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@@ -2,9 +2,10 @@ import sys, os, shlex
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import contextlib
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import torch
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from modules import errors
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+from packaging import version
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-# has_mps is only available in nightly pytorch (for now) and MasOS 12.3+.
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+# has_mps is only available in nightly pytorch (for now) and macOS 12.3+.
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# check `getattr` and try it for compatibility
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def has_mps() -> bool:
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if not getattr(torch, 'has_mps', False):
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@@ -94,3 +95,28 @@ def autocast(disable=False):
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return contextlib.nullcontext()
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return torch.autocast("cuda")
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+
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+
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+# MPS workaround for https://github.com/pytorch/pytorch/issues/79383
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+orig_tensor_to = torch.Tensor.to
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+def tensor_to_fix(self, *args, **kwargs):
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+ if self.device.type != 'mps' and \
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+ ((len(args) > 0 and isinstance(args[0], torch.device) and args[0].type == 'mps') or \
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+ (isinstance(kwargs.get('device'), torch.device) and kwargs['device'].type == 'mps')):
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+ self = self.contiguous()
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+ return orig_tensor_to(self, *args, **kwargs)
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+
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+
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+# MPS workaround for https://github.com/pytorch/pytorch/issues/80800
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+orig_layer_norm = torch.nn.functional.layer_norm
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+def layer_norm_fix(*args, **kwargs):
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+ if len(args) > 0 and isinstance(args[0], torch.Tensor) and args[0].device.type == 'mps':
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+ args = list(args)
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+ args[0] = args[0].contiguous()
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+ return orig_layer_norm(*args, **kwargs)
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+
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+
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+# PyTorch 1.13 doesn't need these fixes but unfortunately is slower and has regressions that prevent training from working
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+if has_mps() and version.parse(torch.__version__) < version.parse("1.13"):
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+ torch.Tensor.to = tensor_to_fix
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+ torch.nn.functional.layer_norm = layer_norm_fix
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