Implementing GPT-2 From Scratch (Transformer Walkthrough Part 2/2)

In this monologue podcast, Neel walks through the process of implementing GPT-2 from scratch, explaining the conceptual underpinnings of transformers and their various components like attention and lanorm. He emphasizes the importance of understanding the internal computations of language models for mechanistic interpretability research, and encourages listeners to code along using a template notebook with tests. Neel also touches on the practical aspects of coding, including debugging, testing, and visualizing attention patterns, and briefly demonstrates how to train a model and generate text, though the training demo is cut short due to technical issues.

Outlines

Part 1: Introduction and Architecture

Part 2: Core Component Implementation

Part 3: Model Assembly and Functionality

Part 4: Training and Conclusion

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Neel Nanda

Part 1: Introduction and Architecture

Introduction to GPT-2 Implementation and Mechanistic Interpretability

Coding GPT-2 from Scratch: Recommendations and High-Level Architecture

Part 2: Core Component Implementation

Implementing Layer Normalization

Writing Tests and Implementing Embedding

Implementing Positional Embedding

Deep Dive into Attention Mechanisms

Coding the Attention Class

Completing Attention and Implementing MLPs and Unembedding

Part 3: Model Assembly and Functionality

Assembling the Transformer and Demonstrating Functionality

Cross-Entropy Loss and Text Generation

Part 4: Training and Conclusion

Training a Model and Conclusion

Implementing GPT-2 From Scratch (Transformer Walkthrough Part 2/2)

Neel Nanda

Part 1: Introduction and Architecture

00:00Introduction to GPT-2 Implementation and Mechanistic Interpretability

Introduction to GPT-2 Implementation and Mechanistic Interpretability

03:11Coding GPT-2 from Scratch: Recommendations and High-Level Architecture

Coding GPT-2 from Scratch: Recommendations and High-Level Architecture

Part 2: Core Component Implementation

05:03Implementing Layer Normalization

Implementing Layer Normalization

13:55Writing Tests and Implementing Embedding

Writing Tests and Implementing Embedding

23:37Implementing Positional Embedding

Implementing Positional Embedding

30:07Deep Dive into Attention Mechanisms

Deep Dive into Attention Mechanisms

38:23Coding the Attention Class

Coding the Attention Class

49:29Completing Attention and Implementing MLPs and Unembedding

Completing Attention and Implementing MLPs and Unembedding

Part 3: Model Assembly and Functionality

58:08Assembling the Transformer and Demonstrating Functionality

Assembling the Transformer and Demonstrating Functionality

1:03:01Cross-Entropy Loss and Text Generation

Cross-Entropy Loss and Text Generation

Part 4: Training and Conclusion

1:11:12Training a Model and Conclusion

Training a Model and Conclusion