neural_machine_translation_with_keras_hub.ipynb

Kernel: Python 3

English-to-Spanish translation with KerasHub

Author: Abheesht Sharma
Date created: 2022/05/26
Last modified: 2024/04/30
Description: Use KerasHub to train a sequence-to-sequence Transformer model on the machine translation task.

Introduction

KerasHub provides building blocks for NLP (model layers, tokenizers, metrics, etc.) and makes it convenient to construct NLP pipelines.

In this example, we'll use KerasHub layers to build an encoder-decoder Transformer model, and train it on the English-to-Spanish machine translation task.

This example is based on the English-to-Spanish NMT example by fchollet. The original example is more low-level and implements layers from scratch, whereas this example uses KerasHub to show some more advanced approaches, such as subword tokenization and using metrics to compute the quality of generated translations.

You'll learn how to:

• Tokenize text using keras_hub.tokenizers.WordPieceTokenizer.

• Implement a sequence-to-sequence Transformer model using KerasHub's keras_hub.layers.TransformerEncoder, keras_hub.layers.TransformerDecoder and keras_hub.layers.TokenAndPositionEmbedding layers, and train it.

• Use keras_hub.samplers to generate translations of unseen input sentences using the top-p decoding strategy!

Don't worry if you aren't familiar with KerasHub. This tutorial will start with the basics. Let's dive right in!

Setup

Before we start implementing the pipeline, let's import all the libraries we need.

!pip install -q --upgrade rouge-score
!pip install -q --upgrade keras-hub
!pip install -q --upgrade keras  # Upgrade to Keras 3.

import keras_hub
import pathlib
import random

import keras
from keras import ops

import tensorflow.data as tf_data
from tensorflow_text.tools.wordpiece_vocab import (
    bert_vocab_from_dataset as bert_vocab,
)

Let's also define our parameters/hyperparameters.

BATCH_SIZE = 64
EPOCHS = 1  # This should be at least 10 for convergence
MAX_SEQUENCE_LENGTH = 40
ENG_VOCAB_SIZE = 15000
SPA_VOCAB_SIZE = 15000

EMBED_DIM = 256
INTERMEDIATE_DIM = 2048
NUM_HEADS = 8

Downloading the data

We'll be working with an English-to-Spanish translation dataset provided by Anki. Let's download it:

text_file = keras.utils.get_file(
    fname="spa-eng.zip",
    origin="http://storage.googleapis.com/download.tensorflow.org/data/spa-eng.zip",
    extract=True,
)
text_file = pathlib.Path(text_file).parent / "spa-eng" / "spa.txt"

Parsing the data

Each line contains an English sentence and its corresponding Spanish sentence. The English sentence is the source sequence and Spanish one is the target sequence. Before adding the text to a list, we convert it to lowercase.

with open(text_file) as f:
    lines = f.read().split("\n")[:-1]
text_pairs = []
for line in lines:
    eng, spa = line.split("\t")
    eng = eng.lower()
    spa = spa.lower()
    text_pairs.append((eng, spa))

Here's what our sentence pairs look like:

for _ in range(5):
    print(random.choice(text_pairs))

Now, let's split the sentence pairs into a training set, a validation set, and a test set.

random.shuffle(text_pairs)
num_val_samples = int(0.15 * len(text_pairs))
num_train_samples = len(text_pairs) - 2 * num_val_samples
train_pairs = text_pairs[:num_train_samples]
val_pairs = text_pairs[num_train_samples : num_train_samples + num_val_samples]
test_pairs = text_pairs[num_train_samples + num_val_samples :]

print(f"{len(text_pairs)} total pairs")
print(f"{len(train_pairs)} training pairs")
print(f"{len(val_pairs)} validation pairs")
print(f"{len(test_pairs)} test pairs")

Tokenizing the data

We'll define two tokenizers - one for the source language (English), and the other for the target language (Spanish). We'll be using keras_hub.tokenizers.WordPieceTokenizer to tokenize the text. keras_hub.tokenizers.WordPieceTokenizer takes a WordPiece vocabulary and has functions for tokenizing the text, and detokenizing sequences of tokens.

Before we define the two tokenizers, we first need to train them on the dataset we have. The WordPiece tokenization algorithm is a subword tokenization algorithm; training it on a corpus gives us a vocabulary of subwords. A subword tokenizer is a compromise between word tokenizers (word tokenizers need very large vocabularies for good coverage of input words), and character tokenizers (characters don't really encode meaning like words do). Luckily, KerasHub makes it very simple to train WordPiece on a corpus with the keras_hub.tokenizers.compute_word_piece_vocabulary utility.

def train_word_piece(text_samples, vocab_size, reserved_tokens):
    word_piece_ds = tf_data.Dataset.from_tensor_slices(text_samples)
    vocab = keras_hub.tokenizers.compute_word_piece_vocabulary(
        word_piece_ds.batch(1000).prefetch(2),
        vocabulary_size=vocab_size,
        reserved_tokens=reserved_tokens,
    )
    return vocab

Every vocabulary has a few special, reserved tokens. We have four such tokens:

• "[PAD]" - Padding token. Padding tokens are appended to the input sequence length when the input sequence length is shorter than the maximum sequence length.

• "[UNK]" - Unknown token.

• "[START]" - Token that marks the start of the input sequence.

• "[END]" - Token that marks the end of the input sequence.

reserved_tokens = ["[PAD]", "[UNK]", "[START]", "[END]"]

eng_samples = [text_pair[0] for text_pair in train_pairs]
eng_vocab = train_word_piece(eng_samples, ENG_VOCAB_SIZE, reserved_tokens)

spa_samples = [text_pair[1] for text_pair in train_pairs]
spa_vocab = train_word_piece(spa_samples, SPA_VOCAB_SIZE, reserved_tokens)

Let's see some tokens!

print("English Tokens: ", eng_vocab[100:110])
print("Spanish Tokens: ", spa_vocab[100:110])

Now, let's define the tokenizers. We will configure the tokenizers with the the vocabularies trained above.

eng_tokenizer = keras_hub.tokenizers.WordPieceTokenizer(
    vocabulary=eng_vocab, lowercase=False
)
spa_tokenizer = keras_hub.tokenizers.WordPieceTokenizer(
    vocabulary=spa_vocab, lowercase=False
)

Let's try and tokenize a sample from our dataset! To verify whether the text has been tokenized correctly, we can also detokenize the list of tokens back to the original text.

eng_input_ex = text_pairs[0][0]
eng_tokens_ex = eng_tokenizer.tokenize(eng_input_ex)
print("English sentence: ", eng_input_ex)
print("Tokens: ", eng_tokens_ex)
print(
    "Recovered text after detokenizing: ",
    eng_tokenizer.detokenize(eng_tokens_ex),
)

print()

spa_input_ex = text_pairs[0][1]
spa_tokens_ex = spa_tokenizer.tokenize(spa_input_ex)
print("Spanish sentence: ", spa_input_ex)
print("Tokens: ", spa_tokens_ex)
print(
    "Recovered text after detokenizing: ",
    spa_tokenizer.detokenize(spa_tokens_ex),
)

Format datasets

Next, we'll format our datasets.

At each training step, the model will seek to predict target words N+1 (and beyond) using the source sentence and the target words 0 to N.

As such, the training dataset will yield a tuple (inputs, targets), where:

• inputs is a dictionary with the keys encoder_inputs and decoder_inputs. encoder_inputs is the tokenized source sentence and decoder_inputs is the target sentence "so far", that is to say, the words 0 to N used to predict word N+1 (and beyond) in the target sentence.

• target is the target sentence offset by one step: it provides the next words in the target sentence -- what the model will try to predict.

We will add special tokens, "[START]" and "[END]", to the input Spanish sentence after tokenizing the text. We will also pad the input to a fixed length. This can be easily done using keras_hub.layers.StartEndPacker.

def preprocess_batch(eng, spa):
    batch_size = ops.shape(spa)[0]

    eng = eng_tokenizer(eng)
    spa = spa_tokenizer(spa)

    # Pad `eng` to `MAX_SEQUENCE_LENGTH`.
    eng_start_end_packer = keras_hub.layers.StartEndPacker(
        sequence_length=MAX_SEQUENCE_LENGTH,
        pad_value=eng_tokenizer.token_to_id("[PAD]"),
    )
    eng = eng_start_end_packer(eng)

    # Add special tokens (`"[START]"` and `"[END]"`) to `spa` and pad it as well.
    spa_start_end_packer = keras_hub.layers.StartEndPacker(
        sequence_length=MAX_SEQUENCE_LENGTH + 1,
        start_value=spa_tokenizer.token_to_id("[START]"),
        end_value=spa_tokenizer.token_to_id("[END]"),
        pad_value=spa_tokenizer.token_to_id("[PAD]"),
    )
    spa = spa_start_end_packer(spa)

    return (
        {
            "encoder_inputs": eng,
            "decoder_inputs": spa[:, :-1],
        },
        spa[:, 1:],
    )


def make_dataset(pairs):
    eng_texts, spa_texts = zip(*pairs)
    eng_texts = list(eng_texts)
    spa_texts = list(spa_texts)
    dataset = tf_data.Dataset.from_tensor_slices((eng_texts, spa_texts))
    dataset = dataset.batch(BATCH_SIZE)
    dataset = dataset.map(preprocess_batch, num_parallel_calls=tf_data.AUTOTUNE)
    return dataset.shuffle(2048).prefetch(16).cache()


train_ds = make_dataset(train_pairs)
val_ds = make_dataset(val_pairs)

Let's take a quick look at the sequence shapes (we have batches of 64 pairs, and all sequences are 40 steps long):

for inputs, targets in train_ds.take(1):
    print(f'inputs["encoder_inputs"].shape: {inputs["encoder_inputs"].shape}')
    print(f'inputs["decoder_inputs"].shape: {inputs["decoder_inputs"].shape}')
    print(f"targets.shape: {targets.shape}")

Building the model

Now, let's move on to the exciting part - defining our model! We first need an embedding layer, i.e., a vector for every token in our input sequence. This embedding layer can be initialised randomly. We also need a positional embedding layer which encodes the word order in the sequence. The convention is to add these two embeddings. KerasHub has a keras_hub.layers.TokenAndPositionEmbedding layer which does all of the above steps for us.

Our sequence-to-sequence Transformer consists of a keras_hub.layers.TransformerEncoder layer and a keras_hub.layers.TransformerDecoder layer chained together.

The source sequence will be passed to keras_hub.layers.TransformerEncoder, which will produce a new representation of it. This new representation will then be passed to the keras_hub.layers.TransformerDecoder, together with the target sequence so far (target words 0 to N). The keras_hub.layers.TransformerDecoder will then seek to predict the next words in the target sequence (N+1 and beyond).

A key detail that makes this possible is causal masking. The keras_hub.layers.TransformerDecoder sees the entire sequence at once, and thus we must make sure that it only uses information from target tokens 0 to N when predicting token N+1 (otherwise, it could use information from the future, which would result in a model that cannot be used at inference time). Causal masking is enabled by default in keras_hub.layers.TransformerDecoder.

We also need to mask the padding tokens ("[PAD]"). For this, we can set the mask_zero argument of the keras_hub.layers.TokenAndPositionEmbedding layer to True. This will then be propagated to all subsequent layers.

# Encoder
encoder_inputs = keras.Input(shape=(None,), name="encoder_inputs")

x = keras_hub.layers.TokenAndPositionEmbedding(
    vocabulary_size=ENG_VOCAB_SIZE,
    sequence_length=MAX_SEQUENCE_LENGTH,
    embedding_dim=EMBED_DIM,
)(encoder_inputs)

encoder_outputs = keras_hub.layers.TransformerEncoder(
    intermediate_dim=INTERMEDIATE_DIM, num_heads=NUM_HEADS
)(inputs=x)
encoder = keras.Model(encoder_inputs, encoder_outputs)


# Decoder
decoder_inputs = keras.Input(shape=(None,), name="decoder_inputs")
encoded_seq_inputs = keras.Input(shape=(None, EMBED_DIM), name="decoder_state_inputs")

x = keras_hub.layers.TokenAndPositionEmbedding(
    vocabulary_size=SPA_VOCAB_SIZE,
    sequence_length=MAX_SEQUENCE_LENGTH,
    embedding_dim=EMBED_DIM,
)(decoder_inputs)

x = keras_hub.layers.TransformerDecoder(
    intermediate_dim=INTERMEDIATE_DIM, num_heads=NUM_HEADS
)(decoder_sequence=x, encoder_sequence=encoded_seq_inputs)
x = keras.layers.Dropout(0.5)(x)
decoder_outputs = keras.layers.Dense(SPA_VOCAB_SIZE, activation="softmax")(x)
decoder = keras.Model(
    [
        decoder_inputs,
        encoded_seq_inputs,
    ],
    decoder_outputs,
)
decoder_outputs = decoder([decoder_inputs, encoder_outputs])

transformer = keras.Model(
    [encoder_inputs, decoder_inputs],
    decoder_outputs,
    name="transformer",
)

Training our model

We'll use accuracy as a quick way to monitor training progress on the validation data. Note that machine translation typically uses BLEU scores as well as other metrics, rather than accuracy. However, in order to use metrics like ROUGE, BLEU, etc. we will have decode the probabilities and generate the text. Text generation is computationally expensive, and performing this during training is not recommended.

Here we only train for 1 epoch, but to get the model to actually converge you should train for at least 10 epochs.

transformer.summary()
transformer.compile(
    "rmsprop", loss="sparse_categorical_crossentropy", metrics=["accuracy"]
)
transformer.fit(train_ds, epochs=EPOCHS, validation_data=val_ds)

Decoding test sentences (qualitative analysis)

Finally, let's demonstrate how to translate brand new English sentences. We simply feed into the model the tokenized English sentence as well as the target token "[START]". The model outputs probabilities of the next token. We then we repeatedly generated the next token conditioned on the tokens generated so far, until we hit the token "[END]".

For decoding, we will use the keras_hub.samplers module from KerasHub. Greedy Decoding is a text decoding method which outputs the most likely next token at each time step, i.e., the token with the highest probability.

def decode_sequences(input_sentences):
    batch_size = 1

    # Tokenize the encoder input.
    encoder_input_tokens = ops.convert_to_tensor(eng_tokenizer(input_sentences))
    if len(encoder_input_tokens[0]) < MAX_SEQUENCE_LENGTH:
        pads = ops.full((1, MAX_SEQUENCE_LENGTH - len(encoder_input_tokens[0])), 0)
        encoder_input_tokens = ops.concatenate(
            [encoder_input_tokens.to_tensor(), pads], 1
        )

    # Define a function that outputs the next token's probability given the
    # input sequence.
    def next(prompt, cache, index):
        logits = transformer([encoder_input_tokens, prompt])[:, index - 1, :]
        # Ignore hidden states for now; only needed for contrastive search.
        hidden_states = None
        return logits, hidden_states, cache

    # Build a prompt of length 40 with a start token and padding tokens.
    length = 40
    start = ops.full((batch_size, 1), spa_tokenizer.token_to_id("[START]"))
    pad = ops.full((batch_size, length - 1), spa_tokenizer.token_to_id("[PAD]"))
    prompt = ops.concatenate((start, pad), axis=-1)

    generated_tokens = keras_hub.samplers.GreedySampler()(
        next,
        prompt,
        stop_token_ids=[spa_tokenizer.token_to_id("[END]")],
        index=1,  # Start sampling after start token.
    )
    generated_sentences = spa_tokenizer.detokenize(generated_tokens)
    return generated_sentences


test_eng_texts = [pair[0] for pair in test_pairs]
for i in range(2):
    input_sentence = random.choice(test_eng_texts)
    translated = decode_sequences([input_sentence])
    translated = translated.numpy()[0].decode("utf-8")
    translated = (
        translated.replace("[PAD]", "")
        .replace("[START]", "")
        .replace("[END]", "")
        .strip()
    )
    print(f"** Example {i} **")
    print(input_sentence)
    print(translated)
    print()

Evaluating our model (quantitative analysis)

There are many metrics which are used for text generation tasks. Here, to evaluate translations generated by our model, let's compute the ROUGE-1 and ROUGE-2 scores. Essentially, ROUGE-N is a score based on the number of common n-grams between the reference text and the generated text. ROUGE-1 and ROUGE-2 use the number of common unigrams and bigrams, respectively.

We will calculate the score over 30 test samples (since decoding is an expensive process).

rouge_1 = keras_hub.metrics.RougeN(order=1)
rouge_2 = keras_hub.metrics.RougeN(order=2)

for test_pair in test_pairs[:30]:
    input_sentence = test_pair[0]
    reference_sentence = test_pair[1]

    translated_sentence = decode_sequences([input_sentence])
    translated_sentence = translated_sentence.numpy()[0].decode("utf-8")
    translated_sentence = (
        translated_sentence.replace("[PAD]", "")
        .replace("[START]", "")
        .replace("[END]", "")
        .strip()
    )

    rouge_1(reference_sentence, translated_sentence)
    rouge_2(reference_sentence, translated_sentence)

print("ROUGE-1 Score: ", rouge_1.result())
print("ROUGE-2 Score: ", rouge_2.result())

After 10 epochs, the scores are as follows:

	ROUGE-1	ROUGE-2
Precision	0.568	0.374
Recall	0.615	0.394
F1 Score	0.579	0.381