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Adds `Axon.Layers.scaled_dot_product_attention/4` and the graph-level
`Axon.scaled_dot_product_attention/4`.
The functional kernel computes `softmax(Q · Kᵀ * scale + mask) · V` over
rank-4 inputs, and supports:
* `:axes` - `:bshd` (default) or `:bhsd` input layouts
* `:scale` - defaults to `1 / sqrt(head_dim)`
* `:mask` - `:causal`, `{:causal, offset}`, `{:sliding_window, w}`,
`{:sliding_window, w, offset}`, or a runtime boolean/float tensor
* `:dropout_rate` - attention-probability dropout in `:train` mode
* `:return_attention_weights`
* grouped-query and multi-query attention, by repeating key/value
heads to match the query
The graph layer wires a tensor mask in as an extra input and, when
dropout is enabled, adds a PRNG key state parameter so the key is
threaded across steps. Mask tensors and dropout keys are passed
positionally, which is why the kernel has 4-, 5- and 6-arity heads.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
polvalente
reviewed
Aug 16, 2026
| maybe_attention_dropout(weights, dropout_key, opts[:dropout_rate], opts[:mode]) | ||
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| output = Nx.dot(weights, [3], [0, 1], value, [2], [0, 1]) | ||
| output = from_bhsd(output, opts[:axes]) |
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Can't we avoid transposes by using the correct reduce axes in dot directly?
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