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new_conv.cpp
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new_conv.cpp
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#include <iostream>
#include <cmath>
#include <Eigen/Dense>
#include <bench/BenchTimer.h>
inline bool is_a_ge_zero_and_a_lt_b(int a, int b) {
return static_cast<unsigned>(a) < static_cast<unsigned>(b);
}
// caffe2 im2col
void Im2col(
const float* data_im,
const int channels,
const int height,
const int width,
const int kernel_h,
const int kernel_w,
const int dilation_h,
const int dilation_w,
const int pad_t,
const int pad_l,
const int pad_b,
const int pad_r,
const int stride_h,
const int stride_w,
float* data_col) {
const int output_h =
(height + pad_b + pad_t - (dilation_h * (kernel_h - 1) + 1)) / stride_h +
1;
const int output_w =
(width + pad_l + pad_r - (dilation_w * (kernel_w - 1) + 1)) / stride_w +
1;
// Fast path for zero padding and no dilation
// From Torch, THNN_(unfolded_copy)
if (dilation_h == 1 && dilation_w == 1 && pad_l == 0 && pad_r == 0 &&
pad_t == 0 && pad_b == 0) {
for (auto k = 0; k < channels * kernel_h * kernel_w; k++) {
const auto nip = k / (kernel_h * kernel_w);
const auto rest = k % (kernel_h * kernel_w);
const auto kh = rest / kernel_w;
const auto kw = rest % kernel_w;
auto* dst = data_col + nip * (kernel_h * kernel_w * output_h * output_w) +
kh * (kernel_w * output_h * output_w) + kw * (output_h * output_w);
const auto* src = data_im + nip * (height * width);
for (auto y = 0; y < output_h; y++) {
const auto iy = y * stride_h + kh;
const auto ix = kw;
if (stride_w == 1) {
memcpy(
dst + (y * output_w),
src + (iy * width + ix),
sizeof(float) * output_w);
} else {
for (auto x = 0; x < output_w; x++) {
memcpy(
dst + (y * output_w + x),
src + (iy * width + ix + x * stride_w),
sizeof(float));
}
}
}
}
return;
}
// Fast path for equal padding
if (pad_l == pad_r && pad_t == pad_b) {
// From Intel, https://github.com/BVLC/caffe/pull/3536
const int pad_h = pad_t;
const int pad_w = pad_l;
const int channel_size = height * width;
for (int channel = channels; channel--; data_im += channel_size) {
for (int kernel_row = 0; kernel_row < kernel_h; kernel_row++) {
for (int kernel_col = 0; kernel_col < kernel_w; kernel_col++) {
int input_row = -pad_h + kernel_row * dilation_h;
for (int output_rows = output_h; output_rows; output_rows--) {
if (!is_a_ge_zero_and_a_lt_b(input_row, height)) {
for (int output_cols = output_w; output_cols; output_cols--) {
*(data_col++) = 0;
}
} else {
int input_col = -pad_w + kernel_col * dilation_w;
for (int output_col = output_w; output_col; output_col--) {
if (is_a_ge_zero_and_a_lt_b(input_col, width)) {
*(data_col++) = data_im[input_row * width + input_col];
} else {
*(data_col++) = 0;
}
input_col += stride_w;
}
}
input_row += stride_h;
}
}
}
}
return;
}
// Baseline
const int dkernel_h = dilation_h * (kernel_h - 1) + 1;
const int dkernel_w = dilation_w * (kernel_w - 1) + 1;
int height_col = (height + pad_t + pad_b - dkernel_h) / stride_h + 1;
int width_col = (width + pad_l + pad_r - dkernel_w) / stride_w + 1;
int channels_col = channels * kernel_h * kernel_w;
for (int c = 0; c < channels_col; ++c) {
int w_offset = c % kernel_w;
int h_offset = (c / kernel_w) % kernel_h;
int c_im = c / kernel_h / kernel_w;
for (int h = 0; h < height_col; ++h) {
for (int w = 0; w < width_col; ++w) {
int h_pad = h * stride_h - pad_t + h_offset * dilation_h;
int w_pad = w * stride_w - pad_l + w_offset * dilation_w;
if (h_pad >= 0 && h_pad < height && w_pad >= 0 && w_pad < width)
data_col[(c * height_col + h) * width_col + w] =
data_im[(c_im * height + h_pad) * width + w_pad];
else
data_col[(c * height_col + h) * width_col + w] = 0;
}
}
}
}
namespace Eigen {
namespace internal {
template<
class T,
class Index>
struct conv_pack_rhs{
void operator()(
T* block,
const T* A,
Index C,
Index H,
Index W,
Index start_c,
Index start_i,
Index start_j,
Index lenC,
Index lenH,
Index lenW){
if(start_c + lenC > C || start_i + lenH > H || start_j + lenW > W){
std::cout << "conv pack rhs out of bound" << std::endl;
exit(0);
}
Index channel_offset = H * W;
const T* Aptr = A + start_c * channel_offset;
Eigen::Map<Eigen::Matrix<T, -1, -1, Eigen::RowMajor>> blockmap(block, lenC * lenH, lenW);
for(Index c = 0; c < lenC; ++c){
Eigen::Map<const Eigen::Matrix<T, -1, -1, Eigen::RowMajor>> Amap(Aptr, H, W);
blockmap.block(c * lenH, 0, lenH, lenW) = Amap.block(start_i, start_j, lenH, lenW);
Aptr += channel_offset;
}
}
};
template<typename _LhsScalar, typename _RhsScalar, class Index>
class conv_level3_blocking
{
typedef _LhsScalar LhsScalar;
typedef _RhsScalar RhsScalar;
protected:
LhsScalar* m_blockA;
RhsScalar* m_blockB;
Index m_mc;
Index m_nc;
Index m_kc;
public:
conv_level3_blocking()
: m_blockA(0), m_blockB(0), m_mc(0), m_nc(0), m_kc(0)
{}
inline Index mc() const { return m_mc; }
inline Index nc() const { return m_nc; }
inline Index kc() const { return m_kc; }
inline LhsScalar* blockA() { return m_blockA; }
inline RhsScalar* blockB() { return m_blockB; }
};
template<typename LhsScalar, typename RhsScalar, int KcFactor, typename Index>
void convEvaluateProductBlockingSizesHeuristic(Index& k, Index& m, Index& n, Index num_threads = 1)
{
typedef gebp_traits<LhsScalar,RhsScalar> Traits;
std::ptrdiff_t l1, l2, l3;
manage_caching_sizes(GetAction, &l1, &l2, &l3);
if (num_threads > 1) {
typedef typename Traits::ResScalar ResScalar;
enum {
kdiv = KcFactor * (Traits::mr * sizeof(LhsScalar) + Traits::nr * sizeof(RhsScalar)),
ksub = Traits::mr * Traits::nr * sizeof(ResScalar),
kr = 8,
mr = Traits::mr,
nr = Traits::nr
};
const Index k_cache = (numext::mini<Index>)((l1-ksub)/kdiv, 320);
if (k_cache < k) {
k = k_cache - (k_cache % kr);
eigen_internal_assert(k > 0);
}
const Index n_cache = (l2-l1) / (nr * sizeof(RhsScalar) * k);
const Index n_per_thread = numext::div_ceil(n, num_threads);
if (n_cache <= n_per_thread) {
eigen_internal_assert(n_cache >= static_cast<Index>(nr));
n = n_cache - (n_cache % nr);
eigen_internal_assert(n > 0);
} else {
n = (numext::mini<Index>)(n, (n_per_thread + nr - 1) - ((n_per_thread + nr - 1) % nr));
}
if (l3 > l2) {
const Index m_cache = (l3-l2) / (sizeof(LhsScalar) * k * num_threads);
const Index m_per_thread = numext::div_ceil(m, num_threads);
if(m_cache < m_per_thread && m_cache >= static_cast<Index>(mr)) {
m = m_cache - (m_cache % mr);
eigen_internal_assert(m > 0);
} else {
m = (numext::mini<Index>)(m, (m_per_thread + mr - 1) - ((m_per_thread + mr - 1) % mr));
}
}
}
else {
#ifdef EIGEN_DEBUG_SMALL_PRODUCT_BLOCKS
l1 = 9*1024;
l2 = 32*1024;
l3 = 512*1024;
#endif
if((numext::maxi)(k,(numext::maxi)(m,n))<48)
return;
typedef typename Traits::ResScalar ResScalar;
enum {
k_peeling = 8,
k_div = KcFactor * (Traits::mr * sizeof(LhsScalar) + Traits::nr * sizeof(RhsScalar)),
k_sub = Traits::mr * Traits::nr * sizeof(ResScalar)
};
const Index max_kc = numext::maxi<Index>(((l1-k_sub)/k_div) & (~(k_peeling-1)),1);
const Index old_k = k;
if(k>max_kc)
{
k = (k%max_kc)==0 ? max_kc
: max_kc - k_peeling * ((max_kc-1-(k%max_kc))/(k_peeling*(k/max_kc+1)));
eigen_internal_assert(((old_k/k) == (old_k/max_kc)) && "the number of sweeps has to remain the same");
}
#ifdef EIGEN_DEBUG_SMALL_PRODUCT_BLOCKS
const Index actual_l2 = l3;
#else
const Index actual_l2 = 1572864; // == 1.5 MB
#endif
Index max_nc;
const Index lhs_bytes = m * k * sizeof(LhsScalar);
const Index remaining_l1 = l1- k_sub - lhs_bytes;
max_nc = (3*actual_l2)/(2*2*max_kc*sizeof(RhsScalar));
Index nc = numext::mini<Index>(actual_l2/(2*k*sizeof(RhsScalar)), max_nc) & (~(Traits::nr-1));
if(n>nc)
{
n = (n%nc)==0 ? nc
: (nc - Traits::nr * ((nc/*-1*/-(n%nc))/(Traits::nr*(n/nc+1))));
}
else if(old_k==k)
{
Index problem_size = k*n*sizeof(LhsScalar);
Index actual_lm = actual_l2;
Index max_mc = m;
if(problem_size<=1024)
{
actual_lm = l1;
}
else if(l3!=0 && problem_size<=32768)
{
actual_lm = l2;
max_mc = (numext::mini<Index>)(576,max_mc);
}
Index mc = (numext::mini<Index>)(actual_lm/(3*k*sizeof(LhsScalar)), max_mc);
if (mc > Traits::mr) mc -= mc % Traits::mr;
else if (mc==0) return;
m = (m%mc)==0 ? mc
: (mc - Traits::mr * ((mc/*-1*/-(m%mc))/(Traits::mr*(m/mc+1))));
}
}
}
template<typename LhsScalar, typename RhsScalar, int KcFactor, typename Index>
void convComputeProductBlockingSizes(Index& k, Index& m, Index& n, Index num_threads = 1)
{
if (!useSpecificBlockingSizes(k, m, n)) {
convEvaluateProductBlockingSizesHeuristic<LhsScalar, RhsScalar, KcFactor, Index>(k, m, n, num_threads);
}
}
template<int StorageOrder, typename LhsScalar, typename RhsScalar, int MaxRows, int MaxCols, int MaxDepth, class Index>
class conv_blocking_space;
template<int StorageOrder, typename _LhsScalar, typename _RhsScalar, int MaxRows, int MaxCols, int MaxDepth, class Index>
class conv_blocking_space
: public conv_level3_blocking<
typename conditional<StorageOrder==RowMajor,_RhsScalar,_LhsScalar>::type,
typename conditional<StorageOrder==RowMajor,_LhsScalar,_RhsScalar>::type,
Index>
{
enum {
Transpose = StorageOrder==RowMajor
};
typedef typename conditional<Transpose,_RhsScalar,_LhsScalar>::type LhsScalar;
typedef typename conditional<Transpose,_LhsScalar,_RhsScalar>::type RhsScalar;
typedef gebp_traits<LhsScalar,RhsScalar> Traits;
Index m_sizeA;
Index m_sizeB;
Index conv_mc;
Index conv_channelsc;
Index conv_ohc;
Index conv_owc;
public:
Index mc(){return conv_mc;}
Index channelsc(){return conv_channelsc;}
Index ohc(){return conv_ohc;}
Index owc(){return conv_owc;}
conv_blocking_space(Index m, Index channels, Index oh, Index ow, Index num_threads = 1, bool l3_blocking = true)
{
Index rows = m;
Index cols = oh * ow;
Index depth = 9 * channels;
this->m_mc = Transpose ? cols : rows;
this->m_nc = Transpose ? rows : cols;
this->m_kc = depth;
if(l3_blocking)
{
convComputeProductBlockingSizes<LhsScalar,RhsScalar,1>(this->m_kc, this->m_mc, this->m_nc, num_threads);
}
else
{
Index n = this->m_nc;
convComputeProductBlockingSizes<LhsScalar,RhsScalar,1>(this->m_kc, this->m_mc, n, num_threads);
}
m_sizeA = this->m_mc * this->m_kc;
m_sizeB = this->m_kc * this->m_nc;
Index mc = this->m_mc;
Index nc = this->m_nc;
Index kc = this->m_kc;
conv_mc = mc;
conv_channelsc = kc / 9;
Index remain = kc * nc / conv_channelsc;
Index sqrtremain = static_cast<Index>(std::sqrt(static_cast<double>(remain)));
conv_owc = std::min(ow,std::max(static_cast<Index>(8), sqrtremain / 8 * 8));
conv_ohc = std::max(static_cast<Index>(1),std::min(oh, remain / (conv_owc + 2) - 2));
}
void initParallel(Index rows, Index cols, Index depth, Index num_threads)
{
this->m_mc = Transpose ? cols : rows;
this->m_nc = Transpose ? rows : cols;
this->m_kc = depth;
eigen_internal_assert(this->m_blockA==0 && this->m_blockB==0);
Index m = this->m_mc;
convComputeProductBlockingSizes<LhsScalar,RhsScalar,1>(this->m_kc, m, this->m_nc, num_threads);
m_sizeA = this->m_mc * this->m_kc;
m_sizeB = this->m_kc * this->m_nc;
}
void allocateA()
{
if(this->m_blockA==0)
this->m_blockA = aligned_new<LhsScalar>(m_sizeA);
}
void allocateB()
{
if(this->m_blockB==0)
this->m_blockB = aligned_new<RhsScalar>(m_sizeB);
}
void allocateAll()
{
allocateA();
allocateB();
}
~conv_blocking_space()
{
aligned_delete(this->m_blockA, m_sizeA);
aligned_delete(this->m_blockB, m_sizeB);
}
};
template<typename _LhsScalar, typename _RhsScalar, bool _ConjLhs, bool _ConjRhs>
class half_gebp_traits
{
public:
typedef _LhsScalar LhsScalar;
typedef _RhsScalar RhsScalar;
typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
enum {
ConjLhs = _ConjLhs,
ConjRhs = _ConjRhs,
Vectorizable = packet_traits<LhsScalar>::Vectorizable && packet_traits<RhsScalar>::Vectorizable,
LhsPacketSize = Vectorizable ? packet_traits<LhsScalar>::size / 2 : 1,
RhsPacketSize = Vectorizable ? packet_traits<RhsScalar>::size / 2 : 1,
ResPacketSize = Vectorizable ? packet_traits<ResScalar>::size / 2 : 1,
NumberOfRegisters = EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS,
nr = 4,
default_mr = (EIGEN_PLAIN_ENUM_MIN(16,NumberOfRegisters)/2/nr)*LhsPacketSize,
#if defined(EIGEN_HAS_SINGLE_INSTRUCTION_MADD) && !defined(EIGEN_VECTORIZE_ALTIVEC) && !defined(EIGEN_VECTORIZE_VSX)
mr = Vectorizable ? 3*LhsPacketSize : default_mr,
#else
mr = default_mr,
#endif
LhsProgress = LhsPacketSize,
RhsProgress = 1
};
typedef typename packet_traits<LhsScalar>::half _LhsPacket;
typedef typename packet_traits<RhsScalar>::half _RhsPacket;
typedef typename packet_traits<ResScalar>::half _ResPacket;
typedef typename conditional<Vectorizable,_LhsPacket,LhsScalar>::type LhsPacket;
typedef typename conditional<Vectorizable,_RhsPacket,RhsScalar>::type RhsPacket;
typedef typename conditional<Vectorizable,_ResPacket,ResScalar>::type ResPacket;
typedef ResPacket AccPacket;
EIGEN_STRONG_INLINE void initAcc(AccPacket& p)
{
p = pset1<ResPacket>(ResScalar(0));
}
EIGEN_STRONG_INLINE void broadcastRhs(const RhsScalar* b, RhsPacket& b0, RhsPacket& b1, RhsPacket& b2, RhsPacket& b3)
{
pbroadcast4(b, b0, b1, b2, b3);
}
template<typename RhsPacketType>
EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketType& dest) const
{
dest = pset1<RhsPacketType>(*b);
}
EIGEN_STRONG_INLINE void loadRhsQuad(const RhsScalar* b, RhsPacket& dest) const
{
dest = ploadquad<RhsPacket>(b);
}
template<typename LhsPacketType>
EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacketType& dest) const
{
dest = pload<LhsPacketType>(a);
}
template<typename LhsPacketType>
EIGEN_STRONG_INLINE void loadLhsUnaligned(const LhsScalar* a, LhsPacketType& dest) const
{
dest = ploadu<LhsPacketType>(a);
}
template<typename LhsPacketType, typename RhsPacketType, typename AccPacketType>
EIGEN_STRONG_INLINE void madd(const LhsPacketType& a, const RhsPacketType& b, AccPacketType& c, AccPacketType& tmp) const
{
conj_helper<LhsPacketType,RhsPacketType,ConjLhs,ConjRhs> cj;
#ifdef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
EIGEN_UNUSED_VARIABLE(tmp);
c = cj.pmadd(a,b,c);
#else
tmp = b; tmp = cj.pmul(a,tmp); c = padd(c,tmp);
#endif
}
EIGEN_STRONG_INLINE void acc(const AccPacket& c, const ResPacket& alpha, ResPacket& r) const
{
r = pmadd(c,alpha,r);
}
template<typename ResPacketHalf>
EIGEN_STRONG_INLINE void acc(const ResPacketHalf& c, const ResPacketHalf& alpha, ResPacketHalf& r) const
{
r = pmadd(c,alpha,r);
}
};
#ifdef EIGEN_HAS_SINGLE_INSTRUCTION_CJMADD
#define CJMADD(CJ,A,B,C,T) C = CJ.pmadd(A,B,C);
#else
#define CJMADD(CJ,A,B,C,T) gebp_madd(CJ,A,B,C,T);
#endif
template<typename LhsScalar, typename RhsScalar, typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
struct new_conv_kernel
{
typedef gebp_traits<LhsScalar,RhsScalar,ConjugateLhs,ConjugateRhs> Traits;
typedef typename Traits::ResScalar ResScalar;
typedef typename Traits::LhsPacket LhsPacket;
typedef typename Traits::RhsPacket RhsPacket;
typedef typename Traits::ResPacket ResPacket;
typedef typename Traits::AccPacket AccPacket;
typedef gebp_traits<RhsScalar,LhsScalar,ConjugateRhs,ConjugateLhs> _SwappedTraits;
typedef half_gebp_traits<RhsScalar,LhsScalar,ConjugateRhs,ConjugateLhs> _HalfSwappedTraits;
typedef typename std::conditional<
_SwappedTraits::LhsProgress >= 8,
_HalfSwappedTraits,
_SwappedTraits>::type SwappedTraits;
typedef typename SwappedTraits::ResScalar SResScalar;
typedef typename SwappedTraits::LhsPacket SLhsPacket;
typedef typename SwappedTraits::RhsPacket SRhsPacket;
typedef typename SwappedTraits::ResPacket SResPacket;
typedef typename SwappedTraits::AccPacket SAccPacket;
typedef typename DataMapper::LinearMapper LinearMapper;
enum {
Vectorizable = Traits::Vectorizable,
LhsProgress = Traits::LhsProgress,
RhsProgress = Traits::RhsProgress,
ResPacketSize = Traits::ResPacketSize
};
EIGEN_DONT_INLINE
void operator()(const DataMapper& res, const LhsScalar* blockA, const RhsScalar* blockB,
Index rows, Index depth, Index oh, Index ow, Index ow_stride,
ResScalar alpha, Index strideA=-1, Index strideB=-1);
};
template<typename LhsScalar, typename RhsScalar, typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
EIGEN_DONT_INLINE
void new_conv_kernel<LhsScalar,RhsScalar,Index,DataMapper,mr,nr,ConjugateLhs,ConjugateRhs>
::operator()(const DataMapper& res, const LhsScalar* blockA, const RhsScalar* blockB,
Index rows, Index depth, Index oh, Index ow, Index ow_stride,
ResScalar alpha, Index strideA, Index strideB)
{
Traits traits;
SwappedTraits straits;
// conv 3x3
if(depth % 9 != 0){
std::cout << "depth must be multiple of 9" << std::endl;
exit(0);
}
Index cols = oh * ow;
if(strideA==-1) strideA = depth;
if(strideB==-1) strideB = depth;
conj_helper<LhsScalar,RhsScalar,ConjugateLhs,ConjugateRhs> cj;
Index packet_cols4 = nr>=4 ? (cols/4) * 4 : 0;
const Index peeled_mc3 = mr>=3*Traits::LhsProgress ? (rows/(3*LhsProgress))*(3*LhsProgress) : 0;
const Index peeled_mc2 = mr>=2*Traits::LhsProgress ? peeled_mc3+((rows-peeled_mc3)/(2*LhsProgress))*(2*LhsProgress) : 0;
const Index peeled_mc1 = mr>=1*Traits::LhsProgress ? (rows/(1*LhsProgress))*(1*LhsProgress) : 0;
enum { pk = 9 };
const Index peeled_kc = depth / pk * pk;
const Index prefetch_res_offset = 32/sizeof(ResScalar);
constexpr Index h_increase = 1;
constexpr Index w_increase = 4;
Index packet_ow4 = nr>=4 ? (ow/4) * 4 : 0;
if(mr>=3*Traits::LhsProgress)
{
const Index l1 = defaultL1CacheSize;
/*to be changed*/const Index actual_panel_rows = (3*LhsProgress) * std::max<Index>(1,( (l1 - sizeof(ResScalar)*mr*nr - depth*nr*sizeof(RhsScalar)) / (depth * sizeof(LhsScalar) * 3*LhsProgress) ));
for(Index i1=0; i1<peeled_mc3; i1+=actual_panel_rows)
{
const Index actual_panel_end = (std::min)(i1+actual_panel_rows, peeled_mc3);
for(Index jh = 0; jh < oh; jh += h_increase)
{
const Index actual_jh_end = (std::min)(jh + h_increase, oh);
Index vert = jh;
for(Index jw = 0; jw < packet_ow4; jw += w_increase)
{
const Index actual_jw_end = (std::min)(jw + w_increase, ow);
Index hori = jw;
for(Index i=i1; i<actual_panel_end; i+=3*LhsProgress)
{
// for(Index vert = jh; vert < actual_jh_end; ++vert)
// for(Index hori = jw; hori < actual_jw_end; hori += nr)
{
const LhsScalar* blA = &blockA[i*strideA];
prefetch(&blA[0]);
AccPacket C0, C1, C2, C3,
C4, C5, C6, C7,
C8, C9, C10, C11;
traits.initAcc(C0); traits.initAcc(C1); traits.initAcc(C2); traits.initAcc(C3);
traits.initAcc(C4); traits.initAcc(C5); traits.initAcc(C6); traits.initAcc(C7);
traits.initAcc(C8); traits.initAcc(C9); traits.initAcc(C10); traits.initAcc(C11);
LinearMapper r0 = res.getLinearMapper(i, vert * ow_stride + hori + 0);
LinearMapper r1 = res.getLinearMapper(i, vert * ow_stride + hori + 1);
LinearMapper r2 = res.getLinearMapper(i, vert * ow_stride + hori + 2);
LinearMapper r3 = res.getLinearMapper(i, vert * ow_stride + hori + 3);
r0.prefetch(0);
r1.prefetch(0);
r2.prefetch(0);
r3.prefetch(0);
const RhsScalar* blB = &blockB[vert * (ow + 2) + hori];
prefetch(&blB[0]);
LhsPacket A0, A1;
for(Index k=0; k<peeled_kc; k+=pk)
{
EIGEN_ASM_COMMENT("begin gebp micro kernel 3pX4");
RhsPacket B_0, T0;
LhsPacket A2;
#define EIGEN_GEBP_ONESTEP(K) \
do { \
EIGEN_ASM_COMMENT("begin step of gebp micro kernel 3pX4"); \
EIGEN_ASM_COMMENT("Note: these asm comments work around bug 935!"); \
internal::prefetch(blA+(3*K+16)*LhsProgress); \
if (EIGEN_ARCH_ARM) { internal::prefetch(blB+(4*K+16)*RhsProgress); } /* Bug 953 */ \
traits.loadLhs(&blA[(0+3*K)*LhsProgress], A0); \
traits.loadLhs(&blA[(1+3*K)*LhsProgress], A1); \
traits.loadLhs(&blA[(2+3*K)*LhsProgress], A2); \
traits.loadRhs(blB + (0+(K / 3) * (ow + 2) + K % 3)*Traits::RhsProgress, B_0); \
traits.madd(A0, B_0, C0, T0); \
traits.madd(A1, B_0, C4, T0); \
traits.madd(A2, B_0, C8, B_0); \
traits.loadRhs(blB + (1+(K / 3) * (ow + 2) + K % 3)*Traits::RhsProgress, B_0); \
traits.madd(A0, B_0, C1, T0); \
traits.madd(A1, B_0, C5, T0); \
traits.madd(A2, B_0, C9, B_0); \
traits.loadRhs(blB + (2+(K / 3) * (ow + 2) + K % 3)*Traits::RhsProgress, B_0); \
traits.madd(A0, B_0, C2, T0); \
traits.madd(A1, B_0, C6, T0); \
traits.madd(A2, B_0, C10, B_0); \
traits.loadRhs(blB + (3+(K / 3) * (ow + 2) + K % 3)*Traits::RhsProgress, B_0); \
traits.madd(A0, B_0, C3 , T0); \
traits.madd(A1, B_0, C7, T0); \
traits.madd(A2, B_0, C11, B_0); \
EIGEN_ASM_COMMENT("end step of gebp micro kernel 3pX4"); \
} while(false)
internal::prefetch(blB);
EIGEN_GEBP_ONESTEP(0);
EIGEN_GEBP_ONESTEP(1);
EIGEN_GEBP_ONESTEP(2);
EIGEN_GEBP_ONESTEP(3);
EIGEN_GEBP_ONESTEP(4);
EIGEN_GEBP_ONESTEP(5);
EIGEN_GEBP_ONESTEP(6);
EIGEN_GEBP_ONESTEP(7);
EIGEN_GEBP_ONESTEP(8);
blB += (ow + 2) * (oh + 2);
blA += pk*3*Traits::LhsProgress;
EIGEN_ASM_COMMENT("end gebp micro kernel 3pX4");
}
#undef EIGEN_GEBP_ONESTEP
ResPacket R0, R1, R2;
ResPacket alphav = pset1<ResPacket>(alpha);
R0 = r0.loadPacket(0 * Traits::ResPacketSize);
R1 = r0.loadPacket(1 * Traits::ResPacketSize);
R2 = r0.loadPacket(2 * Traits::ResPacketSize);
traits.acc(C0, alphav, R0);
traits.acc(C4, alphav, R1);
traits.acc(C8, alphav, R2);
r0.storePacket(0 * Traits::ResPacketSize, R0);
r0.storePacket(1 * Traits::ResPacketSize, R1);
r0.storePacket(2 * Traits::ResPacketSize, R2);
R0 = r1.loadPacket(0 * Traits::ResPacketSize);
R1 = r1.loadPacket(1 * Traits::ResPacketSize);
R2 = r1.loadPacket(2 * Traits::ResPacketSize);
traits.acc(C1, alphav, R0);
traits.acc(C5, alphav, R1);
traits.acc(C9, alphav, R2);
r1.storePacket(0 * Traits::ResPacketSize, R0);
r1.storePacket(1 * Traits::ResPacketSize, R1);
r1.storePacket(2 * Traits::ResPacketSize, R2);
R0 = r2.loadPacket(0 * Traits::ResPacketSize);
R1 = r2.loadPacket(1 * Traits::ResPacketSize);
R2 = r2.loadPacket(2 * Traits::ResPacketSize);
traits.acc(C2, alphav, R0);
traits.acc(C6, alphav, R1);
traits.acc(C10, alphav, R2);
r2.storePacket(0 * Traits::ResPacketSize, R0);
r2.storePacket(1 * Traits::ResPacketSize, R1);
r2.storePacket(2 * Traits::ResPacketSize, R2);
R0 = r3.loadPacket(0 * Traits::ResPacketSize);
R1 = r3.loadPacket(1 * Traits::ResPacketSize);
R2 = r3.loadPacket(2 * Traits::ResPacketSize);
traits.acc(C3, alphav, R0);
traits.acc(C7, alphav, R1);
traits.acc(C11, alphav, R2);
r3.storePacket(0 * Traits::ResPacketSize, R0);
r3.storePacket(1 * Traits::ResPacketSize, R1);
r3.storePacket(2 * Traits::ResPacketSize, R2);
}
}
}
}
if(packet_ow4 != ow)
for(Index vert = 0; vert < oh; ++vert)
for(Index hori=packet_ow4; hori<ow; ++hori)
{
{
for(Index i=i1; i<actual_panel_end; i+=3*LhsProgress)
{
const LhsScalar* blA = &blockA[i*strideA];
prefetch(&blA[0]);
AccPacket C0, C4, C8;
traits.initAcc(C0);
traits.initAcc(C4);
traits.initAcc(C8);
LinearMapper r0 = res.getLinearMapper(i, vert * ow_stride + hori + 0);
r0.prefetch(0);
const RhsScalar* blB = &blockB[vert * (ow + 2) + hori];
LhsPacket A0, A1, A2;
for(Index k=0; k<peeled_kc; k+=pk)
{
EIGEN_ASM_COMMENT("begin gebp micro kernel 3pX1");
RhsPacket B_0;
#define EIGEN_GEBGP_ONESTEP(K) \
do { \
EIGEN_ASM_COMMENT("begin step of gebp micro kernel 3pX1"); \
EIGEN_ASM_COMMENT("Note: these asm comments work around bug 935!"); \
traits.loadLhs(&blA[(0+3*K)*LhsProgress], A0); \
traits.loadLhs(&blA[(1+3*K)*LhsProgress], A1); \
traits.loadLhs(&blA[(2+3*K)*LhsProgress], A2); \
traits.loadRhs(blB + (0+(K / 3) * (ow + 2) + K % 3)*Traits::RhsProgress, B_0); \
traits.madd(A0, B_0, C0, B_0); \
traits.madd(A1, B_0, C4, B_0); \
traits.madd(A2, B_0, C8, B_0); \
EIGEN_ASM_COMMENT("end step of gebp micro kernel 3pX1"); \
} while(false)
EIGEN_GEBGP_ONESTEP(0);
EIGEN_GEBGP_ONESTEP(1);
EIGEN_GEBGP_ONESTEP(2);
EIGEN_GEBGP_ONESTEP(3);
EIGEN_GEBGP_ONESTEP(4);
EIGEN_GEBGP_ONESTEP(5);
EIGEN_GEBGP_ONESTEP(6);
EIGEN_GEBGP_ONESTEP(7);
EIGEN_GEBGP_ONESTEP(8);
blB += (ow + 2) * (oh + 2);
blA += pk*3*Traits::LhsProgress;
EIGEN_ASM_COMMENT("end gebp micro kernel 3pX1");
}
#undef EIGEN_GEBGP_ONESTEP
ResPacket R0, R1, R2;
ResPacket alphav = pset1<ResPacket>(alpha);
R0 = r0.loadPacket(0 * Traits::ResPacketSize);
R1 = r0.loadPacket(1 * Traits::ResPacketSize);
R2 = r0.loadPacket(2 * Traits::ResPacketSize);
traits.acc(C0, alphav, R0);
traits.acc(C4, alphav, R1);
traits.acc(C8, alphav, R2);
r0.storePacket(0 * Traits::ResPacketSize, R0);
r0.storePacket(1 * Traits::ResPacketSize, R1);
r0.storePacket(2 * Traits::ResPacketSize, R2);
}
}
}
}
}
if(mr>=2*Traits::LhsProgress)
{
const Index l1 = defaultL1CacheSize;
Index actual_panel_rows = (2*LhsProgress) * std::max<Index>(1,( (l1 - sizeof(ResScalar)*mr*nr - depth*nr*sizeof(RhsScalar)) / (depth * sizeof(LhsScalar) * 2*LhsProgress) ));
const Index actual_panel_cols = 12 * nr;
for(Index i1=peeled_mc3; i1<peeled_mc2; i1+=actual_panel_rows)
{
Index actual_panel_end = (std::min)(i1+actual_panel_rows, peeled_mc2);
for(Index jh = 0; jh < oh; jh += h_increase)
for(Index jw = 0; jw < packet_ow4; jw += w_increase)
{
const Index actual_jh_end = (std::min)(jh + h_increase, oh);
const Index actual_jw_end = (std::min)(jw + w_increase, ow);
for(Index i=i1; i<actual_panel_end; i+=2*LhsProgress)
{
for(Index vert = jh; vert < actual_jh_end; ++vert)
for(Index hori = jw; hori < actual_jw_end; hori += nr)
{
const LhsScalar* blA = &blockA[i*strideA];
prefetch(&blA[0]);
AccPacket C0, C1, C2, C3,
C4, C5, C6, C7;
traits.initAcc(C0); traits.initAcc(C1); traits.initAcc(C2); traits.initAcc(C3);
traits.initAcc(C4); traits.initAcc(C5); traits.initAcc(C6); traits.initAcc(C7);
LinearMapper r0 = res.getLinearMapper(i, vert * ow_stride + hori + 0);
LinearMapper r1 = res.getLinearMapper(i, vert * ow_stride + hori + 1);
LinearMapper r2 = res.getLinearMapper(i, vert * ow_stride + hori + 2);
LinearMapper r3 = res.getLinearMapper(i, vert * ow_stride + hori + 3);
r0.prefetch(prefetch_res_offset);
r1.prefetch(prefetch_res_offset);
r2.prefetch(prefetch_res_offset);
r3.prefetch(prefetch_res_offset);
const RhsScalar* blB = &blockB[vert * (ow + 2) + hori];
prefetch(&blB[0]);
LhsPacket A0, A1;
for(Index k=0; k<peeled_kc; k+=pk)
{
EIGEN_ASM_COMMENT("begin gebp micro kernel 2pX4");
RhsPacket B_0, B1, B2, B3, T0;
#define EIGEN_GEBGP_ONESTEP(K) \
do { \
EIGEN_ASM_COMMENT("begin step of gebp micro kernel 2pX4"); \
EIGEN_ASM_COMMENT("Note: these asm comments work around bug 935!"); \
traits.loadLhs(&blA[(0+2*K)*LhsProgress], A0); \
traits.loadLhs(&blA[(1+2*K)*LhsProgress], A1); \
traits.broadcastRhs(blB + (0+(K / 3) * (ow + 2) + K % 3)*Traits::RhsProgress, B_0, B1, B2, B3); \
traits.madd(A0, B_0, C0, T0); \
traits.madd(A1, B_0, C4, B_0); \
traits.madd(A0, B1, C1, T0); \
traits.madd(A1, B1, C5, B1); \
traits.madd(A0, B2, C2, T0); \
traits.madd(A1, B2, C6, B2); \
traits.madd(A0, B3, C3, T0); \
traits.madd(A1, B3, C7, B3); \
EIGEN_ASM_COMMENT("end step of gebp micro kernel 2pX4"); \
} while(false)
internal::prefetch(blB+(48+0));
EIGEN_GEBGP_ONESTEP(0);
EIGEN_GEBGP_ONESTEP(1);
EIGEN_GEBGP_ONESTEP(2);
EIGEN_GEBGP_ONESTEP(3);
internal::prefetch(blB+(48+16));
EIGEN_GEBGP_ONESTEP(4);
EIGEN_GEBGP_ONESTEP(5);
EIGEN_GEBGP_ONESTEP(6);
EIGEN_GEBGP_ONESTEP(7);
EIGEN_GEBGP_ONESTEP(8);
blB += (ow + 2) * (oh + 2);
blA += pk*(2*Traits::LhsProgress);
EIGEN_ASM_COMMENT("end gebp micro kernel 2pX4");
}
#undef EIGEN_GEBGP_ONESTEP
ResPacket R0, R1, R2, R3;
ResPacket alphav = pset1<ResPacket>(alpha);
R0 = r0.loadPacket(0 * Traits::ResPacketSize);
R1 = r0.loadPacket(1 * Traits::ResPacketSize);
R2 = r1.loadPacket(0 * Traits::ResPacketSize);
R3 = r1.loadPacket(1 * Traits::ResPacketSize);
traits.acc(C0, alphav, R0);
traits.acc(C4, alphav, R1);
traits.acc(C1, alphav, R2);
traits.acc(C5, alphav, R3);
r0.storePacket(0 * Traits::ResPacketSize, R0);
r0.storePacket(1 * Traits::ResPacketSize, R1);
r1.storePacket(0 * Traits::ResPacketSize, R2);
r1.storePacket(1 * Traits::ResPacketSize, R3);
R0 = r2.loadPacket(0 * Traits::ResPacketSize);
R1 = r2.loadPacket(1 * Traits::ResPacketSize);
R2 = r3.loadPacket(0 * Traits::ResPacketSize);
R3 = r3.loadPacket(1 * Traits::ResPacketSize);
traits.acc(C2, alphav, R0);
traits.acc(C6, alphav, R1);
traits.acc(C3, alphav, R2);
traits.acc(C7, alphav, R3);
r2.storePacket(0 * Traits::ResPacketSize, R0);
r2.storePacket(1 * Traits::ResPacketSize, R1);
r3.storePacket(0 * Traits::ResPacketSize, R2);
r3.storePacket(1 * Traits::ResPacketSize, R3);
}
}
}
for(Index hori=packet_ow4; hori<ow; ++hori)
for(Index vert = 0; vert < oh; ++vert)
{
for(Index i=i1; i<actual_panel_end; i+=2*LhsProgress)
{
const LhsScalar* blA = &blockA[i*strideA];
prefetch(&blA[0]);
AccPacket C0, C4;
traits.initAcc(C0);
traits.initAcc(C4);
LinearMapper r0 = res.getLinearMapper(i, vert * ow_stride + hori + 0);
r0.prefetch(prefetch_res_offset);
const RhsScalar* blB = &blockB[vert * (ow + 2) + hori];
LhsPacket A0, A1;
for(Index k=0; k<peeled_kc; k+=pk)
{
EIGEN_ASM_COMMENT("begin gebp micro kernel 2pX1");
RhsPacket B_0, B1;
#define EIGEN_GEBGP_ONESTEP(K) \
do { \
EIGEN_ASM_COMMENT("begin step of gebp micro kernel 2pX1"); \
EIGEN_ASM_COMMENT("Note: these asm comments work around bug 935!"); \
traits.loadLhs(&blA[(0+2*K)*LhsProgress], A0); \
traits.loadLhs(&blA[(1+2*K)*LhsProgress], A1); \
traits.loadRhs(blB + (0+(K / 3) * (ow + 2) + K % 3)*Traits::RhsProgress, B_0); \
traits.madd(A0, B_0, C0, B1); \
traits.madd(A1, B_0, C4, B_0); \