← Applied intuition Interview Insights
This one took me a minute to scope properly.
Start by explaining the purpose of KV caching in autoregressive decoding and the core data structures involved. Then walk through the per-layer cache API, covering tensor shapes, batched inputs, and memory management. Finally, discuss how the cache integrates with greedy and beam search, highlighting trade-offs and optimizations.
Pro tip: Emphasize that the cache should be pre-allocated to the maximum sequence length to avoid dynamic memory allocation during decoding, which is critical for latency-sensitive inference. Also, mention that beam search requires careful handling of cache reordering to maintain correct associations between beams and cached keys/values.
Ask about expected sequence lengths, batch sizes, memory limits, and whether the system needs to support multiple concurrent requests. This sets the stage for design decisions.
Define a per-layer cache that stores keys and values tensors. Specify shapes: for a batch of size B, H attention heads, and head dimension D, each cache tensor is [B, H, max_seq_len, D]. Provide methods like update (to append new keys/values) and get (to retrieve the full cache).
Explain how the cache is updated incrementally: at each decoding step, new keys/values of shape [B, H, 1, D] are computed and appended to the cache. The attention mechanism then uses the entire cached keys/values up to the current step.
Discuss strategies such as pre-allocating to max length, using a circular buffer for sliding window attention, or evicting old entries. Mention trade-offs between memory usage and recomputation.
For greedy, the cache is straightforward. For beam search, maintain a separate cache per beam and reorder them when beams are pruned or expanded. Explain how to efficiently copy or index into the cache to avoid unnecessary memory duplication.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining the problem: autoregressive decoding where each step predicts the next token. Then contrast the naive full recomputation approach with KV caching, deriving the time complexity for each and highlighting the quadratic vs linear scaling. Conclude with the expected speedup and practical implications.
Pro tip: Quantify the speedup in terms of sequence length L: full recomputation is O(L^2) per step, KV caching is O(L) per step, so the speedup grows linearly with L. Mention that memory becomes the bottleneck, not compute, and that this trade-off is why techniques like PagedAttention exist.
Explain that autoregressive decoding generates one token at a time, and at each step the model computes attention over all previously generated tokens.
Show that without caching, each decoding step recomputes key and value projections for all previous tokens, leading to O(L^2) time per step and O(L^3) total for generating L tokens.
Describe how caching stores the key and value tensors from previous steps, so each new step only computes projections for the new token and attends to cached keys/values.
Show that with caching, each step is O(L) for attention (since it attends to L cached tokens) and O(1) for projections, leading to O(L^2) total for generating L tokens.
Contrast the per-step and total complexities, concluding that KV caching reduces per-step time from O(L^2) to O(L), yielding a speedup factor of O(L) per step and O(L) overall.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining what 'correctness' means for a KV cache—it must produce identical outputs to a no-cache baseline for all sequences, including those with EOS tokens mid-batch. Then outline a testing strategy that combines unit tests for edge cases, integration tests with real models, and property-based tests to cover the combinatorial space of batch compositions and sequence lengths.
Pro tip: Emphasize that you would test with a reference implementation (e.g., a naive forward pass without cache) and use differential testing to catch subtle bugs, especially around EOS handling where attention masks and position IDs can easily go wrong.
Establish that the KV cache must yield bitwise-identical (or within tolerance) outputs to a no-cache baseline for any input sequence, including those with EOS tokens in the middle of a batch. Specify that EOS tokens should not affect subsequent tokens' computations in other sequences.
Create test cases with EOS tokens at various positions: beginning, middle, end, and multiple EOS tokens. Include batches where some sequences have EOS and others don't, and vary sequence lengths to test padding and masking.
Run the same inputs through a reference implementation (no cache) and the cached implementation, comparing outputs at each step. Use random input generation to cover a wide range of scenarios.
Inspect the KV cache contents (keys and values) to ensure they are correctly updated and not polluted by EOS tokens from other sequences. Check that attention masks properly ignore EOS tokens for subsequent steps.
Test with large batches, long sequences, and mixed EOS positions to uncover race conditions or memory issues. Add assertions and logging to catch anomalies during inference.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.