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))
def test_ispc_streaming_stores():
stream_dtype = np.float32
index_dtype = np.int32
knl = lp.make_kernel(
"{[i]: 0<=i<n}",
"a[i] = b[i] + scalar * c[i]",
target=lp.ISPCTarget(), index_dtype=index_dtype,
name="stream_triad")
vars = ["a", "b", "c", "scalar"]
knl = lp.assume(knl, "n>0")
knl = lp.split_iname(
knl, "i", 2**18, outer_tag="g.0", slabs=(0, 1))
knl = lp.split_iname(knl, "i_inner", 8, inner_tag="l.0")
knl = lp.tag_instructions(knl, "!streaming_store")
knl = lp.add_and_infer_dtypes(knl, {
var: stream_dtype
for var in vars
})
knl = lp.set_argument_order(knl, vars + ["n"])
knl = lp.preprocess_kernel(knl)
knl = lp.get_one_scheduled_kernel(knl)
lp.generate_code_v2(knl).all_code()
def test_ispc_streaming_stores():
stream_dtype = np.float32
index_dtype = np.int32
knl = lp.make_kernel(
"{[i]: 0<=i<n}",
"a[i] = b[i] + scalar * c[i]",
target=lp.ISPCTarget(), index_dtype=index_dtype,
name="stream_triad")
vars = ["a", "b", "c", "scalar"]
knl = lp.assume(knl, "n>0")
knl = lp.split_iname(
knl, "i", 2**18, outer_tag="g.0", slabs=(0, 1))
knl = lp.split_iname(knl, "i_inner", 8, inner_tag="l.0")
knl = lp.tag_instructions(knl, "!streaming_store")
knl = lp.add_and_infer_dtypes(knl, {
var: stream_dtype
for var in vars
})
knl = lp.set_argument_order(knl, vars + ["n"])
knl = lp.preprocess_kernel(knl)
knl = lp.get_one_scheduled_kernel(knl)
lp.generate_code_v2(knl).all_code()
def test_forced_iname_deps_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();", assignees="phi")
from pymbolic.primitives import Subscript, Variable
i2 = lp.Assignment("a",
lp.Reduction("sum", "j",
Subscript(Variable("phi"), Variable("j"))),
forced_iname_deps=frozenset(),
forced_iname_deps_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_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 lp.needs_iname_duplication(knl)
assert len(list(lp.get_iname_duplication_options(knl))) == 4
for inames, insns in lp.get_iname_duplication_options(knl):
fixed_knl = lp.duplicate_inames(knl, inames, insns)
assert not lp.needs_iname_duplication(fixed_knl)
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 lp.needs_iname_duplication(knl)
assert len(list(lp.get_iname_duplication_options(knl))) == 10
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 lp.needs_iname_duplication(knl)
assert len(list(lp.get_iname_duplication_options(knl))) == 4
for inames, insns in lp.get_iname_duplication_options(knl):
fixed_knl = lp.duplicate_inames(knl, inames, insns)
assert not lp.needs_iname_duplication(fixed_knl)
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 lp.needs_iname_duplication(knl)
assert len(list(lp.get_iname_duplication_options(knl))) == 10
def test_forced_iname_deps_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();",
assignees="phi")
from pymbolic.primitives import Subscript, Variable
i2 = lp.Assignment("a",
lp.Reduction("sum", "j", Subscript(Variable("phi"), Variable("j"))),
forced_iname_deps=frozenset(),
forced_iname_deps_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_divisibility_assumption(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
"[n] -> {[i]: 0<=i<n}",
[
"b[i] = 2*a[i]"
],
[
lp.GlobalArg("a", np.float32, shape=("n",)),
lp.GlobalArg("b", np.float32, shape=("n",)),
lp.ValueArg("n", np.int32),
],
assumptions="n>=1 and (exists zz: n = 16*zz)")
ref_knl = knl
knl = lp.split_iname(knl, "i", 16)
knl = lp.preprocess_kernel(knl, ctx.devices[0])
for k in lp.generate_loop_schedules(knl):
code = lp.generate_code(k)
assert "if" not in code
lp.auto_test_vs_ref(ref_knl, ctx, knl,
parameters={"n": 16**3})
def test_ilp_race_matmul(ctx_factory):
dtype = np.float32
order = "C"
n = 9
knl = lp.make_kernel(
"{[i,j,k]: 0<=i,j,k<%d}" % n,
[
"c[i, j] = sum(k, a[i, k]*b[k, j])"
],
[
lp.ImageArg("a", dtype, shape=(n, n)),
lp.ImageArg("b", dtype, shape=(n, n)),
lp.GlobalArg("c", dtype, shape=(n, n), order=order),
],
name="matmul")
knl = lp.split_iname(knl, "j", 2, outer_tag="ilp", inner_tag="l.0")
knl = lp.split_iname(knl, "k", 2)
knl = lp.add_prefetch(knl, 'b', ["k_inner"])
with lp.CacheMode(False):
from loopy.diagnostic import WriteRaceConditionWarning
from warnings import catch_warnings
with catch_warnings(record=True) as warn_list:
knl = lp.preprocess_kernel(knl)
list(lp.generate_loop_schedules(knl))
assert any(isinstance(w.message, WriteRaceConditionWarning)
for w in warn_list)
def test_divisibility_assumption(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
"[n] -> {[i]: 0<=i<n}",
[
"b[i] = 2*a[i]"
],
[
lp.GlobalArg("a", np.float32, shape=("n",)),
lp.GlobalArg("b", np.float32, shape=("n",)),
lp.ValueArg("n", np.int32),
],
assumptions="n>=1 and (exists zz: n = 16*zz)")
ref_knl = knl
knl = lp.split_iname(knl, "i", 16)
knl = lp.preprocess_kernel(knl, ctx.devices[0])
for k in lp.generate_loop_schedules(knl):
code = lp.generate_code(k)
assert "if" not in code
lp.auto_test_vs_ref(ref_knl, ctx, knl,
parameters={"n": 16**3})
def test_ilp_write_race_detection_global(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
"[n] -> {[i,j]: 0<=i,j<n }",
[
"a[i] = 5+i+j",
],
[
lp.GlobalArg("a", np.float32),
lp.ValueArg("n", np.int32, approximately=1000),
],
assumptions="n>=1")
knl = lp.tag_inames(knl, dict(j="ilp"))
knl = lp.preprocess_kernel(knl, ctx.devices[0])
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_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))
def test_child_invalid_type_cast():
from pymbolic import var
knl = lp.make_kernel("{[i]: 0<=i<n}", [
"<> ctr = make_uint2(0, 0)",
lp.Assignment("a[i]",
lp.TypeCast(np.int64, var("ctr")) << var("i"))
])
with pytest.raises(lp.LoopyError):
knl = lp.preprocess_kernel(knl)
def test_child_invalid_type_cast():
from pymbolic import var
knl = lp.make_kernel(
"{[i]: 0<=i<n}",
["<> ctr = make_uint2(0, 0)",
lp.Assignment("a[i]", lp.TypeCast(np.int64, var("ctr")) << var("i"))]
)
with pytest.raises(lp.LoopyError):
knl = lp.preprocess_kernel(knl)
def test_nonsense_reduction(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
"{[i]: 0<=i<100}", """
a[i] = sum(i, 2)
""", [lp.GlobalArg("a", np.float32, shape=(100, ))])
import pytest
with pytest.raises(RuntimeError):
knl = lp.preprocess_kernel(knl, ctx.devices[0])
def test_ilp_write_race_avoidance_local(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel("{[i,j]: 0<=i<16 and 0<=j<17 }", [
"<> a[i] = 5+i+j",
], [])
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 }", [
"<> a = 5+j",
], [])
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, )
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)
def test_owed_barriers(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel("{[i]: 0<=i<100}", ["<float32> z[i] = a[i]"],
[lp.GlobalArg("a", np.float32, shape=(100, ))])
knl = lp.tag_inames(knl, dict(i="l.0"))
knl = lp.preprocess_kernel(knl, ctx.devices[0])
kernel_gen = lp.generate_loop_schedules(knl)
for gen_knl in kernel_gen:
compiled = lp.CompiledKernel(ctx, gen_knl)
print(compiled.get_code())
def test_nonsense_reduction(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
"{[i]: 0<=i<100}",
"""
a[i] = sum(i, 2)
""",
[lp.GlobalArg("a", np.float32, shape=(100,))]
)
import pytest
with pytest.raises(RuntimeError):
knl = lp.preprocess_kernel(knl, ctx.devices[0])
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)
def cached_data(params):
data = {}
np.random.seed(17)
logging.basicConfig(level=logging.INFO)
for param in params:
data[param] = {}
expn = _sumpy_kernel_init(param)
data[param]["setup"] = expn
knl = _sumpy_kernel_make(expn, param)
knl = lp.preprocess_kernel(knl)
data[param]["instantiated"] = knl
scheduled = knl.with_kernel(lp.get_one_scheduled_kernel(knl["loopy_kernel"],
knl.callables_table))
data[param]["scheduled"] = scheduled
return data
def test_simple_side_effect(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
"{[i,j]: 0<=i,j<100}", """
a[i] = a[i] + 1
""", [lp.GlobalArg("a", np.float32, shape=(100, ))])
knl = lp.preprocess_kernel(knl, ctx.devices[0])
kernel_gen = lp.generate_loop_schedules(knl)
for gen_knl in kernel_gen:
print(gen_knl)
compiled = lp.CompiledKernel(ctx, gen_knl)
print(compiled.get_code())
def test_ilp_write_race_avoidance_private(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
"{[j]: 0<=j<16 }",
[
"<> a = 5+j",
],
[])
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,)
def test_ilp_write_race_avoidance_local(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
"{[i,j]: 0<=i<16 and 0<=j<17 }",
[
"<> a[i] = 5+i+j",
],
[])
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_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_multiple_writes_to_local_temporary():
# 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"))
knl = lp.preprocess_kernel(knl)
for k in lp.generate_loop_schedules(knl):
code, _ = lp.generate_code(k)
print(code)
def test_empty_reduction(ctx_factory):
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
knl = lp.make_kernel(
["{[i]: 0<=i<20}", "[i] -> {[j]: 0<=j<0}"],
"a[i] = sum(j, j)",
)
knl = lp.preprocess_kernel(knl)
print(knl)
knl = lp.set_options(knl, write_cl=True)
evt, (a, ) = knl(queue)
assert (a.get() == 0).all()
def test_multiple_writes_to_local_temporary():
# 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"))
knl = lp.preprocess_kernel(knl)
for k in lp.generate_loop_schedules(knl):
code, _ = lp.generate_code(k)
print(code)
def test_multi_cse(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel("{[i]: 0<=i<100}",
["<float32> z[i] = a[i] + a[i]**2"],
[lp.GlobalArg("a", np.float32, shape=(100, ))],
local_sizes={0: 16})
knl = lp.split_iname(knl, "i", 16, inner_tag="l.0")
knl = lp.add_prefetch(knl, "a", [])
knl = lp.preprocess_kernel(knl, ctx.devices[0])
kernel_gen = lp.generate_loop_schedules(knl)
for gen_knl in kernel_gen:
compiled = lp.CompiledKernel(ctx, gen_knl)
print(compiled.get_code())
def test_wg_too_small(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel("{[i]: 0<=i<100}",
["<float32> z[i] = a[i] {id=copy}"],
[lp.GlobalArg("a", np.float32, shape=(100, ))],
local_sizes={0: 16})
knl = lp.tag_inames(knl, dict(i="l.0"))
knl = lp.preprocess_kernel(knl, ctx.devices[0])
kernel_gen = lp.generate_loop_schedules(knl)
import pytest
for gen_knl in kernel_gen:
with pytest.raises(RuntimeError):
lp.CompiledKernel(ctx, gen_knl).get_code()
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"])
codegen_result = lp.generate_code_v2(
lp.get_one_scheduled_kernel(lp.preprocess_kernel(knl)))
print(codegen_result.device_code())
print(codegen_result.host_code())
def test_assume(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel("{[i]: 0<=i<n}", "a[i] = a[i] + 1",
[lp.GlobalArg("a", np.float32, shape="n"), "..."])
knl = lp.split_iname(knl, "i", 16)
knl = lp.set_loop_priority(knl, "i_outer,i_inner")
knl = lp.assume(knl, "n mod 16 = 0")
knl = lp.assume(knl, "n > 10")
knl = lp.preprocess_kernel(knl, ctx.devices[0])
kernel_gen = lp.generate_loop_schedules(knl)
for gen_knl in kernel_gen:
print(gen_knl)
compiled = lp.CompiledKernel(ctx, gen_knl)
print(compiled.get_code())
assert "if" not in compiled.get_code()
def test_eq_constraint(ctx_factory):
logging.basicConfig(level=logging.INFO)
ctx = ctx_factory()
knl = lp.make_kernel("{[i,j]: 0<= i,j < 32}", ["a[i] = b[i]"], [
lp.GlobalArg("a", np.float32, shape=(1000, )),
lp.GlobalArg("b", np.float32, shape=(1000, ))
])
knl = lp.split_iname(knl, "i", 16, outer_tag="g.0")
knl = lp.split_iname(knl, "i_inner", 16, outer_tag=None, inner_tag="l.0")
knl = lp.preprocess_kernel(knl, ctx.devices[0])
kernel_gen = lp.generate_loop_schedules(knl)
for knl in kernel_gen:
print(lp.generate_code(knl))
def test_simple_side_effect(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
"{[i,j]: 0<=i,j<100}",
"""
a[i] = a[i] + 1
""",
[lp.GlobalArg("a", np.float32, shape=(100,))]
)
knl = lp.preprocess_kernel(knl, ctx.devices[0])
kernel_gen = lp.generate_loop_schedules(knl)
for gen_knl in kernel_gen:
print(gen_knl)
compiled = lp.CompiledKernel(ctx, gen_knl)
print(compiled.get_code())
def test_owed_barriers(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
"{[i]: 0<=i<100}",
[
"<float32> z[i] = a[i]"
],
[lp.GlobalArg("a", np.float32, shape=(100,))]
)
knl = lp.tag_inames(knl, dict(i="l.0"))
knl = lp.preprocess_kernel(knl, ctx.devices[0])
kernel_gen = lp.generate_loop_schedules(knl)
for gen_knl in kernel_gen:
compiled = lp.CompiledKernel(ctx, gen_knl)
print(compiled.get_code())
def test_type_inference_no_artificial_doubles(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel("{[i]: 0<=i<n}",
"""
<> bb = a[i] - b[i]
c[i] = bb
""", [
lp.GlobalArg("a", np.float32, shape=("n", )),
lp.GlobalArg("b", np.float32, shape=("n", )),
lp.GlobalArg("c", np.float32, shape=("n", )),
lp.ValueArg("n", np.int32),
],
assumptions="n>=1")
knl = lp.preprocess_kernel(knl, ctx.devices[0])
for k in lp.generate_loop_schedules(knl):
code = lp.generate_code(k)
assert "double" not in code
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")
eN = var("eN") # noqa
k = var("k")
u = var("u")
from functools import partial
l_sum = partial(lp.Reduction, "sum", allow_simultaneous=True)
Instr = lp.Assignment # noqa
knl = lp.make_kernel(
"{[I, k]: 0<=I<nSpace and 0<=k<nQuad}",
[
Instr(f[I], l_sum(k, q_v[k, I]*u)),
Instr(df[I], l_sum(k, q_v[k, I])),
],
[
lp.GlobalArg("q_v", np.float64, shape="nQuad, nSpace"),
lp.GlobalArg("f,df", np.float64, shape="nSpace"),
lp.ValueArg("u", np.float64),
"...",
],
target=CTarget(),
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.prioritize_loops(knl, "I,k_outer,k_inner")
knl = lp.preprocess_kernel(knl)
knl = lp.get_one_scheduled_kernel(knl)
print(lp.generate_body(knl))
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")
eN = var("eN") # noqa
k = var("k")
u = var("u")
from functools import partial
l_sum = partial(lp.Reduction, "sum", allow_simultaneous=True)
Instr = lp.Assignment # noqa
knl = lp.make_kernel(
"{[I, k]: 0<=I<nSpace and 0<=k<nQuad}",
[
Instr(f[I], l_sum(k, q_v[k, I]*u)),
Instr(df[I], l_sum(k, q_v[k, I])),
],
[
lp.GlobalArg("q_v", np.float64, shape="nQuad, nSpace"),
lp.GlobalArg("f,df", np.float64, shape="nSpace"),
lp.ValueArg("u", np.float64),
"...",
],
target=CTarget(),
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))
def test_dependent_loop_bounds_3(ctx_factory):
# The point of this test is that it shows a dependency between
# domains that is exclusively mediated by the row_len temporary.
# It also makes sure that row_len gets read before any
# conditionals use it.
dtype = np.dtype(np.float32)
ctx = ctx_factory()
knl = lp.make_kernel(
[
"{[i]: 0<=i<n}",
"{[jj]: 0<=jj<row_len}",
],
[
"<> row_len = a_row_lengths[i]",
"a[i,jj] = 1",
],
[
lp.GlobalArg("a_row_lengths", np.int32, shape=lp.auto),
lp.GlobalArg("a", dtype, shape=("n,n"), order="C"),
lp.ValueArg("n", np.int32),
])
assert knl.parents_per_domain()[1] == 0
knl = lp.split_iname(knl, "i", 128, outer_tag="g.0",
inner_tag="l.0")
cknl = lp.CompiledKernel(ctx, knl)
print("---------------------------------------------------")
print(cknl.get_highlighted_code())
print("---------------------------------------------------")
knl_bad = lp.split_iname(knl, "jj", 128, outer_tag="g.1",
inner_tag="l.1")
knl = lp.preprocess_kernel(knl, ctx.devices[0])
import pytest
with pytest.raises(RuntimeError):
list(lp.generate_loop_schedules(knl_bad))
def test_assume(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
"{[i]: 0<=i<n}",
"a[i] = a[i] + 1",
[lp.GlobalArg("a", np.float32, shape="n"), "..."])
knl = lp.split_iname(knl, "i", 16)
knl = lp.set_loop_priority(knl, "i_outer,i_inner")
knl = lp.assume(knl, "n mod 16 = 0")
knl = lp.assume(knl, "n > 10")
knl = lp.preprocess_kernel(knl, ctx.devices[0])
kernel_gen = lp.generate_loop_schedules(knl)
for gen_knl in kernel_gen:
print(gen_knl)
compiled = lp.CompiledKernel(ctx, gen_knl)
print(compiled.get_code())
assert "if" not in compiled.get_code()
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_wg_too_small(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
"{[i]: 0<=i<100}",
[
"<float32> z[i] = a[i] {id=copy}"
],
[lp.GlobalArg("a", np.float32, shape=(100,))],
local_sizes={0: 16})
knl = lp.tag_inames(knl, dict(i="l.0"))
knl = lp.preprocess_kernel(knl, ctx.devices[0])
kernel_gen = lp.generate_loop_schedules(knl)
import pytest
for gen_knl in kernel_gen:
with pytest.raises(RuntimeError):
lp.CompiledKernel(ctx, gen_knl).get_code()
def test_multi_cse(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
"{[i]: 0<=i<100}",
[
"<float32> z[i] = a[i] + a[i]**2"
],
[lp.GlobalArg("a", np.float32, shape=(100,))],
local_sizes={0: 16})
knl = lp.split_iname(knl, "i", 16, inner_tag="l.0")
knl = lp.add_prefetch(knl, "a", [])
knl = lp.preprocess_kernel(knl, ctx.devices[0])
kernel_gen = lp.generate_loop_schedules(knl)
for gen_knl in kernel_gen:
compiled = lp.CompiledKernel(ctx, gen_knl)
print(compiled.get_code())
def test_dependent_loop_bounds_3(ctx_factory):
# The point of this test is that it shows a dependency between
# domains that is exclusively mediated by the row_len temporary.
# It also makes sure that row_len gets read before any
# conditionals use it.
dtype = np.dtype(np.float32)
ctx = ctx_factory()
knl = lp.make_kernel(
[
"{[i]: 0<=i<n}",
"{[jj]: 0<=jj<row_len}",
],
[
"<> row_len = a_row_lengths[i]",
"a[i,jj] = 1",
],
[
lp.GlobalArg("a_row_lengths", np.int32, shape=lp.auto),
lp.GlobalArg("a", dtype, shape=("n,n"), order="C"),
lp.ValueArg("n", np.int32),
])
assert knl.parents_per_domain()[1] == 0
knl = lp.split_iname(knl, "i", 128, outer_tag="g.0",
inner_tag="l.0")
cknl = lp.CompiledKernel(ctx, knl)
print("---------------------------------------------------")
print(cknl.get_highlighted_code())
print("---------------------------------------------------")
knl_bad = lp.split_iname(knl, "jj", 128, outer_tag="g.1",
inner_tag="l.1")
knl = lp.preprocess_kernel(knl, ctx.devices[0])
with pytest.raises(RuntimeError):
list(lp.generate_loop_schedules(knl_bad))
def test_type_inference_no_artificial_doubles(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
"{[i]: 0<=i<n}",
"""
<> bb = a[i] - b[i]
c[i] = bb
""",
[
lp.GlobalArg("a", np.float32, shape=("n",)),
lp.GlobalArg("b", np.float32, shape=("n",)),
lp.GlobalArg("c", np.float32, shape=("n",)),
lp.ValueArg("n", np.int32),
],
assumptions="n>=1")
knl = lp.preprocess_kernel(knl, ctx.devices[0])
for k in lp.generate_loop_schedules(knl):
code = lp.generate_code(k)
assert "double" not in code
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"])
codegen_result = lp.generate_code_v2(
lp.get_one_scheduled_kernel(
lp.preprocess_kernel(knl)))
print(codegen_result.device_code())
print(codegen_result.host_code())
def test_argmax(ctx_factory):
logging.basicConfig(level=logging.INFO)
dtype = np.dtype(np.float32)
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
n = 10000
knl = lp.make_kernel(
"{[i]: 0<=i<%d}" % n, """
max_val, max_idx = argmax(i, abs(a[i]), i)
""")
knl = lp.add_and_infer_dtypes(knl, {"a": np.float32})
print(lp.preprocess_kernel(knl))
knl = lp.set_options(knl, write_cl=True, highlight_cl=True)
a = np.random.randn(10000).astype(dtype)
evt, (max_idx, max_val) = knl(queue, a=a, out_host=True)
assert max_val == np.max(np.abs(a))
assert max_idx == np.where(np.abs(a) == max_val)[-1]
def test_eq_constraint(ctx_factory):
logging.basicConfig(level=logging.INFO)
ctx = ctx_factory()
knl = lp.make_kernel(
"{[i,j]: 0<= i,j < 32}",
[
"a[i] = b[i]"
],
[
lp.GlobalArg("a", np.float32, shape=(1000,)),
lp.GlobalArg("b", np.float32, shape=(1000,))
])
knl = lp.split_iname(knl, "i", 16, outer_tag="g.0")
knl = lp.split_iname(knl, "i_inner", 16, outer_tag=None, inner_tag="l.0")
knl = lp.preprocess_kernel(knl, ctx.devices[0])
kernel_gen = lp.generate_loop_schedules(knl)
for knl in kernel_gen:
print(lp.generate_code(knl))
def test_argmax(ctx_factory):
logging.basicConfig(level=logging.INFO)
dtype = np.dtype(np.float32)
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
n = 10000
knl = lp.make_kernel(
"{[i]: 0<=i<%d}" % n,
"""
max_val, max_idx = argmax(i, fabs(a[i]))
""")
knl = lp.add_and_infer_dtypes(knl, {"a": np.float32})
print(lp.preprocess_kernel(knl))
knl = lp.set_options(knl, write_cl=True, highlight_cl=True)
a = np.random.randn(10000).astype(dtype)
evt, (max_idx, max_val) = knl(queue, a=a, out_host=True)
assert max_val == np.max(np.abs(a))
assert max_idx == np.where(np.abs(a) == max_val)[-1]
def test_global_mc_parallel_reduction(ctx_factory, size):
ctx = ctx_factory()
import pyopencl.version # noqa
if cl.version.VERSION < (2016, 2):
pytest.skip("Random123 RNG not supported in PyOpenCL < 2016.2")
knl = lp.make_kernel(
"{[i]: 0 <= i < n }", """
for i
<> key = make_uint2(i, 324830944) {inames=i}
<> ctr = make_uint4(0, 1, 2, 3) {inames=i,id=init_ctr}
<> vals, ctr = philox4x32_f32(ctr, key) {dep=init_ctr}
end
z = sum(i, vals.s0 + vals.s1 + vals.s2 + vals.s3)
""")
ref_knl = knl
ref_knl = lp.add_dtypes(ref_knl, {"n": np.int32})
gsize = 128
knl = lp.split_iname(knl, "i", gsize * 20)
knl = lp.split_iname(knl, "i_inner", gsize, outer_tag="l.0")
knl = lp.split_reduction_inward(knl, "i_inner_inner")
knl = lp.split_reduction_inward(knl, "i_inner_outer")
from loopy.transform.data import reduction_arg_to_subst_rule
knl = reduction_arg_to_subst_rule(knl, "i_outer")
knl = lp.precompute(knl,
"red_i_outer_arg",
"i_outer",
temporary_address_space=lp.AddressSpace.GLOBAL,
default_tag="l.auto")
knl = lp.preprocess_kernel(knl)
knl = lp.add_dependency(knl, "writes:acc_i_outer",
"id:red_i_outer_arg_barrier")
lp.auto_test_vs_ref(ref_knl, ctx, knl, parameters={"n": size})
def test_ilp_write_race_detection_global(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel("[n] -> {[i,j]: 0<=i,j<n }", [
"a[i] = 5+i+j",
], [
lp.GlobalArg("a", np.float32),
lp.ValueArg("n", np.int32, approximately=1000),
],
assumptions="n>=1")
knl = lp.tag_inames(knl, dict(j="ilp"))
knl = lp.preprocess_kernel(knl, ctx.devices[0])
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 auto_test_vs_ref(ref_knl,
ctx,
test_knl=None,
op_count=[],
op_label=[],
parameters={},
print_ref_code=False,
print_code=True,
warmup_rounds=2,
dump_binary=False,
fills_entire_output=None,
do_check=True,
check_result=None,
max_test_kernel_count=1,
quiet=False,
blacklist_ref_vendors=[]):
"""Compare results of `ref_knl` to the kernels generated by
scheduling *test_knl*.
:arg check_result: a callable with :class:`numpy.ndarray` arguments
*(result, reference_result)* returning a a tuple (class:`bool`,
message) indicating correctness/acceptability of the result
:arg max_test_kernel_count: Stop testing after this many *test_knl*
"""
import pyopencl as cl
if test_knl is None:
test_knl = ref_knl
do_check = False
if len(ref_knl.args) != len(test_knl.args):
raise LoopyError("ref_knl and test_knl do not have the same number "
"of arguments")
for i, (ref_arg, test_arg) in enumerate(zip(ref_knl.args, test_knl.args)):
if ref_arg.name != test_arg.name:
raise LoopyError(
"ref_knl and test_knl argument lists disagree at index "
"%d (1-based)" % (i + 1))
if ref_arg.dtype != test_arg.dtype:
raise LoopyError(
"ref_knl and test_knl argument lists disagree at index "
"%d (1-based)" % (i + 1))
from loopy.compiled import CompiledKernel
from loopy.target.execution import get_highlighted_code
if isinstance(op_count, (int, float)):
warn("op_count should be a list", stacklevel=2)
op_count = [op_count]
if isinstance(op_label, str):
warn("op_label should be a list", stacklevel=2)
op_label = [op_label]
from time import time
if check_result is None:
check_result = _default_check_result
if fills_entire_output is not None:
warn("fills_entire_output is deprecated",
DeprecationWarning,
stacklevel=2)
# {{{ compile and run reference code
from loopy.type_inference import infer_unknown_types
ref_knl = infer_unknown_types(ref_knl, expect_completion=True)
found_ref_device = False
ref_errors = []
from loopy.kernel.data import ImageArg
need_ref_image_support = any(
isinstance(arg, ImageArg) for arg in ref_knl.args)
for dev in _enumerate_cl_devices_for_ref_test(blacklist_ref_vendors,
need_ref_image_support):
ref_ctx = cl.Context([dev])
ref_queue = cl.CommandQueue(
ref_ctx, properties=cl.command_queue_properties.PROFILING_ENABLE)
pp_ref_knl = lp.preprocess_kernel(ref_knl)
for knl in lp.generate_loop_schedules(pp_ref_knl):
ref_sched_kernel = knl
break
logger.info("{} (ref): trying {} for the reference calculation".format(
ref_knl.name, dev))
ref_compiled = CompiledKernel(ref_ctx, ref_sched_kernel)
if not quiet and print_ref_code:
print(75 * "-")
print("Reference Code:")
print(75 * "-")
print(get_highlighted_code(ref_compiled.get_code()))
print(75 * "-")
ref_kernel_info = ref_compiled.kernel_info(frozenset())
try:
ref_args, ref_arg_data = \
make_ref_args(ref_sched_kernel,
ref_kernel_info.implemented_data_info,
ref_queue, parameters)
ref_args["out_host"] = False
except cl.RuntimeError as e:
if e.code == cl.status_code.IMAGE_FORMAT_NOT_SUPPORTED:
import traceback
ref_errors.append("\n".join([
75 * "-",
"On %s:" % dev, 75 * "-",
traceback.format_exc(), 75 * "-"
]))
continue
else:
raise
found_ref_device = True
if not do_check:
break
ref_queue.finish()
logger.info("{} (ref): using {} for the reference calculation".format(
ref_knl.name, dev))
logger.info("%s (ref): run" % ref_knl.name)
ref_start = time()
if not AUTO_TEST_SKIP_RUN:
ref_evt, _ = ref_compiled(ref_queue, **ref_args)
else:
ref_evt = cl.enqueue_marker(ref_queue)
ref_queue.finish()
ref_stop = time()
ref_elapsed_wall = ref_stop - ref_start
logger.info("%s (ref): run done" % ref_knl.name)
ref_evt.wait()
ref_elapsed_event = 1e-9 * (ref_evt.profile.END -
ref_evt.profile.START)
break
if not found_ref_device:
raise LoopyError("could not find a suitable device for the "
"reference computation.\n"
"These errors were encountered:\n" +
"\n".join(ref_errors))
# }}}
# {{{ compile and run parallel code
need_check = do_check
queue = cl.CommandQueue(
ctx, properties=cl.command_queue_properties.PROFILING_ENABLE)
from loopy.kernel import KernelState
from loopy.target.pyopencl import PyOpenCLTarget
if test_knl.state not in [
KernelState.PREPROCESSED, KernelState.LINEARIZED
]:
if isinstance(test_knl.target, PyOpenCLTarget):
test_knl = test_knl.copy(target=PyOpenCLTarget(ctx.devices[0]))
test_knl = lp.preprocess_kernel(test_knl)
if not test_knl.schedule:
test_kernels = lp.generate_loop_schedules(test_knl)
else:
test_kernels = [test_knl]
test_kernel_count = 0
from loopy.type_inference import infer_unknown_types
for i, kernel in enumerate(test_kernels):
test_kernel_count += 1
if test_kernel_count > max_test_kernel_count:
break
kernel = infer_unknown_types(kernel, expect_completion=True)
compiled = CompiledKernel(ctx, kernel)
kernel_info = compiled.kernel_info(frozenset())
args = make_args(kernel, kernel_info.implemented_data_info, queue,
ref_arg_data, parameters)
args["out_host"] = False
if not quiet:
print(75 * "-")
print("Kernel #%d:" % i)
print(75 * "-")
if print_code:
print(compiled.get_highlighted_code())
print(75 * "-")
if dump_binary:
# {{{ find cl program
for name in dir(kernel_info.cl_kernels):
if name.startswith("__"):
continue
cl_kernel = getattr(kernel_info.cl_kernels, name)
cl_program = cl_kernel.get_info(cl.kernel_info.PROGRAM)
break
else:
assert False, "could not find cl_program"
# }}}
print(type(cl_program))
if hasattr(cl_program, "binaries"):
print(cl_program.binaries[0])
print(75 * "-")
logger.info("%s: run warmup" % (knl.name))
for i in range(warmup_rounds):
if not AUTO_TEST_SKIP_RUN:
compiled(queue, **args)
if need_check and not AUTO_TEST_SKIP_RUN:
for arg_desc in ref_arg_data:
if arg_desc is None:
continue
if not arg_desc.needs_checking:
continue
from pyopencl.compyte.array import as_strided
ref_ary = as_strided(
arg_desc.ref_storage_array.get(),
shape=arg_desc.ref_shape,
strides=arg_desc.ref_numpy_strides).flatten()
test_ary = as_strided(
arg_desc.test_storage_array.get(),
shape=arg_desc.test_shape,
strides=arg_desc.test_numpy_strides).flatten()
common_len = min(len(ref_ary), len(test_ary))
ref_ary = ref_ary[:common_len]
test_ary = test_ary[:common_len]
error_is_small, error = check_result(test_ary, ref_ary)
if not error_is_small:
raise AutomaticTestFailure(error)
need_check = False
events = []
queue.finish()
logger.info("%s: warmup done" % (knl.name))
logger.info("%s: timing run" % (knl.name))
timing_rounds = max(warmup_rounds, 1)
while True:
from time import time
start_time = time()
evt_start = cl.enqueue_marker(queue)
for i in range(timing_rounds):
if not AUTO_TEST_SKIP_RUN:
evt, _ = compiled(queue, **args)
events.append(evt)
else:
events.append(cl.enqueue_marker(queue))
evt_end = cl.enqueue_marker(queue)
queue.finish()
stop_time = time()
for evt in events:
evt.wait()
evt_start.wait()
evt_end.wait()
elapsed_event = (1e-9*events[-1].profile.END
- 1e-9*events[0].profile.START) \
/ timing_rounds
try:
elapsed_event_marker = ((1e-9 * evt_end.profile.START -
1e-9 * evt_start.profile.START) /
timing_rounds)
except cl.RuntimeError:
elapsed_event_marker = None
elapsed_wall = (stop_time - start_time) / timing_rounds
if elapsed_wall * timing_rounds < 0.3:
timing_rounds *= 4
else:
break
logger.info("%s: timing run done" % (knl.name))
rates = ""
for cnt, lbl in zip(op_count, op_label):
rates += " {:g} {}/s".format(cnt / elapsed_wall, lbl)
if not quiet:
def format_float_or_none(v):
if v is None:
return "<unavailable>"
else:
return "%g" % v
print("elapsed: %s s event, %s s marker-event %s s wall "
"(%d rounds)%s" %
(format_float_or_none(elapsed_event),
format_float_or_none(elapsed_event_marker),
format_float_or_none(elapsed_wall), timing_rounds, rates))
if do_check:
ref_rates = ""
for cnt, lbl in zip(op_count, op_label):
ref_rates += " {:g} {}/s".format(cnt / ref_elapsed_event, lbl)
if not quiet:
print("ref: elapsed: {:g} s event, {:g} s wall{}".format(
ref_elapsed_event, ref_elapsed_wall, ref_rates))
# }}}
result_dict = {}
result_dict["elapsed_event"] = elapsed_event
result_dict["elapsed_event_marker"] = elapsed_event_marker
result_dict["elapsed_wall"] = elapsed_wall
result_dict["timing_rounds"] = timing_rounds
if do_check:
result_dict["ref_elapsed_event"] = ref_elapsed_event
result_dict["ref_elapsed_wall"] = ref_elapsed_wall
return result_dict
def gen_code(knl):
knl = lp.preprocess_kernel(knl)
knl = lp.get_one_scheduled_kernel(knl)
codegen_result = lp.generate_code_v2(knl)
return codegen_result.device_code() + "\n" + codegen_result.host_code()
def auto_test_vs_ref(
ref_knl, ctx, test_knl=None, op_count=[], op_label=[], parameters={},
print_ref_code=False, print_code=True, warmup_rounds=2,
dump_binary=False,
fills_entire_output=None, do_check=True, check_result=None,
max_test_kernel_count=1,
quiet=False, blacklist_ref_vendors=[]):
"""Compare results of `ref_knl` to the kernels generated by
scheduling *test_knl*.
:arg check_result: a callable with :class:`numpy.ndarray` arguments
*(result, reference_result)* returning a a tuple (class:`bool`,
message) indicating correctness/acceptability of the result
:arg max_test_kernel_count: Stop testing after this many *test_knl*
"""
import pyopencl as cl
if test_knl is None:
test_knl = ref_knl
do_check = False
if len(ref_knl.args) != len(test_knl.args):
raise LoopyError("ref_knl and test_knl do not have the same number "
"of arguments")
for i, (ref_arg, test_arg) in enumerate(zip(ref_knl.args, test_knl.args)):
if ref_arg.name != test_arg.name:
raise LoopyError("ref_knl and test_knl argument lists disagree at index "
"%d (1-based)" % (i+1))
if ref_arg.dtype != test_arg.dtype:
raise LoopyError("ref_knl and test_knl argument lists disagree at index "
"%d (1-based)" % (i+1))
from loopy.compiled import CompiledKernel, get_highlighted_cl_code
if isinstance(op_count, (int, float)):
warn("op_count should be a list", stacklevel=2)
op_count = [op_count]
if isinstance(op_label, str):
warn("op_label should be a list", stacklevel=2)
op_label = [op_label]
from time import time
if check_result is None:
check_result = _default_check_result
if fills_entire_output is not None:
warn("fills_entire_output is deprecated", DeprecationWarning, stacklevel=2)
# {{{ compile and run reference code
from loopy.preprocess import infer_unknown_types
ref_knl = infer_unknown_types(ref_knl, expect_completion=True)
found_ref_device = False
ref_errors = []
for dev in _enumerate_cl_devices_for_ref_test(blacklist_ref_vendors):
ref_ctx = cl.Context([dev])
ref_queue = cl.CommandQueue(ref_ctx,
properties=cl.command_queue_properties.PROFILING_ENABLE)
pp_ref_knl = lp.preprocess_kernel(ref_knl)
for knl in lp.generate_loop_schedules(pp_ref_knl):
ref_sched_kernel = knl
break
logger.info("%s (ref): trying %s for the reference calculation" % (
ref_knl.name, dev))
ref_compiled = CompiledKernel(ref_ctx, ref_sched_kernel)
if not quiet and print_ref_code:
print(75*"-")
print("Reference Code:")
print(75*"-")
print(get_highlighted_cl_code(ref_compiled.code))
print(75*"-")
ref_cl_kernel_info = ref_compiled.cl_kernel_info(frozenset())
try:
ref_args, ref_arg_data = \
make_ref_args(ref_sched_kernel,
ref_cl_kernel_info.implemented_data_info,
ref_queue, parameters)
ref_args["out_host"] = False
except cl.RuntimeError as e:
if e.code == cl.status_code.IMAGE_FORMAT_NOT_SUPPORTED:
import traceback
ref_errors.append("\n".join([
75*"-",
"On %s:" % dev,
75*"-",
traceback.format_exc(),
75*"-"]))
continue
else:
raise
found_ref_device = True
if not do_check:
break
ref_queue.finish()
logger.info("%s (ref): using %s for the reference calculation" % (
ref_knl.name, dev))
logger.info("%s (ref): run" % ref_knl.name)
ref_start = time()
if not AUTO_TEST_SKIP_RUN:
ref_evt, _ = ref_compiled(ref_queue, **ref_args)
else:
ref_evt = cl.enqueue_marker(ref_queue)
ref_queue.finish()
ref_stop = time()
ref_elapsed_wall = ref_stop-ref_start
logger.info("%s (ref): run done" % ref_knl.name)
ref_evt.wait()
ref_elapsed_event = 1e-9*(ref_evt.profile.END-ref_evt.profile.START)
break
if not found_ref_device:
raise LoopyError("could not find a suitable device for the "
"reference computation.\n"
"These errors were encountered:\n"+"\n".join(ref_errors))
# }}}
# {{{ compile and run parallel code
need_check = do_check
queue = cl.CommandQueue(ctx,
properties=cl.command_queue_properties.PROFILING_ENABLE)
args = None
from loopy.kernel import kernel_state
if test_knl.state not in [
kernel_state.PREPROCESSED,
kernel_state.SCHEDULED]:
test_knl = lp.preprocess_kernel(test_knl)
if not test_knl.schedule:
test_kernels = lp.generate_loop_schedules(test_knl)
else:
test_kernels = [test_knl]
test_kernel_count = 0
from loopy.preprocess import infer_unknown_types
for i, kernel in enumerate(test_kernels):
test_kernel_count += 1
if test_kernel_count > max_test_kernel_count:
break
kernel = infer_unknown_types(kernel, expect_completion=True)
compiled = CompiledKernel(ctx, kernel)
if args is None:
cl_kernel_info = compiled.cl_kernel_info(frozenset())
args = make_args(kernel,
cl_kernel_info.implemented_data_info,
queue, ref_arg_data, parameters)
args["out_host"] = False
if not quiet:
print(75*"-")
print("Kernel #%d:" % i)
print(75*"-")
if print_code:
print(compiled.get_highlighted_code())
print(75*"-")
if dump_binary:
print(type(compiled.cl_program))
print(compiled.cl_program.binaries[0])
print(75*"-")
logger.info("%s: run warmup" % (knl.name))
for i in range(warmup_rounds):
if not AUTO_TEST_SKIP_RUN:
compiled(queue, **args)
if need_check and not AUTO_TEST_SKIP_RUN:
for arg_desc in ref_arg_data:
if arg_desc is None:
continue
if not arg_desc.needs_checking:
continue
from pyopencl.compyte.array import as_strided
ref_ary = as_strided(
arg_desc.ref_storage_array.get(),
shape=arg_desc.ref_shape,
strides=arg_desc.ref_numpy_strides).flatten()
test_ary = as_strided(
arg_desc.test_storage_array.get(),
shape=arg_desc.test_shape,
strides=arg_desc.test_numpy_strides).flatten()
common_len = min(len(ref_ary), len(test_ary))
ref_ary = ref_ary[:common_len]
test_ary = test_ary[:common_len]
error_is_small, error = check_result(test_ary, ref_ary)
if not error_is_small:
raise AutomaticTestFailure(error)
need_check = False
events = []
queue.finish()
logger.info("%s: warmup done" % (knl.name))
logger.info("%s: timing run" % (knl.name))
timing_rounds = warmup_rounds
while True:
from time import time
start_time = time()
evt_start = cl.enqueue_marker(queue)
for i in range(timing_rounds):
if not AUTO_TEST_SKIP_RUN:
evt, _ = compiled(queue, **args)
events.append(evt)
else:
events.append(cl.enqueue_marker(queue))
evt_end = cl.enqueue_marker(queue)
queue.finish()
stop_time = time()
for evt in events:
evt.wait()
evt_start.wait()
evt_end.wait()
elapsed_event = (1e-9*events[-1].profile.END
- 1e-9*events[0].profile.START) \
/ timing_rounds
try:
elapsed_event_marker = ((1e-9*evt_end.profile.START
- 1e-9*evt_start.profile.START)
/ timing_rounds)
except cl.RuntimeError:
elapsed_event_marker = None
elapsed_wall = (stop_time-start_time)/timing_rounds
if elapsed_wall * timing_rounds < 0.3:
timing_rounds *= 4
else:
break
logger.info("%s: timing run done" % (knl.name))
rates = ""
for cnt, lbl in zip(op_count, op_label):
rates += " %g %s/s" % (cnt/elapsed_wall, lbl)
if not quiet:
def format_float_or_none(v):
if v is None:
return "<unavailable>"
else:
return "%g" % v
print("elapsed: %s s event, %s s marker-event %s s wall "
"(%d rounds)%s" % (
format_float_or_none(elapsed_event),
format_float_or_none(elapsed_event_marker),
format_float_or_none(elapsed_wall), timing_rounds, rates))
if do_check:
ref_rates = ""
for cnt, lbl in zip(op_count, op_label):
ref_rates += " %g %s/s" % (cnt/ref_elapsed_event, lbl)
if not quiet:
print("ref: elapsed: %g s event, %g s wall%s" % (
ref_elapsed_event, ref_elapsed_wall, ref_rates))
# }}}
result_dict = {}
result_dict["elapsed_event"] = elapsed_event
result_dict["elapsed_event_marker"] = elapsed_event_marker
result_dict["elapsed_wall"] = elapsed_wall
result_dict["timing_rounds"] = timing_rounds
if do_check:
result_dict["ref_elapsed_event"] = ref_elapsed_event
result_dict["ref_elapsed_wall"] = ref_elapsed_wall
return result_dict
def main():
from argparse import ArgumentParser
parser = ArgumentParser(description="Stand-alone loopy frontend")
parser.add_argument("infile", metavar="INPUT_FILE")
parser.add_argument("outfile", default="-", metavar="OUTPUT_FILE", help="Defaults to stdout ('-').", nargs="?")
parser.add_argument("--lang", metavar="LANGUAGE", help="loopy|fortran")
parser.add_argument("--target", choices=("opencl", "ispc", "ispc-occa", "c", "c-fortran", "cuda"), default="opencl")
parser.add_argument("--name")
parser.add_argument("--transform")
parser.add_argument("--edit-code", action="store_true")
parser.add_argument("--occa-defines")
parser.add_argument("--occa-add-dummy-arg", action="store_true")
parser.add_argument("--print-ir", action="store_true")
args = parser.parse_args()
if args.target == "opencl":
from loopy.target.opencl import OpenCLTarget
target = OpenCLTarget()
elif args.target == "ispc":
from loopy.target.ispc import ISPCTarget
target = ISPCTarget()
elif args.target == "ispc-occa":
from loopy.target.ispc import ISPCTarget
target = ISPCTarget(occa_mode=True)
elif args.target == "c":
from loopy.target.c import CTarget
target = CTarget()
elif args.target == "c-fortran":
from loopy.target.c import CTarget
target = CTarget(fortran_abi=True)
elif args.target == "cuda":
from loopy.target.cuda import CudaTarget
target = CudaTarget()
else:
raise ValueError("unknown target: %s" % target)
lp.set_default_target(target)
lang = None
if args.infile == "-":
infile_content = sys.stdin.read()
else:
from os.path import splitext
_, ext = splitext(args.infile)
lang = {
".py": "loopy",
".loopy": "loopy",
".floopy": "fortran",
".f90": "fortran",
".fpp": "fortran",
".f": "fortran",
".f77": "fortran",
}.get(ext)
with open(args.infile, "r") as infile_fd:
infile_content = infile_fd.read()
if args.lang is not None:
lang = args.lang
if lang is None:
raise RuntimeError("unable to deduce input language " "(wrong input file extension? --lang flag?)")
if lang == "loopy":
# {{{ path wrangling
from os.path import dirname, abspath
from os import getcwd
infile_dirname = dirname(args.infile)
if infile_dirname:
infile_dirname = abspath(infile_dirname)
else:
infile_dirname = getcwd()
sys.path.append(infile_dirname)
# }}}
data_dic = {}
data_dic["lp"] = lp
data_dic["np"] = np
if args.occa_defines:
with open(args.occa_defines, "r") as defines_fd:
occa_define_code = defines_to_python_code(defines_fd.read())
exec(compile(occa_define_code, args.occa_defines, "exec"), data_dic)
with open(args.infile, "r") as infile_fd:
exec(compile(infile_content, args.infile, "exec"), data_dic)
if args.transform:
with open(args.transform, "r") as xform_fd:
exec(compile(xform_fd.read(), args.transform, "exec"), data_dic)
try:
kernel = data_dic["lp_knl"]
except KeyError:
raise RuntimeError("loopy-lang requires 'lp_knl' " "to be defined on exit")
if args.name is not None:
kernel = kernel.copy(name=args.name)
kernels = [kernel]
elif lang in ["fortran", "floopy", "fpp"]:
pre_transform_code = None
if args.transform:
with open(args.transform, "r") as xform_fd:
pre_transform_code = xform_fd.read()
if args.occa_defines:
if pre_transform_code is None:
pre_transform_code = ""
with open(args.occa_defines, "r") as defines_fd:
pre_transform_code = defines_to_python_code(defines_fd.read()) + pre_transform_code
kernels = lp.parse_transformed_fortran(
infile_content, pre_transform_code=pre_transform_code, filename=args.infile
)
if args.name is not None:
kernels = [kernel for kernel in kernels if kernel.name == args.name]
if not kernels:
raise RuntimeError("no kernels found (name specified: %s)" % args.name)
else:
raise RuntimeError("unknown language: '%s'" % args.lang)
if args.print_ir:
for kernel in kernels:
print(kernel, file=sys.stderr)
if args.occa_add_dummy_arg:
new_kernels = []
for kernel in kernels:
new_args = [lp.GlobalArg("occa_info", np.int32, shape=None)] + kernel.args
new_kernels.append(kernel.copy(args=new_args))
kernels = new_kernels
del new_kernels
codes = []
from loopy.codegen import generate_code
for kernel in kernels:
kernel = lp.preprocess_kernel(kernel)
code, impl_arg_info = generate_code(kernel)
codes.append(code)
if args.outfile is not None:
outfile = args.outfile
else:
outfile = "-"
code = "\n\n".join(codes)
# {{{ edit code if requested
import os
edit_kernel_env = os.environ.get("LOOPY_EDIT_KERNEL")
need_edit = args.edit_code
if not need_edit and edit_kernel_env is not None:
# Do not replace with "any()"--Py2.6/2.7 bug doesn't like
# comprehensions in functions with exec().
for k in kernels:
if edit_kernel_env.lower() in k.name.lower():
need_edit = True
if need_edit:
from pytools import invoke_editor
code = invoke_editor(code, filename="edit.cl")
# }}}
if outfile == "-":
sys.stdout.write(code)
else:
with open(outfile, "w") as outfile_fd:
outfile_fd.write(code)
def time_instantiate(self, data, param):
knl = _sumpy_kernel_make(data[param]["setup"], param)
lp.preprocess_kernel(knl)
def main():
from argparse import ArgumentParser
parser = ArgumentParser(description="Stand-alone loopy frontend")
parser.add_argument("infile", metavar="INPUT_FILE")
parser.add_argument("outfile",
default="-",
metavar="OUTPUT_FILE",
help="Defaults to stdout ('-').",
nargs="?")
parser.add_argument("--lang", metavar="LANGUAGE", help="loopy|fortran")
parser.add_argument("--target",
choices=("opencl", "ispc", "ispc-occa", "c",
"c-fortran", "cuda"),
default="opencl")
parser.add_argument("--name")
parser.add_argument("--transform")
parser.add_argument("--edit-code", action="store_true")
parser.add_argument("--occa-defines")
parser.add_argument("--occa-add-dummy-arg", action="store_true")
parser.add_argument("--print-ir", action="store_true")
args = parser.parse_args()
if args.target == "opencl":
from loopy.target.opencl import OpenCLTarget
target = OpenCLTarget()
elif args.target == "ispc":
from loopy.target.ispc import ISPCTarget
target = ISPCTarget()
elif args.target == "ispc-occa":
from loopy.target.ispc import ISPCTarget
target = ISPCTarget(occa_mode=True)
elif args.target == "c":
from loopy.target.c import CTarget
target = CTarget()
elif args.target == "c-fortran":
from loopy.target.c import CTarget
target = CTarget(fortran_abi=True)
elif args.target == "cuda":
from loopy.target.cuda import CudaTarget
target = CudaTarget()
else:
raise ValueError("unknown target: %s" % target)
lp.set_default_target(target)
lang = None
if args.infile == "-":
infile_content = sys.stdin.read()
else:
from os.path import splitext
_, ext = splitext(args.infile)
lang = {
".py": "loopy",
".loopy": "loopy",
".floopy": "fortran",
".f90": "fortran",
".fpp": "fortran",
".f": "fortran",
".f77": "fortran",
}.get(ext)
with open(args.infile) as infile_fd:
infile_content = infile_fd.read()
if args.lang is not None:
lang = args.lang
if lang is None:
raise RuntimeError("unable to deduce input language "
"(wrong input file extension? --lang flag?)")
if lang == "loopy":
# {{{ path wrangling
from os.path import dirname, abspath
from os import getcwd
infile_dirname = dirname(args.infile)
if infile_dirname:
infile_dirname = abspath(infile_dirname)
else:
infile_dirname = getcwd()
sys.path.append(infile_dirname)
# }}}
data_dic = {}
data_dic["lp"] = lp
data_dic["np"] = np
if args.occa_defines:
with open(args.occa_defines) as defines_fd:
occa_define_code = defines_to_python_code(defines_fd.read())
exec(compile(occa_define_code, args.occa_defines, "exec"),
data_dic)
with open(args.infile) as infile_fd:
exec(compile(infile_content, args.infile, "exec"), data_dic)
if args.transform:
with open(args.transform) as xform_fd:
exec(compile(xform_fd.read(), args.transform, "exec"),
data_dic)
try:
kernel = data_dic["lp_knl"]
except KeyError:
raise RuntimeError("loopy-lang requires 'lp_knl' "
"to be defined on exit")
if args.name is not None:
kernel = kernel.copy(name=args.name)
kernels = [kernel]
elif lang in ["fortran", "floopy", "fpp"]:
pre_transform_code = None
if args.transform:
with open(args.transform) as xform_fd:
pre_transform_code = xform_fd.read()
if args.occa_defines:
if pre_transform_code is None:
pre_transform_code = ""
with open(args.occa_defines) as defines_fd:
pre_transform_code = (
defines_to_python_code(defines_fd.read()) +
pre_transform_code)
kernels = lp.parse_transformed_fortran(
infile_content,
pre_transform_code=pre_transform_code,
filename=args.infile)
if args.name is not None:
kernels = [
kernel for kernel in kernels if kernel.name == args.name
]
if not kernels:
raise RuntimeError("no kernels found (name specified: %s)" %
args.name)
else:
raise RuntimeError("unknown language: '%s'" % args.lang)
if args.print_ir:
for kernel in kernels:
print(kernel, file=sys.stderr)
if args.occa_add_dummy_arg:
new_kernels = []
for kernel in kernels:
new_args = [lp.ArrayArg("occa_info", np.int32, shape=None)
] + kernel.args
new_kernels.append(kernel.copy(args=new_args))
kernels = new_kernels
del new_kernels
codes = []
from loopy.codegen import generate_code
for kernel in kernels:
kernel = lp.preprocess_kernel(kernel)
code, impl_arg_info = generate_code(kernel)
codes.append(code)
if args.outfile is not None:
outfile = args.outfile
else:
outfile = "-"
code = "\n\n".join(codes)
# {{{ edit code if requested
import os
edit_kernel_env = os.environ.get("LOOPY_EDIT_KERNEL")
need_edit = args.edit_code
if not need_edit and edit_kernel_env is not None:
# Do not replace with "any()"--Py2.6/2.7 bug doesn't like
# comprehensions in functions with exec().
for k in kernels:
if edit_kernel_env.lower() in k.name.lower():
need_edit = True
if need_edit:
from pytools import invoke_editor
code = invoke_editor(code, filename="edit.cl")
# }}}
if outfile == "-":
sys.stdout.write(code)
else:
with open(outfile, "w") as outfile_fd:
outfile_fd.write(code)