def code_to_function(text, name, args_size, return_size):
from numpy import zeros
from numpy import exp, log
from numpy import sin, cos, tan
from numpy import arcsin as asin
from numpy import arccos as acos
from numpy import arctan as atan
from numpy import sinh, cosh, tanh
from numpy import pi
from numpy import inf
d = locals()
e = {}
print(text)
exec(text, d, e)
fun = e[name]
from numba.vectorize import GUVectorize
from numba import float64
signature = str.join(',',
['(n{})'.format(i) for i in range(len(args_size))])
signature += '->(n)'.format(return_size)
args_types = [float64[:]] * (len(args_size) + 1)
print(signature)
print(args_types)
gufunc = GUVectorize(fun, signature)
gufunc.add(argtypes=args_types)
fun = gufunc.build_ufunc()
return fun
def code_to_function(text, name, args_size, return_size):
from numpy import zeros
from numpy import exp, log
from numpy import sin, cos, tan
from numpy import arcsin as asin
from numpy import arccos as acos
from numpy import arctan as atan
from numpy import sinh, cosh, tanh
from numpy import pi
from numpy import inf
d = locals()
e = {}
print(text)
exec(text, d, e)
fun = e[name]
from numba.vectorize import GUVectorize
from numba import float64
signature = str.join(',',['(n{})'.format(i) for i in range(len(args_size))])
signature += '->(n)'.format(return_size)
args_types = [float64[:]]*(len(args_size)+1)
print(signature)
print(args_types)
gufunc = GUVectorize(fun, signature)
gufunc.add(argtypes=args_types)
fun = gufunc.build_ufunc()
return fun
def test_exp_avg():
vect = GUVectorize(exp_avg, '(t),()->(t)')
vect.add('void(f8[:], f8, f8[:])')
ufunc = vect.build_ufunc()
arr_t = numpy.arange(10, dtype=numpy.float64)
decay_length = numpy.float64(123)
out_t = numpy.empty(10, dtype=numpy.float64)
ufunc(arr_t, decay_length, out_t)
expect = numpy.zeros_like(out_t)
exp_avg(arr_t, decay_length, expect)
numpy.allclose(expect, out_t)
def test_exp_avg():
vect = GUVectorize(exp_avg, '(t),()->(t)')
vect.add('void(f8[:], f8, f8[:])')
ufunc = vect.build_ufunc()
arr_t = numpy.arange(10, dtype=numpy.float64)
decay_length = numpy.float64(123)
out_t = numpy.empty(10, dtype=numpy.float64)
ufunc(arr_t, decay_length, out_t)
expect = numpy.zeros_like(out_t)
exp_avg(arr_t, decay_length, expect)
numpy.allclose(expect, out_t)
def test_gufunc_array_expressions():
gufunc = GUVectorize(array_expr_gufunc, '(m,n),(n,p)->(m,p)')
gufunc.add(argtypes=[float_[:, :], float_[:, :], float_[:, :]])
gufunc = gufunc.build_ufunc()
matrix_ct = 10
A = np.arange(matrix_ct * 2 * 4, dtype=np.float32).reshape(matrix_ct, 2, 4)
B = np.arange(matrix_ct * 4 * 5, dtype=np.float32).reshape(matrix_ct, 4, 5)
C = gufunc(A, B)
Gold = ut.matrix_multiply(A, B)
if (C != Gold).any():
print(C)
print(Gold)
raise ValueError
def test_scalar_output():
gufunc = GUVectorize(mycore, '(m,n)->()')
gufunc.add('void(float32[:,:], float32[:])')
gufunc.add('void(float64[:,:], float64[:])')
myfunc = gufunc.build_ufunc()
A = numpy.arange(2 * 2 * 10).reshape(10, 2, 2)
out = numpy.zeros(10)
myfunc(A, out=out)
expect = numpy.zeros_like(out)
for i in range(A.shape[0]):
expect[i] = A[i].sum()
numpy.allclose(expect, out)
def test_gufunc_array_expressions():
gufunc = GUVectorize(array_expr_gufunc, '(m,n),(n,p)->(m,p)')
gufunc.add(argtypes=[float_[:,:], float_[:,:], float_[:,:]])
gufunc = gufunc.build_ufunc()
matrix_ct = 10
A = np.arange(matrix_ct * 2 * 4, dtype=np.float32).reshape(matrix_ct, 2, 4)
B = np.arange(matrix_ct * 4 * 5, dtype=np.float32).reshape(matrix_ct, 4, 5)
C = gufunc(A, B)
Gold = ut.matrix_multiply(A, B)
if (C != Gold).any():
print(C)
print(Gold)
raise ValueError
def test_scalar_output():
gufunc = GUVectorize(mycore, '(m,n)->()')
gufunc.add('void(float32[:,:], float32[:])')
gufunc.add('void(float64[:,:], float64[:])')
myfunc = gufunc.build_ufunc()
A = numpy.arange(2 * 2 * 10).reshape(10, 2, 2)
out = numpy.zeros(10)
myfunc(A, out=out)
expect = numpy.zeros_like(out)
for i in range(A.shape[0]):
expect[i] = A[i].sum()
numpy.allclose(expect, out)
def _test_gufunc(backend, target):
gufunc = GUVectorize(matmulcore, '(m,n),(n,p)->(m,p)')
gufunc.add(argtypes=[f4[:, :], f4[:, :], f4[:, :]])
gufunc = gufunc.build_ufunc()
matrix_ct = 1001 # an odd number to test thread/block division in CUDA
A = np.arange(matrix_ct * 2 * 4, dtype=np.float32).reshape(matrix_ct, 2, 4)
B = np.arange(matrix_ct * 4 * 5, dtype=np.float32).reshape(matrix_ct, 4, 5)
C = gufunc(A, B)
Gold = ut.matrix_multiply(A, B)
# print(A)
# print(B)
# print(C)
# print(Gold)
assert np.allclose(C, Gold)
def _test_gufunc(backend, target):
gufunc = GUVectorize(matmulcore, '(m,n),(n,p)->(m,p)')
gufunc.add(argtypes=[f4[:,:], f4[:,:], f4[:,:]])
gufunc = gufunc.build_ufunc()
matrix_ct = 1001 # an odd number to test thread/block division in CUDA
A = np.arange(matrix_ct * 2 * 4, dtype=np.float32).reshape(matrix_ct, 2, 4)
B = np.arange(matrix_ct * 4 * 5, dtype=np.float32).reshape(matrix_ct, 4, 5)
C = gufunc(A, B)
Gold = ut.matrix_multiply(A, B)
# print(A)
# print(B)
# print(C)
# print(Gold)
assert np.allclose(C, Gold)
def test_saxpy():
vect = GUVectorize(saxpy, '(),(t),(t)->(t)')
vect.add('void(f4, f4[:], f4[:], f4[:])')
gusaxpy = vect.build_ufunc()
A = numpy.array(numpy.float32(2))
X = numpy.arange(10, dtype=numpy.float32).reshape(5, 2)
Y = numpy.arange(10, dtype=numpy.float32).reshape(5, 2)
out = gusaxpy(A, X, Y)
for j in range(5):
for i in range(2):
exp = A * X[j, i] + Y[j, i]
assert exp == out[j, i], (exp, out[j, i])
A = numpy.arange(5, dtype=numpy.float32)
out = gusaxpy(A, X, Y)
for j in range(5):
for i in range(2):
exp = A[j] * X[j, i] + Y[j, i]
assert exp == out[j, i]
def test_saxpy():
vect = GUVectorize(saxpy, '(),(t),(t)->(t)')
vect.add('void(f4, f4[:], f4[:], f4[:])')
gusaxpy = vect.build_ufunc()
A = numpy.array(numpy.float32(2))
X = numpy.arange(10, dtype=numpy.float32).reshape(5,2)
Y = numpy.arange(10, dtype=numpy.float32).reshape(5,2)
out = gusaxpy(A, X, Y)
for j in range(5):
for i in range(2):
exp = A * X[j, i] + Y[j, i]
assert exp == out[j, i], (exp, out[j, i])
A = numpy.arange(5, dtype=numpy.float32)
out = gusaxpy(A, X, Y)
for j in range(5):
for i in range(2):
exp = A[j] * X[j, i] + Y[j, i]
assert exp == out[j, i]
