In the world of natural language processing (NLP), BERT (Bidirectional Encoder Representations from Transformers) has made significant advancements. In this blog post, we will walk through the process of setting up quantized-aware transfer learning for the bert-base-uncased model using OpenVINO NNCF, specifically on the SQuADv1 dataset. This implementation is designed for users who want to optimize their models for better performance and efficiency without sacrificing accuracy.

What is Quantized-Aware Transfer Learning?

Quantized-aware transfer learning combines the processes of quantization and transfer learning to enhance model performance, minimize memory usage, and cut down on computation time. Imagine you are packing a suitcase efficiently: transferring content from a larger box (a complex model) to a suitcase (a smaller, optimized model) while ensuring everything fits snugly and stays intact.

Prerequisites

• Python 3.6 or above
• Access to GPU with CUDA supported
• Basic understanding of machine learning concepts

Setup Instructions

Follow these steps to get your environment ready for quantized-aware transfer learning:

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

# Clone and setup Hugging Face nn_pruning
git clone https://github.com/vuiseng9/nn_pruning
cd nn_pruning
git checkout reproduce-evaluation
git reset --hard 2d4e196d694c465e43e5fbce6c3836d0a60e1446
pip install -e .[dev]

# Clone and setup Hugging Face Transformers
git clone https://github.com/vuiseng9/transformers
cd transformers
git checkout tld-poc
git reset --hard 10a1e29d84484e48fd106f58957d9ffc89dc43c5
pip install -e .head -n 1 examples/pytorch/question-answering/requirements.txt xargs -i pip install

# Install additional dependencies
pip install onnx

Training the Model

Before training the model, you need to initiate the download of the necessary configurations:

# Download NNCF configuration
wget https://huggingface.co/vuiseng9/bert-base-squadv1-qat-bt/raw/main/nncf_bert_squad_qat.json

# Set up paths and parameters
NNCF_CFG=path/to/downloaded_nncf_cfg_above #to-revise
OUTROOT=path/to/train_output_root #to-revise
WORKDIR=transformers/examples/pytorch/question-answering #to-revise
RUNID=bert-base-squadv1-qat-bt
cd $WORKDIR
OUTDIR=$OUTROOT$RUNID
mkdir -p $OUTDIR
export CUDA_VISIBLE_DEVICES=0
NEPOCH=2

# Run the training process
python run_qa.py \
    --model_name_or_path bert-base-uncased \
    --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 csarron/bert-base-uncased-squad-v1 \
    --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

After completing your training, it’s time to evaluate the model’s performance:

# Clone the repo for evaluation
git clone https://huggingface.co/vuiseng9/bert-base-squadv1-qat-bt

# Set necessary variables
MODELROOT=path/to/cloned_repo_above #to-revise
export CUDA_VISIBLE_DEVICES=0
OUTDIR=eval-bert-base-squadv1-qat-bt
WORKDIR=transformers/examples/pytorch/question-answering #to-revise
cd $WORKDIR
mkdir $OUTDIR

# Begin evaluation
nohup python run_qa.py \
    --model_name_or_path vuiseng9/bert-base-uncased-squad \
    --dataset_name squad \
    --qat_checkpoint $MODELROOT/checkpoint-10750 \
    --nncf_config $MODELROOT/nncf_bert_squad_qat.json \
    --to_onnx $OUTDIR/bert-base-squadv1-qat-bt.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

Troubleshooting Tips

• If you encounter issues related to library versions, ensure all dependencies are compatible with your Python version.
• For CUDA-related errors, verify that the correct CUDA toolkit is installed and configured properly.
• If you see a “model not found” error, double-check the paths to your model and configuration files.
• In case of out-of-memory errors during training or evaluation, consider reducing the batch size or adjusting model parameters.

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Conclusion

You’ve successfully learned how to implement quantized-aware transfer learning for BERT using OpenVINO NNCF! With our guidelines, you should be able to efficiently train and evaluate your NLP models with reduced computational costs while maintaining their performance. Remember that advancements like these enable more comprehensive and effective AI solutions.

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.