BENCH: sync codspeed-benchmarks with BLAS-benchmarks
This commit is contained in:
Evgeni Burovski
parent
0073affe63
commit
37a854718b
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@ -1,66 +1,76 @@
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import pytest
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import numpy as np
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from openblas_wrap import (
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# level 1
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dnrm2, ddot, daxpy,
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# level 3
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dgemm, dsyrk,
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# lapack
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dgesv, # linalg.solve
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dgesdd, dgesdd_lwork, # linalg.svd
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dsyev, dsyev_lwork, # linalg.eigh
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)
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import openblas_wrap as ow
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dtype_map = {
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's': np.float32,
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'd': np.float64,
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'c': np.complex64,
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'z': np.complex128,
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'dz': np.complex128,
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}
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# ### BLAS level 1 ###
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# dnrm2
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dnrm2_sizes = [100, 1000]
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dnrm2_sizes = [100, 200, 400, 600, 800, 1000]
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def run_dnrm2(n, x, incx):
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res = dnrm2(x, n, incx=incx)
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def run_dnrm2(n, x, incx, func):
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res = func(x, n, incx=incx)
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return res
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@pytest.mark.parametrize('variant', ['d', 'dz'])
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@pytest.mark.parametrize('n', dnrm2_sizes)
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def test_nrm2(benchmark, n):
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def test_nrm2(benchmark, n, variant):
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rndm = np.random.RandomState(1234)
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x = np.array(rndm.uniform(size=(n,)), dtype=float)
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result = benchmark(run_dnrm2, n, x, 1)
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dtyp = dtype_map[variant]
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x = np.array(rndm.uniform(size=(n,)), dtype=dtyp)
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nrm2 = ow.get_func('nrm2', variant)
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result = benchmark(run_dnrm2, n, x, 1, nrm2)
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# ddot
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ddot_sizes = [100, 1000]
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ddot_sizes = [100, 200, 400, 600, 800, 1000]
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def run_ddot(x, y,):
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res = ddot(x, y)
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def run_ddot(x, y, func):
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res = func(x, y)
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return res
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@pytest.mark.parametrize('n', ddot_sizes)
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def test_dot(benchmark, n):
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rndm = np.random.RandomState(1234)
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x = np.array(rndm.uniform(size=(n,)), dtype=float)
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y = np.array(rndm.uniform(size=(n,)), dtype=float)
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result = benchmark(run_ddot, x, y)
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dot = ow.get_func('dot', 'd')
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result = benchmark(run_ddot, x, y, dot)
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# daxpy
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daxpy_sizes = [100, 1000]
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daxpy_sizes = [100, 200, 400, 600, 800, 1000]
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def run_daxpy(x, y,):
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res = daxpy(x, y, a=2.0)
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def run_daxpy(x, y, func):
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res = func(x, y, a=2.0)
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return res
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@pytest.mark.parametrize('variant', ['s', 'd', 'c', 'z'])
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@pytest.mark.parametrize('n', daxpy_sizes)
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def test_daxpy(benchmark, n):
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def test_daxpy(benchmark, n, variant):
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rndm = np.random.RandomState(1234)
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x = np.array(rndm.uniform(size=(n,)), dtype=float)
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y = np.array(rndm.uniform(size=(n,)), dtype=float)
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result = benchmark(run_daxpy, x, y)
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dtyp = dtype_map[variant]
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x = np.array(rndm.uniform(size=(n,)), dtype=dtyp)
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y = np.array(rndm.uniform(size=(n,)), dtype=dtyp)
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axpy = ow.get_func('axpy', variant)
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result = benchmark(run_daxpy, x, y, axpy)
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@ -69,40 +79,46 @@ def test_daxpy(benchmark, n):
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# dgemm
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gemm_sizes = [100, 1000]
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gemm_sizes = [100, 200, 400, 600, 800, 1000]
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def run_gemm(a, b, c):
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def run_gemm(a, b, c, func):
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alpha = 1.0
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res = dgemm(alpha, a, b, c=c, overwrite_c=True)
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res = func(alpha, a, b, c=c, overwrite_c=True)
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return res
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@pytest.mark.parametrize('variant', ['s', 'd', 'c', 'z'])
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@pytest.mark.parametrize('n', gemm_sizes)
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def test_gemm(benchmark, n):
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def test_gemm(benchmark, n, variant):
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rndm = np.random.RandomState(1234)
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a = np.array(rndm.uniform(size=(n, n)), dtype=float, order='F')
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b = np.array(rndm.uniform(size=(n, n)), dtype=float, order='F')
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c = np.empty((n, n), dtype=float, order='F')
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result = benchmark(run_gemm, a, b, c)
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dtyp = dtype_map[variant]
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a = np.array(rndm.uniform(size=(n, n)), dtype=dtyp, order='F')
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b = np.array(rndm.uniform(size=(n, n)), dtype=dtyp, order='F')
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c = np.empty((n, n), dtype=dtyp, order='F')
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gemm = ow.get_func('gemm', variant)
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result = benchmark(run_gemm, a, b, c, gemm)
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assert result is c
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# dsyrk
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syrk_sizes = [100, 1000]
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syrk_sizes = [100, 200, 400, 600, 800, 1000]
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def run_syrk(a, c):
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res = dsyrk(1.0, a, c=c, overwrite_c=True)
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def run_syrk(a, c, func):
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res = func(1.0, a, c=c, overwrite_c=True)
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return res
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@pytest.mark.parametrize('variant', ['s', 'd', 'c', 'z'])
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@pytest.mark.parametrize('n', syrk_sizes)
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def test_syrk(benchmark, n):
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def test_syrk(benchmark, n, variant):
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rndm = np.random.RandomState(1234)
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a = np.array(rndm.uniform(size=(n, n)), dtype=float, order='F')
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c = np.empty((n, n), dtype=float, order='F')
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result = benchmark(run_syrk, a, c)
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dtyp = dtype_map[variant]
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a = np.array(rndm.uniform(size=(n, n)), dtype=dtyp, order='F')
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c = np.empty((n, n), dtype=dtyp, order='F')
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syrk = ow.get_func('syrk', variant)
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result = benchmark(run_syrk, a, c, syrk)
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assert result is c
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@ -110,21 +126,25 @@ def test_syrk(benchmark, n):
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# linalg.solve
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gesv_sizes = [100, 1000]
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gesv_sizes = [100, 200, 400, 600, 800, 1000]
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def run_gesv(a, b):
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res = dgesv(a, b, overwrite_a=True, overwrite_b=True)
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def run_gesv(a, b, func):
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res = func(a, b, overwrite_a=True, overwrite_b=True)
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return res
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@pytest.mark.parametrize('variant', ['s', 'd', 'c', 'z'])
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@pytest.mark.parametrize('n', gesv_sizes)
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def test_gesv(benchmark, n):
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def test_gesv(benchmark, n, variant):
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rndm = np.random.RandomState(1234)
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a = (np.array(rndm.uniform(size=(n, n)), dtype=float, order='F') +
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np.eye(n, order='F'))
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b = np.array(rndm.uniform(size=(n, 1)), order='F')
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lu, piv, x, info = benchmark(run_gesv, a, b)
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dtyp = dtype_map[variant]
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a = (np.array(rndm.uniform(size=(n, n)), dtype=dtyp, order='F') +
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np.eye(n, dtype=dtyp, order='F'))
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b = np.array(rndm.uniform(size=(n, 1)), dtype=dtyp, order='F')
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gesv = ow.get_func('gesv', variant)
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lu, piv, x, info = benchmark(run_gesv, a, b, gesv)
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assert lu is a
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assert x is b
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assert info == 0
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@ -135,49 +155,63 @@ def test_gesv(benchmark, n):
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gesdd_sizes = [(100, 5), (1000, 222)]
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def run_gesdd(a, lwork):
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res = dgesdd(a, lwork=lwork, full_matrices=False, overwrite_a=False)
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def run_gesdd(a, lwork, func):
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res = func(a, lwork=lwork, full_matrices=False, overwrite_a=False)
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return res
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@pytest.mark.parametrize('variant', ['s', 'd'])
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@pytest.mark.parametrize('mn', gesdd_sizes)
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def test_gesdd(benchmark, mn):
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def test_gesdd(benchmark, mn, variant):
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m, n = mn
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rndm = np.random.RandomState(1234)
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a = np.array(rndm.uniform(size=(m, n)), dtype=float, order='F')
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dtyp = dtype_map[variant]
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lwork, info = dgesdd_lwork(m, n)
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a = np.array(rndm.uniform(size=(m, n)), dtype=dtyp, order='F')
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gesdd_lwork = ow.get_func('gesdd_lwork', variant)
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lwork, info = gesdd_lwork(m, n)
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lwork = int(lwork)
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assert info == 0
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u, s, vt, info = benchmark(run_gesdd, a, lwork)
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gesdd = ow.get_func('gesdd', variant)
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u, s, vt, info = benchmark(run_gesdd, a, lwork, gesdd)
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assert info == 0
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np.testing.assert_allclose(u @ np.diag(s) @ vt, a, atol=1e-13)
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atol = {'s': 1e-5, 'd': 1e-13}
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np.testing.assert_allclose(u @ np.diag(s) @ vt, a, atol=atol[variant])
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# linalg.eigh
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syev_sizes = [50, 200]
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syev_sizes = [50, 64, 128, 200]
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def run_syev(a, lwork):
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res = dsyev(a, lwork=lwork, overwrite_a=True)
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def run_syev(a, lwork, func):
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res = func(a, lwork=lwork, overwrite_a=True)
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return res
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@pytest.mark.parametrize('variant', ['s', 'd'])
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@pytest.mark.parametrize('n', syev_sizes)
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def test_syev(benchmark, n):
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def test_syev(benchmark, n, variant):
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rndm = np.random.RandomState(1234)
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dtyp = dtype_map[variant]
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a = rndm.uniform(size=(n, n))
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a = np.asarray(a + a.T, dtype=float, order='F')
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a = np.asarray(a + a.T, dtype=dtyp, order='F')
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a_ = a.copy()
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dsyev_lwork = ow.get_func('syev_lwork', variant)
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lwork, info = dsyev_lwork(n)
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lwork = int(lwork)
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assert info == 0
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w, v, info = benchmark(run_syev, a, lwork)
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syev = ow.get_func('syev', variant)
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w, v, info = benchmark(run_syev, a, lwork, syev)
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assert info == 0
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assert a is v # overwrite_a=True
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@ -6,23 +6,12 @@ from benchmarking.
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__version__ = "0.1"
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#from scipy.linalg.blas import (
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from ._flapack import (
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# level 1
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dnrm2 as dnrm2,
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ddot as ddot,
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daxpy as daxpy,
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# level 3
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dgemm as dgemm,
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dsyrk as dsyrk,
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)
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from . import _flapack
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PREFIX = ''
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def get_func(name, variant):
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"""get_func('gesv', 'c') -> cgesv etc."""
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return getattr(_flapack, PREFIX + variant + name)
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#from scipy.linalg.lapack import (
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from openblas_wrap._flapack import (
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# linalg.solve
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dgesv as dgesv,
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# linalg.svd
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dgesdd as dgesdd, dgesdd_lwork as dgesdd_lwork,
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# linalg.eigh
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dsyev as dsyev, dsyev_lwork as dsyev_lwork
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)
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