← Startups.com Interview Insights
The math itself was fine, softmax over QK^T divided by sqrt(d_k) times V.
Start by defining the inputs and their shapes, then walk through the mathematical operations step-by-step: linear projections, dot product, scaling, softmax, and weighted sum. Explain the scaling factor's role in preventing softmax saturation and maintaining stable gradients, and finally clarify the batched tensor shapes.
Pro tip: Mention that the scaling factor is particularly important for large d_k, and that without it, the softmax gradients vanish, leading to slow training. Also, note that in practice, the scaling is often applied before the softmax for numerical stability.
State that Q, K, V are obtained by linear projections of the input, and specify their shapes in a batched setting: (batch_size, seq_len, d_model) for input, and (batch_size, seq_len, d_k) for Q and K, (batch_size, seq_len, d_v) for V.
Explain that attention scores are computed as the dot product of Q and K transposed, resulting in a (batch_size, seq_len, seq_len) matrix. Then apply scaling by 1/sqrt(d_k).
Describe applying softmax over the last dimension to get attention weights, then multiply by V to get the output of shape (batch_size, seq_len, d_v).
Discuss that scaling prevents the dot products from growing large in magnitude, which would push softmax into regions with tiny gradients, hindering learning. The factor 1/sqrt(d_k) assumes Q and K elements are independent with zero mean and unit variance.
Reiterate the shapes: Q: (B, T, d_k), K: (B, T, d_k), V: (B, T, d_v), scores: (B, T, T), output: (B, T, d_v). Mention that d_k and d_v can differ.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining single-head attention and then explain how multi-head attention extends it by projecting queries, keys, and values into multiple subspaces, applying attention in parallel, concatenating the results, and projecting back. Then analyze the computational and memory complexity, showing that multi-head attention with h heads of dimension d_k is equivalent in total dimension to single-head with d_model, but offers representational benefits at similar cost.
Pro tip: Emphasize that multi-head attention allows the model to jointly attend to information from different representation subspaces at different positions, which is crucial for capturing diverse relationships. Also, mention that while the total computation is similar to single-head with full dimension, the parallelizability and expressiveness make it a key innovation in Transformers.
Explain that single-head attention computes a weighted sum of values based on compatibility between queries and keys, using learned projection matrices W_Q, W_K, W_V to project inputs into a single representation space.
Describe how multi-head attention uses h separate sets of projection matrices (W_Q^i, W_K^i, W_V^i) to project inputs into h lower-dimensional subspaces (dimension d_k = d_model / h), applies attention independently in each subspace, and then concatenates the outputs.
After computing attention outputs for each head, concatenate them along the feature dimension to get a vector of size d_model, then apply a final linear projection W_O to mix information across heads and produce the final output.
For sequence length n, number of heads h, and head dimension d_k, the per-head attention scores require O(n^2 d_k) time, and across h heads this sums to O(n^2 h d_k) = O(n^2 d_model). The projections also cost O(n d_model^2) overall, so total time is O(n^2 d_model + n d_model^2).
Memory for storing attention scores per head is O(n^2), and across h heads it's O(h n^2). Storing intermediate activations for all heads requires O(n h d_k) = O(n d_model) memory. The total memory is dominated by the attention matrices, O(h n^2), which can be large for long sequences.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Knew this one cold because I'd been reading about LLaMA and Mistral.
Start by defining standard multi-head attention (MHA) and multi-query attention (MQA), then position GQA as a generalization that interpolates between them by grouping query heads to share key/value heads. Explain the trade-offs in memory, speed, and quality, and give a concrete example of how the number of groups controls this balance.
Pro tip: Mention that GQA is used in production models like Llama 2 and 3 to reduce KV cache memory during inference, and that the number of groups is a hyperparameter you can tune based on latency and quality requirements.
Explain that in MHA, each attention head has its own query, key, and value projections, allowing diverse representation but requiring large memory for KV cache during inference.
Describe MQA as an extreme where all query heads share a single key and value head, drastically reducing KV cache size and speeding up inference but often hurting model quality.
Position GQA as a middle ground: query heads are divided into groups, and each group shares one key/value head. This reduces KV cache compared to MHA while maintaining better quality than MQA.
Discuss how the number of groups controls the trade-off: more groups approach MHA (higher quality, more memory), fewer groups approach MQA (lower memory, faster, but potentially lower quality). Mention that GQA is used in large language models like Llama 2 and 3 for efficient inference.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Honestly the most interesting part of the interview.
Start by clarifying the trade-offs: MHA offers the highest model quality but at the cost of larger KV cache and memory bandwidth, while GQA and MQA reduce memory footprint and increase inference speed at some quality loss. Then, discuss specific scenarios where MHA is preferred, such as when model quality is paramount and memory/bandwidth constraints are less critical, or when the model size is small enough that KV cache is not a bottleneck.
Pro tip: Quantify the trade-offs: for example, mention that MHA can use up to num_heads times more KV cache than MQA, and that in practice, GQA often provides a sweet spot, but MHA might still be chosen for research or when fine-tuning quality is critical.
Briefly explain MHA, GQA, and MQA, focusing on how they differ in KV cache size and computational complexity.
Compare model quality, KV cache memory, and memory bandwidth requirements, noting that MHA has the highest quality but largest memory footprint.
Discuss situations where MHA is preferable, such as when memory is abundant, latency is not critical, or when the highest accuracy is needed.
Mention deployment constraints like hardware limitations, batch size, sequence length, and whether the model is pre-trained or fine-tuned.
Summarize that MHA is chosen when quality outweighs efficiency, but in most production settings, GQA or MQA are preferred for scalability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.