Making TGI deployment optimal # Text Generation Inference GitHub Repo stars Swagger API documentation 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.
## Table of contents - [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 (shm)](#a-note-on-shared-memory-shm) - [Distributed Tracing](#distributed-tracing) - [Architecture](#architecture) - [Local install](#local-install) - [Optimized architectures](#optimized-architectures) - [Run locally](#run-locally) - [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 - [Speculation](https://huggingface.co/docs/text-generation-inference/conceptual/speculation) ~2x latency - [Guidance/JSON](https://huggingface.co/docs/text-generation-inference/conceptual/guidance). Specify output format to speed up inference and make sure the output is valid according to some specs.. - 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) - [Google TPU](https://huggingface.co/docs/optimum-tpu/howto/serving) ## 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 # share a volume with the Docker container to avoid downloading weights every run volume=$PWD/data docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data \ ghcr.io/huggingface/text-generation-inference:2.1.1 --model-id $model ``` And then you can make requests like ```bash 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' ``` **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:2.1.1-rocm --model-id $model` instead of the command above. To see all options to serve your models (in the [code](https://github.com/huggingface/text-generation-inference/blob/main/launcher/src/main.rs) or in the cli): ``` text-generation-launcher --help ``` ### API documentation You can consult the OpenAPI documentation of the `text-generation-inference` REST API using the `/docs` route. The Swagger UI is also available at: [https://huggingface.github.io/text-generation-inference](https://huggingface.github.io/text-generation-inference). ### Using a private or gated model You have the option to utilize the `HF_TOKEN` environment variable for configuring the token employed by `text-generation-inference`. This allows you to gain access to protected resources. For example, if you want to serve the gated Llama V2 model variants: 1. Go to https://huggingface.co/settings/tokens 2. Copy your cli READ token 3. Export `HF_TOKEN=` or with Docker: ```shell model=meta-llama/Llama-2-7b-chat-hf volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run token= docker run --gpus all --shm-size 1g -e HF_TOKEN=$token -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:2.0 --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. The default service name can be overridden with the `--otlp-service-name` argument ### Architecture ![TGI architecture](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/TGI.png) ### 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(, device_map="auto")` or `AutoModelForSeq2SeqLM.from_pretrained(, 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 ```