def passed_test(dtype, as_matrix, provide_C, trans_a, trans_b):
"""
Run one general matrix-matrix multiplication test.
Arguments:
dtype: either 'float64' or 'float32', the NumPy dtype to test
as_matrix: True to test a NumPy matrix, False to test a NumPy ndarray
provide_C: True if C is to be provided to the BLASpy function, False otherwise
trans_a: BLASpy trans_a parameter to test
trans_b: BLASpy trans_b parameter to test
Returns:
True if the expected result is within the margin of error of the actual result,
False otherwise.
"""
# generate random sizes for matrix dimensions
m = randint(N_MIN, N_MAX)
n = randint(N_MIN, N_MAX)
k = randint(N_MIN, N_MAX)
# create random scalars and matrices to test
alpha = uniform(SCAL_MIN, SCAL_MAX)
beta = uniform(SCAL_MIN, SCAL_MAX)
A = random_matrix((m if trans_a == 'n' else k), (k if trans_a == 'n' else m), dtype, as_matrix)
B = random_matrix((k if trans_b == 'n' else n), (n if trans_b == 'n' else k), dtype, as_matrix)
C = random_matrix(m, n, dtype, as_matrix) if provide_C else None
# create copies/views of A, B, and C that can be used to calculate the expected result
A_2 = A if trans_a == 'n' else A.T
B_2 = B if trans_b == 'n' else B.T
C_2 = copy(C) if C is not None else zeros((m, n))
# compute the expected result
C_2 = beta * C_2 + alpha * dot(A_2, B_2)
# get the actual result
C = gemm(A, B, C, trans_a, trans_b, alpha, beta)
# compare the actual result to the expected result and return result of the test
return allclose(C, C_2, RTOL, ATOL)
def timing_test(dtype, trans_a, trans_b, n, k, trials):
"""
Run one general matrix-matrix multiplication test.
Arguments:
dtype: either 'float64' or 'float32', the NumPy dtype to test
as_matrix: True to test a NumPy matrix, False to test a NumPy ndarray
provide_C: True if C is to be provided to the BLASpy function, False otherwise
trans_a: BLASpy trans_a parameter to test
trans_b: BLASpy trans_b parameter to test
Returns:
A tuple of the BLASpy total runtime and NumPy total runtime.
"""
as_matrix = True
np_time = 0.0
bp_time = 0.0
for i in range(trials):
# create random scalars and matrices to test
alpha = uniform(SCAL_MIN, SCAL_MAX)
beta = uniform(SCAL_MIN, SCAL_MAX)
A = random_matrix((n if trans_a == 'n' else k), (k if trans_a == 'n' else n), dtype, as_matrix)
B = random_matrix((k if trans_b == 'n' else n), (n if trans_b == 'n' else k), dtype, as_matrix)
C = random_matrix(n, n, dtype, as_matrix)
# create copies/views for NumPy
A_2 = A if trans_a == 'n' else A.T
B_2 = B if trans_b == 'n' else B.T
C_2 = copy(C)
if i % 2 == 0:
# BLASpy first
start = time.time()
gemm(A, B, C, trans_a, trans_b, alpha, beta)
end = time.time()
bp_time += end - start
# then NumPy
start = time.time()
beta * C_2 + alpha * dot(A_2, B_2)
end = time.time()
np_time += end - start
else:
# NumPy first
start = time.time()
beta * C_2 + alpha * dot(A_2, B_2)
end = time.time()
np_time += end - start
# then BLASpy
start = time.time()
gemm(A, B, C, trans_a, trans_b, alpha, beta)
end = time.time()
bp_time += end - start
return bp_time / trials, np_time / trials
