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https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
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a4cb96d4ae
The test isn't working correctly on macOS 13.3 and the bool tensor fix for cumsum is currently always needed anyway, so enable the fix by default.
53 lines
3.3 KiB
Python
53 lines
3.3 KiB
Python
import torch
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from modules import paths
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from modules.sd_hijack_utils import CondFunc
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from packaging import version
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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 check_for_mps() -> bool:
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if not getattr(torch, 'has_mps', False):
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return False
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try:
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torch.zeros(1).to(torch.device("mps"))
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return True
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except Exception:
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return False
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has_mps = check_for_mps()
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# MPS workaround for https://github.com/pytorch/pytorch/issues/89784
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def cumsum_fix(input, cumsum_func, *args, **kwargs):
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if input.device.type == 'mps':
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output_dtype = kwargs.get('dtype', input.dtype)
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if output_dtype == torch.int64:
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return cumsum_func(input.cpu(), *args, **kwargs).to(input.device)
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elif output_dtype == torch.bool or cumsum_needs_int_fix and (output_dtype == torch.int8 or output_dtype == torch.int16):
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return cumsum_func(input.to(torch.int32), *args, **kwargs).to(torch.int64)
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return cumsum_func(input, *args, **kwargs)
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if has_mps:
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# MPS fix for randn in torchsde
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CondFunc('torchsde._brownian.brownian_interval._randn', lambda _, size, dtype, device, seed: torch.randn(size, dtype=dtype, device=torch.device("cpu"), generator=torch.Generator(torch.device("cpu")).manual_seed(int(seed))).to(device), lambda _, size, dtype, device, seed: device.type == 'mps')
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if version.parse(torch.__version__) < version.parse("1.13"):
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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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# MPS workaround for https://github.com/pytorch/pytorch/issues/79383
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CondFunc('torch.Tensor.to', lambda orig_func, self, *args, **kwargs: orig_func(self.contiguous(), *args, **kwargs),
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lambda _, self, *args, **kwargs: self.device.type != 'mps' and (args and isinstance(args[0], torch.device) and args[0].type == 'mps' or isinstance(kwargs.get('device'), torch.device) and kwargs['device'].type == 'mps'))
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# MPS workaround for https://github.com/pytorch/pytorch/issues/80800
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CondFunc('torch.nn.functional.layer_norm', lambda orig_func, *args, **kwargs: orig_func(*([args[0].contiguous()] + list(args[1:])), **kwargs),
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lambda _, *args, **kwargs: args and isinstance(args[0], torch.Tensor) and args[0].device.type == 'mps')
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# MPS workaround for https://github.com/pytorch/pytorch/issues/90532
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CondFunc('torch.Tensor.numpy', lambda orig_func, self, *args, **kwargs: orig_func(self.detach(), *args, **kwargs), lambda _, self, *args, **kwargs: self.requires_grad)
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elif version.parse(torch.__version__) > version.parse("1.13.1"):
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cumsum_needs_int_fix = not torch.Tensor([1,2]).to(torch.device("mps")).equal(torch.ShortTensor([1,1]).to(torch.device("mps")).cumsum(0))
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cumsum_fix_func = lambda orig_func, input, *args, **kwargs: cumsum_fix(input, orig_func, *args, **kwargs)
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CondFunc('torch.cumsum', cumsum_fix_func, None)
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CondFunc('torch.Tensor.cumsum', cumsum_fix_func, None)
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CondFunc('torch.narrow', lambda orig_func, *args, **kwargs: orig_func(*args, **kwargs).clone(), None)
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