You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
ColossalAI/applications/ColossalMoE
Hongxin Liu 7f8b16635b
[misc] refactor launch API and tensor constructor (#5666)
7 months ago
..
colossal_moe [shardformer, pipeline] add `gradient_checkpointing_ratio` and heterogenous shard policy for llama (#5508) 8 months ago
tests [misc] refactor launch API and tensor constructor (#5666) 7 months ago
README.md [doc] fix ColossalMoE readme (#5599) 8 months ago
infer.py [misc] refactor launch API and tensor constructor (#5666) 7 months ago
infer.sh [moe] init mixtral impl 10 months ago
requirements.txt [moe] init mixtral impl 10 months ago
setup.py [moe] init mixtral impl 10 months ago
train.py [misc] refactor launch API and tensor constructor (#5666) 7 months ago
train.sh [moe] init mixtral impl 10 months ago
version.txt [moe] init mixtral impl 10 months ago

README.md

Mixtral

Usage

1. Installation

Please install the latest ColossalAI from source.

CUDA_EXT=1 pip install -U git+https://github.com/hpcaitech/ColossalAI

Then install dependencies.

cd ColossalAI/applications/ColossalMoE
pip install -e .

Additionally, we recommend you to use torch 1.13.1. We've tested our code on torch 1.13.1 and found it's compatible with our code.

2. Inference

Yon can use colossalai run to launch inference:

bash infer.sh

If you already have downloaded model weights, you can change name to your weights position in infer.sh.

3. Train

You first need to create ./hostfile, listing the ip address of all your devices, such as:

111.111.111.110
111.111.111.111

Then yon can use colossalai run to launch train:

bash train.sh

It requires 16 H100 (80G) to run the training. The number of GPUs should be divided by 8. If you already have downloaded model weights, you can change name to your weights position in train.sh.