def test_Generator_GetOrAdd_only_different_optset(session):
generator_a = deeplearning.deepsmith.generator.Generator.GetOrAdd(
session,
deepsmith_pb2.Generator(name="name", opts={"a": "A", "b": "B", "c": "C",},),
)
generator_b = deeplearning.deepsmith.generator.Generator.GetOrAdd(
session, deepsmith_pb2.Generator(name="name", opts={"d": "D",},)
)
generator_c = deeplearning.deepsmith.generator.Generator.GetOrAdd(
session, deepsmith_pb2.Generator(name="name", opts={},)
)
assert session.query(deeplearning.deepsmith.generator.Generator).count() == 3
assert (
session.query(deeplearning.deepsmith.generator.GeneratorOpt).count() == 4
)
assert (
session.query(deeplearning.deepsmith.generator.GeneratorOptSet).count() == 4
)
assert (
session.query(deeplearning.deepsmith.generator.GeneratorOptName).count()
== 4
)
assert (
session.query(deeplearning.deepsmith.generator.GeneratorOptValue).count()
== 4
)
assert len(generator_a.optset) == 3
assert len(generator_b.optset) == 1
assert len(generator_c.optset) == 0
def _GetOpenCLGenerator(generator_id) -> deepsmith_pb2.Generator:
if generator_id == 0:
return deepsmith_pb2.Generator(
name="clsmith",
opts={
"git_commit": "b637b31c31e0f90ef199ca492af05172400df050",
"git_remote": "https://github.com/ChrisCummins/CLSmith.git",
})
elif generator_id == 1:
return deepsmith_pb2.Generator(
name="clgen",
opts={
"git_commit": "9556e7112ba2bd6f79ee59eef74f0a2304efa007",
"git_remote": "https://github.com/ChrisCummins/clgen.git",
"version": "0.4.0.dev0",
})
elif generator_id == 2:
return deepsmith_pb2.Generator(
name="randchar",
opts={
"url":
"https://github.com/ChrisCummins/dsmith/blob/fd986a36a23b2a398f33d5b5852d930b462401b1/dsmith/opencl/generators.py#L175",
})
else:
raise LookupError
def test_Generator_GetOrAdd_ToProto_equivalence(session):
proto_in = deepsmith_pb2.Testcase(
toolchain="cpp",
generator=deepsmith_pb2.Generator(name="generator", ),
harness=deepsmith_pb2.Harness(name="harness", ),
inputs={
"src": "void main() {}",
"data": "[1,2]"
},
invariant_opts={"config": "opt"},
profiling_events=[
deepsmith_pb2.ProfilingEvent(
client="localhost",
type="generate",
duration_ms=100,
event_start_epoch_ms=101231231,
),
],
)
testcase = deeplearning.deepsmith.testcase.Testcase.GetOrAdd(
session, proto_in)
# NOTE: We have to flush so that SQLAlchemy resolves all of the object IDs.
session.flush()
proto_out = testcase.ToProto()
assert proto_in == proto_out
proto_out.ClearField("toolchain")
assert proto_in != proto_out # Sanity check.
def test_Generator_GetOrAdd(session):
proto = deepsmith_pb2.Generator(
name="name", opts={"version": "1.0.0", "build": "debug+assert",}
)
generator = deeplearning.deepsmith.generator.Generator.GetOrAdd(
session, proto
)
assert (
session.query(deeplearning.deepsmith.generator.GeneratorOptSet).count() == 2
)
assert (
session.query(deeplearning.deepsmith.generator.GeneratorOpt).count() == 2
)
assert (
session.query(deeplearning.deepsmith.generator.GeneratorOptName).count()
== 2
)
assert (
session.query(deeplearning.deepsmith.generator.GeneratorOptValue).count()
== 2
)
assert generator.name == "name"
assert len(generator.optset) == 2
assert len(generator.opts) == 2
assert generator.opts["version"] == "1.0.0"
assert generator.opts["build"] == "debug+assert"
def test_Generator_GetOrAdd_rollback(session):
deeplearning.deepsmith.generator.Generator.GetOrAdd(
session, deepsmith_pb2.Generator(name="name", opts={"a": "1", "b": "2",},)
)
assert session.query(deeplearning.deepsmith.generator.Generator).count() == 1
assert (
session.query(deeplearning.deepsmith.generator.GeneratorOpt).count() == 2
)
assert (
session.query(deeplearning.deepsmith.generator.GeneratorOptSet).count() == 2
)
assert (
session.query(deeplearning.deepsmith.generator.GeneratorOptName).count()
== 2
)
assert (
session.query(deeplearning.deepsmith.generator.GeneratorOptValue).count()
== 2
)
session.rollback()
assert session.query(deeplearning.deepsmith.generator.Generator).count() == 0
assert (
session.query(deeplearning.deepsmith.generator.GeneratorOpt).count() == 0
)
assert (
session.query(deeplearning.deepsmith.generator.GeneratorOptSet).count() == 0
)
assert (
session.query(deeplearning.deepsmith.generator.GeneratorOptName).count()
== 0
)
assert (
session.query(deeplearning.deepsmith.generator.GeneratorOptValue).count()
== 0
)
def ConfigToGenerator(
config: generator_pb2.ClsmithGenerator) -> deepsmith_pb2.Generator:
"""Convert a config proto to a DeepSmith generator proto."""
g = deepsmith_pb2.Generator()
g.name = 'clsmith'
g.opts['opts'] = ' '.join(config.opt)
return g
def test_Generator_GetOrAdd_ToProto_equivalence(session):
proto_in = deepsmith_pb2.Testcase(
toolchain='cpp',
generator=deepsmith_pb2.Generator(name='generator', ),
harness=deepsmith_pb2.Harness(name='harness', ),
inputs={
'src': 'void main() {}',
'data': '[1,2]'
},
invariant_opts={'config': 'opt'},
profiling_events=[
deepsmith_pb2.ProfilingEvent(
client='localhost',
type='generate',
duration_ms=100,
event_start_epoch_ms=101231231,
),
])
testcase = deeplearning.deepsmith.testcase.Testcase.GetOrAdd(
session, proto_in)
# NOTE: We have to flush so that SQLAlchemy resolves all of the object IDs.
session.flush()
proto_out = testcase.ToProto()
assert proto_in == proto_out
proto_out.ClearField('toolchain')
assert proto_in != proto_out # Sanity check.
def test_Generator_GetOrAdd_rollback(session):
deeplearning.deepsmith.generator.Generator.GetOrAdd(
session,
deepsmith_pb2.Generator(
name='name',
opts={
'a': '1',
'b': '2',
},
))
assert session.query(
deeplearning.deepsmith.generator.Generator).count() == 1
assert session.query(
deeplearning.deepsmith.generator.GeneratorOpt).count() == 2
assert session.query(
deeplearning.deepsmith.generator.GeneratorOptSet).count() == 2
assert session.query(
deeplearning.deepsmith.generator.GeneratorOptName).count() == 2
assert session.query(
deeplearning.deepsmith.generator.GeneratorOptValue).count() == 2
session.rollback()
assert session.query(
deeplearning.deepsmith.generator.Generator).count() == 0
assert session.query(
deeplearning.deepsmith.generator.GeneratorOpt).count() == 0
assert session.query(
deeplearning.deepsmith.generator.GeneratorOptSet).count() == 0
assert session.query(
deeplearning.deepsmith.generator.GeneratorOptName).count() == 0
assert session.query(
deeplearning.deepsmith.generator.GeneratorOptValue).count() == 0
def ConfigToGenerator(
config: generator_pb2.ClsmithGenerator, ) -> deepsmith_pb2.Generator:
"""Convert a config proto to a DeepSmith generator proto."""
g = deepsmith_pb2.Generator()
g.name = "clsmith"
g.opts["opts"] = " ".join(config.opt)
return g
def ClgenInstanceToGenerator(
instance: sample.Instance, ) -> deepsmith_pb2.Generator:
"""Convert a CLgen instance to a DeepSmith generator proto."""
g = deepsmith_pb2.Generator()
g.name = "clgen"
g.opts["model"] = str(instance.model.path)
g.opts["sampler"] = instance.sampler.hash
return g
def ClgenInstanceToGenerator(
instance: clgen.Instance) -> deepsmith_pb2.Generator:
"""Convert a CLgen instance to a DeepSmith generator proto."""
g = deepsmith_pb2.Generator()
g.name = 'clgen'
g.opts['model'] = instance.model.hash
g.opts['sampler'] = instance.sampler.hash
return g
def ToProto(self) -> deepsmith_pb2.Generator:
"""Create protocol buffer representation.
Returns:
A Generator message.
"""
proto = deepsmith_pb2.Generator()
return self.SetProto(proto)
def test_duplicate_testcase_testbed_ignored(session):
"""Test that result is ignored if testbed and testcase are not unique."""
proto = deepsmith_pb2.Result(
testcase=deepsmith_pb2.Testcase(
toolchain='cpp',
generator=deepsmith_pb2.Generator(name='generator'),
harness=deepsmith_pb2.Harness(name='harness'),
inputs={
'src': 'void main() {}',
'data': '[1,2]',
},
invariant_opts={
'config': 'opt',
},
profiling_events=[
deepsmith_pb2.ProfilingEvent(
client='localhost',
type='generate',
duration_ms=100,
event_start_epoch_ms=1123123123,
),
]),
testbed=deepsmith_pb2.Testbed(
toolchain='cpp',
name='clang',
opts={'arch': 'x86_64'},
),
returncode=0,
outputs={'stdout': 'Hello, world!'},
profiling_events=[
deepsmith_pb2.ProfilingEvent(
client='localhost',
type='exec',
duration_ms=100,
event_start_epoch_ms=1123123123,
),
],
outcome=deepsmith_pb2.Result.PASS,
)
r1 = deeplearning.deepsmith.result.Result.GetOrAdd(session, proto)
session.add(r1)
session.flush()
# Attempt to add a new result which is identical to the first in all fields
# except for the outputs.
proto.outputs['stdout'] = '!'
r2 = deeplearning.deepsmith.result.Result.GetOrAdd(session, proto)
session.add(r2)
session.flush()
# Check that only one result was added.
assert session.query(deeplearning.deepsmith.result.Result).count() == 1
# Check that only the first result was added.
r3 = session.query(deeplearning.deepsmith.result.Result).first()
assert r3.outputs['stdout'] == 'Hello, world!'
def test_Generator_GetOrAdd_ToProto_equivalence(session):
proto_in = deepsmith_pb2.Result(
testcase=deepsmith_pb2.Testcase(
toolchain="cpp",
generator=deepsmith_pb2.Generator(name="generator"),
harness=deepsmith_pb2.Harness(name="harness"),
inputs={"src": "void main() {}", "data": "[1,2]",},
invariant_opts={"config": "opt",},
profiling_events=[
deepsmith_pb2.ProfilingEvent(
client="localhost",
type="generate",
duration_ms=100,
event_start_epoch_ms=1123123123,
),
deepsmith_pb2.ProfilingEvent(
client="localhost",
type="foo",
duration_ms=100,
event_start_epoch_ms=1123123123,
),
],
),
testbed=deepsmith_pb2.Testbed(
toolchain="cpp",
name="clang",
opts={"arch": "x86_64", "build": "debug+assert",},
),
returncode=0,
outputs={"stdout": "Hello, world!", "stderr": "",},
profiling_events=[
deepsmith_pb2.ProfilingEvent(
client="localhost",
type="exec",
duration_ms=500,
event_start_epoch_ms=1123123123,
),
deepsmith_pb2.ProfilingEvent(
client="localhost",
type="overhead",
duration_ms=100,
event_start_epoch_ms=1123123123,
),
],
outcome=deepsmith_pb2.Result.PASS,
)
result = deeplearning.deepsmith.result.Result.GetOrAdd(session, proto_in)
# NOTE: We have to flush so that SQLAlchemy resolves all of the object IDs.
session.flush()
proto_out = result.ToProto()
assert proto_in == proto_out
proto_out.ClearField("outputs")
assert proto_in != proto_out # Sanity check.
def _GetSolidityGenerator(generator_id) -> deepsmith_pb2.Generator:
if generator_id == -1:
return deepsmith_pb2.Generator(name="github", opts={})
elif generator_id == 1:
return deepsmith_pb2.Generator(
name="clgen",
opts={
"git_commit": "9556e7112ba2bd6f79ee59eef74f0a2304efa007",
"git_remote": "https://github.com/ChrisCummins/clgen.git",
"version": "0.4.0.dev0",
})
elif generator_id == 2:
return deepsmith_pb2.Generator(
name="randchar",
opts={
"url":
"https://github.com/ChrisCummins/dsmith/blob/5181c7c95575d428b5144a25549e5a5a55a3da31/dsmith/sol/generators.py#L203",
})
else:
raise LookupError
def _AddExistingGenerator(session):
deeplearning.deepsmith.generator.Generator.GetOrAdd(
session,
deepsmith_pb2.Generator(
name='name',
opts={
'a': 'a',
'b': 'b',
'c': 'c',
},
))
session.flush()
def _AddRandomNewGenerator(session):
deeplearning.deepsmith.generator.Generator.GetOrAdd(
session,
deepsmith_pb2.Generator(
name=str(random.random()),
opts={
str(random.random()): str(random.random()),
str(random.random()): str(random.random()),
str(random.random()): str(random.random()),
},
))
session.flush()
def test_Testcase_GetOrAdd(session):
proto = deepsmith_pb2.Testcase(
toolchain="cpp",
generator=deepsmith_pb2.Generator(name="generator", ),
harness=deepsmith_pb2.Harness(name="harness", ),
inputs={
"src": "void main() {}",
"data": "[1,2]"
},
invariant_opts={"config": "opt"},
profiling_events=[
deepsmith_pb2.ProfilingEvent(
client="localhost",
type="generate",
duration_ms=100,
event_start_epoch_ms=1021312312,
),
deepsmith_pb2.ProfilingEvent(
client="localhost",
type="foo",
duration_ms=100,
event_start_epoch_ms=1230812312,
),
],
)
testcase = deeplearning.deepsmith.testcase.Testcase.GetOrAdd(
session, proto)
# NOTE: We have to flush so that SQLAlchemy resolves all of the object IDs.
session.flush()
assert testcase.toolchain.string == "cpp"
assert testcase.generator.name == "generator"
assert testcase.harness.name == "harness"
assert len(testcase.inputset) == 2
assert len(testcase.inputs) == 2
assert testcase.inputs["src"] == "void main() {}"
assert testcase.inputs["data"] == "[1,2]"
assert len(testcase.invariant_optset) == 1
assert len(testcase.invariant_opts) == 1
assert testcase.invariant_opts["config"] == "opt"
assert testcase.profiling_events[0].client.string == "localhost"
assert testcase.profiling_events[0].type.string == "generate"
assert testcase.profiling_events[0].duration_ms == 100
assert testcase.profiling_events[
0].event_start == labdate.DatetimeFromMillisecondsTimestamp(1021312312)
assert testcase.profiling_events[1].client.string == "localhost"
assert testcase.profiling_events[1].type.string == "foo"
assert testcase.profiling_events[1].duration_ms == 100
assert testcase.profiling_events[
1].event_start == labdate.DatetimeFromMillisecondsTimestamp(1230812312)
def test_duplicate_testcase_testbed_ignored(session):
"""Test that result is ignored if testbed and testcase are not unique."""
proto = deepsmith_pb2.Result(
testcase=deepsmith_pb2.Testcase(
toolchain="cpp",
generator=deepsmith_pb2.Generator(name="generator"),
harness=deepsmith_pb2.Harness(name="harness"),
inputs={"src": "void main() {}", "data": "[1,2]",},
invariant_opts={"config": "opt",},
profiling_events=[
deepsmith_pb2.ProfilingEvent(
client="localhost",
type="generate",
duration_ms=100,
event_start_epoch_ms=1123123123,
),
],
),
testbed=deepsmith_pb2.Testbed(
toolchain="cpp", name="clang", opts={"arch": "x86_64"},
),
returncode=0,
outputs={"stdout": "Hello, world!"},
profiling_events=[
deepsmith_pb2.ProfilingEvent(
client="localhost",
type="exec",
duration_ms=100,
event_start_epoch_ms=1123123123,
),
],
outcome=deepsmith_pb2.Result.PASS,
)
r1 = deeplearning.deepsmith.result.Result.GetOrAdd(session, proto)
session.add(r1)
session.flush()
# Attempt to add a new result which is identical to the first in all fields
# except for the outputs.
proto.outputs["stdout"] = "!"
r2 = deeplearning.deepsmith.result.Result.GetOrAdd(session, proto)
session.add(r2)
session.flush()
# Check that only one result was added.
assert session.query(deeplearning.deepsmith.result.Result).count() == 1
# Check that only the first result was added.
r3 = session.query(deeplearning.deepsmith.result.Result).first()
assert r3.outputs["stdout"] == "Hello, world!"
def test_Testcase_GetOrAdd(session):
proto = deepsmith_pb2.Testcase(
toolchain='cpp',
generator=deepsmith_pb2.Generator(name='generator', ),
harness=deepsmith_pb2.Harness(name='harness', ),
inputs={
'src': 'void main() {}',
'data': '[1,2]'
},
invariant_opts={'config': 'opt'},
profiling_events=[
deepsmith_pb2.ProfilingEvent(
client='localhost',
type='generate',
duration_ms=100,
event_start_epoch_ms=1021312312,
),
deepsmith_pb2.ProfilingEvent(
client='localhost',
type='foo',
duration_ms=100,
event_start_epoch_ms=1230812312,
),
])
testcase = deeplearning.deepsmith.testcase.Testcase.GetOrAdd(
session, proto)
# NOTE: We have to flush so that SQLAlchemy resolves all of the object IDs.
session.flush()
assert testcase.toolchain.string == 'cpp'
assert testcase.generator.name == 'generator'
assert testcase.harness.name == 'harness'
assert len(testcase.inputset) == 2
assert len(testcase.inputs) == 2
assert testcase.inputs['src'] == 'void main() {}'
assert testcase.inputs['data'] == '[1,2]'
assert len(testcase.invariant_optset) == 1
assert len(testcase.invariant_opts) == 1
assert testcase.invariant_opts['config'] == 'opt'
assert testcase.profiling_events[0].client.string == 'localhost'
assert testcase.profiling_events[0].type.string == 'generate'
assert testcase.profiling_events[0].duration_ms == 100
assert (testcase.profiling_events[0].event_start ==
labdate.DatetimeFromMillisecondsTimestamp(1021312312))
assert testcase.profiling_events[1].client.string == 'localhost'
assert testcase.profiling_events[1].type.string == 'foo'
assert testcase.profiling_events[1].duration_ms == 100
assert (testcase.profiling_events[1].event_start ==
labdate.DatetimeFromMillisecondsTimestamp(1230812312))
def test_Generator_GetOrAdd_ToProto_equivalence(session):
proto_in = deepsmith_pb2.Generator(
name="a", opts={"arch": "x86_64", "build": "debug+assert"},
)
generator = deeplearning.deepsmith.generator.Generator.GetOrAdd(
session, proto_in
)
# NOTE: We have to flush before constructing a proto so that SQLAlchemy
# resolves all of the object IDs.
session.flush()
proto_out = generator.ToProto()
assert proto_in == proto_out
proto_out.ClearField("name")
assert proto_in != proto_out # Sanity check.
def test_Generator_GetOrAdd_only_different_optset(session):
generator_a = deeplearning.deepsmith.generator.Generator.GetOrAdd(
session,
deepsmith_pb2.Generator(
name='name',
opts={
'a': 'A',
'b': 'B',
'c': 'C',
},
))
generator_b = deeplearning.deepsmith.generator.Generator.GetOrAdd(
session, deepsmith_pb2.Generator(
name='name',
opts={
'd': 'D',
},
))
generator_c = deeplearning.deepsmith.generator.Generator.GetOrAdd(
session, deepsmith_pb2.Generator(
name='name',
opts={},
))
assert session.query(
deeplearning.deepsmith.generator.Generator).count() == 3
assert session.query(
deeplearning.deepsmith.generator.GeneratorOpt).count() == 4
assert session.query(
deeplearning.deepsmith.generator.GeneratorOptSet).count() == 4
assert session.query(
deeplearning.deepsmith.generator.GeneratorOptName).count() == 4
assert session.query(
deeplearning.deepsmith.generator.GeneratorOptValue).count() == 4
assert len(generator_a.optset) == 3
assert len(generator_b.optset) == 1
assert len(generator_c.optset) == 0
def __init__(self, config: generator_pb2.RandCharGenerator):
super(RandCharGenerator, self).__init__(config)
self.toolchain = self.config.model.corpus.language
self.generator = deepsmith_pb2.Generator(
name='randchar',
opts={
'toolchain': str(pbutil.AssertFieldConstraint(
self.config, 'toolchain', lambda x: len(x))),
'min_len': str(pbutil.AssertFieldConstraint(
self.config, 'string_min_len', lambda x: x > 0)),
'max_len': str(pbutil.AssertFieldConstraint(
self.config, 'string_max_len',
lambda x: x > 0 and x >= self.config.string_min_len)),
}
)
def test_Generator_GetOrAdd_no_opts(session):
generator = deeplearning.deepsmith.generator.Generator.GetOrAdd(
session, deepsmith_pb2.Generator(
name='name',
opts={},
))
empty_md5 = hashlib.md5().digest()
assert generator.optset_id == empty_md5
assert session.query(
deeplearning.deepsmith.generator.Generator).count() == 1
assert session.query(
deeplearning.deepsmith.generator.GeneratorOpt).count() == 0
assert session.query(
deeplearning.deepsmith.generator.GeneratorOptSet).count() == 0
assert session.query(
deeplearning.deepsmith.generator.GeneratorOptName).count() == 0
assert session.query(
deeplearning.deepsmith.generator.GeneratorOptValue).count() == 0
def test_duplicate_testcases_ignored(session):
"""Test that testcases are only added if they are unique."""
proto = deepsmith_pb2.Testcase(
toolchain="cpp",
generator=deepsmith_pb2.Generator(name="generator"),
harness=deepsmith_pb2.Harness(name="harness"),
inputs={
"src": "void main() {}",
"data": "[1,2]"
},
invariant_opts={"config": "opt"},
profiling_events=[
deepsmith_pb2.ProfilingEvent(
client="localhost",
type="generate",
duration_ms=100,
event_start_epoch_ms=1021312312,
),
deepsmith_pb2.ProfilingEvent(
client="localhost",
type="foo",
duration_ms=100,
event_start_epoch_ms=1230812312,
),
],
)
t1 = deeplearning.deepsmith.testcase.Testcase.GetOrAdd(session, proto)
session.add(t1)
session.flush()
# Attempt to add a new testcase which is identical to the first in all fields
# except for the profiling events.
proto.profiling_events[0].duration_ms = -1
t2 = deeplearning.deepsmith.testcase.Testcase.GetOrAdd(session, proto)
session.add(t2)
session.flush()
# Check that only one testcase was added.
assert session.query(deeplearning.deepsmith.testcase.Testcase).count() == 1
# Check that only the first testcase was added.
t3 = session.query(deeplearning.deepsmith.testcase.Testcase).first()
assert t3.profiling_events[0].duration_ms == 100
assert t3.profiling_events[1].duration_ms == 100
def test_duplicate_testcases_ignored(session):
"""Test that testcases are only added if they are unique."""
proto = deepsmith_pb2.Testcase(
toolchain='cpp',
generator=deepsmith_pb2.Generator(name='generator'),
harness=deepsmith_pb2.Harness(name='harness'),
inputs={
'src': 'void main() {}',
'data': '[1,2]'
},
invariant_opts={'config': 'opt'},
profiling_events=[
deepsmith_pb2.ProfilingEvent(
client='localhost',
type='generate',
duration_ms=100,
event_start_epoch_ms=1021312312,
),
deepsmith_pb2.ProfilingEvent(
client='localhost',
type='foo',
duration_ms=100,
event_start_epoch_ms=1230812312,
),
])
t1 = deeplearning.deepsmith.testcase.Testcase.GetOrAdd(session, proto)
session.add(t1)
session.flush()
# Attempt to add a new testcase which is identical to the first in all fields
# except for the profiling events.
proto.profiling_events[0].duration_ms = -1
t2 = deeplearning.deepsmith.testcase.Testcase.GetOrAdd(session, proto)
session.add(t2)
session.flush()
# Check that only one testcase was added.
assert session.query(deeplearning.deepsmith.testcase.Testcase).count() == 1
# Check that only the first testcase was added.
t3 = session.query(deeplearning.deepsmith.testcase.Testcase).first()
assert t3.profiling_events[0].duration_ms == 100
assert t3.profiling_events[1].duration_ms == 100
def _AddRandomNewTestcase(session):
deeplearning.deepsmith.testcase.Testcase.GetOrAdd(
session,
deepsmith_pb2.Testcase(
toolchain=str(random.random()),
generator=deepsmith_pb2.Generator(
name=str(random.random()),
opts={
str(random.random()): str(random.random()),
str(random.random()): str(random.random()),
str(random.random()): str(random.random()),
},
),
harness=deepsmith_pb2.Harness(
name=str(random.random()),
opts={
str(random.random()): str(random.random()),
str(random.random()): str(random.random()),
str(random.random()): str(random.random()),
},
),
inputs={
str(random.random()): str(random.random()),
str(random.random()): str(random.random()),
str(random.random()): str(random.random()),
},
invariant_opts={
str(random.random()): str(random.random()),
str(random.random()): str(random.random()),
str(random.random()): str(random.random()),
},
profiling_events=[
deepsmith_pb2.ProfilingEvent(
client=str(random.random()),
type=str(random.random()),
duration_ms=int(random.random() * 1000),
event_start_epoch_ms=int(random.random() * 1000000),
),
],
),
)
session.flush()
def _AddExistingTestcase(session):
deeplearning.deepsmith.testcase.Testcase.GetOrAdd(
session,
deepsmith_pb2.Testcase(
toolchain="cpp",
generator=deepsmith_pb2.Generator(
name="name",
opts={
"a": "a",
"b": "b",
"c": "c",
},
),
harness=deepsmith_pb2.Harness(
name="name",
opts={
"a": "a",
"b": "b",
"c": "c",
},
),
inputs={
"src": "void main() {}",
"data": "[1,2]",
"copt": "-DNDEBUG",
},
invariant_opts={
"config": "opt",
"working_dir": "/tmp",
"units": "nanoseconds",
},
profiling_events=[
deepsmith_pb2.ProfilingEvent(
client="localhost",
type="generate",
duration_ms=100,
event_start_epoch_ms=101231231,
),
],
),
)
session.flush()
def _AddExistingTestcase(session):
deeplearning.deepsmith.testcase.Testcase.GetOrAdd(
session,
deepsmith_pb2.Testcase(toolchain='cpp',
generator=deepsmith_pb2.Generator(
name='name',
opts={
'a': 'a',
'b': 'b',
'c': 'c',
},
),
harness=deepsmith_pb2.Harness(
name='name',
opts={
'a': 'a',
'b': 'b',
'c': 'c',
},
),
inputs={
'src': 'void main() {}',
'data': '[1,2]',
'copt': '-DNDEBUG',
},
invariant_opts={
'config': 'opt',
'working_dir': '/tmp',
'units': 'nanoseconds',
},
profiling_events=[
deepsmith_pb2.ProfilingEvent(
client='localhost',
type='generate',
duration_ms=100,
event_start_epoch_ms=101231231,
),
]))
session.flush()
def __init__(self, config: generator_pb2.RandCharGenerator):
super(RandCharGenerator, self).__init__(config)
self.toolchain = self.config.model.corpus.language
self.generator = deepsmith_pb2.Generator(
name="randchar",
opts={
"toolchain":
str(
pbutil.AssertFieldConstraint(self.config, "toolchain",
lambda x: len(x))),
"min_len":
str(
pbutil.AssertFieldConstraint(self.config, "string_min_len",
lambda x: x > 0)),
"max_len":
str(
pbutil.AssertFieldConstraint(
self.config,
"string_max_len",
lambda x: x > 0 and x >= self.config.string_min_len,
)),
},
)
