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import math
import re
from dataclasses import dataclass
import torch
import torch.nn as nn
from ..utils import logging
from .hooks import HookRegistry, ModelHook, StateManager
logger = logging.get_logger(__name__)
_TAYLORSEER_CACHE_HOOK = "taylorseer_cache"
_SPATIAL_ATTENTION_BLOCK_IDENTIFIERS = (
"^blocks.*attn",
"^transformer_blocks.*attn",
"^single_transformer_blocks.*attn",
)
_TEMPORAL_ATTENTION_BLOCK_IDENTIFIERS = ("^temporal_transformer_blocks.*attn",)
_TRANSFORMER_BLOCK_IDENTIFIERS = _SPATIAL_ATTENTION_BLOCK_IDENTIFIERS + _TEMPORAL_ATTENTION_BLOCK_IDENTIFIERS
_BLOCK_IDENTIFIERS = ("^[^.]*block[^.]*\\.[^.]+$",)
_PROJ_OUT_IDENTIFIERS = ("^proj_out$",)
@dataclass
class TaylorSeerCacheConfig:
"""
Configuration for TaylorSeer cache. See: https://huggingface.co/papers/2503.06923
Attributes:
cache_interval (`int`, defaults to `5`):
The interval between full computation steps. After a full computation, the cached (predicted) outputs are
reused for this many subsequent denoising steps before refreshing with a new full forward pass.
disable_cache_before_step (`int`, defaults to `3`):
The denoising step index before which caching is disabled, meaning full computation is performed for the
initial steps (0 to disable_cache_before_step - 1) to gather data for Taylor series approximations. During
these steps, Taylor factors are updated, but caching/predictions are not applied. Caching begins at this
step.
disable_cache_after_step (`int`, *optional*, defaults to `None`):
The denoising step index after which caching is disabled. If set, for steps >= this value, all modules run
full computations without predictions or state updates, ensuring accuracy in later stages if needed.
max_order (`int`, defaults to `1`):
The highest order in the Taylor series expansion for approximating module outputs. Higher orders provide
better approximations but increase computation and memory usage.
taylor_factors_dtype (`torch.dtype`, defaults to `torch.bfloat16`):
Data type used for storing and computing Taylor series factors. Lower precision reduces memory but may
affect stability; higher precision improves accuracy at the cost of more memory.
skip_predict_identifiers (`list[str]`, *optional*, defaults to `None`):
Regex patterns (using `re.fullmatch`) for module names to place as "skip" in "cache" mode. In this mode,
the module computes fully during initial or refresh steps but returns a zero tensor (matching recorded
shape) during prediction steps to skip computation cheaply.
cache_identifiers (`list[str]`, *optional*, defaults to `None`):
Regex patterns (using `re.fullmatch`) for module names to place in Taylor-series caching mode, where
outputs are approximated and cached for reuse.
use_lite_mode (`bool`, *optional*, defaults to `False`):
Enables a lightweight TaylorSeer variant that minimizes memory usage by applying predefined patterns for
skipping and caching (e.g., skipping blocks and caching projections). This overrides any custom
`inactive_identifiers` or `active_identifiers`.
Notes:
- Patterns are matched using `re.fullmatch` on the module name.
- If `skip_predict_identifiers` or `cache_identifiers` are provided, only matching modules are hooked.
- If neither is provided, all attention-like modules are hooked by default.
Example of inactive and active usage:
```py
def forward(x):
x = self.module1(x) # inactive module: returns zeros tensor based on shape recorded during full compute
x = self.module2(x) # active module: caches output here, avoiding recomputation of prior steps
return x
```
"""
cache_interval: int = 5
disable_cache_before_step: int = 3
disable_cache_after_step: int | None = None
max_order: int = 1
taylor_factors_dtype: torch.dtype | None = torch.bfloat16
skip_predict_identifiers: list[str] | None = None
cache_identifiers: list[str] | None = None
use_lite_mode: bool = False
def __repr__(self) -> str:
return (
"TaylorSeerCacheConfig("
f"cache_interval={self.cache_interval}, "
f"disable_cache_before_step={self.disable_cache_before_step}, "
f"disable_cache_after_step={self.disable_cache_after_step}, "
f"max_order={self.max_order}, "
f"taylor_factors_dtype={self.taylor_factors_dtype}, "
f"skip_predict_identifiers={self.skip_predict_identifiers}, "
f"cache_identifiers={self.cache_identifiers}, "
f"use_lite_mode={self.use_lite_mode})"
)
class TaylorSeerState:
def __init__(
self,
taylor_factors_dtype: torch.dtype | None = torch.bfloat16,
max_order: int = 1,
is_inactive: bool = False,
):
self.taylor_factors_dtype = taylor_factors_dtype
self.max_order = max_order
self.is_inactive = is_inactive
self.module_dtypes: tuple[torch.dtype, ...] = ()
self.last_update_step: int | None = None
self.taylor_factors: dict[int, dict[int, torch.Tensor]] = {}
self.inactive_shapes: tuple[tuple[int, ...], ...] | None = None
self.device: torch.device | None = None
self.current_step: int = -1
def reset(self) -> None:
self.current_step = -1
self.last_update_step = None
self.taylor_factors = {}
self.inactive_shapes = None
self.device = None
def update(
self,
outputs: tuple[torch.Tensor, ...],
) -> None:
self.module_dtypes = tuple(output.dtype for output in outputs)
self.device = outputs[0].device
if self.is_inactive:
self.inactive_shapes = tuple(output.shape for output in outputs)
else:
for i, features in enumerate(outputs):
new_factors: dict[int, torch.Tensor] = {0: features}
is_first_update = self.last_update_step is None
if not is_first_update:
delta_step = self.current_step - self.last_update_step
if delta_step == 0:
raise ValueError("Delta step cannot be zero for TaylorSeer update.")
# Recursive divided differences up to max_order
prev_factors = self.taylor_factors.get(i, {})
for j in range(self.max_order):
prev = prev_factors.get(j)
if prev is None:
break
new_factors[j + 1] = (new_factors[j] - prev.to(features.dtype)) / delta_step
self.taylor_factors[i] = {
order: factor.to(self.taylor_factors_dtype) for order, factor in new_factors.items()
}
self.last_update_step = self.current_step
@torch.compiler.disable
def predict(self) -> list[torch.Tensor]:
if self.last_update_step is None:
raise ValueError("Cannot predict without prior initialization/update.")
step_offset = self.current_step - self.last_update_step
outputs = []
if self.is_inactive:
if self.inactive_shapes is None:
raise ValueError("Inactive shapes not set during prediction.")
for i in range(len(self.module_dtypes)):
outputs.append(
torch.zeros(
self.inactive_shapes[i],
dtype=self.module_dtypes[i],
device=self.device,
)
)
else:
if not self.taylor_factors:
raise ValueError("Taylor factors empty during prediction.")
num_outputs = len(self.taylor_factors)
num_orders = len(self.taylor_factors[0])
for i in range(num_outputs):
output_dtype = self.module_dtypes[i]
taylor_factors = self.taylor_factors[i]
output = torch.zeros_like(taylor_factors[0], dtype=output_dtype)
for order in range(num_orders):
coeff = (step_offset**order) / math.factorial(order)
factor = taylor_factors[order]
output = output + factor.to(output_dtype) * coeff
outputs.append(output)
return outputs
class TaylorSeerCacheHook(ModelHook):
_is_stateful = True
def __init__(
self,
cache_interval: int,
disable_cache_before_step: int,
taylor_factors_dtype: torch.dtype,
state_manager: StateManager,
disable_cache_after_step: int | None = None,
):
super().__init__()
self.cache_interval = cache_interval
self.disable_cache_before_step = disable_cache_before_step
self.disable_cache_after_step = disable_cache_after_step
self.taylor_factors_dtype = taylor_factors_dtype
self.state_manager = state_manager
def initialize_hook(self, module: torch.nn.Module):
return module
def reset_state(self, module: torch.nn.Module) -> None:
"""
Reset state between sampling runs.
"""
self.state_manager.reset()
@torch.compiler.disable
def _measure_should_compute(self) -> bool:
state: TaylorSeerState = self.state_manager.get_state()
state.current_step += 1
current_step = state.current_step
is_warmup_phase = current_step < self.disable_cache_before_step
is_compute_interval = (current_step - self.disable_cache_before_step - 1) % self.cache_interval == 0
is_cooldown_phase = self.disable_cache_after_step is not None and current_step >= self.disable_cache_after_step
should_compute = is_warmup_phase or is_compute_interval or is_cooldown_phase
return should_compute, state
def new_forward(self, module: torch.nn.Module, *args, **kwargs):
should_compute, state = self._measure_should_compute()
if should_compute:
outputs = self.fn_ref.original_forward(*args, **kwargs)
wrapped_outputs = (outputs,) if isinstance(outputs, torch.Tensor) else outputs
state.update(wrapped_outputs)
return outputs
outputs_list = state.predict()
return outputs_list[0] if len(outputs_list) == 1 else tuple(outputs_list)
def _resolve_patterns(config: TaylorSeerCacheConfig) -> tuple[list[str], list[str]]:
"""
Resolve effective inactive and active pattern lists from config + templates.
"""
inactive_patterns = config.skip_predict_identifiers if config.skip_predict_identifiers is not None else None
active_patterns = config.cache_identifiers if config.cache_identifiers is not None else None
return inactive_patterns or [], active_patterns or []
def apply_taylorseer_cache(module: torch.nn.Module, config: TaylorSeerCacheConfig):
"""
Applies the TaylorSeer cache to a given pipeline (typically the transformer / UNet).
This function hooks selected modules in the model to enable caching or skipping based on the provided
configuration, reducing redundant computations in diffusion denoising loops.
Args:
module (torch.nn.Module): The model subtree to apply the hooks to.
config (TaylorSeerCacheConfig): Configuration for the cache.
Example:
```python
>>> import torch
>>> from diffusers import FluxPipeline, TaylorSeerCacheConfig
>>> pipe = FluxPipeline.from_pretrained(
... "black-forest-labs/FLUX.1-dev",
... torch_dtype=torch.bfloat16,
... )
>>> pipe.to("cuda")
>>> config = TaylorSeerCacheConfig(
... cache_interval=5,
... max_order=1,
... disable_cache_before_step=3,
... taylor_factors_dtype=torch.float32,
... )
>>> pipe.transformer.enable_cache(config)
```
"""
inactive_patterns, active_patterns = _resolve_patterns(config)
active_patterns = active_patterns or _TRANSFORMER_BLOCK_IDENTIFIERS
if config.use_lite_mode:
logger.info("Using TaylorSeer Lite variant for cache.")
active_patterns = _PROJ_OUT_IDENTIFIERS
inactive_patterns = _BLOCK_IDENTIFIERS
if config.skip_predict_identifiers or config.cache_identifiers:
logger.warning("Lite mode overrides user patterns.")
for name, submodule in module.named_modules():
matches_inactive = any(re.fullmatch(pattern, name) for pattern in inactive_patterns)
matches_active = any(re.fullmatch(pattern, name) for pattern in active_patterns)
if not (matches_inactive or matches_active):
continue
_apply_taylorseer_cache_hook(
module=submodule,
config=config,
is_inactive=matches_inactive,
)
def _apply_taylorseer_cache_hook(
module: nn.Module,
config: TaylorSeerCacheConfig,
is_inactive: bool,
):
"""
Registers the TaylorSeer hook on the specified nn.Module.
Args:
name: Name of the module.
module: The nn.Module to be hooked.
config: Cache configuration.
is_inactive: Whether this module should operate in "inactive" mode.
"""
state_manager = StateManager(
TaylorSeerState,
init_kwargs={
"taylor_factors_dtype": config.taylor_factors_dtype,
"max_order": config.max_order,
"is_inactive": is_inactive,
},
)
registry = HookRegistry.check_if_exists_or_initialize(module)
hook = TaylorSeerCacheHook(
cache_interval=config.cache_interval,
disable_cache_before_step=config.disable_cache_before_step,
taylor_factors_dtype=config.taylor_factors_dtype,
disable_cache_after_step=config.disable_cache_after_step,
state_manager=state_manager,
)
registry.register_hook(hook, _TAYLORSEER_CACHE_HOOK)