chore: import upstream snapshot with attribution

paddle/phi/kernels/xpu/elementwise_divide_kernel.cc

@@ -0,0 +1,55 @@
+// 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/elementwise_divide_kernel.h"
+
+#include <memory>
+#include <string>
+
+#include "paddle/phi/backends/xpu/xpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/funcs/elementwise_base.h"
+#include "paddle/phi/kernels/xpu/elementwise.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void DivideKernel(const Context& dev_ctx,
+                  const DenseTensor& x,
+                  const DenseTensor& y,
+                  DenseTensor* out) {
+  using XPUType = typename XPUTypeTrait<T>::Type;
+  auto f = [](xpu::Context* xpu_ctx,
+              const XPUType* x,
+              const XPUType* y,
+              XPUType* z,
+              const std::vector<int64_t>& xshape,
+              const std::vector<int64_t>& yshape) {
+    return xpu::broadcast_div<XPUType>(xpu_ctx, x, y, z, xshape, yshape);
+  };
+
+  XPUElementwise<T, XPUType>(dev_ctx, x, y, -1, out, f);
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(divide,
+                   XPU,
+                   ALL_LAYOUT,
+                   phi::DivideKernel,
+                   float,
+                   phi::float16,
+                   phi::bfloat16,
+                   int,
+                   int64_t) {}