llama-3-70b-instruct

Self-host LLMs with TensorRT-LLM and BentoML

This is a BentoML example project, showing you how to serve and deploy open-source Large Language Models (LLMs) using TensorRT-LLM, a Python API that optimizes LLM inference on NVIDIA GPUs.

See here for a full list of BentoML example projects.

💡 This example is served as a basis for advanced code customization, such as custom model, inference logic or LMDeploy options. For simple LLM hosting with OpenAI compatible endpoint without writing any code, see OpenLLM.

Prerequisites

• You have installed Python 3.10+ and pip. See the Python downloads page to learn more.
• You have a basic understanding of key concepts in BentoML, such as Services. We recommend you read Quickstart first.
• You need at least 120 GPU memory to perform the quantization for Llama 3 70B. The quantization step may take a long time to finish. Please use a server grade GPU if a GPU out-of-memory error occurs. For more information, see TensorRT-LLM Quantization Toolkit Installation Guide.
• (Optional) We recommend you create a virtual environment for dependency isolation for this project. See the Conda documentation or the Python documentation for details.

Set up the environment

Clone the project repo and TensorRT-LLM repo.

git clone https://github.com/bentoml/BentoTRTLLM.git
cd BentoTRTLLM/llama-3-70b-instruct
git clone -b v0.9.0 https://github.com/NVIDIA/TensorRT-LLM.git
cd TensorRT-LLM

Create the base Docker environment to compile the model.

git lfs install
git clone https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct
docker run --rm --runtime=nvidia --gpus all --volume ${PWD}:/TensorRT-LLM --entrypoint /bin/bash -it --workdir /TensorRT-LLM nvidia/cuda:12.1.0-devel-ubuntu22.04

Install dependencies inside the Docker container. Note that TensorRT-LLM requires Python 3.10.

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

# Install the stable version (corresponding to the cloned branch) of TensorRT-LLM.
pip3 install tensorrt_llm==0.9.0 -U --extra-index-url https://pypi.nvidia.com

# Log in to huggingface-cli
# You can get your token from huggingface.co/settings/token
apt-get install -y git
huggingface-cli login --token *****

Quantize the Llama 3 70B checkpoint into INT4 AWQ format:

python3 ../quantization/quantize.py --model_dir ./Meta-Llama-3-70B-Instruct \
            --output_dir ./tllm_checkpoint_1gpu_bf16 \
            --dtype bfloat16 \
            --qformat int4_awq \
            --awq_block_size 128 \
            --calib_size 32

trtllm-build --checkpoint_dir ./tllm_checkpoint_1gpu_bf16 \
            --output_dir ./tmp/llama/70B/trt_engines/int4_AWQ/1-gpu/ \
            --gpt_attention_plugin bfloat16 \
            --gemm_plugin bfloat16 \
            --max_batch_size 64 \
            --max_input_len 512 \
            --paged_kv_cache enable

The model should be successfully built now. Exit the Docker image.

exit

Clone the tensorrtllm_backend repo.

cd ..
git clone -b v0.9.0 https://github.com/triton-inference-server/tensorrtllm_backend.git

Now, the BentoTRTLLM/ directory should have one TenosrRT-LLM/ directory and one tensorrtllm_backend/ directory.

Copy the model.

cd tensorrtllm_backend
cp ../TensorRT-LLM/tmp/llama/70B/trt_engines/int4_AWQ/1-gpu/* all_models/inflight_batcher_llm/tensorrt_llm/1/

Set the tokenizer_dir and engine_dir paths.

HF_LLAMA_MODEL=TensorRT-LLM/Meta-Llama-3-70B-Instruct
ENGINE_PATH=tensorrtllm_backend/all_models/inflight_batcher_llm/tensorrt_llm/1

python3 tools/fill_template.py -i all_models/inflight_batcher_llm/preprocessing/config.pbtxt tokenizer_dir:${HF_LLAMA_MODEL},tokenizer_type:auto,triton_max_batch_size:64,preprocessing_instance_count:1

python3 tools/fill_template.py -i all_models/inflight_batcher_llm/postprocessing/config.pbtxt tokenizer_dir:${HF_LLAMA_MODEL},tokenizer_type:auto,triton_max_batch_size:64,postprocessing_instance_count:1

python3 tools/fill_template.py -i all_models/inflight_batcher_llm/tensorrt_llm_bls/config.pbtxt triton_max_batch_size:64,decoupled_mode:True,bls_instance_count:1,accumulate_tokens:False

python3 tools/fill_template.py -i all_models/inflight_batcher_llm/ensemble/config.pbtxt triton_max_batch_size:64

python3 tools/fill_template.py -i all_models/inflight_batcher_llm/tensorrt_llm/config.pbtxt triton_max_batch_size:64,decoupled_mode:True,max_beam_width:1,engine_dir:${ENGINE_PATH},max_tokens_in_paged_kv_cache:,max_attention_window_size:2560,kv_cache_free_gpu_mem_fraction:0.95,exclude_input_in_output:True,batching_strategy:inflight_fused_batching,max_queue_delay_microseconds:0

Import the model

Install BentoML.

pip install bentoml

Make sure you are in the llama-3-70b-instruct directory and import the model to the BentoML Model Store.

python pack_model.py

Run the BentoML Service

We have defined a BentoML Service in service.py. To serve it locally, first create a Docker container environment for TensorRT-LLM:

docker run --runtime=nvidia --gpus all --volume ${PWD}:/TensorRT-LLM --entrypoint /bin/bash -it --workdir /TensorRT-LLM nvcr.io/nvidia/tritonserver:24.04-trtllm-python-py3

Install the dependencies.

pip install -r requirements.txt

Start the Service.

$ bentoml serve .
2024-06-07T05:16:38+0000 [INFO] [cli] Starting production HTTP BentoServer from "service:TRTLLM" listening on http://localhost:3000 (Press CTRL+C to quit)

The server is now active at http://localhost:3000. You can interact with it using the Swagger UI or in other different ways.

CURL

curl -X 'POST' \
  'http://localhost:3000/generate' \
  -H 'accept: text/event-stream' \
  -H 'Content-Type: application/json' \
  -d '{
  "prompt": "Explain superconductors like I'\''m five years old",
  "max_tokens": 1024
}'

Python client

import bentoml

with bentoml.SyncHTTPClient("http://localhost:3000") as client:
    response_generator = client.generate(
        prompt="Explain superconductors like I'm five years old",
        max_tokens=1024
    )
    for response in response_generator:
        print(response, end='')

Deploy to BentoCloud

After the Service is ready, you can deploy the application to BentoCloud for better management and scalability. Sign up if you haven't got a BentoCloud account.

Make sure you have logged in to BentoCloud, then run the following command to deploy it. Note that you need to specify the CUDA version in bentofile.yaml.

bentoml deploy .

Once the application is up and running on BentoCloud, you can access it via the exposed URL.

Note: For custom deployment in your own infrastructure, use BentoML to generate an OCI-compliant image.