hf_text-generation-inference/docs/source/installation.md

84 lines
2.3 KiB
Markdown
Raw Normal View History

MI300 compatibility (#1764) Adds support for AMD Instinct MI300 in TGI. Most changes are: * Support PyTorch TunableOp to pick the GEMM/GEMV kernels for decoding https://github.com/pytorch/pytorch/tree/main/aten/src/ATen/cuda/tunable. TunableOp is disabled by default, and can be enabled with `PYTORCH_TUNABLEOP_ENABLED=1`. * Update ROCm dockerfile to PyTorch 2.3 (actually patched with changes from https://github.com/pytorch/pytorch/pull/124362) * Support SILU & Linear custom kernels contributed by AMD * Update vLLM paged attention to https://github.com/fxmarty/rocm-vllm/, branching out of a much more recent commit https://github.com/ROCm/vllm/commit/3489ce7936c5de588916ae3047c44c23c0b0c308 * Support FA2 Triton kernel as recommended by AMD. Can be used by specifying `ROCM_USE_FLASH_ATTN_V2_TRITON=1`. * Update dockerfile to ROCm 6.1 By default, TunableOp tuning results are saved in `/data` (e.g. `/data/tunableop_meta-llama-Llama-2-70b-chat-hf_tp1_rank0.csv`) in order to avoid to have to rerun the tuning at each `docker run`. Example: ``` Validator,PT_VERSION,2.3.0 Validator,ROCM_VERSION,6.1.0.0-82-5fabb4c Validator,HIPBLASLT_VERSION,0.7.0-1549b021 Validator,GCN_ARCH_NAME,gfx942:sramecc+:xnack- Validator,ROCBLAS_VERSION,4.1.0-cefa4a9b-dirty GemmTunableOp_Half_TN,tn_8192_7_28672,Gemm_Rocblas_45475,0.132098 GemmTunableOp_Half_TN,tn_10240_4_8192,Gemm_Rocblas_45546,0.0484431 GemmTunableOp_Half_TN,tn_32000_6_8192,Default,0.149546 GemmTunableOp_Half_TN,tn_32000_3_8192,Gemm_Rocblas_45520,0.147119 GemmTunableOp_Half_TN,tn_8192_3_28672,Gemm_Rocblas_45475,0.132645 GemmTunableOp_Half_TN,tn_10240_3_8192,Gemm_Rocblas_45546,0.0482971 GemmTunableOp_Half_TN,tn_57344_5_8192,Gemm_Rocblas_45520,0.255694 GemmTunableOp_Half_TN,tn_10240_7_8192,Gemm_Rocblas_45517,0.0482522 GemmTunableOp_Half_TN,tn_8192_3_8192,Gemm_Rocblas_45546,0.0444671 GemmTunableOp_Half_TN,tn_8192_5_8192,Gemm_Rocblas_45546,0.0445834 GemmTunableOp_Half_TN,tn_57344_7_8192,Gemm_Rocblas_45520,0.25622 GemmTunableOp_Half_TN,tn_8192_2_28672,Gemm_Rocblas_45475,0.132122 GemmTunableOp_Half_TN,tn_8192_4_8192,Gemm_Rocblas_45517,0.0453191 GemmTunableOp_Half_TN,tn_10240_5_8192,Gemm_Rocblas_45517,0.0482514 GemmTunableOp_Half_TN,tn_8192_5_28672,Gemm_Rocblas_45542,0.133914 GemmTunableOp_Half_TN,tn_8192_2_8192,Gemm_Rocblas_45517,0.0446516 GemmTunableOp_Half_TN,tn_8192_1_28672,Gemm_Hipblaslt_TN_10814,0.131953 GemmTunableOp_Half_TN,tn_10240_2_8192,Gemm_Rocblas_45546,0.0481043 GemmTunableOp_Half_TN,tn_32000_4_8192,Gemm_Rocblas_45520,0.147497 GemmTunableOp_Half_TN,tn_8192_6_28672,Gemm_Rocblas_45529,0.134895 GemmTunableOp_Half_TN,tn_57344_2_8192,Gemm_Rocblas_45520,0.254716 GemmTunableOp_Half_TN,tn_57344_4_8192,Gemm_Rocblas_45520,0.255731 GemmTunableOp_Half_TN,tn_10240_6_8192,Gemm_Rocblas_45517,0.0484816 GemmTunableOp_Half_TN,tn_57344_3_8192,Gemm_Rocblas_45520,0.254701 GemmTunableOp_Half_TN,tn_8192_4_28672,Gemm_Rocblas_45475,0.132159 GemmTunableOp_Half_TN,tn_32000_2_8192,Default,0.147524 GemmTunableOp_Half_TN,tn_32000_5_8192,Default,0.147074 GemmTunableOp_Half_TN,tn_8192_6_8192,Gemm_Rocblas_45546,0.0454045 GemmTunableOp_Half_TN,tn_57344_6_8192,Gemm_Rocblas_45520,0.255582 GemmTunableOp_Half_TN,tn_32000_7_8192,Default,0.146705 GemmTunableOp_Half_TN,tn_8192_7_8192,Gemm_Rocblas_45546,0.0445489 ``` --------- Co-authored-by: Mohit Sharma <mohit21sharma.ms@gmail.com>
2024-05-17 07:30:47 -06:00
# Installation from source
MI300 compatibility (#1764) Adds support for AMD Instinct MI300 in TGI. Most changes are: * Support PyTorch TunableOp to pick the GEMM/GEMV kernels for decoding https://github.com/pytorch/pytorch/tree/main/aten/src/ATen/cuda/tunable. TunableOp is disabled by default, and can be enabled with `PYTORCH_TUNABLEOP_ENABLED=1`. * Update ROCm dockerfile to PyTorch 2.3 (actually patched with changes from https://github.com/pytorch/pytorch/pull/124362) * Support SILU & Linear custom kernels contributed by AMD * Update vLLM paged attention to https://github.com/fxmarty/rocm-vllm/, branching out of a much more recent commit https://github.com/ROCm/vllm/commit/3489ce7936c5de588916ae3047c44c23c0b0c308 * Support FA2 Triton kernel as recommended by AMD. Can be used by specifying `ROCM_USE_FLASH_ATTN_V2_TRITON=1`. * Update dockerfile to ROCm 6.1 By default, TunableOp tuning results are saved in `/data` (e.g. `/data/tunableop_meta-llama-Llama-2-70b-chat-hf_tp1_rank0.csv`) in order to avoid to have to rerun the tuning at each `docker run`. Example: ``` Validator,PT_VERSION,2.3.0 Validator,ROCM_VERSION,6.1.0.0-82-5fabb4c Validator,HIPBLASLT_VERSION,0.7.0-1549b021 Validator,GCN_ARCH_NAME,gfx942:sramecc+:xnack- Validator,ROCBLAS_VERSION,4.1.0-cefa4a9b-dirty GemmTunableOp_Half_TN,tn_8192_7_28672,Gemm_Rocblas_45475,0.132098 GemmTunableOp_Half_TN,tn_10240_4_8192,Gemm_Rocblas_45546,0.0484431 GemmTunableOp_Half_TN,tn_32000_6_8192,Default,0.149546 GemmTunableOp_Half_TN,tn_32000_3_8192,Gemm_Rocblas_45520,0.147119 GemmTunableOp_Half_TN,tn_8192_3_28672,Gemm_Rocblas_45475,0.132645 GemmTunableOp_Half_TN,tn_10240_3_8192,Gemm_Rocblas_45546,0.0482971 GemmTunableOp_Half_TN,tn_57344_5_8192,Gemm_Rocblas_45520,0.255694 GemmTunableOp_Half_TN,tn_10240_7_8192,Gemm_Rocblas_45517,0.0482522 GemmTunableOp_Half_TN,tn_8192_3_8192,Gemm_Rocblas_45546,0.0444671 GemmTunableOp_Half_TN,tn_8192_5_8192,Gemm_Rocblas_45546,0.0445834 GemmTunableOp_Half_TN,tn_57344_7_8192,Gemm_Rocblas_45520,0.25622 GemmTunableOp_Half_TN,tn_8192_2_28672,Gemm_Rocblas_45475,0.132122 GemmTunableOp_Half_TN,tn_8192_4_8192,Gemm_Rocblas_45517,0.0453191 GemmTunableOp_Half_TN,tn_10240_5_8192,Gemm_Rocblas_45517,0.0482514 GemmTunableOp_Half_TN,tn_8192_5_28672,Gemm_Rocblas_45542,0.133914 GemmTunableOp_Half_TN,tn_8192_2_8192,Gemm_Rocblas_45517,0.0446516 GemmTunableOp_Half_TN,tn_8192_1_28672,Gemm_Hipblaslt_TN_10814,0.131953 GemmTunableOp_Half_TN,tn_10240_2_8192,Gemm_Rocblas_45546,0.0481043 GemmTunableOp_Half_TN,tn_32000_4_8192,Gemm_Rocblas_45520,0.147497 GemmTunableOp_Half_TN,tn_8192_6_28672,Gemm_Rocblas_45529,0.134895 GemmTunableOp_Half_TN,tn_57344_2_8192,Gemm_Rocblas_45520,0.254716 GemmTunableOp_Half_TN,tn_57344_4_8192,Gemm_Rocblas_45520,0.255731 GemmTunableOp_Half_TN,tn_10240_6_8192,Gemm_Rocblas_45517,0.0484816 GemmTunableOp_Half_TN,tn_57344_3_8192,Gemm_Rocblas_45520,0.254701 GemmTunableOp_Half_TN,tn_8192_4_28672,Gemm_Rocblas_45475,0.132159 GemmTunableOp_Half_TN,tn_32000_2_8192,Default,0.147524 GemmTunableOp_Half_TN,tn_32000_5_8192,Default,0.147074 GemmTunableOp_Half_TN,tn_8192_6_8192,Gemm_Rocblas_45546,0.0454045 GemmTunableOp_Half_TN,tn_57344_6_8192,Gemm_Rocblas_45520,0.255582 GemmTunableOp_Half_TN,tn_32000_7_8192,Default,0.146705 GemmTunableOp_Half_TN,tn_8192_7_8192,Gemm_Rocblas_45546,0.0445489 ``` --------- Co-authored-by: Mohit Sharma <mohit21sharma.ms@gmail.com>
2024-05-17 07:30:47 -06:00
<Tip warning={true}>
Installing TGI from source is not the recommended usage. We strongly recommend to use TGI through Docker, check the [Quick Tour](./quicktour), [Installation for Nvidia GPUs](./installation_nvidia) and [Installation for AMD GPUs](./installation_amd) to learn how to use TGI with Docker.
</Tip>
## Install CLI
2024-02-16 03:58:58 -07:00
You can use TGI command-line interface (CLI) to download weights, serve and quantize models, or get information on serving parameters.
To install the CLI, you need to first clone the TGI repository and then run `make`.
```bash
git clone https://github.com/huggingface/text-generation-inference.git && cd text-generation-inference
make install
```
If you would like to serve models with custom kernels, run
```bash
BUILD_EXTENSIONS=True make install
```
## Local Installation from Source
Before you start, you will need to setup your environment, and install Text Generation Inference. Text Generation Inference is tested on **Python 3.9+**.
2024-02-16 03:58:58 -07:00
Text Generation Inference is available on pypi, conda and GitHub.
To install and launch locally, first [install Rust](https://rustup.rs/) and create a Python virtual environment with at least
Python 3.9, e.g. using conda:
2023-08-10 06:32:51 -06:00
```bash
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:
2023-08-10 06:32:51 -06:00
```bash
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:
2023-08-10 06:32:51 -06:00
```bash
brew install protobuf
```
Then run to install Text Generation Inference:
2023-08-10 06:32:51 -06:00
```bash
git clone https://github.com/huggingface/text-generation-inference.git && cd text-generation-inference
BUILD_EXTENSIONS=True make install
```
<Tip warning={true}>
On some machines, you may also need the OpenSSL libraries and gcc. On Linux machines, run:
2023-08-10 06:32:51 -06:00
```bash
sudo apt-get install libssl-dev gcc -y
```
</Tip>
Once installation is done, simply run:
2023-08-10 06:32:51 -06:00
```bash
make run-falcon-7b-instruct
```
This will serve Falcon 7B Instruct model from the port 8080, which we can query.