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10a1462 | 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 | #!/usr/bin/env python
# Copyright 2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import math
import os
from dataclasses import asdict, dataclass
from typing import Dict, Optional
import torch
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import ProjectConfiguration, set_seed
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, DataCollatorForLanguageModeling, get_scheduler
from diffusers import BlockRefinementScheduler
from diffusers.training_utils import compute_confidence_aware_loss
logger = get_logger(__name__)
@dataclass
class TrainConfig:
model_name_or_path: str
dataset_name: str
dataset_config_name: Optional[str]
text_column: str
cache_dir: Optional[str]
use_dummy_data: bool
num_dummy_samples: int
output_dir: str
seed: int
max_train_steps: int
checkpointing_steps: int
logging_steps: int
per_device_train_batch_size: int
gradient_accumulation_steps: int
learning_rate: float
weight_decay: float
lr_scheduler: str
lr_warmup_steps: int
max_length: int
prompt_length: int
block_length: int
lambda_conf: float
conf_temperature: float
def parse_args() -> TrainConfig:
parser = argparse.ArgumentParser(description="Train block-refinement with a confidence-aware loss on a causal LM.")
parser.add_argument("--model_name_or_path", type=str, default="Qwen/Qwen2.5-0.5B")
parser.add_argument("--dataset_name", type=str, default="wikitext")
parser.add_argument("--dataset_config_name", type=str, default="wikitext-2-raw-v1")
parser.add_argument("--text_column", type=str, default="text")
parser.add_argument("--cache_dir", type=str, default=None)
parser.add_argument("--use_dummy_data", action="store_true", help="Use random-token data instead of downloading.")
parser.add_argument("--num_dummy_samples", type=int, default=2048)
parser.add_argument("--output_dir", type=str, default="block-refinement-output")
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--max_train_steps", type=int, default=1000)
parser.add_argument("--checkpointing_steps", type=int, default=500)
parser.add_argument("--logging_steps", type=int, default=50)
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=8)
parser.add_argument("--learning_rate", type=float, default=2e-5)
parser.add_argument("--weight_decay", type=float, default=0.0)
parser.add_argument(
"--lr_scheduler", type=str, default="cosine", choices=["linear", "cosine", "cosine_with_restarts"]
)
parser.add_argument("--lr_warmup_steps", type=int, default=100)
parser.add_argument("--max_length", type=int, default=256)
parser.add_argument("--prompt_length", type=int, default=32)
parser.add_argument("--block_length", type=int, default=32)
parser.add_argument("--lambda_conf", type=float, default=2.0)
parser.add_argument("--conf_temperature", type=float, default=0.5)
args = parser.parse_args()
return TrainConfig(**vars(args))
def tokenize_fn(examples: Dict, tokenizer, text_column: str, max_length: int):
texts = examples[text_column]
texts = [t for t in texts if isinstance(t, str) and len(t.strip()) > 0]
return tokenizer(texts, truncation=True, padding=False, max_length=max_length)
class RandomTokenDataset(torch.utils.data.Dataset):
def __init__(self, *, num_samples: int, seq_len: int, vocab_size: int, pad_token_id: int):
self.num_samples = int(num_samples)
self.seq_len = int(seq_len)
self.vocab_size = int(vocab_size)
self.pad_token_id = int(pad_token_id)
def __len__(self):
return self.num_samples
def __getitem__(self, idx):
del idx
input_ids = torch.randint(0, self.vocab_size, (self.seq_len,), dtype=torch.long)
attention_mask = torch.ones_like(input_ids)
return {"input_ids": input_ids, "attention_mask": attention_mask}
def main():
cfg = parse_args()
if cfg.prompt_length >= cfg.max_length:
raise ValueError("`prompt_length` must be < `max_length`.")
if cfg.block_length <= 0:
raise ValueError("`block_length` must be > 0.")
project_config = ProjectConfiguration(project_dir=cfg.output_dir, logging_dir=os.path.join(cfg.output_dir, "logs"))
accelerator = Accelerator(
gradient_accumulation_steps=cfg.gradient_accumulation_steps,
project_config=project_config,
)
if accelerator.is_main_process:
os.makedirs(cfg.output_dir, exist_ok=True)
accelerator.wait_for_everyone()
set_seed(cfg.seed)
logger.info("Training configuration: %s", asdict(cfg))
tokenizer = AutoTokenizer.from_pretrained(cfg.model_name_or_path, use_fast=True, cache_dir=cfg.cache_dir)
if tokenizer.pad_token_id is None:
tokenizer.pad_token = tokenizer.eos_token
if tokenizer.mask_token_id is None:
tokenizer.add_special_tokens({"mask_token": "[MASK]"})
load_dtype = torch.bfloat16 if torch.cuda.is_available() else torch.float32
model = AutoModelForCausalLM.from_pretrained(cfg.model_name_or_path, cache_dir=cfg.cache_dir, dtype=load_dtype)
model.resize_token_embeddings(len(tokenizer))
if load_dtype == torch.float32:
model.to(dtype=torch.float32)
mask_token_id = int(tokenizer.mask_token_id)
if cfg.use_dummy_data:
dataset = RandomTokenDataset(
num_samples=cfg.num_dummy_samples,
seq_len=cfg.max_length,
vocab_size=len(tokenizer),
pad_token_id=int(tokenizer.pad_token_id),
)
train_dataloader = DataLoader(
dataset,
shuffle=True,
batch_size=cfg.per_device_train_batch_size,
drop_last=True,
)
else:
raw_datasets = load_dataset(cfg.dataset_name, cfg.dataset_config_name, cache_dir=cfg.cache_dir)
if "train" not in raw_datasets:
raise ValueError(f"Dataset {cfg.dataset_name} has no 'train' split.")
with accelerator.main_process_first():
tokenized = raw_datasets["train"].map(
lambda ex: tokenize_fn(ex, tokenizer, cfg.text_column, cfg.max_length),
batched=True,
remove_columns=raw_datasets["train"].column_names,
desc="Tokenizing",
)
collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False, return_tensors="pt")
train_dataloader = DataLoader(
tokenized, shuffle=True, collate_fn=collator, batch_size=cfg.per_device_train_batch_size, drop_last=True
)
optimizer = torch.optim.AdamW(model.parameters(), lr=cfg.learning_rate, weight_decay=cfg.weight_decay)
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / cfg.gradient_accumulation_steps)
num_train_epochs = math.ceil(cfg.max_train_steps / num_update_steps_per_epoch)
lr_scheduler = get_scheduler(
name=cfg.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=cfg.lr_warmup_steps,
num_training_steps=cfg.max_train_steps,
)
model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
model, optimizer, train_dataloader, lr_scheduler
)
noise_scheduler = BlockRefinementScheduler(block_length=cfg.block_length)
global_step = 0
model.train()
for _epoch in range(num_train_epochs):
for batch in train_dataloader:
with accelerator.accumulate(model):
input_ids = batch["input_ids"]
attention_mask = batch.get("attention_mask", torch.ones_like(input_ids))
gen = torch.Generator(device=input_ids.device).manual_seed(cfg.seed + global_step)
noisy, noisy_rev, masked, masked_rev = noise_scheduler.add_noise(
input_ids,
attention_mask,
prompt_length=cfg.prompt_length,
block_length=cfg.block_length,
mask_token_id=mask_token_id,
generator=gen,
)
position_ids = (
torch.arange(input_ids.shape[1], device=input_ids.device).unsqueeze(0).expand_as(input_ids)
)
logits = model(input_ids=noisy, attention_mask=attention_mask, position_ids=position_ids).logits
logits_rev = model(
input_ids=noisy_rev, attention_mask=attention_mask, position_ids=position_ids
).logits
logits = logits.clone()
logits[..., mask_token_id] = torch.finfo(logits.dtype).min
logits_rev = logits_rev.clone()
logits_rev[..., mask_token_id] = torch.finfo(logits_rev.dtype).min
valid = attention_mask.to(dtype=torch.bool)
masked = masked & valid
masked_rev = masked_rev & valid
labels = input_ids.clone()
labels[~masked] = -100
labels_rev = input_ids.clone()
labels_rev[~masked_rev] = -100
weights = masked.to(dtype=logits.dtype)
weights_rev = masked_rev.to(dtype=logits.dtype)
loss, loss_sft, loss_conf = compute_confidence_aware_loss(
logits,
labels,
lambda_conf=cfg.lambda_conf,
temperature=cfg.conf_temperature,
per_token_weights=weights,
)
loss_rev, loss_sft_rev, loss_conf_rev = compute_confidence_aware_loss(
logits_rev,
labels_rev,
lambda_conf=cfg.lambda_conf,
temperature=cfg.conf_temperature,
per_token_weights=weights_rev,
)
total_loss = loss + loss_rev
accelerator.backward(total_loss)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad(set_to_none=True)
if accelerator.sync_gradients:
global_step += 1
if global_step % cfg.logging_steps == 0 and accelerator.is_main_process:
logger.info(
"step=%d loss=%.4f sft=%.4f conf=%.4f lr=%.6g",
global_step,
total_loss.item(),
(loss_sft + loss_sft_rev).item(),
(loss_conf + loss_conf_rev).item(),
lr_scheduler.get_last_lr()[0],
)
print(
f"step={global_step} loss={total_loss.item():.4f} "
f"sft={(loss_sft + loss_sft_rev).item():.4f} "
f"conf={(loss_conf + loss_conf_rev).item():.4f} "
f"lr={lr_scheduler.get_last_lr()[0]:.6g}"
)
if cfg.checkpointing_steps > 0 and global_step % cfg.checkpointing_steps == 0:
accelerator.wait_for_everyone()
if accelerator.is_main_process:
save_dir = os.path.join(cfg.output_dir, f"checkpoint-{global_step}")
os.makedirs(save_dir, exist_ok=True)
accelerator.unwrap_model(model).save_pretrained(save_dir, save_function=accelerator.save)
tokenizer.save_pretrained(save_dir)
if global_step >= cfg.max_train_steps:
break
if global_step >= cfg.max_train_steps:
break
accelerator.wait_for_everyone()
if accelerator.is_main_process:
final_dir = os.path.join(cfg.output_dir, "final")
os.makedirs(final_dir, exist_ok=True)
accelerator.unwrap_model(model).save_pretrained(final_dir, save_function=accelerator.save)
tokenizer.save_pretrained(final_dir)
logger.info("Done.")
if __name__ == "__main__":
main()
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