/* 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.
==============================================================================*/

#include "tensorflow/lite/tools/benchmark/benchmark_model.h"

#include <iostream>
#include <sstream>

#include "tensorflow/lite/profiling/time.h"
#include "tensorflow/lite/tools/benchmark/logging.h"

namespace {
void SleepForSeconds(double sleep_seconds) {
  if (sleep_seconds <= 0.0) {
    return;
  }
  // If requested, sleep between runs for an arbitrary amount of time.
  // This can be helpful to determine the effect of mobile processor
  // scaling and thermal throttling.
  return tflite::profiling::time::SleepForMicros(
      static_cast<uint64_t>(sleep_seconds * 1e6));
}

}  // namespace

namespace tflite {
namespace benchmark {
using tensorflow::Stat;

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("run_delay", BenchmarkParam::Create<float>(-1.0f));
  params.AddParam("num_threads", BenchmarkParam::Create<int32_t>(1));
  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));
  return params;
}

BenchmarkModel::BenchmarkModel() : params_(DefaultParams()) {}

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();
  TFLITE_LOG(INFO) << "Average inference timings in us: "
                   << "Warmup: " << warmup_us.avg() << ", "
                   << "Init: " << init_us << ", "
                   << "no stats: " << inference_us.avg();
}

std::vector<Flag> BenchmarkModel::GetFlags() {
  return {
      CreateFlag<int32_t>("num_runs", &params_,
                          "minimum number of runs, see also min_secs"),
      CreateFlag<float>(
          "min_secs", &params_,
          "minimum number of seconds to rerun for, potentially making the "
          "actual number of runs to be greater than num_runs"),
      CreateFlag<float>("run_delay", &params_, "delay between runs in seconds"),
      CreateFlag<int32_t>("num_threads", &params_, "number of threads"),
      CreateFlag<std::string>("benchmark_name", &params_, "benchmark name"),
      CreateFlag<std::string>("output_prefix", &params_,
                              "benchmark output prefix"),
      CreateFlag<int32_t>(
          "warmup_runs", &params_,
          "minimum number of runs performed on initialization, to "
          "allow performance characteristics to settle, see also "
          "warmup_min_secs"),
      CreateFlag<float>(
          "warmup_min_secs", &params_,
          "minimum number of seconds to rerun for, potentially making the "
          "actual number of warm-up runs to be greater than warmup_runs"),
  };
}

void BenchmarkModel::LogParams() {
  TFLITE_LOG(INFO) << "Min num runs: [" << params_.Get<int32_t>("num_runs")
                   << "]";
  TFLITE_LOG(INFO) << "Min runs duration (seconds): ["
                   << params_.Get<float>("min_secs") << "]";
  TFLITE_LOG(INFO) << "Inter-run delay (seconds): ["
                   << params_.Get<float>("run_delay") << "]";
  TFLITE_LOG(INFO) << "Num threads: [" << params_.Get<int32_t>("num_threads")
                   << "]";
  TFLITE_LOG(INFO) << "Benchmark name: ["
                   << params_.Get<std::string>("benchmark_name") << "]";
  TFLITE_LOG(INFO) << "Output prefix: ["
                   << params_.Get<std::string>("output_prefix") << "]";
  TFLITE_LOG(INFO) << "Min warmup runs: ["
                   << params_.Get<int32_t>("warmup_runs") << "]";
  TFLITE_LOG(INFO) << "Min warmup runs duration (seconds): ["
                   << params_.Get<float>("warmup_min_secs") << "]";
}

void BenchmarkModel::PrepareInputData() {}

void BenchmarkModel::ResetInputsAndOutputs() {}

Stat<int64_t> BenchmarkModel::Run(int min_num_times, float min_secs,
                                  RunType run_type) {
  Stat<int64_t> run_stats;
  TFLITE_LOG(INFO) << "Running benchmark for at least " << min_num_times
                   << " iterations and at least " << min_secs << " seconds";
  int64_t min_finish_us =
      profiling::time::NowMicros() + static_cast<int64_t>(min_secs * 1.e6f);
  for (int run = 0;
       run < min_num_times || profiling::time::NowMicros() < min_finish_us;
       run++) {
    ResetInputsAndOutputs();
    listeners_.OnSingleRunStart(run_type);
    int64_t start_us = profiling::time::NowMicros();
    RunImpl();
    int64_t end_us = profiling::time::NowMicros();
    listeners_.OnSingleRunEnd();

    run_stats.UpdateStat(end_us - start_us);
    SleepForSeconds(params_.Get<float>("run_delay"));
  }

  std::stringstream stream;
  run_stats.OutputToStream(&stream);
  TFLITE_LOG(INFO) << stream.str() << std::endl;

  return run_stats;
}

bool BenchmarkModel::ValidateParams() { return true; }

void BenchmarkModel::Run(int argc, char **argv) {
  if (!ParseFlags(argc, argv)) {
    return;
  }
  Run();
}

void BenchmarkModel::Run() {
  ValidateParams();
  LogParams();

  int64_t initialization_start_us = profiling::time::NowMicros();
  Init();
  int64_t initialization_end_us = profiling::time::NowMicros();
  int64_t startup_latency_us = initialization_end_us - initialization_start_us;
  TFLITE_LOG(INFO) << "Initialized session in " << startup_latency_us / 1e3
                   << "ms";

  PrepareInputData();
  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"), WARMUP);
  Stat<int64_t> inference_time_us =
      Run(params_.Get<int32_t>("num_runs"), params_.Get<float>("min_secs"),
          REGULAR);
  listeners_.OnBenchmarkEnd(
      {startup_latency_us, input_bytes, warmup_time_us, inference_time_us});
}

bool 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 false;
  }
  return true;
}

}  // namespace benchmark
}  // namespace tflite
