ColossalAI/applications/Chat/inference

Inference

We provide an online inference server and a benchmark. We aim to run inference on single GPU, so quantization is essential when using large models.

We support 8-bit quantization (RTN), which is powered by bitsandbytes and transformers. And 4-bit quantization (GPTQ), which is powered by gptq and GPTQ-for-LLaMa. We also support FP16 inference.

We only support LLaMA family models now.

Choosing precision (quantization)

FP16: Fastest, best output quality, highest memory usage

8-bit: Slow, easier setup (originally supported by transformers), lower output quality (due to RTN), recommended for first-timers

4-bit: Faster, lowest memory usage, higher output quality (due to GPTQ), but more difficult setup

Hardware requirements for LLaMA

Tha data is from LLaMA Int8 4bit ChatBot Guide v2.

8-bit

Model	Min GPU RAM	Recommended GPU RAM	Min RAM/Swap	Card examples
LLaMA-7B	9.2GB	10GB	24GB	3060 12GB, RTX 3080 10GB, RTX 3090
LLaMA-13B	16.3GB	20GB	32GB	RTX 3090 Ti, RTX 4090
LLaMA-30B	36GB	40GB	64GB	A6000 48GB, A100 40GB
LLaMA-65B	74GB	80GB	128GB	A100 80GB

4-bit

Model	Min GPU RAM	Recommended GPU RAM	Min RAM/Swap	Card examples
LLaMA-7B	3.5GB	6GB	16GB	RTX 1660, 2060, AMD 5700xt, RTX 3050, 3060
LLaMA-13B	6.5GB	10GB	32GB	AMD 6900xt, RTX 2060 12GB, 3060 12GB, 3080, A2000
LLaMA-30B	15.8GB	20GB	64GB	RTX 3080 20GB, A4500, A5000, 3090, 4090, 6000, Tesla V100
LLaMA-65B	31.2GB	40GB	128GB	A100 40GB, 2x3090, 2x4090, A40, RTX A6000, 8000, Titan Ada

General setup

pip install -r requirements.txt

8-bit setup

8-bit quantization is originally supported by the latest transformers. Please install it from source.

Please ensure you have downloaded HF-format model weights of LLaMA models.

Usage:

from transformers import LlamaForCausalLM

USE_8BIT = True # use 8-bit quantization; otherwise, use fp16

model = LlamaForCausalLM.from_pretrained(
            "pretrained/path",
            load_in_8bit=USE_8BIT,
            torch_dtype=torch.float16,
            device_map="auto",
        )
if not USE_8BIT:
    model.half()  # use fp16
model.eval()

Troubleshooting: if you get error indicating your CUDA-related libraries not found when loading 8-bit model, you can check whether your LD_LIBRARY_PATH is correct.

E.g. you can set export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH.

4-bit setup

Please ensure you have downloaded HF-format model weights of LLaMA models first.

Then you can follow GPTQ-for-LLaMa. This lib provides efficient CUDA kernels and weight convertion script.

After installing this lib, we may convert the original HF-format LLaMA model weights to 4-bit version.

CUDA_VISIBLE_DEVICES=0 python llama.py /path/to/pretrained/llama-7b c4 --wbits 4 --groupsize 128 --save llama7b-4bit.pt

Run this command in your cloned GPTQ-for-LLaMa directory, then you will get a 4-bit weight file llama7b-4bit-128g.pt.

Troubleshooting: if you get error about position_ids, you can checkout to commit 50287c3b9ae4a3b66f6b5127c643ec39b769b155(GPTQ-for-LLaMa repo).

Online inference server

In this directory:

export CUDA_VISIBLE_DEVICES=0
# fp16, will listen on 0.0.0.0:7070 by default
python server.py /path/to/pretrained
# 8-bit, will listen on localhost:8080
python server.py /path/to/pretrained --quant 8bit --http_host localhost --http_port 8080
# 4-bit
python server.py /path/to/pretrained --quant 4bit --gptq_checkpoint /path/to/llama7b-4bit-128g.pt --gptq_group_size 128

Benchmark

In this directory:

export CUDA_VISIBLE_DEVICES=0
# fp16
python benchmark.py /path/to/pretrained
# 8-bit
python benchmark.py /path/to/pretrained --quant 8bit
# 4-bit
python benchmark.py /path/to/pretrained --quant 4bit --gptq_checkpoint /path/to/llama7b-4bit-128g.pt --gptq_group_size 128

This benchmark will record throughput and peak CUDA memory usage.