chore: import upstream snapshot with attribution

2026-07-13 12:40:42 +08:00
commit e25996e7db
15472 changed files with 3536181 additions and 0 deletions
@@ -0,0 +1,110 @@
+
+// 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 "paddle/phi/kernels/crop_grad_kernel.h"
+
+#include <vector>
+
+#include "paddle/phi/core/tensor_utils.h"
+#include "paddle/phi/kernels/full_kernel.h"
+#include "paddle/phi/kernels/funcs/eigen/common.h"
+#include "paddle/phi/kernels/funcs/eigen/eigen_function.h"
+namespace phi {
+
+template <typename Context, typename T, size_t D>
+void CropTensorGradFunction(const Context& dev_ctx,
+                            const DenseTensor& x,
+                            const DenseTensor& out_grad,
+                            const IntArray& offsets,
+                            DenseTensor* x_grad) {
+  if (x_grad != nullptr) {
+    x_grad->Resize(x.dims());
+    dev_ctx.template Alloc<T>(x_grad);
+
+    auto offsets_vec = offsets.GetData();
+    std::array<std::pair<int64_t, int64_t>, D> paddings;
+    for (size_t i = 0; i < D; ++i) {
+      paddings[i].first = offsets_vec[i];
+      paddings[i].second =
+          x_grad->dims()[i] - out_grad.dims()[i] - offsets_vec[i];
+    }
+    auto x_grad_tensor = EigenTensor<T, D>::From(*x_grad);
+    auto out_grad_tensor = EigenTensor<T, D>::From(out_grad);
+    auto& place = *dev_ctx.eigen_device();
+
+    funcs::EigenPad<std::decay_t<decltype(place)>, T, D>::Eval(
+        place, x_grad_tensor, out_grad_tensor, paddings, static_cast<T>(0));
+  }
+}
+
+template <typename T, typename Context>
+void CropGradKernel(const Context& dev_ctx,
+                    const DenseTensor& out_grad,
+                    const DenseTensor& x,
+                    const IntArray& offsets,
+                    DenseTensor* x_grad) {
+  // x[3, 5], shape[2, 0], out[2, 0]
+  if (out_grad.numel() == 0 && x_grad != nullptr) {
+    Full<T, Context>(dev_ctx, x_grad->dims(), 0, x_grad);
+    return;
+  }
+  size_t rank = out_grad.dims().size();
+  PADDLE_ENFORCE_GE(
+      rank,
+      1,
+      errors::InvalidArgument(
+          "The number of dimensions of the input 'Out@GRAD' for "
+          "Op(crop_tensor_grad) must be greater than or equal to 1, but the "
+          "value received is %d.",
+          rank));
+  PADDLE_ENFORCE_LE(
+      rank,
+      6,
+      errors::InvalidArgument(
+          "The number of dimensions of the input 'Out@GRAD' for "
+          "Op(crop_tensor_grad) must be less than or equal to 6, but the "
+          "value received is %d.",
+          rank));
+  switch (rank) {
+    case 1:
+      CropTensorGradFunction<Context, T, 1>(
+          dev_ctx, out_grad, x, offsets, x_grad);
+      break;
+    case 2:
+      CropTensorGradFunction<Context, T, 2>(
+          dev_ctx, out_grad, x, offsets, x_grad);
+      break;
+    case 3:
+      CropTensorGradFunction<Context, T, 3>(
+          dev_ctx, out_grad, x, offsets, x_grad);
+      break;
+    case 4:
+      CropTensorGradFunction<Context, T, 4>(
+          dev_ctx, out_grad, x, offsets, x_grad);
+      break;
+    case 5:
+      CropTensorGradFunction<Context, T, 5>(
+          dev_ctx, out_grad, x, offsets, x_grad);
+      break;
+    case 6:
+      CropTensorGradFunction<Context, T, 6>(
+          dev_ctx, out_grad, x, offsets, x_grad);
+      break;
+  }
+}
+
+}  // namespace phi