mirror of https://github.com/alibaba/MNN.git
116 lines
4.2 KiB
C++
116 lines
4.2 KiB
C++
//
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// AVX2Functions.cpp
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// MNN
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//
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// Created by MNN on b'2021/05/17'.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#include "AVX2Functions.hpp"
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#include "AVX2Backend.hpp"
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#include "avx/FunctionSummary.hpp"
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#include "avxfma/FunctionSummary.hpp"
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#include "avx512/FunctionSummary.hpp"
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#include "sse/FunctionSummary.hpp"
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namespace MNN {
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static int geP, glP, ghP;
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static CoreFunctions* gAVX2CoreFunctions = nullptr;
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static CoreInt8Functions* gAVX2CoreInt8Functions = nullptr;
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static void _MNNGetMatMulPackMode(int* eP, int *lP, int* hP) {
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*eP = geP;
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*lP = glP;
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*hP = ghP;
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}
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#ifndef MNN_USE_AVX
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bool AVX2Functions::init(int cpuFlags) {
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return false;
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}
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#else
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bool AVX2Functions::init(int cpuFlags) {
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gAVX2CoreFunctions = new CoreFunctions;
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auto coreFunction = gAVX2CoreFunctions;
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gAVX2CoreInt8Functions = new CoreInt8Functions;
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// Init default functions
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*coreFunction = *MNNGetCoreFunctions();
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*gAVX2CoreInt8Functions = *MNNGetInt8CoreFunctions();
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_AVX_MNNInt8FunctionInit(gAVX2CoreInt8Functions);
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// Init AVX2
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coreFunction->MNNGetMatMulPackMode = _MNNGetMatMulPackMode;
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geP = 24;
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glP = 1;
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ghP = 4;
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_AVX_ReorderInit(coreFunction);
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coreFunction->MNNPackedMatMul = _AVX_MNNPackedMatMul;
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coreFunction->MNNPackedMatMulRemain = _AVX_MNNPackedMatMulRemain;
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#ifdef MNN_CPU_WEIGHT_DEQUANT_GEMM
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coreFunction->MNNPackedMatMul_int8 = _AVX_MNNPackedMatMul_int8;
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coreFunction->MNNPackedMatMulRemain_int8 = _AVX_MNNPackedMatMulRemain_int8;
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#endif
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#ifdef MNN_LOW_MEMORY
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coreFunction->MNNAbsMax = _AVX_MNNAbsMaxFP32;
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coreFunction->MNNDynamicQuant = _AVX_MNNDynamicQuant;
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#endif
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coreFunction->MNNPackC4ForMatMul_A = _AVX_MNNPackC4ForMatMul_A;
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coreFunction->MNNPackForMatMul_B = _AVX_MNNPackForMatMul_B;
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coreFunction->MNNComputeMatMulForE_1 = _AVX_MNNComputeMatMulForE_1;
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coreFunction->MNNComputeMatMulForH_1 = _AVX_MNNComputeMatMulForH_1;
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// Dynamic Quant
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coreFunction->MNNCountMaxMinValue = _AVX_MNNComputeScaleZeroScalar;
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// For Packed Functions
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coreFunction->pack = 8;
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_AVX_ExtraInit(coreFunction);
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// Winograd
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_AVX_WinogradInit(coreFunction);
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if (cpuFlags & libyuv::kCpuHasFMA3) {
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coreFunction->MNNPackedMatMul = _AVX_MNNPackedMatMulFMA;
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coreFunction->MNNPackedMatMulRemain = _AVX_MNNPackedMatMulRemainFMA;
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coreFunction->MNNComputeMatMulForE_1 = _AVX_MNNComputeMatMulForE_1FMA;
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coreFunction->MNNComputeMatMulForH_1 = _AVX_MNNComputeMatMulForH_1FMA;
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_AVX_ExtraInitFMA(coreFunction);
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}
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#ifdef MNN_AVX512
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if ((cpuFlags & libyuv::kCpuHasAVX512VNNI)
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|| (cpuFlags & libyuv::kCpuHasAVX512VL)
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|| (cpuFlags & libyuv::kCpuHasAVX512BW)
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|| (cpuFlags & libyuv::kCpuHasAVX512VBMI)
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|| (cpuFlags & libyuv::kCpuHasAVX512VBITALG)
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|| (cpuFlags & libyuv::kCpuHasAVX512VPOPCNTDQ)
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|| (cpuFlags & libyuv::kCpuHasAVX512VBMI2)
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) {
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coreFunction->pack = 16;
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_AVX512_ReorderInit(coreFunction);
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_AVX512_ExtraInit(coreFunction);
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_AVX512_WinogradInit(coreFunction);
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coreFunction->MNNPackForMatMul_B = _AVX512_MNNPackForMatMul_B;
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coreFunction->MNNPackC4ForMatMul_A = _AVX512_MNNPackC8ForMatMul_A;
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coreFunction->MNNPackedMatMul = _AVX512_MNNPackedMatMul;
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coreFunction->MNNPackedMatMulRemain = _AVX512_MNNPackedMatMulRemain;
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geP = 48;
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ghP = 8;
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glP = 1;
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_AVX512_MNNInt8FunctionInit(gAVX2CoreInt8Functions, cpuFlags & libyuv::kCpuHasAVX512VNNI);
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memcpy(coreFunction->MNNPackedMatMulOC16Functions, _AVX512_MNNPackedMatMulOC16Functions,
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sizeof(MNN::CoreFunctions::MNNPackedMatMulKernel) * AVX512_INPUT_TILE_MAX);
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memcpy(coreFunction->MNNPackedMatMulOC32Functions, _AVX512_MNNPackedMatMulOC32Functions,
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sizeof(MNN::CoreFunctions::MNNPackedMatMulKernel) * AVX512_INPUT_TILE_MAX);
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memcpy(coreFunction->MNNPackedMatMulOC48Functions, _AVX512_MNNPackedMatMulOC48Functions,
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sizeof(MNN::CoreFunctions::MNNPackedMatMulKernel) * AVX512_INPUT_TILE_MAX);
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}
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#endif
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return true;
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}
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#endif
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CoreFunctions* AVX2Functions::get() {
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return gAVX2CoreFunctions;
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}
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CoreInt8Functions* AVX2Functions::getInt8() {
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return gAVX2CoreInt8Functions;
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}
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};
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