Merge pull request #11877 from reyoung/feature/fix_reshape_op_size

User can register a standard C++ functor as Kernel

paddle/fluid/framework/op_registry.h

@@ -76,6 +76,20 @@ class OpRegistry {
 template <typename PlaceType, bool at_end, size_t I, typename... KernelType>
 struct OpKernelRegistrarFunctor;
 
+template <typename PlaceType, typename T, typename Func>
+inline void RegisterKernelClass(const char* op_type, const char* library_type,
+                                Func func) {
+  std::string library(library_type);
+  std::string data_layout = "ANYLAYOUT";
+  if (library == "MKLDNN") {
+    data_layout = "MKLDNNLAYOUT";
+  }
+  OpKernelType key(ToDataType(std::type_index(typeid(T))), PlaceType(),
+                   StringToDataLayout(data_layout),
+                   StringToLibraryType(library_type));
+  OperatorWithKernel::AllOpKernels()[op_type][key] = func;
+}
+
 template <typename PlaceType, size_t I, typename... KernelTypes>
 struct OpKernelRegistrarFunctor<PlaceType, false, I, KernelTypes...> {
   using KERNEL_TYPE =
@@ -83,16 +97,10 @@ struct OpKernelRegistrarFunctor<PlaceType, false, I, KernelTypes...> {
 
   void operator()(const char* op_type, const char* library_type) const {
     using T = typename KERNEL_TYPE::ELEMENT_TYPE;
-    std::string library(library_type);
-    std::string data_layout = "ANYLAYOUT";
-    if (library == "MKLDNN") {
-      data_layout = "MKLDNNLAYOUT";
-    }
-    OpKernelType key(ToDataType(std::type_index(typeid(T))), PlaceType(),
-                     StringToDataLayout(data_layout),
-                     StringToLibraryType(library_type));
-    OperatorWithKernel::AllOpKernels()[op_type][key].reset(new KERNEL_TYPE);
-
+    RegisterKernelClass<PlaceType, T>(
+        op_type, library_type, [](const framework::ExecutionContext& ctx) {
+          KERNEL_TYPE().Compute(ctx);
+        });
     constexpr auto size = std::tuple_size<std::tuple<KernelTypes...>>::value;
     OpKernelRegistrarFunctor<PlaceType, I + 1 == size, I + 1, KernelTypes...>
         func;
@@ -116,6 +124,47 @@ class OpKernelRegistrar : public Registrar {
   }
 };
 
+template <typename PlaceType, bool at_end, size_t I, typename... KernelType>
+struct OpKernelRegistrarFunctorEx;
+
+template <typename PlaceType, typename... DataTypeAndKernelType>
+class OpKernelRegistrarEx : public Registrar {
+ public:
+  explicit OpKernelRegistrarEx(const char* op_type, const char* library_type) {
+    OpKernelRegistrarFunctorEx<PlaceType, false, 0, DataTypeAndKernelType...>
+        func;
+    func(op_type, library_type);
+  }
+};
+
+template <typename PlaceType, size_t I, typename... DataTypeAndKernelType>
+struct OpKernelRegistrarFunctorEx<PlaceType, true, I,
+                                  DataTypeAndKernelType...> {
+  void operator()(const char* op_type, const char* library_type) const {}
+};
+
+template <typename PlaceType, size_t I, typename... DataTypeAndKernelType>
+struct OpKernelRegistrarFunctorEx<PlaceType, false, I,
+                                  DataTypeAndKernelType...> {
+  using Functor =
+      typename std::tuple_element<I + 1,
+                                  std::tuple<DataTypeAndKernelType...>>::type;
+  using T =
+      typename std::tuple_element<I,
+                                  std::tuple<DataTypeAndKernelType...>>::type;
+
+  void operator()(const char* op_type, const char* library_type) const {
+    RegisterKernelClass<PlaceType, T>(op_type, library_type, Functor());
+
+    constexpr auto size =
+        std::tuple_size<std::tuple<DataTypeAndKernelType...>>::value;
+    OpKernelRegistrarFunctorEx<PlaceType, I + 2 >= size, I + 2,
+                               DataTypeAndKernelType...>
+        func;
+    func(op_type, library_type);
+  }
+};
+
 /**
  * check if MACRO is used in GLOBAL NAMESPACE.
  */
@@ -174,6 +223,25 @@ class OpKernelRegistrar : public Registrar {
 #define REGISTER_OP_CPU_KERNEL(op_type, ...) \
   REGISTER_OP_KERNEL(op_type, CPU, ::paddle::platform::CPUPlace, __VA_ARGS__)
 
+#define REGISTER_OP_KERNEL_EX(op_type, library_type, place_class, ...)      \
+  STATIC_ASSERT_GLOBAL_NAMESPACE(                                           \
+      __reg_op_kernel_##op_type##_##library_type##__,                       \
+      "REGISTER_OP_KERNEL_EX must be called in global namespace");          \
+  static ::paddle::framework::OpKernelRegistrarEx<place_class, __VA_ARGS__> \
+      __op_kernel_registrar_##op_type##_##library_type##__(#op_type,        \
+                                                           #library_type);  \
+  int TouchOpKernelRegistrar_##op_type##_##library_type() {                 \
+    __op_kernel_registrar_##op_type##_##library_type##__.Touch();           \
+    return 0;                                                               \
+  }
+
+#define REGISTER_OP_CUDA_KERNEL_FUNCTOR(op_type, ...)                 \
+  REGISTER_OP_KERNEL_EX(op_type, CUDA, ::paddle::platform::CUDAPlace, \
+                        __VA_ARGS__)
+
+#define REGISTER_OP_CPU_KERNEL_FUNCTOR(op_type, ...) \
+  REGISTER_OP_KERNEL_EX(op_type, CPU, ::paddle::platform::CPUPlace, __VA_ARGS__)
+
 /**
  * Macro to mark what Operator and Kernel
  * we will use and tell the compiler to

paddle/fluid/framework/operator.cc

@@ -651,7 +651,7 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
     dev_ctx = pool.Get(expected_kernel_key.place_);
   }
 
-  kernel_iter->second->Compute(ExecutionContext(*this, exec_scope, *dev_ctx));
+  kernel_iter->second(ExecutionContext(*this, exec_scope, *dev_ctx));
 
   if (!transfered_inplace_vars.empty()) {
     // there is inplace variable has been transfered.

paddle/fluid/framework/operator.h

@@ -347,9 +347,9 @@ class OpKernel : public OpKernelBase {
 
 class OperatorWithKernel : public OperatorBase {
  public:
+  using OpKernelFunc = std::function<void(const ExecutionContext&)>;
   using OpKernelMap =
-      std::unordered_map<OpKernelType, std::unique_ptr<OpKernelBase>,
-                         OpKernelType::Hash>;
+      std::unordered_map<OpKernelType, OpKernelFunc, OpKernelType::Hash>;
 
   OperatorWithKernel(const std::string& type, const VariableNameMap& inputs,
                      const VariableNameMap& outputs, const AttributeMap& attrs)

paddle/fluid/operators/fc_mkldnn_op.cc

@@ -115,6 +115,7 @@ class MKLDNNMemory {
 
 template <typename T>
 class FCMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
+ public:
   void Compute(const paddle::framework::ExecutionContext& ctx) const override {
     PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
                    "It must use CPUPlace.");

paddle/fluid/operators/reshape_op.cc

@@ -12,14 +12,108 @@ 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/operators/reshape_op.h"
-
 #include <string>
 #include <vector>
+#include "paddle/fluid/framework/op_registry.h"
 
 namespace paddle {
 namespace operators {
 
+class ReshapeOp : public framework::OperatorWithKernel {
+ public:
+  ReshapeOp(const std::string &type, const framework::VariableNameMap &inputs,
+            const framework::VariableNameMap &outputs,
+            const framework::AttributeMap &attrs)
+      : OperatorWithKernel(type, inputs, outputs, attrs) {}
+
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"),
+                   "Input(X) of ReshapeOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Out"),
+                   "Output(Out) of ReshapeOp should not be null.");
+
+    const std::vector<int> &shape = ctx->Attrs().Get<std::vector<int>>("shape");
+    PADDLE_ENFORCE(!shape.empty(),
+                   "The shape information must be set by Attr(shape).");
+
+    if (ctx->HasInput("Shape") && ctx->IsRuntime()) {
+      // If true, set the shape of Output(Out) according to Input(Shape) in
+      // ReshapeKernel with ExecutionContext. Also check LoD in ReshapeKernel.
+      ctx->ShareLoD("X", /*->*/ "Out");
+      return;
+    }
+
+    auto x_dims = ctx->GetInputDim("X");
+    auto out_dims = ValidateShape(shape, x_dims);
+    ctx->SetOutputDim("Out", out_dims);
+    if (x_dims[0] == out_dims[0]) {
+      // Only pass LoD when the first dimension of output and Input(X)
+      // are the same.
+      ctx->ShareLoD("X", /*->*/ "Out");
+    }
+  }
+
+  static framework::DDim ValidateShape(const std::vector<int> shape,
+                                       const framework::DDim &in_dims) {
+    const int64_t in_size = framework::product(in_dims);
+    // only one dimension can be set to -1, whose size will be automatically
+    // infered.
+    const int64_t unk_dim_val = -1;
+    const int64_t copy_dim_val = 0;
+
+    std::vector<int64_t> output_shape(shape.size(), 0);
+    int64_t capacity = 1;
+    int unk_dim_idx = -1;
+    for (size_t i = 0; i < shape.size(); ++i) {
+      if (shape[i] == unk_dim_val) {
+        PADDLE_ENFORCE(
+            unk_dim_idx == -1,
+            "Only one input dimension of Attr(shape) can be unknown.");
+        unk_dim_idx = i;
+      } else if (shape[i] == copy_dim_val) {
+        PADDLE_ENFORCE(
+            static_cast<int>(i) < in_dims.size(),
+            "The index of dimension to copy from input shape must be less "
+            "than the size of input shape.");
+      } else {
+        PADDLE_ENFORCE(
+            shape[i] > 0,
+            "Each input dimension of Attr(shape) must not be negtive except "
+            "one unknown dimension.");
+      }
+
+      capacity *= (shape[i] ? shape[i] : in_dims[i]);
+      output_shape[i] =
+          (shape[i] ? static_cast<int64_t>(shape[i]) : in_dims[i]);
+    }
+
+    if (unk_dim_idx != -1) {
+      if (in_size > 0) {
+        // in_size < 0 and is un-determinate in compile time, skip the check,
+        // for example, in_dims = [-1, 8, 1, 1], shape = [-1, 3, 8],
+        // capacity = -24, in_size = -8, output_shape[0] = 0
+        // the following check will fail.
+        output_shape[unk_dim_idx] = -in_size / capacity;
+        PADDLE_ENFORCE_EQ(output_shape[unk_dim_idx] * capacity, -in_size,
+                          "Invalid shape is given.");
+      } else {
+        output_shape[unk_dim_idx] = -1;
+      }
+    } else {
+      PADDLE_ENFORCE_EQ(capacity, in_size, "Invalid shape is given.");
+    }
+    return framework::make_ddim(output_shape);
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext &ctx) const override {
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<framework::LoDTensor>("X")->type()),
+        ctx.device_context());
+  }
+};
+
 class ReshapeOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
@@ -107,19 +201,93 @@ class ReshapeGradOp : public framework::OperatorWithKernel {
   }
 };
 
+class ReshapeKernel {
+ public:
+  void operator()(const framework::ExecutionContext &ctx) const {
+    auto *out = ctx.Output<framework::LoDTensor>("Out");
+    auto *in = ctx.Input<framework::LoDTensor>("X");
+
+    auto *shape_tensor = ctx.HasInput("Shape")
+                             ? ctx.Input<framework::LoDTensor>("Shape")
+                             : nullptr;
+
+    framework::DDim out_dims = out->dims();
+
+    if (shape_tensor) {
+      auto *shape_data = shape_tensor->data<int>();
+      framework::Tensor cpu_shape_tensor;
+      if (platform::is_gpu_place(ctx.GetPlace())) {
+        TensorCopySync(*shape_tensor, platform::CPUPlace(), &cpu_shape_tensor);
+        shape_data = cpu_shape_tensor.data<int>();
+      }
+      auto shape =
+          std::vector<int>(shape_data, shape_data + shape_tensor->numel());
+      out_dims = ReshapeOp::ValidateShape(shape, in->dims());
+    }
+    if (!in->lod().empty()) {
+      PADDLE_ENFORCE_EQ(
+          out_dims[0], in->dims()[0],
+          "Reshape operator cannot reshape an input sequence batch "
+          "into an output sequence batch that has a different "
+          "number of time steps. Please consider using "
+          "sequence_reshape op.");
+    }
+
+    bool inplace = ctx.Attr<bool>("inplace");
+    out->Resize(out_dims);
+    if (!inplace) {
+      out->mutable_data(ctx.GetPlace(), in->type());
+      framework::TensorCopySync(*in, ctx.GetPlace(), out);
+      out->Resize(out_dims);
+    } else {
+      out->ShareDataWith(*in);
+      out->Resize(out_dims);
+    }
+  }
+};
+
+class ReshapeGradKernel {
+ public:
+  void operator()(const framework::ExecutionContext &ctx) const {
+    auto *d_out = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
+    auto *d_x = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
+
+    d_x->mutable_data(ctx.GetPlace(), d_out->type());
+    bool inplace = ctx.Attr<bool>("inplace");
+
+    auto in_dims = d_x->dims();
+    if (!inplace) {
+      framework::TensorCopy(*d_out, ctx.GetPlace(), ctx.device_context(), d_x);
+      ctx.device_context().Wait();
+      d_x->Resize(in_dims);
+    } else {
+      d_x->ShareDataWith(*d_out);
+      d_x->Resize(in_dims);
+    }
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 namespace ops = paddle::operators;
-using CPU = paddle::platform::CPUDeviceContext;
 
 REGISTER_OPERATOR(reshape, ops::ReshapeOp, ops::ReshapeOpMaker,
                   paddle::framework::DefaultGradOpDescMaker<true>);
 REGISTER_OPERATOR(reshape_grad, ops::ReshapeGradOp);
-REGISTER_OP_CPU_KERNEL(reshape, ops::ReshapeKernel<CPU, float>,
-                       ops::ReshapeKernel<CPU, double>,
-                       ops::ReshapeKernel<CPU, int>,
-                       ops::ReshapeKernel<CPU, int64_t>);
-REGISTER_OP_CPU_KERNEL(reshape_grad, ops::ReshapeGradKernel<CPU, float>,
-                       ops::ReshapeGradKernel<CPU, double>,
-                       ops::ReshapeGradKernel<CPU, int>,
-                       ops::ReshapeGradKernel<CPU, int64_t>);
+REGISTER_OP_CPU_KERNEL_FUNCTOR(reshape, float, ops::ReshapeKernel, double,
+                               ops::ReshapeKernel, int, ops::ReshapeKernel,
+                               int64_t, ops::ReshapeKernel);
+REGISTER_OP_CPU_KERNEL_FUNCTOR(reshape_grad, float, ops::ReshapeGradKernel,
+                               double, ops::ReshapeGradKernel, int,
+                               ops::ReshapeGradKernel, int64_t,
+                               ops::ReshapeGradKernel);
+
+#ifdef PADDLE_WITH_CUDA
+REGISTER_OP_CUDA_KERNEL_FUNCTOR(reshape, float, ops::ReshapeKernel, double,
+                                ops::ReshapeKernel, int, ops::ReshapeKernel,
+                                int64_t, ops::ReshapeKernel);
+REGISTER_OP_CUDA_KERNEL_FUNCTOR(reshape_grad, float, ops::ReshapeGradKernel,
+                                double, ops::ReshapeGradKernel, int,
+                                ops::ReshapeGradKernel, int64_t,
+                                ops::ReshapeGradKernel);
+#endif

paddle/fluid/operators/reshape_op.cu

-/* Copyright (c) 2016 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/operators/reshape_op.h"
-using CUDA = paddle::platform::CUDADeviceContext;
-
-REGISTER_OP_CUDA_KERNEL(reshape, paddle::operators::ReshapeKernel<CUDA, float>,
-                        paddle::operators::ReshapeKernel<CUDA, double>,
-                        paddle::operators::ReshapeKernel<CUDA, int>,
-                        paddle::operators::ReshapeKernel<CUDA, int64_t>);
-REGISTER_OP_CUDA_KERNEL(reshape_grad,
-                        paddle::operators::ReshapeGradKernel<CUDA, float>,
-                        paddle::operators::ReshapeGradKernel<CUDA, double>,
-                        paddle::operators::ReshapeGradKernel<CUDA, int>,
-                        paddle::operators::ReshapeGradKernel<CUDA, int64_t>);

paddle/fluid/operators/reshape_op.h

-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License. */
-
-#pragma once
-
-#include <string>
-#include <vector>
-
-#include "paddle/fluid/framework/eigen.h"
-#include "paddle/fluid/framework/op_registry.h"
-
-namespace paddle {
-namespace operators {
-
-class ReshapeOp : public framework::OperatorWithKernel {
- public:
-  ReshapeOp(const std::string &type, const framework::VariableNameMap &inputs,
-            const framework::VariableNameMap &outputs,
-            const framework::AttributeMap &attrs)
-      : OperatorWithKernel(type, inputs, outputs, attrs) {}
-
-  void InferShape(framework::InferShapeContext *ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"),
-                   "Input(X) of ReshapeOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("Out"),
-                   "Output(Out) of ReshapeOp should not be null.");
-
-    const std::vector<int> &shape = ctx->Attrs().Get<std::vector<int>>("shape");
-    PADDLE_ENFORCE(!shape.empty(),
-                   "The shape information must be set by Attr(shape).");
-
-    if (ctx->HasInput("Shape") && ctx->IsRuntime()) {
-      // If true, set the shape of Output(Out) according to Input(Shape) in
-      // ReshapeKernel with ExecutionContext. Also check LoD in ReshapeKernel.
-      ctx->ShareLoD("X", /*->*/ "Out");
-      return;
-    }
-
-    auto x_dims = ctx->GetInputDim("X");
-    auto out_dims = ValidateShape(shape, x_dims);
-    ctx->SetOutputDim("Out", out_dims);
-    if (x_dims[0] == out_dims[0]) {
-      // Only pass LoD when the first dimension of output and Input(X)
-      // are the same.
-      ctx->ShareLoD("X", /*->*/ "Out");
-    }
-  }
-
-  static framework::DDim ValidateShape(const std::vector<int> shape,
-                                       const framework::DDim &in_dims) {
-    const int64_t in_size = framework::product(in_dims);
-    // only one dimension can be set to -1, whose size will be automatically
-    // infered.
-    const int64_t unk_dim_val = -1;
-    const int64_t copy_dim_val = 0;
-
-    std::vector<int64_t> output_shape(shape.size(), 0);
-    int64_t capacity = 1;
-    int unk_dim_idx = -1;
-    for (size_t i = 0; i < shape.size(); ++i) {
-      if (shape[i] == unk_dim_val) {
-        PADDLE_ENFORCE(
-            unk_dim_idx == -1,
-            "Only one input dimension of Attr(shape) can be unknown.");
-        unk_dim_idx = i;
-      } else if (shape[i] == copy_dim_val) {
-        PADDLE_ENFORCE(
-            static_cast<int>(i) < in_dims.size(),
-            "The index of dimension to copy from input shape must be less "
-            "than the size of input shape.");
-      } else {
-        PADDLE_ENFORCE(
-            shape[i] > 0,
-            "Each input dimension of Attr(shape) must not be negtive except "
-            "one unknown dimension.");
-      }
-
-      capacity *= (shape[i] ? shape[i] : in_dims[i]);
-      output_shape[i] =
-          (shape[i] ? static_cast<int64_t>(shape[i]) : in_dims[i]);
-    }
-
-    if (unk_dim_idx != -1) {
-      if (in_size > 0) {
-        // in_size < 0 and is un-determinate in compile time, skip the check,
-        // for example, in_dims = [-1, 8, 1, 1], shape = [-1, 3, 8],
-        // capacity = -24, in_size = -8, output_shape[0] = 0
-        // the following check will fail.
-        output_shape[unk_dim_idx] = -in_size / capacity;
-        PADDLE_ENFORCE_EQ(output_shape[unk_dim_idx] * capacity, -in_size,
-                          "Invalid shape is given.");
-      } else {
-        output_shape[unk_dim_idx] = -1;
-      }
-    } else {
-      PADDLE_ENFORCE_EQ(capacity, in_size, "Invalid shape is given.");
-    }
-    return framework::make_ddim(output_shape);
-  }
-
- protected:
-  framework::OpKernelType GetExpectedKernelType(
-      const framework::ExecutionContext &ctx) const override {
-    return framework::OpKernelType(
-        framework::ToDataType(ctx.Input<framework::LoDTensor>("X")->type()),
-        ctx.device_context());
-  }
-};
-
-template <typename DeviceContext, typename T>
-class ReshapeKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext &ctx) const {
-    auto *out = ctx.Output<framework::LoDTensor>("Out");
-    auto *in = ctx.Input<framework::LoDTensor>("X");
-
-    auto *shape_tensor = ctx.HasInput("Shape")
-                             ? ctx.Input<framework::LoDTensor>("Shape")
-                             : nullptr;
-
-    framework::DDim out_dims = out->dims();
-
-    if (shape_tensor) {
-      auto *shape_data = shape_tensor->data<int>();
-      framework::Tensor cpu_shape_tensor;
-      if (platform::is_gpu_place(ctx.GetPlace())) {
-        TensorCopySync(*shape_tensor, platform::CPUPlace(), &cpu_shape_tensor);
-        shape_data = cpu_shape_tensor.data<int>();
-      }
-      auto shape =
-          std::vector<int>(shape_data, shape_data + shape_tensor->numel());
-      out_dims = ReshapeOp::ValidateShape(shape, in->dims());
-    }
-    if (!in->lod().empty()) {
-      PADDLE_ENFORCE_EQ(
-          out_dims[0], in->dims()[0],
-          "Reshape operator cannot reshape an input sequence batch "
-          "into an output sequence batch that has a different "
-          "number of time steps. Please consider using "
-          "sequence_reshape op.");
-    }
-
-    bool inplace = ctx.Attr<bool>("inplace");
-    out->Resize(out_dims);
-    if (!inplace) {
-      out->mutable_data<T>(ctx.GetPlace());
-      framework::TensorCopySync(*in, ctx.GetPlace(), out);
-      out->Resize(out_dims);
-    } else {
-      out->ShareDataWith(*in);
-      out->Resize(out_dims);
-    }
-  }
-};
-
-template <typename DeviceContext, typename T>
-class ReshapeGradKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext &ctx) const {
-    auto *d_out = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
-    auto *d_x = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
-
-    d_x->mutable_data<T>(ctx.GetPlace());
-    bool inplace = ctx.Attr<bool>("inplace");
-
-    auto in_dims = d_x->dims();
-    if (!inplace) {
-      framework::TensorCopy(*d_out, ctx.GetPlace(), ctx.device_context(), d_x);
-      ctx.device_context().Wait();
-      d_x->Resize(in_dims);
-    } else {
-      d_x->ShareDataWith(*d_out);
-      d_x->Resize(in_dims);
-    }
-  }
-};
-}  // namespace operators
-}  // namespace paddle