reference_count_op_handle.h

// Copyright (c) 2018 PaddlePaddle 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.

#pragma once

#include <atomic>
#include <string>
#include <unordered_map>
#include <vector>

#include "paddle/fluid/framework/details/op_handle_base.h"
#include "paddle/fluid/framework/garbage_collector.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/framework/tensor.h"

namespace paddle {
namespace framework {
namespace details {

using ReferenceCountMap = std::unordered_map<std::string, int>;
using AtomicReferenceCountMap =
    std::unordered_map<std::string, std::atomic<int>>;
using DeviceReferenceCountMap =
    std::unordered_map<int, std::unique_ptr<ReferenceCountMap>>;
using AtomicDeviceReferenceCountMap =
    std::unordered_map<int, std::unique_ptr<AtomicReferenceCountMap>>;
using DeviceGarbageCollectorMap =
    std::unordered_map<int,
                       std::unique_ptr<GarbageCollector<framework::Tensor>>>;

class ReferenceCountOpHandle : public OpHandleBase {
 public:
  ReferenceCountOpHandle(ir::Node *node, const Scope *scope,
                         const platform::CUDAPlace &place,
                         const std::vector<std::string> &var_names,
                         GarbageCollector<Tensor> *gc,
                         AtomicReferenceCountMap *ref_cnts)
      : OpHandleBase(node),
        scope_(scope),
        var_names_(var_names),
        gc_(gc),
        ref_cnts_(ref_cnts) {
    dev_ctx_ = static_cast<platform::CUDADeviceContext *>(
        platform::DeviceContextPool::Instance().Get(place));
    if (IsStreamGarabageCollector()) {
      PADDLE_ENFORCE(cudaSetDevice(place.device));
      PADDLE_ENFORCE(cudaEventCreateWithFlags(&event_, cudaEventDisableTiming));
    }
  }

  ~ReferenceCountOpHandle() {
    if (IsStreamGarabageCollector()) {
      auto gpu_place = boost::get<platform::CUDAPlace>(dev_ctx_->GetPlace());
      PADDLE_ENFORCE(cudaSetDevice(gpu_place.device));
      PADDLE_ENFORCE(cudaEventDestroy(event_));
    }
  }

  std::string Name() const override { return "reference_count"; }

  // protected:
  void RunImpl() override {
    auto *exec_scope_ = scope_->FindVar(kLocalExecScopeName)->Get<Scope *>();
    std::vector<LoDTensor *> tensors;
    for (auto &name : var_names_) {
      auto it = ref_cnts_->find(name);
      if (it == ref_cnts_->end()) continue;

      auto *var = exec_scope_->FindVar(name);
      if (var == nullptr || !var->IsType<LoDTensor>()) continue;

      if (it->second.fetch_sub(1) <= 1) {
        tensors.emplace_back(var->GetMutable<LoDTensor>());
      }
    }

    if (!tensors.empty()) {
      ClearTensors(tensors);
    }
  }

 private:
  void ClearTensors(const std::vector<LoDTensor *> &tensors) const {
    auto *gc = dynamic_cast<const StreamGarbageCollector<Tensor> *>(gc_);
    if (gc != nullptr) {
      auto compute_stream = dev_ctx_->stream();
      auto callback_stream = gc->stream();
      auto callback_func = [=]() {
        PADDLE_ENFORCE(cudaEventRecord(event_, compute_stream));
        PADDLE_ENFORCE(cudaStreamWaitEvent(callback_stream, event_, 0));
      };
      gc_->Add(tensors, callback_func);
    } else {
      gc_->Add(tensors);
    }
  }

  bool IsStreamGarabageCollector() const {
    return dynamic_cast<const StreamGarbageCollector<Tensor> *>(gc_) != nullptr;
  }

  const Scope *scope_;
  platform::CUDADeviceContext *dev_ctx_;
  std::vector<std::string> var_names_;
  GarbageCollector<Tensor> *gc_;       // not own
  AtomicReferenceCountMap *ref_cnts_;  // not own
  cudaEvent_t event_;
};

}  // namespace details
}  // namespace framework
}  // namespace paddle