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-# Copyright (c) Meta Platforms, Inc. and affiliates.
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-# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
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-
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-import argparse
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-import json
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-import logging
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-import os
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-import re
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-import sys
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-from pathlib import Path
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-
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-import numpy as np
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-import lm_eval
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-from lm_eval import tasks
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-from lm_eval.utils import make_table
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-
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-
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-def _handle_non_serializable(o):
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- if isinstance(o, np.int64) or isinstance(o, np.int32):
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- return int(o)
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- elif isinstance(o, set):
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- return list(o)
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- else:
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- return str(o)
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-
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-
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-def setup_logging(verbosity):
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- logging.basicConfig(
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- level=verbosity.upper(), format="%(asctime)s - %(levelname)s - %(message)s"
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- )
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- return logging.getLogger(__name__)
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-
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-
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-def handle_output(args, results, logger):
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- if not args.output_path:
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- if args.log_samples:
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- logger.error("Specify --output_path for logging samples.")
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- sys.exit(1)
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- logger.info(json.dumps(results, indent=2, default=_handle_non_serializable))
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- return
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-
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- path = Path(args.output_path)
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- if path.is_file() or path.with_name("results.json").is_file():
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- logger.warning(f"File already exists at {path}. Results will be overwritten.")
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-
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- output_dir = path.parent if path.suffix in (".json", ".jsonl") else path
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- output_dir.mkdir(parents=True, exist_ok=True)
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-
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- results_str = json.dumps(results, indent=2, default=_handle_non_serializable)
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- if args.show_config:
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- logger.info(results_str)
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-
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- file_path = os.path.join(args.output_path, "results.json")
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- with open(file_path , "w", encoding="utf-8") as f:
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- f.write(results_str)
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-
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- if args.log_samples:
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- samples = results.pop("samples", {})
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- for task_name, _ in results.get("configs", {}).items():
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- output_name = re.sub(r"/|=", "__", args.model_args) + "_" + task_name
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- sample_file = output_dir.joinpath(f"{output_name}.jsonl")
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- sample_data = json.dumps(
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- samples.get(task_name, {}), indent=2, default=_handle_non_serializable
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- )
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- sample_file.write_text(sample_data, encoding="utf-8")
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-
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- batch_sizes = ",".join(map(str, results.get("config", {}).get("batch_sizes", [])))
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- summary = f"{args.model} ({args.model_args}), gen_kwargs: ({args.gen_kwargs}), limit: {args.limit}, num_fewshot: {args.num_fewshot}, batch_size: {args.batch_size}{f' ({batch_sizes})' if batch_sizes else ''}"
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- logger.info(summary)
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- logger.info(make_table(results))
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- if "groups" in results:
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- logger.info(make_table(results, "groups"))
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-
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-
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-def load_tasks(args):
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- if args.open_llm_leaderboard_tasks:
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- current_dir = os.getcwd()
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- config_dir = os.path.join(current_dir, "open_llm_leaderboard")
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- task_manager = tasks.TaskManager(include_path=config_dir)
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- return task_manager, [
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- "arc_challenge_25_shot",
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- "hellaswag_10_shot",
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- "truthfulqa_mc2",
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- "winogrande_5_shot",
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- "gsm8k",
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- "mmlu",
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- ]
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- return None, args.tasks.split(",") if args.tasks else []
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-
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-
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-def parse_eval_args():
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- parser = argparse.ArgumentParser(formatter_class=argparse.RawTextHelpFormatter)
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- parser.add_argument(
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- "--model", "-m", default="hf", help="Name of model, e.g., `hf`."
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- )
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- parser.add_argument(
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- "--tasks",
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- "-t",
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- default=None,
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- help="Comma-separated list of tasks, or 'list' to display available tasks.",
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- )
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- parser.add_argument(
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- "--model_args",
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- "-a",
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- default="",
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- help="Comma-separated string arguments for model, e.g., `pretrained=EleutherAI/pythia-160m`.",
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- )
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- parser.add_argument(
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- "--open_llm_leaderboard_tasks",
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- "-oplm",
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- action="store_true",
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- default=False,
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- help="Choose the list of tasks with specification in HF open LLM-leaderboard.",
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- )
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- parser.add_argument(
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- "--num_fewshot",
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- "-f",
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- type=int,
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- default=None,
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- help="Number of examples in few-shot context.",
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- )
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- parser.add_argument(
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- "--batch_size",
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- "-b",
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- default=1,
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- help="Batch size, can be 'auto', 'auto:N', or an integer.",
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- )
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- parser.add_argument(
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- "--max_batch_size",
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- type=int,
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- default=None,
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- help="Maximal batch size with 'auto' batch size.",
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- )
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- parser.add_argument(
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- "--device", default=None, help="Device for evaluation, e.g., 'cuda', 'cpu'."
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- )
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- parser.add_argument(
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- "--output_path", "-o", type=str, default=None, help="Path for saving results."
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- )
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- parser.add_argument(
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- "--limit",
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- "-L",
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- type=float,
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- default=None,
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- help="Limit number of examples per task.",
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- )
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- parser.add_argument(
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- "--use_cache", "-c", default=None, help="Path to cache db file, if used."
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- )
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- parser.add_argument(
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- "--verbosity",
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- "-v",
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- default="INFO",
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- help="Logging level: CRITICAL, ERROR, WARNING, INFO, DEBUG.",
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- )
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- parser.add_argument(
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- "--gen_kwargs",
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- default=None,
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- help="Generation kwargs for tasks that support it.",
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- )
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- parser.add_argument(
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- "--check_integrity",
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- action="store_true",
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- help="Whether to run the relevant part of the test suite for the tasks.",
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- )
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- parser.add_argument(
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- "--write_out",
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- "-w",
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- action="store_true",
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- default=False,
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- help="Prints the prompt for the first few documents.",
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- )
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- parser.add_argument(
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- "--log_samples",
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- "-s",
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- action="store_true",
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- default=False,
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- help="If True, write out all model outputs and documents for per-sample measurement and post-hoc analysis.",
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- )
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- parser.add_argument(
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- "--show_config",
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- action="store_true",
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- default=False,
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- help="If True, shows the full config of all tasks at the end of the evaluation.",
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- )
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- parser.add_argument(
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- "--include_path",
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- type=str,
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- default=None,
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- help="Additional path to include if there are external tasks.",
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- )
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- return parser.parse_args()
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-
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-
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-def evaluate_model(args):
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- try:
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- task_manager, task_list = load_tasks(args)
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- # Customized model such as Quantized model etc.
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- # In case you are working with a custom model, you can use the following guide to add it here:
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- # https://github.com/EleutherAI/lm-evaluation-harness/blob/main/docs/interface.md#external-library-usage
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-
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- # Evaluate
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- results = lm_eval.simple_evaluate(
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- model=args.model,
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- model_args=args.model_args,
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- tasks=task_list,
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- num_fewshot=args.num_fewshot,
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- batch_size=args.batch_size,
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- max_batch_size=args.max_batch_size,
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- device=args.device,
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- use_cache=args.use_cache,
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- limit=args.limit,
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- check_integrity=args.check_integrity,
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- write_out=args.write_out,
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- log_samples=args.log_samples,
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- gen_kwargs=args.gen_kwargs,
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- task_manager=task_manager,
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- )
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- handle_output(args, results, logger)
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-
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- except Exception as e:
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- logger.error(f"An error occurred during evaluation: {e}")
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- sys.exit(1)
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-
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-
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-if __name__ == "__main__":
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- args = parse_eval_args()
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- logger = setup_logging(args.verbosity)
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- evaluate_model(args)
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