def test_divide_array(ctx_factory):
"""Test the division of an array and a scalar. """
context = ctx_factory()
queue = cl.CommandQueue(context)
dtypes = (np.float32, np.complex64)
from pyopencl.characterize import has_double_support
if has_double_support(queue.device):
dtypes = dtypes + (np.float64, np.complex128)
from itertools import product
for dtype_a, dtype_b in product(dtypes, repeat=2):
a = np.array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100]).astype(dtype_a)
b = np.array([10, 10, 10, 10, 10, 10, 10, 10, 10, 10]).astype(dtype_b)
a_gpu = cl_array.to_device(queue, a)
b_gpu = cl_array.to_device(queue, b)
c = a / b
c_gpu = (a_gpu / b_gpu)
assert (np.abs(c_gpu.get() - c) < 1e-3).all()
assert c_gpu.dtype is c.dtype
d = b / a
d_gpu = (b_gpu / a_gpu)
assert (np.abs(d_gpu.get() - d) < 1e-3).all()
assert d_gpu.dtype is d.dtype
def test_dot(ctx_factory):
from pytest import importorskip
importorskip("mako")
context = ctx_factory()
queue = cl.CommandQueue(context)
dtypes = [np.float32, np.complex64]
if has_double_support(context.devices[0]):
dtypes.extend([np.float64, np.complex128])
for a_dtype in dtypes:
for b_dtype in dtypes:
print(a_dtype, b_dtype)
a_gpu = general_clrand(queue, (200000, ), a_dtype)
a = a_gpu.get()
b_gpu = general_clrand(queue, (200000, ), b_dtype)
b = b_gpu.get()
dot_ab = np.dot(a, b)
dot_ab_gpu = cl_array.dot(a_gpu, b_gpu).get()
assert abs(dot_ab_gpu - dot_ab) / abs(dot_ab) < 1e-4
vdot_ab = np.vdot(a, b)
vdot_ab_gpu = cl_array.vdot(a_gpu, b_gpu).get()
assert abs(vdot_ab_gpu - vdot_ab) / abs(vdot_ab) < 1e-4
def get_dot_kernel(ctx,
dtype_out,
dtype_a=None,
dtype_b=None,
conjugate_first=False):
from pyopencl.characterize import has_double_support
map_expr, dtype_out, dtype_b = _get_dot_expr(
dtype_out,
dtype_a,
dtype_b,
conjugate_first,
has_double_support=has_double_support(ctx.devices[0]))
reduce_expr = "a+b"
neutral_expr = "0"
if dtype_out.kind == "c":
from pyopencl.elementwise import complex_dtype_to_name
dtname = complex_dtype_to_name(dtype_out)
reduce_expr = "%s_add(a, b)" % dtname
neutral_expr = "%s_new(0, 0)" % dtname
return ReductionKernel(ctx,
dtype_out,
neutral=neutral_expr,
reduce_expr=reduce_expr,
map_expr=map_expr,
arguments=("const %(tp_a)s *a, "
"const %(tp_b)s *b" % {
"tp_a": dtype_to_ctype(dtype_a),
"tp_b": dtype_to_ctype(dtype_b),
}))
def test_random(ctx_factory):
context = ctx_factory()
queue = cl.CommandQueue(context)
from pyopencl.clrandom import RanluxGenerator
if has_double_support(context.devices[0]):
dtypes = [np.float32, np.float64]
else:
dtypes = [np.float32]
gen = RanluxGenerator(queue, 5120)
for ary_size in [300, 301, 302, 303, 10007]:
for dtype in dtypes:
ran = cl_array.zeros(queue, ary_size, dtype)
gen.fill_uniform(ran)
assert (0 < ran.get()).all()
assert (ran.get() < 1).all()
gen.synchronize(queue)
ran = cl_array.zeros(queue, ary_size, dtype)
gen.fill_uniform(ran, a=4, b=7)
assert (4 < ran.get()).all()
assert (ran.get() < 7).all()
ran = gen.normal(queue, (10007,), dtype, mu=4, sigma=3)
dtypes = [np.int32]
for dtype in dtypes:
ran = gen.uniform(queue, (10000007,), dtype, a=200, b=300)
assert (200 <= ran.get()).all()
assert (ran.get() < 300).all()
def test_astype(ctx_factory):
context = ctx_factory()
queue = cl.CommandQueue(context)
from pyopencl.clrandom import rand as clrand
if not has_double_support(context.devices[0]):
from pytest import skip
skip("double precision not supported on %s" % context.devices[0])
a_gpu = clrand(queue, (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 = clrand(queue, (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
def test_dot(ctx_factory):
from pytest import importorskip
importorskip("mako")
context = ctx_factory()
queue = cl.CommandQueue(context)
dtypes = [np.float32, np.complex64]
if has_double_support(context.devices[0]):
dtypes.extend([np.float64, np.complex128])
for a_dtype in dtypes:
for b_dtype in dtypes:
print(a_dtype, b_dtype)
a_gpu = general_clrand(queue, (200000,), a_dtype)
a = a_gpu.get()
b_gpu = general_clrand(queue, (200000,), b_dtype)
b = b_gpu.get()
dot_ab = np.dot(a, b)
dot_ab_gpu = cl_array.dot(a_gpu, b_gpu).get()
assert abs(dot_ab_gpu - dot_ab) / abs(dot_ab) < 1e-4
vdot_ab = np.vdot(a, b)
vdot_ab_gpu = cl_array.vdot(a_gpu, b_gpu).get()
assert abs(vdot_ab_gpu - vdot_ab) / abs(vdot_ab) < 1e-4
def _get_reduction_source(
ctx, out_type, out_type_size,
neutral, reduce_expr, map_expr, parsed_args,
name="reduce_kernel", preamble="", arg_prep="",
device=None, max_group_size=None):
if device is not None:
devices = [device]
else:
devices = ctx.devices
# {{{ compute group size
def get_dev_group_size(device):
# dirty fix for the RV770 boards
max_work_group_size = device.max_work_group_size
if "RV770" in device.name:
max_work_group_size = 64
# compute lmem limit
from pytools import div_ceil
lmem_wg_size = div_ceil(max_work_group_size, out_type_size)
result = min(max_work_group_size, lmem_wg_size)
# round down to power of 2
from pyopencl.tools import bitlog2
return 2**bitlog2(result)
group_size = min(get_dev_group_size(dev) for dev in devices)
if max_group_size is not None:
group_size = min(max_group_size, group_size)
# }}}
from mako.template import Template
from pytools import all
from pyopencl.characterize import has_double_support
src = str(Template(KERNEL).render(
out_type=out_type,
arguments=", ".join(arg.declarator() for arg in parsed_args),
group_size=group_size,
neutral=neutral,
reduce_expr=_process_code_for_macro(reduce_expr),
map_expr=_process_code_for_macro(map_expr),
name=name,
preamble=preamble,
arg_prep=arg_prep,
double_support=all(has_double_support(dev) for dev in devices),
))
from pytools import Record
class ReductionInfo(Record):
pass
return ReductionInfo(
context=ctx,
source=src,
group_size=group_size)
def get_write_kernel(self, index_dtype):
index_ctype = dtype_to_ctype(index_dtype)
from pyopencl.tools import VectorArg, OtherArg
kernel_list_args = []
kernel_list_arg_values = ""
user_list_args = []
for name, dtype in self.list_names_and_dtypes:
list_name = "plb_%s_list" % name
list_arg = VectorArg(dtype, list_name)
kernel_list_args.append(list_arg)
user_list_args.append(list_arg)
if name in self.count_sharing:
kernel_list_arg_values += "%s, " % list_name
continue
kernel_list_args.append(VectorArg(index_dtype, "plb_%s_start_index" % name))
index_name = "plb_%s_index" % name
user_list_args.append(OtherArg("%s *%s" % (index_ctype, index_name), index_name))
kernel_list_arg_values += "%s, &%s, " % (list_name, index_name)
kernel_name = self.name_prefix + "_write"
from pyopencl.characterize import has_double_support
src = _LIST_BUILDER_TEMPLATE.render(
is_count_stage=False,
kernel_name=kernel_name,
double_support=all(has_double_support(dev) for dev in self.context.devices),
debug=self.debug,
do_not_vectorize=self.do_not_vectorize(),
kernel_list_arg_decl=_get_arg_decl(kernel_list_args),
kernel_list_arg_values=kernel_list_arg_values,
user_list_arg_decl=_get_arg_decl(user_list_args),
user_list_args=_get_arg_list(user_list_args),
user_arg_decl=_get_arg_decl(self.arg_decls),
user_args=_get_arg_list(self.arg_decls),
list_names_and_dtypes=self.list_names_and_dtypes,
count_sharing=self.count_sharing,
name_prefix=self.name_prefix,
generate_template=self.generate_template,
preamble=self.preamble,
index_type=index_ctype,
)
src = str(src)
prg = cl.Program(self.context, src).build(self.options)
knl = getattr(prg, kernel_name)
from pyopencl.tools import get_arg_list_scalar_arg_dtypes
knl.set_scalar_arg_dtypes(get_arg_list_scalar_arg_dtypes(kernel_list_args + self.arg_decls) + [index_dtype])
return knl
def test_bitonic_sort(ctx_factory, size, dtype):
ctx = cl.create_some_context()
queue = cl.CommandQueue(ctx)
dev = ctx.devices[0]
if (dev.platform.name == "Apple" and dev.type & cl.device_type.CPU):
pytest.xfail("Bitonic sort won't work on Apple CPU: no workgroup "
"parallelism")
if (dev.platform.name == "Portable Computing Language"
and dtype == np.float64 and get_pocl_version(dev.platform) <
(1, 0)):
pytest.xfail(
"Double precision bitonic sort doesn't work on POCL < 1.0")
if dtype == np.float64 and not has_double_support(dev):
from pytest import skip
skip("double precision not supported on %s" % dev)
import pyopencl.clrandom as clrandom
from pyopencl.bitonic_sort import BitonicSort
s = clrandom.rand(queue, (
2,
size,
3,
),
dtype,
luxury=None,
a=0,
b=239482333)
sorter = BitonicSort(ctx)
sgs, evt = sorter(s.copy(), axis=1)
assert np.array_equal(np.sort(s.get(), axis=1), sgs.get())
def test_bitonic_argsort(ctx_factory, size, dtype):
import sys
is_pypy = "__pypy__" in sys.builtin_module_names
if not size and is_pypy:
# https://bitbucket.org/pypy/numpy/issues/53/specifying-strides-on-zero-sized-array
pytest.xfail("pypy doesn't seem to handle as_strided "
"on zero-sized arrays very well")
ctx = cl.create_some_context()
queue = cl.CommandQueue(ctx)
device = queue.device
if device.platform.vendor == "The pocl project" \
and device.type & cl.device_type.GPU:
pytest.xfail("bitonic argsort fails on POCL + Nvidia,"
"at least the K40, as of pocl 1.6, 2021-01-20")
dev = ctx.devices[0]
if (dev.platform.name == "Portable Computing Language"
and sys.platform == "darwin"):
pytest.xfail("Bitonic sort crashes on Apple POCL")
if (dev.platform.name == "Apple" and dev.type & cl.device_type.CPU):
pytest.xfail("Bitonic sort won't work on Apple CPU: no workgroup "
"parallelism")
if (dev.platform.name == "Portable Computing Language"
and dtype == np.float64
and get_pocl_version(dev.platform) < (1, 0)):
pytest.xfail("Double precision bitonic sort doesn't work on POCL < 1.0")
if (dev.platform.name == "Intel(R) OpenCL" and size == 0):
pytest.xfail("size-0 arange fails on Intel CL")
if dtype == np.float64 and not has_double_support(dev):
from pytest import skip
skip("double precision not supported on %s" % dev)
import pyopencl.clrandom as clrandom
from pyopencl.bitonic_sort import BitonicSort
index = cl_array.arange(queue, 0, size, 1, dtype=np.int32)
m = clrandom.rand(queue, (size,), dtype, luxury=None, a=0, b=239432234)
sorterm = BitonicSort(ctx)
ms = m.copy()
# enqueue_marker crashes under CL 1.1 pocl if there is anything to wait for
# (no clEnqueueWaitForEvents) https://github.com/inducer/pyopencl/pull/237
if (dev.platform.name == "Portable Computing Language"
and cl.get_cl_header_version() < (1, 2)):
ms.finish()
index.finish()
ms, evt = sorterm(ms, idx=index, axis=0)
assert np.array_equal(np.sort(m.get()), ms.get())
# may be False because of identical values in array
# assert np.array_equal(np.argsort(m.get()), index.get())
# Check values by indices
assert np.array_equal(m.get()[np.argsort(m.get())], m.get()[index.get()])
def test_divide_inplace_scalar(ctx_factory):
"""Test inplace division of arrays and a scalar."""
context = ctx_factory()
queue = cl.CommandQueue(context)
if queue.device.platform.name == "Apple":
pytest.xfail("Apple CL compiler crashes on this.")
dtypes = (np.uint8, np.uint16, np.uint32, np.int8, np.int16, np.int32,
np.float32, np.complex64)
from pyopencl.characterize import has_double_support
if has_double_support(queue.device):
dtypes = dtypes + (np.float64, np.complex128)
from itertools import product
for dtype_a, dtype_s in product(dtypes, repeat=2):
a = np.array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100]).astype(dtype_a)
s = dtype_s(40)
a_gpu = cl_array.to_device(queue, a)
# ensure the same behavior as inplace numpy.ndarray division
try:
a /= s
except TypeError:
with np.testing.assert_raises(TypeError):
a_gpu /= s
else:
a_gpu /= s
assert (np.abs(a_gpu.get() - a) < 1e-3).all()
assert a_gpu.dtype is a.dtype
def test_lin_comb_diff(ctx_factory, arg_type):
ctx = ctx_factory()
dev, = ctx.devices
if not has_double_support(dev):
if arg_type in (np.float64, np.complex128):
pytest.skip('Device does not support double.')
n = 100000
a_np = (np.random.randn(n)).astype(arg_type)
b_np = (np.random.randn(n)).astype(arg_type)
c_np = (np.random.randn(n) * 10).astype(arg_type)
queue = cl.CommandQueue(ctx)
a_g = cl.array.to_device(queue, a_np)
b_g = cl.array.to_device(queue, b_np)
c_g = cl.array.to_device(queue, c_np)
res_g = cl.array.empty_like(a_g)
lin_comb_diff = lin_comb_diff_kernel(ctx, arg_type, arg_type,
arg_type, np.float32, 2)
gs, ls = get_group_sizes(n, dev, lin_comb_diff)
evt = run_elwise_kernel(lin_comb_diff, queue, gs, ls, n, [],
res_g, c_g, a_g, b_g, 2, 3)
evt.wait()
# Check on GPU with PyOpenCL Array:
assert np.linalg.norm((res_g - (c_g + 2 * a_g + 3 * b_g)).get()) <= 2e-4
# Check on CPU with Numpy:
res_np = res_g.get()
assert np.linalg.norm(res_np - (c_np + 2 * a_np + 3 * b_np)) <= 2e-4
def test_divide_inplace_array(ctx_factory):
"""Test inplace division of arrays."""
context = ctx_factory()
queue = cl.CommandQueue(context)
dtypes = (np.uint8, np.uint16, np.uint32, np.int8, np.int16, np.int32,
np.float32, np.complex64)
from pyopencl.characterize import has_double_support
if has_double_support(queue.device):
dtypes = dtypes + (np.float64, np.complex128)
from itertools import product
for dtype_a, dtype_b in product(dtypes, repeat=2):
print(dtype_a, dtype_b)
a = np.array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100]).astype(dtype_a)
b = np.array([10, 10, 10, 10, 10, 10, 10, 10, 10, 10]).astype(dtype_b)
a_gpu = cl_array.to_device(queue, a)
b_gpu = cl_array.to_device(queue, b)
# ensure the same behavior as inplace numpy.ndarray division
try:
a_gpu /= b_gpu
except TypeError:
# pass for now, as numpy casts differently for in-place and out-place
# true_divide
pass
# with np.testing.assert_raises(TypeError):
# a /= b
else:
a /= b
assert (np.abs(a_gpu.get() - a) < 1e-3).all()
assert a_gpu.dtype is a.dtype
def test_bitonic_sort(ctx_factory, size, dtype):
ctx = cl.create_some_context()
queue = cl.CommandQueue(ctx)
dev = ctx.devices[0]
if (dev.platform.name == "Apple" and dev.type & cl.device_type.CPU):
pytest.xfail("Bitonic sort won't work on Apple CPU: no workgroup "
"parallelism")
if (dev.platform.name == "Portable Computing Language"
and dtype == np.float64
and get_pocl_version(dev.platform) < (1, 0)):
pytest.xfail("Double precision bitonic sort doesn't work on POCL < 1.0")
if dtype == np.float64 and not has_double_support(dev):
from pytest import skip
skip("double precision not supported on %s" % dev)
import pyopencl.clrandom as clrandom
from pyopencl.bitonic_sort import BitonicSort
s = clrandom.rand(queue, (2, size, 3,), dtype, luxury=None, a=0, b=239482333)
sgs = s.copy()
# enqueue_marker crashes under CL 1.1 pocl if there is anything to wait for
# (no clEnqueueWaitForEvents) https://github.com/inducer/pyopencl/pull/237
if (dev.platform.name == "Portable Computing Language"
and cl.get_cl_header_version() < (1, 2)):
sgs.finish()
sorter = BitonicSort(ctx)
sgs, evt = sorter(sgs, axis=1)
assert np.array_equal(np.sort(s.get(), axis=1), sgs.get())
def test_astype(ctx_factory):
context = ctx_factory()
queue = cl.CommandQueue(context)
from pyopencl.clrandom import rand as clrand
if not has_double_support(context.devices[0]):
from pytest import skip
skip("double precision not supported on %s" % context.devices[0])
a_gpu = clrand(queue, (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 = clrand(queue, (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
def test(ctx_factory):
context = ctx_factory()
queue = cl.CommandQueue(context)
gpu_func = getattr(clmath, name)
cpu_func = getattr(np, numpy_func_names.get(name, name))
if has_double_support(context.devices[0]):
if use_complex:
dtypes = [np.float32, np.float64, np.complex64, np.complex128]
else:
dtypes = [np.float32, np.float64]
else:
if use_complex:
dtypes = [np.float32, np.complex64]
else:
dtypes = [np.float32]
for s in sizes:
for dtype in dtypes:
dtype = np.dtype(dtype)
args = cl_array.arange(queue, a, b, (b - a) / s, dtype=dtype)
if dtype.kind == "c":
args = args + dtype.type(1j) * args
gpu_results = gpu_func(args).get()
cpu_results = cpu_func(args.get())
my_threshold = threshold
if dtype.kind == "c" and isinstance(use_complex, float):
my_threshold = use_complex
max_err = np.max(np.abs(cpu_results - gpu_results))
assert (max_err <= my_threshold).all(), (max_err, name, dtype)
def test_divide_scalar(ctx_factory):
"""Test the division of an array and a scalar."""
context = ctx_factory()
queue = cl.CommandQueue(context)
if queue.device.platform.name == "Apple":
pytest.xfail("Apple CL compiler crashes on this.")
dtypes = (np.uint8, np.uint16, np.uint32, np.int8, np.int16, np.int32,
np.float32, np.complex64)
from pyopencl.characterize import has_double_support
if has_double_support(queue.device):
dtypes = dtypes + (np.float64, np.complex128)
from itertools import product
for dtype_a, dtype_s in product(dtypes, repeat=2):
a = np.array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100]).astype(dtype_a)
s = dtype_s(40)
a_gpu = cl_array.to_device(queue, a)
b = a / s
b_gpu = a_gpu / s
assert (np.abs(b_gpu.get() - b) < 1e-3).all()
assert b_gpu.dtype is b.dtype
c = s / a
c_gpu = s / a_gpu
assert (np.abs(c_gpu.get() - c) < 1e-3).all()
assert c_gpu.dtype is c.dtype
def get_subset_dot_kernel(ctx,
dtype_out,
dtype_subset,
dtype_a=None,
dtype_b=None,
conjugate_first=False):
from pyopencl.characterize import has_double_support
map_expr, dtype_out, dtype_b = _get_dot_expr(
dtype_out,
dtype_a,
dtype_b,
conjugate_first,
has_double_support=has_double_support(ctx.devices[0]),
index_expr="lookup_tbl[i]")
# important: lookup_tbl must be first--it controls the length
return ReductionKernel(
ctx,
dtype_out,
neutral="0",
reduce_expr="a+b",
map_expr=map_expr,
arguments=("const %(tp_lut)s *lookup_tbl, "
"const %(tp_a)s *a, "
"const %(tp_b)s *b" % {
"tp_lut": dtype_to_ctype(dtype_subset),
"tp_a": dtype_to_ctype(dtype_a),
"tp_b": dtype_to_ctype(dtype_b),
}))
def get_dot_kernel(ctx, dtype_out, dtype_a=None, dtype_b=None):
if dtype_b is None:
if dtype_a is None:
dtype_b = dtype_out
else:
dtype_b = dtype_a
if dtype_out is None:
from pyopencl.compyte.array import get_common_dtype
from pyopencl.characterize import has_double_support
dtype_out = get_common_dtype(dtype_a.type(0), dtype_b.type(0),
has_double_support(ctx.devices[0]))
a_real_dtype = dtype_a.type(0).real.dtype
b_real_dtype = dtype_b.type(0).real.dtype
out_real_dtype = dtype_out.type(0).real.dtype
a_is_complex = dtype_a.kind == "c"
b_is_complex = dtype_b.kind == "c"
out_is_complex = dtype_out.kind == "c"
from pyopencl.elementwise import complex_dtype_to_name
if a_is_complex and b_is_complex:
a = "a[i]"
b = "b[i]"
if dtype_a != dtype_out:
a = "%s_cast(%s)" % (complex_dtype_to_name(dtype_out), a)
if dtype_b != dtype_out:
b = "%s_cast(%s)" % (complex_dtype_to_name(dtype_out), b)
map_expr = "%s_mul(%s, %s)" % (complex_dtype_to_name(dtype_out), a, b)
else:
a = "a[i]"
b = "b[i]"
if out_is_complex:
if a_is_complex and dtype_a != dtype_out:
a = "%s_cast(%s)" % (complex_dtype_to_name(dtype_out), a)
if b_is_complex and dtype_b != dtype_out:
b = "%s_cast(%s)" % (complex_dtype_to_name(dtype_out), b)
if not a_is_complex and a_real_dtype != out_real_dtype:
a = "(%s) (%s)" % (dtype_to_ctype(out_real_dtype), a)
if not b_is_complex and b_real_dtype != out_real_dtype:
b = "(%s) (%s)" % (dtype_to_ctype(out_real_dtype), b)
map_expr = "%s*%s" % (a, b)
return ReductionKernel(ctx,
dtype_out,
neutral="0",
reduce_expr="a+b",
map_expr=map_expr,
arguments="const %(tp_a)s *a, "
"const %(tp_b)s *b" % {
"tp_a": dtype_to_ctype(dtype_a),
"tp_b": dtype_to_ctype(dtype_b),
})
def __init__(self, queue, num_work_items,
luxury=None, seed=None, no_warmup=False,
use_legacy_init=False, max_work_items=None):
if luxury is None:
luxury = 4
if seed is None:
from time import time
seed = int(time()*1e6) % 2<<30
self.context = queue.context
self.luxury = luxury
self.num_work_items = num_work_items
from pyopencl.characterize import has_double_support
self.support_double = has_double_support(queue.device)
self.no_warmup = no_warmup
self.use_legacy_init = use_legacy_init
self.max_work_items = max_work_items
src = """
%(defines)s
#include <pyopencl-ranluxcl.cl>
kernel void init_ranlux(unsigned seeds, global ranluxcl_state_t *ranluxcltab)
{
if (get_global_id(0) < %(num_work_items)d)
ranluxcl_initialization(seeds, ranluxcltab);
}
""" % {
"defines": self.generate_settings_defines(),
"num_work_items": num_work_items
}
prg = cl.Program(queue.context, src).build()
# {{{ compute work group size
wg_size = None
import sys
import platform
if ("darwin" in sys.platform
and "Apple" in queue.device.platform.vendor
and platform.mac_ver()[0].startswith("10.7")
and queue.device.type == cl.device_type.CPU):
wg_size = (1,)
self.wg_size = wg_size
# }}}
self.state = cl_array.empty(queue, (num_work_items, 112), dtype=np.uint8)
self.state.fill(17)
prg.init_ranlux(queue, (num_work_items,), self.wg_size, np.uint32(seed),
self.state.data)
def get_dot_kernel(ctx, dtype_out, dtype_a=None, dtype_b=None):
if dtype_b is None:
if dtype_a is None:
dtype_b = dtype_out
else:
dtype_b = dtype_a
if dtype_out is None:
from pyopencl.compyte.array import get_common_dtype
from pyopencl.characterize import has_double_support
dtype_out = get_common_dtype(
dtype_a.type(0), dtype_b.type(0), has_double_support(ctx.devices[0]))
a_real_dtype = dtype_a.type(0).real.dtype
b_real_dtype = dtype_b.type(0).real.dtype
out_real_dtype = dtype_out.type(0).real.dtype
a_is_complex = dtype_a.kind == "c"
b_is_complex = dtype_b.kind == "c"
out_is_complex = dtype_out.kind == "c"
from pyopencl.elementwise import complex_dtype_to_name
if a_is_complex and b_is_complex:
a = "a[i]"
b = "b[i]"
if dtype_a != dtype_out:
a = "%s_cast(%s)" % (complex_dtype_to_name(dtype_out), a)
if dtype_b != dtype_out:
b = "%s_cast(%s)" % (complex_dtype_to_name(dtype_out), b)
map_expr = "%s_mul(%s, %s)" % (
complex_dtype_to_name(dtype_out), a, b)
else:
a = "a[i]"
b = "b[i]"
if out_is_complex:
if a_is_complex and dtype_a != dtype_out:
a = "%s_cast(%s)" % (complex_dtype_to_name(dtype_out), a)
if b_is_complex and dtype_b != dtype_out:
b = "%s_cast(%s)" % (complex_dtype_to_name(dtype_out), b)
if not a_is_complex and a_real_dtype != out_real_dtype:
a = "(%s) (%s)" % (dtype_to_ctype(out_real_dtype), a)
if not b_is_complex and b_real_dtype != out_real_dtype:
b = "(%s) (%s)" % (dtype_to_ctype(out_real_dtype), b)
map_expr = "%s*%s" % (a, b)
return ReductionKernel(ctx, dtype_out, neutral="0",
reduce_expr="a+b", map_expr=map_expr,
arguments=
"__global const %(tp_a)s *a, "
"__global const %(tp_b)s *b" % {
"tp_a": dtype_to_ctype(dtype_a),
"tp_b": dtype_to_ctype(dtype_b),
})
def test_get_kernels(ctx_factory, res_type, arg_type, weight_type):
ctx = ctx_factory()
dev, = ctx.devices
if not has_double_support(dev):
for t in res_type, arg_type, weight_type:
if t in (np.float64, np.complex128):
pytest.skip('Device does not support double.')
for length in range(1, 3):
lin_comb_kernel(ctx, res_type, arg_type, weight_type, length)
def test_random_float_in_range(ctx_factory,
rng_class,
ary_size,
plot_hist=False):
context = ctx_factory()
queue = cl.CommandQueue(context)
device = queue.device
if device.platform.vendor == "The pocl project" \
and device.type & cl.device_type.GPU \
and rng_class is RanluxGenerator:
pytest.xfail("ranlux test fails on POCL + Nvidia,"
"at least the Titan V, as of pocl 1.6, 2021-01-20")
if has_double_support(context.devices[0]):
dtypes = [np.float32, np.float64]
else:
dtypes = [np.float32]
if rng_class is RanluxGenerator:
gen = rng_class(queue, 5120)
else:
gen = rng_class(context)
for dtype in dtypes:
print(dtype)
ran = cl_array.zeros(queue, ary_size, dtype)
gen.fill_uniform(ran)
if plot_hist:
import matplotlib.pyplot as pt
pt.hist(ran.get(), 30)
pt.show()
assert (0 <= ran.get()).all()
assert (ran.get() <= 1).all()
if rng_class is RanluxGenerator:
gen.synchronize(queue)
ran = cl_array.zeros(queue, ary_size, dtype)
gen.fill_uniform(ran, a=4, b=7)
ran_host = ran.get()
for cond in [4 <= ran_host, ran_host <= 7]:
good = cond.all()
if not good:
print(np.where(~cond))
print(ran_host[~cond])
assert good
ran = gen.normal(queue, ary_size, dtype, mu=10, sigma=3)
if plot_hist:
import matplotlib.pyplot as pt
pt.hist(ran.get(), 30)
pt.show()
def get_count_kernel(self, index_dtype):
index_ctype = dtype_to_ctype(index_dtype)
from pyopencl.tools import VectorArg, OtherArg
kernel_list_args = [
VectorArg(index_dtype, "plb_%s_count" % name)
for name, dtype in self.list_names_and_dtypes
if name not in self.count_sharing]
user_list_args = []
for name, dtype in self.list_names_and_dtypes:
if name in self.count_sharing:
continue
name = "plb_loc_%s_count" % name
user_list_args.append(OtherArg("%s *%s" % (
index_ctype, name), name))
kernel_name = self.name_prefix+"_count"
from pyopencl.characterize import has_double_support
src = _LIST_BUILDER_TEMPLATE.render(
is_count_stage=True,
kernel_name=kernel_name,
double_support=all(has_double_support(dev) for dev in
self.context.devices),
debug=self.debug,
do_not_vectorize=self.do_not_vectorize(),
eliminate_empty_output_lists=self.eliminate_empty_output_lists,
kernel_list_arg_decl=_get_arg_decl(kernel_list_args),
kernel_list_arg_values=_get_arg_list(user_list_args, prefix="&"),
user_list_arg_decl=_get_arg_decl(user_list_args),
user_list_args=_get_arg_list(user_list_args),
user_arg_decl_with_offset=_get_arg_decl(self.arg_decls),
user_arg_decl_no_offset=_get_arg_decl(self.arg_decls_no_offset),
user_args_no_offset=_get_arg_list(self.arg_decls_no_offset),
arg_offset_adjustment=get_arg_offset_adjuster_code(self.arg_decls),
list_names_and_dtypes=self.list_names_and_dtypes,
count_sharing=self.count_sharing,
name_prefix=self.name_prefix,
generate_template=self.generate_template,
preamble=self.preamble,
index_type=index_ctype,
)
src = str(src)
prg = cl.Program(self.context, src).build(self.options)
knl = getattr(prg, kernel_name)
from pyopencl.tools import get_arg_list_scalar_arg_dtypes
knl.set_scalar_arg_dtypes(get_arg_list_scalar_arg_dtypes(
kernel_list_args+self.arg_decls) + [index_dtype])
return knl
def get_count_kernel(self, index_dtype):
index_ctype = dtype_to_ctype(index_dtype)
from pyopencl.tools import VectorArg, OtherArg
kernel_list_args = [
VectorArg(index_dtype, "plb_%s_count" % name)
for name, dtype in self.list_names_and_dtypes
if name not in self.count_sharing]
user_list_args = []
for name, dtype in self.list_names_and_dtypes:
if name in self.count_sharing:
continue
name = "plb_loc_%s_count" % name
user_list_args.append(OtherArg("%s *%s" % (
index_ctype, name), name))
kernel_name = self.name_prefix+"_count"
from pyopencl.characterize import has_double_support
src = _LIST_BUILDER_TEMPLATE.render(
is_count_stage=True,
kernel_name=kernel_name,
double_support=all(has_double_support(dev) for dev in
self.context.devices),
debug=self.debug,
do_not_vectorize=self.do_not_vectorize(),
eliminate_empty_output_lists=self.eliminate_empty_output_lists,
kernel_list_arg_decl=_get_arg_decl(kernel_list_args),
kernel_list_arg_values=_get_arg_list(user_list_args, prefix="&"),
user_list_arg_decl=_get_arg_decl(user_list_args),
user_list_args=_get_arg_list(user_list_args),
user_arg_decl_with_offset=_get_arg_decl(self.arg_decls),
user_arg_decl_no_offset=_get_arg_decl(self.arg_decls_no_offset),
user_args_no_offset=_get_arg_list(self.arg_decls_no_offset),
arg_offset_adjustment=get_arg_offset_adjuster_code(self.arg_decls),
list_names_and_dtypes=self.list_names_and_dtypes,
count_sharing=self.count_sharing,
name_prefix=self.name_prefix,
generate_template=self.generate_template,
preamble=self.preamble,
index_type=index_ctype,
)
src = str(src)
prg = cl.Program(self.context, src).build(self.options)
knl = getattr(prg, kernel_name)
from pyopencl.tools import get_arg_list_scalar_arg_dtypes
knl.set_scalar_arg_dtypes(get_arg_list_scalar_arg_dtypes(
kernel_list_args+self.arg_decls) + [index_dtype])
return knl
def test_clrandom_dtypes(ctx_factory, rng_class, dtype):
cl_ctx = ctx_factory()
if dtype == np.float64 and not has_double_support(cl_ctx.devices[0]):
pytest.skip("double precision not supported on this device")
rng = rng_class(cl_ctx)
size = 10
with cl.CommandQueue(cl_ctx) as queue:
rng.uniform(queue, size, dtype)
if dtype not in (np.int32, np.int64):
rng.normal(queue, size, dtype)
def test_clrandom_dtypes(ctx_factory, rng_class, dtype):
cl_ctx = ctx_factory()
if dtype == np.float64 and not has_double_support(cl_ctx.devices[0]):
pytest.skip("double precision not supported on this device")
rng = rng_class(cl_ctx)
size = 10
with cl.CommandQueue(cl_ctx) as queue:
rng.uniform(queue, size, dtype)
if dtype not in (np.int32, np.int64):
rng.normal(queue, size, dtype)
def test_random_float_in_range(ctx_factory,
rng_class,
ary_size,
plot_hist=False):
context = ctx_factory()
queue = cl.CommandQueue(context)
if has_double_support(context.devices[0]):
dtypes = [np.float32, np.float64]
else:
dtypes = [np.float32]
if rng_class is RanluxGenerator:
gen = rng_class(queue, 5120)
else:
gen = rng_class(context)
for dtype in dtypes:
print(dtype)
ran = cl_array.zeros(queue, ary_size, dtype)
gen.fill_uniform(ran)
if plot_hist:
import matplotlib.pyplot as pt
pt.hist(ran.get(), 30)
pt.show()
assert (0 <= ran.get()).all()
assert (ran.get() <= 1).all()
if rng_class is RanluxGenerator:
gen.synchronize(queue)
ran = cl_array.zeros(queue, ary_size, dtype)
gen.fill_uniform(ran, a=4, b=7)
ran_host = ran.get()
for cond in [4 <= ran_host, ran_host <= 7]:
good = cond.all()
if not good:
print(np.where(~cond))
print(ran_host[~cond])
assert good
ran = gen.normal(queue, ary_size, dtype, mu=10, sigma=3)
if plot_hist:
import matplotlib.pyplot as pt
pt.hist(ran.get(), 30)
pt.show()
def test_hankel_01_complex(ctx_factory, ref_src):
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
if not has_double_support(ctx.devices[0]):
from pytest import skip
skip("no double precision support--cannot test complex bessel function")
n = 10**6
np.random.seed(11)
z = (
np.logspace(-5, 2, n)
* np.exp(1j * 2 * np.pi * np.random.rand(n)))
def get_err(check, ref):
return np.max(np.abs(check-ref)) / np.max(np.abs(ref))
if ref_src == "pyfmmlib":
pyfmmlib = pytest.importorskip("pyfmmlib")
h0_ref, h1_ref = pyfmmlib.hank103_vec(z, ifexpon=1)
elif ref_src == "scipy":
spec = pytest.importorskip("scipy.special")
h0_ref = spec.hankel1(0, z)
h1_ref = spec.hankel1(1, z)
else:
raise ValueError("ref_src")
z_dev = cl_array.to_device(queue, z)
h0_dev, h1_dev = clmath.hankel_01(z_dev)
rel_err_h0 = np.abs(h0_dev.get() - h0_ref)/np.abs(h0_ref)
rel_err_h1 = np.abs(h1_dev.get() - h1_ref)/np.abs(h1_ref)
max_rel_err_h0 = np.max(rel_err_h0)
max_rel_err_h1 = np.max(rel_err_h1)
print("H0", max_rel_err_h0)
print("H1", max_rel_err_h1)
assert max_rel_err_h0 < 4e-13
assert max_rel_err_h1 < 2e-13
if 0:
import matplotlib.pyplot as pt
pt.loglog(np.abs(z), rel_err_h0)
pt.loglog(np.abs(z), rel_err_h1)
pt.show()
def get_dot_kernel(ctx, dtype_out, dtype_a=None, dtype_b=None,
conjugate_first=False):
from pyopencl.characterize import has_double_support
map_expr, dtype_out, dtype_b = _get_dot_expr(
dtype_out, dtype_a, dtype_b, conjugate_first,
has_double_support=has_double_support(ctx.devices[0]))
return ReductionKernel(ctx, dtype_out, neutral="0",
reduce_expr="a+b", map_expr=map_expr,
arguments=
"const %(tp_a)s *a, "
"const %(tp_b)s *b" % {
"tp_a": dtype_to_ctype(dtype_a),
"tp_b": dtype_to_ctype(dtype_b),
})
def get_dot_kernel(ctx, dtype_out, dtype_a=None, dtype_b=None,
conjugate_first=False):
from pyopencl.characterize import has_double_support
map_expr, dtype_out, dtype_b = _get_dot_expr(
dtype_out, dtype_a, dtype_b, conjugate_first,
has_double_support=has_double_support(ctx.devices[0]))
return ReductionKernel(ctx, dtype_out, neutral="0",
reduce_expr="a+b", map_expr=map_expr,
arguments=
"const %(tp_a)s *a, "
"const %(tp_b)s *b" % {
"tp_a": dtype_to_ctype(dtype_a),
"tp_b": dtype_to_ctype(dtype_b),
})
def test_hankel_01_complex(ctx_factory, ref_src):
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
if not has_double_support(ctx.devices[0]):
from pytest import skip
skip(
"no double precision support--cannot test complex bessel function")
n = 10**6
np.random.seed(11)
z = (np.logspace(-5, 2, n) * np.exp(1j * 2 * np.pi * np.random.rand(n)))
def get_err(check, ref):
return np.max(np.abs(check - ref)) / np.max(np.abs(ref))
if ref_src == "pyfmmlib":
pyfmmlib = pytest.importorskip("pyfmmlib")
h0_ref, h1_ref = pyfmmlib.hank103_vec(z, ifexpon=1)
elif ref_src == "scipy":
spec = pytest.importorskip("scipy.special")
h0_ref = spec.hankel1(0, z)
h1_ref = spec.hankel1(1, z)
else:
raise ValueError("ref_src")
z_dev = cl_array.to_device(queue, z)
h0_dev, h1_dev = clmath.hankel_01(z_dev)
rel_err_h0 = np.abs(h0_dev.get() - h0_ref) / np.abs(h0_ref)
rel_err_h1 = np.abs(h1_dev.get() - h1_ref) / np.abs(h1_ref)
max_rel_err_h0 = np.max(rel_err_h0)
max_rel_err_h1 = np.max(rel_err_h1)
print("H0", max_rel_err_h0)
print("H1", max_rel_err_h1)
assert max_rel_err_h0 < 4e-13
assert max_rel_err_h1 < 2e-13
if 0:
import matplotlib.pyplot as pt
pt.loglog(np.abs(z), rel_err_h0)
pt.loglog(np.abs(z), rel_err_h1)
pt.show()
def test_random(ctx_getter):
context = ctx_getter()
queue = cl.CommandQueue(context)
from pyopencl.clrandom import rand as clrand
if has_double_support(context.devices[0]):
dtypes = [np.float32, np.float64]
else:
dtypes = [np.float32]
for dtype in dtypes:
a = clrand(context, queue, (10, 100), dtype=dtype).get()
assert (0 <= a).all()
assert (a < 1).all()
def test_random_float_in_range(ctx_factory, rng_class, ary_size, plot_hist=False):
context = ctx_factory()
queue = cl.CommandQueue(context)
if has_double_support(context.devices[0]):
dtypes = [np.float32, np.float64]
else:
dtypes = [np.float32]
if rng_class is RanluxGenerator:
gen = rng_class(queue, 5120)
else:
gen = rng_class(context)
for dtype in dtypes:
print(dtype)
ran = cl_array.zeros(queue, ary_size, dtype)
gen.fill_uniform(ran)
if plot_hist:
import matplotlib.pyplot as pt
pt.hist(ran.get(), 30)
pt.show()
assert (0 <= ran.get()).all()
assert (ran.get() <= 1).all()
if rng_class is RanluxGenerator:
gen.synchronize(queue)
ran = cl_array.zeros(queue, ary_size, dtype)
gen.fill_uniform(ran, a=4, b=7)
ran_host = ran.get()
for cond in [4 <= ran_host, ran_host <= 7]:
good = cond.all()
if not good:
print(np.where(~cond))
print(ran_host[~cond])
assert good
ran = gen.normal(queue, ary_size, dtype, mu=10, sigma=3)
if plot_hist:
import matplotlib.pyplot as pt
pt.hist(ran.get(), 30)
pt.show()
def test_bitonic_argsort(ctx_factory, size, dtype):
import sys
is_pypy = '__pypy__' in sys.builtin_module_names
if not size and is_pypy:
# https://bitbucket.org/pypy/numpy/issues/53/specifying-strides-on-zero-sized-array
pytest.xfail("pypy doesn't seem to handle as_strided "
"on zero-sized arrays very well")
ctx = cl.create_some_context()
queue = cl.CommandQueue(ctx)
dev = ctx.devices[0]
if (dev.platform.name == "Portable Computing Language"
and sys.platform == "darwin"):
pytest.xfail("Bitonic sort crashes on Apple POCL")
if (dev.platform.name == "Apple" and dev.type & cl.device_type.CPU):
pytest.xfail("Bitonic sort won't work on Apple CPU: no workgroup "
"parallelism")
if (dev.platform.name == "Portable Computing Language"
and dtype == np.float64 and get_pocl_version(dev.platform) <
(1, 0)):
pytest.xfail(
"Double precision bitonic sort doesn't work on POCL < 1.0")
if dtype == np.float64 and not has_double_support(dev):
from pytest import skip
skip("double precision not supported on %s" % dev)
import pyopencl.clrandom as clrandom
from pyopencl.bitonic_sort import BitonicSort
index = cl_array.arange(queue, 0, size, 1, dtype=np.int32)
m = clrandom.rand(queue, (size, ), dtype, luxury=None, a=0, b=239432234)
sorterm = BitonicSort(ctx)
ms, evt = sorterm(m.copy(), idx=index, axis=0)
assert np.array_equal(np.sort(m.get()), ms.get())
# may be False because of identical values in array
# assert np.array_equal(np.argsort(m.get()), index.get())
# Check values by indices
assert np.array_equal(m.get()[np.argsort(m.get())], m.get()[index.get()])
def test_pow_neg1_vs_inv(ctx_factory):
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
device = ctx.devices[0]
if not has_double_support(device):
from py.test import skip
skip("double precision not supported on %s" % device)
a_dev = make_random_array(queue, np.complex128, 20000)
res1 = (a_dev ** (-1)).get()
res2 = (1/a_dev).get()
ref = 1/a_dev.get()
assert la.norm(res1-ref, np.inf) / la.norm(ref) < 1e-13
assert la.norm(res2-ref, np.inf) / la.norm(ref) < 1e-13
def test_bitonic_argsort(ctx_factory, size, dtype):
import sys
is_pypy = '__pypy__' in sys.builtin_module_names
if not size and is_pypy:
# https://bitbucket.org/pypy/numpy/issues/53/specifying-strides-on-zero-sized-array
pytest.xfail("pypy doesn't seem to handle as_strided "
"on zero-sized arrays very well")
ctx = cl.create_some_context()
queue = cl.CommandQueue(ctx)
dev = ctx.devices[0]
if (dev.platform.name == "Portable Computing Language"
and sys.platform == "darwin"):
pytest.xfail("Bitonic sort crashes on Apple POCL")
if (dev.platform.name == "Apple" and dev.type & cl.device_type.CPU):
pytest.xfail("Bitonic sort won't work on Apple CPU: no workgroup "
"parallelism")
if (dev.platform.name == "Portable Computing Language"
and dtype == np.float64
and get_pocl_version(dev.platform) < (1, 0)):
pytest.xfail("Double precision bitonic sort doesn't work on POCL < 1.0")
if dtype == np.float64 and not has_double_support(dev):
from pytest import skip
skip("double precision not supported on %s" % dev)
import pyopencl.clrandom as clrandom
from pyopencl.bitonic_sort import BitonicSort
index = cl_array.arange(queue, 0, size, 1, dtype=np.int32)
m = clrandom.rand(queue, (size,), dtype, luxury=None, a=0, b=239432234)
sorterm = BitonicSort(ctx)
ms, evt = sorterm(m.copy(), idx=index, axis=0)
assert np.array_equal(np.sort(m.get()), ms.get())
# may be False because of identical values in array
# assert np.array_equal(np.argsort(m.get()), index.get())
# Check values by indices
assert np.array_equal(m.get()[np.argsort(m.get())], m.get()[index.get()])
def test_dot(ctx_factory):
from pytest import importorskip
importorskip("mako")
context = ctx_factory()
queue = cl.CommandQueue(context)
dev = context.devices[0]
dtypes = [np.float32, np.complex64]
if has_double_support(dev):
if has_struct_arg_count_bug(dev) == "apple":
dtypes.extend([np.float64])
else:
dtypes.extend([np.float64, np.complex128])
for a_dtype in dtypes:
for b_dtype in dtypes:
print(a_dtype, b_dtype)
a_gpu = general_clrand(queue, (200000,), a_dtype)
a = a_gpu.get()
b_gpu = general_clrand(queue, (200000,), b_dtype)
b = b_gpu.get()
dot_ab = np.dot(a, b)
dot_ab_gpu = cl_array.dot(a_gpu, b_gpu).get()
assert abs(dot_ab_gpu - dot_ab) / abs(dot_ab) < 1e-4
try:
vdot_ab = np.vdot(a, b)
except NotImplementedError:
import sys
is_pypy = '__pypy__' in sys.builtin_module_names
if is_pypy:
print("PYPY: VDOT UNIMPLEMENTED")
continue
else:
raise
vdot_ab_gpu = cl_array.vdot(a_gpu, b_gpu).get()
rel_err = abs(vdot_ab_gpu - vdot_ab) / abs(vdot_ab)
assert rel_err < 1e-4, rel_err
def test_dot(ctx_factory):
from pytest import importorskip
importorskip("mako")
context = ctx_factory()
queue = cl.CommandQueue(context)
dev = context.devices[0]
dtypes = [np.float32, np.complex64]
if has_double_support(dev):
if has_struct_arg_count_bug(dev) == "apple":
dtypes.extend([np.float64])
else:
dtypes.extend([np.float64, np.complex128])
for a_dtype in dtypes:
for b_dtype in dtypes:
print(a_dtype, b_dtype)
a_gpu = general_clrand(queue, (200000,), a_dtype)
a = a_gpu.get()
b_gpu = general_clrand(queue, (200000,), b_dtype)
b = b_gpu.get()
dot_ab = np.dot(a, b)
dot_ab_gpu = cl_array.dot(a_gpu, b_gpu).get()
assert abs(dot_ab_gpu - dot_ab) / abs(dot_ab) < 1e-4
try:
vdot_ab = np.vdot(a, b)
except NotImplementedError:
import sys
is_pypy = "__pypy__" in sys.builtin_module_names
if is_pypy:
print("PYPY: VDOT UNIMPLEMENTED")
continue
else:
raise
vdot_ab_gpu = cl_array.vdot(a_gpu, b_gpu).get()
rel_err = abs(vdot_ab_gpu - vdot_ab) / abs(vdot_ab)
assert rel_err < 1e-4, rel_err
def get_subset_dot_kernel(ctx, dtype_out, dtype_subset, dtype_a=None, dtype_b=None,
conjugate_first=False):
from pyopencl.characterize import has_double_support
map_expr, dtype_out, dtype_b = _get_dot_expr(
dtype_out, dtype_a, dtype_b, conjugate_first,
has_double_support=has_double_support(ctx.devices[0]),
index_expr="lookup_tbl[i]")
# important: lookup_tbl must be first--it controls the length
return ReductionKernel(ctx, dtype_out, neutral="0",
reduce_expr="a+b", map_expr=map_expr,
arguments=(
"const %(tp_lut)s *lookup_tbl, "
"const %(tp_a)s *a, "
"const %(tp_b)s *b" % {
"tp_lut": dtype_to_ctype(dtype_subset),
"tp_a": dtype_to_ctype(dtype_a),
"tp_b": dtype_to_ctype(dtype_b),
}))
def test_minmax(ctx_factory):
context = ctx_factory()
queue = cl.CommandQueue(context)
from pyopencl.clrandom import rand as clrand
if has_double_support(context.devices[0]):
dtypes = [np.float64, np.float32, np.int32]
else:
dtypes = [np.float32, np.int32]
for what in ["min", "max"]:
for dtype in dtypes:
a_gpu = clrand(queue, (200000,), dtype)
a = a_gpu.get()
op_a = getattr(np, what)(a)
op_a_gpu = getattr(cl_array, what)(a_gpu).get()
assert op_a_gpu == op_a, (op_a_gpu, op_a, dtype, what)
def test_clrandom_dtypes(ctx_factory, rng_class, dtype):
cl_ctx = ctx_factory()
if dtype == np.float64 and not has_double_support(cl_ctx.devices[0]):
pytest.skip("double precision not supported on this device")
rng = rng_class(cl_ctx)
size = 10
with cl.CommandQueue(cl_ctx) as queue:
device = queue.device
if device.platform.vendor == "The pocl project" \
and device.type & cl.device_type.GPU \
and rng_class is make_ranlux_generator:
pytest.xfail("ranlux test fails on POCL + Nvidia,"
"at least the K40, as of pocl 1.6, 2021-01-20")
rng.uniform(queue, size, dtype)
if dtype not in (np.int32, np.int64):
rng.normal(queue, size, dtype)
def test_pow_neg1_vs_inv(ctx_factory):
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
device = ctx.devices[0]
if not has_double_support(device):
from pytest import skip
skip("double precision not supported on %s" % device)
if has_struct_arg_count_bug(device) == "apple":
from pytest import xfail
xfail("apple struct arg counting broken")
a_dev = make_random_array(queue, np.complex128, 20000)
res1 = (a_dev**(-1)).get()
res2 = (1 / a_dev).get()
ref = 1 / a_dev.get()
assert la.norm(res1 - ref, np.inf) / la.norm(ref) < 1e-13
assert la.norm(res2 - ref, np.inf) / la.norm(ref) < 1e-13
def test_pow_neg1_vs_inv(ctx_factory):
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
device = ctx.devices[0]
if not has_double_support(device):
from pytest import skip
skip("double precision not supported on %s" % device)
if has_struct_arg_count_bug(device) == "apple":
from pytest import xfail
xfail("apple struct arg counting broken")
a_dev = make_random_array(queue, np.complex128, 20000)
res1 = (a_dev ** (-1)).get()
res2 = (1/a_dev).get()
ref = 1/a_dev.get()
assert la.norm(res1-ref, np.inf) / la.norm(ref) < 1e-13
assert la.norm(res2-ref, np.inf) / la.norm(ref) < 1e-13
def test(ctx_factory):
context = ctx_factory()
queue = cl.CommandQueue(context)
gpu_func = getattr(clmath, name)
cpu_func = getattr(np, numpy_func_names.get(name, name))
dev = context.devices[0]
if has_double_support(dev):
if use_complex and has_struct_arg_count_bug(dev) == "apple":
dtypes = [np.float32, np.float64, np.complex64]
elif use_complex:
dtypes = [np.float32, np.float64, np.complex64, np.complex128]
else:
dtypes = [np.float32, np.float64]
else:
if use_complex:
dtypes = [np.float32, np.complex64]
else:
dtypes = [np.float32]
for s in sizes:
for dtype in dtypes:
dtype = np.dtype(dtype)
args = cl_array.arange(queue, a, b, (b - a) / s, dtype=dtype)
if dtype.kind == "c":
# args = args + dtype.type(1j) * args
args = args + args * dtype.type(1j)
gpu_results = gpu_func(args).get()
cpu_results = cpu_func(args.get())
my_threshold = threshold
if dtype.kind == "c" and isinstance(use_complex, float):
my_threshold = use_complex
max_err = np.max(np.abs(cpu_results - gpu_results))
assert (max_err <= my_threshold).all(), \
(max_err, name, dtype)
def test_subset_minmax(ctx_factory):
from pytest import importorskip
importorskip("mako")
context = ctx_factory()
queue = cl.CommandQueue(context)
from pyopencl.clrandom import rand as clrand
l_a = 200000
gran = 5
l_m = l_a - l_a // gran + 1
if has_double_support(context.devices[0]):
dtypes = [np.float64, np.float32, np.int32]
else:
dtypes = [np.float32, np.int32]
for dtype in dtypes:
a_gpu = clrand(queue, (l_a,), dtype)
a = a_gpu.get()
meaningful_indices_gpu = cl_array.zeros(
queue, 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 = cl_array.to_device(
queue, meaningful_indices)
b = a[meaningful_indices]
min_a = np.min(b)
min_a_gpu = cl_array.subset_min(meaningful_indices_gpu, a_gpu).get()
assert min_a_gpu == min_a
def test_subset_minmax(ctx_factory):
from pytest import importorskip
importorskip("mako")
context = ctx_factory()
queue = cl.CommandQueue(context)
from pyopencl.clrandom import rand as clrand
l_a = 200000
gran = 5
l_m = l_a - l_a // gran + 1
if has_double_support(context.devices[0]):
dtypes = [np.float64, np.float32, np.int32]
else:
dtypes = [np.float32, np.int32]
for dtype in dtypes:
a_gpu = clrand(queue, (l_a,), dtype)
a = a_gpu.get()
meaningful_indices_gpu = cl_array.zeros(
queue, 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 = cl_array.to_device(
queue, meaningful_indices)
b = a[meaningful_indices]
min_a = np.min(b)
min_a_gpu = cl_array.subset_min(meaningful_indices_gpu, a_gpu).get()
assert min_a_gpu == min_a
def test(ctx_getter):
context = ctx_getter()
queue = cl.CommandQueue(context)
gpu_func = getattr(clmath, name)
cpu_func = getattr(np, numpy_func_names.get(name, name))
if has_double_support(context.devices[0]):
dtypes = [np.float32, np.float64]
else:
dtypes = [np.float32]
for s in sizes:
for dtype in dtypes:
args = cl_array.arange(queue, a, b, (b-a)/s,
dtype=np.float32)
gpu_results = gpu_func(args).get()
cpu_results = cpu_func(args.get())
max_err = np.max(np.abs(cpu_results - gpu_results))
assert (max_err <= threshold).all(), \
(max_err, name, dtype)
def get_dot_kernel(ctx, dtype_out, dtype_a=None, dtype_b=None,
conjugate_first=False):
from pyopencl.characterize import has_double_support
map_expr, dtype_out, dtype_b = _get_dot_expr(
dtype_out, dtype_a, dtype_b, conjugate_first,
has_double_support=has_double_support(ctx.devices[0]))
reduce_expr = "a+b"
neutral_expr = "0"
if dtype_out.kind == "c":
from pyopencl.elementwise import complex_dtype_to_name
dtname = complex_dtype_to_name(dtype_out)
reduce_expr = "%s_add(a, b)" % dtname
neutral_expr = "%s_new(0, 0)" % dtname
return ReductionKernel(ctx, dtype_out, neutral=neutral_expr,
reduce_expr=reduce_expr, map_expr=map_expr,
arguments=(
"const %(tp_a)s *a, "
"const %(tp_b)s *b" % {
"tp_a": dtype_to_ctype(dtype_a),
"tp_b": dtype_to_ctype(dtype_b),
}))
def test_astype(ctx_getter):
context = ctx_getter()
queue = cl.CommandQueue(context)
from pyopencl.clrandom import rand as clrand
if not has_double_support(context.devices[0]):
return
a_gpu = clrand(context, queue, (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 = clrand(context, queue, (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
def test_bitonic_sort(ctx_factory, size, dtype):
ctx = cl.create_some_context()
queue = cl.CommandQueue(ctx)
dev = ctx.devices[0]
if (dev.platform.name == "Apple" and dev.type & cl.device_type.CPU):
pytest.xfail("Bitonic sort won't work on Apple CPU: no workgroup "
"parallelism")
if (dev.platform.name == "Portable Computing Language"
and dtype == np.float64
and get_pocl_version(dev.platform) < (1, 0)):
pytest.xfail("Double precision bitonic sort doesn't work on POCL < 1.0")
if dtype == np.float64 and not has_double_support(dev):
from pytest import skip
skip("double precision not supported on %s" % dev)
import pyopencl.clrandom as clrandom
from pyopencl.bitonic_sort import BitonicSort
s = clrandom.rand(queue, (2, size, 3,), dtype, luxury=None, a=0, b=239482333)
sorter = BitonicSort(ctx)
sgs, evt = sorter(s.copy(), axis=1)
assert np.array_equal(np.sort(s.get(), axis=1), sgs.get())
def __init__(self, queue, num_work_items=None,
luxury=None, seed=None, no_warmup=False,
use_legacy_init=False, max_work_items=None):
"""
:param queue: :class:`pyopencl.CommandQueue`, only used for initialization
:param luxury: the "luxury value" of the generator, and should be 0-4,
where 0 is fastest and 4 produces the best numbers. It can also be
>=24, in which case it directly sets the p-value of RANLUXCL.
:param num_work_items: is the number of generators to initialize,
usually corresponding to the number of work-items in the NDRange
RANLUXCL will be used with. May be `None`, in which case a default
value is used.
:param max_work_items: should reflect the maximum number of work-items
that will be used on any parallel instance of RANLUXCL. So for
instance if we are launching 5120 work-items on GPU1 and 10240
work-items on GPU2, GPU1's RANLUXCLTab would be generated by
calling ranluxcl_intialization with numWorkitems = 5120 while
GPU2's RANLUXCLTab would use numWorkitems = 10240. However
maxWorkitems must be at least 10240 for both GPU1 and GPU2, and it
must be set to the same value for both. (may be `None`)
.. versionchanged:: 2013.1
Added default value for `num_work_items`.
"""
if luxury is None:
luxury = 4
if num_work_items is None:
if queue.device.type & cl.device_type.CPU:
num_work_items = 8 * queue.device.max_compute_units
else:
num_work_items = 64 * queue.device.max_compute_units
if seed is None:
from time import time
seed = int(time()*1e6) % 2 << 30
self.context = queue.context
self.luxury = luxury
self.num_work_items = num_work_items
from pyopencl.characterize import has_double_support
self.support_double = has_double_support(queue.device)
self.no_warmup = no_warmup
self.use_legacy_init = use_legacy_init
self.max_work_items = max_work_items
src = """
%(defines)s
#include <pyopencl-ranluxcl.cl>
kernel void init_ranlux(unsigned seeds,
global ranluxcl_state_t *ranluxcltab)
{
if (get_global_id(0) < %(num_work_items)d)
ranluxcl_initialization(seeds, ranluxcltab);
}
""" % {
"defines": self.generate_settings_defines(),
"num_work_items": num_work_items
}
prg = cl.Program(queue.context, src).build()
# {{{ compute work group size
wg_size = None
import sys
import platform
if ("darwin" in sys.platform
and "Apple" in queue.device.platform.vendor
and platform.mac_ver()[0].startswith("10.7")
and queue.device.type & cl.device_type.CPU):
wg_size = (1,)
self.wg_size = wg_size
# }}}
self.state = cl_array.empty(queue, (num_work_items, 112), dtype=np.uint8)
self.state.fill(17)
prg.init_ranlux(queue, (num_work_items,), self.wg_size, np.uint32(seed),
self.state.data)
def test_bessel_j(ctx_factory):
try:
import scipy.special as spec
except ImportError:
from py.test import skip
skip("scipy not present--cannot test Bessel function")
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
if not has_double_support(ctx.devices[0]):
from py.test import skip
skip("no double precision support--cannot test bessel function")
nterms = 30
try:
from hellskitchen._native import jfuns2d
except ImportError:
use_hellskitchen = False
else:
use_hellskitchen = True
if use_hellskitchen:
a = np.logspace(-3, 3, 10 ** 6)
else:
a = np.logspace(-5, 5, 10 ** 6)
if use_hellskitchen:
hellskitchen_result = np.empty((len(a), nterms), dtype=np.complex128)
for i, a_i in enumerate(a):
if i % 10000 == 0:
print "%.1f %%" % (100 * i / len(a))
ier, fjs, _, _ = jfuns2d(nterms, a_i, 1, 0, 10000)
hellskitchen_result[i] = fjs[:nterms]
assert ier == 0
a_dev = cl_array.to_device(queue, a)
for n in range(0, nterms):
cl_bessel = clmath.bessel_jn(n, a_dev).get()
scipy_bessel = spec.jn(n, a)
error_scipy = np.max(np.abs(cl_bessel - scipy_bessel))
assert error_scipy < 1e-10, error_scipy
if use_hellskitchen:
hk_bessel = hellskitchen_result[:, n]
error_hk = np.max(np.abs(cl_bessel - hk_bessel))
assert error_hk < 1e-10, error_hk
error_hk_scipy = np.max(np.abs(scipy_bessel - hk_bessel))
print (n, error_scipy, error_hk, error_hk_scipy)
else:
print (n, error_scipy)
assert not np.isnan(cl_bessel).any()
if 0 and n == 15:
import matplotlib.pyplot as pt
# pt.plot(scipy_bessel)
# pt.plot(cl_bessel)
pt.loglog(a, np.abs(cl_bessel - scipy_bessel), label="vs scipy")
if use_hellskitchen:
pt.loglog(a, np.abs(cl_bessel - hk_bessel), label="vs hellskitchen")
pt.legend()
pt.show()
def get_write_kernel(self, index_dtype):
index_ctype = dtype_to_ctype(index_dtype)
from pyopencl.tools import VectorArg, OtherArg
kernel_list_args = []
kernel_list_arg_values = ""
user_list_args = []
for name, dtype in self.list_names_and_dtypes:
list_name = "plb_%s_list" % name
list_arg = VectorArg(dtype, list_name)
kernel_list_args.append(list_arg)
user_list_args.append(list_arg)
if name in self.count_sharing:
kernel_list_arg_values += "%s, " % list_name
continue
kernel_list_args.append(
VectorArg(index_dtype, "plb_%s_start_index" % name))
index_name = "plb_%s_index" % name
user_list_args.append(
OtherArg("%s *%s" % (index_ctype, index_name), index_name))
kernel_list_arg_values += "%s, &%s, " % (list_name, index_name)
kernel_name = self.name_prefix + "_write"
from pyopencl.characterize import has_double_support
src = _LIST_BUILDER_TEMPLATE.render(
is_count_stage=False,
kernel_name=kernel_name,
double_support=all(
has_double_support(dev) for dev in self.context.devices),
debug=self.debug,
do_not_vectorize=self.do_not_vectorize(),
kernel_list_arg_decl=_get_arg_decl(kernel_list_args),
kernel_list_arg_values=kernel_list_arg_values,
user_list_arg_decl=_get_arg_decl(user_list_args),
user_list_args=_get_arg_list(user_list_args),
user_arg_decl=_get_arg_decl(self.arg_decls),
user_args=_get_arg_list(self.arg_decls),
list_names_and_dtypes=self.list_names_and_dtypes,
count_sharing=self.count_sharing,
name_prefix=self.name_prefix,
generate_template=self.generate_template,
preamble=self.preamble,
index_type=index_ctype,
)
src = str(src)
prg = cl.Program(self.context, src).build(self.options)
knl = getattr(prg, kernel_name)
from pyopencl.tools import get_arg_list_scalar_arg_dtypes
knl.set_scalar_arg_dtypes(
get_arg_list_scalar_arg_dtypes(kernel_list_args + self.arg_decls) +
[index_dtype])
return knl
cl_buffer_datatype_dict = {
np.bool: "bool",
np.uint8: "uchar",
np.uint16: "ushort",
np.uint32: "uint",
np.uint64: "ulong",
np.int8: "char",
np.int16: "short",
np.int32: "int",
np.int64: "long",
np.float32: "float",
np.complex64: "cfloat_t",
}
if characterize.has_double_support(get_device().device):
cl_buffer_datatype_dict[np.float64] = "double"
def abspath(myPath):
""" Get absolute path to resource, works for dev and for PyInstaller """
try:
# PyInstaller creates a temp folder and stores path in _MEIPASS
base_path = sys._MEIPASS
return os.path.join(base_path, os.path.basename(myPath))
except Exception:
base_path = os.path.abspath(os.path.dirname(__file__))
return os.path.join(base_path, myPath)
def test_mix_complex(ctx_factory):
context = ctx_factory()
queue = cl.CommandQueue(context)
size = 10
dtypes = [
(np.float32, np.complex64),
#(np.int32, np.complex64),
]
dev = context.devices[0]
if has_double_support(dev) and has_struct_arg_count_bug(dev) == "apple":
dtypes.extend([
(np.float32, np.float64),
])
elif has_double_support(dev):
dtypes.extend([
(np.float32, np.float64),
(np.float32, np.complex128),
(np.float64, np.complex64),
(np.float64, np.complex128),
])
from operator import add, mul, sub, truediv
for op in [add, sub, mul, truediv, pow]:
for dtype_a0, dtype_b0 in dtypes:
for dtype_a, dtype_b in [
(dtype_a0, dtype_b0),
(dtype_b0, dtype_a0),
]:
for is_scalar_a, is_scalar_b in [
(False, False),
(False, True),
(True, False),
]:
if is_scalar_a:
ary_a = make_random_array(queue, dtype_a, 1).get()[0]
host_ary_a = ary_a
else:
ary_a = make_random_array(queue, dtype_a, size)
host_ary_a = ary_a.get()
if is_scalar_b:
ary_b = make_random_array(queue, dtype_b, 1).get()[0]
host_ary_b = ary_b
else:
ary_b = make_random_array(queue, dtype_b, size)
host_ary_b = ary_b.get()
print(op, dtype_a, dtype_b, is_scalar_a, is_scalar_b)
dev_result = op(ary_a, ary_b).get()
host_result = op(host_ary_a, host_ary_b)
if host_result.dtype != dev_result.dtype:
# This appears to be a numpy bug, where we get
# served a Python complex that is really a
# smaller numpy complex.
print("HOST_DTYPE: %s DEV_DTYPE: %s" % (
host_result.dtype, dev_result.dtype))
dev_result = dev_result.astype(host_result.dtype)
err = la.norm(host_result-dev_result)/la.norm(host_result)
print(err)
correct = err < 1e-4
if not correct:
print(host_result)
print(dev_result)
print(host_result - dev_result)
assert correct