How deeply does Build a Large Language Model (From Scratch) cover MultiHeadAttentionWrapper?

Explains module composition and output concatenation, then defers detailed tensor-splitting internals to an integrated class.

MultiHeadAttentionWrapper in Build a Large Language Model (From Scratch)

A wrapper class that implements multi-head attention by combining several separate single-head attention modules.

MultiHeadAttentionWrapper implements multi-head attention by stacking several single-head CausalAttention modules. Each head performs attention independently, and their results are concatenated. section 3.6.2

Its implementation follows this data flow:

Create a list of CausalAttention objects, with each object representing one attention head. section 3.6.2
Run the input through every head independently. section 3.6.2
Concatenate the outputs from all heads into the multi-head result. section 3.6.2

Two-head comparison

With two heads, the wrapper uses separate query weight matrices, Wq1 and Wq2, to produce Q1 and Q2. The integrated MultiHeadAttention class instead computes one larger query matrix and splits it internally. section 3.6.2

Figure 3.26 In the `MultiHeadAttentionWrapper` class with two attention heads, we initialized two weight matrices, W_q1 and W_q2, and computed two query matrices, Q₁ and Q₂ (top). In the `MultiheadAttention` class, we initialize one larger weight matrix W_q, only perform one matrix multiplication with the inputs to obtain a query matrix Q, and then split the query matrix into Q₁ and Q₂ (bottom). We do the same for the keys and values, which are not shown to reduce visual clutter.

Compare separate per-head projections with the integrated projection-and-split design. section 3.6.2

How Build a Large Language Model (From Scratch) covers it

This explainer was generated by Manning's knowledge graph from the text of Build a Large Language Model (From Scratch). The section links point to the parts of the book it draws on.