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FastDeploy/third_party/eigen/bench/bench_gemm.cpp
Jack Zhou 355382ad63 Move eigen to third party (#282) 
* remove useless statement

* Add eigen to third_party dir

* remove reducdant lines
2022-09-26 19:24:02 +08:00

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// g++-4.4 bench_gemm.cpp -I .. -O2 -DNDEBUG -lrt -fopenmp && OMP_NUM_THREADS=2
// ./a.out
// icpc bench_gemm.cpp -I .. -O3 -DNDEBUG -lrt -openmp && OMP_NUM_THREADS=2
// ./a.out
// Compilation options:
//
// -DSCALAR=std::complex<double>
// -DSCALARA=double or -DSCALARB=double
// -DHAVE_BLAS
// -DDECOUPLED
//
#include <bench/BenchTimer.h>
#include <Eigen/Core>
#include <iostream>
using namespace std;
using namespace Eigen;
#ifndef SCALAR
// #define SCALAR std::complex<float>
#define SCALAR float
#endif
#ifndef SCALARA
#define SCALARA SCALAR
#endif
#ifndef SCALARB
#define SCALARB SCALAR
#endif
#ifdef ROWMAJ_A
const int opt_A = RowMajor;
#else
const int opt_A = ColMajor;
#endif
#ifdef ROWMAJ_B
const int opt_B = RowMajor;
#else
const int opt_B = ColMajor;
#endif
typedef SCALAR Scalar;
typedef NumTraits<Scalar>::Real RealScalar;
typedef Matrix<SCALARA, Dynamic, Dynamic, opt_A> A;
typedef Matrix<SCALARB, Dynamic, Dynamic, opt_B> B;
typedef Matrix<Scalar, Dynamic, Dynamic> C;
typedef Matrix<RealScalar, Dynamic, Dynamic> M;
#ifdef HAVE_BLAS
extern "C" {
#include <Eigen/src/misc/blas.h>
}
static float fone = 1;
static float fzero = 0;
static double done = 1;
static double szero = 0;
static std::complex<float> cfone = 1;
static std::complex<float> cfzero = 0;
static std::complex<double> cdone = 1;
static std::complex<double> cdzero = 0;
static char notrans = 'N';
static char trans = 'T';
static char nonunit = 'N';
static char lower = 'L';
static char right = 'R';
static int intone = 1;
#ifdef ROWMAJ_A
const char transA = trans;
#else
const char transA = notrans;
#endif
#ifdef ROWMAJ_B
const char transB = trans;
#else
const char transB = notrans;
#endif
template <typename A, typename B>
void blas_gemm(const A& a, const B& b, MatrixXf& c) {
int M = c.rows();
int N = c.cols();
int K = a.cols();
int lda = a.outerStride();
int ldb = b.outerStride();
int ldc = c.rows();
sgemm_(&transA, &transB, &M, &N, &K, &fone, const_cast<float*>(a.data()),
&lda, const_cast<float*>(b.data()), &ldb, &fone, c.data(), &ldc);
}
template <typename A, typename B>
void blas_gemm(const A& a, const B& b, MatrixXd& c) {
int M = c.rows();
int N = c.cols();
int K = a.cols();
int lda = a.outerStride();
int ldb = b.outerStride();
int ldc = c.rows();
dgemm_(&transA, &transB, &M, &N, &K, &done, const_cast<double*>(a.data()),
&lda, const_cast<double*>(b.data()), &ldb, &done, c.data(), &ldc);
}
template <typename A, typename B>
void blas_gemm(const A& a, const B& b, MatrixXcf& c) {
int M = c.rows();
int N = c.cols();
int K = a.cols();
int lda = a.outerStride();
int ldb = b.outerStride();
int ldc = c.rows();
cgemm_(&transA, &transB, &M, &N, &K, (float*)&cfone,
const_cast<float*>((const float*)a.data()), &lda,
const_cast<float*>((const float*)b.data()), &ldb, (float*)&cfone,
(float*)c.data(), &ldc);
}
template <typename A, typename B>
void blas_gemm(const A& a, const B& b, MatrixXcd& c) {
int M = c.rows();
int N = c.cols();
int K = a.cols();
int lda = a.outerStride();
int ldb = b.outerStride();
int ldc = c.rows();
zgemm_(&transA, &transB, &M, &N, &K, (double*)&cdone,
const_cast<double*>((const double*)a.data()), &lda,
const_cast<double*>((const double*)b.data()), &ldb, (double*)&cdone,
(double*)c.data(), &ldc);
}
#endif
void matlab_cplx_cplx(const M& ar, const M& ai, const M& br, const M& bi, M& cr,
M& ci) {
cr.noalias() += ar * br;
cr.noalias() -= ai * bi;
ci.noalias() += ar * bi;
ci.noalias() += ai * br;
// [cr ci] += [ar ai] * br + [-ai ar] * bi
}
void matlab_real_cplx(const M& a, const M& br, const M& bi, M& cr, M& ci) {
cr.noalias() += a * br;
ci.noalias() += a * bi;
}
void matlab_cplx_real(const M& ar, const M& ai, const M& b, M& cr, M& ci) {
cr.noalias() += ar * b;
ci.noalias() += ai * b;
}
template <typename A, typename B, typename C>
EIGEN_DONT_INLINE void gemm(const A& a, const B& b, C& c) {
c.noalias() += a * b;
}
int main(int argc, char** argv) {
std::ptrdiff_t l1 = internal::queryL1CacheSize();
std::ptrdiff_t l2 = internal::queryTopLevelCacheSize();
std::cout << "L1 cache size = " << (l1 > 0 ? l1 / 1024 : -1) << " KB\n";
std::cout << "L2/L3 cache size = " << (l2 > 0 ? l2 / 1024 : -1) << " KB\n";
typedef internal::gebp_traits<Scalar, Scalar> Traits;
std::cout << "Register blocking = " << Traits::mr << " x " << Traits::nr
<< "\n";
int rep = 1; // number of repetitions per try
int tries = 2; // number of tries, we keep the best
int s = 2048;
int m = s;
int n = s;
int p = s;
int cache_size1 = -1, cache_size2 = l2, cache_size3 = 0;
bool need_help = false;
for (int i = 1; i < argc;) {
if (argv[i][0] == '-') {
if (argv[i][1] == 's') {
++i;
s = atoi(argv[i++]);
m = n = p = s;
if (argv[i][0] != '-') {
n = atoi(argv[i++]);
p = atoi(argv[i++]);
}
} else if (argv[i][1] == 'c') {
++i;
cache_size1 = atoi(argv[i++]);
if (argv[i][0] != '-') {
cache_size2 = atoi(argv[i++]);
if (argv[i][0] != '-') cache_size3 = atoi(argv[i++]);
}
} else if (argv[i][1] == 't') {
tries = atoi(argv[++i]);
++i;
} else if (argv[i][1] == 'p') {
++i;
rep = atoi(argv[i++]);
}
} else {
need_help = true;
break;
}
}
if (need_help) {
std::cout << argv[0] << " -s <matrix sizes> -c <cache sizes> -t <nb tries> "
"-p <nb repeats>\n";
std::cout << " <matrix sizes> : size\n";
std::cout << " <matrix sizes> : rows columns depth\n";
return 1;
}
#if EIGEN_VERSION_AT_LEAST(3, 2, 90)
if (cache_size1 > 0) setCpuCacheSizes(cache_size1, cache_size2, cache_size3);
#endif
A a(m, p);
a.setRandom();
B b(p, n);
b.setRandom();
C c(m, n);
c.setOnes();
C rc = c;
std::cout << "Matrix sizes = " << m << "x" << p << " * " << p << "x" << n
<< "\n";
std::ptrdiff_t mc(m), nc(n), kc(p);
internal::computeProductBlockingSizes<Scalar, Scalar>(kc, mc, nc);
std::cout << "blocking size (mc x kc) = " << mc << " x " << kc << " x " << nc
<< "\n";
C r = c;
// check the parallel product is correct
#if defined EIGEN_HAS_OPENMP
Eigen::initParallel();
int procs = omp_get_max_threads();
if (procs > 1) {
#ifdef HAVE_BLAS
blas_gemm(a, b, r);
#else
omp_set_num_threads(1);
r.noalias() += a * b;
omp_set_num_threads(procs);
#endif
c.noalias() += a * b;
if (!r.isApprox(c))
std::cerr << "Warning, your parallel product is crap!\n\n";
}
#elif defined HAVE_BLAS
blas_gemm(a, b, r);
c.noalias() += a * b;
if (!r.isApprox(c)) {
std::cout << (r - c).norm() / r.norm() << "\n";
std::cerr << "Warning, your product is crap!\n\n";
}
#else
if (1. * m * n * p < 2000. * 2000 * 2000) {
gemm(a, b, c);
r.noalias() += a.cast<Scalar>().lazyProduct(b.cast<Scalar>());
if (!r.isApprox(c)) {
std::cout << (r - c).norm() / r.norm() << "\n";
std::cerr << "Warning, your product is crap!\n\n";
}
}
#endif
#ifdef HAVE_BLAS
BenchTimer tblas;
c = rc;
BENCH(tblas, tries, rep, blas_gemm(a, b, c));
std::cout << "blas cpu " << tblas.best(CPU_TIMER) / rep << "s \t"
<< (double(m) * n * p * rep * 2 / tblas.best(CPU_TIMER)) * 1e-9
<< " GFLOPS \t(" << tblas.total(CPU_TIMER) << "s)\n";
std::cout << "blas real " << tblas.best(REAL_TIMER) / rep << "s \t"
<< (double(m) * n * p * rep * 2 / tblas.best(REAL_TIMER)) * 1e-9
<< " GFLOPS \t(" << tblas.total(REAL_TIMER) << "s)\n";
#endif
// warm start
if (b.norm() + a.norm() == 123.554) std::cout << "\n";
BenchTimer tmt;
c = rc;
BENCH(tmt, tries, rep, gemm(a, b, c));
std::cout << "eigen cpu " << tmt.best(CPU_TIMER) / rep << "s \t"
<< (double(m) * n * p * rep * 2 / tmt.best(CPU_TIMER)) * 1e-9
<< " GFLOPS \t(" << tmt.total(CPU_TIMER) << "s)\n";
std::cout << "eigen real " << tmt.best(REAL_TIMER) / rep << "s \t"
<< (double(m) * n * p * rep * 2 / tmt.best(REAL_TIMER)) * 1e-9
<< " GFLOPS \t(" << tmt.total(REAL_TIMER) << "s)\n";
#ifdef EIGEN_HAS_OPENMP
if (procs > 1) {
BenchTimer tmono;
omp_set_num_threads(1);
Eigen::setNbThreads(1);
c = rc;
BENCH(tmono, tries, rep, gemm(a, b, c));
std::cout << "eigen mono cpu " << tmono.best(CPU_TIMER) / rep << "s \t"
<< (double(m) * n * p * rep * 2 / tmono.best(CPU_TIMER)) * 1e-9
<< " GFLOPS \t(" << tmono.total(CPU_TIMER) << "s)\n";
std::cout << "eigen mono real " << tmono.best(REAL_TIMER) / rep << "s \t"
<< (double(m) * n * p * rep * 2 / tmono.best(REAL_TIMER)) * 1e-9
<< " GFLOPS \t(" << tmono.total(REAL_TIMER) << "s)\n";
std::cout << "mt speed up x" << tmono.best(CPU_TIMER) / tmt.best(REAL_TIMER)
<< " => "
<< (100.0 * tmono.best(CPU_TIMER) / tmt.best(REAL_TIMER)) / procs
<< "%\n";
}
#endif
if (1. * m * n * p < 30 * 30 * 30) {
BenchTimer tmt;
c = rc;
BENCH(tmt, tries, rep, c.noalias() += a.lazyProduct(b));
std::cout << "lazy cpu " << tmt.best(CPU_TIMER) / rep << "s \t"
<< (double(m) * n * p * rep * 2 / tmt.best(CPU_TIMER)) * 1e-9
<< " GFLOPS \t(" << tmt.total(CPU_TIMER) << "s)\n";
std::cout << "lazy real " << tmt.best(REAL_TIMER) / rep << "s \t"
<< (double(m) * n * p * rep * 2 / tmt.best(REAL_TIMER)) * 1e-9
<< " GFLOPS \t(" << tmt.total(REAL_TIMER) << "s)\n";
}
#ifdef DECOUPLED
if ((NumTraits<A::Scalar>::IsComplex) && (NumTraits<B::Scalar>::IsComplex)) {
M ar(m, p);
ar.setRandom();
M ai(m, p);
ai.setRandom();
M br(p, n);
br.setRandom();
M bi(p, n);
bi.setRandom();
M cr(m, n);
cr.setRandom();
M ci(m, n);
ci.setRandom();
BenchTimer t;
BENCH(t, tries, rep, matlab_cplx_cplx(ar, ai, br, bi, cr, ci));
std::cout << "\"matlab\" cpu " << t.best(CPU_TIMER) / rep << "s \t"
<< (double(m) * n * p * rep * 2 / t.best(CPU_TIMER)) * 1e-9
<< " GFLOPS \t(" << t.total(CPU_TIMER) << "s)\n";
std::cout << "\"matlab\" real " << t.best(REAL_TIMER) / rep << "s \t"
<< (double(m) * n * p * rep * 2 / t.best(REAL_TIMER)) * 1e-9
<< " GFLOPS \t(" << t.total(REAL_TIMER) << "s)\n";
}
if ((!NumTraits<A::Scalar>::IsComplex) && (NumTraits<B::Scalar>::IsComplex)) {
M a(m, p);
a.setRandom();
M br(p, n);
br.setRandom();
M bi(p, n);
bi.setRandom();
M cr(m, n);
cr.setRandom();
M ci(m, n);
ci.setRandom();
BenchTimer t;
BENCH(t, tries, rep, matlab_real_cplx(a, br, bi, cr, ci));
std::cout << "\"matlab\" cpu " << t.best(CPU_TIMER) / rep << "s \t"
<< (double(m) * n * p * rep * 2 / t.best(CPU_TIMER)) * 1e-9
<< " GFLOPS \t(" << t.total(CPU_TIMER) << "s)\n";
std::cout << "\"matlab\" real " << t.best(REAL_TIMER) / rep << "s \t"
<< (double(m) * n * p * rep * 2 / t.best(REAL_TIMER)) * 1e-9
<< " GFLOPS \t(" << t.total(REAL_TIMER) << "s)\n";
}
if ((NumTraits<A::Scalar>::IsComplex) && (!NumTraits<B::Scalar>::IsComplex)) {
M ar(m, p);
ar.setRandom();
M ai(m, p);
ai.setRandom();
M b(p, n);
b.setRandom();
M cr(m, n);
cr.setRandom();
M ci(m, n);
ci.setRandom();
BenchTimer t;
BENCH(t, tries, rep, matlab_cplx_real(ar, ai, b, cr, ci));
std::cout << "\"matlab\" cpu " << t.best(CPU_TIMER) / rep << "s \t"
<< (double(m) * n * p * rep * 2 / t.best(CPU_TIMER)) * 1e-9
<< " GFLOPS \t(" << t.total(CPU_TIMER) << "s)\n";
std::cout << "\"matlab\" real " << t.best(REAL_TIMER) / rep << "s \t"
<< (double(m) * n * p * rep * 2 / t.best(REAL_TIMER)) * 1e-9
<< " GFLOPS \t(" << t.total(REAL_TIMER) << "s)\n";
}
#endif
return 0;
}
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