quad-torch 0.4.5

An implementation of PSGD-QUAD optimizer in PyTorch.

PSGD-QUAD

pip install quad-torch

An implementation of PSGD-QUAD for PyTorch.

import torch
from quad_torch import QUAD

model = torch.nn.Linear(10, 10)
optimizer = QUAD(
    model.parameters(),
    lr=0.001,
    lr_style="adam",
    momentum=0.95,
    weight_decay=0.1,
    max_size_dense=8192,
    max_skew_dense=1.0,
    preconditioner_lr=0.7,
    noise_scale=1e-9,
    dtype=torch.bfloat16,
)

Couple notes:

• lr_style="adam" is the default and scales the update to match adam's behavior LR-wise and weight decay-wise.
• dtype=torch.bfloat16 should be fine for most problems, but if a problem is particularly sensitive then you can try None to default to gradient dtypes or torch.float32 to force f32 precision.

Resources

Xi-Lin Li's repo: https://github.com/lixilinx/psgd_torch

PSGD papers and resources listed from Xi-Lin's repo

1. Xi-Lin Li. Preconditioned stochastic gradient descent, arXiv:1512.04202, 2015. (General ideas of PSGD, preconditioner fitting losses and Kronecker product preconditioners.)
2. Xi-Lin Li. Preconditioner on matrix Lie group for SGD, arXiv:1809.10232, 2018. (Focus on preconditioners with the affine Lie group.)
3. Xi-Lin Li. Black box Lie group preconditioners for SGD, arXiv:2211.04422, 2022. (Mainly about the LRA preconditioner. See these supplementary materials for detailed math derivations.)
4. Xi-Lin Li. Stochastic Hessian fittings on Lie groups, arXiv:2402.11858, 2024. (Some theoretical works on the efficiency of PSGD. The Hessian fitting problem is shown to be strongly convex on set ${\rm GL}(n, \mathbb{R})/R_{\rm polar}$.)
5. Omead Pooladzandi, Xi-Lin Li. Curvature-informed SGD via general purpose Lie-group preconditioners, arXiv:2402.04553, 2024. (Plenty of benchmark results and analyses for PSGD vs. other optimizers.)

License

This work is licensed under a Creative Commons Attribution 4.0 International License.

2024 Evan Walters, Omead Pooladzandi, Xi-Lin Li