[core] Pyramid Attention Broadcast

docs/source/en/_toctree.yml

@@ -592,6 +592,8 @@
       title: Attention Processor
     - local: api/activations
       title: Custom activation functions
+    - local: api/cache
+      title: Caching methods
     - local: api/normalization
       title: Custom normalization layers
     - local: api/utilities

docs/source/en/api/cache.md

@@ -0,0 +1,49 @@
+<!-- Copyright 2024 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. -->
+
+# Caching methods
+
+## Pyramid Attention Broadcast
+
+[Pyramid Attention Broadcast](https://huggingface.co/papers/2408.12588) from Xuanlei Zhao, Xiaolong Jin, Kai Wang, Yang You.
+
+Pyramid Attention Broadcast (PAB) is a method that speeds up inference in diffusion models by systematically skipping attention computations between successive inference steps and reusing cached attention states. The attention states are not very different between successive inference steps. The most prominent difference is in the spatial attention blocks, not as much in the temporal attention blocks, and finally the least in the cross attention blocks. Therefore, many cross attention computation blocks can be skipped, followed by the temporal and spatial attention blocks. By combining other techniques like sequence parallelism and classifier-free guidance parallelism, PAB achieves near real-time video generation.
+
+Enable PAB with [`~PyramidAttentionBroadcastConfig`] on any pipeline. For some benchmarks, refer to [this](https://github.com/huggingface/diffusers/pull/9562) pull request.
+
+```python
+import torch
+from diffusers import CogVideoXPipeline, PyramidAttentionBroadcastConfig
+
+pipe = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-5b", torch_dtype=torch.bfloat16)
+pipe.to("cuda")
+
+# Increasing the value of `spatial_attention_timestep_skip_range[0]` or decreasing the value of
+# `spatial_attention_timestep_skip_range[1]` will decrease the interval in which pyramid attention
+# broadcast is active, leader to slower inference speeds. However, large intervals can lead to
+# poorer quality of generated videos.
+config = PyramidAttentionBroadcastConfig(
+    spatial_attention_block_skip_range=2,
+    spatial_attention_timestep_skip_range=(100, 800),
+    current_timestep_callback=lambda: pipe.current_timestep,
+)
+pipe.transformer.enable_cache(config)
+```
+
+### CacheMixin
+
+[[autodoc]] CacheMixin
+
+### PyramidAttentionBroadcastConfig
+
+[[autodoc]] PyramidAttentionBroadcastConfig
+
+[[autodoc]] apply_pyramid_attention_broadcast

src/diffusers/__init__.py

@@ -28,6 +28,7 @@
 
 _import_structure = {
     "configuration_utils": ["ConfigMixin"],
+    "hooks": [],
     "loaders": ["FromOriginalModelMixin"],
     "models": [],
     "pipelines": [],
@@ -75,6 +76,13 @@
     _import_structure["utils.dummy_pt_objects"] = [name for name in dir(dummy_pt_objects) if not name.startswith("_")]
 
 else:
+    _import_structure["hooks"].extend(
+        [
+            "HookRegistry",
+            "PyramidAttentionBroadcastConfig",
+            "apply_pyramid_attention_broadcast",
+        ]
+    )
     _import_structure["models"].extend(
         [
             "AllegroTransformer3DModel",
@@ -90,6 +98,7 @@
             "AutoencoderKLTemporalDecoder",
             "AutoencoderOobleck",
             "AutoencoderTiny",
+            "CacheMixin",
             "CogVideoXTransformer3DModel",
             "CogView3PlusTransformer2DModel",
             "ConsistencyDecoderVAE",
@@ -586,6 +595,7 @@
     except OptionalDependencyNotAvailable:
         from .utils.dummy_pt_objects import *  # noqa F403
     else:
+        from .hooks import HookRegistry, PyramidAttentionBroadcastConfig, apply_pyramid_attention_broadcast
         from .models import (
             AllegroTransformer3DModel,
             AsymmetricAutoencoderKL,
@@ -600,6 +610,7 @@
             AutoencoderKLTemporalDecoder,
             AutoencoderOobleck,
             AutoencoderTiny,
+            CacheMixin,
             CogVideoXTransformer3DModel,
             CogView3PlusTransformer2DModel,
             ConsistencyDecoderVAE,

src/diffusers/hooks/__init__.py

@@ -0,0 +1,6 @@
+from ..utils import is_torch_available
+
+
+if is_torch_available():
+    from .hooks import HookRegistry
+    from .pyramid_attention_broadcast import PyramidAttentionBroadcastConfig, apply_pyramid_attention_broadcast

src/diffusers/hooks/hooks.py

@@ -0,0 +1,188 @@
+# Copyright 2024 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 functools
+from typing import Any, Dict, Optional, Tuple
+
+import torch
+
+from ..utils.logging import get_logger
+
+
+logger = get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class ModelHook:
+    r"""
+    A hook that contains callbacks to be executed just before and after the forward method of a model.
+    """
+
+    _is_stateful = False
+
+    def initialize_hook(self, module: torch.nn.Module) -> torch.nn.Module:
+        r"""
+        Hook that is executed when a model is initialized.
+
+        Args:
+            module (`torch.nn.Module`):
+                The module attached to this hook.
+        """
+        return module
+
+    def deinitalize_hook(self, module: torch.nn.Module) -> torch.nn.Module:
+        r"""
+        Hook that is executed when a model is deinitalized.
+
+        Args:
+            module (`torch.nn.Module`):
+                The module attached to this hook.
+        """
+        module.forward = module._old_forward
+        del module._old_forward
+        return module
+
+    def pre_forward(self, module: torch.nn.Module, *args, **kwargs) -> Tuple[Tuple[Any], Dict[str, Any]]:
+        r"""
+        Hook that is executed just before the forward method of the model.
+
+        Args:
+            module (`torch.nn.Module`):
+                The module whose forward pass will be executed just after this event.
+            args (`Tuple[Any]`):
+                The positional arguments passed to the module.
+            kwargs (`Dict[Str, Any]`):
+                The keyword arguments passed to the module.
+        Returns:
+            `Tuple[Tuple[Any], Dict[Str, Any]]`:
+                A tuple with the treated `args` and `kwargs`.
+        """
+        return args, kwargs
+
+    def post_forward(self, module: torch.nn.Module, output: Any) -> Any:
+        r"""
+        Hook that is executed just after the forward method of the model.
+
+        Args:
+            module (`torch.nn.Module`):
+                The module whose forward pass been executed just before this event.
+            output (`Any`):
+                The output of the module.
+        Returns:
+            `Any`: The processed `output`.
+        """
+        return output
+
+    def detach_hook(self, module: torch.nn.Module) -> torch.nn.Module:
+        r"""
+        Hook that is executed when the hook is detached from a module.
+
+        Args:
+            module (`torch.nn.Module`):
+                The module detached from this hook.
+        """
+        return module
+
+    def reset_state(self, module: torch.nn.Module):
+        if self._is_stateful:
+            raise NotImplementedError("This hook is stateful and needs to implement the `reset_state` method.")
+        return module
+
+
+class HookRegistry:
+    def __init__(self, module_ref: torch.nn.Module) -> None:
+        super().__init__()
+
+        self.hooks: Dict[str, ModelHook] = {}
+
+        self._module_ref = module_ref
+        self._hook_order = []
+
+    def register_hook(self, hook: ModelHook, name: str) -> None:
+        if name in self.hooks.keys():
+            logger.warning(f"Hook with name {name} already exists, replacing it.")
+
+        if hasattr(self._module_ref, "_old_forward"):
+            old_forward = self._module_ref._old_forward
+        else:
+            old_forward = self._module_ref.forward
+            self._module_ref._old_forward = self._module_ref.forward
+
+        self._module_ref = hook.initialize_hook(self._module_ref)
+
+        if hasattr(hook, "new_forward"):
+            rewritten_forward = hook.new_forward
+
+            def new_forward(module, *args, **kwargs):
+                args, kwargs = hook.pre_forward(module, *args, **kwargs)
+                output = rewritten_forward(module, *args, **kwargs)
+                return hook.post_forward(module, output)
+        else:
+
+            def new_forward(module, *args, **kwargs):
+                args, kwargs = hook.pre_forward(module, *args, **kwargs)
+                output = old_forward(*args, **kwargs)
+                return hook.post_forward(module, output)
+
+        self._module_ref.forward = functools.update_wrapper(
+            functools.partial(new_forward, self._module_ref), old_forward
+        )
+
+        self.hooks[name] = hook
+        self._hook_order.append(name)
+
+    def get_hook(self, name: str) -> Optional[ModelHook]:
+        if name not in self.hooks.keys():
+            return None
+        return self.hooks[name]
+
+    def remove_hook(self, name: str, recurse: bool = True) -> None:
+        if name in self.hooks.keys():
+            hook = self.hooks[name]
+            self._module_ref = hook.deinitalize_hook(self._module_ref)
+            del self.hooks[name]
+            self._hook_order.remove(name)
+
+        if recurse:
+            for module_name, module in self._module_ref.named_modules():
+                if module_name == "":
+                    continue
+                if hasattr(module, "_diffusers_hook"):
+                    module._diffusers_hook.remove_hook(name, recurse=False)
+
+    def reset_stateful_hooks(self, recurse: bool = True) -> None:
+        for hook_name in self._hook_order:
+            hook = self.hooks[hook_name]
+            if hook._is_stateful:
+                hook.reset_state(self._module_ref)
+
+        if recurse:
+            for module_name, module in self._module_ref.named_modules():
+                if module_name == "":
+                    continue
+                if hasattr(module, "_diffusers_hook"):
+                    module._diffusers_hook.reset_stateful_hooks(recurse=False)
+
+    @classmethod
+    def check_if_exists_or_initialize(cls, module: torch.nn.Module) -> "HookRegistry":
+        if not hasattr(module, "_diffusers_hook"):
+            module._diffusers_hook = cls(module)
+        return module._diffusers_hook
+
+    def __repr__(self) -> str:
+        hook_repr = ""
+        for i, hook_name in enumerate(self._hook_order):
+            hook_repr += f"  ({i}) {hook_name} - ({self.hooks[hook_name].__class__.__name__})"
+            if i < len(self._hook_order) - 1:
+                hook_repr += "\n"
+        return f"HookRegistry(\n{hook_repr}\n)"