Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into fix_bug_for_lstmp

test=develop

paddle/fluid/framework/CMakeLists.txt

@@ -128,7 +128,7 @@ cc_test(version_test SRCS version_test.cc DEPS version)
 
 cc_library(proto_desc SRCS var_desc.cc op_desc.cc block_desc.cc program_desc.cc DEPS shape_inference op_info operator glog version)
 
-cc_library(op_registry SRCS op_registry.cc DEPS op_proto_maker op_info operator glog proto_desc)
+cc_library(op_registry SRCS op_registry.cc DEPS op_proto_maker op_info operator glog proto_desc memory_optimize_helper)
 nv_test(op_registry_test SRCS op_registry_test.cc DEPS op_registry)
 
 py_proto_compile(framework_py_proto SRCS framework.proto data_feed.proto)
@@ -192,6 +192,7 @@ cc_library(prune SRCS prune.cc DEPS framework_proto)
 cc_test(prune_test SRCS prune_test.cc DEPS op_info prune recurrent_op device_context)
 cc_test(var_type_inference_test SRCS var_type_inference_test.cc DEPS op_registry
         proto_desc)
+cc_test(inplace_op_inference_test SRCS inplace_op_inference_test.cc DEPS op_registry proto_desc op_info memory_optimize_helper)
 cc_library(selected_rows SRCS selected_rows.cc DEPS tensor)
 cc_test(selected_rows_test SRCS selected_rows_test.cc DEPS selected_rows)
 

paddle/fluid/framework/details/CMakeLists.txt

@@ -50,7 +50,9 @@ cc_library(data_balance_op_handle SRCS data_balance_op_handle.cc DEPS op_handle_
 cc_library(gather_op_handle SRCS gather_op_handle.cc DEPS op_handle_base scope ddim memory variable_visitor)
 cc_library(fuse_vars_op_handle SRCS fuse_vars_op_handle.cc DEPS op_handle_base scope)
 
-cc_library(memory_optimize_pass SRCS analysis_var_pass.cc memory_reuse_types.cc DEPS graph graph_helper pass)
+cc_library(memory_optimize_helper SRCS memory_optimize_helper.cc DEPS graph graph_helper)
+cc_library(memory_optimize_pass SRCS memory_optimize_pass.cc DEPS memory_optimize_helper pass)
+cc_library(inplace_op_pass SRCS inplace_op_pass.cc DEPS memory_optimize_pass op_info)
 cc_library(modify_op_lock_and_record_event_pass SRCS modify_op_lock_and_record_event_pass.cc DEPS computation_op_handle op_graph_view multi_devices_helper)
 cc_library(memory_early_delete_pass SRCS memory_early_delete_pass.cc DEPS memory_optimize_pass computation_op_handle scale_loss_grad_op_handle rpc_op_handle
         all_reduce_op_handle reduce_op_handle broadcast_op_handle data_balance_op_handle graph graph_helper pass)
@@ -65,12 +67,12 @@ cc_library(all_reduce_deps_pass SRCS all_reduce_deps_pass.cc DEPS graph graph_he
 cc_library(multi_devices_graph_pass SRCS multi_devices_graph_pass.cc DEPS multi_devices_helper computation_op_handle
         scale_loss_grad_op_handle rpc_op_handle all_reduce_op_handle reduce_op_handle broadcast_op_handle data_balance_op_handle fused_broadcast_op_handle)
 
-set(SSA_GRAPH_EXECUTOR_DEPS graph framework_proto sequential_execution_pass modify_op_lock_and_record_event_pass all_reduce_deps_pass reference_count_pass eager_deletion_pass memory_optimize_pass memory_early_delete_pass)
+set(SSA_GRAPH_EXECUTOR_DEPS graph framework_proto sequential_execution_pass modify_op_lock_and_record_event_pass all_reduce_deps_pass reference_count_pass eager_deletion_pass memory_optimize_pass memory_early_delete_pass inplace_op_pass)
 if (WITH_GPU)
   list(APPEND SSA_GRAPH_EXECUTOR_DEPS reference_count_pass)
 endif()
-cc_test(memory_reuse_types_test SRCS memory_reuse_types_test.cc memory_reuse_types.cc DEPS framework_proto graph)
-cc_test(analysis_var_pass_test SRCS analysis_var_pass_test.cc analysis_var_pass.cc memory_reuse_types.cc DEPS framework_proto graph graph_helper op_registry pass)
+cc_test(memory_optimize_helper_test SRCS memory_optimize_helper_test.cc memory_optimize_helper.cc DEPS framework_proto graph)
+cc_test(memory_optimize_pass_test SRCS memory_optimize_pass_test.cc memory_optimize_pass.cc memory_optimize_helper.cc DEPS framework_proto graph graph_helper op_registry pass)
 
 cc_library(ssa_graph_executor SRCS ssa_graph_executor.cc DEPS ${SSA_GRAPH_EXECUTOR_DEPS})
 

paddle/fluid/framework/details/build_strategy.cc

@@ -17,7 +17,7 @@ limitations under the License. */
 #include <glog/logging.h>
 #include <memory>
 
-#include "paddle/fluid/framework/details/memory_reuse_types.h"
+#include "paddle/fluid/framework/details/memory_optimize_helper.h"
 #include "paddle/fluid/framework/details/multi_devices_graph_pass.h"
 #include "paddle/fluid/framework/details/multi_devices_graph_print_pass.h"
 #include "paddle/fluid/framework/details/reduce_op_handle.h"
@@ -47,6 +47,22 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
       AppendPass("sequential_execution_pass");
     }
 
+    // Add op fusion.
+    if (strategy.fuse_relu_depthwise_conv_) {
+      AppendPass("fuse_relu_depthwise_conv_pass");
+    }
+
+    // NOTE(dzhwinter): A note for automatical inplace.
+    // 1. modify program desc passes should put
+    // before inplace pass.
+    // 2. manually configured inplace should put
+    // before inplace_pass
+
+    // Add automatically inplace.
+    if (strategy_.enable_inplace_) {
+      AppendPass("inplace_pass");
+    }
+
     // Add a graph viz pass to record a graph.
     if (!strategy_.debug_graphviz_path_.empty()) {
       auto viz_pass = AppendPass("graph_viz_pass");
@@ -55,10 +71,6 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
       viz_pass->Set<std::string>("graph_viz_path", new std::string(graph_path));
     }
 
-    // Add op fusion.
-    if (strategy.fuse_relu_depthwise_conv_) {
-      AppendPass("fuse_relu_depthwise_conv_pass");
-    }
     if (strategy.fuse_elewise_add_act_ops_) {
       auto fuse_elewise_add_act_pass = AppendPass("fuse_elewise_add_act_pass");
       // Add a graph viz pass to record a graph.
@@ -88,7 +100,7 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
     // A side-effect of that, memory optimize cannot forsee the fetched vars
     // , so fetchlist should be set persistable before call the Run interface.
     if (strategy.memory_optimize_) {
-      auto analysis_var_pass = AppendPass("analysis_var_pass");
+      auto memory_optimize_pass = AppendPass("memory_optimize_pass");
     }
 
     AppendMultiDevPass(strategy);
@@ -186,8 +198,10 @@ std::unique_ptr<ir::Graph> BuildStrategy::Apply(
       pass->Erase("nccl_ctxs");
       pass->SetNotOwned<platform::NCCLContextMap>("nccl_ctxs", nctx);
 #endif
-
-    } else if (pass->Type() == "analysis_var_pass") {
+    } else if (pass->Type() == "memory_optimize_pass") {
+      if (graph->Has(kAllOpDescs)) {
+        graph->Erase(kAllOpDescs);
+      }
       const std::vector<OpDesc *> *all_op_descs =
           new std::vector<OpDesc *>(main_program.Block(0).AllOps());
       graph->Set<const std::vector<OpDesc *>>(kAllOpDescs,
@@ -214,6 +228,13 @@ std::unique_ptr<ir::Graph> BuildStrategy::Apply(
       pass->Set<const std::vector<OpDesc *>>(
           kAllOpDescs,
           new std::vector<OpDesc *>(main_program.Block(0).AllOps()));
+    } else if (pass->Type() == "inplace_pass") {
+      if (graph->Has(kAllOpDescs)) {
+        graph->Erase(kAllOpDescs);
+      }
+      graph->Set<const std::vector<OpDesc *>>(
+          kAllOpDescs,
+          new std::vector<OpDesc *>(main_program.Block(0).AllOps()));
     } else if (pass->Type() == "fuse_relu_depthwise_conv_pass") {
       if (!use_cuda) {
         LOG(WARNING) << "fuse_relu_depthwise_conv_pass is only supported on "
@@ -239,9 +260,10 @@ USE_PASS(allreduce_mode_multi_devices_pass);
 USE_PASS(dist_multi_devices_pass);
 USE_PASS(multi_devices_check_pass);
 USE_PASS(multi_devices_print_pass);
-USE_PASS(analysis_var_pass);
+USE_PASS(memory_optimize_pass);
 USE_PASS(sequential_execution_pass);
 USE_PASS(all_reduce_deps_pass);
 USE_PASS(modify_op_lock_and_record_event_pass);
+USE_PASS(inplace_pass);
 USE_PASS(lock_free_optimize_pass);
 USE_PASS(graph_to_program_pass);

paddle/fluid/framework/details/build_strategy.h

@@ -80,6 +80,11 @@ struct BuildStrategy {
 
   bool memory_early_delete_{false};
 
+  // TODO(dzhwinter):
+  // make enable_inplace, memory_optimize_
+  // memory_early_delete_ true by default
+  bool enable_inplace_{false};
+
   bool enable_sequential_execution_{false};
 
   bool fuse_broadcast_op_{false};

paddle/fluid/framework/details/graph_test_base.h

+// Copyright (c) 2019 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 <algorithm>
+#include <iostream>
+#include <iterator>
+#include <string>
+#include "glog/logging.h"
+#include "gtest/gtest.h"
+#include "paddle/fluid/framework/ir/graph.h"
+#include "paddle/fluid/framework/ir/graph_helper.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/program_desc.h"
+
+namespace paddle {
+namespace framework {
+
+class DummyOp : public OperatorBase {
+ public:
+  DummyOp(const std::string& type, const VariableNameMap& inputs,
+          const VariableNameMap& outputs, const AttributeMap& attrs)
+      : OperatorBase(type, inputs, outputs, attrs) {}
+
+ private:
+  void RunImpl(const Scope& scope,
+               const platform::Place& place) const override {}
+};
+
+class SumOpMaker : public OpProtoAndCheckerMaker {
+ public:
+  void Make() {
+    AddInput("X", "").AsDuplicable();
+    AddOutput("Out", "");
+    AddComment("");
+  }
+};
+
+class AssignOpMaker : public OpProtoAndCheckerMaker {
+ public:
+  void Make() {
+    AddInput("X", "").AsDuplicable();
+    AddOutput("Out", "");
+    AddComment("");
+  }
+};
+
+class SplitOpMaker : public OpProtoAndCheckerMaker {
+ public:
+  void Make() {
+    AddInput("X", "");
+    AddOutput("Out", "").AsDuplicable();
+    AddComment("");
+  }
+};
+
+class DummyVarTypeInference : public VarTypeInference {
+ public:
+  void operator()(const OpDesc& op_desc, BlockDesc* block) const override {
+    auto& inputs = op_desc.Input("X");
+    auto type = block->Var(inputs.front())->GetType();
+    auto out_var_name = op_desc.Output("Out").front();
+    block->Var(out_var_name)->SetType(type);
+  }
+};
+
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/details/inplace_op_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/details/inplace_op_pass.h"
+#include <algorithm>
+#include <deque>
+#include <iterator>
+#include <stack>
+#include <string>
+#include <unordered_map>
+#include <unordered_set>
+#include <vector>
+#include "paddle/fluid/framework/details/memory_optimize_pass.h"
+#include "paddle/fluid/framework/ir/graph_helper.h"
+#include "paddle/fluid/framework/op_info.h"
+
+// NOTE(dzhwinter): inplace means one op output variable reuse the input space.
+// By our design, one operator only can read its input(const Variable),
+// write its output(non-const Variable). If one operator is inplaced, means
+// user have chance to write the space before reading happens.
+// Especially when some optimize code writing style is applied.
+//
+//
+// /* wrong case in operator */
+// /*In this case, a larger allocation is allocated, input content is lost*/
+// const Tensor* in = ctx.Input<Tensor>("In")
+// Tensor* out = ctx.Output<Tensor>("Out");
+// auto* out_ptr = out->mutable_data<T>(ctx.GetPlace());
+// out_ptr[0] = 0;  // input contect is overwrited.
+
+// NOTE(dzhwinter):
+// Only for backward compacity and stable. if enable_inplace_whitelist is turn
+// on.
+// only the ops in whitelist will be use inplace strategy.
+// if not, all the op will be inplaced if it registered with InplaceClass
+DEFINE_bool(
+    enable_inplace_whitelist, false,
+    "If this option turns on, only these op in whitelist can be inplaced."
+    "If it turns off, all of the running op can be candidate of inplaced op."
+    "Such as scale, elementwise_add"
+    "By default, it's turned on");
+
+DECLARE_string(memory_optimize_debug);
+
+// clang-format off
+const std::string kInplacedOpWhiteList[] = { // NOLINT
+    "sigmoid",
+    "exp",
+    "relu",
+    "tanh",
+    "sqrt",
+    "ceil",
+    "floor",
+    "reciprocal",
+    "relu6",
+    "soft_relu",
+    "hard_sigmoid",
+    "batch_norm",
+    "batch_norm_grad",
+    "sum",
+    "sum_grad",
+    "scale",
+    "reshape",
+    "elementwise_add",
+    "elementwise_add_grad",
+};
+// clang-format on
+
+namespace paddle {
+namespace framework {
+namespace details {
+
+static inline ir::Node* GetNextCascadeInplacedVar(ir::Node* var) {
+  // if next op is inplaced, then return the output var
+  // otherwise return nullptr
+  PADDLE_ENFORCE(var && var->IsVar() && !var->IsCtrlVar());
+  ir::Node* inplaced_var = nullptr;
+  for (auto* next_op : var->outputs) {
+    for (auto* output : next_op->outputs) {
+      if (output->IsVar() && !output->IsCtrlVar() &&
+          output->Name() == var->Name()) {
+        inplaced_var = output;
+      }
+    }
+  }
+  return inplaced_var;
+}
+
+static inline ir::Node* GetPrevCascadeInplacedVar(ir::Node* var) {
+  PADDLE_ENFORCE(var && var->IsVar() && !var->IsCtrlVar());
+  if (var->inputs.empty()) return nullptr;
+  auto* prev_op = var->inputs.at(0);
+  auto input_it = std::find_if(prev_op->inputs.begin(), prev_op->inputs.end(),
+                               [&](ir::Node* node) {
+                                 if (node->IsVar() && !node->IsCtrlVar() &&
+                                     node->Name() == var->Name()) {
+                                   return true;
+                                 } else {
+                                   return false;
+                                 }
+                               });
+  return input_it == prev_op->inputs.end() ? nullptr : *input_it;
+}
+
+InplacePass::InplacePass() : Pass() {
+  if (FLAGS_enable_inplace_whitelist) {
+    for (auto& s : kInplacedOpWhiteList) {
+      whitelist_.emplace(s);
+    }
+  }
+}
+
+void InplacePass::InitSSAGraphNodes() const {
+  std::unordered_map<std::string, std::unordered_set<ir::Node*>> all_vars;
+  for (auto* op : view_.AllOps()) {
+    for (auto* node : op->inputs) {
+      if (!node->IsVar() || node->IsCtrlVar()) continue;
+      if (all_vars[node->Name()].count(node) == 0) {
+        all_vars[node->Name()].emplace(node);
+        var_nodes_[node->Name()].emplace_back(node);
+      }
+    }
+    for (auto* node : op->outputs) {
+      if (!node->IsVar() || node->IsCtrlVar()) continue;
+      if (all_vars[node->Name()].count(node) == 0) {
+        all_vars[node->Name()].emplace(node);
+        var_nodes_[node->Name()].emplace_back(node);
+      }
+    }
+  }
+}
+
+std::unique_ptr<ir::Graph> InplacePass::ApplyImpl(
+    std::unique_ptr<ir::Graph> graph) const {
+  var_nodes_.clear();
+  view_.Build(graph.get());
+  InitSSAGraphNodes();
+
+  for (auto* op : view_.AllOps()) {
+    if (FLAGS_enable_inplace_whitelist && !whitelist_.count(op->Name()))
+      continue;
+    TryInplaceOpInputOutput(op, graph.get());
+  }
+  graph->ResolveHazard(var_nodes_);
+
+  return graph;
+}
+
+void InplacePass::InplaceModifyDesc(const std::string& var,
+                                    const std::string& cache_var,
+                                    const size_t& idx) const {
+  for (size_t i = idx; i < view_.AllOps().size(); ++i) {
+    ir::Node* op = view_.AllOps()[i];
+    PADDLE_ENFORCE(op->IsOp() && op->Op());
+    auto* op_desc = op->Op();
+    op_desc->RenameInput(var, cache_var);
+    op_desc->RenameOutput(var, cache_var);
+    if (op_desc->Block()->HasVar(var)) op_desc->Block()->RemoveVar(var);
+    op_desc->Flush();
+  }
+}
+
+const SSANodePair InplacePass::TryInplaceModifyVar(const std::string& var,
+                                                   const std::string& cache_var,
+                                                   const size_t& idx,
+                                                   ir::Graph* graph) const {
+  PADDLE_ENFORCE(var_nodes_[var].size() >= 1 &&
+                 var_nodes_[var].at(0)->Var() != nullptr);
+  std::unique_ptr<VarDesc> var_desc(new VarDesc(*var_nodes_[var].at(0)->Var()));
+  var_desc->SetName(cache_var);
+
+  SSANodePair swap_nodes;
+
+  for (size_t i = idx; i < view_.AllOps().size(); ++i) {
+    auto* op = view_.AllOps()[i];
+
+    // redirect the input to the latest version of cache_var
+    for (auto* node : op->inputs) {
+      if (node->Name() == var) {
+        ir::Node* cache_node = graph->CreateVarNode(var_desc.get());
+
+        // swap node to cache_node
+        cache_node->outputs.insert(cache_node->outputs.end(),
+                                   node->outputs.begin(), node->outputs.end());
+        PADDLE_ENFORCE(node->inputs.size() == 1 && node->inputs[0]->IsOp());
+        auto* prev_op = node->inputs[0];
+        std::replace(prev_op->outputs.begin(), prev_op->outputs.end(), node,
+                     cache_node);
+        cache_node->inputs.emplace_back(prev_op);
+        for (auto* next_op : node->outputs) {
+          std::replace(next_op->inputs.begin(), next_op->inputs.end(), node,
+                       cache_node);
+        }
+
+        swap_nodes.emplace_back(std::make_pair(node, cache_node));
+      }
+    }
+
+    // if we need to rename the output,
+    // always create a newer version of cache_var
+    for (auto* node : op->outputs) {
+      if (node->Name() == var) {
+        ir::Node* cache_node = graph->CreateVarNode(var_desc.get());
+        // swap node to cache node
+        cache_node->outputs.insert(cache_node->outputs.end(),
+                                   node->outputs.begin(), node->outputs.end());
+        cache_node->inputs.emplace_back(op);
+        std::replace(op->outputs.begin(), op->outputs.end(), node, cache_node);
+        for (auto* next_op : node->outputs) {
+          std::replace(next_op->inputs.begin(), next_op->inputs.end(), node,
+                       cache_node);
+        }
+
+        swap_nodes.emplace_back(std::make_pair(node, cache_node));
+      }
+    }
+  }
+
+  return swap_nodes;
+}
+
+void InplacePass::CommitModify(const SSANodePair& swap_nodes,
+                               ir::Graph* graph) const {
+  for (auto& pair : swap_nodes) {
+    auto *node = pair.first, *cache_node = pair.second;
+    const std::string var = node->Name(), cache_var = cache_node->Name();
+    var_nodes_[cache_var].emplace_back(cache_node);
+    graph->RemoveNode(node);
+    auto& nodes = var_nodes_.at(var);
+    // release unused var in graph. Because python side memory optimize
+    // may reused the var in same name, so we only clear the var node
+    // after current inplaced index.
+    nodes.erase(std::remove(nodes.begin(), nodes.end(), node), nodes.end());
+  }
+}
+
+void InplacePass::WithdrawModify(const SSANodePair& nodes,
+                                 ir::Graph* graph) const {
+  for (auto& pair : nodes) {
+    auto *node = pair.first, *cache_node = pair.second;
+    const std::string var = node->Name(), cache_var = cache_node->Name();
+    auto* prev_op = node->inputs[0];
+    std::replace(prev_op->outputs.begin(), prev_op->outputs.end(), cache_node,
+                 node);
+    for (auto* next_op : node->outputs) {
+      std::replace(next_op->inputs.begin(), next_op->inputs.end(), cache_node,
+                   node);
+    }
+    graph->RemoveNode(cache_node);
+  }
+}
+
+void InplacePass::TryInplaceOpInputOutput(ir::Node* op,
+                                          ir::Graph* graph) const {
+  VLOG(4) << "Try to inplace op " << op->Name();
+  PADDLE_ENFORCE(op->Op() != nullptr && op->Op()->Block() != nullptr,
+                 "op_desc is nullptr");
+  // some pre-requirments need to meet if the op want to inplaced.
+
+  auto* op_desc = op->Op();
+  auto& infer_inplace =
+      OpInfoMap::Instance().Get(op_desc->Type()).infer_inplace_;
+
+  // 1. infer_inplace_ is registered.
+  if (!static_cast<bool>(infer_inplace)) return;
+  PADDLE_ENFORCE(static_cast<bool>(infer_inplace),
+                 "%s's infer_inplace has not been registered", op_desc->Type());
+
+  auto* block = op_desc->Block();
+  auto in_to_outs = infer_inplace(*op_desc, block);
+
+  auto& all_ops = view_.AllOps();
+  auto cursor = std::find(all_ops.begin(), all_ops.end(), op);
+  size_t idx = std::distance(all_ops.begin(), cursor);
+
+  for (auto& pair : in_to_outs) {
+    auto& in_var_name = pair.first;
+    auto& out_var_name = pair.second;
+    auto* in_node = view_.GetNodeByName(in_var_name, op->inputs);
+    auto* out_node = view_.GetNodeByName(out_var_name, op->outputs);
+
+    // 2. there is no external pending op on the input node
+    if (view_.PendingOpsOnVar(in_node).size() > 1) {
+      VLOG(4) << string::Sprintf(
+          "Skiped pair %s => %s. %s input has external dependency."
+          "inplace such pair will overwrite the memory.",
+          out_var_name, in_var_name, op->Name());
+      continue;
+    }
+
+    // 3. if output has been memory optimize by python(fluid.memory_optmize()).
+    // this candidate  can not be inplaced. Will be deprecated in the future.
+    if (view_.InSkipSet(out_node->Name())) {
+      VLOG(4) << string::Sprintf(
+          "Skiped %s => %s reused previous memory block in python memory "
+          "optmize,"
+          "it inplace may generate a circle",
+          out_var_name, in_var_name, op->Name());
+      continue;
+    }
+
+    // Debug Interface. Which would be skipped by the pass.
+    if (out_node->Name() == FLAGS_memory_optimize_debug) {
+      VLOG(3) << "Skiped var by force. FLAGS_memory_optimize_debug="
+              << out_node->Name();
+      continue;
+    }
+
+    // NOTE(dzhwinter):
+    // two stage commit of inplaced process. if after inplace happens generate a
+    // circle,
+    // then withdraw the changes. Otherwise, safely add the node.
+    auto swap_nodes =
+        TryInplaceModifyVar(out_var_name, in_var_name, idx, graph);
+
+    if (!ir::HasCircle(*graph)) {
+      VLOG(3) << string::Sprintf("!!! %s,  %s => %s inplaced", op->Name(),
+                                 out_var_name, in_var_name);
+      InplaceModifyDesc(out_var_name, in_var_name, idx);
+      CommitModify(swap_nodes, graph);
+    } else {
+      VLOG(3) << string::Sprintf(
+          "Skiped pair %s => %s, inplace will generate a circle. withdraw %s",
+          out_var_name, in_var_name, op->Name());
+      WithdrawModify(swap_nodes, graph);
+    }
+  }
+}
+
+ir::Node* GraphView::GetNodeByName(const std::string& name,
+                                   const std::vector<ir::Node*>& nodes) const {
+  // nodes should be op->inputs/outputs
+  // node in same node do have different name.
+  std::unordered_set<std::string> nodes_in_op;
+  bool has_dup_node =
+      std::all_of(nodes.begin(), nodes.end(), [&nodes_in_op](ir::Node* node) {
+        if (!node->IsVar() || node->IsCtrlVar() || node->Var() == nullptr) {
+          if (nodes_in_op.count(node->Name())) return true;
+          nodes_in_op.emplace(node->Name());
+        }
+        return false;
+      });
+  PADDLE_ENFORCE(has_dup_node == false, "nodes has same name!");
+  ir::Node* node = nullptr;
+  for (auto* it : nodes) {
+    if (!it->IsVar() || it->IsCtrlVar() || it->Var() == nullptr) continue;
+    if (it->Name() == name) {
+      node = it;
+      break;
+    }
+  }
+  PADDLE_ENFORCE(node != nullptr,
+                 string::Sprintf("Not found var %s in nodes!", name));
+  return node;
+}
+
+std::vector<ir::Node*> GraphView::PendingOpsOnVar(ir::Node* node) {
+  // get the pending ops depends on same var node.
+  // because node also maybe a inplaced variable, so need to backtrack all the
+  // previous inplaced vars.
+  std::vector<ir::Node*> pending_ops;
+  ir::Node* p = node;
+  while (p != nullptr) {
+    pending_ops.insert(pending_ops.end(), p->outputs.begin(), p->outputs.end());
+    p = GetPrevCascadeInplacedVar(p);
+  }
+  return pending_ops;
+}
+
+void GraphView::Build(ir::Graph* g) {
+  // track the var nodes in correct order.
+  // Because we insert some new created node. Which may have data race between
+  // nodes.
+  // resolve data harzards depends on the var nodes in right order.
+  ops_ = SortOpLikeDescOrder(*g);
+
+  // 1. track the nodes which reused previous node in Python memory optimize.
+  // these node can not be inplaced, otherwise may generate a circle in graph.
+  std::unordered_set<std::string> all_vars;
+  for (auto& node : g->Nodes()) {
+    if (node->IsVar()) continue;
+    for (auto& out : node->outputs) {
+      if (out->IsCtrlVar() || out->Var() == nullptr) continue;
+      if (all_vars.count(out->Name())) {
+        dup_nodes_.emplace(out->Name());
+      } else {
+        all_vars.emplace(out->Name());
+      }
+    }
+  }
+
+  // 2. track the nodes which used by parameter server.
+  // these node can not be inplaced, otherwise trainer
+  // pserver can not find each other name.
+  for (auto& node : g->Nodes()) {
+    if (!node->IsOp()) continue;
+    if (node->Name() == "send") {
+      for (auto& in : node->inputs) {
+        dup_nodes_.emplace(in->Name());
+      }
+    }
+    if (node->Name() == "recv") {
+      for (auto& out : node->outputs) {
+        dup_nodes_.emplace(out->Name());
+      }
+    }
+  }
+}
+
+const std::vector<ir::Node*>& GraphView::AllOps() { return ops_; }
+
+bool GraphView::InSkipSet(const std::string& var) const {
+  return dup_nodes_.count(var);
+}
+
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle
+
+REGISTER_PASS(inplace_pass, paddle::framework::details::InplacePass);

paddle/fluid/framework/details/inplace_op_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 abtain 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 <map>
+#include <string>
+#include <unordered_map>
+#include <unordered_set>
+#include <utility>
+#include <vector>
+#include "paddle/fluid/framework/details/memory_optimize_helper.h"
+#include "paddle/fluid/framework/ir/graph.h"
+#include "paddle/fluid/framework/ir/pass.h"
+
+namespace paddle {
+namespace framework {
+namespace details {
+
+class GraphView {
+ public:
+  GraphView() = default;
+
+  void Build(ir::Graph* g);
+
+  const std::vector<ir::Node*>& AllOps();
+
+  ir::Node* GetNodeByName(const std::string& name,
+                          const std::vector<ir::Node*>& nodes) const;
+
+  std::vector<ir::Node*> PendingOpsOnVar(ir::Node* var);
+
+  // Will Deperated in the future.
+  // NOTE(dzhwinter) :
+  // 1. Python memory optimize will reuse
+  // memory based var name, so different op output may
+  // have the same variable name. enable inplace on such node
+  // will generate a circle in ssa graph.
+  // 2. DistributeTranspiler will use unique name to
+  // map the parameter and gradient, must be skipped.
+  bool InSkipSet(const std::string& var) const;
+
+ private:
+  std::vector<ir::Node*> ops_;
+  std::unordered_set<std::string> dup_nodes_;  // mem opt affect nodes
+  std::map<ir::Node*, std::unordered_set<ir::Node*>> adj_list_;
+};
+
+typedef std::vector<std::pair<ir::Node*, ir::Node*>> SSANodePair;
+class InplacePass : public ir::Pass {
+ public:
+  InplacePass();
+
+ protected:
+  std::unique_ptr<ir::Graph> ApplyImpl(
+      std::unique_ptr<ir::Graph> graph) const override;
+
+  void InitSSAGraphNodes() const;
+
+ private:
+  const SSANodePair TryInplaceModifyVar(const std::string& var,
+                                        const std::string& cache_var,
+                                        const size_t& idx,
+                                        ir::Graph* graph) const;
+
+  void CommitModify(const SSANodePair&, ir::Graph* graph) const;
+
+  void WithdrawModify(const SSANodePair& nodes, ir::Graph* graph) const;
+
+  void InplaceModifyDesc(const std::string& in_var, const std::string& out_var,
+                         const size_t& idx) const;
+
+  void TryInplaceOpInputOutput(ir::Node* op, ir::Graph* graph) const;
+
+  mutable std::map<std::string, std::vector<ir::Node*>> var_nodes_;
+
+  mutable std::unordered_set<std::string> whitelist_;
+  mutable GraphView view_;
+};
+
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/details/memory_early_delete_pass.cc

@@ -16,7 +16,7 @@
 #include <queue>
 #include <string>
 #include <vector>
-#include "paddle/fluid/framework/details/memory_reuse_types.h"
+#include "paddle/fluid/framework/details/memory_optimize_helper.h"
 #include "paddle/fluid/framework/details/multi_devices_helper.h"
 #include "paddle/fluid/framework/details/reference_count_pass_helper.h"
 #include "paddle/fluid/framework/ir/graph_helper.h"

paddle/fluid/framework/details/memory_reuse_types.cc → paddle/fluid/framework/details/memory_optimize_helper.cc

@@ -12,8 +12,10 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include "paddle/fluid/framework/details/memory_reuse_types.h"
+#include "paddle/fluid/framework/details/memory_optimize_helper.h"
+#include <functional>
 #include <iostream>
+#include <numeric>
 #include <sstream>
 #include <string>
 
@@ -21,15 +23,17 @@ namespace paddle {
 namespace framework {
 namespace details {
 
+size_t NodeSizeInBytes(const VarDesc& node) {
+  auto shape = node.GetShape();
+  int size =
+      std::accumulate(shape.begin(), shape.end(), 1, std::multiplies<int>());
+  size_t type_size = SizeOfType(node.GetDataType());
+  return type_size * std::abs(size);
+}
+
 size_t NodeSizeInBytes(ir::Node* n) {
   auto* desc = FindVarDescInBlock(n);
-  auto shape = desc->GetShape();
-  size_t type_size = SizeOfType(desc->GetDataType());
-  int size = 1;
-  for (auto& s : shape) {
-    size *= s;
-  }
-  return type_size * std::abs(size);
+  return NodeSizeInBytes(*desc);
 }
 
 std::string DebugStringImpl(VarDesc* var) {
@@ -83,7 +87,7 @@ struct NodeComparator {
   }
 };
 
-void OrderedNodePairPool::Insert(ir::Node* var, ir::Node* op) {
+void OrderedNodeList::Insert(ir::Node* var, ir::Node* op) {
   PADDLE_ENFORCE(var->IsVar() && !var->IsCtrlVar());
   PADDLE_ENFORCE(op->IsOp());
   if (mark_table_.count(var->Name()) != 0) {
@@ -119,11 +123,11 @@ void OrderedNodePairPool::Insert(ir::Node* var, ir::Node* op) {
   mark_table_[var->Name()] = it;
 }
 
-int OrderedNodePairPool::GetIndex(ir::Node* var) {
+int OrderedNodeList::GetIndex(ir::Node* var) {
   return std::distance(nodes_.begin(), mark_table_[var->Name()]);
 }
 
-ir::Node* OrderedNodePairPool::NodeMatch(ir::Node* var) const {
+ir::Node* OrderedNodeList::NodeMatch(ir::Node* var) const {
   ir::Node* found_node = nullptr;
   NodeComparator compare_node;
 
@@ -136,13 +140,15 @@ ir::Node* OrderedNodePairPool::NodeMatch(ir::Node* var) const {
   return found_node;
 }
 
-void OrderedNodePairPool::Erase(ir::Node* var) {
-  PADDLE_ENFORCE(mark_table_.count(var->Name()));
-  nodes_.erase(mark_table_[var->Name()]);
-  mark_table_.erase(var->Name());
+void OrderedNodeList::Erase(ir::Node* var) { Erase(var->Name()); }
+
+void OrderedNodeList::Erase(const std::string& var) {
+  PADDLE_ENFORCE(mark_table_.count(var));
+  nodes_.erase(mark_table_[var]);
+  mark_table_.erase(var);
 }
 
-std::string OrderedNodePairPool::ToString() const {
+std::string OrderedNodeList::ToString() const {
   std::stringstream ss;
   for (auto it = nodes_.begin(); it != nodes_.end(); ++it) {
     ss << DebugString(it->first) << " ";
@@ -150,6 +156,43 @@ std::string OrderedNodePairPool::ToString() const {
   return ss.str();
 }
 
+bool NodeCanReused(ir::Node* node) {
+  if (node == nullptr || !node->IsVar() || node->IsCtrlVar()) return false;
+  // auto* desc = node->Var();
+  bool flag = NodeCanReused(*node->Var());
+  for (auto* op : node->inputs) {
+    if (op->Op()->HasAttr("force_cpu")) {
+      // op output force generated in cpu, can not be reused.
+      flag &= framework::AttrReader(op->Op()->GetAttrMap())
+                  .Get<bool>("force_cpu") == 0;
+    }
+  }
+  return flag;
+}
+
+bool NodeCanReused(const VarDesc& node) {
+  auto type = node.GetType();
+  if (node.Persistable() || type != proto::VarType::LOD_TENSOR ||
+      node.GetShape().empty()) {
+    return false;
+  }
+  // vars can be @EMPTY@, @LR_DECAY_REUSE_ID@. For example, while_grad
+  std::string name = node.Name();
+  if (!name.empty() && name[0] == '@' && name[name.size() - 1] == '@')
+    return false;
+  return true;
+}
+
+bool OpHasSubBlock(OpDesc* desc) {
+  const AttributeMap& attrs = desc->GetAttrMap();
+  for (auto& attr : attrs) {
+    if (attr.second.type() == typeid(BlockDesc*) ||             // NOLINT
+        attr.second.type() == typeid(std::vector<BlockDesc*>))  // NOLINT
+      return true;
+  }
+  return false;
+}
+
 }  // namespace details
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/details/memory_reuse_types.h → paddle/fluid/framework/details/memory_optimize_helper.h

@@ -43,7 +43,7 @@ using GraphNodePool = std::vector<
 // For example,
 // node0[-1, 1] node1[-1, 1, 1], node2[1,1], node3[1,1024], ..
 // O(1) insert, delete
-class OrderedNodePairPool {
+class OrderedNodeList {
  public:
   using NodePair = std::pair<ir::Node*, std::unordered_set<ir::Node*>>;
   using Iter = typename std::list<NodePair>::iterator;
@@ -53,8 +53,12 @@ class OrderedNodePairPool {
 
   void Erase(ir::Node* var);
 
+  void Erase(const std::string& var);
+
   bool Has(ir::Node* var) { return mark_table_.count(var->Name()); }
 
+  bool Has(const std::string& var) { return mark_table_.count(var); }
+
   ir::Node* NodeMatch(ir::Node* var) const;
   // map store non-const iterator, can not promise const
   int GetIndex(ir::Node* var);
@@ -67,6 +71,11 @@ class OrderedNodePairPool {
   ConstIter end() const { return nodes_.end(); }
   size_t size() const { return nodes_.size(); }
 
+  void Clear() {
+    mark_table_.clear();
+    nodes_.clear();
+  }
+
  private:
   // for searching.
   std::unordered_map<std::string, Iter> mark_table_;
@@ -74,14 +83,53 @@ class OrderedNodePairPool {
   std::list<NodePair> nodes_;
 };
 
+// valid a tensor can be reuse or not
+bool NodeCanReused(ir::Node* node);
+
+// valid a tensor can be reuse or not.
+bool NodeCanReused(const VarDesc& node);
+
+// check op has subblock or not
+bool OpHasSubBlock(OpDesc* desc);
+
 // node memory size in bytes
 size_t NodeSizeInBytes(ir::Node* n);
 
+// node memory size in bytes
+size_t NodeSizeInBytes(const VarDesc&);
+
 std::string DebugString(ir::Node* var);
 
-// std::string DebugString(VarDesc* var);
 VarDesc* FindVarDescInBlock(ir::Node* n);
 
+template <typename Container, typename Callback>
+class FilterVariableImpl {
+ public:
+  void operator()(const Container& nodes, Callback callback) {
+    for (auto* node : nodes) {
+      callback(node);
+    }
+  }
+};
+
+// filter var node for op->inputs/outputs
+template <typename Callback>
+class FilterVariableImpl<std::vector<ir::Node*>, Callback> {
+ public:
+  void operator()(const std::vector<ir::Node*>& nodes, Callback callback) {
+    for (auto* var : nodes) {
+      if (var->IsVar() && !var->IsCtrlVar()) {
+        callback(var);
+      }
+    }
+  }
+};
+
+template <typename Container, typename Callback>
+void FilterVariables(const Container& nodes, Callback callback) {
+  FilterVariableImpl<Container, Callback>()(nodes, callback);
+}
+
 }  // namespace details
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/details/memory_reuse_types_test.cc → paddle/fluid/framework/details/memory_optimize_helper_test.cc

@@ -12,7 +12,7 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include "paddle/fluid/framework/details/memory_reuse_types.h"
+#include "paddle/fluid/framework/details/memory_optimize_helper.h"
 #include <algorithm>
 #include <iostream>
 #include <memory>
@@ -27,8 +27,8 @@ namespace paddle {
 namespace framework {
 namespace details {
 
-TEST(OrderedNodePairPool, Normal) {
-  OrderedNodePairPool pool;
+TEST(OrderedNodeList, Normal) {
+  OrderedNodeList pool;
   std::vector<std::unique_ptr<ir::Node>> nodes;
 
   // clang-format off

paddle/fluid/framework/details/analysis_var_pass.cc → paddle/fluid/framework/details/memory_optimize_pass.cc

@@ -12,7 +12,7 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include "paddle/fluid/framework/details/analysis_var_pass.h"
+#include "paddle/fluid/framework/details/memory_optimize_pass.h"
 #include <algorithm>
 #include <atomic>
 #include <deque>
@@ -48,35 +48,7 @@ static inline bool IsSameDesc(OpDesc* op1, OpDesc* op2) {
          op1->Outputs() == op2->Outputs();
 }
 
-template <typename Container, typename Callback>
-class FilterVariableImpl {
- public:
-  void operator()(const Container& nodes, Callback callback) {
-    for (auto* node : nodes) {
-      callback(node);
-    }
-  }
-};
-
-// filter var node for op->inputs/outputs
-template <typename Callback>
-class FilterVariableImpl<std::vector<ir::Node*>, Callback> {
- public:
-  void operator()(const std::vector<ir::Node*>& nodes, Callback callback) {
-    for (auto* var : nodes) {
-      if (var->IsVar() && !var->IsCtrlVar()) {
-        callback(var);
-      }
-    }
-  }
-};
-
-template <typename Container, typename Callback>
-void FilterVariables(const Container& nodes, Callback callback) {
-  FilterVariableImpl<Container, Callback>()(nodes, callback);
-}
-
-std::unique_ptr<ir::Graph> AnalysisVarPass::ApplyImpl(
+std::unique_ptr<ir::Graph> MemoryOptimizePass::ApplyImpl(
     std::unique_ptr<ir::Graph> graph) const {
   auto nodes = graph->Nodes();
   auto subblock_vars = GetSubBlockVars(nodes);
@@ -103,48 +75,53 @@ std::unique_ptr<ir::Graph> AnalysisVarPass::ApplyImpl(
     }
 
     for (auto& var : op->outputs) {
-      if (NodeCanReused(var) && cfg_->Use(op).count(var->Name()) == 0) {
-        ir::Node* cache = pool_.NodeMatch(var);
-        if (var->Name() == FLAGS_memory_optimize_debug) {
-          VLOG(3) << "start match var " << DebugString(var) << " of op "
-                  << op->Name();
-          VLOG(3) << pool_.ToString();
-          VLOG(3) << "matched in pool : "
-                  << ((cache == nullptr) ? "False" : "True");
-        }
-        if (cache != nullptr) {
-          if (var->Name() == cache->Name()) {
-            VLOG(3) << "The same cache variable is cascade reused."
-                    << var->Name() << " is re-filled to the pool after"
-                    << "the reused op is finished. Current op can not "
-                    << "replace it again. Skip this candidate.";
-            continue;
-          }
+      if (!NodeCanReused(var) || cfg_->Use(op).count(var->Name()) == 0 ||
+          skip_set_.count(var->Name()))
+        continue;
+      ir::Node* cache = pool_.NodeMatch(var);
+
+      if (var->Name() == FLAGS_memory_optimize_debug) {
+        VLOG(3) << "start match var " << DebugString(var) << " of op "
+                << op->Name();
+        VLOG(3) << pool_.ToString();
+        VLOG(3) << "matched in pool : "
+                << ((cache == nullptr) ? "False" : "True");
+      }
 
-          int node_idx_in_pool = pool_.GetIndex(cache);
-          VLOG(3) << string::Sprintf(
-              "!!! %s,  %s => %s, cache idx %d, pool size %d",
-              std::to_string(reuse_id++), DebugString(var), DebugString(cache),
-              node_idx_in_pool, static_cast<int>(pool_.size()));
-          // update CFG Graph on the fly.
-          // reused var maybe re-fill into the pool
-          cfg_->RenameVarInCFGGraph(var->Name(), cache->Name(), idx);
-          // NOTE(dzhwinter): we need to both update the ProgramDesc
-          // and IR Graph. because op_desc/var_desc is used in CreateOp,
-          // CreateVar when running happens. But IR Graph
-          // define the dependence relationship between nodes.
-          RenameVarInGraphDesc(var->Name(), cache->Name(), idx);
-          RenameVarInGraphNode(var->Name(), cache->Name(), idx, graph.get());
-
-          pool_.Erase(cache);
+      if (cache == nullptr) continue;
+      if (var->Name() == cache->Name()) {
+        VLOG(3) << "The same cache variable is cascade reused." << var->Name()
+                << " is re-filled to the pool after"
+                << "the reused op is finished. Current op can not "
+                << "replace it again. Skip this candidate.";
+        continue;
+
+        int node_idx_in_pool = pool_.GetIndex(cache);
+        VLOG(3) << string::Sprintf(
+            "!!! %s,  %s => %s, cache idx %d, pool size %d",
+            std::to_string(reuse_id++), DebugString(var), DebugString(cache),
+            node_idx_in_pool, static_cast<int>(pool_.size()));
+        // update CFG Graph on the fly.
+        // reused var maybe re-fill into the pool
+        cfg_->RenameVarInCFGGraph(var->Name(), cache->Name(), idx);
+        // NOTE(dzhwinter): we need to both update the ProgramDesc
+        // and IR Graph. because op_desc/var_desc is used in CreateOp,
+        // CreateVar when running happens. But IR Graph
+        // define the dependence relationship between nodes.
+        RenameVarInGraphDesc(var->Name(), cache->Name(), idx);
+        RenameVarInGraphNode(var->Name(), cache->Name(), idx, graph.get());
+
+        pool_.Erase(cache);
+      }
+      // fill the pool
+      std::unordered_set<std::string> unlived_vars;
+      for (auto var : cfg_->LiveIn(op)) {
+        if (cfg_->LiveOut(op).count(var) == 0) {
+          unlived_vars.emplace(var);
         }
       }
-    }
-    // fill the pool
-    for (auto var : cfg_->LiveIn(op)) {
-      if (cfg_->LiveOut(op).count(var) == 0) {
+      for (auto var : unlived_vars) {
         ir::Node* var_node = cfg_->GetNodeFromVarName(var, op);
-        if (var_node == nullptr) continue;
         if (NodeCanReused(var_node) && !pool_.Has(var_node)) {
           pool_.Insert(var_node, op);
         }
@@ -177,7 +154,7 @@ std::unique_ptr<ir::Graph> AnalysisVarPass::ApplyImpl(
   return graph;
 }
 
-void AnalysisVarPass::SubGraphOptimize(OpDesc* op_desc) const {
+void MemoryOptimizePass::SubGraphOptimize(OpDesc* op_desc) const {
   // conditional block, while op and their grad op
   auto* sub_block_desc =
       AttrReader(op_desc->GetAttrMap()).Get<BlockDesc*>("sub_block");
@@ -247,7 +224,7 @@ void AnalysisVarPass::SubGraphOptimize(OpDesc* op_desc) const {
   }
 }
 
-std::unordered_set<std::string> AnalysisVarPass::GetSubBlockVars(
+std::unordered_set<std::string> MemoryOptimizePass::GetSubBlockVars(
     const std::unordered_set<ir::Node*>& nodes) const {
   std::unordered_set<std::string> vars;
   for (auto& op : nodes) {
@@ -263,9 +240,9 @@ std::unordered_set<std::string> AnalysisVarPass::GetSubBlockVars(
   return vars;
 }
 
-void AnalysisVarPass::RenameVarInGraphDesc(const std::string& var,
-                                           const std::string& cache_var,
-                                           size_t idx) const {
+void MemoryOptimizePass::RenameVarInGraphDesc(const std::string& var,
+                                              const std::string& cache_var,
+                                              size_t idx) const {
   for (size_t i = idx; i < cfg_->Ops().size(); ++i) {
     auto* op = cfg_->Ops()[i];
     PADDLE_ENFORCE(op->IsOp() && op->Op());
@@ -277,7 +254,7 @@ void AnalysisVarPass::RenameVarInGraphDesc(const std::string& var,
   }
 }
 
-void AnalysisVarPass::InitSSAGraphNodes() const {
+void MemoryOptimizePass::InitSSAGraphNodes() const {
   std::unordered_map<std::string, std::unordered_set<ir::Node*>> all_vars;
   if (var_nodes_.empty()) {
     for (auto* op : cfg_->Ops()) {
@@ -297,9 +274,10 @@ void AnalysisVarPass::InitSSAGraphNodes() const {
   }
 }
 
-void AnalysisVarPass::RenameVarInGraphNode(const std::string& var,
-                                           const std::string& cache_var,
-                                           size_t idx, ir::Graph* graph) const {
+void MemoryOptimizePass::RenameVarInGraphNode(const std::string& var,
+                                              const std::string& cache_var,
+                                              size_t idx,
+                                              ir::Graph* graph) const {
   // if replace happens, we need to create a newer version cache_var
   // but use the same dims/data_type with var.
   PADDLE_ENFORCE(var_nodes_[var].size() >= 1 &&
@@ -358,39 +336,6 @@ void AnalysisVarPass::RenameVarInGraphNode(const std::string& var,
   var_nodes_.at(var).clear();
 }
 
-bool AnalysisVarPass::NodeCanReused(ir::Node* node) const {
-  if (!node->IsVar() || node->IsCtrlVar()) return false;
-  auto* desc = node->Var();
-  auto type = desc->GetType();
-  if (desc->Persistable() || type != proto::VarType::LOD_TENSOR ||
-      desc->GetShape().empty()) {
-    return false;
-  }
-  // vars can be @EMPTY@, @LR_DECAY_REUSE_ID@. For example, while_grad
-  std::string name = node->Name();
-  if (!name.empty() && name[0] == '@' && name[name.size() - 1] == '@')
-    return false;
-  if (skip_set_.count(name)) return false;
-  for (auto* op : node->inputs) {
-    if (op->Op()->HasAttr("force_cpu")) {
-      // op output force generated in cpu, can not be reused.
-      return framework::AttrReader(op->Op()->GetAttrMap())
-                 .Get<bool>("force_cpu") == 0;
-    }
-  }
-  return true;
-}
-
-bool AnalysisVarPass::OpHasSubBlock(OpDesc* desc) const {
-  const AttributeMap& attrs = desc->GetAttrMap();
-  for (auto& attr : attrs) {
-    if (attr.second.type() == typeid(BlockDesc*) ||             // NOLINT
-        attr.second.type() == typeid(std::vector<BlockDesc*>))  // NOLINT
-      return true;
-  }
-  return false;
-}
-
 std::vector<ir::Node*> SortOpLikeDescOrder(const ir::Graph& graph) {
   PADDLE_ENFORCE(graph.Has(kAllOpDescs),
                  "Graph has no attribute of kAllOpDescs.");
@@ -651,6 +596,7 @@ ir::Node* ControlFlowGraph::GetNodeFromVarName(const std::string& name,
 }  // namespace framework
 }  // namespace paddle
 
-REGISTER_PASS(analysis_var_pass, paddle::framework::details::AnalysisVarPass)
+REGISTER_PASS(memory_optimize_pass,
+              paddle::framework::details::MemoryOptimizePass)
     .RequireGraphAttr(paddle::framework::details::kGraphNodePool)
     .RequireGraphAttr(paddle::framework::details::kAllOpDescs);

paddle/fluid/framework/details/analysis_var_pass.h → paddle/fluid/framework/details/memory_optimize_pass.h

@@ -25,7 +25,7 @@
 #include <vector>
 
 #include "paddle/fluid/framework/data_type.h"
-#include "paddle/fluid/framework/details/memory_reuse_types.h"
+#include "paddle/fluid/framework/details/memory_optimize_helper.h"
 #include "paddle/fluid/framework/ir/graph.h"
 #include "paddle/fluid/framework/ir/pass.h"
 
@@ -35,12 +35,10 @@ namespace details {
 constexpr char kAllOpDescs[] = "all_op_descs";
 
 std::vector<ir::Node*> SortOpLikeDescOrder(const ir::Graph& graph);
-// sort op in bfs order
-std::vector<ir::Node*> BFSSortGraphOps(const ir::Graph& graph);
 
 class ControlFlowGraph;
 
-class AnalysisVarPass : public ir::Pass {
+class MemoryOptimizePass : public ir::Pass {
  protected:
   std::unique_ptr<ir::Graph> ApplyImpl(
       std::unique_ptr<ir::Graph> graph) const override;
@@ -57,17 +55,13 @@ class AnalysisVarPass : public ir::Pass {
                             ir::Graph* graph) const;
 
   void SubGraphOptimize(OpDesc* op_desc) const;
-  // valid a tensor can be reuse or not
-  bool NodeCanReused(ir::Node* node) const;
   // scan subblock and collect the output/input variables.
   std::unordered_set<std::string> GetSubBlockVars(
       const std::unordered_set<ir::Node*>&) const;
-  // check op has subblock or not
-  bool OpHasSubBlock(OpDesc* desc) const;
 
  private:
   // Reuse Node Pool, Owned.
-  mutable OrderedNodePairPool pool_;
+  mutable OrderedNodeList pool_;
   // controlflow Graph
   mutable std::unique_ptr<ControlFlowGraph> cfg_;
   // skip set

paddle/fluid/framework/details/analysis_var_pass_test.cc → paddle/fluid/framework/details/memory_optimize_pass_test.cc

@@ -12,63 +12,19 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include "paddle/fluid/framework/details/analysis_var_pass.h"
+#include "paddle/fluid/framework/details/memory_optimize_pass.h"
 #include <algorithm>
 #include <iostream>
 #include <iterator>
 #include "glog/logging.h"
 #include "gtest/gtest.h"
+#include "paddle/fluid/framework/details/graph_test_base.h"
 #include "paddle/fluid/framework/ir/graph.h"
 #include "paddle/fluid/framework/ir/graph_helper.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/operator.h"
 #include "paddle/fluid/framework/program_desc.h"
 
-namespace paddle {
-namespace framework {
-
-class DummyOp : public OperatorBase {
- public:
-  DummyOp(const std::string& type, const VariableNameMap& inputs,
-          const VariableNameMap& outputs, const AttributeMap& attrs)
-      : OperatorBase(type, inputs, outputs, attrs) {}
-
- private:
-  void RunImpl(const Scope& scope,
-               const platform::Place& place) const override {}
-};
-
-class SumOpMaker : public OpProtoAndCheckerMaker {
- public:
-  void Make() {
-    AddInput("X", "").AsDuplicable();
-    AddOutput("Out", "");
-    AddComment("");
-  }
-};
-
-class AssignOpMaker : public OpProtoAndCheckerMaker {
- public:
-  void Make() {
-    AddInput("X", "").AsDuplicable();
-    AddOutput("Out", "");
-    AddComment("");
-  }
-};
-
-class DummyVarTypeInference : public VarTypeInference {
- public:
-  void operator()(const OpDesc& op_desc, BlockDesc* block) const override {
-    auto& inputs = op_desc.Input("X");
-    auto type = block->Var(inputs.front())->GetType();
-    auto out_var_name = op_desc.Output("Out").front();
-    block->Var(out_var_name)->SetType(type);
-  }
-};
-
-}  // namespace framework
-}  // namespace paddle
-
 REGISTER_OPERATOR(sum, paddle::framework::DummyOp,
                   paddle::framework::SumOpMaker,
                   paddle::framework::DummyVarTypeInference);
@@ -141,15 +97,6 @@ inline static ProgramDesc FillProgramDesc() {
   return prog;
 }
 
-template <typename Container>
-inline static std::string DebugString(const Container& c) {
-  std::stringstream ss;
-  for (auto& item : c) {
-    ss << item << " ";
-  }
-  return ss.str();
-}
-
 TEST(CFGGraph, IRGraph) {
   // prepare ir graph
   auto prog = FillProgramDesc();

paddle/fluid/framework/details/op_registry.h

@@ -18,6 +18,7 @@ limitations under the License. */
 #include <tuple>
 #include <vector>
 #include "paddle/fluid/framework/grad_op_desc_maker.h"
+#include "paddle/fluid/framework/inplace_op_inference.h"
 #include "paddle/fluid/framework/op_info.h"
 #include "paddle/fluid/framework/op_proto_maker.h"
 #include "paddle/fluid/framework/operator.h"
@@ -32,7 +33,8 @@ enum OpInfoFillType {
   kOpProtoAndCheckerMaker = 1,
   kGradOpDescMaker = 2,
   kVarTypeInference = 3,
-  kShapeInference = 4
+  kShapeInference = 4,
+  kInplaceOpInference = 5
 };
 
 template <typename T>
@@ -48,8 +50,11 @@ struct OpInfoFillTypeID {
                                     ? kVarTypeInference
                                     : (std::is_base_of<InferShapeBase, T>::value
                                            ? kShapeInference
-                                           : static_cast<OpInfoFillType>(
-                                                 -1)))));
+                                           : (std::is_base_of<
+                                                  InplaceOpInference, T>::value
+                                                  ? kInplaceOpInference
+                                                  : static_cast<OpInfoFillType>(
+                                                        -1))))));
   }
 };
 
@@ -139,6 +144,16 @@ struct OpInfoFiller<T, kShapeInference> {
   }
 };
 
+template <typename T>
+struct OpInfoFiller<T, kInplaceOpInference> {
+  void operator()(const char* op_type, OpInfo* info) const {
+    info->infer_inplace_ = [](const OpDesc& op_desc, BlockDesc* block) {
+      T infer;
+      return infer(op_desc, block);
+    };
+  }
+};
+
 }  // namespace details
 
 }  // namespace framework

paddle/fluid/framework/inplace_op_inference.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 <functional>
+#include <numeric>
+#include <string>
+#include <unordered_map>
+#include "glog/logging.h"
+#include "paddle/fluid/framework/block_desc.h"
+#include "paddle/fluid/framework/details/memory_optimize_helper.h"
+#include "paddle/fluid/framework/op_desc.h"
+#include "paddle/fluid/framework/type_defs.h"
+
+namespace paddle {
+namespace framework {
+
+/*
+  Inplace Inference for create In->Out pairs for inplaced operator.
+  If we specify a pair of corresponding names. For example, X->Out.
+  then Out will inplaced use X's memory. The base class will do
+  legality validation for both variables.
+*/
+class InplaceOpInference {
+ public:
+  virtual ~InplaceOpInference() {}
+  virtual std::unordered_map<std::string, std::string> operator()(
+      const OpDesc& op_desc, BlockDesc* block) const = 0;
+};
+
+class InplaceInToOut : public InplaceOpInference {
+ public:
+  std::unordered_map<std::string, std::string> operator()(
+      const OpDesc& op_desc, BlockDesc* block) const {
+    std::unordered_map<std::string, std::string> ret;
+    auto in_out_var_names_pair = this->Apply(op_desc, block);
+    for (auto& pair : in_out_var_names_pair) {
+      PADDLE_ENFORCE(!op_desc.Input(pair.first).empty(),
+                     string::Sprintf("op %s do not have input of %s!",
+                                     op_desc.Type(), pair.first));
+      PADDLE_ENFORCE(!op_desc.Output(pair.second).empty(),
+                     string::Sprintf("op %s do not have output of %s!",
+                                     op_desc.Type(), pair.second));
+      auto& in_name = op_desc.Input(pair.first).at(0);
+      auto& out_name = op_desc.Output(pair.second).at(0);
+
+      auto in = block->FindRecursiveOrCreateVar(in_name);
+      auto out = block->FindRecursiveOrCreateVar(out_name);
+      if (TryInplaceInputOutput(in, out)) ret.insert({in_name, out_name});
+    }
+    return ret;
+  }
+
+ protected:
+  virtual std::unordered_map<std::string, std::string> Apply(
+      const OpDesc& op_desc, BlockDesc* block) const = 0;
+
+  bool TryInplaceInputOutput(const VarDesc& in, const VarDesc& out) const {
+    return in.Name() != out.Name() && details::NodeCanReused(in) &&
+           details::NodeCanReused(out) &&
+           details::NodeSizeInBytes(out) <= details::NodeSizeInBytes(in);
+  }
+};
+
+/*
+  Inplace In and Out for operator only have an Input and an Output.
+  For example, activation op.
+ */
+class SingleOpInplaceInToOut : public InplaceInToOut {
+ protected:
+  std::unordered_map<std::string, std::string> Apply(
+      const OpDesc& op_desc, BlockDesc* block) const override {
+    PADDLE_ENFORCE(!op_desc.InputNames().empty(),
+                   "Op inputs must not be empty");
+    PADDLE_ENFORCE(!op_desc.OutputNames().empty(),
+                   "Op outputs must not be empty");
+    auto x_name = op_desc.InputNames().at(0);
+    auto out_name = op_desc.OutputNames().at(0);
+    return std::unordered_map<std::string, std::string>{{x_name, out_name}};
+  }
+};
+
+/*
+  Gradient op. Inplace output use it's Input.
+  For example, Input@Grad->Input reuse strategy.
+ */
+class GradOpInplaceInToOut : public InplaceInToOut {
+ protected:
+  std::unordered_map<std::string, std::string> Apply(
+      const OpDesc& op_desc, BlockDesc* block) const override {
+    std::unordered_map<std::string, std::string> ret;
+    std::unordered_set<std::string> output_names(op_desc.OutputNames().begin(),
+                                                 op_desc.OutputNames().end());
+    for (auto& input_name : op_desc.InputNames()) {
+      if (output_names.count(GradVarName(input_name))) {
+        ret.insert({input_name, GradVarName(input_name)});
+      }
+    }
+    return ret;
+  }
+};
+
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/inplace_op_inference_test.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 <iterator>
+#include <string>
+#include "gtest/gtest.h"
+#include "paddle/fluid/framework/op_info.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/operator.h"
+#include "paddle/fluid/framework/program_desc.h"
+#include "paddle/fluid/framework/var_type_inference.h"
+
+namespace paddle {
+namespace framework {
+
+class NOP : public OperatorBase {
+ public:
+  NOP(const std::string& type, const VariableNameMap& inputs,
+      const VariableNameMap& outputs, const AttributeMap& attrs)
+      : OperatorBase(type, inputs, outputs, attrs) {}
+
+ private:
+  void RunImpl(const Scope& scope,
+               const platform::Place& place) const override {}
+};
+
+class SingleOpMaker : public OpProtoAndCheckerMaker {
+ public:
+  void Make() {
+    AddInput("X", "").AsDuplicable();
+    AddOutput("Out", "");
+    AddComment("");
+  }
+};
+
+class SingleGradOpMaker : public framework::SingleGradOpDescMaker {
+ public:
+  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
+
+ protected:
+  std::unique_ptr<framework::OpDesc> Apply() const override {
+    auto* op = new framework::OpDesc();
+    op->SetType("single_op_grad");
+    op->SetInput("Out", OutputGrad("Out"));
+    op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
+    return std::unique_ptr<OpDesc>(op);
+  }
+};
+
+class SingleOpShapeInference : public framework::InferShapeBase {
+ public:
+  void operator()(framework::InferShapeContext* ctx) const override {
+    ctx->HasInput("X");
+    ctx->HasOutput("Out");
+    ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
+  }
+};
+
+class SingleGradOpShapeInference : public framework::InferShapeBase {
+ public:
+  void operator()(framework::InferShapeContext* ctx) const override {
+    ctx->HasInput(framework::GradVarName("Out"));
+    ctx->HasOutput(framework::GradVarName("X"));
+    ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("Out"));
+  }
+};
+
+class MultiOutOpMaker : public OpProtoAndCheckerMaker {
+ public:
+  void Make() {
+    AddInput("X", "").AsDuplicable();
+    AddInput("Y", "").AsDuplicable();
+    AddInput("Z", "").AsDuplicable();
+    AddOutput("Out", "");
+    AddOutput("YOut", "");
+    AddOutput("ZOut", "");
+    AddOutput("NotReuseOut", "");
+    AddComment("");
+  }
+};
+
+class MultiOutShapeInference : public framework::InferShapeBase {
+ public:
+  void operator()(framework::InferShapeContext* ctx) const override {
+    ctx->ShareDim("X", "Out");
+    ctx->ShareDim("Y", "YOut");
+    ctx->ShareDim("Z", "ZOut");
+  }
+};
+
+class MultiGradOpMaker : public framework::SingleGradOpDescMaker {
+ public:
+  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
+
+ protected:
+  std::unique_ptr<framework::OpDesc> Apply() const override {
+    auto* op = new framework::OpDesc();
+    op->SetType("multi_out_grad");
+    op->SetInput("X", Input("X"));
+    op->SetOutput(framework::GradVarName("Y"), OutputGrad("YOut"));
+    op->SetOutput(framework::GradVarName("X"), OutputGrad("Out"));
+    op->SetOutput(framework::GradVarName("Z"), OutputGrad("ZOut"));
+    return std::unique_ptr<framework::OpDesc>(op);
+  }
+};
+
+class MultiOutGradShapeInference : public framework::InferShapeBase {
+ public:
+  void operator()(framework::InferShapeContext* ctx) const override {
+    ctx->SetOutputDim(framework::GradVarName("Y"),
+                      ctx->GetInputDim(framework::GradVarName("YOut")));
+    ctx->SetOutputDim(framework::GradVarName("X"),
+                      ctx->GetInputDim(framework::GradVarName("Out")));
+    ctx->SetOutputDim(framework::GradVarName("Z"),
+                      ctx->GetInputDim(framework::GradVarName("ZOut")));
+  }
+};
+
+class MultiOutInplaceInToOut : public framework::InplaceInToOut {
+ public:
+  using framework::InplaceInToOut::InplaceInToOut;
+
+ protected:
+  std::unordered_map<std::string, std::string> Apply(
+      const OpDesc& op_desc, BlockDesc* block) const override {
+    return std::unordered_map<std::string, std::string>{
+        {"X", "Out"}, {"Y", "YOut"}, {"Z", "ZOut"},
+    };
+  }
+};
+
+class MultiOutGradInplaceInToOut : public framework::InplaceInToOut {
+ public:
+  using framework::InplaceInToOut::InplaceInToOut;
+
+ protected:
+  std::unordered_map<std::string, std::string> Apply(
+      const OpDesc& op_desc, BlockDesc* block) const override {
+    return std::unordered_map<std::string, std::string>{
+        {framework::GradVarName("YOut"), framework::GradVarName("Y")},
+        {framework::GradVarName("Out"), framework::GradVarName("X")},
+        {framework::GradVarName("ZOut"), framework::GradVarName("Z")},
+    };
+  }
+};
+
+}  // namespace framework
+}  // namespace paddle
+
+namespace f = paddle::framework;
+REGISTER_OPERATOR(single_op, f::NOP, f::SingleOpMaker, f::SingleGradOpMaker,
+                  f::SingleOpInplaceInToOut, f::SingleOpShapeInference);
+REGISTER_OPERATOR(single_op_grad, f::NOP, f::SingleOpInplaceInToOut,
+                  f::SingleGradOpShapeInference);
+REGISTER_OPERATOR(multi_out_op, f::NOP, f::MultiOutOpMaker, f::MultiGradOpMaker,
+                  f::MultiOutInplaceInToOut, f::MultiOutShapeInference);
+REGISTER_OPERATOR(multi_out_grad, f::NOP, f::MultiOutGradInplaceInToOut,
+                  f::MultiOutGradShapeInference);
+
+namespace paddle {
+namespace framework {
+
+TEST(InferInplace, SingleOpInplaceInToOut) {
+  ProgramDesc prog;
+  auto* op = prog.MutableBlock(0)->AppendOp();
+  op->SetType("single_op");
+  op->SetInput("X", {"test2_a", "test2_b", "test2_c"});
+  op->SetOutput("Out", {"test2_out"});
+
+  prog.MutableBlock(0)->Var("test2_a")->SetType(proto::VarType::LOD_TENSOR);
+  prog.MutableBlock(0)->Var("test2_a")->SetShape({32, 64});
+  prog.MutableBlock(0)->Var("test2_b")->SetType(proto::VarType::LOD_TENSOR);
+  prog.MutableBlock(0)->Var("test2_c")->SetType(proto::VarType::LOD_TENSOR);
+  prog.MutableBlock(0)->Var("test2_out");
+  prog.MutableBlock(0)->Var("test2_out")->SetShape({32, 16});
+
+  auto& infer_inplace = OpInfoMap::Instance().Get(op->Type()).infer_inplace_;
+  auto in_to_outs = infer_inplace(*op, op->Block());
+  EXPECT_EQ(in_to_outs.size(), 1ul);
+  auto it = in_to_outs.begin();
+  EXPECT_EQ(it->first, "test2_a");
+  EXPECT_EQ(it->second, "test2_out");
+}
+
+TEST(InferInplace, SingleGradOpInplaceInToOut) {
+  ProgramDesc prog;
+  auto* op = prog.MutableBlock(0)->AppendOp();
+  op->SetType("single_op_grad");
+  op->SetInput(GradVarName("Out"), {"test2_out"});
+  op->SetOutput(GradVarName("X"), {"test2_a", "test2_b", "test2_c"});
+
+  prog.MutableBlock(0)->Var("test2_a")->SetType(proto::VarType::LOD_TENSOR);
+  prog.MutableBlock(0)->Var("test2_a")->SetShape({32, 16});
+  prog.MutableBlock(0)->Var("test2_b")->SetType(proto::VarType::LOD_TENSOR);
+  prog.MutableBlock(0)->Var("test2_c")->SetType(proto::VarType::LOD_TENSOR);
+  prog.MutableBlock(0)->Var("test2_out");
+  prog.MutableBlock(0)->Var("test2_out")->SetShape({32, 16});
+
+  auto& infer_inplace = OpInfoMap::Instance().Get(op->Type()).infer_inplace_;
+  auto in_to_outs = infer_inplace(*op, op->Block());
+  EXPECT_EQ(in_to_outs.size(), 1ul);
+  auto it = in_to_outs.begin();
+  EXPECT_EQ(it->first, "test2_out");
+  EXPECT_EQ(it->second, "test2_a");
+}
+
+TEST(InferInplace, MultiOutInplaceInToOut) {
+  ProgramDesc prog;
+  auto* op = prog.MutableBlock(0)->AppendOp();
+  op->SetType("multi_out_op");
+  op->SetInput("X", {"a0", "a1"});
+  op->SetInput("Y", {"b0"});
+  op->SetInput("Z", {"c0", "c1"});
+  op->SetOutput("Out", {"o0"});
+  op->SetOutput("YOut", {"y0"});
+  op->SetOutput("ZOut", {"z0"});
+
+  prog.MutableBlock(0)->Var("a0")->SetType(proto::VarType::LOD_TENSOR);
+  prog.MutableBlock(0)->Var("b0")->SetType(proto::VarType::LOD_TENSOR);
+  prog.MutableBlock(0)->Var("c0")->SetType(proto::VarType::LOD_TENSOR);
+  prog.MutableBlock(0)->Var("c1")->SetType(proto::VarType::LOD_TENSOR);
+  prog.MutableBlock(0)->Var("o0");
+  prog.MutableBlock(0)->Var("y0");
+  prog.MutableBlock(0)->Var("z0");
+  prog.MutableBlock(0)->Var("a0")->SetShape({32, 16});
+  prog.MutableBlock(0)->Var("b0")->SetShape({32, 16});
+  prog.MutableBlock(0)->Var("c0")->SetShape({32, 16});
+  prog.MutableBlock(0)->Var("o0")->SetShape({32, 16});
+  prog.MutableBlock(0)->Var("y0")->SetShape({32, 16});
+  prog.MutableBlock(0)->Var("z0")->SetShape({32, 16});
+
+  auto& infer_inplace = OpInfoMap::Instance().Get(op->Type()).infer_inplace_;
+  auto in_to_outs = infer_inplace(*op, op->Block());
+  EXPECT_EQ(in_to_outs.size(), 3ul);
+  std::unordered_map<std::string, std::string> expects = {
+      {"a0", "o0"}, {"b0", "y0"}, {"c0", "z0"},
+  };
+  EXPECT_TRUE(expects == in_to_outs);
+}
+
+TEST(InferInplace, MultiGradInplaceInToOut) {
+  ProgramDesc prog;
+  auto* op = prog.MutableBlock(0)->AppendOp();
+  op->SetType("multi_out_grad");
+  op->SetInput(GradVarName("Out"), {"o0"});
+  op->SetInput(GradVarName("YOut"), {"y0"});
+  op->SetInput(GradVarName("ZOut"), {"z0"});
+  op->SetOutput(GradVarName("X"), {"a0", "a1"});
+  op->SetOutput(GradVarName("Y"), {"b0"});
+  op->SetOutput(GradVarName("Z"), {"c0", "c1"});
+
+  prog.MutableBlock(0)->Var("a0")->SetType(proto::VarType::LOD_TENSOR);
+  prog.MutableBlock(0)->Var("b0")->SetType(proto::VarType::LOD_TENSOR);
+  prog.MutableBlock(0)->Var("c0")->SetType(proto::VarType::LOD_TENSOR);
+  prog.MutableBlock(0)->Var("c1")->SetType(proto::VarType::LOD_TENSOR);
+  prog.MutableBlock(0)->Var("o0");
+  prog.MutableBlock(0)->Var("y0");
+  prog.MutableBlock(0)->Var("z0");
+  prog.MutableBlock(0)->Var("a0")->SetShape({32, 16});
+  prog.MutableBlock(0)->Var("b0")->SetShape({32, 16});
+  prog.MutableBlock(0)->Var("c0")->SetShape({32, 16});
+  prog.MutableBlock(0)->Var("o0")->SetShape({32, 16});
+  prog.MutableBlock(0)->Var("y0")->SetShape({32, 16});
+  prog.MutableBlock(0)->Var("z0")->SetShape({32, 16});
+
+  auto& infer_inplace = OpInfoMap::Instance().Get(op->Type()).infer_inplace_;
+  auto in_to_outs = infer_inplace(*op, op->Block());
+  EXPECT_EQ(in_to_outs.size(), 3ul);
+  std::unordered_map<std::string, std::string> expects = {
+      {"o0", "a0"}, {"y0", "b0"}, {"z0", "c0"},
+  };
+  EXPECT_TRUE(expects == in_to_outs);
+}
+
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/ir/graph_helper.cc

@@ -52,16 +52,29 @@ bool HasCircleHelper(
     ir::Node *node,
     const std::map<ir::Node *, std::unordered_set<ir::Node *>> &adj_list,
     std::unordered_set<ir::Node *> *visited,
-    std::unordered_set<ir::Node *> *in_trace) {
+    std::unordered_set<ir::Node *> *in_trace,
+    std::vector<std::vector<ir::Node *>> *circles) {
   if (visited->find(node) == visited->end()) {
     visited->insert(node);
     in_trace->insert(node);
 
     for (ir::Node *in : adj_list.at(node)) {
       if (visited->find(in) == visited->end() &&
-          HasCircleHelper(in, adj_list, visited, in_trace)) {
+          HasCircleHelper(in, adj_list, visited, in_trace, circles)) {
         return true;
       } else if (in_trace->find(in) != in_trace->end()) {
+        if (circles != nullptr) {
+          std::vector<ir::Node *> circle;
+          circle.emplace_back(in);
+          ir::Node *p = in;
+          for (auto &adj : adj_list.at(p)) {
+            if (in_trace->count(adj)) {
+              circle.emplace_back(adj);
+              p = adj;
+            }
+          }
+          circles->emplace_back(circle);
+        }
         return true;
       }
     }
@@ -71,11 +84,12 @@ bool HasCircleHelper(
 }
 
 bool HasCircleInternal(
-    const std::map<ir::Node *, std::unordered_set<ir::Node *>> &adj_list) {
+    const std::map<ir::Node *, std::unordered_set<ir::Node *>> &adj_list,
+    std::vector<std::vector<ir::Node *>> *circles) {
   std::unordered_set<ir::Node *> visited;
   std::unordered_set<ir::Node *> in_trace;
   for (auto &adj : adj_list) {
-    if (HasCircleHelper(adj.first, adj_list, &visited, &in_trace)) {
+    if (HasCircleHelper(adj.first, adj_list, &visited, &in_trace, circles)) {
       return true;
     }
   }
@@ -84,13 +98,18 @@ bool HasCircleInternal(
 }  // namespace
 
 bool HasCircle(const Graph &graph) {
-  return HasCircleInternal(BuildOperationAdjList(graph));
+  return HasCircleInternal(BuildOperationAdjList(graph), nullptr);
+}
+
+bool FindCircleSubGraph(const Graph &graph,
+                        std::vector<std::vector<ir::Node *>> *circles) {
+  return HasCircleInternal(BuildOperationAdjList(graph), circles);
 }
 
 std::vector<ir::Node *> TopologySortOperations(const Graph &graph) {
   std::map<ir::Node *, std::unordered_set<ir::Node *>> adj_list =
       BuildOperationAdjList(graph);
-  PADDLE_ENFORCE(!HasCircleInternal(adj_list));
+  PADDLE_ENFORCE(!HasCircleInternal(adj_list, nullptr));
   std::unordered_set<ir::Node *> visited;
   std::vector<ir::Node *> ret;
   for (auto adj : adj_list) {

paddle/fluid/framework/ir/graph_helper.h

@@ -28,6 +28,11 @@ namespace ir {
 // Test if the graph contains circle.
 bool HasCircle(const Graph &graph);
 
+// Find All Circles for debugging,
+// store all subgraph in circles.
+bool FindCircleSubGraph(const Graph &graph,
+                        std::vector<std::vector<ir::Node *>> *circles);
+
 size_t GraphNum(const Graph &graph);
 
 // Topology Sort the operations in the graph from inputs to outputs.

paddle/fluid/framework/ir/graph_helper_test.cc

@@ -195,6 +195,17 @@ void BuildTwoGraphs(Graph* g) {
   //  v4->outputs.push_back(o5);
 }
 
+TEST(GraphHelperTest, Circles) {
+  ProgramDesc prog;
+
+  Graph g(prog);
+  BuildCircleGraph(&g);
+
+  std::vector<std::vector<ir::Node*>> circles;
+  ASSERT_TRUE(FindCircleSubGraph(g, &circles));
+  ASSERT_EQ(circles.size(), 1UL);
+}
+
 TEST(GraphHelperTest, GraphNum) {
   ProgramDesc prog;
 

paddle/fluid/framework/op_info.h

@@ -38,6 +38,7 @@ struct OpInfo {
   OpAttrChecker* checker_{nullptr};
   InferVarTypeFN infer_var_type_;
   InferShapeFN infer_shape_;
+  InferInplaceOpFN infer_inplace_;
 
   bool HasOpProtoAndChecker() const {
     return proto_ != nullptr && checker_ != nullptr;

paddle/fluid/framework/type_defs.h

@@ -57,5 +57,8 @@ using InferVarTypeFN =
 
 using InferShapeFN = std::function<void(InferShapeContext*)>;
 
+using InplacePair = std::unordered_map<std::string, std::string>;
+using InferInplaceOpFN = std::function<InplacePair(const OpDesc&, BlockDesc*)>;
+
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/inference/utils/benchmark_tester.cc

@@ -34,6 +34,6 @@ TEST(Benchmark, PersistToFile) {
   benchmark.SetLatency(220);
 
   benchmark.PersistToFile("1.log");
-  benchmark.PersistToFile("1.log");
-  benchmark.PersistToFile("1.log");
+  benchmark.PersistToFile("2.log");
+  benchmark.PersistToFile("3.log");
 }

paddle/fluid/operators/activation_op.cc

@@ -547,12 +547,14 @@ namespace ops = paddle::operators;
   __macro(Swish, swish);             \
   __macro(ThresholdedRelu, thresholded_relu);
 
-#define REGISTER_INPLACE_ACTIVATION_OP(OP_NAME, KERNEL_TYPE)        \
-  REGISTER_OPERATOR(KERNEL_TYPE, ::paddle::operators::ActivationOp, \
-                    ::paddle::operators::OP_NAME##OpMaker,          \
-                    ::paddle::operators::ActivationOpInferVarType,  \
-                    ::paddle::operators::OP_NAME##GradMaker);       \
-  REGISTER_OPERATOR(KERNEL_TYPE##_grad, ::paddle::operators::ActivationOpGrad)
+#define REGISTER_INPLACE_ACTIVATION_OP(OP_NAME, KERNEL_TYPE)                   \
+  REGISTER_OPERATOR(KERNEL_TYPE, ::paddle::operators::ActivationOp,            \
+                    ::paddle::operators::OP_NAME##OpMaker,                     \
+                    ::paddle::operators::ActivationOpInferVarType,             \
+                    ::paddle::operators::OP_NAME##GradMaker,                   \
+                    ::paddle::framework::SingleOpInplaceInToOut);              \
+  REGISTER_OPERATOR(KERNEL_TYPE##_grad, ::paddle::operators::ActivationOpGrad, \
+                    ::paddle::framework::SingleOpInplaceInToOut)
 
 #define REGISTER_ACTIVATION_OP(OP_NAME, KERNEL_TYPE)                    \
   REGISTER_OPERATOR(KERNEL_TYPE, ::paddle::operators::ActivationOp,     \

paddle/fluid/operators/batch_norm_op.cc

@@ -604,13 +604,48 @@ class BatchNormGradMaker : public framework::SingleGradOpDescMaker {
   }
 };
 
+class BatchNormInplaceInToOut : public framework::InplaceInToOut {
+ public:
+  using InplaceInToOut::InplaceInToOut;
+
+ protected:
+  std::unordered_map<std::string, std::string> Apply(
+      const framework::OpDesc &op_desc,
+      framework::BlockDesc *block) const override {
+    std::unordered_map<std::string, std::string> inplace_in_to_out = {
+        {"Mean", "MeanOut"}, {"Variance", "VarianceOut"}, {"X", "Y"},
+    };
+    return inplace_in_to_out;
+  }
+};
+
+class BatchNormGradInplaceInToOut : public framework::InplaceInToOut {
+ public:
+  using InplaceInToOut::InplaceInToOut;
+
+ protected:
+  std::unordered_map<std::string, std::string> Apply(
+      const framework::OpDesc &op_desc,
+      framework::BlockDesc *block) const override {
+    std::unordered_map<std::string, std::string> inplace_in_to_out = {
+        // Scale, Bias, SavedMean, SavedVariance shape is [batch_size, C]
+        {framework::GradVarName("Y"), framework::GradVarName("X")},
+        {"SavedMean", framework::GradVarName("Scale")},
+        {"SavedVariance", framework::GradVarName("Bias")},
+    };
+    return inplace_in_to_out;
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
 namespace ops = paddle::operators;
 REGISTER_OPERATOR(batch_norm, ops::BatchNormOp, ops::BatchNormOpMaker,
-                  ops::BatchNormOpInferVarType, ops::BatchNormGradMaker);
-REGISTER_OPERATOR(batch_norm_grad, ops::BatchNormGradOp);
+                  ops::BatchNormOpInferVarType, ops::BatchNormGradMaker,
+                  ops::BatchNormInplaceInToOut);
+REGISTER_OPERATOR(batch_norm_grad, ops::BatchNormGradOp,
+                  ops::BatchNormGradInplaceInToOut);
 
 REGISTER_OP_CPU_KERNEL(
     batch_norm, ops::BatchNormKernel<paddle::platform::CPUDeviceContext, float>,

paddle/fluid/operators/elementwise/elementwise_add_op.cc

@@ -18,6 +18,7 @@ namespace ops = paddle::operators;
 REGISTER_ELEMWISE_GRAD_MAKER(elementwise_add, Add);
 REGISTER_ELEMWISE_EXPLICIT_OP(elementwise_add, "Add", "Out = X + Y", "Out",
                               "X");
+
 REGISTER_OP_CPU_KERNEL(
     elementwise_add,
     ops::ElementwiseAddKernel<paddle::platform::CPUDeviceContext, float>,

paddle/fluid/operators/elementwise/elementwise_op.h

@@ -250,6 +250,20 @@ class ElemwiseGradKernel : public framework::OpKernel<T> {
   }
 };
 
+class ElementwiseOpInplace : public framework::InplaceInToOut {
+ public:
+  using framework::InplaceInToOut::InplaceInToOut;
+
+ protected:
+  std::unordered_map<std::string, std::string> Apply(
+      const framework::OpDesc &op_desc,
+      framework::BlockDesc *block) const override {
+    return std::unordered_map<std::string, std::string>{
+        {"X", "Out"},
+    };
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
@@ -299,6 +313,7 @@ class ElemwiseGradKernel : public framework::OpKernel<T> {
   REGISTER_OPERATOR(op_type, ::paddle::operators::ElementwiseOp,       \
                     __ElemwiseOp##op_type##Maker__,                    \
                     ::paddle::operators::ElementwiseOpInferVarType,    \
-                    op_type##GradMaker);                               \
+                    op_type##GradMaker,                                \
+                    ::paddle::operators::ElementwiseOpInplace);        \
   REGISTER_OPERATOR(op_type##_grad,                                    \
                     ::paddle::operators::ElementwiseOpExplicitGrad)

paddle/fluid/operators/flatten_op.cc

@@ -267,6 +267,35 @@ class Flatten2GradOp : public framework::OperatorBase {
   }
 };
 
+class FlattenOpInplaceInToOut : public framework::InplaceInToOut {
+ public:
+  using InplaceInToOut::InplaceInToOut;
+
+ protected:
+  std::unordered_map<std::string, std::string> Apply(
+      const framework::OpDesc &op_desc,
+      framework::BlockDesc *block) const override {
+    std::unordered_map<std::string, std::string> inplace_in_to_out = {
+        {"X", "Out"},
+    };
+    return inplace_in_to_out;
+  }
+};
+
+class FlattenGradInplaceinToOut : public framework::InplaceInToOut {
+  using InplaceInToOut::InplaceInToOut;
+
+ protected:
+  std::unordered_map<std::string, std::string> Apply(
+      const framework::OpDesc &op_desc,
+      framework::BlockDesc *block) const override {
+    std::unordered_map<std::string, std::string> inplace_in_to_out = {
+        {framework::GradVarName("Out"), framework::GradVarName("X")},
+    };
+    return inplace_in_to_out;
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
@@ -275,10 +304,13 @@ USE_OP(reshape);
 namespace ops = paddle::operators;
 REGISTER_OPERATOR(flatten, ops::FlattenOp, ops::FlattenOpMaker,
                   ops::FlattenOpInferShape,
-                  paddle::framework::DefaultGradOpDescMaker<true>);
-REGISTER_OPERATOR(flatten_grad, ops::FlattenGradOp, ops::FlattenGradInferShape);
+                  paddle::framework::DefaultGradOpDescMaker<true>,
+                  ops::FlattenOpInplaceInToOut);
+REGISTER_OPERATOR(flatten_grad, ops::FlattenGradOp, ops::FlattenGradInferShape,
+                  ops::FlattenGradInplaceinToOut);
 
 REGISTER_OPERATOR(flatten2, ops::Flatten2Op, ops::Flatten2OpMaker,
-                  ops::Flatten2OpInferShape, ops::Flatten2GradOpMaker);
+                  ops::Flatten2OpInferShape, ops::Flatten2GradOpMaker,
+                  ops::FlattenOpInplaceInToOut);
 REGISTER_OPERATOR(flatten2_grad, ops::Flatten2GradOp,
-                  ops::Flatten2GradInferShape);
+                  ops::Flatten2GradInferShape, ops::FlattenGradInplaceinToOut);

paddle/fluid/operators/norm_op.h

@@ -99,10 +99,10 @@ class NormGradKernel : public framework::OpKernel<T> {
     auto dx_e = framework::EigenVector<T>::Flatten(*out_dx);
 
     Eigen::DSizes<int, 3> shape(pre, n, post);
-    Eigen::DSizes<int, 2> norm_shape(pre, post);
+    Eigen::DSizes<int, 3> rshape(pre, 1, post);
     auto x = x_e.reshape(shape);
     auto dy = dy_e.reshape(shape);
-    auto norm = norm_e.reshape(norm_shape);
+    auto norm = norm_e.reshape(rshape);
     auto dx = dx_e.reshape(shape);
 
     framework::Tensor rsum;
@@ -111,7 +111,6 @@ class NormGradKernel : public framework::OpKernel<T> {
 
     Eigen::DSizes<int, 1> rdim(1);
     Eigen::DSizes<int, 3> bcast(1, n, 1);
-    Eigen::DSizes<int, 3> rshape(pre, 1, post);
 
     // dx = ( dy/sqrt(sum(x*x)) ) * [1 - x*sum(x) / (sum(x*x) + e)]
     //    = [dy - dy * x * sum(x) / (sum(x*x) + e)] / sqrt(sum(x*x))

paddle/fluid/operators/reshape_op.cc

@@ -327,14 +327,45 @@ class Reshape2GradOp : public framework::OperatorWithKernel {
   }
 };
 
+class ReshapeOpInplaceInToOut : public framework::InplaceInToOut {
+ public:
+  using InplaceInToOut::InplaceInToOut;
+
+ protected:
+  std::unordered_map<std::string, std::string> Apply(
+      const framework::OpDesc &op_desc,
+      framework::BlockDesc *block) const override {
+    std::unordered_map<std::string, std::string> inplace_in_to_out = {
+        {"X", "Out"},
+    };
+    return inplace_in_to_out;
+  }
+};
+
+class ReshapeGradInplaceInToOut : public framework::InplaceInToOut {
+  using InplaceInToOut::InplaceInToOut;
+
+ protected:
+  std::unordered_map<std::string, std::string> Apply(
+      const framework::OpDesc &op_desc,
+      framework::BlockDesc *block) const override {
+    std::unordered_map<std::string, std::string> inplace_in_to_out = {
+        {framework::GradVarName("Out"), framework::GradVarName("X")},
+    };
+    return inplace_in_to_out;
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 namespace ops = paddle::operators;
 namespace plat = paddle::platform;
 
 REGISTER_OPERATOR(reshape, ops::ReshapeOp, ops::ReshapeOpMaker,
-                  paddle::framework::DefaultGradOpDescMaker<true>);
-REGISTER_OPERATOR(reshape_grad, ops::ReshapeGradOp);
+                  paddle::framework::DefaultGradOpDescMaker<true>,
+                  ops::ReshapeOpInplaceInToOut);
+REGISTER_OPERATOR(reshape_grad, ops::ReshapeGradOp,
+                  ops::ReshapeGradInplaceInToOut);
 REGISTER_OP_CPU_KERNEL_FUNCTOR(reshape, float, ops::ReshapeKernel, double,
                                ops::ReshapeKernel, int, ops::ReshapeKernel,
                                int64_t, ops::ReshapeKernel);
@@ -344,8 +375,9 @@ REGISTER_OP_CPU_KERNEL_FUNCTOR(reshape_grad, float, ops::ReshapeGradKernel,
                                ops::ReshapeGradKernel);
 
 REGISTER_OPERATOR(reshape2, ops::Reshape2Op, ops::Reshape2OpMaker,
-                  ops::Reshape2GradMaker);
-REGISTER_OPERATOR(reshape2_grad, ops::Reshape2GradOp);
+                  ops::Reshape2GradMaker, ops::ReshapeOpInplaceInToOut);
+REGISTER_OPERATOR(reshape2_grad, ops::Reshape2GradOp,
+                  ops::ReshapeGradInplaceInToOut);
 REGISTER_OP_CPU_KERNEL_FUNCTOR(reshape2, float, ops::ReshapeKernel, double,
                                ops::ReshapeKernel, int, ops::ReshapeKernel,
                                int64_t, ops::ReshapeKernel);

paddle/fluid/operators/scale_op.cc

@@ -100,13 +100,14 @@ class ScaleGradMaker : public framework::SingleGradOpDescMaker {
   }
 };
 
+using ScaleOpInplace = framework::SingleOpInplaceInToOut;
 }  // namespace operators
 }  // namespace paddle
 
 namespace ops = paddle::operators;
 
 REGISTER_OPERATOR(scale, ops::ScaleOp, ops::ScaleOpMaker, ops::ScaleGradMaker,
-                  ops::ScaleOpVarTypeInference);
+                  ops::ScaleOpVarTypeInference, ops::ScaleOpInplace);
 REGISTER_OP_CPU_KERNEL(
     scale, ops::ScaleKernel<paddle::platform::CPUDeviceContext, float>,
     ops::ScaleKernel<paddle::platform::CPUDeviceContext, double>,

paddle/fluid/operators/softmax_op.cc

@@ -198,6 +198,21 @@ class SoftmaxOpGradMaker : public framework::SingleGradOpDescMaker {
     return std::unique_ptr<framework::OpDesc>(op);
   }
 };
+
+class SoftmaxInplaceInToOut : public framework::InplaceInToOut {
+ public:
+  using framework::InplaceInToOut::InplaceInToOut;
+
+ protected:
+  std::unordered_map<std::string, std::string> Apply(
+      const framework::OpDesc& op_desc,
+      framework::BlockDesc* block) const override {
+    return std::unordered_map<std::string, std::string>{
+        {"X", "Out"},
+    };
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 

paddle/fluid/pybind/pybind.cc

@@ -1096,6 +1096,10 @@ All parameter, weight, gradient are variables in Paddle.
           "memory_early_delete",
           [](const BuildStrategy &self) { return self.memory_early_delete_; },
           [](BuildStrategy &self, bool b) { self.memory_early_delete_ = b; })
+      .def_property(
+          "enable_inplace",
+          [](const BuildStrategy &self) { return self.enable_inplace_; },
+          [](BuildStrategy &self, bool b) { self.enable_inplace_ = b; })
       .def("_finalize_strategy_and_create_passes",
            [](BuildStrategy &self) -> std::shared_ptr<ir::PassBuilder> {
              return self.CreatePassesFromStrategy(true);

python/paddle/fluid/__init__.py

@@ -158,7 +158,8 @@ def __bootstrap__():
             'enable_cublas_tensor_op_math', 'conv_workspace_size_limit',
             'cudnn_exhaustive_search', 'memory_optimize_debug', 'selected_gpus',
             'sync_nccl_allreduce', 'limit_of_tmp_allocation',
-            'times_excess_than_required_tmp_allocation'
+            'times_excess_than_required_tmp_allocation',
+            'enable_inplace_whitelist'
         ]
 
     core.init_gflags([sys.argv[0]] +

python/paddle/fluid/compiler.py

@@ -174,6 +174,11 @@ class CompiledProgram(object):
                 self._exec_strategy.num_threads = cpu_num * 2
 
         trainers_endpoints = self._program._trainers_endpoints
+
+        # FIXME(dzhwinter): enable_inplace should be after memory_optimize
+        # if turn on python memory optimize, turn off the inplace_pass.
+        self._build_strategy.enable_inplace = False if self._program._is_mem_optimized else True
+
         if self._build_strategy.num_trainers > 1 and trainers_endpoints:
             assert self._build_strategy.num_trainers == len(
                 trainers_endpoints), "num_trainers == len(end_points)"

python/paddle/fluid/framework.py

@@ -1725,6 +1725,19 @@ class Program(object):
         self._trainers_endpoints = []
         # the distributed lookup table names
         self._distributed_lookup_table = None
+        # @deprecated(the python memory optimize transpiler is deprecated)
+        # whether the program is optimized by memory_optimize_transpiler
+        self.__is_mem_optimized = False
+
+    @property
+    def _is_mem_optimized(self):
+        # if the program is optimized, operator input/outputs
+        # maybe same, which conflict with save_inference_model.
+        return self.__is_mem_optimized
+
+    @_is_mem_optimized.setter
+    def _is_mem_optimized(self, target):
+        self.__is_mem_optimized = target
 
     @property
     def op_role(self):
@@ -1744,7 +1757,7 @@ class Program(object):
         return self._current_role
 
     @op_role.setter
-    def set_op_role(self, role):
+    def op_role(self, role):
         self._current_role = role
 
     @property

python/paddle/fluid/io.py

@@ -16,6 +16,7 @@ from __future__ import print_function
 
 import os
 import errno
+import warnings
 import time
 import shutil
 import six
@@ -931,6 +932,13 @@ def save_inference_model(dirname,
 
     if main_program is None:
         main_program = default_main_program()
+        if main_program._is_mem_optimized:
+            warnings.warn(
+                "save_inference_model must put before you call memory_optimize. \
+                                            the memory_optimize will modify the original program, \
+                                            is not suitable for saving inference model \
+                                            we save the original program as inference model.",
+                RuntimeWarning)
 
     # fix the bug that the activation op's output as target will be pruned.
     # will affect the inference performance.

python/paddle/fluid/parallel_executor.py

@@ -146,6 +146,9 @@ class ParallelExecutor(object):
         # step4: get main_program, scope, local_scopes
         main = main_program if main_program \
             else framework.default_main_program()
+        # FIXME(dzhwinter): enable_inplace should be after memory_optimize
+        # if turn on python memory optimize, turn off the inplace_pass.
+        build_strategy.enable_inplace = False if main._is_mem_optimized else True
         scope = scope if scope is not None else executor.global_scope()
 
         if share_vars_from and not isinstance(share_vars_from,

python/paddle/fluid/tests/unittests/CMakeLists.txt

@@ -110,6 +110,10 @@ py_test_modules(test_parallel_executor_transformer MODULES test_parallel_executo
 if(NOT APPLE)
     py_test_modules(test_image_classification_resnet MODULES test_image_classification_resnet SERIAL)
 endif()
+if(CMAKE_BUILD_TYPE STREQUAL "Debug")
+    # change the timeout from 600 to 900, because in debug mode, this test need more time.
+    set_tests_properties(test_image_classification_resnet PROPERTIES TIMEOUT 900)
+endif()
 
 if (WITH_NGRAPH)
     add_subdirectory(ngraph)

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

@@ -40,7 +40,8 @@ class TestParallelExecutorBase(unittest.TestCase):
                                   seed=None,
                                   use_parallel_executor=True,
                                   use_reduce=False,
-                                  use_ir_memory_optimize=False,
+                                  use_ir_memory_optimize=True,
+                                  enable_inplace=True,
                                   fuse_elewise_add_act_ops=False,
                                   fuse_relu_depthwise_conv=False,
                                   optimizer=fluid.optimizer.Adam,
@@ -60,63 +61,65 @@ class TestParallelExecutorBase(unittest.TestCase):
                 main.random_seed = seed
 
             loss = method(use_feed=feed_dict is not None)
-
             if optimizer:
                 optimizer().minimize(loss)
 
             if memory_opt:
                 fluid.memory_optimize(main)
 
-            place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
-            exe = fluid.Executor(place)
-            exe.run(startup)
-            exec_strategy = fluid.ExecutionStrategy()
-            exec_strategy.allow_op_delay = allow_op_delay
-            if use_fast_executor:
-                exec_strategy.use_experimental_executor = True
-            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
-            build_strategy.fuse_relu_depthwise_conv = fuse_relu_depthwise_conv
-            build_strategy.memory_optimize = use_ir_memory_optimize
-            build_strategy.enable_sequential_execution = enable_sequential_execution
-            if use_cuda and core.is_compiled_with_cuda():
-                build_strategy.remove_unnecessary_lock = True
-            if use_parallel_executor:
-                binary = compiler.CompiledProgram(main).with_data_parallel(
-                    loss_name=loss.name,
-                    build_strategy=build_strategy,
-                    exec_strategy=exec_strategy)
-            else:
-                binary = compiler.CompiledProgram(main)
-
-            if batch_size is not None:
-                batch_size *= fluid.core.get_cuda_device_count(
-                ) if use_cuda else int(
-                    os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
-            begin = time.time()
-            first_loss, = run_executor(
-                exe=exe, binary=binary, feed=feed_dict, fetch_list=[loss.name])
-
-            for i in range(iter):
-                run_executor(
-                    exe=exe, binary=binary, feed=feed_dict, fetch_list=[])
-
-            last_loss, = run_executor(
-                exe=exe, binary=binary, feed=feed_dict, fetch_list=[loss.name])
-            end = time.time()
-
-            if batch_size is not None:
-                print("%.4f Instance per second" % (
-                    (batch_size * iter + 2) / (end - begin)))
-
-            avg_last_loss_val = np.array(last_loss).mean()
-            avg_first_loss_val = np.array(first_loss).mean()
-            if math.isnan(float(avg_last_loss_val)) or math.isnan(
-                    float(avg_first_loss_val)):
-                sys.exit("got NaN loss, training failed.")
-
-            print(first_loss, last_loss)
-            # self.assertGreater(first_loss[0], last_loss[0])
-            return first_loss, last_loss
+        place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+        exe = fluid.Executor(place)
+        exe.run(startup)
+        exec_strategy = fluid.ExecutionStrategy()
+        exec_strategy.allow_op_delay = allow_op_delay
+        if use_fast_executor:
+            exec_strategy.use_experimental_executor = True
+        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
+        build_strategy.fuse_relu_depthwise_conv = fuse_relu_depthwise_conv
+        build_strategy.memory_optimize = use_ir_memory_optimize
+        # python memory optimization is conflict with inplace pass.
+        # Use ir graph memory optimization after inplace pass is the correct way.
+        build_strategy.enable_inplace = False if memory_opt else enable_inplace
+        build_strategy.enable_sequential_execution = enable_sequential_execution
+
+        if use_cuda and core.is_compiled_with_cuda():
+            build_strategy.remove_unnecessary_lock = True
+        if use_parallel_executor:
+            binary = compiler.CompiledProgram(main).with_data_parallel(
+                loss_name=loss.name,
+                build_strategy=build_strategy,
+                exec_strategy=exec_strategy)
+        else:
+            binary = compiler.CompiledProgram(main)
+
+        if batch_size is not None:
+            batch_size *= fluid.core.get_cuda_device_count(
+            ) if use_cuda else int(
+                os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
+        begin = time.time()
+        first_loss, = run_executor(
+            exe=exe, binary=binary, feed=feed_dict, fetch_list=[loss.name])
+
+        for i in range(iter):
+            run_executor(exe=exe, binary=binary, feed=feed_dict, fetch_list=[])
+
+        last_loss, = run_executor(
+            exe=exe, binary=binary, feed=feed_dict, fetch_list=[loss.name])
+        end = time.time()
+
+        if batch_size is not None:
+            print("%.4f Instance per second" % (
+                (batch_size * iter + 2) / (end - begin)))
+
+        avg_last_loss_val = np.array(last_loss).mean()
+        avg_first_loss_val = np.array(first_loss).mean()
+        if math.isnan(float(avg_last_loss_val)) or math.isnan(
+                float(avg_first_loss_val)):
+            sys.exit("got NaN loss, training failed.")
+
+        print(first_loss, last_loss)
+        # self.assertGreater(first_loss[0], last_loss[0])
+        return first_loss, last_loss

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

@@ -16,12 +16,10 @@ import os
 import unittest
 os.environ['FLAGS_eager_delete_tensor_gb'] = "0.0"
 
-from test_parallel_executor_transformer import TestTransformer
-
-
-class EagerDeletionTestTransformer(TestTransformer):
-    pass
+os.environ[
+    'RECORDIO_FILENAME'] = '/tmp/eager_deletion_transformer.wmt16.recordio'
 
+from test_parallel_executor_transformer import TestTransformer
 
 if __name__ == '__main__':
     unittest.main()

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

@@ -25,6 +25,7 @@ import paddle.fluid.layers as layers
 import paddle.fluid.optimizer as optimizer
 from paddle.fluid.framework import Program, program_guard
 from paddle.fluid.io import save_inference_model, load_inference_model
+from paddle.fluid.transpiler import memory_optimize
 
 
 class TestBook(unittest.TestCase):
@@ -87,5 +88,31 @@ class TestBook(unittest.TestCase):
         self.assertEqual(expected, actual)
 
 
+class TestSaveInferenceModel(unittest.TestCase):
+    def test_save_inference_model(self):
+        MODEL_DIR = "./tmp/inference_model2"
+        init_program = Program()
+        program = Program()
+
+        # fake program without feed/fetch
+        with program_guard(program, init_program):
+            x = layers.data(name='x', shape=[2], dtype='float32')
+            y = layers.data(name='y', shape=[1], dtype='float32')
+
+            y_predict = layers.fc(input=x, size=1, act=None)
+
+            cost = layers.square_error_cost(input=y_predict, label=y)
+            avg_cost = layers.mean(cost)
+
+        place = core.CPUPlace()
+        exe = executor.Executor(place)
+        exe.run(init_program, feed={}, fetch_list=[])
+
+        memory_optimize(program, print_log=True)
+        self.assertEqual(program._is_mem_optimized, True)
+        # will print warning message
+        save_inference_model(MODEL_DIR, ["x", "y"], [avg_cost], exe, program)
+
+
 if __name__ == '__main__':
     unittest.main()

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

+# Copyright (c) 2019 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 __future__ import print_function
+
+import os
+import unittest
+import numpy as np
+import paddle.fluid.core as core
+import paddle.fluid as fluid
+from parallel_executor_test_base import TestParallelExecutorBase
+
+
+def fc_with_batchnorm(use_feed):
+    img = fluid.layers.data(name='image', shape=[784], dtype='float32')
+    label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+
+    hidden = img
+    for _ in range(3):
+        hidden = fluid.layers.fc(
+            hidden,
+            size=200,
+            act='tanh',
+            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 TestIrInplace(TestParallelExecutorBase):
+    @classmethod
+    def setUpClass(cls):
+        os.environ['CPU_NUM'] = str(4)
+
+    def _fc_with_batchnorm(self,
+                           ir_memory_optimize,
+                           enable_inplace,
+                           memory_opt=False):
+
+        if not core.is_compiled_with_cuda():
+            return
+        np.random.seed(5)
+        img = np.random.random(size=[32, 784]).astype(np.float32)
+        label = np.ones(shape=[32, 1], dtype='int64')
+        self.check_network_convergence(
+            fc_with_batchnorm,
+            feed_dict={"image": img,
+                       "label": label},
+            use_cuda=True,
+            memory_opt=memory_opt,
+            use_ir_memory_optimize=ir_memory_optimize,
+            enable_inplace=enable_inplace)
+
+    def test_fc_with_batchnorm(self, delta=1e-3):
+        loss00 = self._fc_with_batchnorm(False, False)
+        loss10 = self._fc_with_batchnorm(True, False)
+        loss01 = self._fc_with_batchnorm(False, True)
+        loss11 = self._fc_with_batchnorm(True, True)
+        self.assertAlmostEqual(loss00, loss10, delta=delta)
+        self.assertAlmostEqual(loss00, loss01, delta=delta)
+        self.assertAlmostEqual(loss00, loss11, delta=delta)

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

@@ -200,7 +200,7 @@ class TestResnet(TestParallelExecutorBase):
                                       model,
                                       use_cuda,
                                       iter=20,
-                                      delta2=1e-6):
+                                      delta2=1e-5):
         if use_cuda and not core.is_compiled_with_cuda():
             return
 
@@ -228,7 +228,7 @@ class TestResnet(TestParallelExecutorBase):
             optimizer=optimizer)
 
         for loss in zip(all_reduce_first_loss, reduce_first_loss):
-            self.assertAlmostEquals(loss[0], loss[1], delta=1e-6)
+            self.assertAlmostEquals(loss[0], loss[1], delta=1e-5)
         for loss in zip(all_reduce_last_loss, reduce_last_loss):
             self.assertAlmostEquals(loss[0], loss[1], delta=delta2)
 
@@ -258,17 +258,17 @@ class TestResnet(TestParallelExecutorBase):
             enable_sequential_execution=True)
 
         for loss in zip(all_reduce_first_loss, all_reduce_first_loss_seq):
-            self.assertAlmostEquals(loss[0], loss[1], delta=1e-6)
+            self.assertAlmostEquals(loss[0], loss[1], delta=1e-5)
         for loss in zip(all_reduce_last_loss, all_reduce_last_loss_seq):
             self.assertAlmostEquals(loss[0], loss[1], delta=delta2)
 
         for loss in zip(reduce_first_loss, reduce_first_loss_seq):
-            self.assertAlmostEquals(loss[0], loss[1], delta=1e-6)
+            self.assertAlmostEquals(loss[0], loss[1], delta=1e-5)
         for loss in zip(reduce_last_loss, reduce_last_loss_seq):
             self.assertAlmostEquals(loss[0], loss[1], delta=delta2)
 
         for loss in zip(all_reduce_first_loss_seq, reduce_first_loss_seq):
-            self.assertAlmostEquals(loss[0], loss[1], delta=1e-6)
+            self.assertAlmostEquals(loss[0], loss[1], delta=1e-5)
         for loss in zip(all_reduce_last_loss_seq, reduce_last_loss_seq):
             self.assertAlmostEquals(loss[0], loss[1], delta=delta2)
 
@@ -277,7 +277,7 @@ class TestResnet(TestParallelExecutorBase):
                                   use_cuda=True,
                                   use_reduce=False,
                                   iter=20,
-                                  delta2=1e-6):
+                                  delta2=1e-5):
         if use_cuda and not core.is_compiled_with_cuda():
             return
 
@@ -308,7 +308,7 @@ class TestResnet(TestParallelExecutorBase):
             optimizer=optimizer)
 
         self.assertAlmostEquals(
-            np.mean(parallel_first_loss), single_first_loss[0], delta=1e-6)
+            np.mean(parallel_first_loss), single_first_loss[0], delta=1e-5)
         self.assertAlmostEquals(
             np.mean(parallel_last_loss), single_last_loss[0], delta=delta2)
 

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

@@ -24,7 +24,7 @@ import paddle.fluid.core as core
 import paddle.dataset.wmt16 as wmt16
 import os
 
-WMT16_RECORDIO_FILE = "/tmp/wmt16.recordio"
+WMT16_RECORDIO_FILE = os.environ.get('RECORDIO_FILENAME', '/tmp/wmt16.recordio')
 
 
 class ModelHyperParams(object):

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

@@ -17,6 +17,7 @@ from __future__ import print_function
 from functools import partial
 import numpy as np
 
+import os
 import paddle.fluid as fluid
 import paddle.fluid.layers as layers
 from paddle.fluid.layers.io import open_recordio_file
@@ -408,7 +409,7 @@ def transformer(
         trg_pad_idx,
         pos_pad_idx, ):
     file_obj = open_recordio_file(
-        filename='/tmp/wmt16.recordio',
+        filename=os.environ.get('RECORDIO_FILENAME', '/tmp/wmt16.recordio'),
         shapes=[
             [batch_size * max_length, 1],
             [batch_size * max_length, 1],

python/paddle/fluid/transpiler/memory_optimization_transpiler.py

@@ -540,6 +540,7 @@ def memory_optimize(input_program,
     if skip_opt_set is not None:
         skip_opt_set = set(map(to_name_str, skip_opt_set))
     cfgs = _get_cfgs(input_program)
+    input_program._is_mem_optimized = True
     for cfg in cfgs:
         cfg.memory_optimize(skip_opt_set=skip_opt_set, level=level)
 
@@ -559,5 +560,6 @@ def release_memory(input_program, skip_opt_set=None):
         None
     """
     cfgs = _get_cfgs(input_program)
+    input_program._is_mem_optimized = True
     for cfg in cfgs:
         cfg.release_memory(skip_opt_set=skip_opt_set)