/* Copyright 2016 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_CORE_UTIL_STAT_SUMMARIZER_H_
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#define TENSORFLOW_CORE_UTIL_STAT_SUMMARIZER_H_
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#include <stdlib.h>
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#include <cmath>
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#include <limits>
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#include <map>
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#include <memory>
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#include <sstream>
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#include <string>
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#include "tensorflow/core/framework/tensor.h"
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#include "tensorflow/core/framework/types.pb.h"
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#include "tensorflow/core/platform/types.h"
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#include "tensorflow/core/util/stat_summarizer_options.h"
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#include "tensorflow/core/util/stats_calculator.h"
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namespace tensorflow {
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class GraphDef;
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class StepStats;
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class NodeExecStats;
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// A StatSummarizer assists in performance analysis of Graph executions.
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//
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// It summarizes time spent executing (on GPU/CPU), memory used etc. across
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// multiple executions of a single Graph from the StepStats collected during
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// graph execution.
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//
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// See tensorflow/tools/benchmark/benchmark_model.cc for an example usage.
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class StatSummarizer {
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public:
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explicit StatSummarizer(const StatSummarizerOptions& options);
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// Deprecated: Use StatSummarizer(const StatSummarizerOptions&) instead. The
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// GraphDef is not needed by the StatSummarizer.
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explicit StatSummarizer(const tensorflow::GraphDef& tensorflow_graph);
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~StatSummarizer();
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// Adds another run's StepStats output to the aggregate counts.
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void ProcessStepStats(const StepStats& step_stats);
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// Returns a string detailing the accumulated runtime stats in a tab-separated
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// format which can be pasted into a spreadsheet for further analysis.
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std::string GetOutputString() const {
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return stats_calculator_->GetOutputString();
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}
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std::string ShortSummary() const {
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return stats_calculator_->GetShortSummary();
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}
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// Prints the string returned by GetOutputString().
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void PrintStepStats() const;
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// Prints the output tensor sizes and types for each node.
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void PrintOutputs() const;
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void ComputeStatsByType(
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std::map<std::string, int64_t>* node_type_map_count,
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std::map<std::string, int64_t>* node_type_map_time,
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std::map<std::string, int64_t>* node_type_map_memory,
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std::map<std::string, int64_t>* node_type_map_times_called,
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int64_t* accumulated_us) const {
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stats_calculator_->ComputeStatsByType(
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node_type_map_count, node_type_map_time, node_type_map_memory,
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node_type_map_times_called, accumulated_us);
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}
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std::string GetStatsByNodeType() const {
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return stats_calculator_->GetStatsByNodeType();
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}
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std::string GetStatsByMetric(const string& title,
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StatsCalculator::SortingMetric sorting_metric,
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int num_stats) const {
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return stats_calculator_->GetStatsByMetric(title, sorting_metric,
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num_stats);
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}
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int num_runs() const { return stats_calculator_->num_runs(); }
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// Returns stats of total microseconds spent by all nodes in each run.
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const Stat<int64_t>& run_total_us() const {
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return stats_calculator_->run_total_us();
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}
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private:
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void Validate(const std::vector<TensorDescription>* outputs,
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const NodeExecStats& ns) const;
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std::map<std::string, std::vector<TensorDescription> > outputs_;
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std::unique_ptr<StatsCalculator> stats_calculator_;
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};
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} // namespace tensorflow
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#endif // TENSORFLOW_CORE_UTIL_STAT_SUMMARIZER_H_
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