EVOLUTION-MANAGER

Edit File: gpu_utils.h

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
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http://www.apache.org/licenses/LICENSE-2.0

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distributed under the License is distributed on an "AS IS" BASIS,
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==============================================================================*/

#ifndef TENSORFLOW_CORE_KERNELS_GPU_UTILS_H_
#define TENSORFLOW_CORE_KERNELS_GPU_UTILS_H_

#if GOOGLE_CUDA || TENSORFLOW_USE_ROCM

#include <unordered_map>

#include "absl/types/span.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/lib/core/status.h"
#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 stream_executor {
class RedzoneAllocator;
}  // namespace stream_executor

namespace tensorflow {

class NodeDef;
class AutotuneResult;

// Return whether the redzone check is disabled.
//
// Controlled by the TF_DISABLE_RZ_CHECK environment variable.
bool RedzoneCheckDisabled();

// Return an allocated buffer with redzones the size of `buffer`. Does
// *not* copy the contents of the `buffer` into the newly allocated buffer:
// assumes that buffer is a pure out-parameter.
//
// Returns `buffer` if RedzoneCheckDisabled() is true.
//
// On error, return `buffer`, and log an error message (once).
se::DeviceMemoryBase WrapRedzoneBestEffort(se::RedzoneAllocator* rz_allocator,
                                           se::DeviceMemoryBase buffer);

// Check the passed allocator for redzone violations.
// If violations have occurred, mark the corresponding autotune result
// as a failure.
void CheckRedzones(const se::RedzoneAllocator& rz_allocator,
                   tensorflow::AutotuneResult* autotune_result);

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);
    } else if (autotune_global_count_ >= max_autotune_global_count_) {
      // The autotuning exceeds the max iteration threshold and we accept the
      // the winner if it exists in the map, otherwise we accept the current
      // winner.
      auto winner = params_config_map_.find(params);
      if (winner == params_config_map_.end()) {
        VLOG(1) << GetActionSummary("creates", params, config);
        for (int i = 0; i < min_score_threshold_; ++i) {
          VLOG(1) << GetActionSummary("promotes", params, config);
        }
        params_config_map_.insert(
            std::make_pair(params, ValueType{config, min_score_threshold_, 1}));
      } else {
        int promotes_times = min_score_threshold_ - winner->second.score;
        for (int i = 0; i < promotes_times; ++i) {
          VLOG(1) << GetActionSummary("promotes", params, config);
        }
        winner->second.score = min_score_threshold_;
      }
      VLOG(1) << GetActionSummary("accepts", params, config);
    }
    autotune_global_count_++;
  }

private:
  AutoTuneMap(const std::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);
    max_autotune_global_count_ = 2 * max_autotune_count_;
    autotune_global_count_ = 0;
  }

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

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

std::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_
      TF_GUARDED_BY(mu_);
  std::string name_;
  int32 min_score_threshold_;
  int32 max_autotune_count_;
  int32 max_autotune_global_count_;
  int32 autotune_global_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;
  }
};

// Logs convolution results to customized back-storage.
void LogConvAutotuneResults(se::dnn::ConvolutionKind kind,
                            se::dnn::DataType element_type,
                            se::DeviceMemoryBase input_buffer,
                            se::DeviceMemoryBase filter_buffer,
                            se::DeviceMemoryBase output_buffer,
                            const se::dnn::BatchDescriptor& input_desc,
                            const se::dnn::FilterDescriptor& filter_desc,
                            const se::dnn::BatchDescriptor& output_desc,
                            const se::dnn::ConvolutionDescriptor& conv_desc,
                            se::StreamExecutor* stream_exec,
                            absl::Span<const AutotuneResult> results);

// Logs fused convolution results to customized back-storage.
void LogFusedConvForwardAutotuneResults(
    se::dnn::DataType element_type, se::DeviceMemoryBase input_buffer,
    se::DeviceMemoryBase filter_buffer, se::DeviceMemoryBase output_buffer,
    se::DeviceMemoryBase bias_buffer, se::DeviceMemoryBase side_input_buffer,
    const se::dnn::BatchDescriptor& input_desc,
    const se::dnn::FilterDescriptor& filter_desc,
    const se::dnn::BatchDescriptor& output_desc,
    const se::dnn::ConvolutionDescriptor& conv_desc, double conv_scale,
    double side_value_scale, se::dnn::ActivationMode activation_mode,
    se::StreamExecutor* stream_exec, absl::Span<const AutotuneResult> results);

// Returns the best algorithms for the config, one is the fastest, the other is
// other is fastest with 0 scratch space. Unsuccessful autotuning results are
// allowed and ignored.
Status BestCudnnConvAlgorithm(absl::Span<const AutotuneResult> results,
                              se::dnn::AlgorithmConfig* algo);

}  // namespace tensorflow

#endif  // GOOGLE_CUDA || TENSORFLOW_USE_ROCM

#endif  // TENSORFLOW_CORE_KERNELS_GPU_UTILS_H_