finetune_phi3_vision.py

from datasets import Dataset, DatasetDict, Image
import pandas as pd
import os

import torch
from peft import LoraConfig
from transformers import AutoProcessor, BitsAndBytesConfig
from transformers import AutoModelForCausalLM, AutoModelForVision2Seq
from datetime import datetime
import evaluate
from transformers import TrainingArguments, Trainer, Seq2SeqTrainer, Seq2SeqTrainingArguments
from sklearn.model_selection import train_test_split

import random

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, examples):
        texts = []
        images = []
        for example in examples:
            image = example["image"]
            # print(example["query"])
            question = example["query"]
            answer = example["answers"]
            messages = [
                {
                    "role": "user",
                    "content": [
                        {"type": "text", "text": "OCR the text in the image."},
                        {"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] = self.image_token_id
        batch["labels"] = labels

        return batch
        
# Define train and test size.
TRAIN_SAMPLES = 1000
TEST_SAMPLES = 200
TEST_SIZE = 0.166 #
samp_list = [1, 15000, 30000, 45000, 60000, 70000]

# Define the directory containing the images.
df_path = "/mnt/data1/Datasets/AlphaPen/" + "training_data.csv"
df = pd.read_csv(df_path)
df.dropna(inplace=True)
df["id"] = range(df.shape[0])
df["query"] = "What is shown in this image?"
train_df, test_df = train_test_split(df, test_size=0.02, random_state=0)

root_dir = "/mnt/data1/Datasets/OCR/Alphapen/clean_data/final_cropped_rotated_"
image_paths_train = [root_dir + img for img in train_df.filename]
image_paths_test = [root_dir + img for img in test_df.filename]

# New batch
df_path_2 = "/mnt/data1/Datasets/AlphaPen/" + "training_b2.csv"
df_2 = pd.read_csv(df_path_2)
df_2.dropna(inplace=True)
df_2["id"] = range(df_2.shape[0])
df_2["query"] = "What is shown in this image?"
train_df_b2, test_df_b2 = train_test_split(df_2, test_size=0.01, random_state=0)

root_dir_2 = "/mnt/data1/Datasets/OCR/Alphapen/DataBatch2/clean_data/cropped_data/cropped_"
image_paths_2_train = [root_dir_2 + img for img in train_df_b2.filename]
image_paths_2_test = [root_dir_2 + img for img in test_df_b2.filename]


ids_test = range(test_df.shape[0] + test_df_b2.shape[0])
queries_test = test_df['query'].tolist() + test_df_b2['query'].tolist()
answers_test = test_df['text'].tolist() + test_df_b2['text'].tolist()

# Create the dataset dictionary.


eval_dataset_dict = {
    'id': ids_test,
    'image': image_paths_test + image_paths_2_test,
    'query': queries_test,
    'answers': answers_test
}

# Create the dataset.

eval_dataset = Dataset.from_dict(eval_dataset_dict)

# Cast the 'image' column to Image type.

eval_dataset = eval_dataset.cast_column("image", Image())

# Split the dataset into train and test.
# split_dataset = dataset.train_test_split(test_size=TEST_SIZE, shuffle=False)

# train_dataset = split_dataset["train"]
# eval_dataset  = split_dataset["test"]
print(len(eval_dataset))
# Push the dataset on Hugging Face Hub.
# split_dataset.push_to_hub("NSTiwari/DocumentIDEFICS_QA")


# Define model ID
# model_id = "microsoft/Phi-3-vision-128k-instruct"
model_id =  "HuggingFaceM4/idefics2-8b"

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

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

# print(processor.tokenizer.additional_special_tokens.index("<image>"))
if USE_QLORA or USE_LORA:
    lora_config = LoraConfig(
        r=64,
        lora_alpha=16,
        lora_dropout=0.1,
    #     target_modules= [
    #     "q_proj",
    #     "k_proj",
    #     "v_proj",
    #     "o_proj",
    #     "gate_proj",
    #     "up_proj",
    #     # "down_proj",
    # ], 
        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 = AutoModelForVision2Seq.from_pretrained(
        model_id,
        torch_dtype=torch.float16,
        quantization_config=bnb_config if USE_QLORA else None,
        trust_remote_code=True
    )
    model.config.decoder_start_token_id = processor.tokenizer.cls_token_id
    model.config.pad_token_id = processor.tokenizer.pad_token_id
    model.config.max_length= 128
    model.add_adapter(lora_config)
    model.enable_adapters()
else:
    model = AutoModelForVision2Seq.from_pretrained(
        model_id,
        torch_dtype=torch.float16,
        _attn_implementation="flash_attention_2", # Need GPUs like A100 or H100.
        trust_remote_code=True
    ).to(DEVICE)





data_collator = MyDataCollator(processor)


for samp in samp_list:
    os.environ["WANDB_PROJECT"]="Alphapen"
    # Create a list of other columns such as id, query, and answer.
    ids_train = range(train_df.shape[0] + train_df_b2.shape[0])
    queries_train = train_df['query'].tolist() + train_df_b2['query'].tolist()
    answers_train = train_df['text'].tolist() + train_df_b2['text'].tolist()

    train_dataset_dict = {
    'id': ids_train,
    'image': image_paths_train + image_paths_2_train,
    'query': queries_train,
    'answers': answers_train
    }

    train_dataset = Dataset.from_dict(train_dataset_dict)
    train_dataset = train_dataset.cast_column("image", Image())

    training_args = Seq2SeqTrainingArguments(
        predict_with_generate=True,
        output_dir = "idefics2",
        learning_rate = 2e-4,
        fp16 = True,
        per_device_train_batch_size = 8,
        per_device_eval_batch_size = 8,
        gradient_accumulation_steps = 2,
        dataloader_pin_memory = False,
        save_total_limit = 3,
        eval_strategy ="steps",
        save_strategy = "steps",
        eval_steps = 500,
        save_steps = 1000,
        max_steps = 5000,
        logging_steps = 10,
        remove_unused_columns = False,
        push_to_hub=True,
        label_names = ["labels"],
        load_best_model_at_end = False,
        report_to = "wandb",
        optim = "paged_adamw_8bit",
        # run_name=f"idefics2-vision-LoRA-{datetime.now().strftime('%Y-%m-%d-%H-%M-%s')}",
        run_name="idefics2-vision-LoRA-" + str(samp),
        hub_model_id="hadrakey/alphapen_idefics2_" + str(samp),
    )

    def compute_metrics(pred):
        # accuracy_metric = evaluate.load("precision")
        cer_metric = evaluate.load("cer")

        labels_ids = pred.label_ids
        pred_ids = pred.predictions
        # print(pred_ids)
        # print(labels_ids)
        # max_length = max(pred_ids.shape[1], labels_ids.shape[1])
        # generated_texts = processor.batch_decode(generated_ids[:, inputs["input_ids"].size(1):], skip_special_tokens=True)
        pred_str = processor.batch_decode(pred_ids, skip_special_tokens=True)
        pred_str = [word.lower() for word in pred_str]
        # print(pred_str)
        # pred_str = processor.batch_decode(pred_ids, skip_special_tokens=True)
        labels_ids[labels_ids == -100] = processor.tokenizer.pad_token_id
        label_str = processor.batch_decode(labels_ids, skip_special_tokens=True)
        label_str = [word.lower() for word in label_str]
        # print(label_str)
        cer = cer_metric.compute(predictions=pred_str, references=label_str)
        # accuracy = accuracy_metric.compute(predictions=pred_ids.tolist(), references=labels_ids.tolist())

        return {"cer": cer}


    trainer = Seq2SeqTrainer(
        model = model,
        args = training_args,
        data_collator = data_collator,
        train_dataset = train_dataset,
        eval_dataset = eval_dataset,
        compute_metrics=compute_metrics,
    )

    trainer.train()