The label shift one I caught fast because I've been burned by that before.
Start by outlining a systematic debugging strategy: first understand the model architecture and data flow, then isolate each component (loss, positional embeddings, attention mask, and general code) to identify the bugs. For each bug, explain the expected behavior, how to detect it, and the fix, emphasizing root cause analysis and testing.
Pro tip: Demonstrate familiarity with common Transformer pitfalls by mentioning that label shifting is often off-by-one, positional embeddings should not be zero-initialized, and causal masks must prevent attention to future tokens. Also, suggest writing unit tests for each component to catch such bugs early.
Review the Transformer implementation to understand how inputs, positional embeddings, attention, and loss are connected. Identify where each bug might manifest.
Check the label shifting: ensure that the target for each position is the next token, not the current one. Verify that the loss ignores padding tokens if applicable.
Examine how positional embeddings are initialized and added. Common issues include zero initialization or incorrect scaling; ensure they are learnable or properly computed (e.g., sinusoidal).
Check that the mask is upper triangular with -inf on the upper diagonal to prevent attending to future tokens. Ensure it's applied correctly in the attention softmax.
Look for typos such as incorrect variable names, wrong dimensions, or off-by-one errors in loops. Use static analysis or unit tests to catch these.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Ran it on a tiny dataset just to sanity check.
Frame your answer around a systematic verification process: start with a minimal training run to check loss behavior, then qualitatively assess generated text, and finally scale up if needed. Emphasize that this step is critical to catch subtle bugs that unit tests miss, and that you balance speed with thoroughness.
Pro tip: Use a tiny subset of data and a small model for the quick check to iterate fast, but always validate on a held-out set to avoid overfitting to the sanity check. Also, log loss curves and sample outputs to compare against a known-good baseline.
Configure a short training session with a small batch size, few steps, and a subset of data to quickly observe loss trends without long waits.
Confirm that training loss decreases steadily and validation loss doesn't diverge, indicating the model is learning rather than stuck or exploding.
Produce sample outputs from the model and check for coherence, grammaticality, and relevance to the prompt, ensuring it's not gibberish or repetitive.
If available, compare loss and generation quality to a known-good version to detect regressions or improvements from the bug fixes.
Once the brief run looks promising, proceed to a longer training run with full data and monitor for stability and performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
My approach was basically: isolate each module and test it independently before touching the full forward pass.
Structure your answer as a systematic debugging workflow that moves from isolated component tests to end-to-end sanity checks, emphasizing early detection and reproducibility. Explain each step's purpose, how to execute it, and what insights it provides, while highlighting trade-offs between thoroughness and speed.
Pro tip: Always start with a minimal reproducible example and use assertions liberally; this often surfaces the bug faster than diving into complex debugging tools. Also, version your data and code to ensure experiments are comparable.
Test each component (e.g., layers, loss functions, data loaders) in isolation with controlled inputs to verify correct behavior. Use assertions and edge cases to catch obvious errors early.
Print or assert tensor shapes at each stage to catch mismatches. Monitor for NaNs, Infs, or extreme values by checking min/max/mean and using gradient clipping or normalization if needed.
Compute and visualize gradients for each layer; look for vanishing/exploding gradients, zero gradients, or unexpected patterns. Use tools like torch.autograd.gradcheck for numerical gradient checking.
Train on a small subset (e.g., 1-2 batches) and verify the model can overfit, achieving near-zero loss. This confirms the architecture and training loop are functionally correct.
If the bug persists, systematically enable/disable components, compare against a known-good baseline, and use binary search to narrow down the source. Document findings to avoid repeating steps.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.