| import time |
| import torch |
| import contextlib |
| from ldm_patched.modules import model_management |
| from ldm_patched.modules.ops import use_patched_ops |
|
|
|
|
| @contextlib.contextmanager |
| def automatic_memory_management(): |
| model_management.free_memory( |
| memory_required=3 * 1024 * 1024 * 1024, |
| device=model_management.get_torch_device() |
| ) |
|
|
| module_list = [] |
|
|
| original_init = torch.nn.Module.__init__ |
| original_to = torch.nn.Module.to |
|
|
| def patched_init(self, *args, **kwargs): |
| module_list.append(self) |
| return original_init(self, *args, **kwargs) |
|
|
| def patched_to(self, *args, **kwargs): |
| module_list.append(self) |
| return original_to(self, *args, **kwargs) |
|
|
| try: |
| torch.nn.Module.__init__ = patched_init |
| torch.nn.Module.to = patched_to |
| yield |
| finally: |
| torch.nn.Module.__init__ = original_init |
| torch.nn.Module.to = original_to |
|
|
| start = time.perf_counter() |
| module_list = set(module_list) |
|
|
| for module in module_list: |
| module.cpu() |
|
|
| model_management.soft_empty_cache() |
| end = time.perf_counter() |
|
|
| print(f'Automatic Memory Management: {len(module_list)} Modules in {(end - start):.2f} seconds.') |
| return |
|
|