README.md

ALRC

Implementation of Adaptive Learning Rate Clipping by Ede et al https://doi.org/10.1088/2632-2153/ab81e2

The official implementation is in TensorFlow, I have rewritten their implementation in PyTorch using only PyTorch functions. https://github.com/Jeffrey-Ede/ALRC

Code was originally written to stabilise training with massive models and therefore small batch sizes for my thesis project: Paired Diffusion: Generation of 2D and 3D aligned, synthetic PET-CT-Segmentation scans using Linked Denoising Diffusion Probabilistic Models

Testing

Simple tests to show the functionality of the implementation.

1. Normal distribution of values with outliers appended.

losses = np.random.normal(25, 10, 1000)
outliers = np.array([100, 150, 200])

Clipped losses:

2. Showing the clipping of outliers over a long period

Loss values again generated from random normal distribution with several outliers placed at random intervals

losses = np.random.normal(25, 10, 10000)
outliers = np.random.choice(np.arange(len(long_sequence_losses)), size=100, replace=False)
losses[outlier_indices] = losses[outlier_indices] + 100

Longevity test:

Additional simple testing confirms the function does not adjust negative, zero or constant losses.

Usage

Usage is simple

1. Import into your script and make an ALRC object

from alrc_pytorch import ALRC
alrc = ALRC()

2. Use it to clip losses in your training loop

...
loss = alrc(loss)
loss.backward()
...

More advanced, parameter values can be tuned in the original file. Specifically, a prediction for the starting mu1 and mu2 is useful, but it is very robust. See the original implementation for more details.