LLM inference frameworks

Applies to Linux and Windows

2024-06-27

9 min read time

This section discusses how to implement vLLM and Hugging Face TGI using single-accelerator and multi-accelerator systems.

vLLM inference

vLLM is renowned for its paged attention algorithm that can reduce memory consumption and increase throughput thanks to its paging scheme. Instead of allocating GPU high-bandwidth memory (HBM) for the maximum output token lengths of the models, the paged attention of vLLM allocates GPU HBM dynamically for its actual decoding lengths. This paged attention is also effective when multiple requests share the same key and value contents for a large value of beam search or multiple parallel requests.

vLLM also incorporates many modern LLM acceleration and quantization algorithms, such as Flash Attention, HIP and CUDA graphs, tensor parallel multi-GPU, GPTQ, AWQ, and token speculation.

Installing vLLM

1. Run the following commands to build a Docker image vllm-rocm.

   git clone https://github.com/vllm-project/vllm.git
   cd vllm
   docker build -f Dockerfile.rocm -t vllm-rocm .
   

vLLM on a single-accelerator system

2. To use vLLM as an API server to serve reference requests, first start a container using the vllm-rocm Docker image.

   docker run -it \
      --network=host \
      --group-add=video \
      --ipc=host \
      --cap-add=SYS_PTRACE \
      --security-opt seccomp=unconfined \
      --device /dev/kfd \
      --device /dev/dri \
      -v <path/to/model>:/app/model \
      vllm-rocm \
      bash
   

3. Inside the container, start the API server to run on a single accelerator on port 8000 using the following command.

   python -m vllm.entrypoints.api_server --model /app/model --dtype float16 --port 8000 &
   

   The following log message is displayed in your command line indicates that the server is listening for requests.

   

4. To test, send it a curl request containing a prompt.

   curl http://localhost:8000/generate -H "Content-Type: application/json" -d '{"prompt": "What is AMD Instinct?", "max_tokens": 80, "temperature": 0.0 }'
   

   You should receive a response like the following.

   {"text":["What is AMD Instinct?\nAmd Instinct is a brand new line of high-performance computing (HPC) processors from Advanced Micro Devices (AMD). These processors are designed to deliver unparalleled performance for HPC workloads, including scientific simulations, data analytics, and machine learning.\nThe Instinct lineup includes a range of processors, from the entry-level Inst"]}
   

vLLM on a multi-accelerator system

2. To use vLLM as an API server to serve reference requests, first start a container using the vllm-rocm Docker image.

   docker run -it \
      --network=host \
      --group-add=video \
      --ipc=host \
      --cap-add=SYS_PTRACE \
      --security-opt seccomp=unconfined \
      --device /dev/kfd \
      --device /dev/dri \
      -v <path/to/model>:/app/model \
      vllm-rocm \
      bash
   

3. To run API server on multiple GPUs, use the -tp or --tensor-parallel-size parameter. For example, to use two GPUs, start the API server using the following command.

   python -m vllm.entrypoints.api_server --model /app/model --dtype float16 -tp 2 --port 8000 &
   

4. To run multiple instances of API Servers, specify different ports for each server, and use ROCR_VISIBLE_DEVICES to isolate each instance to a different accelerator.

   For example, to run two API servers, one on port 8000 using GPU 0 and 1, one on port 8001 using GPU 2 and 3, use a a command like the following.

   ROCR_VISIBLE_DEVICES=0,1 python -m vllm.entrypoints.api_server --model /data/llama-2-7b-chat-hf --dtype float16 –tp 2 --port 8000 &
   ROCR_VISIBLE_DEVICES=2,3 python -m vllm.entrypoints.api_server --model /data/llama-2-7b-chat-hf --dtype float16 –tp 2--port 8001 &
   

5. To test, send it a curl request containing a prompt.

   curl http://localhost:8000/generate -H "Content-Type: application/json" -d '{"prompt": "What is AMD Instinct?", "max_tokens": 80, "temperature": 0.0 }'
   

   You should receive a response like the following.

   {"text":["What is AMD Instinct?\nAmd Instinct is a brand new line of high-performance computing (HPC) processors from Advanced Micro Devices (AMD). These processors are designed to deliver unparalleled performance for HPC workloads, including scientific simulations, data analytics, and machine learning.\nThe Instinct lineup includes a range of processors, from the entry-level Inst"]}
   

Hugging Face TGI

Text Generation Inference (TGI) is LLM serving framework from Hugging Face, and it also supports the majority of high-performance LLM acceleration algorithms such as Flash Attention, Paged Attention, CUDA/HIP graph, tensor parallel multi-GPU, GPTQ, AWQ, and token speculation.

Tip

In addition to LLM serving capability, TGI also provides the Text Generation Inference benchmarking tool.

Install TGI

1. Launch the TGI Docker container in the host machine.

   docker run --name tgi --rm -it --cap-add=SYS_PTRACE --security-opt seccomp=unconfined
   --device=/dev/kfd --device=/dev/dri --group-add video --ipc=host --shm-size 256g
   --net host -v $PWD:/data
   --entrypoint "/bin/bash"
   --env HUGGINGFACE_HUB_CACHE=/data
   ghcr.io/huggingface/text-generation-inference:latest-rocm
   

TGI on a single-accelerator system

2. Inside the container, launch a model using TGI server on a single accelerator.

   export ROCM_USE_FLASH_ATTN_V2_TRITON=True
   text-generation-launcher --model-id NousResearch/Meta-Llama-3-70B --dtype float16 --port 8000 &
   

3. To test, send it a curl request containing a prompt.

   curl http://localhost:8000/generate_stream -X POST -d '{"inputs":"What is AMD Instinct?","parameters":{"max_new_tokens":20}}' -H 'Content-Type: application/json'
   

   You should receive a response like the following.

   data:{"index":20,"token":{"id":304,"text":" in","logprob":-1.2822266,"special":false},"generated_text":" AMD Instinct is a new family of data center GPUs designed to accelerate the most demanding workloads in","details":null}
   

TGI on a multi-accelerator system

2. Inside the container, launch a model using TGI server on multiple accelerators (4 in this case).

   export ROCM_USE_FLASH_ATTN_V2_TRITON=True
   text-generation-launcher --model-id NousResearch/Meta-Llama-3-8B --dtype float16 --port 8000 --num-shard 4 &
   

3. To test, send it a curl request containing a prompt.

   curl http://localhost:8000/generate_stream -X POST -d '{"inputs":"What is AMD Instinct?","parameters":{"max_new_tokens":20}}' -H 'Content-Type: application/json'
   

   You should receive a response like the following.

   data:{"index":20,"token":{"id":304,"text":" in","logprob":-1.2773438,"special":false},"generated_text":" AMD Instinct is a new family of data center GPUs designed to accelerate the most demanding workloads in","details":null}