chore: import upstream snapshot with attribution

paddle/phi/kernels/xpu/meshgrid_kernel.cc

@@ -0,0 +1,49 @@
+// Copyright (c) 2023 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/meshgrid_kernel.h"
+
+#include "paddle/phi/backends/xpu/enforce_xpu.h"
+#include "paddle/phi/backends/xpu/xpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void MeshgridKernel(const Context& dev_ctx,
+                    const std::vector<const DenseTensor*>& inputs,
+                    std::vector<DenseTensor*> outputs) {
+  using XPUType = typename XPUTypeTrait<T>::Type;
+
+  std::vector<const XPUType*> x_list;
+  std::vector<XPUType*> y_list;
+  std::vector<std::vector<int64_t>> xshape_list;
+
+  for (const auto& x : inputs) {
+    x_list.push_back(reinterpret_cast<const XPUType*>(x->data<T>()));
+    xshape_list.emplace_back(vectorize<int64_t>(x->dims()));
+  }
+  for (auto& x : outputs) {
+    dev_ctx.template Alloc<T>(x);
+    y_list.push_back(reinterpret_cast<XPUType*>(x->data<T>()));
+  }
+  int r =
+      xpu::meshgrid<XPUType>(dev_ctx.x_context(), x_list, y_list, xshape_list);
+  PADDLE_ENFORCE_XDNN_SUCCESS(r, "meshgrid");
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(
+    meshgrid, XPU, ALL_LAYOUT, phi::MeshgridKernel, float, int, int64_t) {}