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Test cleanup
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@davschneller
davschneller committed Oct 1, 2024
1 parent 927e4f6 commit 78eb2c7
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Showing 4 changed files with 60 additions and 65 deletions.
7 changes: 1 addition & 6 deletions tests/sve_testsuite_generator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@
import sys
import os
import testsuite_generator as test_generator
from pspamm.codegen.precision import *

BASEDIR = 'build'

Expand All @@ -30,8 +31,6 @@ def make(kernels, arch):

f.write(test_generator.head_of_testsuite)

include_single_prec = False

for kern in kernels:
arguments = ['pspamm-generator', str(kern.m), str(kern.n), str(kern.k), str(kern.lda),
str(kern.ldb), str(kern.ldc), str(kern.alpha), str(kern.beta)]
Expand All @@ -41,8 +40,6 @@ def make(kernels, arch):

prec = 's' if kern.precision == Precision.SINGLE else 'd'
arguments += ['--precision', prec]
if prec == 's':
include_single_prec = True

block_sizes = list(set(kern.block_sizes))

Expand Down Expand Up @@ -100,8 +97,6 @@ def make(kernels, arch):
bm = bs[0]
bn = bs[1]

prec = 's' if kern.precision == Precision.SINGLE else 'd'

if arch.startswith("arm_sve"):
veclen = int(arch[7:])
assert veclen % 128 == 0 and veclen <= 2048
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42 changes: 21 additions & 21 deletions tests/unit_tests_arm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,27 +11,27 @@
kernels = []

for precision in (Precision.SINGLE, Precision.DOUBLE):
kernels.append(generator.DenseKernel("test4", precision, 4, 4, 4, 4, 4, 4, 2.0, 2.0, [(4, 4)], 0.0000001))

kernels.append(generator.SparseKernel("test1", precision, 8, 56, 56, 8, 0, 8, 1.0, 0.0, [(8, 4), (8,1)] + [x.getBlocksize(8, 56, 1) for x in blocksize_algs], generator.generateMTX(56, 56, 30), 0.0000001))
kernels.append(generator.DenseKernel("test2", precision, 8, 40, 40, 8, 40, 8, 3.0, 2.0, [(8, 5), (8,2)] + [x.getBlocksize(8, 40, 1) for x in blocksize_algs], 0.0000001))
kernels.append(generator.DenseKernel("test3", precision, 8, 56, 56, 8, 56, 8, 0.0, 0.0, [(8, 3), (8, 5)] + [x.getBlocksize(8, 56, 1) for x in blocksize_algs], 0.0000001))

kernels.append(generator.SparseKernel("arm_only_test1", precision, 2, 3, 4, 2, 0, 2, 1.1233, 0.0, [(2, 1), (2,3)] + [x.getBlocksize(2, 3, 1) for x in blocksize_algs], generator.generateMTX(4, 3, 5), 0.0000001))
kernels.append(generator.SparseKernel("arm_only_test2", precision, 2, 3, 4, 20, 0, 14, 1.0, 1.0, [(2, 2), (2,3)] + [x.getBlocksize(2, 3, 1) for x in blocksize_algs], generator.generateMTX(4, 3, 5), 0.0000001))
kernels.append(generator.SparseKernel("arm_only_test3", precision, 32, 80, 50, 32, 0, 32, 1.0, 3.0, [(8, 5)] + [x.getBlocksize(32, 80, 1) for x in blocksize_algs], generator.generateMTX(50, 80, 294), 0.0000001))
kernels.append(generator.SparseKernel("arm_only_test4", precision, 32, 32, 32, 34, 0, 32, 1.0, 0.0, [(4, 4), (4,3)] + [x.getBlocksize(32, 32, 1) for x in blocksize_algs], generator.generateMTX(32, 32, 24), 0.0000001))
kernels.append(generator.SparseKernel("arm_only_test5", precision, 2, 1, 1, 2, 0, 8, 1.0, -1.0, [(2, 1)] + [x.getBlocksize(2, 1, 1) for x in blocksize_algs], generator.generateMTX(1, 1, 1), 0.0000001))
kernels.append(generator.SparseKernel("arm_only_test6", precision, 2, 2, 2, 2, 0, 2, 2.0, 234234.123, [(2, 1)] + [x.getBlocksize(2, 2, 1) for x in blocksize_algs], generator.generateMTX(2, 2, 1), 0.0000001))
kernels.append(generator.SparseKernel("arm_only_test7", precision, 16, 5, 7, 16, 0, 16, 0.0, -1.123, [(8, 1), (8,2)] + [x.getBlocksize(16, 5, 1) for x in blocksize_algs], generator.generateMTX(7, 5, 35), 0.0000001))

kernels.append(generator.DenseKernel("arm_only_test8", precision, 2, 3, 4, 2, 4, 2, 1.0, 0.0, [(2, 1), (2,3)] + [x.getBlocksize(2, 3, 1) for x in blocksize_algs], 0.0000001))
kernels.append(generator.DenseKernel("arm_only_test9", precision, 2, 3, 4, 20, 12, 14, 2.0, 1.123, [(2, 2), (2,3)] + [x.getBlocksize(2, 3, 1) for x in blocksize_algs], 0.0000001))
kernels.append(generator.DenseKernel("arm_only_test10", precision, 32, 80, 50, 32, 50, 32, 0.0, 0.2, [(8, 5)] + [x.getBlocksize(32, 80, 1) for x in blocksize_algs], 0.0000001))
kernels.append(generator.DenseKernel("arm_only_test11", precision, 32, 32, 32, 33, 68, 32, 1231.0, 14443.0, [(4, 4), (4,3)] + [x.getBlocksize(32, 32, 1) for x in blocksize_algs], 0.0000001))
kernels.append(generator.DenseKernel("arm_only_test12", precision, 2, 1, 1, 2, 1, 8, 1.0, 3.0, [(2, 1)] + [x.getBlocksize(2, 1, 1) for x in blocksize_algs], 0.0000001))
kernels.append(generator.DenseKernel("arm_only_test13", precision, 2, 3, 3, 2, 3, 2, 1.0, 0.0, [(2, 1)] + [x.getBlocksize(2, 3, 1) for x in blocksize_algs], 0.0000001))
kernels.append(generator.DenseKernel("arm_only_test14", precision, 16, 5, 7, 16, 7, 16, 1.0, 1.0, [(8, 1), (8,2)] + [x.getBlocksize(16, 5, 1) for x in blocksize_algs], 0.0000001))
kernels.append(generator.DenseKernel(f"test4_{precision}", precision, 4, 4, 4, 4, 4, 4, 2.0, 2.0, [(4, 4)], 0.0000001))

kernels.append(generator.SparseKernel(f"test1_{precision}", precision, 8, 56, 56, 8, 0, 8, 1.0, 0.0, [(8, 4), (8,1)] + [x.getBlocksize(8, 56, 1) for x in blocksize_algs], generator.generateMTX(56, 56, 30), 0.0000001))
kernels.append(generator.DenseKernel(f"test2_{precision}", precision, 8, 40, 40, 8, 40, 8, 3.0, 2.0, [(8, 5), (8,2)] + [x.getBlocksize(8, 40, 1) for x in blocksize_algs], 0.0000001))
kernels.append(generator.DenseKernel(f"test3_{precision}", precision, 8, 56, 56, 8, 56, 8, 0.0, 0.0, [(8, 3), (8, 5)] + [x.getBlocksize(8, 56, 1) for x in blocksize_algs], 0.0000001))

kernels.append(generator.SparseKernel(f"arm_only_test1_{precision}", precision, 2, 3, 4, 2, 0, 2, 1.1233, 0.0, [(2, 1), (2,3)] + [x.getBlocksize(2, 3, 1) for x in blocksize_algs], generator.generateMTX(4, 3, 5), 0.0000001))
kernels.append(generator.SparseKernel(f"arm_only_test2_{precision}", precision, 2, 3, 4, 20, 0, 14, 1.0, 1.0, [(2, 2), (2,3)] + [x.getBlocksize(2, 3, 1) for x in blocksize_algs], generator.generateMTX(4, 3, 5), 0.0000001))
kernels.append(generator.SparseKernel(f"arm_only_test3_{precision}", precision, 32, 80, 50, 32, 0, 32, 1.0, 3.0, [(8, 5)] + [x.getBlocksize(32, 80, 1) for x in blocksize_algs], generator.generateMTX(50, 80, 294), 0.0000001))
kernels.append(generator.SparseKernel(f"arm_only_test4_{precision}", precision, 32, 32, 32, 34, 0, 32, 1.0, 0.0, [(4, 4), (4,3)] + [x.getBlocksize(32, 32, 1) for x in blocksize_algs], generator.generateMTX(32, 32, 24), 0.0000001))
kernels.append(generator.SparseKernel(f"arm_only_test5_{precision}", precision, 2, 1, 1, 2, 0, 8, 1.0, -1.0, [(2, 1)] + [x.getBlocksize(2, 1, 1) for x in blocksize_algs], generator.generateMTX(1, 1, 1), 0.0000001))
kernels.append(generator.SparseKernel(f"arm_only_test6_{precision}", precision, 2, 2, 2, 2, 0, 2, 2.0, 234234.123, [(2, 1)] + [x.getBlocksize(2, 2, 1) for x in blocksize_algs], generator.generateMTX(2, 2, 1), 0.0000001))
kernels.append(generator.SparseKernel(f"arm_only_test7_{precision}", precision, 16, 5, 7, 16, 0, 16, 0.0, -1.123, [(8, 1), (8,2)] + [x.getBlocksize(16, 5, 1) for x in blocksize_algs], generator.generateMTX(7, 5, 35), 0.0000001))

kernels.append(generator.DenseKernel(f"arm_only_test8_{precision}", precision, 2, 3, 4, 2, 4, 2, 1.0, 0.0, [(2, 1), (2,3)] + [x.getBlocksize(2, 3, 1) for x in blocksize_algs], 0.0000001))
kernels.append(generator.DenseKernel(f"arm_only_test9_{precision}", precision, 2, 3, 4, 20, 12, 14, 2.0, 1.123, [(2, 2), (2,3)] + [x.getBlocksize(2, 3, 1) for x in blocksize_algs], 0.0000001))
kernels.append(generator.DenseKernel(f"arm_only_test10_{precision}", precision, 32, 80, 50, 32, 50, 32, 0.0, 0.2, [(8, 5)] + [x.getBlocksize(32, 80, 1) for x in blocksize_algs], 0.0000001))
kernels.append(generator.DenseKernel(f"arm_only_test11_{precision}", precision, 32, 32, 32, 33, 68, 32, 1231.0, 14443.0, [(4, 4), (4,3)] + [x.getBlocksize(32, 32, 1) for x in blocksize_algs], 0.0000001))
kernels.append(generator.DenseKernel(f"arm_only_test12_{precision}", precision, 2, 1, 1, 2, 1, 8, 1.0, 3.0, [(2, 1)] + [x.getBlocksize(2, 1, 1) for x in blocksize_algs], 0.0000001))
kernels.append(generator.DenseKernel(f"arm_only_test13_{precision}", precision, 2, 3, 3, 2, 3, 2, 1.0, 0.0, [(2, 1)] + [x.getBlocksize(2, 3, 1) for x in blocksize_algs], 0.0000001))
kernels.append(generator.DenseKernel(f"arm_only_test14_{precision}", precision, 16, 5, 7, 16, 7, 16, 1.0, 1.0, [(8, 1), (8,2)] + [x.getBlocksize(16, 5, 1) for x in blocksize_algs], 0.0000001))


generator.make(kernels, "arm")
38 changes: 19 additions & 19 deletions tests/unit_tests_hsw.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,25 +10,25 @@

kernels = []
for precision in (Precision.SINGLE, Precision.DOUBLE):
kernels.append(generator.SparseKernel("test1", precision, 8, 56, 56, 8, 0, 8, 2.0, 0.0, [(8, 4), (8,1)] + [x.getBlocksize(8, 56, 2) for x in blocksize_algs], generator.generateMTX(56, 56, 30), 0.0000001))
kernels.append(generator.DenseKernel("test2", precision, 8, 40, 40, 8, 40, 8, 2.5, 1.0, [(8,2)] + [x.getBlocksize(8, 40, 2) for x in blocksize_algs], 0.0000001))
kernels.append(generator.DenseKernel("test3", precision, 8, 56, 56, 8, 56, 8, 1.0, 5.0, [(8, 3)] + [x.getBlocksize(8, 56, 2) for x in blocksize_algs], 0.0000001))
kernels.append(generator.SparseKernel("hsw_only_test1", precision, 8, 2, 1, 8, 0, 8, 1.0, 0.0, [(8,1)] + [x.getBlocksize(8, 2, 2) for x in blocksize_algs], generator.generateMTX(1, 2, 1), 0.0000001))
kernels.append(generator.SparseKernel("hsw_only_test2", precision, 24, 40, 40, 32, 0, 24, 1000, 1.0, [(8, 2)] + [x.getBlocksize(24, 40, 2) for x in blocksize_algs], generator.generateMTX(40, 40, 20), 0.0000001))

kernels.append(generator.SparseKernel("hsw_only_test3", precision, 8, 2, 1, 8, 0, 16, -2.0, 0.0, [(8, 1)] + [x.getBlocksize(8, 2, 2) for x in blocksize_algs], generator.generateMTX(1, 2, 2), 0.0000001))
kernels.append(generator.SparseKernel("hsw_only_test4", precision, 24, 20, 10, 40, 0, 24, 35.222, 0.0, [] + [x.getBlocksize(8, 20, 2) for x in blocksize_algs], generator.generateMTX(10, 20, 1), 0.0000001))
kernels.append(generator.SparseKernel("hsw_only_test5", precision, 64, 5, 10, 64, 0, 64, 2.3, 0.0, [] + [x.getBlocksize(64, 5, 2) for x in blocksize_algs], generator.generateMTX(10, 5, 1), 0.0000001))
kernels.append(generator.SparseKernel("hsw_only_test6", precision, 8, 1, 1, 16, 0, 56, 1.0, 0.0, [(8, 1)] + [x.getBlocksize(8, 1, 2) for x in blocksize_algs], generator.generateMTX(1, 1, 1), 0.0000001))
kernels.append(generator.SparseKernel("hsw_only_test7", precision, 8, 24, 40, 8, 0, 8, 1.0, 333333.2222222, [(8,1)] + [x.getBlocksize(8, 24, 2) for x in blocksize_algs], generator.generateMTX(40, 24, 1), 0.0000001))

kernels.append(generator.DenseKernel("hsw_only_test8", precision, 8, 2, 1, 8, 1, 8, 2.5, 0.0, [(8,1)] + [x.getBlocksize(8, 2, 2) for x in blocksize_algs], 0.0000001))
kernels.append(generator.DenseKernel("hsw_only_test9", precision, 32, 40, 40, 32, 60, 32, 2.0, -4.33, [(8,2)] + [x.getBlocksize(32, 40, 2) for x in blocksize_algs], 0.0000001))
kernels.append(generator.DenseKernel("hsw_only_test10", precision, 56, 28, 56, 56, 56, 56, 0.1, 3.0, [x.getBlocksize(56, 28, 2) for x in blocksize_algs], 0.0000001))
kernels.append(generator.DenseKernel("hsw_only_test11", precision, 8, 20, 8, 40, 10, 8, 234234.123123, 0.0, [(8,3)] + [x.getBlocksize(8, 20, 2) for x in blocksize_algs], 0.0000001))
kernels.append(generator.DenseKernel("hsw_only_test12", precision, 64, 5, 10, 64, 12, 64, 1.0, 1.0, [] + [x.getBlocksize(64, 5, 2) for x in blocksize_algs], 0.0000001))
kernels.append(generator.DenseKernel("hsw_only_test13", precision, 8, 1, 1, 16, 1, 56, 0.0, 123.0, [(8, 1)] + [x.getBlocksize(8, 1, 2) for x in blocksize_algs], 0.0000001))
kernels.append(generator.DenseKernel("hsw_only_test14", precision, 8, 24, 40, 8, 41, 8, 2.0, 1.0, [] + [x.getBlocksize(8, 24, 2) for x in blocksize_algs], 0.0000001))
kernels.append(generator.SparseKernel(f"test1_{precision}", precision, 8, 56, 56, 8, 0, 8, 2.0, 0.0, [(8, 4), (8,1)] + [x.getBlocksize(8, 56, 2) for x in blocksize_algs], generator.generateMTX(56, 56, 30), 0.0000001))
kernels.append(generator.DenseKernel(f"test2_{precision}", precision, 8, 40, 40, 8, 40, 8, 2.5, 1.0, [(8,2)] + [x.getBlocksize(8, 40, 2) for x in blocksize_algs], 0.0000001))
kernels.append(generator.DenseKernel(f"test3_{precision}", precision, 8, 56, 56, 8, 56, 8, 1.0, 5.0, [(8, 3)] + [x.getBlocksize(8, 56, 2) for x in blocksize_algs], 0.0000001))
kernels.append(generator.SparseKernel(f"hsw_only_test1_{precision}", precision, 8, 2, 1, 8, 0, 8, 1.0, 0.0, [(8,1)] + [x.getBlocksize(8, 2, 2) for x in blocksize_algs], generator.generateMTX(1, 2, 1), 0.0000001))
kernels.append(generator.SparseKernel(f"hsw_only_test2_{precision}", precision, 24, 40, 40, 32, 0, 24, 1000, 1.0, [(8, 2)] + [x.getBlocksize(24, 40, 2) for x in blocksize_algs], generator.generateMTX(40, 40, 20), 0.0000001))

kernels.append(generator.SparseKernel(f"hsw_only_test3_{precision}", precision, 8, 2, 1, 8, 0, 16, -2.0, 0.0, [(8, 1)] + [x.getBlocksize(8, 2, 2) for x in blocksize_algs], generator.generateMTX(1, 2, 2), 0.0000001))
kernels.append(generator.SparseKernel(f"hsw_only_test4_{precision}", precision, 24, 20, 10, 40, 0, 24, 35.222, 0.0, [] + [x.getBlocksize(8, 20, 2) for x in blocksize_algs], generator.generateMTX(10, 20, 1), 0.0000001))
kernels.append(generator.SparseKernel(f"hsw_only_test5_{precision}", precision, 64, 5, 10, 64, 0, 64, 2.3, 0.0, [] + [x.getBlocksize(64, 5, 2) for x in blocksize_algs], generator.generateMTX(10, 5, 1), 0.0000001))
kernels.append(generator.SparseKernel(f"hsw_only_test6_{precision}", precision, 8, 1, 1, 16, 0, 56, 1.0, 0.0, [(8, 1)] + [x.getBlocksize(8, 1, 2) for x in blocksize_algs], generator.generateMTX(1, 1, 1), 0.0000001))
kernels.append(generator.SparseKernel(f"hsw_only_test7_{precision}", precision, 8, 24, 40, 8, 0, 8, 1.0, 333333.2222222, [(8,1)] + [x.getBlocksize(8, 24, 2) for x in blocksize_algs], generator.generateMTX(40, 24, 1), 0.0000001))

kernels.append(generator.DenseKernel(f"hsw_only_test8_{precision}", precision, 8, 2, 1, 8, 1, 8, 2.5, 0.0, [(8,1)] + [x.getBlocksize(8, 2, 2) for x in blocksize_algs], 0.0000001))
kernels.append(generator.DenseKernel(f"hsw_only_test9_{precision}", precision, 32, 40, 40, 32, 60, 32, 2.0, -4.33, [(8,2)] + [x.getBlocksize(32, 40, 2) for x in blocksize_algs], 0.0000001))
kernels.append(generator.DenseKernel(f"hsw_only_test10_{precision}", precision, 56, 28, 56, 56, 56, 56, 0.1, 3.0, [x.getBlocksize(56, 28, 2) for x in blocksize_algs], 0.0000001))
kernels.append(generator.DenseKernel(f"hsw_only_test11_{precision}", precision, 8, 20, 8, 40, 10, 8, 234234.123123, 0.0, [(8,3)] + [x.getBlocksize(8, 20, 2) for x in blocksize_algs], 0.0000001))
kernels.append(generator.DenseKernel(f"hsw_only_test12_{precision}", precision, 64, 5, 10, 64, 12, 64, 1.0, 1.0, [] + [x.getBlocksize(64, 5, 2) for x in blocksize_algs], 0.0000001))
kernels.append(generator.DenseKernel(f"hsw_only_test13_{precision}", precision, 8, 1, 1, 16, 1, 56, 0.0, 123.0, [(8, 1)] + [x.getBlocksize(8, 1, 2) for x in blocksize_algs], 0.0000001))
kernels.append(generator.DenseKernel(f"hsw_only_test14_{precision}", precision, 8, 24, 40, 8, 41, 8, 2.0, 1.0, [] + [x.getBlocksize(8, 24, 2) for x in blocksize_algs], 0.0000001))

generator.make(kernels, "hsw")

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