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warn on startup if cuda/rocm are available and torch-cpu is installed

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Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
2025-10-01 12:24:55 -04:00
parent 81bb2b99ef
commit ec7b22e383
2 changed files with 46 additions and 40 deletions
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CHANGELOG.md
+1
View File
@@ -31,6 +31,7 @@
`torch-directml` received no updates in over 1 year and its currently superceded by `rocm` or `zluda`
- command line params `--use-zluda` and `--use-rocm` will attempt desired operation or fail if not possible
previously sdnext was performing a fallback to `torch-cpu` which is not desired
- **installer**: warn if cuda or rocm are available and `torch-cpu` is installed
- **Extensions**
- [Agent-Scheduler](https://github.com/SipherAGI/sd-webui-agent-scheduler)
was a high-value built-in extension, but it has not been maintained for 1.5 years
installer.py
+45 -40
View File
@@ -935,54 +935,59 @@ def check_torch():
else:
log.warning('Torch: CPU-only version installed')
torch_command = os.environ.get('TORCH_COMMAND', 'torch torchvision')
if 'torch' in torch_command and not args.version:
if args.version:
return
if 'torch' in torch_command:
if not installed('torch'):
log.info(f'Torch: download and install in progress... cmd="{torch_command}"')
install('--upgrade pip', 'pip', reinstall=True) # pytorch rocm is too large for older pip
install(torch_command, 'torch torchvision', quiet=True)
else:
try:
import torch
try:
import torch
log.info(f'Torch {torch.__version__}')
try:
import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
log.info(f'Torch backend: Intel IPEX {ipex.__version__}')
except Exception:
pass
if hasattr(torch, "xpu") and torch.xpu.is_available() and allow_ipex:
if shutil.which('icpx') is not None:
log.info(f'{os.popen("icpx --version").read().rstrip()}')
for device in range(torch.xpu.device_count()):
log.info(f'Torch detected GPU: {torch.xpu.get_device_name(device)} VRAM {round(torch.xpu.get_device_properties(device).total_memory / 1024 / 1024)} Compute Units {torch.xpu.get_device_properties(device).max_compute_units}')
elif torch.cuda.is_available() and (allow_cuda or allow_rocm):
# log.debug(f'Torch allocator: {torch.cuda.get_allocator_backend()}')
if torch.version.cuda and allow_cuda:
log.info(f'Torch backend: nVidia CUDA {torch.version.cuda} cuDNN {torch.backends.cudnn.version() if torch.backends.cudnn.is_available() else "N/A"}')
elif torch.version.hip and allow_rocm:
log.info(f'Torch backend: AMD ROCm HIP {torch.version.hip}')
else:
log.warning('Unknown Torch backend')
for device in [torch.cuda.device(i) for i in range(torch.cuda.device_count())]:
log.info(f'Torch detected GPU: {torch.cuda.get_device_name(device)} VRAM {round(torch.cuda.get_device_properties(device).total_memory / 1024 / 1024)} Arch {torch.cuda.get_device_capability(device)} Cores {torch.cuda.get_device_properties(device).multi_processor_count}')
import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
log.info(f'Torch backend: type=IPEX version={ipex.__version__}')
except Exception:
pass
if 'cpu' in torch.__version__:
if is_cuda_available:
log.warning(f'Torch: version="{torch.__version__}" CPU version installed and CUDA is available - consider reinstalling')
elif is_rocm_available:
log.warning(f'Torch: version="{torch.__version__}" CPU version installed and ROCm is available - consider reinstalling')
if hasattr(torch, "xpu") and torch.xpu.is_available() and allow_ipex:
if shutil.which('icpx') is not None:
log.info(f'{os.popen("icpx --version").read().rstrip()}')
for device in range(torch.xpu.device_count()):
log.info(f'Torch detected: gpu="{torch.xpu.get_device_name(device)}" vram={round(torch.xpu.get_device_properties(device).total_memory / 1024 / 1024)} units={torch.xpu.get_device_properties(device).max_compute_units}')
elif torch.cuda.is_available() and (allow_cuda or allow_rocm):
if torch.version.cuda and allow_cuda:
log.info(f'Torch backend: version="{torch.__version__}" type=CUDA CUDA={torch.version.cuda} cuDNN={torch.backends.cudnn.version() if torch.backends.cudnn.is_available() else "N/A"}')
elif torch.version.hip and allow_rocm:
log.info(f'Torch backend: version="{torch.__version__}" type=ROCm HIP={torch.version.hip}')
else:
try:
if args.use_directml and allow_directml:
import torch_directml # pylint: disable=import-error
dml_ver = pkg_resources.get_distribution("torch-directml")
log.warning(f'Torch backend: DirectML ({dml_ver})')
log.warning('DirectML: end-of-life')
for i in range(0, torch_directml.device_count()):
log.info(f'Torch detected GPU: {torch_directml.device_name(i)}')
except Exception:
log.warning("Torch reports CUDA not available")
except Exception as e:
log.error(f'Torch cannot load: {e}')
if not args.ignore:
sys.exit(1)
log.warning('Unknown Torch backend')
for device in [torch.cuda.device(i) for i in range(torch.cuda.device_count())]:
log.info(f'Torch detected: gpu="{torch.cuda.get_device_name(device)}" vram={round(torch.cuda.get_device_properties(device).total_memory / 1024 / 1024)} arch={torch.cuda.get_device_capability(device)} cores={torch.cuda.get_device_properties(device).multi_processor_count}')
else:
try:
if args.use_directml and allow_directml:
import torch_directml # pylint: disable=import-error
dml_ver = pkg_resources.get_distribution("torch-directml")
log.warning(f'Torch backend: DirectML ({dml_ver})')
log.warning('DirectML: end-of-life')
for i in range(0, torch_directml.device_count()):
log.info(f'Torch detected GPU: {torch_directml.device_name(i)}')
except Exception:
log.warning("Torch reports CUDA not available")
except Exception as e:
log.error(f'Torch cannot load: {e}')
if not args.ignore:
sys.exit(1)
if rocm.is_installed:
rocm.postinstall()
if args.version:
return
if not args.skip_all:
install_torch_addons()
check_cudnn()