model_sft.py

from dataclasses import dataclass, field
from typing import Optional
import pandas as pd

import torch
from accelerate import Accelerator
from datasets import load_dataset, Dataset, load_metric
from peft import LoraConfig
from tqdm import tqdm
from transformers import AutoModelForCausalLM, BitsAndBytesConfig, HfArgumentParser, TrainingArguments, VisionEncoderDecoderModel, TrOCRProcessor, Seq2SeqTrainer, Seq2SeqTrainingArguments, default_data_collator, EarlyStoppingCallback


from trl import SFTTrainer, is_xpu_available
from data import AphaPenDataset
import evaluate
from sklearn.model_selection import train_test_split

import torchvision.transforms as transforms
# from utils import compute_metrics 

tqdm.pandas()


# Define and parse arguments.
@dataclass
class ScriptArguments:
    """
    The name of the OCR model we wish to fine with Seq2SeqTrainer
    """

    model_name: Optional[str] = field(default="microsoft/trocr-base-handwritten", metadata={"help": "the model name"})
    dataset_name: Optional[str] = field(
        default="Anthropic/hh-rlhf", metadata={"help": "the dataset name"}
    )
    log_with: Optional[str] = field(default="none", metadata={"help": "use 'wandb' to log with wandb"})
    learning_rate: Optional[float] = field(default=1.41e-5, metadata={"help": "the learning rate"})
    batch_size: Optional[int] = field(default=8, metadata={"help": "the batch size"})
    seq_length: Optional[int] = field(default=512, metadata={"help": "Input sequence length"})
    gradient_accumulation_steps: Optional[int] = field(
        default=16, metadata={"help": "the number of gradient accumulation steps"}
    )
    load_in_8bit: Optional[bool] = field(default=False, metadata={"help": "load the model in 8 bits precision"})
    load_in_4bit: Optional[bool] = field(default=False, metadata={"help": "load the model in 4 bits precision"})
    use_peft: Optional[bool] = field(default=False, metadata={"help": "Wether to use PEFT or not to train adapters"})
    trust_remote_code: Optional[bool] = field(default=False, metadata={"help": "Enable `trust_remote_code`"})
    output_dir: Optional[str] = field(default="output", metadata={"help": "the output directory"})
    peft_lora_r: Optional[int] = field(default=64, metadata={"help": "the r parameter of the LoRA adapters"})
    peft_lora_alpha: Optional[int] = field(default=16, metadata={"help": "the alpha parameter of the LoRA adapters"})
    logging_steps: Optional[int] = field(default=1, metadata={"help": "the number of logging steps"})
    use_auth_token: Optional[bool] = field(default=True, metadata={"help": "Use HF auth token to access the model"})
    num_train_epochs: Optional[int] = field(default=3, metadata={"help": "the number of training epochs"})
    max_steps: Optional[int] = field(default=-1, metadata={"help": "the number of training steps"})
    max_length: Optional[int] = field(default=10, metadata={"help": "the maximum length"})
    no_repeat_ngram_size: Optional[int] = field(default=3, metadata={"help": "the number of repeat"})
    length_penalty: Optional[float] = field(default=2.0, metadata={"help": "the length of penalty"})
    num_beams: Optional[int] = field(default=3, metadata={"help": "the number of beam search"})
    early_stopping: Optional[bool] = field(default=True, metadata={"help": "Early stopping"})
    save_steps: Optional[int] = field(
        default=1000, metadata={"help": "Number of updates steps before two checkpoint saves"}
    )
    save_total_limit: Optional[int] = field(default=10, metadata={"help": "Limits total number of checkpoints."})
    push_to_hub: Optional[bool] = field(default=False, metadata={"help": "Push the model to HF Hub"})
    gradient_checkpointing: Optional[bool] = field(
        default=False, metadata={"help": "Whether to use gradient checkpointing or no"}
    )
    gradient_checkpointing_kwargs: Optional[dict] = field(
        default=None,
        metadata={
            "help": "key word arguments to be passed along `torch.utils.checkpoint.checkpoint` method - e.g. `use_reentrant=False`"
        },
    )
    hub_model_id: Optional[str] = field(default=None, metadata={"help": "The name of the model on HF Hub"})

parser = HfArgumentParser(ScriptArguments)
script_args = parser.parse_args_into_dataclasses()[0]

# # Step 1: Load the dataset
df_path = "/mnt/data1/Datasets/AlphaPen/" + "training_data.csv"
df = pd.read_csv(df_path)
df.dropna(inplace=True)
train_df, test_df = train_test_split(df, test_size=0.15, random_state=0)
# we reset the indices to start from zero
train_df.reset_index(drop=True, inplace=True)
test_df.reset_index(drop=True, inplace=True)
root_dir = "/mnt/data1/Datasets/OCR/Alphapen/clean_data/final_cropped_rotated_"
processor = TrOCRProcessor.from_pretrained(script_args.model_name)

 # Transformation for training including augmentations
transform = transforms.Compose([ 
    transforms.PILToTensor(),
    transforms.RandomRotation(degrees=(0, 180)) 
]) 
  


train_dataset = AphaPenDataset(root_dir=root_dir, df=train_df,  processor=processor, transform=transform)
eval_dataset = AphaPenDataset(root_dir=root_dir, df=test_df,  processor=processor)

# Step 2: Load the model
if script_args.load_in_8bit and script_args.load_in_4bit:
    raise ValueError("You can't load the model in 8 bits and 4 bits at the same time")
elif script_args.load_in_8bit or script_args.load_in_4bit:
    quantization_config = BitsAndBytesConfig(
        load_in_8bit=script_args.load_in_8bit, load_in_4bit=script_args.load_in_4bit
    )
    # Copy the model to each device
    device_map = (
        {"": f"xpu:{Accelerator().local_process_index}"}
        if is_xpu_available()
        else {"": Accelerator().local_process_index}
    )
    torch_dtype = torch.bfloat16
else:
    device_map = None
    quantization_config = None
    torch_dtype = None

model = VisionEncoderDecoderModel.from_pretrained(
    script_args.model_name,
    quantization_config=quantization_config,
    device_map=device_map,
    trust_remote_code=script_args.trust_remote_code,
    torch_dtype=torch_dtype,
    token=script_args.use_auth_token,
)

# set special tokens used for creating the decoder_input_ids from the labels
model.config.decoder_start_token_id = processor.tokenizer.cls_token_id
model.config.pad_token_id = processor.tokenizer.pad_token_id
# make sure vocab size is set correctly
model.config.vocab_size = model.config.decoder.vocab_size

# set beam search parameters
model.config.eos_token_id = processor.tokenizer.sep_token_id
model.config.max_length = script_args.max_length
model.config.early_stopping = script_args.early_stopping
model.config.no_repeat_ngram_size = script_args.no_repeat_ngram_size
model.config.length_penalty = script_args.length_penalty
model.config.num_beams = script_args.num_beams




# # Step 3: Define the training arguments
training_args = Seq2SeqTrainingArguments(
    predict_with_generate=True,
    evaluation_strategy="steps",
    # per_device_train_batch_size=script_args.batch_size,
    # per_device_eval_batch_size=script_args.batch_size,
    fp16=True, 
    output_dir=script_args.output_dir,
    logging_steps=script_args.logging_steps,
    save_steps=script_args.save_steps,
    eval_steps=100,
    save_total_limit=script_args.save_total_limit,
    load_best_model_at_end = True,
    report_to=script_args.log_with,
    num_train_epochs=script_args.num_train_epochs,
    push_to_hub=script_args.push_to_hub,
    hub_model_id=script_args.hub_model_id,
    gradient_checkpointing=script_args.gradient_checkpointing,
    auto_find_batch_size=True,
    metric_for_best_model="eval/cer"
    # TODO: uncomment that on the next release 
    # gradient_checkpointing_kwargs=script_args.gradient_checkpointing_kwargs,
)


# Step 4: Define a metric

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

    labels_ids = pred.label_ids
    pred_ids = pred.predictions

    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)

    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}

early_stop = EarlyStoppingCallback(10, .001)

# Step 5: Define the LoraConfig
if script_args.use_peft:
    peft_config = LoraConfig(
        r=script_args.peft_lora_r,
        lora_alpha=script_args.peft_lora_alpha,
        bias="none",
        task_type="CAUSAL_LM",
        target_modules="all-linear"
    )
else:
    peft_config = None
# # Step 6: Define the Trainer
trainer = SFTTrainer(
    model=model,
    tokenizer=processor.feature_extractor,
    args=training_args,
    compute_metrics=compute_metrics,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    data_collator=default_data_collator,
    peft_config=peft_config,
    callbacks=[EarlyStoppingCallback(early_stopping_patience=10)]
)

trainer.train()

# # Step 6: Save the model
# trainer.save_model(script_args.output_dir)