trainer_lora.py

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

import torch
from transformers import VisionEncoderDecoderModel, TrOCRProcessor, Seq2SeqTrainer, Seq2SeqTrainingArguments, default_data_collator, EarlyStoppingCallback, TrainerCallback, TrainingArguments, TrainerState, TrainerControl
from peft import LoraConfig, get_peft_model
from transformers import VisionEncoderDecoderConfig

from data import AphaPenDataset
import evaluate
from sklearn.model_selection import train_test_split

from src.calibrator import EncoderDecoderCalibrator
from src.loss import MarginLoss, KLRegularization
from src.similarity import CERSimilarity
from datetime import datetime
from torch.utils.data import ConcatDataset
import wandb



# @dataclass
# class ScriptArguments:
#     """
#     The name of the OCR model we wish to fine with Seq2SeqTrainer
#     """
#     samp_size: Optional[int] = field(default=0, metadata={"help": "the additional sample size"})

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

samp_list = [1, 15000, 30000, 45000, 60000, 70000]


model_name = "microsoft/trocr-large-handwritten"
# # 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=0.02, 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)

df_path_b2= "/mnt/data1/Datasets/AlphaPen/" + "training_b2.csv"
df_b2 = pd.read_csv(df_path_b2)
df_b2.dropna(inplace=True)
train_df_b2, test_df_b2 = train_test_split(df_b2, test_size=0.01, random_state=0)
# we reset the indices to start from zero
train_df_b2.reset_index(drop=True, inplace=True)
test_df_b2.reset_index(drop=True, inplace=True)

root_dir = "/mnt/data1/Datasets/OCR/Alphapen/clean_data/final_cropped_rotated_"
root_dir_b2 = "/mnt/data1/Datasets/OCR/Alphapen/DataBatch2/clean_data/cropped_data/cropped_"
processor = TrOCRProcessor.from_pretrained(model_name)

train_dataset_b1 = AphaPenDataset(root_dir=root_dir, df=train_df,  processor=processor)
eval_dataset_b1 = AphaPenDataset(root_dir=root_dir, df=test_df,  processor=processor)

eval_dataset_b2 = AphaPenDataset(root_dir=root_dir_b2, df=test_df_b2,  processor=processor)

# train_dataset = ConcatDataset([train_dataset_b1, train_dataset_b2])
eval_dataset = ConcatDataset([eval_dataset_b1, eval_dataset_b2])


# config = VisionEncoderDecoderConfig.from_pretrained(model_name)
# config.decoder.vocab_size = config.decoder.decoder_vocab_size
# Step 2: Load the model
model = VisionEncoderDecoderModel.from_pretrained(model_name)

# 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
# for peft
# model.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 = 64
model.config.early_stopping = True
model.config.no_repeat_ngram_size = 3
model.config.length_penalty = 2.0
model.config.num_beams = 4

# print(model.config)
# LoRa
lora_config = LoraConfig(
    r=1,
    lora_alpha=8,
    lora_dropout=0.1,
    target_modules=[
        'query',
        'key',
        'value',
        'intermediate.dense',
        'output.dense',
        #'wte',
        #'wpe',
        #'c_attn',
        #'c_proj',
        #'q_attn',
        #'c_fc'
    ],
    # task_type="SEQ_2_SEQ_LM"
)
model = get_peft_model(model, lora_config)
# model.add_adapter(lora_config)
# print(model.config)

# tokenizer = processor.tokenizer
# sim = CERSimilarity(tokenizer)
# loss = MarginLoss(sim, beta=0.1, num_samples=60)
# reg = KLRegularization(model)
# calibrator = EncoderDecoderCalibrator(model, loss, reg, 15, 15)

# from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR
for samp in samp_list:
    os.environ["WANDB_PROJECT"] = "Alphapen-TrOCR"
    train_dataset_b2 = AphaPenDataset(root_dir=root_dir_b2, df=train_df_b2.iloc[:samp,:],  processor=processor)

    train_dataset = ConcatDataset([train_dataset_b1, train_dataset_b2])

        
    # # Step 3: Define the training arguments
    training_args = Seq2SeqTrainingArguments(
        predict_with_generate=True,
        evaluation_strategy="steps",
        per_device_train_batch_size=8,
        per_device_eval_batch_size=8,
        bf16=True,
        bf16_full_eval=True,
        output_dir="./",
        logging_steps=100,
        save_steps=1000,
        eval_steps=500,
        report_to="wandb",
        optim="adamw_torch_fused",
        lr_scheduler_type="cosine",
        gradient_accumulation_steps=2,
        learning_rate=1.0e-4,
        max_steps=15000,
        # run_name=f"trocr-LoRA-{datetime.now().strftime('%Y-%m-%d-%H-%M-%s')}",
        run_name="trocr-LoRA-large_" + str(samp),
        push_to_hub=True,
        hub_model_id="hadrakey/alphapen_trocr_large_" + str(samp),
    )

    # 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)
        pred_str = [word.lower() for word in pred_str]
        label_str = [word.lower() for word in 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}

    # # Step 5: Define the Trainer
    trainer = Seq2SeqTrainer(
        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,
        # callbacks=[SavePeftModelCallback]
    )

    trainer.train()
    wandb.finish()