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# Apache-2.0 License
# 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
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