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test_linalg.py
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test_linalg.py
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# Copyright (c) 2016-2020, The Bifrost Authors. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of The Bifrost Authors nor the names of its
# contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
# **TODO: Add tests with beta != 0
# Python2 compatibility
from __future__ import print_function
import ctypes
import unittest
import numpy as np
from numpy import matmul as gold_matmul
from bifrost.linalg import LinAlg
import bifrost as bf
from bifrost.libbifrost_generated import BF_CUDA_ENABLED
import time
RTOL = 1e-4
ATOL = 1e-5
def H(c):
return np.swapaxes(c, -1, -2).conj()
@unittest.skipUnless(BF_CUDA_ENABLED, "requires GPU support")
class TestLinAlg(unittest.TestCase):
def setUp(self):
self.linalg = LinAlg()
np.random.seed(1234)
def run_test_matmul_aa_ci8_shape(self, shape, transpose=False):
# **TODO: This currently never triggers the transpose path in the backend
shape_complex = shape[:-1] + (shape[-1] * 2,)
# Note: The xGPU-like correlation kernel does not support input values of -128 (only [-127:127])
a8 = ((np.random.random(size=shape_complex) * 2 - 1) * 127).astype(np.int8)
a_gold = a8.astype(np.float32).view(np.complex64)
if transpose:
a_gold = H(a_gold)
# Note: np.matmul seems to be slow and inaccurate when there are batch dims
c_gold = np.matmul(a_gold, H(a_gold))
triu = np.triu_indices(shape[-2] if not transpose else shape[-1], 1)
c_gold[..., triu[0], triu[1]] = 0
a = a8.view(bf.DataType.ci8)
a = bf.asarray(a, space='cuda')
if transpose:
a = H(a)
c = bf.zeros_like(c_gold, space='cuda')
self.linalg.matmul(1, a, None, 0, c)
c = c.copy('system')
np.testing.assert_allclose(c, c_gold, RTOL, ATOL)
def run_test_matmul_aa_dtype_shape(self, shape, dtype, axes=None, conj=False):
a = ((np.random.random(size=shape)) * 127).astype(dtype)
if axes is None:
axes = range(len(shape))
aa = a.transpose(axes)
if conj:
aa = aa.conj()
c_gold = np.matmul(aa, H(aa))
triu = np.triu_indices(shape[axes[-2]], 1)
c_gold[..., triu[0], triu[1]] = 0
a = bf.asarray(a, space='cuda')
aa = a.transpose(axes)
if conj:
aa = aa.conj()
c = bf.zeros_like(c_gold, space='cuda')
self.linalg.matmul(1, aa, None, 0, c)
c = c.copy('system')
np.testing.assert_allclose(c, c_gold, RTOL, ATOL)
def run_test_matmul_ab_ci8_shape(self, shape, k, transpose=False):
ashape_complex = shape[:-2] + (shape[-2], k * 2)
bshape_complex = shape[:-2] + (k, shape[-1] * 2)
a8 = (np.random.random(size=ashape_complex) * 255).astype(np.int8)
b8 = (np.random.random(size=bshape_complex) * 255).astype(np.int8)
a_gold = a8.astype(np.float32).view(np.complex64)
b_gold = b8.astype(np.float32).view(np.complex64)
if transpose:
a_gold, b_gold = H(b_gold), H(a_gold)
c_gold = np.matmul(a_gold, b_gold)
a = a8.view(bf.DataType.ci8)
b = b8.view(bf.DataType.ci8)
a = bf.asarray(a, space='cuda')
b = bf.asarray(b, space='cuda')
if transpose:
a, b = H(b), H(a)
c = bf.zeros_like(c_gold, space='cuda')
self.linalg.matmul(1, a, b, 0, c)
c = c.copy('system')
np.testing.assert_allclose(c, c_gold, RTOL, ATOL)
def run_test_matmul_ab_dtype_shape(self, shape, k, dtype,
axes_a=None, axes_b=None, transpose=False):
# TODO: Allow testing separate transpose_a, transpose_b
ashape = shape[:-2] + (shape[-2], k)
bshape = shape[:-2] + (k, shape[-1])
a = ((np.random.random(size=ashape)) * 127).astype(dtype)
b = ((np.random.random(size=bshape)) * 127).astype(dtype)
if axes_a is None:
axes_a = range(len(ashape))
if axes_b is None:
axes_b = range(len(bshape))
aa = a.transpose(axes_a)
bb = b.transpose(axes_b)
if transpose:
aa, bb = H(bb), H(aa)
c_gold = np.matmul(aa, bb)
a = bf.asarray(a, space='cuda')
b = bf.asarray(b, space='cuda')
aa = a.transpose(axes_a)
bb = b.transpose(axes_b)
if transpose:
aa, bb = H(bb), H(aa)
c = bf.zeros_like(c_gold, space='cuda')
self.linalg.matmul(1, aa, bb, 0, c)
c = c.copy('system')
np.testing.assert_allclose(c, c_gold, RTOL, ATOL)
def run_test_matmul_ab_beamformer_kernel(self, ntime, nbeam, nstand, nchan):
x_shape = (ntime, nchan, nstand*2)
w_shape = (nbeam, nchan, nstand*2)
x8 = ((np.random.random(size=x_shape+(2,))*2-1)*127).astype(np.int8)
x = x8.astype(np.float32).view(np.complex64).reshape(x_shape)
w = ((np.random.random(size=w_shape+(2,))*2-1)*127).astype(np.int8).astype(np.float32).view(np.complex64).reshape(w_shape)
b_gold = np.matmul(w.transpose(1,0,2), x.transpose(1,2,0))
x = x8.view(bf.DataType.ci8).reshape(x_shape)
x = bf.asarray(x, space='cuda')
w = bf.asarray(w, space='cuda')
b = bf.zeros_like(b_gold, space='cuda')
self.linalg.matmul(1, w.transpose(1,0,2), x.transpose(1,2,0), 0, b)
b_ = b.copy('system')
np.testing.assert_allclose(b_, b_gold, RTOL, ATOL)
"""
# Benchmarking
nrep = 30
bf.device.stream_synchronize()
t0 = time.time()
for _ in range(nrep):
self.linalg.matmul(1, w.transpose(1,0,2), x.transpose(1,2,0), 0, b)
bf.device.stream_synchronize()
dt = time.time() - t0
nflop = nrep * ntime * nbeam * nstand*2 * nchan * 8
nbyte = nrep * (x.nbytes + w.nbytes + b.nbytes)
nsamp = nrep * ntime * nchan
print(nbeam, '\t'*1, nflop / dt / 1e9, 'GFLOP/s')
print(nbeam, '\t'*2, nbyte / dt / 1e9, 'GB/s')
print(nbeam, '\t'*3, nsamp / dt / 1e6, 'MHz/s')
"""
def run_test_matmul_aa_correlator_kernel(self, ntime, nstand, nchan, misalign=0):
x_shape = (ntime, nchan, nstand*2)
perm = [1,0,2]
x8 = ((np.random.random(size=x_shape+(2,))*2-1)*127).astype(np.int8)
x = x8.astype(np.float32).view(np.complex64).reshape(x_shape)
x = x.transpose(perm)
x = x[..., misalign:]
b_gold = np.matmul(H(x), x)
triu = np.triu_indices(x.shape[-1], 1)
b_gold[..., triu[0], triu[1]] = 0
x = x8.view(bf.DataType.ci8).reshape(x_shape)
x = bf.asarray(x, space='cuda')
x = x.transpose(perm)
x = x[..., misalign:]
b = bf.zeros_like(b_gold, space='cuda')
self.linalg.matmul(1, None, x, 0, b)
b = b.copy('system')
np.testing.assert_allclose(b, b_gold, RTOL*10, ATOL)
def run_benchmark_matmul_aa_correlator_kernel(self, ntime, nstand, nchan):
x_shape = (ntime, nchan, nstand*2)
perm = [1,0,2]
x8 = ((np.random.random(size=x_shape+(2,))*2-1)*127).astype(np.int8)
x = x8.astype(np.float32).view(np.complex64).reshape(x_shape)
x = x.transpose(perm)
b_gold = np.matmul(H(x[:,[0],:]), x[:,[0],:])
triu = np.triu_indices(x_shape[-1], 1)
b_gold[..., triu[0], triu[1]] = 0
x = x8.view(bf.DataType.ci8).reshape(x_shape)
x = bf.asarray(x, space='cuda')
x = x.transpose(perm)
b = bf.zeros_like(b_gold, space='cuda')
bf.device.stream_synchronize();
t0 = time.time()
nrep = 200
for _ in range(nrep):
self.linalg.matmul(1, None, x, 0, b)
bf.device.stream_synchronize();
dt = time.time() - t0
nflop = nrep * nchan * ntime * nstand*(nstand+1)/2 * 2*2 * 8
print(nstand, '\t', nflop / dt / 1e9, 'GFLOP/s')
print('\t\t', nrep*ntime*nchan / dt / 1e6, 'MHz')
def test_matmul_ab_beamformer_kernel_small(self):
for nchan in range(1, 1+3):
for ntime in range(1, 1+8):
for nstand in [16, 64, 256]:
for nbeam in range(1, 1+12):
self.run_test_matmul_ab_beamformer_kernel(
ntime=ntime, nbeam=nbeam, nstand=nstand, nchan=nchan)
def test_matmul_ab_beamformer_kernel_large(self):
for nbeam in range(1, 1+12):
#print("--------------", nbeam, "---------------")
self.run_test_matmul_ab_beamformer_kernel(ntime=512, nbeam=nbeam,
nstand=256, nchan=10)
def test_matmul_aa_correlator_kernel_small(self):
for nchan in range(1, 1+5):
for ntime in [1, 2, 3, 4, 8, 12]:
for nstand in range(1, 1+65):
for misalign in range(0, min(2 * (nstand - 1), 3), 2):
self.run_test_matmul_aa_correlator_kernel(
ntime=ntime, nstand=nstand, nchan=nchan,
misalign=misalign)
def test_matmul_aa_correlator_kernel_large(self):
self.run_test_matmul_aa_correlator_kernel(ntime=100, nstand=200, nchan=1)
self.run_test_matmul_aa_correlator_kernel(ntime=99, nstand=200, nchan=1)
self.run_test_matmul_aa_correlator_kernel(ntime=100, nstand=200, nchan=3)
self.run_test_matmul_aa_correlator_kernel(ntime=99, nstand=200, nchan=3)
self.run_test_matmul_aa_correlator_kernel(ntime=400, nstand=100, nchan=7)
self.run_test_matmul_aa_correlator_kernel(ntime=399, nstand=100, nchan=7)
self.run_test_matmul_aa_correlator_kernel(ntime=36, nstand=97, nchan=31)
self.run_test_matmul_aa_correlator_kernel(ntime=35, nstand=97, nchan=31)
self.run_test_matmul_aa_correlator_kernel(ntime=4, nstand=512, nchan=1)
self.run_test_matmul_aa_correlator_kernel(ntime=512, nstand=256, nchan=3)
self.run_test_matmul_aa_correlator_kernel(ntime=1000, nstand=256, nchan=1)
# Benchmarks
#self.run_benchmark_matmul_aa_correlator_kernel(ntime=4096, nstand=256, nchan=64)
#for nstand in [16, 28, 64, 256, 1024]:
# self.run_benchmark_matmul_aa_correlator_kernel(ntime=512, nstand=nstand, nchan=256*256*48//2//(nstand*nstand))
#print
#self.run_benchmark_matmul_aa_correlator_kernel(ntime=512, nstand=256, nchan=96)
#print
#self.run_benchmark_matmul_aa_correlator_kernel(ntime=2048, nstand=2048, nchan=2)
def run_test_matmul_aa_dtype(self, dtype):
self.run_test_matmul_aa_dtype_shape((3,2), dtype)
self.run_test_matmul_aa_dtype_shape((11,23), dtype)
# Note: Only Hermitian transposes are supported
self.run_test_matmul_aa_dtype_shape((11,23), dtype, [1,0], conj=True)
self.run_test_matmul_aa_dtype_shape((111,223), dtype)
self.run_test_matmul_aa_dtype_shape((111,223), dtype, [1,0], conj=True)
self.run_test_matmul_aa_dtype_shape((1111,2223), dtype)
self.run_test_matmul_aa_dtype_shape((3,111,223), dtype)
self.run_test_matmul_aa_dtype_shape((3,111,223), dtype, [0,2,1], conj=True)
self.run_test_matmul_aa_dtype_shape((3,111,223), dtype, [1,2,0], conj=True)
self.run_test_matmul_aa_dtype_shape((3,111,223), dtype, [1,0,2])
# Note: The fastest dim can't be a batch dim, so these aren't supported
#self.run_test_matmul_aa_dtype_shape((3,111,223), dtype, [2,0,1])
#self.run_test_matmul_aa_dtype_shape((3,111,223), dtype, [2,1,0])
self.run_test_matmul_aa_dtype_shape((5,3,111,57), dtype)
self.run_test_matmul_aa_dtype_shape((5,3,111,57), dtype, [0,1,3,2], conj=True)
self.run_test_matmul_aa_dtype_shape((5,3,111,57), dtype, [1,0,2,3])
self.run_test_matmul_aa_dtype_shape((5,3,111,57), dtype, [1,0,3,2], conj=True)
self.run_test_matmul_aa_dtype_shape((5,3,111,57), dtype, [1,2,3,0], conj=True)
self.run_test_matmul_aa_dtype_shape((5,3,111,57), dtype, [1,2,0,3])
self.run_test_matmul_aa_dtype_shape((5,3,111,57), dtype, [2,1,0,3])
self.run_test_matmul_aa_dtype_shape((5,3,111,57), dtype, [2,1,3,0], conj=True)
self.run_test_matmul_aa_dtype_shape((5,3,111,57), dtype, [2,0,3,1], conj=True)
self.run_test_matmul_aa_dtype_shape((5,3,111,57), dtype, [2,0,1,3])
self.run_test_matmul_aa_dtype_shape((5,7,3,111,223), dtype)
def run_test_matmul_ab_dtype_transpose(self, dtype, transpose):
self.run_test_matmul_ab_dtype_shape((11,23), 7, dtype, transpose=transpose)
self.run_test_matmul_ab_dtype_shape((11,23), 11, dtype, transpose=transpose)
self.run_test_matmul_ab_dtype_shape((11,23), 23, dtype, transpose=transpose)
self.run_test_matmul_ab_dtype_shape((11,11), 11, dtype, transpose=transpose)
self.run_test_matmul_ab_dtype_shape((111,223), 77, dtype, transpose=transpose)
self.run_test_matmul_ab_dtype_shape((111,2223), 777, dtype, transpose=transpose)
self.run_test_matmul_ab_dtype_shape((3,111,223), 77, dtype, transpose=transpose)
def run_test_matmul_ab_dtype(self, dtype):
self.run_test_matmul_ab_dtype_transpose(dtype, False)
self.run_test_matmul_ab_dtype_transpose(dtype, True)
def run_test_matmul_aa_ci8_transpose(self, transpose):
# Note: The xGPU-like correlation kernel is only invoked when k%4 == 0
for kp in [0, 1]:
self.run_test_matmul_aa_ci8_shape((99+kp,3+kp), transpose=transpose)
self.run_test_matmul_aa_ci8_shape((11+kp,3+kp), transpose=transpose)
self.run_test_matmul_aa_ci8_shape((11+kp,23+kp), transpose=transpose)
self.run_test_matmul_aa_ci8_shape((111+kp,223+kp), transpose=transpose)
self.run_test_matmul_aa_ci8_shape((1111+kp,2223+kp), transpose=transpose)
self.run_test_matmul_aa_ci8_shape((3,111+kp,223+kp), transpose=transpose)
self.run_test_matmul_aa_ci8_shape((5,3,111+kp,223+kp), transpose=transpose)
self.run_test_matmul_aa_ci8_shape((5,7,3,111+kp,223+kp), transpose=transpose)
def test_matmul_aa_ci8(self):
self.run_test_matmul_aa_ci8_transpose(False)
self.run_test_matmul_aa_ci8_transpose(True)
def run_test_matmul_ab_ci8_transpose(self, transpose):
self.run_test_matmul_ab_ci8_shape((11,23), 7777, transpose=transpose)
self.run_test_matmul_ab_ci8_shape((111,223), 777, transpose=transpose)
self.run_test_matmul_ab_ci8_shape((1111,2223), 77, transpose=transpose)
self.run_test_matmul_ab_ci8_shape((3,111,223), 77, transpose=transpose)
self.run_test_matmul_ab_ci8_shape((5,3,111,223), 77, transpose=transpose)
self.run_test_matmul_ab_ci8_shape((5,7,3,111,223), 77, transpose=transpose)
def test_matmul_ab_ci8(self):
self.run_test_matmul_ab_ci8_transpose(False)
self.run_test_matmul_ab_ci8_transpose(True)
def test_matmul_aa_f32(self):
self.run_test_matmul_aa_dtype(np.float32)
def test_matmul_aa_f64(self):
self.run_test_matmul_aa_dtype(np.float64)
def test_matmul_aa_cf32(self):
self.run_test_matmul_aa_dtype(np.complex64)
def test_matmul_aa_cf64(self):
self.run_test_matmul_aa_dtype(np.complex128)
def test_matmul_ab_f32(self):
self.run_test_matmul_ab_dtype(np.float32)
def test_matmul_ab_f64(self):
self.run_test_matmul_ab_dtype(np.float64)
def test_matmul_ab_cf32(self):
self.run_test_matmul_ab_dtype(np.complex64)
def test_matmul_ab_cf64(self):
self.run_test_matmul_ab_dtype(np.complex128)