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import torch
import torch.nn as 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().__init__()
self.seq_len = seq_len
self.ds = ds
self.tokenizer_src = tokenizer_src
self.tokenizer_tgt = tokenizer_tgt
self.src_lang = src_lang
self.tgt_lang = tgt_lang
self.sos_token = torch.tensor([tokenizer_tgt.token_to_id("[SOS]")], dtype=torch.int64)
self.eos_token = torch.tensor([tokenizer_tgt.token_to_id("[EOS]")], dtype=torch.int64)
self.pad_token = torch.tensor([tokenizer_tgt.token_to_id("[PAD]")], dtype=torch.int64)
def __len__(self):
return len(self.ds)
def __getitem__(self, idx):
src_target_pair = self.ds[idx]
src_text = src_target_pair['translation'][self.src_lang]
tgt_text = src_target_pair['translation'][self.tgt_lang]
# Transform the text into tokens
enc_input_tokens = self.tokenizer_src.encode(src_text).ids
dec_input_tokens = self.tokenizer_tgt.encode(tgt_text).ids
# Add sos, eos and padding to each sentence
enc_num_padding_tokens = self.seq_len - len(enc_input_tokens) - 2 # We will add <s> and </s>
# We will only add <s>, and </s> only on the label
dec_num_padding_tokens = self.seq_len - len(dec_input_tokens) - 1
# Make sure the number of padding tokens is not negative. If it is, the sentence is too long
if enc_num_padding_tokens < 0 or dec_num_padding_tokens < 0:
raise ValueError("Sentence is too long")
# Add <s> and </s> token
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),
],
dim=0,
)
# Add only <s> token
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),
],
dim=0,
)
# Add only </s> token
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),
],
dim=0,
)
# Double check the size of the tensors to make sure they are all seq_len long
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, # (seq_len)
"decoder_input": decoder_input, # (seq_len)
"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).int() & causal_mask(decoder_input.size(0)), # (1, seq_len) & (1, seq_len, seq_len),
"label": label, # (seq_len)
"src_text": src_text,
"tgt_text": tgt_text,
}
def causal_mask(size):
mask = torch.triu(torch.ones((1, size, size)), diagonal=1).type(torch.int)
return mask == 0 |