chore: import upstream snapshot with attribution

paddle/phi/kernels/gpu/conv_transpose_kernel.cu

@@ -0,0 +1,120 @@
+// 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_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/funcs/math_function.h"
+#include "paddle/phi/kernels/gpu/depthwise_conv.h"
+#include "paddle/phi/kernels/impl/conv_transpose_kernel_impl.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void DepthwiseConv2dTransposeKernel(const Context& dev_ctx,
+                                    const DenseTensor& x,
+                                    const DenseTensor& filter,
+                                    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* out) {
+  if (x.numel() == 0 || filter.numel() == 0) {
+    Full<T, Context>(dev_ctx, out->dims(), 0, out);
+    return;
+  }
+  const DataLayout data_layout = StringToDataLayout(data_format);
+  DenseTensor filter_ = filter;
+  dev_ctx.template Alloc<T>(out);
+
+  PADDLE_ENFORCE_EQ(
+      groups,
+      filter_.dims()[0],
+      errors::InvalidArgument(
+          "groups should be error to the 1st dimension of filter_. But "
+          "received groups is %d and filter dimension[0] is %d",
+          groups,
+          filter_.dims()[0]));
+
+  std::vector<int> paddings_ = paddings;
+  std::vector<int> dilations_ = dilations;
+
+  for (auto v : dilations_) {
+    PADDLE_ENFORCE_EQ(
+        v,
+        1,
+        errors::InvalidArgument("dilations should be 1 in depthwise conv. "
+                                "But received dilations is %d",
+                                v));
+  }
+
+  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);
+
+  dev_ctx.template Alloc<T>(out);
+
+  funcs::SetConstant<Context, T> set_zero;
+  set_zero(dev_ctx, out, static_cast<T>(0));
+
+  math::DepthwiseConvInputGradFunctor<Context, T> depthwiseConvInputGrad;
+  depthwiseConvInputGrad(
+      dev_ctx,
+      *out,
+      filter,
+      x,
+      strides,
+      std::vector<int>{paddings_[0], paddings_[2], paddings_[1], paddings_[3]},
+      dilations_,
+      out,
+      data_layout);
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(conv2d_transpose,
+                   GPU,
+                   ALL_LAYOUT,
+                   phi::Conv2dTransposeKernel,
+                   float,
+                   double) {}
+PD_REGISTER_KERNEL(conv3d_transpose,
+                   GPU,
+                   ALL_LAYOUT,
+                   phi::Conv3dTransposeKernel,
+                   float,
+                   double) {}
+PD_REGISTER_KERNEL(depthwise_conv2d_transpose,
+                   GPU,
+                   ALL_LAYOUT,
+                   phi::DepthwiseConv2dTransposeKernel,
+                   float,
+                   double) {}