# triton-llm service runner

`tio` is a tool for exploring the triton-distributed and triton-llm components.

## Install and start pre-requisites

Rust:
```bash
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
```

## Build

- CUDA:

`cargo build --release --features mistralrs,cuda`

- MAC w/ Metal:

`cargo build --release --features mistralrs,metal`

- CPU only:

`cargo build --release --features mistralrs`

## Download a model from Hugging Face

For example one of these should be fast and good quality on almost any machine: https://huggingface.co/bartowski/Llama-3.2-3B-Instruct-GGUF

## Run

*Text interface*

`./target/release/tio Llama-3.2-1B-Instruct-Q4_K_M.gguf` or path to a Hugging Face repo checkout instead of the GGUF.

*HTTP interface*

`./target/release/tio in=http --model-path Llama-3.2-1B-Instruct-Q4_K_M.gguf`

List the models: `curl localhost:8080/v1/models`

Send a request:
```
curl -d '{"model": "Llama-3.2-1B-Instruct-Q4_K_M", "max_tokens": 2049, "messages":[{"role":"user", "content": "What is the capital of South Africa?" }]}' -H 'Content-Type: application/json' http://localhost:8080/v1/chat/completions
```

*Multi-node*

Node 1:
```
tio in=http out=tdr://llama3B_pool
```

Node 2:
```
tio in=tdr://llama3B_pool out=mistralrs ~/llm_models/Llama-3.2-3B-Instruct
```

This will use etcd to auto-discover the model and NATS to talk to it. You can run multiple workers on the same endpoint and it will pick one at random each time.

The `ns/backend/mistralrs` are purely symbolic, pick anything as long as it has three parts, and it matches the other node.

Run `tio --help` for more options.

## sglang

1. Setup the python virtual env:

```
uv venv
source .venv/bin/activate
uv pip install pip
uv pip install sgl-kernel --force-reinstall --no-deps
uv pip install "sglang[all]==0.4.2" --find-links https://flashinfer.ai/whl/cu124/torch2.4/flashinfer/
```

2. Build

```
cargo build --release --features sglang
```

3. Run

Any example above using `out=sglang` will work, but our sglang backend is also multi-gpu and multi-node.

Node 1:
```
tio in=http out=sglang --model-path ~/llm_models/DeepSeek-R1-Distill-Llama-70B/ --tensor-parallel-size 8 --num-nodes 2 --node-rank 0 --dist-init-addr 10.217.98.122:9876
```

Node 2:
```
tio in=none out=sglang --model-path ~/llm_models/DeepSeek-R1-Distill-Llama-70B/ --tensor-parallel-size 8 --num-nodes 2 --node-rank 1 --dist-init-addr 10.217.98.122:9876
```

## llama_cpp

- `cargo build --release --features llamacpp,cuda`

- `tio out=llama_cpp --model-path ~/llm_models/Llama-3.2-3B-Instruct-Q6_K.gguf --model-config ~/llm_models/Llama-3.2-3B-Instruct/`

The extra `--model-config` flag is because:
- llama_cpp only runs GGUF
- We send it tokens, meaning we do the tokenization ourself, so we need a tokenizer
- We don't yet read it out of the GGUF (TODO), so we need an HF repo with `tokenizer.json` et al

If the build step also builds llama_cpp libraries into `target/release` ("libllama.so", "libggml.so", "libggml-base.so", "libggml-cpu.so", "libggml-cuda.so"), then `tio` will need to find those at runtime. Set `LD_LIBRARY_PATH`, and be sure to deploy them alongside the `tio` binary.

## vllm

Using the [vllm](https://github.com/vllm-project/vllm) Python library. We only use the back half of vllm, talking to it over `zmq`. Slow startup, fast inference. Supports both safetensors from HF and GGUF files.

We use [uv](https://docs.astral.sh/uv/) but any virtualenv manager should work.

Setup:
```
uv venv
source .venv/bin/activate
uv pip install pip
uv pip install vllm setuptools
```

**Note: If you're on Ubuntu 22.04 or earlier, you will need to add `--python=python3.10` to your `uv venv` command**

Build:
```
cargo build --release --features vllm
```

Run (still inside that virtualenv) - HF repo:
```
./target/release/tio in=http out=vllm --model-path ~/llm_models/Llama-3.2-3B-Instruct/

```

Run (still inside that virtualenv) - GGUF:
```
./target/release/tio in=http out=vllm --model-path ~/llm_models/Llama-3.2-3B-Instruct-Q6_K.gguf --model-config ~/llm_models/Llama-3.2-3B-Instruct/
```

## trtllm

TensorRT-LLM. Requires `clang` and `libclang-dev`.

Build:
```
cargo build --release --features trtllm
```

Run:
```
tio in=text out=trtllm --model-path /app/trtllm_engine/ --model-config ~/llm_models/Llama-3.2-3B-Instruct/
```

Note that TRT-LLM uses it's own `.engine` format for weights. Repo models must be converted like so:

+ Get the build container
```
docker run --gpus all -it nvcr.io/nvidian/nemo-llm/trtllm-engine-builder:0.2.0 bash
```

+ Fetch the model and convert
```
mkdir /tmp/model
huggingface-cli download meta-llama/Llama-3.2-3B-Instruct --local-dir /tmp/model
python convert_checkpoint.py --model_dir /tmp/model/ --output_dir ./converted --dtype [float16|bfloat16|whatever you want] --tp_size X --pp_size Y
trtllm-build --checkpoint_dir ./converted --output_dir ./final/trtllm_engine --use_paged_context_fmha enable --gemm_plugin auto
```

The `--model-path` you give to `tio` must contain the `config.json` (TRT-LLM's , not the model's) and `rank0.engine` (plus other ranks if relevant).

+ Execute
TRT-LLM is a C++ library that must have been previously built and installed. It needs a lot of memory to compile. Gitlab builds a container you can try:
```
sudo docker run --gpus all -it -v /home/graham:/outside-home gitlab-master.nvidia.com:5005/dl/ai-services/libraries/rust/nim-nvllm/tensorrt_llm_runtime:85fa4a6f
```

Copy the trt-llm engine, the model's `.json` files (for the model deployment card) and the `nio` binary built for the correct glibc (container is Ubuntu 22.04 currently) into that container.
