fix ContextProjectFunctor parameter order

paddle/operators/math/context_project.h

@@ -88,9 +88,10 @@ template <typename Place, typename T>
 class ContextProjectFunctor {
  public:
   void operator()(const platform::DeviceContext& context, const LoDTensor& in,
-                  const Tensor& padding_data, Tensor& col,
-                  bool padding_trainable, int context_start, int context_length,
-                  int context_stride, int up_pad, int down_pad) {
+                  const Tensor& padding_data, bool padding_trainable,
+                  const int context_start, const int context_length,
+                  const int context_stride, const int up_pad,
+                  const int down_pad, Tensor* col) {
     auto lod_level_0 = in.lod()[0];
 
     math::Im2ColFunctor<math::ColFormat::kOCF, Place, float> im2col_ocf;
@@ -109,7 +110,7 @@ class ContextProjectFunctor {
                             : static_cast<int>(lod_level_0[i]);
       input_row_end = static_cast<int>(lod_level_0[i + 1]);
 
-      Tensor out_t = col.Slice(static_cast<int>(lod_level_0[i]),
+      Tensor out_t = col->Slice(static_cast<int>(lod_level_0[i]),
                                 static_cast<int>(lod_level_0[i + 1]));
 
       sequence_height = static_cast<int>(out_t.dims()[0]);
@@ -133,7 +134,7 @@ class ContextProjectFunctor {
     }
     if (padding_trainable) {
       for (int i = 0; i < static_cast<int>(lod_level_0.size()) - 1; ++i) {
-        Tensor out_t = col.Slice(static_cast<int>(lod_level_0[i]),
+        Tensor out_t = col->Slice(static_cast<int>(lod_level_0[i]),
                                   static_cast<int>(lod_level_0[i + 1]));
 
         sequence_height = static_cast<int>(out_t.dims()[0]);
@@ -197,10 +198,11 @@ class ContextProjectFunctor {
 template <typename Place, typename T>
 class ContextProjectGradFunctor {
  public:
-  void operator()(const platform::DeviceContext& context, LoDTensor& in,
-                  Tensor& padding_data, Tensor& col, bool padding_trainable,
-                  int context_start, int context_length, int context_stride,
-                  int up_pad, int down_pad, bool input_grad, bool pad_grad) {
+  void operator()(const platform::DeviceContext& context, const LoDTensor& in,
+                  bool padding_trainable, const int context_start,
+                  const int context_length, const int context_stride,
+                  const int up_pad, const int down_pad, bool pad_grad,
+                  bool input_grad, Tensor* padding_data, Tensor* col) {
     auto lod_level_0 = in.lod()[0];
 
     math::Col2ImFunctor<math::ColFormat::kOCF, Place, float> col2im_ocf;
@@ -220,7 +222,7 @@ class ContextProjectGradFunctor {
                               : static_cast<int>(lod_level_0[i]);
         input_row_end = static_cast<int>(lod_level_0[i + 1]);
 
-        Tensor out_t = col.Slice(static_cast<int>(lod_level_0[i]),
+        Tensor out_t = col->Slice(static_cast<int>(lod_level_0[i]),
                                   static_cast<int>(lod_level_0[i + 1]));
 
         sequence_height = static_cast<int>(out_t.dims()[0]);
@@ -247,7 +249,7 @@ class ContextProjectGradFunctor {
     if (pad_grad) {
       if (padding_trainable) {
         for (int i = 0; i < static_cast<int>(lod_level_0.size()) - 1; ++i) {
-          Tensor out_t = col.Slice(static_cast<int>(lod_level_0[i]),
+          Tensor out_t = col->Slice(static_cast<int>(lod_level_0[i]),
                                     static_cast<int>(lod_level_0[i + 1]));
 
           sequence_height = static_cast<int>(out_t.dims()[0]);
@@ -262,7 +264,7 @@ class ContextProjectGradFunctor {
                   k + context_length < up_pad ? context_length : up_pad - k;
               Tensor out_t_sub = out_t.Slice(k * context_length,
                                              k * context_length + padding_size);
-              Tensor w_sub = padding_data.Slice(k, k + padding_size);
+              Tensor w_sub = padding_data->Slice(k, k + padding_size);
               auto out_t_sub_e = EigenMatrix<T>::From(out_t_sub);
               auto w_sub_e = EigenMatrix<T>::From(w_sub);
               w_sub_e.device(*context.GetEigenDevice<Place>()) =
@@ -295,7 +297,7 @@ class ContextProjectGradFunctor {
               Tensor out_t_sub = out_t.Slice(
                   (down_pad_begin_row + t) * context_length - padding_size,
                   (down_pad_begin_row + t) * context_length);
-              Tensor w_sub = padding_data.Slice(
+              Tensor w_sub = padding_data->Slice(
                   up_pad + padding_idx, up_pad + padding_idx + padding_size);
               auto out_t_sub_e = EigenMatrix<T>::From(out_t_sub);
               auto w_sub_e = EigenMatrix<T>::From(w_sub);

paddle/operators/math/vol2col.cu

@@ -174,10 +174,9 @@ __global__ void col2vol(int num_kernels, const T* data_col, int depth,
             int data_col_index =
                 (((((c * filter_depth + d_off) * filter_height + h_off) *
                        filter_width +
-                   w_off) *
-                      output_detph +
-                  d_col) *
-                     output_height +
+                   w_off)));
+            data_col_index =
+                ((data_col_index * output_detph + d_col) * output_height +
                  h_col) *
                     output_width +
                 w_col;

paddle/operators/sequence_conv_op.h

@@ -62,9 +62,9 @@ class SequenceConvKernel : public framework::OpKernel<T> {
 
     math::ContextProjectFunctor<Place, T> seq_project_functor;
 
-    seq_project_functor(context.device_context(), *in, *padding_data, col,
+    seq_project_functor(context.device_context(), *in, *padding_data,
                         padding_trainable, context_start, context_length,
-                        context_stride, up_pad, down_pad);
+                        context_stride, up_pad, down_pad, &col);
 
     math::matmul<Place, T>(context.device_context(), col, false, filter, false,
                            static_cast<T>(1.0), out, static_cast<T>(0.0));
@@ -117,10 +117,10 @@ class SequenceConvGradKernel : public framework::OpKernel<T> {
       in_g->set_lod(in->lod());
       set_zero(context.device_context(), in_g, static_cast<T>(0));
 
-      seq_project_grad_functor(context.device_context(), *in_g, *padding_data_g,
-                               col, padding_trainable, context_start,
-                               context_length, context_stride, up_pad, down_pad,
-                               true, false);
+      seq_project_grad_functor(context.device_context(), *in_g,
+                               padding_trainable, context_start, context_length,
+                               context_stride, up_pad, down_pad, false, true,
+                               padding_data_g, &col);
     }
 
     if (padding_trainable && padding_data_g) {
@@ -129,9 +129,9 @@ class SequenceConvGradKernel : public framework::OpKernel<T> {
 
       LoDTensor* input = const_cast<LoDTensor*>(in);
       seq_project_grad_functor(context.device_context(), *input,
-                               *padding_data_g, col, padding_trainable,
-                               context_start, context_length, context_stride,
-                               up_pad, down_pad, false, true);
+                               padding_trainable, context_start, context_length,
+                               context_stride, up_pad, down_pad, true, false,
+                               padding_data_g, &col);
     }
 
     if (filter_g) {
@@ -146,9 +146,9 @@ class SequenceConvGradKernel : public framework::OpKernel<T> {
         padding_data = context.Input<Tensor>("PaddingData");
       }
 
-      seq_project_functor(context.device_context(), *in, *padding_data, col,
+      seq_project_functor(context.device_context(), *in, *padding_data,
                           padding_trainable, context_start, context_length,
-                          context_stride, up_pad, down_pad);
+                          context_stride, up_pad, down_pad, &col);
 
       math::matmul<Place, T>(context.device_context(), col, true, out_grad,
                              false, T(1.0), &filter_grad, T(1.0));