chore: import upstream snapshot with attribution

paddle/phi/kernels/xpu/unbind_kernel.cc

@@ -0,0 +1,48 @@
+//   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/unbind_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 UnbindKernel(const Context& dev_ctx,
+                  const DenseTensor& x,
+                  int axis,
+                  std::vector<DenseTensor*> outs) {
+  using XPUType = typename XPUTypeTrait<T>::Type;
+  auto x_dims = x.dims();
+  axis = axis < 0 ? x_dims.size() + axis : axis;
+
+  std::vector<XPUType*> y_ptrs;
+  for (size_t j = 0; j < outs.size(); ++j) {
+    dev_ctx.template Alloc<T>(outs[j]);
+    y_ptrs.emplace_back(reinterpret_cast<XPUType*>(outs[j]->data<T>()));
+  }
+  auto x_shape = vectorize<int64_t>(x.dims());
+  int r = xpu::unbind(dev_ctx.x_context(),
+                      reinterpret_cast<const XPUType*>(x.data<T>()),
+                      y_ptrs,
+                      x_shape,
+                      static_cast<int64_t>(axis));
+  PADDLE_ENFORCE_XDNN_SUCCESS(r, "unbind");
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(
+    unbind, XPU, ALL_LAYOUT, phi::UnbindKernel, float, phi::bfloat16) {}