The causal mask part is where I fumbled a bit.
Start by clarifying the requirements and assumptions (e.g., batch-first, learned projections, causal mask). Then walk through the implementation step-by-step: linear projections, head splitting, scaled dot-product attention with causal masking, concatenation, and output projection. Emphasize numerical stability, efficiency, and correctness of the causal mask.
Pro tip: Mention that you would use PyTorch's scaled_dot_product_attention with is_causal=True for efficiency and numerical stability, but also be prepared to implement it manually to demonstrate understanding. Also, discuss the importance of masking before softmax to avoid attending to future positions.
Confirm input shape (B, T, d_model), number of heads, and whether projections are learned. Ask about masking requirements (causal) and any performance constraints.
Use linear layers to project input into Q, K, V of shape (B, T, d_model). Reshape to (B, num_heads, T, head_dim) by splitting d_model into num_heads * head_dim.
Compute attention scores = Q @ K^T / sqrt(head_dim). Apply causal mask (upper triangular -inf) before softmax. Compute attention weights and output = weights @ V.
Reshape attention output back to (B, T, d_model) by concatenating heads. Apply final linear projection to get output of shape (B, T, d_model).
Mention using fused kernels (e.g., PyTorch's scaled_dot_product_attention), handling variable sequence lengths, and ensuring numerical stability (e.g., subtracting max before softmax).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the input/output shapes and the requirement for position-wise independence. Then implement the class with two linear layers and a configurable activation, ensuring the same weights are applied across all positions. Finally, discuss trade-offs like activation choice, parameter count, and computational efficiency.
Pro tip: Mention that this is the standard FFN block in Transformers and that using a larger intermediate dimension (e.g., 4x) is common. Also, note that applying linear layers position-wise is equivalent to a 1x1 convolution, which can be more efficient in some frameworks.
Confirm input dimensions (batch, sequence length, hidden size) and expected output shape. Ask about activation function preference and whether dropout or layer normalization should be included.
Define two linear layers: first expands hidden size to intermediate size (often 4x), second projects back to hidden size. Choose a nonlinear activation (e.g., ReLU, GELU) to apply between them.
Ensure the linear layers are applied independently to each position. This can be done by reshaping the input to (batch*seq_len, hidden_size), applying the layers, then reshaping back.
Talk about parameter count, computational complexity, and alternatives like using 1x1 convolutions. Mention how activation choice affects performance and training dynamics.
Describe how to test with sample inputs, check output shapes, and verify that positions are processed independently (e.g., by permuting sequence order).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I initially wrote post-norm by reflex because that's what I learned first, and the interviewer asked me to clarify which normalization order I was using.
Start by clarifying the architecture: a single decoder block with pre-norm residual connections, masked self-attention, and a feed-forward network. Then walk through the implementation step-by-step, explaining the purpose of each component and how they interact. Finally, discuss trade-offs such as computational efficiency, training stability, and scalability.
Pro tip: Emphasize that pre-norm residual connections improve gradient flow and training stability, especially for deep networks, and mention that this is a key design choice in modern transformers like GPT. Also, note that masking ensures autoregressive property, which is crucial for generation tasks.
Confirm the components: masked self-attention, feed-forward network, and pre-norm residual connections. Explain that pre-norm means LayerNorm is applied before each sub-layer, and residuals are added after.
Describe how to compute queries, keys, and values, apply scaling, and use a causal mask to prevent attending to future positions. Mention softmax and dropout.
Explain the two linear layers with a ReLU activation in between, and possibly dropout. Note that it operates position-wise.
Show how to apply LayerNorm to the input before each sub-layer, then add the residual connection. Emphasize that this differs from post-norm.
Talk about why pre-norm is preferred for deep networks, potential issues like increased memory, and how to optimize (e.g., using fused kernels).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Tying it all together was fine conceptually but I spent too long debating out loud whether to use learned vs fixed positional encodings.
Start by outlining the overall architecture and the shape transformations at each stage, then implement the model in a modular way with clear separation of embeddings, decoder layers, and output projection. Emphasize the importance of weight tying, positional encoding choice, and handling of the causal mask to ensure autoregressive generation.
Pro tip: Mention that you would tie the weights of the token embedding and the final output projection to reduce parameters and improve performance, a common practice in GPT models. Also, note that you would use a learned positional embedding for simplicity, but be prepared to discuss the trade-offs with sinusoidal or relative positional encodings.
Specify the hyperparameters: vocabulary size (V), embedding dimension (d_model), number of layers (N), number of attention heads (h), and maximum sequence length (T_max). Explain that the model consists of token embeddings, positional embeddings, N decoder layers, and a final linear layer.
Create an embedding layer for tokens (shape V x d_model) and a positional embedding layer (shape T_max x d_model). In the forward pass, sum the token embeddings with the positional embeddings for the input token IDs.
Each decoder layer should contain masked multi-head self-attention and a position-wise feed-forward network, with residual connections and layer normalization. Stack N such layers, ensuring the causal mask is applied to prevent attending to future tokens.
Apply a final linear layer (d_model x V) to project the hidden states to vocabulary logits. Optionally tie the weights with the token embedding matrix. The output shape should be (B, T, V).
Mention that during training, the model processes the entire sequence in parallel with teacher forcing, while during inference, it generates tokens autoregressively. Also, note the use of dropout for regularization and the importance of initializing weights properly.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.