Inference LLaMA models on desktops using CPU only

This repository is intended as a minimal, hackable and readable example to load LLaMA (arXiv) models and run inference by using only CPU. Thus requires no videocard, but 64 (better 128 Gb) of RAM and modern processor is required. Make sure you have enough swap space (128Gb should be ok :).

Conda Environment Setup Example for Windows 10+

Download and install Anaconda Python https://www.anaconda.com and run Anaconda Prompt

conda create -n llama python=3.10
conda activate llama
conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia

Setup

In a conda env with pytorch / cuda available, run

pip install -r requirements.txt

Then in this repository

pip install -e .

Download tokenizer and models

magnet:?xt=urn:btih:ZXXDAUWYLRUXXBHUYEMS6Q5CE5WA3LVA&dn=LLaMA

magnet:xt=urn:btih:b8287ebfa04f879b048d4d4404108cf3e8014352&dn=LLaMA&tr=udp%3a%2f%2ftracker.opentrackr.org%3a1337%2fannounce

CPU Inference

Place tokenizer.model file from torrent into repo's [/tokenizer] folder.

Place model files from torrent folder (for example, [/13B]) into repo's [/model] folder.

Run the example:

python example-cpu.py

Interactive chat with LLaMA

python example-chat.py

Some measurements

Running model with single prompt on Windows computer equipped with 12700k, fast nvme and 128 Gb of RAM.

model	RAM usage, fp32	RAM usage, bf16	fp32 inference	bf16 inference	fp32 load model
7B	44 Gb, peak 56 Gb	22 Gb	170 seconds	850 seconds	23 seconds
13B	77 Gb, peak 100 Gb	38 Gb	340 seconds	38 minutes	61 seconds
30B	180 Gb, peak 258 Gb	89 Gb	48 minutes	67 minutes	372 seconds

Bfloat16 RAM usage optimization

By default, torch uses Float32 precision while running on CPU, which leads, for example, to use 44 GB of RAM for 7B model. We may use Bfloat16 precision on CPU too, which decreases RAM consumption/2, down to 22 GB for 7B model, but inference processing much slower.

An optimized checkpoints loader breaks compatibility with Bfloat16, so I decided to add example-bfloat16.py runner.

To use Bfloat16 precision, first you need to unshard checkpoints to a single one.

python merge_weights.py --input_dir D:\Downloads\LLaMA --model_size 13B

In this example, D:\Downloads\LLaMA is a root folder of downloaded torrent with weights.

This will create merged.pth file in the root folder of this repo. Place this file and corresponding params.json of model into [/model] folder. File tokenizer.model should be in [/tokenizer] folder of this repo. Now you are ready to go.

python example-bfloat16.py

For hipda user

pan.baidu.com download url pan.baidu.com

Thanks for xialer, 白胡子

Model Card

See MODEL_CARD.md

License

See the LICENSE file.

quakerzhang / llama-cpu