def test_meta_schedule_integration_apply_history_best():
mod, _, _ = get_network(name="resnet_18", input_shape=[1, 3, 224, 224])
database = DummyDatabase()
env = ApplyHistoryBest(database)
target = Target("llvm")
workload = database.commit_workload(MockModule)
database.commit_tuning_record(
TuningRecord(Schedule(MockModule).trace, [1.0], workload, target, [])
)
mod = env.query(task_name="mock-task", mod=mod, target=target, dispatched=[MockModule])
assert tvm.ir.structural_equal(mod, workload.mod)
def test_meta_schedule_task_scheduler_override_next_task_id_only(): # pylint: disable=invalid-name
num_trials_per_iter = 6
max_trials_per_task = 101
tasks = [
TuneContext(
MatmulModule,
target=tvm.target.Target("llvm"),
space_generator=ScheduleFn(sch_fn=_schedule_matmul),
search_strategy=ReplayTrace(num_trials_per_iter,
max_trials_per_task),
task_name="Matmul",
rand_state=42,
),
TuneContext(
MatmulReluModule,
target=tvm.target.Target("llvm"),
space_generator=ScheduleFn(sch_fn=_schedule_matmul),
search_strategy=ReplayTrace(num_trials_per_iter,
max_trials_per_task),
task_name="MatmulRelu",
rand_state=0xDEADBEEF,
),
TuneContext(
BatchMatmulModule,
target=tvm.target.Target("llvm"),
space_generator=ScheduleFn(sch_fn=_schedule_batch_matmul),
search_strategy=ReplayTrace(num_trials_per_iter,
max_trials_per_task),
task_name="BatchMatmul",
rand_state=0x114514,
),
]
database = DummyDatabase()
scheduler = MyTaskScheduler(
tasks,
DummyBuilder(),
DummyRunner(),
database,
measure_callbacks=[
measure_callback.AddToDatabase(),
],
max_trials=max_trials_per_task * len(tasks),
)
scheduler.tune()
assert len(database) == max_trials_per_task * len(tasks)
for task in tasks:
assert (len(
database.get_top_k(
database.commit_workload(task.mod),
100000,
)) == max_trials_per_task)
def test_meta_schedule_task_scheduler_multiple():
num_trials_per_iter = 6
max_trials_per_task = 101
tasks = [
TuneContext(
MatmulModule,
target=tvm.target.Target("llvm"),
space_generator=ScheduleFn(sch_fn=_schedule_matmul),
search_strategy=ReplayTrace(num_trials_per_iter,
max_trials_per_task),
task_name="Matmul",
rand_state=42,
),
TuneContext(
MatmulReluModule,
target=tvm.target.Target("llvm"),
space_generator=ScheduleFn(sch_fn=_schedule_matmul),
search_strategy=ReplayTrace(num_trials_per_iter,
max_trials_per_task),
task_name="MatmulRelu",
rand_state=0xDEADBEEF,
),
TuneContext(
BatchMatmulModule,
target=tvm.target.Target("llvm"),
space_generator=ScheduleFn(sch_fn=_schedule_batch_matmul),
search_strategy=ReplayTrace(num_trials_per_iter,
max_trials_per_task),
task_name="BatchMatmul",
rand_state=0x114514,
),
]
database = DummyDatabase()
round_robin = RoundRobin(
tasks,
[1.0],
DummyBuilder(),
DummyRunner(),
database,
measure_callbacks=[measure_callback.AddToDatabase()],
max_trials=max_trials_per_task * len(tasks),
)
round_robin.tune()
assert len(database) == max_trials_per_task * len(tasks)
for task in tasks:
assert (len(
database.get_top_k(
database.commit_workload(task.mod),
100000,
)) == max_trials_per_task)
def test_meta_schedule_task_scheduler_single():
num_trials_per_iter = 3
max_trials_per_task = 10
sch_fn = ScheduleFn(sch_fn=_schedule_matmul)
replay = ReplayTrace(num_trials_per_iter, max_trials_per_task)
task = TuneContext(
MatmulModule,
target=tvm.target.Target("llvm"),
space_generator=sch_fn,
search_strategy=replay,
task_name="Test",
rand_state=42,
)
database = DummyDatabase()
round_robin = RoundRobin(
[task],
[1.0],
DummyBuilder(),
DummyRunner(),
database,
measure_callbacks=[measure_callback.AddToDatabase()],
max_trials=max_trials_per_task,
)
round_robin.tune()
assert len(database) == max_trials_per_task
def test_meta_schedule_measure_callback_fail():
@derived_object
class FailingMeasureCallback(PyMeasureCallback):
def apply(
self,
task_scheduler: TaskScheduler,
task_id: int,
measure_candidates: List[MeasureCandidate],
builds: List[BuilderResult],
results: List[RunnerResult],
) -> None:
raise ValueError("test")
measure_callback = FailingMeasureCallback()
with pytest.raises(ValueError, match="test"):
measure_callback.apply(
RoundRobin([], [],
DummyBuilder(),
DummyRunner(),
DummyDatabase(),
max_trials=1),
0,
[MeasureCandidate(Schedule(Matmul), None)],
[BuilderResult("test_build", None)],
[RunnerResult([1.0, 2.1], None)],
)
def test_meta_schedule_measure_callback():
@derived_object
class FancyMeasureCallback(PyMeasureCallback):
def apply(
self,
task_scheduler: TaskScheduler,
task_id: int,
measure_candidates: List[MeasureCandidate],
builds: List[BuilderResult],
results: List[RunnerResult],
) -> None:
assert len(measure_candidates) == 1
assert_structural_equal(measure_candidates[0].sch.mod, Matmul)
assert (len(builds) == 1 and builds[0].error_msg is None
and builds[0].artifact_path == "test_build")
assert (len(results) == 1 and results[0].error_msg is None
and len(results[0].run_secs) == 2)
measure_callback = FancyMeasureCallback()
measure_callback.apply(
RoundRobin([], [],
DummyBuilder(),
DummyRunner(),
DummyDatabase(),
max_trials=1),
0,
[MeasureCandidate(Schedule(Matmul), None)],
[BuilderResult("test_build", None)],
[RunnerResult([1.0, 2.1], None)],
)
def test_meta_schedule_task_scheduler_NIE(): # pylint: disable=invalid-name
@derived_object
class NIETaskScheduler(PyTaskScheduler):
pass
with pytest.raises(
TVMError,
match="PyTaskScheduler's NextTaskId method not implemented!"):
scheduler = NIETaskScheduler([], DummyBuilder(), DummyRunner(),
DummyDatabase(), 1)
scheduler.next_task_id()
def test_meta_schedule_task_scheduler_avoid_cyclic(): # pylint: disable=invalid-name
database = DummyDatabase()
scheduler = MyTaskScheduler(
[],
DummyBuilder(),
DummyRunner(),
database,
measure_callbacks=[
measure_callback.AddToDatabase(),
],
)
test = weakref.ref(scheduler) # test if it can be destructed successfully
del scheduler
assert test() is None
def test_meta_schedule_evolutionary_search(
): # pylint: disable = invalid-name]
num_trials_per_iter = 10
num_trials_total = 100
strategy = EvolutionarySearch(
num_trials_per_iter=num_trials_per_iter,
num_trials_total=num_trials_total,
population_size=5,
init_measured_ratio=0.1,
init_min_unmeasured=50,
genetic_num_iters=3,
genetic_mutate_prob=0.5,
genetic_max_fail_count=10,
eps_greedy=0.9,
)
context = TuneContext(
mod=Matmul,
space_generator=ScheduleFn(sch_fn=_schedule_matmul),
mutator_probs={
DummyMutator(): 1.0,
},
target=tvm.target.Target("llvm"),
num_threads=1, # because we are using a mutator from the python side
)
_scheduler = RoundRobin(
tasks=[context],
builder=LocalBuilder(),
runner=LocalRunner(),
database=DummyDatabase(),
cost_model=RandomModel(),
measure_callbacks=[],
)
context.space_generator.initialize_with_tune_context(context)
spaces = context.space_generator.generate_design_space(context.mod)
strategy.initialize_with_tune_context(context)
strategy.pre_tuning(spaces)
(correct_sch, ) = ScheduleFn(
sch_fn=_schedule_matmul).generate_design_space(Matmul)
num_trials_each_iter: List[int] = []
candidates = strategy.generate_measure_candidates()
while candidates is not None:
num_trials_each_iter.append(len(candidates))
runner_results: List[RunnerResult] = []
for candidate in candidates:
_is_trace_equal(
candidate.sch,
correct_sch,
remove_decisions=(isinstance(strategy, ReplayTrace)),
)
runner_results.append(
RunnerResult(run_secs=[0.11, 0.41, 0.54], error_msg=None))
strategy.notify_runner_results(context, candidates, runner_results)
candidates = strategy.generate_measure_candidates()
strategy.post_tuning()
print(num_trials_each_iter)
correct_count = 10 # For each iteration except the last one
assert num_trials_each_iter == [correct_count] * (
num_trials_total // correct_count) + (
[num_trials_total %
correct_count] if num_trials_total % correct_count != 0 else [])
del _scheduler
