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#!/usr/bin/env python3
import argparse
import json
import os
import torch
from datasets import Dataset, load_dataset
from functools import partial
from glob import glob
from peft import LoraConfig
from transformers import (
AutoProcessor,
AutoModelForImageTextToText,
TrainingArguments,
BitsAndBytesConfig,
)
from trl import SFTTrainer
def add_chat_text(example, processor):
messages = [
{"role": "user", "content": example["user"]},
{"role": "assistant", "content": example["assistant"]},
]
example["text"] = processor.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=False,
)
return example
def collate(batch, processor, max_length):
images = [[i["image"]] for i in batch]
texts = [i["text"] for i in batch]
processor_output = processor(
text=texts,
images=images,
padding=True,
truncation=True,
max_length=max_length,
return_tensors="pt",
)
image_token_id = [
processor.tokenizer.convert_tokens_to_ids(
processor.tokenizer.special_tokens_map["boi_token"]
)
]
labels = processor_output["input_ids"].clone()
labels[labels == processor.tokenizer.pad_token_id] = -100
labels[labels == image_token_id] = -100
labels[labels == 262144] = -100
processor_output["labels"] = labels
return processor_output
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--model", default="google/gemma-3-4b-it")
parser.add_argument("--optimizer", default="adamw_torch_fused")
parser.add_argument("--gradient-checkpointing", action="store_true")
parser.add_argument("--batch-size", default=1, type=int)
parser.add_argument("--gradient-accumulation", default=4, type=int)
parser.add_argument("--learning-rate", default=2e-4, type=float)
parser.add_argument("--epochs", default=1, type=int)
parser.add_argument("--warmup-ratio", default=0.03, type=float)
parser.add_argument("--scheduler-type", default="constant", type=str)
parser.add_argument("--logging-steps", default=10, type=int)
parser.add_argument("--lora-alpha", default=32, type=int)
parser.add_argument("--lora-dropout", default=0.05, type=float)
parser.add_argument("--lora-r", default=16, type=int)
parser.add_argument("--bnb-4bit", action="store_true")
parser.add_argument("--max-grad-norm", default=1.0, type=float)
parser.add_argument("--max-length", default=512, type=int)
parser.add_argument("--lora-config", choices=["S", "M", "L"], default="M", type=str)
parser.add_argument("--dataset", default="asdf2k/caption_translation", type=str)
args = parser.parse_args()
if args.bnb_4bit:
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_quant_storage=torch.bfloat16,
)
else:
bnb_config = None
processor = AutoProcessor.from_pretrained(args.model, use_fast=True)
model = AutoModelForImageTextToText.from_pretrained(
args.model,
quantization_config=bnb_config,
device_map="auto",
low_cpu_mem_usage=True,
)
lora_kwargs = { "lora_alpha": args.lora_alpha,
"lora_dropout": args.lora_dropout,
"r": args.lora_r,
"task_type": "CAUSAL_LM",
"bias": "none",
}
if args.lora_config == "S":
lora_kwargs["target_modules"] = ["q_proj", "o_proj", "k_proj", "v_proj", "gate_proj", "up_proj", "down_proj"]
elif args.lora_config == "M":
lora_kwargs["target_modules"] = "all-linear"
elif args.lora_config == "L":
lora_kwargs["target_modules"] = "all-linear"
lora_kwargs["modules_to_save"] = ["lm_head", "embed_tokens"]
else:
sys.stderr.write(f"Unknown LoRa config: '{args.lora_config}'\n")
exit(1)
peft_config = LoraConfig(**lora_kwargs)
dataset = load_dataset(args.dataset)
dev_ds = dataset["dev"].map(partial(add_chat_text, processor=processor))
train_ds = dataset["train"].map(partial(add_chat_text, processor=processor))
training_args = TrainingArguments(
output_dir="gemma3-mm-sft-lora",
per_device_train_batch_size=args.batch_size,
gradient_accumulation_steps=args.gradient_accumulation,
num_train_epochs=args.epochs,
learning_rate=args.learning_rate,
warmup_ratio=args.warmup_ratio,
lr_scheduler_type=args.scheduler_type,
fp16=False,
bf16=True,
gradient_checkpointing=args.gradient_checkpointing,
gradient_checkpointing_kwargs={"use_reentrant": False},
optim=args.optimizer,
max_grad_norm=args.max_grad_norm,
remove_unused_columns=False,
logging_steps=args.logging_steps,
save_strategy="epoch",
)
trainer = SFTTrainer(
model=model,
train_dataset=train_ds,
eval_dataset=dev_ds,
data_collator=partial(collate, processor=processor, max_length=args.max_length),
args=training_args,
peft_config=peft_config,
)
trainer.train()
trainer.save_model()
if __name__ == "__main__":
main()
|
