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| // 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/attention_lstm_fuse_pass.h" | ||
| #include "paddle/fluid/framework/ir/graph_pattern_detector.h" | ||
| #include "paddle/fluid/framework/ir/graph_viz_pass.h" | ||
| #include "paddle/fluid/framework/lod_tensor.h" | ||
| #include "paddle/fluid/inference/api/helper.h" | ||
|
|
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| namespace paddle { | ||
| namespace framework { | ||
| namespace ir { | ||
|
|
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| struct Param { | ||
| std::string X = "concat_0.tmp_0"; | ||
| std::string C0 = "cell_init"; | ||
| std::string H0 = "hidden_init"; | ||
| std::string AttentionWeight = "attention_fc.w_0"; | ||
| std::string AttentionBias = "attention_fc.b_0"; | ||
| std::string AttentionScalar = "attention_output.w_0"; | ||
| std::string AttentionScalarBias = "attention_output.b_0"; | ||
| std::string LSTMWeight = "attention_w.new"; | ||
| std::string LSTMBias = "attention_b.new"; | ||
| std::string Hidden = "array_to_lod_tensor_0.tmp_0"; | ||
| std::string Cell = "at.cell.new"; | ||
| std::string AttentionedX = "at.x.new"; | ||
| std::string AttentionFCOut = "at.fc.new"; | ||
| std::string LSTMX = "at.lstmx.new"; | ||
| std::string LSTMOUT = "at.lstmout.new"; | ||
| }; | ||
|
|
||
| void PrepareParameters(Graph* graph, const Param& param); | ||
|
|
||
| void FindWhileOp(Graph* graph) { | ||
| GraphPatternDetector gpd; | ||
| std::unordered_set<int> fused_external_ops( | ||
| {35, 36, 37, 38, 43, 44, 49, 45, 46, 47, 41, 42, 53, 54, 48, | ||
| 57, 55, 56, 52, 74, 80, 77, 78, 79, 50, 77, 39, 40, 51}); | ||
|
|
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| gpd.mutable_pattern()->NewNode( | ||
| [&](Node* n) { return fused_external_ops.count(n->id()); }, "while"); | ||
|
|
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| if (!graph->Has(kGraphvizMarkedNodeAttr)) { | ||
| graph->Set(kGraphvizMarkedNodeAttr, new GraphVizPass::marked_nodes_t); | ||
| } | ||
| auto& marked_nodes = | ||
| graph->Get<GraphVizPass::marked_nodes_t>(kGraphvizMarkedNodeAttr); | ||
|
|
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| auto handle = [&](const GraphPatternDetector::subgraph_t& subgraph, | ||
| Graph* g) { | ||
| auto* while_pat_node = gpd.pattern().RetriveNode("while"); | ||
| auto* while_node = subgraph.at(while_pat_node); | ||
| marked_nodes.insert(while_node); | ||
| }; | ||
| gpd(graph, handle); | ||
|
|
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| Param param; | ||
| // Add AttentionLSTM node | ||
| OpDesc op_desc; | ||
| op_desc.SetType("attention_lstm"); | ||
|
|
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| #define OP_SET_IN(x) op_desc.SetInput(#x, {param.x}); | ||
| #define OP_SET_OUT(x) op_desc.SetOutput(#x, {param.x}); | ||
| OP_SET_IN(X); | ||
| OP_SET_IN(C0); | ||
| OP_SET_IN(H0); | ||
| OP_SET_IN(AttentionWeight); | ||
| OP_SET_IN(AttentionBias); | ||
| OP_SET_IN(AttentionScalar); | ||
| OP_SET_IN(AttentionScalarBias); | ||
| OP_SET_IN(LSTMWeight); | ||
| OP_SET_IN(LSTMBias); | ||
|
|
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| OP_SET_OUT(Hidden); | ||
| OP_SET_OUT(Cell); | ||
| OP_SET_OUT(AttentionedX); | ||
| OP_SET_OUT(AttentionFCOut); | ||
| OP_SET_OUT(LSTMX); | ||
| OP_SET_OUT(LSTMOUT); | ||
| #undef OP_SET_IN | ||
| #undef OP_SET_OUT | ||
|
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| auto* X = graph->RetriveNode(34); | ||
| auto* LSTMOUT = graph->RetriveNode(81); | ||
| auto* cell_init = graph->RetriveNode(6); | ||
| auto* hidden_init = graph->RetriveNode(8); | ||
|
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| #define LINK_TO(node0, node1) \ | ||
| node0->outputs.push_back(node1); \ | ||
| node1->inputs.push_back(node0); | ||
|
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| auto* lstm_op = graph->CreateOpNode(&op_desc); | ||
| PrepareParameters(graph, param); | ||
|
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| LINK_TO(X, lstm_op); | ||
| LINK_TO(cell_init, lstm_op); | ||
| LINK_TO(hidden_init, lstm_op); | ||
| LINK_TO(lstm_op, LSTMOUT); | ||
|
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| GraphSafeRemoveNodes(graph, marked_nodes); | ||
| } | ||
|
|
||
| #define CHECK_P1(x) PADDLE_ENFORCE_NOT_NULL(x); | ||
| #define CHECK_P2(x0, x1) \ | ||
| CHECK_P1(x0); \ | ||
| CHECK_P1(x1); | ||
| #define CHECK_P3(x0, x1, x2) \ | ||
| CHECK_P2(x0, x1); \ | ||
| CHECK_P1(x2); | ||
| #define CHECK_P4(x0, x1, x2, x3) \ | ||
| CHECK_P3(x0, x1, x2); \ | ||
| CHECK_P1(x3); | ||
| #define CHECK_P5(x0, x1, x2, x3, x4) \ | ||
| CHECK_P4(x0, x1, x2, x3); \ | ||
| CHECK_P1(x4); | ||
|
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| void PrepareLSTMWeight(const LoDTensor& W_forget_w0, | ||
| const LoDTensor& W_forget_w1, | ||
| const LoDTensor& W_input_w0, const LoDTensor& W_input_w1, | ||
| const LoDTensor& W_output_w0, | ||
| const LoDTensor& W_output_w1, const LoDTensor& W_cell_w0, | ||
| const LoDTensor& W_cell_w1, LoDTensor* out); | ||
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| void PrepareLSTMBias(const LoDTensor& B_forget, const LoDTensor& B_input, | ||
| const LoDTensor& B_output, const LoDTensor& B_cell, | ||
| LoDTensor* out); | ||
|
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| void PrepareParameters(Graph* graph, const Param& param) { | ||
| // Check parameters | ||
| PADDLE_ENFORCE(graph->Has(kParamScopeAttr)); | ||
| auto* scope = graph->Get<Scope*>(kParamScopeAttr); | ||
|
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| // Create new parameters. | ||
| scope->Var(param.LSTMWeight)->GetMutable<LoDTensor>(); | ||
| scope->Var(param.LSTMBias)->GetMutable<LoDTensor>(); | ||
| scope->Var(param.Hidden)->GetMutable<LoDTensor>(); | ||
| scope->Var(param.Cell)->GetMutable<LoDTensor>(); | ||
| scope->Var(param.AttentionedX)->GetMutable<LoDTensor>(); | ||
| scope->Var(param.AttentionFCOut)->GetMutable<LoDTensor>(); | ||
| scope->Var(param.LSTMX)->GetMutable<LoDTensor>(); | ||
| scope->Var(param.LSTMOUT)->GetMutable<LoDTensor>(); | ||
|
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||
| #define GATE_W(name__) \ | ||
| auto* W_##name__##_w0 = scope->FindVar(#name__ ".w_0"); \ | ||
| auto* W_##name__##_w1 = scope->FindVar(#name__ ".w_1"); \ | ||
| auto* W_##name__##_b0 = scope->FindVar(#name__ ".b_0"); \ | ||
| CHECK_P3(W_##name__##_w0, W_##name__##_w1, W_##name__##_b0); \ | ||
| VLOG(4) << #name__ "_w0" \ | ||
| << " shape: " << W_##name__##_w0->Get<LoDTensor>().dims(); \ | ||
| VLOG(4) << #name__ "_w1" \ | ||
| << " shape: " << W_##name__##_w1->Get<LoDTensor>().dims(); \ | ||
| VLOG(4) << #name__ "_b0" \ | ||
| << " shape: " << W_##name__##_b0->Get<LoDTensor>().dims(); \ | ||
| auto& W_##name__##_w0_t = W_##name__##_w0->Get<LoDTensor>(); \ | ||
| auto& W_##name__##_w1_t = W_##name__##_w1->Get<LoDTensor>(); \ | ||
| auto& W_##name__##_b0_t = W_##name__##_b0->Get<LoDTensor>(); | ||
|
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| GATE_W(forget); | ||
| GATE_W(input); | ||
| GATE_W(output); | ||
| GATE_W(c); | ||
| #undef GATE_W | ||
|
|
||
| auto* attention_fc_w = scope->FindVar("attention_fc.w_0"); | ||
| auto* attention_fc_b = scope->FindVar("attention_fc.b_0"); | ||
| auto* attention_output_w = scope->FindVar("attention_output.w_0"); | ||
| auto* attention_output_b = scope->FindVar("attention_output.b_0"); | ||
| CHECK_P4(attention_fc_w, attention_fc_b, attention_output_w, | ||
| attention_output_b); | ||
|
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||
| auto* lstm_weight = scope->Var(param.LSTMWeight); | ||
| auto* lstm_weight_t = lstm_weight->GetMutable<LoDTensor>(); | ||
| auto* lstm_bias = scope->Var(param.LSTMBias); | ||
| auto* lstm_bias_t = lstm_bias->GetMutable<LoDTensor>(); | ||
|
|
||
| // reshape attention_bias | ||
| auto* attention_bias_t = | ||
| scope->FindVar(param.AttentionBias)->GetMutable<LoDTensor>(); | ||
| PADDLE_ENFORCE_EQ(attention_bias_t->dims().size(), 1); | ||
| attention_bias_t->Resize(make_ddim({1, attention_bias_t->dims()[0]})); | ||
|
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| auto* attention_scalar_bias_t = | ||
| scope->FindVar(param.AttentionScalarBias)->GetMutable<LoDTensor>(); | ||
| attention_scalar_bias_t->Resize( | ||
| make_ddim({1, attention_scalar_bias_t->dims()[0]})); | ||
|
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| PrepareLSTMWeight(W_forget_w0_t, W_forget_w1_t, W_input_w0_t, W_input_w1_t, | ||
| W_output_w0_t, W_output_w1_t, W_c_w0_t, W_c_w1_t, | ||
| lstm_weight_t); | ||
| PrepareLSTMBias(W_forget_b0_t, W_input_b0_t, W_output_b0_t, W_c_b0_t, | ||
| lstm_bias_t); | ||
| } | ||
|
|
||
| // Prepare parameters | ||
| void PrepareLSTMWeight(const LoDTensor& W_forget_w0, | ||
| const LoDTensor& W_forget_w1, | ||
| const LoDTensor& W_input_w0, const LoDTensor& W_input_w1, | ||
| const LoDTensor& W_output_w0, | ||
| const LoDTensor& W_output_w1, const LoDTensor& W_cell_w0, | ||
| const LoDTensor& W_cell_w1, LoDTensor* out) { | ||
| int D = W_forget_w0.dims()[0]; | ||
| int M = W_forget_w1.dims()[0]; | ||
| out->Resize(make_ddim({D + M, 4 * D})); | ||
| VLOG(3) << "LSTMWeight resized to " << out->dims(); | ||
|
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| float* out_data = out->mutable_data<float>(platform::CPUPlace()); | ||
| std::array<const float*, 4> tensors( | ||
| {W_forget_w0.data<float>(), W_input_w0.data<float>(), | ||
| W_output_w0.data<float>(), W_cell_w0.data<float>()}); | ||
| std::array<const float*, 4> tensors1( | ||
| {W_forget_w1.data<float>(), W_input_w1.data<float>(), | ||
| W_output_w1.data<float>(), W_cell_w1.data<float>()}); | ||
|
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| for (int row = 0; row < D; row++) { | ||
| for (int col = 0; col < 4; col++) { | ||
| float* dst = out_data + 4 * D * row + D * col; | ||
| const float* src = tensors[col] + D * row; | ||
| memcpy(dst, src, D * sizeof(float)); | ||
| } | ||
| } | ||
|
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| for (int row = 0; row < M; row++) { | ||
| for (int col = 0; col < 4; col++) { | ||
| float* dst = out_data + 4 * D * (D + row) + D * col; | ||
| const float* src = tensors1[col] + D * row; | ||
| memcpy(dst, src, D * sizeof(float)); | ||
| } | ||
| } | ||
| } | ||
|
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| void PrepareLSTMBias(const LoDTensor& B_forget, const LoDTensor& B_input, | ||
| const LoDTensor& B_output, const LoDTensor& B_cell, | ||
| LoDTensor* out) { | ||
| std::array<const float*, 4> tensors( | ||
| {B_forget.data<float>(), B_input.data<float>(), B_output.data<float>(), | ||
| B_cell.data<float>()}); | ||
|
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| PADDLE_ENFORCE_EQ(B_forget.dims().size(), 1); | ||
| int D = B_forget.dims()[0]; | ||
| out->Resize(make_ddim({1, 4 * D})); | ||
| auto* out_data = out->mutable_data<float>(platform::CPUPlace()); | ||
| for (size_t i = 0; i < tensors.size(); i++) { | ||
| memcpy(out_data + D * i, tensors[i], D * sizeof(float)); | ||
| } | ||
| } | ||
|
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| // Parameters | ||
|
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| std::unique_ptr<ir::Graph> AttentionLSTMFusePass::ApplyImpl( | ||
| std::unique_ptr<ir::Graph> graph) const { | ||
| PDPattern external_pattern, subblock_pattern; | ||
|
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| FindWhileOp(graph.get()); | ||
| return graph; | ||
| } | ||
|
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| } // namespace ir | ||
| } // namespace framework | ||
| } // namespace paddle | ||
|
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| REGISTER_PASS(attention_lstm_fuse_pass, | ||
| paddle::framework::ir::AttentionLSTMFusePass); |
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