llvm-project/clang/test/CodeGen/builtins-nvptx-mma.py

344 lines
12 KiB
Python

# This script generates all variants of wmma builtins, verifies that clang calls
# correct LLVM instrinsics, and checks that availability of specific builtins is
# constrained by the correct PTX version and the target GPU variant.
# Dummy test run to avoid lit warnings.
# RUN: echo "This is not a real test. It's a generator for builtins-nvpts-mma.cu" >/dev/null
from __future__ import print_function
import argparse
from collections import defaultdict
from itertools import product
from string import Template
class MMAFrag:
def __init__(self, geom, frag, ptx_elt_type):
self.geom = geom
self.frag = frag
self.ptx_type = ptx_elt_type;
def __repr__(self):
return "%s:%s:%s" % (self.geom, self.frag, self.ptx_type)
class MMAOp:
def __init__(self, a, b, c, d):
self.a = a
self.b = b
self.c = c
self.d = d
def __repr__(self):
return ("{A:%s, B:%s, C:%s, D:%s}" % (self.a, self.b, self.c, self.d ))
def make_mma_ops(geoms, types_a, types_b, types_c, types_d):
ops = []
for geom, type_a, type_c in product( geoms, types_a, types_c):
for type_b, type_d in product(types_b if types_b else [type_a],
types_d if types_d else [type_c]):
ops.append(MMAOp(MMAFrag(geom, "a", type_a),
MMAFrag(geom, "b", type_b),
MMAFrag(geom, "c", type_c),
MMAFrag(geom, "d", type_d)))
return ops
def make_ldst_ops(geoms, frags, types):
return [MMAFrag(geom, frag, ptx_type) for (geom, frag, ptx_type)
in product(geoms, frags, types)]
def get_mma_ops():
return (make_mma_ops(["m16n16k16", "m32n8k16", "m8n32k16"],
["f16"], [], ["f16", "f32"], ["f16", "f32"]) +
make_mma_ops(["m16n16k16", "m32n8k16", "m8n32k16"],
["s8", "u8"], [], ["s32"], []) +
make_mma_ops(["m8n8k32"],
["s4", "u4"], [], ["s32"], []) +
make_mma_ops(["m8n8k128"],
["b1"], [], ["s32"], []))
def get_ldst_ops():
return (make_ldst_ops(["m16n16k16", "m32n8k16", "m8n32k16"],
["a", "b"], ["f16", "u8", "s8"]) +
make_ldst_ops(["m16n16k16", "m32n8k16", "m8n32k16"],
["c", "d"], ["f16", "f32", "s32"]) +
make_ldst_ops(["m8n8k32"], ["a", "b"], ["s4","u4"]) +
make_ldst_ops(["m8n8k128"], ["a", "b"], ["b1"]) +
make_ldst_ops(["m8n8k32", "m8n8k128"], ["c", "d"], ["s32"]))
def is_geom_supported(geom):
# geometries for FP and ints.
if geom in ["m8n32k16", "m32n8k16"]:
return ptx_version >= 61
# geometries for sub-ints.
if geom in ["m8n8k32", "m8n8k128"]:
return ptx_version >= 63 and gpu_arch >= 75
if geom == "m16n16k16":
return ptx_version >= 60
assert(False) # Unexpected geometry.
def is_type_supported(ptx_type):
if ptx_type in ["s8", "u8", "s32"]:
return ptx_version >= 63 and gpu_arch >= 72
if ptx_type in ["s4", "u4", "b1"]:
return ptx_version >= 63 and gpu_arch >= 75
return ptx_version >= 60 and gpu_arch >= 70
def is_mma_variant_supported(op, layout_a, layout_b, satf):
if not (is_type_supported(op.a.ptx_type)
and is_geom_supported(op.a.geom)):
return False
# sub-integer require row/col layout, and no satf.
if op.a.ptx_type in ["s4", "u4", "b1"]:
if op.a.ptx_type == "b1" and satf:
return False
return layout_a == "row" and layout_b == "col"
return True
def is_ldst_variant_supported(frag, layout):
if not (is_type_supported(frag.ptx_type)
and is_geom_supported(frag.geom)):
return False
if frag.ptx_type in ["s4", "u4", "b1"]:
# sub-integer require sm_75 and ptx63, row/col layout for a/b.
return ((frag.frag == "a" and layout == "row")
or (frag.frag == "b" and layout == "col")
or frag.frag in ["c", "d"])
return True
def get_builtin_prefix(frag):
prefix = None
if frag.geom in ["m16n16k16", "m32n8k16", "m8n32k16"]:
if frag.ptx_type in ["f16", "f32"]:
prefix = "__hmma"
else:
prefix = "__imma"
elif frag.geom == "m8n8k32":
prefix = "__imma" # sub-integers
elif frag.geom == "m8n8k128":
prefix = "__bmma"
assert prefix
return prefix
def get_ldst_builtin_name(frag):
prefix = get_builtin_prefix(frag)
if prefix == "__hmma":
suffix = "" if frag.frag in ["a","b"] else frag.ptx_type
elif prefix in ["__imma", "__bmma"]:
suffix = "" if frag.frag in ["c"] else frag.ptx_type
if suffix == "s32":
suffix = "i32"
if frag.frag == "d":
ifrag = "c"
op = "st"
else:
ifrag = frag.frag
op = "ld"
name = "%s_%s_%s_%s%s" % (prefix, frag.geom, op, ifrag,
"_" + suffix if suffix else "")
return name
def get_mma_builtin_name(op):
prefix = get_builtin_prefix(op.a)
if prefix == "__hmma":
suffix = op.d.ptx_type + op.c.ptx_type
else:
suffix = op.a.ptx_type
name = "%s_%s_mma%s_%s" % (prefix, op.a.geom,
"_xor_popc" if op.a.ptx_type == "b1" else "",
suffix)
return name
def get_required_sm(frag):
if frag.ptx_type in ["u4", "s4", "b1"]:
return 75
if frag.ptx_type in ["s8", "u8"]:
return 72
if frag.ptx_type == "s32":
if frag.geom in ["m8n8k32", "m8n8k128"]: # s4/u4/b1
return 75
else: # s8/u8
return 72
if frag.ptx_type in ["f16", "f32"]:
return 70
assert(False)
def get_required_ptx(frag):
if frag.ptx_type in ["f16", "f32"]:
return 60 if frag.geom == "m16n16k16" else 61
return 63
def gen_wmma_ldst_tests(results):
load_template = """
// CHECK${check_suffix}: call {{.*}} @${intrinsic}
// expected-error-re@+1 {{'${builtin}' needs target feature sm_${min_sm}{{.*}},ptx${min_ptx}{{.*}}}}
${builtin}(${dst}, ${src}, ldm, ${blayout});
""".rstrip()
intrinsic_template = "llvm.nvvm.wmma.${geom}.${op}.${frag}.${ilayout}.stride.${itype}"
for frag, layout in sorted(product(get_ldst_ops(), ["row","col"]), key=str):
if not is_ldst_variant_supported(frag, layout):
continue
is_fp = frag.ptx_type == "f32"
min_sm = get_required_sm(frag)
min_ptx = get_required_ptx(frag)
params = {
"check_suffix" : "_PTX%d_SM%d" % (min_ptx, min_sm),
"builtin" : get_ldst_builtin_name(frag),
"min_ptx" : min_ptx,
"min_sm" : min_sm,
"dst": "fdst" if is_fp else "dst",
"src": "fsrc" if is_fp else "src",
"blayout" : 0 if layout == "row" else 1,
"intrinsic" : Template(intrinsic_template).substitute({
"frag" : frag.frag,
"geom" : frag.geom,
"ilayout" : layout,
"itype" : frag.ptx_type,
"op" : "store" if frag.frag == "d" else "load",
})
}
results[(min_ptx,min_sm)] += Template(load_template).substitute(params)
return results
def mma_signature(op):
if op.a.ptx_type in ["s8", "u8", "s4", "u4", "b1"]:
# int and sub-int ops are identified by input type.
return op.a.ptx_type
else:
# the rest are FP ops identified by accumulator & result type.
return "%s.%s" % (op.d.ptx_type, op.c.ptx_type)
# Get numeric value for rowcol parameter of the builtin
# AFAICT it uses the encoding accepted by NVVM intrinsics:
# https://docs.nvidia.com/cuda/nvvm-ir-spec/index.html#nvvm-intrin-warp-level-matrix-mma
def get_ilayout(a, b):
return {
"row.row" : 0,
"row.col" : 1,
"col.row" : 2,
"col.col" : 3
}[a + "." + b]
def gen_wmma_mma_tests(results):
mma_template = """
// CHECK${check_suffix}: call {{.*}} @${intrinsic}
// expected-error-re@+1 {{'${builtin}' needs target feature sm_${min_sm}{{.*}},ptx${min_ptx}{{.*}}}}
${builtin}(${dst}, ${asrc}, ${asrc}, ${csrc}, ${ilayout}${maybe_isatf});
""".rstrip()
intrinsic_template = "llvm.nvvm.wmma.${geom}.mma.${alayout}.${blayout}.${intrinsic_signature}${satf}"
for op, alayout, blayout, satf in sorted(product( get_mma_ops(),
["row","col"],
["row","col"],
[".satfinite", ""]),
key=str):
if not is_mma_variant_supported(op, alayout, blayout, satf):
continue
a_is_fp = op.a.ptx_type == "f32"
c_is_fp = op.c.ptx_type == "f32"
d_is_fp = op.d.ptx_type == "f32"
min_sm = get_required_sm(op.a)
min_ptx = get_required_ptx(op.a)
if op.a.ptx_type == "b1": # .b1 MMA has no satf argument.
isatf_arg = ""
else:
isatf_arg = ", 1" if satf else ", 0"
params = {
"check_suffix" : "_PTX%d_SM%d" % (min_ptx, min_sm),
"builtin" : get_mma_builtin_name(op),
"min_ptx" : min_ptx,
"min_sm" : min_sm,
"dst": "fdst" if d_is_fp else "dst",
"asrc": "fsrc" if a_is_fp else "src",
"csrc": "fsrc" if c_is_fp else "src",
"ilayout" : get_ilayout(alayout, blayout),
"maybe_isatf" : isatf_arg,
"intrinsic" : Template(intrinsic_template).substitute({
"geom" : op.a.geom,
"alayout" : alayout,
"blayout" : blayout,
"intrinsic_signature" : mma_signature(op),
"satf" : satf,
})
}
results[(min_ptx, min_sm)] += Template(mma_template).substitute(params)
return results
def gen_tests():
results = gen_wmma_ldst_tests(defaultdict(str))
results = gen_wmma_mma_tests(results)
run_template = r"""
//
// *** DO NOT EDIT ***
//
// This test has been automatically generated by
// builtins-nvtx-mma.py --ptx=${ptx} --gpu-arch=${sm}
//
// Make sure we can handle all builtins available on sm_${sm} with PTX${ptx}
// ${run}: %clang_cc1 -triple nvptx64-unknown-unknown -target-cpu sm_${sm} \
// ${run}: -fcuda-is-device -target-feature +ptx${ptx} \
// ${run}: -DPTX=${ptx} -DSM=${sm} \
// ${run}: -S -emit-llvm -o - -x cuda %s \
// ${run}: | FileCheck -check-prefixes=${check_labels} %s
// Verify that all builtins have correct constraints.
// ${run}: %clang_cc1 -triple nvptx-unknown-unknown \
// ${run}: -target-cpu sm_60 -target-feature +ptx42 \
// ${run}: -DPTX=${ptx} -DSM=${sm} -fcuda-is-device -S -o /dev/null -x cuda \
// ${run}: -verify %s
"""
def supported_variants(ptx, sm, results):
return [(ptx_, sm_) for ptx_, sm_ in results if ptx_ <= ptx and sm_ <= sm]
print(Template(run_template).substitute({
"run" : "RUN", # To avoid lit misinterpreting the template
"ptx" : ptx_version,
"sm" : gpu_arch,
"check_labels" : ",".join(["CHECK_PTX%d_SM%d" % (ptx_, sm_)
for ptx_, sm_
in supported_variants(ptx_version, gpu_arch,
results)])
}))
print("""
#if !defined(CUDA_VERSION)
#define __device__ __attribute__((device))
#define __global__ __attribute__((global))
#define __shared__ __attribute__((shared))
#define __constant__ __attribute__((constant))
typedef unsigned long long uint64_t;
#endif
// CHECK-LABEL: test_wmma_buitins
__device__ void test_wmma_buitins(int *src, int *dst,
float *fsrc, float *fdst, int ldm) {
""");
for (ptx, sm), tests in sorted(results.items()):
print()
print("#if (PTX >= %d) && (SM >= %d)" % (ptx, sm))
print(tests)
print("#endif // (PTX >= %d) && (SM >= %d) "% (ptx, sm))
print("}")
parser = argparse.ArgumentParser()
parser.add_argument("--ptx", type=int, default=60)
parser.add_argument("--gpu-arch", type=int, default=70)
args = parser.parse_args()
ptx_version = args.ptx
gpu_arch = args.gpu_arch
gen_tests()