Stanford CS25: V2 I Introduction to Transformers w/ Andrej Karpathy

In the inaugural lecture of CS25 Transformers United at Stanford, instructors introduced the transformative impact of deep learning models, particularly Transformers, on various fields such as natural language processing, computer vision, reinforcement learning, and more. The course promises to delve into the mechanics of Transformers and their wide applications, featuring guest speakers from diverse research areas. The instructors, including a PhD student leading an AI robotics startup, a first-year CS PhD student with NLP and computer vision interests, and an enthusiastic previous course participant, aim to unravel the evolution of Transformers, their advantages over RNNs and LSTMs, and their future potential in areas like understanding and generation, finance, and domain-specific models. They also discuss the challenges of external memory, computational complexity, controllability, and alignment with human brain function. The course will cover foundational concepts such as self-attention mechanisms, the history of attention in AI, and the practical implementation of Transformers.

• The course was taught at Stanford during Winter 2023.
• It focuses on deep learning models known as Transformers and their impact on various fields such as natural language processing (NLP), computer vision, reinforcement learning, biology, and robotics.
• The course features guest speakers discussing Transformer applications in their research areas.

"This course is not about robots that can transform into cars... it's about deep learning models that have taken the world by the storm and have revolutionized the field of AI and others starting from natural language processing."

• The quote explains that the course is not about literal transforming robots, but about transformative deep learning models impacting AI and related fields.

• Currently on a temporary departure from a Ph.D. program.
• Leading an AI to robotics startup focused on general-purpose robots.
• Research interests include deep reinforcement learning, competition in remodeling, and robotics.
• Has publications in robotic peer numbers driving and other areas.
• Holds an undergraduate degree from Cornell.

"I'm currently on a temporary data pro from the Ph.D. program and I'm leading AI to robotic startup... I'm very passionate about Robotics and building assistive learning algorithms."

• The instructor is taking a break from their Ph.D. to lead a startup and is passionate about robotics and assistive learning algorithms.

• First-year CS Ph.D. student at Stanford.
• Completed a master's at CMU and an undergraduate degree elsewhere.
• Research interests have shifted from mainly NLP to include computer vision.
• Personal interests include music (piano), martial arts, bodybuilding, k-dramas, anime, and gaming.
• Involved in starting a Stanford piano club.

"I'm Stephen... I've been getting more into computer vision as well... I post a lot on my insta YouTube and Tick Tock so if you guys want to check it out."

• Stephen is a Ph.D. student with a background in NLP and computer vision, who also engages in music and social media activities.

• Rylan is excited to teach and was the most outspoken student in the previous year.
• Looks forward to a fun quarter.

"Instead of talking about myself I just went super excited... I'm thankful you're all here. and I'm looking forward to a really fun quarter again thank you."

• Rylan expresses enthusiasm for teaching the course and is grateful for the students' presence.

• Students will learn how Transformers work and their applications in machine learning.
• Introduction to the basics of Transformers and the self-attention mechanism.
• Future lectures will cover models like BERT and GPT.

"So what we hope you will learn in this class is first of all how do Transformers work... we're just talking about the basics of Transformers introducing them talking about the self-attention mechanism."

• The quote outlines the course's aim to teach the workings of Transformers and their foundational self-attention mechanism.

• Transformers originated with the paper "Attention is All You Need" by Vaswani et al. in 2017.
• Prior models included RNNs and LSTMs with simple attention mechanisms.
• Post-2017 saw Transformers dominate NLP and expand into other fields like computer vision and biology.

"Attention all started with this one paper transform attention is all you need at l in 2017 that was the beginning of Transformers."

• The quote marks the 2017 paper as the inception point for the widespread use of Transformers in AI.

• 2018-2020: Expansion into various fields such as computer vision and biology (e.g., AlphaFold).
• 2021: Emergence of models for alternative tasks, multimodal tasks, and long sequence problems.

"After 2018 to 2020 we saw this explosion of Transformers into other fields... in last year 2021 was the start of alternative era where we got like a lot of opportunity modeling."

• This quote discusses the rapid expansion of Transformer applications into new fields and tasks.

• Transformers are being used for unique applications in audio generation, art, music, and storytelling.
• They are exhibiting reasoning capabilities and interactive learning.
• Future models may focus on understanding and generation, finance, business, and domain-specific tasks.

"And now we are seeing unique applications and audio generation art music storytelling... we also want domain specific models so you might want like a GPT model."

• The quote indicates the current trends and future aspirations for Transformer applications, including domain-specific models.

• External memory: Current models lack long-term memory for conversations.
• Computation complexity: The quadratic complexity of attention mechanisms needs to be reduced.
• Controllability: Stochastic models should have mechanisms to control outputs.
• Human brain alignment: More research is needed to align AI models with brain functions.

"There's still a lot of missing ingredients... first of all is external memory... second is reducing the computation complexity... another thing you want to do is we want to enhance the controllability of these models."

• The quote lists the challenges that need to be addressed to improve Transformer models, including memory, complexity, controllability, and biological plausibility.

• AI, once a taboo term, has become mainstream.
• The shift from algorithmic focus to data and compute scalability.
• Neural networks have become a common framework across AI subfields since 2012.
• The Transformer model has further unified architecture across various applications.

"Basically do you even realize how lucky you are potentially entering this area in roughly going through three... it's pretty crazy to me what I found kind of interesting is."

• The quote reflects on the transformative change in AI and the speaker's amazement at the current state of the field.

• The brain's homogeneous structure across different regions may hint at a uniform learning algorithm, similar to how Transformers are being applied uniformly across AI tasks.

"I think this is some kind of a hint that we're maybe converging to something that maybe the brain is doing... and so maybe we're converging to some kind of a uniform powerful learning algorithm here."

• The quote suggests that the convergence of AI architectures to the Transformer model may parallel the brain's uniform structure, hinting at a fundamental learning algorithm.

• 2003: Neural networks applied to language modeling with a focus on predicting the next word in a sequence.
• Multi-layer perceptrons used to predict word sequences.

"I want to start in 2003 I like this paper... it was the first sort of um popular application of neural networks to the problem of language modeling."

• The quote references a significant paper from 2003 that applied neural networks to language modeling, marking an early step in AI's evolution.

• 2014: Sequence to sequence paper introduced architectures for translating variably sized input sequences.
• Encoder-decoder lstm models were used for machine translation.

"So this was uh well and good at this point now over time people started to apply this to a machine translation so that brings us to sequence to sequence paper from 2014 that was pretty influential."

• The quote discusses the influential 2014 paper that applied sequence-to-sequence models to machine translation, a major milestone in AI development.

• Encoder bottleneck issue: Packing an entire sentence into a single vector was problematic.
• 2017: Introduction of the Transformer model in a machine translation paper, which then became a universal architecture across AI.

"So this entire English sentence that we are trying to condition on is packed into a single Vector that goes from the encoder for the decoder... that didn't seem correct and so people are looking around for ways to alleviate the attention of sorry the um encoded bottleneck."

• The quote explains the limitations of the encoder-decoder lstm model and the search for solutions that led to the development of the Transformer architecture.

• The use of a fixed-length vector is identified as a bottleneck in improving encoder-decoder models.
• Proposes a model that can soft search for relevant parts of the source sentence when predicting a target word.
• This eliminates the need for forming explicit hard segments.

"We conjectured that the use of a fixed-length vector is a bottleneck in improving the performance of the basic encoder-decoder architecture and propose to extend this by allowing the model to automatically soft search for parts of the source sentence that are relevant to predicting a target word."

• The quote suggests that a fixed-length vector hinders the performance of the encoder-decoder architecture and that an automatic soft search for relevant source sentence parts could improve target word prediction.

• Soft search allows for looking back at words from the encoder during the decoding process.
• Attention mechanism introduced where the context vector is a weighted sum of hidden states.
• Weights are determined by a softmax function based on compatibility between the current state and encoder hidden states.

"As you are decoding the words, you are allowed to look back at the words at the encoder via this soft attention mechanism proposed in this paper."

• The quote explains that the decoding process is enhanced by the ability to reference the encoder's words through a soft attention mechanism.

• The concept of attention in the context of machine learning was first used in the paper discussed.
• Email correspondence with the first author, Dimitri, revealed the inspiration for the attention mechanism.
• The term "attention" was coined by Yann LeCun in the final review stages of the paper.

"Dimitri... talks about how he was looking for a way to avoid this bottleneck between the encoder and decoder... and then one day I had this thought that it would be nice to enable the decoder RNN to learn to search where to put the cursor on the source sequence."

• Dimitri's quote from the email explains the conceptual inception of the attention mechanism, inspired by the natural process of translating languages.

• This paper suggests that the attention component alone is sufficient for model performance.
• It proposes the removal of other components like RNNs, keeping only the attention mechanism.
• The paper is considered a landmark due to its non-incremental, combined approach to architecture.
• Introduced concepts like positional encoding, residual networks, multi-layer perceptrons, layer norms, and multi-headed attention.

"You can actually delete everything, like what's making this work very well is just attention by itself."

• The quote highlights the paper's revolutionary idea that the attention mechanism is the core component driving model performance.

• The Transformer consists of two phases: communication (multi-headed attention) and computation (multi-layer perceptron).
• Communication phase involves data-dependent message passing on directed graphs.
• Computation phase involves individual processing of each node with a multi-layer perceptron.

"To me, attention is kind of like the communication phase of the Transformer and the Transformer interleaves two phases: the communication phase which is the multi-headed attention and the computation stage which is this multi-layer perceptron."

• The quote describes the two distinct phases in the Transformer model, emphasizing attention as a key component of the communication phase.

• Nodes in the graph represent tokens and communicate through the attention mechanism.
• In the encoder, tokens are fully connected and communicate freely.
• In the decoder, tokens are only connected to past tokens and the top encoder states to prevent future information leakage.
• The decoder uses cross-attention to utilize features from the encoder.

"All these tokens that are in the encoder that we want to condition on, they are fully connected to each other... but in the decoder... the tokens in the decoder are fully connected from all the encoder states and then they are only connected from everything that is to the past."

• The quote explains the connectivity and communication restrictions within the Transformer model to ensure proper sequence modeling.

• Self-attention refers to nodes producing keys, queries, and values from their own data.
• Cross-attention involves queries from one set of nodes (e.g., decoder) and keys/values from another (e.g., encoder).
• Multi-headed attention means applying the attention mechanism in parallel with different weights.

"Self-attention and multi-headed attention... the multi-headed attention is just this attention scheme but it's just applied multiple times in parallel."

• The quote clarifies the distinction between self-attention and multi-headed attention, with the latter being parallel applications of the attention mechanism.

• Describes a minimal implementation of a Transformer, Nano GPT, reproducing GPT-2.
• Focuses on a decoder-only Transformer, which is essentially a language model predicting the next word or character.
• The model processes text as sequences of integers, representing encoded characters.
• Batches of data are created from chunks of the text sequence, with block size indicating context length.

"Let's try to have a decoder-only Transformer... the data that we train on is always some kind of text."

• The quote introduces the concept of a decoder-only Transformer model that is trained on text data to predict subsequent text sequences.

• Transformers use batches of examples for training; each batch contains many individual examples processed in parallel.
• The model's real batch size is effectively B times d, where B is the batch size and d is the dimensionality.
• Training involves parallel processing along the time dimension.
• Transformers are decoder-only models without encoders, as they generate sequences without translating from another language or conditioning on external information.
• PyTorch is used for the implementation.

"When the input is the sequence 4758, the target is one, and when it's 47.581, the target is 51, and so on. So actually, the single batch of examples that score by eight actually has a ton of individual examples that we are expecting the Transformer to learn on in parallel."

• This quote explains how a single batch contains numerous examples for the Transformer to learn from simultaneously.

"Your real batch size is more like B times d. It's just that the context grows linearly for the predictions that you make along the T Direction in the model."

• The effective batch size is larger than the nominal batch size due to parallel processing across the time dimension.

"This is a decoder only model. We're just trying to produce a sequence of words that follow each other are likely to."

• The model is designed to generate sequences without the need for an encoder, focusing on producing likely word sequences.

• The forward pass involves encoding token identities using an embedding lookup table.
• Positional encoding is applied to provide information about each token's place in the sequence.
• Token and positional embeddings are combined additively.
• A series of Transformer blocks process the input, followed by layer normalization and decoding logits for the next word or integer using a linear projection.
• Targets for training are the inputs offset by one in time, fed into a cross-entropy loss function.

"We both encode the identity of the indices just via an embedding lookup table so every single integer has a word vector for that token."

• Each token is represented by a unique vector obtained from an embedding table, encoding the token's identity.

"Because the Transformer by itself doesn't actually process sets natively, so we need to also positionally encode these vectors."

• Positional encoding is necessary because Transformers naturally process sets and need to understand the sequential order of tokens.

"This x here basically just contains the set of words and their positions and that feeds into the blocks of Transformer."

• The variable x represents the combination of word and positional information that serves as input to the Transformer blocks.

• Transformer blocks consist of a communication phase (multi-headed self-attention) and a compute phase (multi-layer perceptron, MLP).
• In the communication phase, nodes (representing tokens) communicate with each other based on a fixed connectivity pattern.
• The MLP in the compute phase processes each node individually, applying a two-layer neural network with a nonlinearity.
• Causal self-attention ensures that no information from the future is used when predicting a token.

"These blocks that are applied sequentially, there's a communicate phase and the compute phase."

• The blocks within a Transformer model have two main phases for processing information: communication (self-attention) and computation (MLP).

"The MLP here is fairly straightforward, just individual processing on each node."

• The MLP performs individual transformations on each node's feature representation.

"This is the causal self-attention part, the communication phase."

• Causal self-attention is a critical component of the communication phase, preventing future information from influencing predictions.

• Transformers generate sequences by starting with a sum token and incrementally adding tokens while respecting the block size limit.
• The context size is finite, with a typical model handling 1024 or 2048 tokens.
• To generate beyond the block size, the model must crop the input, as Transformers have a fixed context length in their naive implementation.

"Once you run out of the block size, which is eight, you start to crop because you can never have block size more than eight in the way you've trained this Transformer."

• The model must truncate the sequence to the maximum block size during generation due to training limitations.

"All of these Transformers in the naive setting have a finite block size or context line."

• Transformers are limited by a predetermined context size, which restricts the length of sequences they can process in one pass.

• To implement encoder attention, the masking of the attention mechanism is removed, allowing all nodes to communicate freely.
• Encoder-decoder models add cross-attention, where queries come from the decoder, and keys and values come from the encoder.
• Models like BERT use an encoder-only architecture and are trained with different objectives, such as sentiment classification, using masking and denoising techniques.

"If you want to implement cross attention, so you have a full encoder-decoder Transformer, not just a decoder only Transformer like GPT, then we need to also add cross attention in the middle."

• Cross-attention is necessary for encoder-decoder models to enable information flow between the encoder and decoder.

"You'll hear people talk that you can have an encoder-only model like BERT or you can have an encoder-decoder model like T5 doing things like machine translation."

• Different Transformer architectures serve various purposes, with encoder-only, decoder-only, and encoder-decoder models being used for specific tasks.

• The Transformer architecture has remained largely unchanged despite numerous attempts at modification, indicating its robustness.
• Authors of the original Transformer paper were unaware of the significant impact their work would have.
• Future directions could involve hybrid models with diffusion processes, which allow for iterative refinement of sequences.

"It's kind of interesting to me that it's kind of like a package in like a package, which I think is really interesting historically."

• The Transformer model has been packaged into a robust framework that has stood the test of time in the field of machine learning.

"Maybe there's some ways to maybe there's some ways some hybrids with diffusion as an example which I think would be really cool."

• The speaker suggests that incorporating diffusion processes into Transformer models could be a promising avenue for future research.

• Transformers have been applied to various fields, often in unconventional ways.
• For image processing, Transformers treat small squares of an image as tokens, which are then fed into the model.

"Transformers have been applied to all the other fields and the way this was done is in my opinion kind of ridiculous ways honestly."

• The application of Transformers to other domains, such as computer vision, is seen as somewhat unconventional by the speaker.

"You take an image and you chop it up into little squares and then those squares literally feed into a Transformer and that's it."

• Images are processed by Transformers by segmenting them into patches and treating each patch as a token in the sequence.

• Transformers are effective at processing large images by dividing them into smaller squares (patches) and using them as input.
• Positional encoding is important but Transformers must rediscover image structure.
• Transformers allow patches to communicate throughout the entire network.
• Similar approaches are used in speech recognition with Mel spectrograms and in reinforcement learning, treating states and actions as language.
• Transformers are flexible and can integrate various types of data, such as radar, maps, or audio, by chopping them up and feeding them into the system.
• The self-attention mechanism in Transformers figures out how different pieces of information should communicate.
• Transformers are not constrained by the need to conform to Euclidean space computation.

"The simplest baseline of just chopping up big images into small squares and feeding them in as like the individual notes actually works fairly well."

This quote explains that a basic approach to handling large images with Transformers is effective.

"With the Transformer, it's much easier because you just take whatever you want, you chop it up into pieces and feed it in with a set of what you had before and you let the self-attention figure out how everything should communicate."

This quote highlights the ease of integrating various data types into a Transformer model due to its self-attention mechanism.

• Transformers are capable of in-context learning or meta-learning.
• The GPT-3 paper demonstrates that Transformers improve in accuracy with more examples provided in the context.
• Transformers can learn from activations without gradient descent, hinting at potential in-weights meta-learning.
• Recent studies suggest that Transformers may implement operations similar to gradient-based learning within their activations.
• The architecture optimizes expressiveness, optimizability, and efficiency, particularly on GPUs.
• Transformers are designed to be general-purpose and can be reconfigured at runtime to perform various tasks.

"Transformers are capable of in-context learning or like meta-learning that's kind of like what makes them really special."

This quote emphasizes the unique ability of Transformers to learn from the context within which they operate.

"The Transformer is very expressive in the forward pass... it is very optimizable thanks to things like residual connections... it's extremely efficient."

This quote outlines the three desirable properties of Transformers that contribute to their effectiveness.

• Positional encoders are vectors that indicate the location of input elements and are trainable.
• Transformers work better with less encoded inductive bias when there is sufficient data.
• Inductive biases can be introduced to Transformers through attention mechanisms and positional encoding to structure the model.
• The flexibility of Transformers allows for experimentation with different encoding methods.

"If you have Infinity data then you actually want to encode less and less that turns out to work better."

This quote suggests that with ample data, it is better for Transformers to have less inductive bias.

"You can slowly bring in more inductive bias... but the inductive biases are sort of like they're factored out from the core Transformer."

This quote explains that inductive biases can be introduced to Transformers, but they are separate from the core architecture.

• Transformers can be taught to use external memory, similar to humans using a notepad.
• This external memory allows Transformers to handle larger contexts than they could internally.
• The concept is implemented by teaching the Transformer to use a "scratch pad" to store and retrieve information.

"You can teach the Transformer just dynamically because it's so meta-learned you can teach it dynamically to use other gizmos and gadgets and allow it to expand its memory that way."

This quote explains how the meta-learning capability of Transformers allows them to utilize external tools to extend their memory.

• The speaker is transitioning from computer vision to language models.
• Work on Nano GPT aims to reproduce and incrementally improve GPT models.
• There is interest in building a more efficient and effective version of GPT, referred to as "Google plus plus."

"I'm going basically slightly from computer vision and like part uh kind of like the immersion-based products to a little bit in language domain."

This quote indicates the speaker's shift in focus towards language models.

"Something like a Google plus plus to build that I think is really interesting can we give our speed run."

This quote suggests an ambition to develop an advanced and efficient GPT model.