learn.py

# Copyright (C) 2024 Ronan Le Meillat
# License: Apache License 2.0
# Description: Train the model on the dataset
import os
import torch

from huggingface_hub import login as hf_login
from datasets import load_dataset
from peft import LoraConfig
from transformers import AutoProcessor, BitsAndBytesConfig, Idefics3ForConditionalGeneration, TrainingArguments, Trainer
from datasets.utils.logging import disable_progress_bar
disable_progress_bar()

HF_TOKEN = ""

if os.environ.get('HF_TOKEN') is not None:
  HF_TOKEN = os.environ.get('HF_TOKEN')
  print(f"Hugging Face token found in environment variable")

hf_login(
  token=HF_TOKEN,
  add_to_git_credential=True
)
dataset_id = "eltorio/ROCO-radiology"
prompt= "You are an expert radiologist certified with over 15 years of experience in diagnostic imaging, describe this image"
source_model_id = "HuggingFaceM4/Idefics3-8B-Llama3"
destination_model_id = "eltorio/ROCO-idefics3-8B"
output_dir = "IDEFICS3_ROCO"
cache_dir = "/workspace/data"
train_dataset = load_dataset(dataset_id, split="train", cache_dir=cache_dir)

DEVICE = "cuda:0"
USE_LORA = False
USE_QLORA = True

processor = AutoProcessor.from_pretrained(
    source_model_id,
    do_image_splitting=False
)

if USE_QLORA or USE_LORA:
    lora_config = LoraConfig(
        r=8,
        lora_alpha=8,
        lora_dropout=0.1,
        target_modules='.*(text_model|modality_projection|perceiver_resampler).*(down_proj|gate_proj|up_proj|k_proj|q_proj|v_proj|o_proj).*$',
        use_dora=False if USE_QLORA else True,
        init_lora_weights="gaussian"
    )
    if USE_QLORA:
        bnb_config = BitsAndBytesConfig(
            load_in_4bit=True,
            bnb_4bit_quant_type="nf4",
            bnb_4bit_compute_dtype=torch.float16
        )
    model = Idefics3ForConditionalGeneration.from_pretrained(
        source_model_id,
        torch_dtype=torch.float16,
        quantization_config=bnb_config if USE_QLORA else None,
    )
    model.add_adapter(lora_config)
    model.enable_adapters()
else:
    model = Idefics3ForConditionalGeneration.from_pretrained(
        source_model_id,
        torch_dtype=torch.float16,
        _attn_implementation="flash_attention_2", # This works for A100 or H100
    ).to(DEVICE)
    
class MyDataCollator:
    def __init__(self, processor):
        self.processor = processor
        self.image_token_id = processor.tokenizer.additional_special_tokens_ids[
            processor.tokenizer.additional_special_tokens.index("<image>")
        ]

    def __call__(self, samples):
        texts = []
        images = []
        for sample in samples:
            image = sample["image"]
            answer = sample["caption"]
            messages = [
                {
                    "role": "system",
                    "content": [
                        {"type": "text", "text": prompt}
                    ]

                },
                {
                    "role": "user",
                    "content": [
                        {"type": "image"},
                    ]
                },
                {
                    "role": "assistant",
                    "content": [
                        {"type": "text", "text": answer}
                    ]
                }
            ]
            text = processor.apply_chat_template(messages, add_generation_prompt=False)
            texts.append(text.strip())
            images.append([image.convert('RGB')])

        batch = processor(text=texts, images=images, return_tensors="pt", padding=True)

        labels = batch["input_ids"].clone()
        labels[labels == processor.tokenizer.pad_token_id] = self.image_token_id
        batch["labels"] = labels

        return batch

data_collator = MyDataCollator(processor)


training_args = TrainingArguments(
    output_dir = output_dir,
    overwrite_output_dir = False,
    auto_find_batch_size = True,
    learning_rate = 2e-4,
    fp16 = True,
    per_device_train_batch_size = 2,
    per_device_eval_batch_size = 2,
    gradient_accumulation_steps = 8,
    dataloader_pin_memory = False,
    save_total_limit = 3,
    evaluation_strategy = None,
    save_strategy = "steps",
    eval_steps = 100,
    save_steps = 10, # checkpoint each 10 steps
    resume_from_checkpoint = True,
    logging_steps = 5,
    remove_unused_columns = False,
    push_to_hub = True,
    label_names = ["labels"],
    load_best_model_at_end = False,
    report_to = "none",
    optim = "paged_adamw_8bit",
)

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

trainer.train()