add fuse fc gru pass

paddle/fluid/framework/ir/fc_gru_fuse_pass.cc

+// 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_gru_fuse_pass.h"
+#include <string>
+#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::GRU(pattern, name_scope, fc_out);
+  VLOG(3) << "\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 gru_creater = [&](int gru, int x, int weight_x, int weight_h, int bias,
+                         int hidden, int fc_bias) {
+#define GET_NODE(x) auto* x##_n = graph->RetriveNode(x);
+    GET_NODE(x);
+    GET_NODE(weight_x);
+    GET_NODE(weight_h);
+    GET_NODE(bias);
+    GET_NODE(hidden);
+    GET_NODE(gru);
+
+    OpDesc op_desc;
+    op_desc.SetType("fusion_gru");
+#define SET_IN(Key, node__) op_desc.SetInput(#Key, {node__##_n->Name()});
+    SET_IN(X, x);
+    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<framework::LoDTensor>();
+      auto* gru_bias_var = scope->FindVar(bias_n->Name());
+      PADDLE_ENFORCE(gru_bias_var);
+      const auto& gru_bias_tenosr = gru_bias_var->Get<framework::LoDTensor>();
+      bias_tensor->Resize(gru_bias_tenosr.dims());
+
+      GET_NODE(fc_bias);
+      auto* fc_bias_var = scope->FindVar(fc_bias_n->Name());
+      const auto& fc_bias_tensor = fc_bias_var->Get<framework::LoDTensor>();
+      // new bias = fc bias + gru bias
+      auto* data = bias_tensor->mutable_data<float>(platform::CPUPlace());
+      for (int i = 0; i < bias_tensor->numel(); i++) {
+        data[i] =
+            fc_bias_tensor.data<float>()[i] + gru_bias_tenosr.data<float>()[i];
+      }
+      op_desc.SetInput("Bias", {new_bias_var});
+    }
+#undef GET_NODE
+
+    op_desc.SetInput("H0", {});
+    op_desc.SetOutput("Hidden", {hidden_n->Name()});
+    op_desc.SetAttr("is_reverse", gru_n->Op()->GetAttr("is_reverse"));
+    // TODO(TJ): This should be a option for infer
+    op_desc.SetAttr("use_seq", true);
+
+    // Create temp variables.
+    // TODO(TJ): clean code
+    scope->Var(name_scope + "/ReorderedH0.new")
+        ->GetMutable<framework::LoDTensor>();
+    scope->Var(name_scope + "/XX.new")->GetMutable<framework::LoDTensor>();
+    scope->Var(name_scope + "/BatchedInput.new")
+        ->GetMutable<framework::LoDTensor>();
+    scope->Var(name_scope + "/BatchedOut.new")
+        ->GetMutable<framework::LoDTensor>();
+    op_desc.SetOutput("ReorderedH0", {name_scope + "/ReorderedH0.new"});
+    op_desc.SetOutput("XX", {name_scope + "/XX.new"});
+    op_desc.SetOutput("BatchedInput", {name_scope + "/BatchedInput.new"});
+    op_desc.SetOutput("BatchedOut", {name_scope + "/BatchedOut.new"});
+
+    auto* op = graph->CreateOpNode(&op_desc);
+    PADDLE_ENFORCE(graph->Has(kParamScopeAttr));
+    auto* scope = graph->Get<Scope*>(kParamScopeAttr);
+
+    IR_NODE_LINK_TO(x_n, op);
+    IR_NODE_LINK_TO(weight_x_n, op);
+    IR_NODE_LINK_TO(weight_h_n, op);
+    IR_NODE_LINK_TO(bias_n, op);
+    IR_NODE_LINK_TO(op, hidden_n);
+    // h0?
+    return op;
+  };
+
+  int fusion_count{0};
+  auto 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(gru);
+    GET_NODE(Bias);
+    GET_NODE(Hidden);
+
+    if (with_fc_bias) {
+      GET_NODE(fc_bias);
+      GET_NODE(elementwise_add);
+      gru_creater(gru, x, w, Weight, Bias, Hidden, fc_bias);
+      // Remove unneeded nodes.
+      std::unordered_set<const Node*> marked_nodes(
+          {mul_n, gru_n, elementwise_add_n});
+      GraphSafeRemoveNodes(graph, marked_nodes);
+    } else {
+      gru_creater(gru, x, w, Weight, Bias, Hidden, -1);
+      // Remove unneeded nodes.
+      std::unordered_set<const Node*> marked_nodes({mul_n, gru_n});
+      GraphSafeRemoveNodes(graph, marked_nodes);
+    }
+#undef GET_NODE
+
+    ++fusion_count;
+  };
+
+  gpd(graph, handler);
+
+  return fusion_count;
+}
+
+std::unique_ptr<ir::Graph> MulGRUFusePass::ApplyImpl(
+    std::unique_ptr<ir::Graph> 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<ir::Graph> FCGRUFusePass::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(mul_lstm_fuse_pass, paddle::framework::ir::MulGRUFusePass);
+REGISTER_PASS(fc_lstm_fuse_pass, paddle::framework::ir::FCGRUFusePass);

paddle/fluid/framework/ir/fc_gru_fuse_pass.h

+// 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.
+
+#pragma once
+
+#include <string>
+#include "paddle/fluid/framework/ir/fuse_pass_base.h"
+#include "paddle/fluid/framework/ir/graph.h"
+#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+// The MulGRUFusePass and MulGRUFusePass will fuse to the same FusionGRU op.
+
+class FCGRUFusePass : public FusePassBase {
+ public:
+  virtual ~FCGRUFusePass() {}
+
+ protected:
+  std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
+
+  const std::string name_scope_{"fc_gru_fuse"};
+};
+
+// Just FC without bias
+class MulGRUFusePass : public FusePassBase {
+ public:
+  virtual ~MulGRUFusePass() {}
+
+ protected:
+  std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
+  const std::string name_scope_{"fc_nobias_gru_fuse"};
+};
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/ir/graph_pattern_detector.cc

@@ -565,6 +565,7 @@ PDNode* patterns::FC(PDPattern* pattern, const std::string& name_scope,
 
   return fc_out;
 }
+
 PDNode* patterns::LSTM(PDPattern* pattern, const std::string& name_scope,
                        PDNode* x) {
   x->assert_is_op_input("lstm", "Input");
@@ -589,6 +590,32 @@ PDNode* patterns::LSTM(PDPattern* pattern, const std::string& name_scope,
   lstm_op->LinksTo({Hidden, Cell, BatchGate, BatchCellPreAct});
   return Hidden;
 }
+
+PDNode* patterns::GRU(PDPattern* pattern, const std::string& name_scope,
+                      PDNode* x) {
+  x->assert_is_op_input("gru", "Input");
+  auto* gru_op = pattern->NewNode(name_scope, "gru")->assert_is_op("gru");
+#define NEW_NODE(arg__, io__)                        \
+  auto* arg__ = pattern->NewNode(name_scope, #arg__) \
+                    ->assert_is_op_##io__("gru", #arg__);
+
+  NEW_NODE(Weight, input);
+  // TODO(Superjomn): upgrade the fuse framework to support optional.
+  // H0 and bias are optional
+  NEW_NODE(Bias, input);  // also optional
+  // NEW_NODE(H0, input);
+
+  NEW_NODE(Hidden, output);
+  // below are intermediate
+  NEW_NODE(BatchGate, output);
+  NEW_NODE(BatchResetHiddenPrev, output);
+  NEW_NODE(BatchHidden, output);
+
+  gru_op->LinksFrom({x, Weight, Bias});
+  gru_op->LinksTo({Hidden, BatchGate, BatchResetHiddenPrev, BatchHidden});
+  return Hidden;
+}
+
 }  // namespace ir
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/ir/graph_pattern_detector.h

@@ -298,6 +298,8 @@ PDNode* FC(PDPattern* pattern, const std::string& name_scope, PDNode* x,
 
 PDNode* LSTM(PDPattern* pattern, const std::string& name_scope, PDNode* x);
 
+PDNode* GRU(PDPattern* pattern, const std::string& name_scope, PDNode* x);
+
 }  // namespace patterns
 
 #define IR_NODE_LINK_TO(a, b) \