parallel_executor.h 2.4 KB
Newer Older
Y
Yang Yang 已提交
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
/* Copyright (c) 2016 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 <unordered_set>

#include "paddle/fluid/framework/executor.h"
#include "paddle/fluid/framework/op_info.h"
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/framework/tensor.h"

#include "paddle/fluid/operators/nccl/nccl_gpu_common.h"
#include "paddle/fluid/platform/device_context.h"

namespace paddle {
namespace framework {

Y
Yu Yang 已提交
31 32 33
class ParallelExecutorPrivate;
class VarHandle;
class OpHandle;
Y
Yu Yang 已提交
34
class VarHandleBase;
Y
Yang Yang 已提交
35
class ParallelExecutor {
Y
Yu Yang 已提交
36
 public:
Y
Yang Yang 已提交
37
  explicit ParallelExecutor(const std::vector<platform::Place>& places,
Y
Yu Yang 已提交
38 39 40 41 42
                            const std::unordered_set<std::string>& params,
                            const ProgramDesc& startup_program,
                            const ProgramDesc& main_program,
                            const std::string& loss_var_name, Scope* scope);

Y
Yu Yang 已提交
43 44
  void Run(const std::vector<std::string>& fetch_tensors,
           const std::string& fetched_var_name = "fetched_var");
Y
Yang Yang 已提交
45 46

 private:
Y
Yu Yang 已提交
47 48 49 50 51 52 53 54 55 56 57 58 59
  ParallelExecutorPrivate* member_;

  void BCastParamsToGPUs(const ProgramDesc& startup_program) const;

  VarHandle* GetVarHandle(const std::string& each_var_name,
                          const platform::Place& place) const;

  void GenerateVar(OpHandle* op_handle, const std::string& each_var_name,
                   const platform::Place& place) const;

  void ConstructDependencyGraph(const std::unordered_set<std::string>& params,
                                const ProgramDesc& main_program,
                                const std::string& loss_var_name) const;
Y
Yu Yang 已提交
60 61

  void BuildNCCLCommunicator() const;
Y
Yu Yang 已提交
62 63 64

  void RunOp(std::unordered_map<VarHandleBase*, bool>& pending_vars,
             OpHandle* op) const;
Y
Yu Yang 已提交
65 66

  void PolishGraphToSupportDataHarzaeds() const;
Y
Yang Yang 已提交
67 68 69 70
};

}  // namespace framework
}  // namespace paddle