m-LoRA: An Efficient LLM Fine-tuning Framework

m-LoRA (short for Multi-LoRA) is an open-source LLMOps framework developed by the IDs Lab at Sichuan University. It is designed for high-throughput fine-tuning, evaluation, and inference of Large Language Models (LLMs) using techniques such as LoRA, DoRA, MixLoRA, and others. Key features of mLoRA include:

• Concurrent fine-tuning of multiple adapters with a shared pre-trained model.

• Support for multiple PEFT algorithms and various pre-trained models.

• Mo-LoRA (Mixture of LoRAs) optimization, mainly for MixLoRA.

You can try m-LoRA with Google Colab before local installation.

Note from the maintainer of this repository

This repository has transferred to https://github.com/TUDB-Labs/MoE-PEFT, please use MoE-PEFT instead of this software.

This is an actively developing fork of the official m-LoRA repository, focusing on the PEFT algorithm and its related improvements. It is maintained by the authors of m-LoRA. Currently, this fork does not support pipeline parallelism and can only utilize a single compute device, such as a GPU or NPU, for each m-LoRA process. Please note that the functions, interfaces, and performance of this fork differ from those of the original m-LoRA. Compatibility is not guaranteed. For production use, please prefer the original m-LoRA.

Supported Platform

OS	Backend	Model Precision	Quantization	Flash Attention
Linux	CUDA	FP32, FP16, TF32, BF16	8bit and 4bit	✓
Windows	CUDA	FP32, FP16, TF32, BF16	8bit and 4bit	-
macOS	MPS	FP32, FP16, BF16	✗	✗
All	CPU	FP32, FP16, BF16	✗	✗

You can use the MLORA_BACKEND_TYPE environment variable to force m-LoRA to use a specific backend. For example, if you want m-LoRA to run only on CPU, you can set MLORA_BACKEND_TYPE=CPU before importing mlora.

Supported Pre-trained Models

	Model	Model Size
✓	LLaMA 1/2	7B/13B/70B
✓	LLaMA 3/3.1	8B/70B
✓	Yi 1/1.5	6B/9B/34B
✓	TinyLLaMA	1.1B
✓	Qwen 1.5/2	0.5B ~ 72B
✓	Gemma	2B/7B
✓	Gemma 2	9B/27B
✓	Mistral	7B
✓	Phi 1.5/2	2.7B
✓	Phi 3	3.8B/7B/14B
✓	ChatGLM 1/2/3	6B
✓	GLM 4	6B

Supported PEFT Methods

	PEFT Methods	Arguments*
✓	QLoRA	See Quantize Methods
✓	LoRA+	"loraplus_lr_ratio": 20.0
✓	DoRA	"use_dora": true
✓	rsLoRA	"use_rslora": true
✓	MoLA	"routing_strategy": "mola", "num_experts": 8
✓	LoRAMoE	"routing_strategy": "loramoe", "num_experts": 8
✓	MixLoRA	"routing_strategy": "mixlora", "num_experts": 8
✓	MixLoRA-Dynamic	"routing_strategy": "mixlora-dynamic", "num_experts": 8
✓	MixLoRA-Switch	"routing_strategy": "mixlora-switch", "num_experts": 8

*: Arguments of configuration file

Notice of PEFT supports

1. m-LoRA supports specific optimized operators for these PEFT methods, which can effectively improve the computing performance during training, evaluation and inference. However, these operators may cause a certain degree of accuracy loss (less than 5%). You can disable the optimized operators by defining the MLORA_EVALUATE_MODE environment variable in advance.
2. Auxiliary Loss is not currently supported for Mo-LoRA (Mixture of LoRAs) methods other than MixLoRA.
3. You can check detailed arguments of MixLoRA in TUDB-Labs/MixLoRA.

Supported Attention Methods

	Attention Methods	Name	Arguments*
✓	Scaled Dot Product	"eager"	--attn_impl eager
✓	Flash Attention 2	"flash_attn"	--attn_impl flash_attn
✓	Sliding Window Attention	-	--sliding_window

*: Arguments of mlora.py

m-LoRA only supports scaled-dot product attention (eager) by default. Additional requirements are necessary for flash attention.

For flash attention, manual installation of the following dependencies is required:

pip3 install ninja
pip3 install flash-attn==2.5.8 --no-build-isolation

If any attention method is not specified, flash attention is used if available.

Supported Quantize Methods

	Quantize Methods	Arguments*
✓	Full Precision (FP32)	by default
✓	Tensor Float 32	--tf32
✓	Half Precision (FP16)	--fp16
✓	Brain Float 16	--bf16
✓	8bit Quantize	--load_8bit
✓	4bit Quantize	--load_4bit

*: Arguments of mlora.py

m-LoRA offers support for various model accuracy and quantization methods. By default, m-LoRA utilizes full precision (Float32), but users can opt for half precision (Float16) using --fp16 or BrainFloat16 using --bf16. Enabling half precision reduces the model size by half, and for further reduction, quantization methods can be employed.

Quantization can be activated using --load_4bit for 4-bit quantization or --load_8bit for 8-bit quantization. However, when only quantization is enabled, m-LoRA utilizes Float32 for calculations. To achieve memory savings during training, users can combine quantization and half-precision modes.

To enable quantization support, please manually install bitsandbytes:

pip3 install bitsandbytes==0.43.1

It's crucial to note that regardless of the settings, LoRA weights are always calculated and stored at full precision. For maintaining calculation accuracy, m-LoRA framework mandates the use of full precision for calculations when accuracy is imperative.

For users with NVIDIA Ampere or newer GPU architectures, the --tf32 option can be utilized to enable full-precision calculation acceleration.

Offline Configuration

m-LoRA relies on HuggingFace Hub to download necessary models, datasets, etc. If you cannot access the Internet or need to deploy m-LoRA in an offline environment, please refer to the following guide.

1. Use git-lfs manually downloads models and datasets from HuggingFace Hub.
2. Set --data_path to the local path to datasets when executing launch.py gen.
3. Clone the evaluate code repository locally.
4. Set environment variable MLORA_METRIC_PATH to the local path to metrics folder of evaluate code repository.
5. Set --base_model to the local path to models when executing launch.py run.

Example of (4): export MLORA_METRIC_PATH=/path-to-your-git-repo/evaluate/metrics

Known issues

• Quantization with Qwen2 have no effect (same with transformers).
• Applying quantization with DoRA will result in higher memory and computation cost (same with PEFT).
• Sliding window attention with generate cache may product abnormal output.
• Lack of Long RoPE support.

Installation

Please refer to m-LoRA Install Guide.

Quickstart

You can conveniently utilize m-LoRA via launch.py. The following example demonstrates a streamlined approach to training a dummy model with m-LoRA.

# Generating configuration
python launch.py gen --template lora --tasks ./tests/dummy_data.json

# Running the training task
python launch.py run --base_model TinyLlama/TinyLlama_v1.1

# Try with gradio web ui
python inference.py \
  --base_model TinyLlama/TinyLlama_v1.1 \
  --template alpaca \
  --lora_weights ./casual_0

For further detailed usage information, please refer to the help command:

python launch.py help

m-LoRA

The mlora.py code is a starting point for finetuning on various datasets.

Basic command for finetuning a baseline model on the Alpaca Cleaned dataset:

# Generating configuration
python launch.py gen \
  --template lora \
  --tasks yahma/alpaca-cleaned

python mlora.py \
  --base_model meta-llama/Llama-2-7b-hf \
  --config mlora.json \
  --bf16

You can check the template finetune configuration in templates folder.

For further detailed usage information, please use --help option:

python mlora.py --help

Use Docker

Firstly, ensure that you have installed Docker Engine and NVIDIA Container Toolkit correctly.

After that, you can launch the container using the following typical command:

docker run --gpus all -it --rm mikecovlee/mlora

You can check all available tags from: mikecovlee/mlora/tags

Please note that this container only provides a proper environment to run m-LoRA. The codes of m-LoRA are not included.

Copyright

Copyright © 2023-2024 IDs Lab, Sichuan University

This project is licensed under the Apache 2.0 License.