train_idefics2.py

import torch
from peft import LoraConfig, prepare_model_for_kbit_training, get_peft_model
from transformers import AutoProcessor, BitsAndBytesConfig, Idefics3ForConditionalGeneration
from datasets import load_dataset


DEVICE = "cuda:4"
PCI_BUS_ID=4
CUDA_VISIBLE_DEVICES=4
USE_LORA = False
USE_QLORA = True
model_id = "HuggingFaceM4/Idefics3-8B-Llama3"

processor = AutoProcessor.from_pretrained(
    model_id
)


if USE_QLORA or USE_LORA:
    lora_config = LoraConfig(
        r=8,
        lora_alpha=8,
        lora_dropout=0.1,
        target_modules=['down_proj','o_proj','k_proj','q_proj','gate_proj','up_proj','v_proj'],
        use_dora=False if USE_QLORA else True,
        init_lora_weights="gaussian"
    )
    lora_config.inference_mode = False
    if USE_QLORA:
        bnb_config = BitsAndBytesConfig(
            load_in_4bit=True,
            bnb_4bit_use_double_quant=True,
            bnb_4bit_quant_type="nf4",
            bnb_4bit_compute_dtype=torch.bfloat16
        )
        
    model = Idefics3ForConditionalGeneration.from_pretrained(
        model_id,
        quantization_config=bnb_config if USE_QLORA else None,
        _attn_implementation="flash_attention_2",
        device_map="auto"
    )
    model.add_adapter(lora_config)
    model.enable_adapters()
    model = prepare_model_for_kbit_training(model)
    model = get_peft_model(model, lora_config)
    print(model.get_nb_trainable_parameters())
    

else:
    model = Idefics3ForConditionalGeneration.from_pretrained(
        model_id,
        torch_dtype=torch.bfloat16,
        _attn_implementation="flash_attention_2",
    ).to(DEVICE)
    
    # if you'd like to only fine-tune LLM
    for param in model.model.vision_model.parameters():
        param.requires_grad = False

ds = load_dataset('merve/vqav2-small', trust_remote_code=True)
split_ds = ds["validation"].train_test_split(test_size=0.8)
train_ds = split_ds["train"]

image_token_id = processor.tokenizer.additional_special_tokens_ids[
            processor.tokenizer.additional_special_tokens.index("<image>")]

def collate_fn(examples):
  texts = []
  images = []
  for example in examples:
      image = example["image"]
      question = example["question"]
      answer = example["multiple_choice_answer"]
      messages = [
          {
              "role": "user",
              "content": [
                  {"type": "text", "text": "Answer briefly."},
                  {"type": "image"},
                  {"type": "text", "text": question}
              ]
          },
          {
              "role": "assistant",
              "content": [
                  {"type": "text", "text": answer}
              ]
          }
      ]
      text = processor.apply_chat_template(messages, add_generation_prompt=False)
      texts.append(text.strip())
      images.append([image])

  batch = processor(text=texts, images=images, return_tensors="pt", padding=True)
  labels = batch["input_ids"].clone()
  labels[labels == processor.tokenizer.pad_token_id] = -100
  labels[labels == image_token_id] = -100 
  batch["labels"] = labels

  return batch

from transformers import TrainingArguments, Trainer

training_args = TrainingArguments(
    num_train_epochs=1,
    per_device_train_batch_size=1, # increase for QLoRA
    gradient_accumulation_steps=8,
    warmup_steps=50,
    learning_rate=1e-4,
    weight_decay=0.01,
    logging_steps=25,
    save_strategy="steps",
    save_steps=250,
    save_total_limit=1,
    optim="adamw_hf", # for 8-bit, pick paged_adamw_hf
    #evaluation_strategy="epoch",
    bf16=True,
    output_dir="./idefics3-llama-vqav2",
    hub_model_id="idefics3-llama-vqav2",
    remove_unused_columns=False,
)

trainer = Trainer(
    model=model,
    args=training_args,
    data_collator=collate_fn,
    train_dataset=train_ds,
)

trainer.train()
trainer.push_to_hub()