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# coding=utf-8
# Copyright 2026 HuggingFace Inc.
#
# 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 gc
import pytest
import torch
from diffusers.hooks import (
FasterCacheConfig,
FirstBlockCacheConfig,
MagCacheConfig,
PyramidAttentionBroadcastConfig,
TaylorSeerCacheConfig,
)
from diffusers.hooks.faster_cache import _FASTER_CACHE_BLOCK_HOOK, _FASTER_CACHE_DENOISER_HOOK
from diffusers.hooks.first_block_cache import _FBC_BLOCK_HOOK, _FBC_LEADER_BLOCK_HOOK
from diffusers.hooks.mag_cache import _MAG_CACHE_BLOCK_HOOK, _MAG_CACHE_LEADER_BLOCK_HOOK
from diffusers.hooks.pyramid_attention_broadcast import _PYRAMID_ATTENTION_BROADCAST_HOOK
from diffusers.hooks.taylorseer_cache import _TAYLORSEER_CACHE_HOOK
from diffusers.models.cache_utils import CacheMixin
from ...testing_utils import assert_tensors_close, backend_empty_cache, is_cache, torch_device
def require_cache_mixin(func):
"""Decorator to skip tests if model doesn't use CacheMixin."""
def wrapper(self, *args, **kwargs):
if not issubclass(self.model_class, CacheMixin):
pytest.skip(f"{self.model_class.__name__} does not use CacheMixin.")
return func(self, *args, **kwargs)
return wrapper
class CacheTesterMixin:
"""
Base mixin class providing common test implementations for cache testing.
Cache-specific mixins should:
1. Inherit from their respective config mixin (e.g., PyramidAttentionBroadcastConfigMixin)
2. Inherit from this mixin
3. Define the cache config to use for tests
Expected class attributes:
- model_class: The model class to test (must use CacheMixin)
Expected methods in test classes:
- get_init_dict(): Returns dict of arguments to initialize the model
- get_dummy_inputs(): Returns dict of inputs to pass to the model forward pass
Optional overrides:
- cache_input_key: Property returning the input tensor key to vary between passes (default: "hidden_states")
"""
@property
def cache_input_key(self):
return "hidden_states"
def setup_method(self):
gc.collect()
backend_empty_cache(torch_device)
def teardown_method(self):
gc.collect()
backend_empty_cache(torch_device)
def _get_cache_config(self):
"""
Get the cache config for testing.
Should be implemented by subclasses.
"""
raise NotImplementedError("Subclass must implement _get_cache_config")
def _get_hook_names(self):
"""
Get the hook names to check for this cache type.
Should be implemented by subclasses.
Returns a list of hook name strings.
"""
raise NotImplementedError("Subclass must implement _get_hook_names")
def _test_cache_enable_disable_state(self):
"""Test that cache enable/disable updates the is_cache_enabled state correctly."""
init_dict = self.get_init_dict()
model = self.model_class(**init_dict).to(torch_device)
# Initially cache should not be enabled
assert not model.is_cache_enabled, "Cache should not be enabled initially."
config = self._get_cache_config()
# Enable cache
model.enable_cache(config)
assert model.is_cache_enabled, "Cache should be enabled after enable_cache()."
# Disable cache
model.disable_cache()
assert not model.is_cache_enabled, "Cache should not be enabled after disable_cache()."
def _test_cache_double_enable_raises_error(self):
"""Test that enabling cache twice raises an error."""
init_dict = self.get_init_dict()
model = self.model_class(**init_dict).to(torch_device)
config = self._get_cache_config()
model.enable_cache(config)
# Trying to enable again should raise ValueError
with pytest.raises(ValueError, match="Caching has already been enabled"):
model.enable_cache(config)
# Cleanup
model.disable_cache()
def _test_cache_hooks_registered(self):
"""Test that cache hooks are properly registered and removed."""
init_dict = self.get_init_dict()
model = self.model_class(**init_dict).to(torch_device)
config = self._get_cache_config()
hook_names = self._get_hook_names()
model.enable_cache(config)
# Check that at least one hook was registered
hook_count = 0
for module in model.modules():
if hasattr(module, "_diffusers_hook"):
for hook_name in hook_names:
hook = module._diffusers_hook.get_hook(hook_name)
if hook is not None:
hook_count += 1
assert hook_count > 0, f"At least one cache hook should be registered. Hook names: {hook_names}"
# Disable and verify hooks are removed
model.disable_cache()
hook_count_after = 0
for module in model.modules():
if hasattr(module, "_diffusers_hook"):
for hook_name in hook_names:
hook = module._diffusers_hook.get_hook(hook_name)
if hook is not None:
hook_count_after += 1
assert hook_count_after == 0, "Cache hooks should be removed after disable_cache()."
@torch.no_grad()
def _test_cache_inference(self):
"""Test that model can run inference with cache enabled."""
init_dict = self.get_init_dict()
inputs_dict = self.get_dummy_inputs()
model = self.model_class(**init_dict).to(torch_device)
model.eval()
config = self._get_cache_config()
model.enable_cache(config)
# First pass populates the cache
_ = model(**inputs_dict, return_dict=False)[0]
# Create modified inputs for second pass (vary input tensor to simulate denoising)
inputs_dict_step2 = inputs_dict.copy()
if self.cache_input_key in inputs_dict_step2:
inputs_dict_step2[self.cache_input_key] = inputs_dict_step2[self.cache_input_key] + torch.randn_like(
inputs_dict_step2[self.cache_input_key]
)
# Second pass uses cached attention with different inputs (produces approximated output)
output_with_cache = model(**inputs_dict_step2, return_dict=False)[0]
assert output_with_cache is not None, "Model output should not be None with cache enabled."
assert not torch.isnan(output_with_cache).any(), "Model output contains NaN with cache enabled."
# Run same inputs without cache to compare
model.disable_cache()
output_without_cache = model(**inputs_dict_step2, return_dict=False)[0]
# Cached output should be different from non-cached output (due to approximation)
assert not torch.allclose(output_without_cache, output_with_cache, atol=1e-5), (
"Cached output should be different from non-cached output due to cache approximation."
)
@torch.no_grad()
def _test_cache_context_manager(self, atol=1e-5, rtol=0):
"""Test the cache_context context manager properly isolates cache state."""
init_dict = self.get_init_dict()
inputs_dict = self.get_dummy_inputs()
model = self.model_class(**init_dict).to(torch_device)
model.eval()
config = self._get_cache_config()
model.enable_cache(config)
# Run inference in first context
with model.cache_context("context_1"):
output_ctx1 = model(**inputs_dict, return_dict=False)[0]
# Run same inference in second context (cache should be reset)
with model.cache_context("context_2"):
output_ctx2 = model(**inputs_dict, return_dict=False)[0]
# Both contexts should produce the same output (first pass in each)
assert_tensors_close(
output_ctx1,
output_ctx2,
atol=atol,
rtol=rtol,
msg="First pass in different cache contexts should produce the same output.",
)
model.disable_cache()
@torch.no_grad()
def _test_reset_stateful_cache(self):
"""Test that _reset_stateful_cache resets the cache state."""
init_dict = self.get_init_dict()
inputs_dict = self.get_dummy_inputs()
model = self.model_class(**init_dict).to(torch_device)
model.eval()
config = self._get_cache_config()
model.enable_cache(config)
_ = model(**inputs_dict, return_dict=False)[0]
model._reset_stateful_cache()
model.disable_cache()
@is_cache
class PyramidAttentionBroadcastConfigMixin:
"""
Base mixin providing PyramidAttentionBroadcast cache config.
Expected class attributes:
- model_class: The model class to test (must use CacheMixin)
"""
# Default PAB config - can be overridden by subclasses
PAB_CONFIG = {
"spatial_attention_block_skip_range": 2,
}
# Store timestep for callback (must be within default range (100, 800) for skipping to trigger)
_current_timestep = 500
def _get_cache_config(self):
config_kwargs = self.PAB_CONFIG.copy()
config_kwargs["current_timestep_callback"] = lambda: self._current_timestep
return PyramidAttentionBroadcastConfig(**config_kwargs)
def _get_hook_names(self):
return [_PYRAMID_ATTENTION_BROADCAST_HOOK]
@is_cache
class PyramidAttentionBroadcastTesterMixin(PyramidAttentionBroadcastConfigMixin, CacheTesterMixin):
"""
Mixin class for testing PyramidAttentionBroadcast caching on models.
Expected class attributes:
- model_class: The model class to test (must use CacheMixin)
Expected methods to be implemented by subclasses:
- get_init_dict(): Returns dict of arguments to initialize the model
- get_dummy_inputs(): Returns dict of inputs to pass to the model forward pass
Pytest mark: cache
Use `pytest -m "not cache"` to skip these tests
"""
@require_cache_mixin
def test_pab_cache_enable_disable_state(self):
self._test_cache_enable_disable_state()
@require_cache_mixin
def test_pab_cache_double_enable_raises_error(self):
self._test_cache_double_enable_raises_error()
@require_cache_mixin
def test_pab_cache_hooks_registered(self):
self._test_cache_hooks_registered()
@require_cache_mixin
def test_pab_cache_inference(self):
self._test_cache_inference()
@require_cache_mixin
def test_pab_cache_context_manager(self):
self._test_cache_context_manager()
@require_cache_mixin
def test_pab_reset_stateful_cache(self):
self._test_reset_stateful_cache()
@is_cache
class FirstBlockCacheConfigMixin:
"""
Base mixin providing FirstBlockCache config.
Expected class attributes:
- model_class: The model class to test (must use CacheMixin)
"""
# Default FBC config - can be overridden by subclasses
# Higher threshold makes FBC more aggressive about caching (skips more often)
FBC_CONFIG = {
"threshold": 1.0,
}
def _get_cache_config(self):
return FirstBlockCacheConfig(**self.FBC_CONFIG)
def _get_hook_names(self):
return [_FBC_LEADER_BLOCK_HOOK, _FBC_BLOCK_HOOK]
@is_cache
class FirstBlockCacheTesterMixin(FirstBlockCacheConfigMixin, CacheTesterMixin):
"""
Mixin class for testing FirstBlockCache on models.
Expected class attributes:
- model_class: The model class to test (must use CacheMixin)
Expected methods to be implemented by subclasses:
- get_init_dict(): Returns dict of arguments to initialize the model
- get_dummy_inputs(): Returns dict of inputs to pass to the model forward pass
Pytest mark: cache
Use `pytest -m "not cache"` to skip these tests
"""
@torch.no_grad()
def _test_cache_inference(self):
"""Test that model can run inference with FBC cache enabled (requires cache_context)."""
init_dict = self.get_init_dict()
inputs_dict = self.get_dummy_inputs()
model = self.model_class(**init_dict).to(torch_device)
model.eval()
config = self._get_cache_config()
model.enable_cache(config)
# FBC requires cache_context to be set for inference
with model.cache_context("fbc_test"):
# First pass populates the cache
_ = model(**inputs_dict, return_dict=False)[0]
# Create modified inputs for second pass
inputs_dict_step2 = inputs_dict.copy()
if self.cache_input_key in inputs_dict_step2:
inputs_dict_step2[self.cache_input_key] = inputs_dict_step2[self.cache_input_key] + torch.randn_like(
inputs_dict_step2[self.cache_input_key]
)
# Second pass - FBC should skip remaining blocks and use cached residuals
output_with_cache = model(**inputs_dict_step2, return_dict=False)[0]
assert output_with_cache is not None, "Model output should not be None with cache enabled."
assert not torch.isnan(output_with_cache).any(), "Model output contains NaN with cache enabled."
# Run same inputs without cache to compare
model.disable_cache()
output_without_cache = model(**inputs_dict_step2, return_dict=False)[0]
# Cached output should be different from non-cached output (due to approximation)
assert not torch.allclose(output_without_cache, output_with_cache, atol=1e-5), (
"Cached output should be different from non-cached output due to cache approximation."
)
@torch.no_grad()
def _test_reset_stateful_cache(self):
"""Test that _reset_stateful_cache resets the FBC cache state (requires cache_context)."""
init_dict = self.get_init_dict()
inputs_dict = self.get_dummy_inputs()
model = self.model_class(**init_dict).to(torch_device)
model.eval()
config = self._get_cache_config()
model.enable_cache(config)
with model.cache_context("fbc_test"):
_ = model(**inputs_dict, return_dict=False)[0]
model._reset_stateful_cache()
model.disable_cache()
@require_cache_mixin
def test_fbc_cache_enable_disable_state(self):
self._test_cache_enable_disable_state()
@require_cache_mixin
def test_fbc_cache_double_enable_raises_error(self):
self._test_cache_double_enable_raises_error()
@require_cache_mixin
def test_fbc_cache_hooks_registered(self):
self._test_cache_hooks_registered()
@require_cache_mixin
def test_fbc_cache_inference(self):
self._test_cache_inference()
@require_cache_mixin
def test_fbc_cache_context_manager(self):
self._test_cache_context_manager()
@require_cache_mixin
def test_fbc_reset_stateful_cache(self):
self._test_reset_stateful_cache()
@is_cache
class FasterCacheConfigMixin:
"""
Base mixin providing FasterCache config.
Expected class attributes:
- model_class: The model class to test (must use CacheMixin)
"""
# Default FasterCache config - can be overridden by subclasses
FASTER_CACHE_CONFIG = {
"spatial_attention_block_skip_range": 2,
"spatial_attention_timestep_skip_range": (-1, 901),
"tensor_format": "BCHW",
}
def _get_cache_config(self, current_timestep_callback=None):
config_kwargs = self.FASTER_CACHE_CONFIG.copy()
if current_timestep_callback is None:
current_timestep_callback = lambda: 1000 # noqa: E731
config_kwargs["current_timestep_callback"] = current_timestep_callback
return FasterCacheConfig(**config_kwargs)
def _get_hook_names(self):
return [_FASTER_CACHE_DENOISER_HOOK, _FASTER_CACHE_BLOCK_HOOK]
@is_cache
class FasterCacheTesterMixin(FasterCacheConfigMixin, CacheTesterMixin):
"""
Mixin class for testing FasterCache on models.
Note: FasterCache is designed for pipeline-level inference with proper CFG batch handling
and timestep management. Inference tests are skipped at model level - FasterCache should
be tested via pipeline tests (e.g., FluxPipeline, HunyuanVideoPipeline).
Expected class attributes:
- model_class: The model class to test (must use CacheMixin)
Expected methods to be implemented by subclasses:
- get_init_dict(): Returns dict of arguments to initialize the model
- get_dummy_inputs(): Returns dict of inputs to pass to the model forward pass
Pytest mark: cache
Use `pytest -m "not cache"` to skip these tests
"""
@torch.no_grad()
def _test_cache_inference(self):
"""Test that model can run inference with FasterCache enabled."""
init_dict = self.get_init_dict()
inputs_dict = self.get_dummy_inputs()
model = self.model_class(**init_dict).to(torch_device)
model.eval()
current_timestep = [1000]
config = self._get_cache_config(current_timestep_callback=lambda: current_timestep[0])
model.enable_cache(config)
# First pass with timestep outside skip range - computes and populates cache
current_timestep[0] = 1000
_ = model(**inputs_dict, return_dict=False)[0]
# Move timestep inside skip range so subsequent passes use cache
current_timestep[0] = 500
# Create modified inputs for second pass
inputs_dict_step2 = inputs_dict.copy()
if self.cache_input_key in inputs_dict_step2:
inputs_dict_step2[self.cache_input_key] = inputs_dict_step2[self.cache_input_key] + torch.randn_like(
inputs_dict_step2[self.cache_input_key]
)
# Second pass uses cached attention with different inputs
output_with_cache = model(**inputs_dict_step2, return_dict=False)[0]
assert output_with_cache is not None, "Model output should not be None with cache enabled."
assert not torch.isnan(output_with_cache).any(), "Model output contains NaN with cache enabled."
# Run same inputs without cache to compare
model.disable_cache()
output_without_cache = model(**inputs_dict_step2, return_dict=False)[0]
# Cached output should be different from non-cached output (due to approximation)
assert not torch.allclose(output_without_cache, output_with_cache, atol=1e-5), (
"Cached output should be different from non-cached output due to cache approximation."
)
@torch.no_grad()
def _test_reset_stateful_cache(self):
"""Test that _reset_stateful_cache resets the FasterCache state."""
init_dict = self.get_init_dict()
inputs_dict = self.get_dummy_inputs()
model = self.model_class(**init_dict).to(torch_device)
model.eval()
config = self._get_cache_config()
model.enable_cache(config)
_ = model(**inputs_dict, return_dict=False)[0]
model._reset_stateful_cache()
model.disable_cache()
@require_cache_mixin
def test_faster_cache_enable_disable_state(self):
self._test_cache_enable_disable_state()
@require_cache_mixin
def test_faster_cache_double_enable_raises_error(self):
self._test_cache_double_enable_raises_error()
@require_cache_mixin
def test_faster_cache_hooks_registered(self):
self._test_cache_hooks_registered()
@require_cache_mixin
def test_faster_cache_inference(self):
self._test_cache_inference()
@require_cache_mixin
def test_faster_cache_context_manager(self):
self._test_cache_context_manager()
@require_cache_mixin
def test_faster_cache_reset_stateful_cache(self):
self._test_reset_stateful_cache()
@is_cache
class MagCacheConfigMixin:
"""
Base mixin providing MagCache config.
Expected class attributes:
- model_class: The model class to test (must use CacheMixin)
"""
# Default MagCache config - can be overridden by subclasses.
# Uses neutral ratios [1.0, 1.0] and a high threshold so the second
# inference step is always skipped, which is required by _test_cache_inference.
MAG_CACHE_CONFIG = {
"num_inference_steps": 2,
"retention_ratio": 0.0,
"threshold": 100.0,
"mag_ratios": [1.0, 1.0],
}
def _get_cache_config(self):
return MagCacheConfig(**self.MAG_CACHE_CONFIG)
def _get_hook_names(self):
return [_MAG_CACHE_LEADER_BLOCK_HOOK, _MAG_CACHE_BLOCK_HOOK]
@is_cache
class MagCacheTesterMixin(MagCacheConfigMixin, CacheTesterMixin):
"""
Mixin class for testing MagCache on models.
Expected class attributes:
- model_class: The model class to test (must use CacheMixin)
Expected methods to be implemented by subclasses:
- get_init_dict(): Returns dict of arguments to initialize the model
- get_dummy_inputs(): Returns dict of inputs to pass to the model forward pass
Pytest mark: cache
Use `pytest -m "not cache"` to skip these tests
"""
@require_cache_mixin
def test_mag_cache_enable_disable_state(self):
self._test_cache_enable_disable_state()
@require_cache_mixin
def test_mag_cache_double_enable_raises_error(self):
self._test_cache_double_enable_raises_error()
@require_cache_mixin
def test_mag_cache_hooks_registered(self):
self._test_cache_hooks_registered()
@require_cache_mixin
def test_mag_cache_inference(self):
self._test_cache_inference()
@require_cache_mixin
def test_mag_cache_context_manager(self):
self._test_cache_context_manager()
@require_cache_mixin
def test_mag_cache_reset_stateful_cache(self):
self._test_reset_stateful_cache()
@is_cache
class TaylorSeerCacheConfigMixin:
"""
Base mixin providing TaylorSeerCache config.
Expected class attributes:
- model_class: The model class to test (must use CacheMixin)
"""
# Default TaylorSeerCache config - can be overridden by subclasses.
# Uses a low cache_interval and disable_cache_before_step=0 so the second
# inference step is always predicted, which is required by _test_cache_inference.
TAYLORSEER_CACHE_CONFIG = {
"cache_interval": 3,
"disable_cache_before_step": 1,
"max_order": 1,
}
def _get_cache_config(self):
return TaylorSeerCacheConfig(**self.TAYLORSEER_CACHE_CONFIG)
def _get_hook_names(self):
return [_TAYLORSEER_CACHE_HOOK]
@is_cache
class TaylorSeerCacheTesterMixin(TaylorSeerCacheConfigMixin, CacheTesterMixin):
"""
Mixin class for testing TaylorSeerCache on models.
Expected class attributes:
- model_class: The model class to test (must use CacheMixin)
Expected methods to be implemented by subclasses:
- get_init_dict(): Returns dict of arguments to initialize the model
- get_dummy_inputs(): Returns dict of inputs to pass to the model forward pass
Pytest mark: cache
Use `pytest -m "not cache"` to skip these tests
"""
@torch.no_grad()
def _test_cache_inference(self):
"""Test that model can run inference with TaylorSeer cache enabled (requires cache_context)."""
init_dict = self.get_init_dict()
inputs_dict = self.get_dummy_inputs()
model = self.model_class(**init_dict).to(torch_device)
model.eval()
config = self._get_cache_config()
model.enable_cache(config)
# TaylorSeer requires cache_context to be set for inference
with model.cache_context("taylorseer_test"):
# First pass populates the cache
_ = model(**inputs_dict, return_dict=False)[0]
# Create modified inputs for second pass
inputs_dict_step2 = inputs_dict.copy()
if self.cache_input_key in inputs_dict_step2:
inputs_dict_step2[self.cache_input_key] = inputs_dict_step2[self.cache_input_key] + torch.randn_like(
inputs_dict_step2[self.cache_input_key]
)
# Second pass - TaylorSeer should use cached Taylor series predictions
output_with_cache = model(**inputs_dict_step2, return_dict=False)[0]
assert output_with_cache is not None, "Model output should not be None with cache enabled."
assert not torch.isnan(output_with_cache).any(), "Model output contains NaN with cache enabled."
# Run same inputs without cache to compare
model.disable_cache()
output_without_cache = model(**inputs_dict_step2, return_dict=False)[0]
# Cached output should be different from non-cached output (due to approximation)
assert not torch.allclose(output_without_cache, output_with_cache, atol=1e-5), (
"Cached output should be different from non-cached output due to cache approximation."
)
@torch.no_grad()
def _test_reset_stateful_cache(self):
"""Test that _reset_stateful_cache resets the TaylorSeer cache state (requires cache_context)."""
init_dict = self.get_init_dict()
inputs_dict = self.get_dummy_inputs()
model = self.model_class(**init_dict).to(torch_device)
model.eval()
config = self._get_cache_config()
model.enable_cache(config)
with model.cache_context("taylorseer_test"):
_ = model(**inputs_dict, return_dict=False)[0]
model._reset_stateful_cache()
model.disable_cache()
@require_cache_mixin
def test_taylorseer_cache_enable_disable_state(self):
self._test_cache_enable_disable_state()
@require_cache_mixin
def test_taylorseer_cache_double_enable_raises_error(self):
self._test_cache_double_enable_raises_error()
@require_cache_mixin
def test_taylorseer_cache_hooks_registered(self):
self._test_cache_hooks_registered()
@require_cache_mixin
def test_taylorseer_cache_inference(self):
self._test_cache_inference()
@require_cache_mixin
def test_taylorseer_cache_context_manager(self):
self._test_cache_context_manager()
@require_cache_mixin
def test_taylorseer_cache_reset_stateful_cache(self):
self._test_reset_stateful_cache()