| | 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 |
| |
|