forked from mrq/tortoise-tts
88 lines
2.9 KiB
Python
Executable File
88 lines
2.9 KiB
Python
Executable File
import torch
|
|
import psutil
|
|
import importlib
|
|
|
|
def has_dml():
|
|
loader = importlib.find_loader('torch_directml')
|
|
if loader is None:
|
|
return False
|
|
|
|
import torch_directml
|
|
return torch_directml.is_available()
|
|
|
|
def get_device_name():
|
|
name = 'cpu'
|
|
|
|
if has_dml():
|
|
name = 'dml'
|
|
elif torch.cuda.is_available():
|
|
name = 'cuda'
|
|
|
|
return name
|
|
|
|
def get_device(verbose=False):
|
|
name = get_device_name()
|
|
|
|
if verbose:
|
|
if name == 'cpu':
|
|
print("No hardware acceleration is available, falling back to CPU...")
|
|
else:
|
|
print(f"Hardware acceleration found: {name}")
|
|
|
|
if name == "dml":
|
|
import torch_directml
|
|
return torch_directml.device()
|
|
|
|
return torch.device(name)
|
|
|
|
def get_device_batch_size():
|
|
available = 1
|
|
name = get_device_name()
|
|
|
|
if name == "dml":
|
|
# there's nothing publically accessible in the DML API that exposes this
|
|
# there's a method to get currently used RAM statistics... as tiles
|
|
available = 1
|
|
elif name == "cuda":
|
|
_, available = torch.cuda.mem_get_info()
|
|
elif name == "cpu":
|
|
available = psutil.virtual_memory()[4]
|
|
|
|
availableGb = available / (1024 ** 3)
|
|
if availableGb > 14:
|
|
return 16
|
|
elif availableGb > 10:
|
|
return 8
|
|
elif availableGb > 7:
|
|
return 4
|
|
return 1
|
|
|
|
def get_device_count():
|
|
name = get_device_name()
|
|
if name == "cuda":
|
|
return torch.cuda.device_count()
|
|
if name == "dml":
|
|
import torch_directml
|
|
return torch_directml.device_count()
|
|
|
|
return 1
|
|
|
|
|
|
if has_dml():
|
|
_cumsum = torch.cumsum
|
|
_repeat_interleave = torch.repeat_interleave
|
|
_multinomial = torch.multinomial
|
|
|
|
_Tensor_new = torch.Tensor.new
|
|
_Tensor_cumsum = torch.Tensor.cumsum
|
|
_Tensor_repeat_interleave = torch.Tensor.repeat_interleave
|
|
_Tensor_multinomial = torch.Tensor.multinomial
|
|
|
|
torch.cumsum = lambda input, *args, **kwargs: ( _cumsum(input.to("cpu"), *args, **kwargs).to(input.device) )
|
|
torch.repeat_interleave = lambda input, *args, **kwargs: ( _repeat_interleave(input.to("cpu"), *args, **kwargs).to(input.device) )
|
|
torch.multinomial = lambda input, *args, **kwargs: ( _multinomial(input.to("cpu"), *args, **kwargs).to(input.device) )
|
|
|
|
torch.Tensor.new = lambda self, *args, **kwargs: ( _Tensor_new(self.to("cpu"), *args, **kwargs).to(self.device) )
|
|
torch.Tensor.cumsum = lambda self, *args, **kwargs: ( _Tensor_cumsum(self.to("cpu"), *args, **kwargs).to(self.device) )
|
|
torch.Tensor.repeat_interleave = lambda self, *args, **kwargs: ( _Tensor_repeat_interleave(self.to("cpu"), *args, **kwargs).to(self.device) )
|
|
torch.Tensor.multinomial = lambda self, *args, **kwargs: ( _Tensor_multinomial(self.to("cpu"), *args, **kwargs).to(self.device) ) |