Large Language Model Text Generation Inference
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README.md

Text Generation Inference

GitHub Repo stars License Swagger API documentation

architecture

A Rust, Python and gRPC server for text generation inference. Used in production at HuggingFace to power LLMs api-inference widgets.

Table of contents

Features

  • Token streaming using Server Side Events (SSE)
  • Dynamic batching of incoming requests for increased total throughput
  • Quantization with bitsandbytes
  • Safetensors weight loading
  • 45ms per token generation for BLOOM with 8xA100 80GB
  • Logits warpers (temperature scaling, topk, repetition penalty ...)
  • Stop sequences
  • Log probabilities

Officially supported models

Other models are supported on a best effort basis using:

AutoModelForCausalLM.from_pretrained(<model>, device_map="auto")

or

AutoModelForSeq2SeqLM.from_pretrained(<model>, device_map="auto")

Get started

Docker

The easiest way of getting started is using the official Docker container:

model=bigscience/bloom-560m
num_shard=2
volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run

docker run --gpus all -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:latest --model-id $model --num-shard $num_shard

You can then query the model using either the /generate or /generate_stream routes:

curl 127.0.0.1:8080/generate \
    -X POST \
    -d '{"inputs":"Testing API","parameters":{"max_new_tokens":9}}' \
    -H 'Content-Type: application/json'
curl 127.0.0.1:8080/generate_stream \
    -X POST \
    -d '{"inputs":"Testing API","parameters":{"max_new_tokens":9}}' \
    -H 'Content-Type: application/json'

To use GPUs, you will need to install the NVIDIA Container Toolkit.

API documentation

You can consult the OpenAPI documentation of the text-generation-inference REST API using the /docs route. The Swagger UI is also available at: https://huggingface.github.io/text-generation-inference.

Local install

You can also opt to install text-generation-inference locally. You will need to have cargo and Python installed on your machine

BUILD_EXTENSIONS=True make install # Install repository and HF/transformer fork with CUDA kernels
make run-bloom-560m

CUDA Kernels

The custom CUDA kernels are only tested on NVIDIA A100s. If you have any installation or runtime issues, you can remove the kernels by using the BUILD_EXTENSIONS=False environment variable.

Be aware that the official Docker image has them enabled by default.

Run BLOOM

Download

First you need to download the weights:

make download-bloom

Run

make run-bloom # Requires 8xA100 80GB

Quantization

You can also quantize the weights with bitsandbytes to reduce the VRAM requirement:

make run-bloom-quantize # Requires 8xA100 40GB

Develop

make server-dev
make router-dev

Testing

make python-tests
make integration-tests