// Copyright 2018 The Chromium OS Authors. All rights reserved.
// Use of this source code is governed by a BSD-style license that can be
// found in the LICENSE file.

#ifndef ML_REQUEST_METRICS_H_
#define ML_REQUEST_METRICS_H_

#include <algorithm>
#include <memory>
#include <string>
#include <vector>

#include <base/bind.h>
#include <base/files/file_path.h>
#include <base/logging.h>
#include <base/macros.h>
#include <base/process/process_metrics.h>
#include <base/system/sys_info.h>
#include <base/time/time.h>
#include <metrics/metrics_library.h>

#include "metrics/timer.h"
#include "ml/util.h"

namespace ml {

// Performs UMA metrics logging for model loading (LoadBuiltinModel or
// LoadFlatBufferModel), CreateGraphExecutor and Execute. Metrics includes
// events(enumerators defined by RequestEventEnum), memory_usage, and cpu_time.
// RequestEventEnum is an enum class which defines different events for some
// specific actions, currently we reuse the enum classes defined in mojoms. The
// enum class generally contains an OK and several different Errors, besides,
// there should be a kMax which shares the value of the highest enumerator.
template <class RequestEventEnum>
class RequestMetrics {
 public:
  // Creates a RequestMetrics with the specified model and request names.
  // Records UMA metrics named with the prefix
  // "MachineLearningService.`model_name`.`request_name`."
  RequestMetrics(const std::string& model_name,
                 const std::string& request_name);

  // Logs (to UMA) the specified `event` associated with this request.
  void RecordRequestEvent(RequestEventEnum event);

  // When you want to record metrics of some action, call Start func at the
  // beginning of it.
  void StartRecordingPerformanceMetrics();

  // Send performance metrics(memory_usage, cpu_time) to UMA
  // This would usually be called only if the action completes successfully.
  void FinishRecordingPerformanceMetrics();

 private:
  MetricsLibrary metrics_library_;

  const std::string name_base_;
  std::unique_ptr<base::ProcessMetrics> process_metrics_;
  chromeos_metrics::Timer timer_;
  int64_t initial_memory_;

  DISALLOW_COPY_AND_ASSIGN(RequestMetrics);
};

// UMA metric names:
constexpr char kGlobalMetricsPrefix[] = "MachineLearningService.";
constexpr char kEventSuffix[] = ".Event";
constexpr char kTotalMemoryDeltaSuffix[] = ".TotalMemoryDeltaKb";
constexpr char kCpuTimeSuffix[] = ".CpuTimeMicrosec";

// UMA histogram ranges:
constexpr int kMemoryDeltaMinKb = 1;         // 1 KB
constexpr int kMemoryDeltaMaxKb = 10000000;  // 10 GB
constexpr int kMemoryDeltaBuckets = 100;
constexpr int kCpuTimeMinMicrosec = 1;           // 1 μs
constexpr int kCpuTimeMaxMicrosec = 1800000000;  // 30 min
constexpr int kCpuTimeBuckets = 100;

template <class RequestEventEnum>
RequestMetrics<RequestEventEnum>::RequestMetrics(
    const std::string& model_name, const std::string& request_name)
    : name_base_(std::string(kGlobalMetricsPrefix) + model_name + "." +
                 request_name),
      process_metrics_(nullptr) {}

template <class RequestEventEnum>
void RequestMetrics<RequestEventEnum>::RecordRequestEvent(
    RequestEventEnum event) {
  metrics_library_.SendEnumToUMA(
      name_base_ + kEventSuffix, static_cast<int>(event),
      static_cast<int>(RequestEventEnum::kMaxValue) + 1);
  process_metrics_.reset(nullptr);
}

template <class RequestEventEnum>
void RequestMetrics<RequestEventEnum>::StartRecordingPerformanceMetrics() {
  DCHECK(process_metrics_ == nullptr);
  process_metrics_ = base::ProcessMetrics::CreateCurrentProcessMetrics();
  // Call GetPlatformIndependentCPUUsage in order to set the "zero" point of the
  // CPU usage counter of process_metrics_.
  process_metrics_->GetPlatformIndependentCPUUsage();
  timer_.Start();
  // Query memory usage.
  size_t usage = 0;
  if (!GetTotalProcessMemoryUsage(&usage)) {
    LOG(DFATAL) << "Getting process memory usage failed.";
    return;
  }
  initial_memory_ = static_cast<int64_t>(usage);
}

template <class RequestEventEnum>
void RequestMetrics<RequestEventEnum>::FinishRecordingPerformanceMetrics() {
  DCHECK(process_metrics_ != nullptr);
  // To get CPU time, we multiply elapsed (wall) time by CPU usage percentage.
  timer_.Stop();
  base::TimeDelta elapsed_time;
  DCHECK(timer_.GetElapsedTime(&elapsed_time));
  const int64_t elapsed_time_microsec = elapsed_time.InMicroseconds();

  // CPU usage, 12.34 means 12.34%, and the range is 0 to 100 * numCPUCores.
  // That's to say it can exceed 100 when there're multi CPUs.
  // For example, if the device has 4 CPUs and the process fully uses 2 of
  // them, the percent will be 200%.
  const double cpu_usage_percent =
      process_metrics_->GetPlatformIndependentCPUUsage();

  // CPU time, as mentioned above, "100 microseconds" means "1 CPU core fully
  // utilized for 100 microseconds".
  const int64_t cpu_time_microsec =
      static_cast<int64_t>(cpu_usage_percent * elapsed_time_microsec / 100.);

  // Memory usage
  size_t usage = 0;
  if (!GetTotalProcessMemoryUsage(&usage)) {
    LOG(DFATAL) << "Getting process memory usage failed.";
    return;
  }
  const int64_t memory_usage_kb =
      static_cast<int64_t>(usage) - initial_memory_;

  metrics_library_.SendToUMA(name_base_ + kTotalMemoryDeltaSuffix,
                             memory_usage_kb,
                             kMemoryDeltaMinKb,
                             kMemoryDeltaMaxKb,
                             kMemoryDeltaBuckets);
  metrics_library_.SendToUMA(name_base_ + kCpuTimeSuffix,
                             cpu_time_microsec,
                             kCpuTimeMinMicrosec,
                             kCpuTimeMaxMicrosec,
                             kCpuTimeBuckets);
}

// Records a generic model specification error event during a model loading
// (LoadBuiltinModel or LoadFlatBufferModel) request.
void RecordModelSpecificationErrorEvent();

}  // namespace ml

#endif  // ML_REQUEST_METRICS_H_