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update dependencies and docs to cu118

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This commit is contained in:
Lincoln Stein 2023-08-09 13:38:58 -04:00 committed by Kent Keirsey
parent d42b45116f
commit 7bad9bcf53
6 changed files with 41 additions and 32 deletions
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2
README.md
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@ -161,7 +161,7 @@ the command `npm install -g yarn` if needed)
_For Windows/Linux with an NVIDIA GPU:_
```terminal
pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117
pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu118
```
_For Linux with an AMD GPU:_

2
docs/installation/010_INSTALL_AUTOMATED.md
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@ -471,7 +471,7 @@ Then type the following commands:
=== "NVIDIA System"
```bash
pip install torch torchvision --force-reinstall --extra-index-url https://download.pytorch.org/whl/cu117
pip install torch torchvision --force-reinstall --extra-index-url https://download.pytorch.org/whl/cu118
pip install xformers
```

6
docs/installation/020_INSTALL_MANUAL.md
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@ -148,7 +148,7 @@ manager, please follow these steps:
=== "CUDA (NVidia)"
```bash
pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117
pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu118
```
=== "ROCm (AMD)"
@ -312,7 +312,7 @@ installation protocol (important!)
=== "CUDA (NVidia)"
```bash
pip install -e .[xformers] --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117
pip install -e .[xformers] --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu118
```
=== "ROCm (AMD)"
@ -356,7 +356,7 @@ you can do so using this unsupported recipe:
mkdir ~/invokeai
conda create -n invokeai python=3.10
conda activate invokeai
pip install InvokeAI[xformers] --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117
pip install InvokeAI[xformers] --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu118
invokeai-configure --root ~/invokeai
invokeai --root ~/invokeai --web
```

12
docs/installation/030_INSTALL_CUDA_AND_ROCM.md
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@ -34,11 +34,11 @@ directly from NVIDIA. **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.**
Go to [CUDA Toolkit 11.7
Downloads](https://developer.nvidia.com/cuda-11-7-0-download-archive),
and use the target selection wizard to choose your operating system,
hardware platform, and preferred installation method (e.g. "local"
versus "network").
Go to [CUDA Toolkit
Downloads](https://developer.nvidia.com/cuda-downloads), and use the
target selection wizard to choose your operating system, hardware
platform, and preferred installation method (e.g. "local" versus
"network").
This will provide you with a downloadable install file or, depending
on your choices, a recipe for downloading and running a install shell
@ -61,7 +61,7 @@ Runtime Site](https://developer.nvidia.com/nvidia-container-runtime)
When installing torch and torchvision manually with `pip`, remember to provide
the argument `--extra-index-url
https://download.pytorch.org/whl/cu117` as described in the [Manual
https://download.pytorch.org/whl/cu118` as described in the [Manual
Installation Guide](020_INSTALL_MANUAL.md).
## :simple-amd: ROCm

47
docs/installation/070_INSTALL_XFORMERS.md
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@ -28,18 +28,21 @@ command line, then just be sure to activate it's virtual environment.
Then run the following three commands:
```sh
pip install xformers==0.0.16rc425
pip install triton
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. If all goes well, you'll see a report like the
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:
```sh
xFormers 0.0.16rc425
xFormers 0.0.20
memory_efficient_attention.cutlassF: available
memory_efficient_attention.cutlassB: available
memory_efficient_attention.flshattF: available
@ -48,22 +51,28 @@ 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: 1.13.1+cu117
pytorch.version: 2.0.1+cu118
pytorch.cuda: available
gpu.compute_capability: 8.6
gpu.name: NVIDIA RTX A2000 12GB
gpu.compute_capability: 8.9
gpu.name: NVIDIA GeForce RTX 4070
build.info: available
build.cuda_version: 1107
build.python_version: 3.10.9
build.torch_version: 1.13.1+cu117
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-v0.0.16rc425
build.env.XFORMERS_PACKAGE_FROM: wheel-v0.0.20
build.nvcc_version: 11.8.89
source.privacy: open source
```
@ -83,14 +92,14 @@ 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 11.7
#### 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 11.7
Downloads](https://developer.nvidia.com/cuda-11-7-0-download-archive)
by NVIDIA itself. Go to [CUDA Toolkit 11.8
Downloads](https://developer.nvidia.com/cuda-11-8-0-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.
@ -101,17 +110,17 @@ example, the install script recipe for Ubuntu 22.04 running on a
x86_64 system is:
```
wget https://developer.download.nvidia.com/compute/cuda/11.7.0/local_installers/cuda_11.7.0_515.43.04_linux.run
sudo sh cuda_11.7.0_515.43.04_linux.run
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 1.13 with CUDA 11.7 support
#### 2. Confirm/Install pyTorch 2.01 with CUDA 11.8 support
If you are using InvokeAI 2.3 or higher, these will already be
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
@ -124,7 +133,7 @@ Then run the command:
python -c 'exec("import torch\nprint(torch.__version__)")'
```
If it prints __1.13.1+cu117__ you're good. If not, you can install the
If it prints __1.13.1+cu118__ you're good. If not, you can install the
most up to date libraries with this command:
```sh

4
installer/lib/installer.py
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@ -463,10 +463,10 @@ def get_torch_source() -> (Union[str, None], str):
url = "https://download.pytorch.org/whl/cpu"
if device == "cuda":
url = "https://download.pytorch.org/whl/cu117"
url = "https://download.pytorch.org/whl/cu118"
optional_modules = "[xformers,onnx-cuda]"
if device == "cuda_and_dml":
url = "https://download.pytorch.org/whl/cu117"
url = "https://download.pytorch.org/whl/cu118"
optional_modules = "[xformers,onnx-directml]"
# in all other cases, Torch wheels should be coming from PyPi as of Torch 1.13