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118 lines
4.2 KiB
118 lines
4.2 KiB
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "paddle/fluid/framework/details/broadcast_op_handle.h"
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namespace paddle {
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namespace framework {
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namespace details {
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Tensor *GetTensorFromVar(Variable *in_var) {
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if (in_var->IsType<LoDTensor>()) {
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return in_var->GetMutable<LoDTensor>();
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} else if (in_var->IsType<SelectedRows>()) {
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return in_var->GetMutable<SelectedRows>()->mutable_value();
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} else {
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PADDLE_THROW("Var should be LoDTensor or SelectedRows");
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}
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return nullptr;
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}
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BroadcastOpHandle::BroadcastOpHandle(const std::vector<Scope *> &local_scopes,
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const std::vector<platform::Place> &places)
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: local_scopes_(local_scopes), places_(places) {}
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void BroadcastOpHandle::RunImpl() {
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// the input and output may have dummy var.
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std::vector<VarHandle *> in_var_handle = GetValidVarHandles(inputs_);
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std::vector<VarHandle *> out_var_handles = GetValidVarHandles(outputs_);
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PADDLE_ENFORCE_EQ(in_var_handle.size(), 1,
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"The number of input should be one.");
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PADDLE_ENFORCE_EQ(
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out_var_handles.size(), places_.size(),
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"The number of output should equal to the number of places.");
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// Wait input done, this Wait is asynchronous operationplatform::Place
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// &in_place;
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WaitEvents(out_var_handles, in_var_handle);
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auto in_place = in_var_handle[0]->place_;
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auto in_scope_idx = in_var_handle[0]->scope_idx_;
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auto in_var =
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local_scopes_.at(in_scope_idx)->FindVar(in_var_handle[0]->name_);
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Tensor *in_tensor = GetTensorFromVar(in_var);
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for (auto *out : out_var_handles) {
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auto &out_p = out->place_;
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auto out_var = local_scopes_.at(out->scope_idx_)->FindVar(out->name_);
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PADDLE_ENFORCE_EQ(out_p.which(), in_place.which(),
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"Places must be all on CPU or all on CUDA.");
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if (in_var->IsType<framework::SelectedRows>()) {
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auto &in_sr = in_var->Get<framework::SelectedRows>();
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auto out_sr = out_var->GetMutable<framework::SelectedRows>();
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if (&in_sr == out_sr) continue;
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out_sr->set_height(in_sr.height());
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out_sr->set_rows(in_sr.rows());
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out_sr->mutable_value()->Resize(in_sr.value().dims());
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out_sr->mutable_value()->mutable_data(out_p, in_sr.value().type());
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} else if (in_var->IsType<framework::LoDTensor>()) {
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auto in_lod = in_var->Get<framework::LoDTensor>();
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auto out_lod = out_var->GetMutable<framework::LoDTensor>();
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if (&in_lod == out_lod) continue;
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out_lod->set_lod(in_lod.lod());
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out_lod->Resize(in_lod.dims());
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out_lod->mutable_data(out_p, in_lod.type());
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} else {
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PADDLE_THROW("Var should be LoDTensor or SelectedRows.");
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}
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auto dev_ctx = dev_ctxes_[out_p];
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RunAndRecordEvent(out_p, [in_tensor, out_var, dev_ctx, out_p] {
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Tensor *out_tensor = GetTensorFromVar(out_var);
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paddle::framework::TensorCopy(*in_tensor, out_p, *(dev_ctx), out_tensor);
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});
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}
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}
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void BroadcastOpHandle::WaitEvents(
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const std::vector<VarHandle *> &out_var_handles,
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const std::vector<VarHandle *> &in_var_handle) {
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if (in_var_handle[0]->generated_op_) {
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for (auto *out : out_var_handles) {
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auto &out_p = out->place_;
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in_var_handle[0]->generated_op_->Wait(dev_ctxes_[out_p]);
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}
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}
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}
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std::vector<VarHandle *> BroadcastOpHandle::GetValidVarHandles(
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const std::vector<VarHandleBase *> &inputs) {
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std::vector<VarHandle *> in_var_handle;
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for (auto *in : inputs) {
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auto *out_handle = dynamic_cast<VarHandle *>(in);
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if (out_handle) {
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in_var_handle.push_back(out_handle);
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}
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}
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return in_var_handle;
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}
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std::string BroadcastOpHandle::Name() const { return "broadcast"; }
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} // namespace details
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} // namespace framework
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} // namespace paddle
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