llm_judge.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

"""

This script is used to evaluate a set of responses using LLM as a judge for a 6-point Likert scale assessment.

Example usage:
    
    # direct eval in original language
    python llm_judge.py \
        --input_file "data/alpaca_eval_outputs/llama_2_7b_hf_ml6_merged/alpaca_eval_instructions_hi-none-guanaco_prompt-s0-k50-p0.9-t0.8-b8.jsonl" \
        --eval_model_name "gpt-3.5-turbo-1106" \
        --src_key "source" \
        --tgt_key "system" \
        --api_seed 42 --data_seed 42

    # translated eval via English
    python llm_judge.py \
        --input_file "data/alpaca_eval_outputs_translated/llama_2_7b_hf_ml6_merged/alpaca_eval_instructions_hi-none-guanaco_prompt-s0-k50-p0.9-t0.8-b8.jsonl" \
        --eval_model_name "gpt-3.5-turbo-1106" \
        --src_key "source_en" \
        --tgt_key "system_en" \
        --api_seed 42 --data_seed 42

"""

import sys
import argparse
from pathlib import Path
import time
import pandas as pd
import json
from tqdm import tqdm

from helpers import logger
from api_request_parallel_processor import run_api_request_parallel_process

system_message = """You are an expert language evaluator."""

user_message = """You are evaluating a response that has been submitted for a particular task, using a specific set of standards. Below is the data: 

[BEGIN DATA]
***
[Task]: {prompt} 
***
[Submission]: {response}
***
[Criterion]: helpfulness: 

"1": "Not helpful - The generated text is completely irrelevant, unclear, or incomplete. It does not provide any useful information to the user." 

"2": "Somewhat helpful - The generated text has some relevance to the user's question, but it may be unclear or incomplete. It provides only partial information, logical inconsistencies, or the information provided may not be useful for the user's needs." 

"3": "Moderately helpful - The generated text is relevant to the user's question, and it provides a clear and complete answer. However, it may lack detail or explanation that would be helpful for the user." 

"4": "Helpful - The generated text is quite relevant to the user's question, and it provides a clear, complete, and detailed answer. It offers additional information or explanations that are useful for the user. However, some of the points of the response are somewhat repetitive or could be combined for greater clarity and concision" 

"5": "Very helpful - The generated text is highly relevant to the user's question, and it provides a clear, complete, and detailed answer. It offers additional information, explanations, or analogies that are not only useful but also insightful and valuable to the user. However, the structured of the response is not well-organized and there is no clear progression or logical sequence of different points in the response." 

"6": "Highly helpful - The generated text provides a clear, complete, and detailed answer. It offers additional information or explanations that are not only useful but also insightful and valuable to the user. The response is also in a logical and easy-to-follow manner by explicitly using headings, bullet points, or numbered lists to break up the information and make it easier to read." 
***
[END DATA]

Does the submission meet the criterion? First, write out in a step by step manner your reasoning about the criterion to be sure that your conclusion is correct. Avoid simply stating the correct answers at the outset. 
Provide your response as a RFC8259 compliant JSON following this schema:

{{"reasoning": str, "score": int}}

"""

def set_args():
    ap = argparse.ArgumentParser()
    ap.add_argument("--input_file", required=True, type=str, help="Path to input file")
    ap.add_argument("--output_file", required=False, type=str, help="Path to output file")
    ap.add_argument("--output_dir_base", required=False, default='data/llm_judge', type=str, help="Path to output directory")
    ap.add_argument("--eval_model_name", required=False, type=str, default="gpt-3.5-turbo-1106", help="Name of the model to use")
    ap.add_argument("--eval_type", required=False, type=str, default="likert", help="Evaluation type (abs|likert)")
    ap.add_argument("--verbose", action="store_true", default=False, help="Verbose")
    ap.add_argument("--limit", required=False, type=int, default=-1, help="Limit number of items to evaluate")
    ap.add_argument("--src_key", required=False, type=str, default="source", help="Source key")
    ap.add_argument("--tgt_key", required=False, type=str, default="system", help="Target key")
    ap.add_argument("--force", action="store_true", default=False, help="Overwrites existing outputs if found, otherwise skip.")
    ap.add_argument("--data_seed", required=False, type=int, default=42, help="Random seed for sampling items from data")
    ap.add_argument("--api_seed", required=False, type=int, default=42, help="Random seed for generation with API")
    ap.add_argument("--temperature", required=False, type=float, default=0.0, help="Temperature for generation with API")
    # ap.add_argument("--max_tokens", required=False, type=int, default=1000, help="Max tokens to use")
    ap.add_argument("--timeout", required=False, type=int, default=90, help="Timeout for API calls")
    ap.add_argument("--max_retries", required=False, type=int, default=10, help="Max retries for API calls for each item")
    ap.add_argument("--max_parallel_calls", required=False, type=int, default=10, help="Max parallel calls to API")
    return ap.parse_args()    

def prepare_prompt(src, tgt, system_message=system_message, user_message=user_message):
    return [
        {"role": "system", "content": system_message},
        {"role": "user", "content": user_message.format(prompt=src, response=tgt)},
    ]
    
def parse_string_to_json(string):
    """Parse a string to a JSON object."""
    try:
        json_object = json.loads(string)
    except ValueError as e:
        logger.error(f"Error parsing string to JSON: {e}")
        json_object = {}

    return json_object

if __name__ == "__main__":

    args = set_args()

    data = pd.read_json(args.input_file, lines=True)

    # add id column if not present    
    if 'id' not in data.columns:
        data['id'] = data.index
    
    # limit number of items to evaluate
    if args.limit > 0:
        data = data.sample(args.limit, random_state=args.data_seed).reset_index(drop=True)

    logger.info(f"Evaluating {len(data)} items from {args.input_file}")

    # convert dataframe to dict
    data = data.to_dict(orient='records')

    # set output file
    if args.output_file:
        output_file = args.output_file
    
    elif args.output_dir_base:
        # e.g. data/llm_evals/likert/gpt-3.5-turbo-1106/llama_2_7b_hf_de_merged/alpaca_eval_instructions_de-none-guanaco_prompt-s0-k50-p0.9-t0.8-b8-translated.jsonl
        output_dir = Path(args.output_dir_base) / f"{args.eval_type}" / args.eval_model_name / Path(args.input_file).parent.name
        output_file = Path(output_dir) / f"{Path(args.input_file).stem}-l{len(data)}-ds{args.data_seed}-as{args.api_seed}-{args.src_key}-{args.tgt_key}.jsonl"
    
    else:
        raise NotImplementedError("Please specify either --output_file or --output_dir_base.")
    
    logger.info(f"Output file: {output_file}")
    
    if Path(output_file).exists() and not args.force:
        logger.error(f"Output file already exists. Use --force to overwrite.")
        sys.exit(0)

    if args.eval_type != "likert":
        raise NotImplementedError

    # if args.eval_type == "abs":
    #     query_prompts = [prepare_prompts(strip_quotes(item[args.src_key]), strip_quotes(item[args.tgt_key])) for item in data]
    # elif args.eval_type == "likert":
    #     query_prompts = [prepare_prompts(strip_quotes(item[args.src_key]), strip_quotes(item[args.tgt_key]), human_template=human_template_likert) for item in data]

    total_cost = 0
    Path(output_file).parent.mkdir(parents=True, exist_ok=True)

    # prepare prompts
    prompts = [prepare_prompt(item[args.src_key], item[args.tgt_key]) for item in data]
    
    # run api requests in parallel -> results is a list of dicts containing 'content', 'system_fingerprint', 'prompt_tokens', 'completion_tokens', 'cost', 'model_name'
    results = run_api_request_parallel_process(
        args.eval_model_name, 
        prompts, 
        max_parallel_calls=args.max_parallel_calls, 
        timeout=args.timeout, 
        seed=args.api_seed, 
        temperature=args.temperature, 
        expects_json=True
        )
                    
    # check that results match data
    assert len(data) == len(results), f"Length of data ({len(data)}) and results ({len(results)}) do not match!"

    c = 0
    with open(output_file, "w", encoding="utf8") as outf:
        for item, result in zip(data, results):

            # parse result content to JSON and remove from result dict
            try:
                eval_result = parse_string_to_json(result.pop('content'))
                # prefix keys with 'eval_'
                eval_result = {f"eval_{k}": v for k, v in eval_result.items()}

                # update item with eval_result    
                item.update(eval_result)
                # add remaining key to eval_meta
                item['eval_meta'] = result

            except:
                logger.warning(f"Error parsing result content to JSON: {result} - Skipping {item['id']}")
            
            outf.write(f"{json.dumps(item, ensure_ascii=False)}\n")
            c += 1

    logger.info(f"Done. Wrote {c} items to {output_file}")