No learning rates needed: Introducing SALSA - Stable Armijo Line Search Adaptation

The Repository to the Paper Faster Convergence for No learning rates needed: Introducing SALSA - Stable Armijo Line Search Adaptation

Youtube Link:

Install

Please use:

pip install SaLSa-Optimizer

Dependencies:

• pytorch <3
• numpy <3

for replicating the results (not needed for using the optimizer):

• pip install transformers for huggingface transformers <3
• pip install datasets for huggingface datasets <3
• pip install tensorflow-datasets for tensorflow datasets <3
• pip install wandb for optional logging <3
• for easy replication use conda and environment.yml eg: $ conda env create -f environment.yml and $ conda activate sls3

Use in own projects

The custom optimizer is in \salsa\SaLSA.py and the comparison version are in \salsa\adam_sls.py \ Example Usage:

from salsa.SaLSA import SaLSA
self.optimizer = SaLSA(model.parameters())

The typical pytorch forward pass needs to be changed from :

optimizer.zero_grad()
y_pred = model(x)
loss = criterion(y_pred, y)    
loss.backward()
optimizer.step()
scheduler.step() 

to:

def closure(backwards = False):
    y_pred = model(x)
    loss = criterion(y_pred, y)
    if backwards: loss.backward()
    return loss
optimizer.zero_grad()
loss = optimizer.step(closure = closure)

This code change is necessary since, the optimizers needs to perform additional forward passes and thus needs to have the forward pass encapsulated in a function. See embedder.py in the fit() method for more details

Replicating Results

The results of the Line Search Algorithm are:

on average a 50% reduction in final loss, while only needing about 3% extra compute time on average.

For replicating the main Results of the Paper run:

$ python test/run_multiple.py
$ python test/run_multiple_img.py

For replicating specific runs or trying out different hyperparameters use:

$ python test/main.py 

and change the test/config.json file appropriately

Older Versions of this Optimizer:

https://github.com/TheMody/Faster-Convergence-for-Transformer-Fine-tuning-with-Line-Search-Methods https://github.com/TheMody/Improving-Line-Search-Methods-for-Large-Scale-Neural-Network-Training

Please cite:

No learning rates needed: Introducing SALSA - Stable ArmijoLine Search Adaptation from Philip Kenneweg, Tristan Kenneweg, Fabian Fumagalli Barbara Hammer published in IJCNN 2024 and on arvix

@INPROCEEDINGS{10650124,
  author={Kenneweg, Philip and Kenneweg, Tristan and Fumagalli, Fabian and Hammer, Barbara},
  booktitle={2024 International Joint Conference on Neural Networks (IJCNN)}, 
  title={No learning rates needed: Introducing SALSA - Stable Armijo Line Search Adaptation}, 
  year={2024},
  volume={},
  number={},
  pages={1-8},
  keywords={Training;Schedules;Codes;Search methods;Source coding;Computer architecture;Transformers},
  doi={10.1109/IJCNN60899.2024.10650124}}