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2026-07-13 12:40:42 +08:00

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// 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/where_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 WhereKernel(const Context& dev_ctx,
const DenseTensor& condition,
const DenseTensor& x,
const DenseTensor& y,
DenseTensor* out) {
using XPUType = typename XPUTypeTrait<T>::Type;
const bool* cond_data = condition.data<bool>();
const XPUType* x_data = reinterpret_cast<const XPUType*>(x.data<T>());
const XPUType* y_data = reinterpret_cast<const XPUType*>(y.data<T>());
XPUType* out_data =
reinterpret_cast<XPUType*>(dev_ctx.template Alloc<T>(out));
if (out && out->numel() == 0) {
return;
}
auto cond_dims = vectorize<int64_t>(condition.dims());
auto x_dims = vectorize<int64_t>(x.dims());
// use [1] to replace [], because xpu not support []
if (cond_dims.size() == 0) {
cond_dims = std::vector<int64_t>({1});
}
if (x_dims.size() == 0) {
x_dims = std::vector<int64_t>({1});
}
int ret = xpu::where(dev_ctx.x_context(),
cond_data,
x_data,
y_data,
out_data,
cond_dims,
x_dims);
PADDLE_ENFORCE_XDNN_SUCCESS(ret, "where");
}
} // namespace phi
PD_REGISTER_KERNEL(where,
XPU,
ALL_LAYOUT,
phi::WhereKernel,
float,
double,
int,
int64_t,
phi::float16,
phi::bfloat16) {}