//   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"

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(), dev_ctxes_.cbegin()->second);

// FIXME(typhoonzero): If scope0(global scope) have NCCL_ID_VAR,
// this is a distributed or inter-process call, find a better way.
#ifdef PADDLE_WITH_CUDA
  if (NoDummyInputSize() == 1 &&
      local_scopes_[0]->FindVar(NCCL_ID_VARNAME) == nullptr) {
#else
  if (NoDummyInputSize() == 1) {
#endif
    return;  // No need to all reduce when GPU count = 1;
  } else {
    // Wait input done
    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([&] {
        // TODO(Yancey1989): need allreduce operator to avoid this flag
        if (nccl_ctxs_->need_group_call_) {
          platform::NCCLGroupGuard guard;
          for (auto &call : all_reduce_calls) {
            call();
          }
        } else {
          // only used in executor_type == ParallalGraph, one thread one GPU
          // TODO(Yancey1989): use allreduce operator to avoid this tricky.
          PADDLE_ENFORCE(all_reduce_calls.size() == 1UL);
          all_reduce_calls[0]();
        }
      });

#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(ToDataType(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