Merge pull request #4763 from ev-br/sync-codspeed

BENCH: sync codspeed-benchmarks with BLAS-benchmarks

benchmark/pybench/benchmarks/bench_blas.py

@@ -1,15 +1,15 @@
 import pytest
 import numpy as np
-from openblas_wrap import (
-    # level 1
-    dnrm2, ddot, daxpy,
-    # level 3
-    dgemm, dsyrk,
-    # lapack
-    dgesv,                   # linalg.solve
-    dgesdd, dgesdd_lwork,    # linalg.svd
-    dsyev, dsyev_lwork,      # linalg.eigh
-)
+import openblas_wrap as ow
+
+dtype_map = {
+    's': np.float32,
+    'd': np.float64,
+    'c': np.complex64,
+    'z': np.complex128,
+    'dz': np.complex128,
+}
+
 
 # ### BLAS level 1 ###
 
@@ -17,53 +17,119 @@ from openblas_wrap import (
 
 dnrm2_sizes = [100, 1000]
 
-def run_dnrm2(n, x, incx):
-    res = dnrm2(x, n, incx=incx)
+def run_dnrm2(n, x, incx, func):
+    res = func(x, n, incx=incx)
     return res
 
 
+@pytest.mark.parametrize('variant', ['d', 'dz'])
 @pytest.mark.parametrize('n', dnrm2_sizes)
-def test_nrm2(benchmark, n):
+def test_nrm2(benchmark, n, variant):
     rndm = np.random.RandomState(1234)
-    x = np.array(rndm.uniform(size=(n,)), dtype=float)
-    result = benchmark(run_dnrm2, n, x, 1)
+    dtyp = dtype_map[variant]
+
+    x = np.array(rndm.uniform(size=(n,)), dtype=dtyp)
+    nrm2 = ow.get_func('nrm2', variant)
+    result = benchmark(run_dnrm2, n, x, 1, nrm2)
 
 
 # ddot
 
 ddot_sizes = [100, 1000]
 
-def run_ddot(x, y,):
-    res = ddot(x, y)
+def run_ddot(x, y, func):
+    res = func(x, y)
     return res
 
 
 @pytest.mark.parametrize('n', ddot_sizes)
 def test_dot(benchmark, n):
     rndm = np.random.RandomState(1234)
+
     x = np.array(rndm.uniform(size=(n,)), dtype=float)
     y = np.array(rndm.uniform(size=(n,)), dtype=float)
-    result = benchmark(run_ddot, x, y)
+    dot = ow.get_func('dot', 'd')
+    result = benchmark(run_ddot, x, y, dot)
 
 
 # daxpy
 
 daxpy_sizes = [100, 1000]
 
-def run_daxpy(x, y,):
-    res = daxpy(x, y, a=2.0)
+def run_daxpy(x, y, func):
+    res = func(x, y, a=2.0)
     return res
 
 
+@pytest.mark.parametrize('variant', ['s', 'd', 'c', 'z'])
 @pytest.mark.parametrize('n', daxpy_sizes)
-def test_daxpy(benchmark, n):
+def test_daxpy(benchmark, n, variant):
     rndm = np.random.RandomState(1234)
-    x = np.array(rndm.uniform(size=(n,)), dtype=float)
-    y = np.array(rndm.uniform(size=(n,)), dtype=float)
-    result = benchmark(run_daxpy, x, y)
+    dtyp = dtype_map[variant]
+
+    x = np.array(rndm.uniform(size=(n,)), dtype=dtyp)
+    y = np.array(rndm.uniform(size=(n,)), dtype=dtyp)
+    axpy = ow.get_func('axpy', variant)
+    result = benchmark(run_daxpy, x, y, axpy)
+
+
+# ### BLAS level 2 ###
+
+gemv_sizes = [100, 1000]
+
+def run_gemv(a, x, y, func):
+    res = func(1.0, a, x, y=y, overwrite_y=True)
+    return res
+
+
+@pytest.mark.parametrize('variant', ['s', 'd', 'c', 'z'])
+@pytest.mark.parametrize('n', gemv_sizes)
+def test_dgemv(benchmark, n, variant):
+    rndm = np.random.RandomState(1234)
+    dtyp = dtype_map[variant]
+
+    x = np.array(rndm.uniform(size=(n,)), dtype=dtyp)
+    y = np.empty(n, dtype=dtyp)
+
+    a = np.array(rndm.uniform(size=(n,n)), dtype=dtyp)
+    x = np.array(rndm.uniform(size=(n,)), dtype=dtyp)
+    y = np.zeros(n, dtype=dtyp)
+
+    gemv = ow.get_func('gemv', variant)
+    result = benchmark(run_gemv, a, x, y, gemv)
 
+    assert result is y
 
 
+# dgbmv
+
+dgbmv_sizes = [100, 1000]
+
+def run_gbmv(m, n, kl, ku, a, x, y, func):
+    res = func(m, n, kl, ku, 1.0, a, x, y=y, overwrite_y=True)
+    return res
+
+
+
+@pytest.mark.parametrize('variant', ['s', 'd', 'c', 'z'])
+@pytest.mark.parametrize('n', dgbmv_sizes)
+@pytest.mark.parametrize('kl', [1])
+def test_dgbmv(benchmark, n, kl, variant):
+    rndm = np.random.RandomState(1234)
+    dtyp = dtype_map[variant]
+
+    x = np.array(rndm.uniform(size=(n,)), dtype=dtyp)
+    y = np.empty(n, dtype=dtyp)
+
+    m = n
+
+    a = rndm.uniform(size=(2*kl + 1, n))
+    a = np.array(a, dtype=dtyp, order='F')
+
+    gbmv = ow.get_func('gbmv', variant)
+    result = benchmark(run_gbmv, m, n, kl, kl, a, x, y, gbmv)
+    assert result is y
+
 
 # ### BLAS level 3 ###
 
@@ -71,19 +137,22 @@ def test_daxpy(benchmark, n):
 
 gemm_sizes = [100, 1000]
 
-def run_gemm(a, b, c):
+def run_gemm(a, b, c, func):
     alpha = 1.0
-    res = dgemm(alpha, a, b, c=c, overwrite_c=True)
+    res = func(alpha, a, b, c=c, overwrite_c=True)
     return res
 
 
+@pytest.mark.parametrize('variant', ['s', 'd', 'c', 'z'])
 @pytest.mark.parametrize('n', gemm_sizes)
-def test_gemm(benchmark, n):
+def test_gemm(benchmark, n, variant):
     rndm = np.random.RandomState(1234)
-    a = np.array(rndm.uniform(size=(n, n)), dtype=float, order='F')
-    b = np.array(rndm.uniform(size=(n, n)), dtype=float, order='F')
-    c = np.empty((n, n), dtype=float, order='F')
-    result = benchmark(run_gemm, a, b, c)
+    dtyp = dtype_map[variant]
+    a = np.array(rndm.uniform(size=(n, n)), dtype=dtyp, order='F')
+    b = np.array(rndm.uniform(size=(n, n)), dtype=dtyp, order='F')
+    c = np.empty((n, n), dtype=dtyp, order='F')
+    gemm = ow.get_func('gemm', variant)
+    result = benchmark(run_gemm, a, b, c, gemm)
     assert result is c
 
 
@@ -92,17 +161,20 @@ def test_gemm(benchmark, n):
 syrk_sizes = [100, 1000]
 
 
-def run_syrk(a, c):
-    res = dsyrk(1.0, a, c=c, overwrite_c=True)
+def run_syrk(a, c, func):
+    res = func(1.0, a, c=c, overwrite_c=True)
     return res
 
 
+@pytest.mark.parametrize('variant', ['s', 'd', 'c', 'z'])
 @pytest.mark.parametrize('n', syrk_sizes)
-def test_syrk(benchmark, n):
+def test_syrk(benchmark, n, variant):
     rndm = np.random.RandomState(1234)
-    a = np.array(rndm.uniform(size=(n, n)), dtype=float, order='F')
-    c = np.empty((n, n), dtype=float, order='F')
-    result = benchmark(run_syrk, a, c)
+    dtyp = dtype_map[variant]
+    a = np.array(rndm.uniform(size=(n, n)), dtype=dtyp, order='F')
+    c = np.empty((n, n), dtype=dtyp, order='F')
+    syrk = ow.get_func('syrk', variant)
+    result = benchmark(run_syrk, a, c, syrk)
     assert result is c
 
 
@@ -113,18 +185,22 @@ def test_syrk(benchmark, n):
 gesv_sizes = [100, 1000]
 
 
-def run_gesv(a, b):
-    res = dgesv(a, b, overwrite_a=True, overwrite_b=True)
+def run_gesv(a, b, func):
+    res = func(a, b, overwrite_a=True, overwrite_b=True)
     return res
 
 
+@pytest.mark.parametrize('variant', ['s', 'd', 'c', 'z'])
 @pytest.mark.parametrize('n', gesv_sizes)
-def test_gesv(benchmark, n):
+def test_gesv(benchmark, n, variant):
     rndm = np.random.RandomState(1234)
-    a = (np.array(rndm.uniform(size=(n, n)), dtype=float, order='F') +
-         np.eye(n, order='F'))
-    b = np.array(rndm.uniform(size=(n, 1)), order='F')
-    lu, piv, x, info = benchmark(run_gesv, a, b)
+    dtyp = dtype_map[variant]
+
+    a = (np.array(rndm.uniform(size=(n, n)), dtype=dtyp, order='F') +
+         np.eye(n, dtype=dtyp, order='F'))
+    b = np.array(rndm.uniform(size=(n, 1)), dtype=dtyp, order='F')
+    gesv = ow.get_func('gesv', variant)
+    lu, piv, x, info = benchmark(run_gesv, a, b, gesv)
     assert lu is a
     assert x is b
     assert info == 0
@@ -135,25 +211,34 @@ def test_gesv(benchmark, n):
 gesdd_sizes = [(100, 5), (1000, 222)]
 
 
-def run_gesdd(a, lwork):
-    res = dgesdd(a, lwork=lwork, full_matrices=False, overwrite_a=False)
+def run_gesdd(a, lwork, func):
+    res = func(a, lwork=lwork, full_matrices=False, overwrite_a=False)
     return res
 
 
+@pytest.mark.parametrize('variant', ['s', 'd'])
 @pytest.mark.parametrize('mn', gesdd_sizes)
-def test_gesdd(benchmark, mn):
+def test_gesdd(benchmark, mn, variant):
     m, n = mn
     rndm = np.random.RandomState(1234)
-    a = np.array(rndm.uniform(size=(m, n)), dtype=float, order='F')
+    dtyp = dtype_map[variant]
+
+    a = np.array(rndm.uniform(size=(m, n)), dtype=dtyp, order='F')
 
-    lwork, info = dgesdd_lwork(m, n)
+    gesdd_lwork = ow.get_func('gesdd_lwork', variant)
+
+    lwork, info = gesdd_lwork(m, n)
     lwork = int(lwork)
     assert info == 0
 
-    u, s, vt, info = benchmark(run_gesdd, a, lwork)
+    gesdd = ow.get_func('gesdd', variant)
+    u, s, vt, info = benchmark(run_gesdd, a, lwork, gesdd)
 
     assert info == 0
-    np.testing.assert_allclose(u @ np.diag(s) @ vt, a, atol=1e-13)
+
+    atol = {'s': 1e-5, 'd': 1e-13}
+
+    np.testing.assert_allclose(u @ np.diag(s) @ vt, a, atol=atol[variant])
 
 
 # linalg.eigh
@@ -161,23 +246,28 @@ def test_gesdd(benchmark, mn):
 syev_sizes = [50, 200]
 
 
-def run_syev(a, lwork):
-    res = dsyev(a, lwork=lwork, overwrite_a=True)
+def run_syev(a, lwork, func):
+    res = func(a, lwork=lwork, overwrite_a=True)
     return res
 
 
+@pytest.mark.parametrize('variant', ['s', 'd'])
 @pytest.mark.parametrize('n', syev_sizes)
-def test_syev(benchmark, n):
+def test_syev(benchmark, n, variant):
     rndm = np.random.RandomState(1234)
+    dtyp = dtype_map[variant]
+
     a = rndm.uniform(size=(n, n))
-    a = np.asarray(a + a.T, dtype=float, order='F')
+    a = np.asarray(a + a.T, dtype=dtyp, order='F')
     a_ = a.copy()
 
+    dsyev_lwork = ow.get_func('syev_lwork', variant)
     lwork, info = dsyev_lwork(n)
     lwork = int(lwork)
     assert info == 0
 
-    w, v, info = benchmark(run_syev, a, lwork)
+    syev = ow.get_func('syev', variant)
+    w, v, info = benchmark(run_syev, a, lwork, syev)
 
     assert info == 0
     assert a is v  # overwrite_a=True

benchmark/pybench/openblas_wrap/__init__.py

@@ -6,23 +6,12 @@ from benchmarking.
 __version__ = "0.1"  
 
 
-#from scipy.linalg.blas import (
-from ._flapack import (
-    # level 1
-    dnrm2 as dnrm2,
-    ddot as ddot,
-    daxpy as daxpy,
-    # level 3
-    dgemm as dgemm,
-    dsyrk as dsyrk,
-)
+from . import _flapack
+
+PREFIX = ''
+
+
+def get_func(name, variant):
+    """get_func('gesv', 'c') -> cgesv etc."""
+    return getattr(_flapack, PREFIX + variant + name)
 
-#from scipy.linalg.lapack import (
-from openblas_wrap._flapack import (
-    # linalg.solve
-    dgesv as dgesv,
-    # linalg.svd
-    dgesdd as dgesdd, dgesdd_lwork as dgesdd_lwork,
-    # linalg.eigh
-    dsyev as dsyev, dsyev_lwork as dsyev_lwork
-)

benchmark/pybench/openblas_wrap/blas_lapack.pyf.src

@@ -111,6 +111,97 @@ function <prefix4>nrm2(n,x,offx,incx) result(n2)
 
 end function <prefix4>nrm2
 
+
+!
+! Level 2 BLAS
+!
+
+
+subroutine <prefix>gemv(m,n,alpha,a,x,beta,y,offx,incx,offy,incy,trans,rows,cols,ly)
+  ! Computes a matrix-vector product using a general matrix
+  !
+  ! y = gemv(alpha,a,x,beta=0,y=0,offx=0,incx=1,offy=0,incy=0,trans=0)
+  ! Calculate y <- alpha * op(A) * x + beta * y
+
+  callstatement (*f2py_func)((trans?(trans==2?"C":"T"):"N"),&m,&n,&alpha,a,&m, &
+       x+offx,&incx,&beta,y+offy,&incy)
+  callprotoargument char*,F_INT*,F_INT*,<ctype>*,<ctype>*,F_INT*,<ctype>*,F_INT*,<ctype>*, &
+       <ctype>*,F_INT*
+
+  integer optional, intent(in), check(trans>=0 && trans <=2) :: trans = 0
+  integer optional, intent(in), check(incx>0||incx<0) :: incx = 1
+  integer optional, intent(in), check(incy>0||incy<0) :: incy = 1
+  <ftype> intent(in) :: alpha
+  <ftype> intent(in), optional :: beta = <0.0,\0,(0.0\,0.0),\2>
+
+  <ftype> dimension(*), intent(in) :: x
+  <ftype> dimension(ly), intent(in,copy,out), depend(ly),optional :: y
+  integer intent(hide), depend(incy,rows,offy) :: ly = &
+       (y_capi==Py_None?1+offy+(rows-1)*abs(incy):-1)
+  <ftype> dimension(m,n), intent(in) :: a
+  integer depend(a), intent(hide):: m = shape(a,0)
+  integer depend(a), intent(hide):: n = shape(a,1)
+
+  integer optional, intent(in) :: offx=0
+  integer optional, intent(in) :: offy=0
+  check(offx>=0 && offx<len(x)) :: x
+  check(len(x)>offx+(cols-1)*abs(incx)) :: x
+  depend(offx,cols,incx) :: x
+
+  check(offy>=0 && offy<len(y)) :: y
+  check(len(y)>offy+(rows-1)*abs(incy)) :: y
+  depend(offy,rows,incy) :: y
+
+  integer depend(m,n,trans), intent(hide) :: rows = (trans?n:m)
+  integer depend(m,n,trans), intent(hide) :: cols = (trans?m:n)
+
+end subroutine <prefix>gemv
+
+
+subroutine <prefix>gbmv(m,n,kl,ku,alpha,a,lda,x,incx,offx,beta,y,incy,offy,trans,ly)
+  ! Performs one of the matrix-vector operations
+  !
+  !    y := alpha*A*x + beta*y,   or   y := alpha*A**T*x + beta*y,
+  !                               or   y := alpha*A**H*x + beta*y,
+  !
+  ! where alpha and beta are scalars, x and y are vectors and A is an
+  ! m by n band matrix, with kl sub-diagonals and ku super-diagonals.
+
+  callstatement (*f2py_func)((trans?(trans==2?"C":"T"):"N"),&m,&n,&kl,&ku,&alpha,a,&lda,x+offx,&incx,&beta,y+offy,&incy)
+  callprotoargument char*,F_INT*,F_INT*,F_INT*,F_INT*,<ctype>*,<ctype>*,F_INT*,<ctype>*,F_INT*,<ctype>*,<ctype>*,F_INT*
+
+  integer optional,intent(in),check(trans>=0 && trans <=2) :: trans = 0
+  integer intent(in), depend(ku,kl),check(m>=ku+kl+1) :: m
+  integer intent(in),check(n>=0&&n==shape(a,1)),depend(a) :: n
+  integer intent(in),check(kl>=0) :: kl
+  integer intent(in),check(ku>=0) :: ku
+  integer intent(hide),depend(a) :: lda = MAX(shape(a,0),1)
+  integer optional, intent(in),check(incx>0||incx<0) :: incx = 1
+  integer optional, intent(in),check(incy>0||incy<0) :: incy = 1
+  integer intent(hide),depend(m,n,incy,offy,trans) :: ly = &
+      (y_capi==Py_None?1+offy+(trans==0?m-1:n-1)*abs(incy):-1)
+  integer optional, intent(in) :: offx=0
+  integer optional, intent(in) :: offy=0
+
+  <ftype> intent(in) :: alpha
+  <ftype> intent(in),optional :: beta = <0.0,\0,(0.0\,0.0),\2>
+
+  <ftype> dimension(lda,n),intent(in) :: a
+
+  <ftype> dimension(ly), intent(in,out,copy,out=yout),depend(ly),optional :: y
+  check(offy>=0 && offy<len(y)) :: y
+  check(len(y)>offy+(trans==0?m-1:n-1)*abs(incy)) :: y
+  depend(offy,n,incy) :: y
+
+  <ftype> dimension(*), intent(in) :: x
+  check(offx>=0 && offx<len(x)) :: x
+  check(len(x)>offx+(trans==0?n-1:m-1)*abs(incx)) :: x
+  depend(offx,n,incx) :: x
+
+end subroutine <prefix>gbmv
+
+
+
 !
 ! Level 3 BLAS
 !