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# Copyright 2025 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 math
from dataclasses import asdict, dataclass
from typing import Callable
import torch
from ..utils import get_logger
from ..utils.torch_utils import unwrap_module
from ._common import (
_ALL_TRANSFORMER_BLOCK_IDENTIFIERS,
_ATTENTION_CLASSES,
_FEEDFORWARD_CLASSES,
_get_submodule_from_fqn,
)
from ._helpers import AttentionProcessorRegistry, TransformerBlockRegistry
from .hooks import HookRegistry, ModelHook
logger = get_logger(__name__) # pylint: disable=invalid-name
_LAYER_SKIP_HOOK = "layer_skip_hook"
# Aryan/YiYi TODO: we need to make guider class a config mixin so I think this is not needed
# either remove or make it serializable
@dataclass
class LayerSkipConfig:
r"""
Configuration for skipping internal transformer blocks when executing a transformer model.
Args:
indices (`list[int]`):
The indices of the layer to skip. This is typically the first layer in the transformer block.
fqn (`str`, defaults to `"auto"`):
The fully qualified name identifying the stack of transformer blocks. Typically, this is
`transformer_blocks`, `single_transformer_blocks`, `blocks`, `layers`, or `temporal_transformer_blocks`.
For automatic detection, set this to `"auto"`. "auto" only works on DiT models. For UNet models, you must
provide the correct fqn.
skip_attention (`bool`, defaults to `True`):
Whether to skip attention blocks.
skip_ff (`bool`, defaults to `True`):
Whether to skip feed-forward blocks.
skip_attention_scores (`bool`, defaults to `False`):
Whether to skip attention score computation in the attention blocks. This is equivalent to using `value`
projections as the output of scaled dot product attention.
dropout (`float`, defaults to `1.0`):
The dropout probability for dropping the outputs of the skipped layers. By default, this is set to `1.0`,
meaning that the outputs of the skipped layers are completely ignored. If set to `0.0`, the outputs of the
skipped layers are fully retained, which is equivalent to not skipping any layers.
"""
indices: list[int]
fqn: str = "auto"
skip_attention: bool = True
skip_attention_scores: bool = False
skip_ff: bool = True
dropout: float = 1.0
def __post_init__(self):
if not (0 <= self.dropout <= 1):
raise ValueError(f"Expected `dropout` to be between 0.0 and 1.0, but got {self.dropout}.")
if not math.isclose(self.dropout, 1.0) and self.skip_attention_scores:
raise ValueError(
"Cannot set `skip_attention_scores` to True when `dropout` is not 1.0. Please set `dropout` to 1.0."
)
def to_dict(self):
return asdict(self)
@staticmethod
def from_dict(data: dict) -> "LayerSkipConfig":
return LayerSkipConfig(**data)
class AttentionScoreSkipFunctionMode(torch.overrides.TorchFunctionMode):
def __torch_function__(self, func, types, args=(), kwargs=None):
if kwargs is None:
kwargs = {}
if func is torch.nn.functional.scaled_dot_product_attention:
query = kwargs.get("query", None)
key = kwargs.get("key", None)
value = kwargs.get("value", None)
query = query if query is not None else args[0]
key = key if key is not None else args[1]
value = value if value is not None else args[2]
# If the Q sequence length does not match KV sequence length, methods like
# Perturbed Attention Guidance cannot be used (because the caller expects
# the same sequence length as Q, but if we return V here, it will not match).
# When Q.shape[2] != V.shape[2], PAG will essentially not be applied and
# the overall effect would that be of normal CFG with a scale of (guidance_scale + perturbed_guidance_scale).
if query.shape[2] == value.shape[2]:
return value
return func(*args, **kwargs)
class AttentionProcessorSkipHook(ModelHook):
def __init__(self, skip_processor_output_fn: Callable, skip_attention_scores: bool = False, dropout: float = 1.0):
self.skip_processor_output_fn = skip_processor_output_fn
self.skip_attention_scores = skip_attention_scores
self.dropout = dropout
def new_forward(self, module: torch.nn.Module, *args, **kwargs):
if self.skip_attention_scores:
if not math.isclose(self.dropout, 1.0):
raise ValueError(
"Cannot set `skip_attention_scores` to True when `dropout` is not 1.0. Please set `dropout` to 1.0."
)
with AttentionScoreSkipFunctionMode():
output = self.fn_ref.original_forward(*args, **kwargs)
else:
if math.isclose(self.dropout, 1.0):
output = self.skip_processor_output_fn(module, *args, **kwargs)
else:
output = self.fn_ref.original_forward(*args, **kwargs)
output = torch.nn.functional.dropout(output, p=self.dropout)
return output
class FeedForwardSkipHook(ModelHook):
def __init__(self, dropout: float):
super().__init__()
self.dropout = dropout
def new_forward(self, module: torch.nn.Module, *args, **kwargs):
if math.isclose(self.dropout, 1.0):
output = kwargs.get("hidden_states", None)
if output is None:
output = kwargs.get("x", None)
if output is None and len(args) > 0:
output = args[0]
else:
output = self.fn_ref.original_forward(*args, **kwargs)
output = torch.nn.functional.dropout(output, p=self.dropout)
return output
class TransformerBlockSkipHook(ModelHook):
def __init__(self, dropout: float):
super().__init__()
self.dropout = dropout
def initialize_hook(self, module):
self._metadata = TransformerBlockRegistry.get(unwrap_module(module).__class__)
return module
def new_forward(self, module: torch.nn.Module, *args, **kwargs):
if math.isclose(self.dropout, 1.0):
original_hidden_states = self._metadata._get_parameter_from_args_kwargs("hidden_states", args, kwargs)
if self._metadata.return_encoder_hidden_states_index is None:
output = original_hidden_states
else:
original_encoder_hidden_states = self._metadata._get_parameter_from_args_kwargs(
"encoder_hidden_states", args, kwargs
)
output = (original_hidden_states, original_encoder_hidden_states)
else:
output = self.fn_ref.original_forward(*args, **kwargs)
output = torch.nn.functional.dropout(output, p=self.dropout)
return output
def apply_layer_skip(module: torch.nn.Module, config: LayerSkipConfig) -> None:
r"""
Apply layer skipping to internal layers of a transformer.
Args:
module (`torch.nn.Module`):
The transformer model to which the layer skip hook should be applied.
config (`LayerSkipConfig`):
The configuration for the layer skip hook.
Example:
```python
>>> from diffusers import apply_layer_skip_hook, CogVideoXTransformer3DModel, LayerSkipConfig
>>> transformer = CogVideoXTransformer3DModel.from_pretrained("THUDM/CogVideoX-5b", torch_dtype=torch.bfloat16)
>>> config = LayerSkipConfig(layer_index=[10, 20], fqn="transformer_blocks")
>>> apply_layer_skip_hook(transformer, config)
```
"""
_apply_layer_skip_hook(module, config)
def _apply_layer_skip_hook(module: torch.nn.Module, config: LayerSkipConfig, name: str | None = None) -> None:
name = name or _LAYER_SKIP_HOOK
if config.skip_attention and config.skip_attention_scores:
raise ValueError("Cannot set both `skip_attention` and `skip_attention_scores` to True. Please choose one.")
if not math.isclose(config.dropout, 1.0) and config.skip_attention_scores:
raise ValueError(
"Cannot set `skip_attention_scores` to True when `dropout` is not 1.0. Please set `dropout` to 1.0."
)
if config.fqn == "auto":
for identifier in _ALL_TRANSFORMER_BLOCK_IDENTIFIERS:
if hasattr(module, identifier):
config.fqn = identifier
break
else:
raise ValueError(
"Could not find a suitable identifier for the transformer blocks automatically. Please provide a valid "
"`fqn` (fully qualified name) that identifies a stack of transformer blocks."
)
transformer_blocks = _get_submodule_from_fqn(module, config.fqn)
if transformer_blocks is None or not isinstance(transformer_blocks, torch.nn.ModuleList):
raise ValueError(
f"Could not find {config.fqn} in the provided module, or configured `fqn` (fully qualified name) does not identify "
f"a `torch.nn.ModuleList`. Please provide a valid `fqn` that identifies a stack of transformer blocks."
)
if len(config.indices) == 0:
raise ValueError("Layer index list is empty. Please provide a non-empty list of layer indices to skip.")
blocks_found = False
for i, block in enumerate(transformer_blocks):
if i not in config.indices:
continue
blocks_found = True
if config.skip_attention and config.skip_ff:
logger.debug(f"Applying TransformerBlockSkipHook to '{config.fqn}.{i}'")
registry = HookRegistry.check_if_exists_or_initialize(block)
hook = TransformerBlockSkipHook(config.dropout)
registry.register_hook(hook, name)
elif config.skip_attention or config.skip_attention_scores:
for submodule_name, submodule in block.named_modules():
if isinstance(submodule, _ATTENTION_CLASSES) and not submodule.is_cross_attention:
logger.debug(f"Applying AttentionProcessorSkipHook to '{config.fqn}.{i}.{submodule_name}'")
output_fn = AttentionProcessorRegistry.get(submodule.processor.__class__).skip_processor_output_fn
registry = HookRegistry.check_if_exists_or_initialize(submodule)
hook = AttentionProcessorSkipHook(output_fn, config.skip_attention_scores, config.dropout)
registry.register_hook(hook, name)
if config.skip_ff:
for submodule_name, submodule in block.named_modules():
if isinstance(submodule, _FEEDFORWARD_CLASSES):
logger.debug(f"Applying FeedForwardSkipHook to '{config.fqn}.{i}.{submodule_name}'")
registry = HookRegistry.check_if_exists_or_initialize(submodule)
hook = FeedForwardSkipHook(config.dropout)
registry.register_hook(hook, name)
if not blocks_found:
raise ValueError(
f"Could not find any transformer blocks matching the provided indices {config.indices} and "
f"fully qualified name '{config.fqn}'. Please check the indices and fqn for correctness."
)