cherry-pick 22551. test=develop test=release/1.7 (#22609)

[cherry-pick] #22551

当一个模型中有多个fc_lstm子图的时候，且其中fc共用了同一个persistable的bias，此时不应该将bias节点删除，只将非persistable的节点去除即可。

paddle/fluid/framework/ir/CMakeLists.txt

@@ -116,6 +116,8 @@ cc_test(graph_helper_test SRCS graph_helper_test.cc DEPS graph graph_helper op_r
 cc_test(graph_to_program_pass_test SRCS graph_to_program_pass_test.cc DEPS graph_to_program_pass)
 cc_test(test_graph_pattern_detector SRCS graph_pattern_detector_tester.cc DEPS graph_pattern_detector)
 cc_test(test_fc_fuse_pass SRCS fc_fuse_pass_tester.cc DEPS fc_fuse_pass framework_proto)
+cc_test(test_fc_lstm_fuse_pass SRCS fc_lstm_fuse_pass_tester.cc DEPS fc_lstm_fuse_pass framework_proto)
+cc_test(test_fc_gru_fuse_pass SRCS fc_gru_fuse_pass_tester.cc DEPS fc_gru_fuse_pass framework_proto)
 cc_test(test_seqpool_concat_fuse_pass SRCS seqpool_concat_fuse_pass_tester.cc DEPS seqpool_concat_fuse_pass framework_proto)
 cc_test(test_seqpool_cvm_concat_fuse_pass SRCS seqpool_cvm_concat_fuse_pass_tester.cc DEPS seqpool_cvm_concat_fuse_pass framework_proto)
 cc_test(test_repeated_fc_relu_fuse_pass SRCS repeated_fc_relu_fuse_pass_tester.cc DEPS repeated_fc_relu_fuse_pass framework_proto)

paddle/fluid/framework/ir/fc_gru_fuse_pass.cc

@@ -127,8 +127,9 @@ static int BuildFusion(Graph* graph, const std::string& name_scope,
     GET_IR_NODE_FROM_SUBGRAPH(Hidden, Hidden, gru_pattern);
     // nodes need be removed
     GET_IR_NODE_FROM_SUBGRAPH(BatchGate, BatchGate, gru_pattern);
-    GET_IR_NODE_FROM_SUBGRAPH(BatchResetHiddenPrev, BatchGate, gru_pattern);
-    GET_IR_NODE_FROM_SUBGRAPH(BatchHidden, BatchGate, gru_pattern);
+    GET_IR_NODE_FROM_SUBGRAPH(BatchResetHiddenPrev, BatchResetHiddenPrev,
+                              gru_pattern);
+    GET_IR_NODE_FROM_SUBGRAPH(BatchHidden, BatchHidden, gru_pattern);
 
     if (with_fc_bias) {
       GET_IR_NODE_FROM_SUBGRAPH(mul_out, mul_out, fc_pattern);
@@ -138,7 +139,7 @@ static int BuildFusion(Graph* graph, const std::string& name_scope,
       gru_creater(gru, x_n, w, Weight, Bias, Hidden, fc_bias);
       // Remove unneeded nodes.
       std::unordered_set<const Node*> marked_nodes(
-          {mul, gru, elementwise_add, fc_bias, fc_out, mul_out, BatchGate,
+          {mul, gru, elementwise_add, fc_out, mul_out, BatchGate,
            BatchResetHiddenPrev, BatchHidden});
       GraphSafeRemoveNodes(graph, marked_nodes);
     } else {

paddle/fluid/framework/ir/fc_gru_fuse_pass_tester.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 <gtest/gtest.h>
+#include "paddle/fluid/framework/ir/pass_tester_helper.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+void AddVarToScope(Scope* param_scope, const std::string& name,
+                   const DDim& dims) {
+  auto* tensor = param_scope->Var(name)->GetMutable<LoDTensor>();
+  tensor->Resize(dims);
+  tensor->mutable_data<float>(platform::CPUPlace());
+}
+
+Scope* CreateParamScope() {
+  auto param_scope = new Scope();
+  AddVarToScope(param_scope, "gru_fc_w", {});
+  AddVarToScope(param_scope, "gru_fc_b", {});
+  AddVarToScope(param_scope, "gru_w", {});
+  AddVarToScope(param_scope, "gru_b", {});
+  AddVarToScope(param_scope, "gru_batch_gate_0", {});
+  AddVarToScope(param_scope, "gru_batch_reset_hidden_prev_0", {});
+  AddVarToScope(param_scope, "gru_batch_hidden_0", {});
+  AddVarToScope(param_scope, "gru_hidden_0", {});
+  AddVarToScope(param_scope, "gru_batch_gate_1", {});
+  AddVarToScope(param_scope, "gru_batch_reset_hidden_prev_1", {});
+  AddVarToScope(param_scope, "gru_batch_hidden_1", {});
+  AddVarToScope(param_scope, "gru_hidden_1", {});
+  return param_scope;
+}
+
+TEST(FCFusePass, basic) {
+  // inputs                     operator            output
+  // --------------------------------------------------------
+  // (a, gru_fc_w)                mul         ->   fc_0_tmp_0
+  // (fc_0_tmp_0, gru_fc_b)  elementwise_add  ->   fc_0_tmp_1
+  // (fc_0_tmp_1,gru_w,gru_b      gru         ->   gru_out_0
+
+  // (b, gru_fc_w)                mul         ->   fc_1_tmp_0
+  // (fc_1_tmp_0, gru_fc_b)  elementwise_add  ->   fc_1_tmp_1
+  // (fc_1_tmp_1,gru_w,gru_b)     gru         ->   gru_out_1
+  Layers layers;
+  auto* a = layers.data("a");
+  auto* b = layers.data("b");
+  auto* fc_w = layers.data("gru_fc_w", {}, true);
+  auto* fc_b = layers.data("gru_fc_b", {}, true);
+  auto* gru_w = layers.data("gru_w", {}, true);
+  auto* gru_b = layers.data("gru_b", {}, true);
+  auto* fc_0_tmp0 = layers.mul(a, fc_w);
+  auto* fc_0_tmp1 = layers.elementwise_add(fc_0_tmp0, fc_b);
+  auto* gru_batch_gate_0 = layers.data("gru_batch_gate_0", {}, false);
+  auto* gru_batch_reset_hidden_prev_0 =
+      layers.data("gru_batch_reset_hidden_prev_0", {}, false);
+  auto* gru_batch_hidden_0 = layers.data("gru_batch_hidden_0", {}, false);
+  auto* gru_hidden_0 = layers.data("gru_hidden_0", {}, false);
+  layers.gru(fc_0_tmp1, gru_w, gru_b, gru_batch_gate_0,
+             gru_batch_reset_hidden_prev_0, gru_batch_hidden_0, gru_hidden_0);
+
+  auto* fc_1_tmp0 = layers.mul(b, fc_w);
+  auto* fc_1_tmp1 = layers.elementwise_add(fc_1_tmp0, fc_b);
+  auto* gru_batch_gate_1 = layers.data("gru_batch_gate_1", {}, false);
+  auto* gru_batch_reset_hidden_prev_1 =
+      layers.data("gru_batch_reset_hidden_prev_1", {}, false);
+  auto* gru_batch_hidden_1 = layers.data("gru_batch_hidden_1", {}, false);
+  auto* gru_hidden_1 = layers.data("gru_hidden_1", {}, false);
+  layers.gru(fc_1_tmp1, gru_w, gru_b, gru_batch_gate_1,
+             gru_batch_reset_hidden_prev_1, gru_batch_hidden_1, gru_hidden_1);
+
+  std::unique_ptr<ir::Graph> graph(new ir::Graph(layers.main_program()));
+  auto pass = PassRegistry::Instance().Get("fc_gru_fuse_pass");
+  pass->Set("use_gpu", new bool(true));
+  graph->Set("__param_scope__", CreateParamScope());
+  int num_nodes_before = graph->Nodes().size();
+  int num_gru_nodes_before = GetNumOpNodes(graph, "gru");
+  VLOG(3) << DebugString(graph);
+
+  graph.reset(pass->Apply(graph.release()));
+  int num_nodes_after = graph->Nodes().size();
+  int num_fuse_gru_nodes_after = GetNumOpNodes(graph, "fusion_gru");
+  VLOG(3) << DebugString(graph);
+
+  PADDLE_ENFORCE_EQ(num_nodes_before, num_nodes_after + 6,
+                    platform::errors::PreconditionNotMet(
+                        "The number of nodes before and after "
+                        "the fuse does not meet expectations"));
+  PADDLE_ENFORCE_EQ(
+      num_fuse_gru_nodes_after, 2,
+      platform::errors::PreconditionNotMet("The number of gru nodes before the "
+                                           "fuse does not meet expectations"));
+  PADDLE_ENFORCE_EQ(num_gru_nodes_before, num_fuse_gru_nodes_after,
+                    platform::errors::PreconditionNotMet(
+                        "The number of fusion_gru nodes does not meet "
+                        "expectations after fuse"));
+}
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle
+
+USE_PASS(fc_gru_fuse_pass);

paddle/fluid/framework/ir/fc_lstm_fuse_pass.cc

@@ -133,26 +133,30 @@ int BuildFusion(Graph* graph, const std::string& name_scope, Scope* scope,
     GET_IR_NODE_FROM_SUBGRAPH(lstm, lstm, lstm_pattern);
     GET_IR_NODE_FROM_SUBGRAPH(Weight, Weight, lstm_pattern);
     GET_IR_NODE_FROM_SUBGRAPH(Bias, Bias, lstm_pattern);
-    GET_IR_NODE_FROM_SUBGRAPH(Cell, Cell, lstm_pattern);
     GET_IR_NODE_FROM_SUBGRAPH(Hidden, Hidden, lstm_pattern);
+    GET_IR_NODE_FROM_SUBGRAPH(BatchCellPreAct, BatchCellPreAct, lstm_pattern);
+    GET_IR_NODE_FROM_SUBGRAPH(BatchGate, BatchGate, lstm_pattern);
+    GET_IR_NODE_FROM_SUBGRAPH(Cell, Cell, lstm_pattern);
     GET_IR_NODE_FROM_SUBGRAPH(w, w, fc_pattern);
     GET_IR_NODE_FROM_SUBGRAPH(mul, mul, fc_pattern);
     if (with_fc_bias) {
       GET_IR_NODE_FROM_SUBGRAPH(fc_out, elementwise_add_out, fc_pattern);
       GET_IR_NODE_FROM_SUBGRAPH(fc_bias, bias, fc_pattern);
+      GET_IR_NODE_FROM_SUBGRAPH(mul_out, mul_out, fc_pattern);
       GET_IR_NODE_FROM_SUBGRAPH(elementwise_add, elementwise_add, fc_pattern);
       lstm_creator(lstm, subgraph.at(x), w, Weight, Bias, Hidden, Cell, fc_out,
                    fc_bias);
       // Remove unneeded nodes.
       std::unordered_set<const Node*> marked_nodes(
-          {mul, lstm, elementwise_add, fc_bias});
+          {mul, lstm, elementwise_add, mul_out, BatchGate, BatchCellPreAct});
       GraphSafeRemoveNodes(graph, marked_nodes);
     } else {
       GET_IR_NODE_FROM_SUBGRAPH(fc_out, mul_out, fc_pattern);
       lstm_creator(lstm, subgraph.at(x), w, Weight, Bias, Hidden, Cell, fc_out,
                    nullptr);
       // Remove unneeded nodes.
-      std::unordered_set<const Node*> marked_nodes({mul, lstm});
+      std::unordered_set<const Node*> marked_nodes(
+          {mul, lstm, BatchGate, BatchCellPreAct});
       GraphSafeRemoveNodes(graph, marked_nodes);
     }
 

paddle/fluid/framework/ir/fc_lstm_fuse_pass_tester.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_lstm_fuse_pass.h"
+
+#include <gtest/gtest.h>
+#include "paddle/fluid/framework/ir/pass_tester_helper.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+void AddVarToScope(Scope* param_scope, const std::string& name,
+                   const DDim& dims) {
+  auto* tensor = param_scope->Var(name)->GetMutable<LoDTensor>();
+  tensor->Resize(dims);
+  tensor->mutable_data<float>(platform::CPUPlace());
+}
+
+Scope* CreateParamScope() {
+  auto param_scope = new Scope();
+  AddVarToScope(param_scope, "lstm_fc_w", {});
+  AddVarToScope(param_scope, "lstm_fc_b", {});
+  AddVarToScope(param_scope, "lstm_w", {});
+  AddVarToScope(param_scope, "lstm_b", {});
+  AddVarToScope(param_scope, "lstm_cell_0", {});
+  AddVarToScope(param_scope, "lstm_batch_gate_0", {});
+  AddVarToScope(param_scope, "lstm_batch_cell_pre_gate_0", {});
+  AddVarToScope(param_scope, "lstm_hidden_0", {});
+  AddVarToScope(param_scope, "lstm_cell_1", {});
+  AddVarToScope(param_scope, "lstm_batch_gate_1", {});
+  AddVarToScope(param_scope, "lstm_batch_cell_pre_gate_1", {});
+  AddVarToScope(param_scope, "lstm_hidden_1", {});
+  return param_scope;
+}
+
+TEST(FCLSTMFusePass, basic) {
+  // inputs                     operator            output
+  // --------------------------------------------------------
+  // (a, lstm_fc_w)                mul         ->   fc_0_tmp_0
+  // (fc_0_tmp_0, lstm_fc_b)  elementwise_add  ->   fc_0_tmp_1
+  // fc_0_tmp_1,lstm_w,lstm_b     lstm         ->   lstm_out_0
+
+  // (b, lstm_fc_w)                mul         ->   fc_1_tmp_0
+  // (fc_1_tmp_0, lstm_fc_b)  elementwise_add  ->   fc_1_tmp_1
+  // (fc_1_tmp_1,lstm_w,lstm_b)   lstm         ->   lstm_out_1
+  Layers layers;
+  auto* a = layers.data("a");
+  auto* b = layers.data("b");
+  auto* fc_w = layers.data("lstm_fc_w", {}, true);
+  auto* fc_b = layers.data("lstm_fc_b", {}, true);
+  auto* lstm_w = layers.data("lstm_w", {}, true);
+  auto* lstm_b = layers.data("lstm_b", {}, true);
+  auto* fc_0_tmp0 = layers.mul(a, fc_w);
+  auto* fc_0_tmp1 = layers.elementwise_add(fc_0_tmp0, fc_b);
+  auto* lstm_cell_0 = layers.data("lstm_cell_0", {}, false);
+  auto* lstm_batch_gate_0 = layers.data("lstm_batch_gate_0", {}, false);
+  auto* lstm_batch_cell_pre_gate_0 =
+      layers.data("lstm_batch_cell_pre_gate_0", {}, false);
+  auto* lstm_hidden_0 = layers.data("lstm_hidden_0", {}, false);
+  layers.lstm(fc_0_tmp1, lstm_w, lstm_b, lstm_cell_0, lstm_batch_gate_0,
+              lstm_hidden_0, lstm_batch_cell_pre_gate_0);
+
+  auto* fc_1_tmp0 = layers.mul(b, fc_w);
+  auto* fc_1_tmp1 = layers.elementwise_add(fc_1_tmp0, fc_b);
+  auto* lstm_cell_1 = layers.data("lstm_cell_1", {}, false);
+  auto* lstm_batch_gate_1 = layers.data("lstm_batch_gate_1", {}, false);
+  auto* lstm_batch_cell_pre_gate_1 =
+      layers.data("lstm_batch_cell_pre_gate_1", {}, false);
+  auto* lstm_hidden_1 = layers.data("lstm_hidden_1", {}, false);
+  layers.lstm(fc_1_tmp1, lstm_w, lstm_b, lstm_cell_1, lstm_batch_gate_1,
+              lstm_hidden_1, lstm_batch_cell_pre_gate_1);
+
+  std::unique_ptr<ir::Graph> graph(new ir::Graph(layers.main_program()));
+  auto pass = PassRegistry::Instance().Get("fc_lstm_fuse_pass");
+  pass->Set("use_gpu", new bool(false));
+  graph->Set("__param_scope__", CreateParamScope());
+  int num_nodes_before = graph->Nodes().size();
+  int num_lstm_nodes_before = GetNumOpNodes(graph, "lstm");
+  VLOG(3) << DebugString(graph);
+
+  graph.reset(pass->Apply(graph.release()));
+  int num_nodes_after = graph->Nodes().size();
+  int num_fusion_lstm_nodes_after = GetNumOpNodes(graph, "fusion_lstm");
+  VLOG(3) << DebugString(graph);
+
+  PADDLE_ENFORCE_EQ(num_nodes_before, num_nodes_after - 6,
+                    platform::errors::PreconditionNotMet(
+                        "The number of nodes before and after "
+                        "the fuse does not meet expectations"));
+  PADDLE_ENFORCE_EQ(num_fusion_lstm_nodes_after, 2,
+                    platform::errors::PreconditionNotMet(
+                        "The number of lstm nodes before the "
+                        "fuse does not meet expectations"));
+  PADDLE_ENFORCE_EQ(num_lstm_nodes_before, num_fusion_lstm_nodes_after,
+                    platform::errors::PreconditionNotMet(
+                        "The number of fusion_gru nodes does "
+                        "not meet expectations after fuse"));
+}
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle
+
+USE_PASS(fc_lstm_fuse_pass);

paddle/fluid/framework/ir/pass_tester_helper.h

@@ -120,6 +120,62 @@ struct Layers {
     return out;
   }
 
+  void lstm(VarDesc* input, VarDesc* w, VarDesc* bias, VarDesc* cell,
+            VarDesc* batch_gate, VarDesc* hidden, VarDesc* batch_cell_pre_act,
+            VarDesc* h0 = nullptr, VarDesc* c0 = nullptr,
+            bool use_peepholes = true, bool is_reverse = false,
+            std::string gate_activation = "sigmoid",
+            std::string cell_activation = "tanh",
+            std::string candidate_activation = "tanh") {
+    OpDesc* op = program_.MutableBlock(0)->AppendOp();
+    op->SetType("lstm");
+    op->SetInput("Input", {input->Name()});
+    op->SetInput("Weight", {w->Name()});
+    op->SetInput("Bias", {bias->Name()});
+    if (h0) {
+      op->SetInput("H0", {h0->Name()});
+    }
+    if (c0) {
+      op->SetInput("C0", {c0->Name()});
+    }
+    op->SetOutput("Hidden", {hidden->Name()});
+    op->SetOutput("Cell", {cell->Name()});
+    op->SetOutput("BatchGate", {batch_gate->Name()});
+    op->SetOutput("BatchCellPreAct", {batch_cell_pre_act->Name()});
+    op->SetAttr("use_peepholes", use_peepholes);
+    op->SetAttr("is_reverse", is_reverse);
+    op->SetAttr("gate_activation", gate_activation);
+    op->SetAttr("cell_activation", cell_activation);
+    op->SetAttr("candidate_activation", candidate_activation);
+    op->SetAttr(OpProtoAndCheckerMaker::OpRoleAttrName(),
+                static_cast<int>(OpRole::kForward));
+  }
+
+  void gru(VarDesc* input, VarDesc* w, VarDesc* bias, VarDesc* batch_gate,
+           VarDesc* batch_reset_hidden_prev, VarDesc* batch_hidden,
+           VarDesc* hidden, VarDesc* h0 = nullptr, bool origin_mode = false,
+           bool is_reverse = false, std::string activation = "tanh",
+           std::string gate_activation = "sigmoid") {
+    OpDesc* op = program_.MutableBlock(0)->AppendOp();
+    op->SetType("gru");
+    op->SetInput("Input", {input->Name()});
+    op->SetInput("Weight", {w->Name()});
+    op->SetInput("Bias", {bias->Name()});
+    if (h0) {
+      op->SetInput("H0", {h0->Name()});
+    }
+    op->SetOutput("BatchGate", {batch_gate->Name()});
+    op->SetOutput("BatchResetHiddenPrev", {batch_reset_hidden_prev->Name()});
+    op->SetOutput("BatchHidden", {batch_hidden->Name()});
+    op->SetOutput("Hidden", {hidden->Name()});
+    op->SetAttr("origin_mode", origin_mode);
+    op->SetAttr("is_reverse", is_reverse);
+    op->SetAttr("activation", activation);
+    op->SetAttr("gate_activation", gate_activation);
+    op->SetAttr(OpProtoAndCheckerMaker::OpRoleAttrName(),
+                static_cast<int>(OpRole::kForward));
+  }
+
   VarDesc* mul(VarDesc* x, VarDesc* y, VarDesc* out = nullptr,
                int x_num_col_dims = 1) {
     AttributeMap attrs;