Transformer Models Explained

A transformer model is a type of deep learning architecture introduced by Vaswani et al. in the paper “Attention is All You Need” in 2017. It has since revolutionized the field of natural language processing (NLP) and is the basis for many state-of-the-art models like GPT, BERT, and T5. It is primarily used in natural language processing tasks, like machine translation and text summarization, but we use different versions in computer vision.

Innovations of Transformer Models

Transformer models have two innovational aspects. One of them is the self-attention mechanism, and the other one is enabling training parallel.

The transformer model was created as an alternative to traditional sequence-to-sequence models, which relied on recurrent neural networks (RNNs) or long short-term memory (LSTM) networks. RNNs and LSTMs suffered from issues like long training times and difficulty in capturing long-range dependencies in sequences, and they can not be trained in parallel efficiently.

Transformers addressed these limitations by introducing the self-attention mechanism, which allows the model to weigh and consider the importance of all tokens in the input sequence when making predictions.

How Does Self-Attention Mechanism Work?

It is designed to help models capture and weigh the relationships between elements in a sequence, such as words in a sentence or characters in a text. Each element in the input sequence computes an attention score with every other element in the sequence.

These attention scores are used to weigh the importance of each element in the context of the current element. The scores are then combined to create a new representation for each element, reflecting the sequence’s contextual relationships.

The self-attention mechanism consists of three main steps:

1. Calculate query, key, and value vectors for each element in the input sequence using learnable weight matrices. These vectors represent the current element’s relationship with other elements in the sequence.
2. Compute attention scores by taking the dot product of the query and key vectors, followed by scaling and normalization. This results in a probability distribution that represents the importance of each element relative to the current element.
3. Combine the value vectors using the attention scores to generate the output representation for each element.

The self-attention mechanism allows models like the Transformer to capture long-range dependencies in sequences efficiently.

Parallel Training with Transformer Models

RNNs and LSTMs are designed to process input sequences sequentially. At each time step, the network processes an element of the input sequence and updates its internal state. Due to this sequential nature, the computation of the next state depends on the previous state, which inherently limits parallelism.

While you can parallelize across different sequences in a batch, the sequential nature of RNNs and LSTMs makes it difficult to fully take advantage of the parallel computing capabilities of modern hardware within a single sequence.

On the other hand, transformer models can simultaneously attend to all input sequence positions, allowing for parallel computation. The self-attention mechanism enables the model to weigh the importance of relative positions rather than relying on a fixed order like RNNs and LSTMs. This parallel processing capability makes it possible to train large transformer models efficiently on modern hardware like GPUs and TPUs.

Components of Transformer Models

The transformer model consists of an encoder-decoder structure, relying heavily on self-attention mechanisms to process and generate sequences in parallel.

1. Encoder: The encoder comprises a stack of identical layers containing two sub-layers: a multi-head self-attention mechanism and a position-wise fully connected feed-forward network. The input sequence is first embedded into continuous vectors and then combined with positional encoding to account for the sequence order. The encoded input is then passed through multiple layers of the encoder, and the output of the last encoder layer is fed into the decoder.
2. Decoder: Similar to the encoder, the decoder comprises a stack of identical layers. Each decoder layer has three sub-layers: a multi-head self-attention mechanism, a multi-head cross-attention mechanism, and a position-wise fully connected feed-forward network. The self-attention and cross-attention sub-layers allow the decoder to focus on relevant parts of the input and its generated output. Finally, a linear layer followed by a softmax function generates the probabilities for each token in the output vocabulary.
3. Multi-Head Self-Attention: This mechanism allows the model to weigh the importance of different parts of the input sequence concerning each other. It consists of several attention heads, each computing a scaled dot-product attention. The outputs from all heads are concatenated and passed through a linear layer. The attention mechanism enables the model to learn complex dependencies and relationships between input tokens.
4. Positional Encoding: Since the transformer architecture lacks inherent sequential processing, positional encoding is added to the input embeddings to provide information about the position of the tokens in the sequence. This is achieved by injecting sinusoidal functions with different frequencies, which allows the model to learn and utilize positional information.

Pseudocode Explanation of the Transformer Model

function Transformer(N_layers, d_model, n_heads, d_ff, dropout_rate):
    Initialize Encoder with N_layers, d_model, n_heads, d_ff, dropout_rate
    Initialize Decoder with N_layers, d_model, n_heads, d_ff, dropout_rate
    Initialize Linear layer for final output prediction
    function forward(src, tgt, src_mask, tgt_mask):
        src = AddPositionalEncoding(src)
        tgt = AddPositionalEncoding(tgt)
        
        encoder_output = Encoder(src, src_mask)
        decoder_output = Decoder(tgt, encoder_output, tgt_mask, src_mask)
        output = Linear(decoder_output)
        return output
function AddPositionalEncoding(x):
    positional_encoding = CalculatePositionalEncoding(x.shape)
    return x + positional_encoding
function CalculatePositionalEncoding(shape):
    Initialize positional_encoding with zeros of given shape
    for i in range(shape[1]):
        for j in range(0, shape[2], 2):
            positional_encoding[:, i, j] = sin(i / 10000^(2j/d_model))
            positional_encoding[:, i, j+1] = cos(i / 10000^(2j/d_model))
    return positional_encoding
class Encoder:
    function __init__(N_layers, d_model, n_heads, d_ff, dropout_rate):
        Initialize N_layers EncoderLayer instances
    function forward(src, src_mask):
        for each EncoderLayer:
            src = EncoderLayer(src, src_mask)
        return src
class EncoderLayer:
    function __init__(d_model, n_heads, d_ff, dropout_rate):
        Initialize MultiHeadAttention, PositionwiseFeedForward, LayerNorm, and Dropout instances
    function forward(src, src_mask):
        attn_output = MultiHeadAttention(src, src, src, src_mask)
        src = LayerNorm(src + Dropout(attn_output))
        ff_output = PositionwiseFeedForward(src)
        src = LayerNorm(src + Dropout(ff_output))
        return src
class Decoder:
    function __init__(N_layers, d_model, n_heads, d_ff, dropout_rate):
        Initialize N_layers DecoderLayer instances
    function forward(tgt, encoder_output, tgt_mask, src_mask):
        for each DecoderLayer:
            tgt = DecoderLayer(tgt, encoder_output, tgt_mask, src_mask)
        return tgt
class DecoderLayer:
    function __init__(d_model, n_heads, d_ff, dropout_rate):
        Initialize MultiHeadAttention, PositionwiseFeedForward, LayerNorm, and Dropout instances
    function forward(tgt, encoder_output, tgt_mask, src_mask):
        attn_output = MultiHeadAttention(tgt, tgt, tgt, tgt_mask)
        tgt = LayerNorm(tgt + Dropout(attn_output))
        attn_output = MultiHeadAttention(tgt, encoder_output, encoder_output, src_mask)
        tgt = LayerNorm(tgt + Dropout(attn_output))
        ff_output = PositionwiseFeedForward(tgt)
        tgt = LayerNorm(tgt + Dropout(ff_output))
        return tgt
class MultiHeadAttention:
    function __init__(d_model, n_heads, dropout_rate):
        Initialize Linear layers for query, key, value, and output
        Initialize Dropout instance
    function forward(query, key, value, mask):
        Split query, key, and value into multiple heads
        Scaled dot-product attention for each head
        Concatenate all head outputs
        Apply linear layer and dropout
        return output
class PositionwiseFeedForward:
    function __init__(d_model, d_ff, dropout_rate):
        Initialize Linear layers and Dropout instance
    function forward(x):
        Apply first Linear layer with ReLU activation
        Apply Dropout
        Apply second Linear layer
        return output
function ScaledDotProductAttention(Q, K, V, mask):
    Calculate attention scores with Q * K^T / sqrt(d_k)
    Apply mask if provided
    Softmax on attention scores
    Multiply attention scores with V
    return output

Limitations of Transformer Models

While revolutionary in their impact on natural language processing, transformer models have some limitations. Transformers have a quadratic complexity concerning sequence length, which makes it difficult to process long input sequences. The self-attention mechanism’s computational requirements grow rapidly as the sequence length increases, leading to inefficiencies in handling longer texts.

Transformer models, particularly large-scale ones like GPT-3, require significant amounts of memory to store the model parameters and intermediate activations during training and inference. This can limit their accessibility for researchers and developers with limited computational resources.

With millions or even billions of parameters, Transformer models can be prone to overfitting, especially when training on smaller datasets. Regularization techniques like dropout can help mitigate this, but the challenge remains.

Transformer models can be vulnerable to adversarial attacks, where small perturbations in the input can lead to incorrect predictions or generate misleading responses.

Conclusion

In conclusion, the original Transformer model has revolutionized the field of natural language processing and set the stage for a new era of AI research. Its groundbreaking attention mechanism, parallel processing capabilities, and scalability have paved the way for advanced successors such as GPT and BERT.

Despite its challenges and limitations, the Transformer has laid the foundation for innovations in machine translation, sentiment analysis, summarization, and other critical NLP tasks. As the technology continues to evolve, the Transformer model will undoubtedly be remembered as a pivotal moment in AI history, propelling our understanding of human language and enabling countless applications across industries.