optimuon 0.1.1

A performance-optimized Muon optimizer with foreach support, auto-routing, and composite optimizer patterns.

optimuon

A performance-optimized Muon optimizer for PyTorch.

Features:

• Foreach-native: uses torch._foreach_* ops for momentum, weight decay, and parameter updates.
• Batched Newton-Schulz: groups matrices by shape for parallel orthogonalization.
• Auto-parameter routing: automatically partitions model parameters into Muon-eligible (≥2D hidden weights) and auxiliary (embeddings, heads, norms, biases).
• Composite optimizer: CompositeMuon combines Muon with any arbitrary auxiliary optimizer (not just AdamW).
• Three LR modes: Keller Jordan's "original" (with aspect-ratio scaling), Moonshot AI's "match_rms_adamw", and "none" (no scaling).
• Momentum conventions: "ema" (m = beta*m + (1-beta)*g, default) and "classical" (m = beta*m + g).
• Corrections: MARS, cautious updates, cautious weight decay, NorMuon, gradient/update clipping (all toggleable).
• Weight normalization: optional Frobenius-norm clamping to sqrt(fan_out) (from KJ's original Muon).
• Half-precision momentum: optional lower-precision momentum buffers for memory savings.
• Polar Express: optimal per-step Newton-Schulz coefficients (default).
• Distributed: torch.distributed gradient sharding via all_gather.

Installation

uv pip install git+https://github.com/emaballarin/optimuon

Quick start

Standalone Muon (manual parameter selection)

from optimuon import Muon

# Muon for ≥2D hidden weight matrices only
muon = Muon(muon_params, lr=0.02, momentum=0.95, weight_decay=0.01)

# Separate AdamW for everything else
import torch
adamw = torch.optim.AdamW(other_params, lr=3e-4)

# Training loop
for batch in dataloader:
    loss = model(batch).loss
    loss.backward()
    muon.step()
    adamw.step()
    muon.zero_grad()
    adamw.zero_grad()

CompositeMuon with auto-routing (recommended)

from optimuon import CompositeMuon

optimizer = CompositeMuon(
    model,
    muon_lr=0.02,
    muon_kwargs={"weight_decay": 0.01, "foreach": True},
    aux_optimizer_class=torch.optim.AdamW,
    aux_optimizer_kwargs={"lr": 3e-4, "betas": (0.9, 0.95), "weight_decay": 0.01},
    verbose=True,
)

for batch in dataloader:
    loss = model(batch).loss
    loss.backward()
    optimizer.step()
    optimizer.zero_grad()

With corrections

from optimuon import CompositeMuon

optimizer = CompositeMuon(
    model,
    muon_lr=0.02,
    muon_kwargs={
        "weight_decay": 0.01,
        "mars": True,           # MARS gradient correction
        "cautious": True,       # cautious update masking
        "grad_clip": 1.0,       # gradient norm clipping
        "weight_norm": True,    # Frobenius-norm clamping
    },
    aux_optimizer_class=torch.optim.AdamW,
    aux_optimizer_kwargs={"lr": 3e-4},
)

With a custom auxiliary optimizer

from optimuon import CompositeMuon

optimizer = CompositeMuon(
    model,
    muon_lr=0.02,
    aux_optimizer_factory=lambda param_groups: SomeExoticOptimizer(param_groups, lr=1e-3),
)

Manual routing utilities

from optimuon import partition_params

result = partition_params(model)
print(f"Muon: {result.muon_names}")
print(f"Aux:  {result.aux_names}")

References

• Keller Jordan et al., Muon: An optimizer for hidden layers in neural networks (2024)
• Huizhuo Yuan et al., MARS: Unleashing the Power of Variance Reduction for Training Large Models (2024)
• Kaizhao Liang et al., Cautious Optimizers: Improving Training with One Line of Code (2024)
• Moonshot AI, Muon is Scalable for LLM Training (2025)
• Essential AI, Practical Efficiency of Muon for Pretraining (2025)
• Noah Amsel et al., The Polar Express: Optimal Matrix Sign Methods and Their Application to the Muon Algorithm (2025)
• Zichong Li et al., NorMuon: Making Muon more efficient and scalable (2025)
• Lizhang Chen et al., Cautious Weight Decay (2025)

License

MIT