Nvidia TensorRT-LLM

TensorRT-LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and build TensorRT engines that contain state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs.

TensorRT-LLM Github

TensorRT-LLM Environment Setup

Since TensorRT-LLM is a SDK for interacting with local models in process there are a few environment steps that must be followed to ensure that the TensorRT-LLM setup can be used. Please note, that Nvidia Cuda 12.2 or higher is currently required to run TensorRT-LLM.

In this tutorial we will show how to use the connector with GPT2 model. For the best experience, we recommend following Installation process on the official TensorRT-LLM Github.

The following steps are showing how to set up your model with TensorRT-LLM v0.8.0 for x86_64 users.

Obtain and start the basic docker image environment.

docker run --rm --runtime=nvidia --gpus all --entrypoint /bin/bash -it nvidia/cuda:12.1.0-devel-ubuntu22.04

Install dependencies, TensorRT-LLM requires Python 3.10

apt-get update && apt-get -y install python3.10 python3-pip openmpi-bin libopenmpi-dev git git-lfs wget

Install the latest stable version (corresponding to the release branch) of TensorRT-LLM. We are using version 0.8.0, but for the most up to date release, please refer to official release page.

pip3 install tensorrt_llm==0.8.0 -U --extra-index-url https://pypi.nvidia.com

Check installation

python3 -c "import tensorrt_llm"

The above command should not produce any errors.

For this example we will use GPT2. The GPT2 model files need to be created via scripts following the instructions here

First, inside the container, we’ve started during stage 1, clone TensorRT-LLM repository:

git clone --branch v0.8.0 https://github.com/NVIDIA/TensorRT-LLM.git

Install requirements for GPT2 model with:

cd TensorRT-LLM/examples/gpt/ && pip install -r requirements.txt

Download hf gpt2 model

rm -rf gpt2 && git clone https://huggingface.co/gpt2-medium gpt2
cd gpt2
rm pytorch_model.bin model.safetensors
wget -q https://huggingface.co/gpt2-medium/resolve/main/pytorch_model.bin
cd ..

Convert weights from HF Transformers to TensorRT-LLM format

python3 hf_gpt_convert.py -i gpt2 -o ./c-model/gpt2 --tensor-parallelism 1 --storage-type float16

Build TensorRT engine

python3 build.py --model_dir=./c-model/gpt2/1-gpu --use_gpt_attention_plugin --remove_input_padding

Install llama-index-llms-nvidia-tensorrt package

pip install llama-index-llms-nvidia-tensorrt

Basic Usage

Call `complete` with a prompt

from llama_index.llms.nvidia_tensorrt import LocalTensorRTLLM

llm = LocalTensorRTLLM(
    model_path="./engine_outputs",
    engine_name="gpt_float16_tp1_rank0.engine",
    tokenizer_dir="gpt2",
    max_new_tokens=40,
)

resp = llm.complete("Who is Harry Potter?")
print(str(resp))

The expected response should look like:

Harry Potter is a fictional character created by J.K. Rowling in her first novel, Harry Potter and the Philosopher's Stone. The character is a wizard who lives in the fictional town#

Nvidia TensorRT-LLM

TensorRT-LLM Environment Setup

Basic Usage

Call complete with a prompt

Call `complete` with a prompt