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# 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)"
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