GPT2 implemented in Excel (Spreadsheets-are-all-you-need) at AI Tinkerers Seattle

Spreadsheets are all you need

26 Oct 202310:13

Summary

TLDRIn this engaging presentation, Isan showcases a personal project that demonstrates the power of AI and edge computing. He introduces a Python script using GPT-2 and a spreadsheet model that emulates the Transformer architecture without any coding, allowing users to interact with and understand the mechanics of AI models like GPT-2. The project, which he describes as both a teaching tool and a fascinating exploration of AI's inner workings, provides insights into the attention mechanism and the Chain of Thought prompting technique. Isan also shares his experience with the challenges of implementing such a model in a spreadsheet and offers resources for further exploration.

Takeaways

👨‍💻 The speaker, Isan, works at an edge compute platform that powers 4% of the internet and is open to discussing AI and Edge projects.
🚀 Isan has a personal side project called 'Spreadsheets Are All You Need', demonstrating the use of spreadsheets for complex tasks like running AI models.
🐍 The project involves using around 40-50 lines of Python code to integrate with the GPT-2 model, using a very short prompt at zero temperature.
📊 Isan has created a spreadsheet that implements GPT-2 without any API calls or Python, using only spreadsheet functions.
⏱️ The spreadsheet's computation is resource-intensive and can take about a minute to process, with a warning against running it on a Mac due to potential UI lockups.
🛠️ The spreadsheet serves as a teaching tool, analogous to the way computer architecture courses help understand system building and programming.
🎥 Isan is creating a series of videos that walk through every step of the GPT-2 implementation, focusing on the inference pass.
🔍 The spreadsheet allows for hands-on exploration of the Transformer model, including attention mechanisms and the Chain of Thought prompting technique.
🤖 The project provides insights into why certain aspects of the model are named the way they are, such as 'attention is all you need'.
📈 The speaker shares the 'weights' tab of the spreadsheet, which contains a massive amount of data, including all 124 million parameters of GPT-2.
🚫 The project has limitations, such as a maximum of 10 tokens due to the size of the weight matrices, and the difficulty of programmatically expanding the model.

Q & A

What is Isan's day job?
-Isan works at an edge compute platform that powers 4% of the internet.
What is the purpose of Isan's personal side project?
-Isan's personal side project aims to demonstrate and teach the concepts of AI and Edge computing using a unique tool: a spreadsheet that implements GPT-2 without any API calls or Python code.
How does Isan's spreadsheet project work?
-The spreadsheet project uses Excel functions to implement GPT-2, allowing users to input prompts and receive outputs without writing any code. It's designed to be an educational tool for understanding the Transformer model and its mechanisms.
What are the benefits of using a spreadsheet to teach about GPT-2?
-Using a spreadsheet to teach about GPT-2 provides a more approachable and tangible way for both non-developers and developers to understand the model's architecture and functionality. It allows users to visually track the information flow and make changes to see the effects in real-time.
What is the 'Chain of Thought' prompting technique mentioned in the script?
-Chain of Thought prompting is a technique where the AI is given more context or steps to reason through a problem. It helps the AI provide more detailed and accurate responses by simulating a step-by-step reasoning process similar to human thought.
What issues did Isan encounter while implementing GPT-2 in a spreadsheet?
-Isan faced challenges such as the spreadsheet's large size causing the Mac UI to lock up randomly, and dealing with the complexity of implementing bite pair encoding and positional embeddings within Excel's limitations.
How can one access Isan's spreadsheet project?
-The spreadsheet project can be accessed by visiting the website 'spreadsheets are all you need' where users can find videos, download the spreadsheet, and report any bugs or ask questions.
What is the significance of the attention mechanism in Transformers?
-The attention mechanism in Transformers allows the model to focus on different parts of the input sequence when generating each output element. It helps the model to handle long-range dependencies and understand the context better, which is crucial for tasks like text understanding and generation.
How does the spreadsheet demonstrate the 'attention is all you need' concept?
-The spreadsheet visually shows how the attention mechanism works by allowing users to see how tokens interact with each other at each layer. It provides a clear demonstration of how the model attends to different parts of the input, which is a core concept in Transformer models.
What was Isan's experience with running GPT-2 from source?
-Isan found running GPT-2 from source to be a challenging experience due to the complexity of the process and the difficulty in getting a working environment set up, particularly with TensorFlow 1.x.
What is the size of the spreadsheet in terms of parameters and file size?
-The spreadsheet contains all 124 million parameters of GPT-2 and is 1.5 GB in size in Excel binary format.

Outlines

00:00

🚀 Introduction to AI and Edge Computing

The speaker, Isan, introduces himself and his work at an edge compute platform that powers 4% of the internet. He invites those with AI and Edge-related projects to connect. The main focus of the video, however, is a personal project called 'Spreadsheets Are All You Need,' showcasing a Python code snippet using the GPT-2 model with a short prompt at zero temperature. The speaker emphasizes the simplicity of the prompt and its obvious completion, providing an example of how the model's predictions work based on the input given.

05:02

📊 Spreadsheet Implementation of GPT-2

Isan demonstrates a spreadsheet that implements GPT-2 without any API calls or Python code, using only spreadsheet functions. He explains the process of running the model within Excel, including the need to manually recalculate due to the time-consuming nature of the operation. He warns against running it on a Mac due to potential UI lockups and shares his experience with the threading issues. The speaker positions this tool as an educational resource, akin to computer architecture courses, to understand the underlying mechanisms of Transformers and LMS models. He also discusses the benefits of this approach, such as its accessibility to non-developers and the practical insights it offers to developers, including a deeper understanding of concepts like the attention mechanism in Transformers.

10:09

🔍 Deep Dive into GPT-2's Architecture in Spreadsheet

Isan continues to explore the intricacies of GPT-2's architecture within the spreadsheet. He walks through the various components, such as the attention mechanism, residual connections, and multi-layer perceptron, explaining how they function and interact. He also touches on the potential for using the model for Chain of Thought prompting, providing a technical explanation for its effectiveness. The speaker shares his experience with the model's performance, including the challenges of running it from source and the limitations of the Excel implementation, such as the character limit and the need for manual rearrangement of matrices.

Mindmap

Keywords

💡Edge Computing

Edge computing refers to the practice of processing data closer to where it is generated, rather than in a centralized data center or cloud. This can reduce latency and improve the speed of data processing. In the video, the speaker mentions working at an edge compute platform, highlighting the significance of edge computing in powering a substantial portion of the internet.

💡AI

Artificial Intelligence (AI) is the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. In the context of the video, AI is used in conjunction with edge computing to enhance various applications, and the speaker expresses interest in discussing how AI and edge can assist in projects.

💡Transformers

Transformers are a type of deep learning model used for natural language processing. They are known for their ability to handle sequences of data and are particularly effective for tasks such as translation, summarization, and text generation. In the video, the speaker discusses using Transformers in conjunction with GPT-2, a specific implementation of a Transformer model.

💡GPT-2

GPT-2, or Generative Pre-trained Transformer 2, is an open-source language model developed by OpenAI. It is known for its ability to generate human-like text based on a given prompt. The speaker in the video has implemented GPT-2 within a spreadsheet, showcasing the model's versatility and accessibility.

💡Spreadsheets

Spreadsheets are software applications used for organizing, analyzing, and storing data in tabular form. They are commonly used for tasks such as accounting, data analysis, and project management. In the video, the speaker has creatively used spreadsheets as a platform to implement and interact with complex AI models like GPT-2, demonstrating the potential for educational and analytical purposes.

💡Inference

In the context of machine learning and AI, inference refers to the process of using a trained model to make predictions or draw conclusions on new data. The speaker in the video has created a series of videos that walk through the inference process of GPT-2, explaining each step from input encoding to prediction.

💡Attention Mechanism

The attention mechanism is a crucial component of Transformer models, allowing the model to weigh the importance of different parts of the input data when generating a response. It helps the model focus on relevant information and is often likened to how humans pay attention to specific parts of a conversation or task. In the video, the speaker discusses the attention mechanism and its role in the Transformer architecture.

💡Chain of Thought Prompting

Chain of Thought prompting is a technique used in AI where the model is encouraged to 'think aloud' during the problem-solving process. This can lead to more logical and step-by-step responses, similar to how a human might reason through a problem. The speaker in the video relates this concept to the information flow observed in the spreadsheet implementation of GPT-2.

💡Positional Encoding

Positional encoding is a method used in Transformer models to incorporate the order of the input sequence into the model's processing. It is essential because the self-attention mechanism in Transformers does not inherently account for the order of the input elements. In the video, the speaker discusses the impact of positional encoding on the embeddings within the spreadsheet model.

💡Layer Normalization

Layer normalization is a technique used to improve the training of deep neural networks by normalizing the input to each layer's activation function. It helps in stabilizing the learning process and can lead to faster convergence during training. In the video, layer normalization is mentioned as part of the GPT-2 model's architecture, which the speaker has implemented in the spreadsheet.

💡Residual Connections

Residual connections are a type of skip connection used in deep neural networks, where the output of one layer is added back to the input of the same layer or an earlier layer. This technique helps in mitigating the vanishing gradient problem and allows for the training of deeper networks. In the context of the video, the speaker discusses residual connections as part of the GPT-2 model's structure.

Highlights

Isan works at an edge compute platform that powers 4% of the internet.

Isan's personal side project is called 'Spreadsheets are all you need'.

The project involves using 40-50 lines of Python code with Hugging Face Transformers and GPT-2 small at zero temperature.

Isan demonstrates a spreadsheet that implements GPT-2 without any API calls or Python, using only spreadsheet functions.

The spreadsheet requires manual recalculation due to its computational intensity.

Running the spreadsheet on a Mac may cause the UI to lock up due to threading issues.

The project serves as a teaching tool for understanding Transformers and LMS.

Isan is creating videos that walk through every step of the GPT-2 implementation for the inference pass.

The spreadsheet allows for a hands-on, approachable understanding of AI models, even for non-developers.

Isan explains the attention mechanism in Transformers, how information flows through the model.

Chain of Thought prompting is practically demonstrated by giving the model more space and vectors to compute against.

The spreadsheet contains the entire GPT-2 model, including all 124 million parameters.

The spreadsheet is 1.5 GB in size and hosted on GitHub due to its large file size.

Isan shares his experience of implementing GPT-2 from source and the challenges faced.

The project aims to provide a visual and interactive learning experience for computer science concepts related to AI.

Isan's project has the potential to demystify AI models and make them more accessible to a wider audience.

The spreadsheet includes detailed components such as token embeddings, positional embeddings, and attention values.

Isan's project showcases the complexity and depth of AI models in a tangible and understandable way.