Awesome

Batched LoRAs

Maximize GPU util by routing inference through multiple LoRAs in the same batch.

<table> <tr> <td align="center"> <img src="https://github.com/sabetAI/BLoRA/assets/28828395/a99a7503-e022-4012-84fb-4626d8a15cc5" alt="Image 1" /> <p>Trainable parameters for low rank layer adapters are small, and can all be held simultaneously in VRAM. Meaning, you can have the same base model, and change its behavior by swapping LoRAs. Huggingface's PEFT allows swapping adapters over their API.</p> </td> <td align="center"> <img src="https://github.com/sabetAI/BLoRA/assets/28828395/759326cb-d4da-402c-940b-ad479144b6e4" alt="Image 2"/> <p>But what if you wanted to inference all of your adapters at the same time? The LoRA operation is pretty simple! It creates an output of the same shape as the adapted layer, and then adds them together. That has got to be broadcastable, right?</p> </td> <td align="center"> <img src="https://github.com/sabetAI/BLoRA/assets/28828395/b335b30c-438c-494b-ad74-65debcd1910e" alt="Image 3" /> <p>It is! If you have a matching number of LoRA adapters, you can fashion an operation to apply on each respective batch. Multiple models, that share the same weights.</p> </td> </tr> </table>

Usage:

0. Clone the Repository

To clone the repository using git, run:

git clone https://github.com/sabetAI/BLoRA.git
cd BLoRA

Set Up a Virtual Environment (Recommended) and install required packages

pip install -r requirements.txt

1. Load base model

from transformers import LlamaForCausalLM, LlamaTokenizer

model_path = "decapoda-research/llama-7b-hf"
model = transformers.LlamaForCausalLM.from_pretrained(model_path, trust_remote_code=True)
tokenizer = transformers.LlamaTokenizer.from_pretrained(model_path)
tokenizer.pad_token = 0

2. Inject loras into base model from checkpoint paths

from blora_utils import load_loras

loras = ["jondurbin/airoboros-7b-gpt4-1.2-peft", 
         "trl-lib/llama-7b-se-rl-peft",
         "winddude/wizardLM-LlaMA-LoRA-7B"]
model, lora_map = load_loras(model, loras)

3. Prepare batch by side-loading lora batch ids into the model (hack)

from blora_utils import prepare_batch

inputs = [('Outline a five sentence short story where a character stumbles upon a secret room in their house that contains relics from their future.',
  'jondurbin/airoboros-7b-gpt4-1.2-peft'),
 ('Write a 6 line dialogue between a character and a magical creature that only they can see.',
  'trl-lib/llama-7b-se-rl-peft'),
 ('Describe a four sentence scene where a character discovers a hidden talent that changes their life forever.',
  'winddude/wizardLM-LlaMA-LoRA-7B'),
 ('Sculpt a three verse poem about the feeling of walking through a lush, vibrant garden in full bloom.',
  'trl-lib/llama-7b-se-rl-peft'),
 ('Develop an eight sentence short story about a character who can bring their dreams into reality, but only for a limited time.',
  'winddude/wizardLM-LlaMA-LoRA-7B')]

batch = prepare_batch(inputs, tokenizer, model, lora_map)

4. Stream outputs

outputs = []

for out in model.generate(**batch, max_length=200, stream_output=True):
    outputs.append(out)
    batch_decoded = tokenizer.batch_decode(
        torch.cat([out.reshape(-1, 1) for out in outputs], dim=1)
    )
    print(
        "\n\n".join(
            [
                lora + ":\n" + prompt + "\n" + decoded
                for (prompt, lora), decoded in zip(inputs, batch_decoded)
            ]
        )
    )

https://github.com/sabetAI/BLoRA/assets/28828395/287b6cce-555e-4626-852c-1ad79672f27e

Acknowledgements

Thanks to @yacineMTB for reviewing 🙏.