ class TFRecordDataset(Dataset): return dtypes.string +class ParallelInterleaveDataset(dataset_ops.Dataset): + """A `Dataset` that maps a function over its input and flattens the result.""" + + def __init__(self, input_dataset, map_func, cycle_length, block_length, + sloppy, buffer_output_elements, prefetch_input_elements): + """See `tf.contrib.data.parallel_interleave()` for details.""" + super(ParallelInterleaveDataset, self).__init__() + self._input_dataset = input_dataset + + @function.Defun(*nest.flatten( + sparse.as_dense_types(input_dataset.output_types, + input_dataset.output_classes))) + def tf_map_func(*args): + """A wrapper for Defun that facilitates shape inference.""" + # Pass in shape information from the input_dataset. + dense_shapes = sparse.as_dense_shapes(input_dataset.output_shapes, + input_dataset.output_classes) + for arg, shape in zip(args, nest.flatten(dense_shapes)): + arg.set_shape(shape) + + nested_args = nest.pack_sequence_as(input_dataset.output_types, args) + nested_args = sparse.deserialize_sparse_tensors( + nested_args, input_dataset.output_types, input_dataset.output_shapes, + input_dataset.output_classes) + if dataset_ops._should_unpack_args(nested_args): # pylint: disable=protected-access + dataset = map_func(*nested_args) + else: + dataset = map_func(nested_args) + + if not isinstance(dataset, dataset_ops.Dataset): + raise TypeError("`map_func` must return a `Dataset` object.") + + self._output_classes = dataset.output_classes + self._output_types = dataset.output_types + self._output_shapes = dataset.output_shapes + + return dataset._as_variant_tensor() # pylint: disable=protected-access + + self._map_func = tf_map_func + self._map_func.add_to_graph(ops.get_default_graph()) + + self._cycle_length = ops.convert_to_tensor( + cycle_length, dtype=dtypes.int64, name="cycle_length") + self._block_length = ops.convert_to_tensor( + block_length, dtype=dtypes.int64, name="block_length") + self._sloppy = ops.convert_to_tensor( + sloppy, dtype=dtypes.bool, name="sloppy") + self._buffer_output_elements = convert.optional_param_to_tensor( + "buffer_output_elements", + buffer_output_elements, + argument_default=2 * block_length) + self._prefetch_input_elements = convert.optional_param_to_tensor( + "prefetch_input_elements", + prefetch_input_elements, + argument_default=2 * cycle_length) + + def _as_variant_tensor(self): + return gen_dataset_ops.parallel_interleave_dataset( + self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access + self._map_func.captured_inputs, + self._cycle_length, + self._block_length, + self._sloppy, + self._buffer_output_elements, + self._prefetch_input_elements, + f=self._map_func, + output_types=nest.flatten( + sparse.as_dense_types(self.output_types, self.output_classes)), + output_shapes=nest.flatten( + sparse.as_dense_shapes(self.output_shapes, self.output_classes))) + + @property + def output_classes(self): + return self._output_classes + + @property + def output_shapes(self): + return self._output_shapes + + @property + def output_types(self): + return self._output_types + + +@tf_export("data.TFRecordDataset") +class TFRecordDataset(dataset_ops.Dataset): + """A `Dataset` comprising records from one or more TFRecord files.""" + + def __init__(self, filenames, compression_type=None, buffer_size=None, + num_parallel_reads=None): + """Creates a `TFRecordDataset` to read for one or more TFRecord files. + + NOTE: The `num_parallel_reads` argument can be used to improve performance + when reading from a remote filesystem. + + Args: + filenames: A `tf.string` tensor or `tf.data.Dataset` containing one or + more filenames. + compression_type: (Optional.) A `tf.string` scalar evaluating to one of + `""` (no compression), `"ZLIB"`, or `"GZIP"`. + buffer_size: (Optional.) A `tf.int64` scalar representing the number of + bytes in the read buffer. 0 means no buffering. + num_parallel_reads: (Optional.) A `tf.int64` scalar representing the + number of files to read in parallel. Defaults to reading files + sequentially. + + Raises: + TypeError: If any argument does not have the expected type. + ValueError: If any argument does not have the expected shape. + """ + super(TFRecordDataset, self).__init__() + if isinstance(filenames, dataset_ops.Dataset): + if filenames.output_types != dtypes.string: + raise TypeError( + "`filenames` must be a `tf.data.Dataset` of `tf.string` elements.") + if not filenames.output_shapes.is_compatible_with(tensor_shape.scalar()): + raise ValueError( + "`filenames` must be a `tf.data.Dataset` of scalar `tf.string` " + "elements.") + else: + filenames = ops.convert_to_tensor(filenames, dtype=dtypes.string) + filenames = array_ops.reshape(filenames, [-1], name="flat_filenames") + filenames = dataset_ops.Dataset.from_tensor_slices(filenames) + + def read_one_file(filename): + return _TFRecordDataset(filename, compression_type, buffer_size) + + if num_parallel_reads is None: + self._impl = filenames.flat_map(read_one_file) + else: + self._impl = ParallelInterleaveDataset( + filenames, read_one_file, cycle_length=num_parallel_reads, + block_length=1, sloppy=False, buffer_output_elements=None, + prefetch_input_elements=None) + + def _as_variant_tensor(self): + return self._impl._as_variant_tensor() # pylint: disable=protected-access + + @property + def output_classes(self): + return self._impl.output_classes + + @property + def output_shapes(self): + return self._impl.output_shapes + + @property + def output_types(self): + return self._impl.output_types + + @tf_export("data.FixedLengthRecordDataset") -class FixedLengthRecordDataset(Dataset): +class FixedLengthRecordDataset(dataset_ops.Dataset): """A `Dataset` of fixed-length records from one or more binary files.""" def __init__(self, diff --git a/tensorflow/python/data/util/BUILD b/tensorflow/python/data/util/BUILD index e32c7b54a48dd887c2748897c3ce3661aab9f497..b1bdbdab37b63667b475c732df7a47d9e57f2b19 100644 --- a/tensorflow/python/data/util/BUILD +++ b/tensorflow/python/data/util/BUILD @@ -86,6 +86,30 @@ py_test( ], ) +py_library( + name = "random_seed", + srcs = ["random_seed.py"], + srcs_version = "PY2AND3", + deps = [ + "//tensorflow/python:constant_op", + "//tensorflow/python:dtypes", + "//tensorflow/python:framework", + ], +) + +py_test( + name = "random_seed_test", + size = "small", + srcs = ["random_seed_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":random_seed", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:util", + ], +) + filegroup( name = "all_files", srcs = glob( diff --git a/tensorflow/python/data/util/random_seed.py b/tensorflow/python/data/util/random_seed.py new file mode 100644 index 0000000000000000000000000000000000000000..e2c9d8672f94587fd3164f25f97b44a97526be07 --- /dev/null +++ b/tensorflow/python/data/util/random_seed.py @@ -0,0 +1,58 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Utilities for generating Tensor-valued random seeds.""" + +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 random_seed +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import math_ops + + +def get_seed(seed): + """Returns the local seeds an operation should use given an op-specific seed. + + See @{tf.get_seed} for more details. This wrapper adds support for the case + where `seed` may be a tensor. + + Args: + seed: An integer or a @{tf.int64} scalar tensor. + + Returns: + A tuple of two @{tf.int64} scalar tensors that should be used for the local + seed of the calling dataset. + """ + seed, seed2 = random_seed.get_seed(seed) + if seed is None: + seed = constant_op.constant(0, dtype=dtypes.int64, name="seed") + else: + seed = ops.convert_to_tensor(seed, dtype=dtypes.int64, name="seed") + if seed2 is None: + seed2 = constant_op.constant(0, dtype=dtypes.int64, name="seed2") + else: + with ops.name_scope("seed2") as scope: + seed2 = ops.convert_to_tensor(seed2, dtype=dtypes.int64) + seed2 = array_ops.where( + math_ops.logical_and( + math_ops.equal(seed, 0), math_ops.equal(seed2, 0)), + constant_op.constant(2**31 - 1, dtype=dtypes.int64), + seed2, + name=scope) + return seed, seed2 diff --git a/tensorflow/python/data/util/random_seed_test.py b/tensorflow/python/data/util/random_seed_test.py new file mode 100644 index 0000000000000000000000000000000000000000..33227e82afe6fe1c748693d107d4e9844abb8e09 --- /dev/null +++ b/tensorflow/python/data/util/random_seed_test.py @@ -0,0 +1,83 @@ +# 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 utilities working with arbitrarily nested structures.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.data.util import random_seed as data_random_seed +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 random_seed +from tensorflow.python.framework import test_util +from tensorflow.python.platform import test + + +class RandomSeedTest(test.TestCase): + + @test_util.run_in_graph_and_eager_modes() + def testRandomSeed(self): + zero_t = constant_op.constant(0, dtype=dtypes.int64, name='zero') + one_t = constant_op.constant(1, dtype=dtypes.int64, name='one') + intmax_t = constant_op.constant( + 2**31 - 1, dtype=dtypes.int64, name='intmax') + test_cases = [ + # Each test case is a tuple with input to get_seed: + # (input_graph_seed, input_op_seed) + # and output from get_seed: + # (output_graph_seed, output_op_seed) + ((None, None), (0, 0)), + ((None, 1), (random_seed.DEFAULT_GRAPH_SEED, 1)), + ((1, 1), (1, 1)), + ((0, 0), (0, 2**31 - 1)), # Avoid nondeterministic (0, 0) output + ((2**31 - 1, 0), (0, 2**31 - 1)), # Don't wrap to (0, 0) either + ((0, 2**31 - 1), (0, 2**31 - 1)), # Wrapping for the other argument + # Once more, with tensor-valued arguments + ((None, one_t), (random_seed.DEFAULT_GRAPH_SEED, 1)), + ((1, one_t), (1, 1)), + ((0, zero_t), (0, 2**31 - 1)), # Avoid nondeterministic (0, 0) output + ((2**31 - 1, zero_t), (0, 2**31 - 1)), # Don't wrap to (0, 0) either + ((0, intmax_t), (0, 2**31 - 1)), # Wrapping for the other argument + ] + for tc in test_cases: + tinput, toutput = tc[0], tc[1] + random_seed.set_random_seed(tinput[0]) + g_seed, op_seed = data_random_seed.get_seed(tinput[1]) + g_seed = self.evaluate(g_seed) + op_seed = self.evaluate(op_seed) + msg = 'test_case = {0}, got {1}, want {2}'.format( + tinput, (g_seed, op_seed), toutput) + self.assertEqual((g_seed, op_seed), toutput, msg=msg) + random_seed.set_random_seed(None) + + if not context.executing_eagerly(): + random_seed.set_random_seed(1) + tinput = (1, None) + toutput = (1, ops.get_default_graph()._last_id) # pylint: disable=protected-access + random_seed.set_random_seed(tinput[0]) + g_seed, op_seed = data_random_seed.get_seed(tinput[1]) + g_seed = self.evaluate(g_seed) + op_seed = self.evaluate(op_seed) + msg = 'test_case = {0}, got {1}, want {2}'.format(1, (g_seed, op_seed), + toutput) + self.assertEqual((g_seed, op_seed), toutput, msg=msg) + random_seed.set_random_seed(None) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/python/debug/BUILD b/tensorflow/python/debug/BUILD index 253588fc3b2986af3ab8c6be5b0b85f178c06336..c60f6923900fb1c43e6028c844260ee2aa981ff2 100644 --- a/tensorflow/python/debug/BUILD +++ b/tensorflow/python/debug/BUILD @@ -913,6 +913,7 @@ cuda_py_test( "//tensorflow/python:util", "//tensorflow/python:variables", ], + tags = ["no_windows"], # TODO: needs investigation on Windows ) py_test( @@ -957,7 +958,7 @@ cuda_py_test( cuda_py_test( name = "session_debug_grpc_test", - size = "large", + size = "medium", srcs = ["lib/session_debug_grpc_test.py"], additional_deps = [ ":debug_data", @@ -967,7 +968,6 @@ cuda_py_test( ":grpc_wrapper", ":hooks", ":session_debug_testlib", - "//third_party/py/numpy", "//tensorflow/python:client", "//tensorflow/python:client_testlib", "//tensorflow/python:framework_for_generated_wrappers", @@ -983,6 +983,29 @@ cuda_py_test( ], ) +cuda_py_test( + name = "grpc_large_data_test", + size = "medium", + srcs = ["lib/grpc_large_data_test.py"], + additional_deps = [ + ":dumping_wrapper", + ":grpc_debug_test_server", + ":grpc_wrapper", + ":session_debug_testlib", + "//third_party/py/numpy", + "//tensorflow/python:client", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:platform_test", + "//tensorflow/python:variables", + ], + tags = [ + "no_oss", # Test flaky due to port collisions. + "no_windows", + "oss_serial", + ], +) + # TODO(cais): Run the test in OSS, perhaps through a sh_test. cuda_py_test( name = "dist_session_debug_grpc_test", diff --git a/tensorflow/python/debug/README.md b/tensorflow/python/debug/README.md index a2273b050bb1ecd5a35938c3de57fb8562f1d26d..269bbb19bdb898d1d81d0b9c618a284a437e68b9 100644 --- a/tensorflow/python/debug/README.md +++ b/tensorflow/python/debug/README.md @@ -37,12 +37,18 @@ models: * Association of nodes and tensors in graphs with Python source lines * Profiling of models at the level of graph nodes and Python source lines. (Omitted internal-only feature) +* A [gRPC](https://grpc.io/)-based remote debugging protocol, which allows us to + build a browser-based graphical user interface (GUI) for TFDBG: the + [TensorBoard Debugger Plugin](https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/debugger/README.md). ## How to use TFDBG? * For a walkthrough of TFDBG command-line interface, see https://www.tensorflow.org/programmers_guide/debugger. +* For information on the web GUI of TFDBG (TensorBoard Debugger Plugin), see + [this README](https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/debugger/README.md). * For programmatic use of the API of TFDBG, see https://www.tensorflow.org/api_docs/python/tfdbg. + ## Related Publications * Cai, S., Breck E., Nielsen E., Salib M., Sculley D. (2016) TensorFlow Debugger: diff --git a/tensorflow/python/debug/lib/debug_gradients.py b/tensorflow/python/debug/lib/debug_gradients.py index 16f51a4b32f711b97077643cec669bb8970e0b21..589a13db7f798aef3bb82dfbd442deabfbcf2a41 100644 --- a/tensorflow/python/debug/lib/debug_gradients.py +++ b/tensorflow/python/debug/lib/debug_gradients.py @@ -156,11 +156,12 @@ class GradientsDebugger(object): # TODO(cais): Implement value_stack. grad_debug_op_name = _tensor_to_grad_debug_op_name(input_tensor, self._uuid) # pylint: disable=protected-access - identity_op = (gen_array_ops._debug_gradient_ref_identity - if input_tensor.dtype._is_ref_dtype - else gen_array_ops._debug_gradient_identity) - debug_grad_identity = identity_op(input_tensor, name=grad_debug_op_name) + identity_op = ( + gen_array_ops.debug_gradient_ref_identity + if input_tensor.dtype._is_ref_dtype else + gen_array_ops.debug_gradient_identity) # pylint: enable=protected-access + debug_grad_identity = identity_op(input_tensor, name=grad_debug_op_name) assert debug_grad_identity.dtype == input_tensor.dtype if debug_grad_identity.op.name != grad_debug_op_name: raise ValueError( diff --git a/tensorflow/python/debug/lib/grpc_large_data_test.py b/tensorflow/python/debug/lib/grpc_large_data_test.py new file mode 100644 index 0000000000000000000000000000000000000000..5bc477a9baeb7116530fc9122b926458c1a6c08e --- /dev/null +++ b/tensorflow/python/debug/lib/grpc_large_data_test.py @@ -0,0 +1,210 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for sending large-size data through tfdbg grpc channels. + +"Large-size data" includes large GraphDef protos and large Tensor protos. +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +from six.moves import xrange # pylint: disable=redefined-builtin + +from tensorflow.python.debug.lib import grpc_debug_test_server +from tensorflow.python.debug.lib import session_debug_testlib +from tensorflow.python.debug.wrappers import framework +from tensorflow.python.debug.wrappers import grpc_wrapper +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 variables +from tensorflow.python.platform import googletest +from tensorflow.python.platform import test +from tensorflow.python.platform import tf_logging + + +class LargeGraphAndLargeTensorsDebugTest(test_util.TensorFlowTestCase): + + @classmethod + def setUpClass(cls): + (cls.debug_server_port, cls.debug_server_url, _, cls.debug_server_thread, + cls.debug_server + ) = grpc_debug_test_server.start_server_on_separate_thread( + dump_to_filesystem=False) + tf_logging.info("debug server url: %s", cls.debug_server_url) + + @classmethod + def tearDownClass(cls): + cls.debug_server.stop_server().wait() + cls.debug_server_thread.join() + + def tearDown(self): + ops.reset_default_graph() + self.debug_server.clear_data() + + def testSendingLargeGraphDefsWorks(self): + with self.test_session( + use_gpu=True, + config=session_debug_testlib.no_rewrite_session_config()) as sess: + u = variables.Variable(42.0, name="original_u") + for _ in xrange(50 * 1000): + u = array_ops.identity(u) + sess.run(variables.global_variables_initializer()) + + def watch_fn(fetches, feeds): + del fetches, feeds + return framework.WatchOptions( + debug_ops=["DebugIdentity"], + node_name_regex_whitelist=r"original_u") + sess = grpc_wrapper.GrpcDebugWrapperSession( + sess, "localhost:%d" % self.debug_server_port, watch_fn=watch_fn) + self.assertAllClose(42.0, sess.run(u)) + + self.assertAllClose( + [42.0], + self.debug_server.debug_tensor_values["original_u:0:DebugIdentity"]) + self.assertEqual(2 if test.is_gpu_available() else 1, + len(self.debug_server.partition_graph_defs)) + max_graph_def_size = max([ + len(graph_def.SerializeToString()) + for graph_def in self.debug_server.partition_graph_defs]) + self.assertGreater(max_graph_def_size, 4 * 1024 * 1024) + + def testSendingLargeFloatTensorWorks(self): + with self.test_session( + use_gpu=True, + config=session_debug_testlib.no_rewrite_session_config()) as sess: + u_init_val_array = list(xrange(1200 * 1024)) + # Size: 4 * 1200 * 1024 = 4800k > 4M + + u_init = constant_op.constant( + u_init_val_array, dtype=dtypes.float32, name="u_init") + u = variables.Variable(u_init, name="u") + + def watch_fn(fetches, feeds): + del fetches, feeds # Unused by this watch_fn. + return framework.WatchOptions( + debug_ops=["DebugIdentity"], + node_name_regex_whitelist=r"u_init") + sess = grpc_wrapper.GrpcDebugWrapperSession( + sess, "localhost:%d" % self.debug_server_port, watch_fn=watch_fn) + sess.run(u.initializer) + + self.assertAllEqual( + u_init_val_array, + self.debug_server.debug_tensor_values["u_init:0:DebugIdentity"][0]) + + def testSendingStringTensorWithAlmostTooLargeStringsWorks(self): + with self.test_session( + use_gpu=True, + config=session_debug_testlib.no_rewrite_session_config()) as sess: + u_init_val = [ + b"", b"spam", b"A" * 2500 * 1024, b"B" * 2500 * 1024, b"egg", b""] + u_init = constant_op.constant( + u_init_val, dtype=dtypes.string, name="u_init") + u = variables.Variable(u_init, name="u") + + def watch_fn(fetches, feeds): + del fetches, feeds + return framework.WatchOptions( + debug_ops=["DebugIdentity"], + node_name_regex_whitelist=r"u_init") + sess = grpc_wrapper.GrpcDebugWrapperSession( + sess, "localhost:%d" % self.debug_server_port, watch_fn=watch_fn) + sess.run(u.initializer) + + self.assertAllEqual( + u_init_val, + self.debug_server.debug_tensor_values["u_init:0:DebugIdentity"][0]) + + def testSendingLargeStringTensorWorks(self): + with self.test_session( + use_gpu=True, + config=session_debug_testlib.no_rewrite_session_config()) as sess: + strs_total_size_threshold = 5000 * 1024 + cum_size = 0 + u_init_val_array = [] + while cum_size < strs_total_size_threshold: + strlen = np.random.randint(200) + u_init_val_array.append(b"A" * strlen) + cum_size += strlen + + u_init = constant_op.constant( + u_init_val_array, dtype=dtypes.string, name="u_init") + u = variables.Variable(u_init, name="u") + + def watch_fn(fetches, feeds): + del fetches, feeds + return framework.WatchOptions( + debug_ops=["DebugIdentity"], + node_name_regex_whitelist=r"u_init") + sess = grpc_wrapper.GrpcDebugWrapperSession( + sess, "localhost:%d" % self.debug_server_port, watch_fn=watch_fn) + sess.run(u.initializer) + + self.assertAllEqual( + u_init_val_array, + self.debug_server.debug_tensor_values["u_init:0:DebugIdentity"][0]) + + def testSendingEmptyFloatTensorWorks(self): + with self.test_session( + use_gpu=True, + config=session_debug_testlib.no_rewrite_session_config()) as sess: + u_init = constant_op.constant( + [], dtype=dtypes.float32, shape=[0], name="u_init") + u = variables.Variable(u_init, name="u") + + def watch_fn(fetches, feeds): + del fetches, feeds + return framework.WatchOptions( + debug_ops=["DebugIdentity"], + node_name_regex_whitelist=r"u_init") + sess = grpc_wrapper.GrpcDebugWrapperSession( + sess, "localhost:%d" % self.debug_server_port, watch_fn=watch_fn) + sess.run(u.initializer) + + u_init_value = self.debug_server.debug_tensor_values[ + "u_init:0:DebugIdentity"][0] + self.assertEqual(np.float32, u_init_value.dtype) + self.assertEqual(0, len(u_init_value)) + + def testSendingEmptyStringTensorWorks(self): + with self.test_session( + use_gpu=True, + config=session_debug_testlib.no_rewrite_session_config()) as sess: + u_init = constant_op.constant( + [], dtype=dtypes.string, shape=[0], name="u_init") + u = variables.Variable(u_init, name="u") + + def watch_fn(fetches, feeds): + del fetches, feeds + return framework.WatchOptions( + debug_ops=["DebugIdentity"], + node_name_regex_whitelist=r"u_init") + sess = grpc_wrapper.GrpcDebugWrapperSession( + sess, "localhost:%d" % self.debug_server_port, watch_fn=watch_fn) + sess.run(u.initializer) + + u_init_value = self.debug_server.debug_tensor_values[ + "u_init:0:DebugIdentity"][0] + self.assertEqual(np.object, u_init_value.dtype) + self.assertEqual(0, len(u_init_value)) + + +if __name__ == "__main__": + googletest.main() diff --git a/tensorflow/python/debug/lib/session_debug_file_test.py b/tensorflow/python/debug/lib/session_debug_file_test.py index 1a6bedbbcbf94eb95e49d43e2d03c85b53bebb7b..ba0f15b4e2ff23295eae764088144a3d1b533f01 100644 --- a/tensorflow/python/debug/lib/session_debug_file_test.py +++ b/tensorflow/python/debug/lib/session_debug_file_test.py @@ -22,7 +22,6 @@ import shutil import tempfile from tensorflow.core.protobuf import config_pb2 -from tensorflow.core.protobuf import rewriter_config_pb2 from tensorflow.python.client import session from tensorflow.python.debug.lib import debug_data from tensorflow.python.debug.lib import debug_utils @@ -36,13 +35,6 @@ from tensorflow.python.platform import googletest class SessionDebugFileTest(session_debug_testlib.SessionDebugTestBase): - def _no_rewrite_session_config(self): - rewriter_config = rewriter_config_pb2.RewriterConfig( - disable_model_pruning=True, - arithmetic_optimization=rewriter_config_pb2.RewriterConfig.OFF) - graph_options = config_pb2.GraphOptions(rewrite_options=rewriter_config) - return config_pb2.ConfigProto(graph_options=graph_options) - def _debug_urls(self, run_number=None): return ["file://%s" % self._debug_dump_dir(run_number=run_number)] @@ -55,7 +47,8 @@ class SessionDebugFileTest(session_debug_testlib.SessionDebugTestBase): def testAllowsDifferentWatchesOnDifferentRuns(self): """Test watching different tensors on different runs of the same graph.""" - with session.Session(config=self._no_rewrite_session_config()) as sess: + with session.Session( + config=session_debug_testlib.no_rewrite_session_config()) as sess: u_init_val = [[5.0, 3.0], [-1.0, 0.0]] v_init_val = [[2.0], [-1.0]] diff --git a/tensorflow/python/debug/lib/session_debug_grpc_test.py b/tensorflow/python/debug/lib/session_debug_grpc_test.py index b623ee31c5dc59894373ec7952e53acd0f6e1126..ff49b6954776264ccb2eceeceab7da5a881081f0 100644 --- a/tensorflow/python/debug/lib/session_debug_grpc_test.py +++ b/tensorflow/python/debug/lib/session_debug_grpc_test.py @@ -24,11 +24,9 @@ from __future__ import print_function import os import shutil -import numpy as np from six.moves import xrange # pylint: disable=redefined-builtin from tensorflow.core.protobuf import config_pb2 -from tensorflow.core.protobuf import rewriter_config_pb2 from tensorflow.python.client import session from tensorflow.python.debug.lib import debug_data from tensorflow.python.debug.lib import debug_utils @@ -38,28 +36,15 @@ from tensorflow.python.debug.wrappers import framework from tensorflow.python.debug.wrappers import grpc_wrapper from tensorflow.python.debug.wrappers import hooks 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 state_ops from tensorflow.python.ops import variables from tensorflow.python.platform import googletest -from tensorflow.python.platform import test -from tensorflow.python.platform import tf_logging from tensorflow.python.training import monitored_session -def no_rewrite_session_config(): - rewriter_config = rewriter_config_pb2.RewriterConfig( - disable_model_pruning=True, - arithmetic_optimization=rewriter_config_pb2.RewriterConfig.OFF, - dependency_optimization=rewriter_config_pb2.RewriterConfig.OFF) - graph_options = config_pb2.GraphOptions(rewrite_options=rewriter_config) - return config_pb2.ConfigProto(graph_options=graph_options) - - class GrpcDebugServerTest(test_util.TensorFlowTestCase): def testRepeatedRunServerRaisesException(self): @@ -142,19 +127,22 @@ class SessionDebugGrpcTest(session_debug_testlib.SessionDebugTestBase): return os.path.join(self._dump_root, "run_%d" % run_number) def testConstructGrpcDebugWrapperSessionWithInvalidTypeRaisesException(self): - sess = session.Session(config=no_rewrite_session_config()) + sess = session.Session( + config=session_debug_testlib.no_rewrite_session_config()) with self.assertRaisesRegexp( TypeError, "Expected type str or list in grpc_debug_server_addresses"): grpc_wrapper.GrpcDebugWrapperSession(sess, 1337) def testConstructGrpcDebugWrapperSessionWithInvalidTypeRaisesException2(self): - sess = session.Session(config=no_rewrite_session_config()) + sess = session.Session( + config=session_debug_testlib.no_rewrite_session_config()) with self.assertRaisesRegexp( TypeError, "Expected type str in list grpc_debug_server_addresses"): grpc_wrapper.GrpcDebugWrapperSession(sess, ["localhost:1337", 1338]) def testUseInvalidWatchFnTypeWithGrpcDebugWrapperSessionRaisesException(self): - sess = session.Session(config=no_rewrite_session_config()) + sess = session.Session( + config=session_debug_testlib.no_rewrite_session_config()) with self.assertRaises(TypeError): grpc_wrapper.GrpcDebugWrapperSession( sess, "localhost:%d" % self._server_port, watch_fn="foo") @@ -164,7 +152,8 @@ class SessionDebugGrpcTest(session_debug_testlib.SessionDebugTestBase): v = variables.Variable(20.0, name="v") w = math_ops.multiply(u, v, name="w") - sess = session.Session(config=no_rewrite_session_config()) + sess = session.Session( + config=session_debug_testlib.no_rewrite_session_config()) sess.run(u.initializer) sess.run(v.initializer) @@ -190,7 +179,8 @@ class SessionDebugGrpcTest(session_debug_testlib.SessionDebugTestBase): v = variables.Variable(20.0, name="v") w = math_ops.multiply(u, v, name="w") - sess = session.Session(config=no_rewrite_session_config()) + sess = session.Session( + config=session_debug_testlib.no_rewrite_session_config()) sess.run(u.initializer) sess.run(v.initializer) @@ -223,7 +213,8 @@ class SessionDebugGrpcTest(session_debug_testlib.SessionDebugTestBase): v = variables.Variable(20.0, name="v") w = math_ops.multiply(u, v, name="w") - sess = session.Session(config=no_rewrite_session_config()) + sess = session.Session( + config=session_debug_testlib.no_rewrite_session_config()) sess.run(u.initializer) sess.run(v.initializer) @@ -254,7 +245,8 @@ class SessionDebugGrpcTest(session_debug_testlib.SessionDebugTestBase): v = variables.Variable(20.0, name="v") w = math_ops.multiply(u, v, name="w") - sess = session.Session(config=no_rewrite_session_config()) + sess = session.Session( + config=session_debug_testlib.no_rewrite_session_config()) sess.run(u.initializer) sess.run(v.initializer) @@ -298,7 +290,8 @@ class SessionDebugGrpcTest(session_debug_testlib.SessionDebugTestBase): v = variables.Variable(20.0, name="v") w = math_ops.multiply(u, v, name="w") - sess = session.Session(config=no_rewrite_session_config()) + sess = session.Session( + config=session_debug_testlib.no_rewrite_session_config()) sess.run(variables.global_variables_initializer()) grpc_debug_hook = hooks.TensorBoardDebugHook( @@ -324,168 +317,6 @@ class SessionDebugGrpcTest(session_debug_testlib.SessionDebugTestBase): hooks.GrpcDebugHook(["foo:42424"]) -class LargeGraphAndLargeTensorsDebugTest(test_util.TensorFlowTestCase): - - @classmethod - def setUpClass(cls): - (cls.debug_server_port, cls.debug_server_url, _, cls.debug_server_thread, - cls.debug_server - ) = grpc_debug_test_server.start_server_on_separate_thread( - dump_to_filesystem=False) - tf_logging.info("debug server url: %s", cls.debug_server_url) - - @classmethod - def tearDownClass(cls): - cls.debug_server.stop_server().wait() - cls.debug_server_thread.join() - - def tearDown(self): - ops.reset_default_graph() - self.debug_server.clear_data() - - def testSendingLargeGraphDefsWorks(self): - with self.test_session( - use_gpu=True, config=no_rewrite_session_config()) as sess: - u = variables.Variable(42.0, name="original_u") - for _ in xrange(50 * 1000): - u = array_ops.identity(u) - sess.run(variables.global_variables_initializer()) - - def watch_fn(fetches, feeds): - del fetches, feeds - return framework.WatchOptions( - debug_ops=["DebugIdentity"], - node_name_regex_whitelist=r"original_u") - sess = grpc_wrapper.GrpcDebugWrapperSession( - sess, "localhost:%d" % self.debug_server_port, watch_fn=watch_fn) - self.assertAllClose(42.0, sess.run(u)) - - self.assertAllClose( - [42.0], - self.debug_server.debug_tensor_values["original_u:0:DebugIdentity"]) - self.assertEqual(2 if test.is_gpu_available() else 1, - len(self.debug_server.partition_graph_defs)) - max_graph_def_size = max([ - len(graph_def.SerializeToString()) - for graph_def in self.debug_server.partition_graph_defs]) - self.assertGreater(max_graph_def_size, 4 * 1024 * 1024) - - def testSendingLargeFloatTensorWorks(self): - with self.test_session( - use_gpu=True, config=no_rewrite_session_config()) as sess: - u_init_val_array = list(xrange(1200 * 1024)) - # Size: 4 * 1200 * 1024 = 4800k > 4M - - u_init = constant_op.constant( - u_init_val_array, dtype=dtypes.float32, name="u_init") - u = variables.Variable(u_init, name="u") - - def watch_fn(fetches, feeds): - del fetches, feeds # Unused by this watch_fn. - return framework.WatchOptions( - debug_ops=["DebugIdentity"], - node_name_regex_whitelist=r"u_init") - sess = grpc_wrapper.GrpcDebugWrapperSession( - sess, "localhost:%d" % self.debug_server_port, watch_fn=watch_fn) - sess.run(u.initializer) - - self.assertAllEqual( - u_init_val_array, - self.debug_server.debug_tensor_values["u_init:0:DebugIdentity"][0]) - - def testSendingStringTensorWithAlmostTooLargeStringsWorks(self): - with self.test_session( - use_gpu=True, config=no_rewrite_session_config()) as sess: - u_init_val = [ - b"", b"spam", b"A" * 2500 * 1024, b"B" * 2500 * 1024, b"egg", b""] - u_init = constant_op.constant( - u_init_val, dtype=dtypes.string, name="u_init") - u = variables.Variable(u_init, name="u") - - def watch_fn(fetches, feeds): - del fetches, feeds - return framework.WatchOptions( - debug_ops=["DebugIdentity"], - node_name_regex_whitelist=r"u_init") - sess = grpc_wrapper.GrpcDebugWrapperSession( - sess, "localhost:%d" % self.debug_server_port, watch_fn=watch_fn) - sess.run(u.initializer) - - self.assertAllEqual( - u_init_val, - self.debug_server.debug_tensor_values["u_init:0:DebugIdentity"][0]) - - def testSendingLargeStringTensorWorks(self): - with self.test_session( - use_gpu=True, config=no_rewrite_session_config()) as sess: - strs_total_size_threshold = 5000 * 1024 - cum_size = 0 - u_init_val_array = [] - while cum_size < strs_total_size_threshold: - strlen = np.random.randint(200) - u_init_val_array.append(b"A" * strlen) - cum_size += strlen - - u_init = constant_op.constant( - u_init_val_array, dtype=dtypes.string, name="u_init") - u = variables.Variable(u_init, name="u") - - def watch_fn(fetches, feeds): - del fetches, feeds - return framework.WatchOptions( - debug_ops=["DebugIdentity"], - node_name_regex_whitelist=r"u_init") - sess = grpc_wrapper.GrpcDebugWrapperSession( - sess, "localhost:%d" % self.debug_server_port, watch_fn=watch_fn) - sess.run(u.initializer) - - self.assertAllEqual( - u_init_val_array, - self.debug_server.debug_tensor_values["u_init:0:DebugIdentity"][0]) - - def testSendingEmptyFloatTensorWorks(self): - with self.test_session( - use_gpu=True, config=no_rewrite_session_config()) as sess: - u_init = constant_op.constant( - [], dtype=dtypes.float32, shape=[0], name="u_init") - u = variables.Variable(u_init, name="u") - - def watch_fn(fetches, feeds): - del fetches, feeds - return framework.WatchOptions( - debug_ops=["DebugIdentity"], - node_name_regex_whitelist=r"u_init") - sess = grpc_wrapper.GrpcDebugWrapperSession( - sess, "localhost:%d" % self.debug_server_port, watch_fn=watch_fn) - sess.run(u.initializer) - - u_init_value = self.debug_server.debug_tensor_values[ - "u_init:0:DebugIdentity"][0] - self.assertEqual(np.float32, u_init_value.dtype) - self.assertEqual(0, len(u_init_value)) - - def testSendingEmptyStringTensorWorks(self): - with self.test_session( - use_gpu=True, config=no_rewrite_session_config()) as sess: - u_init = constant_op.constant( - [], dtype=dtypes.string, shape=[0], name="u_init") - u = variables.Variable(u_init, name="u") - - def watch_fn(fetches, feeds): - del fetches, feeds - return framework.WatchOptions( - debug_ops=["DebugIdentity"], - node_name_regex_whitelist=r"u_init") - sess = grpc_wrapper.GrpcDebugWrapperSession( - sess, "localhost:%d" % self.debug_server_port, watch_fn=watch_fn) - sess.run(u.initializer) - - u_init_value = self.debug_server.debug_tensor_values[ - "u_init:0:DebugIdentity"][0] - self.assertEqual(np.object, u_init_value.dtype) - self.assertEqual(0, len(u_init_value)) - - class SessionDebugConcurrentTest( session_debug_testlib.DebugConcurrentRunCallsTest): @@ -548,7 +379,8 @@ class SessionDebugGrpcGatingTest(test_util.TensorFlowTestCase): self._server_2.clear_data() def testToggleEnableTwoDebugWatchesNoCrosstalkBetweenDebugNodes(self): - with session.Session(config=no_rewrite_session_config()) as sess: + with session.Session( + config=session_debug_testlib.no_rewrite_session_config()) as sess: v_1 = variables.Variable(50.0, name="v_1") v_2 = variables.Variable(-50.0, name="v_1") delta_1 = constant_op.constant(5.0, name="delta_1") @@ -617,7 +449,8 @@ class SessionDebugGrpcGatingTest(test_util.TensorFlowTestCase): ("toggled_2", 0, "DebugIdentity")]) self._servers_and_threads.append((server, server_thread)) - with session.Session(config=no_rewrite_session_config()) as sess: + with session.Session( + config=session_debug_testlib.no_rewrite_session_config()) as sess: v_1 = variables.Variable(50.0, name="v_1") v_2 = variables.Variable(-50.0, name="v_1") # These two nodes have names that match those in the @@ -656,7 +489,8 @@ class SessionDebugGrpcGatingTest(test_util.TensorFlowTestCase): self.assertEqual(0, len(server.debug_tensor_values)) def testToggleEnableTwoDebugWatchesNoCrosstalkBetweenServers(self): - with session.Session(config=no_rewrite_session_config()) as sess: + with session.Session( + config=session_debug_testlib.no_rewrite_session_config()) as sess: v = variables.Variable(50.0, name="v") delta = constant_op.constant(5.0, name="delta") inc_v = state_ops.assign_add(v, delta, name="inc_v") @@ -698,7 +532,8 @@ class SessionDebugGrpcGatingTest(test_util.TensorFlowTestCase): self.assertEqual(0, len(self._server_2.debug_tensor_values)) def testToggleBreakpointsWorks(self): - with session.Session(config=no_rewrite_session_config()) as sess: + with session.Session( + config=session_debug_testlib.no_rewrite_session_config()) as sess: v_1 = variables.Variable(50.0, name="v_1") v_2 = variables.Variable(-50.0, name="v_2") delta_1 = constant_op.constant(5.0, name="delta_1") @@ -755,7 +590,8 @@ class SessionDebugGrpcGatingTest(test_util.TensorFlowTestCase): self.assertSetEqual(set(), self._server_1.breakpoints) def testTensorBoardDebuggerWrapperToggleBreakpointsWorks(self): - with session.Session(config=no_rewrite_session_config()) as sess: + with session.Session( + config=session_debug_testlib.no_rewrite_session_config()) as sess: v_1 = variables.Variable(50.0, name="v_1") v_2 = variables.Variable(-50.0, name="v_2") delta_1 = constant_op.constant(5.0, name="delta_1") @@ -827,7 +663,8 @@ class SessionDebugGrpcGatingTest(test_util.TensorFlowTestCase): self._server_1.query_source_file_line(__file__, 1) def testTensorBoardDebuggerWrapperDisablingTracebackSourceSendingWorks(self): - with session.Session(config=no_rewrite_session_config()) as sess: + with session.Session( + config=session_debug_testlib.no_rewrite_session_config()) as sess: v_1 = variables.Variable(50.0, name="v_1") v_2 = variables.Variable(-50.0, name="v_2") delta_1 = constant_op.constant(5.0, name="delta_1") diff --git a/tensorflow/python/eager/BUILD b/tensorflow/python/eager/BUILD index ab81d40148476735492890f608315b19eaa0a33f..5bedf9c6fdfcf630a72ef8a34bca38417b738d4f 100644 --- a/tensorflow/python/eager/BUILD +++ b/tensorflow/python/eager/BUILD @@ -42,7 +42,6 @@ py_library( ":backprop", ":context", ":core", - ":custom_gradient", ":execute", ":function", ":graph_callable", @@ -103,7 +102,6 @@ cuda_py_test( additional_deps = [ ":backprop", ":context", - ":custom_gradient", ":test", "//tensorflow/python:embedding_ops", "//tensorflow/python:array_ops", @@ -206,21 +204,6 @@ cc_library( ], ) -py_library( - name = "custom_gradient", - srcs = ["custom_gradient.py"], - srcs_version = "PY2AND3", - visibility = ["//tensorflow:internal"], - deps = [ - ":context", - ":tape", - "//tensorflow/python:array_ops", - "//tensorflow/python:framework_ops", - "//tensorflow/python:resource_variable_ops", - "//tensorflow/python:util", - ], -) - py_library( name = "graph_only_ops", srcs = ["graph_only_ops.py"], @@ -364,7 +347,6 @@ py_test( deps = [ ":backprop", ":context", - ":custom_gradient", ":test", "//tensorflow/python:array_ops", "//tensorflow/python:constant_op", diff --git a/tensorflow/python/eager/backprop.py b/tensorflow/python/eager/backprop.py index 14bcc60006228eeaabea241ee18d960174a9dbea..88de1a951ffd85b4ca97a084c8616d804dc10b45 100644 --- a/tensorflow/python/eager/backprop.py +++ b/tensorflow/python/eager/backprop.py @@ -18,7 +18,6 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import collections import functools import operator import threading @@ -43,26 +42,6 @@ from tensorflow.python.util import nest from tensorflow.python.util import tf_inspect -class _TensorCache(object): - """Simple cache which evicts items based on length in a FIFO manner.""" - - def __init__(self, max_items=256): - self._data = collections.OrderedDict() - self._max_items = max_items if max_items else 256 - - def put(self, key, value): - self._data[key] = value - - if len(self._data) > self._max_items: - self._data.popitem(last=False) - - def get(self, key): - return self._data.get(key, None) - - def flush(self): - self._data = {} - - _op_attr_type_cache = {} @@ -622,7 +601,7 @@ def _num_elements(grad): raise ValueError("`grad` not a Tensor or IndexedSlices.") -_zeros_cache = _TensorCache() +_zeros_cache = context._TensorCache() # pylint: disable=protected-access def _fast_fill(value, shape, dtype): diff --git a/tensorflow/python/eager/backprop_test.py b/tensorflow/python/eager/backprop_test.py index 734558dee2b35813810341480eb38a4bace2936b..07a2155d24bd9e00340273b3af36712216f6e8be 100644 --- a/tensorflow/python/eager/backprop_test.py +++ b/tensorflow/python/eager/backprop_test.py @@ -23,7 +23,6 @@ import numpy as np from tensorflow.python import pywrap_tensorflow from tensorflow.python.eager import backprop from tensorflow.python.eager import context -from tensorflow.python.eager import custom_gradient from tensorflow.python.eager import tape from tensorflow.python.eager import test from tensorflow.python.framework import constant_op @@ -32,6 +31,7 @@ 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 custom_gradient from tensorflow.python.ops import embedding_ops from tensorflow.python.ops import gradients from tensorflow.python.ops import math_ops @@ -115,6 +115,19 @@ class BackpropTest(test.TestCase): with self.assertRaises(RuntimeError): backprop.gradients_function(f)(constant_op.constant(1.0)) + def testGradientsFunctionInCustomGradient(self): + + @custom_gradient.custom_gradient + def f(x): + (y,) = backprop.gradients_function(lambda x: x * x)(x) + + def grad(dy): + return [2 * dy] + + return y, grad + + self.assertAllEqual(f(1.0), 2.0) + def testImplicitGradOverEmbeddingLookup(self): batch_size = 8 embedding_size = 512 diff --git a/tensorflow/python/eager/benchmarks_test.py b/tensorflow/python/eager/benchmarks_test.py index b56cbe80a7ab6b90d715187b0f0a44847038fc37..4255677a68836decce1aaf8d4d7d2df2bf86315b 100644 --- a/tensorflow/python/eager/benchmarks_test.py +++ b/tensorflow/python/eager/benchmarks_test.py @@ -35,7 +35,6 @@ from tensorflow.python import pywrap_tensorflow from tensorflow.python.eager import backprop # pylint: disable=unused-import from tensorflow.python.eager import context from tensorflow.python.eager import core -from tensorflow.python.eager import execute from tensorflow.python.eager import function from tensorflow.python.eager import test from tensorflow.python.framework import dtypes @@ -56,11 +55,11 @@ def c_tfe_py_fastpath_execute(a, transpose_b=False, name=None): ctx = context.context() - assert not ctx.in_graph_mode( + assert ctx.in_eager_mode( ), "The prototype doesn't contain C code for graph construction" try: return pywrap_tensorflow.TFE_Py_FastPathExecute( - ctx._handle, ctx.device_name, "MatMul", execute.record_gradient, name, + ctx._handle, ctx.device_name, "MatMul", name, ctx._post_execution_callbacks, a, b, "transpose_a", transpose_a, "transpose_b", transpose_b) except core._NotOkStatusException as e: @@ -243,7 +242,8 @@ class MicroBenchmarks(test.Benchmark): def _benchmark_gen_math_ops_matmul(self, m, transpose_b, num_iters): def func(): - gen_math_ops._mat_mul(m, m, transpose_b=transpose_b) + gen_math_ops.mat_mul(m, m, transpose_b=transpose_b) + self._run(func, num_iters) def _benchmark_tfe_py_fastpath_execute_matmul(self, m, transpose_b, @@ -275,6 +275,16 @@ class MicroBenchmarks(test.Benchmark): def _benchmark_read_variable(self, m, num_iters): self._run(m.value, num_iters) + def _benchmark_matmul_read_variable(self, m, num_iters): + self._benchmark_gen_math_ops_matmul( + m, transpose_b=False, num_iters=num_iters) + + def _benchmark_matmul_read_variable_with_tape(self, m, num_iters): + with backprop.GradientTape() as tape: + tape.watch(m) + self._benchmark_gen_math_ops_matmul( + m, transpose_b=False, num_iters=num_iters) + def _benchmark_read_variable_with_tape(self, m, num_iters): with backprop.GradientTape() as tape: tape.watch(m) @@ -416,6 +426,17 @@ class MicroBenchmarks(test.Benchmark): self._benchmark_defun_matmul( m, transpose_b=True, num_iters=self._num_iters_100_by_784) + def benchmark_matmul_read_variable_op_2_by_2_CPU(self): + with context.device(CPU): + m = resource_variable_ops.ResourceVariable(self._m_2_by_2) + self._benchmark_matmul_read_variable(m, num_iters=self._num_iters_2_by_2) + + def benchmark_matmul_read_variable_op_with_tape_2_by_2_CPU(self): + with context.device(CPU): + m = resource_variable_ops.ResourceVariable(self._m_2_by_2) + self._benchmark_matmul_read_variable_with_tape( + m, num_iters=self._num_iters_2_by_2) + def benchmark_read_variable_op_2_by_2_CPU(self): with context.device(CPU): m = resource_variable_ops.ResourceVariable(self._m_2_by_2) diff --git a/tensorflow/python/eager/context.py b/tensorflow/python/eager/context.py index 07652d3e02b6364e23b6579a64dcadf02dc5eb99..87d3ed880a88e040f02b957da99a094ec2d783ef 100644 --- a/tensorflow/python/eager/context.py +++ b/tensorflow/python/eager/context.py @@ -32,6 +32,7 @@ from tensorflow.python.framework import errors from tensorflow.python.util import compat from tensorflow.python.util import is_in_graph_mode from tensorflow.python.util import tf_contextlib +from tensorflow.python.util.tf_export import tf_export GRAPH_MODE = 0 EAGER_MODE = 1 @@ -54,20 +55,40 @@ DEVICE_PLACEMENT_SILENT_FOR_INT32 = ( pywrap_tensorflow.TFE_DEVICE_PLACEMENT_SILENT_FOR_INT32) +class _TensorCache(object): + """Simple cache which evicts items based on length in a FIFO manner.""" + + def __init__(self, max_items=256): + self._data = collections.OrderedDict() + self._max_items = max_items if max_items else 256 + + def put(self, key, value): + self._data[key] = value + + if len(self._data) > self._max_items: + self._data.popitem(last=False) + + def get(self, key): + return self._data.get(key, None) + + def flush(self): + self._data = {} + + # TODO(agarwal): better name ? class _EagerContext(threading.local): """Thread local eager context.""" def __init__(self): super(_EagerContext, self).__init__() - self.device_spec = pydev.DeviceSpec.from_string( - "/job:localhost/replica:0/task:0/device:CPU:0") + self.device_spec = pydev.DeviceSpec.from_string("") self.device_name = self.device_spec.to_string() self.mode = _default_mode self.scope_name = "" self.recording_summaries = False self.summary_writer_resource = None self.scalar_cache = {} + self.ones_rank_cache = _TensorCache() ContextStackEntry = collections.namedtuple( @@ -240,18 +261,18 @@ class Context(object): if mode == EAGER_MODE: context_stack.pop() - def in_graph_mode(self): - """Returns True if current thread is in GRAPH mode.""" - return self._eager_context.mode == GRAPH_MODE - - def in_eager_mode(self): - """Returns True if current thread is in EAGER mode.""" + def executing_eagerly(self): + """Returns True if current thread has eager executing enabled.""" return self._eager_context.mode == EAGER_MODE def scalar_cache(self): """Per-device cache for scalars.""" return self._eager_context.scalar_cache + def ones_rank_cache(self): + """Per-device cache for scalars.""" + return self._eager_context.ones_rank_cache + @property def scope_name(self): """Returns scope name for the current thread.""" @@ -498,23 +519,29 @@ def internal_operation_seed(): return context()._internal_operation_seed() # pylint: disable=protected-access -def in_graph_mode(): - """Returns True if current thread is in GRAPH mode for default context.""" - return context().in_graph_mode() +@tf_export("executing_eagerly") +def executing_eagerly(): + """Returns True if the current thread has eager execution enabled. + + Eager execution is typically enabled via @{tf.enable_eager_execution}, + but may also be enabled within the context of a Python function via + tf.contrib.eager.py_func. + """ + return context().executing_eagerly() def in_eager_mode(): - """Returns True if current thread is in EAGER mode for default context.""" - return context().in_eager_mode() + """Use executing_eagerly() instead. This function will be removed.""" + return executing_eagerly() def graph_mode(): - """Context-manager to enable GRAPH mode for current thread.""" + """Context-manager to disable eager execution for the current thread.""" return context()._mode(GRAPH_MODE) # pylint: disable=protected-access def eager_mode(): - """Context-manager to enable EAGER mode for current thread.""" + """Context-manager to enable eager execution for the current thread.""" return context()._mode(EAGER_MODE) # pylint: disable=protected-access @@ -607,4 +634,8 @@ def export_run_metadata(): # (for example, enable_eager_execution in python/framework/ops.py), # but they do all import this file. Note that IS_IN_GRAPH_MODE and # in_graph_mode are both parameterless functions. -is_in_graph_mode.IS_IN_GRAPH_MODE = in_graph_mode +def _tmp_in_graph_mode(): + return not executing_eagerly() + + +is_in_graph_mode.IS_IN_GRAPH_MODE = _tmp_in_graph_mode diff --git a/tensorflow/python/eager/core_test.py b/tensorflow/python/eager/core_test.py index c68e2f422eb81a915d4f941ffb920f221d9be250..61c5526d484b08010a96b0a3de283098aef7a90a 100644 --- a/tensorflow/python/eager/core_test.py +++ b/tensorflow/python/eager/core_test.py @@ -33,7 +33,10 @@ 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 gen_resource_variable_ops from tensorflow.python.ops import nn_ops +from tensorflow.python.ops import resource_variable_ops def execute(op_name, num_outputs, inputs, attrs=None): @@ -54,8 +57,7 @@ class TFETest(test_util.TensorFlowTestCase): def testContext(self): ctx = context.Context() - self.assertFalse(ctx.in_graph_mode()) - self.assertTrue(ctx.in_eager_mode()) + self.assertTrue(ctx.executing_eagerly()) self.assertEqual('', ctx.scope_name) ctx.scope_name = 'foo' @@ -65,8 +67,7 @@ class TFETest(test_util.TensorFlowTestCase): ctx.summary_writer_resource = 'mock' self.assertEqual('mock', ctx.summary_writer_resource) - self.assertEqual('/job:localhost/replica:0/task:0/device:CPU:0', - ctx.device_name) + self.assertEqual('', ctx.device_name) self.assertEqual(ctx.device_name, ctx.device_spec.to_string()) with ctx.device('GPU:0'): self.assertEqual('/job:localhost/replica:0/task:0/device:GPU:0', @@ -100,6 +101,18 @@ class TFETest(test_util.TensorFlowTestCase): self.assertEqual(len(cpu_stats.node_stats), 1) self.assertEqual(cpu_stats.node_stats[0].node_name, 'Add') + def testShouldCopy(self): + if not context.context().num_gpus(): + self.skipTest('No devices other than CPUs found') + with ops.device('gpu:0'): + x = constant_op.constant(1.0) + y = array_ops.identity(x) + # The value we're testing y.device against will depend on what the behavior + # of not explicitly specifying a device in the context is. This behavior is + # subject to change (for example, in the future we may want to use GPUs, if + # available, when no device is explicitly provided) + self.assertEqual(y.device, '/job:localhost/replica:0/task:0/device:CPU:0') + def testContextStackContainsEagerMode(self): # Eager execution has been enabled, and no other context # switch has occurred, so `context_stack` should contain @@ -136,9 +149,9 @@ class TFETest(test_util.TensorFlowTestCase): def get_context_values(ctx): return [ - ctx.in_graph_mode(), - ctx.in_eager_mode(), ctx.scope_name, ctx.summary_writer_resource, - ctx.device_name, ctx.num_gpus() + ctx.executing_eagerly(), ctx.scope_name, ctx.summary_writer_resource, + ctx.device_name, + ctx.num_gpus() ] def get_values(ctx, values): @@ -169,6 +182,18 @@ class TFETest(test_util.TensorFlowTestCase): attrs=('T', x.dtype.as_datatype_enum))[0].cpu().numpy() self.assertEqual(3, result) + def testResourceTensorPlacement(self): + if not context.context().num_gpus(): + self.skipTest('No GPUs found') + + with context.device('gpu:0'): + v = resource_variable_ops.ResourceVariable(1.0) + with context.device('cpu:0'): + # Check that even though we specified the cpu device we'll run the read op + # in the device where the handle is. + self.assertAllEqual( + gen_resource_variable_ops.read_variable_op(v.handle, v.dtype), 1.0) + def testCopyBetweenDevices(self): if not context.context().num_gpus(): self.skipTest('No GPUs found') @@ -225,14 +250,24 @@ class TFETest(test_util.TensorFlowTestCase): def testExecuteTooManyNumOutputs(self): # num_outputs provided is 50, but only one output is produced. - # That should be okay. product = execute( b'Mul', num_outputs=50, - inputs=[constant_op.constant(3), constant_op.constant(5)], + inputs=[constant_op.constant(3), + constant_op.constant(5)], attrs=('T', dtypes.int32.as_datatype_enum))[0] self.assertAllEqual(15, product) + def testExecuteTooFewNumOutputs(self): + # num_outputs provided is 0, but one output is produced. + with self.assertRaises(errors.InvalidArgumentError): + _ = execute( + b'Mul', + num_outputs=0, + inputs=[constant_op.constant(3), + constant_op.constant(5)], + attrs=('T', dtypes.int32.as_datatype_enum))[0] + def testMatMulGPU(self): if not context.context().num_gpus(): self.skipTest('No GPUs found') @@ -520,5 +555,61 @@ class TFETest(test_util.TensorFlowTestCase): self.assertIsInstance(t, ops.EagerTensor) +class SendRecvTest(test_util.TensorFlowTestCase): + + cpu_device = '/job:localhost/replica:0/task:0/device:CPU:0' + + def _send(self, tensor, tensor_name, to_device): + return execute( + b'_Send', num_outputs=0, inputs=[tensor], + attrs=('T', tensor.dtype.as_datatype_enum, + 'tensor_name', tensor_name, + 'send_device', tensor.device, + 'send_device_incarnation', 0, + 'recv_device', to_device, + 'client_terminated', True)) + + def _recv(self, dtype, tensor_name, from_device): + device_name = context.context().device_name + if not device_name: + device_name = self.cpu_device + return execute( + b'_Recv', num_outputs=1, inputs=[], + attrs=('tensor_type', dtype.as_datatype_enum, + 'tensor_name', tensor_name, + 'send_device', from_device, + 'send_device_incarnation', 0, + 'recv_device', device_name, + 'client_terminated', False))[0] + + def testBasic(self): + t0 = constant_op.constant(1.0) + t1 = constant_op.constant(2.0) + self._send(t0, 't0', self.cpu_device) + self._send(t1, 't1', self.cpu_device) + self.assertAllEqual( + self._recv(dtypes.float32, 't0', self.cpu_device), + 1.0) + self.assertAllEqual( + self._recv(dtypes.float32, 't1', self.cpu_device), + 2.0) + + def testLocalCrossDevice(self): + if not context.context().num_gpus(): + self.skipTest('No GPUs found') + gpu_device_name = '/job:localhost/replica:0/task:0/device:GPU:0' + with ops.device('GPU:0'): + t0 = constant_op.constant(1.0) + self._send(t0, 't0', self.cpu_device) + self.assertAllEqual( + self._recv(dtypes.float32, 't0', gpu_device_name), + 1.0) + self._send(constant_op.constant(2.0), 't1', gpu_device_name) + with ops.device('GPU:0'): + self.assertAllEqual( + self._recv(dtypes.float32, 't1', self.cpu_device), + 2.0) + + if __name__ == '__main__': test.main() diff --git a/tensorflow/python/eager/custom_gradient.py b/tensorflow/python/eager/custom_gradient.py deleted file mode 100644 index 05460ff9968312528d87f5fc2ad0495b4da2ad1a..0000000000000000000000000000000000000000 --- a/tensorflow/python/eager/custom_gradient.py +++ /dev/null @@ -1,91 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Decorator to overrides the gradient for a function.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from tensorflow.python.eager import context -from tensorflow.python.eager import tape -from tensorflow.python.framework import ops as tf_ops -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import gen_array_ops -from tensorflow.python.util import nest -from tensorflow.python.util import tf_decorator - - -def custom_gradient(f): - """Decorator to define a function with a custom gradient. - - The input function is expected to return the tuple - (results, gradient_function). - - The output function will return results while possibly recording the - gradient_function and inputs in the tape. - - Args: - f: function to be decorated. - - Returns: - decorated function. - """ - - def decorated(*args, **kwargs): - """Decorated function with custom gradient.""" - if context.in_graph_mode(): - if kwargs: - raise ValueError( - "custom_gradient in graph mode doesn't support keyword arguments.") - name = "CustomGradient-%s" % tf_ops.uid() - args = [tf_ops.convert_to_tensor(x) for x in args] - result, grad_fn = f(*args) - flat_result = nest.flatten(result) - all_tensors = flat_result + args - - @tf_ops.RegisterGradient(name) - def internal_grad_fn(unused_op, *result_grads): # pylint: disable=unused-variable - gradients = nest.flatten(grad_fn(*result_grads[:len(flat_result)])) - # Need to return one value per input to the IdentityN, so pad the - # gradients of the inputs of the custom_gradient function with the - # gradients of the outputs as well. - return ([None] * len(flat_result)) + gradients - - with tf_ops.get_default_graph().gradient_override_map( - {"IdentityN": name}): - all_tensors = array_ops.identity_n(all_tensors) - return nest.pack_sequence_as( - structure=result, flat_sequence=all_tensors[:len(flat_result)]) - - input_tensors = [tf_ops.convert_to_tensor(x) for x in args] - - with tape.stop_recording(): - result, grad_fn = f(*args, **kwargs) - flat_result = nest.flatten(result) - # TODO(apassos) consider removing the identity below. - flat_result = [gen_array_ops.identity(x) for x in flat_result] - - def actual_grad_fn(*outputs): - return nest.flatten(grad_fn(*outputs)) - - tape.record_operation( - f.__name__, - flat_result, - input_tensors, - actual_grad_fn) - flat_result = list(flat_result) - return nest.pack_sequence_as(result, flat_result) - - return tf_decorator.make_decorator(f, decorated) diff --git a/tensorflow/python/eager/function.py b/tensorflow/python/eager/function.py index b3317bd3235f432220d9d5d135f1af18a6f43310..343012e552592a6f8bb1255118add3e938aa443c 100644 --- a/tensorflow/python/eager/function.py +++ b/tensorflow/python/eager/function.py @@ -36,6 +36,7 @@ from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes as dtypes_module from tensorflow.python.framework import errors 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 gradients_impl from tensorflow.python.util import compat @@ -111,7 +112,7 @@ def _convert_to_graph_tensor(value, dtype=None, name=None, as_ref=False): """ del as_ref # Unused. - if context.in_eager_mode(): + if context.executing_eagerly(): return value default_graph = ops.get_default_graph() @@ -162,31 +163,15 @@ class CapturingGraph(ops.Graph): op_def=None, compute_shapes=True, compute_device=True): - # TODO(apassos) probably control flow has to be handled delicately here as - # in if a resource is accessed inside a control flow context we need the - # control dependency to point to something outside the context which is - # guaranteed to happen after the access. - # # TODO(apassos) this should do some form of alias analysis as ops which # forward the resources such as Identity and Switch can cause serialization # to fail. - resource_inputs = set() - control_inputs = set() for i, inp in enumerate(inputs): if inp.graph is not self: inputs[i] = capture_value(self.captures, inp, inp.dtype, inp.op.name) - inp = inputs[i] - if inp.dtype == dtypes_module.resource: - if inp.name in self._last_op_using_resource_tensor: - control_inputs.add(self._last_op_using_resource_tensor[inp.name]) - resource_inputs.add(inp.name) - with self.control_dependencies(list(control_inputs)): - op = super(CapturingGraph, self).create_op( - op_type, inputs, dtypes, input_types, name, attrs, op_def, - compute_shapes, compute_device) - for name in resource_inputs: - self._last_op_using_resource_tensor[name] = op - return op + return super(CapturingGraph, self).create_op( + op_type, inputs, dtypes, input_types, name, attrs, op_def, + compute_shapes, compute_device) # TODO(apassos): it'd be really nice if we could scope this registration. @@ -310,7 +295,7 @@ class _EagerDefinedFunction(object): proto_data = pywrap_tensorflow.TF_GetBuffer(buffer_) function_def = function_pb2.FunctionDef() function_def.ParseFromString(compat.as_bytes(proto_data)) - if context.in_eager_mode(): + if context.executing_eagerly(): _register(fn) self.definition = function_def self.name = function_def.signature.name @@ -453,7 +438,14 @@ class GraphModeFunction(object): all_args = args + self._extra_inputs signature = self._forward_fdef.signature ctx = context.context() - if ctx.in_graph_mode(): + if ctx.executing_eagerly(): + outputs = execute.execute( + str(signature.name), + num_outputs=len(signature.output_arg), + inputs=all_args, + attrs=None, + ctx=ctx) + else: g = ops.get_default_graph() g._add_function(self._forward_fdef) # pylint: disable=protected-access op = g.create_op( @@ -468,13 +460,6 @@ class GraphModeFunction(object): outputs, (ops.Tensor, type(None))) else list(outputs) for i, s in enumerate(self._output_shapes): outputs[i].set_shape(s) - else: - outputs = execute.execute( - str(signature.name), - num_outputs=len(signature.output_arg), - inputs=all_args, - attrs=None, - ctx=ctx) real_outputs = outputs[:len(self._returns)] side_outputs = outputs[len(self._returns):] @@ -545,7 +530,14 @@ class GraphModeFunction(object): return self._backprop_call(tensor_inputs) ctx = context.context() - if ctx.in_graph_mode(): + if ctx.executing_eagerly(): + result = execute.execute( + str(self._func_name), + num_outputs=self._num_outputs, + inputs=tensor_inputs + self._extra_inputs, + attrs=None, + ctx=ctx) + else: g = ops.get_default_graph() self.add_to_graph(g) signature = self._function_def.definition.signature @@ -562,13 +554,6 @@ class GraphModeFunction(object): return op for i, s in enumerate(self._output_shapes): result[i].set_shape(s) - else: - result = execute.execute( - str(self._func_name), - num_outputs=self._num_outputs, - inputs=tensor_inputs + self._extra_inputs, - attrs=None, - ctx=ctx) return self._build_call_outputs(result) @@ -636,13 +621,15 @@ def _defun_internal(name, func, args, kwds): for collection in curr_graph.collections: tmp_graph.get_collection_ref(collection)[:] = curr_graph.get_collection( collection) - with tmp_graph.as_default(): + with tmp_graph.as_default(), AutomaticControlDependencies() as a: func_inputs = _get_defun_inputs(args) def convert(x): if x is None: return None - return ops.convert_to_tensor_or_indexed_slices(x) + x = ops.convert_to_tensor_or_indexed_slices(x) + x = a.mark_as_return(x) + return x with capture_tensors(captures): this_tape = tape.push_new_tape() @@ -679,7 +666,7 @@ def _defun_internal(name, func, args, kwds): if x not in all_ignored_ops) # Register any other functions defined in the graph # TODO(ashankar): Oh lord, forgive me for this lint travesty. - if context.in_eager_mode(): + if context.executing_eagerly(): for f in tmp_graph._functions.values(): # pylint: disable=protected-access # TODO(ashankar): What about the gradient registry? _register(f._c_func) # pylint: disable=protected-access @@ -887,10 +874,39 @@ class AutomaticControlDependencies(object): self._returned_tensors = set() def mark_as_return(self, tensor): + """Acts like identity but marks the `Tensor` as a return value. + + This will possibly return a copy of the `Tensor`. Usage: + + ``` + with AutomaticControlDependencies() as a: + ... + t = a.mark_as_return(t) + _ = ...(t...) # i.e. it's safe to use t here + ``` + + Args: + tensor: the `Tensor` to be marked + + Returns: + a copy of the `Tensor`. + """ + if isinstance(tensor, ops.IndexedSlices): + values = array_ops.identity(tensor.values) + indices = array_ops.identity(tensor.indices) + self._returned_tensors.add(indices) + self._returned_tensors.add(values) + return ops.IndexedSlices(values, indices, dense_shape=tensor.dense_shape) + # We want to make the return values depend on the stateful operations, but + # we don't want to introduce a cycle, so we make the return value the result + # of a new identity operation that the stateful operations definitely don't + # depend on. + tensor = array_ops.identity(tensor) self._returned_tensors.add(tensor) + return tensor def __enter__(self): - if context.in_eager_mode(): + if context.executing_eagerly(): return self # This code assumes no other thread is adding ops to the graph while # we're adding ops to the graph. @@ -961,7 +977,7 @@ class AutomaticControlDependencies(object): merge_for_resource[o] = new_merge[0].op def __exit__(self, unused_type, unused_value, unused_traceback): - if context.in_eager_mode(): + if context.executing_eagerly(): return if self._graph is not ops.get_default_graph(): @@ -1008,7 +1024,8 @@ class AutomaticControlDependencies(object): for op in new_operations: control_inputs = set() # Ensure stateful ops run - if self._graph._registered_ops[op.type].is_stateful: # pylint: disable=protected-access + if (op.type not in self._graph._registered_ops # pylint: disable=protected-access + or self._graph._registered_ops[op.type].is_stateful): # pylint: disable=protected-access ops_which_must_run.add(op) # Ignore switches (they're handled separately) if op.type == "Switch" and op.inputs[0].dtype == dtypes_module.resource: @@ -1044,9 +1061,10 @@ class AutomaticControlDependencies(object): # Ensure all ops which must run do run for r in self._returned_tensors: - r.op._add_control_inputs( # pylint: disable=protected-access - [o for o in ops_which_must_run - if o._control_flow_context is r.op._control_flow_context]) # pylint: disable=protected-access + if ops_which_must_run: + r.op._add_control_inputs( # pylint: disable=protected-access + [o for o in ops_which_must_run + if o._control_flow_context is r.op._control_flow_context]) # pylint: disable=protected-access def automatic_control_dependencies(f): @@ -1066,8 +1084,7 @@ def automatic_control_dependencies(f): def wrapper(*args, **kwds): with AutomaticControlDependencies() as a: result = f(*args, **kwds) - for t in nest.flatten(result): - a.mark_as_return(t) - return result + result_flat = [a.mark_as_return(t) for t in nest.flatten(result)] + return nest.pack_sequence_as(result, result_flat) return tf_decorator.make_decorator(f, wrapper) diff --git a/tensorflow/python/eager/function_test.py b/tensorflow/python/eager/function_test.py index 431d9388c0ee97eda197142ec97b9448d985b04b..b9cde16867d498d73715535f028a5eb2bea97ea6 100644 --- a/tensorflow/python/eager/function_test.py +++ b/tensorflow/python/eager/function_test.py @@ -606,7 +606,7 @@ class AutomaticControlDependenciesTest(test.TestCase): v.assign(v + 1) v.assign(2 * v) val = v.read_value() - c.mark_as_return(val) + val = c.mark_as_return(val) self.assertAllEqual(val.eval(), 4.0) def testCondMustRun(self): @@ -626,7 +626,7 @@ class AutomaticControlDependenciesTest(test.TestCase): control_flow_ops.cond(p, true_fn, false_fn) val = v.read_value() - c.mark_as_return(val) + val = c.mark_as_return(val) self.assertAllEqual(val.eval(feed_dict={p: False}), 5.0) self.assertAllEqual(val.eval(feed_dict={p: True}), 6.0) @@ -647,7 +647,7 @@ class AutomaticControlDependenciesTest(test.TestCase): control_flow_ops.cond(p, true_fn, false_fn) one = constant_op.constant(1.0) - c.mark_as_return(one) + one = c.mark_as_return(one) one.eval(feed_dict={p: False}) self.assertAllEqual(v.read_value().eval(), 5.0) one.eval(feed_dict={p: True}) @@ -681,7 +681,7 @@ class AutomaticControlDependenciesTest(test.TestCase): control_flow_ops.cond(p, true_fn, false_fn) with ops.name_scope('final'): val = v.read_value() - c.mark_as_return(val) + val = c.mark_as_return(val) self.assertAllEqual(val.eval(feed_dict={p: False, q: False}), 3.0) self.assertAllEqual(val.eval(feed_dict={p: False, q: True}), 6.0) self.assertAllEqual(val.eval(feed_dict={p: True, q: True}), 7.0) @@ -703,7 +703,7 @@ class AutomaticControlDependenciesTest(test.TestCase): control_flow_ops.cond(p, true_fn, false_fn) val = v.read_value() - c.mark_as_return(val) + val = c.mark_as_return(val) self.assertAllEqual(val.eval(feed_dict={p: False}), 5.0) self.assertAllEqual(val.eval(feed_dict={p: True}), 5.0) @@ -724,7 +724,7 @@ class AutomaticControlDependenciesTest(test.TestCase): control_flow_ops.cond(p, true_fn, false_fn) val = v.read_value() - c.mark_as_return(val) + val = c.mark_as_return(val) self.assertAllEqual(val.eval(feed_dict={p: False}), 6.0) self.assertAllEqual(val.eval(feed_dict={p: True}), 12.0) @@ -745,7 +745,7 @@ class AutomaticControlDependenciesTest(test.TestCase): control_flow_ops.cond(p, true_fn, false_fn) v.assign(v * 2) val = v.read_value() - c.mark_as_return(val) + val = c.mark_as_return(val) self.assertAllEqual(val.eval(feed_dict={p: False}), 10.0) self.assertAllEqual(val.eval(feed_dict={p: True}), 20.0) diff --git a/tensorflow/python/eager/graph_callable.py b/tensorflow/python/eager/graph_callable.py index 62106bf0e2809e3c056e4a357f3d05251b7dca68..ee5d87f0835a8e70e0ce14537a51ea5418db41b9 100644 --- a/tensorflow/python/eager/graph_callable.py +++ b/tensorflow/python/eager/graph_callable.py @@ -279,9 +279,12 @@ def _graph_callable_internal(func, shape_and_dtypes): # scope's view of which variables exist. variable_captures = _VariableCapturingScope() with variable_captures.initializing_scope(), function.capture_tensors( - captures): + captures), function.AutomaticControlDependencies() as a: func_outputs = func(*func_inputs) - outputs_list = nest.flatten(func_outputs) + outputs_list = nest.flatten(func_outputs) + for i, x in enumerate(outputs_list): + if x is not None: + outputs_list[i] = a.mark_as_return(x) if len(outputs_list) == 1 and outputs_list[0] is None: outputs_list = [] output_shapes = [x.shape for x in outputs_list] @@ -294,9 +297,12 @@ def _graph_callable_internal(func, shape_and_dtypes): # knows about all variables. tmp_graph.clear_resource_control_flow_state() with variable_captures.capturing_scope(), function.capture_tensors( - captures): + captures), function.AutomaticControlDependencies() as a: captured_outputs = func(*func_inputs) captured_outlist = nest.flatten(captured_outputs) + for i, x in enumerate(captured_outlist): + if x is not None: + captured_outlist[i] = a.mark_as_return(x) capturing_operations = tmp_graph.get_operations()[ len(initializing_operations):] @@ -400,7 +406,7 @@ def graph_callable(shape_and_dtypes): A callable graph object. """ # TODO(alive,apassos): support initialized_value and friends from tf.Variable. - assert context.in_eager_mode(), ( + assert context.executing_eagerly(), ( "graph_callable can only be used when Eager execution is enabled.") def decorator(func): return tf_decorator.make_decorator(func, diff --git a/tensorflow/python/eager/ops_test.py b/tensorflow/python/eager/ops_test.py index f2e70341d975fb06bce7f2ce6cba7d8c3bc9826c..f70c7544d6c9e8095e95d0629b94384bc1cbe35b 100644 --- a/tensorflow/python/eager/ops_test.py +++ b/tensorflow/python/eager/ops_test.py @@ -19,6 +19,7 @@ from __future__ import print_function import numpy as np +from tensorflow.core.protobuf import config_pb2 from tensorflow.python.eager import context from tensorflow.python.eager import execute from tensorflow.python.eager import test @@ -130,8 +131,12 @@ class OpsTest(test_util.TensorFlowTestCase): dtype=dtypes.int64) values = constant_op.constant([2, 3, 5, 7, 11]) shape = constant_op.constant([2, 7], dtype=dtypes.int64) - result = sparse_ops.gen_sparse_ops._sparse_split( # pylint: disable=protected-access - split_dim, indices, values, shape, num_split=2) + result = sparse_ops.gen_sparse_ops.sparse_split( + split_dim, + indices, + values, + shape, + num_split=2) output_indices, output_values, output_shape = result self.assertEqual(2, len(output_indices)) self.assertEqual(2, len(output_values)) @@ -277,6 +282,25 @@ class OpsTest(test_util.TensorFlowTestCase): context._context = context.Context() # pylint: enable=protected-access + def testSoftPlacement(self): + if not context.context().num_gpus(): + self.skipTest('No GPUs found') + # Temporarily replace the context + # pylint: disable=protected-access + del context._context + try: + context._context = context.Context( + device_policy=context.DEVICE_PLACEMENT_SILENT, + config=config_pb2.ConfigProto(allow_soft_placement=True)) + cpu_tensor = constant_op.constant(1.0) + result = cpu_tensor + cpu_tensor + self.assertEqual(result.device, + '/job:localhost/replica:0/task:0/device:GPU:0') + finally: + del context._context + context._context = context.Context() + # pylint: enable=protected-access + def testRandomUniform(self): scalar_shape = constant_op.constant([], dtype=dtypes.int32) diff --git a/tensorflow/python/eager/python_eager_op_gen.cc b/tensorflow/python/eager/python_eager_op_gen.cc index e6d03297e0b85856ff165af310149c79e494ab36..c2ce8efd7f70c6ba93b6d444f88ddbb9aa51ccdb 100644 --- a/tensorflow/python/eager/python_eager_op_gen.cc +++ b/tensorflow/python/eager/python_eager_op_gen.cc @@ -367,7 +367,7 @@ void GenEagerPythonOp::HandleGraphMode(const string& function_setup) { // Handle graph-mode case strings::StrAppend(&result_, " _ctx = _context.context()\n" - " if _ctx.in_graph_mode():\n", + " if not _ctx.executing_eagerly():\n", function_setup, " _, _, _op = _op_def_lib._apply_op_helper(\n"); AddBodyNoReturn(" "); @@ -712,9 +712,9 @@ bool GenEagerPythonOp::AddEagerFallbackCode( } void GenEagerPythonOp::AddEagerFastPathExecute() { - string fastpath_execute_params = strings::StrCat( - "_ctx._handle, _ctx.device_name, \"", op_def_.name(), "\", ", - "_execute.record_gradient, name, _ctx._post_execution_callbacks"); + string fastpath_execute_params = + strings::StrCat("_ctx._handle, _ctx.device_name, \"", op_def_.name(), + "\", ", "name, _ctx._post_execution_callbacks"); string fallback_params; for (int i = 0; i < api_def_.in_arg_size(); i++) { @@ -955,10 +955,10 @@ from tensorflow.python.util.tf_export import tf_export if (api_def->visibility() == ApiDef::SKIP) { continue; } - // An op is hidden if either its ApiDef visibility is HIDDEN // or it is in the hidden_ops list. bool is_hidden = api_def->visibility() == ApiDef::HIDDEN; + bool hidden_by_api_def = is_hidden; if (!is_hidden) { for (const string& hidden : hidden_ops) { if (op_def.name() == hidden) { @@ -971,13 +971,22 @@ from tensorflow.python.util.tf_export import tf_export string function_name; python_op_gen_internal::GenerateLowerCaseOpName(op_def.name(), &function_name); - if (is_hidden) function_name = strings::StrCat("_", function_name); - - // When users create custom python wrappers, they may link in the - // default op registry by accident, and because they can't - // enumerate all 'hidden' symbols, this guard is to prevent - // instantiating a python reserved word in their wrapper. - if (python_op_gen_internal::IsPythonReserved(function_name)) { + bool is_reserved = python_op_gen_internal::IsPythonReserved(function_name); + + // Prefix an op with underscore if the op is listed in hidden_ops or + // name is reserved or it is of the exceptions in IsOpWithUnderscorePrefix. + // Do not add underscores to ops set to HIDDEN in ApiDef otherwise. + // TODO(annarev): don't prefix with underscores even if op is in hidden_ops. + if (is_hidden) { + if (!hidden_by_api_def || is_reserved || + python_op_gen_internal::IsOpWithUnderscorePrefix(function_name)) { + function_name = strings::StrCat("_", function_name); + } + } else if (is_reserved) { + // When users create custom python wrappers, they may link in the + // default op registry by accident, and because they can't + // enumerate all 'hidden' symbols, this guard is to prevent + // instantiating a python reserved word in their wrapper. continue; } diff --git a/tensorflow/python/eager/pywrap_tensor.cc b/tensorflow/python/eager/pywrap_tensor.cc index 6fa076507d11ab9c88891cbeb0a4fb3959e4e99d..105c09e81fefc09bca630534cbe044027dac860b 100644 --- a/tensorflow/python/eager/pywrap_tensor.cc +++ b/tensorflow/python/eager/pywrap_tensor.cc @@ -185,6 +185,16 @@ typedef struct EagerTensor { // This stores `_keras_mask` object and is set by Tensorflow layers. PyObject* keras_mask; + + // This stores `_tensor_shape`, a cached `TensorShape` object, and is set the + // first time that `_EagerTensorBase`'s `shape` property is called. + PyObject* tensor_shape; + + // We store a status object here as an optimization to avoid allocating a new + // Status objects on different functions that operate on EagerTensor and need + // to use a TF_Status object. However note that accesses to `status` are not + // thread-safe. + TF_Status* status; } EagerTensor; // tp_init for EagerTensor. @@ -195,6 +205,9 @@ int EagerTensor_init(EagerTensor* self, PyObject* args, PyObject* kwds) { self->handle_data = Py_None; Py_INCREF(Py_None); self->keras_mask = Py_None; + Py_INCREF(Py_None); + self->tensor_shape = Py_None; + self->status = TF_NewStatus(); PyObject* value; PyObject* context = nullptr; PyObject* device = nullptr; @@ -269,17 +282,17 @@ int EagerTensor_init(EagerTensor* self, PyObject* args, PyObject* kwds) { } TF_DataType handle_dtype = TFE_TensorHandleDataType(handle.get()); if (desired_dtype >= 0 && desired_dtype != handle_dtype) { - auto out_status = tensorflow::make_safe(TF_NewStatus()); handle = tensorflow::make_safe( EagerCast(GetContext(context), handle.get(), handle_dtype, - static_cast(desired_dtype), out_status.get())); - if (TF_GetCode(out_status.get()) != TF_OK) { - PyErr_SetString( - PyExc_ValueError, - tensorflow::strings::StrCat("Error while casting from DataType ", - handle_dtype, " to ", desired_dtype, ". ", - TF_Message(out_status.get())) - .c_str()); + static_cast(desired_dtype), self->status)); + if (TF_GetCode(self->status) != TF_OK) { + PyErr_SetString(PyExc_ValueError, + tensorflow::strings::StrCat( + "Error while casting from DataType ", handle_dtype, + " to ", desired_dtype, ". ", TF_Message(self->status)) + .c_str()); + // Cleanup self->status before returning. + TF_SetStatus(self->status, TF_OK, ""); return -1; } handle_dtype = TFE_TensorHandleDataType(handle.get()); @@ -323,10 +336,14 @@ int EagerTensor_init(EagerTensor* self, PyObject* args, PyObject* kwds) { // tp_dealloc for EagerTensor. void EagerTensor_dealloc(EagerTensor* self) { + TF_DeleteStatus(self->status); Py_DECREF(self->handle_data); Py_DECREF(self->keras_mask); - TFE_DeleteTensorHandle(self->handle); - self->handle = nullptr; + Py_DECREF(self->tensor_shape); + if (self->handle != nullptr) { + TFE_DeleteTensorHandle(self->handle); + self->handle = nullptr; + } // We have the global interpreter lock, so use this chance to perform delayed // refcount decrements. tensorflow::ClearDecrefCache(); @@ -348,12 +365,21 @@ static PyObject* EagerTensor_datatype_enum(EagerTensor* self) { // Getter for `_shape_tuple`. static PyObject* EagerTensor_shape_tuple(EagerTensor* self) { auto handle = self->handle; - int n = TFE_TensorHandleNumDims(handle); + int n = TFE_TensorHandleNumDims(handle, self->status); + if (MaybeRaiseExceptionFromTFStatus(self->status, PyExc_ValueError)) { + // Cleanup self->status before returning. + TF_SetStatus(self->status, TF_OK, ""); + return nullptr; + } PyObject* shape = PyTuple_New(n); if (PyErr_Occurred()) return nullptr; for (int i = 0; i < n; ++i) { - PyObject* dim = PyLong_FromLongLong(TFE_TensorHandleDim(handle, i)); - if (dim == nullptr || PyTuple_SetItem(shape, i, dim) != 0) { + PyObject* dim = + PyLong_FromLongLong(TFE_TensorHandleDim(handle, i, self->status)); + if (MaybeRaiseExceptionFromTFStatus(self->status, PyExc_ValueError) || + dim == nullptr || PyTuple_SetItem(shape, i, dim) != 0) { + // Cleanup self->status before returning. + TF_SetStatus(self->status, TF_OK, ""); Py_DECREF(shape); if (dim != nullptr) Py_DECREF(dim); PyErr_SetString(PyExc_RuntimeError, "Error while creating shape"); @@ -365,10 +391,16 @@ static PyObject* EagerTensor_shape_tuple(EagerTensor* self) { // Getter for `_rank`. static PyObject* EagerTensor_rank(EagerTensor* self) { + int num_dims = TFE_TensorHandleNumDims(self->handle, self->status); + if (MaybeRaiseExceptionFromTFStatus(self->status, PyExc_ValueError)) { + // Cleanup self->status before returning. + TF_SetStatus(self->status, TF_OK, ""); + return nullptr; + } #if PY_MAJOR_VERSION < 3 - return PyInt_FromLong(TFE_TensorHandleNumDims(self->handle)); + return PyInt_FromLong(num_dims); #else - return PyLong_FromLong(TFE_TensorHandleNumDims(self->handle)); + return PyLong_FromLong(num_dims); #endif } @@ -397,6 +429,19 @@ static int EagerTensor_setkeras_mask(EagerTensor* self, PyObject* value, self->keras_mask = value; return 0; } + +static PyObject* EagerTensor_tensor_shape(EagerTensor* self, void* unused) { + Py_INCREF(self->tensor_shape); + return self->tensor_shape; +} + +static int EagerTensor_settensor_shape(EagerTensor* self, PyObject* value, + void* unused) { + Py_DECREF(self->tensor_shape); + Py_INCREF(value); + self->tensor_shape = value; + return 0; +} // Function `_copy_to_device`. static PyObject* EagerTensor_copy_to_device(EagerTensor* self, PyObject* args, PyObject* kwds) { @@ -437,10 +482,16 @@ static PyObject* EagerTensor_numpy(EagerTensor* self) { // Getter `device`. static PyObject* EagerTensor_device(EagerTensor* self) { + const char* device = TFE_TensorHandleDeviceName(self->handle, self->status); + if (MaybeRaiseExceptionFromTFStatus(self->status, PyExc_ValueError)) { + // Cleanup self->status before returning. + TF_SetStatus(self->status, TF_OK, ""); + return nullptr; + } #if PY_MAJOR_VERSION >= 3 - return PyUnicode_FromString(TFE_TensorHandleDeviceName(self->handle)); + return PyUnicode_FromString(device); #else - return PyBytes_FromString(TFE_TensorHandleDeviceName(self->handle)); + return PyBytes_FromString(device); #endif } @@ -455,6 +506,9 @@ static PyGetSetDef EagerTensor_getseters[] = { {const_cast("_keras_mask"), (getter)EagerTensor_keras_mask, (setter)EagerTensor_setkeras_mask, const_cast("_keras_mask"), nullptr}, + {const_cast("_tensor_shape"), (getter)EagerTensor_tensor_shape, + (setter)EagerTensor_settensor_shape, const_cast("_tensor_shape"), + nullptr}, {nullptr} /* Sentinel */ }; @@ -491,16 +545,11 @@ PyTypeObject* EagerTensorType = nullptr; #if PY_MAJOR_VERSION >= 3 static PyType_Slot EagerTensor_Type_slots[] = { - Py_tp_dealloc, - reinterpret_cast(EagerTensor_dealloc), - Py_tp_methods, - reinterpret_cast(EagerTensor_methods), - Py_tp_getset, - reinterpret_cast(EagerTensor_getseters), - Py_tp_init, - reinterpret_cast(EagerTensor_init), - 0, - nullptr, + {Py_tp_dealloc, reinterpret_cast(EagerTensor_dealloc)}, + {Py_tp_methods, reinterpret_cast(EagerTensor_methods)}, + {Py_tp_getset, reinterpret_cast(EagerTensor_getseters)}, + {Py_tp_init, reinterpret_cast(EagerTensor_init)}, + {0, nullptr}, }; PyType_Spec EagerTensor_Type_spec = {"EagerTensor", sizeof(EagerTensor), 0, @@ -575,7 +624,10 @@ PyObject* EagerTensorFromHandle(TFE_TensorHandle* handle) { t->handle_data = Py_None; Py_INCREF(Py_None); t->keras_mask = Py_None; + Py_INCREF(Py_None); + t->tensor_shape = Py_None; t->handle = handle; + t->status = TF_NewStatus(); } return reinterpret_cast(t); } @@ -673,6 +725,7 @@ PyObject* TFE_Py_TensorShapeSlice(PyObject* tensor_list, int slice_dim) { auto tensor = tensorflow::make_safe(TF_AllocateTensor( TF_INT32, &num_tensors_int, /*num_dims=*/1, /*len=*/4 * num_tensors_int)); int32_t* data = reinterpret_cast(TF_TensorData(tensor.get())); + auto status = tensorflow::make_safe(TF_NewStatus()); for (Py_ssize_t i = 0; i < num_tensors; ++i) { PyObject* tensor_obj = PyList_GET_ITEM(tensor_list, i); if (!EagerTensor_CheckExact(tensor_obj)) { @@ -687,21 +740,27 @@ PyObject* TFE_Py_TensorShapeSlice(PyObject* tensor_list, int slice_dim) { EagerTensor* t = reinterpret_cast(tensor_obj); TFE_TensorHandle* handle = t->handle; - if (slice_dim >= TFE_TensorHandleNumDims(handle)) { - PyErr_SetString(PyExc_IndexError, - tensorflow::strings::StrCat( - "Slice dimension (", slice_dim, - ") must be smaller than rank of all " - "tensors, but tensor at index ", - i, " has rank ", TFE_TensorHandleNumDims(handle)) - .c_str()); + int num_dims = TFE_TensorHandleNumDims(handle, status.get()); + if (MaybeRaiseExceptionFromTFStatus(status.get(), PyExc_ValueError)) { + return nullptr; + } + if (slice_dim >= num_dims) { + PyErr_SetString( + PyExc_IndexError, + tensorflow::strings::StrCat("Slice dimension (", slice_dim, + ") must be smaller than rank of all " + "tensors, but tensor at index ", + i, " has rank ", num_dims) + .c_str()); + return nullptr; + } + int64_t dim = TFE_TensorHandleDim(handle, slice_dim, status.get()); + if (MaybeRaiseExceptionFromTFStatus(status.get(), PyExc_ValueError)) { return nullptr; } - int64_t dim = TFE_TensorHandleDim(handle, slice_dim); data[i] = dim; } - auto status = tensorflow::make_safe(TF_NewStatus()); TFE_TensorHandle* handle = TFE_NewTensorHandle(tensor.get(), status.get()); if (TF_GetCode(status.get()) != TF_OK) { PyErr_SetString( diff --git a/tensorflow/python/eager/pywrap_tfe.h b/tensorflow/python/eager/pywrap_tfe.h index f9692a8910aa6354c7ed81c7e88aed882058f276..32d731d0f68910b8e41a57cb32ae60c3ea6742f7 100644 --- a/tensorflow/python/eager/pywrap_tfe.h +++ b/tensorflow/python/eager/pywrap_tfe.h @@ -51,6 +51,13 @@ void TFE_Py_Execute(TFE_Context* ctx, const char* device_name, // This function is not thread-safe. PyObject* TFE_Py_RegisterExceptionClass(PyObject* e); +// Registers e as the type of the ResourceVariable class. +// Returns Py_None if registration succeeds, else throws a TypeError and returns +// NULL. +// +// This function is not thread-safe. +PyObject* TFE_Py_RegisterResourceVariableType(PyObject* e); + // Registers e as the Exception to be raised when the conditions of // TFE_Py_FastPathExecute_C have not been met. When this exception is set, it // is a signal to the calling code that it should fall back to the safer (and @@ -160,13 +167,10 @@ PyObject* TFE_Py_TapeGradient(PyObject* tape, PyObject* vspace, // Item 2: device_name: Name of the device on which to execute the operation, // or NULL for automatic selection. // Item 3: op_name: Name of the TensorFlow op to execute. -// Item 4: record_gradient_callback: Callback that records the gradient of the -// result. The callback takes (op_name, inputs, attrs, result, name) -// - all sequences and records the gradient. -// Item 5: name: An optional name for the operation. -// Item 6: List representing all callbacks to execute after successful +// Item 4: name: An optional name for the operation. +// Item 5: List representing all callbacks to execute after successful // op execute. -// Item 7 onwards: inputs - This is a list of inputs followed by a list of +// Item 6 onwards: inputs - This is a list of inputs followed by a list of // attrs. It is not necessary for type attrs to be present. // // This is named _C since there doesn't seem to be any way to make it visible diff --git a/tensorflow/python/eager/pywrap_tfe_src.cc b/tensorflow/python/eager/pywrap_tfe_src.cc index 30e08c8e6531739e3db66a94308e4ce2aff61f11..fe9785dc667e948c760946d41faa9188574a2180 100644 --- a/tensorflow/python/eager/pywrap_tfe_src.cc +++ b/tensorflow/python/eager/pywrap_tfe_src.cc @@ -31,12 +31,30 @@ limitations under the License. #include "tensorflow/core/platform/protobuf.h" #include "tensorflow/core/platform/types.h" #include "tensorflow/python/eager/pywrap_tensor.h" +#include "tensorflow/python/lib/core/safe_ptr.h" using tensorflow::string; using tensorflow::strings::Printf; namespace { +struct FastPathOpExecInfo { + TFE_Context* ctx; + const char* device_name; + // The op def of the main op being executed. + const tensorflow::OpDef* op_def; + + bool run_callbacks; + bool run_post_exec_callbacks; + bool run_gradient_callback; + + // The op name of the main op being executed. + PyObject* name; + // The op type name of the main op being executed. + PyObject* op_name; + PyObject* callbacks; +}; + #define PARSE_VALUE(fn_name, type, check_fn, parse_fn) \ bool fn_name(const string& key, PyObject* py_value, TF_Status* status, \ type* value) { \ @@ -75,6 +93,34 @@ Py_ssize_t TensorShapeNumDims(PyObject* value) { return size; } +bool IsInteger(PyObject* py_value) { +#if PY_MAJOR_VERSION >= 3 + return PyLong_Check(py_value); +#else + return PyInt_Check(py_value); +#endif +} + +bool ParseDimensionValue(const string& key, PyObject* py_value, + TF_Status* status, int64_t* value) { + if (IsInteger(py_value)) { + return ParseInt64Value(key, py_value, status, value); + } + + tensorflow::Safe_PyObjectPtr dimension_value( + PyObject_GetAttrString(py_value, "_value")); + if (dimension_value == nullptr) { + TF_SetStatus( + status, TF_INVALID_ARGUMENT, + tensorflow::strings::StrCat("Expecting a Dimension for attr ", key, + ", got ", py_value->ob_type->tp_name) + .c_str()); + return false; + } + + return ParseInt64Value(key, dimension_value.get(), status, value); +} + bool ParseStringValue(const string& key, PyObject* py_value, TF_Status* status, const char** value) { if (PyBytes_Check(py_value)) { @@ -101,14 +147,6 @@ bool ParseBoolValue(const string& key, PyObject* py_value, TF_Status* status, return true; } -bool IsInteger(PyObject* py_value) { -#if PY_MAJOR_VERSION >= 3 - return PyLong_Check(py_value); -#else - return PyInt_Check(py_value); -#endif -} - // The passed in py_value is expected to be an object of the python type // dtypes.DType or an int. bool ParseTypeValue(const string& key, PyObject* py_value, TF_Status* status, @@ -117,18 +155,18 @@ bool ParseTypeValue(const string& key, PyObject* py_value, TF_Status* status, return ParseIntValue(key, py_value, status, value); } - PyObject* py_type_enum = PyObject_GetAttrString(py_value, "_type_enum"); + tensorflow::Safe_PyObjectPtr py_type_enum( + PyObject_GetAttrString(py_value, "_type_enum")); if (py_type_enum == nullptr) { + TF_SetStatus( + status, TF_INVALID_ARGUMENT, + tensorflow::strings::StrCat("Expecting a DType.dtype for attr ", key, + ", got ", py_value->ob_type->tp_name) + .c_str()); return false; } - if (!ParseIntValue(key, py_type_enum, status, value)) { - Py_DECREF(py_type_enum); - return false; - } - - Py_DECREF(py_type_enum); - return true; + return ParseIntValue(key, py_type_enum.get(), status, value); } bool SetOpAttrList( @@ -146,11 +184,11 @@ bool SetOpAttrList( const int num_values = PySequence_Size(py_list); if (attr_list_sizes != nullptr) (*attr_list_sizes)[key] = num_values; -#define PARSE_LIST(c_type, parse_fn) \ - std::unique_ptr values(new c_type[num_values]); \ - for (int i = 0; i < num_values; ++i) { \ - auto py_value = PySequence_ITEM(py_list, i); \ - if (!parse_fn(key, py_value, status, &values[i])) return false; \ +#define PARSE_LIST(c_type, parse_fn) \ + std::unique_ptr values(new c_type[num_values]); \ + for (int i = 0; i < num_values; ++i) { \ + tensorflow::Safe_PyObjectPtr py_value(PySequence_ITEM(py_list, i)); \ + if (!parse_fn(key, py_value.get(), status, &values[i])) return false; \ } if (type == TF_ATTR_STRING) { @@ -175,9 +213,9 @@ bool SetOpAttrList( // dims across all the input lists. int total_dims = 0; for (int i = 0; i < num_values; ++i) { - auto py_value = PySequence_ITEM(py_list, i); - if (py_value != Py_None) { - if (!PySequence_Check(py_value)) { + tensorflow::Safe_PyObjectPtr py_value(PySequence_ITEM(py_list, i)); + if (py_value.get() != Py_None) { + if (!PySequence_Check(py_value.get())) { TF_SetStatus( status, TF_INVALID_ARGUMENT, tensorflow::strings::StrCat( @@ -186,7 +224,7 @@ bool SetOpAttrList( .c_str()); return false; } - const auto size = TensorShapeNumDims(py_value); + const auto size = TensorShapeNumDims(py_value.get()); if (size >= 0) { total_dims += size; } @@ -200,12 +238,12 @@ bool SetOpAttrList( std::unique_ptr num_dims(new int[num_values]); int64_t* offset = buffer.get(); for (int i = 0; i < num_values; ++i) { - auto py_value = PySequence_ITEM(py_list, i); - if (py_value == Py_None) { + tensorflow::Safe_PyObjectPtr py_value(PySequence_ITEM(py_list, i)); + if (py_value.get() == Py_None) { dims[i] = nullptr; num_dims[i] = -1; } else { - const auto size = TensorShapeNumDims(py_value); + const auto size = TensorShapeNumDims(py_value.get()); if (size == -1) { dims[i] = nullptr; num_dims[i] = -1; @@ -214,10 +252,12 @@ bool SetOpAttrList( dims[i] = offset; num_dims[i] = size; for (int j = 0; j < size; ++j) { - auto inner_py_value = PySequence_ITEM(py_value, j); - if (inner_py_value == Py_None) { + tensorflow::Safe_PyObjectPtr inner_py_value( + PySequence_ITEM(py_value.get(), j)); + if (inner_py_value.get() == Py_None) { *offset = -1; - } else if (!ParseInt64Value(key, inner_py_value, status, offset)) { + } else if (!ParseDimensionValue(key, inner_py_value.get(), status, + offset)) { return false; } ++offset; @@ -238,21 +278,12 @@ bool SetOpAttrList( return true; } -// This is only declared here since GetFunc makes a recursive call to -// SetOpAttrScalarDefault. -void SetOpAttrScalarDefault( - TFE_Context* ctx, TFE_Op* op, const tensorflow::AttrValue& default_value, - const char* attr_name, - tensorflow::gtl::FlatMap* attr_list_sizes, - TF_Status* status); - TFE_Op* GetFunc(TFE_Context* ctx, const tensorflow::NameAttrList& func, TF_Status* status) { TFE_Op* func_op = TFE_NewOp(ctx, func.name().data(), status); for (const auto& attr : func.attr()) { if (TF_GetCode(status) != TF_OK) return nullptr; - SetOpAttrScalarDefault(ctx, func_op, attr.second, attr.first.data(), - nullptr, status); + SetOpAttrValueScalar(ctx, func_op, attr.second, attr.first.data(), status); if (TF_GetCode(status) != TF_OK) return nullptr; } return func_op; @@ -398,10 +429,12 @@ bool SetOpAttrScalar( } std::unique_ptr dims(new int64_t[num_dims]); for (int i = 0; i < num_dims; ++i) { - auto inner_py_value = PySequence_ITEM(py_value, i); - if (inner_py_value == Py_None) { + tensorflow::Safe_PyObjectPtr inner_py_value( + PySequence_ITEM(py_value, i)); + if (inner_py_value.get() == Py_None) { dims[i] = -1; - } else if (!ParseInt64Value(key, inner_py_value, status, &dims[i])) { + } else if (!ParseDimensionValue(key, inner_py_value.get(), status, + &dims[i])) { return false; } } @@ -452,57 +485,9 @@ void SetOpAttrScalarDefault( const char* attr_name, tensorflow::gtl::FlatMap* attr_list_sizes, TF_Status* status) { - switch (default_value.value_case()) { - case tensorflow::AttrValue::kS: - TFE_OpSetAttrString(op, attr_name, default_value.s().data()); - break; - case tensorflow::AttrValue::kI: - TFE_OpSetAttrInt(op, attr_name, static_cast(default_value.i())); - (*attr_list_sizes)[attr_name] = default_value.i(); - break; - case tensorflow::AttrValue::kF: - TFE_OpSetAttrFloat(op, attr_name, default_value.f()); - break; - case tensorflow::AttrValue::kB: - TFE_OpSetAttrBool(op, attr_name, default_value.b()); - break; - case tensorflow::AttrValue::kType: - TFE_OpSetAttrType(op, attr_name, - static_cast(default_value.type())); - break; - case tensorflow::AttrValue::kShape: { - const auto& tensor_shape = default_value.shape(); - if (tensor_shape.unknown_rank()) { - TFE_OpSetAttrShape(op, attr_name, nullptr, -1, status); - } else { - const auto num_dims = tensor_shape.dim_size(); - std::unique_ptr dims(new int64_t[num_dims]); - for (int i = 0; i < num_dims; ++i) { - dims[i] = tensor_shape.dim(i).size(); - } - TFE_OpSetAttrShape(op, attr_name, dims.get(), num_dims, status); - } - } break; - case tensorflow::AttrValue::kFunc: { - const auto func_op = GetFunc(ctx, default_value.func(), status); - if (TF_GetCode(status) != TF_OK) return; - // TODO(nareshmodi): TFE_OpSetAttrFunction and TFE_OpSetAttrFunctionList - // require TFE_Op* and just convert it internally a NameAttrValue, so - // consider adding an overload to the C API to make this case easier. - TFE_OpSetAttrFunction(op, attr_name, func_op); - } break; - case tensorflow::AttrValue::kList: - TF_FALLTHROUGH_INTENDED; - case tensorflow::AttrValue::kTensor: - TF_FALLTHROUGH_INTENDED; - case tensorflow::AttrValue::kPlaceholder: - TF_FALLTHROUGH_INTENDED; - case tensorflow::AttrValue::VALUE_NOT_SET: - TF_SetStatus( - status, TF_UNIMPLEMENTED, - tensorflow::strings::StrCat("Unable to get setfor default value: ", - default_value.DebugString()) - .data()); + SetOpAttrValueScalar(ctx, op, default_value, attr_name, status); + if (default_value.value_case() == tensorflow::AttrValue::kI) { + (*attr_list_sizes)[attr_name] = default_value.i(); } } @@ -579,6 +564,8 @@ PyObject* fallback_exception_class = nullptr; // Python function that returns a backward_function. PyObject* backward_function_getter = nullptr; +PyTypeObject* resource_variable_type = nullptr; + tensorflow::mutex _uid_mutex(tensorflow::LINKER_INITIALIZED); tensorflow::int64 _uid GUARDED_BY(_uid_mutex) = 0; @@ -627,11 +614,28 @@ PyObject* TFE_Py_RegisterExceptionClass(PyObject* e) { "TFE_Py_RegisterExceptionClass: " "Registered class should be subclass of Exception."); return nullptr; - } else { - Py_INCREF(e); - exception_class = e; - Py_RETURN_NONE; } + + Py_INCREF(e); + exception_class = e; + Py_RETURN_NONE; +} + +PyObject* TFE_Py_RegisterResourceVariableType(PyObject* e) { + if (!PyType_Check(e)) { + PyErr_SetString( + PyExc_TypeError, + "TFE_Py_RegisterResourceVariableType: Need to register a type."); + return nullptr; + } + + if (resource_variable_type != nullptr) { + Py_DECREF(resource_variable_type); + } + + Py_INCREF(e); + resource_variable_type = reinterpret_cast(e); + Py_RETURN_NONE; } PyObject* TFE_Py_RegisterFallbackExceptionClass(PyObject* e) { @@ -1008,7 +1012,14 @@ static tensorflow::eager::TapeTensor TapeTensorFromTensor(PyObject* tensor) { if (EagerTensor_CheckExact(tensor)) { TFE_TensorHandle* t = EagerTensor_Handle(tensor); tensorflow::int64 id = EagerTensor_id(tensor); - return tensorflow::eager::TapeTensor{id, t->t.dtype(), t->t.shape()}; + const tensorflow::Tensor* tensor = nullptr; + const tensorflow::Status status = t->Tensor(&tensor); + if (MaybeRaiseExceptionFromStatus(status, nullptr)) { + return tensorflow::eager::TapeTensor{id, t->dtype, + tensorflow::TensorShape({})}; + } else { + return tensorflow::eager::TapeTensor{id, t->dtype, tensor->shape()}; + } } tensorflow::int64 id = FastTensorId(tensor); if (PyErr_Occurred()) { @@ -1364,7 +1375,7 @@ PyObject* TFE_Py_TapeGradient(PyObject* tape, PyObject* vspace, } namespace { -static const int kFastPathExecuteInputStartIndex = 6; +static const int kFastPathExecuteInputStartIndex = 5; PyObject* GetPythonObjectFromString(const char* s) { #if PY_MAJOR_VERSION >= 3 @@ -1374,8 +1385,12 @@ PyObject* GetPythonObjectFromString(const char* s) { #endif } -bool CheckEagerTensors(PyObject* seq, int start_index, - const tensorflow::OpDef& op_def) { +bool CheckResourceVariable(PyObject* item) { + return PyObject_TypeCheck(item, resource_variable_type); +} + +bool CheckInputsOk(PyObject* seq, int start_index, + const tensorflow::OpDef& op_def) { for (int i = 0; i < op_def.input_arg_size(); i++) { PyObject* item = PyTuple_GET_ITEM(seq, i + start_index); if (!op_def.input_arg(i).number_attr().empty() || @@ -1383,9 +1398,13 @@ bool CheckEagerTensors(PyObject* seq, int start_index, // This item should be a list input. if (!PyList_Check(item)) return false; for (Py_ssize_t j = 0; j < PyList_Size(item); j++) { - if (!EagerTensor_CheckExact(PyList_GET_ITEM(item, j))) return false; + PyObject* inner_item = PyList_GET_ITEM(item, j); + if (!EagerTensor_CheckExact(inner_item) && + !CheckResourceVariable(inner_item)) { + return false; + } } - } else if (!EagerTensor_CheckExact(item)) { + } else if (!EagerTensor_CheckExact(item) && !CheckResourceVariable(item)) { return false; } } @@ -1393,71 +1412,6 @@ bool CheckEagerTensors(PyObject* seq, int start_index, return true; } -// Adds input and type attr to the op, and to the list of flattened -// inputs/attrs. -bool AddInputToOp(PyObject* input, const tensorflow::OpDef::ArgDef* input_arg, - std::vector* flattened_attrs, - std::vector* flattened_inputs, TFE_Op* op, - TF_Status* status) { - TFE_TensorHandle* input_handle = EagerTensor_Handle(input); - if (input_arg != nullptr && !input_arg->type_attr().empty()) { - auto dtype = TFE_TensorHandleDataType(input_handle); - TFE_OpSetAttrType(op, input_arg->type_attr().data(), dtype); - if (flattened_attrs != nullptr) { - flattened_attrs->push_back( - GetPythonObjectFromString(input_arg->type_attr().data())); - flattened_attrs->push_back(PyLong_FromLong(dtype)); - } - } - - if (flattened_inputs != nullptr) { - flattened_inputs->push_back(input); - } - TFE_OpAddInput(op, input_handle, status); - if (MaybeRaiseExceptionFromTFStatus(status, nullptr)) { - return false; - } - return true; -} - -const tensorflow::OpDef* GetOpDef(PyObject* py_op_name) { - const char* op_name = TFE_GetPythonString(py_op_name); - if (op_name == nullptr) { - PyErr_SetString(PyExc_TypeError, - Printf("expected a string for op_name, got %s instead", - py_op_name->ob_type->tp_name) - .c_str()); - return nullptr; - } - - const tensorflow::OpRegistrationData* op_reg_data = nullptr; - const tensorflow::Status lookup_status = - tensorflow::OpRegistry::Global()->LookUp(op_name, &op_reg_data); - if (MaybeRaiseExceptionFromStatus(lookup_status, nullptr)) { - return nullptr; - } - return &op_reg_data->op_def; -} - -const char* GetDeviceName(PyObject* py_device_name) { - if (py_device_name != Py_None) { - return TFE_GetPythonString(py_device_name); - } - return nullptr; -} - -bool RaiseIfNotPyList(PyObject* list, const string& attr_name) { - if (!PyList_Check(list)) { - PyErr_SetString(PyExc_TypeError, - Printf("expected a list for attr %s, got %s instead", - attr_name.data(), list->ob_type->tp_name) - .data()); - - return false; - } - return true; -} - bool OpDoesntRequireOutput(const string& op_name) { static tensorflow::gtl::FlatSet* ops_that_dont_require_outputs = new tensorflow::gtl::FlatSet({ @@ -1582,7 +1536,6 @@ PyObject* RecordGradient(PyObject* op_name, PyObject* inputs, PyObject* attrs, break; } } - if (!should_record) Py_RETURN_NONE; string c_op_name = TFE_GetPythonString(op_name); @@ -1616,53 +1569,212 @@ PyObject* RecordGradient(PyObject* op_name, PyObject* inputs, PyObject* attrs, Py_RETURN_NONE; } -bool RunCallbacks(bool run_gradient_callback, bool run_post_exec_callbacks, - const tensorflow::OpDef* op_def, PyObject* args, - const std::vector& flattened_inputs, - const std::vector& flattened_attrs, - PyObject* flattened_result, PyObject* op_name, PyObject* name, - PyObject* record_gradient_callback, PyObject* callbacks) { - PyObject* inputs = PyTuple_New(flattened_inputs.size()); +void MaybeWatchVariable(PyObject* input) { + DCHECK(CheckResourceVariable(input)); + DCHECK(PyObject_HasAttrString(input, "_trainable")); + + tensorflow::Safe_PyObjectPtr trainable( + PyObject_GetAttrString(input, "_trainable")); + if (trainable.get() == Py_False) return; + TFE_Py_TapeSetWatchVariable(input); +} + +bool ReadVariableOp(const FastPathOpExecInfo& parent_op_exec_info, + PyObject* input, tensorflow::Safe_PyObjectPtr* output, + TF_Status* status) { + MaybeWatchVariable(input); + + TFE_Op* op = TFE_NewOp(parent_op_exec_info.ctx, "ReadVariableOp", status); + auto cleaner = tensorflow::gtl::MakeCleanup([op] { TFE_DeleteOp(op); }); + if (MaybeRaiseExceptionFromTFStatus(status, nullptr)) return false; + + // Set dtype + DCHECK(PyObject_HasAttrString(input, "_dtype")); + tensorflow::Safe_PyObjectPtr dtype(PyObject_GetAttrString(input, "_dtype")); + int value; + if (!ParseTypeValue("_dtype", dtype.get(), status, &value)) { + return false; + } + TFE_OpSetAttrType(op, "dtype", static_cast(value)); + + TFE_OpSetDevice(op, parent_op_exec_info.device_name, status); + if (MaybeRaiseExceptionFromTFStatus(status, nullptr)) return false; + + // Get handle + tensorflow::Safe_PyObjectPtr handle(PyObject_GetAttrString(input, "_handle")); + if (!EagerTensor_CheckExact(handle.get())) return false; + TFE_OpAddInput(op, EagerTensor_Handle(handle.get()), status); + if (MaybeRaiseExceptionFromTFStatus(status, nullptr)) return false; + + int num_retvals = 1; + TFE_TensorHandle* output_handle; + TFE_Execute(op, &output_handle, &num_retvals, status); + if (MaybeRaiseExceptionFromTFStatus(status, nullptr)) return false; + + // Always create the py object (and correctly DECREF it) from the returned + // value, else the data will leak. + output->reset(EagerTensorFromHandle(output_handle)); + + // TODO(nareshmodi): Should we run post exec callbacks here? + if (parent_op_exec_info.run_gradient_callback) { + tensorflow::Safe_PyObjectPtr inputs(PyTuple_New(1)); + PyTuple_SET_ITEM(inputs.get(), 0, handle.release()); + + tensorflow::Safe_PyObjectPtr outputs(PyTuple_New(1)); + Py_INCREF(output->get()); // stay alive after since tuple steals. + PyTuple_SET_ITEM(outputs.get(), 0, output->get()); + + if (!RecordGradient(GetPythonObjectFromString("ReadVariableOp"), + inputs.get(), Py_None, outputs.get(), Py_None)) { + return false; + } + } + + return true; +} + +// Supports only 2 cases at the moment: +// i) input is an EagerTensor +// ii) input is a ResourceVariable - in this case, the is_variable param is set +// to true. +bool ConvertToTensor(const FastPathOpExecInfo& op_exec_info, PyObject* input, + tensorflow::Safe_PyObjectPtr* output_handle, + TF_Status* status) { + if (CheckResourceVariable(input)) { + return ReadVariableOp(op_exec_info, input, output_handle, status); + } + + Py_INCREF(input); + output_handle->reset(input); + + return true; +} + +// Adds input and type attr to the op, and to the list of flattened +// inputs/attrs. +bool AddInputToOp(const FastPathOpExecInfo& op_exec_info, PyObject* input, + const tensorflow::OpDef::ArgDef* input_arg, + std::vector* flattened_attrs, + std::vector* flattened_inputs, + TFE_Op* op, TF_Status* status) { + // py_eager_tensor's ownership is transferred to flattened_inputs if it is + // required, else the object is destroyed and DECREF'd when the object goes + // out of scope in this function. + tensorflow::Safe_PyObjectPtr py_eager_tensor = nullptr; + + if (!ConvertToTensor(op_exec_info, input, &py_eager_tensor, status)) { + return false; + } + + TFE_TensorHandle* input_handle = EagerTensor_Handle(py_eager_tensor.get()); + + if (input_arg != nullptr && !input_arg->type_attr().empty()) { + auto dtype = TFE_TensorHandleDataType(input_handle); + TFE_OpSetAttrType(op, input_arg->type_attr().data(), dtype); + if (flattened_attrs != nullptr) { + flattened_attrs->emplace_back( + GetPythonObjectFromString(input_arg->type_attr().data())); + flattened_attrs->emplace_back(PyLong_FromLong(dtype)); + } + } + + if (flattened_inputs != nullptr) { + flattened_inputs->emplace_back(std::move(py_eager_tensor)); + } + + TFE_OpAddInput(op, input_handle, status); + if (MaybeRaiseExceptionFromTFStatus(status, nullptr)) { + return false; + } + + return true; +} + +const tensorflow::OpDef* GetOpDef(PyObject* py_op_name) { + const char* op_name = TFE_GetPythonString(py_op_name); + if (op_name == nullptr) { + PyErr_SetString(PyExc_TypeError, + Printf("expected a string for op_name, got %s instead", + py_op_name->ob_type->tp_name) + .c_str()); + return nullptr; + } + + const tensorflow::OpRegistrationData* op_reg_data = nullptr; + const tensorflow::Status lookup_status = + tensorflow::OpRegistry::Global()->LookUp(op_name, &op_reg_data); + if (MaybeRaiseExceptionFromStatus(lookup_status, nullptr)) { + return nullptr; + } + return &op_reg_data->op_def; +} + +const char* GetDeviceName(PyObject* py_device_name) { + if (py_device_name != Py_None) { + return TFE_GetPythonString(py_device_name); + } + return nullptr; +} + +bool RaiseIfNotPyList(PyObject* list, const string& attr_name) { + if (!PyList_Check(list)) { + PyErr_SetString(PyExc_TypeError, + Printf("expected a list for attr %s, got %s instead", + attr_name.data(), list->ob_type->tp_name) + .data()); + + return false; + } + return true; +} + +bool RunCallbacks( + const FastPathOpExecInfo& op_exec_info, PyObject* args, + const std::vector& flattened_inputs, + const std::vector& flattened_attrs, + PyObject* flattened_result) { + if (!op_exec_info.run_callbacks) return true; + + tensorflow::Safe_PyObjectPtr inputs(PyTuple_New(flattened_inputs.size())); for (int i = 0; i < flattened_inputs.size(); i++) { - PyObject* input = flattened_inputs[i]; + PyObject* input = flattened_inputs[i].get(); Py_INCREF(input); - PyTuple_SET_ITEM(inputs, i, input); + PyTuple_SET_ITEM(inputs.get(), i, input); } int num_non_inferred_attrs = PyTuple_GET_SIZE(args) - - op_def->input_arg_size() - + op_exec_info.op_def->input_arg_size() - kFastPathExecuteInputStartIndex; int num_attrs = flattened_attrs.size() + num_non_inferred_attrs; - PyObject* attrs = PyTuple_New(num_attrs); + tensorflow::Safe_PyObjectPtr attrs(PyTuple_New(num_attrs)); for (int i = 0; i < num_non_inferred_attrs; i++) { - auto* attr = PyTuple_GET_ITEM( - args, kFastPathExecuteInputStartIndex + op_def->input_arg_size() + i); + auto* attr = + PyTuple_GET_ITEM(args, kFastPathExecuteInputStartIndex + + op_exec_info.op_def->input_arg_size() + i); Py_INCREF(attr); - PyTuple_SET_ITEM(attrs, i, attr); + PyTuple_SET_ITEM(attrs.get(), i, attr); } for (int i = num_non_inferred_attrs; i < num_attrs; i++) { - // Not INCREFing anything in flattened_attrs as each of those is a new - // reference, so allow the attrs tuple to steal the reference. - PyTuple_SET_ITEM(attrs, i, flattened_attrs.at(i - num_non_inferred_attrs)); + PyObject* attr_or_name = + flattened_attrs.at(i - num_non_inferred_attrs).get(); + Py_INCREF(attr_or_name); + PyTuple_SET_ITEM(attrs.get(), i, attr_or_name); } - PyObject* callback_args = - Py_BuildValue("OOOOO", op_name, inputs, attrs, flattened_result, name); - - auto cleaner = tensorflow::gtl::MakeCleanup([inputs, attrs, callback_args] { - Py_DECREF(inputs); - Py_DECREF(attrs); - Py_DECREF(callback_args); - }); - - if (run_gradient_callback) { - RecordGradient(op_name, inputs, attrs, flattened_result, name); + if (op_exec_info.run_gradient_callback) { + if (!RecordGradient(op_exec_info.op_name, inputs.get(), attrs.get(), + flattened_result, op_exec_info.name)) { + return false; + } } - if (run_post_exec_callbacks) { - for (Py_ssize_t i = 0; i < PyList_Size(callbacks); i++) { - PyObject* callback_fn = PyList_GET_ITEM(callbacks, i); + if (op_exec_info.run_post_exec_callbacks) { + tensorflow::Safe_PyObjectPtr callback_args( + Py_BuildValue("OOOOO", op_exec_info.op_name, inputs.get(), attrs.get(), + flattened_result, op_exec_info.name)); + for (Py_ssize_t i = 0; i < PyList_Size(op_exec_info.callbacks); i++) { + PyObject* callback_fn = PyList_GET_ITEM(op_exec_info.callbacks, i); if (!PyCallable_Check(callback_fn)) { PyErr_SetString( PyExc_TypeError, @@ -1673,7 +1785,7 @@ bool RunCallbacks(bool run_gradient_callback, bool run_post_exec_callbacks, return false; } PyObject* callback_result = - PyObject_CallObject(callback_fn, callback_args); + PyObject_CallObject(callback_fn, callback_args.get()); if (!callback_result) { return false; } @@ -1697,15 +1809,30 @@ PyObject* TFE_Py_FastPathExecute_C(PyObject*, PyObject* args) { return nullptr; } - TFE_Context* ctx = reinterpret_cast( + FastPathOpExecInfo op_exec_info; + + op_exec_info.ctx = reinterpret_cast( PyCapsule_GetPointer(PyTuple_GET_ITEM(args, 0), nullptr)); - const char* device_name = GetDeviceName(PyTuple_GET_ITEM(args, 1)); - PyObject* op_name = PyTuple_GET_ITEM(args, 2); - const tensorflow::OpDef* op_def = GetOpDef(op_name); - if (op_def == nullptr) return nullptr; - PyObject* record_gradient_callback = PyTuple_GET_ITEM(args, 3); - PyObject* name = PyTuple_GET_ITEM(args, 4); - PyObject* callbacks = PyTuple_GET_ITEM(args, 5); + op_exec_info.device_name = GetDeviceName(PyTuple_GET_ITEM(args, 1)); + op_exec_info.op_name = PyTuple_GET_ITEM(args, 2); + op_exec_info.op_def = GetOpDef(op_exec_info.op_name); + if (op_exec_info.op_def == nullptr) return nullptr; + op_exec_info.name = PyTuple_GET_ITEM(args, 3); + op_exec_info.callbacks = PyTuple_GET_ITEM(args, 4); + + const tensorflow::OpDef* op_def = op_exec_info.op_def; + + // TODO(nareshmodi): Add a benchmark for the fast-path with gradient callbacks + // (similar to benchmark_tf_gradient_function_*). Also consider using an + // InlinedVector for flattened_attrs and flattened_inputs if the benchmarks + // point out problems with heap allocs. + op_exec_info.run_gradient_callback = + !*ThreadTapeIsStopped() && !GetTapeSet()->empty(); + op_exec_info.run_post_exec_callbacks = + op_exec_info.callbacks != Py_None && + PyList_Size(op_exec_info.callbacks) > 0; + op_exec_info.run_callbacks = op_exec_info.run_gradient_callback || + op_exec_info.run_post_exec_callbacks; if (args_size < kFastPathExecuteInputStartIndex + op_def->input_arg_size()) { PyErr_SetString( @@ -1718,7 +1845,7 @@ PyObject* TFE_Py_FastPathExecute_C(PyObject*, PyObject* args) { return nullptr; } - if (!CheckEagerTensors(args, kFastPathExecuteInputStartIndex, *op_def)) { + if (!CheckInputsOk(args, kFastPathExecuteInputStartIndex, *op_def)) { RaiseFallbackException( "This function does not handle the case of the path where " "all inputs are not already EagerTensors."); @@ -1726,7 +1853,7 @@ PyObject* TFE_Py_FastPathExecute_C(PyObject*, PyObject* args) { } TF_Status* status = TF_NewStatus(); - TFE_Op* op = TFE_NewOp(ctx, op_def->name().c_str(), status); + TFE_Op* op = TFE_NewOp(op_exec_info.ctx, op_def->name().c_str(), status); auto cleaner = tensorflow::gtl::MakeCleanup([status, op] { TF_DeleteStatus(status); TFE_DeleteOp(op); @@ -1753,8 +1880,8 @@ PyObject* TFE_Py_FastPathExecute_C(PyObject*, PyObject* args) { // OpRegistrationData. for (const auto& attr : op_def->attr()) { if (attr_name == attr.name()) { - SetOpAttrWithDefaults(ctx, op, attr, attr_name.data(), py_attr_value, - &attr_list_sizes, status); + SetOpAttrWithDefaults(op_exec_info.ctx, op, attr, attr_name.data(), + py_attr_value, &attr_list_sizes, status); if (TF_GetCode(status) != TF_OK) { RaiseFallbackException(TF_Message(status)); @@ -1766,34 +1893,28 @@ PyObject* TFE_Py_FastPathExecute_C(PyObject*, PyObject* args) { } } - TFE_OpSetDevice(op, device_name, status); + TFE_OpSetDevice(op, op_exec_info.device_name, status); if (MaybeRaiseExceptionFromTFStatus(status, nullptr)) { return nullptr; } - // TODO(nareshmodi): Add a benchmark for the fast-path with gradient callbacks - // (similar to benchmark_tf_gradient_function_*). Also consider using an - // InlinedVector for flattened_attrs and flattened_inputs if the benchmarks - // point out problems with heap allocs. - bool run_gradient_callback = !*ThreadTapeIsStopped() && - !GetTapeSet()->empty() && - record_gradient_callback != Py_None; - bool run_post_exec_callbacks = - callbacks != Py_None && PyList_Size(callbacks) > 0; - bool run_callbacks = run_gradient_callback || run_post_exec_callbacks; // Flat attrs and inputs as required by the record_gradient call. The attrs // here only contain inferred attrs (non-inferred attrs are added directly // from the input args). - // All items in flattened_attrs contain new references. - // All items in flattened_inputs contain borrowed references. + // All items in flattened_attrs and flattened_inputs contain + // Safe_PyObjectPtr - any time something steals a reference to this, it must + // INCREF. // TODO(nareshmodi): figure out why PyList_New/PyList_Append don't work // directly. - std::unique_ptr> flattened_attrs = nullptr; - std::unique_ptr> flattened_inputs = nullptr; + std::unique_ptr> flattened_attrs = + nullptr; + std::unique_ptr> flattened_inputs = + nullptr; - if (run_callbacks) { - flattened_attrs.reset(new std::vector); - flattened_inputs.reset(new std::vector); + // TODO(nareshmodi): Encapsulate callbacks information into a struct. + if (op_exec_info.run_callbacks) { + flattened_attrs.reset(new std::vector); + flattened_inputs.reset(new std::vector); } // Add inferred attrs and inputs. @@ -1813,16 +1934,16 @@ PyObject* TFE_Py_FastPathExecute_C(PyObject*, PyObject* args) { Py_ssize_t len = PyList_Size(input); TFE_OpSetAttrInt(op, input_arg.number_attr().data(), len); - if (run_callbacks) { - flattened_attrs->push_back( + if (op_exec_info.run_callbacks) { + flattened_attrs->emplace_back( GetPythonObjectFromString(input_arg.number_attr().data())); - flattened_attrs->push_back(PyLong_FromLong(len)); + flattened_attrs->emplace_back(PyLong_FromLong(len)); } attr_list_sizes[input_arg.number_attr()] = len; if (len > 0) { // First item adds the type attr. - if (!AddInputToOp(PyList_GET_ITEM(input, 0), &input_arg, + if (!AddInputToOp(op_exec_info, PyList_GET_ITEM(input, 0), &input_arg, flattened_attrs.get(), flattened_inputs.get(), op, status)) { return nullptr; @@ -1830,7 +1951,8 @@ PyObject* TFE_Py_FastPathExecute_C(PyObject*, PyObject* args) { for (Py_ssize_t j = 1; j < len; j++) { // Since the list is homogeneous, we don't need to re-add the attr. - if (!AddInputToOp(PyList_GET_ITEM(input, j), nullptr /* input_arg */, + if (!AddInputToOp(op_exec_info, PyList_GET_ITEM(input, j), + nullptr /* input_arg */, nullptr /* flattened_attrs */, flattened_inputs.get(), op, status)) { return nullptr; @@ -1844,12 +1966,20 @@ PyObject* TFE_Py_FastPathExecute_C(PyObject*, PyObject* args) { Py_ssize_t len = PyList_Size(input); tensorflow::gtl::InlinedVector attr_value(len); PyObject* py_attr_value = nullptr; - if (run_callbacks) { + if (op_exec_info.run_callbacks) { py_attr_value = PyTuple_New(len); } for (Py_ssize_t j = 0; j < len; j++) { PyObject* py_input = PyList_GET_ITEM(input, j); - TFE_TensorHandle* input_handle = EagerTensor_Handle(py_input); + tensorflow::Safe_PyObjectPtr py_eager_tensor; + if (!ConvertToTensor(op_exec_info, py_input, &py_eager_tensor, + status)) { + return nullptr; + } + + TFE_TensorHandle* input_handle = + EagerTensor_Handle(py_eager_tensor.get()); + attr_value[j] = TFE_TensorHandleDataType(input_handle); TFE_OpAddInput(op, input_handle, status); @@ -1857,22 +1987,23 @@ PyObject* TFE_Py_FastPathExecute_C(PyObject*, PyObject* args) { return nullptr; } - if (run_callbacks) { - flattened_inputs->push_back(py_input); + if (op_exec_info.run_callbacks) { + flattened_inputs->emplace_back(std::move(py_eager_tensor)); PyTuple_SET_ITEM(py_attr_value, j, PyLong_FromLong(attr_value[j])); } } - if (run_callbacks) { - flattened_attrs->push_back(GetPythonObjectFromString(attr_name.data())); - flattened_attrs->push_back(py_attr_value); + if (op_exec_info.run_callbacks) { + flattened_attrs->emplace_back( + GetPythonObjectFromString(attr_name.data())); + flattened_attrs->emplace_back(py_attr_value); } TFE_OpSetAttrTypeList(op, attr_name.data(), attr_value.data(), attr_value.size()); attr_list_sizes[attr_name] = len; } else { // The item is a single item. - if (!AddInputToOp(input, &input_arg, flattened_attrs.get(), + if (!AddInputToOp(op_exec_info, input, &input_arg, flattened_attrs.get(), flattened_inputs.get(), op, status)) { return nullptr; } @@ -1896,27 +2027,27 @@ PyObject* TFE_Py_FastPathExecute_C(PyObject*, PyObject* args) { Py_BEGIN_ALLOW_THREADS; TFE_Execute(op, retvals.data(), &num_retvals, status); Py_END_ALLOW_THREADS; + if (TF_GetCode(status) != TF_OK) { // Augment the status with the op_name for easier debugging similar to // TFE_Py_Execute. TF_SetStatus(status, TF_GetCode(status), - tensorflow::strings::StrCat(TF_Message(status), " [Op:", - TFE_GetPythonString(op_name), "]") + tensorflow::strings::StrCat( + TF_Message(status), + " [Op:", TFE_GetPythonString(op_exec_info.op_name), "]") .c_str()); MaybeRaiseExceptionFromTFStatus(status, nullptr); return nullptr; } - PyObject* flat_result = PyList_New(num_retvals); + tensorflow::Safe_PyObjectPtr flat_result(PyList_New(num_retvals)); for (int i = 0; i < num_retvals; ++i) { - PyList_SET_ITEM(flat_result, i, EagerTensorFromHandle(retvals[i])); + PyList_SET_ITEM(flat_result.get(), i, EagerTensorFromHandle(retvals[i])); } - if (run_callbacks && - !RunCallbacks(run_gradient_callback, run_post_exec_callbacks, op_def, - args, *flattened_inputs, *flattened_attrs, flat_result, - op_name, name, record_gradient_callback, callbacks)) { + if (!RunCallbacks(op_exec_info, args, *flattened_inputs, *flattened_attrs, + flat_result.get())) { return nullptr; } @@ -1928,11 +2059,10 @@ PyObject* TFE_Py_FastPathExecute_C(PyObject*, PyObject* args) { if (op_def->output_arg_size() == 1) { if (!op_def->output_arg(0).number_attr().empty() || !op_def->output_arg(0).type_list_attr().empty()) { - return flat_result; + return flat_result.release(); } else { - auto* result = PyList_GET_ITEM(flat_result, 0); + auto* result = PyList_GET_ITEM(flat_result.get(), 0); Py_INCREF(result); - Py_DECREF(flat_result); return result; } } @@ -1945,7 +2075,7 @@ PyObject* TFE_Py_FastPathExecute_C(PyObject*, PyObject* args) { int list_length = attr_list_sizes[op_def->output_arg(i).number_attr()]; PyObject* inner_list = PyList_New(list_length); for (int j = 0; j < list_length; j++) { - PyObject* obj = PyList_GET_ITEM(flat_result, flat_result_index++); + PyObject* obj = PyList_GET_ITEM(flat_result.get(), flat_result_index++); Py_INCREF(obj); PyList_SET_ITEM(inner_list, j, obj); } @@ -1954,18 +2084,17 @@ PyObject* TFE_Py_FastPathExecute_C(PyObject*, PyObject* args) { int list_length = attr_list_sizes[op_def->output_arg(i).type_list_attr()]; PyObject* inner_list = PyList_New(list_length); for (int j = 0; j < list_length; j++) { - PyObject* obj = PyList_GET_ITEM(flat_result, flat_result_index++); + PyObject* obj = PyList_GET_ITEM(flat_result.get(), flat_result_index++); Py_INCREF(obj); PyList_SET_ITEM(inner_list, j, obj); } PyList_SET_ITEM(result, i, inner_list); } else { - PyObject* obj = PyList_GET_ITEM(flat_result, flat_result_index++); + PyObject* obj = PyList_GET_ITEM(flat_result.get(), flat_result_index++); Py_INCREF(obj); PyList_SET_ITEM(result, i, obj); } } - Py_DECREF(flat_result); return result; } diff --git a/tensorflow/python/eager/pywrap_tfe_test.py b/tensorflow/python/eager/pywrap_tfe_test.py index 49323e6640e664ef5f98b227964f9dd4e248ca39..faaae40b3f1ef02984a7a75c23ae4acae65ac335 100644 --- a/tensorflow/python/eager/pywrap_tfe_test.py +++ b/tensorflow/python/eager/pywrap_tfe_test.py @@ -21,13 +21,13 @@ from __future__ import print_function from tensorflow.python import pywrap_tensorflow from tensorflow.python.eager import backprop from tensorflow.python.eager import context -from tensorflow.python.eager import execute from tensorflow.python.eager import test 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 random_ops +from tensorflow.python.ops import resource_variable_ops class Tests(test.TestCase): @@ -46,15 +46,28 @@ class Tests(test.TestCase): self.assertAllClose( math_ops.matmul(a_2_by_2, b_2_by_2), pywrap_tensorflow.TFE_Py_FastPathExecute( - ctx._handle, ctx.device_name, "MatMul", execute.record_gradient, - None, None, a_2_by_2, b_2_by_2, "transpose_a", False, "transpose_b", - False)) + ctx._handle, ctx.device_name, "MatMul", None, None, a_2_by_2, + b_2_by_2, "transpose_a", False, "transpose_b", False)) self.assertAllClose( math_ops.matmul(a_100_by_784, b_100_by_784, transpose_b=True), pywrap_tensorflow.TFE_Py_FastPathExecute( - ctx._handle, ctx.device_name, "MatMul", execute.record_gradient, - None, None, a_100_by_784, b_100_by_784, "transpose_a", False, - "transpose_b", True)) + ctx._handle, ctx.device_name, "MatMul", None, None, a_100_by_784, + b_100_by_784, "transpose_a", False, "transpose_b", True)) + + @test_util.assert_no_new_tensors + @test_util.assert_no_garbage_created + def testFastpathExecute_ResourceVariableMatMulCorrectResponse(self): + ctx = context.context() + a_2_by_2 = constant_op.constant(1.0, shape=[2, 2]) + m = resource_variable_ops.ResourceVariable(a_2_by_2) + x = pywrap_tensorflow.TFE_Py_FastPathExecute( + ctx._handle, ctx.device_name, "MatMul", None, None, m, m, "transpose_a", + False, "transpose_b", False) + y = pywrap_tensorflow.TFE_Py_FastPathExecute( + ctx._handle, ctx.device_name, "MatMul", None, None, a_2_by_2, a_2_by_2, + "transpose_a", False, "transpose_b", False) + + self.assertAllEqual(x, y) @test_util.assert_no_new_tensors @test_util.assert_no_garbage_created @@ -64,12 +77,27 @@ class Tests(test.TestCase): a_2_by_2 = constant_op.constant(1.0, shape=[2, 2]) tape.watch(a_2_by_2) z = pywrap_tensorflow.TFE_Py_FastPathExecute( - ctx._handle, ctx.device_name, "MatMul", execute.record_gradient, None, - None, a_2_by_2, a_2_by_2, "transpose_a", False, "transpose_b", False) + ctx._handle, ctx.device_name, "MatMul", None, None, a_2_by_2, + a_2_by_2, "transpose_a", False, "transpose_b", False) dz_dy = tape.gradient(z, [a_2_by_2])[0] self.assertAllEqual(dz_dy.numpy(), constant_op.constant(4.0, shape=[2, 2]).numpy()) + @test_util.assert_no_new_tensors + @test_util.assert_no_garbage_created + def testFastpathExecute_ResourceVariableTapeWrite(self): + ctx = context.context() + with backprop.GradientTape(persistent=True) as tape: + a_2_by_2 = constant_op.constant(1.0, shape=[2, 2]) + m = resource_variable_ops.ResourceVariable(a_2_by_2) + tape.watch(m) + z = pywrap_tensorflow.TFE_Py_FastPathExecute( + ctx._handle, ctx.device_name, "MatMul", None, None, m, m, + "transpose_a", False, "transpose_b", False) + dz_dy = tape.gradient(z, [m])[0] + self.assertAllEqual(dz_dy.numpy(), + constant_op.constant(4.0, shape=[2, 2]).numpy()) + # Tests homogeneous list op @test_util.assert_no_new_tensors @test_util.assert_no_garbage_created @@ -80,9 +108,9 @@ class Tests(test.TestCase): self.assertAllClose( math_ops.add_n([a_2_by_2, b_2_by_2]), - pywrap_tensorflow.TFE_Py_FastPathExecute( - ctx._handle, ctx.device_name, "AddN", execute.record_gradient, None, - None, [a_2_by_2, b_2_by_2])) + pywrap_tensorflow.TFE_Py_FastPathExecute(ctx._handle, ctx.device_name, + "AddN", None, None, + [a_2_by_2, b_2_by_2])) # Tests homogeneous list op @test_util.assert_no_new_tensors @@ -96,8 +124,8 @@ class Tests(test.TestCase): tape.watch(a_2_by_2) tape.watch(b_2_by_2) z1 = pywrap_tensorflow.TFE_Py_FastPathExecute( - ctx._handle, ctx.device_name, "AddN", execute.record_gradient, None, - None, [a_2_by_2, b_2_by_2]) + ctx._handle, ctx.device_name, "AddN", None, None, + [a_2_by_2, b_2_by_2]) z2 = math_ops.add_n([a_2_by_2, b_2_by_2]) dz1_dy = tape.gradient(z1, [a_2_by_2])[0] dz2_dy = tape.gradient(z2, [a_2_by_2])[0] @@ -113,9 +141,9 @@ class Tests(test.TestCase): self.assertAllClose( array_ops.identity_n([a_2_by_2, b_2_by_2]), - pywrap_tensorflow.TFE_Py_FastPathExecute( - ctx._handle, ctx.device_name, "IdentityN", execute.record_gradient, - None, None, [a_2_by_2, b_2_by_2])) + pywrap_tensorflow.TFE_Py_FastPathExecute(ctx._handle, ctx.device_name, + "IdentityN", None, None, + [a_2_by_2, b_2_by_2])) # Tests heterogeneous list op @test_util.assert_no_new_tensors @@ -129,8 +157,8 @@ class Tests(test.TestCase): tape.watch(a_2_by_2) tape.watch(b_2_by_2) z1 = pywrap_tensorflow.TFE_Py_FastPathExecute( - ctx._handle, ctx.device_name, "IdentityN", execute.record_gradient, - None, None, [a_2_by_2, b_2_by_2]) + ctx._handle, ctx.device_name, "IdentityN", None, None, + [a_2_by_2, b_2_by_2]) z2 = array_ops.identity_n([a_2_by_2, b_2_by_2]) dz1_dy = tape.gradient(z1[0], [a_2_by_2])[0] dz2_dy = tape.gradient(z2[0], [a_2_by_2])[0] @@ -141,28 +169,26 @@ class Tests(test.TestCase): def testFastpathExecute_InvalidInputs(self): a_2_by_2 = random_ops.random_uniform((2, 2)) ctx = context.context() - assert not ctx.in_graph_mode( + assert ctx.executing_eagerly( ), "The prototype doesn't contain C code for graph construction" ctx_handle = ctx._handle # pylint: disable=protected-access # Not enough base params with self.assertRaisesRegexp(ValueError, - "at least 6 items in the input tuple"): + "at least 5 items in the input tuple"): pywrap_tensorflow.TFE_Py_FastPathExecute(ctx_handle, ctx.device_name, "Identity") # Not enough inputs with self.assertRaisesRegexp(ValueError, - "Expected to be at least 7, was 6"): - pywrap_tensorflow.TFE_Py_FastPathExecute( - ctx_handle, ctx_handle, "Identity", backprop._record_gradient, None, - []) + "Expected to be at least 6, was 5"): + pywrap_tensorflow.TFE_Py_FastPathExecute(ctx_handle, ctx_handle, + "Identity", None, []) # Bad type with self.assertRaisesRegexp(TypeError, "expected a string for op_name"): - pywrap_tensorflow.TFE_Py_FastPathExecute( - ctx_handle, ctx.device_name, ctx_handle, backprop._record_gradient, - None, [], a_2_by_2) + pywrap_tensorflow.TFE_Py_FastPathExecute(ctx_handle, ctx.device_name, + ctx_handle, None, [], a_2_by_2) if __name__ == "__main__": diff --git a/tensorflow/python/eager/tape_test.py b/tensorflow/python/eager/tape_test.py index b490bac66db03b0a61a8852f45f1f558cccaf121..4326d5efa3d362e883815eb2d3dafb27df25afd4 100644 --- a/tensorflow/python/eager/tape_test.py +++ b/tensorflow/python/eager/tape_test.py @@ -21,11 +21,11 @@ from __future__ import print_function from tensorflow.python.eager import backprop from tensorflow.python.eager import context -from tensorflow.python.eager import custom_gradient from tensorflow.python.eager import test from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.ops import array_ops +from tensorflow.python.ops import custom_gradient from tensorflow.python.ops import gradients_impl from tensorflow.python.ops import math_ops # Importing nn_grad for the registration functions. @@ -165,21 +165,6 @@ class TapeTest(test.TestCase): g, = backprop.gradients_function(fn, [0])(t) self.assertAllEqual(g, 1.0) - def testCustomGradientGraphMode(self): - with context.graph_mode(), self.test_session(): - - @custom_gradient.custom_gradient - def f(x): - - def grad(dresult): - return dresult * 10.0 - - return x, grad - - inp = constant_op.constant(1.0) - grad = gradients_impl.gradients(f(inp), inp) - self.assertAllEqual(grad[0].eval(), 10.0) - if __name__ == '__main__': test.main() diff --git a/tensorflow/python/estimator/BUILD b/tensorflow/python/estimator/BUILD index c519fd557a9319d6ef5522b26198e5b4202917fc..04fcbb0e874bcc5583cd5f607de3a3764f4ba6b0 100644 --- a/tensorflow/python/estimator/BUILD +++ b/tensorflow/python/estimator/BUILD @@ -7,6 +7,7 @@ package( licenses(["notice"]) # Apache 2.0 load("//tensorflow:tensorflow.bzl", "py_test") +load("//tensorflow:tensorflow.bzl", "cuda_py_test") filegroup( name = "all_files", @@ -35,9 +36,9 @@ py_library( ":linear", ":model_fn", ":parsing_utils", + ":replicate_model_fn", ":run_config", ":training", - ":warm_starting_util", "//tensorflow/python:util", ], ) @@ -278,12 +279,12 @@ py_library( srcs = ["canned/dnn_testing_utils.py"], srcs_version = "PY2AND3", deps = [ + ":estimator", ":head", ":metric_keys", ":model_fn", ":numpy_io", ":prediction_keys", - ":warm_starting_util", "//tensorflow/core:protos_all_py", "//tensorflow/python:array_ops", "//tensorflow/python:check_ops", @@ -427,7 +428,6 @@ py_library( ":model_fn", ":run_config", ":util", - ":warm_starting_util", "//tensorflow/core:protos_all_py", "//tensorflow/python:client", "//tensorflow/python:control_flow_ops", @@ -870,37 +870,64 @@ py_test( ) py_library( - name = "warm_starting_util", - srcs = ["warm_starting_util.py"], + name = "replicate_model_fn", + srcs = [ + "replicate_model_fn.py", + ], srcs_version = "PY2AND3", deps = [ + ":export_output", + ":model_fn", + ":util", + "//tensorflow/core:protos_all_py", "//tensorflow/python:array_ops", + "//tensorflow/python:control_flow_ops", + "//tensorflow/python:device", + "//tensorflow/python:device_lib", "//tensorflow/python:framework_ops", + "//tensorflow/python:math_ops", "//tensorflow/python:platform", + "//tensorflow/python:sparse_ops", + "//tensorflow/python:sparse_tensor", "//tensorflow/python:state_ops", "//tensorflow/python:training", "//tensorflow/python:variable_scope", - "//tensorflow/python:variables", - "//tensorflow/python/feature_column", + "//tensorflow/python/ops/losses", + "@six_archive//:six", ], ) -py_test( - name = "warm_starting_util_test", - size = "small", - srcs = ["warm_starting_util_test.py"], - srcs_version = "PY2AND3", - deps = [ - ":warm_starting_util", +cuda_py_test( + name = "replicate_model_fn_test", + size = "medium", + srcs = ["replicate_model_fn_test.py"], + additional_deps = [ + "//tensorflow/python/estimator", + ":dnn", + ":export_export", + ":export_output", + ":model_fn", + ":numpy_io", + ":optimizers", + ":prediction_keys", + "//tensorflow/python/feature_column", + "//tensorflow/python/ops/losses", + "//tensorflow/python/saved_model:signature_constants", "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", - "//tensorflow/python:dtypes", - "//tensorflow/python:framework_ops", - "//tensorflow/python:init_ops", + "//tensorflow/python:control_flow_ops", + "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:framework_test_lib", + "//tensorflow/python:math_ops", + "//tensorflow/python:metrics", + "//tensorflow/python:platform", + "//tensorflow/python:summary", "//tensorflow/python:training", "//tensorflow/python:variable_scope", "//tensorflow/python:variables", - "//tensorflow/python/feature_column", - "//third_party/py/numpy", + ":replicate_model_fn", + ], + tags = [ + "multi_gpu", ], ) diff --git a/tensorflow/python/estimator/canned/baseline_test.py b/tensorflow/python/estimator/canned/baseline_test.py index 18c955f5a0e998de983b31fc4cc595895e6bbcbd..96639e88ea4a07e14121049d78f07e03fcb22156 100644 --- a/tensorflow/python/estimator/canned/baseline_test.py +++ b/tensorflow/python/estimator/canned/baseline_test.py @@ -1075,7 +1075,7 @@ class BaselineClassifierEvaluationTest(test.TestCase): metric_keys.MetricKeys.LABEL_MEAN: 1., metric_keys.MetricKeys.ACCURACY_BASELINE: 1, metric_keys.MetricKeys.AUC: 0., - metric_keys.MetricKeys.AUC_PR: 0.5, + metric_keys.MetricKeys.AUC_PR: 1., } else: # Multi classes: loss = 1 * -log ( softmax(logits)[label] ) @@ -1136,7 +1136,7 @@ class BaselineClassifierEvaluationTest(test.TestCase): metric_keys.MetricKeys.LABEL_MEAN: 0.5, metric_keys.MetricKeys.ACCURACY_BASELINE: 0.5, metric_keys.MetricKeys.AUC: 0.5, - metric_keys.MetricKeys.AUC_PR: 0.25, + metric_keys.MetricKeys.AUC_PR: 0.75, } else: # Expand logits since batch_size=2 @@ -1212,7 +1212,7 @@ class BaselineClassifierEvaluationTest(test.TestCase): metric_keys.MetricKeys.ACCURACY_BASELINE: ( max(label_mean, 1-label_mean)), metric_keys.MetricKeys.AUC: 0.5, - metric_keys.MetricKeys.AUC_PR: 0.16666645, + metric_keys.MetricKeys.AUC_PR: 2. / (1. + 2.), } else: # Multi classes: unweighted_loss = 1 * -log ( soft_max(logits)[label] ) diff --git a/tensorflow/python/estimator/canned/dnn_linear_combined_test.py b/tensorflow/python/estimator/canned/dnn_linear_combined_test.py index 84675bf2a4a1655026bbba37c5d7a63d2f788c46..d275695eb319117cf94aefd7038ab5ee685e05a9 100644 --- a/tensorflow/python/estimator/canned/dnn_linear_combined_test.py +++ b/tensorflow/python/estimator/canned/dnn_linear_combined_test.py @@ -26,7 +26,7 @@ import six from tensorflow.core.example import example_pb2 from tensorflow.core.example import feature_pb2 -from tensorflow.python.estimator import warm_starting_util +from tensorflow.python.estimator import estimator from tensorflow.python.estimator.canned import dnn_linear_combined from tensorflow.python.estimator.canned import dnn_testing_utils from tensorflow.python.estimator.canned import linear_testing_utils @@ -866,7 +866,7 @@ class DNNLinearCombinedWarmStartingTest(test.TestCase): learning_rate=0.0), # The provided regular expression will only warm-start the deep # portion of the model. - warm_start_from=warm_starting_util.WarmStartSettings( + warm_start_from=estimator.WarmStartSettings( ckpt_to_initialize_from=dnn_lc_classifier.model_dir, vars_to_warm_start='.*(dnn).*'))) diff --git a/tensorflow/python/estimator/canned/dnn_testing_utils.py b/tensorflow/python/estimator/canned/dnn_testing_utils.py index cbae43e4f7fef0271de20a4ec54449989455d4bd..9a7d088778e440b8aa4d9bc4f05a72aa86e7c106 100644 --- a/tensorflow/python/estimator/canned/dnn_testing_utils.py +++ b/tensorflow/python/estimator/canned/dnn_testing_utils.py @@ -27,8 +27,8 @@ import six from tensorflow.core.framework import summary_pb2 from tensorflow.python.client import session as tf_session +from tensorflow.python.estimator import estimator from tensorflow.python.estimator import model_fn -from tensorflow.python.estimator import warm_starting_util from tensorflow.python.estimator.canned import head as head_lib from tensorflow.python.estimator.canned import metric_keys from tensorflow.python.estimator.canned import prediction_keys @@ -828,7 +828,7 @@ class BaseDNNWarmStartingTest(object): optimizer=gradient_descent.GradientDescentOptimizer(learning_rate=0.0), # The provided regular expression will only warm-start the city # embedding, not the kernels and biases of the hidden weights. - warm_start_from=warm_starting_util.WarmStartSettings( + warm_start_from=estimator.WarmStartSettings( ckpt_to_initialize_from=dnn_classifier.model_dir, vars_to_warm_start='.*(city).*')) @@ -892,7 +892,7 @@ class BaseDNNWarmStartingTest(object): dimension=2) # We can create our VocabInfo object from the new and old occupation # FeatureColumn's. - occupation_vocab_info = warm_starting_util.VocabInfo( + occupation_vocab_info = estimator.VocabInfo( new_vocab=new_occupation.categorical_column.vocabulary_file, new_vocab_size=new_occupation.categorical_column.vocabulary_size, num_oov_buckets=new_occupation.categorical_column.num_oov_buckets, @@ -907,7 +907,7 @@ class BaseDNNWarmStartingTest(object): feature_columns=[occupation], n_classes=4, optimizer=gradient_descent.GradientDescentOptimizer(learning_rate=0.0), - warm_start_from=warm_starting_util.WarmStartSettings( + warm_start_from=estimator.WarmStartSettings( ckpt_to_initialize_from=dnn_classifier.model_dir, var_name_to_vocab_info={ OCCUPATION_EMBEDDING_NAME: occupation_vocab_info @@ -978,7 +978,7 @@ class BaseDNNWarmStartingTest(object): optimizer=gradient_descent.GradientDescentOptimizer(learning_rate=0.0), # The 'city' variable correspond to the 'locality' variable in the # previous model. - warm_start_from=warm_starting_util.WarmStartSettings( + warm_start_from=estimator.WarmStartSettings( ckpt_to_initialize_from=dnn_classifier.model_dir, var_name_to_prev_var_name={ CITY_EMBEDDING_NAME: @@ -1041,7 +1041,7 @@ class BaseDNNClassifierEvaluateTest(object): # There is no good way to calculate AUC for only two data points. But # that is what the algorithm returns. metric_keys.MetricKeys.AUC: 0.5, - metric_keys.MetricKeys.AUC_PR: 0.25, + metric_keys.MetricKeys.AUC_PR: 0.75, ops.GraphKeys.GLOBAL_STEP: global_step }, dnn_classifier.evaluate(input_fn=_input_fn, steps=1)) diff --git a/tensorflow/python/estimator/canned/head_test.py b/tensorflow/python/estimator/canned/head_test.py index a300f315c18f60e77f262a3b961c5ef6306bc235..b40758f8fef13bad939b3fc2f296e848ade46f63 100644 --- a/tensorflow/python/estimator/canned/head_test.py +++ b/tensorflow/python/estimator/canned/head_test.py @@ -300,7 +300,12 @@ class MultiClassHeadWithSoftmaxCrossEntropyLoss(test.TestCase): features = {'x': values_2x3} # Static shape. - with self.assertRaisesRegexp(ValueError, 'Dimensions must be equal'): + with self.assertRaisesRegexp( + ValueError, + r'Shape mismatch: The shape of labels \(received \(3,\)\) should equal ' + r'the shape of logits except for the last dimension ' + r'\(received \(2, 3\)\)\.' + ): head.create_loss( features=features, mode=model_fn.ModeKeys.EVAL, @@ -1558,7 +1563,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): keys.LABEL_MEAN: 2./2, keys.ACCURACY_BASELINE: 2./2, keys.AUC: 0., - keys.AUC_PR: 0.74999905, + keys.AUC_PR: 1., } # Assert spec contains expected tensors. @@ -1636,7 +1641,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): keys.LABEL_MEAN: 2./2, keys.ACCURACY_BASELINE: 2./2, keys.AUC: 0., - keys.AUC_PR: 0.75, + keys.AUC_PR: 1., } # Assert predictions, loss, and metrics. @@ -1741,7 +1746,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): keys.LABEL_MEAN: 2./2, keys.ACCURACY_BASELINE: 2./2, keys.AUC: 0., - keys.AUC_PR: 0.74999905, + keys.AUC_PR: 1., keys.ACCURACY_AT_THRESHOLD % thresholds[0]: 1., keys.PRECISION_AT_THRESHOLD % thresholds[0]: 1., keys.RECALL_AT_THRESHOLD % thresholds[0]: 1., @@ -2188,7 +2193,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): keys.LABEL_MEAN: expected_label_mean, keys.ACCURACY_BASELINE: 1 - expected_label_mean, keys.AUC: .45454565, - keys.AUC_PR: .21923049, + keys.AUC_PR: .6737757325172424, } # Assert spec contains expected tensors. @@ -2487,7 +2492,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): # We cannot reliably calculate AUC with only 4 data points, but the # values should not change because of backwards-compatibility. keys.AUC: 0.5222, - keys.AUC_PR: 0.5119, + keys.AUC_PR: 0.7341, } tol = 1e-2 diff --git a/tensorflow/python/estimator/canned/linear_testing_utils.py b/tensorflow/python/estimator/canned/linear_testing_utils.py index e88fcbbd2e0e3617dde428662e58b1d86c4eddd0..8e506a763133c3f8c15f49f253f84de5470088c5 100644 --- a/tensorflow/python/estimator/canned/linear_testing_utils.py +++ b/tensorflow/python/estimator/canned/linear_testing_utils.py @@ -31,7 +31,6 @@ from tensorflow.core.example import feature_pb2 from tensorflow.python.client import session as tf_session from tensorflow.python.estimator import estimator from tensorflow.python.estimator import run_config -from tensorflow.python.estimator import warm_starting_util from tensorflow.python.estimator.canned import linear from tensorflow.python.estimator.canned import metric_keys from tensorflow.python.estimator.export import export @@ -1342,7 +1341,7 @@ class BaseLinearClassifierEvaluationTest(object): metric_keys.MetricKeys.LABEL_MEAN: 1., metric_keys.MetricKeys.ACCURACY_BASELINE: 1, metric_keys.MetricKeys.AUC: 0., - metric_keys.MetricKeys.AUC_PR: 0.5, + metric_keys.MetricKeys.AUC_PR: 1., } else: # Multi classes: loss = 1 * -log ( soft_max(logits)[label] ) @@ -1968,7 +1967,7 @@ class BaseLinearWarmStartingTest(object): optimizer=gradient_descent.GradientDescentOptimizer(learning_rate=0.0), # The provided regular expression will only warm-start the age variable # and not the bias. - warm_start_from=warm_starting_util.WarmStartSettings( + warm_start_from=estimator.WarmStartSettings( ckpt_to_initialize_from=linear_classifier.model_dir, vars_to_warm_start='.*(age).*')) @@ -2016,7 +2015,7 @@ class BaseLinearWarmStartingTest(object): vocabulary_size=len(new_vocab_list)) # We can create our VocabInfo object from the new and old occupation # FeatureColumn's. - occupation_vocab_info = warm_starting_util.VocabInfo( + occupation_vocab_info = estimator.VocabInfo( new_vocab=new_occupation.vocabulary_file, new_vocab_size=new_occupation.vocabulary_size, num_oov_buckets=new_occupation.num_oov_buckets, @@ -2030,7 +2029,7 @@ class BaseLinearWarmStartingTest(object): feature_columns=[occupation], n_classes=4, optimizer=gradient_descent.GradientDescentOptimizer(learning_rate=0.0), - warm_start_from=warm_starting_util.WarmStartSettings( + warm_start_from=estimator.WarmStartSettings( ckpt_to_initialize_from=linear_classifier.model_dir, var_name_to_vocab_info={ OCCUPATION_WEIGHT_NAME: occupation_vocab_info @@ -2082,7 +2081,7 @@ class BaseLinearWarmStartingTest(object): optimizer=gradient_descent.GradientDescentOptimizer(learning_rate=0.0), # The 'age' variable correspond to the 'age_in_years' variable in the # previous model. - warm_start_from=warm_starting_util.WarmStartSettings( + warm_start_from=estimator.WarmStartSettings( ckpt_to_initialize_from=linear_classifier.model_dir, var_name_to_prev_var_name={ AGE_WEIGHT_NAME: AGE_WEIGHT_NAME.replace('age', 'age_in_years') diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py index 1167b3834eb6a79abf670f629ec2cbc37957d191..5245a050a1ed87af157dbd2008b52122798f9491 100644 --- a/tensorflow/python/estimator/estimator.py +++ b/tensorflow/python/estimator/estimator.py @@ -19,6 +19,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import collections import copy import os import tempfile @@ -35,7 +36,6 @@ from tensorflow.python.eager import context from tensorflow.python.estimator import model_fn as model_fn_lib from tensorflow.python.estimator import run_config from tensorflow.python.estimator import util -from tensorflow.python.estimator import warm_starting_util from tensorflow.python.estimator.export.export import build_all_signature_defs from tensorflow.python.estimator.export.export import get_temp_export_dir from tensorflow.python.estimator.export.export import get_timestamped_export_dir @@ -49,11 +49,13 @@ from tensorflow.python.saved_model import builder as saved_model_builder from tensorflow.python.saved_model import tag_constants from tensorflow.python.summary import summary from tensorflow.python.summary.writer import writer_cache +from tensorflow.python.training import device_setter from tensorflow.python.training import evaluation from tensorflow.python.training import monitored_session from tensorflow.python.training import saver from tensorflow.python.training import training from tensorflow.python.training import training_util +from tensorflow.python.training import warm_starting_util from tensorflow.python.util import compat from tensorflow.python.util import compat_internal from tensorflow.python.util import nest @@ -137,8 +139,8 @@ class Estimator(object): to configure Estimators from hyper parameter tuning. * `config`: Optional configuration object. Will receive what is passed to Estimator in `config` parameter, or the default `config`. - Allows updating things in your model_fn based on configuration - such as `num_ps_replicas`, or `model_dir`. + Allows updating things in your `model_fn` based on + configuration such as `num_ps_replicas`, or `model_dir`. * Returns: `EstimatorSpec` @@ -165,7 +167,7 @@ class Estimator(object): ValueError: if this is called via a subclass and if that class overrides a member of `Estimator`. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError( 'Estimators are not supported when eager execution is enabled.') @@ -216,8 +218,8 @@ class Estimator(object): self._params = copy.deepcopy(params or {}) # pylint: disable=protected-access - self._warm_start_settings = ( - warm_starting_util._get_default_warm_start_settings(warm_start_from)) + self._warm_start_settings = _get_default_warm_start_settings( + warm_start_from) # pylint: enable=protected-access @property @@ -299,11 +301,11 @@ class Estimator(object): * A 'tf.data.Dataset' object: Outputs of `Dataset` object must be a tuple (features, labels) with same constraints as below. - * A tuple (features, labels): Where features is a `Tensor` or a - dictionary of string feature name to `Tensor` and labels is a + * A tuple (features, labels): Where `features` is a `Tensor` or a + dictionary of string feature name to `Tensor` and `labels` is a `Tensor` or a dictionary of string label name to `Tensor`. Both - features and labels are consumed by `model_fn`. They should satisfy - the expectation of `model_fn` from inputs. + `features` and `labels` are consumed by `model_fn`. They should + satisfy the expectation of `model_fn` from inputs. hooks: List of `SessionRunHook` subclass instances. Used for callbacks inside the training loop. @@ -379,11 +381,11 @@ class Estimator(object): * A 'tf.data.Dataset' object: Outputs of `Dataset` object must be a tuple (features, labels) with same constraints as below. - * A tuple (features, labels): Where features is a `Tensor` or a - dictionary of string feature name to `Tensor` and labels is a + * A tuple (features, labels): Where `features` is a `Tensor` or a + dictionary of string feature name to `Tensor` and `labels` is a `Tensor` or a dictionary of string label name to `Tensor`. Both - features and labels are consumed by `model_fn`. They should satisfy - the expectation of `model_fn` from inputs. + `features` and `labels` are consumed by `model_fn`. They should + satisfy the expectation of `model_fn` from inputs. steps: Number of steps for which to evaluate model. If `None`, evaluates until `input_fn` raises an end-of-input exception. @@ -455,17 +457,17 @@ class Estimator(object): checkpoint_path: Path of a specific checkpoint to predict. If `None`, the latest checkpoint in `model_dir` is used. yield_single_examples: If False, yield the whole batch as returned by the - model_fn instead of decomposing the batch into individual elements. This - is useful if model_fn return some tensor with first dimension not - equal to the batch size + `model_fn` instead of decomposing the batch into individual elements. + This is useful if `model_fn` returns some tensors whose first dimension + is not equal to the batch size. Yields: Evaluated values of `predictions` tensors. Raises: - ValueError: Could not find a trained model in model_dir. - ValueError: if batch length of predictions are not same and - yield_single_examples is True. + ValueError: Could not find a trained model in `model_dir`. + ValueError: If batch length of predictions is not the same and + `yield_single_examples` is True. ValueError: If there is a conflict between `predict_keys` and `predictions`. For example if `predict_keys` is not `None` but `EstimatorSpec.predictions` is not a `dict`. @@ -515,7 +517,7 @@ class Estimator(object): allowed_overrides = set([ '_call_input_fn', '_create_global_step', '_convert_train_steps_to_hooks', '_convert_eval_steps_to_hooks', - '_tf_api_names' + '_tf_api_names', '_validate_features_in_predict_input' ]) estimator_members = set([m for m in Estimator.__dict__.keys() if not m.startswith('__')]) @@ -570,7 +572,7 @@ class Estimator(object): export_dir_base: A string containing a directory in which to create timestamped subdirectories containing exported SavedModels. serving_input_receiver_fn: A function that takes no argument and - returns a `ServingInputReceiver`. + returns a `ServingInputReceiver` or `TensorServingInputReceiver`. assets_extra: A dict specifying how to populate the assets.extra directory within the exported SavedModel, or `None` if no extra assets are needed. as_text: whether to write the SavedModel proto in text format. @@ -668,11 +670,14 @@ class Estimator(object): # Unconditionally drop the label (the second element of result). result = result[0] + self._validate_features_in_predict_input(result) + return result, input_hooks + + def _validate_features_in_predict_input(self, result): if not _has_dataset_or_queue_runner(result): logging.warning('Input graph does not use tf.data.Dataset or contain a ' 'QueueRunner. That means predict yields forever. ' 'This is probably a mistake.') - return result, input_hooks def _get_features_and_labels_from_input_fn(self, input_fn, mode): """Extracts the `features` and labels from return values of `input_fn`.""" @@ -720,7 +725,7 @@ class Estimator(object): """Creates the global step tensor in graph. The global step tensor must be an integer type with name 'global_step' and - be added to the collection ${tf.GraphKeys.GLOBAL_STEP}. + be added to the collection @{tf.GraphKeys.GLOBAL_STEP}. Args: graph: The graph in which to create the global step tensor. @@ -826,7 +831,7 @@ class Estimator(object): logging.info('Warm-starting with WarmStartSettings: %s' % (self._warm_start_settings,)) # pylint: disable=protected-access - warm_starting_util._warm_start(self._warm_start_settings) + warm_starting_util.warm_start(*self._warm_start_settings) # pylint: enable=protected-access # Check if the user created a loss summary, and add one if they didn't. # We assume here that the summary is called 'loss'. If it is not, we will @@ -1007,13 +1012,6 @@ def _get_replica_device_setter(config): Returns: A replica device setter, or None. """ - ps_ops = [ - 'Variable', 'VariableV2', 'AutoReloadVariable', 'MutableHashTable', - 'MutableHashTableV2', 'MutableHashTableOfTensors', - 'MutableHashTableOfTensorsV2', 'MutableDenseHashTable', - 'MutableDenseHashTableV2', 'VarHandleOp' - ] - if config.task_type: worker_device = '/job:%s/task:%d' % (config.task_type, config.task_id) else: @@ -1024,7 +1022,7 @@ def _get_replica_device_setter(config): ps_tasks=config.num_ps_replicas, worker_device=worker_device, merge_devices=True, - ps_ops=ps_ops, + ps_ops=list(device_setter.STANDARD_PS_OPS), cluster=config.cluster_spec) else: return None @@ -1118,7 +1116,7 @@ def _write_dict_to_summary(output_dir, try: summ = summary_pb2.Summary.FromString(dictionary[key]) for i, _ in enumerate(summ.value): - summ.value[i].tag = key + summ.value[i].tag = '%s/%d' % (key, i) summary_proto.value.extend(summ.value) except message.DecodeError: logging.warn('Skipping summary for %s, cannot parse string to Summary.', @@ -1155,3 +1153,187 @@ class _DatasetInitializerHook(training.SessionRunHook): def after_create_session(self, session, coord): del coord session.run(self._initializer) + +VocabInfo = warm_starting_util.VocabInfo # pylint: disable=invalid-name + + +@tf_export('estimator.WarmStartSettings') +class WarmStartSettings( + collections.namedtuple('WarmStartSettings', [ + 'ckpt_to_initialize_from', + 'vars_to_warm_start', + 'var_name_to_vocab_info', + 'var_name_to_prev_var_name', + ])): + """Settings for warm-starting in Estimators. + + Example Use with canned `DNNEstimator`: + + ``` + emb_vocab_file = tf.feature_column.embedding_column( + tf.feature_column.categorical_column_with_vocabulary_file( + "sc_vocab_file", "new_vocab.txt", vocab_size=100), + dimension=8) + emb_vocab_list = tf.feature_column.embedding_column( + tf.feature_column.categorical_column_with_vocabulary_list( + "sc_vocab_list", vocabulary_list=["a", "b"]), + dimension=8) + estimator = tf.estimator.DNNClassifier( + hidden_units=[128, 64], feature_columns=[emb_vocab_file, emb_vocab_list], + warm_start_from=ws) + ``` + + where `ws` could be defined as: + + Warm-start all weights in the model (input layer and hidden weights). + Either the directory or a specific checkpoint can be provided (in the case + of the former, the latest checkpoint will be used): + + ``` + ws = WarmStartSettings(ckpt_to_initialize_from="/tmp") + ws = WarmStartSettings(ckpt_to_initialize_from="/tmp/model-1000") + ``` + + Warm-start only the embeddings (input layer): + + ``` + ws = WarmStartSettings(ckpt_to_initialize_from="/tmp", + vars_to_warm_start=".*input_layer.*") + ``` + + Warm-start all weights but the embedding parameters corresponding to + `sc_vocab_file` have a different vocab from the one used in the current + model: + + ``` + vocab_info = tf.estimator.VocabInfo( + new_vocab=sc_vocab_file.vocabulary_file, + new_vocab_size=sc_vocab_file.vocabulary_size, + num_oov_buckets=sc_vocab_file.num_oov_buckets, + old_vocab="old_vocab.txt" + ) + ws = WarmStartSettings( + ckpt_to_initialize_from="/tmp", + var_name_to_vocab_info={ + "input_layer/sc_vocab_file_embedding/embedding_weights": vocab_info + }) + ``` + + Warm-start only `sc_vocab_file` embeddings (and no other variables), which + have a different vocab from the one used in the current model: + + ``` + vocab_info = tf.estimator.VocabInfo( + new_vocab=sc_vocab_file.vocabulary_file, + new_vocab_size=sc_vocab_file.vocabulary_size, + num_oov_buckets=sc_vocab_file.num_oov_buckets, + old_vocab="old_vocab.txt" + ) + ws = WarmStartSettings( + ckpt_to_initialize_from="/tmp", + vars_to_warm_start=None, + var_name_to_vocab_info={ + "input_layer/sc_vocab_file_embedding/embedding_weights": vocab_info + }) + ``` + + Warm-start all weights but the parameters corresponding to `sc_vocab_file` + have a different vocab from the one used in current checkpoint, and only + 100 of those entries were used: + + ``` + vocab_info = tf.estimator.VocabInfo( + new_vocab=sc_vocab_file.vocabulary_file, + new_vocab_size=sc_vocab_file.vocabulary_size, + num_oov_buckets=sc_vocab_file.num_oov_buckets, + old_vocab="old_vocab.txt", + old_vocab_size=100 + ) + ws = WarmStartSettings( + ckpt_to_initialize_from="/tmp", + var_name_to_vocab_info={ + "input_layer/sc_vocab_file_embedding/embedding_weights": vocab_info + }) + ``` + + Warm-start all weights but the parameters corresponding to `sc_vocab_file` + have a different vocab from the one used in current checkpoint and the + parameters corresponding to `sc_vocab_list` have a different name from the + current checkpoint: + + ``` + vocab_info = tf.estimator.VocabInfo( + new_vocab=sc_vocab_file.vocabulary_file, + new_vocab_size=sc_vocab_file.vocabulary_size, + num_oov_buckets=sc_vocab_file.num_oov_buckets, + old_vocab="old_vocab.txt", + old_vocab_size=100 + ) + ws = WarmStartSettings( + ckpt_to_initialize_from="/tmp", + var_name_to_vocab_info={ + "input_layer/sc_vocab_file_embedding/embedding_weights": vocab_info + }, + var_name_to_prev_var_name={ + "input_layer/sc_vocab_list_embedding/embedding_weights": + "old_tensor_name" + }) + ``` + + Attributes: + ckpt_to_initialize_from: [Required] A string specifying the directory with + checkpoint file(s) or path to checkpoint from which to warm-start the + model parameters. + vars_to_warm_start: [Optional] A regular expression that captures which + variables to warm-start (see tf.get_collection). Defaults to `'.*'`, + which warm-starts all variables. If `None` is explicitly given, only + variables specified in `var_name_to_vocab_info` will be warm-started. + var_name_to_vocab_info: [Optional] Dict of variable names (strings) to + VocabInfo. The variable names should be "full" variables, not the names + of the partitions. If not explicitly provided, the variable is assumed to + have no vocabulary. + var_name_to_prev_var_name: [Optional] Dict of variable names (strings) to + name of the previously-trained variable in `ckpt_to_initialize_from`. If + not explicitly provided, the name of the variable is assumed to be same + between previous checkpoint and current model. + """ + + def __new__(cls, + ckpt_to_initialize_from, + vars_to_warm_start='.*', + var_name_to_vocab_info=None, + var_name_to_prev_var_name=None): + if not ckpt_to_initialize_from: + raise ValueError( + '`ckpt_to_initialize_from` MUST be set in WarmStartSettings') + return super(WarmStartSettings, cls).__new__( + cls, + ckpt_to_initialize_from, + vars_to_warm_start, + var_name_to_vocab_info or {}, + var_name_to_prev_var_name or {}, + ) + + +def _get_default_warm_start_settings(warm_start_from): + """Returns default WarmStartSettings. + + Args: + warm_start_from: Either a string representing the filepath of a checkpoint + to initialize from, or an instance of WarmStartSettings. + + Returns: + Either None or an instance of WarmStartSettings. + + Raises: + ValueError: If warm_start_from is not None but is neither a string nor an + instance of WarmStartSettings. + """ + if warm_start_from is None: + return None + if isinstance(warm_start_from, six.string_types): + return WarmStartSettings(ckpt_to_initialize_from=warm_start_from) + elif isinstance(warm_start_from, WarmStartSettings): + return warm_start_from + else: + raise ValueError('warm_start_from must be a string or a WarmStartSettings') diff --git a/tensorflow/python/estimator/estimator_lib.py b/tensorflow/python/estimator/estimator_lib.py index 01699e7399c4089281e9ece76e534e1f82692257..be8930b3cbcd89dbb31dffde0a7a5ecfb64fcd8b 100644 --- a/tensorflow/python/estimator/estimator_lib.py +++ b/tensorflow/python/estimator/estimator_lib.py @@ -30,6 +30,8 @@ from tensorflow.python.estimator.canned.linear import LinearRegressor from tensorflow.python.estimator.canned.parsing_utils import classifier_parse_example_spec from tensorflow.python.estimator.canned.parsing_utils import regressor_parse_example_spec from tensorflow.python.estimator.estimator import Estimator +from tensorflow.python.estimator.estimator import VocabInfo +from tensorflow.python.estimator.estimator import WarmStartSettings from tensorflow.python.estimator.export import export_lib as export from tensorflow.python.estimator.exporter import Exporter from tensorflow.python.estimator.exporter import FinalExporter @@ -41,8 +43,6 @@ from tensorflow.python.estimator.run_config import RunConfig from tensorflow.python.estimator.training import EvalSpec from tensorflow.python.estimator.training import train_and_evaluate from tensorflow.python.estimator.training import TrainSpec -from tensorflow.python.estimator.warm_starting_util import VocabInfo -from tensorflow.python.estimator.warm_starting_util import WarmStartSettings from tensorflow.python.util.all_util import remove_undocumented diff --git a/tensorflow/python/estimator/estimator_test.py b/tensorflow/python/estimator/estimator_test.py index 7a0745b1d0d5ae932fa59be56a4952e82922a584..f4255091bf6c44916789a182e60e583171ad5e6b 100644 --- a/tensorflow/python/estimator/estimator_test.py +++ b/tensorflow/python/estimator/estimator_test.py @@ -48,6 +48,7 @@ from tensorflow.python.ops import check_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import lookup_ops +from tensorflow.python.ops import math_ops from tensorflow.python.ops import metrics as metrics_lib from tensorflow.python.ops import parsing_ops from tensorflow.python.ops import state_ops @@ -1268,10 +1269,10 @@ class EstimatorEvaluateTest(test.TestCase): _, _ = features, labels global_step = training.get_global_step() - image = array_ops.zeros([1, 3, 3, 1]) + image = array_ops.zeros([5, 3, 3, 1]) eval_metric_ops = { - 'image': (summary.image('image', image, max_outputs=1), - constant_op.constant(1)) + 'foo': (summary.image('image', image, max_outputs=3), + constant_op.constant(1)) } return model_fn_lib.EstimatorSpec( mode, @@ -1291,10 +1292,10 @@ class EstimatorEvaluateTest(test.TestCase): writer_cache.FileWriterCache.clear() # Get last evaluation Event written. - if check_eventfile_for_keyword('image', os.path.join(est.model_dir, - 'eval')): - return - self.fail('{} should be part of reported summaries.'.format('image')) + for key in ['foo/0', 'foo/1', 'foo/2']: + self.assertTrue( + check_eventfile_for_keyword(key, os.path.join(est.model_dir, 'eval')), + '{} should be part of reported summaries.'.format(key)) class EstimatorPredictTest(test.TestCase): @@ -1936,6 +1937,60 @@ class EstimatorExportTest(test.TestCase): # cleanup gfile.DeleteRecursively(tmpdir) + def test_export_savedmodel_tensor_features(self): + """Test that models accepting a single raw Tensor can be exported. + + See https://github.com/tensorflow/tensorflow/issues/11674 + + If the model_fn and receiver_fn accept raw tensors rather than dictionaries + as input, export_savedmodel should be okay with that, too. + + """ + + tmpdir = tempfile.mkdtemp() + + def _input_fn_tensor_features(): + t = array_ops.constant([1, 2, 3], dtype=dtypes.float32, shape=[1, 3]) + return (t, None) + + def _model_fn_tensor_features(features, labels, mode): + _ = labels + prediction = math_ops.matmul(features, features, transpose_b=True) + + return model_fn_lib.EstimatorSpec( + mode, + predictions=prediction, + loss=constant_op.constant(1.), + train_op=state_ops.assign_add(training.get_global_step(), 1), + export_outputs={ + 'test': export_output.PredictOutput({'prediction': prediction}) + }) + + def _serving_input_receiver_fn(): + feat = array_ops.placeholder(dtype=dtypes.float32) + return export.TensorServingInputReceiver( + features=feat, receiver_tensors=feat) + + est = estimator.Estimator(model_fn=_model_fn_tensor_features) + est.train(input_fn=_input_fn_tensor_features, steps=1) + + # Perform the export. + export_dir_base = os.path.join( + compat.as_bytes(tmpdir), compat.as_bytes('export')) + export_dir = est.export_savedmodel( + export_dir_base, _serving_input_receiver_fn) + + # Restore, to validate that the export was well-formed. + with ops.Graph().as_default() as graph: + with session.Session(graph=graph) as sess: + loader.load(sess, [tag_constants.SERVING], export_dir) + graph_ops = [x.name.lower() for x in graph.get_operations()] + self.assertTrue('const' in graph_ops) + self.assertTrue('matmul' in graph_ops) + + # Clean up. + gfile.DeleteRecursively(tmpdir) + def test_scaffold_is_used_for_saver(self): tmpdir = tempfile.mkdtemp() diff --git a/tensorflow/python/estimator/export/export.py b/tensorflow/python/estimator/export/export.py index 83251c79fc561e16ebddb638668b92b3c69b8af4..9206a4964b3b7a6e3cc1e0f9e965a197be78c4ba 100644 --- a/tensorflow/python/estimator/export/export.py +++ b/tensorflow/python/estimator/export/export.py @@ -21,17 +21,16 @@ from __future__ import print_function import collections import os -import time import six +from tensorflow.python.estimator import util 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 parsing_ops -from tensorflow.python.platform import gfile from tensorflow.python.platform import tf_logging as logging from tensorflow.python.saved_model import signature_constants from tensorflow.python.saved_model import signature_def_utils @@ -120,6 +119,62 @@ class ServingInputReceiver(collections.namedtuple( receiver_tensors_alternatives=receiver_tensors_alternatives) +@tf_export('estimator.export.TensorServingInputReceiver') +class TensorServingInputReceiver(collections.namedtuple( + 'TensorServingInputReceiver', + ['features', 'receiver_tensors', 'receiver_tensors_alternatives'])): + """A return type for a serving_input_receiver_fn. + + This is for use with models that expect a single `Tensor` or `SparseTensor` + as an input feature, as opposed to a dict of features. + + The normal `ServingInputReceiver` always returns a feature dict, even if it + contains only one entry, and so can be used only with models that accept such + a dict. For models that accept only a single raw feature, the + `serving_input_receiver_fn` provided to `Estimator.export_savedmodel()` should + return this `TensorServingInputReceiver` instead. See: + https://github.com/tensorflow/tensorflow/issues/11674 + + Note that the receiver_tensors and receiver_tensor_alternatives arguments + will be automatically converted to the dict representation in either case, + because the SavedModel format requires each input `Tensor` to have a name + (provided by the dict key). + + The expected return values are: + features: A single `Tensor` or `SparseTensor`, representing the feature + to be passed to the model. + receiver_tensors: a `Tensor`, or a dict of string to `Tensor`, specifying + input nodes where this receiver expects to be fed by default. Typically, + this is a single placeholder expecting serialized `tf.Example` protos. + receiver_tensors_alternatives: a dict of string to additional + groups of receiver tensors, each of which may be a `Tensor` or a dict of + string to `Tensor`. These named receiver tensor alternatives generate + additional serving signatures, which may be used to feed inputs at + different points within the input receiver subgraph. A typical usage is + to allow feeding raw feature `Tensor`s *downstream* of the + tf.parse_example() op. Defaults to None. + """ + + def __new__(cls, features, receiver_tensors, + receiver_tensors_alternatives=None): + if features is None: + raise ValueError('features must be defined.') + if not (isinstance(features, ops.Tensor) + or isinstance(features, sparse_tensor.SparseTensor)): + raise ValueError('feature must be a Tensor or SparseTensor.') + + receiver = ServingInputReceiver( + features=features, + receiver_tensors=receiver_tensors, + receiver_tensors_alternatives=receiver_tensors_alternatives) + + return super(TensorServingInputReceiver, cls).__new__( + cls, + features=receiver.features[_SINGLE_FEATURE_DEFAULT_NAME], + receiver_tensors=receiver.receiver_tensors, + receiver_tensors_alternatives=receiver.receiver_tensors_alternatives) + + @tf_export('estimator.export.build_parsing_serving_input_receiver_fn') def build_parsing_serving_input_receiver_fn(feature_spec, default_batch_size=None): @@ -273,13 +328,6 @@ def _log_signature_report(signature_def_map, excluded_signatures): logging.warn('Export includes no default signature!') -# When we create a timestamped directory, there is a small chance that the -# directory already exists because another worker is also writing exports. -# In this case we just wait one second to get a new timestamp and try again. -# If this fails several times in a row, then something is seriously wrong. -MAX_DIRECTORY_CREATION_ATTEMPTS = 10 - - def get_timestamped_export_dir(export_dir_base): """Builds a path to a new subdirectory within the base directory. @@ -298,25 +346,7 @@ def get_timestamped_export_dir(export_dir_base): RuntimeError: if repeated attempts fail to obtain a unique timestamped directory name. """ - attempts = 0 - while attempts < MAX_DIRECTORY_CREATION_ATTEMPTS: - export_timestamp = int(time.time()) - - export_dir = os.path.join( - compat.as_bytes(export_dir_base), - compat.as_bytes(str(export_timestamp))) - if not gfile.Exists(export_dir): - # Collisions are still possible (though extremely unlikely): this - # directory is not actually created yet, but it will be almost - # instantly on return from this function. - return export_dir - time.sleep(1) - attempts += 1 - logging.warn( - 'Export directory {} already exists; retrying (attempt {}/{})'.format( - export_dir, attempts, MAX_DIRECTORY_CREATION_ATTEMPTS)) - raise RuntimeError('Failed to obtain a unique export directory name after ' - '{} attempts.'.format(MAX_DIRECTORY_CREATION_ATTEMPTS)) + return util.get_timestamped_dir(export_dir_base) def get_temp_export_dir(timestamped_export_dir): diff --git a/tensorflow/python/estimator/export/export_lib.py b/tensorflow/python/estimator/export/export_lib.py index 99cd81d678bc04e7ed52de721a1fdf799c116795..226fc97fd3a3aefe61c4b88088873ce7489168c7 100644 --- a/tensorflow/python/estimator/export/export_lib.py +++ b/tensorflow/python/estimator/export/export_lib.py @@ -22,6 +22,7 @@ from __future__ import print_function from tensorflow.python.estimator.export.export import build_parsing_serving_input_receiver_fn from tensorflow.python.estimator.export.export import build_raw_serving_input_receiver_fn from tensorflow.python.estimator.export.export import ServingInputReceiver +from tensorflow.python.estimator.export.export import TensorServingInputReceiver from tensorflow.python.estimator.export.export_output import ClassificationOutput from tensorflow.python.estimator.export.export_output import ExportOutput from tensorflow.python.estimator.export.export_output import PredictOutput @@ -34,6 +35,7 @@ _allowed_symbols = [ 'build_parsing_serving_input_receiver_fn', 'build_raw_serving_input_receiver_fn', 'ServingInputReceiver', + 'TensorServingInputReceiver', 'ClassificationOutput', 'ExportOutput', 'PredictOutput', diff --git a/tensorflow/python/estimator/export/export_test.py b/tensorflow/python/estimator/export/export_test.py index 8442bf04accbd0bc15f5958069bf3060debd42bc..eb9688bc973666554b6057f5f546b9a2d18461d6 100644 --- a/tensorflow/python/estimator/export/export_test.py +++ b/tensorflow/python/estimator/export/export_test.py @@ -385,5 +385,67 @@ class ExportTest(test_util.TensorFlowTestCase): self.assertTrue(int(time_2) < int(time_3)) +class TensorServingReceiverTest(test_util.TensorFlowTestCase): + + def test_tensor_serving_input_receiver_constructor(self): + features = constant_op.constant([0]) + receiver_tensors = { + "example0": array_ops.placeholder(dtypes.string, name="example0"), + u"example1": array_ops.placeholder(dtypes.string, name="example1"), + } + r = export.TensorServingInputReceiver(features, receiver_tensors) + self.assertTrue(isinstance(r.features, ops.Tensor)) + self.assertTrue(isinstance(r.receiver_tensors, dict)) + + def test_tensor_serving_input_receiver_sparse(self): + features = sparse_tensor.SparseTensor( + indices=[[0, 0]], values=[1], dense_shape=[1, 1]) + receiver_tensors = { + "example0": array_ops.placeholder(dtypes.string, name="example0"), + u"example1": array_ops.placeholder(dtypes.string, name="example1"), + } + r = export.TensorServingInputReceiver(features, receiver_tensors) + self.assertTrue(isinstance(r.features, sparse_tensor.SparseTensor)) + self.assertTrue(isinstance(r.receiver_tensors, dict)) + + def test_serving_input_receiver_features_invalid(self): + receiver_tensors = { + "example0": array_ops.placeholder(dtypes.string, name="example0"), + u"example1": array_ops.placeholder(dtypes.string, name="example1"), + } + + with self.assertRaisesRegexp(ValueError, "features must be defined"): + export.TensorServingInputReceiver( + features=None, + receiver_tensors=receiver_tensors) + + with self.assertRaisesRegexp(ValueError, "feature must be a Tensor"): + export.TensorServingInputReceiver( + features={"1": constant_op.constant([1])}, + receiver_tensors=receiver_tensors) + + def test_serving_input_receiver_receiver_tensors_invalid(self): + features = constant_op.constant([0]) + + with self.assertRaisesRegexp( + ValueError, "receiver_tensors must be defined"): + export.TensorServingInputReceiver( + features=features, + receiver_tensors=None) + + with self.assertRaisesRegexp( + ValueError, "receiver_tensors keys must be strings"): + export.TensorServingInputReceiver( + features=features, + receiver_tensors={ + 1: array_ops.placeholder(dtypes.string, name="example0")}) + + with self.assertRaisesRegexp( + ValueError, "receiver_tensor example1 must be a Tensor"): + export.TensorServingInputReceiver( + features=features, + receiver_tensors={"example1": [1]}) + + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/estimator/replicate_model_fn.py b/tensorflow/python/estimator/replicate_model_fn.py new file mode 100644 index 0000000000000000000000000000000000000000..144d89abf3444062927d9261301fe50f4a63b280 --- /dev/null +++ b/tensorflow/python/estimator/replicate_model_fn.py @@ -0,0 +1,824 @@ +# 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. +# ============================================================================== +"""Utilities to replicate model_fn's over local GPUs. + +This file contains util that allow to replicate `Estimator.model_fn` over +GPUs. Replicated version of a `model_fn` is returned that can subsequently +be used with `Estimator`. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from collections import defaultdict +from contextlib import contextmanager +import copy + +import six + +from tensorflow.core.framework import node_def_pb2 +from tensorflow.python.client import device_lib +from tensorflow.python.estimator import model_fn as model_fn_lib +from tensorflow.python.estimator import util +from tensorflow.python.estimator.export import export_output as export_output_lib +from tensorflow.python.framework import device as framework_device +from tensorflow.python.framework import ops as ops_lib +from tensorflow.python.framework import sparse_tensor +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 sparse_ops +from tensorflow.python.ops import state_ops +from tensorflow.python.ops import variable_scope +from tensorflow.python.ops.losses import losses +from tensorflow.python.platform import tf_logging +from tensorflow.python.training import device_setter as device_setter_lib +from tensorflow.python.training import optimizer as optimizer_lib + + +def _replicate_model_fn(model_fn, + devices=None): + """Replicate `Estimator.model_fn` over GPUs. + + The given `model_fn` specifies a single forward pass of a model. To replicate + such a model over GPUs, each GPU gets its own instance of the forward pass + (a.k.a. a tower). The input features and labels get sharded into the chunks + that correspond to the number of GPUs. Each tower computes a loss based + on its input. For each such loss, gradients are computed. After that, the + available losses are aggregated to form aggregated loss. Available + gradients are summed. Then, they update weights using the specified + optimizer. + + If `devices` are `None`, then all available GPUs are going to be used for + replication. If no GPUs are available, then the model is going to be + placed on the CPU. + + Two modes of local replication over available GPUs are supported: + 1) If exactly 1 GPU is detected, then variables and operations are placed + onto the GPU. + 2) If more than 1 GPU is detected, then variables are going to be placed on + the CPU. Replicas of operations are placed on each individual GPU. + + Here is an example of how one might use their `model_fn` to run over GPUs: + ```python + ... + def model_fn(...): # See `model_fn` in `Estimator`. + loss = ... + optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001) + optimizer = tf.contrib.estimator._TowerOptimizer(optimizer) + if mode == tf.estimator.ModeKeys.TRAIN: + # See the section below on `EstimatorSpec.train_op`. + return EstimatorSpec(mode=mode, loss=loss, + train_op=optimizer.minimize(loss)) + + # No change for `ModeKeys.EVAL` or `ModeKeys.PREDICT`. + return EstimatorSpec(...) + ... + classifier = tf.estimator.Estimator( + model_fn=tf.contrib.estimator.replicate_model_fn(model_fn)) + ``` + + Please see `DNNClassifierIntegrationTest` for an example with a canned + Estimator. + + On `EstimatorSpec.train_op`: + `model_fn` returns `EstimatorSpec.train_op` for + `tf.estimator.GraphKeys.TRAIN`. It is typically derived using an optimizer. + Towers are expected to populate it in the same way. Gradients from all towers + are reduced and applied in the last tower. To achieve that in the case of + multiple towers, `_TowerOptimizer` needs to be used. See `_TowerOptimizer`. + + On sharding input features and labels: + Input features and labels are split for consumption by each tower. They are + split across the dimension 0. Features and labels need to be batch major. + + On reduction algorithms: + Certain algorithms were chosen for aggregating results of computations on + multiple towers: + - Losses from all towers are reduced according to `loss_reduction` argument + to TowerOptimizer.. + - Gradients from all towers are reduced according to the `loss_reduction` + for each trainable variable. + - `eval_metrics_ops` are reduced per metric using `reduce_mean`. + - `EstimatorSpec.predictions` and `EstimatorSpec.export_outputs` are + reduced using concatenation. + - For all other fields of `EstimatorSpec` the values of the first tower + are taken. + + On distribution of variables: + Variables are not duplicated between towers. Instead, they are placed on a + single device as defined above and shared across towers. + + On overhead: + If only one device is specified, then aggregation of loss and gradients + doesn't happen. Replication consists of placing `model_fn` onto the + specified device. + + On current limitations: + - `predictions` are not supported for `ModeKeys.EVAL`. They are required + for `tf.contrib.estimator.add_metrics`. + + Args: + model_fn: `model_fn` as defined in `Estimator`. See the section above about + the train_op argument of `EstimatorSpec`. + devices: Optional list of devices to replicate the model across. This + argument can be used to replice only on the subset of available GPUs. + If `None`, then all available GPUs are going to be used for replication. + If no GPUs are available, then the model is going to be placed on the CPU. + + Returns: + A replicated version of the supplied `model_fn`. Returned function that + conforms to the requirements of `Estimator`'s `model_fn` and can be used + instead of the supplied `model_fn`. + """ + return _replicate_model_fn_with_mode( + model_fn, + devices, + # TODO(isaprykin): Query the system configuration to choose modes other + # than `SHARED_LOCAL_PARAMETER_SERVER`, even though it is often + # appropriate. + mode=_VariableDistributionMode.SHARED_LOCAL_PARAMETER_SERVER) + + +class _VariableDistributionMode(object): + """Modes for variable distribution used for forcing a particular one. + + Forcing a mode is meant for performance experimentation purposes rather than + for general use cases. + """ + + SHARED_LOCAL_PARAMETER_SERVER = 1 + """Variables are placed on a single device and shared across all devices. + + Two ways to achieve this distribution over available GPUs are supported: + 1) If exactly 1 GPU is detected, then variables and operations are placed + onto GPU. + 2) If more than 1 GPU is detected, then variables are going to be placed on + the CPU. Replicas of operations are placed on each individual GPU. + """ + + SHARED_ROUND_ROBIN = 2 + """Variables are placed on all devices in a round-robin fashion. + + Every subsequent variable is placed on the next device. There is only one + copy of each variable that is shared across all devices. + """ + + +def _replicate_model_fn_with_mode( + model_fn, + devices=None, + mode=_VariableDistributionMode.SHARED_LOCAL_PARAMETER_SERVER): + """A version of `replicate_model_fn` that allows to specify a `mode`.""" + if not devices: + devices = _get_local_devices('GPU') or _get_local_devices('CPU') + + is_a_single_gpu_case = len(devices) == 1 and 'GPU' in devices[0].upper() + consolidation_device = devices[0] if is_a_single_gpu_case else '/CPU:0' + + ps_devices = [consolidation_device] + if mode == _VariableDistributionMode.SHARED_ROUND_ROBIN: + ps_devices = devices + + tf_logging.info('Replicating the `model_fn` across {}. Variables are going ' + 'to be placed on {}. Consolidation device is going to be {}.' + .format(devices, ps_devices, consolidation_device)) + + def single_device_model_fn(features, labels, mode, params=None, config=None): + """`model_fn` on a single device without reduction overhead.""" + return _get_loss_towers( + model_fn=model_fn, + mode=mode, + features=[features], + labels=[labels], + params=params, + config=config, + devices=devices, + local_ps_devices=ps_devices)[0] # One device, so one spec is out. + + def replicated_model_fn(features, labels, mode, params=None, config=None): + """Replicated version of `model_fn` to be used instead.""" + feature_shards, label_shards = _split_batch( + features, labels, len(devices), device=consolidation_device) + tower_specs = _get_loss_towers( + model_fn=model_fn, + mode=mode, + features=feature_shards, + labels=label_shards, + params=params, + config=config, + devices=devices, + local_ps_devices=ps_devices) + + if mode == model_fn_lib.ModeKeys.TRAIN: + train_op = _minimize_towers(tower_specs) + return _train_spec( + tower_specs, train_op, aggregation_device=consolidation_device) + elif mode == model_fn_lib.ModeKeys.EVAL: + return _eval_spec(tower_specs, aggregation_device=consolidation_device) + elif mode == model_fn_lib.ModeKeys.PREDICT: + return _predict_spec(tower_specs, aggregation_device=consolidation_device) + + if len(devices) == 1: + return single_device_model_fn + else: + return replicated_model_fn + + +class _TowerOptimizer(optimizer_lib.Optimizer): + """Gathers gradients from all towers and reduces them in the last one.""" + + COLLECTION_FOR_GRAPH_STATES = 'replicate_model_fn_graph_states' + + def __init__(self, optimizer_or_optimizer_fn, + loss_reduction=losses.Reduction.SUM_OVER_BATCH_SIZE): + """Wrap an existing optimizer for gathering gradients across towers. + + Each invocation of model_fn has to call the same optimizers in the same + order. + + Multiple optimizers that use the same or different losses are supported. + + If _TowerOptimizer is used but `replicate_model_fn` isn't, then no + aggregation will happen. All calls will simply be forwarded to the + underlying optimizer. The behavior is similar if there is only one tower. + + If _TowerOptimizer is used together with SyncReplicasOptimizer that wraps + the user's optimizer, then it's the SyncReplicasOptimizer that needs to be + wrapped with _TowerOptimizer. + + Args: + optimizer_or_optimizer_fn: an instance of optimizer to wrap. That + instance is going to be used for optimizer-specific logic. This can + also be a no-argument function that returns such an optimizer instance. + loss_reduction: controls whether losses are summed or averaged. + """ + self._optimizer_or_optimizer_fn = optimizer_or_optimizer_fn + self._loss_reduction = loss_reduction + + @staticmethod + def has_been_used(): + return _TowerOptimizer._graph_state().has_tower_optimizer_been_used + + def get_slot(self, *args, **kwargs): + return self._get_optimizer().get_slot(*args, **kwargs) + + def get_slot_names(self, *args, **kwargs): + return self._get_optimizer().get_slot_names(*args, **kwargs) + + def get_name(self, *args, **kwargs): + return self._get_optimizer().get_name(*args, **kwargs) + + def variables(self, *args, **kwargs): + return self._get_optimizer().variables(*args, **kwargs) + + def compute_gradients(self, loss, *args, **kwargs): + """Compute gradients, but first, if needed, scale the loss.""" + _TowerOptimizer._graph_state().set_loss_reduction(self._loss_reduction) + loss = _scale_loss(loss, + self._loss_reduction, + self._graph_state().number_of_towers) + return self._get_optimizer().compute_gradients(loss, *args, **kwargs) + + def apply_gradients(self, grads_and_vars, global_step=None, **kwargs): + """Collect gradients updates to apply them with the last tower.""" + if self._graph_state().number_of_towers == 1: + # Avoid the overhead of reduction if there's only one tower. + # + # There assumed to be only one tower if aggregation-related methods were + # not called by `_get_loss_towers`, for example if the model_fn uses + # TowerEstimator, but `replicate_model_fn` isn't used. + return self._get_optimizer().apply_gradients(grads_and_vars, global_step, + **kwargs) + + self._graph_state().collect_gradients(grads_and_vars) + + if not self._graph_state().is_the_last_tower: + with ops_lib.control_dependencies(_extract_tensors(grads_and_vars)): + return self._construct_no_op_train_op() + else: + # Gradients need to be gathered and applied in the scope of the first + # tower, so that the tensors are accessible via names without prefixes. + var_scope, name_scope = self._graph_state().scopes_of_the_first_tower + with variable_scope.variable_scope(var_scope): + with ops_lib.name_scope(name_scope): + return self._apply_gathered_gradients(global_step, **kwargs) + + def _apply_gathered_gradients(self, global_step, **kwargs): + graph_state = self._graph_state() + optimizer = self._get_optimizer() + + grad_lists = {} + for grad, var in graph_state.get_latest_gradients_from_all_towers(): + if grad is not None: + grad_lists.setdefault(var, []).append(grad) + + aggregated_grads = [] + with ops_lib.name_scope('gradient_aggregating'): + for var, grads in six.iteritems(grad_lists): + grad = _compute_sum_on_device(grads, var.device) + aggregated_grads.append((grad, var)) + return optimizer.apply_gradients( + aggregated_grads, global_step=global_step, **kwargs) + + def _get_optimizer(self): + if callable(self._optimizer_or_optimizer_fn): + # If optimizer is given as a function then we need to wait till we are + # under the right graph context before constructing it. That's why the + # optimizer is constructed in _get_optimizer() rather than __init__(). + self._optimizer_or_optimizer_fn = self._optimizer_or_optimizer_fn() + self._graph_state().has_tower_optimizer_been_used = True + return self._optimizer_or_optimizer_fn + + def _construct_no_op_train_op(self): + return control_flow_ops.no_op(name='train_op_placeholder') + + @staticmethod + def _graph_state(): + graph_states = ops_lib.get_default_graph().get_collection_ref( + _TowerOptimizer.COLLECTION_FOR_GRAPH_STATES) + if not graph_states: + graph_states.append(_TowerOptimizer._PerGraphState()) + return graph_states[-1] + + @staticmethod + def _did_towers_have_same_optimizer_calls(): + graph_state = _TowerOptimizer._graph_state() + return graph_state.did_towers_have_same_optimizer_calls() + + @staticmethod + def _clear_graph_state(): + # Clearing the Graph collection will prevent _PerGraphState from being + # serialized. + ops_lib.get_default_graph().clear_collection( + _TowerOptimizer.COLLECTION_FOR_GRAPH_STATES) + + class _PerGraphState(object): + """Gradient reduction related state of a Tensorflow graph.""" + + def __init__(self): + self._collected_grads_and_vars = defaultdict(list) + self._current_tower_index = 0 + self._number_of_towers = 1 + self._loss_reduction = None + # Scopes of the first tower that don't have a prefix: + self._variable_scope = None + self._name_scope = None + # If needed, alert that _TowerOptimizer needs to be used with model_fn. + self._has_tower_optimizer_been_used = False + + def collect_gradients(self, grads_and_vars): + self._collected_grads_and_vars[self._current_tower_index].append( + grads_and_vars) + + def get_latest_gradients_from_all_towers(self): + """Get gradients across towers for the last called optimizer.""" + grads_and_vars = [] + index_of_last_gradients = len( + self._collected_grads_and_vars[self._current_tower_index]) - 1 + for tower_id in range(self._current_tower_index + 1): + grads_and_vars.extend( + self._collected_grads_and_vars[tower_id][index_of_last_gradients]) + return grads_and_vars + + def set_number_of_towers(self, number_of_towers): + self._number_of_towers = number_of_towers + + def set_loss_reduction(self, loss_reduction): + self._loss_reduction = loss_reduction + + @contextmanager + def tower(self, tower_id, var_scope, name_scope): + if tower_id == 0: + self._variable_scope = var_scope + self._name_scope = name_scope + self._current_tower_index = tower_id + yield + + @property + def scopes_of_the_first_tower(self): + return self._variable_scope, self._name_scope + + @property + def is_the_last_tower(self): + return self._current_tower_index == (self._number_of_towers - 1) + + @property + def number_of_towers(self): + return self._number_of_towers + + @property + def loss_reduction(self): + return self._loss_reduction + + @property + def has_tower_optimizer_been_used(self): + return self._has_tower_optimizer_been_used + + @has_tower_optimizer_been_used.setter + def has_tower_optimizer_been_used(self, value): + self._has_tower_optimizer_been_used = value + + def did_towers_have_same_optimizer_calls(self): + total_number_of_grads = sum([ + len(grads) + for _, grads in six.iteritems(self._collected_grads_and_vars) + ]) + return total_number_of_grads % self._number_of_towers == 0 + + +def _get_local_devices(device_type): + local_device_protos = device_lib.list_local_devices() + return [ + device.name + for device in local_device_protos + if device.device_type == device_type + ] + + +def _split_batch(features, labels, number_of_shards, device): + """Split input features and labes into batches.""" + + def ensure_divisible_by_shards(sequence): + batch_size = ops_lib.convert_to_tensor(sequence).get_shape()[0] + if batch_size % number_of_shards != 0: + raise ValueError( + 'Batch size {} needs to be divisible by the number of GPUs, which ' + 'is {}.'.format(batch_size, number_of_shards)) + + def split_dictionary(dictionary): + """Split a dictionary into shards.""" + shards = [{} for _ in range(number_of_shards)] + for name, tensor in six.iteritems(dictionary): + if isinstance(tensor, sparse_tensor.SparseTensor): + for i, shard in enumerate( + sparse_ops.sparse_split( + sp_input=tensor, num_split=number_of_shards, axis=0)): + shards[i][name] = shard + else: + ensure_divisible_by_shards(tensor) + for i, shard in enumerate(array_ops.split(tensor, number_of_shards)): + shards[i][name] = shard + return shards + + with ops_lib.name_scope('split_inputs'): + with ops_lib.device(device): + if isinstance(features, dict): + feature_shards = split_dictionary(features) + else: + ensure_divisible_by_shards(features) + feature_shards = array_ops.split(features, number_of_shards) + + if labels is None: + label_shards = None + elif isinstance(labels, dict): + label_shards = split_dictionary(labels) + else: + ensure_divisible_by_shards(labels) + label_shards = array_ops.split(labels, number_of_shards) + return feature_shards, label_shards + + +_DEFAULT_NAME_SCOPE_PATTERN = 'tower_{}' + + +def _get_loss_towers(model_fn, + mode, + features, + labels, + params, + config, + devices, + local_ps_devices, + name_scope_pattern=_DEFAULT_NAME_SCOPE_PATTERN): + """Replicate the loss computation across devices.""" + tower_specs = [] + + model_fn_args = util.fn_args(model_fn) + optional_params = {} + if 'params' in model_fn_args: + optional_params['params'] = copy.deepcopy(params) + if 'config' in model_fn_args: + optional_params['config'] = copy.deepcopy(config) + + # pylint: disable=protected-access + round_robin_strategy = device_setter_lib._RoundRobinStrategy( + num_tasks=len(local_ps_devices)) + _TowerOptimizer._graph_state().set_number_of_towers(len(devices)) + + for i, device in enumerate(devices): + is_the_first_tower = (i == 0) + + device_setter = _local_device_setter( + worker_device=device, + ps_devices=local_ps_devices, + ps_strategy=round_robin_strategy) + + # We would like to preserve the names of the variables and ops that the user + # might be relying on. Names without a prefix are going to resolve to + # variables and ops of the first tower. + name_scope = name_scope_pattern + if is_the_first_tower: + name_scope = '' + + with variable_scope.variable_scope( + '', reuse=not is_the_first_tower) as var_scope: + with ops_lib.name_scope(name_scope.format(i)) as name_scope: + with _TowerOptimizer._graph_state().tower( + tower_id=i, var_scope=var_scope, name_scope=name_scope): + with ops_lib.device(device_setter): + labels_shard = None + if labels: + labels_shard = labels[i] + + tower_spec = model_fn( + mode=mode, + features=features[i], + labels=labels_shard, + **optional_params) + + if (tower_spec.train_op is not None and len(devices) > 1 and + not _TowerOptimizer.has_been_used()): + raise ValueError('Please wrap optimizers with _TowerOptimizer' + ' in order to use replicate_model_fn with' + ' multiple `devices`.') + + # Scaling the loss here doesn't actually affect gradients. Another + # instance of scaling happens inside the _TowerOptimizer. + tower_spec = _scale_tower_loss( + tower_spec, + _TowerOptimizer._graph_state().loss_reduction, + number_of_towers=len(devices)) + tower_specs.append(tower_spec) + + if not _TowerOptimizer._did_towers_have_same_optimizer_calls(): + raise ValueError('Each invocation of model_fn was supposed to make the same' + ' optimizer calls.') + _TowerOptimizer._clear_graph_state() + # pylint: enable=protected-access + return tower_specs + + +def _local_device_setter(worker_device, ps_devices, ps_strategy): + """A device setter that puts distributes Var/Ops to PS/workers.""" + ps_ops = ['Variable', 'VariableV2', 'VarHandleOp'] + + def local_device_chooser(op): + current_device = framework_device.DeviceSpec.from_string(op.device or '') + + node_def = op if isinstance(op, node_def_pb2.NodeDef) else op.node_def + if node_def.op in ps_ops: + ps_device_spec = framework_device.DeviceSpec.from_string( + '{}'.format(ps_devices[ps_strategy(op)])) + + ps_device_spec.merge_from(current_device) + return ps_device_spec.to_string() + else: + worker_device_spec = framework_device.DeviceSpec.from_string( + worker_device or '') + worker_device_spec.merge_from(current_device) + return worker_device_spec.to_string() + + return local_device_chooser + + +def _scale_tower_loss(tower_spec, loss_reduction, number_of_towers): + """Produce an EstimatorSpec with approproriately scaled loss.""" + if tower_spec.loss is None: + return tower_spec + + estimator_spec = _asdict(tower_spec) + estimator_spec['loss'] = _scale_loss( + tower_spec.loss, + loss_reduction, + number_of_towers, + reduced_loss_name='averaged_loss') + return model_fn_lib.EstimatorSpec(**estimator_spec) + + +def _scale_loss(loss, loss_reduction, number_of_towers, reduced_loss_name=None): + """If needed, scale down the loss for averaging loss by summing.""" + if loss is None: + return None + if number_of_towers == 1: + return loss + + if loss_reduction == losses.Reduction.NONE: + raise ValueError('Tower losses need to be reduced in some way, yet {} ' + 'reduction is specified.'.format(loss_reduction)) + + if loss_reduction != losses.Reduction.SUM: + return math_ops.div(loss, 1.0 * number_of_towers, name=reduced_loss_name) + else: + return loss + + +def _minimize_towers(tower_specs): + """`train_op` of the last tower applies aggregated gradients.""" + return tower_specs[-1].train_op + + +def _compute_sum_on_device(values, device, name=None): + with ops_lib.device(device): + if isinstance(values[0], ops_lib.IndexedSlices): + if name: + raise ValueError('The name {} is not expected to be given to ' + 'IndexedSlices {}'.format(name, values)) + + values_concat = array_ops.concat([v.values for v in values], axis=0) + indices_concat = array_ops.concat([v.indices for v in values], axis=0) + return ops_lib.IndexedSlices(values_concat, indices_concat, + values[0].dense_shape) + else: + return math_ops.add_n(values, name=name) + + +def _train_spec(tower_specs, + train_op, + aggregation_device, + aggregated_loss_name='loss'): + """Populate replicated EstimatorSpec for `GraphKeys.TRAIN`.""" + # Spec of the last tower is used as the template for the final spec, because + # some `EstimatorSpec.training_hooks` rely on calls made in model_fn. For + # example, `SyncReplicasOptimizerHook` validates the + # `SyncReplicasOptimizer.apply_gradients` call. `TowerEstimator` makes that + # call only in the last tower. + estimator_spec = _asdict(tower_specs[-1]) + estimator_spec['mode'] = model_fn_lib.ModeKeys.TRAIN + estimator_spec['train_op'] = train_op + estimator_spec['loss'] = _compute_sum_on_device( + [spec.loss for spec in tower_specs], aggregation_device, + aggregated_loss_name) + return model_fn_lib.EstimatorSpec(**estimator_spec) + + +def _eval_spec(tower_specs, aggregation_device, aggregated_loss_name='loss'): + """Populate replicated EstimatorSpec for `GraphKeys.EVAL`.""" + estimator_spec = _asdict(tower_specs[0]) + estimator_spec['mode'] = model_fn_lib.ModeKeys.EVAL + estimator_spec['loss'] = _compute_sum_on_device( + [spec.loss for spec in tower_specs], aggregation_device, + aggregated_loss_name) + + update_ops = [] + for tower_spec in tower_specs: + for name, (_, update_op) in six.iteritems(tower_spec.eval_metric_ops): + update_ops.append(update_op) + + with ops_lib.control_dependencies(update_ops): + reduced_update_op = _reduce_metric_variables(len(tower_specs)) + + eval_metric_ops = {} + for name, (metric_tensor, _) in six.iteritems(tower_specs[0].eval_metric_ops): + eval_metric_ops[name] = (metric_tensor, reduced_update_op) + estimator_spec['eval_metric_ops'] = eval_metric_ops + return model_fn_lib.EstimatorSpec(**estimator_spec) + + +def _reduce_metric_variables(number_of_towers): + """Aggregate local variables used in metrics into the first tower.""" + if number_of_towers == 1: + return control_flow_ops.no_op(name='no_eval_metric_reduction') + + metric_variables = ops_lib.get_collection(ops_lib.GraphKeys.METRIC_VARIABLES) + variables_per_tower = len(metric_variables) // number_of_towers + + if len(metric_variables) % number_of_towers != 0: + raise ValueError( + 'Different `EstimatorSpec.eval_metric_ops` across `model_fn()` calls.' + ' Expected {} local variables, but got {} instead.'.format( + variables_per_tower * number_of_towers, len(metric_variables))) + + # `metric_variables` has the size of `variables_per_tower` x + # number_of_towers. Each tower is produced by calling the same model_fn. + # First `variables_per_tower` correspond to the first tower. Each such + # variable has an replica at the `(variables_per_tower * i)` position, where + # `i` is `[1.. number_of_towers]`. We are going to add values from replicas + # to each variable of the first tower. We then zero out replica values, so + # that `_reduce_metric_variables` operation is idempotent. If a metric + # is then computed based on local variables from the first tower, then the + # resulting metric is an estimate for all `number_of_towers` towers. + ops = [] + for i in range(0, variables_per_tower): + next_replica_id = i + variables_per_tower + replicas = [ + metric_variables[replica_id] + for replica_id in range(next_replica_id, len(metric_variables), + variables_per_tower) + ] # `replicas` doesn't contain the first-tower variable. + + reduce_op = state_ops.assign_add(metric_variables[i], + math_ops.add_n(replicas)) + + with ops_lib.control_dependencies([reduce_op]): + for replica in replicas: + zeros_for_replica = array_ops.zeros( + array_ops.shape(replica), dtype=replica.dtype) + zero_out_replica_op = state_ops.assign(replica, zeros_for_replica) + ops.append(zero_out_replica_op) + + return control_flow_ops.group(*ops) + + +def _predict_spec(tower_specs, aggregation_device): + """Populate replicated EstimatorSpec for `GraphKeys.PREDICT`.""" + estimator_spec = _asdict(tower_specs[0]) + estimator_spec['mode'] = model_fn_lib.ModeKeys.PREDICT + + with ops_lib.device(aggregation_device): + estimator_spec['predictions'] = _concat_tensor_dicts( + *[tower_spec.predictions for tower_spec in tower_specs]) + + export_outputs_dict = _dict_concat( + *[tower_spec.export_outputs for tower_spec in tower_specs]) + + export_outputs = {} + for name, export_output_list in six.iteritems(export_outputs_dict): + if isinstance(export_output_list[0], export_output_lib.PredictOutput): + export_outputs[name] = export_output_lib.PredictOutput( + outputs=_concat_tensor_dicts(*[ + export_output.outputs for export_output in export_output_list + ])) + elif isinstance(export_output_list[0], + export_output_lib.RegressionOutput): + export_outputs[name] = export_output_lib.RegressionOutput( + value=array_ops.concat( + [export_output.value for export_output in export_output_list], + axis=0)) + elif isinstance(export_output_list[0], + export_output_lib.ClassificationOutput): + scores = None + if export_output_list[0].scores is not None: + scores = array_ops.concat( + [export_output.scores for export_output in export_output_list], + axis=0) + + classes = None + if export_output_list[0].classes is not None: + classes = array_ops.stack( + [export_output.classes for export_output in export_output_list], + axis=0) + + export_outputs[name] = export_output_lib.ClassificationOutput( + scores=scores, classes=classes) + + estimator_spec['export_outputs'] = export_outputs + return model_fn_lib.EstimatorSpec(**estimator_spec) + + +def _concat_tensor_dicts(*tensor_dicts): + return { + name: array_ops.concat(tensors, axis=0, name=name) + for name, tensors in six.iteritems(_dict_concat(*tensor_dicts)) + } + + +def _extract_tensors(tensors_and_vars): + tensors = [] + for tensor_and_var in tensors_and_vars: + tensor, _ = tensor_and_var + if isinstance(tensor, ops_lib.IndexedSlices): + tensors.append(tensor.values) + elif tensor is not None: + tensors.append(tensor) + return tensors + + +def _dict_concat(*dicts): + list_dict = {} + for d in dicts: + if d is None: + continue + + for k, v in six.iteritems(d): + list_dict.setdefault(k, []).append(v) + return list_dict + + +def _asdict(namedtuple): + """Returns a namedtuple as a dictionary. + + This is required because `_asdict()` in Python 3.x.x is broken in classes + that inherit from `collections.namedtuple`. See + https://bugs.python.org/issue24931 for more details. + + Args: + namedtuple: An object that inherits from `collections.namedtuple`. + + Returns: + A dictionary version of the tuple. + """ + return {k: getattr(namedtuple, k) for k in namedtuple._fields} diff --git a/tensorflow/python/estimator/replicate_model_fn_test.py b/tensorflow/python/estimator/replicate_model_fn_test.py new file mode 100644 index 0000000000000000000000000000000000000000..ad1f9c02b92d7b1ce929494f4b6fbf636762a7fd --- /dev/null +++ b/tensorflow/python/estimator/replicate_model_fn_test.py @@ -0,0 +1,1739 @@ +# 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 utilities that replicate `Estimator.model_fn` over GPUs.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import re +import shutil +import tempfile +import numpy as np +import six + +from tensorflow.python.estimator import estimator as estimator_lib +from tensorflow.python.estimator import model_fn as model_fn_lib +from tensorflow.python.estimator import replicate_model_fn +from tensorflow.python.estimator.canned import dnn +from tensorflow.python.estimator.canned import optimizers +from tensorflow.python.estimator.canned import prediction_keys +from tensorflow.python.estimator.export import export +from tensorflow.python.estimator.export import export_output +from tensorflow.python.estimator.inputs import numpy_io +from tensorflow.python.feature_column import feature_column +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops as ops_lib +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 losses +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import metrics as metrics_lib +from tensorflow.python.ops import variable_scope +from tensorflow.python.ops import variables +from tensorflow.python.ops.losses import losses +from tensorflow.python.platform import gfile +from tensorflow.python.platform import test +from tensorflow.python.saved_model import signature_constants +from tensorflow.python.summary.writer import writer_cache +from tensorflow.python.training import adam +from tensorflow.python.training import device_setter +from tensorflow.python.training import gradient_descent +from tensorflow.python.training import training + + +# TODO(isaprykin): Parametrize all the tests on +# replicate_model_fn._VariableDistributionMode when it's supported. +class DNNClassifierIntegrationTest(test_util.TensorFlowTestCase): + + def setUp(self): + self._model_dir = tempfile.mkdtemp() + + def test_complete_flow_with_public_version(self): + return self._complete_flow_with_mode(mode=None) + + def test_complete_flow_with_mode_local_ps_server(self): + return self._complete_flow_with_mode( + replicate_model_fn._VariableDistributionMode. + SHARED_LOCAL_PARAMETER_SERVER) + + def test_complete_flow_with_mode_round_robin(self): + return self._complete_flow_with_mode( + replicate_model_fn._VariableDistributionMode.SHARED_ROUND_ROBIN) + + def _complete_flow_with_mode(self, mode): + n_classes = 3 + input_dimension = 2 + batch_size = 12 + + data = np.linspace( + 0., n_classes - 1., batch_size * input_dimension, dtype=np.float32) + x_data = data.reshape(batch_size, input_dimension) + categorical_data = np.random.random_integers( + 0, len(x_data), size=len(x_data)) + y_data = np.reshape(self._as_label(data[:batch_size]), (batch_size, 1)) + train_input_fn = numpy_io.numpy_input_fn( + x={'x': x_data, + 'categories': categorical_data}, + y=y_data, + batch_size=batch_size, + num_epochs=None, + shuffle=True) + eval_input_fn = numpy_io.numpy_input_fn( + x={'x': x_data, + 'categories': categorical_data}, + y=y_data, + batch_size=batch_size, + shuffle=False) + predict_input_fn = numpy_io.numpy_input_fn( + x={'x': x_data, + 'categories': categorical_data}, + batch_size=batch_size, + shuffle=False) + + feature_columns = [ + feature_column.numeric_column('x', shape=(input_dimension,)), + feature_column.embedding_column( + feature_column.categorical_column_with_vocabulary_list( + 'categories', + vocabulary_list=np.linspace( + 0., len(x_data), len(x_data), dtype=np.int64)), 1) + ] + + def optimizer_fn(): + return optimizers.get_optimizer_instance('Adagrad', learning_rate=0.05) + + estimator = dnn.DNNClassifier( + hidden_units=(2, 2), + # Adagrad is configured with `get_optimizer_instance`, so the function + # form of `TowerOptimizer.__init__` is used. + optimizer=replicate_model_fn._TowerOptimizer( + optimizer_fn, loss_reduction=losses.Reduction.SUM), + feature_columns=feature_columns, + n_classes=n_classes, + model_dir=self._model_dir) + + if not mode: # Use the public `replicate_model_fn`. + model_fn = replicate_model_fn._replicate_model_fn( + estimator.model_fn, devices=['/gpu:0', '/gpu:1', '/gpu:2']) + else: + model_fn = replicate_model_fn._replicate_model_fn_with_mode( + estimator.model_fn, + devices=['/gpu:0', '/gpu:1', '/gpu:2'], + mode=mode) + + estimator = estimator_lib.Estimator( + model_fn=model_fn, + model_dir=estimator.model_dir, + config=estimator.config, + params=estimator.params) + + num_steps = 10 + estimator.train(train_input_fn, steps=num_steps) + + scores = estimator.evaluate(eval_input_fn) + self.assertEqual(num_steps, scores[ops_lib.GraphKeys.GLOBAL_STEP]) + self.assertIn('loss', six.iterkeys(scores)) + + predicted_proba = np.array([ + x[prediction_keys.PredictionKeys.PROBABILITIES] + for x in estimator.predict(predict_input_fn) + ]) + self.assertAllEqual((batch_size, n_classes), predicted_proba.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)) + + # Nothing should be left in the graph so that it doesn't get serialized. + self.assertFalse(ops_lib.get_default_graph().get_collection_ref( + replicate_model_fn._TowerOptimizer.COLLECTION_FOR_GRAPH_STATES)) + + def _as_label(self, data_in_float): + return np.rint(data_in_float).astype(np.int64) + + def tearDown(self): + if self._model_dir: + writer_cache.FileWriterCache.clear() + shutil.rmtree(self._model_dir) + + +class ReplicateModelTest(test_util.TensorFlowTestCase): + + def create_model_fn_with_loss_reduction(self, loss_reduction): + + def model_fn(mode, features, labels, params): + c = variable_scope.get_variable( + 'c', + initializer=constant_op.constant(10, dtype=dtypes.float64), + dtype=dtypes.float64) + + predictions = math_ops.multiply(features, c) + + loss = losses.absolute_difference( + labels=labels, + predictions=predictions, + reduction=losses.Reduction.SUM) + loss = math_ops.reduce_sum(loss) + + metrics = { + 'accuracy': metrics_lib.accuracy(labels, predictions), + 'auc': metrics_lib.auc(labels, predictions) + } + + optimizer = replicate_model_fn._TowerOptimizer( + gradient_descent.GradientDescentOptimizer(params['learning_rate']), + loss_reduction=loss_reduction) + + return model_fn_lib.EstimatorSpec( + mode=mode, + loss=loss, + eval_metric_ops=metrics, + predictions={'probabilities': predictions}, + train_op=optimizer.minimize(loss)) + + return model_fn + + @property + def params(self): + params = {} + params['learning_rate'] = 1.0 + return params + + def test_train(self): + features = np.array([[1.0], [2.0]]) + labels = np.array([[1.0], [2.0]]) + + with self.test_session() as session: + replicated_model_fn = replicate_model_fn._replicate_model_fn( + self.create_model_fn_with_loss_reduction(losses.Reduction.SUM), + devices=['/gpu:0', '/gpu:1']) + estimator_spec = replicated_model_fn( + features, labels, model_fn_lib.ModeKeys.TRAIN, self.params) + session.run(variables.global_variables_initializer()) + + # loss = feature * c - label + total_loss = (1.0 * 10 - 1.0) + (2.0 * 10 - 2.0) + self.assertEqual(total_loss, session.run(estimator_spec.loss)) + + # derivative of loss = (1*c - 1) + (2*c - 2) is 3. + # new value of c = 10 - learning rate * 3 = 7.0. + session.run(estimator_spec.train_op) + with variable_scope.variable_scope('', reuse=True): + c = variable_scope.get_variable('c', dtype=dtypes.float64) + self.assertEqual(7.0, session.run(c)) + + def test_train_with_mean_reduction(self): + features = np.array([[1.0], [2.0]]) + labels = np.array([[1.0], [2.0]]) + + with self.test_session() as session: + # Add another trainable variable that doesn't produce a gradient to + # verify that None gradients are supported. + _ = variable_scope.get_variable( + 'another_variable', + initializer=constant_op.constant(1, dtype=dtypes.float64), + dtype=dtypes.float64) + + replicated_model_fn = replicate_model_fn._replicate_model_fn( + self.create_model_fn_with_loss_reduction(losses.Reduction.MEAN), + devices=['/gpu:0', '/gpu:1']) + estimator_spec = replicated_model_fn( + features, labels, model_fn_lib.ModeKeys.TRAIN, self.params) + session.run(variables.global_variables_initializer()) + + # loss = feature * c - label + total_loss = ((1.0 * 10 - 1.0) + (2.0 * 10 - 2.0)) / 2.0 + self.assertEqual(total_loss, session.run(estimator_spec.loss)) + + # derivative of loss = (1*c - 1)/2 + (2*c - 2)/2 is 1.5. + # It's the same computation as without mean reduction, but the + # loss from every tower is scaled by 1/. + # new value of c = 10 - learning rate * 1.5 = 8.5 + session.run(estimator_spec.train_op) + with variable_scope.variable_scope('', reuse=True): + c = variable_scope.get_variable('c', dtype=dtypes.float64) + self.assertEqual(8.5, session.run(c)) + + def test_train_two_steps_collected_gradients_are_reset_between_steps(self): + with ops_lib.Graph().as_default(): + features = array_ops.placeholder(dtypes.float64) + labels = array_ops.placeholder(dtypes.float64) + + feature_inputs = np.array([[1.0], [2.0]]), np.array([[1.5], [2.5]]) + label_inputs = np.array([[1.0], [2.0]]), np.array([[1.5], [2.5]]) + + # loss = feature * c - label + expected_losses = ((1.0 * 10 - 1.0) + (2.0 * 10 - 2.0), + (1.5 * 7.0 - 1.5) + (2.5 * 7.0 - 2.5)) + # Derivative of the loss is 1.0 + 2.0 for the first step and 1.5 + 2.5 + # for the second. + expected_c = 10.0 - 3.0, 7.0 - 4.0 + + with self.test_session() as session, variable_scope.variable_scope( + '', reuse=variable_scope.AUTO_REUSE): + replicated_model_fn = replicate_model_fn._replicate_model_fn( + self.create_model_fn_with_loss_reduction(losses.Reduction.SUM), + devices=['/gpu:0', '/gpu:1']) + estimator_spec = replicated_model_fn( + features, labels, model_fn_lib.ModeKeys.TRAIN, self.params) + session.run(variables.global_variables_initializer()) + + for feature_input, label_input, loss, weight in zip( + feature_inputs, label_inputs, expected_losses, expected_c): + feeds = {features: feature_input, labels: label_input} + + self.assertEqual(loss, session.run(estimator_spec.loss, feeds)) + + session.run(estimator_spec.train_op, feeds) + c = variable_scope.get_variable('c', dtype=dtypes.float64) + self.assertEqual(weight, session.run(c, feeds)) + + def test_eval(self): + features = np.array([[0.01], [0.002]]) + labels = np.array([[0.01], [0.02]]) + + with self.test_session() as session: + replicated_model_fn = replicate_model_fn._replicate_model_fn( + self.create_model_fn_with_loss_reduction(losses.Reduction.SUM), + devices=['/gpu:0', '/gpu:1']) + estimator_spec = replicated_model_fn( + features, labels, model_fn_lib.ModeKeys.EVAL, self.params) + session.run(variables.local_variables_initializer()) + session.run(variables.global_variables_initializer()) + + accuracy, a = estimator_spec.eval_metric_ops['accuracy'] + auc, b = estimator_spec.eval_metric_ops['auc'] + + session.run([a, b]) + accuracy = session.run(accuracy) + auc = session.run(auc) + + # loss[i] = features[i] * 10 - labels[i]. + # Accuracy is 0.0 (no match) in the first tower. + # Accuracy is 1.0 (match) in the second tower, since the feature + # times weight "c" happened to be equal to the label. + total_loss = ((0.01 * 10 - 0.01) + (0.002 * 10 - 0.02)) + + self.assertNear((0.0 + 1.0) / 2.0, accuracy, 0.01) + self.assertEqual(0, auc) + self.assertNear(total_loss, session.run(estimator_spec.loss), 0.01) + + def test_eval_with_mean_reduction(self): + features = np.array([[0.01], [0.002]]) + labels = np.array([[0.01], [0.02]]) + + with self.test_session() as session: + replicated_model_fn = replicate_model_fn._replicate_model_fn( + self.create_model_fn_with_loss_reduction(losses.Reduction.MEAN), + devices=['/gpu:0', '/gpu:1']) + estimator_spec = replicated_model_fn( + features, labels, model_fn_lib.ModeKeys.EVAL, self.params) + session.run(variables.local_variables_initializer()) + session.run(variables.global_variables_initializer()) + + accuracy, a = estimator_spec.eval_metric_ops['accuracy'] + auc, b = estimator_spec.eval_metric_ops['auc'] + + session.run([a, b]) + accuracy = session.run(accuracy) + auc = session.run(auc) + + # loss[i] = features[i] * 10 - labels[i]. + # Accuracy is 0.0 (no match) in the first tower. + # Accuracy is 1.0 (match) in the second tower, since the feature + # times weight "c" happened to be equal to the label. + total_loss = ((0.01 * 10 - 0.01) + (0.002 * 10 - 0.02)) / 2.0 + + self.assertNear((0.0 + 1.0) / 2.0, accuracy, 0.01) + self.assertEqual(0, auc) + self.assertNear(total_loss, session.run(estimator_spec.loss), 0.01) + + def test_predict(self): + features = np.array([[0.01], [0.002]]) + labels = np.array([[0.01], [0.02]]) + + with self.test_session() as session: + replicated_model_fn = replicate_model_fn._replicate_model_fn( + self.create_model_fn_with_loss_reduction(losses.Reduction.SUM), + devices=['/gpu:0', '/gpu:1']) + estimator_spec = replicated_model_fn( + features, labels, model_fn_lib.ModeKeys.PREDICT, self.params) + session.run(variables.global_variables_initializer()) + + self.assertAllClose({ + 'probabilities': np.array([[0.1], [0.02]]) + }, session.run(estimator_spec.predictions)) + + def test_train_single_tower(self): + features = np.array([[1.0], [2.0]]) + labels = np.array([[1.0], [2.0]]) + + with self.test_session() as session: + replicated_model_fn = replicate_model_fn._replicate_model_fn( + self.create_model_fn_with_loss_reduction(losses.Reduction.SUM), + devices=['/gpu:0']) + estimator_spec = replicated_model_fn( + features, labels, model_fn_lib.ModeKeys.TRAIN, self.params) + session.run(variables.global_variables_initializer()) + + # loss = feature * c - label + total_loss = (1.0 * 10 - 1.0) + (2.0 * 10 - 2.0) + self.assertEqual(total_loss, session.run(estimator_spec.loss)) + + # loss' of c is 3. + # new value of c = 10 - learning rate * 3 = 7.0. + session.run(estimator_spec.train_op) + with variable_scope.variable_scope('', reuse=True): + c = variable_scope.get_variable('c', dtype=dtypes.float64) + self.assertEqual(7.0, session.run(c)) + + def test_eval_single_tower(self): + features = np.array([[0.01], [0.002]]) + labels = np.array([[0.01], [0.02]]) + + with self.test_session() as session: + replicated_model_fn = replicate_model_fn._replicate_model_fn( + self.create_model_fn_with_loss_reduction(losses.Reduction.SUM), + devices=['/gpu:0']) + estimator_spec = replicated_model_fn( + features, labels, model_fn_lib.ModeKeys.EVAL, self.params) + session.run(variables.local_variables_initializer()) + session.run(variables.global_variables_initializer()) + + accuracy, a = estimator_spec.eval_metric_ops['accuracy'] + auc, b = estimator_spec.eval_metric_ops['auc'] + + session.run([a, b]) + accuracy = session.run(accuracy) + auc = session.run(auc) + + # Accuracy is 0.0 (no match) in the first tower. + # Accuracy is 1.0 (match) in the second tower, since the feature + # times weight "c" happened to be equal to the label. + total_loss = ((0.01 * 10 - 0.01) + (0.002 * 10 - 0.02)) + + self.assertNear((0.0 + 1.0) / 2.0, accuracy, 0.01) + self.assertEqual(0, auc) + self.assertNear(total_loss, session.run(estimator_spec.loss), 0.01) + + def test_predict_single_tower(self): + features = np.array([[0.01], [0.002]]) + labels = np.array([[0.01], [0.02]]) + + with self.test_session() as session: + replicated_model_fn = replicate_model_fn._replicate_model_fn( + self.create_model_fn_with_loss_reduction(losses.Reduction.SUM), + devices=['/gpu:0']) + estimator_spec = replicated_model_fn( + features, labels, model_fn_lib.ModeKeys.PREDICT, self.params) + session.run(variables.global_variables_initializer()) + + self.assertAllClose({ + 'probabilities': np.array([[0.1], [0.02]]) + }, session.run(estimator_spec.predictions)) + + def test_batch_size_that_is_not_divisible_by_the_number_of_gpus(self): + features = np.array([[1.0], [2.0], [3.0]]) + labels = np.array([[1.0], [2.0], [3.0]]) + + with self.assertRaisesRegexp( + ValueError, '.*Batch.+size.+needs.+to.+be.+divisible.+by.+GPUs.+'): + replicated_model_fn = replicate_model_fn._replicate_model_fn( + self.create_model_fn_with_loss_reduction(losses.Reduction.SUM), + devices=['/gpu:0', '/gpu:1']) + _ = replicated_model_fn( + features, labels, model_fn_lib.ModeKeys.TRAIN, self.params) + + def test_unsupported_loss_reduction(self): + features = np.array([[1.0], [2.0], [3.0]]) + labels = np.array([[1.0], [2.0], [3.0]]) + + with self.assertRaisesRegexp(ValueError, + '.+none.+reduction.+is.+specified.+'): + replicated_model_fn = replicate_model_fn._replicate_model_fn( + self.create_model_fn_with_loss_reduction(losses.Reduction.NONE), + devices=['/gpu:0', '/gpu:1', '/gpu:2']) + _ = replicated_model_fn( + features, labels, model_fn_lib.ModeKeys.TRAIN, self.params) + + def test_places_on_gpu_with_upper_case_spelling(self): + features = np.array([[0.01], [0.002]]) + labels = np.array([[0.01], [0.02]]) + + with self.test_session(): + replicated_model_fn = replicate_model_fn._replicate_model_fn( + self.create_model_fn_with_loss_reduction(losses.Reduction.SUM), + devices=['/GPU:0']) + _ = replicated_model_fn( + features, labels, model_fn_lib.ModeKeys.TRAIN, self.params) + + with variable_scope.variable_scope('', reuse=True): + c = variable_scope.get_variable('c', dtype=dtypes.float64) + self.assertEqual('/device:GPU:0', c.device) + + def test_places_on_gpu_with_lower_case_spelling(self): + features = np.array([[0.01], [0.002]]) + labels = np.array([[0.01], [0.02]]) + + with self.test_session(): + replicated_model_fn = replicate_model_fn._replicate_model_fn( + self.create_model_fn_with_loss_reduction(losses.Reduction.SUM), + devices=['/gpu:0']) + _ = replicated_model_fn( + features, labels, model_fn_lib.ModeKeys.TRAIN, self.params) + + with variable_scope.variable_scope('', reuse=True): + c = variable_scope.get_variable('c', dtype=dtypes.float64) + self.assertEqual('/device:GPU:0', c.device) + + +class ReplicateAcrossASingleDeviceWithoutTowerOptimizer( + test_util.TensorFlowTestCase): + + def model_fn(self, mode, features, labels, params): + c = variable_scope.get_variable( + 'c', + initializer=constant_op.constant(10, dtype=dtypes.float64), + dtype=dtypes.float64) + + predictions = math_ops.multiply(features, c) + + loss = losses.absolute_difference( + labels=labels, predictions=predictions, reduction=losses.Reduction.SUM) + loss = math_ops.reduce_sum(loss) + + metrics = { + 'accuracy': metrics_lib.accuracy(labels, predictions), + 'auc': metrics_lib.auc(labels, predictions) + } + + optimizer = gradient_descent.GradientDescentOptimizer( + params['learning_rate']) + + return model_fn_lib.EstimatorSpec( + mode=mode, + loss=loss, + eval_metric_ops=metrics, + predictions={'probabilities': predictions}, + train_op=optimizer.minimize(loss)) + + @property + def params(self): + params = {} + params['learning_rate'] = 1.0 + return params + + def test_train_single_tower(self): + features = np.array([[1.0], [2.0]]) + labels = np.array([[1.0], [2.0]]) + + with self.test_session() as session: + replicated_model_fn = replicate_model_fn._replicate_model_fn( + self.model_fn, devices=['/gpu:0']) + estimator_spec = replicated_model_fn( + features, labels, model_fn_lib.ModeKeys.TRAIN, self.params) + session.run(variables.global_variables_initializer()) + + # loss = feature * c - label + total_loss = (1.0 * 10 - 1.0) + (2.0 * 10 - 2.0) + self.assertEqual(total_loss, session.run(estimator_spec.loss)) + + # loss' of c is 3. + # new value of c = 10 - learning rate * 3 = 7.0. + session.run(estimator_spec.train_op) + with variable_scope.variable_scope('', reuse=True): + c = variable_scope.get_variable('c', dtype=dtypes.float64) + self.assertEqual(7.0, session.run(c)) + + +class UseTowerEstimatorWithoutReplication(test_util.TensorFlowTestCase): + + def model_fn(self, mode, features, labels, params): + c = variable_scope.get_variable( + 'c', + initializer=constant_op.constant(10, dtype=dtypes.float64), + dtype=dtypes.float64) + + features = features['features'] + predictions = math_ops.multiply(features, c) + + loss = losses.absolute_difference( + labels=labels, predictions=predictions, reduction=losses.Reduction.SUM) + loss = math_ops.reduce_sum(loss) + + metrics = { + 'accuracy': metrics_lib.accuracy(labels, predictions), + 'auc': metrics_lib.auc(labels, predictions) + } + + optimizer = replicate_model_fn._TowerOptimizer( + gradient_descent.GradientDescentOptimizer(params['learning_rate'])) + + return model_fn_lib.EstimatorSpec( + mode=mode, + loss=loss, + eval_metric_ops=metrics, + predictions={'probabilities': predictions}, + train_op=optimizer.minimize(loss)) + + @property + def params(self): + params = {} + params['learning_rate'] = 1.0 + return params + + def test_train_single_tower(self): + features = np.array([[1.0], [2.0]]) + labels = np.array([[1.0], [2.0]]) + + train_input_fn = numpy_io.numpy_input_fn( + x={'features': features}, y=labels, batch_size=2, shuffle=False) + + with self.test_session(): + estimator = estimator_lib.Estimator( + model_fn=self.model_fn, + model_dir=tempfile.mkdtemp(), + params=self.params) + estimator.train(train_input_fn, steps=1) + + self.assertEqual(7.0, estimator.get_variable_value('c')) + + +class MakeSureSyncReplicasOptimizerWorks(test_util.TensorFlowTestCase): + + def model_fn(self, mode, features, labels, params): + c = variable_scope.get_variable( + 'c', + initializer=constant_op.constant(10, dtype=dtypes.float64), + dtype=dtypes.float64) + + features = features['features'] + predictions = math_ops.multiply(features, c) + + loss = losses.absolute_difference( + labels=labels, predictions=predictions, reduction=losses.Reduction.SUM) + loss = math_ops.reduce_sum(loss) + + metrics = { + 'accuracy': metrics_lib.accuracy(labels, predictions), + 'auc': metrics_lib.auc(labels, predictions) + } + + optimizer = gradient_descent.GradientDescentOptimizer( + params['learning_rate']) + optimizer = training.SyncReplicasOptimizer( + optimizer, replicas_to_aggregate=1) + sync_hook = optimizer.make_session_run_hook(True) + optimizer = replicate_model_fn._TowerOptimizer( + optimizer, loss_reduction=losses.Reduction.SUM) + + return model_fn_lib.EstimatorSpec( + mode=mode, + loss=loss, + eval_metric_ops=metrics, + training_hooks=[sync_hook], + predictions={'probabilities': predictions}, + train_op=optimizer.minimize( + loss, global_step=training.get_global_step())) + + @property + def params(self): + params = {} + params['learning_rate'] = 1.0 + return params + + def test_train_multiple_towers(self): + features = np.array([[1.0], [2.0]]) + labels = np.array([[1.0], [2.0]]) + + train_input_fn = numpy_io.numpy_input_fn( + x={'features': features}, y=labels, batch_size=2, shuffle=False) + + model_fn = replicate_model_fn._replicate_model_fn( + self.model_fn, + devices=['/gpu:0', '/gpu:1']) + + estimator = estimator_lib.Estimator( + model_fn=model_fn, model_dir=tempfile.mkdtemp(), params=self.params) + estimator.train(train_input_fn, steps=1) + + self.assertEqual(7.0, estimator.get_variable_value('c')) + + +class ReplicateWithTwoOptimizersTest(test_util.TensorFlowTestCase): + + def model_fn(self, mode, features, labels, params): + c = variable_scope.get_variable( + 'c', + initializer=constant_op.constant(10, dtype=dtypes.float64), + dtype=dtypes.float64) + + side_effects = variable_scope.get_variable( + 'side_effects', + initializer=constant_op.constant(0, dtype=dtypes.float64), + dtype=dtypes.float64, + use_resource=True, + trainable=False) + + predictions = math_ops.multiply(features, c) + + loss = losses.absolute_difference( + labels=labels, predictions=predictions, reduction=losses.Reduction.SUM) + loss = math_ops.reduce_sum(loss) + + metrics = { + 'accuracy': metrics_lib.accuracy(labels, predictions), + 'auc': metrics_lib.auc(labels, predictions) + } + + first_optimizer = replicate_model_fn._TowerOptimizer( + gradient_descent.GradientDescentOptimizer(1.0), + loss_reduction=losses.Reduction.SUM) + second_optimizer = replicate_model_fn._TowerOptimizer( + adam.AdamOptimizer(1.0), loss_reduction=losses.Reduction.SUM) + + with ops_lib.control_dependencies([side_effects.assign_add(1.0)]): + first_grads_and_vars = first_optimizer.compute_gradients(loss) + + train_op = control_flow_ops.group( + [first_optimizer.apply_gradients(first_grads_and_vars), + second_optimizer.minimize(loss)]) + + return model_fn_lib.EstimatorSpec( + mode=mode, + loss=loss, + eval_metric_ops=metrics, + predictions={'probabilities': predictions}, + train_op=train_op) + + def test_train(self): + features = np.array([[1.0], [2.0]]) + labels = np.array([[1.0], [2.0]]) + + with self.test_session() as session: + replicated_model_fn = replicate_model_fn._replicate_model_fn( + self.model_fn, + devices=['/gpu:0', '/gpu:1']) + estimator_spec = replicated_model_fn(features, labels, + model_fn_lib.ModeKeys.TRAIN, {}) + session.run(variables.global_variables_initializer()) + + # loss = feature * c - label + total_loss = (1.0 * 10 - 1.0) + (2.0 * 10 - 2.0) + self.assertEqual(total_loss, session.run(estimator_spec.loss)) + + # loss' of c is 3. + # new value of c = 10 - learning rate * 3 = 7.0. + # Adam subtracts another ~1. + session.run(estimator_spec.train_op) + with variable_scope.variable_scope('', reuse=True): + c = variable_scope.get_variable('c', dtype=dtypes.float64) + self.assertNear(6.0, session.run(c), 0.000001) + + side_effects = variable_scope.get_variable( + 'side_effects', dtype=dtypes.float64) + self.assertNear(2.0, session.run(side_effects), 0.000001) + + +class ReplicateWithTwoLossesAndOneOptimizer(test_util.TensorFlowTestCase): + + def setUp(self): + self._should_skip_optimizer = False + self._towers_left_before_skipping_optimizer = -1 + + def incorrectly_skip_optimizer_for_tower(self, tower_number): + self._should_skip_optimizer = True + self._towers_left_before_skipping_optimizer = tower_number + + def should_skip_optimizer(self): + if not self._should_skip_optimizer: + return False + if self._towers_left_before_skipping_optimizer == 0: + return True + else: + self._towers_left_before_skipping_optimizer -= 1 + return False + + def model_fn(self, mode, features, labels, params): + c = variable_scope.get_variable( + 'c', + initializer=constant_op.constant(10, dtype=dtypes.float64), + dtype=dtypes.float64) + d = variable_scope.get_variable( + 'd', + initializer=constant_op.constant(2, dtype=dtypes.float64), + dtype=dtypes.float64) + + predictions = math_ops.multiply(features, c) + + loss = losses.absolute_difference( + labels=labels, predictions=predictions, reduction=losses.Reduction.SUM) + loss = math_ops.reduce_sum(loss) + + another_predictions = math_ops.multiply(features, d) + another_loss = losses.absolute_difference( + labels=labels, + predictions=another_predictions, + reduction=losses.Reduction.SUM) + another_loss = math_ops.reduce_sum(another_loss) + + total_loss = math_ops.add(loss, another_loss) + + metrics = { + 'accuracy': metrics_lib.accuracy(labels, predictions), + 'auc': metrics_lib.auc(labels, predictions) + } + + train_ops = [] + + optimizer = replicate_model_fn._TowerOptimizer( + gradient_descent.GradientDescentOptimizer(1.0), + loss_reduction=losses.Reduction.SUM) + train_ops.append(optimizer.minimize(loss, var_list=[c])) + if not self.should_skip_optimizer(): + another_optimizer = replicate_model_fn._TowerOptimizer( + gradient_descent.GradientDescentOptimizer(1.0), + loss_reduction=losses.Reduction.SUM) + train_ops.append(another_optimizer.minimize(another_loss, var_list=[d])) + + train_op = control_flow_ops.group(train_ops) + return model_fn_lib.EstimatorSpec( + mode=mode, + loss=total_loss, + eval_metric_ops=metrics, + predictions={'probabilities': predictions}, + train_op=train_op) + + def test_train(self): + features = np.array([[1.0], [2.0]]) + labels = np.array([[1.0], [2.0]]) + + with ops_lib.Graph().as_default(), self.test_session() as session: + replicated_model_fn = replicate_model_fn._replicate_model_fn( + self.model_fn, + devices=['/gpu:0', '/gpu:1']) + estimator_spec = replicated_model_fn(features, labels, + model_fn_lib.ModeKeys.TRAIN, {}) + session.run(variables.global_variables_initializer()) + + # For each tower, loss = (feature * c - label) + (feature * d - label). + total_loss = (1.0 * 10 - 1.0 + 1.0 * 2.0 - 1.0) + ( + 2.0 * 10 - 2.0 + 2.0 * 2.0 - 2.0) + self.assertEqual(total_loss, session.run(estimator_spec.loss)) + + session.run(estimator_spec.train_op) + + # loss' of c or loss' of d is 3. + # new value of c = 10 - learning rate * 3 = 7.0. + # new value of d = 2 - learning rate * 3 = -1.0. + with variable_scope.variable_scope('', reuse=True): + c = variable_scope.get_variable('c', dtype=dtypes.float64) + self.assertNear(7.0, session.run(c), 0.000001) + d = variable_scope.get_variable('d', dtype=dtypes.float64) + self.assertNear(-1.0, session.run(d), 0.000001) + + def test_different_optimizer_calls_within_towers(self): + self.incorrectly_skip_optimizer_for_tower(1) + + features = np.array([[1.0], [2.0]]) + labels = np.array([[1.0], [2.0]]) + + with self.test_session(), ops_lib.Graph().as_default(): + with self.assertRaisesRegexp( + ValueError, '.+was.+supposed.+to.+make.+same.+optimizer.+calls.+'): + replicated_model_fn = replicate_model_fn._replicate_model_fn( + self.model_fn, devices=['/gpu:0', '/gpu:1']) + _ = replicated_model_fn(features, labels, model_fn_lib.ModeKeys.TRAIN, + {}) + + +class FailToWrapOptimizerInTheModelFn(test_util.TensorFlowTestCase): + + def model_fn(self, mode, features, labels, params): + c = variable_scope.get_variable( + 'c', + initializer=constant_op.constant(10, dtype=dtypes.float64), + dtype=dtypes.float64) + + predictions = math_ops.multiply(features, c) + + loss = losses.absolute_difference( + labels=labels, predictions=predictions, reduction=losses.Reduction.SUM) + loss = math_ops.reduce_sum(loss) + + metrics = { + 'accuracy': metrics_lib.accuracy(labels, predictions), + 'auc': metrics_lib.auc(labels, predictions) + } + + optimizer = gradient_descent.GradientDescentOptimizer(1.0) + train_op = optimizer.minimize(loss) + + return model_fn_lib.EstimatorSpec( + mode=mode, + loss=loss, + eval_metric_ops=metrics, + predictions={'probabilities': predictions}, + train_op=train_op) + + def test_train(self): + features = np.array([[1.0], [2.0]]) + labels = np.array([[1.0], [2.0]]) + + with self.test_session(): + with self.assertRaisesRegexp(ValueError, + 'Please.+wrap.+with.+TowerOptimizer'): + replicated_model_fn = replicate_model_fn._replicate_model_fn( + self.model_fn, devices=['/gpu:0', '/gpu:1']) + _ = replicated_model_fn(features, labels, model_fn_lib.ModeKeys.TRAIN, + {}) + + +class GetLossTowersTest(test_util.TensorFlowTestCase): + + def create_model_fn_with_loss_reduction(self, loss_reduction): + + def model_fn(mode, features, labels, params): + del params + c = variable_scope.get_variable( + 'c', + initializer=constant_op.constant(0.25, dtype=dtypes.float64), + dtype=dtypes.float64) + + predictions = math_ops.add(np.array([0.1, 0.2, 0.3, features[0]]), c) + labels = np.array([0.1, 0.2, 0.3, labels[0]]) + + loss = losses.absolute_difference( + labels=labels, + predictions=predictions, + reduction=losses.Reduction.SUM) + + optimizer = replicate_model_fn._TowerOptimizer( + gradient_descent.GradientDescentOptimizer(1.0), + loss_reduction) + + return model_fn_lib.EstimatorSpec( + mode=mode, + loss=math_ops.reduce_sum(loss), + train_op=optimizer.minimize(loss)) + + return model_fn + + def test_gradients_are_computed(self): + with self.test_session() as session: + tower_specs = replicate_model_fn._get_loss_towers( + self.create_model_fn_with_loss_reduction(losses.Reduction.SUM), + mode=None, + features=[[0.6], [1.6]], + labels=[[0.6], [0.6]], + params=None, + config=None, + devices=['/gpu:0', '/gpu:1'], + local_ps_devices=['/gpu:0'], + name_scope_pattern='test_tower_{}') + session.run(variables.global_variables_initializer()) + + self.assertEqual(len(tower_specs), 2) + + self.assertEqual('/device:GPU:0', tower_specs[0].loss.device) + self.assertEqual('Sum:0', tower_specs[0].loss.name) + self.assertEqual(1.0, session.run(tower_specs[0].loss)) + + self.assertEqual('/device:GPU:1', tower_specs[1].loss.device) + self.assertEqual('test_tower_1/Sum:0', tower_specs[1].loss.name) + # The input batch for the second tower had a loss that is 1.0 + # bigger: 0.6 vs 1.6. + self.assertEqual(2.0, session.run(tower_specs[1].loss)) + + self.assertEqual(1, len(variables.global_variables())) + self.assertEqual(1, len(variables.trainable_variables())) + + with variable_scope.variable_scope('', reuse=True): + c = variable_scope.get_variable('c', dtype=dtypes.float64) + self.assertEqual(0.25, session.run(c)) + + def test_gradients_are_computed_with_mean_reduction(self): + with self.test_session() as session: + tower_specs = replicate_model_fn._get_loss_towers( + self.create_model_fn_with_loss_reduction(losses.Reduction.MEAN), + mode=model_fn_lib.ModeKeys.EVAL, + features=[[0.6], [1.6]], + labels=[[0.6], [0.6]], + params=None, + config=None, + devices=['/gpu:0', '/gpu:1'], + local_ps_devices=['/gpu:0'], + name_scope_pattern='test_tower_{}') + session.run(variables.global_variables_initializer()) + + self.assertEqual(len(tower_specs), 2) + + self.assertEqual('/device:GPU:0', tower_specs[0].loss.device) + self.assertEqual('averaged_loss:0', tower_specs[0].loss.name) + self.assertEqual(0.5, session.run(tower_specs[0].loss)) + + self.assertEqual('/device:GPU:1', tower_specs[1].loss.device) + self.assertEqual('test_tower_1/averaged_loss:0', tower_specs[1].loss.name) + # The input batch for the second tower had a loss that is 1.0 + # bigger: 0.6 vs 1.6. + self.assertEqual(1.0, session.run(tower_specs[1].loss)) + + self.assertEqual(1, len(variables.global_variables())) + self.assertEqual(1, len(variables.trainable_variables())) + + with variable_scope.variable_scope('', reuse=True): + c = variable_scope.get_variable('c', dtype=dtypes.float64) + self.assertEqual(0.25, session.run(c)) + + def test_variables_are_round_robined_correctly(self): + """Test that creates multiple variables and tests round-robin placement.""" + + def model_fn(mode, features, labels, params): + del params + for variable_name in ['a', 'b', 'c', 'd']: + c = variable_scope.get_variable( + variable_name, + initializer=constant_op.constant(0.25, dtype=dtypes.float64), + dtype=dtypes.float64) + + predictions = math_ops.add(np.array([0.1, 0.2, 0.3, features[0]]), c) + labels = np.array([0.1, 0.2, 0.3, labels[0]]) + loss = losses.absolute_difference( + labels=labels, + predictions=predictions, + reduction=losses.Reduction.SUM) + return model_fn_lib.EstimatorSpec( + mode=mode, loss=math_ops.reduce_sum(loss)) + + with self.test_session() as session: + tower_specs = replicate_model_fn._get_loss_towers( + model_fn, + mode=None, + features=[[0.6], [1.6], [2.6]], + labels=[[0.6], [0.6], [2.6]], + params=None, + config=None, + devices=['/gpu:0', '/gpu:1', '/gpu:3'], + local_ps_devices=['/gpu:0', '/gpu:1', '/gpu:3'], + name_scope_pattern='test_tower_{}') + session.run(variables.global_variables_initializer()) + + self.assertEqual(len(tower_specs), 3) + self.assertEqual('/device:GPU:0', tower_specs[0].loss.device) + self.assertEqual('/device:GPU:1', tower_specs[1].loss.device) + self.assertEqual('/device:GPU:3', tower_specs[2].loss.device) + + with variable_scope.variable_scope('', reuse=True): + a = variable_scope.get_variable('a', dtype=dtypes.float64) + self.assertEqual('/device:GPU:0', a.device) + b = variable_scope.get_variable('b', dtype=dtypes.float64) + self.assertEqual('/device:GPU:1', b.device) + c = variable_scope.get_variable('c', dtype=dtypes.float64) + self.assertEqual('/device:GPU:3', c.device) + d = variable_scope.get_variable('d', dtype=dtypes.float64) + self.assertEqual('/device:GPU:0', d.device) + + +class SplitBatchTest(test_util.TensorFlowTestCase): + + def evaluate_shards(self, first_list, second_list): + evaluate_items = lambda x: x.eval() + return list(map(evaluate_items, first_list)), list( + map(evaluate_items, second_list)) + + def assertSparseValuesEqual(self, a, b): + self.assertAllEqual(a.indices, b.indices) + self.assertAllEqual(a.values, b.values) + self.assertAllEqual(a.dense_shape, b.dense_shape) + + def test_simple_half_split(self): + with self.test_session(): + features = [0.0, 1.0, 2.0, 3.0] + labels = [10.0, 11.0, 12.0, 13.0] + feature_shards, label_shards = replicate_model_fn._split_batch( + features, labels, 2, device='/gpu:0') + + feature_shards, label_shards = self.evaluate_shards( + feature_shards, label_shards) + + self.assertAllEqual([[0.0, 1.0], [2.0, 3.0]], feature_shards) + self.assertAllEqual([[10.0, 11.0], [12.0, 13.0]], label_shards) + + def test_to_each_their_own(self): + with self.test_session(): + features = [0.0, 1.0, 2.0, 3.0] + labels = [10.0, 11.0, 12.0, 13.0] + feature_shards, label_shards = replicate_model_fn._split_batch( + features, labels, 4, device='/gpu:0') + + feature_shards, label_shards = self.evaluate_shards( + feature_shards, label_shards) + + self.assertAllEqual([[0.0], [1.0], [2.0], [3.0]], feature_shards) + self.assertAllEqual([[10.0], [11.0], [12.0], [13.0]], label_shards) + + def test_one_batch(self): + with self.test_session(): + features = [0.0, 1.0, 2.0, 3.0] + labels = [10.0, 11.0, 12.0, 13.0] + feature_shards, label_shards = replicate_model_fn._split_batch( + features, labels, 1, device='/gpu:0') + + feature_shards, label_shards = self.evaluate_shards( + feature_shards, label_shards) + + self.assertAllEqual([[0.0, 1.0, 2.0, 3.0]], feature_shards) + self.assertAllEqual([[10.0, 11.0, 12.0, 13.0]], label_shards) + + def test_half_split_in_dictionary(self): + with self.test_session(): + features = {'first': [0.0, 1.0, 2.0, 3.0], 'second': [4.0, 5.0, 6.0, 7.0]} + labels = [10.0, 11.0, 12.0, 13.0] + + feature_shards, label_shards = replicate_model_fn._split_batch( + features, labels, 2, device='/gpu:0') + + self.assertAllEqual([0.0, 1.0], feature_shards[0]['first'].eval()) + self.assertAllEqual([4.0, 5.0], feature_shards[0]['second'].eval()) + self.assertAllEqual([2.0, 3.0], feature_shards[1]['first'].eval()) + self.assertAllEqual([6.0, 7.0], feature_shards[1]['second'].eval()) + self.assertAllEqual([10.0, 11.0], label_shards[0].eval()) + self.assertAllEqual([12.0, 13.0], label_shards[1].eval()) + + def test_sparse_tensor_can_be_split_unevenly(self): + with self.test_session(): + features = { + 'x': + sparse_tensor.SparseTensor( + indices=[[0, 0], [1, 2], [2, 2]], + values=[1.0, 2.0, 3.0], + dense_shape=[3, 4]) + } + labels = np.array([[1.0], [2.0]]) + + feature_shards, label_shards = replicate_model_fn._split_batch( + features, labels, 2, device='/gpu:0') + + self.assertSparseValuesEqual( + sparse_tensor.SparseTensorValue( + indices=[[0, 0], [1, 2]], values=[1., 2.], dense_shape=[2, 4]), + feature_shards[0]['x'].eval()) + self.assertSparseValuesEqual( + sparse_tensor.SparseTensorValue( + indices=[[0, 2]], values=[3.], dense_shape=[1, 4]), + feature_shards[1]['x'].eval()) + self.assertAllEqual([[1.0]], label_shards[0].eval()) + self.assertAllEqual([[2.0]], label_shards[1].eval()) + + def test_sparse_tensor_can_be_split_unevenly_repeated_row(self): + with self.test_session(): + features = { + 'x': + sparse_tensor.SparseTensor( + indices=[[0, 0], [1, 0], [1, 1]], + values=[1.0, 2.0, 3.0], + dense_shape=[3, 4]) + } + labels = np.array([[1.0], [2.0]]) + + feature_shards, label_shards = replicate_model_fn._split_batch( + features, labels, 2, device='/gpu:0') + + self.assertSparseValuesEqual( + sparse_tensor.SparseTensorValue( + indices=[[0, 0], [1, 0], [1, 1]], + values=[1., 2., 3.], + dense_shape=[2, 4]), feature_shards[0]['x'].eval()) + + second_batch = feature_shards[1]['x'].eval() + self.assertFalse(len(second_batch.indices)) + self.assertFalse(len(second_batch.values)) + self.assertAllEqual([1, 4], second_batch.dense_shape) + self.assertAllEqual([[1.0]], label_shards[0].eval()) + self.assertAllEqual([[2.0]], label_shards[1].eval()) + + def test_one_batch_in_dictionary(self): + with self.test_session() as session: # pylint: disable=unused-variable + features = {'first': [0.0, 1.0, 2.0, 3.0], 'second': [4.0, 5.0, 6.0, 7.0]} + labels = [10.0, 11.0, 12.0, 13.0] + + feature_shards, label_shards = replicate_model_fn._split_batch( + features, labels, 1, device='/gpu:0') + + self.assertAllEqual([0.0, 1.0, 2.0, 3.0], + feature_shards[0]['first'].eval()) + self.assertAllEqual([4.0, 5.0, 6.0, 7.0], + feature_shards[0]['second'].eval()) + self.assertAllEqual([10.0, 11.0, 12.0, 13.0], label_shards[0].eval()) + + def test_feature_and_label_dictionaries(self): + with self.test_session() as session: # pylint: disable=unused-variable + features = {'first': [0.0, 1.0, 2.0, 3.0], 'second': [4.0, 5.0, 6.0, 7.0]} + labels = {'first': [10.0, 11.0], 'second': [12.0, 13.0]} + + feature_shards, label_shards = replicate_model_fn._split_batch( + features, labels, 2, device='/gpu:0') + + self.assertAllEqual([0.0, 1.0], feature_shards[0]['first'].eval()) + self.assertAllEqual([4.0, 5.0], feature_shards[0]['second'].eval()) + self.assertAllEqual([2.0, 3.0], feature_shards[1]['first'].eval()) + self.assertAllEqual([6.0, 7.0], feature_shards[1]['second'].eval()) + self.assertAllEqual([10.0], label_shards[0]['first'].eval()) + self.assertAllEqual([12.0], label_shards[0]['second'].eval()) + self.assertAllEqual([11], label_shards[1]['first'].eval()) + self.assertAllEqual([13.0], label_shards[1]['second'].eval()) + + +class TrainSpecTest(test_util.TensorFlowTestCase): + + expected_predictions = {} + + def create_estimator_spec(self, loss): + return model_fn_lib.EstimatorSpec( + mode=model_fn_lib.ModeKeys.TRAIN, + loss=loss, + train_op=loss, # Not used; currently required. + predictions=self.expected_predictions) + + def create_constant_loss(self, loss_value): + return constant_op.constant(loss_value, dtype=dtypes.float64) + + def test_example(self): + with self.test_session() as session: + tower_losses = list(map(self.create_constant_loss, [2, 4, 6])) + tower_specs = list(map(self.create_estimator_spec, tower_losses)) + + expected_train_op = tower_losses[1] + + estimator_spec = replicate_model_fn._train_spec( + tower_specs, expected_train_op, aggregation_device='/gpu:0') + + self.assertEqual(expected_train_op, estimator_spec.train_op) + self.assertEqual(2 + 4 + 6, session.run(estimator_spec.loss)) + self.assertEqual(self.expected_predictions, estimator_spec.predictions) + + +class EvalSpecTest(test_util.TensorFlowTestCase): + + def create_estimator_spec(self, loss, metrics): + return model_fn_lib.EstimatorSpec( + mode=model_fn_lib.ModeKeys.EVAL, loss=loss, eval_metric_ops=metrics) + + def create_constant_loss(self, loss_value): + return constant_op.constant(loss_value, dtype=dtypes.float64) + + def create_eval_metrics(self, noise): + predictions = np.array([0.1, 0.2, 0.3, 0.6 + noise]) + labels = np.array([0.1, 0.2, 0.3, 0.6]) + + metrics = { + 'accuracy': metrics_lib.accuracy(labels, predictions), + 'auc': metrics_lib.auc(labels, predictions) + } + return metrics + + def test_example(self): + with self.test_session() as session: + tower_losses = map(self.create_constant_loss, [2, 4, 6]) + tower_metrics = map(self.create_eval_metrics, [0, 0.2, 0.3]) + tower_specs = [ + self.create_estimator_spec(l, m) + for l, m in zip(tower_losses, tower_metrics) + ] + session.run(variables.local_variables_initializer()) + + estimator_spec = replicate_model_fn._eval_spec( + tower_specs, aggregation_device='/device:GPU:0') + + accuracy, a = estimator_spec.eval_metric_ops['accuracy'] + auc, b = estimator_spec.eval_metric_ops['auc'] + + self.assertEqual('/device:CPU:0', accuracy.device) + self.assertEqual('/device:CPU:0', auc.device) + + session.run([a, b]) + accuracy, auc = session.run([accuracy, auc]) + + self.assertNear((12 - 2) / 12, accuracy, 0.01) + self.assertEqual(0, auc) + self.assertEqual(2 + 4 + 6, session.run(estimator_spec.loss)) + + def test_handles_single_tower(self): + with self.test_session() as session: + tower_losses = map(self.create_constant_loss, [5]) + tower_metrics = map(self.create_eval_metrics, [0.2]) + tower_specs = [ + self.create_estimator_spec(l, m) + for l, m in zip(tower_losses, tower_metrics) + ] + session.run(variables.local_variables_initializer()) + + estimator_spec = replicate_model_fn._eval_spec( + tower_specs, aggregation_device='/device:GPU:0') + + accuracy, a = estimator_spec.eval_metric_ops['accuracy'] + auc, b = estimator_spec.eval_metric_ops['auc'] + + self.assertEqual('/device:CPU:0', accuracy.device) + self.assertEqual('/device:CPU:0', auc.device) + + session.run([a, b]) + accuracy = session.run(accuracy) + auc = session.run(auc) + + self.assertNear((4 - 1) / 4, accuracy, 0.01) + self.assertEqual(0, auc) + self.assertEqual(5, session.run(estimator_spec.loss)) + + +class PredictSpecTest(test_util.TensorFlowTestCase): + + def model_fn(self, mode, features, labels, params): + c = variable_scope.get_variable( + 'c', + initializer=constant_op.constant(0.25, dtype=dtypes.float64), + dtype=dtypes.float64) + + predictions = math_ops.add(np.array([features[0], features[0]]), c) + + return model_fn_lib.EstimatorSpec( + mode=model_fn_lib.ModeKeys.PREDICT, + predictions={ + 'probabilities': predictions + }) + + def test_example(self): + with self.test_session() as session: + tower_specs = replicate_model_fn._get_loss_towers( + self.model_fn, + mode=None, + features=[[0.1], [0.2]], + labels=[[], []], + params=None, + config=None, + devices=['/gpu:0', '/gpu:1'], + local_ps_devices=['/gpu:0'], + ) + session.run(variables.global_variables_initializer()) + + estimator_spec = replicate_model_fn._predict_spec( + tower_specs, aggregation_device='/gpu:0') + + self.assertEqual('/device:GPU:0', + estimator_spec.predictions['probabilities'].device) + self.assertAllClose({ + 'probabilities': np.array([0.35, 0.35, 0.45, 0.45]) + }, session.run(estimator_spec.predictions)) + + +class ReduceMetricVariablesTest(test_util.TensorFlowTestCase): + + def create_metric_variable(self, initial_value, name): + return variable_scope.variable( + initial_value, + trainable=False, + collections=[ops_lib.GraphKeys.METRIC_VARIABLES], + validate_shape=True, + name=name) + + def create_tower_metrics(self, tower_id): + with variable_scope.variable_scope('', reuse=(tower_id != 0)): + self.create_metric_variable(1.3 * (tower_id + 1), 'total') + self.create_metric_variable(2.3 * (tower_id + 1), 'count') + self.create_metric_variable( + np.array([3.3, 3.5, 3.7]) * (tower_id + 1), 'total') + + def test_example(self): + with self.test_session() as session: + for tower_id in range(3): + self.create_tower_metrics(tower_id) + + session.run( + variables.variables_initializer( + ops_lib.get_collection(ops_lib.GraphKeys.METRIC_VARIABLES))) + + session.run( + replicate_model_fn._reduce_metric_variables(number_of_towers=3)) + + # 1st tower = 1.3, 2.3, [3.3, 3.5, 3.7] + # 2nd tower = 2.6, 4.6, [6.6, 7.0, 7.4] + # 3rd tower = 3.9, 6.9, [9.9, 10.5, 11.1] + # Reduced = 7.8, 13.8, [19.8, 21.0, 22.2] + # Towers are accumulated in the first tower. + local_metrics = session.run( + ops_lib.get_collection(ops_lib.GraphKeys.METRIC_VARIABLES)) + + self.assertNear(7.8, local_metrics[0], 0.01) + self.assertNear(13.8, local_metrics[1], 0.01) + self.assertAllClose([19.8, 21., 22.1], local_metrics[2], 0.01) + self.assertNear(0.0, local_metrics[3], 0.01) + self.assertNear(0.0, local_metrics[4], 0.01) + self.assertAllClose([0.0, 0.0, 0.0], local_metrics[5], 0.01) + self.assertNear(0.0, local_metrics[6], 0.01) + self.assertNear(0.0, local_metrics[7], 0.01) + self.assertAllClose([0.0, 0.0, 0.0], local_metrics[8], 0.01) + + def test_reduce_is_idempotent(self): + with self.test_session() as session: + for tower_id in range(3): + self.create_tower_metrics(tower_id) + + session.run( + variables.variables_initializer( + ops_lib.get_collection(ops_lib.GraphKeys.METRIC_VARIABLES))) + + for _ in range(20): + session.run( + replicate_model_fn._reduce_metric_variables(number_of_towers=3)) + + local_metrics = session.run( + ops_lib.get_collection(ops_lib.GraphKeys.METRIC_VARIABLES)) + + self.assertNear(7.8, local_metrics[0], 0.01) + self.assertNear(13.8, local_metrics[1], 0.01) + self.assertAllClose([19.8, 21., 22.1], local_metrics[2], 0.01) + self.assertNear(0.0, local_metrics[3], 0.01) + self.assertNear(0.0, local_metrics[4], 0.01) + self.assertAllClose([0.0, 0.0, 0.0], local_metrics[5], 0.01) + self.assertNear(0.0, local_metrics[6], 0.01) + self.assertNear(0.0, local_metrics[7], 0.01) + self.assertAllClose([0.0, 0.0, 0.0], local_metrics[8], 0.01) + + def test_handles_single_tower(self): + with self.test_session() as session: + self.create_tower_metrics(0) + session.run( + variables.variables_initializer( + ops_lib.get_collection(ops_lib.GraphKeys.METRIC_VARIABLES))) + + session.run( + replicate_model_fn._reduce_metric_variables(number_of_towers=1)) + + local_metrics = session.run( + ops_lib.get_collection(ops_lib.GraphKeys.METRIC_VARIABLES)) + + self.assertNear(1.3, local_metrics[0], 0.01) + self.assertNear(2.3, local_metrics[1], 0.01) + self.assertAllClose([3.3, 3.5, 3.7], local_metrics[2], 0.01) + + def test_doesnt_accept_uneven_number_of_variables(self): + with self.test_session() as session: + for tower_id in range(3): + self.create_tower_metrics(tower_id) + self.create_metric_variable(-1.0, 'oddball') + + session.run( + variables.variables_initializer( + ops_lib.get_collection(ops_lib.GraphKeys.METRIC_VARIABLES))) + + with self.assertRaisesRegexp( + ValueError, '.+Expected.+local.+variables.+but.+got.+instead.+'): + session.run( + replicate_model_fn._reduce_metric_variables(number_of_towers=3)) + + +class MergeExportOutputsTest(test_util.TensorFlowTestCase): + + def model_fn(self, mode, features, labels, params): + c = variable_scope.get_variable( + 'c', + initializer=constant_op.constant(10, dtype=dtypes.float64), + dtype=dtypes.float64) + + predictions = {'probabilities': math_ops.multiply(features, c)} + loss = losses.absolute_difference( + labels=labels, + predictions=predictions['probabilities'], + reduction=losses.Reduction.SUM) + + metrics = { + 'accuracy': metrics_lib.accuracy(labels, predictions['probabilities']), + 'auc': metrics_lib.auc(labels, predictions['probabilities']) + } + tensor_string_repr = str(features) + classes = constant_op.constant( + re.search('(split_inputs/split:[0-9])', tensor_string_repr).group(1), + dtype=dtypes.string) + + export_outputs = { + signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: + export_output.PredictOutput(predictions), + 'classification_output': + export_output.ClassificationOutput(predictions['probabilities'], + classes), + 'classification_scores': + export_output.ClassificationOutput( + scores=predictions['probabilities']), + 'classification_classes': + export_output.ClassificationOutput(classes=classes), + 'regression_output': + export_output.RegressionOutput(predictions['probabilities']), + } + + return model_fn_lib.EstimatorSpec( + mode=mode, + loss=math_ops.reduce_sum(loss), + eval_metric_ops=metrics, + predictions=predictions, + export_outputs=export_outputs) + + def replicate_estimator_spec(self, session): + features = np.array([0.01, 0.002]) + labels = np.array([0.01, 0.02]) + + replicated_model_fn = replicate_model_fn._replicate_model_fn( + self.model_fn, devices=['/gpu:0', '/gpu:1']) + estimator_spec = replicated_model_fn(features, labels, + model_fn_lib.ModeKeys.PREDICT, {}) + session.run(variables.global_variables_initializer()) + return estimator_spec + + def test_merge_predict_output(self): + with self.test_session() as session: + estimator_spec = self.replicate_estimator_spec(session) + self.assertAllClose( + { + 'probabilities': np.array([0.1, 0.02]) + }, + session.run(estimator_spec.export_outputs[ + signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY].outputs)) + + def test_merge_classification_output_scores_classes(self): + with self.test_session() as session: + estimator_spec = self.replicate_estimator_spec(session) + self.assertAllClose( + [0.1, 0.02], + session.run( + estimator_spec.export_outputs['classification_output'].scores)) + self.assertAllEqual( + [b'split_inputs/split:0', b'split_inputs/split:1'], + session.run( + estimator_spec.export_outputs['classification_output'].classes)) + + def test_merge_classification_output_scores(self): + with self.test_session() as session: + estimator_spec = self.replicate_estimator_spec(session) + self.assertAllClose( + [0.1, 0.02], + session.run( + estimator_spec.export_outputs['classification_scores'].scores)) + self.assertEqual( + None, estimator_spec.export_outputs['classification_scores'].classes) + + def test_merge_classification_output_classes(self): + with self.test_session() as session: + estimator_spec = self.replicate_estimator_spec(session) + self.assertAllEqual( + [b'split_inputs/split:0', b'split_inputs/split:1'], + session.run( + estimator_spec.export_outputs['classification_classes'].classes)) + self.assertEqual( + None, estimator_spec.export_outputs['classification_classes'].scores) + + def test_merge_regression_output(self): + with self.test_session() as session: + estimator_spec = self.replicate_estimator_spec(session) + self.assertAllClose( + [0.1, 0.02], + session.run(estimator_spec.export_outputs['regression_output'].value)) + + +class GetLocalDevicesTest(test_util.TensorFlowTestCase): + + def test_there_is_at_least_a_cpu(self): + self.assertTrue(replicate_model_fn._get_local_devices('CPU')) + + def test_there_is_no_xpu(self): + self.assertFalse( + replicate_model_fn._get_local_devices('XPU')) # XPU doesn't exist. + + def test_whether_there_is_a_gpu(self): + if test.is_gpu_available(): + self.assertTrue(len(replicate_model_fn._get_local_devices('GPU'))) + + +class LocalDeviceSetterTest(test_util.TensorFlowTestCase): + + def test_vars_are_on_ps_but_ops_are_on_workers(self): + ps_devices = ['/device:GPU:3'] + round_robin = device_setter._RoundRobinStrategy(num_tasks=len(ps_devices)) + + local_device_setter = replicate_model_fn._local_device_setter( + ps_devices=ps_devices, + ps_strategy=round_robin, + worker_device='/device:GPU:2') + + with ops_lib.device(local_device_setter): + a = variables.Variable(0.01) + self.assertEqual('/device:GPU:3', a.device) + + b = variables.Variable(0.02) + self.assertEqual('/device:GPU:3', b.device) + + c = variables.Variable(0.03) + self.assertEqual('/device:GPU:3', c.device) + + a_op = array_ops.concat(a, axis=0) + self.assertEqual('/device:GPU:2', a_op.device) + + b_op = array_ops.concat(b, axis=0) + self.assertEqual('/device:GPU:2', b_op.device) + + def test_round_robin_placement(self): + ps_devices = [ + '/device:GPU:0', '/device:GPU:1', '/device:GPU:3', '/device:GPU:4' + ] + round_robin = device_setter._RoundRobinStrategy(num_tasks=len(ps_devices)) + + local_device_setter = replicate_model_fn._local_device_setter( + ps_devices=ps_devices, + ps_strategy=round_robin, + worker_device='/device:GPU:2') + + with ops_lib.device(local_device_setter): + a = variables.Variable(0.01) + self.assertEqual('/device:GPU:0', a.device) + + b = variables.Variable(0.02) + self.assertEqual('/device:GPU:1', b.device) + + c = variables.Variable(0.03) + self.assertEqual('/device:GPU:3', c.device) + + a_op = array_ops.concat(a, axis=0) + self.assertEqual('/device:GPU:2', a_op.device) + + b_op = array_ops.concat(b, axis=0) + self.assertEqual('/device:GPU:2', b_op.device) + + c = variables.Variable(0.03) + self.assertEqual('/device:GPU:4', c.device) + + d = variables.Variable(0.03) + self.assertEqual('/device:GPU:0', d.device) + + c_op = array_ops.concat(c, axis=0) + self.assertEqual('/device:GPU:2', c_op.device) + + +class ComputeSumWithDevicePlacementTest(test_util.TensorFlowTestCase): + + def test_vectors(self): + with self.test_session() as session: + total = replicate_model_fn._compute_sum_on_device( + [1.0, 2.0, 3.0, 4.0], device='/device:GPU:0', name='test_sum') + + self.assertEqual('/device:GPU:0', total.device) + self.assertEqual('test_sum', total.op.name) + self.assertEqual(10.0, session.run(total)) + + def test_tensors(self): + with self.test_session() as session: + total = replicate_model_fn._compute_sum_on_device( + [[1.0, 2.0], [3.0, 4.0]], device='/device:GPU:0', name='test_sum') + + self.assertEqual('/device:GPU:0', total.device) + self.assertEqual('test_sum', total.op.name) + self.assertAllEqual([4.0, 6.0], session.run(total)) + + def test_indexedslices(self): + with self.test_session() as session: + a = ops_lib.IndexedSlices( + constant_op.constant([1.0, 2.0]), [0, 1], + dense_shape=constant_op.constant([2])) + b = ops_lib.IndexedSlices(constant_op.constant([3.0, 4.0]), [0, 1]) + + total = replicate_model_fn._compute_sum_on_device( + [a, b], device='/device:GPU:0') + + self.assertEqual('/device:GPU:0', total.device) + self.assertAllEqual([4.0, 6.0], + session.run(ops_lib.convert_to_tensor(total))) + + def test_indexedslices_higher_dimensions(self): + with self.test_session() as session: + a = ops_lib.IndexedSlices( + constant_op.constant([[1.0, 5.0], [2.0, 6.0]]), [0, 1], + dense_shape=constant_op.constant([2, 4])) + b = ops_lib.IndexedSlices( + constant_op.constant([[3.0, 7.0], [4.0, 8.0]]), [0, 1]) + + total = replicate_model_fn._compute_sum_on_device( + [a, b], device='/device:GPU:0') + + self.assertEqual('/device:GPU:0', total.device) + self.assertAllEqual([[4.0, 12.0], [6.0, 14.0]], + session.run(ops_lib.convert_to_tensor(total))) + + def test_indexedslices_some_dont_overlap(self): + with self.test_session() as session: + a = ops_lib.IndexedSlices( + constant_op.constant([1.0, 2.0]), [0, 3], + dense_shape=constant_op.constant([4])) + b = ops_lib.IndexedSlices(constant_op.constant([3.0, 4.0]), [0, 1]) + + total = replicate_model_fn._compute_sum_on_device( + [a, b], device='/device:GPU:0') + + self.assertEqual('/device:GPU:0', total.device) + self.assertAllEqual([4.0, 4.0, 0.0, 2.0], + session.run(ops_lib.convert_to_tensor(total))) + + def test_no_name_for_indexslices(self): + a = ops_lib.IndexedSlices( + constant_op.constant([1.0, 2.0]), [0, 1], + dense_shape=constant_op.constant([2])) + b = ops_lib.IndexedSlices(constant_op.constant([3.0, 4.0]), [0, 1]) + + with self.assertRaisesRegexp(ValueError, '.+name.+not.+expected.+'): + _ = replicate_model_fn._compute_sum_on_device( + [a, b], device='/device:GPU:0', name='cant_name_indexslices') + + +class ConcatTensorDictsTest(test_util.TensorFlowTestCase): + + def test_example(self): + tensor_dicts = [ + { + 'a': np.array([1.0, 2.0]), + 'b': np.array([11.0]), + 'c': np.array([21.0]), + }, + { + 'a': np.array([3.0]), + 'b': np.array([12.0, 13.0]), + }, + { + 'b': np.array([14.0]), + }, + ] + + with self.test_session() as session: + self.assertAllClose({ + 'a': np.array([1.0, 2.0, 3.0]), + 'b': np.array([11.0, 12.0, 13.0, 14.0]), + 'c': np.array([21.0]), + }, session.run(replicate_model_fn._concat_tensor_dicts(*tensor_dicts))) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/python/estimator/run_config.py b/tensorflow/python/estimator/run_config.py index 3e021242c4cc914990c6b38736b8f725213b5b7e..62f035bce558f57c5fd39d60b44cf8eb0130ce38 100644 --- a/tensorflow/python/estimator/run_config.py +++ b/tensorflow/python/estimator/run_config.py @@ -345,7 +345,7 @@ class RunConfig(object): os.environ['TF_CONFIG'] = json.dumps( {'cluster': cluster, 'task': {'type': 'worker', 'index': 1}}) - config = ClusterConfig() + config = RunConfig() assert config.master == 'host4:2222' assert config.task_id == 1 assert config.num_ps_replicas == 2 @@ -363,7 +363,7 @@ class RunConfig(object): os.environ['TF_CONFIG'] = json.dumps( {'cluster': cluster, 'task': {'type': 'chief', 'index': 0}}) - config = ClusterConfig() + config = RunConfig() assert config.master == 'host0:2222' assert config.task_id == 0 assert config.num_ps_replicas == 2 @@ -381,7 +381,7 @@ class RunConfig(object): os.environ['TF_CONFIG'] = json.dumps( {'cluster': cluster, 'task': {'type': 'evaluator', 'index': 0}}) - config = ClusterConfig() + config = RunConfig() assert config.master == '' assert config.evaluator_master == '' assert config.task_id == 0 diff --git a/tensorflow/python/estimator/training.py b/tensorflow/python/estimator/training.py index 2cc3331a15867e9a984847391857bf84baee7424..e38b765da52a7b6957a4fb8a02087c5d1fd5a781 100644 --- a/tensorflow/python/estimator/training.py +++ b/tensorflow/python/estimator/training.py @@ -128,9 +128,16 @@ class TrainSpec( """Creates a validated `TrainSpec` instance. Args: - input_fn: Training input function returning a tuple of: - features - `Tensor` or dictionary of string feature name to `Tensor`. - labels - `Tensor` or dictionary of `Tensor` with labels. + input_fn: A function that provides input data for training as minibatches. + See @{$get_started/premade_estimators#create_input_functions} for more + information. The function should construct and return one of + the following: + * A 'tf.data.Dataset' object: Outputs of `Dataset` object must be a + tuple (features, labels) with same constraints as below. + * A tuple (features, labels): Where features is a `Tensor` or a + dictionary of string feature name to `Tensor` and labels is a + `Tensor` or a dictionary of string label name to `Tensor`. + max_steps: Int. Positive number of total steps for which to train model. If `None`, train forever. The training `input_fn` is not expected to generate `OutOfRangeError` or `StopIteration` exceptions. See the @@ -185,9 +192,16 @@ class EvalSpec( """Creates a validated `EvalSpec` instance. Args: - input_fn: Evaluation input function returning a tuple of: - features - `Tensor` or dictionary of string feature name to `Tensor`. - labels - `Tensor` or dictionary of `Tensor` with labels. + input_fn: A function that constructs the input data for evaluation. + See @{$get_started/premade_estimators#create_input_functions} for more + information. The function should construct and return one of + the following: + * A 'tf.data.Dataset' object: Outputs of `Dataset` object must be a + tuple (features, labels) with same constraints as below. + * A tuple (features, labels): Where features is a `Tensor` or a + dictionary of string feature name to `Tensor` and labels is a + `Tensor` or a dictionary of string label name to `Tensor`. + steps: Int. Positive number of steps for which to evaluate model. If `None`, evaluates until `input_fn` raises an end-of-input exception. See `Estimator.evaluate` for details. diff --git a/tensorflow/python/estimator/util.py b/tensorflow/python/estimator/util.py index 3ce8eea84b6bf601ce89dfaa7d8e3a5d193468b3..bb4bdd3fdfb2e19dbc1c581d7771f2e1ac4442ba 100644 --- a/tensorflow/python/estimator/util.py +++ b/tensorflow/python/estimator/util.py @@ -20,7 +20,12 @@ from __future__ import division from __future__ import print_function import functools +import os +import time +from tensorflow.python.platform import gfile +from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.util import compat from tensorflow.python.util import tf_decorator from tensorflow.python.util import tf_inspect @@ -56,3 +61,48 @@ def fn_args(fn): if _is_bounded_method(fn): args.remove('self') return tuple(args) + + +# When we create a timestamped directory, there is a small chance that the +# directory already exists because another process is also creating these +# directories. In this case we just wait one second to get a new timestamp and +# try again. If this fails several times in a row, then something is seriously +# wrong. +MAX_DIRECTORY_CREATION_ATTEMPTS = 10 + + +def get_timestamped_dir(dir_base): + """Builds a path to a new subdirectory within the base directory. + + The subdirectory will be named using the current time. + This guarantees monotonically increasing directory numbers even across + multiple runs of the pipeline. + The timestamp used is the number of seconds since epoch UTC. + + Args: + dir_base: A string containing a directory to create the subdirectory under. + + Returns: + The full path of the new subdirectory (which is not actually created yet). + + Raises: + RuntimeError: if repeated attempts fail to obtain a unique timestamped + directory name. + """ + attempts = 0 + while attempts < MAX_DIRECTORY_CREATION_ATTEMPTS: + timestamp = int(time.time()) + + result_dir = os.path.join( + compat.as_bytes(dir_base), compat.as_bytes(str(timestamp))) + if not gfile.Exists(result_dir): + # Collisions are still possible (though extremely unlikely): this + # directory is not actually created yet, but it will be almost + # instantly on return from this function. + return result_dir + time.sleep(1) + attempts += 1 + logging.warn('Directory {} already exists; retrying (attempt {}/{})'.format( + result_dir, attempts, MAX_DIRECTORY_CREATION_ATTEMPTS)) + raise RuntimeError('Failed to obtain a unique export directory name after ' + '{} attempts.'.format(MAX_DIRECTORY_CREATION_ATTEMPTS)) diff --git a/tensorflow/python/feature_column/BUILD b/tensorflow/python/feature_column/BUILD index a758f8a4fc4898713772c4e919acda48b0f6ad0b..238a90b67d9d0039c25a6f3800aad25a2db9e36f 100644 --- a/tensorflow/python/feature_column/BUILD +++ b/tensorflow/python/feature_column/BUILD @@ -74,7 +74,10 @@ py_test( srcs = ["feature_column_test.py"], data = [":vocabulary_testdata"], srcs_version = "PY2AND3", - tags = ["no_pip"], + tags = [ + "no_cuda_on_cpu_tap", + "no_pip", + ], deps = [ ":feature_column", ":feature_column_py", diff --git a/tensorflow/python/feature_column/feature_column.py b/tensorflow/python/feature_column/feature_column.py index c416881c3119c160d28f4b8e37cd2aeb22f239a6..381153c66a561e0ef9c494a50e0de3b85a0b2879 100644 --- a/tensorflow/python/feature_column/feature_column.py +++ b/tensorflow/python/feature_column/feature_column.py @@ -16,7 +16,7 @@ FeatureColumns provide a high level abstraction for ingesting and representing features. FeatureColumns are also the primary way of encoding features for -canned ${tf.estimator.Estimator}s. +canned @{tf.estimator.Estimator}s. When using FeatureColumns with `Estimators`, the type of feature column you should choose depends on (1) the feature type and (2) the model type. @@ -1626,7 +1626,7 @@ class _FeatureColumn(object): It is used for get_parsing_spec for `tf.parse_example`. Returned spec is a dict from keys ('string') to `VarLenFeature`, `FixedLenFeature`, and other - supported objects. Please check documentation of ${tf.parse_example} for all + supported objects. Please check documentation of @{tf.parse_example} for all supported spec objects. Let's say a Feature column depends on raw feature ('raw') and another @@ -1677,7 +1677,7 @@ class _DenseColumn(_FeatureColumn): weight_collections: List of graph collections to which Variables (if any will be created) are added. trainable: If `True` also add variables to the graph collection - `GraphKeys.TRAINABLE_VARIABLES` (see ${tf.Variable}). + `GraphKeys.TRAINABLE_VARIABLES` (see @{tf.Variable}). Returns: `Tensor` of shape [batch_size] + `_variable_shape`. @@ -1735,7 +1735,7 @@ class _CategoricalColumn(_FeatureColumn): WARNING: Do not subclass this layer unless you know what you are doing: the API is subject to future changes. - A categorical feature typically handled with a ${tf.SparseTensor} of IDs. + A categorical feature typically handled with a @{tf.SparseTensor} of IDs. """ __metaclass__ = abc.ABCMeta @@ -1770,7 +1770,7 @@ class _CategoricalColumn(_FeatureColumn): weight_collections: List of graph collections to which variables (if any will be created) are added. trainable: If `True` also add variables to the graph collection - `GraphKeys.TRAINABLE_VARIABLES` (see ${tf.get_variable}). + `GraphKeys.TRAINABLE_VARIABLES` (see @{tf.get_variable}). """ pass @@ -1804,6 +1804,21 @@ def _create_categorical_column_weighted_sum( name='weighted_sum') +class _SequenceDenseColumn(_FeatureColumn): + """Represents dense sequence data.""" + + __metaclass__ = abc.ABCMeta + + TensorSequenceLengthPair = collections.namedtuple( # pylint: disable=invalid-name + 'TensorSequenceLengthPair', ['dense_tensor', 'sequence_length']) + + @abc.abstractmethod + def _get_sequence_dense_tensor( + self, inputs, weight_collections=None, trainable=None): + """Returns a `TensorSequenceLengthPair`.""" + pass + + class _LazyBuilder(object): """Handles caching of transformations while building the model. @@ -2152,7 +2167,7 @@ class _BucketizedColumn(_DenseColumn, _CategoricalColumn, class _EmbeddingColumn( - _DenseColumn, + _DenseColumn, _SequenceDenseColumn, collections.namedtuple('_EmbeddingColumn', ( 'categorical_column', 'dimension', 'combiner', 'initializer', 'ckpt_to_load_from', 'tensor_name_in_ckpt', 'max_norm', 'trainable' @@ -2178,7 +2193,9 @@ class _EmbeddingColumn( self._shape = tensor_shape.vector(self.dimension) return self._shape - def _get_dense_tensor(self, inputs, weight_collections=None, trainable=None): + def _get_dense_tensor_internal( + self, inputs, weight_collections=None, trainable=None): + """Private method that follows the signature of _get_dense_tensor.""" # Get sparse IDs and weights. sparse_tensors = self.categorical_column._get_sparse_tensors( # pylint: disable=protected-access inputs, weight_collections=weight_collections, trainable=trainable) @@ -2210,6 +2227,43 @@ class _EmbeddingColumn( name='%s_weights' % self.name, max_norm=self.max_norm) + def _get_dense_tensor(self, inputs, weight_collections=None, trainable=None): + if isinstance(self.categorical_column, _SequenceCategoricalColumn): + raise ValueError( + 'In embedding_column: {}. ' + 'categorical_column must not be of type _SequenceCategoricalColumn. ' + 'Suggested fix A: If you wish to use input_layer, use a ' + 'non-sequence categorical_column_with_*. ' + 'Suggested fix B: If you wish to create sequence input, use ' + 'sequence_input_layer instead of input_layer. ' + 'Given (type {}): {}'.format( + self.name, type(self.categorical_column), + self.categorical_column)) + return self._get_dense_tensor_internal( + inputs=inputs, weight_collections=weight_collections, + trainable=trainable) + + def _get_sequence_dense_tensor( + self, inputs, weight_collections=None, trainable=None): + if not isinstance(self.categorical_column, _SequenceCategoricalColumn): + raise ValueError( + 'In embedding_column: {}. ' + 'categorical_column must be of type _SequenceCategoricalColumn ' + 'to use sequence_input_layer. ' + 'Suggested fix: Use one of sequence_categorical_column_with_*. ' + 'Given (type {}): {}'.format( + self.name, type(self.categorical_column), + self.categorical_column)) + dense_tensor = self._get_dense_tensor_internal( # pylint: disable=protected-access + inputs=inputs, + weight_collections=weight_collections, + trainable=trainable) + sparse_tensors = self.categorical_column._get_sparse_tensors(inputs) # pylint: disable=protected-access + sequence_length = _sequence_length_from_sparse_tensor( + sparse_tensors.id_tensor) + return _SequenceDenseColumn.TensorSequenceLengthPair( + dense_tensor=dense_tensor, sequence_length=sequence_length) + class _SharedEmbeddingColumn( _DenseColumn, @@ -2890,7 +2944,7 @@ def _prune_invalid_ids(sparse_ids, sparse_weights): return sparse_ids, sparse_weights -class _IndicatorColumn(_DenseColumn, +class _IndicatorColumn(_DenseColumn, _SequenceDenseColumn, collections.namedtuple('_IndicatorColumn', ['categorical_column'])): """Represents a one-hot column for use in deep networks. @@ -2966,15 +3020,53 @@ class _IndicatorColumn(_DenseColumn, Returns: Dense `Tensor` created within `_transform_feature`. + + Raises: + ValueError: If `categorical_column` is a `_SequenceCategoricalColumn`. """ # Do nothing with weight_collections and trainable since no variables are # created in this function. del weight_collections del trainable + if isinstance(self.categorical_column, _SequenceCategoricalColumn): + raise ValueError( + 'In indicator_column: {}. ' + 'categorical_column must not be of type _SequenceCategoricalColumn. ' + 'Suggested fix A: If you wish to use input_layer, use a ' + 'non-sequence categorical_column_with_*. ' + 'Suggested fix B: If you wish to create sequence input, use ' + 'sequence_input_layer instead of input_layer. ' + 'Given (type {}): {}'.format( + self.name, type(self.categorical_column), + self.categorical_column)) # Feature has been already transformed. Return the intermediate # representation created by _transform_feature. return inputs.get(self) + def _get_sequence_dense_tensor( + self, inputs, weight_collections=None, trainable=None): + # Do nothing with weight_collections and trainable since no variables are + # created in this function. + del weight_collections + del trainable + if not isinstance(self.categorical_column, _SequenceCategoricalColumn): + raise ValueError( + 'In indicator_column: {}. ' + 'categorical_column must be of type _SequenceCategoricalColumn ' + 'to use sequence_input_layer. ' + 'Suggested fix: Use one of sequence_categorical_column_with_*. ' + 'Given (type {}): {}'.format( + self.name, type(self.categorical_column), + self.categorical_column)) + # Feature has been already transformed. Return the intermediate + # representation created by _transform_feature. + dense_tensor = inputs.get(self) + sparse_tensors = self.categorical_column._get_sparse_tensors(inputs) # pylint: disable=protected-access + sequence_length = _sequence_length_from_sparse_tensor( + sparse_tensors.id_tensor) + return _SequenceDenseColumn.TensorSequenceLengthPair( + dense_tensor=dense_tensor, sequence_length=sequence_length) + def _verify_static_batch_size_equality(tensors, columns): # bath_size is a tf.Dimension object. @@ -2990,3 +3082,68 @@ def _verify_static_batch_size_equality(tensors, columns): 'Batch size of columns ({}, {}): ({}, {})'.format( columns[bath_size_column_index].name, columns[i].name, expected_batch_size, tensors[i].shape[0])) + + +def _sequence_length_from_sparse_tensor(sp_tensor, num_elements=1): + """Returns a [batch_size] Tensor with per-example sequence length.""" + with ops.name_scope(None, 'sequence_length') as name_scope: + row_ids = sp_tensor.indices[:, 0] + column_ids = sp_tensor.indices[:, 1] + column_ids += array_ops.ones_like(column_ids) + seq_length = math_ops.to_int64( + math_ops.segment_max(column_ids, segment_ids=row_ids) / num_elements) + # If the last n rows do not have ids, seq_length will have shape + # [batch_size - n]. Pad the remaining values with zeros. + n_pad = array_ops.shape(sp_tensor)[:1] - array_ops.shape(seq_length)[:1] + padding = array_ops.zeros(n_pad, dtype=seq_length.dtype) + return array_ops.concat([seq_length, padding], axis=0, name=name_scope) + + +class _SequenceCategoricalColumn( + _CategoricalColumn, + collections.namedtuple( + '_SequenceCategoricalColumn', ['categorical_column'])): + """Represents sequences of categorical data.""" + + @property + def name(self): + return self.categorical_column.name + + @property + def _parse_example_spec(self): + return self.categorical_column._parse_example_spec # pylint: disable=protected-access + + def _transform_feature(self, inputs): + return self.categorical_column._transform_feature(inputs) # pylint: disable=protected-access + + @property + def _num_buckets(self): + return self.categorical_column._num_buckets # pylint: disable=protected-access + + def _get_sparse_tensors(self, inputs, weight_collections=None, + trainable=None): + sparse_tensors = self.categorical_column._get_sparse_tensors(inputs) # pylint: disable=protected-access + id_tensor = sparse_tensors.id_tensor + weight_tensor = sparse_tensors.weight_tensor + # Expands final dimension, so that embeddings are not combined during + # embedding lookup. + check_id_rank = check_ops.assert_equal( + array_ops.rank(id_tensor), 2, + data=[ + 'Column {} expected ID tensor of rank 2. '.format(self.name), + 'id_tensor shape: ', array_ops.shape(id_tensor)]) + with ops.control_dependencies([check_id_rank]): + id_tensor = sparse_ops.sparse_reshape( + id_tensor, + shape=array_ops.concat([id_tensor.dense_shape, [1]], axis=0)) + if weight_tensor is not None: + check_weight_rank = check_ops.assert_equal( + array_ops.rank(weight_tensor), 2, + data=[ + 'Column {} expected weight tensor of rank 2.'.format(self.name), + 'weight_tensor shape:', array_ops.shape(weight_tensor)]) + with ops.control_dependencies([check_weight_rank]): + weight_tensor = sparse_ops.sparse_reshape( + weight_tensor, + shape=array_ops.concat([weight_tensor.dense_shape, [1]], axis=0)) + return _CategoricalColumn.IdWeightPair(id_tensor, weight_tensor) diff --git a/tensorflow/python/framework/constant_op.py b/tensorflow/python/framework/constant_op.py index d3d8c9c154fbfcc9613acce4e1bdab7df2e7d56d..782b505d6c1d0b576b7734f088c4d2c9625f4be2 100644 --- a/tensorflow/python/framework/constant_op.py +++ b/tensorflow/python/framework/constant_op.py @@ -181,7 +181,7 @@ def constant(value, dtype=None, shape=None, name="Const", verify_shape=False): TypeError: if shape is incorrectly specified or unsupported. """ ctx = context.context() - if not ctx.in_graph_mode(): + if ctx.executing_eagerly(): t = convert_to_eager_tensor(value, ctx, dtype) if shape is None: return t diff --git a/tensorflow/python/framework/dtypes.py b/tensorflow/python/framework/dtypes.py index 99ae8b24f11c4955379ae532ba7b921ebec63385..0edae92fd4a86e7d10a180ce64364d3ea552bf60 100644 --- a/tensorflow/python/framework/dtypes.py +++ b/tensorflow/python/framework/dtypes.py @@ -343,7 +343,9 @@ tf_export("uint8").export_constant(__name__, "uint8") uint16 = DType(types_pb2.DT_UINT16) tf_export("uint16").export_constant(__name__, "uint16") uint32 = DType(types_pb2.DT_UINT32) +tf_export("uint32").export_constant(__name__, "uint32") uint64 = DType(types_pb2.DT_UINT64) +tf_export("uint64").export_constant(__name__, "uint32") int16 = DType(types_pb2.DT_INT16) tf_export("int16").export_constant(__name__, "int16") int8 = DType(types_pb2.DT_INT8) diff --git a/tensorflow/python/framework/framework_lib.py b/tensorflow/python/framework/framework_lib.py index 3172f3c2c3d259d2c3f2b340b101aef043d0fc33..392a4f65c6e62c3cb70f8e02a9b24f015a09f649 100644 --- a/tensorflow/python/framework/framework_lib.py +++ b/tensorflow/python/framework/framework_lib.py @@ -48,6 +48,7 @@ ## Graph collections @@add_to_collection +@@add_to_collections @@get_collection @@get_collection_ref @@GraphKeys @@ -92,6 +93,7 @@ from tensorflow.python.framework.ops import get_default_graph from tensorflow.python.framework.ops import reset_default_graph from tensorflow.python.framework.ops import GraphKeys from tensorflow.python.framework.ops import add_to_collection +from tensorflow.python.framework.ops import add_to_collections from tensorflow.python.framework.ops import get_collection from tensorflow.python.framework.ops import get_collection_ref from tensorflow.python.framework.ops import convert_to_tensor diff --git a/tensorflow/python/framework/function.py b/tensorflow/python/framework/function.py index caa604999c2fad4ce111d910a77e4b99399c11ca..14d72d8a3de7e22bee4f9961c2f66044c217f641 100644 --- a/tensorflow/python/framework/function.py +++ b/tensorflow/python/framework/function.py @@ -489,10 +489,10 @@ class _DefinedFunction(object): # Adds this function into 'g'. # pylint: disable=protected-access - if context.in_graph_mode(): - g._add_function(self) - else: + if context.executing_eagerly(): context.context().add_function_def(self.definition) + else: + g._add_function(self) # pylint: enable=protected-access # Ensures related sub-routines are defined in 'g', too. diff --git a/tensorflow/python/framework/function_test.py b/tensorflow/python/framework/function_test.py index 52052ba77d42fa91692e7699f49898d0c01c22be..65ca801cbe922b36e3bc72bc2fbcd88f66aa5290 100644 --- a/tensorflow/python/framework/function_test.py +++ b/tensorflow/python/framework/function_test.py @@ -193,7 +193,7 @@ class FunctionTest(test.TestCase): @function.Defun(dtypes.float32, dtypes.float32) def XSquarePlusOneGrad(x, dy): - dx = functional_ops._symbolic_gradient( + dx = functional_ops.symbolic_gradient( input=[x, dy], Tout=[dtypes.float32], f="XSquarePlusOneFn", name="dx") return dx @@ -295,7 +295,7 @@ class FunctionTest(test.TestCase): # gradient function is (x, y, dz) -> (dx, dy). dx's shape # should be the same as x's; and dy's shape should be the same # as y's. - dx, dy = functional_ops._symbolic_gradient( + dx, dy = functional_ops.symbolic_gradient( input=[x, y, dz], Tout=[dtypes.float32] * 2, f="Foo") self.assertEqual(x.get_shape(), dx.get_shape()) self.assertEqual(y.get_shape(), dy.get_shape()) diff --git a/tensorflow/python/framework/graph_util_impl.py b/tensorflow/python/framework/graph_util_impl.py index 5a543317e665a940841714fd72d834a430f8406a..910364364c8be84b1a629dbdaae5e69443d07e75 100644 --- a/tensorflow/python/framework/graph_util_impl.py +++ b/tensorflow/python/framework/graph_util_impl.py @@ -235,7 +235,7 @@ def convert_variables_to_constants(sess, variable_names = [] variable_dict_names = [] for node in inference_graph.node: - if node.op in ["Variable", "VariableV2"]: + if node.op in ["Variable", "VariableV2", "VarHandleOp"]: variable_name = node.name if ((variable_names_whitelist is not None and variable_name not in variable_names_whitelist) or @@ -243,7 +243,10 @@ def convert_variables_to_constants(sess, variable_name in variable_names_blacklist)): continue variable_dict_names.append(variable_name) - variable_names.append(variable_name + ":0") + if node.op == "VarHandleOp": + variable_names.append(variable_name + "/Read/ReadVariableOp:0") + else: + variable_names.append(variable_name + ":0") if variable_names: returned_variables = sess.run(variable_names) else: @@ -266,6 +269,17 @@ def convert_variables_to_constants(sess, tensor=tensor_util.make_tensor_proto( data, dtype=dtype.type, shape=data.shape))) how_many_converted += 1 + elif input_node.op == "ReadVariableOp" and ( + input_node.input[0] in found_variables): + # The preceding branch converts all VarHandleOps of ResourceVariables to + # constants, so we need to convert the associated ReadVariableOps to + # Identity ops. + output_node.op = "Identity" + output_node.name = input_node.name + output_node.input.extend([input_node.input[0]]) + output_node.attr["T"].CopyFrom(input_node.attr["dtype"]) + if "_class" in input_node.attr: + output_node.attr["_class"].CopyFrom(input_node.attr["_class"]) else: output_node.CopyFrom(input_node) output_graph_def.node.extend([output_node]) diff --git a/tensorflow/python/framework/graph_util_test.py b/tensorflow/python/framework/graph_util_test.py index 0421837d49de753d642aed59d1524619a243dcb8..b618152b0256fd043dc7259960d867278ba55b0a 100644 --- a/tensorflow/python/framework/graph_util_test.py +++ b/tensorflow/python/framework/graph_util_test.py @@ -32,6 +32,7 @@ from tensorflow.python.framework import tensor_util from tensorflow.python.ops import gen_state_ops from tensorflow.python.ops import math_ops # pylint: disable=unused-import from tensorflow.python.ops import math_ops as math_ops_lib +from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables from tensorflow.python.platform import test @@ -47,46 +48,46 @@ class DeviceFunctionsTest(test.TestCase): def testTwoDeviceFunctions(self): with ops.Graph().as_default() as g: - var_0 = gen_state_ops._variable( + var_0 = gen_state_ops.variable( shape=[1], dtype=dtypes.float32, name="var_0", container="", shared_name="") with g.device(test_device_func_pin_variable_to_cpu): - var_1 = gen_state_ops._variable( + var_1 = gen_state_ops.variable( shape=[1], dtype=dtypes.float32, name="var_1", container="", shared_name="") - var_2 = gen_state_ops._variable( + var_2 = gen_state_ops.variable( shape=[1], dtype=dtypes.float32, name="var_2", container="", shared_name="") - var_3 = gen_state_ops._variable( + var_3 = gen_state_ops.variable( shape=[1], dtype=dtypes.float32, name="var_3", container="", shared_name="") with g.device(test_device_func_pin_variable_to_cpu): - var_4 = gen_state_ops._variable( + var_4 = gen_state_ops.variable( shape=[1], dtype=dtypes.float32, name="var_4", container="", shared_name="") with g.device("/device:GPU:0"): - var_5 = gen_state_ops._variable( + var_5 = gen_state_ops.variable( shape=[1], dtype=dtypes.float32, name="var_5", container="", shared_name="") - var_6 = gen_state_ops._variable( + var_6 = gen_state_ops.variable( shape=[1], dtype=dtypes.float32, name="var_6", @@ -226,52 +227,62 @@ class DeviceFunctionsTest(test.TestCase): constant_graph_def.library) def testConvertVariablesToConsts(self): - with ops.Graph().as_default(): - variable_node = variables.Variable(1.0, name="variable_node") - _ = variables.Variable(1.0, name="unused_variable_node") - output_node = math_ops_lib.multiply( - variable_node, 2.0, name="output_node") - with session.Session() as sess: - init = variables.initialize_variables([variable_node]) - sess.run(init) - output = sess.run(output_node) - self.assertNear(2.0, output, 0.00001) - variable_graph_def = sess.graph.as_graph_def() - # First get the constant_graph_def when variable_names_whitelist is set, - # note that if variable_names_whitelist is not set an error will be - # thrown because unused_variable_node is not initialized. - constant_graph_def = graph_util.convert_variables_to_constants( - sess, - variable_graph_def, ["output_node"], - variable_names_whitelist=set(["variable_node"])) + self._test_variable_to_const_conversion(use_resource=False) - # Then initialize the unused variable, and get another - # constant_graph_def when variable_names_whitelist is not set. - sess.run(variables.global_variables_initializer()) - constant_graph_def_without_variable_whitelist = ( - graph_util.convert_variables_to_constants(sess, variable_graph_def, - ["output_node"])) - - # The unused variable should be cleared so the two graphs should be - # equivalent. - self.assertEqual( - str(constant_graph_def), - str(constant_graph_def_without_variable_whitelist)) - - # Test variable name black list. This should result in the variable not - # being a const. - sess.run(variables.global_variables_initializer()) - constant_graph_def_with_blacklist = ( - graph_util.convert_variables_to_constants( - sess, - variable_graph_def, ["output_node"], - variable_names_blacklist=set(["variable_node"]))) - variable_node = None - for node in constant_graph_def_with_blacklist.node: - if node.name == "variable_node": - variable_node = node - self.assertIsNotNone(variable_node) - self.assertEqual(variable_node.op, "VariableV2") + def testConvertResourceVariablesToConsts(self): + self._test_variable_to_const_conversion(use_resource=True) + + def _test_variable_to_const_conversion(self, use_resource): + with ops.Graph().as_default(): + with variable_scope.variable_scope("", use_resource=use_resource): + variable_node = variable_scope.get_variable( + "variable_node", initializer=1.0) + another_variable = variable_scope.get_variable( + "unused_variable_node", initializer=1.0) + output_node = math_ops_lib.multiply( + variable_node, 2.0, name="output_node") + with session.Session() as sess: + sess.run(variable_node.initializer) + output = sess.run(output_node) + self.assertNear(2.0, output, 0.00001) + variable_graph_def = sess.graph.as_graph_def() + # First get the constant_graph_def when variable_names_whitelist is + # set, note that if variable_names_whitelist is not set an error will + # be thrown because unused_variable_node is not initialized. + constant_graph_def = graph_util.convert_variables_to_constants( + sess, + variable_graph_def, ["output_node"], + variable_names_whitelist=set(["variable_node"])) + + # Then initialize the unused variable, and get another + # constant_graph_def when variable_names_whitelist is not set. + sess.run(another_variable.initializer) + constant_graph_def_without_variable_whitelist = ( + graph_util.convert_variables_to_constants( + sess, variable_graph_def, ["output_node"])) + + # The unused variable should be cleared so the two graphs should be + # equivalent. + self.assertEqual( + str(constant_graph_def), + str(constant_graph_def_without_variable_whitelist)) + + # Test variable name black list. This should result in the variable + # not being a const. + constant_graph_def_with_blacklist = ( + graph_util.convert_variables_to_constants( + sess, + variable_graph_def, ["output_node"], + variable_names_blacklist=set(["variable_node"]))) + variable_node = None + for node in constant_graph_def_with_blacklist.node: + if node.name == "variable_node": + variable_node = node + self.assertIsNotNone(variable_node) + if use_resource: + self.assertEqual(variable_node.op, "VarHandleOp") + else: + self.assertEqual(variable_node.op, "VariableV2") # Now we make sure the variable is now a constant, and that the graph still # produces the expected result. @@ -279,8 +290,9 @@ class DeviceFunctionsTest(test.TestCase): _ = importer.import_graph_def(constant_graph_def, name="") self.assertEqual(4, len(constant_graph_def.node)) for node in constant_graph_def.node: - self.assertNotEqual("Variable", node.op) - self.assertNotEqual("VariableV2", node.op) + self.assertNotIn( + node.op, + ["Variable", "VariableV2", "VarHandleOp", "ReadVariableOp"]) with session.Session() as sess: output_node = sess.graph.get_tensor_by_name("output_node:0") output = sess.run(output_node) diff --git a/tensorflow/python/framework/importer.py b/tensorflow/python/framework/importer.py index 6ecc1a40ae14760dd39242aaf595b32a9decdc9f..783e9259ad1fae4eb05fc7beeb5be83ae3f7fdce 100644 --- a/tensorflow/python/framework/importer.py +++ b/tensorflow/python/framework/importer.py @@ -475,16 +475,21 @@ def import_graph_def(graph_def, _PopulateTFImportGraphDefOptions(options, prefix, input_map, return_elements) - with c_api_util.tf_buffer(graph_def.SerializeToString()) as serialized: - try: - with errors.raise_exception_on_not_ok_status() as status: - results = c_api.TF_GraphImportGraphDefWithResults( - graph._c_graph, serialized, options, status) # pylint: disable=protected-access - except errors.InvalidArgumentError as e: - # Convert to ValueError for backwards compatibility. - raise ValueError(str(e)) - - _ProcessNewOps(graph) + # _ProcessNewOps mutates the new operations. _lock ensures a Session.run + # call cannot occur between creating the TF_Operations in the + # TF_GraphImportGraphDefWithResults call and mutating the them in + # _ProcessNewOps. + with graph._lock: # pylint: disable=protected-access + with c_api_util.tf_buffer(graph_def.SerializeToString()) as serialized: + try: + with errors.raise_exception_on_not_ok_status() as status: + results = c_api.TF_GraphImportGraphDefWithResults( + graph._c_graph, serialized, options, status) # pylint: disable=protected-access + except errors.InvalidArgumentError as e: + # Convert to ValueError for backwards compatibility. + raise ValueError(str(e)) + + _ProcessNewOps(graph) # Create _DefinedFunctions for any imported functions. # diff --git a/tensorflow/python/framework/importer_test.py b/tensorflow/python/framework/importer_test.py index bf5d9fe0936882c242198bdc7118f9f3a4e79260..c39191e6d90f5f00806eba021ea41dd6ebe86f98 100644 --- a/tensorflow/python/framework/importer_test.py +++ b/tensorflow/python/framework/importer_test.py @@ -31,7 +31,6 @@ from tensorflow.python.framework import function from tensorflow.python.framework import importer from tensorflow.python.framework import ops from tensorflow.python.framework import test_ops # pylint: disable=unused-import -from tensorflow.python.framework import test_util from tensorflow.python.framework import versions from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops @@ -44,7 +43,8 @@ import tensorflow.python.ops.nn_grad # pylint: disable=unused-import from tensorflow.python.platform import test -@test_util.with_c_api +# TODO(skyewm): reenable when this works with _USE_C_SHAPES=False +# @test_util.with_c_api class ImportGraphDefTest(test.TestCase): def _MakeGraphDef(self, diff --git a/tensorflow/python/framework/meta_graph.py b/tensorflow/python/framework/meta_graph.py index 4c1bd736d727e974375ad9008a579361137fb9d6..4bb9941bb778ae9b4022ef9d376cb031223ddb1c 100644 --- a/tensorflow/python/framework/meta_graph.py +++ b/tensorflow/python/framework/meta_graph.py @@ -695,7 +695,7 @@ def import_scoped_meta_graph(meta_graph_or_file, Raises: ValueError: If the graph_def contains unbound inputs. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise ValueError("Exporting/importing meta graphs is not supported when " "eager execution is enabled.") if isinstance(meta_graph_or_file, meta_graph_pb2.MetaGraphDef): @@ -856,7 +856,7 @@ def export_scoped_meta_graph(filename=None, Raises: ValueError: When the `GraphDef` is larger than 2GB. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise ValueError("Exporting/importing meta graphs is not supported when " "Eager Execution is enabled.") graph = graph or ops.get_default_graph() diff --git a/tensorflow/python/framework/meta_graph_test.py b/tensorflow/python/framework/meta_graph_test.py index 19dcd6a1b34741290b2578d93b79883c103fdb1b..06cec504e407301c7cfbceaa04a07674fe87c712 100644 --- a/tensorflow/python/framework/meta_graph_test.py +++ b/tensorflow/python/framework/meta_graph_test.py @@ -285,7 +285,8 @@ class SimpleMetaGraphTest(test.TestCase): self.assertIs(global_vars[0], trainable_vars[0]) -@test_util.with_c_api +# TODO(skyewm): reenable when this works with _USE_C_SHAPES=False +# @test_util.with_c_api class ScopedMetaGraphTest(test.TestCase): def _testScopedExport(self, test_dir, exported_filenames): @@ -905,20 +906,6 @@ class ExportImportAcrossScopesTest(test.TestCase): with variable_scope.variable_scope("importA/keepA"): graph_fn(use_resource=use_resource) - if use_resource: - # Bringing in collections that contain ResourceVariables will adds ops - # to the graph the first time a variable is encountered, so mimic the - # same behavior. - seen_variables = set() - for collection_key in sorted([ - ops.GraphKeys.GLOBAL_VARIABLES, - ops.GraphKeys.TRAINABLE_VARIABLES, - ]): - for var in expected_graph.get_collection(collection_key): - if var not in seen_variables: - var._read_variable_op() - seen_variables.add(var) - result = meta_graph.export_scoped_meta_graph(graph=imported_graph)[0] expected = meta_graph.export_scoped_meta_graph(graph=expected_graph)[0] diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py index 5a14ea417626265b8cee4bbec025f2bb9f5d4307..44df22cf58bfa8da2f9820e041d02668278ad018 100644 --- a/tensorflow/python/framework/ops.py +++ b/tensorflow/python/framework/ops.py @@ -63,6 +63,7 @@ from tensorflow.python.util.tf_export import tf_export # in code or via the environment variable. This will be removed once all # functionality is supported and there's no performance penalty with it enabled. _USE_C_API = os.getenv("TF_C_API_GRAPH_CONSTRUCTION", "0") is not "0" +_USE_C_SHAPES = os.getenv("TF_C_API_GRAPH_CONSTRUCTION_SHAPES", "0") is not "0" def tensor_id(tensor): @@ -369,7 +370,7 @@ class Tensor(_TensorLike): """ graph = self._op._graph._c_graph # pylint: disable=protected-access - if graph: + if graph and _USE_C_SHAPES: with errors.raise_exception_on_not_ok_status() as status: num_dims = c_api.TF_GraphGetTensorNumDims(graph, self._as_tf_output(), status) @@ -395,10 +396,10 @@ class Tensor(_TensorLike): "Tensor._shape cannot be assigned, use Tensor.set_shape instead.") def __iter__(self): - if context.in_graph_mode(): + if not context.executing_eagerly(): raise TypeError( - "`Tensor` objects are not iterable when eager execution is not " - "enabled. To iterate over this tensor use `tf.map_fn`.") + "Tensor objects are not iterable when eager execution is not " + "enabled. To iterate over this tensor use tf.map_fn.") shape = self._shape_tuple() if shape is None: raise TypeError("Cannot iterate over a tensor with unknown shape.") @@ -466,9 +467,13 @@ class Tensor(_TensorLike): ValueError: If `shape` is not compatible with the current shape of this tensor. """ - if not self._op._graph._c_graph: # pylint: disable=protected-access # ASIM + if not _USE_C_SHAPES: # pylint: disable=protected-access self._shape_val = self._shape_val.merge_with(shape) - return + + if not self._op._graph._c_graph: return + + # Update C shape even if _USE_C_SHAPES = False, since we still want + # set_shape to be reflected in the C API graph for when we run it. if not isinstance(shape, tensor_shape.TensorShape): shape = tensor_shape.TensorShape(shape) dim_list = [] @@ -772,7 +777,7 @@ class _EagerTensorBase(Tensor): six.raise_from(core._status_to_exception(e.code, e.message), None) # Record the copy on tape and define backprop copy as well. - if not context.in_graph_mode(): + if context.executing_eagerly(): self_device = self.device def grad_fun(dresult): return [dresult._copy(device_name=self_device)] @@ -782,7 +787,11 @@ class _EagerTensorBase(Tensor): @property def shape(self): - return tensor_shape.TensorShape(self._shape_tuple()) + if self._tensor_shape is None: # pylint: disable=access-member-before-definition + # `_tensor_shape` is declared and defined in the definition of + # `EagerTensor`, in C. + self._tensor_shape = tensor_shape.TensorShape(self._shape_tuple()) + return self._tensor_shape def get_shape(self): """Alias of Tensor.shape.""" @@ -989,7 +998,7 @@ def internal_convert_to_tensor(value, """ if ctx is None: ctx = context.context() - if ctx.in_eager_mode(): + if ctx.executing_eagerly(): # Fast path for EagerTensors that don't need any conversion. if isinstance(value, EagerTensor): # Note that we don't check that value's dtype matches the dtype @@ -2486,7 +2495,7 @@ def _set_shapes_for_outputs(op): def set_shapes_for_outputs(op): """Set the shapes for op's outputs.""" - if op._c_op: # pylint: disable=protected-access + if op._c_op and _USE_C_SHAPES: # pylint: disable=protected-access return _set_shapes_for_outputs_c_api(op) else: return _set_shapes_for_outputs(op) @@ -2690,15 +2699,20 @@ class Graph(object): def __init__(self): """Creates a new, empty Graph.""" - # Protects the core state that may be accessed by multiple readers. - # Only state that can be returned via public accessors (`as_graph_def()`, - # `get_operations()`, `as_graph_element()`, `get_collection()`, and - # `get_collection_ref()`) is by the lock. Thread-safety is provided on a - # best-effort basis to support buggy programs, and is not guaranteed by the - # public `tf.Graph` API. + # Protects core state that can be returned via public accessors, as well as + # synchronizes Session.run calls with methods that create and mutate ops + # (e.g. Graph.create_op()). This synchronization is necessary because it's + # illegal to modify an operation after it's been run. Thread-safety is + # provided on a best-effort basis to support buggy programs, and is not + # guaranteed by the public `tf.Graph` API. + # + # The lock must be reentrant because create_op can be called recursively due + # to control flow. Without a reentrant lock, many methods would also need a + # "locked" version or parameter (including generated code). + # # NOTE(mrry): This does not protect the various stacks. A warning will # be reported if these are used from multiple threads - self._lock = threading.Lock() + self._lock = threading.RLock() self._nodes_by_id = dict() # GUARDED_BY(self._lock) self._next_id_counter = 0 # GUARDED_BY(self._lock) self._nodes_by_name = dict() # GUARDED_BY(self._lock) @@ -2776,7 +2790,6 @@ class Graph(object): c_api.SetRequireShapeInferenceFns(self._c_graph, False) else: self._scoped_c_graph = None - self._variable_creator_stack = [] # TODO(apassos) remove once the C API is used by default. def _use_c_api_hack(self): @@ -2817,17 +2830,26 @@ class Graph(object): # frozen, and this functionality is still not ready for public visibility. @tf_contextlib.contextmanager def _variable_creator_scope(self, creator): + # This step makes a copy of the existing stack, and it also initializes + # self._thread_local._variable_creator_stack if it doesn't exist yet. old = list(self._variable_creator_stack) - self._variable_creator_stack.append(creator) + self._thread_local._variable_creator_stack.append(creator) try: yield finally: - self._variable_creator_stack = old + self._thread_local._variable_creator_stack = old # Note: this method is private because the API of tf.Graph() is public and # frozen, and this functionality is still not ready for public visibility. - def _get_variable_creator_stack(self): - return list(self._variable_creator_stack) + @property + def _variable_creator_stack(self): + if not hasattr(self._thread_local, "_variable_creator_stack"): + self._thread_local._variable_creator_stack = [] + return list(self._thread_local._variable_creator_stack) + + @_variable_creator_stack.setter + def _variable_creator_stack(self, variable_creator_stack): + self._thread_local._variable_creator_stack = variable_creator_stack def _extract_stack(self): """A lightweight, extensible re-implementation of traceback.extract_stack. @@ -3259,17 +3281,20 @@ class Graph(object): input_ops = set([t.op for t in inputs]) control_inputs = self._control_dependencies_for_inputs(input_ops) - ret = Operation( - node_def, - self, - inputs=inputs, - output_types=dtypes, - control_inputs=control_inputs, - input_types=input_types, - original_op=self._default_original_op, - op_def=op_def) - self._create_op_helper(ret, compute_shapes=compute_shapes, - compute_device=compute_device) + # _create_op_helper mutates the new Operation. _lock ensures a Session.run + # call cannot occur between creating and mutating the op. + with self._lock: + ret = Operation( + node_def, + self, + inputs=inputs, + output_types=dtypes, + control_inputs=control_inputs, + input_types=input_types, + original_op=self._default_original_op, + op_def=op_def) + self._create_op_helper(ret, compute_shapes=compute_shapes, + compute_device=compute_device) return ret def _create_op_from_tf_operation(self, c_op, compute_device=True): @@ -3308,7 +3333,7 @@ class Graph(object): # always computes shape information (even for function calls, which the # original Python shape inference code doesn't handle). Deprecate the # compute_shapes argument. - if op._c_op or compute_shapes: # pylint: disable=protected-access + if (op._c_op and _USE_C_SHAPES) or compute_shapes: # pylint: disable=protected-access set_shapes_for_outputs(op) # TODO(b/XXXX): move to Operation.__init__ once _USE_C_API flag is removed. self._add_op(op) @@ -4777,15 +4802,15 @@ def device(device_name_or_function): Raises: RuntimeError: If eager execution is enabled and a function is passed in. """ - if context.in_graph_mode(): - return get_default_graph().device(device_name_or_function) - else: + if context.executing_eagerly(): # TODO(agarwal): support device functions in EAGER mode. if callable(device_name_or_function): raise RuntimeError( "tf.device does not support functions when eager execution " "is enabled.") return context.device(device_name_or_function) + else: + return get_default_graph().device(device_name_or_function) @tf_export("container") @@ -4804,13 +4829,20 @@ def container(container_name): @tf_export("colocate_with") def colocate_with(op, ignore_existing=False): - if context.in_graph_mode(): - return get_default_graph().colocate_with(op, ignore_existing) - else: + if context.executing_eagerly(): if op is not None: return device(op.device) else: return _NullContextmanager() + else: + default_graph = get_default_graph() + if isinstance(op, EagerTensor): + if default_graph.building_function: + op = internal_convert_to_tensor(op) + else: + raise ValueError("Encountered an Eager-defined Tensor during graph " + "construction, but a function was not being built.") + return default_graph.colocate_with(op, ignore_existing) @tf_export("control_dependencies") @@ -4820,20 +4852,29 @@ def control_dependencies(control_inputs): See @{tf.Graph.control_dependencies} for more details. + When eager execution is enabled, any callable object in the `control_inputs` + list will be called. + Args: control_inputs: A list of `Operation` or `Tensor` objects which must be executed or computed before running the operations defined in the context. Can also be `None` to clear the control - dependencies. + dependencies. If eager execution is enabled, any callable object in the + `control_inputs` list will be called. Returns: A context manager that specifies control dependencies for all operations constructed within the context. """ - if context.in_graph_mode(): - return get_default_graph().control_dependencies(control_inputs) - else: + if context.executing_eagerly(): + if control_inputs: + # Excute any pending callables. + for control in control_inputs: + if callable(control): + control() return _NullContextmanager() + else: + return get_default_graph().control_dependencies(control_inputs) class _DefaultStack(threading.local): @@ -5096,75 +5137,106 @@ def init_scope(): """ # pylint: enable=g-doc-return-or-yield,line-too-long - in_graph_mode = context.in_graph_mode() - # Retrieve the active name scope: entering an `init_scope` preserves - # the name scope of the current context. - if in_graph_mode: + if context.executing_eagerly(): + # Fastpath. + with tape.stop_recording(): + yield + else: + # Retrieve the active name scope: entering an `init_scope` preserves + # the name scope of the current context. default_graph = get_default_graph() scope = default_graph.get_name_scope() - else: - scope = context.context().scope_name - if scope and scope[-1] != '/': - # Names that end with trailing slashes are treated by `name_scope` as - # absolute. - scope = scope + '/' - - outer_context = None - if in_graph_mode and not _default_graph_stack.stack: - outer_context = default_graph.as_default - else: - for stack_entry in reversed(context.context_stack.stack): - if not stack_entry.is_building_function: - outer_context = stack_entry.enter_context_fn - break + if scope and scope[-1] != '/': + # Names that end with trailing slashes are treated by `name_scope` as + # absolute. + scope = scope + '/' + + outer_context = None + if not _default_graph_stack.stack: + # If the default graph stack is empty, then we cannot be building a + # function. Install the global graph (which, in this case, is also the + # default graph) as the outer context. + if default_graph.building_function: + raise RuntimeError("The global graph is building a function.") + outer_context = default_graph.as_default + else: + # Find a context that is not building a function. + for stack_entry in reversed(context.context_stack.stack): + if not stack_entry.is_building_function: + outer_context = stack_entry.enter_context_fn + break + + if outer_context is None: + # As a last resort, obtain the global default graph; this graph doesn't + # necessarily live on the graph stack (and hence it doesn't necessarily + # live on the context stack), but it is stored in the graph stack's + # encapsulating object. + outer_context = _default_graph_stack._GetGlobalDefaultGraph().as_default # pylint: disable=protected-access - if outer_context is None: - raise AssertionError("All graphs are building functions, and no " + if outer_context is None: + # Sanity check; this shouldn't be triggered. + raise RuntimeError("All graphs are building functions, and no " "eager context was previously active.") - try: with outer_context(), name_scope(scope), control_dependencies( None), tape.stop_recording(): yield - finally: - pass +@tf_export("enable_eager_execution") def enable_eager_execution(config=None, device_policy=None): - """Enables, for the rest of the lifetime of this program, eager execution. + """Enables eager execution for the lifetime of this program. - If not called immediately on startup risks creating breakage and bugs. + Eager execution provides an imperative interface to TensorFlow. With eager + execution enabled, TensorFlow functions execute operations immediately (as + opposed to adding to a graph to be executed later in a @{tf.Session}) and + return concrete values (as opposed to symbolic references to a node in a + computational graph). - Example: + For example: ```python - tfe.enable_eager_execution() + tf.enable_eager_execution() # After eager execution is enabled, operations are executed as they are - # defined and `Tensor`s hold concrete values, which can be accessed as - # `numpy.ndarray`s through the `numpy()` method. + # defined and Tensor objects hold concrete values, which can be accessed as + # numpy.ndarray`s through the numpy() method. assert tf.multiply(6, 7).numpy() == 42 ``` + Eager execution cannot be enabled after TensorFlow APIs have been used to + create or execute graphs. It is typically recommended to invoke this function + at program startup and not in a library (as most libraries should be usable + both with and without eager execution). + Args: - config: (Optional.) A `ConfigProto` protocol buffer with configuration - options for the Context. Note that a lot of these options may be - currently unimplemented or irrelevant when eager execution is enabled. - device_policy: (Optional.) What policy to use when trying to run an - operation on a device with inputs which are not on that device. + config: (Optional.) A @{tf.ConfigProto} to use to configure the environment + in which operations are executed. Note that @{tf.ConfigProto} is also + used to configure graph execution (via @{tf.Session}) and many options + within `tf.ConfigProto` are not implemented (or are irrelevant) when + eager execution is enabled. + device_policy: (Optional.) Policy controlling how operations requiring + inputs on a specific device (e.g., a GPU 0) handle inputs on a different + device (e.g. GPU 1 or CPU). Valid values: - tfe.DEVICE_PLACEMENT_EXPLICIT: raises an error if the placement is not - correct. - tfe.DEVICE_PLACEMENT_WARN: copies the tensors which are not on the - right device but raises a warning. - tfe.DEVICE_PLACEMENT_SILENT: silently copies the tensors. This might - hide performance problems. - tfe.DEVICE_PLACEMENT_SILENT_FOR_INT32: silently copies int32 tensors, - raising errors on the other ones. + + - tf.contrib.eager.DEVICE_PLACEMENT_EXPLICIT: raises an error if the + placement is not correct. + + - tf.contrib.eager.DEVICE_PLACEMENT_WARN: copies the tensors which are not + on the right device but logs a warning. + + - tf.contrib.eager.DEVICE_PLACEMENT_SILENT: silently copies the tensors. + Note that this may hide performance problems as there is no notification + provided when operations are blocked on the tensor being copied between + devices. + + - tf.contrib.eager.DEVICE_PLACEMENT_SILENT_FOR_INT32: silently copies + int32 tensors, raising errors on the other ones. Raises: - ValueError: If trying to create a context after using graph operations - or if trying to create a context with nontrivial options which differ - from those of the existing context. + ValueError: If eager execution is enabled after creating/executing a + TensorFlow graph, or if options provided conflict with a previous call + to this function. """ if config is not None and not isinstance(config, config_pb2.ConfigProto): raise TypeError( @@ -5174,7 +5246,7 @@ def enable_eager_execution(config=None, device_policy=None): context.DEVICE_PLACEMENT_SILENT, context.DEVICE_PLACEMENT_SILENT_FOR_INT32): raise ValueError( - "device_policy must be one of None, tfe.DEVICE_PLACEMENT_*" + "device_policy must be one of None, tf.contrib.eager.DEVICE_PLACEMENT_*" ) # pylint: disable=protected-access if context._default_mode == context.GRAPH_MODE: @@ -5183,7 +5255,7 @@ def enable_eager_execution(config=None, device_policy=None): _default_graph_stack._global_default_graph is not None) if graph_mode_has_been_used: raise ValueError( - "tfe.enable_eager_execution has to be called at program startup.") + "tf.enable_eager_execution must be called at program startup.") context._default_mode = context.EAGER_MODE if context._context is None: context._context = context.Context(config=config, @@ -5206,7 +5278,7 @@ def enable_eager_execution(config=None, device_policy=None): context._context._device_policy)) else: raise ValueError( - "tfe.enable_eager_execution has to be called at program startup.") + "tf.enable_eager_execution must be called at program startup.") def eager_run(main=None, argv=None): @@ -5290,6 +5362,8 @@ def get_name_scope(): Returns: A string representing the current name scope. """ + if context.in_eager_mode(): + return context.context().scope_name.rstrip("/") return get_default_graph().get_name_scope() @@ -5544,7 +5618,7 @@ def add_to_collection(name, value): """ get_default_graph().add_to_collection(name, value) - +@tf_export("add_to_collections") def add_to_collections(names, value): """Wrapper for `Graph.add_to_collections()` using the default graph. @@ -5666,7 +5740,7 @@ class name_scope(object): # pylint: disable=invalid-name self._default_name = default_name self._values = values self._ctx = context.context() - self._in_eager_mode = self._ctx.in_eager_mode() + self._in_eager_mode = self._ctx.executing_eagerly() def __enter__(self): """Start the scope block. @@ -5845,7 +5919,7 @@ def get_from_proto_function(collection_name): def _assert_collection_is_ok(collection_name): - if context.in_eager_mode(): + if context.executing_eagerly(): if collection_name in GraphKeys._VARIABLE_COLLECTIONS: # pylint: disable=protected-access raise ValueError("When Eager Execution is enabled, variable " "collections are not supported.") diff --git a/tensorflow/python/framework/ops_test.py b/tensorflow/python/framework/ops_test.py index a141fe6340c70efde84411db4efb1f80cb0a61c5..6daab80408c79f95323c7976bd101c0765d7f566 100644 --- a/tensorflow/python/framework/ops_test.py +++ b/tensorflow/python/framework/ops_test.py @@ -763,6 +763,7 @@ class CreateOpFromTFOperationTest(test_util.TensorFlowTestCase): self.assertEqual(g.get_operation_by_name("myop"), op) self.assertEqual(g.get_tensor_by_name("myop:0"), op.outputs[0]) + @test_util.enable_c_shapes def testShape(self): g = ops.Graph() with g.as_default(): @@ -1763,7 +1764,13 @@ class ControlDependenciesTest(test_util.TensorFlowTestCase): return constant_op.constant(2.0) future.calls = 0 - if context.in_graph_mode(): + if context.executing_eagerly(): + a = constant_op.constant(1.0) + b = future + with ops.control_dependencies([a, b]): + c = constant_op.constant(3.0) + self.assertEqual(future.calls, 1) + else: g = ops.Graph() with g.as_default(): a = constant_op.constant(1.0) @@ -1772,12 +1779,6 @@ class ControlDependenciesTest(test_util.TensorFlowTestCase): c = constant_op.constant(3.0) self.assertEqual(c.op.control_inputs, [a.op, b.op]) self.assertEqual(future.calls, 1) - else: - a = constant_op.constant(1.0) - b = future() - with ops.control_dependencies([a, b]): - c = constant_op.constant(3.0) - self.assertEqual(future.calls, 1) def testBasicWithConversion(self): g = ops.Graph() @@ -2150,19 +2151,11 @@ class InitScopeTest(test_util.TensorFlowTestCase): with ops.init_scope(): # Because g is building a function, init_scope should # escape out to the eager context. - self.assertTrue(context.in_eager_mode()) + self.assertTrue(context.executing_eagerly()) # g should be reinstated as the default graph, and the # graph context should be re-entered. self.assertIs(g, ops.get_default_graph()) - self.assertTrue(context.in_graph_mode()) - - def testAllGraphsBuildingFunctionsRaisesError(self): - g = ops.Graph() - g._building_function = True # pylint: disable=protected-access - with g.as_default(): - with self.assertRaises(AssertionError): - with ops.init_scope(): - pass + self.assertFalse(context.executing_eagerly()) def testStaysInEagerWhenOnlyEagerContextActive(self): with context.eager_mode(): @@ -2241,6 +2234,29 @@ class InitScopeTest(test_util.TensorFlowTestCase): self.assertEqual(4, int(compiled_outer(inner=compiled_inner))) self.assertEqual(7, int(compiled_outer(inner=compiled_inner))) + def testFallsBackToGlobalGraphWhenAllGraphsAreBuildingFunctions(self): + with context.graph_mode(): + ops.reset_default_graph() + # This doesn't push anything onto the graph stack, but it does + # set the stack's global graph. + global_graph = ops.get_default_graph() + fn_graph = ops.Graph() + + # pylint: disable=protected-access + fn_graph._building_function = True + self.assertEqual(len(ops._default_graph_stack.stack), 0) + with fn_graph.as_default(): + self.assertEqual(len(ops._default_graph_stack.stack), 1) + with ops.init_scope(): + self.assertGreater(len(ops._default_graph_stack.stack), 1) + dummy = constant_op.constant(1.0) + self.assertEqual(len(ops._default_graph_stack.stack), 1) + # Note that the global graph is _not_ on the graph stack. + self.assertEqual(len(ops._default_graph_stack.stack), 0) + # Ensure that `dummy` was added to the global graph. + self.assertEqual(global_graph, dummy.graph) + # pylint: enable=protected-access + def testInstallsDefaultGraphWhenGraphStackIsEmptyInGraphMode(self): with context.graph_mode(): # pylint: disable=protected-access @@ -2262,12 +2278,13 @@ class InitScopeTest(test_util.TensorFlowTestCase): with context.eager_mode(): def foo(): with ops.name_scope("inner"), ops.init_scope(): - if context.in_graph_mode(): - self.assertEqual(ops.get_name_scope(), "inner") - else: + if context.executing_eagerly(): # A trailing slash is always appended when eager execution is # enabled. self.assertEqual(context.context().scope_name, "inner/") + else: + self.assertEqual(ops.get_name_scope(), "inner") + foo() self.assertEqual(ops.get_name_scope(), "") foo_compiled = eager_function.defun(foo) @@ -2877,7 +2894,7 @@ class OutputTypesTest(test_util.TensorFlowTestCase): with g.as_default(): x = constant_op.constant([1, 1, 2, 4, 4, 4, 7, 8, 8], dtype=dtypes.double) - y, _ = gen_array_ops._unique(x) + y, _ = gen_array_ops.unique(x) self.assertEqual([types_pb2.DT_DOUBLE, types_pb2.DT_INT32], y.op._output_types) # pylint: disable=protected-access diff --git a/tensorflow/python/framework/python_op_gen.cc b/tensorflow/python/framework/python_op_gen.cc index c95149d177990e364c3d6b9daeae5dc535cf0070..00a63650b7c8ee6ec56e0d7f92525a87e0100696 100644 --- a/tensorflow/python/framework/python_op_gen.cc +++ b/tensorflow/python/framework/python_op_gen.cc @@ -75,6 +75,36 @@ bool IsPythonReserved(const string& s) { return kPythonReserved->count(s) > 0; } +bool IsOpWithUnderscorePrefix(const string& s) { + static const std::set* const kUnderscoreOps = new std::set( + {// Lowercase built-in functions and types in Python, from: + // [x for x in dir(__builtins__) if x[0].islower()] + // These need to be excluded so they don't conflict with actual built-in + // functions since we use '*' imports. + "abs", "all", "any", "apply", "bin", "bool", "buffer", "bytearray", + "bytes", "callable", "chr", "classmethod", "cmp", "coerce", "compile", + "complex", "copyright", "credits", "delattr", "dict", "dir", "divmod", + "enumerate", "eval", "execfile", "exit", "file", "filter", "float", + "format", "frozenset", "getattr", "globals", "hasattr", "hash", "help", + "hex", "id", "input", "int", "intern", "isinstance", "issubclass", + "iter", "len", "license", "list", "locals", "long", "map", "max", + "memoryview", "min", "next", "object", "oct", "open", "ord", "pow", + "print", "property", "quit", "range", "raw_input", "reduce", "reload", + "repr", "reversed", "round", "set", "setattr", "slice", "sorted", + "staticmethod", "str", "sum", "super", "tuple", "type", "unichr", + "unicode", "vars", "xrange", "zip", + // These have the same name as ops defined in Python and might be used + // incorrectly depending on order of '*' imports. + // TODO(annarev): reduce usage of '*' imports and remove these from the + // list. + "fused_batch_norm", "histogram_fixed_width", "stack", + "batch_norm_with_global_normalization", + // TODO(annarev): replace these ops in the next change. + "broadcast_gradient_args", "enter", "histogram_summary", "ref_enter", + "ref_identity", "scalar_summary"}); + return kUnderscoreOps->count(s) > 0; +} + string AvoidPythonReserved(const string& s) { if (IsPythonReserved(s)) return strings::StrCat(s, "_"); return s; @@ -816,6 +846,7 @@ from tensorflow.python.util.tf_export import tf_export // An op is hidden if either its ApiDef visibility is HIDDEN // or it is in the hidden_ops list. bool is_hidden = api_def->visibility() == ApiDef::HIDDEN; + bool hidden_by_api_def = is_hidden; if (!is_hidden) { for (const string& hidden : hidden_ops) { if (op_def.name() == hidden) { @@ -828,13 +859,22 @@ from tensorflow.python.util.tf_export import tf_export string function_name; python_op_gen_internal::GenerateLowerCaseOpName(op_def.name(), &function_name); - if (is_hidden) function_name = strings::StrCat("_", function_name); - - // When users create custom python wrappers, they may link in the - // default op registry by accident, and because they can't - // enumerate all 'hidden' symbols, this guard is to prevent - // instantiating a python reserved word in their wrapper. - if (python_op_gen_internal::IsPythonReserved(function_name)) { + bool is_reserved = python_op_gen_internal::IsPythonReserved(function_name); + + // Prefix an op with underscore if the op is listed in hidden_ops or + // name is reserved or it is of the exceptions in IsOpWithUnderscorePrefix. + // Do not add underscores to ops set to HIDDEN in ApiDef otherwise. + // TODO(annarev): don't prefix with underscores even if op is in hidden_ops. + if (is_hidden) { + if (!hidden_by_api_def || is_reserved || + python_op_gen_internal::IsOpWithUnderscorePrefix(function_name)) { + function_name = strings::StrCat("_", function_name); + } + } else if (is_reserved) { + // When users create custom python wrappers, they may link in the + // default op registry by accident, and because they can't + // enumerate all 'hidden' symbols, this guard is to prevent + // instantiating a python reserved word in their wrapper. continue; } diff --git a/tensorflow/python/framework/python_op_gen_internal.h b/tensorflow/python/framework/python_op_gen_internal.h index 4319e5a7820b33283df8153fdc76e0e567813a17..e0cfb05f4bdf8afd09957c62a9ba3af1fd0882a6 100644 --- a/tensorflow/python/framework/python_op_gen_internal.h +++ b/tensorflow/python/framework/python_op_gen_internal.h @@ -29,6 +29,9 @@ namespace python_op_gen_internal { // Returns true if s is a Python keyword or built-in. bool IsPythonReserved(const string& s); +// Whether the op should be prefixed with underscore. +bool IsOpWithUnderscorePrefix(const string& s); + // Add a _ to the end of s if necessary to avoid a Python keyword or built-in. string AvoidPythonReserved(const string& s); diff --git a/tensorflow/python/framework/random_seed.py b/tensorflow/python/framework/random_seed.py index 1e74a790a3fb0c72b7c0fb1127ffac95f386d85e..b724432e00b0d11de86a0fff9ff31758ad36479f 100644 --- a/tensorflow/python/framework/random_seed.py +++ b/tensorflow/python/framework/random_seed.py @@ -52,20 +52,20 @@ def get_seed(op_seed): A tuple of two integers that should be used for the local seed of this operation. """ - is_graph_mode = context.in_graph_mode() + eager = context.executing_eagerly() - if is_graph_mode: - global_seed = ops.get_default_graph().seed - else: + if eager: global_seed = context.global_seed() + else: + global_seed = ops.get_default_graph().seed if global_seed is not None: if op_seed is None: # pylint: disable=protected-access - if is_graph_mode: - op_seed = ops.get_default_graph()._last_id - else: + if eager: op_seed = context.internal_operation_seed() + else: + op_seed = ops.get_default_graph()._last_id seeds = _truncate_seed(global_seed), _truncate_seed(op_seed) else: @@ -176,7 +176,7 @@ def set_random_seed(seed): Args: seed: integer. """ - if context.in_graph_mode(): - ops.get_default_graph().seed = seed - else: + if context.executing_eagerly(): context.set_global_seed(seed) + else: + ops.get_default_graph().seed = seed diff --git a/tensorflow/python/framework/random_seed_test.py b/tensorflow/python/framework/random_seed_test.py index b4c98ab8b289c850c6171425167bb17606a4162d..194492268631abfa911bd45f13a302c09a2c8bda 100644 --- a/tensorflow/python/framework/random_seed_test.py +++ b/tensorflow/python/framework/random_seed_test.py @@ -40,13 +40,13 @@ class RandomSeedTest(test.TestCase): ((2**31 - 1, 0), (0, 2**31 - 1)), # Don't wrap to (0, 0) either ((0, 2**31 - 1), (0, 2**31 - 1)), # Wrapping for the other argument ] - if context.in_graph_mode(): - # 0 will be the default_graph._lastid. - test_cases.append(((1, None), (1, 0))) - else: + if context.executing_eagerly(): # operation seed is random number generated based on global seed. # it's not tested due to possibility of platform or version difference. pass + else: + # 0 will be the default_graph._lastid. + test_cases.append(((1, None), (1, 0))) for tc in test_cases: tinput, toutput = tc[0], tc[1] random_seed.set_random_seed(tinput[0]) diff --git a/tensorflow/python/framework/smart_cond.py b/tensorflow/python/framework/smart_cond.py new file mode 100644 index 0000000000000000000000000000000000000000..c7ff23e4ff809ed7bc57259fa3ec9feb921b5a71 --- /dev/null +++ b/tensorflow/python/framework/smart_cond.py @@ -0,0 +1,123 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""smart_cond and related utilties.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python import pywrap_tensorflow as c_api +from tensorflow.python.framework import errors +from tensorflow.python.framework import ops +from tensorflow.python.framework import tensor_util +from tensorflow.python.ops import control_flow_ops + + +def smart_cond(pred, true_fn=None, false_fn=None, name=None): + """Return either `true_fn()` if predicate `pred` is true else `false_fn()`. + + If `pred` is a bool or has a constant value, we return either `true_fn()` + or `false_fn()`, otherwise we use `tf.cond` to dynamically route to both. + + Arguments: + pred: A scalar determining whether to return the result of `true_fn` or + `false_fn`. + true_fn: The callable to be performed if pred is true. + false_fn: The callable to be performed if pred is false. + name: Optional name prefix when using `tf.cond`. + + Returns: + Tensors returned by the call to either `true_fn` or `false_fn`. + + Raises: + TypeError: If `true_fn` or `false_fn` is not callable. + """ + if not callable(true_fn): + raise TypeError("`true_fn` must be callable.") + if not callable(false_fn): + raise TypeError("`false_fn` must be callable.") + + pred_value = smart_constant_value(pred) + if pred_value is not None: + if pred_value: + return true_fn() + else: + return false_fn() + else: + return control_flow_ops.cond(pred, true_fn=true_fn, false_fn=false_fn, + name=name) + + +def smart_constant_value(pred): + """Return the bool value for `pred`, or None if `pred` had a dynamic value. + + Arguments: + pred: A scalar, either a Python bool or tensor. + + Returns: + True or False if `pred` has a constant boolean value, None otherwise. + + Raises: + TypeError: If `pred` is not a Tensor or bool. + """ + if pred in {0, 1}: # Accept 1/0 as valid boolean values + pred_value = bool(pred) + elif isinstance(pred, bool): + pred_value = pred + elif isinstance(pred, ops.Tensor): + pred_value = tensor_util.constant_value(pred) + # TODO(skyewm): consider folding this into tensor_util.constant_value when + # _USE_C_API is removed (there may be performance and correctness bugs, so I + # wanted to limit the change hidden behind _USE_C_API). + # pylint: disable=protected-access + if pred_value is None and ops._USE_C_API: + with errors.raise_exception_on_not_ok_status() as status: + pred_value = c_api.TF_TryEvaluateConstant_wrapper( + pred.graph._c_graph, pred._as_tf_output(), status) + # pylint: enable=protected-access + + else: + raise TypeError("`pred` must be a Tensor, or a Python bool, or 1 or 0. " + "Found instead: %s" % pred) + return pred_value + + +def smart_case(pred_fn_pairs, default=None, exclusive=False, name="smart_case"): + """Like tf.case, except attempts to statically evaluate predicates. + + If any predicate in `pred_fn_pairs` is a bool or has a constant value, the + associated callable will be called or omitted depending on its value. + Otherwise this functions like tf.case. + + Args: + pred_fn_pairs: Dict or list of pairs of a boolean scalar tensor and a + callable which returns a list of tensors. + default: Optional callable that returns a list of tensors. + exclusive: True iff at most one predicate is allowed to evaluate to `True`. + name: A name for this operation (optional). + + Returns: + The tensors returned by the first pair whose predicate evaluated to True, or + those returned by `default` if none does. + + Raises: + TypeError: If `pred_fn_pairs` is not a list/dictionary. + TypeError: If `pred_fn_pairs` is a list but does not contain 2-tuples. + TypeError: If `fns[i]` is not callable for any i, or `default` is not + callable. + """ + return control_flow_ops._case_helper( # pylint: disable=protected-access + smart_cond, pred_fn_pairs, default, exclusive, name, + allow_python_preds=True) diff --git a/tensorflow/python/framework/smart_cond_test.py b/tensorflow/python/framework/smart_cond_test.py new file mode 100644 index 0000000000000000000000000000000000000000..1170a41c99995ae875e58a2d5491e05bc1e40df6 --- /dev/null +++ b/tensorflow/python/framework/smart_cond_test.py @@ -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. +# ============================================================================== + +from __future__ import absolute_import +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 smart_cond +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 math_ops +from tensorflow.python.platform import googletest + + +def raise_exception(): + raise RuntimeError("did not expect to be called") + + +@test_util.with_c_api +class SmartCondTest(test_util.TensorFlowTestCase): + + def testTrue(self): + with ops.Graph().as_default(): + with session.Session(): + x = constant_op.constant(2) + y = constant_op.constant(5) + z = smart_cond.smart_cond(True, lambda: math_ops.multiply(x, 16), + lambda: math_ops.multiply(y, 5)) + self.assertEqual(z.eval(), 32) + + def testFalse(self): + with ops.Graph().as_default(): + with session.Session(): + x = constant_op.constant(4) + y = constant_op.constant(3) + z = smart_cond.smart_cond(False, lambda: math_ops.multiply(x, 16), + lambda: math_ops.multiply(y, 3)) + self.assertEqual(z.eval(), 9) + + def testUnknown(self): + with ops.Graph().as_default(): + with session.Session(): + x = array_ops.placeholder(dtype=dtypes.int32) + y = smart_cond.smart_cond(x > 0, lambda: constant_op.constant(1), + lambda: constant_op.constant(2)) + self.assertEqual(y.eval(feed_dict={x: 1}), 1) + self.assertEqual(y.eval(feed_dict={x: -1}), 2) + + def testEval(self): + # Constant expression evaluation only works with the C API enabled. + if not ops._USE_C_API: return + + with ops.Graph().as_default(): + with session.Session(): + x = constant_op.constant(1) + y = constant_op.constant(2) + # x * y > 0 can be evaluated at graph construction time, so the false + # branch shouldn't be evaluated at all. + z = smart_cond.smart_cond(x * y > 0, lambda: constant_op.constant(1), + raise_exception) + self.assertEqual(z.eval(feed_dict={x: 1}), 1) + + def testPlaceholderWithDefault(self): + with ops.Graph().as_default(): + with session.Session(): + x = array_ops.placeholder_with_default(1, shape=()) + y = smart_cond.smart_cond(x > 0, lambda: constant_op.constant(1), + lambda: constant_op.constant(2)) + self.assertEqual(y.eval(), 1) + self.assertEqual(y.eval(feed_dict={x: -1}), 2) + + def testMissingArg1(self): + with ops.Graph().as_default(): + with session.Session(): + x = constant_op.constant(1) + with self.assertRaises(TypeError): + smart_cond.smart_cond(True, false_fn=lambda: x) + + def testMissingArg2(self): + with ops.Graph().as_default(): + with session.Session(): + x = constant_op.constant(1) + with self.assertRaises(TypeError): + smart_cond.smart_cond(True, lambda: x) + + +@test_util.with_c_api +class SmartCaseTest(test_util.TensorFlowTestCase): + + def testTrue(self): + x = array_ops.placeholder(dtype=dtypes.int32, shape=[]) + conditions = [(True, lambda: constant_op.constant(1)), + (x == 0, raise_exception)] + y = smart_cond.smart_case(conditions, default=raise_exception, + exclusive=False) + z = smart_cond.smart_case(conditions, default=raise_exception, + exclusive=True) + with session.Session() as sess: + # No feed_dict necessary + self.assertEqual(sess.run(y), 1) + self.assertEqual(sess.run(z), 1) + + def testFalse(self): + conditions = [(False, raise_exception)] + y = smart_cond.smart_case(conditions, + default=lambda: constant_op.constant(1), + exclusive=False) + z = smart_cond.smart_case(conditions, + default=lambda: constant_op.constant(1), + exclusive=True) + with session.Session() as sess: + self.assertEqual(sess.run(y), 1) + self.assertEqual(sess.run(z), 1) + + def testMix(self): + # Constant expression evaluation only works with the C API enabled. + if not ops._USE_C_API: return + + x = array_ops.placeholder(dtype=dtypes.int32, shape=[]) + y = constant_op.constant(10) + conditions = [(x > 1, lambda: constant_op.constant(1)), + (y < 1, raise_exception), + (False, raise_exception), + (True, lambda: constant_op.constant(3))] + z = smart_cond.smart_case(conditions, default=raise_exception) + with session.Session() as sess: + self.assertEqual(sess.run(z, feed_dict={x: 2}), 1) + self.assertEqual(sess.run(z, feed_dict={x: 0}), 3) + + +@test_util.with_c_api +class SmartConstantValueTest(test_util.TensorFlowTestCase): + + # TODO(skyewm): this is essentially a regression test for + # TF_TryEvaluateConstant, and is not really a valid smart_constant_value test + # (smart_constant_value is only supposed to return bools). Move the + # TF_TryEvaluateConstant call to tensor_util.constant_value and make this a + # constant_value test instead. + def testCond(self): + with ops.Graph().as_default(): + pred = array_ops.placeholder_with_default(True, shape=()) + x = control_flow_ops.cond(pred, + lambda: constant_op.constant(1), + lambda: constant_op.constant(2)) + self.assertIsNone(smart_cond.smart_constant_value(x)) + + +if __name__ == "__main__": + googletest.main() diff --git a/tensorflow/python/framework/tensor_shape.py b/tensorflow/python/framework/tensor_shape.py index 222071cb9e87aa0fdd9788d1c72df4c66ea61547..af2a5b1a7ef9a70c0baf5d02257951803a7a76fa 100644 --- a/tensorflow/python/framework/tensor_shape.py +++ b/tensorflow/python/framework/tensor_shape.py @@ -156,7 +156,7 @@ class Dimension(object): ``` Args: - other: Another Dimension. + other: Another Dimension, or a value accepted by `as_dimension`. Returns: A Dimension whose value is the sum of `self` and `other`. @@ -167,6 +167,17 @@ class Dimension(object): else: return Dimension(self._value + other.value) + def __radd__(self, other): + """Returns the sum of `other` and `self`. + + Args: + other: Another Dimension, or a value accepted by `as_dimension`. + + Returns: + A Dimension whose value is the sum of `self` and `other`. + """ + return self + other + def __sub__(self, other): """Returns the subtraction of `other` from `self`. @@ -180,10 +191,10 @@ class Dimension(object): ``` Args: - other: Another Dimension. + other: Another Dimension, or a value accepted by `as_dimension`. Returns: - A Dimension whose value is the subtraction of sum of `other` from `self`. + A Dimension whose value is the subtraction of `other` from `self`. """ other = as_dimension(other) if self._value is None or other.value is None: @@ -191,6 +202,21 @@ class Dimension(object): else: return Dimension(self._value - other.value) + def __rsub__(self, other): + """Returns the subtraction of `self` from `other`. + + Args: + other: Another Dimension, or a value accepted by `as_dimension`. + + Returns: + A Dimension whose value is the subtraction of `self` from `other`. + """ + other = as_dimension(other) + if self._value is None or other.value is None: + return Dimension(None) + else: + return Dimension(other.value - self._value) + def __mul__(self, other): """Returns the product of `self` and `other`. @@ -204,17 +230,32 @@ class Dimension(object): ``` Args: - other: Another Dimension. + other: Another Dimension, or a value accepted by `as_dimension`. Returns: A Dimension whose value is the product of `self` and `other`. """ - other = as_dimension(other) + try: + other = as_dimension(other) + except (TypeError, ValueError): + return NotImplemented + if self._value is None or other.value is None: return Dimension(None) else: return Dimension(self._value * other.value) + def __rmul__(self, other): + """Returns the product of `self` and `other`. + + Args: + other: Another Dimension, or a value accepted by `as_dimension`. + + Returns: + A Dimension whose value is the product of `self` and `other`. + """ + return self * other + def __floordiv__(self, other): """Returns the quotient of `self` and `other` rounded down. @@ -228,17 +269,35 @@ class Dimension(object): ``` Args: - other: Another `Dimension`. + other: Another Dimension, or a value accepted by `as_dimension`. Returns: A `Dimension` whose value is the integer quotient of `self` and `other`. """ - other = as_dimension(other) + try: + other = as_dimension(other) + except (TypeError, ValueError): + return NotImplemented if self._value is None or other.value is None: return Dimension(None) else: return Dimension(self._value // other.value) + def __rfloordiv__(self, other): + """Returns the quotient of `other` and `self` rounded down. + + Args: + other: Another Dimension, or a value accepted by `as_dimension`. + + Returns: + A `Dimension` whose value is the integer quotient of `self` and `other`. + """ + other = as_dimension(other) + if self._value is None or other.value is None: + return Dimension(None) + else: + return Dimension(other.value // self._value) + def __div__(self, other): """DEPRECATED: Use `__floordiv__` via `x // y` instead. @@ -256,7 +315,7 @@ class Dimension(object): return self // other def __mod__(self, other): - """Returns `self` modulo `other. + """Returns `self` modulo `other`. Dimension moduli are computed as follows: @@ -268,17 +327,35 @@ class Dimension(object): ``` Args: - other: Another Dimension. + other: Another Dimension, or a value accepted by `as_dimension`. Returns: A Dimension whose value is `self` modulo `other`. """ - other = as_dimension(other) + try: + other = as_dimension(other) + except (TypeError, ValueError): + return NotImplemented if self._value is None or other.value is None: return Dimension(None) else: return Dimension(self._value % other.value) + def __rmod__(self, other): + """Returns `other` modulo `self`. + + Args: + other: Another Dimension, or a value accepted by `as_dimension`. + + Returns: + A Dimension whose value is `other` modulo `self`. + """ + try: + other = as_dimension(other) + except (TypeError, ValueError): + return NotImplemented + return other % self + def __lt__(self, other): """Returns True if `self` is known to be less than `other`. @@ -456,6 +533,7 @@ class TensorShape(object): else: # Got a list of dimensions self._dims = [as_dimension(d) for d in dims_iter] + self._ndims = None def __repr__(self): return "TensorShape(%r)" % self._dims @@ -473,19 +551,26 @@ class TensorShape(object): """Returns a list of Dimensions, or None if the shape is unspecified.""" return self._dims + @dims.setter + def dims(self, dims): + self._dims = dims + self._ndims = None + @property def ndims(self): """Returns the rank of this shape, or None if it is unspecified.""" if self._dims is None: return None else: - return len(self._dims) + if self._ndims is None: + self._ndims = len(self._dims) + return self._ndims def __len__(self): """Returns the rank of this shape, or raises ValueError if unspecified.""" if self._dims is None: raise ValueError("Cannot take the length of Shape with unknown rank.") - return len(self._dims) + return self.ndims def __bool__(self): """Returns True if this shape contains non-zero information.""" diff --git a/tensorflow/python/framework/tensor_shape_test.py b/tensorflow/python/framework/tensor_shape_test.py index fffd86c7a6241b8be92ad33852da244ab9b5284d..4e8ce4d889c4ef0c6e56806587a64e8f9be7e10a 100644 --- a/tensorflow/python/framework/tensor_shape_test.py +++ b/tensorflow/python/framework/tensor_shape_test.py @@ -34,12 +34,20 @@ class DimensionTest(test_util.TensorFlowTestCase): self.assertEqual(tensor_shape.Dimension(15), dim + tensor_shape.Dimension(3)) self.assertEqual(tensor_shape.Dimension(15), dim + 3) + self.assertEqual(tensor_shape.Dimension(15), 3 + dim) + self.assertEqual(tensor_shape.Dimension(9), dim - 3) + self.assertEqual(tensor_shape.Dimension(1), 13 - dim) self.assertEqual(tensor_shape.Dimension(24), dim * tensor_shape.Dimension(2)) self.assertEqual(tensor_shape.Dimension(24), dim * 2) + self.assertEqual(tensor_shape.Dimension(24), 2 * dim) + self.assertEqual([4] * 12, [4] * dim) + self.assertEqual(12 * [4], dim * [4]) + self.assertEqual(tensor_shape.Dimension(24), 2 * dim) self.assertEqual( tensor_shape.Dimension(6), dim // tensor_shape.Dimension(2)) self.assertEqual(tensor_shape.Dimension(6), dim // 2) + self.assertEqual(tensor_shape.Dimension(0), 2 // dim) self.assertEqual(tensor_shape.Dimension(12), dim.merge_with(tensor_shape.Dimension(12))) self.assertEqual(tensor_shape.Dimension(12), dim.merge_with(12)) @@ -176,6 +184,14 @@ class DimensionTest(test_util.TensorFlowTestCase): self.assertEqual(str(tensor_shape.Dimension(7)), "7") self.assertEqual(str(tensor_shape.Dimension(None)), "?") + def testMod(self): + four = tensor_shape.Dimension(4) + nine = tensor_shape.Dimension(9) + self.assertEqual(nine % four, 1) + # test both __mod__ and __rmod__. + self.assertEqual(nine % 4, 1) + self.assertEqual(4 % nine, 4) + class ShapeTest(test_util.TensorFlowTestCase): diff --git a/tensorflow/python/framework/tensor_spec.py b/tensorflow/python/framework/tensor_spec.py index a0411bc3d9b4b2b87e5a31e9f201154f28ccf1cc..546c48adbae7a97b404b2507994dfff77dd69ec6 100644 --- a/tensorflow/python/framework/tensor_spec.py +++ b/tensorflow/python/framework/tensor_spec.py @@ -65,6 +65,11 @@ class TensorSpec(object): else: raise ValueError("`tensor` should be a tf.Tensor") + @classmethod + def is_bounded(cls): + del cls + return False + @property def shape(self): """Returns the `TensorShape` that represents the shape of the tensor.""" @@ -80,6 +85,16 @@ class TensorSpec(object): """Returns the name of the described tensor.""" return self._name + @property + def is_discrete(self): + """Whether spec is discrete.""" + return self.dtype.is_integer + + @property + def is_continuous(self): + """Whether spec is continuous.""" + return self.dtype.is_floating + def is_compatible_with(self, spec_or_tensor): """True if the shape and dtype of `spec_or_tensor` are compatible.""" return (self._dtype.is_compatible_with(spec_or_tensor.dtype) and @@ -163,19 +178,16 @@ class BoundedTensorSpec(TensorSpec): self._maximum = np.array(maximum, dtype=self.dtype.as_numpy_dtype()) self._maximum.setflags(write=False) + @classmethod + def is_bounded(cls): + del cls + return True + @classmethod def from_spec(cls, spec): dtype = dtypes.as_dtype(spec.dtype) - if dtype in [dtypes.float64, dtypes.float32]: - # Avoid under/over-flow for `dtype.maximum - dtype.minimum`. - low = dtype.min / 2 - high = dtype.max / 2 - else: - low = dtype.min - high = dtype.max - - minimum = getattr(spec, "minimum", low) - maximum = getattr(spec, "maximum", high) + minimum = getattr(spec, "minimum", dtype.min) + maximum = getattr(spec, "maximum", dtype.max) return BoundedTensorSpec(spec.shape, dtype, minimum, maximum, spec.name) @property diff --git a/tensorflow/python/framework/tensor_spec_test.py b/tensorflow/python/framework/tensor_spec_test.py index 54ca4d9a19c2e1c879c05cfb828085951bdd8444..b33d769d86d3ea03f6a59082a132b0a94d5921ea 100644 --- a/tensorflow/python/framework/tensor_spec_test.py +++ b/tensorflow/python/framework/tensor_spec_test.py @@ -127,6 +127,22 @@ class TensorSpecTest(test_util.TensorFlowTestCase): self.assertEqual(bounded_spec.dtype, spec.dtype) self.assertEqual(bounded_spec.name, spec.name) + def testIsDiscrete(self): + discrete_spec = tensor_spec.TensorSpec((1, 2), dtypes.int32) + continuous_spec = tensor_spec.TensorSpec((1, 2), dtypes.float32) + self.assertTrue(discrete_spec.is_discrete) + self.assertFalse(continuous_spec.is_discrete) + + def testIsContinuous(self): + discrete_spec = tensor_spec.TensorSpec((1, 2), dtypes.int32) + continuous_spec = tensor_spec.TensorSpec((1, 2), dtypes.float32) + self.assertFalse(discrete_spec.is_continuous) + self.assertTrue(continuous_spec.is_continuous) + + def testIsBounded(self): + unbounded_spec = tensor_spec.TensorSpec((1, 2), dtypes.int32) + self.assertFalse(unbounded_spec.is_bounded()) + class BoundedTensorSpecTest(test_util.TensorFlowTestCase): @@ -138,6 +154,11 @@ class BoundedTensorSpecTest(test_util.TensorFlowTestCase): with self.assertRaisesRegexp(ValueError, "not compatible"): tensor_spec.BoundedTensorSpec((3, 5), dtypes.uint8, 0, (1, 1, 1)) + def testIsBounded(self): + bounded_spec = tensor_spec.BoundedTensorSpec( + (1, 2), dtypes.int32, minimum=0, maximum=1) + self.assertTrue(bounded_spec.is_bounded()) + def testMinimumMaximumAttributes(self): spec = tensor_spec.BoundedTensorSpec( (1, 2, 3), dtypes.float32, 0, (5, 5, 5)) diff --git a/tensorflow/python/framework/tensor_util.py b/tensorflow/python/framework/tensor_util.py index 27afaa074a6becd5c8b7db94be59e8da1611c13a..984bcecdfe05efd79bdf218197c410b14abe3516 100644 --- a/tensorflow/python/framework/tensor_util.py +++ b/tensorflow/python/framework/tensor_util.py @@ -559,16 +559,16 @@ def MakeNdarray(tensor): if tensor.tensor_content: return (np.frombuffer(tensor.tensor_content, dtype=dtype).copy() .reshape(shape)) - elif tensor_dtype == dtypes.float16: + elif tensor_dtype == dtypes.float16 or tensor_dtype == dtypes.bfloat16: # the half_val field of the TensorProto stores the binary representation # of the fp16: we need to reinterpret this as a proper float16 if len(tensor.half_val) == 1: tmp = np.array(tensor.half_val[0], dtype=np.uint16) - tmp.dtype = np.float16 + tmp.dtype = tensor_dtype.as_numpy_dtype return np.repeat(tmp, num_elements).reshape(shape) else: tmp = np.fromiter(tensor.half_val, dtype=np.uint16) - tmp.dtype = np.float16 + tmp.dtype = tensor_dtype.as_numpy_dtype return tmp.reshape(shape) elif tensor_dtype == dtypes.float32: if len(tensor.float_val) == 1: @@ -586,8 +586,7 @@ def MakeNdarray(tensor): return np.fromiter(tensor.double_val, dtype=dtype).reshape(shape) elif tensor_dtype in [ dtypes.int32, dtypes.uint8, dtypes.uint16, dtypes.int16, dtypes.int8, - dtypes.qint32, dtypes.quint8, dtypes.qint8, dtypes.qint16, dtypes.quint16, - dtypes.bfloat16 + dtypes.qint32, dtypes.quint8, dtypes.qint8, dtypes.qint16, dtypes.quint16 ]: if len(tensor.int_val) == 1: return np.repeat(np.array(tensor.int_val[0], dtype=dtype), @@ -829,7 +828,7 @@ def constant_value_as_shape(tensor): # pylint: disable=invalid-name Returns: A `TensorShape` based on the constant value of the given `tensor`. """ - if context.in_eager_mode(): + if context.executing_eagerly(): return tensor_shape.as_shape( [dim if dim != -1 else None for dim in tensor.numpy()]) diff --git a/tensorflow/python/framework/tensor_util_test.py b/tensorflow/python/framework/tensor_util_test.py index bea0ee34fd4900cc9d4d5d52348ba4512368e81f..35fff80c61b98e7603d3b7b5df3cabdb59059a72 100644 --- a/tensorflow/python/framework/tensor_util_test.py +++ b/tensorflow/python/framework/tensor_util_test.py @@ -235,6 +235,26 @@ class TensorUtilTest(test.TestCase): self.assertEquals(np.float16, a.dtype) self.assertAllClose(np.array([10.0, 20.0], dtype=np.float16), a) + def testBfloat16(self): + test_type = dtypes.bfloat16.as_numpy_dtype + t = tensor_util.make_tensor_proto(np.array([10.0, 20.0], dtype=test_type)) + # 10.0: 16672 = 010000010(130) 0100000: (1+0/2+1/4) * 2^(130-127) + # 20.0: 16800 = 010000011(131) 0100000: (1+0/2+1/4) * 2^(131-127) + self.assertProtoEquals(""" + dtype: DT_BFLOAT16 + tensor_shape { + dim { + size: 2 + } + } + half_val: 16672 + half_val: 16800 + """, t) + + a = tensor_util.MakeNdarray(t) + self.assertEquals(test_type, a.dtype) + self.assertAllClose(np.array([10.0, 20.0], dtype=test_type), a) + def testInt(self): t = tensor_util.make_tensor_proto(10) self.assertProtoEquals(""" @@ -768,7 +788,7 @@ class ConstantValueTest(test.TestCase): self.assertAllClose(np_val, tensor_util.constant_value(tf_val)) def testUnknown(self): - tf_val = gen_state_ops._variable( + tf_val = gen_state_ops.variable( shape=[3, 4, 7], dtype=dtypes.float32, name="tf_val", diff --git a/tensorflow/python/framework/test_util.py b/tensorflow/python/framework/test_util.py index 4fd70039817ad7e0e7077077cb1037625330121c..e9e86e452b19fd23b4eddfb4f6a985458b503eff 100644 --- a/tensorflow/python/framework/test_util.py +++ b/tensorflow/python/framework/test_util.py @@ -56,6 +56,7 @@ from tensorflow.python.framework import errors from tensorflow.python.framework import importer from tensorflow.python.framework import ops from tensorflow.python.framework import random_seed +from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import versions from tensorflow.python.ops import array_ops from tensorflow.python.ops import resource_variable_ops @@ -200,11 +201,14 @@ def _strip_checkpoint_v2_randomized(graph_def): def IsGoogleCudaEnabled(): return pywrap_tensorflow.IsGoogleCudaEnabled() - def CudaSupportsHalfMatMulAndConv(): return pywrap_tensorflow.CudaSupportsHalfMatMulAndConv() +def IsMklEnabled(): + return pywrap_tensorflow.IsMklEnabled() + + def InstallStackTraceHandler(): pywrap_tensorflow.InstallStacktraceHandler() @@ -403,6 +407,31 @@ def enable_c_api(fn): return wrapper +def enable_c_shapes(fn): + """Decorator for enabling C shapes on a test. + + Note this enables the C shapes after running the test class's setup/teardown + methods. + + Args: + fn: the function to be wrapped + + Returns: + The wrapped function + """ + + def wrapper(*args, **kwargs): + prev_value = ops._USE_C_SHAPES + # Only use C shapes if the C API is already enabled. + ops._USE_C_SHAPES = ops._USE_C_API + try: + fn(*args, **kwargs) + finally: + ops._USE_C_SHAPES = prev_value + + return wrapper + + # This decorator is a hacky way to run all the test methods in a decorated # class with and without C API enabled. # TODO(iga): Remove this and its uses once we switch to using C API by default. @@ -430,6 +459,32 @@ def with_c_api(cls): return cls +def assert_no_new_pyobjects_executing_eagerly(f): + """Decorator for asserting that no new Python objects persist after a test. + + Runs the test multiple times executing eagerly, first as a warmup and then + several times to let objects accumulate. The warmup helps ignore caches which + do not grow as the test is run repeatedly. + + Useful for checking that there are no missing Py_DECREFs in the C exercised by + a bit of Python. + """ + def decorator(self, **kwargs): + """Warms up, gets an object count, runs the test, checks for new objects.""" + with context.eager_mode(): + gc.disable() + f(self, **kwargs) + gc.collect() + previous_count = len(gc.get_objects()) + for _ in range(3): + f(self, **kwargs) + gc.collect() + # There should be no new Python objects hanging around. + new_count = len(gc.get_objects()) + self.assertEqual(previous_count, new_count) + gc.enable() + return decorator + def assert_no_new_tensors(f): """Decorator for asserting that no new Tensors persist after a test. @@ -451,15 +506,19 @@ def assert_no_new_tensors(f): def decorator(self, **kwargs): """Finds existing Tensors, runs the test, checks for new Tensors.""" - def _is_tensor(obj): + def _is_tensorflow_object(obj): try: - return (isinstance(obj, ops.Tensor) or - isinstance(obj, variables.Variable)) + return isinstance(obj, ( + ops.Tensor, + variables.Variable, + tensor_shape.Dimension, + tensor_shape.TensorShape)) except ReferenceError: # If the object no longer exists, we don't care about it. return False - tensors_before = set(id(obj) for obj in gc.get_objects() if _is_tensor(obj)) + tensors_before = set(id(obj) for obj in gc.get_objects() + if _is_tensorflow_object(obj)) outside_graph_key = ops.get_default_graph()._graph_key with ops.Graph().as_default(): # Run the test in a new graph so that collections get cleared when it's @@ -469,11 +528,12 @@ def assert_no_new_tensors(f): # Make an effort to clear caches, which would otherwise look like leaked # Tensors. backprop._zeros_cache.flush() + context.get_default_context().ones_rank_cache().flush() context.get_default_context().scalar_cache().clear() gc.collect() tensors_after = [ obj for obj in gc.get_objects() - if _is_tensor(obj) and id(obj) not in tensors_before + if _is_tensorflow_object(obj) and id(obj) not in tensors_before ] if tensors_after: raise AssertionError(("%d Tensors not deallocated after test: %s" % ( @@ -506,6 +566,30 @@ def assert_no_garbage_created(f): previous_garbage = len(gc.garbage) f(self, **kwargs) gc.collect() + if len(gc.garbage) > previous_garbage: + logging.error( + "The decorated test created work for Python's garbage collector, " + "likely due to a reference cycle. New objects in cycle(s):") + for i, obj in enumerate(gc.garbage[previous_garbage:]): + try: + logging.error( + "Object %d of %d" % (i, len(gc.garbage) - previous_garbage)) + def _safe_object_str(obj): + return "<%s %d>" % (obj.__class__.__name__, id(obj)) + logging.error(" Object type: %s" % (_safe_object_str(obj),)) + logging.error(" Referrer types: %s" % ( + ', '.join([_safe_object_str(ref) + for ref in gc.get_referrers(obj)]),)) + logging.error(" Referent types: %s" % ( + ', '.join([_safe_object_str(ref) + for ref in gc.get_referents(obj)]),)) + logging.error(" Object attribute names: %s" % (dir(obj),)) + logging.error(" Object __str__:") + logging.error(obj) + logging.error(" Object __repr__:") + logging.error(repr(obj)) + except Exception: + logging.error("(Exception while printing object)") # This will fail if any garbage has been created, typically because of a # reference cycle. self.assertEqual(previous_garbage, len(gc.garbage)) @@ -564,6 +648,7 @@ def run_in_graph_and_eager_modes(__unused__=None, # This decorator runs the wrapped test twice. # Reset the test environment between runs. self.tearDown() + self._tempdir = None self.setUp() def run_eager_mode(self, **kwargs): @@ -787,7 +872,7 @@ class TensorFlowTestCase(googletest.TestCase): Returns: tensors numpy values. """ - if context.in_eager_mode(): + if context.executing_eagerly(): return self._eval_helper(tensors) else: sess = ops.get_default_session() diff --git a/tensorflow/python/framework/test_util_test.py b/tensorflow/python/framework/test_util_test.py index a717eb39513ac3369ae133b6090ff82597f12eb7..02ffa93baee5c643ebdceaa274710f9d58e6eecb 100644 --- a/tensorflow/python/framework/test_util_test.py +++ b/tensorflow/python/framework/test_util_test.py @@ -82,6 +82,14 @@ class TestUtilTest(test_util.TensorFlowTestCase): else: print("GoogleCuda is disabled") + def testIsMklEnabled(self): + # This test doesn't assert anything. + # It ensures the py wrapper function is generated correctly. + if test_util.IsMklEnabled(): + print("MKL is enabled") + else: + print("MKL is disabled") + def testAssertProtoEqualsStr(self): graph_str = "node { name: 'w1' op: 'params' }" @@ -440,6 +448,26 @@ class GarbageCollectionTest(test_util.TensorFlowTestCase): LeakedTensorTest().test_has_no_leak() + def test_no_new_objects_decorator(self): + + class LeakedObjectTest(object): + + def __init__(inner_self): # pylint: disable=no-self-argument + inner_self.assertEqual = self.assertEqual # pylint: disable=invalid-name + inner_self.accumulation = [] + + @test_util.assert_no_new_pyobjects_executing_eagerly + def test_has_leak(self): + self.accumulation.append([1.]) + + @test_util.assert_no_new_pyobjects_executing_eagerly + def test_has_no_leak(self): + self.not_accumulating = [1.] + + with self.assertRaises(AssertionError): + LeakedObjectTest().test_has_leak() + + LeakedObjectTest().test_has_no_leak() if __name__ == "__main__": googletest.main() diff --git a/tensorflow/python/grappler/hierarchical_controller.py b/tensorflow/python/grappler/hierarchical_controller.py index 655e43e78f3e1e02fc5dc1982487f7291ab1e23d..c0866c1069ac7f7e25cbd12cb5a490e2ed5e4bec 100644 --- a/tensorflow/python/grappler/hierarchical_controller.py +++ b/tensorflow/python/grappler/hierarchical_controller.py @@ -258,9 +258,11 @@ class HierarchicalController(Controller): "attn_w_2", [self.hparams.hidden_size, self.hparams.hidden_size]) variable_scope.get_variable("attn_v", [self.hparams.hidden_size, 1]) seq2seq_input_layer = array_ops.placeholder_with_default( - array_ops.zeros([1, self.num_groups, self.group_emb_size], + array_ops.zeros([self.hparams.num_children, + self.num_groups, + self.group_emb_size], dtypes.float32), - shape=(1, self.num_groups, self.group_emb_size)) + shape=(self.hparams.num_children, self.num_groups, self.group_emb_size)) self.seq2seq_input_layer = seq2seq_input_layer def compute_reward(self, run_time): @@ -585,12 +587,29 @@ class HierarchicalController(Controller): """Approximating the blocks of a TF graph from a graph_def. Args: - grouping_actions: grouping predictions + grouping_actions: grouping predictions. verbose: print stuffs. Returns: groups: list of groups. """ + groups = [ + self._create_group_embeddings(grouping_actions, i, verbose) for + i in range(self.hparams.num_children) + ] + return np.stack(groups, axis=0) + + def _create_group_embeddings(self, grouping_actions, child_id, verbose=False): + """Approximating the blocks of a TF graph from a graph_def for each child. + + Args: + grouping_actions: grouping predictions. + child_id: child_id for the group. + verbose: print stuffs. + + Returns: + groups: group embedding for the child_id. + """ if verbose: print("Processing input_graph") @@ -599,23 +618,23 @@ class HierarchicalController(Controller): dag_matrix = np.zeros([self.num_groups, self.num_groups], dtype=np.float32) for op in self.important_ops: topo_op_index = self.name_to_topo_order_index[op.name] - # TODO(agoldie) child_id - group_index = grouping_actions[0][topo_op_index] + group_index = grouping_actions[child_id][topo_op_index] for output_op in self.get_node_fanout(op): if output_op.name not in self.important_op_names: continue - output_group_index = grouping_actions[0][self.name_to_topo_order_index[ - output_op.name]] + output_group_index = ( + grouping_actions[child_id][self.name_to_topo_order_index[ + output_op.name]]) dag_matrix[group_index, output_group_index] += 1.0 num_connections = np.sum(dag_matrix) num_intra_group_connections = dag_matrix.trace() num_inter_group_connections = num_connections - num_intra_group_connections if verbose: print("grouping evaluation metric") - print("num_connections={} num_intra_group_connections={} " - "num_inter_group_connections={}").format( - num_connections, num_intra_group_connections, - num_inter_group_connections) + print(("num_connections={} num_intra_group_connections={} " + "num_inter_group_connections={}").format( + num_connections, num_intra_group_connections, + num_inter_group_connections)) self.dag_matrix = dag_matrix # output_shape @@ -648,7 +667,8 @@ class HierarchicalController(Controller): ], dtype=np.float32) for op_index, op in enumerate(self.important_ops): - group_index = grouping_actions[0][self.name_to_topo_order_index[op.name]] + group_index = grouping_actions[child_id][ + self.name_to_topo_order_index[op.name]] type_name = str(op.op) type_index = self.type_dict[type_name] group_embedding[group_index, type_index] += 1 @@ -675,7 +695,7 @@ class HierarchicalController(Controller): shape=[num_children, self.num_groups], trainable=False) - x = array_ops.tile(self.seq2seq_input_layer, [num_children, 1, 1]) + x = self.seq2seq_input_layer last_c, last_h, attn_mem = self.encode(x) actions, log_probs = {}, {} actions["sample"], log_probs["sample"] = ( @@ -972,8 +992,8 @@ class HierarchicalController(Controller): controller_ops["reward"]["ph"][child_id]: reward, }) if verbose: - print("run_time={:<.5f} reward={:<.5f} " - "best_reward={:<.5f}").format(run_time, reward, best_reward) + print(("run_time={:<.5f} reward={:<.5f} " + "best_reward={:<.5f}").format(run_time, reward, best_reward)) # Reward is a double, best_reward a float: allow for some slack in the # comparison. @@ -988,8 +1008,7 @@ class HierarchicalController(Controller): def generate_placement(self, grouping, sess): controller_ops = self.ops["controller"] feed_seq2seq_input_dict = {} - feed_seq2seq_input_dict[self.seq2seq_input_layer] = np.expand_dims( - grouping, axis=0) + feed_seq2seq_input_dict[self.seq2seq_input_layer] = grouping sess.run( controller_ops["y_preds"]["sample"], feed_dict=feed_seq2seq_input_dict) diff --git a/tensorflow/python/grappler/layout_optimizer_test.py b/tensorflow/python/grappler/layout_optimizer_test.py index 0f5150174049250e86bbac0a49eb998339058326..5a84b16a23f567fba6d08aaefd3b816a76907735 100644 --- a/tensorflow/python/grappler/layout_optimizer_test.py +++ b/tensorflow/python/grappler/layout_optimizer_test.py @@ -321,7 +321,7 @@ class LayoutOptimizerTest(test.TestCase): conv = _two_layer_model(x) dim = array_ops.placeholder(dtype='int32') sizes = constant_op.constant([50, 10, 4], shape=[3]) - split = gen_array_ops._split_v( + split = gen_array_ops.split_v( value=conv, size_splits=sizes, axis=dim, num_split=3) output = math_ops.reduce_sum(split[0]) @@ -896,7 +896,7 @@ class LayoutOptimizerTest(test.TestCase): add = math_ops.add(conv, conv) mean = math_ops.reduce_mean(conv) condition = math_ops.less(conv, mean) - select = gen_math_ops._select(condition, conv, add) + select = gen_math_ops.select(condition, conv, add) output = array_ops.identity(select) with session.Session(config=_get_config(False)) as sess: @@ -926,7 +926,7 @@ class LayoutOptimizerTest(test.TestCase): conv = _two_layer_model(x) add = math_ops.add(conv, conv) condition = array_ops.placeholder(dtype='bool') - select = gen_math_ops._select(condition, conv, add) + select = gen_math_ops.select(condition, conv, add) output = array_ops.identity(select) condition_val = np.zeros((1, 7, 7, 64)) @@ -957,7 +957,7 @@ class LayoutOptimizerTest(test.TestCase): conv = _two_layer_model(x) add = math_ops.add(conv, conv) condition = constant_op.constant(True) - select = gen_math_ops._select(condition, conv, add) + select = gen_math_ops.select(condition, conv, add) output = array_ops.identity(select) with session.Session(config=_get_config(False)) as sess: @@ -1023,7 +1023,7 @@ class LayoutOptimizerTest(test.TestCase): conv = _two_layer_model(x) ksize = constant_op.constant([1, 2, 3, 1], shape=[4]) strides = array_ops.placeholder(dtype='int32', shape=[4]) - max_pool = gen_nn_ops._max_pool_v2(conv, ksize, strides, 'VALID') + max_pool = gen_nn_ops.max_pool_v2(conv, ksize, strides, 'VALID') output = array_ops.identity(max_pool) strides_val = [1, 3, 2, 1] diff --git a/tensorflow/python/grappler/memory_optimizer_test.py b/tensorflow/python/grappler/memory_optimizer_test.py index 948911f099674af4c6dd19bfdac75e5fc1f75c78..4df959ce04169395589aeebaef9e3e7839e2300c 100644 --- a/tensorflow/python/grappler/memory_optimizer_test.py +++ b/tensorflow/python/grappler/memory_optimizer_test.py @@ -162,7 +162,8 @@ class MemoryOptimizerRecomputeTest(test.TestCase): arithmetic_optimization=rewriter_config_pb2.RewriterConfig.OFF, memory_optimization=rewriter_config_pb2.RewriterConfig. RECOMPUTATION_HEURISTICS, - memory_optimizer_target_node_name_prefix='optimizer/gradients/'), + # Checks that name scope "gradients/" also match sub-scope. + memory_optimizer_target_node_name_scope='gradients/'), original_metagraph) self.assertGreater( len(rewritten_graph_def.node), @@ -176,6 +177,35 @@ class MemoryOptimizerRecomputeTest(test.TestCase): len([node for node in rewritten_graph_def.node if 'Recomputed/' in node.name])) + def testRewritingNameScopedGradientNamesScope(self): + """Tests that rewriting occurs with non-standard gradient names.""" + (original_metagraph, _, _, + _) = self._GetMetaGraph(optimizer_scope_name='foo/bar') + rewritten_graph_def = tf_optimizer.OptimizeGraph( + rewriter_config_pb2.RewriterConfig( + disable_model_pruning=True, + constant_folding=rewriter_config_pb2.RewriterConfig.OFF, + dependency_optimization=rewriter_config_pb2.RewriterConfig.OFF, + layout_optimizer=rewriter_config_pb2.RewriterConfig.OFF, + arithmetic_optimization=rewriter_config_pb2.RewriterConfig.OFF, + memory_optimization=rewriter_config_pb2.RewriterConfig. + RECOMPUTATION_HEURISTICS, + # This should not match anything. + memory_optimizer_target_node_name_scope='r/gradients/'), + original_metagraph) + self.assertEqual( + len(rewritten_graph_def.node), len(original_metagraph.graph_def.node)) + self.assertEqual(0, + len([ + node for node in original_metagraph.graph_def.node + if 'Recomputed/' in node.name + ])) + self.assertEqual(0, + len([ + node for node in rewritten_graph_def.node + if 'Recomputed/' in node.name + ])) + def _GetMemoryOptimizerSessionConfig(self): rewrite_options = rewriter_config_pb2.RewriterConfig( disable_model_pruning=True, diff --git a/tensorflow/python/grappler/tf_optimizer.i b/tensorflow/python/grappler/tf_optimizer.i index 1b657983a4690dd0ddb7f569ce514b08cb10400a..de9326ccfc1653c2afd0833dcdca2cc4bfdabed5 100644 --- a/tensorflow/python/grappler/tf_optimizer.i +++ b/tensorflow/python/grappler/tf_optimizer.i @@ -100,6 +100,7 @@ PyObject* TF_OptimizeGraph( tensorflow::grappler::ItemConfig item_config; item_config.inline_functions = false; item_config.apply_optimizations = false; + item_config.ignore_user_placement = false; std::unique_ptr grappler_item = tensorflow::grappler::GrapplerItemFromMetaGraphDef(graph_id, metagraph, item_config); diff --git a/tensorflow/python/keras/BUILD b/tensorflow/python/keras/BUILD index 16738066ced26cd02267806af54171044b20ccce..f47be178864a12249bbe6440fc02f2381903776d 100755 --- a/tensorflow/python/keras/BUILD +++ b/tensorflow/python/keras/BUILD @@ -9,6 +9,11 @@ package(default_visibility = ["//visibility:public"]) load("//tensorflow:tensorflow.bzl", "py_test") +config_setting( + name = "empty_condition", + values = {"define": "UNUSED=unused"}, +) + py_library( name = "keras", srcs = [ @@ -39,9 +44,16 @@ py_library( "_impl/keras/datasets/mnist.py", "_impl/keras/datasets/reuters.py", "_impl/keras/engine/__init__.py", - "_impl/keras/engine/topology.py", + "_impl/keras/engine/base_layer.py", + "_impl/keras/engine/input_layer.py", + "_impl/keras/engine/network.py", + "_impl/keras/engine/saving.py", + "_impl/keras/engine/sequential.py", "_impl/keras/engine/training.py", + "_impl/keras/engine/training_arrays.py", "_impl/keras/engine/training_eager.py", + "_impl/keras/engine/training_generator.py", + "_impl/keras/engine/training_utils.py", "_impl/keras/estimator.py", "_impl/keras/initializers.py", "_impl/keras/layers/__init__.py", @@ -74,8 +86,8 @@ py_library( "_impl/keras/utils/generic_utils.py", "_impl/keras/utils/io_utils.py", "_impl/keras/utils/layer_utils.py", + "_impl/keras/utils/multi_gpu_utils.py", "_impl/keras/utils/np_utils.py", - "_impl/keras/utils/training_utils.py", "_impl/keras/utils/vis_utils.py", "_impl/keras/wrappers/__init__.py", "_impl/keras/wrappers/scikit_learn.py", @@ -119,7 +131,11 @@ py_library( ], srcs_version = "PY2AND3", visibility = ["//visibility:public"], - deps = [ + deps = select({ + ":empty_condition": [], + "//conditions:default": [], + }) + [ + "@six_archive//:six", "//tensorflow/core:protos_all_py", "//tensorflow/python:array_ops", "//tensorflow/python:check_ops", @@ -158,7 +174,6 @@ py_library( "//tensorflow/python/estimator", "//tensorflow/python/estimator:model_fn", "//tensorflow/python/saved_model", - "@six_archive//:six", ], ) @@ -621,7 +636,10 @@ py_test( size = "small", srcs = ["_impl/keras/utils/io_utils_test.py"], srcs_version = "PY2AND3", - tags = ["notsan"], + tags = [ + "no_windows", # TODO: needs investigation on Windows + "notsan", + ], deps = [ ":keras", "//tensorflow/python:client_testlib", @@ -642,9 +660,9 @@ py_test( ) py_test( - name = "training_utils_test", + name = "multi_gpu_utils_test", size = "medium", - srcs = ["_impl/keras/utils/training_utils_test.py"], + srcs = ["_impl/keras/utils/multi_gpu_utils_test.py"], srcs_version = "PY2AND3", tags = ["multi_gpu"], deps = [ @@ -761,9 +779,31 @@ py_test( srcs_version = "PY2AND3", deps = [ ":keras", - "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", - "//tensorflow/python:dtypes", + "//third_party/py/numpy", + ], +) + +py_test( + name = "saving_test", + size = "small", + srcs = ["_impl/keras/engine/saving_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":keras", + "//tensorflow/python:client_testlib", + "//third_party/py/numpy", + ], +) + +py_test( + name = "sequential_test", + size = "small", + srcs = ["_impl/keras/engine/sequential_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":keras", + "//tensorflow/python:client_testlib", "//third_party/py/numpy", ], ) diff --git a/tensorflow/python/keras/_impl/keras/__init__.py b/tensorflow/python/keras/_impl/keras/__init__.py index b63907b2e60acfc80ee411b9193b2829f0224c3e..53f5d31e9c5b861c551a7a9ca3700c383ea679d7 100644 --- a/tensorflow/python/keras/_impl/keras/__init__.py +++ b/tensorflow/python/keras/_impl/keras/__init__.py @@ -40,4 +40,4 @@ from tensorflow.python.keras._impl.keras.layers import Input from tensorflow.python.keras._impl.keras.models import Model from tensorflow.python.keras._impl.keras.models import Sequential -__version__ = '2.1.4-tf' +__version__ = '2.1.5-tf' diff --git a/tensorflow/python/keras/_impl/keras/applications/densenet.py b/tensorflow/python/keras/_impl/keras/applications/densenet.py index 6521f8410435fd13393b9991d3ee9a6342a912d0..ca83e8691237216e799f2ca738dcb6822506e2cb 100644 --- a/tensorflow/python/keras/_impl/keras/applications/densenet.py +++ b/tensorflow/python/keras/_impl/keras/applications/densenet.py @@ -31,7 +31,7 @@ from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras.applications import imagenet_utils from tensorflow.python.keras._impl.keras.applications.imagenet_utils import _obtain_input_shape from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions -from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs +from tensorflow.python.keras._impl.keras.engine.network import get_source_inputs from tensorflow.python.keras._impl.keras.layers import Activation from tensorflow.python.keras._impl.keras.layers import AveragePooling2D from tensorflow.python.keras._impl.keras.layers import BatchNormalization diff --git a/tensorflow/python/keras/_impl/keras/applications/inception_resnet_v2.py b/tensorflow/python/keras/_impl/keras/applications/inception_resnet_v2.py index bf3901fc54419c2b401bf9c4d6311b39a18f1aba..17e407dd58460e6d6802a3e137a96faf38a6f576 100644 --- a/tensorflow/python/keras/_impl/keras/applications/inception_resnet_v2.py +++ b/tensorflow/python/keras/_impl/keras/applications/inception_resnet_v2.py @@ -31,7 +31,7 @@ from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras.applications import imagenet_utils from tensorflow.python.keras._impl.keras.applications.imagenet_utils import _obtain_input_shape from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions -from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs +from tensorflow.python.keras._impl.keras.engine.network import get_source_inputs from tensorflow.python.keras._impl.keras.layers import Activation from tensorflow.python.keras._impl.keras.layers import AveragePooling2D from tensorflow.python.keras._impl.keras.layers import BatchNormalization diff --git a/tensorflow/python/keras/_impl/keras/applications/inception_v3.py b/tensorflow/python/keras/_impl/keras/applications/inception_v3.py index e268e97bc663773a218f01b958b08f8e43c74ee2..2897c6058eb445ceacc34084b53dc89f556e3e9c 100644 --- a/tensorflow/python/keras/_impl/keras/applications/inception_v3.py +++ b/tensorflow/python/keras/_impl/keras/applications/inception_v3.py @@ -37,7 +37,7 @@ from tensorflow.python.keras._impl.keras import layers from tensorflow.python.keras._impl.keras.applications import imagenet_utils from tensorflow.python.keras._impl.keras.applications.imagenet_utils import _obtain_input_shape from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions -from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs +from tensorflow.python.keras._impl.keras.engine.network import get_source_inputs from tensorflow.python.keras._impl.keras.layers import Activation from tensorflow.python.keras._impl.keras.layers import AveragePooling2D from tensorflow.python.keras._impl.keras.layers import BatchNormalization diff --git a/tensorflow/python/keras/_impl/keras/applications/mobilenet.py b/tensorflow/python/keras/_impl/keras/applications/mobilenet.py index 1bbbedb85e47902b9e6d3dd741e9d52ab9209080..ad96b53a4528d99a014a0214b52a78d6a60076f8 100644 --- a/tensorflow/python/keras/_impl/keras/applications/mobilenet.py +++ b/tensorflow/python/keras/_impl/keras/applications/mobilenet.py @@ -79,8 +79,8 @@ from tensorflow.python.keras._impl.keras.applications import imagenet_utils from tensorflow.python.keras._impl.keras.applications.imagenet_utils import _obtain_input_shape from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions from tensorflow.python.keras._impl.keras.engine import InputSpec -from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs -from tensorflow.python.keras._impl.keras.engine.topology import shape_type_conversion +from tensorflow.python.keras._impl.keras.engine.base_layer import shape_type_conversion +from tensorflow.python.keras._impl.keras.engine.network import get_source_inputs from tensorflow.python.keras._impl.keras.layers import Activation from tensorflow.python.keras._impl.keras.layers import BatchNormalization from tensorflow.python.keras._impl.keras.layers import Conv2D diff --git a/tensorflow/python/keras/_impl/keras/applications/nasnet.py b/tensorflow/python/keras/_impl/keras/applications/nasnet.py index 08dae57f006c64021cbca26404770cd89b1ce176..dd33230a7eb9272f8fc60daee63e1f92574cf5e3 100644 --- a/tensorflow/python/keras/_impl/keras/applications/nasnet.py +++ b/tensorflow/python/keras/_impl/keras/applications/nasnet.py @@ -49,7 +49,7 @@ from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras.applications.imagenet_utils import _obtain_input_shape from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions from tensorflow.python.keras._impl.keras.applications.inception_v3 import preprocess_input -from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs +from tensorflow.python.keras._impl.keras.engine.network import get_source_inputs from tensorflow.python.keras._impl.keras.layers import Activation from tensorflow.python.keras._impl.keras.layers import add from tensorflow.python.keras._impl.keras.layers import AveragePooling2D diff --git a/tensorflow/python/keras/_impl/keras/applications/resnet50.py b/tensorflow/python/keras/_impl/keras/applications/resnet50.py index a47dd657bb9ea0627d82831b7ee5d0b33788b5b7..46c0e635578c7f4707b027247943d75b16d703ad 100644 --- a/tensorflow/python/keras/_impl/keras/applications/resnet50.py +++ b/tensorflow/python/keras/_impl/keras/applications/resnet50.py @@ -34,7 +34,7 @@ from tensorflow.python.keras._impl.keras import layers from tensorflow.python.keras._impl.keras.applications.imagenet_utils import _obtain_input_shape from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions from tensorflow.python.keras._impl.keras.applications.imagenet_utils import preprocess_input -from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs +from tensorflow.python.keras._impl.keras.engine.network import get_source_inputs from tensorflow.python.keras._impl.keras.layers import Activation from tensorflow.python.keras._impl.keras.layers import AveragePooling2D from tensorflow.python.keras._impl.keras.layers import BatchNormalization diff --git a/tensorflow/python/keras/_impl/keras/applications/vgg16.py b/tensorflow/python/keras/_impl/keras/applications/vgg16.py index 9da74253abc2124844ab89b7727ddda4f754d8e2..cefb25063e30505c9c34b49fd2df6eb7210d7ca8 100644 --- a/tensorflow/python/keras/_impl/keras/applications/vgg16.py +++ b/tensorflow/python/keras/_impl/keras/applications/vgg16.py @@ -32,7 +32,7 @@ from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras.applications.imagenet_utils import _obtain_input_shape from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions from tensorflow.python.keras._impl.keras.applications.imagenet_utils import preprocess_input -from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs +from tensorflow.python.keras._impl.keras.engine.network import get_source_inputs from tensorflow.python.keras._impl.keras.layers import Conv2D from tensorflow.python.keras._impl.keras.layers import Dense from tensorflow.python.keras._impl.keras.layers import Flatten diff --git a/tensorflow/python/keras/_impl/keras/applications/vgg19.py b/tensorflow/python/keras/_impl/keras/applications/vgg19.py index 961c1f991893dbc0df858e9f72b61202c9fee500..dadaf4fdf0cc5922752c6867720c5d8cdbcab19a 100644 --- a/tensorflow/python/keras/_impl/keras/applications/vgg19.py +++ b/tensorflow/python/keras/_impl/keras/applications/vgg19.py @@ -32,7 +32,7 @@ from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras.applications.imagenet_utils import _obtain_input_shape from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions from tensorflow.python.keras._impl.keras.applications.imagenet_utils import preprocess_input -from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs +from tensorflow.python.keras._impl.keras.engine.network import get_source_inputs from tensorflow.python.keras._impl.keras.layers import Conv2D from tensorflow.python.keras._impl.keras.layers import Dense from tensorflow.python.keras._impl.keras.layers import Flatten diff --git a/tensorflow/python/keras/_impl/keras/applications/xception.py b/tensorflow/python/keras/_impl/keras/applications/xception.py index 7e7ca5a18a31622ac79d61ab01ce65341a4a46c5..971063a16d1f5ba0e25189f1ef2f6c24eb5f8d61 100644 --- a/tensorflow/python/keras/_impl/keras/applications/xception.py +++ b/tensorflow/python/keras/_impl/keras/applications/xception.py @@ -44,7 +44,7 @@ from tensorflow.python.keras._impl.keras import layers from tensorflow.python.keras._impl.keras.applications import imagenet_utils from tensorflow.python.keras._impl.keras.applications.imagenet_utils import _obtain_input_shape from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions -from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs +from tensorflow.python.keras._impl.keras.engine.network import get_source_inputs from tensorflow.python.keras._impl.keras.layers import Activation from tensorflow.python.keras._impl.keras.layers import BatchNormalization from tensorflow.python.keras._impl.keras.layers import Conv2D diff --git a/tensorflow/python/keras/_impl/keras/backend.py b/tensorflow/python/keras/_impl/keras/backend.py index a2db05f6cfd0c20fef3b18832834d06990b7a512..04866fbe0f48dabd57fd0336258420765b24a793 100644 --- a/tensorflow/python/keras/_impl/keras/backend.py +++ b/tensorflow/python/keras/_impl/keras/backend.py @@ -55,10 +55,10 @@ from tensorflow.python.ops import tensor_array_grad # pylint: disable=unused-im from tensorflow.python.ops import tensor_array_ops from tensorflow.python.ops import variables as variables_module from tensorflow.python.training import moving_averages +from tensorflow.python.util import tf_contextlib from tensorflow.python.util import tf_inspect from tensorflow.python.util.tf_export import tf_export - py_all = all py_sum = sum @@ -343,7 +343,7 @@ def learning_phase(): Returns: Learning phase (scalar integer tensor or Python integer). """ - if context.in_eager_mode(): + if context.executing_eagerly(): if 'eager' not in _GRAPH_LEARNING_PHASES: # Fallback to inference mode as default. return 0 @@ -369,13 +369,42 @@ def set_learning_phase(value): """ global _GRAPH_LEARNING_PHASES # pylint: disable=global-variable-not-assigned if value not in {0, 1}: - raise ValueError('Expected learning phase to be ' '0 or 1.') - if context.in_eager_mode(): + raise ValueError('Expected learning phase to be 0 or 1.') + if context.executing_eagerly(): _GRAPH_LEARNING_PHASES['eager'] = value else: _GRAPH_LEARNING_PHASES[ops.get_default_graph()] = value +@tf_contextlib.contextmanager +def learning_phase_scope(value): + """Provides a scope within which the learning phase is equal to `value`. + + The learning phase gets restored to its original value upon exiting the scope. + + Arguments: + value: Learning phase value, either 0 or 1 (integers). + + Yields: + The provided value. + + Raises: + ValueError: if `value` is neither `0` nor `1`. + """ + if value not in {0, 1}: + raise ValueError('Expected learning phase to be 0 or 1.') + previous_value = learning_phase() + try: + set_learning_phase(value) + yield value + finally: + # Restore learning phase to initial value. + if context.executing_eagerly(): + _GRAPH_LEARNING_PHASES['eager'] = previous_value + else: + _GRAPH_LEARNING_PHASES[ops.get_default_graph()] = previous_value + + @tf_export('keras.backend.get_session') def get_session(): """Returns the TF session to be used by the backend. @@ -394,8 +423,9 @@ def get_session(): A TensorFlow session. """ global _SESSION - if ops.get_default_session() is not None: - session = ops.get_default_session() + default_session = ops.get_default_session() + if default_session is not None: + session = default_session else: if _SESSION is None: if not os.environ.get('OMP_NUM_THREADS'): @@ -466,7 +496,7 @@ def _is_current_explicit_device(device_type): """ device_type = device_type.upper() if device_type not in ['CPU', 'GPU']: - raise ValueError('device_type should be either "CPU" or "GPU".') + raise ValueError('`device_type` should be either "CPU" or "GPU".') device = _get_current_tf_device() return device is not None and device.device_type == device_type.upper() @@ -2596,7 +2626,7 @@ def get_value(x): Returns: A Numpy array. """ - if context.in_eager_mode(): + if context.executing_eagerly(): return x.numpy() return x.eval(session=get_session()) @@ -2611,7 +2641,7 @@ def batch_get_value(tensors): Returns: A list of Numpy arrays. """ - if context.in_eager_mode(): + if context.executing_eagerly(): return [x.numpy() for x in tensors] if tensors: return get_session().run(tensors) @@ -2629,7 +2659,7 @@ def set_value(x, value): (of the same shape). """ value = np.asarray(value, dtype=dtype(x)) - if context.in_eager_mode(): + if context.executing_eagerly(): x.assign(value) else: tf_dtype = dtypes_module.as_dtype(x.dtype.name.split('_')[0]) @@ -2652,7 +2682,7 @@ def batch_set_value(tuples): tuples: a list of tuples `(tensor, value)`. `value` should be a Numpy array. """ - if context.in_eager_mode(): + if context.executing_eagerly(): for x, value in tuples: x.assign(np.asarray(value, dtype=dtype(x))) else: @@ -2749,7 +2779,7 @@ class Function(object): self.updates_op = control_flow_ops.group(*updates_ops) self.name = name # additional tensor substitutions - self.feed_dict = session_kwargs.pop('feed_dict', {}) + self.feed_dict = session_kwargs.pop('feed_dict', None) # additional operations self.fetches = session_kwargs.pop('fetches', []) if not isinstance(self.fetches, list): @@ -2759,8 +2789,15 @@ class Function(object): def __call__(self, inputs): if not isinstance(inputs, (list, tuple)): raise TypeError('`inputs` should be a list or tuple.') - feed_dict = self.feed_dict.copy() + + if self.feed_dict: + feed_dict = self.feed_dict.copy() + else: + feed_dict = {} + for tensor, value in zip(self.inputs, inputs): + if value is None: + continue if is_sparse(tensor): sparse_coo = value.tocoo() indices = np.concatenate((np.expand_dims(sparse_coo.row, 1), @@ -3087,7 +3124,7 @@ def rnn(step_function, outputs_shape[1] = inputs_shape[1] outputs.set_shape(outputs_shape) - if not context.in_eager_mode(): + if not context.executing_eagerly(): last_output._uses_learning_phase = uses_learning_phase return last_output, outputs, new_states @@ -3478,7 +3515,7 @@ def l2_normalize(x, axis=None): Returns: A tensor. """ - return nn.l2_normalize(x, dim=axis) + return nn.l2_normalize(x, axis=axis) @tf_export('keras.backend.in_top_k') diff --git a/tensorflow/python/keras/_impl/keras/backend_test.py b/tensorflow/python/keras/_impl/keras/backend_test.py index f29ca49378bc43385b9e90d3f1cefb7937df64cd..fb4b2a0e1dc06c904d4b93038840dbf688d42ed4 100644 --- a/tensorflow/python/keras/_impl/keras/backend_test.py +++ b/tensorflow/python/keras/_impl/keras/backend_test.py @@ -128,6 +128,22 @@ class BackendUtilsTest(test.TestCase): sess.run(variables.global_variables_initializer()) sess.run(y, feed_dict={x: np.random.random((2, 3))}) + def test_learning_phase_scope(self): + with self.test_session(): + initial_learning_phase = keras.backend.learning_phase() + with keras.backend.learning_phase_scope(1) as lp: + self.assertEqual(lp, 1) + self.assertEqual(keras.backend.learning_phase(), 1) + self.assertEqual(keras.backend.learning_phase(), initial_learning_phase) + with keras.backend.learning_phase_scope(0) as lp: + self.assertEqual(lp, 0) + self.assertEqual(keras.backend.learning_phase(), 0) + self.assertEqual(keras.backend.learning_phase(), initial_learning_phase) + with self.assertRaises(ValueError): + with keras.backend.learning_phase_scope(None): + pass + self.assertEqual(keras.backend.learning_phase(), initial_learning_phase) + def test_int_shape(self): x = keras.backend.placeholder(shape=(3, 4)) self.assertEqual(keras.backend.int_shape(x), (3, 4)) diff --git a/tensorflow/python/keras/_impl/keras/callbacks.py b/tensorflow/python/keras/_impl/keras/callbacks.py index f6c466142522927135d66f73f9f5c697671649ec..deb1e8867dba3d52816ebda02bd9a3bf2ec7bc09 100644 --- a/tensorflow/python/keras/_impl/keras/callbacks.py +++ b/tensorflow/python/keras/_impl/keras/callbacks.py @@ -778,16 +778,24 @@ class TensorBoard(Callback): while i < val_size: step = min(self.batch_size, val_size - i) batch_val = [] - batch_val.append(val_data[0][i:i + step]) - batch_val.append(val_data[1][i:i + step]) - batch_val.append(val_data[2][i:i + step]) + batch_val.append(val_data[0][i:i + step] + if val_data[0] is not None else None) + batch_val.append(val_data[1][i:i + step] + if val_data[1] is not None else None) + batch_val.append(val_data[2][i:i + step] + if val_data[2] is not None else None) if self.model.uses_learning_phase: # do not slice the learning phase - batch_val = [x[i:i + step] for x in val_data[:-1]] + batch_val = [x[i:i + step] if x is not None else None + for x in val_data[:-1]] batch_val.append(val_data[-1]) else: - batch_val = [x[i:i + step] for x in val_data] - feed_dict = dict(zip(tensors, batch_val)) + batch_val = [x[i:i + step] if x is not None else None + for x in val_data] + feed_dict = {} + for key, val in zip(tensors, batch_val): + if val is not None: + feed_dict[key] = val result = self.sess.run([self.merged], feed_dict=feed_dict) summary_str = result[0] self.writer.add_summary(summary_str, epoch) diff --git a/tensorflow/python/keras/_impl/keras/datasets/fashion_mnist.py b/tensorflow/python/keras/_impl/keras/datasets/fashion_mnist.py index b9ae41a0d4d0e8d9df70e3fc1952e81c5f57e8d9..508e95f719a02977960b80c283495ced642293c5 100644 --- a/tensorflow/python/keras/_impl/keras/datasets/fashion_mnist.py +++ b/tensorflow/python/keras/_impl/keras/datasets/fashion_mnist.py @@ -24,8 +24,10 @@ import os import numpy as np from tensorflow.python.keras._impl.keras.utils.data_utils import get_file +from tensorflow.python.util.tf_export import tf_export +@tf_export('keras.datasets.fashion_mnist.load_data') def load_data(): """Loads the Fashion-MNIST dataset. diff --git a/tensorflow/python/keras/_impl/keras/engine/__init__.py b/tensorflow/python/keras/_impl/keras/engine/__init__.py index 31f624f9af65cac60b6466d4eb5753cbdee984c6..1bc533ab8f7ba37948d82bc69fe1c9bfe00d6834 100644 --- a/tensorflow/python/keras/_impl/keras/engine/__init__.py +++ b/tensorflow/python/keras/_impl/keras/engine/__init__.py @@ -18,13 +18,10 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs -from tensorflow.python.keras._impl.keras.engine.topology import Input -from tensorflow.python.keras._impl.keras.engine.topology import InputLayer -from tensorflow.python.keras._impl.keras.engine.topology import InputSpec -from tensorflow.python.keras._impl.keras.engine.topology import Layer +from tensorflow.python.keras._impl.keras.engine.base_layer import InputSpec +from tensorflow.python.keras._impl.keras.engine.base_layer import Layer +from tensorflow.python.keras._impl.keras.engine.input_layer import Input +from tensorflow.python.keras._impl.keras.engine.input_layer import InputLayer +from tensorflow.python.keras._impl.keras.engine.network import get_source_inputs +from tensorflow.python.keras._impl.keras.engine.network import Network from tensorflow.python.keras._impl.keras.engine.training import Model - - -# Note: topology.Node is an internal class, -# it isn't meant to be used by Keras users. diff --git a/tensorflow/python/keras/_impl/keras/engine/base_layer.py b/tensorflow/python/keras/_impl/keras/engine/base_layer.py new file mode 100644 index 0000000000000000000000000000000000000000..5615241ae3077102ef40f9c0619161964a62a335 --- /dev/null +++ b/tensorflow/python/keras/_impl/keras/engine/base_layer.py @@ -0,0 +1,505 @@ +# 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=protected-access +"""Base layer code (`Layer`). +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from six.moves import zip # pylint: disable=redefined-builtin + +from tensorflow.python.eager import context +from tensorflow.python.framework import tensor_shape +from tensorflow.python.keras._impl.keras import backend as K +from tensorflow.python.keras._impl.keras import constraints +from tensorflow.python.keras._impl.keras import initializers +from tensorflow.python.keras._impl.keras import regularizers +from tensorflow.python.keras._impl.keras.utils import generic_utils +from tensorflow.python.layers import base as tf_base_layers +from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.util.tf_export import tf_export + + +# pylint: disable=invalid-name +InputSpec = tf_base_layers.InputSpec +Node = tf_base_layers.Node +TFBaseLayer = tf_base_layers.Layer +# pylint: enable=invalid-name + + +@tf_export('keras.layers.Layer') +class Layer(tf_base_layers.Layer): + """Abstract base layer class. + + # Properties + name: String, must be unique within a model. + input_spec: List of InputSpec class instances + each entry describes one required input: + - ndim + - dtype + A layer with `n` input tensors must have + an `input_spec` of length `n`. + trainable: Boolean, whether the layer weights + will be updated during training. + uses_learning_phase: Whether any operation + of the layer uses `K.in_training_phase()` + or `K.in_test_phase()`. + input_shape: Shape tuple. Provided for convenience, + but note that there may be cases in which this + attribute is ill-defined (e.g. a shared layer + with multiple input shapes), in which case + requesting `input_shape` will raise an Exception. + Prefer using `layer.get_input_shape_for(input_shape)`, + or `layer.get_input_shape_at(node_index)`. + output_shape: Shape tuple. See above. + inbound_nodes: List of nodes. + outbound_nodes: List of nodes. + input, output: Input/output tensor(s). Note that if the layer is used + more than once (shared layer), this is ill-defined + and will raise an exception. In such cases, use + `layer.get_input_at(node_index)`. + input_mask, output_mask: Same as above, for masks. + trainable_weights: List of variables. + non_trainable_weights: List of variables. + weights: The concatenation of the lists trainable_weights and + non_trainable_weights (in this order). + + # Methods + call(x, mask=None): Where the layer's logic lives. + __call__(x, mask=None): Wrapper around the layer logic (`call`). + If x is a Keras tensor: + - Connect current layer with last layer from tensor: + `self._add_inbound_node(last_layer)` + - Add layer to tensor history + If layer is not built: + - Build from inputs shape + get_weights() + set_weights(weights) + get_config() + count_params() + compute_output_shape(input_shape) + compute_mask(x, mask) + get_input_at(node_index) + get_output_at(node_index) + get_input_shape_at(node_index) + get_output_shape_at(node_index) + get_input_mask_at(node_index) + get_output_mask_at(node_index) + + # Class Methods + from_config(config) + + # Internal methods: + build(input_shape) + _add_inbound_node(layer, index=0) + """ + + def __init__(self, **kwargs): + # These properties should be set by the user via keyword arguments. + # note that 'dtype', 'input_shape' and 'batch_input_shape' + # are only applicable to input layers: do not pass these keywords + # to non-input layers. + allowed_kwargs = { + 'activity_regularizer', + 'input_shape', + 'batch_input_shape', + 'batch_size', + 'dtype', + 'name', + 'trainable', + 'weights', + } + # Validate optional keyword arguments. + for kwarg in kwargs: + if kwarg not in allowed_kwargs: + raise TypeError('Keyword argument not understood:', kwarg) + + # Get layer name. + name = kwargs.get('name') + + # Get `trainable` status. + trainable = kwargs.get('trainable', True) + + # Get `dtype`. + dtype = kwargs.get('dtype') + if dtype is None: + dtype = K.floatx() + + # Call super, which will set all properties common to Keras layers + # and core TF layers. + super(Layer, self).__init__( + name=name, dtype=dtype, trainable=trainable, + activity_regularizer=kwargs.get('activity_regularizer')) + + # Add properties that are Keras-only for now. + self.supports_masking = False + + # Manage input shape information if passed. + if 'input_shape' in kwargs or 'batch_input_shape' in kwargs: + # In this case we will later create an input layer + # to insert before the current layer + if 'batch_input_shape' in kwargs: + batch_input_shape = tuple(kwargs['batch_input_shape']) + elif 'input_shape' in kwargs: + if 'batch_size' in kwargs: + batch_size = kwargs['batch_size'] + else: + batch_size = None + batch_input_shape = (batch_size,) + tuple(kwargs['input_shape']) + self._batch_input_shape = batch_input_shape + + # Manage initial weight values if passed. + if 'weights' in kwargs: + self._initial_weights = kwargs['weights'] + else: + self._initial_weights = None + + def add_weight(self, + name, + shape, + dtype=None, + initializer=None, + regularizer=None, + trainable=True, + constraint=None): + """Adds a weight variable to the layer. + + Arguments: + name: String, the name for the weight variable. + shape: The shape tuple of the weight. + dtype: The dtype of the weight. + initializer: An Initializer instance (callable). + regularizer: An optional Regularizer instance. + trainable: A boolean, whether the weight should + be trained via backprop or not (assuming + that the layer itself is also trainable). + constraint: An optional Constraint instance. + + Returns: + The created weight variable. + """ + if dtype is None: + dtype = K.floatx() + weight = self.add_variable(name, shape, + dtype=dtype, + initializer=initializers.get(initializer), + regularizer=regularizers.get(regularizer), + constraint=constraints.get(constraint), + trainable=trainable) + return weight + + def call(self, inputs, **kwargs): # pylint: disable=unused-argument + """This is where the layer's logic lives. + + Arguments: + inputs: Input tensor, or list/tuple of input tensors. + **kwargs: Additional keyword arguments. + + Returns: + A tensor or list/tuple of tensors. + """ + return inputs + + def __call__(self, inputs, **kwargs): + """Wrapper around self.call(), for handling internal references. + + If a Keras tensor is passed: + - We call self._add_inbound_node(). + - If necessary, we `build` the layer to match + the shape of the input(s). + - We update the _keras_history of the output tensor(s) + with the current layer. + This is done as part of _add_inbound_node(). + + Arguments: + inputs: Can be a tensor or list/tuple of tensors. + **kwargs: Additional keyword arguments to be passed to `call()`. + + Returns: + Output of the layer's `call` method. + + Raises: + ValueError: in case the layer is missing shape information + for its `build` call. + """ + # Actually call the layer (optionally building it). + output = super(Layer, self).__call__(inputs, **kwargs) + if context.executing_eagerly(): + return output + + if hasattr(self, '_symbolic_set_inputs') and not self.inputs: + # Subclassed network: explicitly set metadata normally set by a call to + # self._set_inputs(). + self._symbolic_set_inputs(inputs, output) + + # Update learning phase info. + output_tensors = generic_utils.to_list(output) + uses_lp = any( + [getattr(x, '_uses_learning_phase', False) + for x in generic_utils.to_list(inputs)]) + uses_lp = getattr(self, 'uses_learning_phase', False) or uses_lp + for i in range(len(output_tensors)): + output_tensors[i]._uses_learning_phase = getattr( + output_tensors[i], '_uses_learning_phase', False) or uses_lp + + # Optionally load weight values that were specified at layer instantiation. + if hasattr(self, '_initial_weights') and self._initial_weights is not None: + self.set_weights(self._initial_weights) + del self._initial_weights + return output + + def compute_output_shape(self, input_shape): + """Computes the output shape of the layer. + + Assumes that the layer will be built + to match that input shape provided. + + Arguments: + input_shape: Shape tuple (tuple of integers) + or list of shape tuples (one per output tensor of the layer). + Shape tuples can include None for free dimensions, + instead of an integer. + + Returns: + An input shape tuple. + """ + logging.warning( + 'All custom layers should implement the ' + '`compute_output_shape` method. This layer (' + self.name + ') ' + 'is relying on the base `Layer.compute_output_shape` implementation, ' + 'which will start raising a `NotImplementedError` ' + 'as of July 1st, 2018.') + return input_shape + + def compute_mask(self, inputs, mask=None): # pylint: disable=unused-argument + """Computes an output mask tensor. + + Arguments: + inputs: Tensor or list of tensors. + mask: Tensor or list of tensors. + + Returns: + None or a tensor (or list of tensors, + one per output tensor of the layer). + """ + if not self.supports_masking: + if mask is not None: + if isinstance(mask, list): + if any(m is not None for m in mask): + raise TypeError('Layer ' + self.name + ' does not support masking, ' + 'but was passed an input_mask: ' + str(mask)) + else: + raise TypeError('Layer ' + self.name + ' does not support masking, ' + 'but was passed an input_mask: ' + str(mask)) + # masking not explicitly supported: return None as mask + return None + # if masking is explicitly supported, by default + # carry over the input mask + return mask + + def get_input_mask_at(self, node_index): + """Retrieves the input mask tensor(s) of a layer at a given node. + + Arguments: + node_index: Integer, index of the node + from which to retrieve the attribute. + E.g. `node_index=0` will correspond to the + first time the layer was called. + + Returns: + A mask tensor + (or list of tensors if the layer has multiple inputs). + """ + inputs = self.get_input_at(node_index) + if isinstance(inputs, list): + return [getattr(x, '_keras_mask', None) for x in inputs] + else: + return getattr(inputs, '_keras_mask', None) + + def get_output_mask_at(self, node_index): + """Retrieves the output mask tensor(s) of a layer at a given node. + + Arguments: + node_index: Integer, index of the node + from which to retrieve the attribute. + E.g. `node_index=0` will correspond to the + first time the layer was called. + + Returns: + A mask tensor + (or list of tensors if the layer has multiple outputs). + """ + output = self.get_output_at(node_index) + if isinstance(output, list): + return [getattr(x, '_keras_mask', None) for x in output] + else: + return getattr(output, '_keras_mask', None) + + @property + def input_mask(self): + """Retrieves the input mask tensor(s) of a layer. + + Only applicable if the layer has exactly one inbound node, + i.e. if it is connected to one incoming layer. + + Returns: + Input mask tensor (potentially None) or list of input + mask tensors. + + Raises: + AttributeError: if the layer is connected to + more than one incoming layers. + """ + inputs = self.input + if isinstance(inputs, list): + return [getattr(x, '_keras_mask', None) for x in inputs] + else: + return getattr(inputs, '_keras_mask', None) + + @property + def output_mask(self): + """Retrieves the output mask tensor(s) of a layer. + + Only applicable if the layer has exactly one inbound node, + i.e. if it is connected to one incoming layer. + + Returns: + Output mask tensor (potentially None) or list of output + mask tensors. + + Raises: + AttributeError: if the layer is connected to + more than one incoming layers. + """ + output = self.output + if isinstance(output, list): + return [getattr(x, '_keras_mask', None) for x in output] + else: + return getattr(output, '_keras_mask', None) + + def set_weights(self, weights): + """Sets the weights of the layer, from Numpy arrays. + + Arguments: + weights: a list of Numpy arrays. The number + of arrays and their shape must match + number of the dimensions of the weights + of the layer (i.e. it should match the + output of `get_weights`). + + Raises: + ValueError: If the provided weights list does not match the + layer's specifications. + """ + params = self.weights + if len(params) != len(weights): + raise ValueError('You called `set_weights(weights)` on layer "' + + self.name + '" with a weight list of length ' + + str(len(weights)) + ', but the layer was expecting ' + + str(len(params)) + ' weights. Provided weights: ' + + str(weights)[:50] + '...') + if not params: + return + weight_value_tuples = [] + param_values = K.batch_get_value(params) + for pv, p, w in zip(param_values, params, weights): + if pv.shape != w.shape: + raise ValueError('Layer weight shape ' + str(pv.shape) + + ' not compatible with ' + 'provided weight shape ' + str(w.shape)) + weight_value_tuples.append((p, w)) + K.batch_set_value(weight_value_tuples) + + def get_weights(self): + """Returns the current weights of the layer. + + Returns: + Weights values as a list of numpy arrays. + """ + params = self.weights + return K.batch_get_value(params) + + def get_config(self): + """Returns the config of the layer. + + A layer config is a Python dictionary (serializable) + containing the configuration of a layer. + The same layer can be reinstantiated later + (without its trained weights) from this configuration. + + The config of a layer does not include connectivity + information, nor the layer class name. These are handled + by `Network` (one layer of abstraction above). + + Returns: + Python dictionary. + """ + config = {'name': self.name, 'trainable': self.trainable} + if hasattr(self, '_batch_input_shape'): + config['batch_input_shape'] = self._batch_input_shape + if hasattr(self, 'dtype'): + config['dtype'] = self.dtype + return config + + @classmethod + def from_config(cls, config): + """Creates a layer from its config. + + This method is the reverse of `get_config`, + capable of instantiating the same layer from the config + dictionary. It does not handle layer connectivity + (handled by Network), nor weights (handled by `set_weights`). + + Arguments: + config: A Python dictionary, typically the + output of get_config. + + Returns: + A layer instance. + """ + return cls(**config) + + @tf_base_layers.Layer.activity_regularizer.setter + def activity_regularizer(self, activity_regularizer): + self._activity_regularizer = activity_regularizer + + +def shape_type_conversion(fn): + """Decorator that handles tuple/TensorShape conversion. + + Used in `compute_output_shape` and `build`. + + Arguments: + fn: function to wrap. + + Returns: + Wrapped function. + """ + + def wrapper(instance, input_shape): + if input_shape is not None: + if isinstance(input_shape, list): + input_shape = [ + tuple(tensor_shape.TensorShape(x).as_list()) for x in input_shape] + else: + input_shape = tuple(tensor_shape.TensorShape(input_shape).as_list()) + output_shape = fn(instance, input_shape) + if output_shape is not None: + if isinstance(output_shape, list): + return [tensor_shape.TensorShape(x) for x in output_shape] + return tensor_shape.TensorShape(output_shape) + + return wrapper diff --git a/tensorflow/python/keras/_impl/keras/engine/input_layer.py b/tensorflow/python/keras/_impl/keras/engine/input_layer.py new file mode 100644 index 0000000000000000000000000000000000000000..b51dd8a2189d0c8542c84dfeac9be0d72b96ff1b --- /dev/null +++ b/tensorflow/python/keras/_impl/keras/engine/input_layer.py @@ -0,0 +1,231 @@ +# 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=protected-access +"""Input layer code (`Input` and `InputLayer`). +""" +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 tensor_shape +from tensorflow.python.keras._impl.keras import backend as K +from tensorflow.python.keras._impl.keras.engine import base_layer +from tensorflow.python.layers import base as tf_base_layers +from tensorflow.python.ops import array_ops +from tensorflow.python.util.tf_export import tf_export + + +@tf_export('keras.layers.InputLayer') +class InputLayer(base_layer.Layer): + """Layer to be used as an entry point into a Network (a graph of layers). + + It can either wrap an existing tensor (pass an `input_tensor` argument) + or create its a placeholder tensor (pass arguments `input_shape`, and + optionally, `dtype`). + + It is generally recommend to use the functional layer API via `Input`, + (which creates an `InputLayer`) without directly using `InputLayer`. + + Arguments: + input_shape: Shape tuple (not including the batch axis), or `TensorShape` + instance (not including the batch axis). + batch_size: Optional input batch size (integer or None). + dtype: Datatype of the input. + input_tensor: Optional tensor to use as layer input + instead of creating a placeholder. + sparse: Boolean, whether the placeholder created + is meant to be sparse. + name: Name of the layer (string). + """ + + def __init__(self, + input_shape=None, + batch_size=None, + dtype=None, + input_tensor=None, + sparse=False, + name=None, + **kwargs): + if 'batch_input_shape' in kwargs: + batch_input_shape = kwargs.pop('batch_input_shape') + if input_shape and batch_input_shape: + raise ValueError('Only provide the input_shape OR ' + 'batch_input_shape argument to ' + 'InputLayer, not both at the same time.') + batch_size = batch_input_shape[0] + input_shape = batch_input_shape[1:] + if kwargs: + raise ValueError('Unrecognized keyword arguments:', kwargs.keys()) + + if not name: + prefix = 'input' + name = prefix + '_' + str(K.get_uid(prefix)) + + if not dtype: + if input_tensor is None: + dtype = K.floatx() + else: + dtype = K.dtype(input_tensor) + super(InputLayer, self).__init__(dtype=dtype, name=name) + self.built = True + self.sparse = sparse + self.batch_size = batch_size + + if isinstance(input_shape, tensor_shape.TensorShape): + input_shape = tuple(input_shape.as_list()) + + if input_tensor is None: + if input_shape is not None: + batch_input_shape = (batch_size,) + tuple(input_shape) + else: + batch_input_shape = None + + if context.executing_eagerly(): + # In eager mode, create a temporary placeholder to call the layer on. + input_tensor = tf_base_layers._DeferredTensor( # pylint: disable=protected-access + shape=batch_input_shape, + dtype=dtype, + name=self.name) + else: + # In graph mode, create a graph placeholder to call the layer on. + if sparse: + input_tensor = array_ops.sparse_placeholder( + shape=batch_input_shape, + dtype=dtype, + name=self.name) + else: + input_tensor = array_ops.placeholder( + shape=batch_input_shape, + dtype=dtype, + name=self.name) + + # For compatibility with Keras API. + self.is_placeholder = True + self._batch_input_shape = batch_input_shape + else: + # For compatibility with Keras API. + self.is_placeholder = False + self._batch_input_shape = tuple(input_tensor.get_shape().as_list()) + + # Create an input node to add to self.outbound_node + # and set output_tensors' _keras_history. + input_tensor._keras_history = (self, 0, 0) # pylint: disable=protected-access + tf_base_layers.Node( + self, + inbound_layers=[], + node_indices=[], + tensor_indices=[], + input_tensors=[input_tensor], + output_tensors=[input_tensor]) + + def get_config(self): + config = { + 'batch_input_shape': self._batch_input_shape, + 'dtype': self.dtype, + 'sparse': self.sparse, + 'name': self.name + } + return config + + +@tf_export('keras.layers.Input', 'keras.Input') +def Input( # pylint: disable=invalid-name + shape=None, + batch_size=None, + name=None, + dtype=None, + sparse=False, + tensor=None, + **kwargs): + """`Input()` is used to instantiate a Keras tensor. + + A Keras tensor is a tensor object from the underlying backend + (Theano or TensorFlow), which we augment with certain + attributes that allow us to build a Keras model + just by knowing the inputs and outputs of the model. + + For instance, if a, b and c are Keras tensors, + it becomes possible to do: + `model = Model(input=[a, b], output=c)` + + The added Keras attribute is: + `_keras_history`: Last layer applied to the tensor. + the entire layer graph is retrievable from that layer, + recursively. + + Arguments: + shape: A shape tuple (integers), not including the batch size. + For instance, `shape=(32,)` indicates that the expected input + will be batches of 32-dimensional vectors. + batch_size: optional static batch size (integer). + name: An optional name string for the layer. + Should be unique in a model (do not reuse the same name twice). + It will be autogenerated if it isn't provided. + dtype: The data type expected by the input, as a string + (`float32`, `float64`, `int32`...) + sparse: A boolean specifying whether the placeholder + to be created is sparse. + tensor: Optional existing tensor to wrap into the `Input` layer. + If set, the layer will not create a placeholder tensor. + **kwargs: deprecated arguments support. + + Returns: + A tensor. + + Example: + + ```python + # this is a logistic regression in Keras + x = Input(shape=(32,)) + y = Dense(16, activation='softmax')(x) + model = Model(x, y) + ``` + + Raises: + ValueError: in case of invalid arguments. + """ + if 'batch_shape' in kwargs: + batch_shape = kwargs.pop('batch_shape') + if shape and batch_shape: + raise ValueError('Only provide the shape OR ' + 'batch_shape argument to ' + 'Input, not both at the same time.') + batch_size = batch_shape[0] + shape = batch_shape[1:] + if kwargs: + raise ValueError('Unrecognized keyword arguments:', kwargs.keys()) + + if dtype is None: + dtype = K.floatx() + if not shape and tensor is None: + raise ValueError('Please provide to Input either a `shape`' + ' or a `tensor` argument. Note that ' + '`shape` does not include the batch ' + 'dimension.') + input_layer = InputLayer( + input_shape=shape, + batch_size=batch_size, + name=name, + dtype=dtype, + sparse=sparse, + input_tensor=tensor) + # Return tensor including `_keras_history`. + # Note that in this case train_output and test_output are the same pointer. + outputs = input_layer._inbound_nodes[0].output_tensors + if len(outputs) == 1: + return outputs[0] + else: + return outputs diff --git a/tensorflow/python/keras/_impl/keras/engine/topology.py b/tensorflow/python/keras/_impl/keras/engine/network.py similarity index 59% rename from tensorflow/python/keras/_impl/keras/engine/topology.py rename to tensorflow/python/keras/_impl/keras/engine/network.py index f562a19cf5ea5c3573962f565c2a92be28bbd591..bf8239043874794c6617937cfc9c619d743502a9 100644 --- a/tensorflow/python/keras/_impl/keras/engine/topology.py +++ b/tensorflow/python/keras/_impl/keras/engine/network.py @@ -13,7 +13,7 @@ # limitations under the License. # ============================================================================== # pylint: disable=protected-access -"""Base layer code and base model (Network) code. +"""A `Network` is way to compose layers: the topological form of a `Model`. """ from __future__ import absolute_import from __future__ import division @@ -30,19 +30,17 @@ from tensorflow.python.eager import context from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.keras._impl.keras import backend as K -from tensorflow.python.keras._impl.keras import constraints -from tensorflow.python.keras._impl.keras import initializers -from tensorflow.python.keras._impl.keras import regularizers -from tensorflow.python.keras._impl.keras.utils import conv_utils +from tensorflow.python.keras._impl.keras.engine import base_layer +from tensorflow.python.keras._impl.keras.engine import saving +from tensorflow.python.keras._impl.keras.utils import generic_utils from tensorflow.python.keras._impl.keras.utils.io_utils import ask_to_proceed_with_overwrite from tensorflow.python.keras._impl.keras.utils.layer_utils import print_summary as print_layer_summary from tensorflow.python.layers import base as tf_base_layers from tensorflow.python.layers import utils as tf_layers_util -from tensorflow.python.ops import array_ops from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.training import checkpointable from tensorflow.python.util import nest from tensorflow.python.util import tf_inspect -from tensorflow.python.util.tf_export import tf_export # pylint: disable=g-import-not-at-top @@ -57,684 +55,12 @@ except ImportError: yaml = None # pylint: enable=g-import-not-at-top -# pylint: disable=invalid-name -InputSpec = tf_base_layers.InputSpec -Node = tf_base_layers.Node -TFBaseLayer = tf_base_layers.Layer -# pylint: enable=invalid-name - - -@tf_export('keras.layers.Layer') -class Layer(tf_base_layers.Layer): - """Abstract base layer class. - - # Properties - name: String, must be unique within a model. - input_spec: List of InputSpec class instances - each entry describes one required input: - - ndim - - dtype - A layer with `n` input tensors must have - an `input_spec` of length `n`. - trainable: Boolean, whether the layer weights - will be updated during training. - uses_learning_phase: Whether any operation - of the layer uses `K.in_training_phase()` - or `K.in_test_phase()`. - input_shape: Shape tuple. Provided for convenience, - but note that there may be cases in which this - attribute is ill-defined (e.g. a shared layer - with multiple input shapes), in which case - requesting `input_shape` will raise an Exception. - Prefer using `layer.get_input_shape_for(input_shape)`, - or `layer.get_input_shape_at(node_index)`. - output_shape: Shape tuple. See above. - inbound_nodes: List of nodes. - outbound_nodes: List of nodes. - input, output: Input/output tensor(s). Note that if the layer is used - more than once (shared layer), this is ill-defined - and will raise an exception. In such cases, use - `layer.get_input_at(node_index)`. - input_mask, output_mask: Same as above, for masks. - trainable_weights: List of variables. - non_trainable_weights: List of variables. - weights: The concatenation of the lists trainable_weights and - non_trainable_weights (in this order). - - # Methods - call(x, mask=None): Where the layer's logic lives. - __call__(x, mask=None): Wrapper around the layer logic (`call`). - If x is a Keras tensor: - - Connect current layer with last layer from tensor: - `self._add_inbound_node(last_layer)` - - Add layer to tensor history - If layer is not built: - - Build from inputs shape - get_weights() - set_weights(weights) - get_config() - count_params() - compute_output_shape(input_shape) - compute_mask(x, mask) - get_input_at(node_index) - get_output_at(node_index) - get_input_shape_at(node_index) - get_output_shape_at(node_index) - get_input_mask_at(node_index) - get_output_mask_at(node_index) - - # Class Methods - from_config(config) - - # Internal methods: - build(input_shape) - _add_inbound_node(layer, index=0) - """ - - def __init__(self, **kwargs): - # These properties should be set by the user via keyword arguments. - # note that 'dtype', 'input_shape' and 'batch_input_shape' - # are only applicable to input layers: do not pass these keywords - # to non-input layers. - allowed_kwargs = { - 'activity_regularizer', - 'input_shape', - 'batch_input_shape', - 'batch_size', - 'dtype', - 'name', - 'trainable', - 'weights', - } - # Validate optional keyword arguments. - for kwarg in kwargs: - if kwarg not in allowed_kwargs: - raise TypeError('Keyword argument not understood:', kwarg) - - # Get layer name. - name = kwargs.get('name') - - # Get `trainable` status. - trainable = kwargs.get('trainable', True) - - # Get `dtype`. - dtype = kwargs.get('dtype') - if dtype is None: - dtype = K.floatx() - - # Call super, which will set all properties common to Keras layers - # and core TF layers. - super(Layer, self).__init__( - name=name, dtype=dtype, trainable=trainable, - activity_regularizer=kwargs.get('activity_regularizer')) - - # Add properties that are Keras-only for now. - self.supports_masking = False - - # Manage input shape information if passed. - if 'input_shape' in kwargs or 'batch_input_shape' in kwargs: - # In this case we will later create an input layer - # to insert before the current layer - if 'batch_input_shape' in kwargs: - batch_input_shape = tuple(kwargs['batch_input_shape']) - elif 'input_shape' in kwargs: - if 'batch_size' in kwargs: - batch_size = kwargs['batch_size'] - else: - batch_size = None - batch_input_shape = (batch_size,) + tuple(kwargs['input_shape']) - self._batch_input_shape = batch_input_shape - - # Manage initial weight values if passed. - if 'weights' in kwargs: - self._initial_weights = kwargs['weights'] - else: - self._initial_weights = None - - def add_weight(self, - name, - shape, - dtype=None, - initializer=None, - regularizer=None, - trainable=True, - constraint=None): - """Adds a weight variable to the layer. - - Arguments: - name: String, the name for the weight variable. - shape: The shape tuple of the weight. - dtype: The dtype of the weight. - initializer: An Initializer instance (callable). - regularizer: An optional Regularizer instance. - trainable: A boolean, whether the weight should - be trained via backprop or not (assuming - that the layer itself is also trainable). - constraint: An optional Constraint instance. - - Returns: - The created weight variable. - """ - if dtype is None: - dtype = K.floatx() - weight = self.add_variable(name, shape, - dtype=dtype, - initializer=initializers.get(initializer), - regularizer=regularizers.get(regularizer), - constraint=constraints.get(constraint), - trainable=trainable) - return weight - - def call(self, inputs, **kwargs): # pylint: disable=unused-argument - """This is where the layer's logic lives. - - Arguments: - inputs: Input tensor, or list/tuple of input tensors. - **kwargs: Additional keyword arguments. - - Returns: - A tensor or list/tuple of tensors. - """ - return inputs - - def __call__(self, inputs, **kwargs): - """Wrapper around self.call(), for handling internal references. - - If a Keras tensor is passed: - - We call self._add_inbound_node(). - - If necessary, we `build` the layer to match - the shape of the input(s). - - We update the _keras_history of the output tensor(s) - with the current layer. - This is done as part of _add_inbound_node(). - - Arguments: - inputs: Can be a tensor or list/tuple of tensors. - **kwargs: Additional keyword arguments to be passed to `call()`. - - Returns: - Output of the layer's `call` method. - - Raises: - ValueError: in case the layer is missing shape information - for its `build` call. - """ - # Actually call the layer (optionally building it). - output = super(Layer, self).__call__(inputs, **kwargs) - if context.in_eager_mode(): - return output - - # Un-built subclassed network: build it - if isinstance(self, Network) and not self.inputs: - self._set_inputs(inputs, training=kwargs.get('training')) - - # Update learning phase info. - output_tensors = to_list(output) - uses_lp = any( - [getattr(x, '_uses_learning_phase', False) for x in to_list(inputs)]) - uses_lp = getattr(self, 'uses_learning_phase', False) or uses_lp - for i in range(len(output_tensors)): - output_tensors[i]._uses_learning_phase = getattr( - output_tensors[i], '_uses_learning_phase', False) or uses_lp - - # Optionally load weight values that were specified at layer instantiation. - if hasattr(self, '_initial_weights') and self._initial_weights is not None: - self.set_weights(self._initial_weights) - del self._initial_weights - return output - - def compute_output_shape(self, input_shape): - """Computes the output shape of the layer. - - Assumes that the layer will be built - to match that input shape provided. - - Arguments: - input_shape: Shape tuple (tuple of integers) - or list of shape tuples (one per output tensor of the layer). - Shape tuples can include None for free dimensions, - instead of an integer. - - Returns: - An input shape tuple. - """ - logging.warning( - 'All custom layers should implement the ' - '`compute_output_shape` method. This layer (' + self.name + ') ' - 'is relying on the base `Layer.compute_output_shape` implementation, ' - 'which will start raising a `NotImplementedError` ' - 'as of July 1st, 2018.') - return input_shape - - def compute_mask(self, inputs, mask=None): # pylint: disable=unused-argument - """Computes an output mask tensor. - - Arguments: - inputs: Tensor or list of tensors. - mask: Tensor or list of tensors. - - Returns: - None or a tensor (or list of tensors, - one per output tensor of the layer). - """ - if not self.supports_masking: - if mask is not None: - if isinstance(mask, list): - if any(m is not None for m in mask): - raise TypeError('Layer ' + self.name + ' does not support masking, ' - 'but was passed an input_mask: ' + str(mask)) - else: - raise TypeError('Layer ' + self.name + ' does not support masking, ' - 'but was passed an input_mask: ' + str(mask)) - # masking not explicitly supported: return None as mask - return None - # if masking is explicitly supported, by default - # carry over the input mask - return mask - - def get_input_mask_at(self, node_index): - """Retrieves the input mask tensor(s) of a layer at a given node. - - Arguments: - node_index: Integer, index of the node - from which to retrieve the attribute. - E.g. `node_index=0` will correspond to the - first time the layer was called. - - Returns: - A mask tensor - (or list of tensors if the layer has multiple inputs). - """ - inputs = self.get_input_at(node_index) - if isinstance(inputs, list): - return [getattr(x, '_keras_mask', None) for x in inputs] - else: - return getattr(inputs, '_keras_mask', None) - - def get_output_mask_at(self, node_index): - """Retrieves the output mask tensor(s) of a layer at a given node. - - Arguments: - node_index: Integer, index of the node - from which to retrieve the attribute. - E.g. `node_index=0` will correspond to the - first time the layer was called. - - Returns: - A mask tensor - (or list of tensors if the layer has multiple outputs). - """ - output = self.get_output_at(node_index) - if isinstance(output, list): - return [getattr(x, '_keras_mask', None) for x in output] - else: - return getattr(output, '_keras_mask', None) - - @property - def input_mask(self): - """Retrieves the input mask tensor(s) of a layer. - - Only applicable if the layer has exactly one inbound node, - i.e. if it is connected to one incoming layer. - - Returns: - Input mask tensor (potentially None) or list of input - mask tensors. - - Raises: - AttributeError: if the layer is connected to - more than one incoming layers. - """ - inputs = self.input - if isinstance(inputs, list): - return [getattr(x, '_keras_mask', None) for x in inputs] - else: - return getattr(inputs, '_keras_mask', None) - - @property - def output_mask(self): - """Retrieves the output mask tensor(s) of a layer. - - Only applicable if the layer has exactly one inbound node, - i.e. if it is connected to one incoming layer. - - Returns: - Output mask tensor (potentially None) or list of output - mask tensors. - - Raises: - AttributeError: if the layer is connected to - more than one incoming layers. - """ - output = self.output - if isinstance(output, list): - return [getattr(x, '_keras_mask', None) for x in output] - else: - return getattr(output, '_keras_mask', None) - - def set_weights(self, weights): - """Sets the weights of the layer, from Numpy arrays. - - Arguments: - weights: a list of Numpy arrays. The number - of arrays and their shape must match - number of the dimensions of the weights - of the layer (i.e. it should match the - output of `get_weights`). - - Raises: - ValueError: If the provided weights list does not match the - layer's specifications. - """ - params = self.weights - if len(params) != len(weights): - raise ValueError('You called `set_weights(weights)` on layer "' + - self.name + '" with a weight list of length ' + - str(len(weights)) + ', but the layer was expecting ' + - str(len(params)) + ' weights. Provided weights: ' + - str(weights)[:50] + '...') - if not params: - return - weight_value_tuples = [] - param_values = K.batch_get_value(params) - for pv, p, w in zip(param_values, params, weights): - if pv.shape != w.shape: - raise ValueError('Layer weight shape ' + str(pv.shape) + - ' not compatible with ' - 'provided weight shape ' + str(w.shape)) - weight_value_tuples.append((p, w)) - K.batch_set_value(weight_value_tuples) - - def get_weights(self): - """Returns the current weights of the layer. - - Returns: - Weights values as a list of numpy arrays. - """ - params = self.weights - return K.batch_get_value(params) - - def get_config(self): - """Returns the config of the layer. - - A layer config is a Python dictionary (serializable) - containing the configuration of a layer. - The same layer can be reinstantiated later - (without its trained weights) from this configuration. - - The config of a layer does not include connectivity - information, nor the layer class name. These are handled - by `Network` (one layer of abstraction above). - - Returns: - Python dictionary. - """ - config = {'name': self.name, 'trainable': self.trainable} - if hasattr(self, '_batch_input_shape'): - config['batch_input_shape'] = self._batch_input_shape - if hasattr(self, 'dtype'): - config['dtype'] = self.dtype - return config - - @classmethod - def from_config(cls, config): - """Creates a layer from its config. - - This method is the reverse of `get_config`, - capable of instantiating the same layer from the config - dictionary. It does not handle layer connectivity - (handled by Network), nor weights (handled by `set_weights`). - - Arguments: - config: A Python dictionary, typically the - output of get_config. - - Returns: - A layer instance. - """ - return cls(**config) - - @tf_base_layers.Layer.activity_regularizer.setter - def activity_regularizer(self, activity_regularizer): - self._activity_regularizer = activity_regularizer - - -class InputLayer(Layer): - """Layer to be used as an entry point into a Network (a graph of layers). - - It can either wrap an existing tensor (pass an `input_tensor` argument) - or create its a placeholder tensor (pass arguments `input_shape`, and - optionally, `dtype`). - - It is generally recommend to use the functional layer API via `Input`, - (which creates an `InputLayer`) without directly using `InputLayer`. - - Arguments: - input_shape: Shape tuple (not including the batch axis), or `TensorShape` - instance (not including the batch axis). - batch_size: Optional input batch size (integer or None). - dtype: Datatype of the input. - input_tensor: Optional tensor to use as layer input - instead of creating a placeholder. - sparse: Boolean, whether the placeholder created - is meant to be sparse. - name: Name of the layer (string). - """ - - def __init__(self, - input_shape=None, - batch_size=None, - dtype=None, - input_tensor=None, - sparse=False, - name=None, - **kwargs): - if 'batch_input_shape' in kwargs: - batch_input_shape = kwargs.pop('batch_input_shape') - if input_shape and batch_input_shape: - raise ValueError('Only provide the input_shape OR ' - 'batch_input_shape argument to ' - 'InputLayer, not both at the same time.') - batch_size = batch_input_shape[0] - input_shape = batch_input_shape[1:] - if kwargs: - raise ValueError('Unrecognized keyword arguments:', kwargs.keys()) - - if not name: - prefix = 'input' - name = prefix + '_' + str(K.get_uid(prefix)) - - if not dtype: - if input_tensor is None: - dtype = K.floatx() - else: - dtype = K.dtype(input_tensor) - super(InputLayer, self).__init__(dtype=dtype, name=name) - self.built = True - self.sparse = sparse - self.batch_size = batch_size - - if isinstance(input_shape, tensor_shape.TensorShape): - input_shape = tuple(input_shape.as_list()) - - if input_tensor is None: - if input_shape is not None: - batch_input_shape = (batch_size,) + tuple(input_shape) - else: - batch_input_shape = None - - if context.in_eager_mode(): - # In eager mode, create a temporary placeholder to call the layer on. - input_tensor = tf_base_layers._DeferredTensor( # pylint: disable=protected-access - shape=batch_input_shape, - dtype=dtype, - name=self.name) - else: - # In graph mode, create a graph placeholder to call the layer on. - if sparse: - input_tensor = array_ops.sparse_placeholder( - shape=batch_input_shape, - dtype=dtype, - name=self.name) - else: - input_tensor = array_ops.placeholder( - shape=batch_input_shape, - dtype=dtype, - name=self.name) - - # For compatibility with Keras API. - self.is_placeholder = True - self._batch_input_shape = batch_input_shape - else: - # For compatibility with Keras API. - self.is_placeholder = False - self._batch_input_shape = tuple(input_tensor.get_shape().as_list()) - - # Create an input node to add to self.outbound_node - # and set output_tensors' _keras_history. - input_tensor._keras_history = (self, 0, 0) # pylint: disable=protected-access - tf_base_layers.Node( - self, - inbound_layers=[], - node_indices=[], - tensor_indices=[], - input_tensors=[input_tensor], - output_tensors=[input_tensor]) - - def get_config(self): - config = { - 'batch_input_shape': self._batch_input_shape, - 'dtype': self.dtype, - 'sparse': self.sparse, - 'name': self.name - } - return config - - -@tf_export('keras.layers.Input', 'keras.Input') -def Input( # pylint: disable=invalid-name - shape=None, - batch_size=None, - name=None, - dtype=None, - sparse=False, - tensor=None, - **kwargs): - """`Input()` is used to instantiate a Keras tensor. - - A Keras tensor is a tensor object from the underlying backend - (Theano or TensorFlow), which we augment with certain - attributes that allow us to build a Keras model - just by knowing the inputs and outputs of the model. - - For instance, if a, b and c are Keras tensors, - it becomes possible to do: - `model = Model(input=[a, b], output=c)` - - The added Keras attribute is: - `_keras_history`: Last layer applied to the tensor. - the entire layer graph is retrievable from that layer, - recursively. - - Arguments: - shape: A shape tuple (integers), not including the batch size. - For instance, `shape=(32,)` indicates that the expected input - will be batches of 32-dimensional vectors. - batch_size: optional static batch size (integer). - name: An optional name string for the layer. - Should be unique in a model (do not reuse the same name twice). - It will be autogenerated if it isn't provided. - dtype: The data type expected by the input, as a string - (`float32`, `float64`, `int32`...) - sparse: A boolean specifying whether the placeholder - to be created is sparse. - tensor: Optional existing tensor to wrap into the `Input` layer. - If set, the layer will not create a placeholder tensor. - **kwargs: deprecated arguments support. - - Returns: - A tensor. - - Example: - ```python - # this is a logistic regression in Keras - x = Input(shape=(32,)) - y = Dense(16, activation='softmax')(x) - model = Model(x, y) - ``` +class Network(base_layer.Layer): + """A `Network` is a composition of layers. - Raises: - ValueError: in case of invalid arguments. - """ - if 'batch_shape' in kwargs: - batch_shape = kwargs.pop('batch_shape') - if shape and batch_shape: - raise ValueError('Only provide the shape OR ' - 'batch_shape argument to ' - 'Input, not both at the same time.') - batch_size = batch_shape[0] - shape = batch_shape[1:] - if kwargs: - raise ValueError('Unrecognized keyword arguments:', kwargs.keys()) - - if dtype is None: - dtype = K.floatx() - if not shape and tensor is None: - raise ValueError('Please provide to Input either a `shape`' - ' or a `tensor` argument. Note that ' - '`shape` does not include the batch ' - 'dimension.') - input_layer = InputLayer( - input_shape=shape, - batch_size=batch_size, - name=name, - dtype=dtype, - sparse=sparse, - input_tensor=tensor) - # Return tensor including `_keras_history`. - # Note that in this case train_output and test_output are the same pointer. - outputs = input_layer._inbound_nodes[0].output_tensors - if len(outputs) == 1: - return outputs[0] - else: - return outputs - - -class Network(Layer): - """A Network is a directed acyclic graph of layers. - - It is the topological form of a "model". A Model - is simply a Network with added training routines. - - # Properties - name - inputs - outputs - input_layers - output_layers - input_spec (list of class instances) - each entry describes one required input: - - ndim - - dtype - trainable (boolean) - input_shape - output_shape - inbound_nodes: list of nodes - outbound_nodes: list of nodes - trainable_weights (list of variables) - non_trainable_weights (list of variables) - - # Methods - summary - get_layer - get_weights - set_weights - get_config - compute_output_shape - - # Class Methods - from_config + It is the topological form of a "model". A `Model` + is simply a `Network` with added training routines. """ def __init__(self, *args, **kwargs): # pylint: disable=super-init-not-called @@ -773,11 +99,11 @@ class Network(Layer): self._losses = [] # Used in symbolic mode only. self._scope = None # Never used. self._reuse = None # Never used. - if context.in_eager_mode: + if context.executing_eagerly(): self._graph = None else: self._graph = ops.get_default_graph() # Used in symbolic mode only. - # A Network does not create weights of its own, thus has no dtype. + # A Network does not create weights of its own, thus has no dtype. self._dtype = None # All layers in order of horizontal graph traversal. @@ -800,7 +126,7 @@ class Network(Layer): self.outputs = [outputs] # User-prodived argument validation. - if context.in_eager_mode(): + if context.executing_eagerly(): # Check that all inputs/outputs are DeferredTensors. for tensor in self.inputs: if not isinstance(tensor, tf_base_layers._DeferredTensor): # pylint: disable=protected-access @@ -949,7 +275,7 @@ class Network(Layer): self._feed_input_names.append(layer.name) self._feed_input_shapes.append(K.int_shape(self.inputs[i])) # layer.input gives an error in eager mode - if context.in_graph_mode(): + if not context.executing_eagerly(): self._feed_inputs.append(layer.input) for layer in self._output_layers: self.output_names.append(layer.name) @@ -977,6 +303,13 @@ class Network(Layer): if not is_graph_network: if value not in self._layers: self._layers.append(value) + if isinstance(value, checkpointable.CheckpointableBase): + # Layer (and therefore Network/Model) inherit from CheckpointableBase + # rather than Checkpointable, which means there is no Checkpointable + # __setattr__ override (it would be a performance issue for functional + # layers). Therefore Model tracks Checkpointable objects itself. + self._track_checkpointable( + checkpointable=value, name=name, overwrite=True) super(Network, self).__setattr__(name, value) def add_variable(self, name, shape, dtype=None, initializer=None, @@ -984,7 +317,7 @@ class Network(Layer): raise NotImplementedError('`add_variable` is not supported on Networks.') def add_loss(self, *args, **kwargs): - if context.in_eager_mode(): + if context.executing_eagerly(): raise NotImplementedError('`add_loss` is not supported on Networks ' 'when eager execution is enabled.') super(Network, self).add_loss(*args, **kwargs) @@ -1053,17 +386,17 @@ class Network(Layer): if not self._is_graph_network: return None - inputs = to_list(inputs) + inputs = generic_utils.to_list(inputs) if mask is None: masks = [None for _ in range(len(inputs))] else: - masks = to_list(mask) + masks = generic_utils.to_list(mask) cache_key = (tf_layers_util.object_list_uid(inputs) + '_' + tf_layers_util.object_list_uid(masks)) if cache_key in self._output_mask_cache: return self._output_mask_cache[cache_key] else: - _, output_masks = self._run_internal_graph(inputs, masks) + _, output_masks = self._run_internal_graph(inputs, mask=masks) return output_masks @property @@ -1073,6 +406,7 @@ class Network(Layer): def get_layer(self, name=None, index=None): """Retrieves a layer based on either its name (unique) or index. + If `name` and `index` are both provided, `index` will take precedence. Indices are based on order of horizontal graph traversal (bottom-up). Arguments: @@ -1104,7 +438,7 @@ class Network(Layer): @property def updates(self): - """Retrieve the network's updates. + """Retrieves the network's updates. Will only include updates that are either unconditional, or conditional on inputs to this model @@ -1150,7 +484,7 @@ class Network(Layer): Returns: A list of update ops. """ - if context.in_eager_mode(): + if context.executing_eagerly(): return [] if not self.trainable and not self.stateful: @@ -1162,7 +496,10 @@ class Network(Layer): # `updates` might contain irrelevant updates, so it needs to be filtered # with respect to inputs the model has been called on. - relevant_inputs = self.inputs or [] + if self.inputs: + relevant_inputs = self.inputs[:] + else: + relevant_inputs = [] for i in range(1, len(self._inbound_nodes)): inputs = self.get_input_at(i) if isinstance(inputs, list): @@ -1181,7 +518,7 @@ class Network(Layer): @property def losses(self): - """Retrieve the network's losses. + """Retrieves the network's losses. Will only include losses that are either unconditional, or conditional on inputs to this model @@ -1194,10 +531,13 @@ class Network(Layer): losses = [] for layer in self.layers: losses += layer.losses - if context.in_eager_mode(): + if context.executing_eagerly(): return losses - relevant_inputs = self.inputs or [] + if self.inputs: + relevant_inputs = self.inputs[:] + else: + relevant_inputs = [] for i in range(1, len(self._inbound_nodes)): inputs = self.get_input_at(i) if isinstance(inputs, list): @@ -1261,7 +601,7 @@ class Network(Layer): return specs def call(self, inputs, training=None, mask=None): - """Call the model on new inputs. + """Calls the model on new inputs. In this case `call` just reapplies all ops in the graph to the new inputs @@ -1284,7 +624,7 @@ class Network(Layer): else: masks = nest.flatten(mask) - if context.in_graph_mode(): + if not context.executing_eagerly(): # Try to retrieve cached outputs if the layer has already been called # on these exact inputs. cache_key = (tf_layers_util.object_list_uid(inputs) @@ -1490,7 +830,7 @@ class Network(Layer): else: output_masks = [None for _ in range(len(output_tensors))] - if context.in_graph_mode(): + if not context.executing_eagerly(): if layer.activity_regularizer is not None: regularization_losses = [ layer.activity_regularizer(x) for x in output_tensors @@ -1520,7 +860,7 @@ class Network(Layer): if output_masks is not None: output_masks = output_masks[0] - if context.in_graph_mode(): + if not context.executing_eagerly(): # Update cache; # keys are based on ids on input tensors and inputs masks. cache_key = (tf_layers_util.object_list_uid(inputs) @@ -1691,7 +1031,7 @@ class Network(Layer): layer(input_tensors, **kwargs) def process_layer(layer_data): - """Deserialize a layer, then call it on appropriate inputs. + """Deserializes a layer, then call it on appropriate inputs. Arguments: layer_data: layer config dict. @@ -1748,7 +1088,7 @@ class Network(Layer): return cls(inputs=input_tensors, outputs=output_tensors, name=name) def save(self, filepath, overwrite=True, include_optimizer=True): - """Save the model to a single HDF5 file. + """Saves the model to a single HDF5 file. The savefile includes: - The model architecture, allowing to re-instantiate the model. @@ -1818,7 +1158,7 @@ class Network(Layer): if not proceed: return with h5py.File(filepath, 'w') as f: - save_weights_to_hdf5_group(f, self.layers) + saving.save_weights_to_hdf5_group(f, self.layers) def load_weights(self, filepath, by_name=False): """Loads all layer weights from a HDF5 save file. @@ -1849,12 +1189,12 @@ class Network(Layer): if 'layer_names' not in f.attrs and 'model_weights' in f: f = f['model_weights'] if by_name: - load_weights_from_hdf5_group_by_name(f, self.layers) + saving.load_weights_from_hdf5_group_by_name(f, self.layers) else: - load_weights_from_hdf5_group(f, self.layers) + saving.load_weights_from_hdf5_group(f, self.layers) def _updated_config(self): - """Util hared between different serialization methods. + """Util shared between different serialization methods. Returns: Model config with Keras version information added. @@ -1989,370 +1329,12 @@ def get_source_inputs(tensor, layer=None, node_index=None): return source_tensors -def to_list(x): - """Normalizes a list/tensor into a list. - - If a tensor is passed, we return - a list of size 1 containing the tensor. - - Arguments: - x: target object to be normalized. - - Returns: - A list. - """ - if isinstance(x, list): - return x - return [x] - - -def save_weights_to_hdf5_group(f, layers): - from tensorflow.python.keras._impl.keras import __version__ as keras_version # pylint: disable=g-import-not-at-top - - f.attrs['layer_names'] = [layer.name.encode('utf8') for layer in layers] - f.attrs['backend'] = K.backend().encode('utf8') - f.attrs['keras_version'] = str(keras_version).encode('utf8') - - for layer in layers: - g = f.create_group(layer.name) - symbolic_weights = layer.weights - weight_values = K.batch_get_value(symbolic_weights) - weight_names = [] - for i, (w, val) in enumerate(zip(symbolic_weights, weight_values)): - if hasattr(w, 'name') and w.name: - name = str(w.name) - else: - name = 'param_' + str(i) - weight_names.append(name.encode('utf8')) - g.attrs['weight_names'] = weight_names - for name, val in zip(weight_names, weight_values): - param_dset = g.create_dataset(name, val.shape, dtype=val.dtype) - if not val.shape: - # scalar - param_dset[()] = val - else: - param_dset[:] = val - - -def preprocess_weights_for_loading(layer, - weights, - original_keras_version=None, - original_backend=None): - """Converts layers weights from Keras 1 format to Keras 2. - - Arguments: - layer: Layer instance. - weights: List of weights values (Numpy arrays). - original_keras_version: Keras version for the weights, as a string. - original_backend: Keras backend the weights were trained with, - as a string. - - Returns: - A list of weights values (Numpy arrays). - """ - if layer.__class__.__name__ == 'Bidirectional': - num_weights_per_layer = len(weights) // 2 - forward_weights = preprocess_weights_for_loading( - layer.forward_layer, weights[:num_weights_per_layer], - original_keras_version, original_backend) - backward_weights = preprocess_weights_for_loading( - layer.backward_layer, weights[num_weights_per_layer:], - original_keras_version, original_backend) - weights = forward_weights + backward_weights - - if original_keras_version == '1': - if layer.__class__.__name__ == 'TimeDistributed': - weights = preprocess_weights_for_loading( - layer.layer, weights, original_keras_version, original_backend) - - if layer.__class__.__name__ == 'Conv1D': - shape = weights[0].shape - # Handle Keras 1.1 format - if shape[:2] != (layer.kernel_size[0], 1) or shape[3] != layer.filters: - # Legacy shape: - # (filters, input_dim, filter_length, 1) - assert shape[0] == layer.filters and shape[2:] == (layer.kernel_size[0], - 1) - weights[0] = np.transpose(weights[0], (2, 3, 1, 0)) - weights[0] = weights[0][:, 0, :, :] - - if layer.__class__.__name__ == 'Conv2D': - if layer.data_format == 'channels_first': - # old: (filters, stack_size, kernel_rows, kernel_cols) - # new: (kernel_rows, kernel_cols, stack_size, filters) - weights[0] = np.transpose(weights[0], (2, 3, 1, 0)) - - if layer.__class__.__name__ == 'Conv2DTranspose': - if layer.data_format == 'channels_last': - # old: (kernel_rows, kernel_cols, stack_size, filters) - # new: (kernel_rows, kernel_cols, filters, stack_size) - weights[0] = np.transpose(weights[0], (0, 1, 3, 2)) - if layer.data_format == 'channels_first': - # old: (filters, stack_size, kernel_rows, kernel_cols) - # new: (kernel_rows, kernel_cols, filters, stack_size) - weights[0] = np.transpose(weights[0], (2, 3, 0, 1)) - - if layer.__class__.__name__ == 'Conv3D': - if layer.data_format == 'channels_first': - # old: (filters, stack_size, ...) - # new: (..., stack_size, filters) - weights[0] = np.transpose(weights[0], (2, 3, 4, 1, 0)) - - if layer.__class__.__name__ == 'GRU': - if len(weights) == 9: - kernel = np.concatenate([weights[0], weights[3], weights[6]], axis=-1) - recurrent_kernel = np.concatenate( - [weights[1], weights[4], weights[7]], axis=-1) - bias = np.concatenate([weights[2], weights[5], weights[8]], axis=-1) - weights = [kernel, recurrent_kernel, bias] - - if layer.__class__.__name__ == 'LSTM': - if len(weights) == 12: - # old: i, c, f, o - # new: i, f, c, o - kernel = np.concatenate( - [weights[0], weights[6], weights[3], weights[9]], axis=-1) - recurrent_kernel = np.concatenate( - [weights[1], weights[7], weights[4], weights[10]], axis=-1) - bias = np.concatenate( - [weights[2], weights[8], weights[5], weights[11]], axis=-1) - weights = [kernel, recurrent_kernel, bias] - - if layer.__class__.__name__ == 'ConvLSTM2D': - if len(weights) == 12: - kernel = np.concatenate( - [weights[0], weights[6], weights[3], weights[9]], axis=-1) - recurrent_kernel = np.concatenate( - [weights[1], weights[7], weights[4], weights[10]], axis=-1) - bias = np.concatenate( - [weights[2], weights[8], weights[5], weights[11]], axis=-1) - if layer.data_format == 'channels_first': - # old: (filters, stack_size, kernel_rows, kernel_cols) - # new: (kernel_rows, kernel_cols, stack_size, filters) - kernel = np.transpose(kernel, (2, 3, 1, 0)) - recurrent_kernel = np.transpose(recurrent_kernel, (2, 3, 1, 0)) - weights = [kernel, recurrent_kernel, bias] - - if layer.__class__.__name__ in ['Model', 'Sequential']: - new_weights = [] - # trainable weights - for sublayer in layer.layers: - num_weights = len(sublayer.trainable_weights) - if num_weights > 0: - new_weights.extend( - preprocess_weights_for_loading( - layer=sublayer, - weights=weights[:num_weights], - original_keras_version=original_keras_version, - original_backend=original_backend)) - weights = weights[num_weights:] - - # non-trainable weights - for sublayer in layer.layers: - num_weights = len([ - l for l in sublayer.weights if l not in sublayer.trainable_weights - ]) - if num_weights > 0: - new_weights.extend( - preprocess_weights_for_loading( - layer=sublayer, - weights=weights[:num_weights], - original_keras_version=original_keras_version, - original_backend=original_backend)) - weights = weights[num_weights:] - weights = new_weights - - conv_layers = ['Conv1D', 'Conv2D', 'Conv3D', 'Conv2DTranspose', 'ConvLSTM2D'] - if layer.__class__.__name__ in conv_layers: - if original_backend == 'theano': - weights[0] = conv_utils.convert_kernel(weights[0]) - if layer.__class__.__name__ == 'ConvLSTM2D': - weights[1] = conv_utils.convert_kernel(weights[1]) - if K.int_shape(layer.weights[0]) != weights[0].shape: - weights[0] = np.transpose(weights[0], (3, 2, 0, 1)) - if layer.__class__.__name__ == 'ConvLSTM2D': - weights[1] = np.transpose(weights[1], (3, 2, 0, 1)) - - # Convert the weights of CuDNNLSTM so that they could be loaded into LSTM - if layer.__class__.__name__ == 'LSTM' and len(weights) == 3: - # Determine if loading a CuDNNLSTM layer from the number of bias weights: - # CuDNNLSTM has (units * 8) weights; while LSTM has (units * 4) - # if there's no bias weight in the file, skip this conversion - units = weights[1].shape[0] - bias = weights[2] - if len(bias) == units * 8: - # reshape the kernels - kernels = np.split(weights[0], 4, axis=1) - kernels = [ - kernel.reshape(-1).reshape(kernel.shape, order='F') - for kernel in kernels - ] - weights[0] = np.concatenate(kernels, axis=1) - - # transpose the recurrent kernels - recurrent_kernels = np.split(weights[1], 4, axis=1) - recurrent_kernels = [kernel.T for kernel in recurrent_kernels] - weights[1] = np.concatenate(recurrent_kernels, axis=1) - - # split the bias into half and merge - weights[2] = bias[:units * 4] + bias[units * 4:] - - return weights - - -def load_weights_from_hdf5_group(f, layers): - """Implements topological (order-based) weight loading. - - Arguments: - f: A pointer to a HDF5 group. - layers: a list of target layers. - - Raises: - ValueError: in case of mismatch between provided layers - and weights file. - """ - if 'keras_version' in f.attrs: - original_keras_version = f.attrs['keras_version'].decode('utf8') - else: - original_keras_version = '1' - if 'backend' in f.attrs: - original_backend = f.attrs['backend'].decode('utf8') - else: - original_backend = None - - filtered_layers = [] - for layer in layers: - weights = layer.weights - if weights: - filtered_layers.append(layer) - - layer_names = [n.decode('utf8') for n in f.attrs['layer_names']] - filtered_layer_names = [] - for name in layer_names: - g = f[name] - weight_names = [n.decode('utf8') for n in g.attrs['weight_names']] - if weight_names: - filtered_layer_names.append(name) - layer_names = filtered_layer_names - if len(layer_names) != len(filtered_layers): - raise ValueError('You are trying to load a weight file ' - 'containing ' + str(len(layer_names)) + - ' layers into a model with ' + str(len(filtered_layers)) + - ' layers.') - - # We batch weight value assignments in a single backend call - # which provides a speedup in TensorFlow. - weight_value_tuples = [] - for k, name in enumerate(layer_names): - g = f[name] - weight_names = [n.decode('utf8') for n in g.attrs['weight_names']] - weight_values = [g[weight_name] for weight_name in weight_names] - layer = filtered_layers[k] - symbolic_weights = layer.weights - weight_values = preprocess_weights_for_loading( - layer, weight_values, original_keras_version, original_backend) - if len(weight_values) != len(symbolic_weights): - raise ValueError('Layer #' + str(k) + ' (named "' + layer.name + - '" in the current model) was found to ' - 'correspond to layer ' + name + ' in the save file. ' - 'However the new layer ' + layer.name + ' expects ' + - str(len(symbolic_weights)) + - ' weights, but the saved weights have ' + - str(len(weight_values)) + ' elements.') - weight_value_tuples += zip(symbolic_weights, weight_values) - K.batch_set_value(weight_value_tuples) - - -def load_weights_from_hdf5_group_by_name(f, layers): - """Implements name-based weight loading. - - (instead of topological weight loading). - - Layers that have no matching name are skipped. - - Arguments: - f: A pointer to a HDF5 group. - layers: a list of target layers. - - Raises: - ValueError: in case of mismatch between provided layers - and weights file. - """ - if 'keras_version' in f.attrs: - original_keras_version = f.attrs['keras_version'].decode('utf8') - else: - original_keras_version = '1' - if 'backend' in f.attrs: - original_backend = f.attrs['backend'].decode('utf8') - else: - original_backend = None - - # New file format. - layer_names = [n.decode('utf8') for n in f.attrs['layer_names']] - - # Reverse index of layer name to list of layers with name. - index = {} - for layer in layers: - if layer.name: - index.setdefault(layer.name, []).append(layer) - - # We batch weight value assignments in a single backend call - # which provides a speedup in TensorFlow. - weight_value_tuples = [] - for k, name in enumerate(layer_names): - g = f[name] - weight_names = [n.decode('utf8') for n in g.attrs['weight_names']] - weight_values = [g[weight_name] for weight_name in weight_names] - - for layer in index.get(name, []): - symbolic_weights = layer.weights - weight_values = preprocess_weights_for_loading( - layer, weight_values, original_keras_version, original_backend) - if len(weight_values) != len(symbolic_weights): - raise ValueError('Layer #' + str(k) + ' (named "' + layer.name + - '") expects ' + str(len(symbolic_weights)) + - ' weight(s), but the saved weights' + ' have ' + - str(len(weight_values)) + ' element(s).') - # Set values. - for i in range(len(weight_values)): - weight_value_tuples.append((symbolic_weights[i], weight_values[i])) - K.batch_set_value(weight_value_tuples) - - -def shape_type_conversion(fn): - """Decorator that handles tuple/TensorShape conversion. - - Used in `compute_output_shape` and `build`. - - Arguments: - fn: function to wrap. - - Returns: - Wrapped function. - """ - - def wrapper(instance, input_shape): - if input_shape is not None: - if isinstance(input_shape, list): - input_shape = [ - tuple(tensor_shape.TensorShape(x).as_list()) for x in input_shape] - else: - input_shape = tuple(tensor_shape.TensorShape(input_shape).as_list()) - output_shape = fn(instance, input_shape) - if output_shape is not None: - if isinstance(output_shape, list): - return [tensor_shape.TensorShape(x) for x in output_shape] - return tensor_shape.TensorShape(output_shape) - - return wrapper - - def _make_node_key(layer_name, node_index): return layer_name + '_ib-' + str(node_index) def _map_graph_network(inputs, outputs): - """Validate a network's topology and gather its layers and nodes. + """Validates a network's topology and gather its layers and nodes. Arguments: inputs: List of input tensors. diff --git a/tensorflow/python/keras/_impl/keras/engine/saving.py b/tensorflow/python/keras/_impl/keras/engine/saving.py new file mode 100644 index 0000000000000000000000000000000000000000..2ad06ca4fdcd55c12ba3ba192751f2f05aacc7ec --- /dev/null +++ b/tensorflow/python/keras/_impl/keras/engine/saving.py @@ -0,0 +1,844 @@ +# 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. +# ============================================================================== +# pylint: disable=protected-access +"""Model saving utilities. +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import json +import os + +import numpy as np +from six.moves import zip # pylint: disable=redefined-builtin + +from tensorflow.python.keras._impl.keras import backend as K +from tensorflow.python.keras._impl.keras import optimizers +from tensorflow.python.keras._impl.keras.utils import conv_utils +from tensorflow.python.keras._impl.keras.utils.io_utils import ask_to_proceed_with_overwrite +from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.util.tf_export import tf_export + +# pylint: disable=g-import-not-at-top +try: + import h5py + HDF5_OBJECT_HEADER_LIMIT = 64512 +except ImportError: + h5py = None + +try: + import yaml +except ImportError: + yaml = None +# pylint: enable=g-import-not-at-top + + +@tf_export('keras.models.save_model') +def save_model(model, filepath, overwrite=True, include_optimizer=True): + """Saves a model to a HDF5 file. + + The saved model contains: + - the model's configuration (topology) + - the model's weights + - the model's optimizer's state (if any) + + Thus the saved model can be reinstantiated in + the exact same state, without any of the code + used for model definition or training. + + Arguments: + model: Keras model instance to be saved. + filepath: String, path where to save the model. + overwrite: Whether we should overwrite any existing + model at the target location, or instead + ask the user with a manual prompt. + include_optimizer: If True, save optimizer's state together. + + Raises: + ImportError: if h5py is not available. + """ + + if h5py is None: + raise ImportError('`save_model` requires h5py.') + + def get_json_type(obj): + """Serializes any object to a JSON-serializable structure. + + Arguments: + obj: the object to serialize + + Returns: + JSON-serializable structure representing `obj`. + + Raises: + TypeError: if `obj` cannot be serialized. + """ + # if obj is a serializable Keras class instance + # e.g. optimizer, layer + if hasattr(obj, 'get_config'): + return {'class_name': obj.__class__.__name__, 'config': obj.get_config()} + + # if obj is any numpy type + if type(obj).__module__ == np.__name__: + if isinstance(obj, np.ndarray): + return {'type': type(obj), 'value': obj.tolist()} + else: + return obj.item() + + # misc functions (e.g. loss function) + if callable(obj): + return obj.__name__ + + # if obj is a python 'type' + if type(obj).__name__ == type.__name__: + return obj.__name__ + + raise TypeError('Not JSON Serializable:', obj) + + from tensorflow.python.keras._impl.keras import __version__ as keras_version # pylint: disable=g-import-not-at-top + + # If file exists and should not be overwritten. + if not overwrite and os.path.isfile(filepath): + proceed = ask_to_proceed_with_overwrite(filepath) + if not proceed: + return + + with h5py.File(filepath, mode='w') as f: + f.attrs['keras_version'] = str(keras_version).encode('utf8') + f.attrs['backend'] = K.backend().encode('utf8') + f.attrs['model_config'] = json.dumps( + { + 'class_name': model.__class__.__name__, + 'config': model.get_config() + }, + default=get_json_type).encode('utf8') + + model_weights_group = f.create_group('model_weights') + model_layers = model.layers + save_weights_to_hdf5_group(model_weights_group, model_layers) + + if include_optimizer and hasattr(model, 'optimizer'): + if isinstance(model.optimizer, optimizers.TFOptimizer): + logging.warning( + 'TensorFlow optimizers do not ' + 'make it possible to access ' + 'optimizer attributes or optimizer state ' + 'after instantiation. ' + 'As a result, we cannot save the optimizer ' + 'as part of the model save file.' + 'You will have to compile your model again after loading it. ' + 'Prefer using a Keras optimizer instead ' + '(see keras.io/optimizers).') + else: + f.attrs['training_config'] = json.dumps( + { + 'optimizer_config': { + 'class_name': model.optimizer.__class__.__name__, + 'config': model.optimizer.get_config() + }, + 'loss': model.loss, + 'metrics': model.metrics, + 'sample_weight_mode': model.sample_weight_mode, + 'loss_weights': model.loss_weights, + }, + default=get_json_type).encode('utf8') + + # Save optimizer weights. + symbolic_weights = getattr(model.optimizer, 'weights') + if symbolic_weights: + optimizer_weights_group = f.create_group('optimizer_weights') + weight_values = K.batch_get_value(symbolic_weights) + weight_names = [] + for w, val in zip(symbolic_weights, weight_values): + name = str(w.name) + weight_names.append(name.encode('utf8')) + optimizer_weights_group.attrs['weight_names'] = weight_names + for name, val in zip(weight_names, weight_values): + param_dset = optimizer_weights_group.create_dataset( + name, val.shape, dtype=val.dtype) + if not val.shape: + # scalar + param_dset[()] = val + else: + param_dset[:] = val + f.flush() + + +@tf_export('keras.models.load_model') +def load_model(filepath, custom_objects=None, compile=True): # pylint: disable=redefined-builtin + """Loads a model saved via `save_model`. + + Arguments: + filepath: String, path to the saved model. + custom_objects: Optional dictionary mapping names + (strings) to custom classes or functions to be + considered during deserialization. + compile: Boolean, whether to compile the model + after loading. + + Returns: + A Keras model instance. If an optimizer was found + as part of the saved model, the model is already + compiled. Otherwise, the model is uncompiled and + a warning will be displayed. When `compile` is set + to False, the compilation is omitted without any + warning. + + Raises: + ImportError: if h5py is not available. + ValueError: In case of an invalid savefile. + """ + if h5py is None: + raise ImportError('`load_model` requires h5py.') + + if not custom_objects: + custom_objects = {} + + def convert_custom_objects(obj): + """Handles custom object lookup. + + Arguments: + obj: object, dict, or list. + + Returns: + The same structure, where occurrences + of a custom object name have been replaced + with the custom object. + """ + if isinstance(obj, list): + deserialized = [] + for value in obj: + deserialized.append(convert_custom_objects(value)) + return deserialized + if isinstance(obj, dict): + deserialized = {} + for key, value in obj.items(): + deserialized[key] = convert_custom_objects(value) + return deserialized + if obj in custom_objects: + return custom_objects[obj] + return obj + + with h5py.File(filepath, mode='r') as f: + # instantiate model + model_config = f.attrs.get('model_config') + if model_config is None: + raise ValueError('No model found in config file.') + model_config = json.loads(model_config.decode('utf-8')) + model = model_from_config(model_config, custom_objects=custom_objects) + + # set weights + load_weights_from_hdf5_group(f['model_weights'], model.layers) + + # Early return if compilation is not required. + if not compile: + return model + + # instantiate optimizer + training_config = f.attrs.get('training_config') + if training_config is None: + logging.warning('No training configuration found in save file: ' + 'the model was *not* compiled. Compile it manually.') + return model + training_config = json.loads(training_config.decode('utf-8')) + optimizer_config = training_config['optimizer_config'] + optimizer = optimizers.deserialize( + optimizer_config, custom_objects=custom_objects) + + # Recover loss functions and metrics. + loss = convert_custom_objects(training_config['loss']) + metrics = convert_custom_objects(training_config['metrics']) + sample_weight_mode = training_config['sample_weight_mode'] + loss_weights = training_config['loss_weights'] + + # Compile model. + model.compile( + optimizer=optimizer, + loss=loss, + metrics=metrics, + loss_weights=loss_weights, + sample_weight_mode=sample_weight_mode) + + # Set optimizer weights. + if 'optimizer_weights' in f: + # Build train function (to get weight updates). + model._make_train_function() + optimizer_weights_group = f['optimizer_weights'] + optimizer_weight_names = [ + n.decode('utf8') + for n in optimizer_weights_group.attrs['weight_names'] + ] + optimizer_weight_values = [ + optimizer_weights_group[n] for n in optimizer_weight_names + ] + try: + model.optimizer.set_weights(optimizer_weight_values) + except ValueError: + logging.warning('Error in loading the saved optimizer ' + 'state. As a result, your model is ' + 'starting with a freshly initialized ' + 'optimizer.') + return model + + +@tf_export('keras.models.model_from_config') +def model_from_config(config, custom_objects=None): + """Instantiates a Keras model from its config. + + Arguments: + config: Configuration dictionary. + custom_objects: Optional dictionary mapping names + (strings) to custom classes or functions to be + considered during deserialization. + + Returns: + A Keras model instance (uncompiled). + + Raises: + TypeError: if `config` is not a dictionary. + """ + if isinstance(config, list): + raise TypeError('`model_from_config` expects a dictionary, not a list. ' + 'Maybe you meant to use ' + '`Sequential.from_config(config)`?') + from tensorflow.python.keras._impl.keras.layers import deserialize # pylint: disable=g-import-not-at-top + return deserialize(config, custom_objects=custom_objects) + + +@tf_export('keras.models.model_from_yaml') +def model_from_yaml(yaml_string, custom_objects=None): + """Parses a yaml model configuration file and returns a model instance. + + Arguments: + yaml_string: YAML string encoding a model configuration. + custom_objects: Optional dictionary mapping names + (strings) to custom classes or functions to be + considered during deserialization. + + Returns: + A Keras model instance (uncompiled). + + Raises: + ImportError: if yaml module is not found. + """ + if yaml is None: + raise ImportError('Requires yaml module installed.') + config = yaml.load(yaml_string) + from tensorflow.python.keras._impl.keras.layers import deserialize # pylint: disable=g-import-not-at-top + return deserialize(config, custom_objects=custom_objects) + + +@tf_export('keras.models.model_from_json') +def model_from_json(json_string, custom_objects=None): + """Parses a JSON model configuration file and returns a model instance. + + Arguments: + json_string: JSON string encoding a model configuration. + custom_objects: Optional dictionary mapping names + (strings) to custom classes or functions to be + considered during deserialization. + + Returns: + A Keras model instance (uncompiled). + """ + config = json.loads(json_string) + from tensorflow.python.keras._impl.keras.layers import deserialize # pylint: disable=g-import-not-at-top + return deserialize(config, custom_objects=custom_objects) + + +def preprocess_weights_for_loading(layer, + weights, + original_keras_version=None, + original_backend=None): + """Converts layers weights from Keras 1 format to Keras 2. + + Arguments: + layer: Layer instance. + weights: List of weights values (Numpy arrays). + original_keras_version: Keras version for the weights, as a string. + original_backend: Keras backend the weights were trained with, + as a string. + + Returns: + A list of weights values (Numpy arrays). + """ + if layer.__class__.__name__ == 'Bidirectional': + num_weights_per_layer = len(weights) // 2 + forward_weights = preprocess_weights_for_loading( + layer.forward_layer, weights[:num_weights_per_layer], + original_keras_version, original_backend) + backward_weights = preprocess_weights_for_loading( + layer.backward_layer, weights[num_weights_per_layer:], + original_keras_version, original_backend) + weights = forward_weights + backward_weights + + if original_keras_version == '1': + if layer.__class__.__name__ == 'TimeDistributed': + weights = preprocess_weights_for_loading( + layer.layer, weights, original_keras_version, original_backend) + + if layer.__class__.__name__ == 'Conv1D': + shape = weights[0].shape + # Handle Keras 1.1 format + if shape[:2] != (layer.kernel_size[0], 1) or shape[3] != layer.filters: + # Legacy shape: + # (filters, input_dim, filter_length, 1) + assert shape[0] == layer.filters and shape[2:] == (layer.kernel_size[0], + 1) + weights[0] = np.transpose(weights[0], (2, 3, 1, 0)) + weights[0] = weights[0][:, 0, :, :] + + if layer.__class__.__name__ == 'Conv2D': + if layer.data_format == 'channels_first': + # old: (filters, stack_size, kernel_rows, kernel_cols) + # new: (kernel_rows, kernel_cols, stack_size, filters) + weights[0] = np.transpose(weights[0], (2, 3, 1, 0)) + + if layer.__class__.__name__ == 'Conv2DTranspose': + if layer.data_format == 'channels_last': + # old: (kernel_rows, kernel_cols, stack_size, filters) + # new: (kernel_rows, kernel_cols, filters, stack_size) + weights[0] = np.transpose(weights[0], (0, 1, 3, 2)) + if layer.data_format == 'channels_first': + # old: (filters, stack_size, kernel_rows, kernel_cols) + # new: (kernel_rows, kernel_cols, filters, stack_size) + weights[0] = np.transpose(weights[0], (2, 3, 0, 1)) + + if layer.__class__.__name__ == 'Conv3D': + if layer.data_format == 'channels_first': + # old: (filters, stack_size, ...) + # new: (..., stack_size, filters) + weights[0] = np.transpose(weights[0], (2, 3, 4, 1, 0)) + + if layer.__class__.__name__ == 'GRU': + if len(weights) == 9: + kernel = np.concatenate([weights[0], weights[3], weights[6]], axis=-1) + recurrent_kernel = np.concatenate( + [weights[1], weights[4], weights[7]], axis=-1) + bias = np.concatenate([weights[2], weights[5], weights[8]], axis=-1) + weights = [kernel, recurrent_kernel, bias] + + if layer.__class__.__name__ == 'LSTM': + if len(weights) == 12: + # old: i, c, f, o + # new: i, f, c, o + kernel = np.concatenate( + [weights[0], weights[6], weights[3], weights[9]], axis=-1) + recurrent_kernel = np.concatenate( + [weights[1], weights[7], weights[4], weights[10]], axis=-1) + bias = np.concatenate( + [weights[2], weights[8], weights[5], weights[11]], axis=-1) + weights = [kernel, recurrent_kernel, bias] + + if layer.__class__.__name__ == 'ConvLSTM2D': + if len(weights) == 12: + kernel = np.concatenate( + [weights[0], weights[6], weights[3], weights[9]], axis=-1) + recurrent_kernel = np.concatenate( + [weights[1], weights[7], weights[4], weights[10]], axis=-1) + bias = np.concatenate( + [weights[2], weights[8], weights[5], weights[11]], axis=-1) + if layer.data_format == 'channels_first': + # old: (filters, stack_size, kernel_rows, kernel_cols) + # new: (kernel_rows, kernel_cols, stack_size, filters) + kernel = np.transpose(kernel, (2, 3, 1, 0)) + recurrent_kernel = np.transpose(recurrent_kernel, (2, 3, 1, 0)) + weights = [kernel, recurrent_kernel, bias] + + if layer.__class__.__name__ in ['Model', 'Sequential']: + new_weights = [] + # trainable weights + for sublayer in layer.layers: + num_weights = len(sublayer.trainable_weights) + if num_weights > 0: + new_weights.extend( + preprocess_weights_for_loading( + layer=sublayer, + weights=weights[:num_weights], + original_keras_version=original_keras_version, + original_backend=original_backend)) + weights = weights[num_weights:] + + # non-trainable weights + for sublayer in layer.layers: + num_weights = len([ + l for l in sublayer.weights if l not in sublayer.trainable_weights + ]) + if num_weights > 0: + new_weights.extend( + preprocess_weights_for_loading( + layer=sublayer, + weights=weights[:num_weights], + original_keras_version=original_keras_version, + original_backend=original_backend)) + weights = weights[num_weights:] + weights = new_weights + + conv_layers = ['Conv1D', 'Conv2D', 'Conv3D', 'Conv2DTranspose', 'ConvLSTM2D'] + if layer.__class__.__name__ in conv_layers: + if original_backend == 'theano': + weights[0] = conv_utils.convert_kernel(weights[0]) + if layer.__class__.__name__ == 'ConvLSTM2D': + weights[1] = conv_utils.convert_kernel(weights[1]) + if K.int_shape(layer.weights[0]) != weights[0].shape: + weights[0] = np.transpose(weights[0], (3, 2, 0, 1)) + if layer.__class__.__name__ == 'ConvLSTM2D': + weights[1] = np.transpose(weights[1], (3, 2, 0, 1)) + + # Convert the weights of CuDNNLSTM so that they could be loaded into LSTM + if layer.__class__.__name__ == 'LSTM' and len(weights) == 3: + # Determine if loading a CuDNNLSTM layer from the number of bias weights: + # CuDNNLSTM has (units * 8) weights; while LSTM has (units * 4) + # if there's no bias weight in the file, skip this conversion + units = weights[1].shape[0] + bias = weights[2] + if len(bias) == units * 8: + # reshape the kernels + kernels = np.split(weights[0], 4, axis=1) + kernels = [ + kernel.reshape(-1).reshape(kernel.shape, order='F') + for kernel in kernels + ] + weights[0] = np.concatenate(kernels, axis=1) + + # transpose the recurrent kernels + recurrent_kernels = np.split(weights[1], 4, axis=1) + recurrent_kernels = [kernel.T for kernel in recurrent_kernels] + weights[1] = np.concatenate(recurrent_kernels, axis=1) + + # split the bias into half and merge + weights[2] = bias[:units * 4] + bias[units * 4:] + + return convert_rnn_weights(layer, weights) + + +def convert_rnn_weights(layer, weights): + """Converts weights for RNN layers between native and CuDNN format. + + Input kernels for each gate are transposed and converted between Fortran + and C layout, recurrent kernels are transposed. For LSTM biases are summed/ + split in half, for GRU biases are reshaped. + + Weights can be converted in both directions between `LSTM` and`CuDNNSLTM` + and between `CuDNNGRU` and `GRU(reset_after=True)`. Default `GRU` is not + compatible with `CuDNNGRU`. + + For missing biases in `LSTM`/`GRU` (`use_bias=False`) no conversion is made. + + Arguments: + layer: Target layer instance. + weights: List of source weights values (input kernels, recurrent + kernels, [biases]) (Numpy arrays). + + Returns: + A list of converted weights values (Numpy arrays). + + Raises: + ValueError: for incompatible GRU layer/weights or incompatible biases + """ + + def transform_kernels(kernels, func, n_gates): + """Transforms kernel for each gate separately using given function. + + Arguments: + kernels: Stacked array of kernels for individual gates. + func: Function applied to kernel of each gate. + n_gates: Number of gates (4 for LSTM, 3 for GRU). + Returns: + Stacked array of transformed kernels. + """ + return np.hstack([func(k) for k in np.hsplit(kernels, n_gates)]) + + def transpose_input(from_cudnn): + """Makes a function that transforms input kernels from/to CuDNN format. + + It keeps the shape, but changes between the layout (Fortran/C). Eg.: + + ``` + Keras CuDNN + [[0, 1, 2], <---> [[0, 2, 4], + [3, 4, 5]] [1, 3, 5]] + ``` + + It can be passed to `transform_kernels()`. + + Arguments: + from_cudnn: `True` if source weights are in CuDNN format, `False` + if they're in plain Keras format. + Returns: + Function that converts input kernel to the other format. + """ + order = 'F' if from_cudnn else 'C' + + def transform(kernel): + return kernel.T.reshape(kernel.shape, order=order) + + return transform + + target_class = layer.__class__.__name__ + + # convert the weights between CuDNNLSTM and LSTM + if target_class in ['LSTM', 'CuDNNLSTM'] and len(weights) == 3: + # determine if we're loading a CuDNNLSTM layer + # from the number of bias weights: + # CuDNNLSTM has (units * 8) weights; while LSTM has (units * 4) + # if there's no bias weight in the file, skip this conversion + units = weights[1].shape[0] + bias_shape = weights[2].shape + n_gates = 4 + + if bias_shape == (2 * units * n_gates,): + source = 'CuDNNLSTM' + elif bias_shape == (units * n_gates,): + source = 'LSTM' + else: + raise ValueError('Invalid bias shape: ' + str(bias_shape)) + + def convert_lstm_weights(weights, from_cudnn=True): + # Transpose (and reshape) input and recurrent kernels. + kernels = transform_kernels(weights[0], transpose_input(from_cudnn), + n_gates) + recurrent_kernels = transform_kernels(weights[1], lambda k: k.T, n_gates) + if from_cudnn: # Merge input and recurrent biases into a single set. + biases = np.sum(np.split(weights[2], 2, axis=0), axis=0) + else: + # Split single set of biases evenly to two sets. + biases = np.tile(0.5 * weights[2], 2) + return [kernels, recurrent_kernels, biases] + + if source != target_class: + weights = convert_lstm_weights(weights, from_cudnn=source == 'CuDNNLSTM') + + # TODO(fchollet): add feature after GRU is refactored: + # convert the weights between `CuDNNGRU` and `GRU(reset_after=True)` + return weights + + +def save_weights_to_hdf5_group(f, layers): + from tensorflow.python.keras._impl.keras import __version__ as keras_version # pylint: disable=g-import-not-at-top + + save_attributes_to_hdf5_group( + f, 'layer_names', [layer.name.encode('utf8') for layer in layers]) + f.attrs['backend'] = K.backend().encode('utf8') + f.attrs['keras_version'] = str(keras_version).encode('utf8') + + for layer in layers: + g = f.create_group(layer.name) + symbolic_weights = layer.weights + weight_values = K.batch_get_value(symbolic_weights) + weight_names = [] + for i, (w, val) in enumerate(zip(symbolic_weights, weight_values)): + if hasattr(w, 'name') and w.name: + name = str(w.name) + else: + name = 'param_' + str(i) + weight_names.append(name.encode('utf8')) + save_attributes_to_hdf5_group(g, 'weight_names', weight_names) + for name, val in zip(weight_names, weight_values): + param_dset = g.create_dataset(name, val.shape, dtype=val.dtype) + if not val.shape: + # scalar + param_dset[()] = val + else: + param_dset[:] = val + + +def load_weights_from_hdf5_group(f, layers): + """Implements topological (order-based) weight loading. + + Arguments: + f: A pointer to a HDF5 group. + layers: a list of target layers. + + Raises: + ValueError: in case of mismatch between provided layers + and weights file. + """ + if 'keras_version' in f.attrs: + original_keras_version = f.attrs['keras_version'].decode('utf8') + else: + original_keras_version = '1' + if 'backend' in f.attrs: + original_backend = f.attrs['backend'].decode('utf8') + else: + original_backend = None + + filtered_layers = [] + for layer in layers: + weights = layer.weights + if weights: + filtered_layers.append(layer) + + layer_names = load_attributes_from_hdf5_group(f, 'layer_names') + filtered_layer_names = [] + for name in layer_names: + g = f[name] + weight_names = load_attributes_from_hdf5_group(g, 'weight_names') + if weight_names: + filtered_layer_names.append(name) + layer_names = filtered_layer_names + if len(layer_names) != len(filtered_layers): + raise ValueError('You are trying to load a weight file ' + 'containing ' + str(len(layer_names)) + + ' layers into a model with ' + str(len(filtered_layers)) + + ' layers.') + + # We batch weight value assignments in a single backend call + # which provides a speedup in TensorFlow. + weight_value_tuples = [] + for k, name in enumerate(layer_names): + g = f[name] + weight_names = load_attributes_from_hdf5_group(g, 'weight_names') + weight_values = [g[weight_name] for weight_name in weight_names] + layer = filtered_layers[k] + symbolic_weights = layer.weights + weight_values = preprocess_weights_for_loading( + layer, weight_values, original_keras_version, original_backend) + if len(weight_values) != len(symbolic_weights): + raise ValueError('Layer #' + str(k) + ' (named "' + layer.name + + '" in the current model) was found to ' + 'correspond to layer ' + name + ' in the save file. ' + 'However the new layer ' + layer.name + ' expects ' + + str(len(symbolic_weights)) + + ' weights, but the saved weights have ' + + str(len(weight_values)) + ' elements.') + weight_value_tuples += zip(symbolic_weights, weight_values) + K.batch_set_value(weight_value_tuples) + + +def load_weights_from_hdf5_group_by_name(f, layers): + """Implements name-based weight loading. + + (instead of topological weight loading). + + Layers that have no matching name are skipped. + + Arguments: + f: A pointer to a HDF5 group. + layers: a list of target layers. + + Raises: + ValueError: in case of mismatch between provided layers + and weights file. + """ + if 'keras_version' in f.attrs: + original_keras_version = f.attrs['keras_version'].decode('utf8') + else: + original_keras_version = '1' + if 'backend' in f.attrs: + original_backend = f.attrs['backend'].decode('utf8') + else: + original_backend = None + + # New file format. + layer_names = load_attributes_from_hdf5_group(f, 'layer_names') + + # Reverse index of layer name to list of layers with name. + index = {} + for layer in layers: + if layer.name: + index.setdefault(layer.name, []).append(layer) + + # We batch weight value assignments in a single backend call + # which provides a speedup in TensorFlow. + weight_value_tuples = [] + for k, name in enumerate(layer_names): + g = f[name] + weight_names = load_attributes_from_hdf5_group(g, 'weight_names') + weight_values = [g[weight_name] for weight_name in weight_names] + + for layer in index.get(name, []): + symbolic_weights = layer.weights + weight_values = preprocess_weights_for_loading( + layer, weight_values, original_keras_version, original_backend) + if len(weight_values) != len(symbolic_weights): + raise ValueError('Layer #' + str(k) + ' (named "' + layer.name + + '") expects ' + str(len(symbolic_weights)) + + ' weight(s), but the saved weights' + ' have ' + + str(len(weight_values)) + ' element(s).') + # Set values. + for i in range(len(weight_values)): + weight_value_tuples.append((symbolic_weights[i], weight_values[i])) + K.batch_set_value(weight_value_tuples) + + +def save_attributes_to_hdf5_group(group, name, data): + """Saves attributes (data) of the specified name into the HDF5 group. + + This method deals with an inherent problem of HDF5 file which is not + able to store data larger than HDF5_OBJECT_HEADER_LIMIT bytes. + + Arguments: + group: A pointer to a HDF5 group. + name: A name of the attributes to save. + data: Attributes data to store. + + Raises: + RuntimeError: If any single attribute is too large to be saved. + """ + # Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT` + # because in that case even chunking the array would not make the saving + # possible. + bad_attributes = [x for x in data if len(x) > HDF5_OBJECT_HEADER_LIMIT] + + # Expecting this to never be true. + if bad_attributes: + raise RuntimeError('The following attributes cannot be saved to HDF5 ' + 'file because they are larger than %d bytes: %s' % + (HDF5_OBJECT_HEADER_LIMIT, + ', '.join([x for x in bad_attributes]))) + + data_npy = np.asarray(data) + + num_chunks = 1 + chunked_data = np.array_split(data_npy, num_chunks) + + # This will never loop forever thanks to the test above. + while any([x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data]): + num_chunks += 1 + chunked_data = np.array_split(data_npy, num_chunks) + + if num_chunks > 1: + for chunk_id, chunk_data in enumerate(chunked_data): + group.attrs['%s%d' % (name, chunk_id)] = chunk_data + else: + group.attrs[name] = data + + +def load_attributes_from_hdf5_group(group, name): + """Loads attributes of the specified name from the HDF5 group. + + This method deals with an inherent problem + of HDF5 file which is not able to store + data larger than HDF5_OBJECT_HEADER_LIMIT bytes. + + Arguments: + group: A pointer to a HDF5 group. + name: A name of the attributes to load. + + Returns: + data: Attributes data. + """ + if name in group.attrs: + data = [n.decode('utf8') for n in group.attrs[name]] + else: + data = [] + chunk_id = 0 + while '%s%d' % (name, chunk_id) in group.attrs: + data.extend( + [n.decode('utf8') for n in group.attrs['%s%d' % (name, chunk_id)]]) + chunk_id += 1 + return data diff --git a/tensorflow/python/keras/_impl/keras/engine/saving_test.py b/tensorflow/python/keras/_impl/keras/engine/saving_test.py new file mode 100644 index 0000000000000000000000000000000000000000..4a18cc2e119d7cfb3f15da593d4944abd445905b --- /dev/null +++ b/tensorflow/python/keras/_impl/keras/engine/saving_test.py @@ -0,0 +1,461 @@ +# 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 model saving.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import shutil +import tempfile + +import numpy as np + +from tensorflow.python.keras._impl import keras +from tensorflow.python.platform import test +from tensorflow.python.training import training as training_module + +try: + import h5py # pylint:disable=g-import-not-at-top +except ImportError: + h5py = None + + +class TestWeightSavingAndLoading(test.TestCase): + + def test_weight_loading(self): + with self.test_session(): + a = keras.layers.Input(shape=(2,)) + x = keras.layers.Dense(3)(a) + b = keras.layers.Dense(1)(x) + model = keras.models.Model(a, b) + + x = np.random.random((3, 2)) + ref_y = model.predict(x) + weights = model.get_weights() + model.set_weights(weights) + y = model.predict(x) + self.assertAllClose(ref_y, y) + + with self.assertRaises(ValueError): + model.set_weights(weights[1:]) + with self.assertRaises(ValueError): + model.set_weights(weights[::-1]) + + if h5py is None: + return # Skip rest of test if H5py isn't available. + + temp_dir = self.get_temp_dir() + self.addCleanup(shutil.rmtree, temp_dir) + + h5_path = os.path.join(temp_dir, 'test.h5') + model.save_weights(h5_path) + model.load_weights(h5_path) + y = model.predict(x) + self.assertAllClose(ref_y, y) + + model.load_weights(h5_path, by_name=True) + y = model.predict(x) + self.assertAllClose(ref_y, y) + + def test_weight_preprocessing(self): + input_dim = 3 + output_dim = 3 + size = 2 + cases = [ + [ + (keras.layers.Bidirectional(keras.layers.SimpleRNN(2))), + [np.random.random((2, 1)), np.random.random((2, 1))], + (None, 3, 2), + ], + [ + (keras.layers.TimeDistributed(keras.layers.Dense(1))), + [np.random.random((2, 1)), np.random.random((1,))], + (None, 3, 2), + ], + [ + (keras.layers.Conv1D(output_dim, size, use_bias=False)), + [np.random.random((output_dim, input_dim, size, 1))], + (None, 4, input_dim), + ], + [ + (keras.layers.Conv2D(output_dim, size, + use_bias=False, data_format='channels_first')), + [np.random.random((output_dim, input_dim, size, size))], + (None, input_dim, 4, 4), + ], + [ + (keras.layers.Conv2DTranspose(output_dim, size, + use_bias=False, + data_format='channels_first')), + [np.random.random((output_dim, input_dim, size, size))], + (None, input_dim, 4, 4), + ], + [ + (keras.layers.Conv2DTranspose(output_dim, size, + use_bias=False, + data_format='channels_last')), + [np.random.random((size, size, input_dim, output_dim))], + (None, 4, 4, input_dim), + ], + [ + (keras.layers.Conv3D(output_dim, size, + use_bias=False, data_format='channels_first')), + [np.random.random((output_dim, input_dim, size, size, size))], + (None, input_dim, 4, 4, 4), + ], + [ + (keras.layers.GRU(output_dim)), + [np.random.random((input_dim, output_dim)), + np.random.random((output_dim, output_dim)), + np.random.random((output_dim,)), + np.random.random((input_dim, output_dim)), + np.random.random((output_dim, output_dim)), + np.random.random((output_dim,)), + np.random.random((input_dim, output_dim)), + np.random.random((output_dim, output_dim)), + np.random.random((output_dim,))], + (None, 4, input_dim), + ], + [ + (keras.layers.LSTM(output_dim)), + [np.random.random((input_dim, output_dim)), + np.random.random((output_dim, output_dim)), + np.random.random((output_dim,)), + np.random.random((input_dim, output_dim)), + np.random.random((output_dim, output_dim)), + np.random.random((output_dim,)), + np.random.random((input_dim, output_dim)), + np.random.random((output_dim, output_dim)), + np.random.random((output_dim,)), + np.random.random((input_dim, output_dim)), + np.random.random((output_dim, output_dim)), + np.random.random((output_dim,))], + (None, 4, input_dim), + ], + ] + for layer, weights, input_shape in cases: + layer.build(input_shape) + _ = keras.engine.saving.preprocess_weights_for_loading( + layer, weights, original_keras_version='1') + + model = keras.models.Sequential([keras.layers.Dense(2, input_dim=2)]) + _ = keras.engine.saving.preprocess_weights_for_loading( + model, model.weights, original_keras_version='1') + + x = keras.Input((2,)) + y = keras.layers.Dense(2)(x) + model = keras.models.Model(x, y) + _ = keras.engine.saving.preprocess_weights_for_loading( + model, model.weights, original_keras_version='1') + + def test_sequential_weight_loading(self): + 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') + + num_hidden = 5 + input_dim = 3 + batch_size = 5 + num_classes = 2 + + with self.test_session(): + model = keras.models.Sequential() + model.add(keras.layers.Dense(num_hidden, input_dim=input_dim)) + model.add(keras.layers.Dense(num_classes)) + + x = np.random.random((batch_size, input_dim)) + ref_y = model.predict(x) + + model.save_weights(h5_path) + + model = keras.models.Sequential() + model.add(keras.layers.Dense(num_hidden, input_dim=input_dim)) + model.add(keras.layers.Dense(num_classes)) + model.load_weights(h5_path) + y = model.predict(x) + + self.assertAllClose(y, ref_y) + + +class TestWholeModelSaving(test.TestCase): + + def test_sequential_model_saving(self): + if h5py is None: + return # Skip test if models cannot be saved. + + with self.test_session(): + model = keras.models.Sequential() + model.add(keras.layers.Dense(2, input_shape=(3,))) + model.add(keras.layers.RepeatVector(3)) + model.add(keras.layers.TimeDistributed(keras.layers.Dense(3))) + model.compile(loss=keras.losses.MSE, + optimizer=keras.optimizers.RMSprop(lr=0.0001), + metrics=[keras.metrics.categorical_accuracy], + sample_weight_mode='temporal') + x = np.random.random((1, 3)) + y = np.random.random((1, 3, 3)) + model.train_on_batch(x, y) + + out = model.predict(x) + fd, fname = tempfile.mkstemp('.h5') + keras.models.save_model(model, fname) + + new_model = keras.models.load_model(fname) + os.close(fd) + os.remove(fname) + + out2 = new_model.predict(x) + self.assertAllClose(out, out2, atol=1e-05) + + # test that new updates are the same with both models + x = np.random.random((1, 3)) + y = np.random.random((1, 3, 3)) + model.train_on_batch(x, y) + new_model.train_on_batch(x, y) + out = model.predict(x) + out2 = new_model.predict(x) + self.assertAllClose(out, out2, atol=1e-05) + + def test_sequential_model_saving_2(self): + if h5py is None: + return # Skip test if models cannot be saved. + + with self.test_session(): + # test with custom optimizer, loss + + class CustomOp(keras.optimizers.RMSprop): + pass + + def custom_loss(y_true, y_pred): + return keras.losses.mse(y_true, y_pred) + + model = keras.models.Sequential() + model.add(keras.layers.Dense(2, input_shape=(3,))) + model.add(keras.layers.Dense(3)) + model.compile(loss=custom_loss, optimizer=CustomOp(), metrics=['acc']) + + x = np.random.random((1, 3)) + y = np.random.random((1, 3)) + model.train_on_batch(x, y) + + out = model.predict(x) + fd, fname = tempfile.mkstemp('.h5') + keras.models.save_model(model, fname) + + model = keras.models.load_model( + fname, + custom_objects={'CustomOp': CustomOp, + 'custom_loss': custom_loss}) + os.close(fd) + os.remove(fname) + + out2 = model.predict(x) + self.assertAllClose(out, out2, atol=1e-05) + + def test_functional_model_saving(self): + if h5py is None: + return # Skip test if models cannot be saved. + + with self.test_session(): + inputs = keras.layers.Input(shape=(3,)) + x = keras.layers.Dense(2)(inputs) + output = keras.layers.Dense(3)(x) + + model = keras.models.Model(inputs, output) + model.compile(loss=keras.losses.MSE, + optimizer=keras.optimizers.RMSprop(lr=0.0001), + metrics=[keras.metrics.categorical_accuracy]) + x = np.random.random((1, 3)) + y = np.random.random((1, 3)) + model.train_on_batch(x, y) + + out = model.predict(x) + fd, fname = tempfile.mkstemp('.h5') + keras.models.save_model(model, fname) + + model = keras.models.load_model(fname) + os.close(fd) + os.remove(fname) + + out2 = model.predict(x) + self.assertAllClose(out, out2, atol=1e-05) + + def test_saving_without_compilation(self): + if h5py is None: + return # Skip test if models cannot be saved. + + with self.test_session(): + model = keras.models.Sequential() + model.add(keras.layers.Dense(2, input_shape=(3,))) + model.add(keras.layers.Dense(3)) + model.compile(loss='mse', optimizer='sgd', metrics=['acc']) + + fd, fname = tempfile.mkstemp('.h5') + keras.models.save_model(model, fname) + model = keras.models.load_model(fname) + os.close(fd) + os.remove(fname) + + def test_saving_with_tf_optimizer(self): + if h5py is None: + return # Skip test if models cannot be saved. + + with self.test_session(): + model = keras.models.Sequential() + model.add(keras.layers.Dense(2, input_shape=(3,))) + model.add(keras.layers.Dense(3)) + model.compile(loss='mse', + optimizer=training_module.AdadeltaOptimizer(0.1), + metrics=['acc']) + + fd, fname = tempfile.mkstemp('.h5') + keras.models.save_model(model, fname) + model = keras.models.load_model(fname) + os.close(fd) + os.remove(fname) + + def test_saving_right_after_compilation(self): + if h5py is None: + return # Skip test if models cannot be saved. + + with self.test_session(): + model = keras.models.Sequential() + model.add(keras.layers.Dense(2, input_shape=(3,))) + model.add(keras.layers.Dense(3)) + model.compile(loss='mse', optimizer='sgd', metrics=['acc']) + model.model._make_train_function() + + fd, fname = tempfile.mkstemp('.h5') + keras.models.save_model(model, fname) + model = keras.models.load_model(fname) + os.close(fd) + os.remove(fname) + + def test_saving_lambda_numpy_array_arguments(self): + if h5py is None: + return # Skip test if models cannot be saved. + + mean = np.random.random((4, 2, 3)) + std = np.abs(np.random.random((4, 2, 3))) + 1e-5 + inputs = keras.layers.Input(shape=(4, 2, 3)) + output = keras.layers.Lambda(lambda image, mu, std: (image - mu) / std, + arguments={'mu': mean, 'std': std})(inputs) + model = keras.models.Model(inputs, output) + model.compile(loss='mse', optimizer='sgd', metrics=['acc']) + + fd, fname = tempfile.mkstemp('.h5') + keras.models.save_model(model, fname) + + model = keras.models.load_model(fname) + os.close(fd) + os.remove(fname) + + self.assertAllClose(mean, model.layers[1].arguments['mu']) + self.assertAllClose(std, model.layers[1].arguments['std']) + + def test_saving_model_with_long_layer_names(self): + if h5py is None: + return # Skip test if models cannot be saved. + + with self.test_session(): + # This layer name will make the `layers_name` HDF5 attribute blow + # out of proportion. Note that it fits into the internal HDF5 + # attribute memory limit on its own but because h5py converts + # the list of layer names into numpy array, which uses the same + # amout of memory for every item, it increases the memory + # requirements substantially. + x = keras.Input(shape=(2,), name='input_' + ('x' * (2**15))) + f = x + for i in range(4): + f = keras.layers.Dense(2, name='dense_%d' % (i,))(f) + model = keras.Model(inputs=[x], outputs=[f]) + model.compile(loss='mse', optimizer='adam', metrics=['acc']) + + x = np.random.random((1, 2)) + y = np.random.random((1, 2)) + model.train_on_batch(x, y) + out = model.predict(x) + + fd, fname = tempfile.mkstemp('.h5') + keras.models.save_model(model, fname) + model = keras.models.load_model(fname) + + # Check that the HDF5 files contains chunked array + # of layer names. + with h5py.File(fname, 'r') as h5file: + num_names_arrays = len([attr for attr in h5file['model_weights'].attrs + if attr.startswith('layer_names')]) + # The chunking of layer names array should have happend. + self.assertGreater(num_names_arrays, 0) + out2 = model.predict(x) + self.assertAllClose(out, out2, atol=1e-05) + + # Cleanup + os.close(fd) + os.remove(fname) + + def test_saving_model_with_long_weights_names(self): + if h5py is None: + return # Skip test if models cannot be saved. + + with self.test_session(): + x = keras.Input(shape=(2,), name='nested_model_input') + f = x + for i in range(4): + f = keras.layers.Dense(2, name='nested_model_dense_%d' % (i,))(f) + # This layer name will make the `weights_name` + # HDF5 attribute blow out of proportion. + f = keras.layers.Dense(2, name='nested_model_output' + ('x' * (2**15)))(f) + nested_model = keras.Model(inputs=[x], outputs=[f], name='nested_model') + + x = keras.Input(shape=(2,), name='outer_model_input') + f = nested_model(x) + f = keras.layers.Dense(2, name='outer_model_output')(f) + + model = keras.Model(inputs=[x], outputs=[f]) + model.compile(loss='mse', optimizer='adam', metrics=['acc']) + + x = np.random.random((1, 2)) + y = np.random.random((1, 2)) + model.train_on_batch(x, y) + out = model.predict(x) + + fd, fname = tempfile.mkstemp('.h5') + keras.models.save_model(model, fname) + model = keras.models.load_model(fname) + + # Check that the HDF5 files contains chunked array + # of weight names. + with h5py.File(fname, 'r') as h5file: + num_weight_arrays = len( + [attr for attr in h5file['model_weights']['nested_model'].attrs + if attr.startswith('weight_names')]) + # The chunking of layer names array should have happend. + self.assertGreater(num_weight_arrays, 0) + out2 = model.predict(x) + self.assertAllClose(out, out2, atol=1e-05) + + # Cleanup + os.close(fd) + os.remove(fname) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/python/keras/_impl/keras/engine/sequential.py b/tensorflow/python/keras/_impl/keras/engine/sequential.py new file mode 100644 index 0000000000000000000000000000000000000000..db5e7754bc22ba360dbf635f1bd80334f58e8509 --- /dev/null +++ b/tensorflow/python/keras/_impl/keras/engine/sequential.py @@ -0,0 +1,997 @@ +# 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=protected-access +"""Home of the `Sequential` model. +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import copy +import os + +from tensorflow.python.framework import ops +from tensorflow.python.keras._impl.keras import backend as K +from tensorflow.python.keras._impl.keras import layers as layer_module +from tensorflow.python.keras._impl.keras.engine import base_layer +from tensorflow.python.keras._impl.keras.engine import network +from tensorflow.python.keras._impl.keras.engine import saving +from tensorflow.python.keras._impl.keras.engine.input_layer import Input +from tensorflow.python.keras._impl.keras.engine.input_layer import InputLayer +from tensorflow.python.keras._impl.keras.engine.training import Model +from tensorflow.python.keras._impl.keras.utils.io_utils import ask_to_proceed_with_overwrite +from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.util.tf_export import tf_export + +try: + import h5py # pylint: disable=g-import-not-at-top +except ImportError: + h5py = None + + +@tf_export('keras.models.Sequential', 'keras.Sequential') +class Sequential(Model): + """Linear stack of layers. + + Arguments: + layers: list of layers to add to the model. + + # Note + The first layer passed to a Sequential model + should have a defined input shape. What that + means is that it should have received an `input_shape` + or `batch_input_shape` argument, + or for some type of layers (recurrent, Dense...) + an `input_dim` argument. + + Example: + + ```python + model = Sequential() + # first layer must have a defined input shape + model.add(Dense(32, input_dim=500)) + # afterwards, Keras does automatic shape inference + model.add(Dense(32)) + + # also possible (equivalent to the above): + model = Sequential() + model.add(Dense(32, input_shape=(500,))) + model.add(Dense(32)) + + # also possible (equivalent to the above): + model = Sequential() + # here the batch dimension is None, + # which means any batch size will be accepted by the model. + model.add(Dense(32, batch_input_shape=(None, 500))) + model.add(Dense(32)) + ``` + """ + + def __init__(self, layers=None, name=None): + self._is_graph_network = True + self._is_compiled = False + self._layers = [] # Stack of layers. + self.model = None # Internal Model instance. + self.inputs = [] # List of input tensors + self.outputs = [] # List of length 1: the output tensor (unique). + self._trainable = True + self._initial_weights = None + self._input_layers = [] + + # Model attributes. + self._inbound_nodes = [] + self._outbound_nodes = [] + self.built = False + + # Set model name. + if not name: + prefix = 'sequential_' + name = prefix + str(K.get_uid(prefix)) + self._name = name + + # Used by Layer base class. + self._dtype = None + self._activity_regularizer = None + + # The following properties are not actually used by Keras; + # they exist for compatibility with TF's variable scoping mechanism. + self._updates = [] + self._losses = [] + self._scope = None + self._reuse = None + self._base_name = name + self._graph = ops.get_default_graph() + + # Add to the model any layers passed to the constructor. + if layers: + for layer in layers: + self.add(layer) + + def add(self, layer): + """Adds a layer instance on top of the layer stack. + + Arguments: + layer: layer instance. + + Raises: + TypeError: If `layer` is not a layer instance. + ValueError: In case the `layer` argument does not + know its input shape. + ValueError: In case the `layer` argument has + multiple output tensors, or is already connected + somewhere else (forbidden in `Sequential` models). + """ + if not isinstance(layer, (base_layer.Layer, base_layer.TFBaseLayer)): + raise TypeError('The added layer must be ' + 'an instance of class Layer. ' + 'Found: ' + str(layer)) + if not self.outputs: + # First layer in model: check that it is an input layer. + if not isinstance(layer, InputLayer): + # Create an input layer. + # First, we need to infer its expected input shape and dtype. + if isinstance(layer, (Model, Sequential)): + # We were passed a model as first layer. + # This requires a specific way to figure out the + # input shape and dtype. + if not layer.layers: + raise ValueError('Cannot add an empty model ' + 'to a `Sequential` model.') + # In case of nested models: recover the first layer + # of the deepest model to infer input shape and dtype. + first_layer = layer.layers[0] + while isinstance(first_layer, (Model, Sequential)): + first_layer = first_layer.layers[0] + batch_shape = first_layer._batch_input_shape + dtype = first_layer.dtype + else: + # We were passed a regular layer, and it should + # know about its input shape. Otherwise, that's an error. + if not hasattr(layer, '_batch_input_shape'): + raise ValueError('The first layer in a ' + 'Sequential model must ' + 'get an `input_shape` argument.') + batch_shape = layer._batch_input_shape + dtype = layer.dtype + # Instantiate the input layer. + x = Input( + batch_shape=batch_shape, dtype=dtype, name=layer.name + '_input') + # This will build the current layer + # and create the node connecting the current layer + # to the input layer we just created. + layer(x) + + if len(layer._inbound_nodes[-1].output_tensors) != 1: + raise ValueError('All layers in a Sequential model ' + 'should have a single output tensor. ' + 'For multi-output layers, ' + 'use the functional API.') + + self.outputs = [layer._inbound_nodes[-1].output_tensors[0]] + self.inputs = network.get_source_inputs(self.outputs[0]) + + # We create an input node, which we will keep updated + # as we add more layers + base_layer.Node( + outbound_layer=self, + inbound_layers=[], + node_indices=[], + tensor_indices=[], + input_tensors=self.inputs, + output_tensors=self.outputs) + else: + output_tensor = layer(self.outputs[0]) + if isinstance(output_tensor, list): + raise TypeError('All layers in a Sequential model ' + 'should have a single output tensor. ' + 'For multi-output layers, ' + 'use the functional API.') + self.outputs = [output_tensor] + # update self._inbound_nodes + self._inbound_nodes[0].output_tensors = self.outputs + self._inbound_nodes[0].output_shapes = [K.int_shape(self.outputs[0])] + + self._layers.append(layer) + self.built = False + + def pop(self): + """Removes the last layer in the model. + + Raises: + TypeError: if there are no layers in the model. + """ + if not self.layers: + raise TypeError('There are no layers in the model.') + + self.layers.pop() + if not self.layers: + self.outputs = [] + self._inbound_nodes = [] + self._outbound_nodes = [] + else: + self.layers[-1]._outbound_nodes = [] + self.outputs = [self.layers[-1].output] + # update self._inbound_nodes + self._inbound_nodes[0].output_tensors = self.outputs + self._inbound_nodes[0].output_shapes = [K.int_shape(self.outputs[0])] + self.built = False + + def get_layer(self, name=None, index=None): + """Retrieve a layer that is part of the model. + + Returns a layer based on either its name (unique) + or its index in the graph. Indices are based on + order of horizontal graph traversal (bottom-up). + + Arguments: + name: string, name of layer. + index: integer, index of layer. + + Returns: + A layer instance. + """ + if not self.built: + self.build() + return self.model.get_layer(name, index) + + def call(self, inputs, **kwargs): + if not self.built: + self.build() + return self.model.call(inputs, **kwargs) + + def build(self, input_shape=None): + if not self.inputs or not self.outputs: + raise TypeError('Sequential model cannot be built: model is empty.' + ' Add some layers first.') + # actually create the model + self.model = Model(self.inputs, self.outputs[0], name=self.name + '_model') + self.model.trainable = self.trainable + + # mirror model attributes + self.supports_masking = self.model.supports_masking + self._output_mask_cache = self.model._output_mask_cache + self._output_tensor_cache = self.model._output_tensor_cache + self._output_shape_cache = self.model._output_shape_cache + self._input_layers = self.model._input_layers + self._output_layers = self.model._output_layers + self._input_coordinates = self.model._input_coordinates + self._output_coordinates = self.model._output_coordinates + self._nodes_by_depth = self.model._nodes_by_depth + self._network_nodes = self.model._network_nodes + self.output_names = self.model.output_names + self.input_names = self.model.input_names + self._feed_input_names = self.model._feed_input_names + self._feed_inputs = self.model._feed_inputs + + # Make sure child model callbacks + # will call the parent Sequential model. + self.model.callback_model = self + + self.built = True + + @property + def uses_learning_phase(self): + if not self.built: + self.build() + return self.model.uses_learning_phase + + def _gather_list_attr(self, attr): + all_attrs = [] + for layer in self.layers: + all_attrs += getattr(layer, attr, []) + return all_attrs + + def _make_train_function(self): + self.model._make_train_function() + + def _make_test_function(self): + self.model._make_test_function() + + def _make_predict_function(self): + self.model._make_predict_function() + + @property + def trainable(self): + return self._trainable + + @trainable.setter + def trainable(self, value): + if self.model: + self.model.trainable = value + self._trainable = value + + @property + def trainable_weights(self): + if not self.trainable: + return [] + return self._gather_list_attr('trainable_weights') + + @property + def non_trainable_weights(self): + weights = self._gather_list_attr('non_trainable_weights') + if not self.trainable: + trainable_weights = self._gather_list_attr('trainable_weights') + return trainable_weights + weights + return weights + + @property + def regularizers(self): + if not self.built: + self.build() + return self.model.regularizers + + def get_weights(self): + """Retrieves the weights of the model. + + Returns: + A flat list of Numpy arrays + (one array per model weight). + """ + if not self.built: + self.build() + return self.model.get_weights() + + def set_weights(self, weights): + """Sets the weights of the model. + + Arguments: + weights: Should be a list + of Numpy arrays with shapes and types matching + the output of `model.get_weights()`. + """ + if not self.built: + self.build() + self.model.set_weights(weights) + + def load_weights(self, filepath, by_name=False): + if h5py is None: + raise ImportError('`load_weights` requires h5py.') + f = h5py.File(filepath, mode='r') + if 'layer_names' not in f.attrs and 'model_weights' in f: + f = f['model_weights'] + layers = self.layers + if by_name: + saving.load_weights_from_hdf5_group_by_name(f, layers) + else: + saving.load_weights_from_hdf5_group(f, layers) + if hasattr(f, 'close'): + f.close() + + def save_weights(self, filepath, overwrite=True): + if h5py is None: + raise ImportError('`save_weights` requires h5py.') + # If file exists and should not be overwritten: + if not overwrite and os.path.isfile(filepath): + proceed = ask_to_proceed_with_overwrite(filepath) + if not proceed: + return + layers = self.layers + f = h5py.File(filepath, 'w') + saving.save_weights_to_hdf5_group(f, layers) + f.flush() + f.close() + + def compile(self, + optimizer, + loss, + metrics=None, + sample_weight_mode=None, + weighted_metrics=None, + target_tensors=None, + **kwargs): + """Configures the model for training. + + Arguments: + optimizer: String (name of optimizer) or optimizer object. + See [optimizers](/optimizers). + loss: String (name of objective function) or objective function. + See [losses](/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'}`. + 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 a placeholder 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 tensor (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 should be a single tensor + (for a single-output `Sequential` model). + **kwargs: These arguments are passed into `tf.Session.run`. + + Example: + ```python + model = Sequential() + model.add(Dense(32, input_shape=(500,))) + model.add(Dense(10, activation='softmax')) + model.compile(optimizer='rmsprop', + loss='categorical_crossentropy', + metrics=['accuracy']) + ``` + """ + # create the underlying model + self.build() + # call compile method of Model class + self.model.compile( + optimizer, + loss, + metrics=metrics, + sample_weight_mode=sample_weight_mode, + weighted_metrics=weighted_metrics, + target_tensors=target_tensors, + **kwargs) + self.optimizer = self.model.optimizer + self.loss = self.model.loss + self.metrics = self.model.metrics + self.loss_weights = self.model.loss_weights + self.sample_weight_mode = self.model.sample_weight_mode + self.weighted_metrics = self.model.weighted_metrics + self.targets = self.model.targets + self.metrics_tensors = self.model.metrics_tensors + self.metrics_names = self.model.metrics_names + self.sample_weights = self.model.sample_weights + self.total_loss = self.model.total_loss + + 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, + **kwargs): + """Trains the model for a fixed number of epochs. + + Arguments: + x: Numpy array of training data. + If the input layer in the model is named, you can also pass a + dictionary mapping the input name to a Numpy array. + `x` can be `None` (default) if feeding from + TensorFlow data tensors. + y: Numpy array of target (label) data. + If the output layer in the model is named, you can also pass a + dictionary mapping the output name to a Numpy array. + `y` can be `None` (default) if feeding from + TensorFlow data tensors. + batch_size: Integer or `None`. + Number of samples per gradient update. + If unspecified, it will default to 32. + 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: 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](/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. + validation_data: tuple `(x_val, y_val)` or tuple + `(x_val, y_val, val_sample_weights)` 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. + This will override `validation_split`. + 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()`. + initial_epoch: Epoch at which to start training + (useful for resuming a previous training run). + steps_per_epoch: 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 unique samples in your dataset divided by + the batch size, or 1 if that cannot be determined. + validation_steps: Only relevant if `steps_per_epoch` + is specified. Total number of steps (batches of samples) + to validate before stopping. + **kwargs: Used for backwards compatibility support. + + 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. + """ + if not self.built: + raise RuntimeError('The model needs to be compiled before being used.') + return self.model.fit( + x, + y, + batch_size=batch_size, + epochs=epochs, + verbose=verbose, + callbacks=callbacks, + validation_split=validation_split, + validation_data=validation_data, + shuffle=shuffle, + class_weight=class_weight, + sample_weight=sample_weight, + initial_epoch=initial_epoch, + steps_per_epoch=steps_per_epoch, + validation_steps=validation_steps) + + def evaluate(self, x, y, batch_size=32, verbose=1, sample_weight=None): + """Computes the loss on some input data, batch by batch. + + Arguments: + x: input data, as a Numpy array or list of Numpy arrays + (if the model has multiple inputs). + y: labels, as a Numpy array. + batch_size: integer. Number of samples per gradient update. + verbose: verbosity mode, 0 or 1. + sample_weight: sample weights, as a Numpy array. + + Returns: + Scalar test loss (if the model has no metrics) + or list of scalars (if the model computes other metrics). + The attribute `model.metrics_names` will give you + the display labels for the scalar outputs. + + Raises: + RuntimeError: if the model was never compiled. + """ + if not self.built: + raise RuntimeError('The model needs to be compiled before being used.') + return self.model.evaluate( + x, + y, + batch_size=batch_size, + verbose=verbose, + sample_weight=sample_weight) + + def predict(self, x, batch_size=32, verbose=0): + """Generates output predictions for the input samples. + + The input samples are processed batch by batch. + + Arguments: + x: the input data, as a Numpy array. + batch_size: integer. + verbose: verbosity mode, 0 or 1. + + Returns: + A Numpy array of predictions. + """ + if not self.built: + self.build() + return self.model.predict(x, batch_size=batch_size, verbose=verbose) + + def predict_on_batch(self, x): + """Returns predictions for a single batch of samples. + + Arguments: + x: input data, as a Numpy array or list of Numpy arrays + (if the model has multiple inputs). + + Returns: + A Numpy array of predictions. + """ + if not self.built: + self.build() + return self.model.predict_on_batch(x) + + def train_on_batch(self, x, y, class_weight=None, sample_weight=None): + """Single gradient update over one batch of samples. + + Arguments: + x: input data, as a Numpy array or list of Numpy arrays + (if the model has multiple inputs). + y: labels, as a Numpy array. + class_weight: dictionary mapping classes to a weight value, + used for scaling the loss function (during training only). + sample_weight: sample weights, as a Numpy array. + + Returns: + Scalar training loss (if the model has no metrics) + or list of scalars (if the model computes other metrics). + The attribute `model.metrics_names` will give you + the display labels for the scalar outputs. + + Raises: + RuntimeError: if the model was never compiled. + """ + if not self.built: + raise RuntimeError('The model needs to be compiled before being used.') + return self.model.train_on_batch( + x, y, sample_weight=sample_weight, class_weight=class_weight) + + def test_on_batch(self, x, y, sample_weight=None): + """Evaluates the model over a single batch of samples. + + Arguments: + x: input data, as a Numpy array or list of Numpy arrays + (if the model has multiple inputs). + y: labels, as a Numpy array. + sample_weight: sample weights, as a Numpy array. + + Returns: + Scalar test loss (if the model has no metrics) + or list of scalars (if the model computes other metrics). + The attribute `model.metrics_names` will give you + the display labels for the scalar outputs. + + Raises: + RuntimeError: if the model was never compiled. + """ + if not self.built: + raise RuntimeError('The model needs to be compiled before being used.') + return self.model.test_on_batch(x, y, sample_weight=sample_weight) + + def predict_proba(self, x, batch_size=32, verbose=0): + """Generates class probability predictions for the input samples. + + The input samples are processed batch by batch. + + Arguments: + x: input data, as a Numpy array or list of Numpy arrays + (if the model has multiple inputs). + batch_size: integer. + verbose: verbosity mode, 0 or 1. + + Returns: + A Numpy array of probability predictions. + """ + preds = self.predict(x, batch_size, verbose) + if preds.min() < 0. or preds.max() > 1.: + logging.warning('Network returning invalid probability values. ' + 'The last layer might not normalize predictions ' + 'into probabilities ' + '(like softmax or sigmoid would).') + return preds + + def predict_classes(self, x, batch_size=32, verbose=0): + """Generate class predictions for the input samples. + + The input samples are processed batch by batch. + + Arguments: + x: input data, as a Numpy array or list of Numpy arrays + (if the model has multiple inputs). + batch_size: integer. + verbose: verbosity mode, 0 or 1. + + Returns: + A numpy array of class predictions. + """ + proba = self.predict(x, batch_size=batch_size, verbose=verbose) + if proba.shape[-1] > 1: + return proba.argmax(axis=-1) + else: + return (proba > 0.5).astype('int32') + + 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, + **kwargs): + """Fits the model on data generated 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. + + Arguments: + generator: A generator. + The output of the generator must be either + - a tuple (inputs, targets) + - a tuple (inputs, targets, sample_weights). + All arrays should contain the same number of samples. + 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. + Note that in conjunction with initial_epoch, the parameter + epochs is to be understood as "final epoch". The model is + not trained for n steps given by epochs, but until the + epoch epochs is reached. + 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. + Number of steps to yield from validation generator + at the end of every epoch. It should typically + be equal to the number of samples of your + validation dataset divided by the batch size. + 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: Maximum size for the generator queue + workers: Maximum number of processes to spin up + use_multiprocessing: If True, use process based threading. + 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: Whether to shuffle the order of the batches at + the beginning of each epoch. Only used with instances + of `Sequence` (keras.utils.Sequence). + initial_epoch: Epoch at which to start training + (useful for resuming a previous training run) + **kwargs: support for legacy arguments. + + Returns: + A `History` object. + + Raises: + RuntimeError: if the model was never compiled. + ValueError: In case the generator yields + data in an invalid format. + + Example: + + ```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 + x, y = process_line(line) + yield (x, y) + f.close() + + model.fit_generator(generate_arrays_from_file('/my_file.txt'), + steps_per_epoch=1000, epochs=10) + ``` + """ + # Legacy support + if 'max_q_size' in kwargs: + max_queue_size = kwargs.pop('max_q_size') + logging.warning('The argument `max_q_size` has been renamed ' + '`max_queue_size`. Update your method calls accordingly.') + if 'pickle_safe' in kwargs: + use_multiprocessing = kwargs.pop('pickle_safe') + logging.warning('The argument `pickle_safe` has been renamed ' + '`use_multiprocessing`. ' + 'Update your method calls accordingly.') + if kwargs: + raise ValueError('Unrecognized keyword arguments: ' + str(kwargs)) + + if not self.built: + raise RuntimeError('The model needs to be compiled before being used.') + return self.model.fit_generator( + generator, + steps_per_epoch, + 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 evaluate_generator(self, + generator, + steps=None, + max_queue_size=10, + workers=1, + use_multiprocessing=False, + **kwargs): + """Evaluates the model on a data generator. + + The generator should return the same kind of data + as accepted by `test_on_batch`. + + Arguments: + generator: Generator yielding tuples (inputs, targets) + or (inputs, targets, sample_weights) + 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: maximum number of processes to spin up + use_multiprocessing: if True, use process based threading. + 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: support for legacy arguments. + + Returns: + Scalar test loss (if the model has no metrics) + or list of scalars (if the model computes other metrics). + The attribute `model.metrics_names` will give you + the display labels for the scalar outputs. + + Raises: + RuntimeError: if the model was never compiled. + ValueError: In case the generator yields + data in an invalid format. + """ + # Legacy support + if 'max_q_size' in kwargs: + max_queue_size = kwargs.pop('max_q_size') + logging.warning('The argument `max_q_size` has been renamed ' + '`max_queue_size`. Update your method calls accordingly.') + if 'pickle_safe' in kwargs: + use_multiprocessing = kwargs.pop('pickle_safe') + logging.warning('The argument `pickle_safe` has been renamed ' + '`use_multiprocessing`. ' + 'Update your method calls accordingly.') + if kwargs: + raise ValueError('Unrecognized keyword arguments: ' + str(kwargs)) + + if not self.built: + raise RuntimeError('The model needs to be compiled before being used.') + return self.model.evaluate_generator( + generator, + steps, + max_queue_size=max_queue_size, + workers=workers, + use_multiprocessing=use_multiprocessing) + + def predict_generator(self, + generator, + steps=None, + max_queue_size=10, + workers=1, + use_multiprocessing=False, + verbose=0, + **kwargs): + """Generates predictions for the input samples from a data generator. + + The generator should return the same kind of data as accepted by + `predict_on_batch`. + + Arguments: + generator: generator yielding batches of input samples. + 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: maximum number of processes to spin up + use_multiprocessing: if True, use process based threading. + 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. + **kwargs: support for legacy arguments. + + Returns: + A Numpy array of predictions. + + Raises: + ValueError: In case the generator yields + data in an invalid format. + """ + # Legacy support + if 'max_q_size' in kwargs: + max_queue_size = kwargs.pop('max_q_size') + logging.warning('The argument `max_q_size` has been renamed ' + '`max_queue_size`. Update your method calls accordingly.') + if 'pickle_safe' in kwargs: + use_multiprocessing = kwargs.pop('pickle_safe') + logging.warning('The argument `pickle_safe` has been renamed ' + '`use_multiprocessing`. ' + 'Update your method calls accordingly.') + if kwargs: + raise ValueError('Unrecognized keyword arguments: ' + str(kwargs)) + + if not self.built: + self.build() + return self.model.predict_generator( + generator, + steps, + max_queue_size=max_queue_size, + workers=workers, + use_multiprocessing=use_multiprocessing, + verbose=verbose) + + def get_config(self): + config = [] + for layer in self.layers: + config.append({ + 'class_name': layer.__class__.__name__, + 'config': layer.get_config() + }) + return copy.deepcopy(config) + + @classmethod + def from_config(cls, config, custom_objects=None): + model = cls() + for conf in config: + layer = layer_module.deserialize(conf, custom_objects=custom_objects) + model.add(layer) + return model diff --git a/tensorflow/python/keras/_impl/keras/engine/sequential_test.py b/tensorflow/python/keras/_impl/keras/engine/sequential_test.py new file mode 100644 index 0000000000000000000000000000000000000000..166634bd8219b831ce212ba983a4ab695b00c3b7 --- /dev/null +++ b/tensorflow/python/keras/_impl/keras/engine/sequential_test.py @@ -0,0 +1,152 @@ +# 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 specific to `Sequential` model.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.python.keras._impl import keras +from tensorflow.python.platform import test + + +class TestSequential(test.TestCase): + """Most Sequential model API tests are covered in `training_test.py`. + """ + + def test_basic_methods(self): + model = keras.models.Sequential() + model.add(keras.layers.Dense(1, input_dim=2)) + model.add(keras.layers.Dropout(0.3, name='dp')) + model.add(keras.layers.Dense(2, kernel_regularizer='l2', + kernel_constraint='max_norm')) + model.build() + self.assertEqual(model.state_updates, model.model.state_updates) + self.assertEqual(model.get_layer(name='dp').name, 'dp') + + def test_sequential_pop(self): + num_hidden = 5 + input_dim = 3 + batch_size = 5 + num_classes = 2 + with self.test_session(): + model = keras.models.Sequential() + model.add(keras.layers.Dense(num_hidden, input_dim=input_dim)) + model.add(keras.layers.Dense(num_classes)) + model.compile(loss='mse', optimizer='sgd') + x = np.random.random((batch_size, input_dim)) + y = np.random.random((batch_size, num_classes)) + model.fit(x, y, epochs=1) + model.pop() + self.assertEqual(len(model.layers), 1) + self.assertEqual(model.output_shape, (None, num_hidden)) + model.compile(loss='mse', optimizer='sgd') + y = np.random.random((batch_size, num_hidden)) + model.fit(x, y, epochs=1) + + # Test popping single-layer model + model = keras.models.Sequential() + model.add(keras.layers.Dense(num_hidden, input_dim=input_dim)) + model.pop() + self.assertEqual(len(model.layers), 0) + self.assertEqual(len(model.outputs), 0) + + # Invalid use case + model = keras.models.Sequential() + with self.assertRaises(TypeError): + model.pop() + + def test_invalid_use_cases(self): + with self.test_session(): + # Added objects must be layer instances + with self.assertRaises(TypeError): + model = keras.models.Sequential() + model.add(None) + + # Added layers must have an inputs shape + with self.assertRaises(ValueError): + model = keras.models.Sequential() + model.add(keras.layers.Dense(1)) + + # Added layers cannot have multiple outputs + class MyLayer(keras.layers.Layer): + + def call(self, inputs): + return [3 * inputs, 2 * inputs] + + def compute_output_shape(self, input_shape): + return [input_shape, input_shape] + + with self.assertRaises(ValueError): + model = keras.models.Sequential() + model.add(MyLayer(input_shape=(3,))) + with self.assertRaises(TypeError): + model = keras.models.Sequential() + model.add(keras.layers.Dense(1, input_dim=1)) + model.add(MyLayer()) + + # Building empty model + model = keras.models.Sequential() + with self.assertRaises(TypeError): + model.build() + + def test_nested_sequential_trainability(self): + input_dim = 20 + num_units = 10 + num_classes = 2 + + inner_model = keras.models.Sequential() + inner_model.add(keras.layers.Dense(num_units, input_shape=(input_dim,))) + + model = keras.models.Sequential() + model.add(inner_model) + model.add(keras.layers.Dense(num_classes)) + + self.assertEqual(len(model.trainable_weights), 4) + inner_model.trainable = False + self.assertEqual(len(model.trainable_weights), 2) + inner_model.trainable = True + self.assertEqual(len(model.trainable_weights), 4) + + def test_sequential_update_disabling(self): + val_a = np.random.random((10, 4)) + val_out = np.random.random((10, 4)) + + with self.test_session(): + model = keras.models.Sequential() + model.add(keras.layers.BatchNormalization(input_shape=(4,))) + + model.trainable = False + assert not model.updates + + model.compile('sgd', 'mse') + assert not model.updates + assert not model.model.updates + + x1 = model.predict(val_a) + model.train_on_batch(val_a, val_out) + x2 = model.predict(val_a) + self.assertAllClose(x1, x2, atol=1e-7) + + model.trainable = True + model.compile('sgd', 'mse') + assert model.updates + assert model.model.updates + + model.train_on_batch(val_a, val_out) + x2 = model.predict(val_a) + assert np.abs(np.sum(x1 - x2)) > 1e-5 diff --git a/tensorflow/python/keras/_impl/keras/engine/topology_test.py b/tensorflow/python/keras/_impl/keras/engine/topology_test.py index 139621db6db1d8fee69bcc93a873eaa7e31dd8dc..b50277c8fff917d77694903c989fd02ea98b1711 100644 --- a/tensorflow/python/keras/_impl/keras/engine/topology_test.py +++ b/tensorflow/python/keras/_impl/keras/engine/topology_test.py @@ -18,9 +18,6 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import os -import shutil - import numpy as np from tensorflow.python.eager import context @@ -28,7 +25,7 @@ from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import test_util from tensorflow.python.keras._impl import keras -from tensorflow.python.layers import base as base_layers +from tensorflow.python.layers import base as tf_base_layers from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import state_ops @@ -39,11 +36,6 @@ try: except ImportError: yaml = None -try: - import h5py # pylint:disable=g-import-not-at-top -except ImportError: - h5py = None - class TopologyConstructionTest(test.TestCase): @@ -84,7 +76,7 @@ class TopologyConstructionTest(test.TestCase): self.assertEqual(len(layer.get_updates_for(x2)), 1) self.assertEqual(len(layer.get_updates_for(None)), 1) - network = keras.engine.topology.Network(x2, y2) + network = keras.engine.Network(x2, y2) self.assertEqual(len(network.updates), 2) self.assertEqual(len(network.get_updates_for(x1)), 0) self.assertEqual(len(network.get_updates_for(x2)), 1) @@ -146,7 +138,7 @@ class TopologyConstructionTest(test.TestCase): self.assertEqual(len(layer.get_losses_for(x2)), 1) self.assertEqual(len(layer.get_losses_for(None)), 1) - network = keras.engine.topology.Network(x2, y2) + network = keras.engine.Network(x2, y2) self.assertEqual(len(network.losses), 2) self.assertEqual(len(network.get_losses_for(x1)), 0) self.assertEqual(len(network.get_losses_for(x2)), 1) @@ -267,7 +259,7 @@ class TopologyConstructionTest(test.TestCase): x = keras.Input(shape=(32,)) dense = keras.layers.Dense(2) y = dense(x) - network = keras.engine.topology.Network(x, y, name='dense_network') + network = keras.engine.Network(x, y, name='dense_network') # test basic attributes self.assertEqual(network.name, 'dense_network') @@ -502,7 +494,7 @@ class TopologyConstructionTest(test.TestCase): self.assertListEqual([x.shape for x in fn_outputs], [(10, 64), (10, 5)]) # test get_source_inputs - self.assertListEqual(keras.engine.topology.get_source_inputs(c), [a, b]) + self.assertListEqual(keras.engine.network.get_source_inputs(c), [a, b]) # serialization / deserialization json_config = model.to_json() @@ -539,7 +531,9 @@ class TopologyConstructionTest(test.TestCase): e = keras.layers.Input(shape=(32,), name='input_e') f = keras.layers.Input(shape=(32,), name='input_f') + self.assertEqual(len(model.inputs), 2) g, h = model([e, f]) + self.assertEqual(len(model.inputs), 2) self.assertEqual(g.name, 'model/dense_2/BiasAdd:0') self.assertListEqual(g.get_shape().as_list(), c.get_shape().as_list()) @@ -721,7 +715,9 @@ class TopologyConstructionTest(test.TestCase): j = keras.layers.Input(shape=(32,), name='input_j') k = keras.layers.Input(shape=(32,), name='input_k') + self.assertEqual(len(model.inputs), 2) m, n = model([j, k]) + self.assertEqual(len(model.inputs), 2) tf_model = keras.models.Model([j, k], [m, n]) j_tf = array_ops.placeholder(dtype=dtypes.float32, shape=(None, 32)) @@ -759,10 +755,20 @@ class TopologyConstructionTest(test.TestCase): def compute_mask(self, inputs, mask=None): return array_ops.ones_like(inputs) - if context.in_graph_mode(): + if context.executing_eagerly(): + a = constant_op.constant([2] * 32) + mask = constant_op.constant([0, 1] * 16) + a._keras_mask = mask + b = MaskedLayer().apply(a) + self.assertTrue(hasattr(b, '_keras_mask')) + self.assertAllEqual( + self.evaluate(array_ops.ones_like(mask)), + self.evaluate(getattr(b, '_keras_mask'))) + self.assertAllEqual(self.evaluate(a * mask), self.evaluate(b)) + else: x = keras.Input(shape=(32,)) y = MaskedLayer()(x) # pylint: disable=not-callable - network = keras.engine.topology.Network(x, y) + network = keras.engine.Network(x, y) # test callability on Input x_2 = keras.Input(shape=(32,)) @@ -773,15 +779,6 @@ class TopologyConstructionTest(test.TestCase): x_2 = array_ops.placeholder(dtype='float32', shape=(None, 32)) y_2 = network(x_2) self.assertEqual(y_2.get_shape().as_list(), [None, 32]) - else: - a = constant_op.constant([2] * 32) - mask = constant_op.constant([0, 1] * 16) - a._keras_mask = mask - b = MaskedLayer().apply(a) - self.assertTrue(hasattr(b, '_keras_mask')) - self.assertAllEqual(self.evaluate(array_ops.ones_like(mask)), - self.evaluate(getattr(b, '_keras_mask'))) - self.assertAllEqual(self.evaluate(a * mask), self.evaluate(b)) def test_activity_regularization_with_model_composition(self): @@ -875,139 +872,12 @@ class TopologyConstructionTest(test.TestCase): self.assertEqual(np.min(preds), 0.) # At least one unit was dropped. -class TestSaving(test.TestCase): - - def test_weight_loading(self): - with self.test_session(): - a = keras.layers.Input(shape=(2,)) - x = keras.layers.Dense(3)(a) - b = keras.layers.Dense(1)(x) - model = keras.models.Model(a, b) - - x = np.random.random((3, 2)) - ref_y = model.predict(x) - weights = model.get_weights() - model.set_weights(weights) - y = model.predict(x) - self.assertAllClose(ref_y, y) - - with self.assertRaises(ValueError): - model.set_weights(weights[1:]) - with self.assertRaises(ValueError): - model.set_weights(weights[::-1]) - - if h5py is None: - return # Skip rest of test if H5py isn't available. - - temp_dir = self.get_temp_dir() - self.addCleanup(shutil.rmtree, temp_dir) - - h5_path = os.path.join(temp_dir, 'test.h5') - model.save_weights(h5_path) - model.load_weights(h5_path) - y = model.predict(x) - self.assertAllClose(ref_y, y) - - model.load_weights(h5_path, by_name=True) - y = model.predict(x) - self.assertAllClose(ref_y, y) - - def test_weight_preprocessing(self): - input_dim = 3 - output_dim = 3 - size = 2 - cases = [ - [ - (keras.layers.Bidirectional(keras.layers.SimpleRNN(2))), - [np.random.random((2, 1)), np.random.random((2, 1))], - (None, 3, 2), - ], - [ - (keras.layers.TimeDistributed(keras.layers.Dense(1))), - [np.random.random((2, 1)), np.random.random((1,))], - (None, 3, 2), - ], - [ - (keras.layers.Conv1D(output_dim, size, use_bias=False)), - [np.random.random((output_dim, input_dim, size, 1))], - (None, 4, input_dim), - ], - [ - (keras.layers.Conv2D(output_dim, size, - use_bias=False, data_format='channels_first')), - [np.random.random((output_dim, input_dim, size, size))], - (None, input_dim, 4, 4), - ], - [ - (keras.layers.Conv2DTranspose(output_dim, size, - use_bias=False, - data_format='channels_first')), - [np.random.random((output_dim, input_dim, size, size))], - (None, input_dim, 4, 4), - ], - [ - (keras.layers.Conv2DTranspose(output_dim, size, - use_bias=False, - data_format='channels_last')), - [np.random.random((size, size, input_dim, output_dim))], - (None, 4, 4, input_dim), - ], - [ - (keras.layers.Conv3D(output_dim, size, - use_bias=False, data_format='channels_first')), - [np.random.random((output_dim, input_dim, size, size, size))], - (None, input_dim, 4, 4, 4), - ], - [ - (keras.layers.GRU(output_dim)), - [np.random.random((input_dim, output_dim)), - np.random.random((output_dim, output_dim)), - np.random.random((output_dim,)), - np.random.random((input_dim, output_dim)), - np.random.random((output_dim, output_dim)), - np.random.random((output_dim,)), - np.random.random((input_dim, output_dim)), - np.random.random((output_dim, output_dim)), - np.random.random((output_dim,))], - (None, 4, input_dim), - ], - [ - (keras.layers.LSTM(output_dim)), - [np.random.random((input_dim, output_dim)), - np.random.random((output_dim, output_dim)), - np.random.random((output_dim,)), - np.random.random((input_dim, output_dim)), - np.random.random((output_dim, output_dim)), - np.random.random((output_dim,)), - np.random.random((input_dim, output_dim)), - np.random.random((output_dim, output_dim)), - np.random.random((output_dim,)), - np.random.random((input_dim, output_dim)), - np.random.random((output_dim, output_dim)), - np.random.random((output_dim,))], - (None, 4, input_dim), - ], - ] - for layer, weights, input_shape in cases: - layer.build(input_shape) - _ = keras.engine.topology.preprocess_weights_for_loading( - layer, weights, original_keras_version='1') - - model = keras.models.Sequential([keras.layers.Dense(2, input_dim=2)]) - _ = keras.engine.topology.preprocess_weights_for_loading( - model, model.weights, original_keras_version='1') - - x = keras.Input((2,)) - y = keras.layers.Dense(2)(x) - model = keras.models.Model(x, y) - _ = keras.engine.topology.preprocess_weights_for_loading( - model, model.weights, original_keras_version='1') - - class DeferredModeTest(test.TestCase): def testDeferredTensorAttributes(self): - x = base_layers._DeferredTensor(shape=(None, 2), dtype='float32', name='x') + x = tf_base_layers._DeferredTensor(shape=(None, 2), + dtype='float32', + name='x') self.assertEqual(str(x), 'DeferredTensor(\'x\', shape=(?, 2), dtype=float32)') self.assertEqual(repr(x), @@ -1015,23 +885,23 @@ class DeferredModeTest(test.TestCase): @test_util.run_in_graph_and_eager_modes() def testSimpleNetworkBuilding(self): - inputs = keras.engine.topology.Input(shape=(32,)) - if context.in_eager_mode(): - self.assertIsInstance(inputs, base_layers._DeferredTensor) + inputs = keras.engine.Input(shape=(32,)) + if context.executing_eagerly(): + self.assertIsInstance(inputs, tf_base_layers._DeferredTensor) self.assertEqual(inputs.dtype.name, 'float32') self.assertEqual(inputs.shape.as_list(), [None, 32]) x = keras.layers.Dense(2)(inputs) - if context.in_eager_mode(): - self.assertIsInstance(x, base_layers._DeferredTensor) + if context.executing_eagerly(): + self.assertIsInstance(x, tf_base_layers._DeferredTensor) self.assertEqual(x.dtype.name, 'float32') self.assertEqual(x.shape.as_list(), [None, 2]) outputs = keras.layers.Dense(4)(x) - network = keras.engine.topology.Network(inputs, outputs) - self.assertIsInstance(network, keras.engine.topology.Network) + network = keras.engine.Network(inputs, outputs) + self.assertIsInstance(network, keras.engine.Network) - if context.in_eager_mode(): + if context.executing_eagerly(): # It should be possible to call such a network on EagerTensors. inputs = constant_op.constant( np.random.random((10, 32)).astype('float32')) @@ -1040,8 +910,8 @@ class DeferredModeTest(test.TestCase): @test_util.run_in_graph_and_eager_modes() def testMultiIONetworkbuilding(self): - input_a = keras.engine.topology.Input(shape=(32,)) - input_b = keras.engine.topology.Input(shape=(16,)) + input_a = keras.engine.Input(shape=(32,)) + input_b = keras.engine.Input(shape=(16,)) a = keras.layers.Dense(16)(input_a) class AddLayer(keras.layers.Layer): @@ -1055,8 +925,8 @@ class DeferredModeTest(test.TestCase): c = AddLayer()([a, input_b]) # pylint: disable=not-callable c = keras.layers.Dense(2)(c) - network = keras.engine.topology.Network([input_a, input_b], [a, c]) - if context.in_eager_mode(): + network = keras.engine.Network([input_a, input_b], [a, c]) + if context.executing_eagerly(): a_val = constant_op.constant( np.random.random((10, 32)).astype('float32')) b_val = constant_op.constant( diff --git a/tensorflow/python/keras/_impl/keras/engine/training.py b/tensorflow/python/keras/_impl/keras/engine/training.py index d8ea2fe3db500d3b52d80e46b0cff22a3d1c5915..4acb41553eda2e07962f6ac510f08988a5adb90c 100644 --- a/tensorflow/python/keras/_impl/keras/engine/training.py +++ b/tensorflow/python/keras/_impl/keras/engine/training.py @@ -18,500 +18,28 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import copy - import numpy as np from tensorflow.python.eager import context from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_util from tensorflow.python.keras._impl.keras import backend as K -from tensorflow.python.keras._impl.keras import callbacks as cbks from tensorflow.python.keras._impl.keras import losses from tensorflow.python.keras._impl.keras import metrics as metrics_module from tensorflow.python.keras._impl.keras import optimizers +from tensorflow.python.keras._impl.keras.engine import training_arrays from tensorflow.python.keras._impl.keras.engine import training_eager -from tensorflow.python.keras._impl.keras.engine.topology import Layer -from tensorflow.python.keras._impl.keras.engine.topology import Network -from tensorflow.python.keras._impl.keras.utils.data_utils import GeneratorEnqueuer -from tensorflow.python.keras._impl.keras.utils.data_utils import OrderedEnqueuer -from tensorflow.python.keras._impl.keras.utils.data_utils import Sequence -from tensorflow.python.keras._impl.keras.utils.generic_utils import make_batches -from tensorflow.python.keras._impl.keras.utils.generic_utils import Progbar +from tensorflow.python.keras._impl.keras.engine import training_generator +from tensorflow.python.keras._impl.keras.engine import training_utils +from tensorflow.python.keras._impl.keras.engine.base_layer import Layer +from tensorflow.python.keras._impl.keras.engine.network import Network from tensorflow.python.keras._impl.keras.utils.generic_utils import slice_arrays from tensorflow.python.layers.base import _DeferredTensor +from tensorflow.python.ops import array_ops from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import optimizer as tf_optimizer_module from tensorflow.python.util.tf_export import tf_export -try: - from scipy.sparse import issparse # pylint: disable=g-import-not-at-top -except ImportError: - issparse = None - - -def _standardize_input_data(data, - names, - shapes=None, - check_batch_axis=True, - exception_prefix=''): - """Normalizes inputs and targets provided by users. - - Users may pass data as a list of arrays, dictionary of arrays, - or as a single array. We normalize this to an ordered list of - arrays (same order as `names`), while checking that the provided - arrays have shapes that match the network's expectations. - - Arguments: - data: User-provided input data (polymorphic). - names: List of expected array names. - shapes: Optional list of expected array shapes. - check_batch_axis: Boolean; whether to check that - the batch axis of the arrays matches the expected - value found in `shapes`. - exception_prefix: String prefix used for exception formatting. - - Returns: - List of standardized input arrays (one array per model input). - - Raises: - ValueError: in case of improperly formatted user-provided data. - """ - if not names: - if data is not None and hasattr(data, '__len__') and len(data): - raise ValueError('Error when checking model ' + exception_prefix + ': ' - 'expected no data, but got:', data) - return [] - if data is None: - return [None for _ in range(len(names))] - - if isinstance(data, dict): - try: - data = [ - data[x].values - if data[x].__class__.__name__ == 'DataFrame' else data[x] - for x in names - ] - except KeyError as e: - raise ValueError('No data provided for "' + e.args[0] + '". Need data ' - 'for each key in: ' + str(names)) - elif isinstance(data, list): - if isinstance(data[0], list): - data = [np.asarray(d) for d in data] - elif len(names) == 1 and isinstance(data[0], (float, int)): - data = [np.asarray(data)] - else: - data = [ - x.values if x.__class__.__name__ == 'DataFrame' else x for x in data - ] - else: - data = data.values if data.__class__.__name__ == 'DataFrame' else data - data = [data] - data = [ - np.expand_dims(x, 1) if x is not None and x.ndim == 1 else x for x in data - ] - - if len(data) != len(names): - if data and hasattr(data[0], 'shape'): - raise ValueError('Error when checking model ' + exception_prefix + - ': the list of Numpy arrays that you are passing to ' - 'your model is not the size the model expected. ' - 'Expected to see ' + str(len(names)) + ' array(s), ' - 'but instead got the following list of ' + - str(len(data)) + ' arrays: ' + str(data)[:200] + '...') - elif len(names) > 1: - raise ValueError( - 'Error when checking model ' + exception_prefix + - ': you are passing a list as input to your model, ' - 'but the model expects a list of ' + str(len(names)) + - ' Numpy arrays instead. The list you passed was: ' + str(data)[:200]) - elif len(data) == 1 and not hasattr(data[0], 'shape'): - raise TypeError('Error when checking model ' + exception_prefix + - ': data should be a Numpy array, or list/dict of ' - 'Numpy arrays. Found: ' + str(data)[:200] + '...') - elif len(names) == 1: - data = [np.asarray(data)] - - # Check shapes compatibility. - if shapes: - for i in range(len(names)): - if shapes[i] is not None: - data_shape = data[i].shape - shape = shapes[i] - if data[i].ndim != len(shape): - raise ValueError('Error when checking ' + exception_prefix + - ': expected ' + names[i] + ' to have ' + - str(len(shape)) + ' dimensions, but got array ' - 'with shape ' + str(data_shape)) - if not check_batch_axis: - data_shape = data_shape[1:] - shape = shape[1:] - for dim, ref_dim in zip(data_shape, shape): - if ref_dim != dim and ref_dim: - raise ValueError( - 'Error when checking ' + exception_prefix + ': expected ' + - names[i] + ' to have shape ' + str(shape) + - ' but got array with shape ' + str(data_shape)) - return data - - -def _standardize_sample_or_class_weights(x_weight, output_names, weight_type): - """Maps `sample_weight` or `class_weight` to model outputs. - - Arguments: - x_weight: User-provided `sample_weight` or `class_weight` argument. - output_names: List of output names (strings) in the model. - weight_type: A string used purely for exception printing. - - Returns: - A list of `sample_weight` or `class_weight` where there are exactly - one element per model output. - - Raises: - ValueError: In case of invalid user-provided argument. - """ - if x_weight is None or len(x_weight) == 0: # pylint: disable=g-explicit-length-test - return [None for _ in output_names] - if len(output_names) == 1: - if isinstance(x_weight, list) and len(x_weight) == 1: - return x_weight - if isinstance(x_weight, dict) and output_names[0] in x_weight: - return [x_weight[output_names[0]]] - else: - return [x_weight] - if isinstance(x_weight, list): - if len(x_weight) != len(output_names): - raise ValueError('Provided `' + weight_type + '` was a list of ' + - str(len(x_weight)) + ' elements, but the model has ' + - str(len(output_names)) + ' outputs. ' - 'You should provide one `' + weight_type + '`' - 'array per model output.') - return x_weight - if isinstance(x_weight, dict): - x_weights = [] - for name in output_names: - x_weights.append(x_weight.get(name)) - return x_weights - else: - raise TypeError( - 'The model has multiple outputs, so `' + weight_type + '` ' - 'should be either a list or a dict. ' - 'Provided `' + weight_type + '` type not understood: ' + str(x_weight)) - - -def _standardize_class_weights(class_weight, output_names): - return _standardize_sample_or_class_weights(class_weight, output_names, - 'class_weight') - - -def _standardize_sample_weights(sample_weight, output_names): - return _standardize_sample_or_class_weights(sample_weight, output_names, - 'sample_weight') - - -def _check_array_lengths(inputs, targets, weights=None): - """Does user input validation for numpy arrays. - - Arguments: - inputs: list of Numpy arrays of inputs. - targets: list of Numpy arrays of targets. - weights: list of Numpy arrays of sample weights. - - Raises: - ValueError: in case of incorrectly formatted data. - """ - - def set_of_lengths(x): - # return a set with the variation between - # different shapes, with None => 0 - if x is None: - return {0} - else: - return set([0 if y is None else y.shape[0] for y in x]) - - set_x = set_of_lengths(inputs) - set_y = set_of_lengths(targets) - set_w = set_of_lengths(weights) - if len(set_x) > 1: - raise ValueError('All input arrays (x) should have ' - 'the same number of samples. Got array shapes: ' + - str([x.shape for x in inputs])) - if len(set_y) > 1: - raise ValueError('All target arrays (y) should have ' - 'the same number of samples. Got array shapes: ' + - str([y.shape for y in targets])) - if set_x and set_y and list(set_x)[0] != list(set_y)[0]: - raise ValueError('Input arrays should have ' - 'the same number of samples as target arrays. ' - 'Found ' + str(list(set_x)[0]) + ' input samples ' - 'and ' + str(list(set_y)[0]) + ' target samples.') - if len(set_w) > 1: - raise ValueError('All sample_weight arrays should have ' - 'the same number of samples. Got array shapes: ' + - str([w.shape for w in weights])) - if set_y and set_w and list(set_y)[0] != list(set_w)[0]: - raise ValueError('Sample_weight arrays should have ' - 'the same number of samples as target arrays. Got ' + - str(list(set_y)[0]) + ' input samples and ' + - str(list(set_w)[0]) + ' target samples.') - - -def _check_loss_and_target_compatibility(targets, loss_fns, output_shapes): - """Does validation on the compatibility of targets and loss functions. - - This helps prevent users from using loss functions incorrectly. - - Arguments: - targets: list of Numpy arrays of targets. - loss_fns: list of loss functions. - output_shapes: list of shapes of model outputs. - - Raises: - ValueError: if a loss function or target array - is incompatible with an output. - """ - key_losses = { - losses.mean_squared_error, losses.binary_crossentropy, - losses.categorical_crossentropy - } - for y, loss, shape in zip(targets, loss_fns, output_shapes): - if y is None or loss is None: - continue - if loss is losses.categorical_crossentropy: - if y.shape[-1] == 1: - raise ValueError('You are passing a target array of shape ' + str( - y.shape) + ' while using as loss `categorical_crossentropy`. ' - '`categorical_crossentropy` expects ' - 'targets to be binary matrices (1s and 0s) ' - 'of shape (samples, classes). ' - 'If your targets are integer classes, ' - 'you can convert them to the expected format via:\n' - '```\n' - 'from keras.utils import to_categorical\n' - 'y_binary = to_categorical(y_int)\n' - '```\n' - '\n' - 'Alternatively, you can use the loss function ' - '`sparse_categorical_crossentropy` instead, ' - 'which does expect integer targets.') - if loss in key_losses: - for target_dim, out_dim in zip(y.shape[1:], shape[1:]): - if out_dim is not None and target_dim != out_dim: - raise ValueError('A target array with shape ' + str(y.shape) + - ' was passed for an output of shape ' + str(shape) + - ' while using as loss `' + loss.__name__ + '`. ' - 'This loss expects ' - 'targets to have the same shape ' - 'as the output.') - - -def _collect_metrics(metrics, output_names): - """Maps metric functions to model outputs. - - Arguments: - metrics: a list or dict of metric functions. - output_names: a list of the names (strings) of model outputs. - - Returns: - A list (one entry per model output) of lists of metric functions. - For instance, if the model has 2 outputs, and for the first output - we want to compute "binary_accuracy" and "binary_crossentropy", - and just "binary_accuracy" for the second output, - the list would look like: - `[[binary_accuracy, binary_crossentropy], [binary_accuracy]]` - - Raises: - TypeError: if an incorrect type is passed for the `metrics` argument. - """ - if not metrics: - return [[] for _ in output_names] - if isinstance(metrics, list): - # we then apply all metrics to all outputs. - return [copy.copy(metrics) for _ in output_names] - elif isinstance(metrics, dict): - nested_metrics = [] - for name in output_names: - output_metrics = metrics.get(name, []) - if not isinstance(output_metrics, list): - output_metrics = [output_metrics] - nested_metrics.append(output_metrics) - return nested_metrics - else: - raise TypeError('Type of `metrics` argument not understood. ' - 'Expected a list or dictionary, found: ' + str(metrics)) - - -def _batch_shuffle(index_array, batch_size): - """Shuffles an array in a batch-wise fashion. - - Useful for shuffling HDF5 arrays - (where one cannot access arbitrary indices). - - Arguments: - index_array: array of indices to be shuffled. - batch_size: integer. - - Returns: - The `index_array` array, shuffled in a batch-wise fashion. - """ - batch_count = int(len(index_array) / batch_size) - # to reshape we need to be cleanly divisible by batch size - # we stash extra items and reappend them after shuffling - last_batch = index_array[batch_count * batch_size:] - index_array = index_array[:batch_count * batch_size] - index_array = index_array.reshape((batch_count, batch_size)) - np.random.shuffle(index_array) - index_array = index_array.flatten() - return np.append(index_array, last_batch) - - -def _weighted_masked_objective(fn): - """Adds support for masking and sample-weighting to an objective function. - - It transforms an objective function `fn(y_true, y_pred)` - into a sample-weighted, cost-masked objective function - `fn(y_true, y_pred, weights, mask)`. - - Arguments: - fn: The objective function to wrap, - with signature `fn(y_true, y_pred)`. - - Returns: - A function with signature `fn(y_true, y_pred, weights, mask)`. - """ - if fn is None: - return None - - def weighted(y_true, y_pred, weights, mask=None): - """Wrapper function. - - Arguments: - y_true: `y_true` argument of `fn`. - y_pred: `y_pred` argument of `fn`. - weights: Weights tensor. - mask: Mask tensor. - - Returns: - Scalar tensor. - """ - # score_array has ndim >= 2 - score_array = fn(y_true, y_pred) - if mask is not None: - # Cast the mask to floatX to avoid float64 upcasting in theano - mask = K.cast(mask, K.floatx()) - # mask should have the same shape as score_array - score_array *= mask - # the loss per batch should be proportional - # to the number of unmasked samples. - score_array /= K.mean(mask) - - # apply sample weighting - if weights is not None: - # reduce score_array to same ndim as weight array - ndim = K.ndim(score_array) - weight_ndim = K.ndim(weights) - score_array = K.mean(score_array, axis=list(range(weight_ndim, ndim))) - score_array *= weights - score_array /= K.mean(K.cast(K.not_equal(weights, 0), K.floatx())) - return K.mean(score_array) - - return weighted - - -def _standardize_weights(y, - sample_weight=None, - class_weight=None, - sample_weight_mode=None): - """Performs sample weight validation and standardization. - - Everything gets normalized to a single sample-wise (or timestep-wise) - weight array. - - Arguments: - y: Numpy array of model targets to be weighted. - sample_weight: User-provided `sample_weight` argument. - class_weight: User-provided `class_weight` argument. - sample_weight_mode: One of `None` or `"temporal"`. - `"temporal"` indicated that we expect 2D weight data - that will be applied to the last 2 dimensions of - the targets (i.e. we are weighting timesteps, not samples). - - Returns: - A numpy array of target weights, one entry per sample to weight. - - Raises: - ValueError: In case of invalid user-provided arguments. - """ - if sample_weight_mode is not None: - if sample_weight_mode != 'temporal': - raise ValueError('"sample_weight_mode ' - 'should be None or "temporal". ' - 'Found: ' + str(sample_weight_mode)) - if len(y.shape) < 3: - raise ValueError('Found a sample_weight array for ' - 'an input with shape ' + str(y.shape) + '. ' - 'Timestep-wise sample weighting (use of ' - 'sample_weight_mode="temporal") is restricted to ' - 'outputs that are at least 3D, i.e. that have ' - 'a time dimension.') - if sample_weight is not None and len(sample_weight.shape) != 2: - raise ValueError('Found a sample_weight array with shape ' + - str(sample_weight.shape) + '. ' - 'In order to use timestep-wise sample weighting, ' - 'you should pass a 2D sample_weight array.') - else: - if sample_weight is not None and len(sample_weight.shape) != 1: - raise ValueError('Found a sample_weight array with shape ' + - str(sample_weight.shape) + '. ' - 'In order to use timestep-wise sample weights, ' - 'you should specify ' - 'sample_weight_mode="temporal" ' - 'in compile(). If you just mean to use ' - 'sample-wise weights, make sure your ' - 'sample_weight array is 1D.') - - if sample_weight is not None: - if len(sample_weight.shape) > len(y.shape): - raise ValueError( - 'Found a sample_weight with shape' + str(sample_weight.shape) + '.' - 'Expected sample_weight with rank ' - 'less than or equal to ' + str(len(y.shape))) - - if y.shape[:sample_weight.ndim] != sample_weight.shape: - raise ValueError( - 'Found a sample_weight array with shape ' + str(sample_weight.shape) + - ' for an input with shape ' + str(y.shape) + '. ' - 'sample_weight cannot be broadcast.') - return sample_weight - elif isinstance(class_weight, dict): - if len(y.shape) > 2: - raise ValueError('`class_weight` not supported for ' - '3+ dimensional targets.') - if y.shape[1] > 1: - y_classes = np.argmax(y, axis=1) - elif y.shape[1] == 1: - y_classes = np.reshape(y, y.shape[0]) - else: - y_classes = y - - weights = np.asarray( - [class_weight[cls] for cls in y_classes if cls in class_weight]) - - if len(weights) != len(y_classes): - # subtract the sets to pick all missing classes - existing_classes = set(y_classes) - existing_class_weight = set(class_weight.keys()) - raise ValueError('`class_weight` must contain all classes in the data.' - ' The classes %s exist in the data but not in ' - '`class_weight`.' % - (existing_classes - existing_class_weight)) - return weights - else: - if sample_weight_mode is None: - return np.ones((y.shape[0],), dtype=K.floatx()) - else: - return np.ones((y.shape[0], y.shape[1]), dtype=K.floatx()) - @tf_export('keras.models.Model', 'keras.Model') class Model(Network): @@ -634,7 +162,7 @@ class Model(Network): `optimizer`, `loss`, `metrics` or `sample_weight_mode`. """ loss = loss or {} - if context.in_eager_mode() and not isinstance( + if context.executing_eagerly() and not isinstance( optimizer, (tf_optimizer_module.Optimizer, optimizers.TFOptimizer)): raise ValueError('Only TF native optimizers are supported in Eager mode.') @@ -642,13 +170,13 @@ class Model(Network): self.loss = loss self.metrics = metrics or [] self.loss_weights = loss_weights - if context.in_eager_mode() and sample_weight_mode is not None: + if context.executing_eagerly() and sample_weight_mode is not None: raise ValueError('sample_weight_mode is not supported in Eager mode.') self.sample_weight_mode = sample_weight_mode - if context.in_eager_mode() and weighted_metrics is not None: + if context.executing_eagerly() and weighted_metrics is not None: raise ValueError('weighted_metrics is not supported in Eager mode.') self.weighted_metrics = weighted_metrics - if context.in_eager_mode() and target_tensors is not None: + if context.executing_eagerly() and target_tensors is not None: raise ValueError('target_tensors is not supported in Eager mode.') self.target_tensors = target_tensors @@ -688,7 +216,8 @@ class Model(Network): loss_functions = [loss_function for _ in range(len(self.outputs))] self.loss_functions = loss_functions - weighted_losses = [_weighted_masked_objective(fn) for fn in loss_functions] + weighted_losses = [training_utils.weighted_masked_objective(fn) + for fn in loss_functions] skip_target_indices = [] skip_target_weighing_indices = [] self._feed_outputs = [] @@ -701,7 +230,7 @@ class Model(Network): skip_target_weighing_indices.append(i) # Prepare output masks. - if context.in_graph_mode(): + if not context.executing_eagerly(): masks = self.compute_mask(self.inputs, mask=None) if masks is None: masks = [None for _ in self.outputs] @@ -735,9 +264,9 @@ class Model(Network): self.loss_weights_list = loss_weights_list # initialization for Eager mode execution - if context.in_eager_mode(): + if context.executing_eagerly(): if target_tensors is not None: - raise ValueError('target_tensors are not currently supported in Eager' + raise ValueError('target_tensors are not currently supported in Eager ' 'mode.') self.total_loss = None self.metrics_tensors = [] @@ -745,7 +274,8 @@ class Model(Network): for i in range(len(self.outputs)): if len(self.outputs) > 1: self.metrics_names.append(self.output_names[i] + '_loss') - self.nested_metrics = _collect_metrics(metrics, self.output_names) + self.nested_metrics = training_utils.collect_metrics(metrics, + self.output_names) self._feed_sample_weight_modes = [] for i in range(len(self.outputs)): self._feed_sample_weight_modes.append(None) @@ -862,12 +392,12 @@ class Model(Network): sample_weights.append(None) else: if sample_weight_mode == 'temporal': - sample_weights.append( - K.placeholder(ndim=2, name=name + '_sample_weights')) + sample_weights.append(array_ops.placeholder_with_default( + [[1.]], shape=[None, None], name=name + '_sample_weights')) sample_weight_modes.append('temporal') else: - sample_weights.append( - K.placeholder(ndim=1, name=name + '_sample_weights')) + sample_weights.append(array_ops.placeholder_with_default( + [1.], shape=[None], name=name + '_sample_weights')) sample_weight_modes.append(None) self.sample_weight_modes = sample_weight_modes self._feed_sample_weight_modes = [] @@ -915,9 +445,9 @@ class Model(Network): # List of same size as output_names. # contains tuples (metrics for output, names of metrics). - nested_metrics = _collect_metrics(metrics, self.output_names) - nested_weighted_metrics = _collect_metrics(weighted_metrics, - self.output_names) + nested_metrics = training_utils.collect_metrics(metrics, self.output_names) + nested_weighted_metrics = training_utils.collect_metrics(weighted_metrics, + self.output_names) self.metrics_updates = [] self.stateful_metric_names = [] with K.name_scope('metrics'): @@ -963,11 +493,13 @@ class Model(Network): suffix = 'acc' elif metric in ('crossentropy', 'ce'): suffix = 'ce' - weighted_metric_fn = _weighted_masked_objective(metric_fn) + weighted_metric_fn = training_utils.weighted_masked_objective( + metric_fn) metric_name = metric_name_prefix + suffix else: metric_fn = metrics_module.get(metric) - weighted_metric_fn = _weighted_masked_objective(metric_fn) + weighted_metric_fn = training_utils.weighted_masked_objective( + metric_fn) # Get metric name as string if hasattr(metric_fn, 'name'): metric_name = metric_fn.name @@ -1105,451 +637,6 @@ class Model(Network): name='predict_function', **kwargs) - def _check_num_samples(self, - ins, - batch_size=None, - steps=None, - steps_name='steps'): - """Determine the number of samples provided for training and evaluation. - - The number of samples is not defined when running with `steps`, - in which case the number of samples is set to `None`. - - Arguments: - ins: List of tensors to be fed to the Keras function. - batch_size: Integer batch size or `None` if not defined. - steps: Total number of steps (batches of samples) - before declaring `_predict_loop` finished. - Ignored with the default value of `None`. - steps_name: The public API's parameter name for `steps`. - - Raises: - ValueError: when `steps` is `None` and the attribute `ins.shape` - does not exist. Also raises ValueError when `steps` is not `None` - and `batch_size` is not `None` because they are mutually - exclusive. - - Returns: - When steps is `None`, returns the number of samples to be - processed based on the size of the first dimension of the - first input numpy array. When steps is not `None` and - `batch_size` is `None`, returns `None`. - - Raises: - ValueError: In case of invalid arguments. - """ - if steps is not None: - num_samples = None - if batch_size is not None: - raise ValueError( - 'If ' + steps_name + ' is set, the `batch_size` must be None.') - elif ins and hasattr(ins[0], 'shape'): - num_samples = ins[0].shape[0] - else: - raise ValueError( - 'Either the input data should have ' - 'a defined shape, or ' + steps_name + ' should be specified.') - return num_samples - - def _fit_loop(self, - f, - ins, - out_labels=None, - batch_size=None, - epochs=100, - verbose=1, - callbacks=None, - val_f=None, - val_ins=None, - shuffle=True, - callback_metrics=None, - initial_epoch=0, - steps_per_epoch=None, - validation_steps=None): - """Abstract fit function for `f(ins)`. - - Assume that f returns a list, labeled by out_labels. - - Arguments: - f: Keras function returning a list of tensors - ins: List of tensors to be fed to `f` - out_labels: List of strings, display names of - the outputs of `f` - batch_size: Integer batch size or None if unknown. - epochs: Number of times to iterate over the data - verbose: Verbosity mode, 0, 1 or 2 - callbacks: List of callbacks to be called during training - val_f: Keras function to call for validation - val_ins: List of tensors to be fed to `val_f` - shuffle: Whether to shuffle the data at the beginning of each epoch - callback_metrics: List of strings, the display names of the metrics - passed to the callbacks. They should be the - concatenation of list the display names of the outputs of - `f` and the list of display names of the outputs of `f_val`. - initial_epoch: Epoch at which to start training - (useful for resuming a previous training run) - steps_per_epoch: Total number of steps (batches of samples) - before declaring one epoch finished and starting the - next epoch. Ignored with the default value of `None`. - validation_steps: Number of steps to run validation for - (only if doing validation from data tensors). - Ignored with the default value of `None`. - - Returns: - `History` object. - - Raises: - ValueError: in case of invalid arguments. - """ - do_validation = False - if val_f and val_ins: - do_validation = True - if verbose and ins and hasattr(ins[0], 'shape') and hasattr( - val_ins[0], 'shape'): - print('Train on %d samples, validate on %d samples' % - (ins[0].shape[0], val_ins[0].shape[0])) - if validation_steps: - do_validation = True - if steps_per_epoch is None: - raise ValueError('Can only use `validation_steps` ' - 'when doing step-wise ' - 'training, i.e. `steps_per_epoch` ' - 'must be set.') - - num_train_samples = self._check_num_samples( - ins, batch_size, steps_per_epoch, 'steps_per_epoch') - if num_train_samples is not None: - index_array = np.arange(num_train_samples) - - self.history = cbks.History() - all_callbacks = [cbks.BaseLogger( - stateful_metrics=self.stateful_metric_names)] - if verbose: - if steps_per_epoch is not None: - count_mode = 'steps' - else: - count_mode = 'samples' - all_callbacks.append( - cbks.ProgbarLogger( - count_mode, stateful_metrics=self.stateful_metric_names)) - all_callbacks += (callbacks or []) + [self.history] - callbacks = cbks.CallbackList(all_callbacks) - out_labels = out_labels or [] - - # it's possible to callback a different model than self - # (used by Sequential models) - if hasattr(self, 'callback_model') and self.callback_model: - callback_model = self.callback_model - else: - callback_model = self - - callbacks.set_model(callback_model) - - callbacks.set_params({ - 'batch_size': batch_size, - 'epochs': epochs, - 'steps': steps_per_epoch, - 'samples': num_train_samples, - 'verbose': verbose, - 'do_validation': do_validation, - 'metrics': callback_metrics or [], - }) - callbacks.on_train_begin() - callback_model.stop_training = False - for cbk in callbacks: - cbk.validation_data = val_ins - - # To prevent a slowdown, we find beforehand the arrays that need conversion. - feed = self._feed_inputs + self._feed_targets + self._feed_sample_weights - indices_for_conversion_to_dense = [] - for i in range(len(feed)): - if issparse is not None and issparse(ins[i]) and not K.is_sparse(feed[i]): - indices_for_conversion_to_dense.append(i) - - for epoch in range(initial_epoch, epochs): - # Reset stateful metrics - for m in self.metrics: - if isinstance(m, Layer): - m.reset_states() - # Update callbacks - callbacks.on_epoch_begin(epoch) - epoch_logs = {} - if steps_per_epoch is not None: - for step_index in range(steps_per_epoch): - batch_logs = {} - batch_logs['batch'] = step_index - batch_logs['size'] = 1 - callbacks.on_batch_begin(step_index, batch_logs) - outs = f(ins) - - if not isinstance(outs, list): - outs = [outs] - for l, o in zip(out_labels, outs): - batch_logs[l] = o - - callbacks.on_batch_end(step_index, batch_logs) - if callback_model.stop_training: - break - - if do_validation: - val_outs = self._test_loop( - val_f, - val_ins, - batch_size=batch_size, - steps=validation_steps, - verbose=0) - if not isinstance(val_outs, list): - val_outs = [val_outs] - # Same labels assumed. - for l, o in zip(out_labels, val_outs): - epoch_logs['val_' + l] = o - else: - if shuffle == 'batch': - index_array = _batch_shuffle(index_array, batch_size) - elif shuffle: - np.random.shuffle(index_array) - - batches = make_batches(num_train_samples, batch_size) - - for batch_index, (batch_start, batch_end) in enumerate(batches): - batch_ids = index_array[batch_start:batch_end] - try: - if isinstance(ins[-1], float): - # Do not slice the training phase flag. - ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]] - else: - ins_batch = slice_arrays(ins, batch_ids) - except TypeError: - raise TypeError('TypeError while preparing batch. ' - 'If using HDF5 input data, ' - 'pass shuffle="batch".') - batch_logs = {} - batch_logs['batch'] = batch_index - batch_logs['size'] = len(batch_ids) - callbacks.on_batch_begin(batch_index, batch_logs) - for i in indices_for_conversion_to_dense: - ins_batch[i] = ins_batch[i].toarray() - - outs = f(ins_batch) - if not isinstance(outs, list): - outs = [outs] - for l, o in zip(out_labels, outs): - batch_logs[l] = o - - callbacks.on_batch_end(batch_index, batch_logs) - if callback_model.stop_training: - break - - if batch_index == len(batches) - 1: # Last batch. - if do_validation: - val_outs = self._test_loop( - val_f, val_ins, batch_size=batch_size, verbose=0) - if not isinstance(val_outs, list): - val_outs = [val_outs] - # Same labels assumed. - for l, o in zip(out_labels, val_outs): - epoch_logs['val_' + l] = o - callbacks.on_epoch_end(epoch, epoch_logs) - if callback_model.stop_training: - break - callbacks.on_train_end() - return self.history - - def _predict_loop(self, f, ins, batch_size=32, verbose=0, steps=None): - """Abstract method to loop over some data in batches. - - Arguments: - f: Keras function returning a list of tensors. - ins: list of tensors to be fed to `f`. - batch_size: integer batch size. - verbose: verbosity mode. - steps: Total number of steps (batches of samples) - before declaring `_predict_loop` finished. - Ignored with the default value of `None`. - - Returns: - Array of predictions (if the model has a single output) - or list of arrays of predictions - (if the model has multiple outputs). - """ - if hasattr(self, 'metrics'): - for m in self.metrics: - if isinstance(m, Layer): - m.reset_states() - - num_samples = self._check_num_samples(ins, batch_size, steps, 'steps') - if verbose == 1: - if steps is not None: - progbar = Progbar(target=steps, - stateful_metrics=self.stateful_metric_names) - else: - progbar = Progbar(target=num_samples, - stateful_metrics=self.stateful_metric_names) - - indices_for_conversion_to_dense = [] - for i in range(len(self._feed_inputs)): - if (issparse is not None and issparse(ins[i]) and - not K.is_sparse(self._feed_inputs[i])): - indices_for_conversion_to_dense.append(i) - - if steps is not None: - # Step-based predictions. - # 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 - # and concatenate them upon returning. - unconcatenated_outs = [] - for step in range(steps): - batch_outs = f(ins) - if not isinstance(batch_outs, list): - batch_outs = [batch_outs] - if step == 0: - for batch_out in batch_outs: - unconcatenated_outs.append([]) - for i, batch_out in enumerate(batch_outs): - unconcatenated_outs[i].append(batch_out) - if verbose == 1: - progbar.update(step + 1) - if len(unconcatenated_outs) == 1: - return np.concatenate(unconcatenated_outs[0], axis=0) - return [ - np.concatenate(unconcatenated_outs[i], axis=0) - for i in range(len(unconcatenated_outs)) - ] - else: - # Sample-based predictions. - outs = [] - batches = make_batches(num_samples, batch_size) - index_array = np.arange(num_samples) - for batch_index, (batch_start, batch_end) in enumerate(batches): - batch_ids = index_array[batch_start:batch_end] - if ins and isinstance(ins[-1], float): - # Do not slice the training phase flag. - ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]] - else: - ins_batch = slice_arrays(ins, batch_ids) - for i in indices_for_conversion_to_dense: - ins_batch[i] = ins_batch[i].toarray() - - batch_outs = f(ins_batch) - if not isinstance(batch_outs, list): - batch_outs = [batch_outs] - if batch_index == 0: - # Pre-allocate the results arrays. - for batch_out in batch_outs: - shape = (num_samples,) + batch_out.shape[1:] - outs.append(np.zeros(shape, dtype=batch_out.dtype)) - for i, batch_out in enumerate(batch_outs): - outs[i][batch_start:batch_end] = batch_out - if verbose == 1: - progbar.update(batch_end) - if len(outs) == 1: - return outs[0] - return outs - - def _test_loop(self, f, ins, batch_size=None, verbose=0, steps=None): - """Abstract method to loop over some data in batches. - - Arguments: - f: Keras function returning a list of tensors. - ins: list of tensors to be fed to `f`. - batch_size: integer batch size or `None`. - verbose: verbosity mode. - steps: Total number of steps (batches of samples) - before declaring predictions finished. - Ignored with the default value of `None`. - - Returns: - Scalar 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. - """ - if hasattr(self, 'metrics'): - for m in self.metrics: - if isinstance(m, Layer): - m.reset_states() - stateful_metric_indices = [ - i for i, name in enumerate(self.metrics_names) - if str(name) in self.stateful_metric_names - ] - else: - stateful_metric_indices = [] - - num_samples = self._check_num_samples(ins, batch_size, steps, 'steps') - outs = [] - if verbose == 1: - if steps is not None: - progbar = Progbar(target=steps) - else: - progbar = Progbar(target=num_samples) - - # To prevent a slowdown, we find beforehand the arrays that need conversion. - feed = self._feed_inputs + self._feed_targets + self._feed_sample_weights - indices_for_conversion_to_dense = [] - for i in range(len(feed)): - if issparse is not None and issparse(ins[i]) and not K.is_sparse(feed[i]): - indices_for_conversion_to_dense.append(i) - - if steps is not None: - for step in range(steps): - batch_outs = f(ins) - if isinstance(batch_outs, list): - if step == 0: - for _ in enumerate(batch_outs): - outs.append(0.) - for i, batch_out in enumerate(batch_outs): - if i in stateful_metric_indices: - outs[i] = batch_out - else: - outs[i] += batch_out - else: - if step == 0: - outs.append(0.) - outs[0] += batch_outs - if verbose == 1: - progbar.update(step + 1) - for i in range(len(outs)): - if i not in stateful_metric_indices: - outs[i] /= steps - else: - batches = make_batches(num_samples, batch_size) - index_array = np.arange(num_samples) - for batch_index, (batch_start, batch_end) in enumerate(batches): - batch_ids = index_array[batch_start:batch_end] - if isinstance(ins[-1], float): - # Do not slice the training phase flag. - ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]] - else: - ins_batch = slice_arrays(ins, batch_ids) - for i in indices_for_conversion_to_dense: - ins_batch[i] = ins_batch[i].toarray() - - batch_outs = f(ins_batch) - - if isinstance(batch_outs, list): - if batch_index == 0: - for batch_out in enumerate(batch_outs): - outs.append(0.) - for i, batch_out in enumerate(batch_outs): - if i in stateful_metric_indices: - outs[i] = batch_out - else: - outs[i] += batch_out * len(batch_ids) - else: - if batch_index == 0: - outs.append(0.) - outs[0] += batch_outs * len(batch_ids) - if verbose == 1: - progbar.update(batch_end) - for i in range(len(outs)): - if i not in stateful_metric_indices: - outs[i] /= num_samples - if len(outs) == 1: - return outs[0] - return outs - def _standardize_user_data(self, x, y=None, @@ -1651,13 +738,13 @@ class Model(Network): 'TensorFlow tensors. ' 'You passed: x=' + str(x) + '; y=' + str(y)) - if context.in_graph_mode(): + if context.executing_eagerly(): + 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 tensor_util.is_tensor(v)] - else: - target_tensors = None self.compile(optimizer=self.optimizer, loss=self.loss, metrics=self.metrics, @@ -1674,7 +761,7 @@ class Model(Network): # What follows is input validation and standardization to list format, # in the case where all inputs are value arrays. - if context.in_eager_mode(): + if context.executing_eagerly(): # In eager mode, do not do shape validation. feed_input_names = self.input_names feed_input_shapes = None @@ -1689,7 +776,7 @@ class Model(Network): feed_input_shapes = self._feed_input_shapes # Standardize the inputs. - x = _standardize_input_data( + x = training_utils.standardize_input_data( x, feed_input_names, feed_input_shapes, @@ -1697,7 +784,7 @@ class Model(Network): exception_prefix='input') if y is not None: - if context.in_eager_mode(): + if context.executing_eagerly(): feed_output_names = self.output_names feed_output_shapes = None # Sample weighting not supported in this case. @@ -1728,7 +815,7 @@ class Model(Network): feed_output_shapes.append(output_shape) # Standardize the outputs. - y = _standardize_input_data( + y = training_utils.standardize_input_data( y, feed_output_names, feed_output_shapes, @@ -1737,21 +824,21 @@ class Model(Network): # Generate sample-wise weight values given the `sample_weight` and # `class_weight` arguments. - sample_weights = _standardize_sample_weights(sample_weight, - feed_output_names) - class_weights = _standardize_class_weights(class_weight, - feed_output_names) + 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 = [ - _standardize_weights(ref, sw, cw, mode) + 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. - _check_array_lengths(x, y, sample_weights) - if self._is_graph_network and not context.in_eager_mode(): + training_utils.check_array_lengths(x, y, sample_weights) + if self._is_graph_network and not context.executing_eagerly(): # Additional checks to avoid users mistakenly using improper loss fns. - _check_loss_and_target_compatibility(y, self._feed_loss_fns, - feed_output_shapes) + training_utils.check_loss_and_target_compatibility( + y, self._feed_loss_fns, feed_output_shapes) else: y = [] sample_weights = [] @@ -1787,11 +874,20 @@ class Model(Network): whether to build the model's graph in inference mode (False), training mode (True), or using the Keras learning phase (None). """ - if context.in_eager_mode(): + if context.executing_eagerly(): self._eager_set_inputs(inputs) else: self._symbolic_set_inputs(inputs, training=training) + def _set_scope(self, scope=None): + """Modify the Layer scope creation logic to create ResourceVariables.""" + super(Model, self)._set_scope(scope=scope) + # Subclassed Models create ResourceVariables by default. This makes it + # easier to use Models in an eager/graph agnostic way (since eager execution + # always uses ResourceVariables). + if not self._is_graph_network: + self._scope.set_use_resource(True) + def _eager_set_inputs(self, inputs): """Set model's input and output specs based on the input data received. @@ -1807,7 +903,7 @@ class Model(Network): Raises: ValueError: If the model's inputs are already set. """ - assert context.in_eager_mode() + assert context.executing_eagerly() if self.inputs: raise ValueError('Model inputs are already set.') # On-the-fly setting of model inputs/outputs as DeferredTensors, @@ -1836,14 +932,17 @@ class Model(Network): 'output_%d' % (i + 1) for i in range(len(dummy_output_values))] self.built = True - def _symbolic_set_inputs(self, inputs, training=None): - """Set model's inputs based on the input data received from the user. + def _symbolic_set_inputs(self, inputs, outputs=None, training=None): + """Set model's inputs and output specs based. This is to be used for Model subclasses, which do not know at instantiation time what their inputs look like. Args: inputs: Argument `x` (input data) passed by the user upon first model use. + outputs: None, a data tensor, or a list of data 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). @@ -1851,7 +950,7 @@ class Model(Network): Raises: ValueError: If the model's inputs are already set. """ - assert context.in_graph_mode() + assert not context.executing_eagerly() if self.inputs: raise ValueError('Model inputs are already set.') @@ -1893,17 +992,18 @@ class Model(Network): self._feed_input_names.append(name) self._feed_input_shapes.append(K.int_shape(v)) - # Obtain symbolic outputs by calling the model. - if len(self.inputs) == 1: - if self._expects_training_arg: - outputs = self.call(self.inputs[0], training=training) - else: - outputs = self.call(self.inputs[0]) - else: - if self._expects_training_arg: - outputs = self.call(self.inputs, training=training) + if outputs is None: + # Obtain symbolic outputs by calling the model. + if len(self.inputs) == 1: + if self._expects_training_arg: + outputs = self.call(self.inputs[0], training=training) + else: + outputs = self.call(self.inputs[0]) else: - outputs = self.call(self.inputs) + if self._expects_training_arg: + outputs = self.call(self.inputs, training=training) + else: + outputs = self.call(self.inputs) if isinstance(outputs, (list, tuple)): outputs = list(outputs) else: @@ -2049,10 +1149,7 @@ class Model(Network): class_weight=class_weight, batch_size=batch_size) # Prepare validation data. - do_validation = False - val_ins = [] if validation_data: - do_validation = True if len(validation_data) == 2: val_x, val_y = validation_data # pylint: disable=unpacking-non-sequence val_sample_weight = None @@ -2070,13 +1167,8 @@ class Model(Network): val_y, sample_weight=val_sample_weight, batch_size=batch_size) - if self.uses_learning_phase and not isinstance(K.learning_phase(), int): - val_ins = val_x + val_y + val_sample_weights + [0.] - else: - val_ins = val_x + val_y + val_sample_weights elif validation_split and 0. < validation_split < 1.: - do_validation = True if hasattr(x[0], 'shape'): split_at = int(x[0].shape[0] * (1. - validation_split)) else: @@ -2085,77 +1177,44 @@ class Model(Network): 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)) - if self.uses_learning_phase and not isinstance(K.learning_phase(), int): - val_ins = val_x + val_y + val_sample_weights + [0.] - else: - val_ins = val_x + val_y + val_sample_weights - elif validation_steps: - do_validation = True - if self.uses_learning_phase and not isinstance(K.learning_phase(), int): - val_ins = [0.] - - # Prepare input arrays and training function. - if self.uses_learning_phase and not isinstance(K.learning_phase(), int): - ins = x + y + sample_weights + [1.] + val_x = [] + val_y = [] + val_sample_weights = [] else: - ins = x + y + sample_weights - - # Prepare display labels. - out_labels = self.metrics_names - - if context.in_eager_mode(): - if do_validation: - callback_metrics = copy.copy(out_labels) + [ - 'val_' + n for n in out_labels - ] - else: - callback_metrics = copy.copy(out_labels) + val_x = None + val_y = None + val_sample_weights = None + if context.executing_eagerly(): return training_eager.fit_loop( self, - ins, - out_labels=out_labels, + inputs=x, + targets=y, + sample_weights=sample_weights, batch_size=batch_size, epochs=epochs, verbose=verbose, callbacks=callbacks, - val_ins=val_ins, + val_inputs=val_x, + val_targets=val_y, + val_sample_weights=val_sample_weights, shuffle=shuffle, - callback_metrics=callback_metrics, initial_epoch=initial_epoch, steps_per_epoch=steps_per_epoch, validation_steps=validation_steps) else: - self._make_train_function() - f = self.train_function - - if do_validation: - if context.in_graph_mode(): - self._make_test_function() - val_f = self.test_function - else: - val_f = None - callback_metrics = copy.copy(out_labels) + [ - 'val_' + n for n in out_labels - ] - else: - val_f = None - callback_metrics = copy.copy(out_labels) - - # Delegate logic to `_fit_loop`. - return self._fit_loop( - f, - ins, - out_labels=out_labels, + return training_arrays.fit_loop( + self, x, y, + sample_weights=sample_weights, batch_size=batch_size, epochs=epochs, verbose=verbose, callbacks=callbacks, - val_f=val_f, - val_ins=val_ins, + val_inputs=val_x, + val_targets=val_y, + val_sample_weights=val_sample_weights, shuffle=shuffle, - callback_metrics=callback_metrics, initial_epoch=initial_epoch, steps_per_epoch=steps_per_epoch, validation_steps=validation_steps) @@ -2229,20 +1288,15 @@ class Model(Network): y, sample_weight=sample_weight, batch_size=batch_size) - # Prepare inputs, delegate logic to `_test_loop`. - if self.uses_learning_phase and not isinstance(K.learning_phase(), int): - ins = x + y + sample_weights + [0.] - else: - ins = x + y + sample_weights - if context.in_eager_mode(): + if context.executing_eagerly(): return training_eager.test_loop( - self, ins, batch_size=batch_size, verbose=verbose, steps=steps) + self, inputs=x, targets=y, sample_weights=sample_weights, + batch_size=batch_size, verbose=verbose, steps=steps) else: - self._make_test_function() - f = self.test_function - return self._test_loop( - f, ins, batch_size=batch_size, verbose=verbose, steps=steps) + return training_arrays.test_loop( + self, inputs=x, targets=y, sample_weights=sample_weights, + batch_size=batch_size, verbose=verbose, steps=steps) def predict(self, x, batch_size=None, verbose=0, steps=None): """Generates output predictions for the input samples. @@ -2276,21 +1330,12 @@ class Model(Network): 'argument.') x, _, _ = self._standardize_user_data(x) - # Prepare inputs, delegate logic to `_predict_loop`. - if self.uses_learning_phase and not isinstance(K.learning_phase(), int): - ins = x + [0.] - else: - ins = x - - if context.in_eager_mode(): + if context.executing_eagerly(): return training_eager.predict_loop( - self, ins, batch_size=batch_size, verbose=verbose, steps=steps) + self, x, batch_size=batch_size, verbose=verbose, steps=steps) else: - self._make_predict_function() - f = self.predict_function - - return self._predict_loop( - f, ins, batch_size=batch_size, verbose=verbose, steps=steps) + return training_arrays.predict_loop( + self, x, batch_size=batch_size, verbose=verbose, steps=steps) def train_on_batch(self, x, y, sample_weight=None, class_weight=None): """Runs a single gradient update on a single batch of data. @@ -2327,20 +1372,24 @@ class Model(Network): 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 arguments. """ x, y, sample_weights = self._standardize_user_data( x, y, sample_weight=sample_weight, class_weight=class_weight) - if self.uses_learning_phase and not isinstance(K.learning_phase(), int): - ins = x + y + sample_weights + [1.] - else: - ins = x + y + sample_weights - if context.in_eager_mode(): - outputs = training_eager.train_on_batch(self, ins) + if context.executing_eagerly(): + outputs = training_eager.train_on_batch( + self, x, y, sample_weights=sample_weights) else: + if self.uses_learning_phase and not isinstance(K.learning_phase(), int): + ins = x + y + sample_weights + [1] + else: + ins = x + y + sample_weights + self._make_train_function() outputs = self.train_function(ins) @@ -2377,18 +1426,19 @@ class Model(Network): the display labels for the scalar outputs. Raises: - ValueError: in case of invalid arguments. + ValueError: In case of invalid user-provided arguments. """ x, y, sample_weights = self._standardize_user_data( x, y, sample_weight=sample_weight) - if self.uses_learning_phase and not isinstance(K.learning_phase(), int): - ins = x + y + sample_weights + [0.] - else: - ins = x + y + sample_weights - if context.in_eager_mode(): - outputs = training_eager.test_on_batch(self, ins) + if context.executing_eagerly(): + outputs = training_eager.test_on_batch( + self, x, y, sample_weights=sample_weights) else: + if self.uses_learning_phase and not isinstance(K.learning_phase(), int): + ins = x + y + sample_weights + [0] + else: + ins = x + y + sample_weights self._make_test_function() outputs = self.test_function(ins) @@ -2408,26 +1458,19 @@ class Model(Network): """ x, _, _ = self._standardize_user_data(x) - if self.uses_learning_phase and not isinstance(K.learning_phase(), int): - ins = x + [0.] - else: - ins = x - - if context.in_eager_mode(): - ins_batch_converted = [] - for ib in ins: - ins_batch_converted.append(ops.convert_to_tensor(ib, dtype=K.floatx())) - - eager_model_inputs = [] - for i in range(len(self.inputs)): - eager_model_inputs.append(ins_batch_converted[i]) + if context.executing_eagerly(): + inputs = [ops.convert_to_tensor(val, dtype=K.floatx()) for val in x] + return self(inputs) # pylint: disable=not-callable - outs = self(eager_model_inputs) # pylint: disable=not-callable - return outs + if not context.executing_eagerly(): + if self.uses_learning_phase and not isinstance(K.learning_phase(), int): + ins = x + [0] + else: + ins = x - if context.in_graph_mode(): self._make_predict_function() outputs = self.predict_function(ins) + if len(outputs) == 1: return outputs[0] return outputs @@ -2499,20 +1542,19 @@ class Model(Network): 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. + 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, `workers` 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: Whether to shuffle the order of the batches at + 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). + 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) @@ -2543,213 +1585,21 @@ class Model(Network): raise NotImplementedError( '`fit_generator` is not yet enabled for Model subclasses') - wait_time = 0.01 # in seconds - epoch = initial_epoch - - do_validation = bool(validation_data) - self._make_train_function() - if do_validation: - self._make_test_function() - - is_sequence = isinstance(generator, Sequence) - if not is_sequence and use_multiprocessing and workers > 1: - logging.warning( - UserWarning('Using a generator with `use_multiprocessing=True`' - ' and multiple workers may duplicate your data.' - ' Please consider using the`keras.utils.Sequence' - ' class.')) - if steps_per_epoch is None: - if is_sequence: - steps_per_epoch = len(generator) - else: - raise ValueError('`steps_per_epoch=None` is only valid for a' - ' generator based on the `keras.utils.Sequence`' - ' class. Please specify `steps_per_epoch` or use' - ' the `keras.utils.Sequence` class.') - - # python 2 has 'next', 3 has '__next__' - # avoid any explicit version checks - val_gen = ( - hasattr(validation_data, 'next') or - hasattr(validation_data, '__next__') or - isinstance(validation_data, Sequence)) - if (val_gen and not isinstance(validation_data, Sequence) and - not validation_steps): - raise ValueError('`validation_steps=None` is only valid for a' - ' generator based on the `keras.utils.Sequence`' - ' class. Please specify `validation_steps` or use' - ' the `keras.utils.Sequence` class.') - - # Prepare display labels. - out_labels = self.metrics_names - callback_metrics = out_labels + ['val_%s' % n for n in out_labels] - - # prepare callbacks - self.history = cbks.History() - callbacks = [cbks.BaseLogger()] + (callbacks or []) + [self.history] - if verbose: - callbacks += [cbks.ProgbarLogger(count_mode='steps')] - callbacks = cbks.CallbackList(callbacks) - - # it's possible to callback a different model than self: - if hasattr(self, 'callback_model') and self.callback_model: - callback_model = self.callback_model - else: - callback_model = self - callbacks.set_model(callback_model) - callbacks.set_params({ - 'epochs': epochs, - 'steps': steps_per_epoch, - 'verbose': verbose, - 'do_validation': do_validation, - 'metrics': callback_metrics, - }) - callbacks.on_train_begin() - - enqueuer = None - val_enqueuer = None - - try: - if do_validation: - if val_gen: - if workers > 0: - if isinstance(validation_data, Sequence): - val_enqueuer = OrderedEnqueuer( - validation_data, use_multiprocessing=use_multiprocessing) - if validation_steps is None: - validation_steps = len(validation_data) - else: - val_enqueuer = GeneratorEnqueuer( - validation_data, - use_multiprocessing=use_multiprocessing, - wait_time=wait_time) - val_enqueuer.start(workers=workers, max_queue_size=max_queue_size) - validation_generator = val_enqueuer.get() - else: - validation_generator = validation_data - else: - if 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( - '`validation_data` should be a tuple ' - '`(val_x, val_y, val_sample_weight)` ' - 'or `(val_x, val_y)`. Found: ' + str(validation_data)) - val_x, val_y, val_sample_weights = self._standardize_user_data( - val_x, val_y, val_sample_weight) - val_data = val_x + val_y + val_sample_weights - if self.uses_learning_phase and not isinstance( - K.learning_phase(), int): - val_data += [0.] - for cbk in callbacks: - cbk.validation_data = val_data - - if workers > 0: - if is_sequence: - enqueuer = OrderedEnqueuer( - generator, - use_multiprocessing=use_multiprocessing, - shuffle=shuffle) - else: - enqueuer = GeneratorEnqueuer( - generator, - use_multiprocessing=use_multiprocessing, - wait_time=wait_time) - enqueuer.start(workers=workers, max_queue_size=max_queue_size) - output_generator = enqueuer.get() - else: - output_generator = generator - - callback_model.stop_training = False - # Construct epoch logs. - epoch_logs = {} - while epoch < epochs: - callbacks.on_epoch_begin(epoch) - steps_done = 0 - batch_index = 0 - while steps_done < steps_per_epoch: - generator_output = next(output_generator) - - if not hasattr(generator_output, '__len__'): - raise ValueError('Output of generator should be ' - 'a tuple `(x, y, sample_weight)` ' - 'or `(x, y)`. Found: ' + str(generator_output)) - - if len(generator_output) == 2: - x, y = generator_output - sample_weight = None - elif len(generator_output) == 3: - x, y, sample_weight = generator_output - else: - raise ValueError('Output of generator should be ' - 'a tuple `(x, y, sample_weight)` ' - 'or `(x, y)`. Found: ' + str(generator_output)) - # build batch logs - batch_logs = {} - if isinstance(x, list): - batch_size = x[0].shape[0] - elif isinstance(x, dict): - batch_size = list(x.values())[0].shape[0] - else: - batch_size = x.shape[0] - batch_logs['batch'] = batch_index - batch_logs['size'] = batch_size - callbacks.on_batch_begin(batch_index, batch_logs) - - outs = self.train_on_batch( - x, y, sample_weight=sample_weight, class_weight=class_weight) - - if not isinstance(outs, list): - outs = [outs] - for l, o in zip(out_labels, outs): - batch_logs[l] = o - - callbacks.on_batch_end(batch_index, batch_logs) - - batch_index += 1 - steps_done += 1 - - # Epoch finished. - if steps_done >= steps_per_epoch and do_validation: - if val_gen: - val_outs = self.evaluate_generator( - validation_generator, validation_steps, workers=0) - else: - # No need for try/except because - # data has already been validated. - val_outs = self.evaluate( - val_x, - val_y, - batch_size=batch_size, - sample_weight=val_sample_weights, - verbose=0) - if not isinstance(val_outs, list): - val_outs = [val_outs] - # Same labels assumed. - for l, o in zip(out_labels, val_outs): - epoch_logs['val_' + l] = o - - if callback_model.stop_training: - break - - callbacks.on_epoch_end(epoch, epoch_logs) - epoch += 1 - if callback_model.stop_training: - break - - finally: - try: - if enqueuer is not None: - enqueuer.stop() - finally: - if val_enqueuer is not None: - val_enqueuer.stop() - - callbacks.on_train_end() - return self.history + 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 evaluate_generator(self, generator, @@ -2774,16 +1624,15 @@ class Model(Network): 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. + when using process-based threading. If unspecified, `workers` will default to 1. If 0, will execute the generator on the main thread. - use_multiprocessing: if True, use process based threading. - 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. + 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. Returns: Scalar test loss (if the model has a single output and no metrics) @@ -2802,87 +1651,13 @@ class Model(Network): raise NotImplementedError( '`evaluate_generator` is not yet enabled for Model subclasses') - self._make_test_function() - - steps_done = 0 - wait_time = 0.01 - all_outs = [] - batch_sizes = [] - is_sequence = isinstance(generator, Sequence) - if not is_sequence and use_multiprocessing and workers > 1: - logging.warning( - UserWarning('Using a generator with `use_multiprocessing=True`' - ' and multiple workers may duplicate your data.' - ' Please consider using the`keras.utils.Sequence' - ' class.')) - if steps is None: - if is_sequence: - steps = len(generator) - else: - raise ValueError('`steps=None` is only valid for a generator' - ' based on the `keras.utils.Sequence` class.' - ' Please specify `steps` or use the' - ' `keras.utils.Sequence` class.') - enqueuer = None - - try: - if workers > 0: - if is_sequence: - enqueuer = OrderedEnqueuer( - generator, use_multiprocessing=use_multiprocessing) - else: - enqueuer = GeneratorEnqueuer( - generator, - use_multiprocessing=use_multiprocessing, - wait_time=wait_time) - enqueuer.start(workers=workers, max_queue_size=max_queue_size) - output_generator = enqueuer.get() - else: - output_generator = generator - - while steps_done < steps: - generator_output = next(output_generator) - if not hasattr(generator_output, '__len__'): - raise ValueError('Output of generator should be a tuple ' - '(x, y, sample_weight) ' - 'or (x, y). Found: ' + str(generator_output)) - if len(generator_output) == 2: - x, y = generator_output - sample_weight = None - elif len(generator_output) == 3: - x, y, sample_weight = generator_output - else: - raise ValueError('Output of generator should be a tuple ' - '(x, y, sample_weight) ' - 'or (x, y). Found: ' + str(generator_output)) - outs = self.test_on_batch(x, y, sample_weight=sample_weight) - - if isinstance(x, list): - batch_size = x[0].shape[0] - elif isinstance(x, dict): - batch_size = list(x.values())[0].shape[0] - else: - batch_size = x.shape[0] - if batch_size == 0: - raise ValueError('Received an empty batch. ' - 'Batches should at least contain one item.') - all_outs.append(outs) - - steps_done += 1 - batch_sizes.append(batch_size) - - finally: - if enqueuer is not None: - enqueuer.stop() - - if not isinstance(outs, list): - return np.average(np.asarray(all_outs), weights=batch_sizes) - else: - averages = [] - for i in range(len(outs)): - averages.append( - np.average([out[i] for out in all_outs], weights=batch_sizes)) - return averages + return training_generator.evaluate_generator( + self, + generator, + steps=steps, + max_queue_size=max_queue_size, + workers=workers, + use_multiprocessing=use_multiprocessing) def predict_generator(self, generator, @@ -2907,16 +1682,15 @@ class Model(Network): 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. + when using process-based threading. If unspecified, `workers` will default to 1. If 0, will execute the generator on the main thread. - use_multiprocessing: If `True`, use process based threading. - 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. + 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: @@ -2930,88 +1704,11 @@ class Model(Network): raise NotImplementedError( '`predict_generator` is not yet enabled for Model subclasses') - self._make_predict_function() - - steps_done = 0 - wait_time = 0.01 - all_outs = [] - is_sequence = isinstance(generator, Sequence) - if not is_sequence and use_multiprocessing and workers > 1: - logging.warning( - UserWarning('Using a generator with `use_multiprocessing=True`' - ' and multiple workers may duplicate your data.' - ' Please consider using the`keras.utils.Sequence' - ' class.')) - if steps is None: - if is_sequence: - steps = len(generator) - else: - raise ValueError('`steps=None` is only valid for a generator' - ' based on the `keras.utils.Sequence` class.' - ' Please specify `steps` or use the' - ' `keras.utils.Sequence` class.') - enqueuer = None - - try: - if workers > 0: - if is_sequence: - enqueuer = OrderedEnqueuer( - generator, use_multiprocessing=use_multiprocessing) - else: - enqueuer = GeneratorEnqueuer( - generator, - use_multiprocessing=use_multiprocessing, - wait_time=wait_time) - enqueuer.start(workers=workers, max_queue_size=max_queue_size) - output_generator = enqueuer.get() - else: - output_generator = generator - - if verbose == 1: - progbar = Progbar(target=steps) - - while steps_done < steps: - generator_output = next(output_generator) - if isinstance(generator_output, tuple): - # Compatibility with the generators - # used for training. - if len(generator_output) == 2: - x, _ = generator_output - elif len(generator_output) == 3: - x, _, _ = generator_output - else: - raise ValueError('Output of generator should be ' - 'a tuple `(x, y, sample_weight)` ' - 'or `(x, y)`. Found: ' + str(generator_output)) - else: - # Assumes a generator that only - # yields inputs (not targets and sample weights). - x = generator_output - - outs = self.predict_on_batch(x) - if not isinstance(outs, list): - outs = [outs] - - if not all_outs: - for out in outs: - all_outs.append([]) - - for i, out in enumerate(outs): - all_outs[i].append(out) - steps_done += 1 - if verbose == 1: - progbar.update(steps_done) - - finally: - if enqueuer is not None: - enqueuer.stop() - - if len(all_outs) == 1: - if steps_done == 1: - return all_outs[0][0] - else: - return np.concatenate(all_outs[0]) - if steps_done == 1: - return [out[0] for out in all_outs] - else: - return [np.concatenate(out) for out in all_outs] + return training_generator.predict_generator( + self, + generator, + steps=steps, + max_queue_size=max_queue_size, + workers=workers, + use_multiprocessing=use_multiprocessing, + verbose=verbose) diff --git a/tensorflow/python/keras/_impl/keras/engine/training_arrays.py b/tensorflow/python/keras/_impl/keras/engine/training_arrays.py new file mode 100644 index 0000000000000000000000000000000000000000..18116e3a14d6b1365f1a9db1a23243cd07763a62 --- /dev/null +++ b/tensorflow/python/keras/_impl/keras/engine/training_arrays.py @@ -0,0 +1,488 @@ +# 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. +# ============================================================================== +"""Part of the Keras training engine related to plain array data. +""" +# pylint: disable=protected-access +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import copy + +import numpy as np + +from tensorflow.python.keras._impl.keras import backend as K +from tensorflow.python.keras._impl.keras import callbacks as cbks +from tensorflow.python.keras._impl.keras.engine import training_utils +from tensorflow.python.keras._impl.keras.engine.base_layer import Layer +from tensorflow.python.keras._impl.keras.utils.generic_utils import make_batches +from tensorflow.python.keras._impl.keras.utils.generic_utils import Progbar +from tensorflow.python.keras._impl.keras.utils.generic_utils import slice_arrays + +try: + from scipy.sparse import issparse # pylint: disable=g-import-not-at-top +except ImportError: + issparse = None + + +def fit_loop(model, + inputs, + targets, + sample_weights=None, + batch_size=None, + epochs=100, + verbose=1, + callbacks=None, + val_inputs=None, + val_targets=None, + val_sample_weights=None, + shuffle=True, + callback_metrics=None, + initial_epoch=0, + steps_per_epoch=None, + validation_steps=None): + """Abstract fit function for arrays of data. + + Arguments: + model: Keras Model instance. + inputs: List of input arrays. + targets: List of target arrays. + sample_weights: Optional list of sample weight arrays. + batch_size: Integer batch size or None if unknown. + epochs: Number of times to iterate over the data + verbose: Verbosity mode, 0, 1 or 2 + callbacks: List of callbacks to be called during training + val_inputs: List of input arrays. + val_targets: List of target arrays. + val_sample_weights: Optional list of sample weight arrays. + shuffle: Whether to shuffle the data at the beginning of each epoch + callback_metrics: List of strings, the display names of the metrics + passed to the callbacks. They should be the + concatenation of list the display names of the outputs of + `f` and the list of display names of the outputs of `f_val`. + initial_epoch: Epoch at which to start training + (useful for resuming a previous training run) + steps_per_epoch: Total number of steps (batches of samples) + before declaring one epoch finished and starting the + next epoch. Ignored with the default value of `None`. + validation_steps: Number of steps to run validation for + (only if doing validation from data tensors). + Ignored with the default value of `None`. + + Returns: + `History` object. + + Raises: + ValueError: in case of invalid arguments. + """ + model._make_train_function() + f = model.train_function + + sample_weights = sample_weights or [] + val_sample_weights = val_sample_weights or [] + if model.uses_learning_phase and not isinstance(K.learning_phase(), int): + ins = inputs + targets + sample_weights + [1] + if val_inputs: + val_ins = val_inputs + val_targets + val_sample_weights + [1] + else: + ins = inputs + targets + sample_weights + if val_inputs: + val_ins = val_inputs + val_targets + val_sample_weights + if not val_inputs: + val_ins = [] + + do_validation = False + if val_inputs: + do_validation = True + if verbose and inputs and hasattr(inputs[0], 'shape') and hasattr( + val_inputs[0], 'shape'): + print('Train on %d samples, validate on %d samples' % + (inputs[0].shape[0], val_inputs[0].shape[0])) + if validation_steps: + do_validation = True + if steps_per_epoch is None: + raise ValueError('Can only use `validation_steps` ' + 'when doing step-wise ' + 'training, i.e. `steps_per_epoch` ' + 'must be set.') + + out_labels = model.metrics_names + if do_validation: + callback_metrics = copy.copy(out_labels) + [ + 'val_' + n for n in out_labels + ] + else: + callback_metrics = copy.copy(out_labels) + + num_train_samples = training_utils.check_num_samples( + ins, batch_size, steps_per_epoch, 'steps_per_epoch') + if num_train_samples is not None: + index_array = np.arange(num_train_samples) + + model.history = cbks.History() + all_callbacks = [cbks.BaseLogger( + stateful_metrics=model.stateful_metric_names)] + if verbose: + if steps_per_epoch is not None: + count_mode = 'steps' + else: + count_mode = 'samples' + all_callbacks.append( + cbks.ProgbarLogger( + count_mode, stateful_metrics=model.stateful_metric_names)) + all_callbacks += (callbacks or []) + [model.history] + callbacks = cbks.CallbackList(all_callbacks) + out_labels = out_labels or [] + + # it's possible to callback a different model than self + # (used by Sequential models) + if hasattr(model, 'callback_model') and model.callback_model: + callback_model = model.callback_model + else: + callback_model = model + + callbacks.set_model(callback_model) + + callbacks.set_params({ + 'batch_size': batch_size, + 'epochs': epochs, + 'steps': steps_per_epoch, + 'samples': num_train_samples, + 'verbose': verbose, + 'do_validation': do_validation, + 'metrics': callback_metrics or [], + }) + callbacks.on_train_begin() + callback_model.stop_training = False + for cbk in callbacks: + cbk.validation_data = val_ins + + # To prevent a slowdown, we find beforehand the arrays that need conversion. + feed = model._feed_inputs + model._feed_targets + model._feed_sample_weights + indices_for_conversion_to_dense = [] + for i in range(len(feed)): + if issparse is not None and issparse(ins[i]) and not K.is_sparse(feed[i]): + indices_for_conversion_to_dense.append(i) + + for epoch in range(initial_epoch, epochs): + # Reset stateful metrics + for m in model.metrics: + if isinstance(m, Layer): + m.reset_states() + # Update callbacks + callbacks.on_epoch_begin(epoch) + epoch_logs = {} + if steps_per_epoch is not None: + for step_index in range(steps_per_epoch): + batch_logs = {} + batch_logs['batch'] = step_index + batch_logs['size'] = 1 + callbacks.on_batch_begin(step_index, batch_logs) + outs = f(ins) + + if not isinstance(outs, list): + outs = [outs] + for l, o in zip(out_labels, outs): + batch_logs[l] = o + + callbacks.on_batch_end(step_index, batch_logs) + if callback_model.stop_training: + break + + if do_validation: + val_outs = test_loop( + model, + val_inputs, + val_targets, + sample_weights=val_sample_weights, + batch_size=batch_size, + steps=validation_steps, + verbose=0) + if not isinstance(val_outs, list): + val_outs = [val_outs] + # Same labels assumed. + for l, o in zip(out_labels, val_outs): + epoch_logs['val_' + l] = o + else: + if shuffle == 'batch': + index_array = training_utils.batch_shuffle(index_array, batch_size) + elif shuffle: + np.random.shuffle(index_array) + + batches = make_batches(num_train_samples, batch_size) + + for batch_index, (batch_start, batch_end) in enumerate(batches): + batch_ids = index_array[batch_start:batch_end] + try: + if isinstance(ins[-1], int): + # Do not slice the training phase flag. + ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]] + else: + ins_batch = slice_arrays(ins, batch_ids) + except TypeError: + raise TypeError('TypeError while preparing batch. ' + 'If using HDF5 input data, ' + 'pass shuffle="batch".') + batch_logs = {} + batch_logs['batch'] = batch_index + batch_logs['size'] = len(batch_ids) + callbacks.on_batch_begin(batch_index, batch_logs) + for i in indices_for_conversion_to_dense: + ins_batch[i] = ins_batch[i].toarray() + + outs = f(ins_batch) + if not isinstance(outs, list): + outs = [outs] + for l, o in zip(out_labels, outs): + batch_logs[l] = o + + callbacks.on_batch_end(batch_index, batch_logs) + if callback_model.stop_training: + break + + if batch_index == len(batches) - 1: # Last batch. + if do_validation: + val_outs = test_loop( + model, + val_inputs, + val_targets, + sample_weights=val_sample_weights, + batch_size=batch_size, + verbose=0) + if not isinstance(val_outs, list): + val_outs = [val_outs] + # Same labels assumed. + for l, o in zip(out_labels, val_outs): + epoch_logs['val_' + l] = o + callbacks.on_epoch_end(epoch, epoch_logs) + if callback_model.stop_training: + break + callbacks.on_train_end() + return model.history + + +def predict_loop(model, inputs, batch_size=32, verbose=0, steps=None): + """Abstract method to loop over some data in batches. + + Arguments: + model: Keras Model instance. + inputs: list of tensors to be fed to `f`. + batch_size: integer batch size. + verbose: verbosity mode. + steps: Total number of steps (batches of samples) + before declaring `_predict_loop` finished. + Ignored with the default value of `None`. + + Returns: + Array of predictions (if the model has a single output) + or list of arrays of predictions + (if the model has multiple outputs). + """ + model._make_predict_function() + f = model.predict_function + + if model.uses_learning_phase and not isinstance(K.learning_phase(), int): + ins = inputs + [0] + else: + ins = inputs + + num_samples = training_utils.check_num_samples( + inputs, batch_size, steps, 'steps') + if verbose == 1: + if steps is not None: + progbar = Progbar(target=steps) + else: + progbar = Progbar(target=num_samples) + + indices_for_conversion_to_dense = [] + for i in range(len(model._feed_inputs)): + if (issparse is not None and issparse(inputs[i]) and + not K.is_sparse(model._feed_inputs[i])): + indices_for_conversion_to_dense.append(i) + + if steps is not None: + # Step-based predictions. + # 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 + # and concatenate them upon returning. + unconcatenated_outs = [] + for step in range(steps): + batch_outs = f(ins) + if not isinstance(batch_outs, list): + batch_outs = [batch_outs] + if step == 0: + for batch_out in batch_outs: + unconcatenated_outs.append([]) + for i, batch_out in enumerate(batch_outs): + unconcatenated_outs[i].append(batch_out) + if verbose == 1: + progbar.update(step + 1) + if len(unconcatenated_outs) == 1: + return np.concatenate(unconcatenated_outs[0], axis=0) + return [ + np.concatenate(unconcatenated_outs[i], axis=0) + for i in range(len(unconcatenated_outs)) + ] + else: + # Sample-based predictions. + outs = [] + batches = make_batches(num_samples, batch_size) + index_array = np.arange(num_samples) + for batch_index, (batch_start, batch_end) in enumerate(batches): + batch_ids = index_array[batch_start:batch_end] + if ins and isinstance(ins[-1], int): + # Do not slice the training phase flag. + ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]] + else: + ins_batch = slice_arrays(ins, batch_ids) + for i in indices_for_conversion_to_dense: + ins_batch[i] = ins_batch[i].toarray() + + batch_outs = f(ins_batch) + if not isinstance(batch_outs, list): + batch_outs = [batch_outs] + if batch_index == 0: + # Pre-allocate the results arrays. + for batch_out in batch_outs: + shape = (num_samples,) + batch_out.shape[1:] + outs.append(np.zeros(shape, dtype=batch_out.dtype)) + for i, batch_out in enumerate(batch_outs): + outs[i][batch_start:batch_end] = batch_out + if verbose == 1: + progbar.update(batch_end) + if len(outs) == 1: + return outs[0] + return outs + + +def test_loop(model, inputs, targets, + sample_weights=None, + batch_size=None, + verbose=0, + steps=None): + """Abstract method to loop over some data in batches. + + Arguments: + model: Keras Model instance. + inputs: List of input arrays. + targets: List of target arrays. + sample_weights: Optional list of sample weight arrays. + batch_size: integer batch size or `None`. + verbose: verbosity mode. + steps: Total number of steps (batches of samples) + before declaring predictions finished. + Ignored with the default value of `None`. + + Returns: + Scalar 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. + """ + model._make_test_function() + f = model.test_function + + sample_weights = sample_weights or [] + if model.uses_learning_phase and not isinstance(K.learning_phase(), int): + ins = inputs + targets + sample_weights + [0] + else: + ins = inputs + targets + sample_weights + + if hasattr(model, 'metrics'): + for m in model.metrics: + if isinstance(m, Layer): + m.reset_states() + stateful_metric_indices = [ + i for i, name in enumerate(model.metrics_names) + if str(name) in model.stateful_metric_names + ] + else: + stateful_metric_indices = [] + + num_samples = training_utils.check_num_samples( + ins, batch_size, steps, 'steps') + outs = [] + if verbose == 1: + if steps is not None: + progbar = Progbar(target=steps) + else: + progbar = Progbar(target=num_samples) + + # To prevent a slowdown, we find beforehand the arrays that need conversion. + feed = model._feed_inputs + model._feed_targets + model._feed_sample_weights + indices_for_conversion_to_dense = [] + for i in range(len(feed)): + if issparse is not None and issparse(ins[i]) and not K.is_sparse(feed[i]): + indices_for_conversion_to_dense.append(i) + + if steps is not None: + for step in range(steps): + batch_outs = f(ins) + if isinstance(batch_outs, list): + if step == 0: + for _ in enumerate(batch_outs): + outs.append(0.) + for i, batch_out in enumerate(batch_outs): + if i in stateful_metric_indices: + outs[i] = batch_out + else: + outs[i] += batch_out + else: + if step == 0: + outs.append(0.) + outs[0] += batch_outs + if verbose == 1: + progbar.update(step + 1) + for i in range(len(outs)): + if i not in stateful_metric_indices: + outs[i] /= steps + else: + batches = make_batches(num_samples, batch_size) + index_array = np.arange(num_samples) + for batch_index, (batch_start, batch_end) in enumerate(batches): + batch_ids = index_array[batch_start:batch_end] + if isinstance(ins[-1], int): + # Do not slice the training phase flag. + ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]] + else: + ins_batch = slice_arrays(ins, batch_ids) + for i in indices_for_conversion_to_dense: + ins_batch[i] = ins_batch[i].toarray() + + batch_outs = f(ins_batch) + + if isinstance(batch_outs, list): + if batch_index == 0: + for batch_out in enumerate(batch_outs): + outs.append(0.) + for i, batch_out in enumerate(batch_outs): + if i in stateful_metric_indices: + outs[i] = batch_out + else: + outs[i] += batch_out * len(batch_ids) + else: + if batch_index == 0: + outs.append(0.) + outs[0] += batch_outs * len(batch_ids) + if verbose == 1: + progbar.update(batch_end) + for i in range(len(outs)): + if i not in stateful_metric_indices: + outs[i] /= num_samples + if len(outs) == 1: + return outs[0] + return outs diff --git a/tensorflow/python/keras/_impl/keras/engine/training_eager.py b/tensorflow/python/keras/_impl/keras/engine/training_eager.py index 282dd0dc0dbd440455cfade6952eda669aeaf2df..67858a578c5c95b3099e1e6713f3287748fc861f 100644 --- a/tensorflow/python/keras/_impl/keras/engine/training_eager.py +++ b/tensorflow/python/keras/_impl/keras/engine/training_eager.py @@ -12,20 +12,25 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Keras training and evaluation routines. +"""Keras training and evaluation routines for eager execution. """ # pylint: disable=protected-access from __future__ import absolute_import from __future__ import division from __future__ import print_function + +import copy + import numpy as np + from tensorflow.python.eager.backprop import GradientTape from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_util -from tensorflow.python.keras._impl.keras import backend as K +from tensorflow.python.keras._impl.keras import backend from tensorflow.python.keras._impl.keras import callbacks as cbks from tensorflow.python.keras._impl.keras import losses from tensorflow.python.keras._impl.keras import metrics as metrics_module +from tensorflow.python.keras._impl.keras.engine import training_utils from tensorflow.python.keras._impl.keras.utils.generic_utils import make_batches from tensorflow.python.keras._impl.keras.utils.generic_utils import Progbar from tensorflow.python.keras._impl.keras.utils.generic_utils import slice_arrays @@ -55,7 +60,7 @@ def _get_metrics_info(metric, internal_output_shapes=None, loss_func=None): def _eager_loss_fn(outputs, targets, loss_fn, output_name): - with K.name_scope(output_name + '_loss'): + with backend.name_scope(output_name + '_loss'): loss = loss_fn(targets, outputs) return loss @@ -83,7 +88,7 @@ def _eager_metrics_fn(model, outputs, targets): output_metrics = model.nested_metrics[i] for nested_output_metric in output_metrics: metric_name, metric_fn = _get_metrics_info( - nested_output_metric, K.int_shape(model.outputs[i]), + nested_output_metric, backend.int_shape(model.outputs[i]), model.loss_functions[i]) if len(model.output_names) > 1: @@ -91,23 +96,23 @@ def _eager_metrics_fn(model, outputs, targets): if metric_name not in model.metrics_names: model.metrics_names.append(metric_name) - with K.name_scope(metric_name): + with backend.name_scope(metric_name): metric_result = metric_fn(outputs[i], targets[i]) metric_names.append(metric_name) - metric_results.append(K.mean(metric_result)) + metric_results.append(backend.mean(metric_result)) return metric_names, metric_results -def _model_loss(model, inputs, targets, training=False): +def _model_loss(model, inputs, targets, sample_weights=None, training=False): """Calculates the loss for a given model. Arguments: - model: The model on which metrics are being calculated. - inputs: The inputs of the given model. This is typically the mini batch of - data that is fed to the model. - targets: The predictions or targets of the given model. - training: Whether the model should be run in inference or training mode. + model: The model on which metrics are being calculated. + inputs: List of input arrays. + targets: List of target arrays. + sample_weights: Optional list of sample weight arrays. + training: Whether the model should be run in inference or training mode. Returns: Returns the model output, total loss and loss value calculated using the @@ -132,33 +137,22 @@ def _model_loss(model, inputs, targets, training=False): targets = [targets] loss_metrics = [] - with K.name_scope('loss'): + with backend.name_scope('loss'): for i, loss_fn in enumerate(model.loss_functions): - # compute the loss - output_loss = _eager_loss_fn(outs[i], targets[i], loss_fn, - model.output_names[i]) - loss_metrics.append(K.mean(output_loss)) + if sample_weights: + weights = sample_weights[i] + else: + weights = None + # TODO(fchollet): support masking; in practice `_keras_mask` is never + # set in this context currently. mask = outs[i]._keras_mask - # adapted from weighted_loss_fn - if mask is not None: - # mask should have the same shape as output_loss - output_loss *= mask - # the loss per batch should be proportional - # to the number of unmasked samples. - output_loss /= K.mean(mask) - - # adapted from weighted_loss_fn - # apply sample weighting - if model.sample_weights: - # reduce score_array to same ndim as weight array - ndim = K.ndim(output_loss) - weight_ndim = K.ndim(model.sample_weights) - output_loss = K.mean(output_loss, axis=list(range(weight_ndim, ndim))) - output_loss *= model.sample_weights - output_loss /= K.mean(K.cast(K.not_equal(model.sample_weights, 0), - K.floatx())) - output_loss = K.mean(output_loss) + + weighted_masked_fn = training_utils.weighted_masked_objective(loss_fn) + with backend.name_scope(model.output_names[i] + '_loss'): + output_loss = weighted_masked_fn( + outs[i], targets[i], weights, mask=mask) + loss_metrics.append(backend.mean(output_loss)) loss_weight = model.loss_weights_list[i] if total_loss is None: @@ -166,7 +160,7 @@ def _model_loss(model, inputs, targets, training=False): else: total_loss += loss_weight * output_loss - total_loss = K.mean(total_loss) + total_loss = backend.mean(total_loss) # Add regularization losses custom_losses = [] for layer in model.layers: @@ -179,16 +173,20 @@ def _model_loss(model, inputs, targets, training=False): return outs, total_loss, loss_metrics -def _process_single_batch(eager_model_inputs, eager_model_outputs, model, +def _process_single_batch(model, + inputs, + targets, + sample_weights=None, training=False): """Calculate the loss and gradient for one input batch. The model weights are updated if training is set to True. Arguments: - eager_model_inputs: Input batch data. - eager_model_outputs: Output batch data. model: Model whose loss has to be calculated. + inputs: List of input arrays. + targets: List of target arrays. + sample_weights: Optional list of sample weight arrays. training: The boolean represents if the weights of the model are updated. 'fit' methods will set this to True while 'evaluate' methods will set this to False. @@ -199,81 +197,81 @@ def _process_single_batch(eager_model_inputs, eager_model_outputs, model, Raises: ValueError: If the model has no loss to optimize. """ - K.set_learning_phase(training) - with GradientTape() as tape: - outs, loss, loss_metrics = _model_loss(model, eager_model_inputs, - eager_model_outputs, - training=training) - if loss is None: - raise ValueError('The model cannot be run ' - 'because it has no loss to optimize.') - if training: - if not model._collected_trainable_weights: - logging.warning('The list of trainable weights is empty. Make sure that ' - 'you are not setting model.trainable to False before ' - 'compiling the model.') - else: - grads = tape.gradient(loss, model._collected_trainable_weights) - model.optimizer.apply_gradients(zip(grads, - model._collected_trainable_weights)) - return outs, loss, loss_metrics + with backend.learning_phase_scope(1 if training else 0): + with GradientTape() as tape: + outs, loss, loss_metrics = _model_loss(model, inputs, targets, + sample_weights=sample_weights, + training=training) + if loss is None: + raise ValueError('The model cannot be run ' + 'because it has no loss to optimize.') + if training: + if not model._collected_trainable_weights: + logging.warning('The list of trainable weights is empty. Make sure that' + ' you are not setting model.trainable to False before ' + 'compiling the model.') + else: + grads = tape.gradient(loss, model._collected_trainable_weights) + model.optimizer.apply_gradients(zip(grads, + model._collected_trainable_weights)) + return outs, loss, loss_metrics -def train_on_batch(model, ins): +def train_on_batch(model, inputs, targets, sample_weights=None): """Calculates the loss and gradient updates for one input batch. Arguments: - model: Given model on which loss and gradients are calculated. - ins: Input and output batch numpy arrays. + model: Model whose loss has to be calculated. + inputs: Input batch data. + targets: Target batch data. + sample_weights: Sample weight batch data. Returns: total loss and the loss associated with each output. """ - ins_batch_converted = [] - for ib in ins: - ins_batch_converted.append(ops.convert_to_tensor(ib, dtype=K.floatx())) - eager_model_inputs = [] - eager_model_outputs = [] - for i in range(len(model.inputs)): - eager_model_inputs.append(ins_batch_converted[i]) - for i in range(len(model.inputs), len(ins_batch_converted)): - eager_model_outputs.append(ins_batch_converted[i]) + inputs = [ + ops.convert_to_tensor(val, dtype=backend.floatx()) for val in inputs] + targets = [ + ops.convert_to_tensor(val, dtype=backend.floatx()) for val in targets] + sample_weights = [ + ops.convert_to_tensor(val, dtype=backend.floatx()) + if val is not None else None for val in sample_weights] outs, loss, _ = _process_single_batch( - eager_model_inputs, eager_model_outputs, model, training=True) + model, inputs, targets, sample_weights=sample_weights, training=True) if not isinstance(outs, list): outs = [outs] _, metrics_results = _eager_metrics_fn( - model, outs, eager_model_outputs) + model, outs, targets) if not isinstance(loss, list): loss = [loss] return loss + metrics_results -def test_on_batch(model, ins): +def test_on_batch(model, inputs, targets, sample_weights=None): """Calculates the loss for one input batch. Arguments: - model: Given model on which loss is calculated. - ins: Input and output batch numpy arrays. + model: Model whose loss has to be calculated. + inputs: Input batch data. + targets: Target batch data. + sample_weights: Sample weight batch data. Returns: total loss, loss and metrics associated with each output. """ - ins_batch_converted = [] - for ib in ins: - ins_batch_converted.append(ops.convert_to_tensor(ib, dtype=K.floatx())) - eager_model_inputs = [] - eager_model_outputs = [] - for i in range(len(model.inputs)): - eager_model_inputs.append(ins_batch_converted[i]) - for i in range(len(model.inputs), len(ins_batch_converted)): - eager_model_outputs.append(ins_batch_converted[i]) + inputs = [ + ops.convert_to_tensor(val, dtype=backend.floatx()) for val in inputs] + targets = [ + ops.convert_to_tensor(val, dtype=backend.floatx()) for val in targets] + sample_weights = [ + ops.convert_to_tensor(val, dtype=backend.floatx()) + if val is not None else None for val in sample_weights] outs, loss, loss_metrics = _process_single_batch( - eager_model_inputs, eager_model_outputs, model, training=False) + model, inputs, targets, sample_weights=sample_weights, training=False) if not isinstance(outs, list): outs = [outs] metric_names, metrics_results = _eager_metrics_fn( - model, outs, eager_model_outputs) + model, outs, targets) model.metrics_names.append(metric_names) if not isinstance(loss, list): loss = [loss] @@ -282,32 +280,35 @@ def test_on_batch(model, ins): def fit_loop( model, - ins, - out_labels=None, + inputs, + targets, + sample_weights=None, + val_inputs=None, + val_targets=None, + val_sample_weights=None, batch_size=None, epochs=100, verbose=1, callbacks=None, - val_ins=None, shuffle=True, callback_metrics=None, initial_epoch=0, steps_per_epoch=None, validation_steps=None): - """Abstract fit function for `f(ins)`. - - Assume that f returns a list, labeled by out_labels. + """Abstract fit function for eager execution. Arguments: model: Instance of the model that is being executed in Eager mode. - ins: List of tensors to be fed to `f` - out_labels: List of strings, display names of - the outputs of `f` + inputs: List of input arrays. + targets: List of target arrays. + sample_weights: Optional list of sample weight arrays. + val_inputs: Input data for validation. + val_targets: Target data for validation. + val_sample_weights: Sample weight data for validation. batch_size: Integer batch size or None if unknown. epochs: Number of times to iterate over the data verbose: Verbosity mode, 0, 1 or 2 callbacks: List of callbacks to be called during training - val_ins: List of tensors to be fed to `val_f` shuffle: Whether to shuffle the data at the beginning of each epoch callback_metrics: List of strings, the display names of the metrics passed to the callbacks. They should be the @@ -328,165 +329,196 @@ def fit_loop( ValueError: In case of invalid argument values. """ # Required for Eager mode - K.set_learning_phase(True) - - do_validation = False - if val_ins: - do_validation = True - if (verbose and ins and hasattr(ins[0], 'shape') and - hasattr(val_ins[0], 'shape')): - print('Train on %d samples, validate on %d samples' % - (ins[0].shape[0], val_ins[0].shape[0])) - if validation_steps: - if steps_per_epoch is None: - raise ValueError('Can only use `validation_steps` when doing step-wise ' - 'training, i.e. `steps_per_epoch` must be set.') - do_validation = True - - num_train_samples = model._check_num_samples( - ins, batch_size, steps_per_epoch, 'steps_per_epoch') - - if num_train_samples is not None: - index_array = np.arange(num_train_samples) - - model.history = cbks.History() - callbacks = [cbks.BaseLogger()] + (callbacks or []) + [model.history] - if verbose: - if steps_per_epoch is not None: - count_mode = 'steps' + with backend.learning_phase_scope(1): + do_validation = False + if val_inputs: + do_validation = True + if (verbose and inputs and hasattr(inputs[0], 'shape') and + hasattr(val_inputs[0], 'shape')): + print('Train on %d samples, validate on %d samples' % + (inputs[0].shape[0], val_inputs[0].shape[0])) + if validation_steps: + if steps_per_epoch is None: + raise ValueError('Can only use `validation_steps` when doing step-wise ' + 'training, i.e. `steps_per_epoch` must be set.') + do_validation = True + + out_labels = model.metrics_names + if do_validation: + callback_metrics = copy.copy(out_labels) + [ + 'val_' + n for n in out_labels + ] else: - count_mode = 'samples' - callbacks += [cbks.ProgbarLogger(count_mode)] - callbacks = cbks.CallbackList(callbacks) - out_labels = out_labels or [] - - # it's possible to callback a different model than self - # (used by Sequential models) - if hasattr(model, 'callback_model') and model.callback_model: - callback_model = model.callback_model - else: - callback_model = model - - callbacks.set_model(callback_model) - - callbacks.set_params({ - 'batch_size': batch_size, - 'epochs': epochs, - 'steps': steps_per_epoch, - 'samples': num_train_samples, - 'verbose': verbose, - 'do_validation': do_validation, - 'metrics': callback_metrics or [], - }) - callbacks.on_train_begin() - callback_model.stop_training = False - for cbk in callbacks: - cbk.validation_data = val_ins - - for epoch in range(initial_epoch, epochs): - callbacks.on_epoch_begin(epoch) - epoch_logs = {} - if shuffle == 'batch': - index_array = model._batch_shuffle(index_array, batch_size) - elif shuffle: - np.random.shuffle(index_array) - - batches = make_batches(num_train_samples, batch_size) + callback_metrics = copy.copy(out_labels) - for batch_index, (batch_start, batch_end) in enumerate(batches): - batch_ids = index_array[batch_start:batch_end] - try: - if isinstance(ins[-1], float): - # Do not slice the training phase flag. - ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]] - else: - ins_batch = slice_arrays(ins, batch_ids) - except TypeError: - raise TypeError('TypeError while preparing batch. ' - 'If using HDF5 input data, ' - 'pass shuffle="batch".') - batch_logs = {} - batch_logs['batch'] = batch_index - batch_logs['size'] = len(batch_ids) - - callbacks.on_batch_begin(batch_index, batch_logs) - - ins_batch_converted = [] - for ib in ins_batch: - ins_batch_converted.append(ops.convert_to_tensor(ib, dtype=K.floatx())) - eager_model_inputs = [] - eager_model_outputs = [] - for i in range(len(model.inputs)): - eager_model_inputs.append(ins_batch_converted[i]) - - for i in range(len(model.inputs), len(ins_batch_converted)): - eager_model_outputs.append(ins_batch_converted[i]) - - outs, loss, loss_metrics = _process_single_batch(eager_model_inputs, - eager_model_outputs, - model, - training=True) - - if not isinstance(outs, list): - outs = [outs] - - for l, o in zip(out_labels, outs): - batch_logs[l] = o - # Required for Eager mode - metrics_names, metrics_results = _eager_metrics_fn(model, outs, - eager_model_outputs) - batch_logs['loss'] = tensor_util.constant_value(K.mean(loss)) - - # TODO(anjalisridhar): Move this to compile to avoid duplicate code. - # In graph mode we set the metric names in compile. However in - # Eager mode we calculate the metrics for each batch in fit_loop. - # We could calculate the metric names and functions in compile. - # This would avoid setting the callback parameters separately. - # We need to do this for the first iteration alone - for m in metrics_names: - if m not in callback_metrics: - callback_metrics.append(m) - - callbacks.set_params({ - 'batch_size': batch_size, - 'epochs': epochs, - 'steps': steps_per_epoch, - 'samples': num_train_samples, - 'verbose': verbose, - 'do_validation': do_validation, - 'metrics': callback_metrics or [], - }) - - for k, v in zip(model.metrics_names, - [K.mean(loss)] + loss_metrics + metrics_results): - batch_logs[k] = tensor_util.constant_value(v) - - callbacks.on_batch_end(batch_index, batch_logs) + if sample_weights: + feed_data = inputs + targets + sample_weights + else: + feed_data = inputs + targets + num_train_samples = training_utils.check_num_samples( + feed_data, + batch_size=batch_size, + steps=steps_per_epoch, + steps_name='steps_per_epoch') + + if num_train_samples is not None: + index_array = np.arange(num_train_samples) + + model.history = cbks.History() + callbacks = [cbks.BaseLogger()] + (callbacks or []) + [model.history] + if verbose: + if steps_per_epoch is not None: + count_mode = 'steps' + else: + count_mode = 'samples' + callbacks += [cbks.ProgbarLogger(count_mode)] + callbacks = cbks.CallbackList(callbacks) + + # it's possible to callback a different model than self + # (used by Sequential models) + if hasattr(model, 'callback_model') and model.callback_model: + callback_model = model.callback_model + else: + callback_model = model + + callbacks.set_model(callback_model) + + callbacks.set_params({ + 'batch_size': batch_size, + 'epochs': epochs, + 'steps': steps_per_epoch, + 'samples': num_train_samples, + 'verbose': verbose, + 'do_validation': do_validation, + 'metrics': callback_metrics or [], + }) + callbacks.on_train_begin() + callback_model.stop_training = False + for cbk in callbacks: + if not val_inputs: + cbk.validation_data = [] + elif val_sample_weights: + cbk.validation_data = val_inputs + val_targets + val_sample_weights + else: + cbk.validation_data = val_inputs + val_targets + + for epoch in range(initial_epoch, epochs): + callbacks.on_epoch_begin(epoch) + epoch_logs = {} + if shuffle == 'batch': + index_array = model._batch_shuffle(index_array, batch_size) + elif shuffle: + np.random.shuffle(index_array) + + batches = make_batches(num_train_samples, batch_size) + + for batch_index, (batch_start, batch_end) in enumerate(batches): + batch_ids = index_array[batch_start:batch_end] + try: + inputs_batch = slice_arrays(inputs, batch_ids) + targets_batch = slice_arrays(targets, batch_ids) + if sample_weights: + sample_weights_batch = slice_arrays(sample_weights, batch_ids) + else: + sample_weights_batch = None + except TypeError: + raise TypeError('TypeError while preparing batch. ' + 'If using HDF5 input data, ' + 'pass shuffle="batch".') + batch_logs = {} + batch_logs['batch'] = batch_index + batch_logs['size'] = len(batch_ids) + + callbacks.on_batch_begin(batch_index, batch_logs) + + inputs_batch = [ + ops.convert_to_tensor(val, dtype=backend.floatx()) + for val in inputs_batch] + targets_batch = [ + ops.convert_to_tensor(val, dtype=backend.floatx()) + for val in targets_batch] + if sample_weights: + sample_weights_batch = [ + ops.convert_to_tensor(val, dtype=backend.floatx()) + if val is not None else None + for val in sample_weights_batch] + + outs, loss, loss_metrics = _process_single_batch( + model, + inputs_batch, + targets_batch, + sample_weights=sample_weights_batch, + training=True) + + if not isinstance(outs, list): + outs = [outs] + + for l, o in zip(out_labels, outs): + batch_logs[l] = o + # Required for Eager mode + metrics_names, metrics_results = _eager_metrics_fn( + model, outs, targets_batch) + batch_logs['loss'] = tensor_util.constant_value(backend.mean(loss)) + + # TODO(anjalisridhar): Move this to compile to avoid duplicate code. + # In graph mode we set the metric names in compile. However in + # Eager mode we calculate the metrics for each batch in fit_loop. + # We could calculate the metric names and functions in compile. + # This would avoid setting the callback parameters separately. + # We need to do this for the first iteration alone + for m in metrics_names: + if m not in callback_metrics: + callback_metrics.append(m) + + callbacks.set_params({ + 'batch_size': batch_size, + 'epochs': epochs, + 'steps': steps_per_epoch, + 'samples': num_train_samples, + 'verbose': verbose, + 'do_validation': do_validation, + 'metrics': callback_metrics or [], + }) + + for k, v in zip(model.metrics_names, + [backend.mean(loss)] + loss_metrics + metrics_results): + batch_logs[k] = tensor_util.constant_value(v) + + callbacks.on_batch_end(batch_index, batch_logs) + if callback_model.stop_training: + break + + if batch_index == len(batches) - 1: # Last batch. + if do_validation: + val_outs = test_loop( + model, val_inputs, val_targets, + sample_weights=val_sample_weights, + batch_size=batch_size, + verbose=0) + if not isinstance(val_outs, list): + val_outs = [val_outs] + # Same labels assumed. + for l, o in zip(out_labels, val_outs): + epoch_logs['val_' + l] = o + callbacks.on_epoch_end(epoch, epoch_logs) if callback_model.stop_training: break + callbacks.on_train_end() + return model.history + - if batch_index == len(batches) - 1: # Last batch. - if do_validation: - val_outs = test_loop( - model, val_ins, batch_size=batch_size, verbose=0) - if not isinstance(val_outs, list): - val_outs = [val_outs] - # Same labels assumed. - for l, o in zip(out_labels, val_outs): - epoch_logs['val_' + l] = o - callbacks.on_epoch_end(epoch, epoch_logs) - if callback_model.stop_training: - break - callbacks.on_train_end() - return model.history - - -def test_loop(model, ins, batch_size=None, verbose=0, steps=None): +def test_loop(model, inputs, targets, + sample_weights=None, + batch_size=None, + verbose=0, + steps=None): """Abstract method to loop over some data in batches. Arguments: model: Model instance that is being evaluated in Eager mode. - ins: list of tensors to be fed to `f`. + inputs: List of input arrays. + targets: List of target arrays. + sample_weights: Optional list of sample weight arrays. batch_size: integer batch size or `None`. verbose: verbosity mode. steps: Total number of steps (batches of samples) @@ -499,69 +531,79 @@ def test_loop(model, ins, batch_size=None, verbose=0, steps=None): and/or metrics). The attribute `model.metrics_names` will give you the display labels for the scalar outputs. """ - K.set_learning_phase(False) - num_samples = model._check_num_samples(ins, batch_size, steps, 'steps') - outs = [] - if verbose == 1: - progbar = Progbar(target=num_samples) - batches = make_batches(num_samples, batch_size) - index_array = np.arange(num_samples) - for batch_index, (batch_start, batch_end) in enumerate(batches): - batch_ids = index_array[batch_start:batch_end] - if isinstance(ins[-1], float): - # Do not slice the training phase flag. - ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]] - else: - ins_batch = slice_arrays(ins, batch_ids) - - ins_batch_converted = [] - for ib in ins_batch: - ins_batch_converted.append(ops.convert_to_tensor(ib, dtype=K.floatx())) - - eager_model_inputs = [] - eager_model_outputs = [] - for i in range(len(model.inputs)): - eager_model_inputs.append(ins_batch_converted[i]) - - for i in range(len(model.inputs), len(ins_batch_converted)): - eager_model_outputs.append(ins_batch_converted[i]) - - loss_outs, loss, loss_metrics = _model_loss(model, eager_model_inputs, - eager_model_outputs, - training=False) - _, metrics_results = _eager_metrics_fn(model, loss_outs, - eager_model_outputs) - batch_outs = [] - for _, v in zip(model.metrics_names, - [K.mean(loss)] + loss_metrics + metrics_results): - batch_outs.append(tensor_util.constant_value(v)) - - if isinstance(batch_outs, list): - if batch_index == 0: - for batch_out in enumerate(batch_outs): + with backend.learning_phase_scope(0): + feed_data = inputs + targets + if sample_weights: + feed_data += sample_weights + num_samples = training_utils.check_num_samples( + feed_data, batch_size=batch_size, steps=steps, steps_name='steps') + outs = [] + if verbose == 1: + progbar = Progbar(target=num_samples) + batches = make_batches(num_samples, batch_size) + index_array = np.arange(num_samples) + for batch_index, (batch_start, batch_end) in enumerate(batches): + batch_ids = index_array[batch_start:batch_end] + inputs_batch = slice_arrays(inputs, batch_ids) + targets_batch = slice_arrays(targets, batch_ids) + if sample_weights: + sample_weights_batch = slice_arrays(sample_weights, batch_ids) + else: + sample_weights_batch = None + + inputs_batch = [ + ops.convert_to_tensor(val, dtype=backend.floatx()) + for val in inputs_batch] + targets_batch = [ + ops.convert_to_tensor(val, dtype=backend.floatx()) + for val in targets_batch] + if sample_weights: + sample_weights_batch = [ + ops.convert_to_tensor(val, dtype=backend.floatx()) + if val is not None else None + for val in sample_weights_batch] + + loss_outs, loss, loss_metrics = _model_loss( + model, + inputs_batch, + targets_batch, + sample_weights=sample_weights_batch, + training=False) + _, metrics_results = _eager_metrics_fn(model, loss_outs, targets_batch) + batch_outs = [] + for _, v in zip(model.metrics_names, + [backend.mean(loss)] + loss_metrics + metrics_results): + batch_outs.append(tensor_util.constant_value(v)) + + if isinstance(batch_outs, list): + if batch_index == 0: + for batch_out in enumerate(batch_outs): + outs.append(0.) + for i, batch_out in enumerate(batch_outs): + outs[i] += batch_out * len(batch_ids) + else: + if batch_index == 0: outs.append(0.) - for i, batch_out in enumerate(batch_outs): - outs[i] += batch_out * len(batch_ids) - else: - if batch_index == 0: - outs.append(0.) - outs[0] += batch_outs * len(batch_ids) + outs[0] += batch_outs * len(batch_ids) - if verbose == 1: - progbar.update(batch_end) - for i in range(len(outs)): - outs[i] /= num_samples - if len(outs) == 1: - return outs[0] - return outs + if verbose == 1: + progbar.update(batch_end) + for i in range(len(outs)): + outs[i] /= num_samples + if len(outs) == 1: + return outs[0] + return outs -def predict_loop(model, ins, batch_size=32, verbose=0, steps=None): +def predict_loop(model, inputs, + batch_size=32, + verbose=0, + steps=None): """Abstract method to loop over some data in batches. Arguments: model: - ins: list of tensors to be fed to `f`. + inputs: List of input arrays. batch_size: integer batch size. verbose: verbosity mode. steps: Total number of steps (batches of samples) @@ -573,57 +615,50 @@ def predict_loop(model, ins, batch_size=32, verbose=0, steps=None): or list of arrays of predictions (if the model has multiple outputs). """ - K.set_learning_phase(False) - num_samples = model._check_num_samples(ins, batch_size, steps, 'steps') - if verbose == 1: - if steps is not None: - progbar = Progbar(target=steps) - else: - progbar = Progbar(target=num_samples) - - outs = [] - batches = make_batches(num_samples, batch_size) - index_array = np.arange(num_samples) - for batch_index, (batch_start, batch_end) in enumerate(batches): - batch_ids = index_array[batch_start:batch_end] - if ins and isinstance(ins[-1], float): - # Do not slice the training phase flag. - ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]] - else: - ins_batch = slice_arrays(ins, batch_ids) + with backend.learning_phase_scope(0): + num_samples = training_utils.check_num_samples( + inputs, batch_size, steps, 'steps') + if verbose == 1: + if steps is not None: + progbar = Progbar(target=steps) + else: + progbar = Progbar(target=num_samples) - ins_batch_converted = [] - for ib in ins_batch: - ins_batch_converted.append(ops.convert_to_tensor(ib, dtype=K.floatx())) + outs = [] + batches = make_batches(num_samples, batch_size) + index_array = np.arange(num_samples) + for batch_index, (batch_start, batch_end) in enumerate(batches): + batch_ids = index_array[batch_start:batch_end] + inputs_batch = slice_arrays(inputs, batch_ids) - eager_model_inputs = [] - for i in range(len(model.inputs)): - eager_model_inputs.append(ins_batch_converted[i]) + inputs_batch = [ + ops.convert_to_tensor(val, dtype=backend.floatx()) + for val in inputs_batch] - if len(eager_model_inputs) == 1: - if model._expects_training_arg: - batch_outs = model.call(eager_model_inputs[0], training=False) - else: - batch_outs = model.call(eager_model_inputs[0]) - else: - if model._expects_training_arg: - batch_outs = model.call(eager_model_inputs, training=False) + if len(inputs_batch) == 1: + if model._expects_training_arg: + batch_outs = model.call(inputs_batch[0], training=False) + else: + batch_outs = model.call(inputs_batch[0]) else: - batch_outs = model.call(eager_model_inputs) - - if not isinstance(batch_outs, list): - batch_outs = [batch_outs] - if batch_index == 0: - # Pre-allocate the results arrays. - for batch_out in batch_outs: - dims = batch_out.shape[1:].dims - dims_list = [d.value for d in dims] - shape = (num_samples,) + tuple(dims_list) - outs.append(np.zeros(shape, dtype=batch_out.dtype.as_numpy_dtype)) - for i, batch_out in enumerate(batch_outs): - outs[i][batch_start:batch_end] = batch_out - if verbose == 1: - progbar.update(batch_end) - if len(outs) == 1: - return outs[0] - return outs + if model._expects_training_arg: + batch_outs = model.call(inputs_batch, training=False) + else: + batch_outs = model.call(inputs_batch) + + if not isinstance(batch_outs, list): + batch_outs = [batch_outs] + if batch_index == 0: + # Pre-allocate the results arrays. + for batch_out in batch_outs: + dims = batch_out.shape[1:].dims + dims_list = [d.value for d in dims] + shape = (num_samples,) + tuple(dims_list) + outs.append(np.zeros(shape, dtype=batch_out.dtype.as_numpy_dtype)) + for i, batch_out in enumerate(batch_outs): + outs[i][batch_start:batch_end] = batch_out + if verbose == 1: + progbar.update(batch_end) + if len(outs) == 1: + return outs[0] + return outs diff --git a/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py b/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py index 3d94b7537ff354351d3f4adde6e0819ce66ea377..8848b393d5e602e564cb357c32a937eaabd68203 100644 --- a/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py +++ b/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py @@ -24,9 +24,7 @@ import numpy as np from tensorflow.python.framework import ops from tensorflow.python.keras._impl import keras from tensorflow.python.keras._impl.keras import testing_utils -from tensorflow.python.keras._impl.keras.utils.generic_utils import slice_arrays from tensorflow.python.platform import test -from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training.rmsprop import RMSPropOptimizer @@ -316,10 +314,9 @@ class LossWeightingTest(test.TestCase): def test_class_weights(self): num_classes = 5 batch_size = 5 - epochs = 5 weighted_class = 3 - train_samples = 3000 - test_samples = 3000 + train_samples = 300 + test_samples = 300 input_dim = 5 model = keras.models.Sequential() @@ -344,16 +341,16 @@ class LossWeightingTest(test.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] = 4. sample_weight = np.ones((y_train.shape[0])) - sample_weight[int_y_train == weighted_class] = 2. + sample_weight[int_y_train == weighted_class] = 4. model.fit( x_train, y_train, batch_size=batch_size, - epochs=epochs // 3, + epochs=2, verbose=0, class_weight=class_weight, validation_data=(x_train, y_train, sample_weight)) @@ -361,14 +358,14 @@ class LossWeightingTest(test.TestCase): x_train, y_train, batch_size=batch_size, - epochs=epochs // 2, + epochs=2, verbose=0, class_weight=class_weight) model.fit( x_train, y_train, batch_size=batch_size, - epochs=epochs // 2, + epochs=2, verbose=0, class_weight=class_weight, validation_split=0.1) @@ -383,10 +380,9 @@ class LossWeightingTest(test.TestCase): def test_sample_weights(self): num_classes = 5 batch_size = 5 - epochs = 5 weighted_class = 3 - train_samples = 3000 - test_samples = 3000 + train_samples = 300 + test_samples = 300 input_dim = 5 model = keras.models.Sequential() @@ -407,23 +403,23 @@ class LossWeightingTest(test.TestCase): y_train = keras.utils.to_categorical(y_train, num_classes) class_weight = dict([(i, 1.) for i in range(num_classes)]) - class_weight[weighted_class] = 2. + class_weight[weighted_class] = 4. sample_weight = np.ones((y_train.shape[0])) - sample_weight[int_y_train == weighted_class] = 2. + sample_weight[int_y_train == weighted_class] = 4. model.fit( x_train, y_train, batch_size=batch_size, - epochs=epochs // 3, + epochs=2, verbose=0, sample_weight=sample_weight) model.fit( x_train, y_train, batch_size=batch_size, - epochs=epochs // 3, + epochs=2, verbose=0, sample_weight=sample_weight, validation_split=0.1) @@ -536,215 +532,6 @@ class LossWeightingTest(test.TestCase): model.fit(x_np, [y_np, y_np], epochs=1, sample_weight={'1': bad_w_np}) -class TestDynamicTrainability(test.TestCase): - - def test_trainable_warning(self): - x = np.random.random((5, 3)) - y = np.random.random((5, 2)) - model = keras.models.Sequential() - model.add(keras.layers.Dense(2, input_dim=3)) - model.trainable = False - model.compile(RMSPropOptimizer(learning_rate=0.001), 'mse') - model.trainable = True - with test.mock.patch.object(logging, 'warning') as mock_log: - model.train_on_batch(x, y) - self.assertRegexpMatches(str(mock_log.call_args), - 'trainable weights is empty') - - def test_trainable_argument(self): - x = np.random.random((5, 3)) - y = np.random.random((5, 2)) - - model = keras.models.Sequential() - model.add(keras.layers.Dense(2, input_dim=3, trainable=False)) - model.compile(RMSPropOptimizer(learning_rate=0.001), 'mse') - out = model.predict(x) - with test.mock.patch.object(logging, 'warning') as mock_log: - model.train_on_batch(x, y) - self.assertRegexpMatches(str(mock_log.call_args), - 'trainable weights is empty') - out_2 = model.predict(x) - self.assertAllClose(out, out_2) - - # test with nesting - inputs = keras.layers.Input(shape=(3,)) - output = model(inputs) - model = keras.models.Model(inputs, output) - model.compile(RMSPropOptimizer(learning_rate=0.001), 'mse') - out = model.predict(x) - with test.mock.patch.object(logging, 'warning') as mock_log: - model.train_on_batch(x, y) - self.assertRegexpMatches(str(mock_log.call_args), - 'trainable weights is empty') - out_2 = model.predict(x) - self.assertAllClose(out, out_2) - - def test_layer_trainability_switch(self): - # with constructor argument, in Sequential - model = keras.models.Sequential() - model.add(keras.layers.Dense(2, trainable=False, input_dim=1)) - self.assertListEqual(model.trainable_weights, []) - - # by setting the `trainable` argument, in Sequential - model = keras.models.Sequential() - layer = keras.layers.Dense(2, input_dim=1) - model.add(layer) - self.assertListEqual(model.trainable_weights, layer.trainable_weights) - layer.trainable = False - self.assertListEqual(model.trainable_weights, []) - - # with constructor argument, in Model - x = keras.layers.Input(shape=(1,)) - y = keras.layers.Dense(2, trainable=False)(x) - model = keras.models.Model(x, y) - self.assertListEqual(model.trainable_weights, []) - - # by setting the `trainable` argument, in Model - x = keras.layers.Input(shape=(1,)) - layer = keras.layers.Dense(2) - y = layer(x) - model = keras.models.Model(x, y) - self.assertListEqual(model.trainable_weights, layer.trainable_weights) - layer.trainable = False - self.assertListEqual(model.trainable_weights, []) - - def test_model_trainability_switch(self): - # a non-trainable model has no trainable weights - x = keras.layers.Input(shape=(1,)) - y = keras.layers.Dense(2)(x) - model = keras.models.Model(x, y) - model.trainable = False - self.assertListEqual(model.trainable_weights, []) - - # same for Sequential - model = keras.models.Sequential() - model.add(keras.layers.Dense(2, input_dim=1)) - model.trainable = False - self.assertListEqual(model.trainable_weights, []) - - def test_nested_model_trainability(self): - - # a Sequential inside a Model - inner_model = keras.models.Sequential() - inner_model.add(keras.layers.Dense(2, input_dim=1)) - - x = keras.layers.Input(shape=(1,)) - y = inner_model(x) - outer_model = keras.models.Model(x, y) - self.assertListEqual(outer_model.trainable_weights, - inner_model.trainable_weights) - inner_model.trainable = False - self.assertListEqual(outer_model.trainable_weights, []) - inner_model.trainable = True - inner_model.layers[-1].trainable = False - self.assertListEqual(outer_model.trainable_weights, []) - - # a Sequential inside a Sequential - inner_model = keras.models.Sequential() - inner_model.add(keras.layers.Dense(2, input_dim=1)) - outer_model = keras.models.Sequential() - outer_model.add(inner_model) - self.assertListEqual(outer_model.trainable_weights, - inner_model.trainable_weights) - inner_model.trainable = False - self.assertListEqual(outer_model.trainable_weights, []) - inner_model.trainable = True - inner_model.layers[-1].trainable = False - self.assertListEqual(outer_model.trainable_weights, []) - - # a Model inside a Model - x = keras.layers.Input(shape=(1,)) - y = keras.layers.Dense(2)(x) - inner_model = keras.models.Model(x, y) - x = keras.layers.Input(shape=(1,)) - y = inner_model(x) - outer_model = keras.models.Model(x, y) - self.assertListEqual(outer_model.trainable_weights, - inner_model.trainable_weights) - inner_model.trainable = False - self.assertListEqual(outer_model.trainable_weights, []) - inner_model.trainable = True - inner_model.layers[-1].trainable = False - self.assertListEqual(outer_model.trainable_weights, []) - - # a Model inside a Sequential - x = keras.layers.Input(shape=(1,)) - y = keras.layers.Dense(2)(x) - inner_model = keras.models.Model(x, y) - outer_model = keras.models.Sequential() - outer_model.add(inner_model) - self.assertListEqual(outer_model.trainable_weights, - inner_model.trainable_weights) - inner_model.trainable = False - self.assertListEqual(outer_model.trainable_weights, []) - inner_model.trainable = True - inner_model.layers[-1].trainable = False - self.assertListEqual(outer_model.trainable_weights, []) - - -class TestTrainingUtils(test.TestCase): - - def test_check_array_lengths(self): - keras.engine.training._check_array_lengths(None, None, None) - a_np = np.random.random((4, 3, 3)) - keras.engine.training._check_array_lengths(a_np, a_np, a_np) - keras.engine.training._check_array_lengths( - [a_np, a_np], [a_np, a_np], [a_np, a_np]) - keras.engine.training._check_array_lengths([None], [None], [None]) - - b_np = np.random.random((3, 4)) - with self.assertRaises(ValueError): - keras.engine.training._check_array_lengths(a_np, None, None) - with self.assertRaises(ValueError): - keras.engine.training._check_array_lengths(a_np, a_np, None) - with self.assertRaises(ValueError): - keras.engine.training._check_array_lengths([a_np], [None], None) - with self.assertRaises(ValueError): - keras.engine.training._check_array_lengths([a_np], [b_np], None) - with self.assertRaises(ValueError): - keras.engine.training._check_array_lengths([a_np], None, [b_np]) - - def test_slice_arrays(self): - input_a = np.random.random((10, 3)) - slice_arrays(None) - slice_arrays(input_a, 0) - slice_arrays(input_a, 0, 1) - slice_arrays(input_a, stop=2) - input_a = [None, [1, 1], None, [1, 1]] - slice_arrays(input_a, 0) - slice_arrays(input_a, 0, 1) - slice_arrays(input_a, stop=2) - input_a = [None] - slice_arrays(input_a, 0) - slice_arrays(input_a, 0, 1) - slice_arrays(input_a, stop=2) - input_a = None - slice_arrays(input_a, 0) - slice_arrays(input_a, 0, 1) - slice_arrays(input_a, stop=2) - - def test_fit_with_BatchNorm(self): - model = keras.models.Sequential() - model.add(keras.layers.Dense(10, input_dim=4)) - model.add(keras.layers.BatchNormalization()) - model.add(keras.layers.Activation('tanh')) - model.add(keras.layers.Dropout(0.2)) - - input_a_np = np.random.random((10, 4)) - output_b_np = np.random.random((10, 10)) - - model.compile(loss='binary_crossentropy', optimizer=RMSPropOptimizer(0.001)) - model.fit(input_a_np, output_b_np, epochs=1, batch_size=5, verbose=0) - - def test_fit_with_regularization(self): - model = keras.models.Sequential() - with self.assertRaises(ValueError): - model.add( - keras.layers.Dense(4, input_dim=3, - kernel_regularizer=keras.regularizers.l2(0.01), - activity_regularizer=keras.regularizers.l1(0.01))) - - if __name__ == '__main__': # Bazel sets these environment variables to very long paths. # Tempfile uses them to create long paths, and in turn multiprocessing diff --git a/tensorflow/python/keras/_impl/keras/engine/training_generator.py b/tensorflow/python/keras/_impl/keras/engine/training_generator.py new file mode 100644 index 0000000000000000000000000000000000000000..58b5bc39c10ea06f680eb030e14ecd19a3888588 --- /dev/null +++ b/tensorflow/python/keras/_impl/keras/engine/training_generator.py @@ -0,0 +1,436 @@ +# 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. +# ============================================================================== +"""Part of the Keras training engine related to Python generators of array data. +""" +# pylint: disable=protected-access +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.python.keras._impl.keras import backend as K +from tensorflow.python.keras._impl.keras import callbacks as cbks +from tensorflow.python.keras._impl.keras.utils.data_utils import GeneratorEnqueuer +from tensorflow.python.keras._impl.keras.utils.data_utils import OrderedEnqueuer +from tensorflow.python.keras._impl.keras.utils.data_utils import Sequence +from tensorflow.python.keras._impl.keras.utils.generic_utils import Progbar +from tensorflow.python.platform import tf_logging as logging + + +def fit_generator(model, + 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): + """See docstring for `Model.fit_generator`.""" + wait_time = 0.01 # in seconds + epoch = initial_epoch + + do_validation = bool(validation_data) + model._make_train_function() + if do_validation: + model._make_test_function() + + is_sequence = isinstance(generator, Sequence) + if not is_sequence and use_multiprocessing and workers > 1: + logging.warning( + UserWarning('Using a generator with `use_multiprocessing=True`' + ' and multiple workers may duplicate your data.' + ' Please consider using the`keras.utils.Sequence' + ' class.')) + if steps_per_epoch is None: + if is_sequence: + steps_per_epoch = len(generator) + else: + raise ValueError('`steps_per_epoch=None` is only valid for a' + ' generator based on the `keras.utils.Sequence`' + ' class. Please specify `steps_per_epoch` or use' + ' the `keras.utils.Sequence` class.') + + # python 2 has 'next', 3 has '__next__' + # avoid any explicit version checks + val_gen = ( + hasattr(validation_data, 'next') or + hasattr(validation_data, '__next__') or + isinstance(validation_data, Sequence)) + if (val_gen and not isinstance(validation_data, Sequence) and + not validation_steps): + raise ValueError('`validation_steps=None` is only valid for a' + ' generator based on the `keras.utils.Sequence`' + ' class. Please specify `validation_steps` or use' + ' the `keras.utils.Sequence` class.') + + # Prepare display labels. + out_labels = model.metrics_names + callback_metrics = out_labels + ['val_%s' % n for n in out_labels] + + # prepare callbacks + model.history = cbks.History() + callbacks = [cbks.BaseLogger()] + (callbacks or []) + [model.history] + if verbose: + callbacks += [cbks.ProgbarLogger(count_mode='steps')] + callbacks = cbks.CallbackList(callbacks) + + # it's possible to callback a different model than self: + if hasattr(model, 'callback_model') and model.callback_model: + callback_model = model.callback_model + else: + callback_model = model + callbacks.set_model(callback_model) + callbacks.set_params({ + 'epochs': epochs, + 'steps': steps_per_epoch, + 'verbose': verbose, + 'do_validation': do_validation, + 'metrics': callback_metrics, + }) + callbacks.on_train_begin() + + enqueuer = None + val_enqueuer = None + + try: + if do_validation and not val_gen: + # Prepare data for validation + if 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( + '`validation_data` should be a tuple ' + '`(val_x, val_y, val_sample_weight)` ' + 'or `(val_x, val_y)`. Found: ' + str(validation_data)) + val_x, val_y, val_sample_weights = model._standardize_user_data( + val_x, val_y, val_sample_weight) + val_data = val_x + val_y + val_sample_weights + if model.uses_learning_phase and not isinstance(K.learning_phase(), int): + val_data += [0.] + for cbk in callbacks: + cbk.validation_data = val_data + + if workers > 0: + if is_sequence: + enqueuer = OrderedEnqueuer( + generator, + use_multiprocessing=use_multiprocessing, + shuffle=shuffle) + else: + enqueuer = GeneratorEnqueuer( + generator, + use_multiprocessing=use_multiprocessing, + wait_time=wait_time) + enqueuer.start(workers=workers, max_queue_size=max_queue_size) + output_generator = enqueuer.get() + else: + if is_sequence: + output_generator = iter(generator) + else: + output_generator = generator + + callback_model.stop_training = False + # Construct epoch logs. + epoch_logs = {} + while epoch < epochs: + callbacks.on_epoch_begin(epoch) + steps_done = 0 + batch_index = 0 + while steps_done < steps_per_epoch: + generator_output = next(output_generator) + + if not hasattr(generator_output, '__len__'): + raise ValueError('Output of generator should be ' + 'a tuple `(x, y, sample_weight)` ' + 'or `(x, y)`. Found: ' + str(generator_output)) + + if len(generator_output) == 2: + x, y = generator_output + sample_weight = None + elif len(generator_output) == 3: + x, y, sample_weight = generator_output + else: + raise ValueError('Output of generator should be ' + 'a tuple `(x, y, sample_weight)` ' + 'or `(x, y)`. Found: ' + str(generator_output)) + # build batch logs + batch_logs = {} + if isinstance(x, list): + batch_size = x[0].shape[0] + elif isinstance(x, dict): + batch_size = list(x.values())[0].shape[0] + else: + batch_size = x.shape[0] + batch_logs['batch'] = batch_index + batch_logs['size'] = batch_size + callbacks.on_batch_begin(batch_index, batch_logs) + + outs = model.train_on_batch( + x, y, sample_weight=sample_weight, class_weight=class_weight) + + if not isinstance(outs, list): + outs = [outs] + for l, o in zip(out_labels, outs): + batch_logs[l] = o + + callbacks.on_batch_end(batch_index, batch_logs) + + batch_index += 1 + steps_done += 1 + + # Epoch finished. + if steps_done >= steps_per_epoch and do_validation: + if val_gen: + val_outs = evaluate_generator( + model, + validation_data, + validation_steps, + workers=workers, + use_multiprocessing=use_multiprocessing, + max_queue_size=max_queue_size) + else: + # No need for try/except because + # data has already been validated. + val_outs = model.evaluate( + val_x, + val_y, + batch_size=batch_size, + sample_weight=val_sample_weights, + verbose=0) + if not isinstance(val_outs, list): + val_outs = [val_outs] + # Same labels assumed. + for l, o in zip(out_labels, val_outs): + epoch_logs['val_' + l] = o + + if callback_model.stop_training: + break + + callbacks.on_epoch_end(epoch, epoch_logs) + epoch += 1 + if callback_model.stop_training: + break + + finally: + try: + if enqueuer is not None: + enqueuer.stop() + finally: + if val_enqueuer is not None: + val_enqueuer.stop() + + callbacks.on_train_end() + return model.history + + +def evaluate_generator(model, + generator, + steps=None, + max_queue_size=10, + workers=1, + use_multiprocessing=False): + """See docstring for `Model.evaluate_generator`.""" + model._make_test_function() + + steps_done = 0 + wait_time = 0.01 + all_outs = [] + batch_sizes = [] + is_sequence = isinstance(generator, Sequence) + if not is_sequence and use_multiprocessing and workers > 1: + logging.warning( + UserWarning('Using a generator with `use_multiprocessing=True`' + ' and multiple workers may duplicate your data.' + ' Please consider using the`keras.utils.Sequence' + ' class.')) + if steps is None: + if is_sequence: + steps = len(generator) + else: + raise ValueError('`steps=None` is only valid for a generator' + ' based on the `keras.utils.Sequence` class.' + ' Please specify `steps` or use the' + ' `keras.utils.Sequence` class.') + enqueuer = None + + try: + if workers > 0: + if is_sequence: + enqueuer = OrderedEnqueuer( + generator, use_multiprocessing=use_multiprocessing) + else: + enqueuer = GeneratorEnqueuer( + generator, + use_multiprocessing=use_multiprocessing, + wait_time=wait_time) + enqueuer.start(workers=workers, max_queue_size=max_queue_size) + output_generator = enqueuer.get() + else: + if is_sequence: + output_generator = iter(generator) + else: + output_generator = generator + + while steps_done < steps: + generator_output = next(output_generator) + if not hasattr(generator_output, '__len__'): + raise ValueError('Output of generator should be a tuple ' + '(x, y, sample_weight) ' + 'or (x, y). Found: ' + str(generator_output)) + if len(generator_output) == 2: + x, y = generator_output + sample_weight = None + elif len(generator_output) == 3: + x, y, sample_weight = generator_output + else: + raise ValueError('Output of generator should be a tuple ' + '(x, y, sample_weight) ' + 'or (x, y). Found: ' + str(generator_output)) + outs = model.test_on_batch(x, y, sample_weight=sample_weight) + + if isinstance(x, list): + batch_size = x[0].shape[0] + elif isinstance(x, dict): + batch_size = list(x.values())[0].shape[0] + else: + batch_size = x.shape[0] + if batch_size == 0: + raise ValueError('Received an empty batch. ' + 'Batches should at least contain one item.') + all_outs.append(outs) + + steps_done += 1 + batch_sizes.append(batch_size) + + finally: + if enqueuer is not None: + enqueuer.stop() + + if not isinstance(outs, list): + return np.average(np.asarray(all_outs), weights=batch_sizes) + else: + averages = [] + for i in range(len(outs)): + averages.append( + np.average([out[i] for out in all_outs], weights=batch_sizes)) + return averages + + +def predict_generator(model, + generator, + steps=None, + max_queue_size=10, + workers=1, + use_multiprocessing=False, + verbose=0): + """See docstring for `Model.predict_generator`.""" + model._make_predict_function() + + steps_done = 0 + wait_time = 0.01 + all_outs = [] + is_sequence = isinstance(generator, Sequence) + if not is_sequence and use_multiprocessing and workers > 1: + logging.warning( + UserWarning('Using a generator with `use_multiprocessing=True`' + ' and multiple workers may duplicate your data.' + ' Please consider using the`keras.utils.Sequence' + ' class.')) + if steps is None: + if is_sequence: + steps = len(generator) + else: + raise ValueError('`steps=None` is only valid for a generator' + ' based on the `keras.utils.Sequence` class.' + ' Please specify `steps` or use the' + ' `keras.utils.Sequence` class.') + enqueuer = None + + try: + if workers > 0: + if is_sequence: + enqueuer = OrderedEnqueuer( + generator, use_multiprocessing=use_multiprocessing) + else: + enqueuer = GeneratorEnqueuer( + generator, + use_multiprocessing=use_multiprocessing, + wait_time=wait_time) + enqueuer.start(workers=workers, max_queue_size=max_queue_size) + output_generator = enqueuer.get() + else: + if is_sequence: + output_generator = iter(generator) + else: + output_generator = generator + + if verbose == 1: + progbar = Progbar(target=steps) + + while steps_done < steps: + generator_output = next(output_generator) + if isinstance(generator_output, tuple): + # Compatibility with the generators + # used for training. + if len(generator_output) == 2: + x, _ = generator_output + elif len(generator_output) == 3: + x, _, _ = generator_output + else: + raise ValueError('Output of generator should be ' + 'a tuple `(x, y, sample_weight)` ' + 'or `(x, y)`. Found: ' + str(generator_output)) + else: + # Assumes a generator that only + # yields inputs (not targets and sample weights). + x = generator_output + + outs = model.predict_on_batch(x) + if not isinstance(outs, list): + outs = [outs] + + if not all_outs: + for out in outs: + all_outs.append([]) + + for i, out in enumerate(outs): + all_outs[i].append(out) + steps_done += 1 + if verbose == 1: + progbar.update(steps_done) + + finally: + if enqueuer is not None: + enqueuer.stop() + + if len(all_outs) == 1: + if steps_done == 1: + return all_outs[0][0] + else: + return np.concatenate(all_outs[0]) + if steps_done == 1: + return [out[0] for out in all_outs] + else: + return [np.concatenate(out) for out in all_outs] diff --git a/tensorflow/python/keras/_impl/keras/engine/training_test.py b/tensorflow/python/keras/_impl/keras/engine/training_test.py index 9651eb9f14f1275dc79c8d3b1fb54690772086a1..fd91dbba52ff7d152335514085ef3b057ae5eec4 100644 --- a/tensorflow/python/keras/_impl/keras/engine/training_test.py +++ b/tensorflow/python/keras/_impl/keras/engine/training_test.py @@ -25,7 +25,7 @@ import numpy as np from tensorflow.python.keras._impl import keras from tensorflow.python.keras._impl.keras import testing_utils -from tensorflow.python.keras._impl.keras.engine.training import _weighted_masked_objective +from tensorflow.python.keras._impl.keras.engine.training_utils import weighted_masked_objective from tensorflow.python.keras._impl.keras.utils.generic_utils import slice_arrays from tensorflow.python.platform import test @@ -340,20 +340,21 @@ class TrainingTest(test.TestCase): if scipy_sparse is None: return - test_inputs = [ - scipy_sparse.random(6, 3, density=0.25).tocsr() for _ in range(2)] - test_outputs = [ - scipy_sparse.random(6, i, density=0.25).tocsr() for i in range(3, 5)] - in1 = keras.layers.Input(shape=(3,)) - in2 = keras.layers.Input(shape=(3,)) - out1 = keras.layers.Dropout(0.5, name='dropout')(in1) - out2 = keras.layers.Dense(4, name='dense_1')(in2) - model = keras.Model([in1, in2], [out1, out2]) - model.predict(test_inputs, batch_size=2) - model.compile('rmsprop', 'mse') - model.fit(test_inputs, test_outputs, - epochs=1, batch_size=2, validation_split=0.5) - model.evaluate(test_inputs, test_outputs, batch_size=2) + with self.test_session(): + test_inputs = [ + scipy_sparse.random(6, 3, density=0.25).tocsr() for _ in range(2)] + test_outputs = [ + scipy_sparse.random(6, i, density=0.25).tocsr() for i in range(3, 5)] + in1 = keras.layers.Input(shape=(3,)) + in2 = keras.layers.Input(shape=(3,)) + out1 = keras.layers.Dropout(0.5, name='dropout')(in1) + out2 = keras.layers.Dense(4, name='dense_1')(in2) + model = keras.Model([in1, in2], [out1, out2]) + model.predict(test_inputs, batch_size=2) + model.compile('rmsprop', 'mse') + model.fit(test_inputs, test_outputs, + epochs=1, batch_size=2, validation_split=0.5) + model.evaluate(test_inputs, test_outputs, batch_size=2) def test_that_trainable_disables_updates(self): val_a = np.random.random((10, 4)) @@ -705,7 +706,7 @@ class LossMaskingTest(test.TestCase): def test_loss_masking(self): with self.test_session(): - weighted_loss = _weighted_masked_objective(keras.losses.get('mae')) + weighted_loss = weighted_masked_objective(keras.losses.get('mae')) shape = (3, 4, 2) x = np.arange(24).reshape(shape) y = 2 * x @@ -876,9 +877,9 @@ class TestGeneratorMethods(test.TestCase): def custom_generator(): batch_size = 10 - n_samples = 50 + num_samples = 50 while True: - batch_index = np.random.randint(0, n_samples - batch_size) + batch_index = np.random.randint(0, num_samples - batch_size) start = batch_index end = start + batch_size x = arr_data[start: end] @@ -957,9 +958,9 @@ class TestGeneratorMethods(test.TestCase): def custom_generator(): batch_size = 10 - n_samples = 50 + num_samples = 50 while True: - batch_index = np.random.randint(0, n_samples - batch_size) + batch_index = np.random.randint(0, num_samples - batch_size) start = batch_index end = start + batch_size x = arr_data[start: end] @@ -1033,28 +1034,66 @@ class TestGeneratorMethods(test.TestCase): max_queue_size=10, use_multiprocessing=False) + def test_training_with_sequences(self): + + class DummySequence(keras.utils.Sequence): + + def __getitem__(self, idx): + return np.zeros([10, 2]), np.ones([10]) + + def __len__(self): + return 10 + + arr_data = np.random.random((50, 2)) + arr_labels = np.random.random((50,)) + arr_sample_weights = np.random.random((50,)) + + def custom_generator(): + batch_size = 10 + num_samples = 50 + while True: + batch_index = np.random.randint(0, num_samples - batch_size) + start = batch_index + end = start + batch_size + x = arr_data[start: end] + y = arr_labels[start: end] + w = arr_sample_weights[start: end] + yield x, y, w + + with self.test_session(): + model = keras.models.Sequential() + model.add(keras.layers.Dense(1, input_shape=(2,))) + model.compile(loss='mse', optimizer='sgd') + + model.fit_generator(DummySequence(), + steps_per_epoch=10, + validation_data=custom_generator(), + validation_steps=1, + max_queue_size=10, + workers=0, + use_multiprocessing=True) + model.fit_generator(DummySequence(), + steps_per_epoch=10, + validation_data=custom_generator(), + validation_steps=1, + max_queue_size=10, + workers=0, + use_multiprocessing=False) + class TestTrainingUtils(test.TestCase): def test_check_array_lengths(self): - keras.engine.training._check_array_lengths(None, None, None) + keras.engine.training_utils.check_array_lengths(None, None, None) a_np = np.random.random((4, 3, 3)) - keras.engine.training._check_array_lengths(a_np, a_np, a_np) - keras.engine.training._check_array_lengths( + keras.engine.training_utils.check_array_lengths(a_np, a_np, a_np) + keras.engine.training_utils.check_array_lengths( [a_np, a_np], [a_np, a_np], [a_np, a_np]) - keras.engine.training._check_array_lengths([None], [None], [None]) + keras.engine.training_utils.check_array_lengths([None], [None], [None]) b_np = np.random.random((3, 4)) with self.assertRaises(ValueError): - keras.engine.training._check_array_lengths(a_np, None, None) - with self.assertRaises(ValueError): - keras.engine.training._check_array_lengths(a_np, a_np, None) - with self.assertRaises(ValueError): - keras.engine.training._check_array_lengths([a_np], [None], None) - with self.assertRaises(ValueError): - keras.engine.training._check_array_lengths([a_np], [b_np], None) - with self.assertRaises(ValueError): - keras.engine.training._check_array_lengths([a_np], None, [b_np]) + keras.engine.training_utils.check_array_lengths([a_np], [b_np], None) def test_slice_arrays(self): input_a = np.random.random((10, 3)) diff --git a/tensorflow/python/keras/_impl/keras/engine/training_utils.py b/tensorflow/python/keras/_impl/keras/engine/training_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..105638ce1087e8668b49b6653a847667e8f9157d --- /dev/null +++ b/tensorflow/python/keras/_impl/keras/engine/training_utils.py @@ -0,0 +1,534 @@ +# 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. +# ============================================================================== +"""Training-related utilities. +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import copy + +import numpy as np + +from tensorflow.python.framework import tensor_util +from tensorflow.python.keras._impl.keras import backend as K +from tensorflow.python.keras._impl.keras import losses + + +def check_num_samples(ins, + batch_size=None, + steps=None, + steps_name='steps'): + """Determine the number of samples provided for training and evaluation. + + The number of samples is not defined when running with `steps`, + in which case the number of samples is set to `None`. + + Arguments: + ins: List of tensors to be fed to the Keras function. + batch_size: Integer batch size or `None` if not defined. + steps: Total number of steps (batches of samples) + before declaring `_predict_loop` finished. + Ignored with the default value of `None`. + steps_name: The public API's parameter name for `steps`. + + Raises: + ValueError: when `steps` is `None` and the attribute `ins.shape` + does not exist. Also raises ValueError when `steps` is not `None` + and `batch_size` is not `None` because they are mutually + exclusive. + + Returns: + When steps is `None`, returns the number of samples to be + processed based on the size of the first dimension of the + first input numpy array. When steps is not `None` and + `batch_size` is `None`, returns `None`. + + Raises: + ValueError: In case of invalid arguments. + """ + if steps is not None: + num_samples = None + if batch_size is not None: + raise ValueError( + 'If ' + steps_name + ' is set, the `batch_size` must be None.') + elif ins and hasattr(ins[0], 'shape'): + num_samples = ins[0].shape[0] + else: + raise ValueError( + 'Either the input data should have ' + 'a defined shape, or ' + steps_name + ' should be specified.') + return num_samples + + +def standardize_input_data(data, + names, + shapes=None, + check_batch_axis=True, + exception_prefix=''): + """Normalizes inputs and targets provided by users. + + Users may pass data as a list of arrays, dictionary of arrays, + or as a single array. We normalize this to an ordered list of + arrays (same order as `names`), while checking that the provided + arrays have shapes that match the network's expectations. + + Arguments: + data: User-provided input data (polymorphic). + names: List of expected array names. + shapes: Optional list of expected array shapes. + check_batch_axis: Boolean; whether to check that + the batch axis of the arrays matches the expected + value found in `shapes`. + exception_prefix: String prefix used for exception formatting. + + Returns: + List of standardized input arrays (one array per model input). + + Raises: + ValueError: in case of improperly formatted user-provided data. + """ + if not names: + if data is not None and hasattr(data, '__len__') and len(data): + raise ValueError('Error when checking model ' + exception_prefix + ': ' + 'expected no data, but got:', data) + return [] + if data is None: + return [None for _ in range(len(names))] + + if isinstance(data, dict): + try: + data = [ + data[x].values + if data[x].__class__.__name__ == 'DataFrame' else data[x] + for x in names + ] + except KeyError as e: + raise ValueError('No data provided for "' + e.args[0] + '". Need data ' + 'for each key in: ' + str(names)) + elif isinstance(data, list): + if isinstance(data[0], list): + data = [np.asarray(d) for d in data] + elif len(names) == 1 and isinstance(data[0], (float, int)): + data = [np.asarray(data)] + else: + data = [ + x.values if x.__class__.__name__ == 'DataFrame' else x for x in data + ] + else: + data = data.values if data.__class__.__name__ == 'DataFrame' else data + data = [data] + data = [ + np.expand_dims(x, 1) if x is not None and x.ndim == 1 else x for x in data + ] + + if len(data) != len(names): + if data and hasattr(data[0], 'shape'): + raise ValueError('Error when checking model ' + exception_prefix + + ': the list of Numpy arrays that you are passing to ' + 'your model is not the size the model expected. ' + 'Expected to see ' + str(len(names)) + ' array(s), ' + 'but instead got the following list of ' + + str(len(data)) + ' arrays: ' + str(data)[:200] + '...') + elif len(names) > 1: + raise ValueError( + 'Error when checking model ' + exception_prefix + + ': you are passing a list as input to your model, ' + 'but the model expects a list of ' + str(len(names)) + + ' Numpy arrays instead. The list you passed was: ' + str(data)[:200]) + elif len(data) == 1 and not hasattr(data[0], 'shape'): + raise TypeError('Error when checking model ' + exception_prefix + + ': data should be a Numpy array, or list/dict of ' + 'Numpy arrays. Found: ' + str(data)[:200] + '...') + elif len(names) == 1: + data = [np.asarray(data)] + + # Check shapes compatibility. + if shapes: + for i in range(len(names)): + if shapes[i] is not None: + data_shape = data[i].shape + shape = shapes[i] + if data[i].ndim != len(shape): + raise ValueError('Error when checking ' + exception_prefix + + ': expected ' + names[i] + ' to have ' + + str(len(shape)) + ' dimensions, but got array ' + 'with shape ' + str(data_shape)) + if not check_batch_axis: + data_shape = data_shape[1:] + shape = shape[1:] + for dim, ref_dim in zip(data_shape, shape): + if ref_dim != dim and ref_dim: + raise ValueError( + 'Error when checking ' + exception_prefix + ': expected ' + + names[i] + ' to have shape ' + str(shape) + + ' but got array with shape ' + str(data_shape)) + return data + + +def standardize_sample_or_class_weights(x_weight, output_names, weight_type): + """Maps `sample_weight` or `class_weight` to model outputs. + + Arguments: + x_weight: User-provided `sample_weight` or `class_weight` argument. + output_names: List of output names (strings) in the model. + weight_type: A string used purely for exception printing. + + Returns: + A list of `sample_weight` or `class_weight` where there are exactly + one element per model output. + + Raises: + ValueError: In case of invalid user-provided argument. + """ + if x_weight is None or len(x_weight) == 0: # pylint: disable=g-explicit-length-test + return [None for _ in output_names] + if len(output_names) == 1: + if isinstance(x_weight, list) and len(x_weight) == 1: + return x_weight + if isinstance(x_weight, dict) and output_names[0] in x_weight: + return [x_weight[output_names[0]]] + else: + return [x_weight] + if isinstance(x_weight, list): + if len(x_weight) != len(output_names): + raise ValueError('Provided `' + weight_type + '` was a list of ' + + str(len(x_weight)) + ' elements, but the model has ' + + str(len(output_names)) + ' outputs. ' + 'You should provide one `' + weight_type + '`' + 'array per model output.') + return x_weight + if isinstance(x_weight, dict): + x_weights = [] + for name in output_names: + x_weights.append(x_weight.get(name)) + return x_weights + else: + raise TypeError( + 'The model has multiple outputs, so `' + weight_type + '` ' + 'should be either a list or a dict. ' + 'Provided `' + weight_type + '` type not understood: ' + str(x_weight)) + + +def standardize_class_weights(class_weight, output_names): + return standardize_sample_or_class_weights(class_weight, output_names, + 'class_weight') + + +def standardize_sample_weights(sample_weight, output_names): + return standardize_sample_or_class_weights(sample_weight, output_names, + 'sample_weight') + + +def check_array_lengths(inputs, targets, weights=None): + """Does user input validation for numpy arrays. + + Arguments: + inputs: list of Numpy arrays of inputs. + targets: list of Numpy arrays of targets. + weights: list of Numpy arrays of sample weights. + + Raises: + ValueError: in case of incorrectly formatted data. + """ + + def set_of_lengths(x): + # return a set with the variation between + # different shapes, with None => 0 + if x is None: + return {} + else: + return set([y.shape[0] for y in x if y is not None]) + + set_x = set_of_lengths(inputs) + set_y = set_of_lengths(targets) + set_w = set_of_lengths(weights) + if len(set_x) > 1: + raise ValueError('All input arrays (x) should have ' + 'the same number of samples. Got array shapes: ' + + str([x.shape for x in inputs])) + if len(set_y) > 1: + raise ValueError('All target arrays (y) should have ' + 'the same number of samples. Got array shapes: ' + + str([y.shape for y in targets])) + if set_x and set_y and list(set_x)[0] != list(set_y)[0]: + raise ValueError('Input arrays should have ' + 'the same number of samples as target arrays. ' + 'Found ' + str(list(set_x)[0]) + ' input samples ' + 'and ' + str(list(set_y)[0]) + ' target samples.') + if len(set_w) > 1: + raise ValueError('All sample_weight arrays should have ' + 'the same number of samples. Got array shapes: ' + + str([w.shape for w in weights])) + if set_y and set_w and list(set_y)[0] != list(set_w)[0]: + raise ValueError('Sample_weight arrays should have ' + 'the same number of samples as target arrays. Got ' + + str(list(set_y)[0]) + ' input samples and ' + + str(list(set_w)[0]) + ' target samples.') + + +def check_loss_and_target_compatibility(targets, loss_fns, output_shapes): + """Does validation on the compatibility of targets and loss functions. + + This helps prevent users from using loss functions incorrectly. This check + is purely for UX purposes. + + Arguments: + targets: list of Numpy arrays of targets. + loss_fns: list of loss functions. + output_shapes: list of shapes of model outputs. + + Raises: + ValueError: if a loss function or target array + is incompatible with an output. + """ + key_losses = { + losses.mean_squared_error, losses.binary_crossentropy, + losses.categorical_crossentropy + } + for y, loss, shape in zip(targets, loss_fns, output_shapes): + if y is None or loss is None or tensor_util.is_tensor(y): + continue + if loss is losses.categorical_crossentropy: + if y.shape[-1] == 1: + raise ValueError('You are passing a target array of shape ' + str( + y.shape) + ' while using as loss `categorical_crossentropy`. ' + '`categorical_crossentropy` expects ' + 'targets to be binary matrices (1s and 0s) ' + 'of shape (samples, classes). ' + 'If your targets are integer classes, ' + 'you can convert them to the expected format via:\n' + '```\n' + 'from keras.utils import to_categorical\n' + 'y_binary = to_categorical(y_int)\n' + '```\n' + '\n' + 'Alternatively, you can use the loss function ' + '`sparse_categorical_crossentropy` instead, ' + 'which does expect integer targets.') + if loss in key_losses: + for target_dim, out_dim in zip(y.shape[1:], shape[1:]): + if out_dim is not None and target_dim != out_dim: + raise ValueError('A target array with shape ' + str(y.shape) + + ' was passed for an output of shape ' + str(shape) + + ' while using as loss `' + loss.__name__ + '`. ' + 'This loss expects ' + 'targets to have the same shape ' + 'as the output.') + + +def collect_metrics(metrics, output_names): + """Maps metric functions to model outputs. + + Arguments: + metrics: a list or dict of metric functions. + output_names: a list of the names (strings) of model outputs. + + Returns: + A list (one entry per model output) of lists of metric functions. + For instance, if the model has 2 outputs, and for the first output + we want to compute "binary_accuracy" and "binary_crossentropy", + and just "binary_accuracy" for the second output, + the list would look like: + `[[binary_accuracy, binary_crossentropy], [binary_accuracy]]` + + Raises: + TypeError: if an incorrect type is passed for the `metrics` argument. + """ + if not metrics: + return [[] for _ in output_names] + if isinstance(metrics, list): + # we then apply all metrics to all outputs. + return [copy.copy(metrics) for _ in output_names] + elif isinstance(metrics, dict): + nested_metrics = [] + for name in output_names: + output_metrics = metrics.get(name, []) + if not isinstance(output_metrics, list): + output_metrics = [output_metrics] + nested_metrics.append(output_metrics) + return nested_metrics + else: + raise TypeError('Type of `metrics` argument not understood. ' + 'Expected a list or dictionary, found: ' + str(metrics)) + + +def batch_shuffle(index_array, batch_size): + """Shuffles an array in a batch-wise fashion. + + Useful for shuffling HDF5 arrays + (where one cannot access arbitrary indices). + + Arguments: + index_array: array of indices to be shuffled. + batch_size: integer. + + Returns: + The `index_array` array, shuffled in a batch-wise fashion. + """ + batch_count = int(len(index_array) / batch_size) + # to reshape we need to be cleanly divisible by batch size + # we stash extra items and reappend them after shuffling + last_batch = index_array[batch_count * batch_size:] + index_array = index_array[:batch_count * batch_size] + index_array = index_array.reshape((batch_count, batch_size)) + np.random.shuffle(index_array) + index_array = index_array.flatten() + return np.append(index_array, last_batch) + + +def weighted_masked_objective(fn): + """Adds support for masking and sample-weighting to an objective function. + + It transforms an objective function `fn(y_true, y_pred)` + into a sample-weighted, cost-masked objective function + `fn(y_true, y_pred, weights, mask)`. + + Arguments: + fn: The objective function to wrap, + with signature `fn(y_true, y_pred)`. + + Returns: + A function with signature `fn(y_true, y_pred, weights, mask)`. + """ + if fn is None: + return None + + def weighted(y_true, y_pred, weights, mask=None): + """Wrapper function. + + Arguments: + y_true: `y_true` argument of `fn`. + y_pred: `y_pred` argument of `fn`. + weights: Weights tensor. + mask: Mask tensor. + + Returns: + Scalar tensor. + """ + # score_array has ndim >= 2 + score_array = fn(y_true, y_pred) + if mask is not None: + # Cast the mask to floatX to avoid float64 upcasting in theano + mask = K.cast(mask, K.floatx()) + # mask should have the same shape as score_array + score_array *= mask + # the loss per batch should be proportional + # to the number of unmasked samples. + score_array /= K.mean(mask) + + # apply sample weighting + if weights is not None: + # reduce score_array to same ndim as weight array + ndim = K.ndim(score_array) + weight_ndim = K.ndim(weights) + score_array = K.mean(score_array, axis=list(range(weight_ndim, ndim))) + score_array *= weights + score_array /= K.mean(K.cast(K.not_equal(weights, 0), K.floatx())) + return K.mean(score_array) + + return weighted + + +def standardize_weights(y, + sample_weight=None, + class_weight=None, + sample_weight_mode=None): + """Performs sample weight validation and standardization. + + Everything gets normalized to a single sample-wise (or timestep-wise) + weight array. + + Arguments: + y: Numpy array of model targets to be weighted. + sample_weight: User-provided `sample_weight` argument. + class_weight: User-provided `class_weight` argument. + sample_weight_mode: One of `None` or `"temporal"`. + `"temporal"` indicated that we expect 2D weight data + that will be applied to the last 2 dimensions of + the targets (i.e. we are weighting timesteps, not samples). + + Returns: + A numpy array of target weights, one entry per sample to weight. + + Raises: + ValueError: In case of invalid user-provided arguments. + """ + if sample_weight_mode is not None: + if sample_weight_mode != 'temporal': + raise ValueError('"sample_weight_mode ' + 'should be None or "temporal". ' + 'Found: ' + str(sample_weight_mode)) + if len(y.shape) < 3: + raise ValueError('Found a sample_weight array for ' + 'an input with shape ' + str(y.shape) + '. ' + 'Timestep-wise sample weighting (use of ' + 'sample_weight_mode="temporal") is restricted to ' + 'outputs that are at least 3D, i.e. that have ' + 'a time dimension.') + if sample_weight is not None and len(sample_weight.shape) != 2: + raise ValueError('Found a sample_weight array with shape ' + + str(sample_weight.shape) + '. ' + 'In order to use timestep-wise sample weighting, ' + 'you should pass a 2D sample_weight array.') + else: + if sample_weight is not None and len(sample_weight.shape) != 1: + raise ValueError('Found a sample_weight array with shape ' + + str(sample_weight.shape) + '. ' + 'In order to use timestep-wise sample weights, ' + 'you should specify ' + 'sample_weight_mode="temporal" ' + 'in compile(). If you just mean to use ' + 'sample-wise weights, make sure your ' + 'sample_weight array is 1D.') + + if sample_weight is not None: + if len(sample_weight.shape) > len(y.shape): + raise ValueError( + 'Found a sample_weight with shape' + str(sample_weight.shape) + '.' + 'Expected sample_weight with rank ' + 'less than or equal to ' + str(len(y.shape))) + + if y.shape[:sample_weight.ndim] != sample_weight.shape: + raise ValueError( + 'Found a sample_weight array with shape ' + str(sample_weight.shape) + + ' for an input with shape ' + str(y.shape) + '. ' + 'sample_weight cannot be broadcast.') + return sample_weight + elif isinstance(class_weight, dict): + if len(y.shape) > 2: + raise ValueError('`class_weight` not supported for ' + '3+ dimensional targets.') + if y.shape[1] > 1: + y_classes = np.argmax(y, axis=1) + elif y.shape[1] == 1: + y_classes = np.reshape(y, y.shape[0]) + else: + y_classes = y + + weights = np.asarray( + [class_weight[cls] for cls in y_classes if cls in class_weight]) + + if len(weights) != len(y_classes): + # subtract the sets to pick all missing classes + existing_classes = set(y_classes) + existing_class_weight = set(class_weight.keys()) + raise ValueError('`class_weight` must contain all classes in the data.' + ' The classes %s exist in the data but not in ' + '`class_weight`.' % + (existing_classes - existing_class_weight)) + return weights + else: + return None diff --git a/tensorflow/python/keras/_impl/keras/estimator.py b/tensorflow/python/keras/_impl/keras/estimator.py index 0bf5bd41dc915fbecbce4c3a6191e925612dbebb..081f25e91452582afabee1e43f6e15f7e5d0013d 100644 --- a/tensorflow/python/keras/_impl/keras/estimator.py +++ b/tensorflow/python/keras/_impl/keras/estimator.py @@ -25,11 +25,15 @@ from tensorflow.python.client import session from tensorflow.python.estimator import estimator as estimator_lib from tensorflow.python.estimator import export as export_lib from tensorflow.python.estimator import model_fn as model_fn_lib +from tensorflow.python.estimator import run_config as run_config_lib from tensorflow.python.framework import ops from tensorflow.python.framework import random_seed from tensorflow.python.framework import sparse_tensor as sparse_tensor_lib from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras import models +from tensorflow.python.keras._impl.keras import optimizers +from tensorflow.python.keras._impl.keras.engine.base_layer import Layer +from tensorflow.python.keras._impl.keras.engine.network import Network from tensorflow.python.keras._impl.keras.utils.generic_utils import CustomObjectScope from tensorflow.python.ops import math_ops from tensorflow.python.ops import metrics as metrics_module @@ -50,36 +54,174 @@ def _cast_tensor_to_floatx(x): return math_ops.cast(x, K.floatx()) -def _create_ordered_io(keras_model, estimator_io_dict, is_input=True): +def _create_ordered_io(keras_model, estimator_io, is_input=True): """Create a list of tensors from IO dictionary based on Keras IO order. Args: - keras_model: an instance of compiled keras model. - estimator_io_dict: features or labels dictionary from model_fn. + keras_model: An instance of compiled keras model. + estimator_io: The features or labels (dict or plain array) from model_fn. is_input: True if dictionary is for inputs. Returns: - a list of tensors based on Keras IO order. + A list of tensors based on Keras IO order. Raises: ValueError: if dictionary keys cannot be found in Keras model input_names or output_names. """ - if is_input: - keras_io_names = keras_model.input_names + if isinstance(estimator_io, (list, tuple)): + # Case currently not supported by most built-in input_fn, + # but it's good to have for sanity + return [_cast_tensor_to_floatx(x) for x in estimator_io] + elif isinstance(estimator_io, dict): + if is_input: + if keras_model._is_graph_network: + keras_io_names = keras_model.input_names + else: + keras_io_names = [ + 'input_%d' % i for i in range(1, len(estimator_io) + 1)] + else: + if keras_model._is_graph_network: + keras_io_names = keras_model.output_names + else: + keras_io_names = [ + 'output_%d' % i for i in range(1, len(estimator_io) + 1)] + + for key in estimator_io: + if key not in keras_io_names: + raise ValueError( + 'Cannot find %s with name "%s" in Keras Model. ' + 'It needs to match one ' + 'of the following: %s' % ('input' if is_input else 'output', key, + ', '.join(keras_io_names))) + tensors = [_cast_tensor_to_floatx(estimator_io[io_name]) + for io_name in keras_io_names] + return tensors else: - keras_io_names = keras_model.output_names + # Plain array. + return _cast_tensor_to_floatx(estimator_io) - for key in estimator_io_dict: - if key not in keras_io_names: - raise ValueError( - 'Cannot find %s with name "%s" in Keras Model. It needs to match ' - 'one of the following: %s' % ('input' if is_input else 'output', key, - ', '.join(keras_io_names))) - tensors = [] - for io_name in keras_io_names: - tensors.append(_cast_tensor_to_floatx(estimator_io_dict[io_name])) - return tensors + +def _in_place_subclassed_model_reset(model): + """Substitute for model cloning that works for subclassed models. + + Subclassed models cannot be cloned because their topology is not serializable. + To "instantiate" an identical model in a new TF graph, we reuse the original + model object, but we clear its state. + + After calling this function on a model intance, you can use the model instance + as if it were a model clone (in particular you can use it in a new graph). + + This method clears the state of the input model. It is thus destructive. + However the original state can be restored fully by calling + `_in_place_subclassed_model_state_restoration`. + + Args: + model: Instance of a Keras model created via subclassing. + + Raises: + ValueError: In case the model uses a subclassed model as inner layer. + """ + assert not model._is_graph_network # Only makes sense for subclassed networks + # Retrieve all layers tracked by the model as well as their attribute names + attributes_cache = {} + for name in dir(model): + try: + value = getattr(model, name) + except (AttributeError, ValueError, TypeError): + continue + if isinstance(value, Layer): + attributes_cache[name] = value + assert value in model._layers + elif isinstance(value, (list, tuple)) and name not in ('layers', '_layers'): + # Handle case: list/tuple of layers (also tracked by the Network API). + if value and all(isinstance(val, Layer) for val in value): + raise ValueError('We do not support the use of list-of-layers ' + 'attributes in subclassed models used with ' + '`model_to_estimator` at this time. Found list ' + 'model: %s' % name) + + # Replace layers on the model with fresh layers + layers_to_names = {value: key for key, value in attributes_cache.items()} + original_layers = model._layers[:] + model._layers = [] + for layer in original_layers: # We preserve layer order. + config = layer.get_config() + # This will not work for nested subclassed models used as layers. + # This would be theoretically possible to support, but would add complexity. + # Only do it if users complain. + if isinstance(layer, Network) and not layer._is_graph_network: + raise ValueError('We do not support the use of nested subclassed models ' + 'in `model_to_estimator` at this time. Found nested ' + 'model: %s' % layer) + fresh_layer = layer.__class__.from_config(config) + name = layers_to_names[layer] + setattr(model, name, fresh_layer) + + # Cache original model build attributes (in addition to layers) + if (not hasattr(model, '_original_attributes_cache') or + model._original_attributes_cache is None): + if model.built: + attributes_to_cache = [ + 'inputs', + 'outputs', + '_feed_outputs', + '_feed_output_names', + '_feed_output_shapes', + '_feed_loss_fns', + 'loss_weights_list', + 'targets', + '_feed_targets', + 'sample_weight_modes', + 'weighted_metrics', + 'metrics_names', + 'metrics_tensors', + 'metrics_updates', + 'stateful_metric_names', + 'total_loss', + 'sample_weights', + '_feed_sample_weights', + 'train_function', + 'test_function', + 'predict_function', + '_collected_trainable_weights', + '_feed_inputs', + '_feed_input_names', + '_feed_input_shapes', + 'optimizer', + ] + for name in attributes_to_cache: + attributes_cache[name] = getattr(model, name) + model._original_attributes_cache = attributes_cache + + # Reset built state + model.built = False + model.inputs = None + model.outputs = None + + +def _in_place_subclassed_model_state_restoration(model): + """Restores the original state of a model after it was "reset". + + This undoes this action of `_in_place_subclassed_model_reset`. + + Args: + model: Instance of a Keras model created via subclassing, on which + `_in_place_subclassed_model_reset` was previously called. + """ + assert not model._is_graph_network + # Restore layers and build attributes + if (hasattr(model, '_original_attributes_cache') and + model._original_attributes_cache is not None): + model._layers = [] + for name, value in model._original_attributes_cache.items(): + setattr(model, name, value) + model._original_attributes_cache = None + else: + # Restore to the state of a never-called model. + model.built = False + model.inputs = None + model.outputs = None def _clone_and_build_model(mode, @@ -93,8 +235,8 @@ def _clone_and_build_model(mode, mode: training mode. keras_model: an instance of compiled keras model. custom_objects: Dictionary for custom objects. - features: - labels: + features: Dict of tensors. + labels: Dict of tensors, or single tensor instance. Returns: The newly built model. @@ -102,33 +244,49 @@ def _clone_and_build_model(mode, # Set to True during training, False for inference. K.set_learning_phase(mode == model_fn_lib.ModeKeys.TRAIN) - # Clone keras model. - input_tensors = None if features is None else _create_ordered_io( - keras_model, features) - if custom_objects: - with CustomObjectScope(custom_objects): + # Get list of inputs. + if features is None: + input_tensors = None + else: + input_tensors = _create_ordered_io(keras_model, + estimator_io=features, + is_input=True) + # Get list of outputs. + if labels is None: + target_tensors = None + elif isinstance(labels, dict): + target_tensors = _create_ordered_io(keras_model, + estimator_io=labels, + is_input=False) + else: + target_tensors = [ + _cast_tensor_to_floatx( + sparse_tensor_lib.convert_to_tensor_or_sparse_tensor(labels)) + ] + + if keras_model._is_graph_network: + if custom_objects: + with CustomObjectScope(custom_objects): + model = models.clone_model(keras_model, input_tensors=input_tensors) + else: model = models.clone_model(keras_model, input_tensors=input_tensors) else: - model = models.clone_model(keras_model, input_tensors=input_tensors) + model = keras_model + _in_place_subclassed_model_reset(model) + if input_tensors is not None: + model._set_inputs(input_tensors) # Compile/Build model - if mode is model_fn_lib.ModeKeys.PREDICT and not model.built: - model.build() + if mode is model_fn_lib.ModeKeys.PREDICT: + if isinstance(model, models.Sequential): + model.build() else: - optimizer_config = keras_model.optimizer.get_config() - optimizer = keras_model.optimizer.__class__.from_config(optimizer_config) - optimizer.iterations = training_util.get_or_create_global_step() - - # Get list of outputs. - if labels is None: - target_tensors = None - elif isinstance(labels, dict): - target_tensors = _create_ordered_io(keras_model, labels, is_input=False) + if isinstance(keras_model.optimizer, optimizers.TFOptimizer): + optimizer = keras_model.optimizer else: - target_tensors = [ - _cast_tensor_to_floatx( - sparse_tensor_lib.convert_to_tensor_or_sparse_tensor(labels)) - ] + optimizer_config = keras_model.optimizer.get_config() + optimizer = keras_model.optimizer.__class__.from_config(optimizer_config) + optimizer.iterations = training_util.get_or_create_global_step() model.compile( optimizer, @@ -168,10 +326,14 @@ def _create_keras_model_fn(keras_model, custom_objects=None): # Set loss and metric only during train and evaluate. if mode is not model_fn_lib.ModeKeys.PREDICT: - model._make_train_function() # pylint: disable=protected-access + if mode is model_fn_lib.ModeKeys.TRAIN: + model._make_train_function() # pylint: disable=protected-access + else: + model._make_test_function() # pylint: disable=protected-access loss = model.total_loss if model.metrics: + # TODO(fchollet): support stateful metrics eval_metric_ops = {} # When each metric maps to an output if isinstance(model.metrics, dict): @@ -195,6 +357,10 @@ def _create_keras_model_fn(keras_model, custom_objects=None): if mode is model_fn_lib.ModeKeys.TRAIN: train_op = model.train_function.updates_op + if not model._is_graph_network: + # Reset model state to original state, + # to avoid `model_fn` being destructive for the initial model argument. + _in_place_subclassed_model_state_restoration(keras_model) return model_fn_lib.EstimatorSpec( mode=mode, predictions=predictions, @@ -274,10 +440,11 @@ def model_to_estimator(keras_model=None, """ if (not keras_model) and (not keras_model_path): raise ValueError( - 'Either keras_model or keras_model_path needs to be provided.') + 'Either `keras_model` or `keras_model_path` needs to be provided.') if keras_model and keras_model_path: raise ValueError( - 'Please specity either keras_model or keras_model_path but not both.') + 'Please specity either `keras_model` or `keras_model_path`, ' + 'but not both.') if not keras_model: if keras_model_path.startswith( @@ -288,18 +455,42 @@ def model_to_estimator(keras_model=None, logging.info('Loading models from %s', keras_model_path) keras_model = models.load_model(keras_model_path) else: - logging.info('Using the Keras model from memory.') + logging.info('Using the Keras model provided.') keras_model = keras_model - if not hasattr(keras_model, 'optimizer'): + if not hasattr(keras_model, 'optimizer') or not keras_model.optimizer: raise ValueError( - 'Given keras model has not been compiled yet. Please compile first ' - 'before creating the estimator.') + 'The given keras model has not been compiled yet. Please compile first ' + 'before calling `model_to_estimator`.') + + if isinstance(config, dict): + config = run_config_lib.RunConfig(**config) - keras_weights = keras_model.get_weights() keras_model_fn = _create_keras_model_fn(keras_model, custom_objects) - est = estimator_lib.Estimator( + estimator = estimator_lib.Estimator( keras_model_fn, model_dir=model_dir, config=config) - # TODO(yifeif): move checkpoint initialization to scaffold.init_fn - _save_first_checkpoint(keras_model, est, custom_objects, keras_weights) - return est + + # Pass the config into keras backend's default session. + with session.Session(config=estimator._session_config) as sess: + K.set_session(sess) + + keras_weights = keras_model.get_weights() + if keras_model._is_graph_network: + # TODO(yifeif): move checkpoint initialization to scaffold.init_fn + _save_first_checkpoint(keras_model, + estimator, + custom_objects, + keras_weights) + elif keras_model.built: + logging.warning('You are creating an Estimator from a Keras model ' + 'manually subclassed from `Model`, that was ' + 'already called on some inputs (and thus already had ' + 'weights). We are currently unable to preserve ' + 'the model\'s state (its weights) ' + 'as part of the estimator ' + 'in this case. Be warned that the estimator ' + 'has been created using ' + 'a freshly initialized version of your model.\n' + 'Note that this doesn\'t affect the state of the ' + 'model instance you passed as `keras_model` argument.') + return estimator diff --git a/tensorflow/python/keras/_impl/keras/estimator_test.py b/tensorflow/python/keras/_impl/keras/estimator_test.py index 88dd14b856a4ee9dfbee61d6fd1bdb96af24b50c..e076dc25b16900636313f0ddd85a61b8d917fc91 100644 --- a/tensorflow/python/keras/_impl/keras/estimator_test.py +++ b/tensorflow/python/keras/_impl/keras/estimator_test.py @@ -24,6 +24,7 @@ import tempfile import numpy as np +from tensorflow.core.protobuf import config_pb2 from tensorflow.python.estimator import run_config as run_config_lib from tensorflow.python.estimator.inputs import numpy_io from tensorflow.python.framework import test_util @@ -33,6 +34,7 @@ from tensorflow.python.keras._impl.keras.applications import mobilenet from tensorflow.python.platform import gfile from tensorflow.python.platform import test from tensorflow.python.summary.writer import writer_cache +from tensorflow.python.training import rmsprop try: @@ -63,12 +65,42 @@ def simple_functional_model(): return model -def get_resource_for_simple_model(is_sequential=True, is_evaluate=False): - model = simple_sequential_model( - ) if is_sequential else simple_functional_model() - if is_sequential: +def simple_subclassed_model(): + + class SimpleModel(keras.Model): + + def __init__(self): + super(SimpleModel, self).__init__() + self.dense1 = keras.layers.Dense(16, activation='relu') + self.dp = keras.layers.Dropout(0.1) + self.dense2 = keras.layers.Dense(_NUM_CLASS, activation='softmax') + + def call(self, inputs): + x = self.dense1(inputs) + x = self.dp(x) + return self.dense2(x) + + return SimpleModel() + + +def get_resource_for_simple_model(model_type='sequential', + is_evaluate=False,): + if model_type == 'sequential': + model = simple_sequential_model() model.build() - input_name = model.input_names[0] + elif model_type == 'subclass': + model = simple_subclassed_model() + else: + assert model_type == 'functional' + model = simple_functional_model() + + if model_type == 'subclass': + input_name = 'input_1' + output_name = 'output_1' + else: + input_name = model.input_names[0] + output_name = model.output_names[0] + np.random.seed(_RANDOM_SEED) (x_train, y_train), (x_test, y_test) = testing_utils.get_test_data( train_samples=_TRAIN_SIZE, @@ -79,17 +111,19 @@ def get_resource_for_simple_model(is_sequential=True, is_evaluate=False): y_test = keras.utils.to_categorical(y_test) train_input_fn = numpy_io.numpy_input_fn( - x={input_name: x_train}, - y=y_train, + x=randomize_io_type(x_train, input_name), + y=randomize_io_type(y_train, output_name), shuffle=False, num_epochs=None, batch_size=16) evaluate_input_fn = numpy_io.numpy_input_fn( - x={input_name: x_test}, y=y_test, num_epochs=1, shuffle=False) + x=randomize_io_type(x_test, input_name), + y=randomize_io_type(y_test, output_name), + num_epochs=1, shuffle=False) predict_input_fn = numpy_io.numpy_input_fn( - x={input_name: x_test}, num_epochs=1, shuffle=False) + x=randomize_io_type(x_test, input_name), num_epochs=1, shuffle=False) inference_input_fn = evaluate_input_fn if is_evaluate else predict_input_fn @@ -97,6 +131,14 @@ def get_resource_for_simple_model(is_sequential=True, is_evaluate=False): y_test), train_input_fn, inference_input_fn +def randomize_io_type(array, name): + switch = np.random.random() + if switch > 0.5: + return array + else: + return {name: array} + + def multi_inputs_multi_outputs_model(): # test multi-input layer a = keras.layers.Input(shape=(16,), name='input_a') @@ -133,10 +175,10 @@ class TestKerasEstimator(test_util.TensorFlowTestCase): gfile.DeleteRecursively(self._base_dir) def test_train(self): - for is_sequential in [True, False]: + for model_type in ['sequential', 'functional']: keras_model, (_, _), ( _, _), train_input_fn, eval_input_fn = get_resource_for_simple_model( - is_sequential=is_sequential, is_evaluate=True) + model_type=model_type, is_evaluate=True) keras_model.compile( loss='categorical_crossentropy', optimizer='rmsprop', @@ -154,10 +196,87 @@ class TestKerasEstimator(test_util.TensorFlowTestCase): writer_cache.FileWriterCache.clear() gfile.DeleteRecursively(self._config.model_dir) + def test_train_with_tf_optimizer(self): + for model_type in ['sequential', 'functional']: + keras_model, (_, _), ( + _, _), train_input_fn, eval_input_fn = get_resource_for_simple_model( + model_type=model_type, is_evaluate=True) + keras_model.compile( + loss='categorical_crossentropy', + optimizer=rmsprop.RMSPropOptimizer(1e-3), + metrics=['mse', keras.metrics.categorical_accuracy]) + + with self.test_session(): + est_keras = keras.estimator.model_to_estimator( + keras_model=keras_model, + # Also use dict config argument to get test coverage for that line. + config={ + 'tf_random_seed': _RANDOM_SEED, + 'model_dir': self._base_dir, + }) + before_eval_results = est_keras.evaluate( + input_fn=eval_input_fn, steps=1) + est_keras.train(input_fn=train_input_fn, steps=_TRAIN_SIZE / 16) + after_eval_results = est_keras.evaluate(input_fn=eval_input_fn, steps=1) + self.assertLess(after_eval_results['loss'], before_eval_results['loss']) + + writer_cache.FileWriterCache.clear() + gfile.DeleteRecursively(self._config.model_dir) + + def test_train_with_subclassed_model(self): + keras_model, (_, _), ( + _, _), train_input_fn, eval_input_fn = get_resource_for_simple_model( + model_type='subclass', is_evaluate=True) + keras_model.compile( + loss='categorical_crossentropy', + optimizer=rmsprop.RMSPropOptimizer(1e-3), + metrics=['mse', keras.metrics.categorical_accuracy]) + + with self.test_session(): + est_keras = keras.estimator.model_to_estimator( + keras_model=keras_model, config=self._config) + est_keras.train(input_fn=train_input_fn, steps=_TRAIN_SIZE / 16) + before_eval_results = est_keras.evaluate( + input_fn=eval_input_fn, steps=1) + est_keras.train(input_fn=train_input_fn, steps=_TRAIN_SIZE / 16) + after_eval_results = est_keras.evaluate(input_fn=eval_input_fn, steps=1) + self.assertLess(after_eval_results['loss'], before_eval_results['loss']) + + def test_train_with_subclassed_model_with_existing_state(self): + keras_model, (_, _), ( + _, _), train_input_fn, eval_input_fn = get_resource_for_simple_model( + model_type='subclass', is_evaluate=True) + keras_model.compile( + loss='categorical_crossentropy', + optimizer=rmsprop.RMSPropOptimizer(1e-3), + metrics=['mse', keras.metrics.categorical_accuracy]) + + with self.test_session(): + # Create state + keras_model.train_on_batch(np.random.random((10,) + _INPUT_SIZE), + np.random.random((10, _NUM_CLASS))) + original_preds = keras_model.predict(np.ones((10,) + _INPUT_SIZE)) + + est_keras = keras.estimator.model_to_estimator( + keras_model=keras_model, config=self._config) + est_keras.train(input_fn=train_input_fn, steps=_TRAIN_SIZE / 16) + before_eval_results = est_keras.evaluate( + input_fn=eval_input_fn, steps=1) + est_keras.train(input_fn=train_input_fn, steps=_TRAIN_SIZE / 16) + after_eval_results = est_keras.evaluate(input_fn=eval_input_fn, steps=1) + self.assertLess(after_eval_results['loss'], before_eval_results['loss']) + + # Check that original model state was not altered + preds = keras_model.predict(np.ones((10,) + _INPUT_SIZE)) + self.assertAllClose(original_preds, preds, atol=1e-5) + # Check that the original model compilation did not break + keras_model.train_on_batch(np.random.random((10,) + _INPUT_SIZE), + np.random.random((10, _NUM_CLASS))) + def test_evaluate(self): keras_model, (x_train, y_train), ( x_test, y_test), _, eval_input_fn = get_resource_for_simple_model( - is_sequential=False, is_evaluate=True) + model_type='functional', is_evaluate=True) with self.test_session(): metrics = [ @@ -199,7 +318,7 @@ class TestKerasEstimator(test_util.TensorFlowTestCase): # Check that predict on a pretrained model yield the same result. keras_model, (x_train, y_train), ( x_test, _), _, pred_input_fn = get_resource_for_simple_model( - is_sequential=True, is_evaluate=False) + model_type='sequential', is_evaluate=False) with self.test_session(): keras_model.compile( @@ -261,7 +380,7 @@ class TestKerasEstimator(test_util.TensorFlowTestCase): keras_model, (x_train, y_train), ( x_test, _), _, pred_input_fn = get_resource_for_simple_model( - is_sequential=False, is_evaluate=False) + model_type='functional', is_evaluate=False) with self.test_session(): keras_model.compile( @@ -377,6 +496,22 @@ class TestKerasEstimator(test_util.TensorFlowTestCase): keras_model=keras_model, model_dir=tempfile.mkdtemp(dir=self._base_dir)) + def test_gpu_config(self): + keras_model, (_, _), (_, _), _, _ = get_resource_for_simple_model() + keras_model.compile( + loss='categorical_crossentropy', + optimizer='rmsprop', + metrics=['mse', keras.metrics.categorical_accuracy]) + + gpu_options = config_pb2.GPUOptions(per_process_gpu_memory_fraction=0.3) + sess_config = config_pb2.ConfigProto(gpu_options=gpu_options) + self._config._session_config = sess_config + keras.estimator.model_to_estimator( + keras_model=keras_model, config=self._config) + self.assertEqual(keras.backend.get_session() + ._config.gpu_options.per_process_gpu_memory_fraction, + gpu_options.per_process_gpu_memory_fraction) + if __name__ == '__main__': test.main() diff --git a/tensorflow/python/keras/_impl/keras/layers/advanced_activations.py b/tensorflow/python/keras/_impl/keras/layers/advanced_activations.py index 7cac17c51a9adcf8fc62154b6633de60bab18387..c40ee109aaea7dacea72e095b1d8cea3ed2e9bf8 100644 --- a/tensorflow/python/keras/_impl/keras/layers/advanced_activations.py +++ b/tensorflow/python/keras/_impl/keras/layers/advanced_activations.py @@ -25,7 +25,7 @@ from tensorflow.python.keras._impl.keras import initializers from tensorflow.python.keras._impl.keras import regularizers from tensorflow.python.keras._impl.keras.engine import InputSpec from tensorflow.python.keras._impl.keras.engine import Layer -from tensorflow.python.keras._impl.keras.engine.topology import shape_type_conversion +from tensorflow.python.keras._impl.keras.engine.base_layer import shape_type_conversion from tensorflow.python.util.tf_export import tf_export diff --git a/tensorflow/python/keras/_impl/keras/layers/convolutional_recurrent.py b/tensorflow/python/keras/_impl/keras/layers/convolutional_recurrent.py index d2792b9636214d21e9658018f853fb6c0808abb4..d95a0942452afa82e277c358be5c3b2ba061ac98 100644 --- a/tensorflow/python/keras/_impl/keras/layers/convolutional_recurrent.py +++ b/tensorflow/python/keras/_impl/keras/layers/convolutional_recurrent.py @@ -26,7 +26,7 @@ from tensorflow.python.keras._impl.keras import constraints from tensorflow.python.keras._impl.keras import initializers from tensorflow.python.keras._impl.keras import regularizers from tensorflow.python.keras._impl.keras.engine import InputSpec -from tensorflow.python.keras._impl.keras.engine.topology import shape_type_conversion +from tensorflow.python.keras._impl.keras.engine.base_layer import shape_type_conversion from tensorflow.python.keras._impl.keras.layers.recurrent import Recurrent from tensorflow.python.keras._impl.keras.utils import conv_utils from tensorflow.python.util.tf_export import tf_export diff --git a/tensorflow/python/keras/_impl/keras/layers/convolutional_test.py b/tensorflow/python/keras/_impl/keras/layers/convolutional_test.py index c612e97a9d67f7398c78a7da1107f8e067bf9371..f4a134b96cec0385cb24a208f3403db944b68edc 100644 --- a/tensorflow/python/keras/_impl/keras/layers/convolutional_test.py +++ b/tensorflow/python/keras/_impl/keras/layers/convolutional_test.py @@ -553,7 +553,7 @@ class ZeroPaddingTest(test.TestCase): layer = keras.layers.ZeroPadding1D(padding=2) layer.build(shape) output = layer(keras.backend.variable(inputs)) - if context.in_eager_mode(): + if context.executing_eagerly(): np_output = output.numpy() else: np_output = keras.backend.eval(output) @@ -564,7 +564,7 @@ class ZeroPaddingTest(test.TestCase): layer = keras.layers.ZeroPadding1D(padding=(1, 2)) layer.build(shape) output = layer(keras.backend.variable(inputs)) - if context.in_eager_mode(): + if context.executing_eagerly(): np_output = output.numpy() else: np_output = keras.backend.eval(output) @@ -610,7 +610,7 @@ class ZeroPaddingTest(test.TestCase): padding=(2, 2), data_format=data_format) layer.build(inputs.shape) output = layer(keras.backend.variable(inputs)) - if context.in_eager_mode(): + if context.executing_eagerly(): np_output = output.numpy() else: np_output = keras.backend.eval(output) @@ -629,7 +629,7 @@ class ZeroPaddingTest(test.TestCase): padding=((1, 2), (3, 4)), data_format=data_format) layer.build(inputs.shape) output = layer(keras.backend.variable(inputs)) - if context.in_eager_mode(): + if context.executing_eagerly(): np_output = output.numpy() else: np_output = keras.backend.eval(output) @@ -683,7 +683,7 @@ class ZeroPaddingTest(test.TestCase): layer = keras.layers.ZeroPadding3D(padding=(2, 2, 2)) layer.build(inputs.shape) output = layer(keras.backend.variable(inputs)) - if context.in_eager_mode(): + if context.executing_eagerly(): np_output = output.numpy() else: np_output = keras.backend.eval(output) @@ -737,7 +737,7 @@ class UpSamplingTest(test.TestCase): size=(length_row, length_col), data_format=data_format) layer.build(inputs.shape) output = layer(keras.backend.variable(inputs)) - if context.in_eager_mode(): + if context.executing_eagerly(): np_output = output.numpy() else: np_output = keras.backend.eval(output) @@ -790,7 +790,7 @@ class UpSamplingTest(test.TestCase): data_format=data_format) layer.build(inputs.shape) output = layer(keras.backend.variable(inputs)) - if context.in_eager_mode(): + if context.executing_eagerly(): np_output = output.numpy() else: np_output = keras.backend.eval(output) @@ -865,7 +865,7 @@ class CroppingTest(test.TestCase): cropping=cropping, data_format=data_format) layer.build(inputs.shape) output = layer(keras.backend.variable(inputs)) - if context.in_eager_mode(): + if context.executing_eagerly(): np_output = output.numpy() else: np_output = keras.backend.eval(output) @@ -892,7 +892,7 @@ class CroppingTest(test.TestCase): cropping=cropping, data_format=data_format) layer.build(inputs.shape) output = layer(keras.backend.variable(inputs)) - if context.in_eager_mode(): + if context.executing_eagerly(): np_output = output.numpy() else: np_output = keras.backend.eval(output) @@ -937,7 +937,7 @@ class CroppingTest(test.TestCase): cropping=cropping, data_format=data_format) layer.build(inputs.shape) output = layer(keras.backend.variable(inputs)) - if context.in_eager_mode(): + if context.executing_eagerly(): np_output = output.numpy() else: np_output = keras.backend.eval(output) @@ -954,7 +954,7 @@ class CroppingTest(test.TestCase): cropping[2][0]:-cropping[2][1], :] np.testing.assert_allclose(np_output, expected_out) - # test incorrect use + # test incorrect use with self.assertRaises(ValueError): keras.layers.Cropping3D(cropping=(1, 1)) with self.assertRaises(ValueError): diff --git a/tensorflow/python/keras/_impl/keras/layers/core.py b/tensorflow/python/keras/_impl/keras/layers/core.py index 50a197c80c3d97f47a071a24297301dddf78a27e..73e4f15f7e259211c892fdc663e14dcb14aec58d 100644 --- a/tensorflow/python/keras/_impl/keras/layers/core.py +++ b/tensorflow/python/keras/_impl/keras/layers/core.py @@ -124,7 +124,7 @@ class Dropout(tf_core_layers.Dropout, Layer): training = K.learning_phase() output = super(Dropout, self).call(inputs, training=training) # EagerTensor object has no attribute _uses_learning_phase - if not context.in_eager_mode() and training is K.learning_phase(): + if not context.executing_eagerly() and training is K.learning_phase(): output._uses_learning_phase = True # pylint: disable=protected-access return output diff --git a/tensorflow/python/keras/_impl/keras/layers/embeddings.py b/tensorflow/python/keras/_impl/keras/layers/embeddings.py index ca92899a455cd28a756e9efff63655d7c43c9f45..006ecd3135be25d43133daed1603734ecd1be955 100644 --- a/tensorflow/python/keras/_impl/keras/layers/embeddings.py +++ b/tensorflow/python/keras/_impl/keras/layers/embeddings.py @@ -23,7 +23,7 @@ from tensorflow.python.keras._impl.keras import constraints from tensorflow.python.keras._impl.keras import initializers from tensorflow.python.keras._impl.keras import regularizers from tensorflow.python.keras._impl.keras.engine import Layer -from tensorflow.python.keras._impl.keras.engine.topology import shape_type_conversion +from tensorflow.python.keras._impl.keras.engine.base_layer import shape_type_conversion from tensorflow.python.util.tf_export import tf_export diff --git a/tensorflow/python/keras/_impl/keras/layers/local.py b/tensorflow/python/keras/_impl/keras/layers/local.py index df0efe6b8b7eaa0259eb6f4e246269551b3e0c15..13d96e939220c11a4090cf535e3efa4365fe8b62 100644 --- a/tensorflow/python/keras/_impl/keras/layers/local.py +++ b/tensorflow/python/keras/_impl/keras/layers/local.py @@ -25,7 +25,7 @@ from tensorflow.python.keras._impl.keras import initializers from tensorflow.python.keras._impl.keras import regularizers from tensorflow.python.keras._impl.keras.engine import InputSpec from tensorflow.python.keras._impl.keras.engine import Layer -from tensorflow.python.keras._impl.keras.engine.topology import shape_type_conversion +from tensorflow.python.keras._impl.keras.engine.base_layer import shape_type_conversion from tensorflow.python.keras._impl.keras.utils import conv_utils from tensorflow.python.util.tf_export import tf_export diff --git a/tensorflow/python/keras/_impl/keras/layers/merge.py b/tensorflow/python/keras/_impl/keras/layers/merge.py index cdf2878e83e32147d30d6b29742b7e9013a1facb..c660cbd449b11a139f64cfa8b3a35310a597491c 100644 --- a/tensorflow/python/keras/_impl/keras/layers/merge.py +++ b/tensorflow/python/keras/_impl/keras/layers/merge.py @@ -21,8 +21,8 @@ from __future__ import division from __future__ import print_function from tensorflow.python.keras._impl.keras import backend as K -from tensorflow.python.keras._impl.keras.engine.topology import Layer -from tensorflow.python.keras._impl.keras.engine.topology import shape_type_conversion +from tensorflow.python.keras._impl.keras.engine.base_layer import Layer +from tensorflow.python.keras._impl.keras.engine.base_layer import shape_type_conversion from tensorflow.python.util.tf_export import tf_export diff --git a/tensorflow/python/keras/_impl/keras/layers/noise.py b/tensorflow/python/keras/_impl/keras/layers/noise.py index 9010f4961585af58b7eae43dcd224e0c39606239..e309d160e5a9be97ff5f5356dad9dfaf85430233 100644 --- a/tensorflow/python/keras/_impl/keras/layers/noise.py +++ b/tensorflow/python/keras/_impl/keras/layers/noise.py @@ -22,7 +22,7 @@ import numpy as np from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras.engine import Layer -from tensorflow.python.keras._impl.keras.engine.topology import shape_type_conversion +from tensorflow.python.keras._impl.keras.engine.base_layer import shape_type_conversion from tensorflow.python.util.tf_export import tf_export diff --git a/tensorflow/python/keras/_impl/keras/layers/normalization.py b/tensorflow/python/keras/_impl/keras/layers/normalization.py index 0dedd5e8daa2974038c90ae2e8c68ca6516ba725..3b44b20bf822429351002c0f81fe8f9596d595d3 100644 --- a/tensorflow/python/keras/_impl/keras/layers/normalization.py +++ b/tensorflow/python/keras/_impl/keras/layers/normalization.py @@ -111,7 +111,7 @@ class BatchNormalization(tf_normalization_layers.BatchNormalization, Layer): if training is None: training = K.learning_phase() output = super(BatchNormalization, self).call(inputs, training=training) - if context.in_graph_mode() and training is K.learning_phase(): + if not context.executing_eagerly() and training is K.learning_phase(): output._uses_learning_phase = True # pylint: disable=protected-access return output diff --git a/tensorflow/python/keras/_impl/keras/layers/pooling_test.py b/tensorflow/python/keras/_impl/keras/layers/pooling_test.py index 70049f0976b7170005183bb4b028079b39a23afb..bb003c1dddf80e2a745c1268a3a7d045f4e8b036 100644 --- a/tensorflow/python/keras/_impl/keras/layers/pooling_test.py +++ b/tensorflow/python/keras/_impl/keras/layers/pooling_test.py @@ -105,7 +105,7 @@ class Pooling2DTest(test.TestCase): # This part of the test can only run on GPU but doesn't appear # to be properly assigned to a GPU when running in eager mode. - if not context.in_eager_mode(): + if not context.executing_eagerly(): # Only runs on GPU with CUDA, channels_first is not supported on CPU. # TODO(b/62340061): Support channels_first on CPU. if test.is_gpu_available(cuda_only=True): diff --git a/tensorflow/python/keras/_impl/keras/layers/recurrent.py b/tensorflow/python/keras/_impl/keras/layers/recurrent.py index a81971d9eef0070245c31704b527a7f8edbbe9e9..791f9b311300ed05591083d551c040eb25ac8e22 100644 --- a/tensorflow/python/keras/_impl/keras/layers/recurrent.py +++ b/tensorflow/python/keras/_impl/keras/layers/recurrent.py @@ -31,7 +31,7 @@ from tensorflow.python.keras._impl.keras import initializers from tensorflow.python.keras._impl.keras import regularizers from tensorflow.python.keras._impl.keras.engine import InputSpec from tensorflow.python.keras._impl.keras.engine import Layer -from tensorflow.python.keras._impl.keras.engine.topology import shape_type_conversion +from tensorflow.python.keras._impl.keras.engine.base_layer import shape_type_conversion from tensorflow.python.keras._impl.keras.utils.generic_utils import has_arg from tensorflow.python.platform import tf_logging as logging from tensorflow.python.util.tf_export import tf_export @@ -546,8 +546,8 @@ class RNN(Layer): raise ValueError('The initial state or constants of an RNN' ' layer cannot be specified with a mix of' ' Keras tensors and non-Keras tensors' - '(a "Keras tensor" is a tensor that was' - 'returned by a Keras layer, or by `Input`)') + ' (a "Keras tensor" is a tensor that was' + ' returned by a Keras layer, or by `Input`)') if is_keras_tensor: # Compute the full input spec, including state and constants @@ -936,7 +936,7 @@ class SimpleRNNCell(Layer): # Properly set learning phase on output tensor. if 0 < self.dropout + self.recurrent_dropout: - if training is None and not context.in_eager_mode(): + if training is None and not context.executing_eagerly(): # This would be harmless to set in eager mode, but eager tensors # disallow setting arbitrary attributes. output._uses_learning_phase = True @@ -1384,7 +1384,7 @@ class GRUCell(Layer): hh = self.activation(x_h + recurrent_h) h = z * h_tm1 + (1 - z) * hh if 0 < self.dropout + self.recurrent_dropout: - if training is None and not context.in_eager_mode(): + if training is None and not context.executing_eagerly(): # This would be harmless to set in eager mode, but eager tensors # disallow setting arbitrary attributes. h._uses_learning_phase = True @@ -1877,7 +1877,7 @@ class LSTMCell(Layer): h = o * self.activation(c) if 0 < self.dropout + self.recurrent_dropout: - if training is None and not context.in_eager_mode(): + if training is None and not context.executing_eagerly(): # This would be harmless to set in eager mode, but eager tensors # disallow setting arbitrary attributes. h._uses_learning_phase = True diff --git a/tensorflow/python/keras/_impl/keras/layers/wrappers.py b/tensorflow/python/keras/_impl/keras/layers/wrappers.py index 61f1a758e4701e6925af88b7fed9c48cf42ca735..76ddd9299dd669da35d89a6fe8fc521ce4c26337 100644 --- a/tensorflow/python/keras/_impl/keras/layers/wrappers.py +++ b/tensorflow/python/keras/_impl/keras/layers/wrappers.py @@ -25,7 +25,7 @@ from tensorflow.python.framework import tensor_shape from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras.engine import InputSpec from tensorflow.python.keras._impl.keras.engine import Layer -from tensorflow.python.keras._impl.keras.engine.topology import shape_type_conversion +from tensorflow.python.keras._impl.keras.engine.base_layer import shape_type_conversion from tensorflow.python.keras._impl.keras.utils.generic_utils import has_arg from tensorflow.python.layers import utils as tf_layers_util from tensorflow.python.util.tf_export import tf_export diff --git a/tensorflow/python/keras/_impl/keras/model_subclassing_test.py b/tensorflow/python/keras/_impl/keras/model_subclassing_test.py index 3d71a620fcb34d21c41f920eed99b1fe22668899..58b144365be6cd8ea5b2ea82e275eacdee6b6c84 100644 --- a/tensorflow/python/keras/_impl/keras/model_subclassing_test.py +++ b/tensorflow/python/keras/_impl/keras/model_subclassing_test.py @@ -174,19 +174,18 @@ class ModelSubclassingTest(test.TestCase): num_samples = 100 input_dim = 50 - with self.test_session(): - model = SimpleTestModel(num_classes=num_classes, - use_dp=True, - use_bn=True) - model.compile(loss='mse', - optimizer=RMSPropOptimizer(learning_rate=0.001), - metrics=['acc']) + model = SimpleTestModel(num_classes=num_classes, + use_dp=True, + use_bn=True) + model.compile(loss='mse', + optimizer=RMSPropOptimizer(learning_rate=0.001), + metrics=['acc']) - x = np.ones((num_samples, input_dim)) - y = np.zeros((num_samples, num_classes)) + x = np.ones((num_samples, input_dim)) + y = np.zeros((num_samples, num_classes)) - model.fit(x, y, epochs=2, batch_size=32, verbose=0) - _ = model.evaluate(x, y, verbose=0) + model.fit(x, y, epochs=2, batch_size=32, verbose=0) + _ = model.evaluate(x, y, verbose=0) @test_util.run_in_graph_and_eager_modes() def test_multi_io_workflow_with_np_arrays(self): @@ -194,21 +193,20 @@ class ModelSubclassingTest(test.TestCase): num_samples = 1000 input_dim = 50 - with self.test_session(): - model = MultiIOTestModel(num_classes=num_classes, - use_dp=True, - use_bn=True) - model.compile(loss='mse', - optimizer=RMSPropOptimizer(learning_rate=0.001), - metrics=['acc']) + model = MultiIOTestModel(num_classes=num_classes, + use_dp=True, + use_bn=True) + model.compile(loss='mse', + optimizer=RMSPropOptimizer(learning_rate=0.001), + metrics=['acc']) - x1 = np.ones((num_samples, input_dim)) - x2 = np.ones((num_samples, input_dim)) - y1 = np.zeros((num_samples, num_classes[0])) - y2 = np.zeros((num_samples, num_classes[1])) + x1 = np.ones((num_samples, input_dim)) + x2 = np.ones((num_samples, input_dim)) + y1 = np.zeros((num_samples, num_classes[0])) + y2 = np.zeros((num_samples, num_classes[1])) - model.fit([x1, x2], [y1, y2], epochs=2, batch_size=32, verbose=0) - _ = model.evaluate([x1, x2], [y1, y2], verbose=0) + model.fit([x1, x2], [y1, y2], epochs=2, batch_size=32, verbose=0) + _ = model.evaluate([x1, x2], [y1, y2], verbose=0) def test_single_io_workflow_with_tensors(self): @@ -321,14 +319,13 @@ class ModelSubclassingTest(test.TestCase): x = np.ones((num_samples, input_dim)) y = np.ones((num_samples, input_dim)) - with self.test_session(): - model = BNNet() - model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001)) - y_ref = model.predict(x) + model = BNNet() + model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001)) + y_ref = model.predict(x) - model.train_on_batch(x, y) - y_new = model.predict(x) - self.assertGreater(np.sum(np.abs(y_ref - y_new)), 0.1) + model.train_on_batch(x, y) + y_new = model.predict(x) + self.assertGreater(np.sum(np.abs(y_ref - y_new)), 0.1) @test_util.run_in_graph_and_eager_modes() def test_training_and_inference_behavior(self): @@ -350,14 +347,13 @@ class ModelSubclassingTest(test.TestCase): x = self.dp(inputs) return self.dense(x) - with self.test_session(): - model = DPNet() - x = np.ones((num_samples, input_dim)) - y = model.predict(x) - self.assertEqual(np.sum(y), np.sum(x)) - model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001)) - loss = model.train_on_batch(x, y) - self.assertGreater(loss, 0.1) + model = DPNet() + x = np.ones((num_samples, input_dim)) + y = model.predict(x) + self.assertEqual(np.sum(y), np.sum(x)) + model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001)) + loss = model.train_on_batch(x, y) + self.assertGreater(loss, 0.1) @test_util.run_in_graph_and_eager_modes() def test_training_methods(self): @@ -373,21 +369,20 @@ class ModelSubclassingTest(test.TestCase): y1 = np.zeros((num_samples, num_classes[0])) y2 = np.zeros((num_samples, num_classes[1])) - with self.test_session(): - model = MultiIOTestModel(num_classes=num_classes, use_bn=True) - model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001)) - model.fit([x1, x2], [y1, y2], epochs=2, batch_size=32, verbose=0) - model.fit({'input_1': x1, 'input_2': x2}, - {'output_1': y1, 'output_2': y2}, - epochs=2, batch_size=32) - model.fit([x1, x2], [y1, y2], epochs=2, batch_size=32, verbose=0, - validation_data=([x1, x2], [y1, y2])) + model = MultiIOTestModel(num_classes=num_classes, use_bn=True) + model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001)) + model.fit([x1, x2], [y1, y2], epochs=2, batch_size=32, verbose=0) + model.fit({'input_1': x1, 'input_2': x2}, + {'output_1': y1, 'output_2': y2}, + epochs=2, batch_size=32) + model.fit([x1, x2], [y1, y2], epochs=2, batch_size=32, verbose=0, + validation_data=([x1, x2], [y1, y2])) - model = MultiIOTestModel(num_classes=num_classes, use_bn=True) - model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001)) - model.train_on_batch([x1, x2], [y1, y2]) - model.train_on_batch({'input_1': x1, 'input_2': x2}, - {'output_1': y1, 'output_2': y2}) + model = MultiIOTestModel(num_classes=num_classes, use_bn=True) + model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001)) + model.train_on_batch([x1, x2], [y1, y2]) + model.train_on_batch({'input_1': x1, 'input_2': x2}, + {'output_1': y1, 'output_2': y2}) @test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True) def test_inference_methods(self): @@ -402,17 +397,16 @@ class ModelSubclassingTest(test.TestCase): y1 = np.zeros((num_samples, num_classes[0])) y2 = np.zeros((num_samples, num_classes[1])) - with self.test_session(): - model = MultiIOTestModel(num_classes=num_classes, use_bn=True) - model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001)) - model.evaluate([x1, x2], [y1, y2]) - model.test_on_batch([x1, x2], [y1, y2]) + model = MultiIOTestModel(num_classes=num_classes, use_bn=True) + model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001)) + model.evaluate([x1, x2], [y1, y2]) + model.test_on_batch([x1, x2], [y1, y2]) - model = MultiIOTestModel(num_classes=num_classes, use_bn=True) - model.predict([x1, x2]) + model = MultiIOTestModel(num_classes=num_classes, use_bn=True) + model.predict([x1, x2]) - model = MultiIOTestModel(num_classes=num_classes, use_bn=True) - model.predict_on_batch([x1, x2]) + model = MultiIOTestModel(num_classes=num_classes, use_bn=True) + model.predict_on_batch([x1, x2]) @test_util.run_in_graph_and_eager_modes() def test_trainable_mutation(self): @@ -435,26 +429,25 @@ class ModelSubclassingTest(test.TestCase): y1 = np.zeros((num_samples, num_classes[0])) y2 = np.zeros((num_samples, num_classes[1])) - with self.test_session(): - model = MultiIOTestModel(num_classes=num_classes, use_bn=True) - model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001)) - model.fit([x1, x2], [y1, y2], epochs=2, batch_size=32, verbose=0) - y_ref_1, y_ref_2 = model.predict([x1, x2]) + model = MultiIOTestModel(num_classes=num_classes, use_bn=True) + model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001)) + model.fit([x1, x2], [y1, y2], epochs=2, batch_size=32, verbose=0) + y_ref_1, y_ref_2 = model.predict([x1, x2]) - fd, fname = tempfile.mkstemp('.h5') - model.save_weights(fname) + fd, fname = tempfile.mkstemp('.h5') + model.save_weights(fname) - model = MultiIOTestModel(num_classes=num_classes, use_bn=True) - # need to build the model before loading weights - # (otherwise no weights to load) - model._set_inputs([x1, x2]) - model.load_weights(fname) + model = MultiIOTestModel(num_classes=num_classes, use_bn=True) + # need to build the model before loading weights + # (otherwise no weights to load) + model._set_inputs([x1, x2]) + model.load_weights(fname) - y1, y2 = model.predict([x1, x2]) - self.assertAllClose(y_ref_1, y1, atol=1e-5) - self.assertAllClose(y_ref_2, y2, atol=1e-5) - os.close(fd) - os.remove(fname) + y1, y2 = model.predict([x1, x2]) + self.assertAllClose(y_ref_1, y1, atol=1e-5) + self.assertAllClose(y_ref_2, y2, atol=1e-5) + os.close(fd) + os.remove(fname) @test_util.run_in_graph_and_eager_modes() def test_summary(self): @@ -488,23 +481,22 @@ class ModelSubclassingTest(test.TestCase): num_samples = 100 input_dim = 50 - with self.test_session(): - model = NestedTestModel1(num_classes=num_classes) - model.compile(loss='mse', - optimizer=RMSPropOptimizer(learning_rate=0.001), - metrics=['acc']) + model = NestedTestModel1(num_classes=num_classes) + model.compile(loss='mse', + optimizer=RMSPropOptimizer(learning_rate=0.001), + metrics=['acc']) - x = np.ones((num_samples, input_dim)) - y = np.zeros((num_samples, num_classes)) + x = np.ones((num_samples, input_dim)) + y = np.zeros((num_samples, num_classes)) - model.fit(x, y, epochs=2, batch_size=32, verbose=0) - _ = model.evaluate(x, y, verbose=0) + model.fit(x, y, epochs=2, batch_size=32, verbose=0) + _ = model.evaluate(x, y, verbose=0) - self.assertEqual(len(model.weights), 8 + len(model.test_net.weights)) - self.assertEqual(len(model.non_trainable_weights), - 2 + len(model.test_net.non_trainable_weights)) - self.assertEqual(len(model.trainable_weights), - 6 + len(model.test_net.trainable_weights)) + self.assertEqual(len(model.weights), 8 + len(model.test_net.weights)) + self.assertEqual(len(model.non_trainable_weights), + 2 + len(model.test_net.non_trainable_weights)) + self.assertEqual(len(model.trainable_weights), + 6 + len(model.test_net.trainable_weights)) @test_util.run_in_graph_and_eager_modes() def test_graph_nested_in_subclass(self): @@ -512,23 +504,22 @@ class ModelSubclassingTest(test.TestCase): num_samples = 100 input_dim = 50 - with self.test_session(): - model = NestedTestModel2(num_classes=num_classes) - model.compile(loss='mse', - optimizer=RMSPropOptimizer(learning_rate=0.001), - metrics=['acc']) + model = NestedTestModel2(num_classes=num_classes) + model.compile(loss='mse', + optimizer=RMSPropOptimizer(learning_rate=0.001), + metrics=['acc']) - x = np.ones((num_samples, input_dim)) - y = np.zeros((num_samples, num_classes)) + x = np.ones((num_samples, input_dim)) + y = np.zeros((num_samples, num_classes)) - model.fit(x, y, epochs=2, batch_size=32, verbose=0) - _ = model.evaluate(x, y, verbose=0) + model.fit(x, y, epochs=2, batch_size=32, verbose=0) + _ = model.evaluate(x, y, verbose=0) - self.assertEqual(len(model.weights), 8 + len(model.test_net.weights)) - self.assertEqual(len(model.non_trainable_weights), - 2 + len(model.test_net.non_trainable_weights)) - self.assertEqual(len(model.trainable_weights), - 6 + len(model.test_net.trainable_weights)) + self.assertEqual(len(model.weights), 8 + len(model.test_net.weights)) + self.assertEqual(len(model.non_trainable_weights), + 2 + len(model.test_net.non_trainable_weights)) + self.assertEqual(len(model.trainable_weights), + 6 + len(model.test_net.trainable_weights)) @test_util.run_in_graph_and_eager_modes() def test_subclass_nested_in_graph(self): @@ -536,22 +527,21 @@ class ModelSubclassingTest(test.TestCase): num_samples = 100 input_dim = 50 - with self.test_session(): - model = get_nested_model_3(input_dim=input_dim, num_classes=num_classes) - model.compile(loss='mse', - optimizer=RMSPropOptimizer(learning_rate=0.001), - metrics=['acc']) + model = get_nested_model_3(input_dim=input_dim, num_classes=num_classes) + model.compile(loss='mse', + optimizer=RMSPropOptimizer(learning_rate=0.001), + metrics=['acc']) - x = np.ones((num_samples, input_dim)) - y = np.zeros((num_samples, num_classes)) + x = np.ones((num_samples, input_dim)) + y = np.zeros((num_samples, num_classes)) - model.fit(x, y, epochs=2, batch_size=32, verbose=0) - _ = model.evaluate(x, y, verbose=0) + model.fit(x, y, epochs=2, batch_size=32, verbose=0) + _ = model.evaluate(x, y, verbose=0) - self.assertEqual(len(model.weights), 16) - self.assertEqual( - len(model.non_trainable_weights), 4) - self.assertEqual(len(model.trainable_weights), 12) + self.assertEqual(len(model.weights), 16) + self.assertEqual( + len(model.non_trainable_weights), 4) + self.assertEqual(len(model.trainable_weights), 12) @test_util.run_in_graph_and_eager_modes() def test_support_for_manual_training_arg(self): @@ -575,14 +565,13 @@ class ModelSubclassingTest(test.TestCase): x = self.dp(inputs, training=training) return self.dense(x) - with self.test_session(): - model = DPNet() - x = np.ones((10, 10)) - y = model.predict(x) - self.assertEqual(np.sum(y), np.sum(x)) - model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001)) - loss = model.train_on_batch(x, y) - self.assertGreater(loss, 0.1) + model = DPNet() + x = np.ones((10, 10)) + y = model.predict(x) + self.assertEqual(np.sum(y), np.sum(x)) + model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001)) + loss = model.train_on_batch(x, y) + self.assertGreater(loss, 0.1) if __name__ == '__main__': diff --git a/tensorflow/python/keras/_impl/keras/models.py b/tensorflow/python/keras/_impl/keras/models.py index 8000eaabab48dd225b7132af3ffeb11798c3096a..9602e7ba39b290f33c7ca9d0d1b5b35838667531 100644 --- a/tensorflow/python/keras/_impl/keras/models.py +++ b/tensorflow/python/keras/_impl/keras/models.py @@ -13,1305 +13,30 @@ # limitations under the License. # ============================================================================== # pylint: disable=protected-access -"""Home of the Sequential model, and the `save_model`/`load_model` functions. +"""Code for model cloning, plus model-related API entries. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import copy -import json -import os - -import numpy as np - -from tensorflow.python.framework import ops from tensorflow.python.keras._impl.keras import backend as K -from tensorflow.python.keras._impl.keras import layers as layer_module -from tensorflow.python.keras._impl.keras import optimizers -from tensorflow.python.keras._impl.keras.engine import topology -from tensorflow.python.keras._impl.keras.engine.topology import Input -from tensorflow.python.keras._impl.keras.engine.topology import InputLayer -from tensorflow.python.keras._impl.keras.engine.topology import Layer -from tensorflow.python.keras._impl.keras.engine.topology import TFBaseLayer -from tensorflow.python.keras._impl.keras.engine.training import Model +from tensorflow.python.keras._impl.keras.engine import saving +from tensorflow.python.keras._impl.keras.engine import sequential +from tensorflow.python.keras._impl.keras.engine import training +from tensorflow.python.keras._impl.keras.engine.input_layer import Input +from tensorflow.python.keras._impl.keras.engine.input_layer import InputLayer +from tensorflow.python.keras._impl.keras.utils import generic_utils from tensorflow.python.keras._impl.keras.utils.generic_utils import has_arg -from tensorflow.python.keras._impl.keras.utils.io_utils import ask_to_proceed_with_overwrite -from tensorflow.python.platform import tf_logging as logging -from tensorflow.python.util.tf_export import tf_export - - -# pylint: disable=g-import-not-at-top -try: - import h5py -except ImportError: - h5py = None - -try: - import yaml -except ImportError: - yaml = None -# pylint: enable=g-import-not-at-top - - -@tf_export('keras.models.save_model') -def save_model(model, filepath, overwrite=True, include_optimizer=True): - """Save a model to a HDF5 file. - - The saved model contains: - - the model's configuration (topology) - - the model's weights - - the model's optimizer's state (if any) - - Thus the saved model can be reinstantiated in - the exact same state, without any of the code - used for model definition or training. - - Arguments: - model: Keras model instance to be saved. - filepath: String, path where to save the model. - overwrite: Whether we should overwrite any existing - model at the target location, or instead - ask the user with a manual prompt. - include_optimizer: If True, save optimizer's state together. - - Raises: - ImportError: if h5py is not available. - """ - - if h5py is None: - raise ImportError('`save_model` requires h5py.') - - def get_json_type(obj): - """Serialize any object to a JSON-serializable structure. - - Arguments: - obj: the object to serialize - - Returns: - JSON-serializable structure representing `obj`. - - Raises: - TypeError: if `obj` cannot be serialized. - """ - # if obj is a serializable Keras class instance - # e.g. optimizer, layer - if hasattr(obj, 'get_config'): - return {'class_name': obj.__class__.__name__, 'config': obj.get_config()} - - # if obj is any numpy type - if type(obj).__module__ == np.__name__: - if isinstance(obj, np.ndarray): - return {'type': type(obj), 'value': obj.tolist()} - else: - return obj.item() - - # misc functions (e.g. loss function) - if callable(obj): - return obj.__name__ - - # if obj is a python 'type' - if type(obj).__name__ == type.__name__: - return obj.__name__ - - raise TypeError('Not JSON Serializable:', obj) - - from tensorflow.python.keras._impl.keras import __version__ as keras_version # pylint: disable=g-import-not-at-top - - # If file exists and should not be overwritten. - if not overwrite and os.path.isfile(filepath): - proceed = ask_to_proceed_with_overwrite(filepath) - if not proceed: - return - - with h5py.File(filepath, mode='w') as f: - f.attrs['keras_version'] = str(keras_version).encode('utf8') - f.attrs['backend'] = K.backend().encode('utf8') - f.attrs['model_config'] = json.dumps( - { - 'class_name': model.__class__.__name__, - 'config': model.get_config() - }, - default=get_json_type).encode('utf8') - - model_weights_group = f.create_group('model_weights') - model_layers = model.layers - topology.save_weights_to_hdf5_group(model_weights_group, model_layers) - - if include_optimizer and hasattr(model, 'optimizer'): - if isinstance(model.optimizer, optimizers.TFOptimizer): - logging.warning( - 'TensorFlow optimizers do not ' - 'make it possible to access ' - 'optimizer attributes or optimizer state ' - 'after instantiation. ' - 'As a result, we cannot save the optimizer ' - 'as part of the model save file.' - 'You will have to compile your model again after loading it. ' - 'Prefer using a Keras optimizer instead ' - '(see keras.io/optimizers).') - else: - f.attrs['training_config'] = json.dumps( - { - 'optimizer_config': { - 'class_name': model.optimizer.__class__.__name__, - 'config': model.optimizer.get_config() - }, - 'loss': model.loss, - 'metrics': model.metrics, - 'sample_weight_mode': model.sample_weight_mode, - 'loss_weights': model.loss_weights, - }, - default=get_json_type).encode('utf8') - - # Save optimizer weights. - symbolic_weights = getattr(model.optimizer, 'weights') - if symbolic_weights: - optimizer_weights_group = f.create_group('optimizer_weights') - weight_values = K.batch_get_value(symbolic_weights) - weight_names = [] - for w, val in zip(symbolic_weights, weight_values): - name = str(w.name) - weight_names.append(name.encode('utf8')) - optimizer_weights_group.attrs['weight_names'] = weight_names - for name, val in zip(weight_names, weight_values): - param_dset = optimizer_weights_group.create_dataset( - name, val.shape, dtype=val.dtype) - if not val.shape: - # scalar - param_dset[()] = val - else: - param_dset[:] = val - f.flush() - - -@tf_export('keras.models.load_model') -def load_model(filepath, custom_objects=None, compile=True): # pylint: disable=redefined-builtin - """Loads a model saved via `save_model`. - - Arguments: - filepath: String, path to the saved model. - custom_objects: Optional dictionary mapping names - (strings) to custom classes or functions to be - considered during deserialization. - compile: Boolean, whether to compile the model - after loading. - - Returns: - A Keras model instance. If an optimizer was found - as part of the saved model, the model is already - compiled. Otherwise, the model is uncompiled and - a warning will be displayed. When `compile` is set - to False, the compilation is omitted without any - warning. - - Raises: - ImportError: if h5py is not available. - ValueError: In case of an invalid savefile. - """ - if h5py is None: - raise ImportError('`load_model` requires h5py.') - - if not custom_objects: - custom_objects = {} - - def convert_custom_objects(obj): - """Handles custom object lookup. - - Arguments: - obj: object, dict, or list. - - Returns: - The same structure, where occurrences - of a custom object name have been replaced - with the custom object. - """ - if isinstance(obj, list): - deserialized = [] - for value in obj: - deserialized.append(convert_custom_objects(value)) - return deserialized - if isinstance(obj, dict): - deserialized = {} - for key, value in obj.items(): - deserialized[key] = convert_custom_objects(value) - return deserialized - if obj in custom_objects: - return custom_objects[obj] - return obj - - with h5py.File(filepath, mode='r') as f: - # instantiate model - model_config = f.attrs.get('model_config') - if model_config is None: - raise ValueError('No model found in config file.') - model_config = json.loads(model_config.decode('utf-8')) - model = model_from_config(model_config, custom_objects=custom_objects) - - # set weights - topology.load_weights_from_hdf5_group(f['model_weights'], model.layers) - - # Early return if compilation is not required. - if not compile: - return model - - # instantiate optimizer - training_config = f.attrs.get('training_config') - if training_config is None: - logging.warning('No training configuration found in save file: ' - 'the model was *not* compiled. Compile it manually.') - return model - training_config = json.loads(training_config.decode('utf-8')) - optimizer_config = training_config['optimizer_config'] - optimizer = optimizers.deserialize( - optimizer_config, custom_objects=custom_objects) - - # Recover loss functions and metrics. - loss = convert_custom_objects(training_config['loss']) - metrics = convert_custom_objects(training_config['metrics']) - sample_weight_mode = training_config['sample_weight_mode'] - loss_weights = training_config['loss_weights'] - - # Compile model. - model.compile( - optimizer=optimizer, - loss=loss, - metrics=metrics, - loss_weights=loss_weights, - sample_weight_mode=sample_weight_mode) - - # Set optimizer weights. - if 'optimizer_weights' in f: - # Build train function (to get weight updates). - if isinstance(model, Sequential): - model.model._make_train_function() - else: - model._make_train_function() - optimizer_weights_group = f['optimizer_weights'] - optimizer_weight_names = [ - n.decode('utf8') - for n in optimizer_weights_group.attrs['weight_names'] - ] - optimizer_weight_values = [ - optimizer_weights_group[n] for n in optimizer_weight_names - ] - try: - model.optimizer.set_weights(optimizer_weight_values) - except ValueError: - logging.warning('Error in loading the saved optimizer ' - 'state. As a result, your model is ' - 'starting with a freshly initialized ' - 'optimizer.') - return model - - -@tf_export('keras.models.model_from_config') -def model_from_config(config, custom_objects=None): - """Instantiates a Keras model from its config. - - Arguments: - config: Configuration dictionary. - custom_objects: Optional dictionary mapping names - (strings) to custom classes or functions to be - considered during deserialization. - - Returns: - A Keras model instance (uncompiled). - - Raises: - TypeError: if `config` is not a dictionary. - """ - if isinstance(config, list): - raise TypeError('`model_from_config` expects a dictionary, not a list. ' - 'Maybe you meant to use ' - '`Sequential.from_config(config)`?') - return layer_module.deserialize(config, custom_objects=custom_objects) - - -@tf_export('keras.models.model_from_yaml') -def model_from_yaml(yaml_string, custom_objects=None): - """Parses a yaml model configuration file and returns a model instance. - - Arguments: - yaml_string: YAML string encoding a model configuration. - custom_objects: Optional dictionary mapping names - (strings) to custom classes or functions to be - considered during deserialization. - - Returns: - A Keras model instance (uncompiled). - - Raises: - ImportError: if yaml module is not found. - """ - if yaml is None: - raise ImportError('Requires yaml module installed.') - config = yaml.load(yaml_string) - return layer_module.deserialize(config, custom_objects=custom_objects) - - -@tf_export('keras.models.model_from_json') -def model_from_json(json_string, custom_objects=None): - """Parses a JSON model configuration file and returns a model instance. - - Arguments: - json_string: JSON string encoding a model configuration. - custom_objects: Optional dictionary mapping names - (strings) to custom classes or functions to be - considered during deserialization. - - Returns: - A Keras model instance (uncompiled). - """ - config = json.loads(json_string) - return layer_module.deserialize(config, custom_objects=custom_objects) - - -@tf_export('keras.models.Sequential', 'keras.Sequential') -class Sequential(Model): - """Linear stack of layers. - - Arguments: - layers: list of layers to add to the model. - - # Note - The first layer passed to a Sequential model - should have a defined input shape. What that - means is that it should have received an `input_shape` - or `batch_input_shape` argument, - or for some type of layers (recurrent, Dense...) - an `input_dim` argument. - - Example: - - ```python - model = Sequential() - # first layer must have a defined input shape - model.add(Dense(32, input_dim=500)) - # afterwards, Keras does automatic shape inference - model.add(Dense(32)) - - # also possible (equivalent to the above): - model = Sequential() - model.add(Dense(32, input_shape=(500,))) - model.add(Dense(32)) - - # also possible (equivalent to the above): - model = Sequential() - # here the batch dimension is None, - # which means any batch size will be accepted by the model. - model.add(Dense(32, batch_input_shape=(None, 500))) - model.add(Dense(32)) - ``` - """ - - def __init__(self, layers=None, name=None): - self._is_graph_network = True - self._is_compiled = False - self._layers = [] # Stack of layers. - self.model = None # Internal Model instance. - self.inputs = [] # List of input tensors - self.outputs = [] # List of length 1: the output tensor (unique). - self._trainable = True - self._initial_weights = None - self._input_layers = [] - - # Model attributes. - self._inbound_nodes = [] - self._outbound_nodes = [] - self.built = False - - # Set model name. - if not name: - prefix = 'sequential_' - name = prefix + str(K.get_uid(prefix)) - self._name = name - - # Used by Layer base class. - self._dtype = None - self._activity_regularizer = None - - # The following properties are not actually used by Keras; - # they exist for compatibility with TF's variable scoping mechanism. - self._updates = [] - self._losses = [] - self._scope = None - self._reuse = None - self._base_name = name - self._graph = ops.get_default_graph() - - # Add to the model any layers passed to the constructor. - if layers: - for layer in layers: - self.add(layer) - - def add(self, layer): - """Adds a layer instance on top of the layer stack. - - Arguments: - layer: layer instance. - - Raises: - TypeError: If `layer` is not a layer instance. - ValueError: In case the `layer` argument does not - know its input shape. - ValueError: In case the `layer` argument has - multiple output tensors, or is already connected - somewhere else (forbidden in `Sequential` models). - """ - if not isinstance(layer, (Layer, TFBaseLayer)): - raise TypeError('The added layer must be ' - 'an instance of class Layer. ' - 'Found: ' + str(layer)) - if not self.outputs: - # First layer in model: check that it is an input layer. - if not isinstance(layer, InputLayer): - # Create an input layer. - # First, we need to infer its expected input shape and dtype. - if isinstance(layer, (Model, Sequential)): - # We were passed a model as first layer. - # This requires a specific way to figure out the - # input shape and dtype. - if not layer.layers: - raise ValueError('Cannot add an empty model ' - 'to a `Sequential` model.') - # In case of nested models: recover the first layer - # of the deepest model to infer input shape and dtype. - first_layer = layer.layers[0] - while isinstance(first_layer, (Model, Sequential)): - first_layer = first_layer.layers[0] - batch_shape = first_layer._batch_input_shape - dtype = first_layer.dtype - else: - # We were passed a regular layer, and it should - # know about its input shape. Otherwise, that's an error. - if not hasattr(layer, '_batch_input_shape'): - raise ValueError('The first layer in a ' - 'Sequential model must ' - 'get an `input_shape` argument.') - batch_shape = layer._batch_input_shape - dtype = layer.dtype - # Instantiate the input layer. - x = Input( - batch_shape=batch_shape, dtype=dtype, name=layer.name + '_input') - # This will build the current layer - # and create the node connecting the current layer - # to the input layer we just created. - layer(x) - - if len(layer._inbound_nodes[-1].output_tensors) != 1: - raise ValueError('All layers in a Sequential model ' - 'should have a single output tensor. ' - 'For multi-output layers, ' - 'use the functional API.') - - self.outputs = [layer._inbound_nodes[-1].output_tensors[0]] - self.inputs = topology.get_source_inputs(self.outputs[0]) - - # We create an input node, which we will keep updated - # as we add more layers - topology.Node( - outbound_layer=self, - inbound_layers=[], - node_indices=[], - tensor_indices=[], - input_tensors=self.inputs, - output_tensors=self.outputs) - else: - output_tensor = layer(self.outputs[0]) - if isinstance(output_tensor, list): - raise TypeError('All layers in a Sequential model ' - 'should have a single output tensor. ' - 'For multi-output layers, ' - 'use the functional API.') - self.outputs = [output_tensor] - # update self._inbound_nodes - self._inbound_nodes[0].output_tensors = self.outputs - self._inbound_nodes[0].output_shapes = [K.int_shape(self.outputs[0])] - - self._layers.append(layer) - self.built = False - - def pop(self): - """Removes the last layer in the model. - - Raises: - TypeError: if there are no layers in the model. - """ - if not self.layers: - raise TypeError('There are no layers in the model.') - - self.layers.pop() - if not self.layers: - self.outputs = [] - self._inbound_nodes = [] - self._outbound_nodes = [] - else: - self.layers[-1]._outbound_nodes = [] - self.outputs = [self.layers[-1].output] - # update self._inbound_nodes - self._inbound_nodes[0].output_tensors = self.outputs - self._inbound_nodes[0].output_shapes = [K.int_shape(self.outputs[0])] - self.built = False - - def get_layer(self, name=None, index=None): - """Retrieve a layer that is part of the model. - - Returns a layer based on either its name (unique) - or its index in the graph. Indices are based on - order of horizontal graph traversal (bottom-up). - - Arguments: - name: string, name of layer. - index: integer, index of layer. - - Returns: - A layer instance. - """ - if not self.built: - self.build() - return self.model.get_layer(name, index) - - def call(self, inputs, **kwargs): - if not self.built: - self.build() - return self.model.call(inputs, **kwargs) - - def build(self, input_shape=None): - if not self.inputs or not self.outputs: - raise TypeError('Sequential model cannot be built: model is empty.' - ' Add some layers first.') - # actually create the model - self.model = Model(self.inputs, self.outputs[0], name=self.name + '_model') - self.model.trainable = self.trainable - - # mirror model attributes - self.supports_masking = self.model.supports_masking - self._output_mask_cache = self.model._output_mask_cache - self._output_tensor_cache = self.model._output_tensor_cache - self._output_shape_cache = self.model._output_shape_cache - self._input_layers = self.model._input_layers - self._output_layers = self.model._output_layers - self._input_coordinates = self.model._input_coordinates - self._output_coordinates = self.model._output_coordinates - self._nodes_by_depth = self.model._nodes_by_depth - self._network_nodes = self.model._network_nodes - self.output_names = self.model.output_names - self.input_names = self.model.input_names - self._feed_input_names = self.model._feed_input_names - self._feed_inputs = self.model._feed_inputs - - # Make sure child model callbacks - # will call the parent Sequential model. - self.model.callback_model = self - - self.built = True - - @property - def uses_learning_phase(self): - if not self.built: - self.build() - return self.model.uses_learning_phase - - def _gather_list_attr(self, attr): - all_attrs = [] - for layer in self.layers: - all_attrs += getattr(layer, attr, []) - return all_attrs - - @property - def trainable(self): - return self._trainable - - @trainable.setter - def trainable(self, value): - if self.model: - self.model.trainable = value - self._trainable = value - - @property - def trainable_weights(self): - if not self.trainable: - return [] - return self._gather_list_attr('trainable_weights') - - @property - def non_trainable_weights(self): - weights = self._gather_list_attr('non_trainable_weights') - if not self.trainable: - trainable_weights = self._gather_list_attr('trainable_weights') - return trainable_weights + weights - return weights - - @property - def regularizers(self): - if not self.built: - self.build() - return self.model.regularizers - - def get_weights(self): - """Retrieves the weights of the model. - - Returns: - A flat list of Numpy arrays - (one array per model weight). - """ - if not self.built: - self.build() - return self.model.get_weights() - - def set_weights(self, weights): - """Sets the weights of the model. - - Arguments: - weights: Should be a list - of Numpy arrays with shapes and types matching - the output of `model.get_weights()`. - """ - if not self.built: - self.build() - self.model.set_weights(weights) - - def load_weights(self, filepath, by_name=False): - if h5py is None: - raise ImportError('`load_weights` requires h5py.') - f = h5py.File(filepath, mode='r') - if 'layer_names' not in f.attrs and 'model_weights' in f: - f = f['model_weights'] - layers = self.layers - if by_name: - topology.load_weights_from_hdf5_group_by_name(f, layers) - else: - topology.load_weights_from_hdf5_group(f, layers) - if hasattr(f, 'close'): - f.close() - - def save_weights(self, filepath, overwrite=True): - if h5py is None: - raise ImportError('`save_weights` requires h5py.') - # If file exists and should not be overwritten: - if not overwrite and os.path.isfile(filepath): - proceed = ask_to_proceed_with_overwrite(filepath) - if not proceed: - return - layers = self.layers - f = h5py.File(filepath, 'w') - topology.save_weights_to_hdf5_group(f, layers) - f.flush() - f.close() - - def compile(self, - optimizer, - loss, - metrics=None, - sample_weight_mode=None, - weighted_metrics=None, - target_tensors=None, - **kwargs): - """Configures the model for training. - - Arguments: - optimizer: String (name of optimizer) or optimizer object. - See [optimizers](/optimizers). - loss: String (name of objective function) or objective function. - See [losses](/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'}`. - 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 a placeholder 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 tensor (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 should be a single tensor - (for a single-output `Sequential` model). - **kwargs: These arguments are passed into `tf.Session.run`. - - Example: - ```python - model = Sequential() - model.add(Dense(32, input_shape=(500,))) - model.add(Dense(10, activation='softmax')) - model.compile(optimizer='rmsprop', - loss='categorical_crossentropy', - metrics=['accuracy']) - ``` - """ - # create the underlying model - self.build() - # call compile method of Model class - self.model.compile( - optimizer, - loss, - metrics=metrics, - sample_weight_mode=sample_weight_mode, - weighted_metrics=weighted_metrics, - target_tensors=target_tensors, - **kwargs) - self.optimizer = self.model.optimizer - self.loss = self.model.loss - self.metrics = self.model.metrics - self.loss_weights = self.model.loss_weights - self.sample_weight_mode = self.model.sample_weight_mode - self.weighted_metrics = self.model.weighted_metrics - self.targets = self.model.targets - self.metrics_tensors = self.model.metrics_tensors - self.metrics_names = self.model.metrics_names - self.sample_weights = self.model.sample_weights - self.total_loss = self.model.total_loss - - 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, - **kwargs): - """Trains the model for a fixed number of epochs. - - Arguments: - x: Numpy array of training data. - If the input layer in the model is named, you can also pass a - dictionary mapping the input name to a Numpy array. - `x` can be `None` (default) if feeding from - TensorFlow data tensors. - y: Numpy array of target (label) data. - If the output layer in the model is named, you can also pass a - dictionary mapping the output name to a Numpy array. - `y` can be `None` (default) if feeding from - TensorFlow data tensors. - batch_size: Integer or `None`. - Number of samples per gradient update. - If unspecified, it will default to 32. - 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: 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](/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. - validation_data: tuple `(x_val, y_val)` or tuple - `(x_val, y_val, val_sample_weights)` 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. - This will override `validation_split`. - 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()`. - initial_epoch: Epoch at which to start training - (useful for resuming a previous training run). - steps_per_epoch: 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 unique samples in your dataset divided by - the batch size, or 1 if that cannot be determined. - validation_steps: Only relevant if `steps_per_epoch` - is specified. Total number of steps (batches of samples) - to validate before stopping. - **kwargs: Used for backwards compatibility support. - - 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. - """ - if not self.built: - raise RuntimeError('The model needs to be compiled before being used.') - return self.model.fit( - x, - y, - batch_size=batch_size, - epochs=epochs, - verbose=verbose, - callbacks=callbacks, - validation_split=validation_split, - validation_data=validation_data, - shuffle=shuffle, - class_weight=class_weight, - sample_weight=sample_weight, - initial_epoch=initial_epoch, - steps_per_epoch=steps_per_epoch, - validation_steps=validation_steps) - - def evaluate(self, x, y, batch_size=32, verbose=1, sample_weight=None): - """Computes the loss on some input data, batch by batch. - - Arguments: - x: input data, as a Numpy array or list of Numpy arrays - (if the model has multiple inputs). - y: labels, as a Numpy array. - batch_size: integer. Number of samples per gradient update. - verbose: verbosity mode, 0 or 1. - sample_weight: sample weights, as a Numpy array. - - Returns: - Scalar test loss (if the model has no metrics) - or list of scalars (if the model computes other metrics). - The attribute `model.metrics_names` will give you - the display labels for the scalar outputs. - - Raises: - RuntimeError: if the model was never compiled. - """ - if not self.built: - raise RuntimeError('The model needs to be compiled before being used.') - return self.model.evaluate( - x, - y, - batch_size=batch_size, - verbose=verbose, - sample_weight=sample_weight) - - def predict(self, x, batch_size=32, verbose=0): - """Generates output predictions for the input samples. - - The input samples are processed batch by batch. - - Arguments: - x: the input data, as a Numpy array. - batch_size: integer. - verbose: verbosity mode, 0 or 1. - - Returns: - A Numpy array of predictions. - """ - if not self.built: - self.build() - return self.model.predict(x, batch_size=batch_size, verbose=verbose) - - def predict_on_batch(self, x): - """Returns predictions for a single batch of samples. - - Arguments: - x: input data, as a Numpy array or list of Numpy arrays - (if the model has multiple inputs). - - Returns: - A Numpy array of predictions. - """ - if not self.built: - self.build() - return self.model.predict_on_batch(x) - - def train_on_batch(self, x, y, class_weight=None, sample_weight=None): - """Single gradient update over one batch of samples. - - Arguments: - x: input data, as a Numpy array or list of Numpy arrays - (if the model has multiple inputs). - y: labels, as a Numpy array. - class_weight: dictionary mapping classes to a weight value, - used for scaling the loss function (during training only). - sample_weight: sample weights, as a Numpy array. - - Returns: - Scalar training loss (if the model has no metrics) - or list of scalars (if the model computes other metrics). - The attribute `model.metrics_names` will give you - the display labels for the scalar outputs. - - Raises: - RuntimeError: if the model was never compiled. - """ - if not self.built: - raise RuntimeError('The model needs to be compiled before being used.') - return self.model.train_on_batch( - x, y, sample_weight=sample_weight, class_weight=class_weight) - - def test_on_batch(self, x, y, sample_weight=None): - """Evaluates the model over a single batch of samples. - - Arguments: - x: input data, as a Numpy array or list of Numpy arrays - (if the model has multiple inputs). - y: labels, as a Numpy array. - sample_weight: sample weights, as a Numpy array. - - Returns: - Scalar test loss (if the model has no metrics) - or list of scalars (if the model computes other metrics). - The attribute `model.metrics_names` will give you - the display labels for the scalar outputs. - - Raises: - RuntimeError: if the model was never compiled. - """ - if not self.built: - raise RuntimeError('The model needs to be compiled before being used.') - return self.model.test_on_batch(x, y, sample_weight=sample_weight) - - def predict_proba(self, x, batch_size=32, verbose=0): - """Generates class probability predictions for the input samples. - - The input samples are processed batch by batch. - - Arguments: - x: input data, as a Numpy array or list of Numpy arrays - (if the model has multiple inputs). - batch_size: integer. - verbose: verbosity mode, 0 or 1. - - Returns: - A Numpy array of probability predictions. - """ - preds = self.predict(x, batch_size, verbose) - if preds.min() < 0. or preds.max() > 1.: - logging.warning('Network returning invalid probability values. ' - 'The last layer might not normalize predictions ' - 'into probabilities ' - '(like softmax or sigmoid would).') - return preds - - def predict_classes(self, x, batch_size=32, verbose=0): - """Generate class predictions for the input samples. - - The input samples are processed batch by batch. - - Arguments: - x: input data, as a Numpy array or list of Numpy arrays - (if the model has multiple inputs). - batch_size: integer. - verbose: verbosity mode, 0 or 1. - - Returns: - A numpy array of class predictions. - """ - proba = self.predict(x, batch_size=batch_size, verbose=verbose) - if proba.shape[-1] > 1: - return proba.argmax(axis=-1) - else: - return (proba > 0.5).astype('int32') - - 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, - **kwargs): - """Fits the model on data generated 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. - - Arguments: - generator: A generator. - The output of the generator must be either - - a tuple (inputs, targets) - - a tuple (inputs, targets, sample_weights). - All arrays should contain the same number of samples. - 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. - Note that in conjunction with initial_epoch, the parameter - epochs is to be understood as "final epoch". The model is - not trained for n steps given by epochs, but until the - epoch epochs is reached. - 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. - Number of steps to yield from validation generator - at the end of every epoch. It should typically - be equal to the number of samples of your - validation dataset divided by the batch size. - 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: Maximum size for the generator queue - workers: Maximum number of processes to spin up - use_multiprocessing: If True, use process based threading. - 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: Whether to shuffle the order of the batches at - the beginning of each epoch. Only used with instances - of `Sequence` (keras.utils.Sequence). - initial_epoch: Epoch at which to start training - (useful for resuming a previous training run) - **kwargs: support for legacy arguments. - - Returns: - A `History` object. - - Raises: - RuntimeError: if the model was never compiled. - ValueError: In case the generator yields - data in an invalid format. - - Example: - - ```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 - x, y = process_line(line) - yield (x, y) - f.close() - - model.fit_generator(generate_arrays_from_file('/my_file.txt'), - steps_per_epoch=1000, epochs=10) - ``` - """ - # Legacy support - if 'max_q_size' in kwargs: - max_queue_size = kwargs.pop('max_q_size') - logging.warning('The argument `max_q_size` has been renamed ' - '`max_queue_size`. Update your method calls accordingly.') - if 'pickle_safe' in kwargs: - use_multiprocessing = kwargs.pop('pickle_safe') - logging.warning('The argument `pickle_safe` has been renamed ' - '`use_multiprocessing`. ' - 'Update your method calls accordingly.') - if kwargs: - raise ValueError('Unrecognized keyword arguments: ' + str(kwargs)) - - if not self.built: - raise RuntimeError('The model needs to be compiled before being used.') - return self.model.fit_generator( - generator, - steps_per_epoch, - 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 evaluate_generator(self, - generator, - steps=None, - max_queue_size=10, - workers=1, - use_multiprocessing=False, - **kwargs): - """Evaluates the model on a data generator. - - The generator should return the same kind of data - as accepted by `test_on_batch`. - - Arguments: - generator: Generator yielding tuples (inputs, targets) - or (inputs, targets, sample_weights) - 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: maximum number of processes to spin up - use_multiprocessing: if True, use process based threading. - 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: support for legacy arguments. - - Returns: - Scalar test loss (if the model has no metrics) - or list of scalars (if the model computes other metrics). - The attribute `model.metrics_names` will give you - the display labels for the scalar outputs. - - Raises: - RuntimeError: if the model was never compiled. - ValueError: In case the generator yields - data in an invalid format. - """ - # Legacy support - if 'max_q_size' in kwargs: - max_queue_size = kwargs.pop('max_q_size') - logging.warning('The argument `max_q_size` has been renamed ' - '`max_queue_size`. Update your method calls accordingly.') - if 'pickle_safe' in kwargs: - use_multiprocessing = kwargs.pop('pickle_safe') - logging.warning('The argument `pickle_safe` has been renamed ' - '`use_multiprocessing`. ' - 'Update your method calls accordingly.') - if kwargs: - raise ValueError('Unrecognized keyword arguments: ' + str(kwargs)) - - if not self.built: - raise RuntimeError('The model needs to be compiled before being used.') - return self.model.evaluate_generator( - generator, - steps, - max_queue_size=max_queue_size, - workers=workers, - use_multiprocessing=use_multiprocessing) - - def predict_generator(self, - generator, - steps=None, - max_queue_size=10, - workers=1, - use_multiprocessing=False, - verbose=0, - **kwargs): - """Generates predictions for the input samples from a data generator. - - The generator should return the same kind of data as accepted by - `predict_on_batch`. - - Arguments: - generator: generator yielding batches of input samples. - 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: maximum number of processes to spin up - use_multiprocessing: if True, use process based threading. - 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. - **kwargs: support for legacy arguments. - - Returns: - A Numpy array of predictions. - - Raises: - ValueError: In case the generator yields - data in an invalid format. - """ - # Legacy support - if 'max_q_size' in kwargs: - max_queue_size = kwargs.pop('max_q_size') - logging.warning('The argument `max_q_size` has been renamed ' - '`max_queue_size`. Update your method calls accordingly.') - if 'pickle_safe' in kwargs: - use_multiprocessing = kwargs.pop('pickle_safe') - logging.warning('The argument `pickle_safe` has been renamed ' - '`use_multiprocessing`. ' - 'Update your method calls accordingly.') - if kwargs: - raise ValueError('Unrecognized keyword arguments: ' + str(kwargs)) - - if not self.built: - self.build() - return self.model.predict_generator( - generator, - steps, - max_queue_size=max_queue_size, - workers=workers, - use_multiprocessing=use_multiprocessing, - verbose=verbose) - def get_config(self): - config = [] - for layer in self.layers: - config.append({ - 'class_name': layer.__class__.__name__, - 'config': layer.get_config() - }) - return copy.deepcopy(config) - @classmethod - def from_config(cls, config, custom_objects=None): - model = cls() - for conf in config: - layer = layer_module.deserialize(conf, custom_objects=custom_objects) - model.add(layer) - return model +# API entries importable from `keras.models`: +Model = training.Model # pylint: disable=invalid-name +Sequential = sequential.Sequential # pylint: disable=invalid-name +save_model = saving.save_model +load_model = saving.load_model +model_from_config = saving.model_from_config +model_from_yaml = saving.model_from_yaml +model_from_json = saving.model_from_json def _clone_functional_model(model, input_tensors=None): @@ -1365,7 +90,7 @@ def _clone_functional_model(model, input_tensors=None): else: # Make sure that all input tensors come from a Keras layer. # If tensor comes from an input layer: cache the input layer. - input_tensors = topology.to_list(input_tensors) + input_tensors = generic_utils.to_list(input_tensors) input_tensors_ = [] for i, x in enumerate(input_tensors): if not K.is_keras_tensor(x): @@ -1402,7 +127,7 @@ def _clone_functional_model(model, input_tensors=None): # Reuse previously cloned layer. layer = layer_map[layer] # Don't call InputLayer multiple times. - if isinstance(layer, topology.InputLayer): + if isinstance(layer, InputLayer): continue # Gather inputs to call the new layer. @@ -1427,8 +152,9 @@ def _clone_functional_model(model, input_tensors=None): if has_arg(layer.call, 'mask'): if 'mask' not in kwargs: kwargs['mask'] = computed_mask - output_tensors = topology.to_list(layer(computed_tensor, **kwargs)) - output_masks = topology.to_list( + output_tensors = generic_utils.to_list(layer(computed_tensor, + **kwargs)) + output_masks = generic_utils.to_list( layer.compute_mask(computed_tensor, computed_mask)) computed_tensors = [computed_tensor] computed_masks = [computed_mask] @@ -1438,8 +164,9 @@ def _clone_functional_model(model, input_tensors=None): if has_arg(layer.call, 'mask'): if 'mask' not in kwargs: kwargs['mask'] = computed_masks - output_tensors = topology.to_list(layer(computed_tensors, **kwargs)) - output_masks = topology.to_list( + output_tensors = generic_utils.to_list(layer(computed_tensors, + **kwargs)) + output_masks = generic_utils.to_list( layer.compute_mask(computed_tensors, computed_masks)) # Update tensor_map. for x, y, mask in zip(reference_output_tensors, output_tensors, @@ -1489,14 +216,14 @@ def _clone_sequential_model(model, input_tensors=None): if input_tensors is None: return Sequential(layers=layers, name=model.name) else: - if len(topology.to_list(input_tensors)) != 1: + if len(generic_utils.to_list(input_tensors)) != 1: raise ValueError('To clone a `Sequential` model, we expect ' ' at most one tensor ' 'as part of `input_tensors`.') - x = topology.to_list(input_tensors)[0] + x = generic_utils.to_list(input_tensors)[0] if K.is_keras_tensor(x): origin_layer = x._keras_history[0] - if isinstance(origin_layer, topology.InputLayer): + if isinstance(origin_layer, InputLayer): return Sequential(layers=[origin_layer] + layers, name=model.name) else: raise ValueError('Cannot clone a `Sequential` model on top ' diff --git a/tensorflow/python/keras/_impl/keras/models_test.py b/tensorflow/python/keras/_impl/keras/models_test.py index 04017e4b28b27e52f88a7746fc44510c29edffce..5978ddd987c63b9d87a31be6837172f08512ef73 100644 --- a/tensorflow/python/keras/_impl/keras/models_test.py +++ b/tensorflow/python/keras/_impl/keras/models_test.py @@ -12,362 +12,16 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Tests for training routines.""" +"""Tests for `models.py` (model cloning, mainly).""" from __future__ import absolute_import from __future__ import division from __future__ import print_function -import os -import shutil -import tempfile - import numpy as np from tensorflow.python.keras._impl import keras from tensorflow.python.platform import test -from tensorflow.python.training import training as training_module - -try: - import h5py # pylint:disable=g-import-not-at-top -except ImportError: - h5py = None - - -class TestModelSaving(test.TestCase): - - def test_sequential_model_saving(self): - if h5py is None: - return # Skip test if models cannot be saved. - - with self.test_session(): - model = keras.models.Sequential() - model.add(keras.layers.Dense(2, input_shape=(3,))) - model.add(keras.layers.RepeatVector(3)) - model.add(keras.layers.TimeDistributed(keras.layers.Dense(3))) - model.compile(loss=keras.losses.MSE, - optimizer=keras.optimizers.RMSprop(lr=0.0001), - metrics=[keras.metrics.categorical_accuracy], - sample_weight_mode='temporal') - x = np.random.random((1, 3)) - y = np.random.random((1, 3, 3)) - model.train_on_batch(x, y) - - out = model.predict(x) - fd, fname = tempfile.mkstemp('.h5') - keras.models.save_model(model, fname) - - new_model = keras.models.load_model(fname) - os.close(fd) - os.remove(fname) - - out2 = new_model.predict(x) - self.assertAllClose(out, out2, atol=1e-05) - - # test that new updates are the same with both models - x = np.random.random((1, 3)) - y = np.random.random((1, 3, 3)) - model.train_on_batch(x, y) - new_model.train_on_batch(x, y) - out = model.predict(x) - out2 = new_model.predict(x) - self.assertAllClose(out, out2, atol=1e-05) - - def test_sequential_model_saving_2(self): - if h5py is None: - return # Skip test if models cannot be saved. - - with self.test_session(): - # test with custom optimizer, loss - - class CustomOp(keras.optimizers.RMSprop): - pass - - def custom_loss(y_true, y_pred): - return keras.losses.mse(y_true, y_pred) - - model = keras.models.Sequential() - model.add(keras.layers.Dense(2, input_shape=(3,))) - model.add(keras.layers.Dense(3)) - model.compile(loss=custom_loss, optimizer=CustomOp(), metrics=['acc']) - - x = np.random.random((1, 3)) - y = np.random.random((1, 3)) - model.train_on_batch(x, y) - - out = model.predict(x) - fd, fname = tempfile.mkstemp('.h5') - keras.models.save_model(model, fname) - - model = keras.models.load_model( - fname, - custom_objects={'CustomOp': CustomOp, - 'custom_loss': custom_loss}) - os.close(fd) - os.remove(fname) - - out2 = model.predict(x) - self.assertAllClose(out, out2, atol=1e-05) - - def test_functional_model_saving(self): - if h5py is None: - return # Skip test if models cannot be saved. - - with self.test_session(): - inputs = keras.layers.Input(shape=(3,)) - x = keras.layers.Dense(2)(inputs) - output = keras.layers.Dense(3)(x) - - model = keras.models.Model(inputs, output) - model.compile(loss=keras.losses.MSE, - optimizer=keras.optimizers.RMSprop(lr=0.0001), - metrics=[keras.metrics.categorical_accuracy]) - x = np.random.random((1, 3)) - y = np.random.random((1, 3)) - model.train_on_batch(x, y) - - out = model.predict(x) - fd, fname = tempfile.mkstemp('.h5') - keras.models.save_model(model, fname) - - model = keras.models.load_model(fname) - os.close(fd) - os.remove(fname) - - out2 = model.predict(x) - self.assertAllClose(out, out2, atol=1e-05) - - def test_saving_without_compilation(self): - if h5py is None: - return # Skip test if models cannot be saved. - - with self.test_session(): - model = keras.models.Sequential() - model.add(keras.layers.Dense(2, input_shape=(3,))) - model.add(keras.layers.Dense(3)) - model.compile(loss='mse', optimizer='sgd', metrics=['acc']) - - fd, fname = tempfile.mkstemp('.h5') - keras.models.save_model(model, fname) - model = keras.models.load_model(fname) - os.close(fd) - os.remove(fname) - - def test_saving_with_tf_optimizer(self): - if h5py is None: - return # Skip test if models cannot be saved. - - with self.test_session(): - model = keras.models.Sequential() - model.add(keras.layers.Dense(2, input_shape=(3,))) - model.add(keras.layers.Dense(3)) - model.compile(loss='mse', - optimizer=training_module.AdadeltaOptimizer(0.1), - metrics=['acc']) - - fd, fname = tempfile.mkstemp('.h5') - keras.models.save_model(model, fname) - model = keras.models.load_model(fname) - os.close(fd) - os.remove(fname) - - def test_saving_right_after_compilation(self): - if h5py is None: - return # Skip test if models cannot be saved. - - with self.test_session(): - model = keras.models.Sequential() - model.add(keras.layers.Dense(2, input_shape=(3,))) - model.add(keras.layers.Dense(3)) - model.compile(loss='mse', optimizer='sgd', metrics=['acc']) - model.model._make_train_function() - - fd, fname = tempfile.mkstemp('.h5') - keras.models.save_model(model, fname) - model = keras.models.load_model(fname) - os.close(fd) - os.remove(fname) - - def test_saving_lambda_numpy_array_arguments(self): - if h5py is None: - return # Skip test if models cannot be saved. - - mean = np.random.random((4, 2, 3)) - std = np.abs(np.random.random((4, 2, 3))) + 1e-5 - inputs = keras.layers.Input(shape=(4, 2, 3)) - output = keras.layers.Lambda(lambda image, mu, std: (image - mu) / std, - arguments={'mu': mean, 'std': std})(inputs) - model = keras.models.Model(inputs, output) - model.compile(loss='mse', optimizer='sgd', metrics=['acc']) - - fd, fname = tempfile.mkstemp('.h5') - keras.models.save_model(model, fname) - - model = keras.models.load_model(fname) - os.close(fd) - os.remove(fname) - - self.assertAllClose(mean, model.layers[1].arguments['mu']) - self.assertAllClose(std, model.layers[1].arguments['std']) - - -class TestSequential(test.TestCase): - """Most Sequential model API tests are covered in `training_test.py`. - """ - - def test_basic_methods(self): - model = keras.models.Sequential() - model.add(keras.layers.Dense(1, input_dim=2)) - model.add(keras.layers.Dropout(0.3, name='dp')) - model.add(keras.layers.Dense(2, kernel_regularizer='l2', - kernel_constraint='max_norm')) - model.build() - self.assertEqual(model.state_updates, model.model.state_updates) - self.assertEqual(model.get_layer(name='dp').name, 'dp') - - def test_sequential_pop(self): - num_hidden = 5 - input_dim = 3 - batch_size = 5 - num_classes = 2 - with self.test_session(): - model = keras.models.Sequential() - model.add(keras.layers.Dense(num_hidden, input_dim=input_dim)) - model.add(keras.layers.Dense(num_classes)) - model.compile(loss='mse', optimizer='sgd') - x = np.random.random((batch_size, input_dim)) - y = np.random.random((batch_size, num_classes)) - model.fit(x, y, epochs=1) - model.pop() - self.assertEqual(len(model.layers), 1) - self.assertEqual(model.output_shape, (None, num_hidden)) - model.compile(loss='mse', optimizer='sgd') - y = np.random.random((batch_size, num_hidden)) - model.fit(x, y, epochs=1) - - # Test popping single-layer model - model = keras.models.Sequential() - model.add(keras.layers.Dense(num_hidden, input_dim=input_dim)) - model.pop() - self.assertEqual(len(model.layers), 0) - self.assertEqual(len(model.outputs), 0) - - # Invalid use case - model = keras.models.Sequential() - with self.assertRaises(TypeError): - model.pop() - - def test_sequential_weight_loading(self): - 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') - - num_hidden = 5 - input_dim = 3 - batch_size = 5 - num_classes = 2 - - with self.test_session(): - model = keras.models.Sequential() - model.add(keras.layers.Dense(num_hidden, input_dim=input_dim)) - model.add(keras.layers.Dense(num_classes)) - - x = np.random.random((batch_size, input_dim)) - ref_y = model.predict(x) - - model.save_weights(h5_path) - - model = keras.models.Sequential() - model.add(keras.layers.Dense(num_hidden, input_dim=input_dim)) - model.add(keras.layers.Dense(num_classes)) - model.load_weights(h5_path) - y = model.predict(x) - - self.assertAllClose(y, ref_y) - - def test_invalid_use_cases(self): - with self.test_session(): - # Added objects must be layer instances - with self.assertRaises(TypeError): - model = keras.models.Sequential() - model.add(None) - - # Added layers must have an inputs shape - with self.assertRaises(ValueError): - model = keras.models.Sequential() - model.add(keras.layers.Dense(1)) - - # Added layers cannot have multiple outputs - class MyLayer(keras.layers.Layer): - - def call(self, inputs): - return [3 * inputs, 2 * inputs] - - def compute_output_shape(self, input_shape): - return [input_shape, input_shape] - - with self.assertRaises(ValueError): - model = keras.models.Sequential() - model.add(MyLayer(input_shape=(3,))) - with self.assertRaises(TypeError): - model = keras.models.Sequential() - model.add(keras.layers.Dense(1, input_dim=1)) - model.add(MyLayer()) - - # Building empty model - model = keras.models.Sequential() - with self.assertRaises(TypeError): - model.build() - - def test_nested_sequential_trainability(self): - input_dim = 20 - num_units = 10 - num_classes = 2 - - inner_model = keras.models.Sequential() - inner_model.add(keras.layers.Dense(num_units, input_shape=(input_dim,))) - - model = keras.models.Sequential() - model.add(inner_model) - model.add(keras.layers.Dense(num_classes)) - - self.assertEqual(len(model.trainable_weights), 4) - inner_model.trainable = False - self.assertEqual(len(model.trainable_weights), 2) - inner_model.trainable = True - self.assertEqual(len(model.trainable_weights), 4) - - def test_sequential_update_disabling(self): - val_a = np.random.random((10, 4)) - val_out = np.random.random((10, 4)) - - with self.test_session(): - model = keras.models.Sequential() - model.add(keras.layers.BatchNormalization(input_shape=(4,))) - - model.trainable = False - assert not model.updates - - model.compile('sgd', 'mse') - assert not model.updates - assert not model.model.updates - - x1 = model.predict(val_a) - model.train_on_batch(val_a, val_out) - x2 = model.predict(val_a) - self.assertAllClose(x1, x2, atol=1e-7) - - model.trainable = True - model.compile('sgd', 'mse') - assert model.updates - assert model.model.updates - - model.train_on_batch(val_a, val_out) - x2 = model.predict(val_a) - assert np.abs(np.sum(x1 - x2)) > 1e-5 class TestModelCloning(test.TestCase): diff --git a/tensorflow/python/keras/_impl/keras/optimizers.py b/tensorflow/python/keras/_impl/keras/optimizers.py index 6520128c5b65451aef20ec9626245fba5ef29927..b715d722b98b9db3bdf0985da0954356a2facdfe 100644 --- a/tensorflow/python/keras/_impl/keras/optimizers.py +++ b/tensorflow/python/keras/_impl/keras/optimizers.py @@ -95,7 +95,26 @@ class Optimizer(object): raise NotImplementedError 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). + """ grads = K.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') and self.clipnorm > 0: norm = K.sqrt(sum([K.sum(K.square(g)) for g in grads])) grads = [clip_norm(g, self.clipnorm, norm) for g in grads] @@ -120,6 +139,11 @@ class Optimizer(object): ValueError: in case of incompatible weight shapes. """ params = self.weights + if len(params) != len(weights): + raise ValueError( + 'Length of the specified weight list (' + str(len(weights)) + + ') does not match the number of weights ' + 'of the optimizer (' + str(len(params)) + ')') weight_value_tuples = [] param_values = K.batch_get_value(params) for pv, p, w in zip(param_values, params, weights): diff --git a/tensorflow/python/keras/_impl/keras/utils/__init__.py b/tensorflow/python/keras/_impl/keras/utils/__init__.py index 370ae0dd0f0d00059f1b0cc79459abe75c8ca494..0c9f19a0c8dcf3bf929e102b31679a03b27728f7 100644 --- a/tensorflow/python/keras/_impl/keras/utils/__init__.py +++ b/tensorflow/python/keras/_impl/keras/utils/__init__.py @@ -31,8 +31,8 @@ from tensorflow.python.keras._impl.keras.utils.generic_utils import serialize_ke from tensorflow.python.keras._impl.keras.utils.io_utils import HDF5Matrix from tensorflow.python.keras._impl.keras.utils.layer_utils import convert_all_kernels_in_model from tensorflow.python.keras._impl.keras.utils.layer_utils import print_summary +from tensorflow.python.keras._impl.keras.utils.multi_gpu_utils import multi_gpu_model from tensorflow.python.keras._impl.keras.utils.np_utils import normalize from tensorflow.python.keras._impl.keras.utils.np_utils import to_categorical -from tensorflow.python.keras._impl.keras.utils.training_utils import multi_gpu_model from tensorflow.python.keras._impl.keras.utils.vis_utils import plot_model diff --git a/tensorflow/python/keras/_impl/keras/utils/data_utils.py b/tensorflow/python/keras/_impl/keras/utils/data_utils.py index e87c8f48ef0967d561db1ab841a669d783f9b1ec..4c49544c6a63c4e5a0b79d31b074ad352c512bfa 100644 --- a/tensorflow/python/keras/_impl/keras/utils/data_utils.py +++ b/tensorflow/python/keras/_impl/keras/utils/data_utils.py @@ -393,6 +393,16 @@ class Sequence(object): """ pass + def __iter__(self): + """Creates an infinite generator that iterate over the Sequence. + + Yields: + Sequence items. + """ + while True: + for item in (self[i] for i in range(len(self))): + yield item + # Global variables to be shared across processes _SHARED_SEQUENCES = {} @@ -400,6 +410,11 @@ _SHARED_SEQUENCES = {} _SEQUENCE_COUNTER = None +def init_pool(seqs): + global _SHARED_SEQUENCES + _SHARED_SEQUENCES = seqs + + def get_index(uid, i): """Get the value from the Sequence `uid` at index `i`. @@ -532,9 +547,11 @@ class OrderedEnqueuer(SequenceEnqueuer): (when full, workers could block on `put()`) """ if self.use_multiprocessing: - self.executor_fn = lambda: multiprocessing.Pool(workers) + self.executor_fn = lambda seqs: multiprocessing.Pool( # pylint: disable=g-long-lambda + workers, initializer=init_pool, initargs=(seqs,)) else: - self.executor_fn = lambda: ThreadPool(workers) + # We do not need the init since it's threads. + self.executor_fn = lambda _: ThreadPool(workers) self.workers = workers self.queue = queue.Queue(max_queue_size) self.stop_signal = threading.Event() @@ -557,7 +574,7 @@ class OrderedEnqueuer(SequenceEnqueuer): if self.shuffle: random.shuffle(sequence) - with closing(self.executor_fn()) as executor: + with closing(self.executor_fn(_SHARED_SEQUENCES)) as executor: for i in sequence: if self.stop_signal.is_set(): return diff --git a/tensorflow/python/keras/_impl/keras/utils/generic_utils.py b/tensorflow/python/keras/_impl/keras/utils/generic_utils.py index 462d600bf827768b0f2e6265aebdaad48e70fcd9..3bbe87f92d8f7eac27033344550ca65397eab986 100644 --- a/tensorflow/python/keras/_impl/keras/utils/generic_utils.py +++ b/tensorflow/python/keras/_impl/keras/utils/generic_utils.py @@ -490,8 +490,8 @@ def slice_arrays(arrays, start=None, stop=None): if arrays is None: return [None] if isinstance(start, list) and stop is not None: - raise ValueError('The stop argument has to be None if the value of start is' - 'a list.') + raise ValueError('The stop argument has to be None if the value of start ' + 'is a list.') elif isinstance(arrays, list): if hasattr(start, '__len__'): # hdf5 datasets only support list objects as indices @@ -509,3 +509,20 @@ def slice_arrays(arrays, start=None, stop=None): return arrays[start:stop] else: return [None] + + +def to_list(x): + """Normalizes a list/tensor into a list. + + If a tensor is passed, we return + a list of size 1 containing the tensor. + + Arguments: + x: target object to be normalized. + + Returns: + A list. + """ + if isinstance(x, list): + return x + return [x] diff --git a/tensorflow/python/keras/_impl/keras/utils/training_utils.py b/tensorflow/python/keras/_impl/keras/utils/multi_gpu_utils.py similarity index 98% rename from tensorflow/python/keras/_impl/keras/utils/training_utils.py rename to tensorflow/python/keras/_impl/keras/utils/multi_gpu_utils.py index ce7402e9d279278eaaf5aab58a3973eec6de8e99..231ace2a0b4a4f25cebf06a5216cf3d30aadc49b 100644 --- a/tensorflow/python/keras/_impl/keras/utils/training_utils.py +++ b/tensorflow/python/keras/_impl/keras/utils/multi_gpu_utils.py @@ -125,7 +125,7 @@ def multi_gpu_model(model, gpus): if gpus <= 1: raise ValueError('For multi-gpu usage to be effective, ' 'call `multi_gpu_model` with `gpus >= 2`. ' - 'Received: `gpus=%d`' % gpus) + 'Received: `gpus=%s`' % gpus) num_gpus = gpus target_gpu_ids = range(num_gpus) @@ -136,7 +136,7 @@ def multi_gpu_model(model, gpus): ] for device in target_devices: if device not in available_devices: - raise ValueError('To call `multi_gpu_model` with `gpus=%d`, ' + raise ValueError('To call `multi_gpu_model` with `gpus=%s`, ' 'we expect the following devices to be available: %s. ' 'However this machine only has: %s. ' 'Try reducing `gpus`.' % (gpus, target_devices, diff --git a/tensorflow/python/keras/_impl/keras/utils/training_utils_test.py b/tensorflow/python/keras/_impl/keras/utils/multi_gpu_utils_test.py similarity index 64% rename from tensorflow/python/keras/_impl/keras/utils/training_utils_test.py rename to tensorflow/python/keras/_impl/keras/utils/multi_gpu_utils_test.py index 12354c49ca72cddc0f395bcfcfabab18c1189227..0a38d6b5228fe791ce14adc7e37e0b7a6926fadf 100644 --- a/tensorflow/python/keras/_impl/keras/utils/training_utils_test.py +++ b/tensorflow/python/keras/_impl/keras/utils/multi_gpu_utils_test.py @@ -19,21 +19,34 @@ from __future__ import print_function import numpy as np - +from tensorflow.python import data from tensorflow.python.keras._impl import keras from tensorflow.python.platform import test +def check_if_compatible_devices(gpus=2): + available_devices = [ + keras.utils.multi_gpu_utils._normalize_device_name(name) + for name in keras.utils.multi_gpu_utils._get_available_devices() + ] + if '/gpu:%d' % (gpus - 1) not in available_devices: + return False + return True + + class TestMultiGPUModel(test.TestCase): - def multi_gpu_test_simple_model(self): + def test_multi_gpu_test_simple_model(self): gpus = 2 num_samples = 1000 input_dim = 10 output_dim = 1 hidden_dim = 10 epochs = 2 - target_gpu_id = [0, 2, 4] + target_gpu_id = [0, 1] + + if not check_if_compatible_devices(gpus=gpus): + return with self.test_session(): model = keras.models.Sequential() @@ -47,12 +60,11 @@ class TestMultiGPUModel(test.TestCase): parallel_model = keras.utils.multi_gpu_model(model, gpus=gpus) parallel_model.compile(loss='mse', optimizer='rmsprop') parallel_model.fit(x, y, epochs=epochs) - parallel_model = keras.utils.multi_gpu_model(model, gpus=target_gpu_id) parallel_model.compile(loss='mse', optimizer='rmsprop') parallel_model.fit(x, y, epochs=epochs) - def multi_gpu_test_multi_io_model(self): + def test_multi_gpu_test_multi_io_model(self): gpus = 2 num_samples = 1000 input_dim_a = 10 @@ -61,7 +73,10 @@ class TestMultiGPUModel(test.TestCase): output_dim_b = 2 hidden_dim = 10 epochs = 2 - target_gpu_id = [0, 2, 4] + target_gpu_id = [0, 1] + + if not check_if_compatible_devices(gpus=gpus): + return with self.test_session(): input_a = keras.Input((input_dim_a,)) @@ -86,7 +101,10 @@ class TestMultiGPUModel(test.TestCase): parallel_model.compile(loss='mse', optimizer='rmsprop') parallel_model.fit([a_x, b_x], [a_y, b_y], epochs=epochs) - def multi_gpu_test_invalid_devices(self): + def test_multi_gpu_test_invalid_devices(self): + if not check_if_compatible_devices(gpus=2): + return + with self.test_session(): input_shape = (1000, 10) model = keras.models.Sequential() @@ -115,3 +133,53 @@ class TestMultiGPUModel(test.TestCase): with self.assertRaises(ValueError): parallel_model = keras.utils.multi_gpu_model(model, gpus=[0]) parallel_model.fit(x, y, epochs=2) + + def test_nested_model_with_tensor_input(self): + gpus = 2 + input_dim = 10 + shape = (input_dim,) + num_samples = 16 + num_classes = 10 + + if not check_if_compatible_devices(gpus=gpus): + return + + with self.test_session(): + input_shape = (num_samples,) + shape + x_train = np.random.randint(0, 255, input_shape) + y_train = np.random.randint(0, num_classes, (input_shape[0],)) + keras.backend.set_learning_phase(True) + + y_train = keras.utils.to_categorical(y_train, num_classes) + + x_train = x_train.astype('float32') + y_train = y_train.astype('float32') + + dataset = data.Dataset.from_tensor_slices((x_train, y_train)) + dataset = dataset.repeat() + dataset = dataset.batch(4) + iterator = dataset.make_one_shot_iterator() + + inputs, targets = iterator.get_next() + + input_tensor = keras.layers.Input(tensor=inputs) + + model = keras.models.Sequential() + model.add(keras.layers.Dense(3, + input_shape=(input_dim,))) + model.add(keras.layers.Dense(num_classes)) + + output = model(input_tensor) + outer_model = keras.Model(input_tensor, output) + parallel_model = keras.utils.multi_gpu_model(outer_model, gpus=gpus) + + parallel_model.compile( + loss='categorical_crossentropy', + optimizer=keras.optimizers.RMSprop(lr=0.0001, decay=1e-6), + metrics=['accuracy'], + target_tensors=[targets]) + parallel_model.fit(epochs=1, steps_per_epoch=3) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/python/keras/datasets/fashion_mnist/__init__.py b/tensorflow/python/keras/datasets/fashion_mnist/__init__.py index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..7f5ddecc4707334d52ebf4966f2ec6141cce0d46 100644 --- a/tensorflow/python/keras/datasets/fashion_mnist/__init__.py +++ b/tensorflow/python/keras/datasets/fashion_mnist/__init__.py @@ -0,0 +1,25 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Fashion-MNIST dataset.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.keras._impl.keras.datasets.fashion_mnist import load_data + +del absolute_import +del division +del print_function diff --git a/tensorflow/python/keras/utils/__init__.py b/tensorflow/python/keras/utils/__init__.py index 91cc8607274a80a14dd27a64274da7f8f0aafab1..2f74cf031d0520c8d874b7269c52e3b9e1b9931b 100644 --- a/tensorflow/python/keras/utils/__init__.py +++ b/tensorflow/python/keras/utils/__init__.py @@ -30,9 +30,9 @@ from tensorflow.python.keras._impl.keras.utils.generic_utils import Progbar from tensorflow.python.keras._impl.keras.utils.generic_utils import serialize_keras_object from tensorflow.python.keras._impl.keras.utils.io_utils import HDF5Matrix from tensorflow.python.keras._impl.keras.utils.layer_utils import convert_all_kernels_in_model +from tensorflow.python.keras._impl.keras.utils.multi_gpu_utils import multi_gpu_model from tensorflow.python.keras._impl.keras.utils.np_utils import normalize from tensorflow.python.keras._impl.keras.utils.np_utils import to_categorical -from tensorflow.python.keras._impl.keras.utils.training_utils import multi_gpu_model from tensorflow.python.keras._impl.keras.utils.vis_utils import plot_model del absolute_import diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD index d4ceb2e489c8a20d26eaf9d89b12992d2b8673d7..f27ca5c2051be81eb94488ba388c11bcfebafc4b 100644 --- a/tensorflow/python/kernel_tests/BUILD +++ b/tensorflow/python/kernel_tests/BUILD @@ -295,7 +295,6 @@ tf_py_test( "//tensorflow/python:nn_grad", ], data = ["//tensorflow/core:image_testdata"], - tags = ["no_windows"], ) tf_py_test( @@ -712,6 +711,18 @@ cuda_py_test( ], ) +tf_py_test( + name = "regex_replace_op_test", + size = "small", + srcs = ["regex_replace_op_test.py"], + additional_deps = [ + "//tensorflow/python:client_testlib", + "//tensorflow/python:constant_op", + "//tensorflow/python:dtypes", + "//tensorflow/python:string_ops", + ], +) + tf_py_test( name = "save_restore_ops_test", size = "small", @@ -1075,6 +1086,7 @@ cuda_py_test( tags = [ "no_windows", "noasan", + "notap", ], ) @@ -1126,7 +1138,6 @@ tf_py_test( "//tensorflow/python:variables", ], data = ["//tensorflow/core:lmdb_testdata"], - tags = ["no_windows"], ) cuda_py_test( @@ -1559,12 +1570,15 @@ cuda_py_test( "//third_party/py/numpy", "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", + "//tensorflow/python:layers", "//tensorflow/python:framework", "//tensorflow/python:framework_for_generated_wrappers", "//tensorflow/python:init_ops", + "//tensorflow/python:linalg_ops", "//tensorflow/python:math_ops", "//tensorflow/python:nn_ops", "//tensorflow/python:partitioned_variables", + "//tensorflow/python:random_ops", "//tensorflow/python:variable_scope", "//tensorflow/python:variables", ], @@ -2313,7 +2327,6 @@ cuda_py_test( "//tensorflow/python:variables", ], shard_count = 4, - tags = ["no_windows"], ) cuda_py_test( @@ -2444,7 +2457,6 @@ cuda_py_test( "//tensorflow/python/eager:context", ], shard_count = 10, - tags = ["no_windows"], ) cuda_py_test( @@ -2892,6 +2904,40 @@ tf_py_test( ], ) +tf_py_test( + name = "accumulate_n_test", + size = "small", + srcs = ["accumulate_n_test.py"], + additional_deps = [ + "//third_party/py/numpy", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:framework_test_lib", + "//tensorflow/python:gradients", + "//tensorflow/python:math_ops", + "//tensorflow/python:platform_test", + "//tensorflow/python:variables", + ], +) + +tf_py_test( + name = "accumulate_n_eager_test", + size = "small", + srcs = ["accumulate_n_eager_test.py"], + additional_deps = [ + "//third_party/py/numpy", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:framework_test_lib", + "//tensorflow/python:gradients", + "//tensorflow/python:math_ops", + "//tensorflow/python:resource_variable_ops", + "//tensorflow/python/eager:backprop", + "//tensorflow/python/eager:context", + "//tensorflow/python/eager:tape", + ], +) + filegroup( name = "all_files", srcs = glob( diff --git a/tensorflow/contrib/framework/python/ops/accumulate_n_v2_eager_test.py b/tensorflow/python/kernel_tests/accumulate_n_eager_test.py similarity index 72% rename from tensorflow/contrib/framework/python/ops/accumulate_n_v2_eager_test.py rename to tensorflow/python/kernel_tests/accumulate_n_eager_test.py index 35974b9e21d2d7423777a95a99f51c9cb4b453b2..dc11b7deceb9040584aca1f629f4d003aef39428 100644 --- a/tensorflow/contrib/framework/python/ops/accumulate_n_v2_eager_test.py +++ b/tensorflow/python/kernel_tests/accumulate_n_eager_test.py @@ -12,48 +12,41 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Tests for new version of accumulate_n op that will eventually go into -`ops.math_ops`. - -These test cases spefically exercise the `eager` APIs. They need to be in a -separate file from the remaining tests because eager mode is currently something -you can turn on but can't turn off for the lifetime of the current process.""" +"""Tests for new version of accumulate_n op.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np -from tensorflow.contrib.framework.python.ops import accumulate_n_v2 as av2 - from tensorflow.python.eager import backprop from tensorflow.python.framework import constant_op from tensorflow.python.framework import ops from tensorflow.python.framework import test_util +from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.platform import test - class AccumulateNV2EagerTest(test_util.TensorFlowTestCase): - """Tests of the new, differentiable version of accumulate_n""" + """Tests of the new, differentiable version of accumulate_n.""" def testMinimalEagerMode(self): forty = constant_op.constant(40) two = constant_op.constant(2) - answer = av2.accumulate_n_v2([forty, two]) + answer = math_ops.accumulate_n([forty, two]) self.assertEqual(42, answer.numpy()) - def testFloat(self): np.random.seed(12345) x = [np.random.random((1, 2, 3, 4, 5)) - 0.5 for _ in range(5)] tf_x = ops.convert_n_to_tensor(x) with self.test_session(use_gpu=True): - self.assertAllClose(sum(x), av2.accumulate_n_v2(tf_x).numpy()) - self.assertAllClose(x[0] * 5, av2.accumulate_n_v2([tf_x[0]] * 5).numpy()) + self.assertAllClose(sum(x), math_ops.accumulate_n(tf_x).numpy()) + self.assertAllClose(x[0] * 5, + math_ops.accumulate_n([tf_x[0]] * 5).numpy()) def testGrad(self): np.random.seed(42) @@ -65,16 +58,14 @@ class AccumulateNV2EagerTest(test_util.TensorFlowTestCase): ] def fn(first, second, third): - return av2.accumulate_n_v2([first, second, third]) + return math_ops.accumulate_n([first, second, third]) grad_fn = backprop.gradients_function(fn) grad = grad_fn(input_vars[0], input_vars[1], input_vars[2]) - self.assertAllEqual(np.repeat(1.0, num_inputs), # d/dx (x + y + ...) = 1 + self.assertAllEqual(np.repeat(1.0, num_inputs), # d/dx (x + y + ...) = 1 [elem.numpy() for elem in grad]) - if __name__ == "__main__": ops.enable_eager_execution() test.main() - diff --git a/tensorflow/contrib/framework/python/ops/accumulate_n_v2_test.py b/tensorflow/python/kernel_tests/accumulate_n_test.py similarity index 75% rename from tensorflow/contrib/framework/python/ops/accumulate_n_v2_test.py rename to tensorflow/python/kernel_tests/accumulate_n_test.py index 45962098e93acfac414396ddbeaa847701ff2b4b..b793906fac2cd12a5c0c663dd169000ad6067759 100644 --- a/tensorflow/contrib/framework/python/ops/accumulate_n_v2_test.py +++ b/tensorflow/python/kernel_tests/accumulate_n_test.py @@ -12,42 +12,49 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Tests for new version of accumulate_n op that will eventually go into -`ops.math_ops`.""" +"""Tests for new version of accumulate_n op.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np -from tensorflow.contrib.framework.python.ops import accumulate_n_v2 as av2 - 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 gradients +from tensorflow.python.ops import math_ops from tensorflow.python.ops import variables from tensorflow.python.platform import googletest class AccumulateNV2Test(test_util.TensorFlowTestCase): - """Tests of the new, differentiable version of accumulate_n""" + """Tests of the new, differentiable version of accumulate_n.""" def testFloat(self): np.random.seed(12345) x = [np.random.random((1, 2, 3, 4, 5)) - 0.5 for _ in range(5)] tf_x = ops.convert_n_to_tensor(x) with self.test_session(use_gpu=True): - self.assertAllClose(sum(x), av2.accumulate_n_v2(tf_x).eval()) - self.assertAllClose(x[0] * 5, av2.accumulate_n_v2([tf_x[0]] * 5).eval()) + self.assertAllClose(sum(x), math_ops.accumulate_n(tf_x).eval()) + self.assertAllClose(x[0] * 5, + math_ops.accumulate_n([tf_x[0]] * 5).eval()) def testInt(self): np.random.seed(54321) x = [np.random.randint(-128, 128, (5, 4, 3, 2, 1)) for _ in range(6)] tf_x = ops.convert_n_to_tensor(x) with self.test_session(use_gpu=True): - self.assertAllEqual(sum(x), av2.accumulate_n_v2(tf_x).eval()) - self.assertAllEqual(x[0] * 6, av2.accumulate_n_v2([tf_x[0]] * 6).eval()) + self.assertAllEqual(sum(x), math_ops.accumulate_n(tf_x).eval()) + self.assertAllEqual(x[0] * 6, + math_ops.accumulate_n([tf_x[0]] * 6).eval()) + + def testUnknownShape(self): + with self.test_session(use_gpu=True): + x0 = array_ops.placeholder(dtype=dtypes_lib.int32, shape=[None]) + acc = math_ops.accumulate_n([x0, x0], shape=[None]) + self.assertAllEqual([2, 4], acc.eval(feed_dict={x0: [1, 2]})) def testGrad(self): np.random.seed(42) @@ -55,9 +62,9 @@ class AccumulateNV2Test(test_util.TensorFlowTestCase): with self.test_session(use_gpu=True) as sess: input_vars = [ variables.Variable(10.0 * np.random.random()) - for i in range(0, num_inputs) + for _ in range(0, num_inputs) ] - accum_n = av2.accumulate_n_v2(input_vars) + accum_n = math_ops.accumulate_n(input_vars) sess.run(variables.global_variables_initializer()) accum_n_grad = gradients.gradients(accum_n, input_vars) self.assertAllEqual( @@ -77,7 +84,7 @@ class AccumulateNV2Test(test_util.TensorFlowTestCase): ops.convert_to_tensor(x, dtype=dtypes_lib.float32) for x in random_arrays ] - tf_val = av2.accumulate_n_v2(random_tensors) + tf_val = math_ops.accumulate_n(random_tensors) np_val = random_arrays[0] for random_array in random_arrays[1:]: np_val += random_array @@ -86,7 +93,7 @@ class AccumulateNV2Test(test_util.TensorFlowTestCase): def testZeroArgs(self): with self.test_session(): with self.assertRaises(ValueError): - tf_val = av2.accumulate_n_v2([]) + tf_val = math_ops.accumulate_n([]) tf_val.eval() def testWrongShape(self): @@ -94,28 +101,28 @@ class AccumulateNV2Test(test_util.TensorFlowTestCase): with self.assertRaises(ValueError): a = variables.Variable(0.2) b = variables.Variable(0.1) - tf_val = av2.accumulate_n_v2([a, b], shape=[2, 2]) # Should be shape=[] + math_ops.accumulate_n([a, b], shape=[2, 2]) # Should be shape=[] def testIncompatibleShapes(self): with self.test_session(): with self.assertRaises(ValueError): a = variables.Variable(np.array([0.1, 0.2])) b = variables.Variable(np.array([[0.3], [0.4]])) - tf_val = av2.accumulate_n_v2([a, b]) + math_ops.accumulate_n([a, b]) def testWrongType(self): with self.test_session(): with self.assertRaises(TypeError): a = variables.Variable(0.2, dtype=np.float32) b = variables.Variable(0.1, dtype=np.float32) - tf_val = av2.accumulate_n_v2([a, b], tensor_dtype=np.int32) + math_ops.accumulate_n([a, b], tensor_dtype=np.int32) def testWrongTypeOneInput(self): # Scenario that used to trigger a bug, even when testWrongType() worked with self.test_session(): with self.assertRaises(TypeError): a = variables.Variable(0.2, dtype=np.float32) - tf_val = av2.accumulate_n_v2([a], tensor_dtype=np.int32) + math_ops.accumulate_n([a], tensor_dtype=np.int32) if __name__ == "__main__": diff --git a/tensorflow/python/kernel_tests/array_ops_test.py b/tensorflow/python/kernel_tests/array_ops_test.py index 365cf72108de5a1e5e1eb47891a6ad64151add22..d0ba8020c1eaee74ded5ad67cae39b51d44097bd 100644 --- a/tensorflow/python/kernel_tests/array_ops_test.py +++ b/tensorflow/python/kernel_tests/array_ops_test.py @@ -1024,6 +1024,7 @@ class SequenceMaskTest(test_util.TensorFlowTestCase): [[True, False, False, False, False], [True, True, True, False, False], [True, True, False, False, False]]) + @test_util.enable_c_shapes def testOneDimensionalDtypeWithoutMaxlen(self): with self.test_session(): # test dtype and default maxlen: @@ -1037,6 +1038,7 @@ class SequenceMaskTest(test_util.TensorFlowTestCase): res.eval(), [[0.0, 0.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0], [1.0, 1.0, 1.0, 1.0]]) + @test_util.enable_c_shapes def testOneDimensionalWithoutMaxlen(self): with self.test_session(): res = array_ops.sequence_mask( @@ -1051,6 +1053,7 @@ class SequenceMaskTest(test_util.TensorFlowTestCase): [True, False, False, False], [True, True, True, True]]) + @test_util.enable_c_shapes def testTwoDimensional(self): with self.test_session(): res = array_ops.sequence_mask(constant_op.constant([[1, 3, 2]]), 5) @@ -1223,7 +1226,7 @@ class SnapshotOpTest(test_util.TensorFlowTestCase): for dtype in [dtypes.int32, dtypes.int64, dtypes.float32, dtypes.float64]: with self.test_session(use_gpu=True): x = constant_op.constant([0, 1, 2, 3], dtype=dtype) - y = gen_array_ops._snapshot(x) + y = gen_array_ops.snapshot(x) self.assertAllEqual(y.eval(), [0, 1, 2, 3]) diff --git a/tensorflow/python/kernel_tests/atrous_convolution_test.py b/tensorflow/python/kernel_tests/atrous_convolution_test.py index 2d1b3d9b7e836591646a2d0e59742bf6139446d1..0ef08581c9f931b991ef0c1218dc503345e248c2 100644 --- a/tensorflow/python/kernel_tests/atrous_convolution_test.py +++ b/tensorflow/python/kernel_tests/atrous_convolution_test.py @@ -83,14 +83,14 @@ class AtrousConvolutionTest(test.TestCase): checks = [] def add_check(check, *args, **kwargs): - if context.in_eager_mode(): + if context.executing_eagerly(): args_val, kwargs_val = self.evaluate([args, kwargs]) check(*args_val, **kwargs_val) else: checks.append((check, args, kwargs)) yield add_check - if context.in_graph_mode(): + if not context.executing_eagerly(): all_values = self.evaluate([[args, kwargs] for _, args, kwargs in checks]) for (check, _, _), (args, kwargs) in zip(checks, all_values): check(*args, **kwargs) diff --git a/tensorflow/python/kernel_tests/batchtospace_op_test.py b/tensorflow/python/kernel_tests/batchtospace_op_test.py index 0c802476a0e788aff3de84ab736fa8f1de5daab4..6143cd3baa6317fc512d80f94b494710037d4082 100644 --- a/tensorflow/python/kernel_tests/batchtospace_op_test.py +++ b/tensorflow/python/kernel_tests/batchtospace_op_test.py @@ -44,7 +44,7 @@ class CppOpImpl(object): @staticmethod def batch_to_space(*args, **kwargs): - return gen_array_ops._batch_to_space(*args, **kwargs) + return gen_array_ops.batch_to_space(*args, **kwargs) class BatchToSpaceDepthToSpace(test.TestCase, PythonOpImpl): diff --git a/tensorflow/python/kernel_tests/bcast_ops_test.py b/tensorflow/python/kernel_tests/bcast_ops_test.py index 9e512346053a4c3af089170f47313606c4a307c2..cb46fcb0076c1ca437089f5b9d87100667e2a404 100644 --- a/tensorflow/python/kernel_tests/bcast_ops_test.py +++ b/tensorflow/python/kernel_tests/bcast_ops_test.py @@ -20,8 +20,8 @@ from __future__ import print_function from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes -from tensorflow.python.ops.gen_array_ops import _broadcast_args from tensorflow.python.ops.gen_array_ops import _broadcast_gradient_args +from tensorflow.python.ops.gen_array_ops import broadcast_args from tensorflow.python.platform import test @@ -29,7 +29,7 @@ class BcastOpsTest(test.TestCase): def _GetBroadcastShape(self, xs, ys): with self.test_session() as sess: - return sess.run(_broadcast_args(xs, ys)) + return sess.run(broadcast_args(xs, ys)) def _GetGradientArgs(self, xs, ys): with self.test_session() as sess: diff --git a/tensorflow/python/kernel_tests/check_ops_test.py b/tensorflow/python/kernel_tests/check_ops_test.py index 2e94603a3f3d4ca9074320cfb4e9bf06b6640e82..26d3df9e635ea49c6f25e3bb1df25ea72df27304 100644 --- a/tensorflow/python/kernel_tests/check_ops_test.py +++ b/tensorflow/python/kernel_tests/check_ops_test.py @@ -102,17 +102,15 @@ class AssertEqualTest(test.TestCase): with self.assertRaisesRegexp(errors.InvalidArgumentError, "fail"): check_ops.assert_equal(static_big, static_small, message="fail") - # Dynamic check - if context.in_graph_mode(): - with self.test_session(): - small = array_ops.placeholder(dtypes.int32, name="small") - big = array_ops.placeholder(dtypes.int32, name="big") - with ops.control_dependencies( - [check_ops.assert_equal( - big, small, message="fail")]): - out = array_ops.identity(small) - with self.assertRaisesOpError("fail.*big.*small"): - out.eval(feed_dict={small: [1, 2], big: [3, 4]}) + def test_raises_when_greater_dynamic(self): + with self.test_session(): + small = array_ops.placeholder(dtypes.int32, name="small") + big = array_ops.placeholder(dtypes.int32, name="big") + with ops.control_dependencies( + [check_ops.assert_equal(big, small, message="fail")]): + out = array_ops.identity(small) + with self.assertRaisesOpError("fail.*big.*small"): + out.eval(feed_dict={small: [1, 2], big: [3, 4]}) def test_error_message_eager(self): expected_error_msg_full = r"""big does not equal small @@ -182,15 +180,14 @@ First 2 elements of y: with self.assertRaisesRegexp(errors.InvalidArgumentError, "fail"): check_ops.assert_equal(static_big, static_small, message="fail") - # Dynamic check - if context.in_graph_mode(): - with self.test_session(): - small = array_ops.placeholder(dtypes.int32, name="small") - big = array_ops.placeholder(dtypes.int32, name="big") - with ops.control_dependencies([check_ops.assert_equal(small, big)]): - out = array_ops.identity(small) - with self.assertRaisesOpError("small.*big"): - out.eval(feed_dict={small: [3, 1], big: [4, 2]}) + def test_raises_when_less_dynamic(self): + with self.test_session(): + small = array_ops.placeholder(dtypes.int32, name="small") + big = array_ops.placeholder(dtypes.int32, name="big") + with ops.control_dependencies([check_ops.assert_equal(small, big)]): + out = array_ops.identity(small) + with self.assertRaisesOpError("small.*big"): + out.eval(feed_dict={small: [3, 1], big: [4, 2]}) @test_util.run_in_graph_and_eager_modes() def test_doesnt_raise_when_equal_and_broadcastable_shapes(self): diff --git a/tensorflow/python/kernel_tests/checkpoint_ops_test.py b/tensorflow/python/kernel_tests/checkpoint_ops_test.py index a786d0a47e569f71812086fb93c21dc12660a2a5..7f147ba53a71539962f424158731e359724f664f 100644 --- a/tensorflow/python/kernel_tests/checkpoint_ops_test.py +++ b/tensorflow/python/kernel_tests/checkpoint_ops_test.py @@ -50,7 +50,7 @@ class GenerateVocabRemappingTest(test.TestCase): def test_generate_remapping_with_no_vocab_changes(self): """Tests where vocab does not change at all.""" - remapping, num_present = gen_checkpoint_ops._generate_vocab_remapping( + remapping, num_present = gen_checkpoint_ops.generate_vocab_remapping( new_vocab_file=self.old_vocab_file, old_vocab_file=self.old_vocab_file, num_new_vocab=3, @@ -63,7 +63,7 @@ class GenerateVocabRemappingTest(test.TestCase): def test_generate_remapping_with_shifted_vocab(self): """Tests where vocab is the same, but shifted / ordered differently.""" - remapping, num_present = gen_checkpoint_ops._generate_vocab_remapping( + remapping, num_present = gen_checkpoint_ops.generate_vocab_remapping( new_vocab_file=self.new_vocab_file, old_vocab_file=self.old_vocab_file, num_new_vocab=3, @@ -76,7 +76,7 @@ class GenerateVocabRemappingTest(test.TestCase): def test_generate_remapping_with_offset(self): """Tests offset and num_new_vocab logic.""" - remapping, num_present = gen_checkpoint_ops._generate_vocab_remapping( + remapping, num_present = gen_checkpoint_ops.generate_vocab_remapping( new_vocab_file=self.new_vocab_file, old_vocab_file=self.old_vocab_file, num_new_vocab=1, @@ -89,7 +89,7 @@ class GenerateVocabRemappingTest(test.TestCase): def test_generate_remapping_with_old_vocab_size(self): """Tests where old_vocab_size is specified.""" - remapping, num_present = gen_checkpoint_ops._generate_vocab_remapping( + remapping, num_present = gen_checkpoint_ops.generate_vocab_remapping( new_vocab_file=self.new_vocab_file, old_vocab_file=self.old_vocab_file, num_new_vocab=3, @@ -132,7 +132,7 @@ class LoadAndRemapMatrixTest(test.TestCase): # No column remapping, new weight matrix has second row, then first row. row_remapping = [1, 0] - remapped_matrix = gen_checkpoint_ops._load_and_remap_matrix( + remapped_matrix = gen_checkpoint_ops.load_and_remap_matrix( ckpt_path=[self.bundle_file], old_tensor_name=self.old_tensor_name, row_remapping=row_remapping, @@ -147,7 +147,7 @@ class LoadAndRemapMatrixTest(test.TestCase): # No row remapping, new weight matrix has third col, then first col. row_remapping = list(range(self.old_num_rows)) col_remapping = [2, 0] - remapped_matrix = gen_checkpoint_ops._load_and_remap_matrix( + remapped_matrix = gen_checkpoint_ops.load_and_remap_matrix( ckpt_path=[self.bundle_file], old_tensor_name=self.old_tensor_name, row_remapping=row_remapping, @@ -162,7 +162,7 @@ class LoadAndRemapMatrixTest(test.TestCase): # Both row and column remappings. row_remapping = [1, 0, 4] col_remapping = [1, 15] - remapped_matrix = gen_checkpoint_ops._load_and_remap_matrix( + remapped_matrix = gen_checkpoint_ops.load_and_remap_matrix( ckpt_path=[self.bundle_file], old_tensor_name=self.old_tensor_name, row_remapping=row_remapping, @@ -177,7 +177,7 @@ class LoadAndRemapMatrixTest(test.TestCase): def test_load_and_remap_with_init(self): """Tests the op's load and remap where there are missing entries.""" init_val = 42 - remapped_matrix = gen_checkpoint_ops._load_and_remap_matrix( + remapped_matrix = gen_checkpoint_ops.load_and_remap_matrix( ckpt_path=[self.bundle_file], old_tensor_name=self.old_tensor_name, row_remapping=[2, -1, 0], @@ -196,7 +196,7 @@ class LoadAndRemapMatrixTest(test.TestCase): """Tests when all the rows are missing and need to be initialized.""" num_rows = 7 initializing_values = [42] * num_rows * self.old_num_cols - remapped_matrix = gen_checkpoint_ops._load_and_remap_matrix( + remapped_matrix = gen_checkpoint_ops.load_and_remap_matrix( ckpt_path=[self.bundle_file], old_tensor_name=self.old_tensor_name, row_remapping=[-1] * num_rows, @@ -214,7 +214,7 @@ class LoadAndRemapMatrixTest(test.TestCase): num_rows = 7 num_cols = 4 initializing_values = [42] * num_rows * num_cols - remapped_matrix = gen_checkpoint_ops._load_and_remap_matrix( + remapped_matrix = gen_checkpoint_ops.load_and_remap_matrix( ckpt_path=[self.bundle_file], old_tensor_name=self.old_tensor_name, row_remapping=[-1] * num_rows, @@ -235,7 +235,7 @@ class LoadAndRemapMatrixTest(test.TestCase): invalid_remapping = [1, 0, 0, 0, 1, 2] # Invalid row remapping. - remapped_matrix = gen_checkpoint_ops._load_and_remap_matrix( + remapped_matrix = gen_checkpoint_ops.load_and_remap_matrix( ckpt_path=[self.bundle_file], old_tensor_name=self.old_tensor_name, row_remapping=invalid_remapping, @@ -247,7 +247,7 @@ class LoadAndRemapMatrixTest(test.TestCase): remapped_matrix.eval() # Invalid column remapping. - remapped_matrix = gen_checkpoint_ops._load_and_remap_matrix( + remapped_matrix = gen_checkpoint_ops.load_and_remap_matrix( ckpt_path=[self.bundle_file], old_tensor_name=self.old_tensor_name, row_remapping=list(range(self.old_num_rows)), @@ -260,7 +260,7 @@ class LoadAndRemapMatrixTest(test.TestCase): 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( + remapped_matrix = gen_checkpoint_ops.load_and_remap_matrix( ckpt_path=[self.bundle_file], old_tensor_name=self.old_tensor_name, row_remapping=[2, -1, 0], @@ -275,7 +275,7 @@ class LoadAndRemapMatrixTest(test.TestCase): with self.test_session(), self.assertRaises(errors.InvalidArgumentError): remapped_matrix.eval() - remapped_matrix = gen_checkpoint_ops._load_and_remap_matrix( + remapped_matrix = gen_checkpoint_ops.load_and_remap_matrix( ckpt_path=[self.bundle_file], old_tensor_name=self.old_tensor_name, row_remapping=[2, -1, 0], @@ -314,7 +314,7 @@ class LoadAndRemapMatrixWithMaxRowsTest(test.TestCase): num_rows, num_cols = np_value.shape # Tests loading the entire tensor (except reversed). - remapped_matrix = gen_checkpoint_ops._load_and_remap_matrix( + remapped_matrix = gen_checkpoint_ops.load_and_remap_matrix( ckpt_path=ckpt_path, old_tensor_name=old_tensor_name, # Simply reverses the rows of the matrix. @@ -332,7 +332,7 @@ class LoadAndRemapMatrixWithMaxRowsTest(test.TestCase): self.assertGreater(num_rows, 2) prefix_rows = 2 suffix_rows = 3 - remapped_matrix = gen_checkpoint_ops._load_and_remap_matrix( + remapped_matrix = gen_checkpoint_ops.load_and_remap_matrix( ckpt_path=ckpt_path, old_tensor_name=old_tensor_name, # Reverses the rows of the matrix, then prepends and appends @@ -353,7 +353,7 @@ class LoadAndRemapMatrixWithMaxRowsTest(test.TestCase): # Tests when everything is taken from initializing_values. new_rows = 7 initializing_values = [42] * new_rows * num_cols - remapped_matrix = gen_checkpoint_ops._load_and_remap_matrix( + remapped_matrix = gen_checkpoint_ops.load_and_remap_matrix( ckpt_path=ckpt_path, old_tensor_name=old_tensor_name, # Nothing is loaded from the old tensor. diff --git a/tensorflow/python/kernel_tests/concat_op_test.py b/tensorflow/python/kernel_tests/concat_op_test.py index 127bc6bb20ae6b415da94672de68cc4b8ceaa287..81c6a4aa6e6edc4b49338a0bfd354302b1dfac0b 100644 --- a/tensorflow/python/kernel_tests/concat_op_test.py +++ b/tensorflow/python/kernel_tests/concat_op_test.py @@ -526,7 +526,7 @@ class ConcatOpTest(test.TestCase): with self.test_session(use_gpu=True): t1 = [] t2 = [] - output = gen_array_ops._concat_v2([t1, t2], 0).eval() + output = gen_array_ops.concat_v2([t1, t2], 0).eval() self.assertFalse(output) # Checks that output is empty def testConcatInvalidAxis(self): @@ -534,20 +534,20 @@ class ConcatOpTest(test.TestCase): with self.test_session(use_gpu=True): t1 = [1] t2 = [2] - gen_array_ops._concat_v2([t1, t2], 1).eval() + gen_array_ops.concat_v2([t1, t2], 1).eval() def testConcatNegativeAxis(self): with self.test_session(use_gpu=True): t1 = [[1, 2, 3], [4, 5, 6]] t2 = [[7, 8, 9], [10, 11, 12]] - c = gen_array_ops._concat_v2([t1, t2], -2) + c = gen_array_ops.concat_v2([t1, t2], -2) self.assertEqual([4, 3], c.get_shape().as_list()) output = c.eval() self.assertAllEqual([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]], output) - c = gen_array_ops._concat_v2([t1, t2], -1) + c = gen_array_ops.concat_v2([t1, t2], -1) self.assertEqual([2, 6], c.get_shape().as_list()) output = c.eval() self.assertAllEqual([[1, 2, 3, 7, 8, 9], [4, 5, 6, 10, 11, 12]], output) @@ -615,7 +615,7 @@ class ConcatOffsetTest(test.TestCase): s0 = constant_op.constant([2, 3, 5], dtypes.int32) s1 = constant_op.constant([2, 7, 5], dtypes.int32) s2 = constant_op.constant([2, 20, 5], dtypes.int32) - off = gen_array_ops._concat_offset(cdim, [s0, s1, s2]) + off = gen_array_ops.concat_offset(cdim, [s0, s1, s2]) ans = sess.run(off) self.assertAllEqual(ans, [[0, 0, 0], [0, 3, 0], [0, 10, 0]]) @@ -624,7 +624,7 @@ class ConcatOffsetTest(test.TestCase): cdim = constant_op.constant(1, dtypes.int32) s0 = constant_op.constant([[2, 3, 5]], dtypes.int32) s1 = constant_op.constant([[2, 7, 5]], dtypes.int32) - off = gen_array_ops._concat_offset(cdim, [s0, s1]) + off = gen_array_ops.concat_offset(cdim, [s0, s1]) with self.assertRaisesRegexp(errors_impl.InvalidArgumentError, r"should be a vector"): sess.run(off) @@ -634,7 +634,7 @@ class ConcatOffsetTest(test.TestCase): cdim = constant_op.constant(4, dtypes.int32) s0 = constant_op.constant([2, 3, 5], dtypes.int32) s1 = constant_op.constant([2, 7, 5], dtypes.int32) - off = gen_array_ops._concat_offset(cdim, [s0, s1]) + off = gen_array_ops.concat_offset(cdim, [s0, s1]) with self.assertRaisesRegexp(errors_impl.InvalidArgumentError, r"Concat dim is out of range: 4 vs. 3"): sess.run(off) @@ -644,7 +644,7 @@ class ConcatOffsetTest(test.TestCase): cdim = constant_op.constant(1, dtypes.int32) s0 = constant_op.constant([2, 3, 5], dtypes.int32) s1 = constant_op.constant([2, 7, 5, 10], dtypes.int32) - off = gen_array_ops._concat_offset(cdim, [s0, s1]) + off = gen_array_ops.concat_offset(cdim, [s0, s1]) with self.assertRaisesRegexp(errors_impl.InvalidArgumentError, r"should contain 3 elem"): sess.run(off) @@ -654,7 +654,7 @@ class ConcatOffsetTest(test.TestCase): cdim = constant_op.constant(1, dtypes.int32) s0 = constant_op.constant([2, 3, 5], dtypes.int32) s1 = constant_op.constant([2, 7, 10], dtypes.int32) - off = gen_array_ops._concat_offset(cdim, [s0, s1]) + off = gen_array_ops.concat_offset(cdim, [s0, s1]) with self.assertRaisesRegexp( errors_impl.InvalidArgumentError, r"All dimensions except 1 must match. Input 1 has shape \[2 7 10\] " @@ -667,7 +667,7 @@ class ConcatOffsetTest(test.TestCase): s0 = constant_op.constant([2, 3, 5], dtypes.int32) s1 = constant_op.constant([2, 7, 5], dtypes.int32) s2 = constant_op.constant([2, 20, 5], dtypes.int32) - off = gen_array_ops._concat_offset(cdim, [s0, s1, s2]) + off = gen_array_ops.concat_offset(cdim, [s0, s1, s2]) ans = sess.run(off) self.assertAllEqual(ans, [[0, 0, 0], [0, 3, 0], [0, 10, 0]]) @@ -675,7 +675,7 @@ class ConcatOffsetTest(test.TestCase): s0 = constant_op.constant([2, 3, 5], dtypes.int32) s1 = constant_op.constant([1, 3, 5], dtypes.int32) s2 = constant_op.constant([3, 3, 5], dtypes.int32) - off = gen_array_ops._concat_offset(cdim, [s0, s1, s2]) + off = gen_array_ops.concat_offset(cdim, [s0, s1, s2]) ans = sess.run(off) self.assertAllEqual(ans, [[0, 0, 0], [2, 0, 0], [3, 0, 0]]) diff --git a/tensorflow/python/kernel_tests/constant_op_test.py b/tensorflow/python/kernel_tests/constant_op_test.py index 16e56349c45dd56a335f6f881826d975e24bd110..ffbdb0e61a8403fe7ccd36bad40887543023ad40 100644 --- a/tensorflow/python/kernel_tests/constant_op_test.py +++ b/tensorflow/python/kernel_tests/constant_op_test.py @@ -30,6 +30,7 @@ from tensorflow.python.framework import errors_impl from tensorflow.python.framework import importer 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 gradient_checker from tensorflow.python.ops import logging_ops @@ -180,6 +181,11 @@ class ConstantTest(test.TestCase): shape=[2, 3, 5]) self.assertEqual(c.get_shape(), [2, 3, 5]) + @test_util.assert_no_new_pyobjects_executing_eagerly + def testEagerMemory(self): + """Tests PyObject refs are managed correctly when executing eagerly.""" + constant_op.constant([[1.]]) + def testImplicitShapeNumPy(self): with ops.Graph().as_default(): c = constant_op.constant( 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 58f38650eb526e98edf35b2425e0e9e1296ab353..b429fa5c423effce0f0ccb0ad34875dab2808777 100644 --- a/tensorflow/python/kernel_tests/control_flow_ops_py_test.py +++ b/tensorflow/python/kernel_tests/control_flow_ops_py_test.py @@ -552,7 +552,7 @@ class ControlFlowTest(test.TestCase): def testCondRef(self): with self.test_session(): - x = gen_state_ops._variable( + x = gen_state_ops.variable( shape=[1], dtype=dtypes.float32, name="x", @@ -580,7 +580,7 @@ class ControlFlowTest(test.TestCase): def testUninitializedRefIdentity(self): with self.test_session() as sess: - v = gen_state_ops._variable( + v = gen_state_ops.variable( shape=[1], dtype=dtypes.float32, name="v", @@ -1620,7 +1620,7 @@ class ControlFlowTest(test.TestCase): def testWhileStack_1(self): with self.test_session(): - s = gen_data_flow_ops._stack_v2(-1, dtypes.int32, stack_name="foo") + s = gen_data_flow_ops.stack_v2(-1, dtypes.int32, stack_name="foo") i = constant_op.constant(0) def c(i): @@ -1629,7 +1629,7 @@ class ControlFlowTest(test.TestCase): def b(i): ni = math_ops.add(i, 1) ni = control_flow_ops.with_dependencies( - [gen_data_flow_ops._stack_push_v2(s, i)], ni) + [gen_data_flow_ops.stack_push_v2(s, i)], ni) return ni r = control_flow_ops.while_loop(c, b, [i], parallel_iterations=1) @@ -1641,7 +1641,7 @@ class ControlFlowTest(test.TestCase): def b1(i, x): ni = math_ops.subtract(i, 1) - nx = x + gen_data_flow_ops._stack_pop_v2(s, dtypes.int32) + nx = x + gen_data_flow_ops.stack_pop_v2(s, dtypes.int32) return [ni, nx] _, rx = control_flow_ops.while_loop( diff --git a/tensorflow/python/kernel_tests/conv_ops_test.py b/tensorflow/python/kernel_tests/conv_ops_test.py index f4fe01f868da25660171c614bbf84390aead3ade..25525cc1285d8dbc305fdcf838143b6e1c420316 100644 --- a/tensorflow/python/kernel_tests/conv_ops_test.py +++ b/tensorflow/python/kernel_tests/conv_ops_test.py @@ -970,7 +970,7 @@ class Conv2DTest(test.TestCase): self.assertArrayNear(value_2.flatten(), value.flatten(), err) def testConv2D2x2Depth3ValidBackpropFilterStride1x1Dilation2x1(self): - if test.is_gpu_available(cuda_only=True): + if test.is_gpu_available(cuda_only=True) or test_util.IsMklEnabled(): for (data_format, use_gpu) in GetTestConfigs(): self._RunAndVerifyBackpropFilterDilation( input_sizes=[1, 3, 6, 1], @@ -984,7 +984,7 @@ class Conv2DTest(test.TestCase): err=1e-5) def testConv2D2x2Depth1ValidBackpropFilterDilation1x2(self): - if test.is_gpu_available(cuda_only=True): + if test.is_gpu_available(cuda_only=True) or test_util.IsMklEnabled(): for (data_format, use_gpu) in GetTestConfigs(): self._RunAndVerifyBackpropFilterDilation( input_sizes=[1, 2, 3, 1], @@ -998,7 +998,7 @@ class Conv2DTest(test.TestCase): err=1e-5) def testConv2DEmptyBackpropFilterDilation1x2(self): - if test.is_gpu_available(cuda_only=True): + if test.is_gpu_available(cuda_only=True) or test_util.IsMklEnabled(): for (data_format, use_gpu) in GetTestConfigs(): self._RunAndVerifyBackpropFilterDilation( input_sizes=[1, 2, 3, 1], @@ -1012,7 +1012,7 @@ class Conv2DTest(test.TestCase): err=1e-5) def testConv2D2x2Depth3ValidBackpropFilterDilation2x2(self): - if test.is_gpu_available(cuda_only=True): + if test.is_gpu_available(cuda_only=True) or test_util.IsMklEnabled(): for (data_format, use_gpu) in GetTestConfigs(): self._RunAndVerifyBackpropFilterDilation( input_sizes=[1, 3, 4, 3], @@ -1026,7 +1026,7 @@ class Conv2DTest(test.TestCase): err=1e-5) def testConv2DKernelSizeMatchesInputSizeBackpropFilterDilation2x2(self): - if test.is_gpu_available(cuda_only=True): + if test.is_gpu_available(cuda_only=True) or test_util.IsMklEnabled(): for (data_format, use_gpu) in GetTestConfigs(): self._RunAndVerifyBackpropFilterDilation( input_sizes=[1, 3, 3, 1], @@ -1040,7 +1040,7 @@ class Conv2DTest(test.TestCase): err=1e-5) def testConv2D2x2Depth3ValidBackpropInputStride1x1Dilation2x1(self): - if test.is_gpu_available(cuda_only=True): + if test.is_gpu_available(cuda_only=True) or test_util.IsMklEnabled(): for (data_format, use_gpu) in GetTestConfigs(): self._RunAndVerifyBackpropInputDilation( input_sizes=[1, 3, 6, 1], @@ -1054,7 +1054,7 @@ class Conv2DTest(test.TestCase): err=1e-5) def testConv2D2x2Depth1ValidBackpropInputDilation1x2(self): - if test.is_gpu_available(cuda_only=True): + if test.is_gpu_available(cuda_only=True) or test_util.IsMklEnabled(): for (data_format, use_gpu) in GetTestConfigs(): self._RunAndVerifyBackpropInputDilation( input_sizes=[1, 2, 3, 1], @@ -1068,7 +1068,7 @@ class Conv2DTest(test.TestCase): err=1e-5) def testConv2DEmptyBackpropInputDilation1x2(self): - if test.is_gpu_available(cuda_only=True): + if test.is_gpu_available(cuda_only=True) or test_util.IsMklEnabled(): for (data_format, use_gpu) in GetTestConfigs(): self._RunAndVerifyBackpropInputDilation( input_sizes=[0, 2, 3, 1], @@ -1082,7 +1082,7 @@ class Conv2DTest(test.TestCase): err=1e-5) def testConv2D2x2Depth3ValidBackpropInputDilation2x1(self): - if test.is_gpu_available(cuda_only=True): + if test.is_gpu_available(cuda_only=True) or test_util.IsMklEnabled(): for (data_format, use_gpu) in GetTestConfigs(): # The GPU version of this test is not very stable. So adjusting the # error threshold to 1e-4. @@ -1098,7 +1098,7 @@ class Conv2DTest(test.TestCase): err=1e-4) def testConv2DKernelSizeMatchesInputSizeBackpropInputDilation2x2(self): - if test.is_gpu_available(cuda_only=True): + if test.is_gpu_available(cuda_only=True) or test_util.IsMklEnabled(): for (data_format, use_gpu) in GetTestConfigs(): self._RunAndVerifyBackpropInputDilation( input_sizes=[1, 3, 3, 1], diff --git a/tensorflow/python/kernel_tests/cwise_ops_test.py b/tensorflow/python/kernel_tests/cwise_ops_test.py index 0d9b46c30dbbed20dd940e0427fbf6f6d5415106..8db0bb6f0dc495e7be2cd717787acf87156f42af 100644 --- a/tensorflow/python/kernel_tests/cwise_ops_test.py +++ b/tensorflow/python/kernel_tests/cwise_ops_test.py @@ -495,11 +495,11 @@ class UnaryOpTest(test.TestCase): dtype_tols = [(np.float32, 5e-4), (np.float64, 1e-6), (np.complex64, 5e-4), (np.complex128, 1e-6)] op_range = [ - (gen_math_ops._reciprocal_grad, [-2, 2]), - (gen_math_ops._rsqrt_grad, [0.1, 3]), - (gen_math_ops._sigmoid_grad, [-2, 2]), - (gen_math_ops._sqrt_grad, [0.1, 3]), - (gen_math_ops._tanh_grad, [-2, 2]), + (gen_math_ops.reciprocal_grad, [-2, 2]), + (gen_math_ops.rsqrt_grad, [0.1, 3]), + (gen_math_ops.sigmoid_grad, [-2, 2]), + (gen_math_ops.sqrt_grad, [0.1, 3]), + (gen_math_ops.tanh_grad, [-2, 2]), ] def rand(dtype): diff --git a/tensorflow/python/kernel_tests/depthtospace_op_test.py b/tensorflow/python/kernel_tests/depthtospace_op_test.py index 7df2366954f3a6f3f37aef447479ba67c263025f..f0beabb4e20e4ec0a2fc7a487bf2541d19568927 100644 --- a/tensorflow/python/kernel_tests/depthtospace_op_test.py +++ b/tensorflow/python/kernel_tests/depthtospace_op_test.py @@ -35,8 +35,8 @@ from tensorflow.python.platform import tf_logging class DepthToSpaceTest(test.TestCase): - def _testOne(self, inputs, block_size, outputs): - input_nhwc = math_ops.to_float(inputs) + def _testOne(self, inputs, block_size, outputs, dtype=dtypes.float32): + input_nhwc = math_ops.cast(inputs, dtype) with self.test_session(use_gpu=False): # test NHWC (default) on CPU x_tf = array_ops.depth_to_space(input_nhwc, block_size) @@ -59,6 +59,12 @@ class DepthToSpaceTest(test.TestCase): x_out = [[[[1], [2]], [[3], [4]]]] self._testOne(x_np, block_size, x_out) + def testBasicFloat16(self): + x_np = [[[[1, 2, 3, 4]]]] + block_size = 2 + x_out = [[[[1], [2]], [[3], [4]]]] + self._testOne(x_np, block_size, x_out, dtype=dtypes.float16) + # Tests for larger input dimensions. To make sure elements are # correctly ordered spatially. def testBlockSize2(self): @@ -90,6 +96,24 @@ class DepthToSpaceTest(test.TestCase): x_out = [batch_output_elt(i) for i in range(batch_size)] self._testOne(x_np, block_size, x_out) + def testBatchSize0(self): + block_size = 2 + batch_size = 0 + input_nhwc = array_ops.ones([batch_size, 2, 3, 12]) + x_out = array_ops.ones([batch_size, 4, 6, 3]) + + with self.test_session(use_gpu=False): + # test NHWC (default) on CPU + x_tf = array_ops.depth_to_space(input_nhwc, block_size) + self.assertAllEqual(x_tf.shape, x_out.shape) + x_tf.eval() + if test.is_gpu_available(): + with self.test_session(use_gpu=True): + # test NHWC (default) on GPU + x_tf = array_ops.depth_to_space(input_nhwc, block_size) + self.assertAllEqual(x_tf.shape, x_out.shape) + x_tf.eval() + # Tests for different width and height. def testNonSquare(self): x_np = [[[[1, 10, 2, 20, 3, 30, 4, 40]], diff --git a/tensorflow/python/kernel_tests/determinant_op_test.py b/tensorflow/python/kernel_tests/determinant_op_test.py index 222038b22ef3c766efd14fd9b1c9044a0b6e9125..a52b2c0dc32c26ecd5ef08aa3f8678f0006cd4fe 100644 --- a/tensorflow/python/kernel_tests/determinant_op_test.py +++ b/tensorflow/python/kernel_tests/determinant_op_test.py @@ -65,7 +65,7 @@ class DeterminantOpTest(test.TestCase): self._compareDeterminantBase(matrix_x, linalg_ops.matrix_determinant(matrix_x)) self._compareLogDeterminantBase( - matrix_x, gen_linalg_ops._log_matrix_determinant(matrix_x)) + matrix_x, gen_linalg_ops.log_matrix_determinant(matrix_x)) def testBasic(self): # 2x2 matrices diff --git a/tensorflow/python/kernel_tests/dynamic_partition_op_test.py b/tensorflow/python/kernel_tests/dynamic_partition_op_test.py index fedbf9e696923a34968e7a907e4099c520d1447b..5e8937ad2c36afb2b1ddb58ffb238a45e09e4b30 100644 --- a/tensorflow/python/kernel_tests/dynamic_partition_op_test.py +++ b/tensorflow/python/kernel_tests/dynamic_partition_op_test.py @@ -326,6 +326,18 @@ class DynamicPartitionTest(test.TestCase): with self.assertRaises(ValueError): data_flow_ops.dynamic_partition(data, indices, num_partitions=4) + # see https://github.com/tensorflow/tensorflow/issues/17106 + def testCUBBug(self): + x = constant_op.constant(np.random.randn(3072)) + inds = [0]*189 + [1]*184 + [2]*184 + [3]*191 + [4]*192 + [5]*195 + [6]*195 + inds += [7]*195 + [8]*188 + [9]*195 + [10]*188 + [11]*202 + [12]*194 + inds += [13]*194 + [14]*194 + [15]*192 + self.assertEqual(len(inds), x.shape[0]) + partitioned = data_flow_ops.dynamic_partition(x, inds, 16) + with self.test_session() as sess: + res = sess.run(partitioned) + self.assertEqual(res[-1].shape[0], 192) + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/kernel_tests/fractional_avg_pool_op_test.py b/tensorflow/python/kernel_tests/fractional_avg_pool_op_test.py index feec9934e459590bb1dd0bc5c7cf40013d3d8b88..faac7d8365dfaa1b6b32f8fe66a76c3694aa0d5b 100644 --- a/tensorflow/python/kernel_tests/fractional_avg_pool_op_test.py +++ b/tensorflow/python/kernel_tests/fractional_avg_pool_op_test.py @@ -347,7 +347,7 @@ class FractionalAvgPoolGradTest(test.TestCase): Two types of tests for FractionalAvgPoolGrad. 1) Test fractional_avg_pool_grad() directly. - This type of test relies on gen_nn_ops._avg_pool_grad() returns the + This type of test relies on gen_nn_ops.avg_pool_grad() returns the correct result. For example: * input_tensor_shape = (1, 10, 10, 1) * window_size = (1, 2, 2, 1) @@ -404,13 +404,13 @@ class FractionalAvgPoolGradTest(test.TestCase): num_elements *= dim_size output_backprop = (self._PRNG.rand(num_elements) * 1000).reshape(output_data.shape) - input_backprop_tensor = gen_nn_ops._avg_pool_grad( + input_backprop_tensor = gen_nn_ops.avg_pool_grad( input_tensor.get_shape(), output_backprop, window_size, stride_size, padding) input_backprop = input_backprop_tensor.eval() row_seq = list(range(0, num_rows + 1, row_window_size)) col_seq = list(range(0, num_cols + 1, col_window_size)) - fap_input_backprop_tensor = gen_nn_ops._fractional_avg_pool_grad( + fap_input_backprop_tensor = gen_nn_ops.fractional_avg_pool_grad( input_tensor.get_shape(), output_backprop, row_seq, @@ -443,7 +443,7 @@ class FractionalAvgPoolGradTest(test.TestCase): num_elements *= dim_size output_backprop = (self._PRNG.rand(num_elements) * 1000).reshape(output_data.shape) - input_backprop_tensor = gen_nn_ops._avg_pool_grad( + input_backprop_tensor = gen_nn_ops.avg_pool_grad( input_tensor.get_shape(), output_backprop, window_size, stride_size, padding) input_backprop = input_backprop_tensor.eval() @@ -451,7 +451,7 @@ class FractionalAvgPoolGradTest(test.TestCase): col_seq = list(range(0, num_cols, col_window_size - 1)) row_seq[-1] += 1 col_seq[-1] += 1 - fap_input_backprop_tensor = gen_nn_ops._fractional_avg_pool_grad( + fap_input_backprop_tensor = gen_nn_ops.fractional_avg_pool_grad( input_tensor.get_shape(), output_backprop, row_seq, diff --git a/tensorflow/python/kernel_tests/fractional_max_pool_op_test.py b/tensorflow/python/kernel_tests/fractional_max_pool_op_test.py index 5983ae7759dbf3eb2db9867def829ce8dbeb4b73..6477c9ebc4c35fcc5963b27a0f5c50624a73fa09 100644 --- a/tensorflow/python/kernel_tests/fractional_max_pool_op_test.py +++ b/tensorflow/python/kernel_tests/fractional_max_pool_op_test.py @@ -318,7 +318,7 @@ class FractionalMaxPoolGradTest(test.TestCase): Two types of tests for FractionalMaxPoolGrad. 1) Test fractional_max_pool_grad() directly. - This type of test relies on gen_nn_ops._max_pool_grad() returns the correct + This type of test relies on gen_nn_ops.max_pool_grad() returns the correct result. For example: * input_tensor_shape = (1, 10, 10, 1) * window_size = (1, 2, 2, 1) @@ -384,16 +384,13 @@ class FractionalMaxPoolGradTest(test.TestCase): stride_size, padding) output_data = output_tensor.eval() output_backprop = self._PRNG.randint(100, size=output_data.shape) - input_backprop_tensor = gen_nn_ops._max_pool_grad(input_tensor, - output_tensor, - output_backprop, - window_size, - stride_size, - padding) + input_backprop_tensor = gen_nn_ops.max_pool_grad( + input_tensor, output_tensor, output_backprop, window_size, + stride_size, padding) input_backprop = input_backprop_tensor.eval() row_seq = list(range(0, num_rows + 1, row_window_size)) col_seq = list(range(0, num_cols + 1, col_window_size)) - fmp_input_backprop_tensor = gen_nn_ops._fractional_max_pool_grad( + fmp_input_backprop_tensor = gen_nn_ops.fractional_max_pool_grad( input_tensor, output_tensor, output_backprop, @@ -422,18 +419,15 @@ class FractionalMaxPoolGradTest(test.TestCase): stride_size, padding) output_data = output_tensor.eval() output_backprop = self._PRNG.randint(100, size=output_data.shape) - input_backprop_tensor = gen_nn_ops._max_pool_grad(input_tensor, - output_tensor, - output_backprop, - window_size, - stride_size, - padding) + input_backprop_tensor = gen_nn_ops.max_pool_grad( + input_tensor, output_tensor, output_backprop, window_size, + stride_size, padding) input_backprop = input_backprop_tensor.eval() row_seq = list(range(0, num_rows, row_window_size - 1)) col_seq = list(range(0, num_cols, col_window_size - 1)) row_seq[-1] += 1 col_seq[-1] += 1 - fmp_input_backprop_tensor = gen_nn_ops._fractional_max_pool_grad( + fmp_input_backprop_tensor = gen_nn_ops.fractional_max_pool_grad( input_tensor, output_tensor, output_backprop, @@ -591,7 +585,7 @@ class FractionalMaxPoolGradTest(test.TestCase): output_tensor = constant_op.constant( output_data_not_overlapping, shape=output_size) grad = constant_op.constant(output_backprop, shape=output_size) - r = gen_nn_ops._fractional_max_pool_grad( + r = gen_nn_ops.fractional_max_pool_grad( input_tensor, output_tensor, grad, @@ -606,7 +600,7 @@ class FractionalMaxPoolGradTest(test.TestCase): # Test when overlapping is True output_tensor = constant_op.constant( output_data_overlapping, shape=output_size) - r = gen_nn_ops._fractional_max_pool_grad( + r = gen_nn_ops.fractional_max_pool_grad( input_tensor, output_tensor, grad, row_seq, col_seq, overlapping=True) input_backprop_overlapping = r.eval() self.assertShapeEqual( diff --git a/tensorflow/python/kernel_tests/init_ops_test.py b/tensorflow/python/kernel_tests/init_ops_test.py index 19a7d2f9d51fff46ee817ad03ef62383f6727791..c1755985ee85c62005c8d3d5fb916859193aa5f3 100644 --- a/tensorflow/python/kernel_tests/init_ops_test.py +++ b/tensorflow/python/kernel_tests/init_ops_test.py @@ -25,10 +25,13 @@ from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import random_seed +from tensorflow.python.layers import convolutional from tensorflow.python.ops import array_ops from tensorflow.python.ops import init_ops +from tensorflow.python.ops import linalg_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import partitioned_variables +from tensorflow.python.ops import random_ops from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables from tensorflow.python.platform import test @@ -571,6 +574,82 @@ class OrthogonalInitializerTest(test.TestCase): np.dot(t, t.T), np.eye(t.shape[0]), rtol=tol, atol=tol) +class ConvolutionDeltaOrthogonalInitializerTest(test.TestCase): + + def testInitializerIdentical(self): + for dtype in [dtypes.float32, dtypes.float64]: + init1 = init_ops.convolutional_delta_orthogonal(seed=1, dtype=dtype) + init2 = init_ops.convolutional_delta_orthogonal(seed=1, dtype=dtype) + self.assertTrue(identicaltest(self, init1, init2, (3, 3, 10, 10))) + + def testInitializerDifferent(self): + for dtype in [dtypes.float32, dtypes.float64]: + init1 = init_ops.convolutional_delta_orthogonal(seed=1, dtype=dtype) + init2 = init_ops.convolutional_delta_orthogonal(seed=2, dtype=dtype) + self.assertFalse(identicaltest(self, init1, init2, (3, 3, 10, 10))) + + def testDuplicatedInitializer(self): + init = init_ops.convolutional_delta_orthogonal() + self.assertFalse(duplicated_initializer(self, init, 1, (3, 3, 10, 10))) + + def testInvalidDataType(self): + self.assertRaises( + ValueError, init_ops.convolutional_delta_orthogonal, + dtype=dtypes.string) + + def testInvalidShape(self): + init1 = init_ops.convolutional_delta_orthogonal() + with self.test_session(graph=ops.Graph(), use_gpu=True): + self.assertRaises(ValueError, init1, shape=[3, 3, 6, 5]) + + def testGain(self): + shape = (3, 3, 10, 10) + for dtype in [dtypes.float32, dtypes.float64]: + init1 = init_ops.convolutional_delta_orthogonal(seed=1, dtype=dtype) + init2 = init_ops.convolutional_delta_orthogonal(gain=3.14, + seed=1, dtype=dtype) + with self.test_session(graph=ops.Graph(), use_gpu=True): + t1 = init1(shape).eval() + with self.test_session(graph=ops.Graph(), use_gpu=True): + t2 = init2(shape).eval() + return np.allclose(t1, t2 / 3.14, rtol=1e-15, atol=1e-15) + + def testShapesValues(self): + for dtype in [dtypes.float32]: + for kernel_size in [[3], [8], [3, 5], [2, 4], [3, 3, 3], [2, 2, 2]]: + tol = 1e-2 + # Check orthogonality by computing the 2-norms of the inputs and ouputs. + if len(kernel_size) == 1: + shape = [4, 32, 64] + convolution = convolutional.conv1d + elif len(kernel_size) == 2: + convolution = convolutional.conv2d + shape = [4, 32, 32, 64] + else: + shape = [4, 16, 16, 16, 64] + convolution = convolutional.conv3d + inputs = random_ops.random_normal(shape, dtype=dtype) + inputs_2norm = linalg_ops.norm(inputs) + outputs = convolution( + inputs, padding="same", filters=128, + kernel_size=kernel_size, use_bias=False, + kernel_initializer=init_ops.convolutional_delta_orthogonal( + gain=3.14)) + outputs_shape = shape[0:-1] + [128] + outputs_2norm = linalg_ops.norm(outputs) + my_ops = variables.global_variables_initializer() + with self.test_session(use_gpu=True) as sess: + sess.run(my_ops) + # Check the shape of the outputs + t = outputs.eval() + self.assertAllEqual(t.shape, outputs_shape) + # Check isometry of the delta-orthogonal kernel. + self.assertAllClose( + sess.run(inputs_2norm)/np.sqrt(np.prod(shape)), + sess.run(outputs_2norm)/(np.sqrt(np.prod(shape))*np.sqrt(3.14)), + rtol=tol, atol=tol) + + class IdentityInitializerTest(test.TestCase): def testInvalidDataType(self): diff --git a/tensorflow/python/kernel_tests/linalg/linear_operator_composition_test.py b/tensorflow/python/kernel_tests/linalg/linear_operator_composition_test.py index 4d79365dbefc74fe8412b65ec089fb2af4255aea..f96b9ccdaacae7d8e0552ed3d74ce53808fed963 100644 --- a/tensorflow/python/kernel_tests/linalg/linear_operator_composition_test.py +++ b/tensorflow/python/kernel_tests/linalg/linear_operator_composition_test.py @@ -44,9 +44,9 @@ class SquareLinearOperatorCompositionTest( self._rtol[dtypes.float32] = 1e-4 self._rtol[dtypes.complex64] = 1e-4 - def _operator_and_mat_and_feed_dict(self, shape, dtype, use_placeholder): + def _operator_and_mat_and_feed_dict(self, build_info, dtype, use_placeholder): sess = ops.get_default_session() - shape = list(shape) + shape = list(build_info.shape) # Either 1 or 2 matrices, depending. num_operators = rng.randint(low=1, high=3) @@ -148,9 +148,9 @@ class NonSquareLinearOperatorCompositionTest( self._rtol[dtypes.float32] = 1e-4 self._rtol[dtypes.complex64] = 1e-4 - def _operator_and_mat_and_feed_dict(self, shape, dtype, use_placeholder): + def _operator_and_mat_and_feed_dict(self, build_info, dtype, use_placeholder): sess = ops.get_default_session() - shape = list(shape) + shape = list(build_info.shape) # Test only the case of 2 matrices. # The Square test uses either 1 or 2, so we have tested the case of 1 matrix diff --git a/tensorflow/python/kernel_tests/linalg/linear_operator_diag_test.py b/tensorflow/python/kernel_tests/linalg/linear_operator_diag_test.py index 8cb9f9e6213cda8daae7b629fc31d4721fd48fa7..0a0e31c716ecfa10ed93cff92fa908a240f8495e 100644 --- a/tensorflow/python/kernel_tests/linalg/linear_operator_diag_test.py +++ b/tensorflow/python/kernel_tests/linalg/linear_operator_diag_test.py @@ -34,7 +34,8 @@ class LinearOperatorDiagTest( linear_operator_test_util.SquareLinearOperatorDerivedClassTest): """Most tests done in the base class LinearOperatorDerivedClassTest.""" - def _operator_and_mat_and_feed_dict(self, shape, dtype, use_placeholder): + def _operator_and_mat_and_feed_dict(self, build_info, dtype, use_placeholder): + shape = list(build_info.shape) diag = linear_operator_test_util.random_sign_uniform( shape[:-1], minval=1., maxval=2., dtype=dtype) if use_placeholder: diff --git a/tensorflow/python/kernel_tests/linalg/linear_operator_full_matrix_test.py b/tensorflow/python/kernel_tests/linalg/linear_operator_full_matrix_test.py index 50d6f524e9ad75715d7a57348638fdfeee667f40..b3da623b5e8d8c99c6777e75e2d49f24dab1c96b 100644 --- a/tensorflow/python/kernel_tests/linalg/linear_operator_full_matrix_test.py +++ b/tensorflow/python/kernel_tests/linalg/linear_operator_full_matrix_test.py @@ -36,11 +36,11 @@ class SquareLinearOperatorFullMatrixTest( linear_operator_test_util.SquareLinearOperatorDerivedClassTest): """Most tests done in the base class LinearOperatorDerivedClassTest.""" - def _operator_and_mat_and_feed_dict(self, shape, dtype, use_placeholder): - shape = list(shape) + def _operator_and_mat_and_feed_dict(self, build_info, dtype, use_placeholder): + shape = list(build_info.shape) - matrix = linear_operator_test_util.random_positive_definite_matrix(shape, - dtype) + matrix = linear_operator_test_util.random_positive_definite_matrix( + shape, dtype) if use_placeholder: matrix_ph = array_ops.placeholder(dtype=dtype) @@ -136,8 +136,8 @@ class SquareLinearOperatorFullMatrixSymmetricPositiveDefiniteTest( def _dtypes_to_test(self): return [dtypes.float32, dtypes.float64] - def _operator_and_mat_and_feed_dict(self, shape, dtype, use_placeholder): - shape = list(shape) + def _operator_and_mat_and_feed_dict(self, build_info, dtype, use_placeholder): + shape = list(build_info.shape) matrix = linear_operator_test_util.random_positive_definite_matrix( shape, dtype, force_well_conditioned=True) @@ -210,7 +210,8 @@ class NonSquareLinearOperatorFullMatrixTest( linear_operator_test_util.NonSquareLinearOperatorDerivedClassTest): """Most tests done in the base class LinearOperatorDerivedClassTest.""" - def _operator_and_mat_and_feed_dict(self, shape, dtype, use_placeholder): + def _operator_and_mat_and_feed_dict(self, build_info, dtype, use_placeholder): + shape = list(build_info.shape) matrix = linear_operator_test_util.random_normal(shape, dtype=dtype) if use_placeholder: matrix_ph = array_ops.placeholder(dtype=dtype) diff --git a/tensorflow/python/kernel_tests/linalg/linear_operator_identity_test.py b/tensorflow/python/kernel_tests/linalg/linear_operator_identity_test.py index 6d635707683f4500919073a4f43c320a44b65018..59f63f949e96991193412d3574603e58a75cb6e5 100644 --- a/tensorflow/python/kernel_tests/linalg/linear_operator_identity_test.py +++ b/tensorflow/python/kernel_tests/linalg/linear_operator_identity_test.py @@ -43,8 +43,8 @@ class LinearOperatorIdentityTest( # 16bit. return [dtypes.float32, dtypes.float64, dtypes.complex64, dtypes.complex128] - def _operator_and_mat_and_feed_dict(self, shape, dtype, use_placeholder): - shape = list(shape) + def _operator_and_mat_and_feed_dict(self, build_info, dtype, use_placeholder): + shape = list(build_info.shape) assert shape[-1] == shape[-2] batch_shape = shape[:-2] @@ -261,8 +261,8 @@ class LinearOperatorScaledIdentityTest( # 16bit. return [dtypes.float32, dtypes.float64, dtypes.complex64, dtypes.complex128] - def _operator_and_mat_and_feed_dict(self, shape, dtype, use_placeholder): - shape = list(shape) + def _operator_and_mat_and_feed_dict(self, build_info, dtype, use_placeholder): + shape = list(build_info.shape) assert shape[-1] == shape[-2] batch_shape = shape[:-2] diff --git a/tensorflow/python/kernel_tests/linalg/linear_operator_low_rank_update_test.py b/tensorflow/python/kernel_tests/linalg/linear_operator_low_rank_update_test.py index d3a47da946b12277c4c390a4a320d7c91ed81b32..8095f6419ef0d9543339cf1f4ee9cd4783f852b9 100644 --- a/tensorflow/python/kernel_tests/linalg/linear_operator_low_rank_update_test.py +++ b/tensorflow/python/kernel_tests/linalg/linear_operator_low_rank_update_test.py @@ -55,16 +55,22 @@ class BaseLinearOperatorLowRankUpdatetest(object): return [dtypes.float32, dtypes.float64] @property - def _shapes_to_test(self): + def _operator_build_infos(self): + build_info = linear_operator_test_util.OperatorBuildInfo # Previously we had a (2, 10, 10) shape at the end. We did this to test the # inversion and determinant lemmas on not-tiny matrices, since these are # known to have stability issues. This resulted in test timeouts, so this # shape has been removed, but rest assured, the tests did pass. - return [(0, 0), (1, 1), (1, 3, 3), (3, 4, 4), (2, 1, 4, 4)] - - def _operator_and_mat_and_feed_dict(self, shape, dtype, use_placeholder): + return [ + build_info((0, 0)), + build_info((1, 1)), + build_info((1, 3, 3)), + build_info((3, 4, 4)), + build_info((2, 1, 4, 4))] + + def _operator_and_mat_and_feed_dict(self, build_info, dtype, use_placeholder): # Recall A = L + UDV^H - shape = list(shape) + shape = list(build_info.shape) diag_shape = shape[:-1] k = shape[-2] // 2 + 1 u_perturbation_shape = shape[:-1] + [k] diff --git a/tensorflow/python/kernel_tests/linalg/linear_operator_lower_triangular_test.py b/tensorflow/python/kernel_tests/linalg/linear_operator_lower_triangular_test.py index db3918f9983c5b7d05fa4ba3bc85b26a485f2f00..a57d2f085e089fb913f09fdd9b07cf13aa7f3c35 100644 --- a/tensorflow/python/kernel_tests/linalg/linear_operator_lower_triangular_test.py +++ b/tensorflow/python/kernel_tests/linalg/linear_operator_lower_triangular_test.py @@ -38,7 +38,8 @@ class LinearOperatorLowerTriangularTest( # matrix_triangular_solve. return [dtypes.float32, dtypes.float64] - def _operator_and_mat_and_feed_dict(self, shape, dtype, use_placeholder): + def _operator_and_mat_and_feed_dict(self, build_info, dtype, use_placeholder): + shape = list(build_info.shape) # Upper triangle will be nonzero, but ignored. # Use a diagonal that ensures this matrix is well conditioned. tril = linear_operator_test_util.random_tril_matrix( diff --git a/tensorflow/python/kernel_tests/list_ops_test.py b/tensorflow/python/kernel_tests/list_ops_test.py index 1577b7bc8021a326eb720bdf059b8d1c568c0cc1..8865e165fd6bb15b53d10f336e526f8e830500b1 100644 --- a/tensorflow/python/kernel_tests/list_ops_test.py +++ b/tensorflow/python/kernel_tests/list_ops_test.py @@ -30,7 +30,9 @@ 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 list_ops +from tensorflow.python.ops import math_ops from tensorflow.python.platform import test from tensorflow.python.training import server_lib @@ -123,6 +125,56 @@ class ListOpsTest(test_util.TensorFlowTestCase): l_cpu, element_dtype=dtypes.float32)[1], 2.0) + def testGraphStack(self): + with context.graph_mode(), self.test_session(): + tl = list_ops.empty_tensor_list( + element_shape=constant_op.constant([1], dtype=dtypes.int32), + element_dtype=dtypes.int32) + tl = list_ops.tensor_list_push_back(tl, [1]) + self.assertAllEqual( + list_ops.tensor_list_stack(tl, element_dtype=dtypes.int32).eval(), + [[1]]) + + def testGraphStackInLoop(self): + with context.graph_mode(), self.test_session(): + t1 = list_ops.empty_tensor_list( + element_shape=constant_op.constant([], dtype=dtypes.int32), + element_dtype=dtypes.int32) + i = constant_op.constant(0, dtype=dtypes.int32) + + def body(i, t1): + t1 = list_ops.tensor_list_push_back(t1, i) + i += 1 + return i, t1 + + i, t1 = control_flow_ops.while_loop(lambda i, t1: math_ops.less(i, 4), + body, [i, t1]) + s1 = list_ops.tensor_list_stack(t1, element_dtype=dtypes.int32).eval() + self.assertAllEqual(s1, [0, 1, 2, 3]) + + def testGraphStackInLoopSwitchDtype(self): + with context.graph_mode(), self.test_session(): + t1 = list_ops.empty_tensor_list( + element_shape=constant_op.constant([], dtype=dtypes.int32), + element_dtype=dtypes.int32) + i = constant_op.constant(0, dtype=dtypes.float32) + m = constant_op.constant([1, 2, 3], dtype=dtypes.float32) + + def body(i, m, t1): + t1 = control_flow_ops.cond( + math_ops.equal(list_ops.tensor_list_length(t1), 0), + lambda: list_ops.empty_tensor_list(m.shape, m.dtype), lambda: t1) + + t1 = list_ops.tensor_list_push_back(t1, m * i) + i += 1.0 + return i, m, t1 + + i, m, t1 = control_flow_ops.while_loop( + lambda i, m, t1: math_ops.less(i, 4), body, [i, m, t1]) + s1 = list_ops.tensor_list_stack(t1, element_dtype=dtypes.float32).eval() + np_s1 = np.vstack([np.arange(1, 4) * i for i in range(4)]) + self.assertAllEqual(s1, np_s1) + def testSerialize(self): # pylint: disable=g-import-not-at-top try: diff --git a/tensorflow/python/kernel_tests/matrix_exponential_op_test.py b/tensorflow/python/kernel_tests/matrix_exponential_op_test.py index 6203a412d7faec4fe9f6179141301579b5900291..a0c66c77d8850d3144678870983730537a253556 100644 --- a/tensorflow/python/kernel_tests/matrix_exponential_op_test.py +++ b/tensorflow/python/kernel_tests/matrix_exponential_op_test.py @@ -48,7 +48,7 @@ class ExponentialOpTest(test.TestCase): def _verifyExponential(self, x, np_type): inp = x.astype(np_type) with self.test_session(use_gpu=True): - tf_ans = gen_linalg_ops._matrix_exponential(inp) + tf_ans = gen_linalg_ops.matrix_exponential(inp) if x.size == 0: np_ans = np.empty(x.shape, dtype=np_type) else: @@ -116,13 +116,13 @@ class ExponentialOpTest(test.TestCase): # When the exponential of a non-square matrix is attempted we should return # an error with self.assertRaises(ValueError): - gen_linalg_ops._matrix_exponential(np.array([[1., 2., 3.], [3., 4., 5.]])) + gen_linalg_ops.matrix_exponential(np.array([[1., 2., 3.], [3., 4., 5.]])) def testWrongDimensions(self): # The input to the exponential should be at least a 2-dimensional tensor. tensor3 = constant_op.constant([1., 2.]) with self.assertRaises(ValueError): - gen_linalg_ops._matrix_exponential(tensor3) + gen_linalg_ops.matrix_exponential(tensor3) def testEmpty(self): self._verifyExponentialReal(np.empty([0, 2, 2])) @@ -143,8 +143,8 @@ class ExponentialOpTest(test.TestCase): with self.test_session(use_gpu=True) as sess: matrix1 = random_ops.random_normal([5, 5], seed=42) matrix2 = random_ops.random_normal([5, 5], seed=42) - expm1 = gen_linalg_ops._matrix_exponential(matrix1) - expm2 = gen_linalg_ops._matrix_exponential(matrix2) + expm1 = gen_linalg_ops.matrix_exponential(matrix1) + expm2 = gen_linalg_ops.matrix_exponential(matrix2) expm = sess.run([expm1, expm2]) self.assertAllEqual(expm[0], expm[1]) @@ -180,7 +180,7 @@ class MatrixExponentialBenchmark(test.Benchmark): session.Session() as sess, \ ops.device("/cpu:0"): matrix = self._GenerateMatrix(shape) - expm = gen_linalg_ops._matrix_exponential(matrix) + expm = gen_linalg_ops.matrix_exponential(matrix) variables.global_variables_initializer().run() self.run_op_benchmark( sess, diff --git a/tensorflow/python/kernel_tests/matrix_logarithm_op_test.py b/tensorflow/python/kernel_tests/matrix_logarithm_op_test.py index 18ed59828c15f5ad21fe054cd6e40991c02bb356..24edc4f59fe6dd84da6732036eb53e2ad367bd06 100644 --- a/tensorflow/python/kernel_tests/matrix_logarithm_op_test.py +++ b/tensorflow/python/kernel_tests/matrix_logarithm_op_test.py @@ -39,8 +39,8 @@ class LogarithmOpTest(test.TestCase): inp = x.astype(np_type) with self.test_session(use_gpu=True): # Verify that expm(logm(A)) == A. - tf_ans = gen_linalg_ops._matrix_exponential( - gen_linalg_ops._matrix_logarithm(inp)) + tf_ans = gen_linalg_ops.matrix_exponential( + gen_linalg_ops.matrix_logarithm(inp)) out = tf_ans.eval() self.assertAllClose(inp, out, rtol=1e-4, atol=1e-3) @@ -85,14 +85,14 @@ class LogarithmOpTest(test.TestCase): # When the logarithm of a non-square matrix is attempted we should return # an error with self.assertRaises(ValueError): - gen_linalg_ops._matrix_logarithm( + gen_linalg_ops.matrix_logarithm( np.array([[1., 2., 3.], [3., 4., 5.]], dtype=np.complex64)) 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) with self.assertRaises(ValueError): - gen_linalg_ops._matrix_logarithm(tensor3) + gen_linalg_ops.matrix_logarithm(tensor3) def testEmpty(self): self._verifyLogarithmComplex(np.empty([0, 2, 2], dtype=np.complex64)) @@ -115,8 +115,8 @@ class LogarithmOpTest(test.TestCase): random_ops.random_normal([5, 5], seed=42), dtypes.complex64) matrix2 = math_ops.cast( random_ops.random_normal([5, 5], seed=42), dtypes.complex64) - logm1 = gen_linalg_ops._matrix_logarithm(matrix1) - logm2 = gen_linalg_ops._matrix_logarithm(matrix2) + logm1 = gen_linalg_ops.matrix_logarithm(matrix1) + logm2 = gen_linalg_ops.matrix_logarithm(matrix2) logm = sess.run([logm1, logm2]) self.assertAllEqual(logm[0], logm[1]) @@ -152,7 +152,7 @@ class MatrixLogarithmBenchmark(test.Benchmark): session.Session() as sess, \ ops.device("/cpu:0"): matrix = self._GenerateMatrix(shape) - logm = gen_linalg_ops._matrix_logarithm(matrix) + logm = gen_linalg_ops.matrix_logarithm(matrix) variables.global_variables_initializer().run() self.run_op_benchmark( sess, diff --git a/tensorflow/python/kernel_tests/metrics_test.py b/tensorflow/python/kernel_tests/metrics_test.py index 59e7afa2dcb1e02ed9c66e5cf75753f96552b4e0..ad802f7e1f72f6cbc3dda1ca98e46e6da4e5110a 100644 --- a/tensorflow/python/kernel_tests/metrics_test.py +++ b/tensorflow/python/kernel_tests/metrics_test.py @@ -1132,9 +1132,9 @@ class AUCTest(test.TestCase): auc, update_op = metrics.auc(labels, predictions, curve='PR') sess.run(variables.local_variables_initializer()) - self.assertAlmostEqual(0.54166, sess.run(update_op), delta=1e-3) + self.assertAlmostEqual(0.79166, sess.run(update_op), delta=1e-3) - self.assertAlmostEqual(0.54166, auc.eval(), delta=1e-3) + self.assertAlmostEqual(0.79166, auc.eval(), delta=1e-3) def testAnotherAUCPRSpecialCase(self): with self.test_session() as sess: @@ -1146,9 +1146,9 @@ class AUCTest(test.TestCase): auc, update_op = metrics.auc(labels, predictions, curve='PR') sess.run(variables.local_variables_initializer()) - self.assertAlmostEqual(0.44365042, sess.run(update_op), delta=1e-3) + self.assertAlmostEqual(0.610317, sess.run(update_op), delta=1e-3) - self.assertAlmostEqual(0.44365042, auc.eval(), delta=1e-3) + self.assertAlmostEqual(0.610317, auc.eval(), delta=1e-3) def testThirdAUCPRSpecialCase(self): with self.test_session() as sess: @@ -1160,26 +1160,9 @@ class AUCTest(test.TestCase): auc, update_op = metrics.auc(labels, predictions, curve='PR') sess.run(variables.local_variables_initializer()) - self.assertAlmostEqual(0.73611039, sess.run(update_op), delta=1e-3) + self.assertAlmostEqual(0.90277, sess.run(update_op), delta=1e-3) - self.assertAlmostEqual(0.73611039, auc.eval(), delta=1e-3) - - def testFourthAUCPRSpecialCase(self): - # Create the labels and data. - labels = np.array([ - 0, 0, 0, 0, 0, 0, 0, 1, 0, 1]) - predictions = np.array([ - 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35]) - - with self.test_session() as sess: - auc, _ = metrics.auc( - labels, predictions, curve='PR', num_thresholds=11) - - sess.run(variables.local_variables_initializer()) - # Since this is only approximate, we can't expect a 6 digits match. - # Although with higher number of samples/thresholds we should see the - # accuracy improving - self.assertAlmostEqual(0.0, auc.eval(), delta=0.001) + self.assertAlmostEqual(0.90277, auc.eval(), delta=1e-3) def testAllIncorrect(self): inputs = np.random.randint(0, 2, size=(100, 1)) @@ -1205,16 +1188,16 @@ class AUCTest(test.TestCase): self.assertAlmostEqual(1, auc.eval(), 6) - def testRecallOneAndPrecisionOne(self): + def testRecallOneAndPrecisionOneGivesOnePRAUC(self): with self.test_session() as sess: predictions = array_ops.ones([4], dtype=dtypes_lib.float32) labels = array_ops.ones([4]) auc, update_op = metrics.auc(labels, predictions, curve='PR') sess.run(variables.local_variables_initializer()) - self.assertAlmostEqual(0.5, sess.run(update_op), 6) + self.assertAlmostEqual(1, sess.run(update_op), 6) - self.assertAlmostEqual(0.5, auc.eval(), 6) + self.assertAlmostEqual(1, auc.eval(), 6) def np_auc(self, predictions, labels, weights): """Computes the AUC explicitly using Numpy. diff --git a/tensorflow/python/kernel_tests/pad_op_test.py b/tensorflow/python/kernel_tests/pad_op_test.py index 2c766e364073fc8c92156f19d08753367982e7fc..14632ec29a5e8224de5bf85673904aac4eb482a7 100644 --- a/tensorflow/python/kernel_tests/pad_op_test.py +++ b/tensorflow/python/kernel_tests/pad_op_test.py @@ -215,13 +215,13 @@ class PadOpTest(test.TestCase): def testIntTypes(self): # TODO(touts): Figure out why the padding tests do not work on GPU # for int types and rank > 2. - for t in [np.int32, np.int64]: + for t in [np.int8, np.int32, np.int64]: self._testAll( np.random.randint(-100, 100, (4, 4, 3)).astype(t), [[1, 0], [2, 3], [0, 2]], 0) self._testAll( np.random.randint(-100, 100, (4, 2, 1, 3)).astype(t), - [[0, 0], [0, 0], [0, 0], [0, 0]], -1234) + [[0, 0], [0, 0], [0, 0], [0, 0]], -123) def testFloatTypes(self): for t in [np.float32, np.float64]: @@ -238,6 +238,29 @@ class PadOpTest(test.TestCase): x = np.random.rand(3, 2, 1, 1).astype(t) self._testAll(x + 1j * x, [[0, 0], [0, 0], [0, 0], [0, 0]], 0 + 0j) + def testString(self): + # Numpy does not support padding strings so we compare padding manually. + x = ops.convert_to_tensor([["Hello", "World"], + ["Goodnight", "Moon"]]) + + constant = array_ops.pad(x, [[1, 0], [0, 1]], mode="CONSTANT", + constant_values="PAD") + reflect = array_ops.pad(x, [[1, 0], [0, 1]], mode="REFLECT", + constant_values="PAD") + symmetric = array_ops.pad(x, [[1, 0], [0, 1]], mode="SYMMETRIC", + constant_values="PAD") + with self.test_session(use_gpu=True): + self.assertAllEqual([[b"PAD", b"PAD", b"PAD"], + [b"Hello", b"World", b"PAD"], + [b"Goodnight", b"Moon", b"PAD"]], constant.eval()) + self.assertAllEqual([[b"Goodnight", b"Moon", b"Goodnight"], + [b"Hello", b"World", b"Hello"], + [b"Goodnight", b"Moon", b"Goodnight"]], + reflect.eval()) + self.assertAllEqual([[b"Hello", b"World", b"World"], + [b"Hello", b"World", b"World"], + [b"Goodnight", b"Moon", b"Moon"]], symmetric.eval()) + def testShapeFunctionEdgeCases(self): # Unknown paddings shape. inp = constant_op.constant(0.0, shape=[4, 4, 4, 4]) @@ -313,5 +336,30 @@ class PadOpTest(test.TestCase): self.assertAllEqual(inp, out) self.assertShapeEqual(inp, tf_val) + def testCollapseAdjacentNonPaddedDimensions(self): + # pyformat: disable + for paddings_value in [[[0, 0], [0, 0], [0, 0], [0, 1]], + [[0, 0], [2, 3], [0, 0], [0, 0]], + [[0, 0], [0, 0], [0, 0], [0, 0]]]: + # pyformat: enable + inp = constant_op.constant(1.0, shape=[8, 28, 28, 3]) + paddings = constant_op.constant(paddings_value, dtype=dtypes.int32) + padded = array_ops.pad(inp, paddings) + middle = array_ops.slice(padded, [row[0] for row in paddings_value], + [dim.value for dim in inp.shape.dims]) + left = array_ops.slice(padded, [0, 0, 0, 0], + [row[0] for row in paddings_value]) + right = array_ops.slice( + padded, + [paddings_value[i][0] + inp.shape.dims[i].value for i in range(4)], + [-1, -1, -1, -1]) + with self.test_session(use_gpu=True): + self.assertAllEqual(inp.eval(), middle.eval()) + self.assertAllEqual( + np.zeros([row[0] for row in paddings_value]), left.eval()) + self.assertAllEqual( + np.zeros([row[1] for row in paddings_value]), right.eval()) + + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/kernel_tests/pooling_ops_test.py b/tensorflow/python/kernel_tests/pooling_ops_test.py index 4466beeec96509b3761e34d885276e1510c62d10..2f3bea5825f8889c5880c819ebf6b17aaa613f08 100644 --- a/tensorflow/python/kernel_tests/pooling_ops_test.py +++ b/tensorflow/python/kernel_tests/pooling_ops_test.py @@ -31,6 +31,7 @@ from tensorflow.python.ops import gen_nn_ops from tensorflow.python.ops import gradient_checker from tensorflow.python.ops import gradients_impl from tensorflow.python.ops import nn_ops +from tensorflow.python.ops import variables import tensorflow.python.ops.nn_grad # pylint: disable=unused-import from tensorflow.python.platform import test from tensorflow.python.platform import tf_logging @@ -405,7 +406,7 @@ class PoolingTest(test.TestCase): for v2 in [True, False]: self._VerifyValues( - gen_nn_ops._max_pool_v2, + gen_nn_ops.max_pool_v2, input_sizes=[1, 3, 3, 3], ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], @@ -427,7 +428,7 @@ class PoolingTest(test.TestCase): for v2 in [True, False]: self._VerifyValues( - gen_nn_ops._max_pool_v2, + gen_nn_ops.max_pool_v2, input_sizes=[1, 2, 3, 3], ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], @@ -456,7 +457,7 @@ class PoolingTest(test.TestCase): for v2 in [True, False]: self._VerifyValues( - gen_nn_ops._max_pool_v2, + gen_nn_ops.max_pool_v2, input_sizes=[1, 2, 2, 1], ksize=[1, 1, 2, 1], strides=[1, 1, 1, 1], @@ -485,7 +486,7 @@ class PoolingTest(test.TestCase): for v2 in [True, False]: self._VerifyValues( - gen_nn_ops._max_pool_v2, + gen_nn_ops.max_pool_v2, input_sizes=[1, 4, 4, 1], ksize=[1, 2, 2, 1], strides=[1, 1, 2, 1], @@ -494,7 +495,7 @@ class PoolingTest(test.TestCase): use_gpu=use_gpu, v2=v2) self._VerifyValues( - gen_nn_ops._max_pool_v2, + gen_nn_ops.max_pool_v2, input_sizes=[1, 4, 4, 1], ksize=[1, 2, 2, 1], strides=[1, 2, 1, 1], @@ -519,7 +520,7 @@ class PoolingTest(test.TestCase): for v2 in [True, False]: self._VerifyValues( - gen_nn_ops._max_pool_v2, + gen_nn_ops.max_pool_v2, input_sizes=[1, 4, 4, 4], ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], @@ -554,7 +555,7 @@ class PoolingTest(test.TestCase): for v2 in [True, False]: self._VerifyValues( - gen_nn_ops._max_pool_v2, + gen_nn_ops.max_pool_v2, input_sizes=[1, 8, 8, 8], ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], @@ -565,7 +566,7 @@ class PoolingTest(test.TestCase): def _testMaxPoolEmptyInput(self, use_gpu): self._VerifyValues( - gen_nn_ops._max_pool_v2, + gen_nn_ops.max_pool_v2, input_sizes=[0, 8, 8, 8], ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], @@ -600,7 +601,7 @@ class PoolingTest(test.TestCase): for v2 in [True, False]: self._VerifyValues( - gen_nn_ops._max_pool_v2, + gen_nn_ops.max_pool_v2, input_sizes=[1, 1, 1, 10], ksize=[1, 1, 1, 2], strides=[1, 1, 1, 2], @@ -626,7 +627,7 @@ class PoolingTest(test.TestCase): for v2 in [True, False]: self._VerifyValues( - gen_nn_ops._max_pool_v2, + gen_nn_ops.max_pool_v2, input_sizes=[1, 2, 2, 6], ksize=[1, 1, 1, 3], strides=[1, 1, 1, 3], @@ -648,7 +649,7 @@ class PoolingTest(test.TestCase): for v2 in [True, False]: self._VerifyValues( - gen_nn_ops._max_pool_v2, + gen_nn_ops.max_pool_v2, input_sizes=[1, 7, 7, 1], ksize=[1, 2, 2, 1], strides=[1, 3, 3, 1], @@ -689,7 +690,7 @@ class PoolingTest(test.TestCase): for v2 in [True, False]: self._VerifyValues( - gen_nn_ops._max_pool_v2, + gen_nn_ops.max_pool_v2, input_sizes=[1, 3, 3, 1], ksize=[1, 1, 1, 1], strides=[1, 2, 2, 1], @@ -699,7 +700,7 @@ class PoolingTest(test.TestCase): v2=v2) self._VerifyValues( - gen_nn_ops._max_pool_v2, + gen_nn_ops.max_pool_v2, input_sizes=[1, 4, 4, 1], ksize=[1, 1, 1, 1], strides=[1, 2, 2, 1], @@ -731,7 +732,8 @@ class PoolingTest(test.TestCase): [1, 1, 1, 3], "evenly divide") if test.is_gpu_available(): with self.test_session(use_gpu=True): - t = constant_op.constant(1.0, shape=[1, 2, 2, 4]) + t = variables.Variable(np.ones([1, 2, 2, 4])) + variables.global_variables_initializer().run() with self.assertRaisesOpError("for CPU devices"): nn_ops.max_pool( t, ksize=[1, 1, 1, 2], strides=[1, 1, 1, 2], @@ -764,8 +766,8 @@ class PoolingTest(test.TestCase): _, argmax_op = nn_ops.max_pool_with_argmax(t, ksize, strides, padding) argmax = argmax_op.eval() grad_in = constant_op.constant(tensor_output, shape=output_shape) - out_op = gen_nn_ops._max_pool_grad_with_argmax(t, grad_in, argmax, - ksize, strides, padding) + out_op = gen_nn_ops.max_pool_grad_with_argmax(t, grad_in, argmax, ksize, + strides, padding) gpu_val = out_op.eval() self.assertShapeEqual(gpu_val, out_op) with self.test_session(use_gpu=False): @@ -773,8 +775,8 @@ class PoolingTest(test.TestCase): out_op = nn_ops.max_pool(t, ksize, strides, padding) orig_out = out_op.eval() grad_in = constant_op.constant(tensor_output, shape=output_shape) - out_op = gen_nn_ops._max_pool_grad(t, orig_out, grad_in, ksize, strides, - padding) + out_op = gen_nn_ops.max_pool_grad(t, orig_out, grad_in, ksize, strides, + padding) cpu_val = out_op.eval() self.assertShapeEqual(cpu_val, out_op) # The CPU version accumulates its gradient on fp16, so it's less @@ -793,7 +795,7 @@ class PoolingTest(test.TestCase): _, argmax_op = nn_ops.max_pool_with_argmax(t, ksize, strides, padding) argmax = argmax_op.eval() grad_in = constant_op.constant(tensor_input, shape=input_shape) - out_op = gen_nn_ops._max_pool_grad_grad_with_argmax( + out_op = gen_nn_ops.max_pool_grad_grad_with_argmax( t, grad_in, argmax, ksize, strides, padding) gpu_val = out_op.eval() self.assertShapeEqual(gpu_val, out_op) @@ -802,8 +804,8 @@ class PoolingTest(test.TestCase): out_op = nn_ops.max_pool(t, ksize, strides, padding) orig_out = out_op.eval() grad_in = constant_op.constant(tensor_input, shape=input_shape) - out_op = gen_nn_ops._max_pool_grad_grad(t, orig_out, grad_in, ksize, - strides, padding) + out_op = gen_nn_ops.max_pool_grad_grad(t, orig_out, grad_in, ksize, + strides, padding) cpu_val = out_op.eval() self.assertShapeEqual(cpu_val, out_op) # The CPU version accumulates its gradient on fp16, so it's less @@ -842,7 +844,7 @@ class PoolingTest(test.TestCase): t = constant_op.constant(tensor_input, shape=[1, 2, 2, 1]) argmax = constant_op.constant( tensor_argmax, shape=[1, 2, 2, 1], dtype=dtypes.int64) - out_op = gen_nn_ops._max_pool_grad_with_argmax( + out_op = gen_nn_ops.max_pool_grad_with_argmax( orig_in, t, argmax, @@ -865,7 +867,7 @@ class PoolingTest(test.TestCase): t = constant_op.constant(tensor_input, shape=[1, 3, 3, 1]) argmax = constant_op.constant( tensor_argmax, shape=[1, 2, 2, 1], dtype=dtypes.int64) - out_op = gen_nn_ops._max_pool_grad_grad_with_argmax( + out_op = gen_nn_ops.max_pool_grad_grad_with_argmax( orig_in, t, argmax, @@ -1029,7 +1031,7 @@ class PoolingTest(test.TestCase): self.assertLess(err, err_tolerance) def _testMaxPoolGradValidPadding1_1(self, data_format, use_gpu): - for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]: + for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]: self._ConstructAndTestGradient( pool_func, input_sizes=[1, 3, 3, 1], @@ -1043,7 +1045,7 @@ class PoolingTest(test.TestCase): use_gpu=use_gpu) def _testMaxPoolGradValidPadding2_1_6(self, data_format, use_gpu): - for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]: + for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]: self._ConstructAndTestGradient( pool_func, input_sizes=[2, 6, 6, 3], @@ -1057,7 +1059,7 @@ class PoolingTest(test.TestCase): use_gpu=use_gpu) def _testMaxPoolGradValidPadding2_1_7(self, data_format, use_gpu): - for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]: + for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]: self._ConstructAndTestGradient( pool_func, input_sizes=[2, 7, 7, 3], @@ -1071,7 +1073,7 @@ class PoolingTest(test.TestCase): use_gpu=use_gpu) def _testMaxPoolGradValidPadding1_2(self, data_format, use_gpu): - for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]: + for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]: self._ConstructAndTestGradient( pool_func, input_sizes=[1, 3, 3, 1], @@ -1085,7 +1087,7 @@ class PoolingTest(test.TestCase): use_gpu=use_gpu) def _testMaxPoolGradValidPadding2_2(self, data_format, use_gpu): - for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]: + for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]: self._ConstructAndTestGradient( pool_func, input_sizes=[2, 2, 2, 3], @@ -1099,7 +1101,7 @@ class PoolingTest(test.TestCase): use_gpu=use_gpu) def _testMaxPoolGradSamePadding1_1(self, data_format, use_gpu): - for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]: + for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]: self._ConstructAndTestGradient( pool_func, input_sizes=[2, 2, 4, 3], @@ -1113,7 +1115,7 @@ class PoolingTest(test.TestCase): use_gpu=use_gpu) def _testMaxPoolGradSamePadding1_2(self, data_format, use_gpu): - for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]: + for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]: self._ConstructAndTestGradient( pool_func, input_sizes=[2, 2, 4, 3], @@ -1127,7 +1129,7 @@ class PoolingTest(test.TestCase): use_gpu=use_gpu) def _testMaxPoolGradSamePadding2_1(self, data_format, use_gpu): - for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]: + for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]: self._ConstructAndTestGradient( pool_func, input_sizes=[2, 2, 4, 3], @@ -1141,7 +1143,7 @@ class PoolingTest(test.TestCase): use_gpu=use_gpu) def _testMaxPoolGradSamePadding2_2(self, data_format, use_gpu): - for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]: + for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]: self._ConstructAndTestGradient( pool_func, input_sizes=[2, 2, 4, 3], @@ -1155,7 +1157,7 @@ class PoolingTest(test.TestCase): use_gpu=use_gpu) def _testMaxPoolGradSamePadding3_1(self, data_format, use_gpu): - for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]: + for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]: self._ConstructAndTestGradient( pool_func, input_sizes=[1, 7, 7, 1], @@ -1199,7 +1201,7 @@ class PoolingTest(test.TestCase): Returns: A Tensor. """ - pool_func = gen_nn_ops.max_pool_grad_v2 if v2 else gen_nn_ops._max_pool_grad + pool_func = gen_nn_ops.max_pool_grad_v2 if v2 else gen_nn_ops.max_pool_grad return pool_func(orig_input, orig_output, grad, [1, window_rows, window_cols, 1], [1, row_stride, col_stride, 1], padding) @@ -1208,9 +1210,11 @@ class PoolingTest(test.TestCase): expected_input_backprop, input_sizes, output_sizes, window_rows, window_cols, row_stride, col_stride, padding, use_gpu, v2): - pool_func = gen_nn_ops._max_pool_v2 if v2 else nn_ops.max_pool + pool_func = gen_nn_ops.max_pool_v2 if v2 else nn_ops.max_pool with self.test_session(use_gpu=use_gpu): - input_tensor = constant_op.constant(input_data, shape=input_sizes) + input_tensor = variables.Variable( + np.array(input_data, dtype=np.float32).reshape(input_sizes)) + variables.global_variables_initializer().run() output_tensor = pool_func(input_tensor, [1, window_rows, window_cols, 1], [1, row_stride, col_stride, 1], padding) output_backprop_tensor = constant_op.constant( @@ -1504,7 +1508,7 @@ class PoolingTest(test.TestCase): self._testMaxPoolGradDirectWithNans2_2() def _testMaxPoolGradGradValidPadding1_1(self, data_format, use_gpu): - for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]: + for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]: self._ConstructAndTestSecondGradient( pool_func, input_sizes=[1, 3, 3, 1], @@ -1518,7 +1522,7 @@ class PoolingTest(test.TestCase): use_gpu=use_gpu) def _testMaxPoolGradGradValidPadding2_1_6(self, data_format, use_gpu): - for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]: + for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]: self._ConstructAndTestSecondGradient( pool_func, input_sizes=[2, 6, 6, 3], @@ -1532,7 +1536,7 @@ class PoolingTest(test.TestCase): use_gpu=use_gpu) def _testMaxPoolGradGradValidPadding2_1_7(self, data_format, use_gpu): - for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]: + for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]: self._ConstructAndTestSecondGradient( pool_func, input_sizes=[2, 7, 7, 3], @@ -1546,7 +1550,7 @@ class PoolingTest(test.TestCase): use_gpu=use_gpu) def _testMaxPoolGradGradValidPadding2_2(self, data_format, use_gpu): - for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]: + for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]: self._ConstructAndTestSecondGradient( pool_func, input_sizes=[2, 2, 2, 3], @@ -1560,7 +1564,7 @@ class PoolingTest(test.TestCase): use_gpu=use_gpu) def _testMaxPoolGradGradSamePadding1_1(self, data_format, use_gpu): - for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]: + for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]: self._ConstructAndTestSecondGradient( pool_func, input_sizes=[2, 2, 4, 3], @@ -1574,7 +1578,7 @@ class PoolingTest(test.TestCase): use_gpu=use_gpu) def _testMaxPoolGradGradSamePadding2_1(self, data_format, use_gpu): - for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]: + for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]: self._ConstructAndTestSecondGradient( pool_func, input_sizes=[2, 2, 4, 3], @@ -1588,7 +1592,7 @@ class PoolingTest(test.TestCase): use_gpu=use_gpu) def _testMaxPoolGradGradSamePadding2_2(self, data_format, use_gpu): - for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]: + for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]: self._ConstructAndTestSecondGradient( pool_func, input_sizes=[2, 2, 4, 3], @@ -1602,7 +1606,7 @@ class PoolingTest(test.TestCase): use_gpu=use_gpu) def _testMaxPoolGradGradSamePadding3_1(self, data_format, use_gpu): - for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]: + for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]: self._ConstructAndTestSecondGradient( pool_func, input_sizes=[1, 7, 7, 1], @@ -1644,7 +1648,7 @@ class PoolingTest(test.TestCase): Returns: A Tensor. """ - return gen_nn_ops._max_pool_grad_grad( + return gen_nn_ops.max_pool_grad_grad( orig_input, orig_output, grad, [1, window_rows, window_cols, 1], [1, row_stride, col_stride, 1], padding) diff --git a/tensorflow/python/kernel_tests/py_func_test.py b/tensorflow/python/kernel_tests/py_func_test.py index 61fb3f12e45ea5ae3bc4f0a26c2116b54c003624..36142801d682d4e99bc1beceee7aee76206db4cf 100644 --- a/tensorflow/python/kernel_tests/py_func_test.py +++ b/tensorflow/python/kernel_tests/py_func_test.py @@ -19,6 +19,8 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import re + import numpy as np from six.moves import queue from six.moves import xrange # pylint: disable=redefined-builtin @@ -356,12 +358,22 @@ class PyFuncTest(test.TestCase): def _testExceptionHandling(self, py_exp, tf_exp, eager=False): - def raise_exception(): + def inner_exception(): raise py_exp("blah") # pylint: disable=not-callable + def raise_exception(): + inner_exception() + + expected_regexp = r": blah.*" # Error at the top + expected_regexp += r"in raise_exception.*" # Stacktrace outer + expected_regexp += r"in inner_exception.*" # Stacktrace inner + expected_regexp += r": blah" # Stacktrace of raise + def expected_error_check(exception): + return re.search(expected_regexp, str(exception), re.DOTALL) + if eager: - if context.in_eager_mode(): - with self.assertRaisesRegexp(tf_exp, "blah"): + if context.executing_eagerly(): + with self.assertRaisesWithPredicateMatch(tf_exp, expected_error_check): f = script_ops.eager_py_func(raise_exception, [], []) return else: @@ -370,7 +382,7 @@ class PyFuncTest(test.TestCase): f = script_ops.py_func(raise_exception, [], []) with self.test_session(): - with self.assertRaisesRegexp(tf_exp, "blah"): + with self.assertRaisesWithPredicateMatch(tf_exp, expected_error_check): self.evaluate(f) def testExceptionHandling(self): @@ -432,7 +444,7 @@ class PyFuncTest(test.TestCase): output = script_ops.eager_py_func(no_return_value, inp=[], Tout=[]) ret = self.evaluate(output) - if context.in_eager_mode(): + if context.executing_eagerly(): self.assertEquals(len(ret), 0) else: self.assertIsNone(ret) diff --git a/tensorflow/python/kernel_tests/reduction_ops_test.py b/tensorflow/python/kernel_tests/reduction_ops_test.py index d306d1b8d64f292dc299deee2e3c36981b933d1e..589ea54973c10902c461f552d5c54b6fad6ecf67 100644 --- a/tensorflow/python/kernel_tests/reduction_ops_test.py +++ b/tensorflow/python/kernel_tests/reduction_ops_test.py @@ -30,6 +30,7 @@ from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import array_ops from tensorflow.python.ops import gradient_checker from tensorflow.python.ops import math_ops +from tensorflow.python.ops import variables from tensorflow.python.platform import test # The maximum input rank to test. @@ -212,7 +213,8 @@ class SumReductionTest(BaseReductionTest): arr = np.ones([68000], dtype=np.float16) with self.test_session(graph=ops.Graph(), use_gpu=True) as sess: - tf_arr = array_ops.constant(arr) + tf_arr = variables.Variable(arr) + variables.global_variables_initializer().run() tf_mean = math_ops.reduce_mean(tf_arr, 0, False) tf_out_mean = sess.run(tf_mean) self.assertAllClose(tf_out_mean, 1.) diff --git a/tensorflow/python/kernel_tests/regex_replace_op_test.py b/tensorflow/python/kernel_tests/regex_replace_op_test.py new file mode 100644 index 0000000000000000000000000000000000000000..6739ac32245668e98d37673fe9e9fe9d55cc0c5f --- /dev/null +++ b/tensorflow/python/kernel_tests/regex_replace_op_test.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. +# ============================================================================== +"""Tests for RegexReplace op from string_ops.""" + +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.ops import string_ops +from tensorflow.python.platform import test + + +class RegexReplaceOpTest(test.TestCase): + + def testRemovePrefix(self): + values = ["a:foo", "a:bar", "a:foo", "b:baz", "b:qux", "ca:b"] + with self.test_session(): + input_vector = constant_op.constant(values, dtypes.string) + stripped = string_ops.regex_replace( + input_vector, "^(a:|b:)", "", replace_global=False).eval() + self.assertAllEqual([b"foo", b"bar", b"foo", b"baz", b"qux", b"ca:b"], + stripped) + + def testRegexReplace(self): + values = ["aba\naba", "abcdabcde"] + with self.test_session(): + input_vector = constant_op.constant(values, dtypes.string) + stripped = string_ops.regex_replace(input_vector, "a.*a", "(\\0)").eval() + self.assertAllEqual([b"(aba)\n(aba)", b"(abcda)bcde"], stripped) + + def testEmptyMatch(self): + values = ["abc", "1"] + with self.test_session(): + input_vector = constant_op.constant(values, dtypes.string) + stripped = string_ops.regex_replace(input_vector, "", "x").eval() + self.assertAllEqual([b"xaxbxcx", b"x1x"], stripped) + + def testInvalidPattern(self): + values = ["abc", "1"] + with self.test_session(): + input_vector = constant_op.constant(values, dtypes.string) + invalid_pattern = "A[" + replace = string_ops.regex_replace(input_vector, invalid_pattern, "x") + with self.assertRaisesOpError("Invalid pattern"): + replace.eval() + + def testGlobal(self): + values = ["ababababab", "abcabcabc", ""] + with self.test_session(): + input_vector = constant_op.constant(values, dtypes.string) + stripped = string_ops.regex_replace(input_vector, "ab", "abc", + True).eval() + self.assertAllEqual([b"abcabcabcabcabc", b"abccabccabcc", b""], stripped) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/python/kernel_tests/relu_op_test.py b/tensorflow/python/kernel_tests/relu_op_test.py index 6b4091ae5d3c6e469a9cd5237b978eae4c75485f..25e947f09e137b37ea129ba6015a060aa01f02e4 100644 --- a/tensorflow/python/kernel_tests/relu_op_test.py +++ b/tensorflow/python/kernel_tests/relu_op_test.py @@ -19,12 +19,14 @@ from __future__ import division from __future__ import print_function import numpy as np +from six.moves import xrange # pylint: disable=redefined-builtin from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.ops import array_ops from tensorflow.python.ops import gradient_checker from tensorflow.python.ops import gradients_impl +from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn_ops from tensorflow.python.ops import random_ops from tensorflow.python.ops import variables @@ -87,6 +89,35 @@ class ReluTest(test.TestCase): print("relu (float32) gradient err = ", err) self.assertLess(err, 1e-4) + # The gradient for fp16 is inaccurate due to the low-precision. + # Instead of relying on compute_gradient_error, we compare the fp16 analytical + # gradient against their fp32 counterpart. + def testGradientFloat16(self): + with self.test_session(use_gpu=True) as sess: + # Randomly construct a 1D shape from [1, 40) + shape = random_ops.random_uniform( + [1], minval=1, maxval=40, dtype=dtypes.int32) + + # Construct the fp32 graph and its gradient. + x = random_ops.random_uniform(shape, minval=-1, maxval=1, name="x") + y1 = nn_ops.relu(x, name="relu_fp32") + l1 = nn_ops.l2_loss(y1) + dx_f32 = gradients_impl.gradients(l1, x) + + # Construct the fp16 graph and its gradient. + # It starts with the same x, in fp32. But before it reaches Relu, it is + # cast into fp16. So during backprop, the gradient computation is in fp16. + x2 = math_ops.cast(x, dtype=dtypes.float16, name="cast") + y2 = nn_ops.relu(x2, name="relu_fp16") + l2 = nn_ops.l2_loss(y2) + dx_f16 = gradients_impl.gradients(l2, x) + + # Repeat the experiment for 100 times. All tensor shapes and its tensor + # values are randomly generated for each run. + for _ in xrange(100): + dx_f32_v, dx_f16_v = sess.run([dx_f32, dx_f16]) + self.assertAllClose(dx_f32_v, dx_f16_v, atol=3e-4) + def testGradientFloat64(self): with self.test_session(): x = constant_op.constant( diff --git a/tensorflow/python/kernel_tests/resource_variable_ops_test.py b/tensorflow/python/kernel_tests/resource_variable_ops_test.py index 8503f3e0310125bb714942b32bbbf46596f9bddb..2dc993f8117a41de8f15663ce763ec1d5b7ecdb4 100644 --- a/tensorflow/python/kernel_tests/resource_variable_ops_test.py +++ b/tensorflow/python/kernel_tests/resource_variable_ops_test.py @@ -277,6 +277,17 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): self.evaluate(v.assign(2.0)) self.assertEqual(2.0, self.evaluate(v.value())) + # Tests for the 'read_value' argument: + assign_with_read = v.assign(3.0, read_value=True) + self.assertEqual(3.0, self.evaluate(assign_with_read)) + assign_without_read = v.assign(4.0, read_value=False) + if context.executing_eagerly(): + self.assertIsNone(assign_without_read) + else: + self.assertIsInstance(assign_without_read, ops.Operation) + self.evaluate(assign_without_read) + self.assertEqual(4.0, self.evaluate(v.value())) + @test_util.run_in_graph_and_eager_modes() def testLoad(self): v = resource_variable_ops.ResourceVariable(1.0, name="var0") @@ -329,6 +340,9 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): w = resource_variable_ops.ResourceVariable.from_proto(v.to_proto()) self.assertEquals(2, math_ops.add(w, 1).eval()) + self.assertEquals(v._handle, w._handle) + self.assertEquals(v._graph_element, w._graph_element) + @test_util.run_in_graph_and_eager_modes() def testAssignAddMethod(self): v = resource_variable_ops.ResourceVariable(1.0, name="var0") @@ -336,6 +350,17 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): self.evaluate(v.assign_add(1.0)) self.assertEqual(2.0, self.evaluate(v.value())) + # Tests for the 'read_value' argument: + assign_with_read = v.assign_add(1.0, read_value=True) + self.assertEqual(3.0, self.evaluate(assign_with_read)) + assign_without_read = v.assign_add(1.0, read_value=False) + if context.executing_eagerly(): + self.assertIsNone(assign_without_read) + else: + self.assertIsInstance(assign_without_read, ops.Operation) + self.evaluate(assign_without_read) + self.assertEqual(4.0, self.evaluate(v.value())) + @test_util.run_in_graph_and_eager_modes() def testAssignSubMethod(self): v = resource_variable_ops.ResourceVariable(3.0, name="var0") @@ -343,6 +368,17 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): self.evaluate(v.assign_sub(1.0)) self.assertEqual(2.0, self.evaluate(v.value())) + # Tests for the 'read_value' argument: + assign_with_read = v.assign_sub(1.0, read_value=True) + self.assertEqual(1.0, self.evaluate(assign_with_read)) + assign_without_read = v.assign_sub(1.0, read_value=False) + if context.executing_eagerly(): + self.assertIsNone(assign_without_read) + else: + self.assertIsInstance(assign_without_read, ops.Operation) + self.evaluate(assign_without_read) + self.assertEqual(0.0, self.evaluate(v.value())) + @test_util.run_in_graph_and_eager_modes() def testDestroyResource(self): v = resource_variable_ops.ResourceVariable(3.0, name="var0") @@ -440,7 +476,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): self.assertEqual("(10, 20, 35)", str(v.get_shape())) self.assertEqual("(10, 20, 35)", str(v.value().shape)) self.assertEqual("(3, 20, 35)", str(v.sparse_read([0, 1, 2]).shape)) - if context.in_graph_mode(): + if not context.executing_eagerly(): self.assertEqual( "", str(v.sparse_read(array_ops.placeholder(dtypes.int32)).shape)) @@ -481,7 +517,6 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): self.assertEqual(dtypes.int32, v.dtype) self.assertEqual("foo/var7:0", v.name) self.assertAllEqual([10, 20, 35], v.shape.as_list()) - self.assertEqual(context.get_default_context().device_name, v.device) self.assertTrue(isinstance(v.handle, ops.EagerTensor)) self.assertEqual(constraint, v.constraint) self.assertAllEqual(init.numpy(), v.read_value().numpy()) @@ -551,6 +586,15 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): state_ops.scatter_update(v, [1], [3]) self.assertAllEqual([1.0, 3.0], v.numpy()) + @test_util.run_in_graph_and_eager_modes() + def testScatterUpdateInvalidArgs(self): + v = resource_variable_ops.ResourceVariable([0, 1, 2, 3], name="update") + # The exact error and message differ between graph construction (where the + # error is realized during shape inference at graph construction time) and + # eager execution (where the error is realized during kernel execution). + with self.assertRaisesRegexp(Exception, r"shape.*2.*3"): + state_ops.scatter_update(v, [0, 1], [0, 1, 2]) + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/kernel_tests/rnn_test.py b/tensorflow/python/kernel_tests/rnn_test.py index daa42938e6af205425d7e423ce162294b9002be4..9a0409c796ab60da3d47cf7d46ef6fbd5bd82394 100644 --- a/tensorflow/python/kernel_tests/rnn_test.py +++ b/tensorflow/python/kernel_tests/rnn_test.py @@ -111,10 +111,10 @@ class RNNTest(test.TestCase): @test_util.run_in_graph_and_eager_modes() def testInvalidSequenceLengthShape(self): cell = Plus1RNNCell() - if context.in_graph_mode(): - inputs = [array_ops.placeholder(dtypes.float32, shape=(3, 4))] - else: + if context.executing_eagerly(): inputs = [constant_op.constant(np.ones((3, 4)))] + else: + inputs = [array_ops.placeholder(dtypes.float32, shape=(3, 4))] with self.assertRaisesRegexp(ValueError, "must be a vector"): rnn.dynamic_rnn( cell, @@ -125,38 +125,30 @@ class RNNTest(test.TestCase): @test_util.run_in_graph_and_eager_modes() def testBatchSizeFromInput(self): cell = Plus1RNNCell() - in_graph_mode = context.in_graph_mode() + in_eager_mode = context.executing_eagerly() # With static batch size - if in_graph_mode: - inputs = array_ops.placeholder(dtypes.float32, shape=(3, 4, 5)) - initial_state = array_ops.placeholder(dtypes.float32, shape=(3, 5)) - else: + if in_eager_mode: inputs = np.zeros((3, 4, 5), dtype=np.float32) initial_state = np.zeros((3, 5), dtype=np.float32) + else: + inputs = array_ops.placeholder(dtypes.float32, shape=(3, 4, 5)) + initial_state = array_ops.placeholder(dtypes.float32, shape=(3, 5)) # - Without initial_state outputs, state = rnn.dynamic_rnn(cell, inputs, dtype=dtypes.float32) - if in_graph_mode: - self.assertEqual(3, outputs.shape[0].value) - self.assertEqual(3, state.shape[0].value) - else: - self.assertEqual(3, outputs.shape[0]) - self.assertEqual(3, state.shape[0]) + self.assertEqual(3, outputs.shape[0]) + self.assertEqual(3, state.shape[0]) # - With initial_state outputs, state = rnn.dynamic_rnn( cell, inputs, initial_state=initial_state) - if in_graph_mode: - self.assertEqual(3, outputs.shape[0].value) - self.assertEqual(3, state.shape[0].value) - else: - self.assertEqual(3, outputs.shape[0]) - self.assertEqual(3, state.shape[0]) + self.assertEqual(3, outputs.shape[0]) + self.assertEqual(3, state.shape[0]) # Without static batch size - # Tensor shapes are fully determined in Eager mode, so only run this - # test in graph mode. - if in_graph_mode: + # Tensor shapes are fully determined with eager execution enabled, + # so only run this test for graph construction. + if not in_eager_mode: inputs = array_ops.placeholder(dtypes.float32, shape=(None, 4, 5)) # - Without initial_state outputs, state = rnn.dynamic_rnn(cell, inputs, dtype=dtypes.float32) @@ -173,56 +165,46 @@ class RNNTest(test.TestCase): @test_util.run_in_graph_and_eager_modes() def testScalarStateIsAccepted(self): cell = ScalarStateRNNCell() - in_graph_mode = context.in_graph_mode() + in_eager_mode = context.executing_eagerly() - if in_graph_mode: - inputs = array_ops.placeholder(dtypes.float32, shape=(1, 4, 1)) - else: + if in_eager_mode: inputs = np.array([[[1], [2], [3], [4]]], dtype=np.float32) + else: + inputs = array_ops.placeholder(dtypes.float32, shape=(1, 4, 1)) with self.test_session() as sess: outputs, state = rnn.dynamic_rnn( cell, inputs, dtype=dtypes.float32, sequence_length=[4]) - if in_graph_mode: + if not in_eager_mode: outputs, state = sess.run( [outputs, state], feed_dict={inputs: [[[1], [2], [3], [4]]]}) - if in_graph_mode: - self.assertAllEqual(outputs, np.array([[[1], [2], [3], [4]]])) - self.assertEqual(state, 4) - else: - self.assertAllEqual(outputs.numpy(), np.array([[[1], [2], [3], [4]]])) - self.assertEqual(state.numpy(), 4) + self.assertAllEqual([[[1], [2], [3], [4]]], outputs) + self.assertAllEqual(4, state) @test_util.run_in_graph_and_eager_modes() def testTensorArrayStateIsAccepted(self): cell = TensorArrayStateRNNCell() - in_graph_mode = context.in_graph_mode() + in_eager_mode = context.executing_eagerly() - if in_graph_mode: - inputs = array_ops.placeholder(dtypes.float32, shape=(1, 4, 1)) - else: + if in_eager_mode: inputs = np.array([[[1], [2], [3], [4]]], dtype=np.float32) + else: + inputs = array_ops.placeholder(dtypes.float32, shape=(1, 4, 1)) with self.test_session() as sess: outputs, state = rnn.dynamic_rnn( cell, inputs, dtype=dtypes.float32, sequence_length=[4]) state = (state[0], state[1].stack()) - if in_graph_mode: + if not in_eager_mode: outputs, state = sess.run( [outputs, state], feed_dict={ inputs: [[[1], [2], [3], [4]]] }) - if in_graph_mode: - self.assertAllEqual(outputs, np.array([[[1], [2], [3], [4]]])) - self.assertEqual(state[0], 4) - self.assertAllEqual(state[1], np.array([[[1]], [[2]], [[3]], [[4]]])) - else: - self.assertAllEqual(outputs.numpy(), np.array([[[1], [2], [3], [4]]])) - self.assertEqual(state[0].numpy(), 4) - self.assertAllEqual(state[1].numpy(), - np.array([[[1]], [[2]], [[3]], [[4]]])) + self.assertAllEqual([[[1], [2], [3], [4]]], outputs) + self.assertAllEqual(4, state[0]) + self.assertAllEqual([[[1]], [[2]], [[3]], [[4]]], state[1]) ######### Benchmarking RNN code diff --git a/tensorflow/python/kernel_tests/save_restore_ops_test.py b/tensorflow/python/kernel_tests/save_restore_ops_test.py index 1bdfa9ebd8e1a4495e67004f59adfb51bf3a6602..cb9aa1e34d6eb82efa94e60e7b56c26b181cef04 100644 --- a/tensorflow/python/kernel_tests/save_restore_ops_test.py +++ b/tensorflow/python/kernel_tests/save_restore_ops_test.py @@ -31,11 +31,10 @@ class ShardedFileOpsTest(test.TestCase): with session.Session( target="", config=config_pb2.ConfigProto(device_count={"CPU": 2})): self.assertEqual( - gen_io_ops._sharded_filename("foo", 4, 100).eval(), + gen_io_ops.sharded_filename("foo", 4, 100).eval(), b"foo-00004-of-00100") self.assertEqual( - gen_io_ops._sharded_filespec("foo", 100).eval(), - b"foo-?????-of-00100") + gen_io_ops.sharded_filespec("foo", 100).eval(), b"foo-?????-of-00100") class ShapeInferenceTest(test.TestCase): @@ -53,7 +52,7 @@ class ShapeInferenceTest(test.TestCase): [dtypes.float32, dtypes.float32]) def testRestoreSlice(self): - op = gen_io_ops._restore_slice("model", "var", "3 4 0,1:-", dtypes.float32) + op = gen_io_ops.restore_slice("model", "var", "3 4 0,1:-", dtypes.float32) self.assertEqual([1, 4], op.get_shape()) diff --git a/tensorflow/python/kernel_tests/scalar_test.py b/tensorflow/python/kernel_tests/scalar_test.py index e65241981eac2d42207c1de7a261f7936e588f2a..0d8fd232946883ac1d95c4c2d9744af69175ab90 100644 --- a/tensorflow/python/kernel_tests/scalar_test.py +++ b/tensorflow/python/kernel_tests/scalar_test.py @@ -92,11 +92,11 @@ class ScalarTest(test.TestCase): self.check(array_ops.reshape, (7, 1), 'sizes input must be 1-D', [7]) def testShardedFilename(self): - self.check(gen_io_ops._sharded_filename, ('foo', 4, [100]), + self.check(gen_io_ops.sharded_filename, ('foo', 4, [100]), 'must be a scalar', b'foo-00004-of-00100') def testShardedFilespec(self): - self.check(gen_io_ops._sharded_filespec, ('foo', [100]), 'must be a scalar', + self.check(gen_io_ops.sharded_filespec, ('foo', [100]), 'must be a scalar', b'foo-?????-of-00100') def testUnsortedSegmentSum(self): diff --git a/tensorflow/python/kernel_tests/segment_reduction_ops_test.py b/tensorflow/python/kernel_tests/segment_reduction_ops_test.py index bbce6b7d47325b8209815230426672ec6894147f..3bca5fadc42693f514911c7ffa8f078de8ef9bcd 100644 --- a/tensorflow/python/kernel_tests/segment_reduction_ops_test.py +++ b/tensorflow/python/kernel_tests/segment_reduction_ops_test.py @@ -542,6 +542,25 @@ class SparseSegmentReductionOpTest(SparseSegmentReductionHelper): tf_ans = s.eval() self.assertAllClose(np_ans, tf_ans) + def testWithEmptySegments(self): + tf_x = constant_op.constant([], shape=[0, 4], dtype=dtypes_lib.float32) + ops_list = [ + math_ops.sparse_segment_sum_with_num_segments, + math_ops.sparse_segment_mean_with_num_segments + ] + segment_indices = [] + tf_indices = [] + num_segments = 5 + with self.test_session(use_gpu=False): + for tf_op in ops_list: + s = tf_op( + data=tf_x, + indices=tf_indices, + segment_ids=segment_indices, + num_segments=num_segments) + tf_ans = s.eval() + self.assertAllClose(np.zeros([5, 4]), tf_ans) + def testSegmentIdsGreaterThanZero(self): tf_x, np_x = self._input([10, 4], dtype=dtypes_lib.float32) ops_list = [(np.add, None, math_ops.sparse_segment_sum), ( diff --git a/tensorflow/python/kernel_tests/slice_op_test.py b/tensorflow/python/kernel_tests/slice_op_test.py index 051a25080b826de05ee3e24a82fbcd1f47995544..5fc9bef21816e3a12f0d274bab1fc82a83546422 100644 --- a/tensorflow/python/kernel_tests/slice_op_test.py +++ b/tensorflow/python/kernel_tests/slice_op_test.py @@ -283,7 +283,7 @@ class SliceTest(test.TestCase): # unintended behavior is prevented. c = constant_op.constant(5.0) with self.assertRaisesWithPredicateMatch( - TypeError, lambda e: "`Tensor` objects are not iterable" in str(e)): + TypeError, lambda e: "Tensor objects are not iterable" in str(e)): for _ in c: pass diff --git a/tensorflow/python/kernel_tests/softmax_op_test.py b/tensorflow/python/kernel_tests/softmax_op_test.py index 4d89831aae9a5e95210a8defb180e09c9d38f4d6..2b8e99e18e6881143ee77c4f1ec5096635e5c1b2 100644 --- a/tensorflow/python/kernel_tests/softmax_op_test.py +++ b/tensorflow/python/kernel_tests/softmax_op_test.py @@ -20,7 +20,6 @@ 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 errors_impl from tensorflow.python.framework import test_util @@ -166,7 +165,7 @@ class SoftmaxTest(test.TestCase): def testEmptyInput(self): with self.test_session(): - x = constant_op.constant([[]], shape=[0, 3]) + x = array_ops.placeholder(dtypes.float32, shape=[0, 3]) self.assertEqual(0, array_ops.size(x).eval()) # reshape would raise if logits is empty with self.assertRaises(errors_impl.InvalidArgumentError): diff --git a/tensorflow/python/kernel_tests/spacetobatch_op_test.py b/tensorflow/python/kernel_tests/spacetobatch_op_test.py index b943dfa4e5f2a06eddcb3af03764e5e046b715f4..2a9232b6aecb66328f10a62f2251246c4fcec6e6 100644 --- a/tensorflow/python/kernel_tests/spacetobatch_op_test.py +++ b/tensorflow/python/kernel_tests/spacetobatch_op_test.py @@ -86,11 +86,11 @@ class CppOpImpl(object): @staticmethod def space_to_batch(*args, **kwargs): - return gen_array_ops._space_to_batch(*args, **kwargs) + return gen_array_ops.space_to_batch(*args, **kwargs) @staticmethod def batch_to_space(*args, **kwargs): - return gen_array_ops._batch_to_space(*args, **kwargs) + return gen_array_ops.batch_to_space(*args, **kwargs) class SpaceToBatchTest(test.TestCase, PythonOpImpl): diff --git a/tensorflow/python/kernel_tests/spacetodepth_op_test.py b/tensorflow/python/kernel_tests/spacetodepth_op_test.py index 3c98a685e07a1f2d55c3c1035a99ffaa593d35b3..cd90d16aacb4325ed426b0466266d9616b574401 100644 --- a/tensorflow/python/kernel_tests/spacetodepth_op_test.py +++ b/tensorflow/python/kernel_tests/spacetodepth_op_test.py @@ -34,8 +34,8 @@ from tensorflow.python.platform import tf_logging class SpaceToDepthTest(test.TestCase): - def _testOne(self, inputs, block_size, outputs): - input_nhwc = math_ops.to_float(inputs) + def _testOne(self, inputs, block_size, outputs, dtype=dtypes.float32): + input_nhwc = math_ops.cast(inputs, dtype) with self.test_session(use_gpu=False): # test NHWC (default) on CPU x_tf = array_ops.space_to_depth(input_nhwc, block_size) @@ -58,6 +58,12 @@ class SpaceToDepthTest(test.TestCase): x_out = [[[[1, 2, 3, 4]]]] self._testOne(x_np, block_size, x_out) + def testBasicFloat16(self): + x_np = [[[[1], [2]], [[3], [4]]]] + block_size = 2 + x_out = [[[[1, 2, 3, 4]]]] + self._testOne(x_np, block_size, x_out, dtype=dtypes.float16) + # Tests for larger input dimensions. To make sure elements are # correctly ordered spatially. def testLargerInput2x2(self): @@ -126,6 +132,24 @@ class SpaceToDepthTest(test.TestCase): x_out = [batch_output_elt(i) for i in range(batch_size)] self._testOne(x_np, block_size, x_out) + def testBatchSize0(self): + block_size = 2 + batch_size = 0 + input_nhwc = array_ops.ones([batch_size, 4, 6, 3]) + x_out = array_ops.ones([batch_size, 2, 3, 12]) + + with self.test_session(use_gpu=False): + # test NHWC (default) on CPU + x_tf = array_ops.space_to_depth(input_nhwc, block_size) + self.assertAllEqual(x_tf.shape, x_out.shape) + x_tf.eval() + if test.is_gpu_available(): + with self.test_session(use_gpu=True): + # test NHWC (default) on GPU + x_tf = array_ops.space_to_depth(input_nhwc, block_size) + self.assertAllEqual(x_tf.shape, x_out.shape) + x_tf.eval() + # Tests for different width and height. def testNonSquare(self): x_np = [[[[1, 10], [2, 20]], [[3, 30], [4, 40]], [[5, 50], [6, 60]], diff --git a/tensorflow/python/kernel_tests/sparse_xent_op_test.py b/tensorflow/python/kernel_tests/sparse_xent_op_test.py index cd5b711a0ed18aabff543aa4b6ecb1a885618caf..a841fe83a7f585a69ef33c437570359797484a4a 100644 --- a/tensorflow/python/kernel_tests/sparse_xent_op_test.py +++ b/tensorflow/python/kernel_tests/sparse_xent_op_test.py @@ -64,7 +64,7 @@ class SparseXentTest(test.TestCase): def _testXent(self, np_features, np_labels): np_loss, np_backprop = self._npXent(np_features, np_labels) with self.test_session(use_gpu=True) as sess: - loss, backprop = gen_nn_ops._sparse_softmax_cross_entropy_with_logits( + loss, backprop = gen_nn_ops.sparse_softmax_cross_entropy_with_logits( np_features, np_labels) tf_loss, tf_backprop = sess.run([loss, backprop]) self.assertAllCloseAccordingToType(np_loss, tf_loss) @@ -73,7 +73,7 @@ class SparseXentTest(test.TestCase): def testSingleClass(self): for label_dtype in np.int32, np.int64: with self.test_session(use_gpu=True) as sess: - loss, backprop = gen_nn_ops._sparse_softmax_cross_entropy_with_logits( + loss, backprop = gen_nn_ops.sparse_softmax_cross_entropy_with_logits( np.array([[1.], [-1.], [0.]]).astype(np.float32), np.array([0, 0, 0]).astype(label_dtype)) tf_loss, tf_backprop = sess.run([loss, backprop]) @@ -87,8 +87,9 @@ class SparseXentTest(test.TestCase): if test.is_built_with_cuda() and test.is_gpu_available(): with self.test_session(use_gpu=True) as sess: - loss, backprop = (gen_nn_ops._sparse_softmax_cross_entropy_with_logits( - features, labels)) + loss, backprop = ( + gen_nn_ops.sparse_softmax_cross_entropy_with_logits( + features, labels)) tf_loss, tf_backprop = sess.run([loss, backprop]) self.assertAllClose( [[np.nan] * 4, [0.25, 0.25, 0.25, -0.75], @@ -100,8 +101,8 @@ class SparseXentTest(test.TestCase): [np.nan, 1.3862, 3.4420, np.nan], tf_loss, rtol=1e-3, atol=1e-3) with self.test_session(use_gpu=False) as sess: - loss, backprop = (gen_nn_ops._sparse_softmax_cross_entropy_with_logits( - features, labels)) + loss, backprop = ( + gen_nn_ops.sparse_softmax_cross_entropy_with_logits(features, labels)) with self.assertRaisesOpError("Received a label value of"): sess.run([loss, backprop]) diff --git a/tensorflow/python/kernel_tests/split_op_test.py b/tensorflow/python/kernel_tests/split_op_test.py index 6171793b148f8d8f195b9548a13df89d29c5e96e..8cfee3eb933afcea7a58d5632948b87b0c4c10df 100644 --- a/tensorflow/python/kernel_tests/split_op_test.py +++ b/tensorflow/python/kernel_tests/split_op_test.py @@ -336,6 +336,20 @@ class SplitOpTest(test.TestCase): for s in splits: self.assertEqual(None, s.get_shape().ndims) + def testNonexistentDimTensor(self): + x = array_ops.placeholder(dtypes.int32) + values = np.zeros([5, 30]) + splits = array_ops.placeholder(dtypes.int32) + with self.assertRaisesRegexp(ValueError, "Cannot infer"): + y = array_ops.split(values, splits, axis=x) + + splits = array_ops.placeholder(dtypes.int32, [3]) + y = array_ops.split(values, splits, axis=x) + with self.test_session(use_gpu=True) as sess: + with self.assertRaisesRegexp(errors_impl.InvalidArgumentError, + "must have exactly one element"): + sess.run(y, {x: np.array([], dtype=np.int32), splits: [4, 11, 15]}) + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/kernel_tests/stack_ops_test.py b/tensorflow/python/kernel_tests/stack_ops_test.py index aa409336f5c50178e4d0ca946190119fb0e4188e..afd2eaffab992bca4b3ae7b4f65e0370f325b548 100644 --- a/tensorflow/python/kernel_tests/stack_ops_test.py +++ b/tensorflow/python/kernel_tests/stack_ops_test.py @@ -34,11 +34,11 @@ class StackOpTest(test.TestCase): def _testStackPushPop(self, use_gpu): with self.test_session(use_gpu=use_gpu): - h = gen_data_flow_ops._stack_v2( + h = gen_data_flow_ops.stack_v2( -1, elem_type=dtypes.float32, stack_name="foo") - c = gen_data_flow_ops._stack_push_v2(h, [[4.0, 5.0]]) + c = gen_data_flow_ops.stack_push_v2(h, [[4.0, 5.0]]) with ops.control_dependencies([c]): - c1 = gen_data_flow_ops._stack_pop_v2(h, dtypes.float32) + c1 = gen_data_flow_ops.stack_pop_v2(h, dtypes.float32) self.assertAllClose([[4.0, 5.0]], c1.eval()) def testStackPushPop(self): @@ -49,11 +49,11 @@ class StackOpTest(test.TestCase): with self.test_session(use_gpu=use_gpu): a = np.arange(2000) x = constant_op.constant(a, dtype=dtypes.float32) - h = gen_data_flow_ops._stack_v2( + h = gen_data_flow_ops.stack_v2( -1, elem_type=dtypes.float32, stack_name="foo") - c = gen_data_flow_ops._stack_push_v2(h, x, swap_memory=True) + c = gen_data_flow_ops.stack_push_v2(h, x, swap_memory=True) with ops.control_dependencies([c]): - c1 = gen_data_flow_ops._stack_pop_v2(h, dtypes.float32) + c1 = gen_data_flow_ops.stack_pop_v2(h, dtypes.float32) self.assertAllClose(a, c1.eval()) def testStackPushPopSwap(self): @@ -63,7 +63,7 @@ class StackOpTest(test.TestCase): def _testStackWhileSwap(self, use_gpu): with self.test_session(use_gpu=use_gpu): n = constant_op.constant(0) - h = gen_data_flow_ops._stack_v2( + h = gen_data_flow_ops.stack_v2( -1, elem_type=dtypes.float32, stack_name="foo") def c(x): @@ -72,7 +72,7 @@ class StackOpTest(test.TestCase): def b(x): with ops.control_dependencies([x]): a = constant_op.constant(np.ones(2000), dtype=dtypes.float32) - v = gen_data_flow_ops._stack_push_v2(h, a, swap_memory=True) + v = gen_data_flow_ops.stack_push_v2(h, a, swap_memory=True) with ops.control_dependencies([v]): return math_ops.add(x, 1) @@ -86,7 +86,7 @@ class StackOpTest(test.TestCase): def b1(x, y): nx = math_ops.subtract(x, 1) - ny = y + gen_data_flow_ops._stack_pop_v2(h, dtypes.float32) + ny = y + gen_data_flow_ops.stack_pop_v2(h, dtypes.float32) return [nx, ny] _, ry = control_flow_ops.while_loop( @@ -99,16 +99,16 @@ class StackOpTest(test.TestCase): def _testMultiStack(self, use_gpu): with self.test_session(use_gpu=use_gpu): - h1 = gen_data_flow_ops._stack_v2( + h1 = gen_data_flow_ops.stack_v2( -1, elem_type=dtypes.float32, stack_name="foo") - c1 = gen_data_flow_ops._stack_push_v2(h1, 4.0) + c1 = gen_data_flow_ops.stack_push_v2(h1, 4.0) with ops.control_dependencies([c1]): - c1 = gen_data_flow_ops._stack_pop_v2(h1, dtypes.float32) - h2 = gen_data_flow_ops._stack_v2( + c1 = gen_data_flow_ops.stack_pop_v2(h1, dtypes.float32) + h2 = gen_data_flow_ops.stack_v2( -1, elem_type=dtypes.float32, stack_name="bar") - c2 = gen_data_flow_ops._stack_push_v2(h2, 5.0) + c2 = gen_data_flow_ops.stack_push_v2(h2, 5.0) with ops.control_dependencies([c2]): - c2 = gen_data_flow_ops._stack_pop_v2(h2, dtypes.float32) + c2 = gen_data_flow_ops.stack_pop_v2(h2, dtypes.float32) r = c1 + c2 self.assertAllClose(9.0, r.eval()) @@ -119,17 +119,17 @@ class StackOpTest(test.TestCase): def _testSameNameStacks(self, use_gpu): """Different stacks with the same name do not interfere.""" with self.test_session(use_gpu=use_gpu) as sess: - h1 = gen_data_flow_ops._stack_v2( + h1 = gen_data_flow_ops.stack_v2( -1, elem_type=dtypes.float32, stack_name="foo") - h2 = gen_data_flow_ops._stack_v2( + h2 = gen_data_flow_ops.stack_v2( -1, elem_type=dtypes.float32, stack_name="foo") - c1 = gen_data_flow_ops._stack_push_v2(h1, 4.0) + c1 = gen_data_flow_ops.stack_push_v2(h1, 4.0) with ops.control_dependencies([c1]): - c2 = gen_data_flow_ops._stack_push_v2(h2, 5.0) + c2 = gen_data_flow_ops.stack_push_v2(h2, 5.0) with ops.control_dependencies([c2]): - pop1 = gen_data_flow_ops._stack_pop_v2(h1, dtypes.float32) - pop2 = gen_data_flow_ops._stack_pop_v2(h2, dtypes.float32) + pop1 = gen_data_flow_ops.stack_pop_v2(h1, dtypes.float32) + pop2 = gen_data_flow_ops.stack_pop_v2(h2, dtypes.float32) out1, out2 = sess.run([pop1, pop2]) self.assertAllClose(out1, 4.0) @@ -141,9 +141,9 @@ class StackOpTest(test.TestCase): def _testCloseStack(self, use_gpu): with self.test_session(use_gpu=use_gpu) as sess: - h = gen_data_flow_ops._stack_v2( + h = gen_data_flow_ops.stack_v2( -1, elem_type=dtypes.float32, stack_name="foo") - c1 = gen_data_flow_ops._stack_close_v2(h) + c1 = gen_data_flow_ops.stack_close_v2(h) sess.run(c1) def testCloseStack(self): @@ -152,11 +152,11 @@ class StackOpTest(test.TestCase): def _testPushCloseStack(self, use_gpu): with self.test_session(use_gpu=use_gpu) as sess: - h = gen_data_flow_ops._stack_v2( + h = gen_data_flow_ops.stack_v2( -1, elem_type=dtypes.float32, stack_name="foo") - c = gen_data_flow_ops._stack_push_v2(h, [[4.0, 5.0]]) + c = gen_data_flow_ops.stack_push_v2(h, [[4.0, 5.0]]) with ops.control_dependencies([c]): - c1 = gen_data_flow_ops._stack_close_v2(h) + c1 = gen_data_flow_ops.stack_close_v2(h) sess.run(c1) def testPushCloseStack(self): @@ -170,9 +170,9 @@ class StackOpRefTest(test.TestCase): def _testStackPushPop(self, use_gpu): with self.test_session(use_gpu=use_gpu): h = gen_data_flow_ops._stack(dtypes.float32, stack_name="foo") - c = gen_data_flow_ops._stack_push(h, [[4.0, 5.0]]) + c = gen_data_flow_ops.stack_push(h, [[4.0, 5.0]]) with ops.control_dependencies([c]): - c1 = gen_data_flow_ops._stack_pop(h, dtypes.float32) + c1 = gen_data_flow_ops.stack_pop(h, dtypes.float32) self.assertAllClose([[4.0, 5.0]], c1.eval()) def testStackPushPop(self): @@ -184,9 +184,9 @@ class StackOpRefTest(test.TestCase): a = np.arange(2000) x = constant_op.constant(a, dtype=dtypes.float32) h = gen_data_flow_ops._stack(dtypes.float32, stack_name="foo") - c = gen_data_flow_ops._stack_push(h, x, swap_memory=True) + c = gen_data_flow_ops.stack_push(h, x, swap_memory=True) with ops.control_dependencies([c]): - c1 = gen_data_flow_ops._stack_pop(h, dtypes.float32) + c1 = gen_data_flow_ops.stack_pop(h, dtypes.float32) self.assertAllClose(a, c1.eval()) def testStackPushPopSwap(self): @@ -196,13 +196,13 @@ class StackOpRefTest(test.TestCase): def _testMultiStack(self, use_gpu): with self.test_session(use_gpu=use_gpu): h1 = gen_data_flow_ops._stack(dtypes.float32, stack_name="foo") - c1 = gen_data_flow_ops._stack_push(h1, 4.0) + c1 = gen_data_flow_ops.stack_push(h1, 4.0) with ops.control_dependencies([c1]): - c1 = gen_data_flow_ops._stack_pop(h1, dtypes.float32) + c1 = gen_data_flow_ops.stack_pop(h1, dtypes.float32) h2 = gen_data_flow_ops._stack(dtypes.float32, stack_name="bar") - c2 = gen_data_flow_ops._stack_push(h2, 5.0) + c2 = gen_data_flow_ops.stack_push(h2, 5.0) with ops.control_dependencies([c2]): - c2 = gen_data_flow_ops._stack_pop(h2, dtypes.float32) + c2 = gen_data_flow_ops.stack_pop(h2, dtypes.float32) r = c1 + c2 self.assertAllClose(9.0, r.eval()) @@ -217,7 +217,7 @@ class StackOpRefTest(test.TestCase): def b(x): with ops.control_dependencies([x]): a = constant_op.constant(np.ones(2000), dtype=dtypes.float32) - v = gen_data_flow_ops._stack_push(h, a, swap_memory=True) + v = gen_data_flow_ops.stack_push(h, a, swap_memory=True) with ops.control_dependencies([v]): return math_ops.add(x, 1) @@ -231,7 +231,7 @@ class StackOpRefTest(test.TestCase): def b1(x, y): nx = math_ops.subtract(x, 1) - ny = y + gen_data_flow_ops._stack_pop(h, dtypes.float32) + ny = y + gen_data_flow_ops.stack_pop(h, dtypes.float32) return [nx, ny] _, ry = control_flow_ops.while_loop( @@ -249,9 +249,9 @@ class StackOpRefTest(test.TestCase): def _testSameNameStacks(self, use_gpu): with self.test_session(use_gpu=use_gpu): h1 = gen_data_flow_ops._stack(dtypes.float32, stack_name="foo") - c1 = gen_data_flow_ops._stack_push(h1, 4.0) + c1 = gen_data_flow_ops.stack_push(h1, 4.0) h2 = gen_data_flow_ops._stack(dtypes.float32, stack_name="foo") - c2 = gen_data_flow_ops._stack_push(h2, 5.0) + c2 = gen_data_flow_ops.stack_push(h2, 5.0) _ = c1 + c2 self.assertNotEqual(h1.eval()[1], h2.eval()[1]) @@ -262,7 +262,7 @@ class StackOpRefTest(test.TestCase): def _testCloseStack(self, use_gpu): with self.test_session(use_gpu=use_gpu) as sess: h = gen_data_flow_ops._stack(dtypes.float32, stack_name="foo") - c1 = gen_data_flow_ops._stack_close(h) + c1 = gen_data_flow_ops.stack_close(h) sess.run(c1) def testCloseStack(self): @@ -272,9 +272,9 @@ class StackOpRefTest(test.TestCase): def _testPushCloseStack(self, use_gpu): with self.test_session(use_gpu=use_gpu) as sess: h = gen_data_flow_ops._stack(dtypes.float32, stack_name="foo") - c = gen_data_flow_ops._stack_push(h, [[4.0, 5.0]]) + c = gen_data_flow_ops.stack_push(h, [[4.0, 5.0]]) with ops.control_dependencies([c]): - c1 = gen_data_flow_ops._stack_close(h) + c1 = gen_data_flow_ops.stack_close(h) sess.run(c1) def testPushCloseStack(self): diff --git a/tensorflow/python/kernel_tests/template_test.py b/tensorflow/python/kernel_tests/template_test.py index a519b69b22cf51ab4f4173b215c21a71d83e9f99..1b935d5286729e9e802c56e90e2ae7ab72a6e080 100644 --- a/tensorflow/python/kernel_tests/template_test.py +++ b/tensorflow/python/kernel_tests/template_test.py @@ -356,6 +356,10 @@ class TemplateTest(test.TestCase): self.assertEqual("s1_1/nested/dummy:0", v5.name) self.assertEqual("s1_1/nested_1/dummy:0", v6.name) + self.assertEqual(2, len(tmpl1._checkpoint_dependencies)) + self.assertEqual("nested", tmpl1._checkpoint_dependencies[0].name) + self.assertEqual("nested_1", tmpl1._checkpoint_dependencies[1].name) + @test_util.run_in_graph_and_eager_modes() def test_nested_templates_with_defun(self): @@ -558,7 +562,7 @@ class TemplateTest(test.TestCase): outputs_b, _ = linear1(inputs) self.assertEquals("foo", linear1.variable_scope.name) self.assertEquals("foo/w:0", w1.name) - if context.in_graph_mode(): + if not context.executing_eagerly(): self.assertEquals("foo/add:0", outputs_a.name, "First application of template should get " "same name scope as variables.") @@ -573,7 +577,7 @@ class TemplateTest(test.TestCase): "New template gets a freshly uniquified variable scope " "because 'foo' is already taken.") self.assertEquals("foo_1/w:0", w2.name) - if context.in_graph_mode(): + if not context.executing_eagerly(): self.assertEquals("foo_1_1/add:0", outputs_c.name, "First application of template would get " "same name scope as variables, but 'foo_1' is already " @@ -588,7 +592,7 @@ class TemplateTest(test.TestCase): with variable_scope.variable_scope("foo"): # Create two templates with the same name, ensure scopes are made unique. ta = template.make_template("bar", variable_scoped_function, True) - if context.in_eager_mode(): + if context.executing_eagerly(): tb = template.make_template("s", function_with_side_create, trainable=False) else: diff --git a/tensorflow/python/kernel_tests/tensor_array_ops_test.py b/tensorflow/python/kernel_tests/tensor_array_ops_test.py index aad2443eea7ad87faf481973e91ca3df32ccfb44..a834675828b67aed4057d1857c546a586cee69c9 100644 --- a/tensorflow/python/kernel_tests/tensor_array_ops_test.py +++ b/tensorflow/python/kernel_tests/tensor_array_ops_test.py @@ -399,28 +399,14 @@ class TensorArrayTest(test.TestCase): def testTensorArrayWriteWrongIndexOrDataTypeFails(self): with self.test_session(use_gpu=True): ta = _make_ta(3, "foo", dtype=dtypes.float32) - in_graph_mode = context.in_graph_mode() # Test writing the wrong datatype - if in_graph_mode: - with self.assertRaisesOpError( - "TensorArray dtype is float but Op is trying to write " - "dtype string"): - self.evaluate(ta.write(0, "wrong_type_scalar").flow) - else: - with self.assertRaisesOpError( - "TensorArray dtype is float32 but Op is trying to write " - "dtype string"): - self.evaluate(ta.write(0, "wrong_type_scalar").flow) + with self.assertRaisesOpError( + "TensorArray dtype is (float|float32) but Op is trying to write " + "dtype string"): + self.evaluate(ta.write(0, "wrong_type_scalar").flow) - if context.in_graph_mode(): - with self.assertRaisesOpError( - "Tried to write to index -1 but array is not " - "resizeable and size is: 3"): - self.evaluate(ta.write(-1, 3.0).flow) - else: - with self.assertRaisesOpError( - r"Writing to negative indices \(index -1\) is not allowed."): - self.evaluate(ta.write(-1, 3.0).flow) + with self.assertRaisesOpError("index -1"): + self.evaluate(ta.write(-1, 3.0).flow) # Test reading from too large an index with self.assertRaisesOpError( @@ -435,23 +421,17 @@ class TensorArrayTest(test.TestCase): w0 = ta.write(0, [[4.0, 5.0]]) - # Test reading wrong datatype, which is only possible in graph mode - if context.in_graph_mode(): - r0_bad = gen_data_flow_ops._tensor_array_read_v3( + # Test reading wrong datatype (only possible when constructing graphs). + if not context.executing_eagerly(): + 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."): r0_bad.eval() # Test reading from a negative index, which is not allowed - if context.in_graph_mode(): - with self.assertRaisesOpError( - r"Tried to read from index -1 but array size is: 3"): - self.evaluate(ta.read(-1)) - else: - with self.assertRaisesOpError( - r"Reading from negative indices \(index -1\) is not allowed."): - self.evaluate(ta.read(-1)) + with self.assertRaisesOpError("index -1"): + self.evaluate(ta.read(-1)) # Test reading from too large an index with self.assertRaisesOpError( @@ -467,10 +447,7 @@ class TensorArrayTest(test.TestCase): with self.assertRaisesOpError( "Could not write to TensorArray index 2 because " "it has already been written to."): - if context.in_graph_mode(): - self.evaluate(ta.write(2, 3.0).write(2, 3.0).flow) - else: - self.evaluate(ta.write(2, 3.0).write(2, 3.0)) + self.evaluate(ta.write(2, 3.0).write(2, 3.0).flow) @test_util.run_in_graph_and_eager_modes() def testTensorArrayConcatIncompatibleShapesFails(self): @@ -499,58 +476,40 @@ class TensorArrayTest(test.TestCase): w2 = w1.write(1, [4.0]) w3 = w2.write(2, [[3.0]]) - # The eager-mode implementation just passes up array_op.concat's error - # message. - if context.in_graph_mode(): - with self.assertRaisesOpError( - r"TensorArray has inconsistent shapes. Index 0 has " - r"\(excepting dimension 0\) shape: \[\] but index 2 has " - r"\(excepting dimension 0\) shape: \[1\]"): - self.evaluate(w3.concat()) - else: - with self.assertRaisesOpError( - r".*Ranks of all input tensors should match: shape\[0\] " - r"= \[1\] vs\. shape\[2\] = \[1,1\].*"): - self.evaluate(w3.concat()) + # The exact error messages differ between eager execution and graph + # construction as the former bubbles up the error from array_op.concat. + with self.assertRaisesOpError("shape"): + self.evaluate(w3.concat()) @test_util.run_in_graph_and_eager_modes() def testTensorArraySplitIncompatibleShapesFails(self): with self.test_session(use_gpu=True): - in_graph_mode = context.in_graph_mode() + in_eager_mode = context.executing_eagerly() ta = _make_ta(3, "foo") with self.assertRaisesOpError( r"Expected lengths to be a vector, received shape: \[\]"): - if in_graph_mode: + if in_eager_mode: + self.evaluate(ta.split([1.0, 2.0, 3.0], 1)) + else: lengths = array_ops.placeholder(dtypes.int64) ta.split([1.0, 2.0, 3.0], lengths).flow.eval(feed_dict={lengths: 1}) - else: - self.evaluate(ta.split([1.0, 2.0, 3.0], 1)) with self.assertRaisesOpError( r"Expected sum of lengths to be equal to values.shape\[0\], " r"but sum of lengths is 1 and value's shape is: \[3\]"): - if in_graph_mode: - self.evaluate(ta.split([1.0, 2.0, 3.0], [1]).flow) - else: - self.evaluate(ta.split([1.0, 2.0, 3.0], [1])) + self.evaluate(ta.split([1.0, 2.0, 3.0], [1]).flow) ta = _make_ta(1, "baz") with self.assertRaisesOpError( r"Expected value to be at least a vector, but received shape: \[\]"): - if in_graph_mode: - self.evaluate(ta.split(1.0, [1]).flow) - else: - self.evaluate(ta.split(1.0, [1])) + self.evaluate(ta.split(1.0, [1]).flow) ta = _make_ta(2, "buz") with self.assertRaisesOpError( r"TensorArray's size is not equal to the size of lengths " r"\(2 vs. 1\), and the TensorArray is not marked as " r"dynamically resizeable"): - if in_graph_mode: - self.evaluate(ta.split([1.0], [1]).flow) - else: - self.evaluate(ta.split([1.0], [1])) + self.evaluate(ta.split([1.0], [1]).flow) def _testTensorArrayWriteGradientAddMultipleAdds(self, dtype): with self.test_session(use_gpu=True): @@ -868,14 +827,14 @@ class TensorArrayTest(test.TestCase): vout = func(v0, state0, var) grad_val = -np.arange(3 * 5, dtype=np_dtype).reshape(3, 5) - if context.in_graph_mode(): + if context.executing_eagerly(): + grad_fn = backprop.gradients_function(func) + v0_grad, state0_grad, var_grad = grad_fn(v0, state0, var, dy=grad_val) + else: 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() - else: - grad_fn = backprop.gradients_function(func) - v0_grad, state0_grad, var_grad = grad_fn(v0, state0, var, dy=grad_val) state0_t, var_t, v0_t, vout_t, v0_grad_t, var_grad_t, state0_grad_t = ( self.evaluate( @@ -959,10 +918,10 @@ class TensorArrayTest(test.TestCase): return r x = constant_op.constant(2.0, name="x") - if context.in_graph_mode(): - grad = gradients_impl.gradients(loop(x), [x])[0] - else: + if context.executing_eagerly(): grad = backprop.gradients_function(loop)(x)[0] + else: + grad = gradients_impl.gradients(loop(x), [x])[0] self.assertAllClose(31.0, self.evaluate(grad)) def testSumOfTwoReadVariablesWithoutRepeatGrad(self): @@ -1158,14 +1117,14 @@ class TensorArrayTest(test.TestCase): infer_shape=True) w0 = ta1.split(value, [1, 2]) r0 = w0.read(0) - if context.in_graph_mode(): + if context.executing_eagerly(): + self.assertEqual((1, 2), r0.get_shape()) + self.assertEqual((2, 2), w0.read(1).get_shape()) + else: self.assertEqual(r0.get_shape().ndims, None) self.assertEqual( tensor_shape.TensorShape( ta1.handle.op.get_attr("element_shape")).ndims, None) - else: - self.assertEqual((1, 2), r0.get_shape()) - self.assertEqual((2, 2), w0.read(1).get_shape()) def testWriteUnknownShape(self): with self.test_session(use_gpu=True): @@ -1297,13 +1256,13 @@ class TensorArrayTest(test.TestCase): g = func(values) grad_ys = [[[2.0, 3.0], [4.0, 5.0]]] # Test combined gradients + aggregation of read(0) - if context.in_graph_mode(): - grad = gradients_impl.gradients(ys=[g], xs=[values], grad_ys=grad_ys) - g_vals, grad_vals = session.run([[g], grad]) - else: + if context.executing_eagerly(): g_vals = [g] grad_vals = backprop.gradients_function(func)( values, dy=constant_op.constant(grad_ys[0], dtype=dtypes.float32)) + else: + grad = gradients_impl.gradients(ys=[g], xs=[values], grad_ys=grad_ys) + g_vals, grad_vals = session.run([[g], grad]) # Gradients for 8 of the 10 unread components are zero. expected_grad = np.zeros((10, 2)) @@ -1453,13 +1412,13 @@ class TensorArrayTest(test.TestCase): # Tests correct properties on new TensorArrays. self.assertEqual(dtypes.float32, ta0.dtype) self.assertEqual(dtypes.int32, ta1.dtype) - if context.in_graph_mode(): - self.assertEqual(tensor_shape.unknown_shape(), read0.get_shape()) + if context.executing_eagerly(): + self.assertEqual(tensor_shape.scalar(), read0.get_shape()) else: - self.assertEqual(tensor_shape.scalar(), read1.get_shape()) + self.assertEqual(tensor_shape.unknown_shape(), read0.get_shape()) self.assertEqual(tensor_shape.scalar(), read1.get_shape()) - if context.in_graph_mode(): + if not context.executing_eagerly(): variables.global_variables_initializer().run() read0_v, read1_v, size0_v, size1_v = self.evaluate((read0, read1, size0, diff --git a/tensorflow/python/kernel_tests/unique_op_test.py b/tensorflow/python/kernel_tests/unique_op_test.py index 4498fd9fe9986c134b92aed192a6de6f06109bd9..bbc040dc13fc151b970f130eeb76fa1639245416 100644 --- a/tensorflow/python/kernel_tests/unique_op_test.py +++ b/tensorflow/python/kernel_tests/unique_op_test.py @@ -66,9 +66,9 @@ class UniqueTest(test.TestCase): for dtype in [np.int32, np.int64]: x = np.array([[1, 0, 0], [1, 0, 0], [2, 0, 0]]) with self.test_session() as sess: - y0, idx0 = gen_array_ops._unique_v2(x, axis=np.array([0], dtype)) + y0, idx0 = gen_array_ops.unique_v2(x, axis=np.array([0], dtype)) tf_y0, tf_idx0 = sess.run([y0, idx0]) - y1, idx1 = gen_array_ops._unique_v2(x, axis=np.array([1], dtype)) + y1, idx1 = gen_array_ops.unique_v2(x, axis=np.array([1], dtype)) tf_y1, tf_idx1 = sess.run([y1, idx1]) self.assertAllEqual(tf_y0, np.array([[1, 0, 0], [2, 0, 0]])) self.assertAllEqual(tf_idx0, np.array([0, 0, 1])) @@ -80,7 +80,7 @@ class UniqueTest(test.TestCase): # by default, the axis will be wrapped to allow `axis=None`. x = np.random.randint(2, high=10, size=7000) with self.test_session() as sess: - y, idx = gen_array_ops._unique_v2(x, axis=np.array([], np.int32)) + y, idx = gen_array_ops.unique_v2(x, axis=np.array([], np.int32)) tf_y, tf_idx = sess.run([y, idx]) self.assertEqual(len(x), len(tf_idx)) @@ -137,10 +137,10 @@ class UniqueWithCountsTest(test.TestCase): for dtype in [np.int32, np.int64]: x = np.array([[1, 0, 0], [1, 0, 0], [2, 0, 0]]) with self.test_session() as sess: - y0, idx0, count0 = gen_array_ops._unique_with_counts_v2( + y0, idx0, count0 = gen_array_ops.unique_with_counts_v2( x, axis=np.array([0], dtype)) tf_y0, tf_idx0, tf_count0 = sess.run([y0, idx0, count0]) - y1, idx1, count1 = gen_array_ops._unique_with_counts_v2( + y1, idx1, count1 = gen_array_ops.unique_with_counts_v2( x, axis=np.array([1], dtype)) tf_y1, tf_idx1, tf_count1 = sess.run([y1, idx1, count1]) self.assertAllEqual(tf_y0, np.array([[1, 0, 0], [2, 0, 0]])) @@ -155,7 +155,7 @@ class UniqueWithCountsTest(test.TestCase): # by default, the axis will be wrapped to allow `axis=None`. x = np.random.randint(2, high=10, size=7000) with self.test_session() as sess: - y, idx, count = gen_array_ops._unique_with_counts_v2( + y, idx, count = gen_array_ops.unique_with_counts_v2( x, axis=np.array([], np.int32)) tf_y, tf_idx, tf_count = sess.run([y, idx, count]) diff --git a/tensorflow/python/kernel_tests/variable_ops_test.py b/tensorflow/python/kernel_tests/variable_ops_test.py index 79071029fd42374964d12f513e9c510bdc7400eb..cf369c071813120fef685b7220292d50b966cf11 100644 --- a/tensorflow/python/kernel_tests/variable_ops_test.py +++ b/tensorflow/python/kernel_tests/variable_ops_test.py @@ -165,26 +165,26 @@ class VariableOpTest(test.TestCase): def testTemporaryVariable(self): with self.test_session(use_gpu=True): - var = gen_state_ops._temporary_variable( + var = gen_state_ops.temporary_variable( [1, 2], dtypes.float32, var_name="foo") var = state_ops.assign(var, [[4.0, 5.0]]) var = state_ops.assign_add(var, [[6.0, 7.0]]) - final = gen_state_ops._destroy_temporary_variable(var, var_name="foo") + final = gen_state_ops.destroy_temporary_variable(var, var_name="foo") self.assertAllClose([[10.0, 12.0]], final.eval()) def testDestroyNonexistentTemporaryVariable(self): with self.test_session(use_gpu=True): - var = gen_state_ops._temporary_variable([1, 2], dtypes.float32) - final = gen_state_ops._destroy_temporary_variable(var, var_name="bad") + var = gen_state_ops.temporary_variable([1, 2], dtypes.float32) + final = gen_state_ops.destroy_temporary_variable(var, var_name="bad") with self.assertRaises(errors.NotFoundError): final.eval() def testDuplicateTemporaryVariable(self): with self.test_session(use_gpu=True): - var1 = gen_state_ops._temporary_variable( + var1 = gen_state_ops.temporary_variable( [1, 2], dtypes.float32, var_name="dup") var1 = state_ops.assign(var1, [[1.0, 2.0]]) - var2 = gen_state_ops._temporary_variable( + var2 = gen_state_ops.temporary_variable( [1, 2], dtypes.float32, var_name="dup") var2 = state_ops.assign(var2, [[3.0, 4.0]]) final = var1 + var2 @@ -193,25 +193,25 @@ class VariableOpTest(test.TestCase): def testDestroyTemporaryVariableTwice(self): with self.test_session(use_gpu=True): - var = gen_state_ops._temporary_variable([1, 2], dtypes.float32) - val1 = gen_state_ops._destroy_temporary_variable(var, var_name="dup") - val2 = gen_state_ops._destroy_temporary_variable(var, var_name="dup") + var = gen_state_ops.temporary_variable([1, 2], dtypes.float32) + val1 = gen_state_ops.destroy_temporary_variable(var, var_name="dup") + val2 = gen_state_ops.destroy_temporary_variable(var, var_name="dup") final = val1 + val2 with self.assertRaises(errors.NotFoundError): final.eval() def testTemporaryVariableNoLeak(self): with self.test_session(use_gpu=True): - var = gen_state_ops._temporary_variable( + var = gen_state_ops.temporary_variable( [1, 2], dtypes.float32, var_name="bar") final = array_ops.identity(var) final.eval() def testTwoTemporaryVariablesNoLeaks(self): with self.test_session(use_gpu=True): - var1 = gen_state_ops._temporary_variable( + var1 = gen_state_ops.temporary_variable( [1, 2], dtypes.float32, var_name="var1") - var2 = gen_state_ops._temporary_variable( + var2 = gen_state_ops.temporary_variable( [1, 2], dtypes.float32, var_name="var2") final = var1 + var2 final.eval() diff --git a/tensorflow/python/kernel_tests/variable_scope_test.py b/tensorflow/python/kernel_tests/variable_scope_test.py index 8527f116f9541942e52ba2ab635ca1212ea38583..531d0cdf9002d966909fe29ffcd538c3510c60ab 100644 --- a/tensorflow/python/kernel_tests/variable_scope_test.py +++ b/tensorflow/python/kernel_tests/variable_scope_test.py @@ -166,12 +166,10 @@ class VariableScopeTest(test.TestCase): self.evaluate(variables_lib.variables_initializer([w])) self.assertAllClose(self.evaluate(w.value()), [1, 2, 3]) - if context.in_graph_mode(): - with self.assertRaises(TypeError): - variable_scope.get_variable("x4", initializer={}) - else: - with self.assertRaises(ValueError): - variable_scope.get_variable("x4", initializer={}) + # A quirk to be revisited? + error = ValueError if context.executing_eagerly() else TypeError + with self.assertRaises(error): + variable_scope.get_variable("x4", initializer={}) @test_util.run_in_graph_and_eager_modes() def testInitFromNonInitializer(self): @@ -267,7 +265,7 @@ class VariableScopeTest(test.TestCase): self.assertAllClose(self.evaluate(losses[2]), 0.5) with variable_scope.variable_scope("foo", reuse=True): # reuse=True is for now only supported when eager execution is disabled. - if context.in_graph_mode(): + if not context.executing_eagerly(): v = variable_scope.get_variable("v", []) # "v" is alredy there, reused losses = ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES) @@ -374,7 +372,7 @@ class VariableScopeTest(test.TestCase): v = variable_scope.get_variable("v", []) self.evaluate(variables_lib.variables_initializer([v])) self.assertAllClose(self.evaluate(v.value()), 0.3) - if context.in_graph_mode(): + if not context.executing_eagerly(): # Check that we can set reuse. variable_scope.get_variable_scope().reuse_variables() with self.assertRaises(ValueError): # Fail, w does not exist yet. @@ -408,7 +406,7 @@ class VariableScopeTest(test.TestCase): with variable_scope.variable_scope("tower") as tower: with ops.name_scope("scope2") as sc2: self.assertEqual(sc2, "testVarScopeNameScope1/tower/scope2/") - if context.in_graph_mode(): + if not context.executing_eagerly(): with variable_scope.variable_scope( tower): # Re-entering acts like another "tower". with ops.name_scope("scope2") as sc2: @@ -422,7 +420,7 @@ class VariableScopeTest(test.TestCase): with variable_scope.variable_scope("tower"): with ops.name_scope("scope2") as sc2: self.assertEqual(sc2, "testVarScopeNameScope2/tower/scope2/") - if context.in_graph_mode(): + if not context.executing_eagerly(): with variable_scope.variable_scope(tower): with ops.name_scope("scope2") as sc2: self.assertEqual(sc2, "testVarScopeNameScope2/tower_1/scope2/") @@ -903,17 +901,15 @@ class VariableScopeTest(test.TestCase): "w", [], collections=["foo"]) self.assertEqual(local_var.name, "outer/w:0") - # Since variable is local, it should be in the local variable collection - # but not the trainable collection. - if context.in_graph_mode(): + if not context.executing_eagerly(): + # Since variable is local, it should be in the local variable collection + # but not the trainable collection. self.assertIn(local_var, ops.get_collection(ops.GraphKeys.LOCAL_VARIABLES)) self.assertIn(local_var, ops.get_collection("foo")) self.assertNotIn(local_var, ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)) - - # Check that local variable respects `reuse`. - if context.in_graph_mode(): + # Check that local variable respects `reuse`. with variable_scope.variable_scope(outer, "default", reuse=True): self.assertEqual( variable_scope.get_local_variable("w", []).name, "outer/w:0") diff --git a/tensorflow/python/kernel_tests/variables_test.py b/tensorflow/python/kernel_tests/variables_test.py index b16c8c002c98a0351d1fc55fce061695327a18c9..27599868b74be323189b872c2147c6a33f84d170 100644 --- a/tensorflow/python/kernel_tests/variables_test.py +++ b/tensorflow/python/kernel_tests/variables_test.py @@ -687,7 +687,7 @@ class VariableContainerTest(test.TestCase): v1 = variables.Variable([1]) with ops.container("l2"): v2 = variables.Variable([2]) - special_v = gen_state_ops._variable( + special_v = gen_state_ops.variable( shape=[1], dtype=dtypes.float32, name="VariableInL3", diff --git a/tensorflow/python/kernel_tests/xent_op_test.py b/tensorflow/python/kernel_tests/xent_op_test.py index e152f02d8e983364603053dc5c8d14b5dfaf3605..e3e120a4eb01885ac5ac5e41f82ad3e480a83a77 100644 --- a/tensorflow/python/kernel_tests/xent_op_test.py +++ b/tensorflow/python/kernel_tests/xent_op_test.py @@ -48,7 +48,7 @@ class XentTest(test.TestCase): def _testXent(self, np_features, np_labels, use_gpu=False): np_loss, np_backprop = self._npXent(np_features, np_labels) with self.test_session(use_gpu=use_gpu) as sess: - loss, backprop = gen_nn_ops._softmax_cross_entropy_with_logits( + loss, backprop = gen_nn_ops.softmax_cross_entropy_with_logits( np_features, np_labels) tf_loss, tf_backprop = sess.run([loss, backprop]) self.assertAllCloseAccordingToType(np_loss, tf_loss) @@ -71,7 +71,7 @@ class XentTest(test.TestCase): def _testSingleClass(self, use_gpu=False): for dtype in np.float16, np.float32: with self.test_session(use_gpu=use_gpu) as sess: - loss, backprop = gen_nn_ops._softmax_cross_entropy_with_logits( + loss, backprop = gen_nn_ops.softmax_cross_entropy_with_logits( np.array([[1.], [-1.], [0.]]).astype(dtype), np.array([[-1.], [0.], [1.]]).astype(dtype)) tf_loss, tf_backprop = sess.run([loss, backprop]) @@ -89,7 +89,7 @@ class XentTest(test.TestCase): np_labels = np.array([[[0., 0., 0., 1.]], [[0., .5, .5, 0.]]]).astype(dtype) self.assertRaisesRegexp(ValueError, "must be rank 2", - gen_nn_ops._softmax_cross_entropy_with_logits, + gen_nn_ops.softmax_cross_entropy_with_logits, np_features, np_labels) def testNpXent(self): @@ -131,14 +131,14 @@ class XentTest(test.TestCase): def testShapeMismatch(self): with self.test_session(): with self.assertRaises(ValueError): - gen_nn_ops._softmax_cross_entropy_with_logits( + gen_nn_ops.softmax_cross_entropy_with_logits( [[0., 1.], [2., 3.]], [[0., 1., 0.], [1., 0., 0.]]) def testNotMatrix(self): with self.test_session(): with self.assertRaises(ValueError): - gen_nn_ops._softmax_cross_entropy_with_logits([0., 1., 2., 3.], - [0., 1., 0., 1.]) + gen_nn_ops.softmax_cross_entropy_with_logits([0., 1., 2., 3.], + [0., 1., 0., 1.]) def testHalf(self): self._testAll( diff --git a/tensorflow/python/layers/base.py b/tensorflow/python/layers/base.py index 8314c4aa87a5b54effc44c371703267517ffa07d..e4395bea92961d348ed3841a31cacb91aaa282ec 100644 --- a/tensorflow/python/layers/base.py +++ b/tensorflow/python/layers/base.py @@ -36,12 +36,13 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import variable_scope as vs from tensorflow.python.ops import variables as tf_variables from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.training import checkpointable from tensorflow.python.util import nest from tensorflow.python.util.tf_export import tf_export @tf_export('layers.Layer') -class Layer(object): +class Layer(checkpointable.CheckpointableBase): """Base layer class. This is the class from which all layers inherit, implementing common @@ -114,7 +115,7 @@ class Layer(object): # Provides information about which inputs are compatible with the layer. self.input_spec = None - if activity_regularizer and context.in_eager_mode(): + if activity_regularizer and context.executing_eagerly(): raise ValueError( ('Activity regularization is not supported when executing eagerly. ' 'Got activity_regularizer=%s') % (activity_regularizer,)) @@ -126,12 +127,12 @@ class Layer(object): # return tensors. When using graph execution, _losses is a list of ops. self._losses = [] self._reuse = kwargs.get('_reuse') - self._graph = ops.get_default_graph() + self._graph = None # Will be set at build time. self._dtype = None if dtype is None else dtypes.as_dtype(dtype).name - call_fn_args = estimator_util.fn_args(self.call) - self._compute_previous_mask = ('mask' in call_fn_args or + self._call_fn_args = estimator_util.fn_args(self.call) + self._compute_previous_mask = ('mask' in self._call_fn_args or hasattr(self, 'compute_mask')) - self._call_has_scope_arg = 'scope' in call_fn_args + self._call_has_scope_arg = 'scope' in self._call_fn_args # These lists will be filled via successive calls # to self._add_inbound_node(). @@ -227,7 +228,7 @@ class Layer(object): @property def updates(self): - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('Layer.updates not supported in Eager mode.') if not self.trainable and not self.stateful: return [] @@ -259,7 +260,7 @@ class Layer(object): have is available at runtime. A step counter might fall into this category. """ - if context.in_eager_mode(): + if context.executing_eagerly(): return # Updates already applied when in eager mode. updates = _to_list(updates) @@ -285,7 +286,7 @@ class Layer(object): Raises: RuntimeError: If called in Eager mode. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('`get_updates_for()` not supported in Eager mode.') # Updates disabled if layer is not trainable and not explicitly stateful. @@ -316,7 +317,7 @@ class Layer(object): Returns: A list of tensors. """ - if context.in_eager_mode(): + if context.executing_eagerly(): # _losses may only contain variable regularization losses when executing # eagerly, and they have been saved as lambdas to be executed when # requested. @@ -354,7 +355,7 @@ class Layer(object): Raises: RuntimeError: If called in Eager mode. """ - if context.in_eager_mode(): + if context.executing_eagerly(): # TODO(fchollet): it should be possible (and highly desirable) to support # `add_loss` in eager mode. This allows great convenience and flexibility # in defining custom losses on the fly (e.g. in VAEs). @@ -388,7 +389,7 @@ class Layer(object): Raises: RuntimeError: If called in Eager mode. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('Layer.get_losses_for not supported in Eager mode.') if inputs is None: @@ -508,7 +509,7 @@ class Layer(object): # will occur; it should be None if and only if initialization will take # place in the eager context. init_graph = None - if context.in_graph_mode(): + if not context.executing_eagerly(): default_graph = ops.get_default_graph() if default_graph.building_function: with ops.init_scope(): @@ -516,7 +517,7 @@ class Layer(object): # will be lifted; if initialization ops will be lifted into # the eager context, then there is nothing to retrieve, since variable # collections are not supported when eager execution is enabled. - if context.in_graph_mode(): + if not context.executing_eagerly(): init_graph = ops.get_default_graph() existing_variables = set(tf_variables.global_variables()) else: @@ -532,13 +533,17 @@ class Layer(object): with vs.variable_scope( self._scope, reuse=reuse, auxiliary_name_scope=False) as scope: with ops.name_scope(self._name_scope_name(scope)): - variable = vs.get_variable(name, - shape=shape, - initializer=initializer, - dtype=dtypes.as_dtype(dtype), - constraint=constraint, - trainable=trainable and self.trainable, - partitioner=partitioner) + variable = self._add_variable_with_custom_getter( + name=name, + shape=shape, + getter=vs.get_variable, + # Manage errors in Layer rather than Checkpointable. + overwrite=True, + initializer=initializer, + dtype=dtypes.as_dtype(dtype), + constraint=constraint, + trainable=trainable and self.trainable, + partitioner=partitioner) if init_graph is not None: # pylint: disable=protected-access # The variable was created and initialized in a graph. @@ -573,7 +578,7 @@ class Layer(object): if isinstance(variable, tf_variables.PartitionedVariable): raise RuntimeError( 'Partitioned variable regularization is not yet ' - 'supported when executing eagerly. File a feature request' + 'supported when executing eagerly. File a feature request ' 'if this is important to you.') # Save a zero-argument lambda which runs the regularizer on the # variable, to be executed when `Layer.losses` is requested. @@ -619,16 +624,17 @@ class Layer(object): self._set_scope(kwargs.pop('scope', None)) input_list = nest.flatten(inputs) - in_graph_mode = context.in_graph_mode() + build_graph = not context.executing_eagerly() in_deferred_mode = isinstance(input_list[0], _DeferredTensor) # Ensure the Layer, if being reused, is working with inputs from # the same graph as where it was created. - if in_graph_mode: + if build_graph: try: - ops._get_graph_from_inputs(input_list, graph=self.graph) # pylint: disable=protected-access + # Set layer's "graph" at build time + self._graph = ops._get_graph_from_inputs(input_list, graph=self._graph) # pylint: disable=protected-access except ValueError as e: raise ValueError('Input graph and Layer graph are not the same: %s' % e) - if in_graph_mode or in_deferred_mode: + if build_graph or in_deferred_mode: user_kwargs = copy.copy(kwargs) # Handle Keras mask propagation from previous layer to current layer. @@ -636,8 +642,9 @@ class Layer(object): if (not hasattr(self, '_compute_previous_mask') or self._compute_previous_mask): previous_mask = _collect_previous_mask(inputs) - if ('mask' in estimator_util.fn_args(self.call) and - 'mask' not in kwargs and + if not hasattr(self, '_call_fn_args'): + self._call_fn_args = estimator_util.fn_args(self.call) + if ('mask' in self._call_fn_args and 'mask' not in kwargs and not _is_all_none(previous_mask)): # The previous layer generated a mask, and mask was not explicitly pass # to __call__, hence we set previous_mask as the default value. @@ -662,13 +669,14 @@ class Layer(object): with scope_context_manager as scope: with ops.name_scope(self._name_scope_name(scope)): if not self.built: - if not in_graph_mode: + if not build_graph: # Activity regularization is currently unsupported in Eager mode. if self._activity_regularizer: - raise ValueError('activity_regularizer currently unsupported in ' - 'Eager mode. Found an activity_regularizer in ' - '%s(%s).' % (self.__class__.__name__, self)) - if not in_graph_mode and not in_deferred_mode: + raise ValueError( + 'activity_regularizer currently unsupported with ' + 'eager execution enabled. Found an activity_regularizer in ' + '%s(%s).' % (self.__class__.__name__, self)) + if not build_graph and not in_deferred_mode: # TODO(agarwal): support _keras_history in Eager mode. for x in input_list: if hasattr(x, '_keras_history'): @@ -693,11 +701,13 @@ class Layer(object): # TODO(agarwal): Fix the sub-classes and avoid this complexity. call_has_scope_arg = self._call_has_scope_arg except AttributeError: - call_has_scope_arg = 'scope' in estimator_util.fn_args(self.call) + self._call_fn_args = estimator_util.fn_args(self.call) + self._call_has_scope_arg = 'scope' in self._call_fn_args + call_has_scope_arg = self._call_has_scope_arg if call_has_scope_arg: kwargs['scope'] = scope # Check input assumptions set after layer building, e.g. input shape. - if in_graph_mode or in_deferred_mode: + if build_graph or in_deferred_mode: self._assert_input_compatibility(inputs) if not in_deferred_mode: @@ -721,7 +731,7 @@ class Layer(object): if len(outputs) == 1: outputs = outputs[0] - if in_graph_mode: + if build_graph: # Apply activity regularization. # Note that it should be applied every time the layer creates a new # output, since it is output-specific. @@ -743,7 +753,7 @@ class Layer(object): else: outputs._keras_mask = output_mask # pylint: disable=protected-access - if in_graph_mode: + if build_graph: # If all input tensors have history metadata, # we update the output tensors # with corresponding history metadata, thus eventually allowing to use @@ -766,7 +776,7 @@ class Layer(object): # Update global default collections. _add_elements_to_collection(self.updates, ops.GraphKeys.UPDATE_OPS) - if in_deferred_mode or in_graph_mode: + if in_deferred_mode or build_graph: if _have_all_keras_metadata(inputs): # Add an inbound node to the layer, so it can keep track of this call. # This updates the layer history of the output tensor(s). @@ -778,7 +788,7 @@ class Layer(object): @property def graph(self): - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('Layer.graph not supported in Eager mode.') return self._graph @@ -882,7 +892,7 @@ class Layer(object): mode. ValueError: If the index provided does not match any node. """ - assert context.in_graph_mode() + assert not context.executing_eagerly() if not self._inbound_nodes: raise RuntimeError('The layer has never been called ' 'and thus has no defined ' + attr_name + '.') @@ -912,7 +922,7 @@ class Layer(object): Raises: RuntimeError: If called in Eager mode. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError( 'Layer.get_input_shape_at not supported in Eager mode.') return self._get_node_attribute_at_index(node_index, 'input_shapes', @@ -934,7 +944,7 @@ class Layer(object): Raises: RuntimeError: If called in Eager mode. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError( 'Layer.get_output_shape_at not supported in Eager mode.') return self._get_node_attribute_at_index(node_index, 'output_shapes', @@ -955,7 +965,7 @@ class Layer(object): Raises: RuntimeError: If called in Eager mode. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('Layer.get_input_at not supported in Eager mode.') return self._get_node_attribute_at_index(node_index, 'input_tensors', 'input') @@ -975,7 +985,7 @@ class Layer(object): Raises: RuntimeError: If called in Eager mode. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('Layer.get_output_at not supported in Eager mode.') return self._get_node_attribute_at_index(node_index, 'output_tensors', 'output') @@ -998,7 +1008,7 @@ class Layer(object): RuntimeError: If called in Eager mode. AttributeError: If no inbound nodes are found. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('Layer.input not supported in Eager mode.') if not self._inbound_nodes: raise AttributeError('Layer ' + self.name + @@ -1020,7 +1030,7 @@ class Layer(object): layers. RuntimeError: if called in Eager mode. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('Layer.output not supported in Eager mode.') if not self._inbound_nodes: raise AttributeError('Layer ' + self.name + ' has no inbound nodes.') @@ -1042,7 +1052,7 @@ class Layer(object): AttributeError: if the layer has no defined input_shape. RuntimeError: if called in Eager mode. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('Layer.input_shape not supported in Eager mode.') if not self._inbound_nodes: raise AttributeError('The layer has never been called ' @@ -1103,7 +1113,7 @@ class Layer(object): AttributeError: if the layer has no defined output shape. RuntimeError: if called in Eager mode. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('Layer.output_shape not supported in Eager mode.') if not self._inbound_nodes: raise AttributeError('The layer has never been called ' @@ -1461,7 +1471,7 @@ def _to_list(x): def _add_elements_to_collection(elements, collection_list): - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('Using collections from Layers not supported in Eager ' 'mode. Tried to add %s to %s' % (elements, collection_list)) diff --git a/tensorflow/python/layers/base_test.py b/tensorflow/python/layers/base_test.py index 91b8988d31c1f04be8134733e5e919c738ccb74f..9ed4afeaba931c47d2a1e65f08489773f0b9eb1b 100644 --- a/tensorflow/python/layers/base_test.py +++ b/tensorflow/python/layers/base_test.py @@ -44,7 +44,7 @@ class BaseLayerTest(test.TestCase): self.assertEqual(layer.variables, []) self.assertEqual(layer.trainable_variables, []) self.assertEqual(layer.non_trainable_variables, []) - if context.in_graph_mode(): + if not context.executing_eagerly(): # updates, losses only supported in GRAPH mode self.assertEqual(layer.updates, []) self.assertEqual(layer.losses, []) @@ -63,7 +63,7 @@ class BaseLayerTest(test.TestCase): self.assertEqual(layer.variables, [variable]) self.assertEqual(layer.trainable_variables, [variable]) self.assertEqual(layer.non_trainable_variables, []) - if context.in_graph_mode(): + if not context.executing_eagerly(): self.assertEqual( layer.variables, ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)) @@ -77,7 +77,7 @@ class BaseLayerTest(test.TestCase): self.assertEqual(layer.variables, [variable, variable_2]) self.assertEqual(layer.trainable_variables, [variable]) self.assertEqual(layer.non_trainable_variables, [variable_2]) - if context.in_graph_mode(): + if not context.executing_eagerly(): self.assertEqual( len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 1) @@ -161,7 +161,7 @@ class BaseLayerTest(test.TestCase): inputs = random_ops.random_uniform((5,), seed=1) outputs = layer.apply(inputs) self.assertEqual(layer.built, True) - if context.in_graph_mode(): + if not context.executing_eagerly(): # op is only supported in GRAPH mode self.assertEqual(outputs.op.name, 'my_layer/Square') @@ -210,7 +210,7 @@ class BaseLayerTest(test.TestCase): inputs = random_ops.random_uniform((5,), seed=1) outputs = layer.apply(inputs) self.assertEqual(layer.built, True) - if context.in_graph_mode(): + if not context.executing_eagerly(): # op only supported in GRAPH mode. self.assertEqual(outputs.op.name, 'my_layer/Square') @@ -280,7 +280,7 @@ class BaseLayerTest(test.TestCase): def call(self, inputs): return inputs - if context.in_graph_mode(): + if not context.executing_eagerly(): layer = CustomerLayer() with self.assertRaisesRegexp(ValueError, r'requires a defined rank'): layer.apply(array_ops.placeholder('int32')) @@ -307,7 +307,7 @@ class BaseLayerTest(test.TestCase): def call(self, inputs): return inputs - if context.in_graph_mode(): + if not context.executing_eagerly(): layer = CustomerLayer() with self.assertRaisesRegexp(ValueError, r'requires a defined rank'): layer.apply(array_ops.placeholder('int32')) @@ -335,7 +335,7 @@ class BaseLayerTest(test.TestCase): def call(self, inputs): return inputs - if context.in_graph_mode(): + if not context.executing_eagerly(): layer = CustomerLayer() with self.assertRaisesRegexp(ValueError, r'requires a defined rank'): layer.apply(array_ops.placeholder('int32')) @@ -430,7 +430,7 @@ class BaseLayerTest(test.TestCase): layer.apply(constant_op.constant(1)) # Works - if context.in_graph_mode(): + if not context.executing_eagerly(): layer.apply(array_ops.placeholder('int32')) layer.apply(array_ops.placeholder('int32', shape=(2, 3))) @@ -453,13 +453,7 @@ class BaseLayerTest(test.TestCase): return {'l' + key: inputs[key] for key in inputs} layer = DictLayer() - if context.in_graph_mode(): - i1 = array_ops.placeholder('int32') - i2 = array_ops.placeholder('float32') - result = layer.apply({'abel': i1, 'ogits': i2}) - self.assertTrue(isinstance(result, dict)) - self.assertEqual(set(['label', 'logits']), set(result.keys())) - else: + if context.executing_eagerly(): i1 = constant_op.constant(3) i2 = constant_op.constant(4.0) result = layer.apply({'abel': i1, 'ogits': i2}) @@ -467,6 +461,12 @@ class BaseLayerTest(test.TestCase): self.assertEqual(set(['label', 'logits']), set(result.keys())) self.assertEqual(3, result['label'].numpy()) self.assertEqual(4.0, result['logits'].numpy()) + else: + i1 = array_ops.placeholder('int32') + i2 = array_ops.placeholder('float32') + result = layer.apply({'abel': i1, 'ogits': i2}) + self.assertTrue(isinstance(result, dict)) + self.assertEqual(set(['label', 'logits']), set(result.keys())) def testActivityRegularizer(self): regularizer = math_ops.reduce_sum @@ -643,6 +643,16 @@ class BaseLayerTest(test.TestCase): self.assertEqual(len(layer.get_losses_for([intermediate_inputs])), 1) self.assertEqual(len(layer.get_losses_for([outputs])), 0) + def testLayerGraphSetInFirstApply(self): + with ops.Graph().as_default(): + layer = core_layers.Dense(1) # Graph at construction time is ignored + with ops.Graph().as_default(): + layer.apply(constant_op.constant([[1]])) + # layer is now bound to second Graph + with ops.Graph().as_default(), self.assertRaisesRegexp( + ValueError, 'Input graph and Layer graph are not the same'): + layer.apply(constant_op.constant([[1]])) + if __name__ == '__main__': test.main() diff --git a/tensorflow/python/layers/convolutional.py b/tensorflow/python/layers/convolutional.py index bb10fe5e8bfd26e4877fb6aef73980a30f62bb5d..74e7c63fb364d9c4475af5efe7d5db95cccf8166 100644 --- a/tensorflow/python/layers/convolutional.py +++ b/tensorflow/python/layers/convolutional.py @@ -1664,7 +1664,7 @@ class Conv2DTranspose(Conv2D): padding=self.padding.upper(), data_format=utils.convert_data_format(self.data_format, ndim=4)) - if context.in_graph_mode(): + if not context.executing_eagerly(): # Infer the static output shape: out_shape = inputs.get_shape().as_list() out_shape[c_axis] = self.filters @@ -1969,7 +1969,7 @@ class Conv3DTranspose(Conv3D): data_format=utils.convert_data_format(self.data_format, ndim=5), padding=self.padding.upper()) - if context.in_graph_mode(): + if not context.executing_eagerly(): # Infer the static output shape: out_shape = inputs.get_shape().as_list() out_shape[c_axis] = self.filters diff --git a/tensorflow/python/layers/core.py b/tensorflow/python/layers/core.py index 6970bf9234f5a31ee8093069ac1c933bcdb6f103..e598d9f83ab21f2dd5fabb3dd37fa0bfb5f003a4 100644 --- a/tensorflow/python/layers/core.py +++ b/tensorflow/python/layers/core.py @@ -35,6 +35,7 @@ from tensorflow.python.layers import utils from tensorflow.python.ops import array_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import math_ops +from tensorflow.python.ops import gen_math_ops from tensorflow.python.ops import nn from tensorflow.python.ops import nn_ops from tensorflow.python.ops import standard_ops @@ -155,11 +156,11 @@ class Dense(base.Layer): outputs = standard_ops.tensordot(inputs, self.kernel, [[len(shape) - 1], [0]]) # Reshape the output back to the original ndim of the input. - if context.in_graph_mode(): + if not context.executing_eagerly(): output_shape = shape[:-1] + [self.units] outputs.set_shape(output_shape) else: - outputs = standard_ops.matmul(inputs, self.kernel) + outputs = gen_math_ops.mat_mul(inputs, self.kernel) if self.use_bias: outputs = nn.bias_add(outputs, self.bias) if self.activation is not None: @@ -373,7 +374,7 @@ class Flatten(base.Layer): def call(self, inputs): outputs = array_ops.reshape(inputs, (array_ops.shape(inputs)[0], -1)) - if context.in_graph_mode(): + if not context.executing_eagerly(): outputs.set_shape(self.compute_output_shape(inputs.get_shape())) return outputs diff --git a/tensorflow/python/layers/core_test.py b/tensorflow/python/layers/core_test.py index 15ce6cba21fcc78126f7db58ab18934db69c15fd..cf45b07637108422f1c612390bb01efdad6d5bcf 100644 --- a/tensorflow/python/layers/core_test.py +++ b/tensorflow/python/layers/core_test.py @@ -67,7 +67,7 @@ class DenseTest(test.TestCase): variables.global_variables_initializer().run() self.assertAllEqual(x.eval(), [[0.0]]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True) def testCall(self): dense = core_layers.Dense(2, activation=nn_ops.relu, name='my_dense') inputs = random_ops.random_uniform((5, 4), seed=1) @@ -77,12 +77,20 @@ class DenseTest(test.TestCase): self.assertListEqual(dense.trainable_variables, [dense.kernel, dense.bias]) self.assertListEqual(dense.non_trainable_variables, []) - if context.in_graph_mode(): + if not context.executing_eagerly(): self.assertEqual( len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 2) self.assertEqual(dense.kernel.name, 'my_dense/kernel:0') self.assertEqual(dense.bias.name, 'my_dense/bias:0') + @test_util.assert_no_new_pyobjects_executing_eagerly + def testNoEagerLeak(self): + # Tests that repeatedly constructing and building a Layer does not leak + # Python objects. + inputs = random_ops.random_uniform((5, 4), seed=1) + core_layers.Dense(5)(inputs) + core_layers.Dense(2, activation=nn_ops.relu, name='my_dense')(inputs) + @test_util.run_in_graph_and_eager_modes() def testCallTensorDot(self): dense = core_layers.Dense(2, activation=nn_ops.relu, name='my_dense') @@ -98,7 +106,7 @@ class DenseTest(test.TestCase): self.assertListEqual(dense.variables, [dense.kernel]) self.assertListEqual(dense.trainable_variables, [dense.kernel]) self.assertListEqual(dense.non_trainable_variables, []) - if context.in_graph_mode(): + if not context.executing_eagerly(): self.assertEqual( len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 1) self.assertEqual(dense.kernel.name, 'my_dense/kernel:0') @@ -113,7 +121,7 @@ class DenseTest(test.TestCase): self.assertListEqual(dense.non_trainable_variables, [dense.kernel, dense.bias]) self.assertListEqual(dense.trainable_variables, []) - if context.in_graph_mode(): + if not context.executing_eagerly(): self.assertEqual( len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 0) @@ -162,13 +170,13 @@ class DenseTest(test.TestCase): dense = core_layers.Dense(2, activation=nn_ops.relu, name='dense1') inputs = random_ops.random_uniform((5, 3), seed=1) outputs = dense(inputs) - if context.in_graph_mode(): + if not context.executing_eagerly(): self.assertEqual(outputs.op.name, 'dense1/Relu') dense = core_layers.Dense(2, name='dense2') inputs = random_ops.random_uniform((5, 3), seed=1) outputs = dense(inputs) - if context.in_graph_mode(): + if not context.executing_eagerly(): self.assertEqual(outputs.op.name, 'dense2/BiasAdd') def testActivityRegularizer(self): @@ -374,7 +382,7 @@ class DropoutTest(test.TestCase): dp = core_layers.Dropout(0.5) inputs = array_ops.ones((5, 3)) dropped = dp.apply(inputs, training=True) - if context.in_graph_mode(): + if not context.executing_eagerly(): self.evaluate(variables.global_variables_initializer()) np_output = self.evaluate(dropped) self.assertAlmostEqual(0., np_output.min()) diff --git a/tensorflow/python/layers/normalization.py b/tensorflow/python/layers/normalization.py index d83292b80963d942023b5d086a089af53008efe0..3aafdbb14b56bbba3c4c9928e09a91d51f18db73 100644 --- a/tensorflow/python/layers/normalization.py +++ b/tensorflow/python/layers/normalization.py @@ -338,8 +338,9 @@ class BatchNormalization(base.Layer): return var with ops.device(None): - device = ((lambda _: self.moving_mean.device) - if context.in_graph_mode() else self.moving_mean.device) + device = ( + self.moving_mean.device if context.executing_eagerly() else + (lambda _: self.moving_mean.device)) with ops.device(device): self.renorm_mean = _renorm_variable('renorm_mean', param_shape) self.renorm_mean_weight = _renorm_variable('renorm_mean_weight', ()) @@ -347,8 +348,9 @@ class BatchNormalization(base.Layer): # renorm_stddev_weight. This allows us to (1) mix the average # stddev with the minibatch stddev early in training, and (2) compute # the unbiased average stddev by dividing renorm_stddev by the weight. - device = ((lambda _: self.moving_variance.device) - if context.in_graph_mode() else self.moving_variance.device) + device = ( + self.moving_variance.device if context.executing_eagerly() else + (lambda _: self.moving_variance.device)) with ops.device(device): self.renorm_stddev = _renorm_variable('renorm_stddev', param_shape) self.renorm_stddev_weight = _renorm_variable( @@ -420,7 +422,7 @@ class BatchNormalization(base.Layer): one_minus_decay) variance_update = self._assign_moving_average(self.moving_variance, variance, one_minus_decay) - if context.in_graph_mode(): + if not context.executing_eagerly(): # Note that in Eager mode, the updates are already executed when running # assign_moving_averages. So we do not need to put them into # collections. @@ -493,7 +495,7 @@ class BatchNormalization(base.Layer): return (r, d, new_mean, new_variance) def call(self, inputs, training=False): - in_eager_mode = context.in_eager_mode() + in_eager_mode = context.executing_eagerly() if self.virtual_batch_size is not None: # Virtual batches (aka ghost batches) can be simulated by reshaping the # Tensor and reusing the existing batch norm implementation @@ -610,7 +612,7 @@ class BatchNormalization(base.Layer): training, lambda: _do_update(self.moving_variance, new_variance), lambda: self.moving_variance) - if context.in_graph_mode(): + if not context.executing_eagerly(): self.add_update(mean_update, inputs=inputs) self.add_update(variance_update, inputs=inputs) @@ -671,9 +673,16 @@ def batch_normalization(inputs, Note: when training, the moving_mean and moving_variance need to be updated. By default the update ops are placed in `tf.GraphKeys.UPDATE_OPS`, so they - need to be added as a dependency to the `train_op`. For example: + need to be added as a dependency to the `train_op`. Also, be sure to add + any batch_normalization ops before getting the update_ops collection. + Otherwise, update_ops will be empty, and training/inference will not work + properly. For example: ```python + x_norm = tf.layers.batch_normalization(x, training=training) + + # ... + update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) with tf.control_dependencies(update_ops): train_op = optimizer.minimize(loss) diff --git a/tensorflow/python/layers/utils.py b/tensorflow/python/layers/utils.py index 484c6fc466558dc274740955594cc279a175d638..3b156c36a2ff35fb9e05af1406d7b3f6cf883394 100644 --- a/tensorflow/python/layers/utils.py +++ b/tensorflow/python/layers/utils.py @@ -24,6 +24,7 @@ from tensorflow.python.eager import context from tensorflow.python.ops import variables from tensorflow.python.ops import control_flow_ops from tensorflow.python.framework import ops +from tensorflow.python.framework import smart_cond as smart_module from tensorflow.python.framework import tensor_util from tensorflow.python.util import nest @@ -201,7 +202,7 @@ def smart_cond(pred, true_fn=None, false_fn=None, name=None): if isinstance(pred, variables.Variable): return control_flow_ops.cond( pred, true_fn=true_fn, false_fn=false_fn, name=name) - return control_flow_ops.smart_cond( + return smart_module.smart_cond( pred, true_fn=true_fn, false_fn=false_fn, name=name) @@ -228,7 +229,7 @@ def constant_value(pred): if isinstance(pred, variables.Variable): return None - return control_flow_ops.smart_constant_value(pred) + return smart_module.smart_constant_value(pred) def object_list_uid(object_list): diff --git a/tensorflow/python/lib/core/py_func.cc b/tensorflow/python/lib/core/py_func.cc index e0422ef80add42307268be2743e668eb8c8acb68..02eafd42b35231195a6405c8c3cc11871ed55772 100644 --- a/tensorflow/python/lib/core/py_func.cc +++ b/tensorflow/python/lib/core/py_func.cc @@ -79,10 +79,11 @@ Status MakeArgTuple(const PyCall* call, PyObject** tuple) { const Tensor& t = call->ins[i]; if (call->eager) { if (call->gpu) { - arg = EagerTensorFromHandle(new TFE_TensorHandle(t, call->device)); + arg = EagerTensorFromHandle( + new TFE_TensorHandle(t, call->device, call->device)); } else { // TFE_TensorHandle assumes that CPU is identified by `nullptr`. - arg = EagerTensorFromHandle(new TFE_TensorHandle(t, nullptr)); + arg = EagerTensorFromHandle(new TFE_TensorHandle(t, nullptr, nullptr)); } if (arg == nullptr) { return errors::Internal("Unable to procure EagerTensor from Tensor."); @@ -163,9 +164,9 @@ bool IsSingleNone(PyObject* obj) { } // Retrieves a Tensor from `eager_tensor` and stores it in `output_tensor`. -void ExtractTensorFromEagerTensor(const PyObject* eager_tensor, - Tensor* output_tensor) { - *output_tensor = EagerTensor_Handle(eager_tensor)->t; +tensorflow::Status ExtractTensorFromEagerTensor(const PyObject* eager_tensor, + const Tensor** output_tensor) { + return EagerTensor_Handle(eager_tensor)->Tensor(output_tensor); } // Calls the registered py function through the trampoline. @@ -219,7 +220,9 @@ Status DoCallPyFunc(PyCall* call, bool* out_log_on_error) { if (call->eager) { const PyObject* item = PyList_GetItem(result, i); if (EagerTensor_CheckExact(item)) { - ExtractTensorFromEagerTensor(item, &t); + const Tensor* tensor = nullptr; + s = ExtractTensorFromEagerTensor(item, &tensor); + if (s.ok()) t = *tensor; } else { s = errors::FailedPrecondition( "Expected EagerTensor, found PyObject of type: ", @@ -237,10 +240,10 @@ Status DoCallPyFunc(PyCall* call, bool* out_log_on_error) { } else if (EagerTensor_CheckExact(result) || result == Py_None) { // result is an `EagerTensor` or `None`. DCHECK(call->eager); - Tensor t; if (result != Py_None) { - ExtractTensorFromEagerTensor(result, &t); - call->out.push_back(t); + const Tensor* t = nullptr; + s = ExtractTensorFromEagerTensor(result, &t); + if (s.ok()) call->out.push_back(*t); } } else if (PyArray_Check(result)) { // `result` is a NumPy array. diff --git a/tensorflow/python/lib/core/py_seq_tensor.cc b/tensorflow/python/lib/core/py_seq_tensor.cc index 317bdc2e14747583f372808f48a5928273f5570a..8247d354db62532c10c5acc9875cc08289cd31bf 100644 --- a/tensorflow/python/lib/core/py_seq_tensor.cc +++ b/tensorflow/python/lib/core/py_seq_tensor.cc @@ -84,6 +84,7 @@ bool IsPyDimension(PyObject* obj) { } Status InferShapeAndType(PyObject* obj, TensorShape* shape, DataType* dtype) { + std::vector refs_to_clean; while (true) { // We test strings first, in case a string is considered a sequence. if (IsPyString(obj)) { @@ -93,6 +94,7 @@ Status InferShapeAndType(PyObject* obj, TensorShape* shape, DataType* dtype) { if (length > 0) { shape->AddDim(length); obj = PySequence_GetItem(obj, 0); + refs_to_clean.push_back(make_safe(obj)); continue; } else if (length == 0) { shape->AddDim(length); @@ -167,14 +169,15 @@ const char ErrorFoundFloat[] = if (shape.dims() > 1) { \ /* Iterate over outer dim, and recursively convert each element. */ \ const int64 s = shape.dim_size(0); \ - if (TF_PREDICT_FALSE(s != PySequence_Length(obj))) { \ + Safe_PyObjectPtr seq = make_safe(PySequence_Fast(obj, "")); \ + if (TF_PREDICT_FALSE(s != PySequence_Fast_GET_SIZE(seq.get()))) { \ return ErrorRectangular; \ } \ TensorShape rest = shape; \ rest.RemoveDim(0); \ for (int64 i = 0; i < s; ++i) { \ - const char* error = \ - FUNCTION##Helper(PySequence_GetItem(obj, i), rest, buf); \ + const char* error = FUNCTION##Helper( \ + PySequence_Fast_GET_ITEM(seq.get(), i), rest, buf); \ if (TF_PREDICT_FALSE(error != nullptr)) return error; \ } \ } else { \ diff --git a/tensorflow/python/lib/core/py_util.cc b/tensorflow/python/lib/core/py_util.cc index 2635694e23c07dd8e75d4bb0cfb9e83a2042d921..00cbf0c532cf80d3bb27afe168ecde963ba3591d 100644 --- a/tensorflow/python/lib/core/py_util.cc +++ b/tensorflow/python/lib/core/py_util.cc @@ -41,6 +41,55 @@ const char* ClassName(PyObject* py) { } // end namespace +// Returns a PyObject containing a string, or null +void TryAppendTraceback(PyObject* ptype, PyObject* pvalue, PyObject* ptraceback, + string* out) { + // The "traceback" module is assumed to be imported already by script_ops.py. + PyObject* tb_module = PyImport_AddModule("traceback"); + + if (!tb_module) { + return; + } + + PyObject* format_exception = + PyObject_GetAttrString(tb_module, "format_exception"); + + if (!format_exception) { + return; + } + + if (!PyCallable_Check(format_exception)) { + Py_DECREF(format_exception); + return; + } + + PyObject* ret_val = PyObject_CallFunctionObjArgs(format_exception, ptype, + pvalue, ptraceback, nullptr); + Py_DECREF(format_exception); + + if (!ret_val) { + return; + } + + if (!PyList_Check(ret_val)) { + Py_DECREF(ret_val); + return; + } + + Py_ssize_t n = PyList_GET_SIZE(ret_val); + for (Py_ssize_t i = 0; i < n; ++i) { + PyObject* v = PyList_GET_ITEM(ret_val, i); +#if PY_MAJOR_VERSION < 3 + strings::StrAppend(out, PyString_AS_STRING(v), "\n"); +#else + strings::StrAppend(out, PyUnicode_AsUTF8(v), "\n"); +#endif + } + + // Iterate through ret_val. + Py_DECREF(ret_val); +} + string PyExceptionFetch() { CHECK(PyErr_Occurred()) << "Must only call PyExceptionFetch after an exception."; @@ -52,14 +101,20 @@ string PyExceptionFetch() { string err = ClassName(ptype); if (pvalue) { PyObject* str = PyObject_Str(pvalue); + if (str) { #if PY_MAJOR_VERSION < 3 - strings::StrAppend(&err, ": ", PyString_AS_STRING(str)); + strings::StrAppend(&err, ": ", PyString_AS_STRING(str), "\n"); #else - strings::StrAppend(&err, ": ", PyUnicode_AsUTF8(str)); + strings::StrAppend(&err, ": ", PyUnicode_AsUTF8(str), "\n"); #endif Py_DECREF(str); + } else { + strings::StrAppend(&err, "(unknown error message)\n"); } + + TryAppendTraceback(ptype, pvalue, ptraceback, &err); + Py_DECREF(pvalue); } Py_DECREF(ptype); diff --git a/tensorflow/python/lib/io/file_io_test.py b/tensorflow/python/lib/io/file_io_test.py index a751607aaa1f47ca7c08674eca2b27ee0cafa3d2..223858edfa84eaa1c7879a9774dcc836de4f4672 100644 --- a/tensorflow/python/lib/io/file_io_test.py +++ b/tensorflow/python/lib/io/file_io_test.py @@ -485,6 +485,11 @@ class FileIoTest(test.TestCase): f.flush() self.assertEqual(content, f.read(len(content) + 1)) + def testUTF8StringPathExists(self): + file_path = os.path.join(self._base_dir, "UTF8测试_file_exist") + file_io.write_string_to_file(file_path, "testing") + v = file_io.file_exists(file_path) + self.assertEqual(v, True) if __name__ == "__main__": test.main() diff --git a/tensorflow/python/lib/io/tf_record.py b/tensorflow/python/lib/io/tf_record.py index 48ea107a146c2714f7b59f53abbcd8b60dbf2fd4..6fcf9c91d831e3a89552b522040e8e8647114a2f 100644 --- a/tensorflow/python/lib/io/tf_record.py +++ b/tensorflow/python/lib/io/tf_record.py @@ -75,14 +75,16 @@ def tf_record_iterator(path, options=None): if reader is None: raise IOError("Could not open %s." % path) - while True: - try: - with errors.raise_exception_on_not_ok_status() as status: - reader.GetNext(status) - except errors.OutOfRangeError: - break - yield reader.record() - reader.Close() + try: + while True: + try: + with errors.raise_exception_on_not_ok_status() as status: + reader.GetNext(status) + except errors.OutOfRangeError: + break + yield reader.record() + finally: + reader.Close() @tf_export("python_io.TFRecordWriter") diff --git a/tensorflow/python/ops/accumulate_n_benchmark.py b/tensorflow/python/ops/accumulate_n_benchmark.py index c58d36f39705ecf0f24214ce4ba4574e70a93e77..a709066cae4da2811b3e98d2e93bf44ec12dcee6 100644 --- a/tensorflow/python/ops/accumulate_n_benchmark.py +++ b/tensorflow/python/ops/accumulate_n_benchmark.py @@ -39,7 +39,7 @@ from tensorflow.python.platform import test class AccumulateNBenchmark(test.Benchmark): def _AccumulateNTemplate(self, inputs, init, shape, validate_shape): - var = gen_state_ops._temporary_variable( + var = gen_state_ops.temporary_variable( shape=shape, dtype=inputs[0].dtype.base_dtype) ref = state_ops.assign(var, init, validate_shape=validate_shape) update_ops = [ @@ -47,8 +47,7 @@ class AccumulateNBenchmark(test.Benchmark): ref, tensor, use_locking=True).op for tensor in inputs ] with ops.control_dependencies(update_ops): - return gen_state_ops._destroy_temporary_variable( - ref, var_name=var.op.name) + return gen_state_ops.destroy_temporary_variable(ref, var_name=var.op.name) def _AccumulateNInitializedWithFirst(self, inputs): return self._AccumulateNTemplate( @@ -60,7 +59,7 @@ class AccumulateNBenchmark(test.Benchmark): def _AccumulateNInitializedWithMerge(self, inputs): return self._AccumulateNTemplate( inputs, - init=array_ops.zeros_like(gen_control_flow_ops._merge(inputs)[0]), + init=array_ops.zeros_like(gen_control_flow_ops.merge(inputs)[0]), shape=tensor_shape.vector(0), validate_shape=False) diff --git a/tensorflow/python/ops/array_grad.py b/tensorflow/python/ops/array_grad.py index 9745d38dc23dba806a2d0dd2ef588a5a950aa05c..3c6a5c9e562ff9765c2ef47555871c94cd6feb1e 100644 --- a/tensorflow/python/ops/array_grad.py +++ b/tensorflow/python/ops/array_grad.py @@ -80,7 +80,7 @@ def _ConcatGradHelper(op, grad, start_value_index, end_value_index, dim_index): def _ExtractInputShapes(inputs): """Extract the shapes of a set of input tensors.""" - if not context.in_graph_mode(): + if context.executing_eagerly(): return array_ops.shape_n(inputs) sizes = [] fully_known = True @@ -106,7 +106,7 @@ def _ConcatGradHelper(op, grad, start_value_index, end_value_index, dim_index): out_grads = [] if isinstance(grad, ops.Tensor): - if context.in_eager_mode(): + if context.executing_eagerly(): # Using mod here for convenience since concat_dim is already verified # in concat implementation to be within the allowed [-rank, rank) range. non_neg_concat_dim = ( @@ -139,7 +139,6 @@ def _ConcatGradHelper(op, grad, start_value_index, end_value_index, dim_index): # on CPUs and a Maxwell TitanX. A speedup was seen in a large majority of # cases when switching implementations at N=16, but it is possible that # there will be a small number of performance regressions. - # pylint: disable=protected-access if len(sizes) > 16: # extract the size of each input along the concat dimension sizes = array_ops.squeeze( @@ -148,10 +147,9 @@ def _ConcatGradHelper(op, grad, start_value_index, end_value_index, dim_index): [1, -1])) out_grads = array_ops.split(grad, sizes, non_neg_concat_dim) else: - offset = gen_array_ops._concat_offset(non_neg_concat_dim, sizes) + offset = gen_array_ops.concat_offset(non_neg_concat_dim, sizes) for (begin, size) in zip(offset, sizes): out_grads.append(array_ops.slice(grad, begin, size)) - # pylint: enable=protected-access elif isinstance(grad, ops.IndexedSlices): # Using mod here for convenience since concat_dim is already verified # in concat implementation to be within the allowed [-rank, rank) range. @@ -430,7 +428,7 @@ def _GatherV2Grad(op, grad): # For axis 0 gathers, build an appropriately shaped IndexedSlices. if axis_static == 0: - if context.in_eager_mode(): + if context.executing_eagerly(): params_tail_shape = params_shape.cpu()[1:] else: params_tail_shape = params_shape[1:] @@ -580,7 +578,7 @@ def _TileGrad(op, grad): axes = math_ops.range(0, array_ops.size(split_shape), 2) input_grad = math_ops.reduce_sum(array_ops.reshape(grad, split_shape), axes) # Fix shape inference - if context.in_graph_mode(): + if not context.executing_eagerly(): input_grad.set_shape(op.inputs[0].get_shape()) return [input_grad, None] @@ -627,9 +625,7 @@ def _ReverseSequenceGrad(op, grad): @ops.RegisterGradient("Reverse") def _ReverseGrad(op, grad): reverse_dims = op.inputs[1] - # pylint: disable=protected-access - return gen_array_ops._reverse(grad, reverse_dims), None - # pylint: enable=protected-access + return gen_array_ops.reverse(grad, reverse_dims), None @ops.RegisterGradient("ReverseV2") @@ -700,17 +696,13 @@ ops.NotDifferentiable("OneHot") @ops.RegisterGradient("MirrorPad") def _MirrorPadGrad(op, grad): mode = op.get_attr("mode") - # pylint: disable=protected-access - return [gen_array_ops._mirror_pad_grad(grad, op.inputs[1], mode=mode), None] - # pylint: enable=protected-access + return [gen_array_ops.mirror_pad_grad(grad, op.inputs[1], mode=mode), None] @ops.RegisterGradient("MirrorPadGrad") def _MirrorPadGradGrad(op, grad): mode = op.get_attr("mode") - # pylint: disable=protected-access - return [gen_array_ops._mirror_pad(grad, op.inputs[1], mode=mode), None] - # pylint: enable=protected-access + return [gen_array_ops.mirror_pad(grad, op.inputs[1], mode=mode), None] @ops.RegisterGradient("QuantizeAndDequantize") diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py index 14824962eabaecdc2cca348b27ce7f9f8868af82..ec7c14f7d8697e61d2acb25a82c0ac9b2fcf28f4 100644 --- a/tensorflow/python/ops/array_ops.py +++ b/tensorflow/python/ops/array_ops.py @@ -128,15 +128,18 @@ def identity(input, name=None): # pylint: disable=redefined-builtin Returns: A `Tensor`. Has the same type as `input`. """ - if context.in_graph_mode(): - return gen_array_ops.identity(input, name=name) - else: + if context.executing_eagerly(): input = ops.convert_to_tensor(input) in_device = input.device # TODO(ashankar): Does 'identity' need to invoke execution callbacks? - if context.context().device_name != in_device: + context_device = context.context().device_name + if not context_device: + context_device = "/job:localhost/replica:0/task:0/device:CPU:0" + if context_device != in_device: return input._copy() # pylint: disable=protected-access return input + else: + return gen_array_ops.identity(input, name=name) # pylint: disable=redefined-builtin,protected-access @@ -195,7 +198,7 @@ def expand_dims(input, axis=None, name=None, dim=None): if axis is not None: raise ValueError("can't specify both 'dim' and 'axis'") axis = dim - return gen_array_ops._expand_dims(input, axis, name) + return gen_array_ops.expand_dims(input, axis, name) # pylint: enable=redefined-builtin,protected-access @@ -208,28 +211,25 @@ def expand_dims(input, axis=None, name=None, dim=None): "This op will be removed after the deprecation date. " "Please switch to tf.setdiff1d().") def listdiff(x, y, out_idx=None, name=None): - return gen_array_ops._list_diff(x, y, out_idx, name) + return gen_array_ops.list_diff(x, y, out_idx, name) -listdiff.__doc__ = gen_array_ops._list_diff.__doc__ + "\n" + listdiff.__doc__ +listdiff.__doc__ = gen_array_ops.list_diff.__doc__ + "\n" + listdiff.__doc__ # pylint: enable=protected-access -# pylint: disable=undefined-variable,protected-access +# pylint: disable=undefined-variable @tf_export("setdiff1d") def setdiff1d(x, y, index_dtype=dtypes.int32, name=None): - return gen_array_ops._list_diff(x, y, index_dtype, name) + return gen_array_ops.list_diff(x, y, index_dtype, name) -setdiff1d.__doc__ = gen_array_ops._list_diff.__doc__ - -# pylint: enable=protected-access +setdiff1d.__doc__ = gen_array_ops.list_diff.__doc__ @tf_export("broadcast_dynamic_shape") def broadcast_dynamic_shape(shape_x, shape_y): - # pylint: disable=protected-access """Returns the broadcasted dynamic shape between `shape_x` and `shape_y`. Args: @@ -239,8 +239,7 @@ def broadcast_dynamic_shape(shape_x, shape_y): Returns: A rank 1 integer `Tensor` representing the broadcasted shape. """ - return gen_array_ops._broadcast_args(shape_x, shape_y) - # pylint: enable=protected-access + return gen_array_ops.broadcast_args(shape_x, shape_y) @tf_export("broadcast_static_shape") @@ -306,7 +305,7 @@ def shape_internal(input, name=None, optimize=True, out_type=dtypes.int32): sparse_tensor.SparseTensorValue)): return gen_math_ops.cast(input.dense_shape, out_type) else: - if context.in_graph_mode(): + if not context.executing_eagerly(): input_tensor = ops.convert_to_tensor(input) input_shape = input_tensor.get_shape() if optimize and input_shape.is_fully_defined(): @@ -331,7 +330,7 @@ def shape_n(input, out_type=dtypes.int32, name=None): """ output = gen_array_ops.shape_n(input, out_type=out_type, name=name) - if context.in_graph_mode(): + if not context.executing_eagerly(): for i, input_tensor in enumerate(input): input_tensor = ops.convert_to_tensor(input_tensor) input_shape = input_tensor.get_shape() @@ -386,23 +385,22 @@ def size_internal(input, name=None, optimize=True, out_type=dtypes.int32): Returns: A `Tensor` of type `out_type`. Defaults to `tf.int32`. """ - if context.in_eager_mode() and not isinstance( - input, (sparse_tensor.SparseTensor, - sparse_tensor.SparseTensorValue)): - size_ = 1 - for dim in ops.convert_to_tensor(input)._shape_tuple(): # pylint: disable=protected-access - size_ *= dim - return size_ + if context.executing_eagerly() and not isinstance( + input, (sparse_tensor.SparseTensor, sparse_tensor.SparseTensorValue)): + return np.prod(ops.convert_to_tensor(input)._shape_tuple()) # pylint: disable=protected-access with ops.name_scope(name, "Size", [input]) as name: if isinstance(input, (sparse_tensor.SparseTensor, sparse_tensor.SparseTensorValue)): - return gen_math_ops._prod( + return gen_math_ops.prod( gen_math_ops.cast(input.dense_shape, out_type), 0, name=name) else: input_tensor = ops.convert_to_tensor(input) input_shape = input_tensor.get_shape() - if optimize and input_shape.is_fully_defined(): - return constant(input_shape.num_elements(), out_type, name=name) + if optimize: + if input_shape.is_fully_defined(): + return constant(input_shape.num_elements(), out_type, name=name) + if input_shape.dims and any(dim == 0 for dim in input_shape.dims): + return constant(0, out_type, name=name) return gen_array_ops.size(input, name=name, out_type=out_type) @@ -784,7 +782,7 @@ def strided_slice(input_, new_axis_mask=new_axis_mask, shrink_axis_mask=shrink_axis_mask) - if context.in_graph_mode(): + if not context.executing_eagerly(): # TODO(apassos) In eager mode assignment will be done by overriding # __setitem__ instead. op.assign = assign @@ -795,8 +793,8 @@ def _SliceHelperVar(var, slice_spec): """Creates a slice helper object given a variable. This allows creating a sub-tensor from part of the current contents - of a variable. See ${tf.Tensor$`Tensor.__getitem__`} - for detailed examples of slicing. + of a variable. See @{tf.Tensor.__getitem__} for detailed examples + of slicing. This function in addition also allows assignment to a sliced range. This is similar to `__setitem__` functionality in Python. However, @@ -886,7 +884,7 @@ def parallel_stack(values, name="parallel_stack"): output_shape = tensor_shape.TensorShape([len(values)]) output_shape = output_shape.concatenate(value_shape) # expand_dims converts concat to stack. - return gen_array_ops._parallel_concat( + return gen_array_ops.parallel_concat( [expand_dims(value, 0) for value in values], shape=output_shape) @@ -944,7 +942,7 @@ def stack(values, axis=0, name="stack"): raise ValueError("axis = %d not in [%d, %d)" % (axis, -expanded_num_dims, expanded_num_dims)) - return gen_array_ops._pack(values, axis=axis, name=name) + return gen_array_ops.pack(values, axis=axis, name=name) # pylint: disable=invalid-name @@ -988,7 +986,7 @@ def _autopacking_helper(list_or_tuple, dtype, name): # convertible-to-tensor types, such as numpy arrays. elems_as_tensors.append( constant_op.constant(elem, dtype=dtype, name=str(i))) - return gen_array_ops._pack(elems_as_tensors, name=scope) + return gen_array_ops.pack(elems_as_tensors, name=scope) else: return converted_elems @@ -1083,7 +1081,7 @@ def unstack(value, num=None, axis=0, name="unstack"): num = value_shape[axis].value if num is None: raise ValueError("Cannot infer num from shape %s" % value_shape) - return gen_array_ops._unpack(value, num=num, axis=axis, name=name) + return gen_array_ops.unpack(value, num=num, axis=axis, name=name) @tf_export("concat") @@ -1180,7 +1178,7 @@ def concat(values, axis, name="concat"): dtype=dtypes.int32).get_shape().assert_is_compatible_with( tensor_shape.scalar()) return identity(values[0], name=scope) - return gen_array_ops._concat_v2(values=values, axis=axis, name=name) + return gen_array_ops.concat_v2(values=values, axis=axis, name=name) @tf_export("boolean_mask") @@ -1248,8 +1246,7 @@ def boolean_mask(tensor, mask, name="boolean_mask", axis=None): axis = 0 if axis is None else axis shape_tensor[axis:axis + ndims_mask].assert_is_compatible_with(shape_mask) - leading_size = gen_math_ops._prod( - shape(tensor)[axis:axis + ndims_mask], [0]) + leading_size = gen_math_ops.prod(shape(tensor)[axis:axis + ndims_mask], [0]) tensor = reshape(tensor, concat([ shape(tensor)[:axis], [leading_size], @@ -1313,10 +1310,10 @@ def unique(x, out_idx=dtypes.int32, name=None): # period (3 weeks) pass. # TODO(yongtang): The documentation should also # be updated when switch to v2. - return gen_array_ops._unique(x, out_idx, name) + return gen_array_ops.unique(x, out_idx, name) -unique.__doc__ = gen_array_ops._unique.__doc__ +unique.__doc__ = gen_array_ops.unique.__doc__ @tf_export("unique_with_counts") @@ -1325,10 +1322,10 @@ def unique_with_counts(x, out_idx=dtypes.int32, name=None): # period (3 weeks) pass. # TODO(yongtang): The documentation should also # be updated when switch to v2. - return gen_array_ops._unique_with_counts(x, out_idx, name) + return gen_array_ops.unique_with_counts(x, out_idx, name) -unique_with_counts.__doc__ = gen_array_ops._unique_with_counts.__doc__ +unique_with_counts.__doc__ = gen_array_ops.unique_with_counts.__doc__ @tf_export("split") @@ -1382,20 +1379,18 @@ def split(value, num_or_size_splits, axis=0, num=None, name="split"): """ size_splits = ops.convert_to_tensor(num_or_size_splits) if size_splits._rank() == 0 and size_splits.dtype.is_integer: - return gen_array_ops._split( + return gen_array_ops.split( axis=axis, num_split=num_or_size_splits, value=value, name=name) if num is None: - num = size_splits._shape_tuple()[0] + size_splits_shape = size_splits._shape_tuple() + if size_splits_shape: + num = size_splits_shape[0] if num is None: raise ValueError("Cannot infer num from shape %s" % num_or_size_splits) - return gen_array_ops._split_v( - value=value, - size_splits=size_splits, - axis=axis, - num_split=num, - name=name) + return gen_array_ops.split_v( + value=value, size_splits=size_splits, axis=axis, num_split=num, name=name) @tf_export("transpose") @@ -1465,7 +1460,7 @@ def transpose(a, perm=None, name="transpose", conjugate=False): """ with ops.name_scope(name, "transpose", [a]) as name: transpose_fn = ( - gen_array_ops._conjugate_transpose + gen_array_ops.conjugate_transpose if (conjugate and a.dtype.is_complex) else gen_array_ops.transpose) if perm is None: rank = gen_array_ops.rank(a) @@ -1473,7 +1468,7 @@ def transpose(a, perm=None, name="transpose", conjugate=False): ret = transpose_fn(a, perm, name=name) # NOTE(mrry): Setting the shape explicitly because # reverse is not handled by the shape function. - if context.in_graph_mode(): + if not context.executing_eagerly(): input_shape = ret.op.inputs[0].get_shape().dims if input_shape is not None: ret.set_shape(input_shape[::-1]) @@ -1638,12 +1633,12 @@ def zeros_like(tensor, dtype=None, name=None, optimize=True): with ops.name_scope(name, "zeros_like", [tensor]) as name: tensor = ops.convert_to_tensor(tensor, name="tensor") - if context.in_eager_mode(): + if context.executing_eagerly(): if dtype is not None and dtype != tensor.dtype: return zeros( shape_internal(tensor, optimize=optimize), dtype=dtype, name=name) with ops.device(tensor.device): - return gen_array_ops._zeros_like(tensor, name=name) + return gen_array_ops.zeros_like(tensor, name=name) # For now, variant types must be created via zeros_like; as we need to # pass the input variant object to the proper zeros callback. @@ -1658,7 +1653,7 @@ def zeros_like(tensor, dtype=None, name=None, optimize=True): return zeros( shape_internal(tensor, optimize=optimize), dtype=dtype, name=name) else: - return gen_array_ops._zeros_like(tensor, name=name) + return gen_array_ops.zeros_like(tensor, name=name) @tf_export("ones_like") @@ -1694,7 +1689,7 @@ def ones_like(tensor, dtype=None, name=None, optimize=True): if dtype is None: dtype = tensor.dtype ret = ones(ones_shape, dtype=dtype, name=name) - if context.in_graph_mode(): + if not context.executing_eagerly(): ret.set_shape(tensor.get_shape()) return ret @@ -1775,11 +1770,11 @@ def placeholder(dtype, shape=None, name=None): Raises: RuntimeError: if eager execution is enabled """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError("tf.placeholder() is not compatible with " "eager execution.") - return gen_array_ops._placeholder(dtype=dtype, shape=shape, name=name) + return gen_array_ops.placeholder(dtype=dtype, shape=shape, name=name) # pylint: disable=redefined-outer-name @@ -1838,7 +1833,7 @@ def sparse_placeholder(dtype, shape=None, name=None): Raises: RuntimeError: if eager execution is enabled """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError("tf.placeholder() is not compatible with " "eager execution.") @@ -1923,21 +1918,21 @@ def pad(tensor, paddings, mode="CONSTANT", name=None, constant_values=0): # pyl # TODO(rjryan): Once the forward compatibility period (3 weeks) have passed # remove the "Pad" fallback here. if constant_values != 0: - result = gen_array_ops._pad_v2( + result = gen_array_ops.pad_v2( tensor, paddings, constant_values, name=name) else: - result = gen_array_ops._pad(tensor, paddings, name=name) + result = gen_array_ops.pad(tensor, paddings, name=name) elif mode == "REFLECT": - result = gen_array_ops._mirror_pad( + result = gen_array_ops.mirror_pad( tensor, paddings, mode="REFLECT", name=name) elif mode == "SYMMETRIC": - result = gen_array_ops._mirror_pad( + result = gen_array_ops.mirror_pad( tensor, paddings, mode="SYMMETRIC", name=name) else: raise ValueError("Unknown padding mode: %s" % mode) # Restore shape information where possible. - if context.in_graph_mode(): + if not context.executing_eagerly(): paddings_constant = tensor_util.constant_value( result.op.inputs[1], partial=True) input_shape = result.op.inputs[0].shape @@ -2161,7 +2156,7 @@ def edit_distance(hypothesis, truth, normalize=True, name="edit_distance"): sparse_tensor.SparseTensorValue)): raise TypeError("Truth must be a SparseTensor.") - return gen_array_ops._edit_distance( + return gen_array_ops.edit_distance( hypothesis.indices, hypothesis.values, hypothesis.dense_shape, @@ -2298,7 +2293,7 @@ def space_to_batch(input, paddings, block_size, name=None): # pylint: disable=r return result -space_to_batch.__doc__ = gen_array_ops._space_to_batch.__doc__ +space_to_batch.__doc__ = gen_array_ops.space_to_batch.__doc__ @tf_export("space_to_depth") @@ -2328,7 +2323,7 @@ def batch_to_space(input, crops, block_size, name=None): # pylint: disable=rede return result -batch_to_space.__doc__ = gen_array_ops._batch_to_space.__doc__ +batch_to_space.__doc__ = gen_array_ops.batch_to_space.__doc__ @tf_export("one_hot") @@ -2472,8 +2467,8 @@ def one_hot(indices, raise TypeError("dtype {0} of on_value does not match " "dtype {1} of off_value".format(on_dtype, off_dtype)) - return gen_array_ops._one_hot(indices, depth, on_value, off_value, axis, - name) + return gen_array_ops.one_hot(indices, depth, on_value, off_value, axis, + name) def _all_dimensions(x): @@ -2601,7 +2596,7 @@ def squeeze(input, axis=None, name=None, squeeze_dims=None): axis = squeeze_dims if np.isscalar(axis): axis = [axis] - return gen_array_ops._squeeze(input, axis, name) + return gen_array_ops.squeeze(input, axis, name) @tf_export("where") @@ -2652,7 +2647,7 @@ def where(condition, x=None, y=None, name=None): condition, preferred_dtype=dtypes.bool, name="condition") return gen_array_ops.where(condition=condition, name=name) elif x is not None and y is not None: - return gen_math_ops._select(condition=condition, x=x, y=y, name=name) + return gen_math_ops.select(condition=condition, x=x, y=y, name=name) else: raise ValueError("x and y must both be non-None or both be None.") diff --git a/tensorflow/python/ops/batch_norm_benchmark.py b/tensorflow/python/ops/batch_norm_benchmark.py index c2ee2b383231333239c6e2d4e874a0ad1cdf493e..5d68b47aeaef3a90973387ecd5b265eef1e96a5f 100644 --- a/tensorflow/python/ops/batch_norm_benchmark.py +++ b/tensorflow/python/ops/batch_norm_benchmark.py @@ -41,9 +41,8 @@ def batch_norm_op(tensor, mean, variance, beta, gamma, scale): # _batch_norm_with_global_normalization is deprecated in v9 ops.get_default_graph().graph_def_versions.producer = 8 # pylint: disable=protected-access - return gen_nn_ops._batch_norm_with_global_normalization(tensor, mean, - variance, beta, gamma, - 0.001, scale) + return gen_nn_ops._batch_norm_with_global_normalization( + tensor, mean, variance, beta, gamma, 0.001, scale) # pylint: enable=protected-access diff --git a/tensorflow/python/ops/candidate_sampling_ops.py b/tensorflow/python/ops/candidate_sampling_ops.py index 220ef1754d2e1a2d54a8962148b47806df48e98f..9ea1ea9c92c9b016a3f9126c89ee4dc1e73c9f27 100644 --- a/tensorflow/python/ops/candidate_sampling_ops.py +++ b/tensorflow/python/ops/candidate_sampling_ops.py @@ -77,7 +77,7 @@ def uniform_candidate_sampler(true_classes, num_true, num_sampled, unique, of each of `sampled_candidates`. """ seed1, seed2 = random_seed.get_seed(seed) - return gen_candidate_sampling_ops._uniform_candidate_sampler( + return gen_candidate_sampling_ops.uniform_candidate_sampler( true_classes, num_true, num_sampled, unique, range_max, seed=seed1, seed2=seed2, name=name) @@ -136,7 +136,7 @@ def log_uniform_candidate_sampler(true_classes, num_true, num_sampled, unique, of each of `sampled_candidates`. """ seed1, seed2 = random_seed.get_seed(seed) - return gen_candidate_sampling_ops._log_uniform_candidate_sampler( + return gen_candidate_sampling_ops.log_uniform_candidate_sampler( true_classes, num_true, num_sampled, unique, range_max, seed=seed1, seed2=seed2, name=name) @@ -193,7 +193,7 @@ def learned_unigram_candidate_sampler(true_classes, num_true, num_sampled, """ seed1, seed2 = random_seed.get_seed(seed) - return gen_candidate_sampling_ops._learned_unigram_candidate_sampler( + return gen_candidate_sampling_ops.learned_unigram_candidate_sampler( true_classes, num_true, num_sampled, unique, range_max, seed=seed1, seed2=seed2, name=name) @@ -283,7 +283,7 @@ def fixed_unigram_candidate_sampler(true_classes, """ seed1, seed2 = random_seed.get_seed(seed) - return gen_candidate_sampling_ops._fixed_unigram_candidate_sampler( + return gen_candidate_sampling_ops.fixed_unigram_candidate_sampler( true_classes, num_true, num_sampled, unique, range_max, vocab_file=vocab_file, distortion=distortion, num_reserved_ids=num_reserved_ids, num_shards=num_shards, shard=shard, @@ -321,7 +321,7 @@ def all_candidate_sampler(true_classes, num_true, num_sampled, unique, of each of `sampled_candidates`. All returned values are 1.0. """ seed1, seed2 = random_seed.get_seed(seed) - return gen_candidate_sampling_ops._all_candidate_sampler( + return gen_candidate_sampling_ops.all_candidate_sampler( true_classes, num_true, num_sampled, unique, seed=seed1, seed2=seed2, name=name) @@ -370,6 +370,6 @@ def compute_accidental_hits(true_classes, sampled_candidates, num_true, """ seed1, seed2 = random_seed.get_seed(seed) - return gen_candidate_sampling_ops._compute_accidental_hits( + return gen_candidate_sampling_ops.compute_accidental_hits( true_classes, sampled_candidates, num_true, seed=seed1, seed2=seed2, name=name) diff --git a/tensorflow/python/ops/check_ops.py b/tensorflow/python/ops/check_ops.py index 64567ac54ae43acf6f8b674c46525db7a6c4fab7..d6d75e4ef9874e2ed4706d48b7bdeed62baef414 100644 --- a/tensorflow/python/ops/check_ops.py +++ b/tensorflow/python/ops/check_ops.py @@ -169,7 +169,7 @@ def assert_negative(x, data=None, summarize=None, message=None, name=None): with ops.name_scope(name, 'assert_negative', [x, data]): x = ops.convert_to_tensor(x, name='x') if data is None: - if context.in_eager_mode(): + if context.executing_eagerly(): name = _shape_and_dtype_str(x) else: name = x.name @@ -210,7 +210,7 @@ def assert_positive(x, data=None, summarize=None, message=None, name=None): with ops.name_scope(name, 'assert_positive', [x, data]): x = ops.convert_to_tensor(x, name='x') if data is None: - if context.in_eager_mode(): + if context.executing_eagerly(): name = _shape_and_dtype_str(x) else: name = x.name @@ -251,7 +251,7 @@ def assert_non_negative(x, data=None, summarize=None, message=None, name=None): with ops.name_scope(name, 'assert_non_negative', [x, data]): x = ops.convert_to_tensor(x, name='x') if data is None: - if context.in_eager_mode(): + if context.executing_eagerly(): name = _shape_and_dtype_str(x) else: name = x.name @@ -293,7 +293,7 @@ def assert_non_positive(x, data=None, summarize=None, message=None, name=None): with ops.name_scope(name, 'assert_non_positive', [x, data]): x = ops.convert_to_tensor(x, name='x') if data is None: - if context.in_eager_mode(): + if context.executing_eagerly(): name = _shape_and_dtype_str(x) else: name = x.name @@ -343,7 +343,7 @@ def assert_equal(x, y, data=None, summarize=None, message=None, name=None): x = ops.convert_to_tensor(x, name='x') y = ops.convert_to_tensor(y, name='y') - if context.in_eager_mode(): + if context.executing_eagerly(): eq = math_ops.equal(x, y) condition = math_ops.reduce_all(eq) if not condition: @@ -435,7 +435,7 @@ def assert_none_equal( with ops.name_scope(name, 'assert_none_equal', [x, y, data]): x = ops.convert_to_tensor(x, name='x') y = ops.convert_to_tensor(y, name='y') - if context.in_eager_mode(): + if context.executing_eagerly(): x_name = _shape_and_dtype_str(x) y_name = _shape_and_dtype_str(y) else: @@ -512,7 +512,7 @@ def assert_near( rtol = ops.convert_to_tensor(rtol, name='rtol', dtype=x.dtype) atol = ops.convert_to_tensor(atol, name='atol', dtype=x.dtype) - if context.in_eager_mode(): + if context.executing_eagerly(): x_name = _shape_and_dtype_str(x) y_name = _shape_and_dtype_str(y) else: @@ -562,7 +562,7 @@ def assert_less(x, y, data=None, summarize=None, message=None, name=None): with ops.name_scope(name, 'assert_less', [x, y, data]): x = ops.convert_to_tensor(x, name='x') y = ops.convert_to_tensor(y, name='y') - if context.in_eager_mode(): + if context.executing_eagerly(): x_name = _shape_and_dtype_str(x) y_name = _shape_and_dtype_str(y) else: @@ -610,7 +610,7 @@ def assert_less_equal(x, y, data=None, summarize=None, message=None, name=None): with ops.name_scope(name, 'assert_less_equal', [x, y, data]): x = ops.convert_to_tensor(x, name='x') y = ops.convert_to_tensor(y, name='y') - if context.in_eager_mode(): + if context.executing_eagerly(): x_name = _shape_and_dtype_str(x) y_name = _shape_and_dtype_str(y) else: @@ -658,7 +658,7 @@ def assert_greater(x, y, data=None, summarize=None, message=None, name=None): with ops.name_scope(name, 'assert_greater', [x, y, data]): x = ops.convert_to_tensor(x, name='x') y = ops.convert_to_tensor(y, name='y') - if context.in_eager_mode(): + if context.executing_eagerly(): x_name = _shape_and_dtype_str(x) y_name = _shape_and_dtype_str(y) else: @@ -708,7 +708,7 @@ def assert_greater_equal(x, y, data=None, summarize=None, message=None, with ops.name_scope(name, 'assert_greater_equal', [x, y, data]): x = ops.convert_to_tensor(x, name='x') y = ops.convert_to_tensor(y, name='y') - if context.in_eager_mode(): + if context.executing_eagerly(): x_name = _shape_and_dtype_str(x) y_name = _shape_and_dtype_str(y) else: @@ -808,7 +808,7 @@ def assert_rank(x, rank, data=None, summarize=None, message=None, name=None): static_condition = lambda actual_rank, given_rank: actual_rank == given_rank dynamic_condition = math_ops.equal - if context.in_eager_mode(): + if context.executing_eagerly(): name = '' else: name = x.name @@ -873,7 +873,7 @@ def assert_rank_at_least( static_condition = lambda actual_rank, given_rank: actual_rank >= given_rank dynamic_condition = math_ops.greater_equal - if context.in_eager_mode(): + if context.executing_eagerly(): name = '' else: name = x.name @@ -1001,7 +1001,7 @@ def assert_rank_in( ranks = tuple([ops.convert_to_tensor(rank, name='rank') for rank in ranks]) message = message or '' - if context.in_eager_mode(): + if context.executing_eagerly(): name = '' else: name = x.name @@ -1054,7 +1054,7 @@ def assert_integer(x, message=None, name=None): with ops.name_scope(name, 'assert_integer', [x]): x = ops.convert_to_tensor(x, name='x') if not x.dtype.is_integer: - if context.in_eager_mode(): + if context.executing_eagerly(): name = 'tensor' else: name = x.name @@ -1087,12 +1087,11 @@ def assert_type(tensor, tf_type, message=None, name=None): with ops.name_scope(name, 'assert_type', [tensor]): tensor = ops.convert_to_tensor(tensor, name='tensor') if tensor.dtype != tf_type: - if context.in_graph_mode(): - raise TypeError( - '%s %s must be of type %s' % (message, tensor.name, tf_type)) + if context.executing_eagerly(): + raise TypeError('%s tensor must be of type %s' % (message, tf_type)) else: - raise TypeError( - '%s tensor must be of type %s' % (message, tf_type)) + raise TypeError('%s %s must be of type %s' % (message, tensor.name, + tf_type)) return control_flow_ops.no_op('statically_determined_correct_type') @@ -1240,7 +1239,7 @@ def assert_scalar(tensor, name=None): tensor = ops.convert_to_tensor(tensor, name=name_scope) shape = tensor.get_shape() if shape.ndims != 0: - if context.in_eager_mode(): + if context.executing_eagerly(): raise ValueError('Expected scalar shape, saw shape: %s.' % (shape,)) else: diff --git a/tensorflow/python/ops/control_flow_grad.py b/tensorflow/python/ops/control_flow_grad.py index 97b57177b29986a006df992f4c0c2b79e11467aa..21354b5ae8ff1724bbb2539aff370b3df6da2598 100644 --- a/tensorflow/python/ops/control_flow_grad.py +++ b/tensorflow/python/ops/control_flow_grad.py @@ -28,7 +28,6 @@ from tensorflow.python.ops import math_ops # go/tf-wildcard-import # pylint: disable=wildcard-import,undefined-variable from tensorflow.python.ops.control_flow_ops import * -from tensorflow.python.ops.gen_control_flow_ops import * # pylint: enable=wildcard-import diff --git a/tensorflow/python/ops/control_flow_ops.py b/tensorflow/python/ops/control_flow_ops.py index 8218e60b53450a500df71719e533fb0c1cbeb5b5..ff4f452bed673372cf5e63597ecb2378efea9f36 100644 --- a/tensorflow/python/ops/control_flow_ops.py +++ b/tensorflow/python/ops/control_flow_ops.py @@ -23,7 +23,6 @@ See the @{$python/control_flow_ops} guide. @@no_op @@count_up_to @@cond -@@smart_cond @@case @@while_loop @@logical_and @@ -153,7 +152,7 @@ def Assert(condition, data, summarize=None, name=None): @compatibility{eager} `tf.errors.InvalidArgumentError` if `condition` is not true """ - if context.in_eager_mode(): + if context.executing_eagerly(): if not condition: xs = ops.convert_n_to_tensor(data) data_str = [_summarize_eager(x, summarize) for x in xs] @@ -179,6 +178,8 @@ def Assert(condition, data, summarize=None, name=None): condition, data, summarize, name="Assert") guarded_assert = cond(condition, no_op, true_assert, name="AssertGuard") + if context.executing_eagerly(): + return return guarded_assert.op @@ -328,7 +329,7 @@ def exit(data, name=None): # pylint: disable=redefined-builtin data = ops.internal_convert_to_tensor_or_indexed_slices(data, as_ref=True) if isinstance(data, ops.Tensor): if data.dtype._is_ref_dtype: # pylint: disable=protected-access - return gen_control_flow_ops._ref_exit(data, name) + return gen_control_flow_ops.ref_exit(data, name) else: return gen_control_flow_ops._exit(data, name) else: @@ -370,17 +371,17 @@ def switch(data, pred, dtype=None, name=None): data, dtype=dtype, name="data", as_ref=True) pred = ops.convert_to_tensor(pred, name="pred") if isinstance(data, ops.Tensor): - return gen_control_flow_ops._switch(data, pred, name=name) + return gen_control_flow_ops.switch(data, pred, name=name) else: if not isinstance(data, (ops.IndexedSlices, sparse_tensor.SparseTensor)): raise TypeError("Type %s not supported" % type(data)) val, ind = data.values, data.indices - val_f, val_t = gen_control_flow_ops._switch(val, pred, name=name) - ind_f, ind_t = gen_control_flow_ops._switch(ind, pred, name="indices") + val_f, val_t = gen_control_flow_ops.switch(val, pred, name=name) + ind_f, ind_t = gen_control_flow_ops.switch(ind, pred, name="indices") if isinstance(data, ops.IndexedSlices): dense_shape = data.dense_shape if dense_shape is not None: - dense_shape_f, dense_shape_t = gen_control_flow_ops._switch( + dense_shape_f, dense_shape_t = gen_control_flow_ops.switch( dense_shape, pred, name="dense_shape") else: dense_shape_f, dense_shape_t = None, None @@ -388,7 +389,7 @@ def switch(data, pred, dtype=None, name=None): ops.IndexedSlices(val_t, ind_t, dense_shape_t)) else: dense_shape = data.dense_shape - dense_shape_f, dense_shape_t = gen_control_flow_ops._switch( + dense_shape_f, dense_shape_t = gen_control_flow_ops.switch( data.dense_shape, pred, name="dense_shape") return (sparse_tensor.SparseTensor(ind_f, val_f, dense_shape_f), sparse_tensor.SparseTensor(ind_t, val_t, dense_shape_t)) @@ -472,15 +473,15 @@ def merge(inputs, name=None): ] if all([isinstance(v, ops.Tensor) for v in inputs]): if all([v.dtype._is_ref_dtype for v in inputs]): # pylint: disable=protected-access - return gen_control_flow_ops._ref_merge(inputs, name) + return gen_control_flow_ops.ref_merge(inputs, name) else: - return gen_control_flow_ops._merge(inputs, name) + return gen_control_flow_ops.merge(inputs, name) elif all([isinstance(v, sparse_tensor.SparseTensor) for v in inputs]): # Only handle the case when all inputs are SparseTensor. values, _ = merge([inp.values for inp in inputs], name=name) - indices, chosen_index = gen_control_flow_ops._merge( + indices, chosen_index = gen_control_flow_ops.merge( [inp.indices for inp in inputs], name="indices") - dense_shape, _ = gen_control_flow_ops._merge( + dense_shape, _ = gen_control_flow_ops.merge( [inp.dense_shape for inp in inputs], name="dense_shape") return (sparse_tensor.SparseTensor(indices, values, dense_shape), chosen_index) @@ -488,13 +489,13 @@ def merge(inputs, name=None): # For now convert all the inputs as IndexedSlices. inputs = math_ops._as_indexed_slices_list(inputs, optimize=False) values, _ = merge([inp.values for inp in inputs], name=name) - indices, chosen_index = gen_control_flow_ops._merge( + indices, chosen_index = gen_control_flow_ops.merge( [inp.indices for inp in inputs], name="indices") if any(inp.dense_shape is not None for inp in inputs): if any(inp.dense_shape is None for inp in inputs): raise ValueError("Either all merged IndexedSlices must have a " "dense_shape, or none must have a dense_shape.") - dense_shape, _ = gen_control_flow_ops._merge( + dense_shape, _ = gen_control_flow_ops.merge( [inp.dense_shape for inp in inputs], name="dense_shape") else: dense_shape = None @@ -1014,10 +1015,8 @@ class GradLoopState(object): else: max_size = GetMaxSizeFromNestedMaximumIterations( value, self.forward_context) - # pylint: disable=protected-access - acc = gen_data_flow_ops._stack_v2( + acc = gen_data_flow_ops.stack_v2( max_size=max_size, elem_type=value.dtype.base_dtype, name="f_acc") - # pylint: enable=protected-access if curr_ctxt: curr_ctxt.Exit() @@ -1030,10 +1029,8 @@ class GradLoopState(object): if value_ctxt == self.forward_context: # value is not nested in the forward context. self.forward_context.Enter() - # pylint: disable=protected-access - push = gen_data_flow_ops._stack_push_v2( + push = gen_data_flow_ops.stack_push_v2( enter_acc, value, swap_memory=swap_enabled) - # pylint: enable=protected-access self.forward_context.Exit() # Protect stack push and order it before forward_index. self.forward_index.op._add_control_input(push.op) @@ -1045,18 +1042,14 @@ class GradLoopState(object): # The special case for creating a zero tensor for a dead # branch of a switch. See ControlFlowState.ZerosLike(). value_ctxt.outer_context.Enter() - # pylint: disable=protected-access - push = gen_data_flow_ops._stack_push_v2( + push = gen_data_flow_ops.stack_push_v2( enter_acc, value, swap_memory=swap_enabled) - # pylint: enable=protected-access value_ctxt.outer_context.Exit() push.op._set_control_flow_context(value_ctxt) else: value_ctxt.Enter() - # pylint: disable=protected-access - push = gen_data_flow_ops._stack_push_v2( + push = gen_data_flow_ops.stack_push_v2( enter_acc, value, swap_memory=swap_enabled) - # pylint: enable=protected-access value_ctxt.Exit() # Protect stack push and order it before forward_sync. self.forward_sync._add_control_input(push.op) @@ -1103,10 +1096,8 @@ class GradLoopState(object): pred = cond_ctxt.pred branch = (1 - cond_ctxt.branch) if dead_branch else cond_ctxt.branch history_value = _SwitchRefOrTensor(history_value, pred)[branch] - # pylint: disable=protected-access - pop = gen_data_flow_ops._stack_pop_v2(history_value, - value.dtype.base_dtype) - # pylint: enable=protected-access + pop = gen_data_flow_ops.stack_pop_v2(history_value, + value.dtype.base_dtype) pop.set_shape(value.get_shape()) self.grad_context.Exit() parallel_iterations = self.grad_context.parallel_iterations @@ -1508,9 +1499,11 @@ class ControlFlowContext(object): if values_def: self._init_values_from_proto(values_def, import_scope=import_scope) else: - # Values that have been already seen in this context. + # The names of tensors that have been already seen in this context. self._values = set() - # Values referenced by but external to this context. + # The keys are the names of tensors referenced by but external to this + # context. Each value is the Tensor that should be used by this context to + # access the key value (e.g. a switch output guarding a cond input value). self._external_values = {} def _init_values_from_proto(self, values_def, import_scope=None): @@ -1697,9 +1690,12 @@ class CondContext(ControlFlowContext): self._pivot = pivot # The predicate tensor in this branch self._branch = branch # 0 or 1 representing this branch - # Values considered to have been already seen in this context. + # Values considered to have been already seen in this context. They are + # not included in this context. self._values.add(pred.name) + self._external_values[pred.name] = pred self._values.add(pivot.name) + self._external_values[pivot.name] = pivot def _init_from_proto(self, context_def, import_scope=None): """Creates a new `CondContext` from protocol buffer. @@ -1717,8 +1713,8 @@ class CondContext(ControlFlowContext): self._pivot = g.as_graph_element( ops.prepend_name_scope(context_def.pivot_name, import_scope)) self._branch = context_def.branch - super(CondContext, self).__init__( - values_def=context_def.values_def, import_scope=import_scope) + super(CondContext, self).__init__(values_def=context_def.values_def, + import_scope=import_scope) @property def pred(self): @@ -1766,13 +1762,9 @@ class CondContext(ControlFlowContext): context_def.branch = self._branch context_def.values_def.MergeFrom(super(CondContext, self)._to_values_def( export_scope)) - # TODO(b/72868227): enable this once the corresponding control_flow.proto - # changes have been checked in (they aren't checked in and this is - # disabled for now to ensure forwards compatibility). - if False: # pylint: disable=using-constant-test - for nested in self._nested_contexts: - nested_def = context_def.nested_contexts.add() - nested.to_control_flow_context_def(nested_def) + for nested in self._nested_contexts: + nested_def = context_def.nested_contexts.add() + nested.to_control_flow_context_def(nested_def) return context_def else: @@ -1784,14 +1776,10 @@ class CondContext(ControlFlowContext): ret = CondContext(context_def=context_def, import_scope=import_scope) - # TODO(b/72868227): remove "if hasattr(...)" once the corresponding - # control_flow.proto changes have been checked in (they aren't checked in - # and this is here for now to ensure forwards compatibility). - if hasattr(context_def, "nested_contexts"): - ret.Enter() - for nested_def in context_def.nested_contexts: - from_control_flow_context_def(nested_def) - ret.Exit() + ret.Enter() + for nested_def in context_def.nested_contexts: + from_control_flow_context_def(nested_def, import_scope=import_scope) + ret.Exit() return ret def to_control_flow_context_def(self, context_def, export_scope=None): @@ -1810,6 +1798,7 @@ class CondContext(ControlFlowContext): if self._outer_context: result = self._outer_context.AddValue(val) self._values.add(result.name) + self._external_values[result.name] = result with ops.control_dependencies(None): result = _SwitchRefOrTensor(result, self._pred)[self._branch] if self._outer_context: @@ -1874,6 +1863,7 @@ class CondContext(ControlFlowContext): if self._outer_context: real_val = self._outer_context.AddValue(val) self._values.add(real_val.name) + self._external_values[real_val.name] = real_val real_val = _SwitchRefOrTensor(real_val, self._pred)[self._branch] self._external_values[val.name] = real_val else: @@ -2035,7 +2025,7 @@ def cond(pred, raise TypeError("false_fn must be callable.") with ops.name_scope(name, "cond", [pred]): - if context.in_eager_mode(): + if context.executing_eagerly(): if pred: return _UnpackIfSingleton(true_fn()) return _UnpackIfSingleton(false_fn()) @@ -2109,10 +2099,7 @@ def cond(pred, # Only add non-nested conds to the collection. Any nested control flow will # be encapsulated in the root context. assert context_t.outer_context == context_f.outer_context - # TODO(b/72868227): remove "if True..." once the corresponding - # control_flow.proto changes have been checked in (they aren't checked in - # and this is disabled for now to ensure forwards compatibility). - if True or context_t.outer_context is None: + if context_t.outer_context is None: ops.add_to_collection(ops.GraphKeys.COND_CONTEXT, context_t) ops.add_to_collection(ops.GraphKeys.COND_CONTEXT, context_f) @@ -2128,61 +2115,6 @@ def cond(pred, # pylint: enable=redefined-outer-name -def smart_cond(pred, true_fn=None, false_fn=None, name=None): - """Return either `true_fn()` if predicate `pred` is true else `false_fn()`. - - If `pred` is a bool or has a constant value, we return either `true_fn()` - or `false_fn()`, otherwise we use `tf.cond` to dynamically route to both. - - Arguments: - pred: A scalar determining whether to return the result of `true_fn` or - `false_fn`. - true_fn: The callable to be performed if pred is true. - false_fn: The callable to be performed if pred is false. - name: Optional name prefix when using `tf.cond`. - - Returns: - Tensors returned by the call to either `true_fn` or `false_fn`. - - Raises: - TypeError: If `true_fn` or `false_fn` is not callable. - """ - if not callable(true_fn): - raise TypeError("`true_fn` must be callable.") - if not callable(false_fn): - raise TypeError("`false_fn` must be callable.") - - pred_value = smart_constant_value(pred) - if pred_value is not None: - if pred_value: - return true_fn() - else: - return false_fn() - else: - return cond(pred, true_fn=true_fn, false_fn=false_fn, name=name) - - -def smart_constant_value(pred): - """Return the bool value for `pred`, or None if `pred` had a dynamic value. - - Arguments: - pred: A scalar, either a Python bool or tensor. - - Returns: - True or False if `pred` has a constant boolean value, None otherwise. - - Raises: - TypeError: If `pred` is not a Tensor or bool. - """ - if isinstance(pred, bool): - pred_value = pred - elif isinstance(pred, ops.Tensor): - pred_value = tensor_util.constant_value(pred) - else: - raise TypeError("`pred` must be a Tensor or a Python bool.") - return pred_value - - def _resource_safe_shape(t): """Returns the shape of t or the variable it points to.""" if t.dtype == dtypes.resource: @@ -2390,13 +2322,9 @@ class WhileContext(ControlFlowContext): context_def.values_def.MergeFrom( super(WhileContext, self)._to_values_def( export_scope=export_scope)) - # TODO(b/72868227): remove "if True..." once the corresponding - # control_flow.proto changes have been checked in (they aren't checked in - # and this is disabled for now to ensure forwards compatibility). - if False: # pylint: disable=using-constant-test - for nested in self._nested_contexts: - nested_def = context_def.nested_contexts.add() - nested.to_control_flow_context_def(nested_def) + for nested in self._nested_contexts: + nested_def = context_def.nested_contexts.add() + nested.to_control_flow_context_def(nested_def) return context_def else: @@ -2418,14 +2346,10 @@ class WhileContext(ControlFlowContext): """ ret = WhileContext(context_def=context_def, import_scope=import_scope) - # TODO(b/72868227): remove "if hasattr(...)" once the corresponding - # control_flow.proto changes have been checked in (they aren't checked in - # and this is disabled for now to ensure forwards compatibility). - if hasattr(context_def, "nested_contexts"): - ret.Enter() - for nested_def in context_def.nested_contexts: - from_control_flow_context_def(nested_def, import_scope=import_scope) - ret.Exit() + ret.Enter() + for nested_def in context_def.nested_contexts: + from_control_flow_context_def(nested_def, import_scope=import_scope) + ret.Exit() return ret def GetWhileContext(self): @@ -3009,8 +2933,11 @@ class WhileContext(ControlFlowContext): loop_vars = ops.convert_n_to_tensor_or_indexed_slices(loop_vars) try: self.Enter() - original_body_result, exit_vars = self._BuildLoop( - pred, body, original_loop_vars, loop_vars, shape_invariants) + # _BuildLoop calls _update_input in several places. _lock ensures a + # Session.run call cannot occur between creating and mutating new ops. + with ops.get_default_graph()._lock: # pylint: disable=protected-access + original_body_result, exit_vars = self._BuildLoop( + pred, body, original_loop_vars, loop_vars, shape_invariants) finally: self.Exit() @@ -3250,7 +3177,7 @@ def while_loop(cond, math_ops.logical_and(i < maximum_iterations, orig_cond(*lv))) body = lambda i, lv: (i + 1, orig_body(*lv)) - if context.in_eager_mode(): + if context.executing_eagerly(): while cond(*loop_vars): loop_vars = body(*loop_vars) if maximum_iterations is not None: @@ -3270,10 +3197,7 @@ def while_loop(cond, swap_memory=swap_memory) # Only add non-nested loops to the collection. Any nested control flow will # be encapsulated in the root context. - # TODO(b/72868227): enable condition once the corresponding - # control_flow.proto changes have been checked in (they aren't checked in - # and this is disabled for now to ensure forwards compatibility). - if True or loop_context.outer_context is None: + if loop_context.outer_context is None: ops.add_to_collection(ops.GraphKeys.WHILE_CONTEXT, loop_context) result = loop_context.BuildLoop(cond, body, loop_vars, shape_invariants) if maximum_iterations is not None: @@ -3347,7 +3271,7 @@ def with_dependencies(dependencies, output_tensor, name=None): Raises: TypeError: if `output_tensor` is not a `Tensor` or `IndexedSlices`. """ - if context.in_eager_mode(): + if context.executing_eagerly(): return output_tensor with ops.name_scope(name, "control_dependency", list(dependencies) + [output_tensor]) as name: @@ -3392,7 +3316,7 @@ def group(*inputs, **kwargs): Raises: ValueError: If an unknown keyword argument is provided. """ - if context.in_eager_mode(): + if context.executing_eagerly(): return None name = kwargs.pop("name", None) if kwargs: @@ -3472,7 +3396,7 @@ def tuple(tensors, name=None, control_inputs=None): # pylint: disable=redefined objects. """ - if context.in_eager_mode(): + if context.executing_eagerly(): return tensors with ops.name_scope(name, "tuple", tensors) as name: tensors = [t if (isinstance(t, ops.Operation) @@ -3560,15 +3484,17 @@ def _case_create_default_action(predicates, actions): return default_action, other_predicates, other_actions -def _case_verify_and_canonicalize_args(pred_fn_pairs, exclusive, name): +def _case_verify_and_canonicalize_args(pred_fn_pairs, exclusive, name, + allow_python_preds): """Verifies input arguments for the case function. Args: - pred_fn_pairs: Dict or list of pairs of a boolean scalar tensor and a - callable which returns a list of tensors. + pred_fn_pairs: Dict or list of pairs of a boolean scalar tensor, + and a callable which returns a list of tensors. exclusive: True iff at most one predicate is allowed to evaluate to `True`. name: A name for the case operation. - + allow_python_preds: if true, pred_fn_pairs may contain Python bools in + addition to boolean Tensors Raises: TypeError: If `pred_fn_pairs` is not a list/dictionary. TypeError: If `pred_fn_pairs` is a list but does not contain 2-tuples. @@ -3593,14 +3519,69 @@ def _case_verify_and_canonicalize_args(pred_fn_pairs, exclusive, name): if not isinstance(pred_fn_pair, _basetuple) or len(pred_fn_pair) != 2: raise TypeError("Each entry in pred_fn_pairs must be a 2-tuple") pred, fn = pred_fn_pair - if pred.dtype != dtypes.bool: - raise TypeError("pred must be of type bool: %s", pred.name) + + if isinstance(pred, ops.Tensor): + if pred.dtype != dtypes.bool: + raise TypeError("pred must be Tensor of type bool: %s" % pred.name) + elif not allow_python_preds: + raise TypeError("pred must be a Tensor, got: %s" % pred) + elif not isinstance(pred, bool): + raise TypeError("pred must be a Tensor or bool, got: %s" % pred) + if not callable(fn): raise TypeError("fn for pred %s must be callable." % pred.name) + predicates, actions = zip(*pred_fn_pairs) return predicates, actions +def _case_helper(cond_fn, pred_fn_pairs, default, + exclusive, name, allow_python_preds=False, **cond_kwargs): + """Implementation of case that allows for different cond functions. + + Args: + cond_fn: method that has signature and semantics of `cond` above. + pred_fn_pairs: Dict or list of pairs of a boolean scalar tensor, and a + callable which returns a list of tensors. + default: Optional callable that returns a list of tensors. + exclusive: True iff at most one predicate is allowed to evaluate to `True`. + name: A name for this operation (optional). + allow_python_preds: if true, pred_fn_pairs may contain Python bools in + addition to boolean Tensors + **cond_kwargs: keyword arguments that will be passed to `cond_fn`. + + Returns: + The tensors returned by the first pair whose predicate evaluated to True, or + those returned by `default` if none does. + + Raises: + TypeError: If `pred_fn_pairs` is not a list/dictionary. + TypeError: If `pred_fn_pairs` is a list but does not contain 2-tuples. + TypeError: If `fns[i]` is not callable for any i, or `default` is not + callable. + """ + predicates, actions = _case_verify_and_canonicalize_args( + pred_fn_pairs, exclusive, name, allow_python_preds) + with ops.name_scope(name, "case", [predicates]): + if default is None: + default, predicates, actions = _case_create_default_action( + predicates, actions) + fn = default + # To eval conditions in direct order we create nested conditions in reverse: + # cond_fn(c[0], true_fn=.., false_fn=cond_fn(c[1], ...)) + for predicate, action in reversed(list(zip(predicates, actions))): + fn = functools.partial( + cond_fn, predicate, true_fn=action, false_fn=fn, **cond_kwargs) + if exclusive: + with ops.control_dependencies([ + _assert_at_most_n_true( + predicates, n=1, msg="Input error: exclusive=True") + ]): + return fn() + else: + return fn() + + @tf_export("case") def case(pred_fn_pairs, default=None, @@ -3691,26 +3672,8 @@ def case(pred_fn_pairs, TypeError: If `fns[i]` is not callable for any i, or `default` is not callable. """ - predicates, actions = _case_verify_and_canonicalize_args( - pred_fn_pairs, exclusive, name) - with ops.name_scope(name, "case", [predicates]): - if default is None: - default, predicates, actions = _case_create_default_action( - predicates, actions) - fn = default - # To eval conditions in direct order we create nested conditions in reverse: - # cond(c[0], true_fn=.., false_fn=cond(c[1], ...)) - for predicate, action in reversed(list(zip(predicates, actions))): - fn = functools.partial( - cond, predicate, true_fn=action, false_fn=fn, strict=strict) - if exclusive: - with ops.control_dependencies([ - _assert_at_most_n_true( - predicates, n=1, msg="Input error: exclusive=True") - ]): - return fn() - else: - return fn() + return _case_helper(cond, pred_fn_pairs, default, exclusive, name, + allow_python_preds=False, strict=strict) class XLAControlFlowContext(ControlFlowContext): diff --git a/tensorflow/python/ops/control_flow_ops_test.py b/tensorflow/python/ops/control_flow_ops_test.py index adc8c51e11191c4dbf29ac8294f3390bff37bc6c..f22f3059d139d1bb7c7db57a2939184f1089f397 100644 --- a/tensorflow/python/ops/control_flow_ops_test.py +++ b/tensorflow/python/ops/control_flow_ops_test.py @@ -349,42 +349,6 @@ class SwitchTestCase(test_util.TensorFlowTestCase): self.assertEquals(grad_x_false.eval(), 0.) -@test_util.with_c_api -class SmartCondTest(test_util.TensorFlowTestCase): - - def testSmartCondTrue(self): - with ops.Graph().as_default(): - with session.Session(): - x = constant_op.constant(2) - y = constant_op.constant(5) - z = control_flow_ops.smart_cond(True, lambda: math_ops.multiply(x, 16), - lambda: math_ops.multiply(y, 5)) - self.assertEqual(z.eval(), 32) - - def testSmartCondFalse(self): - with ops.Graph().as_default(): - with session.Session(): - x = constant_op.constant(4) - y = constant_op.constant(3) - z = control_flow_ops.smart_cond(False, lambda: math_ops.multiply(x, 16), - lambda: math_ops.multiply(y, 3)) - self.assertEqual(z.eval(), 9) - - def testSmartCondMissingArg1(self): - with ops.Graph().as_default(): - with session.Session(): - x = constant_op.constant(1) - with self.assertRaises(TypeError): - control_flow_ops.smart_cond(True, false_fn=lambda: x) - - def testSmartCondMissingArg2(self): - with ops.Graph().as_default(): - with session.Session(): - x = constant_op.constant(1) - with self.assertRaises(TypeError): - control_flow_ops.smart_cond(True, lambda: x) - - @test_util.with_c_api class CondTest(test_util.TensorFlowTestCase): diff --git a/tensorflow/python/ops/ctc_ops.py b/tensorflow/python/ops/ctc_ops.py index 83da6739db673644f59fda3044769b18b2138fbc..4b57e2de790af13499bc73cfcfa98e999eab1603 100644 --- a/tensorflow/python/ops/ctc_ops.py +++ b/tensorflow/python/ops/ctc_ops.py @@ -148,7 +148,7 @@ def ctc_loss(labels, inputs, sequence_length, if not time_major: inputs = array_ops.transpose(inputs, [1, 0, 2]) # (B,T,N) => (T,B,N) - loss, _ = gen_ctc_ops._ctc_loss( + loss, _ = gen_ctc_ops.ctc_loss( inputs, labels.indices, labels.values, @@ -224,7 +224,7 @@ def ctc_greedy_decoder(inputs, sequence_length, merge_repeated=True): sequence found, the negative of the sum of the greatest logit at each timeframe. """ - outputs = gen_ctc_ops._ctc_greedy_decoder( + outputs = gen_ctc_ops.ctc_greedy_decoder( inputs, sequence_length, merge_repeated=merge_repeated) (decoded_ix, decoded_val, decoded_shape, log_probabilities) = outputs return ([sparse_tensor.SparseTensor(decoded_ix, decoded_val, decoded_shape)], @@ -272,7 +272,7 @@ def ctc_beam_search_decoder(inputs, sequence_length, beam_width=100, """ decoded_ixs, decoded_vals, decoded_shapes, log_probabilities = ( - gen_ctc_ops._ctc_beam_search_decoder( + gen_ctc_ops.ctc_beam_search_decoder( inputs, sequence_length, beam_width=beam_width, top_paths=top_paths, merge_repeated=merge_repeated)) diff --git a/tensorflow/python/ops/custom_gradient.py b/tensorflow/python/ops/custom_gradient.py new file mode 100644 index 0000000000000000000000000000000000000000..9eacac1b3704c43cbeb5ecd0cbe827cac3a7cc8b --- /dev/null +++ b/tensorflow/python/ops/custom_gradient.py @@ -0,0 +1,134 @@ +# 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. +# ============================================================================== +"""Decorator to overrides the gradient for a function.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.eager import context +from tensorflow.python.eager import tape +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import gen_array_ops +from tensorflow.python.util import nest +from tensorflow.python.util import tf_decorator +from tensorflow.python.util.tf_export import tf_export + + +@tf_export("custom_gradient") +def custom_gradient(f): + """Decorator to define a function with a custom gradient. + + This decorator allows fine grained control over the gradients of a sequence + for operations. This may be useful for multiple reasons, including providing + a more efficient or numerically stable gradient for a sequence of operations. + + For example, consider the following function that commonly occurs in the + computation of cross entropy and log likelihoods: + + ```python + def log1pexp(x): + return tf.log(1 + tf.exp(x)) + ``` + + Due to numerical instability, the gradient this function evaluated at x=100 is + NaN. For example: + + ```python + x = tf.constant(100.) + y = log1pexp(x) + dy = tf.gradients(y, x) # Will be NaN when evaluated. + ``` + + The gradient expression can be analytically simplified to provide numerical + stability: + + ```python + @tf.custom_gradient + def log1pexp(x): + e = tf.exp(x) + def grad(dy): + return dy * (1 - 1 / (1 + e)) + return tf.log(1 + e), grad + ``` + + With this definition, the gradient at x=100 will be correctly evaluated as + 1.0. + + See also @{tf.RegisterGradient} which registers a gradient function for a + primitive TensorFlow operation. `tf.custom_gradient` on the other hand allows + for fine grained control over the gradient computation of a sequence of + operations. + + Args: + f: function `f(x)` that returns a tuple `(y, grad_fn)` where: + - `x` is a `Tensor` or sequence of `Tensor` inputs to the function. + - `y` is a `Tensor` or sequence of `Tensor` outputs of applying + TensorFlow + operations in `f` to `x`. + - `grad_fn` is a function with the signature `g(grad_ys)` which returns + a list of `Tensor`s - the derivatives of `Tensor`s in `y` with respect + to the `Tensor`s in `x. `grad_ys` is a `Tensor` or sequence of + `Tensor`s the same size as `y` holding the initial value gradients for + each `Tensor` in `y`. + + Returns: + A function `h(x)` which returns the same value as `f(x)[0]` and whose + gradient (as calculated by @{tf.gradients}) is determined by `f(x)[1]`. + """ + + def decorated(*args, **kwargs): + """Decorated function with custom gradient.""" + if not context.executing_eagerly(): + if kwargs: + raise ValueError( + "The custom_gradient decorator currently suports keywords " + "arguments only when eager execution is enabled.") + name = "CustomGradient-%s" % ops.uid() + args = [ops.convert_to_tensor(x) for x in args] + result, grad_fn = f(*args) + flat_result = nest.flatten(result) + all_tensors = flat_result + args + + @ops.RegisterGradient(name) + def internal_grad_fn(unused_op, *result_grads): # pylint: disable=unused-variable + gradients = nest.flatten(grad_fn(*result_grads[:len(flat_result)])) + # Need to return one value per input to the IdentityN, so pad the + # gradients of the inputs of the custom_gradient function with the + # gradients of the outputs as well. + return ([None] * len(flat_result)) + gradients + + with ops.get_default_graph().gradient_override_map({"IdentityN": name}): + all_tensors = array_ops.identity_n(all_tensors) + return nest.pack_sequence_as( + structure=result, flat_sequence=all_tensors[:len(flat_result)]) + + input_tensors = [ops.convert_to_tensor(x) for x in args] + + result, grad_fn = f(*args, **kwargs) + flat_result = nest.flatten(result) + # TODO(apassos) consider removing the identity below. + flat_result = [gen_array_ops.identity(x) for x in flat_result] + + def actual_grad_fn(*outputs): + return nest.flatten(grad_fn(*outputs)) + + tape.record_operation(f.__name__, flat_result, input_tensors, + actual_grad_fn) + flat_result = list(flat_result) + return nest.pack_sequence_as(result, flat_result) + + return tf_decorator.make_decorator(f, decorated) diff --git a/tensorflow/python/ops/data_flow_ops.py b/tensorflow/python/ops/data_flow_ops.py index 03ed537cfcf27151a0200d7a17f63b1a2bc7ba1a..d2cc87555f6321432261b32f08431c23ce707eff 100644 --- a/tensorflow/python/ops/data_flow_ops.py +++ b/tensorflow/python/ops/data_flow_ops.py @@ -159,7 +159,7 @@ class QueueBase(object): ValueError: If one of the arguments is invalid. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError( "Queues are not supported when eager execution is enabled. " "Instead, please use tf.data to get data into your model.") @@ -177,10 +177,10 @@ class QueueBase(object): else: self._names = None self._queue_ref = queue_ref - if context.in_graph_mode(): - self._name = self._queue_ref.op.name.split("/")[-1] - else: + if context.executing_eagerly(): self._name = context.context().scope_name + else: + self._name = self._queue_ref.op.name.split("/")[-1] @staticmethod def from_list(index, queues): @@ -231,9 +231,9 @@ class QueueBase(object): @property def name(self): """The name of the underlying queue.""" - if context.in_graph_mode(): - return self._queue_ref.op.name - return self._name + if context.executing_eagerly(): + return self._name + return self._queue_ref.op.name @property def dtypes(self): @@ -342,10 +342,10 @@ class QueueBase(object): val.get_shape().assert_is_compatible_with(shape) if self._queue_ref.dtype == _dtypes.resource: - return gen_data_flow_ops._queue_enqueue_v2( + return gen_data_flow_ops.queue_enqueue_v2( self._queue_ref, vals, name=scope) else: - return gen_data_flow_ops._queue_enqueue( + return gen_data_flow_ops.queue_enqueue( self._queue_ref, vals, name=scope) def enqueue_many(self, vals, name=None): @@ -387,7 +387,7 @@ class QueueBase(object): val.get_shape().with_rank_at_least(1)[0]) val.get_shape()[1:].assert_is_compatible_with(shape) - return gen_data_flow_ops._queue_enqueue_many_v2( + return gen_data_flow_ops.queue_enqueue_many_v2( self._queue_ref, vals, name=scope) def _dequeue_return_value(self, tensors): @@ -436,15 +436,15 @@ class QueueBase(object): if name is None: name = "%s_Dequeue" % self._name if self._queue_ref.dtype == _dtypes.resource: - ret = gen_data_flow_ops._queue_dequeue_v2( + ret = gen_data_flow_ops.queue_dequeue_v2( self._queue_ref, self._dtypes, name=name) else: - ret = gen_data_flow_ops._queue_dequeue( + ret = gen_data_flow_ops.queue_dequeue( self._queue_ref, self._dtypes, name=name) # NOTE(mrry): Not using a shape function because we need access to # the `QueueBase` object. - if context.in_graph_mode(): + if not context.executing_eagerly(): op = ret[0].op for output, shape in zip(op.values(), self._shapes): output.set_shape(shape) @@ -479,12 +479,12 @@ class QueueBase(object): if name is None: name = "%s_DequeueMany" % self._name - ret = gen_data_flow_ops._queue_dequeue_many_v2( + ret = gen_data_flow_ops.queue_dequeue_many_v2( self._queue_ref, n=n, component_types=self._dtypes, name=name) # NOTE(mrry): Not using a shape function because we need access to # the Queue object. - if context.in_graph_mode(): + if not context.executing_eagerly(): op = ret[0].op batch_dim = tensor_shape.Dimension( tensor_util.constant_value(op.inputs[1])) @@ -523,12 +523,12 @@ class QueueBase(object): if name is None: name = "%s_DequeueUpTo" % self._name - ret = gen_data_flow_ops._queue_dequeue_up_to_v2( + ret = gen_data_flow_ops.queue_dequeue_up_to_v2( self._queue_ref, n=n, component_types=self._dtypes, name=name) # NOTE(mrry): Not using a shape function because we need access to # the Queue object. - if context.in_graph_mode(): + if not context.executing_eagerly(): op = ret[0].op for output, shape in zip(op.values(), self._shapes): output.set_shape(tensor_shape.TensorShape([None]).concatenate(shape)) @@ -560,12 +560,12 @@ class QueueBase(object): if name is None: name = "%s_Close" % self._name if self._queue_ref.dtype == _dtypes.resource: - return gen_data_flow_ops._queue_close_v2( + return gen_data_flow_ops.queue_close_v2( self._queue_ref, cancel_pending_enqueues=cancel_pending_enqueues, name=name) else: - return gen_data_flow_ops._queue_close( + return gen_data_flow_ops.queue_close( self._queue_ref, cancel_pending_enqueues=cancel_pending_enqueues, name=name) @@ -601,9 +601,9 @@ class QueueBase(object): if name is None: name = "%s_Size" % self._name if self._queue_ref.dtype == _dtypes.resource: - return gen_data_flow_ops._queue_size_v2(self._queue_ref, name=name) + return gen_data_flow_ops.queue_size_v2(self._queue_ref, name=name) else: - return gen_data_flow_ops._queue_size(self._queue_ref, name=name) + return gen_data_flow_ops.queue_size(self._queue_ref, name=name) @tf_export("RandomShuffleQueue") @@ -683,7 +683,7 @@ class RandomShuffleQueue(QueueBase): # the id of the last op created.) string = (str(seed1) + shared_name).encode("utf-8") seed2 = int(hashlib.md5(string).hexdigest()[:8], 16) & 0x7FFFFFFF - queue_ref = gen_data_flow_ops._random_shuffle_queue_v2( + queue_ref = gen_data_flow_ops.random_shuffle_queue_v2( component_types=dtypes, shapes=shapes, capacity=capacity, @@ -748,7 +748,7 @@ class FIFOQueue(QueueBase): dtypes = _as_type_list(dtypes) shapes = _as_shape_list(shapes, dtypes) names = _as_name_list(names, dtypes) - queue_ref = gen_data_flow_ops._fifo_queue_v2( + queue_ref = gen_data_flow_ops.fifo_queue_v2( component_types=dtypes, shapes=shapes, capacity=capacity, @@ -827,7 +827,7 @@ class PaddingFIFOQueue(QueueBase): "but received %d dtypes and %d shapes." % (len(dtypes), len(shapes))) - queue_ref = gen_data_flow_ops._padding_fifo_queue_v2( + queue_ref = gen_data_flow_ops.padding_fifo_queue_v2( component_types=dtypes, shapes=shapes, capacity=capacity, @@ -895,7 +895,7 @@ class PriorityQueue(QueueBase): types = _as_type_list(types) shapes = _as_shape_list(shapes, types) - queue_ref = gen_data_flow_ops._priority_queue_v2( + queue_ref = gen_data_flow_ops.priority_queue_v2( component_types=types, shapes=shapes, capacity=capacity, @@ -985,15 +985,15 @@ class Barrier(object): else: self._shapes = [tensor_shape.unknown_shape() for _ in self._types] - self._barrier_ref = gen_data_flow_ops._barrier( + self._barrier_ref = gen_data_flow_ops.barrier( component_types=self._types, shapes=self._shapes, shared_name=shared_name, name=name) - if context.in_graph_mode(): - self._name = self._barrier_ref.op.name.split("/")[-1] - else: + if context.executing_eagerly(): self._name = context.context().scope_name + else: + self._name = self._barrier_ref.op.name.split("/")[-1] @property def barrier_ref(self): @@ -1003,9 +1003,9 @@ class Barrier(object): @property def name(self): """The name of the underlying barrier.""" - if context.in_graph_mode(): - return self._barrier_ref.op.name - return self._name + if context.executing_eagerly(): + return self._name + return self._barrier_ref.op.name def insert_many(self, component_index, keys, values, name=None): """For each key, assigns the respective value to the specified component. @@ -1026,7 +1026,7 @@ class Barrier(object): """ if name is None: name = "%s_BarrierInsertMany" % self._name - return gen_data_flow_ops._barrier_insert_many( + return gen_data_flow_ops.barrier_insert_many( self._barrier_ref, keys, values, component_index, name=name) def take_many(self, @@ -1073,7 +1073,7 @@ class Barrier(object): """ if name is None: name = "%s_BarrierTakeMany" % self._name - ret = gen_data_flow_ops._barrier_take_many( + ret = gen_data_flow_ops.barrier_take_many( self._barrier_ref, num_elements, self._types, @@ -1083,7 +1083,7 @@ class Barrier(object): # NOTE(mrry): Not using a shape function because we need access to # the Barrier object. - if context.in_graph_mode(): + if not context.executing_eagerly(): op = ret[0].op if allow_small_batch: batch_dim = None @@ -1122,7 +1122,7 @@ class Barrier(object): """ if name is None: name = "%s_BarrierClose" % self._name - return gen_data_flow_ops._barrier_close( + return gen_data_flow_ops.barrier_close( self._barrier_ref, cancel_pending_enqueues=cancel_pending_enqueues, name=name) @@ -1139,7 +1139,7 @@ class Barrier(object): """ if name is None: name = "%s_BarrierReadySize" % self._name - return gen_data_flow_ops._barrier_ready_size(self._barrier_ref, name=name) + return gen_data_flow_ops.barrier_ready_size(self._barrier_ref, name=name) def incomplete_size(self, name=None): """Compute the number of incomplete elements in the given barrier. @@ -1153,7 +1153,7 @@ class Barrier(object): """ if name is None: name = "%s_BarrierIncompleteSize" % self._name - return gen_data_flow_ops._barrier_incomplete_size( + return gen_data_flow_ops.barrier_incomplete_size( self._barrier_ref, name=name) @@ -1183,10 +1183,10 @@ class ConditionalAccumulatorBase(object): else: self._shape = tensor_shape.unknown_shape() self._accumulator_ref = accumulator_ref - if context.in_graph_mode(): - self._name = self._accumulator_ref.op.name.split("/")[-1] - else: + if context.executing_eagerly(): self._name = context.context().scope_name + else: + self._name = self._accumulator_ref.op.name.split("/")[-1] @property def accumulator_ref(self): diff --git a/tensorflow/python/ops/distributions/gamma.py b/tensorflow/python/ops/distributions/gamma.py index 8fb218be3ac7e17e18d85b8e1c100ccd58aa1034..adb1f4f9a879e44cf8cb4cafd22b92554f487712 100644 --- a/tensorflow/python/ops/distributions/gamma.py +++ b/tensorflow/python/ops/distributions/gamma.py @@ -193,12 +193,6 @@ class Gamma(distribution.Distribution): def _log_prob(self, x): return self._log_unnormalized_prob(x) - self._log_normalization() - def _prob(self, x): - return math_ops.exp(self._log_prob(x)) - - def _log_cdf(self, x): - return math_ops.log(self._cdf(x)) - def _cdf(self, x): x = self._maybe_assert_valid_sample(x) # Note that igamma returns the regularized incomplete gamma function, diff --git a/tensorflow/python/ops/distributions/normal.py b/tensorflow/python/ops/distributions/normal.py index e7f120ea2da525e20a1ae42e6418cf2ac83686af..32e8a49c81bc4b23d8897639998dd33942b41a80 100644 --- a/tensorflow/python/ops/distributions/normal.py +++ b/tensorflow/python/ops/distributions/normal.py @@ -188,9 +188,6 @@ class Normal(distribution.Distribution): def _log_prob(self, x): return self._log_unnormalized_prob(x) - self._log_normalization() - def _prob(self, x): - return math_ops.exp(self._log_prob(x)) - def _log_cdf(self, x): return special_math.log_ndtr(self._z(x)) diff --git a/tensorflow/python/ops/distributions/student_t.py b/tensorflow/python/ops/distributions/student_t.py index 778fefb8c2991153b7e7a1f20df61680153dab2a..9d9e65b4e8d6d2e40bf9c263339f899439c842c3 100644 --- a/tensorflow/python/ops/distributions/student_t.py +++ b/tensorflow/python/ops/distributions/student_t.py @@ -248,9 +248,6 @@ class StudentT(distribution.Distribution): math_ops.lgamma(0.5 * self.df) - math_ops.lgamma(0.5 * (self.df + 1.))) - def _prob(self, x): - return math_ops.exp(self._log_prob(x)) - def _cdf(self, x): # Take Abs(scale) to make subsequent where work correctly. y = (x - self.loc) / math_ops.abs(self.scale) diff --git a/tensorflow/python/ops/distributions/uniform.py b/tensorflow/python/ops/distributions/uniform.py index 3580af18f241d777c81340f1c565074914838029..ec623b55eb0067e16599c18c9c504635da863907 100644 --- a/tensorflow/python/ops/distributions/uniform.py +++ b/tensorflow/python/ops/distributions/uniform.py @@ -45,11 +45,12 @@ class Uniform(distribution.Distribution): Z = b - a ``` - where: - * `low = a`, - * `high = b`, - * `Z` is the normalizing constant, and, - * `I[predicate]` is the [indicator function]( + where + + - `low = a`, + - `high = b`, + - `Z` is the normalizing constant, and + - `I[predicate]` is the [indicator function]( https://en.wikipedia.org/wiki/Indicator_function) for `predicate`. The parameters `low` and `high` must be shaped in a way that supports @@ -164,9 +165,6 @@ class Uniform(distribution.Distribution): seed=seed) return self.low + self.range() * samples - def _log_prob(self, x): - return math_ops.log(self._prob(x)) - def _prob(self, x): broadcasted_x = x * array_ops.ones(self.batch_shape_tensor()) return array_ops.where( @@ -178,9 +176,6 @@ class Uniform(distribution.Distribution): array_ops.zeros_like(broadcasted_x), array_ops.ones_like(broadcasted_x) / self.range())) - def _log_cdf(self, x): - return math_ops.log(self.cdf(x)) - def _cdf(self, x): broadcast_shape = array_ops.broadcast_dynamic_shape( array_ops.shape(x), self.batch_shape_tensor()) diff --git a/tensorflow/python/ops/embedding_ops.py b/tensorflow/python/ops/embedding_ops.py index 3826585f59c31133b12c365816729e090c9ab561..20e4a28b9c0f5427e331a69cba52503492a8420a 100644 --- a/tensorflow/python/ops/embedding_ops.py +++ b/tensorflow/python/ops/embedding_ops.py @@ -396,8 +396,8 @@ def embedding_lookup_sparse(params, with `combiner`="mean", then the output will be a 3x20 matrix where output[0, :] = (params[1, :] * 2.0 + params[3, :] * 0.5) / (2.0 + 0.5) - output[1, :] = params[0, :] * 1.0 - output[2, :] = params[1, :] * 3.0 + output[1, :] = (params[0, :] * 1.0) / 1.0 + output[2, :] = (params[1, :] * 3.0) / 3.0 Raises: TypeError: If sp_ids is not a SparseTensor, or if sp_weights is neither diff --git a/tensorflow/python/ops/functional_ops.py b/tensorflow/python/ops/functional_ops.py index ac03d30fcd2e65f032937d9259bc8fff18626619..a840b1eddfc6922dc310490e8166efd73480c437 100644 --- a/tensorflow/python/ops/functional_ops.py +++ b/tensorflow/python/ops/functional_ops.py @@ -41,7 +41,7 @@ from tensorflow.python.ops import variable_scope as vs from tensorflow.python.ops.gen_functional_ops import * # pylint: enable=wildcard-import # pylint: disable=unused-import -from tensorflow.python.ops.gen_functional_ops import _symbolic_gradient +from tensorflow.python.ops.gen_functional_ops import symbolic_gradient # pylint: enable=unused-import from tensorflow.python.util import nest from tensorflow.python.util.tf_export import tf_export @@ -90,7 +90,7 @@ def foldl(fn, elems, initializer=None, parallel_iterations=10, back_prop=True, if not callable(fn): raise TypeError("fn must be callable.") - in_graph_mode = context.in_graph_mode() + in_graph_mode = not context.executing_eagerly() with ops.name_scope(name, "foldl", [elems]): # TODO(akshayka): Remove the in_graph_mode check once caching devices are # supported in Eager @@ -178,7 +178,7 @@ def foldr(fn, elems, initializer=None, parallel_iterations=10, back_prop=True, if not callable(fn): raise TypeError("fn must be callable.") - in_graph_mode = context.in_graph_mode() + in_graph_mode = not context.executing_eagerly() with ops.name_scope(name, "foldr", [elems]): # TODO(akshayka): Remove the in_graph_mode check once caching devices are # supported in Eager @@ -343,7 +343,7 @@ def map_fn(fn, elems, dtype=None, parallel_iterations=10, back_prop=True, elems_flat = input_flatten(elems) - in_graph_mode = context.in_graph_mode() + in_graph_mode = not context.executing_eagerly() with ops.name_scope(name, "map", elems_flat): # TODO(akshayka): Remove the in_graph_mode check once caching devices are # supported in Eager @@ -364,8 +364,8 @@ def map_fn(fn, elems, dtype=None, parallel_iterations=10, back_prop=True, dtype = dtype or input_pack([elem.dtype for elem in elems_flat]) dtype_flat = output_flatten(dtype) - # Convert elems to tensor array. - n = array_ops.shape(elems_flat[0])[0] + # Convert elems to tensor array. n may be known statically. + n = elems_flat[0].shape[0].value or array_ops.shape(elems_flat[0])[0] # TensorArrays are always flat elems_ta = [ @@ -536,7 +536,7 @@ def scan(fn, elems, initializer=None, parallel_iterations=10, back_prop=True, elems_flat = input_flatten(elems) - in_graph_mode = context.in_graph_mode() + in_graph_mode = not context.executing_eagerly() with ops.name_scope(name, "scan", elems_flat): # TODO(akshayka): Remove the in_graph_mode check once caching devices are # supported in Eager @@ -555,7 +555,8 @@ def scan(fn, elems, initializer=None, parallel_iterations=10, back_prop=True, elems_flat = [ ops.convert_to_tensor(elem, name="elem") for elem in elems_flat] - n = array_ops.shape(elems_flat[0])[0] + # Convert elems to tensor array. n may be known statically. + n = elems_flat[0].shape[0].value or array_ops.shape(elems_flat[0])[0] # TensorArrays are always flat elems_ta = [ @@ -615,7 +616,8 @@ def scan(fn, elems, initializer=None, parallel_iterations=10, back_prop=True, _, _, r_a = control_flow_ops.while_loop( lambda i, _1, _2: i < n, compute, (i, a_flat, accs_ta), parallel_iterations=parallel_iterations, - back_prop=back_prop, swap_memory=swap_memory) + back_prop=back_prop, swap_memory=swap_memory, + maximum_iterations=n) results_flat = [r.stack() for r in r_a] diff --git a/tensorflow/python/ops/gradients.py b/tensorflow/python/ops/gradients.py index 921fd50aa9fdd1a1e493708f4bc8c66996e26e2c..63d9a232224cc70c9ce6f04787a8dcc921b3e2f3 100644 --- a/tensorflow/python/ops/gradients.py +++ b/tensorflow/python/ops/gradients.py @@ -19,6 +19,7 @@ from __future__ import division from __future__ import print_function # pylint: disable=unused-import +from tensorflow.python.ops.custom_gradient import custom_gradient from tensorflow.python.ops.gradients_impl import AggregationMethod from tensorflow.python.ops.gradients_impl import gradients from tensorflow.python.ops.gradients_impl import hessians @@ -28,6 +29,7 @@ from tensorflow.python.util.all_util import remove_undocumented _allowed_symbols = [ # TODO(drpng): find a good place to reference this. "AggregationMethod", + "custom_gradient", "gradients", # tf.gradients.gradients. "hessians", # tf.gradients.hessians ] diff --git a/tensorflow/python/ops/gradients_impl.py b/tensorflow/python/ops/gradients_impl.py index 1418c0b10fb60601e7c3024891b89aadb53e6873..44473ec69c8ac6cf565f635621eebff7bc403225 100644 --- a/tensorflow/python/ops/gradients_impl.py +++ b/tensorflow/python/ops/gradients_impl.py @@ -86,17 +86,19 @@ def _IndexedSlicesToTensor(value, dtype=None, name=None, as_ref=False): % str(value)) # TODO(mrry): Consider adding static shape information to # IndexedSlices, to avoid using numpy here. - dense_shape_value = tensor_util.constant_value(value.dense_shape) - if dense_shape_value is not None: - num_elements = np.prod(dense_shape_value) - if num_elements >= _LARGE_SPARSE_NUM_ELEMENTS: + if not context.executing_eagerly(): + dense_shape_value = tensor_util.constant_value(value.dense_shape) + if dense_shape_value is not None: + num_elements = np.prod(dense_shape_value) + if num_elements >= _LARGE_SPARSE_NUM_ELEMENTS: + warnings.warn( + "Converting sparse IndexedSlices to a dense Tensor with %d " + "elements. This may consume a large amount of memory." % + num_elements) + else: warnings.warn( - "Converting sparse IndexedSlices to a dense Tensor with %d elements. " - "This may consume a large amount of memory." % num_elements) - else: - warnings.warn( - "Converting sparse IndexedSlices to a dense Tensor of unknown shape. " - "This may consume a large amount of memory.") + "Converting sparse IndexedSlices to a dense Tensor of unknown shape. " + "This may consume a large amount of memory.") return math_ops.unsorted_segment_sum( value.values, value.indices, value.dense_shape[0], name=name) @@ -354,7 +356,7 @@ def _SymGrad(op, out_grads): for k in op.node_def.attr: f.attr[k].CopyFrom(op.node_def.attr[k]) # pylint: disable=protected-access - in_grads = functional_ops._symbolic_gradient(input=f_in, Tout=f_types, f=f) + in_grads = functional_ops.symbolic_gradient(input=f_in, Tout=f_types, f=f) # pylint: enable=protected-access return in_grads @@ -478,9 +480,21 @@ def gradients(ys, RuntimeError: if called in Eager mode. """ - if context.in_eager_mode(): - raise RuntimeError("tf.gradients not supported in EAGER mode. Use " - "functions in tf.contrib.eager.backprop instead.") + # Creating the gradient graph for control flow mutates Operations. _lock + # ensures a Session.run call cannot occur between creating and mutating new + # ops. + with ops.get_default_graph()._lock: # pylint: disable=protected-access + return _GradientsHelper(ys, xs, grad_ys, name, colocate_gradients_with_ops, + gate_gradients, aggregation_method, stop_gradients) + + +def _GradientsHelper(ys, xs, grad_ys, name, colocate_gradients_with_ops, + gate_gradients, aggregation_method, stop_gradients): + """Implementation of gradients().""" + if context.executing_eagerly(): + raise RuntimeError("tf.gradients not supported when eager execution " + "is enabled. Use tf.contrib.eager.GradientTape " + "instead.") ys = _AsList(ys) xs = _AsList(xs) stop_gradients = [] if stop_gradients is None else _AsList(stop_gradients) diff --git a/tensorflow/python/ops/gradients_test.py b/tensorflow/python/ops/gradients_test.py index d39b934819177e3c15af95a0777ba96869c5e9cf..c94f1396b28e2124c6e5123cf711ac86abf174ab 100644 --- a/tensorflow/python/ops/gradients_test.py +++ b/tensorflow/python/ops/gradients_test.py @@ -35,6 +35,7 @@ from tensorflow.python.ops import array_grad # pylint: disable=unused-import from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_grad # pylint: disable=unused-import from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import custom_gradient from tensorflow.python.ops import data_flow_grad # pylint: disable=unused-import from tensorflow.python.ops import data_flow_ops # pylint: disable=unused-import from tensorflow.python.ops import functional_ops # pylint: disable=unused-import @@ -661,6 +662,7 @@ class HessianTest(test_util.TensorFlowTestCase): self.assertAllEqual((m, n, m, n), hess_actual.shape) self.assertAllClose(hess_value, hess_actual.reshape((m * n, m * n))) + @test_util.with_c_api class IndexedSlicesToTensorTest(test_util.TensorFlowTestCase): @@ -741,6 +743,59 @@ class IndexedSlicesToTensorTest(test_util.TensorFlowTestCase): "of unknown shape. This may consume a large amount of memory." in str(w[0].message)) + def testCustomGradientTrivial(self): + + @custom_gradient.custom_gradient + def MyIdentity(x): + + def Grad(dy): + return [3 * dy] + + return x, Grad + + with ops.Graph().as_default(): + x = constant(3.) + y = MyIdentity(MyIdentity(x)) + dy = gradients.gradients(y, x)[0] + with session.Session(): + self.assertEqual(9., dy.eval()) + + def testCustomGradient(self): + + @custom_gradient.custom_gradient + def MyMultiply(x1, x2): + result = x1 * x2 + + def Grad(dy): + # Switched the ordering here. + return [dy * x1, dy * x2] + + return result, Grad + + with ops.Graph().as_default(): + x1 = constant(3.) + x2 = constant(5.) + y = MyMultiply(x1, x2) + dy = gradients.gradients(y, [x1, x2]) + with session.Session() as sess: + self.assertAllEqual([3., 5.], sess.run(dy)) + + def testCustomGradientErrors(self): + + @custom_gradient.custom_gradient + def F(x): + + def Grad(_): + raise RuntimeError("x") + + return x, Grad + + with ops.Graph().as_default(): + x = constant(1.0) + y = F(x) + with self.assertRaises(RuntimeError): + gradients.gradients(y, x) + @test_util.with_c_api class OnlyRealGradientsTest(test_util.TensorFlowTestCase): diff --git a/tensorflow/python/ops/hidden_ops.txt b/tensorflow/python/ops/hidden_ops.txt deleted file mode 100644 index 9b8172bf2639cca0efb663ff4075b36d6f4f2245..0000000000000000000000000000000000000000 --- a/tensorflow/python/ops/hidden_ops.txt +++ /dev/null @@ -1,394 +0,0 @@ -# array_ops -BatchToSpace -BroadcastArgs -BroadcastGradientArgs -ConcatOffset -Concat -ConcatV2 -ConjugateTranspose -Const -DebugGradientIdentity -DebugGradientRefIdentity -EditDistance -ExpandDims -ListDiff -MirrorPad -MirrorPadGrad -OneHot -Pack -Pad -PadV2 -ParallelConcat -Placeholder -RefIdentity -Reverse -Snapshot -SpaceToBatch -Split -SplitV -Squeeze -Slice -TileGrad # Exported through array_grad instead of array_ops. -ZerosLike # TODO(josh11b): Use this instead of the Python version. -Unique -UniqueV2 -UniqueWithCounts -UniqueWithCountsV2 -Unpack - -# candidate_sampling_ops -AllCandidateSampler -ComputeAccidentalHits -FixedUnigramCandidateSampler -LearnedUnigramCandidateSampler -LogUniformCandidateSampler -ThreadUnsafeUnigramCandidateSampler -UniformCandidateSampler - -# checkpoint_ops -GenerateVocabRemapping -LoadAndRemapMatrix - - -# control_flow_ops -Switch -Merge -RefMerge -Exit -RefExit - -# ctc_ops -CTCLoss -CTCGreedyDecoder -CTCBeamSearchDecoder - -# data_flow_ops -Barrier -BarrierClose -BarrierIncompleteSize -BarrierInsertMany -BarrierReadySize -BarrierTakeMany -DeleteSessionTensor -FakeQueue -FIFOQueue -FIFOQueueV2 -GetSessionHandle -GetSessionHandleV2 -GetSessionTensor -HashTable -HashTableV2 -InitializeTable -InitializeTableV2 -InitializeTableFromTextFile -InitializeTableFromTextFileV2 -LookupTableExport -LookupTableExportV2 -LookupTableFind -LookupTableFindV2 -LookupTableImport -LookupTableImportV2 -LookupTableInsert -LookupTableInsertV2 -LookupTableSize -LookupTableSizeV2 -MutableDenseHashTable -MutableDenseHashTableV2 -MutableHashTable -MutableHashTableV2 -MutableHashTableOfTensors -MutableHashTableOfTensorsV2 -Mutex -MutexAcquire -MutexRelease -PaddingFIFOQueue -PaddingFIFOQueueV2 -PriorityQueue -PriorityQueueV2 -QueueClose -QueueCloseV2 -QueueDequeue -QueueDequeueV2 -QueueDequeueMany -QueueDequeueManyV2 -QueueDequeueUpTo -QueueDequeueUpToV2 -QueueEnqueue -QueueEnqueueV2 -QueueEnqueueMany -QueueEnqueueManyV2 -QueueSize -QueueSizeV2 -RandomShuffleQueue -RandomShuffleQueueV2 -Stack -StackClose -StackPop -StackPush -StackV2 -StackCloseV2 -StackPopV2 -StackPushV2 -TensorArray -TensorArrayClose -TensorArrayCloseV2 -TensorArrayConcat -TensorArrayConcatV2 -TensorArrayGather -TensorArrayGatherV2 -TensorArrayGrad -TensorArrayGradV2 -TensorArrayPack -TensorArrayPackV2 -TensorArrayRead -TensorArrayReadV2 -TensorArrayScatter -TensorArrayScatterV2 -TensorArraySize -TensorArraySizeV2 -TensorArraySplit -TensorArraySplitV2 -TensorArrayUnpack -TensorArrayUnpackV2 -TensorArrayV2 -TensorArrayWrite -TensorArrayWriteV2 -TensorArrayV3 -TensorArrayCloseV3 -TensorArrayConcatV3 -TensorArrayGatherV3 -TensorArrayGradV3 -TensorArrayReadV3 -TensorArrayPackV3 -TensorArrayScatterV3 -TensorArraySizeV3 -TensorArraySplitV3 -TensorArrayUnpackV3 -TensorArrayWriteV3 - -# functional_ops -SymbolicGradient - -# image_ops -AdjustContrastv2 -NonMaxSuppression -NonMaxSuppressionV2 -RandomCrop -ResizeBilinearGrad -ResizeBicubicGrad -ResizeNearestNeighborGrad -SampleDistortedBoundingBox -SampleDistortedBoundingBoxV2 -ScaleImageGrad - -# io_ops -FixedLengthRecordReader -IdentityReader -ReaderNumRecordsProduced -ReaderNumWorkUnitsCompleted -ReaderRead -ReaderReadUpTo -ReaderReset -ReaderRestoreState -ReaderSerializeState -ReaderWorkQueueLength -FixedLengthRecordReaderV2 -IdentityReaderV2 -ReaderNumRecordsProducedV2 -ReaderNumWorkUnitsCompletedV2 -ReaderReadV2 -ReaderReadUpToV2 -ReaderResetV2 -ReaderRestoreStateV2 -ReaderSerializeStateV2 -ReaderWorkQueueLengthV2 -Restore -RestoreSlice -Save -SaveSlices -ShardedFilename -ShardedFilespec -TextLineReader -TFRecordReader -WholeFileReader -TextLineReaderV2 -TFRecordReaderV2 -WholeFileReaderV2 -LMDBReader -DecodeCSV - -# linalg_ops -BatchCholesky -BatchCholeskyGrad -BatchMatrixDeterminant -BatchMatrixInverse -BatchMatrixSolve -BatchMatrixSolveLs -BatchMatrixTriangularSolve -BatchSelfAdjointEig -BatchSelfAdjointEigV2 -BatchSvd -LogMatrixDeterminant -MatrixExponential -MatrixLogarithm -MatrixSolveLs -SelfAdjointEig -SelfAdjointEigV2 -Svd - -# logging_ops -Assert -AudioSummary -AudioSummaryV2 -HistogramSummary -ImageSummary -MergeSummary -Print -ScalarSummary -TensorSummary -TensorSummaryV2 - -# math_ops -Abs -AccumulateNV2 -AddN -AddV2 -All -Any -BatchMatMul -BatchFFT -BatchFFT2D -BatchFFT3D -BatchIFFT -BatchIFFT2D -BatchIFFT3D -Bucketize -Complex -ComplexAbs -Conj -FloorDiv -FloorMod -HistogramFixedWidth -Max -Mean -Min -Mul -Neg -Pow -Prod -Range -RealDiv -Select -SparseMatMul -Sub -Sum -MatMul -Sigmoid -Tanh -SigmoidGrad -TanhGrad -InvGrad -ReciprocalGrad -SqrtGrad -RsqrtGrad -TruncateDiv -TruncateMod - -# nn_ops -AvgPoolGrad # "*Grad" accessible through nn_grad instead of nn_ops. -AvgPool3DGrad -BatchNormWithGlobalNormalization -BatchNormWithGlobalNormalizationGrad -FusedBatchNorm -FusedBatchNormV2 -SoftmaxCrossEntropyWithLogits -SparseSoftmaxCrossEntropyWithLogits -LRNGrad -MaxPoolGrad -MaxPoolGradWithArgmax -MaxPoolGradGrad -MaxPoolGradGradWithArgmax -MaxPool3DGrad -MaxPool3DGradGrad -ReluGrad -Relu6Grad -EluGrad -SeluGrad -SoftplusGrad -SoftsignGrad -TopK -TopKV2 -BiasAdd -BiasAddV1 -Relu6 -AvgPool -MaxPool -MaxPoolV2 -Softmax -LogSoftmax -FractionalAvgPoolGrad -FractionalMaxPoolGrad -InTopK -InTopKV2 - -# parsing_ops -ParseExample -ParseSingleSequenceExample - -# random_ops -RandomGamma -RandomPoisson -RandomUniform -RandomUniformInt -RandomShuffle -RandomStandardNormal -ParameterizedTruncatedNormal -TruncatedNormal - -# script_ops -PyFunc -PyFuncStateless -EagerPyFunc - -# sdca_ops - -# state_ops -Variable -VariableV2 -TemporaryVariable -DestroyTemporaryVariable - -# sparse_ops -AddSparseToTensorsMap -AddManySparseToTensorsMap -TakeManySparseFromTensorsMap -DeserializeManySparse -DeserializeSparse -SerializeManySparse -SerializeSparse -SparseAdd -SparseAddGrad -SparseConcat -SparseCross -SparseFillEmptyRows -SparseFillEmptyRowsGrad -SparseSplit -SparseSelectLastK -SparseReorder -SparseReshape -SparseToDense -SparseTensorDenseAdd -SparseTensorDenseMatMul - -# string_ops -StringSplit - -# user_ops -Fact - -# training_ops -# (None) - -# word2vec deprecated ops -NegTrain -Skipgram diff --git a/tensorflow/python/ops/histogram_ops.py b/tensorflow/python/ops/histogram_ops.py index 6a975160b0698270dfc9ce9140e8b3ff633cdb9e..4a1ef54fb50013881aa832f83674ac66ecccd9bc 100644 --- a/tensorflow/python/ops/histogram_ops.py +++ b/tensorflow/python/ops/histogram_ops.py @@ -141,5 +141,7 @@ def histogram_fixed_width(values, """ with ops.name_scope(name, 'histogram_fixed_width', [values, value_range, nbins]) as name: - return gen_math_ops._histogram_fixed_width( # pylint: disable=protected-access + # pylint: disable=protected-access + return gen_math_ops._histogram_fixed_width( values, value_range, nbins, dtype=dtype, name=name) + # pylint: enable=protected-access diff --git a/tensorflow/python/ops/image_grad.py b/tensorflow/python/ops/image_grad.py index 093843cd5bc0b7c2281a0c9ddf52d93ea3faede3..9f43e3f1466d900ae6d39f3b9ef48043421cb777 100644 --- a/tensorflow/python/ops/image_grad.py +++ b/tensorflow/python/ops/image_grad.py @@ -41,12 +41,10 @@ def _ResizeNearestNeighborGrad(op, grad): else: image_shape = array_ops.shape(image)[1:3] - # pylint: disable=protected-access - grads = gen_image_ops._resize_nearest_neighbor_grad( + grads = gen_image_ops.resize_nearest_neighbor_grad( grad, image_shape, align_corners=op.get_attr("align_corners")) - # pylint: enable=protected-access return [grads, None] @@ -61,10 +59,8 @@ def _ResizeBilinearGrad(op, grad): Returns: The gradients w.r.t. the input. """ - # pylint: disable=protected-access - grad0 = gen_image_ops._resize_bilinear_grad( + grad0 = gen_image_ops.resize_bilinear_grad( grad, op.inputs[0], align_corners=op.get_attr("align_corners")) - # pylint: enable=protected-access return [grad0, None] @@ -82,10 +78,8 @@ def _ResizeBicubicGrad(op, grad): allowed_types = [dtypes.float32, dtypes.float64] grad0 = None if op.inputs[0].dtype in allowed_types: - # pylint: disable=protected-access - grad0 = gen_image_ops._resize_bicubic_grad( + grad0 = gen_image_ops.resize_bicubic_grad( grad, op.inputs[0], align_corners=op.get_attr("align_corners")) - # pylint: enable=protected-access return [grad0, None] diff --git a/tensorflow/python/ops/image_ops.py b/tensorflow/python/ops/image_ops.py index ae52d32fea1c872e588c4122f5e73198e4dfe9ad..68be9ccdd642823e7a9c2294f209accd16f45be5 100644 --- a/tensorflow/python/ops/image_ops.py +++ b/tensorflow/python/ops/image_ops.py @@ -69,6 +69,11 @@ See the @{$python/image} guide. @@non_max_suppression @@sample_distorted_bounding_box @@total_variation +@@psnr +@@ssim +@@ssim_multiscale +@@image_gradients +@@sobel_edges """ from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/python/ops/image_ops_impl.py b/tensorflow/python/ops/image_ops_impl.py index 58c18c6696d64ccca4ebfaa07242d3c7789116e4..3369fe3c9b37ca05311c5548dbfa3228ba04ee80 100644 --- a/tensorflow/python/ops/image_ops_impl.py +++ b/tensorflow/python/ops/image_ops_impl.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 constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops @@ -29,6 +31,8 @@ from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import gen_image_ops from tensorflow.python.ops import gen_nn_ops from tensorflow.python.ops import math_ops +from tensorflow.python.ops import nn +from tensorflow.python.ops import nn_ops from tensorflow.python.ops import random_ops from tensorflow.python.ops import string_ops from tensorflow.python.ops import variables @@ -1113,10 +1117,8 @@ def adjust_contrast(images, contrast_factor): orig_dtype = images.dtype flt_images = convert_image_dtype(images, dtypes.float32) - # pylint: disable=protected-access - adjusted = gen_image_ops._adjust_contrastv2( + adjusted = gen_image_ops.adjust_contrastv2( flt_images, contrast_factor=contrast_factor, name=name) - # pylint: enable=protected-access return convert_image_dtype(adjusted, orig_dtype, saturate=True) @@ -1730,7 +1732,7 @@ def sample_distorted_bounding_box(image_size, Provide as input to `tf.image.draw_bounding_boxes`. """ with ops.name_scope(name, 'sample_distorted_bounding_box'): - return gen_image_ops._sample_distorted_bounding_box_v2( # pylint: disable=protected-access + return gen_image_ops.sample_distorted_bounding_box_v2( image_size, bounding_boxes, seed=seed, @@ -1784,10 +1786,8 @@ def non_max_suppression(boxes, """ with ops.name_scope(name, 'non_max_suppression'): iou_threshold = ops.convert_to_tensor(iou_threshold, name='iou_threshold') - # pylint: disable=protected-access - return gen_image_ops._non_max_suppression_v2(boxes, scores, max_output_size, - iou_threshold) - # pylint: enable=protected-access + return gen_image_ops.non_max_suppression_v2(boxes, scores, max_output_size, + iou_threshold) _rgb_to_yiq_kernel = [[0.299, 0.59590059, @@ -1795,6 +1795,7 @@ _rgb_to_yiq_kernel = [[0.299, 0.59590059, [0.114, -0.32134392, 0.31119955]] +@tf_export('image.rgb_to_yiq') def rgb_to_yiq(images): """Converts one or more images from RGB to YIQ. @@ -1820,6 +1821,7 @@ _yiq_to_rgb_kernel = [[1, 1, 1], [0.95598634, -0.27201283, -1.10674021], [0.6208248, -0.64720424, 1.70423049]] +@tf_export('image.yiq_to_rgb') def yiq_to_rgb(images): """Converts one or more images from YIQ to RGB. @@ -1847,6 +1849,7 @@ _rgb_to_yuv_kernel = [[0.299, -0.14714119, [0.114, 0.43601035, -0.10001026]] +@tf_export('image.rgb_to_yuv') def rgb_to_yuv(images): """Converts one or more images from RGB to YUV. @@ -1872,6 +1875,7 @@ _yuv_to_rgb_kernel = [[1, 1, 1], [0, -0.394642334, 2.03206185], [1.13988303, -0.58062185, 0]] +@tf_export('image.yuv_to_rgb') def yuv_to_rgb(images): """Converts one or more images from YUV to RGB. @@ -1892,3 +1896,489 @@ def yuv_to_rgb(images): _yuv_to_rgb_kernel, dtype=images.dtype, name='kernel') ndims = images.get_shape().ndims return math_ops.tensordot(images, kernel, axes=[[ndims - 1], [0]]) + + +def _verify_compatible_image_shapes(img1, img2): + """Checks if two image tensors are compatible for applying SSIM or PSNR. + + This function checks if two sets of images have ranks at least 3, and if the + last three dimensions match. + + Args: + img1: Tensor containing the first image batch. + img2: Tensor containing the second image batch. + + Returns: + A tuple containing: the first tensor shape, the second tensor shape, and a + list of control_flow_ops.Assert() ops implementing the checks. + + Raises: + ValueError: When static shape check fails. + """ + shape1 = img1.get_shape().with_rank_at_least(3) + shape2 = img2.get_shape().with_rank_at_least(3) + shape1[-3:].assert_is_compatible_with(shape2[-3:]) + + if shape1.ndims is not None and shape2.ndims is not None: + for dim1, dim2 in zip(reversed(shape1[:-3]), reversed(shape2[:-3])): + if not (dim1 == 1 or dim2 == 1 or dim1.is_compatible_with(dim2)): + raise ValueError( + 'Two images are not compatible: %s and %s' % (shape1, shape2)) + + # Now assign shape tensors. + shape1, shape2 = array_ops.shape_n([img1, img2]) + + # TODO(sjhwang): Check if shape1[:-3] and shape2[:-3] are broadcastable. + checks = [] + checks.append(control_flow_ops.Assert( + math_ops.greater_equal(array_ops.size(shape1), 3), + [shape1, shape2], summarize=10)) + checks.append(control_flow_ops.Assert( + math_ops.reduce_all(math_ops.equal(shape1[-3:], shape2[-3:])), + [shape1, shape2], summarize=10)) + return shape1, shape2, checks + + +@tf_export('image.psnr') +def psnr(a, b, max_val, name=None): + """Returns the Peak Signal-to-Noise Ratio between a and b. + + This is intended to be used on signals (or images). Produces a PSNR value for + each image in batch. + + The last three dimensions of input are expected to be [height, width, depth]. + + Example: + + ```python + # Read images from file. + im1 = tf.decode_png('path/to/im1.png') + im2 = tf.decode_png('path/to/im2.png') + # Compute PSNR over tf.uint8 Tensors. + psnr1 = tf.image.psnr(im1, im2, max_val=255) + + # Compute PSNR over tf.float32 Tensors. + im1 = tf.image.convert_image_dtype(im1, tf.float32) + im2 = tf.image.convert_image_dtype(im2, tf.float32) + psnr2 = tf.image.psnr(im1, im2, max_val=1.0) + # psnr1 and psnr2 both have type tf.float32 and are almost equal. + ``` + + Arguments: + a: First set of images. + b: Second set of images. + max_val: The dynamic range of the images (i.e., the difference between the + maximum the and minimum allowed values). + name: Namespace to embed the computation in. + + Returns: + The scalar PSNR between a and b. The returned tensor has type `tf.float32` + and shape [batch_size, 1]. + """ + with ops.name_scope(name, 'PSNR', [a, b]): + # Need to convert the images to float32. Scale max_val accordingly so that + # PSNR is computed correctly. + max_val = math_ops.cast(max_val, a.dtype) + max_val = convert_image_dtype(max_val, dtypes.float32) + a = convert_image_dtype(a, dtypes.float32) + b = convert_image_dtype(b, dtypes.float32) + mse = math_ops.reduce_mean(math_ops.squared_difference(a, b), [-3, -2, -1]) + psnr_val = math_ops.subtract( + 20 * math_ops.log(max_val) / math_ops.log(10.0), + np.float32(10 / np.log(10)) * math_ops.log(mse), + name='psnr') + + _, _, checks = _verify_compatible_image_shapes(a, b) + with ops.control_dependencies(checks): + return array_ops.identity(psnr_val) + +_SSIM_K1 = 0.01 +_SSIM_K2 = 0.03 + + +def _ssim_helper(x, y, reducer, max_val, compensation=1.0): + r"""Helper function for computing SSIM. + + SSIM estimates covariances with weighted sums. The default parameters + use a biased estimate of the covariance: + Suppose `reducer` is a weighted sum, then the mean estimators are + \mu_x = \sum_i w_i x_i, + \mu_y = \sum_i w_i y_i, + where w_i's are the weighted-sum weights, and covariance estimator is + cov_{xy} = \sum_i w_i (x_i - \mu_x) (y_i - \mu_y) + with assumption \sum_i w_i = 1. This covariance estimator is biased, since + E[cov_{xy}] = (1 - \sum_i w_i ^ 2) Cov(X, Y). + For SSIM measure with unbiased covariance estimators, pass as `compensation` + argument (1 - \sum_i w_i ^ 2). + + Arguments: + x: First set of images. + y: Second set of images. + reducer: Function that computes 'local' averages from set of images. + For non-covolutional version, this is usually tf.reduce_mean(x, [1, 2]), + and for convolutional version, this is usually tf.nn.avg_pool or + tf.nn.conv2d with weighted-sum kernel. + max_val: The dynamic range (i.e., the difference between the maximum + possible allowed value and the minimum allowed value). + compensation: Compensation factor. See above. + + Returns: + A pair containing the luminance measure, and the contrast-structure measure. + """ + c1 = (_SSIM_K1 * max_val) ** 2 + c2 = (_SSIM_K2 * max_val) ** 2 + + # SSIM luminance measure is + # (2 * mu_x * mu_y + c1) / (mu_x ** 2 + mu_y ** 2 + c1). + mean0 = reducer(x) + mean1 = reducer(y) + num0 = mean0 * mean1 * 2.0 + den0 = math_ops.square(mean0) + math_ops.square(mean1) + luminance = (num0 + c1) / (den0 + c1) + + # SSIM contrast-structure measure is + # (2 * cov_{xy} + c2) / (cov_{xx} + cov_{yy} + c2). + # Note that `reducer` is a weighted sum with weight w_k, \sum_i w_i = 1, then + # cov_{xy} = \sum_i w_i (x_i - \mu_x) (y_i - \mu_y) + # = \sum_i w_i x_i y_i - (\sum_i w_i x_i) (\sum_j w_j y_j). + num1 = reducer(x * y) * 2.0 + den1 = reducer(math_ops.square(x) + math_ops.square(y)) + c2 *= compensation + cs = (num1 - num0 + c2) / (den1 - den0 + c2) + + # SSIM score is the product of the luminance and contrast-structure measures. + return luminance, cs + + +def _fspecial_gauss(size, sigma): + """Function to mimic the 'fspecial' gaussian MATLAB function.""" + size = ops.convert_to_tensor(size, dtypes.int32) + sigma = ops.convert_to_tensor(sigma) + + coords = math_ops.cast(math_ops.range(size), sigma.dtype) + coords -= math_ops.cast(size - 1, sigma.dtype) / 2.0 + + g = math_ops.square(coords) + g *= -0.5 / math_ops.square(sigma) + + g = array_ops.reshape(g, shape=[1, -1]) + array_ops.reshape(g, shape=[-1, 1]) + g = array_ops.reshape(g, shape=[1, -1]) # For tf.nn.softmax(). + g = nn_ops.softmax(g) + return array_ops.reshape(g, shape=[size, size, 1, 1]) + + +def _ssim_per_channel(img1, img2, max_val=1.0): + """Computes SSIM index between img1 and img2 per color channel. + + This function matches the standard SSIM implementation from: + Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image + quality assessment: from error visibility to structural similarity. IEEE + transactions on image processing. + + Details: + - 11x11 Gaussian filter of width 1.5 is used. + - k1 = 0.01, k2 = 0.03 as in the original paper. + + Args: + img1: First image batch. + img2: Second image batch. + max_val: The dynamic range of the images (i.e., the difference between the + maximum the and minimum allowed values). + + Returns: + A pair of tensors containing and channel-wise SSIM and contrast-structure + values. The shape is [..., channels]. + """ + filter_size = constant_op.constant(11, dtype=dtypes.int32) + filter_sigma = constant_op.constant(1.5, dtype=img1.dtype) + + shape1, shape2 = array_ops.shape_n([img1, img2]) + checks = [ + control_flow_ops.Assert(math_ops.reduce_all(math_ops.greater_equal( + shape1[-3:-1], filter_size)), [shape1, filter_size], summarize=8), + control_flow_ops.Assert(math_ops.reduce_all(math_ops.greater_equal( + shape2[-3:-1], filter_size)), [shape2, filter_size], summarize=8)] + + # Enforce the check to run before computation. + with ops.control_dependencies(checks): + img1 = array_ops.identity(img1) + + # TODO(sjhwang): Try to cache kernels and compensation factor. + kernel = _fspecial_gauss(filter_size, filter_sigma) + kernel = array_ops.tile(kernel, multiples=[1, 1, shape1[-1], 1]) + + # The correct compensation factor is `1.0 - tf.reduce_sum(tf.square(kernel))`, + # but to match MATLAB implementation of MS-SSIM, we use 1.0 instead. + compensation = 1.0 + + # TODO(sjhwang): Try FFT. + # TODO(sjhwang): Gaussian kernel is separable in space. Consider applying + # 1-by-n and n-by-1 Gaussain filters instead of an n-by-n filter. + def reducer(x): + shape = array_ops.shape(x) + x = array_ops.reshape(x, shape=array_ops.concat([[-1], shape[-3:]], 0)) + y = nn.depthwise_conv2d(x, kernel, strides=[1, 1, 1, 1], padding='VALID') + return array_ops.reshape(y, array_ops.concat([shape[:-3], + array_ops.shape(y)[1:]], 0)) + + luminance, cs = _ssim_helper(img1, img2, reducer, max_val, compensation) + + # Average over the second and the third from the last: height, width. + axes = constant_op.constant([-3, -2], dtype=dtypes.int32) + ssim_val = math_ops.reduce_mean(luminance * cs, axes) + cs = math_ops.reduce_mean(cs, axes) + return ssim_val, cs + + +@tf_export('image.ssim') +def ssim(img1, img2, max_val): + """Computes SSIM index between img1 and img2. + + This function is based on the standard SSIM implementation from: + Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image + quality assessment: from error visibility to structural similarity. IEEE + transactions on image processing. + + Note: The true SSIM is only defined on grayscale. This function does not + perform any colorspace transform. (If input is already YUV, then it will + compute YUV SSIM average.) + + Details: + - 11x11 Gaussian filter of width 1.5 is used. + - k1 = 0.01, k2 = 0.03 as in the original paper. + + The image sizes must be at least 11x11 because of the filter size. + + Example: + + ```python + # Read images from file. + im1 = tf.decode_png('path/to/im1.png') + im2 = tf.decode_png('path/to/im2.png') + # Compute SSIM over tf.uint8 Tensors. + ssim1 = tf.image.ssim(im1, im2, max_val=255) + + # Compute SSIM over tf.float32 Tensors. + im1 = tf.image.convert_image_dtype(im1, tf.float32) + im2 = tf.image.convert_image_dtype(im2, tf.float32) + ssim2 = tf.image.ssim(im1, im2, max_val=1.0) + # ssim1 and ssim2 both have type tf.float32 and are almost equal. + ``` + + Args: + img1: First image batch. + img2: Second image batch. + max_val: The dynamic range of the images (i.e., the difference between the + maximum the and minimum allowed values). + + Returns: + A tensor containing an SSIM value for each image in batch. Returned SSIM + values are in range (-1, 1], when pixel values are non-negative. Returns + a tensor with shape: broadcast(img1.shape[:-3], img2.shape[:-3]). + """ + _, _, checks = _verify_compatible_image_shapes(img1, img2) + with ops.control_dependencies(checks): + img1 = array_ops.identity(img1) + + # Need to convert the images to float32. Scale max_val accordingly so that + # SSIM is computed correctly. + max_val = math_ops.cast(max_val, img1.dtype) + max_val = convert_image_dtype(max_val, dtypes.float32) + img1 = convert_image_dtype(img1, dtypes.float32) + img2 = convert_image_dtype(img2, dtypes.float32) + ssim_per_channel, _ = _ssim_per_channel(img1, img2, max_val) + # Compute average over color channels. + return math_ops.reduce_mean(ssim_per_channel, [-1]) + + +# Default values obtained by Wang et al. +_MSSSIM_WEIGHTS = (0.0448, 0.2856, 0.3001, 0.2363, 0.1333) + + +@tf_export('image.ssim_multiscale') +def ssim_multiscale(img1, img2, max_val, power_factors=_MSSSIM_WEIGHTS): + """Computes the MS-SSIM between img1 and img2. + + This function assumes that `img1` and `img2` are image batches, i.e. the last + three dimensions are [height, width, channels]. + + Note: The true SSIM is only defined on grayscale. This function does not + perform any colorspace transform. (If input is already YUV, then it will + compute YUV SSIM average.) + + Original paper: Wang, Zhou, Eero P. Simoncelli, and Alan C. Bovik. "Multiscale + structural similarity for image quality assessment." Signals, Systems and + Computers, 2004. + + Arguments: + img1: First image batch. + img2: Second image batch. Must have the same rank as img1. + max_val: The dynamic range of the images (i.e., the difference between the + maximum the and minimum allowed values). + power_factors: Iterable of weights for each of the scales. The number of + scales used is the length of the list. Index 0 is the unscaled + resolution's weight and each increasing scale corresponds to the image + being downsampled by 2. Defaults to (0.0448, 0.2856, 0.3001, 0.2363, + 0.1333), which are the values obtained in the original paper. + + Returns: + A tensor containing an MS-SSIM value for each image in batch. The values + are in range [0, 1]. Returns a tensor with shape: + broadcast(img1.shape[:-3], img2.shape[:-3]). + """ + # Shape checking. + shape1 = img1.get_shape().with_rank_at_least(3) + shape2 = img2.get_shape().with_rank_at_least(3) + shape1[-3:].merge_with(shape2[-3:]) + + with ops.name_scope(None, 'MS-SSIM', [img1, img2]): + shape1, shape2, checks = _verify_compatible_image_shapes(img1, img2) + with ops.control_dependencies(checks): + img1 = array_ops.identity(img1) + + # Need to convert the images to float32. Scale max_val accordingly so that + # SSIM is computed correctly. + max_val = math_ops.cast(max_val, img1.dtype) + max_val = convert_image_dtype(max_val, dtypes.float32) + img1 = convert_image_dtype(img1, dtypes.float32) + img2 = convert_image_dtype(img2, dtypes.float32) + + imgs = [img1, img2] + shapes = [shape1, shape2] + + # img1 and img2 are assumed to be a (multi-dimensional) batch of + # 3-dimensional images (height, width, channels). `heads` contain the batch + # dimensions, and `tails` contain the image dimensions. + heads = [s[:-3] for s in shapes] + tails = [s[-3:] for s in shapes] + + divisor = [1, 2, 2, 1] + divisor_tensor = constant_op.constant(divisor[1:], dtype=dtypes.int32) + + def do_pad(images, remainder): + padding = array_ops.expand_dims(remainder, -1) + padding = array_ops.pad(padding, [[1, 0], [1, 0]]) + return [array_ops.pad(x, padding, mode='SYMMETRIC') for x in images] + + mcs = [] + for k in range(len(power_factors)): + with ops.name_scope(None, 'Scale%d' % k, imgs): + if k > 0: + # Avg pool takes rank 4 tensors. Flatten leading dimensions. + flat_imgs = [ + array_ops.reshape(x, array_ops.concat([[-1], t], 0)) + for x, t in zip(imgs, tails) + ] + + remainder = tails[0] % divisor_tensor + need_padding = math_ops.reduce_any(math_ops.not_equal(remainder, 0)) + # pylint: disable=cell-var-from-loop + padded = control_flow_ops.cond(need_padding, + lambda: do_pad(flat_imgs, remainder), + lambda: flat_imgs) + # pylint: enable=cell-var-from-loop + + downscaled = [nn_ops.avg_pool(x, ksize=divisor, strides=divisor, + padding='VALID') + for x in padded] + tails = [x[1:] for x in array_ops.shape_n(downscaled)] + imgs = [ + array_ops.reshape(x, array_ops.concat([h, t], 0)) + for x, h, t in zip(downscaled, heads, tails) + ] + + # Overwrite previous ssim value since we only need the last one. + ssim_per_channel, cs = _ssim_per_channel(*imgs, max_val=max_val) + mcs.append(nn_ops.relu(cs)) + + # Remove the cs score for the last scale. In the MS-SSIM calculation, + # we use the l(p) at the highest scale. l(p) * cs(p) is ssim(p). + mcs.pop() # Remove the cs score for the last scale. + mcs_and_ssim = array_ops.stack(mcs + [nn_ops.relu(ssim_per_channel)], + axis=-1) + # Take weighted geometric mean across the scale axis. + ms_ssim = math_ops.reduce_prod(math_ops.pow(mcs_and_ssim, power_factors), + [-1]) + + return math_ops.reduce_mean(ms_ssim, [-1]) # Avg over color channels. + + +@tf_export('image.image_gradients') +def image_gradients(image): + """Returns image gradients (dy, dx) for each color channel. + + Both output tensors have the same shape as the input: [batch_size, h, w, + d]. The gradient values are organized so that [I(x+1, y) - I(x, y)] is in + location (x, y). That means that dy will always have zeros in the last row, + and dx will always have zeros in the last column. + + Arguments: + image: Tensor with shape [batch_size, h, w, d]. + + Returns: + Pair of tensors (dy, dx) holding the vertical and horizontal image + gradients (1-step finite difference). + + Raises: + ValueError: If `image` is not a 4D tensor. + """ + if image.get_shape().ndims != 4: + raise ValueError('image_gradients expects a 4D tensor ' + '[batch_size, h, w, d], not %s.', image.get_shape()) + image_shape = array_ops.shape(image) + batch_size, height, width, depth = array_ops.unstack(image_shape) + dy = image[:, 1:, :, :] - image[:, :-1, :, :] + dx = image[:, :, 1:, :] - image[:, :, :-1, :] + + # Return tensors with same size as original image by concatenating + # zeros. Place the gradient [I(x+1,y) - I(x,y)] on the base pixel (x, y). + shape = array_ops.stack([batch_size, 1, width, depth]) + dy = array_ops.concat([dy, array_ops.zeros(shape, image.dtype)], 1) + dy = array_ops.reshape(dy, image_shape) + + shape = array_ops.stack([batch_size, height, 1, depth]) + dx = array_ops.concat([dx, array_ops.zeros(shape, image.dtype)], 2) + dx = array_ops.reshape(dx, image_shape) + + return dy, dx + + +@tf_export('image.sobel_edges') +def sobel_edges(image): + """Returns a tensor holding Sobel edge maps. + + Arguments: + image: Image tensor with shape [batch_size, h, w, d] and type float32 or + float64. The image(s) must be 2x2 or larger. + + Returns: + Tensor holding edge maps for each channel. Returns a tensor with shape + [batch_size, h, w, d, 2] where the last two dimensions hold [[dy[0], dx[0]], + [dy[1], dx[1]], ..., [dy[d-1], dx[d-1]]] calculated using the Sobel filter. + """ + # Define vertical and horizontal Sobel filters. + static_image_shape = image.get_shape() + image_shape = array_ops.shape(image) + kernels = [[[-1, -2, -1], [0, 0, 0], [1, 2, 1]], + [[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]] + num_kernels = len(kernels) + kernels = np.transpose(np.asarray(kernels), (1, 2, 0)) + kernels = np.expand_dims(kernels, -2) + kernels_tf = constant_op.constant(kernels, dtype=image.dtype) + + kernels_tf = array_ops.tile(kernels_tf, [1, 1, image_shape[-1], 1], + name='sobel_filters') + + # Use depth-wise convolution to calculate edge maps per channel. + pad_sizes = [[0, 0], [1, 1], [1, 1], [0, 0]] + padded = array_ops.pad(image, pad_sizes, mode='REFLECT') + + # Output tensor has shape [batch_size, h, w, d * num_kernels]. + strides = [1, 1, 1, 1] + output = nn.depthwise_conv2d(padded, kernels_tf, strides, 'VALID') + + # Reshape to [batch_size, h, w, d, num_kernels]. + shape = array_ops.concat([image_shape, [num_kernels]], 0) + output = array_ops.reshape(output, shape=shape) + output.set_shape(static_image_shape.concatenate([num_kernels])) + return output diff --git a/tensorflow/python/ops/image_ops_test.py b/tensorflow/python/ops/image_ops_test.py index b8c4b27c162acdd86d88da641ff8afffaa5a9e6a..c437c12c2744792eaee197bf7d2a5f2b75d280bf 100644 --- a/tensorflow/python/ops/image_ops_test.py +++ b/tensorflow/python/ops/image_ops_test.py @@ -20,6 +20,7 @@ from __future__ import print_function import colorsys import functools +import itertools import math import os import time @@ -37,7 +38,9 @@ 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 gen_image_ops +from tensorflow.python.ops import gradients from tensorflow.python.ops import image_ops +from tensorflow.python.ops import image_ops_impl from tensorflow.python.ops import io_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import random_ops @@ -3328,5 +3331,420 @@ class NonMaxSuppressionTest(test_util.TensorFlowTestCase): image_ops.non_max_suppression(boxes, scores, 3, [[0.5]]) +class VerifyCompatibleImageShapesTest(test_util.TensorFlowTestCase): + """Tests utility function used by ssim() and psnr().""" + + def testWrongDims(self): + img = array_ops.placeholder(dtype=dtypes.float32) + img_np = np.array((2, 2)) + + with self.test_session(use_gpu=True) as sess: + _, _, checks = image_ops_impl._verify_compatible_image_shapes(img, img) + with self.assertRaises(errors.InvalidArgumentError): + sess.run(checks, {img: img_np}) + + def testShapeMismatch(self): + img1 = array_ops.placeholder(dtype=dtypes.float32) + img2 = array_ops.placeholder(dtype=dtypes.float32) + + img1_np = np.array([1, 2, 2, 1]) + img2_np = np.array([1, 3, 3, 1]) + + with self.test_session(use_gpu=True) as sess: + _, _, checks = image_ops_impl._verify_compatible_image_shapes(img1, img2) + with self.assertRaises(errors.InvalidArgumentError): + sess.run(checks, {img1: img1_np, img2: img2_np}) + + +class PSNRTest(test_util.TensorFlowTestCase): + """Tests for PSNR.""" + + def _LoadTestImage(self, sess, filename): + content = io_ops.read_file(os.path.join( + "tensorflow/core/lib/psnr/testdata", filename)) + im = image_ops.decode_jpeg(content, dct_method="INTEGER_ACCURATE") + im = image_ops.convert_image_dtype(im, dtypes.float32) + im, = sess.run([im]) + return np.expand_dims(im, axis=0) + + def _LoadTestImages(self): + with self.test_session(use_gpu=True) as sess: + q20 = self._LoadTestImage(sess, "cat_q20.jpg") + q72 = self._LoadTestImage(sess, "cat_q72.jpg") + q95 = self._LoadTestImage(sess, "cat_q95.jpg") + return q20, q72, q95 + + def _PSNR_NumPy(self, orig, target, max_value): + """Numpy implementation of PSNR.""" + mse = ((orig - target) ** 2).mean(axis=(-3, -2, -1)) + return 20 * np.log10(max_value) - 10 * np.log10(mse) + + def _RandomImage(self, shape, max_val): + """Returns an image or image batch with given shape.""" + return np.random.rand(*shape).astype(np.float32) * max_val + + def testPSNRSingleImage(self): + image1 = self._RandomImage((8, 8, 1), 1) + image2 = self._RandomImage((8, 8, 1), 1) + psnr = self._PSNR_NumPy(image1, image2, 1) + + with self.test_session(use_gpu=True): + tf_image1 = constant_op.constant(image1, shape=image1.shape, + dtype=dtypes.float32) + tf_image2 = constant_op.constant(image2, shape=image2.shape, + dtype=dtypes.float32) + tf_psnr = image_ops.psnr(tf_image1, tf_image2, 1.0, "psnr").eval() + self.assertAllClose(psnr, tf_psnr, atol=0.001) + + def testPSNRMultiImage(self): + image1 = self._RandomImage((10, 8, 8, 1), 1) + image2 = self._RandomImage((10, 8, 8, 1), 1) + psnr = self._PSNR_NumPy(image1, image2, 1) + + with self.test_session(use_gpu=True): + tf_image1 = constant_op.constant(image1, shape=image1.shape, + dtype=dtypes.float32) + tf_image2 = constant_op.constant(image2, shape=image2.shape, + dtype=dtypes.float32) + tf_psnr = image_ops.psnr(tf_image1, tf_image2, 1, "psnr").eval() + self.assertAllClose(psnr, tf_psnr, atol=0.001) + + def testGoldenPSNR(self): + q20, q72, q95 = self._LoadTestImages() + + # Verify NumPy implementation first. + # Golden values are generated using GNU Octave's psnr() function. + psnr1 = self._PSNR_NumPy(q20, q72, 1) + self.assertNear(30.321, psnr1, 0.001, msg="q20.dtype=" + str(q20.dtype)) + psnr2 = self._PSNR_NumPy(q20, q95, 1) + self.assertNear(29.994, psnr2, 0.001) + psnr3 = self._PSNR_NumPy(q72, q95, 1) + self.assertNear(35.302, psnr3, 0.001) + + # Test TensorFlow implementation. + with self.test_session(use_gpu=True): + tf_q20 = constant_op.constant(q20, shape=q20.shape, dtype=dtypes.float32) + tf_q72 = constant_op.constant(q72, shape=q72.shape, dtype=dtypes.float32) + tf_q95 = constant_op.constant(q95, shape=q95.shape, dtype=dtypes.float32) + tf_psnr1 = image_ops.psnr(tf_q20, tf_q72, 1, "psnr1").eval() + tf_psnr2 = image_ops.psnr(tf_q20, tf_q95, 1, "psnr2").eval() + tf_psnr3 = image_ops.psnr(tf_q72, tf_q95, 1, "psnr3").eval() + self.assertAllClose(psnr1, tf_psnr1, atol=0.001) + self.assertAllClose(psnr2, tf_psnr2, atol=0.001) + self.assertAllClose(psnr3, tf_psnr3, atol=0.001) + + def testInfinity(self): + q20, _, _ = self._LoadTestImages() + psnr = self._PSNR_NumPy(q20, q20, 1) + with self.test_session(use_gpu=True): + tf_q20 = constant_op.constant(q20, shape=q20.shape, dtype=dtypes.float32) + tf_psnr = image_ops.psnr(tf_q20, tf_q20, 1, "psnr").eval() + self.assertAllClose(psnr, tf_psnr, atol=0.001) + + def testInt(self): + img1 = self._RandomImage((10, 8, 8, 1), 255) + img2 = self._RandomImage((10, 8, 8, 1), 255) + img1 = constant_op.constant(img1, dtypes.uint8) + img2 = constant_op.constant(img2, dtypes.uint8) + psnr_uint8 = image_ops.psnr(img1, img2, 255) + img1 = image_ops.convert_image_dtype(img1, dtypes.float32) + img2 = image_ops.convert_image_dtype(img2, dtypes.float32) + psnr_float32 = image_ops.psnr(img1, img2, 1.0) + with self.test_session(use_gpu=True): + self.assertAllClose(psnr_uint8.eval(), psnr_float32.eval(), atol=0.001) + + +class SSIMTest(test_util.TensorFlowTestCase): + """Tests for SSIM.""" + + _filenames = ["checkerboard1.png", + "checkerboard2.png", + "checkerboard3.png",] + + _ssim = np.asarray([[1.000000, 0.230880, 0.231153], + [0.230880, 1.000000, 0.996828], + [0.231153, 0.996828, 1.000000]]) + + def _LoadTestImage(self, sess, filename): + content = io_ops.read_file(os.path.join( + "tensorflow/core/lib/ssim/testdata", filename)) + im = image_ops.decode_png(content) + im = image_ops.convert_image_dtype(im, dtypes.float32) + im, = sess.run([im]) + return np.expand_dims(im, axis=0) + + def _LoadTestImages(self): + with self.test_session(use_gpu=True) as sess: + return [self._LoadTestImage(sess, f) for f in self._filenames] + + def _RandomImage(self, shape, max_val): + """Returns an image or image batch with given shape.""" + return np.random.rand(*shape).astype(np.float32) * max_val + + def testAgainstMatlab(self): + """Tests against values produced by Matlab.""" + img = self._LoadTestImages() + expected = self._ssim[np.triu_indices(3)] + + ph = [array_ops.placeholder(dtype=dtypes.float32) for _ in range(2)] + ssim = image_ops.ssim(*ph, max_val=1.0) + with self.test_session(use_gpu=True): + scores = [ssim.eval(dict(zip(ph, t))) + for t in itertools.combinations_with_replacement(img, 2)] + self.assertAllClose(expected, np.squeeze(scores), atol=1e-4) + + def testBatch(self): + img = self._LoadTestImages() + expected = self._ssim[np.triu_indices(3, k=1)] + + img1, img2 = zip(*itertools.combinations(img, 2)) + img1 = np.concatenate(img1) + img2 = np.concatenate(img2) + + ssim = image_ops.ssim(constant_op.constant(img1), + constant_op.constant(img2), 1.0) + with self.test_session(use_gpu=True): + self.assertAllClose(expected, ssim.eval(), atol=1e-4) + + def testBroadcast(self): + img = self._LoadTestImages()[:2] + expected = self._ssim[:2, :2] + + img = constant_op.constant(np.concatenate(img)) + img1 = array_ops.expand_dims(img, axis=0) # batch dims: 1, 2. + img2 = array_ops.expand_dims(img, axis=1) # batch dims: 2, 1. + + ssim = image_ops.ssim(img1, img2, 1.0) + with self.test_session(use_gpu=True): + self.assertAllClose(expected, ssim.eval(), atol=1e-4) + + def testNegative(self): + """Tests against negative SSIM index.""" + step = np.expand_dims(np.arange(0, 256, 16, dtype=np.uint8), axis=0) + img1 = np.tile(step, (16, 1)) + img2 = np.fliplr(img1) + + img1 = img1.reshape((1, 16, 16, 1)) + img2 = img2.reshape((1, 16, 16, 1)) + + ssim = image_ops.ssim(constant_op.constant(img1), + constant_op.constant(img2), 255) + with self.test_session(use_gpu=True): + self.assertLess(ssim.eval(), 0) + + def testInt(self): + img1 = self._RandomImage((1, 16, 16, 3), 255) + img2 = self._RandomImage((1, 16, 16, 3), 255) + img1 = constant_op.constant(img1, dtypes.uint8) + img2 = constant_op.constant(img2, dtypes.uint8) + ssim_uint8 = image_ops.ssim(img1, img2, 255) + img1 = image_ops.convert_image_dtype(img1, dtypes.float32) + img2 = image_ops.convert_image_dtype(img2, dtypes.float32) + ssim_float32 = image_ops.ssim(img1, img2, 1.0) + with self.test_session(use_gpu=True): + self.assertAllClose(ssim_uint8.eval(), ssim_float32.eval(), atol=0.001) + + +class MultiscaleSSIMTest(test_util.TensorFlowTestCase): + """Tests for MS-SSIM.""" + + _filenames = ["checkerboard1.png", + "checkerboard2.png", + "checkerboard3.png",] + + _msssim = np.asarray([[1.000000, 0.091016, 0.091025], + [0.091016, 1.000000, 0.999567], + [0.091025, 0.999567, 1.000000]]) + + def _LoadTestImage(self, sess, filename): + content = io_ops.read_file(os.path.join( + "tensorflow/core/lib/ssim/testdata", filename)) + im = image_ops.decode_png(content) + im = image_ops.convert_image_dtype(im, dtypes.float32) + im, = sess.run([im]) + return np.expand_dims(im, axis=0) + + def _LoadTestImages(self): + with self.test_session(use_gpu=True) as sess: + return [self._LoadTestImage(sess, f) for f in self._filenames] + + def _RandomImage(self, shape, max_val): + """Returns an image or image batch with given shape.""" + return np.random.rand(*shape).astype(np.float32) * max_val + + def testAgainstMatlab(self): + """Tests against MS-SSIM computed with Matlab implementation. + + For color images, MS-SSIM scores are averaged over color channels. + """ + img = self._LoadTestImages() + expected = self._msssim[np.triu_indices(3)] + + ph = [array_ops.placeholder(dtype=dtypes.float32) for _ in range(2)] + msssim = image_ops.ssim_multiscale(*ph, max_val=1.0) + with self.test_session(use_gpu=True): + scores = [msssim.eval(dict(zip(ph, t))) + for t in itertools.combinations_with_replacement(img, 2)] + + self.assertAllClose(expected, np.squeeze(scores), atol=1e-4) + + def testUnweightedIsDifferentiable(self): + img = self._LoadTestImages() + ph = [array_ops.placeholder(dtype=dtypes.float32) for _ in range(2)] + scalar = constant_op.constant(1.0, dtype=dtypes.float32) + scaled_ph = [x * scalar for x in ph] + msssim = image_ops.ssim_multiscale(*scaled_ph, max_val=1.0, + power_factors=(1, 1, 1, 1, 1)) + grads = gradients.gradients(msssim, scalar) + with self.test_session(use_gpu=True) as sess: + np_grads = sess.run(grads, feed_dict={ph[0]: img[0], ph[1]: img[1]}) + self.assertTrue(np.isfinite(np_grads).all()) + + def testBatch(self): + """Tests MS-SSIM computed in batch.""" + img = self._LoadTestImages() + expected = self._msssim[np.triu_indices(3, k=1)] + + img1, img2 = zip(*itertools.combinations(img, 2)) + img1 = np.concatenate(img1) + img2 = np.concatenate(img2) + + msssim = image_ops.ssim_multiscale(constant_op.constant(img1), + constant_op.constant(img2), 1.0) + with self.test_session(use_gpu=True): + self.assertAllClose(expected, msssim.eval(), 1e-4) + + def testBroadcast(self): + """Tests MS-SSIM broadcasting.""" + img = self._LoadTestImages()[:2] + expected = self._msssim[:2, :2] + + img = constant_op.constant(np.concatenate(img)) + img1 = array_ops.expand_dims(img, axis=0) # batch dims: 1, 2. + img2 = array_ops.expand_dims(img, axis=1) # batch dims: 2, 1. + + score_tensor = image_ops.ssim_multiscale(img1, img2, 1.0) + with self.test_session(use_gpu=True): + self.assertAllClose(expected, score_tensor.eval(), 1e-4) + + def testRange(self): + """Tests against low MS-SSIM score. + + MS-SSIM is a geometric mean of SSIM and CS scores of various scales. + If any of the value is negative so that the geometric mean is not + well-defined, then treat the MS-SSIM score as zero. + """ + with self.test_session(use_gpu=True) as sess: + img1 = self._LoadTestImage(sess, "checkerboard1.png") + img2 = self._LoadTestImage(sess, "checkerboard3.png") + images = [img1, img2, np.zeros_like(img1), + np.full_like(img1, fill_value=255)] + + images = [ops.convert_to_tensor(x, dtype=dtypes.float32) for x in images] + msssim_ops = [image_ops.ssim_multiscale(x, y, 1.0) + for x, y in itertools.combinations(images, 2)] + msssim = sess.run(msssim_ops) + msssim = np.squeeze(msssim) + + self.assertTrue(np.all(msssim >= 0.0)) + self.assertTrue(np.all(msssim <= 1.0)) + + def testInt(self): + img1 = self._RandomImage((1, 180, 240, 3), 255) + img2 = self._RandomImage((1, 180, 240, 3), 255) + img1 = constant_op.constant(img1, dtypes.uint8) + img2 = constant_op.constant(img2, dtypes.uint8) + ssim_uint8 = image_ops.ssim_multiscale(img1, img2, 255) + img1 = image_ops.convert_image_dtype(img1, dtypes.float32) + img2 = image_ops.convert_image_dtype(img2, dtypes.float32) + ssim_float32 = image_ops.ssim_multiscale(img1, img2, 1.0) + with self.test_session(use_gpu=True): + self.assertAllClose(ssim_uint8.eval(), ssim_float32.eval(), atol=0.001) + + +class ImageGradientsTest(test_util.TensorFlowTestCase): + + def testImageGradients(self): + shape = [1, 2, 4, 1] + img = constant_op.constant([[1, 3, 4, 2], [8, 7, 5, 6]]) + img = array_ops.reshape(img, shape) + + expected_dy = np.reshape([[7, 4, 1, 4], [0, 0, 0, 0]], shape) + expected_dx = np.reshape([[2, 1, -2, 0], [-1, -2, 1, 0]], shape) + + dy, dx = image_ops.image_gradients(img) + with self.test_session(): + actual_dy = dy.eval() + actual_dx = dx.eval() + self.assertAllClose(expected_dy, actual_dy) + self.assertAllClose(expected_dx, actual_dx) + + def testImageGradientsMultiChannelBatch(self): + batch = [[[[1, 2], [2, 5], [3, 3]], + [[8, 4], [5, 1], [9, 8]]], + [[[5, 3], [7, 9], [1, 6]], + [[1, 2], [6, 3], [6, 3]]]] + + expected_dy = [[[[7, 2], [3, -4], [6, 5]], + [[0, 0], [0, 0], [0, 0]]], + [[[-4, -1], [-1, -6], [5, -3]], + [[0, 0], [0, 0], [0, 0]]]] + + expected_dx = [[[[1, 3], [1, -2], [0, 0]], + [[-3, -3], [4, 7], [0, 0]]], + [[[2, 6], [-6, -3], [0, 0]], + [[5, 1], [0, 0], [0, 0]]]] + + batch = constant_op.constant(batch) + assert batch.get_shape().as_list() == [2, 2, 3, 2] + dy, dx = image_ops.image_gradients(batch) + with self.test_session(use_gpu=True): + actual_dy = dy.eval() + actual_dx = dx.eval() + self.assertAllClose(expected_dy, actual_dy) + self.assertAllClose(expected_dx, actual_dx) + + def testImageGradientsBadShape(self): + # [2 x 4] image but missing batch and depth dimensions. + img = constant_op.constant([[1, 3, 4, 2], [8, 7, 5, 6]]) + with self.assertRaises(ValueError): + image_ops.image_gradients(img) + + +class SobelEdgesTest(test_util.TensorFlowTestCase): + + def testSobelEdges1x2x3x1(self): + img = constant_op.constant([[1, 3, 6], [4, 1, 5]], + dtype=dtypes.float32, shape=[1, 2, 3, 1]) + expected = np.reshape([[[0, 0], [0, 12], [0, 0]], + [[0, 0], [0, 12], [0, 0]]], [1, 2, 3, 1, 2]) + sobel = image_ops.sobel_edges(img) + with self.test_session(use_gpu=True): + actual_sobel = sobel.eval() + self.assertAllClose(expected, actual_sobel) + + def testSobelEdges5x3x4x2(self): + batch_size = 5 + plane = np.reshape([[1, 3, 6, 2], [4, 1, 5, 7], [2, 5, 1, 4]], + [1, 3, 4, 1]) + two_channel = np.concatenate([plane, plane], axis=3) + batch = np.concatenate([two_channel] * batch_size, axis=0) + img = constant_op.constant(batch, dtype=dtypes.float32, + shape=[batch_size, 3, 4, 2]) + + expected_plane = np.reshape([[[0, 0], [0, 12], [0, 10], [0, 0]], + [[6, 0], [0, 6], [-6, 10], [-6, 0]], + [[0, 0], [0, 0], [0, 10], [0, 0]]], + [1, 3, 4, 1, 2]) + expected_two_channel = np.concatenate( + [expected_plane, expected_plane], axis=3) + expected_batch = np.concatenate([expected_two_channel] * batch_size, axis=0) + + sobel = image_ops.sobel_edges(img) + with self.test_session(use_gpu=True): + actual_sobel = sobel.eval() + self.assertAllClose(expected_batch, actual_sobel) + + if __name__ == "__main__": googletest.main() diff --git a/tensorflow/python/ops/init_ops.py b/tensorflow/python/ops/init_ops.py index c7502d0fda5c38079362d30877a917e3965e6ca0..40ab22951b1aa04a61e09aac155b6449ae358d7b 100644 --- a/tensorflow/python/ops/init_ops.py +++ b/tensorflow/python/ops/init_ops.py @@ -542,6 +542,62 @@ class Orthogonal(Initializer): return {"gain": self.gain, "seed": self.seed, "dtype": self.dtype.name} +class ConvolutionDeltaOrthogonal(Initializer): + """Initializer that generates a delta orthogonal kernel for ConvNets. + + The shape of the tensor must have length 3, 4 or 5. The number of input + filters must not exceed the number of output filters. The center pixels of the + tensor form an orthogonal matrix. Other pixels are set to be zero. + + Args: + gain: multiplicative factor to apply to the orthogonal matrix. Default is 1. + The 2-norm of an input is multiplied by a factor of 'sqrt(gain)' after + applying this convolution. + dtype: The type of the output. + seed: A Python integer. Used to create random seeds. See + @{tf.set_random_seed} + for behavior. + """ + + def __init__(self, gain=1.0, seed=None, dtype=dtypes.float32): + self.gain = gain + self.dtype = _assert_float_dtype(dtypes.as_dtype(dtype)) + self.seed = seed + + def __call__(self, shape, dtype=None, partition_info=None): + if dtype is None: + dtype = self.dtype + # Check the shape + if len(shape) < 3 or len(shape) > 5: + raise ValueError("The tensor to initialize must be at least " + "three-dimensional and at most five-dimensional") + + if shape[-2] > shape[-1]: + raise ValueError("In_filters cannot be greater than out_filters.") + + # Generate a random matrix + a = random_ops.random_normal([shape[-1], shape[-1]], + dtype=dtype, seed=self.seed) + # Compute the qr factorization + q, _ = linalg_ops.qr(a, full_matrices=False) + q = q[:shape[-2], :] + q *= math_ops.sqrt(math_ops.cast(self.gain, dtype=dtype)) + if len(shape) == 3: + weight = array_ops.scatter_nd([[(shape[0]-1)//2]], + array_ops.expand_dims(q, 0), shape) + elif len(shape) == 4: + weight = array_ops.scatter_nd([[(shape[0]-1)//2, (shape[1]-1)//2]], + array_ops.expand_dims(q, 0), shape) + else: + weight = array_ops.scatter_nd([[(shape[0]-1)//2, (shape[1]-1)//2, + (shape[2]-1)//2]], + array_ops.expand_dims(q, 0), shape) + return weight + + def get_config(self): + return {"gain": self.gain, "seed": self.seed, "dtype": self.dtype.name} + + @tf_export("keras.initializers.Identity", "initializers.identity") class Identity(Initializer): """Initializer that generates the identity matrix. @@ -586,7 +642,7 @@ uniform_unit_scaling_initializer = UniformUnitScaling variance_scaling_initializer = VarianceScaling orthogonal_initializer = Orthogonal identity_initializer = Identity - +convolutional_delta_orthogonal = ConvolutionDeltaOrthogonal # pylint: enable=invalid-name diff --git a/tensorflow/python/ops/initializers_ns.py b/tensorflow/python/ops/initializers_ns.py index c21079f2971a4bdd76b4be1a803055c12b243903..e7996efe93eb2f33306a52ded91c273009192789 100644 --- a/tensorflow/python/ops/initializers_ns.py +++ b/tensorflow/python/ops/initializers_ns.py @@ -39,5 +39,8 @@ global_variables = _variables.global_variables_initializer local_variables = _variables.local_variables_initializer # Seal API. +del absolute_import +del division +del print_function del init_ops del _variables diff --git a/tensorflow/python/ops/io_ops.py b/tensorflow/python/ops/io_ops.py index 5e70b3186f382a0c795b1795b2db27bb2058ee41..f6a25610c5a2ee8b76d06e286365cb957ab643cd 100644 --- a/tensorflow/python/ops/io_ops.py +++ b/tensorflow/python/ops/io_ops.py @@ -111,10 +111,10 @@ def _save(filename, tensor_names, tensors, tensor_slices=None, name="save"): An Operation that saves the tensors. """ if tensor_slices is None: - return gen_io_ops._save(filename, tensor_names, tensors, name=name) + return gen_io_ops.save(filename, tensor_names, tensors, name=name) else: - return gen_io_ops._save_slices(filename, tensor_names, tensor_slices, - tensors, name=name) + return gen_io_ops.save_slices(filename, tensor_names, tensor_slices, + tensors, name=name) def _restore_slice(file_pattern, tensor_name, shape_and_slice, tensor_type, @@ -136,7 +136,7 @@ def _restore_slice(file_pattern, tensor_name, shape_and_slice, tensor_type, A tensor of type "tensor_type". """ base_type = dtypes.as_dtype(tensor_type).base_dtype - return gen_io_ops._restore_slice( + return gen_io_ops.restore_slice( file_pattern, tensor_name, shape_and_slice, base_type, preferred_shard, name=name) @@ -173,7 +173,7 @@ class ReaderBase(object): Raises: RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError( "Readers are not supported when eager execution is enabled. " "Instead, please use tf.data to get data into your model.") @@ -208,12 +208,12 @@ class ReaderBase(object): else: queue_ref = queue.queue_ref if self._reader_ref.dtype == dtypes.resource: - return gen_io_ops._reader_read_v2(self._reader_ref, queue_ref, name=name) + return gen_io_ops.reader_read_v2(self._reader_ref, queue_ref, name=name) else: # For compatibility with pre-resource queues, create a ref(string) tensor # which can be looked up as the same queue by a resource manager. - old_queue_op = gen_data_flow_ops._fake_queue(queue_ref) - return gen_io_ops._reader_read(self._reader_ref, old_queue_op, name=name) + old_queue_op = gen_data_flow_ops.fake_queue(queue_ref) + return gen_io_ops.reader_read(self._reader_ref, old_queue_op, name=name) def read_up_to(self, queue, num_records, # pylint: disable=invalid-name name=None): @@ -240,18 +240,18 @@ class ReaderBase(object): else: queue_ref = queue.queue_ref if self._reader_ref.dtype == dtypes.resource: - return gen_io_ops._reader_read_up_to_v2(self._reader_ref, - queue_ref, - num_records, - name=name) + return gen_io_ops.reader_read_up_to_v2(self._reader_ref, + queue_ref, + num_records, + name=name) else: # For compatibility with pre-resource queues, create a ref(string) tensor # which can be looked up as the same queue by a resource manager. - old_queue_op = gen_data_flow_ops._fake_queue(queue_ref) - return gen_io_ops._reader_read_up_to(self._reader_ref, - old_queue_op, - num_records, - name=name) + old_queue_op = gen_data_flow_ops.fake_queue(queue_ref) + return gen_io_ops.reader_read_up_to(self._reader_ref, + old_queue_op, + num_records, + name=name) def num_records_produced(self, name=None): """Returns the number of records this reader has produced. @@ -267,11 +267,11 @@ class ReaderBase(object): """ if self._reader_ref.dtype == dtypes.resource: - return gen_io_ops._reader_num_records_produced_v2(self._reader_ref, - name=name) + return gen_io_ops.reader_num_records_produced_v2(self._reader_ref, + name=name) else: - return gen_io_ops._reader_num_records_produced(self._reader_ref, - name=name) + return gen_io_ops.reader_num_records_produced(self._reader_ref, + name=name) def num_work_units_completed(self, name=None): """Returns the number of work units this reader has finished processing. @@ -283,11 +283,11 @@ class ReaderBase(object): An int64 Tensor. """ if self._reader_ref.dtype == dtypes.resource: - return gen_io_ops._reader_num_work_units_completed_v2(self._reader_ref, - name=name) + return gen_io_ops.reader_num_work_units_completed_v2(self._reader_ref, + name=name) else: - return gen_io_ops._reader_num_work_units_completed(self._reader_ref, - name=name) + return gen_io_ops.reader_num_work_units_completed(self._reader_ref, + name=name) def serialize_state(self, name=None): """Produce a string tensor that encodes the state of a reader. @@ -302,9 +302,9 @@ class ReaderBase(object): A string Tensor. """ if self._reader_ref.dtype == dtypes.resource: - return gen_io_ops._reader_serialize_state_v2(self._reader_ref, name=name) + return gen_io_ops.reader_serialize_state_v2(self._reader_ref, name=name) else: - return gen_io_ops._reader_serialize_state(self._reader_ref, name=name) + return gen_io_ops.reader_serialize_state(self._reader_ref, name=name) def restore_state(self, state, name=None): """Restore a reader to a previously saved state. @@ -321,11 +321,10 @@ class ReaderBase(object): The created Operation. """ if self._reader_ref.dtype == dtypes.resource: - return gen_io_ops._reader_restore_state_v2( + return gen_io_ops.reader_restore_state_v2( self._reader_ref, state, name=name) else: - return gen_io_ops._reader_restore_state( - self._reader_ref, state, name=name) + return gen_io_ops.reader_restore_state(self._reader_ref, state, name=name) @property def supports_serialize(self): @@ -342,9 +341,9 @@ class ReaderBase(object): The created Operation. """ if self._reader_ref.dtype == dtypes.resource: - return gen_io_ops._reader_reset_v2(self._reader_ref, name=name) + return gen_io_ops.reader_reset_v2(self._reader_ref, name=name) else: - return gen_io_ops._reader_reset(self._reader_ref, name=name) + return gen_io_ops.reader_reset(self._reader_ref, name=name) ops.NotDifferentiable("ReaderRead") @@ -377,7 +376,7 @@ class WholeFileReader(ReaderBase): Args: name: A name for the operation (optional). """ - rr = gen_io_ops._whole_file_reader_v2(name=name) + rr = gen_io_ops.whole_file_reader_v2(name=name) super(WholeFileReader, self).__init__(rr, supports_serialize=True) @@ -406,8 +405,8 @@ class TextLineReader(ReaderBase): to skip from the beginning of every file. name: A name for the operation (optional). """ - rr = gen_io_ops._text_line_reader_v2(skip_header_lines=skip_header_lines, - name=name) + rr = gen_io_ops.text_line_reader_v2(skip_header_lines=skip_header_lines, + name=name) super(TextLineReader, self).__init__(rr) @@ -444,7 +443,7 @@ class FixedLengthRecordReader(ReaderBase): name: A name for the operation (optional). encoding: The type of encoding for the file. Defaults to none. """ - rr = gen_io_ops._fixed_length_record_reader_v2( + rr = gen_io_ops.fixed_length_record_reader_v2( record_bytes=record_bytes, header_bytes=header_bytes, footer_bytes=footer_bytes, @@ -480,7 +479,7 @@ class TFRecordReader(ReaderBase): compression_type = python_io.TFRecordOptions.get_compression_type_string( options) - rr = gen_io_ops._tf_record_reader_v2( + rr = gen_io_ops.tf_record_reader_v2( name=name, compression_type=compression_type) super(TFRecordReader, self).__init__(rr) @@ -506,7 +505,7 @@ class LMDBReader(ReaderBase): name: A name for the operation (optional). options: A LMDBRecordOptions object (optional). """ - rr = gen_io_ops._lmdb_reader(name=name) + rr = gen_io_ops.lmdb_reader(name=name) super(LMDBReader, self).__init__(rr) @@ -534,7 +533,7 @@ class IdentityReader(ReaderBase): Args: name: A name for the operation (optional). """ - rr = gen_io_ops._identity_reader_v2(name=name) + rr = gen_io_ops.identity_reader_v2(name=name) super(IdentityReader, self).__init__(rr, supports_serialize=True) diff --git a/tensorflow/python/ops/linalg/linalg_impl.py b/tensorflow/python/ops/linalg/linalg_impl.py index d5bd916f80d8a03e5423c43d1ca039bc4dceff5e..8343c62816c6aeadc77dae701ae9917a86e68954 100644 --- a/tensorflow/python/ops/linalg/linalg_impl.py +++ b/tensorflow/python/ops/linalg/linalg_impl.py @@ -31,18 +31,19 @@ band_part = array_ops.matrix_band_part cholesky = linalg_ops.cholesky cholesky_solve = linalg_ops.cholesky_solve det = linalg_ops.matrix_determinant -# pylint: disable=protected-access -slogdet = gen_linalg_ops._log_matrix_determinant -# pylint: disable=protected-access +slogdet = gen_linalg_ops.log_matrix_determinant +tf_export('linalg.slogdet')(slogdet) diag = array_ops.matrix_diag diag_part = array_ops.matrix_diag_part eigh = linalg_ops.self_adjoint_eig eigvalsh = linalg_ops.self_adjoint_eigvals einsum = special_math_ops.einsum -expm = gen_linalg_ops._matrix_exponential +expm = gen_linalg_ops.matrix_exponential +tf_export('linalg.expm')(expm) eye = linalg_ops.eye inv = linalg_ops.matrix_inverse -logm = gen_linalg_ops._matrix_logarithm +logm = gen_linalg_ops.matrix_logarithm +tf_export('linalg.logm')(logm) lstsq = linalg_ops.matrix_solve_ls norm = linalg_ops.norm qr = linalg_ops.qr diff --git a/tensorflow/python/ops/linalg/linear_operator_test_util.py b/tensorflow/python/ops/linalg/linear_operator_test_util.py index 2c11f90e6d9de280e6020edfaa4d8ef237126705..ce1a112ad584a14298be6e471578858ef31573d5 100644 --- a/tensorflow/python/ops/linalg/linear_operator_test_util.py +++ b/tensorflow/python/ops/linalg/linear_operator_test_util.py @@ -35,6 +35,18 @@ from tensorflow.python.ops.linalg import linalg_impl as linalg from tensorflow.python.platform import test +class OperatorBuildInfo(object): + """Object encoding expected shape for a test. + + Encodes the expected shape of a matrix for a test. Also + allows additional metadata for the test harness. + """ + + def __init__(self, shape, **kwargs): + self.shape = shape + self.__dict__.update(kwargs) + + @six.add_metaclass(abc.ABCMeta) # pylint: disable=no-init class LinearOperatorDerivedClassTest(test.TestCase): """Tests for derived classes. @@ -84,19 +96,20 @@ class LinearOperatorDerivedClassTest(test.TestCase): return [False, True] @abc.abstractproperty - def _shapes_to_test(self): - """Returns list of tuples, each is one shape that will be tested.""" - raise NotImplementedError("shapes_to_test has not been implemented.") + def _operator_build_infos(self): + """Returns list of OperatorBuildInfo, encapsulating the shape to test.""" + raise NotImplementedError("operator_build_infos has not been implemented.") @abc.abstractmethod - def _operator_and_mat_and_feed_dict(self, shape, dtype, use_placeholder): + def _operator_and_mat_and_feed_dict(self, build_info, dtype, use_placeholder): """Build a batch matrix and an Operator that should have similar behavior. Every operator acts like a (batch) matrix. This method returns both together, and is used by tests. Args: - shape: List-like of Python integers giving full shape of operator. + build_info: `OperatorBuildInfo`, encoding shape information about the + operator. dtype: Numpy dtype. Data type of returned array/operator. use_placeholder: Python bool. If True, initialize the operator with a placeholder of undefined shape and correct dtype. @@ -164,30 +177,30 @@ class LinearOperatorDerivedClassTest(test.TestCase): def test_to_dense(self): self._skip_if_tests_to_skip_contains("to_dense") for use_placeholder in self._use_placeholder_options: - for shape in self._shapes_to_test: + for build_info in self._operator_build_infos: for dtype in self._dtypes_to_test: with self.test_session(graph=ops.Graph()) as sess: sess.graph.seed = random_seed.DEFAULT_GRAPH_SEED operator, mat, feed_dict = self._operator_and_mat_and_feed_dict( - shape, dtype, use_placeholder=use_placeholder) + build_info, dtype, use_placeholder=use_placeholder) op_dense = operator.to_dense() if not use_placeholder: - self.assertAllEqual(shape, op_dense.get_shape()) + self.assertAllEqual(build_info.shape, op_dense.get_shape()) op_dense_v, mat_v = sess.run([op_dense, mat], feed_dict=feed_dict) self.assertAC(op_dense_v, mat_v) def test_det(self): self._skip_if_tests_to_skip_contains("det") for use_placeholder in self._use_placeholder_options: - for shape in self._shapes_to_test: + for build_info in self._operator_build_infos: for dtype in self._dtypes_to_test: with self.test_session(graph=ops.Graph()) as sess: sess.graph.seed = random_seed.DEFAULT_GRAPH_SEED operator, mat, feed_dict = self._operator_and_mat_and_feed_dict( - shape, dtype, use_placeholder=use_placeholder) + build_info, dtype, use_placeholder=use_placeholder) op_det = operator.determinant() if not use_placeholder: - self.assertAllEqual(shape[:-2], op_det.get_shape()) + self.assertAllEqual(build_info.shape[:-2], op_det.get_shape()) op_det_v, mat_det_v = sess.run( [op_det, linalg_ops.matrix_determinant(mat)], feed_dict=feed_dict) @@ -196,16 +209,17 @@ class LinearOperatorDerivedClassTest(test.TestCase): def test_log_abs_det(self): self._skip_if_tests_to_skip_contains("log_abs_det") for use_placeholder in self._use_placeholder_options: - for shape in self._shapes_to_test: + for build_info in self._operator_build_infos: for dtype in self._dtypes_to_test: with self.test_session(graph=ops.Graph()) as sess: sess.graph.seed = random_seed.DEFAULT_GRAPH_SEED operator, mat, feed_dict = self._operator_and_mat_and_feed_dict( - shape, dtype, use_placeholder=use_placeholder) + build_info, dtype, use_placeholder=use_placeholder) op_log_abs_det = operator.log_abs_determinant() _, mat_log_abs_det = linalg.slogdet(mat) if not use_placeholder: - self.assertAllEqual(shape[:-2], op_log_abs_det.get_shape()) + self.assertAllEqual( + build_info.shape[:-2], op_log_abs_det.get_shape()) op_log_abs_det_v, mat_log_abs_det_v = sess.run( [op_log_abs_det, mat_log_abs_det], feed_dict=feed_dict) self.assertAC(op_log_abs_det_v, mat_log_abs_det_v) @@ -213,14 +227,14 @@ class LinearOperatorDerivedClassTest(test.TestCase): def test_matmul(self): self._skip_if_tests_to_skip_contains("matmul") for use_placeholder in self._use_placeholder_options: - for shape in self._shapes_to_test: + for build_info in self._operator_build_infos: for dtype in self._dtypes_to_test: for adjoint in self._adjoint_options: for adjoint_arg in self._adjoint_arg_options: with self.test_session(graph=ops.Graph()) as sess: sess.graph.seed = random_seed.DEFAULT_GRAPH_SEED operator, mat, feed_dict = self._operator_and_mat_and_feed_dict( - shape, dtype, use_placeholder=use_placeholder) + build_info, dtype, use_placeholder=use_placeholder) x = self._make_x(operator, adjoint=adjoint) # If adjoint_arg, compute A X^H^H = A X. if adjoint_arg: @@ -241,14 +255,14 @@ class LinearOperatorDerivedClassTest(test.TestCase): def test_solve(self): self._skip_if_tests_to_skip_contains("solve") for use_placeholder in self._use_placeholder_options: - for shape in self._shapes_to_test: + for build_info in self._operator_build_infos: for dtype in self._dtypes_to_test: for adjoint in self._adjoint_options: for adjoint_arg in self._adjoint_arg_options: with self.test_session(graph=ops.Graph()) as sess: sess.graph.seed = random_seed.DEFAULT_GRAPH_SEED operator, mat, feed_dict = self._operator_and_mat_and_feed_dict( - shape, dtype, use_placeholder=use_placeholder) + build_info, dtype, use_placeholder=use_placeholder) rhs = self._make_rhs(operator, adjoint=adjoint) # If adjoint_arg, solve A X = (rhs^H)^H = rhs. if adjoint_arg: @@ -270,12 +284,12 @@ class LinearOperatorDerivedClassTest(test.TestCase): def test_trace(self): self._skip_if_tests_to_skip_contains("trace") for use_placeholder in self._use_placeholder_options: - for shape in self._shapes_to_test: + for build_info in self._operator_build_infos: for dtype in self._dtypes_to_test: with self.test_session(graph=ops.Graph()) as sess: sess.graph.seed = random_seed.DEFAULT_GRAPH_SEED operator, mat, feed_dict = self._operator_and_mat_and_feed_dict( - shape, dtype, use_placeholder=use_placeholder) + build_info, dtype, use_placeholder=use_placeholder) op_trace = operator.trace() mat_trace = math_ops.trace(mat) if not use_placeholder: @@ -287,16 +301,16 @@ class LinearOperatorDerivedClassTest(test.TestCase): def test_add_to_tensor(self): self._skip_if_tests_to_skip_contains("add_to_tensor") for use_placeholder in self._use_placeholder_options: - for shape in self._shapes_to_test: + for build_info in self._operator_build_infos: for dtype in self._dtypes_to_test: with self.test_session(graph=ops.Graph()) as sess: sess.graph.seed = random_seed.DEFAULT_GRAPH_SEED operator, mat, feed_dict = self._operator_and_mat_and_feed_dict( - shape, dtype, use_placeholder=use_placeholder) + build_info, dtype, use_placeholder=use_placeholder) op_plus_2mat = operator.add_to_tensor(2 * mat) if not use_placeholder: - self.assertAllEqual(shape, op_plus_2mat.get_shape()) + self.assertAllEqual(build_info.shape, op_plus_2mat.get_shape()) op_plus_2mat_v, mat_v = sess.run( [op_plus_2mat, mat], feed_dict=feed_dict) @@ -306,12 +320,12 @@ class LinearOperatorDerivedClassTest(test.TestCase): def test_diag_part(self): self._skip_if_tests_to_skip_contains("diag_part") for use_placeholder in self._use_placeholder_options: - for shape in self._shapes_to_test: + for build_info in self._operator_build_infos: for dtype in self._dtypes_to_test: with self.test_session(graph=ops.Graph()) as sess: sess.graph.seed = random_seed.DEFAULT_GRAPH_SEED operator, mat, feed_dict = self._operator_and_mat_and_feed_dict( - shape, dtype, use_placeholder=use_placeholder) + build_info, dtype, use_placeholder=use_placeholder) op_diag_part = operator.diag_part() mat_diag_part = array_ops.matrix_diag_part(mat) @@ -334,9 +348,15 @@ class SquareLinearOperatorDerivedClassTest(LinearOperatorDerivedClassTest): """ @property - def _shapes_to_test(self): + def _operator_build_infos(self): + build_info = OperatorBuildInfo # non-batch operators (n, n) and batch operators. - return [(0, 0), (1, 1), (1, 3, 3), (3, 4, 4), (2, 1, 4, 4)] + return [ + build_info((0, 0)), + build_info((1, 1)), + build_info((1, 3, 3)), + build_info((3, 4, 4)), + build_info((2, 1, 4, 4))] def _make_rhs(self, operator, adjoint): # This operator is square, so rhs and x will have same shape. @@ -387,9 +407,15 @@ class NonSquareLinearOperatorDerivedClassTest(LinearOperatorDerivedClassTest): return ["solve", "det", "log_abs_det"] @property - def _shapes_to_test(self): + def _operator_build_infos(self): + build_info = OperatorBuildInfo # non-batch operators (n, n) and batch operators. - return [(2, 1), (1, 2), (1, 3, 2), (3, 3, 4), (2, 1, 2, 4)] + return [ + build_info((2, 1)), + build_info((1, 2)), + build_info((1, 3, 2)), + build_info((3, 3, 4)), + build_info((2, 1, 2, 4))] def _make_rhs(self, operator, adjoint): # TODO(langmore) Add once we're testing solve_ls. diff --git a/tensorflow/python/ops/linalg_ops.py b/tensorflow/python/ops/linalg_ops.py index 9803eed6aefe072cbe0841dff2de3f640a440dd5..37470e00d7f11b66c7db0785a530721b07c4b859 100644 --- a/tensorflow/python/ops/linalg_ops.py +++ b/tensorflow/python/ops/linalg_ops.py @@ -248,7 +248,7 @@ def matrix_solve_ls(matrix, rhs, l2_regularizer=0.0, fast=True, name=None): and l2_regularizer != 0 due to poor accuracy. """ - # pylint: disable=protected-access,long-lambda + # pylint: disable=long-lambda def _use_composite_impl(fast, tensor_shape): """Determines whether to use the composite or specialized CPU kernel. @@ -323,9 +323,8 @@ def matrix_solve_ls(matrix, rhs, l2_regularizer=0.0, fast=True, name=None): if _use_composite_impl(fast, tensor_shape): return _composite_impl(matrix, rhs, l2_regularizer) else: - return gen_linalg_ops._matrix_solve_ls( + return gen_linalg_ops.matrix_solve_ls( matrix, rhs, l2_regularizer, fast=fast, name=name) - # pylint: enable=protected-access @tf_export('self_adjoint_eig', 'linalg.eigh') @@ -346,8 +345,7 @@ def self_adjoint_eig(tensor, name=None): v: Eigenvectors. Shape is `[..., N, N]`. The columns of the inner most matrices contain eigenvectors of the corresponding matrices in `tensor` """ - # pylint: disable=protected-access - e, v = gen_linalg_ops._self_adjoint_eig_v2(tensor, compute_v=True, name=name) + e, v = gen_linalg_ops.self_adjoint_eig_v2(tensor, compute_v=True, name=name) return e, v @@ -369,8 +367,7 @@ def self_adjoint_eigvals(tensor, name=None): e: Eigenvalues. Shape is `[..., N]`. The vector `e[..., :]` contains the `N` eigenvalues of `tensor[..., :, :]`. """ - # pylint: disable=protected-access - e, _ = gen_linalg_ops._self_adjoint_eig_v2(tensor, compute_v=False, name=name) + e, _ = gen_linalg_ops.self_adjoint_eig_v2(tensor, compute_v=False, name=name) return e @@ -435,10 +432,8 @@ def svd(tensor, full_matrices=False, compute_uv=True, name=None): ```` @end_compatibility """ - # pylint: disable=protected-access - s, u, v = gen_linalg_ops._svd( + s, u, v = gen_linalg_ops.svd( tensor, compute_uv=compute_uv, full_matrices=full_matrices, name=name) - # pylint: enable=protected-access if compute_uv: return math_ops.real(s), u, v else: diff --git a/tensorflow/python/ops/logging_ops.py b/tensorflow/python/ops/logging_ops.py index 3757109c956dfedc64ac4cda4ad13a4cfa601418..a7ea7dc6e100e809caebed5f03027c4d694cfdd0 100644 --- a/tensorflow/python/ops/logging_ops.py +++ b/tensorflow/python/ops/logging_ops.py @@ -170,7 +170,7 @@ def image_summary(tag, tensor, max_images=3, collections=None, name=None): buffer. """ with ops.name_scope(name, "ImageSummary", [tag, tensor]) as scope: - val = gen_logging_ops._image_summary( + val = gen_logging_ops.image_summary( tag=tag, tensor=tensor, max_images=max_images, name=scope) _Collect(val, collections, [ops.GraphKeys.SUMMARIES]) return val @@ -226,11 +226,12 @@ def audio_summary(tag, with ops.name_scope(name, "AudioSummary", [tag, tensor]) as scope: sample_rate = ops.convert_to_tensor(sample_rate, dtype=dtypes.float32, name="sample_rate") - val = gen_logging_ops._audio_summary_v2(tag=tag, - tensor=tensor, - max_outputs=max_outputs, - sample_rate=sample_rate, - name=scope) + val = gen_logging_ops.audio_summary_v2( + tag=tag, + tensor=tensor, + max_outputs=max_outputs, + sample_rate=sample_rate, + name=scope) _Collect(val, collections, [ops.GraphKeys.SUMMARIES]) return val @@ -263,7 +264,7 @@ def merge_summary(inputs, collections=None, name=None): buffer resulting from the merging. """ with ops.name_scope(name, "MergeSummary", inputs): - val = gen_logging_ops._merge_summary(inputs=inputs, name=name) + val = gen_logging_ops.merge_summary(inputs=inputs, name=name) _Collect(val, collections, []) return val diff --git a/tensorflow/python/ops/lookup_ops.py b/tensorflow/python/ops/lookup_ops.py index f539a7bb68da57e31746bc80fb25339a03a4fafe..6f043f60e677eac560004619464905cd616256b2 100644 --- a/tensorflow/python/ops/lookup_ops.py +++ b/tensorflow/python/ops/lookup_ops.py @@ -157,10 +157,10 @@ class InitializableLookupTableBase(LookupInterface): default_value: The value to use if a key is missing in the table. initializer: The table initializer to use. """ - if context.in_graph_mode(): - name = table_ref.op.name.split("/")[-1] - else: + if context.executing_eagerly(): name = context.context().scope_name + else: + name = table_ref.op.name.split("/")[-1] super(InitializableLookupTableBase, self).__init__(initializer.key_dtype, initializer.value_dtype, name) @@ -196,9 +196,7 @@ class InitializableLookupTableBase(LookupInterface): """ with ops.name_scope(name, "%s_Size" % self._name, [self._table_ref]) as scope: - # pylint: disable=protected-access - return gen_lookup_ops._lookup_table_size_v2(self._table_ref, name=scope) - # pylint: enable=protected-access + return gen_lookup_ops.lookup_table_size_v2(self._table_ref, name=scope) def lookup(self, keys, name=None): """Looks up `keys` in a table, outputs the corresponding values. @@ -227,10 +225,8 @@ class InitializableLookupTableBase(LookupInterface): with ops.name_scope(name, "%s_Lookup" % self._name, (self._table_ref, key_tensor, self._default_value)) as scope: - # pylint: disable=protected-access - values = gen_lookup_ops._lookup_table_find_v2( + values = gen_lookup_ops.lookup_table_find_v2( self._table_ref, key_tensor, self._default_value, name=scope) - # pylint: enable=protected-access values.set_shape(key_tensor.get_shape()) if isinstance(keys, sparse_tensor.SparseTensor): @@ -274,13 +270,11 @@ class HashTable(InitializableLookupTableBase): """ with ops.name_scope(name, "hash_table", (initializer, default_value)) as scope: - # pylint: disable=protected-access - table_ref = gen_lookup_ops._hash_table_v2( + table_ref = gen_lookup_ops.hash_table_v2( shared_name=shared_name, key_dtype=initializer.key_dtype, value_dtype=initializer.value_dtype, name=scope) - # pylint: enable=protected-access super(HashTable, self).__init__(table_ref, default_value, initializer) @@ -352,10 +346,8 @@ class KeyValueTensorInitializer(TableInitializerBase): with ops.name_scope( self._name, values=(table.table_ref, self._keys, self._values)) as scope: - # pylint: disable=protected-access - init_op = gen_lookup_ops._initialize_table_v2( + init_op = gen_lookup_ops.initialize_table_v2( table.table_ref, self._keys, self._values, name=scope) - # pylint: enable=protected-access ops.add_to_collection(ops.GraphKeys.TABLE_INITIALIZERS, init_op) return init_op @@ -518,8 +510,7 @@ class TextFileInitializer(TableInitializerBase): (table.table_ref,)) as scope: filename = ops.convert_to_tensor( self._filename, dtypes.string, name="asset_filepath") - # pylint: disable=protected-access - init_op = gen_lookup_ops._initialize_table_from_text_file_v2( + init_op = gen_lookup_ops.initialize_table_from_text_file_v2( table.table_ref, filename, self._key_index, @@ -527,11 +518,10 @@ class TextFileInitializer(TableInitializerBase): -1 if self._vocab_size is None else self._vocab_size, self._delimiter, name=scope) - # pylint: enable=protected-access ops.add_to_collection(ops.GraphKeys.TABLE_INITIALIZERS, init_op) # If the filename tensor is anything other than a string constant (e.g., if # it is a placeholder) then it does not make sense to track it as an asset. - if context.in_graph_mode() and constant_op.is_constant(filename): + if not context.executing_eagerly() and constant_op.is_constant(filename): ops.add_to_collection(ops.GraphKeys.ASSET_FILEPATHS, filename) return init_op diff --git a/tensorflow/python/ops/losses/losses_impl.py b/tensorflow/python/ops/losses/losses_impl.py index 7386976e93fbb82f38550f50429af878fadda813..0840760810c86a6393ea6b4ab0b9410233275f11 100644 --- a/tensorflow/python/ops/losses/losses_impl.py +++ b/tensorflow/python/ops/losses/losses_impl.py @@ -89,14 +89,6 @@ def _safe_div(numerator, denominator, name="value"): Returns: The element-wise value of the numerator divided by the denominator. """ - if isinstance(denominator, float): - if math_ops.equal(denominator, 0.0): - return ops.convert_to_tensor(0.0, dtype=numerator.dtype) - return math_ops.div(numerator, denominator) - if context.in_eager_mode() and denominator._rank() == 0: # pylint: disable=protected-access - if math_ops.equal(denominator, 0.0): - return ops.convert_to_tensor(0.0, dtype=numerator.dtype) - return math_ops.div(numerator, denominator) return array_ops.where( math_ops.greater(denominator, 0), math_ops.div(numerator, array_ops.where( @@ -144,7 +136,7 @@ def _num_present(losses, weights, per_batch=False): `[batch_size]`. Otherwise, a single scalar tensor is returned. """ if ((isinstance(weights, float) and weights != 0.0) or - (context.in_eager_mode() and weights._rank() == 0 # pylint: disable=protected-access + (context.executing_eagerly() and weights._rank() == 0 # pylint: disable=protected-access and not math_ops.equal(weights, 0.0))): return _num_elements(losses) with ops.name_scope(None, "num_present", (losses, weights)) as scope: diff --git a/tensorflow/python/ops/manip_ops.py b/tensorflow/python/ops/manip_ops.py index 91e15b47b9400f29425af2f186c7c44ee6a5a622..6d335cdc212f368e7667a030791c7b634113a9c6 100644 --- a/tensorflow/python/ops/manip_ops.py +++ b/tensorflow/python/ops/manip_ops.py @@ -23,9 +23,11 @@ from __future__ import print_function from tensorflow.python.ops import gen_manip_ops as _gen_manip_ops from tensorflow.python.util.all_util import remove_undocumented +from tensorflow.python.util.tf_export import tf_export # pylint: disable=protected-access +@tf_export('manip.roll') def roll(input, shift, axis): # pylint: disable=redefined-builtin return _gen_manip_ops.roll(input, shift, axis) diff --git a/tensorflow/python/ops/math_grad.py b/tensorflow/python/ops/math_grad.py index 9e7f37d80fdd71e84516ab450d145d79519ae47a..d220fe3cce186d731803d3d1c9587d9fe9ea3a28 100644 --- a/tensorflow/python/ops/math_grad.py +++ b/tensorflow/python/ops/math_grad.py @@ -35,6 +35,12 @@ def _safe_shape_div(x, y): return x // math_ops.maximum(y, 1) +@ops.RegisterGradient("ArgMax") +def _ArgMaxGrad(op, grad): + del op, grad + return [None, None] + + @ops.RegisterGradient("Sum") def _SumGrad(op, grad): """Gradient for Sum.""" @@ -46,10 +52,18 @@ def _SumGrad(op, grad): if axes is not None: rank = len(input_0_shape) if np.array_equal(axes, np.arange(rank)): # Reduce all dims. - grad = array_ops.reshape(grad, [1] * rank) + if context.executing_eagerly(): + ctx = context.context() + new_shape = ctx.ones_rank_cache().get(rank) + if new_shape is None: + new_shape = constant_op.constant([1] * rank, dtype=dtypes.int32) + ctx.ones_rank_cache().put(rank, new_shape) + else: + new_shape = [1] * rank + grad = array_ops.reshape(grad, new_shape) # If shape is not fully defined (but rank is), we use Shape. if None not in input_0_shape: - input_shape = input_0_shape + input_shape = constant_op.constant(input_0_shape, dtype=dtypes.int32) else: input_shape = array_ops.shape(op.inputs[0]) return [array_ops.tile(grad, input_shape), None] @@ -382,16 +396,14 @@ def _NegGrad(_, grad): def _InvGrad(op, grad): """Returns -grad * (1 / x^2).""" y = op.outputs[0] # y = 1 / x - # pylint: disable=protected-access - return gen_math_ops._reciprocal_grad(y, grad) + return gen_math_ops.reciprocal_grad(y, grad) @ops.RegisterGradient("Reciprocal") def _ReciprocalGrad(op, grad): """Returns -grad * (1 / x^2).""" y = op.outputs[0] # y = 1 / x - # pylint: disable=protected-access - return gen_math_ops._reciprocal_grad(y, grad) + return gen_math_ops.reciprocal_grad(y, grad) @ops.RegisterGradient("InvGrad") @@ -401,8 +413,7 @@ def _InvGradGrad(op, grad): with ops.control_dependencies([grad]): ca = math_ops.conj(op.inputs[0]) cg = math_ops.conj(grad) - # pylint: disable=protected-access - return cg * -2.0 * b * ca, gen_math_ops._reciprocal_grad(ca, grad) + return cg * -2.0 * b * ca, gen_math_ops.reciprocal_grad(ca, grad) @ops.RegisterGradient("ReciprocalGrad") @@ -412,8 +423,7 @@ def _ReciprocalGradGrad(op, grad): with ops.control_dependencies([grad]): ca = math_ops.conj(op.inputs[0]) cg = math_ops.conj(grad) - # pylint: disable=protected-access - return cg * -2.0 * b * ca, gen_math_ops._reciprocal_grad(ca, grad) + return cg * -2.0 * b * ca, gen_math_ops.reciprocal_grad(ca, grad) @ops.RegisterGradient("Square") @@ -422,15 +432,14 @@ def _SquareGrad(op, grad): # Added control dependencies to prevent 2*x from being computed too early. with ops.control_dependencies([grad]): x = math_ops.conj(x) - return math_ops.multiply(grad, math_ops.multiply(x, 2.0)) + y = constant_op.constant(2.0, dtype=x.dtype) + return math_ops.multiply(grad, math_ops.multiply(x, y)) @ops.RegisterGradient("Sqrt") def _SqrtGrad(op, grad): y = op.outputs[0] # y = x^(1/2) - # pylint: disable=protected-access - return gen_math_ops._sqrt_grad(y, grad) - # pylint: enable=protected-access + return gen_math_ops.sqrt_grad(y, grad) @ops.RegisterGradient("SqrtGrad") @@ -446,9 +455,7 @@ def _SqrtGradGrad(op, grad): def _RsqrtGrad(op, grad): """Returns -0.5 * grad * conj(y)^3.""" y = op.outputs[0] # y = x^(-1/2) - # pylint: disable=protected-access - return gen_math_ops._rsqrt_grad(y, grad) - # pylint: enable=protected-access + return gen_math_ops.rsqrt_grad(y, grad) @ops.RegisterGradient("RsqrtGrad") @@ -460,8 +467,7 @@ def _RsqrtGradGrad(op, grad): ca = math_ops.conj(a) cg = math_ops.conj(grad) grad_a = -1.5 * cg * b * math_ops.square(ca) - # pylint: disable=protected-access - grad_b = gen_math_ops._rsqrt_grad(ca, grad) + grad_b = gen_math_ops.rsqrt_grad(ca, grad) return grad_a, grad_b @@ -526,8 +532,7 @@ def _TanhGrad(op, grad): y = op.outputs[0] # y = tanh(x) with ops.control_dependencies([grad]): y = math_ops.conj(y) - # pylint: disable=protected-access - return gen_math_ops._tanh_grad(y, grad) + return gen_math_ops.tanh_grad(y, grad) @ops.RegisterGradient("Asinh") @@ -565,8 +570,7 @@ def _TanhGradGrad(op, grad): with ops.control_dependencies([grad]): a = math_ops.conj(op.inputs[0]) b = math_ops.conj(op.inputs[1]) - # pylint: disable=protected-access - return grad * -2.0 * b * a, gen_math_ops._tanh_grad(a, grad) + return grad * -2.0 * b * a, gen_math_ops.tanh_grad(a, grad) @ops.RegisterGradient("Erf") @@ -714,8 +718,7 @@ def _SigmoidGrad(op, grad): y = op.outputs[0] # y = sigmoid(x) with ops.control_dependencies([grad]): y = math_ops.conj(y) - # pylint: disable=protected-access - return gen_math_ops._sigmoid_grad(y, grad) + return gen_math_ops.sigmoid_grad(y, grad) @ops.RegisterGradient("SigmoidGrad") @@ -724,8 +727,7 @@ def _SigmoidGradGrad(op, grad): a = math_ops.conj(op.inputs[0]) b = math_ops.conj(op.inputs[1]) gb = grad * b - # pylint: disable=protected-access - return gb - 2.0 * gb * a, gen_math_ops._sigmoid_grad(a, grad) + return gb - 2.0 * gb * a, gen_math_ops.sigmoid_grad(a, grad) @ops.RegisterGradient("Sign") @@ -872,7 +874,7 @@ def _MulGrad(op, grad): if (isinstance(grad, ops.Tensor) and _ShapesFullySpecifiedAndEqual(x, y, grad) and grad.dtype in (dtypes.int32, dtypes.float32)): - return gen_math_ops._mul(grad, y), gen_math_ops._mul(grad, x) + return gen_math_ops.mul(grad, y), gen_math_ops.mul(grad, x) assert x.dtype.base_dtype == y.dtype.base_dtype, (x.dtype, " vs. ", y.dtype) sx = array_ops.shape(x) sy = array_ops.shape(y) @@ -880,9 +882,9 @@ def _MulGrad(op, grad): x = math_ops.conj(x) y = math_ops.conj(y) return (array_ops.reshape( - math_ops.reduce_sum(gen_math_ops._mul(grad, y), rx), sx), + math_ops.reduce_sum(gen_math_ops.mul(grad, y), rx), sx), array_ops.reshape( - math_ops.reduce_sum(gen_math_ops._mul(x, grad), ry), sy)) + math_ops.reduce_sum(gen_math_ops.mul(x, grad), ry), sy)) # pylint: enable=protected-access @@ -1056,20 +1058,18 @@ def _MatMulGrad(op, grad): t_b = op.get_attr("transpose_b") a = math_ops.conj(op.inputs[0]) b = math_ops.conj(op.inputs[1]) - # pylint: disable=protected-access if not t_a and not t_b: - grad_a = gen_math_ops._mat_mul(grad, b, transpose_b=True) - grad_b = gen_math_ops._mat_mul(a, grad, transpose_a=True) + grad_a = gen_math_ops.mat_mul(grad, b, transpose_b=True) + grad_b = gen_math_ops.mat_mul(a, grad, transpose_a=True) elif not t_a and t_b: - grad_a = gen_math_ops._mat_mul(grad, b) - grad_b = gen_math_ops._mat_mul(grad, a, transpose_a=True) + grad_a = gen_math_ops.mat_mul(grad, b) + grad_b = gen_math_ops.mat_mul(grad, a, transpose_a=True) elif t_a and not t_b: - grad_a = gen_math_ops._mat_mul(b, grad, transpose_b=True) - grad_b = gen_math_ops._mat_mul(a, grad) + grad_a = gen_math_ops.mat_mul(b, grad, transpose_b=True) + grad_b = gen_math_ops.mat_mul(a, grad) elif t_a and t_b: - grad_a = gen_math_ops._mat_mul(b, grad, transpose_a=True, transpose_b=True) - grad_b = gen_math_ops._mat_mul(grad, a, transpose_a=True, transpose_b=True) - # pylint: enable=protected-access + grad_a = gen_math_ops.mat_mul(b, grad, transpose_a=True, transpose_b=True) + grad_b = gen_math_ops.mat_mul(grad, a, transpose_a=True, transpose_b=True) return grad_a, grad_b @@ -1083,7 +1083,7 @@ def _SparseMatMulGrad(op, grad): op.inputs[0]: op.get_attr("a_is_sparse"), op.inputs[1]: op.get_attr("b_is_sparse"), # Use heuristic to figure out if grad might be sparse - grad: context.in_graph_mode() and (grad.op.type == "ReluGrad") + grad: not context.executing_eagerly() and (grad.op.type == "ReluGrad") } def _SparseMatMul(t1, t2, out_dtype, transpose_a=False, transpose_b=False): diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py index 2ae8b610da04d9762284d51b9c9f28a8c07e24f7..e18d0e95015556acc66b0b50f0c406c0e2cf41aa 100644 --- a/tensorflow/python/ops/math_ops.py +++ b/tensorflow/python/ops/math_ops.py @@ -89,8 +89,6 @@ See the @{$python/math_ops} guide. @@matrix_inverse @@cholesky @@cholesky_solve -@@matrix_exponential -@@matrix_logarithm @@matrix_solve @@matrix_triangular_solve @@matrix_solve_ls @@ -129,8 +127,11 @@ See the @{$python/math_ops} guide. @@segment_min @@segment_max @@segment_mean +@@to_complex128 +@@to_complex64 @@unsorted_segment_sum @@unsorted_segment_max +@@unsorted_segment_mean @@unsorted_segment_min @@unsorted_segment_prod @@unsorted_segment_sqrt_n @@ -161,14 +162,11 @@ 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 gen_control_flow_ops from tensorflow.python.ops import gen_data_flow_ops from tensorflow.python.ops import gen_math_ops from tensorflow.python.ops import gen_nn_ops from tensorflow.python.ops import gen_sparse_ops from tensorflow.python.ops import gen_spectral_ops -from tensorflow.python.ops import gen_state_ops -from tensorflow.python.ops import state_ops # go/tf-wildcard-import # pylint: disable=wildcard-import from tensorflow.python.ops.gen_math_ops import * @@ -184,6 +182,11 @@ arg_max = deprecation.deprecated(None, "Use `argmax` instead")(arg_max) # pylin arg_min = deprecation.deprecated(None, "Use `argmin` instead")(arg_min) # pylint: disable=used-before-assignment +# This is set by resource_variable_ops.py. It is included in this way since +# there is a circular dependency between math_ops and resource_variable_ops +_resource_variable_type = None + + def _set_doc(doc): def _decorator(func): @@ -266,7 +269,7 @@ def abs(x, name=None): # pylint: disable=redefined-builtin with ops.name_scope(name, "Abs", [x]) as name: if isinstance(x, sparse_tensor.SparseTensor): if x.values.dtype.is_complex: - x_abs = gen_math_ops._complex_abs( + x_abs = gen_math_ops.complex_abs( x.values, Tout=x.values.dtype.real_dtype, name=name) return sparse_tensor.SparseTensor( indices=x.indices, values=x_abs, dense_shape=x.dense_shape) @@ -276,7 +279,7 @@ def abs(x, name=None): # pylint: disable=redefined-builtin else: x = ops.convert_to_tensor(x, name="x") if x.dtype.is_complex: - return gen_math_ops._complex_abs(x, Tout=x.dtype.real_dtype, name=name) + return gen_math_ops.complex_abs(x, Tout=x.dtype.real_dtype, name=name) return gen_math_ops._abs(x, name=name) @@ -285,7 +288,7 @@ def abs(x, name=None): # pylint: disable=redefined-builtin # pylint: disable=redefined-builtin def _bucketize(input, boundaries, name=None): - return gen_math_ops._bucketize(input=input, boundaries=boundaries, name=name) + return gen_math_ops.bucketize(input=input, boundaries=boundaries, name=name) # pylint: enable=redefined-builtin @@ -328,10 +331,10 @@ def divide(x, y, name=None): @tf_export("multiply") def multiply(x, y, name=None): - return gen_math_ops._mul(x, y, name) + return gen_math_ops.mul(x, y, name) -multiply.__doc__ = gen_math_ops._mul.__doc__.replace("Mul", "`tf.multiply`") +multiply.__doc__ = gen_math_ops.mul.__doc__.replace("Multiply", "`tf.multiply`") # TODO(aselle): put deprecation in after another round of global code changes @@ -339,19 +342,19 @@ multiply.__doc__ = gen_math_ops._mul.__doc__.replace("Mul", "`tf.multiply`") "2016-12-30", "`tf.mul(x, y)` is deprecated, please use `tf.multiply(x, y)` or `x * y`") def _mul(x, y, name=None): - return gen_math_ops._mul(x, y, name) + return gen_math_ops.mul(x, y, name) _mul.__doc__ = ( - gen_math_ops._mul.__doc__ + ("" if _mul.__doc__ is None else _mul.__doc__)) + gen_math_ops.mul.__doc__ + ("" if _mul.__doc__ is None else _mul.__doc__)) @tf_export("subtract") def subtract(x, y, name=None): - return gen_math_ops._sub(x, y, name) + return gen_math_ops.sub(x, y, name) -subtract.__doc__ = gen_math_ops._sub.__doc__.replace("`Sub`", "`tf.subtract`") +subtract.__doc__ = gen_math_ops.sub.__doc__.replace("`Sub`", "`tf.subtract`") # TODO(aselle): put deprecation in after another round of global code changes @@ -359,11 +362,11 @@ subtract.__doc__ = gen_math_ops._sub.__doc__.replace("`Sub`", "`tf.subtract`") "2016-12-30", "`tf.sub(x, y)` is deprecated, please use `tf.subtract(x, y)` or `x - y`") def _sub(x, y, name=None): - return gen_math_ops._sub(x, y, name) + return gen_math_ops.sub(x, y, name) _sub.__doc__ = ( - gen_math_ops._sub.__doc__ + ("" if _sub.__doc__ is None else _sub.__doc__)) + gen_math_ops.sub.__doc__ + ("" if _sub.__doc__ is None else _sub.__doc__)) # pylint: disable=g-docstring-has-escape @@ -383,11 +386,11 @@ def negative(x, name=None): """ with ops.name_scope(name, "Neg", [x]) as name: if isinstance(x, sparse_tensor.SparseTensor): - x_neg = gen_math_ops._neg(x.values, name=name) + x_neg = gen_math_ops.neg(x.values, name=name) return sparse_tensor.SparseTensor( indices=x.indices, values=x_neg, dense_shape=x.dense_shape) else: - return gen_math_ops._neg(x, name=name) + return gen_math_ops.neg(x, name=name) # pylint: enable=g-docstring-has-escape @@ -935,7 +938,7 @@ def to_complex128(x, name="ToComplex128"): return cast(x, dtypes.complex128, name=name) -ops.Tensor._override_operator("__neg__", gen_math_ops._neg) +ops.Tensor._override_operator("__neg__", gen_math_ops.neg) ops.Tensor._override_operator("__abs__", abs) # __invert__ corresponds to the ~ operator. Here we follow the numpy convention # ~ marks an elementwise bit-wise inverse. This is only implemented for boolean @@ -1064,7 +1067,7 @@ def _truediv_python3(x, y, name=None): if dtype is not None: x = cast(x, dtype) y = cast(y, dtype) - return gen_math_ops._real_div(x, y, name=name) + return gen_math_ops.real_div(x, y, name=name) def _div_python2(x, y, name=None): @@ -1087,9 +1090,9 @@ def _div_python2(x, y, name=None): raise TypeError("x and y must have the same dtype, got %r != %r" % (x_dtype, y_dtype)) if x_dtype.is_floating or x_dtype.is_complex: - return gen_math_ops._real_div(x, y, name=name) + return gen_math_ops.real_div(x, y, name=name) else: - return gen_math_ops._floor_div(x, y, name=name) + return gen_math_ops.floor_div(x, y, name=name) @tf_export("truediv") @@ -1147,7 +1150,7 @@ def div(x, y, name=None): # TODO(aselle): This should be removed -mod = gen_math_ops._floor_mod +mod = gen_math_ops.floor_mod # TODO(aselle): Deprecate this once all internal functionality uses @@ -1180,22 +1183,27 @@ def floordiv(x, y, name=None): TypeError: If the inputs are complex. """ with ops.name_scope(name, "floordiv", [x, y]) as name: - return gen_math_ops._floor_div(x, y, name=name) + return gen_math_ops.floor_div(x, y, name=name) -realdiv = gen_math_ops._real_div -truncatediv = gen_math_ops._truncate_div +realdiv = gen_math_ops.real_div +tf_export("realdiv")(realdiv) +truncatediv = gen_math_ops.truncate_div +tf_export("truncatediv")(truncatediv) # TODO(aselle): Rename this to floordiv when we can. -floor_div = gen_math_ops._floor_div -truncatemod = gen_math_ops._truncate_mod -floormod = gen_math_ops._floor_mod +floor_div = gen_math_ops.floor_div +tf_export("floor_div")(floor_div) +truncatemod = gen_math_ops.truncate_mod +tf_export("truncatemod")(truncatemod) +floormod = gen_math_ops.floor_mod +tf_export("floormod")(floormod) def _mul_dispatch(x, y, name=None): """Dispatches cwise mul for "Dense*Dense" and "Dense*Sparse".""" is_tensor_y = isinstance(y, ops.Tensor) if is_tensor_y: - return gen_math_ops._mul(x, y, name=name) + return gen_math_ops.mul(x, y, name=name) else: assert isinstance(y, sparse_tensor.SparseTensor) # Case: Dense * Sparse. new_vals = gen_sparse_ops.sparse_dense_cwise_mul(y.indices, y.values, @@ -1214,12 +1222,12 @@ _OverrideBinaryOperatorHelper(gen_sparse_ops.sparse_dense_cwise_mul, "mul", sparse_tensor.SparseTensor) _OverrideBinaryOperatorHelper(gen_math_ops.add, "add") -_OverrideBinaryOperatorHelper(gen_math_ops._sub, "sub") +_OverrideBinaryOperatorHelper(gen_math_ops.sub, "sub") _OverrideBinaryOperatorHelper(_mul_dispatch, "mul") _OverrideBinaryOperatorHelper(_div_python2, "div") _OverrideBinaryOperatorHelper(_truediv_python3, "truediv") _OverrideBinaryOperatorHelper(floordiv, "floordiv") -_OverrideBinaryOperatorHelper(gen_math_ops._floor_mod, "mod") +_OverrideBinaryOperatorHelper(gen_math_ops.floor_mod, "mod") _OverrideBinaryOperatorHelper(pow, "pow") @@ -1541,7 +1549,7 @@ def reduce_mean(input_tensor, if keepdims is None: keepdims = False return _may_reduce_to_scalar(keepdims, axis, reduction_indices, - gen_math_ops._mean( + gen_math_ops.mean( input_tensor, _ReductionDims(input_tensor, axis, reduction_indices), @@ -1591,7 +1599,7 @@ def reduce_prod(input_tensor, if keepdims is None: keepdims = False return _may_reduce_to_scalar(keepdims, axis, reduction_indices, - gen_math_ops._prod( + gen_math_ops.prod( input_tensor, _ReductionDims(input_tensor, axis, reduction_indices), @@ -2044,8 +2052,15 @@ def matmul(a, if transpose_b and adjoint_b: raise ValueError("Only one of transpose_b and adjoint_b can be True.") - a = ops.convert_to_tensor(a, name="a") - b = ops.convert_to_tensor(b, name="b") + if context.executing_eagerly(): + if not isinstance(a, (ops.EagerTensor, _resource_variable_type)): + a = ops.convert_to_tensor(a, name="a") + if not isinstance(b, (ops.EagerTensor, _resource_variable_type)): + b = ops.convert_to_tensor(b, name="b") + else: + a = ops.convert_to_tensor(a, name="a") + b = ops.convert_to_tensor(b, name="b") + # TODO(apassos) remove _shape_tuple here when it is not needed. a_shape = a._shape_tuple() # pylint: disable=protected-access b_shape = b._shape_tuple() # pylint: disable=protected-access @@ -2060,7 +2075,7 @@ def matmul(a, if transpose_b: b = conj(b) adjoint_b = True - return gen_math_ops._batch_mat_mul( + return gen_math_ops.batch_mat_mul( a, b, adj_x=adjoint_a, adj_y=adjoint_b, name=name) # Neither matmul nor sparse_matmul support adjoint, so we conjugate @@ -2097,13 +2112,14 @@ def matmul(a, ret = cast(ret, dtypes.bfloat16) return ret else: - return gen_math_ops._mat_mul( + return gen_math_ops.mat_mul( a, b, transpose_a=transpose_a, transpose_b=transpose_b, name=name) _OverrideBinaryOperatorHelper(matmul, "matmul") -sparse_matmul = gen_math_ops._sparse_mat_mul +sparse_matmul = gen_math_ops.sparse_mat_mul +tf_export("sparse_matmul")(sparse_matmul) @ops.RegisterStatistics("MatMul", "flops") @@ -2208,7 +2224,7 @@ def add_n(inputs, name=None): if name: return array_ops.identity(inputs[0], name=name) return inputs[0] - return gen_math_ops._add_n(inputs, name=name) + return gen_math_ops.add_n(inputs, name=name) @tf_export("accumulate_n") @@ -2218,14 +2234,12 @@ def accumulate_n(inputs, shape=None, tensor_dtype=None, name=None): Optionally, pass `shape` and `tensor_dtype` for shape and type checking, otherwise, these are inferred. - NOTE: This operation is not differentiable and cannot be used if inputs depend - on trainable variables. Please use `tf.add_n` for such cases. + `tf.accumulate_n` performs the same operation as `tf.add_n`, but does not + wait for all of its inputs to be ready before beginning to sum. This can + save memory if inputs are ready at different times, since minimum temporary + storage is proportional to the output size rather than the inputs size. - Aside from differentiability, `tf.accumulate_n` performs the same operation as - `tf.add_n`, but does not wait for all of its inputs to be ready before - beginning to sum. This can save memory if inputs are ready at different times, - since minimum temporary storage is proportional to the output size rather than - the inputs size. + `accumulate_n` is differentiable (but wasn't previous to TensorFlow 1.7). For example: @@ -2235,8 +2249,9 @@ def accumulate_n(inputs, shape=None, tensor_dtype=None, name=None): tf.accumulate_n([a, b, a]) # [[7, 4], [6, 14]] # Explicitly pass shape and type - tf.accumulate_n([a, b, a], shape=[2, 2], tensor_dtype=tf.int32) # [[7, 4], - # [6, 14]] + tf.accumulate_n([a, b, a], shape=[2, 2], tensor_dtype=tf.int32) + # [[7, 4], + # [6, 14]] ``` Args: @@ -2252,20 +2267,17 @@ def accumulate_n(inputs, shape=None, tensor_dtype=None, name=None): ValueError: If `inputs` don't all have same shape and dtype or the shape cannot be inferred. """ - if context.in_eager_mode(): - # TODO(apassos) remove this once the lifetime of eager variables gets - # addressed. - raise ValueError("accumulate_n not supported in eager mode") + def _input_error(): + return ValueError( + "inputs must be a list of at least one Tensor with the " + "same dtype and shape") if not inputs or not isinstance(inputs, (list, tuple)): - raise ValueError("inputs must be a list of at least one Tensor with the " - "same dtype and shape") + raise _input_error() inputs = ops.convert_n_to_tensor_or_indexed_slices(inputs) if not all(isinstance(x, ops.Tensor) for x in inputs): - raise ValueError("inputs must be a list of at least one Tensor with the " - "same dtype and shape") + raise _input_error() if not all(x.dtype == inputs[0].dtype for x in inputs): - raise ValueError("inputs must be a list of at least one Tensor with the " - "same dtype and shape") + raise _input_error() if shape is not None: shape = tensor_shape.as_shape(shape) else: @@ -2273,27 +2285,31 @@ def accumulate_n(inputs, shape=None, tensor_dtype=None, name=None): for input_tensor in inputs: if isinstance(input_tensor, ops.Tensor): shape = shape.merge_with(input_tensor.get_shape()) - if tensor_dtype is None: - tensor_dtype = inputs[0].dtype - if tensor_dtype != inputs[0].dtype: - raise TypeError("tensor_dtype is {}, but input is of type {}".format( - tensor_dtype, inputs[0].dtype)) - if len(inputs) == 1: + + # tensor_dtype is for safety only; operator's output type computed in C++ + if tensor_dtype is not None and tensor_dtype != inputs[0].dtype: + raise TypeError("tensor_dtype is {}, but input is of type {}" + .format(tensor_dtype, inputs[0].dtype)) + + if len(inputs) == 1 and name is None: return inputs[0] - with ops.name_scope(name, "AccumulateN", inputs) as name: - var = gen_state_ops._temporary_variable( - shape=tensor_shape.vector(0), dtype=tensor_dtype) - with ops.colocate_with(var): - zeros = array_ops.zeros_like(gen_control_flow_ops._merge(inputs)[0]) - zeros.set_shape(shape) - ref = state_ops.assign(var, zeros, validate_shape=False) - update_ops = [ - state_ops.assign_add(ref, input_tensor, use_locking=True) - for input_tensor in inputs - ] - with ops.control_dependencies(update_ops): - return gen_state_ops._destroy_temporary_variable( - ref, var_name=var.op.name, name=name) + elif len(inputs) == 1 and name is not None: + return array_ops.identity(inputs[0], name=name) + elif context.executing_eagerly(): + # TemporaryVariable not currently supported in eager mode; fall back + # onto AddN for now. + # TODO(frreiss) remove this once the lifetime of eager variables gets + # addressed + return add_n(inputs, name=name) + else: + return gen_math_ops.accumulate_nv2(inputs, name=name, shape=shape) # pylint: disable=protected-access + + +@ops.RegisterGradient("AccumulateNV2") +def _accumulate_n_grad(op, grad): + """Same as gradient for AddN. Copies the gradient to all inputs.""" + # Not broadcasting. + return [grad] * len(op.inputs) @tf_export("nn.sigmoid", "sigmoid") @@ -2316,7 +2332,7 @@ def sigmoid(x, name=None): """ with ops.name_scope(name, "Sigmoid", [x]) as name: x = ops.convert_to_tensor(x, name="x") - return gen_math_ops._sigmoid(x, name=name) + return gen_math_ops.sigmoid(x, name=name) @tf_export("log_sigmoid") @@ -2335,7 +2351,7 @@ def log_sigmoid(x, name=None): """ with ops.name_scope(name, "LogSigmoid", [x]) as name: x = ops.convert_to_tensor(x, name="x") - return gen_math_ops._neg(gen_nn_ops.softplus(-x), name=name) + return gen_math_ops.neg(gen_nn_ops.softplus(-x), name=name) @tf_export("nn.tanh", "tanh") @@ -2352,11 +2368,11 @@ def tanh(x, name=None): """ with ops.name_scope(name, "Tanh", [x]) as name: if isinstance(x, sparse_tensor.SparseTensor): - x_tanh = gen_math_ops._tanh(x.values, name=name) + x_tanh = gen_math_ops.tanh(x.values, name=name) return sparse_tensor.SparseTensor( indices=x.indices, values=x_tanh, dense_shape=x.dense_shape) else: - return gen_math_ops._tanh(x, name=name) + return gen_math_ops.tanh(x, name=name) @tf_export("bincount") @@ -2545,7 +2561,7 @@ def conj(x, name=None): with ops.name_scope(name, "Conj", [x]) as name: x = ops.convert_to_tensor(x, name="x") if x.dtype.is_complex or x.dtype == dtypes.variant: - return gen_math_ops._conj(x, name=name) + return gen_math_ops.conj(x, name=name) elif x.dtype.is_floating or x.dtype.is_integer: return x else: diff --git a/tensorflow/python/ops/math_ops_test.py b/tensorflow/python/ops/math_ops_test.py index d314124ccd9bc8b7676e6926830a8eb1e0315f5f..9f85188b3513563a7444f7a0e908f11af985498b 100644 --- a/tensorflow/python/ops/math_ops_test.py +++ b/tensorflow/python/ops/math_ops_test.py @@ -60,7 +60,7 @@ class ReduceTest(test_util.TensorFlowTestCase): @test_util.run_in_graph_and_eager_modes() def testReduceInvalidAxis(self): - if context.in_eager_mode(): + if context.executing_eagerly(): # The shape check is in run a graph construction time. In eager mode, # it misses the check, magically return result given wrong shape. return @@ -249,7 +249,7 @@ class ScalarMulTest(test_util.TensorFlowTestCase): @test_util.run_in_graph_and_eager_modes() def testAcceptsRefs(self): - if context.in_eager_mode(): + if context.executing_eagerly(): var = resource_variable_ops.ResourceVariable(10, name="var") else: var = variables.Variable(10) diff --git a/tensorflow/python/ops/metrics_impl.py b/tensorflow/python/ops/metrics_impl.py index 043c0e30cd8476b1a91e136df60edfbedf85ab24..9ec49545796cfa7a603b31c23bfd0d495639898d 100644 --- a/tensorflow/python/ops/metrics_impl.py +++ b/tensorflow/python/ops/metrics_impl.py @@ -308,7 +308,7 @@ def mean(values, or tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.mean is not supported when eager execution ' 'is enabled.') @@ -394,7 +394,7 @@ def accuracy(labels, tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.accuracy is not supported when eager ' 'execution is enabled.') @@ -644,7 +644,7 @@ def auc(labels, tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.auc is not supported when eager execution ' 'is enabled.') @@ -672,7 +672,7 @@ def auc(labels, x = fp_rate y = rec else: # curve == 'PR'. - prec = math_ops.div(tp, tp + fp + epsilon) + prec = math_ops.div(tp + epsilon, tp + fp + epsilon) x = rec y = prec if summation_method == 'trapezoidal': @@ -758,7 +758,7 @@ def mean_absolute_error(labels, tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.mean_absolute_error is not supported ' 'when eager execution is enabled.') @@ -818,7 +818,7 @@ def mean_cosine_distance(labels, tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.mean_cosine_distance is not supported when ' 'eager execution is enabled.') @@ -891,7 +891,7 @@ def mean_per_class_accuracy(labels, tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.mean_per_class_accuracy is not supported ' 'when eager execution is enabled.') @@ -923,8 +923,8 @@ def mean_per_class_accuracy(labels, weights = array_ops.reshape(weights, [-1]) weights = math_ops.to_float(weights) - is_correct *= weights - ones *= weights + is_correct = is_correct * weights + ones = ones * weights update_total_op = state_ops.scatter_add(total, labels, ones) update_count_op = state_ops.scatter_add(count, labels, is_correct) @@ -996,7 +996,7 @@ def mean_iou(labels, tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.mean_iou is not supported when ' 'eager execution is enabled.') @@ -1098,7 +1098,7 @@ def mean_relative_error(labels, tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.mean_relative_error is not supported when ' 'eager execution is enabled.') @@ -1165,7 +1165,7 @@ def mean_squared_error(labels, tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.mean_squared_error is not supported when ' 'eager execution is enabled.') @@ -1223,7 +1223,7 @@ def mean_tensor(values, or tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.mean_tensor is not supported when ' 'eager execution is enabled.') @@ -1304,7 +1304,7 @@ def percentage_below(values, or tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.percentage_below is not supported when ' 'eager execution is enabled.') @@ -1397,7 +1397,7 @@ def false_negatives(labels, or tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.false_negatives is not supported when ' 'eager execution is enabled.') @@ -1453,7 +1453,7 @@ def false_negatives_at_thresholds(labels, tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.false_negatives_at_thresholds is not ' 'supported when eager execution is enabled.') @@ -1507,7 +1507,7 @@ def false_positives(labels, tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.false_positives is not supported when ' 'eager execution is enabled.') @@ -1563,7 +1563,7 @@ def false_positives_at_thresholds(labels, tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.false_positives_at_thresholds is not ' 'supported when eager execution is enabled.') @@ -1617,7 +1617,7 @@ def true_negatives(labels, tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.true_negatives is not ' 'supported when eager execution is enabled.') @@ -1673,7 +1673,7 @@ def true_negatives_at_thresholds(labels, tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.true_negatives_at_thresholds is not ' 'supported when eager execution is enabled.') @@ -1727,7 +1727,7 @@ def true_positives(labels, tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.true_positives is not ' 'supported when eager execution is enabled.') @@ -1783,7 +1783,7 @@ def true_positives_at_thresholds(labels, tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.true_positives_at_thresholds is not ' 'supported when eager execution is enabled.') @@ -1851,7 +1851,7 @@ def precision(labels, tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.precision is not ' 'supported when eager execution is enabled.') @@ -1947,7 +1947,7 @@ def precision_at_thresholds(labels, tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.precision_at_thresholds is not ' 'supported when eager execution is enabled.') @@ -2023,7 +2023,7 @@ def recall(labels, tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.recall is not supported is not ' 'supported when eager execution is enabled.') @@ -2400,7 +2400,7 @@ def recall_at_k(labels, are not a list or tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.recall_at_k is not ' 'supported when eager execution is enabled.') @@ -2549,7 +2549,7 @@ def recall_at_thresholds(labels, tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.recall_at_thresholds is not ' 'supported when eager execution is enabled.') @@ -2626,7 +2626,7 @@ def root_mean_squared_error(labels, tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.root_mean_squared_error is not ' 'supported when eager execution is enabled.') @@ -2707,7 +2707,7 @@ def sensitivity_at_specificity(labels, or `updates_collections` are not a list or tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.sensitivity_at_specificity is not ' 'supported when eager execution is enabled.') @@ -3098,7 +3098,7 @@ def average_precision_at_k(labels, ValueError: if k is invalid. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.sparse_average_precision_at_k is not ' 'supported when eager execution is enabled.') @@ -3267,7 +3267,7 @@ def precision_at_top_k(labels, are not a list or tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.precision_at_top_k is not ' 'supported when eager execution is enabled.') @@ -3396,7 +3396,7 @@ def precision_at_k(labels, are not a list or tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.sparse_precision_at_k is not ' 'supported when eager execution is enabled.') @@ -3473,7 +3473,7 @@ def specificity_at_sensitivity(labels, or `updates_collections` are not a list or tuple. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError('tf.metrics.specificity_at_sensitivity is not ' 'supported when eager execution is enabled.') diff --git a/tensorflow/python/ops/nn_batchnorm_test.py b/tensorflow/python/ops/nn_batchnorm_test.py index eebfb17085a568f48769f6df7dddd3ae2f799efc..3ac2c8eb17ef31b46638ce50e0e9f9705adce189 100644 --- a/tensorflow/python/ops/nn_batchnorm_test.py +++ b/tensorflow/python/ops/nn_batchnorm_test.py @@ -57,7 +57,6 @@ class BatchNormalizationTest(test.TestCase): test_util.set_producer_version(ops.get_default_graph(), 8) return gen_nn_ops._batch_norm_with_global_normalization( x, m, v, beta, gamma, epsilon, scale_after_normalization) - # pylint: enable=protected-access def _tfBatchNormV1BW(self, x, m, v, beta, gamma, epsilon, scale_after_normalization): @@ -223,7 +222,7 @@ class BatchNormalizationTest(test.TestCase): for scale_after_normalization in [True, False]: # _batch_norm_with_global_normalization_grad is deprecated in v9 test_util.set_producer_version(ops.get_default_graph(), 8) - grad = gen_nn_ops._batch_norm_with_global_normalization_grad( + grad = gen_nn_ops.batch_norm_with_global_normalization_grad( x, m, v, gamma, backprop, epsilon, scale_after_normalization) dx, dm, dv, db, dg = grad self.assertEqual(grad.dx, dx) diff --git a/tensorflow/python/ops/nn_grad.py b/tensorflow/python/ops/nn_grad.py index dc24b821a5580e3581f153f3cbf63ad2868b8a18..4af5bd26dd80b984b1c898411c2a23827bed1b4b 100644 --- a/tensorflow/python/ops/nn_grad.py +++ b/tensorflow/python/ops/nn_grad.py @@ -150,7 +150,7 @@ def _Conv3DBackpropFilterGrad(op, grad): @ops.RegisterGradient("AvgPool3D") def _AvgPool3DGrad(op, grad): - return gen_nn_ops._avg_pool3d_grad( + return gen_nn_ops.avg_pool3d_grad( array_ops.shape(op.inputs[0]), grad, ksize=op.get_attr("ksize"), @@ -172,7 +172,7 @@ def _AvgPool3DGradGrad(op, grad): @ops.RegisterGradient("MaxPool3D") def _MaxPool3DGrad(op, grad): - return gen_nn_ops._max_pool3d_grad( + return gen_nn_ops.max_pool3d_grad( op.inputs[0], op.outputs[0], grad, @@ -188,7 +188,7 @@ def _MaxPool3DGradGrad(op, grad): shape=array_ops.shape(op.inputs[0]), dtype=op.inputs[0].dtype), array_ops.zeros( shape=array_ops.shape(op.inputs[1]), dtype=op.inputs[1].dtype), - gen_nn_ops._max_pool3d_grad_grad( + gen_nn_ops.max_pool3d_grad_grad( op.inputs[0], op.inputs[1], grad, @@ -204,7 +204,7 @@ def _MaxPool3DGradGradGrad(op, grad): shape=array_ops.shape(op.inputs[0]), dtype=op.inputs[0].dtype), array_ops.zeros( shape=array_ops.shape(op.inputs[1]), dtype=op.inputs[1].dtype), - gen_nn_ops._max_pool3d_grad( + gen_nn_ops.max_pool3d_grad( op.inputs[0], op.inputs[1], grad, @@ -352,13 +352,13 @@ def _BiasAddGradV1(unused_bias_op, received_grad): @ops.RegisterGradient("Relu") def _ReluGrad(op, grad): - return gen_nn_ops._relu_grad(grad, op.outputs[0]) + return gen_nn_ops.relu_grad(grad, op.outputs[0]) @ops.RegisterGradient("EluGrad") def _EluGradGrad(op, grad): elu_x = op.inputs[1] - return (gen_nn_ops._elu_grad(grad, op.outputs[0]), + return (gen_nn_ops.elu_grad(grad, op.outputs[0]), array_ops.where(elu_x < 0, grad * op.inputs[0], array_ops.zeros( shape=array_ops.shape(elu_x), dtype=elu_x.dtype))) @@ -368,63 +368,63 @@ def _EluGradGrad(op, grad): def _SeluGradGrad(op, grad): x = op.inputs[1] scale_alpha = 1.7580993408473768599402175208123 - return (gen_nn_ops._elu_grad(grad, op.outputs[0]), + return (gen_nn_ops.elu_grad(grad, op.outputs[0]), array_ops.where(x < 0., - gen_nn_ops._elu_grad(grad, - op.outputs[0] + scale_alpha), + gen_nn_ops.elu_grad(grad, + op.outputs[0] + scale_alpha), array_ops.zeros( shape=array_ops.shape(x), dtype=x.dtype))) @ops.RegisterGradient("Relu6") def _Relu6Grad(op, grad): - return gen_nn_ops._relu6_grad(grad, op.outputs[0]) # pylint: disable=protected-access + return gen_nn_ops.relu6_grad(grad, op.outputs[0]) @ops.RegisterGradient("Relu6Grad") def _Relu6GradGrad(op, grad): x = op.inputs[1] - return (gen_nn_ops._relu6_grad(grad, x), + return (gen_nn_ops.relu6_grad(grad, x), array_ops.zeros(shape=array_ops.shape(x), dtype=x.dtype)) @ops.RegisterGradient("Elu") def _EluGrad(op, grad): - return gen_nn_ops._elu_grad(grad, op.outputs[0]) + return gen_nn_ops.elu_grad(grad, op.outputs[0]) @ops.RegisterGradient("Selu") def _SeluGrad(op, grad): - return gen_nn_ops._selu_grad(grad, op.outputs[0]) + return gen_nn_ops.selu_grad(grad, op.outputs[0]) @ops.RegisterGradient("Softplus") def _SoftplusGrad(op, grad): - return gen_nn_ops._softplus_grad(grad, op.inputs[0]) + return gen_nn_ops.softplus_grad(grad, op.inputs[0]) @ops.RegisterGradient("SoftplusGrad") def _SoftplusGradGrad(op, grad): # Let: # y = tf.nn.softplus(x) - # dx = gen_nn_ops._softplus_grad(dy, x) = dy / (1 + exp(-x)) + # dx = gen_nn_ops.softplus_grad(dy, x) = dy / (1 + exp(-x)) # This op computes (ddy, d2x) from op.inputs == [dy, x] and grad == ddx. dy, x = op.inputs with ops.control_dependencies([grad]): - ddy = gen_nn_ops._softplus_grad(grad, x) # pylint: disable=protected-access + ddy = gen_nn_ops.softplus_grad(grad, x) d2x = grad * dy / (math_ops.exp(-x) + 2.0 + math_ops.exp(x)) return (ddy, d2x) @ops.RegisterGradient("Softsign") def _SoftsignGrad(op, grad): - return gen_nn_ops._softsign_grad(grad, op.inputs[0]) + return gen_nn_ops.softsign_grad(grad, op.inputs[0]) @ops.RegisterGradient("ReluGrad") def _ReluGradGrad(op, grad): x = op.inputs[1] - return (gen_nn_ops._relu_grad(grad, x), + return (gen_nn_ops.relu_grad(grad, x), array_ops.zeros(shape=array_ops.shape(x), dtype=x.dtype)) @@ -456,7 +456,7 @@ def _SoftmaxCrossEntropyWithLogitsGrad(op, grad_loss, grad_grad): def IsZero(g): # Some introspection to check if the gradient is feeding zeros - if context.in_eager_mode(): + if context.executing_eagerly(): # TODO(apassos) add an efficient way to detect eager zeros here. return False if g.op.type in ("ZerosLike", "Zeros"): @@ -565,14 +565,14 @@ def _LRNGrad(op, grad): alpha = op.get_attr("alpha") beta = op.get_attr("beta") return [ - gen_nn_ops._lrn_grad(grad, op.inputs[0], op.outputs[0], depth_radius, - bias, alpha, beta) + gen_nn_ops.lrn_grad(grad, op.inputs[0], op.outputs[0], depth_radius, bias, + alpha, beta) ] @ops.RegisterGradient("AvgPool") def _AvgPoolGrad(op, grad): - return gen_nn_ops._avg_pool_grad( + return gen_nn_ops.avg_pool_grad( array_ops.shape(op.inputs[0]), grad, op.get_attr("ksize"), @@ -584,7 +584,7 @@ def _AvgPoolGrad(op, grad): @ops.RegisterGradient("AvgPoolGrad") def _AvgPoolGradGrad(op, grad): return (array_ops.stop_gradient(op.inputs[0]), - gen_nn_ops._avg_pool( + gen_nn_ops.avg_pool( grad, op.get_attr("ksize"), op.get_attr("strides"), @@ -594,7 +594,7 @@ def _AvgPoolGradGrad(op, grad): @ops.RegisterGradient("MaxPool") def _MaxPoolGrad(op, grad): - return gen_nn_ops._max_pool_grad( + return gen_nn_ops.max_pool_grad( op.inputs[0], op.outputs[0], grad, @@ -620,7 +620,7 @@ def _MaxPoolGradV2(op, grad): @ops.RegisterGradient("MaxPoolWithArgmax") def _MaxPoolGradWithArgmax(op, grad, unused_argmax_grad): - return gen_nn_ops._max_pool_grad_with_argmax( + return gen_nn_ops.max_pool_grad_with_argmax( op.inputs[0], grad, op.outputs[1], @@ -635,7 +635,7 @@ def _MaxPoolGradGrad(op, grad): shape=array_ops.shape(op.inputs[0]), dtype=op.inputs[0].dtype), array_ops.zeros( shape=array_ops.shape(op.inputs[1]), dtype=op.inputs[1].dtype), - gen_nn_ops._max_pool_grad_grad( + gen_nn_ops.max_pool_grad_grad( op.inputs[0], op.inputs[1], grad, @@ -669,7 +669,7 @@ def _MaxPoolGradGradGrad(op, grad): shape=array_ops.shape(op.inputs[0]), dtype=op.inputs[0].dtype), array_ops.zeros( shape=array_ops.shape(op.inputs[1]), dtype=op.inputs[1].dtype), - gen_nn_ops._max_pool_grad( + gen_nn_ops.max_pool_grad( op.inputs[0], op.inputs[1], grad, @@ -696,8 +696,7 @@ def _FractionalMaxPoolGrad(op, grad_0, unused_grad_1, unused_grad_2): Returns: Input backprop for FractionalMaxPool op. """ - # pylint: disable=protected-access - return gen_nn_ops._fractional_max_pool_grad( + return gen_nn_ops.fractional_max_pool_grad( op.inputs[0], op.outputs[0], grad_0, op.outputs[1], op.outputs[2], op.get_attr("overlapping")) @@ -719,10 +718,9 @@ def _FractionalAvgPoolGrad(op, grad_0, unused_grad_1, unused_grad_2): Returns: Input backprop for FractionalAvgPool op. """ - # pylint: disable=protected-access - return gen_nn_ops._fractional_avg_pool_grad(op.inputs[0].get_shape(), grad_0, - op.outputs[1], op.outputs[2], - op.get_attr("overlapping")) + return gen_nn_ops.fractional_avg_pool_grad(op.inputs[0].get_shape(), grad_0, + op.outputs[1], op.outputs[2], + op.get_attr("overlapping")) @ops.RegisterGradient("BatchNormWithGlobalNormalization") @@ -746,7 +744,7 @@ def _BatchNormWithGlobalNormalizationGrad(op, grad): last dimension. dg: Backprop for gamma, which is (grad * ((x - m) * rsqrt(v + epsilon))) """ - dx, dm, dv, db, dg = gen_nn_ops._batch_norm_with_global_normalization_grad( + dx, dm, dv, db, dg = gen_nn_ops.batch_norm_with_global_normalization_grad( op.inputs[0], op.inputs[1], op.inputs[2], op.inputs[4], grad, op.get_attr("variance_epsilon"), op.get_attr("scale_after_normalization")) return dx, dm, dv, db, dg diff --git a/tensorflow/python/ops/nn_impl.py b/tensorflow/python/ops/nn_impl.py index 5fa5708114fd5cda6afbca78fa0debf68f0252cc..47cc4da7f2abd1f5b00e193a76c8391be94ca27d 100644 --- a/tensorflow/python/ops/nn_impl.py +++ b/tensorflow/python/ops/nn_impl.py @@ -303,12 +303,12 @@ def _swish_grad(features, grad): # @Defun decorator with noinline=True so that sigmoid(features) is re-computed # during backprop, and we can free the sigmoid(features) expression immediately # after use during the forward pass. +@tf_export("nn.swish") @function.Defun( grad_func=_swish_grad, shape_func=_swish_shape, func_name="swish", noinline=True) -@tf_export("nn.swish") def swish(features): # pylint: disable=g-doc-args """Computes the Swish activation function: `x * sigmoid(x)`. @@ -888,12 +888,10 @@ def fused_batch_norm( # TODO(reedwm): In a few weeks, switch to using the V2 version exclusively. We # currently only use the V2 version for float16 inputs, which is not supported # by the V1 version. - # pylint: disable=protected-access if x.dtype == dtypes.float16 or x.dtype == dtypes.bfloat16: - fused_batch_norm_func = gen_nn_ops._fused_batch_norm_v2 + fused_batch_norm_func = gen_nn_ops.fused_batch_norm_v2 else: - fused_batch_norm_func = gen_nn_ops._fused_batch_norm - # pylint: enable=protected-access + fused_batch_norm_func = gen_nn_ops._fused_batch_norm # pylint: disable=protected-access y, batch_mean, batch_var, _, _ = fused_batch_norm_func( x, scale, diff --git a/tensorflow/python/ops/nn_ops.py b/tensorflow/python/ops/nn_ops.py index 8fbe698914e5f2fa8f821feed82c33fc77e35e21..9b2aaa4c1c913ca01a6eff14798438da9ba2be05 100644 --- a/tensorflow/python/ops/nn_ops.py +++ b/tensorflow/python/ops/nn_ops.py @@ -29,6 +29,7 @@ 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 check_ops from tensorflow.python.ops import gen_nn_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import random_ops @@ -698,7 +699,7 @@ def convolution( `padded_input` is obtained by zero padding the input using an effective spatial filter shape of `(spatial_filter_shape-1) * dilation_rate + 1` and output striding `strides` as described in the - @{tf.nn.convolution$comment here}. + @{$python/nn#Convolution$comment here}. In the case that `data_format` does start with `"NC"`, the `input` and output (but not the `filter`) are simply transposed as follows: @@ -1042,9 +1043,7 @@ def pool( @tf_export("nn.atrous_conv2d") def atrous_conv2d(value, filters, rate, padding, name=None): - """Atrous convolution (a.k.a. - - convolution with holes or dilated convolution). + """Atrous convolution (a.k.a. convolution with holes or dilated convolution). This function is a simpler wrapper around the more general @{tf.nn.convolution}, and exists only for backwards compatibility. You can @@ -1481,7 +1480,6 @@ def conv3d_transpose( name=name) -# pylint: disable=protected-access @tf_export("nn.bias_add") def bias_add(value, bias, data_format=None, name=None): """Adds `bias` to `value`. @@ -1504,12 +1502,12 @@ def bias_add(value, bias, data_format=None, name=None): A `Tensor` with the same type as `value`. """ with ops.name_scope(name, "BiasAdd", [value, bias]) as name: - value = ops.convert_to_tensor(value, name="input") - bias = ops.convert_to_tensor(bias, dtype=value.dtype, name="bias") - return gen_nn_ops._bias_add(value, bias, data_format=data_format, name=name) + if not context.executing_eagerly(): + value = ops.convert_to_tensor(value, name="input") + bias = ops.convert_to_tensor(bias, dtype=value.dtype, name="bias") + return gen_nn_ops.bias_add(value, bias, data_format=data_format, name=name) -# pylint: disable=protected-access def bias_add_v1(value, bias, name=None): """Adds `bias` to `value`. @@ -1534,7 +1532,7 @@ def bias_add_v1(value, bias, name=None): with ops.name_scope(name, "BiasAddV1", [value, bias]) as name: value = ops.convert_to_tensor(value, name="input") bias = ops.convert_to_tensor(bias, dtype=value.dtype, name="bias") - return gen_nn_ops._bias_add_v1(value, bias, name=name) + return gen_nn_ops.bias_add_v1(value, bias, name=name) @tf_export("nn.crelu") @@ -1580,7 +1578,7 @@ def relu6(features, name=None): """ with ops.name_scope(name, "Relu6", [features]) as name: features = ops.convert_to_tensor(features, name="features") - return gen_nn_ops._relu6(features, name=name) + return gen_nn_ops.relu6(features, name=name) @tf_export("nn.leaky_relu") @@ -1616,7 +1614,7 @@ def _flatten_outer_dims(logits): output = array_ops.reshape(logits, array_ops.concat([[-1], last_dim_size], 0)) # Set output shape if known. - if context.in_graph_mode(): + if not context.executing_eagerly(): shape = logits.get_shape() if shape is not None and shape.dims is not None: shape = shape.as_list() @@ -1645,7 +1643,7 @@ def _softmax(logits, compute_op, dim=-1, name=None): Args: logits: A non-empty `Tensor`. Must be one of the following types: `half`, `float32`, `float64`. - compute_op: Either gen_nn_ops._softmax or gen_nn_ops._log_softmax + compute_op: Either gen_nn_ops.softmax or gen_nn_ops.log_softmax dim: The dimension softmax would be performed on. The default is -1 which indicates the last dimension. name: A name for the operation (optional). @@ -1739,7 +1737,7 @@ def softmax(logits, axis=None, name=None, dim=None): axis = deprecation.deprecated_argument_lookup("axis", axis, "dim", dim) if axis is None: axis = -1 - return _softmax(logits, gen_nn_ops._softmax, axis, name) + return _softmax(logits, gen_nn_ops.softmax, axis, name) @tf_export("nn.log_softmax") @@ -1769,7 +1767,7 @@ def log_softmax(logits, axis=None, name=None, dim=None): axis = deprecation.deprecated_argument_lookup("axis", axis, "dim", dim) if axis is None: axis = -1 - return _softmax(logits, gen_nn_ops._log_softmax, axis, name) + return _softmax(logits, gen_nn_ops.log_softmax, axis, name) def _ensure_xent_args(name, sentinel, labels, logits): @@ -1871,7 +1869,7 @@ def softmax_cross_entropy_with_logits_v2( # Do the actual op computation. # The second output tensor contains the gradients. We use it in # _CrossEntropyGrad() in nn_grad but not here. - cost, unused_backprop = gen_nn_ops._softmax_cross_entropy_with_logits( + cost, unused_backprop = gen_nn_ops.softmax_cross_entropy_with_logits( precise_logits, labels, name=name) # The output cost shape should be the input minus dim. @@ -1881,7 +1879,8 @@ def softmax_cross_entropy_with_logits_v2( # Make shape inference work since reshape and transpose may erase its static # shape. - if context.in_graph_mode() and shape is not None and shape.dims is not None: + if not context.executing_eagerly( + ) and shape is not None and shape.dims is not None: shape = shape.as_list() del shape[dim] cost.set_shape(shape) @@ -2027,6 +2026,9 @@ def sparse_softmax_cross_entropy_with_logits( # Store label shape for result later. labels_static_shape = labels.get_shape() labels_shape = array_ops.shape(labels) + static_shapes_fully_defined = ( + labels_static_shape.is_fully_defined() and + logits.get_shape()[:-1].is_fully_defined()) if logits.get_shape().ndims is not None and logits.get_shape().ndims == 0: raise ValueError( "Logits cannot be scalars - received shape %s." % logits.get_shape()) @@ -2036,29 +2038,44 @@ def sparse_softmax_cross_entropy_with_logits( raise ValueError("Rank mismatch: Rank of labels (received %s) should " "equal rank of logits minus 1 (received %s)." % (labels_static_shape.ndims, logits.get_shape().ndims)) + if (static_shapes_fully_defined and + labels_static_shape != logits.get_shape()[:-1]): + raise ValueError("Shape mismatch: The shape of labels (received %s) " + "should equal the shape of logits except for the last " + "dimension (received %s)." % (labels_static_shape, + logits.get_shape())) # Check if no reshapes are required. if logits.get_shape().ndims == 2: - cost, _ = gen_nn_ops._sparse_softmax_cross_entropy_with_logits( + cost, _ = gen_nn_ops.sparse_softmax_cross_entropy_with_logits( precise_logits, labels, name=name) if logits.dtype == dtypes.float16: return math_ops.cast(cost, dtypes.float16) else: return cost - # Reshape logits to 2 dim, labels to 1 dim. - num_classes = array_ops.shape(logits)[array_ops.rank(logits) - 1] - precise_logits = array_ops.reshape(precise_logits, [-1, num_classes]) - labels = array_ops.reshape(labels, [-1]) - # The second output tensor contains the gradients. We use it in - # _CrossEntropyGrad() in nn_grad but not here. - cost, _ = gen_nn_ops._sparse_softmax_cross_entropy_with_logits( - precise_logits, labels, name=name) - cost = array_ops.reshape(cost, labels_shape) - cost.set_shape(labels_static_shape) - if logits.dtype == dtypes.float16: - return math_ops.cast(cost, dtypes.float16) - else: - return cost + # Perform a check of the dynamic shapes if the static shapes are not fully + # defined. + shape_checks = [] + if not static_shapes_fully_defined: + shape_checks.append( + check_ops.assert_equal( + array_ops.shape(labels), + array_ops.shape(logits)[:-1])) + with ops.control_dependencies(shape_checks): + # Reshape logits to 2 dim, labels to 1 dim. + num_classes = array_ops.shape(logits)[array_ops.rank(logits) - 1] + precise_logits = array_ops.reshape(precise_logits, [-1, num_classes]) + labels = array_ops.reshape(labels, [-1]) + # The second output tensor contains the gradients. We use it in + # _CrossEntropyGrad() in nn_grad but not here. + cost, _ = gen_nn_ops.sparse_softmax_cross_entropy_with_logits( + precise_logits, labels, name=name) + cost = array_ops.reshape(cost, labels_shape) + cost.set_shape(labels_static_shape) + if logits.dtype == dtypes.float16: + return math_ops.cast(cost, dtypes.float16) + else: + return cost @tf_export("nn.avg_pool") @@ -2086,7 +2103,7 @@ def avg_pool(value, ksize, strides, padding, data_format="NHWC", name=None): """ with ops.name_scope(name, "AvgPool", [value]) as name: value = ops.convert_to_tensor(value, name="input") - return gen_nn_ops._avg_pool( + return gen_nn_ops.avg_pool( value, ksize=ksize, strides=strides, @@ -2116,12 +2133,13 @@ def max_pool(value, ksize, strides, padding, data_format="NHWC", name=None): """ with ops.name_scope(name, "MaxPool", [value]) as name: value = ops.convert_to_tensor(value, name="input") - return gen_nn_ops._max_pool(value, - ksize=ksize, - strides=strides, - padding=padding, - data_format=data_format, - name=name) + return gen_nn_ops.max_pool( + value, + ksize=ksize, + strides=strides, + padding=padding, + data_format=data_format, + name=name) @ops.RegisterStatistics("Conv2D", "flops") @@ -2299,7 +2317,7 @@ def dropout(x, keep_prob, noise_shape=None, seed=None, name=None): # pylint: di # 0. if [keep_prob, 1.0) and 1. if [1.0, 1.0 + keep_prob) binary_tensor = math_ops.floor(random_tensor) ret = math_ops.div(x, keep_prob) * binary_tensor - if context.in_graph_mode(): + if not context.executing_eagerly(): ret.set_shape(x.get_shape()) return ret @@ -2331,7 +2349,7 @@ def top_k(input, k=1, sorted=True, name=None): # pylint: disable=redefined-buil values: The `k` largest elements along each last dimensional slice. indices: The indices of `values` within the last dimension of `input`. """ - return gen_nn_ops._top_kv2(input, k=k, sorted=sorted, name=name) + return gen_nn_ops.top_kv2(input, k=k, sorted=sorted, name=name) def nth_element(input, n, reverse=False, name=None): # pylint: disable=redefined-builtin @@ -2650,4 +2668,4 @@ def in_top_k(predictions, targets, k, name=None): A `Tensor` of type `bool`. Computed Precision at `k` as a `bool Tensor`. """ with ops.name_scope(name, "in_top_k"): - return gen_nn_ops._in_top_kv2(predictions, targets, k, name=name) + return gen_nn_ops.in_top_kv2(predictions, targets, k, name=name) diff --git a/tensorflow/python/ops/nn_test.py b/tensorflow/python/ops/nn_test.py index 21eea3db25af0d1bcfbc7496665f5535c3f660ea..af9dae2aa64f0994f403ac81dcba800699d3c960 100644 --- a/tensorflow/python/ops/nn_test.py +++ b/tensorflow/python/ops/nn_test.py @@ -1049,6 +1049,22 @@ class DataFormatVectorPermuteTest(test_lib.TestCase): y_val = sess.run(y) self.assertAllEqual(y_val, [7, 9, 3, 4]) + def testNHWCToHWNC(self): + x_val = [7, 4, 9, 3] + x = constant_op.constant(x_val) + y = nn_ops.data_format_vec_permute(x, src_format="NHWC", dst_format="HWNC") + with self.test_session(use_gpu=test_lib.is_gpu_available()) as sess: + y_val = sess.run(y) + self.assertAllEqual(y_val, [4, 9, 7, 3]) + + def testHWNCToNHWC(self): + x_val = [7, 4, 9, 3] + x = constant_op.constant(x_val) + y = nn_ops.data_format_vec_permute(x, src_format="HWNC", dst_format="NHWC") + with self.test_session(use_gpu=test_lib.is_gpu_available()) as sess: + y_val = sess.run(y) + self.assertAllEqual(y_val, [9, 7, 4, 3]) + def testNHWCToNCHW2D(self): x_val = [[7, 4], [9, 3], [4, 5], [5, 1]] x = constant_op.constant(x_val) @@ -1057,6 +1073,22 @@ class DataFormatVectorPermuteTest(test_lib.TestCase): y_val = sess.run(y) self.assertAllEqual(y_val, [[7, 4], [5, 1], [9, 3], [4, 5]]) + def testNHWCToHWNC2D(self): + x_val = [[7, 4], [9, 3], [4, 5], [5, 1]] + x = constant_op.constant(x_val) + y = nn_ops.data_format_vec_permute(x, src_format="NHWC", dst_format="HWNC") + with self.test_session(use_gpu=test_lib.is_gpu_available()) as sess: + y_val = sess.run(y) + self.assertAllEqual(y_val, [[9, 3], [4, 5], [7, 4], [5, 1]]) + + def testHWNCToNHWC2D(self): + x_val = [[7, 4], [9, 3], [4, 5], [5, 1]] + x = constant_op.constant(x_val) + y = nn_ops.data_format_vec_permute(x, src_format="HWNC", dst_format="NHWC") + with self.test_session(use_gpu=test_lib.is_gpu_available()) as sess: + y_val = sess.run(y) + self.assertAllEqual(y_val, [[4, 5], [7, 4], [9, 3], [5, 1]]) + def testNCHWToNHWC2D(self): x_val = [[7, 4], [9, 3], [4, 5], [5, 1]] x = constant_op.constant(x_val) diff --git a/tensorflow/python/ops/numerics.py b/tensorflow/python/ops/numerics.py index b4ce1cbf25346412e2781a520b7e2cdcf720bcd5..d348e47f57b703138aabfc3463e750b795113335 100644 --- a/tensorflow/python/ops/numerics.py +++ b/tensorflow/python/ops/numerics.py @@ -74,7 +74,7 @@ def add_check_numerics_ops(): the checked operations. @enc_compatibility """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError( "add_check_numerics_ops() is not compatible with eager execution. " "To check for Inf's and NaN's under eager execution, call " diff --git a/tensorflow/python/ops/parsing_ops.py b/tensorflow/python/ops/parsing_ops.py index b0315ceee268be8ac1813dae5a262a7d9496e154..075b38d743d13329e646c0b268e938b5c5704e47 100644 --- a/tensorflow/python/ops/parsing_ops.py +++ b/tensorflow/python/ops/parsing_ops.py @@ -700,8 +700,7 @@ def _parse_example_raw(serialized, # Finally, convert dense_shapes to TensorShapeProto dense_shapes = [shape.as_proto() for shape in dense_shapes] - # pylint: disable=protected-access - outputs = gen_parsing_ops._parse_example( + outputs = gen_parsing_ops.parse_example( serialized=serialized, names=names, dense_defaults=dense_defaults_vec, @@ -710,7 +709,6 @@ def _parse_example_raw(serialized, dense_keys=dense_keys, dense_shapes=dense_shapes, name=name) - # pylint: enable=protected-access (sparse_indices, sparse_values, sparse_shapes, dense_values) = outputs @@ -1132,8 +1130,7 @@ def _parse_single_sequence_example_raw(serialized, feature_list_dense_shapes = [tensor_shape.as_shape(shape).as_proto() for shape in feature_list_dense_shapes] - # pylint: disable=protected-access - outputs = gen_parsing_ops._parse_single_sequence_example( + outputs = gen_parsing_ops.parse_single_sequence_example( serialized=serialized, debug_name=debug_name, context_dense_defaults=context_dense_defaults_vec, @@ -1149,7 +1146,6 @@ def _parse_single_sequence_example_raw(serialized, feature_list_dense_missing_assumed_empty=( feature_list_dense_missing_assumed_empty), name=name) - # pylint: enable=protected-access (context_sparse_indices, context_sparse_values, context_sparse_shapes, context_dense_values, @@ -1182,7 +1178,6 @@ def _parse_single_sequence_example_raw(serialized, @tf_export("decode_csv") def decode_csv(records, record_defaults, field_delim=",", use_quote_delim=True, name=None, na_value=""): - # pylint: disable=protected-access """Convert CSV records to tensors. Each column maps to one tensor. RFC 4180 format is expected for the CSV records. @@ -1211,11 +1206,13 @@ def decode_csv(records, record_defaults, field_delim=",", Each tensor will have the same shape as records. """ # TODO(martinwicke), remove the wrapper when new Python API generator is done. - return gen_parsing_ops._decode_csv( - records=records, record_defaults=record_defaults, - field_delim=field_delim, use_quote_delim=use_quote_delim, - na_value=na_value, name=name) - # pylint: enable=protected-access + return gen_parsing_ops.decode_csv( + records=records, + record_defaults=record_defaults, + field_delim=field_delim, + use_quote_delim=use_quote_delim, + na_value=na_value, + name=name) # TODO(b/70890287): Combine the implementation of this op and @@ -1391,7 +1388,6 @@ def _parse_single_example_v2_raw(serialized, sparse_keys, sparse_types, # Finally, convert dense_shapes to TensorShapeProto dense_shapes = [shape.as_proto() for shape in dense_shapes] - # pylint: disable=protected-access outputs = gen_parsing_ops.parse_single_example( serialized=serialized, dense_defaults=dense_defaults_vec, @@ -1401,7 +1397,6 @@ def _parse_single_example_v2_raw(serialized, sparse_keys, sparse_types, dense_keys=dense_keys, dense_shapes=dense_shapes, name=name) - # pylint: enable=protected-access (sparse_indices, sparse_values, sparse_shapes, dense_values) = outputs diff --git a/tensorflow/python/ops/random_ops.py b/tensorflow/python/ops/random_ops.py index 2c86358d21b1c280b8d7ade625fd4b7a44c5de26..db8159579a21d9b98b06b6172f8d0df7e8ff95ca 100644 --- a/tensorflow/python/ops/random_ops.py +++ b/tensorflow/python/ops/random_ops.py @@ -43,7 +43,6 @@ def _ShapeTensor(shape): return ops.convert_to_tensor(shape, dtype=dtype, name="shape") -# pylint: disable=protected-access @tf_export("random_normal") def random_normal(shape, mean=0.0, @@ -74,7 +73,7 @@ def random_normal(shape, mean_tensor = ops.convert_to_tensor(mean, dtype=dtype, name="mean") stddev_tensor = ops.convert_to_tensor(stddev, dtype=dtype, name="stddev") seed1, seed2 = random_seed.get_seed(seed) - rnd = gen_random_ops._random_standard_normal( + rnd = gen_random_ops.random_standard_normal( shape_tensor, dtype, seed=seed1, seed2=seed2) mul = rnd * stddev_tensor value = math_ops.add(mul, mean_tensor, name=name) @@ -126,7 +125,7 @@ def parameterized_truncated_normal(shape, minvals_tensor = ops.convert_to_tensor(minvals, dtype=dtype, name="minvals") maxvals_tensor = ops.convert_to_tensor(maxvals, dtype=dtype, name="maxvals") seed1, seed2 = random_seed.get_seed(seed) - rnd = gen_random_ops._parameterized_truncated_normal( + rnd = gen_random_ops.parameterized_truncated_normal( shape_tensor, means_tensor, stddevs_tensor, @@ -171,7 +170,7 @@ def truncated_normal(shape, mean_tensor = ops.convert_to_tensor(mean, dtype=dtype, name="mean") stddev_tensor = ops.convert_to_tensor(stddev, dtype=dtype, name="stddev") seed1, seed2 = random_seed.get_seed(seed) - rnd = gen_random_ops._truncated_normal( + rnd = gen_random_ops.truncated_normal( shape_tensor, dtype, seed=seed1, seed2=seed2) mul = rnd * stddev_tensor value = math_ops.add(mul, mean_tensor, name=name) @@ -237,11 +236,10 @@ def random_uniform(shape, maxval = ops.convert_to_tensor(maxval, dtype=dtype, name="max") seed1, seed2 = random_seed.get_seed(seed) if dtype.is_integer: - return gen_random_ops._random_uniform_int( + return gen_random_ops.random_uniform_int( shape, minval, maxval, seed=seed1, seed2=seed2, name=name) else: - rnd = gen_random_ops._random_uniform( - shape, dtype, seed=seed1, seed2=seed2) + rnd = gen_random_ops.random_uniform(shape, dtype, seed=seed1, seed2=seed2) return math_ops.add(rnd * (maxval - minval), minval, name=name) @@ -275,7 +273,7 @@ def random_shuffle(value, seed=None, name=None): dimension. """ seed1, seed2 = random_seed.get_seed(seed) - return gen_random_ops._random_shuffle( + return gen_random_ops.random_shuffle( value, seed=seed1, seed2=seed2, name=name) @@ -420,7 +418,7 @@ def random_gamma(shape, seed1, seed2 = random_seed.get_seed(seed) return math_ops.maximum( np.finfo(dtype.as_numpy_dtype).tiny, - gen_random_ops._random_gamma( + gen_random_ops.random_gamma( shape, alpha_broadcast, seed=seed1, seed2=seed2) / beta) ops.NotDifferentiable("RandomGamma") diff --git a/tensorflow/python/ops/resource_variable_ops.py b/tensorflow/python/ops/resource_variable_ops.py index 2d6d0672e03d9435175b0accd7c20dfddae16bcc..54191ee765c642a9fe8f040eb3c22d1abdff293b 100644 --- a/tensorflow/python/ops/resource_variable_ops.py +++ b/tensorflow/python/ops/resource_variable_ops.py @@ -21,6 +21,7 @@ from __future__ import print_function from tensorflow.core.framework import attr_value_pb2 from tensorflow.core.framework import variable_pb2 +from tensorflow.python import pywrap_tensorflow from tensorflow.python.eager import context from tensorflow.python.eager import tape from tensorflow.python.framework import dtypes @@ -30,6 +31,7 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import gen_array_ops from tensorflow.python.ops import gen_resource_variable_ops from tensorflow.python.ops import gen_state_ops +from tensorflow.python.ops import math_ops from tensorflow.python.ops import variables # go/tf-wildcard-import # pylint: disable=wildcard-import @@ -133,10 +135,10 @@ class EagerResourceDeleter(object): # valid, and so on. Printing warnings in these cases is silly # (exceptions raised from __del__ are printed as warnings to stderr). pass # 'NoneType' object is not callable when the handle has been - # partially unloaded. + # partially unloaded. except AttributeError: pass # 'NoneType' object has no attribute 'eager_mode' when context has - # been unloaded. Will catch other module unloads as well. + # been unloaded. Will catch other module unloads as well. def shape_safe_assign_variable_handle(handle, shape, value, name=None): @@ -151,7 +153,7 @@ def shape_safe_assign_variable_handle(handle, shape, value, name=None): class ResourceVariable(variables.Variable): """Variable based on resource handles. - See the ${variables} documentation for more details. + See the @{$python/state_ops$`Variables`} documentation for more details. A `ResourceVariable` allows you to maintain state across subsequent calls to session.run. @@ -265,9 +267,9 @@ class ResourceVariable(variables.Variable): if initial_value is not None: raise ValueError("variable_def and initial_value are mutually " "exclusive.") - if not context.in_graph_mode(): - raise ValueError("Creating ResourceVariable from variable_def" - " only supported in GRAPH mode.") + if context.executing_eagerly(): + raise ValueError("Creating ResourceVariable from variable_def is " + "not supported when eager execution is enabled.") self._init_from_proto(variable_def, import_scope=import_scope) else: self._init_from_args( @@ -361,7 +363,7 @@ class ResourceVariable(variables.Variable): # this graph. self._graph_key = ops.get_default_graph()._graph_key # pylint: disable=protected-access with ops.init_scope(): - self._in_graph_mode = context.in_graph_mode() + self._in_graph_mode = not context.executing_eagerly() with ops.name_scope(name, "Variable", [] if init_from_fn else [initial_value]) as name: # pylint: disable=protected-access @@ -384,9 +386,6 @@ class ResourceVariable(variables.Variable): shared_name=handle_name, name=name, graph_mode=self._in_graph_mode) - self._handle_device = ( - self._handle.device if self._in_graph_mode else - context.get_default_context().device_name) self._shape = initial_value.get_shape() else: initial_value = initial_value() @@ -399,9 +398,6 @@ class ResourceVariable(variables.Variable): shared_name=handle_name, name=name, graph_mode=False) - self._handle_device = ( - self._handle.device if self._in_graph_mode else - context.get_default_context().device_name) self._shape = initial_value.get_shape() # pylint: enable=protected-access @@ -425,8 +421,6 @@ class ResourceVariable(variables.Variable): shared_name=handle_name, name=name, graph_mode=self._in_graph_mode) - self._handle_device = (self._handle.device if self._in_graph_mode else - context.get_default_context().device_name) self._shape = initial_value.get_shape() self._initial_value = initial_value if self._in_graph_mode else None @@ -449,7 +443,7 @@ class ResourceVariable(variables.Variable): with ops.name_scope("Read"), ops.colocate_with(self._handle): # Manually assign reads to the handle's device to avoid log # messages. - with ops.device(self._handle_device): + with ops.device(self._handle.device): value = self._read_variable_op() self._graph_element = value if caching_device is not None: @@ -476,7 +470,7 @@ class ResourceVariable(variables.Variable): self._cached_value = self._read_variable_op() else: self._cached_value = None - if context.in_graph_mode(): + if not context.executing_eagerly(): ops.add_to_collections(collections, self) elif ops.GraphKeys.GLOBAL_STEP in collections: ops.add_to_collections(ops.GraphKeys.GLOBAL_STEP, self) @@ -489,12 +483,13 @@ class ResourceVariable(variables.Variable): # cycles being uncollectable, and means that no __del__ will be defined at # all in graph mode. self._handle_deleter = EagerResourceDeleter( - handle=self._handle, handle_device=self._handle_device) + handle=self._handle, handle_device=self._handle.device) + self._cached_shape_as_list = None def _init_from_proto(self, variable_def, import_scope=None): """Initializes from `VariableDef` proto.""" # Note that init_from_proto is currently not supported in Eager mode. - assert context.in_graph_mode() + assert not context.executing_eagerly() self._in_graph_mode = True assert isinstance(variable_def, variable_pb2.VariableDef) if not variable_def.is_resource: @@ -507,7 +502,6 @@ class ResourceVariable(variables.Variable): variable_def.variable_name, import_scope=import_scope)) self._shape = tensor_shape.TensorShape( self._handle.op.get_attr("shape")) - self._handle_device = self._handle.device self._handle_name = self._handle.name self._initializer_op = g.as_graph_element( ops.prepend_name_scope( @@ -534,8 +528,10 @@ class ResourceVariable(variables.Variable): self._save_slice_info = None self._caching_device = None self._dtype = dtypes.as_dtype(self._handle.op.get_attr("dtype")) - self._graph_element = self.value() + self._graph_element = g.get_tensor_by_name( + self._handle.op.name + "/Read/ReadVariableOp:0") self._constraint = None + self._cached_shape_as_list = None def __nonzero__(self): return self.__bool__() @@ -551,7 +547,7 @@ class ResourceVariable(variables.Variable): @property def device(self): """The device this variable is on.""" - return self._handle_device + return self._handle.device @property def graph(self): @@ -568,11 +564,26 @@ class ResourceVariable(variables.Variable): """The shape of this variable.""" return self._shape + def _shape_as_list(self): + if self._cached_shape_as_list: + return self._cached_shape_as_list + if self.shape.ndims is None: + return None + self._cached_shape_as_list = [dim.value for dim in self.shape.dims] + return self._cached_shape_as_list + + def _shape_tuple(self): + shape = self._shape_as_list() + if shape is None: + return None + return tuple(shape) + @property def create(self): """The op responsible for initializing this variable.""" if not self._in_graph_mode: - raise RuntimeError("Calling create in EAGER mode not supported.") + raise RuntimeError("Calling create is not supported when eager execution" + " is enabled.") return self._initializer_op @property @@ -585,7 +596,7 @@ class ResourceVariable(variables.Variable): if self._cached_value is not None: return self._cached_value with ops.colocate_with(None, ignore_existing=True): - with ops.device(self._handle_device): + with ops.device(self._handle.device): return self._read_variable_op() def _as_graph_element(self): @@ -600,7 +611,7 @@ class ResourceVariable(variables.Variable): @property def initial_value(self): """Returns the Tensor used as the initial value for the variable.""" - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError("initial_value not supported in EAGER mode.") return self._initial_value @@ -621,15 +632,15 @@ class ResourceVariable(variables.Variable): def eval(self, session=None): """Evaluates and returns the value of this variable.""" - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError("Trying to eval in EAGER mode") return self._graph_element.eval(session=session) def numpy(self): - if context.in_graph_mode(): - raise NotImplementedError( - "numpy() is only available when eager execution is enabled.") - return self.read_value().numpy() + if context.executing_eagerly(): + return self.read_value().numpy() + raise NotImplementedError( + "numpy() is only available when eager execution is enabled.") def count_up_to(self, limit): """Increments this variable until it reaches `limit`. @@ -682,7 +693,7 @@ class ResourceVariable(variables.Variable): """ with ops.name_scope("Read"): # Ensure we read the variable in the same device as the handle. - with ops.device(self._handle_device): + with ops.device(self._handle.device): value = self._read_variable_op() # Return an identity so it can get placed on whatever device the context # specifies instead of the device where the variable is. @@ -710,7 +721,7 @@ class ResourceVariable(variables.Variable): A `VariableDef` protocol buffer, or `None` if the `Variable` is not in the specified name scope. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError("to_proto not supported in EAGER mode.") if export_scope is None or self.handle.name.startswith(export_scope): var_def = variable_pb2.VariableDef() @@ -737,7 +748,7 @@ class ResourceVariable(variables.Variable): @staticmethod def from_proto(variable_def, import_scope=None): - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError("from_proto not supported in EAGER mode.") return ResourceVariable( variable_def=variable_def, import_scope=import_scope) @@ -788,37 +799,83 @@ class ResourceVariable(variables.Variable): __array_priority__ = 100 - def assign_sub(self, delta, use_locking=None, name=None): + def assign_sub(self, delta, use_locking=None, name=None, read_value=True): + """Subtracts a value from this variable. + + Args: + delta: A `Tensor`. The value to subtract from this variable. + use_locking: If `True`, use locking during the operation. + name: The name to use for the operation. + read_value: A `bool`. Whether to read and return the new value of the + variable or not. + + Returns: + If `read_value` is `True`, this method will return the new value of the + variable after the assignment has completed. Otherwise, when in graph mode + it will return the `Operation` that does the assignment, and when in eager + mode it will return `None`. + """ # TODO(apassos): this here and below is not atomic. Consider making it # atomic if there's a way to do so without a performance cost for those who # don't need it. - return self._lazy_read(gen_resource_variable_ops.assign_sub_variable_op( - self.handle, - ops.convert_to_tensor(delta, dtype=self.dtype), - name=name)) + assign_sub_op = gen_resource_variable_ops.assign_sub_variable_op( + self.handle, ops.convert_to_tensor(delta, dtype=self.dtype), name=name) + if read_value: + return self._lazy_read(assign_sub_op) + return assign_sub_op - def assign_add(self, delta, use_locking=None, name=None): - return self._lazy_read(gen_resource_variable_ops.assign_add_variable_op( - self.handle, - ops.convert_to_tensor(delta, dtype=self.dtype), - name=name)) + def assign_add(self, delta, use_locking=None, name=None, read_value=True): + """Adds a value to this variable. + + Args: + delta: A `Tensor`. The value to add to this variable. + use_locking: If `True`, use locking during the operation. + name: The name to use for the operation. + read_value: A `bool`. Whether to read and return the new value of the + variable or not. + + Returns: + If `read_value` is `True`, this method will return the new value of the + variable after the assignment has completed. Otherwise, when in graph mode + it will return the `Operation` that does the assignment, and when in eager + mode it will return `None`. + """ + assign_add_op = gen_resource_variable_ops.assign_add_variable_op( + self.handle, ops.convert_to_tensor(delta, dtype=self.dtype), name=name) + if read_value: + return self._lazy_read(assign_add_op) + return assign_add_op def _lazy_read(self, op): if hasattr(self, "_trainable") and self._trainable: tape.watch_variable(self) return _UnreadVariable( - self._handle, self.dtype, self._handle_device, self._shape, - self._in_graph_mode, + self._handle, self.dtype, self._shape, self._in_graph_mode, self._handle_deleter if not self._in_graph_mode else None, op) - def assign(self, value, use_locking=None, name=None): + def assign(self, value, use_locking=None, name=None, read_value=True): + """Assigns a new value to this variable. + + Args: + value: A `Tensor`. The new value for this variable. + use_locking: If `True`, use locking during the assignment. + name: The name to use for the assignment. + read_value: A `bool`. Whether to read and return the new value of the + variable or not. + + Returns: + If `read_value` is `True`, this method will return the new value of the + variable after the assignment has completed. Otherwise, when in graph mode + it will return the `Operation` that does the assignment, and when in eager + mode it will return `None`. + """ value_tensor = ops.convert_to_tensor(value, dtype=self.dtype) self._shape.assert_is_compatible_with(value_tensor.shape) - return self._lazy_read( - gen_resource_variable_ops.assign_variable_op( - self.handle, - value_tensor, - name=name)) + assign_op = gen_resource_variable_ops.assign_variable_op( + self.handle, value_tensor, name=name) + if read_value: + return self._lazy_read(assign_op) + return assign_op def _strided_slice_assign(self, begin, end, strides, value, name, begin_mask, end_mask, ellipsis_mask, new_axis_mask, @@ -894,6 +951,10 @@ class ResourceVariable(variables.Variable): "Tensor object.") +pywrap_tensorflow.TFE_Py_RegisterResourceVariableType(ResourceVariable) +math_ops._resource_variable_type = ResourceVariable # pylint: disable=protected-access + + def _dense_var_to_tensor(var, dtype=None, name=None, as_ref=False): return var._dense_var_to_tensor(dtype=dtype, name=name, as_ref=as_ref) # pylint: disable=protected-access @@ -904,7 +965,7 @@ class _UnreadVariable(ResourceVariable): Pretends to be the tensor if anyone looks. """ - def __init__(self, handle, dtype, handle_device, # pylint: disable=super-init-not-called + def __init__(self, handle, dtype, # pylint: disable=super-init-not-called shape, in_graph_mode, deleter, parent_op): # We do not call super init on purpose. self._trainable = False @@ -912,7 +973,6 @@ class _UnreadVariable(ResourceVariable): self._graph_key = ops.get_default_graph()._graph_key # pylint: disable=protected-access self._in_graph_mode = in_graph_mode self._handle = handle - self._handle_device = handle_device self._shape = shape self._initial_value = None if isinstance(self._handle, ops.EagerTensor): @@ -925,10 +985,10 @@ class _UnreadVariable(ResourceVariable): self._is_initialized_op = None self._initializer_op = None self._parent_op = parent_op - if context.in_graph_mode(): - self._graph_element = self.read_value() - else: + if context.executing_eagerly(): self._graph_element = None + else: + self._graph_element = self.read_value() self._handle_deleter = deleter def value(self): @@ -944,6 +1004,7 @@ class _UnreadVariable(ResourceVariable): def set_shape(self, shape): self._shape = shape + self._cached_shape_as_list = None @property def op(self): diff --git a/tensorflow/python/ops/rnn.py b/tensorflow/python/ops/rnn.py index aa8d4327d2f0e93768728744d5cce3fed385393f..42af7f8b274c555e6375ab8e937a8cc06ffbaa8e 100644 --- a/tensorflow/python/ops/rnn.py +++ b/tensorflow/python/ops/rnn.py @@ -45,7 +45,6 @@ from tensorflow.python.util.tf_export import tf_export # pylint: disable=protected-access _concat = rnn_cell_impl._concat -_like_rnncell = rnn_cell_impl._like_rnncell # pylint: enable=protected-access @@ -403,11 +402,8 @@ def bidirectional_dynamic_rnn(cell_fw, cell_bw, inputs, sequence_length=None, Raises: TypeError: If `cell_fw` or `cell_bw` is not an instance of `RNNCell`. """ - - if not _like_rnncell(cell_fw): - raise TypeError("cell_fw must be an instance of RNNCell") - if not _like_rnncell(cell_bw): - raise TypeError("cell_bw must be an instance of RNNCell") + rnn_cell_impl.assert_like_rnncell("cell_fw", cell_fw) + rnn_cell_impl.assert_like_rnncell("cell_bw", cell_bw) with vs.variable_scope(scope or "bidirectional_rnn"): # Forward direction @@ -568,14 +564,13 @@ def dynamic_rnn(cell, inputs, sequence_length=None, initial_state=None, TypeError: If `cell` is not an instance of RNNCell. ValueError: If inputs is None or an empty list. """ - if not _like_rnncell(cell): - raise TypeError("cell must be an instance of RNNCell") + rnn_cell_impl.assert_like_rnncell("cell", cell) with vs.variable_scope(scope or "rnn") as varscope: # Create a new scope in which the caching device is either # determined by the parent scope, or is set to place the cached # Variable using the same placement as for the rest of the RNN. - if context.in_graph_mode(): + if not context.executing_eagerly(): if varscope.caching_device is None: varscope.set_caching_device(lambda op: op.device) @@ -616,7 +611,7 @@ def dynamic_rnn(cell, inputs, sequence_length=None, initial_state=None, ["Expected shape for Tensor %s is " % x.name, packed_shape, " but saw shape: ", x_shape]) - if context.in_graph_mode() and sequence_length is not None: + if not context.executing_eagerly() and sequence_length is not None: # Perform some shape validation with ops.control_dependencies( [_assert_has_shape(sequence_length, [batch_size])]): @@ -742,7 +737,7 @@ def _dynamic_rnn_loop(cell, element_shape=element_shape, tensor_array_name=base_name + name) - in_graph_mode = context.in_graph_mode() + in_graph_mode = not context.executing_eagerly() if in_graph_mode: output_ta = tuple( _create_ta( @@ -872,7 +867,7 @@ def raw_rnn(cell, loop_fn, ```python time = tf.constant(0, dtype=tf.int32) - (finished, next_input, initial_state, _, loop_state) = loop_fn( + (finished, next_input, initial_state, emit_structure, loop_state) = loop_fn( time=time, cell_output=None, cell_state=None, loop_state=None) emit_ta = TensorArray(dynamic_size=True, dtype=initial_state.dtype) state = initial_state @@ -883,7 +878,7 @@ def raw_rnn(cell, loop_fn, loop_state=loop_state) # Emit zeros and copy forward state for minibatch entries that are finished. state = tf.where(finished, state, next_state) - emit = tf.where(finished, tf.zeros_like(emit), emit) + emit = tf.where(finished, tf.zeros_like(emit_structure), emit) emit_ta = emit_ta.write(time, emit) # If any new minibatch entries are marked as finished, mark these. finished = tf.logical_or(finished, next_finished) @@ -943,10 +938,15 @@ def raw_rnn(cell, loop_fn, and `emit_output`: the output to store for this iteration. Note that `emit_output` should be a `Tensor` or (possibly nested) - tuple of tensors with shapes and structure matching `cell.output_size` - and `cell_output` above. The parameter `cell_state` and output - `next_cell_state` may be either a single or (possibly nested) tuple - of tensors. The parameter `loop_state` and + tuple of tensors which is aggregated in the `emit_ta` inside the + `while_loop`. For the first call to `loop_fn`, the `emit_output` + corresponds to the `emit_structure` which is then used to determine the + size of the `zero_tensor` for the `emit_ta` (defaults to + `cell.output_size`). For the subsequent calls to the `loop_fn`, the + `emit_output` corresponds to the actual output tensor + that is to be aggregated in the `emit_ta`. The parameter `cell_state` + and output `next_cell_state` may be either a single or (possibly nested) + tuple of tensors. The parameter `loop_state` and output `next_loop_state` may be either a single or (possibly nested) tuple of `Tensor` and `TensorArray` objects. This last parameter may be ignored by `loop_fn` and the return value may be `None`. If it @@ -1015,9 +1015,8 @@ def raw_rnn(cell, loop_fn, TypeError: If `cell` is not an instance of RNNCell, or `loop_fn` is not a `callable`. """ + rnn_cell_impl.assert_like_rnncell("cell", cell) - if not _like_rnncell(cell): - raise TypeError("cell must be an instance of RNNCell") if not callable(loop_fn): raise TypeError("loop_fn must be a callable") @@ -1027,7 +1026,7 @@ def raw_rnn(cell, loop_fn, # determined by the parent scope, or is set to place the cached # Variable using the same placement as for the rest of the RNN. with vs.variable_scope(scope or "rnn") as varscope: - if context.in_graph_mode(): + if not context.executing_eagerly(): if varscope.caching_device is None: varscope.set_caching_device(lambda op: op.device) @@ -1229,9 +1228,7 @@ def static_rnn(cell, ValueError: If `inputs` is `None` or an empty list, or if the input depth (column size) cannot be inferred from inputs via shape inference. """ - - if not _like_rnncell(cell): - raise TypeError("cell must be an instance of RNNCell") + rnn_cell_impl.assert_like_rnncell("cell", cell) if not nest.is_sequence(inputs): raise TypeError("inputs must be a sequence") if not inputs: @@ -1242,7 +1239,7 @@ def static_rnn(cell, # determined by the parent scope, or is set to place the cached # Variable using the same placement as for the rest of the RNN. with vs.variable_scope(scope or "rnn") as varscope: - if context.in_graph_mode(): + if not context.executing_eagerly(): if varscope.caching_device is None: varscope.set_caching_device(lambda op: op.device) @@ -1469,11 +1466,8 @@ def static_bidirectional_rnn(cell_fw, TypeError: If `cell_fw` or `cell_bw` is not an instance of `RNNCell`. ValueError: If inputs is None or an empty list. """ - - if not _like_rnncell(cell_fw): - raise TypeError("cell_fw must be an instance of RNNCell") - if not _like_rnncell(cell_bw): - raise TypeError("cell_bw must be an instance of RNNCell") + rnn_cell_impl.assert_like_rnncell("cell_fw", cell_fw) + rnn_cell_impl.assert_like_rnncell("cell_bw", cell_bw) if not nest.is_sequence(inputs): raise TypeError("inputs must be a sequence") if not inputs: diff --git a/tensorflow/python/ops/rnn_cell_impl.py b/tensorflow/python/ops/rnn_cell_impl.py index 923348ea44e18a87e09fe1c0424f0323eb967e3d..fe380c44dafdad6dc25d50102bacba610132674d 100644 --- a/tensorflow/python/ops/rnn_cell_impl.py +++ b/tensorflow/python/ops/rnn_cell_impl.py @@ -46,6 +46,7 @@ from tensorflow.python.ops import tensor_array_ops from tensorflow.python.ops import variable_scope as vs from tensorflow.python.ops import variables as tf_variables from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.training import checkpointable from tensorflow.python.util import nest from tensorflow.python.util.tf_export import tf_export @@ -54,6 +55,8 @@ _BIAS_VARIABLE_NAME = "bias" _WEIGHTS_VARIABLE_NAME = "kernel" +# TODO(jblespiau): Remove this function when we are sure there are no longer +# any usage (even if protected, it is being used). Prefer assert_like_rnncell. def _like_rnncell(cell): """Checks that a given object is an RNNCell by using duck typing.""" conditions = [hasattr(cell, "output_size"), hasattr(cell, "state_size"), @@ -61,6 +64,45 @@ def _like_rnncell(cell): return all(conditions) +# This can be used with self.assertRaisesRegexp for assert_like_rnncell. +ASSERT_LIKE_RNNCELL_ERROR_REGEXP = "is not an RNNCell" + + +def assert_like_rnncell(cell_name, cell): + """Raises a TypeError if cell is not like an RNNCell. + + NOTE: Do not rely on the error message (in particular in tests) which can be + subject to change to increase readability. Use + ASSERT_LIKE_RNNCELL_ERROR_REGEXP. + + Args: + cell_name: A string to give a meaningful error referencing to the name + of the functionargument. + cell: The object which should behave like an RNNCell. + + Raises: + TypeError: A human-friendly exception. + """ + conditions = [ + hasattr(cell, "output_size"), + hasattr(cell, "state_size"), + hasattr(cell, "zero_state"), + callable(cell), + ] + errors = [ + "'output_size' property is missing", + "'state_size' property is missing", + "'zero_state' method is missing", + "is not callable" + ] + + if not all(conditions): + + errors = [error for error, cond in zip(errors, conditions) if not cond] + raise TypeError("The argument {!r} ({}) is not an RNNCell: {}.".format( + cell_name, cell, ", ".join(errors))) + + def _concat(prefix, suffix, static=False): """Concat that enables int, Tensor, or TensorShape values. @@ -127,7 +169,7 @@ def _zero_state_tensors(state_size, batch_size, dtype): """Combine s with batch_size to get a proper tensor shape.""" c = _concat(batch_size, s) size = array_ops.zeros(c, dtype=dtype) - if context.in_graph_mode(): + if not context.executing_eagerly(): c_static = _concat(batch_size, s, static=True) size.set_shape(c_static) return size @@ -191,12 +233,13 @@ class RNNCell(base_layer.Layer): def _rnn_get_variable(self, getter, *args, **kwargs): variable = getter(*args, **kwargs) - if context.in_graph_mode(): - trainable = (variable in tf_variables.trainable_variables() or - (isinstance(variable, tf_variables.PartitionedVariable) and - list(variable)[0] in tf_variables.trainable_variables())) - else: + if context.executing_eagerly(): trainable = variable._trainable # pylint: disable=protected-access + else: + trainable = ( + variable in tf_variables.trainable_variables() or + (isinstance(variable, tf_variables.PartitionedVariable) and + list(variable)[0] in tf_variables.trainable_variables())) if trainable and variable not in self._trainable_weights: self._trainable_weights.append(variable) elif not trainable and variable not in self._non_trainable_weights: @@ -240,7 +283,7 @@ class RNNCell(base_layer.Layer): # Try to use the last cached zero_state. This is done to avoid recreating # zeros, especially when eager execution is enabled. state_size = self.state_size - is_eager = context.in_eager_mode() + is_eager = context.executing_eagerly() if is_eager and hasattr(self, "_last_zero_state"): (last_state_size, last_batch_size, last_dtype, last_output) = getattr(self, "_last_zero_state") @@ -912,8 +955,8 @@ class DropoutWrapper(RNNCell): but not `callable`. ValueError: if any of the keep_probs are not between 0 and 1. """ - if not _like_rnncell(cell): - raise TypeError("The parameter cell is not a RNNCell.") + assert_like_rnncell("cell", cell) + if (dropout_state_filter_visitor is not None and not callable(dropout_state_filter_visitor)): raise TypeError("dropout_state_filter_visitor must be callable") @@ -1187,6 +1230,12 @@ class MultiRNNCell(RNNCell): "cells must be a list or tuple, but saw: %s." % cells) self._cells = cells + for cell_number, cell in enumerate(self._cells): + # Add Checkpointable dependencies on these cells so their variables get + # saved with this object when using object-based saving. + if isinstance(cell, checkpointable.CheckpointableBase): + # TODO(allenl): Track down non-Checkpointable callers. + self._track_checkpointable(cell, name="cell-%d" % (cell_number,)) self._state_is_tuple = state_is_tuple if not state_is_tuple: if any(nest.is_sequence(c.state_size) for c in self._cells): diff --git a/tensorflow/python/ops/script_ops.py b/tensorflow/python/ops/script_ops.py index 6fe2f61016775b410045fefcc8764907b8ea39f3..fb59bbba5eb7a668b6b959c8519f2b52f0ce26fb 100644 --- a/tensorflow/python/ops/script_ops.py +++ b/tensorflow/python/ops/script_ops.py @@ -25,6 +25,9 @@ from __future__ import print_function import threading +# Used by py_util.cc to get tracebacks. +import traceback # pylint: disable=unused-import + import numpy as np import six @@ -219,18 +222,16 @@ def _internal_py_func(func, inp, Tout, stateful=None, eager=False, name=None): graph._cleanup_py_funcs_used_in_graph.append(cleanup) # pylint: enable=protected-access - # pylint: disable=protected-access if eager: - result = gen_script_ops._eager_py_func( + result = gen_script_ops.eager_py_func( input=inp, token=token, Tout=Tout, name=name) else: if stateful: - result = gen_script_ops._py_func( + result = gen_script_ops.py_func( input=inp, token=token, Tout=Tout, name=name) else: - result = gen_script_ops._py_func_stateless( + result = gen_script_ops.py_func_stateless( input=inp, token=token, Tout=Tout, name=name) - # pylint: enable=protected-access return result if is_list_or_tuple else result[0] @@ -319,7 +320,7 @@ def py_func(func, inp, Tout, stateful=True, name=None): Returns: A list of `Tensor` or a single `Tensor` which `func` computes. """ - if context.in_eager_mode(): + if context.executing_eagerly(): result = func(*[x.numpy() for x in inp]) result = nest.flatten(result) diff --git a/tensorflow/python/ops/session_ops.py b/tensorflow/python/ops/session_ops.py index cedd36c1deed541adcf601ff9447345e2279e8f9..ad38845153c94e9bb31e6e3ee05ebed0a4313efc 100644 --- a/tensorflow/python/ops/session_ops.py +++ b/tensorflow/python/ops/session_ops.py @@ -16,7 +16,6 @@ """Tensor Handle Operations. See the @{$python/session_ops} guide. @@get_session_handle -@@get_session_handle_v2 @@get_session_tensor @@delete_session_tensor """ @@ -182,7 +181,7 @@ def get_session_handle(data, name=None): # Colocate this operation with data. with ops.colocate_with(data): - return gen_data_flow_ops._get_session_handle(data, name=name) # pylint: disable=protected-access + return gen_data_flow_ops.get_session_handle(data, name=name) @tf_export("get_session_tensor") @@ -222,7 +221,7 @@ def get_session_tensor(handle, dtype, name=None): with ops.device(handle_device): holder = array_ops.placeholder(dtypes.string) _register_handle_feeder(holder.graph, holder, dtype) - tensor = gen_data_flow_ops._get_session_tensor(holder, dtype, name=name) + tensor = gen_data_flow_ops.get_session_tensor(holder, dtype, name=name) return (holder, tensor) @@ -246,7 +245,7 @@ def delete_session_tensor(handle, name=None): handle_device = TensorHandle._get_device_name(handle) with ops.device(handle_device): holder = array_ops.placeholder(dtypes.string) - deleter = gen_data_flow_ops._delete_session_tensor(holder, name=name) + deleter = gen_data_flow_ops.delete_session_tensor(holder, name=name) return (holder, deleter) @@ -268,7 +267,7 @@ def _get_handle_reader(graph, handle, dtype): with graph.as_default(), graph.device(handle_device): holder = array_ops.placeholder(dtypes.string) _register_handle_feeder(holder.graph, holder, dtype) - reader = gen_data_flow_ops._get_session_tensor(holder, dtype) + reader = gen_data_flow_ops.get_session_tensor(holder, dtype) result = (holder, reader) graph._handle_readers[graph_key] = result return result @@ -289,7 +288,7 @@ def _get_handle_mover(graph, feeder, handle): # Create mover if we haven't done it. holder, reader = _get_handle_reader(graph, handle, dtype) with graph.as_default(), graph.device(feeder.op.device): - mover = gen_data_flow_ops._get_session_handle(reader) # pylint: disable=protected-access + mover = gen_data_flow_ops.get_session_handle(reader) result = (holder, mover) graph._handle_movers[graph_key] = result return result @@ -303,7 +302,7 @@ def _get_handle_deleter(graph, deleter_key, handle): handle_device = TensorHandle._get_device_name(handle) with graph.as_default(), graph.device(handle_device): holder = array_ops.placeholder(dtypes.string) - deleter = gen_data_flow_ops._delete_session_tensor(holder) + deleter = gen_data_flow_ops.delete_session_tensor(holder) result = (holder, deleter) graph._handle_deleters[deleter_key] = result return result diff --git a/tensorflow/python/ops/sparse_grad.py b/tensorflow/python/ops/sparse_grad.py index 5295e7d21c2b5810422ec36f5aced63c9039feca..97353d6c747cb7e4d3c1fa92ad61af24fb17de91 100644 --- a/tensorflow/python/ops/sparse_grad.py +++ b/tensorflow/python/ops/sparse_grad.py @@ -88,10 +88,8 @@ def _SparseAddGrad(op, *grads): # the non-zero elements of the sum, and we will peek into `sum_indices` in the # gradient op. - # pylint: disable=protected-access - a_val_grad, b_val_grad = gen_sparse_ops._sparse_add_grad(val_grad, a_indices, - b_indices, - sum_indices) + a_val_grad, b_val_grad = gen_sparse_ops.sparse_add_grad( + val_grad, a_indices, b_indices, sum_indices) a_val_grad.set_shape(op.inputs[1].get_shape()) b_val_grad.set_shape(op.inputs[4].get_shape()) # (a_indices, a_values, a_shape, b_indices, b_values, b_shape, thresh) @@ -151,7 +149,7 @@ def _SparseTensorDenseMatMulGrad(op, grad): "complex gradients.") # gradient w.r.t. dense - b_grad = gen_sparse_ops._sparse_tensor_dense_mat_mul( # pylint: disable=protected-access + b_grad = gen_sparse_ops.sparse_tensor_dense_mat_mul( a_indices, a_values, a_shape, grad, adjoint_a=not adj_a) if adj_b: b_grad = array_ops.transpose(b_grad) @@ -278,8 +276,7 @@ def _SparseFillEmptyRowsGrad(op, unused_grad_output_indices, output_grad_values, """Gradients for SparseFillEmptyRows.""" reverse_index_map = op.outputs[3] - # pylint: disable=protected-access - d_values, d_default_value = gen_sparse_ops._sparse_fill_empty_rows_grad( + d_values, d_default_value = gen_sparse_ops.sparse_fill_empty_rows_grad( reverse_index_map=reverse_index_map, grad_values=output_grad_values) # d_indices, d_values, d_dense_shape, d_default_value. diff --git a/tensorflow/python/ops/sparse_ops.py b/tensorflow/python/ops/sparse_ops.py index 0fbbf5a805f1439d85ad53f02bdb665c04248606..c580052c32c8b61467b857af3d237be41718c1a1 100644 --- a/tensorflow/python/ops/sparse_ops.py +++ b/tensorflow/python/ops/sparse_ops.py @@ -234,7 +234,7 @@ def sparse_concat(axis, ] output_ind, output_val, output_shape = ( - gen_sparse_ops._sparse_concat(inds, vals, shapes, axis, name=name)) + gen_sparse_ops.sparse_concat(inds, vals, shapes, axis, name=name)) return sparse_tensor.SparseTensor(output_ind, output_val, output_shape) @@ -302,8 +302,8 @@ def sparse_add(a, b, thresh=0): thresh = ops.convert_to_tensor( thresh, dtype=a.values.dtype.real_dtype.base_dtype, name="thresh") output_ind, output_val, output_shape = ( - gen_sparse_ops._sparse_add(a.indices, a.values, a.dense_shape, - b.indices, b.values, b.dense_shape, thresh)) + gen_sparse_ops.sparse_add(a.indices, a.values, a.dense_shape, + b.indices, b.values, b.dense_shape, thresh)) # Attempt to get output_shape statically. a.get_shape().assert_is_compatible_with(b.get_shape()) @@ -317,8 +317,8 @@ def sparse_add(a, b, thresh=0): # swap to make `a` the SparseTensor. if isinstance(b, sparse_classes): a, b = b, a - return gen_sparse_ops._sparse_tensor_dense_add(a.indices, a.values, - a.dense_shape, b) + return gen_sparse_ops.sparse_tensor_dense_add(a.indices, a.values, + a.dense_shape, b) def _sparse_cross(inputs, name=None): @@ -402,7 +402,7 @@ def _sparse_cross_internal(inputs, num_buckets=0, hash_key=None, name=None): - """See gen_sparse_ops._sparse_cross.""" + """See gen_sparse_ops.sparse_cross.""" if not isinstance(inputs, list): raise TypeError("Inputs must be a list") if not all( @@ -432,7 +432,7 @@ def _sparse_cross_internal(inputs, dense_inputs[i] = math_ops.to_int64(dense_inputs[i]) internal_type = dtypes.int64 - indices_out, values_out, shape_out = gen_sparse_ops._sparse_cross( + indices_out, values_out, shape_out = gen_sparse_ops.sparse_cross( indices=indices, values=values, shapes=shapes, @@ -511,7 +511,7 @@ def sparse_reorder(sp_input, name=None): sp_input = _convert_to_sparse_tensor(sp_input) reordered_ind, reordered_val = ( - gen_sparse_ops._sparse_reorder( + gen_sparse_ops.sparse_reorder( sp_input.indices, sp_input.values, sp_input.dense_shape, name=name)) if sp_input.get_shape().is_fully_defined(): @@ -575,7 +575,7 @@ def sparse_reshape(sp_input, shape, name=None): shape = math_ops.cast(shape, dtype=dtypes.int64) with ops.name_scope(name, "SparseReshape", [sp_input]) as name: - reshaped_ind, reshaped_shape = gen_sparse_ops._sparse_reshape( + reshaped_ind, reshaped_shape = gen_sparse_ops.sparse_reshape( sp_input.indices, sp_input.dense_shape, shape, name=name) reshaped_shape_const = tensor_util.constant_value(shape) @@ -671,7 +671,7 @@ def sparse_split(keyword_required=KeywordRequired(), sp_input = _convert_to_sparse_tensor(sp_input) output_inds, output_vals, output_shapes = ( - gen_sparse_ops._sparse_split( + gen_sparse_ops.sparse_split( axis, sp_input.indices, sp_input.values, @@ -782,7 +782,7 @@ def sparse_to_dense(sparse_indices, Dense `Tensor` of shape `output_shape`. Has the same type as `sparse_values`. """ - return gen_sparse_ops._sparse_to_dense( + return gen_sparse_ops.sparse_to_dense( sparse_indices, output_shape, sparse_values, @@ -1412,7 +1412,7 @@ def sparse_fill_empty_rows(sp_input, default_value, name=None): default_value = ops.convert_to_tensor( default_value, dtype=sp_input.values.dtype) (output_indices, output_values, empty_row_indicator, - unused_reverse_index_map) = gen_sparse_ops._sparse_fill_empty_rows( + unused_reverse_index_map) = gen_sparse_ops.sparse_fill_empty_rows( indices=sp_input.indices, values=sp_input.values, dense_shape=sp_input.dense_shape, @@ -1441,7 +1441,7 @@ def serialize_sparse(sp_input, name=None, out_type=dtypes.string): """ sp_input = _convert_to_sparse_tensor(sp_input) - return gen_sparse_ops._serialize_sparse( + return gen_sparse_ops.serialize_sparse( sp_input.indices, sp_input.values, sp_input.dense_shape, @@ -1476,7 +1476,7 @@ def serialize_many_sparse(sp_input, name=None, out_type=dtypes.string): """ sp_input = _convert_to_sparse_tensor(sp_input) - return gen_sparse_ops._serialize_many_sparse( + return gen_sparse_ops.serialize_many_sparse( sp_input.indices, sp_input.values, sp_input.dense_shape, @@ -1541,7 +1541,7 @@ def deserialize_sparse(serialized_sparse, dtype, rank=None, name=None): """ output_indices, output_values, output_shape = ( - gen_sparse_ops._deserialize_sparse(serialized_sparse, dtype, name=name)) + gen_sparse_ops.deserialize_sparse(serialized_sparse, dtype, name=name)) # Feed rank data back in, if available output_indices.set_shape([None, rank]) @@ -1610,7 +1610,7 @@ def deserialize_many_sparse(serialized_sparse, dtype, rank=None, name=None): All of the serialized `SparseTensor`s must have had the same rank and type. """ output_indices, output_values, output_shape = ( - gen_sparse_ops._deserialize_many_sparse( + gen_sparse_ops.deserialize_many_sparse( serialized_sparse, dtype, name=name)) # Feed rank data back in, if available @@ -1828,7 +1828,7 @@ def sparse_tensor_dense_matmul(sp_a, with ops.name_scope(name, "SparseTensorDenseMatMul", [sp_a.indices, sp_a.values, b]) as name: b = ops.convert_to_tensor(b, name="b") - return gen_sparse_ops._sparse_tensor_dense_mat_mul( + return gen_sparse_ops.sparse_tensor_dense_mat_mul( a_indices=sp_a.indices, a_values=sp_a.values, a_shape=sp_a.dense_shape, @@ -2046,7 +2046,7 @@ def _add_sparse_to_tensors_map(sp_input, """ sp_input = _convert_to_sparse_tensor(sp_input) - return gen_sparse_ops._add_sparse_to_tensors_map( + return gen_sparse_ops.add_sparse_to_tensors_map( sp_input.indices, sp_input.values, sp_input.dense_shape, @@ -2086,7 +2086,7 @@ def _add_many_sparse_to_tensors_map(sp_input, """ sp_input = _convert_to_sparse_tensor(sp_input) - return gen_sparse_ops._add_many_sparse_to_tensors_map( + return gen_sparse_ops.add_many_sparse_to_tensors_map( sp_input.indices, sp_input.values, sp_input.dense_shape, @@ -2167,7 +2167,7 @@ def _take_many_sparse_from_tensors_map(sparse_map_op, with ops.colocate_with(sparse_map_op): shared_name = sparse_map_op.get_attr("shared_name") or sparse_map_op.name output_indices, output_values, output_shape = ( - gen_sparse_ops._take_many_sparse_from_tensors_map( + gen_sparse_ops.take_many_sparse_from_tensors_map( sparse_handles, dtype=sparse_map_op.get_attr("T"), container=sparse_map_op.get_attr("container"), diff --git a/tensorflow/python/ops/special_math_ops.py b/tensorflow/python/ops/special_math_ops.py index 6d7eaababcd94d687ff20dddc35c68a98320a19b..5e2146b79f08e6671c429f388b05634b1727b4ed 100644 --- a/tensorflow/python/ops/special_math_ops.py +++ b/tensorflow/python/ops/special_math_ops.py @@ -163,7 +163,7 @@ def einsum(equation, *inputs, **kwargs): if '...' in equation: raise ValueError('Subscripts with ellipses are not yet supported.') - match = re.match('([a-z,]+)(->[a-z]*)?', equation) + match = re.match('^([a-zA-Z,]+)(->[a-zA-Z]*)?$', equation) if not match: raise ValueError('Indices have incorrect format: %s' % equation) @@ -402,7 +402,7 @@ def _exponential_space_einsum(equation, *inputs): if '...' in equation: raise ValueError('Subscripts with ellipses are not yet supported.') - match = re.match('([a-z,]+)(->[a-z]*)?', equation) + match = re.match('^([a-zA-Z,]+)(->[a-zA-Z]*)?$', equation) if not match: raise ValueError('Indices have incorrect format: %s' % equation) diff --git a/tensorflow/python/ops/special_math_ops_test.py b/tensorflow/python/ops/special_math_ops_test.py index 2c212f45483eacfd3fd27eecb8d7b2c846b5fe96..d7c3a7e8dc7c2ad611cf47718dddcf38700ce304 100644 --- a/tensorflow/python/ops/special_math_ops_test.py +++ b/tensorflow/python/ops/special_math_ops_test.py @@ -192,6 +192,9 @@ class EinsumTest(test.TestCase): 'abc,cba', 'dba,ead,cad->bce', 'aef,fbc,dca->bde', + 'iJ,Jk->ik', + 'iJ,Ki->JK', + 'iJk,Jklm->Jk' ] long_cases = [ @@ -208,6 +211,8 @@ class EinsumTest(test.TestCase): 'ijk ijk', 'ij.jk->ik', 'ij...,jk...->ik...', + 'ij,k ->kji', + 'ij,k-> kji', # axis in output that does not exist 'ij,jk->im', diff --git a/tensorflow/python/ops/standard_ops.py b/tensorflow/python/ops/standard_ops.py index b62e556967753dac4418add2864ce4e641dc6b58..60a98aca7f822802363de0efd0d2974e45dc810e 100644 --- a/tensorflow/python/ops/standard_ops.py +++ b/tensorflow/python/ops/standard_ops.py @@ -186,7 +186,6 @@ _allowed_symbols_array_ops = [ "quantize_and_dequantize", # to-doc # TODO(drpng): legacy symbols to be removed. - "list_diff", # Use tf.listdiff instead. "batch_matrix_diag", "batch_matrix_band_part", "batch_matrix_diag_part", @@ -219,6 +218,7 @@ _allowed_symbols_gradients = [ # Documented in training.py: # Not importing training.py to avoid complex graph dependencies. "AggregationMethod", + "custom_gradient", "gradients", # tf.gradients = gradients.gradients "hessians", ] diff --git a/tensorflow/python/ops/state_ops.py b/tensorflow/python/ops/state_ops.py index 6c0a090d16bb328de40f02edf9865a0e0a62d385..c3ad5831b4dfa2160a198429c60c7a6ac00f6357 100644 --- a/tensorflow/python/ops/state_ops.py +++ b/tensorflow/python/ops/state_ops.py @@ -99,8 +99,8 @@ def variable_op(shape, dtype, name="Variable", set_shape=True, container="", """Deprecated. Used variable_op_v2 instead.""" if not set_shape: shape = tensor_shape.unknown_shape() - ret = gen_state_ops._variable(shape=shape, dtype=dtype, name=name, - container=container, shared_name=shared_name) + ret = gen_state_ops.variable(shape=shape, dtype=dtype, name=name, + container=container, shared_name=shared_name) # TODO(mrry): Move this to where it is used, so we can get rid of this op # wrapper? if set_shape: @@ -127,11 +127,12 @@ def variable_op_v2(shape, dtype, name="Variable", container="", shared_name=""): Returns: A variable tensor. """ - return gen_state_ops._variable_v2(shape=shape, - dtype=dtype, - name=name, - container=container, - shared_name=shared_name) + return gen_state_ops.variable_v2( + shape=shape, + dtype=dtype, + name=name, + container=container, + shared_name=shared_name) def init_variable(v, init, name="init"): @@ -185,7 +186,7 @@ def is_variable_initialized(ref, name=None): if ref.dtype._is_ref_dtype: return gen_state_ops.is_variable_initialized(ref=ref, name=name) # Handle resource variables. - if context.in_eager_mode() or ref.op.type == "VarHandleOp": + if context.executing_eagerly() or ref.op.type == "VarHandleOp": return gen_resource_variable_ops.var_is_initialized_op(ref.handle, name=name) diff --git a/tensorflow/python/ops/string_ops.py b/tensorflow/python/ops/string_ops.py index b8c39d91b41790c6441594b175e8eaa03620e1ec..5bd75b9215fdbccd5882ea39c2b35ccbbe29d5b0 100644 --- a/tensorflow/python/ops/string_ops.py +++ b/tensorflow/python/ops/string_ops.py @@ -17,6 +17,7 @@ See the @{$python/string_ops} guide. +@@regex_replace @@string_to_hash_bucket_fast @@string_to_hash_bucket_strong @@string_to_hash_bucket @@ -93,10 +94,8 @@ def string_split(source, delimiter=" ", skip_empty=True): # pylint: disable=inv delimiter = ops.convert_to_tensor(delimiter, dtype=dtypes.string) source = ops.convert_to_tensor(source, dtype=dtypes.string) - # pylint: disable=protected-access - indices, values, shape = gen_string_ops._string_split( + indices, values, shape = gen_string_ops.string_split( source, delimiter=delimiter, skip_empty=skip_empty) - # pylint: enable=protected-access indices.set_shape([None, 2]) values.set_shape([None]) shape.set_shape([2]) @@ -141,6 +140,7 @@ def reduce_join(inputs, axis=None, reduce_join.__doc__ = deprecation.rewrite_argument_docstring( gen_string_ops.reduce_join.__doc__, "reduction_indices", "axis") +ops.NotDifferentiable("RegexReplace") ops.NotDifferentiable("StringToHashBucket") ops.NotDifferentiable("StringToHashBucketFast") ops.NotDifferentiable("StringToHashBucketStrong") diff --git a/tensorflow/python/ops/summary_ops.py b/tensorflow/python/ops/summary_ops.py index 7f4f4ce5ab4ee2bd309932cb81f05775996371d6..037bc9845a3f734f65b73b0c4b4ca19fb653731d 100644 --- a/tensorflow/python/ops/summary_ops.py +++ b/tensorflow/python/ops/summary_ops.py @@ -13,7 +13,6 @@ # limitations under the License. # ============================================================================== """Summary Operations.""" -# pylint: disable=protected-access from __future__ import absolute_import from __future__ import division from __future__ import print_function @@ -74,7 +73,7 @@ def tensor_summary(name, with summary_op_util.summary_scope( name, family, values=[tensor]) as (tag, scope): - val = gen_logging_ops._tensor_summary_v2( + val = gen_logging_ops.tensor_summary_v2( tensor=tensor, tag=tag, name=scope, diff --git a/tensorflow/python/ops/template.py b/tensorflow/python/ops/template.py index 424582b348d87d8a5b043ec9b771d8f2768a5994..0a391d896a388f7b40b574f6e1367e9d22fb1d0a 100644 --- a/tensorflow/python/ops/template.py +++ b/tensorflow/python/ops/template.py @@ -26,6 +26,7 @@ from tensorflow.python.eager import function from tensorflow.python.framework import ops from tensorflow.python.ops import variable_scope from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.training import checkpointable from tensorflow.python.util import tf_contextlib from tensorflow.python.util import tf_decorator from tensorflow.python.util.deprecation import deprecated @@ -203,7 +204,7 @@ def make_template_internal(name_, if kwargs: func_ = tf_decorator.make_decorator(func_, functools.partial( func_, **kwargs)) - if context.in_eager_mode(): + if context.executing_eagerly(): if unique_name_ is not None: raise ValueError( "unique_name_ cannot be used when eager exeuction is enabled.") @@ -230,7 +231,7 @@ def _skip_common_stack_elements(stacktrace, base_case): return stacktrace[-1:] -class Template(object): +class Template(checkpointable.CheckpointableBase): """Wrap a function to aid in variable sharing. Templates are functions that create variables the first time they are called @@ -294,12 +295,115 @@ class Template(object): # which is not the same as whether the scope has been created. self._variables_created = False + @property + def _checkpoint_dependencies(self): + """Sanity checking for object-based saving. + + Does not override Checkpointable dependency tracking, but checks that + variables accessible through Checkpointable dependencies on other `Template` + objects include all of the variable_scope-filtered `Template.variables`. + + Returns: + A list of checkpointable.CheckpointableReference objects. + Raises: + ValueError: If this object is not compatible with object-based saving. + """ + dependencies = super(Template, self)._checkpoint_dependencies + dependency_variables = [] + for _, dependency in dependencies: + if isinstance(dependency, Template): + dependency_variables.extend(dependency.variables) + else: + dependency_variables.append(dependency) + dependency_variables = set(dependency_variables) + not_included_variables = [] + for expected_variable in sorted(self.variables, key=lambda v: v.name): + if expected_variable not in dependency_variables: + not_included_variables.append(expected_variable) + if not_included_variables: + # Trying to save a Template which improperly tracks its variables. + raise ValueError( + ("The Template '%s' references variables which are not included via " + "object-based dependency tracking. Most likely a custom " + "getter/creator was registered which does not call Template's " + "custom variable creator (which is responsible for tracking " + "dependencies).\n\nExpected these variables to be dependencies: %s") + % (self, not_included_variables)) + return dependencies + + def _checkpointable_custom_creator(self, next_creator, name, initial_value, + checkpointable_parent=None, **kwargs): + """A variable creation hook which adds Checkpointable dependencies. + + Set during the `Template`'s first wrapped function execution. Ensures that + (a) `Template` objects depend on `Template`s created inside them which + create variables, and (b) that any variables not in a more deeply nested + `Template` are added as dependencies directly. + + The `checkpointable_parent` argument is passed between `Template` custom + creators but ignored when the variable object itself is created. This + argument indicates (if not `None`) that a more deeply nested `Template` has + already added the variable as a dependency, and that parent `Template`s + should add a dependency on that `Template` rather than on the variable + directly. + + Args: + next_creator: See `variable_scope.variable_creator_scope`; the next + creator in the chain. + name: The (full, scope-influenced) name of the variable. The scope name + for the Template itself is stripped for the purposes of object-based + dependency tracking, but scopes within Templates are respected. + initial_value: See `variable_scope.variable_creator_scope`. Taken + explicitly so the argument can be re-named and used with + `Checkpointable._add_variable_with_custom_getter`. + checkpointable_parent: If not None, a more deeply nested Template object + to add a dependency on (rather than depending on the variable directly). + **kwargs: Passed through to the next creator. + Returns: + The output of `next_creator`: the fetched/created variable object. + """ + def _call_next_creator_renaming_initializer(initializer, **inner_kwargs): + inner_kwargs.pop("name") # Ignored; this is the scope-stripped name which + # we don't want to propagate. + return next_creator( + initial_value=initializer, + name=name, + **inner_kwargs) + if name.startswith(self._variable_scope.name): + scope_stripped_name = name[len(self._variable_scope.name) + 1:] + if not checkpointable_parent: + return self._add_variable_with_custom_getter( + initializer=initial_value, + name=scope_stripped_name, + getter=_call_next_creator_renaming_initializer, + # Disable error checking for Checkpointable. Exceptions are instead + # raised if necessary when the object-based saver tries to + # save/restore the object. + overwrite=True, + checkpointable_parent=self, + **kwargs) + else: + self._track_checkpointable( + checkpointable_parent, + name=checkpointable_parent._variable_scope.name[ # pylint: disable=protected-access + len(self._variable_scope.name) + 1:], + overwrite=True) + return next_creator(name=name, initial_value=initial_value, + checkpointable_parent=self, **kwargs) + def _call_func(self, args, kwargs): try: vars_at_start = len(ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)) trainable_at_start = len( ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)) - result = self._func(*args, **kwargs) + if self._variables_created: + result = self._func(*args, **kwargs) + else: + # The first time we run, restore variables if necessary (via + # Checkpointable). + with variable_scope.variable_creator_scope( + self._checkpointable_custom_creator): + result = self._func(*args, **kwargs) if self._variables_created: # Variables were previously created, implying this is not the first @@ -543,7 +647,7 @@ class EagerTemplate(Template): Raises: RuntimeError: if eager execution is not enabled. """ - if not context.in_eager_mode(): + if not context.executing_eagerly(): raise RuntimeError( "{} objects can only be used when eager execution is enabled, use " "tf.Template for graph construction". @@ -563,7 +667,14 @@ class EagerTemplate(Template): try: vars_at_start = self._template_store.variables() trainable_at_start = self._template_store.trainable_variables() - result = self._func(*args, **kwargs) + if self._variables_created: + result = self._func(*args, **kwargs) + else: + # The first time we run, restore variables if necessary (via + # Checkpointable). + with variable_scope.variable_creator_scope( + self._checkpointable_custom_creator): + result = self._func(*args, **kwargs) if self._variables_created: # Variables were previously created, implying this is not the first diff --git a/tensorflow/python/ops/tensor_array_ops.py b/tensorflow/python/ops/tensor_array_ops.py index 3c08870146e447d84d4a5f620cbead633d94751f..2f6badcb532c0ef9d82b211d0c7b11a67e8e3010 100644 --- a/tensorflow/python/ops/tensor_array_ops.py +++ b/tensorflow/python/ops/tensor_array_ops.py @@ -148,7 +148,7 @@ class _GraphTensorArray(object): # will retroactively set the device value of this op. def create(): """Create the TensorArray op.""" - return gen_data_flow_ops._tensor_array_v3( + return gen_data_flow_ops.tensor_array_v3( dtype=dtype, size=size, element_shape=element_shape, @@ -237,7 +237,7 @@ class _GraphTensorArray(object): flow = self.flow with ops.name_scope(name, "TensorArrayGrad", [self._handle]): with ops.colocate_with(self._handle): - g_handle, unused_flow = gen_data_flow_ops._tensor_array_grad_v3( + g_handle, unused_flow = gen_data_flow_ops.tensor_array_grad_v3( handle=self._handle, source=source, flow_in=flow, name=name) with ops.control_dependencies([g_handle]): flow = array_ops.identity(flow, name="gradient_flow") @@ -252,7 +252,7 @@ class _GraphTensorArray(object): def read(self, index, name=None): """See TensorArray.""" - value = gen_data_flow_ops._tensor_array_read_v3( + value = gen_data_flow_ops.tensor_array_read_v3( handle=self._handle, index=index, flow_in=self._flow, @@ -270,7 +270,7 @@ class _GraphTensorArray(object): if self._infer_shape: self._merge_element_shape(value.shape) with self._maybe_colocate_with(value): - flow_out = gen_data_flow_ops._tensor_array_write_v3( + flow_out = gen_data_flow_ops.tensor_array_write_v3( handle=self._handle, index=index, value=value, @@ -296,7 +296,7 @@ class _GraphTensorArray(object): element_shape = self._element_shape[0] else: element_shape = tensor_shape.TensorShape(None) - value = gen_data_flow_ops._tensor_array_gather_v3( + value = gen_data_flow_ops.tensor_array_gather_v3( handle=self._handle, indices=indices, flow_in=self._flow, @@ -314,7 +314,7 @@ class _GraphTensorArray(object): tensor_shape.TensorShape(self._element_shape[0].dims[1:])) else: element_shape_except0 = tensor_shape.TensorShape(None) - value, _ = gen_data_flow_ops._tensor_array_concat_v3( + value, _ = gen_data_flow_ops.tensor_array_concat_v3( handle=self._handle, flow_in=self._flow, dtype=self._dtype, @@ -338,10 +338,10 @@ class _GraphTensorArray(object): with ops.name_scope(name, "TensorArrayScatter", [self._handle, value, indices]): value = ops.convert_to_tensor(value, name="value") - if self._infer_shape and context.in_graph_mode(): + if self._infer_shape and not context.executing_eagerly(): self._merge_element_shape(value.shape[1:]) with self._maybe_colocate_with(value): - flow_out = gen_data_flow_ops._tensor_array_scatter_v3( + flow_out = gen_data_flow_ops.tensor_array_scatter_v3( handle=self._handle, indices=indices, value=value, @@ -363,14 +363,14 @@ class _GraphTensorArray(object): value = ops.convert_to_tensor(value, name="value") with self._maybe_colocate_with(value): lengths_64 = math_ops.to_int64(lengths) - if self._infer_shape and context.in_graph_mode(): + if self._infer_shape and not context.executing_eagerly(): clengths = tensor_util.constant_value(lengths_64) if value.shape.dims is not None: if clengths is not None and clengths.max() == clengths.min(): self._merge_element_shape( tensor_shape.TensorShape([clengths[0]]).concatenate( value.shape[1:])) - flow_out = gen_data_flow_ops._tensor_array_split_v3( + flow_out = gen_data_flow_ops.tensor_array_split_v3( handle=self._handle, value=value, lengths=lengths_64, @@ -386,13 +386,13 @@ class _GraphTensorArray(object): def size(self, name=None): """See TensorArray.""" - return gen_data_flow_ops._tensor_array_size_v3( + return gen_data_flow_ops.tensor_array_size_v3( handle=self._handle, flow_in=self.flow, name=name) @tf_should_use.should_use_result def close(self, name=None): """See TensorArray.""" - return gen_data_flow_ops._tensor_array_close_v3( + return gen_data_flow_ops.tensor_array_close_v3( handle=self._handle, name=name) # pylint: enable=protected-access @@ -774,10 +774,10 @@ class TensorArray(object): ValueError: if both handle and tensor_array_name are provided. TypeError: if handle is provided but is not a Tensor. """ - if context.in_graph_mode(): - implementation = _GraphTensorArray - else: + if context.executing_eagerly(): implementation = _EagerTensorArray + else: + implementation = _GraphTensorArray self._implementation = implementation( dtype, diff --git a/tensorflow/python/ops/variable_scope.py b/tensorflow/python/ops/variable_scope.py index 81565a63774da49628d100ef071b02f6311f6af2..c1af8ff8d3108d60b9007552ef1aadf98fea32e6 100644 --- a/tensorflow/python/ops/variable_scope.py +++ b/tensorflow/python/ops/variable_scope.py @@ -321,7 +321,7 @@ class _VariableStore(object): raise ValueError( "Passed a custom_getter which is not callable: %s" % custom_getter) - if context.in_eager_mode(): + if context.executing_eagerly(): if not self._store_eager_variables and reuse: raise RuntimeError( "When eager execution is enabled variable reuse is only supported" @@ -518,7 +518,7 @@ class _VariableStore(object): when violating reuse during variable creation, or if an existing sharded variable exists for the given name but with different sharding. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise NotImplementedError("Partitioned variables are not yet supported " "when eager execution is enabled.") @@ -798,7 +798,7 @@ class _VariableStore(object): validate_shape=validate_shape, constraint=constraint, use_resource=use_resource) - if context.in_graph_mode() or self._store_eager_variables: + if not context.executing_eagerly() or self._store_eager_variables: # In eager mode we do not want to keep default references to Variable # objects as this will prevent their memory from being released. self._vars[name] = v @@ -811,12 +811,12 @@ class _VariableStore(object): with ops.name_scope(name + "/Regularizer/"): loss = regularizer(v) if loss is not None: - if context.in_graph_mode(): - v_name = v.name - loss_name = loss.name - else: + if context.executing_eagerly(): v_name = "v_%s" % type(v) loss_name = "loss_%s" % type(loss) + else: + v_name = v.name + loss_name = loss.name logging.vlog(1, "Applied regularizer to %s and added the result %s " "to REGULARIZATION_LOSSES.", v_name, loss_name) ops.add_to_collection(ops.GraphKeys.REGULARIZATION_LOSSES, loss) @@ -920,7 +920,7 @@ class VariableScope(object): self._dtype = dtype self._use_resource = use_resource self._constraint = constraint - if context.in_eager_mode(): + if context.executing_eagerly(): if self._caching_device is not None: raise NotImplementedError("Caching devices is not yet supported " "when eager execution is enabled.") @@ -988,7 +988,7 @@ class VariableScope(object): def set_use_resource(self, use_resource): """Sets whether to use ResourceVariables for this scope.""" - if context.in_eager_mode() and not use_resource: + if context.executing_eagerly() and not use_resource: raise ValueError("When eager execution is enabled, " "use_resource cannot be set to false.") self._use_resource = use_resource @@ -999,14 +999,14 @@ class VariableScope(object): def set_caching_device(self, caching_device): """Set caching_device for this scope.""" - if context.in_eager_mode(): + if context.executing_eagerly(): raise NotImplementedError("Caching devices are not yet supported " "when eager execution is enabled.") self._caching_device = caching_device def set_partitioner(self, partitioner): """Set partitioner for this scope.""" - if partitioner and context.in_eager_mode(): + if partitioner and context.executing_eagerly(): raise NotImplementedError("Partitioned variables are not yet supported " "when eager execution is enabled.") self._partitioner = partitioner @@ -1057,14 +1057,14 @@ class VariableScope(object): partitioner = self._partitioner if custom_getter is None: custom_getter = self._custom_getter - if context.in_graph_mode(): + if context.executing_eagerly(): + reuse = False + use_resource = True + else: if reuse is None: reuse = self._reuse if use_resource is None: use_resource = self._use_resource - else: - reuse = False - use_resource = True full_name = self.name + "/" + name if self.name else name # Variable names only depend on variable_scope (full_name here), @@ -1107,7 +1107,7 @@ class VariableScope(object): use_resource=None, constraint=None): """Gets an existing variable with this name or create a new one.""" - if context.in_eager_mode(): + if context.executing_eagerly(): raise NotImplementedError("Partitioned variables are not yet supported " "when eager execution is enabled.") if initializer is None: @@ -1274,6 +1274,9 @@ class EagerVariableStore(object): # pylint: enable=protected-access +# The argument list for get_variable must match arguments to get_local_variable. +# So, if you are updating the arguments, also update arguments to +# get_local_variable below. @tf_export("get_variable") def get_variable(name, shape=None, @@ -1385,15 +1388,32 @@ get_variable.__doc__ = get_variable_or_local_docstring % ( "GraphKeys.GLOBAL_VARIABLES") -@functools.wraps(get_variable) +# The argument list for get_local_variable must match arguments to get_variable. +# So, if you are updating the arguments, also update arguments to get_variable. @tf_export("get_local_variable") -def get_local_variable(*args, **kwargs): - kwargs["trainable"] = False - if "collections" in kwargs: - kwargs["collections"] += [ops.GraphKeys.LOCAL_VARIABLES] +def get_local_variable(name, + shape=None, + dtype=None, + initializer=None, + regularizer=None, + trainable=False, # pylint: disable=unused-argument + collections=None, + caching_device=None, + partitioner=None, + validate_shape=True, + use_resource=None, + custom_getter=None, + constraint=None): + if collections: + collections += [ops.GraphKeys.LOCAL_VARIABLES] else: - kwargs["collections"] = [ops.GraphKeys.LOCAL_VARIABLES] - return get_variable(*args, **kwargs) + collections = [ops.GraphKeys.LOCAL_VARIABLES] + return get_variable( + name, shape=shape, dtype=dtype, initializer=initializer, + regularizer=regularizer, trainable=False, collections=collections, + caching_device=caching_device, partitioner=partitioner, + validate_shape=validate_shape, use_resource=use_resource, + custom_getter=custom_getter, constraint=constraint) get_local_variable.__doc__ = get_variable_or_local_docstring % ( "Gets an existing *local* variable or creates a new one.", "Behavior is the same as in `get_variable`, except that variables are\n" @@ -1871,7 +1891,7 @@ class variable_scope(object): raise ValueError("The reuse parameter must be True or False or None.") if self._values is None: self._values = [] - self._in_graph_mode = not context.in_eager_mode() + self._in_graph_mode = not context.executing_eagerly() if self._in_graph_mode: self._graph = ops._get_graph_from_inputs(self._values) # pylint: disable=protected-access self._cached_pure_variable_scope = None @@ -2111,13 +2131,13 @@ def default_variable_creator(next_creator=None, **kwargs): use_resource = kwargs.get("use_resource", None) if use_resource is None: use_resource = get_variable_scope().use_resource - if use_resource or (use_resource is None and context.in_eager_mode()): + if use_resource or (use_resource is None and context.executing_eagerly()): return resource_variable_ops.ResourceVariable( initial_value=initial_value, trainable=trainable, collections=collections, validate_shape=validate_shape, caching_device=caching_device, name=name, dtype=dtype, constraint=constraint) - elif not use_resource and context.in_eager_mode(): + elif not use_resource and context.executing_eagerly(): raise RuntimeError( "VariableScope should use resource variable when eager execution is" " enabled, but use_resource is False." @@ -2145,7 +2165,7 @@ def variable(initial_value=None, constraint=None, use_resource=None): previous_getter = lambda **kwargs: default_variable_creator(None, **kwargs) - for getter in ops.get_default_graph()._get_variable_creator_stack(): # pylint: disable=protected-access + for getter in ops.get_default_graph()._variable_creator_stack: # pylint: disable=protected-access previous_getter = _make_getter(getter, previous_getter) return previous_getter(initial_value=initial_value, trainable=trainable, diff --git a/tensorflow/python/ops/variables.py b/tensorflow/python/ops/variables.py index d382683858be5d91755ef1a15ebbc6ae2287f8a7..5b9947f4417a0f7621fda885c685021d91701c2b 100644 --- a/tensorflow/python/ops/variables.py +++ b/tensorflow/python/ops/variables.py @@ -210,10 +210,11 @@ class Variable(checkpointable.CheckpointableBase): for details on how variables work in eager execution. @end_compatibility """ - if not context.in_graph_mode(): - raise RuntimeError("tf.Variable not supported in Eager mode. " - "Please use tfe.Variable instead") - self._in_graph_mode = context.in_graph_mode() + if context.executing_eagerly(): + raise RuntimeError( + "tf.Variable not supported when eager execution is enabled. " + "Please use tf.contrib.eager.Variable instead") + self._in_graph_mode = True if variable_def: # If variable_def is provided, recreates the variable from its fields. if initial_value: @@ -234,7 +235,7 @@ class Variable(checkpointable.CheckpointableBase): constraint=constraint) def __repr__(self): - if context.in_eager_mode(): + if context.executing_eagerly(): return "" % ( self.name, self.get_shape(), self.dtype.name, ops.numpy_text(self.read_value(), is_repr=True)) @@ -307,6 +308,9 @@ class Variable(checkpointable.CheckpointableBase): if constraint is not None and not callable(constraint): raise ValueError("The `constraint` argument must be a callable.") + # Store the graph key so optimizers know how to only retrieve variables from + # this graph. + self._graph_key = ops.get_default_graph()._graph_key # pylint: disable=protected-access if isinstance(initial_value, checkpointable.CheckpointInitialValue): self._maybe_initialize_checkpointable() self._update_uid = initial_value.checkpoint_position.restore_uid @@ -737,15 +741,15 @@ class Variable(checkpointable.CheckpointableBase): Raises: ValueError: Session is not passed and no default session """ - if context.in_graph_mode(): + if context.executing_eagerly(): + self.assign(value) + else: session = session or ops.get_default_session() if session is None: raise ValueError( "Either session argument should be provided or default session " "should be established") session.run(self._initializer_op, {self._initializer_op.inputs[1]: value}) - else: - self.assign(value) # Conversion to tensor. @staticmethod @@ -1245,9 +1249,9 @@ class PartitionedVariable(object): information does not match `shape`, or `partitions` has invalid values. RuntimeError: If eager execution is enabled """ - if not context.in_graph_mode(): - raise RuntimeError("tf.PartitionedVariable not supported in " - "eager mode. Please use tfe.Variable instead") + if context.executing_eagerly(): + raise RuntimeError( + "tf.PartitionedVariable not supported with eager execution enabled.") if not isinstance(variable_list, (list, tuple)): raise TypeError( "variable_list is not a list or tuple: %s" % variable_list) @@ -1538,7 +1542,7 @@ def variables_initializer(var_list, name="init"): Returns: An Op that run the initializers of all the specified variables. """ - if var_list and context.in_graph_mode(): + if var_list and not context.executing_eagerly(): return control_flow_ops.group(*[v.initializer for v in var_list], name=name) return control_flow_ops.no_op(name=name) @@ -1560,7 +1564,7 @@ def global_variables_initializer(): Returns: An Op that initializes global variables in the graph. """ - if context.in_eager_mode(): + if context.executing_eagerly(): return control_flow_ops.no_op(name="global_variables_initializer") return variables_initializer(global_variables()) @@ -1582,7 +1586,7 @@ def local_variables_initializer(): Returns: An Op that initializes all local variables in the graph. """ - if context.in_eager_mode(): + if context.executing_eagerly(): return control_flow_ops.no_op(name="local_variables_initializer") return variables_initializer(local_variables()) diff --git a/tensorflow/python/platform/googletest.py b/tensorflow/python/platform/googletest.py index 96219faab719e28a5fa8a9a21c83f81a6f8478e6..8141cf92c568f257a5e9810318182d71f445dfa1 100644 --- a/tensorflow/python/platform/googletest.py +++ b/tensorflow/python/platform/googletest.py @@ -36,6 +36,7 @@ from tensorflow.python.platform import benchmark 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 Benchmark = benchmark.TensorFlowBenchmark # pylint: disable=invalid-name @@ -138,6 +139,7 @@ def StatefulSessionAvailable(): return False +@tf_export('test.StubOutForTesting') class StubOutForTesting(object): """Support class for stubbing methods out for unit testing. diff --git a/tensorflow/python/platform/test.py b/tensorflow/python/platform/test.py index 9b7655722ac5a917f2753617f8e99bf2bd2f8d11..1660791febc9da93f3a3a977a17ca876e772a9a5 100644 --- a/tensorflow/python/platform/test.py +++ b/tensorflow/python/platform/test.py @@ -62,6 +62,8 @@ if sys.version_info.major == 2: else: from unittest import mock # pylint: disable=g-import-not-at-top +tf_export('test.mock')(mock) + # Import Benchmark class Benchmark = _googletest.Benchmark # pylint: disable=invalid-name diff --git a/tensorflow/python/profiler/model_analyzer.py b/tensorflow/python/profiler/model_analyzer.py index 0e20ca35bba606079ed5b0f225dd3029772b5af3..acf02096fffe8b38e68824878fa698ed69d3895c 100644 --- a/tensorflow/python/profiler/model_analyzer.py +++ b/tensorflow/python/profiler/model_analyzer.py @@ -172,7 +172,7 @@ class Profiler(object): op_log: optional. tensorflow::tfprof::OpLogProto proto. Used to define extra op types. """ - if not graph and context.in_graph_mode(): + if not graph and not context.executing_eagerly(): graph = ops.get_default_graph() self._coverage = 0.0 self._graph = graph @@ -336,7 +336,7 @@ def profile(graph=None, If cmd is 'op' or 'code', returns MultiGraphNodeProto proto. Side effect: stdout/file/timeline.json depending on options['output'] """ - if not graph and context.in_graph_mode(): + if not graph and not context.executing_eagerly(): graph = ops.get_default_graph() if options == _DEFAULT_PROFILE_OPTIONS: diff --git a/tensorflow/python/profiler/tfprof_logger.py b/tensorflow/python/profiler/tfprof_logger.py index 8d121064967f2f87cd0aefaa361bfd6f387a3e6e..e651de32ea3bce32a965bfbeefc76ff08a79ac38 100644 --- a/tensorflow/python/profiler/tfprof_logger.py +++ b/tensorflow/python/profiler/tfprof_logger.py @@ -156,7 +156,7 @@ def merge_default_with_oplog(graph, op_log=None, run_meta=None, Returns: tmp_op_log: Merged OpLogProto proto. """ - if not graph and context.in_graph_mode(): + if not graph and not context.executing_eagerly(): graph = ops.get_default_graph() tmp_op_log = tfprof_log_pb2.OpLogProto() @@ -210,7 +210,7 @@ def write_op_log(graph, log_dir, op_log=None, run_meta=None, add_trace=True): add_trace: Whether to add python code trace information. Used to support "code" view. """ - if not graph and context.in_graph_mode(): + if not graph and not context.executing_eagerly(): graph = ops.get_default_graph() op_log = merge_default_with_oplog(graph, op_log, run_meta, add_trace) diff --git a/tensorflow/python/pywrap_tfe.i b/tensorflow/python/pywrap_tfe.i index 7ab0db526881109765adf83749bd01e4d543e5b2..b481ddf5d4798aeed970d435234fd82de3b93a06 100644 --- a/tensorflow/python/pywrap_tfe.i +++ b/tensorflow/python/pywrap_tfe.i @@ -31,6 +31,7 @@ limitations under the License. %rename("%s") TFE_Py_RegisterExceptionClass; %rename("%s") TFE_Py_RegisterBackwardFunctionGetter; %rename("%s") TFE_Py_RegisterFallbackExceptionClass; +%rename("%s") TFE_Py_RegisterResourceVariableType; %rename("%s") TFE_Py_Execute; %rename("%s") TFE_Py_FastPathExecute; %rename("%s") TFE_Py_RecordGradient; diff --git a/tensorflow/python/saved_model/builder_impl.py b/tensorflow/python/saved_model/builder_impl.py index 7347da75364818b95d3f2ad7dfa74a8c3614b161..3447d917e9bf2dace3de784106dadb1fcc3a9647 100644 --- a/tensorflow/python/saved_model/builder_impl.py +++ b/tensorflow/python/saved_model/builder_impl.py @@ -193,7 +193,8 @@ class SavedModelBuilder(object): def _validate_tensor_info(self, tensor_info): """Validates the `TensorInfo` proto. - Checks if the `name` and `dtype` fields exist and are non-empty. + Checks if the `encoding` (`name` or `coo_sparse`) and `dtype` fields exist + and are non-empty. Args: tensor_info: `TensorInfo` protocol buffer to validate. @@ -206,10 +207,12 @@ class SavedModelBuilder(object): raise AssertionError( "All TensorInfo protos used in the SignatureDefs must have the name " "and dtype fields set.") - if not tensor_info.name: + if tensor_info.WhichOneof("encoding") is None: + # TODO(soergel) validate each of the fields of coo_sparse raise AssertionError( - "All TensorInfo protos used in the SignatureDefs must have the name " - "field set: %s" % tensor_info) + "All TensorInfo protos used in the SignatureDefs must have one of " + "the 'encoding' fields (e.g., name or coo_sparse) set: %s" + % tensor_info) if tensor_info.dtype is types_pb2.DT_INVALID: raise AssertionError( "All TensorInfo protos used in the SignatureDefs must have the dtype " diff --git a/tensorflow/python/saved_model/saved_model_test.py b/tensorflow/python/saved_model/saved_model_test.py index d9d316882584470769c14cf0c5f265b58e37ab43..804255375e7c5215597a5dcca02f3b32f2c0a497 100644 --- a/tensorflow/python/saved_model/saved_model_test.py +++ b/tensorflow/python/saved_model/saved_model_test.py @@ -94,7 +94,7 @@ class SavedModelTest(test.TestCase): self.assertEqual(expected_asset_file_name, asset.filename) self.assertEqual(expected_asset_tensor_name, asset.tensor_info.name) - def _validate_inputs_tensor_info(self, builder, tensor_info): + def _validate_inputs_tensor_info_fail(self, builder, tensor_info): with self.test_session(graph=ops.Graph()) as sess: self._init_and_validate_variable(sess, "v", 42) @@ -107,7 +107,18 @@ class SavedModelTest(test.TestCase): sess, ["foo"], signature_def_map={"foo_key": foo_signature}) - def _validate_outputs_tensor_info(self, builder, tensor_info): + def _validate_inputs_tensor_info_accept(self, builder, tensor_info): + with self.test_session(graph=ops.Graph()) as sess: + self._init_and_validate_variable(sess, "v", 42) + + foo_signature = signature_def_utils.build_signature_def({ + "foo_inputs": tensor_info + }, dict(), "foo") + builder.add_meta_graph_and_variables( + sess, ["foo"], + signature_def_map={"foo_key": foo_signature}) + + def _validate_outputs_tensor_info_fail(self, builder, tensor_info): with self.test_session(graph=ops.Graph()) as sess: self._init_and_validate_variable(sess, "v", 42) @@ -119,6 +130,16 @@ class SavedModelTest(test.TestCase): sess, ["foo"], signature_def_map={"foo_key": foo_signature}) + def _validate_outputs_tensor_info_accept(self, builder, tensor_info): + with self.test_session(graph=ops.Graph()) as sess: + self._init_and_validate_variable(sess, "v", 42) + + foo_signature = signature_def_utils.build_signature_def( + dict(), {"foo_outputs": tensor_info}, "foo") + builder.add_meta_graph_and_variables( + sess, ["foo"], + signature_def_map={"foo_key": foo_signature}) + def testMaybeSavedModelDir(self): base_path = test.test_src_dir_path("/python/saved_model") self.assertFalse(loader.maybe_saved_model_directory(base_path)) @@ -538,23 +559,50 @@ class SavedModelTest(test.TestCase): self.assertEqual("bar", bar_signature["bar_key"].method_name) self.assertEqual("foo_new", bar_signature["foo_key"].method_name) - def testSignatureDefValidation(self): - export_dir = self._get_export_dir("test_signature_def_validation") + def testSignatureDefValidationFails(self): + export_dir = self._get_export_dir("test_signature_def_validation_fail") builder = saved_model_builder.SavedModelBuilder(export_dir) - tensor_without_name = meta_graph_pb2.TensorInfo() - tensor_without_name.dtype = types_pb2.DT_FLOAT - self._validate_inputs_tensor_info(builder, tensor_without_name) - self._validate_outputs_tensor_info(builder, tensor_without_name) + tensor_without_encoding = meta_graph_pb2.TensorInfo() + tensor_without_encoding.dtype = types_pb2.DT_FLOAT + self._validate_inputs_tensor_info_fail(builder, tensor_without_encoding) + self._validate_outputs_tensor_info_fail(builder, tensor_without_encoding) tensor_without_dtype = meta_graph_pb2.TensorInfo() tensor_without_dtype.name = "x" - self._validate_inputs_tensor_info(builder, tensor_without_dtype) - self._validate_outputs_tensor_info(builder, tensor_without_dtype) + self._validate_inputs_tensor_info_fail(builder, tensor_without_dtype) + self._validate_outputs_tensor_info_fail(builder, tensor_without_dtype) tensor_empty = meta_graph_pb2.TensorInfo() - self._validate_inputs_tensor_info(builder, tensor_empty) - self._validate_outputs_tensor_info(builder, tensor_empty) + self._validate_inputs_tensor_info_fail(builder, tensor_empty) + self._validate_outputs_tensor_info_fail(builder, tensor_empty) + + def testSignatureDefValidationSucceedsWithName(self): + tensor_with_name = meta_graph_pb2.TensorInfo() + tensor_with_name.name = "foo" + tensor_with_name.dtype = types_pb2.DT_FLOAT + + export_dir = self._get_export_dir("test_signature_def_validation_name_1") + builder = saved_model_builder.SavedModelBuilder(export_dir) + self._validate_inputs_tensor_info_accept(builder, tensor_with_name) + + export_dir = self._get_export_dir("test_signature_def_validation_name_2") + builder = saved_model_builder.SavedModelBuilder(export_dir) + self._validate_outputs_tensor_info_accept(builder, tensor_with_name) + + def testSignatureDefValidationSucceedsWithCoo(self): + tensor_with_coo = meta_graph_pb2.TensorInfo() + # TODO(soergel) test validation of each of the fields of coo_sparse + tensor_with_coo.coo_sparse.values_tensor_name = "foo" + tensor_with_coo.dtype = types_pb2.DT_FLOAT + + export_dir = self._get_export_dir("test_signature_def_validation_coo_1") + builder = saved_model_builder.SavedModelBuilder(export_dir) + self._validate_inputs_tensor_info_accept(builder, tensor_with_coo) + + export_dir = self._get_export_dir("test_signature_def_validation_coo_2") + builder = saved_model_builder.SavedModelBuilder(export_dir) + self._validate_outputs_tensor_info_accept(builder, tensor_with_coo) def testAssets(self): export_dir = self._get_export_dir("test_assets") diff --git a/tensorflow/python/summary/summary.py b/tensorflow/python/summary/summary.py index b80ad79074e85bdeae70148b2822c319c29468bc..2a3918b9b421ea125b327c9f62d098de961fff6b 100644 --- a/tensorflow/python/summary/summary.py +++ b/tensorflow/python/summary/summary.py @@ -152,8 +152,7 @@ def image(name, tensor, max_outputs=3, collections=None, family=None): """ with _summary_op_util.summary_scope( name, family, values=[tensor]) as (tag, scope): - # pylint: disable=protected-access - val = _gen_logging_ops._image_summary( + val = _gen_logging_ops.image_summary( tag=tag, tensor=tensor, max_images=max_outputs, name=scope) _summary_op_util.collect(val, collections, [_ops.GraphKeys.SUMMARIES]) return val @@ -237,10 +236,9 @@ def audio(name, tensor, sample_rate, max_outputs=3, collections=None, """ with _summary_op_util.summary_scope( name, family=family, values=[tensor]) as (tag, scope): - # pylint: disable=protected-access sample_rate = _ops.convert_to_tensor( sample_rate, dtype=_dtypes.float32, name='sample_rate') - val = _gen_logging_ops._audio_summary_v2( + val = _gen_logging_ops.audio_summary_v2( tag=tag, tensor=tensor, max_outputs=max_outputs, sample_rate=sample_rate, name=scope) _summary_op_util.collect(val, collections, [_ops.GraphKeys.SUMMARIES]) @@ -280,14 +278,13 @@ def merge(inputs, collections=None, name=None): @end_compatbility """ # pylint: enable=line-too-long - if _context.in_eager_mode(): + if _context.executing_eagerly(): raise RuntimeError( 'Merging tf.summary.* ops is not compatible with eager execution. ' 'Use tf.contrib.summary instead.') name = _summary_op_util.clean_tag(name) with _ops.name_scope(name, 'Merge', inputs): - # pylint: disable=protected-access - val = _gen_logging_ops._merge_summary(inputs=inputs, name=name) + val = _gen_logging_ops.merge_summary(inputs=inputs, name=name) _summary_op_util.collect(val, collections, []) return val @@ -314,7 +311,7 @@ def merge_all(key=_ops.GraphKeys.SUMMARIES, scope=None): summaries under eager execution, use `tf.contrib.summary` instead. @end_compatbility """ - if _context.in_eager_mode(): + if _context.executing_eagerly(): raise RuntimeError( 'Merging tf.summary.* ops is not compatible with eager execution. ' 'Use tf.contrib.summary instead.') diff --git a/tensorflow/python/summary/writer/writer.py b/tensorflow/python/summary/writer/writer.py index 1f3f2287043c021d636113b5a8807c9f4adf77aa..57f78c156b1334a5486b29f2ddec957e49156e73 100644 --- a/tensorflow/python/summary/writer/writer.py +++ b/tensorflow/python/summary/writer/writer.py @@ -343,7 +343,7 @@ class FileWriter(SummaryToEventTransformer): summaries under eager execution, use `tf.contrib.summary` instead. @end_compatbility """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError( "tf.summary.FileWriter is not compatible with eager execution. " "Use tf.contrib.summary instead.") diff --git a/tensorflow/python/tools/BUILD b/tensorflow/python/tools/BUILD index 63f16c53a29fd65c32077dd29e3b1823c11d457b..5415881cae4a7815e790620f226929278f8c1f12 100644 --- a/tensorflow/python/tools/BUILD +++ b/tensorflow/python/tools/BUILD @@ -14,6 +14,7 @@ py_library( name = "tools_pip", deps = [ ":freeze_graph", + ":import_pb_to_tensorboard", ":inspect_checkpoint", ":optimize_for_inference", ":print_selective_registration_header", diff --git a/tensorflow/python/tools/freeze_graph.py b/tensorflow/python/tools/freeze_graph.py index a52f325ddbcd90ad011c1c056965912b96f27aaa..e9f1def48c462dcd8a5acf0e3d29d562cd1b3d58 100644 --- a/tensorflow/python/tools/freeze_graph.py +++ b/tensorflow/python/tools/freeze_graph.py @@ -56,8 +56,6 @@ from tensorflow.python.saved_model import tag_constants from tensorflow.python.tools import saved_model_utils from tensorflow.python.training import saver as saver_lib -FLAGS = None - def freeze_graph_with_def_protos(input_graph_def, input_saver_def, @@ -256,25 +254,24 @@ def freeze_graph(input_graph, checkpoint_version=checkpoint_version) -def main(unused_args): - if FLAGS.checkpoint_version == 1: +def main(unused_args, flags): + if flags.checkpoint_version == 1: checkpoint_version = saver_pb2.SaverDef.V1 - elif FLAGS.checkpoint_version == 2: + elif flags.checkpoint_version == 2: checkpoint_version = saver_pb2.SaverDef.V2 else: print("Invalid checkpoint version (must be '1' or '2'): %d" % - FLAGS.checkpoint_version) + flags.checkpoint_version) return -1 - freeze_graph(FLAGS.input_graph, FLAGS.input_saver, FLAGS.input_binary, - FLAGS.input_checkpoint, FLAGS.output_node_names, - FLAGS.restore_op_name, FLAGS.filename_tensor_name, - FLAGS.output_graph, FLAGS.clear_devices, FLAGS.initializer_nodes, - FLAGS.variable_names_whitelist, FLAGS.variable_names_blacklist, - FLAGS.input_meta_graph, FLAGS.input_saved_model_dir, - FLAGS.saved_model_tags, checkpoint_version) - + freeze_graph(flags.input_graph, flags.input_saver, flags.input_binary, + flags.input_checkpoint, flags.output_node_names, + flags.restore_op_name, flags.filename_tensor_name, + flags.output_graph, flags.clear_devices, flags.initializer_nodes, + flags.variable_names_whitelist, flags.variable_names_blacklist, + flags.input_meta_graph, flags.input_saved_model_dir, + flags.saved_model_tags, checkpoint_version) -if __name__ == "__main__": +def run_main(): parser = argparse.ArgumentParser() parser.register("type", "bool", lambda v: v.lower() == "true") parser.add_argument( @@ -376,5 +373,10 @@ if __name__ == "__main__": separated by \',\'. For tag-set contains multiple tags, all tags \ must be passed in.\ """) - FLAGS, unparsed = parser.parse_known_args() - app.run(main=main, argv=[sys.argv[0]] + unparsed) + flags, unparsed = parser.parse_known_args() + + my_main = lambda unused_args: main(unused_args, flags) + app.run(main=my_main, argv=[sys.argv[0]] + unparsed) + +if __name__ == '__main__': + run_main() diff --git a/tensorflow/python/tools/inspect_checkpoint.py b/tensorflow/python/tools/inspect_checkpoint.py index dd876cbe7fcd64a8de70eb28f67996df9de1dd7d..6504fbc10755c5c543016b8d56d6d53f3311b249 100644 --- a/tensorflow/python/tools/inspect_checkpoint.py +++ b/tensorflow/python/tools/inspect_checkpoint.py @@ -30,7 +30,7 @@ FLAGS = None def print_tensors_in_checkpoint_file(file_name, tensor_name, all_tensors, - all_tensor_names): + all_tensor_names=False): """Prints tensors in a checkpoint file. If no `tensor_name` is provided, prints the tensor names and shapes @@ -139,7 +139,7 @@ if __name__ == "__main__": const=True, type="bool", default=False, - help="If True, print the values of all the tensors.") + help="If True, print the names and values of all the tensors.") parser.add_argument( "--all_tensor_names", nargs="?", diff --git a/tensorflow/python/tools/print_selective_registration_header_test.py b/tensorflow/python/tools/print_selective_registration_header_test.py index 36978b0860a423569149cd0572629f9f1f280637..4b3d98242caf683693430f08bd8cb74483f4bc74 100644 --- a/tensorflow/python/tools/print_selective_registration_header_test.py +++ b/tensorflow/python/tools/print_selective_registration_header_test.py @@ -24,6 +24,7 @@ import sys from google.protobuf import text_format from tensorflow.core.framework import graph_pb2 +from tensorflow.python.framework import test_util from tensorflow.python.platform import gfile from tensorflow.python.platform import test from tensorflow.python.tools import selective_registration_header_lib @@ -93,11 +94,16 @@ class PrintOpFilegroupTest(test.TestCase): ops_and_kernels = selective_registration_header_lib.get_ops_and_kernels( 'rawproto', self.WriteGraphFiles(graphs), default_ops) + matmul_prefix = '' + if test_util.IsMklEnabled(): + matmul_prefix = 'Mkl' + self.assertListEqual( [ ('BiasAdd', 'BiasOp'), # - ('MatMul', 'MatMulOp'), # - ('MatMul', 'MatMulOp'), # + ('MatMul', + matmul_prefix + 'MatMulOp'), # + ('MatMul', matmul_prefix + 'MatMulOp'), # ('NoOp', 'NoOp'), # ('Reshape', 'ReshapeOp'), # ('_Recv', 'RecvOp'), # @@ -112,8 +118,9 @@ class PrintOpFilegroupTest(test.TestCase): self.assertListEqual( [ ('BiasAdd', 'BiasOp'), # - ('MatMul', 'MatMulOp'), # - ('MatMul', 'MatMulOp'), # + ('MatMul', + matmul_prefix + 'MatMulOp'), # + ('MatMul', matmul_prefix + 'MatMulOp'), # ('NoOp', 'NoOp'), # ('Reshape', 'ReshapeOp'), # ('_Recv', 'RecvOp'), # diff --git a/tensorflow/python/tools/saved_model_cli.py b/tensorflow/python/tools/saved_model_cli.py index 33f6debbcbecb652774c776be54323bbaa824822..b88be4ae04d5dc7a7641fb8dbd7e56e61035869f 100644 --- a/tensorflow/python/tools/saved_model_cli.py +++ b/tensorflow/python/tools/saved_model_cli.py @@ -38,11 +38,15 @@ from tensorflow.core.example import example_pb2 from tensorflow.core.framework import types_pb2 from tensorflow.python.client import session from tensorflow.python.debug.wrappers import local_cli_wrapper +from tensorflow.python.framework import meta_graph as meta_graph_lib from tensorflow.python.framework import ops as ops_lib from tensorflow.python.platform import app # pylint: disable=unused-import from tensorflow.python.saved_model import loader from tensorflow.python.tools import saved_model_utils +# Set of ops to blacklist. +_OP_BLACKLIST = set(['WriteFile', 'ReadFile']) + def _show_tag_sets(saved_model_dir): """Prints the tag-sets stored in SavedModel directory. @@ -115,7 +119,7 @@ def _get_outputs_tensor_info_from_meta_graph_def(meta_graph_def, signature_def_key).outputs -def _show_inputs_outputs(saved_model_dir, tag_set, signature_def_key): +def _show_inputs_outputs(saved_model_dir, tag_set, signature_def_key, indent=0): """Prints input and output TensorInfos. Prints the details of input and output TensorInfos for the SignatureDef mapped @@ -126,6 +130,7 @@ def _show_inputs_outputs(saved_model_dir, tag_set, signature_def_key): tag_set: Group of tag(s) of the MetaGraphDef, in string format, separated by ','. For tag-set contains multiple tags, all tags must be passed in. signature_def_key: A SignatureDef key string. + indent: How far (in increments of 2 spaces) to indent each line of output. """ meta_graph_def = saved_model_utils.get_meta_graph_def(saved_model_dir, tag_set) @@ -134,29 +139,39 @@ def _show_inputs_outputs(saved_model_dir, tag_set, signature_def_key): outputs_tensor_info = _get_outputs_tensor_info_from_meta_graph_def( meta_graph_def, signature_def_key) - print('The given SavedModel SignatureDef contains the following input(s):') + indent_str = " " * indent + def in_print(s): + print(indent_str + s) + + in_print('The given SavedModel SignatureDef contains the following input(s):') for input_key, input_tensor in sorted(inputs_tensor_info.items()): - print('inputs[\'%s\'] tensor_info:' % input_key) - _print_tensor_info(input_tensor) + in_print(' inputs[\'%s\'] tensor_info:' % input_key) + _print_tensor_info(input_tensor, indent+1) - print('The given SavedModel SignatureDef contains the following output(s):') + in_print('The given SavedModel SignatureDef contains the following ' + 'output(s):') for output_key, output_tensor in sorted(outputs_tensor_info.items()): - print('outputs[\'%s\'] tensor_info:' % output_key) - _print_tensor_info(output_tensor) + in_print(' outputs[\'%s\'] tensor_info:' % output_key) + _print_tensor_info(output_tensor, indent+1) - print('Method name is: %s' % - meta_graph_def.signature_def[signature_def_key].method_name) + in_print('Method name is: %s' % + meta_graph_def.signature_def[signature_def_key].method_name) -def _print_tensor_info(tensor_info): +def _print_tensor_info(tensor_info, indent=0): """Prints details of the given tensor_info. Args: tensor_info: TensorInfo object to be printed. + indent: How far (in increments of 2 spaces) to indent each line output """ - print(' dtype: ' + - {value: key - for (key, value) in types_pb2.DataType.items()}[tensor_info.dtype]) + indent_str = " " * indent + def in_print(s): + print(indent_str + s) + + in_print(' dtype: ' + + {value: key + for (key, value) in types_pb2.DataType.items()}[tensor_info.dtype]) # Display shape as tuple. if tensor_info.tensor_shape.unknown_rank: shape = 'unknown_rank' @@ -164,8 +179,8 @@ def _print_tensor_info(tensor_info): dims = [str(dim.size) for dim in tensor_info.tensor_shape.dim] shape = ', '.join(dims) shape = '(' + shape + ')' - print(' shape: ' + shape) - print(' name: ' + tensor_info.name) + in_print(' shape: ' + shape) + in_print(' name: ' + tensor_info.name) def _show_all(saved_model_dir): @@ -186,7 +201,8 @@ def _show_all(saved_model_dir): signature_def_map = get_signature_def_map(saved_model_dir, tag_set) for signature_def_key in sorted(signature_def_map.keys()): print('\nsignature_def[\'' + signature_def_key + '\']:') - _show_inputs_outputs(saved_model_dir, tag_set, signature_def_key) + _show_inputs_outputs(saved_model_dir, tag_set, signature_def_key, + indent=1) def get_meta_graph_def(saved_model_dir, tag_set): @@ -230,6 +246,27 @@ def get_signature_def_map(saved_model_dir, tag_set): return meta_graph.signature_def +def scan_meta_graph_def(meta_graph_def): + """Scans meta_graph_def and reports if there are ops on blacklist. + + Print ops if they are on black list, or print success if no blacklisted ops + found. + + Args: + meta_graph_def: MetaGraphDef protocol buffer. + """ + all_ops_set = set( + meta_graph_lib.ops_used_by_graph_def(meta_graph_def.graph_def)) + blacklisted_ops = _OP_BLACKLIST & all_ops_set + if blacklisted_ops: + # TODO(yifeif): print more warnings + print('MetaGraph with tag set %s contains the following blacklisted ops:' % + meta_graph_def.meta_info_def.tags, blacklisted_ops) + else: + print('MetaGraph with tag set %s does not contain blacklisted ops.' % + meta_graph_def.meta_info_def.tags) + + def run_saved_model_with_feed_dict(saved_model_dir, tag_set, signature_def_key, input_tensor_key_feed_dict, outdir, overwrite_flag, tf_debug=False): @@ -597,6 +634,21 @@ def run(args): args.overwrite, tf_debug=args.tf_debug) +def scan(args): + """Function triggered by scan command. + + Args: + args: A namespace parsed from command line. + """ + if args.tag_set: + scan_meta_graph_def( + saved_model_utils.get_meta_graph_def(args.dir, args.tag_set)) + else: + saved_model = reader.read_saved_model(args.dir) + for meta_graph_def in saved_model.meta_graphs: + scan_meta_graph_def(meta_graph_def) + + def create_parser(): """Creates a parser that parse the command line arguments. @@ -614,19 +666,19 @@ def create_parser(): show_msg = ( 'Usage examples:\n' 'To show all tag-sets in a SavedModel:\n' - '$saved_model_cli show --dir /tmp/saved_model\n' + '$saved_model_cli show --dir /tmp/saved_model\n\n' 'To show all available SignatureDef keys in a ' 'MetaGraphDef specified by its tag-set:\n' - '$saved_model_cli show --dir /tmp/saved_model --tag_set serve\n' + '$saved_model_cli show --dir /tmp/saved_model --tag_set serve\n\n' 'For a MetaGraphDef with multiple tags in the tag-set, all tags must be ' 'passed in, separated by \';\':\n' '$saved_model_cli show --dir /tmp/saved_model --tag_set serve,gpu\n\n' 'To show all inputs and outputs TensorInfo for a specific' ' SignatureDef specified by the SignatureDef key in a' ' MetaGraph.\n' - '$saved_model_cli show --dir /tmp/saved_model --tag_set serve ' - '--signature_def serving_default\n\n' - 'To show all available information in the SavedModel\n:' + '$saved_model_cli show --dir /tmp/saved_model --tag_set serve' + ' --signature_def serving_default\n\n' + 'To show all available information in the SavedModel:\n' '$saved_model_cli show --dir /tmp/saved_model --all') parser_show = subparsers.add_parser( 'show', @@ -658,12 +710,14 @@ def create_parser(): run_msg = ('Usage example:\n' 'To run input tensors from files through a MetaGraphDef and save' ' the output tensors to files:\n' - '$saved_model_cli show --dir /tmp/saved_model --tag_set serve ' - '--signature_def serving_default ' - '--inputs input1_key=/tmp/124.npz[x],input2_key=/tmp/123.npy ' - '--input_exprs \'input3_key=np.ones(2)\' --input_examples ' - '\'input4_key=[{"id":[26],"weights":[0.5, 0.5]}]\' ' - '--outdir=/out\n\n' + '$saved_model_cli show --dir /tmp/saved_model --tag_set serve \\\n' + ' --signature_def serving_default \\\n' + ' --inputs input1_key=/tmp/124.npz[x],input2_key=/tmp/123.npy ' + '\\\n' + ' --input_exprs \'input3_key=np.ones(2)\' \\\n' + ' --input_examples ' + '\'input4_key=[{"id":[26],"weights":[0.5, 0.5]}]\' \\\n' + ' --outdir=/out\n\n' 'For more information about input file format, please see:\n' 'https://www.tensorflow.org/programmers_guide/saved_model_cli\n') parser_run = subparsers.add_parser( @@ -716,6 +770,26 @@ def create_parser(): 'SavedModel.') parser_run.set_defaults(func=run) + # scan command + scan_msg = ('Usage example:\n' + 'To scan for blacklisted ops in SavedModel:\n' + '$saved_model_cli scan --dir /tmp/saved_model\n' + 'To scan a specific MetaGraph, pass in --tag_set\n') + parser_scan = subparsers.add_parser( + 'scan', + description=scan_msg, + formatter_class=argparse.RawTextHelpFormatter) + parser_scan.add_argument( + '--dir', + type=str, + required=True, + help='directory containing the SavedModel to execute') + parser_scan.add_argument( + '--tag_set', + type=str, + help='tag-set of graph in SavedModel to scan, separated by \',\'') + parser_scan.set_defaults(func=scan) + return parser diff --git a/tensorflow/python/tools/saved_model_cli_test.py b/tensorflow/python/tools/saved_model_cli_test.py index d6cbc49ba1e08a6b808b228fb8d69fc14f36e3d2..eedc893a38d3d0857dd49c7ce03f3921da48fdbd 100644 --- a/tensorflow/python/tools/saved_model_cli_test.py +++ b/tensorflow/python/tools/saved_model_cli_test.py @@ -61,83 +61,84 @@ class SavedModelCLITestCase(test.TestCase): exp_out = """MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs: signature_def['classify_x2_to_y3']: -The given SavedModel SignatureDef contains the following input(s): -inputs['inputs'] tensor_info: - dtype: DT_FLOAT - shape: (-1, 1) - name: x2:0 -The given SavedModel SignatureDef contains the following output(s): -outputs['scores'] tensor_info: - dtype: DT_FLOAT - shape: (-1, 1) - name: y3:0 -Method name is: tensorflow/serving/classify + The given SavedModel SignatureDef contains the following input(s): + inputs['inputs'] tensor_info: + dtype: DT_FLOAT + shape: (-1, 1) + name: x2:0 + The given SavedModel SignatureDef contains the following output(s): + outputs['scores'] tensor_info: + dtype: DT_FLOAT + shape: (-1, 1) + name: y3:0 + Method name is: tensorflow/serving/classify signature_def['classify_x_to_y']: -The given SavedModel SignatureDef contains the following input(s): -inputs['inputs'] tensor_info: - dtype: DT_STRING - shape: unknown_rank - name: tf_example:0 -The given SavedModel SignatureDef contains the following output(s): -outputs['scores'] tensor_info: - dtype: DT_FLOAT - shape: (-1, 1) - name: y:0 -Method name is: tensorflow/serving/classify + The given SavedModel SignatureDef contains the following input(s): + inputs['inputs'] tensor_info: + dtype: DT_STRING + shape: unknown_rank + name: tf_example:0 + The given SavedModel SignatureDef contains the following output(s): + outputs['scores'] tensor_info: + dtype: DT_FLOAT + shape: (-1, 1) + name: y:0 + Method name is: tensorflow/serving/classify signature_def['regress_x2_to_y3']: -The given SavedModel SignatureDef contains the following input(s): -inputs['inputs'] tensor_info: - dtype: DT_FLOAT - shape: (-1, 1) - name: x2:0 -The given SavedModel SignatureDef contains the following output(s): -outputs['outputs'] tensor_info: - dtype: DT_FLOAT - shape: (-1, 1) - name: y3:0 -Method name is: tensorflow/serving/regress + The given SavedModel SignatureDef contains the following input(s): + inputs['inputs'] tensor_info: + dtype: DT_FLOAT + shape: (-1, 1) + name: x2:0 + The given SavedModel SignatureDef contains the following output(s): + outputs['outputs'] tensor_info: + dtype: DT_FLOAT + shape: (-1, 1) + name: y3:0 + Method name is: tensorflow/serving/regress signature_def['regress_x_to_y']: -The given SavedModel SignatureDef contains the following input(s): -inputs['inputs'] tensor_info: - dtype: DT_STRING - shape: unknown_rank - name: tf_example:0 -The given SavedModel SignatureDef contains the following output(s): -outputs['outputs'] tensor_info: - dtype: DT_FLOAT - shape: (-1, 1) - name: y:0 -Method name is: tensorflow/serving/regress + The given SavedModel SignatureDef contains the following input(s): + inputs['inputs'] tensor_info: + dtype: DT_STRING + shape: unknown_rank + name: tf_example:0 + The given SavedModel SignatureDef contains the following output(s): + outputs['outputs'] tensor_info: + dtype: DT_FLOAT + shape: (-1, 1) + name: y:0 + Method name is: tensorflow/serving/regress signature_def['regress_x_to_y2']: -The given SavedModel SignatureDef contains the following input(s): -inputs['inputs'] tensor_info: - dtype: DT_STRING - shape: unknown_rank - name: tf_example:0 -The given SavedModel SignatureDef contains the following output(s): -outputs['outputs'] tensor_info: - dtype: DT_FLOAT - shape: (-1, 1) - name: y2:0 -Method name is: tensorflow/serving/regress + The given SavedModel SignatureDef contains the following input(s): + inputs['inputs'] tensor_info: + dtype: DT_STRING + shape: unknown_rank + name: tf_example:0 + The given SavedModel SignatureDef contains the following output(s): + outputs['outputs'] tensor_info: + dtype: DT_FLOAT + shape: (-1, 1) + name: y2:0 + Method name is: tensorflow/serving/regress signature_def['serving_default']: -The given SavedModel SignatureDef contains the following input(s): -inputs['x'] tensor_info: - dtype: DT_FLOAT - shape: (-1, 1) - name: x:0 -The given SavedModel SignatureDef contains the following output(s): -outputs['y'] tensor_info: - dtype: DT_FLOAT - shape: (-1, 1) - name: y:0 -Method name is: tensorflow/serving/predict""" + The given SavedModel SignatureDef contains the following input(s): + inputs['x'] tensor_info: + dtype: DT_FLOAT + shape: (-1, 1) + name: x:0 + The given SavedModel SignatureDef contains the following output(s): + outputs['y'] tensor_info: + dtype: DT_FLOAT + shape: (-1, 1) + name: y:0 + Method name is: tensorflow/serving/predict""" # pylint: enable=line-too-long + self.maxDiff = None # Produce a useful error msg if the comparison fails self.assertMultiLineEqual(output, exp_out) self.assertEqual(err.getvalue().strip(), '') @@ -193,11 +194,11 @@ Method name is: tensorflow/serving/predict""" output = out.getvalue().strip() expected_output = ( 'The given SavedModel SignatureDef contains the following input(s):\n' - 'inputs[\'x\'] tensor_info:\n' - ' dtype: DT_FLOAT\n shape: (-1, 1)\n name: x:0\n' + ' inputs[\'x\'] tensor_info:\n' + ' dtype: DT_FLOAT\n shape: (-1, 1)\n name: x:0\n' 'The given SavedModel SignatureDef contains the following output(s):\n' - 'outputs[\'y\'] tensor_info:\n' - ' dtype: DT_FLOAT\n shape: (-1, 1)\n name: y:0\n' + ' outputs[\'y\'] tensor_info:\n' + ' dtype: DT_FLOAT\n shape: (-1, 1)\n name: y:0\n' 'Method name is: tensorflow/serving/predict') self.assertEqual(output, expected_output) self.assertEqual(err.getvalue().strip(), '') @@ -524,6 +525,28 @@ Method name is: tensorflow/serving/predict""" y_expected = np.array([[2.5], [3.0]]) self.assertAllClose(y_expected, y_actual) + def testScanCommand(self): + self.parser = saved_model_cli.create_parser() + base_path = test.test_src_dir_path(SAVED_MODEL_PATH) + args = self.parser.parse_args(['scan', '--dir', base_path]) + with captured_output() as (out, _): + saved_model_cli.scan(args) + output = out.getvalue().strip() + self.assertTrue('does not contain blacklisted ops' in output) + + def testScanCommandFoundBlacklistedOp(self): + self.parser = saved_model_cli.create_parser() + base_path = test.test_src_dir_path(SAVED_MODEL_PATH) + args = self.parser.parse_args( + ['scan', '--dir', base_path, '--tag_set', 'serve']) + op_blacklist = saved_model_cli._OP_BLACKLIST + saved_model_cli._OP_BLACKLIST = set(['VariableV2']) + with captured_output() as (out, _): + saved_model_cli.scan(args) + saved_model_cli._OP_BLACKLIST = op_blacklist + output = out.getvalue().strip() + self.assertTrue('\'VariableV2\'' in output) + if __name__ == '__main__': test.main() diff --git a/tensorflow/python/training/adam.py b/tensorflow/python/training/adam.py index c92f6fc3015960a2b821651231bb94713e0d53dd..006e360389b404a8edd97c9a8bf4b8876c828004 100644 --- a/tensorflow/python/training/adam.py +++ b/tensorflow/python/training/adam.py @@ -106,10 +106,10 @@ class AdamOptimizer(optimizer.Optimizer): self._updated_lr = None def _get_beta_accumulators(self): - if context.in_graph_mode(): - graph = ops.get_default_graph() - else: + if context.executing_eagerly(): graph = None + else: + graph = ops.get_default_graph() return (self._get_non_slot_variable("beta1_power", graph=graph), self._get_non_slot_variable("beta2_power", graph=graph)) diff --git a/tensorflow/python/training/adam_test.py b/tensorflow/python/training/adam_test.py index a521f1299e035424d1c3897a469655db732b0dcd..af87d6f0e5e74557523c7816d966c8fbcf69a824 100644 --- a/tensorflow/python/training/adam_test.py +++ b/tensorflow/python/training/adam_test.py @@ -184,7 +184,7 @@ class AdamOptimizerTest(test.TestCase): # Shouldn't return non-slot variables from other graphs. self.assertEqual(0, len(opt.variables())) - if context.in_graph_mode(): + if not context.executing_eagerly(): self.evaluate(variables.global_variables_initializer()) # Fetch params to validate initial values self.assertAllClose([1.0, 2.0], self.evaluate(var0)) @@ -194,7 +194,7 @@ class AdamOptimizerTest(test.TestCase): # Run 3 steps of Adam for t in range(1, 4): - if context.in_graph_mode(): + if not context.executing_eagerly(): self.evaluate(update) elif t > 1: opt.apply_gradients(zip([grads0, grads1], [var0, var1])) diff --git a/tensorflow/python/training/checkpoint_ops.py b/tensorflow/python/training/checkpoint_ops.py index 7f92d94d2be369709608d36c109863b0ebfb7bbe..a6e9662b7305a00f1fcf03245685e93b756942d3 100644 --- a/tensorflow/python/training/checkpoint_ops.py +++ b/tensorflow/python/training/checkpoint_ops.py @@ -149,7 +149,7 @@ def _load_and_remap_matrix(ckpt_path, num_rows_present = num_rows_to_load if remap_rows: row_remapping, num_rows_present = ( - gen_checkpoint_ops._generate_vocab_remapping( # pylint: disable=protected-access + gen_checkpoint_ops.generate_vocab_remapping( new_vocab_file=new_row_vocab_file, old_vocab_file=old_row_vocab_file, new_vocab_offset=new_row_vocab_offset, @@ -168,7 +168,7 @@ def _load_and_remap_matrix(ckpt_path, num_cols_present = new_col_vocab_size if remap_cols: col_remapping, num_cols_present = ( - gen_checkpoint_ops._generate_vocab_remapping( # pylint: disable=protected-access + gen_checkpoint_ops.generate_vocab_remapping( new_vocab_file=new_col_vocab_file, old_vocab_file=old_col_vocab_file, new_vocab_offset=0, # Offset is unused for cols (no partitioning). @@ -178,7 +178,7 @@ def _load_and_remap_matrix(ckpt_path, num_rows_to_load * new_col_vocab_size - num_rows_present * num_cols_present, 1 ]) - return_tensor = gen_checkpoint_ops._load_and_remap_matrix( # pylint: disable=protected-access + return_tensor = gen_checkpoint_ops.load_and_remap_matrix( ckpt_path=ckpt_path, old_tensor_name=old_tensor_name, row_remapping=row_remapping, diff --git a/tensorflow/python/training/checkpoint_utils.py b/tensorflow/python/training/checkpoint_utils.py index 0af1cdecfa280f757b253abb2f3408bc5c9416f1..e7f88de1d2290a49f3b7bdf47417016d7e7c9cea 100644 --- a/tensorflow/python/training/checkpoint_utils.py +++ b/tensorflow/python/training/checkpoint_utils.py @@ -23,6 +23,7 @@ import six from tensorflow.python import pywrap_tensorflow from tensorflow.python.framework import 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 variable_scope as vs from tensorflow.python.ops import variables @@ -289,10 +290,18 @@ def _set_checkpoint_initializer(variable, name: Name of the operation. """ base_type = variable.dtype.base_dtype - with ops.colocate_with(variable): + # Do not colocate with variable since RestoreV2 op only runs on CPU and + # colocation will force variable (and other ops that colocate with variable) + # to be on CPU as well. It is okay to place the variable's initializer op on + # CPU since it will only be run once at the start. + with ops.device(variable.device), ops.device("/cpu:0"): restore_op = io_ops.restore_v2( ckpt_file, [tensor_name], [slice_spec], [base_type], name=name)[0] - variable._initializer_op = state_ops.assign(variable, restore_op) # pylint:disable=protected-access + if isinstance(variable, resource_variable_ops.ResourceVariable): + init_op = variable.assign(restore_op, read_value=False) + else: + init_op = state_ops.assign(variable, restore_op) + variable._initializer_op = init_op # pylint:disable=protected-access restore_op.set_shape(variable.shape) variable._initial_value = restore_op # pylint:disable=protected-access diff --git a/tensorflow/python/training/checkpoint_utils_test.py b/tensorflow/python/training/checkpoint_utils_test.py index a461b24cbb1acafe60937f64d1cc0d35eb1bfc55..4e08a1c859fbaac75e7cd09ad498d9fea14c6338 100644 --- a/tensorflow/python/training/checkpoint_utils_test.py +++ b/tensorflow/python/training/checkpoint_utils_test.py @@ -26,6 +26,7 @@ from tensorflow.python.framework import errors_impl from tensorflow.python.framework import ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import partitioned_variables +from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables from tensorflow.python.platform import test @@ -157,23 +158,23 @@ class CheckpointsTest(test.TestCase): "some_scope", initializer=init_ops.zeros_initializer()): my1 = variable_scope.get_variable("my1", [1, 10]) - # At this point, my1.initialized_value() will add ops that reference - # the zeros initializer of my1. - before = variables.Variable(my1.initialized_value(), name="before") + before = my1.initialized_value() checkpoint_utils.init_from_checkpoint(checkpoint_dir, {"var1": my1}) - # At this point, my1.initialized_value() will add ops that reference - # the newly set initializer of my1. - after = variables.Variable(my1.initialized_value(), name="after") + after = my1.initialized_value() + + self.assertAllEqual(session.run(before), [[0.0] * 10]) + self.assertAllEqual(session.run(after), v1) session.run(variables.global_variables_initializer()) + self.assertAllEqual(session.run(my1), v1) self.assertAllEqual(session.run(my1.initialized_value()), v1) - self.assertAllClose(session.run(before), [[0.0] * 10]) + self.assertAllClose(session.run(before), v1) self.assertAllClose(session.run(after), v1) with self.assertRaises(AssertionError): - self.assertAllClose(session.run(before), session.run(after)) + self.assertAllClose(v1, [[0.0] * 10]) def testInitWithScopeDoesNotCaptureSuffixes(self): checkpoint_dir = self.get_temp_dir() @@ -206,7 +207,9 @@ class CheckpointsTest(test.TestCase): checkpoint_utils.init_from_checkpoint(checkpoint_dir, {"useful_scope/": "useful_scope/"}) - self.assertEqual(my4._initializer_op.op.inputs[1].device, "/job:ps") + # initializer runs on the same task but always on CPU. + self.assertEqual(my4._initializer_op.op.inputs[1].device, + "/job:ps/device:CPU:0") def testInitFromRootCheckpoint(self): checkpoint_dir = self.get_temp_dir() @@ -362,6 +365,31 @@ class CheckpointsTest(test.TestCase): checkpoint_utils.init_from_checkpoint(checkpoint_dir, {"useful_scope": "some_scope/"}) + def testNoAdditionalReadOpsForResourceVariables(self): + checkpoint_dir = self.get_temp_dir() + with self.test_session() as session: + v1, _, _, _ = _create_checkpoints(session, checkpoint_dir) + + # New graph and session. + with ops.Graph().as_default() as g: + with self.test_session(graph=g) as session: + my1 = resource_variable_ops.ResourceVariable([[0.0] * 10], name="my1") + + with ops.name_scope("init_from_checkpoint"): + checkpoint_utils.init_from_checkpoint(checkpoint_dir, {"var1": my1}) + + # Basic sanity checks: + session.run(variables.global_variables_initializer()) + self.assertAllEqual(session.run(my1), v1) + + ops_in_init_from_checkpoint_scope = [ + op for op in g.get_operations() + if (op.name.startswith("init_from_checkpoint/") and + not op.name.startswith("init_from_checkpoint/checkpoint_initializer" + ) and op.type != "AssignVariableOp") + ] + self.assertEqual(ops_in_init_from_checkpoint_scope, []) + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/training/checkpointable.py b/tensorflow/python/training/checkpointable.py index 11caa761aec5d631d87a91ec876e0b5032ffdc5b..e49965703e65aea05dc3bddabf8d1e3a24c65a74 100644 --- a/tensorflow/python/training/checkpointable.py +++ b/tensorflow/python/training/checkpointable.py @@ -22,6 +22,7 @@ import collections from tensorflow.python.eager import 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 gen_io_ops as io_ops from tensorflow.python.util import nest @@ -31,8 +32,8 @@ from tensorflow.python.util import nest # creation (avoiding double assignment when executing eagerly). VARIABLE_VALUE_KEY = "VARIABLE_VALUE" -_CheckpointableReference = collections.namedtuple( - "_CheckpointableReference", +CheckpointableReference = collections.namedtuple( + "CheckpointableReference", [ # The local name for this dependency. "name", @@ -181,13 +182,16 @@ class _CheckpointPosition(object): dtype = self._checkpoint.dtype_map[checkpoint_key] base_type = dtype.base_dtype with ops.init_scope(): - value, = io_ops.restore_v2( - prefix=self._checkpoint.save_path, - tensor_names=[checkpoint_key], - shape_and_slices=[""], - dtypes=[base_type], - name="%s_checkpoint_read" % (serialized_tensor.name,)) - value_tensors[serialized_tensor.name] = value + with ops.device("/cpu:0"): + # Run the restore itself on the CPU. + value, = io_ops.restore_v2( + prefix=self._checkpoint.save_path, + tensor_names=[checkpoint_key], + shape_and_slices=[""], + dtypes=[base_type], + name="%s_checkpoint_read" % (serialized_tensor.name,)) + # Copy the value to the current device if necessary. + value_tensors[serialized_tensor.name] = array_ops.identity(value) return value_tensors def restore_ops(self): @@ -204,7 +208,7 @@ class _CheckpointPosition(object): # Name saveables based on the name this object had when it was checkpointed. named_saveables = {} restore_ops = [] - in_graph_mode = context.in_graph_mode() + building_graph = not context.executing_eagerly() for serialized_tensor in self.object_proto.attributes: saveable_object = saveables.get(serialized_tensor.name, None) if saveable_object is None: @@ -215,7 +219,7 @@ class _CheckpointPosition(object): self._checkpoint.unused_attributes.setdefault( self.checkpointable, []).append(serialized_tensor.name) continue - if in_graph_mode: + if building_graph: existing_ops = self._checkpoint.restore_ops_by_name.get( serialized_tensor.name, None) else: @@ -241,7 +245,7 @@ class _CheckpointPosition(object): saveable_index:saveable_index + num_specs] saveable_index += num_specs restore_op = saveable.restore(saveable_tensors, restored_shapes=None) - if in_graph_mode: + if building_graph: 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) @@ -301,14 +305,17 @@ class CheckpointableBase(object): Not __init__, since most objects will forget to call it. """ - if hasattr(self, "_checkpoint_dependencies"): + if hasattr(self, "_unconditional_checkpoint_dependencies"): # __init__ already called. This check means that we don't need # Checkpointable.__init__() in the constructor of every TensorFlow object. return - # A list of _CheckpointableReference objects. - self._checkpoint_dependencies = [] + # A list of CheckpointableReference objects. Some classes implementing + # `Checkpointable`, notably `Optimizer`s, may override the + # _checkpoint_dependencies property with conditional dependencies + # (e.g. based on the current graph when saving). + self._unconditional_checkpoint_dependencies = [] # Maps names -> Checkpointable objects - self._dependency_names = {} + self._unconditional_dependency_names = {} # Restorations for other Checkpointable objects on which this object may # eventually depend. self._deferred_dependencies = {} # local name -> _CheckpointPosition list @@ -320,9 +327,36 @@ class CheckpointableBase(object): "initialization code was run.") self._update_uid = -1 + @property + def _checkpoint_dependencies(self): + """All dependencies of this object. + + May be overridden to include conditional dependencies. + + Returns: + A list of `CheckpointableReference` objects indicating named + `Checkpointable` dependencies which should be saved along with this + object. + """ + return self._unconditional_checkpoint_dependencies + + def _lookup_dependency(self, name): + """Look up a dependency by name. + + May be overridden to include conditional dependencies. + + Args: + name: The local name of the dependency. + Returns: + A `Checkpointable` object, or `None` if no dependency by this name was + found. + """ + return self._unconditional_dependency_names.get(name, None) + def _add_variable_with_custom_getter( self, name, shape=None, dtype=dtypes.float32, - initializer=None, getter=None, **kwargs_for_getter): + initializer=None, getter=None, overwrite=False, + **kwargs_for_getter): """Restore-on-create for a variable be saved with this `Checkpointable`. If the user has requested that this object or another `Checkpointable` which @@ -334,12 +368,11 @@ class CheckpointableBase(object): name: A name for the variable. Must be unique within this object. shape: The shape of the variable. dtype: The data type of the variable. - initializer: The initializer to use. Ignored if there is a deferred restoration left over from a call to `_restore_from_checkpoint_position`. - getter: The getter to wrap which actually fetches the variable. + overwrite: If True, disables unique name and type checks. **kwargs_for_getter: Passed to the getter. Returns: @@ -349,13 +382,13 @@ class CheckpointableBase(object): ValueError: If the variable name is not unique. """ self._maybe_initialize_checkpointable() - if name in self._dependency_names: + if not overwrite and self._lookup_dependency(name) is not None: raise ValueError( ("A variable named '%s' already exists in this Checkpointable, but " "Checkpointable._add_variable called to create another with " "that name. Variable names must be unique within a Checkpointable " "object.") % (name,)) - if context.in_eager_mode(): + if context.executing_eagerly(): # If this is a variable with a single Tensor stored in the checkpoint, we # can set that value as an initializer rather than initializing and then # assigning (when executing eagerly). This call returns None if there is @@ -385,7 +418,13 @@ class CheckpointableBase(object): # assign again. It will add this variable to our dependencies, and if there # is a non-trivial restoration queued, it will handle that. This also # handles slot variables. - return self._track_checkpointable(new_variable, name=name) + if not overwrite or isinstance(new_variable, CheckpointableBase): + return self._track_checkpointable(new_variable, name=name, + overwrite=overwrite) + else: + # TODO(allenl): Some variable types are not yet supported. Remove this + # fallback once all get_variable() return types are Checkpointable. + return new_variable def _preload_simple_restoration(self, name, shape): """Return a dependency's value for restore-on-create. @@ -455,9 +494,10 @@ class CheckpointableBase(object): raise TypeError( ("Checkpointable._track_checkpointable() passed type %s, not a " "Checkpointable.") % (type(checkpointable),)) - new_reference = _CheckpointableReference(name=name, ref=checkpointable) - if (name in self._dependency_names - and self._dependency_names[name] is not checkpointable): + new_reference = CheckpointableReference(name=name, ref=checkpointable) + current_object = self._lookup_dependency(name) + if (current_object is not None + and current_object is not checkpointable): if not overwrite: raise ValueError( ("Called Checkpointable._track_checkpointable() with name='%s', " @@ -465,19 +505,47 @@ class CheckpointableBase(object): "dependency. Names must be unique (or overwrite=True).") % (name,)) # This is a weird thing to do, but we're not going to stop people from # using __setattr__. - for index, (old_name, _) in enumerate(self._checkpoint_dependencies): + for index, (old_name, _) in enumerate( + self._unconditional_checkpoint_dependencies): if name == old_name: - self._checkpoint_dependencies[index] = new_reference + self._unconditional_checkpoint_dependencies[index] = new_reference else: - self._checkpoint_dependencies.append(new_reference) + self._unconditional_checkpoint_dependencies.append(new_reference) - self._dependency_names[name] = checkpointable - deferred_dependency_list = self._deferred_dependencies.pop(name, None) - if deferred_dependency_list is not None: - for checkpoint_position in deferred_dependency_list: - checkpoint_position.restore(checkpointable=checkpointable) + self._unconditional_dependency_names[name] = checkpointable + self._handle_deferred_dependencies(name=name, checkpointable=checkpointable) return checkpointable + def _handle_deferred_dependencies(self, name, checkpointable): + """Pop and load any deferred checkpoint restores into `checkpointable`. + + This method does not add a new dependency on `checkpointable`, but it does + check if any outstanding/deferred dependencies have been queued waiting for + this dependency to be added (matched based on `name`). If so, + `checkpointable` and its dependencies are restored. The restorations are + considered fulfilled and so are deleted. + + `_track_checkpointable` is more appropriate for adding a + normal/unconditional dependency, and includes handling for deferred + restorations. This method allows objects such as `Optimizer` to use the same + restoration logic while managing conditional dependencies themselves, by + overriding `_checkpoint_dependencies` and `_lookup_dependency` to change the + object's dependencies based on the context it is saved/restored in (a single + optimizer instance can have state associated with multiple graphs). + + Args: + name: The name of the dependency within this object (`self`), used to + match `checkpointable` with values saved in a checkpoint. + checkpointable: The Checkpointable object to restore (inheriting from + `CheckpointableBase`). + """ + deferred_dependencies_list = self._deferred_dependencies.pop(name, ()) + for checkpoint_position in sorted( + deferred_dependencies_list, + key=lambda restore: restore.checkpoint.restore_uid, + reverse=True): + checkpoint_position.restore(checkpointable) + def _restore_from_checkpoint_position(self, checkpoint_position): """Restore this object and its dependencies (may be deferred).""" # Attempt a breadth-first traversal, since presumably the user has more @@ -513,7 +581,7 @@ class CheckpointableBase(object): child_position = _CheckpointPosition( checkpoint=checkpoint, proto_id=child.node_id) - local_object = self._dependency_names.get(child.local_name, None) + local_object = self._lookup_dependency(child.local_name) if local_object is None: # We don't yet have a dependency registered with this name. Save it # in case we do. diff --git a/tensorflow/python/training/device_setter.py b/tensorflow/python/training/device_setter.py index 689088bb41edfd94a1d483ed2b5f7447e9e060e7..d31c375b4ce48dcb9bc2918514707636a647c675 100644 --- a/tensorflow/python/training/device_setter.py +++ b/tensorflow/python/training/device_setter.py @@ -25,6 +25,15 @@ from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import server_lib from tensorflow.python.util.tf_export import tf_export +# This is a tuple of PS ops used by tf.estimator.Esitmator which should work in +# almost all of cases. +STANDARD_PS_OPS = ( + "Variable", "VariableV2", "AutoReloadVariable", "MutableHashTable", + "MutableHashTableV2", "MutableHashTableOfTensors", + "MutableHashTableOfTensorsV2", "MutableDenseHashTable", + "MutableDenseHashTableV2", "VarHandleOp" +) + class _RoundRobinStrategy(object): """Returns the next ps task index for placement in round-robin order. @@ -170,8 +179,7 @@ def replica_device_setter(ps_tasks=0, ps_device="/job:ps", than overriding them. cluster: `ClusterDef` proto or `ClusterSpec`. ps_ops: List of strings representing `Operation` types that need to be - placed on `ps` devices. If `None`, defaults to - `["Variable", "VariableV2", "VarHandleOp"]`. + placed on `ps` devices. If `None`, defaults to `STANDARD_PS_OPS`. ps_strategy: A callable invoked for every ps `Operation` (i.e. matched by `ps_ops`), that takes the `Operation` and returns the ps task index to use. If `None`, defaults to a round-robin strategy across all `ps` @@ -201,7 +209,7 @@ def replica_device_setter(ps_tasks=0, ps_device="/job:ps", if ps_ops is None: # TODO(sherrym): Variables in the LOCAL_VARIABLES collection should not be # placed in the parameter server. - ps_ops = ["Variable", "VariableV2", "VarHandleOp"] + ps_ops = list(STANDARD_PS_OPS) if not merge_devices: logging.warning( diff --git a/tensorflow/python/training/ftrl.py b/tensorflow/python/training/ftrl.py index 9d02e694db15637126f37ee5575638908b351def..4fa081fab72df62107cf4957d4ff68240ced9ee0 100644 --- a/tensorflow/python/training/ftrl.py +++ b/tensorflow/python/training/ftrl.py @@ -53,7 +53,7 @@ class FtrlOptimizer(optimizer.Optimizer): learning_rate: A float value or a constant float `Tensor`. learning_rate_power: A float value, must be less or equal to zero. initial_accumulator_value: The starting value for accumulators. - Only positive values are allowed. + Only zero or positive values are allowed. l1_regularization_strength: A float value, must be greater than or equal to zero. l2_regularization_strength: A float value, must be greater than or @@ -84,9 +84,10 @@ class FtrlOptimizer(optimizer.Optimizer): """ super(FtrlOptimizer, self).__init__(use_locking, name) - if initial_accumulator_value <= 0.0: - raise ValueError("initial_accumulator_value %f needs to be positive" % - initial_accumulator_value) + if initial_accumulator_value < 0.0: + raise ValueError( + "initial_accumulator_value %f needs to be be positive or zero" % + initial_accumulator_value) if learning_rate_power > 0.0: raise ValueError("learning_rate_power %f needs to be negative or zero" % learning_rate_power) diff --git a/tensorflow/python/training/gradient_descent.py b/tensorflow/python/training/gradient_descent.py index 380e14e02497fbe3681d6bae03fe9c636c5d13aa..6caf29d83af546f821314179e17f7bf1a693ff1a 100644 --- a/tensorflow/python/training/gradient_descent.py +++ b/tensorflow/python/training/gradient_descent.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 ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops @@ -43,6 +44,7 @@ class GradientDescentOptimizer(optimizer.Optimizer): """ super(GradientDescentOptimizer, self).__init__(use_locking, name) self._learning_rate = learning_rate + self._learning_rate_tensor = None def _apply_dense(self, grad, var): return training_ops.apply_gradient_descent( @@ -69,5 +71,6 @@ class GradientDescentOptimizer(optimizer.Optimizer): return var.scatter_sub(delta, use_locking=self._use_locking) def _prepare(self): - self._learning_rate_tensor = ops.convert_to_tensor(self._learning_rate, - name="learning_rate") + if not context.executing_eagerly() or self._learning_rate_tensor is None: + self._learning_rate_tensor = ops.convert_to_tensor(self._learning_rate, + name="learning_rate") diff --git a/tensorflow/python/training/input.py b/tensorflow/python/training/input.py index bd9985a7c5c181c0431e0c0a91186bc36b11c787..44f00a96deff64012705c4c81b185a9c4fac2295 100644 --- a/tensorflow/python/training/input.py +++ b/tensorflow/python/training/input.py @@ -159,7 +159,7 @@ def input_producer(input_tensor, enabled. Please use the `tf.data` API to ingest data under eager execution. @end_compatibility """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError( "Input pipelines based on Queues are not supported when eager execution" " is enabled. Please use tf.data to ingest data into your model" @@ -737,7 +737,7 @@ def _batch(tensors, batch_size, keep_input, num_threads=1, capacity=32, allow_smaller_final_batch=False, shared_name=None, name=None): """Helper function for `batch` and `maybe_batch`.""" - if context.in_eager_mode(): + if context.executing_eagerly(): raise ValueError( "Input pipelines based on Queues are not supported when eager execution" " is enabled. Please use tf.data to ingest data into your model" @@ -775,7 +775,7 @@ def _batch_join(tensors_list, batch_size, keep_input, capacity=32, enqueue_many=False, shapes=None, dynamic_pad=False, allow_smaller_final_batch=False, shared_name=None, name=None): """Helper function for `batch_join` and `maybe_batch_join`.""" - if context.in_eager_mode(): + if context.executing_eagerly(): raise ValueError( "Input pipelines based on Queues are not supported when eager execution" " is enabled. Please use tf.data to ingest data into your model" @@ -810,7 +810,7 @@ def _shuffle_batch(tensors, batch_size, capacity, min_after_dequeue, shapes=None, allow_smaller_final_batch=False, shared_name=None, name=None): """Helper function for `shuffle_batch` and `maybe_shuffle_batch`.""" - if context.in_eager_mode(): + if context.executing_eagerly(): raise ValueError( "Input pipelines based on Queues are not supported when eager execution" " is enabled. Please use tf.data to ingest data into your model" @@ -855,7 +855,7 @@ def _shuffle_batch_join(tensors_list, batch_size, capacity, allow_smaller_final_batch=False, shared_name=None, name=None): """Helper function for `shuffle_batch_join` and `maybe_shuffle_batch_join`.""" - if context.in_eager_mode(): + if context.executing_eagerly(): raise ValueError( "Input pipelines based on Queues are not supported when eager execution" " is enabled. Please use tf.data to ingest data into your model" diff --git a/tensorflow/python/training/learning_rate_decay_test.py b/tensorflow/python/training/learning_rate_decay_test.py index 1ce8c156a0b126f680bad62267f90e31a23febed..60306e4f1239a759ea1f68492a1211d5f0858997 100644 --- a/tensorflow/python/training/learning_rate_decay_test.py +++ b/tensorflow/python/training/learning_rate_decay_test.py @@ -43,8 +43,8 @@ class LRDecayTest(test_util.TensorFlowTestCase): def testStaircase(self): with self.test_session(): - step = gen_state_ops._variable(shape=[], dtype=dtypes.int32, - name="step", container="", shared_name="") + step = gen_state_ops.variable(shape=[], dtype=dtypes.int32, + name="step", container="", shared_name="") assign_100 = state_ops.assign(step, 100) assign_1 = state_ops.assign(step, 1) assign_2 = state_ops.assign(step, 2) @@ -113,7 +113,7 @@ class LRDecayTest(test_util.TensorFlowTestCase): learning_rate_decay.piecewise_constant(x, boundaries, values) # Test that ref types are valid. - if context.in_graph_mode(): + if not context.executing_eagerly(): x = variables.Variable(0.0) x_ref = x.op.outputs[0] # float32_ref tensor should be accepted boundaries, values = [1.0, 2.0], [1, 2, 3] @@ -264,8 +264,8 @@ class ExponentialDecayTest(test_util.TensorFlowTestCase): initial_lr = 0.1 k = 10 decay_rate = 0.96 - step = gen_state_ops._variable(shape=[], dtype=dtypes.int32, - name="step", container="", shared_name="") + step = gen_state_ops.variable( + shape=[], dtype=dtypes.int32, name="step", container="", shared_name="") assign_step = state_ops.assign(step, 0) increment_step = state_ops.assign_add(step, 1) decayed_lr = learning_rate_decay.natural_exp_decay(initial_lr, step, @@ -281,8 +281,8 @@ class ExponentialDecayTest(test_util.TensorFlowTestCase): initial_lr = 0.1 k = 10 decay_rate = 0.96 - step = gen_state_ops._variable(shape=[], dtype=dtypes.int32, - name="step", container="", shared_name="") + step = gen_state_ops.variable( + shape=[], dtype=dtypes.int32, name="step", container="", shared_name="") assign_step = state_ops.assign(step, 0) increment_step = state_ops.assign_add(step, 1) decayed_lr = learning_rate_decay.natural_exp_decay(initial_lr, @@ -304,8 +304,8 @@ class InverseDecayTest(test_util.TensorFlowTestCase): initial_lr = 0.1 k = 10 decay_rate = 0.96 - step = gen_state_ops._variable(shape=[], dtype=dtypes.int32, - name="step", container="", shared_name="") + step = gen_state_ops.variable( + shape=[], dtype=dtypes.int32, name="step", container="", shared_name="") assign_step = state_ops.assign(step, 0) increment_step = state_ops.assign_add(step, 1) decayed_lr = learning_rate_decay.inverse_time_decay(initial_lr, @@ -323,8 +323,8 @@ class InverseDecayTest(test_util.TensorFlowTestCase): initial_lr = 0.1 k = 10 decay_rate = 0.96 - step = gen_state_ops._variable(shape=[], dtype=dtypes.int32, - name="step", container="", shared_name="") + step = gen_state_ops.variable( + shape=[], dtype=dtypes.int32, name="step", container="", shared_name="") assign_step = state_ops.assign(step, 0) increment_step = state_ops.assign_add(step, 1) decayed_lr = learning_rate_decay.inverse_time_decay(initial_lr, diff --git a/tensorflow/python/training/momentum_test.py b/tensorflow/python/training/momentum_test.py index cda421cef837fa6ab25898208a8dc94d70561048..297a8bbde5447cff9465be36c0bb71f2490c60fc 100644 --- a/tensorflow/python/training/momentum_test.py +++ b/tensorflow/python/training/momentum_test.py @@ -66,7 +66,7 @@ class MomentumOptimizerTest(test.TestCase): mom_update = mom_opt.apply_gradients( zip([grads0, grads1], [var0, var1])) - if context.in_graph_mode(): + if not context.executing_eagerly(): self.evaluate(variables.global_variables_initializer()) # Fetch params to validate initial values self.assertAllClose([1.0, 2.0], self.evaluate(var0)) @@ -78,13 +78,13 @@ class MomentumOptimizerTest(test.TestCase): self.assertEquals(slot0.get_shape(), var0.get_shape()) slot1 = mom_opt.get_slot(var1, "momentum") self.assertEquals(slot1.get_shape(), var1.get_shape()) - if context.in_graph_mode(): + if not context.executing_eagerly(): self.assertFalse(slot0 in variables.trainable_variables()) self.assertFalse(slot1 in variables.trainable_variables()) # Step 1: the momentum accumulators where 0. So we should see a normal # update: v -= grad * learning_rate - if context.in_graph_mode(): + if not context.executing_eagerly(): self.evaluate(mom_update) # Check that the momentum accumulators have been updated. self.assertAllCloseAccordingToType(np.array([0.1, 0.1]), @@ -99,10 +99,10 @@ class MomentumOptimizerTest(test.TestCase): np.array([3.0 - (0.01 * 2.0), 4.0 - (0.01 * 2.0)]), self.evaluate(var1)) # Step 2: the momentum accumulators contain the previous update. - if context.in_graph_mode(): - self.evaluate(mom_update) - else: + if context.executing_eagerly(): mom_opt.apply_gradients(zip([grads0, grads1], [var0, var1])) + else: + self.evaluate(mom_update) # Check that the momentum accumulators have been updated. self.assertAllCloseAccordingToType( np.array([(0.9 * 0.1 + 0.1), (0.9 * 0.1 + 0.1)]), @@ -142,7 +142,7 @@ class MomentumOptimizerTest(test.TestCase): [1.0, 2.0], dtype=dtypes.float32, name="var0") var1 = resource_variable_ops.ResourceVariable( [3.0, 4.0], dtype=dtypes.float32, name="var1") - if context.in_eager_mode(): + if context.executing_eagerly(): loss = lambda: math_ops.reduce_sum(var0 + var1) else: loss = math_ops.reduce_sum(var0 + var1) @@ -157,7 +157,7 @@ class MomentumOptimizerTest(test.TestCase): [1.0, 2.0], dtype=dtypes.float32, name="var2") var3 = resource_variable_ops.ResourceVariable( [3.0, 4.0], dtype=dtypes.float32, name="var3") - if context.in_eager_mode(): + if context.executing_eagerly(): loss = lambda: math_ops.reduce_sum(var2 + var3) else: loss = math_ops.reduce_sum(var2 + var3) diff --git a/tensorflow/python/training/moving_averages_test.py b/tensorflow/python/training/moving_averages_test.py index 6efdeb286657e761a4c46634b9408121765a447b..6717811bbb0f05723a5ad0fbcbfba75249d0d43b 100644 --- a/tensorflow/python/training/moving_averages_test.py +++ b/tensorflow/python/training/moving_averages_test.py @@ -376,7 +376,7 @@ class ExponentialMovingAverageTest(test.TestCase): with ops.device("/job:dev_v0"): v0 = variables.Variable(10.0, name="v0") with ops.device("/job:dev_v1"): - v1 = gen_state_ops._variable( + v1 = gen_state_ops.variable( shape=[1], dtype=dtypes.float32, name="v1", diff --git a/tensorflow/python/training/optimizer.py b/tensorflow/python/training/optimizer.py index 678d6322aa5ecea0a603b6a9858f7619638eae30..7adaedef5b6a01e195a8c0c4a8d77024b4c46c0e 100644 --- a/tensorflow/python/training/optimizer.py +++ b/tensorflow/python/training/optimizer.py @@ -40,19 +40,6 @@ from tensorflow.python.util import nest from tensorflow.python.util.tf_export import tf_export -def _get_variable_for(v): - """Returns the ResourceVariable responsible for v, or v if not necessary.""" - if context.in_eager_mode(): - return v - if v.op.type == "VarHandleOp": - for var in variables.trainable_variables(): - if (isinstance(var, resource_variable_ops.ResourceVariable) - and var.handle.op is v.op): - return var - raise ValueError("Got %s but could not locate source variable." % (str(v))) - return v - - def _deduplicate_indexed_slices(values, indices): """Sums `values` associated with any non-unique `indices`. @@ -73,7 +60,7 @@ def _deduplicate_indexed_slices(values, indices): def _var_key(var): - if context.in_eager_mode(): + if context.executing_eagerly(): return var._shared_name # pylint: disable=protected-access return (var.op.graph, var.op.name) @@ -98,6 +85,9 @@ class _RefVariableProcessor(_OptimizableVariable): def __init__(self, v): self._v = v + def __str__(self): + return "<_RefVariableProcessor(%s)>" % self._v + def target(self): return self._v._ref() # pylint: disable=protected-access @@ -196,7 +186,7 @@ class _TensorProcessor(_OptimizableVariable): def _get_processor(v): """The processor of v.""" - if context.in_eager_mode(): + if context.executing_eagerly(): if isinstance(v, ops.Tensor): return _TensorProcessor(v) else: @@ -213,7 +203,11 @@ def _get_processor(v): @tf_export("train.Optimizer") -class Optimizer(checkpointable.Checkpointable): +class Optimizer( + # Optimizers inherit from CheckpointableBase rather than Checkpointable + # since they do most of their dependency management themselves (slot + # variables are special-cased, and non-slot variables are keyed to graphs). + checkpointable.CheckpointableBase): """Base class for optimizers. This class defines the API to add Ops to train a model. You never use this @@ -453,7 +447,7 @@ class Optimizer(checkpointable.Checkpointable): var_list = tape.watched_variables() grads = tape.gradient(loss_value, var_list, grad_loss) return list(zip(grads, var_list)) - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError( "`loss` passed to Optimizer.compute_gradients should " "be a function when eager execution is enabled.") @@ -542,7 +536,7 @@ class Optimizer(checkpointable.Checkpointable): raise ValueError("No gradients provided for any variable: %s." % ([str(v) for _, _, v in converted_grads_and_vars],)) with ops.init_scope(): - self._create_slots([_get_variable_for(v) for v in var_list]) + self._create_slots(var_list) update_ops = [] with ops.name_scope(name, self._name) as name: self._prepare() @@ -552,7 +546,7 @@ class Optimizer(checkpointable.Checkpointable): # We colocate all ops created in _apply_dense or _apply_sparse # on the same device as the variable. # TODO(apassos): figure out how to get the variable name here. - scope_name = var.op.name if context.in_graph_mode() else "" + scope_name = "" if context.executing_eagerly() else var.op.name with ops.name_scope("update_" + scope_name), ops.colocate_with(var): update_ops.append(processor.update_op(self, grad)) if global_step is None: @@ -570,7 +564,7 @@ class Optimizer(checkpointable.Checkpointable): else: apply_updates = state_ops.assign_add(global_step, 1, name=name) - if context.in_graph_mode(): + if not context.executing_eagerly(): if isinstance(apply_updates, ops.Tensor): apply_updates = apply_updates.op train_op = ops.get_collection_ref(ops.GraphKeys.TRAIN_OP) @@ -620,7 +614,7 @@ class Optimizer(checkpointable.Checkpointable): Returns: A list of variables. """ - executing_eagerly = context.in_eager_mode() + executing_eagerly = context.executing_eagerly() current_graph = ops.get_default_graph() def _from_current_graph(variable): @@ -642,20 +636,54 @@ class Optimizer(checkpointable.Checkpointable): def _create_non_slot_variable(self, initial_value, name, colocate_with): """Add an extra variable, not associated with a slot.""" - if context.in_graph_mode(): - graph = colocate_with.graph - else: - graph = None + eager = context.executing_eagerly() + graph = None if eager else colocate_with.graph key = (name, graph) v = self._non_slot_dict.get(key, None) if v is None: + self._maybe_initialize_checkpointable() with ops.colocate_with(colocate_with): + if eager: + restored_initial_value = self._preload_simple_restoration( + 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) + # 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 + # than unconditionally adding dependencies (since there may be multiple + # non-slot variables with the same name in different graphs, trying to + # save all of them would result in errors). + self._handle_deferred_dependencies(name=name, checkpointable=v) self._non_slot_dict[key] = v return v + @property + def _checkpoint_dependencies(self): + """From Checkpointable. Gather graph-specific non-slot variables to save.""" + current_graph_non_slot_variables = [] + current_graph_key = ops.get_default_graph()._graph_key # pylint: disable=protected-access + for (name, _), variable_object in sorted(self._non_slot_dict.items(), + # Avoid comparing graphs + key=lambda item: item[0][0]): + if variable_object._graph_key == current_graph_key: # pylint: disable=protected-access + current_graph_non_slot_variables.append( + checkpointable.CheckpointableReference( + name=name, ref=variable_object)) + return (super(Optimizer, self)._checkpoint_dependencies + + current_graph_non_slot_variables) + + def _lookup_dependency(self, name): + """From Checkpointable. Find a non-slot variable in the current graph.""" + unconditional = super(Optimizer, self)._lookup_dependency(name) + if unconditional is not None: + return unconditional + graph = None if context.executing_eagerly() else ops.get_default_graph() + return self._get_non_slot_variable(name, graph=graph) + def _get_non_slot_variable(self, name, graph=None): return self._non_slot_dict.get((name, graph), None) @@ -987,9 +1015,8 @@ class Optimizer(checkpointable.Checkpointable): named_slots = self._slot_dict(slot_name) variable_key = _var_key(variable) slot_variable = named_slots.get(variable_key, None) - if (slot_variable is None - and context.in_eager_mode() - and slot_variable_position.is_simple_variable()): + if (slot_variable is None and context.executing_eagerly() and + slot_variable_position.is_simple_variable()): initializer = checkpointable.CheckpointInitialValue( checkpoint_position=slot_variable_position) slot_variable = self._get_or_make_slot( diff --git a/tensorflow/python/training/queue_runner_impl.py b/tensorflow/python/training/queue_runner_impl.py index 07afba79abf4d636c9ec2d53bcf2641594a35733..d38c5499c73e1217effbc907077236cb6c8e0ae8 100644 --- a/tensorflow/python/training/queue_runner_impl.py +++ b/tensorflow/python/training/queue_runner_impl.py @@ -89,7 +89,7 @@ class QueueRunner(object): restoring from `queue_runner_def`. RuntimeError: If eager execution is enabled. """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError( "QueueRunners are not supported when eager execution is enabled. " "Instead, please use tf.data to get data into your model.") @@ -441,7 +441,7 @@ def start_queue_runners(sess=None, coord=None, daemon=True, start=True, use the `tf.data` API instead. @end_compatibility """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError("Queues are not compatible with eager execution.") if sess is None: sess = ops.get_default_session() diff --git a/tensorflow/python/training/saver.py b/tensorflow/python/training/saver.py index 3888e9bba42dc89055638ad0abe2b7e1a9f5b548..cd1baec3cf493834bde01d8ca2bfef2be9623456 100644 --- a/tensorflow/python/training/saver.py +++ b/tensorflow/python/training/saver.py @@ -50,6 +50,7 @@ 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 checkpointable from tensorflow.python.training import training_util from tensorflow.python.training.checkpoint_state_pb2 import CheckpointState from tensorflow.python.util import compat @@ -196,8 +197,8 @@ class BaseSaverBuilder(object): # 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) + return resource_variable_ops.shape_safe_assign_variable_handle( + self.handle_op, self._var_shape, restored_tensor) def __init__(self, write_version=saver_pb2.SaverDef.V2): self._write_version = write_version @@ -310,8 +311,7 @@ class BaseSaverBuilder(object): Returns: A string tensor. """ - # pylint: disable=protected-access - return gen_io_ops._sharded_filename(filename_tensor, shard, num_shards) + return gen_io_ops.sharded_filename(filename_tensor, shard, num_shards) def _AddSaveOps(self, filename_tensor, saveables): """Add ops to save variables that are on the same shard. @@ -420,8 +420,7 @@ class BaseSaverBuilder(object): sharded_saves.append(self._AddSaveOps(sharded_filename, saveables)) # Return the sharded name for the save path. with ops.control_dependencies([x.op for x in sharded_saves]): - # pylint: disable=protected-access - return gen_io_ops._sharded_filespec(filename_tensor, num_shards_tensor) + return gen_io_ops.sharded_filespec(filename_tensor, num_shards_tensor) def _AddRestoreOps(self, filename_tensor, @@ -577,10 +576,31 @@ class BaseSaverBuilder(object): names_to_saveables[name].append(var) else: names_to_saveables[name] = [var] + elif (isinstance(var, checkpointable.CheckpointableBase) + and not isinstance(var, variables.Variable)): + names_to_saveables.update( + BaseSaverBuilder.OpListToDict( + list(var._gather_saveables_for_checkpoint().values()))) else: - if context.in_graph_mode(): + 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: - var = ops.internal_convert_to_tensor(var, as_ref=True) + 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": @@ -591,18 +611,6 @@ class BaseSaverBuilder(object): raise ValueError("At least two variables have the same name: %s" % name) names_to_saveables[name] = var - else: - if not isinstance(var, resource_variable_ops.ResourceVariable): - raise ValueError("Can only save/restore ResourceVariable eager " - "mode 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,)) # pylint: enable=protected-access return names_to_saveables @@ -664,13 +672,16 @@ class BaseSaverBuilder(object): # pylint: enable=protected-access else: # A variable or tensor. - if context.in_eager_mode(): + 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)) saveable = BaseSaverBuilder.ResourceVariableSaveable(op, "", name) else: - variable = ops.internal_convert_to_tensor(op, as_ref=True) + 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" % @@ -768,8 +779,10 @@ class BaseSaverBuilder(object): build_save=True, build_restore=True): """build() with option to only perform save and restore.""" - if context.in_graph_mode() and (not build_save or not build_restore): - raise ValueError("Graph mode needs to build save and restore together.") + if not context.executing_eagerly() and (not build_save or + not build_restore): + raise ValueError("save and restore operations need to be built together " + " when eager execution is not enabled.") saveables = self._ValidateAndSliceInputs(names_to_saveables) if max_to_keep is None: @@ -806,22 +819,22 @@ class BaseSaverBuilder(object): # such usage model makes sense. # # assert restore_op.name.endswith("restore_all"), restore_op.name - if context.in_graph_mode(): + if context.executing_eagerly(): + # Store the tensor values to the tensor_names. + save_tensor_name = save_tensor.numpy() if build_save else "" return saver_pb2.SaverDef( - filename_tensor_name=filename_tensor.name, - save_tensor_name=save_tensor.name, - restore_op_name=restore_op.name, + filename_tensor_name=filename_tensor.numpy(), + save_tensor_name=save_tensor_name, + restore_op_name="", max_to_keep=max_to_keep, sharded=sharded, keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours, version=self._write_version) else: - # Store the tensor values to the tensor_names. - save_tensor_name = save_tensor.numpy() if build_save else "" return saver_pb2.SaverDef( - filename_tensor_name=filename_tensor.numpy(), - save_tensor_name=save_tensor_name, - restore_op_name="", + filename_tensor_name=filename_tensor.name, + save_tensor_name=save_tensor.name, + restore_op_name=restore_op.name, max_to_keep=max_to_keep, sharded=sharded, keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours, @@ -1120,8 +1133,9 @@ class Saver(object): the proliferation of checkpoint files on disk: * `max_to_keep` indicates the maximum number of recent checkpoint files to - keep. As new files are created, older files are deleted. If None or 0, - all checkpoint files are kept. Defaults to 5 (that is, the 5 most recent + keep. As new files are created, older files are deleted. If None or 0, + no checkpoints are deleted from the filesystem but only the last one is + kept in the `checkpoint` file. Defaults to 5 (that is, the 5 most recent checkpoint files are kept.) * `keep_checkpoint_every_n_hours`: In addition to keeping the most recent @@ -1270,7 +1284,7 @@ class Saver(object): raise ValueError( "If `var_list` is provided then build cannot be deferred. " "Either set defer_build=False or var_list=None.") - if context.in_eager_mode() and var_list is None: + if context.executing_eagerly() and var_list is None: raise RuntimeError( "When eager execution is enabled, `var_list` must specify a list or " "dict of variables to save") @@ -1289,7 +1303,12 @@ class Saver(object): self._write_version = write_version self._pad_step_number = pad_step_number self._filename = filename - if not defer_build and context.in_graph_mode(): + self._last_checkpoints = [] + self._checkpoints_to_be_deleted = [] + if context.executing_eagerly(): + self._next_checkpoint_time = ( + time.time() + self._keep_checkpoint_every_n_hours * 3600) + elif not defer_build: self.build() if self.saver_def: self._check_saver_def() @@ -1297,7 +1316,7 @@ class Saver(object): self._save_relative_paths = save_relative_paths def build(self): - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError("Use save/restore instead of build in eager mode.") self._build(self._filename, build_save=True, build_restore=True) @@ -1307,12 +1326,12 @@ class Saver(object): def _build(self, checkpoint_path, build_save, build_restore): """Builds saver_def.""" - if context.in_graph_mode(): + if not context.executing_eagerly(): if self._is_built: return self._is_built = True - if not self.saver_def or context.in_eager_mode(): + if not self.saver_def or context.executing_eagerly(): if self._builder is None: self._builder = BulkSaverBuilder(self._write_version) @@ -1349,17 +1368,17 @@ class Saver(object): self.saver_def.restore_op_name, self._name) self._check_saver_def() - # Updates next checkpoint time. - self._next_checkpoint_time = ( - time.time() + self.saver_def.keep_checkpoint_every_n_hours * 3600) - self._last_checkpoints = [] - self._checkpoints_to_be_deleted = [] + if not context.executing_eagerly(): + # Updates next checkpoint time. + # Set in __init__ when executing eagerly. + self._next_checkpoint_time = ( + time.time() + self.saver_def.keep_checkpoint_every_n_hours * 3600) def _check_saver_def(self): if not isinstance(self.saver_def, saver_pb2.SaverDef): raise ValueError("saver_def must be a saver_pb2.SaverDef: %s" % self.saver_def) - if context.in_graph_mode(): + if not context.executing_eagerly(): if not self.saver_def.save_tensor_name: raise ValueError("saver_def must specify the save_tensor_name: %s" % str(self.saver_def)) @@ -1609,7 +1628,7 @@ class Saver(object): RuntimeError: If save and restore ops weren't built. """ # pylint: enable=line-too-long - if not self._is_built and context.in_graph_mode(): + if not self._is_built and not context.executing_eagerly(): raise RuntimeError( "`build()` should be called before save if defer_build==True") if latest_filename is None: @@ -1641,21 +1660,21 @@ class Saver(object): "'latest_filename' collides with 'save_path': '%s' and '%s'" % (latest_filename, save_path)) - if (context.in_graph_mode() and + if (not context.executing_eagerly() and not isinstance(sess, session.SessionInterface)): raise TypeError("'sess' must be a Session; %s" % sess) save_path_parent = os.path.dirname(save_path) if not self._is_empty: try: - if context.in_graph_mode(): - model_checkpoint_path = sess.run( - self.saver_def.save_tensor_name, - {self.saver_def.filename_tensor_name: checkpoint_file}) - else: + if context.executing_eagerly(): self._build_eager( checkpoint_file, build_save=True, build_restore=False) model_checkpoint_path = self.saver_def.save_tensor_name + else: + model_checkpoint_path = sess.run( + self.saver_def.save_tensor_name, + {self.saver_def.filename_tensor_name: checkpoint_file}) model_checkpoint_path = compat.as_str(model_checkpoint_path) if write_state: @@ -1677,7 +1696,7 @@ class Saver(object): if write_meta_graph: meta_graph_filename = self._MetaGraphFilename( checkpoint_file, meta_graph_suffix=meta_graph_suffix) - if context.in_graph_mode(): + if not context.executing_eagerly(): with sess.graph.as_default(): self.export_meta_graph( meta_graph_filename, strip_default_attrs=strip_default_attrs) @@ -1750,11 +1769,11 @@ class Saver(object): if save_path is None: raise ValueError("Can't load save_path when it is None.") logging.info("Restoring parameters from %s", save_path) - if context.in_graph_mode(): + if context.executing_eagerly(): + self._build_eager(save_path, build_save=False, build_restore=True) + else: sess.run(self.saver_def.restore_op_name, {self.saver_def.filename_tensor_name: save_path}) - else: - self._build_eager(save_path, build_save=False, build_restore=True) @staticmethod def _add_collection_def(meta_graph_def, key, export_scope=None): @@ -1894,7 +1913,7 @@ def import_meta_graph(meta_graph_or_file, clear_devices=False, execution is enabled. @end_compatibility """ # pylint: disable=g-doc-exception - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError("Exporting/importing meta graphs is not supported when " "eager execution is enabled. No graph exists when eager " "execution is enabled.") @@ -1977,7 +1996,7 @@ def export_meta_graph(filename=None, @end_compatibility """ # pylint: enable=line-too-long - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError("Exporting/importing meta graphs is not supported when " "eager execution is enabled. No graph exists when eager " "execution is enabled.") diff --git a/tensorflow/python/training/saver_test.py b/tensorflow/python/training/saver_test.py index c5a6f49df599434ab3bc1a9fe3d85db6f824071e..46fe2735be61758913028f49dfdd44af9e9ec5fe 100644 --- a/tensorflow/python/training/saver_test.py +++ b/tensorflow/python/training/saver_test.py @@ -53,6 +53,7 @@ from tensorflow.python.lib.io import file_io from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import data_flow_ops +from tensorflow.python.ops import gradients_impl from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn_ops from tensorflow.python.ops import partitioned_variables @@ -66,6 +67,7 @@ from tensorflow.python.platform import gfile from tensorflow.python.platform import test from tensorflow.python.summary import summary from tensorflow.python.training import adam +from tensorflow.python.training import checkpointable from tensorflow.python.training import gradient_descent from tensorflow.python.training import queue_runner_impl from tensorflow.python.training import saver as saver_module @@ -89,7 +91,7 @@ class SaverTest(test.TestCase): v2_init = v2.insert("k1", 30.0) # Initialize all variables - if context.in_graph_mode(): + if not context.executing_eagerly(): self.evaluate([variables.global_variables_initializer(), v2_init]) # Check that the parameter nodes have been initialized. @@ -117,7 +119,7 @@ class SaverTest(test.TestCase): v2 = saver_test_utils.CheckpointedOp(name="v2") # Assert that the variables are not initialized. - if context.in_graph_mode(): + if not context.executing_eagerly(): self.assertEqual( len(variables.report_uninitialized_variables().eval()), 2) self.assertEqual(0, len(v2.keys().eval())) @@ -140,7 +142,7 @@ class SaverTest(test.TestCase): v2_init = v2_2.insert("k1000", 3000.0) # Check that the parameter nodes have been initialized. - if context.in_graph_mode(): + if not context.executing_eagerly(): init_all_op = [variables.global_variables_initializer(), v2_init] self.evaluate(init_all_op) # TODO(xpan): Why _mutable_hash_table_v2 doesn't create empty @@ -249,10 +251,10 @@ class SaverTest(test.TestCase): with self.test_session(graph=ops_lib.Graph()) as sess: v = resource_variable_ops.ResourceVariable([1], caching_device="/cpu:0", name="v") - if context.in_graph_mode(): - self.evaluate(variables.global_variables_initializer()) - else: + if context.executing_eagerly(): sess = None + else: + self.evaluate(variables.global_variables_initializer()) save = saver_module.Saver([v]) save.save(sess, save_path) @@ -260,6 +262,24 @@ class SaverTest(test.TestCase): save2.restore(sess, save_path) self.assertEquals(self.evaluate(v), [1]) + def testNoAdditionalOpsAddedBySaverForResourceVariablesOutsideSaveScope(self): + with ops_lib.Graph().as_default() as g: + v = resource_variable_ops.ResourceVariable(1.0, name="v") + with ops_lib.name_scope("saver1"): + saver_module.Saver() + with ops_lib.name_scope("saver2"): + saver_module.Saver({"name": v}) + ops_in_saver1_scope_but_not_save_scope = [ + op for op in g.get_operations() + if (op.name.startswith("saver1/") and + not op.name.startswith("saver1/save/"))] + self.assertEqual(ops_in_saver1_scope_but_not_save_scope, []) + ops_in_saver2_scope_but_not_save_scope = [ + op for op in g.get_operations() + if (op.name.startswith("saver2/") and + not op.name.startswith("saver2/save/"))] + self.assertEqual(ops_in_saver2_scope_but_not_save_scope, []) + def testSaveCopyRestoreWithSaveRelativePaths(self): """Save, copy checkpoint dir and restore from copied dir. @@ -497,7 +517,7 @@ class SaverTest(test.TestCase): with self.test_session(graph=ops_lib.Graph()) as sess: var = resource_variable_ops.ResourceVariable(var_value, name=var_name) save = saver_module.Saver({var_name: var}) - if context.in_graph_mode(): + if not context.executing_eagerly(): self.evaluate(var.initializer) val = save.save(sess, save_path) self.assertEqual(save_path, val) @@ -657,11 +677,11 @@ class SaverTest(test.TestCase): { var._shared_name: var }, pad_step_number=pad_step_number) - if context.in_graph_mode(): + if context.executing_eagerly(): + sess = None + else: self.evaluate(var.initializer) sess = ops_lib.get_default_session() - else: - sess = None if use_tensor: global_step = constant_op.constant(global_step_int) val = save.save(sess, save_path, global_step=global_step) @@ -1039,6 +1059,77 @@ class MaxToKeepTest(test.TestCase): self.assertEqual(checkpoint_state.all_model_checkpoint_paths, all_model_checkpoint_paths) + def testMaxToKeepEager(self): + with context.eager_mode(): + save_dir = self._get_test_dir("max_to_keep_non_sharded") + + v = variable_scope.variable(10.0, name="v") + save = saver_module.Saver({"v": v}, max_to_keep=2) + self.evaluate(variables.global_variables_initializer()) + if not context.executing_eagerly(): + self.assertEqual([], save.last_checkpoints) + + s1 = save.save(None, os.path.join(save_dir, "s1")) + self.assertEqual([s1], save.last_checkpoints) + self.assertTrue(saver_module.checkpoint_exists(s1)) + self.assertCheckpointState( + model_checkpoint_path=s1, + all_model_checkpoint_paths=[s1], + save_dir=save_dir) + + s2 = save.save(None, os.path.join(save_dir, "s2")) + self.assertEqual([s1, s2], save.last_checkpoints) + self.assertTrue(saver_module.checkpoint_exists(s1)) + self.assertTrue(saver_module.checkpoint_exists(s2)) + self.assertCheckpointState( + model_checkpoint_path=s2, + all_model_checkpoint_paths=[s1, s2], + save_dir=save_dir) + + s3 = save.save(None, os.path.join(save_dir, "s3")) + self.assertEqual([s2, s3], save.last_checkpoints) + self.assertFalse(saver_module.checkpoint_exists(s1)) + self.assertTrue(saver_module.checkpoint_exists(s2)) + self.assertTrue(saver_module.checkpoint_exists(s3)) + self.assertCheckpointState( + model_checkpoint_path=s3, + all_model_checkpoint_paths=[s2, s3], + save_dir=save_dir) + + # Create a second helper, identical to the first. + save2 = saver_module.Saver({"v": v}, max_to_keep=2) + save2.set_last_checkpoints(save.last_checkpoints) + + # Exercise the first helper. + + # Adding s2 again (old s2 is removed first, then new s2 appended) + s2 = save.save(None, os.path.join(save_dir, "s2")) + self.assertEqual([s3, s2], save.last_checkpoints) + self.assertFalse(saver_module.checkpoint_exists(s1)) + self.assertTrue(saver_module.checkpoint_exists(s3)) + self.assertTrue(saver_module.checkpoint_exists(s2)) + self.assertCheckpointState( + model_checkpoint_path=s2, + all_model_checkpoint_paths=[s3, s2], + save_dir=save_dir) + + # Adding s1 (s3 should now be deleted as oldest in list) + s1 = save.save(None, os.path.join(save_dir, "s1")) + self.assertEqual([s2, s1], save.last_checkpoints) + self.assertFalse(saver_module.checkpoint_exists(s3)) + self.assertTrue(saver_module.checkpoint_exists(s2)) + self.assertCheckpointState( + model_checkpoint_path=s1, + all_model_checkpoint_paths=[s2, s1], + save_dir=save_dir) + + s2 = save2.save(None, os.path.join(save_dir, "s2")) + self.assertEqual([s3, s2], save2.last_checkpoints) + # Created by the first helper. + self.assertTrue(saver_module.checkpoint_exists(s1)) + # Deleted by the first helper. + self.assertFalse(saver_module.checkpoint_exists(s3)) + def testNonSharded(self): save_dir = self._get_test_dir("max_to_keep_non_sharded") @@ -1301,15 +1392,16 @@ class KeepCheckpointEveryNHoursTest(test.TestCase): gfile.MakeDirs(test_dir) return test_dir + @test_util.run_in_graph_and_eager_modes() @test.mock.patch.object(saver_module, "time") def testNonSharded(self, mock_time): save_dir = self._get_test_dir("keep_checkpoint_every_n_hours") with self.test_session() as sess: - v = variables.Variable([10.0], name="v") + v = variable_scope.variable([10.0], name="v") # Run the initializer NOW to avoid the 0.5s overhead of the first Run() # call, which throws the test timing off in fastbuild mode. - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) # Create a saver that will keep the last 2 checkpoints plus one every 0.7 # seconds. start_time = time.time() @@ -1387,7 +1479,7 @@ class SaveRestoreWithVariableNameMap(test.TestCase): v0 = variable_op(-1.0, name="v0") v1 = variable_op(-1.0, name="v1") - if context.in_graph_mode(): + if not context.executing_eagerly(): with self.assertRaisesOpError("uninitialized"): self.evaluate(v0) with self.assertRaisesOpError("uninitialized"): @@ -1397,7 +1489,7 @@ class SaveRestoreWithVariableNameMap(test.TestCase): save.restore(sess, save_path) # Check that the parameter nodes have been restored. - if context.in_graph_mode(): + if not context.executing_eagerly(): self.assertEqual(10.0, self.evaluate(v0)) self.assertEqual(20.0, self.evaluate(v1)) @@ -1407,7 +1499,7 @@ class SaveRestoreWithVariableNameMap(test.TestCase): v0 = variable_op(-1.0, name="restore_prefix/v0") v1 = variable_op(-1.0, name="restore_prefix/v1") - if context.in_graph_mode(): + if not context.executing_eagerly(): with self.assertRaisesOpError("uninitialized"): self.evaluate(v0) with self.assertRaisesOpError("uninitialized"): @@ -1646,7 +1738,8 @@ class CheckpointStateTest(test.TestCase): os.path.join(save_dir, "./model.ckpt-687529")) -@test_util.with_c_api +# TODO(skyewm): reenable when this works with _USE_C_SHAPES=False +# @test_util.with_c_api class MetaGraphTest(test.TestCase): def _get_test_dir(self, dirname): @@ -2039,6 +2132,107 @@ class MetaGraphTest(test.TestCase): self._testGraphExtensionRestore(test_dir) self._testRestoreFromTrainGraphWithControlContext(test_dir) + def _testGradientSerDes(self, graph_fn): + """Tests that gradients can be computed after exporting and importing. + + Builds a graph, exports it, and verifies that it can be imported and the + gradient can be built and run correctly. + + Args: + graph_fn: takes a single float Tensor argument as input, outputs a single + Tensor + """ + test_dir = self._get_test_dir("nested_control_flow") + filename = os.path.join(test_dir, "metafile") + saver_ckpt = os.path.join(test_dir, "saver.ckpt") + + # Create while loop using `outer_body_fn`. + with ops_lib.Graph().as_default(): + var = variables.Variable(0.0) + var_name = var.name + output = graph_fn(var) + output_name = output.name + init_op = variables.global_variables_initializer() + + # Generate a MetaGraphDef containing the while loop. + with session.Session() as sess: + sess.run(init_op) + sess.run(output) + saver = saver_module.Saver() + saver.save(sess, saver_ckpt) + saver.export_meta_graph(filename) + + # Build and run the gradients of the while loop. We use this below to + # verify that the gradients are correct with an imported MetaGraphDef. + grad = gradients_impl.gradients([output], [var]) + with session.Session() as sess: + sess.run(init_op) + expected_grad_value = sess.run(grad) + + # Restore the MetaGraphDef into a new Graph. + with ops_lib.Graph().as_default(): + with session.Session() as sess: + saver = saver_module.import_meta_graph(filename) + saver.restore(sess, saver_ckpt) + + # Make sure we can still build gradients and get the same result. + var = ops_lib.get_default_graph().get_tensor_by_name(var_name) + output = ops_lib.get_default_graph().get_tensor_by_name(output_name) + grad = gradients_impl.gradients([output], [var]) + + init_op = variables.global_variables_initializer() + + with session.Session() as sess: + sess.run(init_op) + actual_grad_value = sess.run(grad) + self.assertEqual(expected_grad_value, actual_grad_value) + + def _testWhileLoopAndGradientSerDes(self, outer_body_fn): + # Build a while loop with `outer_body_fn`, export it, and verify that it can + # be imported and the gradient can be built and run correctly. + # pylint: disable=g-long-lambda + return self._testGradientSerDes( + lambda x: control_flow_ops.while_loop( + lambda i, y: i < 5, outer_body_fn, [0, x])[1]) + # pylint: enable=g-long-lambda + + def testNestedWhileLoopsSerDes(self): + # Test two simple nested while loops. + def body(i, x): + _, r = control_flow_ops.while_loop(lambda j, y: j < 3, + lambda j, y: (j + 1, y + x), + [0, 0.0]) + return i + 1, x + r + self._testWhileLoopAndGradientSerDes(body) + + def testNestedControlFlowSerDes(self): + # Test while loop in a cond in a while loop. + # pylint: disable=g-long-lambda + def body(i, x): + cond_result = control_flow_ops.cond( + i > 0, + lambda: control_flow_ops.while_loop( + lambda j, y: j < 3, + lambda j, y: (j + 1, y + x), + [0, 0.0])[1], + lambda: x) + return i + 1, cond_result + # pylint: enable=g-long-lambda + self._testWhileLoopAndGradientSerDes(body) + + def testNestedCondsSerDes(self): + # Test conds in a cond. + # pylint: disable=g-long-lambda + self._testGradientSerDes(lambda x: control_flow_ops.cond( + x > 0, + lambda: control_flow_ops.cond(x > 3, + lambda: array_ops.identity(x), + lambda: math_ops.multiply(x, 2.0)), + lambda: control_flow_ops.cond(x < -3, + lambda: constant_op.constant(1.0), + lambda: math_ops.multiply(x, -1.0)))) + # pylint: enable=g-long-lambda + def testStrippedOpListDef(self): with self.test_session(): # Creates a graph. @@ -2660,5 +2854,92 @@ class ScopedGraphTest(test.TestCase): self.assertEqual(2.0, var_dict2["variable2:0"].eval()) +class _OwnsAVariableSimple(checkpointable.CheckpointableBase): + """A Checkpointable object which can be saved using a tf.train.Saver.""" + + def __init__(self): + self.non_dep_variable = variable_scope.get_variable( + name="non_dep_variable", initializer=6., use_resource=True) + + def _gather_saveables_for_checkpoint(self): + return {checkpointable.VARIABLE_VALUE_KEY: self.non_dep_variable} + + # The Saver sorts by name before parsing, so we need a name property. + @property + def name(self): + return self.non_dep_variable.name + + +class _MirroringSaveable( + saver_module.BaseSaverBuilder.ResourceVariableSaveable): + + def __init__(self, primary_variable, mirrored_variable): + self._primary_variable = primary_variable + self._mirrored_variable = mirrored_variable + super(_MirroringSaveable, self).__init__( + self._primary_variable, "", self._primary_variable.name) + + def restore(self, restored_tensors, restored_shapes): + """Restore the same value into both variables.""" + tensor, = restored_tensors + return control_flow_ops.group( + self._primary_variable.assign(tensor), + self._mirrored_variable.assign(tensor)) + + +class _OwnsMirroredVariables(checkpointable.CheckpointableBase): + """A Checkpointable object which returns a more complex SaveableObject.""" + + def __init__(self): + self.non_dep_variable = variable_scope.get_variable( + name="non_dep_variable", initializer=6., use_resource=True) + self.mirrored = variable_scope.get_variable( + name="mirrored", initializer=15., use_resource=True) + + def _gather_saveables_for_checkpoint(self): + saveable = _MirroringSaveable( + primary_variable=self.non_dep_variable, + mirrored_variable=self.mirrored) + return {checkpointable.VARIABLE_VALUE_KEY: saveable} + + # The Saver sorts by name before parsing, so we need a name property. + @property + def name(self): + return self.non_dep_variable.name + + +@test_util.with_c_api +class CheckpointableCompatibilityTests(test.TestCase): + + # TODO(allenl): Track down python3 reference cycles in these tests. + @test_util.run_in_graph_and_eager_modes() + def testNotSaveableButIsCheckpointable(self): + v = _OwnsAVariableSimple() + saver = saver_module.Saver(var_list=[v]) + test_dir = self.get_temp_dir() + prefix = os.path.join(test_dir, "ckpt") + self.evaluate(v.non_dep_variable.assign(42.)) + with self.test_session() as sess: + save_path = saver.save(sess, prefix) + self.evaluate(v.non_dep_variable.assign(43.)) + saver.restore(sess, save_path) + self.assertEqual(42., self.evaluate(v.non_dep_variable)) + + @test_util.run_in_graph_and_eager_modes() + def testMoreComplexSaveableReturned(self): + v = _OwnsMirroredVariables() + saver = saver_module.Saver(var_list=[v]) + test_dir = self.get_temp_dir() + prefix = os.path.join(test_dir, "ckpt") + self.evaluate(v.non_dep_variable.assign(42.)) + with self.test_session() as sess: + save_path = saver.save(sess, prefix) + self.evaluate(v.non_dep_variable.assign(43.)) + self.evaluate(v.mirrored.assign(44.)) + saver.restore(sess, save_path) + self.assertEqual(42., self.evaluate(v.non_dep_variable)) + self.assertEqual(42., self.evaluate(v.mirrored)) + + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/training/saver_test_utils.py b/tensorflow/python/training/saver_test_utils.py index 44b06b357ecbe4c8e330a2ccc49e83ddd4bf8c7d..2bbe5b6d845c304c4dc79fb3619c57211ca0489e 100644 --- a/tensorflow/python/training/saver_test_utils.py +++ b/tensorflow/python/training/saver_test_utils.py @@ -35,12 +35,12 @@ class CheckpointedOp(object): # pylint: disable=protected-access def __init__(self, name, table_ref=None): if table_ref is None: - self.table_ref = gen_lookup_ops._mutable_hash_table_v2( + self.table_ref = gen_lookup_ops.mutable_hash_table_v2( key_dtype=dtypes.string, value_dtype=dtypes.float32, name=name) else: self.table_ref = table_ref self._name = name - if context.in_graph_mode(): + if not context.executing_eagerly(): self._saveable = CheckpointedOp.CustomSaveable(self, name) ops_lib.add_to_collection(ops_lib.GraphKeys.SAVEABLE_OBJECTS, self._saveable) @@ -51,16 +51,16 @@ class CheckpointedOp(object): @property def saveable(self): - if context.in_graph_mode(): - return self._saveable - else: + if context.executing_eagerly(): return CheckpointedOp.CustomSaveable(self, self.name) + else: + return self._saveable def insert(self, keys, values): - return gen_lookup_ops._lookup_table_insert_v2(self.table_ref, keys, values) + return gen_lookup_ops.lookup_table_insert_v2(self.table_ref, keys, values) def lookup(self, keys, default): - return gen_lookup_ops._lookup_table_find_v2(self.table_ref, keys, default) + return gen_lookup_ops.lookup_table_find_v2(self.table_ref, keys, default) def keys(self): return self._export()[0] @@ -69,8 +69,8 @@ class CheckpointedOp(object): return self._export()[1] def _export(self): - return gen_lookup_ops._lookup_table_export_v2(self.table_ref, dtypes.string, - dtypes.float32) + return gen_lookup_ops.lookup_table_export_v2(self.table_ref, dtypes.string, + dtypes.float32) class CustomSaveable(saver_module.BaseSaverBuilder.SaveableObject): """A custom saveable for CheckpointedOp.""" @@ -86,6 +86,6 @@ class CheckpointedOp(object): super(CheckpointedOp.CustomSaveable, self).__init__(table, specs, name) def restore(self, restore_tensors, shapes): - return gen_lookup_ops._lookup_table_import_v2( + return gen_lookup_ops.lookup_table_import_v2( self.op.table_ref, restore_tensors[0], restore_tensors[1]) # pylint: enable=protected-access diff --git a/tensorflow/python/training/slot_creator.py b/tensorflow/python/training/slot_creator.py index 75ef3d5976aba9f0cbe849d9f6984646d71a29ef..9ac52dd0715d7ed15e2e57ed286be973614b01e5 100644 --- a/tensorflow/python/training/slot_creator.py +++ b/tensorflow/python/training/slot_creator.py @@ -106,7 +106,10 @@ def create_slot(primary, val, name, colocate_with_primary=True): # and the same name has been previously used, the scope name will add '_N' # as suffix for unique identifications. validate_shape = val.get_shape().is_fully_defined() - prefix = primary.op.name if context.in_graph_mode() else primary._shared_name # pylint: disable=protected-access + if context.executing_eagerly(): + prefix = primary._shared_name # pylint: disable=protected-access + else: + prefix = primary.op.name with variable_scope.variable_scope(None, prefix + "/" + name): if colocate_with_primary: with ops.colocate_with(primary): @@ -139,7 +142,10 @@ def create_slot_with_initializer(primary, initializer, shape, dtype, name, # and the same name has been previously used, the scope name will add '_N' # as suffix for unique identifications. validate_shape = shape.is_fully_defined() - prefix = primary.op.name if context.in_graph_mode() else primary._shared_name # pylint: disable=protected-access + if context.executing_eagerly(): + prefix = primary._shared_name # pylint: disable=protected-access + else: + prefix = primary.op.name with variable_scope.variable_scope(None, prefix + "/" + name): if colocate_with_primary: with ops.colocate_with(primary): diff --git a/tensorflow/python/training/supervisor.py b/tensorflow/python/training/supervisor.py index d2ad34773e0615256c340826dcc312cc8a00dc23..7389e344c7d8eef8e26c4d24c0985ff66276deea 100644 --- a/tensorflow/python/training/supervisor.py +++ b/tensorflow/python/training/supervisor.py @@ -45,7 +45,7 @@ class Supervisor(object): """A training helper that checkpoints models and computes summaries. This class is deprecated. Please use - ${tf.train.MonitoredTrainingSession} instead. + @{tf.train.MonitoredTrainingSession} instead. The Supervisor is a small wrapper around a `Coordinator`, a `Saver`, and a `SessionManager` that takes care of common needs of TensorFlow @@ -305,7 +305,7 @@ class Supervisor(object): `Supervisor`s are not supported when eager execution is enabled. @end_compatibility """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError("Supervisors are compatible with eager execution.") # Set default values of arguments. if graph is None: @@ -762,7 +762,7 @@ class Supervisor(object): execution is enabled, use the `tf.data` API. @end_compatibility """ - if context.in_eager_mode(): + if context.executing_eagerly(): raise RuntimeError("Queues are not compatible with eager execution.") if queue_runners is None: queue_runners = self._graph.get_collection(ops.GraphKeys.QUEUE_RUNNERS) diff --git a/tensorflow/python/training/training.py b/tensorflow/python/training/training.py index 78c8ce9208efc2f2fa8b5c671d3379e7ca8c70f5..6880cfc4db9b05c02df56c4c1f26f2fea1a7098c 100644 --- a/tensorflow/python/training/training.py +++ b/tensorflow/python/training/training.py @@ -28,6 +28,7 @@ See the @{$python/train} guide. @@ProximalGradientDescentOptimizer @@ProximalAdagradOptimizer @@RMSPropOptimizer +@@custom_gradient @@gradients @@AggregationMethod @@stop_gradient @@ -94,6 +95,8 @@ See the @{$python/train} guide. @@load_variable @@list_variables @@init_from_checkpoint +@@warm_start +@@VocabInfo """ # Optimizers. @@ -187,6 +190,8 @@ from tensorflow.python.training.training_util import get_global_step from tensorflow.python.training.training_util import assert_global_step from tensorflow.python.training.training_util import create_global_step from tensorflow.python.training.training_util import get_or_create_global_step +from tensorflow.python.training.warm_starting_util import VocabInfo +from tensorflow.python.training.warm_starting_util import warm_start from tensorflow.python.pywrap_tensorflow import do_quantize_training_on_graphdef from tensorflow.python.pywrap_tensorflow import NewCheckpointReader from tensorflow.python.util.tf_export import tf_export diff --git a/tensorflow/python/training/training_util.py b/tensorflow/python/training/training_util.py index 499f1feb2dbf8aee26314a43b0a000fb91a1c686..4f1abccc96ff95b6fcf9fe82c7a5d45fc2fd1c0c 100644 --- a/tensorflow/python/training/training_util.py +++ b/tensorflow/python/training/training_util.py @@ -64,7 +64,7 @@ def global_step(sess, global_step_tensor): Returns: The global step value. """ - if context.in_eager_mode(): + if context.executing_eagerly(): return int(global_step_tensor.numpy()) return int(sess.run(global_step_tensor)) @@ -123,7 +123,7 @@ def create_global_step(graph=None): raise ValueError('"global_step" already exists.') # Create in proper graph and base name_scope. with graph.as_default() as g, g.name_scope(None): - if context.in_eager_mode(): + if context.executing_eagerly(): with ops.device('cpu:0'): return variable_scope.get_variable( ops.GraphKeys.GLOBAL_STEP, diff --git a/tensorflow/python/estimator/warm_starting_util.py b/tensorflow/python/training/warm_starting_util.py similarity index 67% rename from tensorflow/python/estimator/warm_starting_util.py rename to tensorflow/python/training/warm_starting_util.py index adb013f5c653c4967a743047fef4e805946e0f59..4d4fb394c1272d2bf510bb594d70b9aa2edb3df2 100644 --- a/tensorflow/python/estimator/warm_starting_util.py +++ b/tensorflow/python/training/warm_starting_util.py @@ -33,7 +33,7 @@ from tensorflow.python.training import saver from tensorflow.python.util.tf_export import tf_export -@tf_export("estimator.VocabInfo") +@tf_export("train.VocabInfo", "estimator.VocabInfo") class VocabInfo( collections.namedtuple("VocabInfo", [ "new_vocab", @@ -43,7 +43,7 @@ class VocabInfo( "old_vocab_size", "backup_initializer", ])): - """Vocabulary information for WarmStartSettings. + """Vocabulary information for warm-starting. See @{tf.estimator.WarmStartSettings$WarmStartSettings} for examples of using VocabInfo to warm-start. @@ -83,164 +83,6 @@ class VocabInfo( ) -@tf_export("estimator.WarmStartSettings") -class WarmStartSettings( - collections.namedtuple("WarmStartSettings", [ - "ckpt_to_initialize_from", - "vars_to_warm_start", - "var_name_to_vocab_info", - "var_name_to_prev_var_name", - ])): - """Settings for warm-starting in Estimators. - - Example Use with canned `DNNEstimator`: - - ``` - emb_vocab_file = tf.feature_column.embedding_column( - tf.feature_column.categorical_column_with_vocabulary_file( - "sc_vocab_file", "new_vocab.txt", vocab_size=100), - dimension=8) - emb_vocab_list = tf.feature_column.embedding_column( - tf.feature_column.categorical_column_with_vocabulary_list( - "sc_vocab_list", vocabulary_list=["a", "b"]), - dimension=8) - estimator = tf.estimator.DNNClassifier( - hidden_units=[128, 64], feature_columns=[emb_vocab_file, emb_vocab_list], - warm_start_from=ws) - ``` - - where `ws` could be defined as: - - Warm-start all weights in the model (input layer and hidden weights). - Either the directory or a specific checkpoint can be provided (in the case - of the former, the latest checkpoint will be used): - - ``` - ws = WarmStartSettings(ckpt_to_initialize_from="/tmp") - ws = WarmStartSettings(ckpt_to_initialize_from="/tmp/model-1000") - ``` - - Warm-start only the embeddings (input layer): - - ``` - ws = WarmStartSettings(ckpt_to_initialize_from="/tmp", - vars_to_warm_start=".*input_layer.*") - ``` - - Warm-start all weights but the embedding parameters corresponding to - `sc_vocab_file` have a different vocab from the one used in the current - model: - - ``` - vocab_info = ws_util.VocabInfo( - new_vocab=sc_vocab_file.vocabulary_file, - new_vocab_size=sc_vocab_file.vocabulary_size, - num_oov_buckets=sc_vocab_file.num_oov_buckets, - old_vocab="old_vocab.txt" - ) - ws = WarmStartSettings( - ckpt_to_initialize_from="/tmp", - var_name_to_vocab_info={ - "input_layer/sc_vocab_file_embedding/embedding_weights": vocab_info - }) - ``` - - Warm-start only `sc_vocab_file` embeddings (and no other variables), which - have a different vocab from the one used in the current model: - - ``` - vocab_info = ws_util.VocabInfo( - new_vocab=sc_vocab_file.vocabulary_file, - new_vocab_size=sc_vocab_file.vocabulary_size, - num_oov_buckets=sc_vocab_file.num_oov_buckets, - old_vocab="old_vocab.txt" - ) - ws = WarmStartSettings( - ckpt_to_initialize_from="/tmp", - vars_to_warm_start=None, - var_name_to_vocab_info={ - "input_layer/sc_vocab_file_embedding/embedding_weights": vocab_info - }) - ``` - - Warm-start all weights but the parameters corresponding to `sc_vocab_file` - have a different vocab from the one used in current checkpoint, and only - 100 of those entries were used: - - ``` - vocab_info = ws_util.VocabInfo( - new_vocab=sc_vocab_file.vocabulary_file, - new_vocab_size=sc_vocab_file.vocabulary_size, - num_oov_buckets=sc_vocab_file.num_oov_buckets, - old_vocab="old_vocab.txt", - old_vocab_size=100 - ) - ws = WarmStartSettings( - ckpt_to_initialize_from="/tmp", - var_name_to_vocab_info={ - "input_layer/sc_vocab_file_embedding/embedding_weights": vocab_info - }) - ``` - - Warm-start all weights but the parameters corresponding to `sc_vocab_file` - have a different vocab from the one used in current checkpoint and the - parameters corresponding to `sc_vocab_list` have a different name from the - current checkpoint: - - ``` - vocab_info = ws_util.VocabInfo( - new_vocab=sc_vocab_file.vocabulary_file, - new_vocab_size=sc_vocab_file.vocabulary_size, - num_oov_buckets=sc_vocab_file.num_oov_buckets, - old_vocab="old_vocab.txt", - old_vocab_size=100 - ) - ws = WarmStartSettings( - ckpt_to_initialize_from="/tmp", - var_name_to_vocab_info={ - "input_layer/sc_vocab_file_embedding/embedding_weights": vocab_info - }, - var_name_to_prev_var_name={ - "input_layer/sc_vocab_list_embedding/embedding_weights": - "old_tensor_name" - }) - ``` - - Attributes: - ckpt_to_initialize_from: [Required] A string specifying the directory with - checkpoint file(s) or path to checkpoint from which to warm-start the - model parameters. - vars_to_warm_start: [Optional] A regular expression that captures which - variables to warm-start (see tf.get_collection). Defaults to `'.*'`, - which warm-starts all variables. If `None` is explicitly given, only - variables specified in `var_name_to_vocab_info` will be warm-started. - var_name_to_vocab_info: [Optional] Dict of variable names (strings) to - VocabInfo. The variable names should be "full" variables, not the names - of the partitions. If not explicitly provided, the variable is assumed to - have no vocabulary. - var_name_to_prev_var_name: [Optional] Dict of variable names (strings) to - name of the previously-trained variable in `ckpt_to_initialize_from`. If - not explicitly provided, the name of the variable is assumed to be same - between previous checkpoint and current model. - """ - - def __new__(cls, - ckpt_to_initialize_from, - vars_to_warm_start=".*", - var_name_to_vocab_info=None, - var_name_to_prev_var_name=None): - if not ckpt_to_initialize_from: - raise ValueError( - "`ckpt_to_initialize_from` MUST be set in WarmStartSettings") - return super(WarmStartSettings, cls).__new__( - cls, - ckpt_to_initialize_from, - vars_to_warm_start, - var_name_to_vocab_info or {}, - var_name_to_prev_var_name or {}, - ) - - def _is_variable(x): return (isinstance(x, variables_lib.Variable) or isinstance(x, resource_variable_ops.ResourceVariable)) @@ -375,8 +217,7 @@ def _warm_start_var_with_vocab(var, full_shape=slice_info.full_shape, var_offset=slice_info.var_offset) - # TODO(eddz): Support WarmStartSettings where class vocabularies need - # remapping too. + # TODO(eddz): Support cases where class vocabularies need remapping too. init = checkpoint_ops._load_and_remap_matrix_initializer( ckpt_path=checkpoint_utils._get_checkpoint_filename(prev_ckpt), old_tensor_name=prev_tensor_name, @@ -396,32 +237,53 @@ def _warm_start_var_with_vocab(var, # pylint: enable=protected-access -def _warm_start(warm_start_settings): +@tf_export("train.warm_start") +def warm_start(ckpt_to_initialize_from, + vars_to_warm_start=".*", + var_name_to_vocab_info=None, + var_name_to_prev_var_name=None): """Warm-starts a model using the given settings. If you are using a tf.estimator.Estimator, this will automatically be called during training. Args: - warm_start_settings: An object of `WarmStartSettings`. + ckpt_to_initialize_from: [Required] A string specifying the directory with + checkpoint file(s) or path to checkpoint from which to warm-start the + model parameters. + vars_to_warm_start: [Optional] A regular expression that captures which + variables to warm-start (see tf.get_collection). Defaults to `'.*'`, + which warm-starts all variables. If `None` is explicitly given, only + variables specified in `var_name_to_vocab_info` will be warm-started. + var_name_to_vocab_info: [Optional] Dict of variable names (strings) to + VocabInfo. The variable names should be "full" variables, not the names + of the partitions. If not explicitly provided, the variable is assumed to + have no vocabulary. + var_name_to_prev_var_name: [Optional] Dict of variable names (strings) to + name of the previously-trained variable in `ckpt_to_initialize_from`. If + not explicitly provided, the name of the variable is assumed to be same + between previous checkpoint and current model. Raises: ValueError: If the WarmStartSettings contains prev_var_name or VocabInfo configuration for variable names that are not used. This is to ensure a stronger check for variable configuration than relying on users to examine the logs. """ - logging.info("Warm-starting from: %s", - (warm_start_settings.ckpt_to_initialize_from,)) + if var_name_to_vocab_info is None: + var_name_to_vocab_info = {} + if var_name_to_prev_var_name is None: + var_name_to_prev_var_name = {} + logging.info("Warm-starting from: %s", (ckpt_to_initialize_from,)) # We have to deal with partitioned variables, since get_collection flattens # out the list. grouped_variables = {} - # Both warm_start_settings.vars_to_warm_start = '.*' and - # warm_start_settings.vars_to_warm_start = None will match everything here. + # Both vars_to_warm_start = '.*' and + # vars_to_warm_start = None will match everything here. for v in ops.get_collection( # TODO(eddz): Allow for different collections here (to support # warm-starting accumulators). ops.GraphKeys.TRAINABLE_VARIABLES, - scope=warm_start_settings.vars_to_warm_start): + scope=vars_to_warm_start): if not isinstance(v, list): var_name = _infer_var_name([v]) else: @@ -437,10 +299,10 @@ def _warm_start(warm_start_settings): vocab_info_used = set() for var_name, variable in six.iteritems(grouped_variables): - prev_var_name = warm_start_settings.var_name_to_prev_var_name.get(var_name) + prev_var_name = var_name_to_prev_var_name.get(var_name) if prev_var_name: prev_var_name_used.add(var_name) - vocab_info = warm_start_settings.var_name_to_vocab_info.get(var_name) + vocab_info = var_name_to_vocab_info.get(var_name) if vocab_info: vocab_info_used.add(var_name) logging.info( @@ -460,16 +322,16 @@ def _warm_start(warm_start_settings): variable, current_vocab_path=vocab_info.new_vocab, current_vocab_size=vocab_info.new_vocab_size, - prev_ckpt=warm_start_settings.ckpt_to_initialize_from, + prev_ckpt=ckpt_to_initialize_from, prev_vocab_path=vocab_info.old_vocab, previous_vocab_size=vocab_info.old_vocab_size, current_oov_buckets=vocab_info.num_oov_buckets, prev_tensor_name=prev_var_name, initializer=vocab_info.backup_initializer) else: - # For the special value of warm_start_settings.vars_to_warm_start = None, + # For the special value of vars_to_warm_start = None, # we only warm-start variables with explicitly specified vocabularies. - if warm_start_settings.vars_to_warm_start: + if vars_to_warm_start: logging.info("Warm-starting variable: {}; prev_var_name: {}".format( var_name, prev_var_name or "Unchanged")) # Because we use a default empty list in grouped_variables, single @@ -477,48 +339,22 @@ def _warm_start(warm_start_settings): # for init_from_checkpoint logic to work correctly. if len(variable) == 1: variable = variable[0] - _warm_start_var(variable, warm_start_settings.ckpt_to_initialize_from, - prev_var_name) + _warm_start_var(variable, ckpt_to_initialize_from, prev_var_name) prev_var_name_not_used = set( - warm_start_settings.var_name_to_prev_var_name.keys()) - prev_var_name_used - vocab_info_not_used = set( - warm_start_settings.var_name_to_vocab_info.keys()) - vocab_info_used + var_name_to_prev_var_name.keys()) - prev_var_name_used + vocab_info_not_used = set(var_name_to_vocab_info.keys()) - vocab_info_used if prev_var_name_not_used: raise ValueError( "You provided the following variables in " - "warm_start_settings.var_name_to_prev_var_name that were not used: " + "var_name_to_prev_var_name that were not used: " "{0}. Perhaps you misspelled them? Here is the list of viable " "variable names: {1}".format(prev_var_name_not_used, grouped_variables.keys())) if vocab_info_not_used: raise ValueError( "You provided the following variables in " - "warm_start_settings.var_name_to_vocab_info that were not used: {0}. " + "var_name_to_vocab_info that were not used: {0}. " " Perhaps you misspelled them? Here is the list of viable variable " "names: {1}".format(vocab_info_not_used, grouped_variables.keys())) - - -def _get_default_warm_start_settings(warm_start_from): - """Returns default WarmStartSettings. - - Args: - warm_start_from: Either a string representing the filepath of a checkpoint - to initialize from, or an instance of WarmStartSettings. - - Returns: - Either None or an instance of WarmStartSettings. - - Raises: - ValueError: If warm_start_from is not None but is neither a string nor an - instance of WarmStartSettings. - """ - if warm_start_from is None: - return None - if isinstance(warm_start_from, six.string_types): - return WarmStartSettings(ckpt_to_initialize_from=warm_start_from) - elif isinstance(warm_start_from, WarmStartSettings): - return warm_start_from - else: - raise ValueError("warm_start_from must be a string or a WarmStartSettings") diff --git a/tensorflow/python/estimator/warm_starting_util_test.py b/tensorflow/python/training/warm_starting_util_test.py similarity index 94% rename from tensorflow/python/estimator/warm_starting_util_test.py rename to tensorflow/python/training/warm_starting_util_test.py index 3985d9ebd04e6963339fcf9999f6367fe4dadc1a..6e445d8bd14cc13010541c1ab0f737f96a4b1e03 100644 --- a/tensorflow/python/estimator/warm_starting_util_test.py +++ b/tensorflow/python/training/warm_starting_util_test.py @@ -22,7 +22,6 @@ import os import numpy as np import six -from tensorflow.python.estimator import warm_starting_util as ws_util from tensorflow.python.feature_column import feature_column as fc from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops @@ -32,6 +31,7 @@ from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables from tensorflow.python.platform import test from tensorflow.python.training import saver as saver_lib +from tensorflow.python.training import warm_starting_util as ws_util ones = init_ops.ones_initializer norms = init_ops.truncated_normal_initializer @@ -330,9 +330,7 @@ class WarmStartingUtilTest(test.TestCase): with ops.Graph().as_default() as g: with self.test_session(graph=g) as sess: cols_to_vars = self._create_linear_model([sc_int], partitioner) - ws_util._warm_start( - ws_util.WarmStartSettings( - self.get_temp_dir(), vars_to_warm_start=".*sc_int.*")) + ws_util.warm_start(self.get_temp_dir(), vars_to_warm_start=".*sc_int.*") sess.run(variables.global_variables_initializer()) # Verify weights were correctly warm-started. self._assert_cols_to_vars(cols_to_vars, {sc_int: [prev_int_val]}, sess) @@ -361,9 +359,8 @@ class WarmStartingUtilTest(test.TestCase): with ops.Graph().as_default() as g: with self.test_session(graph=g) as sess: cols_to_vars = self._create_linear_model([sc_hash], partitioner) - ws_util._warm_start( - ws_util.WarmStartSettings( - self.get_temp_dir(), vars_to_warm_start=".*sc_hash.*")) + ws_util.warm_start( + self.get_temp_dir(), vars_to_warm_start=".*sc_hash.*") sess.run(variables.global_variables_initializer()) # Verify weights were correctly warm-started. self._assert_cols_to_vars(cols_to_vars, {sc_hash: [prev_hash_val]}, @@ -398,9 +395,8 @@ class WarmStartingUtilTest(test.TestCase): cols_to_vars = self._create_linear_model([sc_vocab], partitioner) # Since old vocab is not explicitly set in WarmStartSettings, the old # vocab is assumed to be same as new vocab. - ws_util._warm_start( - ws_util.WarmStartSettings( - self.get_temp_dir(), vars_to_warm_start=".*sc_vocab.*")) + ws_util.warm_start( + self.get_temp_dir(), vars_to_warm_start=".*sc_vocab.*") sess.run(variables.global_variables_initializer()) # Verify weights were correctly warm-started. self._assert_cols_to_vars(cols_to_vars, {sc_vocab: [prev_vocab_val]}, @@ -435,11 +431,10 @@ class WarmStartingUtilTest(test.TestCase): cols_to_vars = self._create_linear_model([sc_vocab], partitioner) # Since old vocab is not explicitly set in WarmStartSettings, the old # vocab is assumed to be same as new vocab. - ws_util._warm_start( - ws_util.WarmStartSettings( - # Explicitly provide the file prefix instead of just the dir. - os.path.join(self.get_temp_dir(), "model-0"), - vars_to_warm_start=".*sc_vocab.*")) + ws_util.warm_start( + # Explicitly provide the file prefix instead of just the dir. + os.path.join(self.get_temp_dir(), "model-0"), + vars_to_warm_start=".*sc_vocab.*") sess.run(variables.global_variables_initializer()) # Verify weights were correctly warm-started. self._assert_cols_to_vars(cols_to_vars, {sc_vocab: [prev_vocab_val]}, @@ -485,13 +480,12 @@ class WarmStartingUtilTest(test.TestCase): num_oov_buckets=sc_vocab.num_oov_buckets, old_vocab=old_vocab_path, old_vocab_size=old_vocab_size) - warm_start_settings = ws_util.WarmStartSettings( + ws_util.warm_start( ckpt_to_initialize_from=self.get_temp_dir(), vars_to_warm_start=".*sc_vocab.*", var_name_to_vocab_info={ "linear_model/sc_vocab/weights": vocab_info }) - ws_util._warm_start(warm_start_settings) sess.run(variables.global_variables_initializer()) # Verify weights were correctly warm-started. 'banana' isn't in the # first two entries of the old vocabulary, so it's newly initialized. @@ -523,9 +517,8 @@ class WarmStartingUtilTest(test.TestCase): with ops.Graph().as_default() as g: with self.test_session(graph=g) as sess: cols_to_vars = self._create_linear_model([real_bucket], partitioner) - ws_util._warm_start( - ws_util.WarmStartSettings( - self.get_temp_dir(), vars_to_warm_start=".*real_bucketized.*")) + ws_util.warm_start( + self.get_temp_dir(), vars_to_warm_start=".*real_bucketized.*") sess.run(variables.global_variables_initializer()) # Verify weights were correctly warm-started. self._assert_cols_to_vars(cols_to_vars, @@ -606,12 +599,11 @@ class WarmStartingUtilTest(test.TestCase): new_vocab_size=sc_vocab.vocabulary_size, num_oov_buckets=sc_vocab.num_oov_buckets, old_vocab=vocab_path) - ws_util._warm_start( - ws_util.WarmStartSettings( - self.get_temp_dir(), - var_name_to_vocab_info={ - "linear_model/sc_vocab/weights": vocab_info - })) + ws_util.warm_start( + self.get_temp_dir(), + var_name_to_vocab_info={ + "linear_model/sc_vocab/weights": vocab_info + }) sess.run(variables.global_variables_initializer()) # Verify weights were correctly warm-started. self._assert_cols_to_vars(cols_to_vars, { @@ -668,7 +660,7 @@ class WarmStartingUtilTest(test.TestCase): new_vocab_size=sc_vocab.vocabulary_size, num_oov_buckets=sc_vocab.num_oov_buckets, old_vocab=prev_vocab_path) - ws_settings = ws_util.WarmStartSettings( + ws_util.warm_start( self.get_temp_dir(), vars_to_warm_start=".*(sc_keys|sc_vocab).*", var_name_to_vocab_info={ @@ -678,7 +670,6 @@ class WarmStartingUtilTest(test.TestCase): ws_util._infer_var_name(cols_to_vars[sc_keys]): "some_other_name" }) - ws_util._warm_start(ws_settings) sess.run(variables.global_variables_initializer()) # Verify weights were correctly warm-started. Var corresponding to # sc_hash should not be warm-started. Var corresponding to sc_vocab @@ -732,7 +723,7 @@ class WarmStartingUtilTest(test.TestCase): new_vocab_size=sc_vocab.vocabulary_size, num_oov_buckets=sc_vocab.num_oov_buckets, old_vocab=prev_vocab_path) - ws_settings = ws_util.WarmStartSettings( + ws_util.warm_start( self.get_temp_dir(), vars_to_warm_start=".*(sc_keys|sc_vocab).*", var_name_to_vocab_info={ @@ -742,7 +733,6 @@ class WarmStartingUtilTest(test.TestCase): ws_util._infer_var_name(cols_to_vars[sc_keys]): "some_other_name" }) - ws_util._warm_start(ws_settings) sess.run(variables.global_variables_initializer()) # Verify weights were correctly warm-started. Var corresponding to # sc_hash should not be warm-started. Var corresponding to sc_vocab @@ -796,7 +786,7 @@ class WarmStartingUtilTest(test.TestCase): new_vocab_size=sc_vocab.vocabulary_size, num_oov_buckets=sc_vocab.num_oov_buckets, old_vocab=prev_vocab_path) - ws_settings = ws_util.WarmStartSettings( + ws_util.warm_start( self.get_temp_dir(), # The special value of None here will ensure that only the variable # specified in var_name_to_vocab_info (sc_vocab embedding) is @@ -812,7 +802,6 @@ class WarmStartingUtilTest(test.TestCase): ws_util._infer_var_name(cols_to_vars[sc_keys]): "some_other_name" }) - ws_util._warm_start(ws_settings) sess.run(variables.global_variables_initializer()) # Verify weights were correctly warm-started. Var corresponding to # sc_vocab should be correctly warm-started after vocab remapping, @@ -874,13 +863,12 @@ class WarmStartingUtilTest(test.TestCase): # use a truncated normal initializer. backup_initializer=init_ops.random_uniform_initializer( minval=0.42, maxval=0.42)) - ws_settings = ws_util.WarmStartSettings( + ws_util.warm_start( self.get_temp_dir(), var_name_to_vocab_info={ ws_util._infer_var_name(cols_to_vars[emb_vocab_column]): vocab_info }) - ws_util._warm_start(ws_settings) sess.run(variables.global_variables_initializer()) # Verify weights were correctly warm-started. Var corresponding to # emb_vocab_column should be correctly warm-started after vocab @@ -947,13 +935,12 @@ class WarmStartingUtilTest(test.TestCase): # use a truncated normal initializer. backup_initializer=init_ops.random_uniform_initializer( minval=0.42, maxval=0.42)) - ws_settings = ws_util.WarmStartSettings( + ws_util.warm_start( self.get_temp_dir(), vars_to_warm_start=".*sc_vocab.*", var_name_to_vocab_info={ "linear_model/sc_vocab_embedding/embedding_weights": vocab_info }) - ws_util._warm_start(ws_settings) sess.run(variables.global_variables_initializer()) # Verify weights were correctly warm-started. Var corresponding to # emb_vocab should be correctly warm-started after vocab remapping. @@ -973,7 +960,6 @@ class WarmStartingUtilTest(test.TestCase): }, sess) def testErrorConditions(self): - self.assertRaises(ValueError, ws_util.WarmStartSettings, None) x = variable_scope.get_variable( "x", shape=[4, 1], @@ -983,9 +969,6 @@ class WarmStartingUtilTest(test.TestCase): # List of PartitionedVariable is invalid type when warm-starting with vocab. self.assertRaises(TypeError, ws_util._warm_start_var_with_vocab, [x], "/tmp", 5, "/tmp", "/tmp") - # Keys of type other than FeatureColumn. - self.assertRaises(TypeError, ws_util._warm_start, {"StringType": x}, - ws_util.WarmStartSettings("/tmp")) # Unused variable names raises ValueError. with ops.Graph().as_default(): @@ -997,18 +980,16 @@ class WarmStartingUtilTest(test.TestCase): partitioner=lambda shape, dtype: [2, 1]) self._write_checkpoint(sess) - self.assertRaises(ValueError, ws_util._warm_start, - ws_util.WarmStartSettings( - self.get_temp_dir(), - var_name_to_vocab_info={ - "y": ws_util.VocabInfo("", 1, 0, "") - })) - self.assertRaises(ValueError, ws_util._warm_start, - ws_util.WarmStartSettings( - self.get_temp_dir(), - var_name_to_prev_var_name={ - "y": "y2" - })) + self.assertRaises( + ValueError, + ws_util.warm_start, + self.get_temp_dir(), + var_name_to_vocab_info={"y": ws_util.VocabInfo("", 1, 0, "")}) + self.assertRaises( + ValueError, + ws_util.warm_start, + self.get_temp_dir(), + var_name_to_prev_var_name={"y": "y2"}) if __name__ == "__main__": diff --git a/tensorflow/python/user_ops/user_ops.py b/tensorflow/python/user_ops/user_ops.py index 17dbab706c9243c5f119dc82cc4428f03b90a18d..6f9b5d92bb2ea662c9a5af279f0fcf71f0efccc5 100644 --- a/tensorflow/python/user_ops/user_ops.py +++ b/tensorflow/python/user_ops/user_ops.py @@ -27,4 +27,4 @@ from tensorflow.python.ops.gen_user_ops import * # pylint: disable=wildcard-imp def my_fact(): """Example of overriding the generated code for an Op.""" - return _gen_user_ops._fact() # pylint: disable=protected-access + return _gen_user_ops.fact() diff --git a/tensorflow/python/util/decorator_utils.py b/tensorflow/python/util/decorator_utils.py index df259c7f7c29f9a4b674d3e980b33d6dcf323769..7b4363c0e40802779cf47c75c5a5e5a901da37e2 100644 --- a/tensorflow/python/util/decorator_utils.py +++ b/tensorflow/python/util/decorator_utils.py @@ -82,7 +82,7 @@ def add_notice_to_docstring( lines = _normalize_docstring(doc).splitlines() lines[0] += ' ' + suffix_str - notice = [''] + notice + [instructions] + notice = [''] + notice + ([instructions] if instructions else []) if len(lines) > 1: # Make sure that we keep our distance from the main body diff --git a/tensorflow/python/util/port.i b/tensorflow/python/util/port.i index cea4d8468afe8816d71da6581635b8a7ab0c2388..2f730732bee373a6e6ead97fe3320645f37ac220 100644 --- a/tensorflow/python/util/port.i +++ b/tensorflow/python/util/port.i @@ -23,5 +23,6 @@ limitations under the License. %unignore tensorflow; %unignore tensorflow::IsGoogleCudaEnabled; %unignore tensorflow::CudaSupportsHalfMatMulAndConv; +%unignore tensorflow::IsMklEnabled; %include "tensorflow/core/util/port.h" %unignoreall diff --git a/tensorflow/python/util/tf_inspect.py b/tensorflow/python/util/tf_inspect.py index c2fe6fc4494428693605a5a7463a9f590a2da39e..4ab8a72a83b466c38c50b1c76004e7a6fe942a04 100644 --- a/tensorflow/python/util/tf_inspect.py +++ b/tensorflow/python/util/tf_inspect.py @@ -46,8 +46,10 @@ def getargspec(object): # pylint: disable=redefined-builtin def getfullargspec(obj): # pylint: disable=redefined-builtin - """TFDecorator-aware replacement for inspect.getfullargspec and fallback to - inspect.getargspec in Python 2. + """TFDecorator-aware replacement for `inspect.getfullargspec`/`getargspec`. + + This wrapper uses `inspect.getfullargspec` if available and falls back to + `inspect.getargspec` in Python 2. Args: obj: A callable, possibly decorated. @@ -149,6 +151,11 @@ def getsource(object): # pylint: disable=redefined-builtin return _inspect.getsource(tf_decorator.unwrap(object)[1]) +def isbuiltin(object): # pylint: disable=redefined-builtin + """TFDecorator-aware replacement for inspect.isbuiltin.""" + return _inspect.isbuiltin(tf_decorator.unwrap(object)[1]) + + def isclass(object): # pylint: disable=redefined-builtin """TFDecorator-aware replacement for inspect.isclass.""" return _inspect.isclass(tf_decorator.unwrap(object)[1]) diff --git a/tensorflow/python/util/tf_inspect_test.py b/tensorflow/python/util/tf_inspect_test.py index 8903e1156b27b3a28543eb5ecfcc6eeb1a04f6ae..129408449ebb45ac3a322f163a13b705cbb31f0c 100644 --- a/tensorflow/python/util/tf_inspect_test.py +++ b/tensorflow/python/util/tf_inspect_test.py @@ -144,6 +144,19 @@ def test_decorated_function_with_defaults(a, b=2, c='Hello'): self.assertEqual( expected, tf_inspect.getsource(test_decorated_function_with_defaults)) + def testIsBuiltin(self): + self.assertEqual( + tf_inspect.isbuiltin(TestDecoratedClass), + inspect.isbuiltin(TestDecoratedClass)) + self.assertEqual( + tf_inspect.isbuiltin(test_decorated_function), + inspect.isbuiltin(test_decorated_function)) + self.assertEqual( + tf_inspect.isbuiltin(test_undecorated_function), + inspect.isbuiltin(test_undecorated_function)) + self.assertEqual(tf_inspect.isbuiltin(range), inspect.isbuiltin(range)) + self.assertEqual(tf_inspect.isbuiltin(max), inspect.isbuiltin(max)) + def testIsClass(self): self.assertTrue(tf_inspect.isclass(TestDecoratedClass)) self.assertFalse(tf_inspect.isclass(test_decorated_function)) diff --git a/tensorflow/python/util/tf_should_use.py b/tensorflow/python/util/tf_should_use.py index 37733152e8ec6d7b026bf74e69e33bfe8f9f4e89..28e49afa023904abed076373685bb38f2537b7d4 100644 --- a/tensorflow/python/util/tf_should_use.py +++ b/tensorflow/python/util/tf_should_use.py @@ -47,7 +47,7 @@ def _add_should_use_warning(x, fatal_error=False): if x is None or x == []: # pylint: disable=g-explicit-bool-comparison return x - if context.in_eager_mode(): + if context.executing_eagerly(): # Typically not needed when executing eagerly (the main use case is for ops # which need to be incorporated into the graph), and even the no-op wrapper # creates reference cycles which require garbage collection. diff --git a/tensorflow/stream_executor/blas.cc b/tensorflow/stream_executor/blas.cc index da09d84921e2dd94942b3a62fe7366211c60aed1..31724cf6c9b97e45975b9e053459f7b8f5918dfa 100644 --- a/tensorflow/stream_executor/blas.cc +++ b/tensorflow/stream_executor/blas.cc @@ -79,6 +79,8 @@ string ComputationTypeString(ComputationType ty) { return "f32"; case ComputationType::kF64: return "f64"; + case ComputationType::kI32: + return "i32"; case ComputationType::kComplexF32: return "complex f32"; case ComputationType::kComplexF64: @@ -88,6 +90,10 @@ string ComputationTypeString(ComputationType ty) { } } +std::ostream& operator<<(std::ostream& os, ComputationType ty) { + return os << ComputationTypeString(ty); +} + } // namespace blas } // namespace gputools } // namespace perftools diff --git a/tensorflow/stream_executor/blas.h b/tensorflow/stream_executor/blas.h index 072f08554688276a05d9be85718de8750bd874c2..c5f778a5c74519c0f35cea5d59aac3d0d4564c56 100644 --- a/tensorflow/stream_executor/blas.h +++ b/tensorflow/stream_executor/blas.h @@ -104,6 +104,8 @@ enum class ComputationType { // Converts a ComputationType to a string. string ComputationTypeString(ComputationType ty); +std::ostream &operator<<(std::ostream &os, ComputationType ty); + // Opaque identifier for an "algorithm" used by a blas routine. This functions // as a hint to the blas library. typedef int64 AlgorithmType; diff --git a/tensorflow/stream_executor/cuda/cuda_blas.cc b/tensorflow/stream_executor/cuda/cuda_blas.cc index 44a3a745ad86dc24f632e4a36691fba06171c9fb..c563f8f931b0a5689268329386d1252f2a45bdd1 100644 --- a/tensorflow/stream_executor/cuda/cuda_blas.cc +++ b/tensorflow/stream_executor/cuda/cuda_blas.cc @@ -13,17 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -// Include cuBLAS headers early, and then set EIGEN_HAS_CUDA_FP16 -// if we have new enough CUDA (which we will only know after including -// cuda.h). This ensures that Eigen's Half.h does not attempt to make its own -// __half typedef if CUDA has already defined one (and conversely, that we do -// not include after Half.h has made its typedef). -#include "cuda/include/cuda.h" #include "cuda/include/cublas_v2.h" - -#if CUDA_VERSION >= 7050 -#define EIGEN_HAS_CUDA_FP16 -#endif +#include "cuda/include/cuda.h" #if CUDA_VERSION >= 8000 #define SE_CUDA_DATA_HALF CUDA_R_16F @@ -33,6 +24,34 @@ limitations under the License. #include "tensorflow/stream_executor/cuda/cuda_blas.h" +// Both Eigen Half.h and CUDA cuda_fp16.h provide similar typedef for __half. As +// such, there are two ways to get the typedef for __half: +// +// (1) Includes cuda_fp16.h and defines EIGEN_HAS_CUDA_FP16. +// (2) Neither includes cuda_fp16.h nor defines EIGEN_HAS_CUDA_FP16. +// +// Due to issue b/73793421, when the first approach is used and NVCC is used to +// compile this file, NVCC will complain duplicated definition for +// EIGEN_HAS_CUDA_FP16. On the other hand, when the second approach is used and +// clang is used to compile this file, clang will not understand __half +// due to missing the definition and macro EIGEN_HAS_CUDA_FP16. +// +// Because this file may be compiled with CLANG but will never be compiled with +// NVCC, we choose the first approach for CUDA < 9.0. For CUDA >= 9.0, we have +// to use the second approach because the data member in the __half defined +// by CUDA > 9.0 is `__x` while Eigen expects it to be `x`. +// +// TODO(b/73793421): Remove the following code block to switch to the second +// approach when the issue is fixed. +#if CUDA_VERSION < 9000 +#include "cuda/include/cuda_fp16.h" +#if CUDA_VERSION >= 7050 +#define EIGEN_HAS_CUDA_FP16 +#endif +#endif + +#include "third_party/eigen3/Eigen/Core" + #include #include @@ -2256,6 +2275,14 @@ bool CUDABlas::DoBlasGemmWithAlgorithm( DeviceMemory *c, int ldc, blas::ComputationType computation_type, blas::AlgorithmType algorithm, blas::ProfileResult *output_profile_result) { + if (computation_type == blas::ComputationType::kF32) { + return DoBlasGemmWithAlgorithmImpl( + stream, transa, transb, m, n, k, static_cast(alpha), a, lda, b, + ldb, static_cast(beta), c, ldc, computation_type, algorithm, + output_profile_result); + } + + CHECK_EQ(computation_type, blas::ComputationType::kF16); return DoBlasGemmWithAlgorithmImpl( stream, transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc, computation_type, algorithm, output_profile_result); diff --git a/tensorflow/stream_executor/cuda/cuda_dnn.cc b/tensorflow/stream_executor/cuda/cuda_dnn.cc index 61cf4ba7eac1f9482e3c1b179f35434a2a65d955..03e3e0857f9f70dcd3062f0766b50f6f75b2fa5e 100644 --- a/tensorflow/stream_executor/cuda/cuda_dnn.cc +++ b/tensorflow/stream_executor/cuda/cuda_dnn.cc @@ -274,7 +274,8 @@ CUDNN_DNN_ROUTINE_EACH_R6(PERFTOOLS_GPUTOOLS_CUDNN_WRAP) // clang-format off #if CUDNN_VERSION >= 7000 #define CUDNN_DNN_ROUTINE_EACH_R7(__macro) \ - __macro(cudnnSetConvolutionMathType) + __macro(cudnnSetConvolutionMathType) \ + __macro(cudnnSetRNNMatrixMathType) // clang-format on CUDNN_DNN_ROUTINE_EACH_R7(PERFTOOLS_GPUTOOLS_CUDNN_WRAP) @@ -586,6 +587,19 @@ static bool TensorOpMathEnabled() { return is_enabled; } +// A helper function to decide whether to enable the TENSOR_OP_MATH math type +// for RNNs. +static bool RnnTensorOpMathEnabled() { + static bool is_enabled = [] { + bool is_disabled = false; + TF_CHECK_OK( + tensorflow::ReadBoolFromEnvVar("TF_DISABLE_CUDNN_RNN_TENSOR_OP_MATH", + /*default_val=*/false, &is_disabled)); + return !is_disabled; + }(); + return is_enabled; +} + // A helper function to decide whether to use CUDNN_BATCHNORM_SPATIAL_PERSISTENT // in batchnorm. This mode can be faster in some tasks because an optimized path // may be selected for CUDNN_DATA_FLOAT and CUDNN_DATA_HALF data types, compute @@ -1124,6 +1138,9 @@ class CudnnRnnDescriptor : public CudnnDescriptorCommon { SetFailure(cudnn_params_desc_->Status()); return; } + if (data_type == CUDNN_DATA_HALF) { + set_use_tensor_op_math(true); + } } ~CudnnRnnDescriptor() override { if (rnn_desc_) { @@ -1132,6 +1149,20 @@ class CudnnRnnDescriptor : public CudnnDescriptorCommon { CUDNN_RETURN_IF_FAIL(status, "Unable to destroy RNN descriptor"); } } + void set_use_tensor_op_math(bool use_tensor_op_math) { +#if CUDNN_VERSION >= 7000 + cudnnMathType_t math_type = + (use_tensor_op_math ? CUDNN_TENSOR_OP_MATH : CUDNN_DEFAULT_MATH); + if (RnnTensorOpMathEnabled()) { + cudnnStatus_t status = + wrap::cudnnSetRNNMatrixMathType(parent_, rnn_desc_, math_type); + if (status != CUDNN_STATUS_SUCCESS) { + LOG(FATAL) << "could not set cudnn RNN math type: " + << ToString(status); + } + } +#endif + } cudnnRNNDescriptor_t handle() const { if (!ok()) return nullptr; return rnn_desc_; @@ -2281,7 +2312,6 @@ struct ConvDoFP32ComputationFP16Input { // A group of helper functions to return the internal compute type for // convolutions in cudnn. -// TODO(yangzihao): Add support for float64. template cudnnDataType_t GetConvComputeType() { return CUDNN_DATA_FLOAT; @@ -2296,6 +2326,11 @@ cudnnDataType_t GetConvComputeType() { } } +template <> +cudnnDataType_t GetConvComputeType() { + return CUDNN_DATA_DOUBLE; +} + } // namespace template @@ -2324,9 +2359,15 @@ bool CudnnSupport::DoConvolveImpl( LOG(FATAL) << "failed to set stream for cudnn handle: " << ToString(status); } // Alpha is the scaling factor for input. - float alpha = 1.0; + float falpha = 1.0; + double dalpha = 1.0; + void* alpha = cudnn_type == CUDNN_DATA_DOUBLE ? static_cast(&dalpha) + : static_cast(&falpha); // Beta is the scaling factor for output. - float beta = 0.0; + float fbeta = 0.0; + double dbeta = 0.0; + void* beta = cudnn_type == CUDNN_DATA_DOUBLE ? static_cast(&dbeta) + : static_cast(&fbeta); const bool is_profiling = output_profile_result != nullptr; cudnnConvolutionFwdAlgo_t algo; @@ -2464,11 +2505,11 @@ bool CudnnSupport::DoConvolveImpl( } status = wrap::cudnnConvolutionForward( parent_, ToHandle(dnn_handle_), - /*alpha=*/&alpha, /*srcDesc=*/input_nd.handle(), + /*alpha=*/alpha, /*srcDesc=*/input_nd.handle(), /*srcData=*/input_data.opaque(), /*filterDesc=*/filter.handle(), /*filterData=*/filter_data.opaque(), /*convDesc=*/conv.handle(), /*algo=*/algo, /*workSpace=*/scratch.opaque(), - /*workSpaceSizeInBytes=*/scratch.size(), /*beta=*/&beta, + /*workSpaceSizeInBytes=*/scratch.size(), /*beta=*/beta, /*destDesc=*/output_nd.handle(), /*destData=*/output_data->opaque()); if (is_profiling) { @@ -2943,10 +2984,14 @@ bool CudnnSupport::DoConvolve( const FilterDescriptor& filter_descriptor, const DeviceMemory& filter_data, const ConvolutionDescriptor& convolution_descriptor, - const BatchDescriptor& output_descriptor, - DeviceMemory* output_data) { - LOG(ERROR) << "double-based DNN not yet implemented"; - return false; + const BatchDescriptor& output_descriptor, DeviceMemory* output_data, + ScratchAllocator* scratch_allocator, + const dnn::AlgorithmConfig& algorithm_config, + dnn::ProfileResult* output_profile_result) { + return DoConvolveImpl( + stream, batch_descriptor, input_data, filter_descriptor, filter_data, + convolution_descriptor, output_descriptor, output_data, scratch_allocator, + algorithm_config, output_profile_result); } bool CudnnSupport::DoConvolve( @@ -3151,10 +3196,17 @@ bool CudnnSupport::DoConvolveBackwardDataImpl( LOG(FATAL) << "failed to set stream for cudnn handle: " << ToString(status); } + cudnnDataType_t cudnn_type = GetCudnnDataType(); // Alpha is the scaling factor for input. - float alpha = 1.0; + float falpha = 1.0; + double dalpha = 1.0; + void* alpha = cudnn_type == CUDNN_DATA_DOUBLE ? static_cast(&dalpha) + : static_cast(&falpha); // Beta is the scaling factor for output. - float beta = 0.0; + float fbeta = 0.0; + double dbeta = 0.0; + void* beta = cudnn_type == CUDNN_DATA_DOUBLE ? static_cast(&dbeta) + : static_cast(&fbeta); // TBD(keveman): remove once cuDNN supports kBatchYXDepth for backward pass. BatchDescriptor output_descriptor; @@ -3163,7 +3215,6 @@ bool CudnnSupport::DoConvolveBackwardDataImpl( backward_output_data = MaybeTransformLayout( stream, &output_descriptor, backward_output_data, &transform_scratch); - cudnnDataType_t cudnn_type = GetCudnnDataType(); ScopedTensorDescriptor out_back_nd{parent_, output_descriptor, cudnn_type}; ScopedTensorDescriptor in_back_nd{parent_, input_descriptor, cudnn_type}; ScopedFilterDescriptor filter{parent_, filter_descriptor, input_descriptor, @@ -3310,7 +3361,7 @@ bool CudnnSupport::DoConvolveBackwardDataImpl( status = wrap::cudnnConvolutionBackwardData_v3( #endif parent_, ToHandle(dnn_handle_), - /*alpha=*/&alpha, + /*alpha=*/alpha, /*filterDesc=*/filter.handle(), /*filterData=*/filter_data.opaque(), /*diffDesc=*/out_back_nd.handle(), @@ -3319,7 +3370,7 @@ bool CudnnSupport::DoConvolveBackwardDataImpl( /*algo=*/algo, /*workSpace=*/scratch.opaque(), /*workSpaceSizeInBytes=*/scratch.size(), - /*beta=*/&beta, + /*beta=*/beta, /*gradDesc=*/in_back_nd.handle(), /*gradData=*/backward_input_data->opaque()); if (is_profiling) { @@ -3344,10 +3395,28 @@ bool CudnnSupport::DoConvolveBackwardDataImpl( return true; } +bool CudnnSupport::DoConvolveBackwardData( + Stream* stream, const FilterDescriptor& filter_descriptor, + const DeviceMemory& filter_data, + const BatchDescriptor& output_descriptor, + DeviceMemory backward_output_data, + const ConvolutionDescriptor& convolution_descriptor, + const BatchDescriptor& input_descriptor, + DeviceMemory* backward_input_data, + ScratchAllocator* scratch_allocator, + const dnn::AlgorithmConfig& algorithm_config, + dnn::ProfileResult* output_profile_result) { + return DoConvolveBackwardDataImpl(stream, filter_descriptor, filter_data, + output_descriptor, backward_output_data, + convolution_descriptor, input_descriptor, + backward_input_data, scratch_allocator, + algorithm_config, output_profile_result); +} + bool CudnnSupport::DoConvolveBackwardData( Stream* stream, const FilterDescriptor& filter_descriptor, const DeviceMemory& filter_data, - const BatchDescriptor& output_descriptor_in, + const BatchDescriptor& output_descriptor, DeviceMemory backward_output_data, const ConvolutionDescriptor& convolution_descriptor, const BatchDescriptor& input_descriptor, @@ -3356,7 +3425,7 @@ bool CudnnSupport::DoConvolveBackwardData( const dnn::AlgorithmConfig& algorithm_config, dnn::ProfileResult* output_profile_result) { return DoConvolveBackwardDataImpl(stream, filter_descriptor, filter_data, - output_descriptor_in, backward_output_data, + output_descriptor, backward_output_data, convolution_descriptor, input_descriptor, backward_input_data, scratch_allocator, algorithm_config, output_profile_result); @@ -3365,7 +3434,7 @@ bool CudnnSupport::DoConvolveBackwardData( bool CudnnSupport::DoConvolveBackwardData( Stream* stream, const FilterDescriptor& filter_descriptor, const DeviceMemory& filter_data, - const BatchDescriptor& output_descriptor_in, + const BatchDescriptor& output_descriptor, DeviceMemory backward_output_data, const ConvolutionDescriptor& convolution_descriptor, const BatchDescriptor& input_descriptor, @@ -3374,7 +3443,7 @@ bool CudnnSupport::DoConvolveBackwardData( const dnn::AlgorithmConfig& algorithm_config, dnn::ProfileResult* output_profile_result) { return DoConvolveBackwardDataImpl(stream, filter_descriptor, filter_data, - output_descriptor_in, backward_output_data, + output_descriptor, backward_output_data, convolution_descriptor, input_descriptor, backward_input_data, scratch_allocator, algorithm_config, output_profile_result); @@ -3398,10 +3467,17 @@ bool CudnnSupport::DoConvolveBackwardFilterImpl( LOG(FATAL) << "failed to set stream for cudnn handle: " << ToString(status); } + cudnnDataType_t cudnn_type = GetCudnnDataType(); // Alpha is the scaling factor for input. - float alpha = 1.0; + float falpha = 1.0; + double dalpha = 1.0; + void* alpha = cudnn_type == CUDNN_DATA_DOUBLE ? static_cast(&dalpha) + : static_cast(&falpha); // Beta is the scaling factor for output. - float beta = 0.0; + float fbeta = 0.0; + double dbeta = 0.0; + void* beta = cudnn_type == CUDNN_DATA_DOUBLE ? static_cast(&dbeta) + : static_cast(&fbeta); // TBD(keveman): remove once cuDNN supports kBatchYXDepth for backward pass. BatchDescriptor output_descriptor; @@ -3410,7 +3486,6 @@ bool CudnnSupport::DoConvolveBackwardFilterImpl( backward_output_data = MaybeTransformLayout( stream, &output_descriptor, backward_output_data, &transform_scratch); - cudnnDataType_t cudnn_type = GetCudnnDataType(); ScopedTensorDescriptor out_back_nd{parent_, output_descriptor, cudnn_type}; ScopedTensorDescriptor input_nd{parent_, input_descriptor, cudnn_type}; ScopedFilterDescriptor filter{parent_, filter_descriptor, input_descriptor, @@ -3557,7 +3632,7 @@ bool CudnnSupport::DoConvolveBackwardFilterImpl( #else status = wrap::cudnnConvolutionBackwardFilter_v3( #endif - parent_, ToHandle(dnn_handle_), /*alpha=*/&alpha, + parent_, ToHandle(dnn_handle_), /*alpha=*/alpha, /*srcDesc=*/input_nd.handle(), /*srcData=*/input_data.opaque(), /*diffDesc=*/out_back_nd.handle(), @@ -3566,7 +3641,7 @@ bool CudnnSupport::DoConvolveBackwardFilterImpl( /*algo=*/algo, /*workSpace=*/scratch.opaque(), /*workSpaceSizeInBytes=*/scratch.size(), - /*beta=*/&beta, + /*beta=*/beta, /*gradDesc=*/filter.handle(), /*gradData=*/backward_filter_data->opaque()); @@ -3592,10 +3667,28 @@ bool CudnnSupport::DoConvolveBackwardFilterImpl( return 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, + ScratchAllocator* scratch_allocator, + const dnn::AlgorithmConfig& algorithm_config, + dnn::ProfileResult* output_profile_result) { + return DoConvolveBackwardFilterImpl(stream, input_descriptor, input_data, + output_descriptor, backward_output_data, + convolution_descriptor, filter_descriptor, + backward_filter_data, scratch_allocator, + algorithm_config, output_profile_result); +} + bool CudnnSupport::DoConvolveBackwardFilter( Stream* stream, const dnn::BatchDescriptor& input_descriptor, const DeviceMemory& input_data, - const dnn::BatchDescriptor& output_descriptor_in, + const dnn::BatchDescriptor& output_descriptor, DeviceMemory backward_output_data, const dnn::ConvolutionDescriptor& convolution_descriptor, const dnn::FilterDescriptor& filter_descriptor, @@ -3603,17 +3696,17 @@ bool CudnnSupport::DoConvolveBackwardFilter( ScratchAllocator* scratch_allocator, const dnn::AlgorithmConfig& algorithm_config, dnn::ProfileResult* output_profile_result) { - return DoConvolveBackwardFilterImpl( - stream, input_descriptor, input_data, output_descriptor_in, - backward_output_data, convolution_descriptor, filter_descriptor, - backward_filter_data, scratch_allocator, algorithm_config, - output_profile_result); + return DoConvolveBackwardFilterImpl(stream, input_descriptor, input_data, + output_descriptor, backward_output_data, + convolution_descriptor, filter_descriptor, + backward_filter_data, scratch_allocator, + algorithm_config, output_profile_result); } bool CudnnSupport::DoConvolveBackwardFilter( Stream* stream, const dnn::BatchDescriptor& input_descriptor, const DeviceMemory& input_data, - const dnn::BatchDescriptor& output_descriptor_in, + const dnn::BatchDescriptor& output_descriptor, DeviceMemory backward_output_data, const dnn::ConvolutionDescriptor& convolution_descriptor, const dnn::FilterDescriptor& filter_descriptor, @@ -3621,11 +3714,11 @@ bool CudnnSupport::DoConvolveBackwardFilter( ScratchAllocator* scratch_allocator, const dnn::AlgorithmConfig& algorithm_config, dnn::ProfileResult* output_profile_result) { - return DoConvolveBackwardFilterImpl( - stream, input_descriptor, input_data, output_descriptor_in, - backward_output_data, convolution_descriptor, filter_descriptor, - backward_filter_data, scratch_allocator, algorithm_config, - output_profile_result); + return DoConvolveBackwardFilterImpl(stream, input_descriptor, input_data, + output_descriptor, backward_output_data, + convolution_descriptor, filter_descriptor, + backward_filter_data, scratch_allocator, + algorithm_config, output_profile_result); } template diff --git a/tensorflow/stream_executor/cuda/cuda_dnn.h b/tensorflow/stream_executor/cuda/cuda_dnn.h index 40aa974dd967df50075da6f2bb34439cd238a113..48d56f71e3195a897b6216ab9f5709326d1b86d3 100644 --- a/tensorflow/stream_executor/cuda/cuda_dnn.h +++ b/tensorflow/stream_executor/cuda/cuda_dnn.h @@ -259,7 +259,10 @@ class CudnnSupport : public dnn::DnnSupport { const DeviceMemory& filter_data, const dnn::ConvolutionDescriptor& convolution_descriptor, const dnn::BatchDescriptor& output_descriptor, - DeviceMemory* output_data) override; + DeviceMemory* output_data, + ScratchAllocator* scratch_allocator, + const dnn::AlgorithmConfig& algorithm_config, + dnn::ProfileResult* output_profile_result) override; bool DoConvolve(Stream* stream, const dnn::BatchDescriptor& batch_descriptor, const DeviceMemory& input_data, @@ -371,6 +374,18 @@ class CudnnSupport : public dnn::DnnSupport { return false; } + 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, + ScratchAllocator* scratch_allocator, + const dnn::AlgorithmConfig& algorithm_config, + dnn::ProfileResult* output_profile_result) override; + bool DoConvolveBackwardData( Stream* stream, const dnn::FilterDescriptor& filter_descriptor, const DeviceMemory& filter_data, @@ -395,6 +410,18 @@ class CudnnSupport : public dnn::DnnSupport { const dnn::AlgorithmConfig& algorithm_config, dnn::ProfileResult* output_profile_result) override; + bool 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, + ScratchAllocator* scratch_allocator, + const dnn::AlgorithmConfig& algorithm_config, + dnn::ProfileResult* output_profile_result) override; + bool DoConvolveBackwardFilter( Stream* stream, const dnn::BatchDescriptor& input_descriptor, const DeviceMemory& input_data, diff --git a/tensorflow/stream_executor/dnn.h b/tensorflow/stream_executor/dnn.h index aa88fe770f3596e5da5e12705c3b706365382134..b41536e638873412a31a0cdbbd3ba3a818dd9cf2 100644 --- a/tensorflow/stream_executor/dnn.h +++ b/tensorflow/stream_executor/dnn.h @@ -1172,7 +1172,9 @@ class DnnSupport { const DeviceMemory& filter_data, const dnn::ConvolutionDescriptor& convolution_descriptor, const dnn::BatchDescriptor& output_descriptor, - DeviceMemory* output_data) = 0; + 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. // See DoConvolve above for argument details. @@ -1273,6 +1275,18 @@ class DnnSupport { bool with_winograd_nonfused, int cc_major, int cc_minor, std::vector* out_algorithms); + 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 BatchDescriptor& input_descriptor, + DeviceMemory* backward_input_data, + ScratchAllocator* scratch_allocator, + const dnn::AlgorithmConfig& algorithm_config, + ProfileResult* output_profile_result) = 0; + virtual bool DoConvolveBackwardData( Stream* stream, const FilterDescriptor& filter_descriptor, const DeviceMemory& filter_data, @@ -1322,6 +1336,18 @@ class DnnSupport { bool with_winograd_nonfused, int cc_major, int cc_minor, std::vector* out_algorithms); + virtual bool DoConvolveBackwardFilter( + 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, + ProfileResult* output_profile_result) = 0; + virtual bool DoConvolveBackwardFilter( Stream* stream, const BatchDescriptor& input_descriptor, const DeviceMemory& input_data, diff --git a/tensorflow/stream_executor/multi_platform_manager.cc b/tensorflow/stream_executor/multi_platform_manager.cc index f23224ae772b9c5915426feaef1155fc9711f075..f9f3737a06dad3f146ef9fc8e2ec50160b3a01b5 100644 --- a/tensorflow/stream_executor/multi_platform_manager.cc +++ b/tensorflow/stream_executor/multi_platform_manager.cc @@ -23,11 +23,37 @@ limitations under the License. namespace perftools { namespace gputools { +/* static */ mutex MultiPlatformManager::platforms_mutex_{LINKER_INITIALIZED}; + +/* static */ port::StatusOr MultiPlatformManager::LookupByNameLocked( + const string& target) { + PlatformMap* platform_map = GetPlatformMap(); + auto it = platform_map->find(port::Lowercase(target)); + if (it == platform_map->end()) { + return port::Status( + port::error::NOT_FOUND, + "could not find registered platform with name: \"" + target + "\""); + } + return it->second; +} + +/* static */ port::StatusOr MultiPlatformManager::LookupByIdLocked( + const Platform::Id& id) { + PlatformIdMap* platform_map = GetPlatformByIdMap(); + auto it = platform_map->find(id); + if (it == platform_map->end()) { + return port::Status( + port::error::NOT_FOUND, + port::Printf("could not find registered platform with id: 0x%p", id)); + } + return it->second; +} + /* static */ port::Status MultiPlatformManager::RegisterPlatform( std::unique_ptr platform) { CHECK(platform != nullptr); string key = port::Lowercase(platform->Name()); - mutex_lock lock(GetPlatformsMutex()); + mutex_lock lock(platforms_mutex_); if (GetPlatformMap()->find(key) != GetPlatformMap()->end()) { return port::Status(port::error::INTERNAL, "platform is already registered with name: \"" + @@ -45,33 +71,63 @@ namespace gputools { /* static */ port::StatusOr MultiPlatformManager::PlatformWithName( const string& target) { - tf_shared_lock lock(GetPlatformsMutex()); - auto it = GetPlatformMap()->find(port::Lowercase(target)); + mutex_lock lock(platforms_mutex_); - if (it == GetPlatformMap()->end()) { - return port::Status( - port::error::NOT_FOUND, - "could not find registered platform with name: \"" + target + "\""); + SE_ASSIGN_OR_RETURN(Platform * platform, LookupByNameLocked(target)); + if (!platform->Initialized()) { + SE_RETURN_IF_ERROR(platform->Initialize({})); } - return it->second; + return platform; } /* static */ port::StatusOr MultiPlatformManager::PlatformWithId( const Platform::Id& id) { - tf_shared_lock lock(GetPlatformsMutex()); - auto it = GetPlatformByIdMap()->find(id); - if (it == GetPlatformByIdMap()->end()) { + mutex_lock lock(platforms_mutex_); + + SE_ASSIGN_OR_RETURN(Platform * platform, LookupByIdLocked(id)); + if (!platform->Initialized()) { + SE_RETURN_IF_ERROR(platform->Initialize({})); + } + + return platform; +} + +/* static */ port::StatusOr +MultiPlatformManager::InitializePlatformWithName( + const string& target, const std::map& options) { + mutex_lock lock(platforms_mutex_); + + SE_ASSIGN_OR_RETURN(Platform * platform, LookupByNameLocked(target)); + if (platform->Initialized()) { + return port::Status(port::error::FAILED_PRECONDITION, + "platform \"" + target + "\" is already initialized"); + } + + SE_RETURN_IF_ERROR(platform->Initialize(options)); + + return platform; +} + +/* static */ port::StatusOr +MultiPlatformManager::InitializePlatformWithId( + const Platform::Id& id, const std::map& options) { + mutex_lock lock(platforms_mutex_); + + SE_ASSIGN_OR_RETURN(Platform * platform, LookupByIdLocked(id)); + if (platform->Initialized()) { return port::Status( - port::error::NOT_FOUND, - port::Printf("could not find registered platform with id: 0x%p", id)); + port::error::FAILED_PRECONDITION, + port::Printf("platform with id 0x%p is already initialized", id)); } - return it->second; + SE_RETURN_IF_ERROR(platform->Initialize(options)); + + return platform; } /* static */ void MultiPlatformManager::ClearPlatformRegistry() { - mutex_lock lock(GetPlatformsMutex()); + mutex_lock lock(platforms_mutex_); GetPlatformMap()->clear(); GetPlatformByIdMap()->clear(); } diff --git a/tensorflow/stream_executor/multi_platform_manager.h b/tensorflow/stream_executor/multi_platform_manager.h index ea6155b4826439b98262530e70e6463eb1fda237..438653ee20bdb1fd83cd9e75c4bcd35af277cc28 100644 --- a/tensorflow/stream_executor/multi_platform_manager.h +++ b/tensorflow/stream_executor/multi_platform_manager.h @@ -67,13 +67,13 @@ limitations under the License. #include #include #include -#include "tensorflow/stream_executor/platform/port.h" #include "tensorflow/stream_executor/lib/status.h" #include "tensorflow/stream_executor/lib/statusor.h" #include "tensorflow/stream_executor/platform.h" #include "tensorflow/stream_executor/platform/mutex.h" #include "tensorflow/stream_executor/platform/port.h" +#include "tensorflow/stream_executor/platform/thread_annotations.h" namespace perftools { namespace gputools { @@ -85,26 +85,43 @@ class MultiPlatformManager { // already registered. The associated listener, if not null, will be used to // trace events for ALL executors for that platform. // Takes ownership of listener. - static port::Status RegisterPlatform(std::unique_ptr platform); + static port::Status RegisterPlatform(std::unique_ptr platform) + LOCKS_EXCLUDED(platforms_mutex_); - // Retrieves the platform registered with the given platform name; e.g. - // "CUDA", "OpenCL", ... + // Retrieves the platform registered with the given platform name (e.g. + // "CUDA", "OpenCL", ...) or id (an opaque, comparable value provided by the + // Platform's Id() method). + // + // If the platform has not already been initialized, it will be initialized + // with a default set of parameters. // // If the requested platform is not registered, an error status is returned. // Ownership of the platform is NOT transferred to the caller -- // the MultiPlatformManager owns the platforms in a singleton-like fashion. - static port::StatusOr PlatformWithName(const string& target); - - // Retrieves the platform registered with the given platform ID, which - // is an opaque (but comparable) value. + static port::StatusOr PlatformWithName(const string& target) + LOCKS_EXCLUDED(platforms_mutex_); + static port::StatusOr PlatformWithId(const Platform::Id& id) + LOCKS_EXCLUDED(platforms_mutex_); + + // Retrieves the platform registered with the given platform name (e.g. + // "CUDA", "OpenCL", ...) or id (an opaque, comparable value provided by the + // Platform's Id() method). + // + // The platform will be initialized with the given options. If the platform + // was already initialized, an error will be returned. // // If the requested platform is not registered, an error status is returned. // Ownership of the platform is NOT transferred to the caller -- // the MultiPlatformManager owns the platforms in a singleton-like fashion. - static port::StatusOr PlatformWithId(const Platform::Id& id); + static port::StatusOr InitializePlatformWithName( + const string& target, const std::map& options) + LOCKS_EXCLUDED(platforms_mutex_); + static port::StatusOr InitializePlatformWithId( + const Platform::Id& id, const std::map& options) + LOCKS_EXCLUDED(platforms_mutex_); // Clears the set of registered platforms, primarily used for testing. - static void ClearPlatformRegistry(); + static void ClearPlatformRegistry() LOCKS_EXCLUDED(platforms_mutex_); // Although the MultiPlatformManager "owns" its platforms, it holds them as // undecorated pointers to prevent races during program exit (between this @@ -122,17 +139,16 @@ class MultiPlatformManager { // Provides access to the available set of platforms under a lock. static port::Status WithPlatforms( - std::function callback) { - mutex_lock lock(GetPlatformsMutex()); + std::function callback) + LOCKS_EXCLUDED(platforms_mutex_) { + mutex_lock lock(platforms_mutex_); return callback(GetPlatformMap()); } private: - // mutex that guards the platform map. - static mutex& GetPlatformsMutex() { - static mutex* platforms_mutex = new mutex; - return *platforms_mutex; - } + using PlatformIdMap = std::map; + + static mutex platforms_mutex_; // TODO(b/22689637): Clean up these two maps; make sure they coexist nicely. // TODO(b/22689637): Move this (whatever the final/"official" map is) to @@ -147,12 +163,21 @@ class MultiPlatformManager { // Holds a Platform::Id-to-object mapping. // Unlike platforms_ above, this map does not own its contents. - static std::map* GetPlatformByIdMap() { - using PlatformIdMap = std::map; + static PlatformIdMap* GetPlatformByIdMap() { static PlatformIdMap* instance = new PlatformIdMap; return instance; } + // Looks up the platform object with the given name. Assumes the Platforms + // mutex is held. + static port::StatusOr LookupByNameLocked(const string& target) + EXCLUSIVE_LOCKS_REQUIRED(platforms_mutex_); + + // Looks up the platform object with the given id. Assumes the Platforms + // mutex is held. + static port::StatusOr LookupByIdLocked(const Platform::Id& id) + EXCLUSIVE_LOCKS_REQUIRED(platforms_mutex_); + SE_DISALLOW_COPY_AND_ASSIGN(MultiPlatformManager); }; diff --git a/tensorflow/stream_executor/platform.cc b/tensorflow/stream_executor/platform.cc index 93f08d06dae862f24b5b533395af63139f344f77..4cdc22bd16a3ea66037696f6a9d70bcb86ef5ebb 100644 --- a/tensorflow/stream_executor/platform.cc +++ b/tensorflow/stream_executor/platform.cc @@ -85,6 +85,17 @@ StreamExecutorConfig::StreamExecutorConfig(int ordinal_in) Platform::~Platform() {} +bool Platform::Initialized() const { return true; } + +port::Status Platform::Initialize( + const std::map &platform_options) { + if (!platform_options.empty()) { + return port::Status(port::error::UNIMPLEMENTED, + "this platform does not support custom initialization"); + } + return port::Status::OK(); +} + port::Status Platform::ForceExecutorShutdown() { return port::Status(port::error::UNIMPLEMENTED, "executor shutdown is not supported on this platform"); diff --git a/tensorflow/stream_executor/platform.h b/tensorflow/stream_executor/platform.h index f0a0e60e02f951018b39ef831cd2f7dd3256f87d..54f8aa86c269ff0d32648e1d4629179cafd5be76 100644 --- a/tensorflow/stream_executor/platform.h +++ b/tensorflow/stream_executor/platform.h @@ -111,6 +111,9 @@ class Platform { // Returns a key uniquely identifying this platform. virtual Id id() const = 0; + // Name of this platform. + virtual const string& Name() const = 0; + // Returns the number of devices accessible on this platform. // // Note that, though these devices are visible, if there is only one userspace @@ -118,8 +121,17 @@ class Platform { // device, a call to ExecutorForDevice may return an error status. virtual int VisibleDeviceCount() const = 0; - // Name of this platform. - virtual const string& Name() const = 0; + // Returns true iff the platform has been initialized. + virtual bool Initialized() const; + + // Initializes the platform with a custom set of options. The platform must be + // initialized before obtaining StreamExecutor objects. The interpretation of + // the platform_options argument is implementation specific. This method may + // return an error if unrecognized options are provided. If using + // MultiPlatformManager, this method will be called automatically by + // InitializePlatformWithId/InitializePlatformWithName. + virtual port::Status Initialize( + const std::map& platform_options); // Returns a device with the given ordinal on this platform with a default // plugin configuration or, if none can be found with the given ordinal or @@ -156,6 +168,8 @@ class Platform { // This is only useful on platforms which bind a device to a single process // that has obtained the device context. May return UNIMPLEMENTED on platforms // that have no reason to destroy device contexts. + // + // The platform must be reinitialized after this is called. virtual port::Status ForceExecutorShutdown(); // Registers a TraceListener to listen to all StreamExecutors for this diff --git a/tensorflow/stream_executor/stream.cc b/tensorflow/stream_executor/stream.cc index ba5001e273632c893b05eea64542f1b156e28c47..6bbb5f0b2e728a855b64beb33d6e28b1b3b399c3 100644 --- a/tensorflow/stream_executor/stream.cc +++ b/tensorflow/stream_executor/stream.cc @@ -681,6 +681,37 @@ Stream &Stream::ThenFusedConvolveWithAlgorithm( return *this; } +Stream &Stream::ThenConvolveWithAlgorithm( + 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, + ScratchAllocator *scratch_allocator, + const dnn::AlgorithmConfig &algorithm_config, + dnn::ProfileResult *output_profile_result) { + VLOG_CALL(PARAM(input_descriptor), PARAM(input_data), + PARAM(filter_descriptor), PARAM(filter_data), + PARAM(convolution_descriptor), PARAM(output_descriptor), + PARAM(output), PARAM(algorithm_config)); + + if (ok()) { + if (dnn::DnnSupport *dnn = parent_->AsDnn()) { + auto status = dnn->DoConvolve( + this, input_descriptor, input_data, filter_descriptor, filter_data, + convolution_descriptor, output_descriptor, output, scratch_allocator, + algorithm_config, output_profile_result); + if (!status && !output_profile_result) { + SetError(); + } + } else { + SetErrorAndLogNoDnnSupport(); + } + } + return *this; +} + Stream &Stream::ThenConvolveWithAlgorithm( const dnn::BatchDescriptor &input_descriptor, const DeviceMemory &input_data, @@ -890,6 +921,39 @@ Stream &Stream::ThenConvolveBackwardDataWithScratch( return *this; } +Stream &Stream::ThenConvolveBackwardDataWithAlgorithm( + 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::ProfileResult *output_profile_result) { + VLOG_CALL(PARAM(filter_descriptor), PARAM(filter_data), + PARAM(output_descriptor), PARAM(backward_output_data), + PARAM(convolution_descriptor), PARAM(input_descriptor), + PARAM(backward_input_data)); + + if (ok()) { + if (dnn::DnnSupport *dnn = parent_->AsDnn()) { + 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, + output_profile_result); + if (!status && !output_profile_result) { + SetError(); + } + } else { + SetErrorAndLogNoDnnSupport(); + } + } + return *this; +} + Stream &Stream::ThenConvolveBackwardDataWithAlgorithm( const dnn::FilterDescriptor &filter_descriptor, const DeviceMemory &filter_data, @@ -1026,6 +1090,39 @@ Stream &Stream::ThenConvolveBackwardFilterWithScratch( return *this; } +Stream &Stream::ThenConvolveBackwardFilterWithAlgorithm( + 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::ProfileResult *output_profile_result) { + VLOG_CALL(PARAM(input_descriptor), PARAM(input_data), + PARAM(output_descriptor), PARAM(backward_output_data), + PARAM(convolution_descriptor), PARAM(filter_descriptor), + PARAM(backward_filter_data)); + + if (ok()) { + if (dnn::DnnSupport *dnn = parent_->AsDnn()) { + 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, + output_profile_result); + if (!status && !output_profile_result) { + SetError(); + } + } else { + SetErrorAndLogNoDnnSupport(); + } + } + return *this; +} + Stream &Stream::ThenConvolveBackwardFilterWithAlgorithm( const dnn::BatchDescriptor &input_descriptor, const DeviceMemory &input_data, @@ -4923,12 +5020,6 @@ Stream &Stream::ThenTransformTensor(const dnn::BatchDescriptor &input_desc, return *this; } -Stream &Stream::ThenDoHostCallbackForTest(std::function callback) { - VLOG_CALL(PARAM(callback)); - - return ThenDoHostCallback(callback); -} - Stream &Stream::ThenDoHostCallback(std::function callback) { VLOG_CALL(PARAM(callback)); diff --git a/tensorflow/stream_executor/stream.h b/tensorflow/stream_executor/stream.h index a2fb2ea2375d0f245ae3bf3ccb04803d01663def..d7d11315699b85cae4d479b79bc8fc2717b2d8fb 100644 --- a/tensorflow/stream_executor/stream.h +++ b/tensorflow/stream_executor/stream.h @@ -358,6 +358,17 @@ class Stream { const dnn::BatchDescriptor &output_descriptor, DeviceMemory *output, ScratchAllocator *scratch_allocator); + Stream &ThenConvolveWithAlgorithm( + 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, ScratchAllocator *scratch_allocator, + const dnn::AlgorithmConfig &algorithm_config, + dnn::ProfileResult *output_profile_result); + Stream &ThenConvolveWithAlgorithm( const dnn::BatchDescriptor &input_descriptor, const DeviceMemory &input_data, @@ -476,6 +487,18 @@ class Stream { DeviceMemory *backward_input_data, ScratchAllocator *scratch_allocator); + Stream &ThenConvolveBackwardDataWithAlgorithm( + 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::ProfileResult *output_profile_result); + Stream &ThenConvolveBackwardDataWithAlgorithm( const dnn::FilterDescriptor &filter_descriptor, const DeviceMemory &filter_data, @@ -529,6 +552,18 @@ class Stream { DeviceMemory *backward_filter_data, ScratchAllocator *scratch_allocator); + Stream &ThenConvolveBackwardFilterWithAlgorithm( + 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::ProfileResult *output_profile_result); + Stream &ThenConvolveBackwardFilterWithAlgorithm( const dnn::BatchDescriptor &input_descriptor, const DeviceMemory &input_data, @@ -1933,16 +1968,15 @@ class Stream { // Entrains onto the stream a callback to the host (from the device). // Host callbacks block/occupy the stream just as device functions // (execute one at a time, block later stream operations). + // // Behavior is undefined when synchronizing using OpenCL user events. // Behavior is undefined if host callbacks call device routines or insert // them into any stream. + // // On certain platforms, ThenDoHostCallback is expected to have significant // negative effects on performance. Stream &ThenDoHostCallback(std::function callback); - // Identical to ThenDoHostCallback; only exposed for testing purposes. - Stream &ThenDoHostCallbackForTest(std::function callback); - // Returns the StreamExecutor (parent object) associated with this stream. StreamExecutor *parent() const { CHECK(parent_ != nullptr); diff --git a/tensorflow/tensorflow.bzl b/tensorflow/tensorflow.bzl index 818d67f7b5be1e8f2db66b24976a529b361a4990..51ef3235b7fa41342a93102a9fa442941c071226 100644 --- a/tensorflow/tensorflow.bzl +++ b/tensorflow/tensorflow.bzl @@ -1185,6 +1185,22 @@ def tf_custom_op_library_additional_deps(): "@nsync//:nsync_headers", clean_dep("//third_party/eigen3"), clean_dep("//tensorflow/core:framework_headers_lib"), + ] + if_windows(["//tensorflow/python:pywrap_tensorflow_import_lib"]) + +# A list of targets that contains the implemenation of +# tf_custom_op_library_additional_deps. It's used to generate a DEF file for +# exporting symbols from _pywrap_tensorflow.dll on Windows. +def tf_custom_op_library_additional_deps_impl(): + return [ + # for @protobuf_archive//:protobuf_headers + "@protobuf_archive//:protobuf", + # for @nsync//:nsync_headers + "@nsync//:nsync_cpp", + # for //third_party/eigen3 + clean_dep("//third_party/eigen3"), + # for //tensorflow/core:framework_headers_lib + clean_dep("//tensorflow/core:framework"), + clean_dep("//tensorflow/core:reader_base"), ] # Traverse the dependency graph along the "deps" attribute of the @@ -1271,6 +1287,7 @@ def tf_custom_op_library(name, srcs=[], gpu_srcs=[], deps=[], linkopts=[]): deps=deps + if_cuda(cuda_deps), data=[name + "_check_deps"], copts=tf_copts(is_external=True), + features = ["windows_export_all_symbols"], linkopts=linkopts + select({ "//conditions:default": [ "-lm", @@ -1417,7 +1434,8 @@ def tf_py_wrap_cc(name, ]) + tf_extension_copts()), linkopts=tf_extension_linkopts() + extra_linkopts, linkstatic=1, - deps=deps + extra_deps) + deps=deps + extra_deps, + **kwargs) native.genrule( name="gen_" + cc_library_pyd_name, srcs=[":" + cc_library_name], diff --git a/tensorflow/tools/api/generator/BUILD b/tensorflow/tools/api/generator/BUILD index e731127a63d792825e15a4b95379517117edebb0..14ce8dbeb3614b177bf4424c3e00ae7dd17f72f0 100644 --- a/tensorflow/tools/api/generator/BUILD +++ b/tensorflow/tools/api/generator/BUILD @@ -1,5 +1,6 @@ # Description: # Scripts used to generate TensorFlow Python API. + licenses(["notice"]) # Apache 2.0 exports_files(["LICENSE"]) @@ -21,7 +22,7 @@ py_binary( srcs = ["create_python_api.py"], srcs_version = "PY2AND3", deps = [ - "//tensorflow:tensorflow_py", + "//tensorflow/python", ], ) @@ -80,6 +81,7 @@ genrule( "api/keras/datasets/boston_housing/__init__.py", "api/keras/datasets/cifar10/__init__.py", "api/keras/datasets/cifar100/__init__.py", + "api/keras/datasets/fashion_mnist/__init__.py", "api/keras/datasets/imdb/__init__.py", "api/keras/datasets/mnist/__init__.py", "api/keras/datasets/reuters/__init__.py", @@ -102,6 +104,7 @@ genrule( "api/linalg/__init__.py", "api/logging/__init__.py", "api/losses/__init__.py", + "api/manip/__init__.py", "api/metrics/__init__.py", "api/nn/__init__.py", "api/nn/rnn_cell/__init__.py", @@ -133,7 +136,9 @@ py_library( name = "python_api", srcs = [":python_api_gen"], srcs_version = "PY2AND3", + visibility = ["//tensorflow:__subpackages__"], deps = [ "//tensorflow/contrib:contrib_py", # keep + "//tensorflow/python", # keep ], ) diff --git a/tensorflow/tools/api/generator/create_python_api.py b/tensorflow/tools/api/generator/create_python_api.py index 1557314939bd85c0467426216f90aa3891ca0ac0..bb7c3e77a3a82b63188fc5a4a21706da7a276fde 100644 --- a/tensorflow/tools/api/generator/create_python_api.py +++ b/tensorflow/tools/api/generator/create_python_api.py @@ -23,15 +23,14 @@ import collections import os import sys -# This import is needed so that we can traverse over TensorFlow modules. -import tensorflow as tf # pylint: disable=unused-import +from tensorflow import python as tf from tensorflow.python.util import tf_decorator _API_CONSTANTS_ATTR = '_tf_api_constants' _API_NAMES_ATTR = '_tf_api_names' _API_DIR = '/api/' -_CONTRIB_IMPORT = 'from tensorflow import contrib' +_OUTPUT_MODULE = 'tensorflow.tools.api.generator.api' _GENERATED_FILE_HEADER = """\"\"\"Imports for Python API. This file is MACHINE GENERATED! Do not edit. @@ -92,7 +91,7 @@ def get_api_imports(): if module_contents_name == _API_CONSTANTS_ATTR: for exports, value in attr: for export in exports: - names = ['tf'] + export.split('.') + names = export.split('.') dest_module = '.'.join(names[:-1]) import_str = format_import(module.__name__, value, names[-1]) module_imports[dest_module].append(import_str) @@ -104,29 +103,43 @@ def get_api_imports(): if hasattr(attr, '__dict__') and _API_NAMES_ATTR in attr.__dict__: # The same op might be accessible from multiple modules. # We only want to consider location where function was defined. - if attr.__module__ != module.__name__: + # Here we check if the op is defined in another TensorFlow module in + # sys.modules. + if (hasattr(attr, '__module__') and + attr.__module__.startswith(tf.__name__) and + attr.__module__ != module.__name__ and + attr.__module__ in sys.modules and + module_contents_name in dir(sys.modules[attr.__module__])): continue for export in attr._tf_api_names: # pylint: disable=protected-access - names = ['tf'] + export.split('.') + names = export.split('.') dest_module = '.'.join(names[:-1]) import_str = format_import( module.__name__, module_contents_name, names[-1]) module_imports[dest_module].append(import_str) # Import all required modules in their parent modules. - # For e.g. if we import 'tf.foo.bar.Value'. Then, we also - # import 'bar' in 'tf.foo'. - dest_modules = set(module_imports.keys()) - for dest_module in dest_modules: - dest_module_split = dest_module.split('.') - for dest_submodule_index in range(1, len(dest_module_split)): - dest_submodule = '.'.join(dest_module_split[:dest_submodule_index]) + # For e.g. if we import 'foo.bar.Value'. Then, we also + # import 'bar' in 'foo'. + imported_modules = set(module_imports.keys()) + for module in imported_modules: + if not module: + continue + module_split = module.split('.') + parent_module = '' # we import submodules in their parent_module + + for submodule_index in range(len(module_split)): + import_from = _OUTPUT_MODULE + if submodule_index > 0: + parent_module += ('.' + module_split[submodule_index-1] if parent_module + else module_split[submodule_index-1]) + import_from += '.' + parent_module submodule_import = format_import( - '', dest_module_split[dest_submodule_index], - dest_module_split[dest_submodule_index]) - if submodule_import not in module_imports[dest_submodule]: - module_imports[dest_submodule].append(submodule_import) + import_from, module_split[submodule_index], + module_split[submodule_index]) + if submodule_import not in module_imports[parent_module]: + module_imports[parent_module].append(submodule_import) return module_imports @@ -151,8 +164,8 @@ def create_api_files(output_files): # First get module directory under _API_DIR. module_dir = os.path.dirname( output_file[output_file.rfind(_API_DIR)+len(_API_DIR):]) - # Convert / to . and prefix with tf. - module_name = '.'.join(['tf', module_dir.replace('/', '.')]).strip('.') + # Convert / to . + module_name = module_dir.replace('/', '.').strip('.') module_name_to_file_path[module_name] = output_file # Create file for each expected output in genrule. @@ -162,16 +175,14 @@ def create_api_files(output_files): open(file_path, 'a').close() module_imports = get_api_imports() - module_imports['tf'].append(_CONTRIB_IMPORT) # Include all of contrib. # Add imports to output files. missing_output_files = [] for module, exports in module_imports.items(): # Make sure genrule output file list is in sync with API exports. if module not in module_name_to_file_path: - module_without_tf = module[len('tf.'):] module_file_path = '"api/%s/__init__.py"' % ( - module_without_tf.replace('.', '/')) + module.replace('.', '/')) missing_output_files.append(module_file_path) continue with open(module_name_to_file_path[module], 'w') as fp: diff --git a/tensorflow/tools/api/golden/tensorflow.data.-dataset.pbtxt b/tensorflow/tools/api/golden/tensorflow.data.-dataset.pbtxt index 42de5c0c80023ad5bd7f33a564780060998307c1..0900adaf762df1415c8db63c3879ca2fabc28d9f 100644 --- a/tensorflow/tools/api/golden/tensorflow.data.-dataset.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.data.-dataset.pbtxt @@ -64,7 +64,7 @@ tf_class { } member_method { name: "list_files" - argspec: "args=[\'file_pattern\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'file_pattern\', \'shuffle\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "make_initializable_iterator" diff --git a/tensorflow/tools/api/golden/tensorflow.data.-fixed-length-record-dataset.pbtxt b/tensorflow/tools/api/golden/tensorflow.data.-fixed-length-record-dataset.pbtxt index e2fc8d6cb1d318cc50828f22e8e575cc28c7aaad..7b16ac90c925beb25e065d26e73ee2a54b06d9dc 100644 --- a/tensorflow/tools/api/golden/tensorflow.data.-fixed-length-record-dataset.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.data.-fixed-length-record-dataset.pbtxt @@ -65,7 +65,7 @@ tf_class { } member_method { name: "list_files" - argspec: "args=[\'file_pattern\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'file_pattern\', \'shuffle\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "make_initializable_iterator" diff --git a/tensorflow/tools/api/golden/tensorflow.data.-t-f-record-dataset.pbtxt b/tensorflow/tools/api/golden/tensorflow.data.-t-f-record-dataset.pbtxt index 9770389e5ef1e29a80ae1da2725d9862f6521ff9..9cf5f2ae2057ab4a16131527cf2ef2fa6ada28e5 100644 --- a/tensorflow/tools/api/golden/tensorflow.data.-t-f-record-dataset.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.data.-t-f-record-dataset.pbtxt @@ -17,7 +17,7 @@ tf_class { } member_method { name: "__init__" - argspec: "args=[\'self\', \'filenames\', \'compression_type\', \'buffer_size\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + argspec: "args=[\'self\', \'filenames\', \'compression_type\', \'buffer_size\', \'num_parallel_reads\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], " } member_method { name: "apply" @@ -65,7 +65,7 @@ tf_class { } member_method { name: "list_files" - argspec: "args=[\'file_pattern\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'file_pattern\', \'shuffle\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "make_initializable_iterator" diff --git a/tensorflow/tools/api/golden/tensorflow.data.-text-line-dataset.pbtxt b/tensorflow/tools/api/golden/tensorflow.data.-text-line-dataset.pbtxt index 7263230c1c7182bb812cb2e433aedd415bcd16c7..8c3d6691439e619c906996a3ddaea4317c4a9597 100644 --- a/tensorflow/tools/api/golden/tensorflow.data.-text-line-dataset.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.data.-text-line-dataset.pbtxt @@ -65,7 +65,7 @@ tf_class { } member_method { name: "list_files" - argspec: "args=[\'file_pattern\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'file_pattern\', \'shuffle\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "make_initializable_iterator" diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-vocab-info.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-vocab-info.pbtxt index a16e3aedae96e7289e73c49ac7890550dd5ddb08..5301b94eb361251a1cb4d02a5d8168f7c8191045 100644 --- a/tensorflow/tools/api/golden/tensorflow.estimator.-vocab-info.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.estimator.-vocab-info.pbtxt @@ -1,7 +1,7 @@ path: "tensorflow.estimator.VocabInfo" tf_class { - is_instance: "" - is_instance: "" + is_instance: "" + is_instance: "" is_instance: "" member { name: "backup_initializer" diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-warm-start-settings.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-warm-start-settings.pbtxt index afdd6bb058353594415cd1abe726070f84ae46b6..43f5343359aff3b856a2b3708e4cda7cec29e146 100644 --- a/tensorflow/tools/api/golden/tensorflow.estimator.-warm-start-settings.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.estimator.-warm-start-settings.pbtxt @@ -1,7 +1,7 @@ path: "tensorflow.estimator.WarmStartSettings" tf_class { - is_instance: "" - is_instance: "" + is_instance: "" + is_instance: "" is_instance: "" member { name: "ckpt_to_initialize_from" diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.export.-tensor-serving-input-receiver.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.export.-tensor-serving-input-receiver.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..4fe92643bf9867765499d7bf475b9cdd1686aec5 --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.estimator.export.-tensor-serving-input-receiver.pbtxt @@ -0,0 +1,27 @@ +path: "tensorflow.estimator.export.TensorServingInputReceiver" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "features" + mtype: "" + } + member { + name: "receiver_tensors" + mtype: "" + } + member { + name: "receiver_tensors_alternatives" + mtype: "" + } + member_method { + name: "__init__" + } + member_method { + name: "count" + } + member_method { + name: "index" + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.export.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.export.pbtxt index 4d0dddb3bc0305a28fab0c95c31e4869f5db0aa8..bd72f6cd79f7dffb9f0a7f8ae43751c4ecba939d 100644 --- a/tensorflow/tools/api/golden/tensorflow.estimator.export.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.estimator.export.pbtxt @@ -20,6 +20,10 @@ tf_module { name: "ServingInputReceiver" mtype: "" } + member { + name: "TensorServingInputReceiver" + mtype: "" + } member_method { name: "build_parsing_serving_input_receiver_fn" argspec: "args=[\'feature_spec\', \'default_batch_size\'], varargs=None, keywords=None, defaults=[\'None\'], " diff --git a/tensorflow/tools/api/golden/tensorflow.image.pbtxt b/tensorflow/tools/api/golden/tensorflow.image.pbtxt index bda1c2bf85977e69b0969bc8b6056710d88ca910..3fc64dae888012169af3ea7695154b73f24d90c8 100644 --- a/tensorflow/tools/api/golden/tensorflow.image.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.image.pbtxt @@ -100,6 +100,10 @@ tf_module { name: "hsv_to_rgb" argspec: "args=[\'images\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } + member_method { + name: "image_gradients" + argspec: "args=[\'image\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "is_jpeg" argspec: "args=[\'contents\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " @@ -116,6 +120,10 @@ tf_module { name: "per_image_standardization" argspec: "args=[\'image\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "psnr" + argspec: "args=[\'a\', \'b\', \'max_val\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } member_method { name: "random_brightness" argspec: "args=[\'image\', \'max_delta\', \'seed\'], varargs=None, keywords=None, defaults=[\'None\'], " @@ -188,6 +196,18 @@ tf_module { name: "sample_distorted_bounding_box" argspec: "args=[\'image_size\', \'bounding_boxes\', \'seed\', \'seed2\', \'min_object_covered\', \'aspect_ratio_range\', \'area_range\', \'max_attempts\', \'use_image_if_no_bounding_boxes\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'0.1\', \'None\', \'None\', \'None\', \'None\', \'None\'], " } + member_method { + name: "sobel_edges" + argspec: "args=[\'image\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "ssim" + argspec: "args=[\'img1\', \'img2\', \'max_val\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "ssim_multiscale" + argspec: "args=[\'img1\', \'img2\', \'max_val\', \'power_factors\'], varargs=None, keywords=None, defaults=[\'(0.0448, 0.2856, 0.3001, 0.2363, 0.1333)\'], " + } member_method { name: "total_variation" argspec: "args=[\'images\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " diff --git a/tensorflow/tools/api/golden/tensorflow.initializers.pbtxt b/tensorflow/tools/api/golden/tensorflow.initializers.pbtxt index 21a0f84d22fc2d06e551c9a709f3963e812333b8..eaf0036cacfadce335a84bcf61f47f9d360be7e2 100644 --- a/tensorflow/tools/api/golden/tensorflow.initializers.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.initializers.pbtxt @@ -1,17 +1,9 @@ path: "tensorflow.initializers" tf_module { - member { - name: "absolute_import" - mtype: "" - } member { name: "constant" mtype: "" } - member { - name: "division" - mtype: "" - } member { name: "identity" mtype: "" @@ -24,10 +16,6 @@ tf_module { name: "orthogonal" mtype: "" } - member { - name: "print_function" - mtype: "" - } member { name: "random_normal" mtype: "" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.-model.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.-model.pbtxt index 04724e3a1af9702e2e0eb8fd8b460d4cc7997a97..7be2f4f61f6b9637f372591e49efc0c93c7a8c0a 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.-model.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.-model.pbtxt @@ -1,9 +1,10 @@ path: "tensorflow.keras.Model" tf_class { is_instance: "" - is_instance: "" - is_instance: "" + is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.-sequential.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.-sequential.pbtxt index c94bd2faa4f3828a7b80388ad6504f7d10b630a1..0f2428d77a537959cf2c46dfa350208abea8cb36 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.-sequential.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.-sequential.pbtxt @@ -1,10 +1,11 @@ path: "tensorflow.keras.Sequential" tf_class { - is_instance: "" + is_instance: "" is_instance: "" - is_instance: "" - is_instance: "" + is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.datasets.fashion_mnist.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.datasets.fashion_mnist.pbtxt index 791cfda23345fea7df1cfb107ae5dec06354bd48..a0e14356fa5e91bc81bd89f6eb8c07087956c392 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.datasets.fashion_mnist.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.datasets.fashion_mnist.pbtxt @@ -1,3 +1,7 @@ path: "tensorflow.keras.datasets.fashion_mnist" tf_module { + member_method { + name: "load_data" + argspec: "args=[], varargs=None, keywords=None, defaults=None" + } } diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-activation.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-activation.pbtxt index f4ab075959906cdf350ec5d49dc86f928b7eb7ae..db8f626b98b70fd99f38e696aa16c72e74e86e25 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-activation.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-activation.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.Activation" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-activity-regularization.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-activity-regularization.pbtxt index eb558cddafc3972127786353072767f0d53bf174..809b3a5430449176a0d7423ec7f4499ceb620890 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-activity-regularization.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-activity-regularization.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.ActivityRegularization" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-add.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-add.pbtxt index 770a107b664d7ab0a8aedf292a34d4258a201859..68d41bb6cc258ca87d4664ac0fb9d5649f89ebaf 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-add.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-add.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.Add" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-alpha-dropout.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-alpha-dropout.pbtxt index 0ce42b706ec20a8ea1cc83ec95cb64d9be2e5710..970b777e514194db4ac49fe58bea737b35436217 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-alpha-dropout.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-alpha-dropout.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.AlphaDropout" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling1-d.pbtxt index d6c98fa225ce924bc8e20f8531516eaed4d32ffb..529c64ab293d596012aefd42e0695bd1eb7e44d1 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling1-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling2-d.pbtxt index 754fd310c6d8ddb994db0590342b29f8cb7abd71..7e7c330d74fe3b71ecd0eb87e34719e47ae70784 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling2-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling3-d.pbtxt index 9b62880c7931d151fb98cc1dc3149dcbd4dd103d..ada8466d7473072b1878861ab36ec40b07fa1914 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling3-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling3-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-average.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-average.pbtxt index b371ad148cee16dd243869d929e0c1c002794682..2a5c1cd530a7a532f6cdd3c184f4ee7eb88d23d3 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-average.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-average.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.Average" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool1-d.pbtxt index 3e2aba55fd63326bb0e232fdce06f32884db7a0a..9a2cb29815d59f3761ea25e9ea36ff6489c85b88 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool1-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool2-d.pbtxt index fb37308cce0124538648c3837e1e802794d7f1ae..f5e991ea42e5ee2723b64574d4598dc8463f1c8c 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool2-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool3-d.pbtxt index 813470ffc7c87727eb0b958e54806f530399806a..31732214a62524017e39776cdfb9ab629746e8ae 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool3-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool3-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-batch-normalization.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-batch-normalization.pbtxt index e251ac18e511b58a49816126d9941b98e4f91088..422eddf10db6763e10405dba5537ca161d1b8994 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-batch-normalization.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-batch-normalization.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.BatchNormalization" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-bidirectional.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-bidirectional.pbtxt index 699208a0b9b665b69f02edaa2b2d2aeed6a83b63..9053a37916314198842bc21b0608a9b69a64c264 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-bidirectional.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-bidirectional.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.Bidirectional" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-concatenate.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-concatenate.pbtxt index ff08def0a08e5201bc01d61be3f2d66d712c384b..3d536d2182fc4480a2ee5fba177543ca21fbd5ac 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-concatenate.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-concatenate.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.Concatenate" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv-l-s-t-m2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv-l-s-t-m2-d.pbtxt index 6db22ca0320519fd9c101456c9c9c0e26a9a11e0..6a7da1aef8db64ad11bb5a5ba357f33eeb99170b 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv-l-s-t-m2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv-l-s-t-m2-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv1-d.pbtxt index 577f206e3510a9995d5d383ac440b4f68ea39fe5..801a0339720919f8b3f6beee0f045d58b2c0a371 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv1-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv2-d-transpose.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv2-d-transpose.pbtxt index 72924c32b43e5edb39938cc0cd909cffefa61be1..13352e264a5305190717bb973a3f2bce4d7f4fff 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv2-d-transpose.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv2-d-transpose.pbtxt @@ -4,8 +4,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv2-d.pbtxt index 16be08d9b2bae8fe1faecf34c4d87ac9b9baf142..f400e4a15c362037e85ac375cee98bb5f6358669 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv2-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d-transpose.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d-transpose.pbtxt index 11e05f884d781166616a9c9a61dacbc8fdae6ae3..b3a9f573b8ba652d2544b21f36f65fe81a6ebb50 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d-transpose.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d-transpose.pbtxt @@ -4,8 +4,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d.pbtxt index 72b72d6b3b1e410dda0b0a529449f0135203fc1b..a9be09c0abd19aeb4df30116ef2befc3948bfbf4 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution1-d.pbtxt index ee93247f63ed700dc6058041bd0ea4ff5c879078..be1ef5eb928d16cc6bf78c289aa20d815c728b23 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution1-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution2-d-transpose.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution2-d-transpose.pbtxt index e5023287e5f38553f3553a37b5a908790072b5c7..30034f7eaf6d9073695353e5c8d9ead0cc8de7cc 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution2-d-transpose.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution2-d-transpose.pbtxt @@ -4,8 +4,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution2-d.pbtxt index ba38cb7121c9d312e7ba9d7147bdc67673d1ad2e..189b38054c004facfeeff8ad2ae87848b89040f2 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution2-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d-transpose.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d-transpose.pbtxt index 58724a1e1661609ef3c000c7ca1dfe9b3235acff..a76d85c629c1fe620dafd62a0f0e05e9009109e2 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d-transpose.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d-transpose.pbtxt @@ -4,8 +4,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d.pbtxt index 98d52c430c659d0fc3e9299f7bede9190dad2fcf..782195d4ad5883d8c0ea6a657cc10258f2080a55 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping1-d.pbtxt index 33b6ebe1af731f66f88a9493502f69049ab34b42..2cb7a39ea595e1ff699b96554cb135377d20a488 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping1-d.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.Cropping1D" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping2-d.pbtxt index 4b241ebb0f68c270a9448b02138d44f82211f418..80803306992bba3b601824a93cb3086ef3947369 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping2-d.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.Cropping2D" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping3-d.pbtxt index 1856a9ee21347ed6ca3dd592517eb644e205a5b7..678f40bbc23db15ff7c1138169478fb4412a449d 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping3-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping3-d.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.Cropping3D" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-dense.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-dense.pbtxt index a8c37af31f649d28ca2ab7614178f2dee58c13fc..fac826109b6a32305ece86c4990f08afe2236ce8 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-dense.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-dense.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.Dense" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-dot.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-dot.pbtxt index 07d3f023e54105c606b198c05750ffa78ee5d0c8..285d544af2d69d564afdec748598b39b6b95670f 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-dot.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-dot.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.Dot" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-dropout.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-dropout.pbtxt index e2e21b5f123f63fa38cb0e344be9a12fc091f20b..b77976974cccb96fc2373c093d2bdf279560c46f 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-dropout.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-dropout.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.Dropout" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-e-l-u.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-e-l-u.pbtxt index 92b9760d53e35d3e5066a730bb5cbda45492cc64..b07714d3f2d158496e0482f8611e55ea0fb0fd51 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-e-l-u.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-e-l-u.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.ELU" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-embedding.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-embedding.pbtxt index 83c528b40117222ac2b3e85ad338459948d0aa8c..e67d4ddfc47077d62319ab097e5333a373cbfc80 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-embedding.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-embedding.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.Embedding" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-flatten.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-flatten.pbtxt index 73609752886c8c57a78f6bc02cc46d2c7ff6e996..b2a668e5a88d312656f48ddd0e9f7aa9f6306991 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-flatten.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-flatten.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.Flatten" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u-cell.pbtxt index b329f1c46bb07ab7684dec6aaf45a20b98c27ed9..1fd3febad26df16576dedca1df7560bf230c08ec 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u-cell.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u-cell.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.GRUCell" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u.pbtxt index c741d4d6e6cf8da9712e68f86abe64e2828823da..f5f41d879dcb840551c00a7272bbcfbe51dbee89 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.GRU" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activation" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-dropout.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-dropout.pbtxt index 57596badf1881950270fa6d3c074afb65daaa8eb..f4f1a5d51c5d5689918af4facf907f79d9ca71ec 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-dropout.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-dropout.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.GaussianDropout" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-noise.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-noise.pbtxt index 3829353cc3c195a750ad862707c5c8563e203fba..e502df5e177d422403d0643c18a9588afb9d9713 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-noise.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-noise.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.GaussianNoise" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling1-d.pbtxt index e53e78a977b32eaf2e31867044aedd39ab2dd34f..9c8d5bfcd8966384230e7d5cdcc1cac53a0eab9a 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling1-d.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.GlobalAveragePooling1D" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling2-d.pbtxt index 48fcd1044e06b2fe61aadb6c3675ce82197ff003..8dd65f1f248daaf120780f19050c45d297b7902e 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling2-d.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.GlobalAveragePooling2D" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling3-d.pbtxt index 66c06ed47289eb2d83d97778a7b13dab821722d2..5e30571cc730ee23767a044036b590460deec00b 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling3-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling3-d.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.GlobalAveragePooling3D" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool1-d.pbtxt index 4f2420f74ab3069952e4a44bf61e5e12b3e80ea3..ba90fa454696d1cb4e77d80a2dc77ff65def4714 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool1-d.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.GlobalAvgPool1D" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool2-d.pbtxt index 7912a6d933b851521358e0246d04688da410b909..8823857758307c208527b144c0cc73b566f2f115 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool2-d.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.GlobalAvgPool2D" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool3-d.pbtxt index d5b2d2c274ad97071497045271c0a595f8e0e062..500ced852ba6b19502769ba9052f2e364af7e283 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool3-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool3-d.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.GlobalAvgPool3D" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool1-d.pbtxt index d88ff17eb6df7bbba7d3af4344fc8ddc367ae44c..cf2717ed46b56e639fb774c1e922648e1653ec0d 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool1-d.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.GlobalMaxPool1D" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool2-d.pbtxt index c8cc5a0ddfdd54cbb47de922591a9842abf63396..a86ff1a46997f19b11e6ef03be432b45687a2df2 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool2-d.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.GlobalMaxPool2D" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool3-d.pbtxt index 7956c5a340d963cfd5976e8af56da222848a164a..e01cc7c1b09ad6a40380613d54b771c6a1c89c1c 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool3-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool3-d.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.GlobalMaxPool3D" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling1-d.pbtxt index 0a7e16413dfbd80d448eb1bad5771915475d96b2..259c1fb37c787f5318570b7aca6935d2f0ed997f 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling1-d.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.GlobalMaxPooling1D" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling2-d.pbtxt index 6c8a58a996f5313ea48e395e7e443a7c21f198ee..0c41bf97f763f1e40e8fac714709ccac1483a00b 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling2-d.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.GlobalMaxPooling2D" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling3-d.pbtxt index 7678ce8aab63fcfa76c0ac61346a723c1dfe1ee7..bec8817aa393ba2d8a6410408938402366cbb01d 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling3-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling3-d.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.GlobalMaxPooling3D" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-input-layer.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-input-layer.pbtxt index 1e9370b02f9c5d198547e27e60bc3acf9e866d89..17be86222901c0f5a9a18c0e5f1c5bcac6c06a17 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-input-layer.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-input-layer.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.InputLayer" tf_class { - is_instance: "" - is_instance: "" + is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m-cell.pbtxt index 3b171b137af699c9608494a17c5651b439fe4545..6d2a8c56196d9b3c80f570c7f1d3ac803253fff6 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m-cell.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m-cell.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.LSTMCell" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m.pbtxt index 29d9cf78ab5ed3bdd1a488359b59cf7171e7e051..490b5b618c65e28f1ae2e01e8d35e7f3973cc180 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.LSTM" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activation" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-lambda.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-lambda.pbtxt index ca0144929942f7024a4e8bac5552bf0547ceb56d..21a65b838af35e2f540eacab823513e7bf54b434 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-lambda.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-lambda.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.Lambda" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-layer.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-layer.pbtxt index c52ad727545c0bf4f199714d71180eac3f1bf62a..127b04738e70c11b2dc1071cf174cf5de23c5133 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-layer.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-layer.pbtxt @@ -1,7 +1,8 @@ path: "tensorflow.keras.layers.Layer" tf_class { - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-leaky-re-l-u.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-leaky-re-l-u.pbtxt index 8134fb738683b79764662d9ea7f721fe04751162..87e49f2ed5b5d73aee5e9aa2511485b1f3f4bcd9 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-leaky-re-l-u.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-leaky-re-l-u.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.LeakyReLU" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected1-d.pbtxt index c5d452300947d7f74e7458e2a04bfdfabb1c1da2..1aa3aad3246b83931a47e69a4aa76fdf2b5aee22 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected1-d.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.LocallyConnected1D" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected2-d.pbtxt index bcbed9241b525a953c8b499197facaefebe8cc44..5e9dc7d4774c651a186a4e320d0cfd088e87b6b3 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected2-d.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.LocallyConnected2D" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-masking.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-masking.pbtxt index 244e79b4ffe60ddd6aa56d2780d80dfd66c494a9..0d101e5b68cdb2cdf24ed472c724cfc885e3d95d 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-masking.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-masking.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.Masking" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool1-d.pbtxt index 56cbf5df785ef0e2614ea7e9e6cfe1335e148eec..c85cd49ac8ce2c1fc0759671865b7174cd1c1480 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool1-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool2-d.pbtxt index 33c2d30e86f9cdc3fb9f4f498bfc2c94497fe2dd..4f59e330c92f96101c65a9a24f66196e84587ccb 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool2-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool3-d.pbtxt index 94f91059b7a1e291c38fe0045accc6c03f226603..c0ea0eb0505d20e70d641f2a646a060d7dbfabda 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool3-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool3-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling1-d.pbtxt index 247230a6d68b8ea93a30a2f5846d8baaa78cb13e..ca37ae51314516ae67c7725eb2ccd3d25154e2ac 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling1-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling2-d.pbtxt index 8d61b67e7ce9564d31b0bd904a58540d19c89172..3ede2378347f5eddb0e8fae775a0200ea484d3f8 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling2-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling3-d.pbtxt index ad2e30802006e934730e5c75247e958329f7121c..d87e25a7ba8e7cce615431723b53a0106c2b5279 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling3-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling3-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-maximum.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-maximum.pbtxt index ff0db15f190675d533c50c277eb1cb60e0b95e55..e4df7b48ae6b41400375920a48ef8577bb69376e 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-maximum.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-maximum.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.Maximum" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-multiply.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-multiply.pbtxt index 1d3f33f04516345ee32f16befe0d7200d2cdad00..6bf7c77743c31b6d74df35d827e9d5bc9a25d303 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-multiply.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-multiply.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.Multiply" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-p-re-l-u.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-p-re-l-u.pbtxt index c86bc49b22a8cc3e004a77f4a21594aacb2c665a..c14be132b7e406c99841576be8d8fa9ab99aa816 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-p-re-l-u.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-p-re-l-u.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.PReLU" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-permute.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-permute.pbtxt index 2043e1a1263f0f0745b7c6446cc670fd6b0f0000..72ffbceae01da900778dba1ec14e646aa17b39e5 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-permute.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-permute.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.Permute" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-r-n-n.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-r-n-n.pbtxt index ad539a7c4c5362500baef0a9c89d054762bbb47d..d3e780c8b22ed580f61ffc3d9b2bad7278391402 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-r-n-n.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-r-n-n.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.RNN" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-repeat-vector.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-repeat-vector.pbtxt index 4b0e98520a0dd86c085fa7345af445e1ae253d3b..a27980a9d17397e558a4b732e3dc332a0c1e8432 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-repeat-vector.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-repeat-vector.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.RepeatVector" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-reshape.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-reshape.pbtxt index 34bc71af8a26ff6e4d7c81a3877751df5209906f..67f991276c6908ff54fd516e84533542a5f60528 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-reshape.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-reshape.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.Reshape" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv1-d.pbtxt index dd67b76523cc50409516e29f963f59d039455bfd..fccea5e8af5ab81e712669ff1b2567d8bde8607e 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv1-d.pbtxt @@ -4,8 +4,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv2-d.pbtxt index 5d898fb2bd86b39cb8fab755382bb96cce231fa6..d20663bdb0bc2eea323d35b1e3d4d27122f50472 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv2-d.pbtxt @@ -4,8 +4,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution1-d.pbtxt index bf62c095e7cc3fbeac95919a0f9fdc545efd3d25..889fa0a1b58bbd3babd293b7b1b45915a9ee3ca4 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution1-d.pbtxt @@ -4,8 +4,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution2-d.pbtxt index c758d87993b3acba88a13c7bc9eaeee929a22652..c850f3fedc814b20f0f95cc3cf4fd5c973446b5b 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution2-d.pbtxt @@ -4,8 +4,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n-cell.pbtxt index 6e3cde3e3eaba4f9985411d66a220f7cdd4ee7ad..526d88ccba60eb25c68432e5baa03fd3a878f718 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n-cell.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n-cell.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.SimpleRNNCell" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n.pbtxt index 6fafc77b947d0df11755e3136ed2e7a14c148081..7fddae34472411f49d42b4d65d12034d056ec818 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.SimpleRNN" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activation" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-softmax.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-softmax.pbtxt index ee4b2fa39ed34a544ee800e9370e4f34c4a17041..5b9b62fc970238e49e6d4849285606d0a7908b23 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-softmax.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-softmax.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.Softmax" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout1-d.pbtxt index e4727072e375b9fc4dc99a1536eaaf3df5415369..769da30999993fad05ae0f7c04e256e6cf01a774 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout1-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout2-d.pbtxt index c5ff7043115ccdd3bc4a1147790b20feda410f65..fca2e42a1519fcf3a9f0ec996c50b148b2df05fd 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout2-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout3-d.pbtxt index 476a7f362cf88e234e964f6f6645ee4ed0cbaff8..36e8de09a967c5940bf8078234f5980a78ec8009 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout3-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout3-d.pbtxt @@ -3,8 +3,9 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-stacked-r-n-n-cells.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-stacked-r-n-n-cells.pbtxt index 3dde1e576918409b106649789443f910775e2f6c..a96f16fae99af9c30959d228202055e9aebfaf58 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-stacked-r-n-n-cells.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-stacked-r-n-n-cells.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.StackedRNNCells" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-thresholded-re-l-u.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-thresholded-re-l-u.pbtxt index ef31c5443efa0c0e5a7a2e0a422d2a9c9c49baaf..e1cbd0e150ed890ae57c1725249d1340fc2cb663 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-thresholded-re-l-u.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-thresholded-re-l-u.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.ThresholdedReLU" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-time-distributed.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-time-distributed.pbtxt index 1e176d8d4b4eb010049f267be3d0683228a7782b..f0d35728fb1c42d563ff0598dd84da51a766a764 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-time-distributed.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-time-distributed.pbtxt @@ -2,8 +2,9 @@ path: "tensorflow.keras.layers.TimeDistributed" tf_class { is_instance: "" is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling1-d.pbtxt index a81b83be49e0073f242efc6890e419b4fe172ab2..74efaea6ddb22ec2fe9d41558978c183b0e06671 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling1-d.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.UpSampling1D" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling2-d.pbtxt index 5403279d45ec7b93bae7907b891c659a043e96d0..dc5bd5fd5319f9bbd601a3c4083ae566b47e1aaa 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling2-d.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.UpSampling2D" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling3-d.pbtxt index 96c337caf28d43fabd0b90df016f4e8ab0c408db..e01ccfb74aead591f1018cdcbb1c888767ecdb20 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling3-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling3-d.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.UpSampling3D" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-wrapper.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-wrapper.pbtxt index ea3bb2f8f567c648cd8b3dfa6f179a108690b0f0..7e6f90f7623677244865ac285c134dc79f7b9b69 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-wrapper.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-wrapper.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.Wrapper" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding1-d.pbtxt index b81a4b1c50b22f13eacb521cfc8bc288bd40c81f..4d0d402dad442ccf52267f5ce40b05400afbfbc7 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding1-d.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.ZeroPadding1D" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding2-d.pbtxt index 1a26f2f3c9bbaa2aa567e76e1aafe14805ecff38..b353a529bcf8e543d334fee57fca26ebc83036a4 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding2-d.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.ZeroPadding2D" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding3-d.pbtxt index 310277fe67433fd870ae3d907984f402576925b2..9fe1256e616dbca4f35101df160dc55bc68bfa8a 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding3-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding3-d.pbtxt @@ -1,8 +1,9 @@ path: "tensorflow.keras.layers.ZeroPadding3D" tf_class { is_instance: "" - is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.models.-model.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.models.-model.pbtxt index 88eb237cec16210a6e290046bb479735e2158971..8ccf15f9ab0fcfa59907ff05a962a84d3d86ccb4 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.models.-model.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.models.-model.pbtxt @@ -1,9 +1,10 @@ path: "tensorflow.keras.models.Model" tf_class { is_instance: "" - is_instance: "" - is_instance: "" + is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.models.-sequential.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.models.-sequential.pbtxt index 34f10f01ad3fda4aa0ec076eff1a0221a2932959..102eb3220334516e0051f952353920f229f4ff20 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.models.-sequential.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.models.-sequential.pbtxt @@ -1,10 +1,11 @@ path: "tensorflow.keras.models.Sequential" tf_class { - is_instance: "" + is_instance: "" is_instance: "" - is_instance: "" - is_instance: "" + is_instance: "" + is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling1-d.pbtxt index de81206bc8b25046cd48c79ff8f154041c0e0cb0..1c4f550d7f05b8be33326cb39d7a5f3bf663f5e6 100644 --- a/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling1-d.pbtxt @@ -3,6 +3,7 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling2-d.pbtxt index 72d5496464210efd9e423996dfb274dd9564f761..d2db0952693f2989e6a9e8748a254eb4db483206 100644 --- a/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling2-d.pbtxt @@ -3,6 +3,7 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling3-d.pbtxt index 595e77ff9f8b64b6606fb075f3edf2281b4c3c1f..34d9a9df281c09a2e2030daf74a2ceb8066085bb 100644 --- a/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling3-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling3-d.pbtxt @@ -3,6 +3,7 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-batch-normalization.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-batch-normalization.pbtxt index 0c4aa2ff2612269727026141574726ad6df5cdbd..21ad0efecf88c42a3a679910ddfe095585a7933a 100644 --- a/tensorflow/tools/api/golden/tensorflow.layers.-batch-normalization.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.layers.-batch-normalization.pbtxt @@ -2,6 +2,7 @@ path: "tensorflow.layers.BatchNormalization" tf_class { is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-conv1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-conv1-d.pbtxt index 5f576d0189309442dc4cea3d3617ab3144420165..ed38747c7671a267bb640ecb96a4c5fcc46c5edf 100644 --- a/tensorflow/tools/api/golden/tensorflow.layers.-conv1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.layers.-conv1-d.pbtxt @@ -3,6 +3,7 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-conv2-d-transpose.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-conv2-d-transpose.pbtxt index 675a7c76e569d3163ecd2c547841b4c36078b21d..ff453c6059477c20528fc768d93c65d208cdfc4a 100644 --- a/tensorflow/tools/api/golden/tensorflow.layers.-conv2-d-transpose.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.layers.-conv2-d-transpose.pbtxt @@ -4,6 +4,7 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-conv2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-conv2-d.pbtxt index eaabbf6aab172aea5c51f8071076890bb6b5bcf7..5583bd22dce18b0a0593b73bde509818b63b3f29 100644 --- a/tensorflow/tools/api/golden/tensorflow.layers.-conv2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.layers.-conv2-d.pbtxt @@ -3,6 +3,7 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-conv3-d-transpose.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-conv3-d-transpose.pbtxt index 838e070d79d2d7cfbd631f1a5e9960412cfdae5a..63f0c32a7c8f7e530c76c64fa619102bc12f9ad9 100644 --- a/tensorflow/tools/api/golden/tensorflow.layers.-conv3-d-transpose.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.layers.-conv3-d-transpose.pbtxt @@ -4,6 +4,7 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-conv3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-conv3-d.pbtxt index 4bd8cfc1a48cd839e2ffa54d0d0ca863060406d8..b77726252ccca30a7c6555fb569eb65b69e34998 100644 --- a/tensorflow/tools/api/golden/tensorflow.layers.-conv3-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.layers.-conv3-d.pbtxt @@ -3,6 +3,7 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-dense.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-dense.pbtxt index 57eccb03ffeb90652b019b5ce8a519797e4a3a3d..92db9f6dcd2f77c4253eb77df4a26fb632b2a766 100644 --- a/tensorflow/tools/api/golden/tensorflow.layers.-dense.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.layers.-dense.pbtxt @@ -2,6 +2,7 @@ path: "tensorflow.layers.Dense" tf_class { is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-dropout.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-dropout.pbtxt index a1ec00eeeaa98a6199e29b187b0760ddc92db09d..80fa846a24c9162d8521bdb4f098b9cd8e34aedb 100644 --- a/tensorflow/tools/api/golden/tensorflow.layers.-dropout.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.layers.-dropout.pbtxt @@ -2,6 +2,7 @@ path: "tensorflow.layers.Dropout" tf_class { is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-flatten.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-flatten.pbtxt index a06943d51a52f1951056136445b0d5786d801b5b..f63213b3dde40aa54b165c1c269c26fd2cd9e3b4 100644 --- a/tensorflow/tools/api/golden/tensorflow.layers.-flatten.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.layers.-flatten.pbtxt @@ -2,6 +2,7 @@ path: "tensorflow.layers.Flatten" tf_class { is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-layer.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-layer.pbtxt index 24fda0c87ed0aeabd0fd4a16bb2efab444f8cd8a..4e45b2d513bb72bb47433d72c310d6a34fbc0c01 100644 --- a/tensorflow/tools/api/golden/tensorflow.layers.-layer.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.layers.-layer.pbtxt @@ -1,6 +1,7 @@ path: "tensorflow.layers.Layer" tf_class { is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling1-d.pbtxt index 4c3d00e0e1ddfe95c56f9ebc7c5d609c79dd44d4..19ec33fce775caa634e71e2295ac945a6f70ade9 100644 --- a/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling1-d.pbtxt @@ -3,6 +3,7 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling2-d.pbtxt index f7e2017b0c9438130f1cfb2431eb73ca4d3103c5..76180c333a21c592a3b53bb445df9b12d3596552 100644 --- a/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling2-d.pbtxt @@ -3,6 +3,7 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling3-d.pbtxt index 84780926a38ff811a5ab35fadfac690a6dbbbbe2..ded75c8ff09efc6746ddd2284f53d2c021cc473c 100644 --- a/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling3-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling3-d.pbtxt @@ -3,6 +3,7 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-separable-conv1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-separable-conv1-d.pbtxt index 05799ecfc9fdb9ff44620a67dcdbdc4426fddced..3dbfa5453f8e0ebb02429df9c4cbdf98de6b8ced 100644 --- a/tensorflow/tools/api/golden/tensorflow.layers.-separable-conv1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.layers.-separable-conv1-d.pbtxt @@ -4,6 +4,7 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-separable-conv2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-separable-conv2-d.pbtxt index c2aeb35c4648bcce22ca73c838a85803a6b9cedf..ab171df1d1650e19836018f3316e6919f6d36def 100644 --- a/tensorflow/tools/api/golden/tensorflow.layers.-separable-conv2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.layers.-separable-conv2-d.pbtxt @@ -4,6 +4,7 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-basic-l-s-t-m-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-basic-l-s-t-m-cell.pbtxt index 44536787f09fc98bba8a4eb0bc562427cfe48b8b..9c71a24d0500e2091e0ae94cc4dd7ed6b788a54f 100644 --- a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-basic-l-s-t-m-cell.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-basic-l-s-t-m-cell.pbtxt @@ -4,6 +4,7 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-basic-r-n-n-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-basic-r-n-n-cell.pbtxt index 768565d3cacbd1313ee5a64c9b15f9ab70683772..9e19f96b7452616956fb7fd3ca62d8f4b25a2122 100644 --- a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-basic-r-n-n-cell.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-basic-r-n-n-cell.pbtxt @@ -4,6 +4,7 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-device-wrapper.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-device-wrapper.pbtxt index 0d253e5dd233d6d2b6ad0070a463c283a8769dab..7540aa62861895a7c41840476d4edb79785a77a9 100644 --- a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-device-wrapper.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-device-wrapper.pbtxt @@ -3,6 +3,7 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-dropout-wrapper.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-dropout-wrapper.pbtxt index 97edf245f6fbed393a6fb8dbf1e83649e9ac4b4e..fc1ff386690f9c7acb11d4cc0770e394f78350ad 100644 --- a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-dropout-wrapper.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-dropout-wrapper.pbtxt @@ -3,6 +3,7 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-g-r-u-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-g-r-u-cell.pbtxt index 6ecc134d4df866ab5d59e238a8157064421579bd..751122cfff3bf9c55dd9fa264fdf2e1960940724 100644 --- a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-g-r-u-cell.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-g-r-u-cell.pbtxt @@ -4,6 +4,7 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-l-s-t-m-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-l-s-t-m-cell.pbtxt index 4b3ca1578ba52f30e3405ff198fb716496a462c6..4b6313f395fd8fd4ec2af78365117620263e7a55 100644 --- a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-l-s-t-m-cell.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-l-s-t-m-cell.pbtxt @@ -4,6 +4,7 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-multi-r-n-n-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-multi-r-n-n-cell.pbtxt index 9a6c73a079884b8ab92be1c9e89b2a9f34aad851..00e8c71140596ecea237ce05a09feff1fbb49001 100644 --- a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-multi-r-n-n-cell.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-multi-r-n-n-cell.pbtxt @@ -3,6 +3,7 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-r-n-n-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-r-n-n-cell.pbtxt index 27488f8e73f20456fae911511ecd2e41a60da351..3852f90dd6c4a254e20e789bdeb7796d61cef6bc 100644 --- a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-r-n-n-cell.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-r-n-n-cell.pbtxt @@ -2,6 +2,7 @@ path: "tensorflow.nn.rnn_cell.RNNCell" tf_class { is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-residual-wrapper.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-residual-wrapper.pbtxt index 3310836ed26387718115c2454300b9edfe930451..8f3f0f7506ef49014b31cd4bc04f1cb1e0d696fc 100644 --- a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-residual-wrapper.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-residual-wrapper.pbtxt @@ -3,6 +3,7 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" + is_instance: "" is_instance: "" member { name: "activity_regularizer" diff --git a/tensorflow/tools/api/golden/tensorflow.pbtxt b/tensorflow/tools/api/golden/tensorflow.pbtxt index f8d08f1d39a8bfa7d78be106e59d88de75a57823..80735cea5d00b93d895c238bc7dc9acd5a6f7237 100644 --- a/tensorflow/tools/api/golden/tensorflow.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.pbtxt @@ -596,6 +596,10 @@ tf_module { name: "add_to_collection" argspec: "args=[\'name\', \'value\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "add_to_collections" + argspec: "args=[\'names\', \'value\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "all_variables" argspec: "args=[], varargs=None, keywords=None, defaults=None" @@ -892,6 +896,10 @@ tf_module { name: "cumsum" argspec: "args=[\'x\', \'axis\', \'exclusive\', \'reverse\', \'name\'], varargs=None, keywords=None, defaults=[\'0\', \'False\', \'False\', \'None\'], " } + member_method { + name: "custom_gradient" + argspec: "args=[\'f\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "decode_base64" argspec: "args=[\'input\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " @@ -964,6 +972,10 @@ tf_module { name: "einsum" argspec: "args=[\'equation\'], varargs=inputs, keywords=kwargs, defaults=None" } + member_method { + name: "enable_eager_execution" + argspec: "args=[\'config\', \'device_policy\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } member_method { name: "encode_base64" argspec: "args=[\'input\', \'pad\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'None\'], " @@ -980,6 +992,10 @@ tf_module { name: "erfc" argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } + member_method { + name: "executing_eagerly" + argspec: "args=[], varargs=None, keywords=None, defaults=None" + } member_method { name: "exp" argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " @@ -1094,7 +1110,7 @@ tf_module { } member_method { name: "get_local_variable" - argspec: "args=[], varargs=args, keywords=kwargs, defaults=None" + argspec: "args=[\'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'collections\', \'caching_device\', \'partitioner\', \'validate_shape\', \'use_resource\', \'custom_getter\', \'constraint\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'False\', \'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\'], " } member_method { name: "get_seed" @@ -1600,6 +1616,10 @@ tf_module { name: "reduce_sum" argspec: "args=[\'input_tensor\', \'axis\', \'keepdims\', \'name\', \'reduction_indices\', \'keep_dims\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\'], " } + member_method { + name: "regex_replace" + argspec: "args=[\'input\', \'pattern\', \'rewrite\', \'replace_global\', \'name\'], varargs=None, keywords=None, defaults=[\'True\', \'None\'], " + } member_method { name: "register_tensor_conversion_function" argspec: "args=[\'base_type\', \'conversion_func\', \'priority\'], varargs=None, keywords=None, defaults=[\'100\'], " @@ -1996,6 +2016,14 @@ tf_module { name: "to_bfloat16" argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'ToBFloat16\'], " } + member_method { + name: "to_complex128" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'ToComplex128\'], " + } + member_method { + name: "to_complex64" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'ToComplex64\'], " + } member_method { name: "to_double" argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'ToDouble\'], " @@ -2060,6 +2088,10 @@ tf_module { name: "unsorted_segment_max" argspec: "args=[\'data\', \'segment_ids\', \'num_segments\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } + member_method { + name: "unsorted_segment_mean" + argspec: "args=[\'data\', \'segment_ids\', \'num_segments\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } member_method { name: "unsorted_segment_min" argspec: "args=[\'data\', \'segment_ids\', \'num_segments\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " diff --git a/tensorflow/tools/api/golden/tensorflow.train.-adadelta-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-adadelta-optimizer.pbtxt index c02e54adfbd9f33e661453767b517a5f0de90d57..16bfbf20d5227d6308248bebcb62f32a2df8ef41 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-adadelta-optimizer.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-adadelta-optimizer.pbtxt @@ -2,7 +2,6 @@ path: "tensorflow.train.AdadeltaOptimizer" tf_class { is_instance: "" is_instance: "" - is_instance: "" is_instance: "" is_instance: "" member { diff --git a/tensorflow/tools/api/golden/tensorflow.train.-adagrad-d-a-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-adagrad-d-a-optimizer.pbtxt index 2b619908fc6aea3f4b8e6a57d0dcf85a9854d466..61cde9181c2367153b7b289b41bd932482bb92fd 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-adagrad-d-a-optimizer.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-adagrad-d-a-optimizer.pbtxt @@ -2,7 +2,6 @@ path: "tensorflow.train.AdagradDAOptimizer" tf_class { is_instance: "" is_instance: "" - is_instance: "" is_instance: "" is_instance: "" member { diff --git a/tensorflow/tools/api/golden/tensorflow.train.-adagrad-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-adagrad-optimizer.pbtxt index 2005cf4677c06cf1f8b4207a444690fdd0c2306e..0a998c1afe4fff6e215360bc1cf8fc135754223c 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-adagrad-optimizer.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-adagrad-optimizer.pbtxt @@ -2,7 +2,6 @@ path: "tensorflow.train.AdagradOptimizer" tf_class { is_instance: "" is_instance: "" - is_instance: "" is_instance: "" is_instance: "" member { diff --git a/tensorflow/tools/api/golden/tensorflow.train.-adam-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-adam-optimizer.pbtxt index 0a2bae1d9021b20707e03ae5786e71f388266c14..cc5954152577796ee7a5a6e1cedc873647d64f7c 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-adam-optimizer.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-adam-optimizer.pbtxt @@ -2,7 +2,6 @@ path: "tensorflow.train.AdamOptimizer" tf_class { is_instance: "" is_instance: "" - is_instance: "" is_instance: "" is_instance: "" member { diff --git a/tensorflow/tools/api/golden/tensorflow.train.-ftrl-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-ftrl-optimizer.pbtxt index 847f9ad75998f1bdda8858650091c70fd0b4015b..1add3a902122341a706c38b19ea6ff5882c26445 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-ftrl-optimizer.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-ftrl-optimizer.pbtxt @@ -2,7 +2,6 @@ path: "tensorflow.train.FtrlOptimizer" tf_class { is_instance: "" is_instance: "" - is_instance: "" is_instance: "" is_instance: "" member { diff --git a/tensorflow/tools/api/golden/tensorflow.train.-gradient-descent-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-gradient-descent-optimizer.pbtxt index 13a58e0608ed269415ba78d84a03f1bae128e80c..ef5bbd6ace29abb5c73516176fcc7594a58d493a 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-gradient-descent-optimizer.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-gradient-descent-optimizer.pbtxt @@ -2,7 +2,6 @@ path: "tensorflow.train.GradientDescentOptimizer" tf_class { is_instance: "" is_instance: "" - is_instance: "" is_instance: "" is_instance: "" member { diff --git a/tensorflow/tools/api/golden/tensorflow.train.-momentum-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-momentum-optimizer.pbtxt index bfbc2357a346c7bfef0242a735ab14c5f4005b22..3d6e87f5eb44de9d6ce1bdd25a54b8df9020cc03 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-momentum-optimizer.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-momentum-optimizer.pbtxt @@ -2,7 +2,6 @@ path: "tensorflow.train.MomentumOptimizer" tf_class { is_instance: "" is_instance: "" - is_instance: "" is_instance: "" is_instance: "" member { diff --git a/tensorflow/tools/api/golden/tensorflow.train.-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-optimizer.pbtxt index 437efa0a2bd04c308db6186e714a5d8785541fa5..e73861ff7cb2d90d8efac72cdd7de3b27395f29e 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-optimizer.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-optimizer.pbtxt @@ -1,7 +1,6 @@ path: "tensorflow.train.Optimizer" tf_class { is_instance: "" - is_instance: "" is_instance: "" is_instance: "" member { diff --git a/tensorflow/tools/api/golden/tensorflow.train.-proximal-adagrad-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-proximal-adagrad-optimizer.pbtxt index 72f224605f67e72dd78699b5f1a703cc3edd566b..301b35b199c87890a0aef4139eb06253592ce0c4 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-proximal-adagrad-optimizer.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-proximal-adagrad-optimizer.pbtxt @@ -2,7 +2,6 @@ path: "tensorflow.train.ProximalAdagradOptimizer" tf_class { is_instance: "" is_instance: "" - is_instance: "" is_instance: "" is_instance: "" member { diff --git a/tensorflow/tools/api/golden/tensorflow.train.-proximal-gradient-descent-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-proximal-gradient-descent-optimizer.pbtxt index 316275b1fb1abd384e193994e35115a1c463f07d..8815befa936a85522011111a4a6270d22cbc25ae 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-proximal-gradient-descent-optimizer.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-proximal-gradient-descent-optimizer.pbtxt @@ -2,7 +2,6 @@ path: "tensorflow.train.ProximalGradientDescentOptimizer" tf_class { is_instance: "" is_instance: "" - is_instance: "" is_instance: "" is_instance: "" member { diff --git a/tensorflow/tools/api/golden/tensorflow.train.-r-m-s-prop-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-r-m-s-prop-optimizer.pbtxt index af50a1986100d830f0809a3f4a0f01faa8821b3b..e9819683ba5ec1bcacb3cdbcb2d787e866a77b6f 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-r-m-s-prop-optimizer.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-r-m-s-prop-optimizer.pbtxt @@ -2,7 +2,6 @@ path: "tensorflow.train.RMSPropOptimizer" tf_class { is_instance: "" is_instance: "" - is_instance: "" is_instance: "" is_instance: "" member { diff --git a/tensorflow/tools/api/golden/tensorflow.train.-sync-replicas-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-sync-replicas-optimizer.pbtxt index 6edc516c9392fa14f23ffc2a6481ec21216f06cf..3db96aff876b88b80b647570cf68b1ebc0b2da3b 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.-sync-replicas-optimizer.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.-sync-replicas-optimizer.pbtxt @@ -2,7 +2,6 @@ path: "tensorflow.train.SyncReplicasOptimizer" tf_class { is_instance: "" is_instance: "" - is_instance: "" is_instance: "" is_instance: "" member { diff --git a/tensorflow/tools/api/golden/tensorflow.train.-vocab-info.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-vocab-info.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..4ce7cb111163e103a1cebe30d5c6f3eeb4234693 --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.train.-vocab-info.pbtxt @@ -0,0 +1,39 @@ +path: "tensorflow.train.VocabInfo" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "backup_initializer" + mtype: "" + } + member { + name: "new_vocab" + mtype: "" + } + member { + name: "new_vocab_size" + mtype: "" + } + member { + name: "num_oov_buckets" + mtype: "" + } + member { + name: "old_vocab" + mtype: "" + } + member { + name: "old_vocab_size" + mtype: "" + } + member_method { + name: "__init__" + } + member_method { + name: "count" + } + member_method { + name: "index" + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.train.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.pbtxt index e49c719a334455d1f8f39fa67332be8bb81f2bc2..c75ee474aa471524e4b5c8a7e2dd4a9da4b08eae 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.pbtxt @@ -224,6 +224,10 @@ tf_module { name: "SyncReplicasOptimizer" mtype: "" } + member { + name: "VocabInfo" + mtype: "" + } member { name: "WorkerSessionCreator" mtype: "" @@ -402,7 +406,7 @@ tf_module { } member_method { name: "sdca_optimizer" - argspec: "args=[\'sparse_example_indices\', \'sparse_feature_indices\', \'sparse_feature_values\', \'dense_features\', \'example_weights\', \'example_labels\', \'sparse_indices\', \'sparse_weights\', \'dense_weights\', \'example_state_data\', \'loss_type\', \'l1\', \'l2\', \'num_loss_partitions\', \'num_inner_iterations\', \'adaptative\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'None\'], " + argspec: "args=[\'sparse_example_indices\', \'sparse_feature_indices\', \'sparse_feature_values\', \'dense_features\', \'example_weights\', \'example_labels\', \'sparse_indices\', \'sparse_weights\', \'dense_weights\', \'example_state_data\', \'loss_type\', \'l1\', \'l2\', \'num_loss_partitions\', \'num_inner_iterations\', \'adaptative\', \'name\'], varargs=None, keywords=None, defaults=[\'True\', \'None\'], " } member_method { name: "sdca_shrink_l1" @@ -436,6 +440,10 @@ tf_module { name: "update_checkpoint_state" argspec: "args=[\'save_dir\', \'model_checkpoint_path\', \'all_model_checkpoint_paths\', \'latest_filename\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " } + member_method { + name: "warm_start" + argspec: "args=[\'ckpt_to_initialize_from\', \'vars_to_warm_start\', \'var_name_to_vocab_info\', \'var_name_to_prev_var_name\'], varargs=None, keywords=None, defaults=[\'.*\', \'None\', \'None\'], " + } member_method { name: "write_graph" argspec: "args=[\'graph_or_graph_def\', \'logdir\', \'name\', \'as_text\'], varargs=None, keywords=None, defaults=[\'True\'], " diff --git a/tensorflow/tools/api/tests/BUILD b/tensorflow/tools/api/tests/BUILD index 608a34ab7b32bdc26cebbe43b383155406fb51b2..15bf1abb5f8f541c435be77b1a3c2f13382f2438 100644 --- a/tensorflow/tools/api/tests/BUILD +++ b/tensorflow/tools/api/tests/BUILD @@ -23,6 +23,7 @@ py_test( ], srcs_version = "PY2AND3", deps = [ + "//tensorflow:experimental_tensorflow_py", "//tensorflow:tensorflow_py", "//tensorflow/python:client_testlib", "//tensorflow/python:lib", diff --git a/tensorflow/tools/api/tests/api_compatibility_test.py b/tensorflow/tools/api/tests/api_compatibility_test.py index c1e09cc531ed8e8995e3e73b87e96b72fba6c038..baa7a0889de2534604f94f34f6852a8fdf8819fa 100644 --- a/tensorflow/tools/api/tests/api_compatibility_test.py +++ b/tensorflow/tools/api/tests/api_compatibility_test.py @@ -34,6 +34,7 @@ import sys import unittest import tensorflow as tf +from tensorflow import experimental_api as api from google.protobuf import text_format @@ -46,6 +47,9 @@ from tensorflow.tools.api.lib import python_object_to_proto_visitor from tensorflow.tools.common import public_api from tensorflow.tools.common import traverse +if hasattr(tf, 'experimental_api'): + del tf.experimental_api + # FLAGS defined at the bottom: FLAGS = None # DEFINE_boolean, update_goldens, default False: @@ -109,7 +113,8 @@ class ApiCompatibilityTest(test.TestCase): expected_dict, actual_dict, verbose=False, - update_goldens=False): + update_goldens=False, + additional_missing_object_message=''): """Diff given dicts of protobufs and report differences a readable way. Args: @@ -120,6 +125,8 @@ class ApiCompatibilityTest(test.TestCase): verbose: Whether to log the full diffs, or simply report which files were different. update_goldens: Whether to update goldens when there are diffs found. + additional_missing_object_message: Message to print when a symbol is + missing. """ diffs = [] verbose_diffs = [] @@ -138,7 +145,8 @@ class ApiCompatibilityTest(test.TestCase): verbose_diff_message = '' # First check if the key is not found in one or the other. if key in only_in_expected: - diff_message = 'Object %s expected but not found (removed).' % key + diff_message = 'Object %s expected but not found (removed). %s' % ( + key, additional_missing_object_message) verbose_diff_message = diff_message elif key in only_in_actual: diff_message = 'New object %s found (added).' % key @@ -165,7 +173,7 @@ class ApiCompatibilityTest(test.TestCase): logging.error('%d differences found between API and golden.', diff_count) messages = verbose_diffs if verbose else diffs for i in range(diff_count): - logging.error('Issue %d\t: %s', i + 1, messages[i]) + print('Issue %d\t: %s' % (i + 1, messages[i]), file=sys.stderr) if update_goldens: # Write files if requested. @@ -229,6 +237,60 @@ class ApiCompatibilityTest(test.TestCase): verbose=FLAGS.verbose_diffs, update_goldens=FLAGS.update_goldens) + @unittest.skipUnless( + sys.version_info.major == 2, + 'API compabitility test goldens are generated using python2.') + def testNewAPIBackwardsCompatibility(self): + # Extract all API stuff. + visitor = python_object_to_proto_visitor.PythonObjectToProtoVisitor() + + public_api_visitor = public_api.PublicAPIVisitor(visitor) + public_api_visitor.do_not_descend_map['tf'].append('contrib') + public_api_visitor.do_not_descend_map['tf.GPUOptions'] = ['Experimental'] + # TODO(annarev): Make slide_dataset available in API. + public_api_visitor.private_map['tf'] = ['slide_dataset'] + traverse.traverse(api, public_api_visitor) + + proto_dict = visitor.GetProtos() + + # Read all golden files. + expression = os.path.join( + resource_loader.get_root_dir_with_all_resources(), + _KeyToFilePath('*')) + golden_file_list = file_io.get_matching_files(expression) + + def _ReadFileToProto(filename): + """Read a filename, create a protobuf from its contents.""" + ret_val = api_objects_pb2.TFAPIObject() + text_format.Merge(file_io.read_file_to_string(filename), ret_val) + return ret_val + + golden_proto_dict = { + _FileNameToKey(filename): _ReadFileToProto(filename) + for filename in golden_file_list + } + + # user_ops is an empty module. It is currently available in TensorFlow API + # but we don't keep empty modules in the new API. + # We delete user_ops from golden_proto_dict to make sure assert passes + # when diffing new API against goldens. + # TODO(annarev): remove user_ops from goldens once we switch to new API. + tf_module = golden_proto_dict['tensorflow'].tf_module + for i in range(len(tf_module.member)): + if tf_module.member[i].name == 'user_ops': + del tf_module.member[i] + break + + # Diff them. Do not fail if called with update. + # If the test is run to update goldens, only report diffs but do not fail. + self._AssertProtoDictEquals( + golden_proto_dict, + proto_dict, + verbose=FLAGS.verbose_diffs, + update_goldens=False, + additional_missing_object_message= + 'Check if tf_export decorator/call is missing for this symbol.') + if __name__ == '__main__': parser = argparse.ArgumentParser() diff --git a/tensorflow/tools/benchmark/benchmark_model.cc b/tensorflow/tools/benchmark/benchmark_model.cc index ecab6f8769ae2d0126f63580030ed6ff756015d0..15523028c726fefa13641a1369cf4274bcfb9973 100644 --- a/tensorflow/tools/benchmark/benchmark_model.cc +++ b/tensorflow/tools/benchmark/benchmark_model.cc @@ -48,33 +48,14 @@ limitations under the License. namespace tensorflow { namespace benchmark_model { -Status InitializeSession(int num_threads, const string& graph, - std::unique_ptr* session, - std::unique_ptr* graph_def) { - LOG(INFO) << "Loading TensorFlow."; +namespace { - tensorflow::SessionOptions options; - tensorflow::ConfigProto& config = options.config; - if (num_threads > 0) { - config.set_intra_op_parallelism_threads(num_threads); +Status InitializeVariables(Session* session, + const std::vector& init_ops) { + LOG(INFO) << "Initializing graph variables"; + for (const string& init_op : init_ops) { + TF_RETURN_IF_ERROR(session->Run({}, {}, {init_op}, nullptr)); } - LOG(INFO) << "Got config, " << config.device_count_size() << " devices"; - - session->reset(tensorflow::NewSession(options)); - graph_def->reset(new GraphDef()); - tensorflow::GraphDef tensorflow_graph; - Status s = ReadBinaryProto(Env::Default(), graph, graph_def->get()); - if (!s.ok()) { - LOG(ERROR) << "Could not create TensorFlow Graph: " << s; - return s; - } - - s = (*session)->Create(*(graph_def->get())); - if (!s.ok()) { - LOG(ERROR) << "Could not create TensorFlow Session: " << s; - return s; - } - return Status::OK(); } @@ -247,8 +228,56 @@ void RecordBenchmarkEntry(const string& output_prefix, TF_QCHECK_OK(node_reporter.Close()); } +void SleepSeconds(double sleep_seconds) { + if (sleep_seconds <= 0.0) { + return; + } +#ifdef PLATFORM_WINDOWS + Sleep(sleep_seconds * 1000); +#else + // Convert the inference_delay string into a timespec. + timespec req; + req.tv_sec = static_cast(sleep_seconds); + req.tv_nsec = (sleep_seconds - req.tv_sec) * 1000000000; + nanosleep(&req, nullptr); +#endif +} + +} // namespace + +Status InitializeSession(int num_threads, const string& graph, + std::unique_ptr* session, + std::unique_ptr* graph_def) { + LOG(INFO) << "Loading TensorFlow."; + + tensorflow::SessionOptions options; + tensorflow::ConfigProto& config = options.config; + if (num_threads > 0) { + config.set_intra_op_parallelism_threads(num_threads); + } + LOG(INFO) << "Got config, " << config.device_count_size() << " devices"; + + session->reset(tensorflow::NewSession(options)); + graph_def->reset(new GraphDef()); + tensorflow::GraphDef tensorflow_graph; + Status s = ReadBinaryProto(Env::Default(), graph, graph_def->get()); + if (!s.ok()) { + LOG(ERROR) << "Could not create TensorFlow Graph: " << s; + return s; + } + + s = (*session)->Create(*(graph_def->get())); + if (!s.ok()) { + LOG(ERROR) << "Could not create TensorFlow Session: " << s; + return s; + } + + return Status::OK(); +} + Status RunBenchmark(const std::vector& inputs, - const std::vector& outputs, Session* session, + const std::vector& outputs, + const std::vector& targets, Session* session, StatSummarizer* stats, int64* inference_time_us) { std::vector > input_tensors; CreateTensorsFromInputInfo(inputs, &input_tensors); @@ -264,8 +293,8 @@ Status RunBenchmark(const std::vector& inputs, RunMetadata run_metadata; const int64 start_time = Env::Default()->NowMicros(); - s = session->Run(run_options, input_tensors, outputs, {}, &output_tensors, - &run_metadata); + s = session->Run(run_options, input_tensors, outputs, targets, + &output_tensors, &run_metadata); const int64 end_time = Env::Default()->NowMicros(); *inference_time_us = end_time - start_time; @@ -283,24 +312,10 @@ Status RunBenchmark(const std::vector& inputs, return s; } -void SleepSeconds(double sleep_seconds) { - if (sleep_seconds <= 0.0) { - return; - } -#ifdef PLATFORM_WINDOWS - Sleep(sleep_seconds * 1000); -#else - // Convert the inference_delay string into a timespec. - timespec req; - req.tv_sec = static_cast(sleep_seconds); - req.tv_nsec = (sleep_seconds - req.tv_sec) * 1000000000; - nanosleep(&req, nullptr); -#endif -} - Status TimeMultipleRuns(double sleep_seconds, int num_runs, double max_time_s, const std::vector& inputs, - const std::vector& outputs, Session* session, + const std::vector& outputs, + const std::vector& targets, Session* session, StatSummarizer* stats, int64* total_time_us, int64* actual_num_runs) { *total_time_us = 0; @@ -315,7 +330,8 @@ Status TimeMultipleRuns(double sleep_seconds, int num_runs, double max_time_s, const bool until_max_time = num_runs <= 0; for (int i = 0; until_max_time || i < num_runs; ++i) { int64 time; - Status run_status = RunBenchmark(inputs, outputs, session, stats, &time); + Status run_status = + RunBenchmark(inputs, outputs, targets, session, stats, &time); stat.UpdateStat(time); (*total_time_us) += time; ++(*actual_num_runs); @@ -345,11 +361,13 @@ Status TimeMultipleRuns(double sleep_seconds, int num_runs, double max_time_s, int Main(int argc, char** argv) { string graph = "/data/local/tmp/tensorflow_inception_graph.pb"; + string init_ops_string = ""; string input_layer_string = "input:0"; string input_layer_shape_string = "1,224,224,3"; string input_layer_type_string = "float"; string input_layer_values_string = ""; string output_layer_string = "output:0"; + string target_layer_string = ""; int max_num_runs = 1000; string max_time = "10.0"; string inference_delay = "-1.0"; @@ -371,12 +389,14 @@ int Main(int argc, char** argv) { std::vector flag_list = { Flag("graph", &graph, "graph file name"), + Flag("init_ops", &init_ops_string, "init ops"), Flag("input_layer", &input_layer_string, "input layer names"), Flag("input_layer_shape", &input_layer_shape_string, "input layer shape"), Flag("input_layer_type", &input_layer_type_string, "input layer type"), Flag("input_layer_values", &input_layer_values_string, "values to initialize the inputs with"), Flag("output_layer", &output_layer_string, "output layer name"), + Flag("target_layer", &target_layer_string, "target layer name"), Flag("max_num_runs", &max_num_runs, "number of runs max"), Flag("max_time", &max_time, "length to run max"), Flag("inference_delay", &inference_delay, @@ -410,6 +430,7 @@ int Main(int argc, char** argv) { return -1; } + std::vector init_ops = str_util::Split(init_ops_string, ','); std::vector input_layers = str_util::Split(input_layer_string, ','); std::vector input_layer_shapes = str_util::Split(input_layer_shape_string, ':'); @@ -418,6 +439,7 @@ int Main(int argc, char** argv) { std::vector input_layer_values = str_util::Split(input_layer_values_string, ':'); std::vector output_layers = str_util::Split(output_layer_string, ','); + std::vector target_layers = str_util::Split(target_layer_string, ','); if ((input_layers.size() != input_layer_shapes.size()) || (input_layers.size() != input_layer_types.size())) { LOG(ERROR) << "There must be the same number of items in --input_layer," @@ -441,10 +463,12 @@ int Main(int argc, char** argv) { } LOG(INFO) << "Graph: [" << graph << "]"; + LOG(INFO) << "Init ops:" << init_ops_string; LOG(INFO) << "Input layers: [" << input_layer_string << "]"; LOG(INFO) << "Input shapes: [" << input_layer_shape_string << "]"; LOG(INFO) << "Input types: [" << input_layer_type_string << "]"; LOG(INFO) << "Output layers: [" << output_layer_string << "]"; + LOG(INFO) << "Target layers: [" << target_layer_string << "]"; LOG(INFO) << "Num runs: [" << max_num_runs << "]"; LOG(INFO) << "Inter-inference delay (seconds): [" << inference_delay << "]"; LOG(INFO) << "Inter-benchmark delay (seconds): [" << inter_benchmark_delay @@ -470,6 +494,16 @@ int Main(int argc, char** argv) { return -1; } + if (!init_ops.empty()) { + Status initialize_variables_status = + InitializeVariables(session.get(), init_ops); + if (!initialize_variables_status.ok()) { + LOG(ERROR) << "Graph variables initialization failed with " + << initialize_variables_status; + return -1; + } + } + StatSummarizerOptions stats_options; stats_options.show_run_order = show_run_order; stats_options.run_order_limit = run_order_limit; @@ -520,9 +554,10 @@ int Main(int argc, char** argv) { int64 warmup_time_us = 0; int64 num_warmup_runs = 0; if (warmup_runs > 0) { - Status warmup_time_status = TimeMultipleRuns( - inter_inference_sleep_seconds, warmup_runs, -1.0, inputs, output_layers, - session.get(), nullptr, &warmup_time_us, &num_warmup_runs); + Status warmup_time_status = + TimeMultipleRuns(inter_inference_sleep_seconds, warmup_runs, -1.0, + inputs, output_layers, target_layers, session.get(), + nullptr, &warmup_time_us, &num_warmup_runs); if (!warmup_time_status.ok()) { LOG(ERROR) << "Timing failed with " << warmup_time_status; return -1; @@ -536,8 +571,8 @@ int Main(int argc, char** argv) { int64 no_stat_num_runs = 0; Status no_stat_time_status = TimeMultipleRuns( inter_inference_sleep_seconds, max_num_runs, max_benchmark_time_seconds, - inputs, output_layers, session.get(), nullptr, &no_stat_time_us, - &no_stat_num_runs); + inputs, output_layers, target_layers, session.get(), nullptr, + &no_stat_time_us, &no_stat_num_runs); const double no_stat_wall_time = no_stat_time_us / 1000000.0; if (!no_stat_time_status.ok()) { LOG(ERROR) << "Timing failed with " << no_stat_time_status; @@ -551,8 +586,8 @@ int Main(int argc, char** argv) { int64 stat_num_runs = 0; Status stat_time_status = TimeMultipleRuns( inter_inference_sleep_seconds, max_num_runs, max_benchmark_time_seconds, - inputs, output_layers, session.get(), stats.get(), &stat_time_us, - &stat_num_runs); + inputs, output_layers, target_layers, session.get(), stats.get(), + &stat_time_us, &stat_num_runs); if (!stat_time_status.ok()) { LOG(ERROR) << "Timing failed with " << stat_time_status; return -1; diff --git a/tensorflow/tools/benchmark/benchmark_model.h b/tensorflow/tools/benchmark/benchmark_model.h index dff62c5b5d518da8f9034295626e46db783f343d..dc5f0080374e70edad52965cc0a95f99751baa48 100644 --- a/tensorflow/tools/benchmark/benchmark_model.h +++ b/tensorflow/tools/benchmark/benchmark_model.h @@ -37,13 +37,15 @@ Status InitializeSession(int num_threads, const string& graph, // Does a single run of the model that's been loaded into the given session. Status RunBenchmark(const std::vector& inputs, - const std::vector& outputs, Session* session, + const std::vector& outputs, + const std::vector& targets, Session* session, StatSummarizer* stats, int64* inference_time_us); // Runs the model multiple time, keeping track of timing information. Status TimeMultipleRuns(double sleep_seconds, int num_runs, double max_time_s, const std::vector& inputs, - const std::vector& outputs, Session* session, + const std::vector& outputs, + const std::vector& targets, Session* session, StatSummarizer* stats, int64* total_time_us, int64* actual_num_runs); diff --git a/tensorflow/tools/benchmark/benchmark_model_test.cc b/tensorflow/tools/benchmark/benchmark_model_test.cc index bb4eb5352039b01a6692621906eff005187cfa36..16ab2ff66e763a0ca5130f075f988bade9c8abd1 100644 --- a/tensorflow/tools/benchmark/benchmark_model_test.cc +++ b/tensorflow/tools/benchmark/benchmark_model_test.cc @@ -64,8 +64,8 @@ TEST(BenchmarkModelTest, InitializeAndRun) { int64 time; int64 num_runs = 0; TF_ASSERT_OK(benchmark_model::TimeMultipleRuns( - 0.0, 10, 0.0, {input}, {output_name}, session.get(), stats.get(), &time, - &num_runs)); + 0.0, 10, 0.0, {input}, {output_name}, {}, session.get(), stats.get(), + &time, &num_runs)); ASSERT_EQ(num_runs, 10); } diff --git a/tensorflow/tools/ci_build/Dockerfile.cmake b/tensorflow/tools/ci_build/Dockerfile.cmake index ec90c83aacd068e8f9c16e5be8eb6e1cef098ea6..d5dea4f3e41841aed5aeac02fcca850dbfdfaeb3 100644 --- a/tensorflow/tools/ci_build/Dockerfile.cmake +++ b/tensorflow/tools/ci_build/Dockerfile.cmake @@ -23,11 +23,12 @@ RUN /install/install_deb_packages.sh RUN apt-get update RUN apt-get install -y --no-install-recommends python-pip +RUN pip install --upgrade wheel RUN pip install --upgrade astor RUN pip install --upgrade gast RUN pip install --upgrade numpy RUN pip install --upgrade termcolor # Install golang -RUN add-apt-repository -y ppa:ubuntu-lxc/lxd-stable -RUN apt-get install -y golang +RUN apt-get install -t xenial-backports -y golang-1.9 +ENV PATH=${PATH}:/usr/lib/go-1.9/bin diff --git a/tensorflow/tools/ci_build/Dockerfile.rbe.cpu b/tensorflow/tools/ci_build/Dockerfile.rbe.cpu new file mode 100644 index 0000000000000000000000000000000000000000..6f0798b1afc34bc08df6f3f8f467a329fcf0fe9b --- /dev/null +++ b/tensorflow/tools/ci_build/Dockerfile.rbe.cpu @@ -0,0 +1,14 @@ +FROM launcher.gcr.io/google/rbe-debian8:r322167 +LABEL maintainer="Yu Yi " + +# Copy install scripts +COPY install/*.sh /install/ + +# Setup envvars +ENV CC /usr/local/bin/clang +ENV CXX /usr/local/bin/clang++ +ENV AR /usr/bin/ar + +# Run pip install script for RBE Debian8 container. +RUN /install/install_pip_packages_remote.sh +RUN /install/install_pip_packages.sh diff --git a/tensorflow/tools/ci_build/Dockerfile.rbe.gpu b/tensorflow/tools/ci_build/Dockerfile.rbe.gpu new file mode 100644 index 0000000000000000000000000000000000000000..24ff4765a619701cd614414d2b06f7fa4ce7d8c0 --- /dev/null +++ b/tensorflow/tools/ci_build/Dockerfile.rbe.gpu @@ -0,0 +1,26 @@ +FROM nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04 + +LABEL maintainer="Nick Lopez " + +# In the Ubuntu 16.04 images, cudnn is placed in system paths. Move them to +# /usr/local/cuda +RUN cp -P /usr/include/cudnn.h /usr/local/cuda/include +RUN cp -P /usr/lib/x86_64-linux-gnu/libcudnn* /usr/local/cuda/lib64 + +# Copy and run the install scripts. +COPY install/*.sh /install/ +ARG DEBIAN_FRONTEND=noninteractive +RUN /install/install_bootstrap_deb_packages.sh +RUN add-apt-repository -y ppa:openjdk-r/ppa && \ + add-apt-repository -y ppa:george-edison55/cmake-3.x +RUN /install/install_deb_packages.sh +RUN /install/install_pip_packages.sh +RUN /install/install_golang.sh + +# Install clang from pre-built package +RUN cd /tmp && \ + wget https://storage.googleapis.com/clang-builds-stable/clang-ubuntu16_04/clang_r323528.tar.gz && \ + echo "26752d9f5785df07193fac8316ba5d5ba3bec36d970c29a1577360848818ac74 clang_r323528.tar.gz" | sha256sum -c && \ + tar -C /usr/local -xf clang_r323528.tar.gz && \ + rm clang_r323528.tar.gz + diff --git a/tensorflow/tools/ci_build/builds/test_tutorials.sh b/tensorflow/tools/ci_build/builds/test_tutorials.sh index 67e5af556405a5c659000a07a79a6bd9a1d1e542..db335f14ca4f88ade7a540ffab7ed9de67f1248e 100755 --- a/tensorflow/tools/ci_build/builds/test_tutorials.sh +++ b/tensorflow/tools/ci_build/builds/test_tutorials.sh @@ -277,17 +277,6 @@ test_ptb_word_lm() { fi } - -# ----------------------------------------------------------- -# translate_test -test_translate_test() { - LOG_FILE=$1 - - run_in_directory "${TEST_DIR}" "${LOG_FILE}" \ - "${TF_MODELS_DIR}/tutorials/rnn/translate/translate.py" --self_test=True -} - - # Run the tutorial tests test_runner "tutorial test-on-install" \ "${TUT_TESTS}" "${TF_BUILD_TUT_TEST_BLACKLIST}" "${LOGS_DIR}" diff --git a/tensorflow/tools/ci_build/remote/remote_docker_build.sh b/tensorflow/tools/ci_build/ci_rbe_docker_build.sh similarity index 58% rename from tensorflow/tools/ci_build/remote/remote_docker_build.sh rename to tensorflow/tools/ci_build/ci_rbe_docker_build.sh index e00a66aabaf1068c772aabce2391616518be44d4..cd811de6bdf9275b799a608381c76713a6c7a65b 100755 --- a/tensorflow/tools/ci_build/remote/remote_docker_build.sh +++ b/tensorflow/tools/ci_build/ci_rbe_docker_build.sh @@ -16,25 +16,19 @@ # Build TensorFlow Docker images for remote build # # Usage: -# remote_docker_build.sh -c # docker image for cpu build -# remote_docker_build.sh -g # docker image for gpu build - +# ci_rbe_docker_build.sh -c # docker image for cpu build +# ci_rbe_docker_build.sh -g # docker image for gpu build function main { - publish=true cpu_build=false gpu_build=false - publish=true + publish=false script_dir=$(dirname "$(readlink -f "$0")") cd $script_dir - trap cleanup_on_finish EXIT - set_script_flags $@ - build_base_image - build_tf_image if [ "$publish" = true ] ; then @@ -50,17 +44,14 @@ function set_script_flags { c) cpu_build=true ;; - f) - base_image_build_script=$OPTARG - ;; g) gpu_build=true ;; h) print_usage ;; - n) - publish=false + p) + publish=true ;; *) print_usage "ERROR: unknown option" @@ -76,7 +67,6 @@ function print_usage { echo "Usage: $(basename $0) -c | -g [options]" echo " -c build image for CPU build (base image debian8-clang)" echo " -g build image for GPU build (base image nvidia-clang)" - echo " -f the script which build the {debian8,nvidia}-clang base image" echo "[option] is one of" echo " -n not publish the locally-built image to GCR;" echo " the build process will publish image to GCR by default" @@ -87,54 +77,22 @@ function print_usage { exit 1 } - -# Build nvidia-cuba-clang base image for GPU image. -# For CPU the `clang-debian8` from Cloud Launcher will be used directly: -# https://console.cloud.google.com/launcher/details/google/clang-debian8?filter=category:developer-tools&q=clang -function build_base_image { - if [ "$gpu_build" = true ] ; then - base_image="nvidia-cuda" - # Run a 2-stage build for clang base image, see - # https://github.com/llvm-mirror/llvm/blob/master/docs/Docker.rst - $base_image_build_script \ - --source $base_image \ - --branch branches/google/stable \ - --docker-repository ${base_image}-clang --docker-tag "latest" \ - -p clang -i stage2-install-clang -i stage2-install-clang-headers \ - -- \ - -DLLVM_TARGETS_TO_BUILD=Native -DCMAKE_BUILD_TYPE=Release \ - -DBOOTSTRAP_CMAKE_BUILD_TYPE=Release \ - -DCLANG_ENABLE_BOOTSTRAP=ON \ - -DCLANG_BOOTSTRAP_TARGETS="install-clang;install-clang-headers" - fi -} - - function build_tf_image { if [ "$cpu_build" = true ] ; then - dockerfile="Dockerfile.cpu" - tf_image="tensorflow-remote" + dockerfile="Dockerfile.rbe.cpu" + tf_image="tensorflow-rbe-cpu" else - dockerfile="Dockerfile.gpu" - tf_image="tensorflow-remote-gpu" + dockerfile="Dockerfile.rbe.gpu" + tf_image="tensorflow-rbe-gpu" fi docker build -f $dockerfile -t $tf_image . } - function publish_tf_image { gcr_tf_image="gcr.io/tensorflow/${tf_image}" docker tag $tf_image $gcr_tf_image gcloud docker -- push $gcr_tf_image } - -function cleanup_on_finish { - cd $script_dir - rm -rf $llvm_docker_src - docker rmi -f ${base_image}-clang ${base_image}-clang-build -} - - main $@ diff --git a/tensorflow/tools/ci_build/copy_binary.py b/tensorflow/tools/ci_build/copy_binary.py index 90fd6a6e71f19649406234bc93025c15e4a5063c..ff26b052f3ea0b4866e12a663c569a3fa39ad698 100755 --- a/tensorflow/tools/ci_build/copy_binary.py +++ b/tensorflow/tools/ci_build/copy_binary.py @@ -43,7 +43,7 @@ BINARY_STRING_TEMPLATE = "%s-%s-%s.whl" def check_existence(filename): """Check the existence of file or dir.""" if not os.path.exists(filename): - raise RuntimeError("%s not found.") + raise RuntimeError("%s not found." % filename) def copy_binary(directory, origin_tag, new_tag, version, gpu=False): diff --git a/tensorflow/tools/ci_build/gpu_build/parallel_gpu_execute.sh b/tensorflow/tools/ci_build/gpu_build/parallel_gpu_execute.sh index cfeaebdbf57c01fef7cd81dae76217429336d0ff..d0816c92b7308a1079579e605ee9af491a0533fb 100755 --- a/tensorflow/tools/ci_build/gpu_build/parallel_gpu_execute.sh +++ b/tensorflow/tools/ci_build/gpu_build/parallel_gpu_execute.sh @@ -54,3 +54,6 @@ for i in `seq 0 $((TF_GPU_COUNT-1))`; do fi done +echo "Cannot find a free GPU to run the test $* on, exiting with failure..." +exit 1 + diff --git a/tensorflow/tools/ci_build/install/install_bazel.sh b/tensorflow/tools/ci_build/install/install_bazel.sh index 1df6a84d7c6f86abfb965063625ac43a3f1a57fb..3e27a94cf2bf3110ac181d6ef5a57366be17255f 100755 --- a/tensorflow/tools/ci_build/install/install_bazel.sh +++ b/tensorflow/tools/ci_build/install/install_bazel.sh @@ -15,7 +15,7 @@ # ============================================================================== # Select bazel version. -BAZEL_VERSION="0.10.0" +BAZEL_VERSION="0.11.0" set +e local_bazel_ver=$(bazel version 2>&1 | grep -i label | awk '{print $3}') diff --git a/tensorflow/tools/integration_tests/gcs_smoke_test/teardown.sh b/tensorflow/tools/ci_build/install/install_pip_packages_remote.sh similarity index 62% rename from tensorflow/tools/integration_tests/gcs_smoke_test/teardown.sh rename to tensorflow/tools/ci_build/install/install_pip_packages_remote.sh index 852486d1677ec597fe56111ffb0e470c333c1cd7..39a6d557d185d8564a79315fc738a054325aa0bc 100755 --- a/tensorflow/tools/integration_tests/gcs_smoke_test/teardown.sh +++ b/tensorflow/tools/ci_build/install/install_pip_packages_remote.sh @@ -1,5 +1,5 @@ -#!/bin/bash -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +#!/usr/bin/env bash +# Copyright 2015 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -13,14 +13,17 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -GSUTIL_BIN="/var/gcloud/google-cloud-sdk/bin/gsutil" -echo "Got teardown argument $1" +set -e -if "${GSUTIL_BIN}" rm "$1" -then - echo "Cleaned up new tfrecord file in GCS: '$1'" -else - echo "FAIL: Unable to clean up new tfrecord file in GCS: '$1'" - exit 1 +if [ ! -f /usr/bin/x86_64-linux-gnu-gcc ]; then + ln -s /usr/local/bin/clang /usr/bin/x86_64-linux-gnu-gcc fi + +pip2 install -U pip +pip3 install -U pip +pip2 install -U setuptools +pip3 install -U setuptools + +# The rest of the pip packages will be installed in +# `install_pip_packages.sh` diff --git a/tensorflow/tools/ci_build/osx/cpu/run_contrib.sh b/tensorflow/tools/ci_build/osx/cpu/run_contrib.sh index 509ee38ec4fd584037f8e43726c01391430c1817..5c5a36139f50e85e70ce4bff5ca8054f7570b0f5 100755 --- a/tensorflow/tools/ci_build/osx/cpu/run_contrib.sh +++ b/tensorflow/tools/ci_build/osx/cpu/run_contrib.sh @@ -31,7 +31,7 @@ export CC_OPT_FLAGS='-mavx' export PYTHON_BIN_PATH=$(which python2) yes "" | $PYTHON_BIN_PATH configure.py which bazel -bazel test --test_tag_filters=-no_oss,-gpu,-benchmark-test,-nomac \ +bazel test --test_tag_filters=-no_oss,-gpu,-benchmark-test,-nomac,-no_mac \ --test_timeout 300,450,1200,3600 \ --test_size_filters=small,medium --config=opt \ --jobs=${N_JOBS} --build_tests_only --test_output=errors -k -- \ diff --git a/tensorflow/tools/ci_build/osx/cpu/run_py2_cc_core.sh b/tensorflow/tools/ci_build/osx/cpu/run_py2_cc_core.sh index 05547136704394ed9262f566a2bfb4160b73c7fd..338066131b5d4511ae9f0646a1269b182cf8e1fa 100755 --- a/tensorflow/tools/ci_build/osx/cpu/run_py2_cc_core.sh +++ b/tensorflow/tools/ci_build/osx/cpu/run_py2_cc_core.sh @@ -31,7 +31,7 @@ export CC_OPT_FLAGS='-mavx' export PYTHON_BIN_PATH=$(which python2) yes "" | $PYTHON_BIN_PATH configure.py which bazel -bazel test --test_tag_filters=-no_oss,-gpu,-benchmark-test,-nomac \ +bazel test --test_tag_filters=-no_oss,-gpu,-benchmark-test,-nomac,-no_mac \ --test_timeout 300,450,1200,3600 --config=opt \ --test_size_filters=small,medium \ --jobs=${N_JOBS} --build_tests_only --test_output=errors -k -- \ diff --git a/tensorflow/tools/ci_build/osx/cpu/run_py3_cc_core.sh b/tensorflow/tools/ci_build/osx/cpu/run_py3_cc_core.sh index 8f839ca110e5bbeba6fb7f0baaeab2fe6f126319..920a261ae3c8d68ec0b0d311fd361e3843eebd86 100755 --- a/tensorflow/tools/ci_build/osx/cpu/run_py3_cc_core.sh +++ b/tensorflow/tools/ci_build/osx/cpu/run_py3_cc_core.sh @@ -30,7 +30,7 @@ export TF_NEED_CUDA=0 export PYTHON_BIN_PATH=$(which python3) yes "" | $PYTHON_BIN_PATH configure.py which bazel -bazel test --test_tag_filters=-no_oss,-gpu,-benchmark-test,-nomac \ +bazel test --test_tag_filters=-no_oss,-gpu,-benchmark-test,-nomac,-no_mac \ --test_timeout 300,450,1200,3600 \ --test_size_filters=small,medium \ --jobs=${N_JOBS} --build_tests_only --test_output=errors -k -- \ diff --git a/tensorflow/tools/ci_build/remote/Dockerfile.cpu b/tensorflow/tools/ci_build/remote/Dockerfile.cpu deleted file mode 100644 index 7b01d8320d26f38c92ad8f404da3188809a6d400..0000000000000000000000000000000000000000 --- a/tensorflow/tools/ci_build/remote/Dockerfile.cpu +++ /dev/null @@ -1,27 +0,0 @@ -FROM launcher.gcr.io/google/clang-debian8:latest - -RUN apt-get update && apt-get --no-install-recommends install -y \ - binutils \ - binutils-gold \ - curl \ - libstdc++-4.9-dev \ - python \ - python-dev \ - python-numpy \ - python-pip \ - unzip \ - zip && \ - rm -rf /var/lib/apt/lists/* - -RUN curl -fSsL -O https://bootstrap.pypa.io/get-pip.py && \ - python get-pip.py && \ - rm get-pip.py - -# Set up grpc -RUN pip install --upgrade enum34 futures mock numpy six backports.weakref portpicker && \ - pip install --pre 'protobuf>=3.0.0a3' && \ - pip install 'grpcio>=1.1.3' - -# TODO: Set up golang which is compatible with clang - -WORKDIR /botexec diff --git a/tensorflow/tools/ci_build/remote/Dockerfile.gpu b/tensorflow/tools/ci_build/remote/Dockerfile.gpu deleted file mode 100644 index 47ffd44163dd3e4b99f06689e1aa6f19f84cc2ca..0000000000000000000000000000000000000000 --- a/tensorflow/tools/ci_build/remote/Dockerfile.gpu +++ /dev/null @@ -1,27 +0,0 @@ -FROM nvidia-cuda-clang:latest - -RUN apt-get update && apt-get --no-install-recommends install -y \ - binutils \ - binutils-gold \ - curl \ - libstdc++-4.9-dev \ - python \ - python-dev \ - python-numpy \ - python-pip \ - unzip \ - zip && \ - rm -rf /var/lib/apt/lists/* - -RUN curl -fSsL -O https://bootstrap.pypa.io/get-pip.py && \ - python get-pip.py && \ - rm get-pip.py - -# Set up grpc -RUN pip install --upgrade \ - enum34 futures astor gast mock numpy six \ - backports.weakref termcolor && \ - pip install --pre 'protobuf>=3.0.0a3' && \ - pip install 'grpcio>=1.1.3' - -WORKDIR /botexec diff --git a/tensorflow/tools/ci_build/windows/bazel/common_env.sh b/tensorflow/tools/ci_build/windows/bazel/common_env.sh index 1c35d74af72ad0a72b0016356888c8cf77e20e56..7d4cc7ac3005f7ff9a79d18228e86d6b74e1e8b0 100644 --- a/tensorflow/tools/ci_build/windows/bazel/common_env.sh +++ b/tensorflow/tools/ci_build/windows/bazel/common_env.sh @@ -34,6 +34,9 @@ export BAZEL_SH=${BAZEL_SH:-"C:/tools/msys64/usr/bin/bash"} export PYTHON_BASE_PATH="${PYTHON_DIRECTORY:-Program Files/Anaconda3}" +# Set the path to find bazel. +export PATH="/c/tools/bazel/:$PATH" + # Set Python path for ./configure export PYTHON_BIN_PATH="C:/${PYTHON_BASE_PATH}/python.exe" export PYTHON_LIB_PATH="C:/${PYTHON_BASE_PATH}/lib/site-packages" 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 8b8ba31a0dda88ad3c43330e0208a9fa6a7d0276..40189a6d1b1388092dc397251f4b581726995a77 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 @@ -65,4 +65,5 @@ bazel test -c opt $BUILD_OPTS -k --test_output=errors \ --define=no_tensorflow_py_deps=true --test_lang_filters=py \ --test_tag_filters=-no_pip,-no_windows,-no_oss \ --build_tag_filters=-no_pip,-no_windows,-no_oss --build_tests_only \ - //${PY_TEST_DIR}/tensorflow/python/... + //${PY_TEST_DIR}/tensorflow/python/... \ + //${PY_TEST_DIR}/tensorflow/contrib/... diff --git a/tensorflow/tools/ci_build/windows/gpu/cmake/run_build.bat b/tensorflow/tools/ci_build/windows/gpu/cmake/run_build.bat index b87e4a9bec41264827d415a11dfa6f23aeda725d..4656afe0256d03540fed6912677c8e93f9cf9eb6 100644 --- a/tensorflow/tools/ci_build/windows/gpu/cmake/run_build.bat +++ b/tensorflow/tools/ci_build/windows/gpu/cmake/run_build.bat @@ -37,7 +37,7 @@ SET CMAKE_DIR=%REPO_ROOT%\tensorflow\contrib\cmake SET MSBUILD_EXE="C:\Program Files (x86)\MSBuild\14.0\Bin\msbuild.exe" :: Run cmake to create Visual Studio Project files. -%CMAKE_EXE% %CMAKE_DIR% -A x64 -DSWIG_EXECUTABLE=%SWIG_EXE% -DPYTHON_EXECUTABLE=%PY_EXE% -DCMAKE_BUILD_TYPE=Release -DPYTHON_LIBRARIES=%PY_LIB% -Dtensorflow_BUILD_PYTHON_TESTS=%BUILD_PYTHON_TESTS% -Dtensorflow_BUILD_CC_TESTS=%BUILD_CC_TESTS% -Dtensorflow_ENABLE_GPU=ON -DCUDNN_HOME=%CUDNN_HOME% -Dtensorflow_TF_NIGHTLY=%TF_NIGHTLY% -Dtensorflow_DISABLE_EIGEN_FORCEINLINE=%DISABLE_FORCEINLINE% -Dtensorflow_WIN_CPU_SIMD_OPTIONS=/arch:AVX +%CMAKE_EXE% %CMAKE_DIR% -A x64 -DSWIG_EXECUTABLE=%SWIG_EXE% -DPYTHON_EXECUTABLE=%PY_EXE% -DCMAKE_BUILD_TYPE=Release -DPYTHON_LIBRARIES=%PY_LIB% -Dtensorflow_BUILD_PYTHON_TESTS=%BUILD_PYTHON_TESTS% -Dtensorflow_BUILD_CC_TESTS=%BUILD_CC_TESTS% -Dtensorflow_ENABLE_GPU=ON -DCUDNN_HOME=%CUDNN_HOME% -Dtensorflow_TF_NIGHTLY=%TF_NIGHTLY% -Dtensorflow_DISABLE_EIGEN_FORCEINLINE=%DISABLE_FORCEINLINE% -Dtensorflow_WIN_CPU_SIMD_OPTIONS=/arch:AVX -G"Visual Studio 14" :: Run msbuild in the resulting VS project files to build a pip package. %MSBUILD_EXE% /p:Configuration=Release /maxcpucount:32 tf_python_build_pip_package.vcxproj diff --git a/tensorflow/tools/compatibility/tf_upgrade.py b/tensorflow/tools/compatibility/tf_upgrade.py index 6e90b286c99f894ddd25268afc69043759571c36..1f8833582af4c922115e637117e775e619439786 100644 --- a/tensorflow/tools/compatibility/tf_upgrade.py +++ b/tensorflow/tools/compatibility/tf_upgrade.py @@ -662,9 +662,9 @@ class TFAPIChangeSpec(APIChangeSpec): def _reverse_handler(file_edit_recorder, node): # TODO(aselle): Could check for a literal list of bools and try to convert # them to indices. - comment = ("ERROR: tf.reverse has had its argument semantics changed\n" - "significantly the converter cannot detect this reliably, so you" - "need to inspect this usage manually.\n") + comment = ("ERROR: tf.reverse has had its argument semantics changed " + "significantly the converter cannot detect this reliably, so " + "you need to inspect this usage manually.\n") file_edit_recorder.add( comment, node.lineno, diff --git a/third_party/cub/BUILD b/tensorflow/tools/def_file_filter/BUILD similarity index 100% rename from third_party/cub/BUILD rename to tensorflow/tools/def_file_filter/BUILD diff --git a/tensorflow/tools/def_file_filter/BUILD.tpl b/tensorflow/tools/def_file_filter/BUILD.tpl new file mode 100644 index 0000000000000000000000000000000000000000..3cb72f49797d8014e4df3f44155c967ab0e2f9b3 --- /dev/null +++ b/tensorflow/tools/def_file_filter/BUILD.tpl @@ -0,0 +1,15 @@ +# Description: +# Tools for filtering DEF file for TensorFlow on Windows +# +# On Windows, we use a DEF file generated by Bazel to export +# symbols from the tensorflow dynamic library(_pywrap_tensorflow.dll). +# The maximum number of symbols that can be exported per DLL is 64K, +# so we have to filter some useless symbols through this python script. + +package(default_visibility = ["//visibility:public"]) + +py_binary( + name = "def_file_filter", + srcs = ["def_file_filter.py"], + srcs_version = "PY2AND3", +) diff --git a/tensorflow/tools/def_file_filter/def_file_filter.py.tpl b/tensorflow/tools/def_file_filter/def_file_filter.py.tpl new file mode 100644 index 0000000000000000000000000000000000000000..8bdc03eb0f19fd6daae826727f429bc1255f0eca --- /dev/null +++ b/tensorflow/tools/def_file_filter/def_file_filter.py.tpl @@ -0,0 +1,168 @@ +# 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. +# ============================================================================== + +"""def_file_filter.py - tool to filter a windows def file. + +The def file can be used to export symbols from the tensorflow dll to enable +tf.load_library(). + +Because the linker allows only 64K symbols to be exported per dll +we filter the symbols down to the essentials. The regular expressions +we use for this are specific to tensorflow. + +TODO: this works fine but there is an issue with exporting +'const char * const' and importing it from a user_ops. The problem is +on the importing end and using __declspec(dllimport) works around it. +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import argparse +import io +import os +import re +import subprocess +import sys +import tempfile + +# External tools we use that come with visual studio sdk +UNDNAME = "%{undname_bin_path}" + +# Exclude if matched +EXCLUDE_RE = re.compile(r"RTTI|deleting destructor|::internal::") + +# Include if matched before exclude +INCLUDEPRE_RE = re.compile(r"google::protobuf::internal::ExplicitlyConstructed|" + r"google::protobuf::internal::ArenaImpl::AllocateAligned|" # for contrib/data/_prefetching_ops + r"google::protobuf::internal::ArenaImpl::AddCleanup|" # for contrib/data/_prefetching_ops + r"google::protobuf::Arena::OnArenaAllocation|" # for contrib/data/_prefetching_ops + r"tensorflow::internal::LogMessage|" + r"tensorflow::internal::LogString|" + r"tensorflow::internal::CheckOpMessageBuilder|" + r"tensorflow::internal::MakeCheckOpValueString|" + r"tensorflow::internal::PickUnusedPortOrDie|" + r"tensorflow::internal::ValidateDevice|" + r"tensorflow::ops::internal::Enter|" + r"tensorflow::strings::internal::AppendPieces|" + r"tensorflow::strings::internal::CatPieces|" + r"tensorflow::io::internal::JoinPathImpl") + +# Include if matched after exclude +INCLUDE_RE = re.compile(r"^(TF_\w*)$|" + r"^(TFE_\w*)$|" + r"nsync::|" + r"tensorflow::|" + r"functor::|" + r"perftools::gputools") + +# We want to identify data members explicitly in the DEF file, so that no one +# can implicitly link against the DLL if they use one of the variables exported +# from the DLL and the header they use does not decorate the symbol with +# __declspec(dllimport). It is easier to detect what a data symbol does +# NOT look like, so doing it with the below regex. +DATA_EXCLUDE_RE = re.compile(r"[)(]|" + r"vftable|" + r"vbtable|" + r"vcall|" + r"RTTI|" + r"protobuf::internal::ExplicitlyConstructed") + +def get_args(): + """Parse command line.""" + filename_list = lambda x: x.split(";") + parser = argparse.ArgumentParser() + parser.add_argument("--input", type=filename_list, + help="paths to input def file", + required=True) + parser.add_argument("--output", help="output deffile", required=True) + parser.add_argument("--target", help="name of the target", required=True) + args = parser.parse_args() + return args + + +def main(): + """main.""" + args = get_args() + + # Pipe dumpbin to extract all linkable symbols from libs. + # Good symbols are collected in candidates and also written to + # a temp file. + candidates = [] + tmpfile = tempfile.NamedTemporaryFile(mode="w", delete=False) + for def_file_path in args.input: + def_file = open(def_file_path, 'r') + for line in def_file: + cols = line.split() + sym = cols[0] + tmpfile.file.write(sym + "\n") + candidates.append(sym) + tmpfile.file.close() + + # Run the symbols through undname to get their undecorated name + # so we can filter on something readable. + with open(args.output, "w") as def_fp: + # track dupes + taken = set() + + # Header for the def file. + def_fp.write("LIBRARY " + args.target + "\n") + def_fp.write("EXPORTS\n") + def_fp.write("\t ??1OpDef@tensorflow@@UEAA@XZ\n") + + # Each symbols returned by undname matches the same position in candidates. + # We compare on undname but use the decorated name from candidates. + dupes = 0 + proc = subprocess.Popen([UNDNAME, tmpfile.name], stdout=subprocess.PIPE) + for idx, line in enumerate(io.TextIOWrapper(proc.stdout, encoding="utf-8")): + decorated = candidates[idx] + if decorated in taken: + # Symbol is already in output, done. + dupes += 1 + continue + + if not INCLUDEPRE_RE.search(line): + if EXCLUDE_RE.search(line): + continue + if not INCLUDE_RE.search(line): + continue + + if "deleting destructor" in line: + # Some of the symbols convered by INCLUDEPRE_RE export deleting + # destructor symbols, which is a bad idea. + # So we filter out such symbols here. + continue + + if DATA_EXCLUDE_RE.search(line): + def_fp.write("\t" + decorated + "\n") + else: + def_fp.write("\t" + decorated + " DATA\n") + taken.add(decorated) + def_fp.close() + + exit_code = proc.wait() + if exit_code != 0: + print("{} failed, exit={}".format(UNDNAME, exit_code)) + return exit_code + + os.unlink(tmpfile.name) + + print("symbols={}, taken={}, dupes={}" + .format(len(candidates), len(taken), dupes)) + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/tensorflow/tools/def_file_filter/def_file_filter_configure.bzl b/tensorflow/tools/def_file_filter/def_file_filter_configure.bzl new file mode 100644 index 0000000000000000000000000000000000000000..47539b2423e602bb9771541ae5b01ba76c79f56f --- /dev/null +++ b/tensorflow/tools/def_file_filter/def_file_filter_configure.bzl @@ -0,0 +1,56 @@ +"""Repository rule for def file filter autoconfiguration. + +This repository reuses Bazel's VC detect mechanism to find undname.exe, +which is a tool used in def_file_filter.py. + +def_file_filter.py is for filtering the DEF file for TensorFlow on Windows. +On Windows, we use a DEF file generated by Bazel to export symbols from the +tensorflow dynamic library(_pywrap_tensorflow.dll). The maximum number of +symbols that can be exported per DLL is 64K, so we have to filter some useless +symbols through this python script. + +`def_file_filter_config` depends on the following environment variables: + * `BAZEL_VC` + * `BAZEL_VS` + * `VS90COMNTOOLS` + * `VS100COMNTOOLS` + * `VS110COMNTOOLS` + * `VS120COMNTOOLS` + * `VS140COMNTOOLS` +""" + +load("@bazel_tools//tools/cpp:windows_cc_configure.bzl", "find_vc_path") +load("@bazel_tools//tools/cpp:windows_cc_configure.bzl", "find_msvc_tool") +load("@bazel_tools//tools/cpp:lib_cc_configure.bzl", "auto_configure_fail") + +def _def_file_filter_configure_impl(repository_ctx): + if repository_ctx.os.name.lower().find("windows") == -1: + repository_ctx.symlink(Label("//tensorflow/tools/def_file_filter:BUILD.tpl"), "BUILD") + repository_ctx.file("def_file_filter.py", "") + return + vc_path = find_vc_path(repository_ctx) + if vc_path == "visual-studio-not-found": + auto_configure_fail("Visual C++ build tools not found on your machine") + undname_bin_path = find_msvc_tool(repository_ctx, vc_path, "undname.exe").replace("\\", "\\\\") + + repository_ctx.template( + "def_file_filter.py", + Label("//tensorflow/tools/def_file_filter:def_file_filter.py.tpl"), + { + "%{undname_bin_path}": undname_bin_path, + }) + repository_ctx.symlink(Label("//tensorflow/tools/def_file_filter:BUILD.tpl"), "BUILD") + + +def_file_filter_configure = repository_rule( + implementation = _def_file_filter_configure_impl, + environ = [ + "BAZEL_VC", + "BAZEL_VS", + "VS90COMNTOOLS", + "VS100COMNTOOLS", + "VS110COMNTOOLS", + "VS120COMNTOOLS", + "VS140COMNTOOLS" + ], +) diff --git a/tensorflow/tools/dist_test/README.md b/tensorflow/tools/dist_test/README.md index c1b1f79bbd4b657768b9bbcab93efa3354774915..228d5ee35d1839c60b51a85bd606c1ba86e46886 100644 --- a/tensorflow/tools/dist_test/README.md +++ b/tensorflow/tools/dist_test/README.md @@ -17,6 +17,14 @@ cesnsu model: ./local_test.sh --model_name CENSUS_WIDENDEEP +You can test specify version of TensorFlow: + +```shell +./local_test.sh ${whl_file_url} +``` + +For example, you can find these TensorFlow python package URLs from [here](https://www.tensorflow.org/install/install_linux#the_url_of_the_tensorflow_python_package) for Ubuntu. + **2) Launch a remote k8s cluster on Google Kubernetes Engine (GKE) and run the test suite on it** diff --git a/tensorflow/tools/dist_test/local_test.sh b/tensorflow/tools/dist_test/local_test.sh index 435f9d0dc9c55a3dcfc45e7e46f279b4679a9086..caae7fd5305af9846628eaf00348dd08df4e827f 100755 --- a/tensorflow/tools/dist_test/local_test.sh +++ b/tensorflow/tools/dist_test/local_test.sh @@ -16,12 +16,11 @@ # # Tests distributed TensorFlow on a locally running TF GRPC cluster. # -# This script peforms the following steps: -# 1) Build the docker-in-docker (dind) image capable of running docker and -# Kubernetes (k8s) cluster inside. +# This script performs the following steps: +# 1) Build the docker image capable of running distributed TensorFlow in docker. # 2) Run a container from the aforementioned image and start docker service # in it -# 3) Call a script to launch a k8s TensorFlow GRPC cluster inside the container +# 3) Call a script to launch a distributed TensorFlow GRPC cluster inside the container # and run the distributed test suite. # # Usage: local_test.sh @@ -64,15 +63,9 @@ die() { # Configurations DOCKER_IMG_NAME="tensorflow/tf-dist-test-local-cluster" -LOCAL_K8S_CACHE=${HOME}/kubernetes -# Helper function -get_container_id_by_image_name() { - # Get the id of a container by image name - # Usage: get_docker_container_id_by_image_name - - docker ps | grep $1 | awk '{print $1}' -} +# Use TensorFlow v1.5.0 for Python 2.7 and CPU only as we set num_gpus to 0 in the below +DEFAULT_WHL_FILE_LOCATION="https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp27-none-linux_x86_64.whl" # Parse input arguments LEAVE_CONTAINER_RUNNING=0 @@ -84,7 +77,8 @@ SYNC_REPLICAS_FLAG="" WHL_FILE_LOCATION=${1} if [[ -z "${WHL_FILE_LOCATION}" ]]; then - die "whl file location is not specified" + WHL_FILE_LOCATION=${DEFAULT_WHL_FILE_LOCATION} + echo "use default whl file location" fi while true; do @@ -121,7 +115,7 @@ DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" # Get utility functions source ${DIR}/scripts/utils.sh -# Build docker-in-docker image for local k8s cluster. +# Build docker image for local distributed TensorFlow cluster. NO_CACHE_FLAG="" if [[ ! -z "${TF_DIST_DOCKER_NO_CACHE}" ]] && [[ "${TF_DIST_DOCKER_NO_CACHE}" != "0" ]]; then diff --git a/tensorflow/tools/dist_test/python/mnist_replica.py b/tensorflow/tools/dist_test/python/mnist_replica.py index a2d12442c44553a287637029843021b7541fa3fa..d6e7f317dd0b52203e354676425dbbbcd53e1973 100644 --- a/tensorflow/tools/dist_test/python/mnist_replica.py +++ b/tensorflow/tools/dist_test/python/mnist_replica.py @@ -56,7 +56,7 @@ flags.DEFINE_integer("task_index", None, flags.DEFINE_integer("num_gpus", 1, "Total number of gpus for each machine." "If you don't use GPU, please set it to '0'") flags.DEFINE_integer("replicas_to_aggregate", None, - "Number of replicas to aggregate before parameter update" + "Number of replicas to aggregate before parameter update " "is applied (For sync_replicas mode only; default: " "num_workers)") flags.DEFINE_integer("hidden_units", 100, diff --git a/tensorflow/tools/docker/Dockerfile.devel b/tensorflow/tools/docker/Dockerfile.devel index d16761c3675942838fd2be0ea6e0b7463a3bf249..22c73c3fe13f2cb763295fa25b43e2f82c0e8962 100644 --- a/tensorflow/tools/docker/Dockerfile.devel +++ b/tensorflow/tools/docker/Dockerfile.devel @@ -57,7 +57,7 @@ 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 0.8.0 +ENV BAZEL_VERSION 0.11.0 WORKDIR / RUN mkdir /bazel && \ cd /bazel && \ diff --git a/tensorflow/tools/docker/Dockerfile.devel-gpu b/tensorflow/tools/docker/Dockerfile.devel-gpu index 4ef37881bc91aaa58bab031c69b4a96c2a9d8ec1..69ba340f9201266fd2c2f86571e83f6acdcda950 100644 --- a/tensorflow/tools/docker/Dockerfile.devel-gpu +++ b/tensorflow/tools/docker/Dockerfile.devel-gpu @@ -66,7 +66,7 @@ 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 0.8.0 +ENV BAZEL_VERSION 0.11.0 WORKDIR / RUN mkdir /bazel && \ cd /bazel && \ diff --git a/tensorflow/tools/docker/Dockerfile.gpu b/tensorflow/tools/docker/Dockerfile.gpu index b6682cd68163ec870ed815b45ac4fdd9233f88c6..625321e1235202f78a2d5e1a5b2d9d05e1e3f9ba 100644 --- a/tensorflow/tools/docker/Dockerfile.gpu +++ b/tensorflow/tools/docker/Dockerfile.gpu @@ -1,11 +1,18 @@ -FROM nvidia/cuda:9.0-cudnn7-runtime-ubuntu16.04 +FROM nvidia/cuda:9.0-base-ubuntu16.04 LABEL maintainer="Craig Citro " # Pick up some TF dependencies RUN apt-get update && apt-get install -y --no-install-recommends \ build-essential \ + cuda-command-line-tools-9-0 \ + cuda-cublas-9-0 \ + cuda-cufft-9-0 \ + cuda-curand-9-0 \ + cuda-cusolver-9-0 \ + cuda-cusparse-9-0 \ curl \ + libcudnn7=7.0.5.15-1+cuda9.0 \ libfreetype6-dev \ libpng12-dev \ libzmq3-dev \ diff --git a/tensorflow/tools/git/gen_git_source.py b/tensorflow/tools/git/gen_git_source.py index 3630dbd740e981971bdc9ff45b756b45095d437d..cbcdbf5b807a585865e2e3f19291e55388d55cb1 100755 --- a/tensorflow/tools/git/gen_git_source.py +++ b/tensorflow/tools/git/gen_git_source.py @@ -114,6 +114,13 @@ def configure(src_base_path, gen_path, debug=False): for target, src in link_map.items(): if src is None: open(os.path.join(gen_path, target), "w").write("") + elif not os.path.exists(src): + # Git repo is configured in a way we don't support such as having + # packed refs. Even though in a git repo, tf.__git_version__ will not + # be accurate. + # TODO(mikecase): Support grabbing git info when using packed refs. + open(os.path.join(gen_path, target), "w").write("") + spec["git"] = False else: try: # In python 3.5, symlink function exists even on Windows. But requires diff --git a/tensorflow/tools/graph_transforms/BUILD b/tensorflow/tools/graph_transforms/BUILD index ad3668fa02e102607c9a03ac312451a147affdda..b7d7fac315367824c6fdb379eaa6b0812d89b28b 100644 --- a/tensorflow/tools/graph_transforms/BUILD +++ b/tensorflow/tools/graph_transforms/BUILD @@ -91,7 +91,6 @@ cc_library( srcs = [ "add_default_attributes.cc", "backports.cc", - "fake_quantize_training.cc", "flatten_atrous.cc", "fold_batch_norms.cc", "fold_constants_lib.cc", @@ -105,7 +104,6 @@ cc_library( "remove_attribute.cc", "remove_control_dependencies.cc", "remove_device.cc", - "remove_ema.cc", "remove_nodes.cc", "rename_attribute.cc", "rename_op.cc", @@ -134,8 +132,8 @@ cc_library( "//tensorflow/core:tensorflow", "//tensorflow/contrib/rnn:gru_ops_op_lib", "//tensorflow/contrib/rnn:lstm_ops_op_lib", + "//tensorflow/core/kernels:quantization_utils", ] + if_not_windows([ - "//tensorflow/core/kernels:quantized_ops", "//tensorflow/core/kernels:remote_fused_graph_rewriter_transform", "//tensorflow/core/kernels/hexagon:hexagon_rewriter_transform", ]), @@ -148,7 +146,6 @@ tf_cc_test( srcs = [ "add_default_attributes_test.cc", "backports_test.cc", - "fake_quantize_training_test.cc", "flatten_atrous_test.cc", "fold_batch_norms_test.cc", "fold_constants_test.cc", @@ -161,7 +158,6 @@ tf_cc_test( "quantize_weights_test.cc", "remove_attribute_test.cc", "remove_device_test.cc", - "remove_ema_test.cc", "remove_nodes_test.cc", "rename_attribute_test.cc", "rename_op_test.cc", diff --git a/tensorflow/tools/graph_transforms/fake_quantize_training.cc b/tensorflow/tools/graph_transforms/fake_quantize_training.cc deleted file mode 100644 index 61aecc6e16d817d245421f18fa39c70aa45b2bef..0000000000000000000000000000000000000000 --- a/tensorflow/tools/graph_transforms/fake_quantize_training.cc +++ /dev/null @@ -1,51 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#define EIGEN_USE_THREADS - -#include "tensorflow/core/graph/quantize_training.h" -#include "tensorflow/tools/graph_transforms/transform_utils.h" - -namespace tensorflow { -namespace graph_transforms { - -// EXPERIMENTAL: This can change without warning. -// Rewrites the GraphDef for quantized training. -// Rewrites the forward pass to include the precision loss with quantization so -// the model can learn to deal with such loss and achieve better accuracy when -// it is quantized later for inference. -// Quantization range information is collected in FakeQuantizeWithMinMaxVars -// ops. -// -// TODO(suharshs): Provide instructions on converting the resulting graph for -// inference. -// TODO(suharshs): Implement this using the GTT rather than calling the old -// prototype function. -Status FakeQuantizeTraining(const GraphDef& input_graph_def, - const TransformFuncContext& context, - GraphDef* output_graph_def) { - // TODO(suharshs): Make num_bits a parameter. - const int32 num_bits = 8; - // TODO(suharshs): Make quantization op a parameter? - const string quant_op_type = "FakeQuantWithMinMaxVars"; - - return DoQuantizeTrainingOnGraphDef(input_graph_def, num_bits, quant_op_type, - output_graph_def); -} - -REGISTER_GRAPH_TRANSFORM("fake_quantize_training", FakeQuantizeTraining); - -} // namespace graph_transforms -} // namespace tensorflow diff --git a/tensorflow/tools/graph_transforms/fake_quantize_training_test.cc b/tensorflow/tools/graph_transforms/fake_quantize_training_test.cc deleted file mode 100644 index 5e4ab209e97808c3f42ecf73fb763ef9d7ab1cfe..0000000000000000000000000000000000000000 --- a/tensorflow/tools/graph_transforms/fake_quantize_training_test.cc +++ /dev/null @@ -1,63 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/cc/ops/const_op.h" -#include "tensorflow/cc/ops/math_ops.h" -#include "tensorflow/core/framework/tensor_testutil.h" -#include "tensorflow/core/lib/core/status_test_util.h" -#include "tensorflow/core/platform/test.h" -#include "tensorflow/tools/graph_transforms/transform_utils.h" - -namespace tensorflow { -namespace graph_transforms { - -// Declare here, so we don't need a public header. -Status FakeQuantizeTraining(const GraphDef& input_graph_def, - const TransformFuncContext& context, - GraphDef* output_graph_def); - -class FakeQuantizeTrainingTest : public ::testing::Test {}; - -// For now, since the fake_quantize_training transform just calls the -// quantize_training rewrite from tensorflow/core/graph/quantize_training.h, -// we just test that the graph has been changed by the transform. -// TODO(suharshs): Once we implement the fake_quantize_training transform -// using the GTT, write proper tests of the transform here. -TEST_F(FakeQuantizeTrainingTest, TransformOccurred) { - auto root = tensorflow::Scope::DisabledShapeInferenceScope(); - using namespace ::tensorflow::ops; // NOLINT(build/namespaces) - - Tensor a_data(DT_FLOAT, TensorShape()); - test::FillIota(&a_data, 1.0f); - Output a_const = Const(root.WithOpName("a"), Input::Initializer(a_data)); - - Tensor b_data(DT_FLOAT, TensorShape()); - test::FillIota(&b_data, 1.0f); - Output b_const = Const(root.WithOpName("b"), Input::Initializer(b_data)); - - Output matmul = MatMul(root.WithOpName("matmul"), a_const, b_const); - GraphDef graph_def; - TF_ASSERT_OK(root.ToGraphDef(&graph_def)); - - GraphDef result; - TransformFuncContext context; - TF_ASSERT_OK(FakeQuantizeTraining(graph_def, context, &result)); - - // Test that the transformation resulted in a graph with more nodes. - EXPECT_GT(result.node_size(), graph_def.node_size()); -} - -} // namespace graph_transforms -} // namespace tensorflow diff --git a/tensorflow/tools/graph_transforms/fold_old_batch_norms.cc b/tensorflow/tools/graph_transforms/fold_old_batch_norms.cc index d89afe85c72883323cec3c14342fd60adebd024d..d86f65325be1c3f5151ab8d0a0c3c64afa3dcf0f 100644 --- a/tensorflow/tools/graph_transforms/fold_old_batch_norms.cc +++ b/tensorflow/tools/graph_transforms/fold_old_batch_norms.cc @@ -182,6 +182,36 @@ Status FuseBatchNormWithConv(const NodeMatch& match, return Status::OK(); } +Status FuseBatchNormWithBatchToSpace(const NodeMatch& match, + std::vector* new_nodes) { + // Calculate the scale and offset values to apply. + std::vector scale_values; + std::vector offset_values; + TF_RETURN_IF_ERROR( + GetScaleAndOffsetValues(match, &scale_values, &offset_values)); + + // Fuse conv weights, and set the final output node name as batch_norm_node. + const NodeDef& batch_norm_node = match.node; + const NodeMatch& batch_to_space_node_match = match.inputs[0]; + const NodeMatch& conv_node_match = batch_to_space_node_match.inputs[0]; + const NodeDef& batch_to_space_node = batch_to_space_node_match.node; + const NodeDef& conv_node = conv_node_match.node; + + string biasadd_name = conv_node.name() + "/biasadd"; + TF_RETURN_IF_ERROR( + FuseScaleOffsetToConvWeights(scale_values, offset_values, conv_node_match, + biasadd_name , new_nodes)); + + NodeDef new_batch_to_space_node = batch_to_space_node; + // reuse batch_norm node name + new_batch_to_space_node.set_name(batch_norm_node.name()); + new_batch_to_space_node.set_input(0, biasadd_name); + new_nodes->push_back(batch_to_space_node_match.inputs[1].node); + new_nodes->push_back(batch_to_space_node_match.inputs[2].node); + new_nodes->push_back(new_batch_to_space_node); + return Status::OK(); +} + Status FuseBatchNormWithConvConcat(const NodeMatch& match, std::vector* new_nodes) { // Calculate the scale and offset values to apply. @@ -284,6 +314,43 @@ Status FoldOldBatchNorms(const GraphDef& input_graph_def, current_graph_def = replaced_graph_def; } while (did_graph_change); + do { + did_graph_change = false; + GraphDef replaced_graph_def; + TF_RETURN_IF_ERROR(ReplaceMatchingOpTypes( + current_graph_def, // clang-format off + {"BatchNormWithGlobalNormalization|FusedBatchNorm", // batch_norm_node + { + {"BatchToSpaceND", // batch_to_space_node + { + {"Conv2D", // conv_node + { + {"*"}, // input_node + {"Const"}, // weights_node + } + }, + {"Const"}, // block_shape + {"Const"}, // crops + } + }, + {"Const"}, // mean_node + {"Const"}, // variance_node + {"Const"}, // beta_node + {"Const"}, // gamma_node + } + }, // clang-format on + [&did_graph_change](const NodeMatch& match, + const std::set& input_nodes, + const std::set& output_nodes, + std::vector* new_nodes) { + TF_RETURN_IF_ERROR(FuseBatchNormWithBatchToSpace(match, new_nodes)); + did_graph_change = true; + return Status::OK(); + }, + {}, &replaced_graph_def)); + current_graph_def = replaced_graph_def; + } while (did_graph_change); + do { did_graph_change = false; GraphDef replaced_graph_def; diff --git a/tensorflow/tools/graph_transforms/fold_old_batch_norms_test.cc b/tensorflow/tools/graph_transforms/fold_old_batch_norms_test.cc index b30ba9ac8b92db68eb3374c51a7f31b69cd1e3cf..272410c693ae8de8bb131ea577700fd16815c42e 100644 --- a/tensorflow/tools/graph_transforms/fold_old_batch_norms_test.cc +++ b/tensorflow/tools/graph_transforms/fold_old_batch_norms_test.cc @@ -16,6 +16,7 @@ limitations under the License. #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/array_ops.h" #include "tensorflow/cc/ops/sendrecv_ops.h" #include "tensorflow/cc/ops/standard_ops.h" #include "tensorflow/core/framework/tensor_testutil.h" @@ -298,6 +299,96 @@ class FoldOldBatchNormsTest : public ::testing::Test { } }; +void TestFoldFusedBatchNormsWithBatchToSpace() { + auto root = tensorflow::Scope::NewRootScope(); + using namespace ::tensorflow::ops; // NOLINT(build/namespaces) + + Tensor input_data(DT_FLOAT, TensorShape({2, 1, 3, 2})); + test::FillValues( + &input_data, {1.0f, 4.0f, 2.0f, 5.0f, 3.0f, 6.0f, -1.0f, -4.0f, -2.0f, + -5.0f, -3.0f, -6.0f}); + Output input_op = + Const(root.WithOpName("input_op"), Input::Initializer(input_data)); + + Tensor weights_data(DT_FLOAT, TensorShape({1, 2, 2, 2})); + test::FillValues(&weights_data, + {1.0f, 2.0f, 3.0f, 4.0f, 0.1f, 0.2f, 0.3f, 0.4f}); + Output weights_op = + Const(root.WithOpName("weights_op"), Input::Initializer(weights_data)); + + Output conv_op = Conv2D(root.WithOpName("conv_op"), input_op, weights_op, + {1, 1, 1, 1}, "VALID"); + + Tensor block_shape_data(DT_INT32, TensorShape({2})); + test::FillValues(&block_shape_data, {1, 2}); + Output block_shape_op = + Const(root.WithOpName("block_shape_op"), Input::Initializer(block_shape_data)); + + Tensor crops_data(DT_INT32, TensorShape({2, 2})); + test::FillValues(&crops_data, {0, 0, 0, 1}); + Output crops_op = + Const(root.WithOpName("crops_op"), Input::Initializer(crops_data)); + + Output batch_to_space_op = BatchToSpaceND(root.WithOpName("batch_to_space_op"), + conv_op, block_shape_op, crops_data); + + Tensor mean_data(DT_FLOAT, TensorShape({2})); + test::FillValues(&mean_data, {10.0f, 20.0f}); + Output mean_op = + Const(root.WithOpName("mean_op"), Input::Initializer(mean_data)); + + Tensor variance_data(DT_FLOAT, TensorShape({2})); + test::FillValues(&variance_data, {0.25f, 0.5f}); + Output variance_op = Const(root.WithOpName("variance_op"), + Input::Initializer(variance_data)); + + Tensor beta_data(DT_FLOAT, TensorShape({2})); + test::FillValues(&beta_data, {0.1f, 0.6f}); + Output beta_op = + Const(root.WithOpName("beta_op"), Input::Initializer(beta_data)); + + Tensor gamma_data(DT_FLOAT, TensorShape({2})); + test::FillValues(&gamma_data, {1.0f, 2.0f}); + Output gamma_op = + Const(root.WithOpName("gamma_op"), Input::Initializer(gamma_data)); + + GraphDef original_graph_def; + TF_ASSERT_OK(root.ToGraphDef(&original_graph_def)); + + NodeDef batch_norm_node; + batch_norm_node.set_op("FusedBatchNorm"); + batch_norm_node.set_name("output"); + AddNodeInput("batch_to_space_op", &batch_norm_node); + AddNodeInput("gamma_op", &batch_norm_node); + AddNodeInput("beta_op", &batch_norm_node); + AddNodeInput("mean_op", &batch_norm_node); + AddNodeInput("variance_op", &batch_norm_node); + SetNodeAttr("T", DT_FLOAT, &batch_norm_node); + SetNodeAttr("epsilon", 0.00001f, &batch_norm_node); + SetNodeAttr("is_training", false, &batch_norm_node); + *(original_graph_def.mutable_node()->Add()) = batch_norm_node; + + std::unique_ptr original_session(NewSession(SessionOptions())); + TF_ASSERT_OK(original_session->Create(original_graph_def)); + std::vector original_outputs; + TF_ASSERT_OK(original_session->Run({}, {"output"}, {}, &original_outputs)); + + GraphDef fused_graph_def; + TF_ASSERT_OK(FoldOldBatchNorms(original_graph_def, {{}, {"output"}}, + &fused_graph_def)); + + std::unique_ptr fused_session(NewSession(SessionOptions())); + TF_ASSERT_OK(fused_session->Create(fused_graph_def)); + std::vector fused_outputs; + TF_ASSERT_OK(fused_session->Run({}, {"output"}, {}, &fused_outputs)); + + test::ExpectTensorNear(original_outputs[0], fused_outputs[0], 1e-5); + + for (const NodeDef& node : fused_graph_def.node()) { + EXPECT_NE("FusedBatchNormWithBatchToSpace", node.op()); + } +} + TEST_F(FoldOldBatchNormsTest, TestFoldOldBatchNorms) { TestFoldOldBatchNorms(); } @@ -315,5 +406,9 @@ TEST_F(FoldOldBatchNormsTest, TestFoldFusedBatchNormsWithConcat) { TestFoldFusedBatchNormsWithConcat(/*split=*/false); } +TEST_F(FoldOldBatchNormsTest, TestFoldFusedBatchNormsWithBatchToSpace) { + TestFoldFusedBatchNormsWithBatchToSpace(); +} + } // namespace graph_transforms } // namespace tensorflow diff --git a/tensorflow/tools/graph_transforms/remove_ema.cc b/tensorflow/tools/graph_transforms/remove_ema.cc deleted file mode 100644 index 22e26267025c3fed4f44ffbc09d55d8d355cc448..0000000000000000000000000000000000000000 --- a/tensorflow/tools/graph_transforms/remove_ema.cc +++ /dev/null @@ -1,146 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#define EIGEN_USE_THREADS - -#include "tensorflow/tools/graph_transforms/transform_utils.h" - -namespace tensorflow { -namespace graph_transforms { - -// EXPERIMENTAL: This can change without warning. -// Given a graph that has gone through the FakeQuantizeTraining transform and -// has been frozen afterwards, RemoveEMA simplifies the FakeQuantize estimated -// moving average subgraphs to make it compatible with the QuantizeNodes -// transform. -Status RemoveEMA(const GraphDef& input_graph_def, - const TransformFuncContext& context, - GraphDef* output_graph_def) { - TF_RETURN_IF_ERROR(ReplaceMatchingOpTypes( - input_graph_def, // clang-format off - {"FakeQuantWithMinMaxVars", - { - {"*"}, - {"Assign", - { - {"Const"}, - {"Merge", - { - {"Switch", - { - {"Min", - { - {"*"}, - {"Range", - { - {"*"}, - {"*"}, - {"*"}, - } - } - } - }, - {"IsVariableInitialized"} - } - }, - {"Sub", - { - {"Const"}, - {"Mul", - { - {"Sub"}, - {"Sub", - { - {"Const"}, - {"Const"} - } - } - } - } - } - } - } - } - } - }, - {"Assign", - { - {"Const"}, - {"Merge", - { - {"Switch", - { - {"Max"}, - {"IsVariableInitialized"} - } - }, - {"Sub", - { - {"Const"}, - {"Mul", - { - {"Sub"}, - {"Sub", - { - {"Const"}, - {"Const"} - } - } - } - } - } - } - } - } - } - }, - } - }, // clang-format on - [](const NodeMatch& match, const std::set& input_nodes, - const std::set& output_nodes, - std::vector* new_nodes) { - const NodeDef& fake_quant_node = match.node; - const NodeDef& input_node = match.inputs[0].node; - const NodeDef& min_var_node = match.inputs[1].inputs[0].node; - const NodeDef& max_var_node = match.inputs[2].inputs[0].node; - - // Make a new FakeQuantizeWithMinMaxVars operation that uses constants - // for its min/max arguments rather than an entire EMA subgraph. - NodeDef new_fake_quant_node; - new_fake_quant_node.set_op(fake_quant_node.op()); - new_fake_quant_node.set_name(fake_quant_node.name()); - AddNodeInput(input_node.name(), &new_fake_quant_node); - AddNodeInput(min_var_node.name(), &new_fake_quant_node); - AddNodeInput(max_var_node.name(), &new_fake_quant_node); - CopyNodeAttr(fake_quant_node, "narrow_range", "narrow_range", - &new_fake_quant_node); - CopyNodeAttr(fake_quant_node, "num_bits", "num_bits", - &new_fake_quant_node); - - new_nodes->push_back(new_fake_quant_node); - new_nodes->push_back(input_node); - new_nodes->push_back(min_var_node); - new_nodes->push_back(max_var_node); - - return Status::OK(); - }, - {}, output_graph_def)); - return Status::OK(); -} - -REGISTER_GRAPH_TRANSFORM("remove_ema", RemoveEMA); - -} // namespace graph_transforms -} // namespace tensorflow diff --git a/tensorflow/tools/graph_transforms/remove_ema_test.cc b/tensorflow/tools/graph_transforms/remove_ema_test.cc deleted file mode 100644 index 27db90e2729487f89324622f7a63aca1c5a58fe7..0000000000000000000000000000000000000000 --- a/tensorflow/tools/graph_transforms/remove_ema_test.cc +++ /dev/null @@ -1,121 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/cc/ops/const_op.h" -#include "tensorflow/cc/ops/math_ops.h" -#include "tensorflow/core/framework/tensor_testutil.h" -#include "tensorflow/core/lib/core/status_test_util.h" -#include "tensorflow/core/platform/test.h" -#include "tensorflow/core/public/session.h" -#include "tensorflow/tools/graph_transforms/transform_utils.h" - -namespace tensorflow { -namespace graph_transforms { - -// Declare transformations here, so we don't need a public header. -Status FakeQuantizeTraining(const GraphDef& input_graph_def, - const TransformFuncContext& context, - GraphDef* output_graph_def); - -Status RemoveEMA(const GraphDef& input_graph_def, - const TransformFuncContext& context, - GraphDef* output_graph_def); - -Status QuantizeNodes(const GraphDef& input_graph_def, - const TransformFuncContext& context, - GraphDef* output_graph_def); - -class RemoveEMATest : public ::testing::Test {}; - -TEST_F(RemoveEMATest, FakeQuant_RemoveEMA_QuantizeTraining) { - // Build a small graph. - auto root = tensorflow::Scope::NewRootScope(); - using namespace ::tensorflow::ops; // NOLINT(build/namespaces) - - Tensor a_data(DT_FLOAT, TensorShape({1, 1})); - test::FillIota(&a_data, 1.0f); - Output a_const = Const(root.WithOpName("a"), Input::Initializer(a_data)); - - Tensor b_data(DT_FLOAT, TensorShape({1, 1})); - test::FillIota(&b_data, 1.0f); - Output b_const = Const(root.WithOpName("b"), Input::Initializer(b_data)); - - Output matmul = MatMul(root.WithOpName("matmul"), a_const, b_const); - GraphDef graph_def; - TF_ASSERT_OK(root.ToGraphDef(&graph_def)); - - // (1) FakeQuantize the graph. - GraphDef fake_quantized_graph_def; - TransformFuncContext context; - TF_ASSERT_OK( - FakeQuantizeTraining(graph_def, context, &fake_quantized_graph_def)); - - // Test that the transformation resulted in a graph with more nodes. - EXPECT_GT(fake_quantized_graph_def.node_size(), graph_def.node_size()); - - // (2) Run the graph to initialize the newly added variables. - std::unique_ptr session(NewSession(SessionOptions())); - TF_ASSERT_OK(session->Create(fake_quantized_graph_def)); - std::vector outputs; - TF_ASSERT_OK(session->Run({}, {"matmul"}, {}, &outputs)); - - // (3) Freeze the graph. Create a "frozen graph" that matches what we would - // expect if we actually froze the above graph. - // TODO(suharshs): Use a c++ freeze graph alternative, when one is available. - GraphDef frozen_graph_def; - for (const NodeDef& node : fake_quantized_graph_def.node()) { - if (node.op() == "Variable" || node.op() == "VariableV2") { - NodeDef const_node; - const_node.set_op("Const"); - const_node.set_name(node.name()); - SetNodeAttr("dtype", DT_FLOAT, &const_node); - Tensor tensor(DT_FLOAT, {}); - tensor.flat()(0) = 1.0f; - SetNodeTensorAttr("value", tensor, &const_node); - *(frozen_graph_def.mutable_node()->Add()) = const_node; - } else { - *(frozen_graph_def.mutable_node()->Add()) = node; - } - } - - // Test that freezing the graph resulted in a graph with the same number of - // nodes. - EXPECT_EQ(frozen_graph_def.node_size(), fake_quantized_graph_def.node_size()); - - // (4) RemoveEMA on the graph to make it compatible with QuantizeNodes. - GraphDef removed_ema_graph_def; - TF_ASSERT_OK(RemoveEMA(frozen_graph_def, context, &removed_ema_graph_def)); - - // Test that the transformation resulted in a graph with less nodes. - EXPECT_LT(removed_ema_graph_def.node_size(), frozen_graph_def.node_size()); - - // (5) QuantizeNodes and inspect the final graph. - // TODO(suharshs): Add a more thorough inspection of the structure of - // the output graph. - GraphDef quantized_graph_def; - TF_ASSERT_OK( - QuantizeNodes(removed_ema_graph_def, context, &quantized_graph_def)); - - // Test that the transformation resulted in a graph with more nodes. - EXPECT_GT(quantized_graph_def.node_size(), removed_ema_graph_def.node_size()); - - // Make sure that the FakeQuantizeWithMinMaxVars op has been removed. - for (const NodeDef& node : quantized_graph_def.node()) { - EXPECT_NE(node.op(), "FakeQuantWithMinMaxVars"); - } -} - -} // namespace graph_transforms -} // namespace tensorflow diff --git a/tensorflow/tools/integration_tests/gcs_smoke_test/BUILD.bazel b/tensorflow/tools/integration_tests/gcs_smoke_test/BUILD.bazel deleted file mode 100755 index 439d86c5d2c10d15f68247c0df42ce488c10d6be..0000000000000000000000000000000000000000 --- a/tensorflow/tools/integration_tests/gcs_smoke_test/BUILD.bazel +++ /dev/null @@ -1,56 +0,0 @@ -package(default_visibility = ["//visibility:public"]) - -load("@rbe_integration_test//skylark:integration_tests.bzl", "sut_component", "integration_test") -load("@rbe_integration_test//skylark:toolchains.bzl", "toolchain_container_images") - -sut_component( - name = "gcs", - docker_image = toolchain_container_images()["tensorflow"], - setups = [{ - "program": "setup.sh", - "args": [ - "gs://tensorflow-test-bucket/tf-gcs-test", - ], - "output_properties": ["gcs_path"], - "timeout_seconds": 100, - }], - teardowns = [{ - "program": "teardown.sh", - "args": ["{gcs_path}"], - "timeout_seconds": 100, - }], -) - -py_binary( - name = "gcs_smoke", - srcs = ["gcs_smoke.py"], -) - -sh_binary( - name = "test_wrapper", - srcs = ["test_wrapper.sh"], - data = [ - "gcs_smoke", - ], -) - -integration_test( - name = "gcs_smoke_test", - sut_deps = { - ":gcs": "gcs", - }, - tags = [ - "manual", - "notap", - ], - test = { - "program": ":test_wrapper", - "args": [ - "--gcs_bucket_url={gcs#gcs_path}", - "--num_examples=20", - ], - "timeout_seconds": 250, - }, - test_docker_image = toolchain_container_images()["tensorflow"], - test_type = "MultiMachine", -) diff --git a/tensorflow/tools/integration_tests/gcs_smoke_test/gcs_smoke.py b/tensorflow/tools/integration_tests/gcs_smoke_test/gcs_smoke.py deleted file mode 100755 index 8438c2156cb09b4d8c9442d9a5f4de67e59272f2..0000000000000000000000000000000000000000 --- a/tensorflow/tools/integration_tests/gcs_smoke_test/gcs_smoke.py +++ /dev/null @@ -1,253 +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. -# ============================================================================== -"""Smoke test for reading records from GCS to TensorFlow.""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import sys -import time - -import numpy as np -import tensorflow as tf -from tensorflow.core.example import example_pb2 -from tensorflow.python.lib.io import file_io - -flags = tf.app.flags -flags.DEFINE_string("gcs_bucket_url", "", - "The URL to the GCS bucket in which the temporary " - "tfrecord file is to be written and read, e.g., " - "gs://my-gcs-bucket/test-directory") -flags.DEFINE_integer("num_examples", 10, "Number of examples to generate") - -FLAGS = flags.FLAGS - - -def create_examples(num_examples, input_mean): - """Create ExampleProto's containing data.""" - ids = np.arange(num_examples).reshape([num_examples, 1]) - inputs = np.random.randn(num_examples, 1) + input_mean - target = inputs - input_mean - examples = [] - for row in range(num_examples): - ex = example_pb2.Example() - ex.features.feature["id"].bytes_list.value.append(str(ids[row, 0])) - ex.features.feature["target"].float_list.value.append(target[row, 0]) - ex.features.feature["inputs"].float_list.value.append(inputs[row, 0]) - examples.append(ex) - return examples - - -def create_dir_test(): - """Verifies file_io directory handling methods.""" - - # Test directory creation. - starttime_ms = int(round(time.time() * 1000)) - dir_name = "%s/tf_gcs_test_%s" % (FLAGS.gcs_bucket_url, starttime_ms) - print("Creating dir %s" % dir_name) - file_io.create_dir(dir_name) - elapsed_ms = int(round(time.time() * 1000)) - starttime_ms - print("Created directory in: %d milliseconds" % elapsed_ms) - - # Check that the directory exists. - dir_exists = file_io.is_directory(dir_name) - assert dir_exists - print("%s directory exists: %s" % (dir_name, dir_exists)) - - # Test recursive directory creation. - starttime_ms = int(round(time.time() * 1000)) - recursive_dir_name = "%s/%s/%s" % (dir_name, - "nested_dir1", - "nested_dir2") - print("Creating recursive dir %s" % recursive_dir_name) - file_io.recursive_create_dir(recursive_dir_name) - elapsed_ms = int(round(time.time() * 1000)) - starttime_ms - print("Created directory recursively in: %d milliseconds" % elapsed_ms) - - # Check that the directory exists. - recursive_dir_exists = file_io.is_directory(recursive_dir_name) - assert recursive_dir_exists - print("%s directory exists: %s" % (recursive_dir_name, recursive_dir_exists)) - - # Create some contents in the just created directory and list the contents. - num_files = 10 - files_to_create = ["file_%d.txt" % n for n in range(num_files)] - for file_num in files_to_create: - file_name = "%s/%s" % (dir_name, file_num) - print("Creating file %s." % file_name) - file_io.write_string_to_file(file_name, "test file.") - - print("Listing directory %s." % dir_name) - starttime_ms = int(round(time.time() * 1000)) - directory_contents = file_io.list_directory(dir_name) - print(directory_contents) - elapsed_ms = int(round(time.time() * 1000)) - starttime_ms - print("Listed directory %s in %s milliseconds" % (dir_name, elapsed_ms)) - assert set(directory_contents) == set(files_to_create + ["nested_dir1/"]) - - # Test directory renaming. - dir_to_rename = "%s/old_dir" % dir_name - new_dir_name = "%s/new_dir" % dir_name - file_io.create_dir(dir_to_rename) - assert file_io.is_directory(dir_to_rename) - assert not file_io.is_directory(new_dir_name) - - starttime_ms = int(round(time.time() * 1000)) - print("Will try renaming directory %s to %s" % (dir_to_rename, new_dir_name)) - file_io.rename(dir_to_rename, new_dir_name) - elapsed_ms = int(round(time.time() * 1000)) - starttime_ms - print("Renamed directory %s to %s in %s milliseconds" % ( - dir_to_rename, new_dir_name, elapsed_ms)) - assert not file_io.is_directory(dir_to_rename) - assert file_io.is_directory(new_dir_name) - - # Test Delete directory recursively. - print("Deleting directory recursively %s." % dir_name) - starttime_ms = int(round(time.time() * 1000)) - file_io.delete_recursively(dir_name) - elapsed_ms = int(round(time.time() * 1000)) - starttime_ms - dir_exists = file_io.is_directory(dir_name) - assert not dir_exists - print("Deleted directory recursively %s in %s milliseconds" % ( - dir_name, elapsed_ms)) - - -def create_object_test(): - """Verifies file_io's object manipulation methods .""" - starttime_ms = int(round(time.time() * 1000)) - dir_name = "%s/tf_gcs_test_%s" % (FLAGS.gcs_bucket_url, starttime_ms) - print("Creating dir %s." % dir_name) - file_io.create_dir(dir_name) - - num_files = 5 - # Create files of 2 different patterns in this directory. - files_pattern_1 = ["%s/test_file_%d.txt" % (dir_name, n) - for n in range(num_files)] - files_pattern_2 = ["%s/testfile%d.txt" % (dir_name, n) - for n in range(num_files)] - - starttime_ms = int(round(time.time() * 1000)) - files_to_create = files_pattern_1 + files_pattern_2 - for file_name in files_to_create: - print("Creating file %s." % file_name) - file_io.write_string_to_file(file_name, "test file creation.") - elapsed_ms = int(round(time.time() * 1000)) - starttime_ms - print("Created %d files in %s milliseconds" % - (len(files_to_create), elapsed_ms)) - - # Listing files of pattern1. - list_files_pattern = "%s/test_file*.txt" % dir_name - print("Getting files matching pattern %s." % list_files_pattern) - starttime_ms = int(round(time.time() * 1000)) - files_list = file_io.get_matching_files(list_files_pattern) - elapsed_ms = int(round(time.time() * 1000)) - starttime_ms - print("Listed files in %s milliseconds" % elapsed_ms) - print(files_list) - assert set(files_list) == set(files_pattern_1) - - # Listing files of pattern2. - list_files_pattern = "%s/testfile*.txt" % dir_name - print("Getting files matching pattern %s." % list_files_pattern) - starttime_ms = int(round(time.time() * 1000)) - files_list = file_io.get_matching_files(list_files_pattern) - elapsed_ms = int(round(time.time() * 1000)) - starttime_ms - print("Listed files in %s milliseconds" % elapsed_ms) - print(files_list) - assert set(files_list) == set(files_pattern_2) - - # Test renaming file. - file_to_rename = "%s/oldname.txt" % dir_name - file_new_name = "%s/newname.txt" % dir_name - file_io.write_string_to_file(file_to_rename, "test file.") - assert file_io.file_exists(file_to_rename) - assert not file_io.file_exists(file_new_name) - - print("Will try renaming file %s to %s" % (file_to_rename, file_new_name)) - starttime_ms = int(round(time.time() * 1000)) - file_io.rename(file_to_rename, file_new_name) - elapsed_ms = int(round(time.time() * 1000)) - starttime_ms - print("File %s renamed to %s in %s milliseconds" % ( - file_to_rename, file_new_name, elapsed_ms)) - assert not file_io.file_exists(file_to_rename) - assert file_io.file_exists(file_new_name) - - # Delete directory. - print("Deleting directory %s." % dir_name) - file_io.delete_recursively(dir_name) - - -def main(argv): - del argv # Unused. - # Sanity check on the GCS bucket URL. - if not FLAGS.gcs_bucket_url or not FLAGS.gcs_bucket_url.startswith("gs://"): - print("ERROR: Invalid GCS bucket URL: \"%s\"" % FLAGS.gcs_bucket_url) - sys.exit(1) - - # Verify that writing to the records file in GCS works. - print("\n=== Testing writing and reading of GCS record file... ===") - example_data = create_examples(FLAGS.num_examples, 5) - with tf.python_io.TFRecordWriter(FLAGS.gcs_bucket_url) as hf: - for e in example_data: - hf.write(e.SerializeToString()) - - print("Data written to: %s" % FLAGS.gcs_bucket_url) - - # Verify that reading from the tfrecord file works and that - # tf_record_iterator works. - record_iter = tf.python_io.tf_record_iterator(FLAGS.gcs_bucket_url) - read_count = 0 - for _ in record_iter: - read_count += 1 - print("Read %d records using tf_record_iterator" % read_count) - - if read_count != FLAGS.num_examples: - print("FAIL: The number of records read from tf_record_iterator (%d) " - "differs from the expected number (%d)" % (read_count, - FLAGS.num_examples)) - sys.exit(1) - - # Verify that running the read op in a session works. - print("\n=== Testing TFRecordReader.read op in a session... ===") - with tf.Graph().as_default() as _: - filename_queue = tf.train.string_input_producer([FLAGS.gcs_bucket_url], - num_epochs=1) - reader = tf.TFRecordReader() - _, serialized_example = reader.read(filename_queue) - - with tf.Session() as sess: - sess.run(tf.global_variables_initializer()) - sess.run(tf.local_variables_initializer()) - tf.train.start_queue_runners() - index = 0 - for _ in range(FLAGS.num_examples): - print("Read record: %d" % index) - sess.run(serialized_example) - index += 1 - - # Reading one more record should trigger an exception. - try: - sess.run(serialized_example) - print("FAIL: Failed to catch the expected OutOfRangeError while " - "reading one more record than is available") - sys.exit(1) - except tf.errors.OutOfRangeError: - print("Successfully caught the expected OutOfRangeError while " - "reading one more record than is available") - - create_dir_test() - create_object_test() - -if __name__ == "__main__": - tf.app.run(main) diff --git a/tensorflow/tools/integration_tests/gcs_smoke_test/test_wrapper.sh b/tensorflow/tools/integration_tests/gcs_smoke_test/test_wrapper.sh deleted file mode 100755 index ef29dee3462c21d6318a6fb7e7e658961f0d88dd..0000000000000000000000000000000000000000 --- a/tensorflow/tools/integration_tests/gcs_smoke_test/test_wrapper.sh +++ /dev/null @@ -1,21 +0,0 @@ -# This is a python2 only test. -#!/bin/bash -# 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. -# ============================================================================== -# Test Tensorflow package installation. -/usr/local/bin/pip install --user tf-nightly - -# Test Tensorflow interaction with GCS. -python tensorflow/tools/integration_test/gcs_smoke_test/gcs_smoke.py "$@" diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD index fb6eaa4faa28b4f6b17e1774907c0c9ff58d6ada..2607b9d7049828db95e7cbdddad30541a26942b0 100644 --- a/tensorflow/tools/pip_package/BUILD +++ b/tensorflow/tools/pip_package/BUILD @@ -48,36 +48,65 @@ py_binary( deps = ["//tensorflow:tensorflow_py"], ) +COMMON_PIP_DEPS = [ + ":licenses", + "MANIFEST.in", + "README", + "setup.py", + ":included_headers", + "//tensorflow:tensorflow_py", + "//tensorflow/contrib/boosted_trees:boosted_trees_pip", + "//tensorflow/contrib/cluster_resolver:cluster_resolver_pip", + "//tensorflow/contrib/data/python/kernel_tests:dataset_serialization_test", + "//tensorflow/contrib/data/python/ops:contrib_op_loader", + "//tensorflow/contrib/eager/python/examples:examples_pip", + "//tensorflow/contrib/eager/python:checkpointable_utils", + "//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/py2tf:py2tf", + "//tensorflow/contrib/py2tf/converters:converters", + "//tensorflow/contrib/py2tf/converters:test_lib", + "//tensorflow/contrib/py2tf/impl:impl", + "//tensorflow/contrib/py2tf/pyct:pyct", + "//tensorflow/contrib/py2tf/pyct/static_analysis:static_analysis", + "//tensorflow/contrib/receptive_field:receptive_field_pip", + "//tensorflow/contrib/session_bundle:session_bundle_pip", + "//tensorflow/contrib/signal:signal_py", + "//tensorflow/contrib/signal:test_util", + "//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/python:distributed_framework_test_lib", + "//tensorflow/python:meta_graph_testdata", + "//tensorflow/python:spectral_ops_test_util", + "//tensorflow/python:util_example_parser_configuration", + "//tensorflow/python/debug:debug_pip", + "//tensorflow/python/eager:eager_pip", + "//tensorflow/python/saved_model:saved_model", + "//tensorflow/python/tools:tools_pip", + "//tensorflow/python:test_ops", + "//tensorflow/tools/dist_test/server:grpc_tensorflow_server", +] + # 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 = [ - "MANIFEST.in", - "README", - "setup.py", - ":included_headers", - "//tensorflow/contrib/nn:nn_py", - "//tensorflow/contrib/session_bundle:session_bundle_pip", - "//tensorflow/contrib/signal:signal_py", - "//tensorflow/contrib/slim/python/slim/data:data_pip", - "//tensorflow/python:util_example_parser_configuration", - "//tensorflow/python/debug:debug_pip", - "//tensorflow/python/saved_model", - "//tensorflow/python:spectral_ops_test_util", - "//tensorflow/python/tools:tools_pip", - "//tensorflow/python/eager:eager_pip", - "//tensorflow/contrib/summary:summary_test_util", - # These targets don't build on Windows yet. Exclude them for now. - # "//tensorflow/contrib/slim", - # "//tensorflow/contrib/slim/python/slim/nets:nets_pip", - # "//tensorflow/contrib/specs", - # "//tensorflow/contrib/tensor_forest:init_py", - # "//tensorflow/contrib/tensor_forest/hybrid:hybrid_pip", - # "//tensorflow/examples/tutorials/mnist:package", - ], + data = COMMON_PIP_DEPS, srcs_version = "PY2AND3", deps = ["//tensorflow:tensorflow_py"], ) @@ -108,6 +137,7 @@ filegroup( "@highwayhash//:LICENSE", "@jemalloc//:COPYING", "@jpeg//:LICENSE.md", + "@kafka//:LICENSE", "@libxsmm_archive//:LICENSE", "@lmdb//:LICENSE", "@local_config_sycl//sycl:LICENSE.text", @@ -137,60 +167,12 @@ sh_binary( data = select({ "//tensorflow:windows": [":simple_console_for_windows"], "//tensorflow:windows_msvc": [":simple_console_for_windows"], - "//conditions:default": [ - ":licenses", - "MANIFEST.in", - "README", - "setup.py", - ":included_headers", + "//conditions:default": COMMON_PIP_DEPS + [ ":simple_console", - "//tensorflow:tensorflow_py", - "//tensorflow/contrib/boosted_trees:boosted_trees_pip", - "//tensorflow/contrib/cluster_resolver:cluster_resolver_pip", - "//tensorflow/contrib/data/python/kernel_tests:dataset_serialization_test", - "//tensorflow/contrib/data/python/ops:contrib_op_loader", - "//tensorflow/contrib/eager/python/examples:examples_pip", - "//tensorflow/contrib/eager/python:checkpointable_utils", - "//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/lite/python:interpreter_test_data", "//tensorflow/contrib/lite/toco:toco", "//tensorflow/contrib/lite/toco/python:toco_wrapper", "//tensorflow/contrib/lite/toco/python:toco_from_protos", - "//tensorflow/contrib/nn:nn_py", - "//tensorflow/contrib/predictor:predictor_pip", - "//tensorflow/contrib/py2tf:py2tf", - "//tensorflow/contrib/py2tf/converters:converters", - "//tensorflow/contrib/py2tf/converters:test_lib", - "//tensorflow/contrib/py2tf/impl:impl", - "//tensorflow/contrib/py2tf/pyct:pyct", - "//tensorflow/contrib/py2tf/pyct/static_analysis:static_analysis", - "//tensorflow/contrib/receptive_field:receptive_field_pip", - "//tensorflow/contrib/session_bundle:session_bundle_pip", - "//tensorflow/contrib/signal:signal_py", - "//tensorflow/contrib/signal:test_util", - "//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/python:distributed_framework_test_lib", - "//tensorflow/python:meta_graph_testdata", - "//tensorflow/python:spectral_ops_test_util", - "//tensorflow/python:util_example_parser_configuration", - "//tensorflow/python/debug:debug_pip", - "//tensorflow/python/eager:eager_pip", - "//tensorflow/python/saved_model:saved_model", - "//tensorflow/python/tools:tools_pip", - "//tensorflow/python:test_ops", - "//tensorflow/tools/dist_test/server:grpc_tensorflow_server", ], }) + if_mkl(["//third_party/mkl:intel_binary_blob"]) + if_tensorrt([ "//tensorflow/contrib/tensorrt:init_py", diff --git a/tensorflow/tools/pip_package/pip_smoke_test.py b/tensorflow/tools/pip_package/pip_smoke_test.py index 73d759eb130633094b402c821cc32eb76c076a44..b66c45ec13067e20fbd3c333cf562001945a63ea 100644 --- a/tensorflow/tools/pip_package/pip_smoke_test.py +++ b/tensorflow/tools/pip_package/pip_smoke_test.py @@ -58,6 +58,10 @@ BLACKLIST = [ # contrib "//tensorflow/contrib/session_bundle:session_bundle_half_plus_two", "//tensorflow/contrib/keras:testing_utils", + "//tensorflow/contrib/lite/python:interpreter", + "//tensorflow/contrib/lite/python:interpreter_test", + "//tensorflow/contrib/lite/python:interpreter.py", + "//tensorflow/contrib/lite/python:interpreter_test.py", "//tensorflow/contrib/ffmpeg:test_data", "//tensorflow/contrib/factorization/examples:mnist", "//tensorflow/contrib/factorization/examples:mnist.py", diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py index 4b6f123daa7b528173234a2bffd30ead2aa9fc0e..7fdf0d8c17790de77595a6979628d93913b6fc93 100644 --- a/tensorflow/tools/pip_package/setup.py +++ b/tensorflow/tools/pip_package/setup.py @@ -29,7 +29,7 @@ from setuptools.dist import Distribution # 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 = '1.6.0-rc1' +_VERSION = '1.6.0' REQUIRED_PACKAGES = [ 'absl-py >= 0.1.6', @@ -72,7 +72,7 @@ if sys.version_info < (3, 4): # pylint: disable=line-too-long CONSOLE_SCRIPTS = [ - 'freeze_graph = tensorflow.python.tools.freeze_graph:main', + 'freeze_graph = tensorflow.python.tools.freeze_graph:run_main', 'toco_from_protos = tensorflow.contrib.lite.toco.python.toco_from_protos:main', 'toco = tensorflow.contrib.lite.toco.python.toco_wrapper:main', 'saved_model_cli = tensorflow.python.tools.saved_model_cli:main', diff --git a/tensorflow/tools/test/upload_test_benchmarks.py b/tensorflow/tools/test/upload_test_benchmarks.py index 77cc9f75f7725438918f681833d58e9ecb4a2f70..edd093510e5483ef946e1177641371f00c92b50b 100644 --- a/tensorflow/tools/test/upload_test_benchmarks.py +++ b/tensorflow/tools/test/upload_test_benchmarks.py @@ -88,6 +88,7 @@ import os import shutil from google.cloud import datastore +from six import text_type def is_real_file(dirpath, fname): @@ -150,7 +151,7 @@ def upload_benchmark_data(client, data): """ test_result = json.loads(data) - test_name = unicode(test_result["name"]) + test_name = text_type(test_result["name"]) start_time = datetime.datetime.utcfromtimestamp( float(test_result["startTime"])) batch = [] @@ -162,7 +163,7 @@ def upload_benchmark_data(client, data): t_val.update({ "test": test_name, "start": start_time, - "info": unicode(data) + "info": text_type(data) }) batch.append(t_val) @@ -170,7 +171,7 @@ def upload_benchmark_data(client, data): # the attribute to be fetched and displayed. The full entry information is # also stored as a non-indexed JSON blob. for ent in test_result["entries"].get("entry", []): - ent_name = unicode(ent["name"]) + ent_name = text_type(ent["name"]) e_key = client.key("Entry") e_val = datastore.Entity(e_key, exclude_from_indexes=["info"]) e_val.update({ @@ -178,7 +179,7 @@ def upload_benchmark_data(client, data): "start": start_time, "entry": ent_name, "timing": ent["wallTime"], - "info": unicode(json.dumps(ent)) + "info": text_type(json.dumps(ent)) }) batch.append(e_val) diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index d5c61baa8b77cbb25a899ba6b34ad9d3b32cc760..a922808a70f8bd082f2aab7d0f01c404b304e121 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -12,6 +12,8 @@ load("//third_party/toolchains/cpus/arm:arm_compiler_configure.bzl", "arm_compil load("//third_party:repo.bzl", "tf_http_archive") load("@io_bazel_rules_closure//closure/private:java_import_external.bzl", "java_import_external") load("@io_bazel_rules_closure//closure:defs.bzl", "filegroup_external") +load("//tensorflow/tools/def_file_filter:def_file_filter_configure.bzl", + "def_file_filter_configure") def _extract_version_number(bazel_version): """Extracts the semantic version number from a version string @@ -67,7 +69,7 @@ def tf_workspace(path_prefix="", tf_repo_name=""): # We must check the bazel version before trying to parse any other BUILD # files, in case the parsing of those build files depends on the bazel # version we require here. - check_bazel_version_at_least("0.5.4") + check_bazel_version_at_least("0.10.0") clang6_configure(name="local_config_clang6") cuda_configure(name="local_config_cuda") tensorrt_configure(name="local_config_tensorrt") @@ -75,6 +77,10 @@ def tf_workspace(path_prefix="", tf_repo_name=""): sycl_configure(name="local_config_sycl") python_configure(name="local_config_python") + # For windows bazel build + # TODO: Remove def file filter when TensorFlow can export symbols properly on Windows. + def_file_filter_configure(name = "local_config_def_file_filter") + # Point //external/local_config_arm_compiler to //external/arm_compiler arm_compiler_configure( name="local_config_arm_compiler", @@ -226,7 +232,6 @@ def tf_workspace(path_prefix="", tf_repo_name=""): urls = [ "https://mirror.bazel.build/github.com/libjpeg-turbo/libjpeg-turbo/archive/1.5.1.tar.gz", "https://github.com/libjpeg-turbo/libjpeg-turbo/archive/1.5.1.tar.gz", - "http://www.nasm.us/pub/nasm/releasebuilds/2.12.02/nasm-2.12.02.tar.bz2", ], sha256 = "c15a9607892113946379ccea3ca8b85018301b200754f209453ab21674268e77", strip_prefix = "libjpeg-turbo-1.5.1", @@ -476,11 +481,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "llvm", urls = [ - "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/fc8ba497cd1a1af4ecae19a5b64bdbd71e065e14.tar.gz", - "https://github.com/llvm-mirror/llvm/archive/fc8ba497cd1a1af4ecae19a5b64bdbd71e065e14.tar.gz", + "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/738ee045416377e8c2094f7f61508ac1c178ff37.tar.gz", + "https://github.com/llvm-mirror/llvm/archive/738ee045416377e8c2094f7f61508ac1c178ff37.tar.gz", ], - sha256 = "f5721d9cc18a9109c9e9f847f48e69b710b961cee83e6691227e310cb3b5da58", - strip_prefix = "llvm-fc8ba497cd1a1af4ecae19a5b64bdbd71e065e14", + sha256 = "4442ed6a05c13752338036b1b9f16b09264de24b6c0bf62325fb9ff75a09340f", + strip_prefix = "llvm-738ee045416377e8c2094f7f61508ac1c178ff37", build_file = str(Label("//third_party/llvm:llvm.BUILD")), ) @@ -644,11 +649,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): ) java_import_external( - name = "javax_validation", - jar_sha256 = "e459f313ebc6db2483f8ceaad39af07086361b474fa92e40f442e8de5d9895dc", + name = "org_checkerframework_qual", + jar_sha256 = "a17501717ef7c8dda4dba73ded50c0d7cde440fd721acfeacbf19786ceac1ed6", jar_urls = [ - "http://mirror.bazel.build/repo1.maven.org/maven2/javax/validation/validation-api/1.0.0.GA/validation-api-1.0.0.GA.jar", - "http://repo1.maven.org/maven2/javax/validation/validation-api/1.0.0.GA/validation-api-1.0.0.GA.jar", + "http://mirror.bazel.build/repo1.maven.org/maven2/org/checkerframework/checker-qual/2.4.0/checker-qual-2.4.0.jar", + "http://repo1.maven.org/maven2/org/checkerframework/checker-qual/2.4.0/checker-qual-2.4.0.jar", ], licenses = ["notice"], # Apache 2.0 ) @@ -667,15 +672,12 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "cub_archive", urls = [ - "https://mirror.bazel.build/github.com/NVlabs/cub/archive/1.7.4.zip", - "https://github.com/NVlabs/cub/archive/1.7.4.zip", + "https://mirror.bazel.build/github.com/NVlabs/cub/archive/1.8.0.zip", + "https://github.com/NVlabs/cub/archive/1.8.0.zip", ], - sha256 = "20a1a39fd97e5da7f40f5f2e7fd73fd2ea59f9dc4bb8a6c5f228aa543e727e31", - strip_prefix = "cub-1.7.4", + sha256 = "6bfa06ab52a650ae7ee6963143a0bbc667d6504822cbd9670369b598f18c58c3", + strip_prefix = "cub-1.8.0", build_file = str(Label("//third_party:cub.BUILD")), - # TODO: remove the patch when upstream fix is accepted and released. - # PR with a fix: https://github.com/NVlabs/cub/pull/125 - patch_file = str(Label("//third_party/cub:fix_compilation_in_clang.patch")), ) tf_http_archive( @@ -700,16 +702,6 @@ def tf_workspace(path_prefix="", tf_repo_name=""): sha256 = "699b55a6916c687f4b7dc092dbbf5f64672cde0dc965f79717735ec4e5416556", ) - tf_http_archive( - name = "rbe_integration_test", - urls = [ - "http://mirror.bazel.build/github.com/google/rbe-integration-test/archive/78a6194c7dda200b9522cf07707e3bc695804d1e.tar.gz", - "https://github.com/google/rbe-integration-test/archive/78a6194c7dda200b9522cf07707e3bc695804d1e.tar.gz", - ], - sha256 = "66d93b3919a165d486c31f5290d312abe9fda2685242f812c110653c124e1db4", - strip_prefix = "rbe-integration-test-78a6194c7dda200b9522cf07707e3bc695804d1e", - ) - tf_http_archive( name = "arm_neon_2_x86_sse", sha256 = "c8d90aa4357f8079d427e87a6f4c493da1fa4140aee926c05902d7ec1533d9a5", diff --git a/third_party/cub/fix_compilation_in_clang.patch b/third_party/cub/fix_compilation_in_clang.patch deleted file mode 100644 index 384e674f2012b2b3ea59c5c1bd205873baa8cf18..0000000000000000000000000000000000000000 --- a/third_party/cub/fix_compilation_in_clang.patch +++ /dev/null @@ -1,23 +0,0 @@ -From 565b77f7c82048871a4d5e3e506dc663d53cd469 Mon Sep 17 00:00:00 2001 -From: Ilya Biryukov -Date: Fri, 26 Jan 2018 18:46:06 +0100 -Subject: [PATCH] Added missing 'template' keyword. - -To unbreak compilation with clang. ---- - cub/device/dispatch/dispatch_radix_sort.cuh | 2 +- - 1 file changed, 1 insertion(+), 1 deletion(-) - -diff --git a/cub/device/dispatch/dispatch_radix_sort.cuh b/cub/device/dispatch/dispatch_radix_sort.cuh -index 7fbc621f..f622e212 100644 ---- a/cub/device/dispatch/dispatch_radix_sort.cuh -+++ b/cub/device/dispatch/dispatch_radix_sort.cuh -@@ -104,7 +104,7 @@ __global__ void DeviceRadixSortUpsweepKernel( - CTA_SYNC(); - - // Write out digit counts (striped) -- upsweep.ExtractCounts(d_spine, gridDim.x, blockIdx.x); -+ upsweep.template ExtractCounts(d_spine, gridDim.x, blockIdx.x); - } - - diff --git a/third_party/gpus/crosstool/CROSSTOOL_clang.tpl b/third_party/gpus/crosstool/CROSSTOOL_clang.tpl index e4363d604577de09241d635b6990c9dd6429efe0..2f09473ee2ddf9a38ca0c7aa11094690607b532f 100644 --- a/third_party/gpus/crosstool/CROSSTOOL_clang.tpl +++ b/third_party/gpus/crosstool/CROSSTOOL_clang.tpl @@ -49,6 +49,7 @@ toolchain { flag_set { action: "c++-link-executable" action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" flag_group { flag: "-lstdc++" } @@ -75,6 +76,7 @@ toolchain { name: "alwayslink" flag_set { action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" action: "c++-link-executable" flag_group { flag: "-Wl,-no-as-needed" @@ -116,6 +118,7 @@ toolchain { } flag_set { action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" flag_group { flag: "-Wl,-z,relro,-z,now" } @@ -161,6 +164,7 @@ toolchain { flag_set { action: "c++-link-executable" action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" flag_group { # Stamp the binary with a unique identifier. flag: "-Wl,--build-id=md5" @@ -176,6 +180,7 @@ toolchain { action: "c++-compile" action: "c++-link-executable" action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" flag_group { flag:"-no-canonical-prefixes" } @@ -199,6 +204,7 @@ toolchain { flag_set { action: "c++-link-executable" action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" flag_group { flag: "-B/usr/bin/" } @@ -246,6 +252,7 @@ toolchain { } flag_set { action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" action: "c++-link-executable" flag_group { flag: "-Wl,--gc-sections" diff --git a/third_party/gpus/cuda/remote.BUILD.tpl b/third_party/gpus/cuda/remote.BUILD.tpl index d88d512b90c352e6a301ed6efe8266d8dd6bf744..f774def5e6cec25e4920ecce0076340a31c70386 100644 --- a/third_party/gpus/cuda/remote.BUILD.tpl +++ b/third_party/gpus/cuda/remote.BUILD.tpl @@ -41,65 +41,65 @@ config_setting( alias( name = "cuda_headers", - actual = "%{remote_cuda_repo}cuda:cuda_headers", + actual = "%{remote_cuda_repo}/cuda:cuda_headers", ) alias( name = "cudart_static", - actual = "%{remote_cuda_repo}cuda:cudart_static", + actual = "%{remote_cuda_repo}/cuda:cudart_static", ) alias( name = "cuda_driver", - actual = "%{remote_cuda_repo}cuda:cuda_driver", + actual = "%{remote_cuda_repo}/cuda:cuda_driver", ) alias( name = "cudart", - actual = "%{remote_cuda_repo}cuda:cudart", + actual = "%{remote_cuda_repo}/cuda:cudart", ) alias( name = "cublas", - actual = "%{remote_cuda_repo}cuda:cublas", + actual = "%{remote_cuda_repo}/cuda:cublas", ) alias( name = "cusolver", - actual = "%{remote_cuda_repo}cuda:cusolver", + actual = "%{remote_cuda_repo}/cuda:cusolver", ) alias( name = "cudnn", - actual = "%{remote_cuda_repo}cuda:cudnn", + actual = "%{remote_cuda_repo}/cuda:cudnn", ) alias( name = "cufft", - actual = "%{remote_cuda_repo}cuda:cufft", + actual = "%{remote_cuda_repo}/cuda:cufft", ) alias( name = "curand", - actual = "%{remote_cuda_repo}cuda:curand", + actual = "%{remote_cuda_repo}/cuda:curand", ) alias( name = "cuda", - actual = "%{remote_cuda_repo}cuda:cuda", + actual = "%{remote_cuda_repo}/cuda:cuda", ) alias( name = "cupti_headers", - actual = "%{remote_cuda_repo}cuda:cupti_headers", + actual = "%{remote_cuda_repo}/cuda:cupti_headers", ) alias( name = "cupti_dsos", - actual = "%{remote_cuda_repo}cuda:cupti_dsos", + actual = "%{remote_cuda_repo}/cuda:cupti_dsos", ) alias( name = "libdevice_root", - actual = "%{remote_cuda_repo}cuda:libdevice_root", + actual = "%{remote_cuda_repo}/cuda:libdevice_root", ) diff --git a/third_party/gpus/cuda_configure.bzl b/third_party/gpus/cuda_configure.bzl index b7c47a19ddcfc69dbee54bf6ca4080489b292c01..6c9c128db635ecb23b7aa930dca1d20bae17f71c 100644 --- a/third_party/gpus/cuda_configure.bzl +++ b/third_party/gpus/cuda_configure.bzl @@ -38,6 +38,64 @@ _DEFAULT_CUDA_TOOLKIT_PATH = "/usr/local/cuda" _DEFAULT_CUDNN_INSTALL_PATH = "/usr/local/cuda" _DEFAULT_CUDA_COMPUTE_CAPABILITIES = ["3.5", "5.2"] +# Lookup paths for CUDA / cuDNN libraries, relative to the install directories. +# +# Paths will be tried out in the order listed below. The first successful path +# will be used. For example, when looking for the cudart libraries, the first +# attempt will be lib64/cudart inside the CUDA toolkit. +CUDA_LIB_PATHS = [ + "lib64/", + "lib64/stubs/", + "lib/x86_64-linux-gnu/", + "lib/x64/", + "lib/", + "", +] + +# Lookup paths for cupti.h, relative to the CUDA toolkit directory. +# +# On most systems, the cupti library is not installed in the same directory as +# the other CUDA libraries but rather in a special extras/CUPTI directory. +CUPTI_HEADER_PATHS = [ + "extras/CUPTI/include/", + "include/cuda/CUPTI/", +] + +# Lookup paths for the cupti library, relative to the +# +# On most systems, the cupti library is not installed in the same directory as +# the other CUDA libraries but rather in a special extras/CUPTI directory. +CUPTI_LIB_PATHS = [ + "extras/CUPTI/lib64/", + "lib/x86_64-linux-gnu", + "lib64/", + "extras/CUPTI/libx64/", + "extras/CUPTI/lib/", + "lib/", +] + +# Lookup paths for CUDA headers (cuda.h) relative to the CUDA toolkit directory. +CUDA_INCLUDE_PATHS = [ + "include/", + "include/cuda/" +] + +# Lookup paths for cudnn.h relative to the CUDNN install directory. +CUDNN_INCLUDE_PATHS = [ + "", + "include/", + "include/cuda/", +] + +# Lookup paths for NVVM libdevice relative to the CUDA directory toolkit. +# +# libdevice implements mathematical functions for GPU kernels, and is provided +# in NVVM bitcode (a subset of LLVM bitcode). +NVVM_LIBDEVICE_PATHS = [ + "nvvm/libdevice/", + "share/cuda/", +] + load(":download_clang.bzl", "download_clang") # TODO(dzc): Once these functions have been factored out of Bazel's @@ -522,31 +580,31 @@ def _find_cuda_lib(lib, repository_ctx, cpu_value, basedir, version="", path: The full path to the library. """ file_name = _lib_name(lib, cpu_value, version, static) - if cpu_value == "Linux": - path = repository_ctx.path("%s/lib64/%s" % (basedir, file_name)) - if path.exists: - return struct(file_name=file_name, path=str(path.realpath)) - path = repository_ctx.path("%s/lib64/stubs/%s" % (basedir, file_name)) - if path.exists: - return struct(file_name=file_name, path=str(path.realpath)) - path = repository_ctx.path( - "%s/lib/x86_64-linux-gnu/%s" % (basedir, file_name)) + for relative_path in CUDA_LIB_PATHS: + path = repository_ctx.path("%s/%s%s" % (basedir, relative_path, file_name)) if path.exists: return struct(file_name=file_name, path=str(path.realpath)) + auto_configure_fail("Cannot find cuda library %s" % file_name) - elif cpu_value == "Windows": - path = repository_ctx.path("%s/lib/x64/%s" % (basedir, file_name)) - if path.exists: - return struct(file_name=file_name, path=str(path.realpath)) - path = repository_ctx.path("%s/lib/%s" % (basedir, file_name)) - if path.exists: - return struct(file_name=file_name, path=str(path.realpath)) - path = repository_ctx.path("%s/%s" % (basedir, file_name)) - if path.exists: - return struct(file_name=file_name, path=str(path.realpath)) +def _find_cupti_header_dir(repository_ctx, cuda_config): + """Returns the path to the directory containing cupti.h - auto_configure_fail("Cannot find cuda library %s" % file_name) + On most systems, the cupti library is not installed in the same directory as + the other CUDA libraries but rather in a special extras/CUPTI directory. + + Args: + repository_ctx: The repository context. + cuda_config: The CUDA config as returned by _get_cuda_config + + Returns: + The path of the directory containing the cupti header. + """ + cuda_toolkit_path = cuda_config.cuda_toolkit_path + for relative_path in CUPTI_HEADER_PATHS: + if repository_ctx.path("%s/%scupti.h" % (cuda_toolkit_path, relative_path)).exists: + return ("%s/%s" % (cuda_toolkit_path, relative_path))[:-1] + auto_configure_fail("Cannot find cupti.h under %s" % cuda_toolkit_path) def _find_cupti_lib(repository_ctx, cuda_config): @@ -566,35 +624,13 @@ def _find_cupti_lib(repository_ctx, cuda_config): """ file_name = _lib_name("cupti", cuda_config.cpu_value, cuda_config.cuda_version) - if cuda_config.cpu_value == "Linux": - path = repository_ctx.path( - "%s/extras/CUPTI/lib64/%s" % (cuda_config.cuda_toolkit_path, file_name)) - if path.exists: - return struct(file_name=file_name, path=str(path.realpath)) - - path = repository_ctx.path( - "%s/lib/x86_64-linux-gnu/%s" % (cuda_config.cuda_toolkit_path, - file_name)) - if path.exists: - return struct(file_name=file_name, path=str(path.realpath)) - - elif cuda_config.cpu_value == "Windows": + cuda_toolkit_path = cuda_config.cuda_toolkit_path + for relative_path in CUPTI_LIB_PATHS: path = repository_ctx.path( - "%s/extras/CUPTI/libx64/%s" % - (cuda_config.cuda_toolkit_path, file_name)) + "%s/%s%s" % (cuda_toolkit_path, relative_path, file_name)) if path.exists: return struct(file_name=file_name, path=str(path.realpath)) - path = repository_ctx.path( - "%s/extras/CUPTI/lib/%s" % (cuda_config.cuda_toolkit_path, file_name)) - if path.exists: - return struct(file_name=file_name, path=str(path.realpath)) - - path = repository_ctx.path( - "%s/lib/%s" % (cuda_config.cuda_toolkit_path, file_name)) - if path.exists: - return struct(file_name=file_name, path=str(path.realpath)) - auto_configure_fail("Cannot find cupti library %s" % file_name) def _find_libs(repository_ctx, cuda_config): @@ -635,6 +671,23 @@ def _find_libs(repository_ctx, cuda_config): } +def _find_cuda_include_path(repository_ctx, cuda_config): + """Returns the path to the directory containing cuda.h + + Args: + repository_ctx: The repository context. + cuda_config: The CUDA config as returned by _get_cuda_config + + Returns: + The path of the directory containing the CUDA headers. + """ + cuda_toolkit_path = cuda_config.cuda_toolkit_path + for relative_path in CUDA_INCLUDE_PATHS: + if repository_ctx.path("%s/%scuda.h" % (cuda_toolkit_path, relative_path)).exists: + return ("%s/%s" % (cuda_toolkit_path, relative_path))[:-1] + auto_configure_fail("Cannot find cuda.h under %s" % cuda_toolkit_path) + + def _find_cudnn_header_dir(repository_ctx, cudnn_install_basedir): """Returns the path to the directory containing cudnn.h @@ -646,15 +699,31 @@ def _find_cudnn_header_dir(repository_ctx, cudnn_install_basedir): Returns: The path of the directory containing the cudnn header. """ - if repository_ctx.path(cudnn_install_basedir + "/cudnn.h").exists: - return cudnn_install_basedir - if repository_ctx.path(cudnn_install_basedir + "/include/cudnn.h").exists: - return cudnn_install_basedir + "/include" + for relative_path in CUDA_INCLUDE_PATHS: + if repository_ctx.path("%s/%scudnn.h" % (cudnn_install_basedir, relative_path)).exists: + return ("%s/%s" % (cudnn_install_basedir, relative_path))[:-1] if repository_ctx.path("/usr/include/cudnn.h").exists: return "/usr/include" auto_configure_fail("Cannot find cudnn.h under %s" % cudnn_install_basedir) +def _find_nvvm_libdevice_dir(repository_ctx, cuda_config): + """Returns the path to the directory containing libdevice in bitcode format. + + Args: + repository_ctx: The repository context. + cuda_config: The CUDA config as returned by _get_cuda_config + + Returns: + The path of the directory containing the CUDA headers. + """ + cuda_toolkit_path = cuda_config.cuda_toolkit_path + for relative_path in NVVM_LIBDEVICE_PATHS: + if repository_ctx.path("%s/%slibdevice.10.bc" % (cuda_toolkit_path, relative_path)).exists: + return ("%s/%s" % (cuda_toolkit_path, relative_path))[:-1] + auto_configure_fail("Cannot find libdevice.10.bc under %s" % cuda_toolkit_path) + + def _cudart_static_linkopt(cpu_value): """Returns additional platform-specific linkopts for cudart.""" return "" if cpu_value == "Darwin" else "\"-lrt\"," @@ -925,21 +994,22 @@ def _create_local_cuda_repository(repository_ctx): """Creates the repository containing files set up to build with CUDA.""" cuda_config = _get_cuda_config(repository_ctx) + cuda_include_path = _find_cuda_include_path(repository_ctx, cuda_config) cudnn_header_dir = _find_cudnn_header_dir(repository_ctx, cuda_config.cudnn_install_basedir) + cupti_header_dir = _find_cupti_header_dir(repository_ctx, cuda_config) + nvvm_libdevice_dir = _find_nvvm_libdevice_dir(repository_ctx, cuda_config) # Set up symbolic links for the cuda toolkit by creating genrules to do # symlinking. We create one genrule for each directory we want to track under # cuda_toolkit_path cuda_toolkit_path = cuda_config.cuda_toolkit_path - cuda_include_path = cuda_toolkit_path + "/include" genrules = [symlink_genrule_for_dir(repository_ctx, cuda_include_path, "cuda/include", "cuda-include")] genrules.append(symlink_genrule_for_dir(repository_ctx, - cuda_toolkit_path + "/nvvm", "cuda/nvvm", "cuda-nvvm")) + nvvm_libdevice_dir, "cuda/nvvm/libdevice", "cuda-nvvm")) genrules.append(symlink_genrule_for_dir(repository_ctx, - cuda_toolkit_path + "/extras/CUPTI/include", - "cuda/extras/CUPTI/include", "cuda-extras")) + cupti_header_dir, "cuda/extras/CUPTI/include", "cuda-extras")) cuda_libs = _find_libs(repository_ctx, cuda_config) cuda_lib_src = [] @@ -1086,6 +1156,7 @@ cuda_configure = repository_rule( _TF_CUDNN_VERSION, _TF_CUDA_COMPUTE_CAPABILITIES, _TF_CUDA_CONFIG_REPO, + "NVVMIR_LIBRARY_DIR", ], ) diff --git a/third_party/jpeg/jpeg.BUILD b/third_party/jpeg/jpeg.BUILD index 87a23925c4316c3ee107af77272300e34b1bb257..4418ac32fc4b08713ff1d1f0d78042803153c886 100644 --- a/third_party/jpeg/jpeg.BUILD +++ b/third_party/jpeg/jpeg.BUILD @@ -526,12 +526,12 @@ config_setting( config_setting( name = "armeabi-v7a", - values = {"android_cpu": "armeabi-v7a"}, + values = {"cpu": "armeabi-v7a"}, ) config_setting( name = "arm64-v8a", - values = {"android_cpu": "arm64-v8a"}, + values = {"cpu": "arm64-v8a"}, ) config_setting( diff --git a/third_party/kafka/BUILD b/third_party/kafka/BUILD index a61a9e1f6c2b29ad3b992e810c0cab463dfd7feb..a839ca717e695f35fac684b510f0a022010e0710 100644 --- a/third_party/kafka/BUILD +++ b/third_party/kafka/BUILD @@ -130,12 +130,16 @@ cc_library( ], hdrs = [ "config.h", + "src-cpp/rdkafkacpp.h", + "src-cpp/rdkafkacpp_int.h", + "src/lz4.c", + "src/snappy_compat.h", ], - defines = [ + copts = [ + "-Iexternal/kafka/src", + "-Iexternal/kafka/src-cpp", ], - includes = [ - "src", - "src-cpp", + defines = [ ], linkopts = [ "-lpthread", @@ -143,5 +147,6 @@ cc_library( visibility = ["//visibility:public"], deps = [ "@boringssl//:ssl", + "@zlib_archive//:zlib", ], ) diff --git a/third_party/py/BUILD.tpl b/third_party/py/BUILD.tpl index de06ad5f27e7c08aade4a8f51ab60ba52d012b7b..1dd8ab433a37a127b98ae7069bffcbfd4f6d8bd1 100644 --- a/third_party/py/BUILD.tpl +++ b/third_party/py/BUILD.tpl @@ -2,20 +2,26 @@ licenses(["restricted"]) package(default_visibility = ["//visibility:public"]) +# To build Python C/C++ extension on Windows, we need to link to python import library pythonXY.lib +# See https://docs.python.org/3/extending/windows.html +cc_import( + name = "python_lib", + interface_library = select({ + ":windows": ":python_import_lib", + # A placeholder for Unix platforms which makes --no_build happy. + "//conditions:default": "not-existing.lib", + }), + system_provided = 1, +) + cc_library( name = "python_headers", hdrs = [":python_include"], - data = select({ - ":windows": [":python_import_lib"], + deps = select({ + ":windows": [":python_lib"], "//conditions:default": [], }), includes = ["python_include"], - linkopts = select({ - # TODO(pcloudy): Ideally, this should just go into deps after resolving - # https://github.com/bazelbuild/bazel/issues/3237, - ":windows": ["$(locations :python_import_lib)"], - "//conditions:default": [], - }), ) cc_library( diff --git a/third_party/tensorrt/tensorrt_configure.bzl b/third_party/tensorrt/tensorrt_configure.bzl index 8e76e5d02aeddab66dacaa495a6c493f18a95a69..9b946505a615372aa7de317c8ee390a2cd4b60e9 100644 --- a/third_party/tensorrt/tensorrt_configure.bzl +++ b/third_party/tensorrt/tensorrt_configure.bzl @@ -57,6 +57,10 @@ def _find_trt_header_dir(repository_ctx, trt_install_path): path = "/usr/include/x86_64-linux-gnu" if _headers_exist(repository_ctx, path): return path + if trt_install_path == "/usr/lib/aarch64-linux-gnu": + path = "/usr/include/aarch64-linux-gnu" + if _headers_exist(repository_ctx, path): + return path path = str(repository_ctx.path("%s/../include" % trt_install_path).realpath) if _headers_exist(repository_ctx, path): return path diff --git a/third_party/toolchains/gpus/crosstool/BUILD b/third_party/toolchains/gpus/crosstool/BUILD index a8c6b0f0291363f3a7576a70e78b3428fb984957..1f9065007ca884a46bfa391d1ee8a8f0333da235 100644 --- a/third_party/toolchains/gpus/crosstool/BUILD +++ b/third_party/toolchains/gpus/crosstool/BUILD @@ -50,3 +50,8 @@ filegroup( name = "empty", srcs = [], ) + +filegroup( + name = "crosstool_wrapper_driver_is_not_gcc", + srcs = ["clang/bin/crosstool_wrapper_driver_is_not_gcc"], +) diff --git a/third_party/toolchains/gpus/crosstool/CROSSTOOL b/third_party/toolchains/gpus/crosstool/CROSSTOOL index a47e0c7cd74edcea777d76854c2d7e97d69897fa..d6ee7e38c414dd59b76c7b2b4c95c55831bb30a8 100644 --- a/third_party/toolchains/gpus/crosstool/CROSSTOOL +++ b/third_party/toolchains/gpus/crosstool/CROSSTOOL @@ -53,6 +53,7 @@ toolchain { flag_set { action: "c++-link-executable" action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" flag_group { flag: "-lstdc++" } @@ -79,6 +80,7 @@ toolchain { name: "alwayslink" flag_set { action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" action: "c++-link-executable" flag_group { flag: "-Wl,-no-as-needed" @@ -120,6 +122,7 @@ toolchain { } flag_set { action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" flag_group { flag: "-Wl,-z,relro,-z,now" } @@ -141,8 +144,8 @@ toolchain { flag_group { # All warnings are enabled. Maybe enable -Werror as well? flag: "-Wall" - # TODO(ngiraldo): Some parts of the codebase set -Werror and hit this - # warning, so switch it off for now. + # Some parts of the codebase set -Werror and hit this warning, so + # switch it off for now. flag: "-Wno-invalid-partial-specialization" } } @@ -165,6 +168,7 @@ toolchain { flag_set { action: "c++-link-executable" action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" flag_group { # Stamp the binary with a unique identifier. flag: "-Wl,--build-id=md5" @@ -180,6 +184,7 @@ toolchain { action: "c++-compile" action: "c++-link-executable" action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" flag_group { flag:"-no-canonical-prefixes" } @@ -203,6 +208,7 @@ toolchain { flag_set { action: "c++-link-executable" action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" flag_group { flag: "-B/usr/bin/" } @@ -250,6 +256,7 @@ toolchain { } flag_set { action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" action: "c++-link-executable" flag_group { flag: "-Wl,--gc-sections" @@ -296,7 +303,7 @@ toolchain { cxx_builtin_include_directory: "/usr/include/x86_64-linux-gnu/c++/5.4.0" cxx_builtin_include_directory: "/usr/include/c++/5.4.0/backward" cxx_builtin_include_directory: "/usr/local/include" - cxx_builtin_include_directory: "/usr/local/lib/clang/6.0.0/include" + cxx_builtin_include_directory: "/usr/local/lib/clang/7.0.0/include" cxx_builtin_include_directory: "/usr/include/x86_64-linux-gnu" cxx_builtin_include_directory: "/usr/include" } diff --git a/third_party/toolchains/gpus/cuda/BUILD b/third_party/toolchains/gpus/cuda/BUILD index 39136de99c901d6d6a9dafefe3163972511ec122..4cb83809383afa52d5a1d98777f8e5bb2d266286 100644 --- a/third_party/toolchains/gpus/cuda/BUILD +++ b/third_party/toolchains/gpus/cuda/BUILD @@ -51,6 +51,7 @@ cc_library( includes = [ ".", "cuda/include", + "cuda/include/crt", ], visibility = ["//visibility:public"], ) @@ -84,8 +85,8 @@ cc_library( cc_library( name = "cudart", - srcs = ["cuda/lib/libcudart.so.8.0"], - data = ["cuda/lib/libcudart.so.8.0"], + srcs = ["cuda/lib/libcudart.so.9.0"], + data = ["cuda/lib/libcudart.so.9.0"], includes = [ ".", "cuda/include", @@ -96,8 +97,8 @@ cc_library( cc_library( name = "cublas", - srcs = ["cuda/lib/libcublas.so.8.0"], - data = ["cuda/lib/libcublas.so.8.0"], + srcs = ["cuda/lib/libcublas.so.9.0"], + data = ["cuda/lib/libcublas.so.9.0"], includes = [ ".", "cuda/include", @@ -108,8 +109,8 @@ cc_library( cc_library( name = "cusolver", - srcs = ["cuda/lib/libcusolver.so.8.0"], - data = ["cuda/lib/libcusolver.so.8.0"], + srcs = ["cuda/lib/libcusolver.so.9.0"], + data = ["cuda/lib/libcusolver.so.9.0"], includes = [ ".", "cuda/include", @@ -121,8 +122,8 @@ cc_library( cc_library( name = "cudnn", - srcs = ["cuda/lib/libcudnn.so.6"], - data = ["cuda/lib/libcudnn.so.6"], + srcs = ["cuda/lib/libcudnn.so.7"], + data = ["cuda/lib/libcudnn.so.7"], includes = [ ".", "cuda/include", @@ -133,8 +134,8 @@ cc_library( cc_library( name = "cufft", - srcs = ["cuda/lib/libcufft.so.8.0"], - data = ["cuda/lib/libcufft.so.8.0"], + srcs = ["cuda/lib/libcufft.so.9.0"], + data = ["cuda/lib/libcufft.so.9.0"], includes = [ ".", "cuda/include", @@ -145,8 +146,8 @@ cc_library( cc_library( name = "curand", - srcs = ["cuda/lib/libcurand.so.8.0"], - data = ["cuda/lib/libcurand.so.8.0"], + srcs = ["cuda/lib/libcurand.so.9.0"], + data = ["cuda/lib/libcurand.so.9.0"], includes = [ ".", "cuda/include", @@ -183,7 +184,7 @@ cc_library( cc_library( name = "cupti_dsos", - data = ["cuda/lib/libcupti.so.8.0"], + data = ["cuda/lib/libcupti.so.9.0"], includes = [ ".", "cuda/include", @@ -200,1063 +201,990 @@ cc_library( genrule( name = "cuda-include", outs = [ - "cuda/include/math_functions.hpp", - "cuda/include/cufft.h", - "cuda/include/nvgraph.h", - "cuda/include/curand_normal.h", - "cuda/include/curand_uniform.h", - "cuda/include/nppi_data_exchange_and_initialization.h", - "cuda/include/cuda_gl_interop.h", - "cuda/include/nppi_compression_functions.h", - "cuda/include/npp.h", + "cuda/include/CL/cl.h", + "cuda/include/CL/cl.hpp", + "cuda/include/CL/cl_egl.h", + "cuda/include/CL/cl_ext.h", + "cuda/include/CL/cl_gl.h", + "cuda/include/CL/cl_gl_ext.h", + "cuda/include/CL/cl_platform.h", + "cuda/include/CL/opencl.h", + "cuda/include/builtin_types.h", + "cuda/include/channel_descriptor.h", + "cuda/include/common_functions.h", + "cuda/include/cooperative_groups.h", + "cuda/include/cooperative_groups_helpers.h", + "cuda/include/crt/common_functions.h", + "cuda/include/crt/device_double_functions.h", + "cuda/include/crt/device_double_functions.hpp", + "cuda/include/crt/device_functions.h", + "cuda/include/crt/device_functions.hpp", + "cuda/include/crt/func_macro.h", + "cuda/include/crt/host_config.h", + "cuda/include/crt/host_defines.h", + "cuda/include/crt/host_runtime.h", + "cuda/include/crt/math_functions.h", + "cuda/include/crt/math_functions.hpp", + "cuda/include/crt/mma.h", + "cuda/include/crt/mma.hpp", + "cuda/include/crt/nvfunctional", + "cuda/include/crt/sm_70_rt.h", + "cuda/include/crt/sm_70_rt.hpp", + "cuda/include/crt/storage_class.h", + "cuda/include/cuComplex.h", + "cuda/include/cublas.h", + "cuda/include/cublasXt.h", + "cuda/include/cublas_api.h", + "cuda/include/cublas_v2.h", "cuda/include/cuda.h", - "cuda/include/nppi_statistics_functions.h", - "cuda/include/vector_functions.hpp", - "cuda/include/sm_32_intrinsics.hpp", - "cuda/include/sm_32_intrinsics.h", - "cuda/include/curand_discrete.h", + "cuda/include/cudaEGL.h", + "cuda/include/cudaGL.h", + "cuda/include/cudaProfiler.h", + "cuda/include/cudaVDPAU.h", + "cuda/include/cuda_device_runtime_api.h", + "cuda/include/cuda_fp16.h", + "cuda/include/cuda_fp16.hpp", + "cuda/include/cuda_gl_interop.h", + "cuda/include/cuda_occupancy.h", + "cuda/include/cuda_profiler_api.h", "cuda/include/cuda_runtime.h", + "cuda/include/cuda_runtime_api.h", + "cuda/include/cuda_surface_types.h", + "cuda/include/cuda_texture_types.h", + "cuda/include/cuda_vdpau_interop.h", + "cuda/include/cudalibxt.h", + "cuda/include/cudnn.h", + "cuda/include/cufft.h", "cuda/include/cufftXt.h", - "cuda/include/sm_61_intrinsics.h", - "cuda/include/texture_fetch_functions.h", + "cuda/include/cufftw.h", + "cuda/include/curand.h", + "cuda/include/curand_discrete.h", + "cuda/include/curand_discrete2.h", + "cuda/include/curand_globals.h", + "cuda/include/curand_kernel.h", + "cuda/include/curand_lognormal.h", "cuda/include/curand_mrg32k3a.h", - "cuda/include/host_defines.h", - "cuda/include/common_functions.h", - "cuda/include/nppi_support_functions.h", - "cuda/include/nppi_linear_transforms.h", - "cuda/include/device_double_functions.hpp", - "cuda/include/math_constants.h", - "cuda/include/nvToolsExtSync.h", - "cuda/include/npps_initialization.h", + "cuda/include/curand_mtgp32.h", + "cuda/include/curand_mtgp32_host.h", + "cuda/include/curand_mtgp32_kernel.h", + "cuda/include/curand_mtgp32dc_p_11213.h", + "cuda/include/curand_normal.h", + "cuda/include/curand_normal_static.h", + "cuda/include/curand_philox4x32_x.h", + "cuda/include/curand_poisson.h", + "cuda/include/curand_precalc.h", + "cuda/include/curand_uniform.h", + "cuda/include/cusolverDn.h", + "cuda/include/cusolverRf.h", + "cuda/include/cusolverSp.h", "cuda/include/cusolverSp_LOWLEVEL_PREVIEW.h", - "cuda/include/texture_indirect_functions.hpp", - "cuda/include/cudaProfiler.h", - "cuda/include/npps_filtering_functions.h", + "cuda/include/cusolver_common.h", + "cuda/include/cusparse.h", "cuda/include/cusparse_v2.h", - "cuda/include/nppi.h", - "cuda/include/surface_indirect_functions.h", - "cuda/include/sm_30_intrinsics.h", + "cuda/include/device_atomic_functions.h", + "cuda/include/device_atomic_functions.hpp", "cuda/include/device_double_functions.h", - "cuda/include/sm_35_intrinsics.h", - "cuda/include/cusolverSp.h", - "cuda/include/library_types.h", - "cuda/include/surface_indirect_functions.hpp", - "cuda/include/cudalibxt.h", - "cuda/include/channel_descriptor.h", + "cuda/include/device_double_functions.hpp", + "cuda/include/device_functions.h", + "cuda/include/device_functions.hpp", "cuda/include/device_functions_decls.h", - "cuda/include/curand_kernel.h", - "cuda/include/curand_mtgp32_host.h", - "cuda/include/nvToolsExtCuda.h", - "cuda/include/nvToolsExt.h", - "cuda/include/cuComplex.h", - "cuda/include/sm_32_atomic_functions.h", - "cuda/include/texture_indirect_functions.h", - "cuda/include/sm_32_atomic_functions.hpp", - "cuda/include/sm_20_intrinsics.hpp", "cuda/include/device_launch_parameters.h", - "cuda/include/curand_mtgp32.h", - "cuda/include/texture_fetch_functions.hpp", - "cuda/include/cuda_occupancy.h", - "cuda/include/CL/opencl.h", - "cuda/include/CL/cl_platform.h", - "cuda/include/CL/cl_egl.h", - "cuda/include/CL/cl_gl.h", - "cuda/include/CL/cl.h", - "cuda/include/CL/cl_gl_ext.h", - "cuda/include/CL/cl_ext.h", - "cuda/include/CL/cl.hpp", + "cuda/include/device_types.h", + "cuda/include/driver_functions.h", + "cuda/include/driver_types.h", + "cuda/include/dynlink_cuda.h", + "cuda/include/dynlink_cuda_cuda.h", + "cuda/include/dynlink_cuviddec.h", + "cuda/include/dynlink_nvcuvid.h", + "cuda/include/fatBinaryCtl.h", + "cuda/include/fatbinary.h", "cuda/include/host_config.h", - "cuda/include/cuda_surface_types.h", + "cuda/include/host_defines.h", + "cuda/include/library_types.h", + "cuda/include/math_constants.h", "cuda/include/math_functions.h", + "cuda/include/math_functions.hpp", + "cuda/include/math_functions_dbl_ptx3.h", + "cuda/include/math_functions_dbl_ptx3.hpp", + "cuda/include/mma.h", + "cuda/include/npp.h", + "cuda/include/nppcore.h", + "cuda/include/nppdefs.h", + "cuda/include/nppi.h", + "cuda/include/nppi_arithmetic_and_logical_operations.h", + "cuda/include/nppi_color_conversion.h", + "cuda/include/nppi_compression_functions.h", + "cuda/include/nppi_computer_vision.h", + "cuda/include/nppi_data_exchange_and_initialization.h", + "cuda/include/nppi_filtering_functions.h", + "cuda/include/nppi_geometry_transforms.h", + "cuda/include/nppi_linear_transforms.h", + "cuda/include/nppi_morphological_operations.h", + "cuda/include/nppi_statistics_functions.h", + "cuda/include/nppi_support_functions.h", + "cuda/include/nppi_threshold_and_compare_operations.h", + "cuda/include/npps.h", + "cuda/include/npps_arithmetic_and_logical_operations.h", + "cuda/include/npps_conversion_functions.h", + "cuda/include/npps_filtering_functions.h", + "cuda/include/npps_initialization.h", + "cuda/include/npps_statistics_functions.h", + "cuda/include/npps_support_functions.h", + "cuda/include/nppversion.h", + "cuda/include/nvToolsExt.h", + "cuda/include/nvToolsExtCuda.h", + "cuda/include/nvToolsExtCudaRt.h", "cuda/include/nvToolsExtMeta.h", + "cuda/include/nvToolsExtSync.h", + "cuda/include/nvblas.h", + "cuda/include/nvfunctional", + "cuda/include/nvgraph.h", + "cuda/include/nvml.h", + "cuda/include/nvrtc.h", + "cuda/include/sm_20_atomic_functions.h", "cuda/include/sm_20_atomic_functions.hpp", - "cuda/include/device_functions.h", - "cuda/include/device_types.h", - "cuda/include/npps_conversion_functions.h", - "cuda/include/curand_precalc.h", - "cuda/include/cusolverRf.h", + "cuda/include/sm_20_intrinsics.h", + "cuda/include/sm_20_intrinsics.hpp", + "cuda/include/sm_30_intrinsics.h", + "cuda/include/sm_30_intrinsics.hpp", + "cuda/include/sm_32_atomic_functions.h", + "cuda/include/sm_32_atomic_functions.hpp", + "cuda/include/sm_32_intrinsics.h", + "cuda/include/sm_32_intrinsics.hpp", + "cuda/include/sm_35_atomic_functions.h", + "cuda/include/sm_35_intrinsics.h", + "cuda/include/sm_60_atomic_functions.h", "cuda/include/sm_60_atomic_functions.hpp", - "cuda/include/cuviddec.h", - "cuda/include/curand_discrete2.h", - "cuda/include/device_functions.hpp", - "cuda/include/thrust/transform_scan.h", - "cuda/include/thrust/system_error.h", - "cuda/include/thrust/device_malloc.h", - "cuda/include/thrust/partition.h", - "cuda/include/thrust/unique.h", - "cuda/include/thrust/device_delete.h", - "cuda/include/thrust/execution_policy.h", + "cuda/include/sm_61_intrinsics.h", + "cuda/include/sm_61_intrinsics.hpp", + "cuda/include/sobol_direction_vectors.h", + "cuda/include/surface_functions.h", + "cuda/include/surface_functions.hpp", + "cuda/include/surface_indirect_functions.h", + "cuda/include/surface_indirect_functions.hpp", + "cuda/include/surface_types.h", + "cuda/include/texture_fetch_functions.h", + "cuda/include/texture_fetch_functions.hpp", + "cuda/include/texture_indirect_functions.h", + "cuda/include/texture_indirect_functions.hpp", + "cuda/include/texture_types.h", "cuda/include/thrust/adjacent_difference.h", - "cuda/include/thrust/sequence.h", - "cuda/include/thrust/merge.h", - "cuda/include/thrust/device_new.h", - "cuda/include/thrust/transform_reduce.h", - "cuda/include/thrust/device_vector.h", - "cuda/include/thrust/gather.h", - "cuda/include/thrust/sort.h", - "cuda/include/thrust/scan.h", - "cuda/include/thrust/detail/temporary_array.h", - "cuda/include/thrust/detail/util/align.h", - "cuda/include/thrust/detail/util/blocking.h", - "cuda/include/thrust/detail/transform.inl", - "cuda/include/thrust/detail/device_vector.inl", + "cuda/include/thrust/advance.h", + "cuda/include/thrust/binary_search.h", + "cuda/include/thrust/complex.h", + "cuda/include/thrust/copy.h", + "cuda/include/thrust/count.h", + "cuda/include/thrust/detail/adjacent_difference.inl", + "cuda/include/thrust/detail/advance.inl", + "cuda/include/thrust/detail/allocator/allocator_traits.h", + "cuda/include/thrust/detail/allocator/allocator_traits.inl", + "cuda/include/thrust/detail/allocator/copy_construct_range.h", + "cuda/include/thrust/detail/allocator/copy_construct_range.inl", + "cuda/include/thrust/detail/allocator/default_construct_range.h", + "cuda/include/thrust/detail/allocator/default_construct_range.inl", + "cuda/include/thrust/detail/allocator/destroy_range.h", + "cuda/include/thrust/detail/allocator/destroy_range.inl", + "cuda/include/thrust/detail/allocator/fill_construct_range.h", + "cuda/include/thrust/detail/allocator/fill_construct_range.inl", + "cuda/include/thrust/detail/allocator/malloc_allocator.h", + "cuda/include/thrust/detail/allocator/malloc_allocator.inl", + "cuda/include/thrust/detail/allocator/no_throw_allocator.h", + "cuda/include/thrust/detail/allocator/tagged_allocator.h", + "cuda/include/thrust/detail/allocator/tagged_allocator.inl", + "cuda/include/thrust/detail/allocator/temporary_allocator.h", + "cuda/include/thrust/detail/allocator/temporary_allocator.inl", "cuda/include/thrust/detail/binary_search.inl", - "cuda/include/thrust/detail/overlapped_copy.h", - "cuda/include/thrust/detail/vector_base.inl", - "cuda/include/thrust/detail/device_reference.inl", - "cuda/include/thrust/detail/functional/actor.h", - "cuda/include/thrust/detail/functional/value.h", - "cuda/include/thrust/detail/functional/operators.h", - "cuda/include/thrust/detail/functional/operators/logical_operators.h", - "cuda/include/thrust/detail/functional/operators/relational_operators.h", - "cuda/include/thrust/detail/functional/operators/assignment_operator.h", - "cuda/include/thrust/detail/functional/operators/bitwise_operators.h", - "cuda/include/thrust/detail/functional/operators/operator_adaptors.h", - "cuda/include/thrust/detail/functional/operators/arithmetic_operators.h", - "cuda/include/thrust/detail/functional/operators/compound_assignment_operators.h", - "cuda/include/thrust/detail/functional/argument.h", - "cuda/include/thrust/detail/functional/placeholder.h", - "cuda/include/thrust/detail/functional/actor.inl", - "cuda/include/thrust/detail/functional/composite.h", - "cuda/include/thrust/detail/static_map.h", - "cuda/include/thrust/detail/type_traits/has_nested_type.h", - "cuda/include/thrust/detail/type_traits/is_call_possible.h", - "cuda/include/thrust/detail/type_traits/function_traits.h", - "cuda/include/thrust/detail/type_traits/pointer_traits.h", - "cuda/include/thrust/detail/type_traits/has_member_function.h", - "cuda/include/thrust/detail/type_traits/algorithm/intermediate_type_from_function_and_iterators.h", - "cuda/include/thrust/detail/type_traits/minimum_type.h", - "cuda/include/thrust/detail/type_traits/has_trivial_assign.h", - "cuda/include/thrust/detail/type_traits/is_metafunction_defined.h", - "cuda/include/thrust/detail/type_traits/iterator/is_discard_iterator.h", - "cuda/include/thrust/detail/type_traits/iterator/is_output_iterator.h", - "cuda/include/thrust/detail/type_traits/result_of_adaptable_function.h", - "cuda/include/thrust/detail/reference.h", - "cuda/include/thrust/detail/inner_product.inl", - "cuda/include/thrust/detail/use_default.h", - "cuda/include/thrust/detail/sequence.inl", - "cuda/include/thrust/detail/sort.inl", - "cuda/include/thrust/detail/equal.inl", - "cuda/include/thrust/detail/execution_policy.h", - "cuda/include/thrust/detail/integer_traits.h", - "cuda/include/thrust/detail/type_traits.h", - "cuda/include/thrust/detail/reverse.inl", - "cuda/include/thrust/detail/tabulate.inl", - "cuda/include/thrust/detail/unique.inl", - "cuda/include/thrust/detail/scatter.inl", - "cuda/include/thrust/detail/set_operations.inl", - "cuda/include/thrust/detail/device_malloc.inl", - "cuda/include/thrust/detail/copy_if.inl", - "cuda/include/thrust/detail/fill.inl", - "cuda/include/thrust/detail/temporary_array.inl", - "cuda/include/thrust/detail/transform_scan.inl", - "cuda/include/thrust/detail/minmax.h", - "cuda/include/thrust/detail/swap.inl", - "cuda/include/thrust/detail/pointer.inl", - "cuda/include/thrust/detail/transform_reduce.inl", - "cuda/include/thrust/detail/config.h", - "cuda/include/thrust/detail/distance.inl", - "cuda/include/thrust/detail/pair.inl", - "cuda/include/thrust/detail/allocator/temporary_allocator.h", - "cuda/include/thrust/detail/allocator/tagged_allocator.h", - "cuda/include/thrust/detail/allocator/destroy_range.inl", - "cuda/include/thrust/detail/allocator/destroy_range.h", - "cuda/include/thrust/detail/allocator/no_throw_allocator.h", - "cuda/include/thrust/detail/allocator/default_construct_range.inl", - "cuda/include/thrust/detail/allocator/fill_construct_range.inl", - "cuda/include/thrust/detail/allocator/tagged_allocator.inl", - "cuda/include/thrust/detail/allocator/malloc_allocator.h", - "cuda/include/thrust/detail/allocator/allocator_traits.h", - "cuda/include/thrust/detail/allocator/copy_construct_range.h", - "cuda/include/thrust/detail/allocator/allocator_traits.inl", - "cuda/include/thrust/detail/allocator/default_construct_range.h", - "cuda/include/thrust/detail/allocator/copy_construct_range.inl", - "cuda/include/thrust/detail/allocator/malloc_allocator.inl", - "cuda/include/thrust/detail/allocator/temporary_allocator.inl", - "cuda/include/thrust/detail/allocator/fill_construct_range.h", - "cuda/include/thrust/detail/temporary_buffer.h", - "cuda/include/thrust/detail/reduce.inl", - "cuda/include/thrust/detail/device_new.inl", - "cuda/include/thrust/detail/pointer.h", - "cuda/include/thrust/detail/for_each.inl", - "cuda/include/thrust/detail/generate.inl", - "cuda/include/thrust/detail/dispatch/is_trivial_copy.h", - "cuda/include/thrust/detail/adjacent_difference.inl", - "cuda/include/thrust/detail/tuple_meta_transform.h", - "cuda/include/thrust/detail/functional.inl", - "cuda/include/thrust/detail/remove.inl", - "cuda/include/thrust/detail/tuple_transform.h", - "cuda/include/thrust/detail/merge.inl", - "cuda/include/thrust/detail/extrema.inl", - "cuda/include/thrust/detail/trivial_sequence.h", - "cuda/include/thrust/detail/vector_base.h", - "cuda/include/thrust/detail/count.inl", - "cuda/include/thrust/detail/uninitialized_copy.inl", - "cuda/include/thrust/detail/function.h", - "cuda/include/thrust/detail/swap_ranges.inl", - "cuda/include/thrust/detail/device_delete.inl", - "cuda/include/thrust/detail/static_assert.h", - "cuda/include/thrust/detail/logical.inl", - "cuda/include/thrust/detail/seq.h", - "cuda/include/thrust/detail/mpl/math.h", - "cuda/include/thrust/detail/mismatch.inl", - "cuda/include/thrust/detail/internal_functional.h", - "cuda/include/thrust/detail/get_iterator_value.h", - "cuda/include/thrust/detail/copy.inl", - "cuda/include/thrust/detail/copy.h", + "cuda/include/thrust/detail/complex/arithmetic.h", + "cuda/include/thrust/detail/complex/c99math.h", + "cuda/include/thrust/detail/complex/catrig.h", "cuda/include/thrust/detail/complex/catrigf.h", - "cuda/include/thrust/detail/complex/cpowf.h", - "cuda/include/thrust/detail/complex/csqrtf.h", + "cuda/include/thrust/detail/complex/ccosh.h", "cuda/include/thrust/detail/complex/ccoshf.h", - "cuda/include/thrust/detail/complex/csinhf.h", + "cuda/include/thrust/detail/complex/cexp.h", + "cuda/include/thrust/detail/complex/cexpf.h", + "cuda/include/thrust/detail/complex/clog.h", "cuda/include/thrust/detail/complex/clogf.h", - "cuda/include/thrust/detail/complex/ccosh.h", - "cuda/include/thrust/detail/complex/arithmetic.h", - "cuda/include/thrust/detail/complex/csqrt.h", - "cuda/include/thrust/detail/complex/cpow.h", "cuda/include/thrust/detail/complex/complex.inl", - "cuda/include/thrust/detail/complex/math_private.h", - "cuda/include/thrust/detail/complex/c99math.h", + "cuda/include/thrust/detail/complex/cpow.h", + "cuda/include/thrust/detail/complex/cpowf.h", "cuda/include/thrust/detail/complex/cproj.h", - "cuda/include/thrust/detail/complex/catrig.h", - "cuda/include/thrust/detail/complex/ctanhf.h", - "cuda/include/thrust/detail/complex/cexpf.h", "cuda/include/thrust/detail/complex/csinh.h", - "cuda/include/thrust/detail/complex/stream.h", + "cuda/include/thrust/detail/complex/csinhf.h", + "cuda/include/thrust/detail/complex/csqrt.h", + "cuda/include/thrust/detail/complex/csqrtf.h", "cuda/include/thrust/detail/complex/ctanh.h", - "cuda/include/thrust/detail/complex/cexp.h", - "cuda/include/thrust/detail/complex/clog.h", - "cuda/include/thrust/detail/range/head_flags.h", - "cuda/include/thrust/detail/range/tail_flags.h", - "cuda/include/thrust/detail/execute_with_allocator.h", - "cuda/include/thrust/detail/integer_math.h", - "cuda/include/thrust/detail/swap.h", - "cuda/include/thrust/detail/uninitialized_fill.inl", - "cuda/include/thrust/detail/scan.inl", - "cuda/include/thrust/detail/gather.inl", - "cuda/include/thrust/detail/reference_forward_declaration.h", - "cuda/include/thrust/detail/numeric_traits.h", - "cuda/include/thrust/detail/reference.inl", - "cuda/include/thrust/detail/cstdint.h", - "cuda/include/thrust/detail/device_free.inl", - "cuda/include/thrust/detail/copy_if.h", - "cuda/include/thrust/detail/partition.inl", - "cuda/include/thrust/detail/find.inl", - "cuda/include/thrust/detail/config/forceinline.h", - "cuda/include/thrust/detail/config/debug.h", - "cuda/include/thrust/detail/config/config.h", - "cuda/include/thrust/detail/config/host_device.h", - "cuda/include/thrust/detail/config/host_system.h", + "cuda/include/thrust/detail/complex/ctanhf.h", + "cuda/include/thrust/detail/complex/math_private.h", + "cuda/include/thrust/detail/complex/stream.h", + "cuda/include/thrust/detail/config.h", "cuda/include/thrust/detail/config/compiler.h", - "cuda/include/thrust/detail/config/device_system.h", "cuda/include/thrust/detail/config/compiler_fence.h", + "cuda/include/thrust/detail/config/config.h", + "cuda/include/thrust/detail/config/debug.h", + "cuda/include/thrust/detail/config/device_system.h", "cuda/include/thrust/detail/config/exec_check_disable.h", - "cuda/include/thrust/detail/config/simple_defines.h", + "cuda/include/thrust/detail/config/forceinline.h", "cuda/include/thrust/detail/config/global_workarounds.h", - "cuda/include/thrust/detail/replace.inl", + "cuda/include/thrust/detail/config/host_device.h", + "cuda/include/thrust/detail/config/host_system.h", + "cuda/include/thrust/detail/config/simple_defines.h", + "cuda/include/thrust/detail/contiguous_storage.h", + "cuda/include/thrust/detail/contiguous_storage.inl", + "cuda/include/thrust/detail/copy.h", + "cuda/include/thrust/detail/copy.inl", + "cuda/include/thrust/detail/copy_if.h", + "cuda/include/thrust/detail/copy_if.inl", + "cuda/include/thrust/detail/count.inl", + "cuda/include/thrust/detail/cstdint.h", + "cuda/include/thrust/detail/device_delete.inl", + "cuda/include/thrust/detail/device_free.inl", + "cuda/include/thrust/detail/device_malloc.inl", + "cuda/include/thrust/detail/device_new.inl", "cuda/include/thrust/detail/device_ptr.inl", - "cuda/include/thrust/detail/tuple.inl", - "cuda/include/thrust/detail/malloc_and_free.h", + "cuda/include/thrust/detail/device_reference.inl", + "cuda/include/thrust/detail/device_vector.inl", + "cuda/include/thrust/detail/dispatch/is_trivial_copy.h", + "cuda/include/thrust/detail/distance.inl", + "cuda/include/thrust/detail/equal.inl", + "cuda/include/thrust/detail/execute_with_allocator.h", + "cuda/include/thrust/detail/execution_policy.h", + "cuda/include/thrust/detail/extrema.inl", + "cuda/include/thrust/detail/fill.inl", + "cuda/include/thrust/detail/find.inl", + "cuda/include/thrust/detail/for_each.inl", + "cuda/include/thrust/detail/function.h", + "cuda/include/thrust/detail/functional.inl", + "cuda/include/thrust/detail/functional/actor.h", + "cuda/include/thrust/detail/functional/actor.inl", + "cuda/include/thrust/detail/functional/argument.h", + "cuda/include/thrust/detail/functional/composite.h", + "cuda/include/thrust/detail/functional/operators.h", + "cuda/include/thrust/detail/functional/operators/arithmetic_operators.h", + "cuda/include/thrust/detail/functional/operators/assignment_operator.h", + "cuda/include/thrust/detail/functional/operators/bitwise_operators.h", + "cuda/include/thrust/detail/functional/operators/compound_assignment_operators.h", + "cuda/include/thrust/detail/functional/operators/logical_operators.h", + "cuda/include/thrust/detail/functional/operators/operator_adaptors.h", + "cuda/include/thrust/detail/functional/operators/relational_operators.h", + "cuda/include/thrust/detail/functional/placeholder.h", + "cuda/include/thrust/detail/functional/value.h", + "cuda/include/thrust/detail/gather.inl", + "cuda/include/thrust/detail/generate.inl", + "cuda/include/thrust/detail/get_iterator_value.h", "cuda/include/thrust/detail/host_vector.inl", + "cuda/include/thrust/detail/inner_product.inl", + "cuda/include/thrust/detail/integer_math.h", + "cuda/include/thrust/detail/integer_traits.h", + "cuda/include/thrust/detail/internal_functional.h", + "cuda/include/thrust/detail/logical.inl", + "cuda/include/thrust/detail/malloc_and_free.h", + "cuda/include/thrust/detail/merge.inl", + "cuda/include/thrust/detail/minmax.h", + "cuda/include/thrust/detail/mismatch.inl", + "cuda/include/thrust/detail/mpl/math.h", + "cuda/include/thrust/detail/numeric_traits.h", + "cuda/include/thrust/detail/overlapped_copy.h", + "cuda/include/thrust/detail/pair.inl", + "cuda/include/thrust/detail/partition.inl", + "cuda/include/thrust/detail/pointer.h", + "cuda/include/thrust/detail/pointer.inl", + "cuda/include/thrust/detail/range/head_flags.h", + "cuda/include/thrust/detail/range/tail_flags.h", "cuda/include/thrust/detail/raw_pointer_cast.h", - "cuda/include/thrust/detail/advance.inl", - "cuda/include/thrust/detail/contiguous_storage.h", "cuda/include/thrust/detail/raw_reference_cast.h", - "cuda/include/thrust/detail/contiguous_storage.inl", - "cuda/include/thrust/reverse.h", - "cuda/include/thrust/device_malloc_allocator.h", - "cuda/include/thrust/scatter.h", - "cuda/include/thrust/pair.h", - "cuda/include/thrust/advance.h", - "cuda/include/thrust/find.h", - "cuda/include/thrust/device_ptr.h", - "cuda/include/thrust/generate.h", - "cuda/include/thrust/uninitialized_fill.h", - "cuda/include/thrust/system/system_error.h", - "cuda/include/thrust/system/detail/bad_alloc.h", - "cuda/include/thrust/system/detail/adl/transform_scan.h", - "cuda/include/thrust/system/detail/adl/unique_by_key.h", - "cuda/include/thrust/system/detail/adl/partition.h", - "cuda/include/thrust/system/detail/adl/unique.h", - "cuda/include/thrust/system/detail/adl/adjacent_difference.h", - "cuda/include/thrust/system/detail/adl/sequence.h", - "cuda/include/thrust/system/detail/adl/merge.h", - "cuda/include/thrust/system/detail/adl/transform_reduce.h", - "cuda/include/thrust/system/detail/adl/gather.h", - "cuda/include/thrust/system/detail/adl/sort.h", - "cuda/include/thrust/system/detail/adl/scan.h", - "cuda/include/thrust/system/detail/adl/temporary_buffer.h", - "cuda/include/thrust/system/detail/adl/scan_by_key.h", - "cuda/include/thrust/system/detail/adl/reverse.h", - "cuda/include/thrust/system/detail/adl/assign_value.h", - "cuda/include/thrust/system/detail/adl/scatter.h", - "cuda/include/thrust/system/detail/adl/find.h", - "cuda/include/thrust/system/detail/adl/generate.h", - "cuda/include/thrust/system/detail/adl/uninitialized_fill.h", - "cuda/include/thrust/system/detail/adl/remove.h", - "cuda/include/thrust/system/detail/adl/tabulate.h", - "cuda/include/thrust/system/detail/adl/for_each.h", - "cuda/include/thrust/system/detail/adl/reduce_by_key.h", - "cuda/include/thrust/system/detail/adl/reduce.h", - "cuda/include/thrust/system/detail/adl/equal.h", - "cuda/include/thrust/system/detail/adl/copy.h", - "cuda/include/thrust/system/detail/adl/swap_ranges.h", - "cuda/include/thrust/system/detail/adl/uninitialized_copy.h", - "cuda/include/thrust/system/detail/adl/binary_search.h", - "cuda/include/thrust/system/detail/adl/set_operations.h", - "cuda/include/thrust/system/detail/adl/mismatch.h", - "cuda/include/thrust/system/detail/adl/extrema.h", - "cuda/include/thrust/system/detail/adl/count.h", - "cuda/include/thrust/system/detail/adl/replace.h", + "cuda/include/thrust/detail/reduce.inl", + "cuda/include/thrust/detail/reference.h", + "cuda/include/thrust/detail/reference.inl", + "cuda/include/thrust/detail/reference_forward_declaration.h", + "cuda/include/thrust/detail/remove.inl", + "cuda/include/thrust/detail/replace.inl", + "cuda/include/thrust/detail/reverse.inl", + "cuda/include/thrust/detail/scan.inl", + "cuda/include/thrust/detail/scatter.inl", + "cuda/include/thrust/detail/seq.h", + "cuda/include/thrust/detail/sequence.inl", + "cuda/include/thrust/detail/set_operations.inl", + "cuda/include/thrust/detail/sort.inl", + "cuda/include/thrust/detail/static_assert.h", + "cuda/include/thrust/detail/static_map.h", + "cuda/include/thrust/detail/swap.h", + "cuda/include/thrust/detail/swap.inl", + "cuda/include/thrust/detail/swap_ranges.inl", + "cuda/include/thrust/detail/tabulate.inl", + "cuda/include/thrust/detail/temporary_array.h", + "cuda/include/thrust/detail/temporary_array.inl", + "cuda/include/thrust/detail/temporary_buffer.h", + "cuda/include/thrust/detail/transform.inl", + "cuda/include/thrust/detail/transform_reduce.inl", + "cuda/include/thrust/detail/transform_scan.inl", + "cuda/include/thrust/detail/trivial_sequence.h", + "cuda/include/thrust/detail/tuple.inl", + "cuda/include/thrust/detail/tuple_meta_transform.h", + "cuda/include/thrust/detail/tuple_transform.h", + "cuda/include/thrust/detail/type_traits.h", + "cuda/include/thrust/detail/type_traits/algorithm/intermediate_type_from_function_and_iterators.h", + "cuda/include/thrust/detail/type_traits/function_traits.h", + "cuda/include/thrust/detail/type_traits/has_member_function.h", + "cuda/include/thrust/detail/type_traits/has_nested_type.h", + "cuda/include/thrust/detail/type_traits/has_trivial_assign.h", + "cuda/include/thrust/detail/type_traits/is_call_possible.h", + "cuda/include/thrust/detail/type_traits/is_metafunction_defined.h", + "cuda/include/thrust/detail/type_traits/iterator/is_discard_iterator.h", + "cuda/include/thrust/detail/type_traits/iterator/is_output_iterator.h", + "cuda/include/thrust/detail/type_traits/minimum_type.h", + "cuda/include/thrust/detail/type_traits/pointer_traits.h", + "cuda/include/thrust/detail/type_traits/result_of_adaptable_function.h", + "cuda/include/thrust/detail/uninitialized_copy.inl", + "cuda/include/thrust/detail/uninitialized_fill.inl", + "cuda/include/thrust/detail/unique.inl", + "cuda/include/thrust/detail/use_default.h", + "cuda/include/thrust/detail/util/align.h", + "cuda/include/thrust/detail/util/blocking.h", + "cuda/include/thrust/detail/vector_base.h", + "cuda/include/thrust/detail/vector_base.inl", + "cuda/include/thrust/device_allocator.h", + "cuda/include/thrust/device_delete.h", + "cuda/include/thrust/device_free.h", + "cuda/include/thrust/device_malloc.h", + "cuda/include/thrust/device_malloc_allocator.h", + "cuda/include/thrust/device_new.h", + "cuda/include/thrust/device_new_allocator.h", + "cuda/include/thrust/device_ptr.h", + "cuda/include/thrust/device_reference.h", + "cuda/include/thrust/device_vector.h", + "cuda/include/thrust/distance.h", + "cuda/include/thrust/equal.h", + "cuda/include/thrust/execution_policy.h", + "cuda/include/thrust/extrema.h", + "cuda/include/thrust/fill.h", + "cuda/include/thrust/find.h", + "cuda/include/thrust/for_each.h", + "cuda/include/thrust/functional.h", + "cuda/include/thrust/gather.h", + "cuda/include/thrust/generate.h", + "cuda/include/thrust/host_vector.h", + "cuda/include/thrust/inner_product.h", + "cuda/include/thrust/iterator/constant_iterator.h", + "cuda/include/thrust/iterator/counting_iterator.h", + "cuda/include/thrust/iterator/detail/any_assign.h", + "cuda/include/thrust/iterator/detail/any_system_tag.h", + "cuda/include/thrust/iterator/detail/constant_iterator_base.h", + "cuda/include/thrust/iterator/detail/counting_iterator.inl", + "cuda/include/thrust/iterator/detail/device_system_tag.h", + "cuda/include/thrust/iterator/detail/discard_iterator_base.h", + "cuda/include/thrust/iterator/detail/distance_from_result.h", + "cuda/include/thrust/iterator/detail/host_system_tag.h", + "cuda/include/thrust/iterator/detail/is_iterator_category.h", + "cuda/include/thrust/iterator/detail/is_trivial_iterator.h", + "cuda/include/thrust/iterator/detail/iterator_adaptor_base.h", + "cuda/include/thrust/iterator/detail/iterator_category_to_system.h", + "cuda/include/thrust/iterator/detail/iterator_category_to_traversal.h", + "cuda/include/thrust/iterator/detail/iterator_category_with_system_and_traversal.h", + "cuda/include/thrust/iterator/detail/iterator_facade_category.h", + "cuda/include/thrust/iterator/detail/iterator_traits.inl", + "cuda/include/thrust/iterator/detail/iterator_traversal_tags.h", + "cuda/include/thrust/iterator/detail/join_iterator.h", + "cuda/include/thrust/iterator/detail/minimum_category.h", + "cuda/include/thrust/iterator/detail/minimum_system.h", + "cuda/include/thrust/iterator/detail/normal_iterator.h", + "cuda/include/thrust/iterator/detail/permutation_iterator_base.h", + "cuda/include/thrust/iterator/detail/retag.h", + "cuda/include/thrust/iterator/detail/reverse_iterator.inl", + "cuda/include/thrust/iterator/detail/reverse_iterator_base.h", + "cuda/include/thrust/iterator/detail/tagged_iterator.h", + "cuda/include/thrust/iterator/detail/transform_iterator.inl", + "cuda/include/thrust/iterator/detail/transform_output_iterator.inl", + "cuda/include/thrust/iterator/detail/tuple_of_iterator_references.h", + "cuda/include/thrust/iterator/detail/universal_categories.h", + "cuda/include/thrust/iterator/detail/zip_iterator.inl", + "cuda/include/thrust/iterator/detail/zip_iterator_base.h", + "cuda/include/thrust/iterator/discard_iterator.h", + "cuda/include/thrust/iterator/iterator_adaptor.h", + "cuda/include/thrust/iterator/iterator_categories.h", + "cuda/include/thrust/iterator/iterator_facade.h", + "cuda/include/thrust/iterator/iterator_traits.h", + "cuda/include/thrust/iterator/permutation_iterator.h", + "cuda/include/thrust/iterator/retag.h", + "cuda/include/thrust/iterator/reverse_iterator.h", + "cuda/include/thrust/iterator/transform_iterator.h", + "cuda/include/thrust/iterator/transform_output_iterator.h", + "cuda/include/thrust/iterator/zip_iterator.h", + "cuda/include/thrust/logical.h", + "cuda/include/thrust/memory.h", + "cuda/include/thrust/merge.h", + "cuda/include/thrust/mismatch.h", + "cuda/include/thrust/pair.h", + "cuda/include/thrust/partition.h", + "cuda/include/thrust/random.h", + "cuda/include/thrust/random/detail/discard_block_engine.inl", + "cuda/include/thrust/random/detail/linear_congruential_engine.inl", + "cuda/include/thrust/random/detail/linear_congruential_engine_discard.h", + "cuda/include/thrust/random/detail/linear_feedback_shift_engine.inl", + "cuda/include/thrust/random/detail/linear_feedback_shift_engine_wordmask.h", + "cuda/include/thrust/random/detail/mod.h", + "cuda/include/thrust/random/detail/normal_distribution.inl", + "cuda/include/thrust/random/detail/normal_distribution_base.h", + "cuda/include/thrust/random/detail/random_core_access.h", + "cuda/include/thrust/random/detail/subtract_with_carry_engine.inl", + "cuda/include/thrust/random/detail/uniform_int_distribution.inl", + "cuda/include/thrust/random/detail/uniform_real_distribution.inl", + "cuda/include/thrust/random/detail/xor_combine_engine.inl", + "cuda/include/thrust/random/detail/xor_combine_engine_max.h", + "cuda/include/thrust/random/discard_block_engine.h", + "cuda/include/thrust/random/linear_congruential_engine.h", + "cuda/include/thrust/random/linear_feedback_shift_engine.h", + "cuda/include/thrust/random/normal_distribution.h", + "cuda/include/thrust/random/subtract_with_carry_engine.h", + "cuda/include/thrust/random/uniform_int_distribution.h", + "cuda/include/thrust/random/uniform_real_distribution.h", + "cuda/include/thrust/random/xor_combine_engine.h", + "cuda/include/thrust/reduce.h", + "cuda/include/thrust/remove.h", + "cuda/include/thrust/replace.h", + "cuda/include/thrust/reverse.h", + "cuda/include/thrust/scan.h", + "cuda/include/thrust/scatter.h", + "cuda/include/thrust/sequence.h", + "cuda/include/thrust/set_operations.h", + "cuda/include/thrust/sort.h", + "cuda/include/thrust/swap.h", + "cuda/include/thrust/system/cpp/detail/adjacent_difference.h", + "cuda/include/thrust/system/cpp/detail/assign_value.h", + "cuda/include/thrust/system/cpp/detail/binary_search.h", + "cuda/include/thrust/system/cpp/detail/copy.h", + "cuda/include/thrust/system/cpp/detail/copy_if.h", + "cuda/include/thrust/system/cpp/detail/count.h", + "cuda/include/thrust/system/cpp/detail/equal.h", + "cuda/include/thrust/system/cpp/detail/execution_policy.h", + "cuda/include/thrust/system/cpp/detail/extrema.h", + "cuda/include/thrust/system/cpp/detail/fill.h", + "cuda/include/thrust/system/cpp/detail/find.h", + "cuda/include/thrust/system/cpp/detail/for_each.h", + "cuda/include/thrust/system/cpp/detail/gather.h", + "cuda/include/thrust/system/cpp/detail/generate.h", + "cuda/include/thrust/system/cpp/detail/get_value.h", + "cuda/include/thrust/system/cpp/detail/inner_product.h", + "cuda/include/thrust/system/cpp/detail/iter_swap.h", + "cuda/include/thrust/system/cpp/detail/logical.h", + "cuda/include/thrust/system/cpp/detail/malloc_and_free.h", + "cuda/include/thrust/system/cpp/detail/memory.inl", + "cuda/include/thrust/system/cpp/detail/merge.h", + "cuda/include/thrust/system/cpp/detail/mismatch.h", + "cuda/include/thrust/system/cpp/detail/par.h", + "cuda/include/thrust/system/cpp/detail/partition.h", + "cuda/include/thrust/system/cpp/detail/reduce.h", + "cuda/include/thrust/system/cpp/detail/reduce_by_key.h", + "cuda/include/thrust/system/cpp/detail/remove.h", + "cuda/include/thrust/system/cpp/detail/replace.h", + "cuda/include/thrust/system/cpp/detail/reverse.h", + "cuda/include/thrust/system/cpp/detail/scan.h", + "cuda/include/thrust/system/cpp/detail/scan_by_key.h", + "cuda/include/thrust/system/cpp/detail/scatter.h", + "cuda/include/thrust/system/cpp/detail/sequence.h", + "cuda/include/thrust/system/cpp/detail/set_operations.h", + "cuda/include/thrust/system/cpp/detail/sort.h", + "cuda/include/thrust/system/cpp/detail/swap_ranges.h", + "cuda/include/thrust/system/cpp/detail/tabulate.h", + "cuda/include/thrust/system/cpp/detail/temporary_buffer.h", + "cuda/include/thrust/system/cpp/detail/transform.h", + "cuda/include/thrust/system/cpp/detail/transform_reduce.h", + "cuda/include/thrust/system/cpp/detail/transform_scan.h", + "cuda/include/thrust/system/cpp/detail/uninitialized_copy.h", + "cuda/include/thrust/system/cpp/detail/uninitialized_fill.h", + "cuda/include/thrust/system/cpp/detail/unique.h", + "cuda/include/thrust/system/cpp/detail/unique_by_key.h", + "cuda/include/thrust/system/cpp/detail/vector.inl", + "cuda/include/thrust/system/cpp/execution_policy.h", + "cuda/include/thrust/system/cpp/memory.h", + "cuda/include/thrust/system/cpp/vector.h", + "cuda/include/thrust/system/cuda/config.h", + "cuda/include/thrust/system/cuda/detail/adjacent_difference.h", + "cuda/include/thrust/system/cuda/detail/assign_value.h", + "cuda/include/thrust/system/cuda/detail/binary_search.h", + "cuda/include/thrust/system/cuda/detail/copy.h", + "cuda/include/thrust/system/cuda/detail/copy_if.h", + "cuda/include/thrust/system/cuda/detail/core/agent_launcher.h", + "cuda/include/thrust/system/cuda/detail/core/alignment.h", + "cuda/include/thrust/system/cuda/detail/core/triple_chevron_launch.h", + "cuda/include/thrust/system/cuda/detail/core/util.h", + "cuda/include/thrust/system/cuda/detail/count.h", + "cuda/include/thrust/system/cuda/detail/cross_system.h", + "cuda/include/thrust/system/cuda/detail/cub/agent/agent_histogram.cuh", + "cuda/include/thrust/system/cuda/detail/cub/agent/agent_radix_sort_downsweep.cuh", + "cuda/include/thrust/system/cuda/detail/cub/agent/agent_radix_sort_upsweep.cuh", + "cuda/include/thrust/system/cuda/detail/cub/agent/agent_reduce.cuh", + "cuda/include/thrust/system/cuda/detail/cub/agent/agent_reduce_by_key.cuh", + "cuda/include/thrust/system/cuda/detail/cub/agent/agent_rle.cuh", + "cuda/include/thrust/system/cuda/detail/cub/agent/agent_scan.cuh", + "cuda/include/thrust/system/cuda/detail/cub/agent/agent_segment_fixup.cuh", + "cuda/include/thrust/system/cuda/detail/cub/agent/agent_select_if.cuh", + "cuda/include/thrust/system/cuda/detail/cub/agent/agent_spmv_csrt.cuh", + "cuda/include/thrust/system/cuda/detail/cub/agent/agent_spmv_orig.cuh", + "cuda/include/thrust/system/cuda/detail/cub/agent/agent_spmv_row_based.cuh", + "cuda/include/thrust/system/cuda/detail/cub/agent/single_pass_scan_operators.cuh", + "cuda/include/thrust/system/cuda/detail/cub/block/block_adjacent_difference.cuh", + "cuda/include/thrust/system/cuda/detail/cub/block/block_discontinuity.cuh", + "cuda/include/thrust/system/cuda/detail/cub/block/block_exchange.cuh", + "cuda/include/thrust/system/cuda/detail/cub/block/block_histogram.cuh", + "cuda/include/thrust/system/cuda/detail/cub/block/block_load.cuh", + "cuda/include/thrust/system/cuda/detail/cub/block/block_radix_rank.cuh", + "cuda/include/thrust/system/cuda/detail/cub/block/block_radix_sort.cuh", + "cuda/include/thrust/system/cuda/detail/cub/block/block_raking_layout.cuh", + "cuda/include/thrust/system/cuda/detail/cub/block/block_reduce.cuh", + "cuda/include/thrust/system/cuda/detail/cub/block/block_scan.cuh", + "cuda/include/thrust/system/cuda/detail/cub/block/block_shuffle.cuh", + "cuda/include/thrust/system/cuda/detail/cub/block/block_store.cuh", + "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_atomic.cuh", + "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_sort.cuh", + "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking.cuh", + "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking_commutative_only.cuh", + "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_warp_reductions.cuh", + "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_raking.cuh", + "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans.cuh", + "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans2.cuh", + "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans3.cuh", + "cuda/include/thrust/system/cuda/detail/cub/cub.cuh", + "cuda/include/thrust/system/cuda/detail/cub/device/device_histogram.cuh", + "cuda/include/thrust/system/cuda/detail/cub/device/device_partition.cuh", + "cuda/include/thrust/system/cuda/detail/cub/device/device_radix_sort.cuh", + "cuda/include/thrust/system/cuda/detail/cub/device/device_reduce.cuh", + "cuda/include/thrust/system/cuda/detail/cub/device/device_run_length_encode.cuh", + "cuda/include/thrust/system/cuda/detail/cub/device/device_scan.cuh", + "cuda/include/thrust/system/cuda/detail/cub/device/device_segmented_radix_sort.cuh", + "cuda/include/thrust/system/cuda/detail/cub/device/device_segmented_reduce.cuh", + "cuda/include/thrust/system/cuda/detail/cub/device/device_select.cuh", + "cuda/include/thrust/system/cuda/detail/cub/device/device_spmv.cuh", + "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_histogram.cuh", + "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_radix_sort.cuh", + "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_reduce.cuh", + "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_reduce_by_key.cuh", + "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_rle.cuh", + "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_scan.cuh", + "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_select_if.cuh", + "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_spmv_csrt.cuh", + "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_spmv_orig.cuh", + "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_spmv_row_based.cuh", + "cuda/include/thrust/system/cuda/detail/cub/grid/grid_barrier.cuh", + "cuda/include/thrust/system/cuda/detail/cub/grid/grid_even_share.cuh", + "cuda/include/thrust/system/cuda/detail/cub/grid/grid_mapping.cuh", + "cuda/include/thrust/system/cuda/detail/cub/grid/grid_queue.cuh", + "cuda/include/thrust/system/cuda/detail/cub/host/mutex.cuh", + "cuda/include/thrust/system/cuda/detail/cub/iterator/arg_index_input_iterator.cuh", + "cuda/include/thrust/system/cuda/detail/cub/iterator/cache_modified_input_iterator.cuh", + "cuda/include/thrust/system/cuda/detail/cub/iterator/cache_modified_output_iterator.cuh", + "cuda/include/thrust/system/cuda/detail/cub/iterator/constant_input_iterator.cuh", + "cuda/include/thrust/system/cuda/detail/cub/iterator/counting_input_iterator.cuh", + "cuda/include/thrust/system/cuda/detail/cub/iterator/discard_output_iterator.cuh", + "cuda/include/thrust/system/cuda/detail/cub/iterator/tex_obj_input_iterator.cuh", + "cuda/include/thrust/system/cuda/detail/cub/iterator/tex_ref_input_iterator.cuh", + "cuda/include/thrust/system/cuda/detail/cub/iterator/transform_input_iterator.cuh", + "cuda/include/thrust/system/cuda/detail/cub/thread/thread_load.cuh", + "cuda/include/thrust/system/cuda/detail/cub/thread/thread_operators.cuh", + "cuda/include/thrust/system/cuda/detail/cub/thread/thread_reduce.cuh", + "cuda/include/thrust/system/cuda/detail/cub/thread/thread_scan.cuh", + "cuda/include/thrust/system/cuda/detail/cub/thread/thread_search.cuh", + "cuda/include/thrust/system/cuda/detail/cub/thread/thread_store.cuh", + "cuda/include/thrust/system/cuda/detail/cub/util_allocator.cuh", + "cuda/include/thrust/system/cuda/detail/cub/util_arch.cuh", + "cuda/include/thrust/system/cuda/detail/cub/util_debug.cuh", + "cuda/include/thrust/system/cuda/detail/cub/util_device.cuh", + "cuda/include/thrust/system/cuda/detail/cub/util_macro.cuh", + "cuda/include/thrust/system/cuda/detail/cub/util_namespace.cuh", + "cuda/include/thrust/system/cuda/detail/cub/util_ptx.cuh", + "cuda/include/thrust/system/cuda/detail/cub/util_type.cuh", + "cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_shfl.cuh", + "cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_smem.cuh", + "cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_shfl.cuh", + "cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_smem.cuh", + "cuda/include/thrust/system/cuda/detail/cub/warp/warp_reduce.cuh", + "cuda/include/thrust/system/cuda/detail/cub/warp/warp_scan.cuh", + "cuda/include/thrust/system/cuda/detail/equal.h", + "cuda/include/thrust/system/cuda/detail/error.inl", + "cuda/include/thrust/system/cuda/detail/execution_policy.h", + "cuda/include/thrust/system/cuda/detail/extrema.h", + "cuda/include/thrust/system/cuda/detail/fill.h", + "cuda/include/thrust/system/cuda/detail/find.h", + "cuda/include/thrust/system/cuda/detail/for_each.h", + "cuda/include/thrust/system/cuda/detail/gather.h", + "cuda/include/thrust/system/cuda/detail/generate.h", + "cuda/include/thrust/system/cuda/detail/get_value.h", + "cuda/include/thrust/system/cuda/detail/guarded_cuda_runtime_api.h", + "cuda/include/thrust/system/cuda/detail/guarded_driver_types.h", + "cuda/include/thrust/system/cuda/detail/inner_product.h", + "cuda/include/thrust/system/cuda/detail/internal/copy_cross_system.h", + "cuda/include/thrust/system/cuda/detail/internal/copy_device_to_device.h", + "cuda/include/thrust/system/cuda/detail/iter_swap.h", + "cuda/include/thrust/system/cuda/detail/logical.h", + "cuda/include/thrust/system/cuda/detail/malloc_and_free.h", + "cuda/include/thrust/system/cuda/detail/memory.inl", + "cuda/include/thrust/system/cuda/detail/memory_buffer.h", + "cuda/include/thrust/system/cuda/detail/merge.h", + "cuda/include/thrust/system/cuda/detail/mismatch.h", + "cuda/include/thrust/system/cuda/detail/par.h", + "cuda/include/thrust/system/cuda/detail/par_to_seq.h", + "cuda/include/thrust/system/cuda/detail/parallel_for.h", + "cuda/include/thrust/system/cuda/detail/partition.h", + "cuda/include/thrust/system/cuda/detail/reduce.h", + "cuda/include/thrust/system/cuda/detail/reduce_by_key.h", + "cuda/include/thrust/system/cuda/detail/remove.h", + "cuda/include/thrust/system/cuda/detail/replace.h", + "cuda/include/thrust/system/cuda/detail/reverse.h", + "cuda/include/thrust/system/cuda/detail/scan.h", + "cuda/include/thrust/system/cuda/detail/scan_by_key.h", + "cuda/include/thrust/system/cuda/detail/scatter.h", + "cuda/include/thrust/system/cuda/detail/sequence.h", + "cuda/include/thrust/system/cuda/detail/set_operations.h", + "cuda/include/thrust/system/cuda/detail/sort.h", + "cuda/include/thrust/system/cuda/detail/swap_ranges.h", + "cuda/include/thrust/system/cuda/detail/tabulate.h", + "cuda/include/thrust/system/cuda/detail/temporary_buffer.h", + "cuda/include/thrust/system/cuda/detail/terminate.h", + "cuda/include/thrust/system/cuda/detail/transform.h", + "cuda/include/thrust/system/cuda/detail/transform_reduce.h", + "cuda/include/thrust/system/cuda/detail/transform_scan.h", + "cuda/include/thrust/system/cuda/detail/uninitialized_copy.h", + "cuda/include/thrust/system/cuda/detail/uninitialized_fill.h", + "cuda/include/thrust/system/cuda/detail/unique.h", + "cuda/include/thrust/system/cuda/detail/unique_by_key.h", + "cuda/include/thrust/system/cuda/detail/util.h", + "cuda/include/thrust/system/cuda/detail/vector.inl", + "cuda/include/thrust/system/cuda/error.h", + "cuda/include/thrust/system/cuda/execution_policy.h", + "cuda/include/thrust/system/cuda/experimental/pinned_allocator.h", + "cuda/include/thrust/system/cuda/memory.h", + "cuda/include/thrust/system/cuda/vector.h", + "cuda/include/thrust/system/detail/adl/adjacent_difference.h", + "cuda/include/thrust/system/detail/adl/assign_value.h", + "cuda/include/thrust/system/detail/adl/binary_search.h", + "cuda/include/thrust/system/detail/adl/copy.h", + "cuda/include/thrust/system/detail/adl/copy_if.h", + "cuda/include/thrust/system/detail/adl/count.h", + "cuda/include/thrust/system/detail/adl/equal.h", + "cuda/include/thrust/system/detail/adl/extrema.h", + "cuda/include/thrust/system/detail/adl/fill.h", + "cuda/include/thrust/system/detail/adl/find.h", + "cuda/include/thrust/system/detail/adl/for_each.h", + "cuda/include/thrust/system/detail/adl/gather.h", + "cuda/include/thrust/system/detail/adl/generate.h", "cuda/include/thrust/system/detail/adl/get_value.h", "cuda/include/thrust/system/detail/adl/inner_product.h", - "cuda/include/thrust/system/detail/adl/copy_if.h", - "cuda/include/thrust/system/detail/adl/logical.h", "cuda/include/thrust/system/detail/adl/iter_swap.h", + "cuda/include/thrust/system/detail/adl/logical.h", "cuda/include/thrust/system/detail/adl/malloc_and_free.h", - "cuda/include/thrust/system/detail/adl/fill.h", + "cuda/include/thrust/system/detail/adl/merge.h", + "cuda/include/thrust/system/detail/adl/mismatch.h", + "cuda/include/thrust/system/detail/adl/partition.h", + "cuda/include/thrust/system/detail/adl/reduce.h", + "cuda/include/thrust/system/detail/adl/reduce_by_key.h", + "cuda/include/thrust/system/detail/adl/remove.h", + "cuda/include/thrust/system/detail/adl/replace.h", + "cuda/include/thrust/system/detail/adl/reverse.h", + "cuda/include/thrust/system/detail/adl/scan.h", + "cuda/include/thrust/system/detail/adl/scan_by_key.h", + "cuda/include/thrust/system/detail/adl/scatter.h", + "cuda/include/thrust/system/detail/adl/sequence.h", + "cuda/include/thrust/system/detail/adl/set_operations.h", + "cuda/include/thrust/system/detail/adl/sort.h", + "cuda/include/thrust/system/detail/adl/swap_ranges.h", + "cuda/include/thrust/system/detail/adl/tabulate.h", + "cuda/include/thrust/system/detail/adl/temporary_buffer.h", "cuda/include/thrust/system/detail/adl/transform.h", + "cuda/include/thrust/system/detail/adl/transform_reduce.h", + "cuda/include/thrust/system/detail/adl/transform_scan.h", + "cuda/include/thrust/system/detail/adl/uninitialized_copy.h", + "cuda/include/thrust/system/detail/adl/uninitialized_fill.h", + "cuda/include/thrust/system/detail/adl/unique.h", + "cuda/include/thrust/system/detail/adl/unique_by_key.h", + "cuda/include/thrust/system/detail/bad_alloc.h", "cuda/include/thrust/system/detail/errno.h", "cuda/include/thrust/system/detail/error_category.inl", - "cuda/include/thrust/system/detail/sequential/transform_scan.h", - "cuda/include/thrust/system/detail/sequential/unique_by_key.h", - "cuda/include/thrust/system/detail/sequential/stable_primitive_sort.h", - "cuda/include/thrust/system/detail/sequential/stable_primitive_sort.inl", - "cuda/include/thrust/system/detail/sequential/stable_merge_sort.h", - "cuda/include/thrust/system/detail/sequential/sort.inl", - "cuda/include/thrust/system/detail/sequential/partition.h", - "cuda/include/thrust/system/detail/sequential/unique.h", - "cuda/include/thrust/system/detail/sequential/execution_policy.h", - "cuda/include/thrust/system/detail/sequential/adjacent_difference.h", - "cuda/include/thrust/system/detail/sequential/sequence.h", - "cuda/include/thrust/system/detail/sequential/merge.h", - "cuda/include/thrust/system/detail/sequential/transform_reduce.h", - "cuda/include/thrust/system/detail/sequential/gather.h", - "cuda/include/thrust/system/detail/sequential/sort.h", - "cuda/include/thrust/system/detail/sequential/copy_backward.h", - "cuda/include/thrust/system/detail/sequential/stable_radix_sort.inl", - "cuda/include/thrust/system/detail/sequential/scan.h", - "cuda/include/thrust/system/detail/sequential/temporary_buffer.h", - "cuda/include/thrust/system/detail/sequential/scan_by_key.h", - "cuda/include/thrust/system/detail/sequential/reverse.h", - "cuda/include/thrust/system/detail/sequential/assign_value.h", - "cuda/include/thrust/system/detail/sequential/scatter.h", - "cuda/include/thrust/system/detail/sequential/find.h", - "cuda/include/thrust/system/detail/sequential/stable_merge_sort.inl", - "cuda/include/thrust/system/detail/sequential/merge.inl", - "cuda/include/thrust/system/detail/sequential/generate.h", - "cuda/include/thrust/system/detail/sequential/uninitialized_fill.h", - "cuda/include/thrust/system/detail/sequential/general_copy.h", - "cuda/include/thrust/system/detail/sequential/insertion_sort.h", - "cuda/include/thrust/system/detail/sequential/remove.h", - "cuda/include/thrust/system/detail/sequential/tabulate.h", - "cuda/include/thrust/system/detail/sequential/for_each.h", - "cuda/include/thrust/system/detail/sequential/reduce_by_key.h", - "cuda/include/thrust/system/detail/sequential/reduce.h", - "cuda/include/thrust/system/detail/sequential/equal.h", - "cuda/include/thrust/system/detail/sequential/stable_radix_sort.h", - "cuda/include/thrust/system/detail/sequential/copy.inl", - "cuda/include/thrust/system/detail/sequential/copy.h", - "cuda/include/thrust/system/detail/sequential/swap_ranges.h", - "cuda/include/thrust/system/detail/sequential/uninitialized_copy.h", - "cuda/include/thrust/system/detail/sequential/binary_search.h", - "cuda/include/thrust/system/detail/sequential/set_operations.h", - "cuda/include/thrust/system/detail/sequential/mismatch.h", - "cuda/include/thrust/system/detail/sequential/extrema.h", - "cuda/include/thrust/system/detail/sequential/count.h", - "cuda/include/thrust/system/detail/sequential/trivial_copy.h", - "cuda/include/thrust/system/detail/sequential/replace.h", - "cuda/include/thrust/system/detail/sequential/get_value.h", - "cuda/include/thrust/system/detail/sequential/inner_product.h", - "cuda/include/thrust/system/detail/sequential/copy_if.h", - "cuda/include/thrust/system/detail/sequential/logical.h", - "cuda/include/thrust/system/detail/sequential/iter_swap.h", - "cuda/include/thrust/system/detail/sequential/malloc_and_free.h", - "cuda/include/thrust/system/detail/sequential/fill.h", - "cuda/include/thrust/system/detail/sequential/transform.h", - "cuda/include/thrust/system/detail/error_condition.inl", - "cuda/include/thrust/system/detail/internal/decompose.h", "cuda/include/thrust/system/detail/error_code.inl", - "cuda/include/thrust/system/detail/generic/transform_scan.h", - "cuda/include/thrust/system/detail/generic/memory.inl", - "cuda/include/thrust/system/detail/generic/transform.inl", - "cuda/include/thrust/system/detail/generic/binary_search.inl", - "cuda/include/thrust/system/detail/generic/scan_by_key.inl", - "cuda/include/thrust/system/detail/generic/unique_by_key.h", - "cuda/include/thrust/system/detail/generic/inner_product.inl", - "cuda/include/thrust/system/detail/generic/select_system.h", - "cuda/include/thrust/system/detail/generic/sequence.inl", - "cuda/include/thrust/system/detail/generic/sort.inl", - "cuda/include/thrust/system/detail/generic/equal.inl", - "cuda/include/thrust/system/detail/generic/partition.h", - "cuda/include/thrust/system/detail/generic/unique.h", + "cuda/include/thrust/system/detail/error_condition.inl", "cuda/include/thrust/system/detail/generic/adjacent_difference.h", - "cuda/include/thrust/system/detail/generic/tag.h", - "cuda/include/thrust/system/detail/generic/unique_by_key.inl", - "cuda/include/thrust/system/detail/generic/sequence.h", - "cuda/include/thrust/system/detail/generic/type_traits.h", - "cuda/include/thrust/system/detail/generic/merge.h", - "cuda/include/thrust/system/detail/generic/reverse.inl", - "cuda/include/thrust/system/detail/generic/tabulate.inl", - "cuda/include/thrust/system/detail/generic/unique.inl", - "cuda/include/thrust/system/detail/generic/scatter.inl", - "cuda/include/thrust/system/detail/generic/set_operations.inl", - "cuda/include/thrust/system/detail/generic/copy_if.inl", - "cuda/include/thrust/system/detail/generic/transform_reduce.h", - "cuda/include/thrust/system/detail/generic/transform_scan.inl", - "cuda/include/thrust/system/detail/generic/gather.h", - "cuda/include/thrust/system/detail/generic/reduce_by_key.inl", - "cuda/include/thrust/system/detail/generic/transform_reduce.inl", - "cuda/include/thrust/system/detail/generic/sort.h", - "cuda/include/thrust/system/detail/generic/distance.inl", - "cuda/include/thrust/system/detail/generic/scan.h", - "cuda/include/thrust/system/detail/generic/temporary_buffer.h", - "cuda/include/thrust/system/detail/generic/reduce.inl", - "cuda/include/thrust/system/detail/generic/scan_by_key.h", - "cuda/include/thrust/system/detail/generic/reverse.h", - "cuda/include/thrust/system/detail/generic/temporary_buffer.inl", - "cuda/include/thrust/system/detail/generic/scatter.h", - "cuda/include/thrust/system/detail/generic/generate.inl", "cuda/include/thrust/system/detail/generic/adjacent_difference.inl", - "cuda/include/thrust/system/detail/generic/remove.inl", "cuda/include/thrust/system/detail/generic/advance.h", - "cuda/include/thrust/system/detail/generic/find.h", - "cuda/include/thrust/system/detail/generic/merge.inl", - "cuda/include/thrust/system/detail/generic/scalar/binary_search.inl", - "cuda/include/thrust/system/detail/generic/scalar/binary_search.h", - "cuda/include/thrust/system/detail/generic/extrema.inl", - "cuda/include/thrust/system/detail/generic/generate.h", - "cuda/include/thrust/system/detail/generic/uninitialized_fill.h", + "cuda/include/thrust/system/detail/generic/advance.inl", + "cuda/include/thrust/system/detail/generic/binary_search.h", + "cuda/include/thrust/system/detail/generic/binary_search.inl", + "cuda/include/thrust/system/detail/generic/copy.h", + "cuda/include/thrust/system/detail/generic/copy.inl", + "cuda/include/thrust/system/detail/generic/copy_if.h", + "cuda/include/thrust/system/detail/generic/copy_if.inl", + "cuda/include/thrust/system/detail/generic/count.h", "cuda/include/thrust/system/detail/generic/count.inl", - "cuda/include/thrust/system/detail/generic/remove.h", - "cuda/include/thrust/system/detail/generic/uninitialized_copy.inl", - "cuda/include/thrust/system/detail/generic/tabulate.h", - "cuda/include/thrust/system/detail/generic/for_each.h", "cuda/include/thrust/system/detail/generic/distance.h", - "cuda/include/thrust/system/detail/generic/swap_ranges.inl", - "cuda/include/thrust/system/detail/generic/reduce_by_key.h", - "cuda/include/thrust/system/detail/generic/reduce.h", + "cuda/include/thrust/system/detail/generic/distance.inl", "cuda/include/thrust/system/detail/generic/equal.h", - "cuda/include/thrust/system/detail/generic/mismatch.inl", - "cuda/include/thrust/system/detail/generic/copy.inl", - "cuda/include/thrust/system/detail/generic/copy.h", - "cuda/include/thrust/system/detail/generic/swap_ranges.h", - "cuda/include/thrust/system/detail/generic/uninitialized_copy.h", - "cuda/include/thrust/system/detail/generic/binary_search.h", - "cuda/include/thrust/system/detail/generic/set_operations.h", - "cuda/include/thrust/system/detail/generic/uninitialized_fill.inl", - "cuda/include/thrust/system/detail/generic/mismatch.h", - "cuda/include/thrust/system/detail/generic/scan.inl", - "cuda/include/thrust/system/detail/generic/gather.inl", + "cuda/include/thrust/system/detail/generic/equal.inl", "cuda/include/thrust/system/detail/generic/extrema.h", - "cuda/include/thrust/system/detail/generic/count.h", - "cuda/include/thrust/system/detail/generic/replace.h", + "cuda/include/thrust/system/detail/generic/extrema.inl", + "cuda/include/thrust/system/detail/generic/fill.h", + "cuda/include/thrust/system/detail/generic/find.h", + "cuda/include/thrust/system/detail/generic/find.inl", + "cuda/include/thrust/system/detail/generic/for_each.h", + "cuda/include/thrust/system/detail/generic/gather.h", + "cuda/include/thrust/system/detail/generic/gather.inl", + "cuda/include/thrust/system/detail/generic/generate.h", + "cuda/include/thrust/system/detail/generic/generate.inl", "cuda/include/thrust/system/detail/generic/inner_product.h", - "cuda/include/thrust/system/detail/generic/copy_if.h", + "cuda/include/thrust/system/detail/generic/inner_product.inl", "cuda/include/thrust/system/detail/generic/logical.h", - "cuda/include/thrust/system/detail/generic/partition.inl", "cuda/include/thrust/system/detail/generic/memory.h", - "cuda/include/thrust/system/detail/generic/find.inl", + "cuda/include/thrust/system/detail/generic/memory.inl", + "cuda/include/thrust/system/detail/generic/merge.h", + "cuda/include/thrust/system/detail/generic/merge.inl", + "cuda/include/thrust/system/detail/generic/mismatch.h", + "cuda/include/thrust/system/detail/generic/mismatch.inl", + "cuda/include/thrust/system/detail/generic/partition.h", + "cuda/include/thrust/system/detail/generic/partition.inl", + "cuda/include/thrust/system/detail/generic/reduce.h", + "cuda/include/thrust/system/detail/generic/reduce.inl", + "cuda/include/thrust/system/detail/generic/reduce_by_key.h", + "cuda/include/thrust/system/detail/generic/reduce_by_key.inl", + "cuda/include/thrust/system/detail/generic/remove.h", + "cuda/include/thrust/system/detail/generic/remove.inl", + "cuda/include/thrust/system/detail/generic/replace.h", "cuda/include/thrust/system/detail/generic/replace.inl", - "cuda/include/thrust/system/detail/generic/advance.inl", - "cuda/include/thrust/system/detail/generic/fill.h", + "cuda/include/thrust/system/detail/generic/reverse.h", + "cuda/include/thrust/system/detail/generic/reverse.inl", + "cuda/include/thrust/system/detail/generic/scalar/binary_search.h", + "cuda/include/thrust/system/detail/generic/scalar/binary_search.inl", + "cuda/include/thrust/system/detail/generic/scan.h", + "cuda/include/thrust/system/detail/generic/scan.inl", + "cuda/include/thrust/system/detail/generic/scan_by_key.h", + "cuda/include/thrust/system/detail/generic/scan_by_key.inl", + "cuda/include/thrust/system/detail/generic/scatter.h", + "cuda/include/thrust/system/detail/generic/scatter.inl", + "cuda/include/thrust/system/detail/generic/select_system.h", + "cuda/include/thrust/system/detail/generic/sequence.h", + "cuda/include/thrust/system/detail/generic/sequence.inl", + "cuda/include/thrust/system/detail/generic/set_operations.h", + "cuda/include/thrust/system/detail/generic/set_operations.inl", + "cuda/include/thrust/system/detail/generic/sort.h", + "cuda/include/thrust/system/detail/generic/sort.inl", + "cuda/include/thrust/system/detail/generic/swap_ranges.h", + "cuda/include/thrust/system/detail/generic/swap_ranges.inl", + "cuda/include/thrust/system/detail/generic/tabulate.h", + "cuda/include/thrust/system/detail/generic/tabulate.inl", + "cuda/include/thrust/system/detail/generic/tag.h", + "cuda/include/thrust/system/detail/generic/temporary_buffer.h", + "cuda/include/thrust/system/detail/generic/temporary_buffer.inl", "cuda/include/thrust/system/detail/generic/transform.h", + "cuda/include/thrust/system/detail/generic/transform.inl", + "cuda/include/thrust/system/detail/generic/transform_reduce.h", + "cuda/include/thrust/system/detail/generic/transform_reduce.inl", + "cuda/include/thrust/system/detail/generic/transform_scan.h", + "cuda/include/thrust/system/detail/generic/transform_scan.inl", + "cuda/include/thrust/system/detail/generic/type_traits.h", + "cuda/include/thrust/system/detail/generic/uninitialized_copy.h", + "cuda/include/thrust/system/detail/generic/uninitialized_copy.inl", + "cuda/include/thrust/system/detail/generic/uninitialized_fill.h", + "cuda/include/thrust/system/detail/generic/uninitialized_fill.inl", + "cuda/include/thrust/system/detail/generic/unique.h", + "cuda/include/thrust/system/detail/generic/unique.inl", + "cuda/include/thrust/system/detail/generic/unique_by_key.h", + "cuda/include/thrust/system/detail/generic/unique_by_key.inl", + "cuda/include/thrust/system/detail/internal/decompose.h", + "cuda/include/thrust/system/detail/sequential/adjacent_difference.h", + "cuda/include/thrust/system/detail/sequential/assign_value.h", + "cuda/include/thrust/system/detail/sequential/binary_search.h", + "cuda/include/thrust/system/detail/sequential/copy.h", + "cuda/include/thrust/system/detail/sequential/copy.inl", + "cuda/include/thrust/system/detail/sequential/copy_backward.h", + "cuda/include/thrust/system/detail/sequential/copy_if.h", + "cuda/include/thrust/system/detail/sequential/count.h", + "cuda/include/thrust/system/detail/sequential/equal.h", + "cuda/include/thrust/system/detail/sequential/execution_policy.h", + "cuda/include/thrust/system/detail/sequential/extrema.h", + "cuda/include/thrust/system/detail/sequential/fill.h", + "cuda/include/thrust/system/detail/sequential/find.h", + "cuda/include/thrust/system/detail/sequential/for_each.h", + "cuda/include/thrust/system/detail/sequential/gather.h", + "cuda/include/thrust/system/detail/sequential/general_copy.h", + "cuda/include/thrust/system/detail/sequential/generate.h", + "cuda/include/thrust/system/detail/sequential/get_value.h", + "cuda/include/thrust/system/detail/sequential/inner_product.h", + "cuda/include/thrust/system/detail/sequential/insertion_sort.h", + "cuda/include/thrust/system/detail/sequential/iter_swap.h", + "cuda/include/thrust/system/detail/sequential/logical.h", + "cuda/include/thrust/system/detail/sequential/malloc_and_free.h", + "cuda/include/thrust/system/detail/sequential/merge.h", + "cuda/include/thrust/system/detail/sequential/merge.inl", + "cuda/include/thrust/system/detail/sequential/mismatch.h", + "cuda/include/thrust/system/detail/sequential/partition.h", + "cuda/include/thrust/system/detail/sequential/reduce.h", + "cuda/include/thrust/system/detail/sequential/reduce_by_key.h", + "cuda/include/thrust/system/detail/sequential/remove.h", + "cuda/include/thrust/system/detail/sequential/replace.h", + "cuda/include/thrust/system/detail/sequential/reverse.h", + "cuda/include/thrust/system/detail/sequential/scan.h", + "cuda/include/thrust/system/detail/sequential/scan_by_key.h", + "cuda/include/thrust/system/detail/sequential/scatter.h", + "cuda/include/thrust/system/detail/sequential/sequence.h", + "cuda/include/thrust/system/detail/sequential/set_operations.h", + "cuda/include/thrust/system/detail/sequential/sort.h", + "cuda/include/thrust/system/detail/sequential/sort.inl", + "cuda/include/thrust/system/detail/sequential/stable_merge_sort.h", + "cuda/include/thrust/system/detail/sequential/stable_merge_sort.inl", + "cuda/include/thrust/system/detail/sequential/stable_primitive_sort.h", + "cuda/include/thrust/system/detail/sequential/stable_primitive_sort.inl", + "cuda/include/thrust/system/detail/sequential/stable_radix_sort.h", + "cuda/include/thrust/system/detail/sequential/stable_radix_sort.inl", + "cuda/include/thrust/system/detail/sequential/swap_ranges.h", + "cuda/include/thrust/system/detail/sequential/tabulate.h", + "cuda/include/thrust/system/detail/sequential/temporary_buffer.h", + "cuda/include/thrust/system/detail/sequential/transform.h", + "cuda/include/thrust/system/detail/sequential/transform_reduce.h", + "cuda/include/thrust/system/detail/sequential/transform_scan.h", + "cuda/include/thrust/system/detail/sequential/trivial_copy.h", + "cuda/include/thrust/system/detail/sequential/uninitialized_copy.h", + "cuda/include/thrust/system/detail/sequential/uninitialized_fill.h", + "cuda/include/thrust/system/detail/sequential/unique.h", + "cuda/include/thrust/system/detail/sequential/unique_by_key.h", "cuda/include/thrust/system/detail/system_error.inl", - "cuda/include/thrust/system/omp/execution_policy.h", - "cuda/include/thrust/system/omp/vector.h", - "cuda/include/thrust/system/omp/detail/transform_scan.h", - "cuda/include/thrust/system/omp/detail/memory.inl", - "cuda/include/thrust/system/omp/detail/reduce_intervals.inl", - "cuda/include/thrust/system/omp/detail/unique_by_key.h", - "cuda/include/thrust/system/omp/detail/sort.inl", - "cuda/include/thrust/system/omp/detail/partition.h", - "cuda/include/thrust/system/omp/detail/unique.h", - "cuda/include/thrust/system/omp/detail/execution_policy.h", + "cuda/include/thrust/system/error_code.h", "cuda/include/thrust/system/omp/detail/adjacent_difference.h", - "cuda/include/thrust/system/omp/detail/unique_by_key.inl", - "cuda/include/thrust/system/omp/detail/sequence.h", - "cuda/include/thrust/system/omp/detail/merge.h", - "cuda/include/thrust/system/omp/detail/unique.inl", + "cuda/include/thrust/system/omp/detail/assign_value.h", + "cuda/include/thrust/system/omp/detail/binary_search.h", + "cuda/include/thrust/system/omp/detail/copy.h", + "cuda/include/thrust/system/omp/detail/copy.inl", + "cuda/include/thrust/system/omp/detail/copy_if.h", "cuda/include/thrust/system/omp/detail/copy_if.inl", - "cuda/include/thrust/system/omp/detail/transform_reduce.h", - "cuda/include/thrust/system/omp/detail/gather.h", - "cuda/include/thrust/system/omp/detail/reduce_by_key.inl", - "cuda/include/thrust/system/omp/detail/sort.h", - "cuda/include/thrust/system/omp/detail/scan.h", - "cuda/include/thrust/system/omp/detail/temporary_buffer.h", + "cuda/include/thrust/system/omp/detail/count.h", "cuda/include/thrust/system/omp/detail/default_decomposition.h", - "cuda/include/thrust/system/omp/detail/reduce.inl", - "cuda/include/thrust/system/omp/detail/scan_by_key.h", - "cuda/include/thrust/system/omp/detail/reverse.h", - "cuda/include/thrust/system/omp/detail/assign_value.h", - "cuda/include/thrust/system/omp/detail/scatter.h", - "cuda/include/thrust/system/omp/detail/for_each.inl", "cuda/include/thrust/system/omp/detail/default_decomposition.inl", - "cuda/include/thrust/system/omp/detail/remove.inl", - "cuda/include/thrust/system/omp/detail/vector.inl", - "cuda/include/thrust/system/omp/detail/find.h", - "cuda/include/thrust/system/omp/detail/generate.h", - "cuda/include/thrust/system/omp/detail/uninitialized_fill.h", - "cuda/include/thrust/system/omp/detail/remove.h", - "cuda/include/thrust/system/omp/detail/tabulate.h", - "cuda/include/thrust/system/omp/detail/for_each.h", - "cuda/include/thrust/system/omp/detail/reduce_by_key.h", - "cuda/include/thrust/system/omp/detail/reduce.h", "cuda/include/thrust/system/omp/detail/equal.h", - "cuda/include/thrust/system/omp/detail/copy.inl", - "cuda/include/thrust/system/omp/detail/copy.h", - "cuda/include/thrust/system/omp/detail/swap_ranges.h", - "cuda/include/thrust/system/omp/detail/uninitialized_copy.h", - "cuda/include/thrust/system/omp/detail/binary_search.h", - "cuda/include/thrust/system/omp/detail/set_operations.h", - "cuda/include/thrust/system/omp/detail/mismatch.h", + "cuda/include/thrust/system/omp/detail/execution_policy.h", "cuda/include/thrust/system/omp/detail/extrema.h", - "cuda/include/thrust/system/omp/detail/count.h", - "cuda/include/thrust/system/omp/detail/replace.h", + "cuda/include/thrust/system/omp/detail/fill.h", + "cuda/include/thrust/system/omp/detail/find.h", + "cuda/include/thrust/system/omp/detail/for_each.h", + "cuda/include/thrust/system/omp/detail/for_each.inl", + "cuda/include/thrust/system/omp/detail/gather.h", + "cuda/include/thrust/system/omp/detail/generate.h", "cuda/include/thrust/system/omp/detail/get_value.h", "cuda/include/thrust/system/omp/detail/inner_product.h", - "cuda/include/thrust/system/omp/detail/copy_if.h", - "cuda/include/thrust/system/omp/detail/logical.h", - "cuda/include/thrust/system/omp/detail/partition.inl", "cuda/include/thrust/system/omp/detail/iter_swap.h", + "cuda/include/thrust/system/omp/detail/logical.h", + "cuda/include/thrust/system/omp/detail/malloc_and_free.h", + "cuda/include/thrust/system/omp/detail/memory.inl", + "cuda/include/thrust/system/omp/detail/merge.h", + "cuda/include/thrust/system/omp/detail/mismatch.h", "cuda/include/thrust/system/omp/detail/par.h", + "cuda/include/thrust/system/omp/detail/partition.h", + "cuda/include/thrust/system/omp/detail/partition.inl", + "cuda/include/thrust/system/omp/detail/reduce.h", + "cuda/include/thrust/system/omp/detail/reduce.inl", + "cuda/include/thrust/system/omp/detail/reduce_by_key.h", + "cuda/include/thrust/system/omp/detail/reduce_by_key.inl", "cuda/include/thrust/system/omp/detail/reduce_intervals.h", - "cuda/include/thrust/system/omp/detail/malloc_and_free.h", - "cuda/include/thrust/system/omp/detail/fill.h", + "cuda/include/thrust/system/omp/detail/reduce_intervals.inl", + "cuda/include/thrust/system/omp/detail/remove.h", + "cuda/include/thrust/system/omp/detail/remove.inl", + "cuda/include/thrust/system/omp/detail/replace.h", + "cuda/include/thrust/system/omp/detail/reverse.h", + "cuda/include/thrust/system/omp/detail/scan.h", + "cuda/include/thrust/system/omp/detail/scan_by_key.h", + "cuda/include/thrust/system/omp/detail/scatter.h", + "cuda/include/thrust/system/omp/detail/sequence.h", + "cuda/include/thrust/system/omp/detail/set_operations.h", + "cuda/include/thrust/system/omp/detail/sort.h", + "cuda/include/thrust/system/omp/detail/sort.inl", + "cuda/include/thrust/system/omp/detail/swap_ranges.h", + "cuda/include/thrust/system/omp/detail/tabulate.h", + "cuda/include/thrust/system/omp/detail/temporary_buffer.h", "cuda/include/thrust/system/omp/detail/transform.h", - "cuda/include/thrust/system/omp/memory.h", - "cuda/include/thrust/system/tbb/execution_policy.h", - "cuda/include/thrust/system/tbb/vector.h", - "cuda/include/thrust/system/tbb/detail/transform_scan.h", - "cuda/include/thrust/system/tbb/detail/memory.inl", - "cuda/include/thrust/system/tbb/detail/unique_by_key.h", - "cuda/include/thrust/system/tbb/detail/sort.inl", - "cuda/include/thrust/system/tbb/detail/partition.h", - "cuda/include/thrust/system/tbb/detail/unique.h", - "cuda/include/thrust/system/tbb/detail/execution_policy.h", + "cuda/include/thrust/system/omp/detail/transform_reduce.h", + "cuda/include/thrust/system/omp/detail/transform_scan.h", + "cuda/include/thrust/system/omp/detail/uninitialized_copy.h", + "cuda/include/thrust/system/omp/detail/uninitialized_fill.h", + "cuda/include/thrust/system/omp/detail/unique.h", + "cuda/include/thrust/system/omp/detail/unique.inl", + "cuda/include/thrust/system/omp/detail/unique_by_key.h", + "cuda/include/thrust/system/omp/detail/unique_by_key.inl", + "cuda/include/thrust/system/omp/detail/vector.inl", + "cuda/include/thrust/system/omp/execution_policy.h", + "cuda/include/thrust/system/omp/memory.h", + "cuda/include/thrust/system/omp/vector.h", + "cuda/include/thrust/system/system_error.h", "cuda/include/thrust/system/tbb/detail/adjacent_difference.h", - "cuda/include/thrust/system/tbb/detail/unique_by_key.inl", - "cuda/include/thrust/system/tbb/detail/sequence.h", - "cuda/include/thrust/system/tbb/detail/merge.h", - "cuda/include/thrust/system/tbb/detail/unique.inl", - "cuda/include/thrust/system/tbb/detail/copy_if.inl", - "cuda/include/thrust/system/tbb/detail/transform_reduce.h", - "cuda/include/thrust/system/tbb/detail/gather.h", - "cuda/include/thrust/system/tbb/detail/reduce_by_key.inl", - "cuda/include/thrust/system/tbb/detail/sort.h", - "cuda/include/thrust/system/tbb/detail/scan.h", - "cuda/include/thrust/system/tbb/detail/temporary_buffer.h", - "cuda/include/thrust/system/tbb/detail/reduce.inl", - "cuda/include/thrust/system/tbb/detail/scan_by_key.h", - "cuda/include/thrust/system/tbb/detail/reverse.h", "cuda/include/thrust/system/tbb/detail/assign_value.h", - "cuda/include/thrust/system/tbb/detail/scatter.h", - "cuda/include/thrust/system/tbb/detail/for_each.inl", - "cuda/include/thrust/system/tbb/detail/remove.inl", - "cuda/include/thrust/system/tbb/detail/vector.inl", - "cuda/include/thrust/system/tbb/detail/find.h", - "cuda/include/thrust/system/tbb/detail/merge.inl", - "cuda/include/thrust/system/tbb/detail/generate.h", - "cuda/include/thrust/system/tbb/detail/uninitialized_fill.h", - "cuda/include/thrust/system/tbb/detail/remove.h", - "cuda/include/thrust/system/tbb/detail/tabulate.h", - "cuda/include/thrust/system/tbb/detail/for_each.h", - "cuda/include/thrust/system/tbb/detail/reduce_by_key.h", - "cuda/include/thrust/system/tbb/detail/reduce.h", - "cuda/include/thrust/system/tbb/detail/equal.h", - "cuda/include/thrust/system/tbb/detail/copy.inl", - "cuda/include/thrust/system/tbb/detail/copy.h", - "cuda/include/thrust/system/tbb/detail/swap_ranges.h", - "cuda/include/thrust/system/tbb/detail/uninitialized_copy.h", "cuda/include/thrust/system/tbb/detail/binary_search.h", - "cuda/include/thrust/system/tbb/detail/set_operations.h", - "cuda/include/thrust/system/tbb/detail/mismatch.h", - "cuda/include/thrust/system/tbb/detail/scan.inl", - "cuda/include/thrust/system/tbb/detail/extrema.h", + "cuda/include/thrust/system/tbb/detail/copy.h", + "cuda/include/thrust/system/tbb/detail/copy.inl", + "cuda/include/thrust/system/tbb/detail/copy_if.h", + "cuda/include/thrust/system/tbb/detail/copy_if.inl", "cuda/include/thrust/system/tbb/detail/count.h", - "cuda/include/thrust/system/tbb/detail/replace.h", + "cuda/include/thrust/system/tbb/detail/equal.h", + "cuda/include/thrust/system/tbb/detail/execution_policy.h", + "cuda/include/thrust/system/tbb/detail/extrema.h", + "cuda/include/thrust/system/tbb/detail/fill.h", + "cuda/include/thrust/system/tbb/detail/find.h", + "cuda/include/thrust/system/tbb/detail/for_each.h", + "cuda/include/thrust/system/tbb/detail/for_each.inl", + "cuda/include/thrust/system/tbb/detail/gather.h", + "cuda/include/thrust/system/tbb/detail/generate.h", "cuda/include/thrust/system/tbb/detail/get_value.h", "cuda/include/thrust/system/tbb/detail/inner_product.h", - "cuda/include/thrust/system/tbb/detail/copy_if.h", - "cuda/include/thrust/system/tbb/detail/logical.h", - "cuda/include/thrust/system/tbb/detail/partition.inl", "cuda/include/thrust/system/tbb/detail/iter_swap.h", + "cuda/include/thrust/system/tbb/detail/logical.h", + "cuda/include/thrust/system/tbb/detail/malloc_and_free.h", + "cuda/include/thrust/system/tbb/detail/memory.inl", + "cuda/include/thrust/system/tbb/detail/merge.h", + "cuda/include/thrust/system/tbb/detail/merge.inl", + "cuda/include/thrust/system/tbb/detail/mismatch.h", "cuda/include/thrust/system/tbb/detail/par.h", + "cuda/include/thrust/system/tbb/detail/partition.h", + "cuda/include/thrust/system/tbb/detail/partition.inl", + "cuda/include/thrust/system/tbb/detail/reduce.h", + "cuda/include/thrust/system/tbb/detail/reduce.inl", + "cuda/include/thrust/system/tbb/detail/reduce_by_key.h", + "cuda/include/thrust/system/tbb/detail/reduce_by_key.inl", "cuda/include/thrust/system/tbb/detail/reduce_intervals.h", - "cuda/include/thrust/system/tbb/detail/malloc_and_free.h", - "cuda/include/thrust/system/tbb/detail/fill.h", + "cuda/include/thrust/system/tbb/detail/remove.h", + "cuda/include/thrust/system/tbb/detail/remove.inl", + "cuda/include/thrust/system/tbb/detail/replace.h", + "cuda/include/thrust/system/tbb/detail/reverse.h", + "cuda/include/thrust/system/tbb/detail/scan.h", + "cuda/include/thrust/system/tbb/detail/scan.inl", + "cuda/include/thrust/system/tbb/detail/scan_by_key.h", + "cuda/include/thrust/system/tbb/detail/scatter.h", + "cuda/include/thrust/system/tbb/detail/sequence.h", + "cuda/include/thrust/system/tbb/detail/set_operations.h", + "cuda/include/thrust/system/tbb/detail/sort.h", + "cuda/include/thrust/system/tbb/detail/sort.inl", + "cuda/include/thrust/system/tbb/detail/swap_ranges.h", + "cuda/include/thrust/system/tbb/detail/tabulate.h", + "cuda/include/thrust/system/tbb/detail/temporary_buffer.h", "cuda/include/thrust/system/tbb/detail/transform.h", - "cuda/include/thrust/system/tbb/memory.h", - "cuda/include/thrust/system/error_code.h", - "cuda/include/thrust/system/cpp/execution_policy.h", - "cuda/include/thrust/system/cpp/vector.h", - "cuda/include/thrust/system/cpp/detail/transform_scan.h", - "cuda/include/thrust/system/cpp/detail/memory.inl", - "cuda/include/thrust/system/cpp/detail/unique_by_key.h", - "cuda/include/thrust/system/cpp/detail/partition.h", - "cuda/include/thrust/system/cpp/detail/unique.h", - "cuda/include/thrust/system/cpp/detail/execution_policy.h", - "cuda/include/thrust/system/cpp/detail/adjacent_difference.h", - "cuda/include/thrust/system/cpp/detail/sequence.h", - "cuda/include/thrust/system/cpp/detail/merge.h", - "cuda/include/thrust/system/cpp/detail/transform_reduce.h", - "cuda/include/thrust/system/cpp/detail/gather.h", - "cuda/include/thrust/system/cpp/detail/sort.h", - "cuda/include/thrust/system/cpp/detail/scan.h", - "cuda/include/thrust/system/cpp/detail/temporary_buffer.h", - "cuda/include/thrust/system/cpp/detail/scan_by_key.h", - "cuda/include/thrust/system/cpp/detail/reverse.h", - "cuda/include/thrust/system/cpp/detail/assign_value.h", - "cuda/include/thrust/system/cpp/detail/scatter.h", - "cuda/include/thrust/system/cpp/detail/vector.inl", - "cuda/include/thrust/system/cpp/detail/find.h", - "cuda/include/thrust/system/cpp/detail/generate.h", - "cuda/include/thrust/system/cpp/detail/uninitialized_fill.h", - "cuda/include/thrust/system/cpp/detail/remove.h", - "cuda/include/thrust/system/cpp/detail/tabulate.h", - "cuda/include/thrust/system/cpp/detail/for_each.h", - "cuda/include/thrust/system/cpp/detail/reduce_by_key.h", - "cuda/include/thrust/system/cpp/detail/reduce.h", - "cuda/include/thrust/system/cpp/detail/equal.h", - "cuda/include/thrust/system/cpp/detail/copy.h", - "cuda/include/thrust/system/cpp/detail/swap_ranges.h", - "cuda/include/thrust/system/cpp/detail/uninitialized_copy.h", - "cuda/include/thrust/system/cpp/detail/binary_search.h", - "cuda/include/thrust/system/cpp/detail/set_operations.h", - "cuda/include/thrust/system/cpp/detail/mismatch.h", - "cuda/include/thrust/system/cpp/detail/extrema.h", - "cuda/include/thrust/system/cpp/detail/count.h", - "cuda/include/thrust/system/cpp/detail/replace.h", - "cuda/include/thrust/system/cpp/detail/get_value.h", - "cuda/include/thrust/system/cpp/detail/inner_product.h", - "cuda/include/thrust/system/cpp/detail/copy_if.h", - "cuda/include/thrust/system/cpp/detail/logical.h", - "cuda/include/thrust/system/cpp/detail/iter_swap.h", - "cuda/include/thrust/system/cpp/detail/par.h", - "cuda/include/thrust/system/cpp/detail/malloc_and_free.h", - "cuda/include/thrust/system/cpp/detail/fill.h", - "cuda/include/thrust/system/cpp/detail/transform.h", - "cuda/include/thrust/system/cpp/memory.h", - "cuda/include/thrust/system/cuda/execution_policy.h", - "cuda/include/thrust/system/cuda/vector.h", - "cuda/include/thrust/system/cuda/error.h", - "cuda/include/thrust/system/cuda/detail/copy_device_to_device.h", - "cuda/include/thrust/system/cuda/detail/transform_scan.h", - "cuda/include/thrust/system/cuda/detail/memory.inl", - "cuda/include/thrust/system/cuda/detail/cub/util_allocator.cuh", - "cuda/include/thrust/system/cuda/detail/cub/grid/grid_mapping.cuh", - "cuda/include/thrust/system/cuda/detail/cub/grid/grid_barrier.cuh", - "cuda/include/thrust/system/cuda/detail/cub/grid/grid_even_share.cuh", - "cuda/include/thrust/system/cuda/detail/cub/grid/grid_queue.cuh", - "cuda/include/thrust/system/cuda/detail/cub/util_device.cuh", - "cuda/include/thrust/system/cuda/detail/cub/device/device_run_length_encode.cuh", - "cuda/include/thrust/system/cuda/detail/cub/device/device_partition.cuh", - "cuda/include/thrust/system/cuda/detail/cub/device/device_radix_sort.cuh", - "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_rle_dispatch.cuh", - "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_histogram_dispatch.cuh", - "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_reduce_by_key_dispatch.cuh", - "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_scan_dispatch.cuh", - "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_select_dispatch.cuh", - "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_reduce_dispatch.cuh", - "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_radix_sort_dispatch.cuh", - "cuda/include/thrust/system/cuda/detail/cub/device/device_scan.cuh", - "cuda/include/thrust/system/cuda/detail/cub/device/device_select.cuh", - "cuda/include/thrust/system/cuda/detail/cub/device/device_reduce.cuh", - "cuda/include/thrust/system/cuda/detail/cub/device/device_histogram.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_reduce.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_histo.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_scan.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_radix_sort_downsweep.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_radix_sort_upsweep.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_satomic.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_sort.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_gatomic.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_select.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_range/block_scan_prefix_operators.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_reduce_by_key.cuh", - "cuda/include/thrust/system/cuda/detail/cub/util_macro.cuh", - "cuda/include/thrust/system/cuda/detail/cub/util_namespace.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_radix_sort_upsweep.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_histogram_sweep.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_rle_sweep.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_select_sweep.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_scan_sweep.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_reduce_sweep.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_satomic_sweep.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_sort_sweep.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_gatomic_sweep.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_radix_sort_downsweep.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_reduce_by_key_sweep.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_scan_prefix_operators.cuh", - "cuda/include/thrust/system/cuda/detail/cub/util_type.cuh", - "cuda/include/thrust/system/cuda/detail/cub/host/spinlock.cuh", - "cuda/include/thrust/system/cuda/detail/cub/warp/warp_reduce.cuh", - "cuda/include/thrust/system/cuda/detail/cub/warp/warp_scan.cuh", - "cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_shfl.cuh", - "cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_smem.cuh", - "cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_shfl.cuh", - "cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_smem.cuh", - "cuda/include/thrust/system/cuda/detail/cub/util_ptx.cuh", - "cuda/include/thrust/system/cuda/detail/cub/util_debug.cuh", - "cuda/include/thrust/system/cuda/detail/cub/cub.cuh", - "cuda/include/thrust/system/cuda/detail/cub/iterator/transform_input_iterator.cuh", - "cuda/include/thrust/system/cuda/detail/cub/iterator/tex_obj_input_iterator.cuh", - "cuda/include/thrust/system/cuda/detail/cub/iterator/tex_ref_input_iterator.cuh", - "cuda/include/thrust/system/cuda/detail/cub/iterator/cache_modified_output_iterator.cuh", - "cuda/include/thrust/system/cuda/detail/cub/iterator/counting_input_iterator.cuh", - "cuda/include/thrust/system/cuda/detail/cub/iterator/cache_modified_input_iterator.cuh", - "cuda/include/thrust/system/cuda/detail/cub/iterator/arg_index_input_iterator.cuh", - "cuda/include/thrust/system/cuda/detail/cub/iterator/constant_input_iterator.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block/block_scan.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block/block_load.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block/block_discontinuity.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block/block_radix_rank.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block/block_shift.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block/block_store.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block/block_reduce.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block/block_exchange.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block/block_radix_sort.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block/block_histogram.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block/block_raking_layout.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_warp_reductions.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking_commutative_only.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_atomic.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_raking.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_sort.cuh", - "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking.cuh", - "cuda/include/thrust/system/cuda/detail/cub/thread/thread_load.cuh", - "cuda/include/thrust/system/cuda/detail/cub/thread/thread_store.cuh", - "cuda/include/thrust/system/cuda/detail/cub/thread/thread_scan.cuh", - "cuda/include/thrust/system/cuda/detail/cub/thread/thread_operators.cuh", - "cuda/include/thrust/system/cuda/detail/cub/thread/thread_reduce.cuh", - "cuda/include/thrust/system/cuda/detail/cub/util_arch.cuh", - "cuda/include/thrust/system/cuda/detail/reduce_intervals.inl", - "cuda/include/thrust/system/cuda/detail/copy_cross_system.inl", - "cuda/include/thrust/system/cuda/detail/unique_by_key.h", - "cuda/include/thrust/system/cuda/detail/bulk.h", - "cuda/include/thrust/system/cuda/detail/sort.inl", - "cuda/include/thrust/system/cuda/detail/partition.h", - "cuda/include/thrust/system/cuda/detail/unique.h", - "cuda/include/thrust/system/cuda/detail/execution_policy.h", - "cuda/include/thrust/system/cuda/detail/cuda_launch_config.h", - "cuda/include/thrust/system/cuda/detail/cub.h", - "cuda/include/thrust/system/cuda/detail/adjacent_difference.h", - "cuda/include/thrust/system/cuda/detail/sequence.h", - "cuda/include/thrust/system/cuda/detail/merge.h", - "cuda/include/thrust/system/cuda/detail/set_symmetric_difference.inl", - "cuda/include/thrust/system/cuda/detail/copy_if.inl", - "cuda/include/thrust/system/cuda/detail/transform_reduce.h", - "cuda/include/thrust/system/cuda/detail/error.inl", - "cuda/include/thrust/system/cuda/detail/gather.h", - "cuda/include/thrust/system/cuda/detail/reduce_by_key.inl", - "cuda/include/thrust/system/cuda/detail/sort.h", - "cuda/include/thrust/system/cuda/detail/synchronize.h", - "cuda/include/thrust/system/cuda/detail/scan.h", - "cuda/include/thrust/system/cuda/detail/temporary_indirect_permutation.h", - "cuda/include/thrust/system/cuda/detail/extern_shared_ptr.h", - "cuda/include/thrust/system/cuda/detail/detail/set_operation.inl", - "cuda/include/thrust/system/cuda/detail/detail/balanced_path.h", - "cuda/include/thrust/system/cuda/detail/detail/virtualized_smem_closure.h", - "cuda/include/thrust/system/cuda/detail/detail/stable_primitive_sort.h", - "cuda/include/thrust/system/cuda/detail/detail/set_operation.h", - "cuda/include/thrust/system/cuda/detail/detail/stable_primitive_sort.inl", - "cuda/include/thrust/system/cuda/detail/detail/stable_merge_sort.h", - "cuda/include/thrust/system/cuda/detail/detail/launch_closure.inl", - "cuda/include/thrust/system/cuda/detail/detail/merge.h", - "cuda/include/thrust/system/cuda/detail/detail/alignment.h", - "cuda/include/thrust/system/cuda/detail/detail/stable_radix_sort.inl", - "cuda/include/thrust/system/cuda/detail/detail/stable_sort_each.h", - "cuda/include/thrust/system/cuda/detail/detail/launch_calculator.inl", - "cuda/include/thrust/system/cuda/detail/detail/stable_merge_sort.inl", - "cuda/include/thrust/system/cuda/detail/detail/launch_closure.h", - "cuda/include/thrust/system/cuda/detail/detail/stable_radix_sort.h", - "cuda/include/thrust/system/cuda/detail/detail/uninitialized.h", - "cuda/include/thrust/system/cuda/detail/detail/cached_temporary_allocator.h", - "cuda/include/thrust/system/cuda/detail/detail/launch_calculator.h", - "cuda/include/thrust/system/cuda/detail/detail/stable_sort_each.inl", - "cuda/include/thrust/system/cuda/detail/temporary_buffer.h", - "cuda/include/thrust/system/cuda/detail/default_decomposition.h", - "cuda/include/thrust/system/cuda/detail/reduce.inl", - "cuda/include/thrust/system/cuda/detail/scan_by_key.h", - "cuda/include/thrust/system/cuda/detail/reverse.h", - "cuda/include/thrust/system/cuda/detail/assign_value.h", - "cuda/include/thrust/system/cuda/detail/scatter.h", - "cuda/include/thrust/system/cuda/detail/reduce_intervals.hpp", - "cuda/include/thrust/system/cuda/detail/for_each.inl", - "cuda/include/thrust/system/cuda/detail/default_decomposition.inl", - "cuda/include/thrust/system/cuda/detail/guarded_cuda_runtime_api.h", - "cuda/include/thrust/system/cuda/detail/adjacent_difference.inl", - "cuda/include/thrust/system/cuda/detail/vector.inl", - "cuda/include/thrust/system/cuda/detail/throw_on_error.h", - "cuda/include/thrust/system/cuda/detail/find.h", - "cuda/include/thrust/system/cuda/detail/terminate.h", - "cuda/include/thrust/system/cuda/detail/merge.inl", - "cuda/include/thrust/system/cuda/detail/trivial_copy.inl", - "cuda/include/thrust/system/cuda/detail/generate.h", - "cuda/include/thrust/system/cuda/detail/execute_on_stream.h", - "cuda/include/thrust/system/cuda/detail/uninitialized_fill.h", - "cuda/include/thrust/system/cuda/detail/remove.h", - "cuda/include/thrust/system/cuda/detail/tabulate.h", - "cuda/include/thrust/system/cuda/detail/for_each.h", - "cuda/include/thrust/system/cuda/detail/reduce_by_key.h", - "cuda/include/thrust/system/cuda/detail/decomposition.h", - "cuda/include/thrust/system/cuda/detail/reduce.h", - "cuda/include/thrust/system/cuda/detail/equal.h", - "cuda/include/thrust/system/cuda/detail/runtime_introspection.h", - "cuda/include/thrust/system/cuda/detail/copy.inl", - "cuda/include/thrust/system/cuda/detail/copy.h", - "cuda/include/thrust/system/cuda/detail/swap_ranges.h", - "cuda/include/thrust/system/cuda/detail/uninitialized_copy.h", - "cuda/include/thrust/system/cuda/detail/binary_search.h", - "cuda/include/thrust/system/cuda/detail/runtime_introspection.inl", - "cuda/include/thrust/system/cuda/detail/set_operations.h", - "cuda/include/thrust/system/cuda/detail/mismatch.h", - "cuda/include/thrust/system/cuda/detail/scan.inl", - "cuda/include/thrust/system/cuda/detail/synchronize.inl", - "cuda/include/thrust/system/cuda/detail/extrema.h", - "cuda/include/thrust/system/cuda/detail/set_union.inl", - "cuda/include/thrust/system/cuda/detail/set_intersection.inl", - "cuda/include/thrust/system/cuda/detail/count.h", - "cuda/include/thrust/system/cuda/detail/trivial_copy.h", - "cuda/include/thrust/system/cuda/detail/copy_device_to_device.inl", - "cuda/include/thrust/system/cuda/detail/replace.h", - "cuda/include/thrust/system/cuda/detail/bulk/malloc.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/algorithm.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/detail/config.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/detail/closure.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/detail/tail_flags.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/detail/terminate.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/detail/alignment.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/detail/guarded_cuda_runtime_api.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/detail/choose_sizes.inl", - "cuda/include/thrust/system/cuda/detail/bulk/detail/tuple_meta_transform.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_task.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/detail/head_flags.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/detail/synchronize.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/detail/throw_on_error.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/parameter_ptr.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/cuda_launcher.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/triple_chevron_launcher.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/runtime_introspection.inl", - "cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/cuda_launch_config.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/runtime_introspection.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/detail/async.inl", - "cuda/include/thrust/system/cuda/detail/bulk/detail/tuple_transform.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/detail/pointer_traits.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/detail/apply_from_tuple.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/detail/is_contiguous_iterator.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/iterator.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/choose_sizes.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/algorithm/copy.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/algorithm/merge.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/algorithm/accumulate.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/algorithm/scan.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/algorithm/detail/stable_merge_sort.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/algorithm/gather.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/algorithm/sort.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/algorithm/reduce.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/algorithm/scatter.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/algorithm/adjacent_difference.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/algorithm/reduce_by_key.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/algorithm/for_each.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/bulk.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/execution_policy.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/iterator/strided_iterator.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/uninitialized.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/async.hpp", - "cuda/include/thrust/system/cuda/detail/bulk/future.hpp", - "cuda/include/thrust/system/cuda/detail/guarded_driver_types.h", - "cuda/include/thrust/system/cuda/detail/get_value.h", - "cuda/include/thrust/system/cuda/detail/inner_product.h", - "cuda/include/thrust/system/cuda/detail/copy_if.h", - "cuda/include/thrust/system/cuda/detail/logical.h", - "cuda/include/thrust/system/cuda/detail/iter_swap.h", - "cuda/include/thrust/system/cuda/detail/block/merge.h", - "cuda/include/thrust/system/cuda/detail/block/inclusive_scan.h", - "cuda/include/thrust/system/cuda/detail/block/merge.inl", - "cuda/include/thrust/system/cuda/detail/block/merging_sort.h", - "cuda/include/thrust/system/cuda/detail/block/exclusive_scan.h", - "cuda/include/thrust/system/cuda/detail/block/reduce.h", - "cuda/include/thrust/system/cuda/detail/block/copy.h", - "cuda/include/thrust/system/cuda/detail/block/odd_even_sort.h", - "cuda/include/thrust/system/cuda/detail/par.h", - "cuda/include/thrust/system/cuda/detail/copy_cross_system.h", - "cuda/include/thrust/system/cuda/detail/reduce_intervals.h", - "cuda/include/thrust/system/cuda/detail/malloc_and_free.h", - "cuda/include/thrust/system/cuda/detail/fill.h", - "cuda/include/thrust/system/cuda/detail/set_difference.inl", - "cuda/include/thrust/system/cuda/detail/transform.h", - "cuda/include/thrust/system/cuda/experimental/pinned_allocator.h", - "cuda/include/thrust/system/cuda/memory.h", - "cuda/include/thrust/remove.h", + "cuda/include/thrust/system/tbb/detail/transform_reduce.h", + "cuda/include/thrust/system/tbb/detail/transform_scan.h", + "cuda/include/thrust/system/tbb/detail/uninitialized_copy.h", + "cuda/include/thrust/system/tbb/detail/uninitialized_fill.h", + "cuda/include/thrust/system/tbb/detail/unique.h", + "cuda/include/thrust/system/tbb/detail/unique.inl", + "cuda/include/thrust/system/tbb/detail/unique_by_key.h", + "cuda/include/thrust/system/tbb/detail/unique_by_key.inl", + "cuda/include/thrust/system/tbb/detail/vector.inl", + "cuda/include/thrust/system/tbb/execution_policy.h", + "cuda/include/thrust/system/tbb/memory.h", + "cuda/include/thrust/system/tbb/vector.h", + "cuda/include/thrust/system_error.h", "cuda/include/thrust/tabulate.h", - "cuda/include/thrust/for_each.h", - "cuda/include/thrust/distance.h", - "cuda/include/thrust/reduce.h", - "cuda/include/thrust/equal.h", - "cuda/include/thrust/complex.h", - "cuda/include/thrust/device_allocator.h", - "cuda/include/thrust/copy.h", + "cuda/include/thrust/transform.h", + "cuda/include/thrust/transform_reduce.h", + "cuda/include/thrust/transform_scan.h", + "cuda/include/thrust/tuple.h", "cuda/include/thrust/uninitialized_copy.h", - "cuda/include/thrust/device_reference.h", - "cuda/include/thrust/binary_search.h", - "cuda/include/thrust/set_operations.h", - "cuda/include/thrust/swap.h", - "cuda/include/thrust/mismatch.h", - "cuda/include/thrust/extrema.h", - "cuda/include/thrust/count.h", - "cuda/include/thrust/device_free.h", - "cuda/include/thrust/random/discard_block_engine.h", - "cuda/include/thrust/random/normal_distribution.h", - "cuda/include/thrust/random/detail/linear_feedback_shift_engine_wordmask.h", - "cuda/include/thrust/random/detail/subtract_with_carry_engine.inl", - "cuda/include/thrust/random/detail/xor_combine_engine_max.h", - "cuda/include/thrust/random/detail/linear_congruential_engine_discard.h", - "cuda/include/thrust/random/detail/uniform_int_distribution.inl", - "cuda/include/thrust/random/detail/discard_block_engine.inl", - "cuda/include/thrust/random/detail/uniform_real_distribution.inl", - "cuda/include/thrust/random/detail/random_core_access.h", - "cuda/include/thrust/random/detail/mod.h", - "cuda/include/thrust/random/detail/linear_feedback_shift_engine.inl", - "cuda/include/thrust/random/detail/linear_congruential_engine.inl", - "cuda/include/thrust/random/detail/xor_combine_engine.inl", - "cuda/include/thrust/random/detail/normal_distribution.inl", - "cuda/include/thrust/random/detail/normal_distribution_base.h", - "cuda/include/thrust/random/uniform_int_distribution.h", - "cuda/include/thrust/random/linear_feedback_shift_engine.h", - "cuda/include/thrust/random/xor_combine_engine.h", - "cuda/include/thrust/random/subtract_with_carry_engine.h", - "cuda/include/thrust/random/linear_congruential_engine.h", - "cuda/include/thrust/random/uniform_real_distribution.h", - "cuda/include/thrust/functional.h", - "cuda/include/thrust/replace.h", - "cuda/include/thrust/device_new_allocator.h", - "cuda/include/thrust/host_vector.h", + "cuda/include/thrust/uninitialized_fill.h", + "cuda/include/thrust/unique.h", "cuda/include/thrust/version.h", - "cuda/include/thrust/inner_product.h", - "cuda/include/thrust/iterator/iterator_traits.h", - "cuda/include/thrust/iterator/discard_iterator.h", - "cuda/include/thrust/iterator/retag.h", - "cuda/include/thrust/iterator/permutation_iterator.h", - "cuda/include/thrust/iterator/transform_iterator.h", - "cuda/include/thrust/iterator/detail/reverse_iterator.inl", - "cuda/include/thrust/iterator/detail/zip_iterator.inl", - "cuda/include/thrust/iterator/detail/counting_iterator.inl", - "cuda/include/thrust/iterator/detail/distance_from_result.h", - "cuda/include/thrust/iterator/detail/host_system_tag.h", - "cuda/include/thrust/iterator/detail/iterator_traversal_tags.h", - "cuda/include/thrust/iterator/detail/retag.h", - "cuda/include/thrust/iterator/detail/tagged_iterator.h", - "cuda/include/thrust/iterator/detail/iterator_traits.inl", - "cuda/include/thrust/iterator/detail/minimum_category.h", - "cuda/include/thrust/iterator/detail/discard_iterator_base.h", - "cuda/include/thrust/iterator/detail/iterator_category_to_traversal.h", - "cuda/include/thrust/iterator/detail/zip_iterator_base.h", - "cuda/include/thrust/iterator/detail/normal_iterator.h", - "cuda/include/thrust/iterator/detail/join_iterator.h", - "cuda/include/thrust/iterator/detail/device_system_tag.h", - "cuda/include/thrust/iterator/detail/universal_categories.h", - "cuda/include/thrust/iterator/detail/reverse_iterator_base.h", - "cuda/include/thrust/iterator/detail/minimum_system.h", - "cuda/include/thrust/iterator/detail/tuple_of_iterator_references.h", - "cuda/include/thrust/iterator/detail/is_iterator_category.h", - "cuda/include/thrust/iterator/detail/permutation_iterator_base.h", - "cuda/include/thrust/iterator/detail/any_assign.h", - "cuda/include/thrust/iterator/detail/any_system_tag.h", - "cuda/include/thrust/iterator/detail/is_trivial_iterator.h", - "cuda/include/thrust/iterator/detail/iterator_category_to_system.h", - "cuda/include/thrust/iterator/detail/iterator_adaptor_base.h", - "cuda/include/thrust/iterator/detail/constant_iterator_base.h", - "cuda/include/thrust/iterator/detail/transform_iterator.inl", - "cuda/include/thrust/iterator/detail/iterator_facade_category.h", - "cuda/include/thrust/iterator/detail/iterator_category_with_system_and_traversal.h", - "cuda/include/thrust/iterator/constant_iterator.h", - "cuda/include/thrust/iterator/counting_iterator.h", - "cuda/include/thrust/iterator/iterator_adaptor.h", - "cuda/include/thrust/iterator/iterator_facade.h", - "cuda/include/thrust/iterator/iterator_categories.h", - "cuda/include/thrust/iterator/reverse_iterator.h", - "cuda/include/thrust/iterator/zip_iterator.h", - "cuda/include/thrust/logical.h", - "cuda/include/thrust/tuple.h", - "cuda/include/thrust/memory.h", - "cuda/include/thrust/random.h", - "cuda/include/thrust/fill.h", - "cuda/include/thrust/transform.h", - "cuda/include/texture_types.h", - "cuda/include/nppversion.h", - "cuda/include/cuda_texture_types.h", - "cuda/include/fatbinary.h", - "cuda/include/cublasXt.h", - "cuda/include/cuda_fp16.h", "cuda/include/vector_functions.h", - "cuda/include/cusparse.h", - "cuda/include/nppi_filtering_functions.h", - "cuda/include/nppi_morphological_operations.h", - "cuda/include/sobol_direction_vectors.h", - "cuda/include/nvblas.h", - "cuda/include/curand_mtgp32dc_p_11213.h", - "cuda/include/nvcuvid.h", - "cuda/include/cuda_runtime_api.h", - "cuda/include/curand_mtgp32_kernel.h", - "cuda/include/cublas_v2.h", - "cuda/include/builtin_types.h", - "cuda/include/nppi_geometry_transforms.h", - "cuda/include/npps_support_functions.h", - "cuda/include/cufftw.h", - "cuda/include/cuda_device_runtime_api.h", - "cuda/include/sm_30_intrinsics.hpp", + "cuda/include/vector_functions.hpp", "cuda/include/vector_types.h", - "cuda/include/sm_35_atomic_functions.h", - "cuda/include/sm_20_intrinsics.h", - "cuda/include/driver_types.h", - "cuda/include/nvToolsExtCudaRt.h", - "cuda/include/curand_globals.h", - "cuda/include/device_atomic_functions.h", - "cuda/include/surface_types.h", - "cuda/include/nvrtc.h", - "cuda/include/nppdefs.h", - "cuda/include/sm_60_atomic_functions.h", - "cuda/include/driver_functions.h", - "cuda/include/cusolver_common.h", - "cuda/include/cublas.h", - "cuda/include/curand_lognormal.h", - "cuda/include/device_atomic_functions.hpp", - "cuda/include/crt/device_runtime.h", - "cuda/include/crt/storage_class.h", - "cuda/include/crt/func_macro.h", - "cuda/include/crt/host_runtime.h", - "cuda/include/nppi_arithmetic_and_logical_operations.h", - "cuda/include/npps_arithmetic_and_logical_operations.h", - "cuda/include/nppi_computer_vision.h", - "cuda/include/surface_functions.hpp", - "cuda/include/surface_functions.h", - "cuda/include/curand_normal_static.h", - "cuda/include/curand.h", - "cuda/include/math_functions_dbl_ptx3.h", - "cuda/include/curand_philox4x32_x.h", - "cuda/include/nppi_threshold_and_compare_operations.h", - "cuda/include/nvml.h", - "cuda/include/npps.h", - "cuda/include/cuda_vdpau_interop.h", - "cuda/include/sm_61_intrinsics.hpp", - "cuda/include/cublas_api.h", - "cuda/include/nppi_color_conversion.h", - "cuda/include/math_functions_dbl_ptx3.hpp", - "cuda/include/nppcore.h", - "cuda/include/cudaGL.h", - "cuda/include/fatBinaryCtl.h", - "cuda/include/npps_statistics_functions.h", - "cuda/include/cudaVDPAU.h", - "cuda/include/curand_poisson.h", - "cuda/include/cusolverDn.h", - "cuda/include/cuda_profiler_api.h", - "cuda/include/sm_20_atomic_functions.h", - "cuda/include/nvfunctional", ], cmd = """ -if [ -d "$(@D)/extras" ]; then rm $(@D)/extras -drf; fi && if [ -d "$(@D)/include" ]; then rm $(@D)/include -drf; fi && if [ -d "$(@D)/lib" ]; then rm $(@D)/lib -drf; fi && if [ -d "$(@D)/nvvm" ]; then rm $(@D)/nvvm -drf; fi && cp "/usr/local/cuda-8.0/include/math_functions.hpp" "$(@D)/cuda/include/math_functions.hpp" && cp "/usr/local/cuda-8.0/include/cufft.h" "$(@D)/cuda/include/cufft.h" && cp "/usr/local/cuda-8.0/include/nvgraph.h" "$(@D)/cuda/include/nvgraph.h" && cp "/usr/local/cuda-8.0/include/curand_normal.h" "$(@D)/cuda/include/curand_normal.h" && cp "/usr/local/cuda-8.0/include/curand_uniform.h" "$(@D)/cuda/include/curand_uniform.h" && cp "/usr/local/cuda-8.0/include/nppi_data_exchange_and_initialization.h" "$(@D)/cuda/include/nppi_data_exchange_and_initialization.h" && cp "/usr/local/cuda-8.0/include/cuda_gl_interop.h" "$(@D)/cuda/include/cuda_gl_interop.h" && cp "/usr/local/cuda-8.0/include/nppi_compression_functions.h" "$(@D)/cuda/include/nppi_compression_functions.h" && cp "/usr/local/cuda-8.0/include/npp.h" "$(@D)/cuda/include/npp.h" && cp "/usr/local/cuda-8.0/include/cuda.h" "$(@D)/cuda/include/cuda.h" && cp "/usr/local/cuda-8.0/include/nppi_statistics_functions.h" "$(@D)/cuda/include/nppi_statistics_functions.h" && cp "/usr/local/cuda-8.0/include/vector_functions.hpp" "$(@D)/cuda/include/vector_functions.hpp" && cp "/usr/local/cuda-8.0/include/sm_32_intrinsics.hpp" "$(@D)/cuda/include/sm_32_intrinsics.hpp" && cp "/usr/local/cuda-8.0/include/sm_32_intrinsics.h" "$(@D)/cuda/include/sm_32_intrinsics.h" && cp "/usr/local/cuda-8.0/include/curand_discrete.h" "$(@D)/cuda/include/curand_discrete.h" && cp "/usr/local/cuda-8.0/include/cuda_runtime.h" "$(@D)/cuda/include/cuda_runtime.h" && cp "/usr/local/cuda-8.0/include/cufftXt.h" "$(@D)/cuda/include/cufftXt.h" && cp "/usr/local/cuda-8.0/include/sm_61_intrinsics.h" "$(@D)/cuda/include/sm_61_intrinsics.h" && cp "/usr/local/cuda-8.0/include/texture_fetch_functions.h" "$(@D)/cuda/include/texture_fetch_functions.h" && cp "/usr/local/cuda-8.0/include/curand_mrg32k3a.h" "$(@D)/cuda/include/curand_mrg32k3a.h" && cp "/usr/local/cuda-8.0/include/host_defines.h" "$(@D)/cuda/include/host_defines.h" && cp "/usr/local/cuda-8.0/include/common_functions.h" "$(@D)/cuda/include/common_functions.h" && cp "/usr/local/cuda-8.0/include/nppi_support_functions.h" "$(@D)/cuda/include/nppi_support_functions.h" && cp "/usr/local/cuda-8.0/include/nppi_linear_transforms.h" "$(@D)/cuda/include/nppi_linear_transforms.h" && cp "/usr/local/cuda-8.0/include/device_double_functions.hpp" "$(@D)/cuda/include/device_double_functions.hpp" && cp "/usr/local/cuda-8.0/include/math_constants.h" "$(@D)/cuda/include/math_constants.h" && cp "/usr/local/cuda-8.0/include/nvToolsExtSync.h" "$(@D)/cuda/include/nvToolsExtSync.h" && cp "/usr/local/cuda-8.0/include/npps_initialization.h" "$(@D)/cuda/include/npps_initialization.h" && cp "/usr/local/cuda-8.0/include/cusolverSp_LOWLEVEL_PREVIEW.h" "$(@D)/cuda/include/cusolverSp_LOWLEVEL_PREVIEW.h" && cp "/usr/local/cuda-8.0/include/texture_indirect_functions.hpp" "$(@D)/cuda/include/texture_indirect_functions.hpp" && cp "/usr/local/cuda-8.0/include/cudaProfiler.h" "$(@D)/cuda/include/cudaProfiler.h" && cp "/usr/local/cuda-8.0/include/npps_filtering_functions.h" "$(@D)/cuda/include/npps_filtering_functions.h" && cp "/usr/local/cuda-8.0/include/cusparse_v2.h" "$(@D)/cuda/include/cusparse_v2.h" && cp "/usr/local/cuda-8.0/include/nppi.h" "$(@D)/cuda/include/nppi.h" && cp "/usr/local/cuda-8.0/include/surface_indirect_functions.h" "$(@D)/cuda/include/surface_indirect_functions.h" && cp "/usr/local/cuda-8.0/include/sm_30_intrinsics.h" "$(@D)/cuda/include/sm_30_intrinsics.h" && cp "/usr/local/cuda-8.0/include/device_double_functions.h" "$(@D)/cuda/include/device_double_functions.h" && cp "/usr/local/cuda-8.0/include/sm_35_intrinsics.h" "$(@D)/cuda/include/sm_35_intrinsics.h" && cp "/usr/local/cuda-8.0/include/cusolverSp.h" "$(@D)/cuda/include/cusolverSp.h" && cp "/usr/local/cuda-8.0/include/library_types.h" "$(@D)/cuda/include/library_types.h" && cp "/usr/local/cuda-8.0/include/surface_indirect_functions.hpp" "$(@D)/cuda/include/surface_indirect_functions.hpp" && cp "/usr/local/cuda-8.0/include/cudalibxt.h" "$(@D)/cuda/include/cudalibxt.h" && cp "/usr/local/cuda-8.0/include/channel_descriptor.h" "$(@D)/cuda/include/channel_descriptor.h" && cp "/usr/local/cuda-8.0/include/device_functions_decls.h" "$(@D)/cuda/include/device_functions_decls.h" && cp "/usr/local/cuda-8.0/include/curand_kernel.h" "$(@D)/cuda/include/curand_kernel.h" && cp "/usr/local/cuda-8.0/include/curand_mtgp32_host.h" "$(@D)/cuda/include/curand_mtgp32_host.h" && cp "/usr/local/cuda-8.0/include/nvToolsExtCuda.h" "$(@D)/cuda/include/nvToolsExtCuda.h" && cp "/usr/local/cuda-8.0/include/nvToolsExt.h" "$(@D)/cuda/include/nvToolsExt.h" && cp "/usr/local/cuda-8.0/include/cuComplex.h" "$(@D)/cuda/include/cuComplex.h" && cp "/usr/local/cuda-8.0/include/sm_32_atomic_functions.h" "$(@D)/cuda/include/sm_32_atomic_functions.h" && cp "/usr/local/cuda-8.0/include/texture_indirect_functions.h" "$(@D)/cuda/include/texture_indirect_functions.h" && cp "/usr/local/cuda-8.0/include/sm_32_atomic_functions.hpp" "$(@D)/cuda/include/sm_32_atomic_functions.hpp" && cp "/usr/local/cuda-8.0/include/sm_20_intrinsics.hpp" "$(@D)/cuda/include/sm_20_intrinsics.hpp" && cp "/usr/local/cuda-8.0/include/device_launch_parameters.h" "$(@D)/cuda/include/device_launch_parameters.h" && cp "/usr/local/cuda-8.0/include/curand_mtgp32.h" "$(@D)/cuda/include/curand_mtgp32.h" && cp "/usr/local/cuda-8.0/include/texture_fetch_functions.hpp" "$(@D)/cuda/include/texture_fetch_functions.hpp" && cp "/usr/local/cuda-8.0/include/cuda_occupancy.h" "$(@D)/cuda/include/cuda_occupancy.h" && cp "/usr/local/cuda-8.0/include/CL/opencl.h" "$(@D)/cuda/include/CL/opencl.h" && cp "/usr/local/cuda-8.0/include/CL/cl_platform.h" "$(@D)/cuda/include/CL/cl_platform.h" && cp "/usr/local/cuda-8.0/include/CL/cl_egl.h" "$(@D)/cuda/include/CL/cl_egl.h" && cp "/usr/local/cuda-8.0/include/CL/cl_gl.h" "$(@D)/cuda/include/CL/cl_gl.h" && cp "/usr/local/cuda-8.0/include/CL/cl.h" "$(@D)/cuda/include/CL/cl.h" && cp "/usr/local/cuda-8.0/include/CL/cl_gl_ext.h" "$(@D)/cuda/include/CL/cl_gl_ext.h" && cp "/usr/local/cuda-8.0/include/CL/cl_ext.h" "$(@D)/cuda/include/CL/cl_ext.h" && cp "/usr/local/cuda-8.0/include/CL/cl.hpp" "$(@D)/cuda/include/CL/cl.hpp" && cp "/usr/local/cuda-8.0/include/host_config.h" "$(@D)/cuda/include/host_config.h" && cp "/usr/local/cuda-8.0/include/cuda_surface_types.h" "$(@D)/cuda/include/cuda_surface_types.h" && cp "/usr/local/cuda-8.0/include/math_functions.h" "$(@D)/cuda/include/math_functions.h" && cp "/usr/local/cuda-8.0/include/nvToolsExtMeta.h" "$(@D)/cuda/include/nvToolsExtMeta.h" && cp "/usr/local/cuda-8.0/include/sm_20_atomic_functions.hpp" "$(@D)/cuda/include/sm_20_atomic_functions.hpp" && cp "/usr/local/cuda-8.0/include/device_functions.h" "$(@D)/cuda/include/device_functions.h" && cp "/usr/local/cuda-8.0/include/device_types.h" "$(@D)/cuda/include/device_types.h" && cp "/usr/local/cuda-8.0/include/npps_conversion_functions.h" "$(@D)/cuda/include/npps_conversion_functions.h" && cp "/usr/local/cuda-8.0/include/curand_precalc.h" "$(@D)/cuda/include/curand_precalc.h" && cp "/usr/local/cuda-8.0/include/cusolverRf.h" "$(@D)/cuda/include/cusolverRf.h" && cp "/usr/local/cuda-8.0/include/sm_60_atomic_functions.hpp" "$(@D)/cuda/include/sm_60_atomic_functions.hpp" && cp "/usr/local/cuda-8.0/include/cuviddec.h" "$(@D)/cuda/include/cuviddec.h" && cp "/usr/local/cuda-8.0/include/curand_discrete2.h" "$(@D)/cuda/include/curand_discrete2.h" && cp "/usr/local/cuda-8.0/include/device_functions.hpp" "$(@D)/cuda/include/device_functions.hpp" && cp "/usr/local/cuda-8.0/include/thrust/transform_scan.h" "$(@D)/cuda/include/thrust/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system_error.h" "$(@D)/cuda/include/thrust/system_error.h" && cp "/usr/local/cuda-8.0/include/thrust/device_malloc.h" "$(@D)/cuda/include/thrust/device_malloc.h" && cp "/usr/local/cuda-8.0/include/thrust/partition.h" "$(@D)/cuda/include/thrust/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/unique.h" "$(@D)/cuda/include/thrust/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/device_delete.h" "$(@D)/cuda/include/thrust/device_delete.h" && cp "/usr/local/cuda-8.0/include/thrust/execution_policy.h" "$(@D)/cuda/include/thrust/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/adjacent_difference.h" "$(@D)/cuda/include/thrust/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/sequence.h" "$(@D)/cuda/include/thrust/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/merge.h" "$(@D)/cuda/include/thrust/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/device_new.h" "$(@D)/cuda/include/thrust/device_new.h" && cp "/usr/local/cuda-8.0/include/thrust/transform_reduce.h" "$(@D)/cuda/include/thrust/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/device_vector.h" "$(@D)/cuda/include/thrust/device_vector.h" && cp "/usr/local/cuda-8.0/include/thrust/gather.h" "$(@D)/cuda/include/thrust/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/sort.h" "$(@D)/cuda/include/thrust/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/scan.h" "$(@D)/cuda/include/thrust/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/temporary_array.h" "$(@D)/cuda/include/thrust/detail/temporary_array.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/util/align.h" "$(@D)/cuda/include/thrust/detail/util/align.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/util/blocking.h" "$(@D)/cuda/include/thrust/detail/util/blocking.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/transform.inl" "$(@D)/cuda/include/thrust/detail/transform.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/device_vector.inl" "$(@D)/cuda/include/thrust/detail/device_vector.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/binary_search.inl" "$(@D)/cuda/include/thrust/detail/binary_search.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/overlapped_copy.h" "$(@D)/cuda/include/thrust/detail/overlapped_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/vector_base.inl" "$(@D)/cuda/include/thrust/detail/vector_base.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/device_reference.inl" "$(@D)/cuda/include/thrust/detail/device_reference.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/actor.h" "$(@D)/cuda/include/thrust/detail/functional/actor.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/value.h" "$(@D)/cuda/include/thrust/detail/functional/value.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators/logical_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/logical_operators.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators/relational_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/relational_operators.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators/assignment_operator.h" "$(@D)/cuda/include/thrust/detail/functional/operators/assignment_operator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators/bitwise_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/bitwise_operators.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators/operator_adaptors.h" "$(@D)/cuda/include/thrust/detail/functional/operators/operator_adaptors.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators/arithmetic_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/arithmetic_operators.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators/compound_assignment_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/compound_assignment_operators.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/argument.h" "$(@D)/cuda/include/thrust/detail/functional/argument.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/placeholder.h" "$(@D)/cuda/include/thrust/detail/functional/placeholder.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/actor.inl" "$(@D)/cuda/include/thrust/detail/functional/actor.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/composite.h" "$(@D)/cuda/include/thrust/detail/functional/composite.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/static_map.h" "$(@D)/cuda/include/thrust/detail/static_map.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/has_nested_type.h" "$(@D)/cuda/include/thrust/detail/type_traits/has_nested_type.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/is_call_possible.h" "$(@D)/cuda/include/thrust/detail/type_traits/is_call_possible.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/function_traits.h" "$(@D)/cuda/include/thrust/detail/type_traits/function_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/pointer_traits.h" "$(@D)/cuda/include/thrust/detail/type_traits/pointer_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/has_member_function.h" "$(@D)/cuda/include/thrust/detail/type_traits/has_member_function.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/algorithm/intermediate_type_from_function_and_iterators.h" "$(@D)/cuda/include/thrust/detail/type_traits/algorithm/intermediate_type_from_function_and_iterators.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/minimum_type.h" "$(@D)/cuda/include/thrust/detail/type_traits/minimum_type.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/has_trivial_assign.h" "$(@D)/cuda/include/thrust/detail/type_traits/has_trivial_assign.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/is_metafunction_defined.h" "$(@D)/cuda/include/thrust/detail/type_traits/is_metafunction_defined.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/iterator/is_discard_iterator.h" "$(@D)/cuda/include/thrust/detail/type_traits/iterator/is_discard_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/iterator/is_output_iterator.h" "$(@D)/cuda/include/thrust/detail/type_traits/iterator/is_output_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/result_of_adaptable_function.h" "$(@D)/cuda/include/thrust/detail/type_traits/result_of_adaptable_function.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/reference.h" "$(@D)/cuda/include/thrust/detail/reference.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/inner_product.inl" "$(@D)/cuda/include/thrust/detail/inner_product.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/use_default.h" "$(@D)/cuda/include/thrust/detail/use_default.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/sequence.inl" "$(@D)/cuda/include/thrust/detail/sequence.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/sort.inl" "$(@D)/cuda/include/thrust/detail/sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/equal.inl" "$(@D)/cuda/include/thrust/detail/equal.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/execution_policy.h" "$(@D)/cuda/include/thrust/detail/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/integer_traits.h" "$(@D)/cuda/include/thrust/detail/integer_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits.h" "$(@D)/cuda/include/thrust/detail/type_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/reverse.inl" "$(@D)/cuda/include/thrust/detail/reverse.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/tabulate.inl" "$(@D)/cuda/include/thrust/detail/tabulate.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/unique.inl" "$(@D)/cuda/include/thrust/detail/unique.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/scatter.inl" "$(@D)/cuda/include/thrust/detail/scatter.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/set_operations.inl" "$(@D)/cuda/include/thrust/detail/set_operations.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/device_malloc.inl" "$(@D)/cuda/include/thrust/detail/device_malloc.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/copy_if.inl" "$(@D)/cuda/include/thrust/detail/copy_if.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/fill.inl" "$(@D)/cuda/include/thrust/detail/fill.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/temporary_array.inl" "$(@D)/cuda/include/thrust/detail/temporary_array.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/transform_scan.inl" "$(@D)/cuda/include/thrust/detail/transform_scan.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/minmax.h" "$(@D)/cuda/include/thrust/detail/minmax.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/swap.inl" "$(@D)/cuda/include/thrust/detail/swap.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/pointer.inl" "$(@D)/cuda/include/thrust/detail/pointer.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/transform_reduce.inl" "$(@D)/cuda/include/thrust/detail/transform_reduce.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/config.h" "$(@D)/cuda/include/thrust/detail/config.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/distance.inl" "$(@D)/cuda/include/thrust/detail/distance.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/pair.inl" "$(@D)/cuda/include/thrust/detail/pair.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/temporary_allocator.h" "$(@D)/cuda/include/thrust/detail/allocator/temporary_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/tagged_allocator.h" "$(@D)/cuda/include/thrust/detail/allocator/tagged_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/destroy_range.inl" "$(@D)/cuda/include/thrust/detail/allocator/destroy_range.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/destroy_range.h" "$(@D)/cuda/include/thrust/detail/allocator/destroy_range.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/no_throw_allocator.h" "$(@D)/cuda/include/thrust/detail/allocator/no_throw_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/default_construct_range.inl" "$(@D)/cuda/include/thrust/detail/allocator/default_construct_range.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/fill_construct_range.inl" "$(@D)/cuda/include/thrust/detail/allocator/fill_construct_range.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/tagged_allocator.inl" "$(@D)/cuda/include/thrust/detail/allocator/tagged_allocator.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/malloc_allocator.h" "$(@D)/cuda/include/thrust/detail/allocator/malloc_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/allocator_traits.h" "$(@D)/cuda/include/thrust/detail/allocator/allocator_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/copy_construct_range.h" "$(@D)/cuda/include/thrust/detail/allocator/copy_construct_range.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/allocator_traits.inl" "$(@D)/cuda/include/thrust/detail/allocator/allocator_traits.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/default_construct_range.h" "$(@D)/cuda/include/thrust/detail/allocator/default_construct_range.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/copy_construct_range.inl" "$(@D)/cuda/include/thrust/detail/allocator/copy_construct_range.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/malloc_allocator.inl" "$(@D)/cuda/include/thrust/detail/allocator/malloc_allocator.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/temporary_allocator.inl" "$(@D)/cuda/include/thrust/detail/allocator/temporary_allocator.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/fill_construct_range.h" "$(@D)/cuda/include/thrust/detail/allocator/fill_construct_range.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/detail/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/reduce.inl" "$(@D)/cuda/include/thrust/detail/reduce.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/device_new.inl" "$(@D)/cuda/include/thrust/detail/device_new.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/pointer.h" "$(@D)/cuda/include/thrust/detail/pointer.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/for_each.inl" "$(@D)/cuda/include/thrust/detail/for_each.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/generate.inl" "$(@D)/cuda/include/thrust/detail/generate.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/dispatch/is_trivial_copy.h" "$(@D)/cuda/include/thrust/detail/dispatch/is_trivial_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/adjacent_difference.inl" "$(@D)/cuda/include/thrust/detail/adjacent_difference.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/tuple_meta_transform.h" "$(@D)/cuda/include/thrust/detail/tuple_meta_transform.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional.inl" "$(@D)/cuda/include/thrust/detail/functional.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/remove.inl" "$(@D)/cuda/include/thrust/detail/remove.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/tuple_transform.h" "$(@D)/cuda/include/thrust/detail/tuple_transform.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/merge.inl" "$(@D)/cuda/include/thrust/detail/merge.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/extrema.inl" "$(@D)/cuda/include/thrust/detail/extrema.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/trivial_sequence.h" "$(@D)/cuda/include/thrust/detail/trivial_sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/vector_base.h" "$(@D)/cuda/include/thrust/detail/vector_base.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/count.inl" "$(@D)/cuda/include/thrust/detail/count.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/uninitialized_copy.inl" "$(@D)/cuda/include/thrust/detail/uninitialized_copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/function.h" "$(@D)/cuda/include/thrust/detail/function.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/swap_ranges.inl" "$(@D)/cuda/include/thrust/detail/swap_ranges.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/device_delete.inl" "$(@D)/cuda/include/thrust/detail/device_delete.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/static_assert.h" "$(@D)/cuda/include/thrust/detail/static_assert.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/logical.inl" "$(@D)/cuda/include/thrust/detail/logical.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/seq.h" "$(@D)/cuda/include/thrust/detail/seq.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/mpl/math.h" "$(@D)/cuda/include/thrust/detail/mpl/math.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/mismatch.inl" "$(@D)/cuda/include/thrust/detail/mismatch.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/internal_functional.h" "$(@D)/cuda/include/thrust/detail/internal_functional.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/get_iterator_value.h" "$(@D)/cuda/include/thrust/detail/get_iterator_value.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/copy.inl" "$(@D)/cuda/include/thrust/detail/copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/copy.h" "$(@D)/cuda/include/thrust/detail/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/catrigf.h" "$(@D)/cuda/include/thrust/detail/complex/catrigf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/cpowf.h" "$(@D)/cuda/include/thrust/detail/complex/cpowf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/csqrtf.h" "$(@D)/cuda/include/thrust/detail/complex/csqrtf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/ccoshf.h" "$(@D)/cuda/include/thrust/detail/complex/ccoshf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/csinhf.h" "$(@D)/cuda/include/thrust/detail/complex/csinhf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/clogf.h" "$(@D)/cuda/include/thrust/detail/complex/clogf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/ccosh.h" "$(@D)/cuda/include/thrust/detail/complex/ccosh.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/arithmetic.h" "$(@D)/cuda/include/thrust/detail/complex/arithmetic.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/csqrt.h" "$(@D)/cuda/include/thrust/detail/complex/csqrt.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/cpow.h" "$(@D)/cuda/include/thrust/detail/complex/cpow.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/complex.inl" "$(@D)/cuda/include/thrust/detail/complex/complex.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/math_private.h" "$(@D)/cuda/include/thrust/detail/complex/math_private.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/c99math.h" "$(@D)/cuda/include/thrust/detail/complex/c99math.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/cproj.h" "$(@D)/cuda/include/thrust/detail/complex/cproj.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/catrig.h" "$(@D)/cuda/include/thrust/detail/complex/catrig.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/ctanhf.h" "$(@D)/cuda/include/thrust/detail/complex/ctanhf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/cexpf.h" "$(@D)/cuda/include/thrust/detail/complex/cexpf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/csinh.h" "$(@D)/cuda/include/thrust/detail/complex/csinh.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/stream.h" "$(@D)/cuda/include/thrust/detail/complex/stream.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/ctanh.h" "$(@D)/cuda/include/thrust/detail/complex/ctanh.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/cexp.h" "$(@D)/cuda/include/thrust/detail/complex/cexp.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/clog.h" "$(@D)/cuda/include/thrust/detail/complex/clog.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/range/head_flags.h" "$(@D)/cuda/include/thrust/detail/range/head_flags.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/range/tail_flags.h" "$(@D)/cuda/include/thrust/detail/range/tail_flags.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/execute_with_allocator.h" "$(@D)/cuda/include/thrust/detail/execute_with_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/integer_math.h" "$(@D)/cuda/include/thrust/detail/integer_math.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/swap.h" "$(@D)/cuda/include/thrust/detail/swap.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/uninitialized_fill.inl" "$(@D)/cuda/include/thrust/detail/uninitialized_fill.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/scan.inl" "$(@D)/cuda/include/thrust/detail/scan.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/gather.inl" "$(@D)/cuda/include/thrust/detail/gather.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/reference_forward_declaration.h" "$(@D)/cuda/include/thrust/detail/reference_forward_declaration.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/numeric_traits.h" "$(@D)/cuda/include/thrust/detail/numeric_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/reference.inl" "$(@D)/cuda/include/thrust/detail/reference.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/cstdint.h" "$(@D)/cuda/include/thrust/detail/cstdint.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/device_free.inl" "$(@D)/cuda/include/thrust/detail/device_free.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/copy_if.h" "$(@D)/cuda/include/thrust/detail/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/partition.inl" "$(@D)/cuda/include/thrust/detail/partition.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/find.inl" "$(@D)/cuda/include/thrust/detail/find.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/forceinline.h" "$(@D)/cuda/include/thrust/detail/config/forceinline.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/debug.h" "$(@D)/cuda/include/thrust/detail/config/debug.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/config.h" "$(@D)/cuda/include/thrust/detail/config/config.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/host_device.h" "$(@D)/cuda/include/thrust/detail/config/host_device.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/host_system.h" "$(@D)/cuda/include/thrust/detail/config/host_system.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/compiler.h" "$(@D)/cuda/include/thrust/detail/config/compiler.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/device_system.h" "$(@D)/cuda/include/thrust/detail/config/device_system.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/compiler_fence.h" "$(@D)/cuda/include/thrust/detail/config/compiler_fence.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/exec_check_disable.h" "$(@D)/cuda/include/thrust/detail/config/exec_check_disable.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/simple_defines.h" "$(@D)/cuda/include/thrust/detail/config/simple_defines.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/global_workarounds.h" "$(@D)/cuda/include/thrust/detail/config/global_workarounds.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/replace.inl" "$(@D)/cuda/include/thrust/detail/replace.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/device_ptr.inl" "$(@D)/cuda/include/thrust/detail/device_ptr.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/tuple.inl" "$(@D)/cuda/include/thrust/detail/tuple.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/detail/malloc_and_free.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/host_vector.inl" "$(@D)/cuda/include/thrust/detail/host_vector.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/raw_pointer_cast.h" "$(@D)/cuda/include/thrust/detail/raw_pointer_cast.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/advance.inl" "$(@D)/cuda/include/thrust/detail/advance.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/contiguous_storage.h" "$(@D)/cuda/include/thrust/detail/contiguous_storage.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/raw_reference_cast.h" "$(@D)/cuda/include/thrust/detail/raw_reference_cast.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/contiguous_storage.inl" "$(@D)/cuda/include/thrust/detail/contiguous_storage.inl" && cp "/usr/local/cuda-8.0/include/thrust/reverse.h" "$(@D)/cuda/include/thrust/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/device_malloc_allocator.h" "$(@D)/cuda/include/thrust/device_malloc_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/scatter.h" "$(@D)/cuda/include/thrust/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/pair.h" "$(@D)/cuda/include/thrust/pair.h" && cp "/usr/local/cuda-8.0/include/thrust/advance.h" "$(@D)/cuda/include/thrust/advance.h" && cp "/usr/local/cuda-8.0/include/thrust/find.h" "$(@D)/cuda/include/thrust/find.h" && cp "/usr/local/cuda-8.0/include/thrust/device_ptr.h" "$(@D)/cuda/include/thrust/device_ptr.h" && cp "/usr/local/cuda-8.0/include/thrust/generate.h" "$(@D)/cuda/include/thrust/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/uninitialized_fill.h" "$(@D)/cuda/include/thrust/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/system_error.h" "$(@D)/cuda/include/thrust/system/system_error.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/bad_alloc.h" "$(@D)/cuda/include/thrust/system/detail/bad_alloc.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/transform_scan.h" "$(@D)/cuda/include/thrust/system/detail/adl/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/unique_by_key.h" "$(@D)/cuda/include/thrust/system/detail/adl/unique_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/partition.h" "$(@D)/cuda/include/thrust/system/detail/adl/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/unique.h" "$(@D)/cuda/include/thrust/system/detail/adl/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/detail/adl/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/sequence.h" "$(@D)/cuda/include/thrust/system/detail/adl/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/merge.h" "$(@D)/cuda/include/thrust/system/detail/adl/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/transform_reduce.h" "$(@D)/cuda/include/thrust/system/detail/adl/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/gather.h" "$(@D)/cuda/include/thrust/system/detail/adl/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/sort.h" "$(@D)/cuda/include/thrust/system/detail/adl/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/scan.h" "$(@D)/cuda/include/thrust/system/detail/adl/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/detail/adl/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/scan_by_key.h" "$(@D)/cuda/include/thrust/system/detail/adl/scan_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/reverse.h" "$(@D)/cuda/include/thrust/system/detail/adl/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/assign_value.h" "$(@D)/cuda/include/thrust/system/detail/adl/assign_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/scatter.h" "$(@D)/cuda/include/thrust/system/detail/adl/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/find.h" "$(@D)/cuda/include/thrust/system/detail/adl/find.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/generate.h" "$(@D)/cuda/include/thrust/system/detail/adl/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/detail/adl/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/remove.h" "$(@D)/cuda/include/thrust/system/detail/adl/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/tabulate.h" "$(@D)/cuda/include/thrust/system/detail/adl/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/for_each.h" "$(@D)/cuda/include/thrust/system/detail/adl/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/detail/adl/reduce_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/reduce.h" "$(@D)/cuda/include/thrust/system/detail/adl/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/equal.h" "$(@D)/cuda/include/thrust/system/detail/adl/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/copy.h" "$(@D)/cuda/include/thrust/system/detail/adl/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/swap_ranges.h" "$(@D)/cuda/include/thrust/system/detail/adl/swap_ranges.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/detail/adl/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/binary_search.h" "$(@D)/cuda/include/thrust/system/detail/adl/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/set_operations.h" "$(@D)/cuda/include/thrust/system/detail/adl/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/mismatch.h" "$(@D)/cuda/include/thrust/system/detail/adl/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/extrema.h" "$(@D)/cuda/include/thrust/system/detail/adl/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/count.h" "$(@D)/cuda/include/thrust/system/detail/adl/count.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/replace.h" "$(@D)/cuda/include/thrust/system/detail/adl/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/get_value.h" "$(@D)/cuda/include/thrust/system/detail/adl/get_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/inner_product.h" "$(@D)/cuda/include/thrust/system/detail/adl/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/copy_if.h" "$(@D)/cuda/include/thrust/system/detail/adl/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/logical.h" "$(@D)/cuda/include/thrust/system/detail/adl/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/iter_swap.h" "$(@D)/cuda/include/thrust/system/detail/adl/iter_swap.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/detail/adl/malloc_and_free.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/fill.h" "$(@D)/cuda/include/thrust/system/detail/adl/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/transform.h" "$(@D)/cuda/include/thrust/system/detail/adl/transform.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/errno.h" "$(@D)/cuda/include/thrust/system/detail/errno.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/error_category.inl" "$(@D)/cuda/include/thrust/system/detail/error_category.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/transform_scan.h" "$(@D)/cuda/include/thrust/system/detail/sequential/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/unique_by_key.h" "$(@D)/cuda/include/thrust/system/detail/sequential/unique_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/stable_primitive_sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_primitive_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/stable_primitive_sort.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_primitive_sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/stable_merge_sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_merge_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/sort.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/partition.h" "$(@D)/cuda/include/thrust/system/detail/sequential/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/unique.h" "$(@D)/cuda/include/thrust/system/detail/sequential/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/execution_policy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/detail/sequential/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/sequence.h" "$(@D)/cuda/include/thrust/system/detail/sequential/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/merge.h" "$(@D)/cuda/include/thrust/system/detail/sequential/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/transform_reduce.h" "$(@D)/cuda/include/thrust/system/detail/sequential/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/gather.h" "$(@D)/cuda/include/thrust/system/detail/sequential/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/copy_backward.h" "$(@D)/cuda/include/thrust/system/detail/sequential/copy_backward.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/stable_radix_sort.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_radix_sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/scan.h" "$(@D)/cuda/include/thrust/system/detail/sequential/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/detail/sequential/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/scan_by_key.h" "$(@D)/cuda/include/thrust/system/detail/sequential/scan_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/reverse.h" "$(@D)/cuda/include/thrust/system/detail/sequential/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/assign_value.h" "$(@D)/cuda/include/thrust/system/detail/sequential/assign_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/scatter.h" "$(@D)/cuda/include/thrust/system/detail/sequential/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/find.h" "$(@D)/cuda/include/thrust/system/detail/sequential/find.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/stable_merge_sort.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_merge_sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/merge.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/merge.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/generate.h" "$(@D)/cuda/include/thrust/system/detail/sequential/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/detail/sequential/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/general_copy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/general_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/insertion_sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/insertion_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/remove.h" "$(@D)/cuda/include/thrust/system/detail/sequential/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/tabulate.h" "$(@D)/cuda/include/thrust/system/detail/sequential/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/for_each.h" "$(@D)/cuda/include/thrust/system/detail/sequential/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/detail/sequential/reduce_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/reduce.h" "$(@D)/cuda/include/thrust/system/detail/sequential/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/equal.h" "$(@D)/cuda/include/thrust/system/detail/sequential/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/stable_radix_sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_radix_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/copy.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/copy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/swap_ranges.h" "$(@D)/cuda/include/thrust/system/detail/sequential/swap_ranges.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/binary_search.h" "$(@D)/cuda/include/thrust/system/detail/sequential/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/set_operations.h" "$(@D)/cuda/include/thrust/system/detail/sequential/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/mismatch.h" "$(@D)/cuda/include/thrust/system/detail/sequential/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/extrema.h" "$(@D)/cuda/include/thrust/system/detail/sequential/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/count.h" "$(@D)/cuda/include/thrust/system/detail/sequential/count.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/trivial_copy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/trivial_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/replace.h" "$(@D)/cuda/include/thrust/system/detail/sequential/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/get_value.h" "$(@D)/cuda/include/thrust/system/detail/sequential/get_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/inner_product.h" "$(@D)/cuda/include/thrust/system/detail/sequential/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/copy_if.h" "$(@D)/cuda/include/thrust/system/detail/sequential/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/logical.h" "$(@D)/cuda/include/thrust/system/detail/sequential/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/iter_swap.h" "$(@D)/cuda/include/thrust/system/detail/sequential/iter_swap.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/detail/sequential/malloc_and_free.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/fill.h" "$(@D)/cuda/include/thrust/system/detail/sequential/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/transform.h" "$(@D)/cuda/include/thrust/system/detail/sequential/transform.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/error_condition.inl" "$(@D)/cuda/include/thrust/system/detail/error_condition.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/internal/decompose.h" "$(@D)/cuda/include/thrust/system/detail/internal/decompose.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/error_code.inl" "$(@D)/cuda/include/thrust/system/detail/error_code.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/transform_scan.h" "$(@D)/cuda/include/thrust/system/detail/generic/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/memory.inl" "$(@D)/cuda/include/thrust/system/detail/generic/memory.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/transform.inl" "$(@D)/cuda/include/thrust/system/detail/generic/transform.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/binary_search.inl" "$(@D)/cuda/include/thrust/system/detail/generic/binary_search.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scan_by_key.inl" "$(@D)/cuda/include/thrust/system/detail/generic/scan_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/unique_by_key.h" "$(@D)/cuda/include/thrust/system/detail/generic/unique_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/inner_product.inl" "$(@D)/cuda/include/thrust/system/detail/generic/inner_product.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/select_system.h" "$(@D)/cuda/include/thrust/system/detail/generic/select_system.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/sequence.inl" "$(@D)/cuda/include/thrust/system/detail/generic/sequence.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/sort.inl" "$(@D)/cuda/include/thrust/system/detail/generic/sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/equal.inl" "$(@D)/cuda/include/thrust/system/detail/generic/equal.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/partition.h" "$(@D)/cuda/include/thrust/system/detail/generic/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/unique.h" "$(@D)/cuda/include/thrust/system/detail/generic/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/detail/generic/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/tag.h" "$(@D)/cuda/include/thrust/system/detail/generic/tag.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/unique_by_key.inl" "$(@D)/cuda/include/thrust/system/detail/generic/unique_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/sequence.h" "$(@D)/cuda/include/thrust/system/detail/generic/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/type_traits.h" "$(@D)/cuda/include/thrust/system/detail/generic/type_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/merge.h" "$(@D)/cuda/include/thrust/system/detail/generic/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/reverse.inl" "$(@D)/cuda/include/thrust/system/detail/generic/reverse.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/tabulate.inl" "$(@D)/cuda/include/thrust/system/detail/generic/tabulate.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/unique.inl" "$(@D)/cuda/include/thrust/system/detail/generic/unique.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scatter.inl" "$(@D)/cuda/include/thrust/system/detail/generic/scatter.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/set_operations.inl" "$(@D)/cuda/include/thrust/system/detail/generic/set_operations.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/copy_if.inl" "$(@D)/cuda/include/thrust/system/detail/generic/copy_if.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/transform_reduce.h" "$(@D)/cuda/include/thrust/system/detail/generic/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/transform_scan.inl" "$(@D)/cuda/include/thrust/system/detail/generic/transform_scan.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/gather.h" "$(@D)/cuda/include/thrust/system/detail/generic/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/reduce_by_key.inl" "$(@D)/cuda/include/thrust/system/detail/generic/reduce_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/transform_reduce.inl" "$(@D)/cuda/include/thrust/system/detail/generic/transform_reduce.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/sort.h" "$(@D)/cuda/include/thrust/system/detail/generic/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/distance.inl" "$(@D)/cuda/include/thrust/system/detail/generic/distance.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scan.h" "$(@D)/cuda/include/thrust/system/detail/generic/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/detail/generic/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/reduce.inl" "$(@D)/cuda/include/thrust/system/detail/generic/reduce.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scan_by_key.h" "$(@D)/cuda/include/thrust/system/detail/generic/scan_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/reverse.h" "$(@D)/cuda/include/thrust/system/detail/generic/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/temporary_buffer.inl" "$(@D)/cuda/include/thrust/system/detail/generic/temporary_buffer.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scatter.h" "$(@D)/cuda/include/thrust/system/detail/generic/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/generate.inl" "$(@D)/cuda/include/thrust/system/detail/generic/generate.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/adjacent_difference.inl" "$(@D)/cuda/include/thrust/system/detail/generic/adjacent_difference.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/remove.inl" "$(@D)/cuda/include/thrust/system/detail/generic/remove.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/advance.h" "$(@D)/cuda/include/thrust/system/detail/generic/advance.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/find.h" "$(@D)/cuda/include/thrust/system/detail/generic/find.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/merge.inl" "$(@D)/cuda/include/thrust/system/detail/generic/merge.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scalar/binary_search.inl" "$(@D)/cuda/include/thrust/system/detail/generic/scalar/binary_search.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scalar/binary_search.h" "$(@D)/cuda/include/thrust/system/detail/generic/scalar/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/extrema.inl" "$(@D)/cuda/include/thrust/system/detail/generic/extrema.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/generate.h" "$(@D)/cuda/include/thrust/system/detail/generic/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/detail/generic/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/count.inl" "$(@D)/cuda/include/thrust/system/detail/generic/count.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/remove.h" "$(@D)/cuda/include/thrust/system/detail/generic/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/uninitialized_copy.inl" "$(@D)/cuda/include/thrust/system/detail/generic/uninitialized_copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/tabulate.h" "$(@D)/cuda/include/thrust/system/detail/generic/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/for_each.h" "$(@D)/cuda/include/thrust/system/detail/generic/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/distance.h" "$(@D)/cuda/include/thrust/system/detail/generic/distance.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/swap_ranges.inl" "$(@D)/cuda/include/thrust/system/detail/generic/swap_ranges.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/detail/generic/reduce_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/reduce.h" "$(@D)/cuda/include/thrust/system/detail/generic/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/equal.h" "$(@D)/cuda/include/thrust/system/detail/generic/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/mismatch.inl" "$(@D)/cuda/include/thrust/system/detail/generic/mismatch.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/copy.inl" "$(@D)/cuda/include/thrust/system/detail/generic/copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/copy.h" "$(@D)/cuda/include/thrust/system/detail/generic/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/swap_ranges.h" "$(@D)/cuda/include/thrust/system/detail/generic/swap_ranges.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/detail/generic/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/binary_search.h" "$(@D)/cuda/include/thrust/system/detail/generic/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/set_operations.h" "$(@D)/cuda/include/thrust/system/detail/generic/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/uninitialized_fill.inl" "$(@D)/cuda/include/thrust/system/detail/generic/uninitialized_fill.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/mismatch.h" "$(@D)/cuda/include/thrust/system/detail/generic/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scan.inl" "$(@D)/cuda/include/thrust/system/detail/generic/scan.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/gather.inl" "$(@D)/cuda/include/thrust/system/detail/generic/gather.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/extrema.h" "$(@D)/cuda/include/thrust/system/detail/generic/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/count.h" "$(@D)/cuda/include/thrust/system/detail/generic/count.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/replace.h" "$(@D)/cuda/include/thrust/system/detail/generic/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/inner_product.h" "$(@D)/cuda/include/thrust/system/detail/generic/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/copy_if.h" "$(@D)/cuda/include/thrust/system/detail/generic/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/logical.h" "$(@D)/cuda/include/thrust/system/detail/generic/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/partition.inl" "$(@D)/cuda/include/thrust/system/detail/generic/partition.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/memory.h" "$(@D)/cuda/include/thrust/system/detail/generic/memory.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/find.inl" "$(@D)/cuda/include/thrust/system/detail/generic/find.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/replace.inl" "$(@D)/cuda/include/thrust/system/detail/generic/replace.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/advance.inl" "$(@D)/cuda/include/thrust/system/detail/generic/advance.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/fill.h" "$(@D)/cuda/include/thrust/system/detail/generic/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/transform.h" "$(@D)/cuda/include/thrust/system/detail/generic/transform.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/system_error.inl" "$(@D)/cuda/include/thrust/system/detail/system_error.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/execution_policy.h" "$(@D)/cuda/include/thrust/system/omp/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/vector.h" "$(@D)/cuda/include/thrust/system/omp/vector.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/transform_scan.h" "$(@D)/cuda/include/thrust/system/omp/detail/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/memory.inl" "$(@D)/cuda/include/thrust/system/omp/detail/memory.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/reduce_intervals.inl" "$(@D)/cuda/include/thrust/system/omp/detail/reduce_intervals.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/unique_by_key.h" "$(@D)/cuda/include/thrust/system/omp/detail/unique_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/sort.inl" "$(@D)/cuda/include/thrust/system/omp/detail/sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/partition.h" "$(@D)/cuda/include/thrust/system/omp/detail/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/unique.h" "$(@D)/cuda/include/thrust/system/omp/detail/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/execution_policy.h" "$(@D)/cuda/include/thrust/system/omp/detail/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/omp/detail/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/unique_by_key.inl" "$(@D)/cuda/include/thrust/system/omp/detail/unique_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/sequence.h" "$(@D)/cuda/include/thrust/system/omp/detail/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/merge.h" "$(@D)/cuda/include/thrust/system/omp/detail/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/unique.inl" "$(@D)/cuda/include/thrust/system/omp/detail/unique.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/copy_if.inl" "$(@D)/cuda/include/thrust/system/omp/detail/copy_if.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/transform_reduce.h" "$(@D)/cuda/include/thrust/system/omp/detail/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/gather.h" "$(@D)/cuda/include/thrust/system/omp/detail/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/reduce_by_key.inl" "$(@D)/cuda/include/thrust/system/omp/detail/reduce_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/sort.h" "$(@D)/cuda/include/thrust/system/omp/detail/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/scan.h" "$(@D)/cuda/include/thrust/system/omp/detail/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/omp/detail/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/default_decomposition.h" "$(@D)/cuda/include/thrust/system/omp/detail/default_decomposition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/reduce.inl" "$(@D)/cuda/include/thrust/system/omp/detail/reduce.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/scan_by_key.h" "$(@D)/cuda/include/thrust/system/omp/detail/scan_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/reverse.h" "$(@D)/cuda/include/thrust/system/omp/detail/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/assign_value.h" "$(@D)/cuda/include/thrust/system/omp/detail/assign_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/scatter.h" "$(@D)/cuda/include/thrust/system/omp/detail/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/for_each.inl" "$(@D)/cuda/include/thrust/system/omp/detail/for_each.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/default_decomposition.inl" "$(@D)/cuda/include/thrust/system/omp/detail/default_decomposition.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/remove.inl" "$(@D)/cuda/include/thrust/system/omp/detail/remove.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/vector.inl" "$(@D)/cuda/include/thrust/system/omp/detail/vector.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/find.h" "$(@D)/cuda/include/thrust/system/omp/detail/find.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/generate.h" "$(@D)/cuda/include/thrust/system/omp/detail/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/omp/detail/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/remove.h" "$(@D)/cuda/include/thrust/system/omp/detail/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/tabulate.h" "$(@D)/cuda/include/thrust/system/omp/detail/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/for_each.h" "$(@D)/cuda/include/thrust/system/omp/detail/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/omp/detail/reduce_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/reduce.h" "$(@D)/cuda/include/thrust/system/omp/detail/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/equal.h" "$(@D)/cuda/include/thrust/system/omp/detail/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/copy.inl" "$(@D)/cuda/include/thrust/system/omp/detail/copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/copy.h" "$(@D)/cuda/include/thrust/system/omp/detail/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/swap_ranges.h" "$(@D)/cuda/include/thrust/system/omp/detail/swap_ranges.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/omp/detail/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/binary_search.h" "$(@D)/cuda/include/thrust/system/omp/detail/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/set_operations.h" "$(@D)/cuda/include/thrust/system/omp/detail/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/mismatch.h" "$(@D)/cuda/include/thrust/system/omp/detail/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/extrema.h" "$(@D)/cuda/include/thrust/system/omp/detail/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/count.h" "$(@D)/cuda/include/thrust/system/omp/detail/count.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/replace.h" "$(@D)/cuda/include/thrust/system/omp/detail/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/get_value.h" "$(@D)/cuda/include/thrust/system/omp/detail/get_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/inner_product.h" "$(@D)/cuda/include/thrust/system/omp/detail/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/copy_if.h" "$(@D)/cuda/include/thrust/system/omp/detail/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/logical.h" "$(@D)/cuda/include/thrust/system/omp/detail/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/partition.inl" "$(@D)/cuda/include/thrust/system/omp/detail/partition.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/iter_swap.h" "$(@D)/cuda/include/thrust/system/omp/detail/iter_swap.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/par.h" "$(@D)/cuda/include/thrust/system/omp/detail/par.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/reduce_intervals.h" "$(@D)/cuda/include/thrust/system/omp/detail/reduce_intervals.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/omp/detail/malloc_and_free.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/fill.h" "$(@D)/cuda/include/thrust/system/omp/detail/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/transform.h" "$(@D)/cuda/include/thrust/system/omp/detail/transform.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/memory.h" "$(@D)/cuda/include/thrust/system/omp/memory.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/execution_policy.h" "$(@D)/cuda/include/thrust/system/tbb/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/vector.h" "$(@D)/cuda/include/thrust/system/tbb/vector.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/transform_scan.h" "$(@D)/cuda/include/thrust/system/tbb/detail/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/memory.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/memory.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/unique_by_key.h" "$(@D)/cuda/include/thrust/system/tbb/detail/unique_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/sort.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/partition.h" "$(@D)/cuda/include/thrust/system/tbb/detail/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/unique.h" "$(@D)/cuda/include/thrust/system/tbb/detail/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/execution_policy.h" "$(@D)/cuda/include/thrust/system/tbb/detail/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/tbb/detail/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/unique_by_key.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/unique_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/sequence.h" "$(@D)/cuda/include/thrust/system/tbb/detail/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/merge.h" "$(@D)/cuda/include/thrust/system/tbb/detail/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/unique.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/unique.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/copy_if.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/copy_if.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/transform_reduce.h" "$(@D)/cuda/include/thrust/system/tbb/detail/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/gather.h" "$(@D)/cuda/include/thrust/system/tbb/detail/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/reduce_by_key.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/sort.h" "$(@D)/cuda/include/thrust/system/tbb/detail/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/scan.h" "$(@D)/cuda/include/thrust/system/tbb/detail/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/tbb/detail/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/reduce.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/scan_by_key.h" "$(@D)/cuda/include/thrust/system/tbb/detail/scan_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/reverse.h" "$(@D)/cuda/include/thrust/system/tbb/detail/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/assign_value.h" "$(@D)/cuda/include/thrust/system/tbb/detail/assign_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/scatter.h" "$(@D)/cuda/include/thrust/system/tbb/detail/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/for_each.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/for_each.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/remove.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/remove.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/vector.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/vector.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/find.h" "$(@D)/cuda/include/thrust/system/tbb/detail/find.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/merge.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/merge.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/generate.h" "$(@D)/cuda/include/thrust/system/tbb/detail/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/tbb/detail/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/remove.h" "$(@D)/cuda/include/thrust/system/tbb/detail/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/tabulate.h" "$(@D)/cuda/include/thrust/system/tbb/detail/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/for_each.h" "$(@D)/cuda/include/thrust/system/tbb/detail/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/reduce.h" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/equal.h" "$(@D)/cuda/include/thrust/system/tbb/detail/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/copy.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/copy.h" "$(@D)/cuda/include/thrust/system/tbb/detail/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/swap_ranges.h" "$(@D)/cuda/include/thrust/system/tbb/detail/swap_ranges.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/tbb/detail/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/binary_search.h" "$(@D)/cuda/include/thrust/system/tbb/detail/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/set_operations.h" "$(@D)/cuda/include/thrust/system/tbb/detail/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/mismatch.h" "$(@D)/cuda/include/thrust/system/tbb/detail/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/scan.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/scan.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/extrema.h" "$(@D)/cuda/include/thrust/system/tbb/detail/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/count.h" "$(@D)/cuda/include/thrust/system/tbb/detail/count.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/replace.h" "$(@D)/cuda/include/thrust/system/tbb/detail/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/get_value.h" "$(@D)/cuda/include/thrust/system/tbb/detail/get_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/inner_product.h" "$(@D)/cuda/include/thrust/system/tbb/detail/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/copy_if.h" "$(@D)/cuda/include/thrust/system/tbb/detail/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/logical.h" "$(@D)/cuda/include/thrust/system/tbb/detail/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/partition.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/partition.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/iter_swap.h" "$(@D)/cuda/include/thrust/system/tbb/detail/iter_swap.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/par.h" "$(@D)/cuda/include/thrust/system/tbb/detail/par.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/reduce_intervals.h" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce_intervals.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/tbb/detail/malloc_and_free.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/fill.h" "$(@D)/cuda/include/thrust/system/tbb/detail/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/transform.h" "$(@D)/cuda/include/thrust/system/tbb/detail/transform.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/memory.h" "$(@D)/cuda/include/thrust/system/tbb/memory.h" && cp "/usr/local/cuda-8.0/include/thrust/system/error_code.h" "$(@D)/cuda/include/thrust/system/error_code.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/execution_policy.h" "$(@D)/cuda/include/thrust/system/cpp/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/vector.h" "$(@D)/cuda/include/thrust/system/cpp/vector.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/transform_scan.h" "$(@D)/cuda/include/thrust/system/cpp/detail/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/memory.inl" "$(@D)/cuda/include/thrust/system/cpp/detail/memory.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/unique_by_key.h" "$(@D)/cuda/include/thrust/system/cpp/detail/unique_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/partition.h" "$(@D)/cuda/include/thrust/system/cpp/detail/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/unique.h" "$(@D)/cuda/include/thrust/system/cpp/detail/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/execution_policy.h" "$(@D)/cuda/include/thrust/system/cpp/detail/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/cpp/detail/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/sequence.h" "$(@D)/cuda/include/thrust/system/cpp/detail/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/merge.h" "$(@D)/cuda/include/thrust/system/cpp/detail/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/transform_reduce.h" "$(@D)/cuda/include/thrust/system/cpp/detail/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/gather.h" "$(@D)/cuda/include/thrust/system/cpp/detail/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/sort.h" "$(@D)/cuda/include/thrust/system/cpp/detail/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/scan.h" "$(@D)/cuda/include/thrust/system/cpp/detail/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/cpp/detail/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/scan_by_key.h" "$(@D)/cuda/include/thrust/system/cpp/detail/scan_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/reverse.h" "$(@D)/cuda/include/thrust/system/cpp/detail/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/assign_value.h" "$(@D)/cuda/include/thrust/system/cpp/detail/assign_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/scatter.h" "$(@D)/cuda/include/thrust/system/cpp/detail/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/vector.inl" "$(@D)/cuda/include/thrust/system/cpp/detail/vector.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/find.h" "$(@D)/cuda/include/thrust/system/cpp/detail/find.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/generate.h" "$(@D)/cuda/include/thrust/system/cpp/detail/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/cpp/detail/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/remove.h" "$(@D)/cuda/include/thrust/system/cpp/detail/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/tabulate.h" "$(@D)/cuda/include/thrust/system/cpp/detail/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/for_each.h" "$(@D)/cuda/include/thrust/system/cpp/detail/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/cpp/detail/reduce_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/reduce.h" "$(@D)/cuda/include/thrust/system/cpp/detail/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/equal.h" "$(@D)/cuda/include/thrust/system/cpp/detail/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/copy.h" "$(@D)/cuda/include/thrust/system/cpp/detail/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/swap_ranges.h" "$(@D)/cuda/include/thrust/system/cpp/detail/swap_ranges.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/cpp/detail/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/binary_search.h" "$(@D)/cuda/include/thrust/system/cpp/detail/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/set_operations.h" "$(@D)/cuda/include/thrust/system/cpp/detail/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/mismatch.h" "$(@D)/cuda/include/thrust/system/cpp/detail/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/extrema.h" "$(@D)/cuda/include/thrust/system/cpp/detail/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/count.h" "$(@D)/cuda/include/thrust/system/cpp/detail/count.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/replace.h" "$(@D)/cuda/include/thrust/system/cpp/detail/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/get_value.h" "$(@D)/cuda/include/thrust/system/cpp/detail/get_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/inner_product.h" "$(@D)/cuda/include/thrust/system/cpp/detail/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/copy_if.h" "$(@D)/cuda/include/thrust/system/cpp/detail/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/logical.h" "$(@D)/cuda/include/thrust/system/cpp/detail/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/iter_swap.h" "$(@D)/cuda/include/thrust/system/cpp/detail/iter_swap.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/par.h" "$(@D)/cuda/include/thrust/system/cpp/detail/par.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/cpp/detail/malloc_and_free.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/fill.h" "$(@D)/cuda/include/thrust/system/cpp/detail/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/transform.h" "$(@D)/cuda/include/thrust/system/cpp/detail/transform.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/memory.h" "$(@D)/cuda/include/thrust/system/cpp/memory.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/execution_policy.h" "$(@D)/cuda/include/thrust/system/cuda/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/vector.h" "$(@D)/cuda/include/thrust/system/cuda/vector.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/error.h" "$(@D)/cuda/include/thrust/system/cuda/error.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy_device_to_device.h" "$(@D)/cuda/include/thrust/system/cuda/detail/copy_device_to_device.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/transform_scan.h" "$(@D)/cuda/include/thrust/system/cuda/detail/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/memory.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/memory.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_allocator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_allocator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/grid/grid_mapping.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/grid/grid_mapping.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/grid/grid_barrier.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/grid/grid_barrier.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/grid/grid_even_share.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/grid/grid_even_share.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/grid/grid_queue.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/grid/grid_queue.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_device.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_device.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/device_run_length_encode.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_run_length_encode.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/device_partition.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_partition.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/device_radix_sort.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_radix_sort.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/dispatch/device_rle_dispatch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_rle_dispatch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/dispatch/device_histogram_dispatch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_histogram_dispatch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/dispatch/device_reduce_by_key_dispatch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_reduce_by_key_dispatch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/dispatch/device_scan_dispatch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_scan_dispatch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/dispatch/device_select_dispatch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_select_dispatch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/dispatch/device_reduce_dispatch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_reduce_dispatch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/dispatch/device_radix_sort_dispatch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_radix_sort_dispatch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/device_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_scan.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/device_select.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_select.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/device_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_reduce.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/device_histogram.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_histogram.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_range_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_reduce.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_range_histo.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_histo.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_range_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_scan.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_range_radix_sort_downsweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_radix_sort_downsweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_range_radix_sort_upsweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_radix_sort_upsweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_satomic.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_satomic.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_sort.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_sort.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_gatomic.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_gatomic.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_range_select.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_select.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_scan_prefix_operators.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_scan_prefix_operators.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_range_reduce_by_key.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_reduce_by_key.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_macro.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_macro.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_namespace.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_namespace.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_radix_sort_upsweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_radix_sort_upsweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_histogram_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_histogram_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_rle_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_rle_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_select_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_select_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_scan_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_scan_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_reduce_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_reduce_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_satomic_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_satomic_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_sort_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_sort_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_gatomic_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_gatomic_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_radix_sort_downsweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_radix_sort_downsweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_reduce_by_key_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_reduce_by_key_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_scan_prefix_operators.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_scan_prefix_operators.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_type.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_type.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/host/spinlock.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/host/spinlock.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/warp/warp_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/warp_reduce.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/warp/warp_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/warp_scan.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_shfl.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_shfl.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_smem.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_smem.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_shfl.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_shfl.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_smem.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_smem.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_ptx.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_ptx.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_debug.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_debug.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/cub.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/cub.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/transform_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/transform_input_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/tex_obj_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/tex_obj_input_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/tex_ref_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/tex_ref_input_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/cache_modified_output_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/cache_modified_output_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/counting_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/counting_input_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/cache_modified_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/cache_modified_input_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/arg_index_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/arg_index_input_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/constant_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/constant_input_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_scan.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_load.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_load.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_discontinuity.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_discontinuity.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_radix_rank.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_radix_rank.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_shift.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_shift.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_store.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_store.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_reduce.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_exchange.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_exchange.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_radix_sort.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_radix_sort.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_histogram.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_histogram.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_raking_layout.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_raking_layout.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_warp_reductions.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_warp_reductions.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking_commutative_only.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking_commutative_only.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_atomic.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_atomic.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_raking.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_raking.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_sort.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_sort.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/thread/thread_load.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_load.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/thread/thread_store.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_store.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/thread/thread_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_scan.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/thread/thread_operators.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_operators.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/thread/thread_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_reduce.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_arch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_arch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reduce_intervals.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce_intervals.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy_cross_system.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/copy_cross_system.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/unique_by_key.h" "$(@D)/cuda/include/thrust/system/cuda/detail/unique_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk.h" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/sort.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/partition.h" "$(@D)/cuda/include/thrust/system/cuda/detail/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/unique.h" "$(@D)/cuda/include/thrust/system/cuda/detail/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/execution_policy.h" "$(@D)/cuda/include/thrust/system/cuda/detail/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cuda_launch_config.h" "$(@D)/cuda/include/thrust/system/cuda/detail/cuda_launch_config.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub.h" "$(@D)/cuda/include/thrust/system/cuda/detail/cub.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/cuda/detail/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/sequence.h" "$(@D)/cuda/include/thrust/system/cuda/detail/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/merge.h" "$(@D)/cuda/include/thrust/system/cuda/detail/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/set_symmetric_difference.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/set_symmetric_difference.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy_if.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/copy_if.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/transform_reduce.h" "$(@D)/cuda/include/thrust/system/cuda/detail/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/error.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/error.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/gather.h" "$(@D)/cuda/include/thrust/system/cuda/detail/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reduce_by_key.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/sort.h" "$(@D)/cuda/include/thrust/system/cuda/detail/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/synchronize.h" "$(@D)/cuda/include/thrust/system/cuda/detail/synchronize.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/scan.h" "$(@D)/cuda/include/thrust/system/cuda/detail/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/temporary_indirect_permutation.h" "$(@D)/cuda/include/thrust/system/cuda/detail/temporary_indirect_permutation.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/extern_shared_ptr.h" "$(@D)/cuda/include/thrust/system/cuda/detail/extern_shared_ptr.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/set_operation.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/set_operation.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/balanced_path.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/balanced_path.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/virtualized_smem_closure.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/virtualized_smem_closure.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_primitive_sort.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_primitive_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/set_operation.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/set_operation.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_primitive_sort.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_primitive_sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_merge_sort.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_merge_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/launch_closure.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/launch_closure.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/merge.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/alignment.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/alignment.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_radix_sort.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_radix_sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_sort_each.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_sort_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/launch_calculator.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/launch_calculator.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_merge_sort.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_merge_sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/launch_closure.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/launch_closure.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_radix_sort.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_radix_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/uninitialized.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/uninitialized.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/cached_temporary_allocator.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/cached_temporary_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/launch_calculator.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/launch_calculator.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_sort_each.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_sort_each.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/cuda/detail/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/default_decomposition.h" "$(@D)/cuda/include/thrust/system/cuda/detail/default_decomposition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reduce.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/scan_by_key.h" "$(@D)/cuda/include/thrust/system/cuda/detail/scan_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reverse.h" "$(@D)/cuda/include/thrust/system/cuda/detail/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/assign_value.h" "$(@D)/cuda/include/thrust/system/cuda/detail/assign_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/scatter.h" "$(@D)/cuda/include/thrust/system/cuda/detail/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reduce_intervals.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce_intervals.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/for_each.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/for_each.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/default_decomposition.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/default_decomposition.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/guarded_cuda_runtime_api.h" "$(@D)/cuda/include/thrust/system/cuda/detail/guarded_cuda_runtime_api.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/adjacent_difference.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/adjacent_difference.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/vector.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/vector.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/throw_on_error.h" "$(@D)/cuda/include/thrust/system/cuda/detail/throw_on_error.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/find.h" "$(@D)/cuda/include/thrust/system/cuda/detail/find.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/terminate.h" "$(@D)/cuda/include/thrust/system/cuda/detail/terminate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/merge.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/merge.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/trivial_copy.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/trivial_copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/generate.h" "$(@D)/cuda/include/thrust/system/cuda/detail/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/execute_on_stream.h" "$(@D)/cuda/include/thrust/system/cuda/detail/execute_on_stream.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/cuda/detail/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/remove.h" "$(@D)/cuda/include/thrust/system/cuda/detail/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/tabulate.h" "$(@D)/cuda/include/thrust/system/cuda/detail/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/for_each.h" "$(@D)/cuda/include/thrust/system/cuda/detail/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/decomposition.h" "$(@D)/cuda/include/thrust/system/cuda/detail/decomposition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reduce.h" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/equal.h" "$(@D)/cuda/include/thrust/system/cuda/detail/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/runtime_introspection.h" "$(@D)/cuda/include/thrust/system/cuda/detail/runtime_introspection.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy.h" "$(@D)/cuda/include/thrust/system/cuda/detail/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/swap_ranges.h" "$(@D)/cuda/include/thrust/system/cuda/detail/swap_ranges.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/cuda/detail/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/binary_search.h" "$(@D)/cuda/include/thrust/system/cuda/detail/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/runtime_introspection.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/runtime_introspection.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/set_operations.h" "$(@D)/cuda/include/thrust/system/cuda/detail/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/mismatch.h" "$(@D)/cuda/include/thrust/system/cuda/detail/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/scan.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/scan.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/synchronize.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/synchronize.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/extrema.h" "$(@D)/cuda/include/thrust/system/cuda/detail/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/set_union.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/set_union.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/set_intersection.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/set_intersection.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/count.h" "$(@D)/cuda/include/thrust/system/cuda/detail/count.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/trivial_copy.h" "$(@D)/cuda/include/thrust/system/cuda/detail/trivial_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy_device_to_device.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/copy_device_to_device.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/replace.h" "$(@D)/cuda/include/thrust/system/cuda/detail/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/malloc.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/malloc.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/config.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/config.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/closure.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/closure.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/tail_flags.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/tail_flags.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/terminate.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/terminate.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/alignment.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/alignment.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/guarded_cuda_runtime_api.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/guarded_cuda_runtime_api.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/choose_sizes.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/choose_sizes.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/tuple_meta_transform.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/tuple_meta_transform.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/cuda_task.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_task.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/head_flags.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/head_flags.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/synchronize.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/synchronize.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/throw_on_error.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/throw_on_error.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/parameter_ptr.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/parameter_ptr.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/cuda_launcher.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/cuda_launcher.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/triple_chevron_launcher.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/triple_chevron_launcher.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/runtime_introspection.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/runtime_introspection.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/cuda_launch_config.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/cuda_launch_config.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/runtime_introspection.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/runtime_introspection.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/async.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/async.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/tuple_transform.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/tuple_transform.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/pointer_traits.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/pointer_traits.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/apply_from_tuple.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/apply_from_tuple.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/is_contiguous_iterator.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/is_contiguous_iterator.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/iterator.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/iterator.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/choose_sizes.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/choose_sizes.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/copy.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/copy.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/merge.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/merge.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/accumulate.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/accumulate.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/scan.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/scan.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/detail/stable_merge_sort.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/detail/stable_merge_sort.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/gather.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/gather.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/sort.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/sort.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/reduce.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/reduce.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/scatter.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/scatter.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/adjacent_difference.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/adjacent_difference.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/reduce_by_key.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/reduce_by_key.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/for_each.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/for_each.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/bulk.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/bulk.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/execution_policy.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/execution_policy.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/iterator/strided_iterator.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/iterator/strided_iterator.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/uninitialized.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/uninitialized.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/async.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/async.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/future.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/future.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/guarded_driver_types.h" "$(@D)/cuda/include/thrust/system/cuda/detail/guarded_driver_types.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/get_value.h" "$(@D)/cuda/include/thrust/system/cuda/detail/get_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/inner_product.h" "$(@D)/cuda/include/thrust/system/cuda/detail/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy_if.h" "$(@D)/cuda/include/thrust/system/cuda/detail/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/logical.h" "$(@D)/cuda/include/thrust/system/cuda/detail/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/iter_swap.h" "$(@D)/cuda/include/thrust/system/cuda/detail/iter_swap.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/merge.h" "$(@D)/cuda/include/thrust/system/cuda/detail/block/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/inclusive_scan.h" "$(@D)/cuda/include/thrust/system/cuda/detail/block/inclusive_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/merge.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/block/merge.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/merging_sort.h" "$(@D)/cuda/include/thrust/system/cuda/detail/block/merging_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/exclusive_scan.h" "$(@D)/cuda/include/thrust/system/cuda/detail/block/exclusive_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/reduce.h" "$(@D)/cuda/include/thrust/system/cuda/detail/block/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/copy.h" "$(@D)/cuda/include/thrust/system/cuda/detail/block/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/odd_even_sort.h" "$(@D)/cuda/include/thrust/system/cuda/detail/block/odd_even_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/par.h" "$(@D)/cuda/include/thrust/system/cuda/detail/par.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy_cross_system.h" "$(@D)/cuda/include/thrust/system/cuda/detail/copy_cross_system.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reduce_intervals.h" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce_intervals.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/cuda/detail/malloc_and_free.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/fill.h" "$(@D)/cuda/include/thrust/system/cuda/detail/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/set_difference.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/set_difference.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/transform.h" "$(@D)/cuda/include/thrust/system/cuda/detail/transform.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/experimental/pinned_allocator.h" "$(@D)/cuda/include/thrust/system/cuda/experimental/pinned_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/memory.h" "$(@D)/cuda/include/thrust/system/cuda/memory.h" && cp "/usr/local/cuda-8.0/include/thrust/remove.h" "$(@D)/cuda/include/thrust/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/tabulate.h" "$(@D)/cuda/include/thrust/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/for_each.h" "$(@D)/cuda/include/thrust/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/distance.h" "$(@D)/cuda/include/thrust/distance.h" && cp "/usr/local/cuda-8.0/include/thrust/reduce.h" "$(@D)/cuda/include/thrust/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/equal.h" "$(@D)/cuda/include/thrust/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/complex.h" "$(@D)/cuda/include/thrust/complex.h" && cp "/usr/local/cuda-8.0/include/thrust/device_allocator.h" "$(@D)/cuda/include/thrust/device_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/copy.h" "$(@D)/cuda/include/thrust/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/uninitialized_copy.h" "$(@D)/cuda/include/thrust/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/device_reference.h" "$(@D)/cuda/include/thrust/device_reference.h" && cp "/usr/local/cuda-8.0/include/thrust/binary_search.h" "$(@D)/cuda/include/thrust/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/set_operations.h" "$(@D)/cuda/include/thrust/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/swap.h" "$(@D)/cuda/include/thrust/swap.h" && cp "/usr/local/cuda-8.0/include/thrust/mismatch.h" "$(@D)/cuda/include/thrust/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/extrema.h" "$(@D)/cuda/include/thrust/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/count.h" "$(@D)/cuda/include/thrust/count.h" && cp "/usr/local/cuda-8.0/include/thrust/device_free.h" "$(@D)/cuda/include/thrust/device_free.h" && cp "/usr/local/cuda-8.0/include/thrust/random/discard_block_engine.h" "$(@D)/cuda/include/thrust/random/discard_block_engine.h" && cp "/usr/local/cuda-8.0/include/thrust/random/normal_distribution.h" "$(@D)/cuda/include/thrust/random/normal_distribution.h" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/linear_feedback_shift_engine_wordmask.h" "$(@D)/cuda/include/thrust/random/detail/linear_feedback_shift_engine_wordmask.h" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/subtract_with_carry_engine.inl" "$(@D)/cuda/include/thrust/random/detail/subtract_with_carry_engine.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/xor_combine_engine_max.h" "$(@D)/cuda/include/thrust/random/detail/xor_combine_engine_max.h" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/linear_congruential_engine_discard.h" "$(@D)/cuda/include/thrust/random/detail/linear_congruential_engine_discard.h" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/uniform_int_distribution.inl" "$(@D)/cuda/include/thrust/random/detail/uniform_int_distribution.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/discard_block_engine.inl" "$(@D)/cuda/include/thrust/random/detail/discard_block_engine.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/uniform_real_distribution.inl" "$(@D)/cuda/include/thrust/random/detail/uniform_real_distribution.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/random_core_access.h" "$(@D)/cuda/include/thrust/random/detail/random_core_access.h" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/mod.h" "$(@D)/cuda/include/thrust/random/detail/mod.h" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/linear_feedback_shift_engine.inl" "$(@D)/cuda/include/thrust/random/detail/linear_feedback_shift_engine.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/linear_congruential_engine.inl" "$(@D)/cuda/include/thrust/random/detail/linear_congruential_engine.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/xor_combine_engine.inl" "$(@D)/cuda/include/thrust/random/detail/xor_combine_engine.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/normal_distribution.inl" "$(@D)/cuda/include/thrust/random/detail/normal_distribution.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/normal_distribution_base.h" "$(@D)/cuda/include/thrust/random/detail/normal_distribution_base.h" && cp "/usr/local/cuda-8.0/include/thrust/random/uniform_int_distribution.h" "$(@D)/cuda/include/thrust/random/uniform_int_distribution.h" && cp "/usr/local/cuda-8.0/include/thrust/random/linear_feedback_shift_engine.h" "$(@D)/cuda/include/thrust/random/linear_feedback_shift_engine.h" && cp "/usr/local/cuda-8.0/include/thrust/random/xor_combine_engine.h" "$(@D)/cuda/include/thrust/random/xor_combine_engine.h" && cp "/usr/local/cuda-8.0/include/thrust/random/subtract_with_carry_engine.h" "$(@D)/cuda/include/thrust/random/subtract_with_carry_engine.h" && cp "/usr/local/cuda-8.0/include/thrust/random/linear_congruential_engine.h" "$(@D)/cuda/include/thrust/random/linear_congruential_engine.h" && cp "/usr/local/cuda-8.0/include/thrust/random/uniform_real_distribution.h" "$(@D)/cuda/include/thrust/random/uniform_real_distribution.h" && cp "/usr/local/cuda-8.0/include/thrust/functional.h" "$(@D)/cuda/include/thrust/functional.h" && cp "/usr/local/cuda-8.0/include/thrust/replace.h" "$(@D)/cuda/include/thrust/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/device_new_allocator.h" "$(@D)/cuda/include/thrust/device_new_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/host_vector.h" "$(@D)/cuda/include/thrust/host_vector.h" && cp "/usr/local/cuda-8.0/include/thrust/version.h" "$(@D)/cuda/include/thrust/version.h" && cp "/usr/local/cuda-8.0/include/thrust/inner_product.h" "$(@D)/cuda/include/thrust/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/iterator_traits.h" "$(@D)/cuda/include/thrust/iterator/iterator_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/discard_iterator.h" "$(@D)/cuda/include/thrust/iterator/discard_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/retag.h" "$(@D)/cuda/include/thrust/iterator/retag.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/permutation_iterator.h" "$(@D)/cuda/include/thrust/iterator/permutation_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/transform_iterator.h" "$(@D)/cuda/include/thrust/iterator/transform_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/reverse_iterator.inl" "$(@D)/cuda/include/thrust/iterator/detail/reverse_iterator.inl" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/zip_iterator.inl" "$(@D)/cuda/include/thrust/iterator/detail/zip_iterator.inl" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/counting_iterator.inl" "$(@D)/cuda/include/thrust/iterator/detail/counting_iterator.inl" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/distance_from_result.h" "$(@D)/cuda/include/thrust/iterator/detail/distance_from_result.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/host_system_tag.h" "$(@D)/cuda/include/thrust/iterator/detail/host_system_tag.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/iterator_traversal_tags.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_traversal_tags.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/retag.h" "$(@D)/cuda/include/thrust/iterator/detail/retag.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/tagged_iterator.h" "$(@D)/cuda/include/thrust/iterator/detail/tagged_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/iterator_traits.inl" "$(@D)/cuda/include/thrust/iterator/detail/iterator_traits.inl" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/minimum_category.h" "$(@D)/cuda/include/thrust/iterator/detail/minimum_category.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/discard_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/discard_iterator_base.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/iterator_category_to_traversal.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_category_to_traversal.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/zip_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/zip_iterator_base.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/normal_iterator.h" "$(@D)/cuda/include/thrust/iterator/detail/normal_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/join_iterator.h" "$(@D)/cuda/include/thrust/iterator/detail/join_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/device_system_tag.h" "$(@D)/cuda/include/thrust/iterator/detail/device_system_tag.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/universal_categories.h" "$(@D)/cuda/include/thrust/iterator/detail/universal_categories.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/reverse_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/reverse_iterator_base.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/minimum_system.h" "$(@D)/cuda/include/thrust/iterator/detail/minimum_system.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/tuple_of_iterator_references.h" "$(@D)/cuda/include/thrust/iterator/detail/tuple_of_iterator_references.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/is_iterator_category.h" "$(@D)/cuda/include/thrust/iterator/detail/is_iterator_category.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/permutation_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/permutation_iterator_base.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/any_assign.h" "$(@D)/cuda/include/thrust/iterator/detail/any_assign.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/any_system_tag.h" "$(@D)/cuda/include/thrust/iterator/detail/any_system_tag.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/is_trivial_iterator.h" "$(@D)/cuda/include/thrust/iterator/detail/is_trivial_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/iterator_category_to_system.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_category_to_system.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/iterator_adaptor_base.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_adaptor_base.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/constant_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/constant_iterator_base.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/transform_iterator.inl" "$(@D)/cuda/include/thrust/iterator/detail/transform_iterator.inl" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/iterator_facade_category.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_facade_category.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/iterator_category_with_system_and_traversal.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_category_with_system_and_traversal.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/constant_iterator.h" "$(@D)/cuda/include/thrust/iterator/constant_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/counting_iterator.h" "$(@D)/cuda/include/thrust/iterator/counting_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/iterator_adaptor.h" "$(@D)/cuda/include/thrust/iterator/iterator_adaptor.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/iterator_facade.h" "$(@D)/cuda/include/thrust/iterator/iterator_facade.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/iterator_categories.h" "$(@D)/cuda/include/thrust/iterator/iterator_categories.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/reverse_iterator.h" "$(@D)/cuda/include/thrust/iterator/reverse_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/zip_iterator.h" "$(@D)/cuda/include/thrust/iterator/zip_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/logical.h" "$(@D)/cuda/include/thrust/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/tuple.h" "$(@D)/cuda/include/thrust/tuple.h" && cp "/usr/local/cuda-8.0/include/thrust/memory.h" "$(@D)/cuda/include/thrust/memory.h" && cp "/usr/local/cuda-8.0/include/thrust/random.h" "$(@D)/cuda/include/thrust/random.h" && cp "/usr/local/cuda-8.0/include/thrust/fill.h" "$(@D)/cuda/include/thrust/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/transform.h" "$(@D)/cuda/include/thrust/transform.h" && cp "/usr/local/cuda-8.0/include/texture_types.h" "$(@D)/cuda/include/texture_types.h" && cp "/usr/local/cuda-8.0/include/nppversion.h" "$(@D)/cuda/include/nppversion.h" && cp "/usr/local/cuda-8.0/include/cuda_texture_types.h" "$(@D)/cuda/include/cuda_texture_types.h" && cp "/usr/local/cuda-8.0/include/fatbinary.h" "$(@D)/cuda/include/fatbinary.h" && cp "/usr/local/cuda-8.0/include/cublasXt.h" "$(@D)/cuda/include/cublasXt.h" && cp "/usr/local/cuda-8.0/include/cuda_fp16.h" "$(@D)/cuda/include/cuda_fp16.h" && cp "/usr/local/cuda-8.0/include/vector_functions.h" "$(@D)/cuda/include/vector_functions.h" && cp "/usr/local/cuda-8.0/include/cusparse.h" "$(@D)/cuda/include/cusparse.h" && cp "/usr/local/cuda-8.0/include/nppi_filtering_functions.h" "$(@D)/cuda/include/nppi_filtering_functions.h" && cp "/usr/local/cuda-8.0/include/nppi_morphological_operations.h" "$(@D)/cuda/include/nppi_morphological_operations.h" && cp "/usr/local/cuda-8.0/include/sobol_direction_vectors.h" "$(@D)/cuda/include/sobol_direction_vectors.h" && cp "/usr/local/cuda-8.0/include/nvblas.h" "$(@D)/cuda/include/nvblas.h" && cp "/usr/local/cuda-8.0/include/curand_mtgp32dc_p_11213.h" "$(@D)/cuda/include/curand_mtgp32dc_p_11213.h" && cp "/usr/local/cuda-8.0/include/nvcuvid.h" "$(@D)/cuda/include/nvcuvid.h" && cp "/usr/local/cuda-8.0/include/cuda_runtime_api.h" "$(@D)/cuda/include/cuda_runtime_api.h" && cp "/usr/local/cuda-8.0/include/curand_mtgp32_kernel.h" "$(@D)/cuda/include/curand_mtgp32_kernel.h" && cp "/usr/local/cuda-8.0/include/cublas_v2.h" "$(@D)/cuda/include/cublas_v2.h" && cp "/usr/local/cuda-8.0/include/builtin_types.h" "$(@D)/cuda/include/builtin_types.h" && cp "/usr/local/cuda-8.0/include/nppi_geometry_transforms.h" "$(@D)/cuda/include/nppi_geometry_transforms.h" && cp "/usr/local/cuda-8.0/include/npps_support_functions.h" "$(@D)/cuda/include/npps_support_functions.h" && cp "/usr/local/cuda-8.0/include/cufftw.h" "$(@D)/cuda/include/cufftw.h" && cp "/usr/local/cuda-8.0/include/cuda_device_runtime_api.h" "$(@D)/cuda/include/cuda_device_runtime_api.h" && cp "/usr/local/cuda-8.0/include/sm_30_intrinsics.hpp" "$(@D)/cuda/include/sm_30_intrinsics.hpp" && cp "/usr/local/cuda-8.0/include/vector_types.h" "$(@D)/cuda/include/vector_types.h" && cp "/usr/local/cuda-8.0/include/sm_35_atomic_functions.h" "$(@D)/cuda/include/sm_35_atomic_functions.h" && cp "/usr/local/cuda-8.0/include/sm_20_intrinsics.h" "$(@D)/cuda/include/sm_20_intrinsics.h" && cp "/usr/local/cuda-8.0/include/driver_types.h" "$(@D)/cuda/include/driver_types.h" && cp "/usr/local/cuda-8.0/include/nvToolsExtCudaRt.h" "$(@D)/cuda/include/nvToolsExtCudaRt.h" && cp "/usr/local/cuda-8.0/include/curand_globals.h" "$(@D)/cuda/include/curand_globals.h" && cp "/usr/local/cuda-8.0/include/device_atomic_functions.h" "$(@D)/cuda/include/device_atomic_functions.h" && cp "/usr/local/cuda-8.0/include/surface_types.h" "$(@D)/cuda/include/surface_types.h" && cp "/usr/local/cuda-8.0/include/nvrtc.h" "$(@D)/cuda/include/nvrtc.h" && cp "/usr/local/cuda-8.0/include/nppdefs.h" "$(@D)/cuda/include/nppdefs.h" && cp "/usr/local/cuda-8.0/include/sm_60_atomic_functions.h" "$(@D)/cuda/include/sm_60_atomic_functions.h" && cp "/usr/local/cuda-8.0/include/driver_functions.h" "$(@D)/cuda/include/driver_functions.h" && cp "/usr/local/cuda-8.0/include/cusolver_common.h" "$(@D)/cuda/include/cusolver_common.h" && cp "/usr/local/cuda-8.0/include/cublas.h" "$(@D)/cuda/include/cublas.h" && cp "/usr/local/cuda-8.0/include/curand_lognormal.h" "$(@D)/cuda/include/curand_lognormal.h" && cp "/usr/local/cuda-8.0/include/device_atomic_functions.hpp" "$(@D)/cuda/include/device_atomic_functions.hpp" && cp "/usr/local/cuda-8.0/include/crt/device_runtime.h" "$(@D)/cuda/include/crt/device_runtime.h" && cp "/usr/local/cuda-8.0/include/crt/storage_class.h" "$(@D)/cuda/include/crt/storage_class.h" && cp "/usr/local/cuda-8.0/include/crt/func_macro.h" "$(@D)/cuda/include/crt/func_macro.h" && cp "/usr/local/cuda-8.0/include/crt/host_runtime.h" "$(@D)/cuda/include/crt/host_runtime.h" && cp "/usr/local/cuda-8.0/include/nppi_arithmetic_and_logical_operations.h" "$(@D)/cuda/include/nppi_arithmetic_and_logical_operations.h" && cp "/usr/local/cuda-8.0/include/npps_arithmetic_and_logical_operations.h" "$(@D)/cuda/include/npps_arithmetic_and_logical_operations.h" && cp "/usr/local/cuda-8.0/include/nppi_computer_vision.h" "$(@D)/cuda/include/nppi_computer_vision.h" && cp "/usr/local/cuda-8.0/include/surface_functions.hpp" "$(@D)/cuda/include/surface_functions.hpp" && cp "/usr/local/cuda-8.0/include/surface_functions.h" "$(@D)/cuda/include/surface_functions.h" && cp "/usr/local/cuda-8.0/include/curand_normal_static.h" "$(@D)/cuda/include/curand_normal_static.h" && cp "/usr/local/cuda-8.0/include/curand.h" "$(@D)/cuda/include/curand.h" && cp "/usr/local/cuda-8.0/include/math_functions_dbl_ptx3.h" "$(@D)/cuda/include/math_functions_dbl_ptx3.h" && cp "/usr/local/cuda-8.0/include/curand_philox4x32_x.h" "$(@D)/cuda/include/curand_philox4x32_x.h" && cp "/usr/local/cuda-8.0/include/nppi_threshold_and_compare_operations.h" "$(@D)/cuda/include/nppi_threshold_and_compare_operations.h" && cp "/usr/local/cuda-8.0/include/nvml.h" "$(@D)/cuda/include/nvml.h" && cp "/usr/local/cuda-8.0/include/npps.h" "$(@D)/cuda/include/npps.h" && cp "/usr/local/cuda-8.0/include/cuda_vdpau_interop.h" "$(@D)/cuda/include/cuda_vdpau_interop.h" && cp "/usr/local/cuda-8.0/include/sm_61_intrinsics.hpp" "$(@D)/cuda/include/sm_61_intrinsics.hpp" && cp "/usr/local/cuda-8.0/include/cublas_api.h" "$(@D)/cuda/include/cublas_api.h" && cp "/usr/local/cuda-8.0/include/nppi_color_conversion.h" "$(@D)/cuda/include/nppi_color_conversion.h" && cp "/usr/local/cuda-8.0/include/math_functions_dbl_ptx3.hpp" "$(@D)/cuda/include/math_functions_dbl_ptx3.hpp" && cp "/usr/local/cuda-8.0/include/nppcore.h" "$(@D)/cuda/include/nppcore.h" && cp "/usr/local/cuda-8.0/include/cudaGL.h" "$(@D)/cuda/include/cudaGL.h" && cp "/usr/local/cuda-8.0/include/fatBinaryCtl.h" "$(@D)/cuda/include/fatBinaryCtl.h" && cp "/usr/local/cuda-8.0/include/npps_statistics_functions.h" "$(@D)/cuda/include/npps_statistics_functions.h" && cp "/usr/local/cuda-8.0/include/cudaVDPAU.h" "$(@D)/cuda/include/cudaVDPAU.h" && cp "/usr/local/cuda-8.0/include/curand_poisson.h" "$(@D)/cuda/include/curand_poisson.h" && cp "/usr/local/cuda-8.0/include/cusolverDn.h" "$(@D)/cuda/include/cusolverDn.h" && cp "/usr/local/cuda-8.0/include/cuda_profiler_api.h" "$(@D)/cuda/include/cuda_profiler_api.h" && cp "/usr/local/cuda-8.0/include/sm_20_atomic_functions.h" "$(@D)/cuda/include/sm_20_atomic_functions.h" && cp "/usr/local/cuda-8.0/include/nvfunctional" "$(@D)/cuda/include/nvfunctional" +if [ -d "$(@D)/extras" ]; then rm $(@D)/extras -drf; fi && if [ -d "$(@D)/include" ]; then rm $(@D)/include -drf; fi && if [ -d "$(@D)/lib" ]; then rm $(@D)/lib -drf; fi && if [ -d "$(@D)/nvvm" ]; then rm $(@D)/nvvm -drf; fi && cp "/usr/local/cuda-9.0/include/CL/cl.h" "$(@D)/cuda/include/CL/cl.h" && cp "/usr/local/cuda-9.0/include/CL/cl.hpp" "$(@D)/cuda/include/CL/cl.hpp" && cp "/usr/local/cuda-9.0/include/CL/cl_egl.h" "$(@D)/cuda/include/CL/cl_egl.h" && cp "/usr/local/cuda-9.0/include/CL/cl_ext.h" "$(@D)/cuda/include/CL/cl_ext.h" && cp "/usr/local/cuda-9.0/include/CL/cl_gl.h" "$(@D)/cuda/include/CL/cl_gl.h" && cp "/usr/local/cuda-9.0/include/CL/cl_gl_ext.h" "$(@D)/cuda/include/CL/cl_gl_ext.h" && cp "/usr/local/cuda-9.0/include/CL/cl_platform.h" "$(@D)/cuda/include/CL/cl_platform.h" && cp "/usr/local/cuda-9.0/include/CL/opencl.h" "$(@D)/cuda/include/CL/opencl.h" && cp "/usr/local/cuda-9.0/include/builtin_types.h" "$(@D)/cuda/include/builtin_types.h" && cp "/usr/local/cuda-9.0/include/channel_descriptor.h" "$(@D)/cuda/include/channel_descriptor.h" && cp "/usr/local/cuda-9.0/include/common_functions.h" "$(@D)/cuda/include/common_functions.h" && cp "/usr/local/cuda-9.0/include/cooperative_groups.h" "$(@D)/cuda/include/cooperative_groups.h" && cp "/usr/local/cuda-9.0/include/cooperative_groups_helpers.h" "$(@D)/cuda/include/cooperative_groups_helpers.h" && cp "/usr/local/cuda-9.0/include/crt/common_functions.h" "$(@D)/cuda/include/crt/common_functions.h" && cp "/usr/local/cuda-9.0/include/crt/device_double_functions.h" "$(@D)/cuda/include/crt/device_double_functions.h" && cp "/usr/local/cuda-9.0/include/crt/device_double_functions.hpp" "$(@D)/cuda/include/crt/device_double_functions.hpp" && cp "/usr/local/cuda-9.0/include/crt/device_functions.h" "$(@D)/cuda/include/crt/device_functions.h" && cp "/usr/local/cuda-9.0/include/crt/device_functions.hpp" "$(@D)/cuda/include/crt/device_functions.hpp" && cp "/usr/local/cuda-9.0/include/crt/func_macro.h" "$(@D)/cuda/include/crt/func_macro.h" && cp "/usr/local/cuda-9.0/include/crt/host_config.h" "$(@D)/cuda/include/crt/host_config.h" && cp "/usr/local/cuda-9.0/include/crt/host_defines.h" "$(@D)/cuda/include/crt/host_defines.h" && cp "/usr/local/cuda-9.0/include/crt/host_runtime.h" "$(@D)/cuda/include/crt/host_runtime.h" && cp "/usr/local/cuda-9.0/include/crt/math_functions.h" "$(@D)/cuda/include/crt/math_functions.h" && cp "/usr/local/cuda-9.0/include/crt/math_functions.hpp" "$(@D)/cuda/include/crt/math_functions.hpp" && cp "/usr/local/cuda-9.0/include/crt/mma.h" "$(@D)/cuda/include/crt/mma.h" && cp "/usr/local/cuda-9.0/include/crt/mma.hpp" "$(@D)/cuda/include/crt/mma.hpp" && cp "/usr/local/cuda-9.0/include/crt/nvfunctional" "$(@D)/cuda/include/crt/nvfunctional" && cp "/usr/local/cuda-9.0/include/crt/sm_70_rt.h" "$(@D)/cuda/include/crt/sm_70_rt.h" && cp "/usr/local/cuda-9.0/include/crt/sm_70_rt.hpp" "$(@D)/cuda/include/crt/sm_70_rt.hpp" && cp "/usr/local/cuda-9.0/include/crt/storage_class.h" "$(@D)/cuda/include/crt/storage_class.h" && cp "/usr/local/cuda-9.0/include/cuComplex.h" "$(@D)/cuda/include/cuComplex.h" && cp "/usr/local/cuda-9.0/include/cublas.h" "$(@D)/cuda/include/cublas.h" && cp "/usr/local/cuda-9.0/include/cublasXt.h" "$(@D)/cuda/include/cublasXt.h" && cp "/usr/local/cuda-9.0/include/cublas_api.h" "$(@D)/cuda/include/cublas_api.h" && cp "/usr/local/cuda-9.0/include/cublas_v2.h" "$(@D)/cuda/include/cublas_v2.h" && cp "/usr/local/cuda-9.0/include/cuda.h" "$(@D)/cuda/include/cuda.h" && cp "/usr/local/cuda-9.0/include/cudaEGL.h" "$(@D)/cuda/include/cudaEGL.h" && cp "/usr/local/cuda-9.0/include/cudaGL.h" "$(@D)/cuda/include/cudaGL.h" && cp "/usr/local/cuda-9.0/include/cudaProfiler.h" "$(@D)/cuda/include/cudaProfiler.h" && cp "/usr/local/cuda-9.0/include/cudaVDPAU.h" "$(@D)/cuda/include/cudaVDPAU.h" && cp "/usr/local/cuda-9.0/include/cuda_device_runtime_api.h" "$(@D)/cuda/include/cuda_device_runtime_api.h" && cp "/usr/local/cuda-9.0/include/cuda_fp16.h" "$(@D)/cuda/include/cuda_fp16.h" && cp "/usr/local/cuda-9.0/include/cuda_fp16.hpp" "$(@D)/cuda/include/cuda_fp16.hpp" && cp "/usr/local/cuda-9.0/include/cuda_gl_interop.h" "$(@D)/cuda/include/cuda_gl_interop.h" && cp "/usr/local/cuda-9.0/include/cuda_occupancy.h" "$(@D)/cuda/include/cuda_occupancy.h" && cp "/usr/local/cuda-9.0/include/cuda_profiler_api.h" "$(@D)/cuda/include/cuda_profiler_api.h" && cp "/usr/local/cuda-9.0/include/cuda_runtime.h" "$(@D)/cuda/include/cuda_runtime.h" && cp "/usr/local/cuda-9.0/include/cuda_runtime_api.h" "$(@D)/cuda/include/cuda_runtime_api.h" && cp "/usr/local/cuda-9.0/include/cuda_surface_types.h" "$(@D)/cuda/include/cuda_surface_types.h" && cp "/usr/local/cuda-9.0/include/cuda_texture_types.h" "$(@D)/cuda/include/cuda_texture_types.h" && cp "/usr/local/cuda-9.0/include/cuda_vdpau_interop.h" "$(@D)/cuda/include/cuda_vdpau_interop.h" && cp "/usr/local/cuda-9.0/include/cudalibxt.h" "$(@D)/cuda/include/cudalibxt.h" && cp "/usr/local/cuda-9.0/include/cudnn.h" "$(@D)/cuda/include/cudnn.h" && cp "/usr/local/cuda-9.0/include/cufft.h" "$(@D)/cuda/include/cufft.h" && cp "/usr/local/cuda-9.0/include/cufftXt.h" "$(@D)/cuda/include/cufftXt.h" && cp "/usr/local/cuda-9.0/include/cufftw.h" "$(@D)/cuda/include/cufftw.h" && cp "/usr/local/cuda-9.0/include/curand.h" "$(@D)/cuda/include/curand.h" && cp "/usr/local/cuda-9.0/include/curand_discrete.h" "$(@D)/cuda/include/curand_discrete.h" && cp "/usr/local/cuda-9.0/include/curand_discrete2.h" "$(@D)/cuda/include/curand_discrete2.h" && cp "/usr/local/cuda-9.0/include/curand_globals.h" "$(@D)/cuda/include/curand_globals.h" && cp "/usr/local/cuda-9.0/include/curand_kernel.h" "$(@D)/cuda/include/curand_kernel.h" && cp "/usr/local/cuda-9.0/include/curand_lognormal.h" "$(@D)/cuda/include/curand_lognormal.h" && cp "/usr/local/cuda-9.0/include/curand_mrg32k3a.h" "$(@D)/cuda/include/curand_mrg32k3a.h" && cp "/usr/local/cuda-9.0/include/curand_mtgp32.h" "$(@D)/cuda/include/curand_mtgp32.h" && cp "/usr/local/cuda-9.0/include/curand_mtgp32_host.h" "$(@D)/cuda/include/curand_mtgp32_host.h" && cp "/usr/local/cuda-9.0/include/curand_mtgp32_kernel.h" "$(@D)/cuda/include/curand_mtgp32_kernel.h" && cp "/usr/local/cuda-9.0/include/curand_mtgp32dc_p_11213.h" "$(@D)/cuda/include/curand_mtgp32dc_p_11213.h" && cp "/usr/local/cuda-9.0/include/curand_normal.h" "$(@D)/cuda/include/curand_normal.h" && cp "/usr/local/cuda-9.0/include/curand_normal_static.h" "$(@D)/cuda/include/curand_normal_static.h" && cp "/usr/local/cuda-9.0/include/curand_philox4x32_x.h" "$(@D)/cuda/include/curand_philox4x32_x.h" && cp "/usr/local/cuda-9.0/include/curand_poisson.h" "$(@D)/cuda/include/curand_poisson.h" && cp "/usr/local/cuda-9.0/include/curand_precalc.h" "$(@D)/cuda/include/curand_precalc.h" && cp "/usr/local/cuda-9.0/include/curand_uniform.h" "$(@D)/cuda/include/curand_uniform.h" && cp "/usr/local/cuda-9.0/include/cusolverDn.h" "$(@D)/cuda/include/cusolverDn.h" && cp "/usr/local/cuda-9.0/include/cusolverRf.h" "$(@D)/cuda/include/cusolverRf.h" && cp "/usr/local/cuda-9.0/include/cusolverSp.h" "$(@D)/cuda/include/cusolverSp.h" && cp "/usr/local/cuda-9.0/include/cusolverSp_LOWLEVEL_PREVIEW.h" "$(@D)/cuda/include/cusolverSp_LOWLEVEL_PREVIEW.h" && cp "/usr/local/cuda-9.0/include/cusolver_common.h" "$(@D)/cuda/include/cusolver_common.h" && cp "/usr/local/cuda-9.0/include/cusparse.h" "$(@D)/cuda/include/cusparse.h" && cp "/usr/local/cuda-9.0/include/cusparse_v2.h" "$(@D)/cuda/include/cusparse_v2.h" && cp "/usr/local/cuda-9.0/include/device_atomic_functions.h" "$(@D)/cuda/include/device_atomic_functions.h" && cp "/usr/local/cuda-9.0/include/device_atomic_functions.hpp" "$(@D)/cuda/include/device_atomic_functions.hpp" && cp "/usr/local/cuda-9.0/include/device_double_functions.h" "$(@D)/cuda/include/device_double_functions.h" && cp "/usr/local/cuda-9.0/include/device_double_functions.hpp" "$(@D)/cuda/include/device_double_functions.hpp" && cp "/usr/local/cuda-9.0/include/device_functions.h" "$(@D)/cuda/include/device_functions.h" && cp "/usr/local/cuda-9.0/include/device_functions.hpp" "$(@D)/cuda/include/device_functions.hpp" && cp "/usr/local/cuda-9.0/include/device_functions_decls.h" "$(@D)/cuda/include/device_functions_decls.h" && cp "/usr/local/cuda-9.0/include/device_launch_parameters.h" "$(@D)/cuda/include/device_launch_parameters.h" && cp "/usr/local/cuda-9.0/include/device_types.h" "$(@D)/cuda/include/device_types.h" && cp "/usr/local/cuda-9.0/include/driver_functions.h" "$(@D)/cuda/include/driver_functions.h" && cp "/usr/local/cuda-9.0/include/driver_types.h" "$(@D)/cuda/include/driver_types.h" && cp "/usr/local/cuda-9.0/include/dynlink_cuda.h" "$(@D)/cuda/include/dynlink_cuda.h" && cp "/usr/local/cuda-9.0/include/dynlink_cuda_cuda.h" "$(@D)/cuda/include/dynlink_cuda_cuda.h" && cp "/usr/local/cuda-9.0/include/dynlink_cuviddec.h" "$(@D)/cuda/include/dynlink_cuviddec.h" && cp "/usr/local/cuda-9.0/include/dynlink_nvcuvid.h" "$(@D)/cuda/include/dynlink_nvcuvid.h" && cp "/usr/local/cuda-9.0/include/fatBinaryCtl.h" "$(@D)/cuda/include/fatBinaryCtl.h" && cp "/usr/local/cuda-9.0/include/fatbinary.h" "$(@D)/cuda/include/fatbinary.h" && cp "/usr/local/cuda-9.0/include/host_config.h" "$(@D)/cuda/include/host_config.h" && cp "/usr/local/cuda-9.0/include/host_defines.h" "$(@D)/cuda/include/host_defines.h" && cp "/usr/local/cuda-9.0/include/library_types.h" "$(@D)/cuda/include/library_types.h" && cp "/usr/local/cuda-9.0/include/math_constants.h" "$(@D)/cuda/include/math_constants.h" && cp "/usr/local/cuda-9.0/include/math_functions.h" "$(@D)/cuda/include/math_functions.h" && cp "/usr/local/cuda-9.0/include/math_functions.hpp" "$(@D)/cuda/include/math_functions.hpp" && cp "/usr/local/cuda-9.0/include/math_functions_dbl_ptx3.h" "$(@D)/cuda/include/math_functions_dbl_ptx3.h" && cp "/usr/local/cuda-9.0/include/math_functions_dbl_ptx3.hpp" "$(@D)/cuda/include/math_functions_dbl_ptx3.hpp" && cp "/usr/local/cuda-9.0/include/mma.h" "$(@D)/cuda/include/mma.h" && cp "/usr/local/cuda-9.0/include/npp.h" "$(@D)/cuda/include/npp.h" && cp "/usr/local/cuda-9.0/include/nppcore.h" "$(@D)/cuda/include/nppcore.h" && cp "/usr/local/cuda-9.0/include/nppdefs.h" "$(@D)/cuda/include/nppdefs.h" && cp "/usr/local/cuda-9.0/include/nppi.h" "$(@D)/cuda/include/nppi.h" && cp "/usr/local/cuda-9.0/include/nppi_arithmetic_and_logical_operations.h" "$(@D)/cuda/include/nppi_arithmetic_and_logical_operations.h" && cp "/usr/local/cuda-9.0/include/nppi_color_conversion.h" "$(@D)/cuda/include/nppi_color_conversion.h" && cp "/usr/local/cuda-9.0/include/nppi_compression_functions.h" "$(@D)/cuda/include/nppi_compression_functions.h" && cp "/usr/local/cuda-9.0/include/nppi_computer_vision.h" "$(@D)/cuda/include/nppi_computer_vision.h" && cp "/usr/local/cuda-9.0/include/nppi_data_exchange_and_initialization.h" "$(@D)/cuda/include/nppi_data_exchange_and_initialization.h" && cp "/usr/local/cuda-9.0/include/nppi_filtering_functions.h" "$(@D)/cuda/include/nppi_filtering_functions.h" && cp "/usr/local/cuda-9.0/include/nppi_geometry_transforms.h" "$(@D)/cuda/include/nppi_geometry_transforms.h" && cp "/usr/local/cuda-9.0/include/nppi_linear_transforms.h" "$(@D)/cuda/include/nppi_linear_transforms.h" && cp "/usr/local/cuda-9.0/include/nppi_morphological_operations.h" "$(@D)/cuda/include/nppi_morphological_operations.h" && cp "/usr/local/cuda-9.0/include/nppi_statistics_functions.h" "$(@D)/cuda/include/nppi_statistics_functions.h" && cp "/usr/local/cuda-9.0/include/nppi_support_functions.h" "$(@D)/cuda/include/nppi_support_functions.h" && cp "/usr/local/cuda-9.0/include/nppi_threshold_and_compare_operations.h" "$(@D)/cuda/include/nppi_threshold_and_compare_operations.h" && cp "/usr/local/cuda-9.0/include/npps.h" "$(@D)/cuda/include/npps.h" && cp "/usr/local/cuda-9.0/include/npps_arithmetic_and_logical_operations.h" "$(@D)/cuda/include/npps_arithmetic_and_logical_operations.h" && cp "/usr/local/cuda-9.0/include/npps_conversion_functions.h" "$(@D)/cuda/include/npps_conversion_functions.h" && cp "/usr/local/cuda-9.0/include/npps_filtering_functions.h" "$(@D)/cuda/include/npps_filtering_functions.h" && cp "/usr/local/cuda-9.0/include/npps_initialization.h" "$(@D)/cuda/include/npps_initialization.h" && cp "/usr/local/cuda-9.0/include/npps_statistics_functions.h" "$(@D)/cuda/include/npps_statistics_functions.h" && cp "/usr/local/cuda-9.0/include/npps_support_functions.h" "$(@D)/cuda/include/npps_support_functions.h" && cp "/usr/local/cuda-9.0/include/nppversion.h" "$(@D)/cuda/include/nppversion.h" && cp "/usr/local/cuda-9.0/include/nvToolsExt.h" "$(@D)/cuda/include/nvToolsExt.h" && cp "/usr/local/cuda-9.0/include/nvToolsExtCuda.h" "$(@D)/cuda/include/nvToolsExtCuda.h" && cp "/usr/local/cuda-9.0/include/nvToolsExtCudaRt.h" "$(@D)/cuda/include/nvToolsExtCudaRt.h" && cp "/usr/local/cuda-9.0/include/nvToolsExtMeta.h" "$(@D)/cuda/include/nvToolsExtMeta.h" && cp "/usr/local/cuda-9.0/include/nvToolsExtSync.h" "$(@D)/cuda/include/nvToolsExtSync.h" && cp "/usr/local/cuda-9.0/include/nvblas.h" "$(@D)/cuda/include/nvblas.h" && cp "/usr/local/cuda-9.0/include/nvfunctional" "$(@D)/cuda/include/nvfunctional" && cp "/usr/local/cuda-9.0/include/nvgraph.h" "$(@D)/cuda/include/nvgraph.h" && cp "/usr/local/cuda-9.0/include/nvml.h" "$(@D)/cuda/include/nvml.h" && cp "/usr/local/cuda-9.0/include/nvrtc.h" "$(@D)/cuda/include/nvrtc.h" && cp "/usr/local/cuda-9.0/include/sm_20_atomic_functions.h" "$(@D)/cuda/include/sm_20_atomic_functions.h" && cp "/usr/local/cuda-9.0/include/sm_20_atomic_functions.hpp" "$(@D)/cuda/include/sm_20_atomic_functions.hpp" && cp "/usr/local/cuda-9.0/include/sm_20_intrinsics.h" "$(@D)/cuda/include/sm_20_intrinsics.h" && cp "/usr/local/cuda-9.0/include/sm_20_intrinsics.hpp" "$(@D)/cuda/include/sm_20_intrinsics.hpp" && cp "/usr/local/cuda-9.0/include/sm_30_intrinsics.h" "$(@D)/cuda/include/sm_30_intrinsics.h" && cp "/usr/local/cuda-9.0/include/sm_30_intrinsics.hpp" "$(@D)/cuda/include/sm_30_intrinsics.hpp" && cp "/usr/local/cuda-9.0/include/sm_32_atomic_functions.h" "$(@D)/cuda/include/sm_32_atomic_functions.h" && cp "/usr/local/cuda-9.0/include/sm_32_atomic_functions.hpp" "$(@D)/cuda/include/sm_32_atomic_functions.hpp" && cp "/usr/local/cuda-9.0/include/sm_32_intrinsics.h" "$(@D)/cuda/include/sm_32_intrinsics.h" && cp "/usr/local/cuda-9.0/include/sm_32_intrinsics.hpp" "$(@D)/cuda/include/sm_32_intrinsics.hpp" && cp "/usr/local/cuda-9.0/include/sm_35_atomic_functions.h" "$(@D)/cuda/include/sm_35_atomic_functions.h" && cp "/usr/local/cuda-9.0/include/sm_35_intrinsics.h" "$(@D)/cuda/include/sm_35_intrinsics.h" && cp "/usr/local/cuda-9.0/include/sm_60_atomic_functions.h" "$(@D)/cuda/include/sm_60_atomic_functions.h" && cp "/usr/local/cuda-9.0/include/sm_60_atomic_functions.hpp" "$(@D)/cuda/include/sm_60_atomic_functions.hpp" && cp "/usr/local/cuda-9.0/include/sm_61_intrinsics.h" "$(@D)/cuda/include/sm_61_intrinsics.h" && cp "/usr/local/cuda-9.0/include/sm_61_intrinsics.hpp" "$(@D)/cuda/include/sm_61_intrinsics.hpp" && cp "/usr/local/cuda-9.0/include/sobol_direction_vectors.h" "$(@D)/cuda/include/sobol_direction_vectors.h" && cp "/usr/local/cuda-9.0/include/surface_functions.h" "$(@D)/cuda/include/surface_functions.h" && cp "/usr/local/cuda-9.0/include/surface_functions.hpp" "$(@D)/cuda/include/surface_functions.hpp" && cp "/usr/local/cuda-9.0/include/surface_indirect_functions.h" "$(@D)/cuda/include/surface_indirect_functions.h" && cp "/usr/local/cuda-9.0/include/surface_indirect_functions.hpp" "$(@D)/cuda/include/surface_indirect_functions.hpp" && cp "/usr/local/cuda-9.0/include/surface_types.h" "$(@D)/cuda/include/surface_types.h" && cp "/usr/local/cuda-9.0/include/texture_fetch_functions.h" "$(@D)/cuda/include/texture_fetch_functions.h" && cp "/usr/local/cuda-9.0/include/texture_fetch_functions.hpp" "$(@D)/cuda/include/texture_fetch_functions.hpp" && cp "/usr/local/cuda-9.0/include/texture_indirect_functions.h" "$(@D)/cuda/include/texture_indirect_functions.h" && cp "/usr/local/cuda-9.0/include/texture_indirect_functions.hpp" "$(@D)/cuda/include/texture_indirect_functions.hpp" && cp "/usr/local/cuda-9.0/include/texture_types.h" "$(@D)/cuda/include/texture_types.h" && cp "/usr/local/cuda-9.0/include/thrust/adjacent_difference.h" "$(@D)/cuda/include/thrust/adjacent_difference.h" && cp "/usr/local/cuda-9.0/include/thrust/advance.h" "$(@D)/cuda/include/thrust/advance.h" && cp "/usr/local/cuda-9.0/include/thrust/binary_search.h" "$(@D)/cuda/include/thrust/binary_search.h" && cp "/usr/local/cuda-9.0/include/thrust/complex.h" "$(@D)/cuda/include/thrust/complex.h" && cp "/usr/local/cuda-9.0/include/thrust/copy.h" "$(@D)/cuda/include/thrust/copy.h" && cp "/usr/local/cuda-9.0/include/thrust/count.h" "$(@D)/cuda/include/thrust/count.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/adjacent_difference.inl" "$(@D)/cuda/include/thrust/detail/adjacent_difference.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/advance.inl" "$(@D)/cuda/include/thrust/detail/advance.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/allocator_traits.h" "$(@D)/cuda/include/thrust/detail/allocator/allocator_traits.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/allocator_traits.inl" "$(@D)/cuda/include/thrust/detail/allocator/allocator_traits.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/copy_construct_range.h" "$(@D)/cuda/include/thrust/detail/allocator/copy_construct_range.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/copy_construct_range.inl" "$(@D)/cuda/include/thrust/detail/allocator/copy_construct_range.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/default_construct_range.h" "$(@D)/cuda/include/thrust/detail/allocator/default_construct_range.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/default_construct_range.inl" "$(@D)/cuda/include/thrust/detail/allocator/default_construct_range.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/destroy_range.h" "$(@D)/cuda/include/thrust/detail/allocator/destroy_range.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/destroy_range.inl" "$(@D)/cuda/include/thrust/detail/allocator/destroy_range.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/fill_construct_range.h" "$(@D)/cuda/include/thrust/detail/allocator/fill_construct_range.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/fill_construct_range.inl" "$(@D)/cuda/include/thrust/detail/allocator/fill_construct_range.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/malloc_allocator.h" "$(@D)/cuda/include/thrust/detail/allocator/malloc_allocator.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/malloc_allocator.inl" "$(@D)/cuda/include/thrust/detail/allocator/malloc_allocator.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/no_throw_allocator.h" "$(@D)/cuda/include/thrust/detail/allocator/no_throw_allocator.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/tagged_allocator.h" "$(@D)/cuda/include/thrust/detail/allocator/tagged_allocator.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/tagged_allocator.inl" "$(@D)/cuda/include/thrust/detail/allocator/tagged_allocator.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/temporary_allocator.h" "$(@D)/cuda/include/thrust/detail/allocator/temporary_allocator.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/temporary_allocator.inl" "$(@D)/cuda/include/thrust/detail/allocator/temporary_allocator.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/binary_search.inl" "$(@D)/cuda/include/thrust/detail/binary_search.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/arithmetic.h" "$(@D)/cuda/include/thrust/detail/complex/arithmetic.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/c99math.h" "$(@D)/cuda/include/thrust/detail/complex/c99math.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/catrig.h" "$(@D)/cuda/include/thrust/detail/complex/catrig.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/catrigf.h" "$(@D)/cuda/include/thrust/detail/complex/catrigf.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/ccosh.h" "$(@D)/cuda/include/thrust/detail/complex/ccosh.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/ccoshf.h" "$(@D)/cuda/include/thrust/detail/complex/ccoshf.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/cexp.h" "$(@D)/cuda/include/thrust/detail/complex/cexp.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/cexpf.h" "$(@D)/cuda/include/thrust/detail/complex/cexpf.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/clog.h" "$(@D)/cuda/include/thrust/detail/complex/clog.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/clogf.h" "$(@D)/cuda/include/thrust/detail/complex/clogf.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/complex.inl" "$(@D)/cuda/include/thrust/detail/complex/complex.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/cpow.h" "$(@D)/cuda/include/thrust/detail/complex/cpow.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/cpowf.h" "$(@D)/cuda/include/thrust/detail/complex/cpowf.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/cproj.h" "$(@D)/cuda/include/thrust/detail/complex/cproj.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/csinh.h" "$(@D)/cuda/include/thrust/detail/complex/csinh.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/csinhf.h" "$(@D)/cuda/include/thrust/detail/complex/csinhf.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/csqrt.h" "$(@D)/cuda/include/thrust/detail/complex/csqrt.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/csqrtf.h" "$(@D)/cuda/include/thrust/detail/complex/csqrtf.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/ctanh.h" "$(@D)/cuda/include/thrust/detail/complex/ctanh.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/ctanhf.h" "$(@D)/cuda/include/thrust/detail/complex/ctanhf.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/math_private.h" "$(@D)/cuda/include/thrust/detail/complex/math_private.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/stream.h" "$(@D)/cuda/include/thrust/detail/complex/stream.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/config.h" "$(@D)/cuda/include/thrust/detail/config.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/config/compiler.h" "$(@D)/cuda/include/thrust/detail/config/compiler.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/config/compiler_fence.h" "$(@D)/cuda/include/thrust/detail/config/compiler_fence.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/config/config.h" "$(@D)/cuda/include/thrust/detail/config/config.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/config/debug.h" "$(@D)/cuda/include/thrust/detail/config/debug.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/config/device_system.h" "$(@D)/cuda/include/thrust/detail/config/device_system.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/config/exec_check_disable.h" "$(@D)/cuda/include/thrust/detail/config/exec_check_disable.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/config/forceinline.h" "$(@D)/cuda/include/thrust/detail/config/forceinline.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/config/global_workarounds.h" "$(@D)/cuda/include/thrust/detail/config/global_workarounds.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/config/host_device.h" "$(@D)/cuda/include/thrust/detail/config/host_device.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/config/host_system.h" "$(@D)/cuda/include/thrust/detail/config/host_system.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/config/simple_defines.h" "$(@D)/cuda/include/thrust/detail/config/simple_defines.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/contiguous_storage.h" "$(@D)/cuda/include/thrust/detail/contiguous_storage.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/contiguous_storage.inl" "$(@D)/cuda/include/thrust/detail/contiguous_storage.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/copy.h" "$(@D)/cuda/include/thrust/detail/copy.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/copy.inl" "$(@D)/cuda/include/thrust/detail/copy.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/copy_if.h" "$(@D)/cuda/include/thrust/detail/copy_if.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/copy_if.inl" "$(@D)/cuda/include/thrust/detail/copy_if.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/count.inl" "$(@D)/cuda/include/thrust/detail/count.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/cstdint.h" "$(@D)/cuda/include/thrust/detail/cstdint.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/device_delete.inl" "$(@D)/cuda/include/thrust/detail/device_delete.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/device_free.inl" "$(@D)/cuda/include/thrust/detail/device_free.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/device_malloc.inl" "$(@D)/cuda/include/thrust/detail/device_malloc.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/device_new.inl" "$(@D)/cuda/include/thrust/detail/device_new.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/device_ptr.inl" "$(@D)/cuda/include/thrust/detail/device_ptr.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/device_reference.inl" "$(@D)/cuda/include/thrust/detail/device_reference.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/device_vector.inl" "$(@D)/cuda/include/thrust/detail/device_vector.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/dispatch/is_trivial_copy.h" "$(@D)/cuda/include/thrust/detail/dispatch/is_trivial_copy.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/distance.inl" "$(@D)/cuda/include/thrust/detail/distance.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/equal.inl" "$(@D)/cuda/include/thrust/detail/equal.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/execute_with_allocator.h" "$(@D)/cuda/include/thrust/detail/execute_with_allocator.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/execution_policy.h" "$(@D)/cuda/include/thrust/detail/execution_policy.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/extrema.inl" "$(@D)/cuda/include/thrust/detail/extrema.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/fill.inl" "$(@D)/cuda/include/thrust/detail/fill.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/find.inl" "$(@D)/cuda/include/thrust/detail/find.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/for_each.inl" "$(@D)/cuda/include/thrust/detail/for_each.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/function.h" "$(@D)/cuda/include/thrust/detail/function.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional.inl" "$(@D)/cuda/include/thrust/detail/functional.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional/actor.h" "$(@D)/cuda/include/thrust/detail/functional/actor.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional/actor.inl" "$(@D)/cuda/include/thrust/detail/functional/actor.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional/argument.h" "$(@D)/cuda/include/thrust/detail/functional/argument.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional/composite.h" "$(@D)/cuda/include/thrust/detail/functional/composite.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional/operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional/operators/arithmetic_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/arithmetic_operators.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional/operators/assignment_operator.h" "$(@D)/cuda/include/thrust/detail/functional/operators/assignment_operator.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional/operators/bitwise_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/bitwise_operators.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional/operators/compound_assignment_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/compound_assignment_operators.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional/operators/logical_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/logical_operators.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional/operators/operator_adaptors.h" "$(@D)/cuda/include/thrust/detail/functional/operators/operator_adaptors.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional/operators/relational_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/relational_operators.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional/placeholder.h" "$(@D)/cuda/include/thrust/detail/functional/placeholder.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional/value.h" "$(@D)/cuda/include/thrust/detail/functional/value.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/gather.inl" "$(@D)/cuda/include/thrust/detail/gather.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/generate.inl" "$(@D)/cuda/include/thrust/detail/generate.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/get_iterator_value.h" "$(@D)/cuda/include/thrust/detail/get_iterator_value.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/host_vector.inl" "$(@D)/cuda/include/thrust/detail/host_vector.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/inner_product.inl" "$(@D)/cuda/include/thrust/detail/inner_product.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/integer_math.h" "$(@D)/cuda/include/thrust/detail/integer_math.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/integer_traits.h" "$(@D)/cuda/include/thrust/detail/integer_traits.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/internal_functional.h" "$(@D)/cuda/include/thrust/detail/internal_functional.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/logical.inl" "$(@D)/cuda/include/thrust/detail/logical.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/detail/malloc_and_free.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/merge.inl" "$(@D)/cuda/include/thrust/detail/merge.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/minmax.h" "$(@D)/cuda/include/thrust/detail/minmax.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/mismatch.inl" "$(@D)/cuda/include/thrust/detail/mismatch.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/mpl/math.h" "$(@D)/cuda/include/thrust/detail/mpl/math.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/numeric_traits.h" "$(@D)/cuda/include/thrust/detail/numeric_traits.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/overlapped_copy.h" "$(@D)/cuda/include/thrust/detail/overlapped_copy.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/pair.inl" "$(@D)/cuda/include/thrust/detail/pair.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/partition.inl" "$(@D)/cuda/include/thrust/detail/partition.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/pointer.h" "$(@D)/cuda/include/thrust/detail/pointer.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/pointer.inl" "$(@D)/cuda/include/thrust/detail/pointer.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/range/head_flags.h" "$(@D)/cuda/include/thrust/detail/range/head_flags.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/range/tail_flags.h" "$(@D)/cuda/include/thrust/detail/range/tail_flags.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/raw_pointer_cast.h" 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"/usr/local/cuda-9.0/include/thrust/detail/scan.inl" "$(@D)/cuda/include/thrust/detail/scan.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/scatter.inl" "$(@D)/cuda/include/thrust/detail/scatter.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/seq.h" "$(@D)/cuda/include/thrust/detail/seq.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/sequence.inl" "$(@D)/cuda/include/thrust/detail/sequence.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/set_operations.inl" "$(@D)/cuda/include/thrust/detail/set_operations.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/sort.inl" "$(@D)/cuda/include/thrust/detail/sort.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/static_assert.h" "$(@D)/cuda/include/thrust/detail/static_assert.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/static_map.h" "$(@D)/cuda/include/thrust/detail/static_map.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/swap.h" "$(@D)/cuda/include/thrust/detail/swap.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/swap.inl" "$(@D)/cuda/include/thrust/detail/swap.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/swap_ranges.inl" "$(@D)/cuda/include/thrust/detail/swap_ranges.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/tabulate.inl" "$(@D)/cuda/include/thrust/detail/tabulate.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/temporary_array.h" "$(@D)/cuda/include/thrust/detail/temporary_array.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/temporary_array.inl" "$(@D)/cuda/include/thrust/detail/temporary_array.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/detail/temporary_buffer.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/transform.inl" "$(@D)/cuda/include/thrust/detail/transform.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/transform_reduce.inl" "$(@D)/cuda/include/thrust/detail/transform_reduce.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/transform_scan.inl" "$(@D)/cuda/include/thrust/detail/transform_scan.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/trivial_sequence.h" "$(@D)/cuda/include/thrust/detail/trivial_sequence.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/tuple.inl" "$(@D)/cuda/include/thrust/detail/tuple.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/tuple_meta_transform.h" "$(@D)/cuda/include/thrust/detail/tuple_meta_transform.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/tuple_transform.h" "$(@D)/cuda/include/thrust/detail/tuple_transform.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/type_traits.h" "$(@D)/cuda/include/thrust/detail/type_traits.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/type_traits/algorithm/intermediate_type_from_function_and_iterators.h" "$(@D)/cuda/include/thrust/detail/type_traits/algorithm/intermediate_type_from_function_and_iterators.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/type_traits/function_traits.h" "$(@D)/cuda/include/thrust/detail/type_traits/function_traits.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/type_traits/has_member_function.h" "$(@D)/cuda/include/thrust/detail/type_traits/has_member_function.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/type_traits/has_nested_type.h" "$(@D)/cuda/include/thrust/detail/type_traits/has_nested_type.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/type_traits/has_trivial_assign.h" "$(@D)/cuda/include/thrust/detail/type_traits/has_trivial_assign.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/type_traits/is_call_possible.h" "$(@D)/cuda/include/thrust/detail/type_traits/is_call_possible.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/type_traits/is_metafunction_defined.h" "$(@D)/cuda/include/thrust/detail/type_traits/is_metafunction_defined.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/type_traits/iterator/is_discard_iterator.h" "$(@D)/cuda/include/thrust/detail/type_traits/iterator/is_discard_iterator.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/type_traits/iterator/is_output_iterator.h" "$(@D)/cuda/include/thrust/detail/type_traits/iterator/is_output_iterator.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/type_traits/minimum_type.h" "$(@D)/cuda/include/thrust/detail/type_traits/minimum_type.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/type_traits/pointer_traits.h" "$(@D)/cuda/include/thrust/detail/type_traits/pointer_traits.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/type_traits/result_of_adaptable_function.h" "$(@D)/cuda/include/thrust/detail/type_traits/result_of_adaptable_function.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/uninitialized_copy.inl" "$(@D)/cuda/include/thrust/detail/uninitialized_copy.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/uninitialized_fill.inl" "$(@D)/cuda/include/thrust/detail/uninitialized_fill.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/unique.inl" "$(@D)/cuda/include/thrust/detail/unique.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/use_default.h" "$(@D)/cuda/include/thrust/detail/use_default.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/util/align.h" "$(@D)/cuda/include/thrust/detail/util/align.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/util/blocking.h" "$(@D)/cuda/include/thrust/detail/util/blocking.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/vector_base.h" "$(@D)/cuda/include/thrust/detail/vector_base.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/vector_base.inl" "$(@D)/cuda/include/thrust/detail/vector_base.inl" && cp "/usr/local/cuda-9.0/include/thrust/device_allocator.h" "$(@D)/cuda/include/thrust/device_allocator.h" && cp "/usr/local/cuda-9.0/include/thrust/device_delete.h" "$(@D)/cuda/include/thrust/device_delete.h" && cp "/usr/local/cuda-9.0/include/thrust/device_free.h" "$(@D)/cuda/include/thrust/device_free.h" && cp "/usr/local/cuda-9.0/include/thrust/device_malloc.h" "$(@D)/cuda/include/thrust/device_malloc.h" && cp "/usr/local/cuda-9.0/include/thrust/device_malloc_allocator.h" "$(@D)/cuda/include/thrust/device_malloc_allocator.h" && cp "/usr/local/cuda-9.0/include/thrust/device_new.h" "$(@D)/cuda/include/thrust/device_new.h" && cp "/usr/local/cuda-9.0/include/thrust/device_new_allocator.h" "$(@D)/cuda/include/thrust/device_new_allocator.h" && cp "/usr/local/cuda-9.0/include/thrust/device_ptr.h" "$(@D)/cuda/include/thrust/device_ptr.h" && cp "/usr/local/cuda-9.0/include/thrust/device_reference.h" "$(@D)/cuda/include/thrust/device_reference.h" && cp "/usr/local/cuda-9.0/include/thrust/device_vector.h" "$(@D)/cuda/include/thrust/device_vector.h" && cp "/usr/local/cuda-9.0/include/thrust/distance.h" "$(@D)/cuda/include/thrust/distance.h" && cp "/usr/local/cuda-9.0/include/thrust/equal.h" "$(@D)/cuda/include/thrust/equal.h" && cp "/usr/local/cuda-9.0/include/thrust/execution_policy.h" "$(@D)/cuda/include/thrust/execution_policy.h" && cp "/usr/local/cuda-9.0/include/thrust/extrema.h" "$(@D)/cuda/include/thrust/extrema.h" && cp "/usr/local/cuda-9.0/include/thrust/fill.h" "$(@D)/cuda/include/thrust/fill.h" && cp "/usr/local/cuda-9.0/include/thrust/find.h" "$(@D)/cuda/include/thrust/find.h" && cp "/usr/local/cuda-9.0/include/thrust/for_each.h" "$(@D)/cuda/include/thrust/for_each.h" && cp "/usr/local/cuda-9.0/include/thrust/functional.h" "$(@D)/cuda/include/thrust/functional.h" && cp "/usr/local/cuda-9.0/include/thrust/gather.h" "$(@D)/cuda/include/thrust/gather.h" && cp "/usr/local/cuda-9.0/include/thrust/generate.h" "$(@D)/cuda/include/thrust/generate.h" && cp "/usr/local/cuda-9.0/include/thrust/host_vector.h" "$(@D)/cuda/include/thrust/host_vector.h" && cp "/usr/local/cuda-9.0/include/thrust/inner_product.h" "$(@D)/cuda/include/thrust/inner_product.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/constant_iterator.h" "$(@D)/cuda/include/thrust/iterator/constant_iterator.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/counting_iterator.h" "$(@D)/cuda/include/thrust/iterator/counting_iterator.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/any_assign.h" "$(@D)/cuda/include/thrust/iterator/detail/any_assign.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/any_system_tag.h" "$(@D)/cuda/include/thrust/iterator/detail/any_system_tag.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/constant_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/constant_iterator_base.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/counting_iterator.inl" "$(@D)/cuda/include/thrust/iterator/detail/counting_iterator.inl" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/device_system_tag.h" "$(@D)/cuda/include/thrust/iterator/detail/device_system_tag.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/discard_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/discard_iterator_base.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/distance_from_result.h" "$(@D)/cuda/include/thrust/iterator/detail/distance_from_result.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/host_system_tag.h" "$(@D)/cuda/include/thrust/iterator/detail/host_system_tag.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/is_iterator_category.h" "$(@D)/cuda/include/thrust/iterator/detail/is_iterator_category.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/is_trivial_iterator.h" "$(@D)/cuda/include/thrust/iterator/detail/is_trivial_iterator.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/iterator_adaptor_base.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_adaptor_base.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/iterator_category_to_system.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_category_to_system.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/iterator_category_to_traversal.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_category_to_traversal.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/iterator_category_with_system_and_traversal.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_category_with_system_and_traversal.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/iterator_facade_category.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_facade_category.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/iterator_traits.inl" "$(@D)/cuda/include/thrust/iterator/detail/iterator_traits.inl" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/iterator_traversal_tags.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_traversal_tags.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/join_iterator.h" "$(@D)/cuda/include/thrust/iterator/detail/join_iterator.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/minimum_category.h" "$(@D)/cuda/include/thrust/iterator/detail/minimum_category.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/minimum_system.h" "$(@D)/cuda/include/thrust/iterator/detail/minimum_system.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/normal_iterator.h" "$(@D)/cuda/include/thrust/iterator/detail/normal_iterator.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/permutation_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/permutation_iterator_base.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/retag.h" "$(@D)/cuda/include/thrust/iterator/detail/retag.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/reverse_iterator.inl" "$(@D)/cuda/include/thrust/iterator/detail/reverse_iterator.inl" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/reverse_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/reverse_iterator_base.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/tagged_iterator.h" "$(@D)/cuda/include/thrust/iterator/detail/tagged_iterator.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/transform_iterator.inl" "$(@D)/cuda/include/thrust/iterator/detail/transform_iterator.inl" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/transform_output_iterator.inl" "$(@D)/cuda/include/thrust/iterator/detail/transform_output_iterator.inl" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/tuple_of_iterator_references.h" "$(@D)/cuda/include/thrust/iterator/detail/tuple_of_iterator_references.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/universal_categories.h" "$(@D)/cuda/include/thrust/iterator/detail/universal_categories.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/zip_iterator.inl" "$(@D)/cuda/include/thrust/iterator/detail/zip_iterator.inl" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/zip_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/zip_iterator_base.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/discard_iterator.h" "$(@D)/cuda/include/thrust/iterator/discard_iterator.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/iterator_adaptor.h" "$(@D)/cuda/include/thrust/iterator/iterator_adaptor.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/iterator_categories.h" "$(@D)/cuda/include/thrust/iterator/iterator_categories.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/iterator_facade.h" "$(@D)/cuda/include/thrust/iterator/iterator_facade.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/iterator_traits.h" "$(@D)/cuda/include/thrust/iterator/iterator_traits.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/permutation_iterator.h" "$(@D)/cuda/include/thrust/iterator/permutation_iterator.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/retag.h" "$(@D)/cuda/include/thrust/iterator/retag.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/reverse_iterator.h" "$(@D)/cuda/include/thrust/iterator/reverse_iterator.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/transform_iterator.h" "$(@D)/cuda/include/thrust/iterator/transform_iterator.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/transform_output_iterator.h" "$(@D)/cuda/include/thrust/iterator/transform_output_iterator.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/zip_iterator.h" "$(@D)/cuda/include/thrust/iterator/zip_iterator.h" && cp "/usr/local/cuda-9.0/include/thrust/logical.h" "$(@D)/cuda/include/thrust/logical.h" && cp "/usr/local/cuda-9.0/include/thrust/memory.h" "$(@D)/cuda/include/thrust/memory.h" && cp "/usr/local/cuda-9.0/include/thrust/merge.h" "$(@D)/cuda/include/thrust/merge.h" && cp "/usr/local/cuda-9.0/include/thrust/mismatch.h" "$(@D)/cuda/include/thrust/mismatch.h" && cp "/usr/local/cuda-9.0/include/thrust/pair.h" "$(@D)/cuda/include/thrust/pair.h" && cp "/usr/local/cuda-9.0/include/thrust/partition.h" "$(@D)/cuda/include/thrust/partition.h" && cp "/usr/local/cuda-9.0/include/thrust/random.h" "$(@D)/cuda/include/thrust/random.h" && cp "/usr/local/cuda-9.0/include/thrust/random/detail/discard_block_engine.inl" "$(@D)/cuda/include/thrust/random/detail/discard_block_engine.inl" && cp "/usr/local/cuda-9.0/include/thrust/random/detail/linear_congruential_engine.inl" "$(@D)/cuda/include/thrust/random/detail/linear_congruential_engine.inl" && cp "/usr/local/cuda-9.0/include/thrust/random/detail/linear_congruential_engine_discard.h" "$(@D)/cuda/include/thrust/random/detail/linear_congruential_engine_discard.h" && cp "/usr/local/cuda-9.0/include/thrust/random/detail/linear_feedback_shift_engine.inl" "$(@D)/cuda/include/thrust/random/detail/linear_feedback_shift_engine.inl" && cp "/usr/local/cuda-9.0/include/thrust/random/detail/linear_feedback_shift_engine_wordmask.h" "$(@D)/cuda/include/thrust/random/detail/linear_feedback_shift_engine_wordmask.h" && cp "/usr/local/cuda-9.0/include/thrust/random/detail/mod.h" "$(@D)/cuda/include/thrust/random/detail/mod.h" && cp "/usr/local/cuda-9.0/include/thrust/random/detail/normal_distribution.inl" "$(@D)/cuda/include/thrust/random/detail/normal_distribution.inl" && cp "/usr/local/cuda-9.0/include/thrust/random/detail/normal_distribution_base.h" "$(@D)/cuda/include/thrust/random/detail/normal_distribution_base.h" && cp "/usr/local/cuda-9.0/include/thrust/random/detail/random_core_access.h" "$(@D)/cuda/include/thrust/random/detail/random_core_access.h" && cp "/usr/local/cuda-9.0/include/thrust/random/detail/subtract_with_carry_engine.inl" "$(@D)/cuda/include/thrust/random/detail/subtract_with_carry_engine.inl" && cp "/usr/local/cuda-9.0/include/thrust/random/detail/uniform_int_distribution.inl" "$(@D)/cuda/include/thrust/random/detail/uniform_int_distribution.inl" && cp "/usr/local/cuda-9.0/include/thrust/random/detail/uniform_real_distribution.inl" "$(@D)/cuda/include/thrust/random/detail/uniform_real_distribution.inl" && cp "/usr/local/cuda-9.0/include/thrust/random/detail/xor_combine_engine.inl" "$(@D)/cuda/include/thrust/random/detail/xor_combine_engine.inl" && cp "/usr/local/cuda-9.0/include/thrust/random/detail/xor_combine_engine_max.h" "$(@D)/cuda/include/thrust/random/detail/xor_combine_engine_max.h" && cp "/usr/local/cuda-9.0/include/thrust/random/discard_block_engine.h" "$(@D)/cuda/include/thrust/random/discard_block_engine.h" && cp "/usr/local/cuda-9.0/include/thrust/random/linear_congruential_engine.h" "$(@D)/cuda/include/thrust/random/linear_congruential_engine.h" && cp "/usr/local/cuda-9.0/include/thrust/random/linear_feedback_shift_engine.h" "$(@D)/cuda/include/thrust/random/linear_feedback_shift_engine.h" && cp "/usr/local/cuda-9.0/include/thrust/random/normal_distribution.h" "$(@D)/cuda/include/thrust/random/normal_distribution.h" && cp "/usr/local/cuda-9.0/include/thrust/random/subtract_with_carry_engine.h" "$(@D)/cuda/include/thrust/random/subtract_with_carry_engine.h" && cp "/usr/local/cuda-9.0/include/thrust/random/uniform_int_distribution.h" "$(@D)/cuda/include/thrust/random/uniform_int_distribution.h" && cp "/usr/local/cuda-9.0/include/thrust/random/uniform_real_distribution.h" "$(@D)/cuda/include/thrust/random/uniform_real_distribution.h" && cp "/usr/local/cuda-9.0/include/thrust/random/xor_combine_engine.h" "$(@D)/cuda/include/thrust/random/xor_combine_engine.h" && cp "/usr/local/cuda-9.0/include/thrust/reduce.h" "$(@D)/cuda/include/thrust/reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/remove.h" "$(@D)/cuda/include/thrust/remove.h" && cp "/usr/local/cuda-9.0/include/thrust/replace.h" "$(@D)/cuda/include/thrust/replace.h" && cp "/usr/local/cuda-9.0/include/thrust/reverse.h" "$(@D)/cuda/include/thrust/reverse.h" && cp "/usr/local/cuda-9.0/include/thrust/scan.h" "$(@D)/cuda/include/thrust/scan.h" && cp "/usr/local/cuda-9.0/include/thrust/scatter.h" "$(@D)/cuda/include/thrust/scatter.h" && cp "/usr/local/cuda-9.0/include/thrust/sequence.h" "$(@D)/cuda/include/thrust/sequence.h" && cp "/usr/local/cuda-9.0/include/thrust/set_operations.h" "$(@D)/cuda/include/thrust/set_operations.h" && cp "/usr/local/cuda-9.0/include/thrust/sort.h" "$(@D)/cuda/include/thrust/sort.h" && cp "/usr/local/cuda-9.0/include/thrust/swap.h" "$(@D)/cuda/include/thrust/swap.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/cpp/detail/adjacent_difference.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/assign_value.h" "$(@D)/cuda/include/thrust/system/cpp/detail/assign_value.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/binary_search.h" "$(@D)/cuda/include/thrust/system/cpp/detail/binary_search.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/copy.h" "$(@D)/cuda/include/thrust/system/cpp/detail/copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/copy_if.h" "$(@D)/cuda/include/thrust/system/cpp/detail/copy_if.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/count.h" "$(@D)/cuda/include/thrust/system/cpp/detail/count.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/equal.h" "$(@D)/cuda/include/thrust/system/cpp/detail/equal.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/execution_policy.h" "$(@D)/cuda/include/thrust/system/cpp/detail/execution_policy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/extrema.h" "$(@D)/cuda/include/thrust/system/cpp/detail/extrema.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/fill.h" "$(@D)/cuda/include/thrust/system/cpp/detail/fill.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/find.h" "$(@D)/cuda/include/thrust/system/cpp/detail/find.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/for_each.h" "$(@D)/cuda/include/thrust/system/cpp/detail/for_each.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/gather.h" "$(@D)/cuda/include/thrust/system/cpp/detail/gather.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/generate.h" "$(@D)/cuda/include/thrust/system/cpp/detail/generate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/get_value.h" "$(@D)/cuda/include/thrust/system/cpp/detail/get_value.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/inner_product.h" "$(@D)/cuda/include/thrust/system/cpp/detail/inner_product.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/iter_swap.h" "$(@D)/cuda/include/thrust/system/cpp/detail/iter_swap.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/logical.h" "$(@D)/cuda/include/thrust/system/cpp/detail/logical.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/cpp/detail/malloc_and_free.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/memory.inl" "$(@D)/cuda/include/thrust/system/cpp/detail/memory.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/merge.h" "$(@D)/cuda/include/thrust/system/cpp/detail/merge.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/mismatch.h" "$(@D)/cuda/include/thrust/system/cpp/detail/mismatch.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/par.h" "$(@D)/cuda/include/thrust/system/cpp/detail/par.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/partition.h" "$(@D)/cuda/include/thrust/system/cpp/detail/partition.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/reduce.h" "$(@D)/cuda/include/thrust/system/cpp/detail/reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/cpp/detail/reduce_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/remove.h" "$(@D)/cuda/include/thrust/system/cpp/detail/remove.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/replace.h" "$(@D)/cuda/include/thrust/system/cpp/detail/replace.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/reverse.h" "$(@D)/cuda/include/thrust/system/cpp/detail/reverse.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/scan.h" "$(@D)/cuda/include/thrust/system/cpp/detail/scan.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/scan_by_key.h" "$(@D)/cuda/include/thrust/system/cpp/detail/scan_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/scatter.h" "$(@D)/cuda/include/thrust/system/cpp/detail/scatter.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/sequence.h" "$(@D)/cuda/include/thrust/system/cpp/detail/sequence.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/set_operations.h" "$(@D)/cuda/include/thrust/system/cpp/detail/set_operations.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/sort.h" "$(@D)/cuda/include/thrust/system/cpp/detail/sort.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/swap_ranges.h" "$(@D)/cuda/include/thrust/system/cpp/detail/swap_ranges.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/tabulate.h" "$(@D)/cuda/include/thrust/system/cpp/detail/tabulate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/cpp/detail/temporary_buffer.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/transform.h" "$(@D)/cuda/include/thrust/system/cpp/detail/transform.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/transform_reduce.h" "$(@D)/cuda/include/thrust/system/cpp/detail/transform_reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/transform_scan.h" "$(@D)/cuda/include/thrust/system/cpp/detail/transform_scan.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/cpp/detail/uninitialized_copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/cpp/detail/uninitialized_fill.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/unique.h" "$(@D)/cuda/include/thrust/system/cpp/detail/unique.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/unique_by_key.h" "$(@D)/cuda/include/thrust/system/cpp/detail/unique_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/vector.inl" "$(@D)/cuda/include/thrust/system/cpp/detail/vector.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/execution_policy.h" "$(@D)/cuda/include/thrust/system/cpp/execution_policy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/memory.h" "$(@D)/cuda/include/thrust/system/cpp/memory.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/vector.h" "$(@D)/cuda/include/thrust/system/cpp/vector.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/config.h" "$(@D)/cuda/include/thrust/system/cuda/config.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/cuda/detail/adjacent_difference.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/assign_value.h" "$(@D)/cuda/include/thrust/system/cuda/detail/assign_value.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/binary_search.h" "$(@D)/cuda/include/thrust/system/cuda/detail/binary_search.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/copy.h" "$(@D)/cuda/include/thrust/system/cuda/detail/copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/copy_if.h" "$(@D)/cuda/include/thrust/system/cuda/detail/copy_if.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/core/agent_launcher.h" "$(@D)/cuda/include/thrust/system/cuda/detail/core/agent_launcher.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/core/alignment.h" "$(@D)/cuda/include/thrust/system/cuda/detail/core/alignment.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/core/triple_chevron_launch.h" "$(@D)/cuda/include/thrust/system/cuda/detail/core/triple_chevron_launch.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/core/util.h" "$(@D)/cuda/include/thrust/system/cuda/detail/core/util.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/count.h" "$(@D)/cuda/include/thrust/system/cuda/detail/count.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cross_system.h" "$(@D)/cuda/include/thrust/system/cuda/detail/cross_system.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/agent/agent_histogram.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/agent/agent_histogram.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/agent/agent_radix_sort_downsweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/agent/agent_radix_sort_downsweep.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/agent/agent_radix_sort_upsweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/agent/agent_radix_sort_upsweep.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/agent/agent_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/agent/agent_reduce.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/agent/agent_reduce_by_key.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/agent/agent_reduce_by_key.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/agent/agent_rle.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/agent/agent_rle.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/agent/agent_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/agent/agent_scan.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/agent/agent_segment_fixup.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/agent/agent_segment_fixup.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/agent/agent_select_if.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/agent/agent_select_if.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/agent/agent_spmv_csrt.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/agent/agent_spmv_csrt.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/agent/agent_spmv_orig.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/agent/agent_spmv_orig.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/agent/agent_spmv_row_based.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/agent/agent_spmv_row_based.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/agent/single_pass_scan_operators.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/agent/single_pass_scan_operators.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/block_adjacent_difference.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_adjacent_difference.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/block_discontinuity.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_discontinuity.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/block_exchange.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_exchange.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/block_histogram.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_histogram.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/block_load.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_load.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/block_radix_rank.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_radix_rank.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/block_radix_sort.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_radix_sort.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/block_raking_layout.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_raking_layout.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/block_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_reduce.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/block_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_scan.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/block_shuffle.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_shuffle.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/block_store.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_store.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_atomic.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_atomic.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_sort.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_sort.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking_commutative_only.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking_commutative_only.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_warp_reductions.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_warp_reductions.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_raking.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_raking.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans2.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans2.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans3.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans3.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/cub.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/cub.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/device_histogram.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_histogram.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/device_partition.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_partition.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/device_radix_sort.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_radix_sort.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/device_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_reduce.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/device_run_length_encode.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_run_length_encode.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/device_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_scan.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/device_segmented_radix_sort.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_segmented_radix_sort.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/device_segmented_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_segmented_reduce.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/device_select.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_select.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/device_spmv.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_spmv.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_histogram.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_histogram.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_radix_sort.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_radix_sort.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_reduce.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_reduce_by_key.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_reduce_by_key.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_rle.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_rle.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_scan.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_select_if.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_select_if.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_spmv_csrt.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_spmv_csrt.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_spmv_orig.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_spmv_orig.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_spmv_row_based.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_spmv_row_based.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/grid/grid_barrier.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/grid/grid_barrier.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/grid/grid_even_share.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/grid/grid_even_share.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/grid/grid_mapping.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/grid/grid_mapping.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/grid/grid_queue.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/grid/grid_queue.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/host/mutex.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/host/mutex.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/iterator/arg_index_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/arg_index_input_iterator.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/iterator/cache_modified_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/cache_modified_input_iterator.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/iterator/cache_modified_output_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/cache_modified_output_iterator.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/iterator/constant_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/constant_input_iterator.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/iterator/counting_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/counting_input_iterator.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/iterator/discard_output_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/discard_output_iterator.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/iterator/tex_obj_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/tex_obj_input_iterator.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/iterator/tex_ref_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/tex_ref_input_iterator.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/iterator/transform_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/transform_input_iterator.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/thread/thread_load.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_load.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/thread/thread_operators.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_operators.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/thread/thread_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_reduce.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/thread/thread_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_scan.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/thread/thread_search.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_search.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/thread/thread_store.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_store.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/util_allocator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_allocator.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/util_arch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_arch.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/util_debug.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_debug.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/util_device.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_device.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/util_macro.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_macro.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/util_namespace.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_namespace.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/util_ptx.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_ptx.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/util_type.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_type.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_shfl.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_shfl.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_smem.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_smem.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_shfl.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_shfl.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_smem.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_smem.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/warp/warp_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/warp_reduce.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/warp/warp_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/warp_scan.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/equal.h" "$(@D)/cuda/include/thrust/system/cuda/detail/equal.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/error.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/error.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/execution_policy.h" "$(@D)/cuda/include/thrust/system/cuda/detail/execution_policy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/extrema.h" "$(@D)/cuda/include/thrust/system/cuda/detail/extrema.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/fill.h" "$(@D)/cuda/include/thrust/system/cuda/detail/fill.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/find.h" "$(@D)/cuda/include/thrust/system/cuda/detail/find.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/for_each.h" "$(@D)/cuda/include/thrust/system/cuda/detail/for_each.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/gather.h" "$(@D)/cuda/include/thrust/system/cuda/detail/gather.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/generate.h" "$(@D)/cuda/include/thrust/system/cuda/detail/generate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/get_value.h" "$(@D)/cuda/include/thrust/system/cuda/detail/get_value.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/guarded_cuda_runtime_api.h" "$(@D)/cuda/include/thrust/system/cuda/detail/guarded_cuda_runtime_api.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/guarded_driver_types.h" "$(@D)/cuda/include/thrust/system/cuda/detail/guarded_driver_types.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/inner_product.h" "$(@D)/cuda/include/thrust/system/cuda/detail/inner_product.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/internal/copy_cross_system.h" "$(@D)/cuda/include/thrust/system/cuda/detail/internal/copy_cross_system.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/internal/copy_device_to_device.h" "$(@D)/cuda/include/thrust/system/cuda/detail/internal/copy_device_to_device.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/iter_swap.h" "$(@D)/cuda/include/thrust/system/cuda/detail/iter_swap.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/logical.h" "$(@D)/cuda/include/thrust/system/cuda/detail/logical.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/cuda/detail/malloc_and_free.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/memory.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/memory.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/memory_buffer.h" "$(@D)/cuda/include/thrust/system/cuda/detail/memory_buffer.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/merge.h" "$(@D)/cuda/include/thrust/system/cuda/detail/merge.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/mismatch.h" "$(@D)/cuda/include/thrust/system/cuda/detail/mismatch.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/par.h" "$(@D)/cuda/include/thrust/system/cuda/detail/par.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/par_to_seq.h" "$(@D)/cuda/include/thrust/system/cuda/detail/par_to_seq.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/parallel_for.h" "$(@D)/cuda/include/thrust/system/cuda/detail/parallel_for.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/partition.h" "$(@D)/cuda/include/thrust/system/cuda/detail/partition.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/reduce.h" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/remove.h" "$(@D)/cuda/include/thrust/system/cuda/detail/remove.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/replace.h" "$(@D)/cuda/include/thrust/system/cuda/detail/replace.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/reverse.h" "$(@D)/cuda/include/thrust/system/cuda/detail/reverse.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/scan.h" "$(@D)/cuda/include/thrust/system/cuda/detail/scan.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/scan_by_key.h" "$(@D)/cuda/include/thrust/system/cuda/detail/scan_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/scatter.h" "$(@D)/cuda/include/thrust/system/cuda/detail/scatter.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/sequence.h" "$(@D)/cuda/include/thrust/system/cuda/detail/sequence.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/set_operations.h" "$(@D)/cuda/include/thrust/system/cuda/detail/set_operations.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/sort.h" "$(@D)/cuda/include/thrust/system/cuda/detail/sort.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/swap_ranges.h" "$(@D)/cuda/include/thrust/system/cuda/detail/swap_ranges.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/tabulate.h" "$(@D)/cuda/include/thrust/system/cuda/detail/tabulate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/cuda/detail/temporary_buffer.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/terminate.h" "$(@D)/cuda/include/thrust/system/cuda/detail/terminate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/transform.h" "$(@D)/cuda/include/thrust/system/cuda/detail/transform.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/transform_reduce.h" "$(@D)/cuda/include/thrust/system/cuda/detail/transform_reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/transform_scan.h" "$(@D)/cuda/include/thrust/system/cuda/detail/transform_scan.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/cuda/detail/uninitialized_copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/cuda/detail/uninitialized_fill.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/unique.h" "$(@D)/cuda/include/thrust/system/cuda/detail/unique.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/unique_by_key.h" "$(@D)/cuda/include/thrust/system/cuda/detail/unique_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/util.h" "$(@D)/cuda/include/thrust/system/cuda/detail/util.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/vector.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/vector.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/error.h" "$(@D)/cuda/include/thrust/system/cuda/error.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/execution_policy.h" "$(@D)/cuda/include/thrust/system/cuda/execution_policy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/experimental/pinned_allocator.h" "$(@D)/cuda/include/thrust/system/cuda/experimental/pinned_allocator.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/memory.h" "$(@D)/cuda/include/thrust/system/cuda/memory.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/vector.h" "$(@D)/cuda/include/thrust/system/cuda/vector.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/detail/adl/adjacent_difference.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/assign_value.h" "$(@D)/cuda/include/thrust/system/detail/adl/assign_value.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/binary_search.h" "$(@D)/cuda/include/thrust/system/detail/adl/binary_search.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/copy.h" "$(@D)/cuda/include/thrust/system/detail/adl/copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/copy_if.h" "$(@D)/cuda/include/thrust/system/detail/adl/copy_if.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/count.h" "$(@D)/cuda/include/thrust/system/detail/adl/count.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/equal.h" "$(@D)/cuda/include/thrust/system/detail/adl/equal.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/extrema.h" "$(@D)/cuda/include/thrust/system/detail/adl/extrema.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/fill.h" "$(@D)/cuda/include/thrust/system/detail/adl/fill.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/find.h" "$(@D)/cuda/include/thrust/system/detail/adl/find.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/for_each.h" "$(@D)/cuda/include/thrust/system/detail/adl/for_each.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/gather.h" "$(@D)/cuda/include/thrust/system/detail/adl/gather.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/generate.h" "$(@D)/cuda/include/thrust/system/detail/adl/generate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/get_value.h" "$(@D)/cuda/include/thrust/system/detail/adl/get_value.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/inner_product.h" "$(@D)/cuda/include/thrust/system/detail/adl/inner_product.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/iter_swap.h" "$(@D)/cuda/include/thrust/system/detail/adl/iter_swap.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/logical.h" "$(@D)/cuda/include/thrust/system/detail/adl/logical.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/detail/adl/malloc_and_free.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/merge.h" "$(@D)/cuda/include/thrust/system/detail/adl/merge.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/mismatch.h" "$(@D)/cuda/include/thrust/system/detail/adl/mismatch.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/partition.h" "$(@D)/cuda/include/thrust/system/detail/adl/partition.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/reduce.h" "$(@D)/cuda/include/thrust/system/detail/adl/reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/detail/adl/reduce_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/remove.h" "$(@D)/cuda/include/thrust/system/detail/adl/remove.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/replace.h" "$(@D)/cuda/include/thrust/system/detail/adl/replace.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/reverse.h" "$(@D)/cuda/include/thrust/system/detail/adl/reverse.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/scan.h" "$(@D)/cuda/include/thrust/system/detail/adl/scan.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/scan_by_key.h" "$(@D)/cuda/include/thrust/system/detail/adl/scan_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/scatter.h" "$(@D)/cuda/include/thrust/system/detail/adl/scatter.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/sequence.h" "$(@D)/cuda/include/thrust/system/detail/adl/sequence.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/set_operations.h" "$(@D)/cuda/include/thrust/system/detail/adl/set_operations.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/sort.h" "$(@D)/cuda/include/thrust/system/detail/adl/sort.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/swap_ranges.h" "$(@D)/cuda/include/thrust/system/detail/adl/swap_ranges.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/tabulate.h" "$(@D)/cuda/include/thrust/system/detail/adl/tabulate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/detail/adl/temporary_buffer.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/transform.h" "$(@D)/cuda/include/thrust/system/detail/adl/transform.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/transform_reduce.h" "$(@D)/cuda/include/thrust/system/detail/adl/transform_reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/transform_scan.h" "$(@D)/cuda/include/thrust/system/detail/adl/transform_scan.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/detail/adl/uninitialized_copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/detail/adl/uninitialized_fill.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/unique.h" "$(@D)/cuda/include/thrust/system/detail/adl/unique.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/unique_by_key.h" "$(@D)/cuda/include/thrust/system/detail/adl/unique_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/bad_alloc.h" "$(@D)/cuda/include/thrust/system/detail/bad_alloc.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/errno.h" "$(@D)/cuda/include/thrust/system/detail/errno.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/error_category.inl" "$(@D)/cuda/include/thrust/system/detail/error_category.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/error_code.inl" "$(@D)/cuda/include/thrust/system/detail/error_code.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/error_condition.inl" "$(@D)/cuda/include/thrust/system/detail/error_condition.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/detail/generic/adjacent_difference.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/adjacent_difference.inl" "$(@D)/cuda/include/thrust/system/detail/generic/adjacent_difference.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/advance.h" "$(@D)/cuda/include/thrust/system/detail/generic/advance.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/advance.inl" "$(@D)/cuda/include/thrust/system/detail/generic/advance.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/binary_search.h" "$(@D)/cuda/include/thrust/system/detail/generic/binary_search.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/binary_search.inl" "$(@D)/cuda/include/thrust/system/detail/generic/binary_search.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/copy.h" "$(@D)/cuda/include/thrust/system/detail/generic/copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/copy.inl" "$(@D)/cuda/include/thrust/system/detail/generic/copy.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/copy_if.h" "$(@D)/cuda/include/thrust/system/detail/generic/copy_if.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/copy_if.inl" "$(@D)/cuda/include/thrust/system/detail/generic/copy_if.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/count.h" "$(@D)/cuda/include/thrust/system/detail/generic/count.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/count.inl" "$(@D)/cuda/include/thrust/system/detail/generic/count.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/distance.h" "$(@D)/cuda/include/thrust/system/detail/generic/distance.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/distance.inl" "$(@D)/cuda/include/thrust/system/detail/generic/distance.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/equal.h" "$(@D)/cuda/include/thrust/system/detail/generic/equal.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/equal.inl" "$(@D)/cuda/include/thrust/system/detail/generic/equal.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/extrema.h" "$(@D)/cuda/include/thrust/system/detail/generic/extrema.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/extrema.inl" "$(@D)/cuda/include/thrust/system/detail/generic/extrema.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/fill.h" "$(@D)/cuda/include/thrust/system/detail/generic/fill.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/find.h" "$(@D)/cuda/include/thrust/system/detail/generic/find.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/find.inl" "$(@D)/cuda/include/thrust/system/detail/generic/find.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/for_each.h" "$(@D)/cuda/include/thrust/system/detail/generic/for_each.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/gather.h" "$(@D)/cuda/include/thrust/system/detail/generic/gather.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/gather.inl" "$(@D)/cuda/include/thrust/system/detail/generic/gather.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/generate.h" "$(@D)/cuda/include/thrust/system/detail/generic/generate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/generate.inl" "$(@D)/cuda/include/thrust/system/detail/generic/generate.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/inner_product.h" "$(@D)/cuda/include/thrust/system/detail/generic/inner_product.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/inner_product.inl" "$(@D)/cuda/include/thrust/system/detail/generic/inner_product.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/logical.h" "$(@D)/cuda/include/thrust/system/detail/generic/logical.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/memory.h" "$(@D)/cuda/include/thrust/system/detail/generic/memory.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/memory.inl" "$(@D)/cuda/include/thrust/system/detail/generic/memory.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/merge.h" "$(@D)/cuda/include/thrust/system/detail/generic/merge.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/merge.inl" "$(@D)/cuda/include/thrust/system/detail/generic/merge.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/mismatch.h" "$(@D)/cuda/include/thrust/system/detail/generic/mismatch.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/mismatch.inl" "$(@D)/cuda/include/thrust/system/detail/generic/mismatch.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/partition.h" "$(@D)/cuda/include/thrust/system/detail/generic/partition.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/partition.inl" "$(@D)/cuda/include/thrust/system/detail/generic/partition.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/reduce.h" "$(@D)/cuda/include/thrust/system/detail/generic/reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/reduce.inl" "$(@D)/cuda/include/thrust/system/detail/generic/reduce.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/detail/generic/reduce_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/reduce_by_key.inl" "$(@D)/cuda/include/thrust/system/detail/generic/reduce_by_key.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/remove.h" "$(@D)/cuda/include/thrust/system/detail/generic/remove.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/remove.inl" "$(@D)/cuda/include/thrust/system/detail/generic/remove.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/replace.h" "$(@D)/cuda/include/thrust/system/detail/generic/replace.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/replace.inl" "$(@D)/cuda/include/thrust/system/detail/generic/replace.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/reverse.h" "$(@D)/cuda/include/thrust/system/detail/generic/reverse.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/reverse.inl" "$(@D)/cuda/include/thrust/system/detail/generic/reverse.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/scalar/binary_search.h" "$(@D)/cuda/include/thrust/system/detail/generic/scalar/binary_search.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/scalar/binary_search.inl" "$(@D)/cuda/include/thrust/system/detail/generic/scalar/binary_search.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/scan.h" "$(@D)/cuda/include/thrust/system/detail/generic/scan.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/scan.inl" "$(@D)/cuda/include/thrust/system/detail/generic/scan.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/scan_by_key.h" "$(@D)/cuda/include/thrust/system/detail/generic/scan_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/scan_by_key.inl" "$(@D)/cuda/include/thrust/system/detail/generic/scan_by_key.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/scatter.h" "$(@D)/cuda/include/thrust/system/detail/generic/scatter.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/scatter.inl" "$(@D)/cuda/include/thrust/system/detail/generic/scatter.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/select_system.h" "$(@D)/cuda/include/thrust/system/detail/generic/select_system.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/sequence.h" "$(@D)/cuda/include/thrust/system/detail/generic/sequence.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/sequence.inl" "$(@D)/cuda/include/thrust/system/detail/generic/sequence.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/set_operations.h" "$(@D)/cuda/include/thrust/system/detail/generic/set_operations.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/set_operations.inl" "$(@D)/cuda/include/thrust/system/detail/generic/set_operations.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/sort.h" "$(@D)/cuda/include/thrust/system/detail/generic/sort.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/sort.inl" "$(@D)/cuda/include/thrust/system/detail/generic/sort.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/swap_ranges.h" "$(@D)/cuda/include/thrust/system/detail/generic/swap_ranges.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/swap_ranges.inl" "$(@D)/cuda/include/thrust/system/detail/generic/swap_ranges.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/tabulate.h" "$(@D)/cuda/include/thrust/system/detail/generic/tabulate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/tabulate.inl" "$(@D)/cuda/include/thrust/system/detail/generic/tabulate.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/tag.h" "$(@D)/cuda/include/thrust/system/detail/generic/tag.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/detail/generic/temporary_buffer.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/temporary_buffer.inl" "$(@D)/cuda/include/thrust/system/detail/generic/temporary_buffer.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/transform.h" "$(@D)/cuda/include/thrust/system/detail/generic/transform.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/transform.inl" "$(@D)/cuda/include/thrust/system/detail/generic/transform.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/transform_reduce.h" "$(@D)/cuda/include/thrust/system/detail/generic/transform_reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/transform_reduce.inl" "$(@D)/cuda/include/thrust/system/detail/generic/transform_reduce.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/transform_scan.h" "$(@D)/cuda/include/thrust/system/detail/generic/transform_scan.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/transform_scan.inl" "$(@D)/cuda/include/thrust/system/detail/generic/transform_scan.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/type_traits.h" "$(@D)/cuda/include/thrust/system/detail/generic/type_traits.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/detail/generic/uninitialized_copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/uninitialized_copy.inl" "$(@D)/cuda/include/thrust/system/detail/generic/uninitialized_copy.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/detail/generic/uninitialized_fill.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/uninitialized_fill.inl" "$(@D)/cuda/include/thrust/system/detail/generic/uninitialized_fill.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/unique.h" "$(@D)/cuda/include/thrust/system/detail/generic/unique.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/unique.inl" "$(@D)/cuda/include/thrust/system/detail/generic/unique.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/unique_by_key.h" "$(@D)/cuda/include/thrust/system/detail/generic/unique_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/unique_by_key.inl" "$(@D)/cuda/include/thrust/system/detail/generic/unique_by_key.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/internal/decompose.h" "$(@D)/cuda/include/thrust/system/detail/internal/decompose.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/detail/sequential/adjacent_difference.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/assign_value.h" "$(@D)/cuda/include/thrust/system/detail/sequential/assign_value.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/binary_search.h" "$(@D)/cuda/include/thrust/system/detail/sequential/binary_search.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/copy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/copy.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/copy.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/copy_backward.h" "$(@D)/cuda/include/thrust/system/detail/sequential/copy_backward.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/copy_if.h" "$(@D)/cuda/include/thrust/system/detail/sequential/copy_if.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/count.h" "$(@D)/cuda/include/thrust/system/detail/sequential/count.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/equal.h" "$(@D)/cuda/include/thrust/system/detail/sequential/equal.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/execution_policy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/execution_policy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/extrema.h" "$(@D)/cuda/include/thrust/system/detail/sequential/extrema.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/fill.h" "$(@D)/cuda/include/thrust/system/detail/sequential/fill.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/find.h" "$(@D)/cuda/include/thrust/system/detail/sequential/find.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/for_each.h" "$(@D)/cuda/include/thrust/system/detail/sequential/for_each.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/gather.h" "$(@D)/cuda/include/thrust/system/detail/sequential/gather.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/general_copy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/general_copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/generate.h" "$(@D)/cuda/include/thrust/system/detail/sequential/generate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/get_value.h" "$(@D)/cuda/include/thrust/system/detail/sequential/get_value.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/inner_product.h" "$(@D)/cuda/include/thrust/system/detail/sequential/inner_product.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/insertion_sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/insertion_sort.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/iter_swap.h" "$(@D)/cuda/include/thrust/system/detail/sequential/iter_swap.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/logical.h" "$(@D)/cuda/include/thrust/system/detail/sequential/logical.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/detail/sequential/malloc_and_free.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/merge.h" "$(@D)/cuda/include/thrust/system/detail/sequential/merge.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/merge.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/merge.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/mismatch.h" "$(@D)/cuda/include/thrust/system/detail/sequential/mismatch.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/partition.h" "$(@D)/cuda/include/thrust/system/detail/sequential/partition.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/reduce.h" "$(@D)/cuda/include/thrust/system/detail/sequential/reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/detail/sequential/reduce_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/remove.h" "$(@D)/cuda/include/thrust/system/detail/sequential/remove.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/replace.h" "$(@D)/cuda/include/thrust/system/detail/sequential/replace.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/reverse.h" "$(@D)/cuda/include/thrust/system/detail/sequential/reverse.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/scan.h" "$(@D)/cuda/include/thrust/system/detail/sequential/scan.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/scan_by_key.h" "$(@D)/cuda/include/thrust/system/detail/sequential/scan_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/scatter.h" "$(@D)/cuda/include/thrust/system/detail/sequential/scatter.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/sequence.h" "$(@D)/cuda/include/thrust/system/detail/sequential/sequence.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/set_operations.h" "$(@D)/cuda/include/thrust/system/detail/sequential/set_operations.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/sort.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/sort.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/sort.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/stable_merge_sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_merge_sort.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/stable_merge_sort.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_merge_sort.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/stable_primitive_sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_primitive_sort.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/stable_primitive_sort.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_primitive_sort.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/stable_radix_sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_radix_sort.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/stable_radix_sort.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_radix_sort.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/swap_ranges.h" "$(@D)/cuda/include/thrust/system/detail/sequential/swap_ranges.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/tabulate.h" "$(@D)/cuda/include/thrust/system/detail/sequential/tabulate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/detail/sequential/temporary_buffer.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/transform.h" "$(@D)/cuda/include/thrust/system/detail/sequential/transform.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/transform_reduce.h" "$(@D)/cuda/include/thrust/system/detail/sequential/transform_reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/transform_scan.h" "$(@D)/cuda/include/thrust/system/detail/sequential/transform_scan.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/trivial_copy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/trivial_copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/uninitialized_copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/detail/sequential/uninitialized_fill.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/unique.h" "$(@D)/cuda/include/thrust/system/detail/sequential/unique.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/unique_by_key.h" "$(@D)/cuda/include/thrust/system/detail/sequential/unique_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/system_error.inl" "$(@D)/cuda/include/thrust/system/detail/system_error.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/error_code.h" "$(@D)/cuda/include/thrust/system/error_code.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/omp/detail/adjacent_difference.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/assign_value.h" "$(@D)/cuda/include/thrust/system/omp/detail/assign_value.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/binary_search.h" "$(@D)/cuda/include/thrust/system/omp/detail/binary_search.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/copy.h" "$(@D)/cuda/include/thrust/system/omp/detail/copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/copy.inl" "$(@D)/cuda/include/thrust/system/omp/detail/copy.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/copy_if.h" "$(@D)/cuda/include/thrust/system/omp/detail/copy_if.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/copy_if.inl" "$(@D)/cuda/include/thrust/system/omp/detail/copy_if.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/count.h" "$(@D)/cuda/include/thrust/system/omp/detail/count.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/default_decomposition.h" "$(@D)/cuda/include/thrust/system/omp/detail/default_decomposition.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/default_decomposition.inl" "$(@D)/cuda/include/thrust/system/omp/detail/default_decomposition.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/equal.h" "$(@D)/cuda/include/thrust/system/omp/detail/equal.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/execution_policy.h" "$(@D)/cuda/include/thrust/system/omp/detail/execution_policy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/extrema.h" "$(@D)/cuda/include/thrust/system/omp/detail/extrema.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/fill.h" "$(@D)/cuda/include/thrust/system/omp/detail/fill.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/find.h" "$(@D)/cuda/include/thrust/system/omp/detail/find.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/for_each.h" "$(@D)/cuda/include/thrust/system/omp/detail/for_each.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/for_each.inl" "$(@D)/cuda/include/thrust/system/omp/detail/for_each.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/gather.h" "$(@D)/cuda/include/thrust/system/omp/detail/gather.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/generate.h" "$(@D)/cuda/include/thrust/system/omp/detail/generate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/get_value.h" "$(@D)/cuda/include/thrust/system/omp/detail/get_value.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/inner_product.h" "$(@D)/cuda/include/thrust/system/omp/detail/inner_product.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/iter_swap.h" "$(@D)/cuda/include/thrust/system/omp/detail/iter_swap.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/logical.h" "$(@D)/cuda/include/thrust/system/omp/detail/logical.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/omp/detail/malloc_and_free.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/memory.inl" "$(@D)/cuda/include/thrust/system/omp/detail/memory.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/merge.h" "$(@D)/cuda/include/thrust/system/omp/detail/merge.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/mismatch.h" "$(@D)/cuda/include/thrust/system/omp/detail/mismatch.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/par.h" "$(@D)/cuda/include/thrust/system/omp/detail/par.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/partition.h" "$(@D)/cuda/include/thrust/system/omp/detail/partition.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/partition.inl" "$(@D)/cuda/include/thrust/system/omp/detail/partition.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/reduce.h" "$(@D)/cuda/include/thrust/system/omp/detail/reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/reduce.inl" "$(@D)/cuda/include/thrust/system/omp/detail/reduce.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/omp/detail/reduce_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/reduce_by_key.inl" "$(@D)/cuda/include/thrust/system/omp/detail/reduce_by_key.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/reduce_intervals.h" "$(@D)/cuda/include/thrust/system/omp/detail/reduce_intervals.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/reduce_intervals.inl" "$(@D)/cuda/include/thrust/system/omp/detail/reduce_intervals.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/remove.h" "$(@D)/cuda/include/thrust/system/omp/detail/remove.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/remove.inl" "$(@D)/cuda/include/thrust/system/omp/detail/remove.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/replace.h" "$(@D)/cuda/include/thrust/system/omp/detail/replace.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/reverse.h" "$(@D)/cuda/include/thrust/system/omp/detail/reverse.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/scan.h" "$(@D)/cuda/include/thrust/system/omp/detail/scan.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/scan_by_key.h" "$(@D)/cuda/include/thrust/system/omp/detail/scan_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/scatter.h" "$(@D)/cuda/include/thrust/system/omp/detail/scatter.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/sequence.h" "$(@D)/cuda/include/thrust/system/omp/detail/sequence.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/set_operations.h" "$(@D)/cuda/include/thrust/system/omp/detail/set_operations.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/sort.h" "$(@D)/cuda/include/thrust/system/omp/detail/sort.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/sort.inl" "$(@D)/cuda/include/thrust/system/omp/detail/sort.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/swap_ranges.h" "$(@D)/cuda/include/thrust/system/omp/detail/swap_ranges.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/tabulate.h" "$(@D)/cuda/include/thrust/system/omp/detail/tabulate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/omp/detail/temporary_buffer.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/transform.h" "$(@D)/cuda/include/thrust/system/omp/detail/transform.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/transform_reduce.h" "$(@D)/cuda/include/thrust/system/omp/detail/transform_reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/transform_scan.h" "$(@D)/cuda/include/thrust/system/omp/detail/transform_scan.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/omp/detail/uninitialized_copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/omp/detail/uninitialized_fill.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/unique.h" "$(@D)/cuda/include/thrust/system/omp/detail/unique.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/unique.inl" "$(@D)/cuda/include/thrust/system/omp/detail/unique.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/unique_by_key.h" "$(@D)/cuda/include/thrust/system/omp/detail/unique_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/unique_by_key.inl" "$(@D)/cuda/include/thrust/system/omp/detail/unique_by_key.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/vector.inl" "$(@D)/cuda/include/thrust/system/omp/detail/vector.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/execution_policy.h" "$(@D)/cuda/include/thrust/system/omp/execution_policy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/memory.h" "$(@D)/cuda/include/thrust/system/omp/memory.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/vector.h" "$(@D)/cuda/include/thrust/system/omp/vector.h" && cp "/usr/local/cuda-9.0/include/thrust/system/system_error.h" "$(@D)/cuda/include/thrust/system/system_error.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/tbb/detail/adjacent_difference.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/assign_value.h" "$(@D)/cuda/include/thrust/system/tbb/detail/assign_value.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/binary_search.h" "$(@D)/cuda/include/thrust/system/tbb/detail/binary_search.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/copy.h" "$(@D)/cuda/include/thrust/system/tbb/detail/copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/copy.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/copy.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/copy_if.h" "$(@D)/cuda/include/thrust/system/tbb/detail/copy_if.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/copy_if.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/copy_if.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/count.h" "$(@D)/cuda/include/thrust/system/tbb/detail/count.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/equal.h" "$(@D)/cuda/include/thrust/system/tbb/detail/equal.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/execution_policy.h" "$(@D)/cuda/include/thrust/system/tbb/detail/execution_policy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/extrema.h" "$(@D)/cuda/include/thrust/system/tbb/detail/extrema.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/fill.h" "$(@D)/cuda/include/thrust/system/tbb/detail/fill.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/find.h" "$(@D)/cuda/include/thrust/system/tbb/detail/find.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/for_each.h" "$(@D)/cuda/include/thrust/system/tbb/detail/for_each.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/for_each.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/for_each.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/gather.h" "$(@D)/cuda/include/thrust/system/tbb/detail/gather.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/generate.h" "$(@D)/cuda/include/thrust/system/tbb/detail/generate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/get_value.h" "$(@D)/cuda/include/thrust/system/tbb/detail/get_value.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/inner_product.h" "$(@D)/cuda/include/thrust/system/tbb/detail/inner_product.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/iter_swap.h" "$(@D)/cuda/include/thrust/system/tbb/detail/iter_swap.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/logical.h" "$(@D)/cuda/include/thrust/system/tbb/detail/logical.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/tbb/detail/malloc_and_free.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/memory.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/memory.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/merge.h" "$(@D)/cuda/include/thrust/system/tbb/detail/merge.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/merge.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/merge.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/mismatch.h" "$(@D)/cuda/include/thrust/system/tbb/detail/mismatch.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/par.h" "$(@D)/cuda/include/thrust/system/tbb/detail/par.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/partition.h" "$(@D)/cuda/include/thrust/system/tbb/detail/partition.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/partition.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/partition.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/reduce.h" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/reduce.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/reduce_by_key.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce_by_key.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/reduce_intervals.h" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce_intervals.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/remove.h" "$(@D)/cuda/include/thrust/system/tbb/detail/remove.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/remove.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/remove.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/replace.h" "$(@D)/cuda/include/thrust/system/tbb/detail/replace.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/reverse.h" "$(@D)/cuda/include/thrust/system/tbb/detail/reverse.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/scan.h" "$(@D)/cuda/include/thrust/system/tbb/detail/scan.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/scan.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/scan.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/scan_by_key.h" "$(@D)/cuda/include/thrust/system/tbb/detail/scan_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/scatter.h" "$(@D)/cuda/include/thrust/system/tbb/detail/scatter.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/sequence.h" "$(@D)/cuda/include/thrust/system/tbb/detail/sequence.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/set_operations.h" "$(@D)/cuda/include/thrust/system/tbb/detail/set_operations.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/sort.h" "$(@D)/cuda/include/thrust/system/tbb/detail/sort.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/sort.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/sort.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/swap_ranges.h" "$(@D)/cuda/include/thrust/system/tbb/detail/swap_ranges.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/tabulate.h" "$(@D)/cuda/include/thrust/system/tbb/detail/tabulate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/tbb/detail/temporary_buffer.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/transform.h" "$(@D)/cuda/include/thrust/system/tbb/detail/transform.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/transform_reduce.h" "$(@D)/cuda/include/thrust/system/tbb/detail/transform_reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/transform_scan.h" "$(@D)/cuda/include/thrust/system/tbb/detail/transform_scan.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/tbb/detail/uninitialized_copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/tbb/detail/uninitialized_fill.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/unique.h" "$(@D)/cuda/include/thrust/system/tbb/detail/unique.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/unique.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/unique.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/unique_by_key.h" "$(@D)/cuda/include/thrust/system/tbb/detail/unique_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/unique_by_key.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/unique_by_key.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/vector.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/vector.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/execution_policy.h" "$(@D)/cuda/include/thrust/system/tbb/execution_policy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/memory.h" "$(@D)/cuda/include/thrust/system/tbb/memory.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/vector.h" "$(@D)/cuda/include/thrust/system/tbb/vector.h" && cp "/usr/local/cuda-9.0/include/thrust/system_error.h" "$(@D)/cuda/include/thrust/system_error.h" && cp "/usr/local/cuda-9.0/include/thrust/tabulate.h" "$(@D)/cuda/include/thrust/tabulate.h" && cp "/usr/local/cuda-9.0/include/thrust/transform.h" "$(@D)/cuda/include/thrust/transform.h" && cp "/usr/local/cuda-9.0/include/thrust/transform_reduce.h" "$(@D)/cuda/include/thrust/transform_reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/transform_scan.h" "$(@D)/cuda/include/thrust/transform_scan.h" && cp "/usr/local/cuda-9.0/include/thrust/tuple.h" "$(@D)/cuda/include/thrust/tuple.h" && cp "/usr/local/cuda-9.0/include/thrust/uninitialized_copy.h" "$(@D)/cuda/include/thrust/uninitialized_copy.h" && cp "/usr/local/cuda-9.0/include/thrust/uninitialized_fill.h" "$(@D)/cuda/include/thrust/uninitialized_fill.h" && cp "/usr/local/cuda-9.0/include/thrust/unique.h" "$(@D)/cuda/include/thrust/unique.h" && cp "/usr/local/cuda-9.0/include/thrust/version.h" "$(@D)/cuda/include/thrust/version.h" && cp "/usr/local/cuda-9.0/include/vector_functions.h" "$(@D)/cuda/include/vector_functions.h" && cp "/usr/local/cuda-9.0/include/vector_functions.hpp" "$(@D)/cuda/include/vector_functions.hpp" && cp "/usr/local/cuda-9.0/include/vector_types.h" "$(@D)/cuda/include/vector_types.h" """, ) @@ -1264,72 +1192,69 @@ genrule( name = "cuda-nvvm", outs = [ "cuda/nvvm/bin/cicc", - "cuda/nvvm/libdevice/libdevice.compute_50.10.bc", - "cuda/nvvm/libdevice/libdevice.compute_30.10.bc", - "cuda/nvvm/libdevice/libdevice.compute_20.10.bc", - "cuda/nvvm/libdevice/libdevice.compute_35.10.bc", - "cuda/nvvm/lib64/libnvvm.so.3", - "cuda/nvvm/lib64/libnvvm.so", - "cuda/nvvm/lib64/libnvvm.so.3.1.0", "cuda/nvvm/include/nvvm.h", - "cuda/nvvm/libnvvm-samples/ptxgen/README.txt", - "cuda/nvvm/libnvvm-samples/ptxgen/ptxgen.c", - "cuda/nvvm/libnvvm-samples/ptxgen/CMakeLists.txt", + "cuda/nvvm/lib64/libnvvm.so", + "cuda/nvvm/lib64/libnvvm.so.3", + "cuda/nvvm/lib64/libnvvm.so.3.2.0", + "cuda/nvvm/libdevice/libdevice.10.bc", + "cuda/nvvm/libnvvm-samples/CMakeLists.txt", + "cuda/nvvm/libnvvm-samples/README.txt", "cuda/nvvm/libnvvm-samples/build.bat", - "cuda/nvvm/libnvvm-samples/cuda-c-linking/README.txt", - "cuda/nvvm/libnvvm-samples/cuda-c-linking/math-funcs.cu", + "cuda/nvvm/libnvvm-samples/build.sh", + "cuda/nvvm/libnvvm-samples/common/include/DDSWriter.h", + "cuda/nvvm/libnvvm-samples/common/include/drvapi_error_string.h", "cuda/nvvm/libnvvm-samples/cuda-c-linking/CMakeLists.txt", + "cuda/nvvm/libnvvm-samples/cuda-c-linking/README.txt", "cuda/nvvm/libnvvm-samples/cuda-c-linking/cuda-c-linking.cpp", - "cuda/nvvm/libnvvm-samples/README.txt", - "cuda/nvvm/libnvvm-samples/simple/simple.c", - "cuda/nvvm/libnvvm-samples/simple/simple-gpu.ll", + "cuda/nvvm/libnvvm-samples/cuda-c-linking/math-funcs.cu", + "cuda/nvvm/libnvvm-samples/ptxgen/CMakeLists.txt", + "cuda/nvvm/libnvvm-samples/ptxgen/README.txt", + "cuda/nvvm/libnvvm-samples/ptxgen/ptxgen.c", + "cuda/nvvm/libnvvm-samples/simple/CMakeLists.txt", "cuda/nvvm/libnvvm-samples/simple/README.txt", + "cuda/nvvm/libnvvm-samples/simple/simple-gpu.ll", "cuda/nvvm/libnvvm-samples/simple/simple-gpu64.ll", - "cuda/nvvm/libnvvm-samples/simple/CMakeLists.txt", - "cuda/nvvm/libnvvm-samples/common/include/DDSWriter.h", - "cuda/nvvm/libnvvm-samples/common/include/drvapi_error_string.h", - "cuda/nvvm/libnvvm-samples/build.sh", - "cuda/nvvm/libnvvm-samples/CMakeLists.txt", + "cuda/nvvm/libnvvm-samples/simple/simple.c", ], cmd = """ -if [ -d "$(@D)/extras" ]; then rm $(@D)/extras -drf; fi && if [ -d "$(@D)/include" ]; then rm $(@D)/include -drf; fi && if [ -d "$(@D)/lib" ]; then rm $(@D)/lib -drf; fi && if [ -d "$(@D)/nvvm" ]; then rm $(@D)/nvvm -drf; fi && cp "/usr/local/cuda-8.0/nvvm/bin/cicc" "$(@D)/cuda/nvvm/bin/cicc" && cp "/usr/local/cuda-8.0/nvvm/libdevice/libdevice.compute_50.10.bc" "$(@D)/cuda/nvvm/libdevice/libdevice.compute_50.10.bc" && cp "/usr/local/cuda-8.0/nvvm/libdevice/libdevice.compute_30.10.bc" "$(@D)/cuda/nvvm/libdevice/libdevice.compute_30.10.bc" && cp "/usr/local/cuda-8.0/nvvm/libdevice/libdevice.compute_20.10.bc" "$(@D)/cuda/nvvm/libdevice/libdevice.compute_20.10.bc" && cp "/usr/local/cuda-8.0/nvvm/libdevice/libdevice.compute_35.10.bc" "$(@D)/cuda/nvvm/libdevice/libdevice.compute_35.10.bc" && cp "/usr/local/cuda-8.0/nvvm/lib64/libnvvm.so.3" "$(@D)/cuda/nvvm/lib64/libnvvm.so.3" && cp "/usr/local/cuda-8.0/nvvm/lib64/libnvvm.so" "$(@D)/cuda/nvvm/lib64/libnvvm.so" && cp "/usr/local/cuda-8.0/nvvm/lib64/libnvvm.so.3.1.0" "$(@D)/cuda/nvvm/lib64/libnvvm.so.3.1.0" && cp "/usr/local/cuda-8.0/nvvm/include/nvvm.h" "$(@D)/cuda/nvvm/include/nvvm.h" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/ptxgen/README.txt" "$(@D)/cuda/nvvm/libnvvm-samples/ptxgen/README.txt" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/ptxgen/ptxgen.c" "$(@D)/cuda/nvvm/libnvvm-samples/ptxgen/ptxgen.c" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/ptxgen/CMakeLists.txt" "$(@D)/cuda/nvvm/libnvvm-samples/ptxgen/CMakeLists.txt" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/build.bat" "$(@D)/cuda/nvvm/libnvvm-samples/build.bat" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/cuda-c-linking/README.txt" "$(@D)/cuda/nvvm/libnvvm-samples/cuda-c-linking/README.txt" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/cuda-c-linking/math-funcs.cu" "$(@D)/cuda/nvvm/libnvvm-samples/cuda-c-linking/math-funcs.cu" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/cuda-c-linking/CMakeLists.txt" "$(@D)/cuda/nvvm/libnvvm-samples/cuda-c-linking/CMakeLists.txt" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/cuda-c-linking/cuda-c-linking.cpp" "$(@D)/cuda/nvvm/libnvvm-samples/cuda-c-linking/cuda-c-linking.cpp" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/README.txt" "$(@D)/cuda/nvvm/libnvvm-samples/README.txt" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/simple/simple.c" "$(@D)/cuda/nvvm/libnvvm-samples/simple/simple.c" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/simple/simple-gpu.ll" "$(@D)/cuda/nvvm/libnvvm-samples/simple/simple-gpu.ll" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/simple/README.txt" "$(@D)/cuda/nvvm/libnvvm-samples/simple/README.txt" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/simple/simple-gpu64.ll" "$(@D)/cuda/nvvm/libnvvm-samples/simple/simple-gpu64.ll" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/simple/CMakeLists.txt" "$(@D)/cuda/nvvm/libnvvm-samples/simple/CMakeLists.txt" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/common/include/DDSWriter.h" "$(@D)/cuda/nvvm/libnvvm-samples/common/include/DDSWriter.h" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/common/include/drvapi_error_string.h" "$(@D)/cuda/nvvm/libnvvm-samples/common/include/drvapi_error_string.h" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/build.sh" "$(@D)/cuda/nvvm/libnvvm-samples/build.sh" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/CMakeLists.txt" "$(@D)/cuda/nvvm/libnvvm-samples/CMakeLists.txt" +if [ -d "$(@D)/extras" ]; then rm $(@D)/extras -drf; fi && if [ -d "$(@D)/include" ]; then rm $(@D)/include -drf; fi && if [ -d "$(@D)/lib" ]; then rm $(@D)/lib -drf; fi && if [ -d "$(@D)/nvvm" ]; then rm $(@D)/nvvm -drf; fi && cp "/usr/local/cuda-9.0/nvvm/bin/cicc" "$(@D)/cuda/nvvm/bin/cicc" && cp "/usr/local/cuda-9.0/nvvm/include/nvvm.h" "$(@D)/cuda/nvvm/include/nvvm.h" && cp "/usr/local/cuda-9.0/nvvm/lib64/libnvvm.so" "$(@D)/cuda/nvvm/lib64/libnvvm.so" && cp "/usr/local/cuda-9.0/nvvm/lib64/libnvvm.so.3" "$(@D)/cuda/nvvm/lib64/libnvvm.so.3" && cp "/usr/local/cuda-9.0/nvvm/lib64/libnvvm.so.3.2.0" "$(@D)/cuda/nvvm/lib64/libnvvm.so.3.2.0" && cp "/usr/local/cuda-9.0/nvvm/libdevice/libdevice.10.bc" "$(@D)/cuda/nvvm/libdevice/libdevice.10.bc" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/CMakeLists.txt" "$(@D)/cuda/nvvm/libnvvm-samples/CMakeLists.txt" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/README.txt" "$(@D)/cuda/nvvm/libnvvm-samples/README.txt" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/build.bat" "$(@D)/cuda/nvvm/libnvvm-samples/build.bat" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/build.sh" "$(@D)/cuda/nvvm/libnvvm-samples/build.sh" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/common/include/DDSWriter.h" "$(@D)/cuda/nvvm/libnvvm-samples/common/include/DDSWriter.h" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/common/include/drvapi_error_string.h" "$(@D)/cuda/nvvm/libnvvm-samples/common/include/drvapi_error_string.h" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/cuda-c-linking/CMakeLists.txt" "$(@D)/cuda/nvvm/libnvvm-samples/cuda-c-linking/CMakeLists.txt" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/cuda-c-linking/README.txt" "$(@D)/cuda/nvvm/libnvvm-samples/cuda-c-linking/README.txt" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/cuda-c-linking/cuda-c-linking.cpp" "$(@D)/cuda/nvvm/libnvvm-samples/cuda-c-linking/cuda-c-linking.cpp" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/cuda-c-linking/math-funcs.cu" "$(@D)/cuda/nvvm/libnvvm-samples/cuda-c-linking/math-funcs.cu" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/ptxgen/CMakeLists.txt" "$(@D)/cuda/nvvm/libnvvm-samples/ptxgen/CMakeLists.txt" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/ptxgen/README.txt" "$(@D)/cuda/nvvm/libnvvm-samples/ptxgen/README.txt" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/ptxgen/ptxgen.c" "$(@D)/cuda/nvvm/libnvvm-samples/ptxgen/ptxgen.c" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/simple/CMakeLists.txt" "$(@D)/cuda/nvvm/libnvvm-samples/simple/CMakeLists.txt" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/simple/README.txt" "$(@D)/cuda/nvvm/libnvvm-samples/simple/README.txt" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/simple/simple-gpu.ll" "$(@D)/cuda/nvvm/libnvvm-samples/simple/simple-gpu.ll" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/simple/simple-gpu64.ll" "$(@D)/cuda/nvvm/libnvvm-samples/simple/simple-gpu64.ll" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/simple/simple.c" "$(@D)/cuda/nvvm/libnvvm-samples/simple/simple.c" """, ) genrule( name = "cuda-extras", outs = [ - "cuda/extras/CUPTI/include/cupti_result.h", + "cuda/extras/CUPTI/include/GL/gl.h", + "cuda/extras/CUPTI/include/GL/glew.h", + "cuda/extras/CUPTI/include/GL/glext.h", + "cuda/extras/CUPTI/include/GL/glu.h", + "cuda/extras/CUPTI/include/GL/glut.h", + "cuda/extras/CUPTI/include/GL/glx.h", + "cuda/extras/CUPTI/include/GL/glxext.h", + "cuda/extras/CUPTI/include/GL/wglew.h", + "cuda/extras/CUPTI/include/GL/wglext.h", + "cuda/extras/CUPTI/include/cuda_stdint.h", + "cuda/extras/CUPTI/include/cupti.h", + "cuda/extras/CUPTI/include/cupti_activity.h", + "cuda/extras/CUPTI/include/cupti_callbacks.h", + "cuda/extras/CUPTI/include/cupti_driver_cbid.h", "cuda/extras/CUPTI/include/cupti_events.h", - "cuda/extras/CUPTI/include/openacc/cupti_openacc.h", + "cuda/extras/CUPTI/include/cupti_metrics.h", + "cuda/extras/CUPTI/include/cupti_nvtx_cbid.h", + "cuda/extras/CUPTI/include/cupti_result.h", + "cuda/extras/CUPTI/include/cupti_runtime_cbid.h", "cuda/extras/CUPTI/include/cupti_version.h", - "cuda/extras/CUPTI/include/generated_cuda_gl_interop_meta.h", + "cuda/extras/CUPTI/include/generated_cudaGL_meta.h", "cuda/extras/CUPTI/include/generated_cudaVDPAU_meta.h", - "cuda/extras/CUPTI/include/cupti_activity.h", - "cuda/extras/CUPTI/include/generated_cuda_runtime_api_meta.h", + "cuda/extras/CUPTI/include/generated_cuda_gl_interop_meta.h", "cuda/extras/CUPTI/include/generated_cuda_meta.h", - "cuda/extras/CUPTI/include/cupti_nvtx_cbid.h", - "cuda/extras/CUPTI/include/cuda_stdint.h", - "cuda/extras/CUPTI/include/generated_cudaGL_meta.h", + "cuda/extras/CUPTI/include/generated_cuda_runtime_api_meta.h", "cuda/extras/CUPTI/include/generated_cuda_vdpau_interop_meta.h", - "cuda/extras/CUPTI/include/cupti_metrics.h", - "cuda/extras/CUPTI/include/cupti_callbacks.h", - "cuda/extras/CUPTI/include/cupti_runtime_cbid.h", - "cuda/extras/CUPTI/include/cupti.h", - "cuda/extras/CUPTI/include/GL/glut.h", - "cuda/extras/CUPTI/include/GL/glu.h", - "cuda/extras/CUPTI/include/GL/glxext.h", - "cuda/extras/CUPTI/include/GL/wglext.h", - "cuda/extras/CUPTI/include/GL/glx.h", - "cuda/extras/CUPTI/include/GL/glext.h", - "cuda/extras/CUPTI/include/GL/wglew.h", - "cuda/extras/CUPTI/include/GL/gl.h", - "cuda/extras/CUPTI/include/GL/glew.h", - "cuda/extras/CUPTI/include/cupti_driver_cbid.h", "cuda/extras/CUPTI/include/generated_nvtx_meta.h", + "cuda/extras/CUPTI/include/openacc/cupti_openacc.h", ], cmd = """ -if [ -d "$(@D)/extras" ]; then rm $(@D)/extras -drf; fi && if [ -d "$(@D)/include" ]; then rm $(@D)/include -drf; fi && if [ -d "$(@D)/lib" ]; then rm $(@D)/lib -drf; fi && if [ -d "$(@D)/nvvm" ]; then rm $(@D)/nvvm -drf; fi && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_result.h" "$(@D)/cuda/extras/CUPTI/include/cupti_result.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_events.h" "$(@D)/cuda/extras/CUPTI/include/cupti_events.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/openacc/cupti_openacc.h" "$(@D)/cuda/extras/CUPTI/include/openacc/cupti_openacc.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_version.h" "$(@D)/cuda/extras/CUPTI/include/cupti_version.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/generated_cuda_gl_interop_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cuda_gl_interop_meta.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/generated_cudaVDPAU_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cudaVDPAU_meta.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_activity.h" "$(@D)/cuda/extras/CUPTI/include/cupti_activity.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/generated_cuda_runtime_api_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cuda_runtime_api_meta.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/generated_cuda_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cuda_meta.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_nvtx_cbid.h" "$(@D)/cuda/extras/CUPTI/include/cupti_nvtx_cbid.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cuda_stdint.h" "$(@D)/cuda/extras/CUPTI/include/cuda_stdint.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/generated_cudaGL_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cudaGL_meta.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/generated_cuda_vdpau_interop_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cuda_vdpau_interop_meta.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_metrics.h" "$(@D)/cuda/extras/CUPTI/include/cupti_metrics.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_callbacks.h" "$(@D)/cuda/extras/CUPTI/include/cupti_callbacks.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_runtime_cbid.h" "$(@D)/cuda/extras/CUPTI/include/cupti_runtime_cbid.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti.h" "$(@D)/cuda/extras/CUPTI/include/cupti.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/glut.h" "$(@D)/cuda/extras/CUPTI/include/GL/glut.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/glu.h" "$(@D)/cuda/extras/CUPTI/include/GL/glu.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/glxext.h" "$(@D)/cuda/extras/CUPTI/include/GL/glxext.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/wglext.h" "$(@D)/cuda/extras/CUPTI/include/GL/wglext.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/glx.h" "$(@D)/cuda/extras/CUPTI/include/GL/glx.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/glext.h" "$(@D)/cuda/extras/CUPTI/include/GL/glext.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/wglew.h" "$(@D)/cuda/extras/CUPTI/include/GL/wglew.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/gl.h" "$(@D)/cuda/extras/CUPTI/include/GL/gl.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/glew.h" "$(@D)/cuda/extras/CUPTI/include/GL/glew.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_driver_cbid.h" "$(@D)/cuda/extras/CUPTI/include/cupti_driver_cbid.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/generated_nvtx_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_nvtx_meta.h" +if [ -d "$(@D)/extras" ]; then rm $(@D)/extras -drf; fi && if [ -d "$(@D)/include" ]; then rm $(@D)/include -drf; fi && if [ -d "$(@D)/lib" ]; then rm $(@D)/lib -drf; fi && if [ -d "$(@D)/nvvm" ]; then rm $(@D)/nvvm -drf; fi && cp "/usr/local/cuda-9.0/extras/CUPTI/include/GL/gl.h" "$(@D)/cuda/extras/CUPTI/include/GL/gl.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/GL/glew.h" "$(@D)/cuda/extras/CUPTI/include/GL/glew.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/GL/glext.h" "$(@D)/cuda/extras/CUPTI/include/GL/glext.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/GL/glu.h" "$(@D)/cuda/extras/CUPTI/include/GL/glu.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/GL/glut.h" "$(@D)/cuda/extras/CUPTI/include/GL/glut.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/GL/glx.h" "$(@D)/cuda/extras/CUPTI/include/GL/glx.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/GL/glxext.h" "$(@D)/cuda/extras/CUPTI/include/GL/glxext.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/GL/wglew.h" "$(@D)/cuda/extras/CUPTI/include/GL/wglew.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/GL/wglext.h" "$(@D)/cuda/extras/CUPTI/include/GL/wglext.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/cuda_stdint.h" "$(@D)/cuda/extras/CUPTI/include/cuda_stdint.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/cupti.h" "$(@D)/cuda/extras/CUPTI/include/cupti.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/cupti_activity.h" "$(@D)/cuda/extras/CUPTI/include/cupti_activity.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/cupti_callbacks.h" "$(@D)/cuda/extras/CUPTI/include/cupti_callbacks.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/cupti_driver_cbid.h" "$(@D)/cuda/extras/CUPTI/include/cupti_driver_cbid.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/cupti_events.h" "$(@D)/cuda/extras/CUPTI/include/cupti_events.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/cupti_metrics.h" "$(@D)/cuda/extras/CUPTI/include/cupti_metrics.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/cupti_nvtx_cbid.h" "$(@D)/cuda/extras/CUPTI/include/cupti_nvtx_cbid.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/cupti_result.h" "$(@D)/cuda/extras/CUPTI/include/cupti_result.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/cupti_runtime_cbid.h" "$(@D)/cuda/extras/CUPTI/include/cupti_runtime_cbid.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/cupti_version.h" "$(@D)/cuda/extras/CUPTI/include/cupti_version.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/generated_cudaGL_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cudaGL_meta.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/generated_cudaVDPAU_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cudaVDPAU_meta.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/generated_cuda_gl_interop_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cuda_gl_interop_meta.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/generated_cuda_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cuda_meta.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/generated_cuda_runtime_api_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cuda_runtime_api_meta.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/generated_cuda_vdpau_interop_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cuda_vdpau_interop_meta.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/generated_nvtx_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_nvtx_meta.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/openacc/cupti_openacc.h" "$(@D)/cuda/extras/CUPTI/include/openacc/cupti_openacc.h" """, ) @@ -1337,26 +1262,21 @@ genrule( name = "cuda-lib", outs = [ "cuda/lib/libcuda.so", - "cuda/lib/libcudart.so.8.0", + "cuda/lib/libcudart.so.9.0", "cuda/lib/libcudart_static.a", - "cuda/lib/libcublas.so.8.0", - "cuda/lib/libcusolver.so.8.0", - "cuda/lib/libcurand.so.8.0", - "cuda/lib/libcufft.so.8.0", - "cuda/lib/libcudnn.so.6", - "cuda/lib/libcupti.so.8.0", + "cuda/lib/libcublas.so.9.0", + "cuda/lib/libcusolver.so.9.0", + "cuda/lib/libcurand.so.9.0", + "cuda/lib/libcufft.so.9.0", + "cuda/lib/libcudnn.so.7", + "cuda/lib/libcupti.so.9.0", ], cmd = """ -if [ -d "$(@D)/extras" ]; then rm $(@D)/extras -drf; fi && if [ -d "$(@D)/include" ]; then rm $(@D)/include -drf; fi && if [ -d "$(@D)/lib" ]; then rm $(@D)/lib -drf; fi && if [ -d "$(@D)/nvvm" ]; then rm $(@D)/nvvm -drf; fi && cp "/usr/local/cuda-8.0/targets/x86_64-linux/lib/stubs/libcuda.so" "$(@D)/cuda/lib/libcuda.so" && cp "/usr/local/cuda-8.0/targets/x86_64-linux/lib/libcudart.so.8.0.61" "$(@D)/cuda/lib/libcudart.so.8.0" && cp "/usr/local/cuda-8.0/targets/x86_64-linux/lib/libcudart_static.a" "$(@D)/cuda/lib/libcudart_static.a" && cp "/usr/local/cuda-8.0/targets/x86_64-linux/lib/libcublas.so.8.0.88" "$(@D)/cuda/lib/libcublas.so.8.0" && cp "/usr/local/cuda-8.0/targets/x86_64-linux/lib/libcusolver.so.8.0.61" "$(@D)/cuda/lib/libcusolver.so.8.0" && cp "/usr/local/cuda-8.0/targets/x86_64-linux/lib/libcurand.so.8.0.61" "$(@D)/cuda/lib/libcurand.so.8.0" && cp "/usr/local/cuda-8.0/targets/x86_64-linux/lib/libcufft.so.8.0.61" "$(@D)/cuda/lib/libcufft.so.8.0" && cp "/usr/lib/x86_64-linux-gnu/libcudnn.so.6.0.21" "$(@D)/cuda/lib/libcudnn.so.6" && cp "/usr/local/cuda-8.0/extras/CUPTI/lib64/libcupti.so.8.0.61" "$(@D)/cuda/lib/libcupti.so.8.0" +if [ -d "$(@D)/extras" ]; then rm $(@D)/extras -drf; fi && if [ -d "$(@D)/include" ]; then rm $(@D)/include -drf; fi && if [ -d "$(@D)/lib" ]; then rm $(@D)/lib -drf; fi && if [ -d "$(@D)/nvvm" ]; then rm $(@D)/nvvm -drf; fi && cp "/usr/local/cuda-9.0/targets/x86_64-linux/lib/stubs/libcuda.so" "$(@D)/cuda/lib/libcuda.so" && cp "/usr/local/cuda-9.0/targets/x86_64-linux/lib/libcudart.so.9.0" "$(@D)/cuda/lib/libcudart.so.9.0" && cp "/usr/local/cuda-9.0/targets/x86_64-linux/lib/libcudart_static.a" "$(@D)/cuda/lib/libcudart_static.a" && cp "/usr/local/cuda-9.0/targets/x86_64-linux/lib/libcublas.so.9.0" "$(@D)/cuda/lib/libcublas.so.9.0" && cp "/usr/local/cuda-9.0/targets/x86_64-linux/lib/libcusolver.so.9.0" "$(@D)/cuda/lib/libcusolver.so.9.0" && cp "/usr/local/cuda-9.0/targets/x86_64-linux/lib/libcurand.so.9.0" "$(@D)/cuda/lib/libcurand.so.9.0" && cp "/usr/local/cuda-9.0/targets/x86_64-linux/lib/libcufft.so.9.0" "$(@D)/cuda/lib/libcufft.so.9.0" && cp "/usr/lib/x86_64-linux-gnu/libcudnn.so.7" "$(@D)/cuda/lib/libcudnn.so.7" && cp "/usr/local/cuda-9.0/extras/CUPTI/lib64/libcupti.so.9.0" "$(@D)/cuda/lib/libcupti.so.9.0" """, ) -genrule( +filegroup( name = "cudnn-include", - outs = [ - "cuda/include/cudnn.h", - ], - cmd = """ -if [ -d "$(@D)/extras" ]; then rm $(@D)/extras -drf; fi && if [ -d "$(@D)/include" ]; then rm $(@D)/include -drf; fi && if [ -d "$(@D)/lib" ]; then rm $(@D)/lib -drf; fi && if [ -d "$(@D)/nvvm" ]; then rm $(@D)/nvvm -drf; fi && cp "/usr/include/cudnn.h" "$(@D)/cudnn.h" - """, + srcs = [], ) diff --git a/third_party/toolchains/gpus/py/BUILD b/third_party/toolchains/gpus/py/BUILD new file mode 100644 index 0000000000000000000000000000000000000000..2d5ace93ff5054927cda61b0302af4edd8fe56c1 --- /dev/null +++ b/third_party/toolchains/gpus/py/BUILD @@ -0,0 +1,171 @@ +# A build file to configure python remote repository used with Bazel remote +# execution service +# DO NOT EDIT: automatically generated BUILD file + +licenses(["restricted"]) + +package(default_visibility = ["//visibility:public"]) + +cc_library( + name = "python_headers", + hdrs = [":python_include"], + data = select({ + ":windows": [":python_import_lib"], + "//conditions:default": [], + }), + includes = ["python_include"], + linkopts = select({ + # TODO(pcloudy): Ideally, this should just go into deps after resolving + # https://github.com/bazelbuild/bazel/issues/3237, + ":windows": ["$(locations :python_import_lib)"], + "//conditions:default": [], + }), +) + +cc_library( + name = "numpy_headers", + hdrs = [":numpy_include"], + includes = ["numpy_include"], +) + +config_setting( + name = "windows", + values = {"cpu": "x64_windows"}, + visibility = ["//visibility:public"], +) + +genrule( + name = "python_include", + outs = [ + "python_include/Python-ast.h", + "python_include/Python.h", + "python_include/abstract.h", + "python_include/asdl.h", + "python_include/ast.h", + "python_include/bitset.h", + "python_include/boolobject.h", + "python_include/bufferobject.h", + "python_include/bytearrayobject.h", + "python_include/bytes_methods.h", + "python_include/bytesobject.h", + "python_include/cStringIO.h", + "python_include/cellobject.h", + "python_include/ceval.h", + "python_include/classobject.h", + "python_include/cobject.h", + "python_include/code.h", + "python_include/codecs.h", + "python_include/compile.h", + "python_include/complexobject.h", + "python_include/datetime.h", + "python_include/descrobject.h", + "python_include/dictobject.h", + "python_include/dtoa.h", + "python_include/enumobject.h", + "python_include/errcode.h", + "python_include/eval.h", + "python_include/fileobject.h", + "python_include/floatobject.h", + "python_include/frameobject.h", + "python_include/funcobject.h", + "python_include/genobject.h", + "python_include/graminit.h", + "python_include/grammar.h", + "python_include/import.h", + "python_include/intobject.h", + "python_include/intrcheck.h", + "python_include/iterobject.h", + "python_include/listobject.h", + "python_include/longintrepr.h", + "python_include/longobject.h", + "python_include/marshal.h", + "python_include/memoryobject.h", + "python_include/metagrammar.h", + "python_include/methodobject.h", + "python_include/modsupport.h", + "python_include/moduleobject.h", + "python_include/node.h", + "python_include/object.h", + "python_include/objimpl.h", + "python_include/opcode.h", + "python_include/osdefs.h", + "python_include/parsetok.h", + "python_include/patchlevel.h", + "python_include/pgen.h", + "python_include/pgenheaders.h", + "python_include/py_curses.h", + "python_include/pyarena.h", + "python_include/pycapsule.h", + "python_include/pyconfig.h", + "python_include/pyctype.h", + "python_include/pydebug.h", + "python_include/pyerrors.h", + "python_include/pyexpat.h", + "python_include/pyfpe.h", + "python_include/pygetopt.h", + "python_include/pymacconfig.h", + "python_include/pymactoolbox.h", + "python_include/pymath.h", + "python_include/pymem.h", + "python_include/pyport.h", + "python_include/pystate.h", + "python_include/pystrcmp.h", + "python_include/pystrtod.h", + "python_include/pythonrun.h", + "python_include/pythread.h", + "python_include/rangeobject.h", + "python_include/setobject.h", + "python_include/sliceobject.h", + "python_include/stringobject.h", + "python_include/structmember.h", + "python_include/structseq.h", + "python_include/symtable.h", + "python_include/sysmodule.h", + "python_include/timefuncs.h", + "python_include/token.h", + "python_include/traceback.h", + "python_include/tupleobject.h", + "python_include/ucnhash.h", + "python_include/unicodeobject.h", + "python_include/warnings.h", + "python_include/weakrefobject.h", + ], + cmd = """ +cp "/usr/include/python2.7/Python-ast.h" "$(@D)/python_include/Python-ast.h" && cp "/usr/include/python2.7/Python.h" "$(@D)/python_include/Python.h" && cp "/usr/include/python2.7/abstract.h" "$(@D)/python_include/abstract.h" && cp "/usr/include/python2.7/asdl.h" "$(@D)/python_include/asdl.h" && cp "/usr/include/python2.7/ast.h" "$(@D)/python_include/ast.h" && cp "/usr/include/python2.7/bitset.h" "$(@D)/python_include/bitset.h" && cp "/usr/include/python2.7/boolobject.h" "$(@D)/python_include/boolobject.h" && cp "/usr/include/python2.7/bufferobject.h" "$(@D)/python_include/bufferobject.h" && cp "/usr/include/python2.7/bytearrayobject.h" "$(@D)/python_include/bytearrayobject.h" && cp "/usr/include/python2.7/bytes_methods.h" "$(@D)/python_include/bytes_methods.h" && cp "/usr/include/python2.7/bytesobject.h" "$(@D)/python_include/bytesobject.h" && cp "/usr/include/python2.7/cStringIO.h" "$(@D)/python_include/cStringIO.h" && cp "/usr/include/python2.7/cellobject.h" "$(@D)/python_include/cellobject.h" && cp "/usr/include/python2.7/ceval.h" "$(@D)/python_include/ceval.h" && cp "/usr/include/python2.7/classobject.h" "$(@D)/python_include/classobject.h" && cp "/usr/include/python2.7/cobject.h" "$(@D)/python_include/cobject.h" && cp "/usr/include/python2.7/code.h" "$(@D)/python_include/code.h" && cp "/usr/include/python2.7/codecs.h" "$(@D)/python_include/codecs.h" && cp "/usr/include/python2.7/compile.h" "$(@D)/python_include/compile.h" && cp "/usr/include/python2.7/complexobject.h" "$(@D)/python_include/complexobject.h" && cp "/usr/include/python2.7/datetime.h" "$(@D)/python_include/datetime.h" && cp "/usr/include/python2.7/descrobject.h" "$(@D)/python_include/descrobject.h" && cp "/usr/include/python2.7/dictobject.h" "$(@D)/python_include/dictobject.h" && cp "/usr/include/python2.7/dtoa.h" "$(@D)/python_include/dtoa.h" && cp "/usr/include/python2.7/enumobject.h" "$(@D)/python_include/enumobject.h" && cp "/usr/include/python2.7/errcode.h" "$(@D)/python_include/errcode.h" && cp "/usr/include/python2.7/eval.h" "$(@D)/python_include/eval.h" && cp "/usr/include/python2.7/fileobject.h" "$(@D)/python_include/fileobject.h" && cp "/usr/include/python2.7/floatobject.h" "$(@D)/python_include/floatobject.h" && cp "/usr/include/python2.7/frameobject.h" "$(@D)/python_include/frameobject.h" && cp "/usr/include/python2.7/funcobject.h" "$(@D)/python_include/funcobject.h" && cp "/usr/include/python2.7/genobject.h" "$(@D)/python_include/genobject.h" && cp "/usr/include/python2.7/graminit.h" "$(@D)/python_include/graminit.h" && cp "/usr/include/python2.7/grammar.h" "$(@D)/python_include/grammar.h" && cp "/usr/include/python2.7/import.h" "$(@D)/python_include/import.h" && cp "/usr/include/python2.7/intobject.h" "$(@D)/python_include/intobject.h" && cp "/usr/include/python2.7/intrcheck.h" "$(@D)/python_include/intrcheck.h" && cp "/usr/include/python2.7/iterobject.h" "$(@D)/python_include/iterobject.h" && cp "/usr/include/python2.7/listobject.h" "$(@D)/python_include/listobject.h" && cp "/usr/include/python2.7/longintrepr.h" "$(@D)/python_include/longintrepr.h" && cp "/usr/include/python2.7/longobject.h" "$(@D)/python_include/longobject.h" && cp "/usr/include/python2.7/marshal.h" "$(@D)/python_include/marshal.h" && cp "/usr/include/python2.7/memoryobject.h" "$(@D)/python_include/memoryobject.h" && cp "/usr/include/python2.7/metagrammar.h" "$(@D)/python_include/metagrammar.h" && cp "/usr/include/python2.7/methodobject.h" "$(@D)/python_include/methodobject.h" && cp "/usr/include/python2.7/modsupport.h" "$(@D)/python_include/modsupport.h" && cp "/usr/include/python2.7/moduleobject.h" "$(@D)/python_include/moduleobject.h" && cp "/usr/include/python2.7/node.h" "$(@D)/python_include/node.h" && cp "/usr/include/python2.7/object.h" "$(@D)/python_include/object.h" && cp "/usr/include/python2.7/objimpl.h" "$(@D)/python_include/objimpl.h" && cp "/usr/include/python2.7/opcode.h" "$(@D)/python_include/opcode.h" && cp "/usr/include/python2.7/osdefs.h" "$(@D)/python_include/osdefs.h" && cp "/usr/include/python2.7/parsetok.h" "$(@D)/python_include/parsetok.h" && cp "/usr/include/python2.7/patchlevel.h" "$(@D)/python_include/patchlevel.h" && cp "/usr/include/python2.7/pgen.h" "$(@D)/python_include/pgen.h" && cp "/usr/include/python2.7/pgenheaders.h" "$(@D)/python_include/pgenheaders.h" && cp "/usr/include/python2.7/py_curses.h" "$(@D)/python_include/py_curses.h" && cp "/usr/include/python2.7/pyarena.h" "$(@D)/python_include/pyarena.h" && cp "/usr/include/python2.7/pycapsule.h" "$(@D)/python_include/pycapsule.h" && cp "/usr/include/python2.7/pyconfig.h" "$(@D)/python_include/pyconfig.h" && cp "/usr/include/python2.7/pyctype.h" "$(@D)/python_include/pyctype.h" && cp "/usr/include/python2.7/pydebug.h" "$(@D)/python_include/pydebug.h" && cp "/usr/include/python2.7/pyerrors.h" "$(@D)/python_include/pyerrors.h" && cp "/usr/include/python2.7/pyexpat.h" "$(@D)/python_include/pyexpat.h" && cp "/usr/include/python2.7/pyfpe.h" "$(@D)/python_include/pyfpe.h" && cp "/usr/include/python2.7/pygetopt.h" "$(@D)/python_include/pygetopt.h" && cp "/usr/include/python2.7/pymacconfig.h" "$(@D)/python_include/pymacconfig.h" && cp "/usr/include/python2.7/pymactoolbox.h" "$(@D)/python_include/pymactoolbox.h" && cp "/usr/include/python2.7/pymath.h" "$(@D)/python_include/pymath.h" && cp "/usr/include/python2.7/pymem.h" "$(@D)/python_include/pymem.h" && cp "/usr/include/python2.7/pyport.h" "$(@D)/python_include/pyport.h" && cp "/usr/include/python2.7/pystate.h" "$(@D)/python_include/pystate.h" && cp "/usr/include/python2.7/pystrcmp.h" "$(@D)/python_include/pystrcmp.h" && cp "/usr/include/python2.7/pystrtod.h" "$(@D)/python_include/pystrtod.h" && cp "/usr/include/python2.7/pythonrun.h" "$(@D)/python_include/pythonrun.h" && cp "/usr/include/python2.7/pythread.h" "$(@D)/python_include/pythread.h" && cp "/usr/include/python2.7/rangeobject.h" "$(@D)/python_include/rangeobject.h" && cp "/usr/include/python2.7/setobject.h" "$(@D)/python_include/setobject.h" && cp "/usr/include/python2.7/sliceobject.h" "$(@D)/python_include/sliceobject.h" && cp "/usr/include/python2.7/stringobject.h" "$(@D)/python_include/stringobject.h" && cp "/usr/include/python2.7/structmember.h" "$(@D)/python_include/structmember.h" && cp "/usr/include/python2.7/structseq.h" "$(@D)/python_include/structseq.h" && cp "/usr/include/python2.7/symtable.h" "$(@D)/python_include/symtable.h" && cp "/usr/include/python2.7/sysmodule.h" "$(@D)/python_include/sysmodule.h" && cp "/usr/include/python2.7/timefuncs.h" "$(@D)/python_include/timefuncs.h" && cp "/usr/include/python2.7/token.h" "$(@D)/python_include/token.h" && cp "/usr/include/python2.7/traceback.h" "$(@D)/python_include/traceback.h" && cp "/usr/include/python2.7/tupleobject.h" "$(@D)/python_include/tupleobject.h" && cp "/usr/include/python2.7/ucnhash.h" "$(@D)/python_include/ucnhash.h" && cp "/usr/include/python2.7/unicodeobject.h" "$(@D)/python_include/unicodeobject.h" && cp "/usr/include/python2.7/warnings.h" "$(@D)/python_include/warnings.h" && cp "/usr/include/python2.7/weakrefobject.h" "$(@D)/python_include/weakrefobject.h" + """, +) + +genrule( + name = "numpy_include", + outs = [ + "numpy_include/numpy/__multiarray_api.h", + "numpy_include/numpy/__ufunc_api.h", + "numpy_include/numpy/_neighborhood_iterator_imp.h", + "numpy_include/numpy/_numpyconfig.h", + "numpy_include/numpy/arrayobject.h", + "numpy_include/numpy/arrayscalars.h", + "numpy_include/numpy/halffloat.h", + "numpy_include/numpy/multiarray_api.txt", + "numpy_include/numpy/ndarrayobject.h", + "numpy_include/numpy/ndarraytypes.h", + "numpy_include/numpy/noprefix.h", + "numpy_include/numpy/npy_1_7_deprecated_api.h", + "numpy_include/numpy/npy_3kcompat.h", + "numpy_include/numpy/npy_common.h", + "numpy_include/numpy/npy_cpu.h", + "numpy_include/numpy/npy_endian.h", + "numpy_include/numpy/npy_interrupt.h", + "numpy_include/numpy/npy_math.h", + "numpy_include/numpy/npy_no_deprecated_api.h", + "numpy_include/numpy/npy_os.h", + "numpy_include/numpy/numpyconfig.h", + "numpy_include/numpy/old_defines.h", + "numpy_include/numpy/oldnumeric.h", + "numpy_include/numpy/ufunc_api.txt", + "numpy_include/numpy/ufuncobject.h", + "numpy_include/numpy/utils.h", + ], + cmd = """ +cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/__multiarray_api.h" "$(@D)/numpy_include/numpy/__multiarray_api.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/__ufunc_api.h" "$(@D)/numpy_include/numpy/__ufunc_api.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/_neighborhood_iterator_imp.h" "$(@D)/numpy_include/numpy/_neighborhood_iterator_imp.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/_numpyconfig.h" "$(@D)/numpy_include/numpy/_numpyconfig.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/arrayobject.h" "$(@D)/numpy_include/numpy/arrayobject.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/arrayscalars.h" "$(@D)/numpy_include/numpy/arrayscalars.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/halffloat.h" "$(@D)/numpy_include/numpy/halffloat.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/multiarray_api.txt" "$(@D)/numpy_include/numpy/multiarray_api.txt" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/ndarrayobject.h" "$(@D)/numpy_include/numpy/ndarrayobject.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/ndarraytypes.h" "$(@D)/numpy_include/numpy/ndarraytypes.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/noprefix.h" "$(@D)/numpy_include/numpy/noprefix.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h" "$(@D)/numpy_include/numpy/npy_1_7_deprecated_api.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_3kcompat.h" "$(@D)/numpy_include/numpy/npy_3kcompat.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_common.h" "$(@D)/numpy_include/numpy/npy_common.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_cpu.h" "$(@D)/numpy_include/numpy/npy_cpu.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_endian.h" "$(@D)/numpy_include/numpy/npy_endian.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_interrupt.h" "$(@D)/numpy_include/numpy/npy_interrupt.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_math.h" "$(@D)/numpy_include/numpy/npy_math.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_no_deprecated_api.h" "$(@D)/numpy_include/numpy/npy_no_deprecated_api.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_os.h" "$(@D)/numpy_include/numpy/npy_os.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/numpyconfig.h" "$(@D)/numpy_include/numpy/numpyconfig.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/old_defines.h" "$(@D)/numpy_include/numpy/old_defines.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/oldnumeric.h" "$(@D)/numpy_include/numpy/oldnumeric.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/ufunc_api.txt" "$(@D)/numpy_include/numpy/ufunc_api.txt" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/ufuncobject.h" "$(@D)/numpy_include/numpy/ufuncobject.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/utils.h" "$(@D)/numpy_include/numpy/utils.h" + """, +)