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"""
Usage:
python requests_caching.py --tasks=comma,separated,list,of,tasks --cache_requests=<true|refresh|delete]>
"""
import argparse
import os
from typing import List
import torch
from transformers import (
pipeline as trans_pipeline,
)
from lm_eval import simple_evaluate
from lm_eval.evaluator import request_caching_arg_to_dict
from lm_eval.utils import eval_logger
MODULE_DIR = os.path.dirname(os.path.realpath(__file__))
# Used to specify alternate cache path, useful if run in a docker container
# NOTE raw datasets will break if you try to transfer the cache from your host to a docker image
LM_HARNESS_CACHE_PATH = os.getenv("LM_HARNESS_CACHE_PATH")
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
MODEL = "EleutherAI/pythia-70m"
TASK = "text-generation"
def run_model_for_task_caching(tasks: List[str], cache_requests: str):
eval_logger.info(f"Loading HF model: {MODEL}")
trans_pipe = trans_pipeline(
task=TASK, model=MODEL, device=DEVICE, trust_remote_code=True
)
model = trans_pipe.model
tokenizer = trans_pipe.tokenizer
eval_logger.info(
f"Running simple_evaluate to cache request objects for tasks: {tasks}"
)
cache_args = request_caching_arg_to_dict(cache_requests=cache_requests)
eval_logger.info(
f"The following operations will be performed on the cache: {cache_requests}"
)
eval_data = simple_evaluate(
model="hf-auto",
model_args={
"pretrained": model,
"tokenizer": tokenizer,
},
limit=1,
device=DEVICE,
tasks=tasks,
write_out=True,
**cache_args,
)
return eval_data
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--tasks",
"-t",
default=None,
metavar="task1,task2",
)
parser.add_argument(
"--cache_requests",
type=str,
default=None,
choices=["true", "refresh", "delete"],
help="Speed up evaluation by caching the building of dataset requests. `None` if not caching.",
)
args = parser.parse_args()
tasks = args.tasks.split(",")
eval_data = run_model_for_task_caching(
tasks=tasks, model=MODEL, device=DEVICE, cache_requests=args.cache_requests
)
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