# `Emily.Bumblebee.FastKernels`
[🔗](https://github.com/ausimian/emily/blob/0.7.2/lib/emily/bumblebee/fast_kernels.ex#L6)

Rewrite RMSNorm, LayerNorm, RoPE, and SDPA Axon layers of a
Bumblebee model so they call `Emily.Fast.*` instead of their stock
defn implementations. When the rewritten model is then evaluated
under `Emily.Compiler`, those `Emily.Fast.*` calls dispatch to
fused MLX kernels via the `:optional`-node mechanism (see
`Emily.Fast`'s moduledoc). On any other backend the helpers fall
back to defn composition and produce mathematically equivalent
results, so applying the shim is safe even if the model is later
evaluated on `Nx.BinaryBackend` or EXLA.

## Optional dependency

This module depends on `:axon` and `:bumblebee`, which are declared
as `optional: true` in Emily's `mix.exs`. Consumers who don't pull
those deps into their own project get a clean build: the whole
module definition is wrapped in `Code.ensure_loaded?/1` and elides
entirely when either dep is missing. To use the shim, add both to
your own `deps/0`:

    {:bumblebee, "~> 0.6"},
    {:axon, "~> 0.7"}

## Usage

    {:ok, model_info} = Bumblebee.load_model({:hf, "openai/whisper-tiny"})

    model_info =
      update_in(model_info.model, &Emily.Bumblebee.FastKernels.apply/1)

    # then proceed with Bumblebee.Audio.speech_to_text_whisper/5
    # (or Text.generation/4 / etc.) as usual.

## Coverage

  * `:rms_norm` (Bumblebee's `rms_norm/2`).
  * `:layer_norm` (Axon's built-in normalization layer).
  * `Bumblebee.Layers.apply_rotary_embedding/5` — supports the
    default schedule plus the `:linear`, `:dynamic`, `:longrope`,
    `:llama3` scaling strategies. Inverse frequencies are
    precomputed Elixir-side using Bumblebee's own helpers and
    passed to `mx::fast::rope` via the `freqs`-override overload.
  * `Bumblebee.Layers.attention_output_impl/3` — coalesced with
    its sibling `attention_weights_impl/7` into a single
    `mx::fast::scaled_dot_product_attention` dispatch. Mask
    translation: causal + window + key/head/bias collapsed to one
    additive `array` mask.

## What's *not* rewritten

  * Norms with `:channel_index` other than `-1` (uncommon outside
    vision-CNN heads — those don't fit the fused kernel's
    last-axis-only contract). The original layer is left in place.
  * Attention layers with `dropout_rate > 0`. Inference path is
    `dropout_rate: 0` everywhere, so this is a no-op in practice;
    training paths continue using composed defn.

# `apply`

```elixir
@spec apply(Axon.t()) :: Axon.t()
```

Apply every available rewrite to `model`.

---

*Consult [api-reference.md](api-reference.md) for complete listing*