Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into rewrite_allocation
Yu Yang
6 years ago
commit
057a682ee9
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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/fused_broadcast_op_handle.h"
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#include "gtest/gtest.h"
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#include "paddle/fluid/framework/details/broadcast_op_handle_test.h"
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namespace paddle {
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namespace framework {
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namespace details {
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struct TestFusedBroadcastOpHandle : TestBroadcastOpHandle {
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std::vector<std::string> out_varnames_;
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void InitFusedBroadcastOp(std::vector<size_t> input_scope_idxes) {
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// initialize scope and var
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for (size_t i = 0; i < place_list_.size(); ++i) {
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local_scopes_.push_back(&(g_scope_.NewScope()));
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Scope& local_scope = local_scopes_.back()->NewScope();
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*local_scopes_.back()
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->Var(details::kLocalExecScopeName)
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->GetMutable<Scope*>() = &local_scope;
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for (size_t j = 0; j < input_scope_idxes.size(); ++j) {
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local_scope.Var("out_var" + j);
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if (i == j) local_scope.Var("in_var" + j);
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}
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param_scopes_.emplace_back(&local_scope);
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}
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// create op handle node
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std::unique_ptr<ir::Node> n =
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ir::CreateNodeForTest("fused_broadcast", ir::Node::Type::kOperation);
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if (use_gpu_) {
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#ifdef PADDLE_WITH_CUDA
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op_handle_.reset(new FusedBroadcastOpHandle(
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n.get(), local_scopes_, place_list_, nccl_ctxs_.get()));
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#else
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PADDLE_THROW("CUDA is not supported.");
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#endif
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} else {
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#ifdef PADDLE_WITH_CUDA
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op_handle_.reset(new FusedBroadcastOpHandle(
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n.get(), local_scopes_, place_list_, nccl_ctxs_.get()));
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#else
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op_handle_.reset(
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new FusedBroadcastOpHandle(n.get(), local_scopes_, place_list_));
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#endif
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}
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for (size_t i = 0; i < input_scope_idxes.size(); ++i) {
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// add input var handle
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std::unique_ptr<ir::Node> in_node =
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ir::CreateNodeForTest("in_node" + i, ir::Node::Type::kVariable);
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VarHandle* in_var_handle =
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new VarHandle(in_node.get(), 1, input_scope_idxes[i], "in_var" + i,
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place_list_[input_scope_idxes[i]]);
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vars_.emplace_back(in_var_handle);
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op_handle_->AddInput(in_var_handle);
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// add output var handle
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for (size_t j = 0; j < place_list_.size(); ++j) {
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std::unique_ptr<ir::Node> out_node =
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ir::CreateNodeForTest("out_node" + i, ir::Node::Type::kVariable);
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VarHandle* out_var_handle =
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new VarHandle(out_node.get(), 2, j, "out_var" + i, place_list_[j]);
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vars_.emplace_back(out_var_handle);
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op_handle_->AddOutput(out_var_handle);
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}
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}
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}
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void TestFusedBroadcastLoDTensor(std::vector<size_t> input_scope_idxes) {
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std::vector<std::vector<float>> send_vec;
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f::LoD lod{{0, 10, 20}};
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for (size_t i = 0; i < input_scope_idxes.size(); ++i) {
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const std::string varname("in_var" + i);
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float val_scalar = static_cast<float>(i);
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send_vec.push_back(
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InitLoDTensor(varname, input_scope_idxes[i], lod, val_scalar));
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}
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op_handle_->Run(false);
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WaitAll();
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for (size_t i = 0; i < input_scope_idxes.size(); ++i) {
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const std::string& varname("out_var" + i);
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for (size_t j = 0; j < place_list_.size(); ++j) {
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LoDTensorEqual(varname, send_vec[i], lod, param_scopes_[j]);
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}
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}
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}
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void TestFusedBroadcastSelectedRows(std::vector<size_t> input_scope_idxes) {
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std::vector<std::vector<float>> send_vector;
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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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int height = static_cast<int>(kDims[0] * 2);
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for (size_t i = 0; i < input_scope_idxes.size(); ++i) {
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const std::string varname("in_var" + i);
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float val_scalar = static_cast<float>(i);
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send_vector.push_back(InitSelectedRows(varname, input_scope_idxes[i],
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rows, height, val_scalar));
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}
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op_handle_->Run(false);
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WaitAll();
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for (size_t i = 0; i < input_scope_idxes.size(); ++i) {
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const std::string& varname("out_var" + i);
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for (size_t j = 0; j < place_list_.size(); ++j) {
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SelectedRowsEqual(varname, input_scope_idxes[i], send_vector[i], rows,
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height);
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}
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}
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}
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};
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TEST(FusedBroadcastTester, CPULodTensor) {
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TestFusedBroadcastOpHandle test_op;
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std::vector<size_t> input_scope_idxes = {0, 1};
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test_op.InitCtxOnGpu(false);
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test_op.InitFusedBroadcastOp(input_scope_idxes);
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test_op.TestFusedBroadcastLoDTensor(input_scope_idxes);
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}
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TEST(FusedBroadcastTester, CPUSelectedRows) {
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TestFusedBroadcastOpHandle test_op;
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std::vector<size_t> input_scope_idxes = {0, 1};
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test_op.InitCtxOnGpu(false);
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test_op.InitFusedBroadcastOp(input_scope_idxes);
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test_op.TestFusedBroadcastSelectedRows(input_scope_idxes);
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}
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#ifdef PADDLE_WITH_CUDA
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TEST(FusedBroadcastTester, GPULodTensor) {
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TestFusedBroadcastOpHandle test_op;
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std::vector<size_t> input_scope_idxes = {0, 1};
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test_op.InitCtxOnGpu(true);
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test_op.InitFusedBroadcastOp(input_scope_idxes);
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test_op.TestFusedBroadcastLoDTensor(input_scope_idxes);
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}
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TEST(FusedBroadcastTester, GPUSelectedRows) {
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TestFusedBroadcastOpHandle test_op;
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std::vector<size_t> input_scope_idxes = {0, 1};
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test_op.InitCtxOnGpu(true);
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test_op.InitFusedBroadcastOp(input_scope_idxes);
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test_op.TestFusedBroadcastSelectedRows(input_scope_idxes);
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}
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#endif
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} // namespace details
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} // namespace framework
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} // namespace paddle
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