mixle.experimental.typed_runtime.torch_optimizer module

Executable PyTorch adapter for statistically typed per-parameter optimizer routes.

build_routed_torch_optimizer(module, plan, *, lr=1.0e-3, betas=(0.9, 0.999), eps=1.0e-8, weight_decay=0.0, precondition_frequency=10, muon_backend='newton_schulz', muon_steps=7, fisher_provider=None, projection_provider=None)

Build a real torch.optim.Optimizer executing a typed geometry plan.

Muon routes use a fixed-step Newton-Schulz polar approximation by default; muon_backend="svd" retains the exact polar factor as an audit reference. Kronecker routes maintain row/column second-moment factors and refresh inverse fourth-roots at precondition_frequency.

Parameters:

• module (Any)

• plan (OptimizerPlan)

• lr (float)

• betas (tuple[float, float])

• eps (float)

• weight_decay (float)

• precondition_frequency (int)

• muon_backend (str)

• muon_steps (int)

• fisher_provider (Callable[[str, Any], Any] | None)

• projection_provider (Callable[[str, Any], None] | None)

Return type:

Any