chengduoZH
7 years ago
parent
124c93081d
commit
e7684911fd
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// 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/gather_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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static 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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GatherOpHandle::GatherOpHandle(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 GatherOpHandle::RunImpl() {
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PADDLE_ENFORCE_EQ(this->inputs_.size(), places_.size());
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PADDLE_ENFORCE_EQ(this->outputs_.size(), 1);
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// Wait input done, this Wait is asynchronous operation
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for (auto *in : inputs_) {
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if (inputs_[0]->generated_op_) {
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auto &p = static_cast<VarHandle *>(in)->place_;
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in->generated_op_->Wait(dev_ctxes_[p]);
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}
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}
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auto in_0_handle = static_cast<VarHandle *>(inputs_[0]);
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auto pre_in_var =
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local_scopes_[in_0_handle->scope_idx_]->FindVar(in_0_handle->name_);
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std::vector<int64_t> out_rows;
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std::vector<Tensor *> in_tensors;
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std::vector<platform::Place> in_places;
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// gather the inputs
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for (auto *in : inputs_) {
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auto in_handle = static_cast<VarHandle *>(in);
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auto in_p = in_handle->place_;
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in_places.push_back(in_p);
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PADDLE_ENFORCE_LT(in_handle->scope_idx_, local_scopes_.size(),
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"%s is not the the local_scopes ", in_handle->name_);
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auto *s = local_scopes_[in_handle->scope_idx_];
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auto in_var = s->FindVar(in_handle->name_);
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PADDLE_ENFORCE_EQ(in_var->Type(), pre_in_var->Type(),
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"The type of input is not consistent.");
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if (in_var->IsType<framework::SelectedRows>()) {
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auto &pre_in = pre_in_var->Get<framework::SelectedRows>();
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auto &in_sr = in_var->Get<framework::SelectedRows>();
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auto in_sr_rows = in_sr.rows();
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out_rows.insert(out_rows.begin(), in_sr_rows.begin(), in_sr_rows.end());
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PADDLE_ENFORCE_EQ(pre_in.height(), in_sr.height(), "");
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PADDLE_ENFORCE_EQ(pre_in.GetCompleteDims(), in_sr.GetCompleteDims(), "");
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} else if (in_var->IsType<framework::LoDTensor>()) {
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auto &pre_in = pre_in_var->Get<framework::LoDTensor>();
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auto &in_lodtensor = in_var->Get<framework::LoDTensor>();
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PADDLE_ENFORCE_EQ(in_lodtensor.lod(), pre_in.lod());
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PADDLE_ENFORCE_EQ(in_lodtensor.dims(), pre_in.dims());
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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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in_tensors.push_back(GetTensorFromVar(in_var));
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pre_in_var = in_var;
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}
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// write the output
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auto out_handle = static_cast<VarHandle *>(this->outputs_[0]);
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auto &out_place = out_handle->place_;
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auto out_scope_idx = out_handle->scope_idx_;
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auto out_var = local_scopes_[out_scope_idx]->FindVar(out_handle->name_);
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if (pre_in_var->IsType<framework::SelectedRows>()) {
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auto &pre_in = pre_in_var->Get<framework::SelectedRows>();
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auto out = out_var->GetMutable<framework::SelectedRows>();
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out->set_height(pre_in.height());
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out->set_rows(out_rows);
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size_t rows = out_rows.size();
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DDim out_dim = pre_in.GetCompleteDims();
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out_dim[0] = static_cast<int64_t>(rows);
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out->mutable_value()->Resize(out_dim);
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out->mutable_value()->mutable_data(out_place, pre_in.value().type());
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auto out_tensor = out->mutable_value();
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// copy
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int s = 0, e = 0;
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for (size_t j = 0; j < in_tensors.size(); ++j) {
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e += in_tensors[j]->dims()[0];
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auto sub_out = out_tensor->Slice(s, e);
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paddle::framework::TensorCopy(*(in_tensors[j]), out_place,
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*(dev_ctxes_[in_places[j]]), &sub_out);
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s = e;
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}
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} else if (pre_in_var->IsType<framework::LoDTensor>()) {
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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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}
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std::string GatherOpHandle::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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@ -0,0 +1,52 @@
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// 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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#pragma once
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#include <map>
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#include <string>
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#include <vector>
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#include "paddle/fluid/framework/details/op_handle_base.h"
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#include "paddle/fluid/framework/lod_tensor.h"
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#include "paddle/fluid/framework/scope.h"
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#include "paddle/fluid/framework/selected_rows.h"
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#include "paddle/fluid/platform/device_context.h"
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namespace paddle {
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namespace framework {
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namespace details {
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/*
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* Broadcast the input to all scope.
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*
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*/
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struct GatherOpHandle : public OpHandleBase {
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const std::vector<Scope *> &local_scopes_;
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const std::vector<platform::Place> &places_;
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GatherOpHandle(const std::vector<Scope *> &local_scopes,
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const std::vector<platform::Place> &places);
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std::string Name() const override;
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bool IsMultiDeviceTransfer() override { return false; };
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protected:
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void RunImpl() override;
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};
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} // namespace details
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} // namespace framework
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} // namespace paddle
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@ -0,0 +1,227 @@
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// 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/gather_op_handle.h"
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#include "gtest/gtest.h"
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#include "paddle/fluid/platform/device_context.h"
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namespace f = paddle::framework;
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namespace p = paddle::platform;
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// test data amount
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const f::DDim kDims = {20, 20};
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class GatherTester : public ::testing::Test {
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public:
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void InitCtx(bool use_gpu) {
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if (use_gpu) {
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#ifdef PADDLE_WITH_CUDA
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int count = p::GetCUDADeviceCount();
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if (count <= 1) {
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LOG(WARNING) << "Cannot test multi-gpu Gather, because the CUDA "
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"device count is "
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<< count;
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exit(0);
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}
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for (int i = 0; i < count; ++i) {
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auto p = p::CUDAPlace(i);
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gpu_list_.push_back(p);
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ctxs_.emplace_back(new p::CUDADeviceContext(p));
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}
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#else
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PADDLE_THROW("CUDA is not support.");
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#endif
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} else {
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int count = 8;
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for (int i = 0; i < count; ++i) {
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auto p = p::CPUPlace();
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gpu_list_.push_back(p);
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ctxs_.emplace_back(new p::CPUDeviceContext(p));
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}
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}
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}
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template <class T>
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void InitGatherOp(int input_scope_idx) {
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for (size_t j = 0; j < gpu_list_.size(); ++j) {
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local_scope_.push_back(&g_scope_.NewScope());
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auto* out_var = local_scope_[j]->Var("input");
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out_var->GetMutable<T>();
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}
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auto* in_var = local_scope_[input_scope_idx]->Var("out");
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in_var->GetMutable<T>();
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gather_op_handle_ = new f::details::GatherOpHandle(local_scope_, gpu_list_);
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f::details::VarHandle* out_var_handle = new f::details::VarHandle();
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out_var_handle->place_ = gpu_list_[input_scope_idx];
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out_var_handle->name_ = "out";
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out_var_handle->version_ = 2;
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out_var_handle->scope_idx_ = input_scope_idx;
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out_var_handle->generated_op_ = gather_op_handle_;
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gather_op_handle_->AddOutput(out_var_handle);
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for (size_t j = 0; j < gpu_list_.size(); ++j) {
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gather_op_handle_->dev_ctxes_[gpu_list_[j]] = ctxs_[j];
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f::details::VarHandle* in_var_handle = new f::details::VarHandle();
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in_var_handle->place_ = gpu_list_[j];
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in_var_handle->name_ = "input";
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in_var_handle->version_ = 1;
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in_var_handle->scope_idx_ = j;
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in_var_handle->generated_op_ = nullptr;
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gather_op_handle_->AddInput(in_var_handle);
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}
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}
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void GatherOpDestroy() {
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for (auto in : gather_op_handle_->inputs_) {
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delete in;
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}
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for (auto out : gather_op_handle_->outputs_) {
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delete out;
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}
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delete gather_op_handle_;
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for (size_t j = 0; j < ctxs_.size(); ++j) {
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delete ctxs_[j];
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}
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}
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void WaitAll() {
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for (size_t j = 0; j < ctxs_.size(); ++j) {
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ctxs_[j]->Wait();
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}
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}
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void TestGatherLodTensor() {
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// int input_scope_idx = 0;
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// InitGatherOp<f::LoDTensor>(input_scope_idx);
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//
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// auto in_var = local_scope_[input_scope_idx]->Var("input");
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// auto in_lod_tensor = in_var->GetMutable<f::LoDTensor>();
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// in_lod_tensor->mutable_data<float>(kDims, gpu_list_[input_scope_idx]);
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//
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// std::vector<float> send_vector(f::product(kDims), input_scope_idx +
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// 12);
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// for (size_t k = 0; k < send_vector.size(); ++k) {
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// send_vector[k] = k;
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// }
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// f::LoD lod{{0, 10, 20}};
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// paddle::framework::TensorFromVector<float>(
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// send_vector, *(ctxs_[input_scope_idx]), in_lod_tensor);
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// in_lod_tensor->set_lod(lod);
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//
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// gather_op_handle_->Run(false);
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//
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// WaitAll();
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//
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// p::CPUPlace cpu_place;
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// for (size_t j = 0; j < gpu_list_.size(); ++j) {
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// auto out_var = local_scope_[j]->Var("out");
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// auto out_tensor = out_var->Get<f::LoDTensor>();
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// PADDLE_ENFORCE_EQ(out_tensor.lod(), lod, "lod is not equal.");
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//
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// f::Tensor result_tensor;
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// f::TensorCopy(out_tensor, cpu_place, *(ctxs_[j]), &result_tensor);
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// float* ct = result_tensor.mutable_data<float>(cpu_place);
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//
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// for (int64_t j = 0; j < f::product(kDims); ++j) {
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// ASSERT_NEAR(ct[j], send_vector[j], 1e-5);
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// }
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// }
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//
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// GatherOpDestroy();
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}
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void TestGatherSelectedRows() {
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int output_scope_idx = 0;
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InitGatherOp<f::SelectedRows>(output_scope_idx);
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int height = kDims[0] * 2;
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std::vector<int64_t> rows{0, 1, 2, 3, 3, 0, 14, 7, 3, 1,
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2, 4, 6, 3, 1, 1, 1, 1, 3, 7};
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std::vector<float> send_vector(f::product(kDims));
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for (size_t k = 0; k < send_vector.size(); ++k) {
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send_vector[k] = k;
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}
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for (size_t input_scope_idx = 0; input_scope_idx < gpu_list_.size();
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++input_scope_idx) {
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auto in_var = local_scope_[input_scope_idx]->Var("input");
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auto in_selected_rows = in_var->GetMutable<f::SelectedRows>();
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auto value = in_selected_rows->mutable_value();
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value->mutable_data<float>(kDims, gpu_list_[input_scope_idx]);
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in_selected_rows->set_height(height);
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in_selected_rows->set_rows(rows);
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paddle::framework::TensorFromVector<float>(
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send_vector, *(ctxs_[input_scope_idx]), value);
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value->Resize(kDims);
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}
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gather_op_handle_->Run(false);
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WaitAll();
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p::CPUPlace cpu_place;
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auto out_var = local_scope_[output_scope_idx]->Var("out");
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auto& out_select_rows = out_var->Get<f::SelectedRows>();
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auto rt = out_select_rows.value();
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PADDLE_ENFORCE_EQ(out_select_rows.height(), height, "height is not equal.");
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for (size_t k = 0; k < out_select_rows.rows().size(); ++k) {
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PADDLE_ENFORCE_EQ(out_select_rows.rows()[k], rows[k % rows.size()]);
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}
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f::Tensor result_tensor;
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f::TensorCopy(rt, cpu_place, *(ctxs_[output_scope_idx]), &result_tensor);
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float* ct = result_tensor.data<float>();
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for (int64_t j = 0; j < f::product(kDims); ++j) {
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ASSERT_NEAR(ct[j], send_vector[j % send_vector.size()], 1e-5);
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}
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GatherOpDestroy();
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}
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public:
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f::Scope g_scope_;
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std::vector<p::DeviceContext*> ctxs_;
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std::vector<f::Scope*> local_scope_;
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std::vector<p::Place> gpu_list_;
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f::details::GatherOpHandle* gather_op_handle_;
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};
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// TEST_F(GatherTester, TestCPUGatherTestLodTensor) {
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// InitCtx(false);
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// TestGatherLodTensor();
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//}
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TEST_F(GatherTester, TestCPUGatherTestSelectedRows) {
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InitCtx(false);
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TestGatherSelectedRows();
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}
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#ifdef PADDLE_WITH_CUDA
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// TEST_F(GatherTester, TestGPUGatherTestLodTensor) {
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// InitCtx(true);
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// TestGatherLodTensor();
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//}
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TEST_F(GatherTester, TestGPUGatherTestSelectedRows) {
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InitCtx(true);
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TestGatherSelectedRows();
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}
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#endif
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