vendor/VideoX-Fun/videox_fun/utils/group_offload.py

# Modified from https://github.com/huggingface/diffusers/blob/v0.36.0/src/diffusers/hooks/group_offloading.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 functools
import hashlib
import os
import types
from contextlib import contextmanager, nullcontext
from dataclasses import dataclass, replace
from enum import Enum
from typing import Any, Dict, List, Optional, Set, Tuple, Union

import safetensors.torch
import torch
from diffusers.utils import get_logger, is_accelerate_available

if is_accelerate_available():
    from accelerate.hooks import AlignDevicesHook, CpuOffload
    from accelerate.utils import send_to_device

logger = get_logger(__name__)  # pylint: disable=invalid-name


# fmt: off
_GROUP_OFFLOADING = "group_offloading"
_LAYER_EXECUTION_TRACKER = "layer_execution_tracker"
_LAZY_PREFETCH_GROUP_OFFLOADING = "lazy_prefetch_group_offloading"
_GROUP_ID_LAZY_LEAF = "lazy_leafs"
# fmt: on

_GO_LC_SUPPORTED_PYTORCH_LAYERS = (
    torch.nn.Conv1d,
    torch.nn.Conv2d,
    torch.nn.Conv3d,
    torch.nn.ConvTranspose1d,
    torch.nn.ConvTranspose2d,
    torch.nn.ConvTranspose3d,
    torch.nn.Linear,
    # TODO(aryan): look into torch.nn.LayerNorm, torch.nn.GroupNorm later, seems to be causing some issues with CogVideoX
    # because of double invocation of the same norm layer in CogVideoXLayerNorm
)


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 __init__(self):
        self.fn_ref: "HookFunctionReference" = None

    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.

        """
        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 HookFunctionReference:
    def __init__(self) -> None:
        """A container class that maintains mutable references to forward pass functions in a hook chain.



        Its mutable nature allows the hook system to modify the execution chain dynamically without rebuilding the

        entire forward pass structure.



        Attributes:

            pre_forward: A callable that processes inputs before the main forward pass.

            post_forward: A callable that processes outputs after the main forward pass.

            forward: The current forward function in the hook chain.

            original_forward: The original forward function, stored when a hook provides a custom new_forward.



        The class enables hook removal by allowing updates to the forward chain through reference modification rather

        than requiring reconstruction of the entire chain. When a hook is removed, only the relevant references need to

        be updated, preserving the execution order of the remaining hooks.

        """
        self.pre_forward = None
        self.post_forward = None
        self.forward = None
        self.original_forward = None


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 = []
        self._fn_refs = []

    def register_hook(self, hook: ModelHook, name: str) -> None:
        if name in self.hooks.keys():
            raise ValueError(
                f"Hook with name {name} already exists in the registry. Please use a different name or "
                f"first remove the existing hook and then add a new one."
            )

        self._module_ref = hook.initialize_hook(self._module_ref)

        def create_new_forward(function_reference: HookFunctionReference):
            def new_forward(module, *args, **kwargs):
                args, kwargs = function_reference.pre_forward(module, *args, **kwargs)
                output = function_reference.forward(*args, **kwargs)
                return function_reference.post_forward(module, output)

            return new_forward

        forward = self._module_ref.forward

        fn_ref = HookFunctionReference()
        fn_ref.pre_forward = hook.pre_forward
        fn_ref.post_forward = hook.post_forward
        fn_ref.forward = forward

        if hasattr(hook, "new_forward"):
            fn_ref.original_forward = forward
            fn_ref.forward = functools.update_wrapper(
                functools.partial(hook.new_forward, self._module_ref), hook.new_forward
            )

        rewritten_forward = create_new_forward(fn_ref)
        self._module_ref.forward = functools.update_wrapper(
            functools.partial(rewritten_forward, self._module_ref), rewritten_forward
        )

        hook.fn_ref = fn_ref
        self.hooks[name] = hook
        self._hook_order.append(name)
        self._fn_refs.append(fn_ref)

    def get_hook(self, name: str) -> Optional[ModelHook]:
        return self.hooks.get(name, None)

    def remove_hook(self, name: str, recurse: bool = True) -> None:
        if name in self.hooks.keys():
            num_hooks = len(self._hook_order)
            hook = self.hooks[name]
            index = self._hook_order.index(name)
            fn_ref = self._fn_refs[index]

            old_forward = fn_ref.forward
            if fn_ref.original_forward is not None:
                old_forward = fn_ref.original_forward

            if index == num_hooks - 1:
                self._module_ref.forward = old_forward
            else:
                self._fn_refs[index + 1].forward = old_forward

            self._module_ref = hook.deinitalize_hook(self._module_ref)
            del self.hooks[name]
            self._hook_order.pop(index)
            self._fn_refs.pop(index)

        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 reversed(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:
        registry_repr = ""
        for i, hook_name in enumerate(self._hook_order):
            if self.hooks[hook_name].__class__.__repr__ is not object.__repr__:
                hook_repr = self.hooks[hook_name].__repr__()
            else:
                hook_repr = self.hooks[hook_name].__class__.__name__
            registry_repr += f"  ({i}) {hook_name} - {hook_repr}"
            if i < len(self._hook_order) - 1:
                registry_repr += "\n"
        return f"HookRegistry(\n{registry_repr}\n)"


class GroupOffloadingType(str, Enum):
    BLOCK_LEVEL = "block_level"
    LEAF_LEVEL = "leaf_level"


@dataclass
class GroupOffloadingConfig:
    onload_device: torch.device
    offload_device: torch.device
    offload_type: GroupOffloadingType
    non_blocking: bool
    record_stream: bool
    low_cpu_mem_usage: bool
    num_blocks_per_group: Optional[int] = None
    offload_to_disk_path: Optional[str] = None
    stream: Optional[Union[torch.cuda.Stream, torch.Stream]] = None
    block_modules: Optional[List[str]] = None
    exclude_kwargs: Optional[List[str]] = None
    module_prefix: Optional[str] = ""


class ModuleGroup:
    def __init__(

        self,

        modules: List[torch.nn.Module],

        offload_device: torch.device,

        onload_device: torch.device,

        offload_leader: torch.nn.Module,

        onload_leader: Optional[torch.nn.Module] = None,

        parameters: Optional[List[torch.nn.Parameter]] = None,

        buffers: Optional[List[torch.Tensor]] = None,

        non_blocking: bool = False,

        stream: Union[torch.cuda.Stream, torch.Stream, None] = None,

        record_stream: Optional[bool] = False,

        low_cpu_mem_usage: bool = False,

        onload_self: bool = True,

        offload_to_disk_path: Optional[str] = None,

        group_id: Optional[Union[int, str]] = None,

    ) -> None:
        self.modules = modules
        self.offload_device = offload_device
        self.onload_device = onload_device
        self.offload_leader = offload_leader
        self.onload_leader = onload_leader
        self.parameters = parameters or []
        self.buffers = buffers or []
        self.non_blocking = non_blocking or stream is not None
        self.stream = stream
        self.record_stream = record_stream
        self.onload_self = onload_self
        self.low_cpu_mem_usage = low_cpu_mem_usage

        self.offload_to_disk_path = offload_to_disk_path
        self._is_offloaded_to_disk = False

        if self.offload_to_disk_path is not None:
            # Instead of `group_id or str(id(self))` we do this because `group_id` can be "" as well.
            self.group_id = group_id if group_id is not None else str(id(self))
            short_hash = _compute_group_hash(self.group_id)
            self.safetensors_file_path = os.path.join(self.offload_to_disk_path, f"group_{short_hash}.safetensors")

            all_tensors = []
            for module in self.modules:
                all_tensors.extend(list(module.parameters()))
                all_tensors.extend(list(module.buffers()))
            all_tensors.extend(self.parameters)
            all_tensors.extend(self.buffers)
            all_tensors = list(dict.fromkeys(all_tensors))  # Remove duplicates

            self.tensor_to_key = {tensor: f"tensor_{i}" for i, tensor in enumerate(all_tensors)}
            self.key_to_tensor = {v: k for k, v in self.tensor_to_key.items()}
            self.cpu_param_dict = {}
        else:
            self.cpu_param_dict = self._init_cpu_param_dict()

        self._torch_accelerator_module = (
            getattr(torch, torch.accelerator.current_accelerator().type)
            if hasattr(torch, "accelerator")
            else torch.cuda
        )

    def _init_cpu_param_dict(self):
        cpu_param_dict = {}
        if self.stream is None:
            return cpu_param_dict

        for module in self.modules:
            for param in module.parameters():
                cpu_param_dict[param] = param.data.cpu() if self.low_cpu_mem_usage else param.data.cpu().pin_memory()
            for buffer in module.buffers():
                cpu_param_dict[buffer] = (
                    buffer.data.cpu() if self.low_cpu_mem_usage else buffer.data.cpu().pin_memory()
                )

        for param in self.parameters:
            cpu_param_dict[param] = param.data.cpu() if self.low_cpu_mem_usage else param.data.cpu().pin_memory()

        for buffer in self.buffers:
            cpu_param_dict[buffer] = buffer.data.cpu() if self.low_cpu_mem_usage else buffer.data.cpu().pin_memory()

        return cpu_param_dict

    @contextmanager
    def _pinned_memory_tensors(self):
        try:
            pinned_dict = {
                param: tensor.pin_memory() if not tensor.is_pinned() else tensor
                for param, tensor in self.cpu_param_dict.items()
            }
            yield pinned_dict
        finally:
            pinned_dict = None

    def _transfer_tensor_to_device(self, tensor, source_tensor, default_stream):
        tensor.data = source_tensor.to(self.onload_device, non_blocking=self.non_blocking)
        if self.record_stream:
            tensor.data.record_stream(default_stream)

    def _process_tensors_from_modules(self, pinned_memory=None, default_stream=None):
        for group_module in self.modules:
            for param in group_module.parameters():
                source = pinned_memory[param] if pinned_memory else param.data
                self._transfer_tensor_to_device(param, source, default_stream)
            for buffer in group_module.buffers():
                source = pinned_memory[buffer] if pinned_memory else buffer.data
                self._transfer_tensor_to_device(buffer, source, default_stream)

        for param in self.parameters:
            source = pinned_memory[param] if pinned_memory else param.data
            self._transfer_tensor_to_device(param, source, default_stream)

        for buffer in self.buffers:
            source = pinned_memory[buffer] if pinned_memory else buffer.data
            self._transfer_tensor_to_device(buffer, source, default_stream)

    def _onload_from_disk(self):
        if self.stream is not None:
            # Wait for previous Host->Device transfer to complete
            self.stream.synchronize()

        context = nullcontext() if self.stream is None else self._torch_accelerator_module.stream(self.stream)
        current_stream = self._torch_accelerator_module.current_stream() if self.record_stream else None

        with context:
            # Load to CPU (if using streams) or directly to target device, pin, and async copy to device
            device = str(self.onload_device) if self.stream is None else "cpu"
            loaded_tensors = safetensors.torch.load_file(self.safetensors_file_path, device=device)

            if self.stream is not None:
                for key, tensor_obj in self.key_to_tensor.items():
                    pinned_tensor = loaded_tensors[key].pin_memory()
                    tensor_obj.data = pinned_tensor.to(self.onload_device, non_blocking=self.non_blocking)
                    if self.record_stream:
                        tensor_obj.data.record_stream(current_stream)
            else:
                onload_device = (
                    self.onload_device.type if isinstance(self.onload_device, torch.device) else self.onload_device
                )
                loaded_tensors = safetensors.torch.load_file(self.safetensors_file_path, device=onload_device)
                for key, tensor_obj in self.key_to_tensor.items():
                    tensor_obj.data = loaded_tensors[key]

    def _onload_from_memory(self):
        if self.stream is not None:
            # Wait for previous Host->Device transfer to complete
            self.stream.synchronize()

        context = nullcontext() if self.stream is None else self._torch_accelerator_module.stream(self.stream)
        default_stream = self._torch_accelerator_module.current_stream() if self.stream is not None else None

        with context:
            if self.stream is not None:
                with self._pinned_memory_tensors() as pinned_memory:
                    self._process_tensors_from_modules(pinned_memory, default_stream=default_stream)
            else:
                self._process_tensors_from_modules(None)

    def _offload_to_disk(self):
        # TODO: we can potentially optimize this code path by checking if the _all_ the desired
        # safetensor files exist on the disk and if so, skip this step entirely, reducing IO
        # overhead. Currently, we just check if the given `safetensors_file_path` exists and if not
        # we perform a write.
        # Check if the file has been saved in this session or if it already exists on disk.
        if not self._is_offloaded_to_disk and not os.path.exists(self.safetensors_file_path):
            os.makedirs(os.path.dirname(self.safetensors_file_path), exist_ok=True)
            tensors_to_save = {key: tensor.data.to(self.offload_device) for tensor, key in self.tensor_to_key.items()}
            safetensors.torch.save_file(tensors_to_save, self.safetensors_file_path)

        # The group is now considered offloaded to disk for the rest of the session.
        self._is_offloaded_to_disk = True

        # We do this to free up the RAM which is still holding the up tensor data.
        for tensor_obj in self.tensor_to_key.keys():
            tensor_obj.data = torch.empty_like(tensor_obj.data, device=self.offload_device)

    def _offload_to_memory(self):
        if self.stream is not None:
            if not self.record_stream:
                self._torch_accelerator_module.current_stream().synchronize()

            for group_module in self.modules:
                for param in group_module.parameters():
                    param.data = self.cpu_param_dict[param]
            for param in self.parameters:
                param.data = self.cpu_param_dict[param]
            for buffer in self.buffers:
                buffer.data = self.cpu_param_dict[buffer]
        else:
            for group_module in self.modules:
                group_module.to(self.offload_device, non_blocking=False)
            for param in self.parameters:
                param.data = param.data.to(self.offload_device, non_blocking=False)
            for buffer in self.buffers:
                buffer.data = buffer.data.to(self.offload_device, non_blocking=False)

    @torch.compiler.disable()
    def onload_(self):
        r"""Onloads the group of parameters to the onload_device."""
        if self.offload_to_disk_path is not None:
            self._onload_from_disk()
        else:
            self._onload_from_memory()

    @torch.compiler.disable()
    def offload_(self):
        r"""Offloads the group of parameters to the offload_device."""
        if self.offload_to_disk_path:
            self._offload_to_disk()
        else:
            self._offload_to_memory()


class GroupOffloadingHook(ModelHook):
    r"""

    A hook that offloads groups of torch.nn.Module to the CPU for storage and onloads to accelerator device for

    computation. Each group has one "onload leader" module that is responsible for onloading, and an "offload leader"

    module that is responsible for offloading. If prefetching is enabled, the onload leader of the previous module

    group is responsible for onloading the current module group.

    """

    _is_stateful = False

    def __init__(self, group: ModuleGroup, *, config: GroupOffloadingConfig) -> None:
        self.group = group
        self.next_group: Optional[ModuleGroup] = None
        self.config = config

    def initialize_hook(self, module: torch.nn.Module) -> torch.nn.Module:
        if self.group.offload_leader == module:
            self.group.offload_()
        return module

    def pre_forward(self, module: torch.nn.Module, *args, **kwargs):
        # If there wasn't an onload_leader assigned, we assume that the submodule that first called its forward
        # method is the onload_leader of the group.
        if self.group.onload_leader is None:
            self.group.onload_leader = module

        # If the current module is the onload_leader of the group, we onload the group if it is supposed
        # to onload itself. In the case of using prefetching with streams, we onload the next group if
        # it is not supposed to onload itself.
        if self.group.onload_leader == module:
            if self.group.onload_self:
                self.group.onload_()

            should_onload_next_group = self.next_group is not None and not self.next_group.onload_self
            if should_onload_next_group:
                self.next_group.onload_()

            should_synchronize = (
                not self.group.onload_self and self.group.stream is not None and not should_onload_next_group
            )
            if should_synchronize:
                # If this group didn't onload itself, it means it was asynchronously onloaded by the
                # previous group. We need to synchronize the side stream to ensure parameters
                # are completely loaded to proceed with forward pass. Without this, uninitialized
                # weights will be used in the computation, leading to incorrect results
                # Also, we should only do this synchronization if we don't already do it from the sync call in
                # self.next_group.onload_, hence the `not should_onload_next_group` check.
                self.group.stream.synchronize()

        args = send_to_device(args, self.group.onload_device, non_blocking=self.group.non_blocking)

        # Some Autoencoder models use a feature cache that is passed through submodules
        # and modified in place. The `send_to_device` call returns a copy of this feature cache object
        # which breaks the inplace updates. Use `exclude_kwargs` to mark these cache features
        exclude_kwargs = self.config.exclude_kwargs or []
        if exclude_kwargs:
            moved_kwargs = send_to_device(
                {k: v for k, v in kwargs.items() if k not in exclude_kwargs},
                self.group.onload_device,
                non_blocking=self.group.non_blocking,
            )
            kwargs.update(moved_kwargs)
        else:
            kwargs = send_to_device(kwargs, self.group.onload_device, non_blocking=self.group.non_blocking)

        return args, kwargs

    def post_forward(self, module: torch.nn.Module, output):
        if self.group.offload_leader == module:
            self.group.offload_()
        return output


class LazyPrefetchGroupOffloadingHook(ModelHook):
    r"""

    A hook, used in conjunction with GroupOffloadingHook, that applies lazy prefetching to groups of torch.nn.Module.

    This hook is used to determine the order in which the layers are executed during the forward pass. Once the layer

    invocation order is known, assignments of the next_group attribute for prefetching can be made, which allows

    prefetching groups in the correct order.

    """

    _is_stateful = False

    def __init__(self):
        self.execution_order: List[Tuple[str, torch.nn.Module]] = []
        self._layer_execution_tracker_module_names = set()

    def initialize_hook(self, module):
        def make_execution_order_update_callback(current_name, current_submodule):
            def callback():
                if not torch.compiler.is_compiling():
                    logger.debug(f"Adding {current_name} to the execution order")
                self.execution_order.append((current_name, current_submodule))

            return callback

        # To every submodule that contains a group offloading hook (at this point, no prefetching is enabled for any
        # of the groups), we add a layer execution tracker hook that will be used to determine the order in which the
        # layers are executed during the forward pass.
        for name, submodule in module.named_modules():
            if name == "" or not hasattr(submodule, "_diffusers_hook"):
                continue

            registry = HookRegistry.check_if_exists_or_initialize(submodule)
            group_offloading_hook = registry.get_hook(_GROUP_OFFLOADING)

            if group_offloading_hook is not None:
                # For the first forward pass, we have to load in a blocking manner
                group_offloading_hook.group.non_blocking = False
                layer_tracker_hook = LayerExecutionTrackerHook(make_execution_order_update_callback(name, submodule))
                registry.register_hook(layer_tracker_hook, _LAYER_EXECUTION_TRACKER)
                self._layer_execution_tracker_module_names.add(name)

        return module

    def post_forward(self, module, output):
        # At this point, for the current modules' submodules, we know the execution order of the layers. We can now
        # remove the layer execution tracker hooks and apply prefetching by setting the next_group attribute for each
        # group offloading hook.
        num_executed = len(self.execution_order)
        execution_order_module_names = {name for name, _ in self.execution_order}

        # It may be possible that some layers were not executed during the forward pass. This can happen if the layer
        # is not used in the forward pass, or if the layer is not executed due to some other reason. In such cases, we
        # may not be able to apply prefetching in the correct order, which can lead to device-mismatch related errors
        # if the missing layers end up being executed in the future.
        if execution_order_module_names != self._layer_execution_tracker_module_names:
            unexecuted_layers = list(self._layer_execution_tracker_module_names - execution_order_module_names)
            if not torch.compiler.is_compiling():
                logger.warning(
                    "It seems like some layers were not executed during the forward pass. This may lead to problems when "
                    "applying lazy prefetching with automatic tracing and lead to device-mismatch related errors. Please "
                    "make sure that all layers are executed during the forward pass. The following layers were not executed:\n"
                    f"{unexecuted_layers=}"
                )

        # Remove the layer execution tracker hooks from the submodules
        base_module_registry = module._diffusers_hook
        registries = [submodule._diffusers_hook for _, submodule in self.execution_order]
        group_offloading_hooks = [registry.get_hook(_GROUP_OFFLOADING) for registry in registries]

        for i in range(num_executed):
            registries[i].remove_hook(_LAYER_EXECUTION_TRACKER, recurse=False)

        # Remove the current lazy prefetch group offloading hook so that it doesn't interfere with the next forward pass
        base_module_registry.remove_hook(_LAZY_PREFETCH_GROUP_OFFLOADING, recurse=False)

        # LazyPrefetchGroupOffloadingHook is only used with streams, so we know that non_blocking should be True.
        # We disable non_blocking for the first forward pass, but need to enable it for the subsequent passes to
        # see the benefits of prefetching.
        for hook in group_offloading_hooks:
            hook.group.non_blocking = True

        # Set required attributes for prefetching
        if num_executed > 0:
            base_module_group_offloading_hook = base_module_registry.get_hook(_GROUP_OFFLOADING)
            base_module_group_offloading_hook.next_group = group_offloading_hooks[0].group
            base_module_group_offloading_hook.next_group.onload_self = False

        for i in range(num_executed - 1):
            name1, _ = self.execution_order[i]
            name2, _ = self.execution_order[i + 1]
            if not torch.compiler.is_compiling():
                logger.debug(f"Applying lazy prefetch group offloading from {name1} to {name2}")
            group_offloading_hooks[i].next_group = group_offloading_hooks[i + 1].group
            group_offloading_hooks[i].next_group.onload_self = False

        return output


class LayerExecutionTrackerHook(ModelHook):
    r"""

    A hook that tracks the order in which the layers are executed during the forward pass by calling back to the

    LazyPrefetchGroupOffloadingHook to update the execution order.

    """

    _is_stateful = False

    def __init__(self, execution_order_update_callback):
        self.execution_order_update_callback = execution_order_update_callback

    def pre_forward(self, module, *args, **kwargs):
        self.execution_order_update_callback()
        return args, kwargs


def apply_group_offloading(

    module: torch.nn.Module,

    onload_device: Union[str, torch.device],

    offload_device: Union[str, torch.device] = torch.device("cpu"),

    offload_type: Union[str, GroupOffloadingType] = "block_level",

    num_blocks_per_group: Optional[int] = None,

    non_blocking: bool = False,

    use_stream: bool = False,

    record_stream: bool = False,

    low_cpu_mem_usage: bool = False,

    offload_to_disk_path: Optional[str] = None,

    block_modules: Optional[List[str]] = None,

    exclude_kwargs: Optional[List[str]] = None,

) -> None:
    r"""

    Applies group offloading to the internal layers of a torch.nn.Module. To understand what group offloading is, and

    where it is beneficial, we need to first provide some context on how other supported offloading methods work.



    Typically, offloading is done at two levels:

    - Module-level: In Diffusers, this can be enabled using the `ModelMixin::enable_model_cpu_offload()` method. It

      works by offloading each component of a pipeline to the CPU for storage, and onloading to the accelerator device

      when needed for computation. This method is more memory-efficient than keeping all components on the accelerator,

      but the memory requirements are still quite high. For this method to work, one needs memory equivalent to size of

      the model in runtime dtype + size of largest intermediate activation tensors to be able to complete the forward

      pass.

    - Leaf-level: In Diffusers, this can be enabled using the `ModelMixin::enable_sequential_cpu_offload()` method. It

      works by offloading the lowest leaf-level parameters of the computation graph to the CPU for storage, and

      onloading only the leafs to the accelerator device for computation. This uses the lowest amount of accelerator

      memory, but can be slower due to the excessive number of device synchronizations.



    Group offloading is a middle ground between the two methods. It works by offloading groups of internal layers,

    (either `torch.nn.ModuleList` or `torch.nn.Sequential`). This method uses lower memory than module-level

    offloading. It is also faster than leaf-level/sequential offloading, as the number of device synchronizations is

    reduced.



    Another supported feature (for CUDA devices with support for asynchronous data transfer streams) is the ability to

    overlap data transfer and computation to reduce the overall execution time compared to sequential offloading. This

    is enabled using layer prefetching with streams, i.e., the layer that is to be executed next starts onloading to

    the accelerator device while the current layer is being executed - this increases the memory requirements slightly.

    Note that this implementation also supports leaf-level offloading but can be made much faster when using streams.



    Args:

        module (`torch.nn.Module`):

            The module to which group offloading is applied.

        onload_device (`torch.device`):

            The device to which the group of modules are onloaded.

        offload_device (`torch.device`, defaults to `torch.device("cpu")`):

            The device to which the group of modules are offloaded. This should typically be the CPU. Default is CPU.

        offload_type (`str` or `GroupOffloadingType`, defaults to "block_level"):

            The type of offloading to be applied. Can be one of "block_level" or "leaf_level". Default is

            "block_level".

        offload_to_disk_path (`str`, *optional*, defaults to `None`):

            The path to the directory where parameters will be offloaded. Setting this option can be useful in limited

            RAM environment settings where a reasonable speed-memory trade-off is desired.

        num_blocks_per_group (`int`, *optional*):

            The number of blocks per group when using offload_type="block_level". This is required when using

            offload_type="block_level".

        non_blocking (`bool`, defaults to `False`):

            If True, offloading and onloading is done with non-blocking data transfer.

        use_stream (`bool`, defaults to `False`):

            If True, offloading and onloading is done asynchronously using a CUDA stream. This can be useful for

            overlapping computation and data transfer.

        record_stream (`bool`, defaults to `False`): When enabled with `use_stream`, it marks the current tensor

            as having been used by this stream. It is faster at the expense of slightly more memory usage. Refer to the

            [PyTorch official docs](https://pytorch.org/docs/stable/generated/torch.Tensor.record_stream.html) more

            details.

        low_cpu_mem_usage (`bool`, defaults to `False`):

            If True, the CPU memory usage is minimized by pinning tensors on-the-fly instead of pre-pinning them. This

            option only matters when using streamed CPU offloading (i.e. `use_stream=True`). This can be useful when

            the CPU memory is a bottleneck but may counteract the benefits of using streams.

        block_modules (`List[str]`, *optional*):

            List of module names that should be treated as blocks for offloading. If provided, only these modules will

            be considered for block-level offloading. If not provided, the default block detection logic will be used.

        exclude_kwargs (`List[str]`, *optional*):

            List of kwarg keys that should not be processed by send_to_device. This is useful for mutable state like

            caching lists that need to maintain their object identity across forward passes. If not provided, will be

            inferred from the module's `_skip_keys` attribute if it exists.



    Example:

        ```python

        >>> from diffusers import CogVideoXTransformer3DModel

        >>> from diffusers.hooks import apply_group_offloading



        >>> transformer = CogVideoXTransformer3DModel.from_pretrained(

        ...     "THUDM/CogVideoX-5b", subfolder="transformer", torch_dtype=torch.bfloat16

        ... )



        >>> apply_group_offloading(

        ...     transformer,

        ...     onload_device=torch.device("cuda"),

        ...     offload_device=torch.device("cpu"),

        ...     offload_type="block_level",

        ...     num_blocks_per_group=2,

        ...     use_stream=True,

        ... )

        ```

    """

    onload_device = torch.device(onload_device) if isinstance(onload_device, str) else onload_device
    offload_device = torch.device(offload_device) if isinstance(offload_device, str) else offload_device
    offload_type = GroupOffloadingType(offload_type)

    stream = None
    if use_stream:
        if torch.cuda.is_available():
            stream = torch.cuda.Stream()
        elif hasattr(torch, "xpu") and torch.xpu.is_available():
            stream = torch.Stream()
        else:
            raise ValueError("Using streams for data transfer requires a CUDA device, or an Intel XPU device.")

    if not use_stream and record_stream:
        raise ValueError("`record_stream` cannot be True when `use_stream=False`.")
    if offload_type == GroupOffloadingType.BLOCK_LEVEL and num_blocks_per_group is None:
        raise ValueError("`num_blocks_per_group` must be provided when using `offload_type='block_level'.")

    _raise_error_if_accelerate_model_or_sequential_hook_present(module)

    if block_modules is None:
        block_modules = getattr(module, "_group_offload_block_modules", None)

    if exclude_kwargs is None:
        exclude_kwargs = getattr(module, "_skip_keys", None)

    config = GroupOffloadingConfig(
        onload_device=onload_device,
        offload_device=offload_device,
        offload_type=offload_type,
        num_blocks_per_group=num_blocks_per_group,
        non_blocking=non_blocking,
        stream=stream,
        record_stream=record_stream,
        low_cpu_mem_usage=low_cpu_mem_usage,
        offload_to_disk_path=offload_to_disk_path,
        block_modules=block_modules,
        exclude_kwargs=exclude_kwargs,
    )
    _apply_group_offloading(module, config)


def _apply_group_offloading(module: torch.nn.Module, config: GroupOffloadingConfig) -> None:
    if config.offload_type == GroupOffloadingType.BLOCK_LEVEL:
        _apply_group_offloading_block_level(module, config)
    elif config.offload_type == GroupOffloadingType.LEAF_LEVEL:
        _apply_group_offloading_leaf_level(module, config)
    else:
        assert False


def _apply_group_offloading_block_level(module: torch.nn.Module, config: GroupOffloadingConfig) -> None:
    r"""

    This function applies offloading to groups of torch.nn.ModuleList or torch.nn.Sequential blocks, and explicitly

    defined block modules. In comparison to the "leaf_level" offloading, which is more fine-grained, this offloading is

    done at the top-level blocks and modules specified in block_modules.



    When block_modules is provided, only those modules will be treated as blocks for offloading. For each specified

    module, recursively apply block offloading to it.

    """
    if config.stream is not None and config.num_blocks_per_group != 1:
        logger.warning(
            f"Using streams is only supported for num_blocks_per_group=1. Got {config.num_blocks_per_group=}. Setting it to 1."
        )
        config.num_blocks_per_group = 1

    block_modules = set(config.block_modules) if config.block_modules is not None else set()

    # Create module groups for ModuleList and Sequential blocks, and explicitly defined block modules
    modules_with_group_offloading = set()
    unmatched_modules = []
    matched_module_groups = []

    for name, submodule in module.named_children():
        # Check if this is an explicitly defined block module
        if name in block_modules:
            # Track submodule using a prefix to avoid filename collisions during disk offload.
            # Without this, submodules sharing the same model class would be assigned identical
            # filenames (derived from the class name).
            prefix = f"{config.module_prefix}{name}." if config.module_prefix else f"{name}."
            submodule_config = replace(config, module_prefix=prefix)

            _apply_group_offloading_block_level(submodule, submodule_config)
            modules_with_group_offloading.add(name)

        elif isinstance(submodule, (torch.nn.ModuleList, torch.nn.Sequential)):
            # Handle ModuleList and Sequential blocks as before
            for i in range(0, len(submodule), config.num_blocks_per_group):
                current_modules = list(submodule[i : i + config.num_blocks_per_group])
                if len(current_modules) == 0:
                    continue

                group_id = f"{config.module_prefix}{name}_{i}_{i + len(current_modules) - 1}"
                group = ModuleGroup(
                    modules=current_modules,
                    offload_device=config.offload_device,
                    onload_device=config.onload_device,
                    offload_to_disk_path=config.offload_to_disk_path,
                    offload_leader=current_modules[-1],
                    onload_leader=current_modules[0],
                    non_blocking=config.non_blocking,
                    stream=config.stream,
                    record_stream=config.record_stream,
                    low_cpu_mem_usage=config.low_cpu_mem_usage,
                    onload_self=True,
                    group_id=group_id,
                )
                matched_module_groups.append(group)
                for j in range(i, i + len(current_modules)):
                    modules_with_group_offloading.add(f"{name}.{j}")
        else:
            # This is an unmatched module
            unmatched_modules.append((name, submodule))

    # Apply group offloading hooks to the module groups
    for i, group in enumerate(matched_module_groups):
        for group_module in group.modules:
            _apply_group_offloading_hook(group_module, group, config=config)

    # Parameters and Buffers of the top-level module need to be offloaded/onloaded separately
    # when the forward pass of this module is called. This is because the top-level module is not
    # part of any group (as doing so would lead to no VRAM savings).
    parameters = _gather_parameters_with_no_group_offloading_parent(module, modules_with_group_offloading)
    buffers = _gather_buffers_with_no_group_offloading_parent(module, modules_with_group_offloading)
    parameters = [param for _, param in parameters]
    buffers = [buffer for _, buffer in buffers]

    # Create a group for the remaining unmatched submodules of the top-level
    # module so that they are on the correct device when the forward pass is called.
    unmatched_modules = [unmatched_module for _, unmatched_module in unmatched_modules]
    if len(unmatched_modules) > 0 or len(parameters) > 0 or len(buffers) > 0:
        unmatched_group = ModuleGroup(
            modules=unmatched_modules,
            offload_device=config.offload_device,
            onload_device=config.onload_device,
            offload_to_disk_path=config.offload_to_disk_path,
            offload_leader=module,
            onload_leader=module,
            parameters=parameters,
            buffers=buffers,
            non_blocking=False,
            stream=None,
            record_stream=False,
            onload_self=True,
            group_id=f"{config.module_prefix}{module.__class__.__name__}_unmatched_group",
        )
        if config.stream is None:
            _apply_group_offloading_hook(module, unmatched_group, config=config)
        else:
            _apply_lazy_group_offloading_hook(module, unmatched_group, config=config)


def _apply_group_offloading_leaf_level(module: torch.nn.Module, config: GroupOffloadingConfig) -> None:
    r"""

    This function applies offloading to groups of leaf modules in a torch.nn.Module. This method has minimal memory

    requirements. However, it can be slower compared to other offloading methods due to the excessive number of device

    synchronizations. When using devices that support streams to overlap data transfer and computation, this method can

    reduce memory usage without any performance degradation.

    """
    # Create module groups for leaf modules and apply group offloading hooks
    modules_with_group_offloading = set()
    for name, submodule in module.named_modules():
        if not isinstance(submodule, _GO_LC_SUPPORTED_PYTORCH_LAYERS):
            continue
        group = ModuleGroup(
            modules=[submodule],
            offload_device=config.offload_device,
            onload_device=config.onload_device,
            offload_to_disk_path=config.offload_to_disk_path,
            offload_leader=submodule,
            onload_leader=submodule,
            non_blocking=config.non_blocking,
            stream=config.stream,
            record_stream=config.record_stream,
            low_cpu_mem_usage=config.low_cpu_mem_usage,
            onload_self=True,
            group_id=name,
        )
        _apply_group_offloading_hook(submodule, group, config=config)
        modules_with_group_offloading.add(name)

    # Parameters and Buffers at all non-leaf levels need to be offloaded/onloaded separately when the forward pass
    # of the module is called
    module_dict = dict(module.named_modules())
    parameters = _gather_parameters_with_no_group_offloading_parent(module, modules_with_group_offloading)
    buffers = _gather_buffers_with_no_group_offloading_parent(module, modules_with_group_offloading)

    # Find closest module parent for each parameter and buffer, and attach group hooks
    parent_to_parameters = {}
    for name, param in parameters:
        parent_name = _find_parent_module_in_module_dict(name, module_dict)
        if parent_name in parent_to_parameters:
            parent_to_parameters[parent_name].append(param)
        else:
            parent_to_parameters[parent_name] = [param]

    parent_to_buffers = {}
    for name, buffer in buffers:
        parent_name = _find_parent_module_in_module_dict(name, module_dict)
        if parent_name in parent_to_buffers:
            parent_to_buffers[parent_name].append(buffer)
        else:
            parent_to_buffers[parent_name] = [buffer]

    parent_names = set(parent_to_parameters.keys()) | set(parent_to_buffers.keys())
    for name in parent_names:
        parameters = parent_to_parameters.get(name, [])
        buffers = parent_to_buffers.get(name, [])
        parent_module = module_dict[name]
        group = ModuleGroup(
            modules=[],
            offload_device=config.offload_device,
            onload_device=config.onload_device,
            offload_leader=parent_module,
            onload_leader=parent_module,
            offload_to_disk_path=config.offload_to_disk_path,
            parameters=parameters,
            buffers=buffers,
            non_blocking=config.non_blocking,
            stream=config.stream,
            record_stream=config.record_stream,
            low_cpu_mem_usage=config.low_cpu_mem_usage,
            onload_self=True,
            group_id=name,
        )
        _apply_group_offloading_hook(parent_module, group, config=config)

    if config.stream is not None:
        # When using streams, we need to know the layer execution order for applying prefetching (to overlap data transfer
        # and computation). Since we don't know the order beforehand, we apply a lazy prefetching hook that will find the
        # execution order and apply prefetching in the correct order.
        unmatched_group = ModuleGroup(
            modules=[],
            offload_device=config.offload_device,
            onload_device=config.onload_device,
            offload_to_disk_path=config.offload_to_disk_path,
            offload_leader=module,
            onload_leader=module,
            parameters=None,
            buffers=None,
            non_blocking=False,
            stream=None,
            record_stream=False,
            low_cpu_mem_usage=config.low_cpu_mem_usage,
            onload_self=True,
            group_id=_GROUP_ID_LAZY_LEAF,
        )
        _apply_lazy_group_offloading_hook(module, unmatched_group, config=config)


def _apply_group_offloading_hook(

    module: torch.nn.Module,

    group: ModuleGroup,

    *,

    config: GroupOffloadingConfig,

) -> None:
    registry = HookRegistry.check_if_exists_or_initialize(module)

    # We may have already registered a group offloading hook if the module had a torch.nn.Parameter whose parent
    # is the current module. In such cases, we don't want to overwrite the existing group offloading hook.
    if registry.get_hook(_GROUP_OFFLOADING) is None:
        hook = GroupOffloadingHook(group, config=config)
        registry.register_hook(hook, _GROUP_OFFLOADING)


def _apply_lazy_group_offloading_hook(

    module: torch.nn.Module,

    group: ModuleGroup,

    *,

    config: GroupOffloadingConfig,

) -> None:
    registry = HookRegistry.check_if_exists_or_initialize(module)

    # We may have already registered a group offloading hook if the module had a torch.nn.Parameter whose parent
    # is the current module. In such cases, we don't want to overwrite the existing group offloading hook.
    if registry.get_hook(_GROUP_OFFLOADING) is None:
        hook = GroupOffloadingHook(group, config=config)
        registry.register_hook(hook, _GROUP_OFFLOADING)

    lazy_prefetch_hook = LazyPrefetchGroupOffloadingHook()
    registry.register_hook(lazy_prefetch_hook, _LAZY_PREFETCH_GROUP_OFFLOADING)


def _gather_parameters_with_no_group_offloading_parent(

    module: torch.nn.Module, modules_with_group_offloading: Set[str]

) -> List[torch.nn.Parameter]:
    parameters = []
    for name, parameter in module.named_parameters():
        has_parent_with_group_offloading = False
        atoms = name.split(".")
        while len(atoms) > 0:
            parent_name = ".".join(atoms)
            if parent_name in modules_with_group_offloading:
                has_parent_with_group_offloading = True
                break
            atoms.pop()
        if not has_parent_with_group_offloading:
            parameters.append((name, parameter))
    return parameters


def _gather_buffers_with_no_group_offloading_parent(

    module: torch.nn.Module, modules_with_group_offloading: Set[str]

) -> List[torch.Tensor]:
    buffers = []
    for name, buffer in module.named_buffers():
        has_parent_with_group_offloading = False
        atoms = name.split(".")
        while len(atoms) > 0:
            parent_name = ".".join(atoms)
            if parent_name in modules_with_group_offloading:
                has_parent_with_group_offloading = True
                break
            atoms.pop()
        if not has_parent_with_group_offloading:
            buffers.append((name, buffer))
    return buffers


def _find_parent_module_in_module_dict(name: str, module_dict: Dict[str, torch.nn.Module]) -> str:
    atoms = name.split(".")
    while len(atoms) > 0:
        parent_name = ".".join(atoms)
        if parent_name in module_dict:
            return parent_name
        atoms.pop()
    return ""


def _raise_error_if_accelerate_model_or_sequential_hook_present(module: torch.nn.Module) -> None:
    if not is_accelerate_available():
        return
    for name, submodule in module.named_modules():
        if not hasattr(submodule, "_hf_hook"):
            continue
        if isinstance(submodule._hf_hook, (AlignDevicesHook, CpuOffload)):
            raise ValueError(
                f"Cannot apply group offloading to a module that is already applying an alternative "
                f"offloading strategy from Accelerate. If you want to apply group offloading, please "
                f"disable the existing offloading strategy first. Offending module: {name} ({type(submodule)})"
            )


def _get_top_level_group_offload_hook(module: torch.nn.Module) -> Optional[GroupOffloadingHook]:
    for submodule in module.modules():
        if hasattr(submodule, "_diffusers_hook"):
            group_offloading_hook = submodule._diffusers_hook.get_hook(_GROUP_OFFLOADING)
            if group_offloading_hook is not None:
                return group_offloading_hook
    return None


def _is_group_offload_enabled(module: torch.nn.Module) -> bool:
    top_level_group_offload_hook = _get_top_level_group_offload_hook(module)
    return top_level_group_offload_hook is not None


def _get_group_onload_device(module: torch.nn.Module) -> torch.device:
    top_level_group_offload_hook = _get_top_level_group_offload_hook(module)
    if top_level_group_offload_hook is not None:
        return top_level_group_offload_hook.config.onload_device
    raise ValueError("Group offloading is not enabled for the provided module.")


def _compute_group_hash(group_id):
    hashed_id = hashlib.sha256(group_id.encode("utf-8")).hexdigest()
    # first 16 characters for a reasonably short but unique name
    return hashed_id[:16]


def _maybe_remove_and_reapply_group_offloading(module: torch.nn.Module) -> None:
    r"""

    Removes the group offloading hook from the module and re-applies it. This is useful when the module has been

    modified in-place and the group offloading hook references-to-tensors needs to be updated. The in-place

    modification can happen in a number of ways, for example, fusing QKV or unloading/loading LoRAs on-the-fly.



    In this implementation, we make an assumption that group offloading has only been applied at the top-level module,

    and therefore all submodules have the same onload and offload devices. If this assumption is not true, say in the

    case where user has applied group offloading at multiple levels, this function will not work as expected.



    There is some performance penalty associated with doing this when non-default streams are used, because we need to

    retrace the execution order of the layers with `LazyPrefetchGroupOffloadingHook`.

    """
    top_level_group_offload_hook = _get_top_level_group_offload_hook(module)

    if top_level_group_offload_hook is None:
        return

    registry = HookRegistry.check_if_exists_or_initialize(module)
    registry.remove_hook(_GROUP_OFFLOADING, recurse=True)
    registry.remove_hook(_LAYER_EXECUTION_TRACKER, recurse=True)
    registry.remove_hook(_LAZY_PREFETCH_GROUP_OFFLOADING, recurse=True)

    _apply_group_offloading(module, top_level_group_offload_hook.config)


def remove_group_offloading(

    module: torch.nn.Module,

    exclude_modules: Optional[Union[str, List[str]]] = None,

) -> None:
    """

    Removes group offloading hooks from a module and its submodules.

    

    Args:

        module (`torch.nn.Module`):

            The module from which to remove group offloading hooks.

        exclude_modules (`Union[str, List[str]]`, *optional*, defaults to `None`):

            List of modules to exclude from hook removal.

    """
    if isinstance(exclude_modules, str):
        exclude_modules = [exclude_modules]
    elif exclude_modules is None:
        exclude_modules = []
    
    # Check if this is a pipeline with components
    if hasattr(module, 'components'):
        unknown = set(exclude_modules) - module.components.keys()
        if unknown:
            logger.info(
                f"The following modules are not present in pipeline: {', '.join(unknown)}. Ignore if this is expected."
            )
        
        # Remove hooks from each component
        for name, component in module.components.items():
            if name not in exclude_modules and isinstance(component, torch.nn.Module):
                registry = HookRegistry.check_if_exists_or_initialize(component)
                registry.remove_hook(_GROUP_OFFLOADING, recurse=True)
                registry.remove_hook(_LAYER_EXECUTION_TRACKER, recurse=True)
                registry.remove_hook(_LAZY_PREFETCH_GROUP_OFFLOADING, recurse=True)
    else:
        # Original behavior for single modules
        registry = HookRegistry.check_if_exists_or_initialize(module)
        registry.remove_hook(_GROUP_OFFLOADING, recurse=True)
        registry.remove_hook(_LAYER_EXECUTION_TRACKER, recurse=True)
        registry.remove_hook(_LAZY_PREFETCH_GROUP_OFFLOADING, recurse=True)


def safe_remove_group_offloading(obj, *args, **kwargs):
    """Safely call remove_group_offloading, and restore _execution_device if it was patched."""
    result = remove_group_offloading(obj, *args, **kwargs)
    # Restore the original _execution_device from the MRO if we had patched it.
    if hasattr(obj, 'components') and hasattr(obj.__class__, '_execution_device_original'):
        obj.__class__._execution_device = obj.__class__._execution_device_original
        del obj.__class__._execution_device_original
        logger.debug("Restored original _execution_device after removing group offload.")
    return result


def enable_group_offload(

    self,

    onload_device: torch.device,

    offload_device: torch.device = torch.device("cpu"),

    offload_type: str = "block_level",

    num_blocks_per_group: Optional[int] = None,

    non_blocking: bool = False,

    use_stream: bool = False,

    record_stream: bool = False,

    low_cpu_mem_usage=False,

    offload_to_disk_path: Optional[str] = None,

    exclude_modules: Optional[Union[str, List[str]]] = None,

) -> None:
    r"""

    Applies group offloading to the internal layers of a torch.nn.Module. To understand what group offloading is,

    and where it is beneficial, we need to first provide some context on how other supported offloading methods

    work.



    Typically, offloading is done at two levels:

    - Module-level: In Diffusers, this can be enabled using the `ModelMixin::enable_model_cpu_offload()` method. It

    works by offloading each component of a pipeline to the CPU for storage, and onloading to the accelerator

    device when needed for computation. This method is more memory-efficient than keeping all components on the

    accelerator, but the memory requirements are still quite high. For this method to work, one needs memory

    equivalent to size of the model in runtime dtype + size of largest intermediate activation tensors to be able

    to complete the forward pass.

    - Leaf-level: In Diffusers, this can be enabled using the `ModelMixin::enable_sequential_cpu_offload()` method.

        It

    works by offloading the lowest leaf-level parameters of the computation graph to the CPU for storage, and

    onloading only the leafs to the accelerator device for computation. This uses the lowest amount of accelerator

    memory, but can be slower due to the excessive number of device synchronizations.



    Group offloading is a middle ground between the two methods. It works by offloading groups of internal layers,

    (either `torch.nn.ModuleList` or `torch.nn.Sequential`). This method uses lower memory than module-level

    offloading. It is also faster than leaf-level/sequential offloading, as the number of device synchronizations

    is reduced.



    Another supported feature (for CUDA devices with support for asynchronous data transfer streams) is the ability

    to overlap data transfer and computation to reduce the overall execution time compared to sequential

    offloading. This is enabled using layer prefetching with streams, i.e., the layer that is to be executed next

    starts onloading to the accelerator device while the current layer is being executed - this increases the

    memory requirements slightly. Note that this implementation also supports leaf-level offloading but can be made

    much faster when using streams.



    Args:

        onload_device (`torch.device`):

            The device to which the group of modules are onloaded.

        offload_device (`torch.device`, defaults to `torch.device("cpu")`):

            The device to which the group of modules are offloaded. This should typically be the CPU. Default is

            CPU.

        offload_type (`str` or `GroupOffloadingType`, defaults to "block_level"):

            The type of offloading to be applied. Can be one of "block_level" or "leaf_level". Default is

            "block_level".

        offload_to_disk_path (`str`, *optional*, defaults to `None`):

            The path to the directory where parameters will be offloaded. Setting this option can be useful in

            limited RAM environment settings where a reasonable speed-memory trade-off is desired.

        num_blocks_per_group (`int`, *optional*):

            The number of blocks per group when using offload_type="block_level". This is required when using

            offload_type="block_level".

        non_blocking (`bool`, defaults to `False`):

            If True, offloading and onloading is done with non-blocking data transfer.

        use_stream (`bool`, defaults to `False`):

            If True, offloading and onloading is done asynchronously using a CUDA stream. This can be useful for

            overlapping computation and data transfer.

        record_stream (`bool`, defaults to `False`): When enabled with `use_stream`, it marks the current tensor

            as having been used by this stream. It is faster at the expense of slightly more memory usage. Refer to

            the [PyTorch official docs](https://pytorch.org/docs/stable/generated/torch.Tensor.record_stream.html)

            more details.

        low_cpu_mem_usage (`bool`, defaults to `False`):

            If True, the CPU memory usage is minimized by pinning tensors on-the-fly instead of pre-pinning them.

            This option only matters when using streamed CPU offloading (i.e. `use_stream=True`). This can be

            useful when the CPU memory is a bottleneck but may counteract the benefits of using streams.

        exclude_modules (`Union[str, List[str]]`, defaults to `None`): List of modules to exclude from offloading.



    Example:

        ```python

        >>> from diffusers import DiffusionPipeline

        >>> import torch



        >>> pipe = DiffusionPipeline.from_pretrained("Qwen/Qwen-Image", torch_dtype=torch.bfloat16)



        >>> pipe.enable_group_offload(

        ...     onload_device=torch.device("cuda"),

        ...     offload_device=torch.device("cpu"),

        ...     offload_type="leaf_level",

        ...     use_stream=True,

        ... )

        >>> image = pipe("a beautiful sunset").images[0]

        ```

    """
    if isinstance(exclude_modules, str):
        exclude_modules = [exclude_modules]
    elif exclude_modules is None:
        exclude_modules = []

    unknown = set(exclude_modules) - self.components.keys()
    if unknown:
        logger.info(
            f"The following modules are not present in pipeline: {', '.join(unknown)}. Ignore if this is expected."
        )

    group_offload_kwargs = {
        "onload_device": onload_device,
        "offload_device": offload_device,
        "offload_type": offload_type,
        "num_blocks_per_group": num_blocks_per_group,
        "non_blocking": non_blocking,
        "use_stream": use_stream,
        "record_stream": record_stream,
        "low_cpu_mem_usage": low_cpu_mem_usage,
        "offload_to_disk_path": offload_to_disk_path,
    }
    for name, component in self.components.items():
        if name not in exclude_modules and isinstance(component, torch.nn.Module):
            apply_group_offloading(module=component, **group_offload_kwargs)

    if exclude_modules:
        for module_name in exclude_modules:
            module = getattr(self, module_name, None)
            if module is not None and isinstance(module, torch.nn.Module):
                module.to(onload_device)
                logger.debug(f"Placed `{module_name}` on {onload_device} device as it was in `exclude_modules`.")


def safe_enable_group_offload(obj, *args, **kwargs):
    """Safely call enable_group_offload, register default implementation if not exists.

    Also patches obj._execution_device so that pipelines using group offload (which does

    not use Accelerate _hf_hook) can still return the correct onload device instead of

    falling back to self.device (which may be CPU after offloading).

    """

    if not hasattr(obj, 'enable_group_offload'):
        obj.enable_group_offload = types.MethodType(enable_group_offload, obj)

    result = obj.enable_group_offload(*args, **kwargs)

    # Patch _execution_device on the pipeline so it correctly returns the
    # onload (GPU) device instead of self.device (CPU) when group offload is active.
    onload_device = kwargs.get('onload_device') or (args[0] if args else None)
    if onload_device is not None and hasattr(obj, 'components'):
        onload_device = torch.device(onload_device) if isinstance(onload_device, str) else onload_device

        # Save the original _execution_device before patching so safe_remove can restore it.
        if not hasattr(obj.__class__, '_execution_device_original'):
            obj.__class__._execution_device_original = obj.__class__._execution_device

        @property
        def _execution_device(self):
            # Dynamically check: if any component still has group offload active,
            # return the onload device; otherwise fall through to the original impl.
            for _, component in self.components.items():
                if isinstance(component, torch.nn.Module) and _is_group_offload_enabled(component):
                    return onload_device
            # Group offload has been removed, delegate to the saved original.
            return self.__class__._execution_device_original.fget(self)

        obj.__class__._execution_device = _execution_device
        logger.debug(f"Patched _execution_device to return {onload_device} for group offload.")

    return result


def register_auto_device_hook(model):
    """

    Register forward pre-hooks for all modules to automatically transfer device

    

    Args:

        model: The model to process

    

    Returns:

        model: The model with registered hooks

    """
    
    def auto_device_hook(module, input: Tuple[Any, ...]):
        """

        Forward pre-hook function to automatically transfer device before forward

        

        Args:

            module: Current module

            input: Forward input arguments (in tuple form)

        """
        # Get the device of input tensor
        input_device = None
        
        # Traverse input tuple to find the first tensor
        for item in input:
            if isinstance(item, torch.Tensor):
                input_device = item.device
                break
            # Handle nested cases (like list, tuple, etc.)
            elif isinstance(item, (list, tuple)):
                for sub_item in item:
                    if isinstance(sub_item, torch.Tensor):
                        input_device = sub_item.device
                        break
                if input_device is not None:
                    break
        
        # If no tensor input found, return directly
        if input_device is None:
            return
        
        # Get current device of the module
        module_device = None
        try:
            # Try to get device from parameters
            module_device = next(module.parameters()).device
        except StopIteration:
            # If no parameters, try to get from buffers
            try:
                module_device = next(module.buffers()).device
            except StopIteration:
                # No parameters or buffers, no need to transfer
                return
        
        # Check if device transfer is needed
        # Condition: module_device is not 'meta' and different from input_device
        if module_device.type != 'meta' and module_device != input_device:
            # print(f"Moving {module.__class__.__name__} from {module_device} to {input_device}")
            module.to(input_device)
    
    # Register hooks for all submodules
    hooks = []
    for module in model.modules():
        hook = module.register_forward_pre_hook(auto_device_hook)
        hooks.append(hook)
    
    # Save hooks to model for later removal
    model._auto_device_hooks = hooks
    
    return model


def remove_auto_device_hook(model):
    """

    Remove previously registered auto device hooks

    

    Args:

        model: The model to process

    """
    if hasattr(model, '_auto_device_hooks'):
        for hook in model._auto_device_hooks:
            hook.remove()
        delattr(model, '_auto_device_hooks')
        print("Auto device hooks removed")