def test_small_batched_matvec(ctx_factory):
dtype = np.float32
ctx = ctx_factory()
order = "C"
K = 9997 # noqa
Np = 36 # noqa
knl = lp.make_kernel(
"{[i,j,k]: 0<=k<K and 0<= i,j < %d}" % Np,
[
"result[k, i] = sum(j, d[i, j]*f[k, j])"
],
[
lp.GlobalArg("d", dtype, shape=(Np, Np), order=order),
lp.GlobalArg("f", dtype, shape=("K", Np), order=order),
lp.GlobalArg("result", dtype, shape=("K", Np), order=order),
lp.ValueArg("K", np.int32, approximately=1000),
], name="batched_matvec", assumptions="K>=1")
seq_knl = knl
align_bytes = 64
knl = lp.add_prefetch(knl, 'd[:,:]')
pad_mult = lp.find_padding_multiple(knl, "f", 0, align_bytes)
knl = lp.split_array_dim(knl, ("f", 0), pad_mult)
knl = lp.add_padding(knl, "f", 0, align_bytes)
lp.auto_test_vs_ref(seq_knl, ctx, knl,
op_count=[K*2*Np**2/1e9], op_label=["GFlops"],
parameters=dict(K=K))
def test_tim2d(ctx_factory):
dtype = np.float32
ctx = ctx_factory()
order = "C"
n = 8
from pymbolic import var
K_sym = var("K")
field_shape = (K_sym, n, n)
# K - run-time symbolic
knl = lp.make_kernel(ctx.devices[0],
"[K] -> {[i,j,e,m,o,gi]: 0<=i,j,m,o<%d and 0<=e<K and 0<=gi<3}" % n,
[
"ur(a,b) := sum_float32(@o, D[a,o]*u[e,o,b])",
"us(a,b) := sum_float32(@o, D[b,o]*u[e,a,o])",
"lap[e,i,j] = "
" sum_float32(m, D[m,i]*(G[0,e,m,j]*ur(m,j) + G[1,e,m,j]*us(m,j)))"
"+ sum_float32(m, D[m,j]*(G[1,e,i,m]*ur(i,m) + G[2,e,i,m]*us(i,m)))"
],
[
lp.ArrayArg("u", dtype, shape=field_shape, order=order),
lp.ArrayArg("lap", dtype, shape=field_shape, order=order),
lp.ArrayArg("G", dtype, shape=(3,)+field_shape, order=order),
# lp.ConstantArrayArg("D", dtype, shape=(n, n), order=order),
lp.ArrayArg("D", dtype, shape=(n, n), order=order),
# lp.ImageArg("D", dtype, shape=(n, n)),
lp.ValueArg("K", np.int32, approximately=1000),
],
name="semlap2D", assumptions="K>=1")
unroll = 32
seq_knl = knl
knl = lp.add_prefetch(knl, "D", ["m", "j", "i","o"], default_tag="l.auto")
knl = lp.add_prefetch(knl, "u", ["i", "j", "o"], default_tag="l.auto")
knl = lp.precompute(knl, "ur", np.float32, ["a", "b"], default_tag="l.auto")
knl = lp.precompute(knl, "us", np.float32, ["a", "b"], default_tag="l.auto")
knl = lp.split_iname(knl, "e", 1, outer_tag="g.0")#, slabs=(0, 1))
knl = lp.tag_inames(knl, dict(i="l.0", j="l.1"))
knl = lp.tag_inames(knl, dict(o="unr"))
knl = lp.tag_inames(knl, dict(m="unr"))
# knl = lp.add_prefetch(knl, "G", [2,3], default_tag=None) # axis/argument indices on G
knl = lp.add_prefetch(knl, "G", [2,3], default_tag="l.auto") # axis/argument indices on G
kernel_gen = lp.generate_loop_schedules(knl)
kernel_gen = lp.check_kernels(kernel_gen, dict(K=1000))
K = 1000
lp.auto_test_vs_ref(seq_knl, ctx, kernel_gen,
op_count=K*(n*n*n*2*2 + n*n*2*3 + n**3 * 2*2)/1e9,
op_label="GFlops",
parameters={"K": K})
def test_troublesome_premagma_fermi_matrix_mul(ctx_factory):
dtype = np.float32
ctx = ctx_factory()
order = "C"
n = 6*16*2
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.GlobalArg("a", dtype, shape=(n, n), order=order),
lp.GlobalArg("b", dtype, shape=(n, n), order=order),
lp.GlobalArg("c", dtype, shape=(n, n), order=order),
],
name="matmul")
seq_knl = knl
i_reg = 2
j_reg = 2
i_chunks = 16
j_chunks = 16
knl = lp.split_iname(knl, "i", i_reg*i_chunks, outer_tag="g.0")
knl = lp.split_iname(knl, "i_inner", i_reg, outer_tag="l.0", inner_tag="ilp")
knl = lp.split_iname(knl, "j", j_reg*j_chunks, outer_tag="g.1")
knl = lp.split_iname(knl, "j_inner", j_reg, outer_tag="l.1", inner_tag="ilp")
knl = lp.split_iname(knl, "k", 16)
knl = lp.add_prefetch(knl, 'a', ["k_inner", "i_inner_inner", "i_inner_outer"])
lp.auto_test_vs_ref(seq_knl, ctx, knl,
op_count=[2*n**3/1e9], op_label=["GFlops"],
parameters={})
def test_variable_size_matrix_mul(ctx_factory):
ctx = ctx_factory()
if (not ctx.devices[0].image_support
or ctx.devices[0].platform.name == "Portable Computing Language"):
pytest.skip("crashes on pocl")
n = get_suitable_size(ctx)
knl = lp.make_kernel(
"{[i,j,k]: 0<=i,j,k<n}",
"c[i, j] = sum(k, a[i, k]*b[k, j])")
knl = lp.add_dtypes(knl, {
"a": np.float32,
"b": np.float32,
})
ref_knl = knl
knl = lp.split_iname(knl, "i", 16,
outer_tag="g.0", inner_tag="l.1",
slabs=(0, 1))
knl = lp.split_iname(knl, "j", 16,
outer_tag="g.1", inner_tag="l.0",
slabs=(0, 1))
knl = lp.split_iname(knl, "k", 8, slabs=(0, 1))
knl = lp.add_prefetch(knl, "a", ["k_inner", "i_inner"], default_tag="l.auto")
knl = lp.add_prefetch(knl, "b", ["j_inner", "k_inner"], default_tag="l.auto")
lp.auto_test_vs_ref(ref_knl, ctx, knl,
op_count=[2*n**3/1e9], op_label=["GFlops"],
parameters={"n": n})
def test_fancy_matrix_mul(ctx_factory):
dtype = np.float32
ctx = ctx_factory()
order = "C"
n = get_suitable_size(ctx)
knl = lp.make_kernel(
"[n] -> {[i,j,k]: 0<=i,j,k<n }",
[
"c[i, j] = sum(k, a[i, k]*b[k, j])"
],
[
lp.GlobalArg("a", dtype, shape="(n, n)", order=order),
lp.GlobalArg("b", dtype, shape="(n, n)", order=order),
lp.GlobalArg("c", dtype, shape="(n, n)", order=order),
lp.ValueArg("n", np.int32, approximately=1000),
], name="fancy_matmul", assumptions="n>=1")
seq_knl = knl
knl = lp.split_iname(knl, "i", 16, outer_tag="g.0", inner_tag="l.1")
knl = lp.split_iname(knl, "j", 16, outer_tag="g.1", inner_tag="l.0")
knl = lp.split_iname(knl, "k", 16, slabs=(0, 1))
knl = lp.add_prefetch(knl, 'a', ["i_inner", "k_inner"])
knl = lp.add_prefetch(knl, 'b', ["k_inner", "j_inner"])
lp.auto_test_vs_ref(seq_knl, ctx, knl,
op_count=[2*n**3/1e9], op_label=["GFlops"],
parameters=dict(n=n))
def test_plain_matrix_mul(ctx_factory):
ctx = ctx_factory()
order = "C"
n = get_suitable_size(ctx)
for dtype, check, vec_size in [
(cl_array.vec.float4, check_float4, 4),
(np.float32, None, 1),
]:
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.GlobalArg("a", dtype, shape=(n, n), order=order),
lp.GlobalArg("b", dtype, shape=(n, n), order=order),
lp.GlobalArg("c", dtype, shape=(n, n), order=order),
],
name="matmul")
ref_knl = knl
knl = lp.split_iname(knl, "i", 16,
outer_tag="g.0", inner_tag="l.1")
knl = lp.split_iname(knl, "j", 16,
outer_tag="g.1", inner_tag="l.0")
knl = lp.split_iname(knl, "k", 16)
knl = lp.add_prefetch(knl, "a", ["k_inner", "i_inner"])
knl = lp.add_prefetch(knl, "b", ["j_inner", "k_inner", ])
lp.auto_test_vs_ref(ref_knl, ctx, knl,
op_count=[vec_size*2*n**3/1e9], op_label=["GFlops"],
parameters={"n": n}, check_result=check)
def test_variable_size_matrix_mul(ctx_factory):
ctx = ctx_factory()
n = get_suitable_size(ctx)
knl = lp.make_kernel(
"{[i,j,k]: 0<=i,j,k<n}",
"c[i, j] = sum(k, a[i, k]*b[k, j])")
knl = lp.add_dtypes(knl, {
"a": np.float32,
"b": np.float32,
})
ref_knl = knl
knl = lp.split_iname(knl, "i", 16,
outer_tag="g.0", inner_tag="l.1",
slabs=(0, 1))
knl = lp.split_iname(knl, "j", 16,
outer_tag="g.1", inner_tag="l.0",
slabs=(0, 1))
knl = lp.split_iname(knl, "k", 8, slabs=(0, 1))
knl = lp.add_prefetch(knl, "a", ["k_inner", "i_inner"])
knl = lp.add_prefetch(knl, "b", ["j_inner", "k_inner"])
lp.auto_test_vs_ref(ref_knl, ctx, knl,
op_count=[2*n**3/1e9], op_label=["GFlops"],
parameters={"n": n})
def test_atomic(ctx_factory, dtype):
ctx = ctx_factory()
if (
np.dtype(dtype).itemsize == 8
and "cl_khr_int64_base_atomics" not in ctx.devices[0].extensions):
pytest.skip("64-bit atomics not supported on device")
import pyopencl.version # noqa
if (
cl.version.VERSION < (2015, 2)
and dtype == np.int64):
pytest.skip("int64 RNG not supported in PyOpenCL < 2015.2")
knl = lp.make_kernel(
"{ [i]: 0<=i<n }",
"out[i%20] = out[i%20] + 2*a[i] {atomic}",
[
lp.GlobalArg("out", dtype, shape=lp.auto, for_atomic=True),
lp.GlobalArg("a", dtype, shape=lp.auto),
"..."
],
assumptions="n>0")
ref_knl = knl
knl = lp.split_iname(knl, "i", 512)
knl = lp.split_iname(knl, "i_inner", 128, outer_tag="unr", inner_tag="g.0")
lp.auto_test_vs_ref(ref_knl, ctx, knl, parameters=dict(n=10000))
def test_precompute_nested_subst(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
"{[i,j]: 0<=i<n and 0<=j<5}",
"""
E:=a[i]
D:=E*E
b[i] = D
""")
knl = lp.add_and_infer_dtypes(knl, dict(a=np.float32))
ref_knl = knl
knl = lp.tag_inames(knl, dict(j="g.1"))
knl = lp.split_iname(knl, "i", 128, outer_tag="g.0", inner_tag="l.0")
from loopy.symbolic import get_dependencies
assert "i_inner" not in get_dependencies(knl.substitutions["D"].expression)
knl = lp.precompute(knl, "D", "i_inner")
# There's only one surviving 'E' rule.
assert len([
rule_name
for rule_name in knl.substitutions
if rule_name.startswith("E")]) == 1
# That rule should use the newly created prefetch inames,
# not the prior 'i_inner'
assert "i_inner" not in get_dependencies(knl.substitutions["E"].expression)
lp.auto_test_vs_ref(
ref_knl, ctx, knl,
parameters=dict(n=12345))
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_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_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_transpose(ctx_factory):
dtype = np.dtype(np.float32)
ctx = ctx_factory()
order = "C"
n = get_suitable_size(ctx)
knl = lp.make_kernel(
"{[i,j]: 0<=i,j<%d}" % n,
[
"b[i, j] = a[j, i]"
],
[
lp.GlobalArg("a", dtype, shape=(n, n), order=order),
lp.GlobalArg("b", dtype, shape=(n, n), order=order),
],
name="transpose")
seq_knl = knl
knl = lp.split_iname(knl, "i", 16,
outer_tag="g.0", inner_tag="l.1")
knl = lp.split_iname(knl, "j", 16,
outer_tag="g.1", inner_tag="l.0")
knl = lp.add_prefetch(knl, 'a', ["i_inner", "j_inner"])
lp.auto_test_vs_ref(seq_knl, ctx, knl,
op_count=[dtype.itemsize*n**2*2/1e9], op_label=["GByte"],
parameters={})
def test_conditional(ctx_factory):
#logging.basicConfig(level=logging.DEBUG)
ctx = ctx_factory()
knl = lp.make_kernel(
"{ [i,j]: 0<=i,j<n }",
"""
<> my_a = a[i,j] {id=read_a}
<> a_less_than_zero = my_a < 0 {dep=read_a,inames=i:j}
my_a = 2*my_a {id=twice_a,dep=read_a,if=a_less_than_zero}
my_a = my_a+1 {id=aplus,dep=twice_a,if=a_less_than_zero}
out[i,j] = 2*my_a {dep=aplus}
""",
[
lp.GlobalArg("a", np.float32, shape=lp.auto),
lp.GlobalArg("out", np.float32, shape=lp.auto),
"..."
])
ref_knl = knl
lp.auto_test_vs_ref(ref_knl, ctx, knl,
parameters=dict(
n=200
))
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_convolution(ctx_factory):
ctx = ctx_factory()
dtype = np.float32
knl = lp.make_kernel(
"{ [iimg, ifeat, icolor, im_x, im_y, f_x, f_y]: \
-f_w <= f_x,f_y <= f_w \
and 0 <= im_x < im_w and 0 <= im_y < im_h \
and 0<=iimg<=nimgs and 0<=ifeat<nfeats and 0<=icolor<ncolors \
}",
"""
out[iimg, ifeat, im_x, im_y] = sum((f_x, f_y, icolor), \
img[iimg, f_w+im_x-f_x, f_w+im_y-f_y, icolor] \
* f[ifeat, f_w+f_x, f_w+f_y, icolor])
""",
[
lp.GlobalArg("f", dtype, shape=lp.auto),
lp.GlobalArg("img", dtype, shape=lp.auto),
lp.GlobalArg("out", dtype, shape=lp.auto),
"..."
],
assumptions="f_w>=1 and im_w, im_h >= 2*f_w+1 and nfeats>=1 and nimgs>=0",
options="annotate_inames")
f_w = 3
knl = lp.fix_parameters(knl, f_w=f_w, ncolors=3)
ref_knl = knl
def variant_0(knl):
#knl = lp.split_iname(knl, "im_x", 16, inner_tag="l.0")
knl = lp.prioritize_loops(knl, "iimg,im_x,im_y,ifeat,f_x,f_y")
return knl
def variant_1(knl):
knl = lp.split_iname(knl, "im_x", 16, inner_tag="l.0")
knl = lp.prioritize_loops(knl, "iimg,im_x_outer,im_y,ifeat,f_x,f_y")
return knl
def variant_2(knl):
knl = lp.split_iname(knl, "im_x", 16, outer_tag="g.0", inner_tag="l.0")
knl = lp.split_iname(knl, "im_y", 16, outer_tag="g.1", inner_tag="l.1")
knl = lp.tag_inames(knl, dict(ifeat="g.2"))
knl = lp.add_prefetch(knl, "f[ifeat,:,:,:]", default_tag="l.auto")
knl = lp.add_prefetch(knl, "img", "im_x_inner, im_y_inner, f_x, f_y",
default_tag="l.auto")
return knl
for variant in [
#variant_0,
#variant_1,
variant_2
]:
lp.auto_test_vs_ref(ref_knl, ctx, variant(knl),
parameters=dict(
im_w=128, im_h=128, f_w=f_w,
nfeats=3, nimgs=3
))
def test_equality_constraints(ctx_factory):
dtype = np.float32
ctx = ctx_factory()
order = "C"
n = 10
knl = lp.make_kernel([
"[n] -> {[i,j]: 0<=i,j<n }",
"{[k]: k =i+5 and k < n}",
],
[
"a[i,j] = 5 {id=set_all}",
"b[i,k] = 22 {dep=set_all}",
],
[
lp.GlobalArg("a,b", dtype, shape="n, n", order=order),
lp.ValueArg("n", np.int32, approximately=1000),
],
name="equality_constraints", assumptions="n>=1")
seq_knl = knl
knl = lp.split_iname(knl, "i", 16, outer_tag="g.0", inner_tag="l.0")
knl = lp.split_iname(knl, "j", 16, outer_tag="g.1", inner_tag="l.1")
#print(knl)
#print(knl.domains[0].detect_equalities())
lp.auto_test_vs_ref(seq_knl, ctx, knl,
parameters=dict(n=n), print_ref_code=True)
def test_global_parallel_reduction_simpler(ctx_factory, size):
ctx = ctx_factory()
pytest.xfail("very sensitive to kernel ordering, fails unused hw-axis check")
knl = lp.make_kernel(
"{[l,g,j]: 0 <= l < nl and 0 <= g,j < ng}",
"""
<> key = make_uint2(l+nl*g, 1234) {inames=l:g}
<> ctr = make_uint4(0, 1, 2, 3) {inames=l:g,id=init_ctr}
<> vals, ctr = philox4x32_f32(ctr, key) {dep=init_ctr}
<> tmp[g] = sum(l, vals.s0 + 1j*vals.s1 + vals.s2 + 1j*vals.s3)
result = sum(j, tmp[j])
""")
ng = 50
knl = lp.fix_parameters(knl, ng=ng)
knl = lp.set_options(knl, write_cl=True)
ref_knl = knl
knl = lp.split_iname(knl, "l", 128, inner_tag="l.0")
knl = lp.split_reduction_outward(knl, "l_inner")
knl = lp.tag_inames(knl, "g:g.0,j:l.0")
lp.auto_test_vs_ref(ref_knl, ctx, knl, parameters={"nl": size})
def test_ilp_loop_bound(ctx_factory):
# The salient bit of this test is that a joint bound on (outer, inner)
# from a split occurs in a setting where the inner loop has been ilp'ed.
# In 'normal' parallel loops, the inner index is available for conditionals
# throughout. In ILP'd loops, not so much.
ctx = ctx_factory()
knl = lp.make_kernel(
"{ [i,j,k]: 0<=i,j,k<n }",
"""
out[i,k] = sum(j, a[i,j]*b[j,k])
""",
[
lp.GlobalArg("a,b", np.float32, shape=lp.auto),
"...",
],
assumptions="n>=1")
ref_knl = knl
knl = lp.set_loop_priority(knl, "j,i,k")
knl = lp.split_iname(knl, "k", 4, inner_tag="ilp")
lp.auto_test_vs_ref(ref_knl, ctx, knl,
parameters=dict(
n=200
))
def test_to_batched_temp(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
''' { [i,j]: 0<=i,j<n } ''',
''' cnst = 2.0
out[i] = sum(j, cnst*a[i,j]*x[j])''',
[lp.TemporaryVariable(
"cnst",
dtype=np.float32,
shape=(),
scope=lp.temp_var_scope.PRIVATE), '...'])
knl = lp.add_and_infer_dtypes(knl, dict(out=np.float32,
x=np.float32,
a=np.float32))
ref_knl = lp.make_kernel(
''' { [i,j]: 0<=i,j<n } ''',
'''out[i] = sum(j, 2.0*a[i,j]*x[j])''')
ref_knl = lp.add_and_infer_dtypes(ref_knl, dict(out=np.float32,
x=np.float32,
a=np.float32))
bknl = lp.to_batched(knl, "nbatches", "out,x")
bref_knl = lp.to_batched(ref_knl, "nbatches", "out,x")
# checking that cnst is not being bathced
assert bknl.temporary_variables['cnst'].shape == ()
a = np.random.randn(5, 5)
x = np.random.randn(7, 5)
# Checking that the program compiles and the logic is correct
lp.auto_test_vs_ref(
bref_knl, ctx, bknl,
parameters=dict(a=a, x=x, n=5, nbatches=7))
def test_assignment_to_subst_indices(ctx_factory):
fortran_src = """
subroutine fill(out, out2, inp, n)
implicit none
real*8 a(n), out(n), out2(n), inp(n)
integer n, i
do i = 1, n
a(i) = 6*inp(i)
enddo
do i = 1, n
out(i) = 5*a(i)
end do
end
"""
knl, = lp.parse_fortran(fortran_src)
knl = lp.fix_parameters(knl, n=5)
ref_knl = knl
assert "a" in knl.temporary_variables
knl = lp.assignment_to_subst(knl, "a")
assert "a" not in knl.temporary_variables
ctx = ctx_factory()
lp.auto_test_vs_ref(ref_knl, ctx, knl)
def test_assignment_to_subst_two_defs(ctx_factory):
fortran_src = """
subroutine fill(out, out2, inp, n)
implicit none
real*8 a, out(n), out2(n), inp(n)
integer n, i
do i = 1, n
a = inp(i)
out(i) = 5*a
a = 3*inp(n)
out2(i) = 6*a
end do
end
"""
knl, = lp.parse_fortran(fortran_src)
ref_knl = knl
knl = lp.assignment_to_subst(knl, "a")
ctx = ctx_factory()
lp.auto_test_vs_ref(ref_knl, ctx, knl, parameters=dict(n=5))
def test_fill(ctx_factory):
fortran_src = """
subroutine fill(out, a, n)
implicit none
real*8 a, out(n)
integer n, i
do i = 1, n
out(i) = a
end do
end
!$loopy begin
!
! fill, = lp.parse_fortran(SOURCE)
! fill = lp.split_iname(fill, "i", split_amount,
! outer_tag="g.0", inner_tag="l.0")
! RESULT = [fill]
!
!$loopy end
"""
knl, = lp.parse_transformed_fortran(fortran_src,
pre_transform_code="split_amount = 128")
assert "i_inner" in knl.all_inames()
ctx = ctx_factory()
lp.auto_test_vs_ref(knl, ctx, knl, parameters=dict(n=5, a=5))
def test_if(ctx_factory):
fortran_src = """
subroutine fill(out, out2, inp, n)
implicit none
real*8 a, b, out(n), out2(n), inp(n)
integer n, i, j
do i = 1, n
a = inp(i)
if (a.ge.3) then
b = 2*a
do j = 1,3
b = 3 * b
end do
out(i) = 5*b
else
out(i) = 4*a
endif
end do
end
"""
knl, = lp.parse_fortran(fortran_src)
ref_knl = knl
knl = lp.assignment_to_subst(knl, "a")
ctx = ctx_factory()
lp.auto_test_vs_ref(ref_knl, ctx, knl, parameters=dict(n=5))
def test_kernel_splitting_with_loop(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
"{ [i,k]: 0<=i<n and 0<=k<3 }",
"""
c[k,i] = a[k, i + 1]
out[k,i] = c[k,i]
""")
knl = lp.add_and_infer_dtypes(knl,
{"a": np.float32, "c": np.float32, "out": np.float32, "n": np.int32})
ref_knl = knl
knl = lp.split_iname(knl, "i", 128, outer_tag="g.0", inner_tag="l.0")
# schedule
from loopy.preprocess import preprocess_kernel
knl = preprocess_kernel(knl)
from loopy.schedule import get_one_scheduled_kernel
knl = get_one_scheduled_kernel(knl)
# map schedule onto host or device
print(knl)
cgr = lp.generate_code_v2(knl)
assert len(cgr.device_programs) == 2
print(cgr.device_code())
print(cgr.host_code())
lp.auto_test_vs_ref(ref_knl, ctx, knl, parameters=dict(n=5))
def test_global_temporary(ctx_factory):
ctx = ctx_factory()
knl = lp.make_kernel(
"{ [i]: 0<=i<n}",
"""
<> c[i] = a[i + 1]
out[i] = c[i]
""")
knl = lp.add_and_infer_dtypes(knl,
{"a": np.float32, "c": np.float32, "out": np.float32, "n": np.int32})
knl = lp.set_temporary_scope(knl, "c", "global")
ref_knl = knl
knl = lp.split_iname(knl, "i", 128, outer_tag="g.0", inner_tag="l.0")
cgr = lp.generate_code_v2(knl)
assert len(cgr.device_programs) == 2
#print(cgr.device_code())
#print(cgr.host_code())
lp.auto_test_vs_ref(ref_knl, ctx, knl, parameters=dict(n=5))
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 test_local_parallel_reduction(ctx_factory, size):
ctx = ctx_factory()
knl = lp.make_kernel(
"{[i, j]: 0 <= i < n and 0 <= j < 5}",
"""
z[j] = sum(i, i+j)
""")
knl = lp.fix_parameters(knl, n=size)
ref_knl = knl
def variant0(knl):
return lp.tag_inames(knl, "i:l.0")
def variant1(knl):
return lp.tag_inames(knl, "i:l.0,j:l.1")
def variant2(knl):
return lp.tag_inames(knl, "i:l.0,j:g.0")
for variant in [
variant0,
variant1,
variant2
]:
knl = variant(ref_knl)
lp.auto_test_vs_ref(ref_knl, ctx, knl)
def test_domain_dependency_via_existentially_quantified_variable(ctx_factory):
dtype = np.float32
ctx = ctx_factory()
order = "C"
n = 10
knl = lp.make_kernel([
"{[i]: 0<=i<n }",
"{[k]: k=i and (exists l: k = 2*l) }",
],
[
"a[i] = 5 {id=set}",
"b[k] = 6 {dep=set}",
],
[
lp.GlobalArg("a,b", dtype, shape="n", order=order),
lp.ValueArg("n", np.int32, approximately=1000),
],
assumptions="n>=1")
seq_knl = knl
lp.auto_test_vs_ref(seq_knl, ctx, knl,
parameters=dict(n=n))
def test_interp_diff(ctx_factory):
1/0 # not ready
dtype = np.float32
ctx = ctx_factory()
order = "C"
N = 8
M = 8
from pymbolic import var
K_sym = var("K")
field_shape = (N, N, N, K_sym)
interim_field_shape = (M, M, M, K_sym)
# 1. direction-by-direction similarity transform on u
# 2. invert diagonal
# 3. transform back (direction-by-direction)
# K - run-time symbolic
knl = lp.make_kernel(ctx.devices[0],
"[K] -> {[i,ip,j,jp,k,kp,e]: 0<=i,j,k<%d AND 0<=ip,jp,kp<%d 0<=e<K}" %M %N
[
"[|i,jp,kp] <float32> u1[i ,jp,kp,e] = sum_float32(ip, I[i,ip]*u [ip,jp,kp,e])",
"[|i,j ,kp] <float32> u2[i ,j ,kp,e] = sum_float32(jp, I[j,jp]*u1[i ,jp,kp,e])",
"[|i,j ,k ] <float32> u3[i ,j ,k ,e] = sum_float32(kp, I[k,kp]*u2[i ,j ,kp,e])",
"[|i,j ,k ] <float32> Pu[i ,j ,k ,e] = P[i,j,k,e]*u3[i,j,k,e]",
"[|i,j ,kp] <float32> Pu3[i ,j ,kp,e] = sum_float32(k, V[kp,k]*Pu[i ,j , k,e])",
"[|i,jp,kp] <float32> Pu2[i ,jp,kp,e] = sum_float32(j, V[jp,j]*Pu[i ,j ,kp,e])",
"Pu[ip,jp,kp,e] = sum_float32(i, V[ip,i]*Pu[i ,jp,kp,e])",
],
[
lp.GlobalArg("u", dtype, shape=field_shape, order=order),
lp.GlobalArg("P", dtype, shape=interim_field_shape, order=order),
lp.GlobalArg("I", dtype, shape=(M, N), order=order),
lp.GlobalArg("V", dtype, shape=(N, M), order=order),
lp.GlobalArg("Pu", dtype, shape=field_shape, order=order),
lp.ValueArg("K", np.int32, approximately=1000),
],
name="sem_lap_precon", assumptions="K>=1")
print(knl)
1/0
knl = lp.split_iname(knl, "e", 16, outer_tag="g.0")#, slabs=(0, 1))
knl = lp.tag_inames(knl, dict(i="l.0", j="l.1"))
print(knl)
#1/0
kernel_gen = lp.generate_loop_schedules(knl)
kernel_gen = lp.check_kernels(kernel_gen, dict(K=1000), kill_level_min=5)
lp.auto_test_vs_ref(seq_knl, ctx, kernel_gen,
op_count=0,
op_label="GFlops",
parameters={"K": K}, print_seq_code=True,)
def test_laplacian(ctx_factory):
1 / 0 # not adapted to new language
dtype = np.float32
ctx = ctx_factory()
order = "C"
n = 8
from pymbolic import var
K_sym = var("K")
field_shape = (K_sym, n, n, n)
# load: 1+6 fields + 1/N D entry
# store: 1 fields
# perform: N*2*6 + 3*5 flops
# ratio: (12*N+15)/8 flops per 4 bytes on bus
# ~ 14 FLOPS per 4 bytes at N=8
# ~ 525 GFLOPS max on a 150GB/s device at N=8 if done perfectly
# K - run-time symbolic
knl = lp.make_kernel(
ctx.devices[0],
"[K] -> {[i,j,k,e,m,o1,o2,o3,gi]: 0<=i,j,k,m,o1,o2,o3<%d and 0<=e<K and 0<=gi<6}"
% n,
[
"CSE: ur(i,j,k) = sum_float32(o1, D[i,o1]*cse(u[e,o1,j,k], urf))",
"CSE: us(i,j,k) = sum_float32(o2, D[j,o2]*cse(u[e,i,o2,k], usf))",
"CSE: ut(i,j,k) = sum_float32(o3, D[k,o3]*cse(u[e,i,j,o3], utf))",
# define function
"CSE: Gu(i,j,k) = G[0,e,i,j,k]*ur(i,j,k) + G[1,e,i,j,k]*us(i,j,k) + G[2,e,i,j,k]*ut(i,j,k)",
"CSE: Gv(i,j,k) = G[1,e,i,j,k]*ur(i,j,k) + G[3,e,i,j,k]*us(i,j,k) + G[4,e,i,j,k]*ut(i,j,k)",
"CSE: Gw(i,j,k) = G[2,e,i,j,k]*ur(i,j,k) + G[4,e,i,j,k]*us(i,j,k) + G[5,e,i,j,k]*ut(i,j,k)",
"lap[e,i,j,k] = "
" sum_float32(m, D[m,i]*Gu(m,j,k))"
"+ sum_float32(m, D[m,j]*Gv(i,m,k))"
"+ sum_float32(m, D[m,k]*Gw(i,j,m))"
],
[
lp.ArrayArg("u", dtype, shape=field_shape, order=order),
lp.ArrayArg("lap", dtype, shape=field_shape, order=order),
lp.ArrayArg("G", dtype, shape=(6, ) + field_shape, order=order),
lp.ArrayArg("D", dtype, shape=(n, n), order=order),
lp.ValueArg("K", np.int32, approximately=1000),
],
name="semlap",
assumptions="K>=1")
#print(lp.preprocess_kernel(knl, cse_ok=True))
#1/0
#
#print(knl)
#1/0
knl = lp.realize_cse(knl, "urf", np.float32, ["o1"])
knl = lp.realize_cse(knl, "usf", np.float32, ["o2"])
knl = lp.realize_cse(knl, "utf", np.float32, ["o3"])
knl = lp.realize_cse(knl, "Gu", np.float32, ["m", "j", "k"])
knl = lp.realize_cse(knl, "Gv", np.float32, ["i", "m", "k"])
knl = lp.realize_cse(knl, "Gw", np.float32, ["i", "j", "m"])
knl = lp.realize_cse(knl, "ur", np.float32, ["k", "j", "m"])
knl = lp.realize_cse(knl, "us", np.float32, ["i", "m", "k"])
knl = lp.realize_cse(knl, "ut", np.float32, ["i", "j", "m"])
if 0:
pass
#seq_knl = lp.add_prefetch(knl, "G", ["gi", "m", "j", "k"], "G[gi,e,m,j,k]", default_tag="l.auto")
#seq_knl = lp.add_prefetch(seq_knl, "D", ["m", "j"], default_tag="l.auto")
#seq_knl = lp.add_prefetch(seq_knl, "u", ["i", "j", "k"], "u[*,i,j,k]", default_tag="l.auto")
else:
seq_knl = knl
knl = lp.split_iname(knl, "e", 16, outer_tag="g.0") #, slabs=(0, 1))
knl = lp.add_prefetch(knl,
"G", ["gi", "m", "j", "k"],
"G[gi,e,m,j,k]",
default_tag="l.auto")
knl = lp.add_prefetch(knl, "D", ["m", "j"], default_tag="l.auto")
#knl = lp.add_prefetch(knl, "u", ["i", "j", "k"], "u[*,i,j,k]", default_tag="l.auto")
#knl = lp.split_iname(knl, "e_inner", 4, inner_tag="ilp")
#print(seq_knl)
#print(lp.preprocess_kernel(knl))
#1/0
knl = lp.tag_inames(knl, dict(i="l.0", j="l.1"))
kernel_gen = lp.generate_loop_schedules(
knl, loop_priority=["m_fetch_G", "i_fetch_u"])
kernel_gen = lp.check_kernels(kernel_gen, dict(K=1000))
K = 1000
lp.auto_test_vs_ref(
seq_knl,
ctx,
kernel_gen,
op_count=K *
(n * n * n * n * 2 * 3 + n * n * n * 5 * 3 + n**4 * 2 * 3) / 1e9,
op_label="GFlops",
parameters={"K": K},
print_seq_code=True)
def test_lbm(ctx_factory):
ctx = ctx_factory()
# D2Q4Q4Q4 lattice Boltzmann scheme for the shallow water equations
# Example by Loic Gouarin <*****@*****.**>
knl = lp.make_kernel(
"{[ii,jj]:0<=ii<nx-2 and 0<=jj<ny-2}",
""" # noqa (silences flake8 line length warning)
i := ii + 1
j := jj + 1
for ii, jj
with {id_prefix=init_m}
<> m[0] = + f[i-1, j, 0] + f[i, j-1, 1] + f[i+1, j, 2] + f[i, j+1, 3]
m[1] = + 4.*f[i-1, j, 0] - 4.*f[i+1, j, 2]
m[2] = + 4.*f[i, j-1, 1] - 4.*f[i, j+1, 3]
m[3] = + f[i-1, j, 0] - f[i, j-1, 1] + f[i+1, j, 2] - f[i, j+1, 3]
m[4] = + f[i-1, j, 4] + f[i, j-1, 5] + f[i+1, j, 6] + f[i, j+1, 7]
m[5] = + 4.*f[i-1, j, 4] - 4.*f[i+1, j, 6]
m[6] = + 4.*f[i, j-1, 5] - 4.*f[i, j+1, 7]
m[7] = + f[i-1, j, 4] - f[i, j-1, 5] + f[i+1, j, 6] - f[i, j+1, 7]
m[8] = + f[i-1, j, 8] + f[i, j-1, 9] + f[i+1, j, 10] + f[i, j+1, 11]
m[9] = + 4.*f[i-1, j, 8] - 4.*f[i+1, j, 10]
m[10] = + 4.*f[i, j-1, 9] - 4.*f[i, j+1, 11]
m[11] = + f[i-1, j, 8] - f[i, j-1, 9] + f[i+1, j, 10] - f[i, j+1, 11]
end
with {id_prefix=update_m,dep=init_m*}
m[1] = m[1] + 2.*(m[4] - m[1])
m[2] = m[2] + 2.*(m[8] - m[2])
m[3] = m[3]*(1. - 1.5)
m[5] = m[5] + 1.5*(0.5*(m[0]*m[0]) + (m[4]*m[4])/m[0] - m[5])
m[6] = m[6] + 1.5*(m[4]*m[8]/m[0] - m[6])
m[7] = m[7]*(1. - 1.2000000000000000)
m[9] = m[9] + 1.5*(m[4]*m[8]/m[0] - m[9])
m[10] = m[10] + 1.5*(0.5*(m[0]*m[0]) + (m[8]*m[8])/m[0] - m[10])
m[11] = m[11]*(1. - 1.2)
end
with {dep=update_m*}
f_new[i, j, 0] = + 0.25*m[0] + 0.125*m[1] + 0.25*m[3]
f_new[i, j, 1] = + 0.25*m[0] + 0.125*m[2] - 0.25*m[3]
f_new[i, j, 2] = + 0.25*m[0] - 0.125*m[1] + 0.25*m[3]
f_new[i, j, 3] = + 0.25*m[0] - 0.125*m[2] - 0.25*m[3]
f_new[i, j, 4] = + 0.25*m[4] + 0.125*m[5] + 0.25*m[7]
f_new[i, j, 5] = + 0.25*m[4] + 0.125*m[6] - 0.25*m[7]
f_new[i, j, 6] = + 0.25*m[4] - 0.125*m[5] + 0.25*m[7]
f_new[i, j, 7] = + 0.25*m[4] - 0.125*m[6] - 0.25*m[7]
f_new[i, j, 8] = + 0.25*m[8] + 0.125*m[9] + 0.25*m[11]
f_new[i, j, 9] = + 0.25*m[8] + 0.125*m[10] - 0.25*m[11]
f_new[i, j, 10] = + 0.25*m[8] - 0.125*m[9] + 0.25*m[11]
f_new[i, j, 11] = + 0.25*m[8] - 0.125*m[10] - 0.25*m[11]
end
end
""")
knl = lp.add_and_infer_dtypes(knl, {"f": np.float32})
ref_knl = knl
knl = lp.split_iname(knl, "ii", 16, outer_tag="g.1", inner_tag="l.1")
knl = lp.split_iname(knl, "jj", 16, outer_tag="g.0", inner_tag="l.0")
knl = lp.expand_subst(knl)
knl = lp.add_prefetch(knl, "f", "ii_inner,jj_inner", fetch_bounding_box=True,
default_tag="l.auto")
lp.auto_test_vs_ref(ref_knl, ctx, knl, parameters={"nx": 20, "ny": 20})
def test_advect(ctx_factory):
1 / 0 # not ready
dtype = np.float32
ctx = ctx_factory()
order = "C"
N = 8
from pymbolic import var
K_sym = var("K")
field_shape = (K_sym, N, N, N)
# 1. direction-by-direction similarity transform on u
# 2. invert diagonal
# 3. transform back (direction-by-direction)
# K - run-time symbolic
# A. updated for CSE: notation.
# B. fixed temp indexing and C ordering
# load: 3+9 fields + 1/N D entry
# store: 3 fields
# perform: N*2*6 + 3*5 + 3*5 flops
# ratio: (12*N+30)/15 flops per 4 bytes on bus
# ~ 8.4 FLOPS per 4 bytes at N=8
# ~ 300 GFLOPS max on a 150GB/s device at N=8 if done perfectly
knl = lp.make_kernel(
ctx.devices[0],
"[K] -> {[i,j,k,m,e]: 0<=i,j,k,m<%d AND 0<=e<K}" % N,
[
# differentiate u
"CSE: ur(i,j,k) = sum_float32(@m, D[i,m]*u[e,m,j,k])",
"CSE: us(i,j,k) = sum_float32(@m, D[j,m]*u[e,i,m,k])",
"CSE: ut(i,j,k) = sum_float32(@m, D[k,m]*u[e,i,j,m])",
# differentiate v
"CSE: vr(i,j,k) = sum_float32(@m, D[i,m]*v[e,m,j,k])",
"CSE: vs(i,j,k) = sum_float32(@m, D[j,m]*v[e,i,m,k])",
"CSE: vt(i,j,k) = sum_float32(@m, D[k,m]*v[e,i,j,m])",
# differentiate w
"CSE: wr(i,j,k) = sum_float32(@m, D[i,m]*w[e,m,j,k])",
"CSE: ws(i,j,k) = sum_float32(@m, D[j,m]*w[e,i,m,k])",
"CSE: wt(i,j,k) = sum_float32(@m, D[k,m]*w[e,i,j,m])",
# find velocity in (r,s,t) coordinates
# CSE?
"CSE: Vr(i,j,k) = G[0,e,i,j,k]*u[e,i,j,k] + G[1,e,i,j,k]*v[e,i,j,k] + G[2,e,i,j,k]*w[e,i,j,k]",
"CSE: Vs(i,j,k) = G[3,e,i,j,k]*u[e,i,j,k] + G[4,e,i,j,k]*v[e,i,j,k] + G[5,e,i,j,k]*w[e,i,j,k]",
"CSE: Vt(i,j,k) = G[6,e,i,j,k]*u[e,i,j,k] + G[7,e,i,j,k]*v[e,i,j,k] + G[8,e,i,j,k]*w[e,i,j,k]",
# form nonlinear term on integration nodes
"Nu[e,i,j,k] = Vr(i,j,k)*ur(i,j,k)+Vs(i,j,k)*us(i,j,k)+Vt(i,j,k)*ut(i,j,k)",
"Nv[e,i,j,k] = Vr(i,j,k)*vr(i,j,k)+Vs(i,j,k)*vs(i,j,k)+Vt(i,j,k)*vt(i,j,k)",
"Nw[e,i,j,k] = Vr(i,j,k)*wr(i,j,k)+Vs(i,j,k)*ws(i,j,k)+Vt(i,j,k)*wt(i,j,k)",
],
[
lp.ArrayArg("u", dtype, shape=field_shape, order=order),
lp.ArrayArg("v", dtype, shape=field_shape, order=order),
lp.ArrayArg("w", dtype, shape=field_shape, order=order),
lp.ArrayArg("Nu", dtype, shape=field_shape, order=order),
lp.ArrayArg("Nv", dtype, shape=field_shape, order=order),
lp.ArrayArg("Nw", dtype, shape=field_shape, order=order),
lp.ArrayArg("G", dtype, shape=(9, ) + field_shape, order=order),
lp.ArrayArg("D", dtype, shape=(N, N), order=order),
lp.ValueArg("K", np.int32, approximately=1000),
],
name="sem_advect",
assumptions="K>=1")
print(knl)
1 / 0
seq_knl = knl
knl = lp.split_iname(knl, "e", 16, outer_tag="g.0") #, slabs=(0, 1))
knl = lp.tag_inames(knl, dict(i="l.0", j="l.1"))
kernel_gen = lp.generate_loop_schedules(knl)
kernel_gen = lp.check_kernels(kernel_gen, dict(K=1000), kill_level_min=5)
K = 1000
lp.auto_test_vs_ref(
seq_knl,
ctx,
kernel_gen,
op_count=0,
op_label="GFlops",
parameters={"K": K},
print_seq_code=True,
)
def test_laplacian_lmem(ctx_factory):
dtype = np.float32
ctx = ctx_factory()
order = "C"
n = 4
from pymbolic import var
K_sym = var("K")
field_shape = (K_sym, n, n, n)
# K - run-time symbolic
knl = lp.make_kernel(
ctx.devices[0],
"[K] -> {[i,j,k,e,m,o,gi]: 0<=i,j,k,m,o<%d and 0<=e<K and 0<=gi<6}" %
n, [
"ur(a,b,c) := sum_float32(@o, D[a,o]*u[e,o,b,c])",
"us(a,b,c) := sum_float32(@o, D[b,o]*u[e,a,o,c])",
"ut(a,b,c) := sum_float32(@o, D[c,o]*u[e,a,b,o])",
"lap[e,i,j,k] = "
" sum_float32(m, D[m,i]*(G[0,e,m,j,k]*ur(m,j,k) + G[1,e,m,j,k]*us(m,j,k) + G[2,e,m,j,k]*ut(m,j,k)))"
"+ sum_float32(m, D[m,j]*(G[1,e,i,m,k]*ur(i,m,k) + G[3,e,i,m,k]*us(i,m,k) + G[4,e,i,m,k]*ut(i,m,k)))"
"+ sum_float32(m, D[m,k]*(G[2,e,i,j,m]*ur(i,j,m) + G[4,e,i,j,m]*us(i,j,m) + G[5,e,i,j,m]*ut(i,j,m)))"
], [
lp.ArrayArg("u", dtype, shape=field_shape, order=order),
lp.ArrayArg("lap", dtype, shape=field_shape, order=order),
lp.ArrayArg("G", dtype, shape=(6, ) + field_shape, order=order),
lp.ArrayArg("D", dtype, shape=(n, n), order=order),
lp.ValueArg("K", np.int32, approximately=1000),
],
name="semlap",
assumptions="K>=1")
seq_knl = knl
if 1:
# original
knl = lp.add_prefetch(knl,
"u", ["i", "j", "k", "o"],
default_tag="l.auto")
knl = lp.precompute(knl,
"ur",
np.float32, ["a", "b", "c"],
default_tag="l.auto")
knl = lp.precompute(knl,
"us",
np.float32, ["a", "b", "c"],
default_tag="l.auto")
knl = lp.precompute(knl,
"ut",
np.float32, ["a", "b", "c"],
default_tag="l.auto")
knl = lp.split_iname(knl, "e", 16, outer_tag="g.0") #, slabs=(0, 1))
knl = lp.add_prefetch(knl,
"D", ["m", "j", "k", "i"],
default_tag="l.auto")
else:
# experiment
# knl = lp.add_prefetch(knl, "u", ["i", "j", "k", "o"], default_tag="l.auto")
knl = lp.precompute(knl,
"eu",
np.float32, ["b", "c"],
default_tag="l.auto")
knl = lp.precompute(knl,
"ur",
np.float32, ["b", "c"],
default_tag="l.auto")
knl = lp.precompute(knl,
"us",
np.float32, ["b", "c"],
default_tag="l.auto")
knl = lp.precompute(knl,
"ut",
np.float32, ["b", "c"],
default_tag="l.auto")
knl = lp.split_iname(knl, "e", 1, outer_tag="g.0") #, slabs=(0, 1))
knl = lp.add_prefetch(knl,
"D", ["m", "j", "k", "i"],
default_tag="l.auto")
#knl = lp.add_prefetch(knl, "G", [2,3,4], default_tag="l.auto") # axis/argument indices on G
#knl = lp.add_prefetch(knl, "G", ["i", "j", "m", "k"], default_tag="l.auto") # axis/argument indices on G
#print(knl)
#1/0
#knl = lp.split_iname(knl, "e_inner", 4, inner_tag="ilp")
# knl = lp.join_dimensions(knl, ["i", "j"], "i_and_j")
#print(seq_knl)
#print(lp.preprocess_kernel(knl))
#1/0
# TW: turned this off since it generated:
# ValueError: cannot tag 'i_and_j'--not known
# knl = lp.tag_inames(knl, dict(i_and_j="l.0", k="l.1"))
kernel_gen = lp.generate_loop_schedules(knl)
kernel_gen = lp.check_kernels(kernel_gen, dict(K=1000))
K = 1000
lp.auto_test_vs_ref(
seq_knl,
ctx,
kernel_gen,
op_count=K *
(n * n * n * n * 2 * 3 + n * n * n * 5 * 3 + n**4 * 2 * 3) / 1e9,
op_label="GFlops",
parameters={"K": K})
def test_advect_dealias(ctx_factory):
1 / 0 # not ready
dtype = np.float32
ctx = ctx_factory()
order = "C"
N = 8
M = 8
from pymbolic import var
K_sym = var("K")
field_shape = (N, N, N, K_sym)
interim_field_shape = (M, M, M, K_sym)
# 1. direction-by-direction similarity transform on u
# 2. invert diagonal
# 3. transform back (direction-by-direction)
# K - run-time symbolic
knl = lp.make_kernel(
ctx.devices[0],
"[K] -> {[i,ip,j,jp,k,kp,m,e]: 0<=i,j,k,m<%d AND 0<=o,ip,jp,kp<%d 0<=e<K}"
% M % N[
# interpolate u to integration nodes
"CSE: u0[i,jp,kp,e] = sum_float32(@o, I[i,o]*u[o,jp,kp,e])",
"CSE: u1[i,j,kp,e] = sum_float32(@o, I[j,o]*u0[i,o,kp,e])",
"CSE: Iu[i,j,k,e] = sum_float32(@o, I[k,o]*u1[i,j,o,e])",
# differentiate u on integration nodes
"CSE: Iur[i,j,k,e] = sum_float32(@m, D[i,m]*Iu[m,j,k,e])",
"CSE: Ius[i,j,k,e] = sum_float32(@m, D[j,m]*Iu[i,m,k,e])",
"CSE: Iut[i,j,k,e] = sum_float32(@m, D[k,m]*Iu[i,j,m,e])",
# interpolate v to integration nodes
"CSE: v0[i,jp,kp,e] = sum_float32(@o, I[i,o]*v[o,jp,kp,e])",
"CSE: v1[i,j,kp,e] = sum_float32(@o, I[j,o]*v0[i,o,kp,e])",
"CSE: Iv[i,j,k,e] = sum_float32(@o, I[k,o]*v1[i,j,o,e])",
# differentiate v on integration nodes
"CSE: Ivr[i,j,k,e] = sum_float32(@m, D[i,m]*Iv[m,j,k,e])",
"CSE: Ivs[i,j,k,e] = sum_float32(@m, D[j,m]*Iv[i,m,k,e])",
"CSE: Ivt[i,j,k,e] = sum_float32(@m, D[k,m]*Iv[i,j,m,e])",
# interpolate w to integration nodes
"CSE: w0[i,jp,kp,e] = sum_float32(@o, I[i,o]*w[o,jp,kp,e])",
"CSE: w1[i,j,kp,e] = sum_float32(@o, I[j,o]*w0[i,o,kp,e])",
"CSE: Iw[i,j,k,e] = sum_float32(@o, I[k,o]*w1[i,j,o,e])",
# differentiate v on integration nodes
"CSE: Iwr[i,j,k,e] = sum_float32(@m, D[i,m]*Iw[m,j,k,e])",
"CSE: Iws[i,j,k,e] = sum_float32(@m, D[j,m]*Iw[i,m,k,e])",
"CSE: Iwt[i,j,k,e] = sum_float32(@m, D[k,m]*Iw[i,j,m,e])",
# find velocity in (r,s,t) coordinates
# QUESTION: should I use CSE here ?
"CSE: Vr[i,j,k,e] = G[i,j,k,0,e]*Iu[i,j,k,e] + G[i,j,k,1,e]*Iv[i,j,k,e] + G[i,j,k,2,e]*Iw[i,j,k,e]",
"CSE: Vs[i,j,k,e] = G[i,j,k,3,e]*Iu[i,j,k,e] + G[i,j,k,4,e]*Iv[i,j,k,e] + G[i,j,k,5,e]*Iw[i,j,k,e]",
"CSE: Vt[i,j,k,e] = G[i,j,k,6,e]*Iu[i,j,k,e] + G[i,j,k,7,e]*Iv[i,j,k,e] + G[i,j,k,8,e]*Iw[i,j,k,e]",
# form nonlinear term on integration nodes
# QUESTION: should I use CSE here ?
"<SE: Nu[i,j,k,e] = Vr[i,j,k,e]*Iur[i,j,k,e]+Vs[i,j,k,e]*Ius[i,j,k,e]+Vt[i,j,k,e]*Iut[i,j,k,e]",
"<SE: Nv[i,j,k,e] = Vr[i,j,k,e]*Ivr[i,j,k,e]+Vs[i,j,k,e]*Ivs[i,j,k,e]+Vt[i,j,k,e]*Ivt[i,j,k,e]",
"<SE: Nw[i,j,k,e] = Vr[i,j,k,e]*Iwr[i,j,k,e]+Vs[i,j,k,e]*Iws[i,j,k,e]+Vt[i,j,k,e]*Iwt[i,j,k,e]",
# L2 project Nu back to Lagrange basis
"CSE: Nu2[ip,j,k,e] = sum_float32(@m, V[ip,m]*Nu[m,j,k,e])",
"CSE: Nu1[ip,jp,k,e] = sum_float32(@m, V[jp,m]*Nu2[ip,m,k,e])",
"INu[ip,jp,kp,e] = sum_float32(@m, V[kp,m]*Nu1[ip,jp,m,e])",
# L2 project Nv back to Lagrange basis
"CSE: Nv2[ip,j,k,e] = sum_float32(@m, V[ip,m]*Nv[m,j,k,e])",
"CSE: Nv1[ip,jp,k,e] = sum_float32(@m, V[jp,m]*Nv2[ip,m,k,e])",
"INv[ip,jp,kp,e] = sum_float32(@m, V[kp,m]*Nv1[ip,jp,m,e])",
# L2 project Nw back to Lagrange basis
"CSE: Nw2[ip,j,k,e] = sum_float32(@m, V[ip,m]*Nw[m,j,k,e])",
"CSE: Nw1[ip,jp,k,e] = sum_float32(@m, V[jp,m]*Nw2[ip,m,k,e])",
"INw[ip,jp,kp,e] = sum_float32(@m, V[kp,m]*Nw1[ip,jp,m,e])", ],
[
lp.ArrayArg("u", dtype, shape=field_shape, order=order),
lp.ArrayArg("v", dtype, shape=field_shape, order=order),
lp.ArrayArg("w", dtype, shape=field_shape, order=order),
lp.ArrayArg("INu", dtype, shape=field_shape, order=order),
lp.ArrayArg("INv", dtype, shape=field_shape, order=order),
lp.ArrayArg("INw", dtype, shape=field_shape, order=order),
lp.ArrayArg("D", dtype, shape=(M, M), order=order),
lp.ArrayArg("I", dtype, shape=(M, N), order=order),
lp.ArrayArg("V", dtype, shape=(N, M), order=order),
lp.ValueArg("K", np.int32, approximately=1000),
],
name="sem_advect",
assumptions="K>=1")
print(knl)
1 / 0
knl = lp.split_iname(knl, "e", 16, outer_tag="g.0") #, slabs=(0, 1))
knl = lp.tag_inames(knl, dict(i="l.0", j="l.1"))
print(knl)
#1/0
kernel_gen = lp.generate_loop_schedules(knl)
kernel_gen = lp.check_kernels(kernel_gen, dict(K=1000), kill_level_min=5)
K = 1000
lp.auto_test_vs_ref(
seq_knl,
ctx,
kernel_gen,
op_count=0,
op_label="GFlops",
parameters={"K": K},
print_seq_code=True,
)
def test_interp_diff(ctx_factory):
1 / 0 # not ready
dtype = np.float32
ctx = ctx_factory()
order = "C"
N = 8
M = 8
from pymbolic import var
K_sym = var("K")
field_shape = (N, N, N, K_sym)
interim_field_shape = (M, M, M, K_sym)
# 1. direction-by-direction similarity transform on u
# 2. invert diagonal
# 3. transform back (direction-by-direction)
# K - run-time symbolic
knl = lp.make_kernel(
ctx.devices[0],
"[K] -> {[i,ip,j,jp,k,kp,e]: 0<=i,j,k<%d AND 0<=ip,jp,kp<%d 0<=e<K}" %
M %
N["[|i,jp,kp] <float32> u1[i ,jp,kp,e] = sum_float32(ip, I[i,ip]*u [ip,jp,kp,e])",
"[|i,j ,kp] <float32> u2[i ,j ,kp,e] = sum_float32(jp, I[j,jp]*u1[i ,jp,kp,e])",
"[|i,j ,k ] <float32> u3[i ,j ,k ,e] = sum_float32(kp, I[k,kp]*u2[i ,j ,kp,e])",
"[|i,j ,k ] <float32> Pu[i ,j ,k ,e] = P[i,j,k,e]*u3[i,j,k,e]",
"[|i,j ,kp] <float32> Pu3[i ,j ,kp,e] = sum_float32(k, V[kp,k]*Pu[i ,j , k,e])",
"[|i,jp,kp] <float32> Pu2[i ,jp,kp,e] = sum_float32(j, V[jp,j]*Pu[i ,j ,kp,e])",
"Pu[ip,jp,kp,e] = sum_float32(i, V[ip,i]*Pu[i ,jp,kp,e])", ], [
lp.ArrayArg("u", dtype, shape=field_shape, order=order),
lp.ArrayArg("P", dtype, shape=interim_field_shape, order=order),
lp.ArrayArg("I", dtype, shape=(M, N), order=order),
lp.ArrayArg("V", dtype, shape=(N, M), order=order),
lp.ArrayArg("Pu", dtype, shape=field_shape, order=order),
lp.ValueArg("K", np.int32, approximately=1000),
],
name="sem_lap_precon",
assumptions="K>=1")
print(knl)
1 / 0
knl = lp.split_iname(knl, "e", 16, outer_tag="g.0") #, slabs=(0, 1))
knl = lp.tag_inames(knl, dict(i="l.0", j="l.1"))
print(knl)
#1/0
kernel_gen = lp.generate_loop_schedules(knl)
kernel_gen = lp.check_kernels(kernel_gen, dict(K=1000), kill_level_min=5)
lp.auto_test_vs_ref(
seq_knl,
ctx,
kernel_gen,
op_count=0,
op_label="GFlops",
parameters={"K": K},
print_seq_code=True,
)
def test_rank_one(ctx_factory):
dtype = np.float32
ctx = ctx_factory()
order = "F"
#n = int(get_suitable_size(ctx)**(2.7/2))
n = 16**3
knl = lp.make_kernel(
"[n] -> {[i,j]: 0<=i,j<n}",
["c[i, j] = a[i]*b[j] {id=mylabel, priority =5}"], [
lp.GlobalArg("a", dtype, shape=("n", ), order=order),
lp.GlobalArg("b", dtype, shape=("n", ), order=order),
lp.GlobalArg("c", dtype, shape=("n, n"), order=order),
lp.ValueArg("n", np.int32, approximately=n),
],
name="rank_one",
assumptions="n >= 16")
def variant_1(knl):
knl = lp.add_prefetch(knl, "a")
knl = lp.add_prefetch(knl, "b")
knl = lp.set_loop_priority(knl, ["i", "j"])
return knl
def variant_2(knl):
knl = lp.split_iname(knl, "i", 16, outer_tag="g.0", inner_tag="l.0")
knl = lp.split_iname(knl, "j", 16, outer_tag="g.1", inner_tag="l.1")
knl = lp.add_prefetch(knl, "a")
knl = lp.add_prefetch(knl, "b")
return knl
def variant_3(knl):
knl = lp.split_iname(knl, "i", 16, outer_tag="g.0", inner_tag="l.0")
knl = lp.split_iname(knl, "j", 16, outer_tag="g.1", inner_tag="l.1")
knl = lp.add_prefetch(knl, "a", ["i_inner"])
knl = lp.add_prefetch(knl, "b", ["j_inner"])
return knl
def variant_4(knl):
knl = lp.split_iname(knl, "i", 256, outer_tag="g.0", slabs=(0, 1))
knl = lp.split_iname(knl, "j", 256, outer_tag="g.1", slabs=(0, 1))
knl = lp.add_prefetch(knl, "a", ["i_inner"], default_tag=None)
knl = lp.add_prefetch(knl, "b", ["j_inner"], default_tag=None)
knl = lp.split_iname(knl, "i_inner", 16, inner_tag="l.0")
knl = lp.split_iname(knl, "j_inner", 16, inner_tag="l.1")
knl = lp.split_iname(knl,
"a_dim_0",
16,
outer_tag="l.1",
inner_tag="l.0")
knl = lp.split_iname(knl,
"b_dim_0",
16,
outer_tag="l.1",
inner_tag="l.0")
return knl
seq_knl = knl
for variant in [variant_1, variant_2, variant_3, variant_4]:
lp.auto_test_vs_ref(seq_knl,
ctx,
variant(knl),
op_count=[np.dtype(dtype).itemsize * n**2 / 1e9],
op_label=["GBytes"],
parameters={"n": n})
def test_axpy(ctx_factory):
logging.basicConfig(level="INFO")
ctx = ctx_factory()
n = 3145182
vec = cl_array.vec
if ctx.devices[0].platform.vendor.startswith("Advanced Micro"):
pytest.skip("crashes on AMD 15.12")
for dtype, check, a, b in [
(np.complex64, None, 5, 7),
(vec.float4, check_float4, vec.make_float4(1, 2, 3, 4),
vec.make_float4(6, 7, 8, 9)),
(np.float32, None, 5, 7),
]:
knl = lp.make_kernel("[n] -> {[i]: 0<=i<n}", ["z[i] = a*x[i]+b*y[i]"],
[
lp.ValueArg("a", dtype),
lp.GlobalArg("x", dtype, shape="n,"),
lp.ValueArg("b", dtype),
lp.GlobalArg("y", dtype, shape="n,"),
lp.GlobalArg("z", dtype, shape="n,"),
lp.ValueArg("n", np.int32, approximately=n),
],
name="axpy",
assumptions="n>=1")
seq_knl = knl
def variant_cpu(knl):
unroll = 16
block_size = unroll * 4096
knl = lp.split_iname(knl,
"i",
block_size,
outer_tag="g.0",
slabs=(0, 1))
knl = lp.split_iname(knl, "i_inner", unroll, inner_tag="unr")
return knl
def variant_gpu(knl):
unroll = 4
block_size = 256
knl = lp.split_iname(knl,
"i",
unroll * block_size,
outer_tag="g.0",
slabs=(0, 1))
knl = lp.split_iname(knl,
"i_inner",
block_size,
outer_tag="unr",
inner_tag="l.0")
return knl
#for variant in [ variant_gpu]:
for variant in [variant_cpu, variant_gpu]:
lp.auto_test_vs_ref(
seq_knl,
ctx,
variant(knl),
op_count=[np.dtype(dtype).itemsize * n * 3 / 1e9],
op_label=["GBytes"],
parameters={
"a": a,
"b": b,
"n": n
},
check_result=check,
blacklist_ref_vendors=["Advanced Micro"])
def test_convolution(ctx_factory):
ctx = ctx_factory()
dtype = np.float32
knl = lp.make_kernel(
"{ [iimg, ifeat, icolor, im_x, im_y, f_x, f_y]: \
-f_w <= f_x,f_y <= f_w \
and 0 <= im_x < im_w and 0 <= im_y < im_h \
and 0<=iimg<=nimgs and 0<=ifeat<nfeats and 0<=icolor<ncolors \
}",
"""
out[iimg, ifeat, im_x, im_y] = sum((f_x, f_y, icolor), \
img[iimg, f_w+im_x-f_x, f_w+im_y-f_y, icolor] \
* f[ifeat, f_w+f_x, f_w+f_y, icolor])
""",
[
lp.GlobalArg("f", dtype, shape=lp.auto),
lp.GlobalArg("img", dtype, shape=lp.auto),
lp.GlobalArg("out", dtype, shape=lp.auto),
"..."
],
assumptions="f_w>=1 and im_w, im_h >= 2*f_w+1 and nfeats>=1 and nimgs>=0",
options="annotate_inames")
f_w = 3
knl = lp.fix_parameters(knl, f_w=f_w, ncolors=3)
ref_knl = knl
def variant_0(knl):
#knl = lp.split_iname(knl, "im_x", 16, inner_tag="l.0")
knl = lp.prioritize_loops(knl, "iimg,im_x,im_y,ifeat,f_x,f_y")
return knl
def variant_1(knl):
knl = lp.split_iname(knl, "im_x", 16, inner_tag="l.0")
knl = lp.prioritize_loops(knl, "iimg,im_x_outer,im_y,ifeat,f_x,f_y")
return knl
def variant_2(knl):
knl = lp.split_iname(knl, "im_x", 16, outer_tag="g.0", inner_tag="l.0")
knl = lp.split_iname(knl, "im_y", 16, outer_tag="g.1", inner_tag="l.1")
knl = lp.tag_inames(knl, dict(ifeat="g.2"))
knl = lp.add_prefetch(knl, "f[ifeat,:,:,:]",
fetch_outer_inames="im_x_outer, im_y_outer, ifeat",
default_tag="l.auto")
knl = lp.add_prefetch(knl, "img", "im_x_inner, im_y_inner, f_x, f_y",
fetch_outer_inames="iimg, im_x_outer, im_y_outer, ifeat, icolor",
default_tag="l.auto")
return knl
for variant in [
#variant_0,
#variant_1,
variant_2
]:
lp.auto_test_vs_ref(ref_knl, ctx, variant(knl),
parameters=dict(
im_w=128, im_h=128, f_w=f_w,
nfeats=3, nimgs=3
))
def test_tim2d(ctx_factory):
dtype = np.float32
ctx = ctx_factory()
order = "C"
n = 8
from pymbolic import var
K_sym = var("K") # noqa
field_shape = (K_sym, n, n)
# K - run-time symbolic
knl = lp.make_kernel(
"{[i,j,e,m,o,o2,gi]: 0<=i,j,m,o,o2<n and 0<=e<K and 0<=gi<3}",
[
"ur(a,b) := simul_reduce(sum, o, D[a,o]*u[e,o,b])",
"us(a,b) := simul_reduce(sum, o2, D[b,o2]*u[e,a,o2])",
#"Gu(mat_entry,a,b) := G[mat_entry,e,m,j]*ur(m,j)",
"Gux(a,b) := G$x[0,e,a,b]*ur(a,b)+G$x[1,e,a,b]*us(a,b)",
"Guy(a,b) := G$y[1,e,a,b]*ur(a,b)+G$y[2,e,a,b]*us(a,b)",
"lap[e,i,j] = "
" simul_reduce(sum, m, D[m,i]*Gux(m,j))"
"+ simul_reduce(sum, m, D[m,j]*Guy(i,m))"
],
[
lp.GlobalArg("u", dtype, shape=field_shape, order=order),
lp.GlobalArg("lap", dtype, shape=field_shape, order=order),
lp.GlobalArg("G", dtype, shape=(3, ) + field_shape, order=order),
# lp.ConstantArrayArg("D", dtype, shape=(n, n), order=order),
lp.GlobalArg("D", dtype, shape=(n, n), order=order),
# lp.ImageArg("D", dtype, shape=(n, n)),
lp.ValueArg("K", np.int32, approximately=1000),
],
name="semlap2D",
assumptions="K>=1")
knl = lp.fix_parameters(knl, n=n)
knl = lp.duplicate_inames(knl, "o", within="id:ur")
knl = lp.duplicate_inames(knl, "o", within="id:us")
seq_knl = knl
def variant_orig(knl):
knl = lp.tag_inames(knl, dict(i="l.0", j="l.1", e="g.0"))
knl = lp.add_prefetch(knl, "D[:,:]", default_tag="l.auto")
knl = lp.add_prefetch(knl, "u[e, :, :]", default_tag="l.auto")
knl = lp.precompute(knl, "ur(m,j)", ["m", "j"], default_tag="l.auto")
knl = lp.precompute(knl, "us(i,m)", ["i", "m"], default_tag="l.auto")
knl = lp.precompute(knl, "Gux(m,j)", ["m", "j"], default_tag="l.auto")
knl = lp.precompute(knl, "Guy(i,m)", ["i", "m"], default_tag="l.auto")
knl = lp.add_prefetch(knl, "G$x[:,e,:,:]", default_tag="l.auto")
knl = lp.add_prefetch(knl, "G$y[:,e,:,:]", default_tag="l.auto")
knl = lp.tag_inames(knl, dict(o="unr"))
knl = lp.tag_inames(knl, dict(m="unr"))
knl = lp.set_instruction_priority(knl, "id:D_fetch", 5)
print(knl)
return knl
for variant in [variant_orig]:
K = 1000 # noqa
lp.auto_test_vs_ref(
seq_knl,
ctx,
variant(knl),
op_count=[
K * (n * n * n * 2 * 2 + n * n * 2 * 3 + n**3 * 2 * 2) / 1e9
],
op_label=["GFlops"],
parameters={"K": K})
def test_dg_volume(ctx_factory):
#logging.basicConfig(level=logging.DEBUG)
dtype = np.float32
dtype4 = cl.array.vec.float4
ctx = ctx_factory()
order = "F"
N = 3 # noqa
Np = (N + 1) * (N + 2) * (N + 3) // 6 # noqa
K = 10000 # noqa
knl = lp.make_kernel(
[
"{[n,m,k]: 0<= n,m < Np and 0<= k < K}",
],
"""
<> du_drst = simul_reduce(sum, m, DrDsDt[n,m]*u[k,m])
<> dv_drst = simul_reduce(sum, m, DrDsDt[n,m]*v[k,m])
<> dw_drst = simul_reduce(sum, m, DrDsDt[n,m]*w[k,m])
<> dp_drst = simul_reduce(sum, m, DrDsDt[n,m]*p[k,m])
# volume flux
rhsu[k,n] = dot(drst_dx[k],dp_drst)
rhsv[k,n] = dot(drst_dy[k],dp_drst)
rhsw[k,n] = dot(drst_dz[k],dp_drst)
rhsp[k,n] = dot(drst_dx[k], du_drst) + dot(drst_dy[k], dv_drst) \
+ dot(drst_dz[k], dw_drst)
""", [
lp.GlobalArg(
"u,v,w,p,rhsu,rhsv,rhsw,rhsp", dtype, shape="K, Np",
order="C"),
lp.GlobalArg("DrDsDt", dtype4, shape="Np, Np", order="C"),
lp.GlobalArg(
"drst_dx,drst_dy,drst_dz", dtype4, shape="K", order=order),
lp.ValueArg("K", np.int32, approximately=1000),
],
name="dg_volume",
assumptions="K>=1")
knl = lp.fix_parameters(knl, Np=Np)
seq_knl = knl
def variant_basic(knl):
knl = lp.tag_inames(knl, dict(k="g.0", n="l.0"))
return knl
def variant_more_per_work_group(knl):
knl = lp.tag_inames(knl, dict(n="l.0"))
knl = lp.split_iname(knl, "k", 3, outer_tag="g.0", inner_tag="l.1")
return knl
def variant_image_d(knl):
knl = lp.tag_inames(knl, dict(n="l.0"))
knl = lp.split_iname(knl, "k", 3, outer_tag="g.0", inner_tag="l.1")
knl = lp.change_arg_to_image(knl, "DrDsDt")
return knl
def variant_prefetch_d(knl):
knl = lp.tag_inames(knl, dict(n="l.0"))
knl = lp.split_iname(knl, "k", 3, outer_tag="g.0", inner_tag="l.1")
knl = lp.add_prefetch(knl,
"DrDsDt[:,:]",
fetch_outer_inames="k_outer",
default_tag="l.auto")
return knl
def variant_prefetch_fields(knl):
knl = lp.tag_inames(knl, dict(n="l.0"))
knl = lp.split_iname(knl, "k", 3, outer_tag="g.0", inner_tag="l.1")
for name in ["u", "v", "w", "p"]:
knl = lp.add_prefetch(knl,
"%s[k,:]" % name, ["k_inner"],
default_tag="l.auto")
return knl
def variant_k_ilp(knl):
knl = lp.tag_inames(knl, dict(n="l.0"))
knl = lp.split_iname(knl, "k", 3, outer_tag="g.0", inner_tag="ilp")
knl = lp.tag_inames(knl, dict(m="unr"))
return knl
def variant_simple_padding(knl):
knl = lp.tag_inames(knl, dict(n="l.0"))
knl = lp.split_iname(knl, "k", 3, outer_tag="g.0", inner_tag="l.1")
arg_names = [
prefix + name for name in ["u", "v", "w", "p"]
for prefix in ["", "rhs"]
]
for name in arg_names:
knl = lp.add_padding(knl, name, axis=0, align_bytes=32)
knl = lp.tag_inames(knl, dict(m="unr"))
return knl
def variant_fancy_padding(knl):
knl = lp.tag_inames(knl, dict(n="l.0"))
pad_mult = lp.find_padding_multiple(knl, "u", 1, 32)
arg_names = [
prefix + name for name in ["u", "v", "w", "p"]
for prefix in ["", "rhs"]
]
knl = lp.split_array_dim(knl, [(nm, 0) for nm in arg_names], pad_mult)
return knl
parameters_dict = dict(K=K)
variants = [
variant_basic, variant_more_per_work_group, variant_prefetch_d,
variant_prefetch_fields, variant_k_ilp, variant_simple_padding,
variant_fancy_padding
]
if (ctx.devices[0].image_support
and ctx.devices[0].platform.name != "Portable Computing Language"):
variants.append(variant_image_d)
for variant in variants:
lp.auto_test_vs_ref(
seq_knl,
ctx,
variant(knl),
parameters=parameters_dict,
#codegen_kwargs=dict(with_annotation=True)
)
def test_tim3d(ctx_factory):
dtype = np.float32
ctx = ctx_factory()
order = "C"
n = 8
from pymbolic import var
K_sym = var("K")
field_shape = (K_sym, n, n, n)
# K - run-time symbolic
knl = lp.make_kernel(
ctx.devices[0],
"[K] -> {[i,j,k,e,m,o,gi]: 0<=i,j,k,m,o<%d and 0<=e<K and 0<=gi<6}" %
n,
[
"ur(a,b,c) := sum_float32(@o, D[a,o]*u[e,o,b,c])",
"us(a,b,c) := sum_float32(@o, D[b,o]*u[e,a,o,c])",
"ut(a,b,c) := sum_float32(@o, D[c,o]*u[e,a,b,o])",
"lap[e,i,j,k] = "
" sum_float32(m, D[m,i]*(G[0,e,m,j,k]*ur(m,j,k) + G[1,e,m,j,k]*us(m,j,k) + G[2,e,m,j,k]*ut(m,j,k)))"
" + sum_float32(m, D[m,j]*(G[1,e,i,m,k]*ur(i,m,k) + G[3,e,i,m,k]*us(i,m,k) + G[4,e,i,m,k]*ut(i,m,k)))"
" + sum_float32(m, D[m,k]*(G[2,e,i,j,m]*ur(i,j,m) + G[4,e,i,j,m]*us(i,j,m) + G[5,e,i,j,m]*ut(i,j,m)))"
],
[
lp.ArrayArg("u", dtype, shape=field_shape, order=order),
lp.ArrayArg("lap", dtype, shape=field_shape, order=order),
lp.ArrayArg("G", dtype, shape=(6, ) + field_shape, order=order),
# lp.ConstantArrayArg("D", dtype, shape=(n, n), order=order),
lp.ArrayArg("D", dtype, shape=(n, n), order=order),
# lp.ImageArg("D", dtype, shape=(n, n)),
lp.ValueArg("K", np.int32, approximately=1000),
],
name="semlap3D",
assumptions="K>=1")
seq_knl = knl
knl = lp.add_prefetch(knl,
"D", ["m", "j", "i", "k", "o"],
default_tag="l.auto")
knl = lp.add_prefetch(knl, "u", ["i", "j", "o", "k"], default_tag="l.auto")
knl = lp.precompute(knl,
"ur",
np.float32, ["a", "b", "c"],
default_tag="l.auto")
knl = lp.precompute(knl,
"us",
np.float32, ["a", "b", "c"],
default_tag="l.auto")
knl = lp.precompute(knl,
"ut",
np.float32, ["a", "b", "c"],
default_tag="l.auto")
knl = lp.split_iname(knl, "e", 1, outer_tag="g.0") #, slabs=(0, 1))
knl = lp.split_iname(knl, "k", n, inner_tag="l.2") #, slabs=(0, 1))
knl = lp.split_iname(knl, "j", n, inner_tag="l.1") #, slabs=(0, 1))
knl = lp.split_iname(knl, "i", n, inner_tag="l.0") #, slabs=(0, 1))
# knl = lp.tag_inames(knl, dict(k_nner="unr"))
knl = lp.tag_inames(knl, dict(o="unr"))
knl = lp.tag_inames(knl, dict(m="unr"))
# knl = lp.tag_inames(knl, dict(i="unr"))
knl = lp.add_prefetch(knl, "G", [2, 3, 4],
default_tag="l.auto") # axis/argument indices on G
kernel_gen = lp.generate_loop_schedules(knl)
kernel_gen = lp.check_kernels(kernel_gen, dict(K=1000))
K = 4000
lp.auto_test_vs_ref(seq_knl,
ctx,
kernel_gen,
op_count=K * ((n**4) * 3 * 2 + (n**3) * 5 * 3 +
(n**4) * 3 * 2) / 1e9,
op_label="GFlops",
parameters={"K": K})
def test_red2d(ctx_factory):
dtype = np.float32
ctx = ctx_factory()
order = "C"
n = 16
from pymbolic import var
K_sym = var("K")
field_shape = (K_sym, n, n)
# K - run-time symbolic
knl = lp.make_kernel(
ctx.devices[0],
"[K] -> {[i,j,e,m,o,gi]: 0<=i,j,m,o<%d and 0<=e<K and 0<=gi<3}" % n, [
"ue(a,b) := u[e,a,b]",
"ur(a,b) := sum_float32(@o, D[a,o]*ue(o,b))",
"us(a,b) := sum_float32(@o, D[b,o]*ue(a,o))", "lap[e,i,j] = "
" sum_float32(m, D[m,i]*(G[0,e,m,j]*ur(m,j)+G[1,e,m,j]*us(m,j)))"
"+ sum_float32(m, D[m,j]*(G[1,e,i,m]*ur(i,m)+G[2,e,i,m]*us(i,m)))"
], [
lp.ArrayArg("u", dtype, shape=field_shape, order=order),
lp.ArrayArg("lap", dtype, shape=field_shape, order=order),
lp.ArrayArg("G", dtype, shape=(3, ) + field_shape, order=order),
lp.ArrayArg("D", dtype, shape=(n, n), order=order),
lp.ValueArg("K", np.int32, approximately=1000),
],
name="semlap2D",
assumptions="K>=1")
unroll = 32
seq_knl = knl
knl = lp.add_prefetch(knl, "D", ["m", "j", "i", "o"], default_tag="l.auto")
knl = lp.add_prefetch(knl, "u", ["i", "j", "o"], default_tag="l.auto")
knl = lp.precompute(knl,
"ue",
np.float32, ["a", "b", "m"],
default_tag="l.auto")
knl = lp.precompute(knl,
"ur",
np.float32, ["a", "b"],
default_tag="l.auto")
knl = lp.precompute(knl,
"us",
np.float32, ["a", "b"],
default_tag="l.auto")
knl = lp.split_iname(knl, "e", 2, outer_tag="g.0")
knl = lp.split_iname(knl, "j", n, inner_tag="l.0") #, slabs=(0, 1))
knl = lp.split_iname(knl, "i", n, inner_tag="l.1") #, slabs=(0, 1))
knl = lp.tag_inames(knl, dict(o="unr"))
knl = lp.tag_inames(knl, dict(m="unr"))
knl = lp.add_prefetch(knl, "G", [2, 3],
default_tag="l.auto") # axis/argument indices on G
kernel_gen = lp.generate_loop_schedules(knl)
kernel_gen = lp.check_kernels(kernel_gen, dict(K=1000))
K = 1000
lp.auto_test_vs_ref(seq_knl,
ctx,
kernel_gen,
op_count=K * ((n**3) * 2 * 2 + n * n * 2 * 3 +
(n**3) * 2 * 2) / 1e9,
op_label="GFlops",
parameters={"K": K})
def test_gnuma_horiz_kernel(ctx_factory, ilp_multiple, Nq, opt_level): # noqa
ctx = ctx_factory()
filename = os.path.join(os.path.dirname(__file__),
"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, seq_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
hsv = lp.add_nosync(hsv, "any", "writes:rhsQ", "writes:rhsQ", force=True)
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.prioritize_loops(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[:,:]", default_tag="l.auto")
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_array_axes(hsv, flux_store_name, "N0,N1,N2?")
else:
hsv = lp.tag_array_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*"),
default_tag="l.auto")
if opt_level == 3:
tap_hsv = hsv
hsv = lp.add_prefetch(hsv,
"Q[ii,jj,k,:,:,e]",
sweep_inames=ilp_inames,
default_tag="l.auto")
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_array_axes(hsv, "rhsQ_buf", "c?,vec,c")
hsv = lp.tag_array_axes(hsv, "Q_fetch", "c?,vec,c")
hsv = lp.tag_array_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_map = lp.get_op_map(hsv)
print(lp.stringify_stats_mapping(op_map))
print("MEM")
gmem_map = lp.get_mem_access_map(hsv, subgroup_size=32).to_bytes()
print(lp.stringify_stats_mapping(gmem_map))
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)
and qbx_forced_limit == 0)
or (in_disk
and qbx_forced_limit != 0
and qbx_forced_limit * center_side[ictr] > 0)
)
<> post_dist_sq = if(matches, dist_sq, HUGE)
end
<> min_dist_sq, <> min_ictr = argmin(ictr, ictr, post_dist_sq)
tgt_to_qbx_center[itgt] = if(min_dist_sq < HUGE, min_ictr, -1)
end
""")
knl = lp.fix_parameters(knl, ambient_dim=2)
knl = lp.add_and_infer_dtypes(
knl, {
"tgt,center,radius,HUGE": np.float32,
"center_side,qbx_forced_limit": np.int32,
})
lp.auto_test_vs_ref(knl,
cl_ctx,
knl,
parameters={
"HUGE": 1e20,
"ncenters": 200,
"ntargets": 300,
"qbx_forced_limit": 1
})
def test_matmul(ctx_factory, buffer_inames):
ctx = ctx_factory()
if (buffer_inames
and ctx.devices[0].platform.name == "Portable Computing Language"):
pytest.skip("crashes on pocl")
logging.basicConfig(level=logging.INFO)
fortran_src = """
subroutine dgemm(m,n,ell,a,b,c)
implicit none
real*8 a(m,ell),b(ell,n),c(m,n)
integer m,n,k,i,j,ell
do j = 1,n
do i = 1,m
do k = 1,ell
c(i,j) = c(i,j) + b(k,j)*a(i,k)
end do
end do
end do
end subroutine
"""
prog = lp.parse_fortran(fortran_src)
assert len(prog["dgemm"].domains) == 1
ref_prog = prog
prog = lp.split_iname(prog, "i", 16, outer_tag="g.0", inner_tag="l.1")
prog = lp.split_iname(prog, "j", 8, outer_tag="g.1", inner_tag="l.0")
prog = lp.split_iname(prog, "k", 32)
prog = lp.assume(prog, "n mod 32 = 0")
prog = lp.assume(prog, "m mod 32 = 0")
prog = lp.assume(prog, "ell mod 16 = 0")
prog = lp.extract_subst(prog, "a_acc", "a[i1,i2]", parameters="i1, i2")
prog = lp.extract_subst(prog, "b_acc", "b[i1,i2]", parameters="i1, i2")
prog = lp.precompute(prog,
"a_acc",
"k_inner,i_inner",
precompute_outer_inames="i_outer, j_outer, k_outer",
default_tag="l.auto")
prog = lp.precompute(prog,
"b_acc",
"j_inner,k_inner",
precompute_outer_inames="i_outer, j_outer, k_outer",
default_tag="l.auto")
prog = lp.buffer_array(prog,
"c",
buffer_inames=buffer_inames,
init_expression="0",
store_expression="base+buffer")
lp.auto_test_vs_ref(ref_prog,
ctx,
prog,
parameters=dict(n=128, m=128, ell=128))
def test_laplacian_stiffness(ctx_factory):
dtype = np.float32
ctx = ctx_factory()
order = "C"
dim = 2 # (baked into code)
Nq = 40 # num. quadrature points (baked into code)
Nb = 20 # num. basis functions (baked into code)
Nc = 100 # num. cells (run-time symbolic)
from pymbolic import var
Nc_sym = var("Nc")
knl = lp.make_kernel(ctx.devices[0],
"[Nc] -> {[K,i,j,q, dx_axis, ax_b]: 0<=K<Nc and 0<=i,j<%(Nb)d and 0<=q<%(Nq)d "
"and 0<= dx_axis, ax_b < %(dim)d}"
% dict(Nb=Nb, Nq=Nq, dim=dim),
[
"dPsi(ij, dxi) := sum_float32(@ax_b,"
" jacInv[ax_b,dxi,K,q] * DPsi[ax_b,ij,q])",
"A[K, i, j] = sum_float32(q, w[q] * jacDet[K,q] * ("
"sum_float32(dx_axis, dPsi$one(i,dx_axis)*dPsi$two(j,dx_axis))))"
],
[
lp.GlobalArg("jacInv", dtype, shape=(dim, dim, Nc_sym, Nq), order=order),
lp.ConstantArg("DPsi", dtype, shape=(dim, Nb, Nq), order=order),
lp.GlobalArg("jacDet", dtype, shape=(Nc_sym, Nq), order=order),
lp.ConstantArg("w", dtype, shape=(Nq,), order=order),
lp.GlobalArg("A", dtype, shape=(Nc_sym, Nb, Nb), order=order),
lp.ValueArg("Nc", np.int32, approximately=1000),
],
name="lapquad", assumptions="Nc>=1")
knl = lp.tag_inames(knl, dict(ax_b="unr"))
seq_knl = knl
def variant_fig31(knl):
# This (mostly) reproduces Figure 3.1.
knl = lp.tag_inames(knl, {"dx_axis": "unr"})
return knl, ["K", "i", "j", "q", "ax_b_insn"]
def variant_pg4(knl):
# This (mostly) reproduces the unlabeled code snippet on pg. 4.
knl = lp.tag_inames(knl, {"dx_axis": "unr"})
Ncloc = 16
knl = lp.split_iname(knl, "K", Ncloc,
outer_iname="Ko", inner_iname="Kloc")
return knl, ["Ko", "Kloc", "i", "j", "q", "ax_b_insn"]
def variant_fig32(knl):
# This (mostly) reproduces Figure 3.2.
Ncloc = 16
knl = lp.split_iname(knl, "K", Ncloc,
outer_iname="Ko", inner_iname="Kloc")
knl = lp.precompute(knl, "dPsi", np.float32, ["i", "q", "dx_axis"],
default_tag=None)
knl = lp.tag_inames(knl, {"dx_axis": "unr", "dxi": "unr"})
return knl, ["Ko", "Kloc", "dPsi_q", "ij", "i", "j", "q", "ax_b_insn"]
def variant_fig33(knl):
# This is meant to (mostly) reproduce Figure 3.3.
Ncloc = 16
knl = lp.split_iname(knl, "K", Ncloc,
outer_iname="Ko", inner_iname="Kloc")
knl = lp.precompute(knl, "dPsi$one", np.float32, ["dx_axis"], default_tag=None)
knl = lp.tag_inames(knl, {"j": "ilp.seq"})
return knl, ["Ko", "Kloc"]
def variant_simple_gpu(knl):
# This is a simple GPU-ish variant.
# It's not the same thing as Matt's code, but I'll need some more time
# to reverse-engineer what is going on there. Some discussion might
# help, too. :)
knl = lp.tag_inames(knl, {"dx_axis": "unr"})
Ncloc = 16
knl = lp.split_iname(knl, "K", Ncloc,
outer_iname="Ko", inner_iname="Kloc",
outer_tag="g.0")
knl = lp.tag_inames(knl, {"i": "l.1", "j": "l.0"})
return knl, ["K", "i", "j", "q", "ax_b_insn"]
def variant_simple_gpu_prefetch(knl):
# This adds prefetching to the GPU variant above.
# In this variant (on my machine), loopy makes a silly choice
# for the upper bound of Kloc (it uses Nc). I'll investigate and
# fix that. (FIXME)
knl = lp.tag_inames(knl, {"dx_axis": "unr"})
Ncloc = 16
knl = lp.split_iname(knl, "K", Ncloc,
outer_iname="Ko", inner_iname="Kloc",
outer_tag="g.0")
knl = lp.tag_inames(knl, {"i": "l.1", "j": "l.0"})
knl = lp.add_prefetch(knl, "w", ["q"])
knl = lp.add_prefetch(knl, "DPsi", [0, 1, 2])
knl = lp.add_prefetch(knl, "jacInv", [0, 1, 3])
knl = lp.add_prefetch(knl, "jacDet", [1])
return knl, ["K", "i", "j", "q", "ax_b_insn"]
# Plug in variant name here
# |
# v
for variant in [variant_fig33]:
var_knl, loop_prio = variant(knl)
kernel_gen = lp.generate_loop_schedules(var_knl,
loop_priority=loop_prio)
kernel_gen = lp.check_kernels(kernel_gen, dict(Nc=Nc))
#print lp.preprocess_kernel(var_knl)
lp.auto_test_vs_ref(seq_knl, ctx, kernel_gen,
op_count=0, op_label="GFlops",
parameters={"Nc": Nc}, print_ref_code=True)
def no_test_dg_surface(ctx_factory):
# tough to test, would need the right index info
dtype = np.float32
ctx = ctx_factory()
order = "F"
N = 3 # noqa
Np = (N + 1) * (N + 2) * (N + 3) // 6 # noqa
Nfp = (N + 1) * (N + 2) // 2 # noqa
Nfaces = 4 # noqa
K = 10000 # noqa
knl = lp.make_kernel(
["{[m,n,k]: 0<= m < NfpNfaces and 0<= n < Np and 0<= k < K }"],
"""
<> idP = vmapP[m,k]
<> idM = vmapM[m,k]
<> du = u[[idP]]-u[[idM]]
<> dv = v[[idP]]-v[[idM]]
<> dw = w[[idP]]-w[[idM]]
<> dp = bc[m,k]*p[[idP]] - p[[idM]]
<> dQ = 0.5*Fscale[m,k]* \
(dp - nx[m,k]*du - ny[m,k]*dv - nz[m,k]*dw)
<> fluxu = -nx[m,k]*dQ
<> fluxv = -ny[m,k]*dQ
<> fluxw = -nz[m,k]*dQ
<> fluxp = dQ
# reduction here
rhsu[n,k] = sum(m, LIFT[n,m]*fluxu)
rhsv[n,k] = sum(m, LIFT[n,m]*fluxv)
rhsw[n,k] = sum(m, LIFT[n,m]*fluxw)
rhsp[n,k] = sum(m, LIFT[n,m]*fluxp)
""", [
lp.GlobalArg(
"vmapP,vmapM", np.int32, shape="NfpNfaces, K", order=order),
lp.GlobalArg("u,v,w,p,rhsu,rhsv,rhsw,rhsp",
dtype,
shape="Np, K",
order=order),
lp.GlobalArg(
"nx,ny,nz,Fscale,bc", dtype, shape="NfpNfaces, K",
order=order),
lp.GlobalArg("LIFT", dtype, shape="Np, NfpNfaces", order="C"),
lp.ValueArg("K", np.int32, approximately=1000),
],
name="dg_surface",
assumptions="K>=1")
knl = lp.fix_parameters(knl,
Np=Np,
Nfp=Nfp,
NfpNfaces=Nfaces * Nfp,
nsurf_dofs=K * Nfp)
seq_knl = knl
def variant_basic(knl):
return knl
parameters_dict = dict(K=K)
for variant in [
variant_basic,
]:
lp.auto_test_vs_ref(seq_knl,
ctx,
variant(knl),
parameters=parameters_dict)
def test_poisson_fem(ctx_factory):
# Stolen from Peter Coogan and Rob Kirby for FEM assembly
ctx = ctx_factory()
nbf = 5
nqp = 5
sdim = 3
knl = lp.make_kernel("{ [c,i,j,k,ell,ell2]: \
0 <= c < nels and \
0 <= i < nbf and \
0 <= j < nbf and \
0 <= k < nqp and \
0 <= ell,ell2 < sdim}",
"""
dpsi(bf,k0,dir) := \
simul_reduce(sum, ell2, DFinv[c,ell2,dir] * DPsi[bf,k0,ell2] )
Ael[c,i,j] = \
J[c] * w[k] * sum(ell, dpsi(i,k,ell) * dpsi(j,k,ell))
""",
assumptions="nels>=1 and nbf >= 1 and nels mod 4 = 0")
print(knl)
knl = lp.fix_parameters(knl, nbf=nbf, sdim=sdim, nqp=nqp)
ref_knl = knl
knl = lp.prioritize_loops(knl, ["c", "j", "i", "k"])
def variant_1(knl):
knl = lp.precompute(knl, "dpsi", "i,k,ell", default_tag='for')
knl = lp.prioritize_loops(knl, "c,i,j")
return knl
def variant_2(knl):
knl = lp.precompute(knl, "dpsi", "i,ell", default_tag='for')
knl = lp.prioritize_loops(knl, "c,i,j")
return knl
def add_types(knl):
return lp.add_and_infer_dtypes(
knl,
dict(
w=np.float32,
J=np.float32,
DPsi=np.float32,
DFinv=np.float32,
))
for variant in [
#variant_1,
variant_2
]:
knl = variant(knl)
lp.auto_test_vs_ref(add_types(ref_knl),
ctx,
add_types(knl),
parameters=dict(n=5, nels=15, nbf=5, sdim=2,
nqp=7))