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assumptions="n>=1")
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knl = lp.tag_inames(knl, dict(j="ilp"))
knl = lp.preprocess_kernel(knl, ctx.devices[0])
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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(
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"<> a[i] = 5+i+j",
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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 }",
[
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"<> a = 5+j",
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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,)
# }}}
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def test_write_parameter(ctx_factory):
dtype = np.float32
ctx = ctx_factory()
knl = lp.make_kernel(
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"{[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")
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import pytest
with pytest.raises(RuntimeError):
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lp.CompiledKernel(ctx, knl).get_code()
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def test_arg_shape_guessing(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
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"{[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),
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lp.ValueArg("n"),
],
assumptions="n>=1")
print(knl)
print(lp.CompiledKernel(ctx, knl).get_highlighted_code())
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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
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def test_vector_ilp_with_prefetch(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
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"{ [i]: 0<=i<n }",
"out[i] = 2*a[i]",
[
# Tests that comma'd arguments interoperate with
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# 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()
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import re
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assert len(list(re.finditer("barrier", code))) == 1
def test_convolution(ctx_factory):
knl = lp.make_kernel(
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"{ [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", """
x = sin((float) i);
""", assignees="x"),
"a[i*i] = x",
],
[
lp.GlobalArg("a", shape="n"),
lp.ValueArg("n"),
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 }",
"""
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<> xi = qpts[1, i2] {inames=+el}
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<> aind = 0 {id=aind_init,inames=+i2:el}
tmp[el,alpha1,i2] = tmp[el,alpha1,i2] + w * coeffs[aind] \
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{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, \
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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(
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"{[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
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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)
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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
))
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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(
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"{ [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.clrandom.rand(queue, 20, np.float32)
a_ref = a.copy()
a_knl = a.copy()
knl = lp.set_options(knl, "write_cl")
knl(queue, a=a_knl)
ref_knl(queue, a=a_ref)
queue.finish()
assert (a_ref == a_knl).get().all()
# 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,j] = u[i, j]**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))
knl = lp.preprocess_kernel(knl)
knl = lp.get_one_scheduled_kernel(knl)
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))
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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)
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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):
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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))
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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")
return knl
def variant_2(knl):
knl = lp.precompute(knl, "dpsi", "i,ell", default_tag='for')
knl = lp.set_loop_priority(knl, "c,i,j")
return knl
def add_types(knl):
return lp.add_and_infer_dtypes(knl, dict(
w=np.float32,
J=np.float32,
DPsi=np.float32,
DFinv=np.float32,
))
for variant in [
#variant_1,
variant_2
]:
knl = variant(knl)
lp.auto_test_vs_ref(
add_types(ref_knl), ctx, add_types(knl),
parameters=dict(n=5, nels=15, nbf=5, sdim=2, nqp=7))
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def test_auto_test_can_detect_problems(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
"{[i,j]: 0<=i,j<n}",
"""
a[i,j] = 25
""")
knl = lp.add_and_infer_dtypes(knl, dict(a=np.float32))
ref_knl = knl
knl = lp.link_inames(knl, "i,j", "i0")
from loopy.diagnostic import AutomaticTestFailure
with pytest.raises(AutomaticTestFailure):
lp.auto_test_vs_ref(
ref_knl, ctx, knl,
parameters=dict(n=123))
def test_generate_c_snippet():
from loopy.target.c import CTarget
from pymbolic import var
I = var("I") # noqa
f = var("f")
df = var("df")
q_v = var("q_v")