//   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.
#include <algorithm>

#include "paddle/fluid/framework/details/all_reduce_op_handle.h"
#include "paddle/fluid/framework/details/container_cast.h"
#include "paddle/fluid/framework/details/reduce_and_gather.h"
#include "paddle/fluid/framework/details/variable_visitor.h"
#include "paddle/fluid/platform/profiler.h"

// asynchronous nccl allreduce or synchronous issue:
// https://github.com/PaddlePaddle/Paddle/issues/15049
DEFINE_bool(
    sync_nccl_allreduce, false,
    "If set true, will call `cudaStreamSynchronize(nccl_stream)`"
    "after allreduce, this mode can get better performance in some scenarios.");

namespace paddle {
namespace framework {
namespace details {

#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
AllReduceOpHandle::AllReduceOpHandle(ir::Node *node,
                                     const std::vector<Scope *> &local_scopes,
                                     const std::vector<platform::Place> &places,
                                     const platform::NCCLContextMap *ctxs)
    : OpHandleBase(node),
      local_scopes_(local_scopes),
      places_(places),
      nccl_ctxs_(ctxs) {
  if (nccl_ctxs_) {
    for (auto &p : places_) {
      this->SetDeviceContext(p, nccl_ctxs_->DevCtx(p));
    }
  }
}
#else
AllReduceOpHandle::AllReduceOpHandle(ir::Node *node,
                                     const std::vector<Scope *> &local_scopes,
                                     const std::vector<platform::Place> &places)
    : OpHandleBase(node), local_scopes_(local_scopes), places_(places) {}
#endif

void AllReduceOpHandle::RunImpl() {
  platform::RecordEvent record_event(Name());

  WaitInputVarGenerated();
  auto in_var_handles = DynamicCast<VarHandle>(this->Inputs());
  auto out_var_handles = DynamicCast<VarHandle>(this->Outputs());
  PADDLE_ENFORCE_EQ(
      in_var_handles.size(), places_.size(),
      "The NoDummyInputSize should be equal to the number of places.");
  PADDLE_ENFORCE_EQ(
      in_var_handles.size(), out_var_handles.size(),
      "The NoDummyInputSize and NoDummyOutputSize should be equal.");

  std::vector<const LoDTensor *> lod_tensors;
  for (size_t i = 0; i < local_scopes_.size(); ++i) {
    auto *s = local_scopes_[i];
    auto &local_scope = *s->FindVar(kLocalExecScopeName)->Get<Scope *>();
    auto &lod_tensor =
        local_scope.FindVar(in_var_handles[i]->name())->Get<LoDTensor>();
    lod_tensors.emplace_back(&lod_tensor);
    PADDLE_ENFORCE_EQ(in_var_handles[i]->name(), out_var_handles[i]->name(),
                      "The name of input and output should be equal.");
  }

  if (platform::is_gpu_place(lod_tensors[0]->place())) {
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
    PADDLE_ENFORCE(nccl_ctxs_, "nccl_ctxs should not be nullptr.");
    int dtype = -1;
    size_t numel = 0;
    std::vector<std::function<void()>> all_reduce_calls;
    for (size_t i = 0; i < local_scopes_.size(); ++i) {
      auto &p = places_[i];
      auto &lod_tensor = *lod_tensors[i];
      void *buffer = const_cast<void *>(lod_tensor.data<void>());

      if (dtype == -1) {
        dtype = platform::ToNCCLDataType(lod_tensor.type());
      }

      if (numel == 0) {
        numel = static_cast<size_t>(lod_tensor.numel());
      }

      int dev_id = boost::get<platform::CUDAPlace>(p).device;
      auto &nccl_ctx = nccl_ctxs_->at(dev_id);
      auto stream = nccl_ctx.stream();
      auto comm = nccl_ctx.comm_;
      all_reduce_calls.emplace_back([=] {
        PADDLE_ENFORCE(platform::dynload::ncclAllReduce(
            buffer, buffer, numel, static_cast<ncclDataType_t>(dtype), ncclSum,
            comm, stream));
      });
    }

    this->RunAndRecordEvent([&] {
      if (all_reduce_calls.size() == 1UL) {
        // Do not use NCCLGroup when manage NCCL by per thread per device
        all_reduce_calls[0]();
      } else {
        platform::NCCLGroupGuard guard;
        for (auto &call : all_reduce_calls) {
          call();
        }
      }
    });

    if (FLAGS_sync_nccl_allreduce) {
      for (auto &p : places_) {
        int dev_id = boost::get<platform::CUDAPlace>(p).device;
        auto &nccl_ctx = nccl_ctxs_->at(dev_id);
        auto stream = nccl_ctx.stream();
        cudaStreamSynchronize(stream);
      }
    }

#else
    PADDLE_THROW("Not compiled with CUDA");
#endif
  } else {  // Special handle CPU only Operator's gradient. Like CRF
    auto &trg = *this->local_scopes_[0]
                     ->FindVar(kLocalExecScopeName)
                     ->Get<Scope *>()
                     ->FindVar(out_var_handles[0]->name())
                     ->GetMutable<framework::LoDTensor>();

    // Reduce All Tensor to trg in CPU
    ReduceLoDTensor func(lod_tensors, &trg);
    VisitDataType(lod_tensors[0]->type(), func);

    for (size_t i = 1; i < local_scopes_.size(); ++i) {
      auto &scope =
          *local_scopes_[i]->FindVar(kLocalExecScopeName)->Get<Scope *>();
      auto &p = places_[i];
      auto *var = scope.FindVar(out_var_handles[i]->name());
      auto *dev_ctx = dev_ctxes_.at(p);

      RunAndRecordEvent(p, [&trg, var, dev_ctx, p] {
        auto &tensor_gpu = *var->GetMutable<framework::LoDTensor>();
        auto &tensor_cpu = trg;
        TensorCopy(tensor_cpu, p, *dev_ctx, &tensor_gpu);
      });
    }
  }
}

std::string AllReduceOpHandle::Name() const { return "all_reduce"; }
}  // namespace details
}  // namespace framework
}  // namespace paddle