This one had been floating around on forums so I'd seen a version of it before, which helped.
Start by understanding the expected behavior of a correct miniGPT and the architecture's data flow. Then systematically debug the implementation by checking each component (data pipeline, model architecture, training loop, generation) against known correct patterns, using small-scale tests and print statements to isolate bugs. Finally, verify fixes by training on a tiny dataset and checking if the model can overfit and generate coherent text.
Pro tip: Before diving into code, articulate the expected tensor shapes and operations at each step; this mental model helps you spot mismatches quickly. Also, use a minimal reproducible example (e.g., a single batch) to test components in isolation, which is faster than full training runs.
Review the miniGPT code to identify the intended model architecture (e.g., transformer decoder), input/output shapes, and training objective. Clarify what 'correct text' means in terms of loss and generation quality.
Check tokenization, vocabulary creation, sequence length, and batching. Common bugs include off-by-one errors in input/target alignment, incorrect padding, or missing special tokens.
Verify each layer: embeddings, positional encodings, multi-head attention (masking, scaling), feed-forward networks, layer norms, and residual connections. Ensure tensor shapes match and operations are correctly implemented.
Check optimizer setup, learning rate, gradient clipping, and loss function (e.g., cross-entropy with correct ignore_index). Ensure gradients flow and parameters update.
After fixing obvious bugs, train on a small dataset to see if loss decreases and the model can overfit. Then test generation with greedy or sampling methods, checking for coherent output. Iterate until correct.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Came right after the debug portion with basically no pause.
Start by clarifying the transformer architecture and inference scenario (batch size, sequence length, precision) to scope the KV cache design. Then walk through the implementation: pre-allocate cache tensors, update them during autoregressive decoding, and manage memory via paging or eviction. Finally, discuss trade-offs like memory vs. latency, and how to handle dynamic batching and long contexts.
Pro tip: Emphasize that KV cache is not just about speed—it's about enabling practical deployment of large models by reducing memory bandwidth and compute. Mention that you'd profile memory usage and latency to validate the cache's effectiveness.
Ask about the model size, number of layers, attention heads, batch size, max sequence length, and hardware (GPU memory). This determines cache shape and memory footprint.
Decide on tensor layout (e.g., [batch, heads, seq_len, head_dim]) and whether to pre-allocate or grow dynamically. Consider using a paged attention approach for efficient memory management.
During autoregressive decoding, append new key/value tensors to the cache and use the full cache for attention computation. Ensure correct masking for padded tokens.
Implement strategies for when cache exceeds memory: eviction policies (e.g., sliding window, attention sinks), or paging to CPU. Discuss trade-offs between memory and accuracy.
Profile memory usage and latency. Consider quantization (e.g., FP8) or compression. Validate that outputs match the non-cached version for correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.