diff --git a/paddle/operators/math/context_project.h b/paddle/operators/math/context_project.h index d9f952c387f2cfd9169ec5bbde54a525d6c438f4..845de82bbcb33d52184f04ae1594738cb4776eca 100644 --- a/paddle/operators/math/context_project.h +++ b/paddle/operators/math/context_project.h @@ -88,9 +88,10 @@ template 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 im2col_ocf; @@ -109,8 +110,8 @@ class ContextProjectFunctor { : static_cast(lod_level_0[i]); input_row_end = static_cast(lod_level_0[i + 1]); - Tensor out_t = col.Slice(static_cast(lod_level_0[i]), - static_cast(lod_level_0[i + 1])); + Tensor out_t = col->Slice(static_cast(lod_level_0[i]), + static_cast(lod_level_0[i + 1])); sequence_height = static_cast(out_t.dims()[0]); @@ -133,8 +134,8 @@ class ContextProjectFunctor { } if (padding_trainable) { for (int i = 0; i < static_cast(lod_level_0.size()) - 1; ++i) { - Tensor out_t = col.Slice(static_cast(lod_level_0[i]), - static_cast(lod_level_0[i + 1])); + Tensor out_t = col->Slice(static_cast(lod_level_0[i]), + static_cast(lod_level_0[i + 1])); sequence_height = static_cast(out_t.dims()[0]); @@ -197,10 +198,11 @@ class ContextProjectFunctor { template 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 col2im_ocf; @@ -220,8 +222,8 @@ class ContextProjectGradFunctor { : static_cast(lod_level_0[i]); input_row_end = static_cast(lod_level_0[i + 1]); - Tensor out_t = col.Slice(static_cast(lod_level_0[i]), - static_cast(lod_level_0[i + 1])); + Tensor out_t = col->Slice(static_cast(lod_level_0[i]), + static_cast(lod_level_0[i + 1])); sequence_height = static_cast(out_t.dims()[0]); @@ -247,8 +249,8 @@ class ContextProjectGradFunctor { if (pad_grad) { if (padding_trainable) { for (int i = 0; i < static_cast(lod_level_0.size()) - 1; ++i) { - Tensor out_t = col.Slice(static_cast(lod_level_0[i]), - static_cast(lod_level_0[i + 1])); + Tensor out_t = col->Slice(static_cast(lod_level_0[i]), + static_cast(lod_level_0[i + 1])); sequence_height = static_cast(out_t.dims()[0]); out_t.Resize({sequence_height * context_length, sequence_width}); @@ -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::From(out_t_sub); auto w_sub_e = EigenMatrix::From(w_sub); w_sub_e.device(*context.GetEigenDevice()) = @@ -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::From(out_t_sub); auto w_sub_e = EigenMatrix::From(w_sub); diff --git a/paddle/operators/math/vol2col.cu b/paddle/operators/math/vol2col.cu index addae3caf89eea2fee784ecb87d3a496ae773ad6..dae3be858e9f47d0133aa37e8a5f90a0addf1dfd 100644 --- a/paddle/operators/math/vol2col.cu +++ b/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; diff --git a/paddle/operators/sequence_conv_op.h b/paddle/operators/sequence_conv_op.h index a57e1752bb8ed4844423f752bf0ad9f8e114486a..adee8d760e1d6bed852236ecee0951656c458901 100644 --- a/paddle/operators/sequence_conv_op.h +++ b/paddle/operators/sequence_conv_op.h @@ -62,9 +62,9 @@ class SequenceConvKernel : public framework::OpKernel { math::ContextProjectFunctor 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(context.device_context(), col, false, filter, false, static_cast(1.0), out, static_cast(0.0)); @@ -117,10 +117,10 @@ class SequenceConvGradKernel : public framework::OpKernel { in_g->set_lod(in->lod()); set_zero(context.device_context(), in_g, static_cast(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 { LoDTensor* input = const_cast(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 { padding_data = context.Input("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(context.device_context(), col, true, out_grad, false, T(1.0), &filter_grad, T(1.0));