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Training the Transformer Model



We have put together the complete Transformer model, and now we are ready to train it for neural machine translation. We shall be making use of a training dataset for this purpose, which contains short English and German sentence pairs. We will also be revisiting the role of masking in computing the accuracy and loss metrics during the training process. 

In this tutorial, you will discover how to train the Transformer model for neural machine translation. 

After completing this tutorial, you will know:

How to prepare the training dataset. 
How to apply a padding mask to the loss and accuracy computations. 
How to train the Transformer model. 

Let’s get started. 

Training the Transformer Model
Photo by v2osk, some rights reserved.

Tutorial Overview

This tutorial is divided into four parts; they are:

Recap of the Transformer Architecture
Preparing the Training Dataset
Applying a Padding Mask to the Loss and Accuracy Computations
Training the Transformer Model

Prerequisites

For this tutorial, we assume that you are already familiar with:

The theory behind the Transformer model
An implementation of the Transformer model

Recap of the Transformer Architecture

Recall having seen that the Transformer architecture follows an encoder-decoder structure: the encoder, on the left-hand side, is tasked with mapping an input sequence to a sequence of continuous representations; the decoder, on the right-hand side, receives the output of the encoder together with the decoder output at the previous time step, to generate an output sequence.

The Encoder-Decoder Structure of the Transformer Architecture
Taken from “Attention Is All You Need

In generating an output sequence, the Transformer does not rely on recurrence and convolutions.

We have seen how to implement the complete Transformer model, and we shall now proceed to train it for neural machine translation. 

Let’s start first by preparing the dataset for training. 

Preparing the Training Dataset

For this purpose we will be referring to a previous tutorial that covers material related to preparing the text data for training. 

We will also be making use of a dataset that contains short English and German sentence pairs, which you may download here. This particular dataset has already been cleaned by removing non-printable and non-alphabetic characters, and punctuation characters, and further normalizing all Unicode characters to ASCII, and all uppercase letters to lowercase ones. Hence, we will be skipping the cleaning step that is typically part of the data preparation process. However, should you be using a dataset that does not come readily cleaned, you may refer to this previous tutorial in order to learn how to do so. 

Let’s proceed by creating the PrepareDataset class that implements the following steps:

Loads the dataset from a specified filename. 

clean_dataset = load(open(filename, ‘rb’))

Selects the number of sentences to use from the dataset. Since the dataset is large, we will be reducing its size to limit the training time. However, you may explore using the full dataset as an extension to this tutorial.

dataset = clean_dataset[:self.n_sentences, :]

Appends start (<START>) and end-of-string (<EOS>) tokens to each sentence. For example, the English sentence, i like to run, now becomes, <START> i like to run <EOS>. This also applies to its corresponding translation in German, ich gehe gerne joggen, which now becomes, <START> ich gehe gerne joggen <EOS>.

for i in range(dataset[:, 0].size):
dataset[i, 0] = “<START> ” + dataset[i, 0] + ” <EOS>”
dataset[i, 1] = “<START> ” + dataset[i, 1] + ” <EOS>”

Shuffles the dataset randomly. 

shuffle(dataset)

Splits the shuffled dataset based on a pre-defined ratio.

train = dataset[:int(self.n_sentences * self.train_split)]

Creates and trains a tokenizer on the text sequences that will be fed into the encoder, and finds the length of the longest sequence as well as the vocabulary size. 

enc_tokenizer = self.create_tokenizer(train[:, 0])
enc_seq_length = self.find_seq_length(train[:, 0])
enc_vocab_size = self.find_vocab_size(enc_tokenizer, train[:, 0])

Tokenizes the sequences of text that will be fed into the encoder, by creating a vocabulary of words and replacing each word by its corresponding vocabulary index. The <START> and <EOS> tokens will also form part of this vocabulary. Each sequence is also padded to the maximum phrase length.  

trainX = enc_tokenizer.texts_to_sequences(train[:, 0])
trainX = pad_sequences(trainX, maxlen=enc_seq_length, padding=’post’)
trainX = convert_to_tensor(trainX, dtype=int64)

Creates and trains a tokenizer on the text sequences that will be fed into the decoder, and finds the length of the longest sequence as well as the vocabulary size.

dec_tokenizer = self.create_tokenizer(train[:, 1])
dec_seq_length = self.find_seq_length(train[:, 1])
dec_vocab_size = self.find_vocab_size(dec_tokenizer, train[:, 1])

Repeats a similar tokenization and padding procedure for the sequences of text that will be fed into the decoder.

trainY = enc_tokenizer.texts_to_sequences(train[:, 1])
trainY = pad_sequences(trainY, maxlen=dec_seq_length, padding=’post’)
trainY = convert_to_tensor(trainY, dtype=int64)

The complete code listing is as follows (refer to this previous tutorial for further details):

from pickle import load
from numpy.random import shuffle
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from tensorflow import convert_to_tensor, int64

class PrepareDataset:
def __init__(self, **kwargs):
super(PrepareDataset, self).__init__(**kwargs)
self.n_sentences = 10000 # Number of sentences to include in the dataset
self.train_split = 0.9 # Ratio of the training data split

# Fit a tokenizer
def create_tokenizer(self, dataset):
tokenizer = Tokenizer()
tokenizer.fit_on_texts(dataset)

return tokenizer

def find_seq_length(self, dataset):
return max(len(seq.split()) for seq in dataset)

def find_vocab_size(self, tokenizer, dataset):
tokenizer.fit_on_texts(dataset)

return len(tokenizer.word_index) + 1

def __call__(self, filename, **kwargs):
# Load a clean dataset
clean_dataset = load(open(filename, ‘rb’))

# Reduce dataset size
dataset = clean_dataset[:self.n_sentences, :]

# Include start and end of string tokens
for i in range(dataset[:, 0].size):
dataset[i, 0] = “<START> ” + dataset[i, 0] + ” <EOS>”
dataset[i, 1] = “<START> ” + dataset[i, 1] + ” <EOS>”

# Random shuffle the dataset
shuffle(dataset)

# Split the dataset
train = dataset[:int(self.n_sentences * self.train_split)]

# Prepare tokenizer for the encoder input
enc_tokenizer = self.create_tokenizer(train[:, 0])
enc_seq_length = self.find_seq_length(train[:, 0])
enc_vocab_size = self.find_vocab_size(enc_tokenizer, train[:, 0])

# Encode and pad the input sequences
trainX = enc_tokenizer.texts_to_sequences(train[:, 0])
trainX = pad_sequences(trainX, maxlen=enc_seq_length, padding=’post’)
trainX = convert_to_tensor(trainX, dtype=int64)

# Prepare tokenizer for the decoder input
dec_tokenizer = self.create_tokenizer(train[:, 1])
dec_seq_length = self.find_seq_length(train[:, 1])
dec_vocab_size = self.find_vocab_size(dec_tokenizer, train[:, 1])

# Encode and pad the input sequences
trainY = enc_tokenizer.texts_to_sequences(train[:, 1])
trainY = pad_sequences(trainY, maxlen=dec_seq_length, padding=’post’)
trainY = convert_to_tensor(trainY, dtype=int64)

return trainX, trainY, train, enc_seq_length, dec_seq_length, enc_vocab_size, dec_vocab_size

Before we move on to train the Transformer model, let’s first have a look at the output of the PrepareDataset class corresponding to the first sentence in the training dataset:

# Prepare the training data
dataset = PrepareDataset()
trainX, trainY, train_orig, enc_seq_length, dec_seq_length, enc_vocab_size, dec_vocab_size = dataset(‘english-german-both.pkl’)

print(train_orig[0, 0], ‘n’, trainX[0, :])

<START> did tom tell you <EOS>
tf.Tensor([ 1 25 4 97 5 2 0], shape=(7,), dtype=int64)

(Note: Since the dataset has been randomly shuffled, you will likely see a different output.)

We can see that, originally, we had a three-word sentence (did tom tell you) to which we have appended the start and end-of-string tokens, and which we then proceeded to vectorize (you may notice that the <START> and <EOS> tokens are assigned the vocabulary indices 1 and 2, respectively). The vectorized text was also padded with zeros, such that the length of the end result matches the maximum sequence length of the encoder:

print(‘Encoder sequence length:’, enc_seq_length)

Encoder sequence length: 7

We may similarly check out the corresponding target data that is fed into the decoder:

print(train_orig[0, 1], ‘n’, trainY[0, :])

<START> hat tom es dir gesagt <EOS>
tf.Tensor([ 1 14 5 7 42 162 2 0 0 0 0 0], shape=(12,), dtype=int64)

Here, the length of the end result matches the maximum sequence length of the decoder:

print(‘Decoder sequence length:’, dec_seq_length)

Decoder sequence length: 12

Applying a Padding Mask to the Loss and Accuracy Computations

Recall seeing that the importance of having a padding mask at the encoder and decoder is to make sure that the zero values that we have just appended to the vectorized inputs, are not processed along with the actual input values. 

This also holds true for the training process, where a padding mask is required so that the zero padding values in the target data are not considered in the computation of the loss and accuracy.

Let’s have a look at the computation of loss first. 

This will be computed by means of a sparse categorical cross-entropy loss function between the target and predicted values, and subsequently multiplied by a padding mask so that only the valid non-zero values are considered. The returned loss is the mean of the unmasked values:

def loss_fcn(target, prediction):
# Create mask so that the zero padding values are not included in the computation of loss
padding_mask = math.logical_not(equal(target, 0))
padding_mask = cast(padding_mask, float32)

# Compute a sparse categorical cross-entropy loss on the unmasked values
loss = sparse_categorical_crossentropy(target, prediction, from_logits=True) * padding_mask

# Compute the mean loss over the unmasked values
return reduce_sum(loss) / reduce_sum(padding_mask)

For the computation of accuracy, the predicted and target values are first compared. The predicted output is a tensor of size, (batch_size, dec_seq_length, dec_vocab_size), and contains probability values (generated by the softmax function on the decoder side) for the tokens in the output. In order to be able to perform the comparison with the target values, only each token with the highest probability value is considered, with its dictionary index being retrieved through the operation: argmax(prediction, axis=2). Following the application of a padding mask, the returned accuracy is the mean of the unmasked values:

def accuracy_fcn(target, prediction):
# Create mask so that the zero padding values are not included in the computation of accuracy
padding_mask = math.logical_not(math.equal(target, 0))

# Find equal prediction and target values, and apply the padding mask
accuracy = equal(target, argmax(prediction, axis=2))
accuracy = math.logical_and(padding_mask, accuracy)

# Cast the True/False values to 32-bit-precision floating-point numbers
padding_mask = cast(padding_mask, float32)
accuracy = cast(accuracy, float32)

# Compute the mean accuracy over the unmasked values
return reduce_sum(accuracy) / reduce_sum(padding_mask)

Training the Transformer Model

Let’s first define the model and training parameters as specified by Vaswani et al. (2017):

# Define the model parameters
h = 8 # Number of self-attention heads
d_k = 64 # Dimensionality of the linearly projected queries and keys
d_v = 64 # Dimensionality of the linearly projected values
d_model = 512 # Dimensionality of model layers’ outputs
d_ff = 2048 # Dimensionality of the inner fully connected layer
n = 6 # Number of layers in the encoder stack

# Define the training parameters
epochs = 2
batch_size = 64
beta_1 = 0.9
beta_2 = 0.98
epsilon = 1e-9
dropout_rate = 0.1

(Note: We are only considering two epochs here to limit the training time. However, you may explore training the model further as an extension to this tutorial.)

We also need to implement a learning rate scheduler that, initially, increases the learning rate linearly for the first warmup_steps, and then decreases it proportionally to the inverse square root of the step number. Vawsani et al. express this by the following formula: 

$$text{learning_rate} = text{d_model}^{−0.5} cdot text{min}(text{step}^{−0.5}, text{step} cdot text{warmup_steps}^{−1.5})$$

 

class LRScheduler(LearningRateSchedule):
def __init__(self, d_model, warmup_steps=4000, **kwargs):
super(LRScheduler, self).__init__(**kwargs)

self.d_model = cast(d_model, float32)
self.warmup_steps = warmup_steps

def __call__(self, step_num):

# Linearly increasing the learning rate for the first warmup_steps, and decreasing it thereafter
arg1 = step_num ** -0.5
arg2 = step_num * (self.warmup_steps ** -1.5)

return (self.d_model ** -0.5) * math.minimum(arg1, arg2)

An instance of the LRScheduler class is subsequently passed on as the learning_rate argument of the Adam optimizer:

optimizer = Adam(LRScheduler(d_model), beta_1, beta_2, epsilon)

Next, we will split the dataset into batches, in preparation for training:

train_dataset = data.Dataset.from_tensor_slices((trainX, trainY))
train_dataset = train_dataset.batch(batch_size)

Followed by the creation of a model instance:

training_model = TransformerModel(enc_vocab_size, dec_vocab_size, enc_seq_length, dec_seq_length, h, d_k, d_v, d_model, d_ff, n, dropout_rate)

In training the Transformer model, we will be writing our own training loop, which incorporates the loss and accuracy functions that we have implemented earlier. 

The default runtime in Tensorflow 2.0 is eager execution, which means that operations execute immediately one after the other. Eager execution is simple and intuitive, and makes debugging easier. Its downside, however, is that it cannot take advantage of the global performance optimizations that come with running the code using graph execution. In graph execution, a graph is first built before the tensor computations may be executed, which gives rise to a computational overhead. For this reason, the use of graph execution is mostly recommended for large model training, rather than for small model training where eager execution may be more suited to perform simpler operations. Since the Transformer model is sufficiently large, we will be applying graph execution to train it. 

In order to do so, we will use the @function decorator as follows:

@function
def train_step(encoder_input, decoder_input, decoder_output):
with GradientTape() as tape:

# Run the forward pass of the model to generate a prediction
prediction = training_model(encoder_input, decoder_input, training=True)

# Compute the training loss
loss = loss_fcn(decoder_output, prediction)

# Compute the training accuracy
accuracy = accuracy_fcn(decoder_output, prediction)

# Retrieve gradients of the trainable variables with respect to the training loss
gradients = tape.gradient(loss, training_model.trainable_weights)

# Update the values of the trainable variables by gradient descent
optimizer.apply_gradients(zip(gradients, training_model.trainable_weights))

train_loss(loss)
train_accuracy(accuracy)

With the addition of the @function decorator, a function that takes tensors as input will be compiled into a graph. If the @function decorator is commented out, the function is, alternatively, run with eager execution. 

The next step is to implement the training loop that will call the train_step function above. The training loop will iterate over the specified number of epochs, and over the dataset batches. For each batch, the train_step function computes the training loss and accuracy measures, and applies the optimizer to update the trainable model parameters. A checkpoint manager is also included to save a checkpoint after every five epochs:

train_loss = Mean(name=’train_loss’)
train_accuracy = Mean(name=’train_accuracy’)

# Create a checkpoint object and manager to manage multiple checkpoints
ckpt = train.Checkpoint(model=training_model, optimizer=optimizer)
ckpt_manager = train.CheckpointManager(ckpt, “./checkpoints”, max_to_keep=3)

for epoch in range(epochs):

train_loss.reset_states()
train_accuracy.reset_states()

print(“nStart of epoch %d” % (epoch + 1))

# Iterate over the dataset batches
for step, (train_batchX, train_batchY) in enumerate(train_dataset):

# Define the encoder and decoder inputs, and the decoder output
encoder_input = train_batchX[:, 1:]
decoder_input = train_batchY[:, :-1]
decoder_output = train_batchY[:, 1:]

train_step(encoder_input, decoder_input, decoder_output)

if step % 50 == 0:
print(f’Epoch {epoch + 1} Step {step} Loss {train_loss.result():.4f} Accuracy {train_accuracy.result():.4f}’)

# Print epoch number and loss value at the end of every epoch
print(“Epoch %d: Training Loss %.4f, Training Accuracy %.4f” % (epoch + 1, train_loss.result(), train_accuracy.result()))

# Save a checkpoint after every five epochs
if (epoch + 1) % 5 == 0:
save_path = ckpt_manager.save()
print(“Saved checkpoint at epoch %d” % (epoch + 1))

An important point to keep in mind is that the input to the decoder is offset by one position to the right with respect to the encoder input. The idea behind this offset, combined with a look-ahead mask in the first multi-head attention block of the decoder, is to ensure that the prediction for the current token can only depend on the previous tokens. 

This masking, combined with fact that the output embeddings are offset by one position, ensures that the predictions for position i can depend only on the known outputs at positions less than i.

Attention Is All You Need, 2017. 

It is for this reason that the encoder and decoder inputs are fed into the Transformer model in the following manner:

encoder_input = train_batchX[:, 1:]

decoder_input = train_batchY[:, :-1]

Putting together the complete code listing produces the following:

from tensorflow.keras.optimizers import Adam
from tensorflow.keras.optimizers.schedules import LearningRateSchedule
from tensorflow.keras.metrics import Mean
from tensorflow import data, train, math, reduce_sum, cast, equal, argmax, float32, GradientTape, TensorSpec, function, int64
from keras.losses import sparse_categorical_crossentropy
from model import TransformerModel
from prepare_dataset import PrepareDataset
from time import time

# Define the model parameters
h = 8 # Number of self-attention heads
d_k = 64 # Dimensionality of the linearly projected queries and keys
d_v = 64 # Dimensionality of the linearly projected values
d_model = 512 # Dimensionality of model layers’ outputs
d_ff = 2048 # Dimensionality of the inner fully connected layer
n = 6 # Number of layers in the encoder stack

# Define the training parameters
epochs = 2
batch_size = 64
beta_1 = 0.9
beta_2 = 0.98
epsilon = 1e-9
dropout_rate = 0.1

# Implementing a learning rate scheduler
class LRScheduler(LearningRateSchedule):
def __init__(self, d_model, warmup_steps=4000, **kwargs):
super(LRScheduler, self).__init__(**kwargs)

self.d_model = cast(d_model, float32)
self.warmup_steps = warmup_steps

def __call__(self, step_num):

# Linearly increasing the learning rate for the first warmup_steps, and decreasing it thereafter
arg1 = step_num ** -0.5
arg2 = step_num * (self.warmup_steps ** -1.5)

return (self.d_model ** -0.5) * math.minimum(arg1, arg2)

# Instantiate an Adam optimizer
optimizer = Adam(LRScheduler(d_model), beta_1, beta_2, epsilon)

# Prepare the training and test splits of the dataset
dataset = PrepareDataset()
trainX, trainY, train_orig, enc_seq_length, dec_seq_length, enc_vocab_size, dec_vocab_size = dataset(‘english-german-both.pkl’)

# Prepare the dataset batches
train_dataset = data.Dataset.from_tensor_slices((trainX, trainY))
train_dataset = train_dataset.batch(batch_size)

# Create model
training_model = TransformerModel(enc_vocab_size, dec_vocab_size, enc_seq_length, dec_seq_length, h, d_k, d_v, d_model, d_ff, n, dropout_rate)

# Defining the loss function
def loss_fcn(target, prediction):
# Create mask so that the zero padding values are not included in the computation of loss
padding_mask = math.logical_not(equal(target, 0))
padding_mask = cast(padding_mask, float32)

# Compute a sparse categorical cross-entropy loss on the unmasked values
loss = sparse_categorical_crossentropy(target, prediction, from_logits=True) * padding_mask

# Compute the mean loss over the unmasked values
return reduce_sum(loss) / reduce_sum(padding_mask)

# Defining the accuracy function
def accuracy_fcn(target, prediction):
# Create mask so that the zero padding values are not included in the computation of accuracy
padding_mask = math.logical_not(equal(target, 0))

# Find equal prediction and target values, and apply the padding mask
accuracy = equal(target, argmax(prediction, axis=2))
accuracy = math.logical_and(padding_mask, accuracy)

# Cast the True/False values to 32-bit-precision floating-point numbers
padding_mask = cast(padding_mask, float32)
accuracy = cast(accuracy, float32)

# Compute the mean accuracy over the unmasked values
return reduce_sum(accuracy) / reduce_sum(padding_mask)

# Include metrics monitoring
train_loss = Mean(name=’train_loss’)
train_accuracy = Mean(name=’train_accuracy’)

# Create a checkpoint object and manager to manage multiple checkpoints
ckpt = train.Checkpoint(model=training_model, optimizer=optimizer)
ckpt_manager = train.CheckpointManager(ckpt, “./checkpoints”, max_to_keep=3)

# Speeding up the training process
@function
def train_step(encoder_input, decoder_input, decoder_output):
with GradientTape() as tape:

# Run the forward pass of the model to generate a prediction
prediction = training_model(encoder_input, decoder_input, training=True)

# Compute the training loss
loss = loss_fcn(decoder_output, prediction)

# Compute the training accuracy
accuracy = accuracy_fcn(decoder_output, prediction)

# Retrieve gradients of the trainable variables with respect to the training loss
gradients = tape.gradient(loss, training_model.trainable_weights)

# Update the values of the trainable variables by gradient descent
optimizer.apply_gradients(zip(gradients, training_model.trainable_weights))

train_loss(loss)
train_accuracy(accuracy)

for epoch in range(epochs):

train_loss.reset_states()
train_accuracy.reset_states()

print(“nStart of epoch %d” % (epoch + 1))

start_time = time()

# Iterate over the dataset batches
for step, (train_batchX, train_batchY) in enumerate(train_dataset):

# Define the encoder and decoder inputs, and the decoder output
encoder_input = train_batchX[:, 1:]
decoder_input = train_batchY[:, :-1]
decoder_output = train_batchY[:, 1:]

train_step(encoder_input, decoder_input, decoder_output)

if step % 50 == 0:
print(f’Epoch {epoch + 1} Step {step} Loss {train_loss.result():.4f} Accuracy {train_accuracy.result():.4f}’)
# print(“Samples so far: %s” % ((step + 1) * batch_size))

# Print epoch number and loss value at the end of every epoch
print(“Epoch %d: Training Loss %.4f, Training Accuracy %.4f” % (epoch + 1, train_loss.result(), train_accuracy.result()))

# Save a checkpoint after every five epochs
if (epoch + 1) % 5 == 0:
save_path = ckpt_manager.save()
print(“Saved checkpoint at epoch %d” % (epoch + 1))

print(“Total time taken: %.2fs” % (time() – start_time))

Running the code produces a similar output to the following (you will likely see different loss and accuracy values because we are training from scratch, whereas the training time depends on the computational resources that you have available for training):

Start of epoch 1
Epoch 1 Step 0 Loss 8.4525 Accuracy 0.0000
Epoch 1 Step 50 Loss 7.6768 Accuracy 0.1234
Epoch 1 Step 100 Loss 7.0360 Accuracy 0.1713
Epoch 1: Training Loss 6.7109, Training Accuracy 0.1924

Start of epoch 2
Epoch 2 Step 0 Loss 5.7323 Accuracy 0.2628
Epoch 2 Step 50 Loss 5.4360 Accuracy 0.2756
Epoch 2 Step 100 Loss 5.2638 Accuracy 0.2839
Epoch 2: Training Loss 5.1468, Training Accuracy 0.2908
Total time taken: 87.98s

It takes 155.13s for the code to run using eager execution alone on the same platform that is making use of only a CPU, which shows the benefit of using graph execution. 

Further Reading

This section provides more resources on the topic if you are looking to go deeper.

Books

Advanced Deep Learning with Python, 2019.
Transformers for Natural Language Processing, 2021. 

Papers

Attention Is All You Need, 2017.

Websites

Writing a training loop from scratch in Keras, https://keras.io/guides/writing_a_training_loop_from_scratch/

Summary

In this tutorial, you discovered how to train the Transformer model for neural machine translation.

Specifically, you learned:

How to prepare the training dataset. 
How to apply a padding mask to the loss and accuracy computations. 
How to train the Transformer model. 

Do you have any questions?
Ask your questions in the comments below and I will do my best to answer.



The post Training the Transformer Model appeared first on Machine Learning Mastery.

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