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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/reduce_max_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"
#include "paddle/phi/kernels/xpu/reduce.h"
namespace phi {
template <typename T, typename Context>
void MaxKernel(const Context& dev_ctx,
const DenseTensor& x,
const IntArray& dims,
bool keep_dim,
DenseTensor* out) {
if (x.numel() == 0) {
dev_ctx.template Alloc<T>(out);
return;
}
bool reduce_all = recompute_reduce_all(x, dims);
using XPUType = typename XPUTypeTrait<T>::Type;
auto f = [](xpu::Context* xpu_ctx,
const T* x,
T* y,
const std::vector<int64_t>& xdims,
const std::vector<int64_t>& reduce_dims) {
#ifndef PADDLE_WITH_XPU_PLUGIN
return xpu::reduce_max<XPUType>(xpu_ctx,
reinterpret_cast<const XPUType*>(x),
reinterpret_cast<XPUType*>(y),
xdims,
reduce_dims);
#else
return xpu::plugin::fast_reduce_max<XPUType>(
xpu_ctx,
reinterpret_cast<const XPUType*>(x),
reinterpret_cast<XPUType*>(y),
std::vector<int>(xdims.begin(), xdims.end()),
std::vector<int>(reduce_dims.begin(), reduce_dims.end()));
#endif
};
int r = XPUReduce<Context, T>(
dev_ctx, x, dims.GetData(), keep_dim, reduce_all, out, f);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "reduce_max");
}
} // namespace phi
PD_REGISTER_KERNEL(max,
XPU,
ALL_LAYOUT,
phi::MaxKernel,
int,
int64_t,
float,
phi::float16,
phi::bfloat16) {}