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4d04310 e5f2a36 4d04310 e5f2a36 4d04310 e5f2a36 4d04310 | 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 | #!/usr/bin/env python3
# /// script
# requires-python = ">=3.10"
# dependencies = [
# "accelerate>=0.33",
# "datasets>=2.20",
# "huggingface_hub>=0.24",
# "safetensors>=0.4",
# "sentencepiece>=0.2",
# "torch>=2.4",
# "transformers>=4.44",
# ]
# ///
"""Scaled GSM8K checkpoint evaluation for IAPO.
This evaluates released merged checkpoints against the base Qwen models using the
IAPO paper's chat prompt style and reports Pass@k, generated-token length, ratio,
and wall-clock generation time.
"""
from __future__ import annotations
import argparse
import csv
import json
import math
import os
import random
import re
import time
from pathlib import Path
from typing import Any
import torch
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer
SYSTEM_PROMPT = """A conversation between User and Assistant. The user asks a question, and the Assistant solves it.
The assistant first thinks about the reasoning process in the mind and then provides the user
with the answer. The reasoning process and answer are enclosed within <think> and
<answer> tags, respectively, i.e., <think> reasoning process here </think>
<answer> answer here </answer>.
The answer must be a single integer."""
FEWSHOT_USER = "What is 2+2?"
FEWSHOT_ASSISTANT = "<think>To calculate 2+2, we simply add the numbers together: 2 + 2 = 4.</think>\n<answer>4</answer>"
MODEL_SPECS = {
"base-0.5b": {"repo": "Qwen/Qwen2.5-0.5B-Instruct", "subfolder": None},
"iapo-0.5b-gsm8k": {"repo": "jonathanhe123/iapo", "subfolder": "Qwen2.5-0.5B-Instruct_GSM8K"},
"base-7b": {"repo": "Qwen/Qwen2.5-7B-Instruct", "subfolder": None},
"iapo-7b-gsm8k": {"repo": "jonathanhe123/iapo", "subfolder": "Qwen2.5-7B-Instruct_GSM8K"},
}
def normalize_answer(s: str | None) -> str:
if s is None:
return ""
s = str(s).strip()
s = s.split("=")[-1]
s = s.replace(",", "").replace("$", "").strip()
s = re.sub(r"\\boxed\{([^{}]+)\}", r"\1", s)
return s.strip().rstrip(".")
def extract_gold(answer: str) -> str:
return normalize_answer(answer.split("####")[-1])
def extract_pred(text: str) -> str:
xml = re.search(r"<answer>(.*?)</answer>", text, flags=re.DOTALL | re.IGNORECASE)
if xml:
return normalize_answer(xml.group(1))
answer_line = re.findall(r"Answer:\s*([-+]?\d[\d,]*(?:\.\d+)?)", text, flags=re.IGNORECASE)
if answer_line:
return normalize_answer(answer_line[-1])
nums = re.findall(r"[-+]?\d[\d,]*(?:\.\d+)?", text)
return normalize_answer(nums[-1]) if nums else ""
def make_prompt(tokenizer: Any, question: str) -> str:
messages = [
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": FEWSHOT_USER},
{"role": "assistant", "content": FEWSHOT_ASSISTANT},
{"role": "user", "content": question},
]
return tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
def load_model(spec: dict[str, str | None], dtype: str):
torch_dtype = {
"auto": "auto",
"float16": torch.float16,
"bfloat16": torch.bfloat16,
"float32": torch.float32,
}[dtype]
kwargs = {"trust_remote_code": True}
if spec["subfolder"]:
kwargs["subfolder"] = spec["subfolder"]
tokenizer = AutoTokenizer.from_pretrained(spec["repo"], **kwargs)
if tokenizer.pad_token_id is None:
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "left"
model = AutoModelForCausalLM.from_pretrained(
spec["repo"],
torch_dtype=torch_dtype,
device_map="auto",
low_cpu_mem_usage=True,
**kwargs,
)
model.eval()
return tokenizer, model
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--models", nargs="+", default=["base-0.5b", "iapo-0.5b-gsm8k"])
parser.add_argument("--sample-size", type=int, default=32)
parser.add_argument("--num-return-sequences", type=int, default=4)
parser.add_argument("--max-new-tokens", type=int, default=384)
parser.add_argument("--batch-size", type=int, default=4)
parser.add_argument("--temperature", type=float, default=1.0)
parser.add_argument("--top-p", type=float, default=1.0)
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--dtype", choices=["auto", "float16", "bfloat16", "float32"], default="bfloat16")
parser.add_argument("--output-dir", default="outputs/hf_gsm8k_eval")
parser.add_argument("--dry-run", action="store_true")
args = parser.parse_args()
random.seed(args.seed)
torch.manual_seed(args.seed)
out_dir = Path(args.output_dir)
out_dir.mkdir(parents=True, exist_ok=True)
print(json.dumps({
"event": "env",
"torch": torch.__version__,
"cuda_available": torch.cuda.is_available(),
"cuda_device_count": torch.cuda.device_count(),
"models": args.models,
"sample_size": args.sample_size,
"num_return_sequences": args.num_return_sequences,
}))
if args.dry_run:
print(json.dumps({"event": "dry_run_ok"}))
return
dataset = load_dataset("openai/gsm8k", "main", split="test")
indices = list(range(len(dataset)))
random.Random(args.seed).shuffle(indices)
indices = sorted(indices[: args.sample_size])
examples = [dataset[i] for i in indices]
golds = [extract_gold(ex["answer"]) for ex in examples]
all_summaries = []
details_path = out_dir / "generations.jsonl"
with details_path.open("w") as details:
for model_key in args.models:
if model_key not in MODEL_SPECS:
raise ValueError(f"Unknown model key: {model_key}")
spec = MODEL_SPECS[model_key]
tokenizer, model = load_model(spec, args.dtype)
prompts = [make_prompt(tokenizer, ex["question"]) for ex in examples]
per_question_correct: list[list[int]] = []
per_question_lengths: list[list[int]] = []
generated_count = 0
gen_seconds = 0.0
for start in range(0, len(prompts), args.batch_size):
batch_prompts = prompts[start : start + args.batch_size]
encoded = tokenizer(batch_prompts, return_tensors="pt", padding=True).to(model.device)
do_sample = args.num_return_sequences > 1 or args.temperature > 0
t0 = time.perf_counter()
with torch.inference_mode():
output_ids = model.generate(
**encoded,
max_new_tokens=args.max_new_tokens,
do_sample=do_sample,
temperature=args.temperature if do_sample else None,
top_p=args.top_p if do_sample else None,
num_return_sequences=args.num_return_sequences,
pad_token_id=tokenizer.pad_token_id,
eos_token_id=tokenizer.eos_token_id,
)
gen_seconds += time.perf_counter() - t0
prompt_lens = encoded["attention_mask"].sum(dim=1).tolist()
input_width = int(encoded["input_ids"].shape[1])
expanded_prompt_lens = [
input_width
for i in range(len(prompt_lens))
for _ in range(args.num_return_sequences)
]
texts = []
lengths = []
for ids, plen in zip(output_ids, expanded_prompt_lens):
new_ids = ids[int(plen) :]
if tokenizer.eos_token_id in new_ids:
eos_pos = (new_ids == tokenizer.eos_token_id).nonzero(as_tuple=True)[0]
if len(eos_pos):
new_ids = new_ids[: int(eos_pos[0]) + 1]
lengths.append(int(len(new_ids)))
texts.append(tokenizer.decode(new_ids, skip_special_tokens=True))
for local_idx in range(len(batch_prompts)):
global_idx = start + local_idx
group_texts = texts[
local_idx * args.num_return_sequences : (local_idx + 1) * args.num_return_sequences
]
group_lengths = lengths[
local_idx * args.num_return_sequences : (local_idx + 1) * args.num_return_sequences
]
preds = [extract_pred(t) for t in group_texts]
correct = [int(pred == golds[global_idx]) for pred in preds]
per_question_correct.append(correct)
per_question_lengths.append(group_lengths)
generated_count += len(group_texts)
for j, (text, pred, ok, length) in enumerate(zip(group_texts, preds, correct, group_lengths)):
details.write(json.dumps({
"model": model_key,
"dataset_index": indices[global_idx],
"sample_in_group": j,
"gold": golds[global_idx],
"prediction": pred,
"correct": ok,
"completion_tokens": length,
"completion": text,
}) + "\n")
details.flush()
k_values = [k for k in [1, 2, 4, 8, 16, 32] if k <= args.num_return_sequences]
summary = {
"model": model_key,
"repo": spec["repo"],
"subfolder": spec["subfolder"],
"sample_size": len(examples),
"num_return_sequences": args.num_return_sequences,
"max_new_tokens": args.max_new_tokens,
"generated_count": generated_count,
"generation_seconds": gen_seconds,
"avg_seconds_per_completion": gen_seconds / max(1, generated_count),
}
for k in k_values:
pass_at_k = sum(int(any(row[:k])) for row in per_question_correct) / len(per_question_correct)
length_at_k = sum(sum(row[:k]) for row in per_question_lengths) / (len(per_question_lengths) * k)
summary[f"pass@{k}"] = pass_at_k
summary[f"length@{k}"] = length_at_k
summary[f"ratio@{k}"] = pass_at_k / length_at_k if length_at_k else math.nan
all_summaries.append(summary)
print(json.dumps({"event": "summary", **summary}))
del model
if torch.cuda.is_available():
torch.cuda.empty_cache()
summary_path = out_dir / "summary.json"
csv_path = out_dir / "summary.csv"
summary_path.write_text(json.dumps(all_summaries, indent=2))
keys = sorted({k for row in all_summaries for k in row})
with csv_path.open("w", newline="") as f:
writer = csv.DictWriter(f, fieldnames=keys)
writer.writeheader()
writer.writerows(all_summaries)
print(json.dumps({"event": "wrote_outputs", "summary": str(summary_path), "csv": str(csv_path), "details": str(details_path)}))
if __name__ == "__main__":
main()
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