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.. _sphx_glr_beginner_translation_transformer.py:
Language Translation with ``nn.Transformer`` and torchtext
==========================================================
This tutorial shows:
- How to train a translation model from scratch using Transformer.
- Use torchtext library to access `Multi30k <http://www.statmt.org/wmt16/multimodal-task.html#task1>`__ dataset to train a German to English translation model.
.. GENERATED FROM PYTHON SOURCE LINES 12-24
Data Sourcing and Processing
----------------------------
`torchtext library <https://pytorch.org/text/stable/>`__ has utilities for creating datasets that can be easily
iterated through for the purposes of creating a language translation
model. In this example, we show how to use torchtext's inbuilt datasets,
tokenize a raw text sentence, build vocabulary, and numericalize tokens into tensor. We will use
`Multi30k dataset from torchtext library <https://pytorch.org/text/stable/datasets.html#multi30k>`__
that yields a pair of source-target raw sentences.
To access torchtext datasets, please install torchdata following instructions at https://github.com/pytorch/data.
.. GENERATED FROM PYTHON SOURCE LINES 24-43
.. code-block:: default
from torchtext.data.utils import get_tokenizer
from torchtext.vocab import build_vocab_from_iterator
from torchtext.datasets import multi30k, Multi30k
from typing import Iterable, List
# We need to modify the URLs for the dataset since the links to the original dataset are broken
# Refer to https://github.com/pytorch/text/issues/1756#issuecomment-1163664163 for more info
multi30k.URL["train"] = "https://raw.githubusercontent.com/neychev/small_DL_repo/master/datasets/Multi30k/training.tar.gz"
multi30k.URL["valid"] = "https://raw.githubusercontent.com/neychev/small_DL_repo/master/datasets/Multi30k/validation.tar.gz"
SRC_LANGUAGE = 'de'
TGT_LANGUAGE = 'en'
# Place-holders
token_transform = {}
vocab_transform = {}
.. GENERATED FROM PYTHON SOURCE LINES 44-52
Create source and target language tokenizer. Make sure to install the dependencies.
.. code-block:: python
pip install -U torchdata
pip install -U spacy
python -m spacy download en_core_web_sm
python -m spacy download de_core_news_sm
.. GENERATED FROM PYTHON SOURCE LINES 52-83
.. code-block:: default
token_transform[SRC_LANGUAGE] = get_tokenizer('spacy', language='de_core_news_sm')
token_transform[TGT_LANGUAGE] = get_tokenizer('spacy', language='en_core_web_sm')
# helper function to yield list of tokens
def yield_tokens(data_iter: Iterable, language: str) -> List[str]:
language_index = {SRC_LANGUAGE: 0, TGT_LANGUAGE: 1}
for data_sample in data_iter:
yield token_transform[language](data_sample[language_index[language]])
# Define special symbols and indices
UNK_IDX, PAD_IDX, BOS_IDX, EOS_IDX = 0, 1, 2, 3
# Make sure the tokens are in order of their indices to properly insert them in vocab
special_symbols = ['<unk>', '<pad>', '<bos>', '<eos>']
for ln in [SRC_LANGUAGE, TGT_LANGUAGE]:
# Training data Iterator
train_iter = Multi30k(split='train', language_pair=(SRC_LANGUAGE, TGT_LANGUAGE))
# Create torchtext's Vocab object
vocab_transform[ln] = build_vocab_from_iterator(yield_tokens(train_iter, ln),
min_freq=1,
specials=special_symbols,
special_first=True)
# Set ``UNK_IDX`` as the default index. This index is returned when the token is not found.
# If not set, it throws ``RuntimeError`` when the queried token is not found in the Vocabulary.
for ln in [SRC_LANGUAGE, TGT_LANGUAGE]:
vocab_transform[ln].set_default_index(UNK_IDX)
.. GENERATED FROM PYTHON SOURCE LINES 84-98
Seq2Seq Network using Transformer
---------------------------------
Transformer is a Seq2Seq model introduced in `“Attention is all you
need” <https://papers.nips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf>`__
paper for solving machine translation tasks.
Below, we will create a Seq2Seq network that uses Transformer. The network
consists of three parts. First part is the embedding layer. This layer converts tensor of input indices
into corresponding tensor of input embeddings. These embedding are further augmented with positional
encodings to provide position information of input tokens to the model. The second part is the
actual `Transformer <https://pytorch.org/docs/stable/generated/torch.nn.Transformer.html>`__ model.
Finally, the output of the Transformer model is passed through linear layer
that gives unnormalized probabilities for each token in the target language.
.. GENERATED FROM PYTHON SOURCE LINES 98-185
.. code-block:: default
from torch import Tensor
import torch
import torch.nn as nn
from torch.nn import Transformer
import math
DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# helper Module that adds positional encoding to the token embedding to introduce a notion of word order.
class PositionalEncoding(nn.Module):
def __init__(self,
emb_size: int,
dropout: float,
maxlen: int = 5000):
super(PositionalEncoding, self).__init__()
den = torch.exp(- torch.arange(0, emb_size, 2)* math.log(10000) / emb_size)
pos = torch.arange(0, maxlen).reshape(maxlen, 1)
pos_embedding = torch.zeros((maxlen, emb_size))
pos_embedding[:, 0::2] = torch.sin(pos * den)
pos_embedding[:, 1::2] = torch.cos(pos * den)
pos_embedding = pos_embedding.unsqueeze(-2)
self.dropout = nn.Dropout(dropout)
self.register_buffer('pos_embedding', pos_embedding)
def forward(self, token_embedding: Tensor):
return self.dropout(token_embedding + self.pos_embedding[:token_embedding.size(0), :])
# helper Module to convert tensor of input indices into corresponding tensor of token embeddings
class TokenEmbedding(nn.Module):
def __init__(self, vocab_size: int, emb_size):
super(TokenEmbedding, self).__init__()
self.embedding = nn.Embedding(vocab_size, emb_size)
self.emb_size = emb_size
def forward(self, tokens: Tensor):
return self.embedding(tokens.long()) * math.sqrt(self.emb_size)
# Seq2Seq Network
class Seq2SeqTransformer(nn.Module):
def __init__(self,
num_encoder_layers: int,
num_decoder_layers: int,
emb_size: int,
nhead: int,
src_vocab_size: int,
tgt_vocab_size: int,
dim_feedforward: int = 512,
dropout: float = 0.1):
super(Seq2SeqTransformer, self).__init__()
self.transformer = Transformer(d_model=emb_size,
nhead=nhead,
num_encoder_layers=num_encoder_layers,
num_decoder_layers=num_decoder_layers,
dim_feedforward=dim_feedforward,
dropout=dropout)
self.generator = nn.Linear(emb_size, tgt_vocab_size)
self.src_tok_emb = TokenEmbedding(src_vocab_size, emb_size)
self.tgt_tok_emb = TokenEmbedding(tgt_vocab_size, emb_size)
self.positional_encoding = PositionalEncoding(
emb_size, dropout=dropout)
def forward(self,
src: Tensor,
trg: Tensor,
src_mask: Tensor,
tgt_mask: Tensor,
src_padding_mask: Tensor,
tgt_padding_mask: Tensor,
memory_key_padding_mask: Tensor):
src_emb = self.positional_encoding(self.src_tok_emb(src))
tgt_emb = self.positional_encoding(self.tgt_tok_emb(trg))
outs = self.transformer(src_emb, tgt_emb, src_mask, tgt_mask, None,
src_padding_mask, tgt_padding_mask, memory_key_padding_mask)
return self.generator(outs)
def encode(self, src: Tensor, src_mask: Tensor):
return self.transformer.encoder(self.positional_encoding(
self.src_tok_emb(src)), src_mask)
def decode(self, tgt: Tensor, memory: Tensor, tgt_mask: Tensor):
return self.transformer.decoder(self.positional_encoding(
self.tgt_tok_emb(tgt)), memory,
tgt_mask)
.. GENERATED FROM PYTHON SOURCE LINES 186-190
During training, we need a subsequent word mask that will prevent the model from looking into
the future words when making predictions. We will also need masks to hide
source and target padding tokens. Below, let's define a function that will take care of both.
.. GENERATED FROM PYTHON SOURCE LINES 190-210
.. code-block:: default
def generate_square_subsequent_mask(sz):
mask = (torch.triu(torch.ones((sz, sz), device=DEVICE)) == 1).transpose(0, 1)
mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
return mask
def create_mask(src, tgt):
src_seq_len = src.shape[0]
tgt_seq_len = tgt.shape[0]
tgt_mask = generate_square_subsequent_mask(tgt_seq_len)
src_mask = torch.zeros((src_seq_len, src_seq_len),device=DEVICE).type(torch.bool)
src_padding_mask = (src == PAD_IDX).transpose(0, 1)
tgt_padding_mask = (tgt == PAD_IDX).transpose(0, 1)
return src_mask, tgt_mask, src_padding_mask, tgt_padding_mask
.. GENERATED FROM PYTHON SOURCE LINES 211-214
Let's now define the parameters of our model and instantiate the same. Below, we also
define our loss function which is the cross-entropy loss and the optimizer used for training.
.. GENERATED FROM PYTHON SOURCE LINES 214-238
.. code-block:: default
torch.manual_seed(0)
SRC_VOCAB_SIZE = len(vocab_transform[SRC_LANGUAGE])
TGT_VOCAB_SIZE = len(vocab_transform[TGT_LANGUAGE])
EMB_SIZE = 512
NHEAD = 8
FFN_HID_DIM = 512
BATCH_SIZE = 128
NUM_ENCODER_LAYERS = 3
NUM_DECODER_LAYERS = 3
transformer = Seq2SeqTransformer(NUM_ENCODER_LAYERS, NUM_DECODER_LAYERS, EMB_SIZE,
NHEAD, SRC_VOCAB_SIZE, TGT_VOCAB_SIZE, FFN_HID_DIM)
for p in transformer.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
transformer = transformer.to(DEVICE)
loss_fn = torch.nn.CrossEntropyLoss(ignore_index=PAD_IDX)
optimizer = torch.optim.Adam(transformer.parameters(), lr=0.0001, betas=(0.9, 0.98), eps=1e-9)
.. GENERATED FROM PYTHON SOURCE LINES 239-247
Collation
---------
As seen in the ``Data Sourcing and Processing`` section, our data iterator yields a pair of raw strings.
We need to convert these string pairs into the batched tensors that can be processed by our ``Seq2Seq`` network
defined previously. Below we define our collate function that converts a batch of raw strings into batch tensors that
can be fed directly into our model.
.. GENERATED FROM PYTHON SOURCE LINES 247-284
.. code-block:: default
from torch.nn.utils.rnn import pad_sequence
# helper function to club together sequential operations
def sequential_transforms(*transforms):
def func(txt_input):
for transform in transforms:
txt_input = transform(txt_input)
return txt_input
return func
# function to add BOS/EOS and create tensor for input sequence indices
def tensor_transform(token_ids: List[int]):
return torch.cat((torch.tensor([BOS_IDX]),
torch.tensor(token_ids),
torch.tensor([EOS_IDX])))
# ``src`` and ``tgt`` language text transforms to convert raw strings into tensors indices
text_transform = {}
for ln in [SRC_LANGUAGE, TGT_LANGUAGE]:
text_transform[ln] = sequential_transforms(token_transform[ln], #Tokenization
vocab_transform[ln], #Numericalization
tensor_transform) # Add BOS/EOS and create tensor
# function to collate data samples into batch tensors
def collate_fn(batch):
src_batch, tgt_batch = [], []
for src_sample, tgt_sample in batch:
src_batch.append(text_transform[SRC_LANGUAGE](src_sample.rstrip("\n")))
tgt_batch.append(text_transform[TGT_LANGUAGE](tgt_sample.rstrip("\n")))
src_batch = pad_sequence(src_batch, padding_value=PAD_IDX)
tgt_batch = pad_sequence(tgt_batch, padding_value=PAD_IDX)
return src_batch, tgt_batch
.. GENERATED FROM PYTHON SOURCE LINES 285-288
Let's define training and evaluation loop that will be called for each
epoch.
.. GENERATED FROM PYTHON SOURCE LINES 288-342
.. code-block:: default
from torch.utils.data import DataLoader
def train_epoch(model, optimizer):
model.train()
losses = 0
train_iter = Multi30k(split='train', language_pair=(SRC_LANGUAGE, TGT_LANGUAGE))
train_dataloader = DataLoader(train_iter, batch_size=BATCH_SIZE, collate_fn=collate_fn)
for src, tgt in train_dataloader:
src = src.to(DEVICE)
tgt = tgt.to(DEVICE)
tgt_input = tgt[:-1, :]
src_mask, tgt_mask, src_padding_mask, tgt_padding_mask = create_mask(src, tgt_input)
logits = model(src, tgt_input, src_mask, tgt_mask,src_padding_mask, tgt_padding_mask, src_padding_mask)
optimizer.zero_grad()
tgt_out = tgt[1:, :]
loss = loss_fn(logits.reshape(-1, logits.shape[-1]), tgt_out.reshape(-1))
loss.backward()
optimizer.step()
losses += loss.item()
return losses / len(list(train_dataloader))
def evaluate(model):
model.eval()
losses = 0
val_iter = Multi30k(split='valid', language_pair=(SRC_LANGUAGE, TGT_LANGUAGE))
val_dataloader = DataLoader(val_iter, batch_size=BATCH_SIZE, collate_fn=collate_fn)
for src, tgt in val_dataloader:
src = src.to(DEVICE)
tgt = tgt.to(DEVICE)
tgt_input = tgt[:-1, :]
src_mask, tgt_mask, src_padding_mask, tgt_padding_mask = create_mask(src, tgt_input)
logits = model(src, tgt_input, src_mask, tgt_mask,src_padding_mask, tgt_padding_mask, src_padding_mask)
tgt_out = tgt[1:, :]
loss = loss_fn(logits.reshape(-1, logits.shape[-1]), tgt_out.reshape(-1))
losses += loss.item()
return losses / len(list(val_dataloader))
.. GENERATED FROM PYTHON SOURCE LINES 343-345
Now we have all the ingredients to train our model. Let's do it!
.. GENERATED FROM PYTHON SOURCE LINES 345-392
.. code-block:: default
from timeit import default_timer as timer
NUM_EPOCHS = 18
for epoch in range(1, NUM_EPOCHS+1):
start_time = timer()
train_loss = train_epoch(transformer, optimizer)
end_time = timer()
val_loss = evaluate(transformer)
print((f"Epoch: {epoch}, Train loss: {train_loss:.3f}, Val loss: {val_loss:.3f}, "f"Epoch time = {(end_time - start_time):.3f}s"))
# function to generate output sequence using greedy algorithm
def greedy_decode(model, src, src_mask, max_len, start_symbol):
src = src.to(DEVICE)
src_mask = src_mask.to(DEVICE)
memory = model.encode(src, src_mask)
ys = torch.ones(1, 1).fill_(start_symbol).type(torch.long).to(DEVICE)
for i in range(max_len-1):
memory = memory.to(DEVICE)
tgt_mask = (generate_square_subsequent_mask(ys.size(0))
.type(torch.bool)).to(DEVICE)
out = model.decode(ys, memory, tgt_mask)
out = out.transpose(0, 1)
prob = model.generator(out[:, -1])
_, next_word = torch.max(prob, dim=1)
next_word = next_word.item()
ys = torch.cat([ys,
torch.ones(1, 1).type_as(src.data).fill_(next_word)], dim=0)
if next_word == EOS_IDX:
break
return ys
# actual function to translate input sentence into target language
def translate(model: torch.nn.Module, src_sentence: str):
model.eval()
src = text_transform[SRC_LANGUAGE](src_sentence).view(-1, 1)
num_tokens = src.shape[0]
src_mask = (torch.zeros(num_tokens, num_tokens)).type(torch.bool)
tgt_tokens = greedy_decode(
model, src, src_mask, max_len=num_tokens + 5, start_symbol=BOS_IDX).flatten()
return " ".join(vocab_transform[TGT_LANGUAGE].lookup_tokens(list(tgt_tokens.cpu().numpy()))).replace("<bos>", "").replace("<eos>", "")
.. GENERATED FROM PYTHON SOURCE LINES 394-398
.. code-block:: default
print(translate(transformer, "Eine Gruppe von Menschen steht vor einem Iglu ."))
.. GENERATED FROM PYTHON SOURCE LINES 399-405
References
----------
1. Attention is all you need paper.
https://papers.nips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf
2. The annotated transformer. https://nlp.seas.harvard.edu/2018/04/03/attention.html#positional-encoding
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.. _sphx_glr_download_beginner_translation_transformer.py:
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