dataset.py

import torch
from torch import nn
from torch.utils.data import Dataset

class BilingualDataset(Dataset):
    def __init__(self, ds, tokenizer_src, tokenizer_tgt, src_lang, tgt_lang, seq_len):
        super(BilingualDataset, self).__init__()


        self.ds = ds
        self.tokenizer_src = tokenizer_src
        self.tokenizer_tgt = tokenizer_tgt
        self.src_lang = src_lang
        self.tgt_lang = tgt_lang
        self.seq_len = seq_len

        self.sos_token = torch.tensor([tokenizer_src.token_to_id('[SOS]')],dtype=torch.int64)
        self.eos_token = torch.tensor([tokenizer_src.token_to_id('[EOS]')],dtype=torch.int64)
        self.pad_token = torch.tensor([tokenizer_src.token_to_id('[PAD]')],dtype=torch.int64)

    def __len__(self):
        return len(self.ds)
    
    def __getitem__(self, index):
        src_target_pair = self.ds[index]
        src_text = src_target_pair['translation'][self.src_lang]
        tgt_text = src_target_pair['translation'][self.tgt_lang]

        enc_input_tokens = self.tokenizer_src.encode(src_text).ids
        dec_input_tokens = self.tokenizer_tgt.encode(tgt_text).ids
        enc_num_padding_tokens = self.seq_len - len(enc_input_tokens) -2
        dec_num_padding_tokens = self.seq_len - len(dec_input_tokens) -1

        if enc_num_padding_tokens < 0 or dec_num_padding_tokens <0:
            raise ValueError('Sentence is too Long')
        

        # Adding SOS and EOS to source text
        encoder_input = torch.cat(
            [
                self.sos_token,
                torch.tensor(enc_input_tokens, dtype=torch.int64),
                self.eos_token,
                torch.tensor([self.pad_token] * enc_num_padding_tokens,dtype=torch.int64)
            ]
        )

        decoder_input = torch.cat(
            [
                self.sos_token,
                torch.tensor(dec_input_tokens,dtype=torch.int64),
                torch.tensor([self.pad_token] * dec_num_padding_tokens,dtype=torch.int64)
            ]
        )
        label = torch.cat(
            [
                torch.tensor(dec_input_tokens,dtype=torch.int64),
                self.eos_token,
                torch.tensor([self.pad_token] * dec_num_padding_tokens,dtype=torch.int64)
            ]
        )

        assert encoder_input.size(0) == self.seq_len
        assert decoder_input.size(0) == self.seq_len
        assert label.size(0) == self.seq_len

        return {
            "encoder_input": encoder_input,
            "decoder_input": decoder_input,
            "encoder_mask":(encoder_input != self.pad_token).unsqueeze(0).unsqueeze(0).int(), # (1,1,seq_len)
            "decoder_mask":(decoder_input != self.pad_token).unsqueeze(0).unsqueeze(0).int() & casual_mask(decoder_input.size(0)), # (1,seq_len) & (1, seq_len, seq_len)
            "label": label,
            "src_text": src_text,
            "tgt_text": tgt_text,
        }
    
def casual_mask(size):
    mask = torch.triu(torch.ones(1,size,size), diagonal=1).type(torch.int)
    return mask == 0