Understanding Transformer Architecture: The Backbone of Modern AI
Aashish Singh
?? Lead ML Engineer @ Orange Business ?? MBA | Generative AI Innovator| Tech Blogger| Helping Community Grow ?? Certified ML Developer | AI Solutions Pioneer
Transformers have revolutionized the field of natural language processing (NLP) and beyond. They power state-of-the-art models like GPT-4, BERT, and T5, enabling impressive feats in language understanding and generation. Let’s dive into how transformers work, from the fundamental architecture to the mathematical principles that drive them.
1. Introduction to Transformers
Transformers were introduced by Vaswani et al. in their 2017 paper, "Attention is All You Need." Unlike RNNs or LSTMs, which process sequential data step by step, transformers handle entire sequences simultaneously, enabling parallelization and significantly speeding up training.
If you are interested in exploring the research paper, here is the link: Attention is all you need
To put it simply:
"In the world of AI, transformers are the key to unlocking the future."
2. The Shift from LSTM/RNN to Transformer: Why It Was Necessary
In the evolution of natural language processing (NLP), Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) played crucial roles by enabling models to capture temporal dependencies in sequential data. However, as the complexity of tasks increased, these models faced limitations, particularly in handling long-range dependencies and parallelization.
Key Challenges with LSTM/RNN:
The Emergence of Transformers:
Transformers introduced a paradigm shift in NLP with the concept of self-attention, allowing models to attend to all parts of a sequence simultaneously. This shift addressed several limitations of LSTM/RNN models:
The Transformer Architecture
Overview
Transformers, initially designed for sequence transduction tasks like neural machine translation, have become foundational in modern AI. These models excel at converting input sequences into output sequences, relying entirely on self-attention mechanisms rather than sequence-aligned RNNs or convolutional networks. A key feature of the transformer architecture is its encoder-decoder structure.
For instance, when used in language translation, a transformer takes a sentence in one language, such as English, and outputs its translation in another language, like French, maintaining a sophisticated understanding of context and semantics throughout the process.
When we delve into the transformer architecture, we find that it consists of two primary components:
Both the encoder and decoder in the transformer architecture are composed of multiple layers stacked on top of each other, with each encoder and decoder layer sharing the same internal structure. The input data passes sequentially through each encoder layer before moving on to the decoders. Similarly, each decoder layer processes the output of the preceding layer.
The original transformer model consisted of 6 layers each for the encoder and decoder, but this can be expanded to any number NNN of layers, depending on the complexity required.
So now that we have a generic idea of the overall Transformer architecture, let’s focus on both Encoders and Decoders to understand better their working flow:
The Encoder WorkFlow
The encoder is a critical component of the Transformer architecture, designed to convert input tokens into contextualized representations. Unlike traditional models that process tokens in isolation, the Transformer encoder captures the context of each token relative to the entire sequence, allowing for a deeper understanding of the input data.
The encoder is composed of multiple identical layers, each with two main sub-layers: a multi-head self-attention mechanism and a position-wise fully connected feed-forward network. Additionally, each sub-layer includes residual connections and layer normalization to enhance stability and performance.
So let’s break its workflow into its most basic steps:
Step 1: Input Embeddings
The process begins in the bottom-most encoder, where input tokens—words or subwords—are transformed into numerical vectors using embedding layers. These embeddings capture the semantic meaning of the tokens and convert them into fixed-sized vectors, typically of size 512.
Each encoder receives a list of these vectors. In the initial encoder, these vectors are the word embeddings, while in subsequent encoders, they are the output from the encoder layer directly beneath them. This hierarchical structure allows for the progressive refinement of token representations.
Step 2: Positional Encoding
Transformers, unlike RNNs, lack a built-in mechanism to capture the order of tokens. To address this, positional encodings are added to the input embeddings, providing information about each token's position within a sequence.
Researchers introduced a method using sine and cosine functions to generate positional vectors that can represent sequences of any length. Each dimension of the positional encoding corresponds to a unique frequency, with values ranging from -1 to 1, effectively encoding the position of each token in the sequence.
Step 3: Stack of Encoder Layers
The Transformer encoder is composed of a stack of identical layers, typically six in the original model. Each encoder layer plays a crucial role in transforming input sequences into continuous, abstract representations that encapsulate information from the entire sequence.
Each layer consists of two key sub-modules:
To enhance stability and performance, residual connections are applied around each sublayer, followed by layer normalization. This ensures that the information flows smoothly through the network while maintaining the integrity of the data.
3.1 Multi-Headed Self-Attention Mechanism
In the encoder architecture, the multi-headed attention mechanism employs a specialized form of attention known as self-attention. This mechanism allows the model to capture dependencies between words in an input sequence, effectively enabling it to relate each word to others. For instance, the model might learn to associate the word "are" with "you" within a given sentence.
Self-attention empowers the encoder to dynamically focus on different parts of the input sequence as it processes each token. This is achieved through the computation of attention scores, which are derived from three primary components:
The self-attention mechanism allows the model to capture contextual information from the entire sequence, enabling it to better understand relationships between words. Rather than applying a single attention function, the queries, keys, and values are linearly projected multiple times, corresponding to the number of heads (h) in the mechanism. The attention function is then executed in parallel on these h projected versions, resulting in h-dimensional output values.
This multi-headed approach allows the model to attend to different aspects of the input sequence simultaneously, thereby enhancing its ability to capture intricate relationships and dependencies across the entire sequence.
The detailed architecture goes as follows:
Matrix Multiplication (MatMul) - Dot Product of Query and Key
Once the query, key, and value vectors are passed through a linear layer, a dot product matrix multiplication is performed between the queries and keys, resulting in the creation of a score matrix.
The score matrix establishes the degree of emphasis each word should place on other words. Therefore, each word is assigned a score in relation to other words within the same time step. A higher score indicates greater focus.
This process effectively maps the queries to their corresponding keys.
Reducing the Magnitude of attention scores
The scores are then scaled down by dividing them by the square root of the dimension of the query and key vectors. This step is implemented to ensure more stable gradients, as the multiplication of values can lead to excessively large effects.
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Applying Softmax to the Adjusted Scores
Subsequently, a softmax function is applied to the computed attention scores to derive the attention weights. This operation transforms the scores into probability values that range between 0 and 1. The softmax function accentuates higher scores while suppressing lower ones, thereby refining the model's ability to prioritize words that should receive greater attention. This process ensures that the most relevant words are given more focus during the encoding process.
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Combining Softmax Results with the Value Vector
The next step in the attention mechanism involves combining the attention weights, derived from the softmax function, with the value vector. Specifically, the attention weights are multiplied by the corresponding value vector, resulting in an output vector that emphasizes the most relevant words based on their attention scores.
In this process, only the words with high softmax scores significantly influence the output, effectively filtering out less relevant information. The resulting output vector is then passed through a linear layer for further processing, allowing the model to refine its understanding of the input sequence.
Final Output of the Attention Mechanism
The culmination of the attention mechanism yields the final output vector. At this point, you might wonder why it is termed "Multi-Head Attention."
Before the attention process begins, the queries, keys, and values are divided into multiple subsets, denoted as h heads. The self-attention mechanism is then applied independently within each of these smaller subsets or 'heads,' allowing each head to generate its own output vector.
These individual output vectors are subsequently combined and passed through a final linear layer, which acts as a filter to fine-tune their collective output. The strength of this approach lies in the diversity of learning that occurs across the different heads, enabling the encoder model to develop a more comprehensive and nuanced understanding of the input sequence.
3.2 Normalization and Residual Connections
In the encoder architecture, each sub-layer is followed by a normalization step. Additionally, a residual connection is employed, where the output of each sub-layer is added to its input. This technique helps mitigate the vanishing gradient problem, facilitating the training of deeper models by ensuring that important information is preserved as it passes through the network.
This process is also applied after the Feed-Forward Neural Network, ensuring consistency in the model's learning process and enhancing its overall stability and performance.
STEP 3.3 Feed-Forward Neural Network
The normalized residual output then progresses through a pointwise feed-forward network, a vital phase for further refinement. This network can be envisioned as two linear layers connected by a ReLU activation function, which serves as an intermediary bridge.
Once the output is processed by the feed-forward network, it undergoes a residual connection, merging with the original input of the network. This integration is followed by another normalization step, ensuring that the output is well-adjusted and harmonized, preparing it for the subsequent stages of the model.
4. Output of the Encoder
The output of the final encoder layer consists of a series of vectors, each providing a rich contextual representation of the input sequence. These vectors are then passed as input to the decoder in a Transformer model, where they play a crucial role in guiding the decoding process.
This meticulous encoding process ensures that the decoder can effectively focus on the relevant parts of the input sequence during translation or generation tasks. The encoder's layered structure, which can consist of multiple stacked layers, allows each layer to explore and learn different aspects of attention. This layered approach not only enhances the model's understanding but also significantly improves the predictive capabilities of the Transformer network.
The Decoder Workflow
The decoder is tasked with generating text sequences. Much like the encoder, the decoder is composed of a series of sub-layers. It includes two multi-headed attention layers, a pointwise feed-forward layer, and utilizes residual connections and layer normalization after each sub-layer. These elements work in concert to refine the decoder's ability to generate coherent and contextually accurate text sequences.
These components operate similarly to the layers of the encoder, but with a distinct purpose: each multi-headed attention layer in the decoder is designed for a specific task. The final stage of the decoder involves a linear layer that acts as a classifier, followed by a softmax function to compute the probabilities of different possible words.
The Transformer decoder is architected to generate output by systematically decoding the encoded information. It functions in an autoregressive manner, beginning with a start token and using previously generated outputs as inputs, along with the encoder's outputs, which are enriched with attention information from the original input.
This sequential decoding process continues until the decoder generates a token that signifies the end of the output sequence, completing the generation task.
1. Output Embeddings
At the outset of the decoder's process, the workflow mirrors that of the encoder. The input sequence first passes through an embedding layer, converting the input tokens into dense vectors that capture semantic information.
2. Positional Encoding
Following the embedding layer, the input is passed through a positional encoding layer, just as in the encoder. This step introduces positional embeddings to the sequence, enabling the model to capture the order of words, which is crucial for understanding context.
These positional embeddings are then fed into the first multi-head attention layer of the decoder, where the attention scores specific to the decoder’s input are carefully calculated.
3. Stack of Decoder Layers
The decoder is composed of a stack of identical layers—six layers in the original Transformer model. Each layer comprises three key sub-components:
3.1 Masked Self-Attention Mechanism
The masked self-attention mechanism in the decoder functions similarly to the self-attention mechanism in the encoder, with one critical difference: it prevents any position in the sequence from attending to subsequent positions. This ensures that each word is only influenced by the words that have come before it in the sequence, not by any future tokens.
For example, when computing the attention scores for the word "are," the mechanism ensures that "are" does not have access to information from "you," which appears later in the sequence. This masking is essential for preserving the autoregressive nature of the decoder's generation process.
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This masking mechanism ensures that predictions for any given position can only depend on known outputs from earlier positions in the sequence.
3.2 Encoder-Decoder Multi-Head Attention (Cross-Attention)
In the second multi-headed attention layer of the decoder, there is a distinct interaction between the components of the encoder and decoder. In this layer, the outputs from the encoder serve as the keys and values, while the outputs from the first multi-headed attention layer of the decoder act as the queries.
This configuration effectively aligns the decoder's understanding with the encoded input, allowing the decoder to identify and emphasize the most relevant parts of the input sequence provided by the encoder.
The output from this cross-attention layer is then further processed through a pointwise feedforward layer, refining the information and enhancing the overall quality of the decoding process.
In this sub-layer, the queries come from the previous decoder layer, and the keys and values come from the output of the encoder. This allows every position in the decoder to attend over all positions in the input sequence, effectively integrating information from the encoder with the information in the decoder.
STEP 3.3 Feed-Forward Neural Network
Similar to the encoder, each decoder layer includes a fully connected feed-forward network, applied to each position separately and identically.
STEP 4 Linear Classifier and Softmax for Generating Output Probabilities
The journey of data through the transformer model culminates in its passage through a final linear layer, which functions as a classifier.
The size of this classifier corresponds to the total number of classes involved (number of words contained in the vocabulary). For instance, in a scenario with 1000 distinct classes representing 1000 different words, the classifier's output will be an array with 1000 elements.
This output is then introduced to a softmax layer, which transforms it into a range of probability scores, each lying between 0 and 1. The highest of these probability scores is key,its corresponding index directly points to the word that the model predicts as the next in the sequence.
Normalization and Residual Connections
Each sub-layer (masked self-attention, encoder-decoder attention, feed-forward network) is followed by a normalization step, and each also includes a residual connection around it.
Output of the Decoder
The final layer's output is transformed into a predicted sequence, typically through a linear layer followed by a softmax to generate probabilities over the vocabulary.
The decoder, in its operational flow, incorporates the freshly generated output into its growing list of inputs, and then proceeds with the decoding process. This cycle repeats until the model predicts a specific token, signaling completion.
The token predicted with the highest probability is assigned as the concluding class, often represented by the end token.
Again remember that the decoder isn't limited to a single layer. It can be structured with N layers, each one building upon the input received from the encoder and its preceding layers. This layered architecture allows the model to diversify its focus and extract varying attention patterns across its attention heads.
Such a multi-layered approach can significantly enhance the model’s ability to predict, as it develops a more nuanced understanding of different attention combinations.
And the final architecture is something similar like this (form the original paper)
Conclusion
The transformer architecture’s ability to handle long-range dependencies and its parallel processing power make it a cornerstone of modern AI. Understanding its inner workings, from self-attention to positional encoding, is key to grasping the power and potential of today's AI models.
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