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
|
#!/usr/bin/env python3
import argparse
import json
import os
import torch
from datasets import Dataset, Image, 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("--lora-small", 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")
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("asdf2k/caption_translation")
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()
|
