Newer
Older
lp.GlobalArg("out", dtype, shape=lp.auto, for_atomic=True),
lp.GlobalArg("a", dtype, shape=lp.auto),
"..."
],
assumptions="n>0")
ref_knl = knl
knl = lp.split_iname(knl, "i", 512)
knl = lp.split_iname(knl, "i_inner", 128, outer_tag="unr", inner_tag="g.0")
lp.auto_test_vs_ref(ref_knl, ctx, knl, parameters=dict(n=10000))
def test_atomic_load(ctx_factory):
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
from loopy.kernel.data import temp_var_scope as scopes
n = 100
if (
np.dtype(dtype).itemsize == 8
and "cl_khr_int64_base_atomics" not in ctx.devices[0].extensions):
pytest.skip("64-bit atomics not supported on device")
import pyopencl.version # noqa
if (
cl.version.VERSION < (2015, 2)
and dtype == np.int64):
pytest.skip("int64 RNG not supported in PyOpenCL < 2015.2")
knl = lp.make_kernel(
for j
<> upper = 0
<> lower = 0
temp[0] = 0 {id=init, atomic}
for i
upper = upper + i * a[i] {id=sum0}
lower = lower - b[i] {id=sum1}
end
Nick Curtis
committed
... lbarrier {id=lb1, dep=sum1:init}
temp[0] = temp[0] + lower {id=temp_sum, dep=sum*:lb1:init, atomic,\
nosync=init}
out[j] = upper / temp[0] {id=final, dep=sum*:temp_sum:lb2, atomic,\
nosync=init:temp_sum}
""",
[
lp.GlobalArg("out", dtype, shape=lp.auto, for_atomic=True),
lp.GlobalArg("a", dtype, shape=lp.auto),
lp.GlobalArg("b", dtype, shape=lp.auto),
lp.TemporaryVariable('temp', dtype, for_atomic=True,
scope=scopes.LOCAL, shape=(vec_width,)),
],
silenced_warnings=["write_race(init)", "write_race(temp_sum)"])
knl = lp.split_iname(knl, "j", vec_width, inner_tag="l.0")
_, out = knl(queue, a=np.arange(n, dtype=dtype), b=np.arange(n, dtype=dtype))
assert np.allclose(out, np.full_like(out, (-(2 * n - 1) / float(3 * vec_width))))
def test_atomic_init():
dtype = np.float32
vec_width = 4
knl = lp.make_kernel(
"{ [i,j]: 0<=i<100 }",
"""
out[i%4] = 0 {id=init, atomic=init}
""",
[
lp.GlobalArg("out", dtype, shape=lp.auto, for_atomic=True),
"..."
],
silenced_warnings=["write_race(init)"])
knl = lp.split_iname(knl, 'i', vec_width, inner_tag='l.0')
print(lp.generate_code_v2(knl).device_code())
def test_within_inames_and_reduction():
# See https://github.com/inducer/loopy/issues/24
# This is (purposefully) somewhat un-idiomatic, to replicate the conditions
# under which the above bug was found. If assignees were phi[i], then the
# iname propagation heuristic would not assume that dependent instructions
# need to run inside of 'i', and hence the forced_iname_* bits below would not
# be needed.
i1 = lp.CInstruction("i",
"doSomethingToGetPhi();",
from pymbolic.primitives import Subscript, Variable
i2 = lp.Assignment("a",
lp.Reduction("sum", "j", Subscript(Variable("phi"), Variable("j"))),
within_inames=frozenset(),
within_inames_is_final=True)
k = lp.make_kernel("{[i,j] : 0<=i,j<n}",
[i1, i2],
[
lp.GlobalArg("a", dtype=np.float32, shape=()),
lp.ValueArg("n", dtype=np.int32),
lp.TemporaryVariable("phi", dtype=np.float32, shape=("n",)),
],
target=lp.CTarget(),
)
k = lp.preprocess_kernel(k)
assert 'i' not in k.insn_inames("insn_0_j_update")
print(k.stringify(with_dependencies=True))
def test_literal_local_barrier(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
"{ [i]: 0<=i<n }",
"""
for i
... lbarrier
end
""", seq_dependencies=True)
knl = lp.fix_parameters(knl, n=128)
ref_knl = knl
lp.auto_test_vs_ref(ref_knl, ctx, knl, parameters=dict(n=5))
def test_kernel_splitting(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
"{ [i]: 0<=i<n }",
"""
for i
c[i] = a[i + 1]
... gbarrier
out[i] = c[i]
end
""", seq_dependencies=True)
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knl = lp.add_and_infer_dtypes(knl,
{"a": np.float32, "c": np.float32, "out": np.float32, "n": np.int32})
ref_knl = knl
knl = lp.split_iname(knl, "i", 128, outer_tag="g.0", inner_tag="l.0")
# schedule
from loopy.preprocess import preprocess_kernel
knl = preprocess_kernel(knl)
from loopy.schedule import get_one_scheduled_kernel
knl = get_one_scheduled_kernel(knl)
# map schedule onto host or device
print(knl)
cgr = lp.generate_code_v2(knl)
assert len(cgr.device_programs) == 2
print(cgr.device_code())
print(cgr.host_code())
lp.auto_test_vs_ref(ref_knl, ctx, knl, parameters=dict(n=5))
def test_kernel_splitting_with_loop(ctx_factory):
knl = lp.make_kernel(
"{ [i,k]: 0<=i<n and 0<=k<3 }",
"""
for i, k
... gbarrier
c[k,i] = a[k, i + 1]
... gbarrier
out[k,i] = c[k,i]
end
""", seq_dependencies=True)
knl = lp.add_and_infer_dtypes(knl,
{"a": np.float32, "c": np.float32, "out": np.float32, "n": np.int32})
knl = lp.split_iname(knl, "i", 128, outer_tag="g.0", inner_tag="l.0")
# schedule
from loopy.preprocess import preprocess_kernel
knl = preprocess_kernel(knl)
from loopy.schedule import get_one_scheduled_kernel
knl = get_one_scheduled_kernel(knl)
# map schedule onto host or device
print(knl)
cgr = lp.generate_code_v2(knl)
assert len(cgr.device_programs) == 2
print(cgr.device_code())
print(cgr.host_code())
lp.auto_test_vs_ref(ref_knl, ctx, knl, parameters=dict(n=5))
def save_and_reload_temporaries_test(queue, knl, out_expect, debug=False):
from loopy.preprocess import preprocess_kernel
from loopy.schedule import get_one_scheduled_kernel
knl = preprocess_kernel(knl)
knl = get_one_scheduled_kernel(knl)
from loopy.transform.save import save_and_reload_temporaries
knl = save_and_reload_temporaries(knl)
knl = get_one_scheduled_kernel(knl)
if debug:
print(knl)
cgr = lp.generate_code_v2(knl)
print(cgr.device_code())
print(cgr.host_code())
1/0
_, (out,) = knl(queue, out_host=True)
assert (out == out_expect).all(), (out, out_expect)
@pytest.mark.parametrize("hw_loop", [True, False])
def test_save_of_private_scalar(ctx_factory, hw_loop, debug=False):
"{ [i]: 0<=i<8 }",
"""
for i
<>t = i
... gbarrier
out[i] = t
end
""", seq_dependencies=True)
if hw_loop:
knl = lp.tag_inames(knl, dict(i="g.0"))
save_and_reload_temporaries_test(queue, knl, np.arange(8), debug)
"{ [i]: 0<=i<8 }",
"""
for i
<>t[i] = i
... gbarrier
out[i] = t[i]
end
""", seq_dependencies=True)
knl = lp.set_temporary_scope(knl, "t", "private")
save_and_reload_temporaries_test(queue, knl, np.arange(8), debug)
def test_save_of_private_array_in_hw_loop(ctx_factory, debug=False):
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
"{ [i,j,k]: 0<=i,j,k<8 }",
"""
for i
for j
<>t[j] = j
... gbarrier
for k
out[i,k] = t[k]
knl = lp.tag_inames(knl, dict(i="g.0"))
knl = lp.set_temporary_scope(knl, "t", "private")
save_and_reload_temporaries_test(
queue, knl, np.vstack((8 * (np.arange(8),))), debug)
def test_save_of_private_multidim_array(ctx_factory, debug=False):
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
knl = lp.make_kernel(
"{ [i,j,k,l,m]: 0<=i,j,k,l,m<8 }",
"""
for i
for j, k
<>t[j,k] = k
end
... gbarrier
for l, m
out[i,l,m] = t[l,m]
end
end
""", seq_dependencies=True)
knl = lp.set_temporary_scope(knl, "t", "private")
result = np.array([np.vstack((8 * (np.arange(8),))) for i in range(8)])
save_and_reload_temporaries_test(queue, knl, result, debug)
def test_save_of_private_multidim_array_in_hw_loop(ctx_factory, debug=False):
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
knl = lp.make_kernel(
"{ [i,j,k,l,m]: 0<=i,j,k,l,m<8 }",
"""
for i
for j, k
<>t[j,k] = k
end
... gbarrier
for l, m
out[i,l,m] = t[l,m]
end
end
""", seq_dependencies=True)
knl = lp.set_temporary_scope(knl, "t", "private")
knl = lp.tag_inames(knl, dict(i="g.0"))
result = np.array([np.vstack((8 * (np.arange(8),))) for i in range(8)])
save_and_reload_temporaries_test(queue, knl, result, debug)
@pytest.mark.parametrize("hw_loop", [True, False])
def test_save_of_multiple_private_temporaries(ctx_factory, hw_loop, debug=False):
for i
for k
<> t_arr[k] = k
end
<> t_scalar = 1
for j
... gbarrier
out[j] = t_scalar
... gbarrier
t_scalar = 10
<> flag = i == 9
out[i] = t_arr[i] {if=flag}
end
""", seq_dependencies=True)
knl = lp.set_temporary_scope(knl, "t_arr", "private")
if hw_loop:
knl = lp.tag_inames(knl, dict(i="g.0"))
result = np.array([1, 10, 10, 10, 10, 10, 10, 10, 10, 9])
save_and_reload_temporaries_test(queue, knl, result, debug)
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
knl = lp.make_kernel(
"{ [i,j]: 0<=i,j<8 }",
"""
for i, j
<>t[2*j] = j
t[2*j+1] = j
... gbarrier
out[i] = t[2*i]
end
""", seq_dependencies=True)
knl = lp.set_temporary_scope(knl, "t", "local")
knl = lp.tag_inames(knl, dict(i="g.0", j="l.0"))
save_and_reload_temporaries_test(queue, knl, np.arange(8), debug)
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def test_save_of_local_array_with_explicit_local_barrier(ctx_factory, debug=False):
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
knl = lp.make_kernel(
"{ [i,j]: 0<=i,j<8 }",
"""
for i, j
<>t[2*j] = j
... lbarrier
t[2*j+1] = t[2*j]
... gbarrier
out[i] = t[2*i]
end
""", seq_dependencies=True)
knl = lp.set_temporary_scope(knl, "t", "local")
knl = lp.tag_inames(knl, dict(i="g.0", j="l.0"))
save_and_reload_temporaries_test(queue, knl, np.arange(8), debug)
def test_save_local_multidim_array(ctx_factory, debug=False):
"{ [i,j,k]: 0<=i<2 and 0<=k<3 and 0<=j<2}",
end
""", seq_dependencies=True)
knl = lp.set_temporary_scope(knl, "t_local", "local")
knl = lp.tag_inames(knl, dict(j="l.0", i="g.0"))
save_and_reload_temporaries_test(queue, knl, 1, debug)
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def test_save_with_base_storage(ctx_factory, debug=False):
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
knl = lp.make_kernel(
"{[i]: 0 <= i < 10}",
"""
<>a[i] = 0
<>b[i] = i
... gbarrier
out[i] = a[i]
""",
"...",
seq_dependencies=True)
knl = lp.tag_inames(knl, dict(i="l.0"))
knl = lp.set_temporary_scope(knl, "a", "local")
knl = lp.set_temporary_scope(knl, "b", "local")
knl = lp.alias_temporaries(knl, ["a", "b"],
synchronize_for_exclusive_use=False)
save_and_reload_temporaries_test(queue, knl, np.arange(10), debug)
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def test_save_ambiguous_storage_requirements():
knl = lp.make_kernel(
"{[i,j]: 0 <= i < 10 and 0 <= j < 10}",
"""
<>a[j] = j
... gbarrier
out[i,j] = a[j]
""",
seq_dependencies=True)
knl = lp.tag_inames(knl, dict(i="g.0", j="l.0"))
knl = lp.duplicate_inames(knl, "j", within="writes:out", tags={"j": "l.0"})
knl = lp.set_temporary_scope(knl, "a", "local")
knl = lp.preprocess_kernel(knl)
knl = lp.get_one_scheduled_kernel(knl)
from loopy.diagnostic import LoopyError
with pytest.raises(LoopyError):
lp.save_and_reload_temporaries(knl)
def test_save_across_inames_with_same_tag(ctx_factory, debug=False):
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
knl = lp.make_kernel(
"{[i]: 0 <= i < 10}",
"""
<>a[i] = i
... gbarrier
out[i] = a[i]
""",
"...",
seq_dependencies=True)
knl = lp.tag_inames(knl, dict(i="l.0"))
knl = lp.duplicate_inames(knl, "i", within="reads:a", tags={"i": "l.0"})
save_and_reload_temporaries_test(queue, knl, np.arange(10), debug)
def test_missing_temporary_definition_detection():
knl = lp.make_kernel(
"{ [i]: 0<=i<10 }",
"""
for i
<> t = 1
... gbarrier
out[i] = t
end
""", seq_dependencies=True)
from loopy.diagnostic import MissingDefinitionError
with pytest.raises(MissingDefinitionError):
lp.generate_code_v2(knl)
def test_missing_definition_check_respects_aliases():
# Based on https://github.com/inducer/loopy/issues/69
knl = lp.make_kernel("{ [i] : 0<=i<n }",
["a[i] = 0",
"c[i] = b[i]"],
temporary_variables={
"a": lp.TemporaryVariable("a",
dtype=np.float64, shape=("n",), base_storage="base"),
"b": lp.TemporaryVariable("b",
dtype=np.float64, shape=("n",), base_storage="base")
},
target=lp.CTarget(),
silenced_warnings=frozenset(["read_no_write(b)"]))
lp.generate_code_v2(knl)
def test_global_temporary(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
"{ [i]: 0<=i<n}",
"""
for i
<> c[i] = a[i + 1]
... gbarrier
out[i] = c[i]
end
""", seq_dependencies=True)
knl = lp.add_and_infer_dtypes(knl,
{"a": np.float32, "c": np.float32, "out": np.float32, "n": np.int32})
knl = lp.set_temporary_scope(knl, "c", "global")
ref_knl = knl
knl = lp.split_iname(knl, "i", 128, outer_tag="g.0", inner_tag="l.0")
cgr = lp.generate_code_v2(knl)
assert len(cgr.device_programs) == 2
#print(cgr.device_code())
#print(cgr.host_code())
lp.auto_test_vs_ref(ref_knl, ctx, knl, parameters=dict(n=5))
def test_assign_to_linear_subscript(ctx_factory):
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
knl1 = lp.make_kernel(
"{ [i]: 0<=i<n}",
"a[i,i] = 1")
knl2 = lp.make_kernel(
"{ [i]: 0<=i<n}",
"a[[i*n + i]] = 1",
[lp.GlobalArg("a", shape="n,n"), "..."])
a1 = cl.array.zeros(queue, (10, 10), np.float32)
knl1(queue, a=a1)
a2 = cl.array.zeros(queue, (10, 10), np.float32)
knl2(queue, a=a2)
assert np.array_equal(a1.get(), a2.get())
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def test_finite_difference_expr_subst(ctx_factory):
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
grid = np.linspace(0, 2*np.pi, 2048, endpoint=False)
h = grid[1] - grid[0]
u = cl.clmath.sin(cl.array.to_device(queue, grid))
fin_diff_knl = lp.make_kernel(
"{[i]: 1<=i<=n}",
"out[i] = -(f[i+1] - f[i-1])/h",
[lp.GlobalArg("out", shape="n+2"), "..."])
flux_knl = lp.make_kernel(
"{[j]: 1<=j<=n}",
"f[j] = u[j]**2/2",
[
lp.GlobalArg("f", shape="n+2"),
lp.GlobalArg("u", shape="n+2"),
])
fused_knl = lp.fuse_kernels([fin_diff_knl, flux_knl],
data_flow=[
("f", 1, 0)
])
fused_knl = lp.set_options(fused_knl, write_cl=True)
evt, _ = fused_knl(queue, u=u, h=np.float32(1e-1))
fused_knl = lp.assignment_to_subst(fused_knl, "f")
fused_knl = lp.set_options(fused_knl, write_cl=True)
# This is the real test here: The automatically generated
# shape expressions are '2+n' and the ones above are 'n+2'.
# Is loopy smart enough to understand that these are equal?
evt, _ = fused_knl(queue, u=u, h=np.float32(1e-1))
fused0_knl = lp.affine_map_inames(fused_knl, "i", "inew", "inew+1=i")
gpu_knl = lp.split_iname(
fused0_knl, "inew", 128, outer_tag="g.0", inner_tag="l.0")
precomp_knl = lp.precompute(
gpu_knl, "f_subst", "inew_inner", fetch_bounding_box=True)
precomp_knl = lp.tag_inames(precomp_knl, {"j_0_outer": "unr"})
precomp_knl = lp.set_options(precomp_knl, return_dict=True)
evt, _ = precomp_knl(queue, u=u, h=h)
# {{{ call without returned values
def test_call_with_no_returned_value(ctx_factory):
import pymbolic.primitives as p
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
knl = lp.make_kernel(
"{:}",
[lp.CallInstruction((), p.Call(p.Variable("f"), ()))]
from library_for_test import no_ret_f_mangler, no_ret_f_preamble_gen
knl = lp.register_function_manglers(knl, [no_ret_f_mangler])
knl = lp.register_preamble_generators(knl, [no_ret_f_preamble_gen])
evt, _ = knl(queue)
knl = lp.make_kernel(
"{:}",
"f() {id=init}"
)
knl = lp.register_function_manglers(knl, [no_ret_f_mangler])
print(lp.generate_code_v2(knl).device_code())
Dominic Kempf
committed
def test_unschedulable_kernel_detection():
knl = lp.make_kernel(["{[i,j]:0<=i,j<n}"],
"""
mat1[i,j] = mat1[i,j] + 1 {inames=i:j, id=i1}
mat2[j] = mat2[j] + 1 {inames=j, id=i2}
mat3[i] = mat3[i] + 1 {inames=i, id=i3}
""")
knl = lp.preprocess_kernel(knl)
# Check that loopy can detect the unschedulability of the kernel
assert not lp.has_schedulable_iname_nesting(knl)
assert len(list(lp.get_iname_duplication_options(knl))) == 4
Dominic Kempf
committed
for inames, insns in lp.get_iname_duplication_options(knl):
fixed_knl = lp.duplicate_inames(knl, inames, insns)
assert lp.has_schedulable_iname_nesting(fixed_knl)
Dominic Kempf
committed
knl = lp.make_kernel(["{[i,j,k,l,m]:0<=i,j,k,l,m<n}"],
"""
mat1[l,m,i,j,k] = mat1[l,m,i,j,k] + 1 {inames=i:j:k:l:m}
mat2[l,m,j,k] = mat2[l,m,j,k] + 1 {inames=j:k:l:m}
mat3[l,m,k] = mat3[l,m,k] + 11 {inames=k:l:m}
mat4[l,m,i] = mat4[l,m,i] + 1 {inames=i:l:m}
""")
assert not lp.has_schedulable_iname_nesting(knl)
assert len(list(lp.get_iname_duplication_options(knl))) == 10
Andreas Klöckner
committed
def test_regression_no_ret_call_removal(ctx_factory):
# https://github.com/inducer/loopy/issues/32
knl = lp.make_kernel(
"{[i] : 0<=i<n}",
"f(sum(i, x[i]))")
knl = lp.add_and_infer_dtypes(knl, {"x": np.float32})
knl = lp.preprocess_kernel(knl)
assert len(knl.instructions) == 3
def test_regression_persistent_hash():
knl1 = lp.make_kernel(
"{[i] : 0<=i<n}",
"cse_exprvar = d[2]*d[2]")
knl2 = lp.make_kernel(
"{[i] : 0<=i<n}",
"cse_exprvar = d[0]*d[0]")
from loopy.tools import LoopyKeyBuilder
lkb = LoopyKeyBuilder()
assert lkb(knl1.instructions[0]) != lkb(knl2.instructions[0])
assert lkb(knl1) != lkb(knl2)
def test_sequential_dependencies(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
"{[i]: 0<=i<n}",
"""
for i
<> aa = 5jf
<> bb = 5j
a[i] = imag(aa)
b[i] = imag(bb)
c[i] = 5f
end
""", seq_dependencies=True)
print(knl.stringify(with_dependencies=True))
lp.auto_test_vs_ref(knl, ctx, knl, parameters=dict(n=5))
def test_nop(ctx_factory):
knl = lp.make_kernel(
"{[i,itrip]: 0<=i<n and 0<=itrip<ntrips}",
"""
for itrip,i
... nop {dep=wr_z:wr_v,id=yoink}
knl = lp.fix_parameters(knl, n=15)
knl = lp.add_and_infer_dtypes(knl, {"z": np.float64})
lp.auto_test_vs_ref(knl, ctx, knl, parameters=dict(ntrips=5))
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def test_global_barrier(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
"{[i,itrip]: 0<=i<n and 0<=itrip<ntrips}",
"""
for i
for itrip
... gbarrier {id=top}
<> z[i] = z[i+1] + z[i] {id=wr_z,dep=top}
<> v[i] = 11 {id=wr_v,dep=top}
... gbarrier {dep=wr_z:wr_v,id=yoink}
z[i] = z[i] - z[i+1] + v[i] {id=iupd}
end
... gbarrier {dep=iupd,id=postloop}
z[i] = z[i] - z[i+1] + v[i] {dep=postloop}
end
""")
knl = lp.fix_parameters(knl, ntrips=3)
knl = lp.add_and_infer_dtypes(knl, {"z": np.float64})
ref_knl = knl
ref_knl = lp.set_temporary_scope(ref_knl, "z", "global")
ref_knl = lp.set_temporary_scope(ref_knl, "v", "global")
knl = lp.split_iname(knl, "i", 256, outer_tag="g.0", inner_tag="l.0")
print(knl)
knl = lp.preprocess_kernel(knl)
assert knl.temporary_variables["z"].scope == lp.temp_var_scope.GLOBAL
assert knl.temporary_variables["v"].scope == lp.temp_var_scope.GLOBAL
print(knl)
lp.auto_test_vs_ref(ref_knl, ctx, knl, parameters=dict(ntrips=5, n=10))
def test_missing_global_barrier():
knl = lp.make_kernel(
"{[i,itrip]: 0<=i<n and 0<=itrip<ntrips}",
"""
for i
for itrip
... gbarrier {id=yoink}
<> z[i] = z[i] - z[i+1] {id=iupd,dep=yoink}
end
# This is where the barrier should be
z[i] = z[i] - z[i+1] + v[i] {dep=iupd}
end
""")
knl = lp.set_temporary_scope(knl, "z", "global")
knl = lp.split_iname(knl, "i", 256, outer_tag="g.0")
knl = lp.preprocess_kernel(knl)
from loopy.diagnostic import MissingBarrierError
with pytest.raises(MissingBarrierError):
lp.get_one_scheduled_kernel(knl)
def test_index_cse(ctx_factory):
knl = lp.make_kernel(["{[i,j,k,l,m]:0<=i,j,k,l,m<n}"],
"""
for i
for j
c[i,j,m] = sum((k,l), a[i,j,l]*b[i,j,k,l])
end
end
""")
knl = lp.tag_inames(knl, "l:unr")
knl = lp.prioritize_loops(knl, "i,j,k,l")
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knl = lp.add_and_infer_dtypes(knl, {"a": np.float32, "b": np.float32})
knl = lp.fix_parameters(knl, n=5)
print(lp.generate_code_v2(knl).device_code())
def test_ilp_and_conditionals(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel('{[k]: 0<=k<n}}',
"""
for k
<> Tcond = T[k] < 0.5
if Tcond
cp[k] = 2 * T[k] + Tcond
end
end
""")
knl = lp.fix_parameters(knl, n=200)
knl = lp.add_and_infer_dtypes(knl, {"T": np.float32})
ref_knl = knl
knl = lp.split_iname(knl, 'k', 2, inner_tag='ilp')
lp.auto_test_vs_ref(ref_knl, ctx, knl)
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def test_unr_and_conditionals(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel('{[k]: 0<=k<n}}',
"""
for k
<> Tcond[k] = T[k] < 0.5
if Tcond[k]
cp[k] = 2 * T[k] + Tcond[k]
end
end
""")
knl = lp.fix_parameters(knl, n=200)
knl = lp.add_and_infer_dtypes(knl, {"T": np.float32})
ref_knl = knl
knl = lp.split_iname(knl, 'k', 2, inner_tag='unr')
lp.auto_test_vs_ref(ref_knl, ctx, knl)
def test_constant_array_args(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel('{[k]: 0<=k<n}}',
"""
for k
<> Tcond[k] = T[k] < 0.5
if Tcond[k]
cp[k] = 2 * T[k] + Tcond[k]
end
end
""",
[lp.ConstantArg('T', shape=(200,), dtype=np.float32),
'...'])
knl = lp.fix_parameters(knl, n=200)
@pytest.mark.parametrize("src_order", ["C"])
@pytest.mark.parametrize("tmp_order", ["C", "F"])
def test_temp_initializer(ctx_factory, src_order, tmp_order):
a = np.random.randn(3, 3).copy(order=src_order)
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
knl = lp.make_kernel(
"{[i,j]: 0<=i,j<n}",
"out[i,j] = tmp[i,j]",
[
lp.TemporaryVariable("tmp",
initializer=a,
shape=lp.auto,
scope=lp.temp_var_scope.PRIVATE,
read_only=True,
order=tmp_order),
"..."
])
knl = lp.set_options(knl, write_cl=True, highlight_cl=True)
knl = lp.fix_parameters(knl, n=a.shape[0])
evt, (a2,) = knl(queue, out_host=True)
assert np.array_equal(a, a2)
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def test_const_temp_with_initializer_not_saved():
knl = lp.make_kernel(
"{[i]: 0<=i<10}",
"""
... gbarrier
out[i] = tmp[i]
""",
[
lp.TemporaryVariable("tmp",
initializer=np.arange(10),
shape=lp.auto,
scope=lp.temp_var_scope.PRIVATE,
read_only=True),
"..."
],
seq_dependencies=True)
knl = lp.preprocess_kernel(knl)
knl = lp.get_one_scheduled_kernel(knl)
knl = lp.save_and_reload_temporaries(knl)
# This ensures no save slot was added.
assert len(knl.temporary_variables) == 1
def test_header_extract():
knl = lp.make_kernel('{[k]: 0<=k<n}}',
"""
for k
T[k] = k**2
end
""",
[lp.GlobalArg('T', shape=(200,), dtype=np.float32),
'...'])
knl = lp.fix_parameters(knl, n=200)
#test C
cknl = knl.copy(target=lp.CTarget())
assert str(lp.generate_header(cknl)[0]) == (