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Grid/Grid/algorithms/blas/BatchedBlas.h
Peter Boyle 9c902e4c2d Batched blas, but not working yet on OneAPI
2024-07-11 15:19:49 +00:00

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/*************************************************************************************
Grid physics library, www.github.com/paboyle/Grid
Source file: BatchedBlas.h
Copyright (C) 2023
Author: Peter Boyle <pboyle@bnl.gov>
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
See the full license in the file "LICENSE" in the top level distribution directory
*************************************************************************************/
/* END LEGAL */
#pragma once
#ifdef GRID_HIP
#include <hipblas/hipblas.h>
#endif
#ifdef GRID_CUDA
#include <cublas_v2.h>
#endif
#ifdef GRID_SYCL
#include <oneapi/mkl.hpp>
#endif
#if 0
#define GRID_ONE_MKL
#endif
#ifdef GRID_ONE_MKL
#include <oneapi/mkl.hpp>
#endif
///////////////////////////////////////////////////////////////////////
// Need to rearrange lattice data to be in the right format for a
// batched multiply. Might as well make these static, dense packed
///////////////////////////////////////////////////////////////////////
NAMESPACE_BEGIN(Grid);
#ifdef GRID_HIP
typedef hipblasHandle_t gridblasHandle_t;
#endif
#ifdef GRID_CUDA
typedef cublasHandle_t gridblasHandle_t;
#endif
#ifdef GRID_SYCL
typedef cl::sycl::queue *gridblasHandle_t;
#endif
#ifdef GRID_ONE_MKL
typedef cl::sycl::queue *gridblasHandle_t;
#endif
#if !defined(GRID_SYCL) && !defined(GRID_CUDA) && !defined(GRID_HIP) && !defined(GRID_ONE_MKL)
typedef int32_t gridblasHandle_t;
#endif
enum GridBLASOperation_t { GridBLAS_OP_N, GridBLAS_OP_T, GridBLAS_OP_C } ;
class GridBLAS {
public:
static gridblasHandle_t gridblasHandle;
static int gridblasInit;
static void Init(void)
{
if ( ! gridblasInit ) {
#ifdef GRID_CUDA
std::cout << "cublasCreate"<<std::endl;
cublasCreate(&gridblasHandle);
cublasSetPointerMode(gridblasHandle, CUBLAS_POINTER_MODE_DEVICE);
#endif
#ifdef GRID_HIP
std::cout << "hipblasCreate"<<std::endl;
hipblasCreate(&gridblasHandle);
#endif
#ifdef GRID_SYCL
gridblasHandle = theGridAccelerator;
#endif
#ifdef GRID_ONE_MKL
cl::sycl::gpu_selector selector;
cl::sycl::device selectedDevice { selector };
cl::sycl::property_list q_prop{cl::sycl::property::queue::in_order()};
gridblasHandle =new sycl::queue (selectedDevice,q_prop);
#endif
gridblasInit=1;
}
}
// Force construct once
GridBLAS() { Init(); };
~GridBLAS() { };
/////////////////////////////////////////////////////////////////////////////////////
// BLAS GEMM conventions:
/////////////////////////////////////////////////////////////////////////////////////
// - C = alpha A * B + beta C
// Dimensions:
// - C_m.n
// - A_m.k
// - B_k.n
// - Flops = 8 M N K
// - Bytes = 2*sizeof(word) * (MN+MK+KN)
// M=60, N=12
// Flop/Byte = 8 . 60.60.12 / (60.12+60.60+60.12)/16 = 4 so expect about 4 TF/s on a GCD
/////////////////////////////////////////////////////////////////////////////////////
void synchronise(void)
{
#ifdef GRID_HIP
auto err = hipDeviceSynchronize();
assert(err==hipSuccess);
#endif
#ifdef GRID_CUDA
auto err = cudaDeviceSynchronize();
assert(err==cudaSuccess);
#endif
#ifdef GRID_SYCL
accelerator_barrier();
#endif
#ifdef GRID_ONE_MKL
gridblasHandle->wait();
#endif
}
void gemmBatched(int m,int n, int k,
ComplexD alpha,
deviceVector<ComplexD*> &Amk, // pointer list to matrices
deviceVector<ComplexD*> &Bkn,
ComplexD beta,
deviceVector<ComplexD*> &Cmn)
{
gemmBatched(GridBLAS_OP_N,GridBLAS_OP_N,
m,n,k,
alpha,
Amk,
Bkn,
beta,
Cmn);
}
void gemmBatched(int m,int n, int k,
ComplexF alpha,
deviceVector<ComplexF*> &Amk, // pointer list to matrices
deviceVector<ComplexF*> &Bkn,
ComplexF beta,
deviceVector<ComplexF*> &Cmn)
{
gemmBatched(GridBLAS_OP_N,GridBLAS_OP_N,
m,n,k,
alpha,
Amk,
Bkn,
beta,
Cmn);
}
void gemmBatched(int m,int n, int k,
RealD alpha,
deviceVector<RealD*> &Amk, // pointer list to matrices
deviceVector<RealD*> &Bkn,
RealD beta,
deviceVector<RealD*> &Cmn)
{
gemmBatched(GridBLAS_OP_N,GridBLAS_OP_N,
m,n,k,
alpha,
Amk,
Bkn,
beta,
Cmn);
}
void gemmBatched(int m,int n, int k,
RealF alpha,
deviceVector<RealF*> &Amk, // pointer list to matrices
deviceVector<RealF*> &Bkn,
RealF beta,
deviceVector<RealF*> &Cmn)
{
gemmBatched(GridBLAS_OP_N,GridBLAS_OP_N,
m,n,k,
alpha,
Amk,
Bkn,
beta,
Cmn);
}
void gemmBatched(GridBLASOperation_t OpA,
GridBLASOperation_t OpB,
int m,int n, int k,
ComplexD alpha,
deviceVector<ComplexD*> &Amk, // pointer list to matrices
deviceVector<ComplexD*> &Bkn,
ComplexD beta,
deviceVector<ComplexD*> &Cmn)
{
RealD t2=usecond();
int32_t batchCount = Amk.size();
assert(Bkn.size()==batchCount);
assert(Cmn.size()==batchCount);
int lda = m; // m x k column major
int ldb = k; // k x n column major
int ldc = m; // m x b column major
if(OpA!=GridBLAS_OP_N)
lda = k;
if(OpB!=GridBLAS_OP_N)
ldb = n;
static deviceVector<ComplexD> alpha_p(1);
static deviceVector<ComplexD> beta_p(1);
// can prestore the 1 and the zero on device
acceleratorCopyToDevice((void *)&alpha,(void *)&alpha_p[0],sizeof(ComplexD));
acceleratorCopyToDevice((void *)&beta ,(void *)&beta_p[0],sizeof(ComplexD));
RealD t0=usecond();
// std::cout << "ZgemmBatched mnk "<<m<<","<<n<<","<<k<<" count "<<batchCount<<std::endl;
#ifdef GRID_HIP
hipblasOperation_t hOpA;
hipblasOperation_t hOpB;
if ( OpA == GridBLAS_OP_N ) hOpA = HIPBLAS_OP_N;
if ( OpA == GridBLAS_OP_T ) hOpA = HIPBLAS_OP_T;
if ( OpA == GridBLAS_OP_C ) hOpA = HIPBLAS_OP_C;
if ( OpB == GridBLAS_OP_N ) hOpB = HIPBLAS_OP_N;
if ( OpB == GridBLAS_OP_T ) hOpB = HIPBLAS_OP_T;
if ( OpB == GridBLAS_OP_C ) hOpB = HIPBLAS_OP_C;
auto err = hipblasZgemmBatched(gridblasHandle,
hOpA,
hOpB,
m,n,k,
(hipblasDoubleComplex *) &alpha_p[0],
(hipblasDoubleComplex **)&Amk[0], lda,
(hipblasDoubleComplex **)&Bkn[0], ldb,
(hipblasDoubleComplex *) &beta_p[0],
(hipblasDoubleComplex **)&Cmn[0], ldc,
batchCount);
// std::cout << " hipblas return code " <<(int)err<<std::endl;
assert(err==HIPBLAS_STATUS_SUCCESS);
#endif
#ifdef GRID_CUDA
cublasOperation_t hOpA;
cublasOperation_t hOpB;
if ( OpA == GridBLAS_OP_N ) hOpA = CUBLAS_OP_N;
if ( OpA == GridBLAS_OP_T ) hOpA = CUBLAS_OP_T;
if ( OpA == GridBLAS_OP_C ) hOpA = CUBLAS_OP_C;
if ( OpB == GridBLAS_OP_N ) hOpB = CUBLAS_OP_N;
if ( OpB == GridBLAS_OP_T ) hOpB = CUBLAS_OP_T;
if ( OpB == GridBLAS_OP_C ) hOpB = CUBLAS_OP_C;
auto err = cublasZgemmBatched(gridblasHandle,
hOpA,
hOpB,
m,n,k,
(cuDoubleComplex *) &alpha_p[0],
(cuDoubleComplex **)&Amk[0], lda,
(cuDoubleComplex **)&Bkn[0], ldb,
(cuDoubleComplex *) &beta_p[0],
(cuDoubleComplex **)&Cmn[0], ldc,
batchCount);
assert(err==CUBLAS_STATUS_SUCCESS);
#endif
#ifdef GRID_SYCL
std::cerr << " Calling SYCL batched ZGEMM "<<std::endl;
int64_t m64=m;
int64_t n64=n;
int64_t k64=k;
int64_t lda64=lda;
int64_t ldb64=ldb;
int64_t ldc64=ldc;
int64_t batchCount64=batchCount;
oneapi::mkl::transpose notransp =oneapi::mkl::transpose::N;
oneapi::mkl::blas::column_major::gemm_batch(*gridblasHandle,
&notransp,
&notransp,
&m64,&n64,&k64,
(ComplexD *) &alpha_p[0],
(const ComplexD **)&Amk[0], (const int64_t *)&lda64,
(const ComplexD **)&Bkn[0], (const int64_t *)&ldb64,
(ComplexD *) &beta_p[0],
(ComplexD **)&Cmn[0], (const int64_t *)&ldc64,
(int64_t)1,&batchCount64,std::vector<sycl::event>());
synchronise();
std::cerr << " Called SYCL batched ZGEMM "<<std::endl;
std::vector<ComplexD> A(m*k); // pointer list to matrices
std::vector<ComplexD> B(k*n);
std::vector<ComplexD> C(m*n);
int sda = lda*k;
int sdb = ldb*k;
int sdc = ldc*n;
for (int p = 0; p < 1; ++p) {
acceleratorCopyFromDevice((void *)&Amk[p][0],(void *)&A[0],m*k*sizeof(ComplexD));
acceleratorCopyFromDevice((void *)&Bkn[p][0],(void *)&B[0],k*n*sizeof(ComplexD));
acceleratorCopyFromDevice((void *)&Cmn[p][0],(void *)&C[0],m*n*sizeof(ComplexD));
for (int mm = 0; mm < m; ++mm) {
for (int nn = 0; nn < n; ++nn) {
ComplexD c_mn(0.0);
for (int kk = 0; kk < k; ++kk)
c_mn += A[mm + kk*lda ] * B[kk + nn*ldb];
std::cout << " beta "<<beta<<" C_"<<mm<<","<<nn<<" "<<c_mn<<" "<<C[mm + nn*ldc]<<std::endl;
}
}
}
#endif
#if !defined(GRID_SYCL) && !defined(GRID_CUDA) && !defined(GRID_HIP)
// Need a default/reference implementation
int sda = lda*k;
int sdb = ldb*k;
int sdc = ldc*n;
for (int p = 0; p < batchCount; ++p) {
for (int mm = 0; mm < m; ++mm) {
for (int nn = 0; nn < n; ++nn) {
ComplexD c_mn(0.0);
for (int kk = 0; kk < k; ++kk)
c_mn += Amk[p][mm + kk*lda ] * Bkn[p][kk + nn*ldb];
Cmn[p][mm + nn*ldc] = (alpha)*c_mn + (beta)*Cmn[p][mm + nn*ldc ];
}
}
}
#endif
RealD t1=usecond();
RealD flops = 8.0*m*n*k*batchCount;
RealD bytes = 1.0*sizeof(ComplexD)*(m*k+k*n+m*n)*batchCount;
// std::cout <<GridLogMessage<< " batched Blas copy "<<(t0-t2)/1.e3 <<" ms "<<std::endl;
// std::cout <<GridLogMessage<< " batched Blas zGemm call "<<m<<","<<n<<","<<k<<" "<< flops/(t1-t0)/1.e3 <<" GF/s "<<(t1-t0)/1.e3<<" ms "<<std::endl;
// std::cout <<GridLogMessage<< " batched Blas zGemm call "<<m<<","<<n<<","<<k<<" "<< bytes/(t1-t0)/1.e3 <<" GB/s "<<(t1-t0)/1.e3<<" ms "<<std::endl;
}
void gemmBatched(GridBLASOperation_t OpA,
GridBLASOperation_t OpB,
int m,int n, int k,
ComplexF alpha,
deviceVector<ComplexF*> &Amk, // pointer list to matrices
deviceVector<ComplexF*> &Bkn,
ComplexF beta,
deviceVector<ComplexF*> &Cmn)
{
RealD t2=usecond();
int32_t batchCount = Amk.size();
int lda = m; // m x k column major
int ldb = k; // k x n column major
int ldc = m; // m x b column major
if(OpA!=GridBLAS_OP_N)
lda = k;
if(OpB!=GridBLAS_OP_N)
ldb = n;
static deviceVector<ComplexF> alpha_p(1);
static deviceVector<ComplexF> beta_p(1);
// can prestore the 1 and the zero on device
acceleratorCopyToDevice((void *)&alpha,(void *)&alpha_p[0],sizeof(ComplexF));
acceleratorCopyToDevice((void *)&beta ,(void *)&beta_p[0],sizeof(ComplexF));
RealD t0=usecond();
assert(Bkn.size()==batchCount);
assert(Cmn.size()==batchCount);
#ifdef GRID_HIP
hipblasOperation_t hOpA;
hipblasOperation_t hOpB;
if ( OpA == GridBLAS_OP_N ) hOpA = HIPBLAS_OP_N;
if ( OpA == GridBLAS_OP_T ) hOpA = HIPBLAS_OP_T;
if ( OpA == GridBLAS_OP_C ) hOpA = HIPBLAS_OP_C;
if ( OpB == GridBLAS_OP_N ) hOpB = HIPBLAS_OP_N;
if ( OpB == GridBLAS_OP_T ) hOpB = HIPBLAS_OP_T;
if ( OpB == GridBLAS_OP_C ) hOpB = HIPBLAS_OP_C;
auto err = hipblasCgemmBatched(gridblasHandle,
hOpA,
hOpB,
m,n,k,
(hipblasComplex *) &alpha_p[0],
(hipblasComplex **)&Amk[0], lda,
(hipblasComplex **)&Bkn[0], ldb,
(hipblasComplex *) &beta_p[0],
(hipblasComplex **)&Cmn[0], ldc,
batchCount);
assert(err==HIPBLAS_STATUS_SUCCESS);
#endif
#ifdef GRID_CUDA
cublasOperation_t hOpA;
cublasOperation_t hOpB;
if ( OpA == GridBLAS_OP_N ) hOpA = CUBLAS_OP_N;
if ( OpA == GridBLAS_OP_T ) hOpA = CUBLAS_OP_T;
if ( OpA == GridBLAS_OP_C ) hOpA = CUBLAS_OP_C;
if ( OpB == GridBLAS_OP_N ) hOpB = CUBLAS_OP_N;
if ( OpB == GridBLAS_OP_T ) hOpB = CUBLAS_OP_T;
if ( OpB == GridBLAS_OP_C ) hOpB = CUBLAS_OP_C;
auto err = cublasCgemmBatched(gridblasHandle,
hOpA,
hOpB,
m,n,k,
(cuComplex *) &alpha_p[0],
(cuComplex **)&Amk[0], lda,
(cuComplex **)&Bkn[0], ldb,
(cuComplex *) &beta_p[0],
(cuComplex **)&Cmn[0], ldc,
batchCount);
assert(err==CUBLAS_STATUS_SUCCESS);
#endif
#ifdef GRID_SYCL
int64_t m64=m;
int64_t n64=n;
int64_t k64=k;
int64_t lda64=lda;
int64_t ldb64=ldb;
int64_t ldc64=ldc;
int64_t batchCount64=batchCount;
oneapi::mkl::transpose notransp =oneapi::mkl::transpose::N;
oneapi::mkl::blas::column_major::gemm_batch(*gridblasHandle,
&notransp,
&notransp,
&m64,&n64,&k64,
(ComplexF *) &alpha_p[0],
(const ComplexF **)&Amk[0], (const int64_t *)&lda64,
(const ComplexF **)&Bkn[0], (const int64_t *)&ldb64,
(ComplexF *) &beta_p[0],
(ComplexF **)&Cmn[0], (const int64_t *)&ldc64,
(int64_t)1,&batchCount64,std::vector<sycl::event>());
synchronise();
#endif
#if !defined(GRID_SYCL) && !defined(GRID_CUDA) && !defined(GRID_HIP)
int sda = lda*k;
int sdb = ldb*k;
int sdc = ldc*n;
ComplexF alphaf(real(alpha),imag(alpha));
ComplexF betaf(real(beta),imag(beta));
// Need a default/reference implementation
for (int p = 0; p < batchCount; ++p) {
for (int mm = 0; mm < m; ++mm) {
for (int nn = 0; nn < n; ++nn) {
ComplexF c_mn(0.0);
for (int kk = 0; kk < k; ++kk)
c_mn += Amk[p][mm + kk*lda ] * Bkn[p][kk + nn*ldb];
Cmn[p][mm + nn*ldc] = (alphaf)*c_mn + (betaf)*Cmn[p][mm + nn*ldc ];
}
}
}
#endif
RealD t1=usecond();
RealD flops = 8.0*m*n*k*batchCount;
RealD bytes = 1.0*sizeof(ComplexF)*(m*k+k*n+m*n)*batchCount;
}
///////////////////////////////////////////////////////////////////////////
// Single precision real GEMM
///////////////////////////////////////////////////////////////////////////
void gemmBatched(GridBLASOperation_t OpA,
GridBLASOperation_t OpB,
int m,int n, int k,
RealF alpha,
deviceVector<RealF*> &Amk, // pointer list to matrices
deviceVector<RealF*> &Bkn,
RealF beta,
deviceVector<RealF*> &Cmn)
{
RealD t2=usecond();
int32_t batchCount = Amk.size();
int lda = m; // m x k column major
int ldb = k; // k x n column major
int ldc = m; // m x b column major
if(OpA!=GridBLAS_OP_N)
lda = k;
if(OpB!=GridBLAS_OP_N)
ldb = n;
static deviceVector<RealF> alpha_p(1);
static deviceVector<RealF> beta_p(1);
// can prestore the 1 and the zero on device
acceleratorCopyToDevice((void *)&alpha,(void *)&alpha_p[0],sizeof(RealF));
acceleratorCopyToDevice((void *)&beta ,(void *)&beta_p[0],sizeof(RealF));
RealD t0=usecond();
assert(Bkn.size()==batchCount);
assert(Cmn.size()==batchCount);
#ifdef GRID_HIP
hipblasOperation_t hOpA;
hipblasOperation_t hOpB;
if ( OpA == GridBLAS_OP_N ) hOpA = HIPBLAS_OP_N;
if ( OpA == GridBLAS_OP_T ) hOpA = HIPBLAS_OP_T;
if ( OpA == GridBLAS_OP_C ) hOpA = HIPBLAS_OP_C;
if ( OpB == GridBLAS_OP_N ) hOpB = HIPBLAS_OP_N;
if ( OpB == GridBLAS_OP_T ) hOpB = HIPBLAS_OP_T;
if ( OpB == GridBLAS_OP_C ) hOpB = HIPBLAS_OP_C;
auto err = hipblasSgemmBatched(gridblasHandle,
hOpA,
hOpB,
m,n,k,
(float *) &alpha_p[0],
(float **)&Amk[0], lda,
(float **)&Bkn[0], ldb,
(float *) &beta_p[0],
(float **)&Cmn[0], ldc,
batchCount);
assert(err==HIPBLAS_STATUS_SUCCESS);
#endif
#ifdef GRID_CUDA
cublasOperation_t hOpA;
cublasOperation_t hOpB;
if ( OpA == GridBLAS_OP_N ) hOpA = CUBLAS_OP_N;
if ( OpA == GridBLAS_OP_T ) hOpA = CUBLAS_OP_T;
if ( OpA == GridBLAS_OP_C ) hOpA = CUBLAS_OP_C;
if ( OpB == GridBLAS_OP_N ) hOpB = CUBLAS_OP_N;
if ( OpB == GridBLAS_OP_T ) hOpB = CUBLAS_OP_T;
if ( OpB == GridBLAS_OP_C ) hOpB = CUBLAS_OP_C;
auto err = cublasSgemmBatched(gridblasHandle,
hOpA,
hOpB,
m,n,k,
(float *) &alpha_p[0],
(float **)&Amk[0], lda,
(float **)&Bkn[0], ldb,
(float *) &beta_p[0],
(float **)&Cmn[0], ldc,
batchCount);
assert(err==CUBLAS_STATUS_SUCCESS);
#endif
#ifdef GRID_SYCL
int64_t m64=m;
int64_t n64=n;
int64_t k64=k;
int64_t lda64=lda;
int64_t ldb64=ldb;
int64_t ldc64=ldc;
int64_t batchCount64=batchCount;
oneapi::mkl::transpose notransp =oneapi::mkl::transpose::N;
oneapi::mkl::blas::column_major::gemm_batch(*gridblasHandle,
&notransp,
&notransp,
&m64,&n64,&k64,
(float *) &alpha_p[0],
(const float **)&Amk[0], (const int64_t *)&lda64,
(const float **)&Bkn[0], (const int64_t *)&ldb64,
(float *) &beta_p[0],
(float **)&Cmn[0], (const int64_t *)&ldc64,
(int64_t)1,&batchCount64,std::vector<sycl::event>());
synchronise();
#endif
#if !defined(GRID_SYCL) && !defined(GRID_CUDA) && !defined(GRID_HIP)
int sda = lda*k;
int sdb = ldb*k;
int sdc = ldc*n;
// Need a default/reference implementation
for (int p = 0; p < batchCount; ++p) {
for (int mm = 0; mm < m; ++mm) {
for (int nn = 0; nn < n; ++nn) {
RealD c_mn(0.0);
for (int kk = 0; kk < k; ++kk)
c_mn += Amk[p][mm + kk*lda ] * Bkn[p][kk + nn*ldb];
Cmn[p][mm + nn*ldc] = (alpha)*c_mn + (beta)*Cmn[p][mm + nn*ldc ];
}
}
}
#endif
RealD t1=usecond();
RealD flops = 2.0*m*n*k*batchCount;
RealD bytes = 1.0*sizeof(RealF)*(m*k+k*n+m*n)*batchCount;
}
///////////////////////////////////////////////////////////////////////////
// Double precision real GEMM
///////////////////////////////////////////////////////////////////////////
void gemmBatched(GridBLASOperation_t OpA,
GridBLASOperation_t OpB,
int m,int n, int k,
RealD alpha,
deviceVector<RealD*> &Amk, // pointer list to matrices
deviceVector<RealD*> &Bkn,
RealD beta,
deviceVector<RealD*> &Cmn)
{
RealD t2=usecond();
int32_t batchCount = Amk.size();
int lda = m; // m x k column major
int ldb = k; // k x n column major
int ldc = m; // m x b column major
if(OpA!=GridBLAS_OP_N)
lda = k;
if(OpB!=GridBLAS_OP_N)
ldb = n;
static deviceVector<RealD> alpha_p(1);
static deviceVector<RealD> beta_p(1);
// can prestore the 1 and the zero on device
acceleratorCopyToDevice((void *)&alpha,(void *)&alpha_p[0],sizeof(RealD));
acceleratorCopyToDevice((void *)&beta ,(void *)&beta_p[0],sizeof(RealD));
RealD t0=usecond();
assert(Bkn.size()==batchCount);
assert(Cmn.size()==batchCount);
#ifdef GRID_HIP
hipblasOperation_t hOpA;
hipblasOperation_t hOpB;
if ( OpA == GridBLAS_OP_N ) hOpA = HIPBLAS_OP_N;
if ( OpA == GridBLAS_OP_T ) hOpA = HIPBLAS_OP_T;
if ( OpA == GridBLAS_OP_C ) hOpA = HIPBLAS_OP_C;
if ( OpB == GridBLAS_OP_N ) hOpB = HIPBLAS_OP_N;
if ( OpB == GridBLAS_OP_T ) hOpB = HIPBLAS_OP_T;
if ( OpB == GridBLAS_OP_C ) hOpB = HIPBLAS_OP_C;
auto err = hipblasDgemmBatched(gridblasHandle,
HIPBLAS_OP_N,
HIPBLAS_OP_N,
m,n,k,
(double *) &alpha_p[0],
(double **)&Amk[0], lda,
(double **)&Bkn[0], ldb,
(double *) &beta_p[0],
(double **)&Cmn[0], ldc,
batchCount);
assert(err==HIPBLAS_STATUS_SUCCESS);
#endif
#ifdef GRID_CUDA
cublasOperation_t hOpA;
cublasOperation_t hOpB;
if ( OpA == GridBLAS_OP_N ) hOpA = CUBLAS_OP_N;
if ( OpA == GridBLAS_OP_T ) hOpA = CUBLAS_OP_T;
if ( OpA == GridBLAS_OP_C ) hOpA = CUBLAS_OP_C;
if ( OpB == GridBLAS_OP_N ) hOpB = CUBLAS_OP_N;
if ( OpB == GridBLAS_OP_T ) hOpB = CUBLAS_OP_T;
if ( OpB == GridBLAS_OP_C ) hOpB = CUBLAS_OP_C;
auto err = cublasDgemmBatched(gridblasHandle,
hOpA,
hOpB,
m,n,k,
(double *) &alpha_p[0],
(double **)&Amk[0], lda,
(double **)&Bkn[0], ldb,
(double *) &beta_p[0],
(double **)&Cmn[0], ldc,
batchCount);
assert(err==CUBLAS_STATUS_SUCCESS);
#endif
#ifdef GRID_SYCL
int64_t m64=m;
int64_t n64=n;
int64_t k64=k;
int64_t lda64=lda;
int64_t ldb64=ldb;
int64_t ldc64=ldc;
int64_t batchCount64=batchCount;
oneapi::mkl::transpose notransp =oneapi::mkl::transpose::N;
oneapi::mkl::blas::column_major::gemm_batch(*gridblasHandle,
&notransp,
&notransp,
&m64,&n64,&k64,
(double *) &alpha_p[0],
(const double **)&Amk[0], (const int64_t *)&lda64,
(const double **)&Bkn[0], (const int64_t *)&ldb64,
(double *) &beta_p[0],
(double **)&Cmn[0], (const int64_t *)&ldc64,
(int64_t)1,&batchCount64,std::vector<sycl::event>());
synchronise();
#endif
#if !defined(GRID_SYCL) && !defined(GRID_CUDA) && !defined(GRID_HIP)
int sda = lda*k;
int sdb = ldb*k;
int sdc = ldc*n;
// Need a default/reference implementation
for (int p = 0; p < batchCount; ++p) {
for (int mm = 0; mm < m; ++mm) {
for (int nn = 0; nn < n; ++nn) {
RealD c_mn(0.0);
for (int kk = 0; kk < k; ++kk)
c_mn += Amk[p][mm + kk*lda ] * Bkn[p][kk + nn*ldb];
Cmn[p][mm + nn*ldc] = (alpha)*c_mn + (beta)*Cmn[p][mm + nn*ldc ];
}
}
}
#endif
RealD t1=usecond();
RealD flops = 2.0*m*n*k*batchCount;
RealD bytes = 1.0*sizeof(RealD)*(m*k+k*n+m*n)*batchCount;
}
////////////////////////////////////////////////////////////////////////////////////////////////
// Strided case used by benchmark, but generally unused in Grid
// Keep a code example in double complex, but don't generate the single and real variants for now
////////////////////////////////////////////////////////////////////////////////////////////////
void gemmStridedBatched(int m,int n, int k,
ComplexD alpha,
ComplexD* Amk, // pointer list to matrices
ComplexD* Bkn,
ComplexD beta,
ComplexD* Cmn,
int batchCount)
{
// Use C-row major storage, so transpose calls
int lda = m; // m x k column major
int ldb = k; // k x n column major
int ldc = m; // m x b column major
int sda = m*k;
int sdb = k*n;
int sdc = m*n;
deviceVector<ComplexD> alpha_p(1);
deviceVector<ComplexD> beta_p(1);
acceleratorCopyToDevice((void *)&alpha,(void *)&alpha_p[0],sizeof(ComplexD));
acceleratorCopyToDevice((void *)&beta ,(void *)&beta_p[0],sizeof(ComplexD));
// std::cout << "blasZgemmStridedBatched mnk "<<m<<","<<n<<","<<k<<" count "<<batchCount<<std::endl;
// std::cout << "blasZgemmStridedBatched ld "<<lda<<","<<ldb<<","<<ldc<<std::endl;
// std::cout << "blasZgemmStridedBatched sd "<<sda<<","<<sdb<<","<<sdc<<std::endl;
#ifdef GRID_HIP
auto err = hipblasZgemmStridedBatched(gridblasHandle,
HIPBLAS_OP_N,
HIPBLAS_OP_N,
m,n,k,
(hipblasDoubleComplex *) &alpha_p[0],
(hipblasDoubleComplex *) Amk, lda, sda,
(hipblasDoubleComplex *) Bkn, ldb, sdb,
(hipblasDoubleComplex *) &beta_p[0],
(hipblasDoubleComplex *) Cmn, ldc, sdc,
batchCount);
assert(err==HIPBLAS_STATUS_SUCCESS);
#endif
#ifdef GRID_CUDA
cublasZgemmStridedBatched(gridblasHandle,
CUBLAS_OP_N,
CUBLAS_OP_N,
m,n,k,
(cuDoubleComplex *) &alpha_p[0],
(cuDoubleComplex *) Amk, lda, sda,
(cuDoubleComplex *) Bkn, ldb, sdb,
(cuDoubleComplex *) &beta_p[0],
(cuDoubleComplex *) Cmn, ldc, sdc,
batchCount);
#endif
#if defined(GRID_SYCL) || defined(GRID_ONE_MKL)
oneapi::mkl::blas::column_major::gemm_batch(*gridblasHandle,
oneapi::mkl::transpose::N,
oneapi::mkl::transpose::N,
m,n,k,
alpha,
(const ComplexD *)Amk,lda,sda,
(const ComplexD *)Bkn,ldb,sdb,
beta,
(ComplexD *)Cmn,ldc,sdc,
batchCount);
synchronise();
#endif
#if !defined(GRID_SYCL) && !defined(GRID_CUDA) && !defined(GRID_HIP) && !defined(GRID_ONE_MKL)
// Need a default/reference implementation
for (int p = 0; p < batchCount; ++p) {
for (int mm = 0; mm < m; ++mm) {
for (int nn = 0; nn < n; ++nn) {
ComplexD c_mn(0.0);
for (int kk = 0; kk < k; ++kk)
c_mn += Amk[mm + kk*lda + p*sda] * Bkn[kk + nn*ldb + p*sdb];
Cmn[mm + nn*ldc + p*sdc] = (alpha)*c_mn + (beta)*Cmn[mm + nn*ldc + p*sdc];
}
}
}
#endif
}
double benchmark(int M, int N, int K, int BATCH)
{
int32_t N_A = M*K*BATCH;
int32_t N_B = K*N*BATCH;
int32_t N_C = M*N*BATCH;
deviceVector<ComplexD> A(N_A); acceleratorMemSet(&A[0],0,N_A*sizeof(ComplexD));
deviceVector<ComplexD> B(N_B); acceleratorMemSet(&B[0],0,N_B*sizeof(ComplexD));
deviceVector<ComplexD> C(N_C); acceleratorMemSet(&C[0],0,N_C*sizeof(ComplexD));
ComplexD alpha(1.0);
ComplexD beta (1.0);
RealD flops = 8.0*M*N*K*BATCH;
int ncall=10;
RealD t0 = usecond();
for(int i=0;i<ncall;i++){
gemmStridedBatched(M,N,K,
alpha,
&A[0], // m x k
&B[0], // k x n
beta,
&C[0], // m x n
BATCH);
}
synchronise();
RealD t1 = usecond();
RealD bytes = 1.0*sizeof(ComplexD)*(M*N*2+N*K+M*K)*BATCH;
flops = 8.0*M*N*K*BATCH*ncall;
flops = flops/(t1-t0)/1.e3;
return flops; // Returns gigaflops
}
};
NAMESPACE_END(Grid);
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