# 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 from tensorflow.python.profiler import option_builder # pylint: disable=g-bad-import-order from tensorflow.python.profiler import model_analyzer from tensorflow.python.profiler.internal import model_analyzer_testlib as lib builder = option_builder.ProfileOptionBuilder class ProfilerTest(test.TestCase): def testProfileBasic(self): ops.reset_default_graph() outfile = os.path.join(test.get_temp_dir(), 'dump') opts = (builder(builder.trainable_variables_parameter()) .with_file_output(outfile) .with_accounted_types(['.*']) .select(['params', 'float_ops', 'micros', 'bytes', 'device', 'op_types', 'occurrence']).build()) # Test the output without run_meta. sess = session.Session() r = lib.BuildFullModel() sess.run(variables.global_variables_initializer()) # 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 = model_analyzer.Profiler(sess.graph) profiler.add_step(1, run_meta) profiler.profile_graph(opts) with gfile.Open(outfile, 'r') as f: profiler_str = f.read() model_analyzer.profile( sess.graph, cmd='graph', run_meta=run_meta, options=opts) with gfile.Open(outfile, 'r') as f: pma_str = f.read() self.assertEqual(pma_str, profiler_str) profiler.profile_name_scope(opts) with gfile.Open(outfile, 'r') as f: profiler_str = f.read() model_analyzer.profile( sess.graph, cmd='scope', run_meta=run_meta, options=opts) with gfile.Open(outfile, 'r') as f: pma_str = f.read() self.assertEqual(pma_str, profiler_str) profiler.profile_python(opts) with gfile.Open(outfile, 'r') as f: profiler_str = f.read() model_analyzer.profile( sess.graph, cmd='code', run_meta=run_meta, 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.profile( sess.graph, cmd='op', run_meta=run_meta, options=opts) with gfile.Open(outfile, 'r') as f: pma_str = f.read() self.assertEqual(pma_str, profiler_str) model_analyzer.profile( sess.graph, cmd='scope', run_meta=run_meta, options=opts) with gfile.Open(outfile, 'r') as f: pma_str = f.read() self.assertNotEqual(pma_str, profiler_str) def testMultiStepProfile(self): ops.reset_default_graph() opts = builder.time_and_memory(min_bytes=0) 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) def testMultipleProfilePerStep(self): ops.reset_default_graph() opts = (builder(builder.trainable_variables_parameter()) .with_empty_output() .with_accounted_types(['.*']) .select(['micros', 'bytes', 'peak_bytes', 'residual_bytes', 'output_bytes']).build()) r = lib.BuildSmallModel() sess = session.Session() profiler = model_analyzer.Profiler(sess.graph) init_var_run_meta = config_pb2.RunMetadata() sess.run(variables.global_variables_initializer(), options=config_pb2.RunOptions( trace_level=config_pb2.RunOptions.FULL_TRACE), run_metadata=init_var_run_meta) train_run_meta = config_pb2.RunMetadata() sess.run(r, options=config_pb2.RunOptions( trace_level=config_pb2.RunOptions.FULL_TRACE), run_metadata=train_run_meta) profiler.add_step(0, train_run_meta) ret1 = profiler.profile_name_scope(opts) n1 = lib.SearchTFProfNode( ret1, 'DW/Initializer/random_normal/RandomStandardNormal') # Without the var initialization run_meta, it doesn't have the # information of var_initialization. self.assertEqual(n1.exec_micros, 0) self.assertEqual(n1.requested_bytes, 0) self.assertEqual(n1.peak_bytes, 0) self.assertEqual(n1.residual_bytes, 0) profiler.add_step(0, init_var_run_meta) ret2 = profiler.profile_name_scope(opts) n2 = lib.SearchTFProfNode( ret2, 'DW/Initializer/random_normal/RandomStandardNormal') # After adding the var initialization run_meta. self.assertGreater(n2.exec_micros, 0) self.assertGreater(n2.requested_bytes, 0) self.assertGreater(n2.peak_bytes, 0) self.assertGreater(n2.residual_bytes, 0) if __name__ == '__main__': test.main()