193 lines
6.3 KiB
Python
193 lines
6.3 KiB
Python
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 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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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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def extract_device_id(args, name):
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for x in range(len(args)):
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if name in args[x]:
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return args[x + 1]
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return None
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def get_cuda_device_string():
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from modules import shared
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if shared.cmd_opts.device_id is not None:
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return f"cuda:{shared.cmd_opts.device_id}"
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return "cuda"
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def get_optimal_device():
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if torch.cuda.is_available():
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return torch.device(get_cuda_device_string())
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if has_mps():
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return torch.device("mps")
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return cpu
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def get_device_for(task):
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from modules import shared
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if task in shared.cmd_opts.use_cpu:
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return cpu
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return get_optimal_device()
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def torch_gc():
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if torch.cuda.is_available():
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with torch.cuda.device(get_cuda_device_string()):
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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def enable_tf32():
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if torch.cuda.is_available():
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# enabling benchmark option seems to enable a range of cards to do fp16 when they otherwise can't
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# see https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/4407
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if any([torch.cuda.get_device_capability(devid) == (7, 5) for devid in range(0, torch.cuda.device_count())]):
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torch.backends.cudnn.benchmark = True
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torch.backends.cuda.matmul.allow_tf32 = True
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torch.backends.cudnn.allow_tf32 = True
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errors.run(enable_tf32, "Enabling TF32")
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cpu = torch.device("cpu")
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device = device_interrogate = device_gfpgan = device_esrgan = device_codeformer = None
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dtype = torch.float16
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dtype_vae = torch.float16
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def randn(seed, shape):
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torch.manual_seed(seed)
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if device.type == 'mps':
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return torch.randn(shape, device=cpu).to(device)
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return torch.randn(shape, device=device)
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def randn_without_seed(shape):
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if device.type == 'mps':
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return torch.randn(shape, device=cpu).to(device)
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return torch.randn(shape, device=device)
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def autocast(disable=False):
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from modules import shared
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if disable:
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return contextlib.nullcontext()
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if dtype == torch.float32 or shared.cmd_opts.precision == "full":
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return contextlib.nullcontext()
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return torch.autocast("cuda")
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class NansException(Exception):
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pass
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def test_for_nans(x, where):
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from modules import shared
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if shared.cmd_opts.disable_nan_check:
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return
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if not torch.all(torch.isnan(x)).item():
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return
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if where == "unet":
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message = "A tensor with all NaNs was produced in Unet."
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if not shared.cmd_opts.no_half:
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message += " This could be either because there's not enough precision to represent the picture, or because your video card does not support half type. Try using --no-half commandline argument to fix this."
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elif where == "vae":
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message = "A tensor with all NaNs was produced in VAE."
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if not shared.cmd_opts.no_half and not shared.cmd_opts.no_half_vae:
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message += " This could be because there's not enough precision to represent the picture. Try adding --no-half-vae commandline argument to fix this."
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else:
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message = "A tensor with all NaNs was produced."
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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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orig_narrow = torch.narrow
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torch.narrow = lambda *args, **kwargs: ( orig_narrow(*args, **kwargs).clone() )
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