Merge branch 'develop' into add_config_helper_for_resize_layer

doc/design/register_grad_op.md

+# Design Doc: Gradient Operators Registration
+
+
+## The Problem Posed
+
+In our current operator registration mechanism, for each operator, the programmer should register a *gradient operator creator* function, which takes a C++ operator instance, and returns the corresponding gradient instance.
+
+However, as we decided to separate the *compilation* and *execution* of DL models, we need to reshape the creator to take a protobuf `OpDesc` message, and returns a corresponding message.
+
+More than that, the new registration mechanism need to support the fact that an operators' gradient computation might be a composition of operators.
+
+## Current Implementation
+
+OpInfos store in a association map which key is the operator type. The `grad_op_type` indicate associated gradient operator type. Operator can create gradient operator by `OpInfo::creator_` of gradient. The pseudo code is
+
+```cpp
+struct OpInfo {
+  std::function<OperatorBase*(...)> creator_;
+  std::string grad_op_type_;
+  ...
+};
+
+map<string, OpInfo> OpInfoMap;
+
+OperatorBase* CreateGradientOperator(const OperatorBase& op) {
+  return OpInfoMap.at(op.Type()).creator_(...);
+}
+```
+
+## Proposed Solution
+
+The mapping relationship between an operator and its gradient operators is a function. The interface of that function is:
+
+```cpp
+// (OpDesc) --> vector<OpDesc>
+using GradOpDescMaker = std::function<std::vector<OpDesc>(const OpDesc&)>;
+```
+
+The function take a `OpDesc` of the forward operator and return one or many gradient operator descriptions.
+
+The `GradOpDescMaker` will be registered in `OpInfo`, to replace `grad_op_type_` field. The `OpInfo` should be
+
+```cpp
+struct OpInfo {
+  GradOpDescMaker grad_op_maker_;
+  ...
+};
+```
+
+The `grad_op_maker_ ` is `nullptr` if the operator does not have associated gradient operators.
+
+We should chagne register macros at the same time. In the current solution, there is no difference between forwarding operators and backward operators. So `REGISTER_OP` just register one operator. If the `REGISTER_OPERATOR ` contains `OpProtoAndCheckerMaker` and `GradOpDescMaker`, we just list them in the same macro. It can be done by a macro contains `__VA_ARGS__`.
+
+The user interface should be
+
+```cpp
+vector<OpDesc> MinusOpGradMaker(OpDesc) {...}
+REGISTER_OPERATOR(minus, MinusOp, MinusOpProtoAndCheckerMaker, SumOpGradMaker);
+// Developers can still manually implement gradient operator.
+REGISTER_OPERATOR(minus_grad, MinusGradOp);
+```
+
+The interface of current `REGISTER_OP` macro could not be changed. In `REGISTER_OP`, it will invoke `REGISTER_OPERATOR` two times and generate GradOpDescMaker inside.
+
+```cpp
+REGISTER_OP(minus, MinusOp, MinusOpProtoAndCheckerMaker, minus_grad, MinusGradOp);
+```

paddle/framework/CMakeLists.txt

@@ -22,14 +22,14 @@ cc_library(attribute SRCS attribute.cc DEPS framework_proto)
 cc_library(proto_desc SRCS var_desc.cc op_desc.cc block_desc.cc program_desc.cc DEPS attribute)
 cc_library(op_proto_maker SRCS op_proto_maker.cc DEPS framework_proto attribute)
 cc_test(op_proto_maker_test SRCS op_proto_maker_test.cc DEPS op_proto_maker)
-cc_library(op_info SRCS op_info.cc DEPS attribute framework_proto)
+cc_library(op_info SRCS op_info.cc DEPS attribute framework_proto proto_desc)
 cc_library(operator SRCS operator.cc DEPS op_info 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 operator proto_desc)
 cc_library(op_registry SRCS op_registry.cc DEPS grad_op_builder op_proto_maker op_info)
 cc_test(op_registry_test SRCS op_registry_test.cc DEPS op_registry)
-cc_test(grad_op_builder_test SRCS grad_op_builder_test.cc DEPS grad_op_builder op_registry add_op)
+cc_test(grad_op_builder_test SRCS grad_op_builder_test.cc DEPS grad_op_builder op_registry sum_op)
 
 py_proto_compile(framework_py_proto SRCS framework.proto)
 # Generate an empty __init__.py to make framework_py_proto as a valid python module.

paddle/framework/details/op_registry.h

+/* 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. */
+
+#pragma once
+
+#include "paddle/framework/op_info.h"
+#include "paddle/framework/op_proto_maker.h"
+#include "paddle/framework/operator.h"
+
+namespace paddle {
+namespace framework {
+namespace details {
+
+enum OpInfoFillType {
+  kOperator = 0,
+  kOpProtoAndCheckerMaker = 1,
+  kGradOpDescMaker = 2
+};
+
+template <typename T>
+struct OpInfoFillTypeID {
+  static constexpr OpInfoFillType ID() {
+    return std::is_base_of<OperatorBase, T>::value
+               ? kOperator
+               : (std::is_base_of<OpProtoAndCheckerMaker, T>::value
+                      ? kOpProtoAndCheckerMaker
+                      : (std::is_base_of<GradOpDescMakerBase, T>::value
+                             ? kGradOpDescMaker
+                             : static_cast<OpInfoFillType>(-1)));
+  }
+};
+
+template <typename T, OpInfoFillType = OpInfoFillTypeID<T>::ID()>
+struct OpInfoFiller;
+
+template <size_t I, bool at_end, typename... ARGS>
+class OperatorRegistrarRecursive;
+
+template <size_t I, typename... ARGS>
+class OperatorRegistrarRecursive<I, false, ARGS...> {
+ public:
+  using T = typename std::tuple_element<I, std::tuple<ARGS...>>::type;
+  OperatorRegistrarRecursive(const char* op_type, OpInfo* info) {
+    OpInfoFiller<T> fill;
+    fill(op_type, info);
+    constexpr auto size = sizeof...(ARGS);
+    OperatorRegistrarRecursive<I + 1, I + 1 == size, ARGS...> reg(op_type,
+                                                                  info);
+    (void)(reg);
+  }
+};
+
+template <size_t I, typename... ARGS>
+class OperatorRegistrarRecursive<I, true, ARGS...> {
+ public:
+  OperatorRegistrarRecursive(const char* op_type, OpInfo* info) {}
+};
+
+template <typename T>
+struct OpInfoFiller<T, kOperator> {
+  void operator()(const char* op_type, OpInfo* info) const {
+    info->creator_ = [](const std::string& type, const VariableNameMap& inputs,
+                        const VariableNameMap& outputs,
+                        const AttributeMap& attrs) {
+      return new T(type, inputs, outputs, attrs);
+    };
+  }
+};
+
+template <typename T>
+struct OpInfoFiller<T, kOpProtoAndCheckerMaker> {
+  void operator()(const char* op_type, OpInfo* info) const {
+    info->proto_ = new OpProto;
+    info->checker_ = new OpAttrChecker();
+    auto maker = T(info->proto_, info->checker_);
+    maker.Validate();
+    info->proto_->set_type(op_type);
+    PADDLE_ENFORCE(
+        info->proto_->IsInitialized(),
+        "Fail to initialize %s's OpProto, because %s is not initialized",
+        op_type, info->proto_->InitializationErrorString());
+  }
+};
+
+template <typename T>
+struct OpInfoFiller<T, kGradOpDescMaker> {
+  void operator()(const char* op_type, OpInfo* info) const {
+    info->grad_op_maker_ = new T();
+  }
+};
+}  // namespace details
+
+}  // namespace framework
+}  // namespace paddle

paddle/framework/grad_op_builder_test.cc

@@ -3,7 +3,7 @@
 #include "paddle/framework/op_registry.h"
 #include "paddle/framework/operator.h"
 
-USE_OP(add);
+USE_OP(sum);
 
 namespace paddle {
 namespace framework {
@@ -41,17 +41,24 @@ namespace f = paddle::framework;
 
 TEST(GradOpBuilder, AddTwo) {
   std::shared_ptr<f::OperatorBase> add_op(f::OpRegistry::CreateOp(
-      "add", {{"X", {"x"}}, {"Y", {"y"}}}, {{"Out", {"out"}}}, {}));
+      "sum", {{"X", {"x", "y"}}}, {{"Out", {"out"}}}, {}));
   std::shared_ptr<f::OperatorBase> grad_add_op =
       f::OpRegistry::CreateGradOp(*add_op);
-  EXPECT_EQ(grad_add_op->Inputs().size(), 4UL);
-  EXPECT_EQ(grad_add_op->Outputs().size(), 2UL);
-  EXPECT_EQ(grad_add_op->Input("X"), "x");
-  EXPECT_EQ(grad_add_op->Input("Y"), "y");
-  EXPECT_EQ(grad_add_op->Input("Out"), "out");
+
+  EXPECT_EQ(grad_add_op->Inputs().size(), 1UL);
+  EXPECT_EQ(grad_add_op->Outputs().size(), 1UL);
   EXPECT_EQ(grad_add_op->Input(f::GradVarName("Out")), f::GradVarName("out"));
-  EXPECT_EQ(grad_add_op->Output(f::GradVarName("X")), f::GradVarName("x"));
-  EXPECT_EQ(grad_add_op->Output(f::GradVarName("Y")), f::GradVarName("y"));
+  auto &outputs = grad_add_op->Outputs(f::GradVarName("X"));
+  EXPECT_EQ(2UL, outputs.size());
+  auto in_output = [&outputs](const std::string &name) {
+    for (auto &output_name : outputs) {
+      if (output_name == name) return true;
+    }
+    return false;
+  };
+
+  EXPECT_TRUE(in_output(f::GradVarName("x")));
+  EXPECT_TRUE(in_output(f::GradVarName("y")));
 }
 
 REGISTER_OP(mult_io, f::NOP, f::MutiInOutOpMaker, mult_io_grad, f::NOP);

paddle/framework/op_info.h

@@ -17,8 +17,8 @@
 #include <map>
 #include <string>
 #include <unordered_map>
-
 #include "paddle/framework/attribute.h"
+#include "paddle/framework/op_desc.h"
 
 namespace paddle {
 namespace framework {
@@ -29,11 +29,18 @@ using OpCreator = std::function<OperatorBase*(
     const std::string& /*type*/, const VariableNameMap& /*inputs*/,
     const VariableNameMap& /*outputs*/, const AttributeMap& /*attrs*/)>;
 
+class GradOpDescMakerBase {
+ public:
+  virtual ~GradOpDescMakerBase() = default;
+  virtual std::vector<OpDescBind> operator()(const OpDescBind&) const = 0;
+};
+
 struct OpInfo {
   OpCreator creator_;
   std::string grad_op_type_;
-  OpProto* proto_;
-  OpAttrChecker* checker_;
+  GradOpDescMakerBase* grad_op_maker_{nullptr};
+  OpProto* proto_{nullptr};
+  OpAttrChecker* checker_{nullptr};
 
   bool HasOpProtoAndChecker() const {
     return proto_ != nullptr && checker_ != nullptr;

paddle/framework/op_registry.h

@@ -21,49 +21,42 @@ limitations under the License. */
 #include <unordered_map>
 #include <unordered_set>
 #include "paddle/framework/attribute.h"
+#include "paddle/framework/details/op_registry.h"
 #include "paddle/framework/framework.pb.h"
 #include "paddle/framework/grad_op_builder.h"
-#include "paddle/framework/op_info.h"
-#include "paddle/framework/op_proto_maker.h"
 #include "paddle/framework/operator.h"
 #include "paddle/framework/scope.h"
 
 namespace paddle {
 namespace framework {
 
+template <typename... ARGS>
+struct OperatorRegistrar {
+  explicit OperatorRegistrar(const char* op_type) : op_type(op_type) {
+    PADDLE_ENFORCE(!OpInfoMap::Instance().Has(op_type),
+                   "'%s' is registered more than once.", op_type);
+    static_assert(sizeof...(ARGS) != 0,
+                  "OperatorRegistrar should be invoked at least by OpClass");
+    details::OperatorRegistrarRecursive<0, false, ARGS...>(op_type, &info);
+  }
+
+  ~OperatorRegistrar() { OpInfoMap::Instance().Insert(op_type, info); }
+
+  const char* op_type;
+
+  OpInfo info;
+};
+
 class OpRegistry {
  public:
   template <typename OpType, typename ProtoMakerType, typename GradOpType>
   static void RegisterOp(const std::string& op_type,
                          const std::string& grad_op_type) {
-    PADDLE_ENFORCE(!OpInfoMap::Instance().Has(op_type),
-                   "'%s' is registered more than once.", op_type);
-    OpInfo op_info;
-    op_info.creator_ = [](
-        const std::string& type, const VariableNameMap& inputs,
-        const VariableNameMap& outputs, const AttributeMap& attrs) {
-      return new OpType(type, inputs, outputs, attrs);
-    };
-    op_info.grad_op_type_ = grad_op_type;
-    if (std::type_index(typeid(ProtoMakerType)) !=
-        std::type_index(typeid(NOPMaker))) {
-      op_info.proto_ = new OpProto;
-      op_info.checker_ = new OpAttrChecker;
-      auto maker = ProtoMakerType(op_info.proto_, op_info.checker_);
-      maker.Validate();
-      op_info.proto_->set_type(op_type);
-      PADDLE_ENFORCE(
-          op_info.proto_->IsInitialized(),
-          "Fail to initialize %s's OpProto, because %s is not initialized",
-          op_type, op_info.proto_->InitializationErrorString());
-    } else {
-      op_info.proto_ = nullptr;
-      op_info.checker_ = nullptr;
-    }
-    OpInfoMap::Instance().Insert(op_type, op_info);
+    OperatorRegistrar<OpType, ProtoMakerType> reg(op_type.c_str());
+    reg.info.grad_op_type_ = grad_op_type;
     // register gradient op
     if (!grad_op_type.empty()) {
-      RegisterOp<GradOpType, NOPMaker, NOP>(grad_op_type, "");
+      OperatorRegistrar<GradOpType> grad_reg(grad_op_type.c_str());
     }
   }
 

paddle/framework/op_registry_test.cc

@@ -173,3 +173,14 @@ TEST(OpRegistry, CustomChecker) {
   int test_attr = op->Attr<int>("test_attr");
   ASSERT_EQ(test_attr, 4);
 }
+
+class CosineOpComplete : public paddle::framework::CosineOp {
+ public:
+  DEFINE_OP_CONSTRUCTOR(CosineOpComplete, paddle::framework::CosineOp);
+  DEFINE_OP_CLONE_METHOD(CosineOpComplete);
+};
+
+TEST(OperatorRegistrar, Test) {
+  using namespace paddle::framework;
+  OperatorRegistrar<CosineOpComplete, CosineOpProtoAndCheckerMaker> reg("cos");
+}
\ No newline at end of file

paddle/framework/operator.cc

@@ -245,5 +245,12 @@ std::vector<Tensor*> InferShapeContext::MultiOutput<Tensor>(
   return res;
 }
 
+std::ostream& operator<<(std::ostream& os,
+                         const OperatorWithKernel::OpKernelKey& kernel_key) {
+  os << "place[" << kernel_key.place_ << "]:data_type[" << kernel_key.data_type_
+     << "]";
+  return os;
+}
+
 }  // namespace framework
 }  // namespace paddle

paddle/framework/operator.h

@@ -478,9 +478,25 @@ class OperatorWithKernel : public OperatorBase {
     this->InferShape(&infer_shape_ctx);
 
     ExecutionContext ctx(*this, scope, dev_ctx);
-    auto& opKernel = AllOpKernels().at(type_).at(
-        OpKernelKey(IndicateDataType(ctx), dev_ctx));
-    opKernel->Compute(ctx);
+
+    // check if op[type] has kernel registered.
+    auto& all_op_kernels = AllOpKernels();
+    auto kernels_iter = all_op_kernels.find(type_);
+    if (kernels_iter == all_op_kernels.end()) {
+      PADDLE_THROW("op[%s] has no kernel", type_);
+    }
+
+    // check if op[type] have kernel for kernel_key
+    OpKernelMap& kernels = kernels_iter->second;
+    auto kernel_key = OpKernelKey(IndicateDataType(ctx), dev_ctx);
+    auto kernel_iter = kernels.find(kernel_key);
+
+    if (kernel_iter == kernels.end()) {
+      PADDLE_THROW("op[%s] has no kernel with kernel_key[%s]", type_,
+                   kernel_key);
+    }
+
+    kernel_iter->second->Compute(ctx);
   }
 
   static std::unordered_map<std::string /* op_type */, OpKernelMap>&
@@ -529,5 +545,8 @@ class OperatorWithKernel : public OperatorBase {
   }
 };
 
+std::ostream& operator<<(std::ostream& os,
+                         const OperatorWithKernel::OpKernelKey& kernel_key);
+
 }  // namespace framework
 }  // namespace paddle

paddle/operators/add_op.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/operators/add_op.h"
-
-namespace paddle {
-namespace operators {
-
-class AddOp : public framework::OperatorWithKernel {
- public:
-  using framework::OperatorWithKernel::OperatorWithKernel;
-
- protected:
-  void InferShape(framework::InferShapeContextBase* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of AddOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) of AddOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("Out"),
-                   "Output(Out) of AddOp should not be null.");
-
-    auto x_dims = ctx->GetInputDim("X");
-    auto y_dims = ctx->GetInputDim("Y");
-    PADDLE_ENFORCE_EQ(x_dims, y_dims,
-                      "Two input of Add Op's dimension must be same.");
-    ctx->SetOutputDim("Out", x_dims);
-  }
-};
-
-class AddOpMaker : public framework::OpProtoAndCheckerMaker {
- public:
-  AddOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
-      : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("X", "The first input of add op");
-    AddInput("Y", "The second input of add op");
-    AddOutput("Out", "The output of add op");
-    AddComment(R"DOC(
-Two Element Add Operator.
-
-The equation is: Out = X + Y
-)DOC");
-  }
-};
-
-class AddOpGrad : public framework::OperatorWithKernel {
- public:
-  using framework::OperatorWithKernel::OperatorWithKernel;
-
- protected:
-  void InferShape(framework::InferShapeContextBase* ctx) const override {}
-};
-
-}  // namespace operators
-}  // namespace paddle
-
-namespace ops = paddle::operators;
-REGISTER_OP(add, ops::AddOp, ops::AddOpMaker, add_grad, ops::AddOpGrad);
-
-REGISTER_OP_CPU_KERNEL(add, ops::AddKernel<paddle::platform::CPUPlace, float>);

paddle/operators/add_op.cu

-/* 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/operators/add_op.h"
-
-namespace ops = paddle::operators;
-REGISTER_OP_GPU_KERNEL(add, ops::AddKernel<paddle::platform::GPUPlace, float>);

paddle/operators/add_op.h

-/* 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. */
-
-#pragma once
-#include "paddle/framework/eigen.h"
-#include "paddle/framework/op_registry.h"
-
-namespace paddle {
-namespace operators {
-
-using Tensor = framework::Tensor;
-template <typename T, int MajorType = Eigen::RowMajor,
-          typename IndexType = Eigen::DenseIndex>
-using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
-
-template <typename Place, typename T>
-class AddKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& context) const override {
-    auto* input0 = context.Input<Tensor>("X");
-    auto* input1 = context.Input<Tensor>("Y");
-    auto* output = context.Output<Tensor>("Out");
-
-    output->mutable_data<T>(context.GetPlace());
-
-    auto X = EigenVector<T>::Flatten(*input0);
-    auto Y = EigenVector<T>::Flatten(*input1);
-    auto Z = EigenVector<T>::Flatten(*output);
-
-    auto place = context.GetEigenDevice<Place>();
-
-    Z.device(place) = X + Y;
-  }
-};
-
-}  // namespace operators
-}  // namespace paddle

paddle/operators/sum_op.cc

@@ -43,8 +43,10 @@ class SumOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   SumOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("X", "the input tensors of sum operator.").AsDuplicable();
-    AddOutput("Out", "the output tensor of sum operator.");
+    AddInput("X", "the input tensors of sum operator.")
+        .AsDuplicable()
+        .NotInGradient();
+    AddOutput("Out", "the output tensor of sum operator.").NotInGradient();
     AddComment(R"DOC(
 Sum the input tensors.
 

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

-import unittest
-import numpy as np
-from op_test import OpTest
-
-
-class TestAddOp(OpTest):
-    def setUp(self):
-        self.op_type = "add"
-        self.inputs = {
-            'X': np.random.random((102, 105)).astype("float32"),
-            'Y': np.random.random((102, 105)).astype("float32")
-        }
-        self.outputs = {'Out': self.inputs['X'] + self.inputs['Y']}
-
-    def test_check_output(self):
-        self.check_output()
-
-
-if __name__ == "__main__":
-    unittest.main()

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

@@ -15,7 +15,7 @@ class PySimpleCond(object):
         for i in range(1, 10, 2):
             array[i] = 0
         self.cond = np.array(array)
-        self.x = np.ones(shape=(10, 1))
+        self.x = np.ones(shape=(10, 1)).astype("float32")
 
     def forward(self):
         self.index_t = np.where(self.cond == 1)

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

-import unittest
-import numpy as np
-import paddle.v2.framework.core as core
-from op_test import get_numeric_gradient
-from op_test import create_op
-
-
-class GetNumericGradientTest(unittest.TestCase):
-    def test_add_op(self):
-        x = np.random.random((10, 1)).astype("float32")
-        y = np.random.random((10, 1)).astype("float32")
-        z = x + y
-        scope = core.Scope()
-        add_op = create_op(scope, "add", {'X': x, 'Y': y}, {'Out': z}, dict())
-        arr = get_numeric_gradient(scope, add_op, {'X': x,
-                                                   'Y': y}, 'X', ['Out'])
-        self.assertAlmostEqual(arr.mean(), 1.0, delta=1e-4)
-
-    def test_softmax_op(self):
-        def stable_softmax(x):
-            """Compute the softmax of vector x in a numerically stable way."""
-            shiftx = x - np.max(x)
-            exps = np.exp(shiftx)
-            return exps / np.sum(exps)
-
-        def label_softmax_grad(Y, dY):
-            dX = Y * 0.0
-            for i in range(Y.shape[0]):
-                d = np.dot(Y[i, :], dY[i, :])
-                dX[i, :] = Y[i, :] * (dY[i, :] - d)
-            return dX
-
-        X = np.random.random((2, 2)).astype("float32")
-        Y = np.apply_along_axis(stable_softmax, 1, X)
-        dY = np.ones(Y.shape)
-        dX = label_softmax_grad(Y, dY)
-
-        scope = core.Scope()
-        softmax_op = create_op(scope, "softmax", {"X": X}, {"Y": Y}, dict())
-
-        arr = get_numeric_gradient(scope, softmax_op, {"X": X}, "X", "Y")
-        np.testing.assert_almost_equal(arr, dX, decimal=1e-2)
-
-
-if __name__ == "__main__":
-    unittest.main()

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

@@ -15,7 +15,7 @@ def fc(X, W, Y):
 class TestNet(unittest.TestCase):
     def test_net_all(self):
         net = core.Net.create()
-        op1 = Operator("add", X="X", Y="Y", Out="Out")
+        op1 = Operator("sum", X=["X", "Y"], Out="Out")
         net.append_op(op1)
 
         net2 = core.Net.create()
@@ -26,7 +26,7 @@ class TestNet(unittest.TestCase):
 
         expected = '''
 Op(plain_net), inputs:{all[W, X, Y]}, outputs:{all[Out, fc.out, pre_activation]}.
-    Op(add), inputs:{X[X], Y[Y]}, outputs:{Out[Out]}.
+    Op(sum), inputs:{X[X, Y]}, outputs:{Out[Out]}.
     Op(plain_net), inputs:{all[W, X]}, outputs:{all[fc.out, pre_activation]}.
         Op(plain_net), inputs:{all[W, X]}, outputs:{all[fc.out, pre_activation]}.
             Op(mul), inputs:{X[X], Y[W]}, outputs:{Out[pre_activation]}.

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

@@ -193,10 +193,10 @@ class TestOpDescCreationMethod(unittest.TestCase):
 
 class TestOpCreations(unittest.TestCase):
     def test_all(self):
-        add_op = op.Operator("add", X="a", Y="b", Out="z")
+        add_op = op.Operator("sum", X=["a", "b"], Out="z")
         self.assertIsNotNone(add_op)
         # Invoke C++ DebugString()
-        self.assertEqual('Op(add), inputs:{X[a], Y[b]}, outputs:{Out[z]}.',
+        self.assertEqual('Op(sum), inputs:{X[a, b]}, outputs:{Out[z]}.',
                          str(add_op))