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# File: lm-evaluation-harness-main/lm_eval/__main__.py
import argparse
import json
import logging
import os
import sys
from functools import partial
from typing import Union
from lm_eval import evaluator, utils
from lm_eval.evaluator import request_caching_arg_to_dict
from lm_eval.loggers import EvaluationTracker, WandbLogger
from lm_eval.tasks import TaskManager
from lm_eval.utils import handle_non_serializable, make_table, simple_parse_args_string
def _int_or_none_list_arg_type(min_len: int, max_len: int, defaults: str, value: str, split_char: str=','):
def parse_value(item):
item = item.strip().lower()
if item == 'none':
return None
try:
return int(item)
except ValueError:
raise argparse.ArgumentTypeError(f'{item} is not an integer or None')
items = [parse_value(v) for v in value.split(split_char)]
num_items = len(items)
if num_items == 1:
items = items * max_len
elif num_items < min_len or num_items > max_len:
raise argparse.ArgumentTypeError(f"Argument requires {max_len} integers or None, separated by '{split_char}'")
elif num_items != max_len:
logging.warning(f"Argument requires {max_len} integers or None, separated by '{split_char}'. Missing values will be filled with defaults.")
default_items = [parse_value(v) for v in defaults.split(split_char)]
items.extend(default_items[num_items:])
return items
def check_argument_types(parser: argparse.ArgumentParser):
for action in parser._actions:
if action.dest != 'help' and (not action.const):
if action.type is None:
raise ValueError(f"Argument '{action.dest}' doesn't have a type specified.")
else:
continue
def setup_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser(formatter_class=argparse.RawTextHelpFormatter)
parser.add_argument('--model', '-m', type=str, default='hf', help='Name of model e.g. `hf`')
parser.add_argument('--tasks', '-t', default=None, type=str, metavar='task1,task2', help='Comma-separated list of task names or task groupings to evaluate on.\nTo get full list of tasks, use one of the commands `lm-eval --tasks {{list_groups,list_subtasks,list_tags,list}}` to list out all available names for task groupings; only (sub)tasks; tags; or all of the above')
parser.add_argument('--model_args', '-a', default='', type=str, help='Comma separated string arguments for model, e.g. `pretrained=EleutherAI/pythia-160m,dtype=float32`')
parser.add_argument('--num_fewshot', '-f', type=int, default=None, metavar='N', help='Number of examples in few-shot context')
parser.add_argument('--batch_size', '-b', type=str, default=1, metavar='auto|auto:N|N', help="Acceptable values are 'auto', 'auto:N' or N, where N is an integer. Default 1.")
parser.add_argument('--max_batch_size', type=int, default=None, metavar='N', help='Maximal batch size to try with --batch_size auto.')
parser.add_argument('--device', type=str, default=None, help='Device to use (e.g. cuda, cuda:0, cpu).')
parser.add_argument('--output_path', '-o', default=None, type=str, metavar='DIR|DIR/file.json', help='The path to the output file where the result metrics will be saved. If the path is a directory and log_samples is true, the results will be saved in the directory. Else the parent directory will be used.')
parser.add_argument('--limit', '-L', type=float, default=None, metavar='N|0<N<1', help='Limit the number of examples per task. If <1, limit is a percentage of the total number of examples.')
parser.add_argument('--use_cache', '-c', type=str, default=None, metavar='DIR', help='A path to a sqlite db file for caching model responses. `None` if not caching.')
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.')
parser.add_argument('--check_integrity', action='store_true', help='Whether to run the relevant part of the test suite for the tasks.')
parser.add_argument('--write_out', '-w', action='store_true', default=False, help='Prints the prompt for the first few documents.')
parser.add_argument('--log_samples', '-s', action='store_true', default=False, help='If True, write out all model outputs and documents for per-sample measurement and post-hoc analysis. Use with --output_path.')
parser.add_argument('--system_instruction', type=str, default=None, help='System instruction to be used in the prompt')
parser.add_argument('--apply_chat_template', action='store_true', default=False, help='If True, applies the chat template to the prompt')
parser.add_argument('--fewshot_as_multiturn', action='store_true', default=False, help='If True, uses the fewshot as a multi-turn conversation')
parser.add_argument('--show_config', action='store_true', default=False, help='If True, shows the the full config of all tasks at the end of the evaluation.')
parser.add_argument('--include_path', type=str, default=None, metavar='DIR', help='Additional path to include if there are external tasks to include.')
parser.add_argument('--gen_kwargs', type=str, default=None, help='String arguments for model generation on greedy_until tasks, e.g. `temperature=0,top_k=0,top_p=0`.')
parser.add_argument('--verbosity', '-v', type=str.upper, default='INFO', metavar='CRITICAL|ERROR|WARNING|INFO|DEBUG', help='Controls the reported logging error level. Set to DEBUG when testing + adding new task configurations for comprehensive log output.')
parser.add_argument('--wandb_args', type=str, default='', help='Comma separated string arguments passed to wandb.init, e.g. `project=lm-eval,job_type=eval')
parser.add_argument('--hf_hub_log_args', type=str, default='', help="Comma separated string arguments passed to Hugging Face Hub's log function, e.g. `hub_results_org=EleutherAI,hub_repo_name=lm-eval-results`")
parser.add_argument('--predict_only', '-x', action='store_true', default=False, help='Use with --log_samples. Only model outputs will be saved and metrics will not be evaluated.')
default_seed_string = '0,1234,1234,1234'
parser.add_argument('--seed', type=partial(_int_or_none_list_arg_type, 3, 4, default_seed_string), default=default_seed_string, help=f"Set seed for python's random, numpy, torch, and fewshot sampling.\nAccepts a comma-separated list of 4 values for python's random, numpy, torch, and fewshot sampling seeds, respectively, or a single integer to set the same seed for all four.\nThe values are either an integer or 'None' to not set the seed. Default is `{default_seed_string}` (for backward compatibility).\nE.g. `--seed 0,None,8,52` sets `random.seed(0)`, `torch.manual_seed(8)`, and fewshot sampling seed to 52. Here numpy's seed is not set since the second value is `None`.\nE.g, `--seed 42` sets all four seeds to 42.")
parser.add_argument('--trust_remote_code', action='store_true', help='Sets trust_remote_code to True to execute code to create HF Datasets from the Hub')
return parser
def parse_eval_args(parser: argparse.ArgumentParser) -> argparse.Namespace:
check_argument_types(parser)
return parser.parse_args()
def cli_evaluate(args: Union[argparse.Namespace, None]=None) -> None:
if not args:
parser = setup_parser()
args = parse_eval_args(parser)
if args.wandb_args:
wandb_logger = WandbLogger(**simple_parse_args_string(args.wandb_args))
eval_logger = utils.eval_logger
eval_logger.setLevel(getattr(logging, f'{args.verbosity}'))
eval_logger.info(f'Verbosity set to {args.verbosity}')
os.environ['TOKENIZERS_PARALLELISM'] = 'false'
if args.output_path:
args.hf_hub_log_args += f',output_path={args.output_path}'
if os.environ.get('HF_TOKEN', None):
args.hf_hub_log_args += f",token={os.environ.get('HF_TOKEN')}"
evaluation_tracker_args = simple_parse_args_string(args.hf_hub_log_args)
evaluation_tracker = EvaluationTracker(**evaluation_tracker_args)
if args.predict_only:
args.log_samples = True
if (args.log_samples or args.predict_only) and (not args.output_path):
raise ValueError('Specify --output_path if providing --log_samples or --predict_only')
if args.fewshot_as_multiturn and args.apply_chat_template is False: