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from typing import List
import pytest
import torch
import lm_eval.tasks as tasks
from lm_eval.api.instance import Instance
task_manager = tasks.TaskManager()
@pytest.mark.skip(reason="requires CUDA")
class TEST_VLLM:
vllm = pytest.importorskip("vllm")
try:
from lm_eval.models.vllm_causallms import VLLM
LM = VLLM(pretrained="EleutherAI/pythia-70m")
except ModuleNotFoundError:
pass
torch.use_deterministic_algorithms(True)
task_list = task_manager.load_task_or_group(["arc_easy", "gsm8k", "wikitext"])
multiple_choice_task = task_list["arc_easy"] # type: ignore
multiple_choice_task.build_all_requests(limit=10, rank=0, world_size=1)
MULTIPLE_CH: List[Instance] = multiple_choice_task.instances
generate_until_task = task_list["gsm8k"] # type: ignore
generate_until_task._config.generation_kwargs["max_gen_toks"] = 10
generate_until_task.build_all_requests(limit=10, rank=0, world_size=1)
generate_until: List[Instance] = generate_until_task.instances
rolling_task = task_list["wikitext"] # type: ignore
rolling_task.build_all_requests(limit=10, rank=0, world_size=1)
ROLLING: List[Instance] = rolling_task.instances
# TODO: make proper tests
def test_logliklihood(self) -> None:
res = self.LM.loglikelihood(self.MULTIPLE_CH)
assert len(res) == len(self.MULTIPLE_CH)
for x in res:
assert isinstance(x[0], float)
def test_generate_until(self) -> None:
res = self.LM.generate_until(self.generate_until)
assert len(res) == len(self.generate_until)
for x in res:
assert isinstance(x, str)
def test_logliklihood_rolling(self) -> None:
res = self.LM.loglikelihood_rolling(self.ROLLING)
for x in res:
assert isinstance(x, float)
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