llcurious / adalora

AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning (ICLR 2023).

AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning

This pytorch package implements Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning (ICLR 2023).

The implementaion of AdaLoRA has been merged to the parameter-efficient fine-tuning repository (🤗PEFT) supported by HuggingFace: 🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning. Feel free to raise any issues when you using AdaLoRA in PEFT or our repository.

Repository Overview

There are several directories in this repo:

• loralib/ contains the source code of the updated package loralib, which include our implementation of AdaLoRA (loralib/adalora.py) and needs to be installed to run the examples;
• NLU/ contains an example implementation of AdaLoRA in DeBERTaV3-base, which produces the results on the GLUE benchmark;
• NLG_QA/ contains an example implementation of AdaLoRA in BART-large and DeBERTaV3-base, which can be used to reproduce the results of summarization and question-answering tasks.

Quickstart of AdaLoRA

1. Install the updated loralib:

pip install -e loralib/ 

2. Then we apply SVD-based adaptation of AdaLoRA. Here is an example (For more examples, please see modeling_debertav2.py for how we adapte DeBERTa):

# ===== Before =====
# layer = nn.Linear(in_features, out_features)

# ===== After ======
import loralib 
# Add a SVD-based adaptation matrices with rank r=12
layer = loralib.SVDLinear(in_features, out_features, r=12)

Also, before the training loop begins, mark only LoRA parameters as trainable.

model = BigModel()
# This sets requires_grad to False for all parameters without the string "lora_" in their names
loralib.mark_only_lora_as_trainable(model)

3. During the training loop, we apply RankAllocator of AdaLoRA to update importance scores of incremental matrices and allocate budget accordingly.

from loralib import RankAllocator
from loralib import compute_orth_regu 
# Initialize the RankAllocator 
rankallocator = RankAllocator(
    model, lora_r=12, target_rank=8,
    init_warmup=500, final_warmup=1500, mask_interval=10, 
    total_step=3000, beta1=0.85, beta2=0.85, 
)

• lora_r: The initial rank of each incremental matrix.

• target_rank: The average target rank of final incremental matrices, i.e. the average number of singular values per matrix.

• init_warmup: The steps of initial warmup for budget scheduler.

• final_warmup: The steps of final warmup for budget scheduler.

• mask_interval: The time internval between two budget allocations.

• beta1 and beta2: The coefficient of exponentional moving average when updating importance scores.

  At each step of back-propagation, we apply an additional regularization to enforce the orthongonality of SVDLinear modules by compute_orth_regu(model). After each step of optimizer.step(), we then call RankAllocator to update importance estimation and allocate the budget accordingly:

  # ===== Before =====
  # loss.backward() 
  # optimizer.step() 
  # global_step += 1 
  
  # ===== After ======
  (loss+compute_orth_regu(model, regu_weight=0.1)).backward
  optimizer.step()
  rankallocator.update_and_mask(model, global_step)
  global_step += 1

GLUE benchmark

Check the folder NLU for more details about reproducing the GLUE results. An example of adapting DeBERTaV3-base on MNLI:

python -m torch.distributed.launch --nproc_per_node=1 \
NLU/examples/text-classification/run_glue.py \
--model_name_or_path microsoft/deberta-v3-base \
--task_name mnli \
--apply_adalora --apply_lora --lora_type svd \
--target_rank 1  --lora_r 3  \
--reg_orth_coef 0.1 \
--init_warmup 8000 --final_warmup 50000 --mask_interval 100 \
--beta1 0.85 --beta2 0.85 \
--lora_module query,key,value,intermediate,layer.output,attention.output \
--lora_alpha 16 \
--do_train --do_eval \
--max_seq_length 256 \
--per_device_train_batch_size 32 --learning_rate 5e-4 --num_train_epochs 7 \
--warmup_steps 1000 \
--cls_dropout 0.15 --weight_decay 0 \
--evaluation_strategy steps --eval_steps 3000 \
--save_strategy steps --save_steps 30000 \
--logging_steps 500 \
--seed 6 \
--root_output_dir ./output/deberta-v3-base/mnli \
--overwrite_output_dir

Please see NLU/scripts for more examples of GLUE.

Summarization and Question Answering Task

Check the folder NLG_QA for more details about reproducing the results of summarization and question-answering tasks.
An example of adapting DeBERTaV3-base on SQuADv2:

python -m torch.distributed.launch --nproc_per_node=1 \
NLG_QA/examples/question-answering/run_qa.py \
--model_name_or_path microsoft/deberta-v3-base \
--dataset_name squad_v2 \
--apply_lora --apply_adalora \
--lora_type svd --target_rank 8   --lora_r 12  \
--reg_orth_coef 0.1 \
--init_warmup 50 --final_warmup 100 --mask_interval 10 \
--beta1 0.85 --beta2 0.85 \
--lora_module query,key,value,intermediate,layer.output,attention.output \
--lora_alpha 16 \
--do_train --do_eval --version_2_with_negative \
--max_seq_length 384 --doc_stride 128 \
--per_device_train_batch_size 16 \
--learning_rate 8e-4 \
--num_train_epochs 1 \
--max_step 300 \
--warmup_steps 1000 --per_device_eval_batch_size 128 \
--evaluation_strategy steps --eval_steps 3000 \
--save_strategy steps --save_steps 100000 \
--logging_steps 300 \
--tb_writter_loginterval 300 \
--report_to tensorboard \
--seed 9 \
--root_output_dir ./output/debertav3-base/squadv2 \
--overwrite_output_dir 

Citation

@inproceedings{
   zhang2023adaptive,
   title={Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning },
   author={Qingru Zhang and Minshuo Chen and Alexander Bukharin and Pengcheng He and Yu Cheng and Weizhu Chen and Tuo Zhao},
   booktitle={The Eleventh International Conference on Learning Representations },
   year={2023},
   url={https://openreview.net/forum?id=lq62uWRJjiY}
}