visitsb/tritonserver

By visitsb

•Updated about 2 years ago

OpenAI API compatible proxy for NVIDIA Triton Inference Server

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visitsb/tritonserver repository overview

⁠Triton Inference Server OpenAI compatible API proxy

This project⁠ provides an OpenAI API compatible proxy for NVIDIA Triton Inference Server⁠. More specifically, LLMs on NVIDIA GPUs can benefit from high performance inference with TensorRT-LLM⁠ backend running on Triton Inference Server compared to using llama.cpp⁠.

Triton Inference Server supports HTTP/REST and GRPC inference protocols⁠ based on the community developed KServe protocol⁠, but that is not useable with existing OpenAI API clients.

This proxy bridges that gap and it currently API supports text generation OpenAI API⁠ endpoints only which are suitable for use with Open WebUI⁠ or similar OpenAI clients-

GET|POST /v1/models           (or /models)
GET      /v1/models/{model}   (or /models/{model})
POST     /v1/chat/completions (or /v1/completions) streaming supported

⁠Docker image

Recommended Use a pre-published Docker image⁠

docker image pull visitsb/tritonserver:24.07-trtllm-python-py3

Alternatively, use the Dockerfile to build a local image. The proxy is built on top of existing Triton Inference Server⁠ docker image which includes the TensorRT-LLM backend.

# Pull upstream NVIDIA docker image
docker image pull nvcr.io/nvidia/tritonserver:24.07-trtllm-python-py3
# Clone this repository
git clone <this repository>
cd triton-inference-server-openai-api
# Build your custom docker image with proxy bundled
docker buildx build --no-cache --tag myimages/tritonserver:24.07-trtllm-python-py3 .

⁠Usage

Once your image is pulled (or built locally) you can run it directly using Docker-

# Run Triton Inference Server alongwith proxy as shoen in `sh -c` command
docker run --rm --tty --interactive \
       --gpus all --shm-size 4g --memory 32g \
       --cpuset-cpus 0-3 --publish 11434:11434/tcp \
       --volume <your Triton models folder>:/models:rw \
       --name triton \
       visitsb/tritonserver:24.07-trtllm-python-py3 \
       sh -c '/opt/tritonserver/bin/tritonserver \
              --model-store /models/mymodel/model \
            & /opt/tritonserver/bin/tritonopenaiserver \
              --tokenizer_dir /models/mymodel/tokenizer \
              --engine_dir /models/mymodel/engine'

Alternatively using docker-compose.yml-

triton:
    image: visitsb/tritonserver:24.07-trtllm-python-py3
    command: >
      sh -c '/opt/tritonserver/bin/tritonserver --model-store /models/mymodel/model & /opt/tritonserver/bin/tritonopenaiserver --tokenizer_dir /models/mymodel/tokenizer --engine_dir /models/mymodel/engine'
    ports:
      - "11434:11434/tcp" # OpenAI API Proxy
      - "8000:8000/tcp"   # HTTP
      - "8001:8001/tcp"   # GRPC
      - "8080:8080/tcp"   # Sagemaker, Vertex
      - "8002:8002/tcp"   # Prometheus metrics
    volumes:
      - <your Triton models folder>:/models:rw
    shm_size: "4G"
    deploy:
      resources:
        limits:
          memory: 32G
        reservations:
          memory: 8G
          devices: 
            - driver: nvidia
              count: all
              capabilities: [compute,video,utility]
    ulimits:
      stack: 67108864
      memlock:
        soft: -1
        hard: -1

⁠Performance

Using GenAI-Perf⁠ to measure performance for meta-llama/Meta-Llama-3-8B⁠ on a NVIDIA RTX 4090 GPU⁠ the following was observed-

Test: meta-llama/Meta-Llama-3-8B-Instruct⁠ evaluated using NVIDIA GenAI-Perf⁠. For llama.cpp evaluation QuantFactory/Meta-Llama-3-8B-GGUF⁠ - Meta-Llama-3-8B.Q8_0.gguf was used.

Backend          Loaded model size     GPU Util Tokens/sec
-------          -----------------     -------- ----------
TensorRT (gRPC)  15879MiB /  24564MiB  91%      97.04
TensorRT (HTTP)  15879MiB /  24564MiB  91%      56.73 
llama.cpp         9491MiB /  24564MiB  74%      70.23

In summary, TensorRT (gRPC) inference is better than llama.cpp, but using TensorRT (HTTP) gave similar performance to llama.cpp.

The raw performance numbers are as below-

⁠TensorRT (gRPC)

[INFO] genai_perf.wrapper:135 - Running Perf Analyzer : 'perf_analyzer -m llama3 --async --service-kind triton -u triton:8001 --measurement-interval 4000 --stability-percentage 999 -i grpc --streaming --shape max_tokens:1 --shape text_input:1 --concurrency-range 1'
                                  LLM Metrics                                   
┏━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━┳━━━━━━━━┓
┃            Statistic ┃    avg ┃    min ┃     max ┃    p99 ┃     p90 ┃    p75 ┃
┡━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━╇━━━━━━━━┩
│ Request latency (ns) │ 1,081… │ 1,048… │ 1,311,… │ 1,284… │ 1,083,… │ 1,064… │
│     Num output token │    105 │    100 │     110 │    110 │     109 │    107 │
│      Num input token │    200 │    200 │     200 │    200 │     200 │    200 │
└──────────────────────┴────────┴────────┴─────────┴────────┴─────────┴────────┘
Output token throughput (per sec): 97.04
Request throughput (per sec): 0.92

⁠TensorRT (HTTP) via this OpenAI API Proxy

[INFO] genai_perf.wrapper:135 - Running Perf Analyzer : 'perf_analyzer -m llama3 --async --endpoint v1/chat/completions --service-kind openai -u triton:11434 --measurement-interval 4000 --stability-percentage 999 -i http --concurrency-range 1'
                                  LLM Metrics                                   
┏━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━┳━━━━━━━━┓
┃            Statistic ┃    avg ┃    min ┃     max ┃    p99 ┃     p90 ┃    p75 ┃
┡━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━╇━━━━━━━━┩
│ Request latency (ns) │ 2,033… │ 1,732… │ 3,856,… │ 3,723… │ 2,525,… │ 1,802… │
│     Num output token │    115 │    110 │     121 │    121 │     120 │    119 │
│      Num input token │    200 │    200 │     200 │    200 │     200 │    200 │
└──────────────────────┴────────┴────────┴─────────┴────────┴─────────┴────────┘
Output token throughput (per sec): 56.73
Request throughput (per sec): 0.49

⁠llama.cpp

[INFO] genai_perf.wrapper:135 - Running Perf Analyzer : 'perf_analyzer -m llama3 --async --endpoint v1/chat/completions --service-kind openai -u llama:11434 --measurement-interval 4000 --stability-percentage 999 -i http --concurrency-range 1'
                                  LLM Metrics                                   
┏━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━┳━━━━━━━━┓
┃            Statistic ┃    avg ┃    min ┃     max ┃    p99 ┃     p90 ┃    p75 ┃
┡━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━╇━━━━━━━━┩
│ Request latency (ns) │ 1,656… │ 1,596… │ 1,822,… │ 1,810… │ 1,701,… │ 1,649… │
│     Num output token │    116 │    104 │     149 │    147 │     132 │    118 │
│      Num input token │    200 │    200 │     200 │    200 │     200 │    200 │
└──────────────────────┴────────┴────────┴─────────┴────────┴─────────┴────────┘
Output token throughput (per sec): 70.23
Request throughput (per sec): 0.60

Note This proxy uses TensorRT (HTTP) currently, so above performance numbers should be considered relative. Performance will vary for TensorRT-LLM models based on build and deployment options⁠ used.

Additional optimizations like speculative sampling and FP8 quantization can further improve throughput. For more on the throughput levels that are possible with TensorRT-LLM for different combinations of model, hardware, and workload, see the official benchmarks⁠.

⁠Build and deploy your own models

The image includes TensorRT-LLM toolbox⁠ and backend⁠ for building your own TensorRT-LLM models. Both can be found under /opt/tritonserver/third-party-src/ inside your Docker image.

The basic steps to build a TensorRT model are outlined here⁠ which essentially involves

Downloading a Hugging Face model⁠ of your choice,
Converting it to a TensorRT format, and
Lastly building a compiled model that can be deployed on Triton Inference Server.

Additionally, you can also use the steps mentioned here⁠ to build your TensorRT model. Once your model is built, you can deploy⁠ and use it through the OpenAI API proxy.

⁠Further references-

Benchmarking NVIDIA TensorRT-LLM⁠ - TensorRT-LLM was 30-70% faster than llama.cpp⁠ on the same hardware, consumes less memory on consecutive runs with marginally more GPU VRAM utilization than llama.cpp and models are 20%+ smaller compiled model sizes than llama.cpp.
Use Llama 3 with NVIDIA TensorRT-LLM and Triton Inference Server⁠ - 30-minute tutorial to show how to use TensorRT-LLM to build TensorRT engines that contain state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs using Llama3 model as an example.
Similar guide can be on Serverless TensorRT-LLM (LLaMA 3 8B)⁠ - how to use the TensorRT-LLM framework to serve Meta’s LLaMA 3 8B model at a total throughput of roughly 4,500 output tokens per second on a single NVIDIA A100 40GB GPU.

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about 2 years ago

docker pull visitsb/tritonserver:24.07-trtllm-python-py3