pytorch

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/**
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 * Copyright (c) 2016-present, Facebook, Inc.
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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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#include <string>
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#include <vector>
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#include <ATen/ATen.h>
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#include "caffe2/core/timer.h"
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#include "caffe2/utils/string_utils.h"
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#include <torch/csrc/autograd/grad_mode.h>
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#include <torch/csrc/jit/mobile/module.h>
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#include <torch/csrc/jit/mobile/import.h>
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#include <torch/csrc/jit/serialization/import.h>
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#include <torch/script.h>
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#include <c10/mobile/CPUCachingAllocator.h>
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#include <chrono>
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using namespace std::chrono;
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C10_DEFINE_string(model, "", "The given torch script model to benchmark.");
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C10_DEFINE_int(iter, 10, "The number of iterations to run.");
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C10_DEFINE_bool(
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  report_pep,
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  true,
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  "Whether to print performance stats for AI-PEP.");
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int main(int argc, char** argv) {
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  c10::SetUsageMessage(
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    "Run model load time benchmark for pytorch model.\n"
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    "Example usage:\n"
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    "./load_benchmark_torch"
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    " --model=<model_file>"
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    " --iter=20");
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  if (!c10::ParseCommandLineFlags(&argc, &argv)) {
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    std::cerr << "Failed to parse command line flags!" << std::endl;
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    return 1;
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  }
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  std::cout << "Starting benchmark." << std::endl;
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  CAFFE_ENFORCE(
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      FLAGS_iter >= 0,
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      "Number of main runs should be non negative, provided ",
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      FLAGS_iter,
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      ".");
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  caffe2::Timer timer;
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  std::vector<long> times;
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  for (int i = 0; i < FLAGS_iter; ++i) {
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    auto start = high_resolution_clock::now();
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#if BUILD_LITE_INTERPRETER
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    auto module = torch::jit::_load_for_mobile(FLAGS_model);
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#else
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    auto module = torch::jit::load(FLAGS_model);
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#endif
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    auto stop = high_resolution_clock::now();
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    auto duration = duration_cast<microseconds>(stop - start);
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    times.push_back(duration.count());
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  }
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  const double micros = static_cast<double>(timer.MicroSeconds());
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  if (FLAGS_report_pep) {
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    for (auto t : times) {
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      std::cout << R"(PyTorchObserver {"type": "NET", "unit": "us", )"
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                << R"("metric": "latency", "value": ")"
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                << t << R"("})" << std::endl;
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    }
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  }
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  const double iters = static_cast<double>(FLAGS_iter);
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  std::cout << "Main run finished. Microseconds per iter: "
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            << micros / iters
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            << ". Iters per second: " << 1000.0 * 1000 * iters / micros
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            << std::endl;
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  return 0;
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}
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