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import sys, os, shlex
import contextlib
import torch
from modules import errors
from packaging import version
# has_mps is only available in nightly pytorch (for now) and macOS 12.3+.
# check `getattr` and try it for compatibility
def has_mps() -> bool:
if not getattr(torch, 'has_mps', False):
return False
try:
torch.zeros(1).to(torch.device("mps"))
return True
except Exception:
return False
def extract_device_id(args, name):
for x in range(len(args)):
if name in args[x]:
return args[x + 1]
return None
def get_cuda_device_string():
from modules import shared
if shared.cmd_opts.device_id is not None:
return f"cuda:{shared.cmd_opts.device_id}"
return "cuda"
def get_optimal_device():
if torch.cuda.is_available():
return torch.device(get_cuda_device_string())
if has_mps():
return torch.device("mps")
return cpu
def torch_gc():
if torch.cuda.is_available():
with torch.cuda.device(get_cuda_device_string()):
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
def enable_tf32():
if torch.cuda.is_available():
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
errors.run(enable_tf32, "Enabling TF32")
cpu = torch.device("cpu")
device = device_interrogate = device_gfpgan = device_swinir = device_esrgan = device_scunet = device_codeformer = None
dtype = torch.float16
dtype_vae = torch.float16
def randn(seed, shape):
torch.manual_seed(seed)
if device.type == 'mps':
return torch.randn(shape, device=cpu).to(device)
return torch.randn(shape, device=device)
def randn_without_seed(shape):
if device.type == 'mps':
return torch.randn(shape, device=cpu).to(device)
return torch.randn(shape, device=device)
def autocast(disable=False):
from modules import shared
if disable:
return contextlib.nullcontext()
if dtype == torch.float32 or shared.cmd_opts.precision == "full":
return contextlib.nullcontext()
return torch.autocast("cuda")
# MPS workaround for https://github.com/pytorch/pytorch/issues/79383
orig_tensor_to = torch.Tensor.to
def tensor_to_fix(self, *args, **kwargs):
if self.device.type != 'mps' and \
((len(args) > 0 and isinstance(args[0], torch.device) and args[0].type == 'mps') or \
(isinstance(kwargs.get('device'), torch.device) and kwargs['device'].type == 'mps')):
self = self.contiguous()
return orig_tensor_to(self, *args, **kwargs)
# MPS workaround for https://github.com/pytorch/pytorch/issues/80800
orig_layer_norm = torch.nn.functional.layer_norm
def layer_norm_fix(*args, **kwargs):
if len(args) > 0 and isinstance(args[0], torch.Tensor) and args[0].device.type == 'mps':
args = list(args)
args[0] = args[0].contiguous()
return orig_layer_norm(*args, **kwargs)
# PyTorch 1.13 doesn't need these fixes but unfortunately is slower and has regressions that prevent training from working
if has_mps() and version.parse(torch.__version__) < version.parse("1.13"):
torch.Tensor.to = tensor_to_fix
torch.nn.functional.layer_norm = layer_norm_fix
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