deconv2d kernel and deconv3d kernel write together

paddle/operators/conv2d_transpose_cudnn_op.cc

@@ -44,7 +44,7 @@ REGISTER_OP(conv2d_transpose_cudnn, ops::ConvTransposeOp,
 
 REGISTER_OP_CPU_KERNEL(
     conv2d_transpose_cudnn,
-    ops::GemmConv2DTransposeKernel<paddle::platform::CPUPlace, float>);
+    ops::GemmConvTransposeKernel<paddle::platform::CPUPlace, float>);
 REGISTER_OP_CPU_KERNEL(
     conv2d_transpose_cudnn_grad,
-    ops::GemmConv2DTransposeGradKernel<paddle::platform::CPUPlace, float>);
+    ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/conv_transpose_op.cc

@@ -187,17 +187,17 @@ REGISTER_OP(conv2d_transpose, ops::ConvTransposeOp, ops::Conv2DTransposeOpMaker,
 
 REGISTER_OP_CPU_KERNEL(
     conv2d_transpose,
-    ops::GemmConv2DTransposeKernel<paddle::platform::CPUPlace, float>);
+    ops::GemmConvTransposeKernel<paddle::platform::CPUPlace, float>);
 REGISTER_OP_CPU_KERNEL(
     conv2d_transpose_grad,
-    ops::GemmConv2DTransposeGradKernel<paddle::platform::CPUPlace, float>);
+    ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, float>);
 
 REGISTER_OP(conv3d_transpose, ops::ConvTransposeOp, ops::Conv3DTransposeOpMaker,
             conv3d_transpose_grad, ops::ConvTransposeOpGrad);
 
 REGISTER_OP_CPU_KERNEL(
     conv3d_transpose,
-    ops::GemmConv3DTransposeKernel<paddle::platform::CPUPlace, float>);
+    ops::GemmConvTransposeKernel<paddle::platform::CPUPlace, float>);
 REGISTER_OP_CPU_KERNEL(
     conv3d_transpose_grad,
-    ops::GemmConv3DTransposeGradKernel<paddle::platform::CPUPlace, float>);
+    ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/conv_transpose_op.cu

@@ -18,14 +18,14 @@ namespace ops = paddle::operators;
 
 REGISTER_OP_GPU_KERNEL(
     conv2d_transpose,
-    ops::GemmConv2DTransposeKernel<paddle::platform::GPUPlace, float>);
+    ops::GemmConvTransposeKernel<paddle::platform::GPUPlace, float>);
 REGISTER_OP_GPU_KERNEL(
     conv2d_transpose_grad,
-    ops::GemmConv2DTransposeGradKernel<paddle::platform::GPUPlace, float>);
+    ops::GemmConvTransposeGradKernel<paddle::platform::GPUPlace, float>);
 
 REGISTER_OP_GPU_KERNEL(
     conv3d_transpose,
-    ops::GemmConv3DTransposeKernel<paddle::platform::GPUPlace, float>);
+    ops::GemmConvTransposeKernel<paddle::platform::GPUPlace, float>);
 REGISTER_OP_GPU_KERNEL(
     conv3d_transpose_grad,
-    ops::GemmConv3DTransposeGradKernel<paddle::platform::GPUPlace, float>);
+    ops::GemmConvTransposeGradKernel<paddle::platform::GPUPlace, float>);

paddle/operators/conv_transpose_op.h

@@ -57,7 +57,7 @@ class ConvTransposeOpGrad : public framework::OperatorWithKernel {
 };
 
 template <typename Place, typename T>
-class GemmConv2DTransposeKernel : public framework::OpKernel<T> {
+class GemmConvTransposeKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     const Tensor* input = context.Input<Tensor>("Input");
@@ -70,24 +70,31 @@ class GemmConv2DTransposeKernel : public framework::OpKernel<T> {
     // groups will alway be disabled in conv2dtranspose.
 
     const int batch_size = static_cast<int>(input->dims()[0]);
-    const int64_t m = input->dims()[1];
-    const int64_t h = input->dims()[2];
-    const int64_t w = input->dims()[3];
 
-    const int64_t k_h = filter.dims()[2];
-    const int64_t k_w = filter.dims()[3];
-
-    const int64_t c = output->dims()[1];  // output channels
-    const int64_t o_h = output->dims()[2];
-    const int64_t o_w = output->dims()[3];
-
-    math::Col2ImFunctor<math::ColFormat::kCFO, Place, T> col2im;
-
-    // use col_shape in the im2col and col2im calculation
-    DDim col_shape = {c, k_h, k_w, h, w};
+    // input_shape_vec: {h, w} or {d, h, w}
+    std::vector<int64_t> input_shape_vec = framework::vectorize(input->dims());
+    input_shape_vec.erase(input_shape_vec.begin(), input_shape_vec.begin() + 2);
+
+    // filter_shape_vec: {k_h, k_w} or {k_d, k_h, k_w}
+    std::vector<int64_t> filter_shape_vec = framework::vectorize(filter.dims());
+    filter_shape_vec.erase(filter_shape_vec.begin(),
+                           filter_shape_vec.begin() + 2);
+
+    // use col_shape in the im2col and col2im (or vol2col and col2vol)
+    // calculation
+    // col_shape_vec: {c, k_h, k_w, h, w} or {c, k_d, k_h, k_w, d, h, w}
+    std::vector<int64_t> col_shape_vec;
+    col_shape_vec.push_back(output->dims()[1]);
+    col_shape_vec.insert(col_shape_vec.end(), filter_shape_vec.begin(),
+                         filter_shape_vec.end());
+    col_shape_vec.insert(col_shape_vec.end(), input_shape_vec.begin(),
+                         input_shape_vec.end());
+    DDim col_shape(framework::make_ddim(col_shape_vec));
 
     // use col_matrix_shape in the gemm calculation
-    DDim col_matrix_shape = {c * k_h * k_w, h * w};
+    // size: (c * k_h * k_w, h * w) or (c * k_d * k_h * k_w, d * h * w)
+    DDim col_matrix_shape =
+        framework::flatten_to_2d(col_shape, filter_shape_vec.size() + 1);
 
     Tensor col;
     col.mutable_data<T>(col_shape, context.GetPlace());
@@ -98,47 +105,61 @@ class GemmConv2DTransposeKernel : public framework::OpKernel<T> {
     col_matrix.ShareDataWith(col);
     col_matrix.Resize(col_matrix_shape);
 
-    DDim output_shape = {c, o_h, o_w};
-    DDim input_matrix_shape = {m, h * w};
+    // output size: (c, o_h, o_w) or (c, o_d, o_h, o_w)
+    DDim output_shape =
+        framework::slice_ddim(output->dims(), 1, output->dims().size());
 
-    // filter size: (m, c * k_h * k_w)
-    DDim filter_matrix_shape = {m, c * k_h * k_w};
+    // input matrix size: (m, h * w) or (m, d * h * w)
+    DDim input_matrix_shape = {input->dims()[1], col_matrix_shape[1]};
+
+    // filter size: (m, c * k_h * k_w) or (m, c * k_d * k_h * k_w)
+    DDim filter_matrix_shape = {input->dims()[1], col_matrix_shape[0]};
     filter.Resize(filter_matrix_shape);
 
     output->mutable_data<T>(context.GetPlace());
     math::SetConstant<Place, T> set_zero;
     set_zero(context.device_context(), output, static_cast<T>(0));
 
-    // convolution transpose: gemm + col2im (similar to conv-backward on input)
+    // convolution transpose: gemm + col2im or col2vol (similar to conv-backward
+    // on input)
     for (int i = 0; i < batch_size; i++) {
-      // batch with size (m, h * w)
+      // batch with size (m, h * w) or (m, d * h * w)
       Tensor input_batch = input->Slice(i, i + 1).Resize(input_matrix_shape);
 
-      // output size: (c, o_h, o_w)
+      // output size: (c, o_h, o_w) or (c, o_d, o_h, o_w)
       Tensor output_batch = output->Slice(i, i + 1).Resize(output_shape);
 
       // col_matrix = filter * input_batch
-      // of shape (c * k_h * k_w, h * w)
+      // of shape (c * k_h * k_w, h * w) or (c * k_d * k_h * k_w, d * h * w)
       math::matmul<Place, T>(context.device_context(), filter, true,
                              input_batch, false, static_cast<T>(1.0),
                              &col_matrix, static_cast<T>(0.0));
 
-      // col2im: col_matrix -> dy
-      // from (c * k_h * k_w, h * w) to (c, o_h, o_w)
-      col2im(context.device_context(), output_batch, col, strides[0],
-             strides[1], 0, 0, 0, 0);
+      if (filter_shape_vec.size() == 2) {
+        // col2im: col_matrix -> dy
+        // from (c * k_h * k_w, h * w) to (c, o_h, o_w)
+        math::Col2ImFunctor<math::ColFormat::kCFO, Place, T> col2im;
+
+        col2im(context.device_context(), output_batch, col, strides[0],
+               strides[1], 0, 0, 0, 0);
+      } else if (filter_shape_vec.size() == 3) {
+        // col2vol: col_matrix -> dy
+        // from (c * k_d * k_h * k_w, d * h * w) to (c, o_d, o_h, o_w)
+        math::Col2VolFunctor<Place, T> col2vol;
+        col2vol(context.device_context(), output_batch, col, strides[0],
+                strides[1], strides[2], 0, 0, 0);
+      }
     }
   }
 };
 
 template <typename Place, typename T>
-class GemmConv2DTransposeGradKernel : public framework::OpKernel<T> {
+class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     const Tensor* input = context.Input<Tensor>("Input");
     const Tensor* output_grad =
         context.Input<Tensor>(framework::GradVarName("Output"));
-
     // For filter, we do not use const pointer b/c we will do reshape,
     // but we should avoid modifying its value.
     Tensor filter = *context.Input<Tensor>("Filter");
@@ -147,38 +168,50 @@ class GemmConv2DTransposeGradKernel : public framework::OpKernel<T> {
     Tensor* filter_grad =
         context.Output<Tensor>(framework::GradVarName("Filter"));
 
+    if ((!input_grad) && (!filter_grad)) return;
+
     std::vector<int> strides = context.Attr<std::vector<int>>("strides");
     // Actually, no paddings and groups allowed in conv transpose.
     std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
 
     const int batch_size = static_cast<int>(input->dims()[0]);
-    const int64_t m = input->dims()[1];
-    const int64_t h = input->dims()[2];
-    const int64_t w = input->dims()[3];
 
-    const int64_t k_h = filter.dims()[2];
-    const int64_t k_w = filter.dims()[3];
+    // input_shape_vec: {h, w} or {d, h, w}
+    std::vector<int64_t> input_shape_vec = framework::vectorize(input->dims());
+    input_shape_vec.erase(input_shape_vec.begin(), input_shape_vec.begin() + 2);
+
+    // filter_shape_vec: {k_h, k_w} or {k_d, k_h, k_w}
+    std::vector<int64_t> filter_shape_vec = framework::vectorize(filter.dims());
+    filter_shape_vec.erase(filter_shape_vec.begin(),
+                           filter_shape_vec.begin() + 2);
+
+    // use col_shape in the im2col and col2im (or vol2col and col2vol)
+    // calculation
+    // col_shape_vec: {c, k_h, k_w, h, w} or {c, k_d, k_h, k_w, d, h, w}
+    std::vector<int64_t> col_shape_vec;
+    col_shape_vec.push_back(output_grad->dims()[1]);
+    col_shape_vec.insert(col_shape_vec.end(), filter_shape_vec.begin(),
+                         filter_shape_vec.end());
+    col_shape_vec.insert(col_shape_vec.end(), input_shape_vec.begin(),
+                         input_shape_vec.end());
+    DDim col_shape(framework::make_ddim(col_shape_vec));
 
-    const int64_t c = output_grad->dims()[1];  // output channels
-    const int64_t o_h = output_grad->dims()[2];
-    const int64_t o_w = output_grad->dims()[3];
-
-    // Only im2col functor required for bp to get to the right shape
-    math::Im2ColFunctor<math::ColFormat::kCFO, Place, T> im2col;
+    // use col_matrix_shape in the gemm calculation
+    // size: (c * k_h * k_w, h * w) or (c * k_d * k_h * k_w, d * h * w)
+    DDim col_matrix_shape =
+        framework::flatten_to_2d(col_shape, filter_shape_vec.size() + 1);
 
-    // use col_shape in the im2col and col2im calculation
-    DDim col_shape = {c, k_h, k_w, h, w};
+    // output size: (c, o_h, o_w) or (c, o_d, o_h, o_w)
+    DDim output_shape = framework::slice_ddim(output_grad->dims(), 1,
+                                              output_grad->dims().size());
 
-    DDim output_shape = {c, o_h, o_w};
-    DDim input_matrix_shape = {m, h * w};
+    // input matrix size: (m, h * w) or (m, d * h * w)
+    DDim input_matrix_shape = {input->dims()[1], col_matrix_shape[1]};
 
-    DDim filter_matrix_shape = {m, c * k_h * k_w};
+    // filter size: (m, c * k_h * k_w) or (m, c * k_d * k_h * k_w)
+    DDim filter_matrix_shape = {input->dims()[1], col_matrix_shape[0]};
     filter.Resize(filter_matrix_shape);
 
-    if ((!input_grad) && (!filter_grad)) {
-      return;
-    }
-
     // convolution transpose grad on input:
     // im2col + gemm (similar to conv-forward)
     // input need to compute gradient
@@ -190,7 +223,6 @@ class GemmConv2DTransposeGradKernel : public framework::OpKernel<T> {
       // to call the matrix multiplication interface.
       Tensor col_matrix;
       col_matrix.ShareDataWith(col);
-      DDim col_matrix_shape = {c * k_h * k_w, h * w};
       col_matrix.Resize(col_matrix_shape);
 
       Tensor filter_grad_;
@@ -212,10 +244,21 @@ class GemmConv2DTransposeGradKernel : public framework::OpKernel<T> {
         Tensor output_grad_batch =
             output_grad->Slice(i, i + 1).Resize(output_shape);
 
-        // im2col: dy -> col matrix
-        // from (c, o_h, o_w) to (c * k_h * k_w, h * w)
-        im2col(context.device_context(), output_grad_batch, col, strides[0],
-               strides[1], paddings[0], paddings[0], paddings[1], paddings[1]);
+        if (filter_shape_vec.size() == 2) {
+          // im2col: dy -> col matrix
+          // from (c, o_h, o_w) to (c * k_h * k_w, h * w)
+          math::Im2ColFunctor<math::ColFormat::kCFO, Place, T> im2col;
+          im2col(context.device_context(), output_grad_batch, col, strides[0],
+                 strides[1], paddings[0], paddings[0], paddings[1],
+                 paddings[1]);
+        } else if (filter_shape_vec.size() == 3) {
+          // vol2col: dy -> col_matrix
+          // from (c, o_d, o_h, o_w) to (c * k_d * k_h * k_w, d * h * w)
+          math::Vol2ColFunctor<Place, T> vol2col;
+          vol2col(context.device_context(), output_grad_batch, col, strides[0],
+                  strides[1], strides[2], paddings[0], paddings[1],
+                  paddings[2]);
+        }
 
         if (input_grad) {
           // batch with size (m, h, w)
@@ -223,197 +266,7 @@ class GemmConv2DTransposeGradKernel : public framework::OpKernel<T> {
               input_grad->Slice(i, i + 1).Resize(input_matrix_shape);
           // gemm: dx = filter * dy
           // (m, c * k_h * k_w) * (c * k_h * k_w, h * w) -> (m, h * w)
-          math::matmul<Place, T>(context.device_context(), filter, false,
-                                 col_matrix, false, static_cast<T>(1.0),
-                                 &input_grad_batch, static_cast<T>(0.0));
-        }
-        if (filter_grad) {
-          // input batch
-          Tensor in_batch = input->Slice(i, i + 1).Resize(input_matrix_shape);
-          // gemm: d_filter = x * dy^T
-          // (m, c * h * w) * (k_h * k_w, c * h * w) -> (m, k_h * k_w)
-          math::matmul<Place, T>(context.device_context(), in_batch, false,
-                                 col_matrix, true, static_cast<T>(1.0),
-                                 &filter_grad_, static_cast<T>(1.0));
-        }
-      }
-    }
-  }
-};
-
-template <typename Place, typename T>
-class GemmConv3DTransposeKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& context) const override {
-    const Tensor* input = context.Input<Tensor>("Input");
-    // The filter will be reshaped, so it should not be constant pointer
-    Tensor filter = *context.Input<Tensor>("Filter");
-    Tensor* output = context.Output<Tensor>("Output");
-
-    std::vector<int> strides = context.Attr<std::vector<int>>("strides");
-    // TODO(chengduo): Paddings can be added in future.
-    // groups will alway be disabled in conv3dtranspose.
-
-    const int batch_size = static_cast<int>(input->dims()[0]);
-    const int64_t m = input->dims()[1];
-    const int64_t d = input->dims()[2];
-    const int64_t h = input->dims()[3];
-    const int64_t w = input->dims()[4];
-
-    const int64_t k_d = filter.dims()[2];
-    const int64_t k_h = filter.dims()[3];
-    const int64_t k_w = filter.dims()[4];
-
-    const int64_t c = output->dims()[1];  // output channels
-    const int64_t o_d = output->dims()[2];
-    const int64_t o_h = output->dims()[3];
-    const int64_t o_w = output->dims()[4];
-
-    math::Col2VolFunctor<Place, T> col2vol;
-
-    // use col_shape in the vol2col and col2vol calculation
-    DDim col_shape = {c, k_d, k_h, k_w, d, h, w};
-    // use col_matrix_shape in the gemm calculation
-    DDim col_matrix_shape = {c * k_d * k_h * k_w, d * h * w};
-
-    Tensor col;
-    col.mutable_data<T>(col_shape, context.GetPlace());
-    // col_matrix shares the same piece of data with col,
-    // but will be reshaped into a two-dimensional matrix shape
-    // to call the matrix multiplication interface.
-    Tensor col_matrix;
-    col_matrix.ShareDataWith(col);
-    col_matrix.Resize(col_matrix_shape);
-
-    DDim output_shape = {c, o_d, o_h, o_w};
-    DDim input_matrix_shape = {m, d * h * w};
-
-    // filter size: (m, c * k_d * k_h * k_w)
-    DDim filter_matrix_shape = {m, c * k_d * k_h * k_w};
-    filter.Resize(filter_matrix_shape);
-
-    output->mutable_data<T>(context.GetPlace());
-    math::SetConstant<Place, T> set_zero;
-    set_zero(context.device_context(), output, static_cast<T>(0));
-
-    // convolution transpose: gemm + col2vol (similar to conv-backward on input)
-    for (int i = 0; i < batch_size; i++) {
-      // batch with size (m, d * h * w)
-      Tensor input_batch = input->Slice(i, i + 1).Resize(input_matrix_shape);
-
-      // output size: (c, o_d, o_h, o_w)
-      Tensor output_batch = output->Slice(i, i + 1).Resize(output_shape);
-
-      // col_matrix = filter * input_batch
-      // of shape (c * k_d * k_h * k_w, d * h * w)
-      math::matmul<Place, T>(context.device_context(), filter, true,
-                             input_batch, false, static_cast<T>(1.0),
-                             &col_matrix, static_cast<T>(0.0));
-      // col2vol: col_matrix -> dy
-      // from (c * k_d * k_h * k_w, d * h * w) to (c, o_d, o_h, o_w)
-      col2vol(context.device_context(), output_batch, col, strides[0],
-              strides[1], strides[2], 0, 0, 0);
-    }
-  }
-};
-
-template <typename Place, typename T>
-class GemmConv3DTransposeGradKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& context) const override {
-    const Tensor* input = context.Input<Tensor>("Input");
-    const Tensor* output_grad =
-        context.Input<Tensor>(framework::GradVarName("Output"));
-
-    // For filter, we do not use const pointer b/c we will do reshape,
-    // but we should avoid modifying its value.
-    Tensor filter = *context.Input<Tensor>("Filter");
-    Tensor* input_grad =
-        context.Output<Tensor>(framework::GradVarName("Input"));
-    Tensor* filter_grad =
-        context.Output<Tensor>(framework::GradVarName("Filter"));
-
-    std::vector<int> strides = context.Attr<std::vector<int>>("strides");
-    // Actually, no paddings and groups allowed in conv transpose.
-    std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
-
-    const int batch_size = static_cast<int>(input->dims()[0]);
-    const int64_t m = input->dims()[1];
-    const int64_t d = input->dims()[2];
-    const int64_t h = input->dims()[3];
-    const int64_t w = input->dims()[4];
-
-    const int64_t k_d = filter.dims()[2];
-    const int64_t k_h = filter.dims()[3];
-    const int64_t k_w = filter.dims()[4];
-
-    const int64_t c = output_grad->dims()[1];  // output channels
-    const int64_t o_d = output_grad->dims()[2];
-    const int64_t o_h = output_grad->dims()[3];
-    const int64_t o_w = output_grad->dims()[4];
-
-    // Only vol2col functor required for bp to get to the right shape
-    math::Vol2ColFunctor<Place, T> vol2col;
-
-    // use col_shape in the vol2col and col2vol calculation
-    DDim col_shape = {c, k_d, k_h, k_w, d, h, w};
-
-    // use col_matrix_shape in the gemm calculation
-    DDim col_matrix_shape_f = {c * d * h * w, k_d * k_h * k_w};
-
-    DDim output_shape = {c, o_d, o_h, o_w};
-    DDim input_matrix_shape = {m, d * h * w};
-
-    DDim filter_matrix_shape = {m, c * k_d * k_h * k_w};
-    filter.Resize(filter_matrix_shape);
-
-    if ((!input_grad) && (!filter_grad)) {
-      return;
-    }
-
-    // convolution transpose grad on input:
-    // vol2col + gemm (similar to conv-forward)
-    // input need to compute gradient
-    if (input_grad || filter_grad) {
-      Tensor col;
-      col.mutable_data<T>(col_shape, context.GetPlace());
-      // col_matrix shares the same piece of data with col,
-      // but will be reshaped into a two-dimensional matrix shape
-      // to call the matrix multiplication interface.
-      Tensor col_matrix;
-      col_matrix.ShareDataWith(col);
-      DDim col_matrix_shape = {c * k_d * k_h * k_w, d * h * w};
-      col_matrix.Resize(col_matrix_shape);
-
-      Tensor filter_grad_;
-      math::SetConstant<Place, T> set_zero;
-
-      if (input_grad) {
-        input_grad->mutable_data<T>(context.GetPlace());
-        set_zero(context.device_context(), input_grad, static_cast<T>(0));
-      }
-      if (filter_grad) {  // filter size (m, c * k_d * k_h * k_w)
-        filter_grad->mutable_data<T>(context.GetPlace());
-        set_zero(context.device_context(), filter_grad, static_cast<T>(0));
-        filter_grad_ = *filter_grad;
-        filter_grad_.Resize(filter_matrix_shape);
-      }
-
-      for (int i = 0; i < batch_size; i++) {
-        // batch with size (c, o_d * o_h * o_w)
-        Tensor output_grad_batch =
-            output_grad->Slice(i, i + 1).Resize(output_shape);
-
-        // vol2col: dy -> col_matrix
-        // from (c, o_d, o_h, o_w) to (c * k_d * k_h * k_w, d * h * w)
-        vol2col(context.device_context(), output_grad_batch, col, strides[0],
-                strides[1], strides[2], paddings[0], paddings[1], paddings[2]);
-
-        if (input_grad) {
-          // batch with size (m, d, h, w)
-          Tensor input_grad_batch =
-              input_grad->Slice(i, i + 1).Resize(input_matrix_shape);
-          // gemm: dx = filter * dy
+          // or
           // (m, c * k_d * k_h * k_w) * (c * k_d * k_h * k_w, d * h * w) -> (m,
           // d, h, w)
           math::matmul<Place, T>(context.device_context(), filter, false,
@@ -424,6 +277,8 @@ class GemmConv3DTransposeGradKernel : public framework::OpKernel<T> {
           // input batch
           Tensor in_batch = input->Slice(i, i + 1).Resize(input_matrix_shape);
           // gemm: d_filter = x * dy^T
+          // (m, c * h * w) * (k_h * k_w, c * h * w) -> (m, k_h * k_w)
+          // or
           // (m, d * h * w) * (d * h * w, c * k_d * k_h * k_w) -> (m, c * k_d *
           // k_h * k_w)
           math::matmul<Place, T>(context.device_context(), in_batch, false,
@@ -434,6 +289,5 @@ class GemmConv3DTransposeGradKernel : public framework::OpKernel<T> {
     }
   }
 };
-
 }  // namespace operators
 }  // namespace paddle