/* Copyright 2015 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.
==============================================================================*/

#ifndef TENSORFLOW_CORE_KERNELS_GPU_UTILS_H_
#define TENSORFLOW_CORE_KERNELS_GPU_UTILS_H_

#if GOOGLE_CUDA

#include <unordered_map>

#include "tensorflow/core/lib/strings/str_util.h"
#include "tensorflow/core/lib/strings/strcat.h"
#include "tensorflow/core/lib/strings/stringprintf.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/stream_executor.h"

namespace tensorflow {

template <typename T>
inline se::DeviceMemory<T> AsDeviceMemory(const T* cuda_memory, uint64 size) {
  se::DeviceMemoryBase wrapped(const_cast<T*>(cuda_memory), size * sizeof(T));
  se::DeviceMemory<T> typed(wrapped);
  return typed;
}

// A helper class that looks up the best autotuned config from parameters.
// Due to the noisy nature of autotune, especially with multiple devices, it
// only accepts a config if its margin exceeds a threshold.
// For the same shape configs, if a new best config matches the previous best,
// they get promoted; otherwise, the winner gets demoted. This process stops
// when the winner's score exceeds the threshold.
// In a bad case when two configs are very close to each other and flips
// back and forth randomly, the expected number of experiments before autotune
// settles is O(threshold ^ 2). So we recommend that number of warmup runs
// for any benchmarks.
template <typename Parameters, typename Config>
class AutoTuneMap {
 public:
  bool Find(const Parameters& params, Config* config) const {
    mutex_lock lock(mu_);
    auto iter = params_config_map_.find(params);
    if (iter == params_config_map_.end() ||
        (iter->second.score < min_score_threshold_ &&
         iter->second.count <= max_autotune_count_)) {
      return false;
    }
    *config = iter->second.config;
    return true;
  }
  void Insert(const Parameters& params, const Config& config) {
    mutex_lock lock(mu_);
    auto iter = params_config_map_.find(params);
    int new_score = 0;
    if (iter == params_config_map_.end()) {
      // Create a new entry if params is new.
      VLOG(1) << GetActionSummary("creates", params, config);
      params_config_map_.insert(
          std::make_pair(params, ValueType{config, 1, 1}));
      new_score = 1;
    } else if (iter->second.score < min_score_threshold_ &&
               iter->second.count <= max_autotune_count_) {
      DCHECK_GT(iter->second.score, 0);
      if (iter->second.config != config) {
        // If it is different from the current winner, demotes the winner.
        VLOG(1) << GetActionSummary("demotes", params, config);
        new_score = --iter->second.score;
        ++iter->second.count;
        if (new_score <= 0) {
          VLOG(1) << GetActionSummary("erases", params, config);
          params_config_map_.erase(iter);
        }
      } else {
        // If it is the same as the current winner, promotes the winner.
        VLOG(1) << GetActionSummary("promotes", params, config);
        new_score = ++iter->second.score;
        ++iter->second.count;
      }
    }
    if (new_score >= min_score_threshold_) {
      VLOG(1) << GetActionSummary("accepts", params, config);
    }
  }

 private:
  AutoTuneMap(const string& name) : name_(name) {
    min_score_threshold_ = 1;
    int min_warmup_iterations = 10;
    const char* threshold_str = getenv("TF_AUTOTUNE_THRESHOLD");
    if (threshold_str != nullptr) {
      strings::safe_strto32(threshold_str, &min_score_threshold_);
    }
    const char* min_warmup_iteration_str =
        getenv("TF_AUTOTUNE_MIN_WARMUP_ITERATIONS");
    if (min_warmup_iteration_str != nullptr) {
      strings::safe_strto32(min_warmup_iteration_str, &min_warmup_iterations);
    }
    min_score_threshold_ = std::max(min_score_threshold_, 1);
    max_autotune_count_ = std::max(
        5 * min_score_threshold_ * min_score_threshold_, min_warmup_iterations);
  }

  template <class Group, class Params, class Cfg>
  friend class AutoTuneSingleton;

  struct Hasher {
    std::size_t operator()(const Parameters& parameter) const {
      return parameter.hash();
    }
  };

  string GetActionSummary(StringPiece action, const Parameters& params,
                          const Config& config) {
    return strings::Printf("autotune_map %s %s: %s -> (%s)", name_.c_str(),
                           string(action).c_str(), params.ToString().c_str(),
                           config.ToString().c_str());
  }

  mutable mutex mu_;
  struct ValueType {
    Config config;
    int32 score;
    int32 count;
  };
  std::unordered_map<Parameters, ValueType, Hasher> params_config_map_
      GUARDED_BY(mu_);
  string name_;
  int32 min_score_threshold_;
  int32 max_autotune_count_;

  TF_DISALLOW_COPY_AND_ASSIGN(AutoTuneMap);
};

// A Singleton helper that manages the global autotune results by groups.
// The caller specified arbitrary Group type that can distinguish between
// different autotune results, even if their Parameters and Configs are the
// same.
template <class Group, typename Parameters, typename Config>
class AutoTuneSingleton {
 public:
  typedef AutoTuneMap<Parameters, Config> AutoTuneType;
  static AutoTuneType* GetInstance() {
    static AutoTuneType* instance = new AutoTuneType(Group::name());
    return instance;
  }
};

}  // namespace tensorflow

#endif  // GOOGLE_CUDA

#endif  // TENSORFLOW_CORE_KERNELS_GPU_UTILS_H_