wehub-resource-sync
406 lines
14 KiB
Python
Executable File
406 lines
14 KiB
Python
Executable File
import random
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import os
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import argparse
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import time
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from vllm import LLM, SamplingParams
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from datetime import datetime
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from tqdm import tqdm
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from evaluate import evaluate
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from utils import set_seed, load_jsonl, save_jsonl, construct_prompt
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from parser import *
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from trajectory import *
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from data_loader import load_data
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from python_executor import PythonExecutor
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from model_utils import load_hf_lm_and_tokenizer, generate_completions
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def parse_args():
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parser = argparse.ArgumentParser()
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parser.add_argument("--data_names", default="gsm8k,math", type=str)
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parser.add_argument("--data_dir", default="./data", type=str)
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parser.add_argument("--model_name_or_path", default="gpt-4", type=str)
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parser.add_argument("--output_dir", default="./output", type=str)
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parser.add_argument("--prompt_type", default="tool-integrated", type=str)
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parser.add_argument("--split", default="test", type=str)
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parser.add_argument("--num_test_sample", default=-1, type=int) # -1 for full data
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parser.add_argument("--seed", default=0, type=int)
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parser.add_argument("--start", default=0, type=int)
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parser.add_argument("--end", default=-1, type=int)
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parser.add_argument("--temperature", default=0, type=float)
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parser.add_argument("--n_sampling", default=1, type=int)
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parser.add_argument("--top_p", default=1, type=float)
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parser.add_argument("--max_tokens_per_call", default=2048, type=int)
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parser.add_argument("--shuffle", action="store_true")
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parser.add_argument("--use_vllm", action="store_true")
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parser.add_argument("--save_outputs", action="store_true")
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parser.add_argument("--overwrite", action="store_true")
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parser.add_argument("--use_safetensors", action="store_true")
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parser.add_argument("--num_shots", type=int, default=0)
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parser.add_argument(
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"--apply_chat_template",
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action="store_true",
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help="Apply chat template to prompt.",
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)
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parser.add_argument("--pipeline_parallel_size", type=int, default=1)
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parser.add_argument(
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"--adapt_few_shot",
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action="store_true",
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help="Few shot for multiple-choice questions, zero shot for others.",
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)
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args = parser.parse_args()
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args.top_p = (
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1 if args.temperature == 0 else args.top_p
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) # top_p must be 1 when using greedy sampling (vllm)
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return args
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def prepare_data(data_name, args):
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examples = load_data(data_name, args.split, args.data_dir)
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# sample `num_test_sample` from dataset
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if args.num_test_sample > 0:
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# examples = random.sample(examples, min(args.num_test_sample, len(examples)))
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examples = examples[: args.num_test_sample]
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# shuffle
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if args.shuffle:
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random.seed(datetime.now().timestamp())
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random.shuffle(examples)
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# select start and end
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examples = examples[args.start : len(examples) if args.end == -1 else args.end]
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# get out_file name
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dt_string = datetime.now().strftime("%m-%d_%H-%M")
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model_name = "/".join(args.model_name_or_path.split("/")[-2:])
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out_file_prefix = f"{args.split}_{args.prompt_type}_{args.num_test_sample}_seed{args.seed}_t{args.temperature}"
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output_dir = args.output_dir
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if not os.path.exists(output_dir):
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output_dir = f"outputs/{output_dir}"
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out_file = f"{output_dir}/{data_name}/{out_file_prefix}_s{args.start}_e{args.end}.jsonl"
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os.makedirs(f"{output_dir}/{data_name}", exist_ok=True)
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# load all processed samples
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processed_samples = []
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if not args.overwrite:
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processed_files = [
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f
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for f in os.listdir(f"{output_dir}/{data_name}/")
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if f.endswith(".jsonl") and f.startswith(out_file_prefix)
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]
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for f in processed_files:
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processed_samples.extend(
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list(load_jsonl(f"{output_dir}/{data_name}/{f}"))
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)
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# dedepulicate
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processed_samples = {sample["idx"]: sample for sample in processed_samples}
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processed_idxs = list(processed_samples.keys())
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processed_samples = list(processed_samples.values())
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examples = [example for example in examples if example["idx"] not in processed_idxs]
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return examples, processed_samples, out_file
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def setup(args):
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# load model
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available_gpus = os.environ["CUDA_VISIBLE_DEVICES"].split(",")
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if args.use_vllm:
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llm = LLM(
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model=args.model_name_or_path,
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tensor_parallel_size=len(available_gpus) // args.pipeline_parallel_size,
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pipeline_parallel_size=args.pipeline_parallel_size,
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trust_remote_code=True,
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)
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tokenizer = None
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if args.apply_chat_template:
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tokenizer = AutoTokenizer.from_pretrained(
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args.model_name_or_path, trust_remote_code=True
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)
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else:
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llm, tokenizer = load_hf_lm_and_tokenizer(
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model_name_or_path=args.model_name_or_path,
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load_in_half=True,
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use_fast_tokenizer=True,
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use_safetensors=args.use_safetensors,
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)
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# infer & eval
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data_list = args.data_names.split(",")
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results = []
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for data_name in data_list:
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results.append(main(llm, tokenizer, data_name, args))
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# add "avg" result to data_list and results
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data_list.append("avg")
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results.append(
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{
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"acc": sum([result["acc"] for result in results]) / len(results),
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}
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)
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# print all results
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pad = max([len(data_name) for data_name in data_list])
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print("\t".join(data_name.ljust(pad, " ") for data_name in data_list))
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print("\t".join([f"{result['acc']:.1f}".ljust(pad, " ") for result in results]))
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def is_multi_choice(answer):
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for c in answer:
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if c not in ["A", "B", "C", "D", "E"]:
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return False
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return True
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def main(llm, tokenizer, data_name, args):
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examples, processed_samples, out_file = prepare_data(data_name, args)
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print("=" * 50)
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print("data:", data_name, " ,remain samples:", len(examples))
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if len(examples) > 0:
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print(examples[0])
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# init python executor
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if "pal" in args.prompt_type:
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executor = PythonExecutor(get_answer_expr="solution()")
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else:
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executor = PythonExecutor(get_answer_from_stdout=True)
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samples = []
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for example in tqdm(examples, total=len(examples)):
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idx = example["idx"]
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# parse question and answer
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example["question"] = parse_question(example, data_name)
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if example["question"] == "":
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continue
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gt_cot, gt_ans = parse_ground_truth(example, data_name)
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example["gt_ans"] = gt_ans
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full_prompt = construct_prompt(example, data_name, args)
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if idx == args.start:
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print(full_prompt)
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sample = {
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"idx": idx,
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"question": example["question"],
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"gt_cot": gt_cot,
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"gt": gt_ans,
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"prompt": full_prompt,
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}
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# add remain fields
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for key in [
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"level",
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"type",
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"unit",
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"solution_type",
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"choices",
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"solution",
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"ques_type",
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"ans_type",
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"answer_type",
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"dataset",
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"subfield",
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"filed",
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"theorem",
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"answer",
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]:
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if key in example:
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sample[key] = example[key]
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samples.append(sample)
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# repeat n times
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input_prompts = [
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sample["prompt"] for sample in samples for _ in range(args.n_sampling)
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]
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if args.apply_chat_template:
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input_prompts = [
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tokenizer.apply_chat_template(
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[{"role": "user", "content": prompt.strip()}],
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tokenize=False,
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add_generation_prompt=True,
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)
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for prompt in input_prompts
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]
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remain_prompts = input_prompts
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remain_prompts = [(i, prompt) for i, prompt in enumerate(remain_prompts)]
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end_prompts = []
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max_func_call = 1 if args.prompt_type in ["cot", "pal"] else 4
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stop_words = ["</s>", "<|im_end|>", "<|endoftext|>"]
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if args.prompt_type in ["cot"]:
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stop_words.append("\n\nQuestion:")
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if args.prompt_type in ["pal", "tool-integrated", "jiuzhang_tora"]:
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stop_words.extend(["\n\n---", "```output"])
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elif args.prompt_type in ["wizard_zs", "platypus_fs"]:
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stop_words.extend(["Instruction", "Response"])
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elif "jiuzhang" in args.prompt_type:
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stop_words.append("\n\n## Question")
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elif "numina" in args.prompt_type:
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stop_words.append("\n### Problem")
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elif "pure" in args.prompt_type:
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stop_words.append("\n\n\n")
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# start inference
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# measure time use
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start_time = time.time()
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for epoch in range(max_func_call):
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print("-" * 20, "Epoch", epoch)
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current_prompts = remain_prompts
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if len(current_prompts) == 0:
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break
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# get all outputs
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prompts = [item[1] for item in current_prompts]
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if args.use_vllm:
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outputs = llm.generate(
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prompts,
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SamplingParams(
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temperature=args.temperature,
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top_p=args.top_p,
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max_tokens=args.max_tokens_per_call,
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n=1,
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stop=stop_words,
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stop_token_ids=(
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[151645, 151643]
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if "qwen2" in args.model_name_or_path.lower()
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else None
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),
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),
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)
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outputs = sorted(
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outputs, key=lambda x: int(x.request_id)
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) # sort outputs by request_id
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outputs = [output.outputs[0].text for output in outputs]
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else:
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outputs = generate_completions(
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model=llm,
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tokenizer=tokenizer,
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prompts=prompts,
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max_new_tokens=args.max_tokens_per_call,
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batch_size=16,
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stop_id_sequences=stop_words,
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)
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assert len(outputs) == len(current_prompts)
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# process all outputs
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remain_prompts = []
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remain_codes = []
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for (i, query), output in zip(current_prompts, outputs):
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output = output.rstrip()
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query += output
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if args.prompt_type == "pal":
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remain_prompts.append((i, query))
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if "```python" in output:
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output = extract_program(query)
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remain_codes.append(output)
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elif args.prompt_type == "cot":
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end_prompts.append((i, query))
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elif "boxed" not in output and output.endswith("```"):
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program = extract_program(query)
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remain_prompts.append((i, query))
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remain_codes.append(program)
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else:
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end_prompts.append((i, query))
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# execute the remain prompts
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remain_results = executor.batch_apply(remain_codes)
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for k in range(len(remain_prompts)):
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i, query = remain_prompts[k]
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res, report = remain_results[k]
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exec_result = res if res else report
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if "pal" in args.prompt_type:
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exec_result = "\\boxed{" + exec_result + "}"
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exec_result = f"\n```output\n{exec_result}\n```\n"
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query += exec_result
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# not end
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if epoch == max_func_call - 1:
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query += "\nReach max function call limit."
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remain_prompts[k] = (i, query)
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# unsolved samples
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print("Unsolved samples:", len(remain_prompts))
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end_prompts.extend(remain_prompts)
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# sort by idx
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end_prompts = sorted(end_prompts, key=lambda x: x[0])
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# remove input_prompt from end_prompt
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codes = []
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assert len(input_prompts) == len(end_prompts)
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for i in range(len(input_prompts)):
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_, end_prompt = end_prompts[i]
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code = end_prompt.split(input_prompts[i])[-1].strip()
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for stop_word in stop_words:
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if stop_word in code:
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code = code.split(stop_word)[0].strip()
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codes.append(code)
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# extract preds
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results = [
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run_execute(executor, code, args.prompt_type, data_name) for code in codes
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]
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time_use = time.time() - start_time
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# put results back to examples
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all_samples = []
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for i, sample in enumerate(samples):
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code = codes[i * args.n_sampling : (i + 1) * args.n_sampling]
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result = results[i * args.n_sampling : (i + 1) * args.n_sampling]
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preds = [item[0] for item in result]
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reports = [item[1] for item in result]
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for j in range(len(preds)):
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if sample["gt"] in ["A", "B", "C", "D", "E"] and preds[j] not in [
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"A",
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"B",
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"C",
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"D",
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"E",
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]:
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preds[j] = choice_answer_clean(code[j])
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elif is_multi_choice(sample["gt"]) and not is_multi_choice(preds[j]):
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# remove any non-choice char
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preds[j] = "".join(
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[c for c in preds[j] if c in ["A", "B", "C", "D", "E"]]
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)
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sample.pop("prompt")
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sample.update({"code": code, "pred": preds, "report": reports})
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all_samples.append(sample)
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# add processed samples
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all_samples.extend(processed_samples)
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all_samples, result_json = evaluate(
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samples=all_samples,
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data_name=data_name,
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prompt_type=args.prompt_type,
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execute=True,
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)
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# save outputs
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if len(processed_samples) < len(all_samples) and args.save_outputs:
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save_jsonl(all_samples, out_file)
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result_json["time_use_in_second"] = time_use
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result_json["time_use_in_minite"] = (
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f"{int(time_use // 60)}:{int(time_use % 60):02d}"
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)
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with open(
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out_file.replace(".jsonl", f"_{args.prompt_type}_metrics.json"), "w"
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) as f:
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json.dump(result_json, f, indent=4)
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return result_json
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if __name__ == "__main__":
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args = parse_args()
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set_seed(args.seed)
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setup(args)
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