Move conv2d_transpose_grad XPU kernel to PHI, test=kunlun (#45466)

paddle/phi/kernels/xpu/conv_transpose_grad_kernel.cc

@@ -0,0 +1,106 @@
+// 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/phi/backends/xpu/enforce_xpu.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/cpu/conv_util.h"
+
+namespace phi {
+template <typename T, typename Context>
+void Conv2dTransposeGradKernel(const Context& 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 std::vector<int>& output_size,
+                               const std::string& padding_algorithm,
+                               int groups,
+                               const std::vector<int>& dilations,
+                               const std::string& data_format,
+                               DenseTensor* dx,
+                               DenseTensor* dfilter) {
+  // The filter and dfilter will be reshaped in the calculations,
+  // so here use an assignment operation,
+  // that avoids modifying the variable in the Scope.
+  DenseTensor filter_ = filter;
+  if (!dx && !dfilter) return;
+
+  std::vector<int> paddings_ = paddings;
+  std::vector<int> dilations_ = dilations;
+
+  PADDLE_ENFORCE_EQ(
+      data_format == "NHWC" || data_format == "NDHWC",
+      false,
+      errors::InvalidArgument(
+          ("XPU do support data_format is NCHW in conv grad op.")));
+
+  DDim in_data_dims = slice_ddim(x.dims(), 2, x.dims().size());
+  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);
+
+  const int batch_size = static_cast<int>(x.dims()[0]);
+  const int img_yc = static_cast<int>(x.dims()[1]);
+  const int img_yh = static_cast<int>(x.dims()[2]);
+  const int img_yw = static_cast<int>(x.dims()[3]);
+  const int img_xc = static_cast<int>(dout.dims()[1]);
+  const int img_xh = static_cast<int>(dout.dims()[2]);
+  const int img_xw = static_cast<int>(dout.dims()[3]);
+  if (dx) {
+    ctx.template Alloc<T>(dx);
+  }
+  if (dfilter) {
+    ctx.template Alloc<T>(dfilter);
+  }
+
+  int r = xpu::conv2d_transpose_grad<float, float, float, int16_t>(
+      ctx.x_context(),
+      x.data<T>(),
+      filter_.data<T>(),
+      dout.data<T>(),
+      dx ? dx->data<T>() : nullptr,
+      dfilter ? dfilter->data<T>() : nullptr,
+      batch_size,
+      img_yc,
+      img_yh,
+      img_yw,
+      img_xc,
+      img_xh,
+      img_xw,
+      ksize,
+      strides,
+      paddings_,
+      dilations_,
+      groups,
+      nullptr,
+      nullptr,
+      nullptr,
+      nullptr,
+      nullptr,
+      true);
+  PADDLE_ENFORCE_XDNN_SUCCESS(r, "conv2d_transpose_grad");
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(conv2d_transpose_grad,
+                   XPU,
+                   ALL_LAYOUT,
+                   phi::Conv2dTransposeGradKernel,
+                   float) {}