Feature/op_fuse_pass (#12440)

* Add Preface

* Add demo code

* Save file

* Refine code

* seems can work

* use elementwise strategy

* Use ElementwiseComputeEx

* Add comments

* extract functions from operator

* Refine code

* Follow comment

* code refine

* add op_fuse  pass

* add backward

* code refine

* use TopologySortOperations

* follow comments

* refine IsFusible

* code enhance

* fix op_fusion_pass

* refine code

* refine fuse_elemwise_act_op

* adjust the input and output

* refine logic

* add intermediate_edge

* disable inplace

* follow comments

* refine logic

* follow comments

* Remove the removable IntermediateOut

* change strategy

* code refine

* enable fuse backward

* code refine

* code refine

* rename unit test

* follow comments

paddle/fluid/framework/CMakeLists.txt

@@ -148,13 +148,13 @@ if(WITH_DISTRIBUTE)
 else()
   cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method graph_to_program_pass)
 endif()
-
+ 
 if (NOT WIN32)
-cc_library(parallel_executor SRCS parallel_executor.cc DEPS
-        threaded_ssa_graph_executor scope_buffered_ssa_graph_executor
-        graph graph_viz_pass multi_devices_graph_pass
-        multi_devices_graph_print_pass multi_devices_graph_check_pass
-        fast_threaded_ssa_graph_executor)
+  cc_library(parallel_executor SRCS parallel_executor.cc DEPS
+          threaded_ssa_graph_executor scope_buffered_ssa_graph_executor
+          graph graph_viz_pass multi_devices_graph_pass
+          multi_devices_graph_print_pass multi_devices_graph_check_pass
+          fast_threaded_ssa_graph_executor fuse_elewise_add_act_pass)
 endif() # NOT WIN32
 
 cc_library(prune SRCS prune.cc DEPS framework_proto)

paddle/fluid/framework/details/build_strategy.h

@@ -54,6 +54,8 @@ struct BuildStrategy {
 
   std::string debug_graphviz_path_{""};
 
+  bool fuse_elewise_add_act_ops_{false};
+
   bool enable_data_balance_{false};
 };
 

paddle/fluid/framework/ir/CMakeLists.txt

@@ -37,6 +37,8 @@ pass_library(fc_lstm_fuse_pass inference)
 pass_library(fc_gru_fuse_pass inference)
 pass_library(seq_concat_fc_fuse_pass inference)
 
+cc_library(fuse_elewise_add_act_pass SRCS fuse_elewise_add_act_pass.cc DEPS pass graph_pattern_detector )
+
 set(GLOB_PASS_LIB ${PASS_LIBRARY} CACHE INTERNAL "Global PASS library")
 
 cc_test(pass_test SRCS pass_test.cc DEPS graph pass graph_helper)

paddle/fluid/framework/ir/fuse_elewise_add_act_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/fuse_elewise_add_act_pass.h"
+#include <algorithm>
+#include <string>
+#include <vector>
+#include "paddle/fluid/framework/operator.h"
+#include "paddle/fluid/platform/enforce.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+std::unique_ptr<ir::Graph> FuseElewiseAddActPass::ApplyImpl(
+    std::unique_ptr<ir::Graph> graph) const {
+  std::unordered_set<std::string> act_types = {"relu", "scale"};
+  graph = FuseActElewiseAdd(std::move(graph), act_types);
+  graph = FuseElewiseAddAct(std::move(graph), act_types);
+  // backward
+  {
+    std::unordered_set<std::string> in_place_act_types = {"relu_grad"};
+    graph = FuseElewiseAddActInplaceGrad(std::move(graph), in_place_act_types);
+  }
+
+  // Remove the removable intermediate_out.
+  RemoveIntermediateOut(graph.get());
+
+  return graph;
+}
+
+// ele_add(x, act(y))
+std::unique_ptr<ir::Graph> FuseElewiseAddActPass::FuseElewiseAddAct(
+    std::unique_ptr<ir::Graph> graph,
+    const std::unordered_set<std::string> &act_types) const {
+  PADDLE_ENFORCE(graph.get());
+  FusePassBase::Init("elewise_add_act", graph.get());
+
+  GraphPatternDetector gpd;
+  auto *x = gpd.mutable_pattern()
+                ->NewNode("elewise_add_act/x")
+                ->AsInput()
+                ->assert_is_op_input("elementwise_add", "X");
+  patterns::ElewiseAddAct elewise_add_act_pattern(gpd.mutable_pattern(),
+                                                  "elementwise_add");
+
+  elewise_add_act_pattern(x, act_types);
+
+  int found_elewise_add_act_count = 0;
+
+  auto handler = [&](const GraphPatternDetector::subgraph_t &subgraph,
+                     Graph *g) {
+    VLOG(4) << "handle FuseElewiseAddAct fuse";
+    GET_IR_NODE_FROM_SUBGRAPH(ele_y, ele_y, elewise_add_act_pattern);
+    GET_IR_NODE_FROM_SUBGRAPH(ele_out, elewise_add_out,
+                              elewise_add_act_pattern);
+    GET_IR_NODE_FROM_SUBGRAPH(act_out, act_out, elewise_add_act_pattern);
+    GET_IR_NODE_FROM_SUBGRAPH(act, act, elewise_add_act_pattern);
+    GET_IR_NODE_FROM_SUBGRAPH(ele_add, ele_add, elewise_add_act_pattern);
+
+    std::string ele_x_n = subgraph.at(x)->Name();
+    std::string ele_y_n = ele_y->Name();
+    std::string ele_out_n = ele_out->Name();
+    std::string act_out_n = act_out->Name();
+
+    Node *elewise_add_act_node = CreateFuseElewiseAddActNode(
+        g, act, ele_add, ele_x_n, ele_y_n, ele_out_n, act_out_n);
+
+    VLOG(4) << "\n\t " << ele_x_n << " and " << ele_y_n << " -> "
+            << ele_add->Name() << " -> " << ele_out_n << "\n"
+            << "\t " << ele_out_n << " -> " << act->Name() << " -> "
+            << act_out_n;
+
+    ReLinkNodes(g, ele_out, ele_add, act, elewise_add_act_node);
+    found_elewise_add_act_count++;
+  };
+
+  gpd(graph.get(), handler);
+
+  AddStatis(found_elewise_add_act_count);
+  return graph;
+}
+
+// act(ele_add(x,y))
+std::unique_ptr<ir::Graph> FuseElewiseAddActPass::FuseActElewiseAdd(
+    std::unique_ptr<ir::Graph> graph,
+    const std::unordered_set<std::string> &act_types) const {
+  PADDLE_ENFORCE(graph.get());
+  FusePassBase::Init("act_elewise_add", graph.get());
+
+  GraphPatternDetector gpd;
+  auto *x = gpd.mutable_pattern()
+                ->NewNode("act_elewise_add/x")
+                ->AsInput()
+                ->assert_is_ops_input(act_types, "X");
+  patterns::ActElewiseAdd act_elewise_add_pattern(gpd.mutable_pattern(),
+                                                  "act_elewise_add");
+
+  act_elewise_add_pattern(x, act_types);
+
+  int found_elewise_add_act_count = 0;
+
+  auto handler = [&](const GraphPatternDetector::subgraph_t &subgraph,
+                     Graph *g) {
+    VLOG(4) << "handle FuseElewiseAddAct fuse";
+    GET_IR_NODE_FROM_SUBGRAPH(act_out, act_out, act_elewise_add_pattern);
+    GET_IR_NODE_FROM_SUBGRAPH(ele_x, ele_x, act_elewise_add_pattern);
+    GET_IR_NODE_FROM_SUBGRAPH(ele_out, elewise_add_out,
+                              act_elewise_add_pattern);
+    GET_IR_NODE_FROM_SUBGRAPH(act, act, act_elewise_add_pattern);
+    GET_IR_NODE_FROM_SUBGRAPH(ele_add, ele_add, act_elewise_add_pattern);
+
+    std::string act_i_n = subgraph.at(x)->Name();
+    std::string act_o_n = act_out->Name();
+    std::string elewise_add_x_n = ele_x->Name();
+    std::string elewise_add_out_n = ele_out->Name();
+
+    Node *elewise_add_act_node = CreateFuseElewiseAddActNode(
+        g, ele_add, act, elewise_add_x_n, act_i_n, act_o_n, elewise_add_out_n);
+
+    VLOG(4) << "\n\t " << act_i_n << " -> " << act->Name() << " -> " << act_o_n
+            << "\n\t " << act_o_n << " and " << elewise_add_x_n << " -> "
+            << ele_add->Name() << " -> " << elewise_add_out_n;
+
+    ReLinkNodes(g, act_out, act, ele_add, elewise_add_act_node);
+    found_elewise_add_act_count++;
+  };
+
+  gpd(graph.get(), handler);
+
+  AddStatis(found_elewise_add_act_count);
+  return graph;
+}
+
+// the backward of act(ele_add(x,y))
+// act_grad: in["Out", "Out@GRAD"], out["X@GRAD"]
+// ele_add_grad: in["Y", "Out@GRAD"], out["X@GRAD", "Y@GRAD"]
+std::unique_ptr<ir::Graph> FuseElewiseAddActPass::FuseElewiseAddActInplaceGrad(
+    std::unique_ptr<ir::Graph> graph,
+    const std::unordered_set<std::string> &act_types) const {
+  PADDLE_ENFORCE(graph.get());
+  FusePassBase::Init("elewise_add_act_grad", graph.get());
+
+  GraphPatternDetector gpd;
+  auto *d_act_out = gpd.mutable_pattern()
+                        ->NewNode("elewise_add_act_grad_inplace/x")
+                        ->AsInput()
+                        ->assert_is_ops_input(act_types, GradVarName("Out"));
+  patterns::ElewiseAddActInplaceGrad elewise_add_act_grad_pattern(
+      gpd.mutable_pattern(), "elewise_add_act_grad_inplace");
+  elewise_add_act_grad_pattern(d_act_out, act_types);
+
+  int found_elewise_add_act_count = 0;
+
+  auto handler = [&](const GraphPatternDetector::subgraph_t &subgraph,
+                     Graph *g) {
+    VLOG(4) << "handle FuseElewiseAddActGrad1 fuse";
+    GET_IR_NODE_FROM_SUBGRAPH(act_out, act_out, elewise_add_act_grad_pattern);
+    GET_IR_NODE_FROM_SUBGRAPH(act_grad, act_grad, elewise_add_act_grad_pattern);
+    GET_IR_NODE_FROM_SUBGRAPH(d_itermediate_out, d_itermediate_out,
+                              elewise_add_act_grad_pattern);
+    GET_IR_NODE_FROM_SUBGRAPH(ele_y, ele_y, elewise_add_act_grad_pattern);
+    GET_IR_NODE_FROM_SUBGRAPH(ele_add_grad, ele_add_grad,
+                              elewise_add_act_grad_pattern);
+    GET_IR_NODE_FROM_SUBGRAPH(d_ele_x, d_ele_x, elewise_add_act_grad_pattern);
+    GET_IR_NODE_FROM_SUBGRAPH(d_ele_y, d_ele_y, elewise_add_act_grad_pattern);
+
+    std::string d_act_out_n = subgraph.at(d_act_out)->Name();
+    std::string act_out_n = act_out->Name();
+    std::string d_itermediate_out_n = d_itermediate_out->Name();
+    std::string ele_y_n = ele_y->Name();
+    std::string d_ele_x_n = d_ele_x->Name();
+    std::string d_ele_y_n = d_ele_y->Name();
+
+    OpDesc desc;
+    desc.SetType("fused_elemwise_activation_grad");
+    desc.SetInput("IntermediateOut", {});
+    desc.SetInput("X", {});
+    desc.SetInput("Y", std::vector<std::string>({ele_y_n}));
+    desc.SetInput("Out", std::vector<std::string>({act_out_n}));
+    desc.SetInput(GradVarName("Out"), std::vector<std::string>({d_act_out_n}));
+    desc.SetOutput(GradVarName("X"), std::vector<std::string>({d_ele_x_n}));
+    desc.SetOutput(GradVarName("Y"), std::vector<std::string>({d_ele_y_n}));
+    desc.SetOutput(GradVarName("IntermediateOut"),
+                   std::vector<std::string>({d_itermediate_out_n}));
+
+    desc.SetAttr("save_intermediate_out", false);
+    desc.SetAttr("functor_list",
+                 std::vector<std::string>(
+                     {act_grad->Op()->Type(), ele_add_grad->Op()->Type()}));
+
+    for (auto &n : {act_grad->Op(), ele_add_grad->Op()}) {
+      for (auto &m_ele : n->GetAttrMap()) {
+        desc.SetAttr(m_ele.first, m_ele.second);
+      }
+    }
+
+    auto fused_node = g->CreateOpNode(&desc);
+
+    VLOG(4) << "\n\t " << d_act_out_n << " and " << act_out_n << " -> "
+            << act_grad->Name() << " -> " << d_itermediate_out_n << "\n\t "
+            << d_itermediate_out_n << " and " << act_out_n << " -> "
+            << ele_add_grad->Name() << " -> " << d_itermediate_out_n;
+
+    ReLinkNodes(g, d_itermediate_out, act_grad, ele_add_grad, fused_node);
+    found_elewise_add_act_count++;
+  };
+
+  gpd(graph.get(), handler);
+
+  AddStatis(found_elewise_add_act_count);
+  return graph;
+}
+
+Node *FuseElewiseAddActPass::CreateFuseElewiseAddActNode(
+    Graph *g, const Node *op_1, const Node *op_2, const std::string &ele_x_n,
+    const std::string &ele_y_n, const std::string &ele_out_n,
+    const std::string &act_out_n) const {
+  OpDesc desc;
+  desc.SetInput("X", std::vector<std::string>({ele_x_n}));
+  desc.SetInput("Y", std::vector<std::string>({ele_y_n}));
+  desc.SetOutput("Out", std::vector<std::string>({act_out_n}));
+  desc.SetOutput("IntermediateOut", std::vector<std::string>({ele_out_n}));
+  desc.SetType("fused_elemwise_activation");
+  desc.SetAttr("save_intermediate_out", true);
+  desc.SetAttr("functor_list", std::vector<std::string>(
+                                   {op_1->Op()->Type(), op_2->Op()->Type()}));
+
+  // Set attrs
+  for (auto &n : {op_1->Op(), op_2->Op()}) {
+    for (auto &m_ele : n->GetAttrMap()) {
+      desc.SetAttr(m_ele.first, m_ele.second);
+    }
+  }
+
+  auto elewise_add_act_node = g->CreateOpNode(&desc);
+  return elewise_add_act_node;
+}
+
+void FuseElewiseAddActPass::RemoveIntermediateOut(Graph *graph) const {
+  std::unordered_set<const Node *> need_removed_nodes;
+  for (auto &cur_node : graph->Nodes()) {
+    if (cur_node->IsVar()) continue;
+    if (cur_node->Name() == "fused_elemwise_activation") {
+      bool save_intermediate_out =
+          boost::get<bool>(cur_node->Op()->GetAttr("save_intermediate_out"));
+      auto intermediate_out_args = cur_node->Op()->Output("IntermediateOut");
+      PADDLE_ENFORCE(
+          save_intermediate_out && !intermediate_out_args.empty(),
+          "The %s should save the intermediate_out in the fusing stage.",
+          cur_node->Name());
+
+      // If the intermediate_out's output is empty, it should be removed.
+      auto cur_node_outputs = cur_node->outputs;
+      for (auto &out : cur_node_outputs) {
+        if (out->Name() == intermediate_out_args[0]) {
+          if (out->outputs.size() == 0) {
+            cur_node->outputs = this->RemoveNode(out, cur_node->outputs);
+            need_removed_nodes.insert(std::move(out));
+            cur_node->Op()->SetAttr("save_intermediate_out", false);
+          }
+        }
+      }
+    } else if (cur_node->Name() == "fused_elemwise_activation_grad") {
+      auto intermediate_out_grad_args =
+          cur_node->Op()->Output(GradVarName("IntermediateOut"));
+      PADDLE_ENFORCE(
+          !intermediate_out_grad_args.empty(),
+          "The %s should save the intermediate_out in the fusing stage.",
+          cur_node->Name());
+      auto cur_node_outputs = cur_node->outputs;
+      // If the intermediate_out_g's output is empty, it should be removed.
+      for (auto &out : cur_node_outputs) {
+        if (out->Name() == intermediate_out_grad_args[0] &&
+            out->outputs.empty()) {
+          cur_node->Op()->SetOutput(GradVarName("IntermediateOut"), {});
+          cur_node->outputs = this->RemoveNode(out, cur_node->outputs);
+          need_removed_nodes.insert(std::move(out));
+        }
+      }
+    }
+  }
+  GraphSafeRemoveNodes(graph, need_removed_nodes);
+}
+
+void FuseElewiseAddActPass::ReLinkNodes(Graph *graph,
+                                        const Node *intermediate_out,
+                                        Node *op_1, Node *op_2,
+                                        Node *fused_op) const {  // delete act
+  for (auto &in : op_1->inputs) {
+    fused_op->inputs.emplace_back(in);
+    in->outputs = this->ReplaceNode(op_1, fused_op, in->outputs);
+  }
+
+  std::unordered_set<const Node *> nodes2delete;
+  for (auto &out : op_1->outputs) {
+    if (out->IsCtrlVar()) {
+      auto result_iter = std::find_if(
+          op_2->inputs.begin(), op_2->inputs.end(),
+          [&out](const Node *node) -> bool { return node == out; });
+
+      if (result_iter == op_2->inputs.end()) {
+        IR_OP_VAR_LINK(fused_op, out);
+      } else {
+        nodes2delete.emplace(out);
+      }
+    } else {
+      PADDLE_ENFORCE(out == intermediate_out);
+      IR_OP_VAR_LINK(fused_op, out);
+    }
+  }
+
+  for (auto &in : op_2->inputs) {
+    if (in == intermediate_out || nodes2delete.count(in)) {
+      continue;
+    }
+    fused_op->inputs.emplace_back(in);
+    in->outputs = this->ReplaceNode(op_2, fused_op, in->outputs);
+  }
+
+  for (auto &out : op_2->outputs) {
+    IR_OP_VAR_LINK(fused_op, out);
+  }
+
+  nodes2delete.insert(std::move(op_1));
+  nodes2delete.insert(std::move(op_2));
+
+  GraphSafeRemoveNodes(graph, nodes2delete);
+}
+
+std::vector<Node *> FuseElewiseAddActPass::ReplaceNode(
+    Node *cur_node, Node *new_node, const std::vector<Node *> &nodes) const {
+  std::vector<Node *> new_list(nodes.size());
+  bool has_replaced = false;
+  std::transform(nodes.begin(), nodes.end(), new_list.begin(),
+                 [&](Node *node) -> Node * {
+                   if (node == cur_node) {
+                     has_replaced = true;
+                     return new_node;
+                   }
+                   return node;
+                 });
+  PADDLE_ENFORCE(has_replaced, "Not find %s in the node list.",
+                 cur_node->Name());
+  return new_list;
+}
+
+std::vector<Node *> FuseElewiseAddActPass::RemoveNode(
+    Node *trg_node, const std::vector<Node *> &nodes) const {
+  std::vector<Node *> new_list(nodes.size());
+  auto end_iter =
+      std::copy_if(nodes.begin(), nodes.end(), new_list.begin(),
+                   [&](Node *node) -> bool { return node != trg_node; });
+  new_list.resize(
+      static_cast<uint64_t>(std::distance(new_list.begin(), end_iter)));
+  return new_list;
+}
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle
+
+REGISTER_PASS(fuse_elewise_add_act_pass,
+              paddle::framework::ir::FuseElewiseAddActPass);

paddle/fluid/framework/ir/fuse_elewise_add_act_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 <vector>
+#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"
+#include "paddle/fluid/framework/ir/pass.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+/*
+ * Fuse the ElewiseAdd and activation
+ */
+class FuseElewiseAddActPass : public FusePassBase {
+ public:
+  virtual ~FuseElewiseAddActPass() {}
+
+ protected:
+  std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
+
+  std::unique_ptr<ir::Graph> FuseElewiseAddAct(
+      std::unique_ptr<ir::Graph> graph,
+      const std::unordered_set<std::string> &act_types) const;
+
+  std::unique_ptr<ir::Graph> FuseActElewiseAdd(
+      std::unique_ptr<ir::Graph> graph,
+      const std::unordered_set<std::string> &act_types) const;
+
+  std::unique_ptr<ir::Graph> FuseElewiseAddActInplaceGrad(
+      std::unique_ptr<ir::Graph> graph,
+      const std::unordered_set<std::string> &act_types) const;
+
+  /**
+   * Remove the removable intermediate_out.
+   *   - If the intermediate_out is only used by the backward op, but the
+   *     backward op doesn't use intermediate_out.
+   *   - If the intermediate_out_grad is not used by any op.
+   */
+  void RemoveIntermediateOut(Graph *graph) const;
+
+  std::vector<Node *> ReplaceNode(Node *cur_node, Node *new_node,
+                                  const std::vector<Node *> &nodes) const;
+
+  std::vector<Node *> RemoveNode(Node *trg_node,
+                                 const std::vector<Node *> &nodes) const;
+
+  void ReLinkNodes(Graph *graph, const Node *intermediate_out, Node *op_1,
+                   Node *op_2, Node *fused_op) const;
+  Node *CreateFuseElewiseAddActNode(Graph *g, const Node *op_1,
+                                    const Node *op_2,
+                                    const std::string &ele_x_n,
+                                    const std::string &ele_y_n,
+                                    const std::string &ele_out_n,
+                                    const std::string &act_out_n) const;
+};
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/ir/graph_pattern_detector.h

@@ -95,6 +95,7 @@ struct PDNode {
   PDNode* assert_is_op();
   PDNode* assert_is_op(const std::string& op_type);
   PDNode* assert_is_var();
+  PDNode* assert_is_not_ctrl_var();
   PDNode* assert_var_not_persistable();
   PDNode* assert_is_persistable_var();
   PDNode* assert_is_op_output(const std::string& op_type);
@@ -113,6 +114,20 @@ struct PDNode {
   PDNode* assert_op_has_n_outputs(const std::string& op_type, size_t n);
   PDNode* assert_more(teller_t&& teller);
 
+  PDNode* assert_is_ops_output(const std::unordered_set<std::string>& op_types);
+  PDNode* assert_is_ops(const std::unordered_set<std::string>& op_types);
+  PDNode* assert_is_ops_output(const std::unordered_set<std::string>& op_types,
+                               const std::string& argument);
+  PDNode* assert_is_ops_nth_input(
+      const std::unordered_set<std::string>& op_types,
+      const std::string& argument, int nth);
+  PDNode* assert_is_ops_input(const std::unordered_set<std::string>& op_types);
+  PDNode* assert_is_ops_input(const std::unordered_set<std::string>& op_types,
+                              const std::string& argument);
+  PDNode* assert_is_ops_nth_output(
+      const std::unordered_set<std::string>& op_types,
+      const std::string& argument, int nth);
+
  private:
   PDNode(PDPattern* pattern, const std::string& name = "",
          Type type = Type::kVar)
@@ -447,6 +462,68 @@ struct GRU : public PatternBase {
   PATTERN_DECL_NODE(Hidden);
 };
 
+// The following patterns are used to fuse elewise_add and act
+// formula: act(ele_add(x, y))
+// op: elementwise_add + act
+// named nodes: elementwise_add, act
+//              ele_x, ele_y, elewise_add_out, act_out
+struct ElewiseAddAct : public PatternBase {
+  ElewiseAddAct(PDPattern* pattern, const std::string& name_scope)
+      : PatternBase(pattern, name_scope, "elewise_add_act") {}
+
+  PDNode* operator()(PDNode* x, std::unordered_set<std::string> acts);
+
+  // declare operator node's name
+  PATTERN_DECL_NODE(ele_add);
+  PATTERN_DECL_NODE(act);
+  // declare variable node's name
+  PATTERN_DECL_NODE(elewise_add_out);
+  PATTERN_DECL_NODE(ele_y);
+  PATTERN_DECL_NODE(act_out);
+};
+
+// formula: ele_add(x, act(y))
+// op: elementwise_add + act
+// named nodes: elementwise_add, act
+//              act_in, act_out, ele_x, elewise_add_out
+struct ActElewiseAdd : public PatternBase {
+  ActElewiseAdd(PDPattern* pattern, const std::string& name_scope)
+      : PatternBase(pattern, name_scope, "act_elewise_add") {}
+
+  PDNode* operator()(PDNode* x, std::unordered_set<std::string> acts);
+
+  // declare operator node's name
+  PATTERN_DECL_NODE(act);
+  PATTERN_DECL_NODE(ele_add);
+  // declare variable node's name
+  PATTERN_DECL_NODE(act_out);
+  PATTERN_DECL_NODE(ele_x);
+  PATTERN_DECL_NODE(elewise_add_out);
+};
+
+// the backward of act(ele_add(x, y))
+// the act is inplace.
+// op: elementwise_add_grad + act_grad
+// named nodes: elementwise_add_grad, act_grad
+//              act_out, act_out_g, ele_y, d_itermediate_out, d_ele_x, d_ele_y
+struct ElewiseAddActInplaceGrad : public PatternBase {
+  ElewiseAddActInplaceGrad(PDPattern* pattern, const std::string& name_scope)
+      : PatternBase(pattern, name_scope, "elewise_add_act_grad1") {}
+
+  // act_grad: in["Out", "Out@GRAD"], out["X@GRAD"]
+  // ele_add_grad: in["Y", "Out@GRAD"], out["X@GRAD", "Y@GRAD"]
+  PDNode* operator()(PDNode* x, std::unordered_set<std::string> acts);
+
+  // declare operator node's name
+  PATTERN_DECL_NODE(act_grad);
+  PATTERN_DECL_NODE(ele_add_grad);
+  // declare variable node's name
+  PATTERN_DECL_NODE(act_out);
+  PATTERN_DECL_NODE(d_itermediate_out);
+  PATTERN_DECL_NODE(d_ele_x);
+  PATTERN_DECL_NODE(d_ele_y);
+  PATTERN_DECL_NODE(ele_y);
+};
 }  // namespace patterns
 
 // Link two ir::Nodes from each other.
@@ -454,6 +531,12 @@ struct GRU : public PatternBase {
   a->outputs.push_back(b);    \
   b->inputs.push_back(a);
 
+// Set the out_var as the output of the op
+#define IR_OP_VAR_LINK(op, out_var) \
+  op->outputs.push_back(out_var);   \
+  out_var->inputs.clear();          \
+  out_var->inputs.push_back(op);
+
 }  // namespace ir
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/ir/node.h

@@ -48,6 +48,10 @@ class Node {
 
   bool IsOp() const { return type_ == Type::kOperation; }
   bool IsVar() const { return type_ == Type::kVariable; }
+  bool IsCtrlVar() const {
+    return type_ == Type::kVariable &&
+           Name().find(ir::Node::kControlDepVarName) != std::string::npos;
+  }
 
   std::vector<Node*> inputs;
   std::vector<Node*> outputs;

paddle/fluid/framework/parallel_executor.cc

@@ -57,6 +57,21 @@ std::unique_ptr<ir::Graph> ApplyParallelExecutorPass(
     graph = viz_pass->Apply(std::move(graph));
   }
 
+  // Apply op fusion.
+  if (strategy.fuse_elewise_add_act_ops_) {
+    auto fuse_elewise_add_act_pass =
+        ir::PassRegistry::Instance().Get("fuse_elewise_add_act_pass");
+    graph = fuse_elewise_add_act_pass->Apply(std::move(graph));
+    // Apply a graph viz pass to record a graph.
+    if (!strategy.debug_graphviz_path_.empty()) {
+      auto viz_pass = ir::PassRegistry::Instance().Get("graph_viz_pass");
+      const std::string graph_path = string::Sprintf(
+          "%s%s", strategy.debug_graphviz_path_.c_str(), "_fused_graph");
+      viz_pass->Set<std::string>("graph_viz_path", new std::string(graph_path));
+      graph = viz_pass->Apply(std::move(graph));
+    }
+  }
+
   // Convert graph to run on multi-devices.
   auto multi_devices_pass =
       ir::PassRegistry::Instance().Get("multi_devices_pass");
@@ -359,6 +374,7 @@ ParallelExecutor::~ParallelExecutor() {
 }  // namespace framework
 }  // namespace paddle
 
+USE_PASS(fuse_elewise_add_act_pass);
 USE_PASS(graph_viz_pass);
 USE_PASS(multi_devices_pass);
 USE_PASS(multi_devices_check_pass);

paddle/fluid/operators/fused_elemwise_activation_op.cc

@@ -13,18 +13,11 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/fused_elemwise_activation_op.h"
-#include <string>
-#include <vector>
 
 namespace paddle {
 namespace operators {
 
-/*
- * Whether the compound function is Unary(Binary(X, Y)).
- * For Unary(Binary(X, Y)), the intermediate_out's shape is the same the final
- * out.
- */
-static bool IsUnaryCompound(const std::vector<std::string> &functor_list) {
+bool IsUnaryCompound(const std::vector<std::string> &functor_list) {
   PADDLE_ENFORCE_EQ(functor_list.size(), 2);
   static std::unordered_set<std::string> binary_fun = {
       "elementwise_add", "elementwise_mul", "elementwise_add_grad",
@@ -32,10 +25,17 @@ static bool IsUnaryCompound(const std::vector<std::string> &functor_list) {
   return binary_fun.count(functor_list[1]) != 0;
 }
 
-/*
- * Whether the Input(X) could be absent.
- */
-static bool InputXCanBeAbsent(const std::vector<std::string> &functor_list) {
+bool HasInPlaceUnary(const std::vector<std::string> &functor_list) {
+  PADDLE_ENFORCE_EQ(functor_list.size(), 2);
+  static std::unordered_set<std::string> InplaceOpSet = {"relu", "relu_grad"};
+  bool is_in_place = false;
+  for (auto &func_name : functor_list) {
+    is_in_place |= (InplaceOpSet.count(func_name) == 1);
+  }
+  return is_in_place;
+}
+
+bool InputXCanBeAbsent(const std::vector<std::string> &functor_list) {
   PADDLE_ENFORCE_EQ(functor_list.size(), 2);
   static std::unordered_set<std::string> binary_fun = {"elementwise_add_grad"};
   return binary_fun.count(functor_list[0]) != 0 ||
@@ -86,20 +86,12 @@ class FusedElemwiseActivationOp : public framework::OperatorWithKernel {
 
     // Whether the shape of Y is a continuous subsequence of X,
     // For more information please refer to the op's introduction.
-    bool bcast_y = x_dim.size() >= y_dim.size();
-    if (x_dim.size() == y_dim.size()) {
-      for (int i = 0; i < x_dim.size(); ++i) {
-        if (x_dim[i] < y_dim[i]) {
-          bcast_y = false;
-          break;
-        }
-      }
-    }
+    bool bcast_y = IsBcastY(x_dim, y_dim);
 
     auto &out_dim = bcast_y ? x_dim : y_dim;
     std::string out_lod = bcast_y ? "X" : "Y";
 
-    if (ctx->Attrs().Get<bool>("keep_intermediate_value")) {
+    if (ctx->Attrs().Get<bool>("save_intermediate_out")) {
       PADDLE_ENFORCE(ctx->HasOutput("IntermediateOut"),
                      "Output(IntermediateOut) of FusedElemwiseActivationOp "
                      "should not be null.");
@@ -123,6 +115,20 @@ class FusedElemwiseActivationOp : public framework::OperatorWithKernel {
     ctx->ShareLoD(out_lod, /*->*/ "Out");
   }
 
+  static bool IsBcastY(const framework::DDim &x_dim,
+                       const framework::DDim &y_dim) {
+    bool bcast_y = x_dim.size() >= y_dim.size();
+    if (x_dim.size() == y_dim.size()) {
+      for (int i = 0; i < x_dim.size(); ++i) {
+        if (x_dim[i] < y_dim[i]) {
+          bcast_y = false;
+          break;
+        }
+      }
+    }
+    return bcast_y;
+  }
+
  protected:
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext &ctx) const override {
@@ -157,17 +163,7 @@ class FusedElemwiseActivationMaker : public framework::OpProtoAndCheckerMaker {
     AddAttr<float>("scale",
                    "scale is used by scale_op, the default value is 0.0.")
         .SetDefault(0.0);
-    AddAttr<bool>(
-        "recomputation",
-        "Whether to recompute the Out."
-        "The computation of fused_elemwise_activation_grad has two methods to "
-        "get the dx and dy, one is to use the 'Out', and the other is not. "
-        "The former method will save the time of recomputing the 'Out', but it "
-        "must occupy the memory to store the 'out'. While, the later method "
-        "can avoid occupying the memory, but it must recompute the 'Out'. "
-        "It is useful for Unary(Binary(X, Y)). The default value is true.")
-        .SetDefault(true);
-    AddAttr<bool>("keep_intermediate_value",
+    AddAttr<bool>("save_intermediate_out",
                   "Whether to save the intermediate_out.")
         .SetDefault(false);
     AddAttr<std::vector<std::string>>("functor_list",
@@ -227,30 +223,38 @@ class FusedElemwiseActivationGradMaker
 
  protected:
   std::unique_ptr<framework::OpDesc> Apply() const override {
-    auto *op_desc_ptr = new framework::OpDesc();
-    op_desc_ptr->SetType(this->ForwardOpType() + "_grad");
+    auto *grad_op = new framework::OpDesc();
+    grad_op->SetType(this->ForwardOpType() + "_grad");
 
     for (auto &input_param : this->InputNames()) {
-      op_desc_ptr->SetInput(input_param, this->Input(input_param));
-      op_desc_ptr->SetOutput(framework::GradVarName(input_param),
-                             this->InputGrad(input_param, true));
+      grad_op->SetInput(input_param, this->Input(input_param));
+      grad_op->SetOutput(framework::GradVarName(input_param),
+                         this->InputGrad(input_param, true));
     }
 
-    for (auto &output_param : this->OutputNames()) {
-      op_desc_ptr->SetInput(output_param, this->Output(output_param));
-      op_desc_ptr->SetInput(framework::GradVarName(output_param),
-                            this->OutputGrad(output_param));
-    }
+    grad_op->SetInput("Out", this->Output("Out"));
+    grad_op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
 
-    op_desc_ptr->SetAttrMap(this->Attrs());
+    grad_op->SetAttrMap(this->Attrs());
 
     std::vector<std::string> functor_names =
-        boost::get<std::vector<std::string>>(
-            op_desc_ptr->GetAttr("functor_list"));
+        boost::get<std::vector<std::string>>(grad_op->GetAttr("functor_list"));
+
     functor_names[0] += "_grad";
     functor_names[1] += "_grad";
-    op_desc_ptr->SetAttr("functor_list", functor_names);
-    return std::unique_ptr<framework::OpDesc>(op_desc_ptr);
+    grad_op->SetAttr("functor_list", functor_names);
+
+    if (boost::get<bool>(grad_op->GetAttr("save_intermediate_out"))) {
+      PADDLE_ENFORCE_NE(Output("IntermediateOut").size(), 0);
+      grad_op->SetInput("IntermediateOut", this->Output("IntermediateOut"));
+      grad_op->SetOutput(framework::GradVarName("IntermediateOut"),
+                         this->OutputGrad("IntermediateOut"));
+    } else {
+      grad_op->SetInput("IntermediateOut", {});
+      grad_op->SetOutput(framework::GradVarName("IntermediateOut"), {});
+    }
+
+    return std::unique_ptr<framework::OpDesc>(grad_op);
   }
 };
 
@@ -261,56 +265,65 @@ class FusedElemwiseActivationOpGrad : public framework::OperatorWithKernel {
   void InferShape(framework::InferShapeContext *ctx) const override {
     PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
                    "Input(Out@Grad) should not be null");
-    if (ctx->Attrs().Get<bool>("keep_intermediate_value")) {
+
+    auto functor_list =
+        ctx->Attrs().Get<std::vector<std::string>>("functor_list");
+
+    if (ctx->Attrs().Get<bool>("save_intermediate_out")) {
       PADDLE_ENFORCE(ctx->HasInput("IntermediateOut"),
                      "Input(IntermediateOut) should not be null");
     } else {
-      PADDLE_ENFORCE_EQ(ctx->Inputs(framework::GradVarName("Out")).size(), 1);
+      if (!InputXCanBeAbsent(functor_list)) {
+        PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
+      }
     }
 
-    auto funtor_list =
-        ctx->Attrs().Get<std::vector<std::string>>("functor_list");
     auto x_grad_name = framework::GradVarName("X");
     auto y_grad_name = framework::GradVarName("Y");
+    auto inter_grad_name = framework::GradVarName("IntermediateOut");
 
     if (ctx->HasOutput(x_grad_name)) {
       if (ctx->HasInputs("X")) {
         ctx->SetOutputDim(x_grad_name, ctx->GetInputDim("X"));
         ctx->ShareLoD("X", x_grad_name);
       } else {
-        // Node: If "X" is absence, the shape of Y should be a continuous
-        // subsequence of X, if not, we could not infer the shape of dx.
-
         // Currently, only when Binary is elementwise_add or elementwise_sub,
         // the "X" could be absent.
-        PADDLE_ENFORCE(InputXCanBeAbsent(funtor_list),
+        PADDLE_ENFORCE(InputXCanBeAbsent(functor_list),
                        "Only when BinaryFunctor is elementwise_add, the 'X' "
                        "could be absent.");
 
-        // For Unary(Binary(X, Y)), IntermediateOut should not be empty.
-        if (IsUnaryCompound(funtor_list)) {
-          PADDLE_ENFORCE(
-              ctx->HasInputs("IntermediateOut"),
-              "If the compound_functor is Unary(Binary(X, Y)) and Binary "
-              "is elementwise_add, the intermediate_out must be not absent.");
-        }
+        // Node: If "X" is absence, the shape of Y should be a continuous
+        // subsequence of X, otherwise, we could not infer the shape of dx.
 
         ctx->SetOutputDim(x_grad_name,
                           ctx->GetInputDim(framework::GradVarName("Out")));
         ctx->ShareLoD(framework::GradVarName("Out"), x_grad_name);
       }
     }
+
     if (ctx->HasOutput(y_grad_name)) {
       PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) should not be null");
       ctx->SetOutputDim(y_grad_name, ctx->GetInputDim("Y"));
       ctx->ShareLoD("Y", y_grad_name);
     }
+
+    if (ctx->HasOutput(inter_grad_name)) {
+      // For Unary(Binary(X, Y)), IntermediateOut should not be empty.
+      if (IsUnaryCompound(functor_list)) {
+        ctx->SetOutputDim(inter_grad_name,
+                          ctx->GetInputDim(framework::GradVarName("Out")));
+        ctx->ShareLoD(framework::GradVarName("Out"), inter_grad_name);
+      } else {
+        ctx->SetOutputDim(inter_grad_name, ctx->GetInputDim("Y"));
+        ctx->ShareLoD("Y", inter_grad_name);
+      }
+    }
   }
 
  protected:
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext &ctx) const override {
-    //    PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) should not be null");
     auto input_data_type_index = ctx.Input<framework::Tensor>("Y")->type();
     auto input_data_type = framework::ToDataType(input_data_type_index);
     return framework::OpKernelType(input_data_type, ctx.GetPlace());

paddle/fluid/operators/fused_elemwise_activation_op.h

@@ -26,6 +26,24 @@ limitations under the License. */
 namespace paddle {
 namespace operators {
 
+/**
+ * Whether the compound function is Unary(Binary(X, Y)).
+ * For Unary(Binary(X, Y)), the intermediate_out's shape is the same the final
+ * out.
+ */
+bool IsUnaryCompound(const std::vector<std::string> &functor_list);
+
+/**
+ *  For the in-place unary functor, the inputs of op_desc only have Out and
+ *  Out@Grad.
+ */
+bool HasInPlaceUnary(const std::vector<std::string> &functor_list);
+
+/**
+ * Whether the Input(X) could be absent.
+ */
+bool InputXCanBeAbsent(const std::vector<std::string> &functor_list);
+
 template <typename DeviceContext, typename T, typename BinaryFunctor,
           typename UnaryFunctor>
 static void RunBinaryCompoundFunctor(
@@ -39,7 +57,7 @@ static void RunBinaryCompoundFunctor(
   paddle::operators::math::BinaryCompoundFunctor<T, BinaryFunctor, UnaryFunctor>
       compound_func(binary_functor, unary_functor);
   int axis = ctx.Attr<int>("axis");
-  if (ctx.Attr<bool>("keep_intermediate_value")) {
+  if (ctx.Attr<bool>("save_intermediate_out")) {
     FusedElemwiseAndActComputeEx<DeviceContext, T,
                                  paddle::operators::math::BinaryCompoundFunctor<
                                      T, BinaryFunctor, UnaryFunctor>,
@@ -71,7 +89,7 @@ static void RunUnaryCompoundFunctors(
   paddle::operators::math::UnaryCompoundFunctor<T, UnaryFunctor, BinaryFunctor>
       compound_func(unary_functor, binary_functor);
 
-  if (ctx.Attr<bool>("keep_intermediate_value")) {
+  if (ctx.Attr<bool>("save_intermediate_out")) {
     FusedElemwiseAndActComputeEx<DeviceContext, T,
                                  paddle::operators::math::UnaryCompoundFunctor<
                                      T, UnaryFunctor, BinaryFunctor>,
@@ -89,7 +107,7 @@ static void RunUnaryCompoundFunctors(
 }
 
 template <typename DeviceContext, typename T, typename BinaryGradFunctor,
-          typename UnaryFunctor, typename UnaryGradFunctor>
+          typename UnaryFunctor, typename UnaryGradFunctor, bool InPlace>
 static void RunBinaryCompoundGradFunctors(
     const framework::ExecutionContext &ctx,
     const BinaryGradFunctor &binary_grad_functor,
@@ -98,7 +116,7 @@ static void RunBinaryCompoundGradFunctors(
     const framework::Tensor *in_y, const framework::Tensor *in_out,
     const framework::Tensor *in_intermediate_out,
     const framework::Tensor *in_out_grad, framework::Tensor *x_grad,
-    framework::Tensor *y_grad) {
+    framework::Tensor *y_grad, framework::Tensor *d_intermediate_out) {
   // Z = Binary(X, Unary(Y))
   int axis = ctx.Attr<int>("axis");
 
@@ -107,32 +125,40 @@ static void RunBinaryCompoundGradFunctors(
                                                            UnaryFunctor>;
   using BinaryCompoundDyFunctor =
       paddle::operators::math::BinaryCompoundGradDyFunctor<
-          T, BinaryGradFunctor, UnaryFunctor, UnaryGradFunctor>;
+          T, BinaryGradFunctor, UnaryFunctor, UnaryGradFunctor, InPlace>;
+  using BinaryCompoundDIntermedaiteOutFunctor =
+      paddle::operators::math::BinaryCompoundGradDIntermedaiteOutFunctor<
+          T, BinaryGradFunctor, UnaryFunctor>;
 
   if (in_intermediate_out) {
     FusedElemwiseAndActGradComputeEx<
         DeviceContext, T, BinaryCompoundDxFunctor, BinaryCompoundDyFunctor,
-        true /*UseIntermediateOut*/,
+        BinaryCompoundDIntermedaiteOutFunctor, true /*UseIntermediateOut*/,
         false /*SameShapeOfIntermediateOutAndOut*/>(
         ctx, in_x, in_y, in_out, in_intermediate_out, in_out_grad, axis, x_grad,
-        y_grad, BinaryCompoundDxFunctor(binary_grad_functor, unary_functor),
+        y_grad, d_intermediate_out,
+        BinaryCompoundDxFunctor(binary_grad_functor, unary_functor),
         BinaryCompoundDyFunctor(binary_grad_functor, unary_functor,
-                                unary_grad_functor));
+                                unary_grad_functor),
+        BinaryCompoundDIntermedaiteOutFunctor(binary_grad_functor,
+                                              unary_functor));
   } else {
     FusedElemwiseAndActGradComputeEx<
         DeviceContext, T, BinaryCompoundDxFunctor, BinaryCompoundDyFunctor,
-        false /*UseIntermediateOut*/,
+        BinaryCompoundDIntermedaiteOutFunctor, false /*UseIntermediateOut*/,
         false /*SameShapeOfIntermediateOutAndOut*/>(
         ctx, in_x, in_y, in_out, in_intermediate_out, in_out_grad, axis, x_grad,
-        y_grad, BinaryCompoundDxFunctor(binary_grad_functor, unary_functor),
+        y_grad, d_intermediate_out,
+        BinaryCompoundDxFunctor(binary_grad_functor, unary_functor),
         BinaryCompoundDyFunctor(binary_grad_functor, unary_functor,
-                                unary_grad_functor));
+                                unary_grad_functor),
+        BinaryCompoundDIntermedaiteOutFunctor(binary_grad_functor,
+                                              unary_functor));
   }
 }
 
 template <typename DeviceContext, typename T, typename UnaryGradFunctor,
-          typename BinaryFunctor, typename BinaryGradFunctor,
-          bool Recomputation = true>
+          typename BinaryFunctor, typename BinaryGradFunctor, bool InPlace>
 static void RunUnaryCompoundGradFunctors(
     const framework::ExecutionContext &ctx,
     const UnaryGradFunctor &unary_grad_functor,
@@ -141,36 +167,44 @@ static void RunUnaryCompoundGradFunctors(
     const framework::Tensor *in_y, const framework::Tensor *in_out,
     const framework::Tensor *in_intermediate_out,
     const framework::Tensor *in_out_grad, framework::Tensor *x_grad,
-    framework::Tensor *y_grad) {
+    framework::Tensor *y_grad, framework::Tensor *d_intermediate_out) {
   // Z = Unary(Binary(X, Y))
   int axis = ctx.Attr<int>("axis");
 
   using UnaryCompoundDxFunctor =
       paddle::operators::math::UnaryCompoundGradDxFunctor<
-          T, UnaryGradFunctor, BinaryFunctor, BinaryGradFunctor, Recomputation>;
+          T, UnaryGradFunctor, BinaryFunctor, BinaryGradFunctor, InPlace>;
   using UnaryCompoundDyFunctor =
       paddle::operators::math::UnaryCompoundGradDyFunctor<
-          T, UnaryGradFunctor, BinaryFunctor, BinaryGradFunctor, Recomputation>;
+          T, UnaryGradFunctor, BinaryFunctor, BinaryGradFunctor, InPlace>;
+  using UnaryCompoundDIntermediateFunctor =
+      paddle::operators::math::UnaryCompoundGradDIntermediateFunctor<
+          T, UnaryGradFunctor, BinaryFunctor, InPlace>;
 
   if (in_intermediate_out) {
     FusedElemwiseAndActGradComputeEx<
         DeviceContext, T, UnaryCompoundDxFunctor, UnaryCompoundDyFunctor,
-        true /*UseIntermediateOut*/, true /*SameShapeOfIntermediateOutAndOut*/>(
+        UnaryCompoundDIntermediateFunctor, true /*UseIntermediateOut*/,
+        true /*SameShapeOfIntermediateOutAndOut*/>(
         ctx, in_x, in_y, in_out, in_intermediate_out, in_out_grad, axis, x_grad,
-        y_grad, UnaryCompoundDxFunctor(unary_grad_functor, binary_functor,
-                                       binary_grad_functor),
+        y_grad, d_intermediate_out,
+        UnaryCompoundDxFunctor(unary_grad_functor, binary_functor,
+                               binary_grad_functor),
         UnaryCompoundDyFunctor(unary_grad_functor, binary_functor,
-                               binary_grad_functor));
+                               binary_grad_functor),
+        UnaryCompoundDIntermediateFunctor(unary_grad_functor, binary_functor));
   } else {
-    FusedElemwiseAndActGradComputeEx<DeviceContext, T, UnaryCompoundDxFunctor,
-                                     UnaryCompoundDyFunctor,
-                                     false /*UseIntermediateOut*/,
-                                     true /*SameShapeOfIntermediateOutAndOut*/>(
+    FusedElemwiseAndActGradComputeEx<
+        DeviceContext, T, UnaryCompoundDxFunctor, UnaryCompoundDyFunctor,
+        UnaryCompoundDIntermediateFunctor, false /*UseIntermediateOut*/,
+        true /*SameShapeOfIntermediateOutAndOut*/>(
         ctx, in_x, in_y, in_out, in_intermediate_out, in_out_grad, axis, x_grad,
-        y_grad, UnaryCompoundDxFunctor(unary_grad_functor, binary_functor,
-                                       binary_grad_functor),
+        y_grad, d_intermediate_out,
+        UnaryCompoundDxFunctor(unary_grad_functor, binary_functor,
+                               binary_grad_functor),
         UnaryCompoundDyFunctor(unary_grad_functor, binary_functor,
-                               binary_grad_functor));
+                               binary_grad_functor),
+        UnaryCompoundDIntermediateFunctor(unary_grad_functor, binary_functor));
   }
 }
 
@@ -226,72 +260,67 @@ static void RunFunctors(const framework::ExecutionContext &ctx,
   }
 }
 
-template <typename DeviceContext, typename T, bool ReComputation>
-static void RunGradFunctors(const framework::ExecutionContext &ctx,
-                            const framework::Tensor *in_x,
-                            const framework::Tensor *in_y,
-                            const framework::Tensor *in_out,
-                            const framework::Tensor *in_intermediate_out,
-                            const framework::Tensor *in_out_grad,
-                            framework::Tensor *x_grad,
-                            framework::Tensor *y_grad) {
+template <typename DeviceContext, typename T, bool InPlace>
+static void RunGradFunctors(
+    const framework::ExecutionContext &ctx, const framework::Tensor *in_x,
+    const framework::Tensor *in_y, const framework::Tensor *in_out,
+    const framework::Tensor *in_intermediate_out,
+    const framework::Tensor *in_out_grad, framework::Tensor *x_grad,
+    framework::Tensor *y_grad, framework::Tensor *d_intermediate_out) {
   auto &functors = ctx.Attr<std::vector<std::string>>("functor_list");
   auto funcs_str = functors[0] + "," + functors[1];
 
-  // TODO(zcd): The following code can be refined. for example, use registrition
   if (funcs_str == "elementwise_add_grad,scale_grad") {
     // The backward of Z = Binary(X, Unary(Y))
     T scale = static_cast<T>(ctx.Attr<float>("scale"));
-    RunBinaryCompoundGradFunctors<DeviceContext, T,
-                                  paddle::operators::math::AddGradFunctor<T>,
-                                  paddle::operators::math::ScaleFunctor<T>,
-                                  paddle::operators::math::ScaleGradFunctor<T>>(
+    RunBinaryCompoundGradFunctors<
+        DeviceContext, T, paddle::operators::math::AddGradFunctor<T>,
+        paddle::operators::math::ScaleFunctor<T>,
+        paddle::operators::math::ScaleGradFunctor<T>, InPlace>(
         ctx, paddle::operators::math::AddGradFunctor<T>(),
         paddle::operators::math::ScaleFunctor<T>(scale),
         paddle::operators::math::ScaleGradFunctor<T>(scale), in_x, in_y, in_out,
-        in_intermediate_out, in_out_grad, x_grad, y_grad);
+        in_intermediate_out, in_out_grad, x_grad, y_grad, d_intermediate_out);
   } else if (funcs_str == "scale_grad,elementwise_add_grad") {
     // The backward of Z = Unary(Binary(X, Y))
     T scale = static_cast<T>(ctx.Attr<float>("scale"));
-    RunUnaryCompoundGradFunctors<DeviceContext, T,
-                                 paddle::operators::math::ScaleGradFunctor<T>,
-                                 paddle::operators::math::AddFunctor<T>,
-                                 paddle::operators::math::AddGradFunctor<T>,
-                                 ReComputation /*Recomputation*/>(
+    RunUnaryCompoundGradFunctors<
+        DeviceContext, T, paddle::operators::math::ScaleGradFunctor<T>,
+        paddle::operators::math::AddFunctor<T>,
+        paddle::operators::math::AddGradFunctor<T>, InPlace>(
         ctx, paddle::operators::math::ScaleGradFunctor<T>(scale),
         paddle::operators::math::AddFunctor<T>(),
         paddle::operators::math::AddGradFunctor<T>(), in_x, in_y, in_out,
-        in_intermediate_out, in_out_grad, x_grad, y_grad);
+        in_intermediate_out, in_out_grad, x_grad, y_grad, d_intermediate_out);
   } else if (funcs_str == "elementwise_add_grad,relu_grad") {
-    RunBinaryCompoundGradFunctors<DeviceContext, T,
-                                  paddle::operators::math::AddGradFunctor<T>,
-                                  paddle::operators::math::ReluFunctor<T>,
-                                  paddle::operators::math::ReluGradFunctor<T>>(
+    RunBinaryCompoundGradFunctors<
+        DeviceContext, T, paddle::operators::math::AddGradFunctor<T>,
+        paddle::operators::math::ReluFunctor<T>,
+        paddle::operators::math::ReluGradFunctor<T>, InPlace>(
         ctx, paddle::operators::math::AddGradFunctor<T>(),
         paddle::operators::math::ReluFunctor<T>(),
         paddle::operators::math::ReluGradFunctor<T>(), in_x, in_y, in_out,
-        in_intermediate_out, in_out_grad, x_grad, y_grad);
+        in_intermediate_out, in_out_grad, x_grad, y_grad, d_intermediate_out);
   } else if (funcs_str == "relu_grad,elementwise_add_grad") {
-    RunUnaryCompoundGradFunctors<DeviceContext, T,
-                                 paddle::operators::math::ReluGradFunctor<T>,
-                                 paddle::operators::math::AddFunctor<T>,
-                                 paddle::operators::math::AddGradFunctor<T>,
-                                 ReComputation /*Recomputation*/>(
+    RunUnaryCompoundGradFunctors<
+        DeviceContext, T, paddle::operators::math::ReluGradFunctor<T>,
+        paddle::operators::math::AddFunctor<T>,
+        paddle::operators::math::AddGradFunctor<T>, InPlace>(
         ctx, paddle::operators::math::ReluGradFunctor<T>(),
         paddle::operators::math::AddFunctor<T>(),
         paddle::operators::math::AddGradFunctor<T>(), in_x, in_y, in_out,
-        in_intermediate_out, in_out_grad, x_grad, y_grad);
+        in_intermediate_out, in_out_grad, x_grad, y_grad, d_intermediate_out);
   } else if (funcs_str == "elementwise_mul_grad,scale_grad") {
     // The backward of Z = Binary(X, Unary(Y))
     T scale = static_cast<T>(ctx.Attr<float>("scale"));
-    RunBinaryCompoundGradFunctors<DeviceContext, T,
-                                  paddle::operators::math::MulGradFunctor<T>,
-                                  paddle::operators::math::ScaleFunctor<T>,
-                                  paddle::operators::math::ScaleGradFunctor<T>>(
+    RunBinaryCompoundGradFunctors<
+        DeviceContext, T, paddle::operators::math::MulGradFunctor<T>,
+        paddle::operators::math::ScaleFunctor<T>,
+        paddle::operators::math::ScaleGradFunctor<T>, InPlace>(
         ctx, paddle::operators::math::MulGradFunctor<T>(),
         paddle::operators::math::ScaleFunctor<T>(scale),
         paddle::operators::math::ScaleGradFunctor<T>(scale), in_x, in_y, in_out,
-        in_intermediate_out, in_out_grad, x_grad, y_grad);
+        in_intermediate_out, in_out_grad, x_grad, y_grad, d_intermediate_out);
   } else {
     PADDLE_THROW("%s has not been implemented.", funcs_str);
   }
@@ -313,9 +342,9 @@ class FusedElemwiseActivationKernel : public framework::OpKernel<T> {
     std::vector<framework::Tensor *> outputs;
     outputs.emplace_back(output);
 
-    if (ctx.Attr<bool>("keep_intermediate_value")) {
+    if (ctx.Attr<bool>("save_intermediate_out")) {
       PADDLE_ENFORCE(ctx.HasOutput("IntermediateOut"),
-                     "The keep_intermediate_value is enable, so the "
+                     "The save_intermediate_out is enable, so the "
                      "IntermediateOut should not be empty.");
       auto intermediate_out = ctx.Output<framework::Tensor>("IntermediateOut");
       outputs.emplace_back(intermediate_out);
@@ -331,65 +360,63 @@ template <typename DeviceContext, typename T>
 class FusedElemwiseActivationGradKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext &ctx) const override {
-    auto x = ctx.Input<framework::Tensor>("X");
-    auto y = ctx.Input<framework::Tensor>("Y");
-
+    auto in_y = ctx.Input<framework::Tensor>("Y");
+    PADDLE_ENFORCE(in_y != nullptr, "Input(Y) should not be nullptr.");
     auto in_out = ctx.Input<framework::Tensor>("Out");
+    PADDLE_ENFORCE(in_out != nullptr, "Input(Out) should not be nullptr.");
     auto in_out_grad =
         ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
-
+    PADDLE_ENFORCE(in_out_grad != nullptr,
+                   "Input(Out@Grad) should not be nullptr.");
+    framework::Tensor *in_x =
+        const_cast<framework::Tensor *>(ctx.Input<framework::Tensor>("X"));
     framework::Tensor *x_grad =
         ctx.Output<framework::Tensor>(framework::GradVarName("X"));
     framework::Tensor *y_grad =
         ctx.Output<framework::Tensor>(framework::GradVarName("Y"));
+    framework::Tensor *d_intermediate_out = ctx.Output<framework::Tensor>(
+        framework::GradVarName("IntermediateOut"));
 
-    PADDLE_ENFORCE(y != nullptr, "Input(Y) should not be nullptr.");
-
-    if (ctx.Attr<bool>("recomputation")) {
-      PADDLE_ENFORCE(
-          x != nullptr,
-          "The recomputation is opened, so Input(X) should not be absent.");
-    } else {
-      PADDLE_ENFORCE(in_out != nullptr,
-                     "The recomputation is disabled, so the Input('Out') "
-                     "should not be empty.");
-    }
-
-    framework::Tensor *in_x;
     auto functor_list = ctx.Attr<std::vector<std::string>>("functor_list");
 
-    // If functor_list contains elementwise_add, the backward doesn't use
-    // in_x, and in_outs.
-    if (x == nullptr) {
-      PADDLE_ENFORCE(functor_list[0] == "elementwise_add_grad" ||
-                         functor_list[1] == "elementwise_add_grad",
-                     "Only when the compoundfunctor contains "
-                     "elementwise_add_grad, the 'X' could be absent.");
-      in_x = const_cast<framework::Tensor *>(in_out_grad);
-      in_out = const_cast<framework::Tensor *>(in_out_grad);
-    } else {
-      in_x = const_cast<framework::Tensor *>(x);
-    }
-
-    framework::Tensor *in_intermediate_out;
-    if (ctx.Attr<bool>("keep_intermediate_value")) {
+    // Get intermediate_out
+    framework::Tensor *in_intermediate_out = nullptr;
+    if (ctx.Attr<bool>("save_intermediate_out")) {
+      // if save_intermediate_out is true, for Unary(Binary(x, y)) and
+      // Binary(x, Unary(y)), the Binary(x, y) and Unary(y) not need to
+      // recompute.
       in_intermediate_out = const_cast<framework::Tensor *>(
           ctx.Input<framework::Tensor>("IntermediateOut"));
       PADDLE_ENFORCE(in_intermediate_out != nullptr,
-                     "The option of 'keep_intermediate_value' is opened, "
+                     "The option of 'save_intermediate_out' is opened, "
                      "so the number of 'Out' should be two.");
     } else {
-      in_intermediate_out = nullptr;
+      if (!InputXCanBeAbsent(functor_list)) {
+        PADDLE_ENFORCE(in_x != nullptr, "Input(X) should not be null.");
+      }
+    }
+
+    // Get in_x
+    if (ctx.HasInput("X")) {
+      PADDLE_ENFORCE(in_x != nullptr, "Input(X) should not be nullptr.");
+    } else {
+      // If functor_list contains elementwise_add, the backward doesn't use
+      // in_x, in_y and in_out.
+      PADDLE_ENFORCE(InputXCanBeAbsent(functor_list),
+                     "Only when the compoundfunctor contains "
+                     "elementwise_add_grad, the 'X' could be absent.");
+      in_x = const_cast<framework::Tensor *>(in_out_grad);
     }
 
-    if (ctx.Attr<bool>("recomputation")) {
-      RunGradFunctors<DeviceContext, T, true /*Recomputation*/>(
-          ctx, in_x, y, in_out, in_intermediate_out, in_out_grad, x_grad,
-          y_grad);
+    bool has_in_place = HasInPlaceUnary(functor_list);
+    if (has_in_place) {
+      RunGradFunctors<DeviceContext, T, true /*InPlace*/>(
+          ctx, in_x, in_y, in_out, in_intermediate_out, in_out_grad, x_grad,
+          y_grad, d_intermediate_out);
     } else {
-      RunGradFunctors<DeviceContext, T, false /*Recomputation*/>(
-          ctx, in_x, y, in_out, in_intermediate_out, in_out_grad, x_grad,
-          y_grad);
+      RunGradFunctors<DeviceContext, T, false /*InPlace*/>(
+          ctx, in_x, in_y, in_out, in_intermediate_out, in_out_grad, x_grad,
+          y_grad, d_intermediate_out);
     }
   }
 };

paddle/fluid/operators/math/compound_functors.h

@@ -22,11 +22,11 @@ namespace paddle {
 namespace operators {
 namespace math {
 
+// Z = BinaryFunctor(X, UnaryFunctor(Y))
 template <typename T, typename BinaryFunctor, typename UnaryFunctor>
 struct BinaryCompoundFunctor {
   BinaryCompoundFunctor(const BinaryFunctor func1, const UnaryFunctor func2)
       : func1_(func1), func2_(func2) {}
-  // Z = BinaryFunctor(X, UnaryFunctor(Y))
 
   inline HOSTDEVICE T GetOut(T x, T y) { return func1_(x, func2_(y)); }
 
@@ -40,11 +40,11 @@ struct BinaryCompoundFunctor {
   UnaryFunctor func2_;
 };
 
+// Z = UnaryFunctor(BinaryFunctor(X, Y))
 template <typename T, typename UnaryFunctor, typename BinaryFunctor>
 struct UnaryCompoundFunctor {
   UnaryCompoundFunctor(const UnaryFunctor func1, const BinaryFunctor func2)
       : func1_(func1), func2_(func2) {}
-  // Z = UnaryFunctor(BinaryFunctor(X, Y))
 
   inline HOSTDEVICE T GetOut(T x, T y) { return func1_(func2_(x, y)); }
 
@@ -58,23 +58,19 @@ struct UnaryCompoundFunctor {
   BinaryFunctor func2_;
 };
 
-// FIXME(zcd): DBinaryFun and DUnaryFun have to method to get
-// the dx, one is to use the 'out', and the other is not to use it.
-// the former method will save the time of recomputing the
-// 'out', but it must occupy the memory to store the 'out'.
-// While the later method can avoid occupying this memory,
-// but it must recompute the 'out'.
+// Z = BinaryFunctor(X, UnaryFunctor(Y))
 template <typename T, typename DBinaryFun, typename UnaryFun>
 struct BinaryCompoundGradDxFunctor {
   BinaryCompoundGradDxFunctor(const DBinaryFun &d_binary_fun,
                               const UnaryFun &unary_fun)
       : d_binary_fun_(d_binary_fun), unary_fun_(unary_fun) {}
 
-  inline HOSTDEVICE T operator()(T x, T y, T out, T dout) {
+  inline HOSTDEVICE T Recompute(T x, T y, T out, T dout) {
     return dout * d_binary_fun_.Dx(x, unary_fun_(y));
   }
 
-  inline HOSTDEVICE T operator()(T x, T y, T intermediate_out, T out, T dout) {
+  inline HOSTDEVICE T UseIntermediateOut(T x, T y, T intermediate_out, T out,
+                                         T dout) {
     return dout * d_binary_fun_.Dx(x, intermediate_out);
   }
 
@@ -83,8 +79,9 @@ struct BinaryCompoundGradDxFunctor {
   UnaryFun unary_fun_;
 };
 
+// Z = BinaryFunctor(X, UnaryFunctor(Y))
 template <typename T, typename DBinaryFun, typename UnaryFun,
-          typename DUnaryFun>
+          typename DUnaryFun, bool InPlace>
 struct BinaryCompoundGradDyFunctor {
   BinaryCompoundGradDyFunctor(const DBinaryFun &d_binary_fun,
                               const UnaryFun &unary_fun,
@@ -93,13 +90,19 @@ struct BinaryCompoundGradDyFunctor {
         unary_fun_(unary_fun),
         d_unary_fun_(d_unary_fun) {}
 
-  inline HOSTDEVICE T operator()(T x, T y, T out, T dout) {
-    return dout * d_binary_fun_.Dy(x, unary_fun_(y)) * d_unary_fun_(y);
+  inline HOSTDEVICE T Recompute(T x, T y, T out, T dout) {
+    return dout * d_binary_fun_.Dy(x, unary_fun_(y)) * d_unary_fun_.UseX(y);
   }
 
-  inline HOSTDEVICE T operator()(T x, T y, T intermediate_out, T out, T dout) {
-    return dout * d_binary_fun_.Dy(x, intermediate_out) *
-           d_unary_fun_(y, intermediate_out);
+  inline HOSTDEVICE T UseIntermediateOut(T x, T y, T intermediate_out, T out,
+                                         T dout) {
+    if (InPlace) {
+      return dout * d_binary_fun_.Dy(x, intermediate_out) *
+             d_unary_fun_.UseOut(intermediate_out);
+    } else {
+      return dout * d_binary_fun_.Dy(x, intermediate_out) *
+             d_unary_fun_.UseXAndOut(y, intermediate_out);
+    }
   }
 
  private:
@@ -108,8 +111,9 @@ struct BinaryCompoundGradDyFunctor {
   DUnaryFun d_unary_fun_;
 };
 
+// Z = UnaryFunctor(BinaryFunctor(X, Y))
 template <typename T, typename DUnaryFun, typename BinaryFun,
-          typename DBinaryFun, bool Recomputation = true>
+          typename DBinaryFun, bool InPlace>
 struct UnaryCompoundGradDxFunctor {
   UnaryCompoundGradDxFunctor(const DUnaryFun &d_unary_fun,
                              const BinaryFun &binary_fun,
@@ -118,22 +122,23 @@ struct UnaryCompoundGradDxFunctor {
         binary_fun_(binary_fun),
         d_binary_fun_(d_binary_fun) {}
 
-  inline HOSTDEVICE T operator()(T x, T y, T out, T dout) {
+  inline HOSTDEVICE T Recompute(T x, T y, T out, T dout) {
     T base;
-    if (Recomputation) {
-      base = dout * d_unary_fun_(binary_fun_(x, y));
+    if (InPlace) {
+      base = dout * d_unary_fun_.UseOut(out);
     } else {
-      base = dout * d_unary_fun_(binary_fun_(x, y), out);
+      base = dout * d_unary_fun_.UseXAndOut(binary_fun_(x, y), out);
     }
     return base * d_binary_fun_.Dx(x, y);
   }
 
-  inline HOSTDEVICE T operator()(T x, T y, T intermediate_out, T out, T dout) {
+  inline HOSTDEVICE T UseIntermediateOut(T x, T y, T intermediate_out, T out,
+                                         T dout) {
     T base;
-    if (Recomputation) {
-      base = dout * d_unary_fun_(intermediate_out);
+    if (InPlace) {
+      base = dout * d_unary_fun_.UseOut(out);
     } else {
-      base = dout * d_unary_fun_(intermediate_out, out);
+      base = dout * d_unary_fun_.UseXAndOut(intermediate_out, out);
     }
     return base * d_binary_fun_.Dx(x, y);
   }
@@ -144,8 +149,9 @@ struct UnaryCompoundGradDxFunctor {
   DBinaryFun d_binary_fun_;
 };
 
+// Z = UnaryFunctor(BinaryFunctor(X, Y))
 template <typename T, typename DUnaryFun, typename BinaryFun,
-          typename DBinaryFun, bool Recomputation = true>
+          typename DBinaryFun, bool InPlace>
 struct UnaryCompoundGradDyFunctor {
   UnaryCompoundGradDyFunctor(const DUnaryFun &d_unary_fun,
                              const BinaryFun &binary_fun,
@@ -154,22 +160,23 @@ struct UnaryCompoundGradDyFunctor {
         binary_fun_(binary_fun),
         d_binary_fun_(d_binary_fun) {}
 
-  inline HOSTDEVICE T operator()(T x, T y, T out, T dout) {
+  inline HOSTDEVICE T Recompute(T x, T y, T out, T dout) {
     T base;
-    if (Recomputation) {
-      base = dout * d_unary_fun_(binary_fun_(x, y));
+    if (InPlace) {
+      base = dout * d_unary_fun_.UseOut(out);
     } else {
-      base = dout * d_unary_fun_(binary_fun_(x, y), out);
+      base = dout * d_unary_fun_.UseXAndOut(binary_fun_(x, y), out);
     }
     return base * d_binary_fun_.Dy(x, y);
   }
 
-  inline HOSTDEVICE T operator()(T x, T y, T intermediate_out, T out, T dout) {
+  inline HOSTDEVICE T UseIntermediateOut(T x, T y, T intermediate_out, T out,
+                                         T dout) {
     T base;
-    if (Recomputation) {
-      base = dout * d_unary_fun_(intermediate_out);
+    if (InPlace) {
+      base = dout * d_unary_fun_.UseOut(out);
     } else {
-      base = dout * d_unary_fun_(intermediate_out, out);
+      base = dout * d_unary_fun_.UseXAndOut(intermediate_out, out);
     }
     return base * d_binary_fun_.Dy(x, y);
   }
@@ -180,6 +187,56 @@ struct UnaryCompoundGradDyFunctor {
   DBinaryFun d_binary_fun_;
 };
 
+// Z = BinaryFunctor(X, UnaryFunctor(Y))
+template <typename T, typename DBinaryFun, typename UnaryFun>
+struct BinaryCompoundGradDIntermedaiteOutFunctor {
+  BinaryCompoundGradDIntermedaiteOutFunctor(const DBinaryFun &d_binary_fun,
+                                            const UnaryFun &unary_fun)
+      : d_binary_fun_(d_binary_fun), unary_fun_(unary_fun) {}
+
+  inline HOSTDEVICE T Recompute(T x, T y, T out, T dout) {
+    return dout * d_binary_fun_.Dy(x, unary_fun_(y));
+  }
+
+  inline HOSTDEVICE T UseIntermediateOut(T x, T intermediate_out, T out,
+                                         T dout) {
+    return dout * d_binary_fun_.Dy(x, intermediate_out);
+  }
+
+ private:
+  DBinaryFun d_binary_fun_;
+  UnaryFun unary_fun_;
+};
+
+// Z = UnaryFunctor(BinaryFunctor(X, Y))
+template <typename T, typename DUnaryFun, typename BinaryFun, bool InPlace>
+struct UnaryCompoundGradDIntermediateFunctor {
+  UnaryCompoundGradDIntermediateFunctor(const DUnaryFun &d_unary_fun,
+                                        const BinaryFun &binary_fun)
+      : d_unary_fun_(d_unary_fun), binary_fun_(binary_fun) {}
+
+  inline HOSTDEVICE T Recompute(T x, T y, T out, T dout) {
+    if (InPlace) {
+      return dout * d_unary_fun_.UseOut(out);
+    } else {
+      return dout * d_unary_fun_.UseXAndOut(binary_fun_(x, y), out);
+    }
+  }
+
+  inline HOSTDEVICE T UseIntermediateOut(T x, T intermediate_out, T out,
+                                         T dout) {
+    if (InPlace) {
+      return dout * d_unary_fun_.UseOut(out);
+    } else {
+      return dout * d_unary_fun_.UseXAndOut(intermediate_out, out);
+    }
+  }
+
+ private:
+  DUnaryFun d_unary_fun_;
+  BinaryFun binary_fun_;
+};
+
 }  // namespace math
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/math/functors.h

@@ -58,9 +58,9 @@ template <typename T>
 struct ScaleGradFunctor {
   explicit ScaleGradFunctor(T coeff) : coeff_(coeff) {}
 
-  inline HOSTDEVICE T operator()(T x) { return coeff_; }
-
-  inline HOSTDEVICE T operator()(T x, T out) { return coeff_; }
+  inline HOSTDEVICE T UseX(T x) { return coeff_; }
+  inline HOSTDEVICE T UseOut(T out) { return coeff_; }
+  inline HOSTDEVICE T UseXAndOut(T x, T out) { return coeff_; }
 
  private:
   T coeff_;
@@ -73,9 +73,9 @@ struct ReluFunctor {
 
 template <typename T>
 struct ReluGradFunctor {
-  inline HOSTDEVICE T operator()(T x) { return x > 0 ? 1 : 0; }
-
-  inline HOSTDEVICE T operator()(T x, T out) { return x > 0 ? 1 : 0; }
+  inline HOSTDEVICE T UseX(T x) { return x > 0 ? 1 : 0; }
+  inline HOSTDEVICE T UseOut(T out) { return out > 0 ? 1 : 0; }
+  inline HOSTDEVICE T UseXAndOut(T x, T out) { return out > 0 ? 1 : 0; }
 };
 
 }  // namespace math

paddle/fluid/pybind/pybind.cc

@@ -670,7 +670,14 @@ All parameter, weight, gradient are variables in Paddle.
       .def_property(
           "enable_data_balance",
           [](const BuildStrategy &self) { return self.enable_data_balance_; },
-          [](BuildStrategy &self, bool b) { self.enable_data_balance_ = b; });
+          [](BuildStrategy &self, bool b) { self.enable_data_balance_ = b; })
+      .def_property("fuse_elewise_add_act_ops",
+                    [](const BuildStrategy &self) {
+                      return self.fuse_elewise_add_act_ops_;
+                    },
+                    [](BuildStrategy &self, bool b) {
+                      self.fuse_elewise_add_act_ops_ = b;
+                    });
 
   pe.def(py::init<const std::vector<platform::Place> &,
                   const std::unordered_set<std::string> &,

python/paddle/fluid/tests/unittests/op_test.py

@@ -47,8 +47,7 @@ def get_numeric_gradient(place,
                          input_to_check,
                          output_names,
                          delta=0.005,
-                         in_place=False,
-                         sum_outputs=None):
+                         in_place=False):
     # FIXME: change this method by compile time concepts
     set_input(scope, op, inputs, place)
 
@@ -59,8 +58,6 @@ def get_numeric_gradient(place,
         sum = []
         op.run(scope, place)
         for output_name in output_names:
-            if sum_outputs and output_name not in sum_outputs:
-                continue
             sum.append(
                 np.array(scope.find_var(output_name).get_tensor()).mean())
         return np.array(sum).sum() / len(output_names)
@@ -407,14 +404,13 @@ class OpTest(unittest.TestCase):
                    numeric_grad_delta=0.005,
                    in_place=False,
                    max_relative_error=0.005,
-                   user_defined_grads=None,
-                   sum_outputs=None):
+                   user_defined_grads=None):
         places = self._get_places()
         for place in places:
             self.check_grad_with_place(place, inputs_to_check, output_names,
                                        no_grad_set, numeric_grad_delta,
                                        in_place, max_relative_error,
-                                       user_defined_grads, sum_outputs)
+                                       user_defined_grads)
 
     def check_grad_with_place(self,
                               place,
@@ -424,8 +420,7 @@ class OpTest(unittest.TestCase):
                               numeric_grad_delta=0.005,
                               in_place=False,
                               max_relative_error=0.005,
-                              user_defined_grads=None,
-                              sum_outputs=None):
+                              user_defined_grads=None):
         self.scope = core.Scope()
         op_inputs = self.inputs if hasattr(self, "inputs") else dict()
         op_outputs = self.outputs if hasattr(self, "outputs") else dict()
@@ -448,8 +443,7 @@ class OpTest(unittest.TestCase):
                 input_to_check,
                 output_names,
                 delta=numeric_grad_delta,
-                in_place=in_place,
-                sum_outputs=sum_outputs) for input_to_check in inputs_to_check
+                in_place=in_place) for input_to_check in inputs_to_check
         ]
         analytic_grads = self._get_gradient(inputs_to_check, place,
                                             output_names, no_grad_set)

python/paddle/fluid/tests/unittests/parallel_executor_test_base.py

@@ -38,6 +38,7 @@ class TestParallelExecutorBase(unittest.TestCase):
                                   seed=None,
                                   use_parallel_executor=True,
                                   use_reduce=False,
+                                  fuse_elewise_add_act_ops=False,
                                   optimizer=fluid.optimizer.Adam,
                                   use_fast_executor=False):
         def run_executor(exe, feed, fetch_list, program=None):
@@ -78,6 +79,7 @@ class TestParallelExecutorBase(unittest.TestCase):
             build_strategy = fluid.BuildStrategy()
             build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce \
                 if use_reduce else fluid.BuildStrategy.ReduceStrategy.AllReduce
+            build_strategy.fuse_elewise_add_act_ops = fuse_elewise_add_act_ops
 
             if use_parallel_executor:
                 exe = fluid.ParallelExecutor(

python/paddle/fluid/tests/unittests/test_fuse_elewise_add_act_pass.py

+# 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.
+
+from parallel_executor_test_base import TestParallelExecutorBase
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+import numpy as np
+import paddle
+import paddle.dataset.mnist as mnist
+import unittest
+import os
+
+MNIST_RECORDIO_FILE = "./mnist_test_pe.recordio"
+
+
+def simple_fc_net(use_feed):
+    if use_feed:
+        img = fluid.layers.data(name='image', shape=[784], dtype='float32')
+        label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+    else:
+        reader = fluid.layers.open_files(
+            filenames=[MNIST_RECORDIO_FILE],
+            shapes=[[-1, 784], [-1, 1]],
+            lod_levels=[0, 0],
+            dtypes=['float32', 'int64'])
+        reader = fluid.layers.io.double_buffer(reader)
+        img, label = fluid.layers.read_file(reader)
+    hidden = img
+    for _ in range(4):
+        hidden = fluid.layers.fc(
+            hidden,
+            size=200,
+            act='relu',
+            bias_attr=fluid.ParamAttr(
+                initializer=fluid.initializer.Constant(value=1.0)))
+    prediction = fluid.layers.fc(hidden, size=10, act='softmax')
+    loss = fluid.layers.cross_entropy(input=prediction, label=label)
+    loss = fluid.layers.mean(loss)
+    return loss
+
+
+def fc_with_batchnorm(use_feed):
+    if use_feed:
+        img = fluid.layers.data(name='image', shape=[784], dtype='float32')
+        label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+    else:
+        reader = fluid.layers.open_files(
+            filenames=[MNIST_RECORDIO_FILE],
+            shapes=[[-1, 784], [-1, 1]],
+            lod_levels=[0, 0],
+            dtypes=['float32', 'int64'])
+        reader = fluid.layers.io.double_buffer(reader)
+        img, label = fluid.layers.read_file(reader)
+
+    hidden = img
+    for _ in range(2):
+        hidden = fluid.layers.fc(
+            hidden,
+            size=200,
+            act='relu',
+            bias_attr=fluid.ParamAttr(
+                initializer=fluid.initializer.Constant(value=1.0)))
+
+        hidden = fluid.layers.batch_norm(input=hidden)
+
+    prediction = fluid.layers.fc(hidden, size=10, act='softmax')
+    loss = fluid.layers.cross_entropy(input=prediction, label=label)
+    loss = fluid.layers.mean(loss)
+    return loss
+
+
+class TestMNIST(TestParallelExecutorBase):
+    @classmethod
+    def setUpClass(cls):
+        os.environ['CPU_NUM'] = str(4)
+        # Convert mnist to recordio file
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            reader = paddle.batch(mnist.train(), batch_size=4)
+            feeder = fluid.DataFeeder(
+                feed_list=[  # order is image and label
+                    fluid.layers.data(
+                        name='image', shape=[784]),
+                    fluid.layers.data(
+                        name='label', shape=[1], dtype='int64'),
+                ],
+                place=fluid.CPUPlace())
+            fluid.recordio_writer.convert_reader_to_recordio_file(
+                MNIST_RECORDIO_FILE, reader, feeder)
+
+    def _init_data(self, random=True):
+        np.random.seed(5)
+        if random:
+            img = np.random.random(size=[32, 784]).astype(np.float32)
+        else:
+            img = np.ones(shape=[32, 784], dtype='float32')
+        label = np.ones(shape=[32, 1], dtype='int64')
+        return img, label
+
+    def _compare_fuse_elewise_add_act_ops(self,
+                                          model,
+                                          use_cuda,
+                                          random_data=True):
+        if use_cuda and not core.is_compiled_with_cuda():
+            return
+        img, label = self._init_data(random_data)
+
+        def _optimizer(learning_rate=1e-6):
+            optimizer = fluid.optimizer.SGD(
+                learning_rate=learning_rate,
+                regularization=fluid.regularizer.L2Decay(1e-6))
+            return optimizer
+
+        not_fuse_op_first_loss, not_fuse_op_last_loss = self.check_network_convergence(
+            model,
+            feed_dict={"image": img,
+                       "label": label},
+            use_cuda=use_cuda,
+            fuse_elewise_add_act_ops=False,
+            memory_opt=False,
+            optimizer=_optimizer)
+        fuse_op_first_loss, fuse_op_last_loss = self.check_network_convergence(
+            model,
+            feed_dict={"image": img,
+                       "label": label},
+            use_cuda=use_cuda,
+            fuse_elewise_add_act_ops=True,
+            memory_opt=False,
+            optimizer=_optimizer)
+
+        for loss in zip(not_fuse_op_first_loss, fuse_op_first_loss):
+            self.assertAlmostEquals(loss[0], loss[1], delta=1e-6)
+        for loss in zip(not_fuse_op_last_loss, fuse_op_last_loss):
+            self.assertAlmostEquals(loss[0], loss[1], delta=1e-6)
+
+    def test_simple_fc_with_fuse_op(self):
+        self._compare_fuse_elewise_add_act_ops(simple_fc_net, True)
+        self._compare_fuse_elewise_add_act_ops(simple_fc_net, False)
+
+    def test_batchnorm_fc_with_fuse_op(self):
+        self._compare_fuse_elewise_add_act_ops(fc_with_batchnorm, True)
+        self._compare_fuse_elewise_add_act_ops(fc_with_batchnorm, False)
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/fluid/tests/unittests/test_fused_elemwise_activation_op.py

@@ -48,7 +48,7 @@ def create_test_class(test_case, callback, attrs):
                 'X': OpTest.np_dtype_to_fluid_dtype(self.x),
                 'Y': OpTest.np_dtype_to_fluid_dtype(self.y)
             }
-            if self.attrs["keep_intermediate_value"]:
+            if self.attrs["save_intermediate_out"]:
                 self.outputs = {
                     'Out': self.out,
                     "IntermediateOut": self.intermediate_out
@@ -73,22 +73,19 @@ def create_test_class(test_case, callback, attrs):
         def test_check_output(self):
             self.check_output()
 
+        # FIXME(zcd): the intermediate_out_grad is not checked.
         def test_check_grad_normal(self):
-            if self.attrs["keep_intermediate_value"]:
-                self.check_grad(
-                    ['X', 'Y'], ['Out', 'IntermediateOut'],
-                    max_relative_error=0.005,
-                    sum_outputs=['Out'])
+            if self.attrs["save_intermediate_out"]:
+                self.check_grad(['X', 'Y'], ['Out'], max_relative_error=0.005)
             else:
                 self.check_grad(['X', 'Y'], ['Out'], max_relative_error=0.005)
 
         def test_check_grad_ingore_x(self):
-            if self.attrs["keep_intermediate_value"]:
+            if self.attrs["save_intermediate_out"]:
                 self.check_grad(
-                    ['Y'], ['Out', 'IntermediateOut'],
+                    ['Y'], ['Out'],
                     max_relative_error=0.005,
-                    no_grad_set=set("X"),
-                    sum_outputs=['Out'])
+                    no_grad_set=set("X"))
             else:
                 self.check_grad(
                     ['Y'], ['Out'],
@@ -96,12 +93,11 @@ def create_test_class(test_case, callback, attrs):
                     no_grad_set=set("X"))
 
         def test_check_grad_ingore_y(self):
-            if self.attrs["keep_intermediate_value"]:
+            if self.attrs["save_intermediate_out"]:
                 self.check_grad(
-                    ['X'], ['Out', 'IntermediateOut'],
+                    ['X'], ['Out'],
                     max_relative_error=0.005,
-                    no_grad_set=set("Y"),
-                    sum_outputs=['Out'])
+                    no_grad_set=set("Y"))
             else:
                 self.check_grad(
                     ['X'], ['Out'],
@@ -303,39 +299,32 @@ for mode in {0, 1}:
     relu_add_func = partial(relu_add_func, mode=mode)
     add_relu_func = partial(add_relu_func, mode=mode)
 
-    for recomputation in {True, False}:
-        for keep_intermediate_value in {True, False}:
-            suffix = ("_keep_intermediate_value" if keep_intermediate_value else "") \
-                     + ("_recomputation" if recomputation else "") \
-                     + ("_mode_"+ str(mode))
-            create_test_class('scale_add' + suffix, scale_add_func, {
-                'scale': scale,
-                'functor_list': ["scale", "elementwise_add"],
-                'keep_intermediate_value': keep_intermediate_value,
-                'recomputation': recomputation
-            })
-            create_test_class('add_scale' + suffix, add_scale_func, {
-                'scale': scale,
-                'functor_list': ["elementwise_add", "scale"],
-                'keep_intermediate_value': keep_intermediate_value,
-                'recomputation': recomputation
-            })
-            create_test_class('add_relu' + suffix, add_relu_func, {
-                'functor_list': ["elementwise_add", "relu"],
-                'keep_intermediate_value': keep_intermediate_value,
-                'recomputation': recomputation
-            })
-            create_test_class('relu_add' + suffix, relu_add_func, {
-                'functor_list': ["relu", "elementwise_add"],
-                'keep_intermediate_value': keep_intermediate_value,
-                'recomputation': recomputation
-            })
-            create_test_class('mul_scale' + suffix, mul_scale_func, {
-                'scale': scale,
-                'functor_list': ["elementwise_mul", "scale"],
-                'keep_intermediate_value': keep_intermediate_value,
-                'recomputation': recomputation
-            })
+    for save_intermediate_out in {True, False}:
+        suffix = ("_save_intermediate_out" if save_intermediate_out else "") \
+                 + ("_mode_"+ str(mode))
+        create_test_class('scale_add' + suffix, scale_add_func, {
+            'scale': scale,
+            'functor_list': ["scale", "elementwise_add"],
+            'save_intermediate_out': save_intermediate_out,
+        })
+        create_test_class('add_scale' + suffix, add_scale_func, {
+            'scale': scale,
+            'functor_list': ["elementwise_add", "scale"],
+            'save_intermediate_out': save_intermediate_out,
+        })
+        create_test_class('add_relu' + suffix, add_relu_func, {
+            'functor_list': ["elementwise_add", "relu"],
+            'save_intermediate_out': save_intermediate_out,
+        })
+        create_test_class('relu_add' + suffix, relu_add_func, {
+            'functor_list': ["relu", "elementwise_add"],
+            'save_intermediate_out': save_intermediate_out,
+        })
+        create_test_class('mul_scale' + suffix, mul_scale_func, {
+            'scale': scale,
+            'functor_list': ["elementwise_mul", "scale"],
+            'save_intermediate_out': save_intermediate_out,
+        })
 
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/test_reshape_op.py

@@ -79,7 +79,7 @@ class TestReshapeOpWithInputShape(OpTest):
         self.check_output(no_check_set=['XShape'])
 
     def test_check_grad(self):
-        self.check_grad(["X"], "Out", sum_outputs=["Out"])
+        self.check_grad(["X"], "Out")
 
 
 if __name__ == "__main__":

python/paddle/fluid/tests/unittests/test_transpose_op.py

@@ -34,7 +34,7 @@ class TestTransposeOp(OpTest):
         self.check_output(no_check_set=['XShape'])
 
     def test_check_grad(self):
-        self.check_grad(['X'], 'Out', sum_outputs=['Out'])
+        self.check_grad(['X'], 'Out')
 
     def initTestCase(self):
         self.shape = (3, 4)