I knew the high-level architecture but writing it out class by class under time pressure was rougher than expected.
Start by clarifying the requirements and constraints, then outline the architecture and data flow. Implement each component (MultiheadAttention, Feedforward, DecoderLayer, GPT) with clear interfaces, and explain how they fit together. Finally, discuss trade-offs and potential optimizations.
Pro tip: Emphasize modularity and testability: design each component to be independently testable, and mention how you would verify correctness (e.g., shape checks, gradient flow). This shows engineering maturity beyond just coding the model.
Ask about expected input/output shapes, performance requirements, and whether to include training or just inference. Confirm the exact parameters and any assumptions.
Sketch the overall GPT architecture: embedding layer, stacked decoder layers (each with masked multi-head attention and feedforward), and output projection. Explain the role of each component.
Code MultiheadAttention (with scaled dot-product attention, masking, and linear projections), Feedforward (two linear layers with activation), and DecoderLayer (combining attention, feedforward, and layer norms with residual connections).
Combine embeddings, positional encoding, stacked decoder layers, and final linear layer. Ensure forward propagation handles input sequences and returns logits.
Talk about parameter initialization, dropout placement, computational complexity, and potential improvements like weight tying or efficient attention mechanisms.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.