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import sys, os, shlex
import contextlib
import torch
from modules import errors
# has_mps is only available in nightly pytorch (for now) and MasOS 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_optimal_device():
if torch.cuda.is_available():
from modules import shared
device_id = shared.cmd_opts.device_id
if device_id is not None:
cuda_device = f"cuda:{device_id}"
return torch.device(cuda_device)
else:
return torch.device("cuda")
if has_mps():
return torch.device("mps")
return cpu
def torch_gc():
if torch.cuda.is_available():
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):
# Pytorch currently doesn't handle setting randomness correctly when the metal backend is used.
if device.type == 'mps':
generator = torch.Generator(device=cpu)
generator.manual_seed(seed)
noise = torch.randn(shape, generator=generator, device=cpu).to(device)
return noise
torch.manual_seed(seed)
return torch.randn(shape, device=device)
def randn_without_seed(shape):
# Pytorch currently doesn't handle setting randomness correctly when the metal backend is used.
if device.type == 'mps':
generator = torch.Generator(device=cpu)
noise = torch.randn(shape, generator=generator, device=cpu).to(device)
return noise
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
def mps_contiguous(input_tensor, device):
return input_tensor.contiguous() if device.type == 'mps' else input_tensor
def mps_contiguous_to(input_tensor, device):
return mps_contiguous(input_tensor, device).to(device)
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