Visualizing transformers and attention | Talk for TNG Big Tech Day '24

The speaker discusses the intricacies of Transformers, focusing on their ability to process large language models efficiently through parallelization, which is essential for handling computation-heavy tasks on GPUs. The talk highlights the flexibility of Transformers, initially designed for language translation, now applicable to various tasks like transcription and image classification. The speaker explains the attention mechanism, which allows words to contextually update each other, and the importance of key, query, and value matrices in this process. The discussion also touches on the challenges of scaling and training these models, emphasizing the role of unsupervised learning and the potential for integrating multiple data types.

• The speaker is working on visually explaining Transformers, primarily in math but extending into adjacent fields.
• Transformers were introduced in the 2017 paper "Attention is All You Need," initially for machine translation but have since expanded to tasks like transcription, speech synthesis, and image classification.
• The focus is on a simpler model used in chatbots, trained to predict the next word in a text sequence.

"Transformers were introduced in this now very famous paper called Attention is All You Need from 2017, and that paper was focused on a specific use case of machine translation."

• The quote emphasizes the origin and initial purpose of Transformers, highlighting their foundational role in machine translation.

• Large language models require significant computational resources and are typically run on GPUs due to their parallelizable nature.
• The goal is to provide a deep understanding of the computations and why parallelization is crucial for their success.

"Large language models consume a lot of computation; they take a lot of resources, and anybody who has looked at the market cap of Nvidia recently will also know that they tend to be run on GPUs because they're very parallelizable."

• This quote underscores the computational demands of large language models and the role of GPUs in managing these demands due to their parallel processing capabilities.

• The model predicts what comes next in a text sequence, assigning probabilities to possible snippets.
• Introducing randomness (temperature) in predictions can lead to more creative and natural outputs.

"If you have a model that does this that simply predicts what word comes next, you can turn it into something that'll generate new text simply by having it randomly sample from that distribution."

• The quote explains how prediction models can be used to generate new text by sampling from probability distributions, highlighting the role of randomness in enhancing output creativity.

• Text is divided into tokens, which could be words, word pieces, or punctuation, and each token is associated with a vector (embedding).
• Tokens allow the model to process text efficiently and encode meaning.

"The first step is to subdivide it into little pieces, and we call these pieces tokens... you pass that through this all-important attention block."

• This quote describes the tokenization process, which is crucial for converting text into a format that the model can process and understand.

• The attention block allows tokens to interact and update their meanings based on context, crucial for disambiguating words.
• Attention is critical for capturing long-range dependencies and context within text.

"You want to give the machine a mechanism by which it can let words talk to each other so that that meaning could get updated."

• The quote highlights the importance of the attention mechanism in updating word meanings based on context, a key feature of Transformers.

• Multi-layer perceptrons (MLPs) store general knowledge and account for the majority of model parameters.
• They are crucial for encoding facts and general world knowledge, as seen in studies associating athletes with their sports.

"In so far as prediction requires context, this is where the attention blocks are relevant, and in so far as prediction requires just general knowledge from the world, these perceptrons give extra capacity to store some more of that."

• The quote distinguishes the roles of attention blocks and MLPs in storing context and general knowledge, respectively.

• Training involves adjusting model parameters to minimize the cost function, representing prediction errors.
• The process is akin to finding a minimum on a high-dimensional cost surface, with no certainty of the behavior that will emerge.

"The process of learning involves tweaking those parameters iteratively so that you're taking little steps downhill on that surface."

• This quote explains the iterative nature of training, where parameters are adjusted to reduce prediction errors, akin to descending a cost surface.

• Understanding the computations and framework is separate from understanding the model's emergent behavior.
• The complexity of the cost surface and the vast number of parameters make predicting model behavior challenging.

"Actually understanding what's going on is extremely challenging because it is an entirely separate question from the design of the network itself."

• The quote emphasizes the difficulty in comprehending model behavior due to the complexity and emergent nature of neural networks.

• Words are associated with vectors to facilitate processing in machine learning models.
• This step is crucial for transforming text into a numerical format suitable for model input and output.

"The reason you have to do this is that if you're doing certainly deep learning but most categories of machine learning, it's very very helpful if your inputs and your outputs are both real numbers."

• The quote underscores the necessity of converting words into vectors for effective processing in machine learning models.

• Word embeddings convert words into vectors in a continuous space, allowing for mathematical operations like gradients and calculus.
• Words with similar meanings tend to cluster together in vector spaces, forming groups of related concepts.
• Directions in vector space can encode meanings not captured by individual words, enabling complex analogies.

"Words with very similar meanings tend to cluster near each other."

• This quote highlights how similar words form clusters in vector spaces, reflecting their related meanings.

"If you take the embedding of woman and you subtract off the embedding of man and then you add that to the embedding of King, what you find is that it's actually quite close to the embedding of Queen."

• Demonstrates the ability of vector spaces to capture gender-related analogies through mathematical operations.

• High-dimensional vector spaces are essential for encoding complex concepts and relationships between words.
• The dimensionality of embeddings, such as the 12,288 dimensions in GPT-3, supports a vast array of distinct directions for encoding concepts.
• Exponential growth in the capacity to fit vectors in high-dimensional spaces allows for encoding numerous concepts efficiently.

"Word vectors in principles are very high dimensional, and you can understand why it's helpful for them to be high dimensional."

• Explains the necessity of high-dimensional spaces for encoding diverse and complex concepts.

"As you scale things up, the way that the answer to this question grows is exponential in the number of dimensions."

• Highlights the exponential increase in capacity to fit vectors as the dimensionality of the space increases.

• Language models aim to capture context, allowing words to adapt their meanings based on surrounding text.
• The embedding process involves soaking in relevant context to predict subsequent words accurately.
• High-dimensional embeddings help encapsulate not just individual word meanings but also broader contextual ideas.

"Once it has the ability to soak in context, if you want to predict what comes next, it would be helpful to somehow know that it's one of two roads."

• Illustrates how contextual understanding is crucial for accurate predictions in language models.

"Anyone who's interacted with these LLMs often gets the feeling that it really does seem to understand something at a deeper level."

• Suggests that language models capture a deeper understanding beyond mere word definitions.

• The attention mechanism enables words in a sentence to influence each other, refining their meanings based on context.
• Words are associated with query, key, and value matrices to facilitate this interaction.
• This mechanism allows for parallel processing and efficient training through matrix multiplication.

"The query Matrix is as if you want to let each word ask a question."

• Describes how the query matrix enables words to seek relevant contextual information.

"Make sure that all the operations that you're working with to somehow ask the question of which ones are associated with which and how should they update other are expressed in the language of matrix multiplication."

• Emphasizes the importance of matrix multiplication for efficient training and processing in language models.

• Matrix multiplication is crucial for the efficient operation of language models, enabling parallel processing on GPUs.
• The use of matrices with tunable parameters allows models to learn and adapt to various linguistic patterns.
• The combination of query, key, and value matrices forms the backbone of the attention mechanism.

"Matrix multiplication can be done very efficiently on GPUs in parallel."

• Highlights the efficiency of matrix multiplication in processing large-scale language models.

"The query, the key, and the value matrices...we'll go through them each one by one."

• Introduces the core components of the attention mechanism, essential for understanding how language models function.

• The concept of keys, queries, and their alignment is fundamental in machine learning, especially in attention mechanisms.
• Dot products are used to measure the alignment of vectors, determining how relevant certain words are to others in a sentence.

"The hope is that you're giving the model capacity to do something like this so if you have all of these keys and you have all of these queries and you've trained everything just you know hoping that a bunch of data will somehow get these patterns that you're hoping for."

• The quote highlights the goal of training models to recognize patterns through keys and queries, enhancing their capacity for understanding relationships in data.

"This dot product is going to be positive whenever two vectors align with each other, it's going to be zero whenever they're perpendicular to each other which we'll think of as meaning unrelated."

• Dot products are crucial for determining the relationship between vectors, indicating alignment or unrelatedness, which is central to attention mechanisms.

• The softmax function is used to convert arbitrary numbers into a probability distribution, making them suitable as weights.
• Weighted sums are calculated based on these weights to update the meanings of words in a sentence.

"The tool in the belt for this another very common function that comes up in machine learning contexts is something called the softmax."

• Softmax is a key function in machine learning, transforming numbers into a normalized probability distribution for further processing.

"What you want to do is take some kind of weighted sum according to how much each of these vectors on the left attends to one of them on the top."

• Weighted sums allow the model to prioritize certain vectors based on their relevance, refining the understanding of word relationships.

• Attention patterns determine which tokens are relevant for updating other tokens, essential for efficient training.
• The concept of masked attention prevents future information from influencing past predictions, maintaining causal relationships.

"This gives you what you would call a masked attention pattern or sometimes people call it causal attention."

• Masked attention ensures that predictions are made without future context, preserving the integrity of sequence predictions.

"If you want to be able to get this training efficiency speed up by having it do all of this in parallel."

• Training efficiency is enhanced by processing multiple predictions simultaneously, leveraging parallelism for faster learning.

• Increasing context size in models presents challenges due to quadratic growth in complexity.
• Redundancy occurs when generating new text, allowing for optimization through caching and other techniques.

"As you increase the context size which is how much text it's incorporating into its prediction this pattern that it's producing grows quadratically."

• Larger context sizes exponentially increase computational demands, necessitating innovative solutions to manage complexity.

"There is a lot of redundancy and you can have a lot of clever caching to basically take advantage of it."

• Redundancy in computations can be mitigated through caching, optimizing the efficiency of text generation processes.

• Tokenization, such as byte pair encoding, balances the granularity of text representation for effective learning.
• Embedding tokens with meaningful representations accelerates the learning process by providing immediate context.

"The pattern that people have found that seems to work quite well is something called byte pair encoding."

• Byte pair encoding is an effective method for tokenizing text, striking a balance between granularity and learning efficiency.

"You let it just kind of jump directly to the meaning of words from that very first step if you do it at tokens."

• Embedding tokens with immediate meaning enhances the model's ability to understand and process text efficiently.

• The value matrix transforms embeddings to update word meanings, using a low-rank transformation for parameter efficiency.
• Multi-headed attention benefits from this optimization, reducing computational load while maintaining accuracy.

"Simplest way to do this is just throw yet another Matrix at it we can call this the value Matrix."

• The value matrix is a fundamental component in updating word embeddings, facilitating nuanced understanding of text.

"If the value is mapping from the embedding space to the embedding space itself it involves almost 100 times as many parameters."

• Optimizing the value matrix with fewer parameters is crucial for efficient model performance, especially in complex networks.

These comprehensive notes encapsulate the intricate mechanisms of machine learning models, focusing on attention mechanisms, training efficiency, and optimization strategies. The detailed exploration of these themes provides a robust foundation for understanding the complexities of modern machine learning systems.

• The attention mechanism in Transformers involves calculating a weighted sum of vectors, which helps in encoding specific directions or meanings relevant to the input.
• Each attention head multiplies input vectors by key, query, and value matrices to produce attention patterns.
• Multiple attention heads operate in parallel within an attention block, allowing for complex context relevance updates.
• Transformers are designed to leverage GPUs efficiently, enabling faster computation despite the complexity of operations.

"The hope is that for this kind of example it takes something that encoded creature and then it spits out something that encloses a more specific direction of that fluffy boo creature."

• This quote explains the role of attention in refining input data into more specific outputs.

"Each one of the heads inside one of these multi-headed attention blocks has its own distinct key and query matrices which are used to produce their own distinct attention patterns."

• Multiple heads in an attention block allow for diverse attention patterns, enhancing the model's ability to capture various aspects of context.

• Transformers consist of multiple layers, including attention blocks and multi-layer perceptrons, which iteratively refine the input data.
• The iterative process aims to enrich the meaning of vectors, allowing the model to predict subsequent tokens effectively.
• The architecture enables the model to draw from an increasingly rich context, enhancing prediction accuracy.

"The loose thought there is you have all of these vectors gaining rich and richer meanings and the context that they're drawing from is itself becoming richer and richer and richer."

• The iterative nature of Transformers enhances the contextual understanding of input data, improving prediction capabilities.

• Scale is a crucial factor in machine learning, with larger models and more training data often leading to qualitative improvements.
• Transformers' architecture, which relies on parallel processing, allows for efficient scaling and processing of large datasets.
• The attention mechanism's independence from sequential processing enables simultaneous consideration of entire passages, optimizing GPU usage.

"A big lesson in machine learning through the last couple decades is that scale alone matters simply making things bigger and simply giving them more training data can sometimes give qualitative improvements to the model performance."

• Scaling up models and data leads to significant improvements in performance, highlighting the importance of scale in machine learning.

"Once people did away with all those mechanisms and just left one them the attention mechanism which allows things to talk to each other but not in a way that relies on sequential processing."

• This highlights the efficiency of Transformers in processing data non-sequentially, allowing for parallel computation.

• Transformers benefit from unsupervised pre-training, enabling the model to learn from vast amounts of data without human labeling.
• The tokenization process in Transformers is versatile, allowing different data types (text, images, sound) to be embedded as vectors and processed together.
• This flexibility supports the integration of multiple data types within a single model, enhancing its applicability across various domains.

"The fact that you can tokenize essentially anything just break up whatever your data type is into little pieces and then embed those as vectors means that you can have lots of distinct data types work in conjunction with each other."

• Tokenization flexibility allows Transformers to process diverse data types, broadening their applicability.

• The speaker emphasizes the importance of understanding model interpretability and engaging with researchers in the field.
• Exploring concepts like the superposition hypothesis and feature directions can provide deeper insights into model behavior.
• The speaker values the pedagogical motivation for model structure and the intellectual curiosity it stimulates.

"I've been especially fond of the interpretability researchers on this front because you know they're motivated to understand what's actually going on from my point of view."

• Collaboration with interpretability researchers enriches understanding of model behavior and structure.

• Transformer models face challenges similar to earlier neural network architectures, such as managing gradients and ensuring training stability.
• Residual connections, which facilitate signal passage without unnecessary transformations, are integral to Transformer architectures.
• The trend towards deeper models continues, with residuality contributing to training stability and hardware efficiency.

"The residuality is sort of baked in from the beginning with how it's even done the thought that as you're going from layer to layer rather than generating an entirely new data type you're kind of adding to whatever there was before."

• Residual connections in Transformers aid in maintaining stability and efficiency during training.

• The speaker prioritizes understanding and clarity over humor in presentations, focusing on delivering content that leads to genuine comprehension.
• Promising a path to understanding is seen as a key motivator for audience engagement in longer presentations.

"The thing that should motivate the next step is centered around understanding and Clarity rather than humor."

• The focus on clarity and understanding drives audience engagement and retention during presentations.

• There is curiosity about the potential integration of analog computing with digital systems to address energy consumption in computations.
• Tokenization in images involves dividing the image into patches and encoding positional information to maintain spatial relevance.
• Different strategies exist for handling the two-dimensional nature of images in tokenization and attention processes.

"Loosely speaking you'd want to think of it as like little patches of the image are going to be the tokens and then you have some notion of positional encoding that will encode that not just the X position but also the Y position."

• Tokenization of images involves patching and positional encoding to retain spatial information, accommodating the two-dimensional nature of images.