evaluation/math_eval/eval/math_eval.py

import random
import os
import argparse
import time
from vllm import LLM, SamplingParams
from datetime import datetime
from tqdm import tqdm

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

from evaluate import evaluate
from utils import set_seed, load_jsonl, save_jsonl, construct_prompt
from parser import *
from trajectory import *
from data_loader import load_data
from python_executor import PythonExecutor
from model_utils import load_hf_lm_and_tokenizer, generate_completions


def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--data_names", default="gsm8k,math", type=str)
    parser.add_argument("--data_dir", default="./data", type=str)
    parser.add_argument("--model_name_or_path", default="gpt-4", type=str)
    parser.add_argument("--output_dir", default="./output", type=str)
    parser.add_argument("--prompt_type", default="tool-integrated", type=str)
    parser.add_argument("--split", default="test", type=str)
    parser.add_argument("--num_test_sample", default=-1, type=int)  # -1 for full data
    parser.add_argument("--seed", default=0, type=int)
    parser.add_argument("--start", default=0, type=int)
    parser.add_argument("--end", default=-1, type=int)
    parser.add_argument("--temperature", default=0, type=float)
    parser.add_argument("--n_sampling", default=1, type=int)
    parser.add_argument("--top_p", default=1, type=float)
    parser.add_argument("--max_tokens_per_call", default=2048, type=int)
    parser.add_argument("--shuffle", action="store_true")
    parser.add_argument("--use_vllm", action="store_true")
    parser.add_argument("--save_outputs", action="store_true")
    parser.add_argument("--overwrite", action="store_true")
    parser.add_argument("--use_safetensors", action="store_true")
    parser.add_argument("--num_shots", type=int, default=0)
    parser.add_argument(
        "--apply_chat_template",
        action="store_true",
        help="Apply chat template to prompt.",
    )
    parser.add_argument("--pipeline_parallel_size", type=int, default=1)
    parser.add_argument(
        "--adapt_few_shot",
        action="store_true",
        help="Few shot for multiple-choice questions, zero shot for others.",
    )
    args = parser.parse_args()
    args.top_p = (
        1 if args.temperature == 0 else args.top_p
    )  # top_p must be 1 when using greedy sampling (vllm)
    return args


def prepare_data(data_name, args):
    examples = load_data(data_name, args.split, args.data_dir)

    # sample `num_test_sample` from dataset
    if args.num_test_sample > 0:
        # examples = random.sample(examples, min(args.num_test_sample, len(examples)))
        examples = examples[: args.num_test_sample]

    # shuffle
    if args.shuffle:
        random.seed(datetime.now().timestamp())
        random.shuffle(examples)

    # select start and end
    examples = examples[args.start : len(examples) if args.end == -1 else args.end]

    # get out_file name
    dt_string = datetime.now().strftime("%m-%d_%H-%M")
    model_name = "/".join(args.model_name_or_path.split("/")[-2:])
    out_file_prefix = f"{args.split}_{args.prompt_type}_{args.num_test_sample}_seed{args.seed}_t{args.temperature}"
    output_dir = args.output_dir
    # if not os.path.exists(output_dir):
    #     output_dir = f"outputs/{output_dir}"
    out_file = f"{output_dir}/{data_name}/{out_file_prefix}_s{args.start}_e{args.end}.jsonl"
    os.makedirs(f"{output_dir}/{data_name}", exist_ok=True)

    # load all processed samples
    processed_samples = []
    if not args.overwrite:
        processed_files = [
            f
            for f in os.listdir(f"{output_dir}/{data_name}/")
            if f.endswith(".jsonl") and f.startswith(out_file_prefix)
        ]
        for f in processed_files:
            processed_samples.extend(
                list(load_jsonl(f"{output_dir}/{data_name}/{f}"))
            )

    # dedepulicate
    processed_samples = {sample["idx"]: sample for sample in processed_samples}
    processed_idxs = list(processed_samples.keys())
    processed_samples = list(processed_samples.values())
    examples = [example for example in examples if example["idx"] not in processed_idxs]
    return examples, processed_samples, out_file


def setup(args):
    # load model
    
    available_gpus = os.environ["CUDA_VISIBLE_DEVICES"].split(",")
    data_list = args.data_names.split(",")
    need_eval_data_list = []
    if not args.overwrite:
        for data_name in data_list:
            out_prefix = f"{args.split}_{args.prompt_type}_{args.num_test_sample}_seed{args.seed}_t{args.temperature}"
            out_file =  f"{args.output_dir}/{data_name}/{out_prefix}_s{args.start}_e{args.end}.jsonl"
            out_metric_json = out_file.replace(".jsonl", f"_metrics.json")
            
            if os.path.exists(out_metric_json):
                print(f"Skipping {data_name} because {out_metric_json} already exists.")
                continue
            else:
                need_eval_data_list.append(data_name)
    
        if len(need_eval_data_list) == 0:
            print("All datasets already evaluated. Exiting.")
            exit(0)
        data_list = need_eval_data_list
    
    if args.use_vllm:
        llm = LLM(
            model=args.model_name_or_path,
            tensor_parallel_size=len(available_gpus) // args.pipeline_parallel_size,
            pipeline_parallel_size=args.pipeline_parallel_size,
            trust_remote_code=True,
            seed=args.seed,
            gpu_memory_utilization=0.9,
            max_model_len=32768,
        )
        tokenizer = None
        if args.apply_chat_template:
            tokenizer = AutoTokenizer.from_pretrained(
                args.model_name_or_path, trust_remote_code=True
            )
    else:
        llm, tokenizer = load_hf_lm_and_tokenizer(
            model_name_or_path=args.model_name_or_path,
            load_in_half=True,
            use_fast_tokenizer=True,
            use_safetensors=args.use_safetensors,
        )

    # delete model_name_or_path
    os.system(f"rm -rf {args.model_name_or_path}")

    # infer & eval
    results = []
    for data_name in data_list:
        results.append(main(llm, tokenizer, data_name, args))

    # add "avg" result to data_list and results
    data_list.append("avg")
    results.append(
        {
            "acc": sum([result["acc"] for result in results]) / len(results),
        }
    )

    # print all results
    pad = max([len(data_name) for data_name in data_list])
    print("\t".join(data_name.ljust(pad, " ") for data_name in data_list))
    print("\t".join([f"{result['acc']:.1f}".ljust(pad, " ") for result in results]))


def is_multi_choice(answer):
    for c in answer:
        if c not in ["A", "B", "C", "D", "E"]:
            return False
    return True


def main(llm, tokenizer, data_name, args):
    azr = False
    print(f"Prompt .... args.prompt_type, {args.prompt_type}")
    if "azr" in args.prompt_type:
        print("AZRed.")
        azr = True
    
    examples, processed_samples, out_file = prepare_data(data_name, args)
    print("=" * 50)
    print("data:", data_name, " ,remain samples:", len(examples))
    # examples = examples[:1]
    if len(examples) > 0:
        print(examples[0])
    # init python executor
    if "pal" in args.prompt_type:
        executor = PythonExecutor(get_answer_expr="solution()")
    else:
        executor = PythonExecutor(get_answer_from_stdout=True)

    samples = []
    for example in tqdm(examples, total=len(examples)):
        idx = example["idx"]

        # parse question and answer
        example["question"] = parse_question(example, data_name)
        if example["question"] == "":
            continue
        gt_cot, gt_ans = parse_ground_truth(example, data_name)
        example["gt_ans"] = gt_ans
        full_prompt = construct_prompt(example, data_name, args)

        if idx == args.start:
            print(full_prompt)

        sample = {
            "idx": idx,
            "question": example["question"],
            "gt_cot": gt_cot,
            "gt": gt_ans,
            "prompt": full_prompt,
        }

        # add remain fields
        for key in [
            "level",
            "type",
            "unit",
            "solution_type",
            "choices",
            "solution",
            "ques_type",
            "ans_type",
            "answer_type",
            "dataset",
            "subfield",
            "filed",
            "theorem",
            "answer",
        ]:
            if key in example:
                sample[key] = example[key]
        samples.append(sample)

    # repeat n times
    input_prompts = [
        sample["prompt"] for sample in samples for _ in range(args.n_sampling)
    ]
    if args.apply_chat_template:
        input_prompts = [
            tokenizer.apply_chat_template(
                [{"role": "user", "content": prompt.strip()}],
                tokenize=False,
                add_generation_prompt=True,
            )
            for prompt in input_prompts
        ]
    remain_prompts = input_prompts
    remain_prompts = [(i, prompt) for i, prompt in enumerate(remain_prompts)]
    end_prompts = []

    max_func_call = 1 if args.prompt_type in ["cot", "pal"] else 4

    stop_words = ["</s>", "<|im_end|>", "<|endoftext|>"]

    if args.prompt_type in ["cot"]:
        stop_words.append("\n\nQuestion:")
    if args.prompt_type in ["pal", "tool-integrated", "jiuzhang_tora"]:
        stop_words.extend(["\n\n---", "```output"])
    elif args.prompt_type in ["wizard_zs", "platypus_fs"]:
        stop_words.extend(["Instruction", "Response"])
    elif "jiuzhang" in args.prompt_type:
        stop_words.append("\n\n## Question")
    elif "numina" in args.prompt_type:
        stop_words.append("\n### Problem")
    elif "pure" in args.prompt_type:
        stop_words.append("\n\n\n")
    elif "deepseek" in args.prompt_type:
        stop_words.extend(["\nProblem", "User:", "Assistant:", "</answer>", "</s>"])
    elif "qwen" in args.prompt_type:
        stop_words.extend(["assistant", "user", "_end", "_start"])
    elif "abel" in args.prompt_type:
        stop_words.extend(["Question:", "Answer:"])


    # start inference
    # measure time use
    start_time = time.time()
    for epoch in range(max_func_call):
        print("-" * 20, "Epoch", epoch)
        current_prompts = remain_prompts
        if len(current_prompts) == 0:
            break
        # assert False
        # if epoch > 0 :
        #     assert False
        # get all outputs
        prompts = [item[1] for item in current_prompts]
        # Initialize stop_token_ids based on the model name
        stop_token_ids = None
        if "mistral" in args.model_name_or_path.lower():
            if "24b" in args.model_name_or_path.lower():
                stop_token_ids = [23836, 19464, 3263, 18993]  # _end, istant, user, _start
            elif "7b-v0.1" in args.model_name_or_path.lower():
                stop_token_ids = [22478, 24994, 26307, 9977]
        elif "qwen2" in args.model_name_or_path.lower():
            stop_token_ids = [151645, 151643]  

        
        if args.use_vllm:
            outputs = llm.generate(
                prompts,
                SamplingParams(
                    temperature=args.temperature,
                    top_p=args.top_p,
                    max_tokens=args.max_tokens_per_call,
                    n=1,
                    stop=stop_words,
                    stop_token_ids=stop_token_ids,
                ),
            )

            outputs = sorted(
                outputs, key=lambda x: int(x.request_id)
            )  # sort outputs by request_id
            outputs = [
                (output.outputs[0].text, output.outputs[0].finish_reason)
                for output in outputs
            ]
        else:
            outputs_text = generate_completions(
                model=llm,
                tokenizer=tokenizer,
                prompts=prompts,
                max_new_tokens=args.max_tokens_per_call,
                batch_size=16,
                stop_id_sequences=stop_words,
            )
            # 对于非 vllm 模型，finish_reason 暂时设为 None
            outputs = [(text, None) for text in outputs_text]

        assert len(outputs) == len(current_prompts)

        # process all outputs
        remain_prompts = []
        remain_codes = []
        for (i, query), (output, finish_reason) in zip(current_prompts, outputs):
            output = output.rstrip()
            query += output
            if args.prompt_type == "pal":
                remain_prompts.append((i, query, finish_reason))
                if "```python" in output:
                    output = extract_program(query)
                remain_codes.append(output)
            elif args.prompt_type == "cot":
                end_prompts.append((i, query, finish_reason))
            # elif "boxed" not in output and output.endswith("```"):
            #     program = extract_program(query)
            #     remain_prompts.append((i, query))
            #     remain_codes.append(program)
            else:
                end_prompts.append((i, query, finish_reason))

        # execute the remain prompts
        # assert len(remain_codes)==0
        remain_results = executor.batch_apply(remain_codes)
        for k in range(len(remain_prompts)):
            # assert False
            i, query, finish_reason = remain_prompts[k]
            res, report = remain_results[k]
            exec_result = res if res else report
            if "pal" in args.prompt_type:
                exec_result = "\\boxed{" + exec_result + "}"
            exec_result = f"\n```output\n{exec_result}\n```\n"
            query += exec_result
            # not end
            if epoch == max_func_call - 1:
                query += "\nReach max function call limit."
            remain_prompts[k] = (i, query,finish_reason)

    # unsolved samples
    print("Unsolved samples:", len(remain_prompts))
    end_prompts.extend(remain_prompts)
    # sort by idx
    end_prompts = sorted(end_prompts, key=lambda x: x[0])

    # remove input_prompt from end_prompt
    codes = []
    finish_reasons = []
    assert len(input_prompts) == len(end_prompts)
    for i in range(len(input_prompts)):
        _, end_prompt, finish_reason = end_prompts[i]
        code = end_prompt.split(input_prompts[i])[-1].strip()
        for stop_word in stop_words:
            if stop_word in code:
                code = code.split(stop_word)[0].strip()
        codes.append(code)
        finish_reasons.append(finish_reason)

    # extract preds
    results = [
        run_execute(executor, code, args.prompt_type, data_name, azr=azr) for code in codes
    ]
    time_use = time.time() - start_time

    # put results back to examples
    all_samples = []
    for i, sample in enumerate(samples):
        code = codes[i * args.n_sampling : (i + 1) * args.n_sampling]
        result = results[i * args.n_sampling : (i + 1) * args.n_sampling]
        preds = [item[0] for item in result]
        reports = [item[1] for item in result]
        finish_reason_list = finish_reasons[i * args.n_sampling : (i + 1) * args.n_sampling]
        for j in range(len(preds)):
            if sample["gt"] in ["A", "B", "C", "D", "E"] and preds[j] not in [
                "A",
                "B",
                "C",
                "D",
                "E",
            ]:
                preds[j] = choice_answer_clean(code[j])
            elif is_multi_choice(sample["gt"]) and not is_multi_choice(preds[j]):
                # remove any non-choice char
                preds[j] = "".join(
                    [c for c in preds[j] if c in ["A", "B", "C", "D", "E"]]
                )

        sample.pop("prompt")
        sample.update({"code": code, "pred": preds, "report": reports, "finish_reason": finish_reason_list  })
        all_samples.append(sample)

    # add processed samples
    all_samples.extend(processed_samples)
    all_samples, result_json = evaluate(
        samples=all_samples,
        data_name=data_name,
        prompt_type=args.prompt_type,
        execute=True,
    )

    # save outputs
    if len(processed_samples) < len(all_samples) and args.save_outputs:
        save_jsonl(all_samples, out_file)

    result_json["time_use_in_second"] = time_use
    result_json["time_use_in_minite"] = (
        f"{int(time_use // 60)}:{int(time_use % 60):02d}"
    )

    with open(
        out_file.replace(".jsonl", f"_metrics.json"), "w"
    ) as f:
        json.dump(result_json, f, indent=4)
    return result_json


if __name__ == "__main__":
    args = parse_args()
    set_seed(args.seed)
    setup(args)