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Older
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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()
def test_arg_guessing(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(ctx.devices[0], [
"{[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(ctx.devices[0], [
"{[i,j]: 0<=i,j<n }",
],
"""
a = 1.5 + sum((i,j), i*j)
d = 1.5 + 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(ctx.devices[0], [
"{[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(ctx.devices[0], [
"{[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(ctx.devices[0], [
"{[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(ctx.devices[0],
"{ [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):
ctx = ctx_factory()
dtype = np.float64
knl = lp.make_kernel(ctx.devices[0],
"{ [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",
defines=dict(ncolors=3))
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")
return knl
for variant in [
variant_0,
#variant_1
]:
lp.auto_test_vs_ref(ref_knl, ctx, variant(knl),
parameters=dict(
im_w=128, im_h=128, f_w=7,
nfeats=12, nimgs=17
))
def test_c_instruction(ctx_factory):
#logging.basicConfig(level=logging.DEBUG)
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ctx = ctx_factory()
knl = lp.make_kernel(ctx.devices[0], [
"{[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(ctx.devices[0], [
"{[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"),
"..."])
print knl
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):
ctx = ctx_factory()
knl = lp.make_kernel(ctx.devices[0], [
"{[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),
"..."])
print knl
assert "i" in knl.insn_inames("myred")
def test_split_reduction(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(ctx.devices[0], [
"{[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")
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def test_modulo_indexing(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(ctx.devices[0], [
"{[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(
dict(
a=np.float32,
))
def test_rob_stroud_bernstein(ctx_factory):
ctx = ctx_factory()
# NOTE: tmp would have to be zero-filled beforehand
knl = lp.make_kernel(ctx.devices[0],
"{[el, i2, alpha1,alpha2]: \
0 <= el < nels and \
0 <= i2 < nqp1d and \
0 <= alpha1 <= deg and 0 <= alpha2 <= deg-alpha1 }",
"""
tmp[el,alpha1,i2] = tmp[el,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, \
# 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 knl
print lp.CompiledKernel(ctx, knl).get_highlighted_code(
dict(
qpts=np.float32,
coeffs=np.float32,
tmp=np.float32,
))
@pytest.mark.parametrize("vec_len", [2, 3, 4, 8])
def test_vector_types(ctx_factory, vec_len):
ctx = cl.create_some_context()
knl = lp.make_kernel(ctx.devices[0],
"{ [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
),
fills_entire_output=False)
if __name__ == "__main__":
if len(sys.argv) > 1:
exec(sys.argv[1])
else:
from py.test.cmdline import main
main([__file__])
# vim: foldmethod=marker