The Decoder-Only Architecture & The Art of Essay Writing

New LLMs are being released at a rapid clip, with examples like Gemini-1.5, Gemma, Claude 3, in just last 30-days, and potentially GPT-5 on the horizon. Despite the flurry of new models, one crucial aspect has remained largely unchanged over the past few years: the decoder-only transformer architecture. This architecture form the backbone of most modern large language models. In this article, I try to demystify the decoder-only transformer architecture in a simple way.

Why?

1. Large language models (LLMs) are advancing rapidly, but their underlying architecture has not deviated much from the original GPT model.
2. The key differences of most LLMs lie in the scale, minor modifications, and more extensive training/alignment processes.
3. The decoder-only transformer architecture has emerged as a fundamental and influential concept in AI research.
4. Given its central role, understanding the decoder-only transformer architecture is important for those involved in or interested in AI.
5. Familiarizing yourself with this architecture will help you better comprehend the foundation of modern LLMs and stay informed as the field evolves.

Let's dive in:

A decoder-only transformer architecture is a variation of the Transformer model, which is widely known for its efficiency in handling sequential data and its ability to scale with data size. Unlike the standard Transformer model that consists of both encoder and decoder components, a decoder-only transformer focuses solely on the decoder part. This architecture is particularly effective for generative tasks where the model predicts subsequent tokens based on the previous tokens in a sequence. (credit: this article is inspired by a research article written by Cameron Wolff . I highly encourage everyone to read it for deeper understanding)

Here are the key components of a decoder-only transformer:

1. Input Embeddings:

The input tokens are converted into dense vector representations called embeddings, which capture the semantic meaning of the tokens.

Let's simplify this:

• Imagine a student who needs to write an essay. Each word in the student's vocabulary is like a distinct color on an artist's palette. The student carefully selects each word, considering its unique shade and the way it blends with others to create a vivid picture in the reader's mind. To organize their palette, the student creates a color-coding system. They assign a specific code to each color, representing its hue, saturation, and brightness. These codes are like the embeddings in the language model, capturing the essence of each word.The student understands that words with similar meanings, like "happy," "joyful," "excited," and "delighted." will have codes close to each other, just as similar colors sit together on the color wheel. Words with contrasting meanings, like "radiant" and "gloomy," will have codes far apart, like colors on opposite sides of the spectrum.By assigning these color codes, the student can easily identify and group words based on their meanings and relationships. They can create harmonious combinations, choosing words that complement each other like adjacent colors on the palette.As the student writes their essay, they refer to their color-coded vocabulary, selecting the perfect shades to paint a vivid picture in the reader's mind. The precise use of color brings their ideas to life, making their writing more engaging and impactful.Just as the artist's palette is the foundation for a beautiful painting, the input embeddings are the building blocks for the language model. They capture the rich meanings and intricate relationships between words, allowing the model to create coherent and expressive text that resonates with the reader.

2. Positional Encoding:

Since the transformer architecture doesn't inherently capture the position of tokens in the sequence, positional encodings are added to the input embeddings to provide this information.

Let's simplify this:

• Going with our student writing the essay analogy, when the student writes their essay, they don't just write a bunch of sentences randomly. They need to structure their thoughts in a logical order, so that the essay makes sense and is easy to follow. This is where positional encoding comes in.Imagine the student starts by writing down the main points they want to make in their essay. They might have something like this:1. Introduction2. Point A3. Point B4. Point C5. ConclusionThese main points are like the key positions in the essay. The student then starts filling in each point with sentences that support or explain it. For example, under "Point A," they might have:2.1 Sentence explaining Point A2.2 Example supporting Point A2.3 Additional detail about Point AThe numbers before each sentence represent their position within the essay. The first number (2) indicates that these sentences belong under the second main point, "Point A." The second number (1, 2, 3) indicates the order of the sentences within that point.By numbering each sentence like this, the student creates a clear structure for their essay. They can easily see how each sentence relates to the main points and how the essay progresses from one idea to the next.In a similar way, positional encoding in a language model helps the model understand the order and structure of the input tokens. Just like the student numbers their sentences, the model assigns a unique positional encoding to each token in the input sequence.These positional encodings are typically represented as vectors, which are added to the token embeddings. The vectors are designed in such a way that the model can easily distinguish the relative positions of the tokens. For example, the positional encoding for the first token might be very different from the positional encoding for the last token, while the encodings for adjacent tokens would be more similar.By incorporating these positional encodings, the language model can learn to pay attention to the order of the tokens, not just their individual meanings. This is crucial for understanding the structure and meaning of the input text, just like how the numbered sentences help the student create a coherent and well-structured essay.

3. Stack of Decoder Layers:

The decoder stack typically consists of 6 to 24 identical layers, depending on the model size. Each decoder layer has three main sub-components:

3.1 Self-Attention Mechanism:

This allows the model to attend to different positions of the input sequence to capture dependencies between tokens.

side bar: since the original paper of Transformer, a newer attention techniques have been developed, prominently FlashAttention. Flash attention can be used instead of standard self-attention for memory and compute efficiency as well as improving parallelization. Like anything in science, choice of one vs. another always depends on specific requirements.

bias alert: In such situation where memory and compute demand is higher, a linear scale solution such as Wafer Scale Engine provided Cerebras Systems would help alleviate such pain.

(https://www.cerebras.net/product-system/)

Let's simplify this:

• As the student progresses through their essay, they need to ensure that each new sentence they write is relevant to the main topic and logically follows from the previous sentences. This is where the self-attention mechanism comes into play.Imagine the student has a thesis statement that serves as the central idea of their essay. As they write each new sentence, they mentally refer back to this thesis statement and ask themselves, "How does this sentence support or relate to my main argument?"To do this, the student looks at all the sentences they have written so far and determines which ones are most relevant to the current sentence they are writing. They might ask themselves questions like:- "Which previous sentences contain information that directly supports this new point I'm making?"- "Are there any sentences that provide important context or background information for this new idea?"- "How does this sentence fit into the overall structure and flow of my essay?"By asking these questions and "attending" to the most relevant parts of their essay, the student can ensure that each new sentence they write is well-connected to the main topic and logically follows from what they have written before.In the context of a language model, the self-attention mechanism works in a similar way. As the model processes each new token in the input sequence, it looks at all the previous tokens and determines which ones are most relevant to the current token it is processing.The model assigns attention weights to each of the previous tokens, indicating how much importance it should give to each one when generating the next token. Tokens that are more relevant to the current token will receive higher attention weights, while less relevant tokens will receive lower weights.By attending to the most important parts of the input sequence at each step, the language model can generate text that is coherent, well-structured, and stays on topic, much like how the student's self-attention helps them write a clear and focused essay..

3.2 Masked Multi-Head Attention:

In the decoder, the self-attention mechanism is masked to prevent the model from attending to future tokens during training, ensuring that the predictions are based only on the tokens seen so far.

Let's simplify this:

• As the student writes, they want to maintain a logical flow without getting ahead of themselves. They use a piece of paper to cover the parts of the essay they haven't written yet. This keeps them focused on the current section, preventing them from jumping to conclusions prematurely.The student divides their attention between different aspects of their essay, such as the main argument, supporting evidence, and counterarguments. They consider each aspect separately, like wearing multiple "thinking hats," to ensure they address each one adequately.By covering the unwritten parts and focusing on each aspect individually, the student can build their argument step by step, ensuring that each new sentence logically follows from the previous ones. This divided focus helps them create a coherent and well-structured essay.In the language model, masked multi-head attention works similarly. The "mask" prevents the model from attending to tokens that come later in the sequence. This maintains the auto-regressive property, ensuring that the model generates each new token based only on the previously generated tokens.The "multi-head" part refers to the model dividing its attention into multiple "heads," each focusing on different aspects of the input sequence. This allows the model to capture various dependencies and relationships between the tokens.By masking future tokens and using multi-head attention, the language model can generate text that follows a logical sequence, with each new token building upon the information provided by the previous tokens.

3.3 Position-wise Feed-Forward Networks:

These are fully connected neural networks applied to each position separately and identically, which help the model learn complex non-linear transformations of the input.

Let's simplify this:

• After completing the initial draft, the student reviews each sentence individually to enhance their writing. They apply various techniques to improve the structure, clarity, and flow of the essay.For example, the student might:- Vary sentence lengths and structures to create a more engaging rhythm.- Use transitional phrases to smoothly connect ideas between sentences.- Replace weak or repetitive words with more precise and descriptive language.- Adjust the tone and style of each sentence to match the overall theme of the essay.By focusing on each sentence separately, the student can make targeted improvements that enhance the quality and coherence of their writing.Similarly, position-wise feed-forward networks in the language model process each token independently. After the self-attention layer, these networks apply a series of transformations to each token's representation.The feed-forward networks consist of multiple layers that can learn complex patterns and relationships. They help refine the token representations based on the context provided by the self-attention layer.By processing each token separately, the feed-forward networks can capture fine-grained details and nuances that contribute to the overall coherence and fluency of the generated text.Just as the student polishes each sentence to create a well-crafted essay, the position-wise feed-forward networks refine each token's representation to produce high-quality, contextually relevant output.

4. Layer Normalization:

This technique is applied after each sublayer (self-attention and feed-forward) to normalize the activations and stabilize the training process.

Let's simplify this:

• After refining individual sentences, the student takes a step back to review the essay as a whole. They want to ensure that the writing style and tone remain consistent throughout the piece.The student checks for balance in various aspects of their writing:- Sentence length: They make sure that the sentences are not overly long or short, maintaining a balanced rhythm.- Vocabulary complexity: They ensure that the vocabulary is appropriate for the intended audience and that complex words are used consistently and not just in certain sections.- Tone and formality: They check that the tone and level of formality remain consistent, avoiding sudden shifts that could distract the reader.By maintaining consistency in these elements, the student creates a cohesive and readable essay that flows smoothly from beginning to end.In the language model, layer normalization serves a similar purpose. It helps maintain consistency in the scale and distribution of the activations (i.e., the intermediate outputs) across the different layers of the model.During training, the activations can sometimes become unstable, with their values varying widely between layers. This can lead to issues like vanishing or exploding gradients, making it difficult for the model to learn effectively.Layer normalization addresses this by normalizing the activations within each layer. It adjusts the scale and mean of the activations, ensuring that they have a consistent distribution throughout the model.By keeping the activations in check, layer normalization helps stabilize the training process and allows the model to learn more efficiently. It promotes a balanced flow of information across the layers, much like how the student maintains consistency in their writing style.Just as the student's consistent writing style enhances the readability of their essay, layer normalization improves the stability and performance of the language model.

5. Residual Connections:

These connections allow the model to learn residual functions with reference to the layer inputs, which helps in training deeper networks.

Let's simplify this:

• As the student reviews their draft, they may identify areas where they omitted important information or details that could strengthen their arguments. However, they don't want to disrupt the main flow of the essay by making extensive changes to the existing sentences.To address this, the student can use techniques similar to residual connections:- Footnotes: The student can add a footnote to provide additional information or clarify a point without interrupting the main text. - Asides: The student can include a brief aside, enclosed in parentheses or dashes, to insert missing information or provide context. - Margin notes: The student can write short notes in the margins to remind themselves of key details or connections they want to incorporate. By using these techniques, the student can effectively add missing information and enhance their essay while preserving the main narrative flow.In the language model, residual connections serve a similar purpose. They allow the model to easily incorporate important information from earlier layers into the current layer's computations.In deep neural networks, as the number of layers increases, the information from the initial layers can sometimes get lost or become diluted as it passes through the network. This can make it difficult for the model to learn and retain important features from the input.Residual connections address this issue by creating shortcuts that skip one or more layers. They take the output of an earlier layer and add it directly to the output of the current layer. This allows the model to learn residual functions, which capture the difference between the desired output and the input from the previous layer.By preserving information from earlier layers, residual connections help the model maintain a strong connection to the original input, even as it goes through multiple transformations. This enables the model to learn more effectively and capture complex dependencies in the data.Just as the student uses footnotes, asides, and margin notes to incorporate missing details without disrupting the main essay, residual connections allow the language model to integrate important information from earlier layers seamlessly.These connections enhance the model's ability to capture and retain key features, leading to improved performance and more coherent generated text.

6. Output Layer:

The final layer of the decoder, which produces the output probabilities over the target vocabulary.

Let's simplify this:

• After completing the writing and revision process, the student carefully reviews their entire essay to ensure it effectively communicates their intended message to the reader. This final review is critical, as it allows the student to make any necessary adjustments and polish their work before submitting it.During this review, the student focuses on several key aspects:- Clarity: They ensure that each sentence and paragraph is clear, concise, and easily understandable.- Coherence: The student checks that the essay flows logically from one point to the next, with smooth transitions between sentences and paragraphs. - Consistency: They verify that the essay maintains a consistent tone, style, and voice throughout.- Accuracy: The student double-checks any facts, figures, or quotes used in the essay to ensure their accuracy. - Impact: Finally, the student assesses whether their essay effectively conveys their intended message and leaves a lasting impact on the reader. By thoroughly reviewing and refining their work, the student ensures that their final essay is polished, effective, and ready for submission.In the language model, the output layer serves a similar purpose. It takes the final hidden state (the representation of the input after passing through all the layers) and transforms it into the actual output of the model.For example, in a language generation task, the output layer would take the final hidden state and produce a probability distribution over the entire vocabulary. This distribution represents the likelihood of each word being the next word in the sequence, given the input and the context captured by the model.The output layer is responsible for translating the abstract representations learned by the model into a concrete, interpretable output. It maps the high-dimensional hidden states to the desired output format, such as word probabilities or class labels.During training, the output layer is optimized to minimize the difference between the predicted output and the actual target output. This process allows the model to learn the appropriate mappings and make accurate predictions.Just as the student carefully reviews and polishes their essay to ensure it effectively conveys their message, the output layer transforms the model's internal representations into a clear, coherent, and accurate output.This final step is crucial in ensuring that the language model generates high-quality, meaningful text that aligns with the intended purpose and effectively communicates the desired information to the user.

These components work together to enable the decoder-only transformer to process and generate sequential data, such as text. The architecture has proven to be highly effective for a wide range of natural language processing tasks, including language modeling, text generation, and machine translation.

Challenges:

The decoder-only transformer architecture, while highly influential in the field of natural language processing (NLP) and beyond, does face ongoing challenges and has sparked numerous areas of research aimed at addressing these limitations:

1. Exposure Bias: In essay writing, students often have to plan and organize their thoughts before writing the actual essay. The exposure bias challenge of decoder-only models is analogous to the difficulty students face when transitioning from the planning stage to the writing stage. Just as the model struggles with relying on its own predictions rather than ground truth, students may find it challenging to maintain coherence and flow when writing based on their initial outline or plan.
2. Lack of Bidirectionality: When writing an essay, students need to consider the overall context, introduction, and conclusion while composing each paragraph. The lack of bidirectionality in decoder-only models, where they cannot leverage future context, is similar to the challenge students face in ensuring that each paragraph fits well within the broader structure and flow of the essay.
3. Scaling Limitations: As essays become longer and more complex, the computational and memory requirements of the self-attention mechanism in decoder-only models can become a bottleneck. Similarly, as students tackle longer and more involved writing assignments, the cognitive load and working memory demands increase, making it more challenging to maintain coherence and consistency throughout the essay.
4. Alignment and Control: In essay writing, students need to ensure that their writing aligns with the prompt, rubric, or assignment guidelines, and that they maintain a consistent tone, style, and level of formality throughout the essay. This challenge is analogous to the alignment and control issues faced by decoder-only models, where it can be difficult to fine-tune their behavior to match specific desired characteristics or constraints.

Each of these challenges represents an exciting avenue for further research and development in the field of AI and NLP. Addressing these issues will not only enhance the capabilities and applicability of decoder-only transformers but also contribute to the broader goal of creating more intelligent, efficient, and ethical AI systems.

What do you think?

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