vendor/diffusers/hooks/layer_skip.py

# 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, List, Optional

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: Optional[str] = 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."
        )