chore: import upstream snapshot with attribution

paddle/phi/kernels/impl/unstack_grad_kernel_impl.h

@@ -0,0 +1,70 @@
+// 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/core/dense_tensor.h"
+#include "paddle/phi/kernels/funcs/stack_functor.h"
+#if defined(__NVCC__) || defined(__HIPCC__)
+#include <thrust/device_vector.h>
+#endif
+namespace phi {
+
+template <typename T, typename Context>
+void UnStackGradKernel(const Context &dev_ctx,
+                       const std::vector<const DenseTensor *> &x,
+                       int axis,
+                       DenseTensor *x_grad) {
+  if (axis < 0) axis += (x[0]->dims().size() + 1);
+
+  int64_t n = static_cast<int64_t>(x.size());
+  auto *x_grad_data = dev_ctx.template Alloc<T>(x_grad);
+  std::vector<const T *> x_datas(n);
+  for (int64_t i = 0; i < n; i++) x_datas[i] = x[i]->data<T>();
+
+  int64_t pre = 1;
+  int64_t post = 1;
+  auto &dim = x[0]->dims();
+  for (auto i = 0; i < axis; ++i) pre *= dim[i];
+  for (auto i = axis; i < dim.size(); ++i) post *= dim[i];
+
+#if defined(__NVCC__) || defined(__HIPCC__)
+  int total_num = pre * n * post;
+
+  thrust::device_vector<const T *> device_x_vec(x_datas);
+  auto x_data_arr = device_x_vec.data().get();
+
+  funcs::StackFunctorForRange(
+      dev_ctx, x_data_arr, x_grad_data, total_num, n, post);
+
+  // Wait() must be called because device_x_vec may be destructed before
+  // kernel ends
+  dev_ctx.Wait();
+#else
+  auto x_data_arr = x_datas.data();
+
+  size_t x_offset = 0;
+  size_t y_offset = 0;
+  for (int64_t i = 0; i < pre; i++) {
+    for (int64_t j = 0; j < n; j++) {
+      std::memcpy(
+          x_grad_data + y_offset, x_data_arr[j] + x_offset, post * sizeof(T));
+      y_offset += post;
+    }
+    x_offset += post;
+  }
+#endif
+}
+
+}  // namespace phi