Zhao Yingzhuo
ec199c2a4f
* [doc]: fix readme * [feat]: support torch.nn.Module cache and add test wf * [refactor]: optimize directory structure
86 lines
2.9 KiB
Python
86 lines
2.9 KiB
Python
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from dataclasses import dataclass, field
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from functools import _make_key
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from typing import Dict, Tuple
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import torch
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import logging
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logger = logging.getLogger(__name__)
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class ModelValidChecker:
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def __init__(self, result):
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self.result = result
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# module dict should be the first place
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self.module = result[0] if isinstance(result, tuple) else result
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self.key_count = self.get_latest_key_count()
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def is_valid(self) -> bool:
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# if tensors on gpu are released, the module keys are incomplete
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if self.key_count == self.get_latest_key_count():
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return True
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return False
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def get_latest_key_count(self) -> int:
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if isinstance(self.module, torch.nn.Module):
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return len(self.module.state_dict().keys())
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elif isinstance(self.module, Dict):
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return len(self.module.keys())
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logger.warning(f"\033[92mModel_Cache: result is not torch.nn.Module or dict, but {type(self.module)}\033[0m")
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logger.warning(f"\033[92mModel_Cache: So cache will never happen for this result! \033[0m")
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# will nevel equal and never cache the result
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return -1
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def get_result(self) -> torch.nn.Module | Tuple[torch.nn.Module, ...]:
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return self.result
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@dataclass
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class ModelCache:
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models: Dict[str, ModelValidChecker] = field(default_factory=dict)
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def generate_cache_key(self, *args, **kwargs):
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args_key = _make_key(args, kwargs, typed=True)
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return args_key
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def cached(self, model_key) -> bool:
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checker = self.models.get(model_key, None)
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if checker:
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return checker.is_valid()
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return False
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def register_model(
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self,
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model_key: str,
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result: torch.nn.Module | Tuple[torch.nn.Module, ...],
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) -> None:
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"""
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Register an cache model result to be used in Comfy.
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:code:`result` can be either:
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- A :class:`torch.nn.Module` class directly referencing the model.
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- B :A tuple with `torch.nn.Module` Dict at the first place and other kwargs follow-up
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"""
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self.models[model_key] = ModelValidChecker(result)
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def get_result(self, model_key):
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logger.info(f"\033[92mModel_Cache: Return a cached module result with args:{model_key}\033[0m")
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return self.models[model_key].get_result()
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_model_cache = ModelCache()
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def get_model_cache():
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global _model_cache
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assert _model_cache is not None, "Model Cache has not been initialized."
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return _model_cache
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def cache_model(func):
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def wrapper(*args, **kwargs):
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model_key = get_model_cache().generate_cache_key(*args, **kwargs)
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if get_model_cache().cached(model_key):
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return get_model_cache().get_result(model_key)
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result = func(*args, **kwargs)
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get_model_cache().register_model(model_key, result)
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return result
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return wrapper |