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# SPDX-License-Identifier: Apache-2.0
# Generate test instances from a large tensor product set of options
GB_TYPE_PREFIX = "GrB"
SUPPORTED_TYPES = {
"int32_t": "INT32",
"uint32_t": "UINT32",
"int64_t": "INT64",
"uint64_t": "UINT64",
"bool": "BOOL",
"float": "FP32",
"double": "FP64"
}
DOT3_BUCKETS = [1, 2] # NBUCKETS, hard-coded
DataShapes ={
"nanoxnano": {'N':32, 'Anz':64, 'Bnz':56, 'Cnz': 256},
"tinyxtiny": {'N':128, 'Anz':1256, 'Bnz':1028, 'Cnz': 1640},
"smallxsmall": {'N':1024, 'Anz': 65_536, 'Bnz':65_536, 'Cnz': 10000},
"ti_denxti_den": {'N':32, 'Anz':1024, 'Bnz':1024, 'Cnz': 1024},
"ti_spaxti_den": {'N':32, 'Anz':256, 'Bnz':1024, 'Cnz': 1024},
"medxmed": {'N':4096, 'Anz': 2**20, 'Bnz':2**20},
"largexlarge": {'N':2**16, 'Anz': 64*2**20, 'Bnz':64*2**20}
}
FORMATS = { "sparse": ["phase1", "phase2", "mxm_sparse"],
"dense": ["dense_phase1", "mxm_dense"],
"sparse_dense": ["dense_phase1", "mxm_sparse_dense"],
"reduce": ["reduce"]}
FORMAT_INPUTS = {
"sparse": [("GxB_SPARSE", "GxB_SPARSE")],
"dense": [("GxB_FULL", "GxB_FULL"), ("GxB_BITMAP", "GxB_BITMAP")],
"sparse_dense": [("GxB_SPARSE", "GxB_FULL")],
"reduce": [("GxB_SPARSE", "GxB_SPARSE")]
}
FORMAT_DATASETS = {
"sparse": ["nanoxnano", "tinyxtiny", "smallxsmall"],
"dense": ["ti_denxti_den"],
"sparse_dense": ["ti_spaxti_den"],
"reduce": ["nanoxnano", "smallxsmall", "ti_denxti_den", "ti_spaxti_den"]
}
def std_type_to_gb_type(t):
return SUPPORTED_TYPES[t]
def build_gb_monioid(t, m):
# Example: GrB_PLUS_MONIOD_UINT64
gb_type = std_type_to_gb_type(t)
return f"{GB_TYPE_PREFIX}_{m}_MONOID_{gb_type}"
def build_gb_binop(t, b):
# Example: GrB_TIMES_UINT64
gb_type = std_type_to_gb_type(t)
return f"{GB_TYPE_PREFIX}_{b}_{gb_type}"
def buildTest(ts="TestsuiteName", ds="tiny-tiny", df=("GxB_SPARSE", "GxB_SPARSE"),
SUM="PLUS", PRODUCT="TIMES",
typeC="int32_t",typeM="int32_t",
typeA="int32_t",typeB="int32_t",
type_x="int32_t", type_y="int32_t",type_z="int32_t"):
# build string interpolation from pieces
format_A, format_B = df
Test_name = f"{ds}{SUM}_{PRODUCT}__{format_A}_{format_B}__C{typeC}M{typeM}A{typeA}B{typeB}X{type_x}Y{type_y}Z{type_z}"
Test_suite = f"{ts}"
N = DataShapes[ds]['N']
Anz = DataShapes[ds]['Anz']
Bnz = DataShapes[ds]['Bnz']
Cnz = DataShapes[ds]['Cnz']
gb_monoid = build_gb_monioid(typeC, SUM)
gb_binop = build_gb_binop(typeC, PRODUCT)
TEST_HEAD = f"""
TEST( {Test_suite}, {Test_name}) {{
/**************************
* Create reference and input data
*/
GrB_Monoid monoid = {gb_monoid};
GrB_BinaryOp binop = {gb_binop};
mxm_problem_spec<{typeC}, {typeM}, {typeA}, {typeB}> problem_spec(monoid, binop, {N}, {Anz}, {Bnz}, {Cnz},
{format_A}, {format_B});
"""
phase1_body= f""" test_AxB_phase1_factory< {typeC}, {typeM}, {typeA}, {typeB}>(problem_spec);"""
phase2_body= f""" test_AxB_phase2_factory< {typeC}, {typeM}, {typeA}, {typeB} >(problem_spec);"""
dense_phase1_body = f""" test_AxB_dense_phase1_factory<{typeC}, {typeM}, {typeA}, {typeB}>(problem_spec);"""
mxm_sparse_body = f""" test_AxB_dot3_sparse_factory< {typeC},{typeM},{typeA},{typeB},{type_x},{type_y},{type_z} > (problem_spec);\n"""
mxm_dense_body = f""" test_AxB_dot3_dense_factory< {typeC},{typeM},{typeA},{typeB},{type_x},{type_y},{type_z} > (problem_spec);\n"""
mxm_sparse_dense_body = f""" test_AxB_dot3_sparse_dense_factory< {typeC},{typeM},{typeA},{typeB},{type_x},{type_y},{type_z} > (problem_spec);\n"""
reduce_body = f""" test_reduce_factory<{typeC}, {typeM}, {typeA}, {typeB}>(problem_spec);"""
phasedict = { "phase1": phase1_body,
"phase2": phase2_body,
"mxm_sparse": mxm_sparse_body,
"mxm_dense": mxm_dense_body,
"mxm_sparse_dense": mxm_sparse_dense_body,
"reduce": reduce_body,
"dense_phase1": dense_phase1_body }
return TEST_HEAD, phasedict
def load_types(argv):
test_suite_name = argv[2]
Monoids = argv[3].split(";")
Binops = argv[4].split(";")
Semirings = argv[5]
DataTypes = argv[6].split(";")
# Hard-coding data shapes for now
Kernels= argv[7]
return argv[1], test_suite_name, Monoids, Binops, Semirings, DataTypes, DataShapes, Kernels
def write_test_instances_header(test_suite_name, mat_format, tests, Monoids, Binops, Semirings, DataTypes, DataShapes, Kernels):
outfile = f'{test_suite_name}_{Semirings}_{mat_format}_test_instances.hpp'
with open(outfile, 'w') as fp:
fp.write("#pragma once\n#include \"problem_spec.hpp\"\n");
m, b = Semirings.split("_")
Test_suite = f'{test_suite_name}_tests_{mat_format}_{m}_{b}'
for dtC in DataTypes:
dtX = dtC
dtY = dtC
dtZ = dtC
for dtM in ["bool", "int32_t", "int64_t", "float", "double"]:
for dtA in DataTypes:
for dtB in DataTypes:
for ds in FORMAT_DATASETS[mat_format]:
for df in FORMAT_INPUTS[mat_format]:
TEST_HEAD, TEST_BODY = buildTest( Test_suite, ds, df, m, b,
dtC, dtM, dtA, dtB, dtX, dtY, dtZ)
fp.write( TEST_HEAD)
for test in tests:
fp.write( TEST_BODY[test] )
fp.write( "}\n")
def write_cuda_test(source_dir, test_suite_name, mat_format, semiring, kernel):
import shutil
shutil.copy(f"{source_dir}/test/cuda_tests_template.cpp", f"{test_suite_name}_{semiring}_{mat_format}_cuda_tests.cpp")
with open(f"{test_suite_name}_{semiring}_{mat_format}_cuda_tests.cpp", "a") as file_object:
# Keeping this as a separate file for now to allow for further nesting
# of test instances for each test_suite_name
file_object.write(f"\n#include \"{test_suite_name}_{semiring}_{mat_format}_test_instances.hpp\"")
if __name__ == "__main__":
import sys
if(len(sys.argv) != 8):
raise ValueError("Expected 7 arguments but only got %s" % len(sys.argv))
"""
First load values
"""
source_dir, test_suite_name, Monoids, Binops, Semirings, DataTypes, DataShapes, Kernels = load_types(sys.argv)
for mat_format, tests in FORMATS.items():
write_test_instances_header(test_suite_name, mat_format, tests, Monoids, Binops, Semirings, DataTypes, DataShapes, DOT3_BUCKETS)
write_cuda_test(source_dir, test_suite_name, mat_format, Semirings, Kernels)
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