File size: 3,891 Bytes
d5049a2 | 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 | from __future__ import annotations
import argparse
import os
from transformers import Trainer, TrainingArguments, set_seed
from .common import load_rows
from .data import DriveDataset, SFTCollator
from .modeling import (
load_base_model,
load_processor,
prepare_trainable_model,
trainable_parameter_summary,
)
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="LoRA SFT for Qwen3.5 VLM")
parser.add_argument("--model", required=True)
parser.add_argument("--data-dir", required=True)
parser.add_argument("--output-dir", required=True)
parser.add_argument("--adapter-path", default=None)
parser.add_argument("--train-split", default="train")
parser.add_argument("--val-split", default="val")
parser.add_argument("--num-views", type=int, default=1)
parser.add_argument("--max-length", type=int, default=2048)
parser.add_argument("--max-steps", type=int, default=1000)
parser.add_argument("--learning-rate", type=float, default=2e-4)
parser.add_argument("--gradient-accumulation-steps", type=int, default=16)
parser.add_argument("--lora-r", type=int, default=16)
parser.add_argument("--lora-alpha", type=int, default=32)
parser.add_argument("--lora-dropout", type=float, default=0.05)
parser.add_argument("--eval-steps", type=int, default=100)
parser.add_argument("--save-steps", type=int, default=100)
parser.add_argument("--logging-steps", type=int, default=5)
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--attn-implementation", default="sdpa")
parser.add_argument("--deepspeed", default=None)
parser.add_argument("--freeze-vision", action=argparse.BooleanOptionalAction, default=True)
return parser.parse_args()
def main() -> None:
args = parse_args()
set_seed(args.seed)
processor = load_processor(args.model)
model = load_base_model(args.model, attn_implementation=args.attn_implementation)
model = prepare_trainable_model(
model,
adapter_path=args.adapter_path,
lora_r=args.lora_r,
lora_alpha=args.lora_alpha,
lora_dropout=args.lora_dropout,
freeze_vision=args.freeze_vision,
)
trainable, total = trainable_parameter_summary(model)
print(f"trainable_parameters={trainable:,}/{total:,} ({trainable / total:.4%})")
train_dataset = DriveDataset(load_rows(args.data_dir, args.train_split))
eval_dataset = DriveDataset(load_rows(args.data_dir, args.val_split))
collator = SFTCollator(processor, args.num_views, args.max_length)
training_args = TrainingArguments(
output_dir=args.output_dir,
per_device_train_batch_size=1,
per_device_eval_batch_size=1,
gradient_accumulation_steps=args.gradient_accumulation_steps,
learning_rate=args.learning_rate,
max_steps=args.max_steps,
warmup_ratio=0.03,
lr_scheduler_type="cosine",
bf16=True,
tf32=True,
gradient_checkpointing=True,
eval_strategy="steps",
eval_steps=args.eval_steps,
save_strategy="steps",
save_steps=args.save_steps,
save_total_limit=2,
logging_steps=args.logging_steps,
report_to="none",
remove_unused_columns=False,
dataloader_num_workers=0,
ddp_find_unused_parameters=False,
deepspeed=args.deepspeed,
seed=args.seed,
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
data_collator=collator,
)
trainer.train(resume_from_checkpoint=False)
final_dir = os.path.join(args.output_dir, "final_adapter")
trainer.save_model(final_dir)
processor.save_pretrained(final_dir)
print(f"SFT_DONE adapter={final_dir}")
if __name__ == "__main__":
main()
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