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| /* Copyright 2018 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. | ||
| ==============================================================================*/ | ||
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| #include "tensorflow/lite/tools/benchmark/benchmark_model.h" | ||
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| #include <iostream> | ||
| #include <sstream> | ||
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| #include "tensorflow/lite/profiling/memory_info.h" | ||
| #include "tensorflow/lite/profiling/time.h" | ||
| #include "tensorflow/lite/tools/benchmark/benchmark_utils.h" | ||
| #include "tensorflow/lite/tools/logging.h" | ||
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| namespace tflite { | ||
| namespace benchmark { | ||
| using tensorflow::Stat; | ||
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| BenchmarkParams BenchmarkModel::DefaultParams() { | ||
| BenchmarkParams params; | ||
| params.AddParam("num_runs", BenchmarkParam::Create<int32_t>(50)); | ||
| params.AddParam("min_secs", BenchmarkParam::Create<float>(1.0f)); | ||
| params.AddParam("max_secs", BenchmarkParam::Create<float>(150.0f)); | ||
| params.AddParam("run_delay", BenchmarkParam::Create<float>(-1.0f)); | ||
| params.AddParam("num_threads", BenchmarkParam::Create<int32_t>(1)); | ||
| params.AddParam("use_caching", BenchmarkParam::Create<bool>(false)); | ||
| params.AddParam("benchmark_name", BenchmarkParam::Create<std::string>("")); | ||
| params.AddParam("output_prefix", BenchmarkParam::Create<std::string>("")); | ||
| params.AddParam("warmup_runs", BenchmarkParam::Create<int32_t>(1)); | ||
| params.AddParam("warmup_min_secs", BenchmarkParam::Create<float>(0.5f)); | ||
| params.AddParam("verbose", BenchmarkParam::Create<bool>(false)); | ||
| return params; | ||
| } | ||
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| BenchmarkModel::BenchmarkModel() : params_(DefaultParams()) {} | ||
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| void BenchmarkLoggingListener::OnBenchmarkEnd(const BenchmarkResults& results) { | ||
| auto inference_us = results.inference_time_us(); | ||
| auto init_us = results.startup_latency_us(); | ||
| auto warmup_us = results.warmup_time_us(); | ||
| auto init_mem_usage = results.init_mem_usage(); | ||
| auto overall_mem_usage = results.overall_mem_usage(); | ||
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| std::cout << "time cost: min=" << inference_us.min() / 1000. << "ms" | ||
| << " max=" << inference_us.max() / 1000. << "ms" | ||
| << " avg=" << inference_us.avg() / 1000. << "ms" | ||
| << " std_dev=" << inference_us.std_deviation() / 1000. << "ms" | ||
| << std::endl; | ||
| TFLITE_LOG(INFO) << "Inference timings in us: " | ||
| << "Init: " << init_us << ", " | ||
| << "First inference: " << warmup_us.first() << ", " | ||
| << "Warmup (avg): " << warmup_us.avg() << ", " | ||
| << "Inference (avg): " << inference_us.avg(); | ||
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| if (!init_mem_usage.IsSupported()) return; | ||
| TFLITE_LOG(INFO) | ||
| << "Note: as the benchmark tool itself affects memory footprint, the " | ||
| "following is only APPROXIMATE to the actual memory footprint of the " | ||
| "model at runtime. Take the information at your discretion."; | ||
| TFLITE_LOG(INFO) << "Peak memory footprint (MB): init=" | ||
| << init_mem_usage.max_rss_kb / 1024.0 | ||
| << " overall=" << overall_mem_usage.max_rss_kb / 1024.0; | ||
| } | ||
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| std::vector<Flag> BenchmarkModel::GetFlags() { | ||
| return { | ||
| CreateFlag<int32_t>( | ||
| "num_runs", ¶ms_, | ||
| "expected number of runs, see also min_secs, max_secs"), | ||
| CreateFlag<float>( | ||
| "min_secs", ¶ms_, | ||
| "minimum number of seconds to rerun for, potentially making the " | ||
| "actual number of runs to be greater than num_runs"), | ||
| CreateFlag<float>( | ||
| "max_secs", ¶ms_, | ||
| "maximum number of seconds to rerun for, potentially making the " | ||
| "actual number of runs to be less than num_runs. Note if --max-secs " | ||
| "is exceeded in the middle of a run, the benchmark will continue to " | ||
| "the end of the run but will not start the next run."), | ||
| CreateFlag<float>("run_delay", ¶ms_, "delay between runs in seconds"), | ||
| CreateFlag<int32_t>("num_threads", ¶ms_, "number of threads"), | ||
| CreateFlag<bool>( | ||
| "use_caching", ¶ms_, | ||
| "Enable caching of prepacked weights matrices in matrix " | ||
| "multiplication routines. Currently implies the use of the Ruy " | ||
| "library."), | ||
| CreateFlag<std::string>("benchmark_name", ¶ms_, "benchmark name"), | ||
| CreateFlag<std::string>("output_prefix", ¶ms_, | ||
| "benchmark output prefix"), | ||
| CreateFlag<int32_t>( | ||
| "warmup_runs", ¶ms_, | ||
| "minimum number of runs performed on initialization, to " | ||
| "allow performance characteristics to settle, see also " | ||
| "warmup_min_secs"), | ||
| CreateFlag<float>( | ||
| "warmup_min_secs", ¶ms_, | ||
| "minimum number of seconds to rerun for, potentially making the " | ||
| "actual number of warm-up runs to be greater than warmup_runs"), | ||
| CreateFlag<bool>("verbose", ¶ms_, | ||
| "Whether to log parameters whose values are not set. " | ||
| "By default, only log those parameters that are set by " | ||
| "parsing their values from the commandline flags."), | ||
| }; | ||
| } | ||
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| void BenchmarkModel::LogParams() { | ||
| const bool verbose = params_.Get<bool>("verbose"); | ||
| TFLITE_LOG(INFO) << "Log parameter values verbosely: [" << verbose << "]"; | ||
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| LOG_BENCHMARK_PARAM(int32_t, "num_runs", "Min num runs", verbose); | ||
| LOG_BENCHMARK_PARAM(float, "min_secs", "Min runs duration (seconds)", | ||
| verbose); | ||
| LOG_BENCHMARK_PARAM(float, "max_secs", "Max runs duration (seconds)", | ||
| verbose); | ||
| LOG_BENCHMARK_PARAM(float, "run_delay", "Inter-run delay (seconds)", verbose); | ||
| LOG_BENCHMARK_PARAM(int32_t, "num_threads", "Num threads", verbose); | ||
| LOG_BENCHMARK_PARAM(bool, "use_caching", "Use caching", verbose); | ||
| LOG_BENCHMARK_PARAM(std::string, "benchmark_name", "Benchmark name", verbose); | ||
| LOG_BENCHMARK_PARAM(std::string, "output_prefix", "Output prefix", verbose); | ||
| LOG_BENCHMARK_PARAM(int32_t, "warmup_runs", "Min warmup runs", verbose); | ||
| LOG_BENCHMARK_PARAM(float, "warmup_min_secs", | ||
| "Min warmup runs duration (seconds)", verbose); | ||
| } | ||
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| TfLiteStatus BenchmarkModel::PrepareInputData() { return kTfLiteOk; } | ||
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| TfLiteStatus BenchmarkModel::ResetInputsAndOutputs() { return kTfLiteOk; } | ||
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| Stat<int64_t> BenchmarkModel::Run(int min_num_times, float min_secs, | ||
| float max_secs, RunType run_type, | ||
| TfLiteStatus* invoke_status) { | ||
| Stat<int64_t> run_stats; | ||
| TFLITE_LOG(INFO) << "Running benchmark for at least " << min_num_times | ||
| << " iterations and at least " << min_secs << " seconds but" | ||
| << " terminate if exceeding " << max_secs << " seconds."; | ||
| int64_t now_us = profiling::time::NowMicros(); | ||
| int64_t min_finish_us = now_us + static_cast<int64_t>(min_secs * 1.e6f); | ||
| int64_t max_finish_us = now_us + static_cast<int64_t>(max_secs * 1.e6f); | ||
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| *invoke_status = kTfLiteOk; | ||
| for (int run = 0; (run < min_num_times || now_us < min_finish_us) && | ||
| now_us <= max_finish_us; | ||
| run++) { | ||
| ResetInputsAndOutputs(); | ||
| listeners_.OnSingleRunStart(run_type); | ||
| int64_t start_us = profiling::time::NowMicros(); | ||
| TfLiteStatus status = RunImpl(); | ||
| int64_t end_us = profiling::time::NowMicros(); | ||
| listeners_.OnSingleRunEnd(); | ||
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| run_stats.UpdateStat(end_us - start_us); | ||
| util::SleepForSeconds(params_.Get<float>("run_delay")); | ||
| now_us = profiling::time::NowMicros(); | ||
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| if (status != kTfLiteOk) { | ||
| *invoke_status = status; | ||
| } | ||
| } | ||
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| std::stringstream stream; | ||
| run_stats.OutputToStream(&stream); | ||
| TFLITE_LOG(INFO) << stream.str() << std::endl; | ||
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| return run_stats; | ||
| } | ||
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| TfLiteStatus BenchmarkModel::ValidateParams() { return kTfLiteOk; } | ||
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| TfLiteStatus BenchmarkModel::Run(int argc, char** argv) { | ||
| TF_LITE_ENSURE_STATUS(ParseFlags(argc, argv)); | ||
| return Run(); | ||
| } | ||
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| TfLiteStatus BenchmarkModel::Run() { | ||
| TF_LITE_ENSURE_STATUS(ValidateParams()); | ||
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| LogParams(); | ||
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| const double model_size_mb = MayGetModelFileSize() / 1e6; | ||
| const auto start_mem_usage = profiling::memory::GetMemoryUsage(); | ||
| int64_t initialization_start_us = profiling::time::NowMicros(); | ||
| TF_LITE_ENSURE_STATUS(Init()); | ||
| const auto init_end_mem_usage = profiling::memory::GetMemoryUsage(); | ||
| int64_t initialization_end_us = profiling::time::NowMicros(); | ||
| int64_t startup_latency_us = initialization_end_us - initialization_start_us; | ||
| const auto init_mem_usage = init_end_mem_usage - start_mem_usage; | ||
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| if (model_size_mb > 0) { | ||
| TFLITE_LOG(INFO) << "The input model file size (MB): " << model_size_mb; | ||
| } | ||
| TFLITE_LOG(INFO) << "Initialized session in " << startup_latency_us / 1e3 | ||
| << "ms."; | ||
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| TF_LITE_ENSURE_STATUS(PrepareInputData()); | ||
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| TfLiteStatus status = kTfLiteOk; | ||
| uint64_t input_bytes = ComputeInputBytes(); | ||
| listeners_.OnBenchmarkStart(params_); | ||
| Stat<int64_t> warmup_time_us = | ||
| Run(params_.Get<int32_t>("warmup_runs"), | ||
| params_.Get<float>("warmup_min_secs"), params_.Get<float>("max_secs"), | ||
| WARMUP, &status); | ||
| if (status != kTfLiteOk) { | ||
| return status; | ||
| } | ||
|
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| Stat<int64_t> inference_time_us = | ||
| Run(params_.Get<int32_t>("num_runs"), params_.Get<float>("min_secs"), | ||
| params_.Get<float>("max_secs"), REGULAR, &status); | ||
| const auto overall_mem_usage = | ||
| profiling::memory::GetMemoryUsage() - start_mem_usage; | ||
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| listeners_.OnBenchmarkEnd({model_size_mb, startup_latency_us, input_bytes, | ||
| warmup_time_us, inference_time_us, init_mem_usage, | ||
| overall_mem_usage}); | ||
| return status; | ||
| } | ||
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| TfLiteStatus BenchmarkModel::ParseFlags(int* argc, char** argv) { | ||
| auto flag_list = GetFlags(); | ||
| const bool parse_result = | ||
| Flags::Parse(argc, const_cast<const char**>(argv), flag_list); | ||
| if (!parse_result) { | ||
| std::string usage = Flags::Usage(argv[0], flag_list); | ||
| TFLITE_LOG(ERROR) << usage; | ||
| return kTfLiteError; | ||
| } | ||
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| std::string unconsumed_args = | ||
| Flags::ArgsToString(*argc, const_cast<const char**>(argv)); | ||
| if (!unconsumed_args.empty()) { | ||
| TFLITE_LOG(WARN) << "Unconsumed cmdline flags: " << unconsumed_args; | ||
| } | ||
|
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| return kTfLiteOk; | ||
| } | ||
|
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| } // namespace benchmark | ||
| } // namespace tflite |
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