Welcome to the world of BERT optimization! In this article, we will dive deep into how to optimize a BERT model using the OpenVINO NNCF toolkit. We will cover the steps needed for implementation, troubleshooting tips, and an analogy that could help you grasp these concepts better.

Understanding the Optimization Process

• We start with a BERT model, specifically the one identified as vuiseng9bert-base-squadv1-block-pruning-hybrid-filled-lt.
• The model undergoes three main optimization techniques:
  • Magnitude Sparsification – Sparks the charm of initializing sparsity at 57.92%, targeting 90% sparsity across linear layers.
  • NNCF Quantize-Aware Training – Adopts a symmetric 8-bit format for both weights and activations across all learnable layers.
  • Custom Distillation – Involves a robust BERT model fine-tuned for the SQuAD dataset.

Setting Up Your Environment

To begin with the optimizations, you need to clone several repositories and set up your environment. Here’s how:

• Clone the necessary GitHub repositories:

git clone https://github.com/vuiseng9/nncf
cd nncf
git checkout tld-poc
git reset --hard 1dec7afe7a4b567c059fcf287ea2c234980fded2
python setup.py develop
pip install -r examples/torch/requirements.txt

• Repeat this process for the pruning and Transformers repositories, modifying the commands as necessary.

Training Your Model

Once the environment is set up, it’s time for training!

• Set your variables accordingly and run the training script using the following commands:

BASE_MODEL=pathtocloned_repo_above
NNCF_CFG=pathtodownloaded_nncf_cfg_above
OUTROOT=pathtotrain_output_root
WORKDIR=transformers/examples/pytorch/question-answering
RUNID=bert-base-squadv1-block-pruning-hybrid-filled-lt-nncf-57.92sparse-qat-lt
cd $WORKDIR
OUTDIR=$OUTROOT$RUNID
mkdir -p $OUTDIR
export CUDA_VISIBLE_DEVICES=0
NEPOCH=5
python run_qa.py --model_name_or_path vuiseng9/bert-base-squadv1-block-pruning-hybrid --optimize_model_before_eval --optimized_checkpoint $BASE_MODEL --dataset_name squad --do_eval --do_train --evaluation_strategy steps --eval_steps 250 --learning_rate 3e-5 --lr_scheduler_type cosine_with_restarts --warmup_ratio 0.25 --cosine_cycles 1 --teacher bert-large-uncased-whole-word-masking-finetuned-squad --teacher_ratio 0.9 --num_train_epochs $NEPOCH --per_device_eval_batch_size 128 --per_device_train_batch_size 16 --max_seq_length 384 --doc_stride 128 --save_steps 250 --nncf_config $NNCF_CFG --logging_steps 1 --overwrite_output_dir --run_name $RUNID --output_dir $OUTDIR

Evaluating the Model

Once the training is complete, you can evaluate the model with the evaluation settings you prefer.

git clone https://huggingface.co/vuiseng9/bert-base-squadv1-block-pruning-hybrid-filled-lt-nncf-57.92sparse-qat-lt
MODELROOT=pathtocloned_repo_above
export CUDA_VISIBLE_DEVICES=0
OUTDIR=eval-bert-base-squadv1-block-pruning-hybrid-filled-lt-nncf-57.92sparse-qat-lt
WORKDIR=transformers/examples/pytorch/question-answering
cd $WORKDIR
mkdir $OUTDIR
nohup python run_qa.py --model_name_or_path vuiseng9/bert-base-squadv1-block-pruning-hybrid --dataset_name squad --optimize_model_before_eval --qat_checkpoint $MODELROOT/checkpoint-21750 --nncf_config $MODELROOT/nncf_bert_squad_sparsity.json --to_onnx $OUTDIR/bert-base-squadv1-block-pruning-hybrid-filled-lt-nncf-57.92sparse-qat-lt.onnx --do_eval --per_device_eval_batch_size 128 --max_seq_length 384 --doc_stride 128 --overwrite_output_dir --output_dir $OUTDIR 21 tee $OUTDIR/run.log

Analogy: The Chef and the Kitchen

Think of the optimization process like a chef preparing a signature dish. The chef starts with an array of ingredients (the model) and, through careful selection and preparation (sparsification and quantization), makes sure every flavor is enhanced while unnecessary elements are minimized. The result is a delicious dish (an efficient model) that’s not only tasty (accurate) but also presented beautifully (well-optimized for deployment).

Troubleshooting Ideas

If you encounter any issues throughout this process, consider the following:

• Check your cloned repositories for the correct branch and commit IDs.
• Ensure that all paths in your commands are accurate and point to the correct locations.
• If you run into errors regarding environments, make sure all dependencies are satisfied by following the instructions carefully.

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Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.