Newer
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data
)
knl = lp.split_iname(knl, 'i', 4, inner_tag='vec')
knl = lp.split_array_axis(knl, names, 0, 4)
knl = lp.tag_array_axes(knl, names, 'N0,vec')
if v is not None:
knl = lp.set_options(knl, write_wrapper=True)
queue = cl.CommandQueue(ctx)
if check is not None:
assert check(knl)
elif exception is not None:
with pytest.raises(exception):
knl(queue, **kwargs)
else:
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if not isinstance(answer, tuple):
answer = (answer,)
result = knl(queue, **kwargs)[1]
for r, a in zip(result, answer):
assert np.array_equal(r.flatten('C'), a)
ans = np.zeros(12, dtype=np.int32)
ans[7:] = 1
from loopy.diagnostic import LoopyError
# 1) test a conditional on a vector iname -- currently unimplemented as it
# would require creating a 'shadow' vector iname temporary
create_and_test('a[i] = 1', 'i > 6', ans, exception=LoopyError)
create_and_test('a[i] = 1', 'b[i] > 6', ans, b=np.arange(
12, dtype=np.int32).reshape((3, 4)))
# 3) condition on a vector temporary -- this is currently broken for the
# same reason as #1
create_and_test('a[i] = 1', 'c', ans, extra_insns='<> c = i < 6',
exception=LoopyError)
# 4) condition on an assigned vector array, this should work as assignment to a
# vector can be safely unrolled
create_and_test('a[i] = 1', 'b[i] > 6', ans, b=np.zeros((3, 4), dtype=np.int32),
extra_insns='b[i] = i')
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# 5) a block of simple assignments, this should be seemlessly translated to
# multiple vector if statements
c_ans = np.ones(12, dtype=np.int32)
c_ans[7:] = 0
create_and_test('a[i] = 1\nc[i] = 0', 'b[i] > 6', (ans, c_ans), b=np.arange(
12, dtype=np.int32).reshape((3, 4)), c=np.ones((3, 4), dtype=np.int32))
# 6) test a negated conditional
ans_negated = np.invert(ans) + 2
create_and_test('a[i] = 1', 'not (b[i] > 6)', ans_negated, b=np.arange(
12, dtype=np.int32).reshape((3, 4)))
# 7) test conditional on differing dtype
ans_negated = np.invert(ans) + 2
create_and_test('a[i] = 1', 'not (b[i] > 6)', ans_negated, b=np.arange(
12, dtype=np.int64).reshape((3, 4)))
# 8) test conditional on differing dtype (float->int) and (int->float)
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ans_negated = np.invert(ans) + 2
create_and_test('a[i] = 1', 'not (b[i] > 6)', ans_negated, b=np.arange(
12, dtype=np.float64).reshape((3, 4)))
create_and_test('a[i] = 1', 'not (b[i] > 6)', ans_negated, b=np.arange(
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12, dtype=np.int64).reshape((3, 4)), a=np.zeros((3, 4), dtype=np.float32))
# 9) test conditional on valuearg, the "test" here is that we can actually
# generate the code
create_and_test('a[i] = 1', 'v', np.ones_like(ans), v=1)
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@pytest.mark.parametrize(('lhs_dtype', 'rhs_dtype'), [
(np.int32, np.int64),
(np.float32, np.float64)])
def test_explicit_vector_dtype_conversion(ctx_factory, lhs_dtype, rhs_dtype):
ctx = ctx_factory()
# test that dtype conversion happens correctly between differing vector-dtypes
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def __make_kernel(insn, has_conversion=True, uses_temp=True):
vw = 4
a_lp = lp.GlobalArg('a', shape=(12,), dtype=rhs_dtype)
temp_lp = lp.TemporaryVariable('temp', dtype=lhs_dtype)
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knl = lp.make_kernel(['{[i]: 0 <= i < 12}'],
"""
for i
{insn}
end
""".format(insn=insn),
[a_lp, temp_lp],
target=lp.PyOpenCLTarget(ctx.devices[0]),
silenced_warnings=['temp_to_write(temp)'] if not uses_temp else [])
knl = lp.split_iname(knl, 'i', vw, inner_tag='vec')
knl = lp.split_array_axis(knl, 'a', 0, 4)
knl = lp.tag_array_axes(knl, 'a', 'N0,vec')
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queue = cl.CommandQueue(ctx)
# check that the kernel compiles correctly
knl(queue, a=np.zeros((12,), dtype=rhs_dtype).reshape((3, 4)))
# check that we have or don't have a conversion
assert ('convert_' in lp.generate_code_v2(knl).device_code()) == \
has_conversion
# test simple dtype conversion
__make_kernel("temp = a[i]")
# test literal assignment
__make_kernel("a[i] = 1", False, False)
# test that a non-vector temporary doesn't trigger conversion
#
# this should generate the code (e.g.,):
# __kernel void __attribute__ ((reqd_work_group_size(1, 1, 1)))
# loopy_kernel(__global long4 *__restrict__ a)
# {
# int temp;
# for (int i_outer = 0; i_outer <= 2; ++i_outer)
# {
# temp = 1;
# a[i_outer] = temp;
# }
# }
#
# that is, temp should _not_ be assigned to "a" w/ convert_long4
__make_kernel("""
temp = 1
a[i] = temp
""", has_conversion=False)
# test that the inverse _does_ result in a convers
__make_kernel("""
temp = a[i] {id=1, dep=*}
a[i] = temp {id=2, dep=1}
""")
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def test_vectorizability():
# check new vectorizability conditions
from loopy.kernel.array import VectorArrayDimTag
from loopy.kernel.data import VectorizeTag, filter_iname_tags_by_type
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def create_and_test(insn, exception=None, a=None, b=None):
a = np.zeros((3, 4), dtype=np.int32) if a is None else a
data = [lp.GlobalArg('a', shape=(12,), dtype=a.dtype)]
kwargs = dict(a=a)
if b is not None:
data += [lp.GlobalArg('b', shape=(12,), dtype=b.dtype)]
kwargs['b'] = b
names = [d.name for d in data]
knl = lp.make_kernel(['{[i]: 0 <= i < 12}'],
"""
for i
%(insn)s
end
""" % dict(insn=insn),
data
)
knl = lp.split_iname(knl, 'i', 4, inner_tag='vec')
knl = lp.split_array_axis(knl, names, 0, 4)
knl = lp.tag_array_axes(knl, names, 'N0,vec')
knl = lp.preprocess_kernel(knl)
lp.generate_code_v2(knl).device_code()
assert knl.instructions[0].within_inames & set(['i_inner'])
assert isinstance(knl.args[0].dim_tags[-1], VectorArrayDimTag)
assert isinstance(knl.args[0].dim_tags[-1], VectorArrayDimTag)
assert filter_iname_tags_by_type(knl.iname_to_tags['i_inner'], VectorizeTag)
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def run(op_list=[], unary_operators=[], func_list=[], unary_funcs=[],
rvals=['1', 'a[i]']):
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for op in op_list:
template = 'a[i] = a[i] %(op)s %(rval)s' \
if op not in unary_operators else 'a[i] = %(op)s a[i]'
for rval in rvals:
create_and_test(template % dict(op=op, rval=rval))
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for func in func_list:
template = 'a[i] = %(func)s(a[i], %(rval)s)' \
if func not in unary_funcs else 'a[i] = %(func)s(a[i])'
for rval in rvals:
create_and_test(template % dict(func=func, rval=rval))
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# 1) comparisons
run(['>', '>=', '<', '<=', '==', '!='])
# 2) logical operators
run(['and', 'or', 'not'], ['not'])
# 3) bitwise operators
# bitwise xor '^' not not implemented in codegen
run(['~', '|', '&'], ['~'])
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# 4) functions -- a random selection of the enabled math functions in opencl
run(func_list=['acos', 'exp10', 'atan2', 'round'],
unary_funcs=['round', 'acos', 'exp10'])
# 5) remainders and floor division (use 4 instead of 1 to avoid pymbolic
# optimizing out the a[i] % 1)
run(['%', '//'], rvals=['a[i]', '4'])
def test_check_for_variable_access_ordering():
knl = lp.make_kernel(
"{[i]: 0<=i<n}",
"""
a[i] = 12
a[i+1] = 13
""")
knl = lp.preprocess_kernel(knl)
from loopy.diagnostic import VariableAccessNotOrdered
with pytest.raises(VariableAccessNotOrdered):
lp.get_one_scheduled_kernel(knl)
def test_check_for_variable_access_ordering_with_aliasing():
knl = lp.make_kernel(
"{[i]: 0<=i<n}",
"""
a[i] = 12
b[i+1] = 13
""",
[
lp.TemporaryVariable("a", shape="n+1", base_storage="tmp"),
lp.TemporaryVariable("b", shape="n+1", base_storage="tmp"),
])
knl = lp.preprocess_kernel(knl)
from loopy.diagnostic import VariableAccessNotOrdered
with pytest.raises(VariableAccessNotOrdered):
lp.get_one_scheduled_kernel(knl)
@pytest.mark.parametrize(("second_index", "expect_barrier"),
[
("2*i", True),
("2*i+1", False),
])
def test_no_barriers_for_nonoverlapping_access(second_index, expect_barrier):
knl = lp.make_kernel(
"{[i]: 0<=i<128}",
"""
a[2*i] = 12 {id=first}
a[%s] = 13 {id=second,dep=first}
""" % second_index,
[
lp.TemporaryVariable("a", lp.auto, shape=(256,),
scope=lp.AddressSpace.LOCAL),
])
knl = lp.tag_inames(knl, "i:l.0")
knl = lp.preprocess_kernel(knl)
knl = lp.get_one_scheduled_kernel(knl)
assert barrier_between(knl, "first", "second") == expect_barrier
def test_half_complex_conditional(ctx_factory):
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
knl = lp.make_kernel(
"{[i]: 0 <= i < 10}",
"""
tmp[i] = if(i < 5, 0, 0j)
""")
knl(queue)
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def test_dep_cycle_printing_and_error():
# https://gitlab.tiker.net/inducer/loopy/issues/140
# This kernel has two dep cycles.
knl = lp.make_kernel('{[i,j,k]: 0 <= i,j,k < 12}',
"""
for j
for i
<> nu = i - 4
if nu > 0
<> P_val = a[i, j] {id=pset0}
else
P_val = 0.1 * a[i, j] {id=pset1}
end
<> B_sum = 0
for k
B_sum = B_sum + k * P_val {id=bset, dep=pset*}
end
# here, we are testing that Kc is properly promoted to a vector dtype
<> Kc = P_val * B_sum {id=kset, dep=bset}
a[i, j] = Kc {dep=kset}
end
end
""",
[lp.GlobalArg('a', shape=(12, 12), dtype=np.int32)])
knl = lp.split_iname(knl, 'j', 4, inner_tag='vec')
knl = lp.split_array_axis(knl, 'a', 1, 4)
knl = lp.tag_array_axes(knl, 'a', 'N1,N0,vec')
knl = lp.preprocess_kernel(knl)
from loopy.diagnostic import DependencyCycleFound
with pytest.raises(DependencyCycleFound):
print(lp.generate_code(knl)[0])
if __name__ == "__main__":
if len(sys.argv) > 1:
exec(sys.argv[1])
else:
main([__file__])