source/backend/cpu/compute/CommonOptFunction.cpp (3,509 lines of code) (raw):
//
// CommonOptFunction.cpp
// MNN
//
// Created by MNN on 2018/09/06.
// Copyright © 2018, Alibaba Group Holding Limited
//
#include "CommonOptFunction.h"
#include "ConvOpt.h"
#include "WinogradOptFunction.hpp"
#include "Int8FunctionsOpt.h"
#include "ImageProcessFunction.hpp"
#include <string.h>
#include <algorithm>
#include <cmath>
#include <math.h>
#include "math/Vec.hpp"
#include <vector>
#include "../CPURuntime.hpp"
#include "core/MemoryFormater.h"
// TODO: Find better way to optimize it
#include "../CPUBinary.hpp"
#include "../CPUUnary.hpp"
#include "../CPUPool.hpp"
#define PACK 4
#define FLOAT float
using Vec = MNN::Math::Vec<float, 4>;
#include "../GridSampler.hpp"
#ifdef MNN_LOW_MEMORY
#ifdef __aarch64__
#include "backend/cpu/arm/arm64/low_memory/MNNDynamicQuantFunctions.hpp"
#endif
#endif
#ifndef MNN_USE_SSE
void MNNInt8ToInt16(int16_t* dest, const int8_t* source, size_t count) {
// Should not be called
MNN_ASSERT(false);
}
#endif
#ifndef __aarch64__
#ifdef MNN_CPU_WEIGHT_DEQUANT_GEMM
static void _MNNPackedMatMulRemain_int4(float* C, const float* A, const float* fB, size_t eSize, const size_t* parameter, const float* postParameters, const float* bias, int aStride, const float* k, const float* b) {
auto B = reinterpret_cast<const uint8_t*>(fB);
auto h = parameter[2];
auto l = parameter[1];
auto cStride = parameter[3] / sizeof(float);
auto hRemain = parameter[4];
float weightBytes = 0.5; // sizeof(int4_t)
auto bExtraStride = static_cast<int32_t>(parameter[5] / weightBytes);
auto bStride = bExtraStride + 4 * l;
auto hC4 = UP_DIV(h, 4);
float minValue = -std::numeric_limits<float>().max();
float maxValue = std::numeric_limits<float>().max();
if (nullptr != postParameters) {
minValue = postParameters[2];
maxValue = postParameters[3];
}
int blockId = parameter[6];
for (int x=0; x<eSize; ++x) {
auto dst = C + 4 * x;
auto src = A + x;
for (int y=0; y<hC4; ++y) {
auto dstY = dst + y * cStride;
auto weight = B + y * bStride / 2;
auto alpha = k + y * 4;
auto qbias = b + y * 4;
float summer[4] = {
0.0f,
0.0f,
0.0f,
0.0f,
};
if (blockId > 0) {
summer[0] = dstY[0];
summer[1] = dstY[1];
summer[2] = dstY[2];
summer[3] = dstY[3];
}
if (nullptr != bias && nullptr != postParameters) {
for (int v=0; v<4; ++v) {
summer[v] += bias[4 * y + v];
}
}
for (int z=0; z<l; ++z) {
auto aZ = src + z * aStride;
auto i4wZ = weight + z * 2;
float wZ[4];
{
auto w01 = i4wZ[0];
auto w23 = i4wZ[1];
int iw01 = w01;
int iw23 = w23;
int iw0 = iw01 / 16;
int iw1 = iw01 % 16;
int iw2 = iw23 / 16;
int iw3 = iw23 % 16;
wZ[0] = iw0 * alpha[0] + qbias[0];
wZ[1] = iw1 * alpha[1] + qbias[1];
wZ[2] = iw2 * alpha[2] + qbias[2];
wZ[3] = iw3 * alpha[3] + qbias[3];
}
summer[0] += wZ[0] * aZ[0];
summer[1] += wZ[1] * aZ[0];
summer[2] += wZ[2] * aZ[0];
summer[3] += wZ[3] * aZ[0];
}
for (int v=0; v<4; ++v) {
auto dstValue = std::min(summer[v], maxValue);
dstValue = std::max(dstValue, minValue);
dstY[v] = dstValue;
}
}
}
}
static void _MNNPackedMatMulRemain_int8(float* C, const float* A, const float* fB, size_t eSize, const size_t* parameter, const float* postParameters, const float* bias, int aStride, const float* k, const float* b) {
auto B = reinterpret_cast<const int8_t*>(fB);
auto h = parameter[2];
auto l = parameter[1];
auto cStride = parameter[3] / sizeof(float);
auto hRemain = parameter[4];
float weightBytes = 1; // sizeof(int8_t)
auto bExtraStride = static_cast<int32_t>(parameter[5] / weightBytes);
auto bStride = bExtraStride + 4 * l;
auto hC4 = UP_DIV(h, 4);
float minValue = -std::numeric_limits<float>().max();
float maxValue = std::numeric_limits<float>().max();
if (nullptr != postParameters) {
minValue = postParameters[2];
maxValue = postParameters[3];
}
int blockId = parameter[6];
for (int x=0; x<eSize; ++x) {
auto dst = C + 4 * x;
auto src = A + x;
for (int y=0; y<hC4; ++y) {
auto dstY = dst + y * cStride;
auto weight = B + y * bStride;
auto alpha = k + y * 4;
auto qbias = b + y * 4;
float summer[4] = {
0.0f,
0.0f,
0.0f,
0.0f,
};
if (blockId > 0) {
summer[0] = dstY[0];
summer[1] = dstY[1];
summer[2] = dstY[2];
summer[3] = dstY[3];
}
if (nullptr != bias && nullptr != postParameters) {
for (int v=0; v<4; ++v) {
summer[v] += bias[4 * y + v];
}
}
for (int z=0; z<l; ++z) {
auto aZ = src + z * aStride;
auto i8wZ = weight + z * 4;
float wZ[4];
{
wZ[0] = i8wZ[0] * alpha[0] + qbias[0];
wZ[1] = i8wZ[1] * alpha[1] + qbias[1];
wZ[2] = i8wZ[2] * alpha[2] + qbias[2];
wZ[3] = i8wZ[3] * alpha[3] + qbias[3];
}
summer[0] += wZ[0] * aZ[0];
summer[1] += wZ[1] * aZ[0];
summer[2] += wZ[2] * aZ[0];
summer[3] += wZ[3] * aZ[0];
}
for (int v=0; v<4; ++v) {
auto dstValue = std::min(summer[v], maxValue);
dstValue = std::max(dstValue, minValue);
dstY[v] = dstValue;
}
}
}
}
void MNNPackedMatMul_int4(float* C, const float* A, const float* B, const size_t* parameter, const float* postParameters, const float* bias, const float* k, const float* b) {
_MNNPackedMatMulRemain_int4(C, A, B, 16, parameter, postParameters, bias, 16, k, b);
}
void MNNPackedMatMulRemain_int4(float* C, const float* A, const float* B, size_t eSize, const size_t* parameter, const float* postParameters, const float* bias, const float* k, const float* b) {
auto aStride = parameter[0] / sizeof(float);
_MNNPackedMatMulRemain_int4(C, A, B, eSize, parameter, postParameters, bias, aStride, k, b);
}
void MNNPackedMatMul_int8(float* C, const float* A, const float* B, const size_t* parameter, const float* postParameters, const float* bias, const float* k, const float* b) {
_MNNPackedMatMulRemain_int8(C, A, B, 16, parameter, postParameters, bias, 16, k, b);
}
void MNNPackedMatMulRemain_int8(float* C, const float* A, const float* B, size_t eSize, const size_t* parameter, const float* postParameters, const float* bias, const float* k, const float* b) {
auto aStride = parameter[0] / sizeof(float);
_MNNPackedMatMulRemain_int8(C, A, B, eSize, parameter, postParameters, bias, aStride, k, b);
}
#endif // MNN_CPU_WEIGHT_DEQUANT_GEMM
#ifdef MNN_LOW_MEMORY
void MNNQuantScaleFP32(float* absmax, float* quant_scale, float* dequant_scale, size_t thread, size_t batch) {
for (int i = 0; i < batch; ++i) {
auto absmaxPtr = absmax + i;
float absVal = 0.f;
for (int t = 0; t < thread; ++t) {
absVal = std::max(absVal, absmaxPtr[t * batch]);
}
if (absVal < 1e-7) {
quant_scale[i] = 1.f;
dequant_scale[i] = 1.f;
} else {
quant_scale[i] = 127.0f / absVal;
dequant_scale[i] = absVal / 127.0f;
}
}
}
void MNNDynamicUpdateConvBiasScale(float* newbias, float* oldbias, float* weightKernelSum, float* inputBias, size_t ocQuad) {
int ocUp4 = 4 * ocQuad;
int pack = 4;
for (int i = 0; i < ocUp4; ++i) {
newbias[i] = oldbias[i] + weightKernelSum[i] * inputBias[0];
}
}
#endif // LOW_MEMORY
#endif // not __aarch64__
static void MNNCountMaxMinValue(const float* source, float* minVal, float* maxVal, size_t size) {
int pack = 4;
float max_ = source[0], min_ = source[0];
for (int i = 1; i < size; ++i) {
if (max_ < source[i]) {
max_ = source[i];
}
if (min_ > source[i]) {
min_ = source[i];
}
}
*minVal = min_;
*maxVal = max_;
}
#ifdef MNN_LOW_MEMORY
static void MNNAbsMaxFP32(const float* source, float* absmax, size_t src_depth_quad, size_t realSize, int pack) {
#ifdef __aarch64__
if (pack == 4) {
MNNAbsMaxFP32_Pack4(source, absmax, src_depth_quad, realSize, pack);
return;
}
if (pack == 8) {
MNNAbsMaxFP32_Pack8(source, absmax, src_depth_quad, realSize, pack);
return;
}
#endif
// source: (ic/4, N, 4)
auto srcStep = pack * realSize;
for (int i = 0; i < realSize; ++i) {
float absmaxVal = 0.f; // absmaxVal>=0
for (int c = 0; c < src_depth_quad; ++c) {
auto src = source + c * srcStep + i * pack;
for (int k = 0; k < pack; ++k) {
absmaxVal = std::max(absmaxVal, std::abs(src[k]));
}
}
absmax[i] = absmaxVal;
}
}
void MNNDynamicQuantFP32(const float* src, int8_t* dst, const float* scale, size_t src_depth_quad, size_t realSize, int pack, const float* bias = nullptr) {
#ifdef __aarch64__
if (pack == 4) {
MNNDynamicQuantFP32_Pack4(src, dst, scale, src_depth_quad, realSize, nullptr, pack);
return;
}
if (pack == 8) {
MNNDynamicQuantFP32_Pack8(src, dst, scale, src_depth_quad, realSize, nullptr, pack);
return;
}
#endif
#ifdef MNN_USE_SSE
uint8_t* dstPtr = reinterpret_cast<uint8_t*>(dst);
int offset = 128;
#else
int8_t* dstPtr = dst;
int offset = 0;
#endif
for (int i = 0; i < realSize; ++i) {
auto scaleVal = scale[i];
for (int c = 0; c < src_depth_quad; ++c) {
auto srcZ = src + c * pack * realSize + i * pack;
auto dstZ = dstPtr + c * pack * realSize + i * pack;
for (int k = 0; k < pack; ++k) {
int val = (int)roundf(srcZ[k] * scaleVal);
dstZ[k] = val + offset;
}
}
}
}
static void MNNAsyQuantFunc(int8_t* dst, const float* src, float* qscale, float* qbias, const size_t* info) {
// input shape: [kernelsize, blockNum, blockLU, EP, LP]
auto blockNum = info[0];
auto EP = info[1]; // real area for data
auto LP = info[2]; // Innermost data layout, may come from backend's pack or gemmint8 units' SRC_UNIT
auto DST_XUNIT = info[3]; // backend gemmint8 units
auto SRC_UNIT = info[4];
auto kernelsize = info[5];
auto blockLU = info[6];
auto stride0 = blockNum * blockLU * EP * LP;
auto stride1 = blockLU * EP * LP;
int int8Max = 127;
int int8Min = -128;
// qscale&qbias [blockNum, EP]
#ifdef __aarch64__
if (LP == 4 || LP == 8) {
for (int k = 0; k < kernelsize; ++k) {
for (int i = 0; i < blockNum; ++i) {
if (LP == 4) {
MNNDynamicQuantFP32_Pack4(src + k * stride0 + i * stride1, dst + k * stride0 + i * stride1, qscale + i * EP, blockLU, EP, qbias + i * EP, LP);
}
if (LP == 8) {
MNNDynamicQuantFP32_Pack8(src + k * stride0 + i * stride1, dst + k * stride0 + i * stride1, qscale + i * EP, blockLU, EP, qbias + i * EP, LP);
}
}
}
return;
}
#endif
for (int i = 0; i < EP; ++i) {
for (int bk = 0; bk < blockNum; ++bk) {
float quant_scale = qscale[i + bk * EP];
float quant_bias = qbias[i + bk * EP];
for (int n = 0; n < kernelsize; ++n) {
for (int k = 0; k < blockLU; ++k) {
for (int j = 0; j < LP; ++j) {
int dataIndx = n * stride0 + bk * stride1 + k * EP * LP + i * LP + j;
float data_ = src[dataIndx];
int qval = static_cast<int32_t>(roundf(data_ * quant_scale + quant_bias));
#ifdef MNN_USE_SSE
((uint8_t*)dst)[dataIndx] = qval + 128;
#else
dst[dataIndx] = ALIMIN(int8Max, ALIMAX(int8Min, qval));
#endif
}
}
}
}
}
}
static void MNNAsyQuantInfo_FP32(float* scale, float* bias, float* qscale, float* qbias, float* dstMin, float* dstMax, const float* src, const size_t* info) {
auto blockNum = info[0];
auto plane = info[1]; // real area for data
auto innerSide = info[2]; // Innermost data layout, may come from backend's pack or gemmint8 units' SRC_UNIT
auto DST_XUNIT = info[3];
auto kernelsize = info[5];
auto blockLU = info[6];
auto stride0 = blockNum * blockLU * plane * innerSide;
auto stride1 = blockLU * plane * innerSide;
if (info[7] == 1) { // scale&bias:[1]
float maxval, minval;
MNNCountMaxMinValue(src, &minval, &maxval, kernelsize * stride0);
if (info[8] == 1 && (maxval -minval) > 1e-7) {
if (minval > 0.f) {
minval = 0;
} else if (maxval < 0.f){
maxval = 0;
}
}
auto range = maxval - minval;
if (range <= 1e-7) {
scale[0] = 0.f;
qscale[0] = 0.f;
qbias[0] = 0.f;
bias[0] = maxval;
} else {
qscale[0] = 255.f / range;
scale[0] = range / 255.f;
qbias[0] = roundf(-minval * 255.f / range)- 128.f;
bias[0] = -qbias[0] * scale[0];
}
return;
}
// input : [kernelsize, blockNum, blockLU, plane, pack]
// dequant scale/bias : [EU, blockNum, step], step=ALIMIN(step, EP), EU=UP_DIV(plane, EP)
// quant scale/bias : [blockNum, plane]
#ifdef __aarch64__
if (DST_XUNIT == 12 && innerSide == 4) { // Arm82,fp32: SRC_UNIT=4, core->pack=4
// max,min shape: [blockNum, EP]
for (int i = 0; i < kernelsize; ++i) {
MNNLocalMinMaxFP32_Pack4(dstMin, dstMax, src + i * stride0, blockNum, blockLU, plane, innerSide, i);
}
// scale, bias
bool success = MNNAsyLocalQuantInfo_EP12_FP32(scale, bias, qscale, qbias, dstMin, dstMax, info);
if (!success) {
MNN_ERROR("Call error for:MNNAsyLocalQuantInfo_EP12\n");
return;
}
return;
}
if (DST_XUNIT == 10) { // Arm86,fp32: SRC_UNIT=8,core->pack=4
// max,min shape: [blockNum, EP]
if (innerSide == 4) {
for (int i = 0; i < kernelsize; ++i) {
MNNLocalMinMaxFP32_Pack4(dstMin, dstMax, src + i * stride0, blockNum, blockLU, plane, innerSide, i);
}
}
if (innerSide == 8) {
for (int i = 0; i < kernelsize; ++i) {
MNNLocalMinMaxFP32_Pack8(dstMin, dstMax, src + i * stride0, blockNum, blockLU, plane, innerSide, i);
}
}
// scale, bias
bool success = MNNAsyLocalQuantInfo_EP10_FP32(scale, bias, qscale, qbias, dstMin, dstMax, info);
if (!success) {
MNN_ERROR("Call error for:MNNAsyLocalQuantInfo_EP10\n");
return;
}
return;
}
#endif
// max,min shape: [blockNum, plane]
for (int i = 0; i < plane; ++i) {
for (int bk = 0; bk < blockNum; ++bk) {
auto idx0 = i *innerSide + bk * stride1;
float max_ = src[idx0];
float min_ = max_;
for (int n = 0; n < kernelsize; ++n) {
for (int k = 0; k < blockLU; ++k) {
for (int j = 0; j < innerSide; ++j) {
auto dataIndx = idx0 + n * stride0 + k * (plane * innerSide) + j;
float data_ = src[dataIndx];
max_ = ALIMAX(max_, data_);
min_ = ALIMIN(min_, data_);
}
}
}
auto sindx = i + bk * plane;
dstMin[sindx] = min_;
dstMax[sindx] = max_;
}
}
// scale, bias
for (int i = 0; i < plane; ++i) {
auto step = ALIMIN(DST_XUNIT, plane - (i / DST_XUNIT) * DST_XUNIT);
auto sind0 = (i / DST_XUNIT) * DST_XUNIT * blockNum + (i % DST_XUNIT);
for (int k = 0; k < blockNum; ++k) {
auto sind = sind0 + k * step;
auto qind = i + k * plane;
auto max_ = dstMax[qind];
auto min_ = dstMin[qind];
if (fabs(max_ - min_) < 1e-7) {
qscale[qind] = 0.f;
qbias[qind] = 0.f;
scale[sind] = 0.f;
bias[sind] = max_;
} else {
qscale[qind] = 255.f / (max_ - min_);
qbias[qind] = roundf(-min_ * 255.f / (max_ - min_)) - 128.0f;
scale[sind] = (max_ - min_) / 255.f;
#ifndef MNN_USE_SSE
bias[sind] = min_ + (128.f / 255.f) * (max_ - min_);
#else
bias[sind] = min_;
#endif
}
}
}
}
#endif // MNN_LOW_MEMORY
static void MNNReorderWeightInt4(uint8_t* dest, const uint8_t* source, int32_t* shape, size_t size, float* kernelsum) {
MNN_ASSERT(size > 4);
auto blocknum = shape[0];
auto hu = shape[1];
auto lu = shape[2];
auto hp = shape[3];
auto lp = shape[4];
auto ic = blocknum * lu * lp;
auto stride0 = blocknum * hp * lu * lp;
auto stride1 = lu * hp * lp;
auto stride2 = hp * lp;
// [oc,ic]->[hu,blocknum,lu,hp,lp]
for (int i = 0; i < hu; ++i) {
for (int k = 0; k < hp; ++k) {
for (int bl = 0; bl < blocknum; ++bl) {
for (int j = 0; j < lu; ++j) {
int srcindex = (i * hp + k) * ic + bl * (lu * lp) + j * lp;
int dstindex = i * stride0 + bl * stride1 + j * stride2 + k * lp;
memcpy(dest + dstindex, source + srcindex, lp);
}
}
}
}
// [hu,blocknum,lu,hp,lp] address [hp,lp] for int4
auto inside = lp * hp;
auto outside = blocknum * hu;
std::vector<uint8_t> buffer(inside);
for (int i = 0; i < outside; ++i) {
std::vector<float> accum(hp, 0);
for (int k = 0; k < lu; ++k) {
for (int j = 0; j < inside / 2; ++j) {
auto w0 = dest[j + (i * lu + k) * inside] >> 4;
auto w1 = dest[j + (i * lu + k) * inside] & 0x0f;
auto w2 = dest[(i * lu + k) * inside + j + inside / 2] >> 4;
auto w3 = dest[(i * lu + k) * inside + j + inside / 2] & 0x0f;
buffer[2 * j + 0] = w0 * 16 + w2;
buffer[2 * j + 1] = w1 * 16 + w3;
// sum
accum[j / lp] += ((float)w0 + (float)w1);
accum[(j + inside / 2) / lp] += ((float)w2 + (float)w3);
}
memcpy(dest + (i * lu + k) * inside, buffer.data(), inside);
}
memcpy(kernelsum + i * hp, accum.data(), hp * sizeof(float));
}
}
#ifdef __aarch64__
static void MNNReorderWeightInt4Arm86(uint8_t* dest, const uint8_t* source, int32_t* shape, size_t size, float* kernelsum) {
MNN_ASSERT(size > 4);
auto blocknum = shape[0];
auto hu = shape[1];
auto lu = shape[2];
auto hp = shape[3];
auto lp = shape[4];
auto ic = blocknum *lu * lp;
auto stride0 = blocknum * hp * lu * lp;
auto stride1 = lu * hp * lp;
auto stride2 = hp * lp;
auto dstPtr = (int32_t*)dest;
auto srcPtr = (int32_t*)source;
int unitpacksize = sizeof(int32_t) / sizeof(uint8_t);
for (int i = 0; i < hu; ++i) {
for (int k = 0; k < hp; ++k) {
for (int bl = 0; bl < blocknum; ++bl) {
int j = 0;
while (j + 7 < lu) {
auto srcindex0 = ((i * hp + k) * ic + bl * (lu * lp) + j * lp) / unitpacksize;
auto srcindex1 = ((i * hp + k) * ic + bl * (lu * lp) + (j + 4) * lp) / unitpacksize;
auto dstindex0 = (bl * stride1 + i * stride0 + j * stride2 + k * lp) / unitpacksize;
auto dstindex1 = (bl * stride1 + i * stride0 + (j + 1) * stride2 + k * lp) / unitpacksize;
auto dstindex2 = (bl * stride1 + i * stride0 + (j + 2) * stride2 + k * lp) / unitpacksize;
auto dstindex3 = (bl * stride1 + i * stride0 + (j + 3) * stride2 + k * lp) / unitpacksize;
auto dstindex4 = (bl * stride1 + i * stride0 + (j + 4) * stride2 + k * lp) / unitpacksize;
auto dstindex5 = (bl * stride1 + i * stride0 + (j + 5) * stride2 + k * lp) / unitpacksize;
auto dstindex6 = (bl * stride1 + i * stride0 + (j + 6) * stride2 + k * lp) / unitpacksize;
auto dstindex7 = (bl * stride1 + i * stride0 + (j + 7) * stride2 + k * lp) / unitpacksize;
j += 8;
auto srcdata0 = vld1q_s32(srcPtr + srcindex0);
auto srcdata1 = vld1q_s32(srcPtr + srcindex1);
vst1q_lane_s32(dstPtr + dstindex0, srcdata0, 0);
vst1q_lane_s32(dstPtr + dstindex1, srcdata0, 1);
vst1q_lane_s32(dstPtr + dstindex2, srcdata0, 2);
vst1q_lane_s32(dstPtr + dstindex3, srcdata0, 3);
vst1q_lane_s32(dstPtr + dstindex4, srcdata1, 0);
vst1q_lane_s32(dstPtr + dstindex5, srcdata1, 1);
vst1q_lane_s32(dstPtr + dstindex6, srcdata1, 2);
vst1q_lane_s32(dstPtr + dstindex7, srcdata1, 3);
}
while (j + 3 < lu) {
auto srcindex = ((i * hp + k) * ic + bl * (lu * lp) + j * lp) / unitpacksize;
auto dstindex0 = (bl * stride1 + i * stride0 + j * stride2 + k * lp) / unitpacksize;
auto dstindex1 = (bl * stride1 + i * stride0 + (j + 1) * stride2 + k * lp) / unitpacksize;
auto dstindex2 = (bl * stride1 + i * stride0 + (j + 2) * stride2 + k * lp) / unitpacksize;
auto dstindex3 = (bl * stride1 + i * stride0 + (j + 3) * stride2 + k * lp) / unitpacksize;
j += 4;
auto srcdata = vld1q_s32(srcPtr + srcindex);
vst1q_lane_s32(dstPtr + dstindex0, srcdata, 0);
vst1q_lane_s32(dstPtr + dstindex1, srcdata, 1);
vst1q_lane_s32(dstPtr + dstindex2, srcdata, 2);
vst1q_lane_s32(dstPtr + dstindex3, srcdata, 3);
}
while (j < lu) {
auto srcindex = ((i * hp + k) * ic + bl * (lu * lp) + j * lp) / unitpacksize;
auto dstindex = (bl * stride1+ i * stride0 + j * stride2 + k * lp) / unitpacksize;
dstPtr[dstindex] = srcPtr[srcindex];
j++;
}
}
}
}
MNNPermuteSumWeightInt4Arm86(dest, dest, blocknum * hu, lu, kernelsum);
}
static void MNNReorderWeightInt4Arm82(uint8_t* dest, const uint8_t* source, int32_t* shape, size_t size, float* kernelsum) {
MNN_ASSERT(size > 4);
// dst shape: [hu, blocknum, kernelCount, lu, hp, lp], kernelCount=1 in this case
auto blocknum = shape[0];
auto hu = shape[1];
auto lu = shape[2];
auto hp = shape[3];
auto lp = shape[4];
auto ic = blocknum *lu * lp;
auto stride0 = blocknum * hp * lu * lp;
auto stride1 = lu * hp * lp;
auto stride2 = hp * lp;
auto dstPtr = (int16_t*)dest;
auto srcPtr = (int16_t*)source;
int unitpacksize = sizeof(int16_t) / sizeof(uint8_t);
for (int i = 0; i < hu; ++i) {
for (int k = 0; k < hp; ++k) {
for (int bl = 0; bl < blocknum; ++bl) {
int j = 0;
while (j + 7 < lu) {
auto srcindex = ((i * hp + k) * ic + bl * (lu * lp) + j * lp) / unitpacksize;
auto dstindex0 = (bl * stride1 + i * stride0 + j * stride2 + k * lp) / unitpacksize;
auto dstindex1 = (bl * stride1 + i * stride0 + (j + 1) * stride2 + k * lp) / unitpacksize;
auto dstindex2 = (bl * stride1 + i * stride0 + (j + 2) * stride2 + k * lp) / unitpacksize;
auto dstindex3 = (bl * stride1 + i * stride0 + (j + 3) * stride2 + k * lp) / unitpacksize;
auto dstindex4 = (bl * stride1 + i * stride0 + (j + 4) * stride2 + k * lp) / unitpacksize;
auto dstindex5 = (bl * stride1 + i * stride0 + (j + 5) * stride2 + k * lp) / unitpacksize;
auto dstindex6 = (bl * stride1 + i * stride0 + (j + 6) * stride2 + k * lp) / unitpacksize;
auto dstindex7 = (bl * stride1 + i * stride0 + (j + 7) * stride2 + k * lp) / unitpacksize;
j += 8;
auto srcdata = vld1q_s16(srcPtr + srcindex);
vst1q_lane_s16(dstPtr + dstindex0, srcdata, 0);
vst1q_lane_s16(dstPtr + dstindex1, srcdata, 1);
vst1q_lane_s16(dstPtr + dstindex2, srcdata, 2);
vst1q_lane_s16(dstPtr + dstindex3, srcdata, 3);
vst1q_lane_s16(dstPtr + dstindex4, srcdata, 4);
vst1q_lane_s16(dstPtr + dstindex5, srcdata, 5);
vst1q_lane_s16(dstPtr + dstindex6, srcdata, 6);
vst1q_lane_s16(dstPtr + dstindex7, srcdata, 7);
}
while (j + 3 < lu) {
auto srcindex = ((i * hp + k) * ic + bl * (lu * lp) + j * lp) / unitpacksize;
auto dstindex0 = (bl * stride1 + i * stride0 + j * stride2 + k * lp) / unitpacksize;
auto dstindex1 = (bl * stride1 + i * stride0 + (j + 1) * stride2 + k * lp) / unitpacksize;
auto dstindex2 = (bl * stride1 + i * stride0 + (j + 2) * stride2 + k * lp) / unitpacksize;
auto dstindex3 = (bl * stride1 + i * stride0 + (j + 3) * stride2 + k * lp) / unitpacksize;
j += 4;
auto srcdata = vld1_s16(srcPtr + srcindex);
vst1_lane_s16(dstPtr + dstindex0, srcdata, 0);
vst1_lane_s16(dstPtr + dstindex1, srcdata, 1);
vst1_lane_s16(dstPtr + dstindex2, srcdata, 2);
vst1_lane_s16(dstPtr + dstindex3, srcdata, 3);
}
while (j < lu)
{
auto srcindex = ((i * hp + k) * ic + bl * (lu * lp) + j * lp) / 2;
auto dstindex = (bl * stride1 + i * stride0 + j * stride2 + k * lp) / 2;
dstPtr[dstindex] = srcPtr[srcindex];
j++;
}
}
}
}
MNNPermuteSumWeightInt4Arm82(dest, dest, blocknum * hu, lu, kernelsum);
}
#endif // __aarch64__
static void MNNSumWeightInt8(float* kernelsum, int8_t* source, size_t outside, size_t reduceAxis, size_t hP, size_t lP) {
// weight shape: [outside, axis, hP, lP]
// outside = blocknum * hU
// reduceAxis = kernelCount * lU
auto inside = hP * lP;
auto stride0 = inside * reduceAxis;
std::vector<float> accum(hP);
for (int i = 0; i < outside; ++i) {
memset(accum.data(), 0, hP * 4);
for (int j = 0; j < reduceAxis; ++j) {
for (int k = 0; k < hP; ++k) {
for (int x = 0; x < lP; ++x) {
accum[k] += (float)source[x + k * lP + j * inside + i * stride0];
}
}
}
memcpy(kernelsum + i * hP, accum.data(), hP * sizeof(float));
}
}
static void MNNSumByAxisLForMatmul_A(float* dest, int8_t* source, const float* scale, ssize_t realDstCount, SumByAxisParams sumParams) {
#ifdef MNN_USE_SSE
uint8_t* srcInt8 = reinterpret_cast<uint8_t*>(source);
#else
int8_t* srcInt8 = source;
#endif
auto scalePtr = scale;
auto blockNum = sumParams.blockNum;
auto EP = sumParams.DST_XUNIT;
auto LP = sumParams.SRC_UNIT;
auto col_buffer_unit_size = sumParams.unitColBufferSize;
auto oneScale = sumParams.oneScale;
auto LU = sumParams.LU;
auto valid = sumParams.valid;
auto kernelxy = sumParams.kernelxy;
auto blockSizeQuad = LU / blockNum;
auto inputBlockQuant = sumParams.inputBlock;
auto lastL = LP;
if (valid) {
lastL = valid;
}
float singlescale = scale[0];
do {
int step = ALIMIN(EP, realDstCount);
int scaleOffset = inputBlockQuant ? (step * blockNum) : step;
for (int k = 0; k < blockNum; ++k) {
const auto src_x = srcInt8 + k * (step * LP * blockSizeQuad * kernelxy);
for (int w = 0; w < step; ++w) {
float dequantScale = singlescale;
if (oneScale == 0 && inputBlockQuant) {
dequantScale = scalePtr[w + k * step];
} else if (oneScale == 0) {
dequantScale = scalePtr[w];
}
int sumint32 = 0;
const auto src_y = src_x + w * LP;
for (int j = 0; j < kernelxy; ++j) {
for (int i = 0; i < blockSizeQuad; ++i) {
auto sumsize = i == (blockSizeQuad - 1) ? lastL : LP;
const auto src_z = src_y + j * (blockSizeQuad * step * LP) + i * step * LP;
for (int x = 0; x < sumsize; ++x) {
sumint32 += src_z[x];
}
}
}
dest[w + k * step] = dequantScale * static_cast<float>(sumint32);
}
}
scalePtr += scaleOffset;
dest += (step * blockNum);
realDstCount -= step;
srcInt8 += col_buffer_unit_size;
} while(realDstCount > 0);
}
template<typename T>
void MNNPackC4Common(T* dst, const T* src, size_t area, size_t depth, int* areaOffset) {
int depthC4 = depth / 4;
int depthRemain = depthC4 * 4;
int remain = depth - depthRemain;
int z, x, y;
const T* srcChannel[4];
const T* srcOffset = src;
for(z = 0; z < depthC4; ++z) {
auto dstZ = dst + z * areaOffset[1] * 4;
for(y = 0; y < 4; ++y) {
srcChannel[y] = srcOffset + areaOffset[0] * y;
}
for(x = 0; x < area; ++x) {
for(y = 0; y < 4; ++y) {
dstZ[0] = srcChannel[y][x];
dstZ++;
}
}
srcOffset += areaOffset[0] * 4;
}
if(remain > 0){
auto dstZ = dst + depthC4 * areaOffset[1] * 4;
for(y = 0; y < remain; ++y) {
srcChannel[y] = srcOffset + areaOffset[0] * y;
}
for(x = 0; x < area; ++x) {
for(y = 0; y < remain; ++y) {
dstZ[0] = srcChannel[y][x];
dstZ++;
}
for(y = remain; y < 4; ++y) {
dstZ[0] = 0;
dstZ++;
}
}
}
}
template<typename T>
void MNNUnpackC4Common(T* dst, const T* src, size_t area, size_t depth, int* areaOffset) {
int depthC4 = depth / 4;
int depthRemain = depthC4 * 4;
int remain = depth - depthRemain;
int z, x, y;
const T* srcChannel[4];
const T* srcOffset = src;
for(z = 0; z < depthC4; ++z) {
for(y = 0; y < 4; ++y) {
auto dstZ = dst + (z * 4 + y) * areaOffset[1];
srcChannel[y] = srcOffset + y;
for(x = 0; x < area; ++x) {
dstZ[x] = srcChannel[y][0];
srcChannel[y] += 4;
}
}
srcOffset += areaOffset[0] * 4;
}
if(remain > 0){
auto dstZ = dst + depthC4 * areaOffset[1] * 4;
for(y = 0; y < remain; ++y) {
srcChannel[y] = srcOffset + y;
for(x = 0; x < area; ++x) {
dstZ[x] = srcChannel[y][0];
srcChannel[y] += 4;
}
dstZ += areaOffset[1];
}
}
}
template<typename T>
void MNNPackC2Common(T* dst, const T* src, size_t area, size_t depth, int* areaOffset) {
int depthC2 = depth / 2;
int depthRemain = depthC2 * 2;
int remain = depth - depthRemain;
int z, x, y;
const T* srcChannel[2];
const T* srcOffset = src;
for(z = 0; z < depthC2; ++z) {
auto dstZ = dst + z * areaOffset[1] * 2;
for(y = 0; y < 2; ++y) {
srcChannel[y] = srcOffset + areaOffset[0] * y;
}
for(x = 0; x < area; ++x) {
for(y = 0; y < 2; ++y) {
dstZ[0] = srcChannel[y][x];
dstZ++;
}
}
srcOffset += areaOffset[0] * 2;
}
if(remain > 0){
auto dstZ = dst + depthC2 * areaOffset[1] * 2;
for(y = 0; y < remain; ++y) {
srcChannel[y] = srcOffset + areaOffset[0] * y;
}
for(x = 0; x < area; ++x) {
for(y = 0; y < remain; ++y) {
dstZ[0] = srcChannel[y][x];
dstZ++;
}
for(y = remain; y < 2; ++y) {
dstZ[0] = 0;
dstZ++;
}
}
}
}
template<typename T>
void MNNUnpackC2Common(T* dst, const T* src, size_t area, size_t depth, int* areaOffset, int pack = 1) {
int depthC2 = depth / 2;
int depthRemain = depthC2 * 2;
int remain = depth - depthRemain;
int z, x, y;
const T* srcChannel[2];
const T* srcOffset = src;
for(z = 0; z < depthC2; ++z) {
for(y = 0; y < 2; ++y) {
auto dstZ = dst + (z * 2 + y) * areaOffset[1] * pack;
srcChannel[y] = srcOffset + y * pack;
for(x = 0; x < area; ++x) {
for (int p = 0; p < pack; ++p) {
dstZ[x * pack + p] = srcChannel[y][p];
}
srcChannel[y] += (2 * pack);
}
}
srcOffset += areaOffset[0] * 2 * pack;
}
if(remain > 0){
auto dstZ = dst + depthC2 * areaOffset[1] * 2 * pack;
for(y = 0; y < remain; ++y) {
srcChannel[y] = srcOffset + y * pack;
for(x = 0; x < area; ++x) {
for (int p = 0; p < pack; ++p) {
dstZ[x * pack + p] = srcChannel[y][p];
}
srcChannel[y] += 2 * pack;
}
dstZ += areaOffset[1] * pack;
}
}
}
void MNN4BitcopyWithStride (uint8_t* dstO, const uint8_t* srcO, int size, int stride, int ds) {
auto src = (uint32_t*)srcO;
auto dst = (uint32_t*)dstO;
for (int i = 0; i < size; ++i) {
dst[0] = *src;
dst += ds;
src += stride;
}
}
void MNN4BitcopyFast (uint8_t* dstO, const uint8_t* srcO, int size, int stride, int ds) {
// ds=1, stride=0||1
auto src = (float*)srcO;
auto dst = (float*)dstO;
int cnt = size;
if (stride == 1) { // stride=1
#ifdef MNN_USE_NEON
for (; cnt >= 8; cnt -= 8) {
auto v4 = vld1q_f32(src);
auto u4 = vld1q_f32(src + 4);
vst1q_f32(dst, v4);
vst1q_f32(dst + 4, u4);
dst += 8;
src += 8;
}
for (; cnt >= 4; cnt -= 4) {
auto v4 = vld1q_f32(src);
vst1q_f32(dst, v4);
dst += 4;
src += 4;
}
#elif defined(MNN_USE_SSE)
for (; cnt >= 8; cnt -= 8) {
__m128 v4 = _mm_loadu_ps(src);
__m128 u4 = _mm_loadu_ps(src + 4);
_mm_storeu_ps(dst, v4);
_mm_storeu_ps(dst + 4, u4);
dst += 8;
src += 8;
}
for (; cnt >= 4; cnt -= 4) {
__m128 v4 = _mm_loadu_ps(src);
_mm_storeu_ps(dst, v4);
dst += 4;
src += 4;
}
#endif
} else { // stride=0
int i = 0;
float val = *src;
#ifdef MNN_USE_NEON
auto val4 = vdupq_n_f32(val);
for (; cnt >= 8; cnt -= 8) {
vst1q_f32(dst, val4);
vst1q_f32(dst + 4, val4);
dst += 8;
}
for (; cnt >= 4; cnt -= 4) {
vst1q_f32(dst, val4);
dst += 4;
}
#elif defined(MNN_USE_SSE)
__m128 val4 = _mm_set_ps(val, val, val, val);
for (; cnt >= 8; cnt -= 8) {
_mm_storeu_ps(dst, val4);
_mm_storeu_ps((dst + 4), val4);
dst += 8;
}
for (; cnt >= 4; cnt -= 4) {
_mm_storeu_ps(dst, val4);
dst += 4;
}
#endif
}
for (; cnt > 0; --cnt) {
dst[0] = *src;
dst += ds;
src += stride;
}
}
void MNN2BitcopyWithStride(uint8_t* dstO, const uint8_t* srcO, int size, int stride, int ds) {
auto src = (uint16_t*)srcO;
auto dst = (uint16_t*)dstO;
for (int i=0; i<size; ++i) {
*dst = *src;
src+=stride;
dst+=ds;
}
}
void MNN2BitcopyFast(uint8_t* dstO, const uint8_t* srcO, int size, int stride, int ds) {
auto src = (uint16_t*)srcO;
auto dst = (uint16_t*)dstO;
int cnt = size;
uint16_t val = *src;
if (stride == 1) {
#ifdef MNN_USE_NEON
for (; cnt >= 8; cnt-=8) {
auto val8 = vld1q_u16(src);
vst1q_u16(dst, val8);
src += 8;
dst += 8;
}
for (; cnt >= 4; cnt-=4) {
auto val4 = vld1_u16(src);
vst1_u16(dst, val4);
src += 4;
dst += 4;
}
#elif defined(MNN_USE_SSE)
for (; cnt >= 8; cnt-=8) {
auto tmp = _mm_loadu_ps((float*)src);
_mm_storeu_ps((float*)dst, tmp);
src += 8;
dst += 8;
}
#endif
} else { // stride=0
#ifdef MNN_USE_NEON
auto val4 = vdup_n_u16(val);
auto val8 = vdupq_n_u16(val);
for (; cnt >= 8; cnt-=8) {
vst1q_u16(dst, val8);
dst += 8;
}
for (; cnt >= 4; cnt-=4) {
vst1_u16(dst, val4);
dst += 4;
}
#elif defined(MNN_USE_SSE)
uint16_t arr[8] = {val, val, val, val, val, val, val, val};
auto val8 = _mm_loadu_ps((float*)arr);
for (; cnt >= 8; cnt-=8) {
_mm_storeu_ps((float*)dst, val8);
dst += 8;
}
#endif
}
for (; cnt > 0; --cnt) {
*dst = *src;
src += stride;
dst += ds;
}
}
void MNN1BitcopyWithStride (uint8_t* dstO, const uint8_t* srcO, int size, int stride, int ds) {
for (int i = 0; i < size; ++i) {
dstO[0] = *srcO;
dstO += ds;
srcO += stride;
}
}
void MNN1BitCopyFast (uint8_t* dstO, const uint8_t* srcO, int size, int stride, int ds) {
int cnt = size;
uint8_t val = *srcO;
if (stride == 1) {
#ifdef MNN_USE_SSE
for (; cnt >= 16; cnt-=16) {
auto tmp = _mm_loadu_ps((float*)srcO);
_mm_storeu_ps((float*)dstO, tmp);
srcO += 16;
dstO += 16;
}
#elif defined(MNN_USE_NEON)
for (; cnt >= 16; cnt-=16) {
auto val16 = vld1q_u8(srcO);
vst1q_u8(dstO, val16);
srcO += 16;
dstO += 16;
}
for (; cnt >= 8; cnt-=8) {
auto val8 = vld1_u8(srcO);
vst1_u8(dstO, val8);
srcO += 8;
dstO += 8;
}
#endif
} else { // stride=0
#ifdef MNN_USE_SSE
std::vector<uint8_t> arr(16, val);
auto val16 = _mm_loadu_ps((float*)arr.data());
for (; cnt >= 16; cnt-=16) {
_mm_storeu_ps((float*)dstO, val16);
dstO += 16;
}
#elif defined(MNN_USE_NEON)
auto val16 = vdupq_n_u8(val);
auto val8 = vdup_n_u8(val);
for (; cnt >= 16; cnt-=16) {
vst1q_u8(dstO, val16);
dstO += 16;
}
for (; cnt >= 8; cnt-=8) {
vst1_u8(dstO, val8);
dstO += 8;
}
#endif
}
for (; cnt > 0; --cnt) {
dstO[0] = *srcO;
dstO += ds;
srcO += stride;
}
}
void MNNAccumulateSequenceNumber (float* dst, const float* src, int size) {
// mode: 0:Add, 1:Sub, 2:Min, 3:Max
int size8 = (size / 8) * 8;
int i = 0;
float sum = 0.f;
float tmp[4];
#ifdef MNN_USE_NEON
if (size >= 8) {
auto sum4_1 = vdupq_n_f32(0.f);
auto sum4_2 = vdupq_n_f32(0.f);
for (; i < size8; i += 8) {
auto v4 = vld1q_f32(src);
auto u4 = vld1q_f32(src + 4);
sum4_1 = vaddq_f32(sum4_1, v4);
sum4_2 = vaddq_f32(sum4_2, u4);
src += 8;
}
sum4_1 = vaddq_f32(sum4_1, sum4_2);
sum = (sum4_1[0] + sum4_1[1]) + (sum4_1[2] + sum4_1[3]);
}
#elif defined(MNN_USE_SSE)
if (size >= 8) {
auto sum4_1 = _mm_set_ps1(0.f);
auto sum4_2 = _mm_set_ps1(0.f);
for (; i < size8; i += 8) {
auto v4 = _mm_loadu_ps(src);
auto u4 = _mm_loadu_ps(src + 4);
sum4_1 = _mm_add_ps(sum4_1, v4);
sum4_2 = _mm_add_ps(sum4_2, u4);
src += 8;
}
sum4_1 = _mm_add_ps(sum4_1, sum4_2);
_mm_storeu_ps(tmp, sum4_1);
sum += (tmp[0] + tmp[1] + tmp[2] + tmp[3]);
}
#endif
for (; i < size; ++i) {
sum += (*src);
src += 1;
}
*dst = sum;
}
#ifndef MNN_USE_NEON
void MNNGetMatMulPackMode(int* eP, int *lP, int* hP) {
*eP = 16;
*lP = 1;
*hP = 4;
}
void MNNGetSparseMatMulPackMode(int* eP, int *lP, int* hP) {
*eP = 16;
*lP = 1;
*hP = 4;
// hp is corresponding to sparse block along right matrix colum dimension. in ramdom sparse, it is 1.
return;
}
void MNNPackForMatMul_B(float* dest, const float* source, size_t h, size_t l, bool transpose) {
auto hP = h / 4;
auto hR = hP * 4;
if (hR != h) {
::memset(dest, 0, UP_DIV(h, 4)*4*l*sizeof(float));
}
if (!transpose) {
for (int y=0; y<hP; ++y) {
auto destY = dest + y * 4 * l;
auto sourceY = source + y * 4;
for (int x=0; x<l; ++x) {
::memcpy(destY + 4 * x, sourceY + x * h, 4 * sizeof(float));
}
}
auto hRemain = h - hR;
if (hRemain > 0) {
auto destY = dest + hP * 4 * l;
auto sourceY = source + hP * 4;
for (int x=0; x<l; ++x) {
::memcpy(destY + 4 * x, sourceY + x * h, hRemain * sizeof(float));
}
}
return;
}
int offset[] = {
(int)l,
(int)l
};
MNNPackC4(dest, source, l, h, offset);
}
static void _MNNPackedMatMulRemain(float* C, const float* A, const float* B, size_t eSize, const size_t* parameter, const float* postParameters, const float* bias, int aStride) {
auto h = parameter[2];
auto l = parameter[1];
auto cStride = parameter[3] / sizeof(float);
auto hRemain = parameter[4];
auto bExtraStride = parameter[5] / sizeof(float);
auto bStride = bExtraStride + l * 4;
auto hC4 = UP_DIV(h, 4);
for (int y=0; y<hC4; ++y) {
::memset(C + y * cStride, 0, eSize * 4 * sizeof(float));
}
float alpha = 1.0f;
float beta = 0.0f;
float minValue = -std::numeric_limits<float>().max();
float maxValue = std::numeric_limits<float>().max();
if (nullptr != postParameters) {
minValue = postParameters[2];
maxValue = postParameters[3];
alpha = postParameters[0];
beta = postParameters[1];
}
for (int x=0; x<eSize; ++x) {
auto dst = C + 4 * x;
auto src = A + x;
for (int y=0; y<hC4; ++y) {
auto dstY = dst + y * cStride;
auto weight = B + y * bStride;
float summer[4] = {
0.0f,
0.0f,
0.0f,
0.0f,
};
if (nullptr != bias) {
for (int v=0; v<4; ++v) {
summer[v] = bias[4 * y + v];
}
}
for (int z=0; z<l; ++z) {
auto aZ = src + z * aStride;
auto wZ = weight + z * 4;
summer[0] += wZ[0] * aZ[0];
summer[1] += wZ[1] * aZ[0];
summer[2] += wZ[2] * aZ[0];
summer[3] += wZ[3] * aZ[0];
}
for (int v=0; v<4; ++v) {
auto dstValue = std::min(summer[v], maxValue);
dstValue = std::max(dstValue, minValue);
dstY[v] = dstValue;
}
}
}
}
void MNNPackedMatMul(float* C, const float* A, const float* B, const size_t* parameter, const float* postParameters, const float* bias, const float* k, const float* b) {
return _MNNPackedMatMulRemain(C, A, B, 16, parameter, postParameters, bias, 16);
}
void MNNPackedMatMulRemain(float* C, const float* A, const float* B, size_t eSize, const size_t* parameter, const float* postParameters, const float* bias, const float* k, const float* b) {
auto aStride = parameter[0] / sizeof(float);
_MNNPackedMatMulRemain(C, A, B, eSize, parameter, postParameters, bias, aStride);
}
void MNNPackC4ForMatMul_A(float* destOrigin, float const** sourceGroup, const int32_t* info, const int32_t* el) {
int number = info[0];
int eReal = info[1];
int eDest = info[2];
int offset = info[3];
for (int n=0; n<number; ++n) {
int e = el[4 * n + 0];
int l = el[4 * n + 1];
int eOffset = el[4 * n + 2];
int lOffset = el[4 * n + 3];
auto dest = destOrigin + lOffset * eDest + eOffset;
auto source = sourceGroup[n];
for (int y=0; y<e; ++y) {
auto yR = y % eDest;
for (int x=0; x<l; ++x) {
auto xR = x % 4;
auto xC = x / 4;
dest[(x) * eDest + yR] = source[xC * eReal * 4 + y * 4 * offset + xR];
}
}
}
}
void MNNPackedSparseMatMulEpx1(float* C, const float* A, const float* B, size_t eSize, const size_t* parameter, const float* postParameters, const float* bias, unsigned int* NNZMap, int* dataOffsetMap) {
auto eP = parameter[0] / sizeof(float);
MNN_ASSERT((eP & 0x03) == 0); // In sparse calculate, eP should be evenly divided by 4
auto h = parameter[2];
auto l = parameter[1];
auto cStride = parameter[3] / sizeof(float);
auto aStride = eP * l;
auto hRemain = parameter[4];
auto bExtraStride = parameter[5] / sizeof(float);
auto bStride = bExtraStride + l * 4;
auto hC4 = UP_DIV(h, 4);
float minValue = -std::numeric_limits<float>().max();
float maxValue = std::numeric_limits<float>().max();
if (nullptr != postParameters) {
minValue = postParameters[2];
maxValue = postParameters[3];
}
// MNN_PRINT("MNNPackedSparseMatMul eP:%lu, eSize:%lu, l:%lu, h:%lu, cStride:%lu, aStride:%lu\n", eP, eSize, l, h, cStride, aStride);
const float* a = A;
size_t ie = 0;
for (ie = 0; ie < eSize && eP <= eSize; ie += eP) {
const int* dataOffset = dataOffsetMap;
const int diff = *dataOffset++;
a += diff;
const float* w = B;
float* blockC = C + (ie << 2);
const unsigned int* nnz = NNZMap;
for (auto ih = 0; ih < h; ih++) {
auto ihPack = ih >> 2;
auto ihSubIndex = ih & 0x03;
auto c = blockC + ihPack * cStride + ihSubIndex;
const float initValue = nullptr != bias ? bias[ih] : 0;
float acc0 = initValue;
float acc1 = initValue;
float acc2 = initValue;
float acc3 = initValue;
float acc4 = initValue;
float acc5 = initValue;
float acc6 = initValue;
float acc7 = initValue;
float acc8 = initValue;
float acc9 = initValue;
float acc10 = initValue;
float acc11 = initValue;
float acc12 = initValue;
float acc13 = initValue;
float acc14 = initValue;
float acc15 = initValue;
const int lElement = *nnz++;
for (auto il = 0; il < lElement; il++) {
const int diff = *dataOffset++;
const float a0 = a[0];
const float a1 = a[1];
const float a2 = a[2];
const float a3 = a[3];
const float a4 = a[4];
const float a5 = a[5];
const float a6 = a[6];
const float a7 = a[7];
const float a8 = a[8];
const float a9 = a[9];
const float a10 = a[10];
const float a11 = a[11];
const float a12 = a[12];
const float a13 = a[13];
const float a14 = a[14];
const float a15 = a[15];
const float oneW = *w++;
// MNN_PRINT("16-loop: ie:%zu, a offset:%ld, w offset:%ld, c offset:%ld, w value:%f, a value[0-15]:", ie, a - A, w - B - 1, c - C, oneW);
// formatMatrix(a, {16});
// MNN_PRINT("\n");
a = a + diff;
acc0 += a0 * oneW;
acc1 += a1 * oneW;
acc2 += a2 * oneW;
acc3 += a3 * oneW;
acc4 += a4 * oneW;
acc5 += a5 * oneW;
acc6 += a6 * oneW;
acc7 += a7 * oneW;
acc8 += a8 * oneW;
acc9 += a9 * oneW;
acc10 += a10 * oneW;
acc11 += a11 * oneW;
acc12 += a12 * oneW;
acc13 += a13 * oneW;
acc14 += a14 * oneW;
acc15 += a15 * oneW;
}
acc0 = std::max(std::min(maxValue, acc0), minValue);
acc1 = std::max(std::min(maxValue, acc1), minValue);
acc2 = std::max(std::min(maxValue, acc2), minValue);
acc3 = std::max(std::min(maxValue, acc3), minValue);
acc4 = std::max(std::min(maxValue, acc4), minValue);
acc5 = std::max(std::min(maxValue, acc5), minValue);
acc6 = std::max(std::min(maxValue, acc6), minValue);
acc7 = std::max(std::min(maxValue, acc7), minValue);
acc8 = std::max(std::min(maxValue, acc8), minValue);
acc9 = std::max(std::min(maxValue, acc9), minValue);
acc10 = std::max(std::min(maxValue, acc10), minValue);
acc11 = std::max(std::min(maxValue, acc11), minValue);
acc12 = std::max(std::min(maxValue, acc12), minValue);
acc13 = std::max(std::min(maxValue, acc13), minValue);
acc14 = std::max(std::min(maxValue, acc14), minValue);
acc15 = std::max(std::min(maxValue, acc15), minValue);
// how to store faster: st4 / transpose /
c[0] = acc0;
c[4] = acc1;
c[4 * 2] = acc2;
c[4 * 3] = acc3;
c[4 * 4] = acc4;
c[4 * 5] = acc5;
c[4 * 6] = acc6;
c[4 * 7] = acc7;
c[4 * 8] = acc8;
c[4 * 9] = acc9;
c[4 * 10] = acc10;
c[4 * 11] = acc11;
c[4 * 12] = acc12;
c[4 * 13] = acc13;
c[4 * 14] = acc14;
c[4 * 15] = acc15;
}
a += aStride;
}
// const float* blockA = A + ie * l;
if (eSize & 0x08) {
const int* dataOffset = dataOffsetMap;
const int diff = *dataOffset++;
// a = blockA + diff;
a += diff;
const float* w = B;
float* blockC = C + (ie << 2);
const unsigned int* nnz = NNZMap;
for (auto ih = 0; ih < h; ih++) {
auto ihPack = ih >> 2;
auto ihSubIndex = ih & 0x03;
auto c = blockC + ihPack * cStride + ihSubIndex;
const float initValue = nullptr != bias ? bias[ih] : 0;
float acc0 = initValue;
float acc1 = initValue;
float acc2 = initValue;
float acc3 = initValue;
float acc4 = initValue;
float acc5 = initValue;
float acc6 = initValue;
float acc7 = initValue;
const int lElement = *nnz++;
for (auto il = 0; il < lElement; il++) {
const int diff = *dataOffset++;
const float a0 = a[0];
const float a1 = a[1];
const float a2 = a[2];
const float a3 = a[3];
const float a4 = a[4];
const float a5 = a[5];
const float a6 = a[6];
const float a7 = a[7];
const float oneW = *w++;
// MNN_PRINT("8-loop: ie:%zu, a offset:%ld, w offset:%ld, c offset:%ld, w value:%f, a value[0-7]:", ie, a - A, w - B - 1, c - C, oneW);
// formatMatrix(a, {8});
// MNN_PRINT("\n");
a = a + diff;
acc0 += a0 * oneW;
acc1 += a1 * oneW;
acc2 += a2 * oneW;
acc3 += a3 * oneW;
acc4 += a4 * oneW;
acc5 += a5 * oneW;
acc6 += a6 * oneW;
acc7 += a7 * oneW;
}
acc0 = std::max(std::min(maxValue, acc0), minValue);
acc1 = std::max(std::min(maxValue, acc1), minValue);
acc2 = std::max(std::min(maxValue, acc2), minValue);
acc3 = std::max(std::min(maxValue, acc3), minValue);
acc4 = std::max(std::min(maxValue, acc4), minValue);
acc5 = std::max(std::min(maxValue, acc5), minValue);
acc6 = std::max(std::min(maxValue, acc6), minValue);
acc7 = std::max(std::min(maxValue, acc7), minValue);
// how to store faster: st4 / transpose /
c[0] = acc0;
c[4] = acc1;
c[4 * 2] = acc2;
c[4 * 3] = acc3;
c[4 * 4] = acc4;
c[4 * 5] = acc5;
c[4 * 6] = acc6;
c[4 * 7] = acc7;
}
ie += 8;
a += 8;
}
if (eSize & 0x04) {
const int* dataOffset = dataOffsetMap;
const int diff = *dataOffset++;
// const float* a = blockA + diff;
a += diff;
const float* w = B;
float* blockC = C + (ie << 2);
const unsigned int* nnz = NNZMap;
for (auto ih = 0; ih < h; ih++) {
auto ihPack = ih >> 2;
auto ihSubIndex = ih & 0x03;
auto c = blockC + ihPack * cStride + ihSubIndex;
const float initValue = nullptr != bias ? bias[ih] : 0;
float acc0 = initValue;
float acc1 = initValue;
float acc2 = initValue;
float acc3 = initValue;
const int lElement = *nnz++;
for (auto il = 0; il < lElement; il++) {
const int diff = *dataOffset++;
const float a0 = a[0];
const float a1 = a[1];
const float a2 = a[2];
const float a3 = a[3];
const float oneW = *w++;
// MNN_PRINT("4-loop: ie:%zu, a offset:%ld, w offset:%ld, c offset:%ld, w value:%f, a value[0-3]:", ie, a - A, w - B - 1, c - C, oneW);
// formatMatrix(a, {4});
// MNN_PRINT("\n");
a = a + diff;
acc0 += a0 * oneW;
acc1 += a1 * oneW;
acc2 += a2 * oneW;
acc3 += a3 * oneW;
}
acc0 = std::max(std::min(maxValue, acc0), minValue);
acc1 = std::max(std::min(maxValue, acc1), minValue);
acc2 = std::max(std::min(maxValue, acc2), minValue);
acc3 = std::max(std::min(maxValue, acc3), minValue);
// how to store faster: st4 / transpose /
c[0] = acc0;
c[4] = acc1;
c[4 * 2] = acc2;
c[4 * 3] = acc3;
}
ie += 4;
a += 4;
}
if (eSize & 0x02) {
const int* dataOffset = dataOffsetMap;
const int diff = *dataOffset++;
// const float* a = blockA + diff;
a += diff;
const float* w = B;
float* blockC = C + (ie << 2);
const unsigned int* nnz = NNZMap;
for (auto ih = 0; ih < h; ih++) {
auto ihPack = ih >> 2;
auto ihSubIndex = ih & 0x03;
auto c = blockC + ihPack * cStride + ihSubIndex;
const float initValue = nullptr != bias ? bias[ih] : 0;
float acc0 = initValue;
float acc1 = initValue;
const int lElement = *nnz++;
for (auto il = 0; il < lElement; il++) {
const int diff = *dataOffset++;
const float a0 = a[0];
const float a1 = a[1];
const float oneW = *w++;
// MNN_PRINT("2-loop: ie:%zu, a offset:%ld, w offset:%ld, c offset:%ld, w value:%f, a value[0-1]:", ie, a - A, w - B - 1, c - C, oneW);
// formatMatrix(a, {2});
// MNN_PRINT("\n");
a = a + diff;
acc0 += a0 * oneW;
acc1 += a1 * oneW;
}
acc0 = std::max(std::min(maxValue, acc0), minValue);
acc1 = std::max(std::min(maxValue, acc1), minValue);
// how to store faster: st4 / transpose /
c[0] = acc0;
c[4] = acc1;
}
ie += 2;
a += 2;
}
if (eSize & 0x01) {
const int* dataOffset = dataOffsetMap;
const int diff = *dataOffset++;
// const float* a = blockA + diff;
a += diff;
const float* w = B;
float* blockC = C + (ie << 2);
const unsigned int* nnz = NNZMap;
for (auto ih = 0; ih < h; ih++) {
auto ihPack = ih >> 2;
auto ihSubIndex = ih & 0x03;
auto c = blockC + ihPack * cStride + ihSubIndex;
const float initValue = nullptr != bias ? bias[ih] : 0;
float acc0 = initValue;
const int lElement = *nnz++;
for (auto il = 0; il < lElement; il++) {
const int diff = *dataOffset++;
const float a0 = a[0];
const float oneW = *w++;
// MNN_PRINT("1-loop: ie:%zu, a offset:%ld, c offset:%ld, w offset:%ld, w value:%f, a value[0]:", ie, a - A, w - B - 1, c - C, oneW);
// formatMatrix(a, {1});
// MNN_PRINT("\n");
a = a + diff;
acc0 += a0 * oneW;
}
acc0 = std::max(std::min(maxValue, acc0), minValue);
// how to store faster: st4 / transpose /
c[0] = acc0;
}
ie += 1;
// a += 1;
}
return;
}
void MNNPackedSparseMatMulEpx4(float* C, const float* A, const float* B, size_t eSize, const size_t* parameter, const float* postParameters, const float* bias, unsigned int* NNZMap, int* dataOffsetMap) {
auto eP = parameter[0] / sizeof(float);
MNN_ASSERT((eP & 0x03) == 0); // In sparse calculate, eP should be evenly divided by 4
auto h = parameter[2];
auto l = parameter[1];
auto cStride = parameter[3] / sizeof(float);
auto aStride = eP * l;
auto hRemain = parameter[4];
auto bExtraStride = parameter[5] / sizeof(float);
auto bStride = bExtraStride + l * 4;
auto hC4 = UP_DIV(h, 4);
float minValue = -std::numeric_limits<float>().max();
float maxValue = std::numeric_limits<float>().max();
if (nullptr != postParameters) {
minValue = postParameters[2];
maxValue = postParameters[3];
}
// MNN_PRINT("MNNPackedSparseMatMul 16x4 eP:%lu, eSize:%lu, l:%lu, h:%lu, cStride:%lu, aStride:%lu\n", eP, eSize, l, h, cStride, aStride);
const int sparseBlockOC = 4;
const float* a = A;
size_t ie = 0;
for (ie = 0; ie < eSize && eP <= eSize; ie += eP) {
const int* dataOffset = dataOffsetMap;
const int diff = *dataOffset++;
a += diff;
const float* w = B;
float* blockC = C + (ie << 2);
const unsigned int* nnz = NNZMap;
size_t ih = 0;
for (; ih < (h & (~0x03)); ih += sparseBlockOC) {
auto ihPack = ih >> 2;
auto c = blockC + ihPack * cStride;
float initValue[4] = {0, 0, 0, 0};
if (nullptr != bias) {
memcpy(initValue, bias + ih, 4 * sizeof(float));
}
float acc0[4];
float acc1[4];
float acc2[4];
float acc3[4];
float acc4[4];
float acc5[4];
float acc6[4];
float acc7[4];
float acc8[4];
float acc9[4];
float acc10[4];
float acc11[4];
float acc12[4];
float acc13[4];
float acc14[4];
float acc15[4];
memcpy(acc0, initValue, 4 * sizeof(float));
memcpy(acc1, initValue, 4 * sizeof(float));
memcpy(acc2, initValue, 4 * sizeof(float));
memcpy(acc3, initValue, 4 * sizeof(float));
memcpy(acc4, initValue, 4 * sizeof(float));
memcpy(acc5, initValue, 4 * sizeof(float));
memcpy(acc6, initValue, 4 * sizeof(float));
memcpy(acc7, initValue, 4 * sizeof(float));
memcpy(acc8, initValue, 4 * sizeof(float));
memcpy(acc9, initValue, 4 * sizeof(float));
memcpy(acc10, initValue, 4 * sizeof(float));
memcpy(acc11, initValue, 4 * sizeof(float));
memcpy(acc12, initValue, 4 * sizeof(float));
memcpy(acc13, initValue, 4 * sizeof(float));
memcpy(acc14, initValue, 4 * sizeof(float));
memcpy(acc15, initValue, 4 * sizeof(float));
const int lElement = *nnz++;
for (auto il = 0; il < lElement; il++) {
const int diff = *dataOffset++;
const float a0 = a[0];
const float a1 = a[1];
const float a2 = a[2];
const float a3 = a[3];
const float a4 = a[4];
const float a5 = a[5];
const float a6 = a[6];
const float a7 = a[7];
const float a8 = a[8];
const float a9 = a[9];
const float a10 = a[10];
const float a11 = a[11];
const float a12 = a[12];
const float a13 = a[13];
const float a14 = a[14];
const float a15 = a[15];
const float wv[4] = {*w++, *w++, *w++, *w++};
// MNN_PRINT("16-loop: ie:%zu, a offset:%ld, w offset:%ld, c offset:%ld, w value:%f, a value[0-15]:", ie, a - A, w - B - 1, c - C, oneW);
// formatMatrix(a, {16});
// MNN_PRINT("\n");
a = a + diff;
for (int lane = 0; lane < 4; lane++) {
acc0[lane] += a0 * wv[lane];
acc1[lane] += a1 * wv[lane];
acc2[lane] += a2 * wv[lane];
acc3[lane] += a3 * wv[lane];
acc4[lane] += a4 * wv[lane];
acc5[lane] += a5 * wv[lane];
acc6[lane] += a6 * wv[lane];
acc7[lane] += a7 * wv[lane];
acc8[lane] += a8 * wv[lane];
acc9[lane] += a9 * wv[lane];
acc10[lane] += a10 * wv[lane];
acc11[lane] += a11 * wv[lane];
acc12[lane] += a12 * wv[lane];
acc13[lane] += a13 * wv[lane];
acc14[lane] += a14 * wv[lane];
acc15[lane] += a15 * wv[lane];
}
}
for (int lane = 0; lane < 4; lane++) {
acc0[lane] = std::max(std::min(maxValue, acc0[lane]), minValue);
acc1[lane] = std::max(std::min(maxValue, acc1[lane]), minValue);
acc2[lane] = std::max(std::min(maxValue, acc2[lane]), minValue);
acc3[lane] = std::max(std::min(maxValue, acc3[lane]), minValue);
acc4[lane] = std::max(std::min(maxValue, acc4[lane]), minValue);
acc5[lane] = std::max(std::min(maxValue, acc5[lane]), minValue);
acc6[lane] = std::max(std::min(maxValue, acc6[lane]), minValue);
acc7[lane] = std::max(std::min(maxValue, acc7[lane]), minValue);
acc8[lane] = std::max(std::min(maxValue, acc8[lane]), minValue);
acc9[lane] = std::max(std::min(maxValue, acc9[lane]), minValue);
acc10[lane] = std::max(std::min(maxValue, acc10[lane]), minValue);
acc11[lane] = std::max(std::min(maxValue, acc11[lane]), minValue);
acc12[lane] = std::max(std::min(maxValue, acc12[lane]), minValue);
acc13[lane] = std::max(std::min(maxValue, acc13[lane]), minValue);
acc14[lane] = std::max(std::min(maxValue, acc14[lane]), minValue);
acc15[lane] = std::max(std::min(maxValue, acc15[lane]), minValue);
}
memcpy(c, acc0, 4 * sizeof(float)); // store continuous c
memcpy(c + 4, acc1, 4 * sizeof(float));
memcpy(c + 4 * 2, acc2, 4 * sizeof(float));
memcpy(c + 4 * 3, acc3, 4 * sizeof(float));
memcpy(c + 4 * 4, acc4, 4 * sizeof(float));
memcpy(c + 4 * 5, acc5, 4 * sizeof(float));
memcpy(c + 4 * 6, acc6, 4 * sizeof(float));
memcpy(c + 4 * 7, acc7, 4 * sizeof(float));
memcpy(c + 4 * 8, acc8, 4 * sizeof(float));
memcpy(c + 4 * 9, acc9, 4 * sizeof(float));
memcpy(c + 4 * 10, acc10, 4 * sizeof(float));
memcpy(c + 4 * 11, acc11, 4 * sizeof(float));
memcpy(c + 4 * 12, acc12, 4 * sizeof(float));
memcpy(c + 4 * 13, acc13, 4 * sizeof(float));
memcpy(c + 4 * 14, acc14, 4 * sizeof(float));
memcpy(c + 4 * 15, acc15, 4 * sizeof(float));
}
blockC += (h >> 2) * cStride;
for (; ih < h; ih++) {
auto ihSubIndex = ih & 0x03;
auto c = blockC + ihSubIndex;
const float initValue = nullptr != bias ? bias[ih] : 0;
float acc0 = initValue;
float acc1 = initValue;
float acc2 = initValue;
float acc3 = initValue;
float acc4 = initValue;
float acc5 = initValue;
float acc6 = initValue;
float acc7 = initValue;
float acc8 = initValue;
float acc9 = initValue;
float acc10 = initValue;
float acc11 = initValue;
float acc12 = initValue;
float acc13 = initValue;
float acc14 = initValue;
float acc15 = initValue;
const int lElement = *nnz++;
for (auto il = 0; il < lElement; il++) {
const int diff = *dataOffset++;
const float a0 = a[0];
const float a1 = a[1];
const float a2 = a[2];
const float a3 = a[3];
const float a4 = a[4];
const float a5 = a[5];
const float a6 = a[6];
const float a7 = a[7];
const float a8 = a[8];
const float a9 = a[9];
const float a10 = a[10];
const float a11 = a[11];
const float a12 = a[12];
const float a13 = a[13];
const float a14 = a[14];
const float a15 = a[15];
const float oneW = *w++;
// MNN_PRINT("16-loop: ie:%zu, a offset:%ld, w offset:%ld, c offset:%ld, w value:%f, a value[0-15]:", ie, a - A, w - B - 1, c - C, oneW);
// formatMatrix(a, {16});
// MNN_PRINT("\n");
a = a + diff;
acc0 += a0 * oneW;
acc1 += a1 * oneW;
acc2 += a2 * oneW;
acc3 += a3 * oneW;
acc4 += a4 * oneW;
acc5 += a5 * oneW;
acc6 += a6 * oneW;
acc7 += a7 * oneW;
acc8 += a8 * oneW;
acc9 += a9 * oneW;
acc10 += a10 * oneW;
acc11 += a11 * oneW;
acc12 += a12 * oneW;
acc13 += a13 * oneW;
acc14 += a14 * oneW;
acc15 += a15 * oneW;
}
acc0 = std::max(std::min(maxValue, acc0), minValue);
acc1 = std::max(std::min(maxValue, acc1), minValue);
acc2 = std::max(std::min(maxValue, acc2), minValue);
acc3 = std::max(std::min(maxValue, acc3), minValue);
acc4 = std::max(std::min(maxValue, acc4), minValue);
acc5 = std::max(std::min(maxValue, acc5), minValue);
acc6 = std::max(std::min(maxValue, acc6), minValue);
acc7 = std::max(std::min(maxValue, acc7), minValue);
acc8 = std::max(std::min(maxValue, acc8), minValue);
acc9 = std::max(std::min(maxValue, acc9), minValue);
acc10 = std::max(std::min(maxValue, acc10), minValue);
acc11 = std::max(std::min(maxValue, acc11), minValue);
acc12 = std::max(std::min(maxValue, acc12), minValue);
acc13 = std::max(std::min(maxValue, acc13), minValue);
acc14 = std::max(std::min(maxValue, acc14), minValue);
acc15 = std::max(std::min(maxValue, acc15), minValue);
// how to store faster: st4 / transpose /
c[0] = acc0;
c[4] = acc1;
c[4 * 2] = acc2;
c[4 * 3] = acc3;
c[4 * 4] = acc4;
c[4 * 5] = acc5;
c[4 * 6] = acc6;
c[4 * 7] = acc7;
c[4 * 8] = acc8;
c[4 * 9] = acc9;
c[4 * 10] = acc10;
c[4 * 11] = acc11;
c[4 * 12] = acc12;
c[4 * 13] = acc13;
c[4 * 14] = acc14;
c[4 * 15] = acc15;
}
a += aStride;
}
// const float* blockA = A + ie * l;
if (eSize & 0x08) {
const int* dataOffset = dataOffsetMap;
const int diff = *dataOffset++;
// a = blockA + diff;
a += diff;
const float* w = B;
float* blockC = C + (ie << 2);
const unsigned int* nnz = NNZMap;
size_t ih = 0;
for (; ih < (h & (~0x03)); ih += sparseBlockOC) {
auto ihPack = ih >> 2;
auto c = blockC + ihPack * cStride;
float initValue[4] = {0, 0, 0, 0};
if (nullptr != bias) {
memcpy(initValue, bias + ih, 4 * sizeof(float));
}
float acc0[4];
float acc1[4];
float acc2[4];
float acc3[4];
float acc4[4];
float acc5[4];
float acc6[4];
float acc7[4];
memcpy(acc0, initValue, 4 * sizeof(float));
memcpy(acc1, initValue, 4 * sizeof(float));
memcpy(acc2, initValue, 4 * sizeof(float));
memcpy(acc3, initValue, 4 * sizeof(float));
memcpy(acc4, initValue, 4 * sizeof(float));
memcpy(acc5, initValue, 4 * sizeof(float));
memcpy(acc6, initValue, 4 * sizeof(float));
memcpy(acc7, initValue, 4 * sizeof(float));
const int lElement = *nnz++;
for (auto il = 0; il < lElement; il++) {
const int diff = *dataOffset++;
const float a0 = a[0];
const float a1 = a[1];
const float a2 = a[2];
const float a3 = a[3];
const float a4 = a[4];
const float a5 = a[5];
const float a6 = a[6];
const float a7 = a[7];
const float wv[4] = {*w++, *w++, *w++, *w++};
// MNN_PRINT("16-loop: ie:%zu, a offset:%ld, w offset:%ld, c offset:%ld, w value:%f, a value[0-15]:", ie, a - A, w - B - 1, c - C, oneW);
// formatMatrix(a, {16});
// MNN_PRINT("\n");
a = a + diff;
for (int lane = 0; lane < 4; lane++) {
acc0[lane] += a0 * wv[lane];
acc1[lane] += a1 * wv[lane];
acc2[lane] += a2 * wv[lane];
acc3[lane] += a3 * wv[lane];
acc4[lane] += a4 * wv[lane];
acc5[lane] += a5 * wv[lane];
acc6[lane] += a6 * wv[lane];
acc7[lane] += a7 * wv[lane];
}
}
for (int lane = 0; lane < 4; lane++) {
acc0[lane] = std::max(std::min(maxValue, acc0[lane]), minValue);
acc1[lane] = std::max(std::min(maxValue, acc1[lane]), minValue);
acc2[lane] = std::max(std::min(maxValue, acc2[lane]), minValue);
acc3[lane] = std::max(std::min(maxValue, acc3[lane]), minValue);
acc4[lane] = std::max(std::min(maxValue, acc4[lane]), minValue);
acc5[lane] = std::max(std::min(maxValue, acc5[lane]), minValue);
acc6[lane] = std::max(std::min(maxValue, acc6[lane]), minValue);
acc7[lane] = std::max(std::min(maxValue, acc7[lane]), minValue);
}
memcpy(c, acc0, 4 * sizeof(float)); // store continuous c
memcpy(c + 4, acc1, 4 * sizeof(float));
memcpy(c + 4 * 2, acc2, 4 * sizeof(float));
memcpy(c + 4 * 3, acc3, 4 * sizeof(float));
memcpy(c + 4 * 4, acc4, 4 * sizeof(float));
memcpy(c + 4 * 5, acc5, 4 * sizeof(float));
memcpy(c + 4 * 6, acc6, 4 * sizeof(float));
memcpy(c + 4 * 7, acc7, 4 * sizeof(float));
}
blockC += (ih >> 2) * cStride;
for (; ih < h; ih++) {
auto ihSubIndex = ih & 0x03;
auto c = blockC + ihSubIndex;
const float initValue = nullptr != bias ? bias[ih] : 0;
float acc0 = initValue;
float acc1 = initValue;
float acc2 = initValue;
float acc3 = initValue;
float acc4 = initValue;
float acc5 = initValue;
float acc6 = initValue;
float acc7 = initValue;
const int lElement = *nnz++;
for (auto il = 0; il < lElement; il++) {
const int diff = *dataOffset++;
const float a0 = a[0];
const float a1 = a[1];
const float a2 = a[2];
const float a3 = a[3];
const float a4 = a[4];
const float a5 = a[5];
const float a6 = a[6];
const float a7 = a[7];
const float oneW = *w++;
// MNN_PRINT("8-loop: ie:%zu, a offset:%ld, w offset:%ld, c offset:%ld, w value:%f, a value[0-7]:", ie, a - A, w - B - 1, c - C, oneW);
// formatMatrix(a, {8});
// MNN_PRINT("\n");
a = a + diff;
acc0 += a0 * oneW;
acc1 += a1 * oneW;
acc2 += a2 * oneW;
acc3 += a3 * oneW;
acc4 += a4 * oneW;
acc5 += a5 * oneW;
acc6 += a6 * oneW;
acc7 += a7 * oneW;
}
acc0 = std::max(std::min(maxValue, acc0), minValue);
acc1 = std::max(std::min(maxValue, acc1), minValue);
acc2 = std::max(std::min(maxValue, acc2), minValue);
acc3 = std::max(std::min(maxValue, acc3), minValue);
acc4 = std::max(std::min(maxValue, acc4), minValue);
acc5 = std::max(std::min(maxValue, acc5), minValue);
acc6 = std::max(std::min(maxValue, acc6), minValue);
acc7 = std::max(std::min(maxValue, acc7), minValue);
// how to store faster: st4 / transpose /
c[0] = acc0;
c[4] = acc1;
c[4 * 2] = acc2;
c[4 * 3] = acc3;
c[4 * 4] = acc4;
c[4 * 5] = acc5;
c[4 * 6] = acc6;
c[4 * 7] = acc7;
}
ie += 8;
a += 8;
}
if (eSize & 0x04) {
const int* dataOffset = dataOffsetMap;
const int diff = *dataOffset++;
// const float* a = blockA + diff;
a += diff;
const float* w = B;
float* blockC = C + (ie << 2);
const unsigned int* nnz = NNZMap;
size_t ih = 0;
for (; ih < (h & (~0x03)); ih += sparseBlockOC) {
auto ihPack = ih >> 2;
auto c = blockC + ihPack * cStride;
float initValue[4] = {0, 0, 0, 0};
if (nullptr != bias) {
memcpy(initValue, bias + ih, 4 * sizeof(float));
}
float acc0[4];
float acc1[4];
float acc2[4];
float acc3[4];
memcpy(acc0, initValue, 4 * sizeof(float));
memcpy(acc1, initValue, 4 * sizeof(float));
memcpy(acc2, initValue, 4 * sizeof(float));
memcpy(acc3, initValue, 4 * sizeof(float));
const int lElement = *nnz++;
for (auto il = 0; il < lElement; il++) {
const int diff = *dataOffset++;
const float a0 = a[0];
const float a1 = a[1];
const float a2 = a[2];
const float a3 = a[3];
const float wv[4] = {*w++, *w++, *w++, *w++};
// MNN_PRINT("16-loop: ie:%zu, a offset:%ld, w offset:%ld, c offset:%ld, w value:%f, a value[0-15]:", ie, a - A, w - B - 1, c - C, oneW);
// formatMatrix(a, {16});
// MNN_PRINT("\n");
a = a + diff;
for (int lane = 0; lane < 4; lane++) {
acc0[lane] += a0 * wv[lane];
acc1[lane] += a1 * wv[lane];
acc2[lane] += a2 * wv[lane];
acc3[lane] += a3 * wv[lane];
}
}
for (int lane = 0; lane < 4; lane++) {
acc0[lane] = std::max(std::min(maxValue, acc0[lane]), minValue);
acc1[lane] = std::max(std::min(maxValue, acc1[lane]), minValue);
acc2[lane] = std::max(std::min(maxValue, acc2[lane]), minValue);
acc3[lane] = std::max(std::min(maxValue, acc3[lane]), minValue);
}
memcpy(c, acc0, 4 * sizeof(float)); // store continuous c
memcpy(c + 4, acc1, 4 * sizeof(float));
memcpy(c + 4 * 2, acc2, 4 * sizeof(float));
memcpy(c + 4 * 3, acc3, 4 * sizeof(float));
}
blockC += (ih >> 2) * cStride;
for (; ih < h; ih++) {
auto ihSubIndex = ih & 0x03;
auto c = blockC + ihSubIndex;
const float initValue = nullptr != bias ? bias[ih] : 0;
float acc0 = initValue;
float acc1 = initValue;
float acc2 = initValue;
float acc3 = initValue;
const int lElement = *nnz++;
for (auto il = 0; il < lElement; il++) {
const int diff = *dataOffset++;
const float a0 = a[0];
const float a1 = a[1];
const float a2 = a[2];
const float a3 = a[3];
const float oneW = *w++;
// MNN_PRINT("4-loop: ie:%zu, a offset:%ld, w offset:%ld, c offset:%ld, w value:%f, a value[0-3]:", ie, a - A, w - B - 1, c - C, oneW);
// formatMatrix(a, {4});
// MNN_PRINT("\n");
a = a + diff;
acc0 += a0 * oneW;
acc1 += a1 * oneW;
acc2 += a2 * oneW;
acc3 += a3 * oneW;
}
acc0 = std::max(std::min(maxValue, acc0), minValue);
acc1 = std::max(std::min(maxValue, acc1), minValue);
acc2 = std::max(std::min(maxValue, acc2), minValue);
acc3 = std::max(std::min(maxValue, acc3), minValue);
// how to store faster: st4 / transpose /
c[0] = acc0;
c[4] = acc1;
c[4 * 2] = acc2;
c[4 * 3] = acc3;
}
ie += 4;
a += 4;
}
if (eSize & 0x02) {
const int* dataOffset = dataOffsetMap;
const int diff = *dataOffset++;
// const float* a = blockA + diff;
a += diff;
const float* w = B;
float* blockC = C + (ie << 2);
const unsigned int* nnz = NNZMap;
size_t ih = 0;
for (; ih < (h & (~0x03)); ih += sparseBlockOC) {
auto ihPack = ih >> 2;
auto c = blockC + ihPack * cStride;
float initValue[4] = {0, 0, 0, 0};
if (nullptr != bias) {
memcpy(initValue, bias + ih, 4 * sizeof(float));
}
float acc0[4];
float acc1[4];
memcpy(acc0, initValue, 4 * sizeof(float));
memcpy(acc1, initValue, 4 * sizeof(float));
const int lElement = *nnz++;
for (auto il = 0; il < lElement; il++) {
const int diff = *dataOffset++;
const float a0 = a[0];
const float a1 = a[1];
const float wv[4] = {*w++, *w++, *w++, *w++};
// MNN_PRINT("16-loop: ie:%zu, a offset:%ld, w offset:%ld, c offset:%ld, w value:%f, a value[0-15]:", ie, a - A, w - B - 1, c - C, oneW);
// formatMatrix(a, {16});
// MNN_PRINT("\n");
a = a + diff;
for (int lane = 0; lane < 4; lane++) {
acc0[lane] += a0 * wv[lane];
acc1[lane] += a1 * wv[lane];
}
}
for (int lane = 0; lane < 4; lane++) {
acc0[lane] = std::max(std::min(maxValue, acc0[lane]), minValue);
acc1[lane] = std::max(std::min(maxValue, acc1[lane]), minValue);
}
memcpy(c, acc0, 4 * sizeof(float)); // store continuous c
memcpy(c + 4, acc1, 4 * sizeof(float));
}
blockC += (ih >> 2) * cStride;
for (; ih < h; ih++) {
auto ihPack = ih >> 2;
auto ihSubIndex = ih & 0x03;
auto c = blockC + ihSubIndex;
const float initValue = nullptr != bias ? bias[ih] : 0;
float acc0 = initValue;
float acc1 = initValue;
const int lElement = *nnz++;
for (auto il = 0; il < lElement; il++) {
const int diff = *dataOffset++;
const float a0 = a[0];
const float a1 = a[1];
const float oneW = *w++;
// MNN_PRINT("2-loop: ie:%zu, a offset:%ld, w offset:%ld, c offset:%ld, w value:%f, a value[0-1]:", ie, a - A, w - B - 1, c - C, oneW);
// formatMatrix(a, {2});
// MNN_PRINT("\n");
a = a + diff;
acc0 += a0 * oneW;
acc1 += a1 * oneW;
}
acc0 = std::max(std::min(maxValue, acc0), minValue);
acc1 = std::max(std::min(maxValue, acc1), minValue);
// how to store faster: st4 / transpose /
c[0] = acc0;
c[4] = acc1;
}
ie += 2;
a += 2;
}
if (eSize & 0x01) {
const int* dataOffset = dataOffsetMap;
const int diff = *dataOffset++;
// const float* a = blockA + diff;
a += diff;
const float* w = B;
float* blockC = C + (ie << 2);
const unsigned int* nnz = NNZMap;
size_t ih = 0;
for (; ih < (h & (~0x03)); ih += sparseBlockOC) {
auto ihPack = ih >> 2;
auto c = blockC + ihPack * cStride;
float initValue[4] = {0, 0, 0, 0};
if (nullptr != bias) {
memcpy(initValue, bias + ih, 4 * sizeof(float));
}
float acc0[4];
memcpy(acc0, initValue, 4 * sizeof(float));
const int lElement = *nnz++;
for (auto il = 0; il < lElement; il++) {
const int diff = *dataOffset++;
const float a0 = a[0];
const float wv[4] = {*w++, *w++, *w++, *w++};
// MNN_PRINT("16-loop: ie:%zu, a offset:%ld, w offset:%ld, c offset:%ld, w value:%f, a value[0-15]:", ie, a - A, w - B - 1, c - C, oneW);
// formatMatrix(a, {16});
// MNN_PRINT("\n");
a = a + diff;
for (int lane = 0; lane < 4; lane++) {
acc0[lane] += a0 * wv[lane];
}
}
for (int lane = 0; lane < 4; lane++) {
acc0[lane] = std::max(std::min(maxValue, acc0[lane]), minValue);
}
memcpy(c, acc0, 4 * sizeof(float)); // store continuous c
}
blockC += (ih >> 2) * cStride;
for (; ih < h; ih++) {
auto ihSubIndex = ih & 0x03;
auto c = blockC + ihSubIndex;
const float initValue = nullptr != bias ? bias[ih] : 0;
float acc0 = initValue;
const int lElement = *nnz++;
for (auto il = 0; il < lElement; il++) {
const int diff = *dataOffset++;
const float a0 = a[0];
const float oneW = *w++;
// MNN_PRINT("1-loop: ie:%zu, a offset:%ld, c offset:%ld, w offset:%ld, w value:%f, a value[0]:", ie, a - A, w - B - 1, c - C, oneW);
// formatMatrix(a, {1});
// MNN_PRINT("\n");
a = a + diff;
acc0 += a0 * oneW;
}
acc0 = std::max(std::min(maxValue, acc0), minValue);
// how to store faster: st4 / transpose /
c[0] = acc0;
}
ie += 1;
// a += 1;
}
return;
}
#endif
#ifndef MNN_USE_SSE
#ifndef MNN_USE_NEON
void MNNTranspose32Bit(int32_t* dstO, const int32_t* srcO, int32_t* dim) {
int w = dim[0];
int h = dim[1];
int srcStride = dim[2];
int dstStride = dim[3];
for (int i=0; i<h; ++i) {
auto si = srcO + i;
auto di = dstO + i * dstStride;
for (int j=0; j<w; ++j) {
auto sj = si + j * srcStride;
auto dj = di + j;
*dj = *sj;
}
}
}
void MNNTranspose16Bit(int16_t* dstO, const int16_t* srcO, int32_t* dim) {
int w = dim[0];
int h = dim[1];
int srcStride = dim[2];
int dstStride = dim[3];
for (int i=0; i<h; ++i) {
auto si = srcO + i;
auto di = dstO + i * dstStride;
for (int j=0; j<w; ++j) {
auto sj = si + j * srcStride;
auto dj = di + j;
*dj = *sj;
}
}
}
#endif
void MNNFunctionInit() {
// Do nothing
}
#endif
#ifdef MNN_USE_NEON
#include <arm_neon.h>
#endif
#define UNIT 4
using Vec4 = MNN::Math::Vec<float, 4>;
#ifndef MNN_USE_NEON
#ifndef MNN_USE_SSE
void MNNCopyC4WithStride(const float* source, float* dest, size_t srcStride, size_t dstStride, size_t count) {
for (int i = 0; i < count; ++i) {
auto s = source + i * srcStride;
auto d = dest + i * dstStride;
for (int j = 0; j < 4; ++j) {
d[j] = s[j];
}
}
}
void MNNAddC4WithStride(const float* source, float* dest, size_t srcStride, size_t dstStride, size_t count) {
for (int i = 0; i < count; ++i) {
auto s = source + i * srcStride;
auto d = dest + i * dstStride;
for (int j = 0; j < 4; ++j) {
d[j] += s[j];
}
}
}
void MNNReluWithSlopeChannel(float* dst, const float* src, const float* slope, size_t sizeQuad, size_t depthQuad) {
for (int j = 0; j < depthQuad; j++) {
const float* slopeZ = slope + 4 * j;
const float* srcZ = src + 4 * j * sizeQuad;
float* dstZ = dst + 4 * j * sizeQuad;
for (int i = 0; i < sizeQuad; i++) {
for (int c = 0; c < 4; c++) {
if (srcZ[4 * i + c] < 0) {
dstZ[4 * i + c] = srcZ[4 * i + c] * slopeZ[c];
} else {
dstZ[4 * i + c] = srcZ[4 * i + c];
}
}
}
}
}
void MNNPackC4(float* dst, const float* src, size_t area, size_t depth, int* areaOffset) {
MNNPackC4Common<float>(dst, src, area, depth, areaOffset);
}
void MNNUnpackC4(float* dst, const float* src, size_t area, size_t depth, int* areaOffset) {
MNNUnpackC4Common<float>(dst, src, area, depth, areaOffset);
}
void MNNExpC8(float* dest, const float* source, float* offset, const float* parameters, size_t countC8) {
auto count = countC8 * 8;
auto param = parameters[0];
float xLimit = 87;
float summer = offset[3];
for (int i = 0; i < count; ++i) {
auto x = source[i] * offset[0] + offset[2];
x = ALIMAX(x, -xLimit);
x = ALIMIN(x, xLimit);
int div = (x * parameters[1]);
int div2 = (div + 127) << 23;
auto xReamin = x - div * param;
float expBasic = *(float*)(&div2);
auto t = xReamin * 0.25f;
auto expRemain =
((((parameters[7] * t + parameters[6]) * t + parameters[5]) * t + parameters[4]) * t + 1.0f) * t +
1.0f;
expRemain = expRemain * expRemain;
expRemain = expRemain * expRemain;
dest[i] = expBasic * expRemain + offset[1];
summer+= dest[i];
}
offset[3] = summer;
}
void MNNSoftmax(float* dest, const float* source, size_t size) {
float maxValue = ALIMAX(source[0], source[1]);
for (int i = 2; i < size; ++i) {
maxValue = ALIMAX(maxValue, source[i]);
}
float xLimit = 87, param = 0.6931471805599453, sumValue = 0.f;
for (int i = 0; i < size; ++i) {
auto x = source[i] - maxValue;
x = x > -xLimit ? x : -xLimit;
x = x < xLimit ? x : xLimit;
int div = (x / param);
int div2 = (div + 127) << 23;
auto xReamin = x - div * param;
float expBasic = *(float*)(&div2);
auto t = xReamin;
auto expRemain = ((((1.0f / 120 * t + 1.0f / 24) * t + 1.0f / 6) * t + 0.5f) * t + 1.0f) * t + 1.0f;
dest[i] = expBasic * expRemain;
sumValue += dest[i];
}
sumValue = 1.f / sumValue;
for (int i = 0; i < size; ++i) {
dest[i] *= sumValue;
}
}
void MNNReluInt8(int8_t* dst, const int8_t* src, size_t size, ssize_t zeroPoint) {
for (int i = 0; i < size; ++i) {
if (src[i] < zeroPoint) {
dst[i] = zeroPoint;
} else {
dst[i] = src[i];
}
}
}
#endif // no MNN_USE_SSE
void MNNMaxFloat(float* input, float* maxBuffer, int32_t inputCountUnit) {
for (int i = 0; i < inputCountUnit; i++) {
for (int j = 0; j < UNIT; j++) {
for (int m = 0; m < 2; m++) {
maxBuffer[j] = std::max(input[i * UNIT * 2 + j * 2 + m], maxBuffer[j]);
}
}
}
}
void MNNMinFloat(float* input, float* minBuffer, int32_t inputCountUnit) {
for (int i = 0; i < inputCountUnit; i++) {
for (int j = 0; j < UNIT; j++) {
for (int m = 0; m < 2; m++) {
minBuffer[j] = std::min(input[i * UNIT * 2 + j * 2 + m], minBuffer[j]);
}
}
}
}
void MNNScaleAndAddBias(float* dst, const float* src, const float* bias, const float* alpha, size_t planeNumber,
size_t biasNumber) {
for (int z = 0; z < biasNumber; ++z) {
float* dstZ = dst + planeNumber * 4 * z;
const float* srcZ = src + planeNumber * 4 * z;
auto biasZ = Vec4::load(bias + 4 * z);
auto alphaZ = Vec4::load(alpha + 4 * z);
for (int p = 0; p < planeNumber; ++p) {
float* dstX = dstZ + 4 * p;
const float* srcX = srcZ + 4 * p;
Vec4::save(dstX, (Vec4::load(srcX) * alphaZ) + biasZ);
}
}
}
void MNNUInt8ToInt16WithOffsetC4Common(int16_t* dst, const uint8_t* src, size_t zeroPoint, size_t sizeQuad,
size_t dstStride, size_t srcStride) {
dstStride /= sizeof(int16_t);
srcStride /= sizeof(uint8_t);
for (int z = 0; z < sizeQuad; ++z) {
auto dstZ = dst + dstStride * z;
auto srcZ = src + srcStride * z;
for (int j = 0; j < 4; ++j) {
dstZ[j] = (int16_t)((int32_t)srcZ[j] - (int32_t)zeroPoint);
}
}
}
void MNNUInt8ToInt16WithOffsetC4Fast(int16_t* colAddr, const uint8_t* srcStart, size_t zeroPoint, size_t sizeQuad,
size_t depthQuad, size_t dstZStep, size_t srcZStep) {
dstZStep /= sizeof(int16_t);
srcZStep /= sizeof(uint8_t);
for (int sz = 0; sz < depthQuad; ++sz) {
auto dstZ = colAddr + sz * dstZStep;
auto srcZ = srcStart + sz * srcZStep;
MNNUInt8ToInt16WithOffsetC4Common(dstZ, srcZ, zeroPoint, sizeQuad, 4 * sizeof(int16_t), 4 * sizeof(uint8_t));
}
}
void MNNPowC8(float* dest, const float* source, const float* powfParam, size_t betaInt, size_t countC8) {
const int count = countC8 * 8;
const float powfConstant = powfParam[6];
for (int i = 0; i < count; ++i) {
float result = 1, x, xInv = 1 / source[i];
for (int j = 0; j < betaInt; result *= xInv, ++j)
;
for (x = source[i]; x >= 1.25; x /= 1.5, result *= powfConstant)
;
float t = x - 1;
float powRemain =
powfParam[0] +
t * (powfParam[1] + t * (powfParam[2] + t * (powfParam[3] + t * (powfParam[4] + t * powfParam[5]))));
result *= powRemain;
dest[i] = result;
}
}
#endif // no MNN_USE_NEON
void MNNGridSampleComputeCord(float* dst, const float* src, size_t inH, size_t inW, size_t outH, size_t outW, bool alignCorners) {
float a = alignCorners ? 1.0f : 0.0f;
float b = alignCorners ? 0.0f : 1.0f;
int area = outH * outW;
float kx = 0.5f * ((float)inW - a);
float bx = 0.5f * ((float)inW - a - b);
float ky = 0.5f * ((float)inH - a);
float by = 0.5f * ((float)inH - a - b);
for (int w = 0; w < area; ++w) {
auto x = src[2 * w + 0];
auto y = src[2 * w + 1];
dst[2 * w + 0] = kx * x + bx;
dst[2 * w + 1] = ky * y + by;
}
}
void MNNGridSampleComputeCord3D(float* dst, const float* src, size_t inD, size_t inH, size_t inW, size_t outD, size_t outH, size_t outW, bool alignCorners) {
int strideD = outH * outW * 3;
int strideH = outW * 3;
float a = alignCorners ? 1.0f : 0.0f;
float b = alignCorners ? 0.0f : 1.0f;
int area = outD * outH * outW;
float kx = 0.5f * ((float)inW - a);
float bx = 0.5f * ((float)inW - a - b);
float ky = 0.5f * ((float)inH - a);
float by = 0.5f * ((float)inH - a - b);
float kz = 0.5f * ((float)inD - a);
float bz = 0.5f * ((float)inD - a - b);
for (int w=0; w<area; ++w) {
auto x = src[3 * w + 0];
auto y = src[3 * w + 1];
auto z = src[3 * w + 2];
dst[3 * w + 0] = kx * x + bx;
dst[3 * w + 1] = ky * y + by;
dst[3 * w + 2] = kz * z + bz;
}
}
#ifndef MNN_USE_SSE
void MNNNorm(float *dst, const float *src, const float *gamma, const float *beta, float epsilon, size_t size, bool RMSNorm) {
float mean = 0;
if(false == RMSNorm){
float sum = 0.f;
for (int j = 0; j < size; ++j) {
sum += src[j];
}
mean = sum / size;
}
float square_sum = 0.f;
for (int j = 0; j < size; ++j) {
square_sum += (src[j] - mean) * (src[j] - mean);
}
#ifdef __aarch64__
auto vs = vadd_f32(vdiv_f32(vdup_n_f32(square_sum), vdup_n_f32(size)), vdup_n_f32(epsilon));
auto vecs = vdiv_f32(vdup_n_f32(1.0f), vsqrt_f32(vs));
float vars[2];
vst1_f32(vars, vecs);
float variable = vars[0];
#else
float variable = square_sum / size;
variable = 1.f / std::sqrt(variable + epsilon);
#endif
if (gamma && beta) {
for (int j = 0; j < size; ++j) {
dst[j] = (src[j] - mean) * variable * gamma[j] + beta[j];
}
} else {
for (int j = 0; j < size; ++j) {
dst[j] = (src[j] - mean) * variable;
}
}
}
#endif
void MNNRoiPoolingMax(float* dst, const float* src, int hLen, int wLen, int iw) {
Vec4 max = Vec4(-FLT_MAX);
for (int h = 0; h < hLen; h++, src += iw * UNIT) {
for (int w = 0; w < wLen; w++) {
Vec4 in = Vec4::load(src + w * UNIT);
max = Vec4::max(max, in);
}
}
Vec4::save(dst, max);
}
void MNNRoiAlignMax(float* dst, const float* src, const std::vector<std::vector<int>> &vecPos, const std::vector<std::vector<float>> &vecArea, int samplingRatioArea, int pooledHeight, int pooledWidth) {
for (int h = 0; h < pooledHeight; ++h, dst += pooledWidth * UNIT) {
int preCalcIdx = h * pooledWidth * samplingRatioArea;
for (int w = 0; w < pooledWidth; ++w) {
Vec4 res = Vec4(-FLT_MAX);
for (int i = 0; i < samplingRatioArea; ++i) {
const std::vector<int>& pos = vecPos[preCalcIdx];
const std::vector<float>& area = vecArea[preCalcIdx];
Vec4 val0 = Vec4::load(src + pos[0] * UNIT);
Vec4 val1 = Vec4::load(src + pos[1] * UNIT);
Vec4 val2 = Vec4::load(src + pos[2] * UNIT);
Vec4 val3 = Vec4::load(src + pos[3] * UNIT);
Vec4 mla = val0 * area[0];
mla = Vec4::fma(mla, val1, area[1]);
mla = Vec4::fma(mla, val2, area[2]);
mla = Vec4::fma(mla, val3, area[3]);
res = Vec4::max(res, mla);
preCalcIdx++;
}
Vec4::save(dst + w * UNIT, res);
}
}
}
void MNNRoiAlignAvg(float* dst, const float* src, const std::vector<std::vector<int>> &vecPos, const std::vector<std::vector<float>> &vecArea, int samplingRatioArea, int pooledHeight, int pooledWidth) {
float invSamplingCnt = 1.f / samplingRatioArea;
for (int h = 0; h < pooledHeight; ++h, dst += pooledWidth * UNIT) {
int preCalcIdx = h * pooledWidth * samplingRatioArea;
for (int w = 0; w < pooledWidth; ++w) {
Vec4 res = Vec4(0.f);
for (int i = 0; i < samplingRatioArea; ++i) {
const std::vector<int>& pos = vecPos[preCalcIdx];
const std::vector<float>& area = vecArea[preCalcIdx];
Vec4 val0 = Vec4::load(src + pos[0] * UNIT);
Vec4 val1 = Vec4::load(src + pos[1] * UNIT);
Vec4 val2 = Vec4::load(src + pos[2] * UNIT);
Vec4 val3 = Vec4::load(src + pos[3] * UNIT);
Vec4 mla = val0 * area[0];
mla = Vec4::fma(mla, val1, area[1]);
mla = Vec4::fma(mla, val2, area[2]);
mla = Vec4::fma(mla, val3, area[3]);
res += mla;
preCalcIdx++;
}
res = res * invSamplingCnt;
Vec4::save(dst + w * UNIT, res);
}
}
}
void MNNPackC4Uint8(uint8_t* dst, const uint8_t* src, size_t area,size_t depth, int* areaOffset) {
MNNPackC4Common(dst, src, area, depth, areaOffset);
}
void MNNUnpackC4Uint8(uint8_t* dst, const uint8_t* src, size_t area,size_t depth, int* areaOffset) {
MNNUnpackC4Common(dst, src, area, depth, areaOffset);
}
void MNNUnpackTransposeUint8(uint8_t* dst, const uint8_t* src, size_t area,size_t depth, int* areaOffset) {
if (depth == 4) {
::memcpy(dst, src, area * depth * sizeof(uint8_t));
return;
}
#ifdef MNN_USE_NEON
if (depth == 3) {
uint8x16x4_t rgba;
rgba.val[3] = vdupq_n_u8(0);
int sta = 0;
int staC16 = (int)area / 16;
for (int i = 0; i < staC16; sta += 16, ++i) {
auto rgb = vld3q_u8(src + sta * 3);
rgba.val[0] = rgb.val[0];
rgba.val[1] = rgb.val[1];
rgba.val[2] = rgb.val[2];
vst4q_u8(dst + 4 * sta, rgba);
}
sta = staC16 * 16;
for (; sta < area; ++sta) {
auto s = src + sta * 3;
auto d = dst + sta * 4;
d[0] = s[0];
d[1] = s[1];
d[2] = s[2];
d[3] = 0;
}
return;
}
if (depth == 1) {
uint8x16x4_t rgba;
rgba.val[1] = vdupq_n_u8(0);
rgba.val[2] = vdupq_n_u8(0);
rgba.val[3] = vdupq_n_u8(0);
int sta = 0;
for (; sta < area; sta += 16) {
rgba.val[0] = vld1q_u8(src + sta);
vst4q_u8(dst + 4 * sta, rgba);
}
for (; sta < area; ++sta) {
auto s = src + sta;
auto d = dst + sta * 4;
d[0] = s[0];
d[1] = 0;
d[2] = 0;
d[3] = 0;
}
return;
}
#endif
int c = (int)depth;
int cDiv4 = c / 4;
int cAlign = cDiv4 * 4;
if (cAlign == c) {
for (int hi = 0; hi < area; ++hi) {
auto srcHeight = reinterpret_cast<const int32_t*>(src + hi * c);
auto dstHeight = reinterpret_cast<int32_t*>(dst + hi * 4);
for (int ci = 0; ci < cDiv4; ++ci) {
dstHeight[ci * areaOffset[1]] = srcHeight[ci];
}
}
return;
} else {
for (int hi = 0; hi < area; ++hi) {
auto srcHeight = src + hi * c;
auto dstHeight = dst + hi * 4;
for (int ci = 0; ci < cDiv4; ++ci) {
dstHeight[ci * areaOffset[1] * 4 + 0] = srcHeight[ci * 4 + 0];
dstHeight[ci * areaOffset[1] * 4 + 1] = srcHeight[ci * 4 + 1];
dstHeight[ci * areaOffset[1] * 4 + 2] = srcHeight[ci * 4 + 2];
dstHeight[ci * areaOffset[1] * 4 + 3] = srcHeight[ci * 4 + 3];
}
}
}
int cReamin = c - cAlign;
auto srcAlign = src + cAlign;
auto dstAlign = dst + areaOffset[1] * cAlign;
for (int hi = 0; hi < area; ++hi) {
auto srcHeight = srcAlign + hi * c;
auto dstHeight = dstAlign + hi * 4;
for (int i = 0; i < 4; ++i) {
dstHeight[i] = 0;
}
for (int ci = 0; ci < cReamin; ++ci) {
dstHeight[ci] = srcHeight[ci];
}
}
}
void MNNUnpackTranspose(float* dst, const float* src, size_t area, size_t depth, int* areaOffset) {
int srcAreaOffset = areaOffset[0];
int dstAreaOffset = areaOffset[1];
#ifdef MNN_USE_NEON
if (1 == depth) {
auto zeroValue = vmovq_n_f32(0.0f);
int areaC4 = (int)area / 4;
int remain = areaC4 * 4;
for (int i = 0; i < areaC4; ++i) {
auto srcCur = src + 4 * i;
auto dstCur = dst + 16 * i;
auto srcValue = vld1q_f32(srcCur);
float32x4x4_t dstValue;
dstValue.val[0] = srcValue;
dstValue.val[1] = zeroValue;
dstValue.val[2] = zeroValue;
dstValue.val[3] = zeroValue;
vst4q_f32(dstCur, dstValue);
}
for (int i = remain; i < area; ++i) {
dst[4 * i + 0] = src[i];
dst[4 * i + 1] = 0.0f;
dst[4 * i + 2] = 0.0f;
dst[4 * i + 3] = 0.0f;
}
return;
}
if (3 == depth) {
auto zeroValue = vmovq_n_f32(0.0f);
int areaC4 = (int)area / 4;
int remain = areaC4 * 4;
for (int i = 0; i < areaC4; ++i) {
auto srcCur = src + 12 * i;
auto dstCur = dst + 16 * i;
auto srcValue = vld3q_f32(srcCur);
float32x4x4_t dstValue;
dstValue.val[0] = srcValue.val[0];
dstValue.val[1] = srcValue.val[1];
dstValue.val[2] = srcValue.val[2];
dstValue.val[3] = zeroValue;
vst4q_f32(dstCur, dstValue);
}
for (int i = remain; i < area; ++i) {
dst[4 * i + 0] = src[3 * i + 0];
dst[4 * i + 1] = src[3 * i + 1];
dst[4 * i + 2] = src[3 * i + 2];
dst[4 * i + 3] = 0.0f;
}
return;
}
#endif
int c = (int)depth;
int cDiv4 = c / 4;
int cAlign = cDiv4 * 4;
for (int hi = 0; hi < area; ++hi) {
const float* srcHeight = src + hi * c;
float* dstHeight = dst + hi * 4;
for (int ci = 0; ci < cDiv4; ++ci) {
Vec4::save(dstHeight + 4 * ci * dstAreaOffset, Vec4::load(srcHeight + 4 * ci));
}
}
if (cAlign == c) {
return;
}
int cReamin = c - cAlign;
auto srcAlign = src + cAlign;
auto dstAlign = dst + dstAreaOffset * cAlign;
#ifdef MNN_USE_NEON
auto zeroVector = vdupq_n_f32(0.0f);
#endif
for (int hi = 0; hi < area; ++hi) {
const float* srcHeight = srcAlign + hi * c;
float* dstHeight = dstAlign + hi * 4;
#ifdef MNN_USE_NEON
vst1q_f32(dstHeight, zeroVector);
#else
for (int i = 0; i < 4; ++i) {
dstHeight[i] = 0;
}
#endif
for (int ci = 0; ci < cReamin; ++ci) {
dstHeight[ci] = srcHeight[ci];
}
}
}
void MNNPackTransposeUint8(uint8_t* dst, const uint8_t* src, size_t area,size_t depth, int* areaOffset) {
int c = (int)depth;
int cDiv4 = c / 4;
int cAlign = cDiv4 * 4;
if (cAlign == c) {
int32_t* dst32 = (int32_t*)dst;
const int32_t* src32 = (int32_t*)src;
for (int hi = 0; hi < area; ++hi) {
auto srcHeight = src32 + hi;
auto dstHeight = dst32 + hi * cDiv4;
for (int ci = 0; ci < cDiv4; ++ci) {
dstHeight[ci] = srcHeight[ci * areaOffset[0]];
}
}
return;
}
for (int hi = 0; hi < area; ++hi) {
auto srcHeight = src + hi * 4;
auto dstHeight = dst + hi * c;
for (int ci = 0; ci < cDiv4; ++ci) {
for (int i = 0; i < 4; ++i) {
dstHeight[ci * 4 + i] = srcHeight[4 * ci * areaOffset[0] + i];
}
}
}
int cReamin = c - cAlign;
auto srcAlign = src + areaOffset[0] * cAlign;
auto dstAlign = dst + cAlign;
for (int hi = 0; hi < area; ++hi) {
auto srcHeight = srcAlign + hi * 4;
auto dstHeight = dstAlign + hi * c;
for (int ci = 0; ci < cReamin; ++ci) {
dstHeight[ci] = srcHeight[ci];
}
}
}
void MNNPackTranspose(float* dst, const float* src, size_t area, size_t depth, int* areaOffset) {
#if defined(MNN_USE_NEON)
if (3 == depth) {
int areaC4 = (int)area / 4;
int remain = areaC4 * 4;
for (int i = 0; i < areaC4; ++i) {
auto srcCur = src + 16 * i;
auto dstCur = dst + 12 * i;
auto srcValue = vld4q_f32(srcCur);
float32x4x3_t dstValue;
dstValue.val[0] = srcValue.val[0];
dstValue.val[1] = srcValue.val[1];
dstValue.val[2] = srcValue.val[2];
vst3q_f32(dstCur, dstValue);
}
for (int i = remain; i < area; ++i) {
dst[3 * i + 0] = src[4 * i + 0];
dst[3 * i + 1] = src[4 * i + 1];
dst[3 * i + 2] = src[4 * i + 2];
}
return;
}
#elif defined(MNN_USE_SSE)
if (3 == depth) {
if (area < 1) return;
for (int i = 0; i < area - 1; ++i) {
auto srcValue = Vec4::load(src + 4 * i);
Vec4::save(dst + 3 * i, srcValue);
}
for (int i = 0; i < 3; ++i) {
dst[3 * (area - 1) + i] = src[4 * (area - 1) + i];
}
return;
}
#endif
int c = (int)depth;
int cDiv4 = c / 4;
int cAlign = cDiv4 * 4;
auto srcArea = areaOffset[0];
for (int hi = 0; hi < area; ++hi) {
const float* srcHeight = src + hi * 4;
float* dstHeight = dst + hi * c;
for (int ci = 0; ci < cDiv4; ++ci) {
Vec4::save(dstHeight + 4 * ci, Vec4::load(srcHeight + 4 * ci * srcArea));
}
}
if (cAlign == c) {
return;
}
int cReamin = c - cAlign;
auto srcAlign = src + srcArea * cAlign;
auto dstAlign = dst + cAlign;
for (int hi = 0; hi < area; ++hi) {
const float* srcHeight = srcAlign + hi * 4;
float* dstHeight = dstAlign + hi * c;
for (int ci = 0; ci < cReamin; ++ci) {
dstHeight[ci] = srcHeight[ci];
}
}
}
void MNNExp(float* dst, const float* src, float* offset, size_t dataSize) {
int countC8 = static_cast<int32_t>(dataSize) / 8;
int remain = static_cast<int32_t>(dataSize) % 8;
static const float parameters[] = {
(float)logf(2.0f), 1.0f / (float)logf(2.0f), 0.25f, 1.0f, 0.5f, 1.0f / 6.0f, 1.0f / 24.0f, 1.0f / 120.0f};
if (countC8 > 0) {
// Align to eight so asm is easier to write
MNNExpC8(dst, src, offset, parameters, countC8);
}
if (remain > 0) {
auto param = parameters[0];
float xLimit = 87;
float summer = offset[3];
auto source = src + countC8 * 8;
auto dest = dst + countC8 * 8;
for (int i = 0; i < remain; ++i) {
auto x = source[i] * offset[0] + offset[2];
x = ALIMAX(x, -xLimit);
x = ALIMIN(x, xLimit);
int div = (x * parameters[1]);
int div2 = (div + 127) << 23;
auto xReamin = x - div * param;
float expBasic = *(float*)(&div2);
auto t = xReamin * 0.25f;
auto expRemain =
((((parameters[7] * t + parameters[6]) * t + parameters[5]) * t + parameters[4]) * t + 1.0f) * t +
1.0f;
expRemain = expRemain * expRemain;
expRemain = expRemain * expRemain;
dest[i] = expBasic * expRemain + offset[1];
summer+= dest[i];
}
offset[3] = summer;
}
}
// Lambert's series with 7 divisions
// reference from
// https://varietyofsound.wordpress.com/2011/02/14/efficient-tanh-computation-using-lamberts-continued-fraction/
inline float tanhf_poly(float value) {
if (value > 5.0) {
return 1.0;
} else if (value <= -5.0) {
return -1.0;
} else {
float x2 = value * value;
float a = value * (135135.0f + x2 * (17325.0f + x2 * (378.0f + x2)));
float b = 135135.0f + x2 * (62370.0f + x2 * (3150.0f + x2 * 28.0f));
return a / b;
}
}
void MNNTanh(float* dst, const float* src, size_t dataSize) {
/* Origin Code
for (int i = 0; i < dataSize; i++) {
// outputData[i] = 1 - 2 / (expf(2 * inputData[i]) + 1);
dst[i] = tanhf_poly(src[i]);
}
*/
float offset[4] = {
-2.0f,
0.0f,
0.0f,
0.0f
};
MNNExp(dst, src, offset, dataSize);
for (int i = 0; i < dataSize; i++) {
// outputData[i] = 1 - 2 / (expf(2 * inputData[i]) + 1);
auto expX2 = dst[i];
dst[i] = (1.0f - expX2) / (1.0f + expX2);
}
}
void MNNReluWithSlope(float* dst, const float* src, size_t sizeQuad, float slope) {
float slopeValue[4];
for (int i=0; i<4; ++i) {
slopeValue[i] = slope;
}
MNNReluWithSlopeChannel(dst, src, slopeValue, sizeQuad, 1);
}
void MNNReluWithSlopeCommon(float* dst, const float* src, size_t size, float slope) {
int sizeQuad = static_cast<int32_t>(size) / 4;
int remain = static_cast<int32_t>(size) % 4;
if (sizeQuad > 0) {
MNNReluWithSlope(dst, src, sizeQuad, slope);
}
if (remain > 0) {
float intmp[4] = {0}, outmp[4] = {0};
::memcpy(intmp, src + sizeQuad * 4, remain * sizeof(float));
MNNReluWithSlope(outmp, intmp, 1, slope);
::memcpy(dst + sizeQuad * 4, outmp, remain * sizeof(float));
}
}
void MNNHardSwishCommon(float* dst, const float* src, size_t size) {
int sizeQuad = static_cast<int32_t>(size / 4);
int remain = static_cast<int32_t>(size) % 4;
#ifdef MNN_USE_SSE
if (sizeQuad > 0) {
MNNHardSwish(dst, src, sizeQuad);
}
if (remain > 0) {
float intmp[4] = {0}, outmp[4] = {0};
::memcpy(intmp, src + sizeQuad * 4, remain * sizeof(float));
MNNHardSwish(outmp, intmp, 1);
::memcpy(dst + sizeQuad * 4, outmp, remain * sizeof(float));
}
#else
#ifdef MNN_USE_NEON
float32x4_t zero = vdupq_n_f32(0.f);
float32x4_t three = vdupq_n_f32(3.f);
float32x4_t six = vdupq_n_f32(6.f);
float32x4_t divsix = vdupq_n_f32(1.0f/6.f);
for (int i = 0; i < sizeQuad; i++) {
auto x = vld1q_f32(src + 4 * i);
auto y = vmulq_f32(vmulq_f32(x, vminq_f32(vmaxq_f32(vaddq_f32(x, three), zero), six)), divsix);
vst1q_f32(dst + 4 * i, y);
}
if (remain > 0) {
float intmp[4] = {0}, outmp[4] = {0};
::memcpy(intmp, src + sizeQuad * 4, remain * sizeof(float));
auto x = vld1q_f32(intmp);
auto y = vmulq_f32(vmulq_f32(x, vminq_f32(vmaxq_f32(vaddq_f32(x, three), zero), six)), divsix);
vst1q_f32(outmp, y);
::memcpy(dst + sizeQuad * 4, outmp, remain * sizeof(float));
}
#else
for (int j = 0; j < size; j++) {
if (src[j] <= -3) {
dst[j] = 0;
} else if (src[j] >= 3){
dst[j] = src[j];
} else {
dst[j] = src[j] * (src[j] + 3) / 6.f;
}
}
#endif
#endif
}
void MNNGeluStandardCommon(float* dst, const float* src, size_t size) {
for (int i = 0; i < size; i++) {
dst[i] = (erf(src[i] * 0.7071067932881648) + 1) * src[i] * 0.5;
}
}
void MNNGeluCommon(float* dst, const float* src, size_t size) {
int sizeQuad = static_cast<int32_t>(size / 8);
int remain = static_cast<int32_t>(size) % 8;
#if defined(MNN_USE_SSE) || defined(MNN_USE_NEON)
float parameters[8] = {0.044715f, 0.79788458f, 378.f, 17325.f, 135135.f, 28.f, 3150.f, 62370.f};
if (sizeQuad > 0) {
MNNGelu(dst, src, sizeQuad, parameters);
}
if (remain > 0) {
float intmp[8] = {0};
float outmp[8] = {0};
::memcpy(intmp, src + 8 * sizeQuad, remain * sizeof(float));
MNNGelu(outmp, intmp, 1, parameters);
::memcpy(dst + 8 * sizeQuad, outmp, remain * sizeof(float));
}
#else
auto tanhf_poly = [](float value) -> float {
if (value > 5.0f) {
return 1.0f;
} else if (value <= -5.0f) {
return -1.0f;
} else {
float x2 = value * value;
float a = value * (135135.0f + x2 * (17325.0f + x2 * (378.0f + x2)));
float b = 135135.0f + x2 * (62370.0f + x2 * (3150.0f + x2 * 28.0f));
return a / b;
}
};
for (int i = 0; i < size; i++) {
float temp = 0.044715f * src[i] * src[i] * src[i];
temp = 0.79788458f * (temp + src[i]);
dst[i] = (1.0f + tanhf_poly(temp)) * src[i] * 0.5f;
}
#endif
}
void MNNScaleAndAddBiasScalar(float* dst, const float* src, float bias, float alpha, size_t number) {
int numberC4 = (int)number / 4;
int start = 0;
if (numberC4 > 0) {
float biasC4[4] = {
bias,
bias,
bias,
bias
};
float alphaC4[4] = {
alpha,
alpha,
alpha,
alpha
};
MNNScaleAndAddBias(dst, src, biasC4, alphaC4, numberC4, 1);
start = numberC4 * 4;
}
for (int i=start; i<number; ++i) {
dst[i] = src[i] * alpha + bias;
}
}
#ifndef MNN_USE_NEON
void MNNAxByClampBroadcastUnit(float* C, const float* A, const float* B, size_t width, size_t cStride, size_t aStride, size_t height, const float* parameters) {
auto minF = Vec4(parameters[2]);
auto maxF = Vec4(parameters[3]);
auto beta = Vec4(parameters[1]);
for (int y = 0; y < height; ++y) {
auto a = A + aStride * y;
auto b = B + 4 * y;
auto bv = Vec4::load(b);
auto c = C + cStride * y;
for (int x = 0; x < width; ++x) {
auto av = Vec4::load(a + 4 * x);
auto cv = av + bv * beta;
cv = Vec4::min(cv, maxF);
cv = Vec4::max(cv, minF);
Vec4::save(c + 4 * x, cv);
}
}
}
void MNNVectorTop1Float(float* input, float* maxValue, int32_t* maxIndex, size_t inputCountUnit) {
float maxV = input[0];
int maxIdx = 0;
for (int i = 0; i < inputCountUnit; i++) {
int offset = i * UNIT;
for (int j = 0; j < UNIT; j++) {
if (input[offset + j] > maxV) {
maxV = input[offset + j];
maxIdx = offset + j;
}
}
}
maxValue[0] = maxV;
maxIndex[0] = maxIdx;
}
void MNNVectorTop1Int32(int32_t* input, int32_t* maxValue, int32_t* maxIndex, size_t inputCountUnit) {
int32_t maxV = input[0];
int maxIdx = 0;
for (int i = 0; i < inputCountUnit; i++) {
int offset = i * UNIT;
for (int j = 0; j < UNIT; j++) {
if (input[offset + j] > maxV) {
maxV = input[offset + j];
maxIdx = offset + j;
}
}
}
maxValue[0] = maxV;
maxIndex[0] = maxIdx;
}
#endif
void MNNComputeMatMulForE_1(const float* A, const float* B, float* C, const float* biasPtr, const MatMulParam* param, size_t tId) {
auto l = param->l;
auto h = param->h;
auto numberThread = param->numberThread;
auto lC4 = l / 4;
auto lR = lC4 * 4;
if (param->BTranspose) {
for (int y=tId; y<h; y+=numberThread) {
Vec4 sumValue = Vec4(0.0f);
auto by = B + y * l;
for (int x=0; x<lC4; ++x) {
sumValue = Vec4::fma(sumValue, Vec4::load(A + x * 4), Vec4::load(by + x * 4));
}
float sumRemain = 0.0f;
for (int x=lR; x<l; ++x) {
sumRemain = sumRemain + A[x] * by[x];
}
if (nullptr != biasPtr) {
sumRemain += biasPtr[y];
}
C[y] = sumRemain + sumValue[0] + sumValue[1] + sumValue[2] + sumValue[3];
}
} else {
auto hC4 = h / 16;
auto hR = hC4 * 16;
for (int y=tId; y<hC4; y+=numberThread) {
auto bs = B + 16 * y;
Vec4 sumValue0 = Vec4(0.0f);
Vec4 sumValue1 = Vec4(0.0f);
Vec4 sumValue2 = Vec4(0.0f);
Vec4 sumValue3 = Vec4(0.0f);
if (biasPtr != nullptr) {
sumValue0 = Vec4::load(biasPtr + 16 * y + 0);
sumValue1 = Vec4::load(biasPtr + 16 * y + 4);
sumValue2 = Vec4::load(biasPtr + 16 * y + 8);
sumValue3 = Vec4::load(biasPtr + 16 * y + 12);
}
auto srcY = A + y * l;
for (int x=0; x<l; ++x) {
auto a = Vec4(A[x]);
sumValue0 = Vec4::fma(sumValue0, a, Vec4::load(bs + h * x));
sumValue1 = Vec4::fma(sumValue1, a, Vec4::load(bs + h * x + 4));
sumValue2 = Vec4::fma(sumValue2, a, Vec4::load(bs + h * x + 8));
sumValue3 = Vec4::fma(sumValue3, a, Vec4::load(bs + h * x + 12));
}
Vec4::save(C + 16 * y, sumValue0);
Vec4::save(C + 16 * y + 4, sumValue1);
Vec4::save(C + 16 * y + 8, sumValue2);
Vec4::save(C + 16 * y + 12, sumValue3);
}
for (int y=hR + tId; y<h; y+=numberThread) {
auto bs = B + y;
float sumValue = 0.0f;
if (biasPtr != nullptr) {
sumValue = biasPtr[y];
}
auto srcY = A + y * l;
for (int x=0; x<l; ++x) {
sumValue = sumValue + A[x] * bs[h * x];
}
C[y] = sumValue;
}
}
}
void MNNComputeMatMulForH_1(const float* A, const float* B, float* C, const float* biasPtr, const MatMulParam* param, size_t tId) {
int e = param->e;
int l = param->l;
int numberThread = param->numberThread;
if (param->ATranspose) {
float biasValue = 0.0f;
if (nullptr != biasPtr) {
biasValue = *biasPtr;
}
auto eC4 = e / 4;
auto eR = eC4 * 4;
for (int y=tId; y<eC4; y+=numberThread) {
Vec4 sumValue = Vec4(biasValue);
auto srcY = A + y * 4;
for (int x=0; x<l; ++x) {
sumValue = sumValue + Vec4::load(srcY + x * e) * Vec4(B[x]);
}
Vec4::save(C + 4 * y, sumValue);
}
if (0 == tId) {
for (int y=eR; y<e; ++y) {
float sumValue = biasValue;
auto srcY = A + y;
for (int x=0; x<l; ++x) {
sumValue = sumValue + srcY[x * e] * B[x];
}
C[y] = sumValue;
}
}
return;
}
float biasValue = 0.0f;
if (nullptr != biasPtr) {
biasValue = *biasPtr;
}
auto lC4 = l / 4;
auto lR = lC4 * 4;
for (int y=tId; y<e; y+=numberThread) {
Vec4 sumValue = Vec4(biasValue);
auto srcY = A + y * l;
for (int x=0; x<lC4; ++x) {
sumValue = sumValue + Vec4::load(srcY + 4 * x) * Vec4::load(B + 4 * x);
}
float sumSingle = sumValue[0] + sumValue[1] + sumValue[2] + sumValue[3];
for (int x=lR; x<l; ++x) {
sumSingle += srcY[x] * B[x];
}
C[y] = sumSingle;
}
}
void MNNPackC4Int16(int16_t* dst, const int16_t* src, size_t area,size_t depth, int* areaOffset) {
MNNPackC4Common(dst, src, area, depth, areaOffset);
}
void MNNUnpackC4Int16(int16_t* dst, const int16_t* src, size_t area,size_t depth, int* areaOffset) {
MNNUnpackC4Common(dst, src, area, depth, areaOffset);
}
void MNNUnpackTransposeInt16(int16_t* dst, const int16_t* src, size_t area,size_t depth, int* areaOffset) {
if (depth == 4) {
::memcpy(dst, src, area * depth * sizeof(int16_t));
return;
}
int c = (int)depth;
int cDiv4 = c / 4;
int cAlign = cDiv4 * 4;
for (int hi = 0; hi < area; ++hi) {
auto srcHeight = (src + hi * c);
auto dstHeight = (dst + hi * 4);
for (int ci = 0; ci < cDiv4; ++ci) {
for (int i = 0; i < 4; ++i) {
dstHeight[ci * areaOffset[1] * 4 + i] = srcHeight[4 * ci + i];
}
}
}
if (cAlign == c) {
return;
}
int cReamin = c - cAlign;
auto srcAlign = src + cAlign;
auto dstAlign = dst + areaOffset[1] * cAlign;
for (int hi = 0; hi < area; ++hi) {
auto srcHeight = srcAlign + hi * c;
auto dstHeight = dstAlign + hi * 4;
for (int i = 0; i < 4; ++i) {
dstHeight[i] = 0;
}
for (int ci = 0; ci < cReamin; ++ci) {
dstHeight[ci] = srcHeight[ci];
}
}
}
void MNNPackTransposeInt16(int16_t* dst, const int16_t* src, size_t area,size_t depth, int* areaOffset) {
int c = (int)depth;
int cDiv4 = c / 4;
int cAlign = cDiv4 * 4;
if (cAlign == c) {
int64_t* dst32 = (int64_t*)dst;
const int64_t* src32 = (int64_t*)src;
for (int hi = 0; hi < area; ++hi) {
auto srcHeight = src32 + hi;
auto dstHeight = dst32 + hi * cDiv4;
for (int ci = 0; ci < cDiv4; ++ci) {
dstHeight[ci] = srcHeight[ci * areaOffset[0]];
}
}
return;
}
for (int hi = 0; hi < area; ++hi) {
auto srcHeight = src + hi * 4;
auto dstHeight = dst + hi * c;
for (int ci = 0; ci < cDiv4; ++ci) {
for (int i = 0; i < 4; ++i) {
dstHeight[ci * 4 + i] = srcHeight[4 * ci * areaOffset[0] + i];
}
}
}
int cReamin = c - cAlign;
auto srcAlign = src + areaOffset[0] * cAlign;
auto dstAlign = dst + cAlign;
for (int hi = 0; hi < area; ++hi) {
auto srcHeight = srcAlign + hi * 4;
auto dstHeight = dstAlign + hi * c;
for (int ci = 0; ci < cReamin; ++ci) {
dstHeight[ci] = srcHeight[ci];
}
}
}
void MNNCopyC4Int16WithStride(const float* sourceF, float* destF, size_t srcStride, size_t dstStride, size_t count) {
auto source = (int16_t*)sourceF;
auto dest = (int16_t*)destF;
for (int i = 0; i < count; ++i) {
auto s = source + i * srcStride;
auto d = dest + i * dstStride;
*(int64_t*)(d) = *((int64_t*)s);
}
}
void MNNSin(float* dst, const float* src, size_t dataSize) {
for (int i = 0; i < dataSize; i++) {
dst[i] = sinf(src[i]);
}
}
void MNNSigmoid(float* dst, const float* src, size_t dataSize) {
float offset[4] = {
-1.0f,
0.0f,
0.0f,
0.0f
};
MNNExp(dst, src, offset, dataSize);
for (int i = 0; i < dataSize; ++i) {
dst[i] = 1.0f / (1.0f + dst[i]);
}
}
void MNNSiLu(float* dst, const float* src, size_t dataSize) {
float offset[4] = {
-1.0f,
0.0f,
0.0f,
0.0f
};
MNNExp(dst, src, offset, dataSize);
for (int i = 0; i < dataSize; ++i) {
dst[i] = src[i] / (1.0f + dst[i]);
}
}
/**
Modified from https://github.com/alibaba/MNN/pull/1359
Thanks for https://github.com/hroken
*/
void MNNSigmoidLowp(float* dst, const float* src, size_t dataSize) {
float offset[4] = {
-1.0f,
0.0f,
0.0f,
0.0f
};
MNNExp(dst, src, offset, dataSize);
#ifdef MNN_USE_NEON
int dataC4 = static_cast<int32_t>(dataSize) / 4;
int remain = static_cast<int32_t>(dataSize) % 4;
float32x4_t value = vdupq_n_f32(1.0f);
if(dataC4 > 0) {
float32x4_t out = vld1q_f32(dst);
// neon optimization for sigmid cpu
for (int i = 1; i < dataC4; ++i) {
out = vrecpeq_f32(vaddq_f32(value,out));
vst1q_f32(dst ,out);
dst += 4;
out = vld1q_f32(dst);
}
out = vrecpeq_f32(vaddq_f32(value,out));
vst1q_f32(dst, out);
dst += 4;
}
if (remain > 0) {
float intmp[4] = {0};
::memcpy(intmp, dst, remain * sizeof(float));
float32x4_t out = vld1q_f32(intmp);
out = vrecpeq_f32(vaddq_f32(value,out));
vst1q_f32(intmp, out);
::memcpy(dst, intmp, remain * sizeof(float));
}
#else
for (int i = 0; i < dataSize; ++i) {
dst[i] = 1.0f / (1.0f + dst[i]);
}
#endif
}
void MNNSiLuLowp(float* dst, const float* src, size_t dataSize) {
float offset[4] = {
-1.0f,
0.0f,
0.0f,
0.0f
};
MNNExp(dst, src, offset, dataSize);
#ifdef __aarch64__
int dataC4 = static_cast<int32_t>(dataSize) / 4;
int remain = static_cast<int32_t>(dataSize) % 4;
float32x4_t one = vdupq_n_f32(1.0f);
if(dataC4 > 0) {
float32x4_t out = vld1q_f32(dst);
float32x4_t in = vld1q_f32(src);
// neon optimization for sigmid cpu
for (int i = 1; i < dataC4; ++i) {
out = vdivq_f32(in, vaddq_f32(one,out));
vst1q_f32(dst ,out);
dst += 4;
src += 4;
out = vld1q_f32(dst);
in = vld1q_f32(src);
}
out = vdivq_f32(in, vaddq_f32(one,out));
vst1q_f32(dst, out);
dst += 4;
src += 4;
}
if (remain > 0) {
float intmp[4] = {0};
float atmp[4] = {0};
::memcpy(intmp, dst, remain * sizeof(float));
::memcpy(atmp, src, remain * sizeof(float));
float32x4_t out = vld1q_f32(intmp);
float32x4_t in = vld1q_f32(atmp);
out = vdivq_f32(in, vaddq_f32(one, out));
vst1q_f32(intmp, out);
::memcpy(dst, intmp, remain * sizeof(float));
}
#else
for (int i = 0; i < dataSize; ++i) {
dst[i] = src[i] / (1.0f + dst[i]);
}
#endif
}
static void _MNNAdjustOptimalSparseKernel(int& sparseBlockOC, MNN::CoreFunctions::MNNPackedSparseMatMul& packedSparseMatMul) {
if(sparseBlockOC == 4) {
packedSparseMatMul = MNNPackedSparseMatMulEpx4;
return;
} else if(sparseBlockOC % 4 == 0) {
sparseBlockOC = 4;
packedSparseMatMul = MNNPackedSparseMatMulEpx4;
// MNN_PRINT("common downgrade sparse to:%d\n",sparseBlockOC);
return;
} else {
sparseBlockOC = 1;
packedSparseMatMul = MNNPackedSparseMatMulEpx1;
return;
}
}
// fp32 <--> fp8
static const int FP32_EXP_BIAS = 127;
static const int FP8_EXP_BIAS = 24; // [0, 31] --> [-24, 7] --> [1 / 2^24, 2^7]
void MNNFp32ToFp8(uint8_t* dst, const float* src, size_t size) {
for (int i = 0; i < size; i++) {
uint32_t rawData = *((uint32_t *)(&src[i]));
uint32_t sign = (rawData >> 31) & 1U;
uint32_t exp = (int)((rawData >> 23) & 0x0ffU);
uint32_t mant = (rawData >> 21) & 3U;
int realExp = (int)exp - FP32_EXP_BIAS;
realExp = ALIMAX(realExp, 0 - FP8_EXP_BIAS);
realExp = ALIMIN(realExp, 31 - FP8_EXP_BIAS);
exp = (uint32_t)(realExp + FP8_EXP_BIAS);
dst[i] = (int8_t)((sign << 7) | (exp << 2) | mant);
}
}
void MNNFp8ToFp32(float* dst, const uint8_t* src, size_t size) {
for (int i = 0; i < size; i++) {
uint32_t sign = (src[i] >> 7) & 1U;
uint32_t exp = (int)((src[i] >> 2) & 0x1fU);
uint32_t mant = (src[i] & 3U) << 21;
int realExp = (int)exp - FP8_EXP_BIAS;
exp = (uint32_t)(realExp + FP32_EXP_BIAS);
uint32_t rawData = (sign << 31) | (exp << 23) | mant;
dst[i] = *((float *)(&rawData));
}
}
// fp16 <--> fp8
void MNNFp16ToFp8(uint8_t* dst, const uint16_t* src, size_t size) {
#ifdef MNN_USE_NEON
#ifdef __aarch64__
int loopN = size / 16;
for (int i = 0; i < loopN; i++) {
uint8x16_t v1 = vld1q_u8((uint8_t*)(src + i * 16));
uint8x16_t v2 = vld1q_u8((uint8_t*)(src + i * 16 + 8));
uint8x16_t res = vuzp2q_u8(v1, v2);
vst1q_u8(dst + i * 16, res);
}
for (int i = loopN * 16; i < size; i++) {
dst[i] = static_cast<int8_t>(src[i] >> 8);
}
#else
int loopN = size / 8;
for (int i = 0; i < loopN; i++) {
uint16x8_t vec = vld1q_u16(src + i * 8);
uint8x8_t res = vshrn_n_u16(vec, 8);
vst1_u8(dst + i * 8, res);
}
for (int i = loopN * 8; i < size; i++) {
dst[i] = static_cast<int8_t>(src[i] >> 8);
}
#endif // ARM64
#else
for (int i = 0; i < size; i++) {
dst[i] = static_cast<int8_t>(src[i] >> 8);
}
#endif // USE_NEON
}
void MNNFp8ToFp16(uint16_t* dst, const uint8_t* src, size_t size) {
#ifdef MNN_USE_NEON
int loopN = size / 8;
for (int i = 0; i < loopN; i++) {
uint8x8_t vec8x8 = vld1_u8(src + i * 8);
uint16x8_t vec16x8 = vshll_n_u8(vec8x8, 8);
vst1q_u16(dst + i * 8, vec16x8);
}
for (int i = loopN * 8; i < size; i++) {
dst[i] = static_cast<int16_t>(src[i]) << 8;
}
#else
for (int i = 0; i < size; i++) {
dst[i] = static_cast<int16_t>(src[i]) << 8;
}
#endif // USE_NEON
}
#ifdef MNN_LOW_MEMORY
static void generalIm2col(float* destOrigin, float const** sourceGroup, const int32_t* info, const int32_t* el, int LP, int pack) {
// LP >= pack
int number = info[0];
int eReal = info[1];
int eDest = info[2];
int offset = info[3];
for (int n=0; n<number; ++n) {
int e = el[4 * n + 0];
int l = el[4 * n + 1];
int eOffset = el[4 * n + 2];
int lOffset = el[4 * n + 3];
int lC = lOffset / LP;
int lR = lOffset % LP;
auto dest = destOrigin + eOffset * LP + lC * eDest * LP + lR;
auto source = sourceGroup[n];
for (int y=0; y<e; ++y) {
auto yR = y % eDest;
for (int x=0; x<l; ++x) {
auto xR = x % pack;
auto xC = x / pack;
auto xOut = x / LP;
auto xIn = x % LP;
dest[xOut * eDest * LP + yR * LP + xIn] = source[xC * eReal * pack + y * pack * offset + xR];
}
}
}
}
#endif // MNN_LOW_MEMORY
namespace MNN {
static CoreFunctions* gCoreFunction = nullptr;
void MNNCoreFunctionInit() {
gCoreFunction = new CoreFunctions;
// fp8
gCoreFunction->MNNFp32ToFp8 = MNNFp32ToFp8;
gCoreFunction->MNNFp16ToFp8 = MNNFp16ToFp8;
gCoreFunction->MNNFp8ToFp32 = MNNFp8ToFp32;
gCoreFunction->MNNFp8ToFp16 = MNNFp8ToFp16;
// MatMul
gCoreFunction->MNNGetMatMulPackMode = MNNGetMatMulPackMode;
gCoreFunction->MNNPackC4ForMatMul_A = MNNPackC4ForMatMul_A;
gCoreFunction->MNNPackForMatMul_B = MNNPackForMatMul_B;
gCoreFunction->MNNPackedMatMul = MNNPackedMatMul;
gCoreFunction->MNNPackedMatMulRemain = MNNPackedMatMulRemain;
gCoreFunction->MNNCountMaxMinValue = MNNCountMaxMinValue;
gCoreFunction->MNNGetSparseMatMulPackMode = MNNGetSparseMatMulPackMode;
gCoreFunction->MNNAdjustOptimalSparseKernel = _MNNAdjustOptimalSparseKernel;
gCoreFunction->MNNComputeMatMulForE_1 = MNNComputeMatMulForE_1;
gCoreFunction->MNNComputeMatMulForH_1 = MNNComputeMatMulForH_1;
// Lowp
gCoreFunction->MNNFp32ToLowp = nullptr;
gCoreFunction->MNNLowpToFp32 = nullptr;
gCoreFunction->bytes = 4;// sizeof(float)
// Packed Function
gCoreFunction->pack = 4;
// FIXME: MNNPackTranspose and MNNUnpackTranspose is reverted
gCoreFunction->MNNPackCUnit = MNNPackC4;
gCoreFunction->MNNUnpackCUnit = MNNUnpackC4;
gCoreFunction->MNNUnpackCUnitTranspose = MNNPackTranspose;
gCoreFunction->MNNPackCUnitTranspose = MNNUnpackTranspose;
gCoreFunction->MNNPackCUnitInt8 = decltype(gCoreFunction->MNNPackCUnitInt8)(MNNPackC4Uint8);
gCoreFunction->MNNUnpackCUnitInt8 = decltype(gCoreFunction->MNNUnpackCUnitInt8)(MNNUnpackC4Uint8);
gCoreFunction->MNNPackCUnitTransposeInt8 = decltype(gCoreFunction->MNNPackCUnitTransposeInt8)(MNNUnpackTransposeUint8);
gCoreFunction->MNNUnpackCUnitTransposeInt8 = decltype(gCoreFunction->MNNUnpackCUnitTransposeInt8)(MNNPackTransposeUint8);
gCoreFunction->MNNPackCUnitInt16 = MNNPackC4Int16;
gCoreFunction->MNNUnpackCUnitInt16 = MNNUnpackC4Int16;
gCoreFunction->MNNPackCUnitTransposeInt16 = MNNUnpackTransposeInt16;
gCoreFunction->MNNUnpackCUnitTransposeInt16 = MNNPackTransposeInt16;
gCoreFunction->MNNAxByClampBroadcastUnit = MNNAxByClampBroadcastUnit;
gCoreFunction->MNNConvRunForLineDepthwise = MNNConvRunForLineDepthwise;
gCoreFunction->MNNMatrixAdd = MNNMatrixAdd;
gCoreFunction->MNNMatrixSub = MNNMatrixSub;
gCoreFunction->MNNStrassenMergeCFunction = MNNStrassenMergeCFunction;
gCoreFunction->penalty = 1.5f;
gCoreFunction->MNNScaleAndAddBias = MNNScaleAndAddBias;
gCoreFunction->MNNGridSampleComputeCord = MNNGridSampleComputeCord;
gCoreFunction->MNNGridSampleInterp = MNNGridSampleInterp;
gCoreFunction->MNNGridSampleInterpGrad = MNNGridSampleInterpGrad;
gCoreFunction->MNNGridSampleComputeCord3D = MNNGridSampleComputeCord3D;
gCoreFunction->MNNGridSampleInterp3D = MNNGridSampleInterp3D;
gCoreFunction->MNNRoiPoolingMax = MNNRoiPoolingMax;
gCoreFunction->MNNRoiAlignMax = MNNRoiAlignMax;
gCoreFunction->MNNRoiAlignAvg = MNNRoiAlignAvg;
gCoreFunction->MNNAddC4WithStride = MNNAddC4WithStride;
gCoreFunction->MNNCopyC4WithStride = MNNCopyC4WithStride;
gCoreFunction->chooseWinoSourceTransformPack = WinogradFunction::chooseWinoSourceTransformPack;
gCoreFunction->chooseWinoSourceUnrollTransform = WinogradFunction::chooseSourceUnrollTransform;
gCoreFunction->chooseWinoDestUnrollTransform = WinogradFunction::chooseWinoDestUnrollTransform;
gCoreFunction->MNNDeconvRunForLineDepthwise = MNNDeconvRunForLineDepthwise;
gCoreFunction->MNNDeconvRunForUnitDepthWise = MNNDeconvRunForUnitDepthWise;
#ifdef MNN_USE_NEON
gCoreFunction->MNNDepthwiseConvFastKernel = MNNDepthwiseConvFastKernel;
#endif
gCoreFunction->MNNSelectBinaryFunctionForFloat = CPUBinary::selectForFloat;
gCoreFunction->MNNSelectUnaryFunctionForFloat = CPUUnary::selectForFloat;
gCoreFunction->MNNSelectUnaryFunctionForInt8 = CPUUnary::selectForInt8;
gCoreFunction->MNNReluWithSlopeChannel = MNNReluWithSlopeChannel;
gCoreFunction->MNNPoolingAvg = (decltype(gCoreFunction->MNNPoolingAvg))(poolingAvg<float, Vec4, 4>);
// Set min value as 1 << 24
gCoreFunction->MNNPoolingMax = (decltype(gCoreFunction->MNNPoolingMax))(poolingMax<float, Vec4, 4, -16777216>);
gCoreFunction->MNNPoolingMaxWithRedice = (decltype(gCoreFunction->MNNPoolingMaxWithRedice))(poolingMaxWithRedice<float, -16777216>);
// ImageProcess Functions
gCoreFunction->MNNRGBAToBGRA = MNNRGBAToBGRA;
gCoreFunction->MNNNV21ToRGBA = MNNNV21ToRGBA;
gCoreFunction->MNNNV21ToRGB = MNNNV21ToRGB;
gCoreFunction->MNNNV21ToBGRA = MNNNV21ToBGRA;
gCoreFunction->MNNNV21ToBGR = MNNNV21ToBGR;
gCoreFunction->MNNC1ToFloatC1 = MNNC1ToFloatC1;
gCoreFunction->MNNC3ToFloatC3 = MNNC3ToFloatC3;
gCoreFunction->MNNC3ToFloatRGBA = MNNC3ToFloatRGBA;
gCoreFunction->MNNSamplerC4Nearest = MNNSamplerC4Nearest;
gCoreFunction->MNNSamplerC4Bilinear = MNNSamplerC4Bilinear;
gCoreFunction->MNN4BitcopyWithStride = MNN4BitcopyWithStride;
gCoreFunction->MNN1BitcopyWithStride = MNN1BitcopyWithStride;
gCoreFunction->MNN2BitcopyWithStride = MNN2BitcopyWithStride;
gCoreFunction->MNN4BitcopyFast = MNN4BitcopyFast;
gCoreFunction->MNN2BitcopyFast = MNN2BitcopyFast;
gCoreFunction->MNN1BitcopyFast = MNN1BitCopyFast;
gCoreFunction->MNNAccumulateSequenceNumber = MNNAccumulateSequenceNumber;
const MNNCPUInfo& gCPUInfo = *MNNGetCPUInfo();
gCoreFunction->supportFp16arith = gCPUInfo.fp16arith;
gCoreFunction->supportSDot = gCPUInfo.dot;
gCoreFunction->supportI8mm = gCPUInfo.i8mm;
gCoreFunction->MNNSumByAxisLForMatmul_A = MNNSumByAxisLForMatmul_A;
gCoreFunction->MNNReorderWeightInt4 = MNNReorderWeightInt4;
gCoreFunction->MNNSumWeightInt8 = MNNSumWeightInt8;
#ifdef __aarch64__
if (gCoreFunction->supportSDot) {
gCoreFunction->MNNReorderWeightInt4 = MNNReorderWeightInt4Arm82;
gCoreFunction->MNNSumWeightInt8 = MNNSumWeightInt8Arm82;
}
if (gCoreFunction->supportI8mm) {
gCoreFunction->MNNReorderWeightInt4 = MNNReorderWeightInt4Arm86;
gCoreFunction->MNNSumWeightInt8 = MNNSumWeightInt8Arm86;
}
#endif
#ifdef MNN_CPU_WEIGHT_DEQUANT_GEMM
// Weight Dequant Gemm Kernels
gCoreFunction->MNNPackedMatMul_int8 = MNNPackedMatMul_int8;
gCoreFunction->MNNPackedMatMulRemain_int8 = MNNPackedMatMulRemain_int8;
#endif
#ifdef MNN_LOW_MEMORY
gCoreFunction->MNNAbsMax = MNNAbsMaxFP32; // abs max value for [icDiv4,plane,4] -> abs max:[plane]
gCoreFunction->MNNDynamicQuant = MNNDynamicQuantFP32; // symmetric 'batch' quant for [icDiv4,plane,4]
gCoreFunction->MNNAsyQuantFunc = MNNAsyQuantFunc; // asymmetric 'batch' quant for [icDiv4,plane,4]
gCoreFunction->MNNAsyQuantInfo = MNNAsyQuantInfo_FP32; // asymmetric quant/dequant scale&bias for [icDiv4,plane,4] -> scale&bias:[blockNum,plane]
gCoreFunction->MNNQuantScale = MNNQuantScaleFP32; // symmetric quant/dequant scale&bias for [icDiv4,plane,4] -> scale&bias:[plane]
gCoreFunction->MNNGeneralIm2Col = generalIm2col; // Im2Col based on float data -> output:[eU,kernelsize,lU,ep,lp]
gCoreFunction->MNNDynamicUpdateConvBiasScale = MNNDynamicUpdateConvBiasScale;
#ifdef __aarch64__
if (gCoreFunction->supportSDot) {
gCoreFunction->MNNGeneralIm2Col = MNNGeneralIm2col_Fp32Arm82;
}
if (gCoreFunction->supportI8mm) {
gCoreFunction->MNNGeneralIm2Col = MNNGeneralIm2col_Fp32Arm86;
}
#endif
#endif
MNNCoreInt8FunctionInit();
MNNFunctionInit();
}
CoreFunctions* MNNGetCoreFunctions() {
return gCoreFunction;
}
};
void MNNUnpackC4Origin(float* dst, const float* src, size_t area, size_t depth, int areaOffset) {
int offset[] = {
areaOffset,
areaOffset,
};
MNNUnpackC4(dst, src, area, depth, offset);
}
void MNNPackC4Origin(float* dst, const float* src, size_t area, size_t depth, int areaOffset) {
int offset[] = {
areaOffset,
areaOffset,
};
MNNPackC4(dst, src, area, depth, offset);
}
void MNNPackC2(double* dst, const double* src, size_t area, size_t depth, int* areaOffset) {
MNNPackC2Common<double>(dst, src, area, depth, areaOffset);
}
void MNNUnpackC2(double* dst, const double* src, size_t area, size_t depth, int* areaOffset) {
MNNUnpackC2Common<double>(dst, src, area, depth, areaOffset);
}
void MNNUnpackC2Float(float* dst, const float* src, size_t area, size_t depth, int* areaOffset, int pack) {
MNNUnpackC2Common<float>(dst, src, area, depth, areaOffset, pack);
}
#ifndef __aarch64__
void MNNPackInt8C2(float* dst, const float* src, size_t area, size_t depth, int* areaOffset) {
MNNPackC2Common<float>(dst, src, area, depth, areaOffset);
}
#endif
void MNNUnpackInt8C2(float* dst, const float* src, size_t area, size_t depth, int* areaOffset) {
MNNUnpackC2Common<float>(dst, src, area, depth, areaOffset);
}
void MNNUnpackC2Origin(double* dst, const double* src, size_t area, size_t depth, int areaOffset) {
int offset[] = {
areaOffset,
areaOffset,
};
MNNUnpackC2(dst, src, area, depth, offset);
}
void MNNPackC2Origin(double* dst, const double* src, size_t area, size_t depth, int areaOffset) {
int offset[] = {
areaOffset,
areaOffset,
};
MNNPackC2(dst, src, area, depth, offset);
}
void MNNUnpackInt8C2Origin(float* dst, const float* src, size_t area, size_t depth, int areaOffset) {
int offset[] = {
areaOffset,
areaOffset,
};
MNNUnpackInt8C2(dst, src, area, depth, offset);
}
void MNNPackInt8C2Origin(float* dst, const float* src, size_t area, size_t depth, int areaOffset) {
int offset[] = {
areaOffset,
areaOffset,
};
MNNPackInt8C2(dst, src, area, depth, offset);
}