Debug Coding (numpy/tensor/distributed)

Question

The interviewer provides pre-written buggy code involving numpy arrays, tensor operations, and distributed computing concepts. The candidate must read and understand the code, identify the bugs, and rewrite the problematic sections. Known bug categories observed include: (1) Matrix.zeros aliasing issues, (2) incorrect or missing axis parameter handling in to_ndarray (e.g., missing axis=1) and from_ndarray usage, and (3) remainder truncation errors in distributed/tensor operations.

AceOffer · Accepted Answer

The interviewer provides pre-written buggy code involving numpy arrays, tensor operations, and distributed computing concepts. The candidate must read and understand the code, identify the bugs, and rewrite the problematic sections. Known bug categories observed include: (1) Matrix.zeros aliasing issues, (2) incorrect or missing axis parameter handling in to_ndarray (e.g., missing axis=1) and from_ndarray usage, and (3) remainder truncation errors in distributed/tensor operations.

Reported follow-ups:
1. Why does this aliasing occur and how would you fix it? (when: After candidate identifies aliasing bug with Matrix.zeros)
2. What is the effect of omitting axis=1 here, and what is the correct behavior? (when: After candidate identifies missing axis parameter in to_ndarray)
3. How would you handle the case where the total workload does not evenly divide across workers? (when: After candidate identifies remainder truncation in distributed logic)

**Alternative approaches:** Test-case driven debugging (Running mental (or written) test cases against the buggy code can help surface incorrect outputs, but may be slow in an interview setting and may miss subtle aliasing or truncation bugs without careful edge-case selection.)

Debug Coding (numpy/tensor/distributed)

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