Mirrors
/
stable-diffusion-webui
mirror of https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Merge pull request #5810 from brkirch/fix-training-mps 

Training fixes for MPS
This commit is contained in:
AUTOMATIC1111 2022-12-24 08:34:46 +03:00 committed by GitHub
parent f0dfed2a17 cca16373de
commit 3bfc6c07ae
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 15 additions and 6 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

9
modules/devices.py
View File

@ -125,7 +125,16 @@ def layer_norm_fix(*args, **kwargs):
return orig_layer_norm(*args, **kwargs) return orig_layer_norm(*args, **kwargs)
# MPS workaround for https://github.com/pytorch/pytorch/issues/90532
orig_tensor_numpy = torch.Tensor.numpy
def numpy_fix(self, *args, **kwargs):
if self.requires_grad:
self = self.detach()
return orig_tensor_numpy(self, *args, **kwargs)
# PyTorch 1.13 doesn't need these fixes but unfortunately is slower and has regressions that prevent training from working # PyTorch 1.13 doesn't need these fixes but unfortunately is slower and has regressions that prevent training from working
if has_mps() and version.parse(torch.__version__) < version.parse("1.13"): if has_mps() and version.parse(torch.__version__) < version.parse("1.13"):
torch.Tensor.to = tensor_to_fix torch.Tensor.to = tensor_to_fix
torch.nn.functional.layer_norm = layer_norm_fix torch.nn.functional.layer_norm = layer_norm_fix
torch.Tensor.numpy = numpy_fix

12
modules/safe.py
View File

@ -37,16 +37,16 @@ class RestrictedUnpickler(pickle.Unpickler):
if module == 'collections' and name == 'OrderedDict': if module == 'collections' and name == 'OrderedDict':
return getattr(collections, name) return getattr(collections, name)
if module == 'torch._utils' and name in ['_rebuild_tensor_v2', '_rebuild_parameter']: if module == 'torch._utils' and name in ['_rebuild_tensor_v2', '_rebuild_parameter', '_rebuild_device_tensor_from_numpy']:
return getattr(torch._utils, name) return getattr(torch._utils, name)
if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage', 'DoubleStorage', 'ByteStorage']: if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage', 'DoubleStorage', 'ByteStorage', 'float32']:
return getattr(torch, name) return getattr(torch, name)
if module == 'torch.nn.modules.container' and name in ['ParameterDict']: if module == 'torch.nn.modules.container' and name in ['ParameterDict']:
return getattr(torch.nn.modules.container, name) return getattr(torch.nn.modules.container, name)
if module == 'numpy.core.multiarray' and name == 'scalar': if module == 'numpy.core.multiarray' and name in ['scalar', '_reconstruct']:
return numpy.core.multiarray.scalar return getattr(numpy.core.multiarray, name)
if module == 'numpy' and name == 'dtype': if module == 'numpy' and name in ['dtype', 'ndarray']:
return numpy.dtype return getattr(numpy, name)
if module == '_codecs' and name == 'encode': if module == '_codecs' and name == 'encode':
return encode return encode
if module == "pytorch_lightning.callbacks" and name == 'model_checkpoint': if module == "pytorch_lightning.callbacks" and name == 'model_checkpoint':