Debug MiniGPT and Backpropagate Matmul
Company: OpenAI
Role: Machine Learning Engineer
Category: Machine Learning
Difficulty: medium
Interview Round: Technical Screen
This interview has two PyTorch-focused tasks.
Part A: Debug a small GPT-style language model. You are given a mini transformer decoder that trains or runs but produces incorrect text. Debug the model until autoregressive generation produces the expected output. Then implement key-value caching for faster generation. Your answer should discuss tensor shapes, causal masking, attention computation, positional information, loss shifting, train versus evaluation mode, and sampling.
Part B: Implement matrix multiplication and its backward pass. Given matrices A and B, implement the forward operation C = A @ B and the backward operation for an upstream gradient dC. Derive and implement dA and dB in PyTorch. As a follow-up, explain how a parallel scan-style algorithm such as Hillis-Steele scan could be used to parallelize associative accumulation steps in a tiled or prefix-based version of the backward computation.
Quick Answer: This question evaluates proficiency in deep learning model debugging and low-level linear-algebra autograd, focusing on transformer internals—tensor shapes, causal masking, attention computation, positional encoding, loss shifting, train versus evaluation mode, autoregressive sampling—and on deriving and implementing forward and backward matrix multiplication. It is commonly asked in Machine Learning interviews to measure practical implementation skills and conceptual understanding of numerical correctness, generation behavior, and performance-aware parallelization (e.g., associative scan techniques), spanning both practical application and conceptual reasoning.