Start by outlining the high-level architecture of a Transformer, then dive into the implementation details of each component, explaining the purpose and design choices. Emphasize modularity, efficiency, and numerical stability, and discuss trade-offs such as layer normalization placement and attention optimizations.
Pro tip: Demonstrate awareness of production constraints by mentioning how you would optimize for Apple's hardware (e.g., using Metal Performance Shaders or Core ML) and ensure the model is efficient for on-device inference.
Describe the Transformer's encoder-decoder structure, highlighting self-attention, feed-forward networks, residual connections, and layer normalization. Explain how these components fit together.
Detail the computation of queries, keys, and values, scaled dot-product attention, and concatenation of multiple heads. Discuss masking for autoregressive decoding and efficient matrix operations.
Explain the two linear transformations with a ReLU activation in between, and how they are applied independently to each position. Mention the importance of dimension expansion and contraction.
Describe how residual connections mitigate vanishing gradients and how layer normalization stabilizes training. Discuss pre-norm vs. post-norm and their trade-offs.
Explain the need for positional encodings (sinusoidal or learned) and how to implement them. Discuss masking for padding and causal attention in decoders.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Came right after the base implementation, no break.
Start by explaining the motivation for multi-head attention: allowing the model to jointly attend to information from different representation subspaces. Then outline the implementation steps: projecting queries, keys, and values into multiple heads, computing scaled dot-product attention in parallel, concatenating the outputs, and applying a final linear projection. Emphasize the trade-offs in computational efficiency and model capacity.
Pro tip: Mention that you would ensure the head dimension is a divisor of the model dimension to avoid reshaping errors, and that you would use efficient batched matrix operations to leverage hardware parallelism. Also, discuss how you would validate the implementation by checking that the output shape matches the input and that gradients flow correctly.
Briefly describe what multi-head attention is and why it's beneficial: it allows the model to focus on different parts of the sequence simultaneously, capturing diverse relationships.
Describe how to split the model dimension into multiple heads: project Q, K, V into h heads with dimension d_k = d_model / h, then compute attention independently for each head.
Explain the scaled dot-product attention for each head: softmax(QK^T / sqrt(d_k)) V, then concatenate the outputs and apply a linear transformation.
Mention efficient tensor operations (e.g., reshaping and transposing for batch matrix multiplication), handling of masks, and ensuring compatibility with existing code.
Talk about trade-offs: increased model capacity vs. computational cost, and how to validate correctness (e.g., shape checks, gradient checks, and comparing to a single-head baseline).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the type of masking needed (padding vs. causal) and how it integrates into scaled dot-product attention. Then, walk through the implementation step-by-step, explaining how masks are applied before softmax and how to handle broadcasting and numerical stability.
Pro tip: Mention that masks should be applied by adding a large negative value (e.g., -1e9) to the attention scores before softmax, rather than multiplying by zero, to avoid NaNs and ensure proper gradient flow.
Ask whether the mask is for padding (ignoring padded tokens) or causal (preventing future token attention), or both. Confirm the expected shape and data type of the mask.
Calculate the scaled dot-product attention scores: Q @ K^T / sqrt(d_k). Ensure the implementation supports batched inputs and multiple heads.
Add a large negative value (e.g., -1e9) to masked positions, or use torch.masked_fill. Ensure the mask broadcasts correctly across batch and head dimensions.
Apply softmax along the last dimension to get attention weights, then multiply by V to get the output. Verify that masked positions have near-zero weights.
Test with simple cases (e.g., all-ones mask, causal mask) to ensure correctness. Check for numerical stability and gradient flow.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.