TensorRT-LLM Backend

The Triton backend for TensorRT-LLM. You can learn more about Triton backends in the backend repo. The goal of TensorRT-LLM Backend is to let you serve TensorRT-LLM models with Triton Inference Server. The inflight_batcher_llm directory contains the C++ implementation of the backend supporting inflight batching, paged attention and more.

Where can I ask general questions about Triton and Triton backends? Be sure to read all the information below as well as the general Triton documentation available in the main server repo. If you don't find your answer there you can ask questions on the issues page.

Building the TensorRT-LLM Backend

There are several ways to access the TensorRT-LLM Backend.

Before Triton 23.10 release, please use Option 3 to build TensorRT-LLM backend via Docker

Option 1. Run the Docker Container

Starting with Triton 23.10 release, Triton includes a container with the TensorRT-LLM Backend and Python Backend. This container should have everything to run a TensorRT-LLM model. You can find this container on the Triton NGC page.

Option 2. Build via the build.py Script in Server Repo

Starting with Triton 23.10 release, you can follow steps described in the Building With Docker guide and use the build.py script.

A sample command to build a Triton Server container with all options enabled is shown below, which will build the same TRT-LLM container as the one on the NGC.

BASE_CONTAINER_IMAGE_NAME=nvcr.io/nvidia/tritonserver:23.10-py3-min
TENSORRTLLM_BACKEND_REPO_TAG=release/0.5.0
PYTHON_BACKEND_REPO_TAG=r23.10

# Run the build script. The flags for some features or endpoints can be removed if not needed.
./build.py -v --no-container-interactive --enable-logging --enable-stats --enable-tracing \
              --enable-metrics --enable-gpu-metrics --enable-cpu-metrics \
              --filesystem=gcs --filesystem=s3 --filesystem=azure_storage \
              --endpoint=http --endpoint=grpc --endpoint=sagemaker --endpoint=vertex-ai \
              --backend=ensemble --enable-gpu --endpoint=http --endpoint=grpc \
              --image=base,${BASE_CONTAINER_IMAGE_NAME} \
              --backend=tensorrtllm:${TENSORRTLLM_BACKEND_REPO_TAG} \
              --backend=python:${PYTHON_BACKEND_REPO_TAG}

The BASE_CONTAINER_IMAGE_NAME is the base image that will be used to build the container. By default it is set to the most recent min image of Triton, on NGC, that matches the Triton release you are building for. You can change it to a different image if needed by setting the --image flag like the command below. The TENSORRTLLM_BACKEND_REPO_TAG and PYTHON_BACKEND_REPO_TAG are the tags of the TensorRT-LLM backend and Python backend repositories that will be used to build the container. You can also remove the features or endpoints that you don't need by removing the corresponding flags.

Option 3. Build via Docker

The version of Triton Server used in this build option can be found in the Dockerfile.

# Update the submodules
cd tensorrtllm_backend
git lfs install
git submodule update --init --recursive

# Use the Dockerfile to build the backend in a container
# For x86_64
DOCKER_BUILDKIT=1 docker build -t triton_trt_llm -f dockerfile/Dockerfile.trt_llm_backend .
# For aarch64
DOCKER_BUILDKIT=1 docker build -t triton_trt_llm --build-arg TORCH_INSTALL_TYPE="src_non_cxx11_abi" -f dockerfile/Dockerfile.trt_llm_backend .

Using the TensorRT-LLM Backend

Below is an example of how to serve a TensorRT-LLM model with the Triton TensorRT-LLM Backend on a 4-GPU environment. The example uses the GPT model from the TensorRT-LLM repository.

Prepare TensorRT-LLM engines

You can skip this step if you already have the engines ready. Follow the guide in TensorRT-LLM repository for more details on how to to prepare the engines for deployment.

# Update the submodule TensorRT-LLM repository
git submodule update --init --recursive

# TensorRT-LLM is required for generating engines. You can skip this step if
# you already have the package installed. If you are generating engines within
# the Triton container, you have to install the TRT-LLM package.
pip install git+https://github.com/NVIDIA/TensorRT-LLM.git
mkdir /usr/local/lib/python3.10/dist-packages/tensorrt_llm/libs/
cp /opt/tritonserver/backends/tensorrtllm/* /usr/local/lib/python3.10/dist-packages/tensorrt_llm/libs/

# Go to the tensorrt_llm/examples/gpt directory
cd tensorrt_llm/examples/gpt

# Download weights from HuggingFace Transformers
rm -rf gpt2 && git clone https://huggingface.co/gpt2-medium gpt2
pushd gpt2 && rm pytorch_model.bin model.safetensors && wget -q https://huggingface.co/gpt2-medium/resolve/main/pytorch_model.bin && popd

# Convert weights from HF Tranformers to FT format
python3 hf_gpt_convert.py -p 8 -i gpt2 -o ./c-model/gpt2 --tensor-parallelism 4 --storage-type float16

# Build TensorRT engines
python3 build.py --model_dir=./c-model/gpt2/4-gpu/ \
                 --world_size=4 \
                 --dtype float16 \
                 --use_inflight_batching \
                 --use_gpt_attention_plugin float16 \
                 --paged_kv_cache \
                 --use_gemm_plugin float16 \
                 --remove_input_padding \
                 --use_layernorm_plugin float16 \
                 --hidden_act gelu \
                 --parallel_build \
                 --output_dir=engines/fp16/4-gpu

Create the model repository

There are four models in the all_models/inflight_batcher_llm directory that will be used in this example:

• "preprocessing": This model is used for tokenizing, meaning the conversion from prompts(string) to input_ids(list of ints).
• "tensorrt_llm": This model is a wrapper of your TensorRT-LLM model and is used for inferencing
• "postprocessing": This model is used for de-tokenizing, meaning the conversion from output_ids(list of ints) to outputs(string).
• "ensemble": This model is used to chain the three models above together: preprocessing -> tensorrt_llm -> postprocessing

To learn more about ensemble model, please see here.

# Create the model repository that will be used by the Triton server
cd tensorrtllm_backend
mkdir triton_model_repo

# Copy the example models to the model repository
cp -r all_models/inflight_batcher_llm/* triton_model_repo/

# Copy the TRT engine to triton_model_repo/tensorrt_llm/1/
cp tensorrt_llm/examples/gpt/engines/fp16/4-gpu/* triton_model_repo/tensorrt_llm/1

Modify the model configuration

The following table shows the fields that need to be modified before deployment:

triton_model_repo/preprocessing/config.pbtxt

Name	Description
tokenizer_dir	The path to the tokenizer for the model. In this example, the path should be set to /tensorrtllm_backend/tensorrt_llm/examples/gpt/gpt2 as the tensorrtllm_backend directory will be mounted to /tensorrtllm_backend within the container
tokenizer_type	The type of the tokenizer for the model, t5, auto and llama are supported. In this example, the type should be set to auto

triton_model_repo/tensorrt_llm/config.pbtxt

Name	Description
decoupled	Controls streaming. Decoupled mode must be set to True if using the streaming option from the client.
gpt_model_type	Set to inflight_fused_batching when enabling in-flight batching support. To disable in-flight batching, set to V1
gpt_model_path	Path to the TensorRT-LLM engines for deployment. In this example, the path should be set to /tensorrtllm_backend/triton_model_repo/tensorrt_llm/1 as the tensorrtllm_backend directory will be mounted to /tensorrtllm_backend within the container

triton_model_repo/postprocessing/config.pbtxt

Name	Description
tokenizer_dir	The path to the tokenizer for the model. In this example, the path should be set to /tensorrtllm_backend/tensorrt_llm/examples/gpt/gpt2 as the tensorrtllm_backend directory will be mounted to /tensorrtllm_backend within the container
tokenizer_type	The type of the tokenizer for the model, t5, auto and llama are supported. In this example, the type should be set to auto

Launch Triton server

Please follow the option corresponding to the way you build the TensorRT-LLM backend.

Option 1. Launch Triton server within Triton NGC container

docker run --rm -it --net host --shm-size=2g --ulimit memlock=-1 --ulimit stack=67108864 --gpus all -v /path/to/tensorrtllm_backend:/tensorrtllm_backend nvcr.io/nvidia/tritonserver:23.10-trtllm-python-py3 bash

Option 2. Launch Triton server within the Triton container built via build.py script

docker run --rm -it --net host --shm-size=2g --ulimit memlock=-1 --ulimit stack=67108864 --gpus all -v /path/to/tensorrtllm_backend:/tensorrtllm_backend tritonserver bash

Option 3. Launch Triton server within the Triton container built via Docker

docker run --rm -it --net host --shm-size=2g --ulimit memlock=-1 --ulimit stack=67108864 --gpus all -v /path/to/tensorrtllm_backend:/tensorrtllm_backend triton_trt_llm bash

Once inside the container, you can launch the Triton server with the following command:

cd /tensorrtllm_backend
# --world_size is the number of GPUs you want to use for serving
python3 scripts/launch_triton_server.py --world_size=4 --model_repo=/tensorrtllm_backend/triton_model_repo

When successfully deployed, the server produces logs similar to the following ones.

I0919 14:52:10.475738 293 grpc_server.cc:2451] Started GRPCInferenceService at 0.0.0.0:8001
I0919 14:52:10.475968 293 http_server.cc:3558] Started HTTPService at 0.0.0.0:8000
I0919 14:52:10.517138 293 http_server.cc:187] Started Metrics Service at 0.0.0.0:8002

Query the server with the Triton generate endpoint

Starting with Triton 23.10 release, you can query the server using Triton's generate endpoint with a curl command based on the following general format within your client environment/container:

curl -X POST localhost:8000/v2/models/${MODEL_NAME}/generate -d '{"{PARAM1_KEY}": "{PARAM1_VALUE}", ... }'

In the case of the models used in this example, you can replace MODEL_NAME with ensemble. Examining the ensemble model's config.pbtxt file, you can see that 4 parameters are required to generate a response for this model:

• "text_input": Input text to generate a response from
• "max_tokens": The number of requested output tokens
• "bad_words": A list of bad words (can be empty)
• "stop_words": A list of stop words (can be empty)

Therefore, we can query the server in the following way:

curl -X POST localhost:8000/v2/models/ensemble/generate -d '{"text_input": "What is machine learning?", "max_tokens": 20, "bad_words": "", "stop_words": ""}'

Which should return a result similar to (formatted for readability):

{
  "model_name": "ensemble",
  "model_version": "1",
  "sequence_end": false,
  "sequence_id": 0,
  "sequence_start": false,
  "text_output": "What is machine learning?\n\nMachine learning is a method of learning by using machine learning algorithms to solve problems.\n\n"
}

Utilize the provided client script to send a request

You can send requests to the "tensorrt_llm" model with the provided python client script as following:

python3 inflight_batcher_llm/client/inflight_batcher_llm_client.py --request-output-len 200 --tokenizer_dir /workspace/tensorrtllm_backend/tensorrt_llm/examples/gpt/gpt2

The result should be similar to the following:

Got completed request
output_ids =  [[28524, 287, 5093, 12, 23316, 4881, 11, 30022, 263, 8776, 355, 257, 21221, 878, 3867, 284, 3576, 287, 262, 1903, 6303, 82, 13, 679, 468, 1201, 3111, 287, 10808, 287, 3576, 11, 6342, 11, 21574, 290, 968, 1971, 13, 198, 198, 1544, 318, 6405, 284, 262, 1966, 2746, 290, 14549, 11, 11735, 12, 44507, 11, 290, 468, 734, 1751, 11, 257, 4957, 11, 18966, 11, 290, 257, 3367, 11, 7806, 13, 198, 198, 50, 726, 263, 338, 3656, 11, 11735, 12, 44507, 11, 318, 257, 1966, 2746, 290, 14549, 13, 198, 198, 1544, 318, 11803, 416, 465, 3656, 11, 11735, 12, 44507, 11, 290, 511, 734, 1751, 11, 7806, 290, 18966, 13, 198, 198, 50, 726, 263, 373, 4642, 287, 6342, 11, 4881, 11, 284, 257, 4141, 2988, 290, 257, 2679, 2802, 13, 198, 198, 1544, 373, 15657, 379, 262, 23566, 38719, 293, 748, 1355, 14644, 12, 3163, 912, 287, 6342, 290, 262, 15423, 4189, 710, 287, 6342, 13, 198, 198, 1544, 373, 257, 2888, 286, 262, 4141, 8581, 286, 13473, 290, 262, 4141, 8581, 286, 11536, 13, 198, 198, 1544, 373, 257, 2888, 286, 262, 4141, 8581, 286, 13473, 290, 262, 4141, 8581, 286, 11536, 13, 198, 198, 50, 726, 263, 373, 257, 2888, 286, 262, 4141, 8581, 286, 13473, 290]]
Input: Born in north-east France, Soyer trained as a
Output:  chef before moving to London in the early 1990s. He has since worked in restaurants in London, Paris, Milan and New York.

He is married to the former model and actress, Anna-Marie, and has two children, a daughter, Emma, and a son, Daniel.

Soyer's wife, Anna-Marie, is a former model and actress.

He is survived by his wife, Anna-Marie, and their two children, Daniel and Emma.

Soyer was born in Paris, France, to a French father and a German mother.

He was educated at the prestigious Ecole des Beaux-Arts in Paris and the Sorbonne in Paris.

He was a member of the French Academy of Sciences and the French Academy of Arts.

He was a member of the French Academy of Sciences and the French Academy of Arts.

Soyer was a member of the French Academy of Sciences and

You can also stop the generation process early by using the --stop-after-ms option to send a stop request after a few milliseconds:

python inflight_batcher_llm/client/inflight_batcher_llm_client.py --stop-after-ms 200 --request-output-len 200 --tokenizer_dir /workspace/tensorrtllm_backend/tensorrt_llm/examples/gpt/gpt2

You will find that the generation process is stopped early and therefore the number of generated tokens is lower than 200. You can have a look at the client code to see how early stopping is achieved.

Launch Triton server within Slurm based clusters

Prepare some scripts

tensorrt_llm_triton.sub

#!/bin/bash
#SBATCH -o logs/tensorrt_llm.out
#SBATCH -e logs/tensorrt_llm.error
#SBATCH -J <REPLACE WITH YOUR JOB's NAME>
#SBATCH -A <REPLACE WITH YOUR ACCOUNT's NAME>
#SBATCH -p <REPLACE WITH YOUR PARTITION's NAME>
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=8
#SBATCH --time=00:30:00

sudo nvidia-smi -lgc 1410,1410

srun --mpi=pmix \
    --container-image triton_trt_llm \
    --container-mounts /path/to/tensorrtllm_backend:/tensorrtllm_backend \
    --container-workdir /tensorrtllm_backend \
    --output logs/tensorrt_llm_%t.out \
    bash /tensorrtllm_backend/tensorrt_llm_triton.sh

tensorrt_llm_triton.sh

TRITONSERVER="/opt/tritonserver/bin/tritonserver"
MODEL_REPO="/tensorrtllm_backend/triton_model_repo"

${TRITONSERVER} --model-repository=${MODEL_REPO} --disable-auto-complete-config --backend-config=python,shm-region-prefix-name=prefix${SLURM_PROCID}_

Submit a Slurm job

sbatch tensorrt_llm_triton.sub

You might have to contact your cluster's administrator to help you customize the above script.

Kill the Triton server

pkill tritonserver

Testing the TensorRT-LLM Backend

Please follow the guide in ci/README.md to see how to run the testing for TensorRT-LLM backend.