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<div align="center">
![image](https://github.com/huggingface/text-generation-inference/assets/3841370/38ba1531-ea0d-4851-b31a-a6d4ddc944b0)
# 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://github.com/huggingface/text-generation-inference/blob/main/LICENSE">
<img alt="License" src="https://img.shields.io/github/license/huggingface/text-generation-inference">
</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>
</div>
A Rust, Python and gRPC server for text generation inference. Used in production at [HuggingFace](https://huggingface.co)
to power LLMs api-inference widgets.
## Table of contents
- [Features](#features)
- [Optimized Architectures](#optimized-architectures)
- [Get Started](#get-started)
- [Docker](#docker)
- [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)
- [Run Falcon](#run-falcon)
- [Run](#run)
- [Quantization](#quantization)
- [Develop](#develop)
- [Testing](#testing)
## Features
- Serve the most popular Large Language Models with a simple launcher
- Tensor Parallelism for faster inference on multiple GPUs
- Token streaming using Server-Sent Events (SSE)
- [Continuous batching of incoming requests](https://github.com/huggingface/text-generation-inference/tree/main/router) 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) and [GPT-Q](https://arxiv.org/abs/2210.17323)
- [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
- Production ready (distributed tracing with Open Telemetry, Prometheus metrics)
## Optimized architectures
- [BLOOM](https://huggingface.co/bigscience/bloom)
- [FLAN-T5](https://huggingface.co/google/flan-t5-xxl)
- [Galactica](https://huggingface.co/facebook/galactica-120b)
- [GPT-Neox](https://huggingface.co/EleutherAI/gpt-neox-20b)
- [Llama](https://github.com/facebookresearch/llama)
- [OPT](https://huggingface.co/facebook/opt-66b)
- [SantaCoder](https://huggingface.co/bigcode/santacoder)
- [Starcoder](https://huggingface.co/bigcode/starcoder)
- [Falcon 7B](https://huggingface.co/tiiuae/falcon-7b)
- [Falcon 40B](https://huggingface.co/tiiuae/falcon-40b)
- [MPT](https://huggingface.co/mosaicml/mpt-30b)
- [Llama V2](https://huggingface.co/meta-llama)
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")`
## Get started
### Docker
The easiest way of getting started is using the official Docker container:
```shell
model=tiiuae/falcon-7b-instruct
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:0.9.4 --model-id $model
```
**Note:** To use 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 11.8 or higher.
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
```
You can then query the model using either the `/generate` or `/generate_stream` routes:
```shell
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'
```
```shell
curl 127.0.0.1:8080/generate_stream \
-X POST \
-d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":20}}' \
-H 'Content-Type: application/json'
```
or from Python:
```shell
pip install text-generation
```
```python
from text_generation import Client
client = Client("http://127.0.0.1:8080")
print(client.generate("What is Deep Learning?", max_new_tokens=20).generated_text)
text = ""
for response in client.generate_stream("What is Deep Learning?", max_new_tokens=20):
if not response.token.special:
text += response.token.text
print(text)
```
### 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:0.9.3 --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.
### Local install ### Local install
You can also opt to install `text-generation-inference` locally. 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 First [install Rust](https://rustup.rs/):
Python 3.9, e.g. using `conda`:
```shell ```bash
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
conda create -n text-generation-inference python=3.9
conda activate text-generation-inference
``` ```
You may also need to install Protoc. Install conda:
On Linux: ```bash
curl https://repo.anaconda.com/pkgs/misc/gpgkeys/anaconda.asc | gpg --dearmor > conda.gpg
sudo install -o root -g root -m 644 conda.gpg /usr/share/keyrings/conda-archive-keyring.gpg
gpg --keyring /usr/share/keyrings/conda-archive-keyring.gpg --no-default-keyring --fingerprint 34161F5BF5EB1D4BFBBB8F0A8AEB4F8B29D82806
echo "deb [arch=amd64 signed-by=/usr/share/keyrings/conda-archive-keyring.gpg] https://repo.anaconda.com/pkgs/misc/debrepo/conda stable main" | sudo tee -a /etc/apt/sources.list.d/conda.list
sudo apt update && sudo apt install conda -y
source /opt/conda/etc/profile.d/conda.sh
conda -V
```
Create Env:
```shell
conda create -n dscb python=3.9
conda activate dscb
```
Install PROTOC
```shell ```shell
PROTOC_ZIP=protoc-21.12-linux-x86_64.zip PROTOC_ZIP=protoc-21.12-linux-x86_64.zip
curl -OL https://github.com/protocolbuffers/protobuf/releases/download/v21.12/$PROTOC_ZIP curl -OL https://github.com/protocolbuffers/protobuf/releases/download/v21.12/$PROTOC_ZIP
@ -211,65 +36,19 @@ sudo unzip -o $PROTOC_ZIP -d /usr/local 'include/*'
rm -f $PROTOC_ZIP rm -f $PROTOC_ZIP
``` ```
On MacOS, using Homebrew: You might need to install these:
```shell
brew install protobuf
```
Then run:
```shell
BUILD_EXTENSIONS=True make install # Install repository and HF/transformer fork with CUDA kernels
make run-falcon-7b-instruct
```
**Note:** on some machines, you may also need the OpenSSL libraries and gcc. On Linux machines, run:
```shell ```shell
sudo apt-get install libssl-dev gcc -y sudo apt-get install libssl-dev gcc -y
sudo apt-get install pkg-config
``` ```
### CUDA Kernels Install DeepSparse:
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 `DISABLE_CUSTOM_KERNELS=True` environment variable.
Be aware that the official Docker image has them enabled by default.
## Run Falcon
### Run
```shell ```shell
make run-falcon-7b-instruct pip install deepsparse-nightly[transformers]
``` ```
### Quantization Install Server / Router
You can also quantize the weights with bitsandbytes to reduce the VRAM requirement:
```shell ```shell
make run-falcon-7b-instruct-quantize make install-server
``` make install-router
```
## 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
```