add gpu kernel by copying inputs/outputs between cpu and gpu.

paddle/framework/operator.cc

@@ -38,7 +38,7 @@ const Tensor* GetTensorFromVar(const Variable* var) {
     return &var->Get<LoDTensor>();
   }
   PADDLE_ENFORCE(var->IsType<Tensor>(),
-                 "The Input must be LoDTensor or Tensor.");
+                 "The Input must be a LoDTensor or a Tensor.");
   return &var->Get<Tensor>();
 }
 
@@ -47,39 +47,39 @@ Tensor* GetTensorFromVar(Variable* var) {
     return var->GetMutable<LoDTensor>();
   }
   PADDLE_ENFORCE(var->IsType<Tensor>(),
-                 "The Input must be LoDTensor or Tensor.");
+                 "The Input must be a LoDTensor or a Tensor.");
   return var->GetMutable<Tensor>();
 }
 
 std::string OperatorBase::Input(const std::string& name) const {
   auto& ins = Inputs(name);
   PADDLE_ENFORCE_LE(ins.size(), 1UL,
-                    "Op %s input %s should contain only one variable", type_,
-                    name);
+                    "Operator %s's input %s should contain only one variable.",
+                    type_, name);
   return ins.empty() ? kEmptyVarName : ins[0];
 }
 
 const std::vector<std::string>& OperatorBase::Inputs(
     const std::string& name) const {
   auto it = inputs_.find(name);
-  PADDLE_ENFORCE(it != inputs_.end(), "Op %s do not have input %s", type_,
-                 name);
+  PADDLE_ENFORCE(it != inputs_.end(), "Operator %s does not have the input %s.",
+                 type_, name);
   return it->second;
 }
 
 std::string OperatorBase::Output(const std::string& name) const {
   auto& outs = Outputs(name);
   PADDLE_ENFORCE_LE(outs.size(), 1UL,
-                    "Op %s output %s should contain only one variable", type_,
-                    name);
+                    "Operator %s's output %s should contain only one variable.",
+                    type_, name);
   return outs.empty() ? kEmptyVarName : outs[0];
 }
 
 const std::vector<std::string>& OperatorBase::Outputs(
     const std::string& name) const {
   auto it = outputs_.find(name);
-  PADDLE_ENFORCE(it != outputs_.end(), "Op %s does not have output called %s",
-                 type_, name);
+  PADDLE_ENFORCE(it != outputs_.end(),
+                 "Operator %s does not have an output called %s.", type_, name);
   return it->second;
 }
 

paddle/framework/tensor_impl.h

@@ -108,9 +108,10 @@ inline void* Tensor::mutable_data(platform::Place place, std::type_index type) {
   if (holder_ != nullptr) {
     holder_->set_type(type);
   }
-  PADDLE_ENFORCE_GT(numel(), 0,
-                    "Tensor's numel must be larger than zero to call "
-                    "Tensor::mutable_data. Call Tensor::set_dim first.");
+  PADDLE_ENFORCE_GT(
+      numel(), 0,
+      "When calling this method, the Tensor's numel must be larger than zero. "
+      "Please check Tensor::Resize has been called first.");
   int64_t size = numel() * SizeOfType(type);
   /* some versions of boost::variant don't have operator!= */
   if (holder_ == nullptr || !(holder_->place() == place) ||

paddle/operators/linear_chain_crf_op.cc

@@ -204,8 +204,7 @@ class LinearChainCRFGradOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE(emission_exps_dims[0],
                    "An empty mini-batch is not allowed.");
 
-    auto transition_exps_dims =
-        ctx->GetInputDim(framework::GradVarName("TransitionExps"));
+    auto transition_exps_dims = ctx->GetInputDim("TransitionExps");
     PADDLE_ENFORCE_EQ(transition_exps_dims.size(), 2UL,
                       "The Input(TransitionExps) should be a 2-D tensor.");
     PADDLE_ENFORCE_EQ(
@@ -240,7 +239,8 @@ class LinearChainCRFGradOp : public framework::OperatorWithKernel {
   // operator is determined by its input: graidents of LogLikelihood.
   framework::DataType IndicateDataType(
       const framework::ExecutionContext& ctx) const override {
-    return framework::ToDataType(ctx.Input<LoDTensor>("LogLikelihood")->type());
+    return framework::ToDataType(
+        ctx.Input<LoDTensor>(framework::GradVarName("LogLikelihood"))->type());
   }
 };
 

paddle/operators/linear_chain_crf_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/linear_chain_crf_op.h"
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_GPU_KERNEL(
+    linear_chain_crf,
+    ops::LinearChainCRFOpKernel<paddle::platform::GPUPlace, float>,
+    ops::LinearChainCRFOpKernel<paddle::platform::GPUPlace, double>);
+REGISTER_OP_GPU_KERNEL(
+    linear_chain_crf_grad,
+    ops::LinearChainCRFGradOpKernel<paddle::platform::GPUPlace, float>,
+    ops::LinearChainCRFGradOpKernel<paddle::platform::GPUPlace, double>);

paddle/operators/linear_chain_crf_op.h

@@ -15,6 +15,7 @@ limitations under the License. */
 #pragma once
 #include "paddle/framework/eigen.h"
 #include "paddle/framework/op_registry.h"
+#include "paddle/operators/math/math_function.h"
 
 namespace paddle {
 namespace operators {
@@ -47,36 +48,90 @@ template <typename Place, typename T>
 class LinearChainCRFOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
-    auto* emission_weights = ctx.Input<LoDTensor>("Emission");
-    auto* transition_weights = ctx.Input<Tensor>("Transition");
-    auto* emission_exps = ctx.Output<LoDTensor>("EmissionExps");
-    emission_exps->mutable_data<T>(ctx.GetPlace());
-    auto* transition_exps = ctx.Output<Tensor>("TransitionExps");
-    transition_exps->mutable_data<T>(ctx.GetPlace());
-    auto* label = ctx.Input<LoDTensor>("Label");
-
-    auto in_lod = emission_weights->lod();
-    PADDLE_ENFORCE(in_lod.size(), "Input(Emission) is not a sequence.");
-
     // TODO(caoying) The checks related to LoD information should be
     // moved into InferShape once after the InferShape is refactored.
-    PADDLE_ENFORCE_EQ(emission_weights->NumLevels(), 1UL,
+    PADDLE_ENFORCE_EQ(ctx.Input<LoDTensor>("Emission")->NumLevels(), 1UL,
                       "The Input(Emission) should be a sequence.");
-    PADDLE_ENFORCE_EQ(label->NumLevels(), 1UL,
+    PADDLE_ENFORCE_EQ(ctx.Input<LoDTensor>("Label")->NumLevels(), 1UL,
                       "The Input(Label) should be a sequence.");
+    auto in_lod = ctx.Input<LoDTensor>("Label")->lod();
+    PADDLE_ENFORCE(in_lod.size(), "Input(Label) must be a sequence.");
     const size_t level = 0;
+    const size_t seq_num = in_lod[level].size() - 1;
+
+    // These local variables hold the inputs and outputs, garanteeing them on
+    // CPU memory, to provide a consistent reference.
+    // TODO(caoying) Fix this by moving all these local variables into the
+    // class's data members once we can profile the whole training process.
+    LoDTensor* emission_weights = nullptr;
+    LoDTensor emission_weight_tensor;
+    Tensor* transition_weights = nullptr;
+    Tensor transition_weight_tensor;
+    LoDTensor* label = nullptr;
+    LoDTensor label_tensor;
+
+    Tensor* emission_exps = nullptr;
+    Tensor emission_exps_tensor;
+    Tensor* transition_exps = nullptr;
+    Tensor transition_exps_tensor;
+    Tensor* alpha = nullptr;
+    Tensor alpha_tensor;
+    Tensor* ll = nullptr;
+    Tensor ll_tensor;
+
+    if (platform::is_gpu_place(ctx.GetPlace())) {
+      emission_weights = &emission_weight_tensor;
+      transition_weights = &transition_weight_tensor;
+      label = &label_tensor;
+
+      CopyInputsToCpuMemory(
+          ctx.device_context(), *ctx.Input<LoDTensor>("Emission"),
+          *ctx.Input<Tensor>("Transition"), *ctx.Input<LoDTensor>("Label"),
+          emission_weights, transition_weights, label);
+
+      emission_exps = &emission_exps_tensor;
+      emission_exps->Resize(emission_weights->dims());
+
+      transition_exps = &transition_exps_tensor;
+      transition_exps->Resize(transition_weights->dims());
+
+      alpha = &alpha_tensor;
+      alpha->Resize(ctx.Output<Tensor>("Alpha")->dims());
+
+      ll = &ll_tensor;
+    } else {
+      emission_weights =
+          const_cast<LoDTensor*>(ctx.Input<LoDTensor>("Emission"));
+      transition_weights = const_cast<Tensor*>(ctx.Input<Tensor>("Transition"));
+      label = const_cast<LoDTensor*>(ctx.Input<LoDTensor>("Label"));
+
+      emission_exps = ctx.Output<Tensor>("EmissionExps");
+      transition_exps = ctx.Output<Tensor>("TransitionExps");
+      alpha = ctx.Output<Tensor>("Alpha");
+      ll = ctx.Output<Tensor>("LogLikelihood");
+    }
+
+    // Because the computation codes only runs on CPU, here the memory for all
+    // the outputs is FIXED to be allocated on the CPU memory.
+    emission_exps->mutable_data<T>(platform::CPUPlace());
+    transition_exps->mutable_data<T>(platform::CPUPlace());
+    alpha->mutable_data<T>(platform::CPUPlace());
 
+    // Resize the output tensor to its correct dimension.
+    ll->Resize({static_cast<int>(seq_num), 1});
+    ll->mutable_data<T>(platform::CPUPlace());
+
+    // Now, all the inputs and outputs should be on the CPU memory.
     auto emission_dims = emission_weights->dims();
     const size_t batch_size = emission_dims[0];
     const size_t tag_num = emission_dims[1];
-    const size_t seq_num = in_lod[level].size() - 1;
 
     Tensor emission_row_max;
     emission_row_max.mutable_data<T>(
         framework::make_ddim({static_cast<int>(batch_size), 1}),
-        ctx.GetPlace());
+        platform::CPUPlace());
 
-    auto place = ctx.GetEigenDevice<Place>();
+    auto place = ctx.GetEigenDevice<platform::CPUPlace>();
     auto x = EigenMatrix<T>::From(*emission_weights);
     auto x_row_max = EigenMatrix<T>::From(emission_row_max);
     x_row_max.device(place) =
@@ -91,12 +146,7 @@ class LinearChainCRFOpKernel : public framework::OpKernel<T> {
     auto w_exps = EigenMatrix<T>::From(*transition_exps);
     w_exps.device(place) = w.exp();
 
-    auto* alpha = ctx.Output<LoDTensor>("Alpha");
-    alpha->mutable_data<T>(ctx.GetPlace());
-    auto* ll = ctx.Output<LoDTensor>("LogLikelihood");
-    // resize the output tensor to the correct dimension.
-    ll->Resize({static_cast<int>(seq_num), 1});
-    T* log_likelihood = ll->mutable_data<T>(ctx.GetPlace());
+    T* log_likelihood = ll->data<T>();
     for (size_t i = 0; i < seq_num; ++i) {
       int start_pos = static_cast<int>(in_lod[level][i]);
       int end_pos = static_cast<int>(in_lod[level][i + 1]);
@@ -116,9 +166,61 @@ class LinearChainCRFOpKernel : public framework::OpKernel<T> {
           one_seq, one_seq_row_max, one_seq_exps, *transition_weights,
           *transition_exps, one_seq_label, &one_seq_alpha);
     }
+
+    if (platform::is_gpu_place(ctx.GetPlace())) {
+      CopyOutputsToGpuMemory(
+          ctx.device_context(), *emission_exps, *transition_exps, *alpha, *ll,
+          ctx.Output<Tensor>("EmissionExps"),
+          ctx.Output<Tensor>("TransitionExps"), ctx.Output<Tensor>("Alpha"),
+          ctx.Output<Tensor>("LogLikelihood"));
+    }
+  };
+
+ private:
+  void CopyInputsToCpuMemory(const platform::DeviceContext& ctx,
+                             const LoDTensor& emission_weights_src,
+                             const Tensor& transition_weights_src,
+                             const LoDTensor& label_src,
+                             LoDTensor* emission_weights_dst,
+                             Tensor* transition_weights_dst,
+                             LoDTensor* label_dst) const {
+    // Copy the inputs from GPU memory to CPU memory if this operators runs on
+    // GPU device.
+    auto copyLoDTensor = [](const platform::DeviceContext& ctx,
+                            const LoDTensor& src, LoDTensor* dst) {
+      dst->mutable_data<T>(src.dims(), platform::CPUPlace());
+      dst->CopyFrom(src, platform::CPUPlace(), ctx);
+
+    };
+    copyLoDTensor(ctx, emission_weights_src, emission_weights_dst);
+    copyLoDTensor(ctx, label_src, label_dst);
+
+    transition_weights_dst->mutable_data<T>(transition_weights_src.dims(),
+                                            platform::CPUPlace());
+    transition_weights_dst->CopyFrom(transition_weights_src,
+                                     platform::CPUPlace(), ctx);
+  }
+
+  void CopyOutputsToGpuMemory(const platform::DeviceContext& ctx,
+                              const Tensor& emission_exps_src,
+                              const Tensor& transition_exps_src,
+                              const Tensor& alpha_src, const Tensor& ll_src,
+                              Tensor* emission_exps_dst,
+                              Tensor* transition_exps_dst, Tensor* alpha_dst,
+                              Tensor* ll_dst) const {
+    // Copy the forward results from CPU memory to GPU memory if this
+    // operators runs on GPU device.
+    auto copyTensor = [](const platform::DeviceContext& ctx, const Tensor& src,
+                         Tensor* dst) {
+      dst->mutable_data<T>(platform::GPUPlace());
+      dst->CopyFrom(src, platform::GPUPlace(), ctx);
+    };
+    copyTensor(ctx, emission_exps_src, emission_exps_dst);
+    copyTensor(ctx, transition_exps_src, transition_exps_dst);
+    copyTensor(ctx, alpha_src, alpha_dst);
+    copyTensor(ctx, ll_src, ll_dst);
   };
 
- protected:
   T ForwardOneSequence(const Tensor& emission, const Tensor& emission_row_max,
                        const Tensor& emission_exps, const Tensor& trans_weights,
                        const Tensor& trans_weight_exps, const Tensor& label,
@@ -183,35 +285,84 @@ template <typename Place, typename T>
 class LinearChainCRFGradOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
-    auto* label = ctx.Input<LoDTensor>("Label");
-    auto* emission_exps = ctx.Input<LoDTensor>("EmissionExps");
-    auto* transition_exps = ctx.Input<Tensor>("TransitionExps");
-    auto* alpha = ctx.Input<LoDTensor>("Alpha");
-    const T* ll_grad =
-        ctx.Input<Tensor>(framework::GradVarName("LogLikelihood"))->data<T>();
-
-    auto place = ctx.GetPlace();
-    auto* emission_grad =
-        ctx.Output<Tensor>(framework::GradVarName("Emission"));
-    emission_grad->mutable_data<T>(place);
-
-    auto* trans_grad = ctx.Output<Tensor>(framework::GradVarName("Transition"));
-    if (trans_grad) {
-      trans_grad->mutable_data<T>(place);
+    const size_t level = 0;  // currently, only support sequence.
+    auto lod = ctx.Input<LoDTensor>("Label")->lod();
+    PADDLE_ENFORCE(lod.size(), "Input(Label) must be a sequence.");
+
+    // These local variables hold the inputs and outputs, garanteeing them on
+    // CPU memory, to provide a consistent reference.
+    // TODO(caoying) Fix this by moving all these local variables into the
+    // class's data members once we can profile the training process.
+    Tensor* label = nullptr;
+    Tensor label_tensor;
+    Tensor* emission_exps = nullptr;
+    Tensor emission_exps_tensor;
+    Tensor* transition_exps = nullptr;
+    Tensor transition_exps_tensor;
+    Tensor* alpha = nullptr;
+    Tensor alpha_tensor;
+    Tensor ll_grad_tensor;
+    T* ll_grad = nullptr;
+
+    Tensor* emission_grad = nullptr;
+    Tensor emission_grad_tensor;
+    Tensor* transition_grad = nullptr;
+    Tensor transition_grad_tensor;
+
+    if (platform::is_gpu_place(ctx.GetPlace())) {
+      label = &label_tensor;
+      emission_exps = &emission_exps_tensor;
+      transition_exps = &transition_exps_tensor;
+      alpha = &alpha_tensor;
+      CopyInputsToCpuMemory(
+          ctx.device_context(), *ctx.Input<LoDTensor>("Label"),
+          *ctx.Input<Tensor>("EmissionExps"),
+          *ctx.Input<Tensor>("TransitionExps"), *ctx.Input<Tensor>("Alpha"),
+          *ctx.Input<Tensor>(framework::GradVarName("LogLikelihood")), label,
+          emission_exps, transition_exps, alpha, &ll_grad_tensor);
+      ll_grad = ll_grad_tensor.data<T>();
+
+      if (ctx.Output<Tensor>(framework::GradVarName("Emission"))) {
+        emission_grad = &emission_grad_tensor;
+        emission_grad->Resize(emission_exps->dims());
       }
 
-    auto emission_dims = emission_exps->dims();
+      if (ctx.Output<Tensor>(framework::GradVarName("Transition"))) {
+        transition_grad = &transition_grad_tensor;
+        transition_grad->Resize(transition_exps->dims());
+      }
+    } else {
+      label = const_cast<LoDTensor*>(ctx.Input<LoDTensor>("Label"));
+      emission_exps = const_cast<Tensor*>(ctx.Input<Tensor>("EmissionExps"));
+      transition_exps =
+          const_cast<Tensor*>(ctx.Input<Tensor>("TransitionExps"));
+      alpha = const_cast<Tensor*>(ctx.Input<Tensor>("Alpha"));
+      ll_grad = const_cast<Tensor*>(
+                    ctx.Input<Tensor>(framework::GradVarName("LogLikelihood")))
+                    ->data<T>();
+
+      emission_grad = ctx.Output<Tensor>(framework::GradVarName("Emission"));
+      transition_grad =
+          ctx.Output<Tensor>(framework::GradVarName("Transition"));
+    }
+    PADDLE_ENFORCE(emission_grad, "Output(Emission@Grad) should not be null.");
+    emission_grad->mutable_data<T>(platform::CPUPlace());
+    math::SetConstant<platform::CPUPlace, T>()(ctx.device_context(),
+                                               emission_grad, 0.);
+    if (transition_grad) {
+      transition_grad->mutable_data<T>(platform::CPUPlace());
+      math::SetConstant<platform::CPUPlace, T>()(ctx.device_context(),
+                                                 transition_grad, 0.);
+    }
+    // Now, all the inputs and outputs should be on the CPU memory.
 
+    auto emission_dims = emission_exps->dims();
     // Beta is the memo table used in dynamic programming to calculate the
     // backwark vectors. For a backward vector i (the i-th row of beta), it
-    // captures the unnormalized probabilities of partial sequences starting at
-    // position i.
+    // captures the unnormalized probabilities of partial sequences starting
+    // at position i.
     Tensor beta;
-    beta.mutable_data<T>(emission_dims, place);
-
-    const size_t level = 0;  // currently, only support sequence.
-    auto lod = label->lod();
-    PADDLE_ENFORCE(lod.size(), "Input(Label) is not a sequence.");
+    beta.mutable_data<T>(emission_dims, platform::CPUPlace());
 
     for (size_t i = 0; i < lod[level].size() - 1; ++i) {
       int start_pos = static_cast<int>(lod[level][i]);
@@ -228,11 +379,60 @@ class LinearChainCRFGradOpKernel : public framework::OpKernel<T> {
       BackwardOneSequence(ctx.device_context(), ll_grad[i],
                           one_seq_emission_exps, *transition_exps,
                           one_seq_alpha, one_seq_label, &one_seq_beta,
-                          trans_grad, &one_seq_emission_grad);
+                          transition_grad, &one_seq_emission_grad);
+    }
+
+    if (platform::is_gpu_place(ctx.GetPlace())) {
+      CopyOutputsToGpuMemory(
+          ctx.device_context(), emission_grad, transition_grad,
+          ctx.Output<Tensor>(framework::GradVarName("Emission")),
+          ctx.Output<Tensor>(framework::GradVarName("Transition")));
     }
   };
 
- protected:
+ private:
+  void CopyInputsToCpuMemory(const platform::DeviceContext& ctx,
+                             const LoDTensor& label_src,
+                             const Tensor& emission_exps_src,
+                             const Tensor& transition_exps_src,
+                             const Tensor& alpha_src, const Tensor& ll_grad_src,
+                             Tensor* label_dst, Tensor* emission_exps_dst,
+                             Tensor* transition_exps_dst, Tensor* alpha_dst,
+                             Tensor* ll_grad_dst) const {
+    // Copy the inputs from GPU memory to CPU memory when this operators runs on
+    // GPU device.
+    label_dst->mutable_data<T>(label_src.dims(), platform::CPUPlace());
+    label_dst->CopyFrom(label_src, platform::CPUPlace(), ctx);
+
+    auto copyTensor = [](const platform::DeviceContext& ctx, const Tensor& src,
+                         Tensor* dst) {
+      dst->mutable_data<T>(src.dims(), platform::CPUPlace());
+      dst->CopyFrom(src, platform::CPUPlace(), ctx);
+    };
+    copyTensor(ctx, emission_exps_src, emission_exps_dst);
+    copyTensor(ctx, transition_exps_src, transition_exps_dst);
+    copyTensor(ctx, alpha_src, alpha_dst);
+    copyTensor(ctx, ll_grad_src, ll_grad_dst);
+  };
+
+  void CopyOutputsToGpuMemory(const platform::DeviceContext& ctx,
+                              const Tensor* emission_grad_src,
+                              const Tensor* transition_grad_src,
+                              Tensor* emission_grad_dst,
+                              Tensor* transition_grad_dst) const {
+    // Copy the backward results from CPU memory to GPU
+    // memory if this operators runs on GPU device.
+    auto copyTensor = [](const platform::DeviceContext& ctx, const Tensor* src,
+                         Tensor* dst) {
+      if (src && dst) {
+        dst->mutable_data<T>(platform::GPUPlace());
+        dst->CopyFrom(*src, platform::GPUPlace(), ctx);
+      }
+    };
+    copyTensor(ctx, emission_grad_src, emission_grad_dst);
+    copyTensor(ctx, transition_grad_src, transition_grad_dst);
+  };
+
   void BackwardOneSequence(const platform::DeviceContext& ctx, const T ll_grad,
                            const Tensor& emission_exps,
                            const Tensor& transition_exps, const Tensor& alpha,
@@ -255,7 +455,6 @@ class LinearChainCRFGradOpKernel : public framework::OpKernel<T> {
       beta_value[(seq_length - 1) * tag_num + i] = w_exps[tag_num + i];
     }
     NormalizeL1<T>(beta_value + (seq_length - 1) * tag_num, tag_num);
-
     for (int k = static_cast<int>(seq_length) - 2; k >= 0; --k) {
       for (size_t i = 0; i < tag_num; ++i) {
         T sum = 0.;
@@ -270,10 +469,11 @@ class LinearChainCRFGradOpKernel : public framework::OpKernel<T> {
       NormalizeL1<T>(beta_value + k * tag_num, tag_num);
     }
 
+    auto x_grad_mat = EigenMatrix<T>::From(*emission_grad);
     auto alpha_mat = EigenMatrix<T>::From(alpha);
     auto beta_mat = EigenMatrix<T>::From(*beta);
-    auto x_grad_mat = EigenMatrix<T>::From(*emission_grad);
-    auto* place = ctx.GetEigenDevice<Place>();
+
+    auto* place = ctx.GetEigenDevice<platform::CPUPlace>();
     auto prob = alpha_mat * beta_mat;
     auto row_sum = prob.sum(Eigen::DSizes<int, 1>(1))
                        .reshape(Eigen::DSizes<int, 2>(seq_length, 1))
@@ -296,7 +496,7 @@ class LinearChainCRFGradOpKernel : public framework::OpKernel<T> {
 
       // TODO(caoying): Fix this to avoid using this local variable.
       Tensor tmp;
-      tmp.mutable_data<T>(beta->dims(), ctx.GetPlace());
+      tmp.mutable_data<T>(beta->dims(), platform::CPUPlace());
       auto tmp_mat = EigenMatrix<T>::From(tmp);
       auto prob = beta_mat * x_exps_mat;
       auto row_sum = prob.sum(Eigen::DSizes<int, 1>(1))