chore: import upstream snapshot with attribution

paddle/phi/kernels/xpu/grid_sample_grad_kernel.cc

@@ -0,0 +1,95 @@
+// Copyright (c) 2023 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/grid_sample_grad_kernel.h"
+
+#include "paddle/phi/backends/xpu/enforce_xpu.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/full_kernel.h"
+namespace phi {
+
+template <typename T, typename Context>
+void GridSampleGradKernel(const Context& dev_ctx,
+                          const DenseTensor& x,
+                          const DenseTensor& grid,
+                          const DenseTensor& out_grad,
+                          const std::string& mode,
+                          const std::string& padding_mode,
+                          bool align_corners,
+                          DenseTensor* x_grad,
+                          DenseTensor* grid_grad) {
+  if (out_grad.numel() == 0) {
+    if (x_grad) {
+      Full<T, Context>(dev_ctx, x_grad->dims(), 0, x_grad);
+    }
+    if (grid_grad) {
+      Full<T, Context>(dev_ctx, grid_grad->dims(), 0, grid_grad);
+    }
+    return;
+  }
+  PADDLE_ENFORCE_EQ(
+      x.dims().size(),
+      4,
+      common::errors::InvalidArgument(
+          ("XPU is only support input_dims == 4 in grid_sample_grad op.")));
+  const int64_t n = grid.dims()[0];
+  const int64_t out_h = grid.dims()[1];
+  const int64_t out_w = grid.dims()[2];
+  const int64_t c = x.dims()[1];
+  const int64_t in_h = x.dims()[2];
+  const int64_t in_w = x.dims()[3];
+
+  x_grad->Resize({n, c, in_h, in_w});
+  T* x_grad_ptr = dev_ctx.template Alloc<T>(x_grad);
+
+  T* grid_grad_ptr = nullptr;
+  if (grid_grad != nullptr) {
+    grid_grad->Resize({n, out_h, out_w, 2});
+    grid_grad_ptr = dev_ctx.template Alloc<T>(grid_grad);
+  }
+
+  bool is_nearest = false;
+  if (mode == "nearest") {
+    is_nearest = true;
+  }
+  int64_t padding_mode_type = 0;
+  if (padding_mode == "border") {
+    padding_mode_type = 1;
+  } else if (padding_mode == "reflection") {
+    padding_mode_type = 2;
+  }
+
+  int r = xpu::grid_sample_grad<T>(dev_ctx.x_context(),
+                                   x.data<T>(),
+                                   grid.data<T>(),
+                                   out_grad.data<T>(),
+                                   x_grad_ptr,
+                                   grid_grad_ptr,
+                                   n,
+                                   c,
+                                   in_h,
+                                   in_w,
+                                   out_h,
+                                   out_w,
+                                   is_nearest,
+                                   align_corners,
+                                   padding_mode_type,
+                                   true);
+  PADDLE_ENFORCE_XDNN_SUCCESS(r, "grid_sample_grad");
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(
+    grid_sample_grad, XPU, ALL_LAYOUT, phi::GridSampleGradKernel, float) {}