Spaces:
Runtime error
Runtime error
File size: 4,322 Bytes
6b803ee |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 |
# By lllyasviel
import torch
cpu = torch.device('cpu')
gpu = torch.device(f'cuda:{torch.cuda.current_device()}')
gpu_complete_modules = []
class DynamicSwapInstaller:
@staticmethod
def _install_module(module: torch.nn.Module, **kwargs):
original_class = module.__class__
module.__dict__['forge_backup_original_class'] = original_class
def hacked_get_attr(self, name: str):
if '_parameters' in self.__dict__:
_parameters = self.__dict__['_parameters']
if name in _parameters:
p = _parameters[name]
if p is None:
return None
if p.__class__ == torch.nn.Parameter:
return torch.nn.Parameter(p.to(**kwargs), requires_grad=p.requires_grad)
else:
return p.to(**kwargs)
if '_buffers' in self.__dict__:
_buffers = self.__dict__['_buffers']
if name in _buffers:
return _buffers[name].to(**kwargs)
return super(original_class, self).__getattr__(name)
module.__class__ = type('DynamicSwap_' + original_class.__name__, (original_class,), {
'__getattr__': hacked_get_attr,
})
return
@staticmethod
def _uninstall_module(module: torch.nn.Module):
if 'forge_backup_original_class' in module.__dict__:
module.__class__ = module.__dict__.pop('forge_backup_original_class')
return
@staticmethod
def install_model(model: torch.nn.Module, **kwargs):
for m in model.modules():
DynamicSwapInstaller._install_module(m, **kwargs)
return
@staticmethod
def uninstall_model(model: torch.nn.Module):
for m in model.modules():
DynamicSwapInstaller._uninstall_module(m)
return
def fake_diffusers_current_device(model: torch.nn.Module, target_device: torch.device):
if hasattr(model, 'scale_shift_table'):
model.scale_shift_table.data = model.scale_shift_table.data.to(target_device)
return
for k, p in model.named_modules():
if hasattr(p, 'weight'):
p.to(target_device)
return
def get_cuda_free_memory_gb(device=None):
if device is None:
device = gpu
memory_stats = torch.cuda.memory_stats(device)
bytes_active = memory_stats['active_bytes.all.current']
bytes_reserved = memory_stats['reserved_bytes.all.current']
bytes_free_cuda, _ = torch.cuda.mem_get_info(device)
bytes_inactive_reserved = bytes_reserved - bytes_active
bytes_total_available = bytes_free_cuda + bytes_inactive_reserved
return bytes_total_available / (1024 ** 3)
def move_model_to_device_with_memory_preservation(model, target_device, preserved_memory_gb=0):
print(f'Moving {model.__class__.__name__} to {target_device} with preserved memory: {preserved_memory_gb} GB')
for m in model.modules():
if get_cuda_free_memory_gb(target_device) <= preserved_memory_gb:
torch.cuda.empty_cache()
return
if hasattr(m, 'weight'):
m.to(device=target_device)
model.to(device=target_device)
torch.cuda.empty_cache()
return
def offload_model_from_device_for_memory_preservation(model, target_device, preserved_memory_gb=0):
print(f'Offloading {model.__class__.__name__} from {target_device} to preserve memory: {preserved_memory_gb} GB')
for m in model.modules():
if get_cuda_free_memory_gb(target_device) >= preserved_memory_gb:
torch.cuda.empty_cache()
return
if hasattr(m, 'weight'):
m.to(device=cpu)
model.to(device=cpu)
torch.cuda.empty_cache()
return
def unload_complete_models(*args):
for m in gpu_complete_modules + list(args):
m.to(device=cpu)
print(f'Unloaded {m.__class__.__name__} as complete.')
gpu_complete_modules.clear()
torch.cuda.empty_cache()
return
def load_model_as_complete(model, target_device, unload=True):
if unload:
unload_complete_models()
model.to(device=target_device)
print(f'Loaded {model.__class__.__name__} to {target_device} as complete.')
gpu_complete_modules.append(model)
return
|