# import logging # # import torch # from torch import Tensor # 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 availability, # # 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) # # # # MPS workaround for https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14046 # def interpolate_with_fp32_fallback(orig_func, *args, **kwargs) -> Tensor: # try: # return orig_func(*args, **kwargs) # except RuntimeError as e: # if "not implemented for" in str(e) and "Half" in str(e): # input_tensor = args[0] # return orig_func(input_tensor.to(torch.float32), *args[1:], **kwargs).to(input_tensor.dtype) # else: # print(f"An unexpected RuntimeError occurred: {str(e)}") # # 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/AUTOMATIC1111/stable-diffusion-webui/pull/14046 # CondFunc('torch.nn.functional.interpolate', interpolate_with_fp32_fallback, None) # # # 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')