refactor new executor (#35537)

* refactor new executor, test=develop

* refine, test=develop

* refine, test=develop

* refine, test=develop

* refine, test=develop

* refine, test=develop

* refine, test=develop

* refine, test=develop

paddle/fluid/framework/new_executor/CMakeLists.txt

+set(INTERPRETERCORE_DEPS op_registry device_context scope framework_proto data_feed_proto heter_service_proto trainer_desc_proto glog 
+lod_rank_table fs shell fleet_wrapper heter_wrapper ps_gpu_wrapper box_wrapper lodtensor_printer feed_fetch_method
+graph_to_program_pass variable_helper timer monitor)
+
 cc_library(workqueue SRCS workqueue.cc DEPS enforce)
-cc_library(interpretercore SRCS interpretercore.cc DEPS op_registry
-            device_context scope framework_proto data_feed_proto heter_service_proto trainer_desc_proto glog
-            lod_rank_table fs shell fleet_wrapper heter_wrapper ps_gpu_wrapper box_wrapper lodtensor_printer feed_fetch_method
-            graph_to_program_pass variable_helper timer monitor workqueue ${DEVICE_EVENT_LIBS})
+cc_library(interpretercore_util SRCS interpretercore_util.cc DEPS ${INTERPRETERCORE_DEPS})
+cc_library(interpretercore SRCS interpretercore.cc DEPS workqueue ${DEVICE_EVENT_LIBS} interpretercore_util)
+
 cc_library(standalone_executor SRCS standalone_executor.cc DEPS interpretercore)
 cc_test(workqueue_test SRCS workqueue_test.cc DEPS workqueue)
 # cc_binary(standalone_executor_test SRCS standalone_executor_test.cc DEPS interpretercore standalone_executor operator op_registry executor ${GLOB_OP_LIB} ${GLOB_OPERATOR_DEPS} profiler)

paddle/fluid/framework/new_executor/interpretercore.h

@@ -19,7 +19,6 @@
 #include <unordered_map>
 #include <vector>
 
-#include "paddle/fluid/framework/new_executor/interpretercore_util.h"
 #include "paddle/fluid/framework/new_executor/new_executor_defs.h"
 #include "paddle/fluid/framework/new_executor/profiler.h"
 #include "paddle/fluid/framework/new_executor/workqueue.h"
@@ -44,18 +43,12 @@ class InterpreterCore {
 
   const CostInfo& DryRun(const std::vector<framework::Tensor>& feed_tensors);
 
-  static void BuildOpFuncList(const platform::Place& place,
-                              const framework::ProgramDesc& pdesc,
-                              std::vector<OperatorBase*>* op_list,
-                              std::vector<OpFuncNode>* vec_func_list,
-                              VariableScope* var_scope);
-
  private:
   void Convert();
 
-  void BuildInstructionCtx(Instruction* instr_node,
-                           const VariableScope& var_scope,
-                           const platform::Place& place);
+  void BuildAndCacheInstructionCtx(Instruction* instr_node,
+                                   const VariableScope& var_scope,
+                                   const platform::Place& place);
 
   void RunInstruction(const Instruction& instr_node);
 
@@ -64,13 +57,7 @@ class InterpreterCore {
                               const platform::Place& place,
                               bool is_dry_run = false);
 
-  std::vector<size_t> MergeVector(const std::vector<size_t>& first,
-                                  const std::vector<size_t>& second);
-
-  void BuildVariableScope(const framework::ProgramDesc& pdesc,
-                          VariableScope* var_scope);
-
-  void Prepare(const std::vector<framework::Tensor>& feed_tensors);
+  void DryRunPrepare(const std::vector<framework::Tensor>& feed_tensors);
 
   void CheckGC(size_t instr_id, const std::vector<size_t>& gc_check_list,
                const VariableScope& var_scope, const platform::Place& place,
@@ -87,26 +74,31 @@ class InterpreterCore {
 
   void StreamWaitEventOrSync(const Instruction& instruction);
 
+  void AddFetch(const std::vector<std::string>& fetch_names);
+
+  bool is_build_;
+
   const platform::Place& place_;
   ProgramDesc main_program_;
   VariableScope* global_scope_;
+
   platform::DeviceContextPool d2h_ctx_pool_;
   platform::DeviceContextPool h2d_ctx_pool_;
-  std::vector<VariableMetaInfo> vec_meta_info_;
-
-  std::vector<paddle::framework::OpFuncNode> vec_func_list_;
-  std::vector<paddle::framework::OperatorBase*> op_list_;
 
   std::vector<Instruction> vec_instruction_;
   InstructionInfo instruction_info_;
   std::vector<size_t> dependecy_count_;
-  std::vector<VariableMetaInfo> ref_coun_info_;
   std::vector<std::vector<size_t>> input_var2op_info_;
+  std::vector<VariableMetaInfo> ref_coun_info_;
+  std::vector<VariableMetaInfo> vec_meta_info_;
 
-  bool is_build_;
+  std::vector<paddle::framework::OpFuncNode> vec_func_list_;
+  std::vector<paddle::framework::OperatorBase*> op_list_;
 
   std::vector<std::string> feed_names_;
-  InterpreterProfiler profiler_;
+
+  InterpreterProfiler dry_run_profiler_;
+
   std::map<size_t, std::shared_ptr<platform::DeviceEvent>> var_id2event_;
 
   std::vector<paddle::platform::DeviceEvent> gc_event_;

paddle/fluid/framework/new_executor/interpretercore_gc_helper.h

-// Copyright (c) 2021 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 <memory>
-#include <string>
-#include <unordered_map>
-#include <vector>
-
-#include "paddle/fluid/framework/garbage_collector.h"
-#include "paddle/fluid/framework/operator.h"
-#include "paddle/fluid/framework/scope.h"
-
-namespace paddle {
-namespace framework {
-
-bool var_can_be_deleted(const std::string &name, const BlockDesc &block) {
-  auto *var_desc = block.FindVar(name);
-  if (var_desc == nullptr || var_desc->Persistable()) {
-    return false;
-  }
-
-  auto type = var_desc->Proto()->type().type();
-
-  return type == proto::VarType::LOD_TENSOR ||
-         type == proto::VarType::SELECTED_ROWS ||
-         type == proto::VarType::LOD_TENSOR_ARRAY;
-}
-
-std::unordered_map<const paddle::framework::OperatorBase *,
-                   std::vector<std::string>>
-get_unused_vars(const BlockDesc &block,
-                const std::vector<OperatorBase *> &ops) {
-  std::unordered_map<std::string, size_t> var_op_idx_map;
-
-  for (size_t i = 0; i < ops.size(); ++i) {
-    auto *op = ops[i];
-
-    OpInOutInfo info;
-    for (auto &name_pair : op->Inputs()) {
-      for (auto &name : name_pair.second) {
-        if (!var_can_be_deleted(name, block)) {
-          continue;
-        }
-
-        // var can be gc-ed
-        if (!info.IsBuilt()) {
-          info.Build(op);
-        }
-
-        if (info.IsInArgBufferNeeded(name)) {
-          // Update the last living op of variable to current op
-          var_op_idx_map[name] = i;
-        } else {
-          VLOG(10) << "Skip reference count computing of variable "
-                   << name_pair.first << "(" << name << ") in Operator "
-                   << op->Type();
-        }
-      }
-    }
-
-    for (auto &name_pair : op->Outputs()) {
-      for (auto &name : name_pair.second) {
-        if (var_can_be_deleted(name, block)) {
-          // Update the last living op of variable to current op
-          var_op_idx_map[name] = i;
-        }
-      }
-    }
-  }
-
-  std::unordered_map<const OperatorBase *, std::vector<std::string>> result;
-  for (auto &name_op_idx_pair : var_op_idx_map) {
-    auto &name = name_op_idx_pair.first;
-    size_t op_idx = name_op_idx_pair.second;
-    result[ops[op_idx]].emplace_back(name);
-  }
-  return result;
-}
-
-}  // namespace framework
-}  // namespace paddle

paddle/fluid/framework/new_executor/interpretercore_util.cc

+// Copyright (c) 2021 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/new_executor/interpretercore_util.h"
+#include "paddle/fluid/framework/executor_gc_helper.h"
+
+namespace paddle {
+namespace framework {
+namespace interpretercore {
+
+bool var_can_be_deleted(const std::string& name, const BlockDesc& block) {
+  auto* var_desc = block.FindVar(name);
+  if (var_desc == nullptr || var_desc->Persistable()) {
+    return false;
+  }
+
+  auto type = var_desc->Proto()->type().type();
+
+  return type == proto::VarType::LOD_TENSOR ||
+         type == proto::VarType::SELECTED_ROWS ||
+         type == proto::VarType::LOD_TENSOR_ARRAY;
+}
+
+std::unordered_map<const paddle::framework::OperatorBase*,
+                   std::vector<std::string>>
+get_unused_vars(const BlockDesc& block, const std::vector<OperatorBase*>& ops) {
+  std::unordered_map<std::string, size_t> var_op_idx_map;
+
+  for (size_t i = 0; i < ops.size(); ++i) {
+    auto* op = ops[i];
+
+    OpInOutInfo info;
+    for (auto& name_pair : op->Inputs()) {
+      for (auto& name : name_pair.second) {
+        if (!var_can_be_deleted(name, block)) {
+          continue;
+        }
+
+        // var can be gc-ed
+        if (!info.IsBuilt()) {
+          info.Build(op);
+        }
+
+        if (info.IsInArgBufferNeeded(name)) {
+          // Update the last living op of variable to current op
+          var_op_idx_map[name] = i;
+        } else {
+          VLOG(10) << "Skip reference count computing of variable "
+                   << name_pair.first << "(" << name << ") in Operator "
+                   << op->Type();
+        }
+      }
+    }
+
+    for (auto& name_pair : op->Outputs()) {
+      for (auto& name : name_pair.second) {
+        if (var_can_be_deleted(name, block)) {
+          // Update the last living op of variable to current op
+          var_op_idx_map[name] = i;
+        }
+      }
+    }
+  }
+
+  std::unordered_map<const OperatorBase*, std::vector<std::string>> result;
+  for (auto& name_op_idx_pair : var_op_idx_map) {
+    auto& name = name_op_idx_pair.first;
+    size_t op_idx = name_op_idx_pair.second;
+    result[ops[op_idx]].emplace_back(name);
+  }
+  return result;
+}
+
+std::string get_memcpy_type(const platform::Place& src_place,
+                            const platform::Place& dst_place) {
+  PADDLE_ENFORCE_EQ(platform::is_same_place(src_place, dst_place), false,
+                    platform::errors::PreconditionNotMet(
+                        "Required src_place shall be different with dst_place, "
+                        "but received same place: %s",
+                        src_place));
+  if (platform::is_gpu_place(dst_place)) {
+    return kMemcpyH2D;
+  } else if (platform::is_gpu_place(src_place)) {
+    return kMemcpyD2H;
+  } else {
+    PADDLE_THROW(platform::errors::PreconditionNotMet(
+        "Not support Memcpy typ : %s -> %s", src_place, dst_place));
+  }
+}
+
+void build_variable_scope(const framework::ProgramDesc& pdesc,
+                          VariableScope* var_scope) {
+  auto& global_block = pdesc.Block(0);
+
+  for (auto& var : global_block.AllVars()) {
+    if (var->Name() == framework::kEmptyVarName) {
+      continue;
+    }
+
+    if (var_scope->name2id.find(var->Name()) == var_scope->name2id.end()) {
+      var_scope->name2id[var->Name()] = var_scope->var_list.size();
+      auto v = new Variable();
+      InitializeVariable(v, var->GetType());
+      var_scope->var_list.push_back(v);
+
+      VariableMetaInfo info;
+      info.var_ref_count_ = 0;
+      info.vardesc_ = var;
+      var_scope->vec_meta_info_.push_back(info);
+    }
+  }
+}
+
+void build_op_func_list(const platform::Place& place,
+                        const framework::ProgramDesc& pdesc,
+                        std::vector<OperatorBase*>* op_list,
+                        std::vector<OpFuncNode>* vec_func_list,
+                        VariableScope* var_scope) {
+  auto& global_block = pdesc.Block(0);
+  auto& all_op_kernels = OperatorWithKernel::AllOpKernels();
+
+  std::vector<OperatorBase*> ops;
+  for (auto& op : global_block.AllOps()) {
+    VLOG(3) << "Build OpFuncNode from : " << op->Type();
+
+    auto& info = OpInfoMap::Instance().Get(op->Type());
+
+    const VariableNameMap& inputs_names = op->Inputs();
+    const VariableNameMap& outputs_names = op->Outputs();
+    AttributeMap op_attr_map = op->GetAttrMap();
+
+    if (info.Checker() != nullptr) {
+      info.Checker()->Check(&op_attr_map);
+    }
+    // step 1. Prepare VariableValueMap of input/output
+    auto op_base =
+        info.Creator()(op->Type(), inputs_names, outputs_names, op_attr_map);
+    ops.push_back(op_base);
+  }
+
+  auto unused_var_map = get_unused_vars(global_block, ops);
+
+  size_t ops_index = 0;
+  for (auto& op : global_block.AllOps()) {
+    VLOG(3) << op->Type();
+    // << op->Type() << endl;
+
+    auto op_base = ops[ops_index++];
+
+    auto inputs_names = op->Inputs();
+    auto outputs_names = op->Outputs();
+
+    VariableValueMap ins_map;
+    std::map<std::string, std::vector<int>> ins_name2id;
+    for (auto& var_name_item : inputs_names) {
+      std::vector<Variable*> input_vars;
+      std::vector<int> vec_ids;
+      input_vars.reserve(var_name_item.second.size());
+      for (auto& var_name : var_name_item.second) {
+        auto it = var_scope->name2id.find(var_name);
+        assert(it != var_scope->name2id.end());
+        input_vars.push_back(var_scope->var_list[it->second]);
+        vec_ids.push_back(it->second);
+      }
+      ins_map[var_name_item.first] = input_vars;
+      ins_name2id[var_name_item.first] = vec_ids;
+    }
+
+    VariableValueMap outs_map;
+    std::map<std::string, std::vector<int>> outs_name2id;
+    for (auto& var_name_item : outputs_names) {
+      std::vector<Variable*> output_vars;
+      std::vector<int> vec_ids;
+      output_vars.reserve(var_name_item.second.size());
+      for (auto& var_name : var_name_item.second) {
+        auto it = var_scope->name2id.find(var_name);
+        assert(it != var_scope->name2id.end());
+        output_vars.push_back(var_scope->var_list[it->second]);
+        vec_ids.push_back(it->second);
+      }
+      outs_map[var_name_item.first] = output_vars;
+      outs_name2id[var_name_item.first] = vec_ids;
+    }
+
+    OpFuncNode op_func_node;
+    op_func_node.input_index = ins_name2id;
+    op_func_node.output_index = outs_name2id;
+    // step 2: construct RuntimeContext and analysis KernelType
+    RuntimeContext runtime_context({}, {});
+    runtime_context.inputs.swap(ins_map);
+    runtime_context.outputs.swap(outs_map);
+    RuntimeInferShapeContext infer_shape_ctx(*op_base, runtime_context);
+    static_cast<const framework::OperatorWithKernel*>(op_base)->InferShape(
+        &infer_shape_ctx);
+    auto kernels_iter = all_op_kernels.find(op->Type());
+    PADDLE_ENFORCE_NE(
+        kernels_iter, all_op_kernels.end(),
+        platform::errors::Unavailable(
+            "There are no kernels which are registered in the %s operator.",
+            op->Type()));
+
+    OpKernelMap& kernels = kernels_iter->second;
+
+    platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
+    auto* dev_ctx = pool.Get(place);
+    Scope scope;
+    auto expected_kernel_key =
+        dynamic_cast<const framework::OperatorWithKernel*>(op_base)
+            ->GetExpectedKernelType(
+                ExecutionContext(*op_base, scope, *dev_ctx, runtime_context));
+
+    // consider device_guard context
+    bool need_change_place =
+        (op_base->HasAttr("op_device") &&
+         (op_base->Attr<std::string>("op_device").length() > 0));
+    if (need_change_place) {
+      auto& op_device = op_base->Attr<std::string>("op_device");
+      if (op_device == "cpu" || platform::is_cpu_place(place)) {
+        VLOG(3) << "Switch into CPUPlace by device_guard.";
+        expected_kernel_key.place_ = platform::CPUPlace();
+      } else if (op_device.find("gpu") != std::string::npos &&
+                 platform::is_gpu_place(place)) {
+        VLOG(3) << "Switch into " << place << " by device_guard.";
+        expected_kernel_key.place_ = place;
+      } else {
+        PADDLE_THROW(
+            platform::errors::Fatal("Unsupported current place %s", op_device));
+      }
+    }
+    VLOG(3) << "expected_kernel_key : " << expected_kernel_key;
+
+    // step 3. Insert memcpy_op if needed
+    VariableValueMap& ins_map_temp = runtime_context.inputs;
+    for (auto& var_name_item : ins_map_temp) {
+      for (size_t i = 0; i < var_name_item.second.size(); ++i) {
+        auto var = var_name_item.second[i];
+        auto tensor_in = static_cast<const Tensor*>(&(var->Get<LoDTensor>()));
+        if (!tensor_in->IsInitialized()) {
+          continue;
+        }
+        auto kernel_type_for_var =
+            static_cast<const framework::OperatorWithKernel*>(op_base)
+                ->GetKernelTypeForVar(var_name_item.first, *tensor_in,
+                                      expected_kernel_key);
+        if (!platform::is_same_place(kernel_type_for_var.place_,
+                                     expected_kernel_key.place_)) {
+          if (op_base->Type() == "fetch_v2") {
+            op_base->SetAttr("deepcopy", false);
+          }
+          // need trans place
+          // 1. add var in scope
+          // 2. add copy op
+          std::string new_var_name =
+              "temp_1" + std::to_string(var_scope->var_list.size() + 1);
+          auto v = new Variable();
+          v->GetMutable<LoDTensor>();
+          var_scope->name2id[new_var_name] = var_scope->var_list.size();
+          var_scope->var_list.push_back(v);
+
+          VariableMetaInfo info;
+          info.var_ref_count_ = 0;
+          info.vardesc_ = nullptr;
+          var_scope->vec_meta_info_.push_back(info);
+
+          VariableNameMap copy_in_map;
+          auto x_iter = inputs_names.find(var_name_item.first);
+          copy_in_map["X"] = {x_iter->second[i]};
+          VariableNameMap copy_out_map;
+          copy_out_map["Out"] = {new_var_name};
+          AttributeMap attr_map;
+          attr_map["dst_place_type"] =
+              is_cpu_place(expected_kernel_key.place_)
+                  ? 0
+                  : is_gpu_place(expected_kernel_key.place_) ? 1 : -1;
+
+          std::map<std::string, std::vector<int>> copy_ins_name2id;
+          copy_ins_name2id["X"] = ins_name2id[var_name_item.first];
+          std::map<std::string, std::vector<int>> copy_out_name2id;
+          copy_out_name2id["Out"] = {var_scope->name2id[new_var_name]};
+
+          op_func_node.input_index[var_name_item.first][i] =
+              var_scope->name2id[new_var_name];
+
+          VariableValueMap copy_ins_value_map;
+          copy_ins_value_map["X"] = {var};
+          VariableValueMap copy_outs_value_map;
+          copy_outs_value_map["Out"] = {v};
+
+          // memcpy_d2h, memcpy_h2d
+          auto memcpy_op_type = get_memcpy_type(kernel_type_for_var.place_,
+                                                expected_kernel_key.place_);
+          VLOG(3) << string::Sprintf("Insert %s with %s(%s) -> %s(%s).",
+                                     memcpy_op_type, x_iter->second[i],
+                                     kernel_type_for_var.place_, new_var_name,
+                                     expected_kernel_key.place_);
+          auto& copy_info = OpInfoMap::Instance().Get(memcpy_op_type);
+          auto copy_op = copy_info.Creator()(memcpy_op_type, copy_in_map,
+                                             copy_out_map, attr_map);
+          OpFuncNode copy_op_func_node;
+          copy_op_func_node.input_index = copy_ins_name2id;
+          copy_op_func_node.output_index = copy_out_name2id;
+
+          RuntimeContext copy_runtime_context({}, {});
+          copy_runtime_context.inputs.swap(copy_ins_value_map);
+          copy_runtime_context.outputs.swap(copy_outs_value_map);
+          RuntimeInferShapeContext copy_infer_shape_ctx(*copy_op,
+                                                        copy_runtime_context);
+          static_cast<const framework::OperatorWithKernel*>(copy_op)
+              ->InferShape(&copy_infer_shape_ctx);
+
+          auto kernels_iter = all_op_kernels.find(memcpy_op_type);
+          PADDLE_ENFORCE_NE(kernels_iter, all_op_kernels.end(),
+                            platform::errors::Unavailable(
+                                "There are no kernels which are registered in "
+                                "the memcpy operator."));
+
+          OpKernelMap& kernels = kernels_iter->second;
+          auto* dev_ctx = pool.Get(place);
+          Scope scope;
+          auto copy_exec_ctx =
+              ExecutionContext(*copy_op, scope, *dev_ctx, copy_runtime_context);
+          auto expected_kernel_key =
+              dynamic_cast<const framework::OperatorWithKernel*>(copy_op)
+                  ->GetExpectedKernelType(copy_exec_ctx);
+          auto kernel_iter = kernels.find(expected_kernel_key);
+          copy_op_func_node.kernel_func_ =
+              OpKernelComputeFunc(kernel_iter->second);
+          copy_op_func_node.kernel_func_(copy_exec_ctx);
+          VLOG(3) << "Run " << memcpy_op_type << " done.";
+          copy_op_func_node.type_ = OpFuncType::kQueueAsync;
+          copy_op_func_node.dev_ctx_ = dev_ctx;
+          op_list->push_back(copy_op);
+          vec_func_list->push_back(copy_op_func_node);
+
+          var_name_item.second[i] = v;
+        }
+      }
+    }
+    // step 4. Run op kernel
+    op_list->push_back(op_base);
+    VLOG(3) << op_base->Type()
+            << " : expected_kernel_key : " << expected_kernel_key;
+
+    if (platform::is_gpu_place(expected_kernel_key.place_)) {
+      op_func_node.type_ = OpFuncType::kQueueAsync;
+    } else if (platform::is_cpu_place(expected_kernel_key.place_)) {
+      op_func_node.type_ = OpFuncType::kQueueSync;
+    } else {
+      PADDLE_THROW(platform::errors::Fatal("Unsupported current place %s",
+                                           expected_kernel_key.place_));
+    }
+
+    if (!(expected_kernel_key.place_ == dev_ctx->GetPlace())) {
+      dev_ctx = pool.Get(expected_kernel_key.place_);
+    }
+    op_func_node.dev_ctx_ = dev_ctx;
+
+    auto exec_ctx =
+        ExecutionContext(*op_base, scope, *dev_ctx, runtime_context);
+
+    auto kernel_iter = kernels.find(expected_kernel_key);
+    PADDLE_ENFORCE_NE(kernel_iter, kernels.end(),
+                      platform::errors::NotFound(
+                          "Operator (%s) does not have kernel for %s.",
+                          op->Type(), KernelTypeToString(expected_kernel_key)));
+
+    op_func_node.kernel_func_ = OpKernelComputeFunc(kernel_iter->second);
+    op_func_node.kernel_func_(exec_ctx);
+    vec_func_list->push_back(op_func_node);
+
+    // gc---------------------------------------------------------------------------
+    auto iter = unused_var_map.find(op_base);
+    if (iter == unused_var_map.end()) {
+      continue;
+    }
+
+    auto& delete_vars = iter->second;
+    std::deque<std::shared_ptr<memory::Allocation>>* garbages =
+        new std::deque<std::shared_ptr<memory::Allocation>>();
+
+    for (auto& var_name : delete_vars) {
+      auto it = var_scope->name2id.find(var_name);
+      assert(it != var_scope->name2id.end());
+      auto* var = var_scope->var_list[it->second];
+      if (var == nullptr) {
+        continue;
+      }
+
+      VLOG(2) << "Erase variable " << var_name;
+      if (var->IsType<LoDTensor>()) {
+        garbages->emplace_back(
+            var->GetMutable<LoDTensor>()->MoveMemoryHolder());
+      } else if (var->IsType<SelectedRows>()) {
+        garbages->emplace_back(var->GetMutable<SelectedRows>()
+                                   ->mutable_value()
+                                   ->MoveMemoryHolder());
+      } else if (var->IsType<LoDTensorArray>()) {
+        auto* lod_tensor_arr = var->GetMutable<LoDTensorArray>();
+        for (auto& t : *lod_tensor_arr) {
+          garbages->emplace_back(t.MoveMemoryHolder());
+        }
+      } else {
+        PADDLE_THROW(platform::errors::Unimplemented(
+            "Type %s of variable %s is not supported eager deletion.",
+            framework::ToTypeName(var->Type()), var_name));
+      }
+    }
+
+    delete garbages;  // free mem
+
+    VLOG(3) << "run " << op_base->Type() << " done.";
+  }
+}
+
+std::vector<size_t> merge_vector(const std::vector<size_t>& first,
+                                 const std::vector<size_t>& second) {
+  std::vector<size_t> out(first.size() + second.size());
+  std::merge(first.begin(), first.end(), second.begin(), second.end(),
+             out.begin());
+
+  std::vector<size_t>::iterator it;
+  it = std::unique(out.begin(), out.end());
+
+  out.resize(std::distance(out.begin(), it));
+
+  return out;
+}
+
+}  // namespace interpretercore
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/new_executor/interpretercore_util.h

@@ -31,6 +31,7 @@
 
 #include "paddle/fluid/framework/executor_gc_helper.h"
 #include "paddle/fluid/framework/garbage_collector.h"
+#include "paddle/fluid/framework/new_executor/new_executor_defs.h"
 #include "paddle/fluid/framework/op_info.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/operator.h"
@@ -468,5 +469,27 @@ class RuntimeInferShapeContext : public InferShapeContext {
   const OperatorBase& op_;
   const RuntimeContext& ctx_;
 };
+
+namespace interpretercore {
+
+static constexpr char kMemcpyH2D[] = "memcpy_h2d";
+static constexpr char kMemcpyD2H[] = "memcpy_d2h";
+
+std::string get_memcpy_type(const platform::Place& src_place,
+                            const platform::Place& dst_place);
+
+void build_variable_scope(const framework::ProgramDesc& pdesc,
+                          VariableScope* var_scope);
+
+void build_op_func_list(const platform::Place& place,
+                        const framework::ProgramDesc& pdesc,
+                        std::vector<OperatorBase*>* op_list,
+                        std::vector<OpFuncNode>* vec_func_list,
+                        VariableScope* var_scope);
+
+std::vector<size_t> merge_vector(const std::vector<size_t>& first,
+                                 const std::vector<size_t>& second);
+
+}  // namespace interpretercore
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/new_executor/new_executor_defs.h

@@ -70,6 +70,8 @@ struct InstructionInfo {
   std::vector<size_t> dependecy_count_;
 };
 
+class RuntimeInferShapeContext;
+
 struct Instruction {
   OpKernelFunc kernel_func_;
   std::shared_ptr<RuntimeContext> runtime_ctx_;

paddle/fluid/framework/new_executor/standalone_executor.cc

@@ -12,6 +12,7 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 #include "paddle/fluid/framework/new_executor/standalone_executor.h"
+#include "paddle/fluid/framework/new_executor/interpretercore_util.h"
 
 namespace paddle {
 namespace framework {
@@ -48,8 +49,8 @@ StandaloneExecutor::StandaloneExecutor(const platform::Place& place,
   // run startup program
   std::vector<paddle::framework::OpFuncNode> vec_func_list;
   std::vector<paddle::framework::OperatorBase*> op_list;
-  InterpreterCore::BuildOpFuncList(place_, startup_prog, &op_list,
-                                   &vec_func_list, &global_scope_);
+  paddle::framework::interpretercore::build_op_func_list(
+      place_, startup_prog, &op_list, &vec_func_list, &global_scope_);
 }
 
 paddle::framework::FetchList StandaloneExecutor::Run(