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@ -1,17 +1,25 @@
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import sys
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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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if sys.platform == "darwin":
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from modules import mac_specific
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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 has_mps() -> bool:
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if sys.platform != "darwin":
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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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else:
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return mac_specific.has_mps
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def has_dml():
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import importlib
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loader = importlib.find_loader('torch_directml')
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return loader is not None
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def extract_device_id(args, name):
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for x in range(len(args)):
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@ -31,16 +39,23 @@ def get_cuda_device_string():
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def get_optimal_device_name():
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if torch.cuda.is_available():
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return get_cuda_device_string()
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if has_dml():
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return "dml"
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if has_mps():
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return "mps"
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if torch.cuda.is_available():
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return get_cuda_device_string()
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return "cpu"
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def get_optimal_device():
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if get_optimal_device_name() == "dml":
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import torch_directml
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return torch_directml.device()
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return torch.device(get_optimal_device_name())
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@ -150,3 +165,75 @@ def test_for_nans(x, where):
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message += " Use --disable-nan-check commandline argument to disable this check."
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raise NansException(message)
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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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# 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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# MPS workaround for https://github.com/pytorch/pytorch/issues/90532
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orig_tensor_numpy = torch.Tensor.numpy
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def numpy_fix(self, *args, **kwargs):
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if self.requires_grad:
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self = self.detach()
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return orig_tensor_numpy(self, *args, **kwargs)
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# MPS workaround for https://github.com/pytorch/pytorch/issues/89784
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orig_cumsum = torch.cumsum
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orig_Tensor_cumsum = torch.Tensor.cumsum
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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 cumsum_needs_bool_fix and 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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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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torch.Tensor.to = tensor_to_fix
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torch.nn.functional.layer_norm = layer_norm_fix
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torch.Tensor.numpy = numpy_fix
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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_needs_bool_fix = not torch.BoolTensor([True,True]).to(device=torch.device("mps"), dtype=torch.int64).equal(torch.BoolTensor([True,False]).to(torch.device("mps")).cumsum(0))
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torch.cumsum = lambda input, *args, **kwargs: ( cumsum_fix(input, orig_cumsum, *args, **kwargs) )
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torch.Tensor.cumsum = lambda self, *args, **kwargs: ( cumsum_fix(self, orig_Tensor_cumsum, *args, **kwargs) )
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if has_dml():
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_cumsum = torch.cumsum
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_repeat_interleave = torch.repeat_interleave
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_multinomial = torch.multinomial
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_Tensor_new = torch.Tensor.new
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_Tensor_cumsum = torch.Tensor.cumsum
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_Tensor_repeat_interleave = torch.Tensor.repeat_interleave
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_Tensor_multinomial = torch.Tensor.multinomial
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torch.cumsum = lambda input, *args, **kwargs: ( _cumsum(input.to("cpu"), *args, **kwargs).to(input.device) )
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torch.repeat_interleave = lambda input, *args, **kwargs: ( _repeat_interleave(input.to("cpu"), *args, **kwargs).to(input.device) )
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torch.multinomial = lambda input, *args, **kwargs: ( _multinomial(input.to("cpu"), *args, **kwargs).to(input.device) )
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torch.Tensor.new = lambda self, *args, **kwargs: ( _Tensor_new(self.to("cpu"), *args, **kwargs).to(self.device) )
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torch.Tensor.cumsum = lambda self, *args, **kwargs: ( _Tensor_cumsum(self.to("cpu"), *args, **kwargs).to(self.device) )
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torch.Tensor.repeat_interleave = lambda self, *args, **kwargs: ( _Tensor_repeat_interleave(self.to("cpu"), *args, **kwargs).to(self.device) )
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torch.Tensor.multinomial = lambda self, *args, **kwargs: ( _Tensor_multinomial(self.to("cpu"), *args, **kwargs).to(self.device) )
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