Start by clarifying the expected behavior of the Transformer component and the input/output shapes. Then systematically review the code for common bugs in attention, masking, normalization, and positional encoding, fixing each and explaining the rationale. Finally, discuss how your fixes affect training and extension, and propose tests to validate correctness.
Pro tip: Demonstrate deep understanding by not just fixing bugs but also explaining how each bug would manifest during training (e.g., loss not decreasing, NaN gradients) and how your fix addresses the root cause. This shows you can debug and reason about model behavior, not just spot syntax errors.
Ask clarifying questions about the specific Transformer component, its intended use (e.g., encoder, decoder, multi-head attention), and the expected input/output shapes. Confirm whether the code is for training or inference.
Check for typical Transformer bugs: incorrect scaling in attention (missing sqrt(d_k)), wrong masking (e.g., not masking padding or future tokens), improper layer normalization placement, incorrect residual connections, and errors in positional encoding (e.g., wrong frequency).
For each identified bug, describe the fix and justify it by referencing the original Transformer paper or standard practices. Explain the impact of the bug on model performance or training stability.
Propose unit tests (e.g., shape checks, gradient checks, attention mask verification) or reason about how the fixes would affect training (e.g., loss convergence). Mention potential follow-up questions about extending the model (e.g., adding layers, changing dimensions).
Explain how the fixed model would be trained (e.g., learning rate warmup, dropout) and how to extend it (e.g., stacking more layers, adding cross-attention). Highlight trade-offs in system design (e.g., memory vs. speed).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.