# Copyright 2024 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import functools from typing import Any, Dict, Optional, Tuple import torch from accelerate.logging import get_logger from ..constants import FINETRAINERS_LOG_LEVEL logger = get_logger("finetrainers") # pylint: disable=invalid-name logger.setLevel(FINETRAINERS_LOG_LEVEL) class ModelHook: r""" A hook that contains callbacks to be executed just before and after the forward method of a model. """ _is_stateful = False def initialize_hook(self, module: torch.nn.Module) -> torch.nn.Module: r""" Hook that is executed when a model is initialized. Args: module (`torch.nn.Module`): The module attached to this hook. """ return module def deinitalize_hook(self, module: torch.nn.Module) -> torch.nn.Module: r""" Hook that is executed when a model is deinitalized. Args: module (`torch.nn.Module`): The module attached to this hook. """ module.forward = module._old_forward del module._old_forward return module def pre_forward(self, module: torch.nn.Module, *args, **kwargs) -> Tuple[Tuple[Any], Dict[str, Any]]: r""" Hook that is executed just before the forward method of the model. Args: module (`torch.nn.Module`): The module whose forward pass will be executed just after this event. args (`Tuple[Any]`): The positional arguments passed to the module. kwargs (`Dict[Str, Any]`): The keyword arguments passed to the module. Returns: `Tuple[Tuple[Any], Dict[Str, Any]]`: A tuple with the treated `args` and `kwargs`. """ return args, kwargs def post_forward(self, module: torch.nn.Module, output: Any) -> Any: r""" Hook that is executed just after the forward method of the model. Args: module (`torch.nn.Module`): The module whose forward pass been executed just before this event. output (`Any`): The output of the module. Returns: `Any`: The processed `output`. """ return output def detach_hook(self, module: torch.nn.Module) -> torch.nn.Module: r""" Hook that is executed when the hook is detached from a module. Args: module (`torch.nn.Module`): The module detached from this hook. """ return module def reset_state(self, module: torch.nn.Module): if self._is_stateful: raise NotImplementedError("This hook is stateful and needs to implement the `reset_state` method.") return module class HookRegistry: def __init__(self, module_ref: torch.nn.Module) -> None: super().__init__() self.hooks: Dict[str, ModelHook] = {} self._module_ref = module_ref self._hook_order = [] def register_hook(self, hook: ModelHook, name: str) -> None: if name in self.hooks.keys(): logger.warning(f"Hook with name {name} already exists, replacing it.") if hasattr(self._module_ref, "_old_forward"): old_forward = self._module_ref._old_forward else: old_forward = self._module_ref.forward self._module_ref._old_forward = self._module_ref.forward self._module_ref = hook.initialize_hook(self._module_ref) if hasattr(hook, "new_forward"): rewritten_forward = hook.new_forward def new_forward(module, *args, **kwargs): args, kwargs = hook.pre_forward(module, *args, **kwargs) output = rewritten_forward(module, *args, **kwargs) return hook.post_forward(module, output) else: def new_forward(module, *args, **kwargs): args, kwargs = hook.pre_forward(module, *args, **kwargs) output = old_forward(*args, **kwargs) return hook.post_forward(module, output) self._module_ref.forward = functools.update_wrapper( functools.partial(new_forward, self._module_ref), old_forward ) self.hooks[name] = hook self._hook_order.append(name) def get_hook(self, name: str) -> Optional[ModelHook]: if name not in self.hooks.keys(): return None return self.hooks[name] def remove_hook(self, name: str) -> None: if name not in self.hooks.keys(): raise ValueError(f"Hook with name {name} not found.") self.hooks[name].deinitalize_hook(self._module_ref) del self.hooks[name] self._hook_order.remove(name) def reset_stateful_hooks(self, recurse: bool = True) -> None: for hook_name in self._hook_order: hook = self.hooks[hook_name] if hook._is_stateful: hook.reset_state(self._module_ref) if recurse: for module in self._module_ref.modules(): if hasattr(module, "_diffusers_hook"): module._diffusers_hook.reset_stateful_hooks(recurse=False) @classmethod def check_if_exists_or_initialize(cls, module: torch.nn.Module) -> "HookRegistry": if not hasattr(module, "_diffusers_hook"): module._diffusers_hook = cls(module) return module._diffusers_hook def __repr__(self) -> str: hook_repr = "" for i, hook_name in enumerate(self._hook_order): hook_repr += f" ({i}) {hook_name} - ({self.hooks[hook_name].__class__.__name__})" if i < len(self._hook_order) - 1: hook_repr += "\n" return f"HookRegistry(\n{hook_repr}\n)"