FINALLY - GOOD NEWS!

I've started to work on reimplementation of the library here: FastTensors

Please star it if you'd like to see GGML-compatible implementation in pure Go.

Looking for LLM Debug and Inference with Golang?

Please check out my related project Booster

Motivation

We dream of a world where fellow ML hackers are grokking REALLY BIG GPT models in their homelabs without having GPU clusters consuming a shit tons of $$$.

The code of the project is based on the legendary ggml.cpp framework of Georgi Gerganov written in C++ with the same attitude to performance and elegance.

We hope using Golang instead of soo-powerful but too-low-level language will allow much greater adoption.

V0 Roadmap

• Tensor math in pure Golang
• Implement LLaMA neural net architecture and model loading
• Test with smaller LLaMA-7B model
• Be sure Go inference works exactly same way as C++
• Let Go shine! Enable multi-threading and messaging to boost performance

V1 Roadmap - Spring'23

• Cross-patform compatibility with Mac, Linux and Windows
• Release first stable version for ML hackers - v1.0
• Enable bigger LLaMA models: 13B, 30B, 65B - v1.1
• ARM NEON support on Apple Silicon (modern Macs) and ARM servers - v1.2
• Performance boost with x64 AVX2 support for Intel and AMD - v1.2
• Better memory use and GC optimizations - v1.3
• Introduce Server Mode (embedded REST API) for use in real projects - v1.4
• Release converted models for free access over the Internet - v1.4

V2 Roadmap - Winter'23

• Support LLaMA V2 7B / 13B models architecture
• Implement LLaMA V2 34B / 70B Qrouped Query Attention
• Support modern GGUF V3 model format
• INT8 quantization to allow x4 bigger models fit same memory
• Benchmark LLaMA.go against some mainstream Python / C++ frameworks
• Enable some popular models of LLaMA family: Vicuna, Alpaca, etc
• Speed-up AVX2 with memory aligned tensors
• Extensive logging for production monitoring
• Interactive mode for real-time chat with GPT
• Automatic CPU / GPU features detection
• Implement metrics for RAM and CPU usage
• Standalone GUI or web interface for better access to framework
• Support popular open models: Open Assistant, StableLM, BLOOM, Anthropic, etc.
• AVX512 support - yet another performance boost for AMD Epyc and Intel Sapphire Rapids
• Nvidia GPUs support (CUDA or Tensor Cores)

V3 Roadmap - Spring'23

• Allow plugins and external APIs for complex projects
• Allow model training and fine-tuning
• Speed up execution on GPU cards and clusters
• FP16 and BF16 math if hardware support is there
• INT4 and GPTQ quantization
• AMD Radeon GPUs support with OpenCL

How to Run?

First, obtain and convert original LLaMA models on your own, or just download ready-to-rock ones:

LLaMA-7B: llama-7b-fp32.bin

LLaMA-13B: llama-13b-fp32.bin

Both models store FP32 weights, so you'll needs at least 32Gb of RAM (not VRAM or GPU RAM) for LLaMA-7B. Double to 64Gb for LLaMA-13B.

Next, build app binary from sources (see instructions below), or just download already built one:

Windows: llama-go-v1.4.0.exe

MacOS: llama-go-v1.4.0-macos

Linux: llama-go-v1.4.0-linux

So now you have both executable and model, go try it for yourself:

llama-go-v1.4.0-macos \
    --model ~/models/llama-7b-fp32.bin \
    --prompt "Why Golang is so popular?" \

Useful command line flags:

--prompt   Text prompt from user to feed the model input
--model    Path and file name of converted .bin LLaMA model [ llama-7b-fp32.bin, etc ]
--server   Start in Server Mode acting as REST API endpoint
--host     Host to allow requests from in Server Mode [ localhost by default ]
--port     Port listen to in Server Mode [ 8080 by default ]
--pods     Maximum pods or units of parallel execution allowed in Server Mode [ 1 by default ]
--threads  Adjust to the number of CPU cores you want to use [ all cores by default ]
--context  Context size in tokens [ 1024 by default ]
--predict  Number of tokens to predict [ 512 by default ]
--temp     Model temperature hyper parameter [ 0.5 by default ]
--silent   Hide welcome logo and other output [ shown by default ]
--chat     Chat with user in interactive mode instead of compute over static prompt
--profile  Profe CPU performance while running and store results to cpu.pprof file
--avx      Enable x64 AVX2 optimizations for Intel and AMD machines
--neon     Enable ARM NEON optimizations for Apple Macs and ARM server

Going Production

LLaMA.go embeds standalone HTTP server exposing REST API. To enable it, run app with special flags:

llama-go-v1.4.0-macos \
    --model ~/models/llama-7b-fp32.bin \
    --server \
    --host 127.0.0.1 \
    --port 8080 \
    --pods 4 \
    --threads 4

Depending on the model size, how many CPU cores available there, how many requests you want to process in parallel, how fast you'd like to get answers, choose pods and threads parameters wisely.

Pods is a number of inference instances that might run in parallel.

Threads parameter sets how many cores will be used for tensor math within a pod.

So for example if you have machine with 16 hardware cores capable running 32 hyper-threads in parallel, you might end up with something like that:

--server --pods 4 --threads 8

When there is no free pod to handle arriving request, it will be placed into the waiting queue and started when some pod gets job finished.

REST API examples

Place new job

Send POST request (with Postman) to your server address with JSON containing unique UUID v4 and prompt:

{
    "id": "5fb8ebd0-e0c9-4759-8f7d-35590f6c9fc3",
    "prompt": "Why Golang is so popular?"
}

Check job status

Send GET request (with Postman or browser) to URL like http://host:port/jobs/status/:id

GET http://localhost:8080/jobs/status/5fb8ebd0-e0c9-4759-8f7d-35590f6c9fcb

Get the results

Send GET request (with Postman or browser) to URL like http://host:port/jobs/:id

GET http://localhost:8080/jobs/5fb8ebd0-e0c9-4759-8f7d-35590f6c9fcb

How to build

First, install Golang and git (you'll need to download installers in case of Windows).

brew install git
brew install golang

Then clone the repo and enter the project folder:

git clone https://github.com/gotzmann/llama.go.git
cd llama.go

Some Go magic to install external dependencies:

go mod tidy
go mod vendor

Now we are ready to build the binary from the source code:

go build -o llama-go-v1.exe -ldflags "-s -w" main.go

FAQ

1) From where I might obtain original LLaMA models?

Contact Meta directly or just look around for some torrent alternatives.

2) How to convert original LLaMA files into supported format?

Place original PyTorch FP16 files into models directory, then convert with command:

python3 ./scripts/convert.py ~/models/LLaMA/7B/ 0