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add vector optimization for loongarch64 (#4242)
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# must define SRC DST CLASS | ||
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file(READ ${SRC} source_data) | ||
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# replace | ||
string(TOUPPER ${CLASS} CLASS_UPPER) | ||
string(TOLOWER ${CLASS} CLASS_LOWER) | ||
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string(REGEX REPLACE "LAYER_${CLASS_UPPER}_LOONGARCH_H" "LAYER_${CLASS_UPPER}_LOONGARCH_LSX_H" source_data "${source_data}") | ||
string(REGEX REPLACE "${CLASS}_loongarch" "${CLASS}_loongarch_lsx" source_data "${source_data}") | ||
string(REGEX REPLACE "#include \"${CLASS_LOWER}_loongarch.h\"" "#include \"${CLASS_LOWER}_loongarch_lsx.h\"" source_data "${source_data}") | ||
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file(WRITE ${DST} "${source_data}") |
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// 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. | ||
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#include "absval_loongarch.h" | ||
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#if __loongarch_sx | ||
#include <lsxintrin.h> | ||
#endif // __loongarch_sx | ||
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namespace ncnn { | ||
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AbsVal_loongarch::AbsVal_loongarch() | ||
{ | ||
#if __loongarch_sx | ||
support_packing = true; | ||
#endif | ||
} | ||
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int AbsVal_loongarch::forward_inplace(Mat& bottom_top_blob, const Option& opt) const | ||
{ | ||
int w = bottom_top_blob.w; | ||
int h = bottom_top_blob.h; | ||
int d = bottom_top_blob.d; | ||
int channels = bottom_top_blob.c; | ||
int elempack = bottom_top_blob.elempack; | ||
int size = w * h * d * elempack; | ||
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#pragma omp parallel for num_threads(opt.num_threads) | ||
for (int q = 0; q < channels; q++) | ||
{ | ||
float* ptr = bottom_top_blob.channel(q); | ||
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int i = 0; | ||
#if __loongarch_sx | ||
for (; i + 3 < size; i += 4) | ||
{ | ||
__builtin_prefetch(ptr + 16); | ||
__m128i _p = __lsx_vld(ptr, 0); | ||
__m128i _outp = __lsx_vbitclri_w(_p, 31); | ||
__lsx_vst(_outp, ptr, 0); | ||
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ptr += 4; | ||
} | ||
#endif // __loongarch_sx | ||
for (; i < size; i++) | ||
{ | ||
*ptr = *ptr > 0 ? *ptr : -*ptr; | ||
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ptr++; | ||
} | ||
} | ||
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return 0; | ||
} | ||
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} // namespace ncnn |
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// 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. | ||
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#ifndef LAYER_ABSVAL_LOONGARCH_H | ||
#define LAYER_ABSVAL_LOONGARCH_H | ||
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#include "absval.h" | ||
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namespace ncnn { | ||
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class AbsVal_loongarch : virtual public AbsVal | ||
{ | ||
public: | ||
AbsVal_loongarch(); | ||
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virtual int forward_inplace(Mat& bottom_top_blob, const Option& opt) const; | ||
}; | ||
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} // namespace ncnn | ||
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#endif // LAYER_ABSVAL_LOONGARCH_H |
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// 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. | ||
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#include "batchnorm_loongarch.h" | ||
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#if __loongarch_sx | ||
#include <lsxintrin.h> | ||
#endif // __loongarch_sx | ||
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#include "loongarch_usability.h" | ||
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namespace ncnn { | ||
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BatchNorm_loongarch::BatchNorm_loongarch() | ||
{ | ||
#if __loongarch_sx | ||
support_packing = true; | ||
#endif // __loongarch_sx | ||
} | ||
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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; | ||
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if (dims == 1) | ||
{ | ||
int w = bottom_top_blob.w * elempack; | ||
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#if __loongarch_sx | ||
int nn_w = w / 4; | ||
int remain_w_start = nn_w * 4; | ||
#else | ||
int remain_w_start = 0; | ||
#endif // __loongarch_sx | ||
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float* ptr = bottom_top_blob; | ||
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#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; | ||
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__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 | ||
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#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]; | ||
} | ||
} | ||
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if (dims == 2) | ||
{ | ||
int w = bottom_top_blob.w * elempack; | ||
int h = bottom_top_blob.h; | ||
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#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]; | ||
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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); | ||
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ptr += 4; | ||
} | ||
#endif // __loongarch_sx | ||
for (; j < w; j++) | ||
{ | ||
*ptr = b * *ptr + a; | ||
ptr++; | ||
} | ||
} | ||
} | ||
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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; | ||
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#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]; | ||
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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); | ||
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ptr += 4; | ||
} | ||
#endif // __loongarch_sx | ||
for (; i < size; i++) | ||
{ | ||
*ptr = b * *ptr + a; | ||
ptr++; | ||
} | ||
} | ||
} | ||
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return 0; | ||
} | ||
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} // namespace ncnn |
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