Newer
Older
knl = lp.make_kernel(
"{[i,j]: 0<=i<n and 0<=j<m }",
[
"res[i] = reduce(%s, j, a[i,j])" % op_name,
],
assumptions="n>=1")
a = np.random.randn(20, 10)
evt, (res,) = knl(queue, a=a)
assert np.allclose(res, np_op(a, axis=1))
def test_double_sum_made_unique(ctx_factory):
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
n = 20
knl = lp.make_kernel(
"{[i,j]: 0<=i,j<n }",
[
"a = sum((i,j), i*j)",
"b = sum(i, sum(j, i*j))",
],
assumptions="n>=1")
knl = lp.make_reduction_inames_unique(knl)
print(knl)
assert a.get() == ref
assert b.get() == ref
# {{{ test race detection
@pytest.mark.skipif("sys.version_info < (2,6)")
def test_ilp_write_race_detection_global(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
"[n] -> {[i,j]: 0<=i,j<n }",
[
Andreas Klöckner
committed
"a[i] = 5+i+j",
],
[
lp.GlobalArg("a", np.float32),
lp.ValueArg("n", np.int32, approximately=1000),
],
assumptions="n>=1")
Andreas Klöckner
committed
knl = lp.tag_inames(knl, dict(j="ilp"))
knl = lp.preprocess_kernel(knl, ctx.devices[0])
Andreas Klöckner
committed
with lp.CacheMode(False):
from loopy.diagnostic import WriteRaceConditionWarning
from warnings import catch_warnings
with catch_warnings(record=True) as warn_list:
list(lp.generate_loop_schedules(knl))
assert any(isinstance(w.message, WriteRaceConditionWarning)
for w in warn_list)
def test_ilp_write_race_avoidance_local(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
Andreas Klöckner
committed
"<> a[i] = 5+i+j",
Andreas Klöckner
committed
knl = lp.tag_inames(knl, dict(i="l.0", j="ilp"))
knl = lp.preprocess_kernel(knl, ctx.devices[0])
for k in lp.generate_loop_schedules(knl):
assert k.temporary_variables["a"].shape == (16, 17)
def test_ilp_write_race_avoidance_private(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
"{[j]: 0<=j<16 }",
[
Andreas Klöckner
committed
"<> a = 5+j",
Andreas Klöckner
committed
knl = lp.tag_inames(knl, dict(j="ilp"))
knl = lp.preprocess_kernel(knl, ctx.devices[0])
for k in lp.generate_loop_schedules(knl):
assert k.temporary_variables["a"].shape == (16,)
# }}}
Andreas Klöckner
committed
def test_write_parameter(ctx_factory):
dtype = np.float32
ctx = ctx_factory()
knl = lp.make_kernel(
Andreas Klöckner
committed
"{[i,j]: 0<=i,j<n }",
"""
a = sum((i,j), i*j)
b = sum(i, sum(j, i*j))
n = 15
""",
[
lp.GlobalArg("a", dtype, shape=()),
lp.GlobalArg("b", dtype, shape=()),
lp.ValueArg("n", np.int32, approximately=1000),
],
assumptions="n>=1")
Andreas Klöckner
committed
import pytest
with pytest.raises(RuntimeError):
Andreas Klöckner
committed
lp.CompiledKernel(ctx, knl).get_code()
Andreas Klöckner
committed
def test_arg_shape_guessing(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
Andreas Klöckner
committed
"{[i,j]: 0<=i,j<n }",
"""
a = 1.5 + sum((i,j), i*j)
b[i, j] = i*j
c[i+j, j] = b[j,i]
""",
[
lp.GlobalArg("a", shape=lp.auto),
lp.GlobalArg("b", shape=lp.auto),
lp.GlobalArg("c", shape=lp.auto),
Andreas Klöckner
committed
lp.ValueArg("n"),
],
assumptions="n>=1")
print(knl)
print(lp.CompiledKernel(ctx, knl).get_highlighted_code())
Andreas Klöckner
committed
def test_arg_guessing(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
"{[i,j]: 0<=i,j<n }",
"""
a = 1.5 + sum((i,j), i*j)
b[i, j] = i*j
c[i+j, j] = b[j,i]
""",
assumptions="n>=1")
print(knl)
print(lp.CompiledKernel(ctx, knl).get_highlighted_code())
def test_arg_guessing_with_reduction(ctx_factory):
#logging.basicConfig(level=logging.DEBUG)
ctx = ctx_factory()
knl = lp.make_kernel(
"{[i,j]: 0<=i,j<n }",
"""
a = 1.5 + simul_reduce(sum, (i,j), i*j)
d = 1.5 + simul_reduce(sum, (i,j), b[i,j])
b[i, j] = i*j
c[i+j, j] = b[j,i]
""",
assumptions="n>=1")
print(knl)
print(lp.CompiledKernel(ctx, knl).get_highlighted_code())
def test_nonlinear_index(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
"{[i,j]: 0<=i,j<n }",
"""
a[i*i] = 17
""",
[
lp.GlobalArg("a", shape="n"),
lp.ValueArg("n"),
],
assumptions="n>=1")
print(knl)
print(lp.CompiledKernel(ctx, knl).get_highlighted_code())
def test_triangle_domain(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
"{[i,j]: 0<=i,j<n and i <= j}",
"a[i,j] = 17",
assumptions="n>=1")
print(knl)
print(lp.CompiledKernel(ctx, knl).get_highlighted_code())
def test_offsets_and_slicing(ctx_factory):
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
knl = lp.make_kernel(
"{[i,j]: 0<=i<n and 0<=j<m }",
"""
b[i,j] = 2*a[i,j]
""",
assumptions="n>=1 and m>=1",
default_offset=lp.auto)
knl = lp.tag_data_axes(knl, "a,b", "stride:auto,stride:1")
cknl = lp.CompiledKernel(ctx, knl)
a_full = cl.clrandom.rand(queue, (n, n), np.float64)
a_full_h = a_full.get()
b_full = cl.clrandom.rand(queue, (n, n), np.float64)
b_full_h = b_full.get()
a_sub = (slice(3, 10), slice(5, 10))
a = a_full[a_sub]
b_sub = (slice(3+3, 10+3), slice(5+4, 10+4))
b = b_full[b_sub]
b_full_h[b_sub] = 2*a_full_h[a_sub]
print(cknl.get_highlighted_code({"a": a.dtype}))
import numpy.linalg as la
assert la.norm(b_full.get() - b_full_h) < 1e-13
Andreas Klöckner
committed
def test_vector_ilp_with_prefetch(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
Andreas Klöckner
committed
"{ [i]: 0<=i<n }",
"out[i] = 2*a[i]",
[
# Tests that comma'd arguments interoperate with
Andreas Klöckner
committed
# argument guessing.
lp.GlobalArg("out,a", np.float32, shape=lp.auto),
"..."
])
knl = lp.split_iname(knl, "i", 128, inner_tag="l.0")
knl = lp.split_iname(knl, "i_outer", 4, outer_tag="g.0", inner_tag="ilp")
knl = lp.add_prefetch(knl, "a", ["i_inner", "i_outer_inner"])
cknl = lp.CompiledKernel(ctx, knl)
cknl.cl_kernel_info()
Andreas Klöckner
committed
import re
Andreas Klöckner
committed
assert len(list(re.finditer("barrier", code))) == 1
def test_convolution(ctx_factory):
knl = lp.make_kernel(
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
"{ [iimg, ifeat, icolor, im_x, im_y, f_x, f_y]: \
-f_w <= f_x,f_y <= f_w \
and 0 <= im_x < im_w and 0 <= im_y < im_h \
and 0<=iimg<=nimgs and 0<=ifeat<nfeats and 0<=icolor<ncolors \
}",
"""
out[iimg, ifeat, im_x, im_y] = sum((f_x, f_y, icolor), \
img[iimg, f_w+im_x-f_x, f_w+im_y-f_y, icolor] \
* f[ifeat, f_w+f_x, f_w+f_y, icolor])
""",
[
lp.GlobalArg("f", dtype, shape=lp.auto),
lp.GlobalArg("img", dtype, shape=lp.auto),
lp.GlobalArg("out", dtype, shape=lp.auto),
"..."
],
assumptions="f_w>=1 and im_w, im_h >= 2*f_w+1 and nfeats>=1 and nimgs>=0",
flags="annotate_inames",
defines=dict(ncolors=3))
f_w = 3
knl = lp.fix_parameters(knl, f_w=f_w)
ref_knl = knl
def variant_0(knl):
#knl = lp.split_iname(knl, "im_x", 16, inner_tag="l.0")
knl = lp.set_loop_priority(knl, "iimg,im_x,im_y,ifeat,f_x,f_y")
return knl
def variant_1(knl):
knl = lp.split_iname(knl, "im_x", 16, inner_tag="l.0")
knl = lp.set_loop_priority(knl, "iimg,im_x_outer,im_y,ifeat,f_x,f_y")
return knl
def variant_2(knl):
knl = lp.split_iname(knl, "im_x", 16, outer_tag="g.0", inner_tag="l.0")
knl = lp.split_iname(knl, "im_y", 16, outer_tag="g.1", inner_tag="l.1")
knl = lp.tag_inames(knl, dict(ifeat="g.2"))
knl = lp.add_prefetch(knl, "f[ifeat,:,:,:]")
knl = lp.add_prefetch(knl, "img", "im_x_inner, im_y_inner, f_x, f_y")
return knl
for variant in [
variant_2
]:
lp.auto_test_vs_ref(ref_knl, ctx, variant(knl),
parameters=dict(
im_w=128, im_h=128, f_w=f_w,
))
def test_convolution_with_nonzero_base(ctx_factory):
# This is kept alive as a test for domains that don't start at zero.
# These are a bad idea for split_iname, which places its origin at zero
# and therefore produces a first block that is odd-sized.
#
# Therefore, for real tests, check test_convolution further up.
ctx = ctx_factory()
dtype = np.float32
knl = lp.make_kernel(
"{ [iimg, ifeat, icolor, im_x, im_y, f_x, f_y]: \
-f_w <= f_x,f_y <= f_w \
and f_w <= im_x < im_w-f_w and f_w <= im_y < im_h-f_w \
and 0<=iimg<=nimgs and 0<=ifeat<nfeats and 0<=icolor<ncolors \
}",
out[iimg, ifeat, im_x-f_w, im_y-f_w] = sum((f_x, f_y, icolor), \
img[iimg, im_x-f_x, im_y-f_y, icolor] \
* f[ifeat, f_w+f_x, f_w+f_y, icolor])
""",
[
lp.GlobalArg("f", dtype, shape=lp.auto),
lp.GlobalArg("img", dtype, shape=lp.auto),
lp.GlobalArg("out", dtype, shape=lp.auto),
"..."
],
assumptions="f_w>=1 and im_w, im_h >= 2*f_w+1 and nfeats>=1 and nimgs>=0",
flags="annotate_inames",
def variant_0(knl):
#knl = lp.split_iname(knl, "im_x", 16, inner_tag="l.0")
knl = lp.set_loop_priority(knl, "iimg,im_x,im_y,ifeat,f_x,f_y")
return knl
def variant_1(knl):
knl = lp.split_iname(knl, "im_x", 16, inner_tag="l.0")
knl = lp.set_loop_priority(knl, "iimg,im_x_outer,im_y,ifeat,f_x,f_y")
for variant in [
variant_0,
]:
lp.auto_test_vs_ref(ref_knl, ctx, variant(knl),
parameters=dict(
def test_c_instruction(ctx_factory):
#logging.basicConfig(level=logging.DEBUG)
ctx = ctx_factory()
knl = lp.make_kernel(
"{[i,j]: 0<=i,j<n }",
[
lp.CInstruction("i,j", """
x = sin((float) i*j);
""", assignees="x"),
],
[
lp.GlobalArg("a", shape=lp.auto, dtype=np.float32),
lp.TemporaryVariable("x", np.float32),
],
assumptions="n>=1")
knl = lp.split_iname(knl, "i", 128, outer_tag="g.0", inner_tag="l.0")
print(knl)
print(lp.CompiledKernel(ctx, knl).get_highlighted_code())
def test_dependent_domain_insn_iname_finding(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel([
"{[isrc_box]: 0<=isrc_box<nsrc_boxes}",
"{[isrc,idim]: isrc_start<=isrc<isrc_end and 0<=idim<dim}",
],
"""
<> src_ibox = source_boxes[isrc_box]
<> isrc_start = box_source_starts[src_ibox]
<> isrc_end = isrc_start+box_source_counts_nonchild[src_ibox]
<> strength = strengths[isrc] {id=set_strength}
""",
[
lp.GlobalArg("box_source_starts,box_source_counts_nonchild",
None, shape=None),
lp.GlobalArg("strengths",
None, shape="nsources"),
assert "isrc_box" in knl.insn_inames("set_strength")
print(lp.CompiledKernel(ctx, knl).get_highlighted_code(
dict(
source_boxes=np.int32,
box_source_starts=np.int32,
box_source_counts_nonchild=np.int32,
strengths=np.float64,
def test_inames_deps_from_write_subscript(ctx_factory):
knl = lp.make_kernel(
"{[i,j]: 0<=i,j<n}",
"""
<> src_ibox = source_boxes[i]
<int32> something = 5
a[src_ibox] = sum(j, something) {id=myred}
""",
[
lp.GlobalArg("box_source_starts,box_source_counts_nonchild,a",
None, shape=None),
"..."])
assert "i" in knl.insn_inames("myred")
knl = lp.make_kernel(
"{[i,j,k]: 0<=i,j,k<n}",
"""
b = sum((i,j,k), a[i,j,k])
""",
[
lp.GlobalArg("box_source_starts,box_source_counts_nonchild,a",
None, shape=None),
"..."])
knl = lp.split_reduction_outward(knl, "j,k")
def test_modulo_indexing(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
"{[i,j]: 0<=i<n and 0<=j<5}",
"""
b[i] = sum(j, a[(i+j)%n])
""",
[
lp.GlobalArg("a", None, shape="n"),
"..."
]
)
print(knl)
print(lp.CompiledKernel(ctx, knl).get_highlighted_code(
def test_rob_stroud_bernstein(ctx_factory):
ctx = ctx_factory()
# NOTE: tmp would have to be zero-filled beforehand
knl = lp.make_kernel(
"{[el, i2, alpha1,alpha2]: \
0 <= el < nels and \
0 <= i2 < nqp1d and \
0 <= alpha1 <= deg and 0 <= alpha2 <= deg-alpha1 }",
"""
Andreas Klöckner
committed
<> xi = qpts[1, i2] {inames=+el}
Andreas Klöckner
committed
<> aind = 0 {id=aind_init,inames=+i2:el}
tmp[el,alpha1,i2] = tmp[el,alpha1,i2] + w * coeffs[aind] \
Andreas Klöckner
committed
{id=write_tmp,inames=+alpha2}
w = w * r * ( deg - alpha1 - alpha2 ) / (1 + alpha2) \
{id=update_w,dep=init_w:write_tmp}
aind = aind + 1 \
{id=aind_incr,\
dep=aind_init:write_tmp:update_w, \
Andreas Klöckner
committed
inames=+el:i2:alpha1:alpha2}
# Must declare coeffs to have "no" shape, to keep loopy
# from trying to figure it out the shape automatically.
lp.GlobalArg("coeffs", None, shape=None),
"..."
],
knl = lp.fix_parameters(knl, nqp1d=7, deg=4)
knl = lp.split_iname(knl, "el", 16, inner_tag="l.0")
knl = lp.split_iname(knl, "el_outer", 2, outer_tag="g.0", inner_tag="ilp",
slabs=(0, 1))
knl = lp.tag_inames(knl, dict(i2="l.1", alpha1="unr", alpha2="unr"))
print(lp.CompiledKernel(ctx, knl).get_highlighted_code(
dict(
qpts=np.float32,
coeffs=np.float32,
tmp=np.float32,
def test_rob_stroud_bernstein_full(ctx_factory):
#logging.basicConfig(level=logging.DEBUG)
ctx = ctx_factory()
# NOTE: result would have to be zero-filled beforehand
knl = lp.make_kernel(
1573
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
1593
1594
1595
1596
1597
1598
1599
1600
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1612
1613
1614
1615
1616
1617
1618
1619
1620
1621
1622
1623
1624
1625
1626
1627
"{[el, i2, alpha1,alpha2, i1_2, alpha1_2, i2_2]: \
0 <= el < nels and \
0 <= i2 < nqp1d and \
0 <= alpha1 <= deg and 0 <= alpha2 <= deg-alpha1 and\
\
0 <= i1_2 < nqp1d and \
0 <= alpha1_2 <= deg and \
0 <= i2_2 < nqp1d \
}",
"""
<> xi = qpts[1, i2] {inames=+el}
<> s = 1-xi
<> r = xi/s
<> aind = 0 {id=aind_init,inames=+i2:el}
<> w = s**(deg-alpha1) {id=init_w}
<> tmp[alpha1,i2] = tmp[alpha1,i2] + w * coeffs[aind] \
{id=write_tmp,inames=+alpha2}
w = w * r * ( deg - alpha1 - alpha2 ) / (1 + alpha2) \
{id=update_w,dep=init_w:write_tmp}
aind = aind + 1 \
{id=aind_incr,\
dep=aind_init:write_tmp:update_w, \
inames=+el:i2:alpha1:alpha2}
<> xi2 = qpts[0, i1_2] {dep=aind_incr,inames=+el}
<> s2 = 1-xi2
<> r2 = xi2/s2
<> w2 = s2**deg
result[el, i1_2, i2_2] = result[el, i1_2, i2_2] + \
w2 * tmp[alpha1_2, i2_2] \
{inames=el:alpha1_2:i1_2:i2_2}
w2 = w2 * r2 * (deg-alpha1_2) / (1+alpha1_2)
""",
[
# Must declare coeffs to have "no" shape, to keep loopy
# from trying to figure it out the shape automatically.
lp.GlobalArg("coeffs", None, shape=None),
"..."
],
assumptions="deg>=0 and nels>=1"
)
knl = lp.fix_parameters(knl, nqp1d=7, deg=4)
if 0:
knl = lp.split_iname(knl, "el", 16, inner_tag="l.0")
knl = lp.split_iname(knl, "el_outer", 2, outer_tag="g.0", inner_tag="ilp",
slabs=(0, 1))
knl = lp.tag_inames(knl, dict(i2="l.1", alpha1="unr", alpha2="unr"))
from pickle import dumps, loads
knl = loads(dumps(knl))
knl = lp.CompiledKernel(ctx, knl).get_highlighted_code(
dict(
qpts=np.float32,
tmp=np.float32,
coeffs=np.float32,
result=np.float32,
))
@pytest.mark.parametrize("vec_len", [2, 3, 4, 8, 16])
def test_vector_types(ctx_factory, vec_len):
knl = lp.make_kernel(
"{ [i,j]: 0<=i<n and 0<=j<vec_len }",
"out[i,j] = 2*a[i,j]",
[
lp.GlobalArg("a", np.float32, shape=lp.auto),
lp.GlobalArg("out", np.float32, shape=lp.auto),
"..."
knl = lp.fix_parameters(knl, vec_len=vec_len)
ref_knl = knl
knl = lp.tag_data_axes(knl, "out", "c,vec")
knl = lp.tag_inames(knl, dict(j="unr"))
knl = lp.split_iname(knl, "i", 128, outer_tag="g.0", inner_tag="l.0")
lp.auto_test_vs_ref(ref_knl, ctx, knl,
parameters=dict(
n=20000
1669
1670
1671
1672
1673
1674
1675
1676
1677
1678
1679
1680
1681
1682
1683
1684
1685
1686
1687
1688
1689
1690
def test_tag_data_axes(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
"{ [i,j,k]: 0<=i,j,k<n }",
"out[i,j,k] = 15")
ref_knl = knl
with pytest.raises(lp.LoopyError):
lp.tag_data_axes(knl, "out", "N1,N0,N5")
with pytest.raises(lp.LoopyError):
lp.tag_data_axes(knl, "out", "N1,N0,c")
knl = lp.tag_data_axes(knl, "out", "N1,N0,N2")
knl = lp.tag_inames(knl, dict(j="g.0", i="g.1"))
lp.auto_test_vs_ref(ref_knl, ctx, knl,
parameters=dict(n=20))
def test_conditional(ctx_factory):
#logging.basicConfig(level=logging.DEBUG)
1694
1695
1696
1697
1698
1699
1700
1701
1702
1703
1704
1705
1706
1707
1708
1709
1710
1711
1712
1713
1714
1715
1716
1717
knl = lp.make_kernel(
"{ [i,j]: 0<=i,j<n }",
"""
<> my_a = a[i,j] {id=read_a}
<> a_less_than_zero = my_a < 0 {dep=read_a,inames=i:j}
my_a = 2*my_a {id=twice_a,dep=read_a,if=a_less_than_zero}
my_a = my_a+1 {id=aplus,dep=twice_a,if=a_less_than_zero}
out[i,j] = 2*my_a {dep=aplus}
""",
[
lp.GlobalArg("a", np.float32, shape=lp.auto),
lp.GlobalArg("out", np.float32, shape=lp.auto),
"..."
])
ref_knl = knl
lp.auto_test_vs_ref(ref_knl, ctx, knl,
parameters=dict(
n=200
))
Andreas Klöckner
committed
def test_ilp_loop_bound(ctx_factory):
# The salient bit of this test is that a joint bound on (outer, inner)
# from a split occurs in a setting where the inner loop has been ilp'ed.
# In 'normal' parallel loops, the inner index is available for conditionals
# throughout. In ILP'd loops, not so much.
ctx = ctx_factory()
knl = lp.make_kernel(
Andreas Klöckner
committed
1726
1727
1728
1729
1730
1731
1732
1733
1734
1735
1736
1737
1738
1739
1740
1741
1742
1743
1744
1745
1746
"{ [i,j,k]: 0<=i,j,k<n }",
"""
out[i,k] = sum(j, a[i,j]*b[j,k])
""",
[
lp.GlobalArg("a,b", np.float32, shape=lp.auto),
"...",
],
assumptions="n>=1")
ref_knl = knl
knl = lp.set_loop_priority(knl, "j,i,k")
knl = lp.split_iname(knl, "k", 4, inner_tag="ilp")
lp.auto_test_vs_ref(ref_knl, ctx, knl,
parameters=dict(
n=200
))
def test_arg_shape_uses_assumptions(ctx_factory):
# If arg shape determination does not use assumptions, then it won't find a
# static shape for out, which is at least 1 x 1 in size, but otherwise of
# size n x n.
lp.make_kernel(
"{ [i,j]: 0<=i,j<n }",
"""
out[i,j] = 2*a[i,j]
out[0,0] = 13.0
""", assumptions="n>=1")
def test_slab_decomposition_does_not_double_execute(ctx_factory):
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
knl = lp.make_kernel(
"{ [i]: 0<=i<n }",
ref_knl = knl
for outer_tag in ["for", "g.0"]:
knl = ref_knl
knl = lp.split_iname(knl, "i", 4, slabs=(0, 1), inner_tag="unr",
outer_tag=outer_tag)
knl = lp.set_loop_priority(knl, "i_outer")
a = cl.array.empty(queue, 20, np.float32)
a.fill(17)
a_ref = a.copy()
a_knl = a.copy()
knl = lp.set_options(knl, write_cl=True)
print("TEST-----------------------------------------")
print("REF-----------------------------------------")
print("DONE-----------------------------------------")
print("REF", a_ref)
print("KNL", a_knl)
assert (a_ref == a_knl).get().all()
print("_________________________________")
# Loopy would previously only handle barrier insertion correctly if exactly
# one instruction wrote to each local temporary. This tests that multiple
# writes are OK.
knl = lp.make_kernel(
"{[i,e]: 0<=i<5 and 0<=e<nelements}",
"""
<> temp[i, 0] = 17
temp[i, 1] = 15
""")
knl = lp.tag_inames(knl, dict(i="l.0"))
for k in lp.generate_loop_schedules(knl):
code, _ = lp.generate_code(k)
knl = lp.make_kernel(
"{[i,j]: 0<=i,j<n}",
"result[i+1,j+1] = u[i + 1, j + 1]**2 + -1 + (-4)*u[i + 1, j + 1] \
+ u[i + 1 + 1, j + 1] + u[i + 1 + -1, j + 1] \
+ u[i + 1, j + 1 + 1] + u[i + 1, j + 1 + -1]")
knl = lp.split_iname(knl,
"i", 16, outer_tag="g.1", inner_tag="l.1")
knl = lp.split_iname(knl,
"j", 16, outer_tag="g.0", inner_tag="l.0")
knl = lp.add_prefetch(knl, "u",
["i_inner", "j_inner"],
fetch_bounding_box=True)
#n = 1000
#u = cl.clrandom.rand(queue, (n+2, n+2), dtype=np.float32)
knl = lp.set_options(knl, write_cl=True)
knl = lp.add_and_infer_dtypes(knl, dict(u=np.float32))
code, inf = lp.generate_code(knl)
assert "double" not in code
def test_fd_1d(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
"{[i]: 0<=i<n}",
"result[i] = u[i+1]-u[i]")
knl = lp.add_and_infer_dtypes(knl, {"u": np.float32})
ref_knl = knl
knl = lp.split_iname(knl, "i", 16)
knl = lp.extract_subst(knl, "u_acc", "u[j]", parameters="j")
knl = lp.precompute(knl, "u_acc", "i_inner", default_tag="for")
knl = lp.assume(knl, "n mod 16 = 0")
lp.auto_test_vs_ref(
ref_knl, ctx, knl,
parameters=dict(n=2048))
1861
1862
1863
1864
1865
1866
1867
1868
1869
1870
1871
1872
1873
1874
1875
1876
1877
1878
1879
1880
1881
1882
1883
def test_make_copy_kernel(ctx_factory):
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
intermediate_format = "f,f,sep"
a1 = np.random.randn(1024, 4, 3)
cknl1 = lp.make_copy_kernel(intermediate_format)
cknl1 = lp.fix_parameters(cknl1, n2=3)
cknl1 = lp.set_options(cknl1, write_cl=True)
evt, a2 = cknl1(queue, input=a1)
cknl2 = lp.make_copy_kernel("c,c,c", intermediate_format)
cknl2 = lp.fix_parameters(cknl2, n2=3)
evt, a3 = cknl2(queue, input=a2)
assert (a1 == a3).all()
def test_set_arg_order():
knl = lp.make_kernel(
"{ [i,j]: 0<=i,j<n }",
"out[i,j] = a[i]*b[j]")
knl = lp.set_argument_order(knl, "out,a,n,b")
def test_affine_map_inames():
knl = lp.make_kernel(
"{[e, i,j,n]: 0<=e<E and 0<=i,j,n<N}",
"rhsQ[e, n+i, j] = rhsQ[e, n+i, j] - D[i, n]*x[i,j]")
knl = lp.affine_map_inames(knl,
"i", "i0",
"i0 = n+i")
print(knl)
Andreas Klöckner
committed
1904
1905
1906
1907
1908
1909
1910
1911
1912
1913
1914
1915
1916
1917
1918
1919
1920
1921
1922
1923
1924
1925
1926
1927
1928
1929
1930
def test_precompute_confusing_subst_arguments(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
"{[i,j]: 0<=i<n and 0<=j<5}",
"""
D(i):=a[i+1]-a[i]
b[i,j] = D(j)
""")
knl = lp.add_and_infer_dtypes(knl, dict(a=np.float32))
ref_knl = knl
knl = lp.tag_inames(knl, dict(j="g.1"))
knl = lp.split_iname(knl, "i", 128, outer_tag="g.0", inner_tag="l.0")
from loopy.symbolic import get_dependencies
assert "i_inner" not in get_dependencies(knl.substitutions["D"].expression)
knl = lp.precompute(knl, "D")
lp.auto_test_vs_ref(
ref_knl, ctx, knl,
parameters=dict(n=12345))
def test_precompute_nested_subst(ctx_factory):
1931
1932
1933
1934
1935
1936
1937
1938
1939
1940
1941
1942
1943
1944
1945
1946
1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
ctx = ctx_factory()
knl = lp.make_kernel(
"{[i,j]: 0<=i<n and 0<=j<5}",
"""
E:=a[i]
D:=E*E
b[i] = D
""")
knl = lp.add_and_infer_dtypes(knl, dict(a=np.float32))
ref_knl = knl
knl = lp.tag_inames(knl, dict(j="g.1"))
knl = lp.split_iname(knl, "i", 128, outer_tag="g.0", inner_tag="l.0")
from loopy.symbolic import get_dependencies
assert "i_inner" not in get_dependencies(knl.substitutions["D"].expression)
knl = lp.precompute(knl, "D", "i_inner")
# There's only one surviving 'E' rule.
assert len([
rule_name
for rule_name in knl.substitutions
if rule_name.startswith("E")]) == 1
# That rule should use the newly created prefetch inames,
# not the prior 'i_inner'
assert "i_inner" not in get_dependencies(knl.substitutions["E"].expression)
lp.auto_test_vs_ref(
ref_knl, ctx, knl,
parameters=dict(n=12345))
Andreas Klöckner
committed
def test_poisson(ctx_factory):
# Stolen from Peter Coogan and Rob Kirby for FEM assembly
ctx = ctx_factory()
nbf = 5
nqp = 5
sdim = 3
knl = lp.make_kernel(
0 <= c < nels and \
0 <= i < nbf and \
0 <= j < nbf and \
0 <= k < nqp and \
dpsi(bf,k0,dir) := \
simul_reduce(sum, ell2, DFinv[c,ell2,dir] * DPsi[bf,k0,ell2] )
J[c] * w[k] * sum(ell, dpsi(i,k,ell) * dpsi(j,k,ell))
""",
assumptions="nels>=1 and nbf >= 1 and nels mod 4 = 0")
knl = lp.fix_parameters(knl, nbf=nbf, sdim=sdim, nqp=nqp)
ref_knl = knl
knl = lp.set_loop_priority(knl, ["c", "j", "i", "k"])
def variant_1(knl):
knl = lp.precompute(knl, "dpsi", "i,k,ell", default_tag='for')
knl = lp.set_loop_priority(knl, "c,i,j")