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vendor: OpenCV 5.0.0 snapshot at 40738fb16ceddb5fb3fea747585f7ce6abb0605b

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3rdparty/mlas/lib/s390x/SgemmKernelZVECTOR.cpp
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/*++
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
Module Name:
SgemmKernelZVECTOR.cpp
Abstract:
This module implements the kernels for the single precision matrix/matrix
multiply operation (SGEMM).
--*/
#include "SgemmKernelZVECTOR.h"
#include <vecintrin.h>
struct MlasSgemmBroadcastAElementsZVECTOR
{
template<size_t RowCount, size_t Row>
MLAS_FORCEINLINE
static
void
Iteration(
MLAS_FLOAT32X4 ABroadcast[RowCount],
const float* A,
size_t lda
)
{
ABroadcast[0][Row] = A [Row * lda];
}
};
template<size_t RowCount>
MLAS_FORCEINLINE
void
MlasSgemmComputeAElements(
MLAS_FLOAT32X4 AElements[RowCount],
MLAS_FLOAT32X4 ABroadcast[RowCount]
)
{
const __vector unsigned char mask0 = { 0, 1, 2, 3, 4, 5, 6, 7, 16, 17, 18, 19, 20, 21, 22, 23 };
const __vector unsigned char mask3 = { 8, 9, 10, 11, 12, 13, 14, 15, 24, 25, 26, 27, 28, 29, 30, 31 };
const __vector unsigned char mask_even = { 0, 1, 2, 3, 16, 17, 18, 19, 8, 9, 10, 11, 24, 25, 26, 27 };
const __vector unsigned char mask_odd = { 4, 5, 6, 7, 20, 21, 22, 23, 12, 13, 14, 15, 28, 29, 30, 31 };
__vector float a1,a2;
a1 = vec_perm(AElements[0], AElements[1], mask_even);
a2 = vec_perm(AElements[2], AElements[3], mask_even);
ABroadcast[0] = vec_perm(a1, a2, mask0);
ABroadcast[2] = vec_perm(a1, a2, mask3);
a1 = vec_perm(AElements[0], AElements[1], mask_odd);
a2 = vec_perm(AElements[2], AElements[3], mask_odd);
ABroadcast[1] = vec_perm(a1, a2, mask0);
ABroadcast[3] = vec_perm(a1, a2, mask3);
}
template<size_t RowCount>
MLAS_FORCEINLINE
void
MlasSgemmComputeBlockZVECTOR(
MLAS_FLOAT32X4 acc[32],
MLAS_FLOAT32X4 ABroadcast,
MLAS_FLOAT32X4 A2Broadcast,
const float* B,
size_t CountM
)
{
MLAS_FLOAT32X4 AElements[8];
AElements[0] = vec_splats(ABroadcast[0]);
AElements[1] = vec_splats(ABroadcast[1]);
AElements[2] = vec_splats(ABroadcast[2]);
AElements[3] = vec_splats(ABroadcast[3]);
if (CountM == 8) {
AElements[4] = vec_splats(A2Broadcast[0]);
AElements[5] = vec_splats(A2Broadcast[1]);
AElements[6] = vec_splats(A2Broadcast[2]);
AElements[7] = vec_splats(A2Broadcast[3]);
}
MLAS_FLOAT32X4 BElements[4];
BElements[0] = MlasLoadFloat32x4(B);
BElements[1] = MlasLoadFloat32x4(B + 4);
BElements[2] = MlasLoadFloat32x4(B + 8);
BElements[3] = MlasLoadFloat32x4(B + 12);
acc[0] = __builtin_s390_vfmasb(AElements[0], BElements[0], acc[0]);
acc[1] = __builtin_s390_vfmasb(AElements[1], BElements[0], acc[1]);
acc[2] = __builtin_s390_vfmasb(AElements[2], BElements[0], acc[2]);
acc[3] = __builtin_s390_vfmasb(AElements[3], BElements[0], acc[3]);
acc[4] = __builtin_s390_vfmasb(AElements[0], BElements[1], acc[4]);
acc[5] = __builtin_s390_vfmasb(AElements[1], BElements[1], acc[5]);
acc[6] = __builtin_s390_vfmasb(AElements[2], BElements[1], acc[6]);
acc[7] = __builtin_s390_vfmasb(AElements[3], BElements[1], acc[7]);
acc[8] = __builtin_s390_vfmasb(AElements[0], BElements[2], acc[8]);
acc[9] = __builtin_s390_vfmasb(AElements[1], BElements[2], acc[9]);
acc[10] = __builtin_s390_vfmasb(AElements[2], BElements[2], acc[10]);
acc[11] = __builtin_s390_vfmasb(AElements[3], BElements[2], acc[11]);
acc[12] = __builtin_s390_vfmasb(AElements[0], BElements[3], acc[12]);
acc[13] = __builtin_s390_vfmasb(AElements[1], BElements[3], acc[13]);
acc[14] = __builtin_s390_vfmasb(AElements[2], BElements[3], acc[14]);
acc[15] = __builtin_s390_vfmasb(AElements[3], BElements[3], acc[15]);
if (CountM == 8) {
acc[16] = __builtin_s390_vfmasb(AElements[4], BElements[0], acc[16]);
acc[17] = __builtin_s390_vfmasb(AElements[5], BElements[0], acc[17]);
acc[18] = __builtin_s390_vfmasb(AElements[6], BElements[0], acc[18]);
acc[19] = __builtin_s390_vfmasb(AElements[7], BElements[0], acc[19]);
acc[20] = __builtin_s390_vfmasb(AElements[4], BElements[1], acc[20]);
acc[21] = __builtin_s390_vfmasb(AElements[5], BElements[1], acc[21]);
acc[22] = __builtin_s390_vfmasb(AElements[6], BElements[1], acc[22]);
acc[23] = __builtin_s390_vfmasb(AElements[7], BElements[1], acc[23]);
acc[24] = __builtin_s390_vfmasb(AElements[4], BElements[2], acc[24]);
acc[25] = __builtin_s390_vfmasb(AElements[5], BElements[2], acc[25]);
acc[26] = __builtin_s390_vfmasb(AElements[6], BElements[2], acc[26]);
acc[27] = __builtin_s390_vfmasb(AElements[7], BElements[2], acc[27]);
acc[28] = __builtin_s390_vfmasb(AElements[4], BElements[3], acc[28]);
acc[29] = __builtin_s390_vfmasb(AElements[5], BElements[3], acc[29]);
acc[30] = __builtin_s390_vfmasb(AElements[6], BElements[3], acc[30]);
acc[31] = __builtin_s390_vfmasb(AElements[7], BElements[3], acc[31]);
}
}
template<size_t VectorCount>
struct MlasSgemmStoreVectorZVECTOR
{
template<size_t RowCount, size_t Row>
MLAS_FORCEINLINE
static
void
Iteration(
MLAS_FLOAT32X4 Result[4],
float* C,
size_t ldc,
MLAS_FLOAT32X4 AlphaBroadcast,
bool ZeroMode
)
{
MLAS_FLOAT32X4 *rowC;
if (ZeroMode) {
rowC = reinterpret_cast<MLAS_FLOAT32X4 *>(&C[Row * ldc + VectorCount]);
rowC[0] = Result[Row] * AlphaBroadcast;
} else {
rowC = reinterpret_cast<MLAS_FLOAT32X4 *>(&C[Row * ldc + VectorCount]);
rowC[0] += Result[Row] * AlphaBroadcast;
}
}
};
struct MlasSgemmMultiplyAlphaTrailingZVECTOR
{
template<size_t RowCount, size_t Row>
MLAS_FORCEINLINE
static
void
Iteration(
MLAS_FLOAT32X4 Accumulators[RowCount],
MLAS_FLOAT32X4 AlphaBroadcast
)
{
Accumulators[Row] = MlasMultiplyFloat32x4(Accumulators[Row], AlphaBroadcast);
}
};
template<unsigned Lane>
struct MlasSgemmStoreScalarZVECTOR
{
template<size_t RowCount, size_t Row>
MLAS_FORCEINLINE
static
void
Iteration(
MLAS_FLOAT32X4 Accumulators[RowCount],
float* C,
size_t ldc,
bool ZeroMode
)
{
float* c = C + Row * ldc + Lane;
float Value = Accumulators[Row][Lane];
if (!ZeroMode) {
Value += *c;
}
*c = Value;
}
};
template<size_t RowCount>
MLAS_FORCEINLINE
size_t
MlasSgemmZVECTORProcessCount(
const float* A,
const float* B,
float* C,
size_t CountM,
size_t CountK,
size_t CountN,
size_t lda,
size_t ldc,
MLAS_FLOAT32X4 AlphaBroadcast,
bool ZeroMode
)
{
do {
const float* a = A;
size_t k = CountK;
MLAS_FLOAT32X4 AElements[RowCount];
MLAS_FLOAT32X4 ABroadcast[RowCount] = { 0 };
MLAS_FLOAT32X4 A2Broadcast[RowCount] = { 0 };
MLAS_FLOAT32X4 acc[32] = { 0 };
MLAS_FLOAT32X4 Accumulators[2][RowCount] = {{0}};
//
// Compute the output block.
//
while (k >= 4) {
MlasLoopUnroll<RowCount, MlasFgemmLoadAElements>()(AElements, a, lda);
MlasSgemmComputeAElements<RowCount>(AElements, ABroadcast);
if (CountM == 8) {
MlasLoopUnroll<RowCount, MlasFgemmLoadAElements>()(AElements, a + ( lda * 4), lda);
MlasSgemmComputeAElements<RowCount>(AElements, A2Broadcast);
}
MlasSgemmComputeBlockZVECTOR<RowCount>(&acc[0], ABroadcast[0], A2Broadcast[0], B, CountM);
MlasSgemmComputeBlockZVECTOR<RowCount>(&acc[0], ABroadcast[1], A2Broadcast[1], B+16, CountM);
MlasSgemmComputeBlockZVECTOR<RowCount>(&acc[0], ABroadcast[2], A2Broadcast[2], B+32, CountM);
MlasSgemmComputeBlockZVECTOR<RowCount>(&acc[0], ABroadcast[3], A2Broadcast[3], B+48, CountM);
B += 16 * 4;
a += 4;
k -= 4;
}
while (k > 0) {
MlasLoopUnroll<RowCount, MlasSgemmBroadcastAElementsZVECTOR>()(ABroadcast, a, lda);
if (CountM == 8) {
MlasLoopUnroll<RowCount, MlasSgemmBroadcastAElementsZVECTOR>()(A2Broadcast, a + (lda * 4), lda);
}
MlasSgemmComputeBlockZVECTOR<RowCount>(&acc[0], ABroadcast[0], A2Broadcast[0], B, CountM);
a += 1;
B += 16;
k -= 1;
}
if (CountN >= 16) {
//
// Store the entire output block.
//
MlasLoopUnroll<RowCount, MlasSgemmStoreVectorZVECTOR<0>>()(acc, C, ldc, AlphaBroadcast, ZeroMode);
MlasLoopUnroll<RowCount, MlasSgemmStoreVectorZVECTOR<4>>()(acc + 4, C, ldc, AlphaBroadcast, ZeroMode);
MlasLoopUnroll<RowCount, MlasSgemmStoreVectorZVECTOR<8>>()(acc + 8, C, ldc, AlphaBroadcast, ZeroMode);
MlasLoopUnroll<RowCount, MlasSgemmStoreVectorZVECTOR<12>>()(acc + 12, C, ldc, AlphaBroadcast, ZeroMode);
if (CountM == 8) {
MlasLoopUnroll<RowCount, MlasSgemmStoreVectorZVECTOR<0>>()(acc + 16, C + (ldc*4), ldc, AlphaBroadcast, ZeroMode);
MlasLoopUnroll<RowCount, MlasSgemmStoreVectorZVECTOR<4>>()(acc + 20, C + (ldc*4), ldc, AlphaBroadcast, ZeroMode);
MlasLoopUnroll<RowCount, MlasSgemmStoreVectorZVECTOR<8>>()(acc + 24, C + (ldc*4), ldc, AlphaBroadcast, ZeroMode);
MlasLoopUnroll<RowCount, MlasSgemmStoreVectorZVECTOR<12>>()(acc + 28, C + (ldc*4), ldc, AlphaBroadcast, ZeroMode);
}
} else {
//
// Store the partial output block.
//
if (CountN >= 12) {
MlasLoopUnroll<RowCount, MlasSgemmStoreVectorZVECTOR<0>>()(acc, C, ldc, AlphaBroadcast, ZeroMode);
MlasLoopUnroll<RowCount, MlasSgemmStoreVectorZVECTOR<4>>()(acc + 4, C, ldc, AlphaBroadcast, ZeroMode);
MlasLoopUnroll<RowCount, MlasSgemmStoreVectorZVECTOR<8>>()(acc + 8, C, ldc, AlphaBroadcast, ZeroMode);
if (CountM == 8) {
MlasLoopUnroll<RowCount, MlasSgemmStoreVectorZVECTOR<0>>()(acc + 16, C + (ldc*4), ldc, AlphaBroadcast, ZeroMode);
MlasLoopUnroll<RowCount, MlasSgemmStoreVectorZVECTOR<4>>()(acc + 20, C + (ldc*4), ldc, AlphaBroadcast, ZeroMode);
MlasLoopUnroll<RowCount, MlasSgemmStoreVectorZVECTOR<8>>()(acc + 24, C + (ldc*4), ldc, AlphaBroadcast, ZeroMode);
if (CountN - 12 > 0) {
for (size_t i = 0; i < 4; ++i) {
Accumulators[1][i] = acc[i + 28];
}
}
}
if (CountN - 12 > 0) {
for (size_t i = 0; i < 4; ++i) {
Accumulators[0][i] = acc[i + 12];
}
}
} else if (CountN >= 8) {
MlasLoopUnroll<RowCount, MlasSgemmStoreVectorZVECTOR<0>>()(acc, C, ldc, AlphaBroadcast, ZeroMode);
MlasLoopUnroll<RowCount, MlasSgemmStoreVectorZVECTOR<4>>()(acc + 4, C, ldc, AlphaBroadcast, ZeroMode);
if (CountM == 8) {
MlasLoopUnroll<RowCount, MlasSgemmStoreVectorZVECTOR<0>>()(acc + 16, C + (ldc*4), ldc, AlphaBroadcast, ZeroMode);
MlasLoopUnroll<RowCount, MlasSgemmStoreVectorZVECTOR<4>>()(acc + 20, C + (ldc*4), ldc, AlphaBroadcast, ZeroMode);
if (CountN - 8 > 0) {
for (size_t i = 0; i < 4; ++i) {
Accumulators[1][i] = acc[i + 24];
}
}
}
if (CountN - 8 > 0) {
for (size_t i = 0; i < 4; ++i) {
Accumulators[0][i] = acc[i + 8];
}
}
} else if (CountN >= 4) {
MlasLoopUnroll<RowCount, MlasSgemmStoreVectorZVECTOR<0>>()(acc, C, ldc, AlphaBroadcast, ZeroMode);
if (CountM == 8) {
MlasLoopUnroll<RowCount, MlasSgemmStoreVectorZVECTOR<0>>()(acc + 16, C + (ldc*4), ldc, AlphaBroadcast, ZeroMode);
if (CountN - 4 > 0) {
for (size_t i = 0; i < 4; ++i) {
Accumulators[1][i] = acc[i + 20];
}
}
}
if (CountN - 4 > 0) {
for (size_t i = 0; i < 4; ++i) {
Accumulators[0][i] = acc[i + 4];
}
}
} else {
for (size_t i = 0; i < 4; ++i) {
Accumulators[0][i] = acc[i];
}
if (CountM == 8) {
for (size_t i = 0; i < 4; ++i) {
Accumulators[1][i] = acc[i + 16];
}
}
}
//
// Store the remaining unaligned columns.
//
C += (CountN & ~3);
CountN &= 3;
if (CountN > 0) {
MlasLoopUnroll<RowCount, MlasSgemmMultiplyAlphaTrailingZVECTOR>()(Accumulators[0], AlphaBroadcast);
MlasLoopUnroll<RowCount, MlasSgemmStoreScalarZVECTOR<0>>()(Accumulators[0], C, ldc, ZeroMode);
if (CountM == 8) {
MlasLoopUnroll<RowCount, MlasSgemmMultiplyAlphaTrailingZVECTOR>()(Accumulators[1], AlphaBroadcast);
MlasLoopUnroll<RowCount, MlasSgemmStoreScalarZVECTOR<0>>()(Accumulators[1], C + (ldc*4), ldc, ZeroMode);
}
if (CountN >= 2) {
MlasLoopUnroll<RowCount, MlasSgemmStoreScalarZVECTOR<1>>()(Accumulators[0], C, ldc, ZeroMode);
if (CountM == 8) {
MlasLoopUnroll<RowCount, MlasSgemmStoreScalarZVECTOR<1>>()(Accumulators[1], C + (ldc*4), ldc, ZeroMode);
}
}
if (CountN >= 3) {
MlasLoopUnroll<RowCount, MlasSgemmStoreScalarZVECTOR<2>>()(Accumulators[0], C, ldc, ZeroMode);
if (CountM == 8) {
MlasLoopUnroll<RowCount, MlasSgemmStoreScalarZVECTOR<2>>()(Accumulators[1], C + (ldc*4), ldc, ZeroMode);
}
}
}
break;
}
C += 16;
CountN -= 16;
} while (CountN > 0);
return CountM;
}
size_t
MLASCALL
MlasSgemmKernelZVECTOR(
const float* A,
const float* B,
float* C,
size_t CountK,
size_t CountM,
size_t CountN,
size_t lda,
size_t ldc,
float alpha,
bool ZeroMode
)
/*++
Routine Description:
This routine is an inner kernel to compute matrix multiplication for a
set of rows.
Arguments:
A - Supplies the address of matrix A.
B - Supplies the address of matrix B. The matrix data has been packed using
MlasSgemmCopyPackB or MlasSgemmTransposePackB.
C - Supplies the address of matrix C.
CountK - Supplies the number of columns from matrix A and the number of rows
from matrix B to iterate over.
CountM - Supplies the maximum number of rows that can be processed for
matrix A and matrix C. The actual number of rows handled for this
invocation depends on the kernel implementation.
CountN - Supplies the number of columns from matrix B and matrix C to
iterate over.
lda - Supplies the first dimension of matrix A.
ldc - Supplies the first dimension of matrix C.
alpha - Supplies the scalar multiplier (see SGEMM definition).
ZeroMode - Supplies true if the output matrix must be zero initialized,
else false if the output matrix is accumulated into.
Return Value:
Returns the number of rows handled.
--*/
{
size_t RowsHandled;
MLAS_FLOAT32X4 AlphaBroadcast = MlasBroadcastFloat32x4(alpha);
if (CountM >= 8) {
RowsHandled = MlasSgemmZVECTORProcessCount<4>(A, B, C, 8 ,CountK, CountN, lda, ldc, AlphaBroadcast, ZeroMode);
} else if (CountM >= 4) {
RowsHandled = MlasSgemmZVECTORProcessCount<4>(A, B, C, 4, CountK, CountN, lda, ldc, AlphaBroadcast, ZeroMode);
} else if (CountM >= 2) {
RowsHandled = MlasSgemmProcessCount<2>(A, B, C, CountK, CountN, lda, ldc, AlphaBroadcast, ZeroMode);
} else {
RowsHandled = MlasSgemmProcessCount<1>(A, B, C, CountK, CountN, lda, ldc, AlphaBroadcast, ZeroMode);
}
return RowsHandled;
}