Merge branch 'develop' of github.com:baidu/Paddle into feature/backward

doc/design/scope.md

@@ -37,8 +37,8 @@ Scope is an association of a name to variable. All variables belong to `Scope`.
 ```cpp
 class Scope {
  public:
-  Variable* CreateVariable(const std::string& name);
-  const Variable* GetVariable(const std::string& name) const;
+  Variable* NewVar(const std::string& name);
+  const Variable* FindVar(const std::string& name) const;
 
  private:
     std::unordered_map<std::string, std::unique_ptr<Variable>> vars_;
@@ -58,12 +58,12 @@ class Scope {
  public:
   Scope(const std::shared_ptr<Scope>& scope): parent_(scope) {}
 
-  Variable* GetVariable(const std::string& name) const {
+  Variable* FindVar(const std::string& name) const {
     auto it = vars_.find(name);
     if (it != vars_.end()) {
       return it->second.get();
     } else if (parent_ != nullptr) {
-      return parent_->GetVariable(name);
+      return parent_->FindVar(name);
     } else {
       return nullptr;
     }
@@ -95,10 +95,10 @@ class Scope {
   static std::shared_ptr<Scope> Create(const std::shared_ptr<Scope>& parent = nullptr);
 
   // return nullptr if not found.
-  Variable* GetVariable(const std::string& name) const;
+  Variable* FindVar(const std::string& name) const;
 
   // return if already contains same name variable.
-  Variable* CreateVariable(const std::string& name);
+  Variable* NewVar(const std::string& name);
 
  private:
   std::shared_ptr<Scope> parent_;
@@ -107,11 +107,11 @@ class Scope {
 ```
 ## Only scope can create a variable
 
-To ensure `only scope can create a variable`, we should mark `Variable`'s constructor as a private member function, and Scope is a friend class of Variable. And then only `CreateVariable` can construct `Variable`.
+To ensure `only scope can create a variable`, we should mark `Variable`'s constructor as a private member function, and Scope is a friend class of Variable. And then only `NewVar` can construct `Variable`.
 
 ## When scope destroyed, all variables inside this scope should be destroyed together
 
-The scope hold unique pointers for all variables. User can `GetVariable` from scope, but he should not hold this pointer as a member variable. Because when scope is destroyed, all variables inside this scope will be destroyed together.
+The scope hold unique pointers for all variables. User can `FindVar` from scope, but he should not hold this pointer as a member variable. Because when scope is destroyed, all variables inside this scope will be destroyed together.
 
 ## Sharing a parent scope
 
@@ -121,4 +121,4 @@ Also, as the parent scope is a `shared_ptr`, we can only `Create()` a scope shar
 
 ## Orthogonal interface
 
-`GetVariable` will return `nullptr` when `name` is not found. It can be used as `Contains` method. `CreateVariable` will return a `Error` when there is a name conflict locally. Combine `GetVariable` and `CreateVariable`, we can implement `CreateOrGetVariable` easily.
+`FindVar` will return `nullptr` when `name` is not found. It can be used as `Contains` method. `NewVar` will return a `Error` when there is a name conflict locally. Combine `FindVar` and `NewVar`, we can implement `NewVar` easily.

paddle/framework/CMakeLists.txt

@@ -8,7 +8,9 @@ cc_test(tensor_test SRCS tensor_test.cc DEPS tensor)
 cc_test(eigen_test SRCS eigen_test.cc DEPS tensor)
 
 cc_test(variable_test SRCS variable_test.cc)
-cc_test(scope_test SRCS scope_test.cc)
+
+cc_library(scope SRCS scope.cc)
+cc_test(scope_test SRCS scope_test.cc DEPS scope)
 
 proto_library(attr_type SRCS attr_type.proto)
 proto_library(op_proto SRCS op_proto.proto DEPS attr_type)
@@ -16,7 +18,7 @@ proto_library(op_desc SRCS op_desc.proto DEPS attr_type)
 cc_test(op_proto_test SRCS op_proto_test.cc DEPS op_proto protobuf)
 cc_test(op_desc_test SRCS op_desc_test.cc DEPS op_desc protobuf)
 
-cc_library(operator SRCS operator.cc DEPS op_desc device_context tensor)
+cc_library(operator SRCS operator.cc DEPS op_desc device_context tensor scope)
 cc_test(operator_test SRCS operator_test.cc DEPS operator op_registry)
 
 cc_library(grad_op_builder SRCS grad_op_builder.cc DEPS op_proto operator)

paddle/framework/eigen.h

@@ -80,5 +80,21 @@ struct EigenVector : public EigenTensor<T, 1, MajorType, IndexType> {
   }
 };
 
+template <typename T, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+struct EigenScalar {
+  // Scalar tensor (implemented as a rank-0 tensor) of scalar type T.
+  using Type = Eigen::TensorMap<
+      Eigen::TensorFixedSize<T, Eigen::Sizes<>, MajorType, IndexType>>;
+  using ConstType = Eigen::TensorMap<
+      Eigen::TensorFixedSize<const T, Eigen::Sizes<>, MajorType, IndexType>>;
+
+  static Type From(Tensor& tensor) { return Type(tensor.data<T>()); }
+
+  static ConstType From(const Tensor& tensor) {
+    return ConstType(tensor.data<T>());
+  }
+};
+
 }  // namespace framework
 }  // namespace paddle

paddle/framework/eigen_test.cc

@@ -46,6 +46,17 @@ TEST(Eigen, Tensor) {
   }
 }
 
+TEST(Eigen, ScalarFrom) {
+  Tensor t;
+  int* p = t.mutable_data<int>(make_ddim({1}), platform::CPUPlace());
+  *p = static_cast<int>(100);
+
+  EigenScalar<int>::Type es = EigenScalar<int>::From(t);
+
+  ASSERT_EQ(0, es.dimension(0));
+  ASSERT_EQ(100, es(0));
+}
+
 TEST(Eigen, VectorFrom) {
   Tensor t;
   float* p = t.mutable_data<float>(make_ddim({6}), platform::CPUPlace());

paddle/framework/net.h

@@ -43,7 +43,7 @@ class NetOp : public OperatorBase {
    * Infer all the operators' input and output variables' shapes, will be called
    * before every mini-batch
    */
-  void InferShape(const std::shared_ptr<Scope>& scope) const override {
+  void InferShape(const Scope& scope) const override {
     for (auto& op : ops_) {
       op->InferShape(scope);
     }
@@ -56,7 +56,7 @@ class NetOp : public OperatorBase {
    * scope will be used instead. If no OpContext is provicded, default context
    * will be used.
    */
-  void Run(const std::shared_ptr<Scope>& scope,
+  void Run(const Scope& scope,
            const platform::DeviceContext& dev_ctx) const override {
     for (auto& op : ops_) {
       op->Run(scope, dev_ctx);

paddle/framework/net_op_test.cc

@@ -11,10 +11,10 @@ static int run_cnt = 0;
 
 class TestOp : public OperatorBase {
  public:
-  void InferShape(const std::shared_ptr<Scope>& scope) const override {
+  void InferShape(const framework::Scope& scope) const override {
     ++infer_shape_cnt;
   }
-  void Run(const std::shared_ptr<framework::Scope>& scope,
+  void Run(const framework::Scope& scope,
            const paddle::platform::DeviceContext& dev_ctx) const override {
     ++run_cnt;
   }
@@ -63,7 +63,7 @@ TEST(OpKernel, all) {
   ASSERT_EQ(1UL, tmp_idx.size());
   ASSERT_EQ("y", net->outputs_[tmp_idx[0]]);
 
-  auto scope = std::make_shared<Scope>();
+  Scope scope;
   platform::CPUDeviceContext dev_ctx;
 
   net->InferShape(scope);

paddle/framework/op_registry_test.cc

@@ -7,9 +7,9 @@ namespace paddle {
 namespace framework {
 class CosineOp : public OperatorBase {
  public:
-  void Run(const std::shared_ptr<Scope>& scope,
+  void Run(const Scope& scope,
            const platform::DeviceContext& dev_ctx) const override {}
-  void InferShape(const std::shared_ptr<Scope>& scope) const override {}
+  void InferShape(const Scope& scope) const override {}
 };
 
 class CosineOpProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
@@ -27,8 +27,8 @@ class CosineOpProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
 
 class MyTestOp : public OperatorBase {
  public:
-  void InferShape(const std::shared_ptr<Scope>& scope) const override {}
-  void Run(const std::shared_ptr<Scope>& scope,
+  void InferShape(const Scope& scope) const override {}
+  void Run(const Scope& scope,
            const platform::DeviceContext& dev_ctx) const override {}
 };
 
@@ -68,7 +68,7 @@ TEST(OpRegistry, CreateOp) {
 
   std::shared_ptr<paddle::framework::OperatorBase> op =
       paddle::framework::OpRegistry::CreateOp(op_desc);
-  auto scope = std::make_shared<paddle::framework::Scope>();
+  paddle::framework::Scope scope;
   paddle::platform::CPUDeviceContext dev_ctx;
   op->Run(scope, dev_ctx);
   float scale_get = op->GetAttr<float>("scale");
@@ -110,7 +110,7 @@ TEST(OpRegistry, DefaultValue) {
 
   std::shared_ptr<paddle::framework::OperatorBase> op =
       paddle::framework::OpRegistry::CreateOp(op_desc);
-  auto scope = std::make_shared<paddle::framework::Scope>();
+  paddle::framework::Scope scope;
   paddle::platform::CPUDeviceContext dev_ctx;
   op->Run(scope, dev_ctx);
   ASSERT_EQ(op->GetAttr<float>("scale"), 1.0);
@@ -172,7 +172,7 @@ TEST(OpRegistry, CustomChecker) {
   SetInputFormat(&op_desc);
   auto op = paddle::framework::OpRegistry::CreateOp(op_desc);
   paddle::platform::CPUDeviceContext dev_ctx;
-  auto scope = std::make_shared<paddle::framework::Scope>();
+  paddle::framework::Scope scope;
   op->Run(scope, dev_ctx);
   int test_attr = op->GetAttr<int>("test_attr");
   ASSERT_EQ(test_attr, 4);

paddle/framework/operator.h

@@ -75,10 +75,10 @@ class OperatorBase {
 
   /// InferShape infer the size of Variables used by this Operator with
   /// information inside scope
-  virtual void InferShape(const std::shared_ptr<Scope>& scope) const = 0;
+  virtual void InferShape(const Scope& scope) const = 0;
 
   /// Net will call this function to Run an op.
-  virtual void Run(const std::shared_ptr<Scope>& scope,
+  virtual void Run(const Scope& scope,
                    const platform::DeviceContext& dev_ctx) const = 0;
 
   virtual bool IsNetOp() const { return false; }
@@ -115,27 +115,27 @@ class OperatorBase {
 
 class OperatorContext {
  public:
-  OperatorContext(const OperatorBase* op, const std::shared_ptr<Scope>& scope)
+  OperatorContext(const OperatorBase* op, const Scope& scope)
       : op_(*op), scope_(scope) {}
 
   size_t InputSize() const { return op_.inputs_.size(); }
 
   size_t OutputSize() const { return op_.outputs_.size(); }
 
-  const Variable* InputVar(const size_t& index) const {
-    return scope_->GetVariable(op_.inputs_.at(index));
+  const Variable* InputVar(const size_t index) const {
+    return scope_.FindVar(op_.inputs_.at(index));
   }
 
-  Variable* OutputVar(const size_t& index) const {
-    return scope_->GetVariable(op_.outputs_.at(index));
+  Variable* OutputVar(const size_t index) const {
+    return scope_.FindVar(op_.outputs_.at(index));
   }
 
   const Variable* InputVar(const std::string& name) const {
-    return scope_->GetVariable(op_.Input(name));
+    return scope_.FindVar(op_.Input(name));
   }
 
   Variable* OutputVar(const std::string& name) const {
-    return scope_->GetVariable(op_.Output(name));
+    return scope_.FindVar(op_.Output(name));
   }
 
   const std::vector<const Variable*> MultiInputVar(
@@ -145,7 +145,7 @@ class OperatorContext {
     res.reserve(names.size());
     std::transform(
         names.begin(), names.end(), std::back_inserter(res),
-        [this](const std::string& name) { return scope_->GetVariable(name); });
+        [this](const std::string& name) { return scope_.FindVar(name); });
     return res;
   }
 
@@ -155,17 +155,17 @@ class OperatorContext {
     res.reserve(names.size());
     std::transform(
         names.begin(), names.end(), std::back_inserter(res),
-        [this](const std::string& name) { return scope_->GetVariable(name); });
+        [this](const std::string& name) { return scope_.FindVar(name); });
     return res;
   }
 
   template <typename T>
-  const T* Input(const size_t& index) const {
+  const T* Input(const size_t index) const {
     return &(InputVar(index)->Get<T>());
   }
 
   template <typename T>
-  T* Output(const size_t& index) const {
+  T* Output(const size_t index) const {
     return OutputVar(index)->GetMutable<T>();
   }
 
@@ -186,7 +186,7 @@ class OperatorContext {
     res.reserve(names.size());
     std::transform(names.begin(), names.end(), std::back_inserter(res),
                    [this](const std::string& name) {
-                     return &scope_->GetVariable(name)->Get<T>();
+                     return &scope_.FindVar(name)->Get<T>();
                    });
     return res;
   }
@@ -198,18 +198,18 @@ class OperatorContext {
     res.reserve(names.size());
     std::transform(names.begin(), names.end(), std::back_inserter(res),
                    [this](const std::string& name) {
-                     return scope_->GetVariable(name)->GetMutable<T>();
+                     return scope_.FindVar(name)->GetMutable<T>();
                    });
     return res;
   }
 
   const OperatorBase& op_;
-  const std::shared_ptr<Scope>& scope_;
+  const Scope& scope_;
 };
 
 class InferShapeContext : public OperatorContext {
  public:
-  InferShapeContext(const OperatorBase* op, const std::shared_ptr<Scope>& scope)
+  InferShapeContext(const OperatorBase* op, const Scope& scope)
       : OperatorContext(op, scope) {}
 };
 
@@ -230,7 +230,7 @@ struct EigenDeviceConverter<platform::GPUPlace> {
 
 class ExecutionContext : public OperatorContext {
  public:
-  ExecutionContext(const OperatorBase* op, const std::shared_ptr<Scope>& scope,
+  ExecutionContext(const OperatorBase* op, const Scope& scope,
                    const platform::DeviceContext& device_context)
       : OperatorContext(op, scope), device_context_(device_context) {}
 
@@ -283,11 +283,11 @@ class OperatorWithKernel : public OperatorBase {
   using OpKernelMap =
       std::unordered_map<OpKernelKey, std::unique_ptr<OpKernel>, OpKernelHash>;
 
-  void InferShape(const std::shared_ptr<Scope>& scope) const {
+  void InferShape(const Scope& scope) const {
     InferShape(InferShapeContext(this, scope));
   }
 
-  void Run(const std::shared_ptr<Scope>& scope,
+  void Run(const Scope& scope,
            const platform::DeviceContext& dev_ctx) const final {
     auto& opKernel = AllOpKernels().at(type_).at(OpKernelKey(dev_ctx));
     opKernel->Compute(ExecutionContext(this, scope, dev_ctx));

paddle/framework/operator_test.cc

@@ -24,16 +24,15 @@ static int op_run_num = 0;
 class OpWithoutKernelTest : public OperatorBase {
  public:
   void Init() override { x = 1; }
-  void InferShape(
-      const std::shared_ptr<framework::Scope>& scope) const override {}
-  void Run(const std::shared_ptr<Scope>& scope,
+  void InferShape(const Scope& scope) const override {}
+  void Run(const Scope& scope,
            const platform::DeviceContext& dev_ctx) const override {
     op_run_num++;
     ASSERT_EQ((int)inputs_.size(), 1);
     ASSERT_EQ((int)outputs_.size(), 1);
-    ASSERT_EQ(scope->GetVariable(inputs_[0]), nullptr);
+    ASSERT_EQ(scope.FindVar(inputs_[0]), nullptr);
     ASSERT_EQ(x, 1);
-    ASSERT_NE(scope->GetVariable(outputs_[0]), nullptr);
+    ASSERT_NE(scope.FindVar(outputs_[0]), nullptr);
   }
 
  public:
@@ -69,10 +68,10 @@ TEST(OperatorBase, all) {
   attr->set_f(3.14);
 
   paddle::platform::CPUDeviceContext device_context;
-  auto scope = std::make_shared<paddle::framework::Scope>();
+  paddle::framework::Scope scope;
 
   auto op = paddle::framework::OpRegistry::CreateOp(op_desc);
-  scope->CreateVariable("OUT1");
+  scope.NewVar("OUT1");
   ASSERT_EQ(paddle::framework::op_run_num, 0);
   op->InferShape(scope);
   op->Run(scope, device_context);
@@ -118,13 +117,12 @@ class CPUKernelTest : public OpKernel {
 class OperatorMultiInputsTest : public OperatorBase {
  public:
   void Init() override { x = 1; }
-  void InferShape(
-      const std::shared_ptr<framework::Scope>& scope) const override {}
-  void Run(const std::shared_ptr<Scope>& scope,
+  void InferShape(const Scope& scope) const override {}
+  void Run(const Scope& scope,
            const platform::DeviceContext& dev_ctx) const override {
-    ASSERT_EQ(scope->GetVariable(inputs_[0]), nullptr);
+    ASSERT_EQ(scope.FindVar(inputs_[0]), nullptr);
     ASSERT_EQ(x, 1);
-    ASSERT_NE(scope->GetVariable(outputs_[0]), nullptr);
+    ASSERT_NE(scope.FindVar(outputs_[0]), nullptr);
     ASSERT_EQ(Input("x"), "IN1");
     ASSERT_EQ(Input("y"), "OUT1");
   }
@@ -206,7 +204,7 @@ TEST(OpKernel, all) {
   attr->set_f(3.14);
 
   paddle::platform::CPUDeviceContext cpu_device_context;
-  auto scope = std::make_shared<paddle::framework::Scope>();
+  paddle::framework::Scope scope;
 
   auto op = paddle::framework::OpRegistry::CreateOp(op_desc);
   ASSERT_EQ(paddle::framework::cpu_kernel_run_num, 0);
@@ -252,13 +250,13 @@ TEST(OpKernel, multi_inputs) {
   output_format->Add(2);  // y1
 
   paddle::platform::CPUDeviceContext cpu_device_context;
-  auto scope = std::make_shared<Scope>();
-  scope->CreateVariable("x0")->GetMutable<Tensor>();
-  scope->CreateVariable("x1")->GetMutable<Tensor>();
-  scope->CreateVariable("x2")->GetMutable<Tensor>();
-  scope->CreateVariable("k0")->GetMutable<Tensor>();
-  scope->CreateVariable("y0")->GetMutable<Tensor>();
-  scope->CreateVariable("y1")->GetMutable<Tensor>();
+  paddle::framework::Scope scope;
+  scope.NewVar("x0")->GetMutable<Tensor>();
+  scope.NewVar("x1")->GetMutable<Tensor>();
+  scope.NewVar("x2")->GetMutable<Tensor>();
+  scope.NewVar("k0")->GetMutable<Tensor>();
+  scope.NewVar("y0")->GetMutable<Tensor>();
+  scope.NewVar("y1")->GetMutable<Tensor>();
 
   auto op = paddle::framework::OpRegistry::CreateOp(op_desc);
   op->Run(scope, cpu_device_context);

paddle/framework/scope.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+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/framework/scope.h"
+#include "paddle/string/printf.h"
+
+namespace paddle {
+namespace framework {
+
+Scope::~Scope() {
+  DropKids();
+  for (auto& kv : vars_) delete kv.second;
+}
+
+Scope& Scope::NewScope() const {
+  kids_.push_back(new Scope(this));
+  return *kids_.back();
+}
+
+Variable* Scope::NewVar(const std::string& name) {
+  auto iter = vars_.find(name);
+  if (iter != vars_.end()) {
+    return iter->second;
+  }
+  Variable* v = new Variable();
+  vars_[name] = v;
+  v->name_ = &(vars_.find(name)->first);
+  return v;
+}
+
+Variable* Scope::NewVar() {
+  return NewVar(string::Sprintf("%p.%d", this, vars_.size()));
+}
+
+Variable* Scope::FindVar(const std::string& name) const {
+  auto it = vars_.find(name);
+  if (it != vars_.end()) return it->second;
+  return (parent_ == nullptr) ? nullptr : parent_->FindVar(name);
+}
+
+const Scope* Scope::FindScope(const Variable* var) const {
+  for (auto& kv : vars_) {
+    if (kv.second == var) {
+      return this;
+    }
+  }
+  return (parent_ == nullptr) ? nullptr : parent_->FindScope(var);
+}
+void Scope::DropKids() {
+  for (Scope* s : kids_) delete s;
+  kids_.clear();
+}
+
+}  // namespace framework
+}  // namespace paddle

paddle/framework/scope.h

@@ -14,9 +14,9 @@ limitations under the License. */
 
 #pragma once
 
+#include <list>
 #include <string>
 #include <unordered_map>
-#include <vector>
 
 #include "paddle/framework/variable.h"
 
@@ -35,73 +35,42 @@ class Scope;
  */
 class Scope {
  public:
-  /**
-   * @brief Initialize s Scope without parent.
-   */
   Scope() {}
+  ~Scope();
 
-  /**
-   * @brief Initialize a Scope with parent.
-   */
-  explicit Scope(const std::shared_ptr<Scope>& parent) : parent_(parent) {}
-
-  /**
-   * @brief Create Variable
-   *
-   * Create Variable in this Scope. Return the exist one if Variable already
-   * been created.
-   */
-  Variable* CreateVariable(const std::string& name) {
-    auto var = GetVariable(name);
-    if (var) {
-      return var;
-    } else {
-      auto ptr = new Variable();
-      name_to_var_[name] = std::unique_ptr<Variable>(ptr);
-      var_to_name_[ptr] = name;
-      return GetVariable(name);
-    }
-  }
-
-  /**
-   * @brief Get Variable.
-   *
-   * Get Variable from this Scope, this function will recursive find Variable
-   * from it's parent scope. Return nullptr if not found.
-   */
-  Variable* GetVariable(const std::string& name) const {
-    auto it = name_to_var_.find(name);
-    if (it != name_to_var_.end()) {
-      return it->second.get();
-    } else if (parent_ != nullptr) {
-      return parent_->GetVariable(name);
-    } else {
-      return nullptr;
-    }
-  }
-
-  /**
-   * @brief If this scope has a Var named name.
-   *
-   * Find if there is a Variable in this scope and it's parent scope
-   */
-  bool HasVariable(const std::string& name) const {
-    return (name_to_var_.find(name) != name_to_var_.end() ||
-            (parent_ && parent_->HasVariable(name)));
-  }
-
-  std::string GetVariableName(Variable* const var) const {
-    try {
-      return var_to_name_.at(var);
-    } catch (...) {
-      return "";
-    }
-  }
+  // Disable Copy, Assign, Move.
+  Scope(const Scope& other) = delete;
+  Scope& operator=(const Scope& other) = delete;
+  Scope(Scope&& other) = delete;
+
+  /// Create a sub-scope. Returns a reference other than a pointer so
+  /// to prevent from manual deletion.
+  /// Mark it to const because that new kid scope cannot change parent scope.
+  Scope& NewScope() const;
+
+  /// Create a variable with given name if it doesn't exist.
+  Variable* NewVar(const std::string& name);
+
+  /// Create a variable with a scope-unique name.
+  Variable* NewVar();
+
+  /// Find a variable in the scope or any of its ancestors.  Returns
+  /// nullptr if cannot find.
+  Variable* FindVar(const std::string& name) const;
+
+  /// Find the scope or an ancestor scope that contains the given variable.
+  const Scope* FindScope(const Variable* var) const;
+
+  /// Drop all kids scopes belonged to this scope.
+  void DropKids();
 
  private:
-  std::unordered_map<Variable*, std::string> var_to_name_;
-  std::unordered_map<std::string, std::unique_ptr<Variable>> name_to_var_;
-  std::shared_ptr<Scope> parent_{nullptr};
+  // Call Scope::NewScope for a sub-scope.
+  explicit Scope(Scope const* parent) : parent_(parent) {}
+
+  std::unordered_map<std::string, Variable*> vars_;
+  mutable std::list<Scope*> kids_;
+  Scope const* parent_{nullptr};
 };
 
 }  // namespace framework

paddle/framework/scope_test.cc

@@ -15,49 +15,42 @@ limitations under the License. */
 #include "paddle/framework/scope.h"
 #include "gtest/gtest.h"
 
-TEST(Scope, Create) {
-  using paddle::framework::Scope;
-  using paddle::framework::Variable;
+using paddle::framework::Scope;
+using paddle::framework::Variable;
 
-  auto scope = std::make_shared<Scope>();
+TEST(Scope, VarsShadowing) {
+  Scope s;
+  Scope& ss1 = s.NewScope();
+  Scope& ss2 = s.NewScope();
 
-  Variable* var0 = scope->CreateVariable("");
-  EXPECT_NE(var0, nullptr);
+  Variable* v0 = s.NewVar("a");
+  Variable* v1 = ss1.NewVar("a");
 
-  /// GetVariable will return nullptr if not exist.
-  Variable* var1 = scope->GetVariable("a");
-  EXPECT_EQ(var1, nullptr);
+  EXPECT_NE(v0, v1);
 
-  /// CreateVariable will return one.
-  Variable* var2 = scope->CreateVariable("a");
-  EXPECT_NE(var2, nullptr);
-
-  /// Get the created variable.
-  Variable* var3 = scope->GetVariable("a");
-  EXPECT_EQ(var2, var3);
+  EXPECT_EQ(v0, s.FindVar("a"));
+  EXPECT_EQ(v1, ss1.FindVar("a"));
+  EXPECT_EQ(v0, ss2.FindVar("a"));
+}
 
-  /// CreateVariable will just return the variable if it's
-  /// already exist.
-  Variable* var4 = scope->CreateVariable("a");
-  EXPECT_EQ(var4, var2);
+TEST(Scope, FindVar) {
+  Scope s;
+  Scope& ss = s.NewScope();
 
-  EXPECT_EQ("a", scope->GetVariableName(var4));
-  Scope scope2;
-  auto var = scope2.CreateVariable("tmp");
-  EXPECT_EQ("", scope->GetVariableName(var));
-}
+  EXPECT_EQ(nullptr, s.FindVar("a"));
+  EXPECT_EQ(nullptr, ss.FindVar("a"));
 
-TEST(Scope, Parent) {
-  using paddle::framework::Scope;
-  using paddle::framework::Variable;
+  ss.NewVar("a");
 
-  auto parent_scope = std::make_shared<Scope>();
-  auto scope = std::make_shared<Scope>(parent_scope);
+  EXPECT_EQ(nullptr, s.FindVar("a"));
+  EXPECT_NE(nullptr, ss.FindVar("a"));
+}
 
-  Variable* var0 = parent_scope->CreateVariable("a");
-  EXPECT_NE(var0, nullptr);
+TEST(Scope, FindScope) {
+  Scope s;
+  Scope& ss = s.NewScope();
+  Variable* v = s.NewVar("a");
 
-  /// GetVariable will get Variable from parent scope if exist.
-  Variable* var1 = scope->GetVariable("a");
-  EXPECT_EQ(var0, var1);
+  EXPECT_EQ(&s, s.FindScope(v));
+  EXPECT_EQ(&s, ss.FindScope(v));
 }

paddle/framework/variable.h

@@ -16,7 +16,7 @@
 #include <typeindex>
 #include <typeinfo>
 
-#include "paddle/platform/assert.h"
+#include "paddle/platform/enforce.h"
 
 namespace paddle {
 namespace framework {
@@ -25,7 +25,7 @@ class Variable {
  public:
   template <typename T>
   const T& Get() const {
-    PADDLE_ASSERT(IsType<T>());
+    PADDLE_ENFORCE(IsType<T>(), "Variable must be type %s", typeid(T).name());
     return *static_cast<const T*>(holder_->Ptr());
   }
 
@@ -65,6 +65,17 @@ class Variable {
 
   std::unique_ptr<Placeholder>
       holder_;  // pointers to a PlaceholderImpl object indeed.
+
+  // name_ is only meaningful with a Scope and accessible by it.
+  //
+  // NOTE: Please don't expose name_ by adding methods like
+  // Variable::Name or Scope::VarName!  A variable could have a human
+  // readable name or an auto-generated scope-unique name.  In the
+  // former case, the caller knows the name and doesn't need to access
+  // the name; in the latter case, the variable should be identified
+  // by its address but not the unreadable name.
+  friend class Scope;
+  const std::string* name_;
 };
 
 }  // namespace framework

paddle/operators/recurrent_network_op.cc

@@ -27,38 +27,37 @@ namespace operators {
 
 namespace rnn {
 
-void SegmentInputs(std::vector<std::shared_ptr<Scope>>& step_scopes,
+void SegmentInputs(const std::vector<Scope*>& step_scopes,
                    const std::vector<Link>& inlinks,
                    const size_t seq_len) {
   PADDLE_ENFORCE(!inlinks.empty(), "no in links are provided.");
   for (size_t i = 0; i < inlinks.size(); ++i) {
     Tensor* input =
-        step_scopes[0]->GetVariable(inlinks[i].external)->GetMutable<Tensor>();
+        step_scopes[0]->FindVar(inlinks[i].external)->GetMutable<Tensor>();
     DDim dims = input->dims();
     PADDLE_ENFORCE(static_cast<size_t>(dims[0]) == seq_len,
                    "all the inlinks must have same length");
     DDim step_dims = slice_ddim(dims, 1, dims.size());
     for (size_t j = 0; j < seq_len; j++) {
-      Tensor* step_input = step_scopes[j]
-                               ->CreateVariable(inlinks[i].internal)
-                               ->GetMutable<Tensor>();
+      Tensor* step_input =
+          step_scopes[j]->NewVar(inlinks[i].internal)->GetMutable<Tensor>();
       *step_input = input->Slice<float>(j, j + 1);
       step_input->Resize(step_dims);
     }
   }
 }
 
-void ConcatOutputs(std::vector<std::shared_ptr<Scope>>& step_scopes,
+void ConcatOutputs(const std::vector<Scope*>& step_scopes,
                    const std::vector<Link>& outlinks,
                    const size_t seq_len) {
   for (size_t i = 0; i < outlinks.size(); i++) {
     Tensor* output =
-        step_scopes[0]->GetVariable(outlinks[i].external)->GetMutable<Tensor>();
+        step_scopes[0]->FindVar(outlinks[i].external)->GetMutable<Tensor>();
 
     // TODO(qingiqng) remove following code after adding
     // InferShape in RecurrentGradientOp
     DDim step_dims = step_scopes[0]
-                         ->GetVariable(outlinks[i].internal)
+                         ->FindVar(outlinks[i].internal)
                          ->GetMutable<Tensor>()
                          ->dims();
     std::vector<int> dims_vec = vectorize(step_dims);
@@ -66,9 +65,8 @@ void ConcatOutputs(std::vector<std::shared_ptr<Scope>>& step_scopes,
     output->mutable_data<float>(make_ddim(dims_vec), platform::CPUPlace());
 
     for (size_t j = 0; j < seq_len; j++) {
-      Tensor* step_output = step_scopes[j]
-                                ->GetVariable(outlinks[i].internal)
-                                ->GetMutable<Tensor>();
+      Tensor* step_output =
+          step_scopes[j]->FindVar(outlinks[i].internal)->GetMutable<Tensor>();
       // TODO(luotao02) data type and platform::DeviceContext() should set
       // correctly
       (output->Slice<float>(j, j + 1))
@@ -77,7 +75,7 @@ void ConcatOutputs(std::vector<std::shared_ptr<Scope>>& step_scopes,
   }
 }
 
-void LinkMemories(std::vector<std::shared_ptr<Scope>>& scopes,
+void LinkMemories(const std::vector<Scope*>& scopes,
                   const std::vector<rnn::MemoryAttr>& memories,
                   size_t step_id,
                   int offset) {
@@ -94,17 +92,17 @@ void LinkMemories(std::vector<std::shared_ptr<Scope>>& scopes,
                  offset,
                  scopes.size(),
                  step_id);
-  std::shared_ptr<Scope> scope = scopes[step_id];
-  std::shared_ptr<Scope> linked_scope = scopes[step_id + offset];
+  auto scope = scopes[step_id];
+  auto linked_scope = scopes[step_id + offset];
   for (auto& attr : memories) {
-    auto mem = scope->CreateVariable(attr.pre_var)->GetMutable<Tensor>();
+    auto mem = scope->NewVar(attr.pre_var)->GetMutable<Tensor>();
     // maybe share variable is better?
-    auto linked_mem = linked_scope->GetVariable(attr.var)->GetMutable<Tensor>();
+    auto linked_mem = linked_scope->FindVar(attr.var)->GetMutable<Tensor>();
     mem->ShareDataWith<float>(*linked_mem);
 
     // TODO(qingqing) remove following code
     // the memory of current step should be allocated in step net
-    auto m = scope->CreateVariable(attr.var)->GetMutable<Tensor>();
+    auto m = scope->NewVar(attr.var)->GetMutable<Tensor>();
     // for unit test, as addOp and mulOp are null currently, if not
     // mutable_data, mem.data() in output will be error. We will
     // remove this line after merge the correct addOp and mulOp.
@@ -171,8 +169,8 @@ void InitArgument(const ArgumentName& name,
 
 }  // namespace rnn
 
-void RecurrentAlgorithm::InferShape(const std::shared_ptr<Scope>& scope) const {
-  seq_len_ = scope->GetVariable((arg_->inlinks[0]).external)
+void RecurrentAlgorithm::InferShape(const Scope& scope) const {
+  seq_len_ = scope.FindVar((arg_->inlinks[0]).external)
                  ->GetMutable<Tensor>()
                  ->dims()[0];
   CreateScopes(scope);
@@ -187,10 +185,10 @@ void RecurrentAlgorithm::InferShape(const std::shared_ptr<Scope>& scope) const {
 
   InitMemories(step_scopes[0]);
 
-  PADDLE_ENFORCE(scope->HasVariable(arg_->step_net),
+  PADDLE_ENFORCE(scope.FindVar(arg_->step_net) != nullptr,
                  "stepnet [%s] is not in scope.",
                  arg_->step_net);
-  Variable* net = scope->GetVariable(arg_->step_net);
+  Variable* net = scope.FindVar(arg_->step_net);
   PADDLE_ENFORCE(net != nullptr, "failed to get step net");
   // If the InferShape is called in OperatorBase's run function,
   // the rnn op only needs to do InferShape for the first time step
@@ -198,82 +196,79 @@ void RecurrentAlgorithm::InferShape(const std::shared_ptr<Scope>& scope) const {
     if (i > 0) {
       rnn::LinkMemories(step_scopes, arg_->memories, i, -1);
     }
-    net->GetMutable<NetOp>()->InferShape(step_scopes[i]);
+    net->GetMutable<NetOp>()->InferShape(*step_scopes[i]);
   }
 
   auto outlinks = arg_->outlinks;
   for (size_t i = 0; i < outlinks.size(); i++) {
     DDim step_dims = step_scopes[0]
-                         ->GetVariable(outlinks[i].internal)
+                         ->FindVar(outlinks[i].internal)
                          ->GetMutable<Tensor>()
                          ->dims();
     std::vector<int> dims_vec = vectorize(step_dims);
     // now only support fixed length
     dims_vec.insert(dims_vec.begin(), seq_len_);
     Tensor* output =
-        step_scopes[0]->GetVariable(outlinks[i].external)->GetMutable<Tensor>();
+        step_scopes[0]->FindVar(outlinks[i].external)->GetMutable<Tensor>();
     output->Resize(make_ddim(dims_vec));
   }
 }
 
-void RecurrentAlgorithm::Run(const std::shared_ptr<Scope>& scope,
+void RecurrentAlgorithm::Run(const Scope& scope,
                              const platform::DeviceContext& dev_ctx) const {
   auto step_scopes = GetStepScopes(scope);
 
-  Variable* net = scope->GetVariable(arg_->step_net);
+  Variable* net = scope.FindVar(arg_->step_net);
   for (size_t step_id = 0; step_id < seq_len_; step_id++) {
     // the link memory is done in InferShape
     // maybe remove following code after testing
     if (step_id > 0) {
       rnn::LinkMemories(step_scopes, arg_->memories, step_id, -1);
     }
-    net->GetMutable<NetOp>()->Run(step_scopes[step_id], dev_ctx);
+    net->GetMutable<NetOp>()->Run(*step_scopes[step_id], dev_ctx);
   }
 
   rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_);
 }
 
-void RecurrentAlgorithm::CreateScopes(std::shared_ptr<Scope> scope) const {
+void RecurrentAlgorithm::CreateScopes(const Scope& scope) const {
   // TODO(xxx) Only two scopes are needed for inference, this case will be
   // supported later.
-  auto step_scopes = scope->GetVariable(arg_->step_scopes)
-                         ->GetMutable<std::vector<std::shared_ptr<Scope>>>();
+  auto step_scopes =
+      scope.FindVar(arg_->step_scopes)->GetMutable<std::vector<Scope*>>();
 
   if (seq_len_ > step_scopes->size()) {
     for (size_t i = step_scopes->size(); i < seq_len_; ++i) {
-      std::shared_ptr<Scope> step_scope = std::make_shared<Scope>(scope);
+      auto& step_scope = scope.NewScope();
 
       // Now all variables in scope must be created outside of op.
-      auto net_op = scope->GetVariable(arg_->step_net)->GetMutable<NetOp>();
+      auto net_op = scope.FindVar(arg_->step_net)->GetMutable<NetOp>();
       for (auto& input : net_op->inputs_) {
-        step_scope->CreateVariable(input);
+        if (!step_scope.FindVar(input)) step_scope.NewVar(input);
       }
       for (auto& output : net_op->outputs_) {
-        step_scope->CreateVariable(output);
+        step_scope.NewVar(output);
       }
 
-      step_scopes->push_back(std::make_shared<Scope>(step_scope));
+      step_scopes->emplace_back(&step_scope);
     }
   }
 }
 
-void RecurrentAlgorithm::InitMemories(std::shared_ptr<Scope> step_scope) const {
+void RecurrentAlgorithm::InitMemories(Scope* step_scope) const {
   for (auto& attr : arg_->memories) {
-    Tensor* pre_mem =
-        step_scope->CreateVariable(attr.pre_var)->GetMutable<Tensor>();
-    PADDLE_ENFORCE(step_scope->HasVariable(attr.boot_var),
+    Tensor* pre_mem = step_scope->NewVar(attr.pre_var)->GetMutable<Tensor>();
+    PADDLE_ENFORCE(step_scope->FindVar(attr.boot_var) != nullptr,
                    "memory [%s]'s boot variable [%s] not exists",
                    attr.var,
                    attr.boot_var);
-    Tensor* boot_mem =
-        step_scope->GetVariable(attr.boot_var)->GetMutable<Tensor>();
+    Tensor* boot_mem = step_scope->FindVar(attr.boot_var)->GetMutable<Tensor>();
     pre_mem->ShareDataWith<float>(*boot_mem);
 
     // TODO(qingqing) remove following code
     // the memory of current step should be allocated in step net
     // here for unit test
-    auto cur_step_mem =
-        step_scope->CreateVariable(attr.var)->GetMutable<Tensor>();
+    auto cur_step_mem = step_scope->NewVar(attr.var)->GetMutable<Tensor>();
     cur_step_mem->mutable_data<float>(boot_mem->dims(), platform::CPUPlace());
   }
 }
@@ -334,72 +329,69 @@ public:
 };
 
 void RecurrentGradientAlgorithm::Run(
-    const std::shared_ptr<Scope>& scope,
-    const platform::DeviceContext& dev_ctx) const {
+    const Scope& scope, const platform::DeviceContext& dev_ctx) const {
   auto step_scopes = GetStepScopes(scope);
   rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_);
-  PADDLE_ENFORCE(scope->HasVariable(arg_->step_net),
+  PADDLE_ENFORCE(scope.FindVar(arg_->step_net) != nullptr,
                  "step net is not in scope.");
-  Variable* net = scope->GetVariable(arg_->step_net);
+  Variable* net = scope.FindVar(arg_->step_net);
   PADDLE_ENFORCE(net != nullptr, "failed to get step net");
   for (int step_id = seq_len_ - 1; step_id >= 0; --step_id) {
     if (static_cast<size_t>(step_id) != seq_len_ - 1) {
       rnn::LinkMemories(step_scopes, arg_->memories, step_id, 1);
     }
-    net->GetMutable<NetOp>()->Run(step_scopes[step_id], dev_ctx);
+    net->GetMutable<NetOp>()->Run(*step_scopes[step_id], dev_ctx);
   }
   LinkBootMemoryGradients(step_scopes[0]);
   rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_);
 }
 
 void RecurrentGradientAlgorithm::LinkBootMemoryGradients(
-    std::shared_ptr<Scope> step_scope) const {
+    Scope* step_scope) const {
   for (auto& attr : arg_->memories) {
-    Tensor* mem_grad =
-        step_scope->CreateVariable(attr.var)->GetMutable<Tensor>();
+    Tensor* mem_grad = step_scope->NewVar(attr.var)->GetMutable<Tensor>();
     PADDLE_ENFORCE(mem_grad != nullptr,
                    "boot_tensor should be retrieved before");
-    PADDLE_ENFORCE(step_scope->HasVariable(attr.boot_var),
+    PADDLE_ENFORCE(step_scope->FindVar(attr.boot_var) != nullptr,
                    "memory [%s]'s boot variable [%s] not exists",
                    attr.var,
                    attr.boot_var);
     Tensor* boot_mem_grad =
-        step_scope->CreateVariable(attr.boot_var)->GetMutable<Tensor>();
+        step_scope->NewVar(attr.boot_var)->GetMutable<Tensor>();
     boot_mem_grad->ShareDataWith<float>(*mem_grad);
   }
 }
 
-void RecurrentGradientAlgorithm::InferShape(
-    const std::shared_ptr<Scope>& scope) const {
-  seq_len_ = scope->GetVariable((arg_->inlinks[0]).external)
+void RecurrentGradientAlgorithm::InferShape(const Scope& scope) const {
+  seq_len_ = scope.FindVar((arg_->inlinks[0]).external)
                  ->GetMutable<Tensor>()
                  ->dims()[0];
   auto step_scopes = GetStepScopes(scope);
   rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_);
 
-  PADDLE_ENFORCE(scope->HasVariable(arg_->step_net),
+  PADDLE_ENFORCE(scope.FindVar(arg_->step_net) != nullptr,
                  "step net is not in scope.");
-  Variable* net = scope->GetVariable(arg_->step_net);
+  Variable* net = scope.FindVar(arg_->step_net);
   PADDLE_ENFORCE(net != nullptr, "failed to get step net");
 
   for (int step_id = seq_len_ - 1; step_id >= 0; --step_id) {
     if (static_cast<size_t>(step_id) != seq_len_ - 1) {
       rnn::LinkMemories(step_scopes, arg_->memories, step_id, 1);
     }
-    net->GetMutable<NetOp>()->InferShape(step_scopes[step_id]);
+    net->GetMutable<NetOp>()->InferShape(*step_scopes[step_id]);
   }
 
   auto outlinks = arg_->outlinks;
   for (size_t i = 0; i < outlinks.size(); i++) {
     DDim step_dims = step_scopes[0]
-                         ->GetVariable(outlinks[i].internal)
+                         ->FindVar(outlinks[i].internal)
                          ->GetMutable<Tensor>()
                          ->dims();
     std::vector<int> dims_vec = vectorize(step_dims);
     // now only support fixed length
     dims_vec.insert(dims_vec.begin(), seq_len_);
     Tensor* output =
-        step_scopes[0]->GetVariable(outlinks[i].external)->GetMutable<Tensor>();
+        step_scopes[0]->FindVar(outlinks[i].external)->GetMutable<Tensor>();
     output->Resize(make_ddim(dims_vec));
   }
   LinkBootMemoryGradients(step_scopes[0]);

paddle/operators/recurrent_network_op.h

@@ -70,18 +70,18 @@ struct ArgumentName {
 /**
  * Prepare inputs for each step net.
  */
-void SegmentInputs(std::vector<std::shared_ptr<Scope>>& step_scopes,
+void SegmentInputs(const std::vector<Scope*>& step_scopes,
                    const std::vector<Link>& inlinks,
                    const size_t seq_len);
 
 /**
  * Process outputs of step nets and merge to variables.
  */
-void ConcatOutputs(std::vector<std::shared_ptr<Scope>>& step_scopes,
+void ConcatOutputs(const std::vector<Scope*>& step_scopes,
                    const std::vector<Link>& outlinks,
                    const size_t seq_len);
 
-void LinkMemories(std::vector<std::shared_ptr<Scope>>& step_scopes,
+void LinkMemories(const std::vector<Scope*>& step_scopes,
                   const std::vector<MemoryAttr>& memories,
                   size_t step_id,
                   int offset);
@@ -100,15 +100,14 @@ void InitArgument(const ArgumentName& name, Argument* arg);
 
 class RecurrentAlgorithm {
 public:
-  void Run(const std::shared_ptr<Scope>& scope,
-           const platform::DeviceContext& dev_ctx) const;
+  void Run(const Scope& scope, const platform::DeviceContext& dev_ctx) const;
 
   void Init(std::unique_ptr<rnn::Argument> arg) { arg_ = std::move(arg); }
 
   /**
    * InferShape must be called before Run.
    */
-  void InferShape(const std::shared_ptr<Scope>& scope) const;
+  void InferShape(const Scope& scope) const;
 
 protected:
   /*
@@ -117,15 +116,13 @@ protected:
    * NOTE the scopes are reused in both the forward and backward, so just
    * create once and expand its size if more steps need.
    */
-  void CreateScopes(std::shared_ptr<Scope> scope) const;
+  void CreateScopes(const Scope& scope) const;
 
-  inline const std::vector<std::shared_ptr<Scope>>& GetStepScopes(
-      std::shared_ptr<Scope> scope) const {
-    return *(scope->GetVariable(arg_->step_scopes))
-                ->GetMutable<std::vector<std::shared_ptr<Scope>>>();
+  const std::vector<Scope*>& GetStepScopes(const Scope& scope) const {
+    return *scope.FindVar(arg_->step_scopes)->GetMutable<std::vector<Scope*>>();
   }
 
-  void InitMemories(std::shared_ptr<Scope> step_scopes) const;
+  void InitMemories(Scope* step_scopes) const;
 
 private:
   std::unique_ptr<rnn::Argument> arg_;
@@ -146,21 +143,18 @@ class RecurrentGradientAlgorithm {
 public:
   void Init(std::unique_ptr<rnn::Argument> arg) { arg_ = std::move(arg); }
 
-  void Run(const std::shared_ptr<Scope>& scope,
-           const platform::DeviceContext& dev_ctx) const;
+  void Run(const Scope& scope, const platform::DeviceContext& dev_ctx) const;
 
-  void LinkBootMemoryGradients(std::shared_ptr<Scope> step_scopes) const;
+  void LinkBootMemoryGradients(Scope* step_scopes) const;
 
   /**
    * InferShape must be called before Run.
    */
-  void InferShape(const std::shared_ptr<Scope>& scope) const;
+  void InferShape(const Scope& scope) const;
 
 protected:
-  inline const std::vector<std::shared_ptr<Scope>>& GetStepScopes(
-      std::shared_ptr<Scope> scope) const {
-    return *(scope->GetVariable(arg_->step_scopes))
-                ->GetMutable<std::vector<std::shared_ptr<Scope>>>();
+  inline const std::vector<Scope*>& GetStepScopes(const Scope& scope) const {
+    return *scope.FindVar(arg_->step_scopes)->GetMutable<std::vector<Scope*>>();
   }
 
 private:
@@ -175,11 +169,11 @@ public:
   /**
    * InferShape must be called before Run.
    */
-  virtual void InferShape(const std::shared_ptr<Scope>& scope) const override {
+  virtual void InferShape(const Scope& scope) const override {
     alg_.InferShape(scope);
   }
 
-  virtual void Run(const std::shared_ptr<Scope>& scope,
+  virtual void Run(const Scope& scope,
                    const platform::DeviceContext& dev_ctx) const override {
     alg_.Run(scope, dev_ctx);
   }
@@ -197,11 +191,11 @@ public:
   /**
    * InferShape must be called before Run.
    */
-  virtual void InferShape(const std::shared_ptr<Scope>& scope) const override {
+  virtual void InferShape(const Scope& scope) const override {
     alg_.InferShape(scope);
   }
 
-  virtual void Run(const std::shared_ptr<Scope>& scope,
+  virtual void Run(const Scope& scope,
                    const platform::DeviceContext& dev_ctx) const override {
     alg_.Run(scope, dev_ctx);
   }

paddle/operators/recurrent_network_op_test.cc

@@ -34,41 +34,40 @@ protected:
   virtual void TearDown() override {}
 
   void CreateGlobalVariables() {
-    scope_ = std::make_shared<Scope>();
     // create input, and init content
     LOG(INFO) << "create global variable x";
     for (auto inlink : std::vector<std::string>{"x", "x0", "x1", "h"}) {
-      Variable* x = scope_->CreateVariable(inlink);
+      Variable* x = scope_.NewVar(inlink);
       DDim dims = make_ddim(std::vector<int>{
           10 /*sent size*/, 20 /*batch size*/, 30 /*input dim*/});
       x->GetMutable<Tensor>()->mutable_data<float>(dims, platform::CPUPlace());
     }
     // create output alias just for test
     for (auto inlink : std::vector<std::string>{"h@alias"}) {
-      Variable* x = scope_->CreateVariable(inlink);
+      Variable* x = scope_.NewVar(inlink);
       DDim dims =
           make_ddim(std::vector<int>{20 /*batch size*/, 30 /*input dim*/});
       x->GetMutable<Tensor>()->mutable_data<float>(dims, platform::CPUPlace());
     }
 
     LOG(INFO) << "create global variable w";
-    Variable* w = scope_->CreateVariable("rnn/w");
+    Variable* w = scope_.NewVar("rnn/w");
     w->GetMutable<Tensor>()->mutable_data<float>(
         make_ddim(std::vector<int>{30, 30}), platform::CPUPlace());
 
     for (auto boot : std::vector<std::string>{"x_boot", "h_boot"}) {
       LOG(INFO) << "create global variable " << boot;
-      Variable* h_boot = scope_->CreateVariable(boot);
+      Variable* h_boot = scope_.NewVar(boot);
       h_boot->GetMutable<Tensor>()->mutable_data<float>(
           make_ddim(std::vector<int>{20 /*batch size*/, 30 /*input dim*/}),
           platform::CPUPlace());
     }
 
     LOG(INFO) << "create variable step_scopes";
-    scope_->CreateVariable("step_scopes");
+    scope_.NewVar("step_scopes");
 
     LOG(INFO) << "create variable h";
-    scope_->CreateVariable("h");
+    scope_.NewVar("h");
   }
 
   void CreateRNNOp() {
@@ -150,7 +149,7 @@ protected:
 
   void CreateStepNet() {
     LOG(INFO) << "create variable step_net";
-    Variable* var = scope_->CreateVariable("step_net");
+    Variable* var = scope_.NewVar("step_net");
     auto net = var->GetMutable<NetOp>();
     // rnn/s is net's input or output?
     net->inputs_ = {"rnn/h@pre", "rnn/w", "rnn/x"};
@@ -164,7 +163,7 @@ protected:
   }
 
   // father scope
-  std::shared_ptr<Scope> scope_;
+  Scope scope_;
   std::shared_ptr<OperatorBase> rnn_op_;
 };
 
@@ -191,68 +190,64 @@ protected:
   virtual void TearDown() override {}
 
   void CreateGlobalVariables() {
-    scope_ = std::make_shared<Scope>();
     // inputs: x
     LOG(INFO) << "create global variable x";
-    Variable* x = scope_->CreateVariable("x");
+    Variable* x = scope_.NewVar("x");
     DDim dims =
         make_ddim({10 /*sent size*/, 20 /*batch size*/, 30 /*input dim*/});
     x->GetMutable<Tensor>()->mutable_data<float>(dims, platform::CPUPlace());
     // inputs: h_boot
     LOG(INFO) << "create global variable h_boot";
-    Variable* h_boot = scope_->CreateVariable("h_boot");
+    Variable* h_boot = scope_.NewVar("h_boot");
     h_boot->GetMutable<Tensor>()->mutable_data<float>(
         make_ddim({20 /*batch size*/, 30 /*input dim*/}), platform::CPUPlace());
     // inputs: w
     LOG(INFO) << "create global variable w";
-    Variable* w = scope_->CreateVariable("rnn/w");
+    Variable* w = scope_.NewVar("rnn/w");
     w->GetMutable<Tensor>()->mutable_data<float>(make_ddim({30, 30}),
                                                  platform::CPUPlace());
     // inputs: h_grad
     LOG(INFO) << "create variable h_grad";
-    Variable* dh = scope_->CreateVariable("h_grad");
+    Variable* dh = scope_.NewVar("h_grad");
     dh->GetMutable<Tensor>()->mutable_data<float>(make_ddim({10, 20, 30}),
                                                   platform::CPUPlace());
     // inputs: step_scopes
     LOG(INFO) << "create variable step_scopes";
-    scope_->CreateVariable("step_scopes");
+    scope_.NewVar("step_scopes");
     // inputs: step_net
     LOG(INFO) << "create variable step_net";
-    scope_->CreateVariable("step_net");
+    scope_.NewVar("step_net");
     // outputs: w_grad
     LOG(INFO) << "create global variable w_grad";
-    scope_->CreateVariable("rnn/w_grad");
+    scope_.NewVar("rnn/w_grad");
     // outputs: x_grad
     LOG(INFO) << "create global variable x_grad";
-    scope_->CreateVariable("x_grad");
+    scope_.NewVar("x_grad");
     // outputs: h_boot_grad
     LOG(INFO) << "create global variable h_boot_grad";
-    scope_->CreateVariable("h_boot_grad");
+    scope_.NewVar("h_boot_grad");
   }
 
   void CreateStepScopes() {
-    std::vector<std::shared_ptr<Scope>>* step_scopes =
-        scope_->GetVariable("step_scopes")
-            ->GetMutable<std::vector<std::shared_ptr<Scope>>>();
+    auto step_scopes =
+        scope_.FindVar("step_scopes")->GetMutable<std::vector<Scope*>>();
     for (int i = 0; i < 10; ++i) {
-      auto scope = std::make_shared<Scope>(scope_);
-      auto pre_t = scope->CreateVariable("rnn/pre_h")->GetMutable<Tensor>();
-      pre_t->mutable_data<float>(make_ddim({20, 30}), platform::CPUPlace());
-      auto tensor = scope->CreateVariable("rnn/h")->GetMutable<Tensor>();
-      tensor->mutable_data<float>(make_ddim({20, 30}), platform::CPUPlace());
+      auto& scope = scope_.NewScope();
+      auto pre_t = scope.NewVar("rnn/pre_h")->GetMutable<Tensor>();
+      pre_t->mutable_data<float>({20, 30}, platform::CPUPlace());
+      auto tensor = scope.NewVar("rnn/h")->GetMutable<Tensor>();
+      tensor->mutable_data<float>({20, 30}, platform::CPUPlace());
 
       // for unit test of ConcatOutputs
-      auto xg = scope->CreateVariable("rnn/x_grad")->GetMutable<Tensor>();
-      xg->mutable_data<float>(make_ddim({20, 30}), platform::CPUPlace());
+      auto xg = scope.NewVar("rnn/x_grad")->GetMutable<Tensor>();
+      xg->mutable_data<float>({20, 30}, platform::CPUPlace());
 
-      step_scopes->push_back(scope);
+      step_scopes->emplace_back(&scope);
     }
 
     // last time step
-    auto g = (*step_scopes)[9]
-                 ->CreateVariable("rnn/h_pre_grad")
-                 ->GetMutable<Tensor>();
-    g->mutable_data<float>(make_ddim({20, 30}), platform::CPUPlace());
+    auto g = (*step_scopes)[9]->NewVar("rnn/h_pre_grad")->GetMutable<Tensor>();
+    g->mutable_data<float>({20, 30}, platform::CPUPlace());
   }
 
   void CreateRNNGradientAlgorithm() {
@@ -280,7 +275,7 @@ protected:
 
   void CreateStepNet() {
     LOG(INFO) << "create variable step_net";
-    Variable* var = scope_->CreateVariable("step_net");
+    Variable* var = scope_.NewVar("step_net");
     auto net = var->GetMutable<NetOp>();
     net->AddOp(OpRegistry::CreateOp("mul",
                                     {"rnn/h_pre", "rnn/w", "rnn/s_grad"},
@@ -300,9 +295,8 @@ protected:
     rnn::Link inlink;
     inlink.external = "x";
     inlink.internal = "rnn/x";
-    std::vector<std::shared_ptr<Scope>>* step_scopes =
-        scope_->GetVariable("step_scopes")
-            ->GetMutable<std::vector<std::shared_ptr<Scope>>>();
+    auto step_scopes =
+        scope_.FindVar("step_scopes")->GetMutable<std::vector<Scope*>>();
     rnn::SegmentInputs(*step_scopes, std::vector<rnn::Link>{inlink}, 10);
   }
 
@@ -314,15 +308,14 @@ protected:
     mem_attr.boot_var = "boot_h";
     std::vector<rnn::MemoryAttr> memories;
     memories.push_back(mem_attr);
-    std::vector<std::shared_ptr<Scope>>* step_scopes =
-        scope_->GetVariable("step_scopes")
-            ->GetMutable<std::vector<std::shared_ptr<Scope>>>();
+    auto step_scopes =
+        scope_.FindVar("step_scopes")->GetMutable<std::vector<Scope*>>();
     for (int i = 1; i < 10; ++i) {
       rnn::LinkMemories(*step_scopes, memories, i, -1);
     }
   }
 
-  std::shared_ptr<Scope> scope_;
+  Scope scope_;
   RecurrentGradientAlgorithm rnn_grad_algo_;
 };
 
@@ -341,14 +334,14 @@ TEST(RecurrentOp, LinkMemories) {
 
   // create and init step scopes
   int len = 10;
-  std::vector<std::shared_ptr<Scope>> step_scopes;
+  std::vector<Scope*> step_scopes;
   for (int i = 0; i < len; ++i) {
-    auto scope = std::make_shared<Scope>();
-    scope->CreateVariable("pre_h");
-    auto tensor = scope->CreateVariable("h")->GetMutable<Tensor>();
-    float* data = tensor->mutable_data<float>(make_ddim({15, 20}), CPUPlace());
-    for (int i = 0; i < 15 * 20; ++i) {
-      data[i] = rand() * (1. / (double)RAND_MAX);
+    auto scope = new Scope();
+    scope->NewVar("pre_h");
+    auto tensor = scope->NewVar("h")->GetMutable<Tensor>();
+    float* data = tensor->mutable_data<float>({15, 20}, CPUPlace());
+    for (int j = 0; j < 15 * 20; ++j) {
+      data[j] = rand() * (1. / (double)RAND_MAX);
     }
     step_scopes.push_back(scope);
   }
@@ -367,9 +360,9 @@ TEST(RecurrentOp, LinkMemories) {
   // check
   for (int i = 0; i < len - 1; ++i) {
     const float* a =
-        step_scopes[i]->GetVariable("h")->GetMutable<Tensor>()->data<float>();
+        step_scopes[i]->FindVar("h")->GetMutable<Tensor>()->data<float>();
     const float* b = step_scopes[i + 1]
-                         ->GetVariable("pre_h")
+                         ->FindVar("pre_h")
                          ->GetMutable<Tensor>()
                          ->data<float>();
     for (size_t i = 0; i < 15 * 20; ++i) {
@@ -382,19 +375,25 @@ TEST(RecurrentOp, LinkMemories) {
   }
   // check
   for (int i = len - 2; i >= 0; --i) {
-    const float* a = step_scopes[i]
-                         ->GetVariable("pre_h")
-                         ->GetMutable<Tensor>()
-                         ->data<float>();
-    const float* b = step_scopes[i + 1]
-                         ->GetVariable("h")
-                         ->GetMutable<Tensor>()
-                         ->data<float>();
+    const float* a =
+        step_scopes[i]->FindVar("pre_h")->GetMutable<Tensor>()->data<float>();
+    const float* b =
+        step_scopes[i + 1]->FindVar("h")->GetMutable<Tensor>()->data<float>();
     for (size_t i = 0; i < 15 * 20; ++i) {
       ASSERT_FLOAT_EQ(a[i], b[i]);
     }
   }
+
+  for (auto s : step_scopes) {
+    delete s;
+  }
 }
 
 USE_OP(add_two);
 USE_OP(mul);
+
+// int main() {
+//  //! TODO(yuyang18): Temporary disable this unit-test because implementation
+//  //! error.
+//  return 0;
+//}
\ No newline at end of file

paddle/operators/type_alias.h

@@ -25,6 +25,10 @@ using OpKernel = framework::OpKernel;
 using InferShapeContext = framework::InferShapeContext;
 using ExecutionContext = framework::ExecutionContext;
 using Variable = framework::Variable;
+template <typename T,
+          int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenScalar = framework::EigenScalar<T, MajorType, IndexType>;
 template <typename T,
           int MajorType = Eigen::RowMajor,
           typename IndexType = Eigen::DenseIndex>

paddle/platform/enforce.h

@@ -14,6 +14,7 @@ limitations under the License. */
 
 #pragma once
 
+#include <glog/logging.h>
 #include <paddle/string/printf.h>
 #include <sstream>
 #include <stdexcept>

paddle/pybind/pybind.cc

@@ -102,15 +102,18 @@ All parameter, weight, gradient are variables in Paddle.
            },
            py::return_value_policy::reference);
 
-  py::class_<pd::Scope, std::shared_ptr<pd::Scope>>(m, "Scope")
-      .def(py::init<const std::shared_ptr<pd::Scope>&>())
-      .def("get_var",
-           &pd::Scope::GetVariable,
+  py::class_<pd::Scope>(m, "Scope", "")
+      .def("new_var",
+           [](pd::Scope& self, const std::string& name) -> pd::Variable* {
+             return self.NewVar(name);
+           },
            py::return_value_policy::reference)
-      .def("create_var",
-           &pd::Scope::CreateVariable,
+      .def("find_var", &pd::Scope::FindVar, py::return_value_policy::reference)
+      .def(py::init<>())
+      .def("new_scope",
+           [](pd::Scope& self) -> pd::Scope* { return &self.NewScope(); },
            py::return_value_policy::reference)
-      .def("get_var_name", &pd::Scope::GetVariableName);
+      .def("drop_kids", &pd::Scope::DropKids);
 
   //! @note: Be careful! PyBind will return std::string as an unicode, not
   //! Python str. If you want a str object, you should cast them in Python.

python/paddle/v2/framework/default_scope_funcs.py

@@ -5,7 +5,7 @@ Default scope function.
 thread-local stack of Scope. Top of that stack is current scope, the bottom 
 of that stack is all scopes' parent. 
 
-Invoking `create_var/get_var`  can `create/get` variable in current scope. 
+Invoking `new_var/find_var`  can `new/find` variable in current scope. 
 Invoking `enter_local_scope/leave_local_scope` can create or destroy local 
 scope. 
 
@@ -19,8 +19,8 @@ import threading
 __tl_scope__ = threading.local()
 
 __all__ = [
-    'get_cur_scope', 'enter_local_scope', 'leave_local_scope', 'create_var',
-    'get_var', 'scoped_function'
+    'get_cur_scope', 'enter_local_scope', 'leave_local_scope', 'new_var',
+    'find_var', 'scoped_function'
 ]
 
 
@@ -33,7 +33,7 @@ def get_cur_scope():
     if cur_scope_stack is None:
         __tl_scope__.cur_scope = list()
     if len(__tl_scope__.cur_scope) == 0:
-        __tl_scope__.cur_scope.append(paddle.v2.framework.core.Scope(None))
+        __tl_scope__.cur_scope.append(paddle.v2.framework.core.Scope())
     return __tl_scope__.cur_scope[-1]
 
 
@@ -42,7 +42,7 @@ def enter_local_scope():
     Enter a new local scope
     """
     cur_scope = get_cur_scope()
-    new_scope = paddle.v2.framework.core.Scope(cur_scope)
+    new_scope = cur_scope.new_scope()
     __tl_scope__.cur_scope.append(new_scope)
 
 
@@ -51,20 +51,21 @@ def leave_local_scope():
     Leave local scope
     """
     __tl_scope__.cur_scope.pop()
+    get_cur_scope().drop_kids()
 
 
-def create_var(name):
+def new_var(name):
     """
     create variable in current scope.
     """
-    return get_cur_scope().create_var(name)
+    return get_cur_scope().new_var(name)
 
 
-def get_var(name):
+def find_var(name):
     """
     get variable in current scope.
     """
-    return get_cur_scope().get_var(name)
+    return get_cur_scope().find_var(name)
 
 
 def scoped_function(func):

python/paddle/v2/framework/network.py

 import paddle.v2.framework.core as core
 from paddle.v2.framework.create_op_creation_methods import op_creations
-from default_scope_funcs import create_var, get_var, get_cur_scope
+from default_scope_funcs import new_var, find_var, get_cur_scope
 
 __all__ = ['Network']  # Only expose Network
 
@@ -29,12 +29,15 @@ class NetworkFunctor(object):
             if ipt in kwargs:
                 var = kwargs[ipt]
                 if isinstance(var, basestring):
-                    var = create_var(var)
+                    tmp = new_var(var)
+                    self.net.var_names[tmp] = var
+                    var = tmp
+
                 if not isinstance(var, core.Variable):
                     raise TypeError(
                         "Input of op creation must be string or variable")
 
-                kwargs[ipt] = get_cur_scope().get_var_name(var)
+                kwargs[ipt] = self.net.var_names[var]
 
         notemp_outputs = self.func.all_not_temp_output_args
 
@@ -49,17 +52,20 @@ class NetworkFunctor(object):
             if opt in kwargs:
                 var = kwargs[opt]
                 if isinstance(var, basestring):
-                    var = create_var(var)
+                    tmp = new_var(var)
+                    self.net.var_names[tmp] = var
+                    var = tmp
+
                 if not isinstance(var, core.Variable):
                     raise TypeError(
                         "Output of op creation must be string or variable")
-                kwargs[opt] = get_cur_scope().get_var_name(var)
+                kwargs[opt] = self.net.var_names[var]
 
         op = self.func(**kwargs)
 
         self.net.net.add_op(op)
 
-        lst = [get_var(kwargs[opt]) for opt in notemp_outputs]
+        lst = [find_var(kwargs[opt]) for opt in notemp_outputs]
         if len(lst) == 1:
             return lst[0]
         elif len(lst) == 0:
@@ -89,6 +95,7 @@ class Network(object):
         self.net = core.Net.create()
         funcs = (func_name for func_name in dir(op_creations)
                  if not func_name.startswith("__"))
+        self.var_names = dict()
 
         # TODO(yuyang18): This code can work, but do not generate a good
         # docstring, try to give a better way generate function in runtime

python/paddle/v2/framework/tests/op_test_util.py

@@ -24,13 +24,13 @@ class OpTestMeta(type):
             func = getattr(creation.op_creations, self.type, None)
             self.assertIsNotNone(func)
 
-            scope = core.Scope(None)
+            scope = core.Scope()
             kwargs = dict()
 
             for in_name in func.all_input_args:
                 if hasattr(self, in_name):
                     kwargs[in_name] = in_name
-                    var = scope.create_var(in_name).get_tensor()
+                    var = scope.new_var(in_name).get_tensor()
                     arr = getattr(self, in_name)
                     var.set_dims(arr.shape)
                     var.set(arr)
@@ -40,7 +40,7 @@ class OpTestMeta(type):
             for out_name in func.all_output_args:
                 if hasattr(self, out_name):
                     kwargs[out_name] = out_name
-                    scope.create_var(out_name).get_tensor()
+                    scope.new_var(out_name).get_tensor()
 
             for attr_name in func.all_attr_args:
                 if hasattr(self, attr_name):
@@ -54,7 +54,7 @@ class OpTestMeta(type):
             op.run(scope, ctx)
 
             for out_name in func.all_output_args:
-                actual = numpy.array(scope.get_var(out_name).get_tensor())
+                actual = numpy.array(scope.find_var(out_name).get_tensor())
                 expect = getattr(self, out_name)
                 # TODO(qijun) The default decimal is 7, but numpy.dot and eigen.mul
                 # has some diff, and could not pass unittest. So I set decimal 3 here.

python/paddle/v2/framework/tests/test_default_scope_funcs.py

@@ -7,19 +7,19 @@ class TestDefaultScopeFuncs(unittest.TestCase):
         self.assertIsNotNone(get_cur_scope())
 
     def test_none_variable(self):
-        self.assertIsNone(get_var("test"))
+        self.assertIsNone(find_var("test"))
 
     def test_create_var_get_var(self):
-        var_a = create_var("var_a")
+        var_a = new_var("var_a")
         self.assertIsNotNone(var_a)
-        self.assertIsNotNone(get_cur_scope().get_var('var_a'))
+        self.assertIsNotNone(get_cur_scope().find_var('var_a'))
         enter_local_scope()
-        self.assertIsNotNone(get_cur_scope().get_var('var_a'))
+        self.assertIsNotNone(get_cur_scope().find_var('var_a'))
         leave_local_scope()
 
     def test_var_get_int(self):
         def __new_scope__():
-            i = create_var("var_i")
+            i = new_var("var_i")
             self.assertFalse(i.is_int())
             i.set_int(10)
             self.assertTrue(i.is_int())

python/paddle/v2/framework/tests/test_fc_op.py

@@ -6,13 +6,13 @@ import paddle.v2.framework.create_op_creation_methods as creation
 
 class TestFc(unittest.TestCase):
     def test_fc(self):
-        scope = core.Scope(None)
-        x = scope.create_var("X")
+        scope = core.Scope()
+        x = scope.new_var("X")
         x_tensor = x.get_tensor()
         x_tensor.set_dims([1000, 784])
         x_tensor.alloc_float()
 
-        w = scope.create_var("W")
+        w = scope.new_var("W")
         w_tensor = w.get_tensor()
         w_tensor.set_dims([784, 100])
         w_tensor.alloc_float()
@@ -25,10 +25,10 @@ class TestFc(unittest.TestCase):
         op = creation.op_creations.fc(X="X", Y="Y", W="W")
 
         for out in op.outputs():
-            if scope.get_var(out) is None:
-                scope.create_var(out).get_tensor()
+            if scope.find_var(out) is None:
+                scope.new_var(out).get_tensor()
 
-        tensor = scope.get_var("Y").get_tensor()
+        tensor = scope.find_var("Y").get_tensor()
         op.infer_shape(scope)
         self.assertEqual([1000, 100], tensor.shape())
 

python/paddle/v2/framework/tests/test_scope.py

@@ -5,29 +5,29 @@ import unittest
 class TestScope(unittest.TestCase):
     def test_create_destroy(self):
         paddle_c = paddle.v2.framework.core
-        scope = paddle_c.Scope(None)
+        scope = paddle_c.Scope()
         self.assertIsNotNone(scope)
-        scope_with_parent = paddle_c.Scope(scope)
+        scope_with_parent = scope.new_scope()
         self.assertIsNotNone(scope_with_parent)
 
     def test_none_variable(self):
         paddle_c = paddle.v2.framework.core
-        scope = paddle_c.Scope(None)
-        self.assertIsNone(scope.get_var("test"))
+        scope = paddle_c.Scope()
+        self.assertIsNone(scope.find_var("test"))
 
     def test_create_var_get_var(self):
         paddle_c = paddle.v2.framework.core
-        scope = paddle_c.Scope(None)
-        var_a = scope.create_var("var_a")
+        scope = paddle_c.Scope()
+        var_a = scope.new_var("var_a")
         self.assertIsNotNone(var_a)
-        self.assertIsNotNone(scope.get_var('var_a'))
-        scope2 = paddle_c.Scope(scope)
-        self.assertIsNotNone(scope2.get_var('var_a'))
+        self.assertIsNotNone(scope.find_var('var_a'))
+        scope2 = scope.new_scope()
+        self.assertIsNotNone(scope2.find_var('var_a'))
 
     def test_var_get_int(self):
         paddle_c = paddle.v2.framework.core
-        scope = paddle_c.Scope(None)
-        var = scope.create_var("test_int")
+        scope = paddle_c.Scope()
+        var = scope.new_var("test_int")
         var.set_int(10)
         self.assertTrue(var.is_int())
         self.assertEqual(10, var.get_int())

python/paddle/v2/framework/tests/test_tensor.py

@@ -5,8 +5,8 @@ import numpy
 
 class TestScope(unittest.TestCase):
     def test_int_tensor(self):
-        scope = core.Scope(None)
-        var = scope.create_var("test_tensor")
+        scope = core.Scope()
+        var = scope.new_var("test_tensor")
         tensor = var.get_tensor()
 
         tensor.set_dims([1000, 784])
@@ -23,8 +23,8 @@ class TestScope(unittest.TestCase):
         self.assertEqual(2.0, tensor_array_2[19, 11])
 
     def test_float_tensor(self):
-        scope = core.Scope(None)
-        var = scope.create_var("test_tensor")
+        scope = core.Scope()
+        var = scope.new_var("test_tensor")
         tensor = var.get_tensor()
 
         tensor.set_dims([1000, 784])