Transformer Architectures for Dummies - Part 2 (Decoder Only Architectures)

Decoder-Only Language Models for Dummies and Experts

Welcome back to the 'Transformer Architectures for Dummies' series. In my first article, I introduced you to Encoder-Only Models. These models excel in understanding and interpreting text. Like expert analysts, they dissect language to grasp its meaning but do not engage in creating it.

Now, I turn to Decoder-Only Models. These models stand in contrast to their Encoder counterparts. While encoder-only models specialize in analyzing text, decoder-only models are designed to generate new text. Their role isn't just to read or interpret; it's to create.

In this article, I will explore Decoder-Only Models, such as those used in the GPT (Generative Pre-trained Transformer) series. These models are crucial in text generation and are responsible for everything from crafting conversation responses to creating new content. Unlike Encoder-Only Models that excel in comprehension, Decoder-Only Models have the unique skill of producing coherent, contextually relevant text.

I aim to provide a clear and concise understanding of Decoder-Only Models, their workings, and their applications in artificial intelligence. By the end of this article, you will have a comprehensive grasp of these models and their different Architectures. If you study this article thoroughly, you should be able to determine when to choose which Decoder-only architecture.

2.? What Are Decoder-Only Models?

Decoder-only transformer architectures are central to major language models like GPT-3, ChatGPT, GPT-4, PaLM, LaMDa, and Falcon. These models are unique in their approach to handling language: instead of interpreting or analyzing existing text (which Encoder Only Models are adept at), the Decoder only models focus on creating “new text.” Originally introduced in the 2017 paper "Attention is All You Need," transformers featured both decoder and encoder parts. Now, with the GPT models, the trend is towards decoder-only models due to their impressive performance in text generation.

What sets these models apart is their method of operation. Decoder-only models receive input, which could be anything from a simple prompt to a more complex set of data, and then they generate text that is relevant to that input. This process is akin to responding in a conversation or writing an essay on a given topic. The model takes the input and uses it as a starting point to produce coherent and contextually appropriate text.

The power of Decoder-Only Models lies in their ability to not just mimic human-like text, but to also be creative in their responses. They can craft stories, answer questions, and even engage in dialogue that feels natural and fluid. This capability makes them incredibly useful in a wide range of applications, from chatbots and digital assistants to content creation, abstractive summarization, and storytelling.

3. How Decoder Only Models work?

To understand these models better, let's consider the architecture of the original Transformer, which comprised both Encoder and Decoder parts. In recent developments, we've seen a shift towards models specializing in either encoding, like BERT from Google, or decoding, like GPT. Decoder-Only Models fall into the latter category.

The core architecture of a Decoder-Only Model is relatively straightforward. It typically includes:

1. Token Input Layer: This is where the model receives the input sequence of tokens.
2. Decoder Block Layer: This layer is the core of the model. It consists of several components: Masked Multi-Head Self-Attention Layer: This is crucial for understanding the input sequence. The 'masked' aspect ensures that the prediction for a token doesn’t consider future tokens, maintaining the autoregressive nature of the model. Fully Connected Layers: These layers process the information further. Positional Encoding: This adds information about the position of each token in the sequence, which is essential for understanding the order of words.
3. Last Layer: A fully connected layer followed by a SoftMax function is used over the entire vocabulary to predict the next word in the sequence.

Depending on the requirements, a Decoder-Only Model can have multiple Decoder Blocks stacked on top of each other. The real driving force behind these models is the Masked Self-Attention mechanism. This mechanism allows the model to focus on different parts of the input sequence when predicting each token, facilitating the generation of contextually relevant text.

Decoder-Only Models are usually pre-trained on a vast corpus of language data, often encompassing a substantial portion of text available on the internet. The primary task during this pre-training phase is to predict the next word in each sequence of text. This extensive training enables the model to understand and generate human-like text.

After pre-training, these models can be fine-tuned for specific tasks. This fine-tuning, done through methods like Instruction Tuning or Reinforcement Learning from Human Feedback (RHLF), tailors the model for applications such as question-answering systems, virtual assistants, or dialogue-based systems.

During inference or production, Decoder-Only Models employ algorithms like Greedy Search or Sampling to choose the most appropriate words for generating the next part of the text. This ability to generate text makes Decoder-Only Models particularly useful in creating content that requires a high degree of contextual understanding and coherence, making them ideal for applications that involve human-like interaction and content creation.

Analogy for Dummies

If Encoder-Only Models are the fielders, adept at understanding where the ball is going, then Decoder-Only Models are akin to Sachin Tendulkar in his prime. Just as Sachin skillfully read each bowler's delivery and responded with a perfectly timed shot, Decoder-Only Models process input data (similar to the bowler's ball) and generate text that perfectly fits the situation (much like Sachin's well-chosen shots).

Consider the bowler's ball as the input for a Decoder-Only Model. It's akin to Sachin Tendulkar at the crease, meticulously deciding his response. He wouldn't swing his bat indiscriminately. Instead, he would analyze the type of bowler, assess the pitch conditions, and take note of the fielders' positions. Similarly, a Decoder-Only Model evaluates the input it receives and determines the most appropriate response, considering all the preceding context.

Sachin's proficiency in playing a diverse array of shots was a result of years of practice and experience. Decoder-Only Models exhibit a similar breadth of capability, but in the realm of language. They are oversized neural networks, trained on massive amounts of text data, making them exceptionally adept at predicting the next word in a sequence. This extensive training equips them to handle various text-generation tasks, akin to how Sachin prepared for different bowling styles and match scenarios. Just as Sachin fine-tuned his skills for specific matches, these models are fine-tuned for specialized tasks such as answering questions or assisting in chat applications, adapting their vast training to specific, real-world applications.

Decoder Only Architectures

1. Autoregressive Models (e.g., GPT-3.5,4):

These models predict each subsequent token based on the previously generated tokens. GPT-4 operates on the principle of autoregressive language modeling. This means the model generates text by predicting the next token (e.g., a word or a part of a word) based on the tokens that precede it. The model generates text sequentially, one token at a time, using the probability distribution it learned during training. GPT-4 is trained on a diverse and extensive dataset encompassing a wide range of internet text. This training enables the model to understand and generate human-like text across various topics and styles. It can maintain coherence over longer passages, making it effective for tasks like content creation, conversation, and complex problem-solving.

GPT 4 Architecture: GPT-4 uses the classical Transformer architecture, primarily built upon decoder blocks. This architecture is known for its self-attention mechanism, which allows the model to weigh the importance of different tokens within the input sequence when generating each new token. GPT-4 is significantly larger than its predecessors in terms of the number of parameters. With this increase in scale, the model achieves a higher level of understanding and fluency in text generation.

The self-attention mechanism in GPT-4 allows it to focus on different parts of the input sequence, enabling it to capture subtle nuances in language and context. This mechanism is key to its ability to generate coherent and contextually appropriate text. To scale the model at an enterprise level, GPT4 multiple layers of Transformer blocks have been added, each contributing to processing the input tokens and generating the output. The depth of these layers is a critical factor in the model's ability to handle complex language tasks. GPT-4 utilizes positional encoding to maintain the order of the input tokens, an essential aspect for understanding the sequence and structure of the language.

2. Dilated Convolutional Models:

These models use dilated convolutions, a technique that allows the network to have a wider receptive field without increasing the number of parameters. This approach is especially useful in sequence modeling tasks, as it enables the model to efficiently process longer sequences. Dilated convolutions are particularly effective in generating high-quality text by capturing long-range dependencies within the data.

3. Sequence-to-Sequence GANs (Seq2Seq GANs):

This architecture adapts the Generative Adversarial Network (GAN) framework for sequence generation. In Seq2Seq GANs, the generator is a Decoder-Only model that generates sequences (such as text), while the discriminator evaluates the quality and relevance of the generated sequences. The adversarial training process pushes the generator to produce increasingly realistic and contextually appropriate sequences, enhancing the quality of text generation.

The Architecture Intuition at a high level can be described as below:

1. Generator as Decoder-Only Model: In the Seq2Seq GAN architecture, the generator functions as a Decoder-Only model. It takes an input sequence, which can be thought of as a "partial summary" or starting point, and generates a continuation of that sequence. The input to the generator typically begins with a special start token (e.g., <START> in the diagram) to signify the beginning of generation. The generator then produces a sequence of tokens one after another, building upon the initial input.
2. Context Vector and Attention Mechanism: The generator utilizes a context vector obtained from the attention distribution over the encoder hidden states. This attention mechanism allows the generator to focus on different parts of the input source text while generating each token, thereby integrating relevant context into the generated sequence.
3. Adversarial Training Process: Parallel to the generator is a discriminator, whose role is to evaluate the sequences generated by the decoder (generator). The discriminator assesses how realistic or contextually appropriate the generated text is compared to real data. During training, the generator and discriminator engage in a game-like scenario where the generator aims to produce text that is indistinguishable from genuine text, and the discriminator strives to accurately distinguish between the two.
4. Feedback Loop: The adversarial feedback loop continuously improves the generator's ability to produce high-quality text. The discriminator's assessments inform the generator of the quality of its output, guiding it to adjust and improve through backpropagation. This process enhances the generator's capacity to produce text that is not only grammatically correct but also contextually relevant and realistic.
5. Vocabulary Distribution: The output of the generator at each step is a distribution over the vocabulary, from which a token is sampled. This token becomes part of the generated sequence and influences the subsequent generation steps. The diagram's histogram ("beat" to "zoo") represents the probability of each vocabulary token being the next word in the sequence.
6. Sequence Continuation: The generated sequence by the generator serves as the next input to continue the generation process, creating a loop until an end token is produced or a maximum sequence length is reached.

4.?Sparse Transformer (Sparse Attention):

Sparse attention methods reduce complexity by only considering a subset of computations in the self-attention matrix. The idea is that tokens can focus on the more important tokens and ignore the others. Sparse Transformers like Google’s BigBird improve the concentration of attention on the global context. Sparse attention mechanisms address the inefficiency of sparse operations on modern hardware by transforming sparse local and random attention into dense tensor operations. This is achieved by 'blockifying' the attention and using matrix operations to leverage SIMD capabilities of GPUs/TPUs, thereby reducing memory consumption while maintaining performance.

5.?Transformer-XL (Extended Context)

1. Intuition: The Transformer-XL architecture extends the context window of the standard Transformer model. This extension allows the model to remember and utilize information from a much longer sequence of tokens, thereby learning longer-term dependencies.
2. Segmented Recurrence and Relative Positional Encoding: Transformer-XL introduces two key concepts: segmented recurrence and relative positional encoding. Segmented recurrence connects the hidden states across segments, allowing the model to maintain a longer memory. Relative positional encoding, on the other hand, helps the model in understanding the relative positions of tokens within the sequence.
3. Handling Long Sequences: By leveraging these concepts, Transformer-XL can process text segments in a way that retains information from previous segments. This is crucial for tasks that require understanding over longer texts, such as document summarization or question-answering over long documents.
4. Training and Efficiency: The training of Transformer-XL is more efficient compared to standard Transformers when dealing with long sequences. This efficiency comes from reduced computational redundancy as the model reuses the computations from previous segments.
5. Applicability: Transformer-XL has shown improved performance on various language modeling benchmarks, especially those that involve longer text sequences. It is particularly adept at tasks that require an understanding of context over extended text spans.

In this article, I have provided an overview of Decoder-Only Models, detailing their role in text generation within AI. I've described their structure and how they process input to produce text. The aim was to present their applications in a straightforward manner, making their complex functions clear and accessible.

About the Author:

Bhaskar Tripathi is the Head of Data Science & Research Practices at Multicloud4U? Technologies and is a Ph.D. in Computational & Financial Mathematics. He is a leading open source contributor and creator of several popular open-source libraries on GitHub such as pdfGPT, text2diagram, sanitized gray wolf algorithm, tripathi-sharma low discrepancy sequence, TypeTruth AI Text Detector, HypothesisHub, Improved-CEEMDAN among many others.

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