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be903e2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 | // yala is pleased to support the open source community by making ncnn available.
//
//
// Copyright (C) 2022 yala <zhaojunchao@loongson.cn>;<junchao82@qq.com>. All rights reserved.
// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
// in compliance with the License. You may obtain a copy of the License at
//
// https://opensource.org/licenses/BSD-3-Clause
//
// 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 "batchnorm_loongarch.h"
#if __loongarch_sx
#include <lsxintrin.h>
#endif // __loongarch_sx
#include "loongarch_usability.h"
namespace ncnn {
BatchNorm_loongarch::BatchNorm_loongarch()
{
#if __loongarch_sx
support_packing = true;
#endif // __loongarch_sx
}
int BatchNorm_loongarch::forward_inplace(Mat& bottom_top_blob, const Option& opt) const
{
int dims = bottom_top_blob.dims;
int elempack = bottom_top_blob.elempack;
if (dims == 1)
{
int w = bottom_top_blob.w * elempack;
#if __loongarch_sx
int nn_w = w / 4;
int remain_w_start = nn_w * 4;
#else
int remain_w_start = 0;
#endif // __loongarch_sx
float* ptr = bottom_top_blob;
#if __loongarch_sx
#pragma omp parallel for num_threads(opt.num_threads)
for (int i = 0; i < nn_w; i++)
{
float* ptr0 = ptr + i * 4;
__m128 _p = (__m128)__lsx_vld(ptr0, 0);
__m128 _a = (__m128)__lsx_vld((const float*)a_data + i * 4, 0);
__m128 _b = (__m128)__lsx_vld((const float*)b_data + i * 4, 0);
_p = __lsx_vfmadd_s(_b, _p, _a);
__lsx_vst(_p, ptr0, 0);
}
#endif // __loongarch_sx
#pragma omp parallel for num_threads(opt.num_threads)
for (int i = remain_w_start; i < w; i++)
{
ptr[i] = b_data[i] * ptr[i] + a_data[i];
}
}
if (dims == 2)
{
int w = bottom_top_blob.w * elempack;
int h = bottom_top_blob.h;
#pragma omp parallel for num_threads(opt.num_threads)
for (int i = 0; i < h; i++)
{
float* ptr = bottom_top_blob.row(i);
float a = a_data[i];
float b = b_data[i];
int j = 0;
#if __loongarch_sx
__m128 _a = elempack == 4 ? (__m128)__lsx_vld((const float*)a_data + i * 4, 0) : (__m128)__lsx_vreplfr2vr_s(a);
__m128 _b = elempack == 4 ? (__m128)__lsx_vld((const float*)b_data + i * 4, 0) : (__m128)__lsx_vreplfr2vr_s(b);
for (; j + 3 < w; j += 4)
{
__builtin_prefetch(ptr + 16);
__m128 _p = (__m128)__lsx_vld(ptr, 0);
_p = __lsx_vfmadd_s(_b, _p, _a);
__lsx_vst(_p, ptr, 0);
ptr += 4;
}
#endif // __loongarch_sx
for (; j < w; j++)
{
*ptr = b * *ptr + a;
ptr++;
}
}
}
if (dims == 3 || dims == 4)
{
int w = bottom_top_blob.w;
int h = bottom_top_blob.h;
int d = bottom_top_blob.d;
int c = bottom_top_blob.c;
int size = w * h * d * elempack;
#pragma omp parallel for num_threads(opt.num_threads)
for (int q = 0; q < c; q++)
{
float* ptr = bottom_top_blob.channel(q);
float a = a_data[q];
float b = b_data[q];
int i = 0;
#if __loongarch_sx
__m128 _a = elempack == 4 ? (__m128)__lsx_vld((const float*)a_data + q * 4, 0) : (__m128)__lsx_vreplfr2vr_s(a);
__m128 _b = elempack == 4 ? (__m128)__lsx_vld((const float*)b_data + q * 4, 0) : (__m128)__lsx_vreplfr2vr_s(b);
for (; i + 3 < size; i += 4)
{
__builtin_prefetch(ptr + 16);
__m128 _p = (__m128)__lsx_vld(ptr, 0);
_p = __lsx_vfmadd_s(_b, _p, _a);
__lsx_vst(_p, ptr, 0);
ptr += 4;
}
#endif // __loongarch_sx
for (; i < size; i++)
{
*ptr = b * *ptr + a;
ptr++;
}
}
}
return 0;
}
} // namespace ncnn
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