def test_meta_schedule_random_model_reseed():
model = RandomModel(seed=100)
res = model.predict(TuneContext(), [MeasureCandidate(Schedule(Matmul), []) for i in range(20)])
new_model = RandomModel(seed=100)
new_res = new_model.predict(
TuneContext(), [MeasureCandidate(Schedule(Matmul), []) for i in range(20)]
)
assert (res == new_res).all()
def test_meta_schedule_random_model_reload():
model = RandomModel(seed=25973)
model.predict(
TuneContext(), [MeasureCandidate(Schedule(Matmul), []) for i in range(30)]
) # change state
path = os.path.join(tempfile.mkdtemp(), "test_output_meta_schedule_random_model.npy")
model.save(path)
res1 = model.predict(TuneContext(), [MeasureCandidate(Schedule(Matmul), []) for i in range(70)])
model.load(path)
res2 = model.predict(TuneContext(), [MeasureCandidate(Schedule(Matmul), []) for i in range(70)])
shutil.rmtree(os.path.dirname(path))
assert (res1 == res2).all()
def test_meta_schedule_cost_model():
class FancyCostModel(PyCostModel):
def load(self, path: str) -> None:
pass
def save(self, path: str) -> None:
pass
def update(
self,
tune_context: TuneContext,
candidates: List[MeasureCandidate],
results: List[RunnerResult],
) -> None:
pass
def predict(self, tune_context: TuneContext,
candidates: List[MeasureCandidate]) -> np.ndarray:
return np.random.rand(10)
model = FancyCostModel()
model.save("fancy_test_location")
model.load("fancy_test_location")
model.update(TuneContext(), [], [])
results = model.predict(TuneContext,
[MeasureCandidate(Schedule(mod=Matmul), [])])
assert results.shape == (10, )
def test_meta_schedule_random_model():
model = RandomModel()
model.update(TuneContext(), [], [])
res = model.predict(
TuneContext(),
[MeasureCandidate(Schedule(Matmul), []) for i in range(10)])
assert len(res) == 10
assert min(res) >= 0 and max(res) <= model.max_range
def _dummy_candidate():
return MeasureCandidate(Schedule(Matmul), [])
