diff options
| author |  A. Unique TensorFlower <gardener@tensorflow.org> | 2017-07-04 10:19:42 -0700 |
|---|---|---|
| committer | TensorFlower Gardener <gardener@tensorflow.org> | 2017-07-04 10:23:59 -0700 |
| commit | af23ae65db2585f4a18d0bc5f21f15e94805aa4f (patch) | |
| tree | a805f64d0a85fa29ff69d204634379b80cdbcbf1 /tensorflow/contrib/tfprof | |
| parent | 11ec8b7cfdec0fd498182d0ad8f550b4a8ddaf13 (diff) | |
Migrating tfprof python API to tensorflow/python/profiler
Migrating tfprof c++ to tensorflow/core/profiler
API changes:
New tf.profiler namespace.
Within tf.profiler namespace:
tf.profiler.advise # One-shot advise function
tf.profiler.profile # One-shot profile function
tf.profiler.Profiler # Multi-step profile/advise class
tf.profiler.write_op_log # Write profile for offline analysis
PiperOrigin-RevId: 160901831
Diffstat (limited to 'tensorflow/contrib/tfprof')
18 files changed, 112 insertions, 2800 deletions
diff --git a/tensorflow/contrib/tfprof/BUILD b/tensorflow/contrib/tfprof/BUILD index 944d767e21..9b9215234e 100644 --- a/tensorflow/contrib/tfprof/BUILD +++ b/tensorflow/contrib/tfprof/BUILD @@ -8,12 +8,25 @@ py_library( name = "tfprof", srcs = [ "__init__.py", + "model_analyzer.py", + "tfprof_logger.py", ], srcs_version = "PY2AND3", visibility = ["//tensorflow:__subpackages__"], deps = [ - "//tensorflow/contrib/tfprof/python/tools/tfprof:model_analyzer", - "//tensorflow/contrib/tfprof/python/tools/tfprof:tfprof_logger", - "//tensorflow/python:util", + "//tensorflow/python/profiler:model_analyzer", + "//tensorflow/python/profiler:tfprof_logger", ], ) + +filegroup( + name = "all_files", + srcs = glob( + ["**/*"], + exclude = [ + "**/METADATA", + "**/OWNERS", + ], + ), + visibility = ["//tensorflow:__subpackages__"], +) diff --git a/tensorflow/contrib/tfprof/README.md b/tensorflow/contrib/tfprof/README.md index 4fa1ccea69..eefd88793e 100644 --- a/tensorflow/contrib/tfprof/README.md +++ b/tensorflow/contrib/tfprof/README.md @@ -1,26 +1,24 @@ # tfprof: TensorFlow Profiler and Beyond -# Full Document in tensorflow/tools/tfprof/README.md - -Author: Xin Pan (xpan@google.com, github: panyx0718), Jon Shlens, Yao Zhang - -Consultants: Jon Shlens, Pete Warden - -###Major Features - -1. Measure model parameters, float operations, tensor shapes. -2. Profile op execution times, requested memory size and device placement. -3. Inspect checkpoint tensors' shapes and their values. -4. Selectively group, filter, account and order ops. - -####tfprof supports 3 views to organize TensorFlow model profiles - - * code view: Stats are associated your Python codes and organized as call stacks. - * scope view: Stats are organized as name scope hierarchies. - * graph view: Stats are organized as Tensorflow Op graph. - -####For each view, there are 3 ways to display outputs: - - * stdout: Results are written to stdout. - * timeline: Visualized in chrome browser as time series. - * file: Results are dumped to file. +<h1>Please use `tf.profiler.xxx` instead of `tf.contrib.tfprof.xxx`</h1> +<h1>Full Document in tensorflow/core/profiler/README.md<h1> + +###Features + +* Profile model architectures + * parameters, tensor shapes, float operations, device placement, etc. +* Profile model performance + * execution time, memory consumption + * Profile multiple steps. +* Auto profile and advise. + * accelerator utilization check + * expensive operation check + * operation configuration check + * distributed runtime check (Not OSS) + +###Interfaces + +* Python API +* Command Line +* Visualization +* C++ API (Not public, contact us if needed.) diff --git a/tensorflow/contrib/tfprof/__init__.py b/tensorflow/contrib/tfprof/__init__.py index f3952f6cb5..7a023e5d67 100644 --- a/tensorflow/contrib/tfprof/__init__.py +++ b/tensorflow/contrib/tfprof/__init__.py @@ -17,5 +17,6 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.tfprof.python.tools.tfprof import model_analyzer -from tensorflow.contrib.tfprof.python.tools.tfprof import tfprof_logger +# pylint: disable=unused-import +from tensorflow.contrib.tfprof import model_analyzer +from tensorflow.contrib.tfprof import tfprof_logger diff --git a/tensorflow/contrib/tfprof/model_analyzer.py b/tensorflow/contrib/tfprof/model_analyzer.py new file mode 100644 index 0000000000..04b5063218 --- /dev/null +++ b/tensorflow/contrib/tfprof/model_analyzer.py @@ -0,0 +1,47 @@ +# 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. +# ============================================================================== +"""Model Analyzer. + +Analyze model, including shape, params, time, memory, structure, etc. +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + + +# Import the names here for existing users. +# pylint: disable=unused-import +from tensorflow.python.profiler.model_analyzer import advise as _advise +from tensorflow.python.profiler.model_analyzer import ALL_ADVICE +from tensorflow.python.profiler.model_analyzer import FLOAT_OPS_OPTIONS +from tensorflow.python.profiler.model_analyzer import PRINT_ALL_TIMING_MEMORY +from tensorflow.python.profiler.model_analyzer import profile as _profile +from tensorflow.python.profiler.model_analyzer import Profiler +from tensorflow.python.profiler.model_analyzer import TRAINABLE_VARS_PARAMS_STAT_OPTIONS + +_DEFAULT_PROFILE_OPTIONS = 0 +_DEFAULT_ADVISE_OPTIONS = 0 + + +def advise(graph, run_meta=None, tfprof_options=_DEFAULT_ADVISE_OPTIONS): + return _advise(graph, run_meta, tfprof_options) + + +def print_model_analysis(graph, + run_meta=None, + op_log=None, + tfprof_cmd='scope', + tfprof_options=_DEFAULT_PROFILE_OPTIONS): + return _profile(graph, run_meta, op_log, tfprof_cmd, tfprof_options) diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/BUILD b/tensorflow/contrib/tfprof/python/tools/tfprof/BUILD deleted file mode 100644 index b7c79edfca..0000000000 --- a/tensorflow/contrib/tfprof/python/tools/tfprof/BUILD +++ /dev/null @@ -1,116 +0,0 @@ -package(default_visibility = ["//visibility:public"]) - -licenses(["notice"]) # Apache 2.0 - -load("//tensorflow:tensorflow.bzl", "cuda_py_test") -load("//tensorflow:tensorflow.bzl", "py_test") -load("//tensorflow:tensorflow.bzl", "tf_py_test") - -py_library( - name = "model_analyzer", - srcs = ["model_analyzer.py"], - srcs_version = "PY2AND3", - deps = [ - ":tfprof_logger", - "//tensorflow/python:pywrap_tensorflow", - "//tensorflow/tools/tfprof:protos_all_py", - ], -) - -cuda_py_test( - name = "model_analyzer_test", - srcs = ["model_analyzer_test.py"], - additional_deps = [ - ":model_analyzer", - "//tensorflow/contrib/tfprof/python/tools/tfprof/internal:model_analyzer_testlib", - "//tensorflow/python:client", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:platform", - "//tensorflow/python:variables", - ], - tags = ["no_pip"], -) - -cuda_py_test( - name = "profiler_test", - srcs = ["profiler_test.py"], - additional_deps = [ - ":model_analyzer", - "//tensorflow/contrib/tfprof/python/tools/tfprof/internal:model_analyzer_testlib", - "//tensorflow/python:client", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:platform", - "//tensorflow/python:variables", - ], - tags = ["no_pip"], -) - -py_library( - name = "tfprof_logger", - srcs = ["tfprof_logger.py"], - srcs_version = "PY2AND3", - deps = [ - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:platform", - "//tensorflow/tools/tfprof:protos_all_py", - "@six_archive//:six", - ], -) - -tf_py_test( - name = "tfprof_logger_test", - size = "small", - srcs = ["tfprof_logger_test.py"], - additional_deps = [ - ":tfprof_logger", - "//tensorflow/contrib/copy_graph:copy_graph_py", - "//tensorflow/core:protos_all_py", - "//tensorflow/python:array_ops", - "//tensorflow/python:client", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:math_ops", - "//tensorflow/tools/tfprof:protos_all_py", - ], -) - -py_library( - name = "pprof_profiler", - srcs = ["pprof_profiler.py"], - srcs_version = "PY2AND3", - deps = ["@com_google_pprof//:pprof_proto_py"], -) - -py_test( - name = "pprof_profiler_test", - size = "small", - srcs = ["pprof_profiler_test.py"], - main = "pprof_profiler_test.py", - srcs_version = "PY2AND3", - tags = ["no_pip"], # TODO(annarev): get it working with pip. - deps = [ - ":pprof_profiler", - "//tensorflow/python:client", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework_test_lib", - "//tensorflow/python:platform_test", - "@com_google_pprof//:pprof_proto_py", - ], -) - -# ----------------------------------------------------------------------------- -# Google-internal targets. These must be at the end for syncrepo. - -filegroup( - name = "all_files", - srcs = glob( - ["**/*"], - exclude = [ - "**/METADATA", - "**/OWNERS", - ], - ), - visibility = ["//tensorflow:__subpackages__"], -) diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/__init__.py b/tensorflow/contrib/tfprof/python/tools/tfprof/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 --- a/tensorflow/contrib/tfprof/python/tools/tfprof/__init__.py +++ /dev/null diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/BUILD b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/BUILD deleted file mode 100644 index a498574a95..0000000000 --- a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/BUILD +++ /dev/null @@ -1,80 +0,0 @@ -package(default_visibility = ["//tensorflow/contrib/tfprof/python/tools/tfprof:__subpackages__"]) - -licenses(["notice"]) # Apache 2.0 - -load("//tensorflow:tensorflow.bzl", "cuda_py_test") -load("//tensorflow:tensorflow.bzl", "py_test") -load("//tensorflow:tensorflow.bzl", "tf_py_test") -load("//tensorflow:tensorflow.bzl", "tf_py_wrap_cc") - -py_library( - name = "model_analyzer_testlib", - srcs = ["model_analyzer_testlib.py"], - srcs_version = "PY2AND3", - deps = [ - "//tensorflow/contrib/rnn:rnn_py", - "//tensorflow/contrib/tfprof/python/tools/tfprof:model_analyzer", - "//tensorflow/core:protos_all_py", - "//tensorflow/python:array_ops", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:init_ops", - "//tensorflow/python:math_ops", - "//tensorflow/python:nn_ops", - "//tensorflow/python:platform", - "//tensorflow/python:rnn", - "//tensorflow/python:training", - "//tensorflow/python:variable_scope", - "//tensorflow/python:variables", - ], -) - -py_test( - name = "print_model_analysis_test", - srcs = ["print_model_analysis_test.py"], - srcs_version = "PY2AND3", - deps = [ - "//tensorflow/python:array_ops", - "//tensorflow/python:client", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:framework_test_lib", - "//tensorflow/python:init_ops", - "//tensorflow/python:nn_ops", - "//tensorflow/python:platform_test", - "//tensorflow/python:pywrap_tensorflow", - "//tensorflow/python:variable_scope", - "//tensorflow/tools/tfprof:protos_all_py", - ], -) - -cuda_py_test( - name = "run_metadata_test", - srcs = ["run_metadata_test.py"], - additional_deps = [ - ":model_analyzer_testlib", - "//tensorflow/contrib/tfprof/python/tools/tfprof:model_analyzer", - "//tensorflow/python:array_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:math_ops", - "//tensorflow/python:random_ops", - "//tensorflow/tools/tfprof:protos_all_py", - ], - tags = [ - "no_pip", - ], -) - -# ----------------------------------------------------------------------------- -# Google-internal targets. These must be at the end for syncrepo. - -filegroup( - name = "all_files", - srcs = glob( - ["**/*"], - exclude = [ - "**/METADATA", - "**/OWNERS", - ], - ), - visibility = ["//tensorflow:__subpackages__"], -) diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/model_analyzer_testlib.py b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/model_analyzer_testlib.py deleted file mode 100644 index 42b83fde7c..0000000000 --- a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/model_analyzer_testlib.py +++ /dev/null @@ -1,97 +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. -# ============================================================================== -"""A test lib that defines some models.""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -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_grad # pylint: disable=unused-import -from tensorflow.python.ops import nn_ops -from tensorflow.python.ops import rnn -from tensorflow.python.ops import rnn_cell -from tensorflow.python.ops import tensor_array_grad # pylint: disable=unused-import -from tensorflow.python.ops import variable_scope -from tensorflow.python.training import gradient_descent - - -def BuildSmallModel(): - """Build a small forward conv model.""" - image = array_ops.zeros([2, 6, 6, 3]) - _ = variable_scope.get_variable( - 'ScalarW', [], - dtypes.float32, - initializer=init_ops.random_normal_initializer(stddev=0.001)) - kernel = variable_scope.get_variable( - 'DW', [3, 3, 3, 6], - dtypes.float32, - initializer=init_ops.random_normal_initializer(stddev=0.001)) - x = nn_ops.conv2d(image, kernel, [1, 2, 2, 1], padding='SAME') - kernel = variable_scope.get_variable( - 'DW2', [2, 2, 6, 12], - dtypes.float32, - initializer=init_ops.random_normal_initializer(stddev=0.001)) - x = nn_ops.conv2d(x, kernel, [1, 2, 2, 1], padding='SAME') - return x - - -def BuildFullModel(): - """Build the full model with conv,rnn,opt.""" - seq = [] - for i in range(4): - with variable_scope.variable_scope('inp_%d' % i): - seq.append(array_ops.reshape(BuildSmallModel(), [2, 1, -1])) - - cell = rnn_cell.BasicRNNCell(16) - out = rnn.dynamic_rnn( - cell, array_ops.concat(seq, axis=1), dtype=dtypes.float32)[0] - - target = array_ops.ones_like(out) - loss = nn_ops.l2_loss(math_ops.reduce_mean(target - out)) - sgd_op = gradient_descent.GradientDescentOptimizer(1e-2) - return sgd_op.minimize(loss) - - -def BuildSplitableModel(): - """Build a small model that can be run partially in each step.""" - image = array_ops.zeros([2, 6, 6, 3]) - - kernel1 = variable_scope.get_variable( - 'DW', [3, 3, 3, 6], - dtypes.float32, - initializer=init_ops.random_normal_initializer(stddev=0.001)) - r1 = nn_ops.conv2d(image, kernel1, [1, 2, 2, 1], padding='SAME') - - kernel2 = variable_scope.get_variable( - 'DW2', [2, 3, 3, 6], - dtypes.float32, - initializer=init_ops.random_normal_initializer(stddev=0.001)) - r2 = nn_ops.conv2d(image, kernel2, [1, 2, 2, 1], padding='SAME') - - r3 = r1 + r2 - return r1, r2, r3 - - -def SearchTFProfNode(node, name): - """Search a node in the tree.""" - if node.name == name: - return node - for c in node.children: - r = SearchTFProfNode(c, name) - if r: return r - return None diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/print_model_analysis_test.py b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/print_model_analysis_test.py deleted file mode 100644 index 7ded5e890f..0000000000 --- a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/print_model_analysis_test.py +++ /dev/null @@ -1,475 +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. -# ============================================================================== -"""print_model_analysis test.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from google.protobuf import text_format - -from tensorflow.python.client import session -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 nn_ops -from tensorflow.python.ops import variable_scope -from tensorflow.python.platform import test -from tensorflow.tools.tfprof import tfprof_options_pb2 -from tensorflow.tools.tfprof import tfprof_output_pb2 - -# pylint: disable=g-bad-import-order -# XXX: this depends on pywrap_tensorflow and must come later -from tensorflow.python import pywrap_tensorflow as print_mdl - -# pylint: disable=bad-whitespace -# pylint: disable=bad-continuation -TEST_OPTIONS = { - 'max_depth': 10000, - 'min_bytes': 0, - 'min_micros': 0, - 'min_params': 0, - 'min_float_ops': 0, - 'order_by': 'name', - 'account_type_regexes': ['.*'], - 'start_name_regexes': ['.*'], - 'trim_name_regexes': [], - 'show_name_regexes': ['.*'], - 'hide_name_regexes': [], - 'account_displayed_op_only': True, - 'select': ['params'], - 'output': 'stdout', -} - -# pylint: enable=bad-whitespace -# pylint: enable=bad-continuation - - -class PrintModelAnalysisTest(test.TestCase): - - def _BuildSmallModel(self): - image = array_ops.zeros([2, 6, 6, 3]) - kernel = variable_scope.get_variable( - 'DW', [6, 6, 3, 6], - dtypes.float32, - initializer=init_ops.random_normal_initializer(stddev=0.001)) - x = nn_ops.conv2d(image, kernel, [1, 2, 2, 1], padding='SAME') - return x - - def testPrintModelAnalysis(self): - opts = tfprof_options_pb2.OptionsProto() - opts.max_depth = TEST_OPTIONS['max_depth'] - opts.min_bytes = TEST_OPTIONS['min_bytes'] - opts.min_micros = TEST_OPTIONS['min_micros'] - opts.min_params = TEST_OPTIONS['min_params'] - opts.min_float_ops = TEST_OPTIONS['min_float_ops'] - opts.order_by = TEST_OPTIONS['order_by'] - opts.step = -1 - for p in TEST_OPTIONS['account_type_regexes']: - opts.account_type_regexes.append(p) - for p in TEST_OPTIONS['start_name_regexes']: - opts.start_name_regexes.append(p) - for p in TEST_OPTIONS['trim_name_regexes']: - opts.trim_name_regexes.append(p) - for p in TEST_OPTIONS['show_name_regexes']: - opts.show_name_regexes.append(p) - for p in TEST_OPTIONS['hide_name_regexes']: - opts.hide_name_regexes.append(p) - opts.account_displayed_op_only = TEST_OPTIONS['account_displayed_op_only'] - for p in TEST_OPTIONS['select']: - opts.select.append(p) - opts.output = TEST_OPTIONS['output'] - - with session.Session() as sess, ops.device('/cpu:0'): - _ = self._BuildSmallModel() - tfprof_pb = tfprof_output_pb2.TFGraphNodeProto() - tfprof_pb.ParseFromString( - print_mdl.PrintModelAnalysis( - sess.graph.as_graph_def(add_shapes=True).SerializeToString(), - b'', - b'', - b'scope', - opts.SerializeToString())) - - expected_pb = tfprof_output_pb2.TFGraphNodeProto() - text_format.Merge(r"""name: "_TFProfRoot" - exec_micros: 0 - requested_bytes: 0 - total_exec_micros: 0 - total_requested_bytes: 0 - total_parameters: 648 - children { - name: "Conv2D" - exec_micros: 0 - requested_bytes: 0 - total_exec_micros: 0 - total_requested_bytes: 0 - total_parameters: 0 - float_ops: 0 - total_float_ops: 0 - input_shapes { - key: 0 - value { - dim { - size: 2 - } - dim { - size: 6 - } - dim { - size: 6 - } - dim { - size: 3 - } - } - } - input_shapes { - key: 1 - value { - dim { - size: 6 - } - dim { - size: 6 - } - dim { - size: 3 - } - dim { - size: 6 - } - } - } - accelerator_exec_micros: 0 - cpu_exec_micros: 0 - total_accelerator_exec_micros: 0 - total_cpu_exec_micros: 0 - run_count: 0 - total_run_count: 0 - total_definition_count: 1 - } - children { - name: "DW" - exec_micros: 0 - requested_bytes: 0 - parameters: 648 - total_exec_micros: 0 - total_requested_bytes: 0 - total_parameters: 648 - children { - name: "DW/Assign" - exec_micros: 0 - requested_bytes: 0 - total_exec_micros: 0 - total_requested_bytes: 0 - total_parameters: 0 - float_ops: 0 - total_float_ops: 0 - input_shapes { - key: 0 - value { - dim { - size: 6 - } - dim { - size: 6 - } - dim { - size: 3 - } - dim { - size: 6 - } - } - } - input_shapes { - key: 1 - value { - dim { - size: 6 - } - dim { - size: 6 - } - dim { - size: 3 - } - dim { - size: 6 - } - } - } - accelerator_exec_micros: 0 - cpu_exec_micros: 0 - total_accelerator_exec_micros: 0 - total_cpu_exec_micros: 0 - run_count: 0 - total_run_count: 0 - total_definition_count: 1 - } - children { - name: "DW/Initializer" - exec_micros: 0 - requested_bytes: 0 - total_exec_micros: 0 - total_requested_bytes: 0 - total_parameters: 0 - children { - name: "DW/Initializer/random_normal" - exec_micros: 0 - requested_bytes: 0 - total_exec_micros: 0 - total_requested_bytes: 0 - total_parameters: 0 - children { - name: "DW/Initializer/random_normal/RandomStandardNormal" - exec_micros: 0 - requested_bytes: 0 - total_exec_micros: 0 - total_requested_bytes: 0 - total_parameters: 0 - float_ops: 0 - total_float_ops: 0 - input_shapes { - key: 0 - value { - dim { - size: 4 - } - } - } - accelerator_exec_micros: 0 - cpu_exec_micros: 0 - total_accelerator_exec_micros: 0 - total_cpu_exec_micros: 0 - run_count: 0 - total_run_count: 0 - total_definition_count: 1 - } - children { - name: "DW/Initializer/random_normal/mean" - exec_micros: 0 - requested_bytes: 0 - total_exec_micros: 0 - total_requested_bytes: 0 - total_parameters: 0 - float_ops: 0 - total_float_ops: 0 - accelerator_exec_micros: 0 - cpu_exec_micros: 0 - total_accelerator_exec_micros: 0 - total_cpu_exec_micros: 0 - run_count: 0 - total_run_count: 0 - total_definition_count: 1 - } - children { - name: "DW/Initializer/random_normal/mul" - exec_micros: 0 - requested_bytes: 0 - total_exec_micros: 0 - total_requested_bytes: 0 - total_parameters: 0 - float_ops: 0 - total_float_ops: 0 - input_shapes { - key: 0 - value { - dim { - size: 6 - } - dim { - size: 6 - } - dim { - size: 3 - } - dim { - size: 6 - } - } - } - input_shapes { - key: 1 - value { - dim { - size: 1 - } - } - } - accelerator_exec_micros: 0 - cpu_exec_micros: 0 - total_accelerator_exec_micros: 0 - total_cpu_exec_micros: 0 - run_count: 0 - total_run_count: 0 - total_definition_count: 1 - } - children { - name: "DW/Initializer/random_normal/shape" - exec_micros: 0 - requested_bytes: 0 - total_exec_micros: 0 - total_requested_bytes: 0 - total_parameters: 0 - float_ops: 0 - total_float_ops: 0 - accelerator_exec_micros: 0 - cpu_exec_micros: 0 - total_accelerator_exec_micros: 0 - total_cpu_exec_micros: 0 - run_count: 0 - total_run_count: 0 - total_definition_count: 1 - } - children { - name: "DW/Initializer/random_normal/stddev" - exec_micros: 0 - requested_bytes: 0 - total_exec_micros: 0 - total_requested_bytes: 0 - total_parameters: 0 - float_ops: 0 - total_float_ops: 0 - accelerator_exec_micros: 0 - cpu_exec_micros: 0 - total_accelerator_exec_micros: 0 - total_cpu_exec_micros: 0 - run_count: 0 - total_run_count: 0 - total_definition_count: 1 - } - float_ops: 0 - total_float_ops: 0 - input_shapes { - key: 0 - value { - dim { - size: 6 - } - dim { - size: 6 - } - dim { - size: 3 - } - dim { - size: 6 - } - } - } - input_shapes { - key: 1 - value { - dim { - size: 1 - } - } - } - accelerator_exec_micros: 0 - cpu_exec_micros: 0 - total_accelerator_exec_micros: 0 - total_cpu_exec_micros: 0 - run_count: 0 - total_run_count: 0 - total_definition_count: 6 - } - float_ops: 0 - total_float_ops: 0 - accelerator_exec_micros: 0 - cpu_exec_micros: 0 - total_accelerator_exec_micros: 0 - total_cpu_exec_micros: 0 - run_count: 0 - total_run_count: 0 - total_definition_count: 7 - } - children { - name: "DW/read" - exec_micros: 0 - requested_bytes: 0 - total_exec_micros: 0 - total_requested_bytes: 0 - total_parameters: 0 - float_ops: 0 - total_float_ops: 0 - input_shapes { - key: 0 - value { - dim { - size: 6 - } - dim { - size: 6 - } - dim { - size: 3 - } - dim { - size: 6 - } - } - } - accelerator_exec_micros: 0 - cpu_exec_micros: 0 - total_accelerator_exec_micros: 0 - total_cpu_exec_micros: 0 - run_count: 0 - total_run_count: 0 - total_definition_count: 1 - } - float_ops: 0 - total_float_ops: 0 - accelerator_exec_micros: 0 - cpu_exec_micros: 0 - total_accelerator_exec_micros: 0 - total_cpu_exec_micros: 0 - run_count: 0 - total_run_count: 0 - total_definition_count: 10 - } - children { - name: "zeros" - exec_micros: 0 - requested_bytes: 0 - total_exec_micros: 0 - total_requested_bytes: 0 - total_parameters: 0 - float_ops: 0 - total_float_ops: 0 - accelerator_exec_micros: 0 - cpu_exec_micros: 0 - total_accelerator_exec_micros: 0 - total_cpu_exec_micros: 0 - run_count: 0 - total_run_count: 0 - total_definition_count: 1 - } - float_ops: 0 - total_float_ops: 0 - accelerator_exec_micros: 0 - cpu_exec_micros: 0 - total_accelerator_exec_micros: 0 - total_cpu_exec_micros: 0 - run_count: 0 - total_run_count: 0 - total_definition_count: 13""", expected_pb) - self.assertEqual(expected_pb, tfprof_pb) - - -if __name__ == '__main__': - test.main() diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py b/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py deleted file mode 100644 index 8f3351999f..0000000000 --- a/tensorflow/contrib/tfprof/python/tools/tfprof/internal/run_metadata_test.py +++ /dev/null @@ -1,191 +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. -# ============================================================================== -"""test the RunMetadata proto.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from collections import defaultdict - -import six - -from tensorflow.core.protobuf import config_pb2 -from tensorflow.python.client import session -from tensorflow.python.framework import ops -from tensorflow.python.ops import math_ops -from tensorflow.python.ops import random_ops -from tensorflow.python.ops import variables -from tensorflow.python.platform import test - -# pylint: disable=g-bad-import-order -# XXX: this depends on pywrap_tensorflow and must come later -from tensorflow.contrib.tfprof.python.tools.tfprof import model_analyzer -from tensorflow.contrib.tfprof.python.tools.tfprof.internal import model_analyzer_testlib as lib -SIZE = 1300 - - -def _extract_node(run_meta, node_name): - ret = defaultdict(list) - for dev_stat in run_meta.step_stats.dev_stats: - dev = dev_stat.device - for node_stat in dev_stat.node_stats: - if node_stat.node_name == node_name: - ret[dev].append(node_stat) - return ret - - -def _run_model(): - x = random_ops.random_normal(shape=[1, SIZE]) - w = random_ops.random_normal(shape=[SIZE, 2 * SIZE]) - y = math_ops.matmul(x, w) - - with session.Session() as sess: - run_metadata = config_pb2.RunMetadata() - opts = model_analyzer.PRINT_ALL_TIMING_MEMORY - opts['min_micros'] = 0 - opts['min_bytes'] = 0 - _ = sess.run(y, - options=config_pb2.RunOptions( - trace_level=config_pb2.RunOptions.FULL_TRACE), - run_metadata=run_metadata) - tfprof_node = model_analyzer.print_model_analysis( - sess.graph, - run_meta=run_metadata, - tfprof_options=opts) - - return tfprof_node, run_metadata - - -def _run_loop_model(): - with session.Session() as sess: - x = lib.BuildFullModel() - - sess.run(variables.global_variables_initializer()) - run_meta = config_pb2.RunMetadata() - _ = sess.run(x, - options=config_pb2.RunOptions( - trace_level=config_pb2.RunOptions.FULL_TRACE), - run_metadata=run_meta) - - tfprof_node = model_analyzer.print_model_analysis( - sess.graph, run_meta, - tfprof_options=model_analyzer.PRINT_ALL_TIMING_MEMORY) - return tfprof_node, run_meta - - -class RunMetadataTest(test.TestCase): - - def testGPU(self): - if not test.is_gpu_available(cuda_only=True): - return - - ops.reset_default_graph() - with ops.device('/gpu:0'): - tfprof_node, run_meta = _run_model() - self.assertEqual(tfprof_node.children[0].name, 'MatMul') - self.assertGreater(tfprof_node.children[0].exec_micros, 10) - - ret = _extract_node(run_meta, 'MatMul') - self.assertEqual(len(ret), 1) - self.assertTrue('/job:localhost/replica:0/task:0/gpu:0' in ret) - - ret = _extract_node(run_meta, 'MatMul:MatMul') - self.assertEqual(len(ret), 2) - has_all_stream = False - for k, _ in six.iteritems(ret): - self.assertTrue('gpu:0/stream' in k) - if 'gpu:0/stream:all' in k: - has_all_stream = True - self.assertTrue(has_all_stream) - - def testCPU(self): - ops.reset_default_graph() - with ops.device('/cpu:0'): - tfprof_node, run_meta = _run_model() - self.assertEqual(tfprof_node.children[0].name, 'MatMul') - self.assertGreater(tfprof_node.children[0].exec_micros, 0) - - ret = _extract_node(run_meta, 'MatMul') - self.assertEqual(len(ret), 1) - self.assertTrue('/job:localhost/replica:0/task:0/cpu:0' in ret) - - ret = _extract_node(run_meta, 'MatMul:MatMul') - self.assertEqual(len(ret), 0) - - def testLoopCPU(self): - ops.reset_default_graph() - with ops.device('/cpu:0'): - tfprof_node, run_meta = _run_loop_model() - # The while-loop caused a node to appear 4 times in scheduling. - ret = _extract_node(run_meta, - 'rnn/while/rnn/basic_rnn_cell/basic_rnn_cell/MatMul') - self.assertEqual(len(ret['/job:localhost/replica:0/task:0/cpu:0']), 4) - - total_cpu_execs = 0 - for node in ret['/job:localhost/replica:0/task:0/cpu:0']: - total_cpu_execs += node.op_end_rel_micros - - mm_node = lib.SearchTFProfNode( - tfprof_node, - 'rnn/while/rnn/basic_rnn_cell/basic_rnn_cell/MatMul') - - self.assertEqual(mm_node.run_count, 4) - self.assertEqual(mm_node.cpu_exec_micros, total_cpu_execs) - self.assertEqual(mm_node.exec_micros, total_cpu_execs) - - # pylint: disable=pointless-string-statement - """ - TODO(xpan): This test is flaky because RunMetadata returned from TensorFlow - is random. Still being investigated. - def testLoopGPU(self): - if not test.is_gpu_available(): - return - - ops.reset_default_graph() - with ops.device('/gpu:0'): - tfprof_node, run_meta = _run_loop_model() - # The while-loop caused a node to appear 4 times in scheduling. - ret = _extract_node(run_meta, - 'rnn/while/rnn/basic_rnn_cell/basic_rnn_cell/MatMul') - self.assertEqual(len(ret['/job:localhost/replica:0/task:0/gpu:0']), 4) - - total_cpu_execs = 0 - for node in ret['/job:localhost/replica:0/task:0/gpu:0']: - total_cpu_execs += node.op_end_rel_micros - - ret = _extract_node( - run_meta, - 'rnn/while/rnn/basic_rnn_cell/basic_rnn_cell/MatMul:MatMul') - self.assertGreaterEqual(len(ret['/gpu:0/stream:all']), 4) - - total_accelerator_execs = 0 - for node in ret['/gpu:0/stream:all']: - total_accelerator_execs += node.op_end_rel_micros - - mm_node = lib.SearchTFProfNode( - tfprof_node, - 'rnn/while/rnn/basic_rnn_cell/basic_rnn_cell/MatMul') - - self.assertEqual(mm_node.run_count, 4) - self.assertEqual(mm_node.accelerator_exec_micros, total_accelerator_execs) - self.assertEqual(mm_node.cpu_exec_micros, total_cpu_execs) - self.assertEqual(mm_node.exec_micros, - total_cpu_execs + total_accelerator_execs) - """ - - -if __name__ == '__main__': - test.main() diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer.py b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer.py deleted file mode 100644 index 5b6111efdf..0000000000 --- a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer.py +++ /dev/null @@ -1,429 +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. -# ============================================================================== -"""Model Analyzer. - -Analyze model, including shape, params, time, memory, structure, etc. -""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import six - -from tensorflow.contrib.tfprof.python.tools.tfprof import tfprof_logger -from tensorflow.python import pywrap_tensorflow as print_mdl -from tensorflow.python.framework import errors -from tensorflow.tools.tfprof import tfprof_options_pb2 -from tensorflow.tools.tfprof import tfprof_output_pb2 - -# pylint: disable=bad-whitespace -# pylint: disable=bad-continuation -# 2 example tfprof_options for print_model_analysis API. -# -# Show the parameter statistics of trainable variables. -TRAINABLE_VARS_PARAMS_STAT_OPTIONS = { - 'max_depth': 10000, - 'min_bytes': 0, - 'min_micros': 0, - 'min_params': 0, - 'min_float_ops': 0, - 'order_by': 'name', - 'account_type_regexes': [tfprof_logger.TRAINABLE_VARIABLES], - 'start_name_regexes': ['.*'], - 'trim_name_regexes': [], - 'show_name_regexes': ['.*'], - 'hide_name_regexes': [], - 'account_displayed_op_only': True, - 'select': ['params'], - 'output': 'stdout', - 'dump_to_file': '' -} - -# Show the number float operations. -FLOAT_OPS_OPTIONS = { - 'max_depth': 10000, - 'min_bytes': 0, - 'min_micros': 0, - 'min_params': 0, - 'min_float_ops': 1, - 'order_by': 'float_ops', - 'account_type_regexes': ['.*'], - 'start_name_regexes': ['.*'], - 'trim_name_regexes': [], - 'show_name_regexes': ['.*'], - 'hide_name_regexes': [], - 'account_displayed_op_only': True, - 'select': ['float_ops'], - 'output': 'stdout', - 'dump_to_file': '' -} - -# Show number of parameters on parameter server 0. -# It is recommended to provide`run_meta` argument -# to have complete device placement info. -PRINT_PARAMS_ON_DEVICE = { - 'max_depth': 1, - 'min_bytes': 0, - 'min_micros': 0, - 'min_params': 0, - 'min_float_ops': 0, - 'order_by': 'name', - 'account_type_regexes': ['.*ps.*task:0.*'], - 'start_name_regexes': ['.*'], - 'trim_name_regexes': [], - 'show_name_regexes': ['.*'], - 'hide_name_regexes': [], - 'account_displayed_op_only': False, - 'select': ['device', 'params'], - 'output': 'stdout', - 'dump_to_file': '' -} - -# Show the timing stats and memory demands. -PRINT_ALL_TIMING_MEMORY = { - 'max_depth': 10000, - 'min_bytes': 1, # Only >=1 - 'min_micros': 1, # Only >=1 - 'min_params': 0, - 'min_float_ops': 0, - 'order_by': 'name', - 'account_type_regexes': ['.*'], - 'start_name_regexes': ['.*'], - 'trim_name_regexes': [], - 'show_name_regexes': ['.*'], - 'hide_name_regexes': [], - 'account_displayed_op_only': True, - 'select': ['micros', 'bytes'], - 'output': 'stdout', - 'dump_to_file': '' -} - -# The following options are for 'advise' tfprof_cmd. -# Show all advice. -ALL_ADVICE = { - 'ExpensiveOperationChecker': {}, - 'AcceleratorUtilizationChecker': {}, - 'JobChecker': {}, # Only available internally. - 'OperationChecker': {}, -} - -# pylint: enable=bad-whitespace -# pylint: enable=bad-continuation - - -def _build_options(options): - """Build tfprof.OptionsProto. - - Args: - options: A dictionary of options. - Returns: - tfprof.OptionsProto. - """ - opts = tfprof_options_pb2.OptionsProto() - opts.max_depth = options.get('max_depth', 10) - opts.min_bytes = options.get('min_bytes', 0) - opts.min_micros = options.get('min_micros', 0) - opts.min_params = options.get('min_params', 0) - opts.min_float_ops = options.get('min_float_ops', 0) - opts.min_occurrence = options.get('min_occurrence', 0) - - opts.step = options.get('step', -1) - - opts.order_by = options.get('order_by', 'name') - - for p in options.get('account_type_regexes', []): - opts.account_type_regexes.append(p) - for p in options.get('start_name_regexes', []): - opts.start_name_regexes.append(p) - for p in options.get('trim_name_regexes', []): - opts.trim_name_regexes.append(p) - for p in options.get('show_name_regexes', []): - opts.show_name_regexes.append(p) - for p in options.get('hide_name_regexes', []): - opts.hide_name_regexes.append(p) - opts.account_displayed_op_only = options.get('account_displayed_op_only', - False) - - for p in options.get('select', []): - opts.select.append(p) - - opts.output = options.get('output', 'stdout') - opts.dump_to_file = options.get('dump_to_file', '') - - return opts - - -def _build_advisor_options(options): - """Build tfprof.AdvisorOptionsProto. - - Args: - options: A dictionary of options. See ALL_ADVICE example. - Returns: - tfprof.AdvisorOptionsProto. - """ - opts = tfprof_options_pb2.AdvisorOptionsProto() - if options is None: - return opts - for checker, checker_opts in six.iteritems(options): - checker_ops_pb = tfprof_options_pb2.AdvisorOptionsProto.CheckerOption() - for k, v in six.iteritems(checker_opts): - checker_ops_pb[k] = v - opts.checkers[checker].MergeFrom(checker_ops_pb) - return opts - - -class Profiler(object): - """TensorFlow multi-step profiler. - - See go/tfprof or README for details. - - Typical use case: - # Currently we are only allowed to create 1 profiler per process. - profiler = Profile(sess.graph) - - for i in xrange(total_steps): - if i % 10000 == 0: - run_meta = tf.RunMetadata() - _ = sess.run(..., - options=tf.RunOptions( - trace_level=tf.RunOptions.FULL_TRACE), - run_metadata=run_meta) - profiler.add_step(i, run_meta) - - # Profile the parameters of your model. - profiler.profile_name_scope(options=TRAINABLE_VARS_PARAMS_STAT_OPTIONS) - - # Or profile the timing of your model operations. - opts = PRINT_ALL_TIMING_MEMORY.copy() - opts['order_by'] = 'micros' - opts['select'] = ['micros', 'occurrence'] - opts['max_depth'] = 20 - profiler.profile_operations(options=opts) - - # Or you can generate a timeline: - opts = PRINT_ALL_TIMING_MEMORY.copy() - opts['output'] = 'timeline:outfile=' + filename - opts['step'] = i - profiler.profile_graph(options=opts) - else: - _ = sess.run(...) - # Auto detect problems and generate advice. - profiler.advise(model_analyzer.ALL_ADVICE) - """ - - def __init__(self, graph, op_log=None): - """Constructor. - - Args: - graph: tf.Graph. - op_log: optional. tensorflow::tfprof::OpLog proto. Used to define - extra op types. - """ - self._graph = graph - # pylint: disable=protected-access - op_log = tfprof_logger._merge_default_with_oplog( - self._graph, op_log=op_log) - # pylint: enable=protected-access - - print_mdl.NewProfiler( - self._graph.as_graph_def(add_shapes=True).SerializeToString(), - op_log.SerializeToString()) - - def __del__(self): - print_mdl.DeleteProfiler() - - def add_step(self, step, run_meta): - """Add statistics of a step. - - Args: - step: A step uint64 used to identify the RunMetadata. Must be different - across different AddStep() calls. - run_meta: RunMetadata proto that contains statistics of a session run. - """ - # pylint: disable=protected-access - op_log = tfprof_logger._merge_default_with_oplog( - self._graph, run_meta=run_meta, add_trace=False, - add_trainable_var=False) - # pylint: enable=protected-access - print_mdl.AddStep( - step, run_meta.SerializeToString(), op_log.SerializeToString()) - - def profile_python_codes(self, options): - """Profile the statistics of the Python codes. - - Hint: set options['show_name_regexes'] = ['.*my_code.py.*'] - - Args: - options: A dict of profiler options. - Returns: - a TFMultiGraphNodeProto that records the results. - """ - opts = _build_options(options) - tfprof_node = tfprof_output_pb2.TFMultiGraphNodeProto() - tfprof_node.ParseFromString( - print_mdl.Profile('code'.encode('utf-8'), opts.SerializeToString())) - return tfprof_node - - def profile_operations(self, options): - """Profile the statistics of the Operation types (e.g. MatMul, Conv2D). - - Args: - options: A dict of profiler options. - Returns: - a TFMultiGraphNodeProto that records the results. - """ - opts = _build_options(options) - tfprof_node = tfprof_output_pb2.TFMultiGraphNodeProto() - tfprof_node.ParseFromString( - print_mdl.Profile('op'.encode('utf-8'), opts.SerializeToString())) - return tfprof_node - - def profile_name_scope(self, options): - """Profile the statistics of graph nodes, organized by name scope. - - Args: - options: A dict of profiler options. - Returns: - a TFGraphNodeProto that records the results. - """ - opts = _build_options(options) - tfprof_node = tfprof_output_pb2.TFGraphNodeProto() - tfprof_node.ParseFromString( - print_mdl.Profile('scope'.encode('utf-8'), opts.SerializeToString())) - return tfprof_node - - def profile_graph(self, options): - """Profile the statistics of graph nodes, organized by dataflow graph. - - Args: - options: A dict of profiler options. - Returns: - a TFGraphNodeProto that records the results. - """ - opts = _build_options(options) - tfprof_node = tfprof_output_pb2.TFGraphNodeProto() - tfprof_node.ParseFromString( - print_mdl.Profile('graph'.encode('utf-8'), opts.SerializeToString())) - return tfprof_node - - def advise(self, options=ALL_ADVICE): # pylint: disable=dangerous-default-value - """Automatically detect problems and generate reports. - - Args: - options: A dict of options. - Returns: - A Advise proto that conains the reports from all checkers. - """ - advise_pb = tfprof_output_pb2.AdviceProto() - opts = _build_advisor_options(options) - advise_pb.ParseFromString( - print_mdl.Profile('advise'.encode('utf-8'), opts.SerializeToString())) - return advise_pb - - -def print_model_analysis(graph, - run_meta=None, - op_log=None, - tfprof_cmd='scope', - tfprof_options=TRAINABLE_VARS_PARAMS_STAT_OPTIONS): - """Print model statistics. - - See go/tfprof or README for examples and tutorials. - Run tfprof tool for help: - 'bazel run third_party/tensorflow/tools/tfprof help' - - Args: - graph: tf.Graph. - run_meta: tensorflow::RunMetadata proto. When provided, also shows valid - timing and memory information when 'select' option contains - 'micros' and 'bytes'. - op_log: tensorflow::tfprof::OpLog proto. users can use this proto to - group together ops and use a op_type to select the group. - tfprof_cmd: string. Either 'op', 'scope', 'graph', 'code'. - 'op' view organize outputs using operation type. (e.g. MatMul) - 'scope' view organize outputs using graph node name scope. - 'graph' view organize outputs using graph node inputs/outputs. - 'code' view organize outputs using Python call stack. - tfprof_options: See 'tfprof help' for details. - Returns: - If tfprof_cmd is 'scope' or 'graph', returns TFGraphNodeProto proto. - If tfprof_cmd is 'op' or 'code', returns TFMultiGraphNodeProto proto. - Side effect: stdout/file/timeline.json depending on tfprof_options['output'] - """ - # pylint: disable=protected-access - op_log = tfprof_logger._merge_default_with_oplog( - graph, op_log, run_meta, add_trace=tfprof_cmd == 'code') - # pylint: enable=protected-access - - opts = _build_options(tfprof_options) - - run_meta_str = run_meta.SerializeToString() if run_meta else b'' - - if tfprof_cmd == 'code' or tfprof_cmd == 'op': - tfprof_node = tfprof_output_pb2.TFMultiGraphNodeProto() - tfprof_node.ParseFromString( - print_mdl.PrintModelAnalysis( - graph.as_graph_def(add_shapes=True).SerializeToString(), - run_meta_str, - op_log.SerializeToString(), - tfprof_cmd.encode('utf-8'), - opts.SerializeToString())) - elif tfprof_cmd == 'graph' or tfprof_cmd == 'scope': - tfprof_node = tfprof_output_pb2.TFGraphNodeProto() - tfprof_node.ParseFromString( - print_mdl.PrintModelAnalysis( - graph.as_graph_def(add_shapes=True).SerializeToString(), - run_meta_str, - op_log.SerializeToString(), - tfprof_cmd.encode('utf-8'), - opts.SerializeToString())) - else: - raise errors.InvalidArgumentError( - None, None, 'unknown tfprof_cmd: %s\n' % tfprof_cmd) - - return tfprof_node - - -def advise(graph, run_meta=None, tfprof_options=ALL_ADVICE): # pylint: disable=dangerous-default-value - """Auto profile and advise. - - Builds profiles and automatically check anormalies of various - aspects. See go/tfprof or README for examples and tutorials. - - Args: - graph: tf.Graph. - run_meta: tensorflow::RunMetadata proto. Allows auto-profile - time and memroy. - tfprof_options: see ALL_ADVICE example above. - Returns: - Returns AdviceProto proto - """ - # pylint: disable=protected-access - op_log = tfprof_logger._merge_default_with_oplog( - graph, None, run_meta, add_trace=True) - # pylint: enable=protected-access - - run_meta_str = run_meta.SerializeToString() if run_meta else b'' - - opts = _build_advisor_options(tfprof_options) - ret = tfprof_output_pb2.AdviceProto() - ret.ParseFromString( - print_mdl.PrintModelAnalysis( - graph.as_graph_def(add_shapes=True).SerializeToString(), - run_meta_str, - op_log.SerializeToString(), - 'advise'.encode('utf-8'), - opts.SerializeToString())) - return ret diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py deleted file mode 100644 index 32a6d5fdb2..0000000000 --- a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py +++ /dev/null @@ -1,319 +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. -# ============================================================================== - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import os -from tensorflow.core.protobuf import config_pb2 -from tensorflow.core.protobuf import rewriter_config_pb2 -from tensorflow.python.client import session -from tensorflow.python.framework import ops -from tensorflow.python.ops import variables -from tensorflow.python.platform import gfile -from tensorflow.python.platform import test - -# XXX: this depends on pywrap_tensorflow and must come later -from tensorflow.contrib.tfprof.python.tools.tfprof import model_analyzer -from tensorflow.contrib.tfprof.python.tools.tfprof.internal import model_analyzer_testlib as lib - - -class PrintModelAnalysisTest(test.TestCase): - - def testDumpToFile(self): - ops.reset_default_graph() - opts = model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS.copy() - outfile = os.path.join(test.get_temp_dir(), 'dump') - opts['output'] = 'file:outfile=' + outfile - - with session.Session() as sess: - _ = lib.BuildSmallModel() - model_analyzer.print_model_analysis(sess.graph, tfprof_options=opts) - - with gfile.Open(outfile, 'r') as f: - self.assertEqual(u'node name | # parameters\n' - '_TFProfRoot (--/451 params)\n' - ' DW (3x3x3x6, 162/162 params)\n' - ' DW2 (2x2x6x12, 288/288 params)\n' - ' ScalarW (1, 1/1 params)\n', - f.read()) - - def testSelectEverything(self): - ops.reset_default_graph() - opts = model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS.copy() - outfile = os.path.join(test.get_temp_dir(), 'dump') - opts['output'] = 'file:outfile=' + outfile - opts['account_type_regexes'] = ['.*'] - opts['select'] = [ - 'params', 'float_ops', 'occurrence', 'device', 'op_types', - 'input_shapes' - ] - - rewriter_config = rewriter_config_pb2.RewriterConfig( - disable_model_pruning=True) - graph_options = config_pb2.GraphOptions(rewrite_options=rewriter_config) - config = config_pb2.ConfigProto(graph_options=graph_options) - with session.Session(config=config) as sess, ops.device('/cpu:0'): - x = lib.BuildSmallModel() - - sess.run(variables.global_variables_initializer()) - run_meta = config_pb2.RunMetadata() - _ = sess.run(x, - options=config_pb2.RunOptions( - trace_level=config_pb2.RunOptions.FULL_TRACE), - run_metadata=run_meta) - - model_analyzer.print_model_analysis( - sess.graph, run_meta, tfprof_options=opts) - - with gfile.Open(outfile, 'r') as f: - # pylint: disable=line-too-long - self.assertEqual( - 'node name | # parameters | # float_ops | assigned devices | op types | op count (run|defined) | input shapes\n_TFProfRoot (--/451 params, --/10.44k flops, _kTFScopeParent, --/7|--/35, )\n Conv2D (0/0 params, 5.83k/5.83k flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Conv2D, 1/1|1/1, 0:2x6x6x3|1:3x3x3x6)\n Conv2D_1 (0/0 params, 4.61k/4.61k flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Conv2D, 1/1|1/1, 0:2x3x3x6|1:2x2x6x12)\n DW (3x3x3x6, 162/162 params, 0/0 flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|VariableV2|_trainable_variables, 1/2|1/10, )\n DW/Assign (0/0 params, 0/0 flops, Assign, 0/0|1/1, 0:3x3x3x6|1:3x3x3x6)\n DW/Initializer (0/0 params, 0/0 flops, _kTFScopeParent, 0/0|1/7, )\n DW/Initializer/random_normal (0/0 params, 0/0 flops, Add, 0/0|1/6, 0:3x3x3x6|1:1)\n DW/Initializer/random_normal/RandomStandardNormal (0/0 params, 0/0 flops, RandomStandardNormal, 0/0|1/1, 0:4)\n DW/Initializer/random_normal/mean (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n DW/Initializer/random_normal/mul (0/0 params, 0/0 flops, Mul, 0/0|1/1, 0:3x3x3x6|1:1)\n DW/Initializer/random_normal/shape (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n DW/Initializer/random_normal/stddev (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n DW/read (0/0 params, 0/0 flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Identity, 1/1|1/1, 0:3x3x3x6)\n DW2 (2x2x6x12, 288/288 params, 0/0 flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|VariableV2|_trainable_variables, 1/2|1/10, )\n DW2/Assign (0/0 params, 0/0 flops, Assign, 0/0|1/1, 0:2x2x6x12|1:2x2x6x12)\n DW2/Initializer (0/0 params, 0/0 flops, _kTFScopeParent, 0/0|1/7, )\n DW2/Initializer/random_normal (0/0 params, 0/0 flops, Add, 0/0|1/6, 0:2x2x6x12|1:1)\n DW2/Initializer/random_normal/RandomStandardNormal (0/0 params, 0/0 flops, RandomStandardNormal, 0/0|1/1, 0:4)\n DW2/Initializer/random_normal/mean (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n DW2/Initializer/random_normal/mul (0/0 params, 0/0 flops, Mul, 0/0|1/1, 0:2x2x6x12|1:1)\n DW2/Initializer/random_normal/shape (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n DW2/Initializer/random_normal/stddev (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n DW2/read (0/0 params, 0/0 flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Identity, 1/1|1/1, 0:2x2x6x12)\n ScalarW (1, 1/1 params, 0/0 flops, VariableV2|_trainable_variables, 0/0|1/10, )\n ScalarW/Assign (0/0 params, 0/0 flops, Assign, 0/0|1/1, 0:1|1:1)\n ScalarW/Initializer (0/0 params, 0/0 flops, _kTFScopeParent, 0/0|1/7, )\n ScalarW/Initializer/random_normal (0/0 params, 0/0 flops, Add, 0/0|1/6, 0:1|1:1)\n ScalarW/Initializer/random_normal/RandomStandardNormal (0/0 params, 0/0 flops, RandomStandardNormal, 0/0|1/1, 0:0)\n ScalarW/Initializer/random_normal/mean (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n ScalarW/Initializer/random_normal/mul (0/0 params, 0/0 flops, Mul, 0/0|1/1, 0:1|1:1)\n ScalarW/Initializer/random_normal/shape (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n ScalarW/Initializer/random_normal/stddev (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n ScalarW/read (0/0 params, 0/0 flops, Identity, 0/0|1/1, 0:1)\n init (0/0 params, 0/0 flops, NoOp, 0/0|1/1, 0:1|1:3x3x3x6|2:2x2x6x12)\n zeros (0/0 params, 0/0 flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Const, 1/1|1/1, )\n', - f.read()) - # pylint: enable=line-too-long - - def testSimpleCodeView(self): - ops.reset_default_graph() - opts = model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS.copy() - outfile = os.path.join(test.get_temp_dir(), 'dump') - opts['output'] = 'file:outfile=' + outfile - opts['account_type_regexes'] = ['.*'] - opts['show_name_regexes'] = ['.*model_analyzer_testlib.*'] - opts['account_displayed_op_only'] = False - # TODO(xpan): Test 'micros'. Since the execution time changes each run, - # it's a bit difficult to test it now. - opts['select'] = [ - 'bytes', 'params', 'float_ops', 'num_hidden_ops', 'device', - 'input_shapes' - ] - - with session.Session() as sess: - x = lib.BuildSmallModel() - - sess.run(variables.global_variables_initializer()) - run_meta = config_pb2.RunMetadata() - _ = sess.run(x, - options=config_pb2.RunOptions( - trace_level=config_pb2.RunOptions.FULL_TRACE), - run_metadata=run_meta) - - model_analyzer.print_model_analysis( - sess.graph, run_meta, tfprof_cmd='code', tfprof_options=opts) - - with gfile.Open(outfile, 'r') as f: - # pylint: disable=line-too-long - self.assertEqual( - 'node name | output bytes | # parameters | # float_ops | assigned devices | input', - f.read()[0:80]) - # pylint: enable=line-too-long - - def testComplexCodeView(self): - ops.reset_default_graph() - opts = model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS.copy() - outfile = os.path.join(test.get_temp_dir(), 'dump') - opts['output'] = 'file:outfile=' + outfile - opts['account_type_regexes'] = ['.*'] - opts['show_name_regexes'] = ['.*model_analyzer_testlib.py.*'] - opts['account_displayed_op_only'] = False - opts['select'] = ['params', 'float_ops'] - - with session.Session() as sess: - x = lib.BuildFullModel() - - sess.run(variables.global_variables_initializer()) - run_meta = config_pb2.RunMetadata() - _ = sess.run(x, - options=config_pb2.RunOptions( - trace_level=config_pb2.RunOptions.FULL_TRACE), - run_metadata=run_meta) - - tfprof_node = model_analyzer.print_model_analysis( - sess.graph, run_meta, tfprof_cmd='code', tfprof_options=opts) - - # pylint: disable=line-too-long - with gfile.Open(outfile, 'r') as f: - lines = f.read().split('\n') - result = '\n'.join([l[:min(len(l), 80)] for l in lines]) - self.assertEqual('node name | # parameters | # float_ops\n_TFProfRoot (--/2.84k params, --/91.04k flops)\n model_analyzer_testlib.py:58:BuildFullModel:seq.append(array_... (0/1.80k para\n model_analyzer_testlib.py:35:BuildSmallModel:image = array_ops... (0/0 param\n model_analyzer_testlib.py:39:BuildSmallModel:initializer=init_... (0/4 param\n model_analyzer_testlib.py:43:BuildSmallModel:initializer=init_... (0/648 par\n model_analyzer_testlib.py:44:BuildSmallModel:x = nn_ops.conv2d... (0/0 param\n model_analyzer_testlib.py:48:BuildSmallModel:initializer=init_... (0/1.15k p\n model_analyzer_testlib.py:49:BuildSmallModel:x = nn_ops.conv2d... (0/0 param\n model_analyzer_testlib.py:62:BuildFullModel:cell, array_ops.c... (0/1.04k para\n model_analyzer_testlib.py:64:BuildFullModel:target = array_op... (0/0 params, \n model_analyzer_testlib.py:65:BuildFullModel:loss = nn_ops.l2_... (0/0 params, \n model_analyzer_testlib.py:67:BuildFullModel:return sgd_op.min... (0/0 params, \n', - result) - - self.assertLess(0, tfprof_node.total_exec_micros) - self.assertEqual(2844, tfprof_node.total_parameters) - self.assertEqual(91040, tfprof_node.total_float_ops) - self.assertEqual(5, len(tfprof_node.children)) - self.assertEqual('_TFProfRoot', tfprof_node.name) - self.assertEqual( - 'model_analyzer_testlib.py:58:BuildFullModel:seq.append(array_...', - tfprof_node.children[0].name) - self.assertEqual( - 'model_analyzer_testlib.py:62:BuildFullModel:cell, array_ops.c...', - tfprof_node.children[1].name) - self.assertEqual( - 'model_analyzer_testlib.py:64:BuildFullModel:target = array_op...', - tfprof_node.children[2].name) - self.assertEqual( - 'model_analyzer_testlib.py:65:BuildFullModel:loss = nn_ops.l2_...', - tfprof_node.children[3].name) - self.assertEqual( - 'model_analyzer_testlib.py:67:BuildFullModel:return sgd_op.min...', - tfprof_node.children[4].name) - # pylint: enable=line-too-long - - def testCodeViewLeafGraphNode(self): - ops.reset_default_graph() - opts = model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS.copy() - opts['account_type_regexes'] = ['.*'] - opts['account_displayed_op_only'] = False - opts['select'] = [ - 'bytes', 'params', 'float_ops', 'device' - ] - opts['output'] = 'none' - - with session.Session() as sess: - x = lib.BuildSmallModel() - - sess.run(variables.global_variables_initializer()) - run_meta = config_pb2.RunMetadata() - _ = sess.run(x, - options=config_pb2.RunOptions( - trace_level=config_pb2.RunOptions.FULL_TRACE), - run_metadata=run_meta) - - tfprof_node = model_analyzer.print_model_analysis( - sess.graph, run_meta, tfprof_cmd='code', tfprof_options=opts) - - leaf = tfprof_node - while leaf.children: - self.assertEqual(0, len(leaf.graph_nodes)) - leaf = leaf.children[0] - self.assertEqual(1, len(leaf.graph_nodes)) - - def testTimeline(self): - ops.reset_default_graph() - opts = model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS.copy() - outfile = os.path.join(test.get_temp_dir(), 'timeline') - opts['output'] = 'timeline:outfile=' + outfile - opts['account_type_regexes'] = ['.*'] - opts['max_depth'] = 100000 - opts['step'] = 0 - - with session.Session() as sess: - x = lib.BuildFullModel() - - sess.run(variables.global_variables_initializer()) - run_meta = config_pb2.RunMetadata() - _ = sess.run( - x, - options=config_pb2.RunOptions( - trace_level=config_pb2.RunOptions.FULL_TRACE), - run_metadata=run_meta) - - _ = model_analyzer.print_model_analysis( - sess.graph, run_meta, tfprof_cmd='graph', tfprof_options=opts) - - with gfile.Open(outfile, 'r') as f: - # Test that a json file is created. - # TODO(xpan): tfprof Timeline isn't quite correct on Windows. - # Investigate why. - if os.name != 'nt': - self.assertLess(1000, len(f.read())) - else: - self.assertLess(1, len(f.read())) - - def testOpView(self): - ops.reset_default_graph() - opts = model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS.copy() - outfile = os.path.join(test.get_temp_dir(), 'dump') - opts['output'] = 'file:outfile=' + outfile - opts['account_type_regexes'] = ['.*'] - opts['min_occurrence'] = 10 - opts['select'] = ['params', 'micros', 'occurrence', 'input_shapes'] - opts['order_by'] = 'occurrence' - - with session.Session() as sess: - x = lib.BuildFullModel() - - sess.run(variables.global_variables_initializer()) - run_meta = config_pb2.RunMetadata() - _ = sess.run(x, - options=config_pb2.RunOptions( - trace_level=config_pb2.RunOptions.FULL_TRACE), - run_metadata=run_meta) - - tfprof_node = model_analyzer.print_model_analysis( - sess.graph, run_meta, tfprof_cmd='op', tfprof_options=opts) - - with gfile.Open(outfile, 'r') as f: - # pylint: disable=line-too-long - self.assertEqual( - 'nodename|totalexecutiontime|acceleratorexecutiontime|cpuexecutiontime|#parameters|opoccurrence(run|defined)|inputshapes\n', - f.read().replace('\t', '').replace(' ', '')[0:120]) - # pylint: enable=line-too-long - - total_children = 0 - last_occurrence = 1e32 - input_shapes = 0 - last_total_micros = tfprof_node.total_exec_micros - last_micros = tfprof_node.exec_micros - while tfprof_node.children: - for gnode in tfprof_node.graph_nodes: - input_shapes += len(gnode.input_shapes) - self.assertEqual(len(tfprof_node.children), 1) - tfprof_node = tfprof_node.children[0] - - self.assertEqual( - last_total_micros, tfprof_node.total_exec_micros + last_micros) - last_total_micros = tfprof_node.total_exec_micros - last_micros = tfprof_node.exec_micros - - total_children += 1 - self.assertLessEqual(len(tfprof_node.graph_nodes), last_occurrence) - last_occurrence = len(tfprof_node.graph_nodes) - - self.assertEqual(total_children, 15) - self.assertGreater(input_shapes, 0) - - def testAdvisor(self): - ops.reset_default_graph() - - with session.Session() as sess: - x = lib.BuildFullModel() - - sess.run(variables.global_variables_initializer()) - run_meta = config_pb2.RunMetadata() - _ = sess.run( - x, - options=config_pb2.RunOptions( - trace_level=config_pb2.RunOptions.FULL_TRACE), - run_metadata=run_meta) - - advice_pb = model_analyzer.advise(sess.graph, run_meta) - self.assertTrue('AcceleratorUtilizationChecker' in advice_pb.checkers) - self.assertTrue('ExpensiveOperationChecker' in advice_pb.checkers) - self.assertTrue('OperationChecker' in advice_pb.checkers) - - checker = advice_pb.checkers['AcceleratorUtilizationChecker'] - if test.is_gpu_available(): - self.assertGreater(len(checker.reports), 0) - else: - self.assertEqual(len(checker.reports), 0) - checker = advice_pb.checkers['ExpensiveOperationChecker'] - self.assertGreater(len(checker.reports), 0) - - -if __name__ == '__main__': - test.main() diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/pprof_profiler.py b/tensorflow/contrib/tfprof/python/tools/tfprof/pprof_profiler.py deleted file mode 100644 index c57e45748d..0000000000 --- a/tensorflow/contrib/tfprof/python/tools/tfprof/pprof_profiler.py +++ /dev/null @@ -1,445 +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. -# ============================================================================== -"""Profiler for TensorFlow models that outputs data in pprof format. - -See https://github.com/google/pprof/blob/master/proto/profile.proto for pprof -profile format. -The following needs to be set for profiler to work: - * trace_level needs to be set to FULL_TRACE - * run_metadata object should be passed in to session.run call - -Sample usage: - options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE) - run_metadata = tf.RunMetadata() - - with tf.Session as sess: - ... - sess.run(computation, run_metadata=run_metadata, options=options) - pprof_profiler.profile(sess.graph, run_metadata, output_dir) - - - The code above would output a pprof profile to separate output_dir/.*.pb.gz - file for each device. These files can be passed to pprof for formatting. - For e.g.: - pprof -png --nodecount=100 --sample_index=1 output_dir/profile_output.pb.gz -""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from collections import defaultdict -from collections import namedtuple -import gzip -import os -import string -import sys -import time - -from proto import profile_pb2 - - -if sys.version_info < (3,): - maketrans = string.maketrans -else: - maketrans = str.maketrans - - -ProfileDatum = namedtuple('ProfileDatum', [ - 'node_exec_stats', 'op_type', 'traceback']) - - -class StringTable(object): - """Keeps track of strings to add to string_table in pprof proto.""" - - def __init__(self): - # Pprof requires first entry in string_table to be ''. - self._string_table = [''] - self._string_to_index = {'': 0} - - def index_of(self, value_str): - """Get index of value_str in the string table. - - If value_str is not in the string table, we will add it at the end - and then return the new index. - Args: - value_str: (string) Value to lookup/add in/to the string table. - - Returns: - Index of value_str in the string table. - """ - if value_str is None: - value_str = '' - if value_str in self._string_to_index: - return self._string_to_index[value_str] - index = len(self._string_table) - self._string_table.append(value_str) - self._string_to_index[value_str] = index - return index - - def next_index(self): - """Gets index that would be assigned to the next added string. - - Returns: - Index of the next string if it was added. - """ - return len(self._string_table) - - def string_table(self): - """Returns a list of strings to store in pprof's string_table.""" - return self._string_table - - -class Functions(object): - """Keeps track of `Function` protos for pprof profile.""" - - def __init__(self, string_table): - """Constructor. - - Args: - string_table: A `StringTable` object. - """ - self._string_table = string_table - # Maps tuples in the form (file_path, function_name, start_line_number) - # to `Function` protos. - self._function_key_to_function = {} - - def index_of(self, file_path, function_name, function_start_line): - """Returns index of the function, adding the function if needed. - - Args: - file_path: (string) Path to file where the function is defined. - function_name: (string) Function name. - function_start_line: (integer) Start line number of function definition. - - Returns: - Function index. - """ - function_key = (file_path, function_name, function_start_line) - if function_key in self._function_key_to_function: - return self._function_key_to_function[function_key].id - else: - # Function indexes should start from 1 - function_index = len(self._function_key_to_function) + 1 - function = profile_pb2.Function() - function.id = function_index - function.name = self._string_table.index_of(function_name) - function.filename = self._string_table.index_of(file_path) - function.start_line = function_start_line - self._function_key_to_function[function_key] = function - return function_index - - def function_protos(self): - """Returns list of `profile_pb2.Function` protos.""" - return self._function_key_to_function.values() - - -class Locations(object): - """Keeps track of `Location` protos for pprof profile. - - `Locations` store information about function call locations. - """ - - def __init__(self, functions): - """Constructor. - - Args: - functions: A `Functions` object. - """ - self._functions = functions - # Maps tuples in the form (file_path, called_function_name, line_number) - # to `Location` protos. - self._location_key_to_location = {} - - def index_of( - self, file_path, line_number, called_function_name, called_file_path, - called_function_start_line): - """Returns index of the location, adding the location if needed. - - Args: - file_path: (string) Path to file that makes the call. - line_number: (integer) Call line number. - called_function_name: (string) Function name of the function called at - `file_path` and `line_number`. - called_file_path: (string) Path to file where the called function is - defined. - called_function_start_line: (integer) Start line number of called - function definition in `called_file_path` file. - - Returns: - Index of location. - """ - location_key = (file_path, called_function_name, line_number) - if location_key in self._location_key_to_location: - location = self._location_key_to_location[location_key] - return location.id - else: - # Location indexes should start from 1 - location_index = len(self._location_key_to_location) + 1 - location = profile_pb2.Location() - location.id = location_index - self._location_key_to_location[location_key] = location - - line = location.line.add() - line.function_id = self._functions.index_of( - called_file_path, called_function_name, called_function_start_line) - line.line = line_number - return location_index - - def location_protos(self): - """Returns list of `profile_pb2.Location` protos.""" - return self._location_key_to_location.values() - - -class Samples(object): - """Keeps track of `Sample` protos for pprof profile. - - Samples store the following statistics in order: - count, all_time, op_time - """ - - def __init__(self, string_table): - """Constructor. - - Args: - string_table: A `StringTable` object. - """ - self._string_table = string_table - # TODO(annarev): figure out if location is unique for each node name. - # If not, also key this dictionary based on location ids. - self._node_name_to_sample = {} - - def add(self, datum, location_ids): - """Adds a sample data point. - - Args: - datum: `ProfileDatum` to add a sample for. - location_ids: List of numberic location ids for this - sample. - """ - node_name = datum.node_exec_stats.node_name - if node_name in self._node_name_to_sample: - sample = self._node_name_to_sample[node_name] - sample.location_id.extend(location_ids) - else: - sample = profile_pb2.Sample() - # Sample stores 3 values: count, all_time, op_time - sample.value.extend([0, 0, 0]) - - label = sample.label.add() - label.key = self._string_table.index_of('node_name') - label.str = self._string_table.index_of(node_name) - label = sample.label.add() - label.key = self._string_table.index_of('op_type') - label.str = self._string_table.index_of(datum.op_type) - self._node_name_to_sample[node_name] = sample - sample.value[0] += 1 - sample.value[1] += datum.node_exec_stats.all_end_rel_micros - sample.value[2] += ( - datum.node_exec_stats.op_end_rel_micros - - datum.node_exec_stats.op_start_rel_micros) - - def get_sample_protos(self): - """Returns list of `Sample` protos for pprof profile.""" - return self._node_name_to_sample.values() - - -class PprofProfiler(object): - """Creates profiles in pprof format.""" - - def __init__(self, graph, run_metadata): - """Constructor. - - Args: - graph: A `Graph` instance. - run_metadata: A list of `RunMetadata` objects. - """ - self._graph = graph - self._run_metadata = run_metadata - self._string_table = StringTable() - self._functions = Functions(self._string_table) - self._locations = Locations(self._functions) - - def profile(self): - """Generates pprof profiles. - - Returns: - Dictionary mapping from device name to proto in `profile_pb2.Profile` - format. - """ - profiles = {} - data_generator_func = self._get_profile_data_generator() - for device_index, device_stats in enumerate( - self._run_metadata.step_stats.dev_stats): - # Create profile - pprof_proto = self._get_pprof_proto(data_generator_func(device_stats)) - if not pprof_proto.sample: - print( - 'Not enough data to create profile for device %s. Did you pass ' - 'RunMetadata to session.run call?' % device_stats.device) - continue - # Add device name comment - device_count = len(self._run_metadata.step_stats.dev_stats) - device_description = ( - 'Device %d of %d: %s' % - (device_index + 1, device_count, device_stats.device)) - device_description_str_index = self._string_table.next_index() - pprof_proto.string_table.append(device_description) - pprof_proto.comment.append(device_description_str_index) - profiles[device_stats.device] = pprof_proto - return profiles - - def _get_pprof_proto(self, profile_datum_generator): - """Returns profile data in pprof proto format. - - Args: - profile_datum_generator: Generator outputting `ProfileDatum` objects. - - Returns: - A proto in pprof format. - """ - pprof_profile = profile_pb2.Profile() - samples = Samples(self._string_table) - - for datum in profile_datum_generator: - if not datum.traceback: - continue - - stack_frame = datum.traceback[-1] - after_apply_op = False - location_ids = [] - - # We add locations from stack trace in bottom-up order. - for stack_frame_index in reversed(range(len(datum.traceback) - 1)): - prev_stack_frame = stack_frame - stack_frame = datum.traceback[stack_frame_index] - - # Call at current frame calls function at previous frame. - prev_file_path = prev_stack_frame[0] - prev_function = prev_stack_frame[2] - prev_function_start_line = prev_stack_frame[4] - curr_file_path = stack_frame[0] - curr_line_number = stack_frame[1] - - # Skip all calls up to apply_op since they are the same for all ops. - if not after_apply_op: - if prev_function == 'apply_op': - after_apply_op = True - continue - location_index = self._locations.index_of( - curr_file_path, curr_line_number, - prev_function, prev_file_path, prev_function_start_line) - location_ids.append(location_index) - samples.add(datum, location_ids) - - sample_type_description = 'count' - sample_type = pprof_profile.sample_type.add() - sample_type.type = self._string_table.index_of(sample_type_description) - sample_type.unit = self._string_table.index_of('count') - sample_type_description = 'all_time' - sample_type = pprof_profile.sample_type.add() - sample_type.type = self._string_table.index_of(sample_type_description) - sample_type.unit = self._string_table.index_of('nanoseconds') - sample_type_description = 'op_time' - sample_type = pprof_profile.sample_type.add() - sample_type.type = self._string_table.index_of(sample_type_description) - sample_type.unit = self._string_table.index_of('nanoseconds') - - pprof_profile.string_table.extend(self._string_table.string_table()) - pprof_profile.sample.extend(samples.get_sample_protos()) - pprof_profile.function.extend(self._functions.function_protos()) - pprof_profile.location.extend(self._locations.location_protos()) - return pprof_profile - - def _get_profile_data_generator(self): - """Get function that generates `ProfileDatum` objects. - - Returns: - A function that generates `ProfileDatum` objects. - """ - node_to_traceback = defaultdict(list) - node_to_op_type = defaultdict(str) - for op in self._graph.get_operations(): - node_to_traceback[op.name] = op.traceback_with_start_lines - node_to_op_type[op.name] = op.type - - def profile_data_generator(device_step_stats): - for node_stats in device_step_stats.node_stats: - if node_stats.node_name == '_SOURCE' or node_stats.node_name == '_SINK': - continue - yield ProfileDatum( - node_stats, - node_to_op_type[node_stats.node_name], - node_to_traceback[node_stats.node_name]) - - return profile_data_generator - - -def get_profiles(graph, run_metadata): - """Generate profiles in pprof format. - - See https://github.com/google/pprof/blob/master/proto/profile.proto - for pprof proto format. - - Args: - graph: A `Graph` object. - run_metadata: A `RunMetadata` proto. - - Returns: - A dictionary mapping from device name to pprof proto for that device. - """ - return PprofProfiler(graph, run_metadata).profile() - - -def profile(graph, run_metadata, output_dir=None): - """Generate profiles in pprof format. - - See https://github.com/google/pprof/blob/master/proto/profile.proto - for pprof proto format. - - Args: - graph: A `Graph` object. - run_metadata: A `RunMetadata` proto. - output_dir: (string) Directory to output pprof profile to. - Profile files for each device will be stored in compressed - serialized proto format. If output_dir is None, profile protos - will be printed to stdout instead. - - Returns: - List of output files created by this profile call. - (Note: this list will be empty if output_dir is None) - """ - profiles = get_profiles(graph, run_metadata) - output_file_template = None - if output_dir: - if not os.path.isdir(output_dir): - os.makedirs(output_dir) - time_suffix = time.strftime('%Y%m%d%H%M%S') - output_file_template = os.path.join( - output_dir, '%s_' + time_suffix + '.pb.gz') - - profile_files = [] - for device, pprof_proto in profiles.items(): - if output_file_template is None: - print('No output directory specified, printing to stdout instead.') - print(pprof_proto) - else: - device_name = str(device).strip('/').translate( - maketrans('/:', '__')) - profile_file = output_file_template % device_name - profile_files.append(profile_file) - with gzip.open(profile_file, 'w') as output_file: - print('Writing profile to %s...' % profile_file) - output_file.write(pprof_proto.SerializeToString()) - return profile_files diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/pprof_profiler_test.py b/tensorflow/contrib/tfprof/python/tools/tfprof/pprof_profiler_test.py deleted file mode 100644 index 6487adf992..0000000000 --- a/tensorflow/contrib/tfprof/python/tools/tfprof/pprof_profiler_test.py +++ /dev/null @@ -1,164 +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 pprof_profiler.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import gzip - -from proto import profile_pb2 -from tensorflow.contrib.tfprof.python.tools.tfprof import pprof_profiler -from tensorflow.core.framework import step_stats_pb2 -from tensorflow.core.protobuf import config_pb2 -from tensorflow.python.framework import constant_op -from tensorflow.python.ops import control_flow_ops -from tensorflow.python.ops import math_ops -from tensorflow.python.platform import test - - -class PprofProfilerTest(test.TestCase): - - def testDataEmpty(self): - output_dir = test.get_temp_dir() - run_metadata = config_pb2.RunMetadata() - graph = test.mock.MagicMock() - graph.get_operations.return_value = [] - - profiles = pprof_profiler.get_profiles(graph, run_metadata) - self.assertEquals(0, len(profiles)) - profile_files = pprof_profiler.profile( - graph, run_metadata, output_dir) - self.assertEquals(0, len(profile_files)) - - def testRunMetadataEmpty(self): - output_dir = test.get_temp_dir() - run_metadata = config_pb2.RunMetadata() - graph = test.mock.MagicMock() - op1 = test.mock.MagicMock() - op1.name = 'Add/123' - op1.traceback = [('a/b/file1', 10, 'some_var')] - op1.type = 'add' - graph.get_operations.return_value = [op1] - - profiles = pprof_profiler.get_profiles(graph, run_metadata) - self.assertEquals(0, len(profiles)) - profile_files = pprof_profiler.profile( - graph, run_metadata, output_dir) - self.assertEquals(0, len(profile_files)) - - def testValidProfile(self): - output_dir = test.get_temp_dir() - run_metadata = config_pb2.RunMetadata() - - node1 = step_stats_pb2.NodeExecStats( - node_name='Add/123', - op_start_rel_micros=3, - op_end_rel_micros=5, - all_end_rel_micros=4) - - run_metadata = config_pb2.RunMetadata() - device1 = run_metadata.step_stats.dev_stats.add() - device1.device = 'deviceA' - device1.node_stats.extend([node1]) - - graph = test.mock.MagicMock() - op1 = test.mock.MagicMock() - op1.name = 'Add/123' - op1.traceback = [ - ('a/b/file1', 10, 'apply_op', 'abc'), ('a/c/file2', 12, 'my_op', 'def')] - op1.type = 'add' - graph.get_operations.return_value = [op1] - - expected_proto = """sample_type { - type: 5 - unit: 5 -} -sample_type { - type: 6 - unit: 7 -} -sample_type { - type: 8 - unit: 7 -} -sample { - value: 1 - value: 4 - value: 2 - label { - key: 1 - str: 2 - } - label { - key: 3 - str: 4 - } -} -string_table: "" -string_table: "node_name" -string_table: "Add/123" -string_table: "op_type" -string_table: "add" -string_table: "count" -string_table: "all_time" -string_table: "nanoseconds" -string_table: "op_time" -string_table: "Device 1 of 1: deviceA" -comment: 9 -""" - # Test with protos - profiles = pprof_profiler.get_profiles(graph, run_metadata) - self.assertEquals(1, len(profiles)) - self.assertTrue('deviceA' in profiles) - self.assertEquals(expected_proto, str(profiles['deviceA'])) - # Test with files - profile_files = pprof_profiler.profile( - graph, run_metadata, output_dir) - self.assertEquals(1, len(profile_files)) - with gzip.open(profile_files[0]) as profile_file: - profile_contents = profile_file.read() - profile = profile_pb2.Profile() - profile.ParseFromString(profile_contents) - self.assertEquals(expected_proto, str(profile)) - - def testProfileWithWhileLoop(self): - options = config_pb2.RunOptions() - options.trace_level = config_pb2.RunOptions.FULL_TRACE - run_metadata = config_pb2.RunMetadata() - - num_iters = 5 - with self.test_session() as sess: - i = constant_op.constant(0) - c = lambda i: math_ops.less(i, num_iters) - b = lambda i: math_ops.add(i, 1) - r = control_flow_ops.while_loop(c, b, [i]) - sess.run(r, options=options, run_metadata=run_metadata) - profiles = pprof_profiler.get_profiles(sess.graph, run_metadata) - self.assertEquals(1, len(profiles)) - profile = next(iter(profiles.values())) - add_samples = [] # Samples for the while/Add node - for sample in profile.sample: - if profile.string_table[sample.label[0].str] == 'while/Add': - add_samples.append(sample) - # Values for same nodes are aggregated. - self.assertEquals(1, len(add_samples)) - # Value of "count" should be equal to number of iterations. - self.assertEquals(num_iters, add_samples[0].value[0]) - - -if __name__ == '__main__': - test.main() diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/profiler_test.py b/tensorflow/contrib/tfprof/python/tools/tfprof/profiler_test.py deleted file mode 100644 index dd25340564..0000000000 --- a/tensorflow/contrib/tfprof/python/tools/tfprof/profiler_test.py +++ /dev/null @@ -1,186 +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. -# ============================================================================== - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import os -from tensorflow.core.protobuf import config_pb2 -from tensorflow.python.client import session -from tensorflow.python.framework import ops -from tensorflow.python.ops import variables -from tensorflow.python.platform import gfile -from tensorflow.python.platform import test - -# pylint: disable=g-bad-import-order -from tensorflow.contrib.tfprof.python.tools.tfprof import model_analyzer -from tensorflow.contrib.tfprof.python.tools.tfprof.internal import model_analyzer_testlib as lib - - -class ProfilerTest(test.TestCase): - - def testProfileBasic(self): - ops.reset_default_graph() - opts = model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS.copy() - opts['account_type_regexes'] = ['.*'] - opts['select'] = ['params', 'float_ops', 'micros', 'bytes', - 'device', 'op_types', 'occurrence'] - outfile = os.path.join(test.get_temp_dir(), 'dump') - opts['output'] = 'file:outfile=' + outfile - - # Test the output without run_meta. - sess = session.Session() - r = lib.BuildFullModel() - sess.run(variables.global_variables_initializer()) - - profiler = model_analyzer.Profiler(sess.graph) - profiler.profile_name_scope(opts) - with gfile.Open(outfile, 'r') as f: - profiler_str = f.read() - - model_analyzer.print_model_analysis( - sess.graph, tfprof_cmd='scope', tfprof_options=opts) - with gfile.Open(outfile, 'r') as f: - pma_str = f.read() - self.assertEqual(pma_str, profiler_str) - - # Test the output with run_meta. - run_meta = config_pb2.RunMetadata() - _ = sess.run(r, - options=config_pb2.RunOptions( - trace_level=config_pb2.RunOptions.FULL_TRACE), - run_metadata=run_meta) - - profiler.add_step(1, run_meta) - profiler.profile_graph(opts) - with gfile.Open(outfile, 'r') as f: - profiler_str = f.read() - - model_analyzer.print_model_analysis( - sess.graph, tfprof_cmd='graph', run_meta=run_meta, tfprof_options=opts) - with gfile.Open(outfile, 'r') as f: - pma_str = f.read() - self.assertEqual(pma_str, profiler_str) - - profiler.profile_python_codes(opts) - with gfile.Open(outfile, 'r') as f: - profiler_str = f.read() - - model_analyzer.print_model_analysis( - sess.graph, tfprof_cmd='code', run_meta=run_meta, tfprof_options=opts) - with gfile.Open(outfile, 'r') as f: - pma_str = f.read() - self.assertEqual(pma_str, profiler_str) - - profiler.profile_operations(opts) - with gfile.Open(outfile, 'r') as f: - profiler_str = f.read() - - model_analyzer.print_model_analysis( - sess.graph, tfprof_cmd='op', run_meta=run_meta, tfprof_options=opts) - with gfile.Open(outfile, 'r') as f: - pma_str = f.read() - self.assertEqual(pma_str, profiler_str) - - model_analyzer.print_model_analysis( - sess.graph, tfprof_cmd='scope', run_meta=run_meta, tfprof_options=opts) - with gfile.Open(outfile, 'r') as f: - pma_str = f.read() - self.assertNotEqual(pma_str, profiler_str) - - opts2 = opts.copy() - opts2['select'] = ['params', 'float_ops'] - profiler.profile_name_scope(opts2) - with gfile.Open(outfile, 'r') as f: - profiler_str = f.read() - - model_analyzer.print_model_analysis( - sess.graph, tfprof_cmd='scope', run_meta=run_meta, tfprof_options=opts2) - with gfile.Open(outfile, 'r') as f: - pma_str = f.read() - self.assertEqual(pma_str, profiler_str) - - def testMultiStepProfile(self): - ops.reset_default_graph() - opts = model_analyzer.PRINT_ALL_TIMING_MEMORY.copy() - opts['account_type_regexes'] = ['.*'] - - with session.Session() as sess: - r1, r2, r3 = lib.BuildSplitableModel() - sess.run(variables.global_variables_initializer()) - - profiler = model_analyzer.Profiler(sess.graph) - pb0 = profiler.profile_name_scope(opts) - - run_meta = config_pb2.RunMetadata() - _ = sess.run(r1, - options=config_pb2.RunOptions( - trace_level=config_pb2.RunOptions.FULL_TRACE), - run_metadata=run_meta) - profiler.add_step(1, run_meta) - pb1 = profiler.profile_name_scope(opts) - - self.assertNotEqual(lib.SearchTFProfNode(pb1, 'DW'), None) - self.assertEqual(lib.SearchTFProfNode(pb1, 'DW2'), None) - self.assertEqual(lib.SearchTFProfNode(pb1, 'add'), None) - - run_meta2 = config_pb2.RunMetadata() - _ = sess.run(r2, - options=config_pb2.RunOptions( - trace_level=config_pb2.RunOptions.FULL_TRACE), - run_metadata=run_meta2) - profiler.add_step(2, run_meta2) - pb2 = profiler.profile_name_scope(opts) - - self.assertNotEqual(lib.SearchTFProfNode(pb2, 'DW'), None) - self.assertNotEqual(lib.SearchTFProfNode(pb2, 'DW2'), None) - self.assertEqual(lib.SearchTFProfNode(pb2, 'add'), None) - - run_meta3 = config_pb2.RunMetadata() - _ = sess.run(r3, - options=config_pb2.RunOptions( - trace_level=config_pb2.RunOptions.FULL_TRACE), - run_metadata=run_meta3) - profiler.add_step(3, run_meta3) - pb3 = profiler.profile_name_scope(opts) - - self.assertNotEqual(lib.SearchTFProfNode(pb3, 'DW'), None) - self.assertNotEqual(lib.SearchTFProfNode(pb3, 'DW2'), None) - self.assertNotEqual(lib.SearchTFProfNode(pb3, 'add'), None) - - self.assertEqual(lib.SearchTFProfNode(pb0, 'Conv2D'), None) - self.assertGreater(lib.SearchTFProfNode(pb1, 'Conv2D').exec_micros, 0) - self.assertEqual(lib.SearchTFProfNode(pb1, 'Conv2D_1'), None) - self.assertGreater(lib.SearchTFProfNode(pb2, 'Conv2D_1').exec_micros, 0) - self.assertEqual(lib.SearchTFProfNode(pb2, 'add'), None) - self.assertGreater(lib.SearchTFProfNode(pb3, 'add').exec_micros, 0) - - advice_pb = profiler.advise(model_analyzer.ALL_ADVICE) - self.assertTrue('AcceleratorUtilizationChecker' in advice_pb.checkers) - self.assertTrue('ExpensiveOperationChecker' in advice_pb.checkers) - self.assertTrue('OperationChecker' in advice_pb.checkers) - - checker = advice_pb.checkers['AcceleratorUtilizationChecker'] - if test.is_gpu_available(): - self.assertGreater(len(checker.reports), 0) - else: - self.assertEqual(len(checker.reports), 0) - checker = advice_pb.checkers['ExpensiveOperationChecker'] - self.assertGreater(len(checker.reports), 0) - - -if __name__ == '__main__': - test.main() diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/tfprof_logger.py b/tensorflow/contrib/tfprof/python/tools/tfprof/tfprof_logger.py deleted file mode 100644 index 52febef26c..0000000000 --- a/tensorflow/contrib/tfprof/python/tools/tfprof/tfprof_logger.py +++ /dev/null @@ -1,187 +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. -# ============================================================================== -"""Logging tensorflow::tfprof::OpLog. - -OpLog is used to add extra model information for offline analysis by tfprof. -""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import os -import sys - -import six -from tensorflow.python.framework import ops -from tensorflow.python.framework import tensor_shape -from tensorflow.python.platform import gfile -from tensorflow.tools.tfprof import tfprof_log_pb2 - -TRAINABLE_VARIABLES = '_trainable_variables' -REGISTERED_FLOP_STATS = 'flops' - - -def _fill_missing_graph_shape(graph, run_meta): - """Fill Tensor shapes in 'graph' with run time shape from 'run_meta'.""" - for dev_stat in run_meta.step_stats.dev_stats: - for node_stat in dev_stat.node_stats: - if not node_stat.output: - continue - try: - op = graph.get_operation_by_name(node_stat.node_name) - except KeyError as e: - # Graph doesn't contains the node_stat, usually RecvTensor. - continue - if len(node_stat.output) != len(op.outputs): - # For example, conditional op has only 1 output at run time. - continue - for (i, node_stat_out) in enumerate(node_stat.output): - if op.outputs[i].get_shape().is_fully_defined(): - continue - node_stat_dims = node_stat_out.tensor_description.shape.dim - node_stat_shape = tensor_shape.TensorShape( - [d.size for d in node_stat_dims]) - try: - op.outputs[i].set_shape(op.outputs[i].get_shape().merge_with( - node_stat_shape)) - except ValueError as e: - sys.stderr.write('Node %s incompatible shapes: %s.\n' % - (node_stat.node_name, e)) - return graph - - -def _get_logged_ops(graph, run_meta=None, add_trace=True, - add_trainable_var=True): - """Extract trainable model parameters and FLOPs for ops from a Graph. - - Args: - graph: tf.Graph. - run_meta: RunMetadata proto used to complete shape information. - add_trace: Whether to add op trace information. - add_trainable_var: Whether to assign tf.trainable_variables() op type - '_trainable_variables'. - Returns: - logged_ops: dict mapping from op_name to OpLogEntry. - """ - if run_meta: - graph = _fill_missing_graph_shape(graph, run_meta) - - op_missing_shape = 0 - logged_ops = {} - # TODO(xpan): Work with Profiler more efficiently. - for op in graph.get_operations(): - try: - stats = ops.get_stats_for_node_def( - graph, op.node_def, REGISTERED_FLOP_STATS) - except ValueError: - # Catch Exception When shape is incomplete. Skip it. - op_missing_shape += 1 - stats = None - - entry = tfprof_log_pb2.OpLogEntry() - entry.name = op.name - add_entry = False - if stats and stats.value: - entry.float_ops = int(stats.value) - add_entry = True - - if add_trace: - for tb in op.traceback: - trace = entry.code_def.traces.add() - trace.file = tb[0] if tb[0] else 'none' - trace.lineno = tb[1] if tb[1] else -1 - trace.function = tb[2] if tb[2] else 'none' - trace.line = tb[3] if tb[3] else 'none' - add_entry = True - - if add_entry: - logged_ops[entry.name] = entry - - if add_trainable_var: - for v in graph.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES): - if v.op.name not in logged_ops: - entry = tfprof_log_pb2.OpLogEntry() - entry.name = v.op.name - entry.types.append(TRAINABLE_VARIABLES) - logged_ops[entry.name] = entry - else: - logged_ops[v.op.name].types.append(TRAINABLE_VARIABLES) - - if op_missing_shape > 0 and not run_meta: - sys.stderr.write('%d ops no flops stats due to incomplete shapes.\n' % - op_missing_shape) - return logged_ops - - -def _merge_default_with_oplog(graph, op_log=None, run_meta=None, - add_trace=True, add_trainable_var=True): - """Merge the tfprof default extra info with caller's op_log. - - Args: - graph: tf.Graph. - op_log: OpLog proto. - run_meta: RunMetadata proto used to complete shape information. - add_trace: Whether to add op trace information. - add_trainable_var: Whether to assign tf.trainable_variables() op type - '_trainable_variables'. - Returns: - tmp_op_log: Merged OpLog proto. - """ - tmp_op_log = tfprof_log_pb2.OpLog() - logged_ops = _get_logged_ops( - graph, run_meta, add_trace=add_trace, add_trainable_var=add_trainable_var) - - if not op_log: - tmp_op_log.log_entries.extend(logged_ops.values()) - else: - all_ops = dict() - for entry in op_log.log_entries: - all_ops[entry.name] = entry - for op_name, entry in six.iteritems(logged_ops): - if op_name in all_ops: - all_ops[op_name].types.extend(entry.types) - if entry.float_ops > 0 and all_ops[op_name].float_ops == 0: - all_ops[op_name].float_ops = entry.float_ops - if entry.code_def.traces and not all_ops[op_name].code_def.traces: - all_ops[op_name].code_def.MergeFrom(entry.code_def) - else: - all_ops[op_name] = entry - tmp_op_log.log_entries.extend(all_ops.values()) - return tmp_op_log - - -def write_op_log(graph, log_dir, op_log=None, run_meta=None, add_trace=True): - """Log provided 'op_log', and add additional model information below. - - The API also assigns ops in tf.trainable_variables() an op type called - '_trainable_variables'. - The API also logs 'flops' statistics for ops with op.RegisterStatistics() - defined. flops calculation depends on Tensor shapes defined in 'graph', - which might not be complete, 'run_meta', if provided, completes the shape - information with best effort. - - Args: - graph: tf.Graph. - log_dir: directory to write the log file. - op_log: (Optional) OpLog proto to be written. If not provided, an new - one is created. - run_meta: (Optional) RunMetadata proto that helps flops computation using - run time shape information. - add_trace: Whether to add op trace information. Used to support "code" view. - """ - op_log = _merge_default_with_oplog(graph, op_log, run_meta, add_trace) - - with gfile.Open(os.path.join(log_dir, 'tfprof_log'), 'w') as log: - log.write(op_log.SerializeToString()) diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/tfprof_logger_test.py b/tensorflow/contrib/tfprof/python/tools/tfprof/tfprof_logger_test.py deleted file mode 100644 index 87dfdc0fc1..0000000000 --- a/tensorflow/contrib/tfprof/python/tools/tfprof/tfprof_logger_test.py +++ /dev/null @@ -1,82 +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. -# ============================================================================== - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from tensorflow.contrib.copy_graph.python.util import copy_elements -from tensorflow.contrib.tfprof.python.tools.tfprof import tfprof_logger -from tensorflow.core.protobuf import config_pb2 -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.ops import array_ops -from tensorflow.python.ops import math_ops -from tensorflow.python.platform import test - - -class TFProfLoggerTest(test.TestCase): - - def _BuildSmallPlaceholderlModel(self): - a = array_ops.placeholder(dtypes.int32, [2, 2]) - b = array_ops.placeholder(dtypes.int32, [2, 2]) - y = math_ops.matmul(a, b) - return a, b, y - - def _BuildSmallModel(self): - a = constant_op.constant([[1, 2], [3, 4]]) - b = constant_op.constant([[1, 2], [3, 4]]) - return math_ops.matmul(a, b) - - def testFillMissingShape(self): - a, b, y = self._BuildSmallPlaceholderlModel() - run_options = config_pb2.RunOptions( - trace_level=config_pb2.RunOptions.FULL_TRACE) - run_metadata = config_pb2.RunMetadata() - sess = session.Session() - sess.run(y, - options=run_options, - run_metadata=run_metadata, - feed_dict={a: [[1, 2], [2, 3]], - b: [[1, 2], [2, 3]]}) - - graph2 = ops.Graph() - # Use copy_op_to_graph to remove shape information. - y2 = copy_elements.copy_op_to_graph(y, graph2, []) - self.assertEquals('<unknown>', str(y2.get_shape())) - - tfprof_logger._fill_missing_graph_shape(graph2, run_metadata) - self.assertEquals('(2, 2)', str(y2.get_shape())) - - def testFailedFillMissingShape(self): - y = self._BuildSmallModel() - run_options = config_pb2.RunOptions( - trace_level=config_pb2.RunOptions.FULL_TRACE) - run_metadata = config_pb2.RunMetadata() - sess = session.Session() - sess.run(y, options=run_options, run_metadata=run_metadata) - - graph2 = ops.Graph() - y2 = copy_elements.copy_op_to_graph(y, graph2, []) - self.assertEquals('<unknown>', str(y2.get_shape())) - # run_metadata has special name for MatMul, hence failed to fill shape. - tfprof_logger._fill_missing_graph_shape(graph2, run_metadata) - self.assertEquals('<unknown>', str(y2.get_shape())) - - -if __name__ == '__main__': - test.main() diff --git a/tensorflow/contrib/tfprof/tfprof_logger.py b/tensorflow/contrib/tfprof/tfprof_logger.py new file mode 100644 index 0000000000..9588bd2985 --- /dev/null +++ b/tensorflow/contrib/tfprof/tfprof_logger.py @@ -0,0 +1,24 @@ +# 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. +# ============================================================================== +"""Logging tensorflow::tfprof::OpLog. + +OpLog is used to add extra model information for offline analysis by tfprof. +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +# pylint: disable=unused-import +from tensorflow.python.profiler.tfprof_logger import write_op_log |