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nanmedian_grad_kernel.cc
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nanmedian_grad_kernel.cc
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// 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/nanmedian_grad_kernel.h"
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/math_function.h"
namespace phi {
template <typename T, typename Context>
void CalcMedianGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& median_index,
const DenseTensor& out_grad,
const IntArray& axes,
DenseTensor* x_grad,
T* x_grad_ptr) {
phi::funcs::SetConstant<Context, T> set_zero;
set_zero(dev_ctx, x_grad, static_cast<T>(0));
if (!x_grad_ptr) return;
const int64_t* m_ptr = median_index.data<int64_t>();
const T* out_grad_ptr = out_grad.data<T>();
int64_t numel = x.numel();
auto x_dim = x.dims();
int64_t rank = x_dim.size();
int64_t stride = x_dim[rank - 1];
int64_t pre_dim = numel / stride;
int64_t i = 0;
int64_t offset = 0;
T div_factor = static_cast<T>(2.0);
for (i = 0; i < pre_dim; i++) {
if (m_ptr[2 * i] >= 0) {
if (m_ptr[2 * i] == m_ptr[2 * i + 1]) {
x_grad_ptr[offset + m_ptr[2 * i]] = out_grad_ptr[i];
} else {
x_grad_ptr[offset + m_ptr[2 * i]] = out_grad_ptr[i] / div_factor;
x_grad_ptr[offset + m_ptr[2 * i + 1]] = out_grad_ptr[i] / div_factor;
}
}
offset += stride;
}
}
template <typename T, typename Context>
void BaseMedianGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& median_index,
const DenseTensor& out_grad,
const IntArray& axes,
DenseTensor* x_grad) {
auto rank = x.dims().size();
T* x_grad_ptr = dev_ctx.template Alloc<T>(x_grad);
if (axes.size() && (rank > 1)) {
DenseTensor tmp_x_grad(*x_grad);
CalcMedianGradKernel<T, Context>(
dev_ctx, x, median_index, out_grad, axes, &tmp_x_grad, x_grad_ptr);
PostprocessMedianGradKernel<T, Context>(dev_ctx, &tmp_x_grad, axes, x_grad);
} else {
CalcMedianGradKernel<T, Context>(
dev_ctx, x, median_index, out_grad, axes, x_grad, x_grad_ptr);
}
}
template <typename T, typename Context>
void NanmedianGradKernel(const Context& dev_ctx,
const DenseTensor& input,
const DenseTensor& median_index,
const DenseTensor& out_grad,
const IntArray& axes,
bool keep_dim,
DenseTensor* x_grad) {
BaseMedianGradKernel<T, Context>(
dev_ctx, input, median_index, out_grad, axes, x_grad);
}
} // namespace phi
PD_REGISTER_KERNEL(nanmedian_grad,
CPU,
ALL_LAYOUT,
phi::NanmedianGradKernel,
float,
double,
int,
int64_t) {}