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Update 070_INSTALL_XFORMERS.md

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Millun Atluri 2023-11-14 11:10:57 +11:00 committed by Kent Keirsey
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docs/installation/070_INSTALL_XFORMERS.md
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@ -28,7 +28,7 @@ command line, then just be sure to activate it's virtual environment.
Then run the following three commands:
```sh
pip install xformers~=0.0.19
pip install xformers~=0.0.22
pip install triton # WON'T WORK ON WINDOWS
python -m xformers.info output
```
@ -42,7 +42,7 @@ If all goes well, you'll see a report like the
following:
```sh
xFormers 0.0.20
xFormers 0.0.22
memory_efficient_attention.cutlassF: available
memory_efficient_attention.cutlassB: available
memory_efficient_attention.flshattF: available
@ -59,14 +59,14 @@ 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.version: 2.1.0+cu121
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.torch_version: 2.1.0+cu121
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
@ -92,33 +92,22 @@ 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
#### 1. Install CUDA Toolkit 12.1
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package.** It is out of date and will cause conflicts among the NVIDIA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](https://developer.nvidia.com/cuda-11-8-0-download-archive)
by NVIDIA itself. Go to [CUDA Toolkit 12.1
Downloads](https://developer.nvidia.com/cuda-12-1-0-download-archive)
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:
install shell script that will install the toolkit and drivers.
```
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
#### 2. Confirm/Install pyTorch 2.1.0 with CUDA 12.1 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
@ -133,7 +122,7 @@ 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
If it prints __2.1.0+cu121__ you're good. If not, you can install the
most up to date libraries with this command:
```sh