256 lines
10 KiB
Markdown
256 lines
10 KiB
Markdown
<div align="center">
|
|
|
|
<a href="https://www.youtube.com/watch?v=jlMAX2Oaht0">
|
|
<img width=560 width=315 alt="Making TGI deployment optimal" src="https://huggingface.co/datasets/Narsil/tgi_assets/resolve/main/thumbnail.png">
|
|
</a>
|
|
|
|
# Text Generation Inference
|
|
|
|
<a href="https://github.com/huggingface/text-generation-inference">
|
|
<img alt="GitHub Repo stars" src="https://img.shields.io/github/stars/huggingface/text-generation-inference?style=social">
|
|
</a>
|
|
<a href="https://huggingface.github.io/text-generation-inference">
|
|
<img alt="Swagger API documentation" src="https://img.shields.io/badge/API-Swagger-informational">
|
|
</a>
|
|
|
|
A Rust, Python and gRPC server for text generation inference. Used in production at [HuggingFace](https://huggingface.co)
|
|
to power Hugging Chat, the Inference API and Inference Endpoint.
|
|
|
|
</div>
|
|
|
|
## Table of contents
|
|
|
|
- [Get Started](#get-started)
|
|
- [API Documentation](#api-documentation)
|
|
- [Using a private or gated model](#using-a-private-or-gated-model)
|
|
- [A note on Shared Memory](#a-note-on-shared-memory-shm)
|
|
- [Distributed Tracing](#distributed-tracing)
|
|
- [Local Install](#local-install)
|
|
- [CUDA Kernels](#cuda-kernels)
|
|
- [Optimized architectures](#optimized-architectures)
|
|
- [Run Mistral](#run-a-model)
|
|
- [Run](#run)
|
|
- [Quantization](#quantization)
|
|
- [Develop](#develop)
|
|
- [Testing](#testing)
|
|
|
|
Text Generation Inference (TGI) is a toolkit for deploying and serving Large Language Models (LLMs). TGI enables high-performance text generation for the most popular open-source LLMs, including Llama, Falcon, StarCoder, BLOOM, GPT-NeoX, and [more](https://huggingface.co/docs/text-generation-inference/supported_models). TGI implements many features, such as:
|
|
|
|
- Simple launcher to serve most popular LLMs
|
|
- Production ready (distributed tracing with Open Telemetry, Prometheus metrics)
|
|
- Tensor Parallelism for faster inference on multiple GPUs
|
|
- Token streaming using Server-Sent Events (SSE)
|
|
- Continuous batching of incoming requests for increased total throughput
|
|
- Optimized transformers code for inference using [Flash Attention](https://github.com/HazyResearch/flash-attention) and [Paged Attention](https://github.com/vllm-project/vllm) on the most popular architectures
|
|
- Quantization with :
|
|
- [bitsandbytes](https://github.com/TimDettmers/bitsandbytes)
|
|
- [GPT-Q](https://arxiv.org/abs/2210.17323)
|
|
- [EETQ](https://github.com/NetEase-FuXi/EETQ)
|
|
- [AWQ](https://github.com/casper-hansen/AutoAWQ)
|
|
- [Safetensors](https://github.com/huggingface/safetensors) weight loading
|
|
- Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
|
|
- Logits warper (temperature scaling, top-p, top-k, repetition penalty, more details see [transformers.LogitsProcessor](https://huggingface.co/docs/transformers/internal/generation_utils#transformers.LogitsProcessor))
|
|
- Stop sequences
|
|
- Log probabilities
|
|
- Custom Prompt Generation: Easily generate text by providing custom prompts to guide the model's output
|
|
- Fine-tuning Support: Utilize fine-tuned models for specific tasks to achieve higher accuracy and performance
|
|
|
|
### Hardware support
|
|
|
|
- [Nvidia](https://github.com/huggingface/text-generation-inference/pkgs/container/text-generation-inference)
|
|
- [AMD](https://github.com/huggingface/text-generation-inference/pkgs/container/text-generation-inference) (-rocm)
|
|
- [Inferentia](https://github.com/huggingface/optimum-neuron/tree/main/text-generation-inference)
|
|
- [Intel GPU](https://github.com/huggingface/text-generation-inference/pull/1475)
|
|
- [Gaudi](https://github.com/huggingface/tgi-gaudi)
|
|
|
|
|
|
## Get Started
|
|
|
|
### Docker
|
|
|
|
For a detailed starting guide, please see the [Quick Tour](https://huggingface.co/docs/text-generation-inference/quicktour). The easiest way of getting started is using the official Docker container:
|
|
|
|
```shell
|
|
model=HuggingFaceH4/zephyr-7b-beta
|
|
volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
|
|
|
|
docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.4 --model-id $model
|
|
```
|
|
|
|
And then you can make requests like
|
|
|
|
```bash
|
|
curl 127.0.0.1:8080/generate \
|
|
-X POST \
|
|
-d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":20}}' \
|
|
-H 'Content-Type: application/json'
|
|
```
|
|
|
|
**Note:** To use NVIDIA GPUs, you need to install the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html). We also recommend using NVIDIA drivers with CUDA version 12.2 or higher. For running the Docker container on a machine with no GPUs or CUDA support, it is enough to remove the `--gpus all` flag and add `--disable-custom-kernels`, please note CPU is not the intended platform for this project, so performance might be subpar.
|
|
|
|
**Note:** TGI supports AMD Instinct MI210 and MI250 GPUs. Details can be found in the [Supported Hardware documentation](https://huggingface.co/docs/text-generation-inference/supported_models#supported-hardware). To use AMD GPUs, please use `docker run --device /dev/kfd --device /dev/dri --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.4-rocm --model-id $model` instead of the command above.
|
|
|
|
To see all options to serve your models (in the [code](https://github.com/huggingface/text-generation-inference/blob/main/launcher/src/main.rs) or in the cli):
|
|
```
|
|
text-generation-launcher --help
|
|
```
|
|
|
|
### API documentation
|
|
|
|
You can consult the OpenAPI documentation of the `text-generation-inference` REST API using the `/docs` route.
|
|
The Swagger UI is also available at: [https://huggingface.github.io/text-generation-inference](https://huggingface.github.io/text-generation-inference).
|
|
|
|
### Using a private or gated model
|
|
|
|
You have the option to utilize the `HUGGING_FACE_HUB_TOKEN` environment variable for configuring the token employed by
|
|
`text-generation-inference`. This allows you to gain access to protected resources.
|
|
|
|
For example, if you want to serve the gated Llama V2 model variants:
|
|
|
|
1. Go to https://huggingface.co/settings/tokens
|
|
2. Copy your cli READ token
|
|
3. Export `HUGGING_FACE_HUB_TOKEN=<your cli READ token>`
|
|
|
|
or with Docker:
|
|
|
|
```shell
|
|
model=meta-llama/Llama-2-7b-chat-hf
|
|
volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
|
|
token=<your cli READ token>
|
|
|
|
docker run --gpus all --shm-size 1g -e HUGGING_FACE_HUB_TOKEN=$token -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.4 --model-id $model
|
|
```
|
|
|
|
### A note on Shared Memory (shm)
|
|
|
|
[`NCCL`](https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/index.html) is a communication framework used by
|
|
`PyTorch` to do distributed training/inference. `text-generation-inference` make
|
|
use of `NCCL` to enable Tensor Parallelism to dramatically speed up inference for large language models.
|
|
|
|
In order to share data between the different devices of a `NCCL` group, `NCCL` might fall back to using the host memory if
|
|
peer-to-peer using NVLink or PCI is not possible.
|
|
|
|
To allow the container to use 1G of Shared Memory and support SHM sharing, we add `--shm-size 1g` on the above command.
|
|
|
|
If you are running `text-generation-inference` inside `Kubernetes`. You can also add Shared Memory to the container by
|
|
creating a volume with:
|
|
|
|
```yaml
|
|
- name: shm
|
|
emptyDir:
|
|
medium: Memory
|
|
sizeLimit: 1Gi
|
|
```
|
|
|
|
and mounting it to `/dev/shm`.
|
|
|
|
Finally, you can also disable SHM sharing by using the `NCCL_SHM_DISABLE=1` environment variable. However, note that
|
|
this will impact performance.
|
|
|
|
### Distributed Tracing
|
|
|
|
`text-generation-inference` is instrumented with distributed tracing using OpenTelemetry. You can use this feature
|
|
by setting the address to an OTLP collector with the `--otlp-endpoint` argument.
|
|
|
|
### Architecture
|
|
|
|
|
|
|
|
### Local install
|
|
|
|
You can also opt to install `text-generation-inference` locally.
|
|
|
|
First [install Rust](https://rustup.rs/) and create a Python virtual environment with at least
|
|
Python 3.9, e.g. using `conda`:
|
|
|
|
```shell
|
|
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
|
|
|
|
conda create -n text-generation-inference python=3.11
|
|
conda activate text-generation-inference
|
|
```
|
|
|
|
You may also need to install Protoc.
|
|
|
|
On Linux:
|
|
|
|
```shell
|
|
PROTOC_ZIP=protoc-21.12-linux-x86_64.zip
|
|
curl -OL https://github.com/protocolbuffers/protobuf/releases/download/v21.12/$PROTOC_ZIP
|
|
sudo unzip -o $PROTOC_ZIP -d /usr/local bin/protoc
|
|
sudo unzip -o $PROTOC_ZIP -d /usr/local 'include/*'
|
|
rm -f $PROTOC_ZIP
|
|
```
|
|
|
|
On MacOS, using Homebrew:
|
|
|
|
```shell
|
|
brew install protobuf
|
|
```
|
|
|
|
Then run:
|
|
|
|
```shell
|
|
BUILD_EXTENSIONS=True make install # Install repository and HF/transformer fork with CUDA kernels
|
|
text-generation-launcher --model-id mistralai/Mistral-7B-Instruct-v0.2
|
|
```
|
|
|
|
**Note:** on some machines, you may also need the OpenSSL libraries and gcc. On Linux machines, run:
|
|
|
|
```shell
|
|
sudo apt-get install libssl-dev gcc -y
|
|
```
|
|
|
|
## Optimized architectures
|
|
|
|
TGI works out of the box to serve optimized models for all modern models. They can be found in [this list](https://huggingface.co/docs/text-generation-inference/supported_models).
|
|
|
|
Other architectures are supported on a best-effort basis using:
|
|
|
|
`AutoModelForCausalLM.from_pretrained(<model>, device_map="auto")`
|
|
|
|
or
|
|
|
|
`AutoModelForSeq2SeqLM.from_pretrained(<model>, device_map="auto")`
|
|
|
|
|
|
|
|
## Run locally
|
|
|
|
### Run
|
|
|
|
```shell
|
|
text-generation-launcher --model-id mistralai/Mistral-7B-Instruct-v0.2
|
|
```
|
|
|
|
### Quantization
|
|
|
|
You can also quantize the weights with bitsandbytes to reduce the VRAM requirement:
|
|
|
|
```shell
|
|
text-generation-launcher --model-id mistralai/Mistral-7B-Instruct-v0.2 --quantize
|
|
```
|
|
|
|
4bit quantization is available using the [NF4 and FP4 data types from bitsandbytes](https://arxiv.org/pdf/2305.14314.pdf). It can be enabled by providing `--quantize bitsandbytes-nf4` or `--quantize bitsandbytes-fp4` as a command line argument to `text-generation-launcher`.
|
|
|
|
## Develop
|
|
|
|
```shell
|
|
make server-dev
|
|
make router-dev
|
|
```
|
|
|
|
## Testing
|
|
|
|
```shell
|
|
# python
|
|
make python-server-tests
|
|
make python-client-tests
|
|
# or both server and client tests
|
|
make python-tests
|
|
# rust cargo tests
|
|
make rust-tests
|
|
# integration tests
|
|
make integration-tests
|
|
```
|