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#!/usr/bin/env python
"""
Data module for Cosmopedia dataset
Author: Shilpaj Bhalerao
Date: 2025-01-20
"""
# Standard Library Imports
from typing import Optional
# Third-Party Imports
import pytorch_lightning as pl
from torch.utils.data import DataLoader
from datasets import load_dataset
from transformers import GPT2Tokenizer
# Local Imports
from config import DataConfig
class CosmopediaDataModule(pl.LightningDataModule):
"""
Data module for Cosmopedia dataset
"""
def __init__(
self,
batch_size: int = DataConfig.batch_size,
num_workers: int = DataConfig.num_workers,
shuffle_buffer_size: int = DataConfig.shuffle_buffer_size,
max_length: int = DataConfig.max_length,
):
"""
Constructor
:param batch_size: Batch size for dataloaders
:param num_workers: Number of workers for dataloaders
:param shuffle_buffer_size: Size of buffer for shuffling streaming data
:param max_length: Maximum sequence length for tokenized text
"""
super().__init__()
self.batch_size = batch_size
self.num_workers = num_workers
self.shuffle_buffer_size = shuffle_buffer_size
self.max_length = max_length
# Dataset path on HuggingFace
self.dataset_path = DataConfig.dataset_path
self.dataset_name = DataConfig.dataset_name
# Initialize tokenizer
self.tokenizer = GPT2Tokenizer.from_pretrained(DataConfig.tokenizer_path)
self.tokenizer.pad_token = self.tokenizer.eos_token
def setup(self, stage: Optional[str] = None):
"""
Setup datasets for training and validation
"""
# Load dataset in streaming mode
self.dataset = load_dataset(
self.dataset_path,
self.dataset_name,
split="train", # Only train split is available
streaming=DataConfig.streaming
)
# Shuffle the streaming dataset
self.dataset = self.dataset.shuffle(buffer_size=self.shuffle_buffer_size)
# Create train/val split using configured validation split
val_size = int(DataConfig.validation_split * self.shuffle_buffer_size)
self.train_dataset = self.dataset.skip(val_size)
self.val_dataset = self.dataset.take(val_size)
def collate_fn(self, batch):
"""
Tokenize and pad the texts in the batch
"""
texts = [item['text'] for item in batch]
# Tokenize all texts in the batch
encodings = self.tokenizer(
texts,
padding=True,
truncation=True,
max_length=self.max_length,
return_tensors='pt'
)
# Prepare inputs and labels for language modeling
input_ids = encodings['input_ids'][:, :-1]
labels = encodings['input_ids'][:, 1:]
attention_mask = encodings['attention_mask'][:, :-1]
return {
'input_ids': input_ids,
'labels': labels,
'attention_mask': attention_mask
}
def train_dataloader(self):
"""
Return train dataloader
"""
return DataLoader(
self.train_dataset,
batch_size=self.batch_size,
num_workers=self.num_workers,
pin_memory=DataConfig.pin_memory,
collate_fn=self.collate_fn
)
def val_dataloader(self):
"""
Return validation dataloader
"""
return DataLoader(
self.val_dataset,
batch_size=self.batch_size,
num_workers=self.num_workers,
pin_memory=DataConfig.pin_memory,
collate_fn=self.collate_fn
)
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