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| Installing xFormers |
:material-image-size-select-large: Installing xformers
xFormers is toolbox that integrates with the pyTorch and CUDA libraries to provide accelerated performance and reduced memory consumption for applications using the transformers machine learning architecture. After installing xFormers, InvokeAI users who have CUDA GPUs will see a noticeable decrease in GPU memory consumption and an increase in speed.
xFormers can be installed into a working InvokeAI installation without any code changes or other updates. This document explains how to install xFormers.
Pip Install
For both Windows and Linux, you can install xformers in just a
couple of steps from the command line.
If you are used to launching invoke.sh or invoke.bat to start
InvokeAI, then run the launcher and select the "developer's console"
to get to the command line. If you run invoke.py directly from the
command line, then just be sure to activate it's virtual environment.
Then run the following three commands:
pip install xformers~=0.0.19
pip install triton # WON'T WORK ON WINDOWS
python -m xformers.info output
The first command installs xformers, the second installs the
triton training accelerator, and the third prints out the xformers
installation status. On Windows, please omit the triton package,
which is not available on that platform.
If all goes well, you'll see a report like the following:
xFormers 0.0.20
memory_efficient_attention.cutlassF: available
memory_efficient_attention.cutlassB: available
memory_efficient_attention.flshattF: available
memory_efficient_attention.flshattB: available
memory_efficient_attention.smallkF: available
memory_efficient_attention.smallkB: available
memory_efficient_attention.tritonflashattF: available
memory_efficient_attention.tritonflashattB: available
indexing.scaled_index_addF: available
indexing.scaled_index_addB: available
indexing.index_select: available
swiglu.dual_gemm_silu: available
swiglu.gemm_fused_operand_sum: available
swiglu.fused.p.cpp: available
is_triton_available: True
is_functorch_available: False
pytorch.version: 2.0.1+cu118
pytorch.cuda: available
gpu.compute_capability: 8.9
gpu.name: NVIDIA GeForce RTX 4070
build.info: available
build.cuda_version: 1108
build.python_version: 3.10.11
build.torch_version: 2.0.1+cu118
build.env.TORCH_CUDA_ARCH_LIST: 5.0+PTX 6.0 6.1 7.0 7.5 8.0 8.6
build.env.XFORMERS_BUILD_TYPE: Release
build.env.XFORMERS_ENABLE_DEBUG_ASSERTIONS: None
build.env.NVCC_FLAGS: None
build.env.XFORMERS_PACKAGE_FROM: wheel-v0.0.20
build.nvcc_version: 11.8.89
source.privacy: open source
Source Builds
xformers is currently under active development and at some point you
may wish to build it from sourcce to get the latest features and
bugfixes.
Source Build on Linux
Note that xFormers only works with true NVIDIA GPUs and will not work properly with the ROCm driver for AMD acceleration.
xFormers is not currently available as a pip binary wheel and must be installed from source. These instructions were written for a system running Ubuntu 22.04, but other Linux distributions should be able to adapt this recipe.
1. Install CUDA Toolkit 11.8
You will need the CUDA developer's toolkit in order to compile and install xFormers. Do not try to install Ubuntu's nvidia-cuda-toolkit package. It is out of date and will cause conflicts among the NVIDIA driver and binaries. Instead install the CUDA Toolkit package provided by NVIDIA itself. Go to CUDA Toolkit 11.8 Downloads and use the target selection wizard to choose your platform and Linux distribution. Select an installer type of "runfile (local)" at the last step.
This will provide you with a recipe for downloading and running a install shell script that will install the toolkit and drivers. For example, the install script recipe for Ubuntu 22.04 running on a x86_64 system is:
wget https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_520.61.05_linux.run
sudo sh cuda_11.8.0_520.61.05_linux.run
Rather than cut-and-paste this example, We recommend that you walk through the toolkit wizard in order to get the most up to date installer for your system.
2. Confirm/Install pyTorch 2.01 with CUDA 11.8 support
If you are using InvokeAI 3.0.2 or higher, these will already be
installed. If not, you can check whether you have the needed libraries
using a quick command. Activate the invokeai virtual environment,
either by entering the "developer's console", or manually with a
command similar to source ~/invokeai/.venv/bin/activate (depending
on where your invokeai directory is.
Then run the command:
python -c 'exec("import torch\nprint(torch.__version__)")'
If it prints 1.13.1+cu118 you're good. If not, you can install the most up to date libraries with this command:
pip install --upgrade --force-reinstall torch torchvision
3. Install the triton module
This module isn't necessary for xFormers image inference optimization, but avoids a startup warning.
pip install triton
4. Install source code build prerequisites
To build xFormers from source, you will need the build-essentials
package. If you don't have it installed already, run:
sudo apt install build-essential
5. Build xFormers
There is no pip wheel package for xFormers at this time (January 2023). Although there is a conda package, InvokeAI no longer officially supports conda installations and you're on your own if you wish to try this route.
Following the recipe provided at the xFormers GitHub page, and with the InvokeAI virtual environment active (see step 1) run the following commands:
pip install ninja
export TORCH_CUDA_ARCH_LIST="6.0;6.1;6.2;7.0;7.2;7.5;8.0;8.6"
pip install -v -U git+https://github.com/facebookresearch/xformers.git@main#egg=xformers
The TORCH_CUDA_ARCH_LIST is a list of GPU architectures to compile xFormer support for. You can speed up compilation by selecting the architecture specific for your system. You'll find the list of GPUs and their architectures at NVIDIA's GPU Compute Capability table.
If the compile and install completes successfully, you can check that xFormers is installed with this command:
python -m xformers.info
If suiccessful, the top of the listing should indicate "available" for
each of the memory_efficient_attention modules, as shown here:
memory_efficient_attention.cutlassF: available
memory_efficient_attention.cutlassB: available
memory_efficient_attention.flshattF: available
memory_efficient_attention.flshattB: available
memory_efficient_attention.smallkF: available
memory_efficient_attention.smallkB: available
memory_efficient_attention.tritonflashattF: available
memory_efficient_attention.tritonflashattB: available
[...]
You can now launch InvokeAI and enjoy the benefits of xFormers.
Windows
To come
(c) Copyright 2023 Lincoln Stein and the InvokeAI Development Team