heavyball 2.1.1

Efficient Optimizers

heavyball

High-performance, extensible, chainable optimizers for PyTorch.

Why heavyball

• Lightning-Fast Training: Batched foreach operations deliver significant speedups on large models.
• Adaptive & Extensible: Built-in AdamW, RMSprop, Schedule-Free algorithms, and PaLM-inspired schedules.
• Plug-and-Play: Drop-in replacements for torch.optim with seamless integration.
• Customizable: Chainable API lets you compose optimizers and transforms (MARS correction, cautious updates, orthogonal updates).
• Battle-Tested: Extensive benchmarks and real-world examples included.

Key Features

• Foreach-based optimizers: ForeachAdamW, ForeachRMSprop, ForeachSFAdamW, Muon, ADOPT, MSAM (Momentum SAM), …
• Schedule-Free optimizers with dynamic learning rate adaptation.
• Advanced update rules: MARS correction, cautious updates, PaLM beta2 scheduling.
• Chainable transforms for custom optimization recipes.
• Comprehensive benchmark suite packaged separately as LightBench (../LightBench).
• Detailed documentation and example-driven tutorials.

Quickstart

Install:

pip install heavyball

Basic usage:

import torch
from torch import nn
from heavyball import ForeachAdamW

model = nn.Sequential(
    nn.Linear(128, 64), nn.ReLU(), nn.Linear(64, 10)
)
optimizer = ForeachAdamW(model.parameters(), lr=1e-3)

for data, target in dataloader:
    optimizer.zero_grad()
    output = model(data)
    loss = torch.nn.functional.cross_entropy(output, target)
    loss.backward()
    optimizer.step()

Benchmarks

Reproduce benchmarks with LightBench (install it via pip install -e ../LightBench from the repo root):

python3 -m lightbench.run_all_benchmarks --opt ForeachSOAP --opt LaProp --opt AdamW --opt Muon --opt ForeachCachedNewtonPSGD  --opt RMSprop --opt OrthoLaProp --opt ForeachSFAdamW --opt ForeachADOPT --opt LaPropOrtho --opt CachedPSGDKron --opt SignLaProp --opt ForeachSOLP --opt PSGDLRA --opt NewtonPSGDLRA --opt NewtonHybrid2PSGDKron --opt NewtonHybrid2PSGDLRA --opt mars-NewtonHybrid2PSGDLRA --opt MSAMLaProp --opt mars-adaptive-NewtonHybrid2PSGDKron  --opt mars-ortho-NewtonHybrid2PSGDKron --opt MuonLaProp --opt mars-unscaled-NewtonHybrid2PSGDKron --opt mars-NewtonHybrid2PSGDKron --opt cautious-AdamW --opt unscaled_cautious-AdamW --opt mars-AdamW  --dtype float32 --steps 1000000 --trials 1000 --parallelism 256 --seeds 1 --difficulties trivial --difficulties easy --difficulties medium --difficulties hard --difficulties extreme --difficulties nightmare --timeout 2880

Migrating from HeavyBall 1.x

• Read the detailed 2.0.0 migration notes for an end-to-end checklist, including guidance for restoring legacy behaviour when needed.
• Use scripts/migrate_optimizer_state.py to rewrite pre-2.0 optimizer checkpoints:

  python scripts/migrate_optimizer_state.py path/to/checkpoint.pt heavyball.ForeachAdamW --state-key optimizer
  

  The utility renames legacy state entries, fans them out per parameter view, and injects the HeavyBall metadata block expected by 2.0.0.

Contributing

We welcome contributions! Please check the issue tracker and follow these steps:

1. Fork the repo and create a feature branch.
2. Install dev dependencies: pip install -e .[dev].
3. Run tests: pytest.
4. Submit a pull request.

License

BSD 3-Clause — see the LICENSE file.

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Made by the HeavyBall team.