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assumptions="nQuad>=1")
if 0: # enable to play with prefetching
# (prefetch currently requires constant sizes)
knl = lp.fix_parameters(knl, nQuad=5, nSpace=3)
knl = lp.add_prefetch(knl, "q_v", "k,I", default_tag=None)
knl = lp.split_iname(knl, "k", 4, inner_tag="unr", slabs=(0, 1))
knl = lp.set_loop_priority(knl, "I,k_outer,k_inner")
knl = lp.preprocess_kernel(knl)
knl = lp.get_one_scheduled_kernel(knl)
print(lp.generate_body(knl))
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def test_precompute_with_preexisting_inames(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
"{[e,i,j,k]: 0<=e<E and 0<=i,j,k<n}",
"""
result[e,i] = sum(j, D1[i,j]*u[e,j])
result2[e,i] = sum(k, D2[i,k]*u[e,k])
""")
knl = lp.add_and_infer_dtypes(knl, {
"u": np.float32,
"D1": np.float32,
"D2": np.float32,
})
knl = lp.fix_parameters(knl, n=13)
ref_knl = knl
knl = lp.extract_subst(knl, "D1_subst", "D1[ii,jj]", parameters="ii,jj")
knl = lp.extract_subst(knl, "D2_subst", "D2[ii,jj]", parameters="ii,jj")
knl = lp.precompute(knl, "D1_subst", "i,j", default_tag="for",
precompute_inames="ii,jj")
knl = lp.precompute(knl, "D2_subst", "i,k", default_tag="for",
precompute_inames="ii,jj")
knl = lp.set_loop_priority(knl, "ii,jj,e,j,k")
lp.auto_test_vs_ref(
ref_knl, ctx, knl,
parameters=dict(E=200))
def test_precompute_with_preexisting_inames_fail():
knl = lp.make_kernel(
"{[e,i,j,k]: 0<=e<E and 0<=i,j<n and 0<=k<2*n}",
"""
result[e,i] = sum(j, D1[i,j]*u[e,j])
result2[e,i] = sum(k, D2[i,k]*u[e,k])
""")
knl = lp.add_and_infer_dtypes(knl, {
"u": np.float32,
"D1": np.float32,
"D2": np.float32,
})
knl = lp.fix_parameters(knl, n=13)
knl = lp.extract_subst(knl, "D1_subst", "D1[ii,jj]", parameters="ii,jj")
knl = lp.extract_subst(knl, "D2_subst", "D2[ii,jj]", parameters="ii,jj")
knl = lp.precompute(knl, "D1_subst", "i,j", default_tag="for",
precompute_inames="ii,jj")
with pytest.raises(lp.LoopyError):
lp.precompute(knl, "D2_subst", "i,k", default_tag="for",
precompute_inames="ii,jj")
def test_vectorize(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
"{[i]: 0<=i<n}",
"""
<> temp = 2*b[i]
a[i] = temp
""")
knl = lp.add_and_infer_dtypes(knl, dict(b=np.float32))
knl = lp.set_array_dim_names(knl, "a,b", "i")
knl = lp.split_array_dim(knl, [("a", 0), ("b", 0)], 4,
split_kwargs=dict(slabs=(0, 1)))
knl = lp.tag_data_axes(knl, "a,b", "c,vec")
ref_knl = knl
ref_knl = lp.tag_inames(ref_knl, {"i_inner": "unr"})
knl = lp.tag_inames(knl, {"i_inner": "vec"})
knl = lp.preprocess_kernel(knl)
knl = lp.get_one_scheduled_kernel(knl)
code, inf = lp.generate_code(knl)
lp.auto_test_vs_ref(
ref_knl, ctx, knl,
parameters=dict(n=30))
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def test_alias_temporaries(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
"{[i]: 0<=i<n}",
"""
times2(i) := 2*a[i]
times3(i) := 3*a[i]
times4(i) := 4*a[i]
x[i] = times2(i)
y[i] = times3(i)
z[i] = times4(i)
""")
knl = lp.add_and_infer_dtypes(knl, {"a": np.float32})
ref_knl = knl
knl = lp.split_iname(knl, "i", 16, outer_tag="g.0", inner_tag="l.0")
knl = lp.precompute(knl, "times2", "i_inner")
knl = lp.precompute(knl, "times3", "i_inner")
knl = lp.precompute(knl, "times4", "i_inner")
knl = lp.alias_temporaries(knl, ["times2_0", "times3_0", "times4_0"])
lp.auto_test_vs_ref(
ref_knl, ctx, knl,
parameters=dict(n=30))
def test_fusion():
exp_kernel = lp.make_kernel(
''' { [i]: 0<=i<n } ''',
''' exp[i] = pow(E, z[i])''',
assumptions="n>0")
sum_kernel = lp.make_kernel(
'{ [j]: 0<=j<n }',
'out2 = sum(j, exp[j])',
assumptions='n>0')
knl = lp.fuse_kernels([exp_kernel, sum_kernel])
print(knl)
def test_sci_notation_literal(ctx_factory):
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
set_kernel = lp.make_kernel(
''' { [i]: 0<=i<12 } ''',
''' out[i] = 1e-12''')
set_kernel = lp.set_options(set_kernel, write_cl=True)
evt, (out,) = set_kernel(queue)
assert (np.abs(out.get() - 1e-12) < 1e-20).all()
def test_rename_argument(ctx_factory):
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
kernel = lp.make_kernel(
'''{ [i]: 0<=i<n }''',
'''out[i] = a + 2''')
kernel = lp.rename_argument(kernel, "a", "b")
evt, (out,) = kernel(queue, b=np.float32(12), n=20)
assert (np.abs(out.get() - 14) < 1e-8).all()
def test_to_batched(ctx_factory):
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
knl = lp.make_kernel(
''' { [i,j]: 0<=i,j<n } ''',
''' out[i] = sum(j, a[i,j]*x[j])''')
bknl = lp.to_batched(knl, "nbatches", "out,x")
a = np.random.randn(5, 5)
x = np.random.randn(7, 5)
bknl(queue, a=a, x=x)
def test_variable_size_temporary():
knl = lp.make_kernel(
''' { [i,j]: 0<=i,j<n } ''',
''' out[i] = sum(j, a[i,j])''')
knl = lp.add_and_infer_dtypes(knl, {"a": np.float32})
knl = lp.add_prefetch(
knl, "a[:,:]", default_tag=None)
# Make sure that code generation succeeds even if
# there are variable-length arrays.
knl = lp.preprocess_kernel(knl)
for k in lp.generate_loop_schedules(knl):
lp.generate_code(k)
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def test_indexof(ctx_factory):
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
knl = lp.make_kernel(
''' { [i,j]: 0<=i,j<5 } ''',
''' out[i,j] = indexof(out[i,j])''')
knl = lp.set_options(knl, write_cl=True)
(evt, (out,)) = knl(queue)
out = out.get()
assert np.array_equal(out.ravel(order="C"), np.arange(25))
def test_indexof_vec(ctx_factory):
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
if ctx.devices[0].platform.name.startswith("Portable"):
# Accurate as of 2015-10-08
pytest.skip("POCL miscompiles vector code")
knl = lp.make_kernel(
''' { [i,j,k]: 0<=i,j,k<4 } ''',
''' out[i,j,k] = indexof_vec(out[i,j,k])''')
knl = lp.tag_inames(knl, {"i": "vec"})
knl = lp.tag_data_axes(knl, "out", "vec,c,c")
knl = lp.set_options(knl, write_cl=True)
(evt, (out,)) = knl(queue)
#out = out.get()
#assert np.array_equal(out.ravel(order="C"), np.arange(25))
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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)
])
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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)
def test_is_expression_equal():
from loopy.symbolic import is_expression_equal
from pymbolic import var
x = var("x")
y = var("y")
assert is_expression_equal(x+2, 2+x)
assert is_expression_equal((x+2)**2, x**2 + 4*x + 4)
assert is_expression_equal((x+y)**2, x**2 + 2*x*y + y**2)
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def test_collect_common_factors(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
"{[i,j,k]: 0<=i,j<n}",
"""
<float32> out_tmp = 0 {id=out_init,inames=i}
out_tmp = out_tmp + alpha[i]*a[i,j]*b1[j] {id=out_up1,dep=out_init}
out_tmp = out_tmp + alpha[i]*a[j,i]*b2[j] {id=out_up2,dep=out_init}
out[i] = out_tmp {dep=out_up1:out_up2}
""")
knl = lp.add_and_infer_dtypes(knl,
dict(a=np.float32, alpha=np.float32, b1=np.float32, b2=np.float32))
ref_knl = knl
knl = lp.split_iname(knl, "i", 256, outer_tag="g.0", inner_tag="l.0")
knl = lp.collect_common_factors_on_increment(knl, "out_tmp")
lp.auto_test_vs_ref(ref_knl, ctx, knl, parameters=dict(n=13))
def test_ispc_target(occa_mode=False):
from loopy.target.ispc import ISPCTarget
knl = lp.make_kernel(
"{ [i]: 0<=i<n }",
"out[i] = 2*a[i]",
[
lp.GlobalArg("out,a", np.float32, shape=lp.auto),
"..."
],
target=ISPCTarget(occa_mode=occa_mode))
knl = lp.split_iname(knl, "i", 8, 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"])
print(
lp.generate_code(
lp.get_one_scheduled_kernel(
lp.preprocess_kernel(knl)))[0])
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def test_cuda_target():
from loopy.target.cuda import CudaTarget
knl = lp.make_kernel(
"{ [i]: 0<=i<n }",
"out[i] = 2*a[i]",
[
lp.GlobalArg("out,a", np.float32, shape=lp.auto),
"..."
],
target=CudaTarget())
knl = lp.split_iname(knl, "i", 8, 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"])
print(
lp.generate_code(
lp.get_one_scheduled_kernel(
lp.preprocess_kernel(knl)))[0])
def test_chunk_iname(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
"{ [i]: 0<=i<n }",
"out[i] = 2*a[i]",
[
lp.GlobalArg("out,a", np.float32, shape=lp.auto),
"..."
],
assumptions="n>0")
ref_knl = knl
knl = lp.chunk_iname(knl, "i", 3, inner_tag="l.0")
knl = lp.set_loop_priority(knl, "i_outer, i_inner")
lp.auto_test_vs_ref(ref_knl, ctx, knl, parameters=dict(n=130))
if __name__ == "__main__":
if len(sys.argv) > 1:
exec(sys.argv[1])
else:
from py.test.cmdline import main
main([__file__])
# vim: foldmethod=marker