50 lines
1.1 KiB
Python
50 lines
1.1 KiB
Python
import torch
|
|
|
|
# has_mps is only available in nightly pytorch (for now), `getattr` for compatibility
|
|
from modules import errors
|
|
|
|
has_mps = getattr(torch, 'has_mps', False)
|
|
|
|
cpu = torch.device("cpu")
|
|
|
|
|
|
def get_optimal_device():
|
|
if torch.cuda.is_available():
|
|
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")
|
|
|
|
|
|
device = get_optimal_device()
|
|
device_codeformer = cpu if has_mps else device
|
|
|
|
|
|
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)
|
|
|