Decoder layer

Q: How deeply does Inside Deep Learning: Math, Algorithms, Models cover decoder layer?

Explains attention routing, residual structure, positional information, and future masking, but stops short of decoder implementation code.

As explained in Inside Deep Learning: Math, Algorithms, Models

A transformer block that uses encoder outputs in a second attention stage for sequence generation.

A decoder layer supports sequence generation by adding a second multi-head attention stage to the self-attention path. It usually works with an encoder: the encoder output supplies keys and values, while the previous attention result supplies queries. Residual connections organize both attention stages, and positional encodings provide sequence information instead of explicit recurrent time connections. section 12.5.2

For autoregressive use, account for these design points:

Use a triangular mask to prevent attention from seeing future inputs. section 12.5.2
Pair the decoder with an encoder for sequence-to-sequence tasks such as machine translation. section 12.5.2

How Inside Deep Learning: Math, Algorithms, Models covers it

Cover of Inside Deep Learning: Math, Algorithms, Models

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Also explained in

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Learn Generative AI with PyTorchA Transformer building block composed of self-attention and feed-forward components, plus normalization and residual connections in this section.

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Related concepts

attentionuses · Attention is a neural-network mechanism that computes input-dependent weights over elements and uses them to combine information when forming representations or outputs.
cross-attentionuses · Cross-attention is an attention mechanism in which queries from one sequence or representation attend to keys and values from another.
transformer architecturepart of · A transformer architecture is a neural-network architecture that processes sequences or other structured inputs using attention mechanisms and related transformations.
decodepart of · Decoding is the process of transforming an encoded or internal representation into an interpretable or target-form output.
decoder-only transformerpart of · A decoder-only transformer is a Transformer architecture without a separate encoder that processes preceding context to predict or generate subsequent sequence elements.
masked attentionuses · Masked attention is an attention mechanism in which a mask restricts which positions can influence each other, often preventing sequence positions from accessing future tokens.

This explainer was generated by Manning's knowledge graph from the text of Inside Deep Learning: Math, Algorithms, Models. The section links point to the parts of the book it draws on.