def main():
import sys
log.init(verbose=True)
m = model.from_json(clgen.load_json_file(sys.argv[1]))
s = sampler.from_json({
"kernels": {
"args": [
"__global float*", "__global float*", "__global float*",
"const int"
],
"max_length":
5000,
"temperature":
1
},
"sampler": {
"batch_size": 1000,
"max_batches": 1,
"static_checker": False,
"dynamic_checker": False
}
})
print("Corpus size:", m.corpus.size)
print("Vocab size: ", m.corpus.vocab_size)
print()
clgen.platform_info()
print()
outpath = "./benchmark-" + fs.basename(sys.argv[1])
info = evaluate(m, s)
clgen.write_file(outpath, clgen.format_json(info))
def main():
import sys
log.init(verbose=True)
m = model.from_json(clgen.load_json_file(sys.argv[1]))
s = sampler.from_json({
"kernels": {
"args": [
"__global float*",
"__global float*",
"__global float*",
"const int"
],
"max_length": 5000,
"temperature": 1
},
"sampler": {
"batch_size": 1000,
"max_batches": 1,
"static_checker": False,
"dynamic_checker": False
}
})
print("Corpus size:", m.corpus.size)
print("Vocab size: ", m.corpus.vocab_size)
print()
clgen.platform_info()
print()
outpath = "./benchmark-" + fs.basename(sys.argv[1])
info = evaluate(m, s)
clgen.write_file(outpath, clgen.format_json(info))
def main():
log.init(verbose=True)
m = model.from_json(clgen.load_json_file(sys.argv[1]))
c = corpus.Corpus.from_json({"path": "~/data/github"})
print("CLgen: ", clgen.version())
print("Corpus size:", c.size)
print("Vocab size: ", c.vocab_size)
m.train()
p, _ = corpus.most_common_prototypes(c, 20)
for i, row in enumerate(p):
outpath = "./inference-p" + str(i + 1) + "-" + fs.basename(sys.argv[1])
if fs.exists(outpath):
continue
_, prototype = row
argspec = [' '.join(x.split()[:-1]) for x in prototype.split(',')]
print("argspec", ','.join([str(x) for x in argspec]))
s = sampler.from_json({
"kernels": {
"args": argspec,
"max_length": 5000
},
"sampler": {
"batch_size": 2000,
"max_batches": 1,
"static_checker": False,
"dynamic_checker": False
}
})
info = evaluate(m, s)
clgen.write_file(outpath, clgen.format_json(info))
def test_sample(self):
m = get_test_model()
m.train()
argspec = [
'__global float*', '__global float*', '__global float*',
'const int'
]
s = sampler.from_json({
"kernels": {
"args": argspec,
"max_length": 300,
},
"sampler": {
"batch_size": 1,
"max_batches": 1
}
})
s.cache(m).empty() # clear old samples
# sample a single kernel:
s.sample(m)
nun_contentfiles = dbutil.num_rows_in(
s.cache(m)["kernels.db"], "ContentFiles")
num_preprocessed = dbutil.num_rows_in(
s.cache(m)["kernels.db"], "PreProcessedFiles")
self.assertEqual(nun_contentfiles, 1)
self.assertEqual(num_preprocessed, 1)
s.sample(m)
nun_contentfiles = dbutil.num_rows_in(
s.cache(m)["kernels.db"], "ContentFiles")
num_preprocessed = dbutil.num_rows_in(
s.cache(m)["kernels.db"], "PreProcessedFiles")
# if sample is the same as previous, then there will still only be a
# single sample in db:
self.assertTrue(nun_contentfiles >= 1)
self.assertTrue(num_preprocessed >= 1)
def main():
log.init(verbose=True)
m = model.from_json(clgen.load_json_file(sys.argv[1]))
c = corpus.Corpus.from_json({"path": "~/data/github"})
print("CLgen: ", clgen.version())
print("Corpus size:", c.size)
print("Vocab size: ", c.vocab_size)
m.train()
p, _ = corpus.most_common_prototypes(c, 20)
for i, row in enumerate(p):
outpath = "./inference-p" + str(i + 1) + "-" + fs.basename(sys.argv[1])
if fs.exists(outpath):
print("skipped result for", outpath)
continue
else:
print("starting result for", outpath)
_, prototype = row
argspec = [' '.join(x.split()[:-1]) for x in prototype.split(',')]
print("argspec", ','.join([str(x) for x in argspec]))
s = sampler.from_json({
"kernels": {
"args": argspec,
"max_length": 5000
},
"sampler": {
"batch_size": 2000,
"max_batches": 1,
"static_checker": False,
"dynamic_checker": False
}
})
info = evaluate(m, s)
clgen.write_file(outpath, clgen.format_json(info))