wehub-resource-sync
123 lines
4.2 KiB
C++
123 lines
4.2 KiB
C++
// 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/kernels/impl/lrn_kernel_impl.h"
|
|
|
|
#include <memory>
|
|
#include <string>
|
|
#include <vector>
|
|
|
|
#include "paddle/phi/backends/onednn/onednn_helper.h"
|
|
#include "paddle/phi/core/kernel_registry.h"
|
|
#include "paddle/phi/kernels/funcs/blas/blas.h"
|
|
#include "paddle/phi/kernels/funcs/math_function.h"
|
|
|
|
namespace phi {
|
|
|
|
template <typename T>
|
|
struct LRNFunctor<CPUContext, T> {
|
|
void operator()(const CPUContext& dev_ctx,
|
|
const DenseTensor& input,
|
|
DenseTensor* out,
|
|
DenseTensor* mid,
|
|
int64_t N,
|
|
int64_t C,
|
|
int64_t H,
|
|
int64_t W,
|
|
int n,
|
|
T k,
|
|
T alpha,
|
|
T beta,
|
|
const DataLayout data_layout) {
|
|
auto blas = funcs::GetBlas<CPUContext, T>(dev_ctx);
|
|
funcs::Transpose<CPUContext, T, 4> transpose;
|
|
DenseTensor in_transpose, mid_transpose, out_transpose;
|
|
// if channel_last, transpose to channel_first
|
|
if (data_layout == DataLayout::NHWC) {
|
|
auto in_dims = input.dims();
|
|
std::vector<int64_t> shape(
|
|
{in_dims[0], in_dims[3], in_dims[1], in_dims[2]});
|
|
in_transpose.Resize(shape);
|
|
mid_transpose.Resize(shape);
|
|
out_transpose.Resize(shape);
|
|
dev_ctx.Alloc<T>(&in_transpose);
|
|
dev_ctx.Alloc<T>(&mid_transpose);
|
|
dev_ctx.Alloc<T>(&out_transpose);
|
|
std::vector<int> axis = {0, 3, 1, 2};
|
|
transpose(dev_ctx, input, &in_transpose, axis);
|
|
} else {
|
|
in_transpose = input;
|
|
mid_transpose = *mid;
|
|
out_transpose = *out;
|
|
mid_transpose.Resize(mid->dims());
|
|
out_transpose.Resize(out->dims());
|
|
dev_ctx.Alloc<T>(&mid_transpose);
|
|
dev_ctx.Alloc<T>(&out_transpose);
|
|
}
|
|
|
|
const T* idata = in_transpose.data<T>();
|
|
T* odata = out_transpose.data<T>();
|
|
T* mdata = mid_transpose.data<T>();
|
|
|
|
DenseTensor squared;
|
|
squared.Resize({1, C + n - 1, H, W});
|
|
T* sdata = dev_ctx.Alloc<T>(&squared);
|
|
std::memset(sdata, 0, sizeof(T) * squared.numel());
|
|
for (int64_t i = 0; i < mid->numel(); ++i) {
|
|
mdata[i] = k;
|
|
}
|
|
int64_t img_size = H * W;
|
|
int64_t fea_size = C * img_size;
|
|
int pre_pad = (n - 1) / 2;
|
|
// compute batches one by one
|
|
for (int64_t i = 0; i < N; ++i) {
|
|
blas.VSQUARE(fea_size, idata + i * fea_size, sdata + pre_pad * img_size);
|
|
// init the first channel of mid
|
|
for (int c = 0; c < n; ++c) {
|
|
blas.AXPY(img_size, alpha, sdata + c * img_size, mdata + i * fea_size);
|
|
}
|
|
for (int64_t c = 1; c < C; ++c) {
|
|
// copy previous scale
|
|
int64_t mid_offset = i * fea_size + c * img_size;
|
|
std::memcpy(mdata + mid_offset,
|
|
mdata + mid_offset - img_size,
|
|
img_size * sizeof(T));
|
|
// add last
|
|
blas.AXPY(img_size,
|
|
alpha,
|
|
sdata + (c + n - 1) * img_size,
|
|
mdata + mid_offset);
|
|
// sub rest
|
|
blas.AXPY(
|
|
img_size, -alpha, sdata + (c - 1) * img_size, mdata + mid_offset);
|
|
}
|
|
}
|
|
// compute the final output
|
|
blas.VPOW(mid->numel(), mdata, -beta, odata);
|
|
blas.VMUL(mid->numel(), odata, idata, odata);
|
|
|
|
// if channel_last, transpose the output(NCHW) to channel_last
|
|
if (data_layout == DataLayout::NHWC) {
|
|
std::vector<int> axis = {0, 2, 3, 1};
|
|
transpose(dev_ctx, mid_transpose, mid, axis);
|
|
transpose(dev_ctx, out_transpose, out, axis);
|
|
}
|
|
}
|
|
};
|
|
template struct LRNFunctor<CPUContext, float>;
|
|
template struct LRNFunctor<CPUContext, double>;
|
|
} // namespace phi
|
|
|
|
PD_REGISTER_KERNEL(lrn, CPU, ALL_LAYOUT, phi::LRNKernel, float) {}
|