Fast-LLaMA: A High-Performance Inference Engine

Descriptions

fast-llama is a super high-performance inference engine for LLMs like LLaMA (2.5x of llama.cpp) written in pure C++. It can run a 8-bit quantized LLaMA2-7B model on a cpu with 56 cores in speed of ~25 tokens / s. It outperforms all current open-source inference engines, especially when compared to the renowned llama.cpp, with ~2.5 times better inference speed on a CPU.

Features

Feature Name	Current Support	Future Suport
Model Types	✅LLaMA2	Others LLMs like Baichuan, StableDiffusion
Quantization	✅INT16, ✅INT8	INT4
Model Formats	✅HuggingFace, ✅gguf(by llama.cpp), ✅flm
Systems	✅Linux, ✅Windows	Macbook, Android, iOS
CPU/GPU	✅X86/64 CPU	ARM, Apple Mx CPUs, GPU, CPU+GPU
Architectures	✅UMA, ✅NUMA

Advantages

Why you should use Fast-LLaMA?

• Fast
  • Extremely fast on CPU. Faster than any other engines on Github including llama.cpp.
• Simple
  • Totally less than 7k lines of C++ codes with well-orgnized code structures and no dependencies except NUMA (if needed for multi-cpus).
• "Easy To Use" (target ☺️）

Quick Start

Compile

Only Linux is supported currently. Support of other platforms including Windows, Mac, GPU is coming soon.

Requirements

• GCC 10.x or newer versions
• libnuma-dev if your computer has more than one physical CPUs
  • Linux Kernel v5.x or higher is needed for NUMA

Compilation

Method 1. Using the provided build script:

bash ./build.sh

Method 2. Using Make:

make -j 4

Run

1. Run with llama2.c models:

Step 1: Download a model

See llama2.c

Step 2: Run the model

./main -c ./models/stories110M.bin -z ./models/tokenizer.bin -j 14 -q int8 -n 200 -i 'That was a long long story happened in the ancient China.'

2. Run with hugging face format models

Step 1: Download a model

See Chinese-LLaMA-Alpaca-2

Step 2: Convert the model info FLM format

python3 ./tools/convert_flm.py -m /path/to/model-directory -o ./models/model-name-int8.flm -t int8

Step 3: Run the model

./main -c ./models/model-name-int8.flm -j 40 -n 200 -i 'That was a long long story happened in the ancient China.'

All supported command-line options are as follows:

• -c: Path to the model file
• -f: Model file format (e.g., gguf)
• -j: Number of threads to use (e.g., 56)
• -q: Quantization mode (e.g., int8)
• -n: Number of tokens to generate (e.g., 200)
• -i: Input text (e.g., 'That was a long long story happened in the ancient China.')
• -h: show usage information

Performance

Below are some incomplete test results

Testing Result:

Model	Model Size	OutputSpeed/8 threads	OutputSpeed/28 threads	OutputSpeed/56 threads
stories110M	110M	237tps	400tps	440tps
Chinese-LLaMA-1.3B	1.3B	38.9tps	127tps	155tps
Chinese-LLaMA-7B	7B	7.4tps	17.4tps	23.5tps

• Note: tps = tokens / second

Testing Conditions

• Testing Prompt: "That was a long long story happened in the ancient Europe. It was about a brave boy name Oliver. Oliver lived in a small village among many big moutains. It was a beautiful village."
• Quantization: int8
• NUMA: 2 sockets
  • Note: Make sure that NUMA is truely available if you expect to accelerate with NUMA)
• System: (uname -a)Linux coderlsf 5.15.0-72-generic #79-Ubuntu SMP Wed Apr 19 08:22:18 UTC 2023 x86_64 x86_64 x86_64 GNU/Linux
• CPU: 56 physical cores, AVX-512

Architecture:            x86_64
Model name:              Intel(R) Xeon(R) Platinum 8350C CPU @ 2.60GHz
CPU(s):                  112 (56 physical cores)
Thread(s) per core:      2
Core(s) per socket:      28
Socket(s):               2

Latancy of first token will be optimized laterly.

Why

Why is it so fast?

• Ultimate memory efficiency
  • Zero memory allocations and frees during inferencing.
  • Maximization of memory locality.
• Well-designed thread scheduling algorithm
• Optimized operators
  • Fuse all operators that can be fused together
  • Optmize calculation of several operators
• Proper Quantizations

License

fast-llama is licensed under the MIT.

Acknowledgements

Special thanks to AlpinDale for his professional, meticulous, and patient guidance and assistance.

Contact

Email: 📩topcoderlsf@gmail.com

Contact me if you any questions.