98 lines
3.7 KiB
C++
98 lines
3.7 KiB
C++
/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#ifndef TENSORFLOW_CC_PROFILER_PROFILER_H_
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#define TENSORFLOW_CC_PROFILER_PROFILER_H_
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#include "tensorflow/core/framework/graph.pb.h"
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#include "tensorflow/core/lib/core/status.h"
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#include "tensorflow/core/profiler/internal/tfprof_stats.h"
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#include "tensorflow/core/profiler/tfprof_options.h"
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#include "tensorflow/core/profiler/tfprof_output.pb.h"
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namespace tensorflow {
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namespace tfprof {
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/// @addtogroup core
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/// @{
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/// A `Profiler` object lets the caller profile the execution of a graph.
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///
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/// Example:
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/// // First build a graph and run tracing.
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/// Scope root = Scope::NewRootScope();
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/// auto a = Placeholder(root, DT_INT32);
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/// auto c = Add(root, a, {41});
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///
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/// ClientSession session(root);
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/// std::vector<Tensor> outputs;
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/// RunOptions run_options;
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/// run_options.set_trace_level(RunOptions::FULL_TRACE);
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/// RunMetadata run_meta;
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/// Status s = session.Run(run_options, { {a, {1}} }, {c}, &outputs,
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/// &run_meta);
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/// if (!s.ok()) { ... }
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///
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/// // Then create profiler to do profiling.
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/// GraphDef graph;
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/// root.ToGraphDef(&graph);
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/// Profiler profiler(graph);
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/// profiler.AddStep(0, run_meta);
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/// Options opts = ... // TODO(xpan): Support option building API.
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/// MultiGraphNodeProto r = profiler.ProfileOperations(opts);
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///
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class Profiler {
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public:
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/// `graph` is the model's GraphDef.
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Profiler(const GraphDef& graph);
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/// Adds tracing information `run_meta` to profiler. A `run_meta` is
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/// generated by a TensorFlow session run call. `step` is the key
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/// to the `run_meta`. When calling ProfileXXX methods, caller can specify
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/// `step` in `options` to selectively profile the corresponding `run_meta`.
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/// Multiple different `run_meta` can be keyed by the same `step` in order
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/// to group them together.
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void AddStep(int64 step, const RunMetadata& run_meta);
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/// Profiles the model by organizing nodes in graph structure.
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/// Each node is an op and the nodes are connected by the op inputs/outputs.
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GraphNodeProto ProfileGraph(const Options& options);
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/// Profiles the model by organizing nodes in name scope structure.
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/// Each node is an op, and nodes are organized by the ops' name
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/// scope, similar to a file system tree.
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/// E.g. /foo is the root of operation /foo/matmul_1 and foo/conv_2.
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GraphNodeProto ProfileNameScope(const Options& options);
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/// Profiles the model by organizing nodes by operation types.
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/// Each node is an operation type (e.g. Conv2D or MatMul), containing all
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/// ops belonging to that type in the model.
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MultiGraphNodeProto ProfileOperations(const Options& options);
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/// Serialize the profile content (ProfileProto) into a binary string,
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/// User can write the string to file for offline analysis by
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/// tfprof command-line tools or graphical user interface.
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Status SerializeToString(string* content);
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private:
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std::unique_ptr<TFStats> stats_;
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};
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/// @}
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} // namespace tfprof
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} // namespace tensorflow
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#endif // TENSORFLOW_CC_PROFILER_PROFILER_H_
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