def test_db_latest_all():
logging.info("test db load w/ multiple results ...")
inp1, res1 = get_sample_records(1)[0]
lis1 = list(tuple(res1))
lis2 = list(tuple(res1))
lis3 = list(tuple(res1))
# set timestamp
lis1[-1] = 0.0
lis2[-1] = 1.1
lis3[-1] = 9999.9999
res1 = MeasureResult(*lis1)
res2 = MeasureResult(*lis2)
res3 = MeasureResult(*lis3)
_db = database.DummyDatabase()
_db.flush()
_db.save(inp1, res1, extend=True)
load1 = _db.load(inp1)
assert load1.timestamp == 0.0
_db.save(inp1, res2, extend=True)
load2 = _db.load(inp1)
assert load2.timestamp == 1.1
_db.save(inp1, res3, extend=True)
load3 = _db.load(inp1)
assert load3.timestamp == 9999.9999
load4 = _db.load(inp1, get_all=True)
assert encode(inp1, load4[0]) == encode(inp1, res1)
assert encode(inp1, load4[1]) == encode(inp1, res2)
assert encode(inp1, load4[2]) == encode(inp1, res3)
def test_db_filter():
logging.info("test db filter ...")
records = get_sample_records(5)
_db = database.DummyDatabase()
_db.flush()
for inp, result in records:
_db.save(inp, result)
records = _db.filter(lambda inp, ress: any(r.costs[0] <= 2 for r in ress))
assert len(records) == 2
def test_db_save_replay():
logging.info("test db save (from measure_batch) and replay ...")
_db = database.DummyDatabase()
_db.flush()
task, target = get_sample_task()
ctx = tvm.context(str(target))
if not ctx.exist:
logging.warning(
"Skip this test because there is no supported device for test")
measure_option = autotvm.measure_option('local',
do_fork=False,
timeout=2,
replay_db=_db)
measure_batch = autotvm.measure.create_measure_batch(task, measure_option)
batch_size = 2
ct = 0
all_inputs = list()
all_results = list()
batches = list()
tuner = autotvm.tuner.RandomTuner(task)
while ct < TRIAL_LIMIT:
inputs = list()
for i in range(batch_size):
cfg = tuner.next_batch(1)[0]
inputs.append((MeasureInput(target, task, cfg)))
all_inputs.append(inputs[-1])
batches.append(inputs)
results = measure_batch(inputs)
all_results += results
ct += 1
callback = autotvm.callback.log_to_database(_db)
callback(None, all_inputs, all_results)
assert len(_db.db.keys()) == batch_size * TRIAL_LIMIT, \
"%d vs %d" % (len(_db.db.keys()), batch_size * TRIAL_LIMIT)
all_results_2 = measure_batch(all_inputs)
all_results_3 = measure_batch(all_inputs)
for i in range(len(all_results)):
encr1 = encode(all_inputs[i], all_results[i])
encr2 = encode(all_inputs[i], all_results_2[i])
encr3 = encode(all_inputs[i], all_results_3[i])
assert encr1 == encr2, "EXPECTED MATCH WITH SAVE REPLAY (first replay), got MISMATCH"
assert encr2 == encr3, "EXPECTED MATCH WITH SAVE REPLAY (second replay), got MISMATCH"
del measure_batch
def test_save_load():
logging.info("test basic db load/save ...")
records = get_sample_records(3)
inp1, res1 = records[0]
inp2, res2 = records[1]
inp3, _ = records[2]
_db = database.DummyDatabase()
_db.flush()
_db.save(inp1, res1)
_db.save(inp2, res2)
load1 = _db.load(inp1)
load2 = _db.load(inp2)
load3 = _db.load(inp3)
assert load1 == res1
assert load2 == res2
assert load3 is None
assert load1 != load2
def test_db_hash():
logging.info("test db hash check ...")
inp1, res1 = get_sample_records(1)[0]
inp2 = copy.deepcopy(inp1)
inp1.config.code_hash = 'cafecafe'
inp2.config.code_hash = 'dbffdbff'
res2l = list(tuple(res1))
# set timestamp
res2l[-1] = -1
res2 = MeasureResult(*res2l)
_db = database.DummyDatabase()
_db.flush()
_db.save(inp1, res1, extend=True)
_db.save(inp2, res2, extend=True)
load1 = _db.load(inp1)
load2 = _db.load(inp2)
assert load1 != load2
assert load1.timestamp != -1
assert load2.timestamp == -1
def test_db_filter():
logging.info("test db filter ...")
# Pick a GPU target because there are more likely to be failures/invalid configs
task, target = get_sample_task()
ctx = tvm.context(str(target))
if not ctx.exist:
logging.warning(
"Skip this test because there is no supported device for test")
batch_size = 2
measure_option = autotvm.measure_option('local', do_fork=False, timeout=2)
measure_batch = autotvm.measure.create_measure_batch(task, measure_option)
ct = 0
all_inputs = list()
all_results = list()
batches = list()
tuner = autotvm.tuner.RandomTuner(task)
while ct < TRIAL_LIMIT:
inputs = list()
for i in range(batch_size):
cfg = tuner.next_batch(1)[0]
inputs.append((MeasureInput(target, task, cfg)))
all_inputs.append(inputs[-1])
batches.append(inputs)
results = measure_batch(inputs)
all_results += results
ct += 1
del measure_batch
db = database.DummyDatabase()
db.flush()
# First setting, memoize one input at a time, check that each is saved and replayed
measure_option = autotvm.measure_option('local',
do_fork=False,
timeout=2,
replay_db=db)
measure_batch = autotvm.measure.create_measure_batch(task, measure_option)
for i in range(len(all_inputs) + 1):
db.flush()
for j in range(i):
db.save(all_inputs[j], all_results[j])
for k in range(len(batches)):
batch = batches[k]
batch_result = measure_batch(batch)
for l in range(batch_size):
all_idx = k * batch_size + l
assert batch_result[l] is not None
if all_idx < i:
assert encode(batch[l], batch_result[l]) == encode(batch[l], all_results[all_idx]), \
"(no retry) EXPECTED MATCH, GOT MISMATCH"
else:
assert encode(batch[l], batch_result[l]) != encode(batch[l], all_results[all_idx]), \
"(no retry) EXPECTED MISMATCH, GOT MATCH"
del measure_batch
