// 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/fc_lstm_fuse_pass.h" #include "paddle/fluid/framework/lod_tensor.h" namespace paddle { namespace framework { namespace ir { std::string GenNodeName(const std::string& prefix, const std::string& name) { return prefix + "/" + name; } void BuildPattern(PDPattern* pattern, const std::string& name_scope, bool with_fc_bias) { PDNode* x = pattern->NewNode(name_scope, "x") ->assert_is_op_input("mul") ->assert_var_not_persistable(); auto* fc_out = patterns::FC(pattern, name_scope, x, with_fc_bias); fc_out->AsIntermediate(); // fc_out is a tmp var, will be removed after fuse. patterns::LSTM(pattern, name_scope, fc_out); // LOG(INFO) << "\n" << pattern->DotString(); } int BuildFusion(Graph* graph, const std::string& name_scope, Scope* scope, bool with_fc_bias) { GraphPatternDetector gpd; auto* pattern = gpd.mutable_pattern(); BuildPattern(pattern, name_scope, with_fc_bias); // Create New OpDesc auto lstm_creator = [&](int lstm, int input, int weight_x, int weight_h, int bias, int hidden, int cell, int xx, int fc_bias) { #define GET_NODE(x) auto* x##_n = graph->RetriveNode(x); GET_NODE(input); GET_NODE(weight_x); GET_NODE(weight_h); GET_NODE(bias); GET_NODE(hidden); GET_NODE(cell); GET_NODE(xx); GET_NODE(lstm); OpDesc op_desc; op_desc.SetType("fusion_lstm"); #define SET_IN(Key, node__) op_desc.SetInput(#Key, {node__##_n->Name()}); SET_IN(X, input); SET_IN(WeightX, weight_x); SET_IN(WeightH, weight_h); SET_IN(Bias, bias); #undef SET_IN if (with_fc_bias) { // Add FC-bias with LSTM-bias and create a new weight PADDLE_ENFORCE(scope); const std::string& new_bias_var = name_scope + "_bias.new"; auto* bias_var = scope->Var(new_bias_var); PADDLE_ENFORCE(bias_var); auto* bias_tensor = bias_var->GetMutable(); auto* lstm_bias_var = scope->FindVar(bias_n->Name()); PADDLE_ENFORCE(lstm_bias_var); const auto& lstm_bias_tensor = lstm_bias_var->Get(); bias_tensor->Resize(lstm_bias_tensor.dims()); GET_NODE(fc_bias); auto* fc_bias_var = scope->FindVar(fc_bias_n->Name()); const auto& fc_bias_tensor = fc_bias_var->Get(); auto* data = bias_tensor->mutable_data(platform::CPUPlace()); for (int i = 0; i < bias_tensor->numel(); i++) { data[i] = fc_bias_tensor.data()[i] + lstm_bias_tensor.data()[i]; } op_desc.SetInput("Bias", {new_bias_var}); } #undef GET_NODE op_desc.SetInput("H0", {}); op_desc.SetInput("C0", {}); op_desc.SetOutput("Hidden", {hidden_n->Name()}); op_desc.SetOutput("Cell", {cell_n->Name()}); op_desc.SetOutput("XX", {xx_n->Name()}); op_desc.SetOutput("BatchedGate", {"blstm_0.tmp_2"}); op_desc.SetOutput("BatchCellPreAct", {"blstm_1.tmp_2"}); op_desc.SetAttr("is_reverse", lstm_n->Op()->GetAttr("is_reverse")); op_desc.SetAttr("use_peepholes", lstm_n->Op()->GetAttr("use_peepholes")); auto* op = graph->CreateOpNode(&op_desc); #define LINK_TO(a, b) \ a->outputs.push_back(b); \ b->inputs.push_back(a); LINK_TO(input_n, op); LINK_TO(weight_x_n, op); LINK_TO(weight_h_n, op); LINK_TO(bias_n, op); LINK_TO(op, hidden_n); #undef LINK_TO return op; }; int fusion_count{0}; auto fc_no_bias_handler = [&]( const GraphPatternDetector::subgraph_t& subgraph, Graph* g) { #define GET_NODE(name__) \ std::string name__##key = name_scope + "/" + #name__; \ auto* name__##n = pattern->RetrieveNode(name__##key); \ PADDLE_ENFORCE(name__##n); \ PADDLE_ENFORCE(subgraph.count(name__##n)); \ Node* name__##_n = subgraph.at(name__##n); \ int name__ __attribute__((unused)) = name__##_n->id(); GET_NODE(x); GET_NODE(w); GET_NODE(mul); GET_NODE(fc_out); GET_NODE(Weight); GET_NODE(lstm); GET_NODE(Bias); GET_NODE(Hidden); GET_NODE(Cell); if (with_fc_bias) { GET_NODE(fc_bias); lstm_creator(lstm, x, w, Weight, Bias, Hidden, Cell, fc_out, fc_bias); } else { lstm_creator(lstm, x, w, Weight, Bias, Hidden, Cell, fc_out, -1); } #undef GET_NODE // Remove unneeded nodes. std::unordered_set marked_nodes({mul_n, lstm_n}); GraphSafeRemoveNodes(graph, marked_nodes); ++fusion_count; }; gpd(graph, fc_no_bias_handler); return fusion_count; } std::unique_ptr MulLstmFusePass::ApplyImpl( std::unique_ptr graph) const { FusePassBase::Init(name_scope_, graph.get()); int fusion_count = BuildFusion(graph.get(), name_scope_, param_scope(), false /*with_fc_bias*/); AddStatis(fusion_count); return graph; } std::unique_ptr FCLstmFusePass::ApplyImpl( std::unique_ptr 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(mul_lstm_fuse_pass, paddle::framework::ir::MulLstmFusePass); REGISTER_PASS(fc_lstm_fuse_pass, paddle::framework::ir::FCLstmFusePass);