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transformer_eng-it / dataset.py

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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