chore: import upstream snapshot with attribution

paddle/phi/kernels/impl/fold_grad_kernel_impl.h

@@ -0,0 +1,71 @@
+// 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.
+
+#pragma once
+
+#include <vector>
+
+#include "paddle/phi/core/dense_tensor.h"
+#include "paddle/phi/kernels/funcs/im2col.h"
+#include "paddle/phi/kernels/funcs/math_function.h"
+#include "paddle/phi/kernels/funcs/unfold_functor.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void FoldGradKernel(const Context& dev_ctx,
+                    const DenseTensor& x UNUSED,
+                    const DenseTensor& out_grad,
+                    const std::vector<int>& output_sizes,
+                    const std::vector<int>& kernel_sizes,
+                    const std::vector<int>& strides,
+                    const std::vector<int>& paddings,
+                    const std::vector<int>& dilations,
+                    DenseTensor* x_grad) {
+  dev_ctx.template Alloc<T>(x_grad);
+
+  if (!x_grad) return;
+
+  const auto& x_dims = x_grad->dims();
+  const int64_t batch_size = x_dims[0];
+
+  int output_height = (output_sizes[0] + 2 * paddings[0] -
+                       (dilations[0] * (kernel_sizes[0] - 1) + 1)) /
+                          strides[0] +
+                      1;
+  int output_width = (output_sizes[1] + 2 * paddings[1] -
+                      (dilations[1] * (kernel_sizes[1] - 1) + 1)) /
+                         strides[1] +
+                     1;
+
+  int64_t n_input_plane = x_dims[1];
+  int64_t n_output_plane = n_input_plane / (kernel_sizes[0] * kernel_sizes[1]);
+
+  DDim out_shape =
+      make_ddim({n_output_plane, output_sizes[0], output_sizes[1]});
+  DDim input_matrix_shape = make_ddim(
+      {1, kernel_sizes[0], kernel_sizes[1], output_height, output_width});
+
+  funcs::Im2ColFunctor<funcs::ColFormat::CFO, Context, T> im2col;
+
+  for (int64_t i = 0; i < batch_size; i++) {
+    DenseTensor out_grad_batch = out_grad.Slice(i, i + 1).Resize(out_shape);
+    DenseTensor x_grad_batch =
+        x_grad->Slice(i, i + 1).Resize(input_matrix_shape);
+    im2col(
+        dev_ctx, out_grad_batch, dilations, strides, paddings, &x_grad_batch);
+  }
+}
+
+}  // namespace phi