Rewrite feed/fetch op (#4815)

* Feed/Fetch op just plain operator, not a OpWithKernel
* Do not register OpInfoMaker since Feed/Fetch will never be
  configured by users
* Feed/Fetch op has empty gradient
* Feed/Fetch op do not hard code `feed_variable`, `fetch_variable` as
  its input and output, make it as a plain Operator input/output

paddle/framework/executor_test.cc

@@ -28,8 +28,8 @@ limitations under the License. */
 
 USE_OP(elementwise_add);
 USE_OP(gaussian_random);
-USE_OP(feed);
-USE_OP(fetch);
+USE_NO_KERNEL_OP(feed);
+USE_NO_KERNEL_OP(fetch);
 USE_OP(mul);
 USE_OP(sum);
 USE_OP(squared_l2_distance);
@@ -37,6 +37,9 @@ USE_OP(fill_constant);
 USE_OP(mean);
 USE_OP(sgd);
 
+constexpr auto kFeedValueName = "feed_value";
+constexpr auto kFetchValueName = "fetch_value";
+
 using namespace paddle::platform;
 using namespace paddle::framework;
 
@@ -77,9 +80,9 @@ void AddOp(const std::string& type, const VariableNameMap& inputs,
 template <typename T>
 void SetFeedVariable(const std::vector<std::vector<T>>& inputs,
                      const std::vector<std::vector<int64_t>>& dims) {
-  Variable* g_feed_value = GetGlobalScope().FindVar("feed_value");
+  Variable* g_feed_value = GetGlobalScope().FindVar(kFeedValueName);
   auto& feed_inputs =
-      *(g_feed_value->GetMutable<std::vector<paddle::framework::Tensor>>());
+      *(g_feed_value->GetMutable<std::vector<paddle::framework::LoDTensor>>());
   size_t size = inputs.size();
   feed_inputs.resize(size);
   for (size_t i = 0; i < size; i++) {
@@ -92,9 +95,9 @@ void SetFeedVariable(const std::vector<std::vector<T>>& inputs,
 // So we can memcpy the data from fetch_value to vector<T>
 template <typename T>
 std::vector<std::vector<T>> GetFetchVariable() {
-  Variable* g_fetch_value = GetGlobalScope().FindVar("fetch_value");
+  Variable* g_fetch_value = GetGlobalScope().FindVar(kFetchValueName);
   auto& fetch_outputs =
-      *(g_fetch_value->GetMutable<std::vector<paddle::framework::Tensor>>());
+      *(g_fetch_value->GetMutable<std::vector<paddle::framework::LoDTensor>>());
 
   size_t size = fetch_outputs.size();
   std::vector<std::vector<T>> result;
@@ -126,8 +129,10 @@ class ExecutorTesterRandom : public ::testing::Test {
           {{"dims", std::vector<int>{input_dim, embed_dim}}}, init_root_block);
     AddOp("gaussian_random", {}, {{"Out", {"w2"}}},
           {{"dims", std::vector<int>{embed_dim, input_dim}}}, init_root_block);
-    AddOp("fetch", {{"Input", {"w1"}}}, {}, {{"col", 0}}, init_root_block);
-    AddOp("fetch", {{"Input", {"w2"}}}, {}, {{"col", 1}}, init_root_block);
+    AddOp("fetch", {{"Input", {"w1"}}}, {{"Out", {kFetchValueName}}},
+          {{"col", 0}}, init_root_block);
+    AddOp("fetch", {{"Input", {"w2"}}}, {{"Out", {kFetchValueName}}},
+          {{"col", 1}}, init_root_block);
 
     // flush
     init_program.Proto();
@@ -143,7 +148,7 @@ class ExecutorTesterRandom : public ::testing::Test {
     // feed data
     inputs_.push_back({1.0, 1.0, 1.0, 1.0, 1.0, 1.0});
     dims_.push_back({batch_size, input_dim});
-    AddOp("feed", {}, {{"Out", {"a"}}},
+    AddOp("feed", {{"Input", {kFeedValueName}}}, {{"Out", {"a"}}},
           {{"dims", std::vector<int>{batch_size, input_dim}}, {"col", 0}},
           root_block);
 
@@ -175,9 +180,12 @@ class ExecutorTesterRandom : public ::testing::Test {
                   {"Grad", {"w2@GRAD"}}},
           {{"ParamOut", {"w2"}}}, {}, root_block);
 
-    AddOp("fetch", {{"Input", {"w1"}}}, {}, {{"col", 0}}, root_block);
-    AddOp("fetch", {{"Input", {"w2"}}}, {}, {{"col", 1}}, root_block);
-    AddOp("fetch", {{"Input", {"l2_distance"}}}, {}, {{"col", 0}}, root_block);
+    AddOp("fetch", {{"Input", {"w1"}}}, {{"Out", {kFetchValueName}}},
+          {{"col", 0}}, root_block);
+    AddOp("fetch", {{"Input", {"w2"}}}, {{"Out", {kFetchValueName}}},
+          {{"col", 1}}, root_block);
+    AddOp("fetch", {{"Input", {"l2_distance"}}}, {{"Out", {kFetchValueName}}},
+          {{"col", 0}}, root_block);
 
     // flush
     program.Proto();
@@ -204,12 +212,14 @@ class ExecutorTesterFeedAndFetch : public ::testing::Test {
 
     std::vector<int> dim{6};
 
-    AddOp("feed", {}, {{"Out", {"a"}}}, {{"dims", dim}, {"col", 0}},
-          root_block);
-    AddOp("feed", {}, {{"Out", {"b"}}}, {{"dims", dim}, {"col", 1}},
-          root_block);
-    AddOp("fetch", {{"Input", {"a"}}}, {}, {{"col", 0}}, root_block);
-    AddOp("fetch", {{"Input", {"b"}}}, {}, {{"col", 1}}, root_block);
+    AddOp("feed", {{"Input", {kFeedValueName}}}, {{"Out", {"a"}}},
+          {{"dims", dim}, {"col", 0}}, root_block);
+    AddOp("feed", {{"Input", {kFeedValueName}}}, {{"Out", {"b"}}},
+          {{"dims", dim}, {"col", 1}}, root_block);
+    AddOp("fetch", {{"Input", {"a"}}}, {{"Out", {kFetchValueName}}},
+          {{"col", 0}}, root_block);
+    AddOp("fetch", {{"Input", {"b"}}}, {{"Out", {kFetchValueName}}},
+          {{"col", 1}}, root_block);
 
     // flush
     program.Proto();
@@ -241,7 +251,6 @@ TEST_F(ExecutorTesterRandom, CPU) {
   paddle::memory::Used(cpu_place);
 
   std::unique_ptr<Executor> executor(new Executor(places));
-
   executor->Run(init_pdesc_, &GetGlobalScope(), 0);
   SetFeedVariable<float>(inputs_, dims_);
   executor->Run(pdesc_, &GetGlobalScope(), 0);
@@ -251,7 +260,7 @@ TEST_F(ExecutorTesterRandom, CPU) {
 TEST_F(ExecutorTesterFeedAndFetch, CPU) {
   std::vector<Place> places;
   CPUPlace cpu_place;
-  places.push_back(cpu_place);
+  places.emplace_back(cpu_place);
 
   // We have a global Scope and BuddyAllocator, and we must ensure
   // global BuddyAllocator is initialized before global Scope. Thus,
@@ -265,11 +274,11 @@ TEST_F(ExecutorTesterFeedAndFetch, CPU) {
     SetFeedVariable<float>(inputs_, dims_);
     executor->Run(pdesc_, &GetGlobalScope(), 0);
     std::vector<std::vector<float>> result = GetFetchVariable<float>();
-    PADDLE_ENFORCE_EQ(result.size(), inputs_.size());
+    ASSERT_EQ(result.size(), inputs_.size());
     for (size_t i = 0; i < result.size(); ++i) {
-      PADDLE_ENFORCE_EQ(result[i].size(), inputs_[i].size());
+      ASSERT_EQ(result[i].size(), inputs_[i].size());
       for (size_t j = 0; j < result[i].size(); ++j) {
-        PADDLE_ENFORCE_EQ(result[i][j], inputs_[i][j]);
+        ASSERT_EQ(result[i][j], inputs_[i][j]);
       }
     }
   }

paddle/framework/feed_fetch_type.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 <vector>
+#include "paddle/framework/lod_tensor.h"
+
+namespace paddle {
+namespace framework {
+using FeedFetchType = LoDTensor;
+using FeedFetchList = std::vector<FeedFetchType>;
+}  // namespace framework
+}  // namespace paddle

paddle/framework/grad_op_desc_maker.h

@@ -149,5 +149,13 @@ class DefaultGradOpDescMaker : public SingleGradOpDescMaker {
   }
 };
 
+class EmptyGradOpMaker : public GradOpDescMakerBase {
+ public:
+  using GradOpDescMakerBase::GradOpDescMakerBase;
+  std::vector<std::unique_ptr<OpDescBind>> operator()() const override {
+    return {};
+  }
+};
+
 }  // namespace framework
 }  // namespace paddle

paddle/framework/op_desc.cc

@@ -220,9 +220,12 @@ static InferShapeFuncMap &InferShapeFuncs() {
 void OpDescBind::CheckAttrs() {
   PADDLE_ENFORCE(!Type().empty(),
                  "CheckAttr() can not be called before type is setted.");
-  const auto *checker = OpInfoMap::Instance().Get(Type()).Checker();
-  PADDLE_ENFORCE_NOT_NULL(checker, "Operator \"%s\" has no registered checker.",
-                          Type());
+  auto *checker = OpInfoMap::Instance().Get(Type()).Checker();
+  if (checker == nullptr) {
+    // checker is not configured. That operator could be generated by Paddle,
+    // not by users.
+    return;
+  }
   checker->Check(attrs_);
 }
 

paddle/framework/tensor.h

@@ -100,6 +100,22 @@ class Tensor {
   inline void CopyFrom(const Tensor& src, const platform::Place& dst_place,
                        const platform::DeviceContext& ctx);
 
+  // FIXME(yuyang18): CopyFrom should without template T, use the replace
+  // `CopyFrom` with `CopyFromTensor`
+  inline void CopyFromTensor(const Tensor& src,
+                             const platform::Place& dst_place,
+                             const platform::DeviceContext& ctx) {
+    // NOLINTNEXTLINES_8 cpplint.py will recognize below lines as functions.
+    // That is a bug of cpplint.py. Just ignore lint these lines.
+    if (src.type() == std::type_index(typeid(double))) {
+      CopyFrom<double>(src, dst_place, ctx);
+    } else if (src.type() == std::type_index(typeid(float))) {
+      CopyFrom<float>(src, dst_place, ctx);
+    } else if (src.type() == std::type_index(typeid(int))) {
+      CopyFrom<int>(src, dst_place, ctx);
+    }
+  }
+
   /**
    * @brief   Copy the content of an external vector to a tensor.
    *

paddle/operators/feed_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
+   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. */
+   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/feed_op.h"
+#include "paddle/framework/feed_fetch_type.h"
+#include "paddle/framework/op_registry.h"
+#include "paddle/framework/operator.h"
 
 namespace paddle {
 namespace operators {
-
-class FeedOp : public framework::OperatorWithKernel {
- public:
-  using framework::OperatorWithKernel::OperatorWithKernel;
-
- protected:
-  void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output should be not null.");
-    auto& shape = ctx->Attrs().Get<std::vector<int>>("dims");
-    std::vector<int64_t> shape_int64(shape.size(), 0);
-    std::transform(shape.begin(), shape.end(), shape_int64.begin(),
-                   [](int a) { return static_cast<int64_t>(a); });
-    ctx->SetOutputDim("Out", framework::make_ddim(shape_int64));
-    // TODO(qijun): need to handle LodTensor later
-  }
-
-  framework::DataType IndicateDataType(
-      const framework::ExecutionContext& ctx) const override {
-    return static_cast<framework::DataType>(Attr<int>("dataType"));
-  }
-};
-
-class FeedOpMaker : public framework::OpProtoAndCheckerMaker {
+class FeedOp : public framework::OperatorBase {
  public:
-  FeedOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
-      : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddAttr<int>("dataType", "output data type")
-        .SetDefault(framework::DataType::FP32);
-    AddAttr<int>("col", "The col in global feed variable").SetDefault(0);
-    AddAttr<std::vector<int>>("dims", "The dimension of feed tensor.");
-    AddOutput("Out", "The output of feed op.");
-    AddComment(R"DOC(Feed data from global feed variable)DOC");
+  FeedOp(const std::string &type, const framework::VariableNameMap &inputs,
+         const framework::VariableNameMap &outputs,
+         const framework::AttributeMap &attrs)
+      : OperatorBase(type, inputs, outputs, attrs) {}
+  void Run(const framework::Scope &scope,
+           const platform::DeviceContext &dev_ctx) const override {
+    auto feed_var_name = Input("Input");
+    auto *feed_var = scope.FindVar(feed_var_name);
+    PADDLE_ENFORCE(feed_var != nullptr,
+                   "Cannot find feed_var in scope, feed_var_name is %s",
+                   feed_var_name);
+
+    auto out_name = this->Output("Out");
+    auto *out_var = scope.FindVar(out_name);
+    PADDLE_ENFORCE(out_var != nullptr,
+                   "Cannot find out_var in scope, out_var_name is %s",
+                   out_name);
+
+    auto col = Attr<int>("col");
+
+    auto &feed_list = feed_var->Get<framework::FeedFetchList>();
+    auto &feed_item = feed_list.at(static_cast<size_t>(col));
+    auto *out_item = out_var->GetMutable<framework::FeedFetchType>();
+    out_item->CopyFromTensor(feed_item, dev_ctx.GetPlace(), dev_ctx);
+    out_item->set_lod(feed_item.lod());
   }
 };
 
 }  // namespace operators
 }  // namespace paddle
 
-namespace ops = paddle::operators;
-REGISTER_OP_WITHOUT_GRADIENT(feed, ops::FeedOp, ops::FeedOpMaker);
-REGISTER_OP_CPU_KERNEL(feed, ops::FeedKernel<float>);
+// We do not need to register OpInfoMaker,
+// since feed operator will not be used by end users directly
+REGISTER_OPERATOR(feed, paddle::operators::FeedOp,
+                  paddle::framework::EmptyGradOpMaker);

paddle/operators/feed_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/feed_op.h"
-
-namespace ops = paddle::operators;
-REGISTER_OP_GPU_KERNEL(feed, ops::FeedKernel<float>);

paddle/operators/feed_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 {
-
-template <typename T>
-class FeedKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& ctx) const override {
-    framework::Tensor* out = ctx.Output<framework::Tensor>("Out");
-    out->mutable_data<T>(ctx.GetPlace());
-    framework::Variable* g_feed_variable =
-        framework::GetGlobalScope().FindVar("feed_value");
-    const auto& tensors =
-        g_feed_variable->Get<std::vector<framework::Tensor>>();
-    int col = ctx.template Attr<int>("col");
-    PADDLE_ENFORCE_GT(tensors.size(), static_cast<size_t>(col));
-    // TODO(qijun):
-    //   check tensors[col].dims() with attribute,
-    //   except the first dimenson.
-    out->CopyFrom<T>(tensors[col], ctx.GetPlace(), ctx.device_context());
-  }
-};
-
-}  // namespace operators
-}  // namespace paddle

paddle/operators/fetch_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
+   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. */
+   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/fetch_op.h"
+#include "paddle/framework/feed_fetch_type.h"
+#include "paddle/framework/op_registry.h"
 
 namespace paddle {
 namespace operators {
 
-class FetchOp : public framework::OperatorWithKernel {
+class FetchOp : public framework::OperatorBase {
  public:
-  using framework::OperatorWithKernel::OperatorWithKernel;
+  FetchOp(const std::string &type, const framework::VariableNameMap &inputs,
+          const framework::VariableNameMap &outputs,
+          const framework::AttributeMap &attrs)
+      : OperatorBase(type, inputs, outputs, attrs) {}
 
- protected:
-  void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("Input"), "Input should be not null.");
-  }
+  void Run(const framework::Scope &scope,
+           const platform::DeviceContext &dev_ctx) const override {
+    auto fetch_var_name = Input("Input");
+    auto *fetch_var = scope.FindVar(fetch_var_name);
+    PADDLE_ENFORCE(fetch_var != nullptr,
+                   "Cannot find fetch variable in scope, fetch_var_name is %s",
+                   fetch_var_name);
+
+    auto out_name = this->Output("Out");
+    auto *out_var = scope.FindVar(out_name);
+    PADDLE_ENFORCE(out_var != nullptr,
+                   "Cannot find out_var in scope, out_var_name is %s",
+                   out_name);
+
+    auto col = static_cast<size_t>(Attr<int>("col"));
 
-  framework::DataType IndicateDataType(
-      const framework::ExecutionContext& ctx) const override {
-    return static_cast<framework::DataType>(Attr<int>("dataType"));
+    auto *fetch_list = out_var->GetMutable<framework::FeedFetchList>();
+    auto &src_item = fetch_var->Get<framework::FeedFetchType>();
+
+    if (col >= fetch_list->size()) {
+      fetch_list->resize(col + 1);
     }
-};
+    auto &dst_item = fetch_list->at(col);
 
-class FetchOpMaker : public framework::OpProtoAndCheckerMaker {
- public:
-  FetchOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
-      : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddAttr<int>("dataType", "output data type")
-        .SetDefault(framework::DataType::FP32);
-    AddAttr<int>("col", "The col in global fetch variable").SetDefault(0);
-    AddInput("Input", "The output of fetch op.");
-    AddComment(R"DOC(Fetch data to global fetch variable)DOC");
+    // FIXME(yuyang18): Should we assume the fetch operator always generate
+    // CPU outputs?
+    dst_item.CopyFromTensor(src_item, platform::CPUPlace(), dev_ctx);
   }
 };
 
 }  // namespace operators
 }  // namespace paddle
 
-namespace ops = paddle::operators;
-REGISTER_OP_WITHOUT_GRADIENT(fetch, ops::FetchOp, ops::FetchOpMaker);
-REGISTER_OP_CPU_KERNEL(fetch, ops::FetchKernel<float>);
+// We do not need to register OpInfoMaker,
+// since fetch operator will not be used by end users directly
+REGISTER_OPERATOR(fetch, paddle::operators::FetchOp,
+                  paddle::framework::EmptyGradOpMaker);

paddle/operators/fetch_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/fetch_op.h"
-
-namespace ops = paddle::operators;
-REGISTER_OP_GPU_KERNEL(fetch, ops::FetchKernel<float>);

paddle/operators/fetch_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 {
-
-template <typename T>
-class FetchKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& ctx) const override {
-    const framework::Tensor* input = ctx.Input<framework::Tensor>("Input");
-    framework::Variable* g_fetch_variable =
-        framework::GetGlobalScope().FindVar("fetch_value");
-    auto* tensors =
-        g_fetch_variable->GetMutable<std::vector<framework::Tensor>>();
-    int col = ctx.template Attr<int>("col");
-    if (tensors->size() < static_cast<size_t>(col + 1)) {
-      tensors->resize(col + 1);
-    }
-    PADDLE_ENFORCE_GT(tensors->size(), static_cast<size_t>(col));
-    (*tensors)[col].Resize(input->dims());
-    (*tensors)[col].mutable_data<T>(platform::CPUPlace());
-    (*tensors)[col].CopyFrom<T>(*input, platform::CPUPlace(),
-                                ctx.device_context());
-    // TODO(qijun): need to handle LodTensor later
-  }
-};
-
-}  // namespace operators
-}  // namespace paddle