This project adds LoRA, Flash attn patch and DS (Deepspeed) to Scaled Rope that can be run multinode.

Flash Attention 2 and LLaMA 2 ready 🚀

1. To install the necessary dependencies, use pip to install the required Python packages:

   pip install -r requirements.txt
   

2. Update the config.yaml file as per your requirements.

To run the application, use the following command:

python --configs defaults <your_override_config>

Replace <your_override_config> with your specific configuration specified in configs/config.yaml. Command line arguments can also be overridden.

Please Note: This uses the HF recent PR, so models are HF compatible. Linear scaling argument: 'interpolation_factor', i.e. how much you want to scale the model. If set to None will scale config.max_position_embeddings / 4096. As this is the default for LLaMA 2.

• Specify packed untokenized datasets on the hub under dataset_names e.g. (Multi-Domain-Expert-Layers/the_pile_books3_packed_128k)
• If pretokenized=True, specify a single pre-tokenized dataset on the hub under dataset_names (conceptofmind/rp-packed-32k-no-filter for OpenLLaMA)

Use the following commands for running on a single node:

1. Export the necessary paths:

   export PYTHONPATH="/mnt/data/jordiclive/scaled-rope:$PYTHONPATH"
   export TRANSFORMERS_CACHE="/mnt/data/jordiclive/transformers_cache"
   export HF_DATASETS_CACHE="/mnt/data/jordiclive/transformers_cache"
   export HF_HOME="/mnt/data/jordiclive/transformers_cache"
   export WANDB_API_KEY=""
   

2. Run the script:

   deepspeed --include=localhost:0,1,2,3,4,5,6,7 --master_port 61500 finetune.py --output_dir saved_ckpts_32k --configs defaults lora-7b-llama2 --deepspeed
   

Example script using the slurm launcher with deepspeed: scripts/juwels_booster.sh