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triton/bench/blas.cpp

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#include "isaac/array.h"
#include "isaac/symbolic/execute.h"
#ifdef BENCH_CLBLAS
#include "isaac/wrap/clBLAS.h"
#endif
#ifdef BENCH_CBLAS
#include "cblas.h"
#endif
#ifdef BENCH_CUBLAS
#include <cublas.h>
#endif
#include <iomanip>
#include <stdlib.h>
#include <cmath>
#include <numeric>
#include <regex>
#include "timer.hpp"
namespace isc = isaac;
typedef isc::int_t int_t;
template<std::size_t> struct int_{};
template <class Tuple, size_t Pos>
std::ostream& print_tuple(std::ostream& out, const Tuple& t, int_<Pos> ) {
out << std::get< std::tuple_size<Tuple>::value-Pos >(t) << ',';
return print_tuple(out, t, int_<Pos-1>());
}
template <class Tuple>
std::ostream& print_tuple(std::ostream& out, const Tuple& t, int_<1> ) {
return out << std::get<std::tuple_size<Tuple>::value-1>(t);
}
template <class... Args>
std::ostream& operator<<(std::ostream& out, const std::tuple<Args...>& t) {
print_tuple(out, t, int_<sizeof...(Args)>());
return out;
}
int ceil(int N, int pad)
{
return (N%pad==0)?N:(N+pad-1)/pad*pad;
}
std::vector<int> create_log_range(int min, int max, int N, int pad)
{
std::vector<int> res(N);
for(int i = 0 ; i < N ; ++i)
{
res[i] = static_cast<int>(std::exp(std::log(min) + (float)(std::log(max) - std::log(min))*i/N));
res[i] = ceil(res[i], pad);
}
return res;
}
std::vector<int> create_full_range(int min, int max, int pad)
{
std::vector<int> N;
for(int i = ceil(min, pad) ; i < ceil(max, pad) ; i+=pad)
N.push_back(i);
return N;
}
template<class T>
T median(std::vector<T> x)
{
size_t size = x.size();
std::sort(x.begin(), x.end());
if (size % 2 == 0)
return (x[size / 2 - 1] + x[size / 2]) / 2;
else
return x[size / 2];
}
template<class T>
T mean(std::vector<T> x)
{
T res = 0;
int N = x.size();
for(int i = 0 ; i < N ; ++i)
res += x[i];
return res/N;
}
static long time_event(long sum, isc::driver::Event const & e)
{ return sum + e.elapsed_time();}
template<class T>
void bench(isc::numeric_type dtype, std::string operation)
{
//
// MACROS FOR BENCHMARKING
//
#define CL_HANDLE(X) X.handle().cl()
#define BENCHMARK_ISAAC(OP, PERF) \
{\
std::vector<double> times;\
double total_time = 0;\
while(total_time*1e-9 < 1e-3){\
std::list<isc::driver::Event> events;\
flush = isc::zeros((isaac::int_t)1e6, 1, dtype);\
queue.synchronize();\
OP;\
queue.synchronize();\
times.push_back((double)std::accumulate(events.begin(), events.end(), 0, &time_event));\
total_time+=times.back();\
}\
double t = median(times);\
std::cout << " " << PERF << std::flush;\
}
#define BENCHMARK_CLBLAS(OP, PERF) \
{\
std::vector<long> times;\
double total_time = 0;\
while(total_time*1e-9 < 1e-3){\
cl_event event;\
flush = isc::zeros(1e6, 1, dtype);\
queue.synchronize();\
OP;\
queue.synchronize();\
times.push_back(isc::driver::Event(event).elapsed_time());\
total_time+=times.back();\
}\
double t = median(times);\
std::cout << " " << PERF << std::flush;\
}
#define BENCHMARK_HOST(OP, PERF) \
{\
isc::tools::timer tmr;\
double total_time = 0;\
std::vector<double> times;\
while(total_time < 1e-2){\
std::vector<int> cache_flusher(10000000, 0);\
tmr.start();\
OP;\
double time = tmr.get();\
times.push_back(time);\
total_time += time;\
}\
double t = 1e9*median(times);\
std::cout << " " << PERF << std::flush;\
}
#define BENCHMARK_CUDA(OP, PERF) \
{\
std::vector<long> times;\
double total_time = 0;\
float time;\
cudaEvent_t start, stop;\
cudaEventCreate(&start);\
cudaEventCreate(&stop);\
OP;\
cudaThreadSynchronize();\
while(total_time*1e-3 < 1e-3){\
flush = isc::zeros(1e6, 1, dtype);\
cudaEventRecord(start,0);\
OP;\
cudaEventRecord(stop,0);\
cudaEventSynchronize(stop);\
cudaEventElapsedTime(&time, start, stop);\
times.push_back(time*1e6);\
total_time+=time;\
}\
double t = median(times);\
std::cout << "\t" << PERF << std::flush;\
}
unsigned int dtsize = isc::size_of(dtype);
isc::driver::CommandQueue & queue = isc::driver::backend::queues::get(isc::driver::backend::contexts::get_default(),0);
std::map<std::string, std::string> metric{ {"axpy", "GB/s"}, {"dot", "GB/s"}, {"gemv", "GB/s"}, {"gemm", "GFLOPS"}};
isc::array flush((int)1e6, isc::FLOAT_TYPE);
std::cout << "#" << operation << " (" << metric[operation] << ")" << std::endl;
std::cout << "N";
std::cout << "\tISAAC";
#ifdef BENCH_CLBLAS
std::cout << "\tclBLAS";
#endif
#ifdef BENCH_CBLAS
std::cout << "\tBLAS";
#endif
#ifdef BENCH_CUBLAS
std::cout << "\tcuBLAS";
#endif
std::cout << std::endl;
//
// RUN BENCHMARKS
//
/*---------*/
/*--BLAS1--*/
/*---------*/
if(operation=="axpy")
{
float alpha = 1;
for(int_t N: create_log_range((int)1e3, (int)2e7, 50, 64))
{
std::cout << N;
isc::array x(N, dtype), y(N, dtype);
/* ISAAC */
std::list<isc::driver::Event> events;
BENCHMARK_ISAAC(y = isc::control(x + alpha*y, isc::execution_options_type(0, &events)), 3*N*dtsize/t)
/* clblas */
#ifdef BENCH_CLBLAS
BENCHMARK_CLBLAS(clblasSaxpy(N, alpha, CL_HANDLE(x.data()), 0, 1, CL_HANDLE(y.data()), 0, 1, 1, &CL_HANDLE(queue), 0, NULL, &event), 3*N*dtsize/t);
#endif
/* BLAS */
#ifdef BENCH_CBLAS
std::vector<float> cx(N), cy(N);
isc::copy(x, cx);
isc::copy(y, cy);
BENCHMARK_HOST(cblas_saxpy(N, alpha, cx.data(), 1, cy.data(), 1), 3*N*dtsize/t);
#endif
/* CuBLAS */
#ifdef BENCH_CUBLAS
T *cux, *cuy;
cudaMalloc((void**) &cux, N * sizeof(T));
cudaMalloc((void**) &cuy, N * sizeof(T));
BENCHMARK_CUDA(cublasSaxpy(N, alpha, cux, 1, cuy, 1), 3*N*dtsize/t)
cudaFree(cux);
cudaFree(cuy);
#endif
std::cout << std::endl;
}
}
if(operation=="dot")
{
for(int_t N: create_log_range((int)1e3, (int)2e7, 50, 64))
{
std::cout << N;
/* ISAAC */
isc::array x(N, dtype), y(N, dtype);
isc::array scratch(N, dtype);
isc::scalar s(dtype);
s = dot(x,y); queue.synchronize();
BENCHMARK_ISAAC(s = isc::control(dot(x,y), isc::execution_options_type(0, &events)), 2*N*dtsize/t)
/* clblas */
#ifdef BENCH_CLBLAS
BENCHMARK_CLBLAS(clblasSdot(N, CL_HANDLE(s.data()), 0, CL_HANDLE(x.data()), 0, 1, CL_HANDLE(y.data()), 0, 1, CL_HANDLE(scratch.data()), 1, &CL_HANDLE(queue), 0, NULL, &event), 2*N*dtsize/t)
#endif
/* BLAS */
#ifdef BENCH_CBLAS
std::vector<float> cx(N), cy(N);
isc::copy(x, cx);
isc::copy(y, cy);
BENCHMARK_HOST(cblas_sdot(N, cx.data(), 1, cy.data(), 1), 2*N*dtsize/t);
#endif
#ifdef BENCH_CUBLAS
T *cux, *cuy;
T result;
cudaMalloc((void**) &cux, N * sizeof(T));
cudaMalloc((void**) &cuy, N * sizeof(T));
BENCHMARK_CUDA(cublasSdot(N, cux, 1, cuy, 1), 2*N*dtsize/t)
cudaFree(cux);
cudaFree(cuy);
#endif
std::cout << std::endl;
}
std::cout << "\n\n" << std::flush;
}
if(operation.substr(0, 4)=="gemv")
{
std::vector<std::tuple<int_t, int_t> > MNs;
MNs.push_back(std::make_tuple(896,896));
MNs.push_back(std::make_tuple(3072,3072));
MNs.push_back(std::make_tuple(64,32000));
MNs.push_back(std::make_tuple(896,32000));
MNs.push_back(std::make_tuple(32000, 64));
MNs.push_back(std::make_tuple(32000, 896));
/*---------*/
/*--BLAS2--*/
/*---------*/
//T-layout
for(std::tuple<int_t, int_t> MN: MNs)
{
int_t M = std::get<0>(MN);
int_t N = std::get<1>(MN);
std::cout << M << "," << N;
/* ISAAC */
isc::array A(N, M, dtype), y(M, dtype), x(N, dtype);
#ifdef HAS_A_BLAS
int_t lda = A.ld();
#endif
y = dot(trans(A),x); queue.synchronize();
BENCHMARK_ISAAC(y = isc::control(dot(trans(A),x), isc::execution_options_type(0, &events)),(M*N + M + N)*dtsize/t);
#ifdef BENCH_CLBLAS
BENCHMARK_CLBLAS(clblasSgemv(clblasColumnMajor, clblasTrans, N, M, 1, CL_HANDLE(A.data()), 0, lda, CL_HANDLE(x.data()), 0, 1, 0, CL_HANDLE(y.data()), 0, 1, 1, &CL_HANDLE(queue),0, NULL, &event), (M*N + M + N)*dtsize/t)
#endif
#ifdef BENCH_CBLAS
std::vector<float> cA(N*M), cx(N), cy(M);
isc::copy(x, cx);
isc::copy(y, cy);
isc::copy(A, cA);
BENCHMARK_HOST(cblas_sgemv(CblasColMajor, CblasTrans, N, M, 1, cA.data(), lda, cx.data(), 1, 0, cy.data(), 1), (M*N + M + N)*dtsize/t);
#endif
#ifdef BENCH_CUBLAS
T *cuA, *cux, *cuy;
cudaMalloc((void**) &cuA, N * M * sizeof(T));
cudaMalloc((void**) &cux, N * sizeof(T));
cudaMalloc((void**) &cuy, M * sizeof(T));
BENCHMARK_CUDA(cublasSgemv('t', N, M, 1, cuA, lda, cux, 1, 0, cuy, 1), (M*N + M + N)*dtsize/t)
cudaFree(cuA);
cudaFree(cux);
cudaFree(cuy);
#endif
std::cout << std::endl;
}
std::cout << "\n\n" << std::flush;
}
if(operation.substr(0,4)=="gemm")
{
std::vector<std::tuple<char, char, int_t, int_t, int_t> > MNKs;
MNKs.push_back(std::make_tuple('N','T',1536,1536,1536));
//AlexNet (Forward)
MNKs.push_back(std::make_tuple('N','N',3025,96,363));
MNKs.push_back(std::make_tuple('N','N',729,128,1200));
MNKs.push_back(std::make_tuple('N','N',169,384,2304));
MNKs.push_back(std::make_tuple('N','N',169,192,1728));
MNKs.push_back(std::make_tuple('N','N',169,128,1728));
//AlexNet (Backward)
MNKs.push_back(std::make_tuple('T','N',1728,128,169));
MNKs.push_back(std::make_tuple('T','N',1728,192,169));
MNKs.push_back(std::make_tuple('T','N',2304,384,169));
MNKs.push_back(std::make_tuple('T','N',1200,128,729));
MNKs.push_back(std::make_tuple('T','N',363,96,3025));
MNKs.push_back(std::make_tuple('N','T',169,1728,128));
MNKs.push_back(std::make_tuple('N','T',169,1728,192));
MNKs.push_back(std::make_tuple('N','T',169,2304,384));
MNKs.push_back(std::make_tuple('N','T',729,1200,128));
//Covariance (e.g., ICA)
MNKs.push_back(std::make_tuple('N','N',64,64,32000));
MNKs.push_back(std::make_tuple('N','N',1024,1024,32000));
/*---------*/
/*--BLAS3--*/
/*---------*/
for(std::tuple<char, char, int_t, int_t, int_t> MNK: MNKs)
{
bool AT = std::get<0>(MNK)=='T';
bool BT = std::get<1>(MNK)=='T';
int_t M = std::get<2>(MNK);
int_t N = std::get<3>(MNK);
int_t K = std::get<4>(MNK);
std::cout << MNK;
std::cout << std::flush;
/* ISAAC */
int_t As1 = M, As2 = K;
if(AT) std::swap(As1, As2);
int_t Bs1 = K, Bs2 = N;
if(BT) std::swap(Bs1, Bs2);
isc::array C(M, N, dtype), A(As1, As2, dtype), B(Bs1, Bs2, dtype);
#ifdef HAS_A_BLAS
int_t lda = A.ld(), ldb = B.ld(), ldc = C.ld();
#endif
BENCHMARK_ISAAC(C = isc::control(AT?(BT?dot(A.T(),B.T()):dot(A.T(),B)):(BT?dot(A,B.T()):dot(A,B)), isc::execution_options_type(0, &events)), (double)2*M*N*K/t);
/* clblas */
#ifdef BENCH_CLBLAS
BENCHMARK_CLBLAS(clblasSgemm(clblasColumnMajor, AT?clblasTrans:clblasNoTrans, BT?clblasTrans:clblasNoTrans, M, N, K, 1, CL_HANDLE(A.data()), 0, lda, CL_HANDLE(B.data()), 0, ldb,
0, CL_HANDLE(C.data()), 0, ldc, 1, &CL_HANDLE(queue),0, NULL, &event), (double)2*M*N*K/t)
#endif
/* BLAS */
#ifdef BENCH_CBLAS
std::vector<float> cC(M*N), cA(M*K), cB(N*K);
isc::copy(C, cC);
isc::copy(A, cA);
isc::copy(B, cB);
BENCHMARK_HOST(cblas_sgemm(CblasColMajor, CblasNoTrans, CblasTrans, M, N, K, 1, cA.data(), lda, cB.data(), ldb, 1, cC.data(), ldc), (double)2*M*N*K/t);
#endif
#ifdef BENCH_CUBLAS
T *cuA, *cuB, *cuC;
cudaMalloc((void**) &cuA, M * K * sizeof(T));
cudaMalloc((void**) &cuB, K * N * sizeof(T));
cudaMalloc((void**) &cuC, M * N * sizeof(T));
BENCHMARK_CUDA(cublasSgemm('n', 't', M, N, K, 1, cuA, lda, cuB, ldb, 1, cuC, ldc), (double)2*M*N*K/t)
cudaFree(cuA);
cudaFree(cuB);
cudaFree(cuC);
#endif
std::cout << std::endl;
}
}
}
int main(int argc, char* argv[])
{
std::vector<std::string> args(argv, argv + argc);
#ifdef BENCH_CLBLAS
clblasSetup();
#endif
isc::driver::backend::queue_properties = CL_QUEUE_PROFILING_ENABLE;
int device_idx = 0;
std::list<isc::driver::Context const *> contexts;
isc::driver::backend::contexts::get(contexts);
std::string operation;
if(contexts.size() > 1)
{
if(args.size() != 3)
{
std::cerr << "usage : blas-bench DEVICE_IDX OPERATION" << std::endl;
std::cout << "Devices available: " << std::endl;
unsigned int current=0;
for(isc::driver::Context const * context: contexts)
{
isc::driver::Device device = isc::driver::backend::queues::get(*context,0).device();
std::cout << current++ << ": " << device.name() << " on " << device.platform().name() << " " << device.platform().version() << std::endl;
}
exit(EXIT_FAILURE);
}
device_idx = atoi(argv[1]);
operation = args[2];
}
else
{
if(args.size() != 2)
{
std::cerr << "usage : blas-bench OPERATION" << std::endl;
exit(EXIT_FAILURE);
}
operation = args[1];
}
isc::driver::backend::default_device = device_idx;
std::cout << "#Benchmark : BLAS" << std::endl;
std::cout << "#----------------" << std::endl;
bench<float>(isc::FLOAT_TYPE, operation);
#ifdef BENCH_CLBLAS
clblasTeardown();
#endif
}
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