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paddlepaddle--paddle/paddle/phi/kernels/xpu/uniform_inplace_grad_kernel.cc
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2026-07-13 12:40:42 +08:00

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// Copyright (c) 2024 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/common/memory_utils.h"
#include "paddle/phi/core/generator.h"
#include "paddle/phi/core/kernel_registry.h"
namespace phi {
template <typename T, typename Context>
void XPUUniformRandomInplaceGradKernel(const Context& dev_ctx,
const DenseTensor& out_grad,
float min UNUSED,
float max UNUSED,
int seed UNUSED,
int diag_num UNUSED,
int diag_step UNUSED,
float diag_val UNUSED,
DenseTensor* x_grad) {
auto* dx = x_grad;
if (dx) {
T* data = dev_ctx.template Alloc<T>(dx);
int64_t size = dx->numel();
std::unique_ptr<T[]> data_cpu(new T[size]);
for (int64_t i = 0; i < size; ++i) {
data_cpu[i] = T(0);
}
phi::memory_utils::Copy(dev_ctx.GetPlace(),
data,
CPUPlace(),
reinterpret_cast<void*>(data_cpu.get()),
size * sizeof(T));
}
}
} // namespace phi
PD_REGISTER_KERNEL(uniform_inplace_grad,
XPU,
ALL_LAYOUT,
phi::XPUUniformRandomInplaceGradKernel,
float) {}