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#! /usr/bin/env python3
from __future__ import absolute_import, print_function
import numpy as np
import numpy.linalg as la
import sys
from pycuda.tools import mark_cuda_test
from pycuda.characterize import has_double_support
from six.moves import range
def have_pycuda():
try:
import pycuda # noqa
return True
except:
return False
if have_pycuda():
import pycuda.gpuarray as gpuarray
import pycuda.driver as drv
from pycuda.compiler import SourceModule
class TestGPUArray:
disabled = not have_pycuda()
@mark_cuda_test
def test_pow_array(self):
a = np.array([1, 2, 3, 4, 5]).astype(np.float32)
a_gpu = gpuarray.to_gpu(a)
result = pow(a_gpu, a_gpu).get()
assert (np.abs(a**a - result) < 1e-3).all()
result = (a_gpu**a_gpu).get()
assert (np.abs(pow(a, a) - result) < 1e-3).all()
a_gpu **= a_gpu
a_gpu = a_gpu.get()
assert (np.abs(pow(a, a) - a_gpu) < 1e-3).all()
@mark_cuda_test
def test_pow_number(self):
a = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]).astype(np.float32)
a_gpu = gpuarray.to_gpu(a)
result = pow(a_gpu, 2).get()
assert (np.abs(a**2 - result) < 1e-3).all()
a_gpu **= 2
a_gpu = a_gpu.get()
assert (np.abs(a**2 - a_gpu) < 1e-3).all()
@mark_cuda_test
def test_numpy_integer_shape(self):
gpuarray.empty(np.int32(17), np.float32)
gpuarray.empty((np.int32(17), np.int32(17)), np.float32)
@mark_cuda_test
def test_ndarray_shape(self):
gpuarray.empty(np.array(3), np.float32)
gpuarray.empty(np.array([3]), np.float32)
gpuarray.empty(np.array([2, 3]), np.float32)
@mark_cuda_test
def test_abs(self):
a = -gpuarray.arange(111, dtype=np.float32)
res = a.get()
for i in range(111):
assert res[i] <= 0
a = abs(a)
res = a.get()
for i in range(111):
assert abs(res[i]) >= 0
assert res[i] == i
@mark_cuda_test
def test_len(self):
a = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]).astype(np.float32)
a_cpu = gpuarray.to_gpu(a)
assert len(a_cpu) == 10
@mark_cuda_test
def test_multiply(self):
"""Test the muliplication of an array with a scalar. """
for sz in [10, 50000]:
for dtype, scalars in [
(np.float32, [2]),
(np.complex64, [2, 2j])
]:
for scalar in scalars:
a = np.arange(sz).astype(dtype)
a_gpu = gpuarray.to_gpu(a)
a_doubled = (scalar * a_gpu).get()
assert (a * scalar == a_doubled).all()
@mark_cuda_test
def test_rmul_yields_right_type(self):
a = np.array([1, 2, 3, 4, 5]).astype(np.float32)
a_gpu = gpuarray.to_gpu(a)
two_a = 2*a_gpu
assert isinstance(two_a, gpuarray.GPUArray)
two_a = np.float32(2)*a_gpu
assert isinstance(two_a, gpuarray.GPUArray)
@mark_cuda_test
def test_multiply_array(self):
"""Test the multiplication of two arrays."""
a = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]).astype(np.float32)
a_gpu = gpuarray.to_gpu(a)
b_gpu = gpuarray.to_gpu(a)
a_squared = (b_gpu*a_gpu).get()
assert (a*a == a_squared).all()
@mark_cuda_test
def test_addition_array(self):
"""Test the addition of two arrays."""
a = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]).astype(np.float32)
a_gpu = gpuarray.to_gpu(a)
a_added = (a_gpu+a_gpu).get()
assert (a+a == a_added).all()
@mark_cuda_test
def test_iaddition_array(self):
"""Test the inplace addition of two arrays."""
a = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]).astype(np.float32)
a_gpu = gpuarray.to_gpu(a)
a_gpu += a_gpu
a_added = a_gpu.get()
assert (a+a == a_added).all()
@mark_cuda_test
def test_addition_scalar(self):
"""Test the addition of an array and a scalar."""
a = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]).astype(np.float32)
a_gpu = gpuarray.to_gpu(a)
a_added = (7+a_gpu).get()
assert (7+a == a_added).all()
@mark_cuda_test
def test_iaddition_scalar(self):
"""Test the inplace addition of an array and a scalar."""
a = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]).astype(np.float32)
a_gpu = gpuarray.to_gpu(a)
a_gpu += 7
a_added = a_gpu.get()
assert (7+a == a_added).all()
@mark_cuda_test
def test_substract_array(self):
"""Test the substraction of two arrays."""
#test data
a = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]).astype(np.float32)
b = np.array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100]).astype(np.float32)
a_gpu = gpuarray.to_gpu(a)
b_gpu = gpuarray.to_gpu(b)
result = (a_gpu-b_gpu).get()
assert (a-b == result).all()
result = (b_gpu-a_gpu).get()
assert (b-a == result).all()
@mark_cuda_test
def test_substract_scalar(self):
"""Test the substraction of an array and a scalar."""
#test data
a = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]).astype(np.float32)
#convert a to a gpu object
a_gpu = gpuarray.to_gpu(a)
result = (a_gpu-7).get()
assert (a-7 == result).all()
result = (7-a_gpu).get()
assert (7-a == result).all()
@mark_cuda_test
def test_divide_scalar(self):
"""Test the division of an array and a scalar."""
a = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]).astype(np.float32)
a_gpu = gpuarray.to_gpu(a)
result = (a_gpu/2).get()
assert (a/2 == result).all()
result = (2/a_gpu).get()
assert (2/a == result).all()
@mark_cuda_test
def test_divide_array(self):
"""Test the division of an array and a scalar. """
#test data
a = np.array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100]).astype(np.float32)
b = np.array([10, 10, 10, 10, 10, 10, 10, 10, 10, 10]).astype(np.float32)
a_gpu = gpuarray.to_gpu(a)
b_gpu = gpuarray.to_gpu(b)
a_divide = (a_gpu/b_gpu).get()
assert (np.abs(a/b - a_divide) < 1e-3).all()
a_divide = (b_gpu/a_gpu).get()
assert (np.abs(b/a - a_divide) < 1e-3).all()
@mark_cuda_test
def test_random(self):
from pycuda.curandom import rand as curand
if has_double_support():
dtypes = [np.float32, np.float64]
else:
dtypes = [np.float32]
for dtype in dtypes:
a = curand((10, 100), dtype=dtype).get()
assert (0 <= a).all()
assert (a < 1).all()
@mark_cuda_test
def test_curand_wrappers(self):
from pycuda.curandom import get_curand_version
if get_curand_version() is None:
from pytest import skip
skip("curand not installed")
generator_types = []
if get_curand_version() >= (3, 2, 0):
from pycuda.curandom import (
XORWOWRandomNumberGenerator,
Sobol32RandomNumberGenerator)
generator_types.extend([
XORWOWRandomNumberGenerator,
Sobol32RandomNumberGenerator])
if get_curand_version() >= (4, 0, 0):
from pycuda.curandom import (
ScrambledSobol32RandomNumberGenerator,
Sobol64RandomNumberGenerator,
ScrambledSobol64RandomNumberGenerator)
generator_types.extend([
ScrambledSobol32RandomNumberGenerator,
Sobol64RandomNumberGenerator,
ScrambledSobol64RandomNumberGenerator])
if get_curand_version() >= (4, 1, 0):
from pycuda.curandom import MRG32k3aRandomNumberGenerator
generator_types.extend([MRG32k3aRandomNumberGenerator])
if has_double_support():
dtypes = [np.float32, np.float64]
else:
dtypes = [np.float32]
for gen_type in generator_types:
gen = gen_type()
for dtype in dtypes:
gen.gen_normal(10000, dtype)
# test non-Box-Muller version, if available
gen.gen_normal(10001, dtype)
if get_curand_version() >= (4, 0, 0):
gen.gen_log_normal(10000, dtype, 10.0, 3.0)
# test non-Box-Muller version, if available
gen.gen_log_normal(10001, dtype, 10.0, 3.0)
x = gen.gen_uniform(10000, dtype)
x_host = x.get()
assert (-1 <= x_host).all()
assert (x_host <= 1).all()
gen.gen_uniform(10000, np.uint32)
if get_curand_version() >= (5, 0, 0):
gen.gen_poisson(10000, np.uint32, 13.0)
for dtype in dtypes + [np.uint32]:
a = gpuarray.empty(1000000, dtype=dtype)
v = 10
a.fill(v)
gen.fill_poisson(a)
tmp = (a.get() == (v-1)).sum() / a.size
# Commented out for CI on the off chance it'd fail
# # Check Poisson statistics (need 1e6 values)
# # Compare with scipy.stats.poisson.pmf(v - 1, v)
# assert np.isclose(0.12511, tmp, atol=0.002)
@mark_cuda_test
def test_array_gt(self):
"""Test whether array contents are > the other array's
contents"""
a = np.array([5, 10]).astype(np.float32)
a_gpu = gpuarray.to_gpu(a)
b = np.array([2, 10]).astype(np.float32)
b_gpu = gpuarray.to_gpu(b)
result = (a_gpu > b_gpu).get()
assert result[0]
assert not result[1]
@mark_cuda_test
def test_array_lt(self):
"""Test whether array contents are < the other array's
contents"""
a = np.array([5, 10]).astype(np.float32)
a_gpu = gpuarray.to_gpu(a)
b = np.array([2, 10]).astype(np.float32)
b_gpu = gpuarray.to_gpu(b)
result = (b_gpu < a_gpu).get()
assert result[0]
assert not result[1]
@mark_cuda_test
def test_array_le(self):
"""Test whether array contents are <= the other array's
contents"""
a = np.array([5, 10, 1]).astype(np.float32)
a_gpu = gpuarray.to_gpu(a)
b = np.array([2, 10, 2]).astype(np.float32)
b_gpu = gpuarray.to_gpu(b)
result = (b_gpu <= a_gpu).get()
assert result[0]
assert result[1]
assert not result[2]
@mark_cuda_test
def test_array_ge(self):
"""Test whether array contents are >= the other array's
contents"""
a = np.array([5, 10, 1]).astype(np.float32)
a_gpu = gpuarray.to_gpu(a)
b = np.array([2, 10, 2]).astype(np.float32)
b_gpu = gpuarray.to_gpu(b)
result = (a_gpu >= b_gpu).get()
assert result[0]
assert result[1]
assert not result[2]
@mark_cuda_test
def test_array_eq(self):
"""Test whether array contents are == the other array's
contents"""
a = np.array([5, 10]).astype(np.float32)
a_gpu = gpuarray.to_gpu(a)
b = np.array([2, 10]).astype(np.float32)
b_gpu = gpuarray.to_gpu(b)
result = (a_gpu == b_gpu).get()
assert not result[0]
assert result[1]
@mark_cuda_test
def test_array_ne(self):
"""Test whether array contents are != the other array's
contents"""
a = np.array([5, 10]).astype(np.float32)
a_gpu = gpuarray.to_gpu(a)
b = np.array([2, 10]).astype(np.float32)
b_gpu = gpuarray.to_gpu(b)
result = (a_gpu != b_gpu).get()
assert result[0]
assert not result[1]
@mark_cuda_test
def test_nan_arithmetic(self):
def make_nan_contaminated_vector(size):
shape = (size,)
a = np.random.randn(*shape).astype(np.float32)
#for i in range(0, shape[0], 3):
#a[i] = float('nan')
from random import randrange
for i in range(size//10):
a[randrange(0, size)] = float('nan')
return a
size = 1 << 20
a = make_nan_contaminated_vector(size)
a_gpu = gpuarray.to_gpu(a)
b = make_nan_contaminated_vector(size)
b_gpu = gpuarray.to_gpu(b)
ab = a*b
ab_gpu = (a_gpu*b_gpu).get()
assert (np.isnan(ab) == np.isnan(ab_gpu)).all()
@mark_cuda_test
def test_elwise_kernel(self):
from pycuda.curandom import rand as curand
a_gpu = curand((50,))
b_gpu = curand((50,))
from pycuda.elementwise import ElementwiseKernel
lin_comb = ElementwiseKernel(
"float a, float *x, float b, float *y, float *z",
"z[i] = a*x[i] + b*y[i]",
"linear_combination")
c_gpu = gpuarray.empty_like(a_gpu)
lin_comb(5, a_gpu, 6, b_gpu, c_gpu)
assert la.norm((c_gpu - (5*a_gpu+6*b_gpu)).get()) < 1e-5
@mark_cuda_test
def test_ranged_elwise_kernel(self):
from pycuda.elementwise import ElementwiseKernel
set_to_seven = ElementwiseKernel(
"float *z",
"z[i] = 7",
"set_to_seven")
for i, slc in enumerate([
slice(5, 20000),
slice(5, 20000, 17),
slice(3000, 5, -1),
slice(1000, -1),
]):
a_gpu = gpuarray.zeros((50000,), dtype=np.float32)
a_cpu = np.zeros(a_gpu.shape, a_gpu.dtype)
a_cpu[slc] = 7
set_to_seven(a_gpu, slice=slc)
drv.Context.synchronize()
assert la.norm(a_cpu - a_gpu.get()) == 0, i
@mark_cuda_test
def test_take(self):
idx = gpuarray.arange(0, 10000, 2, dtype=np.uint32)
for dtype in [np.float32, np.complex64]:
a = gpuarray.arange(0, 600000, dtype=np.uint32).astype(dtype)
a_host = a.get()
result = gpuarray.take(a, idx)
assert (a_host[idx.get()] == result.get()).all()
@mark_cuda_test
def test_arange(self):
a = gpuarray.arange(12, dtype=np.float32)
assert (np.arange(12, dtype=np.float32) == a.get()).all()
@mark_cuda_test
def test_reverse(self):
a = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]).astype(np.float32)
a_cpu = gpuarray.to_gpu(a)
a_cpu = a_cpu.reverse()
b = a_cpu.get()
for i in range(0, 10):
assert a[len(a)-1-i] == b[i]
@mark_cuda_test
def test_sum(self):
from pycuda.curandom import rand as curand
a_gpu = curand((200000,))
a = a_gpu.get()
sum_a = np.sum(a)
sum_a_gpu = gpuarray.sum(a_gpu).get()
assert abs(sum_a_gpu-sum_a)/abs(sum_a) < 1e-4
@mark_cuda_test
def test_minmax(self):
from pycuda.curandom import rand as curand
if has_double_support():
dtypes = [np.float64, np.float32, np.int32]
else:
dtypes = [np.float32, np.int32]
for what in ["min", "max"]:
for dtype in dtypes:
a_gpu = curand((200000,), dtype)
a = a_gpu.get()
op_a = getattr(np, what)(a)
op_a_gpu = getattr(gpuarray, what)(a_gpu).get()
assert op_a_gpu == op_a, (op_a_gpu, op_a, dtype, what)
@mark_cuda_test
def test_subset_minmax(self):
from pycuda.curandom import rand as curand
l_a = 200000
gran = 5
l_m = l_a - l_a // gran + 1
if has_double_support():
dtypes = [np.float64, np.float32, np.int32]
else:
dtypes = [np.float32, np.int32]
for dtype in dtypes:
a_gpu = curand((l_a,), dtype)
a = a_gpu.get()
meaningful_indices_gpu = gpuarray.zeros(l_m, dtype=np.int32)
meaningful_indices = meaningful_indices_gpu.get()
j = 0
for i in range(len(meaningful_indices)):
meaningful_indices[i] = j
j = j + 1
if j % gran == 0:
j = j + 1
meaningful_indices_gpu = gpuarray.to_gpu(meaningful_indices)
b = a[meaningful_indices]
min_a = np.min(b)
min_a_gpu = gpuarray.subset_min(meaningful_indices_gpu, a_gpu).get()
assert min_a_gpu == min_a
@mark_cuda_test
def test_dot(self):
from pycuda.curandom import rand as curand
for l in [2, 3, 4, 5, 6, 7, 31, 32, 33, 127, 128, 129,
255, 256, 257, 16384 - 993,
20000]:
a_gpu = curand((l,))
a = a_gpu.get()
b_gpu = curand((l,))
b = b_gpu.get()
dot_ab = np.dot(a, b)
dot_ab_gpu = gpuarray.dot(a_gpu, b_gpu).get()
assert abs(dot_ab_gpu-dot_ab)/abs(dot_ab) < 1e-4
@mark_cuda_test
def test_slice(self):
from pycuda.curandom import rand as curand
l = 20000
a_gpu = curand((l,))
a = a_gpu.get()
from random import randrange
for i in range(200):
start = randrange(l)
end = randrange(start, l)
a_gpu_slice = a_gpu[start:end]
a_slice = a[start:end]
assert la.norm(a_gpu_slice.get()-a_slice) == 0
@mark_cuda_test
def test_2d_slice_c(self):
from pycuda.curandom import rand as curand
n = 1000
m = 300
a_gpu = curand((n, m))
a = a_gpu.get()
from random import randrange
for i in range(200):
start = randrange(n)
end = randrange(start, n)
a_gpu_slice = a_gpu[start:end]
a_slice = a[start:end]
assert la.norm(a_gpu_slice.get()-a_slice) == 0
@mark_cuda_test
def test_2d_slice_f(self):
from pycuda.curandom import rand as curand
import pycuda.gpuarray as gpuarray
n = 1000
m = 300
a_gpu = curand((n, m))
a_gpu_f = gpuarray.GPUArray((m, n), np.float32,
gpudata=a_gpu.gpudata,
order="F")
a = a_gpu_f.get()
from random import randrange
for i in range(200):
start = randrange(n)
end = randrange(start, n)
a_gpu_slice = a_gpu_f[:, start:end]
a_slice = a[:, start:end]
assert la.norm(a_gpu_slice.get()-a_slice) == 0
@mark_cuda_test
def test_if_positive(self):
from pycuda.curandom import rand as curand
l = 20
a_gpu = curand((l,))
b_gpu = curand((l,))
a = a_gpu.get()
b = b_gpu.get()
import pycuda.gpuarray as gpuarray
max_a_b_gpu = gpuarray.maximum(a_gpu, b_gpu)
min_a_b_gpu = gpuarray.minimum(a_gpu, b_gpu)
print (max_a_b_gpu)
print((np.maximum(a, b)))
assert la.norm(max_a_b_gpu.get() - np.maximum(a, b)) == 0
assert la.norm(min_a_b_gpu.get() - np.minimum(a, b)) == 0
@mark_cuda_test
def test_take_put(self):
for n in [5, 17, 333]:
one_field_size = 8
buf_gpu = gpuarray.zeros(n*one_field_size, dtype=np.float32)
dest_indices = gpuarray.to_gpu(np.array(
[0, 1, 2, 3, 32, 33, 34, 35], dtype=np.uint32))
read_map = gpuarray.to_gpu(
np.array([7, 6, 5, 4, 3, 2, 1, 0], dtype=np.uint32))
gpuarray.multi_take_put(
arrays=[buf_gpu for i in range(n)],
dest_indices=dest_indices,
src_indices=read_map,
src_offsets=[i*one_field_size for i in range(n)],
dest_shape=(96,))
drv.Context.synchronize()
@mark_cuda_test
def test_astype(self):
from pycuda.curandom import rand as curand
if not has_double_support():
return
a_gpu = curand((2000,), dtype=np.float32)
a = a_gpu.get().astype(np.float64)
a2 = a_gpu.astype(np.float64).get()
assert a2.dtype == np.float64
assert la.norm(a - a2) == 0, (a, a2)
a_gpu = curand((2000,), dtype=np.float64)
a = a_gpu.get().astype(np.float32)
a2 = a_gpu.astype(np.float32).get()
assert a2.dtype == np.float32
assert la.norm(a - a2)/la.norm(a) < 1e-7
@mark_cuda_test
def test_complex_bits(self):
from pycuda.curandom import rand as curand
if has_double_support():
dtypes = [np.complex64, np.complex128]
else:
dtypes = [np.complex64]
n = 20
for tp in dtypes:
dtype = np.dtype(tp)
from pytools import match_precision
real_dtype = match_precision(np.dtype(np.float64), dtype)
z = (curand((n,), real_dtype).astype(dtype)
+ 1j*curand((n,), real_dtype).astype(dtype))
assert la.norm(z.get().real - z.real.get()) == 0
assert la.norm(z.get().imag - z.imag.get()) == 0
assert la.norm(z.get().conj() - z.conj().get()) == 0
# verify contiguity is preserved
for order in ["C", "F"]:
# test both zero and non-zero value code paths
z_real = gpuarray.zeros(z.shape, dtype=real_dtype,
order=order)
z2 = z.reshape(z.shape, order=order)
for zdata in [z_real, z2]:
if order == "C":
assert zdata.flags.c_contiguous == True
assert zdata.real.flags.c_contiguous == True
assert zdata.imag.flags.c_contiguous == True
assert zdata.conj().flags.c_contiguous == True
elif order == "F":
assert zdata.flags.f_contiguous == True
assert zdata.real.flags.f_contiguous == True
assert zdata.imag.flags.f_contiguous == True
assert zdata.conj().flags.f_contiguous == True
@mark_cuda_test
def test_pass_slice_to_kernel(self):
mod = SourceModule("""
__global__ void twice(float *a)
{
const int i = threadIdx.x + blockIdx.x * blockDim.x;
a[i] *= 2;
}
""")
multiply_them = mod.get_function("twice")
a = np.ones(256**2, np.float32)
a_gpu = gpuarray.to_gpu(a)
multiply_them(a_gpu[256:-256], block=(256, 1, 1), grid=(254, 1))
a = a_gpu.get()
assert (a[255:257] == np.array([1, 2], np.float32)).all()
assert (a[255*256-1:255*256+1] == np.array([2, 1], np.float32)).all()
@mark_cuda_test
def test_scan(self):
from pycuda.scan import ExclusiveScanKernel, InclusiveScanKernel
for cls in [ExclusiveScanKernel, InclusiveScanKernel]:
scan_kern = cls(np.int32, "a+b", "0")
for n in [
10, 2**10-5, 2**10,
2**20-2**18,
2**20-2**18+5,
2**10+5,
2**20+5,
2**20, 2**24
]:
host_data = np.random.randint(0, 10, n).astype(np.int32)
gpu_data = gpuarray.to_gpu(host_data)
scan_kern(gpu_data)
desired_result = np.cumsum(host_data, axis=0)
if cls is ExclusiveScanKernel:
desired_result -= host_data
assert (gpu_data.get() == desired_result).all()
@mark_cuda_test
def test_stride_preservation(self):
A = np.random.rand(3, 3)
AT = A.T
print((AT.flags.f_contiguous, AT.flags.c_contiguous))
AT_GPU = gpuarray.to_gpu(AT)
print((AT_GPU.flags.f_contiguous, AT_GPU.flags.c_contiguous))
assert np.allclose(AT_GPU.get(), AT)
@mark_cuda_test
def test_vector_fill(self):
a_gpu = gpuarray.GPUArray(100, dtype=gpuarray.vec.float3)
a_gpu.fill(gpuarray.vec.make_float3(0.0, 0.0, 0.0))
a = a_gpu.get()
assert a.dtype == gpuarray.vec.float3
@mark_cuda_test
def test_create_complex_zeros(self):
gpuarray.zeros(3, np.complex64)
@mark_cuda_test
def test_reshape(self):
a = np.arange(128).reshape(8, 16).astype(np.float32)
a_gpu = gpuarray.to_gpu(a)
# different ways to specify the shape
a_gpu.reshape(4, 32)
a_gpu.reshape((4, 32))
a_gpu.reshape([4, 32])
# using -1 as unknown dimension
assert a_gpu.reshape(-1, 32).shape == (4, 32)
assert a_gpu.reshape((32, -1)).shape == (32, 4)
assert a_gpu.reshape(((8, -1, 4))).shape == (8, 4, 4)
throws_exception = False
try:
a_gpu.reshape(-1, -1, 4)
except ValueError:
throws_exception = True
assert throws_exception
# with order specified
a_gpu = a_gpu.reshape((4, 32), order='C')
assert a_gpu.flags.c_contiguous
a_gpu = a_gpu.reshape(4, 32, order='F')
assert a_gpu.flags.f_contiguous
a_gpu = a_gpu.reshape((4, 32), order='F')
assert a_gpu.flags.f_contiguous
# default is C-contiguous
a_gpu = a_gpu.reshape((4, 32))
assert a_gpu.flags.c_contiguous
@mark_cuda_test
def test_view(self):
a = np.arange(128).reshape(8, 16).astype(np.float32)
a_gpu = gpuarray.to_gpu(a)
# same dtype
view = a_gpu.view()
assert view.shape == a_gpu.shape and view.dtype == a_gpu.dtype
# larger dtype
view = a_gpu.view(np.complex64)
assert view.shape == (8, 8) and view.dtype == np.complex64
# smaller dtype
view = a_gpu.view(np.int16)
assert view.shape == (8, 32) and view.dtype == np.int16
@mark_cuda_test
def test_squeeze(self):
shape = (40, 2, 5, 100)
a_cpu = np.random.random(size=shape)
a_gpu = gpuarray.to_gpu(a_cpu)
# Slice with length 1 on dimensions 0 and 1
a_gpu_slice = a_gpu[0:1,1:2,:,:]
assert a_gpu_slice.shape == (1,1,shape[2],shape[3])
assert a_gpu_slice.flags.c_contiguous
# Squeeze it and obtain contiguity
a_gpu_squeezed_slice = a_gpu[0:1,1:2,:,:].squeeze()
assert a_gpu_squeezed_slice.shape == (shape[2],shape[3])
assert a_gpu_squeezed_slice.flags.c_contiguous
# Check that we get the original values out
assert np.all(a_gpu_slice.get().ravel() == a_gpu_squeezed_slice.get().ravel())
# Slice with length 1 on dimensions 2
a_gpu_slice = a_gpu[:,:,2:3,:]
assert a_gpu_slice.shape == (shape[0],shape[1],1,shape[3])
assert not a_gpu_slice.flags.c_contiguous
# Squeeze it, but no contiguity here
a_gpu_squeezed_slice = a_gpu[:,:,2:3,:].squeeze()
assert a_gpu_squeezed_slice.shape == (shape[0],shape[1],shape[3])
assert not a_gpu_squeezed_slice.flags.c_contiguous
# Check that we get the original values out
assert np.all(a_gpu_slice.get().ravel() == a_gpu_squeezed_slice.get().ravel())
@mark_cuda_test
def test_struct_reduce(self):
preamble = """
struct minmax_collector
{
float cur_min;
float cur_max;
__device__
minmax_collector()
{ }
__device__
minmax_collector(float cmin, float cmax)
: cur_min(cmin), cur_max(cmax)
{ }
__device__ minmax_collector(minmax_collector const &src)
: cur_min(src.cur_min), cur_max(src.cur_max)
{ }
__device__ minmax_collector(minmax_collector const volatile &src)
: cur_min(src.cur_min), cur_max(src.cur_max)
{ }
__device__ minmax_collector volatile &operator=(
minmax_collector const &src) volatile
{
cur_min = src.cur_min;
cur_max = src.cur_max;
return *this;
}
};
__device__
minmax_collector agg_mmc(minmax_collector a, minmax_collector b)
{
return minmax_collector(
fminf(a.cur_min, b.cur_min),
fmaxf(a.cur_max, b.cur_max));
}
"""
mmc_dtype = np.dtype([("cur_min", np.float32), ("cur_max", np.float32)])
from pycuda.curandom import rand as curand
a_gpu = curand((20000,), dtype=np.float32)
a = a_gpu.get()
from pycuda.tools import register_dtype
register_dtype(mmc_dtype, "minmax_collector")
from pycuda.reduction import ReductionKernel
red = ReductionKernel(mmc_dtype,
neutral="minmax_collector(10000, -10000)",
# FIXME: needs infinity literal in real use, ok here
reduce_expr="agg_mmc(a, b)", map_expr="minmax_collector(x[i], x[i])",
arguments="float *x", preamble=preamble)
minmax = red(a_gpu).get()
#print minmax["cur_min"], minmax["cur_max"]
#print np.min(a), np.max(a)
assert minmax["cur_min"] == np.min(a)
assert minmax["cur_max"] == np.max(a)
@mark_cuda_test
def test_reduce_out(self):
from pycuda.curandom import rand as curand
a_gpu = curand((10, 200), dtype=np.float32)
a = a_gpu.get()
from pycuda.reduction import ReductionKernel
red = ReductionKernel(np.float32, neutral=0,
reduce_expr="max(a,b)",
arguments="float *in")
max_gpu = gpuarray.empty(10, dtype=np.float32)
for i in range(10):
red(a_gpu[i], out=max_gpu[i])
assert np.alltrue(a.max(axis=1) == max_gpu.get())
@mark_cuda_test
def test_sum_allocator(self):
# FIXME
from pytest import skip
skip("https://github.com/inducer/pycuda/issues/163")
# crashes with terminate called after throwing an instance of 'pycuda::error'
# what(): explicit_context_dependent failed: invalid device context - no currently active context?
import pycuda.tools
pool = pycuda.tools.DeviceMemoryPool()
rng = np.random.randint(low=512,high=1024)
a = gpuarray.arange(rng,dtype=np.int32)
b = gpuarray.sum(a)
c = gpuarray.sum(a, allocator=pool.allocate)
# Test that we get the correct results
assert b.get() == rng*(rng-1)//2
assert c.get() == rng*(rng-1)//2
# Test that result arrays were allocated with the appropriate allocator
assert b.allocator == a.allocator
assert c.allocator == pool.allocate
@mark_cuda_test
def test_dot_allocator(self):
# FIXME
from pytest import skip
skip("https://github.com/inducer/pycuda/issues/163")
import pycuda.tools
pool = pycuda.tools.DeviceMemoryPool()
a_cpu = np.random.randint(low=512,high=1024,size=1024)
b_cpu = np.random.randint(low=512,high=1024,size=1024)
# Compute the result on the CPU
dot_cpu_1 = np.dot(a_cpu, b_cpu)
a_gpu = gpuarray.to_gpu(a_cpu)
b_gpu = gpuarray.to_gpu(b_cpu)
# Compute the result on the GPU using different allocators
dot_gpu_1 = gpuarray.dot(a_gpu, b_gpu)
dot_gpu_2 = gpuarray.dot(a_gpu, b_gpu, allocator=pool.allocate)
# Test that we get the correct results
assert dot_cpu_1 == dot_gpu_1.get()
assert dot_cpu_1 == dot_gpu_2.get()
# Test that result arrays were allocated with the appropriate allocator
assert dot_gpu_1.allocator == a_gpu.allocator
assert dot_gpu_2.allocator == pool.allocate
@mark_cuda_test
def test_view_and_strides(self):
from pycuda.curandom import rand as curand
X = curand((5, 10), dtype=np.float32)
Y = X[:3, :5]
y = Y.view()
assert y.shape == Y.shape
assert y.strides == Y.strides
assert np.array_equal(y.get(), X.get()[:3, :5])
@mark_cuda_test
def test_scalar_comparisons(self):
a = np.array([1.0, 0.25, 0.1, -0.1, 0.0])
a_gpu = gpuarray.to_gpu(a)
x_gpu = a_gpu > 0.25
x = (a > 0.25).astype(a.dtype)
assert (x == x_gpu.get()).all()
x_gpu = a_gpu <= 0.25
x = (a <= 0.25).astype(a.dtype)
assert (x == x_gpu.get()).all()
x_gpu = a_gpu == 0.25
x = (a == 0.25).astype(a.dtype)
assert (x == x_gpu.get()).all()
x_gpu = a_gpu == 1 # using an integer scalar
x = (a == 1).astype(a.dtype)
assert (x == x_gpu.get()).all()
@mark_cuda_test
def test_minimum_maximum_scalar(self):
from pycuda.curandom import rand as curand
l = 20
a_gpu = curand((l,))
a = a_gpu.get()
import pycuda.gpuarray as gpuarray
max_a0_gpu = gpuarray.maximum(a_gpu, 0)
min_a0_gpu = gpuarray.minimum(0, a_gpu)
assert la.norm(max_a0_gpu.get() - np.maximum(a, 0)) == 0
assert la.norm(min_a0_gpu.get() - np.minimum(0, a)) == 0
@mark_cuda_test
def test_transpose(self):
import pycuda.gpuarray as gpuarray
from pycuda.curandom import rand as curand
a_gpu = curand((10,20,30))
a = a_gpu.get()
#assert np.allclose(a_gpu.transpose((1,2,0)).get(), a.transpose((1,2,0))) # not contiguous
assert np.allclose(a_gpu.T.get(), a.T)
@mark_cuda_test
def test_newaxis(self):
import pycuda.gpuarray as gpuarray
from pycuda.curandom import rand as curand
a_gpu = curand((10,20,30))
a = a_gpu.get()
b_gpu = a_gpu[:,np.newaxis]
b = a[:,np.newaxis]
assert b_gpu.shape == b.shape
assert b_gpu.strides == b.strides
@mark_cuda_test
def test_copy(self):
from pycuda.curandom import rand as curand
a_gpu = curand((3,3))
for start, stop, step in [(0,3,1), (1,2,1), (0,3,2), (0,3,3)]:
assert np.allclose(a_gpu[start:stop:step].get(), a_gpu.get()[start:stop:step])
a_gpu = curand((3,1))
for start, stop, step in [(0,3,1), (1,2,1), (0,3,2), (0,3,3)]:
assert np.allclose(a_gpu[start:stop:step].get(), a_gpu.get()[start:stop:step])
a_gpu = curand((3,3,3))
for start, stop, step in [(0,3,1), (1,2,1), (0,3,2), (0,3,3)]:
assert np.allclose(a_gpu[start:stop:step,start:stop:step].get(), a_gpu.get()[start:stop:step,start:stop:step])
a_gpu = curand((3,3,3)).transpose((1,2,0))
a = a_gpu.get()
for start, stop, step in [(0,3,1), (1,2,1), (0,3,2), (0,3,3)]:
assert np.allclose(a_gpu[start:stop:step,:,start:stop:step].get(), a_gpu.get()[start:stop:step,:,start:stop:step])
# 4-d should work as long as only 2 axes are discontiguous
a_gpu = curand((3,3,3,3))
a = a_gpu.get()
for start, stop, step in [(0,3,1), (1,2,1), (0,3,3)]:
assert np.allclose(a_gpu[start:stop:step,:,start:stop:step].get(), a_gpu.get()[start:stop:step,:,start:stop:step])
@mark_cuda_test
def test_get_set(self):
import pycuda.gpuarray as gpuarray
a = np.random.normal(0., 1., (4,4))
a_gpu = gpuarray.to_gpu(a)
assert np.allclose(a_gpu.get(), a)
assert np.allclose(a_gpu[1:3,1:3].get(), a[1:3,1:3])
a = np.random.normal(0., 1., (4,4,4)).transpose((1,2,0))
a_gpu = gpuarray.to_gpu(a)
assert np.allclose(a_gpu.get(), a)
assert np.allclose(a_gpu[1:3,1:3,1:3].get(), a[1:3,1:3,1:3])
@mark_cuda_test
def test_zeros_like_etc(self):
shape = (16, 16)
a = np.random.randn(*shape).astype(np.float32)
z = gpuarray.to_gpu(a)
zf = gpuarray.to_gpu(np.asfortranarray(a))
a_noncontig = np.arange(3*4*5).reshape(3, 4, 5).swapaxes(1, 2)
z_noncontig = gpuarray.to_gpu(a_noncontig)
for func in [gpuarray.empty_like,
gpuarray.zeros_like,
gpuarray.ones_like]:
for arr in [z, zf, z_noncontig]:
contig = arr.flags.c_contiguous or arr.flags.f_contiguous
# Output matches order of input.
# Non-contiguous becomes C-contiguous
new_z = func(arr, order="A")
if contig:
assert new_z.flags.c_contiguous == arr.flags.c_contiguous
assert new_z.flags.f_contiguous == arr.flags.f_contiguous
else:
assert new_z.flags.c_contiguous is True
assert new_z.flags.f_contiguous is False
assert new_z.dtype == arr.dtype
assert new_z.shape == arr.shape
# Force C-ordered output
new_z = func(arr, order="C")
assert new_z.flags.c_contiguous is True
assert new_z.flags.f_contiguous is False
assert new_z.dtype == arr.dtype
assert new_z.shape == arr.shape
# Force Fortran-orded output
new_z = func(arr, order="F")
assert new_z.flags.c_contiguous is False
assert new_z.flags.f_contiguous is True
assert new_z.dtype == arr.dtype
assert new_z.shape == arr.shape
# Change the dtype, but otherwise match order & strides
# order = "K" so non-contiguous array remains non-contiguous
new_z = func(arr, dtype=np.complex64, order="K")
assert new_z.flags.c_contiguous == arr.flags.c_contiguous
assert new_z.flags.f_contiguous == arr.flags.f_contiguous
assert new_z.dtype == np.complex64
assert new_z.shape == arr.shape
if __name__ == "__main__":
# make sure that import failures get reported, instead of skipping the tests.
import pycuda.autoinit # noqa
if len(sys.argv) > 1:
exec (sys.argv[1])
else:
from pytest import main
main([__file__])
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