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Commit ed956410 authored by Vadim Pisarevsky's avatar Vadim Pisarevsky
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reuse AVX2-optimized kernels for AVX1 CPUs (like IvyBridge)

parent f670a992
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Showing with 447 additions and 306 deletions
modules/dnn/src/layers/convolution_layer.cpp
View file @ ed956410
......@@ -285,11 +285,12 @@ public:
const std::vector<float>* reluslope_;
const ActivationLayer* activ_;
bool is1x1_;
bool useAVX;
bool useAVX2;
ParallelConv()
: input_(0), weights_(0), output_(0), ngroups_(0), nstripes_(0),
biasvec_(0), reluslope_(0), activ_(0), is1x1_(false), useAVX2(false)
biasvec_(0), reluslope_(0), activ_(0), is1x1_(false), useAVX(false), useAVX2(false)
{}
static void run( const Mat& input, Mat& output, const Mat& weights,
......@@ -322,6 +323,7 @@ public:
int inpCnAll = input.size[1], width = input.size[3], height = input.size[2];
int inpCn = inpCnAll / ngroups;
p.is1x1_ = kernel == Size(0,0) && pad == Size(0, 0);
p.useAVX = checkHardwareSupport(CPU_AVX);
p.useAVX2 = checkHardwareSupport(CPU_AVX2);
int ncn = std::min(inpCn, (int)BLK_SIZE_CN);
......@@ -507,6 +509,12 @@ public:
fastConv_avx2(wptr, wstep, biasptr, rowbuf0, data_out0 + ofs0,
outShape, bsz, vsz, vsz_a, relu, cn0 == 0);
else
#endif
#if CV_TRY_AVX
if(useAVX)
fastConv_avx(wptr, wstep, biasptr, rowbuf0, data_out0 + ofs0,
outShape, bsz, vsz, vsz_a, relu, cn0 == 0);
else
#endif
for( int i = 0; i < outCn; i += 2 )
{
......@@ -795,6 +803,7 @@ public:
b_ = &b;
c_ = &c;
nstripes_ = nstripes;
useAVX = checkHardwareSupport(CPU_AVX);
useAVX2 = checkHardwareSupport(CPU_AVX2);
}
......@@ -817,6 +826,11 @@ public:
if( useAVX2 )
fastGEMM_avx2( aptr, astep, bptr, bstep, cptr, cstep, mmax, kmax, nmax );
else
#endif
#if CV_TRY_AVX
if( useAVX )
fastGEMM_avx( aptr, astep, bptr, bstep, cptr, cstep, mmax, kmax, nmax );
else
#endif
for( m = 0; m < mmax; m += 2 )
{
......@@ -910,6 +924,7 @@ public:
const Mat *a_, *b_;
Mat* c_;
int nstripes_;
bool useAVX;
bool useAVX2;
};
......
modules/dnn/src/layers/fully_connected_layer.cpp
View file @ ed956410
......@@ -119,7 +119,7 @@ public:
class FullyConnected : public ParallelLoopBody
{
public:
FullyConnected() : srcMat(0), weights(0), biasMat(0), activ(0), dstMat(0), nstripes(0), useAVX2(false) {}
FullyConnected() : srcMat(0), weights(0), biasMat(0), activ(0), dstMat(0), nstripes(0), useAVX(false), useAVX2(false) {}
static void run(const Mat& srcMat, const Mat& weights, const Mat& biasMat,
Mat& dstMat, const ActivationLayer* activ, int nstripes)
......@@ -139,6 +139,7 @@ public:
p.dstMat = &dstMat;
p.nstripes = nstripes;
p.activ = activ;
p.useAVX = checkHardwareSupport(CPU_AVX);
p.useAVX2 = checkHardwareSupport(CPU_AVX2);
parallel_for_(Range(0, nstripes), p, nstripes);
......@@ -178,6 +179,11 @@ public:
if( useAVX2 )
fastGEMM1T_avx2( sptr, wptr, wstep, biasptr, dptr, nw, vecsize);
else
#endif
#if CV_TRY_AVX
if( useAVX )
fastGEMM1T_avx( sptr, wptr, wstep, biasptr, dptr, nw, vecsize);
else
#endif
{
int i = 0;
......@@ -228,6 +234,7 @@ public:
const ActivationLayer* activ;
Mat* dstMat;
int nstripes;
bool useAVX;
bool useAVX2;
};
......
modules/dnn/src/layers/layers_common.avx.cpp 0 → 100644
View file @ ed956410
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Copyright (C) 2017, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
#include "layers_common.hpp"
#include "opencv2/core/hal/intrin.hpp"
#define fastConv_some_avx fastConv_avx
#define fastGEMM1T_some_avx fastGEMM1T_avx
#define fastGEMM_some_avx fastGEMM_avx
#undef _mm256_fmadd_ps
#define _mm256_fmadd_ps(a, b, c) _mm256_add_ps(c, _mm256_mul_ps(a, b))
#include "layers_common.simd.hpp"
modules/dnn/src/layers/layers_common.avx2.cpp
View file @ ed956410
......@@ -44,308 +44,8 @@
#include "layers_common.hpp"
#include "opencv2/core/hal/intrin.hpp"
namespace cv {
namespace dnn {
#define fastConv_some_avx fastConv_avx2
#define fastGEMM1T_some_avx fastGEMM1T_avx2
#define fastGEMM_some_avx fastGEMM_avx2
void fastConv_avx2( const float* weights, size_t wstep, const float* bias,
const float* rowbuf, float* output, const int* outShape,
int blockSize, int vecsize, int vecsize_aligned,
const float* relu, bool initOutput )
{
int outCn = outShape[1];
size_t outPlaneSize = outShape[2]*outShape[3];
float r0 = 1.f, r1 = 1.f, r2 = 1.f;
__m256 vr0 = _mm256_set1_ps(1.f), vr1 = vr0, vr2 = vr0, z = _mm256_setzero_ps();
// now compute dot product of the weights
// and im2row-transformed part of the tensor
for( int i = 0; i < outCn; i += 3 )
{
const float* wptr0 = weights + i*wstep;
const float* wptr1 = wptr0 + wstep;
const float* wptr2 = wptr1 + wstep;
float* outptr0 = output + i*outPlaneSize;
float* outptr1 = outptr0 + outPlaneSize;
float* outptr2 = outptr1 + outPlaneSize;
float bias0 = bias[i], bias1 = bias[i+1], bias2 = bias[i+2];
if( i+2 >= outCn )
{
wptr2 = wptr1;
outptr2 = outptr1;
bias2 = bias1;
if( i+1 >= outCn )
{
wptr2 = wptr1 = wptr0;
outptr2 = outptr1 = outptr0;
bias2 = bias1 = bias0;
}
}
if( relu )
{
r0 = relu[i];
r1 = relu[i+1];
r2 = relu[i+2];
vr0 = _mm256_set1_ps(r0);
vr1 = _mm256_set1_ps(r1);
vr2 = _mm256_set1_ps(r2);
}
int j = 0;
for( ; j <= blockSize - 4; j += 4 )
{
const float* rptr = rowbuf + j*vecsize_aligned;
__m256 vs00 = _mm256_setzero_ps(), vs01 = _mm256_setzero_ps(),
vs02 = _mm256_setzero_ps(), vs03 = _mm256_setzero_ps(),
vs10 = _mm256_setzero_ps(), vs11 = _mm256_setzero_ps(),
vs12 = _mm256_setzero_ps(), vs13 = _mm256_setzero_ps(),
vs20 = _mm256_setzero_ps(), vs21 = _mm256_setzero_ps(),
vs22 = _mm256_setzero_ps(), vs23 = _mm256_setzero_ps();
for( int k = 0; k < vecsize; k += 8, rptr += 8 )
{
__m256 w0 = _mm256_load_ps(wptr0 + k);
__m256 w1 = _mm256_load_ps(wptr1 + k);
__m256 w2 = _mm256_load_ps(wptr2 + k);
__m256 r0 = _mm256_load_ps(rptr);
vs00 = _mm256_fmadd_ps(w0, r0, vs00);
vs10 = _mm256_fmadd_ps(w1, r0, vs10);
vs20 = _mm256_fmadd_ps(w2, r0, vs20);
r0 = _mm256_load_ps(rptr + vecsize_aligned);
vs01 = _mm256_fmadd_ps(w0, r0, vs01);
vs11 = _mm256_fmadd_ps(w1, r0, vs11);
vs21 = _mm256_fmadd_ps(w2, r0, vs21);
r0 = _mm256_load_ps(rptr + vecsize_aligned*2);
vs02 = _mm256_fmadd_ps(w0, r0, vs02);
vs12 = _mm256_fmadd_ps(w1, r0, vs12);
vs22 = _mm256_fmadd_ps(w2, r0, vs22);
r0 = _mm256_load_ps(rptr + vecsize_aligned*3);
vs03 = _mm256_fmadd_ps(w0, r0, vs03);
vs13 = _mm256_fmadd_ps(w1, r0, vs13);
vs23 = _mm256_fmadd_ps(w2, r0, vs23);
}
__m256 t0 = _mm256_hadd_ps(_mm256_hadd_ps(vs00, vs01), _mm256_hadd_ps(vs02, vs03));
__m256 t1 = _mm256_hadd_ps(_mm256_hadd_ps(vs10, vs11), _mm256_hadd_ps(vs12, vs13));
__m256 t2 = _mm256_hadd_ps(_mm256_hadd_ps(vs20, vs21), _mm256_hadd_ps(vs22, vs23));
t0 = _mm256_add_ps(t0, _mm256_permute2f128_ps(t0, t0, 1));
t1 = _mm256_add_ps(t1, _mm256_permute2f128_ps(t1, t1, 1));
t2 = _mm256_add_ps(t2, _mm256_permute2f128_ps(t2, t2, 1));
__m256 s0, s1, s2;
if( initOutput )
{
s0 = _mm256_set1_ps(bias0);
s1 = _mm256_set1_ps(bias1);
s2 = _mm256_set1_ps(bias2);
}
else
{
s0 = _mm256_castps128_ps256(_mm_loadu_ps(outptr0 + j));
s1 = _mm256_castps128_ps256(_mm_loadu_ps(outptr1 + j));
s2 = _mm256_castps128_ps256(_mm_loadu_ps(outptr2 + j));
}
s0 = _mm256_add_ps(s0, t0);
s1 = _mm256_add_ps(s1, t1);
s2 = _mm256_add_ps(s2, t2);
if( relu )
{
__m256 m0 = _mm256_cmp_ps(s0, z, _CMP_GT_OS);
__m256 m1 = _mm256_cmp_ps(s1, z, _CMP_GT_OS);
__m256 m2 = _mm256_cmp_ps(s2, z, _CMP_GT_OS);
s0 = _mm256_xor_ps(s0, _mm256_andnot_ps(m0, _mm256_xor_ps(_mm256_mul_ps(s0, vr0), s0)));
s1 = _mm256_xor_ps(s1, _mm256_andnot_ps(m1, _mm256_xor_ps(_mm256_mul_ps(s1, vr1), s1)));
s2 = _mm256_xor_ps(s2, _mm256_andnot_ps(m2, _mm256_xor_ps(_mm256_mul_ps(s2, vr2), s2)));
}
_mm_storeu_ps(outptr0 + j, _mm256_castps256_ps128(s0));
_mm_storeu_ps(outptr1 + j, _mm256_castps256_ps128(s1));
_mm_storeu_ps(outptr2 + j, _mm256_castps256_ps128(s2));
}
for( ; j < blockSize; j++ )
{
const float* rptr = rowbuf + j*vecsize_aligned;
float s00, s10, s20;
if( initOutput )
{
s00 = bias0;
s10 = bias1;
s20 = bias2;
}
else
{
s00 = outptr0[j];
s10 = outptr1[j];
s20 = outptr2[j];
}
for( int k = 0; k < vecsize; k++ )
{
float r0 = rptr[k];
s00 += wptr0[k]*r0;
s10 += wptr1[k]*r0;
s20 += wptr2[k]*r0;
}
if( relu )
{
s00 = s00 > 0.f ? s00 : s00*r0;
s10 = s10 > 0.f ? s10 : s10*r1;
s20 = s20 > 0.f ? s20 : s20*r2;
}
outptr0[j] = s00;
outptr1[j] = s10;
outptr2[j] = s20;
}
}
_mm256_zeroupper();
}
// dst = vec * weights^t + bias
void fastGEMM1T_avx2( const float* vec, const float* weights,
size_t wstep, const float* bias,
float* dst, int nvecs, int vecsize )
{
int i = 0;
for( ; i <= nvecs - 8; i += 8 )
{
const float* wptr = weights + i*wstep;
__m256 vs0 = _mm256_setzero_ps(), vs1 = _mm256_setzero_ps(),
vs2 = _mm256_setzero_ps(), vs3 = _mm256_setzero_ps(),
vs4 = _mm256_setzero_ps(), vs5 = _mm256_setzero_ps(),
vs6 = _mm256_setzero_ps(), vs7 = _mm256_setzero_ps();
for( int k = 0; k < vecsize; k += 8, wptr += 8 )
{
__m256 v = _mm256_load_ps(vec + k);
vs0 = _mm256_fmadd_ps(_mm256_load_ps(wptr), v, vs0);
vs1 = _mm256_fmadd_ps(_mm256_load_ps(wptr + wstep), v, vs1);
vs2 = _mm256_fmadd_ps(_mm256_load_ps(wptr + wstep*2), v, vs2);
vs3 = _mm256_fmadd_ps(_mm256_load_ps(wptr + wstep*3), v, vs3);
vs4 = _mm256_fmadd_ps(_mm256_load_ps(wptr + wstep*4), v, vs4);
vs5 = _mm256_fmadd_ps(_mm256_load_ps(wptr + wstep*5), v, vs5);
vs6 = _mm256_fmadd_ps(_mm256_load_ps(wptr + wstep*6), v, vs6);
vs7 = _mm256_fmadd_ps(_mm256_load_ps(wptr + wstep*7), v, vs7);
}
__m256 s0 = _mm256_hadd_ps(_mm256_hadd_ps(vs0, vs1), _mm256_hadd_ps(vs2, vs3));
__m256 s1 = _mm256_hadd_ps(_mm256_hadd_ps(vs4, vs5), _mm256_hadd_ps(vs6, vs7));
s0 = _mm256_add_ps(s0, _mm256_permute2f128_ps(s0, s0, 1));
s1 = _mm256_add_ps(s1, _mm256_permute2f128_ps(s1, s1, 1));
s0 = _mm256_add_ps(s0, _mm256_castps128_ps256(_mm_loadu_ps(bias + i)));
s1 = _mm256_add_ps(s1, _mm256_castps128_ps256(_mm_loadu_ps(bias + i + 4)));
_mm_storeu_ps(dst + i, _mm256_castps256_ps128(s0));
_mm_storeu_ps(dst + i + 4, _mm256_castps256_ps128(s1));
}
float temp = 0.f;
for( ; i < nvecs; i++ )
{
const float* wptr = weights + i*wstep;
__m256 vs0 = _mm256_setzero_ps();
for( int k = 0; k < vecsize; k += 8, wptr += 8 )
{
__m256 v = _mm256_load_ps(vec + k);
vs0 = _mm256_fmadd_ps(_mm256_load_ps(wptr), v, vs0);
}
__m256 s0 = _mm256_hadd_ps(_mm256_hadd_ps(vs0, vs0), vs0);
s0 = _mm256_add_ps(s0, _mm256_permute2f128_ps(s0, s0, 1));
_mm_store_ss(&temp, _mm256_castps256_ps128(s0));
dst[i] = temp + bias[i];
}
_mm256_zeroupper();
}
void fastGEMM_avx2( const float* aptr, size_t astep, const float* bptr,
size_t bstep, float* cptr, size_t cstep,
int ma, int na, int nb )
{
int n = 0;
for( ; n <= nb - 16; n += 16 )
{
for( int m = 0; m < ma; m += 4 )
{
const float* aptr0 = aptr + astep*m;
const float* aptr1 = aptr + astep*std::min(m+1, ma-1);
const float* aptr2 = aptr + astep*std::min(m+2, ma-1);
const float* aptr3 = aptr + astep*std::min(m+3, ma-1);
float* cptr0 = cptr + cstep*m;
float* cptr1 = cptr + cstep*std::min(m+1, ma-1);
float* cptr2 = cptr + cstep*std::min(m+2, ma-1);
float* cptr3 = cptr + cstep*std::min(m+3, ma-1);
__m256 d00 = _mm256_setzero_ps(), d01 = _mm256_setzero_ps();
__m256 d10 = _mm256_setzero_ps(), d11 = _mm256_setzero_ps();
__m256 d20 = _mm256_setzero_ps(), d21 = _mm256_setzero_ps();
__m256 d30 = _mm256_setzero_ps(), d31 = _mm256_setzero_ps();
for( int k = 0; k < na; k++ )
{
__m256 a0 = _mm256_set1_ps(aptr0[k]);
__m256 a1 = _mm256_set1_ps(aptr1[k]);
__m256 a2 = _mm256_set1_ps(aptr2[k]);
__m256 a3 = _mm256_set1_ps(aptr3[k]);
__m256 b0 = _mm256_loadu_ps(bptr + k*bstep + n);
__m256 b1 = _mm256_loadu_ps(bptr + k*bstep + n + 8);
d00 = _mm256_fmadd_ps(a0, b0, d00);
d01 = _mm256_fmadd_ps(a0, b1, d01);
d10 = _mm256_fmadd_ps(a1, b0, d10);
d11 = _mm256_fmadd_ps(a1, b1, d11);
d20 = _mm256_fmadd_ps(a2, b0, d20);
d21 = _mm256_fmadd_ps(a2, b1, d21);
d30 = _mm256_fmadd_ps(a3, b0, d30);
d31 = _mm256_fmadd_ps(a3, b1, d31);
}
_mm256_storeu_ps(cptr0 + n, d00);
_mm256_storeu_ps(cptr0 + n + 8, d01);
_mm256_storeu_ps(cptr1 + n, d10);
_mm256_storeu_ps(cptr1 + n + 8, d11);
_mm256_storeu_ps(cptr2 + n, d20);
_mm256_storeu_ps(cptr2 + n + 8, d21);
_mm256_storeu_ps(cptr3 + n, d30);
_mm256_storeu_ps(cptr3 + n + 8, d31);
}
}
for( ; n < nb; n++ )
{
for( int m = 0; m < ma; m++ )
{
const float* aptr0 = aptr + astep*m;
float* cptr0 = cptr + cstep*m;
float d0 = 0.f;
for( int k = 0; k < na; k++ )
d0 += aptr0[k]*bptr[k*bstep + n];
cptr0[n] = d0;
}
}
_mm256_zeroupper();
}
}
}
#include "layers_common.simd.hpp"
modules/dnn/src/layers/layers_common.hpp
View file @ ed956410
......@@ -64,6 +64,19 @@ void getConvPoolPaddings(const Size& inp, const Size& out,
const Size &kernel, const Size &stride,
const String &padMode, Size &pad);
#if CV_TRY_AVX
void fastConv_avx(const float* weights, size_t wstep, const float* bias,
const float* rowbuf, float* output, const int* outShape,
int blockSize, int vecsize, int vecsize_aligned,
const float* relu, bool initOutput);
void fastGEMM1T_avx( const float* vec, const float* weights,
size_t wstep, const float* bias,
float* dst, int nvecs, int vecsize );
void fastGEMM_avx( const float* aptr, size_t astep, const float* bptr0,
size_t bstep, float* cptr, size_t cstep,
int ma, int na, int nb );
#endif
#if CV_TRY_AVX2
void fastConv_avx2(const float* weights, size_t wstep, const float* bias,
const float* rowbuf, float* output, const int* outShape,
......
modules/dnn/src/layers/layers_common.simd.hpp 0 → 100644
View file @ ed956410
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Copyright (C) 2017, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __DNN_LAYERS_COMMON_SIMD_HPP__
#define __DNN_LAYERS_COMMON_SIMD_HPP__
namespace cv {
namespace dnn {
void fastConv_some_avx( const float* weights, size_t wstep, const float* bias,
const float* rowbuf, float* output, const int* outShape,
int blockSize, int vecsize, int vecsize_aligned,
const float* relu, bool initOutput )
{
int outCn = outShape[1];
size_t outPlaneSize = outShape[2]*outShape[3];
float r0 = 1.f, r1 = 1.f, r2 = 1.f;
__m256 vr0 = _mm256_set1_ps(1.f), vr1 = vr0, vr2 = vr0, z = _mm256_setzero_ps();
// now compute dot product of the weights
// and im2row-transformed part of the tensor
for( int i = 0; i < outCn; i += 3 )
{
const float* wptr0 = weights + i*wstep;
const float* wptr1 = wptr0 + wstep;
const float* wptr2 = wptr1 + wstep;
float* outptr0 = output + i*outPlaneSize;
float* outptr1 = outptr0 + outPlaneSize;
float* outptr2 = outptr1 + outPlaneSize;
float bias0 = bias[i], bias1 = bias[i+1], bias2 = bias[i+2];
if( i+2 >= outCn )
{
wptr2 = wptr1;
outptr2 = outptr1;
bias2 = bias1;
if( i+1 >= outCn )
{
wptr2 = wptr1 = wptr0;
outptr2 = outptr1 = outptr0;
bias2 = bias1 = bias0;
}
}
if( relu )
{
r0 = relu[i];
r1 = relu[i+1];
r2 = relu[i+2];
vr0 = _mm256_set1_ps(r0);
vr1 = _mm256_set1_ps(r1);
vr2 = _mm256_set1_ps(r2);
}
int j = 0;
for( ; j <= blockSize - 4; j += 4 )
{
const float* rptr = rowbuf + j*vecsize_aligned;
__m256 vs00 = _mm256_setzero_ps(), vs01 = _mm256_setzero_ps(),
vs02 = _mm256_setzero_ps(), vs03 = _mm256_setzero_ps(),
vs10 = _mm256_setzero_ps(), vs11 = _mm256_setzero_ps(),
vs12 = _mm256_setzero_ps(), vs13 = _mm256_setzero_ps(),
vs20 = _mm256_setzero_ps(), vs21 = _mm256_setzero_ps(),
vs22 = _mm256_setzero_ps(), vs23 = _mm256_setzero_ps();
for( int k = 0; k < vecsize; k += 8, rptr += 8 )
{
__m256 w0 = _mm256_load_ps(wptr0 + k);
__m256 w1 = _mm256_load_ps(wptr1 + k);
__m256 w2 = _mm256_load_ps(wptr2 + k);
__m256 r0 = _mm256_load_ps(rptr);
vs00 = _mm256_fmadd_ps(w0, r0, vs00);
vs10 = _mm256_fmadd_ps(w1, r0, vs10);
vs20 = _mm256_fmadd_ps(w2, r0, vs20);
r0 = _mm256_load_ps(rptr + vecsize_aligned);
vs01 = _mm256_fmadd_ps(w0, r0, vs01);
vs11 = _mm256_fmadd_ps(w1, r0, vs11);
vs21 = _mm256_fmadd_ps(w2, r0, vs21);
r0 = _mm256_load_ps(rptr + vecsize_aligned*2);
vs02 = _mm256_fmadd_ps(w0, r0, vs02);
vs12 = _mm256_fmadd_ps(w1, r0, vs12);
vs22 = _mm256_fmadd_ps(w2, r0, vs22);
r0 = _mm256_load_ps(rptr + vecsize_aligned*3);
vs03 = _mm256_fmadd_ps(w0, r0, vs03);
vs13 = _mm256_fmadd_ps(w1, r0, vs13);
vs23 = _mm256_fmadd_ps(w2, r0, vs23);
}
__m256 t0 = _mm256_hadd_ps(_mm256_hadd_ps(vs00, vs01), _mm256_hadd_ps(vs02, vs03));
__m256 t1 = _mm256_hadd_ps(_mm256_hadd_ps(vs10, vs11), _mm256_hadd_ps(vs12, vs13));
__m256 t2 = _mm256_hadd_ps(_mm256_hadd_ps(vs20, vs21), _mm256_hadd_ps(vs22, vs23));
t0 = _mm256_add_ps(t0, _mm256_permute2f128_ps(t0, t0, 1));
t1 = _mm256_add_ps(t1, _mm256_permute2f128_ps(t1, t1, 1));
t2 = _mm256_add_ps(t2, _mm256_permute2f128_ps(t2, t2, 1));
__m256 s0, s1, s2;
if( initOutput )
{
s0 = _mm256_set1_ps(bias0);
s1 = _mm256_set1_ps(bias1);
s2 = _mm256_set1_ps(bias2);
}
else
{
s0 = _mm256_castps128_ps256(_mm_loadu_ps(outptr0 + j));
s1 = _mm256_castps128_ps256(_mm_loadu_ps(outptr1 + j));
s2 = _mm256_castps128_ps256(_mm_loadu_ps(outptr2 + j));
}
s0 = _mm256_add_ps(s0, t0);
s1 = _mm256_add_ps(s1, t1);
s2 = _mm256_add_ps(s2, t2);
if( relu )
{
__m256 m0 = _mm256_cmp_ps(s0, z, _CMP_GT_OS);
__m256 m1 = _mm256_cmp_ps(s1, z, _CMP_GT_OS);
__m256 m2 = _mm256_cmp_ps(s2, z, _CMP_GT_OS);
s0 = _mm256_xor_ps(s0, _mm256_andnot_ps(m0, _mm256_xor_ps(_mm256_mul_ps(s0, vr0), s0)));
s1 = _mm256_xor_ps(s1, _mm256_andnot_ps(m1, _mm256_xor_ps(_mm256_mul_ps(s1, vr1), s1)));
s2 = _mm256_xor_ps(s2, _mm256_andnot_ps(m2, _mm256_xor_ps(_mm256_mul_ps(s2, vr2), s2)));
}
_mm_storeu_ps(outptr0 + j, _mm256_castps256_ps128(s0));
_mm_storeu_ps(outptr1 + j, _mm256_castps256_ps128(s1));
_mm_storeu_ps(outptr2 + j, _mm256_castps256_ps128(s2));
}
for( ; j < blockSize; j++ )
{
const float* rptr = rowbuf + j*vecsize_aligned;
float s00, s10, s20;
if( initOutput )
{
s00 = bias0;
s10 = bias1;
s20 = bias2;
}
else
{
s00 = outptr0[j];
s10 = outptr1[j];
s20 = outptr2[j];
}
for( int k = 0; k < vecsize; k++ )
{
float r0 = rptr[k];
s00 += wptr0[k]*r0;
s10 += wptr1[k]*r0;
s20 += wptr2[k]*r0;
}
if( relu )
{
s00 = s00 > 0.f ? s00 : s00*r0;
s10 = s10 > 0.f ? s10 : s10*r1;
s20 = s20 > 0.f ? s20 : s20*r2;
}
outptr0[j] = s00;
outptr1[j] = s10;
outptr2[j] = s20;
}
}
_mm256_zeroupper();
}
// dst = vec * weights^t + bias
void fastGEMM1T_some_avx( const float* vec, const float* weights,
size_t wstep, const float* bias,
float* dst, int nvecs, int vecsize )
{
int i = 0;
for( ; i <= nvecs - 8; i += 8 )
{
const float* wptr = weights + i*wstep;
__m256 vs0 = _mm256_setzero_ps(), vs1 = _mm256_setzero_ps(),
vs2 = _mm256_setzero_ps(), vs3 = _mm256_setzero_ps(),
vs4 = _mm256_setzero_ps(), vs5 = _mm256_setzero_ps(),
vs6 = _mm256_setzero_ps(), vs7 = _mm256_setzero_ps();
for( int k = 0; k < vecsize; k += 8, wptr += 8 )
{
__m256 v = _mm256_load_ps(vec + k);
vs0 = _mm256_fmadd_ps(_mm256_load_ps(wptr), v, vs0);
vs1 = _mm256_fmadd_ps(_mm256_load_ps(wptr + wstep), v, vs1);
vs2 = _mm256_fmadd_ps(_mm256_load_ps(wptr + wstep*2), v, vs2);
vs3 = _mm256_fmadd_ps(_mm256_load_ps(wptr + wstep*3), v, vs3);
vs4 = _mm256_fmadd_ps(_mm256_load_ps(wptr + wstep*4), v, vs4);
vs5 = _mm256_fmadd_ps(_mm256_load_ps(wptr + wstep*5), v, vs5);
vs6 = _mm256_fmadd_ps(_mm256_load_ps(wptr + wstep*6), v, vs6);
vs7 = _mm256_fmadd_ps(_mm256_load_ps(wptr + wstep*7), v, vs7);
}
__m256 s0 = _mm256_hadd_ps(_mm256_hadd_ps(vs0, vs1), _mm256_hadd_ps(vs2, vs3));
__m256 s1 = _mm256_hadd_ps(_mm256_hadd_ps(vs4, vs5), _mm256_hadd_ps(vs6, vs7));
s0 = _mm256_add_ps(s0, _mm256_permute2f128_ps(s0, s0, 1));
s1 = _mm256_add_ps(s1, _mm256_permute2f128_ps(s1, s1, 1));
s0 = _mm256_add_ps(s0, _mm256_castps128_ps256(_mm_loadu_ps(bias + i)));
s1 = _mm256_add_ps(s1, _mm256_castps128_ps256(_mm_loadu_ps(bias + i + 4)));
_mm_storeu_ps(dst + i, _mm256_castps256_ps128(s0));
_mm_storeu_ps(dst + i + 4, _mm256_castps256_ps128(s1));
}
float temp = 0.f;
for( ; i < nvecs; i++ )
{
const float* wptr = weights + i*wstep;
__m256 vs0 = _mm256_setzero_ps();
for( int k = 0; k < vecsize; k += 8, wptr += 8 )
{
__m256 v = _mm256_load_ps(vec + k);
vs0 = _mm256_fmadd_ps(_mm256_load_ps(wptr), v, vs0);
}
__m256 s0 = _mm256_hadd_ps(_mm256_hadd_ps(vs0, vs0), vs0);
s0 = _mm256_add_ps(s0, _mm256_permute2f128_ps(s0, s0, 1));
_mm_store_ss(&temp, _mm256_castps256_ps128(s0));
dst[i] = temp + bias[i];
}
_mm256_zeroupper();
}
void fastGEMM_some_avx( const float* aptr, size_t astep, const float* bptr,
size_t bstep, float* cptr, size_t cstep,
int ma, int na, int nb )
{
int n = 0;
for( ; n <= nb - 16; n += 16 )
{
for( int m = 0; m < ma; m += 4 )
{
const float* aptr0 = aptr + astep*m;
const float* aptr1 = aptr + astep*std::min(m+1, ma-1);
const float* aptr2 = aptr + astep*std::min(m+2, ma-1);
const float* aptr3 = aptr + astep*std::min(m+3, ma-1);
float* cptr0 = cptr + cstep*m;
float* cptr1 = cptr + cstep*std::min(m+1, ma-1);
float* cptr2 = cptr + cstep*std::min(m+2, ma-1);
float* cptr3 = cptr + cstep*std::min(m+3, ma-1);
__m256 d00 = _mm256_setzero_ps(), d01 = _mm256_setzero_ps();
__m256 d10 = _mm256_setzero_ps(), d11 = _mm256_setzero_ps();
__m256 d20 = _mm256_setzero_ps(), d21 = _mm256_setzero_ps();
__m256 d30 = _mm256_setzero_ps(), d31 = _mm256_setzero_ps();
for( int k = 0; k < na; k++ )
{
__m256 a0 = _mm256_set1_ps(aptr0[k]);
__m256 a1 = _mm256_set1_ps(aptr1[k]);
__m256 a2 = _mm256_set1_ps(aptr2[k]);
__m256 a3 = _mm256_set1_ps(aptr3[k]);
__m256 b0 = _mm256_loadu_ps(bptr + k*bstep + n);
__m256 b1 = _mm256_loadu_ps(bptr + k*bstep + n + 8);
d00 = _mm256_fmadd_ps(a0, b0, d00);
d01 = _mm256_fmadd_ps(a0, b1, d01);
d10 = _mm256_fmadd_ps(a1, b0, d10);
d11 = _mm256_fmadd_ps(a1, b1, d11);
d20 = _mm256_fmadd_ps(a2, b0, d20);
d21 = _mm256_fmadd_ps(a2, b1, d21);
d30 = _mm256_fmadd_ps(a3, b0, d30);
d31 = _mm256_fmadd_ps(a3, b1, d31);
}
_mm256_storeu_ps(cptr0 + n, d00);
_mm256_storeu_ps(cptr0 + n + 8, d01);
_mm256_storeu_ps(cptr1 + n, d10);
_mm256_storeu_ps(cptr1 + n + 8, d11);
_mm256_storeu_ps(cptr2 + n, d20);
_mm256_storeu_ps(cptr2 + n + 8, d21);
_mm256_storeu_ps(cptr3 + n, d30);
_mm256_storeu_ps(cptr3 + n + 8, d31);
}
}
for( ; n < nb; n++ )
{
for( int m = 0; m < ma; m++ )
{
const float* aptr0 = aptr + astep*m;
float* cptr0 = cptr + cstep*m;
float d0 = 0.f;
for( int k = 0; k < na; k++ )
d0 += aptr0[k]*bptr[k*bstep + n];
cptr0[n] = d0;
}
}
_mm256_zeroupper();
}
}
}
#endif
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