dataload.py

import pandas as pd
import torch
from torch.utils.data import Dataset, DataLoader
from transformers import AutoTokenizer, PreTrainedTokenizer
import os

# --- 1. CONFIGURATION AND FILE LOADING ---

# Replace 'path/to/your/converted_data.csv' with the actual path
FILE_PATH = 'converted_data.csv'
KAGGLE_PATH = '/kaggle/'
MAX_LENGTH = 128  # Maximum sequence length for the model

def load_data(this_dir_path,file_path=FILE_PATH):
    """Loads the CSV, selecting Sinhala as source and English as target."""
    print(f"Loading data from: {this_dir_path}{file_path}")
    
    f_path = this_dir_path+file_path
    print(f"f_path:{f_path}")
    if os.path.exists(f_path):
        print(f"exists?{f_path}")
        df = pd.read_csv(f_path, usecols=['Sinhala', 'English'])
        print(f"df:{len(df)}")
    else:
        print("Note: File not found. Using dummy data for demonstration.")
        data = {
            'Sinhala': [
                "ඔබට කොහොමද?", 
                "හෙට මම එනවා.", 
                "කරුණාකර උදව් කරන්න."
            ],
            'English': [
                "How are you?", 
                "I will come tomorrow.", 
                "Please help."
            ],
            'Singlish': [
                "Oya kohomada?", 
                "Heta mama enawa.", 
                "Karunakara udaw karanna."
            ]
        }
        df = pd.DataFrame(data, columns=['Sinhala', 'English', 'Singlish'])
        # Select the columns you need for the task
        df = df[['Sinhala', 'English']]
        
    print(f"Loaded {len(df)} samples.")
    return df

# --- 2. TOKENIZER SETUP ---

def setup_tokenizer():
    """

    Initializes a GPT-2 tokenizer and adds special tokens for Seq2S

    eq.



    NOTE ON SINHALA:

    GPT-2 was primarily trained on English text. For better Sinhala support, 

    you would ideally use a multilingual tokenizer (like XLM-R or mBART) 

    or train a custom tokenizer using your entire dataset.

    

    For this preliminary code, we use a standard GPT2Tokenizer and add the 

    necessary control tokens.

    """
    
    # Using 'gpt2' base tokenizer
    tokenizer = AutoTokenizer.from_pretrained("gpt2")
    
    # Define custom tokens needed for sequence-to-sequence:
    # <SEP> : Separator between the Source (Sinhala) and Target (English) sentence
    # <PAD> : Padding token (GPT2 doesn't have one by default, crucial for batching)
    
    new_tokens = {
        'pad_token': '<PAD>',
        'sep_token': '<SEP>',
    }
    
    # Add new tokens and resize the tokenizer vocabulary
    num_added_toks = tokenizer.add_special_tokens(new_tokens)
    
    # Set the padding side to 'left' or 'right'. 
    # For decoder-only models (like GPT-2), 'left' padding is often preferred
    # for faster attention, but 'right' is also common. We'll use 'right'.
    tokenizer.padding_side = "right" 
    
    print(f"Added {num_added_toks} custom tokens.")
    print(f"BOS Token: {tokenizer.bos_token} ({tokenizer.bos_token_id})")
    print(f"SEP Token: {tokenizer.sep_token} ({tokenizer.sep_token_id})")
    
    return tokenizer

# --- 3. PYTORCH CUSTOM DATASET ---

class TranslationDataset(Dataset):
    """

    Custom Dataset to prepare data for a decoder-only model (GPT-2) 

    used in a sequence-to-sequence (translation) task.

    """
    def __init__(self, data_frame: pd.DataFrame, tokenizer: PreTrainedTokenizer, max_length: int):
        self.tokenizer = tokenizer
        self.data = data_frame
        self.max_length = max_length

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        # 1. Retrieve the Source (Sinhala) and Target (English) sentences
        source_text = self.data.iloc[idx]['Sinhala']
        target_text = self.data.iloc[idx]['English']

        # 2. Construct the single sequence for CLM (Causal Language Modeling)
        # Format: <BOS> Sinhala_Sentence <SEP> English_Sentence <EOS>
        full_sequence = (
            self.tokenizer.bos_token + 
            source_text + 
            self.tokenizer.sep_token + 
            target_text + 
            self.tokenizer.eos_token
        )

        # 3. Tokenize and truncate
        tokenized_sequence = self.tokenizer(
            full_sequence,
            max_length=self.max_length,
            truncation=True,
            return_tensors='pt'  # Return PyTorch tensors
        )
        
        # Squeeze to remove the batch dimension (which is 1 here)
        input_ids = tokenized_sequence['input_ids'].squeeze(0)
        attention_mask = tokenized_sequence['attention_mask'].squeeze(0)
        
        # 4. Prepare Labels for CLM Loss (Shifted Input)
        # In PyTorch's GPT2 implementation, the model shifts the labels internally.
        # We just need to pass the input_ids as labels.
        # labels = input_ids.clone() 
        
        # --- IMPORTANT: Loss Masking for Translation ---
        # We only want the model to calculate loss over the GENERATED tokens 
        # (the English/target part).
        # PyTorch's CrossEntropyLoss ignores targets with a value of -100.
        
        # Find the index of the <SEP> token (start of the target sequence)
        sep_token_id = self.tokenizer.sep_token_id
        sep_index = (input_ids == sep_token_id).nonzero(as_tuple=True)[0]
        
        # Check if the SEP token exists (it should, unless truncated out)
        if sep_index.numel() > 0:
            # Mask out the Source part and the <SEP> token itself
            # Source tokens, <BOS>, and <SEP> are set to -100
            mask_end_index = sep_index[0].item()
            
            # Initialize labels as a copy of input_ids
            labels = input_ids.clone()
            
            # Set the Source sentence tokens (including <BOS> and <SEP>) to -100
            labels[:mask_end_index + 1] = -100
            
        else:
            # If <SEP> is not found (due to truncation), mask the whole sequence
            labels = torch.full_like(input_ids, -100)
        
        return {
            'input_ids': input_ids,
            'attention_mask': attention_mask,
            'labels': labels
        }


# --- 4. DATA COLLATOR AND DATALOADER SETUP ---

def data_collator_fn(batch_list, pad_token_id):
    """

    Custom collate function to handle padding for the batch.

    """
    
    # Stack the tensors for padding
    input_ids = [item['input_ids'] for item in batch_list]
    attention_mask = [item['attention_mask'] for item in batch_list]
    labels = [item['labels'] for item in batch_list]

    # Padding function
    # NOTE: Since we set padding_side="right" in the tokenizer, 
    # the standard pad_sequence handles this correctly.
    
    input_ids_padded = torch.nn.utils.rnn.pad_sequence(
        input_ids, batch_first=True, padding_value=pad_token_id
    )
    attention_mask_padded = torch.nn.utils.rnn.pad_sequence(
        attention_mask, batch_first=True, padding_value=0 # Attention mask uses 0 for padding
    )
    labels_padded = torch.nn.utils.rnn.pad_sequence(
        labels, batch_first=True, padding_value=-100 # Labels use -100 for padding/masking
    )

    return {
        'input_ids': input_ids_padded,
        'attention_mask': attention_mask_padded,
        'labels': labels_padded
    }


# --- MAIN EXECUTION BLOCK ---
if __name__ == '__main__':
    # 1. Load Data
    data_df = load_data(FILE_PATH)

    # 2. Setup Tokenizer
    tokenizer = setup_tokenizer()

    # 3. Create Dataset
    translation_dataset = TranslationDataset(data_df, tokenizer, MAX_LENGTH)

    # 4. Create DataLoader
    BATCH_SIZE = 4
    
    # Pass a lambda function to the collate_fn argument, 
    # which binds the tokenizer's pad_token_id
    train_dataloader = DataLoader(
        translation_dataset,
        batch_size=BATCH_SIZE,
        shuffle=True,
        collate_fn=lambda batch_list: data_collator_fn(batch_list, tokenizer.pad_token_id)
    )

    print("\n--- DataLoader Example ---")
    print(f"Total batches: {len(train_dataloader)}")

    # 5. Inspect a single batch
    for batch in train_dataloader:
        print(f"\nBatch 'input_ids' shape: {batch['input_ids'].shape}")
        print(f"Batch 'labels' shape: {batch['labels'].shape}")
        
        # Print a sample from the batch to verify masking
        sample_index = 0
        print("\n--- Sample 1 Verification ---")
        
        # Decode the full input sequence
        print("Input Sequence (Decoded):")
        print(tokenizer.decode(batch['input_ids'][sample_index], skip_special_tokens=False))
        
        # Find all tokens that are NOT masked (-100)
        target_tokens = batch['labels'][sample_index].clone()
        target_tokens[target_tokens == -100] = tokenizer.pad_token_id # replace -100 with PAD for decoding
        
        print("\nTarget Labels (Decoded - Only English should be visible):")
        print(tokenizer.decode(target_tokens, skip_special_tokens=True))
        
        print("\nRaw Labels Tensor (Verifying Masking):")
        # Show a snippet of the labels tensor to confirm -100 for the source part
        print(batch['labels'][sample_index]) 
        
        break # Stop after inspecting the first batch

    # You can now iterate over `train_dataloader` to get batches for training.


    if __name__ == '__main__':
        df = load_data(FILE_PATH) # data frame
        df.head()