File size: 45,337 Bytes
a80200a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 |
import random
import os
import argparse
import time
from vllm import LLM, SamplingParams
from datetime import datetime
from tqdm import tqdm
from transformers import AutoTokenizer, AutoModelForCausalLM
from eval_tools import apply_RL_prompt, solve_final_answer
from numba import cuda
import sys
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
import logging
if not os.path.exists(f'{os.environ["modelname"]}'):
os.mkdir(f'{os.environ["modelname"]}')
if not os.path.exists(f'{os.environ["model"]}'):
os.mkdir(f'{os.environ["model"]}')
DATA_NAME = os.environ["DATA_NAME"]
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
datefmt='%Y-%m-%d %H:%M:%S', filename=f'{os.environ["model"]}/{os.environ["mode"]}-{DATA_NAME}.log', filemode='a')
print(f"logging in {os.environ['model']}/{os.environ['mode']}-{DATA_NAME}.log")
logging.info(f"modelname's infor: {os.environ['modelname']}")
logging.info(f"mode's infor: {os.environ['mode']}")
logging.info(f"model's infor: {os.environ['model']}")
with open('./special_tokens.json') as f:
special_tokens = json.load(f)
bins_tokens = [
special_tokens[f"{i}"] for i in range(400)
]
import os
import multiprocessing
from multiprocessing import Process, Manager, Queue
from transformers import AutoTokenizer
from vllm import LLM, SamplingParams
import fcntl
import torch
def process_chunk(chunk, args, available_gpus, stop_words, output_queue, index, two_stage=False):
"""
处理一个数据块的函数
"""
GPU_LOCK_FILES = {gpu_id: f"/tmp/gpu_{gpu_id}.lock" for gpu_id in available_gpus}
# 设置当前进程使用的GPU
os.environ["CUDA_VISIBLE_DEVICES"] = str(available_gpus[index % len(available_gpus)])
gpu_id = available_gpus[index % len(available_gpus)]
lock_file = f"/tmp/gpu_{gpu_id}.lock" # 每个 GPU 对应一个锁文件
with open(lock_file, 'w') as f:
fcntl.flock(f, fcntl.LOCK_EX)
try:
os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_id)
# torch.cuda.synchronize()
# torch.cuda.empty_cache()
# torch.cuda.reset_peak_memory_stats()
# torch.cuda.synchronize()
# # 使用 numba.cuda 强制释放 CUDA 上下文
# cuda.close()
# cuda.select_device(0) # 重新选择设备
# torch.cuda.init()
# # 重置 GPU 状态
# for i in range(torch.cuda.device_count()):
# torch.cuda.set_device(i)
# torch.cuda.ipc_collect()
# torch.cuda.empty_cache()
llm = LLM(
model=args.model_name_or_path,
tensor_parallel_size=1,
pipeline_parallel_size=1,
trust_remote_code=True,
gpu_memory_utilization=0.85, # 降低利用率以提供更多缓冲
# gpu_memory_utilization=0.6, # 降低利用率以提供更多缓冲
enforce_eager=True,
max_seq_len_to_capture=65536,
)
chunk_outputs = []
for i in range(0, len(chunk), 200):
chunk_batch = chunk[i:i + 200]
if args.use_vllm:
if os.environ['stage'] == "add":
budget = args.max_tokens_per_call + (args.max_tokens_per_call // 50) + 50
else:
budget = args.max_tokens_per_call + (args.max_tokens_per_call // 50)
os.environ["position"] = 'start'
chunk_batch_outputs = llm.generate(
chunk_batch,
SamplingParams(
temperature=args.temperature,
# top_p=1,
top_p=0.9,
max_tokens=budget if not two_stage else 20,
n=1,
stop=stop_words,
stop_token_ids=(
[151645, 151643]
if "qwen2" in args.model_name_or_path.lower()
else None
),
skip_special_tokens=False,
),
)
torch.cuda.synchronize()
if os.path.exists('./start_positions.pt'):
os.remove('./start_positions.pt')
if os.path.exists('./early_positions.pt'):
os.remove('./early_positions.pt')
chunk_batch_outputs = sorted(chunk_batch_outputs, key=lambda x: int(x.request_id))
chunk_batch_outputs = [output.outputs[0].text for output in chunk_batch_outputs]
batch_chunk = [single_chunk + chunk_output for single_chunk, chunk_output in zip(chunk_batch, chunk_batch_outputs)]
chunk_outputs.extend(batch_chunk)
# 处理完成后，将结果存入队列
output_queue.put((index, chunk_outputs))
del llm
torch.cuda.empty_cache()
finally:
# 显式销毁 llm 对象
if 'llm' in locals():
del llm # 确保 LLM 引擎被销毁
# torch.cuda.empty_cache() # 清理 GPU 内存
# 释放锁（确保即使发生异常也能释放）
torch.cuda.empty_cache()
torch.cuda.synchronize()
fcntl.flock(f, fcntl.LOCK_UN)
sys.exit(0)
def clean_code(code):
for bin_token in bins_tokens:
if bin_token in code:
code = code.replace(bin_token, "")
return code
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--ratio", type=float, default=-1, help="ratio of cot to use for generation")
parser.add_argument("--data_names", default="math", type=str)
parser.add_argument("--data_dir", default="./data", type=str)
parser.add_argument("--model_name_or_path", default="Qwen/QwQ-32B-Preview", type=str)
parser.add_argument("--output_dir", default="Qwen/QwQ-32B-Preview/math_eval", type=str)
parser.add_argument("--prompt_type", default="qwen25-math-cot", 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=4096, 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)
# if args.ratio > 0:
# args.max_tokens_per_call = 50
return args
def set_output_path(args, data_name):
# args.output_dir defines experiment path,such as outputs/12_25
model_name_list = args.model_name_or_path.split('/')[-1]
model_name = model_name_list
for part in model_name_list:
if 'models' in part:
model_name = part
# print(f"args.output_dir: {args.output_dir}")
# print(f"model_name: {model_name}")
# print(f"args.prompt_type: {args.prompt_type}")
output_dir = os.path.join(args.output_dir, model_name, args.prompt_type)
out_file_prefix = f"{args.split}_{args.prompt_type}_{args.num_test_sample}_seed{args.seed}_t{args.temperature}"
out_file = f"{output_dir}/{data_name}/{out_file_prefix}_s{args.start}_e{args.end}_b{int(args.max_tokens_per_call)}_original.jsonl"
print(out_file)
os.makedirs(f"{output_dir}/{data_name}", exist_ok=True)
return out_file_prefix, output_dir, out_file
def prepare_data(data_name, args):
examples = load_data(data_name, args.split, args.data_dir)
# sample `num_test_sample` from dataset， -1 for full data
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:])
# get out_file_prefix, output_dir and out_file
out_file_prefix, output_dir, out_file = set_output_path(args, data_name)
# 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(",")
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,
# gpu_memory_utilization=0.85,
# enforce_eager=True,
# max_seq_len_to_capture=5000000,
# # enable_flash_attn=True
# )
llm = None
tokenizer = None
# if args.apply_chat_template:
tokenizer = AutoTokenizer.from_pretrained(
args.model_name_or_path, trust_remote_code=True, max_length=16000,
)
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,
)
# infer & eval
data_list = args.data_names.split(",")
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),
"simple_acc": sum([result["simple_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]))
logging.info("\t".join(data_name.ljust(pad, " ") for data_name in data_list))
logging.info(f"os.environ['PE_MODE'] = {os.environ['PE_MODE']}")
logging.info(f"path = {args.model_name_or_path}")
logging.info(f"tip = {os.environ['tip']}")
logging.info(f"BUDGET = {os.environ['BUDGET']}")
logging.info("\t".join([f"{result['acc']:.5f}".ljust(pad, " ") for result in results]))
logging.info("!!!simple_acc!!!")
logging.info("\t".join([f"{result['simple_acc']:.5f}".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):
examples, processed_samples, out_file = prepare_data(data_name, args)
print(examples[0])
print("\n" + "-" * 50)
print("data:", data_name, ", remain samples:", len(examples))
if len(examples) > 0:
print(examples[0])
available_gpus = os.environ["CUDA_VISIBLE_DEVICES"].split(",")
print(f"available_gpus = {available_gpus}")
# init python executor
if "pal" in args.prompt_type:
executor = PythonExecutor(get_answer_expr="solution()")
else:
executor = PythonExecutor(get_answer_from_stdout=True)
# load done samples
if args.ratio > 0 :
done_samples_path = out_file.replace("_r" + str(args.ratio), "")
done_samples = list(load_jsonl(done_samples_path))
else:
done_samples = []
done_samples = {sample["idx"]: sample for sample in done_samples}
samples = []
print("\nProcessing", len(examples), "examples", "=" * 50)
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)
# # add ratio part of complete cot
if args.ratio > 0 :
done_cot = done_samples[idx]["code"][0]
cut_cot = done_cot[:int(len(done_cot)*args.ratio)]
# # 将prompt中的<|im_start|>assistant\n换成新内容
# full_prompt = full_prompt.replace("<|im_start|>assistant\n", "<|im_start|>assistant\n" + cut_cot + "\n\nFinal answer within \\boxed{{}}:\n")
# 直接在prompt的后面添加新内容
full_prompt = full_prompt + cut_cot + "\n\nFinal answer within \\boxed{{}}:\n"
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)]
input_prompts = apply_RL_prompt(input_prompts, args, budget = args.max_tokens_per_call)
# print(input_prompts)
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", "qwen25-math-cot"] else 4
stop_words = ["</s>", "<|im_end|>", "<|endoftext|>", "<｜end▁of▁sentence｜>"]
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")
# start inference
# measure time use
start_time = time.time()
print(f"start_time: {start_time}")
for epoch in range(max_func_call):
print("-" * 20, "Epoch", epoch)
current_prompts = remain_prompts
if len(current_prompts) == 0:
break
prompts = [item[1] for item in current_prompts]
# prompts = apply_RL_prompt(prompts, args, budget = args.max_tokens_per_call)
num_prompts = len(prompts)
chunk_size = 256
#(num_prompts + 4) // 5 # 确保包含所有的 prompts
outputs = []
if os.environ['tip'] == "remaining" or os.environ['tip'] == "ATD_R":
for i in range(0, num_prompts, chunk_size):
# print(prompts[i])
chunk = prompts[i:i + chunk_size] # 获取当前的 chunk
if args.use_vllm:
budget = args.max_tokens_per_call
i = 0
while 50*(2**i) < budget:
i += 1
i -= 1
for k in range(i, -2, -1):
stop_budget = budget - 50*(2**k) if k >= 0 else 50
# print(f"stop_budget: {stop_budget}")
# chunk = [data + f"\n<remaining>[{budget} token]</remaining>\n" for data in chunk]
if budget == args.max_tokens_per_call:
chunk = chunk
else:
# "<｜end▁of▁sentence｜>"
chunk = [data + f"\n<remaining>{budget}</remaining>\n" if "<｜end▁of▁sentence｜>" not in data else data for data in chunk]
print(f"chunk0: {chunk[0]}")
if stop_budget > 0:
chunk_outputs = llm.generate(
chunk,
SamplingParams(
temperature=args.temperature,
top_p=args.top_p,
max_tokens=stop_budget,
n=1,
stop=stop_words,
stop_token_ids=(
[151645, 151643]
if "qwen2" in args.model_name_or_path.lower()
else None
),
skip_special_tokens=False, #G 设置特殊token的可见性
),
)
if os.path.exists('./start_positions.pt'):
os.remove('./start_positions.pt')
print('start_positions.pt removed')
if os.path.exists('./early_positions.pt'):
os.remove('./early_positions.pt')
print('early_positions.pt removed')
chunk_outputs = sorted(chunk_outputs, key=lambda x: int(x.request_id))
chunk_outputs = [output.outputs[0].text for output in chunk_outputs]
### 输出已经被整理过了，不需要再进行排序
chunk = [single_chunk + chunk_output for single_chunk, chunk_output in zip(chunk, chunk_outputs)]
budget = 50*(2**k) if k >= 0 else 0
chunk, end_chunk, open_chunk = solve_final_answer(chunk)
print(f"len of end_chunk: {len(end_chunk)}")
print(f"len of open_chunk: {len(open_chunk)}")
# outputs.extend(end_chunk)
# chunk = open_chunk
# print(f"now budget: {budget}")
# print(f"k = {k}")
chunk_outputs = chunk
outputs.extend(chunk_outputs)
# chunk_outputs = sorted(chunk, key=lambda x: int(x.request_id))
# initial_outputs = [output.outputs[0].text for output in chunk_outputs]
# Add the think/final answer tags and create new prompts
# modified_outputs = []
# for output in chunk_outputs:
# modified_output = output.rstrip() + "</think>\n\n**Final Answer**\n\\boxed"
# modified_outputs.append(modified_output)
# # Second generation with modified outputs
# second_prompts = [p + mo for p, mo in zip(chunk, modified_outputs)]
# second_outputs = llm.generate(
# second_prompts,
# SamplingParams(
# temperature=args.temperature,
# top_p=args.top_p,
# max_tokens=20,
# n=1,
# stop=stop_words,
# stop_token_ids=(
# [151645, 151643]
# if "qwen2" in args.model_name_or_path.lower()
# else None
# ),
# ),
# )
# if os.path.exists('./start_positions.pt'):
# os.remove('./start_positions.pt')
# print('start_positions.pt removed')
# if os.path.exists('./early_positions.pt'):
# os.remove('./early_positions.pt')
# print('early_positions.pt removed')
# second_outputs = sorted(second_outputs, key=lambda x: int(x.request_id))
# second_outputs = [output.outputs[0].text for output in second_outputs]
# # Combine initial and second outputs
# combined_outputs = [init + "</think>\n\n**Final Answer**\n\\boxed" + second for init, second in zip(second_prompts, second_outputs)]
# outputs.extend(combined_outputs)
else:
# Similar modification for non-vllm case
chunk_outputs = generate_completions(
model=llm,
tokenizer=tokenizer,
prompts=chunk,
max_new_tokens=args.max_tokens_per_call,
batch_size=16,
stop_id_sequences=stop_words,
)
# Add the think/final answer tags and create new prompts
modified_outputs = []
for output in chunk_outputs:
modified_output = output.rstrip() + "\n</think>\n\n**Final Answer**\n\\boxed"
modified_outputs.append(modified_output)
# Second generation with modified outputs
second_prompts = [p + mo for p, mo in zip(chunk, modified_outputs)]
second_outputs = generate_completions(
model=llm,
tokenizer=tokenizer,
prompts=second_prompts,
max_new_tokens=args.max_tokens_per_call,
batch_size=16,
stop_id_sequences=stop_words,
)
# Combine initial and second outputs
combined_outputs = [init + second for init, second in zip(chunk_outputs, second_outputs)]
outputs.extend(combined_outputs)
elif os.environ["tip"] == "TCM":
for i in range(0, num_prompts, chunk_size):
chunk = prompts[i:i + chunk_size] # 获取当前的 chunk
if args.use_vllm:
budget = args.max_tokens_per_call
i = budget // 50 + 1
for k in reversed(range(i)):
stop_budget = budget - 50 * k
if budget == args.max_tokens_per_call:
chunk = chunk
else:
chunk = [data + f"\n<remaining>{budget}</remaining>\n" if "<｜end▁of▁sentence｜>" not in data else data for data in chunk]
print(f"chunk0: {chunk[0]}")
if stop_budget > 0:
chunk_outputs = llm.generate(
chunk,
SamplingParams(
temperature=args.temperature,
top_p=args.top_p,
max_tokens=stop_budget,
n=1,
stop=stop_words,
stop_token_ids=(
[151645, 151643]
if "qwen2" in args.model_name_or_path.lower()
else None
),
skip_special_tokens=False, #G 设置特殊token的可见性
),
)
if os.path.exists('./start_positions.pt'):
os.remove('./start_positions.pt')
print('start_positions.pt removed')
if os.path.exists('./early_positions.pt'):
os.remove('./early_positions.pt')
print('early_positions.pt removed')
chunk_outputs = sorted(chunk_outputs, key=lambda x: int(x.request_id))
chunk_outputs = [output.outputs[0].text for output in chunk_outputs]
### 输出已经被整理过了，不需要再进行排序
chunk = [single_chunk + chunk_output for single_chunk, chunk_output in zip(chunk, chunk_outputs)]
budget = 50 * k if k >= 0 else 0
chunk, end_chunk, open_chunk = solve_final_answer(chunk)
print(f"len of end_chunk: {len(end_chunk)}")
print(f"len of open_chunk: {len(open_chunk)}")
print(F"len of chunk: {len(chunk)}s")
# outputs.extend(end_chunk)
# chunk = open_chunk
chunk_outputs = chunk
outputs.extend(chunk_outputs)
else:
raise(ValueError("Not implemented for non-vllm mode while tip == TCM"))
elif os.environ["tip"] == "SST":
for i in range(0, num_prompts, chunk_size):
chunk = prompts[i:i + chunk_size] # 获取当前的 chunk
if args.use_vllm:
budget = args.max_tokens_per_call
i = budget // 50 + 1
for k in reversed(range(i)):
stop_budget = budget - 50 * k
if budget == args.max_tokens_per_call:
chunk = chunk
else:
chunk = [data + f"\n<countdown>\n" if "<｜end▁of▁sentence｜>" not in data else data for data in chunk]
print(f"chunk0: {chunk[0]}")
if stop_budget > 0:
chunk_outputs = llm.generate(
chunk,
SamplingParams(
temperature=args.temperature,
top_p=args.top_p,
max_tokens=stop_budget,
n=1,
stop=stop_words,
stop_token_ids=(
[151645, 151643]
if "qwen2" in args.model_name_or_path.lower()
else None
),
),
)
if os.path.exists('./start_positions.pt'):
os.remove('./start_positions.pt')
print('start_positions.pt removed')
if os.path.exists('./early_positions.pt'):
os.remove('./early_positions.pt')
print('early_positions.pt removed')
chunk_outputs = sorted(chunk_outputs, key=lambda x: int(x.request_id))
chunk_outputs = [output.outputs[0].text for output in chunk_outputs]
### 输出已经被整理过了，不需要再进行排序
chunk = [single_chunk + chunk_output for single_chunk, chunk_output in zip(chunk, chunk_outputs)]
budget = 50 * k if k >= 0 else 0
chunk, end_chunk, open_chunk = solve_final_answer(chunk)
print(f"len of end_chunk: {len(end_chunk)}")
print(f"len of open_chunk: {len(open_chunk)}")
print(F"len of chunk: {len(chunk)}s")
# outputs.extend(end_chunk)
# chunk = open_chunk
chunk_outputs = chunk
outputs.extend(chunk_outputs)
else:
raise(ValueError("Not implemented for non-vllm mode while tip == TTS"))
# elif os.environ["tip"] == "TCMv2":
else:
available_gpus = os.environ["CUDA_VISIBLE_DEVICES"].split(",") # 示例GPU列表，根据实际情况修改
num_gpus = len(available_gpus)
manager = Manager()
output_queue = Queue()
processes = []
multi_outputs = []
chunk_size = len(prompts) // num_gpus + 1
chunks = [prompts[i:i + chunk_size] for i in range(0, len(prompts), chunk_size)]
num_rounds = (len(chunks) + num_gpus - 1) // num_gpus #g 轮次数等于最大GPU上的任务数
for round_idx in range(num_rounds):
start_idx = round_idx * num_gpus
end_idx = min((round_idx + 1) * num_gpus, len(chunks))
for i in range(start_idx, end_idx):
chunk = chunks[i]
p = Process(target=process_chunk, args=(chunk, args, available_gpus, stop_words, output_queue, i))
processes.append(p)
p.start()
# for i, chunk in enumerate(chunks):
# print(f"Processing chunk {i} with size {len(chunk)}")
# p = Process(target=process_chunk, args=(chunk, args, available_gpus, stop_words, output_queue, i))
# processes.append(p)
# p.start()
for _ in range(len(chunks)):
result = output_queue.get()
if isinstance(result, tuple) and len(result) == 2:
multi_outputs.append(result)
else:
print(f"Error: Received non-tuple result: {result}")
# multi_outputs.extend(output_queue.get())
for p in processes:
p.join()
multi_outputs.sort(key=lambda x: x[0])
outputs = []
for _, chunk_output in multi_outputs:
outputs.extend(chunk_output)
print('stage one finished!!!\n' * 20)
# print("Special tokens in tokenizer:", tokenizer.special_tokens_map)
# test_token = "\n<remaining>50</remaining>\n"
# print(f"Encoding '{test_token}':", tokenizer.encode(test_token, add_special_tokens=False))
print(outputs[:3])
#################!
###! stage? 1 or 2 or add
if os.environ['stage'] == "2":
print("stage 2")
two_stage_outputs = []
modified_outputs = []
print(f"len of outputs: {len(outputs)}")
for output in outputs:
# 去除output字符串末尾的换行符，并添加</think>和**Final Answer**\n\\boxed字符串，将结果添加到modified_outputs列表中
if "<｜end▁of▁sentence｜>" in output:
start_index = output.index("<｜end▁of▁sentence｜>")
output = output[:start_index]
# output = output.replace("<｜end▁of▁sentence｜>", "")
modified_output = output + "\n</think>\n\n**Final Answer**\\boxed"
modified_outputs.append(modified_output)
# print(f"modified_output_len: {len(modified_output)}")
available_gpus = os.environ["CUDA_VISIBLE_DEVICES"].split(",") # 示例GPU列表，根据实际情况修改
num_gpus = len(available_gpus)
manager = Manager()
output_queue = Queue()
processes = []
multi_outputs = []
prompts = modified_outputs
chunk_size = len(prompts) // num_gpus + 1
chunks = [prompts[i:i + chunk_size] for i in range(0, len(prompts), chunk_size)]
num_rounds = (len(chunks) + num_gpus - 1) // num_gpus #g 轮次数等于最大GPU上的任务数
for round_idx in range(num_rounds):
start_idx = round_idx * num_gpus
end_idx = min((round_idx + 1) * num_gpus, len(chunks))
for i in range(start_idx, end_idx):
chunk = chunks[i]
p = Process(target=process_chunk, args=(chunk, args, available_gpus, stop_words, output_queue, i))
processes.append(p)
p.start()
# for i, chunk in enumerate(chunks):
# print(f"Processing chunk {i} with size {len(chunk)}")
# p = Process(target=process_chunk, args=(chunk, args, available_gpus, stop_words, output_queue, i))
# processes.append(p)
# p.start()
for _ in range(len(chunks)):
result = output_queue.get()
if isinstance(result, tuple) and len(result) == 2:
multi_outputs.append(result)
else:
print(f"Error: Received non-tuple result: {result}")
# multi_outputs.extend(output_queue.get())
for p in processes:
p.join()
multi_outputs.sort(key=lambda x: x[0])
outputs = []
for _, chunk_output in multi_outputs:
outputs.extend(chunk_output)
elif os.environ['stage'] == "1":
outputs = outputs
# elif os.environ['stage'] == "add":
# llm = LLM(
# model=args.model_name_or_path,
# tensor_parallel_size=1,
# pipeline_parallel_size=1,
# trust_remote_code=True,
# gpu_memory_utilization=0.85,
# enforce_eager=True,
# max_seq_len_to_capture=5000000,
# )
# two_stage_outputs = []
# modified_outputs = []
# print(f"len of outputs: {len(outputs)}")
# for output in outputs:
# # 去除output字符串末尾的换行符，并添加</think>和**Final Answer**\n\\boxed字符串，将结果添加到modified_outputs列表中
# if "<｜end▁of▁sentence｜>" in output:
# start_index = output.index("<｜end▁of▁sentence｜>")
# output = output[:start_index]
# # output = output.replace("<｜end▁of▁sentence｜>", "")
# modified_output = output
# modified_outputs.append(modified_output)
# # print(f"modified_output_len: {len(modified_output)}")
# for i in range(0, num_prompts, chunk_size):
# modified_chunk = outputs[i:i + chunk_size] # 获取当前的 chunk
# if args.use_vllm:
# os.environ["position"] = 'start'
# second_outputs = llm.generate(
# modified_chunk,
# SamplingParams(
# temperature=args.temperature,
# top_p=args.top_p,
# max_tokens=50,
# n=1,
# stop=stop_words,
# stop_token_ids=(
# [151645, 151643]
# ),
# skip_special_tokens=False, #G 设置特殊token的可见性
# ),
# )
# if os.path.exists('./start_positions.pt'):
# os.remove('./start_positions.pt')
# print('start_positions.pt removed')
# if os.path.exists('./early_positions.pt'):
# os.remove('./early_positions.pt')
# print('early_positions.pt removed')
# second_outputs = sorted(second_outputs, key=lambda x: int(x.request_id))
# second_outputs = [output.outputs[0].text for output in second_outputs]
# # Combine initial and second outputs
# combined_outputs = [init + second for init, second in zip(modified_chunk, second_outputs)]
# print(f"len of combined_outputs:{len(combined_outputs)}")
# two_stage_outputs.extend(combined_outputs) ## 直接覆盖掉就好
# outputs = two_stage_outputs
#################!
print(f"outputs:{len(outputs)}")
print(f"current_prompts:{len(current_prompts)}")
assert len(outputs) == len(current_prompts)
#g 以防万一，再次sort回答
# print(f"outputs[:5]: {outputs[:5]}")
# print(f"current_prompts[:5]: {current_prompts[:5]}")
# sorted_outputs = []
# for (index, prompt) in current_prompts:
# for i in range(len(outputs)):
# if prompt in outputs[i]:
# sorted_outputs.append(outputs[i])
# outputs = sorted_outputs
# print(f"outputs[:5]: {outputs[123:128]}")
# print(f"current_prompts[:5]: {current_prompts[123:128]}")
# assert len(outputs) == len(current_prompts)
# print(outputs)
# import pdb
# pdb.set_trace()
# process all outputs
remain_prompts = []
remain_codes = []
for (i, query), output in zip(current_prompts, outputs):
output = output.rstrip()
query += output
if args.prompt_type == "pal":
remain_prompts.append((i, query))
if "```python" in output:
output = extract_program(query)
remain_codes.append(output)
elif args.prompt_type == "cot":
end_prompts.append((i, query))
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))
# execute the remain prompts
remain_results = executor.batch_apply(remain_codes)
for k in range(len(remain_prompts)):
i, query = 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)
# 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 = []
assert len(input_prompts) == len(end_prompts)
for i in range(len(input_prompts)):
if i ==1:
print(f"input_prompts[{i}] = {input_prompts[i]}")
print(f"end_prompts[{i}] = {end_prompts[i]}")
_, end_prompt = 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()
if args.prompt_type == "deepseek3":
# print(f"code = {code.split('<｜Assistant｜>')}")
if '<｜Assistant｜>' in code:
code = code.split("<｜Assistant｜>")[1]
else:
code = code
codes.append(code)
# extract preds
# results = [
# run_execute(executor, clean_code(code), args.prompt_type, data_name) for code in codes
# ]
results = [
run_execute(executor, clean_code(code), args.prompt_type, data_name) 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]
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") # save the prompt for debug
sample.update({"code": code, "pred": preds, "report": reports})
all_samples.append(sample)
# add processed samples
all_samples.extend(processed_samples)#
#G 评估时采用的answer均是从终止符开始截断的。
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", "_metrics.json"), "w"
) as f:
json.dump(result_json, f, indent=4)
return result_json
if __name__ == "__main__":
multiprocessing.set_start_method('spawn')
args = parse_args()
set_seed(args.seed)
setup(args) |