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| import logging | |
| import torch | |
| import platform | |
| from modules.sd_hijack_utils import CondFunc | |
| from packaging import version | |
| from modules import shared | |
| log = logging.getLogger(__name__) | |
| # before torch version 1.13, has_mps is only available in nightly pytorch and macOS 12.3+, | |
| # use check `getattr` and try it for compatibility. | |
| # in torch version 1.13, backends.mps.is_available() and backends.mps.is_built() are introduced in to check mps availabilty, | |
| # since torch 2.0.1+ nightly build, getattr(torch, 'has_mps', False) was deprecated, see https://github.com/pytorch/pytorch/pull/103279 | |
| def check_for_mps() -> bool: | |
| if version.parse(torch.__version__) <= version.parse("2.0.1"): | |
| if not getattr(torch, 'has_mps', False): | |
| return False | |
| try: | |
| torch.zeros(1).to(torch.device("mps")) | |
| return True | |
| except Exception: | |
| return False | |
| else: | |
| return torch.backends.mps.is_available() and torch.backends.mps.is_built() | |
| has_mps = check_for_mps() | |
| def torch_mps_gc() -> None: | |
| try: | |
| if shared.state.current_latent is not None: | |
| log.debug("`current_latent` is set, skipping MPS garbage collection") | |
| return | |
| from torch.mps import empty_cache | |
| empty_cache() | |
| except Exception: | |
| log.warning("MPS garbage collection failed", exc_info=True) | |
| # MPS workaround for https://github.com/pytorch/pytorch/issues/89784 | |
| def cumsum_fix(input, cumsum_func, *args, **kwargs): | |
| if input.device.type == 'mps': | |
| output_dtype = kwargs.get('dtype', input.dtype) | |
| if output_dtype == torch.int64: | |
| return cumsum_func(input.cpu(), *args, **kwargs).to(input.device) | |
| elif output_dtype == torch.bool or cumsum_needs_int_fix and (output_dtype == torch.int8 or output_dtype == torch.int16): | |
| return cumsum_func(input.to(torch.int32), *args, **kwargs).to(torch.int64) | |
| return cumsum_func(input, *args, **kwargs) | |
| if has_mps: | |
| if platform.mac_ver()[0].startswith("13.2."): | |
| # MPS workaround for https://github.com/pytorch/pytorch/issues/95188, thanks to danieldk (https://github.com/explosion/curated-transformers/pull/124) | |
| CondFunc('torch.nn.functional.linear', lambda _, input, weight, bias: (torch.matmul(input, weight.t()) + bias) if bias is not None else torch.matmul(input, weight.t()), lambda _, input, weight, bias: input.numel() > 10485760) | |
| 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 | |
| # MPS workaround for https://github.com/pytorch/pytorch/issues/79383 | |
| CondFunc('torch.Tensor.to', lambda orig_func, self, *args, **kwargs: orig_func(self.contiguous(), *args, **kwargs), | |
| 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')) | |
| # MPS workaround for https://github.com/pytorch/pytorch/issues/80800 | |
| CondFunc('torch.nn.functional.layer_norm', lambda orig_func, *args, **kwargs: orig_func(*([args[0].contiguous()] + list(args[1:])), **kwargs), | |
| lambda _, *args, **kwargs: args and isinstance(args[0], torch.Tensor) and args[0].device.type == 'mps') | |
| # MPS workaround for https://github.com/pytorch/pytorch/issues/90532 | |
| CondFunc('torch.Tensor.numpy', lambda orig_func, self, *args, **kwargs: orig_func(self.detach(), *args, **kwargs), lambda _, self, *args, **kwargs: self.requires_grad) | |
| elif version.parse(torch.__version__) > version.parse("1.13.1"): | |
| cumsum_needs_int_fix = not torch.Tensor([1,2]).to(torch.device("mps")).equal(torch.ShortTensor([1,1]).to(torch.device("mps")).cumsum(0)) | |
| cumsum_fix_func = lambda orig_func, input, *args, **kwargs: cumsum_fix(input, orig_func, *args, **kwargs) | |
| CondFunc('torch.cumsum', cumsum_fix_func, None) | |
| CondFunc('torch.Tensor.cumsum', cumsum_fix_func, None) | |
| CondFunc('torch.narrow', lambda orig_func, *args, **kwargs: orig_func(*args, **kwargs).clone(), None) | |
| # MPS workaround for https://github.com/pytorch/pytorch/issues/96113 | |
| CondFunc('torch.nn.functional.layer_norm', lambda orig_func, x, normalized_shape, weight, bias, eps, **kwargs: orig_func(x.float(), normalized_shape, weight.float() if weight is not None else None, bias.float() if bias is not None else bias, eps).to(x.dtype), lambda _, input, *args, **kwargs: len(args) == 4 and input.device.type == 'mps') | |
| # MPS workaround for https://github.com/pytorch/pytorch/issues/92311 | |
| if platform.processor() == 'i386': | |
| for funcName in ['torch.argmax', 'torch.Tensor.argmax']: | |
| CondFunc(funcName, lambda _, input, *args, **kwargs: torch.max(input.float() if input.dtype == torch.int64 else input, *args, **kwargs)[1], lambda _, input, *args, **kwargs: input.device.type == 'mps') | |