Fix compling for softmax_with_cross_entropy_op.

paddle/operators/CMakeLists.txt

@@ -168,11 +168,12 @@ set(DEPS_OPS
     recurrent_op
     dynamic_recurrent_op
     softmax_with_cross_entropy_op
+    softmax_op
+    sequence_softmax_op
     sum_op
     pool_op
     pool_with_index_op
     conv_op
-    lstm_op
     conv_transpose_op
     nccl_op
     sequence_conv_op
@@ -187,6 +188,8 @@ set(DEPS_OPS
 op_library(cond_op SRCS cond_op.cc DEPS framework_proto tensor operator net_op)
 op_library(cross_entropy_op DEPS cross_entropy)
 op_library(softmax_with_cross_entropy_op DEPS cross_entropy softmax)
+op_library(softmax_op DEPS softmax)
+op_library(sequence_softmax_op DEPS softmax)
 op_library(conv_op DEPS vol2col)
 op_library(sum_op DEPS net_op selected_rows_functor)
 op_library(pool_op DEPS pooling)

paddle/operators/math/CMakeLists.txt

 add_subdirectory(detail)
 
 if(WITH_GPU)
-    nv_library(math_function SRCS math_function.cc math_function.cu im2col.cc im2col.cu DEPS cblas device_context operator)
+    nv_library(math_function SRCS math_function.cc math_function.cu im2col.cc im2col.cu DEPS cblas device_context)
     nv_test(math_function_gpu_test SRCS math_function_test.cu DEPS math_function tensor)
     nv_library(selected_rows_functor SRCS selected_rows_functor.cc selected_rows_functor.cu DEPS selected_rows math_function)
     nv_test(selected_rows_functor_gpu_test SRCS selected_rows_functor_test.cu DEPS selected_rows_functor)
-    nv_library(softmax SRCS softmax.cc softmax.cu DEPS operator)
-    nv_library(cross_entropy SRCS cross_entropy.cc cross_entropy.cu DEPS operator)
+    nv_library(softmax SRCS softmax.cc softmax.cu DEPS device_context)
+    nv_library(cross_entropy SRCS cross_entropy.cc cross_entropy.cu DEPS device_context)
     nv_library(pooling SRCS pooling.cc pooling.cu DEPS device_context)
     nv_library(sequence_pooling SRCS sequence_pooling.cc sequence_pooling.cu DEPS device_context math_function)
     nv_library(vol2col SRCS vol2col.cc vol2col.cu DEPS device_context)
@@ -15,10 +15,10 @@ if(WITH_GPU)
     nv_library(lstm_compute SRCS lstm_compute.cc lstm_compute.cu DEPS device_context activation_functions)
     nv_library(gru_compute SRCS gru_compute.cc gru_compute.cu DEPS device_context activation_functions math_function)
 else()
-    cc_library(math_function SRCS math_function.cc im2col.cc DEPS cblas device_context operator)
+    cc_library(math_function SRCS math_function.cc im2col.cc DEPS cblas device_context)
     cc_library(selected_rows_functor SRCS selected_rows_functor.cc DEPS selected_rows math_function)
-    cc_library(softmax SRCS softmax.cc DEPS operator)
-    cc_library(cross_entropy SRCS cross_entropy.cc DEPS operator)
+    cc_library(softmax SRCS softmax.cc DEPS device_context)
+    cc_library(cross_entropy SRCS cross_entropy.cc DEPS device_context)
     cc_library(pooling SRCS pooling.cc DEPS device_context)
     cc_library(sequence_pooling SRCS sequence_pooling.cc DEPS device_context math_function)
     cc_library(vol2col SRCS vol2col.cc DEPS device_context)

paddle/operators/math/cross_entropy.h

@@ -14,7 +14,6 @@
 
 #pragma once
 #include "paddle/framework/eigen.h"
-#include "paddle/framework/operator.h"
 #include "paddle/framework/tensor.h"
 #include "paddle/platform/hostdevice.h"
 

paddle/operators/math/math_function_impl.h

@@ -12,6 +12,7 @@ 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/data_type.h"
 #include "paddle/operators/math/math_function.h"
 

paddle/operators/math/softmax.cc

@@ -13,13 +13,16 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/operators/math/softmax.h"
+#include "paddle/operators/math/softmax_impl.h"
 
 namespace paddle {
 namespace operators {
 namespace math {
 
 template class SoftmaxFunctor<platform::CPUPlace, float>;
+template class SoftmaxFunctor<platform::CPUPlace, double>;
 template class SoftmaxGradFunctor<platform::CPUPlace, float>;
+template class SoftmaxGradFunctor<platform::CPUPlace, double>;
 
 }  // namespace math
 }  // namespace operators

paddle/operators/math/softmax.cu

@@ -15,13 +15,16 @@ limitations under the License. */
 #define EIGEN_USE_GPU
 
 #include "paddle/operators/math/softmax.h"
+#include "paddle/operators/math/softmax_impl.h"
 
 namespace paddle {
 namespace operators {
 namespace math {
 
 template class SoftmaxFunctor<platform::GPUPlace, float>;
+template class SoftmaxFunctor<platform::GPUPlace, double>;
 template class SoftmaxGradFunctor<platform::GPUPlace, float>;
+template class SoftmaxGradFunctor<platform::GPUPlace, double>;
 
 }  // namespace math
 }  // namespace operators

paddle/operators/math/softmax.h

@@ -13,60 +13,17 @@ 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"
 
 namespace paddle {
 namespace operators {
 namespace math {
 
-template <typename T, int MajorType = Eigen::RowMajor,
-          typename IndexType = Eigen::DenseIndex>
-using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
-
-template <typename T>
-struct ValueClip {
-  HOSTDEVICE T operator()(const T& x) const {
-    const T kThreshold = -64.;
-    return x < kThreshold ? kThreshold : x;
-  }
-};
-
 template <typename Place, typename T>
 class SoftmaxFunctor {
  public:
   void operator()(const platform::DeviceContext& context,
-                  const framework::Tensor* X, framework::Tensor* Y) {
-    auto logits = EigenMatrix<T>::From(*X);
-    auto softmax = EigenMatrix<T>::From(*Y);
-
-    const int kBatchDim = 0;
-    const int kClassDim = 1;
-
-    const int batch_size = logits.dimension(kBatchDim);
-    const int num_classes = logits.dimension(kClassDim);
-
-    Eigen::DSizes<int, 1> along_class(kClassDim);
-    Eigen::DSizes<int, 2> batch_by_one(batch_size, 1);
-    Eigen::DSizes<int, 2> one_by_class(1, num_classes);
-
-    auto shifted_logits = (logits -
-                           logits.maximum(along_class)
-                               .eval()
-                               .reshape(batch_by_one)
-                               .broadcast(one_by_class))
-                              .unaryExpr(ValueClip<T>());
-
-    softmax.device(*context.GetEigenDevice<Place>()) = shifted_logits.exp();
-    softmax.device(*context.GetEigenDevice<Place>()) =
-        (softmax *
-         softmax.sum(along_class)
-             .inverse()
-             .eval()
-             .reshape(batch_by_one)
-             .broadcast(one_by_class));
-  }
+                  const framework::Tensor* X, framework::Tensor* Y);
 };
 
 template <typename Place, typename T>
@@ -74,29 +31,7 @@ class SoftmaxGradFunctor {
  public:
   void operator()(const platform::DeviceContext& context,
                   const framework::Tensor* y, const framework::Tensor* y_grad,
-                  framework::Tensor* x_grad) {
-    auto softmax = EigenMatrix<T>::From(*y);
-    auto softmax_grad = EigenMatrix<T>::From(*y_grad);
-    auto logits_grad = EigenMatrix<T>::From(*x_grad);
-
-    const int kBatchDim = 0;
-    const int kClassDim = 1;
-
-    const int batch_size = softmax.dimension(kBatchDim);
-    const int num_classes = softmax.dimension(kClassDim);
-
-    Eigen::DSizes<int, 1> along_class(kClassDim);
-    Eigen::DSizes<int, 2> batch_by_one(batch_size, 1);
-    Eigen::DSizes<int, 2> one_by_class(1, num_classes);
-
-    auto dot = (softmax * softmax_grad)
-                   .sum(along_class)
-                   .eval()
-                   .reshape(batch_by_one)
-                   .broadcast(one_by_class);
-    logits_grad.device(*context.GetEigenDevice<Place>()) =
-        (softmax_grad - dot) * softmax;
-  }
+                  framework::Tensor* x_grad);
 };
 
 }  // namespace math

paddle/operators/math/softmax_impl.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/tensor.h"
+
+namespace paddle {
+namespace operators {
+namespace math {
+
+template <typename T, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
+
+template <typename T>
+struct ValueClip {
+  HOSTDEVICE T operator()(const T& x) const {
+    const T kThreshold = -64.;
+    return x < kThreshold ? kThreshold : x;
+  }
+};
+
+template <typename Place, typename T>
+void SoftmaxFunctor<Place, T>::operator()(
+    const platform::DeviceContext& context, const framework::Tensor* X,
+    framework::Tensor* Y) {
+  auto logits = EigenMatrix<T>::From(*X);
+  auto softmax = EigenMatrix<T>::From(*Y);
+
+  const int kBatchDim = 0;
+  const int kClassDim = 1;
+
+  const int batch_size = logits.dimension(kBatchDim);
+  const int num_classes = logits.dimension(kClassDim);
+
+  Eigen::DSizes<int, 1> along_class(kClassDim);
+  Eigen::DSizes<int, 2> batch_by_one(batch_size, 1);
+  Eigen::DSizes<int, 2> one_by_class(1, num_classes);
+
+  auto shifted_logits = (logits -
+                         logits.maximum(along_class)
+                             .eval()
+                             .reshape(batch_by_one)
+                             .broadcast(one_by_class))
+                            .unaryExpr(ValueClip<T>());
+
+  softmax.device(*context.GetEigenDevice<Place>()) = shifted_logits.exp();
+  softmax.device(*context.GetEigenDevice<Place>()) =
+      (softmax *
+       softmax.sum(along_class)
+           .inverse()
+           .eval()
+           .reshape(batch_by_one)
+           .broadcast(one_by_class));
+}
+
+template <typename Place, typename T>
+void SoftmaxGradFunctor<Place, T>::operator()(
+    const platform::DeviceContext& context, const framework::Tensor* y,
+    const framework::Tensor* y_grad, framework::Tensor* x_grad) {
+  auto softmax = EigenMatrix<T>::From(*y);
+  auto softmax_grad = EigenMatrix<T>::From(*y_grad);
+  auto logits_grad = EigenMatrix<T>::From(*x_grad);
+
+  const int kBatchDim = 0;
+  const int kClassDim = 1;
+
+  const int batch_size = softmax.dimension(kBatchDim);
+  const int num_classes = softmax.dimension(kClassDim);
+
+  Eigen::DSizes<int, 1> along_class(kClassDim);
+  Eigen::DSizes<int, 2> batch_by_one(batch_size, 1);
+  Eigen::DSizes<int, 2> one_by_class(1, num_classes);
+
+  auto dot = (softmax * softmax_grad)
+                 .sum(along_class)
+                 .eval()
+                 .reshape(batch_by_one)
+                 .broadcast(one_by_class);
+  logits_grad.device(*context.GetEigenDevice<Place>()) =
+      (softmax_grad - dot) * softmax;
+}
+
+}  // namespace math
+}  // namespace operators
+}  // namespace paddle

paddle/operators/softmax_with_cross_entropy_op.cc

@@ -14,7 +14,6 @@ limitations under the License. */
 
 #include "paddle/operators/softmax_with_cross_entropy_op.h"
 #include <paddle/function/TensorType.h>
-#include <iostream>
 
 namespace paddle {
 namespace operators {