Walk through a T5 decoder block in order, list the attention sub-layers and what each one's Q, K, V read from.

**The correct order is: causal self-attention, then cross-attention, then position-wise FFN.** A T5 (and original Transformer 2017) decoder block has three sub-layers, each with its own residual connection and layer norm. ### Sub-layer 1: causal self-attention The decoder looks at its own previously-generated tokens. Q, K, and V are all projected from the decoder's current hidden state. A causal lower-triangular mask ensures position `t` can only attend to positions `0, 1, ..., t`, not to future positions. This is what makes generation autoregressive. ### Sub-layer 2: cross-attention (the encoder-decoder bridge) The decoder consults the encoder's output. Q is projected from the decoder's hidden state (the updated state after self-attention). K and V are projected from the encoder's final-layer output, which is fixed across decoder steps. **No causal mask on the encoder axis** because the encoder is fully observed, the decoder can attend to any encoder position. This is where the encoder and decoder communicate. Cross-attention is what makes encoder-decoder models useful for translation, summarization, and any task with a source-target structure. ### Sub-layer 3: position-wise FFN A two-layer MLP applied independently to each position. No cross-token interaction. Refines each token's representation based on the combined self-attention and cross-attention output. ### Why this order The decoder must process its own context first (what has been generated so far) before forming a useful query against the encoder output. Reversing the order, doing cross-attention before self-attention, would force the cross-attention query to be based on the raw input embedding without any awareness of the decoder's own state. The order causal -> cross -> FFN is what enables coherent generation.

**The correct order is: causal self-attention, then cross-attention, then position-wise FFN.** A T5 decoder block has three sub-layers in this fixed sequence, each wrapped with residual + layer norm. ### Sub-layer 1: decoder self-attention - Q, K, V all projected from the decoder's current hidden state. - Causal lower-triangular mask: position `t` attends only to positions `0, 1, ..., t`. - Updates the decoder's representation based on its own previously-generated tokens. ### Sub-layer 2: cross-attention - Q projected from the decoder's hidden state (post self attention). - K and V projected from the **final-layer encoder output**, which is fixed across all decoder steps. - No causal mask on the encoder axis, the decoder can attend to any encoder position freely. - This is the encoder-decoder bridge. Without it, the decoder would have no access to the source sequence at all. ### Sub-layer 3: position-wise FFN - Two-layer MLP applied independently to each position. - No cross-token interaction. - Refines each token's representation. ### Why this order matters The decoder must process its own context (via causal self-attention) before issuing a query against the encoder output (via cross-attention). The cross-attention Q is built from the decoder's UPDATED hidden state, not its raw input. This lets the query be "what do I need from the source given what I have generated so far?" rather than "what does this raw position need from the source?" The FFN comes last because it has no information to contribute until both attention sub-layers have done their job of moving information between positions and across the encoder-decoder boundary. **Mnemonic**: review your work (self-attn) -> consult the source (cross-attn) -> polish each word (FFN).