BERT-mini model finetuned with M-FAC

This model is finetuned on STS-B dataset with state-of-the-art second-order optimizer M-FAC. Check NeurIPS 2021 paper for more details on M-FAC: https://arxiv.org/pdf/2107.03356.pdf.

Finetuning setup

For fair comparison against default Adam baseline, we finetune the model in the same framework as described here https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification and just swap Adam optimizer with M-FAC. Hyperparameters used by M-FAC optimizer:

learning rate = 1e-4
number of gradients = 512
dampening = 1e-6

Results

We share the best model out of 5 runs with the following score on STS-B validation set:

pearson = 85.03
spearman = 85.06

Mean and standard deviation for 5 runs on STS-B validation set:

	Pearson	Spearman
Adam	82.09 ± 0.54	82.64 ± 0.71
M-FAC	84.66 ± 0.30	84.65 ± 0.30

Results can be reproduced by adding M-FAC optimizer code in https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py and running the following bash script:

CUDA_VISIBLE_DEVICES=0 python run_glue.py \
  --seed 7 \
  --model_name_or_path prajjwal1/bert-mini \
  --task_name stsb \
  --do_train \
  --do_eval \
  --max_seq_length 128 \
  --per_device_train_batch_size 32 \
  --learning_rate 1e-4 \
  --num_train_epochs 5 \
  --output_dir out_dir/ \
  --optim MFAC \
  --optim_args '{"lr": 1e-4, "num_grads": 512, "damp": 1e-6}'

We believe these results could be improved with modest tuning of hyperparameters: per_device_train_batch_size, learning_rate, num_train_epochs, num_grads and damp. For the sake of fair comparison and a robust default setup we use the same hyperparameters across all models (bert-tiny, bert-mini) and all datasets (SQuAD version 2 and GLUE).

Our code for M-FAC can be found here: https://github.com/IST-DASLab/M-FAC. A step-by-step tutorial on how to integrate and use M-FAC with any repository can be found here: https://github.com/IST-DASLab/M-FAC/tree/master/tutorials.

BibTeX entry and citation info

@article{DBLP:journals/corr/abs-2107-03356,
  author    = {Elias Frantar and
               Eldar Kurtic and
               Dan Alistarh},
  title     = {Efficient Matrix-Free Approximations of Second-Order Information,
               with Applications to Pruning and Optimization},
  journal   = {CoRR},
  volume    = {abs/2107.03356},
  year      = {2021},
  url       = {https://arxiv.org/abs/2107.03356},
  eprinttype = {arXiv},
  eprint    = {2107.03356},
  timestamp = {Tue, 20 Jul 2021 15:08:33 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2107-03356.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}