// yala is pleased to support the open source community by making ncnn available. // // // Copyright (C) 2022 yala ;. 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 #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