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| import logging | |
| import os | |
| import pprint | |
| from huggingface_hub import snapshot_download | |
| import subprocess | |
| subprocess.run(["python", "scripts/fix_harness_import.py"]) | |
| logging.getLogger("openai").setLevel(logging.WARNING) | |
| from src.backend.run_eval_suite import run_evaluation | |
| from src.backend.manage_requests import check_completed_evals, get_eval_requests, set_eval_request, EvalRequest | |
| from src.backend.sort_queue import sort_models_by_priority | |
| from src.envs import QUEUE_REPO, EVAL_REQUESTS_PATH_BACKEND, RESULTS_REPO, EVAL_RESULTS_PATH_BACKEND, DEVICE, API, \ | |
| LIMIT, TOKEN, RUN_MODE | |
| from src.about import NUM_FEWSHOT, HarnessTasks | |
| import asyncio | |
| TASKS_HARNESS = [task.value.benchmark for task in HarnessTasks] | |
| logging.basicConfig(level=logging.ERROR) | |
| pp = pprint.PrettyPrinter(width=80) | |
| PENDING_STATUS = "PENDING" | |
| RUNNING_STATUS = "RUNNING" | |
| FINISHED_STATUS = "FINISHED" | |
| FAILED_STATUS = "FAILED" | |
| # TODO: uncomment | |
| if RUN_MODE != "LOCAL": | |
| snapshot_download(repo_id=RESULTS_REPO, revision="main", local_dir=EVAL_RESULTS_PATH_BACKEND, repo_type="dataset", | |
| max_workers=60, token=TOKEN) | |
| snapshot_download(repo_id=QUEUE_REPO, revision="main", local_dir=EVAL_REQUESTS_PATH_BACKEND, repo_type="dataset", | |
| max_workers=60, token=TOKEN) | |
| def run_auto_eval(): | |
| current_pending_status = [PENDING_STATUS] | |
| # pull the eval dataset from the hub and parse any eval requests | |
| # check completed evals and set them to finished | |
| if RUN_MODE != "LOCAL": | |
| check_completed_evals( | |
| api=API, | |
| checked_status=RUNNING_STATUS, | |
| completed_status=FINISHED_STATUS, | |
| failed_status=FAILED_STATUS, | |
| hf_repo=QUEUE_REPO, | |
| local_dir=EVAL_REQUESTS_PATH_BACKEND, | |
| hf_repo_results=RESULTS_REPO, | |
| local_dir_results=EVAL_RESULTS_PATH_BACKEND | |
| ) | |
| # Get all eval request that are PENDING, if you want to run other evals, change this parameter | |
| eval_requests = get_eval_requests(job_status=current_pending_status, hf_repo=QUEUE_REPO, | |
| local_dir=EVAL_REQUESTS_PATH_BACKEND) | |
| # Sort the evals by priority (first submitted first run) | |
| eval_requests = sort_models_by_priority(api=API, models=eval_requests) | |
| else: | |
| local_model_name = os.getenv("LOCAL_MODEL_NAME", "hf-internal-testing/tiny-random-gpt2") | |
| sample_request = { | |
| "model": local_model_name, "json_filepath": "", "base_model": "", "revision": "main", | |
| "private": False, | |
| "precision": "bfloat16", "weight_type": "Original", "status": "PENDING", | |
| "submitted_time": "2023-11-21T18:10:08Z", "model_type": "\ud83d\udfe2 : pretrained", "likes": 0, | |
| "params": 0.1, "license": "custom" | |
| } | |
| eval_requests = [EvalRequest(**sample_request)] | |
| print(f"Found {len(eval_requests)} {','.join(current_pending_status)} eval requests") | |
| if len(eval_requests) == 0: | |
| return | |
| eval_request = eval_requests[0] | |
| pp.pprint(eval_request) | |
| if RUN_MODE != "LOCAL": | |
| set_eval_request( | |
| api=API, | |
| eval_request=eval_request, | |
| set_to_status=RUNNING_STATUS, | |
| hf_repo=QUEUE_REPO, | |
| local_dir=EVAL_REQUESTS_PATH_BACKEND, | |
| ) | |
| asyncio.run( | |
| run_evaluation( | |
| eval_request=eval_request, | |
| task_names=TASKS_HARNESS, | |
| num_fewshot=NUM_FEWSHOT, | |
| local_dir=EVAL_RESULTS_PATH_BACKEND, | |
| results_repo=RESULTS_REPO, | |
| batch_size=1, | |
| device=DEVICE, | |
| no_cache=True, | |
| limit=LIMIT | |
| ) | |
| ) | |
| logging.info("Shopping finished") | |
| if __name__ == "__main__": | |
| run_auto_eval() | |