def copy_targets_kernel(self):
knl = lp.make_kernel(
"""{[dim,i]:
0<=dim<ndims and
0<=i<npoints}""",
"""
targets[dim, i] = points[dim, i]
""",
default_offset=lp.auto, name="copy_targets",
defines=dict(ndims=self.ambient_dim))
knl = lp.split_iname(knl, "i", 128, inner_tag="l.0", outer_tag="g.0")
knl = lp.tag_data_axes(knl, "points", "sep, C")
knl = lp.tag_data_axes(knl, "targets", "stride:auto, stride:1")
return lp.tag_inames(knl, dict(dim="ilp"))
def test_offsets_and_slicing(ctx_factory):
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
n = 20
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}))
cknl(queue, a=a, b=b)
import numpy.linalg as la
assert la.norm(b_full.get() - b_full_h) < 1e-13
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_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_vector_types(ctx_factory, vec_len):
ctx = ctx_factory()
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
))
def set_q_storage_format(kernel, name):
kernel = lp.set_array_dim_names(kernel, name, "i,j,k,field,e")
kernel = lp.split_array_dim(kernel, (name, 3, "F"), 4, auto_split_inames=False)
kernel = lp.tag_data_axes(kernel, name, "N0,N1,N2,vec,N4,N3")
return kernel
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 set_q_storage_format(kernel, name):
kernel = lp.set_array_dim_names(kernel, name, "i,j,k,field,e")
kernel = lp.split_array_dim(kernel, (name, 3, "F"),
4,
auto_split_inames=False)
kernel = lp.tag_data_axes(kernel, name, "N0,N1,N2,vec,N4,N3")
return kernel
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_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)
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)
def test_indexof_vec(ctx_factory):
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
if (
# Accurate as of 2019-11-04
ctx.devices[0].platform.name.startswith("Intel")):
pytest.skip("target ICD 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])""",
[lp.GlobalArg("out", shape=lp.auto, is_input=False)])
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)
def test_vector_types(ctx_factory, vec_len):
ctx = ctx_factory()
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))
def knl():
knl = lp.make_kernel(
"""{[d,k,i,j]:
0<=d<dims and
0<=k<nelements and
0<=i<ndiscr_nodes and
0<=j<nmesh_nodes}""",
"""
result[d, k, i] = \
sum(j, resampling_mat[i, j] * nodes[d, k, j])
""",
name="nodes",
default_offset=lp.auto)
knl = lp.split_iname(knl, "i", 16, inner_tag="l.0")
knl = lp.tag_inames(knl, dict(k="g.0"))
knl = lp.tag_data_axes(knl, "result",
"stride:auto,stride:auto,stride:auto")
return knl
def pick_expansion_centers(self):
knl = lp.make_kernel(
"""{[dim,k,i]:
0<=dim<ndims and
0<=k<nelements and
0<=i<nout_nodes}""",
"""
centers[dim, k, i] = all_centers[dim, k, kept_center_indices[i]]
radii[k, i] = all_radii[k, kept_center_indices[i]]
""",
[
lp.GlobalArg("all_centers", None,
shape="ndims,nelements,nunit_nodes"),
lp.GlobalArg("all_radii", None, shape="nelements,nunit_nodes"),
lp.ValueArg("nunit_nodes", np.int32),
"..."
],
default_offset=lp.auto, name="center_pick",
defines=dict(ndims=self.ambient_dim))
knl = lp.tag_data_axes(knl, "centers,all_centers", "sep, C, C")
knl = lp.split_iname(knl, "i", 16, inner_tag="l.0")
return lp.tag_inames(knl, dict(k="g.0", dim="ilp"))
def test_offsets_and_slicing(ctx_factory):
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
n = 20
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}))
cknl(queue, a=a, b=b)
import numpy.linalg as la
assert la.norm(b_full.get() - b_full_h) < 1e-13
def test_gnuma_horiz_kernel(ctx_factory, ilp_multiple, Nq, opt_level):
ctx = ctx_factory()
filename = "strongVolumeKernels.f90"
with open(filename, "r") as sourcef:
source = sourcef.read()
source = source.replace("datafloat", "real*4")
hsv_r, hsv_s = [
knl for knl in lp.parse_fortran(source, filename, auto_dependencies=False)
if "KernelR" in knl.name or "KernelS" in knl.name
]
hsv_r = lp.tag_instructions(hsv_r, "rknl")
hsv_s = lp.tag_instructions(hsv_s, "sknl")
hsv = lp.fuse_kernels([hsv_r, hsv_s], ["_r", "_s"])
#hsv = hsv_s
from gnuma_loopy_transforms import (
fix_euler_parameters,
set_q_storage_format, set_D_storage_format)
hsv = lp.fix_parameters(hsv, Nq=Nq)
hsv = lp.set_loop_priority(hsv, "e,k,j,i")
hsv = lp.tag_inames(hsv, dict(e="g.0", j="l.1", i="l.0"))
hsv = lp.assume(hsv, "elements >= 1")
hsv = fix_euler_parameters(hsv, p_p0=1, p_Gamma=1.4, p_R=1)
for name in ["Q", "rhsQ"]:
hsv = set_q_storage_format(hsv, name)
hsv = set_D_storage_format(hsv)
#hsv = lp.add_prefetch(hsv, "volumeGeometricFactors")
ref_hsv = hsv
if opt_level == 0:
tap_hsv = hsv
hsv = lp.add_prefetch(hsv, "D[:,:]")
if opt_level == 1:
tap_hsv = hsv
# turn the first reads into subst rules
local_prep_var_names = set()
for insn in lp.find_instructions(hsv, "tag:local_prep"):
assignee, = insn.assignee_var_names()
local_prep_var_names.add(assignee)
hsv = lp.assignment_to_subst(hsv, assignee)
# precompute fluxes
hsv = lp.assignment_to_subst(hsv, "JinvD_r")
hsv = lp.assignment_to_subst(hsv, "JinvD_s")
r_fluxes = lp.find_instructions(hsv, "tag:compute_fluxes and tag:rknl")
s_fluxes = lp.find_instructions(hsv, "tag:compute_fluxes and tag:sknl")
if ilp_multiple > 1:
hsv = lp.split_iname(hsv, "k", 2, inner_tag="ilp")
ilp_inames = ("k_inner",)
flux_ilp_inames = ("kk",)
else:
ilp_inames = ()
flux_ilp_inames = ()
rtmps = []
stmps = []
flux_store_idx = 0
for rflux_insn, sflux_insn in zip(r_fluxes, s_fluxes):
for knl_tag, insn, flux_inames, tmps, flux_precomp_inames in [
("rknl", rflux_insn, ("j", "n",), rtmps, ("jj", "ii",)),
("sknl", sflux_insn, ("i", "n",), stmps, ("ii", "jj",)),
]:
flux_var, = insn.assignee_var_names()
print(insn)
reader, = lp.find_instructions(hsv,
"tag:{knl_tag} and reads:{flux_var}"
.format(knl_tag=knl_tag, flux_var=flux_var))
hsv = lp.assignment_to_subst(hsv, flux_var)
flux_store_name = "flux_store_%d" % flux_store_idx
flux_store_idx += 1
tmps.append(flux_store_name)
hsv = lp.precompute(hsv, flux_var+"_subst", flux_inames + ilp_inames,
temporary_name=flux_store_name,
precompute_inames=flux_precomp_inames + flux_ilp_inames,
default_tag=None)
if flux_var.endswith("_s"):
hsv = lp.tag_data_axes(hsv, flux_store_name, "N0,N1,N2?")
else:
hsv = lp.tag_data_axes(hsv, flux_store_name, "N1,N0,N2?")
n_iname = "n_"+flux_var.replace("_r", "").replace("_s", "")
if n_iname.endswith("_0"):
n_iname = n_iname[:-2]
hsv = lp.rename_iname(hsv, "n", n_iname, within="id:"+reader.id,
existing_ok=True)
hsv = lp.tag_inames(hsv, dict(ii="l.0", jj="l.1"))
for iname in flux_ilp_inames:
hsv = lp.tag_inames(hsv, {iname: "ilp"})
hsv = lp.alias_temporaries(hsv, rtmps)
hsv = lp.alias_temporaries(hsv, stmps)
if opt_level == 2:
tap_hsv = hsv
for prep_var_name in local_prep_var_names:
if prep_var_name.startswith("Jinv") or "_s" in prep_var_name:
continue
hsv = lp.precompute(hsv,
lp.find_one_rule_matching(hsv, prep_var_name+"_*subst*"))
if opt_level == 3:
tap_hsv = hsv
hsv = lp.add_prefetch(hsv, "Q[ii,jj,k,:,:,e]", sweep_inames=ilp_inames)
if opt_level == 4:
tap_hsv = hsv
tap_hsv = lp.tag_inames(tap_hsv, dict(
Q_dim_field_inner="unr",
Q_dim_field_outer="unr"))
hsv = lp.buffer_array(hsv, "rhsQ", ilp_inames,
fetch_bounding_box=True, default_tag="for",
init_expression="0", store_expression="base + buffer")
if opt_level == 5:
tap_hsv = hsv
tap_hsv = lp.tag_inames(tap_hsv, dict(
rhsQ_init_field_inner="unr", rhsQ_store_field_inner="unr",
rhsQ_init_field_outer="unr", rhsQ_store_field_outer="unr",
Q_dim_field_inner="unr",
Q_dim_field_outer="unr"))
# buffer axes need to be vectorized in order for this to work
hsv = lp.tag_data_axes(hsv, "rhsQ_buf", "c?,vec,c")
hsv = lp.tag_data_axes(hsv, "Q_fetch", "c?,vec,c")
hsv = lp.tag_data_axes(hsv, "D_fetch", "f,f")
hsv = lp.tag_inames(hsv,
{"Q_dim_k": "unr", "rhsQ_init_k": "unr", "rhsQ_store_k": "unr"},
ignore_nonexistent=True)
if opt_level == 6:
tap_hsv = hsv
tap_hsv = lp.tag_inames(tap_hsv, dict(
rhsQ_init_field_inner="unr", rhsQ_store_field_inner="unr",
rhsQ_init_field_outer="unr", rhsQ_store_field_outer="unr",
Q_dim_field_inner="unr",
Q_dim_field_outer="unr"))
hsv = lp.tag_inames(hsv, dict(
rhsQ_init_field_inner="vec", rhsQ_store_field_inner="vec",
rhsQ_init_field_outer="unr", rhsQ_store_field_outer="unr",
Q_dim_field_inner="vec",
Q_dim_field_outer="unr"))
if opt_level == 7:
tap_hsv = hsv
hsv = lp.collect_common_factors_on_increment(hsv, "rhsQ_buf",
vary_by_axes=(0,) if ilp_multiple > 1 else ())
if opt_level >= 8:
tap_hsv = hsv
hsv = tap_hsv
if 1:
print("OPS")
op_poly = lp.get_op_poly(hsv)
print(lp.stringify_stats_mapping(op_poly))
print("MEM")
gmem_poly = lp.sum_mem_access_to_bytes(lp.get_gmem_access_poly(hsv))
print(lp.stringify_stats_mapping(gmem_poly))
hsv = lp.set_options(hsv, cl_build_options=[
"-cl-denorms-are-zero",
"-cl-fast-relaxed-math",
"-cl-finite-math-only",
"-cl-mad-enable",
"-cl-no-signed-zeros",
])
hsv = hsv.copy(name="horizontalStrongVolumeKernel")
results = lp.auto_test_vs_ref(ref_hsv, ctx, hsv, parameters=dict(elements=300),
quiet=True)
elapsed = results["elapsed_wall"]
print("elapsed", elapsed)
def set_D_storage_format(kernel):
return lp.tag_data_axes(kernel, "D", "f,f")
def test_gnuma_horiz_kernel(ctx_factory, ilp_multiple, Nq, opt_level):
ctx = ctx_factory()
filename = "strongVolumeKernels.f90"
with open(filename, "r") as sourcef:
source = sourcef.read()
source = source.replace("datafloat", "real*4")
hsv_r, hsv_s = [
knl
for knl in lp.parse_fortran(source, filename, auto_dependencies=False)
if "KernelR" in knl.name or "KernelS" in knl.name
]
hsv_r = lp.tag_instructions(hsv_r, "rknl")
hsv_s = lp.tag_instructions(hsv_s, "sknl")
hsv = lp.fuse_kernels([hsv_r, hsv_s], ["_r", "_s"])
#hsv = hsv_s
from gnuma_loopy_transforms import (fix_euler_parameters,
set_q_storage_format,
set_D_storage_format)
hsv = lp.fix_parameters(hsv, Nq=Nq)
hsv = lp.set_loop_priority(hsv, "e,k,j,i")
hsv = lp.tag_inames(hsv, dict(e="g.0", j="l.1", i="l.0"))
hsv = lp.assume(hsv, "elements >= 1")
hsv = fix_euler_parameters(hsv, p_p0=1, p_Gamma=1.4, p_R=1)
for name in ["Q", "rhsQ"]:
hsv = set_q_storage_format(hsv, name)
hsv = set_D_storage_format(hsv)
#hsv = lp.add_prefetch(hsv, "volumeGeometricFactors")
ref_hsv = hsv
if opt_level == 0:
tap_hsv = hsv
hsv = lp.add_prefetch(hsv, "D[:,:]")
if opt_level == 1:
tap_hsv = hsv
# turn the first reads into subst rules
local_prep_var_names = set()
for insn in lp.find_instructions(hsv, "tag:local_prep"):
assignee, = insn.assignee_var_names()
local_prep_var_names.add(assignee)
hsv = lp.assignment_to_subst(hsv, assignee)
# precompute fluxes
hsv = lp.assignment_to_subst(hsv, "JinvD_r")
hsv = lp.assignment_to_subst(hsv, "JinvD_s")
r_fluxes = lp.find_instructions(hsv, "tag:compute_fluxes and tag:rknl")
s_fluxes = lp.find_instructions(hsv, "tag:compute_fluxes and tag:sknl")
if ilp_multiple > 1:
hsv = lp.split_iname(hsv, "k", 2, inner_tag="ilp")
ilp_inames = ("k_inner", )
flux_ilp_inames = ("kk", )
else:
ilp_inames = ()
flux_ilp_inames = ()
rtmps = []
stmps = []
flux_store_idx = 0
for rflux_insn, sflux_insn in zip(r_fluxes, s_fluxes):
for knl_tag, insn, flux_inames, tmps, flux_precomp_inames in [
("rknl", rflux_insn, (
"j",
"n",
), rtmps, (
"jj",
"ii",
)),
("sknl", sflux_insn, (
"i",
"n",
), stmps, (
"ii",
"jj",
)),
]:
flux_var, = insn.assignee_var_names()
print(insn)
reader, = lp.find_instructions(
hsv,
"tag:{knl_tag} and reads:{flux_var}".format(knl_tag=knl_tag,
flux_var=flux_var))
hsv = lp.assignment_to_subst(hsv, flux_var)
flux_store_name = "flux_store_%d" % flux_store_idx
flux_store_idx += 1
tmps.append(flux_store_name)
hsv = lp.precompute(hsv,
flux_var + "_subst",
flux_inames + ilp_inames,
temporary_name=flux_store_name,
precompute_inames=flux_precomp_inames +
flux_ilp_inames,
default_tag=None)
if flux_var.endswith("_s"):
hsv = lp.tag_data_axes(hsv, flux_store_name, "N0,N1,N2?")
else:
hsv = lp.tag_data_axes(hsv, flux_store_name, "N1,N0,N2?")
n_iname = "n_" + flux_var.replace("_r", "").replace("_s", "")
if n_iname.endswith("_0"):
n_iname = n_iname[:-2]
hsv = lp.rename_iname(hsv,
"n",
n_iname,
within="id:" + reader.id,
existing_ok=True)
hsv = lp.tag_inames(hsv, dict(ii="l.0", jj="l.1"))
for iname in flux_ilp_inames:
hsv = lp.tag_inames(hsv, {iname: "ilp"})
hsv = lp.alias_temporaries(hsv, rtmps)
hsv = lp.alias_temporaries(hsv, stmps)
if opt_level == 2:
tap_hsv = hsv
for prep_var_name in local_prep_var_names:
if prep_var_name.startswith("Jinv") or "_s" in prep_var_name:
continue
hsv = lp.precompute(
hsv, lp.find_one_rule_matching(hsv, prep_var_name + "_*subst*"))
if opt_level == 3:
tap_hsv = hsv
hsv = lp.add_prefetch(hsv, "Q[ii,jj,k,:,:,e]", sweep_inames=ilp_inames)
if opt_level == 4:
tap_hsv = hsv
tap_hsv = lp.tag_inames(
tap_hsv, dict(Q_dim_field_inner="unr", Q_dim_field_outer="unr"))
hsv = lp.buffer_array(hsv,
"rhsQ",
ilp_inames,
fetch_bounding_box=True,
default_tag="for",
init_expression="0",
store_expression="base + buffer")
if opt_level == 5:
tap_hsv = hsv
tap_hsv = lp.tag_inames(
tap_hsv,
dict(rhsQ_init_field_inner="unr",
rhsQ_store_field_inner="unr",
rhsQ_init_field_outer="unr",
rhsQ_store_field_outer="unr",
Q_dim_field_inner="unr",
Q_dim_field_outer="unr"))
# buffer axes need to be vectorized in order for this to work
hsv = lp.tag_data_axes(hsv, "rhsQ_buf", "c?,vec,c")
hsv = lp.tag_data_axes(hsv, "Q_fetch", "c?,vec,c")
hsv = lp.tag_data_axes(hsv, "D_fetch", "f,f")
hsv = lp.tag_inames(hsv, {
"Q_dim_k": "unr",
"rhsQ_init_k": "unr",
"rhsQ_store_k": "unr"
},
ignore_nonexistent=True)
if opt_level == 6:
tap_hsv = hsv
tap_hsv = lp.tag_inames(
tap_hsv,
dict(rhsQ_init_field_inner="unr",
rhsQ_store_field_inner="unr",
rhsQ_init_field_outer="unr",
rhsQ_store_field_outer="unr",
Q_dim_field_inner="unr",
Q_dim_field_outer="unr"))
hsv = lp.tag_inames(
hsv,
dict(rhsQ_init_field_inner="vec",
rhsQ_store_field_inner="vec",
rhsQ_init_field_outer="unr",
rhsQ_store_field_outer="unr",
Q_dim_field_inner="vec",
Q_dim_field_outer="unr"))
if opt_level == 7:
tap_hsv = hsv
hsv = lp.collect_common_factors_on_increment(
hsv, "rhsQ_buf", vary_by_axes=(0, ) if ilp_multiple > 1 else ())
if opt_level >= 8:
tap_hsv = hsv
hsv = tap_hsv
if 1:
print("OPS")
op_poly = lp.get_op_poly(hsv)
print(lp.stringify_stats_mapping(op_poly))
print("MEM")
gmem_poly = lp.sum_mem_access_to_bytes(lp.get_gmem_access_poly(hsv))
print(lp.stringify_stats_mapping(gmem_poly))
hsv = lp.set_options(hsv,
cl_build_options=[
"-cl-denorms-are-zero",
"-cl-fast-relaxed-math",
"-cl-finite-math-only",
"-cl-mad-enable",
"-cl-no-signed-zeros",
])
hsv = hsv.copy(name="horizontalStrongVolumeKernel")
results = lp.auto_test_vs_ref(ref_hsv,
ctx,
hsv,
parameters=dict(elements=300),
quiet=True)
elapsed = results["elapsed_wall"]
print("elapsed", elapsed)
def set_D_storage_format(kernel):
return lp.tag_data_axes(kernel, "D", "f,f")
