Alpaca Lora 4bit

Made some adjust for the code in peft and gptq for llama, and make it possible for lora finetuning with a 4 bits base model. The same adjustment can be made for 2, 3 and 8 bits.

Quick start for running the chat UI

git clone https://github.com/johnsmith0031/alpaca_lora_4bit.git
cd alpaca_lora_4bit
DOCKER_BUILDKIT=1 docker build -t alpaca_lora_4bit . # build step can take 12 min
docker run --gpus=all -p 7860:7860 alpaca_lora_4bit

Point your browser to http://localhost:7860

Results

It's fast on a 3070 Ti mobile. Uses 5-6 GB of GPU RAM.

Development

• Install Manual by s4rduk4r: https://github.com/s4rduk4r/alpaca_lora_4bit_readme/blob/main/README.md (NOTE: don't use the install script, use the requirements.txt instead.)
• Also Remember to create a venv if you do not want the packages be overwritten.

Update Logs

• Resolved numerically unstable issue
• Reconstruct fp16 matrix from 4bit data and call torch.matmul largely increased the inference speed.
• Added install script for windows and linux.
• Added Gradient Checkpointing. Now It can finetune 30b model 4bit on a single GPU with 24G VRAM with Gradient Checkpointing enabled. (finetune.py updated) (but would reduce training speed, so if having enough VRAM this option is not needed)
• Added install manual by s4rduk4r
• Added pip install support by sterlind, preparing to merge changes upstream
• Added V2 model support (with groupsize, both inference + finetune)
• Added some options on finetune: set default to use eos_token instead of padding, add resume_checkpoint to continue training
• Added offload support. load_llama_model_4bit_low_ram_and_offload_to_cpu function can be used.
• Added monkey patch for text generation webui for fixing initial eos token issue.
• Added Flash attention support. (Use --flash-attention)
• Added Triton backend to support model using groupsize and act-order. (Use --backend=triton)
• Added g_idx support in cuda backend (need recompile cuda kernel)
• Added xformers support
• Removed triton, flash-atten from requirements.txt for compatibility

Requirements

gptq-for-llama
peft
The specific version is inside requirements.txt

Install

~copy files from GPTQ-for-LLaMa into GPTQ-for-LLaMa path and re-compile cuda extension~
~copy files from peft/tuners/lora.py to peft path, replace it~

NOTE: Install scripts are no longer needed! requirements.txt now pulls from forks with the necessary patches.

pip install -r requirements.txt

Finetune

~The same finetune script from https://github.com/tloen/alpaca-lora can be used.~

After installation, this script can be used: GPTQv1:

python finetune.py

or

GPTQ_VERSION=1 python finetune.py

GPTQv2:

GPTQ_VERSION=2 python finetune.py

Inference

After installation, this script can be used:

python inference.py

Text Generation Webui Monkey Patch

Clone the latest version of text generation webui and copy all the files into ./text-generation-webui/

git clone https://github.com/oobabooga/text-generation-webui.git

Open server.py and insert a line at the beginning

import custom_monkey_patch # apply monkey patch
import gc
import io
...

Use the command to run

python server.py

Flash Attention

It seems that we can apply a monkey patch for llama model. To use it, simply download the file from MonkeyPatch. And also, flash-attention is needed, and currently do not support pytorch 2.0. Just add --flash-attention to use it for finetuning.

Xformers

• Install

pip install xformers

• Usage

from monkeypatch.llama_attn_hijack_xformers import hijack_llama_attention
hijack_llama_attention()