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Merge pull request #6402 from brkirch/work-with-nightly-local-builds

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Add support for using PyTorch nightly and local builds
This commit is contained in:
AUTOMATIC1111
2023-01-06 07:51:45 +03:00
committed by GitHub
parent 3ea354f274 8111b5569d
commit 85fa4eacea
2 changed files with 29 additions and 6 deletions
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28
modules/devices.py
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@@ -133,8 +133,26 @@ def numpy_fix(self, *args, **kwargs):
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
if has_mps() and version.parse(torch.__version__) < version.parse("1.13"):
torch.Tensor.to = tensor_to_fix
torch.nn.functional.layer_norm = layer_norm_fix
torch.Tensor.numpy = numpy_fix
# MPS workaround for https://github.com/pytorch/pytorch/issues/89784
orig_cumsum = torch.cumsum
orig_Tensor_cumsum = torch.Tensor.cumsum
def cumsum_fix(input, cumsum_func, *args, **kwargs):
if input.device.type == 'mps':
output_dtype = kwargs.get('dtype', input.dtype)
if any(output_dtype == broken_dtype for broken_dtype in [torch.bool, torch.int8, torch.int16, torch.int64]):
return cumsum_func(input.cpu(), *args, **kwargs).to(input.device)
return cumsum_func(input, *args, **kwargs)
if has_mps():
if version.parse(torch.__version__) < version.parse("1.13"):
# PyTorch 1.13 doesn't need these fixes but unfortunately is slower and has regressions that prevent training from working
torch.Tensor.to = tensor_to_fix
torch.nn.functional.layer_norm = layer_norm_fix
torch.Tensor.numpy = numpy_fix
elif version.parse(torch.__version__) > version.parse("1.13.1"):
if not torch.Tensor([1,2]).to(torch.device("mps")).equal(torch.Tensor([1,1]).to(torch.device("mps")).cumsum(0, dtype=torch.int16)):
torch.cumsum = lambda input, *args, **kwargs: ( cumsum_fix(input, orig_cumsum, *args, **kwargs) )
torch.Tensor.cumsum = lambda self, *args, **kwargs: ( cumsum_fix(self, orig_Tensor_cumsum, *args, **kwargs) )
orig_narrow = torch.narrow
torch.narrow = lambda *args, **kwargs: ( orig_narrow(*args, **kwargs).clone() )