[Feat] TaylorSeer Cache

src/diffusers/__init__.py

@@ -169,10 +169,12 @@
             "LayerSkipConfig",
             "PyramidAttentionBroadcastConfig",
             "SmoothedEnergyGuidanceConfig",
+            "TaylorSeerCacheConfig",
             "apply_faster_cache",
             "apply_first_block_cache",
             "apply_layer_skip",
             "apply_pyramid_attention_broadcast",
+            "apply_taylorseer_cache",
         ]
     )
     _import_structure["models"].extend(
@@ -883,10 +885,12 @@
             LayerSkipConfig,
             PyramidAttentionBroadcastConfig,
             SmoothedEnergyGuidanceConfig,
+            TaylorSeerCacheConfig,
             apply_faster_cache,
             apply_first_block_cache,
             apply_layer_skip,
             apply_pyramid_attention_broadcast,
+            apply_taylorseer_cache,
         )
         from .models import (
             AllegroTransformer3DModel,

src/diffusers/hooks/__init__.py

@@ -25,3 +25,4 @@
     from .layerwise_casting import apply_layerwise_casting, apply_layerwise_casting_hook
     from .pyramid_attention_broadcast import PyramidAttentionBroadcastConfig, apply_pyramid_attention_broadcast
     from .smoothed_energy_guidance_utils import SmoothedEnergyGuidanceConfig
+    from .taylorseer_cache import TaylorSeerCacheConfig, apply_taylorseer_cache

src/diffusers/hooks/taylorseer_cache.py

@@ -0,0 +1,311 @@
+import torch
+from dataclasses import dataclass
+from typing import Callable, Optional, List, Dict
+from .hooks import ModelHook
+import math
+from ..models.attention import Attention
+from ..models.attention import AttentionModuleMixin
+from ._common import (
+    _ATTENTION_CLASSES,
+)
+from ..hooks import HookRegistry
+from ..utils import logging
+import re
+from collections import defaultdict
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+_TAYLORSEER_ATTENTION_CACHE_HOOK = "taylorseer_attention_cache"
+
+SPECIAL_CACHE_IDENTIFIERS = {
+    "flux": [
+        r"transformer_blocks\.\d+\.attn",
+        r"transformer_blocks\.\d+\.ff",
+        r"transformer_blocks\.\d+\.ff_context",
+        r"single_transformer_blocks\.\d+\.proj_out",
+    ]
+}
+SKIP_COMPUTE_IDENTIFIERS = {
+    "flux": [
+        r"single_transformer_blocks\.\d+\.attn",
+        r"single_transformer_blocks\.\d+\.proj_mlp",
+        r"single_transformer_blocks\.\d+\.act_mlp",
+    ]
+}
+
+
+@dataclass
+class TaylorSeerCacheConfig:
+    """
+    Configuration for TaylorSeer cache.
+    See: https://huggingface.co/papers/2503.06923
+
+    Attributes:
+        warmup_steps (int, defaults to 3): Number of warmup steps without caching.
+        predict_steps (int, defaults to 5): Number of prediction (cache) steps between non-cached steps.
+        max_order (int, defaults to 1): Maximum order of Taylor series expansion to approximate the features.
+        taylor_factors_dtype (torch.dtype, defaults to torch.float32): Data type for Taylor series expansion factors.
+        architecture (str, defaults to None): Architecture for which the cache is applied. If we know the architecture, we can use the special cache identifiers.
+        skip_compute_identifiers (List[str], defaults to []): Identifiers for modules to skip computation.
+        special_cache_identifiers (List[str], defaults to []): Identifiers for modules to use special cache.
+    """
+
+    warmup_steps: int = 3
+    predict_steps: int = 5
+    max_order: int = 1
+    taylor_factors_dtype: torch.dtype = torch.float32
+    architecture: str | None = None
+    skip_compute_identifiers: List[str] = None
+    special_cache_identifiers: List[str] = None
+
+    def __repr__(self) -> str:
+        return f"TaylorSeerCacheConfig(warmup_steps={self.warmup_steps}, predict_steps={self.predict_steps}, max_order={self.max_order}, taylor_factors_dtype={self.taylor_factors_dtype}, architecture={self.architecture}, skip_compute_identifiers={self.skip_compute_identifiers}, special_cache_identifiers={self.special_cache_identifiers})"
+
+
+class TaylorSeerOutputState:
+    """
+    Manages the state for Taylor series-based prediction of a single attention output.
+    Tracks Taylor expansion factors, last update step, and remaining prediction steps.
+    The Taylor expansion uses the timestep as the independent variable for approximation.
+    """
+
+    def __init__(
+        self, module_name: str, taylor_factors_dtype: torch.dtype, module_dtype: torch.dtype, is_skip: bool = False
+    ):
+        self.module_name = module_name
+        self.remaining_predictions: int = 0
+        self.last_update_step: Optional[int] = None
+        self.taylor_factors: Dict[int, torch.Tensor] = {}
+        self.taylor_factors_dtype = taylor_factors_dtype
+        self.module_dtype = module_dtype
+        self.is_skip = is_skip
+        self.dummy_shape: Optional[Tuple[int, ...]] = None
+        self.device: Optional[torch.device] = None
+        self.dummy_tensor: Optional[torch.Tensor] = None
+
+    def reset(self):
+        self.remaining_predictions = 0
+        self.last_update_step = None
+        self.taylor_factors = {}
+        self.dummy_shape = None
+        self.device = None
+        self.dummy_tensor = None
+
+    def update(
+        self, features: torch.Tensor, current_step: int, max_order: int, predict_steps: int, is_first_update: bool
+    ):
+        """
+        Updates the Taylor factors based on the current features and timestep.
+        Computes finite difference approximations for derivatives using recursive divided differences.
+
+        Args:
+            features (torch.Tensor): The attention output features to update with.
+            current_step (int): The current timestep or step number from the diffusion model.
+            max_order (int): Maximum order of the Taylor expansion.
+            predict_steps (int): Number of prediction steps to set after update.
+            is_first_update (bool): Whether this is the initial update (skips difference computation).
+        """
+        if self.is_skip:
+            self.dummy_shape = features.shape
+            self.device = features.device
+            self.taylor_factors = {}
+            self.last_update_step = current_step
+            self.remaining_predictions = predict_steps
+        else:
+            features = features.to(self.taylor_factors_dtype)
+            new_factors = {0: features}
+            if not is_first_update:
+                if self.last_update_step is None:
+                    raise ValueError("Cannot update without prior initialization.")
+                delta_step = current_step - self.last_update_step
+                if delta_step == 0:
+                    raise ValueError("Delta step cannot be zero for updates.")
+                for i in range(max_order):
+                    if i in self.taylor_factors:
+                        new_factors[i + 1] = (
+                            new_factors[i] - self.taylor_factors[i].to(self.taylor_factors_dtype)
+                        ) / delta_step
+                    else:
+                        break
+
+            # taylor factors will be kept in the taylor_factors_dtype
+            self.taylor_factors = new_factors
+            self.last_update_step = current_step
+            self.remaining_predictions = predict_steps
+
+    def predict(self, current_step: int) -> torch.Tensor:
+        """
+        Predicts the features using the Taylor series expansion at the given timestep.
+
+        Args:
+            current_step (int): The current timestep for prediction.
+
+        Returns:
+            torch.Tensor: The predicted features in the module's dtype.
+        """
+        if self.is_skip:
+            if self.dummy_shape is None or self.device is None:
+                raise ValueError("Cannot predict for skip module without prior update.")
+            self.remaining_predictions -= 1
+            return torch.empty(self.dummy_shape, dtype=self.module_dtype, device=self.device)
+        else:
+            if self.last_update_step is None:
+                raise ValueError("Cannot predict without prior update.")
+            step_offset = current_step - self.last_update_step
+            output = 0
+            for order in range(len(self.taylor_factors)):
+                output += self.taylor_factors[order] * (step_offset**order) * (1 / math.factorial(order))
+            self.remaining_predictions -= 1
+            # output will be converted to the module's dtype
+            return output.to(self.module_dtype)
+
+
+class TaylorSeerAttentionCacheHook(ModelHook):
+    """
+    Hook for caching and predicting attention outputs using Taylor series approximations.
+    Applies to attention modules in diffusion models (e.g., Flux).
+    Performs full computations during warmup, then alternates between predictions and refreshes.
+    """
+
+    _is_stateful = True
+
+    def __init__(
+        self,
+        module_name: str,
+        predict_steps: int,
+        max_order: int,
+        warmup_steps: int,
+        taylor_factors_dtype: torch.dtype,
+        is_skip_compute: bool = False,
+    ):
+        super().__init__()
+        self.module_name = module_name
+        self.predict_steps = predict_steps
+        self.max_order = max_order
+        self.warmup_steps = warmup_steps
+        self.step_counter = -1
+        self.states: Optional[List[TaylorSeerOutputState]] = None
+        self.num_outputs: Optional[int] = None
+        self.taylor_factors_dtype = taylor_factors_dtype
+        self.is_skip_compute = is_skip_compute
+
+    def initialize_hook(self, module: torch.nn.Module):
+        self.step_counter = -1
+        self.states = None
+        self.num_outputs = None
+        return module
+
+    def new_forward(self, module: torch.nn.Module, *args, **kwargs):
+        self.step_counter += 1
+        is_warmup_phase = self.step_counter < self.warmup_steps
+
+        if self.states is None:
+            # First step: always full compute and initialize
+            attention_outputs = self.fn_ref.original_forward(*args, **kwargs)
+            if isinstance(attention_outputs, torch.Tensor):
+                attention_outputs = [attention_outputs]
+            else:
+                attention_outputs = list(attention_outputs)
+            module_dtype = attention_outputs[0].dtype
+            self.num_outputs = len(attention_outputs)
+            self.states = [
+                TaylorSeerOutputState(
+                    self.module_name, self.taylor_factors_dtype, module_dtype, is_skip=self.is_skip_compute
+                )
+                for _ in range(self.num_outputs)
+            ]
+            for i, features in enumerate(attention_outputs):
+                self.states[i].update(
+                    features, self.step_counter, self.max_order, self.predict_steps, is_first_update=True
+                )
+            return attention_outputs[0] if self.num_outputs == 1 else tuple(attention_outputs)
+
+        should_predict = self.states[0].remaining_predictions > 0
+        if is_warmup_phase or not should_predict:
+            # Full compute during warmup or when refresh needed
+            attention_outputs = self.fn_ref.original_forward(*args, **kwargs)
+            if isinstance(attention_outputs, torch.Tensor):
+                attention_outputs = [attention_outputs]
+            else:
+                attention_outputs = list(attention_outputs)
+            is_first_update = self.step_counter == 0  # Only True for the very first step
+            for i, features in enumerate(attention_outputs):
+                self.states[i].update(features, self.step_counter, self.max_order, self.predict_steps, is_first_update)
+            return attention_outputs[0] if self.num_outputs == 1 else tuple(attention_outputs)
+        else:
+            # Predict using Taylor series
+            predicted_outputs = [state.predict(self.step_counter) for state in self.states]
+            return predicted_outputs[0] if self.num_outputs == 1 else tuple(predicted_outputs)
+
+    def reset_state(self, module: torch.nn.Module) -> None:
+        self.states = None
+
+
+def apply_taylorseer_cache(module: torch.nn.Module, config: TaylorSeerCacheConfig):
+    """
+    Applies the TaylorSeer cache to given pipeline.
+
+    Args:
+        module (torch.nn.Module): The model to apply the hook to.
+        config (TaylorSeerCacheConfig): Configuration for the cache.
+
+    Example:
+    ```python
+    >>> import torch
+    >>> from diffusers import FluxPipeline, TaylorSeerCacheConfig, apply_taylorseer_cache
+
+    >>> pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
+    >>> pipe.to("cuda")
+
+    >>> config = TaylorSeerCacheConfig(predict_steps=5, max_order=1, warmup_steps=3, taylor_factors_dtype=torch.float32, architecture="flux")
+    >>> apply_taylorseer_cache(pipe.transformer, config)
+    ```
+    """
+    if config.skip_compute_identifiers:
+        skip_compute_identifiers = config.skip_compute_identifiers
+    else:
+        skip_compute_identifiers = SKIP_COMPUTE_IDENTIFIERS.get(config.architecture, [])
+
+    if config.special_cache_identifiers:
+        special_cache_identifiers = config.special_cache_identifiers
+    else:
+        special_cache_identifiers = SPECIAL_CACHE_IDENTIFIERS.get(config.architecture, [])
+
+    logger.debug(f"Skip compute identifiers: {skip_compute_identifiers}")
+    logger.debug(f"Special cache identifiers: {special_cache_identifiers}")
+
+    for name, submodule in module.named_modules():
+        if (skip_compute_identifiers and special_cache_identifiers) or (special_cache_identifiers):
+            if any(re.fullmatch(identifier, name) for identifier in skip_compute_identifiers) or any(
+                re.fullmatch(identifier, name) for identifier in special_cache_identifiers
+            ):
+                logger.debug(f"Applying TaylorSeer cache to {name}")
+                _apply_taylorseer_cache_hook(name, submodule, config)
+        elif isinstance(submodule, (*_ATTENTION_CLASSES, AttentionModuleMixin)):
+            logger.debug(f"Applying TaylorSeer cache to {name}")
+            _apply_taylorseer_cache_hook(name, submodule, config)
+
+
+def _apply_taylorseer_cache_hook(name: str, module: Attention, config: TaylorSeerCacheConfig):
+    """
+    Registers the TaylorSeer hook on the specified attention module.
+    Args:
+        name (str): Name of the module.
+        module (Attention): The attention module.
+        config (TaylorSeerCacheConfig): Configuration for the cache.
+    """
+
+    is_skip_compute = any(
+        re.fullmatch(identifier, name) for identifier in SKIP_COMPUTE_IDENTIFIERS.get(config.architecture, [])
+    )
+
+    registry = HookRegistry.check_if_exists_or_initialize(module)
+
+    hook = TaylorSeerAttentionCacheHook(
+        name,
+        config.predict_steps,
+        config.max_order,
+        config.warmup_steps,
+        config.taylor_factors_dtype,
+        is_skip_compute=is_skip_compute,
+    )
+
+    registry.register_hook(hook, _TAYLORSEER_ATTENTION_CACHE_HOOK)

src/diffusers/models/cache_utils.py

@@ -67,9 +67,11 @@ def enable_cache(self, config) -> None:
             FasterCacheConfig,
             FirstBlockCacheConfig,
             PyramidAttentionBroadcastConfig,
+            TaylorSeerCacheConfig,
             apply_faster_cache,
             apply_first_block_cache,
             apply_pyramid_attention_broadcast,
+            apply_taylorseer_cache,
         )
 
         if self.is_cache_enabled:
@@ -83,16 +85,19 @@ def enable_cache(self, config) -> None:
             apply_first_block_cache(self, config)
         elif isinstance(config, PyramidAttentionBroadcastConfig):
             apply_pyramid_attention_broadcast(self, config)
+        elif isinstance(config, TaylorSeerCacheConfig):
+            apply_taylorseer_cache(self, config)
         else:
             raise ValueError(f"Cache config {type(config)} is not supported.")
 
         self._cache_config = config
 
     def disable_cache(self) -> None:
-        from ..hooks import FasterCacheConfig, FirstBlockCacheConfig, HookRegistry, PyramidAttentionBroadcastConfig
+        from ..hooks import FasterCacheConfig, FirstBlockCacheConfig, HookRegistry, PyramidAttentionBroadcastConfig, TaylorSeerCacheConfig
         from ..hooks.faster_cache import _FASTER_CACHE_BLOCK_HOOK, _FASTER_CACHE_DENOISER_HOOK
         from ..hooks.first_block_cache import _FBC_BLOCK_HOOK, _FBC_LEADER_BLOCK_HOOK
         from ..hooks.pyramid_attention_broadcast import _PYRAMID_ATTENTION_BROADCAST_HOOK
+        from ..hooks.taylorseer_cache import _TAYLORSEER_ATTENTION_CACHE_HOOK
 
         if self._cache_config is None:
             logger.warning("Caching techniques have not been enabled, so there's nothing to disable.")
@@ -107,6 +112,8 @@ def disable_cache(self) -> None:
             registry.remove_hook(_FBC_BLOCK_HOOK, recurse=True)
         elif isinstance(self._cache_config, PyramidAttentionBroadcastConfig):
             registry.remove_hook(_PYRAMID_ATTENTION_BROADCAST_HOOK, recurse=True)
+        elif isinstance(self._cache_config, TaylorSeerCacheConfig):
+            registry.remove_hook(_TAYLORSEER_ATTENTION_CACHE_HOOK, recurse=True)
         else:
             raise ValueError(f"Cache config {type(self._cache_config)} is not supported.")