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import random
import tempfile
import pytest
from optimum.intel import OVModelForCausalLM
from transformers import AutoTokenizer
import lm_eval.evaluator as evaluator
from lm_eval.api.registry import get_model
SUPPORTED_ARCHITECTURES_TASKS = {
"facebook/opt-125m": "lambada_openai",
"hf-internal-testing/tiny-random-gpt2": "wikitext",
}
@pytest.mark.parametrize("model_id,task", SUPPORTED_ARCHITECTURES_TASKS.items())
def test_evaluator(model_id, task):
with tempfile.TemporaryDirectory() as tmpdirname:
model = OVModelForCausalLM.from_pretrained(
model_id, export=True, use_cache=True
)
model.save_pretrained(tmpdirname)
tokenizer = AutoTokenizer.from_pretrained(model_id)
tokenizer.save_pretrained(tmpdirname)
lm = get_model("openvino").create_from_arg_string(
f"pretrained={tmpdirname}",
{
"batch_size": 1,
"device": "cpu",
},
)
def ll_fn(reqs):
for ctx, cont in [req.args for req in reqs]:
if len(ctx) == 0:
continue
# space convention
assert ctx[-1] != " "
assert cont[0] == " " or ctx[-1] == "\n"
res = []
random.seed(42)
for _ in reqs:
res.append((-random.random(), False))
return res
def ll_perp_fn(reqs):
for (string,) in [req.args for req in reqs]:
assert isinstance(string, str)
res = []
random.seed(42)
for _ in reqs:
res.append(-random.random())
return res
lm.loglikelihood = ll_fn
lm.loglikelihood_rolling = ll_perp_fn
limit = 10
evaluator.simple_evaluate(
model=lm,
tasks=[task],
num_fewshot=0,
limit=limit,
bootstrap_iters=10,
)
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