Fix broken group offloading with block_level for models with standalone layers

src/diffusers/hooks/group_offloading.py

@@ -59,6 +59,7 @@ class GroupOffloadingConfig:
     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
 
 
 class ModuleGroup:
@@ -77,7 +78,7 @@ def __init__(
         low_cpu_mem_usage: bool = False,
         onload_self: bool = True,
         offload_to_disk_path: Optional[str] = None,
-        group_id: Optional[int] = None,
+        group_id: Optional[Union[int, str]] = None,
     ) -> None:
         self.modules = modules
         self.offload_device = offload_device
@@ -453,6 +454,7 @@ def apply_group_offloading(
     record_stream: bool = False,
     low_cpu_mem_usage: bool = False,
     offload_to_disk_path: Optional[str] = None,
+    block_modules: 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
@@ -510,6 +512,9 @@ def apply_group_offloading(
             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.
 
     Example:
         ```python
@@ -561,6 +566,7 @@ def apply_group_offloading(
         record_stream=record_stream,
         low_cpu_mem_usage=low_cpu_mem_usage,
         offload_to_disk_path=offload_to_disk_path,
+        block_modules=block_modules,
     )
     _apply_group_offloading(module, config)
 
@@ -576,28 +582,123 @@ def _apply_group_offloading(module: torch.nn.Module, config: GroupOffloadingConf
 
 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. In comparison to
-    the "leaf_level" offloading, which is more fine-grained, this offloading is done at the top-level blocks.
-    """
+    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, we either offload the entire submodule or 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
 
-    # Create module groups for ModuleList and Sequential blocks
+    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():
-        if not isinstance(submodule, (torch.nn.ModuleList, torch.nn.Sequential)):
+        # Check if this is an explicitly defined block module
+        if name in block_modules:
+            # Apply block offloading to the specified submodule
+            _apply_block_offloading_to_submodule(
+                submodule, name, config, modules_with_group_offloading, matched_module_groups
+            )
+        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"{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))
-            modules_with_group_offloading.add(name)
-            continue
 
+    # 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"{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_block_offloading_to_submodule(
+    submodule: torch.nn.Module,
+    name: str,
+    config: GroupOffloadingConfig,
+    modules_with_group_offloading: Set[str],
+    matched_module_groups: List[ModuleGroup],
+) -> None:
+    r"""
+    Apply block offloading to a explicitly defined submodule. This function either:
+    1. Offloads the entire submodule as a single group ( SIMPLE APPROACH)
+    2. Recursively applies block offloading to the submodule
+
+    For now, we use the simple approach - offload the entire submodule as a single group.
+    """
+    # Simple approach: offload the entire submodule as a single group
+    # Since AEs are typically small, this is usually okay
+    if isinstance(submodule, (torch.nn.ModuleList, torch.nn.Sequential)):
+        # If it's a ModuleList or Sequential, apply the normal block-level logic
         for i in range(0, len(submodule), config.num_blocks_per_group):
-            current_modules = submodule[i : i + 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"{name}_{i}_{i + len(current_modules) - 1}"
             group = ModuleGroup(
                 modules=current_modules,
@@ -616,42 +717,24 @@ def _apply_group_offloading_block_level(module: torch.nn.Module, config: GroupOf
             matched_module_groups.append(group)
             for j in range(i, i + len(current_modules)):
                 modules_with_group_offloading.add(f"{name}.{j}")
-
-    # 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 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]
-    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"{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)
+        # For other modules, treat the entire submodule as a single group
+        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,
+        )
+        matched_module_groups.append(group)
+        modules_with_group_offloading.add(name)
 
 
 def _apply_group_offloading_leaf_level(module: torch.nn.Module, config: GroupOffloadingConfig) -> None:

src/diffusers/models/autoencoders/autoencoder_kl.py

@@ -72,6 +72,7 @@ class AutoencoderKL(ModelMixin, AutoencoderMixin, ConfigMixin, FromOriginalModel
 
     _supports_gradient_checkpointing = True
     _no_split_modules = ["BasicTransformerBlock", "ResnetBlock2D"]
+    _group_offload_block_modules = ["quant_conv", "post_quant_conv", "encoder", "decoder"]
 
     @register_to_config
     def __init__(

src/diffusers/models/autoencoders/autoencoder_kl_wan.py

@@ -964,6 +964,7 @@ class AutoencoderKLWan(ModelMixin, AutoencoderMixin, ConfigMixin, FromOriginalMo
     # keys toignore when AlignDeviceHook moves inputs/outputs between devices
     # these are shared mutable state modified in-place
     _skip_keys = ["feat_cache", "feat_idx"]
+    _group_offload_block_modules = ["quant_conv", "post_quant_conv", "encoder", "decoder"]
 
     @register_to_config
     def __init__(

src/diffusers/models/modeling_utils.py

@@ -570,6 +570,10 @@ def enable_group_offload(
                 f"`_supports_group_offloading` to `True` in the class definition. If you believe this is a mistake, please "
                 f"open an issue at https://github.com/huggingface/diffusers/issues."
             )
+
+        # Get block modules from the model if available
+        block_modules = getattr(self, "_group_offload_block_modules", None)
+
         apply_group_offloading(
             module=self,
             onload_device=onload_device,
@@ -581,6 +585,7 @@ def enable_group_offload(
             record_stream=record_stream,
             low_cpu_mem_usage=low_cpu_mem_usage,
             offload_to_disk_path=offload_to_disk_path,
+            block_modules=block_modules,
         )
 
     def set_attention_backend(self, backend: str) -> None: