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comp-muon-release

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Compositional Muon release

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<p align="center">Partner-whitened steepest descent for the QK and OV attention circuits. <a href="https://blog.tilderesearch.com/blog/compositional-muon">[blog post]</a></p>

Compositional Muon (CM) extends Muon from single-matrix steepest descent to the
**composed operators a transformer actually applies** — the QK product
$M = W_Q W_K^\top$ and the OV product $W_O W_V$. Each factor's gradient is
whitened by its partner's inverse Gram root before the spectral sign and scaled
by it again afterward, so the step size for each matrix adapts to the geometry
of its partner.

## How it works

The loss sees $W_Q$ and $W_K$ only through $M = W_Q W_K^\top$. Constraining the
operator norm of the *composed* update $\Delta M$ and splitting the budget
equally between the two factors gives the partner-whitened half-split rule

$$\Delta W_Q = -\tfrac{\eta}{2}\,\mathrm{msign}\left(G_Q C_K^{-1}\right) C_K^{-1},
\qquad
\Delta W_K = -\tfrac{\eta}{2}\,\mathrm{msign}\left(G_K C_Q^{-1}\right) C_Q^{-1},$$

with $C_K = (W_K^\top W_K + \lambda I)^{1/2}$ and symmetrically $C_Q$. The same
construction applies to the OV product $W_O W_V$ (with $W_V$ per-head and $W_O$
per-matrix). When each partner Gram is near-isotropic the inverse root collapses
to a scalar $C^{-1} \approx c^{-1} I$, recovering a cheap per-head dynamic
learning rate.

## Usage

Requires `torch`. `cm_ov` / `cm_qk` take the attention weights in `nn.Linear`
convention plus caller-managed momentum buffers, and apply one CM update in place:

```python
from compositional_muon import cm_ov, cm_qk

# attn has q_proj/k_proj/v_proj/o_proj as bias-free nn.Linear layers; the momentum
# buffers (zeros at init) are caller-managed, one per weight.
cm_qk(attn.q_proj.weight, attn.k_proj.weight,
      attn.q_proj.weight.grad, attn.k_proj.weight.grad,
      m_q, m_k, head_dim=attn.head_dim, eta=lr)

cm_ov(attn.v_proj.weight, attn.o_proj.weight,
      attn.v_proj.weight.grad, attn.o_proj.weight.grad,
      m_v, m_o, head_dim=attn.head_dim, eta=lr)
```

CM governs only the attention QK and OV pairs; update the other parameters with
your optimizer of choice. `src/main.py` is a runnable demo (a small transformer
trained with CM on attention and Muon on the rest).

## Variants

| argument | default | values | description |
| --- | --- | --- | --- |
| `method` | `"half_split"` | `"half_split"`, `"joint"` | split the budget per factor, or one shared spectral sign over the stacked factors |
| `isotropic` | `False` | `False`, `True` | full matrix partner whitening, or its per-head scalar approximation |
| `hybrid` (OV) | `True` | `True`, `False` | $W_O$ per-matrix spectral sign with $W_V$ per-head, or both per-head |
| `whitening` | `"both"` | `"both"`, `"pre"`, `"post"`, `"none"` | which side(s) of the partner whitening to apply |
| `connection` | `"none"` | `"none"`, `"frobenius"`, `"scale_aware"`, `"frobenius_scalar"`, `"scale_aware_scalar"` | gauge fix removing the vertical (gauge) component of the update |
| `momentum_reproject` | `False` | `False`, `True` | project the momentum onto the horizontal (gauge-fixed) bundle |
| `per_mat_renorm` | `False` | `False`, `True` | restore each leg to its pre-whiten Frobenius norm |

## License

Apache 2.0

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