cross entropy as a functor to avoid duplicated codes.

paddle/operators/CMakeLists.txt

@@ -88,10 +88,14 @@ add_subdirectory(math)
 
 set(DEPS_OPS
     recurrent_op
-    cond_op)
+    cond_op
+    cross_entropy_op
+    softmax_with_cross_entropy_op)
 op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc
   DEPS framework_proto tensor net_op)
 op_library(cond_op SRCS cond_op.cc DEPS framework_proto tensor operator net_op)
+op_library(cross_entropy_op DEPS cross_entropy_function)
+op_library(softmax_with_cross_entropy_op DEPS cross_entropy_function softmax_function)
 
 list(REMOVE_ITEM GENERAL_OPS ${DEPS_OPS})
 foreach(src ${GENERAL_OPS})

paddle/operators/cross_entropy_op.cu

@@ -12,62 +12,12 @@
    See the License for the specific language governing permissions and
    limitations under the License. */
 
-#include "paddle/framework/op_registry.h"
 #include "paddle/operators/cross_entropy_op.h"
-#include "paddle/platform/assert.h"
-#include "paddle/platform/hostdevice.h"
 
 namespace paddle {
 namespace operators {
 
-template <typename T>
-__global__ void CrossEntropyKernel(T* Y, const T* X, const int* label,
-                                   const int N, const int D) {
-  // TOOD(qingqing) define CUDA_1D_KERNEL_LOOP macro in a common file.
-  // CUDA_1D_KERNEL_LOOP(i, N) {
-  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < N;
-       i += blockDim.x * gridDim.x) {
-    PADDLE_ASSERT(label[i] >= 0 && label[i] < D);
-    Y[i] = -TolerableValue<T>()(log(X[i * D + label[i]]));
-  }
-}
-
-template <typename T>
-__device__ __forceinline__ T sum_single_warp(T val) {
-  val += __shfl_down(val, 16);
-  val += __shfl_down(val, 8);
-  val += __shfl_down(val, 4);
-  val += __shfl_down(val, 2);
-  val += __shfl_down(val, 1);
-  return val;
-}
-
-template <typename T>
-__global__ void SoftCrossEntropyKernel(T* Y, const T* X, const T* label,
-                                       const int class_num) {
-  int tid = threadIdx.x;
-  extern __shared__ T d_sum[];
-  d_sum[tid] = 0;
-
-  int cur_idx = tid;
-  int next_idx = blockIdx.x * class_num + tid;
-  while (cur_idx < class_num) {
-    d_sum[tid] += TolerableValue<T>()(std::log(X[next_idx])) * label[next_idx];
-    next_idx += blockDim.x;
-    cur_idx += blockDim.x;
-  }
-  __syncthreads();
-
-  for (unsigned int stride = blockDim.x >> 1; stride >= 32; stride >>= 1) {
-    if (tid < stride) d_sum[tid] += d_sum[tid + stride];
-    __syncthreads();
-  }
-
-  T val = d_sum[tid];
-  val = sum_single_warp<T>(val);
-  if (tid == 0) Y[blockIdx.x] = -val;
-}
-
+namespace {
 // TODO(qingqing): make zero setting a common function.
 template <typename T>
 __global__ void Zero(T* X, const int N) {
@@ -100,6 +50,7 @@ __global__ void SoftCrossEntropyGradientKernel(T* dX, const T* dY, const T* X,
     dX[ids] = -label[ids] * dY[row_ids] / X[ids];
   }
 }
+}  // namespace
 
 template <typename T>
 class CrossEntropyOpCUDAKernel : public framework::OpKernel {
@@ -107,36 +58,13 @@ class CrossEntropyOpCUDAKernel : public framework::OpKernel {
   void Compute(const framework::ExecutionContext& ctx) const override {
     PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
                    "This kernel only runs on GPU device.");
-
     const Tensor* x = ctx.Input<Tensor>("X");
     const Tensor* label = ctx.Input<Tensor>("Label");
     Tensor* y = ctx.Output<Tensor>("Y");
+    y->mutable_data<T>(ctx.GetPlace());
 
-    const T* x_data = x->data<T>();
-    T* y_data = y->mutable_data<T>(ctx.GetPlace());
-
-    int batch_size = x->dims()[0];
-    int class_num = x->dims()[1];
-
-    if (ctx.Attr<bool>("softLabel")) {
-      auto* label_data = ctx.Input<Tensor>("Label")->data<T>();
-      int block = class_num > 512 ? 512 : pow(2, int(std::log2(class_num)));
-
-      SoftCrossEntropyKernel<
-          T><<<batch_size, block, block * sizeof(T),
-               reinterpret_cast<const platform::CUDADeviceContext&>(
-                   ctx.device_context())
-                   .stream()>>>(y_data, x_data, label_data, class_num);
-    } else {
-      auto* label_data = ctx.Input<Tensor>("Label")->data<int>();
-      int block = 512;
-      int grid = (batch_size + block - 1) / block;
-      CrossEntropyKernel<T><<<
-          grid, block, 0, reinterpret_cast<const platform::CUDADeviceContext&>(
-                              ctx.device_context())
-                              .stream()>>>(y_data, x_data, label_data,
-                                           batch_size, class_num);
-    }
+    math::CrossEntropyFunctor<platform::GPUPlace, T>()(
+        ctx, y, x, label, ctx.Attr<bool>("softLabel"));
   }
 };
 
@@ -150,6 +78,7 @@ class CrossEntropyGradientOpCUDAKernel : public framework::OpKernel {
     const Tensor* x = ctx.Input<Tensor>("X");
     const Tensor* label = ctx.Input<Tensor>("Label");
     Tensor* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
+    dx->mutable_data<T>(ctx.GetPlace());
 
     const T* dy_data =
         ctx.Input<Tensor>(framework::GradVarName("Y"))->data<T>();

paddle/operators/cross_entropy_op.h

@@ -15,7 +15,7 @@ limitations under the License. */
 #pragma once
 #include "paddle/framework/eigen.h"
 #include "paddle/framework/op_registry.h"
-#include "paddle/platform/hostdevice.h"
+#include "paddle/operators/math/cross_entropy.h"
 
 namespace paddle {
 namespace operators {
@@ -25,18 +25,6 @@ template <typename T, int MajorType = Eigen::RowMajor,
           typename IndexType = Eigen::DenseIndex>
 using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
 
-template <typename T>
-struct TolerableValue {
-  HOSTDEVICE T operator()(const T& x) const {
-    PADDLE_ASSERT(std::is_floating_point<T>::value);
-    const T kApproInf = 1e20;
-
-    if (x == INFINITY) return kApproInf;
-    if (x == -INFINITY) return -kApproInf;
-    return x;
-  }
-};
-
 template <typename T>
 class CrossEntropyOpKernel : public framework::OpKernel {
  public:
@@ -46,28 +34,10 @@ class CrossEntropyOpKernel : public framework::OpKernel {
     const Tensor* x = ctx.Input<Tensor>("X");
     const Tensor* labels = ctx.Input<Tensor>("Label");
     Tensor* y = ctx.Output<Tensor>("Y");
-    T* y_data = y->mutable_data<T>(ctx.GetPlace());
-
-    const int batch_size = x->dims()[0];
-    if (ctx.Attr<bool>("softLabel")) {
-      auto prob = EigenMatrix<T>::From(*x);
-      auto lbl_mat = EigenMatrix<T>::From(*labels);
-      auto loss = EigenMatrix<T>::From(*y);
+    y->mutable_data<T>(ctx.GetPlace());
 
-      loss.device(ctx.GetEigenDevice<platform::CPUPlace>()) =
-          -((lbl_mat * prob.log().unaryExpr(TolerableValue<T>()))
-                .sum(Eigen::DSizes<int, 1>(1))
-                .reshape(Eigen::DSizes<int, 2>(batch_size, 1)));
-    } else {
-      const int class_num = x->dims()[1];
-      const T* x_data = x->data<T>();
-
-      const int* label_data = labels->data<int>();
-      for (int i = 0; i < batch_size; ++i) {
-        int index = i * class_num + label_data[i];
-        y_data[i] = -TolerableValue<T>()(std::log(x_data[index]));
-      }
-    }
+    math::CrossEntropyFunctor<platform::CPUPlace, T>()(
+        ctx, y, x, labels, ctx.Attr<bool>("softLabel"));
   }
 };
 

paddle/operators/math/CMakeLists.txt

@@ -3,10 +3,13 @@ if(WITH_GPU)
       im2col.cu DEPS cblas device_context operator)
     nv_library(softmax_function SRCS softmax.cc softmax.cu
       DEPS operator)
+    nv_library(cross_entropy_function SRCS cross_entropy.cc cross_entropy.cu
+      DEPS operator)
 else()
     cc_library(math_function SRCS math_function.cc im2col.cc
       DEPS cblas device_context operator)
     cc_library(softmax_function SRCS softmax.cc DEPS operator)
+    cc_library(cross_entropy_function SRCS cross_entropy.cc DEPS operator)
 endif()
 
 nv_test(math_function_test SRCS math_function_test.cc DEPS math_function tensor)

paddle/operators/math/cross_entropy.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/math/cross_entropy.h"
+
+namespace paddle {
+namespace operators {
+namespace math {
+
+using Tensor = framework::Tensor;
+template <typename T, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
+
+template <typename T>
+class CrossEntropyFunctor<platform::CPUPlace, T> {
+ public:
+  void operator()(const framework::ExecutionContext& ctx,
+                  framework::Tensor* out, const framework::Tensor* prob,
+                  const framework::Tensor* labels, const bool softLabel) {
+    const int batch_size = prob->dims()[0];
+    if (softLabel) {
+      auto in = EigenMatrix<T>::From(*prob);
+      auto lbl = EigenMatrix<T>::From(*labels);
+      auto loss = EigenMatrix<T>::From(*out);
+
+      loss.device(ctx.GetEigenDevice<platform::CPUPlace>()) =
+          -((lbl * in.log().unaryExpr(math::TolerableValue<T>()))
+                .sum(Eigen::DSizes<int, 1>(1))
+                .reshape(Eigen::DSizes<int, 2>(batch_size, 1)));
+    } else {
+      const int class_num = prob->dims()[1];
+      const T* prob_data = prob->data<T>();
+      T* loss_data = out->data<T>();
+
+      const int* label_data = labels->data<int>();
+      for (int i = 0; i < batch_size; ++i) {
+        int index = i * class_num + label_data[i];
+        loss_data[i] = -math::TolerableValue<T>()(std::log(prob_data[index]));
+      }
+    }
+  }
+};
+
+template class CrossEntropyFunctor<platform::CPUPlace, float>;
+}  // namespace math
+}  // namespace operators
+}  // namespace paddle

paddle/operators/math/cross_entropy.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/math/cross_entropy.h"
+
+namespace paddle {
+namespace operators {
+namespace math {
+
+namespace {
+template <typename T>
+__global__ void CrossEntropyKernel(T* Y, const T* X, const int* label,
+                                   const int N, const int D) {
+  // TOOD(qingqing) define CUDA_1D_KERNEL_LOOP macro in a common file.
+  // CUDA_1D_KERNEL_LOOP(i, N) {
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < N;
+       i += blockDim.x * gridDim.x) {
+    PADDLE_ASSERT(label[i] >= 0 && label[i] < D);
+    Y[i] = -math::TolerableValue<T>()(log(X[i * D + label[i]]));
+  }
+}
+
+template <typename T>
+__device__ __forceinline__ T sum_single_warp(T val) {
+  val += __shfl_down(val, 16);
+  val += __shfl_down(val, 8);
+  val += __shfl_down(val, 4);
+  val += __shfl_down(val, 2);
+  val += __shfl_down(val, 1);
+  return val;
+}
+
+template <typename T>
+__global__ void SoftCrossEntropyKernel(T* Y, const T* X, const T* label,
+                                       const int class_num) {
+  int tid = threadIdx.x;
+  extern __shared__ T d_sum[];
+  d_sum[tid] = 0;
+
+  int cur_idx = tid;
+  int next_idx = blockIdx.x * class_num + tid;
+  while (cur_idx < class_num) {
+    d_sum[tid] +=
+        math::TolerableValue<T>()(std::log(X[next_idx])) * label[next_idx];
+    next_idx += blockDim.x;
+    cur_idx += blockDim.x;
+  }
+  __syncthreads();
+
+  for (unsigned int stride = blockDim.x >> 1; stride >= 32; stride >>= 1) {
+    if (tid < stride) d_sum[tid] += d_sum[tid + stride];
+    __syncthreads();
+  }
+
+  T val = d_sum[tid];
+  val = sum_single_warp<T>(val);
+  if (tid == 0) Y[blockIdx.x] = -val;
+}
+}  // namespace
+
+using Tensor = framework::Tensor;
+
+template <typename T>
+class CrossEntropyFunctor<platform::GPUPlace, T> {
+ public:
+  void operator()(const framework::ExecutionContext& ctx,
+                  framework::Tensor* out, const framework::Tensor* prob,
+                  const framework::Tensor* labels, bool softLabel) {
+    const T* prob_data = prob->data<T>();
+    T* loss_data = out->mutable_data<T>(ctx.GetPlace());
+
+    int batch_size = prob->dims()[0];
+    int class_num = prob->dims()[1];
+
+    if (softLabel) {
+      const T* label_data = labels->data<T>();
+      int block = class_num > 512 ? 512 : pow(2, int(std::log2(class_num)));
+
+      SoftCrossEntropyKernel<
+          T><<<batch_size, block, block * sizeof(T),
+               reinterpret_cast<const platform::CUDADeviceContext&>(
+                   ctx.device_context())
+                   .stream()>>>(loss_data, prob_data, label_data, class_num);
+    } else {
+      const int* label_data = labels->data<int>();
+      int block = 512;
+      int grid = (batch_size + block - 1) / block;
+      CrossEntropyKernel<T><<<
+          grid, block, 0, reinterpret_cast<const platform::CUDADeviceContext&>(
+                              ctx.device_context())
+                              .stream()>>>(loss_data, prob_data, label_data,
+                                           batch_size, class_num);
+    }
+  }
+};
+
+template class CrossEntropyFunctor<platform::GPUPlace, float>;
+}  // namespace math
+}  // namespace operators
+}  // namespace paddle

paddle/operators/math/cross_entropy.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/operator.h"
+#include "paddle/framework/tensor.h"
+#include "paddle/platform/hostdevice.h"
+
+namespace paddle {
+namespace operators {
+namespace math {
+
+template <typename T>
+struct TolerableValue {
+  HOSTDEVICE T operator()(const T& x) const {
+    PADDLE_ASSERT(std::is_floating_point<T>::value);
+    const T kApproInf = 1e20;
+
+    if (x == INFINITY) return kApproInf;
+    if (x == -INFINITY) return -kApproInf;
+    return x;
+  }
+};
+
+template <typename Place, typename T>
+class CrossEntropyFunctor {
+ public:
+  // (TODO caoying) it is much better to use DeviceContext as the first
+  // parameter.
+  void operator()(const framework::ExecutionContext& context,
+                  framework::Tensor* out, const framework::Tensor* prob,
+                  const framework::Tensor* labels, const bool softLabel);
+};
+}  // namespace math
+}  // namespace operators
+}  // namespace paddle

paddle/operators/softmax_with_cross_entropy_op.cu

@@ -14,26 +14,14 @@
 
 #define EIGEN_USE_GPU
 
-#include "paddle/framework/op_registry.h"
-#include "paddle/operators/cross_entropy_op.h"
-#include "paddle/operators/math/softmax.h"
+#include "paddle/operators/softmax_with_cross_entropy_op.h"
 
 namespace paddle {
 namespace operators {
 
 using Tensor = framework::Tensor;
 
-template <typename T>
-__global__ void CrossEntropy(T* out, const T* softmax_out, const int* labels,
-                             const int batch_size, const int class_num) {
-  int i = blockIdx.x * blockDim.x + threadIdx.x;
-  if (i < batch_size) {
-    PADDLE_ASSERT(labels[i] >= 0 && labels[i] < class_num);
-    out[i] =
-        -TolerableValue<T>()(std::log(softmax_out[i * class_num + labels[i]]));
-  }
-}
-
+namespace {
 template <typename T>
 __global__ void CrossEntropyGrad(T* out_grad, const T* in_grad,
                                  const int* labels, const int batch_size,
@@ -50,42 +38,6 @@ __global__ void CrossEntropyGrad(T* out_grad, const T* in_grad,
   }
 }
 
-template <typename T>
-__device__ __forceinline__ T sum_single_warp(T val) {
-  val += __shfl_down(val, 16);
-  val += __shfl_down(val, 8);
-  val += __shfl_down(val, 4);
-  val += __shfl_down(val, 2);
-  val += __shfl_down(val, 1);
-  return val;
-}
-
-template <typename T>
-__global__ void SoftCrossEntropyKernel(T* Y, const T* X, const T* label,
-                                       const int class_num) {
-  int tid = threadIdx.x;
-  extern __shared__ T d_sum[];
-  d_sum[tid] = 0;
-
-  int cur_idx = tid;
-  int next_idx = blockIdx.x * class_num + tid;
-  while (cur_idx < class_num) {
-    d_sum[tid] += TolerableValue<T>()(std::log(X[next_idx])) * label[next_idx];
-    next_idx += blockDim.x;
-    cur_idx += blockDim.x;
-  }
-  __syncthreads();
-
-  for (unsigned int stride = blockDim.x >> 1; stride >= 32; stride >>= 1) {
-    if (tid < stride) d_sum[tid] += d_sum[tid + stride];
-    __syncthreads();
-  }
-
-  T val = d_sum[tid];
-  val = sum_single_warp<T>(val);
-  if (tid == 0) Y[blockIdx.x] = -val;
-}
-
 template <typename T>
 __global__ void SoftCrossEntropyGradientKernel(T* logit_grad,
                                                const T* loss_grad,
@@ -98,6 +50,7 @@ __global__ void SoftCrossEntropyGradientKernel(T* logit_grad,
     logit_grad[ids] = logit_grad[ids] * loss_grad[row_ids] - labels[ids];
   }
 }
+}  // namespace
 
 template <typename T>
 class SoftmaxWithCrossEntropyCUDAKernel : public framework::OpKernel {
@@ -105,36 +58,17 @@ class SoftmaxWithCrossEntropyCUDAKernel : public framework::OpKernel {
   void Compute(const framework::ExecutionContext& context) const override {
     PADDLE_ENFORCE(platform::is_gpu_place(context.GetPlace()),
                    "This kernel only runs on GPU device.");
-    T* loss_data =
-        context.Output<Tensor>("Loss")->mutable_data<T>(context.GetPlace());
-
     const Tensor* logits = context.Input<Tensor>("Logits");
+    const Tensor* labels = context.Input<Tensor>("Label");
     Tensor* softmax = context.Output<Tensor>("Softmax");
-    T* softmax_out = softmax->mutable_data<T>(context.GetPlace());
-    math::SoftmaxFunctor<platform::GPUPlace, T>()(context, logits, softmax);
-
-    const int batch_size = logits->dims()[0];
-    const int class_num = logits->dims()[1];
-    int block = 512;
-    int grid = (batch_size + block - 1) / block;
 
-    if (context.Attr<bool>("softLabel")) {
-      const T* label_data = context.Input<Tensor>("Label")->data<T>();
-      block = class_num > 512 ? 512 : pow(2, int(std::log2(class_num)));
+    Tensor* loss = context.Output<Tensor>("Loss");
+    softmax->mutable_data<T>(context.GetPlace());
+    loss->mutable_data<T>(context.GetPlace());
 
-      SoftCrossEntropyKernel<
-          T><<<batch_size, block, block * sizeof(T),
-               reinterpret_cast<const platform::CUDADeviceContext&>(
-                   context.device_context())
-                   .stream()>>>(loss_data, softmax_out, label_data, class_num);
-    } else {
-      const int* label_data = context.Input<Tensor>("Label")->data<int>();
-      CrossEntropy<T><<<grid, block, 0,
-                        reinterpret_cast<const platform::CUDADeviceContext&>(
-                            context.device_context())
-                            .stream()>>>(loss_data, softmax_out, label_data,
-                                         batch_size, class_num);
-    }
+    math::SoftmaxFunctor<platform::GPUPlace, T>()(context, logits, softmax);
+    math::CrossEntropyFunctor<platform::GPUPlace, T>()(
+        context, loss, softmax, labels, context.Attr<bool>("softLabel"));
   }
 };
 

paddle/operators/softmax_with_cross_entropy_op.h

@@ -15,7 +15,7 @@
 #pragma once
 #include "paddle/framework/eigen.h"
 #include "paddle/framework/op_registry.h"
-#include "paddle/operators/cross_entropy_op.h"
+#include "paddle/operators/math/cross_entropy.h"
 #include "paddle/operators/math/softmax.h"
 
 namespace paddle {
@@ -37,31 +37,12 @@ class SoftmaxWithCrossEntropyKernel : public framework::OpKernel {
     Tensor* softmax = context.Output<Tensor>("Softmax");
     Tensor* loss = context.Output<Tensor>("Loss");
 
-    T* softmax_data = softmax->mutable_data<T>(context.GetPlace());
-    T* loss_data = loss->mutable_data<T>(context.GetPlace());
+    softmax->mutable_data<T>(context.GetPlace());
+    loss->mutable_data<T>(context.GetPlace());
 
     math::SoftmaxFunctor<platform::CPUPlace, T>()(context, logits, softmax);
-
-    const int batch_size = logits->dims()[0];
-    if (context.Attr<bool>("softLabel")) {
-      // (TODO caoying) the forward implementation can be further optimized.
-      // Current implementation is exactly cross entropy after softmax.
-      auto prob = EigenMatrix<T>::From(*softmax);
-      auto lbl_mat = EigenMatrix<T>::From(*labels);
-      auto loss_mat = EigenMatrix<T>::From(*loss);
-
-      loss_mat.device(context.GetEigenDevice<platform::CPUPlace>()) =
-          -((lbl_mat * prob.log().unaryExpr(TolerableValue<T>()))
-                .sum(Eigen::DSizes<int, 1>(1))
-                .reshape(Eigen::DSizes<int, 2>(batch_size, 1)));
-    } else {
-      const int* label_data = labels->data<int>();
-      const int class_num = logits->dims()[1];
-
-      for (int i = 0; i < batch_size; ++i)
-        loss_data[i] = -TolerableValue<T>()(
-            std::log(softmax_data[i * class_num + label_data[i]]));
-    }
+    math::CrossEntropyFunctor<platform::CPUPlace, T>()(
+        context, loss, softmax, labels, context.Attr<bool>("softLabel"));
   }
 };