//   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 "paddle/fluid/framework/details/broadcast_op_handle.h"
#include "paddle/fluid/framework/details/container_cast.h"
#include "paddle/fluid/framework/details/variable_visitor.h"

namespace paddle {
namespace framework {
namespace details {
BroadcastOpHandle::BroadcastOpHandle(const std::vector<Scope *> &local_scopes,
                                     const std::vector<platform::Place> &places)
    : local_scopes_(local_scopes), places_(places) {}

void BroadcastOpHandle::RunImpl() {
  // the input and output may have dummy var.
  VarHandle *in_var_handle;

  {
    auto in_var_handles = DynamicCast<VarHandle>(inputs_);
    PADDLE_ENFORCE_EQ(in_var_handles.size(), 1,
                      "The number of input should be one.");
    in_var_handle = in_var_handles[0];
  }

  auto out_var_handles = DynamicCast<VarHandle>(outputs_);

  PADDLE_ENFORCE_EQ(
      out_var_handles.size(), places_.size(),
      "The number of output should equal to the number of places.");

  // Wait input done, this Wait is asynchronous operation platform::Place
  // &in_place;
  WaitInputVarGenerated(*in_var_handle);

  std::vector<const Scope *> var_scopes;
  for (auto *s : local_scopes_) {
    var_scopes.emplace_back(s->FindVar(kLocalExecScopeName)->Get<Scope *>());
  }

  auto *in_var =
      var_scopes.at(in_var_handle->scope_idx_)->FindVar(in_var_handle->name_);
  PADDLE_ENFORCE_NOT_NULL(in_var);

  Tensor &in_tensor = VariableVisitor::GetMutableTensor(in_var);

  for (auto *out : out_var_handles) {
    if (*out == *in_var_handle) {
      continue;
    }

    auto &out_p = out->place_;
    auto *out_var = var_scopes.at(out->scope_idx_)->FindVar(out->name_);
    PADDLE_ENFORCE_NOT_NULL(out_var);
    PADDLE_ENFORCE_EQ(out_p.which(), in_var_handle->place_.which(),
                      "Places must be all on CPU or all on CUDA.");

    VariableVisitor::ShareDimsAndLoD(*in_var, out_var);
    VariableVisitor::GetMutableTensor(out_var).mutable_data(out_p,
                                                            in_tensor.type());

    auto dev_ctx = dev_ctxes_.at(out_p);
    RunAndRecordEvent(out_p, [in_tensor, out_var, dev_ctx, out_p] {
      paddle::framework::TensorCopy(
          in_tensor, out_p, *(dev_ctx),
          &VariableVisitor::GetMutableTensor(out_var));
    });
  }
}

void BroadcastOpHandle::WaitInputVarGenerated(const VarHandle &in_var) {
  if (in_var.generated_op_) {
    for (auto &pair : dev_ctxes_) {
      in_var.generated_op_->Wait(pair.second);
    }
  }
}

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