pip3 install -e transformers/
pip3 install torch==2.0.1
pip3 install deepspeed==0.10.0
pip3 install torchvision==0.15.2
pip3 install nltk
pip3 install numerize
pip3 install rouge-score
pip3 install torchtyping
pip3 install rich
pip3 install accelerate
pip3 install datasets
pip3 install sentencepiece
pip3 install protobuf==3.20.3
pip3 install peft

or

bash install.sh

Our code is based in this commit of HuggingFace Transformers.

• The training/evaluation intruction-response data before processing can be downloaded from this link.
• The plain-text corpus $\mathcal{D}_\text{PT}$ can be download from the HugginFace datasets repository. For reproducibility, we recommend you to use the following preprocessed data.
• The processed data can be downloaded from this link.

Get plain-text corpus $\mathcal{D}_\text{PT}$:

python3 tools/get_openwebtext.py

This script will replace the continuous \n in each document with a special token "<@x(x!>" and write each document in OpenWebText in a line, which is covenient for parallel processing. In data/openwebtext/data.txt, we give an example of the resulting format. You can follow this format to prepare other corpus beyond OpenWebText.

Tokenize the data and store them in binary files:

bash scripts/gpt2/tools/process_data_dolly.sh /PATH/TO/MiniLLM # Process Dolly Train / Validation Data
bash scripts/gpt2/tools/process_data_pretrain.sh /PATH/TO/MiniLLM # Process OpenWebText Train / Validation Data

bash scripts/opt/tools/process_data_dolly.sh /PATH/TO/MiniLLM # Process Dolly Train / Validation Data
bash scripts/opt/tools/process_data_pretrain.sh /PATH/TO/MiniLLM # Process RoBERTa Corpus Train / Validation Data

bash scripts/llama/tools/process_data_dolly.sh /PATH/TO/MiniLLM # Process Dolly Train / Validation Data
bash scripts/llama/tools/process_data_pretrain.sh /PATH/TO/MiniLLM # Process RoBERTa Corpus Train / Validation Data

• The baselines and MiniLLM models based on GPT-2 can be downloaded from this link.
• The baselines and MiniLLM models based on OPT can be downloaded from this link.
• The baselines and MiniLLM models based on LLaMA can be downloaded from this link.

To run fine-tuning or standard KD baselines, you need to download the model checkpoints from [Huggingface Model Hub] and put them in checkpoints/. For example, for gpt2-large, you can download the model from this link and put them in checkpoints/gpt2-large.

Alternatively, you can also change the CKPT variable in each script to the corresponding model name to enable Transformers to download the base models automatically. For example, set CKPT="gpt2-large" in scripts/gpt2/sft/sft_large.sh causes download of the gpt2-large base model from the HugginFace model hub.

NOTE:

1. LLaMA models require license and cannot be directly downloaded.
2. If you want to use model parallel for training, it is recommended to download the models to checkpoints because you need to run tools/convert_mp.py to change their model parallel sizes (see next section).

You can increase/decrease the tensor parallel sizes with

python3 tools/convert_mp.py \
    --input_path results/llama/train/minillm/7B-init-13B-sft \
    --source_mp_size 1 \
    --target_mp_size 4 \
    --model_type llama # choose from opt and llama

To use the model with Model Parallel, we provide two example scripts for training and evaluation.

bash scripts/gpt2/eval/run_eval.sh /PATH/TO/MiniLLM
bash scripts/opt/eval/run_eval.sh /PATH/TO/MiniLLM
bash scripts/llama/eval/run_eval.sh /PATH/TO/MiniLLM

We provide example commands for GPT-2 models. Similar scripts for model families can be found in scripts/opt and scripts/llama. All our experiments are conducted on 16 * 32V100, which can be reduced for small models. Some large models require tensor parallel size = 4, which is set in the scripts with --model-parallel and --model-parallel-size options.

The final checkpoints are selected by the Rouge-L scores.

bash scripts/gpt2/sft/sft_xlarge.sh /PATH/TO/MiniLLM

bash scripts/gpt2/sft/sft_base.sh /PATH/TO/MiniLLM
bash scripts/gpt2/sft/sft_medium.sh /PATH/TO/MiniLLM
bash scripts/gpt2/sft/sft_large.sh /PATH/TO/MiniLLM

bash scripts/gpt2/kd/kd_base.sh /PATH/TO/MiniLLM
bash scripts/gpt2/kd/kd_medium.sh /PATH/TO/MiniLLM
bash scripts/gpt2/kd/kd_large.sh /PATH/TO/MiniLLM

Generate and process responses with the teacher:

bash scripts/gpt2/tools/generate_data_seqkd.sh /PATH/TO/MiniLLM
bash scripts/gpt2/tools/process_pseudo_data_seqkd.sh /PATH/TO/MiniLLM

Fine-tune the model with SeqKD:

bash scripts/gpt2/seqkd/seqkd_base.sh /PATH/TO/MiniLLM
bash scripts/gpt2/seqkd/seqkd_medium.sh /PATH/TO/MiniLLM
bash scripts/gpt2/seqkd/seqkd_large.sh /PATH/TO/MiniLLM

The final checkpoints are selected by the validation loss.

bash scripts/gpt2/sft/sft_base.sh /PATH/TO/MiniLLM
bash scripts/gpt2/sft/sft_medium.sh /PATH/TO/MiniLLM
bash scripts/gpt2/sft/sft_large.sh /PATH/TO/MiniLLM

The final checkpoints are selected by the Rouge-L scores.

bash scripts/gpt2/minillm/train_base_xl.sh /PATH/TO/MiniLLM
bash scripts/gpt2/minillm/train_medium_xl.sh /PATH/TO/MiniLLM
bash scripts/gpt2/minillm/train_large_xl.sh /PATH/TO/MiniLLM

Multi-Node training is launched by deepspeed. We provide an example script in scripts/llama/sft/sft_7B_mn.sh for multi-node training. Compared to single-node scripts, some of the DISTRIBUTED_ARGS are changed, and you need to specify a hostfile like configs/hostfiles/node_0_1 to tell the script which nodes to use. For more information, please refer to HuggingFace's tutorial.

@article{minillm,
  title={Knowledge Distillation of Large Language Models},
  author={Gu, Yuxian and Dong, Li and Wei, Furu and Huang, Minlie},
  journal={arXiv preprint arXiv:2306.08543},
  year={2023}
}