File size: 12,112 Bytes
d91766b | 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 | import argparse
import json
import os
import random
import sys
import tempfile
import subprocess
import torch
import torch.nn.functional as F
import pyarrow.parquet as pq
from tqdm import tqdm
from transformers import AutoTokenizer, AutoModel
MASK_ID = 126336
def add_gumbel_noise(logits, temperature):
if temperature == 0:
return logits
logits = logits.to(torch.float64)
noise = torch.rand_like(logits, dtype=torch.float64)
gumbel_noise = (-torch.log(noise)) ** temperature
return logits.exp() / gumbel_noise
def get_num_transfer_tokens(mask_index, steps):
mask_num = mask_index.sum(dim=1, keepdim=True)
base = mask_num // steps
remainder = mask_num % steps
num_transfer_tokens = torch.zeros(mask_num.size(0), steps, device=mask_index.device, dtype=torch.int64) + base
for i in range(mask_num.size(0)):
num_transfer_tokens[i, :remainder[i]] += 1
return num_transfer_tokens
@torch.no_grad()
def generate_trajectory(model, prompt_ids, attention_mask=None, steps=16, gen_length=256, block_length=32, temperature=0.0, remasking="low_confidence"):
x = torch.full((prompt_ids.shape[0], prompt_ids.shape[1] + gen_length), MASK_ID, dtype=torch.long).to(model.device)
x[:, :prompt_ids.shape[1]] = prompt_ids.clone()
if attention_mask is not None:
attention_mask = torch.cat(
[attention_mask, torch.ones((prompt_ids.shape[0], gen_length), dtype=attention_mask.dtype, device=model.device)],
dim=-1,
)
prompt_index = x != MASK_ID
assert gen_length % block_length == 0
num_blocks = gen_length // block_length
assert steps % num_blocks == 0
steps = steps // num_blocks
trajectory = {}
step_idx = 0
trajectory[f"step{step_idx}"] = x[:, prompt_ids.shape[1]:].clone()
step_idx += 1
for num_block in range(num_blocks):
block_mask_index = (x[:, prompt_ids.shape[1] + num_block * block_length: prompt_ids.shape[1] + (num_block + 1) * block_length] == MASK_ID)
num_transfer_tokens = get_num_transfer_tokens(block_mask_index, steps)
for i in range(steps):
mask_index = x == MASK_ID
logits = model(x, attention_mask=attention_mask).logits
logits_with_noise = add_gumbel_noise(logits, temperature=temperature)
x0 = torch.argmax(logits_with_noise, dim=-1)
if remasking == "low_confidence":
p = F.softmax(logits, dim=-1)
x0_p = torch.squeeze(torch.gather(p, dim=-1, index=torch.unsqueeze(x0, -1)), -1)
elif remasking == "random":
x0_p = torch.rand((x0.shape[0], x0.shape[1]), device=x0.device)
else:
raise NotImplementedError(remasking)
x0_p[:, prompt_ids.shape[1] + (num_block + 1) * block_length :] = -torch.inf
x0 = torch.where(mask_index, x0, x)
confidence = torch.where(mask_index, x0_p, -torch.inf)
transfer_index = torch.zeros_like(x0, dtype=torch.bool, device=x0.device)
for j in range(confidence.shape[0]):
k = int(num_transfer_tokens[j, i].item())
if k > 0:
_, select_index = torch.topk(confidence[j], k=k)
transfer_index[j, select_index] = True
x[transfer_index] = x0[transfer_index]
trajectory[f"step{step_idx}"] = x[:, prompt_ids.shape[1]:].clone()
step_idx += 1
return x, trajectory
def build_prompt(tokenizer, text):
messages = [{"role": "user", "content": text}]
return tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
def extract_code_from_text(text):
l = text.split("```")
if len(l) >= 3:
body = l[1]
if body.strip().lower().startswith("python"):
body = body.split("\n", 1)[1] if "\n" in body else ""
return body
return text
def run_tests(solution_code, test_code, timeout=20):
tmp = tempfile.mkdtemp(prefix="kodcode_")
try:
sol_path = os.path.join(tmp, "solution.py")
tst_path = os.path.join(tmp, "test_content.py")
runner_path = os.path.join(tmp, "test_runner.py")
with open(sol_path, "w", encoding="utf-8") as f:
f.write(solution_code)
with open(tst_path, "w", encoding="utf-8") as f:
f.write(test_code)
runner_src = (
"import importlib.util,sys\n"
"sys.path.insert(0,'.')\n"
"spec=importlib.util.spec_from_file_location('solution','solution.py')\n"
"m=importlib.util.module_from_spec(spec)\n"
"spec.loader.exec_module(m)\n"
"g={'__name__':'__main__'}\n"
"exec(open('test_content.py','r',encoding='utf-8').read(),g)\n"
"fails=0\n"
"for k,v in list(g.items()):\n"
" if callable(v) and k.startswith('test_'):\n"
" try:\n"
" v()\n"
" except Exception:\n"
" fails+=1\n"
"import sys\n"
"sys.exit(1 if fails>0 else 0)\n"
)
with open(runner_path, "w", encoding="utf-8") as f:
f.write(runner_src)
p = subprocess.run([sys.executable, runner_path], cwd=tmp, stdout=subprocess.PIPE, stderr=subprocess.PIPE, timeout=timeout)
return p.returncode == 0
except Exception:
return False
finally:
try:
for fn in os.listdir(tmp):
fp = os.path.join(tmp, fn)
if os.path.isfile(fp):
os.remove(fp)
os.rmdir(tmp)
except Exception:
pass
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--model_name", type=str, default="Model Path")
parser.add_argument("--parquet_path", type=str, default="Dataset Path")
parser.add_argument("--output_path", type=str, required=True)
parser.add_argument("--limit", type=int, default=0)
parser.add_argument("--num_samples", type=int, default=1)
parser.add_argument("--steps", type=int, default=256)
parser.add_argument("--max_new_tokens", type=int, default=256)
parser.add_argument("--block_length", type=int, default=32)
parser.add_argument("--temperature", type=float, default=0.0)
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--target_count", type=int, default=500)
args = parser.parse_args()
random.seed(args.seed)
torch.manual_seed(args.seed)
model = AutoModel.from_pretrained(args.model_name, trust_remote_code=True, torch_dtype=torch.bfloat16, device_map="auto").eval()
tokenizer = AutoTokenizer.from_pretrained(args.model_name, trust_remote_code=True)
if tokenizer.padding_side != "left":
tokenizer.padding_side = "left"
os.makedirs(os.path.dirname(args.output_path), exist_ok=True)
existing = set()
if os.path.exists(args.output_path):
with open(args.output_path, "r", encoding="utf-8") as f:
for line in f:
line = line.strip()
if not line:
continue
try:
obj = json.loads(line)
key = (int(obj.get("row_id", -1)), int(obj.get("sample_id", -1)))
existing.add(key)
except Exception:
continue
out_f = open(args.output_path, "a", encoding="utf-8")
pf = pq.ParquetFile(args.parquet_path)
total_rows = 0
for i in range(pf.num_row_groups):
rg = pf.read_row_group(i)
total_rows += rg.num_rows
target = args.target_count
collected = 0
total_attempts = 0 # Total generation attempts (including passed and failed)
processed = 0
progress = tqdm(total=total_rows if args.limit <= 0 else min(args.limit, total_rows), desc="processing")
for i in range(pf.num_row_groups):
rg = pf.read_row_group(i)
tbl = rg.to_pydict()
n = rg.num_rows
for r in range(n):
if args.limit > 0 and processed >= args.limit:
break
processed += 1
progress.update(1)
row = {k: tbl[k][r] for k in tbl.keys()}
row_id = int(row.get("metadata", {}).get("row_id")) if isinstance(row.get("metadata"), dict) and "row_id" in row["metadata"] else processed
for sample_id in range(args.num_samples):
if (row_id, sample_id) in existing:
continue
question = str(row.get("question", ""))
solution_ref = str(row.get("solution", ""))
test_code = str(row.get("test", ""))
teacher_text = question + "\nReference Answer: " + solution_ref + "\nAfter understanding the reference solution, please try to solve this problem using your own approach below and just OUTPUT YOUR SOLUTION CODE, DON'T EXPLAIN IT:"
prompt_text = build_prompt(tokenizer, teacher_text)
encoded = tokenizer([prompt_text], add_special_tokens=False, padding=True, return_tensors="pt")
input_ids = encoded["input_ids"].to(model.device)
attention_mask = encoded["attention_mask"].to(model.device)
final_x, trajectory = generate_trajectory(
model,
input_ids,
attention_mask=attention_mask,
steps=args.steps,
gen_length=args.max_new_tokens,
block_length=args.block_length,
temperature=args.temperature,
remasking="low_confidence",
)
output_text = tokenizer.batch_decode(final_x[:, input_ids.shape[1] :], skip_special_tokens=True)[0]
code_text = extract_code_from_text(output_text)
passed = run_tests(code_text, test_code, timeout=30)
total_attempts += 1
success_rate = collected / total_attempts * 100 if total_attempts > 0 else 0.0
if not passed:
print(f"[idx={row_id} sample={sample_id}] x Test failed, skipped "
f"(success rate: {collected}/{total_attempts} = {success_rate:.2f}%)")
continue
print(f"[idx={row_id} sample={sample_id}] v Test passed, collecting trajectory "
f"(success rate: {collected + 1}/{total_attempts} = {(collected + 1) / total_attempts * 100:.2f}%)")
traj_dict = {k: v.squeeze(0).tolist() for k, v in trajectory.items()}
rec = {
"row_id": int(row_id),
"sample_id": int(sample_id),
"question": question,
"solution_ref": solution_ref,
"teacher_text": teacher_text,
"trajectory": traj_dict,
"output_text": output_text,
"solution_code": code_text,
}
out_f.write(json.dumps(rec, ensure_ascii=False) + "\n")
out_f.flush()
collected += 1
if collected >= target:
break
if collected >= target:
break
if collected >= target:
break
progress.close()
out_f.close()
# -- Summary statistics --
final_rate = collected / total_attempts * 100 if total_attempts > 0 else 0.0
summary = {
"summary": "collection_stats",
"collected": collected,
"total_attempts": total_attempts,
"success_rate": round(final_rate, 4),
"processed_rows": processed,
"target_count": target,
}
with open(args.output_path, "a", encoding="utf-8") as f:
f.write(json.dumps(summary, ensure_ascii=False) + "\n")
print(f"[Done] Collected {collected} trajectories / Total attempts {total_attempts} / "
f"Success rate {final_rate:.2f}% / Processed rows {processed}")
print(f"[Done] Output: {args.output_path}")
if __name__ == "__main__":
main()
|