| # BERT-tiny model finetuned with M-FAC | |
| This model is finetuned on MRPC 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](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](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: | |
| ```bash | |
| 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 MRPC validation set: | |
| ```bash | |
| f1 = 83.12 | |
| accuracy = 73.52 | |
| ``` | |
| Mean and standard deviation for 5 runs on MRPC validation set: | |
| | | F1 | Accuracy | | |
| |:----:|:-----------:|:----------:| | |
| | Adam | 81.68 ± 0.33 | 69.90 ± 0.32 | | |
| | M-FAC | 82.77 ± 0.22 | 72.94 ± 0.37 | | |
| 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](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) and running the following bash script: | |
| ```bash | |
| CUDA_VISIBLE_DEVICES=0 python run_glue.py \ | |
| --seed 42 \ | |
| --model_name_or_path prajjwal1/bert-tiny \ | |
| --task_name mrpc \ | |
| --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](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](https://github.com/IST-DASLab/M-FAC/tree/master/tutorials). | |
| ## BibTeX entry and citation info | |
| ```bibtex | |
| @article{frantar2021m, | |
| title={M-FAC: Efficient Matrix-Free Approximations of Second-Order Information}, | |
| author={Frantar, Elias and Kurtic, Eldar and Alistarh, Dan}, | |
| journal={Advances in Neural Information Processing Systems}, | |
| volume={35}, | |
| year={2021} | |
| } | |
| ``` | |