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 +1,65 @@
+/* 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/unstack_grad_kernel.h"
+
+#include "paddle/phi/backends/xpu/enforce_xpu.h"
+#include "paddle/phi/core/kernel_registry.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void UnStackGradKernel(const Context &dev_ctx,
+                       const std::vector<const DenseTensor *> &x,
+                       int axis,
+                       DenseTensor *x_grad) {
+  using XPUType = typename XPUTypeTrait<T>::Type;
+  if (axis < 0) {
+    axis += x[0]->dims().size() + 1;
+  }
+  dev_ctx.template Alloc<T>(x_grad);
+  auto &dim = x[0]->dims();
+  std::vector<int64_t> xdims;
+  for (auto i = 0; i < dim.size(); ++i) {
+    xdims.push_back(dim[i]);
+  }
+  xdims.push_back(1);
+  std::vector<std::vector<int64_t>> xdims_list;
+  size_t n = static_cast<size_t>(x.size());
+  for (size_t i = 0; i < n; i++) {
+    xdims_list.push_back(xdims);
+  }
+
+  std::vector<const XPUType *> x_list;
+  for (size_t i = 0; i < n; i++) {
+    x_list.push_back(reinterpret_cast<const XPUType *>(x[i]->data<T>()));
+  }
+
+  int r = xpu::concat<XPUType>(dev_ctx.x_context(),
+                               x_list,
+                               reinterpret_cast<XPUType *>(x_grad->data<T>()),
+                               xdims_list,
+                               axis);
+  PADDLE_ENFORCE_XDNN_SUCCESS(r, "concat in unstack_grad op");
+}
+}  // namespace phi
+
+PD_REGISTER_KERNEL(unstack_grad,
+                   XPU,
+                   ALL_LAYOUT,
+                   phi::UnStackGradKernel,
+                   float,
+                   phi::float16,
+                   int,
+                   int64_t) {}