Update Nov 22 2023: The official repo was moved to vip-token-centric-compression

Official Repo for VCC: Scaling Transformers to 128K Tokens or More by Prioritizing Important Tokens

VCC stands for VIP-Token Centric Compression

We are waiting for disclosure approval process in AWS before we can release the code, and it is likely to be finished before Nov 10, 2023.

Paper link

VCC stands for VIP-Token Centric Compression

Dataset Preparation

All codes for dataset preparation are located at src/datasets_scripts

Finetuning

The codes simply download HuggingFace's Dataset or convert json files downloaded from official source to HuggingFace's Dataset Code for downstream datasets is very short and should be self-explanatory.

Pretraining

Use the following command to download wikipedia english dataset from HuggingFace

python3 src/datasets_scripts/pretrain/wiki_en_dataset.py --output <output_path>

Then use the following command to segment or pack articles in wikipedia english dataset to examples of <sequence_length> length and stores examples to multiple jsonl files

python3 src/datasets_scripts/pretrain/example_packing.py \
  --output_folder <jsonl_files_output_folder> \
  --data_file <wiki_en_output_path>/wikipedia.20220301.en/train/ \
  --example_pack_length <sequence_length> \
  --mp

mp option will use all available cpu cores to preprocess the dataset.

Finally, use the following command to combine all jsonl files

python3 src/datasets_scripts/pretrain/jsonl_to_arrow.py \
  --jsonl_folder <jsonl_files_output_folder> \
  --output_file <arrow_file_output_path>

You can move some jsonl files from <jsonl_files_output_folder> to a different folder and use it as validation set.

Training using PyTorch Lightning

Both pretraining and finetuning are launched by main.py. All configuration including training pipeline, model, dataset, data collator, and optimizer are specified in a config file, such as cfgs/roberta/base-512/postnorm-16n.py

python3 main.py --config cfgs/roberta/base-512/postnorm-16n.py