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import os
import re
from typing import List
import pytest
import lm_eval.api as api
import lm_eval.evaluator as evaluator
from lm_eval import tasks
from lm_eval.utils import make_table
os.environ["TOKENIZERS_PARALLELISM"] = "false"
# TODO: more fine grained unit tests rather than this big honking integration
# test once we break evaluator into smaller, more manageable pieces
@pytest.mark.parametrize(
"task_name,limit,model,model_args,bootstrap_iters",
[
(
["arc_easy"],
10,
"hf",
"pretrained=EleutherAI/pythia-160m,dtype=float32,device=cpu",
0,
),
(
["mmlu_abstract_algebra"],
None,
"hf",
"pretrained=EleutherAI/pythia-160m,dtype=float32,device=cpu",
10000,
),
],
ids=lambda d: f"{d}",
)
def test_evaluator(
task_name: List[str], limit: int, model: str, model_args: str, bootstrap_iters: int
):
e1 = evaluator.simple_evaluate(
model=model,
tasks=task_name,
limit=limit,
model_args=model_args,
bootstrap_iters=bootstrap_iters,
)
assert e1 is not None
lm = api.registry.get_model(model).create_from_arg_string(
model_args,
{
"batch_size": None,
"max_batch_size": None,
"device": None,
},
)
task_manager = tasks.TaskManager()
task_dict = tasks.get_task_dict(task_name, task_manager)
e2 = evaluator.evaluate(
lm=lm,
task_dict=task_dict,
limit=limit,
bootstrap_iters=bootstrap_iters,
)
assert e2 is not None
# check that caching is working
def r(x):
if "arc_easy" in x["results"]:
return x["results"]["arc_easy"]
else:
return x["results"]["mmlu_abstract_algebra"]
assert all(
x == y
for x, y in zip([y for _, y in r(e1).items()], [y for _, y in r(e2).items()])
)
@pytest.mark.parametrize(
"task_name,limit,model,model_args",
[
(
["ai2_arc"],
10,
"hf",
"pretrained=EleutherAI/pythia-14m,dtype=float32,device=cpu",
),
(
["mmlu_stem"],
10,
"hf",
"pretrained=EleutherAI/pythia-14m,dtype=float32,device=cpu",
),
(
["lambada_openai"],
10,
"hf",
"pretrained=EleutherAI/pythia-14m,dtype=float32,device=cpu",
),
(
["wikitext"],
10,
"hf",
"pretrained=EleutherAI/pythia-14m,dtype=float32,device=cpu",
),
],
ids=lambda d: f"{d}",
)
def test_printed_results(task_name: List[str], limit: int, model: str, model_args: str):
results = evaluator.simple_evaluate(
model=model,
tasks=task_name,
limit=limit,
model_args=model_args,
bootstrap_iters=0,
random_seed=0,
numpy_random_seed=0,
torch_random_seed=0,
fewshot_random_seed=0,
)
filename = "_".join(
(
"-".join(task_name),
str(limit),
str(model),
re.sub(r"[^a-zA-Z0-9_\-\.]", "-", model_args),
)
)
filepath = f"./tests/testdata/{filename}.txt"
with open(filepath, "r") as f:
t1 = f.read().strip()
t2 = make_table(results).strip()
t1_lines, t2_lines = t1.splitlines(), t2.splitlines()
assert len(t1_lines) == len(t2_lines)
for t1_line, t2_line in zip(t1_lines, t2_lines):
t1_items, t2_items = t1_line.split("|"), t2_line.split("|")
assert len(t1_items) == len(t2_items)
for t1_item, t2_item in zip(t1_items, t2_items):
try:
t1_item = float(t1_item)
t2_item = float(t2_item)
assert abs(t1_item - t2_item) < 0.3
except ValueError:
assert t1_item == t2_item
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