chore: import upstream snapshot with attribution

paddle/phi/kernels/gpu/conv_transpose_grad_kernel.cu

@@ -0,0 +1,170 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// 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/phi/kernels/conv_transpose_grad_kernel.h"
+
+#include "paddle/common/ddim.h"
+#include "paddle/common/layout.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/cpu/conv_util.h"
+#include "paddle/phi/kernels/full_kernel.h"
+#include "paddle/phi/kernels/funcs/math_function.h"
+#include "paddle/phi/kernels/gpu/depthwise_conv.h"
+#include "paddle/phi/kernels/impl/conv_transpose_grad_kernel_impl.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void Conv2dTransposeDoubleGradKernel(const Context& dev_ctx,
+                                     const DenseTensor& x,
+                                     const DenseTensor& filter,
+                                     const DenseTensor& dout,
+                                     const DenseTensor& ddx,
+                                     const DenseTensor& ddfilter,
+                                     const std::vector<int>& strides,
+                                     const std::vector<int>& paddings,
+                                     const std::vector<int>& output_padding,
+                                     const IntArray& output_size,
+                                     const std::string& padding_algorithm,
+                                     int groups,
+                                     const std::vector<int>& dilations,
+                                     const std::string& data_format,
+                                     DenseTensor* dx,
+                                     DenseTensor* dfilter,
+                                     DenseTensor* ddout) {
+  ConvTransposeGradRawKernel<T, Context>(dev_ctx,
+                                         x,
+                                         filter,
+                                         dout,
+                                         strides,
+                                         paddings,
+                                         padding_algorithm,
+                                         groups,
+                                         dilations,
+                                         data_format,
+                                         dx,
+                                         dfilter);
+}
+
+template <typename T, typename Context>
+void DepthwiseConv2dTransposeGradKernel(const Context& dev_ctx,
+                                        const DenseTensor& x,
+                                        const DenseTensor& filter,
+                                        const DenseTensor& dout,
+                                        const std::vector<int>& strides,
+                                        const std::vector<int>& paddings,
+                                        const std::vector<int>& output_padding,
+                                        const IntArray& output_size,
+                                        const std::string& padding_algorithm,
+                                        int groups,
+                                        const std::vector<int>& dilations,
+                                        const std::string& data_format,
+                                        DenseTensor* dx,
+                                        DenseTensor* dfilter) {
+  const DataLayout data_layout = StringToDataLayout(data_format);
+  DenseTensor filter_ = filter;
+
+  if (!dx && !dfilter) {
+    return;
+  }
+  // 0-size
+  if (x.numel() == 0) {
+    if (dx) dev_ctx.template Alloc<T>(dx);
+    if (dfilter) {
+      Full<T, Context>(dev_ctx, dfilter->dims(), 0, dfilter);
+    }
+    return;
+  }
+  if (filter.numel() == 0) {
+    if (dfilter) dev_ctx.template Alloc<T>(dfilter);
+    if (dx) {
+      Full<T, Context>(dev_ctx, dx->dims(), 0, dx);
+    }
+    return;
+  }
+  std::vector<int> paddings_ = paddings;
+  std::vector<int> dilations_ = dilations;
+
+  auto x_dims = x.dims();
+  auto filter_dims = filter_.dims();
+
+  DDim in_data_dims;
+  if (data_layout != DataLayout::NHWC) {
+    in_data_dims = slice_ddim(x_dims, 2, x_dims.size());
+  } else {
+    in_data_dims = slice_ddim(x_dims, 1, x_dims.size() - 1);
+  }
+  DDim filter_data_dims = slice_ddim(filter_dims, 2, filter_dims.size());
+  std::vector<int> ksize = vectorize<int>(filter_data_dims);
+  UpdatePaddingAndDilation(
+      &paddings_, &dilations_, padding_algorithm, in_data_dims, strides, ksize);
+
+  if (dx) {
+    math::DepthwiseConvFunctor<Context, T> depthwiseConv;
+    depthwiseConv(dev_ctx,
+                  dout,
+                  filter_,
+                  strides,
+                  std::vector<int>{
+                      paddings_[0], paddings_[2], paddings_[1], paddings_[3]},
+                  dilations_,
+                  dx,
+                  data_layout);
+  }
+
+  if (dfilter) {
+    funcs::SetConstant<Context, T> set_zero;
+    dev_ctx.template Alloc<T>(dfilter);
+    set_zero(dev_ctx, dfilter, static_cast<T>(0));
+
+    math::DepthwiseConvFilterGradFunctor<Context, T> depthwiseConvFilterGrad;
+    depthwiseConvFilterGrad(
+        dev_ctx,
+        dout,
+        x,
+        strides,
+        std::vector<int>{
+            paddings_[0], paddings_[2], paddings_[1], paddings_[3]},
+        dilations_,
+        dfilter,
+        data_layout);
+  }
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(conv2d_transpose_grad,
+                   GPU,
+                   ALL_LAYOUT,
+                   phi::Conv2dTransposeGradKernel,
+                   float,
+                   double) {}
+PD_REGISTER_KERNEL(conv2d_transpose_double_grad,
+                   GPU,
+                   ALL_LAYOUT,
+                   phi::Conv2dTransposeDoubleGradKernel,
+                   float,
+                   double) {}
+PD_REGISTER_KERNEL(conv3d_transpose_grad,
+                   GPU,
+                   ALL_LAYOUT,
+                   phi::Conv3dTransposeGradKernel,
+                   float,
+                   double) {}
+PD_REGISTER_KERNEL(depthwise_conv2d_transpose_grad,
+                   GPU,
+                   ALL_LAYOUT,
+                   phi::DepthwiseConv2dTransposeGradKernel,
+                   float,
+                   double) {}