bee955699c
This will allow us to start reasoning about more global config options in Grappler. For example, it will allow us to move XLA functionalization rewrites into Grappler, because we'll know when JIT optimization has been turned on. We'll also be able to selectively disable particular grappler rewrites when JIT optimization is on. Additional changes: * PartitionedCall now accepts a ConfigProto instead of a RewriterConfig * Eager context now passes the entire ConfigProto through. PiperOrigin-RevId: 221460335
135 lines
5.1 KiB
Python
135 lines
5.1 KiB
Python
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# =============================================================================
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"""A tool for cost analysis."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import argparse
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import sys
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from google.protobuf import message
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from google.protobuf import text_format
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from tensorflow.contrib.fused_conv.ops import gen_fused_conv2d_bias_activation_op # pylint: disable=unused-import
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from tensorflow.core.framework import graph_pb2
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from tensorflow.core.protobuf import config_pb2
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from tensorflow.core.protobuf import meta_graph_pb2
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from tensorflow.core.protobuf import saved_model_pb2
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from tensorflow.python.framework import importer
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from tensorflow.python.framework import ops
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from tensorflow.python.grappler import cost_analyzer
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from tensorflow.python.grappler import tf_optimizer
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from tensorflow.python.platform import app
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from tensorflow.python.platform import gfile
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from tensorflow.python.training import saver
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def get_metagraph():
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"""Constructs and returns a MetaGraphDef from the input file."""
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with gfile.GFile(FLAGS.input) as input_file:
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input_data = input_file.read()
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try:
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saved_model = saved_model_pb2.SavedModel()
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text_format.Merge(input_data, saved_model)
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meta_graph = saved_model.meta_graphs[0]
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except text_format.ParseError:
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try:
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saved_model.ParseFromString(input_data)
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meta_graph = saved_model.meta_graphs[0]
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except message.DecodeError:
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try:
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meta_graph = meta_graph_pb2.MetaGraphDef()
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text_format.Merge(input_data, meta_graph)
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except text_format.ParseError:
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try:
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meta_graph.ParseFromString(input_data)
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except message.DecodeError:
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try:
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graph_def = graph_pb2.GraphDef()
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text_format.Merge(input_data, graph_def)
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except text_format.ParseError:
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try:
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graph_def.ParseFromString(input_data)
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except message.DecodeError:
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raise ValueError("Invalid input file.")
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importer.import_graph_def(graph_def, name="")
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graph = ops.get_default_graph()
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meta_graph = saver.export_meta_graph(
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graph_def=graph.as_graph_def(), graph=graph)
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if FLAGS.fetch is not None:
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fetch_collection = meta_graph_pb2.CollectionDef()
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for fetch in FLAGS.fetch.split(","):
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fetch_collection.node_list.value.append(fetch)
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meta_graph.collection_def["train_op"].CopyFrom(fetch_collection)
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return meta_graph
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def main(_):
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metagraph = get_metagraph()
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config = config_pb2.ConfigProto()
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if FLAGS.rewriter_config is not None:
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text_format.Merge(FLAGS.rewriter_config,
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config.graph_options.rewrite_options)
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optimized_graph = tf_optimizer.OptimizeGraph(config, metagraph)
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metagraph.graph_def.CopyFrom(optimized_graph)
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report = cost_analyzer.GenerateCostReport(metagraph, FLAGS.per_node_report,
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FLAGS.verbose)
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print(report)
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if FLAGS.memory_report:
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report = cost_analyzer.GenerateMemoryReport(metagraph)
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print(report)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--input",
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type=str,
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default=None,
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help="Input file path. Accept SavedModel, MetaGraphDef, and GraphDef in "
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"either binary or text format.")
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parser.add_argument(
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"--fetch",
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type=str,
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default=None,
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help="The names of the fetch node delimited by comma.")
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parser.add_argument(
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"--rewriter_config",
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type=str,
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default=None,
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help="Configuration for the grappler optimizers, described as a "
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"RewriterConfig protocol buffer. Usage example 1: "
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"--rewriter_config='optimize_tensor_layout: true "
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"disable_model_pruning: true'. Usage example 2: "
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"--rewriter_config='optimizers: \"constfold\" optimizers: \"layout\"'")
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parser.add_argument(
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"--per_node_report",
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action="store_true",
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help="Generate per-node report. By default the report contains stats "
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"aggregated on a per op type basis, per_node_report adds results "
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"for each individual node to the report.")
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parser.add_argument(
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"--memory_report",
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action="store_true",
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help="Generate memory usage report.")
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parser.add_argument(
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"--verbose",
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action="store_true",
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help="Generate verbose reports. By default, succinct reports are used.")
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FLAGS, unparsed = parser.parse_known_args()
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app.run(main=main, argv=[sys.argv[0]] + unparsed)
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