forked from mrq/tortoise-tts
130 lines
3.9 KiB
Python
Executable File
130 lines
3.9 KiB
Python
Executable File
import torch
|
|
import psutil
|
|
import importlib
|
|
|
|
DEVICE_OVERRIDE = None
|
|
DEVICE_BATCH_SIZE_MAP = [(14, 16), (10,8), (7,4)]
|
|
|
|
from inspect import currentframe, getframeinfo
|
|
import gc
|
|
|
|
def do_gc():
|
|
gc.collect()
|
|
try:
|
|
torch.cuda.empty_cache()
|
|
except Exception as e:
|
|
pass
|
|
|
|
def print_stats(collect=False):
|
|
cf = currentframe().f_back
|
|
msg = f'{getframeinfo(cf).filename}:{cf.f_lineno}'
|
|
|
|
if collect:
|
|
do_gc()
|
|
|
|
tot = torch.cuda.get_device_properties(0).total_memory / (1024 ** 3)
|
|
res = torch.cuda.memory_reserved(0) / (1024 ** 3)
|
|
alloc = torch.cuda.memory_allocated(0) / (1024 ** 3)
|
|
print("[{}] Total: {:.3f} | Reserved: {:.3f} | Allocated: {:.3f} | Free: {:.3f}".format( msg, tot, res, alloc, tot-res ))
|
|
|
|
|
|
def has_dml():
|
|
loader = importlib.find_loader('torch_directml')
|
|
if loader is None:
|
|
return False
|
|
|
|
import torch_directml
|
|
return torch_directml.is_available()
|
|
|
|
def set_device_name(name):
|
|
global DEVICE_OVERRIDE
|
|
DEVICE_OVERRIDE = name
|
|
|
|
def get_device_name(attempt_gc=True):
|
|
global DEVICE_OVERRIDE
|
|
if DEVICE_OVERRIDE is not None and DEVICE_OVERRIDE != "":
|
|
return DEVICE_OVERRIDE
|
|
|
|
name = 'cpu'
|
|
|
|
if torch.cuda.is_available():
|
|
name = 'cuda'
|
|
if attempt_gc:
|
|
torch.cuda.empty_cache() # may have performance implications
|
|
elif has_dml():
|
|
name = 'dml'
|
|
|
|
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_vram( name=get_device_name() ):
|
|
available = 1
|
|
|
|
if name == "cuda":
|
|
_, available = torch.cuda.mem_get_info()
|
|
elif name == "cpu":
|
|
available = psutil.virtual_memory()[4]
|
|
|
|
return available / (1024 ** 3)
|
|
|
|
def get_device_batch_size(name=get_device_name()):
|
|
vram = get_device_vram(name)
|
|
|
|
if vram > 14:
|
|
return 16
|
|
elif vram > 10:
|
|
return 8
|
|
elif vram > 7:
|
|
return 4
|
|
"""
|
|
for k, v in DEVICE_BATCH_SIZE_MAP:
|
|
if vram > k:
|
|
return v
|
|
"""
|
|
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 you're getting errors make sure you've updated your torch-directml, and if you're still getting errors then you can uncomment the below block
|
|
"""
|
|
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) )
|
|
""" |