in lm_eval/api/model.py [0:0]
def __getattr__(self, attr: str):
lm_attr = getattr(self.lm, attr)
if attr not in ["loglikelihood", "loglikelihood_rolling", "generate_until"]:
eval_logger.debug(f"Passing through attribute '{attr}' to underlying LM")
return lm_attr
def fn(requests):
res = []
remaining_reqs = []
warned = False
# figure out which ones are cached and which ones are new
eval_logger.info(
f"Loading '{attr}' responses from cache '{self.cache_db}' where possible..."
)
for req in tqdm(requests, desc="Checking cached requests"):
hsh = hash_args(attr, req.args)
if attr == "generate_until" and req.args[1].get("do_sample", False):
# when we are doing non-greedy generation, don't use the cache
# (else every "randomly sampled" generation would be identical for repeats > 1).
if not warned:
eval_logger.warning(
f"Arguments to lm.generate_until() '{req.args[1]}' include non-deterministic sampling. Caching will not be performed for such requests."
)
warned = True
res.append(None)
remaining_reqs.append(req)
elif hsh in self.dbdict:
ob = self.dbdict[hsh]
assert ob is not None
res.append(ob)
else:
res.append(None)
remaining_reqs.append(req)
eval_logger.info(
f"Cached requests: {len(requests) - len(remaining_reqs)}, Requests remaining: {len(remaining_reqs)}"
)
# actually run the LM on the requests that do not have cached results
rem_res = getattr(self.lm, attr)(remaining_reqs)
# stick the new ones back into the list and also cache any of the new ones
resptr = 0
for req, r in zip(remaining_reqs, rem_res):
while res[resptr] is not None:
resptr += 1
res[resptr] = r
# caching
hsh = hash_args(attr, req.args)
self.dbdict[hsh] = r
self.dbdict.commit()
return res
return fn