# Text Generation Inference GitHub Repo stars License Swagger API documentation ![architecture](assets/architecture.jpg)
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) - [A note on Shared Memory](#a-note-on-shared-memory-shm) - [Distributed Tracing](#distributed-tracing) - [Local Install](#local-install) - [CUDA Kernels](#cuda-kernels) - [Run BLOOM](#run-bloom) - [Download](#download) - [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) Other architectures are supported on a best effort basis using: `AutoModelForCausalLM.from_pretrained(, device_map="auto")` or `AutoModelForSeq2SeqLM.from_pretrained(, device_map="auto")` ## Get started ### Docker The easiest way of getting started is using the official Docker container: ```shell 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 --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:0.9 --model-id $model --num-shard $num_shard ``` **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":17}}' \ -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":17}}' \ -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=17).generated_text) text = "" for response in client.generate_stream("What is Deep Learning?", max_new_tokens=17): 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). ### 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. ### 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. ### 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=3.9 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 make run-bloom-560m ``` **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 ``` ### 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 It is advised to download the weights ahead of time with the following command: ```shell make download-bloom ``` ### Run ```shell make run-bloom # Requires 8xA100 80GB ``` ### Quantization You can also quantize the weights with bitsandbytes to reduce the VRAM requirement: ```shell make run-bloom-quantize # Requires 8xA100 40GB ``` ## 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 ```