Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into optimize-opyreader
qiaolongfei
7 years ago
commit
91756a5a90
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/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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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/build_strategy.h"
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#include "paddle/fluid/framework/details/multi_devices_graph_check_pass.h"
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#include "paddle/fluid/framework/details/multi_devices_graph_print_pass.h"
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#include "paddle/fluid/framework/ir/graph.h"
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#include "paddle/fluid/framework/ir/graph_viz_pass.h"
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namespace paddle {
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namespace framework {
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namespace details {
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class ParallelExecutorPassBuilder : public ir::PassBuilder {
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public:
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explicit ParallelExecutorPassBuilder(const BuildStrategy &strategy)
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: ir::PassBuilder(), strategy_(strategy) {
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// Add a graph viz pass to record a graph.
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if (!strategy_.debug_graphviz_path_.empty()) {
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auto viz_pass = AppendPass("graph_viz_pass");
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const std::string graph_path = string::Sprintf(
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"%s%s", strategy_.debug_graphviz_path_.c_str(), "_original_graph");
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viz_pass->Set<std::string>("graph_viz_path", new std::string(graph_path));
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}
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// Add op fusion.
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if (strategy.fuse_elewise_add_act_ops_) {
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auto fuse_elewise_add_act_pass = AppendPass("fuse_elewise_add_act_pass");
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// Add a graph viz pass to record a graph.
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if (!strategy.debug_graphviz_path_.empty()) {
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auto viz_pass = AppendPass("graph_viz_pass");
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const std::string graph_path = string::Sprintf(
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"%s%s", strategy.debug_graphviz_path_.c_str(), "_fused_graph");
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viz_pass->Set<std::string>("graph_viz_path",
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new std::string(graph_path));
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}
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}
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// Convert graph to run on multi-devices.
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auto multi_devices_pass = AppendPass("multi_devices_pass");
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multi_devices_pass->SetNotOwned<const BuildStrategy>("strategy",
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&strategy_);
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// Add a graph print pass to record a graph with device info.
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if (!strategy_.debug_graphviz_path_.empty()) {
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auto multi_devices_print_pass = AppendPass("multi_devices_print_pass");
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multi_devices_print_pass->SetNotOwned<const std::string>(
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"debug_graphviz_path", &strategy_.debug_graphviz_path_);
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multi_devices_print_pass->Set<details::GraphvizSSAGraphPrinter>(
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"graph_printer", new details::GraphvizSSAGraphPrinter);
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}
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// Verify that the graph is correct for multi-device executor.
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AppendPass("multi_devices_check_pass");
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}
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private:
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BuildStrategy strategy_;
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};
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std::shared_ptr<ir::PassBuilder> BuildStrategy::CreatePassesFromStrategy()
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const {
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pass_builder_.reset(new ParallelExecutorPassBuilder(*this));
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return pass_builder_;
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}
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std::unique_ptr<ir::Graph> BuildStrategy::Apply(
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const ProgramDesc &main_program, const std::vector<platform::Place> &places,
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const std::string &loss_var_name,
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const std::unordered_set<std::string> ¶m_names,
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const std::vector<Scope *> &local_scopes,
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#ifdef PADDLE_WITH_CUDA
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const bool use_cuda, platform::NCCLContextMap *nccl_ctxs) const {
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#else
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const bool use_cuda) const {
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#endif
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// Create a default one if not initialized by user.
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if (!pass_builder_) {
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CreatePassesFromStrategy();
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}
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std::unique_ptr<ir::Graph> graph(new ir::Graph(main_program));
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|
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for (std::shared_ptr<ir::Pass> &pass : pass_builder_->AllPasses()) {
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if (pass->Type() == "multi_devices_pass") {
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pass->Erase("places");
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pass->SetNotOwned<const std::vector<platform::Place>>("places", &places);
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pass->Erase("loss_var_name");
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pass->SetNotOwned<const std::string>("loss_var_name", &loss_var_name);
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pass->Erase("params");
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pass->SetNotOwned<const std::unordered_set<std::string>>("params",
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¶m_names);
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pass->Erase("local_scopes");
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pass->SetNotOwned<const std::vector<Scope *>>("local_scopes",
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&local_scopes);
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#ifdef PADDLE_WITH_CUDA
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platform::NCCLContextMap *nctx = use_cuda ? nccl_ctxs : nullptr;
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pass->Erase("nccl_ctxs");
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pass->SetNotOwned<platform::NCCLContextMap>("nccl_ctxs", nctx);
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#endif
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}
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graph = pass->Apply(std::move(graph));
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}
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return graph;
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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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USE_PASS(fuse_elewise_add_act_pass);
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USE_PASS(graph_viz_pass);
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USE_PASS(multi_devices_pass);
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USE_PASS(multi_devices_check_pass);
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USE_PASS(multi_devices_print_pass);
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@ -0,0 +1,243 @@
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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");
|
||||
// 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,
|
||||
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
// See the License for the specific language governing permissions and
|
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// limitations under the License.
|
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|
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#include "paddle/fluid/framework/ir/embedding_fc_lstm_fuse_pass.h"
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#include <algorithm>
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#include <string>
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#include "paddle/fluid/framework/lod_tensor.h"
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#include "paddle/fluid/operators/math/blas.h"
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#include "paddle/fluid/operators/math/cpu_vec.h"
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#include "paddle/fluid/operators/math/fc_compute.h"
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#include "paddle/fluid/platform/cpu_info.h"
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namespace paddle {
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namespace framework {
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namespace ir {
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static int BuildFusion(Graph* graph, const std::string& name_scope,
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Scope* scope, bool with_fc_bias) {
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GraphPatternDetector gpd;
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auto* pattern = gpd.mutable_pattern();
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// Build pattern
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PDNode* x = pattern->NewNode(patterns::PDNodeName(name_scope, "x"))
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->assert_is_op_input("lookup_table")
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->assert_var_not_persistable();
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patterns::Embedding embedding_pattern(pattern, name_scope);
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// TODO(jczaja): Intermediate can only be for val that are not used anywhere
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// but lookup table output may go into other LSTM (for reverse
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// direction)
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auto* embedding_out = embedding_pattern(x);
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patterns::FC fc_pattern(pattern, name_scope);
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// fc_out is a tmp var, will be removed after fuse, so marked as intermediate.
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auto* fc_out = fc_pattern(embedding_out, with_fc_bias)->AsIntermediate();
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patterns::LSTM lstm_pattern(pattern, name_scope);
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lstm_pattern(fc_out);
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// Create New OpDesc
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auto embedding_lstm_creator = [&](Node* embedding, Node* W, Node* lstm,
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Node* input, Node* weight_x, Node* weight_h,
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Node* bias, Node* hidden, Node* cell,
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Node* xx, Node* fc_bias) {
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OpDesc op_desc;
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op_desc.SetType("fused_embedding_fc_lstm");
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#define SET_IN(Key, node__) op_desc.SetInput(#Key, {node__->Name()});
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SET_IN(Ids, input);
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SET_IN(WeightH, weight_h);
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// Neet to have this passed as We need Wc data for peephole connections
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SET_IN(Bias, bias);
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#undef SET_IN
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// Multiply embeddings with Weights
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PADDLE_ENFORCE(scope);
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const std::string& embeddings = patterns::UniqueKey("Embeddings");
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auto* embeddings_var = scope->Var(embeddings);
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PADDLE_ENFORCE(embeddings_var);
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auto* embeddings_tensor =
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embeddings_var->GetMutable<framework::LoDTensor>();
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// Get WeightX size: [single_embedding, fc_size]
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// and embedding size: [dict_size, single_embedding]
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// and create new size of embeddings eg. [dict_size , hidden_size]
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auto* embedding_var = scope->FindVar(W->Name());
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PADDLE_ENFORCE(embedding_var);
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const auto& embedding_tensor = embedding_var->Get<framework::LoDTensor>();
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const auto& weightx_tensor =
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scope->FindVar(weight_x->Name())->Get<framework::LoDTensor>();
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embeddings_tensor->Resize(
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{embedding_tensor.dims()[0], weightx_tensor.dims()[1]});
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// Multiplie embeddings via WeightsX and add bias
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auto embedding_data = embedding_tensor.data<float>();
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auto weightx_data = weightx_tensor.data<float>();
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auto embeddings_data =
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embeddings_tensor->mutable_data<float>(platform::CPUPlace());
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// Adding biases to GEMM result to be
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auto* lstm_bias_var = scope->FindVar(bias->Name());
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PADDLE_ENFORCE(lstm_bias_var);
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const auto& lstm_bias_tensor = lstm_bias_var->Get<framework::LoDTensor>();
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auto alpha = 1.0f;
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auto beta = 1.0f;
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int m = embedding_tensor.dims()[0];
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int n = weightx_tensor.dims()[1];
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int k = embedding_tensor.dims()[1];
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// Copy only gate biases values (only actual bias data, not peephole
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// weights)
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std::vector<float> combined_biases;
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combined_biases.reserve(n);
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std::copy_n(lstm_bias_tensor.data<float>(), n,
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std::back_inserter(combined_biases));
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if (with_fc_bias) {
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// Add FC-bias with LSTM-bias (into GEMM result to be)
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auto* fc_bias_var = scope->FindVar(fc_bias->Name());
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const auto& fc_bias_tensor = fc_bias_var->Get<framework::LoDTensor>();
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for (int i = 0; i < fc_bias_tensor.numel(); i++) {
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combined_biases[i] += fc_bias_tensor.data<float>()[i];
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}
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}
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// broadcast biases
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std::vector<float> ones(m, 1.0f);
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paddle::operators::math::CBlas<float>::GEMM(
|
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CblasRowMajor, CblasNoTrans, CblasNoTrans, m, n, 1, alpha, &ones[0], 1,
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&combined_biases[0], n, 0.0f, embeddings_data, n);
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|
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// Wx*embeddings + biases
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paddle::operators::math::CBlas<float>::GEMM(
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CblasRowMajor, CblasNoTrans, CblasNoTrans, m, n, k, alpha,
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embedding_data, k, weightx_data, n, beta, embeddings_data, n);
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op_desc.SetInput("Embeddings", {embeddings});
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// Create temp variables.
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const std::string BatchedInput = patterns::UniqueKey("BatchedInput");
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const std::string BatchedCellPreAct =
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patterns::UniqueKey("BatchedCellPreAct");
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const std::string BatchedGate = patterns::UniqueKey("BatchedGate");
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|
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scope->Var(BatchedInput)->GetMutable<framework::LoDTensor>();
|
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scope->Var(BatchedCellPreAct)->GetMutable<framework::LoDTensor>();
|
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scope->Var(BatchedGate)->GetMutable<framework::LoDTensor>();
|
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|
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op_desc.SetInput("H0", {});
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op_desc.SetInput("C0", {});
|
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op_desc.SetOutput("Hidden", {hidden->Name()});
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op_desc.SetOutput("Cell", {cell->Name()});
|
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op_desc.SetOutput("XX", {xx->Name()});
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op_desc.SetOutput("BatchedGate", {BatchedGate});
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op_desc.SetOutput("BatchCellPreAct", {BatchedCellPreAct});
|
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op_desc.SetOutput("BatchedInput", {BatchedInput});
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op_desc.SetAttr("is_reverse", lstm->Op()->GetAttr("is_reverse"));
|
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op_desc.SetAttr("use_peepholes", lstm->Op()->GetAttr("use_peepholes"));
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// TODO(TJ): get from attr
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op_desc.SetAttr("use_seq", true);
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PADDLE_ENFORCE(graph->Has(kParamScopeAttr));
|
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auto* scope = graph->Get<Scope*>(kParamScopeAttr);
|
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#define OP_SET_OUT(x) \
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const std::string x = patterns::UniqueKey(#x); \
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op_desc.SetOutput(#x, {x}); \
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scope->Var(x)->GetMutable<LoDTensor>()
|
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OP_SET_OUT(BatchedCell);
|
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OP_SET_OUT(BatchedHidden);
|
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OP_SET_OUT(ReorderedH0);
|
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OP_SET_OUT(ReorderedC0);
|
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#undef OP_SET_OUT
|
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|
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auto* op = graph->CreateOpNode(&op_desc);
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IR_NODE_LINK_TO(input, op);
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IR_NODE_LINK_TO(weight_x, op);
|
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IR_NODE_LINK_TO(weight_h, op);
|
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IR_NODE_LINK_TO(bias, op);
|
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IR_NODE_LINK_TO(op, hidden);
|
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return op;
|
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};
|
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|
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int fusion_count{0};
|
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|
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auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
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Graph* g) {
|
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GET_IR_NODE_FROM_SUBGRAPH(lstm, lstm, lstm_pattern);
|
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GET_IR_NODE_FROM_SUBGRAPH(Weight, Weight, lstm_pattern);
|
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GET_IR_NODE_FROM_SUBGRAPH(Bias, Bias, lstm_pattern);
|
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GET_IR_NODE_FROM_SUBGRAPH(Cell, Cell, lstm_pattern);
|
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GET_IR_NODE_FROM_SUBGRAPH(Hidden, Hidden, lstm_pattern);
|
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GET_IR_NODE_FROM_SUBGRAPH(lookup_table, lookup_table, embedding_pattern);
|
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GET_IR_NODE_FROM_SUBGRAPH(W, W, embedding_pattern);
|
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GET_IR_NODE_FROM_SUBGRAPH(w, w, fc_pattern);
|
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GET_IR_NODE_FROM_SUBGRAPH(mul, mul, fc_pattern);
|
||||
|
||||
// TODO(jczaja): Add support for is_sparse / is_distributed
|
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auto is_sparse = boost::get<bool>(lookup_table->Op()->GetAttr("is_sparse"));
|
||||
auto is_distributed =
|
||||
boost::get<bool>(lookup_table->Op()->GetAttr("is_distributed"));
|
||||
|
||||
if (is_sparse == true || is_distributed == true) {
|
||||
return;
|
||||
}
|
||||
|
||||
if (with_fc_bias) {
|
||||
GET_IR_NODE_FROM_SUBGRAPH(fc_out, Out, fc_pattern);
|
||||
GET_IR_NODE_FROM_SUBGRAPH(fc_bias, bias, fc_pattern);
|
||||
GET_IR_NODE_FROM_SUBGRAPH(elementwise_add, elementwise_add, fc_pattern);
|
||||
embedding_lstm_creator(lookup_table, W, lstm, subgraph.at(x), w, Weight,
|
||||
Bias, Hidden, Cell, fc_out, fc_bias);
|
||||
// Remove unneeded nodes.
|
||||
// TODO(jczaja): Proper removing of lookup table
|
||||
std::unordered_set<const Node*> marked_nodes(
|
||||
//{lookup_table, mul, lstm, elementwise_add, fc_bias, W});
|
||||
{mul, lstm, elementwise_add, fc_bias});
|
||||
GraphSafeRemoveNodes(graph, marked_nodes);
|
||||
} else {
|
||||
GET_IR_NODE_FROM_SUBGRAPH(fc_out, mul_out, fc_pattern);
|
||||
embedding_lstm_creator(lookup_table, W, lstm, subgraph.at(x), w, Weight,
|
||||
Bias, Hidden, Cell, fc_out, nullptr);
|
||||
// Remove unneeded nodes.
|
||||
// TODO(jczaja): Proper removing of lookup table
|
||||
// std::unordered_set<const Node*> marked_nodes({lookup_table, W, mul,
|
||||
// lstm});
|
||||
std::unordered_set<const Node*> marked_nodes({mul, lstm});
|
||||
GraphSafeRemoveNodes(graph, marked_nodes);
|
||||
}
|
||||
|
||||
++fusion_count;
|
||||
};
|
||||
|
||||
gpd(graph, handler);
|
||||
|
||||
return fusion_count;
|
||||
}
|
||||
|
||||
std::unique_ptr<ir::Graph> EmbeddingFCLSTMFusePass::ApplyImpl(
|
||||
std::unique_ptr<ir::Graph> graph) const {
|
||||
FusePassBase::Init(name_scope_, graph.get());
|
||||
|
||||
int fusion_count = BuildFusion(graph.get(), name_scope_, param_scope(),
|
||||
true /*with_fc_bias*/);
|
||||
|
||||
AddStatis(fusion_count);
|
||||
return graph;
|
||||
}
|
||||
|
||||
} // namespace ir
|
||||
} // namespace framework
|
||||
} // namespace paddle
|
||||
|
||||
REGISTER_PASS(embedding_fc_lstm_fuse_pass,
|
||||
paddle::framework::ir::EmbeddingFCLSTMFusePass);
|
||||
@ -0,0 +1,43 @@
|
||||
/* 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/ir/pass_builder.h"
|
||||
|
||||
namespace paddle {
|
||||
namespace framework {
|
||||
namespace ir {
|
||||
|
||||
std::shared_ptr<Pass> PassBuilder::AppendPass(const std::string& pass_type) {
|
||||
auto pass = ir::PassRegistry::Instance().Get(pass_type);
|
||||
passes_.emplace_back(pass.release());
|
||||
return passes_.back();
|
||||
}
|
||||
|
||||
void PassBuilder::RemovePass(size_t idx) {
|
||||
PADDLE_ENFORCE(passes_.size() > idx);
|
||||
passes_.erase(passes_.begin() + idx);
|
||||
}
|
||||
|
||||
std::shared_ptr<Pass> PassBuilder::InsertPass(size_t idx,
|
||||
const std::string& pass_type) {
|
||||
PADDLE_ENFORCE(passes_.size() >= idx);
|
||||
std::shared_ptr<Pass> pass(
|
||||
ir::PassRegistry::Instance().Get(pass_type).release());
|
||||
passes_.insert(passes_.begin() + idx, std::move(pass));
|
||||
return passes_[idx];
|
||||
}
|
||||
|
||||
} // namespace ir
|
||||
} // namespace framework
|
||||
} // namespace paddle
|
||||
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