[refactor] condense group offloading (#11990)

* update

* update

* refactor

* add test

* address review comment

* nit

src/diffusers/hooks/group_offloading.py

@@ -95,7 +95,7 @@ class ModuleGroup:
         self.offload_to_disk_path = offload_to_disk_path
         self._is_offloaded_to_disk = False
 
-        if self.offload_to_disk_path:
+        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)
@@ -115,6 +115,12 @@ class ModuleGroup:
         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:
@@ -138,112 +144,76 @@ class ModuleGroup:
 
     @contextmanager
     def _pinned_memory_tensors(self):
-        pinned_dict = {}
         try:
-            for param, tensor in self.cpu_param_dict.items():
-                if not tensor.is_pinned():
-                    pinned_dict[param] = tensor.pin_memory()
-                else:
-                    pinned_dict[param] = tensor
-
+            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, current_stream=None):
+    def _transfer_tensor_to_device(self, tensor, source_tensor):
         tensor.data = source_tensor.to(self.onload_device, non_blocking=self.non_blocking)
-        if self.record_stream and current_stream is not None:
-            tensor.data.record_stream(current_stream)
+        if self.record_stream:
+            tensor.data.record_stream(self._torch_accelerator_module.current_stream())
 
-    def _process_tensors_from_modules(self, pinned_memory=None, current_stream=None):
+    def _process_tensors_from_modules(self, pinned_memory=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, current_stream)
+                self._transfer_tensor_to_device(param, source)
             for buffer in group_module.buffers():
                 source = pinned_memory[buffer] if pinned_memory else buffer.data
-                self._transfer_tensor_to_device(buffer, source, current_stream)
+                self._transfer_tensor_to_device(buffer, source)
 
         for param in self.parameters:
             source = pinned_memory[param] if pinned_memory else param.data
-            self._transfer_tensor_to_device(param, source, current_stream)
+            self._transfer_tensor_to_device(param, source)
 
         for buffer in self.buffers:
             source = pinned_memory[buffer] if pinned_memory else buffer.data
-            self._transfer_tensor_to_device(buffer, source, current_stream)
+            self._transfer_tensor_to_device(buffer, source)
 
-    def _onload_from_disk(self, current_stream):
+    def _onload_from_disk(self):
         if self.stream is not None:
-            loaded_cpu_tensors = safetensors.torch.load_file(self.safetensors_file_path, device="cpu")
-
-            for key, tensor_obj in self.key_to_tensor.items():
-                self.cpu_param_dict[tensor_obj] = loaded_cpu_tensors[key]
-
-            with self._pinned_memory_tensors() as pinned_memory:
-                for key, tensor_obj in self.key_to_tensor.items():
-                    self._transfer_tensor_to_device(tensor_obj, pinned_memory[tensor_obj], current_stream)
-
-            self.cpu_param_dict.clear()
+            # Wait for previous Host->Device transfer to complete
+            self.stream.synchronize()
 
-        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]
+        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
 
-    def _onload_from_memory(self, current_stream):
-        if self.stream is not None:
-            with self._pinned_memory_tensors() as pinned_memory:
-                self._process_tensors_from_modules(pinned_memory, current_stream)
-        else:
-            self._process_tensors_from_modules(None, current_stream)
-
-    @torch.compiler.disable()
-    def onload_(self):
-        torch_accelerator_module = (
-            getattr(torch, torch.accelerator.current_accelerator().type)
-            if hasattr(torch, "accelerator")
-            else torch.cuda
-        )
-        context = nullcontext() if self.stream is None else torch_accelerator_module.stream(self.stream)
-        current_stream = 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.offload_to_disk_path:
             if self.stream is not None:
-                # Wait for previous Host->Device transfer to complete
-                self.stream.synchronize()
-
-            with context:
-                if self.stream is not None:
-                    # Load to CPU, pin, and async copy to device for overlapping transfer and compute
-                    loaded_cpu_tensors = safetensors.torch.load_file(self.safetensors_file_path, device="cpu")
-                    for key, tensor_obj in self.key_to_tensor.items():
-                        pinned_tensor = loaded_cpu_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:
-                    # Load directly to the target device (synchronous)
-                    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]
-            return
+                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)
         with context:
-            if self.offload_to_disk_path:
-                self._onload_from_disk(current_stream)
+            if self.stream is not None:
+                with self._pinned_memory_tensors() as pinned_memory:
+                    self._process_tensors_from_modules(pinned_memory)
             else:
-                self._onload_from_memory(current_stream)
+                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
@@ -264,14 +234,10 @@ class ModuleGroup:
             tensor_obj.data = torch.empty_like(tensor_obj.data, device=self.offload_device)
 
     def _offload_to_memory(self):
-        torch_accelerator_module = (
-            getattr(torch, torch.accelerator.current_accelerator().type)
-            if hasattr(torch, "accelerator")
-            else torch.cuda
-        )
         if self.stream is not None:
             if not self.record_stream:
-                torch_accelerator_module.current_stream().synchronize()
+                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]
@@ -282,15 +248,23 @@ class ModuleGroup:
 
         else:
             for group_module in self.modules:
-                group_module.to(self.offload_device, non_blocking=self.non_blocking)
+                group_module.to(self.offload_device, non_blocking=False)
             for param in self.parameters:
-                param.data = param.data.to(self.offload_device, non_blocking=self.non_blocking)
+                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=self.non_blocking)
+                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 modules to the offload_device."""
+        r"""Offloads the group of parameters to the offload_device."""
         if self.offload_to_disk_path:
             self._offload_to_disk()
         else:
@@ -307,11 +281,9 @@ class GroupOffloadingHook(ModelHook):
 
     _is_stateful = False
 
-    def __init__(
-        self, group: ModuleGroup, next_group: Optional[ModuleGroup] = None, *, config: GroupOffloadingConfig
-    ) -> None:
+    def __init__(self, group: ModuleGroup, *, config: GroupOffloadingConfig) -> None:
         self.group = group
-        self.next_group = next_group
+        self.next_group: Optional[ModuleGroup] = None
         self.config = config
 
     def initialize_hook(self, module: torch.nn.Module) -> torch.nn.Module:
@@ -459,8 +431,8 @@ class LayerExecutionTrackerHook(ModelHook):
 
 def apply_group_offloading(
     module: torch.nn.Module,
-    onload_device: torch.device,
-    offload_device: torch.device = torch.device("cpu"),
+    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,
@@ -546,6 +518,8 @@ def apply_group_offloading(
         ```
     """
 
+    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
@@ -633,7 +607,7 @@ def _apply_group_offloading_block_level(module: torch.nn.Module, config: GroupOf
     # 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, None, config=config)
+            _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
@@ -662,9 +636,9 @@ def _apply_group_offloading_block_level(module: torch.nn.Module, config: GroupOf
         group_id=f"{module.__class__.__name__}_unmatched_group",
     )
     if config.stream is None:
-        _apply_group_offloading_hook(module, unmatched_group, None, config=config)
+        _apply_group_offloading_hook(module, unmatched_group, config=config)
     else:
-        _apply_lazy_group_offloading_hook(module, unmatched_group, None, config=config)
+        _apply_lazy_group_offloading_hook(module, unmatched_group, config=config)
 
 
 def _apply_group_offloading_leaf_level(module: torch.nn.Module, config: GroupOffloadingConfig) -> None:
@@ -693,7 +667,7 @@ def _apply_group_offloading_leaf_level(module: torch.nn.Module, config: GroupOff
             onload_self=True,
             group_id=name,
         )
-        _apply_group_offloading_hook(submodule, group, None, config=config)
+        _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
@@ -740,7 +714,7 @@ def _apply_group_offloading_leaf_level(module: torch.nn.Module, config: GroupOff
             onload_self=True,
             group_id=name,
         )
-        _apply_group_offloading_hook(parent_module, group, None, config=config)
+        _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
@@ -762,13 +736,12 @@ def _apply_group_offloading_leaf_level(module: torch.nn.Module, config: GroupOff
             onload_self=True,
             group_id=_GROUP_ID_LAZY_LEAF,
         )
-        _apply_lazy_group_offloading_hook(module, unmatched_group, None, config=config)
+        _apply_lazy_group_offloading_hook(module, unmatched_group, config=config)
 
 
 def _apply_group_offloading_hook(
     module: torch.nn.Module,
     group: ModuleGroup,
-    next_group: Optional[ModuleGroup] = None,
     *,
     config: GroupOffloadingConfig,
 ) -> None:
@@ -777,14 +750,13 @@ def _apply_group_offloading_hook(
     # 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, next_group, config=config)
+        hook = GroupOffloadingHook(group, config=config)
         registry.register_hook(hook, _GROUP_OFFLOADING)
 
 
 def _apply_lazy_group_offloading_hook(
     module: torch.nn.Module,
     group: ModuleGroup,
-    next_group: Optional[ModuleGroup] = None,
     *,
     config: GroupOffloadingConfig,
 ) -> None:
@@ -793,7 +765,7 @@ def _apply_lazy_group_offloading_hook(
     # 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, next_group, config=config)
+        hook = GroupOffloadingHook(group, config=config)
         registry.register_hook(hook, _GROUP_OFFLOADING)
 
     lazy_prefetch_hook = LazyPrefetchGroupOffloadingHook()

tests/hooks/test_group_offloading.py

@@ -17,7 +17,9 @@ import gc
 import unittest
 
 import torch
+from parameterized import parameterized
 
+from diffusers.hooks import HookRegistry, ModelHook
 from diffusers.models import ModelMixin
 from diffusers.pipelines.pipeline_utils import DiffusionPipeline
 from diffusers.utils import get_logger
@@ -99,6 +101,29 @@ class DummyModelWithMultipleBlocks(ModelMixin):
         return x
 
 
+# Test for https://github.com/huggingface/diffusers/pull/12077
+class DummyModelWithLayerNorm(ModelMixin):
+    def __init__(self, in_features: int, hidden_features: int, out_features: int, num_layers: int) -> None:
+        super().__init__()
+
+        self.linear_1 = torch.nn.Linear(in_features, hidden_features)
+        self.activation = torch.nn.ReLU()
+        self.blocks = torch.nn.ModuleList(
+            [DummyBlock(hidden_features, hidden_features, hidden_features) for _ in range(num_layers)]
+        )
+        self.layer_norm = torch.nn.LayerNorm(hidden_features, elementwise_affine=True)
+        self.linear_2 = torch.nn.Linear(hidden_features, out_features)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.linear_1(x)
+        x = self.activation(x)
+        for block in self.blocks:
+            x = block(x)
+        x = self.layer_norm(x)
+        x = self.linear_2(x)
+        return x
+
+
 class DummyPipeline(DiffusionPipeline):
     model_cpu_offload_seq = "model"
 
@@ -113,6 +138,16 @@ class DummyPipeline(DiffusionPipeline):
         return x
 
 
+class LayerOutputTrackerHook(ModelHook):
+    def __init__(self):
+        super().__init__()
+        self.outputs = []
+
+    def post_forward(self, module, output):
+        self.outputs.append(output)
+        return output
+
+
 @require_torch_accelerator
 class GroupOffloadTests(unittest.TestCase):
     in_features = 64
@@ -258,6 +293,7 @@ class GroupOffloadTests(unittest.TestCase):
     def test_block_level_stream_with_invocation_order_different_from_initialization_order(self):
         if torch.device(torch_device).type not in ["cuda", "xpu"]:
             return
+
         model = DummyModelWithMultipleBlocks(
             in_features=self.in_features,
             hidden_features=self.hidden_features,
@@ -274,3 +310,54 @@ class GroupOffloadTests(unittest.TestCase):
 
         with context:
             model(self.input)
+
+    @parameterized.expand([("block_level",), ("leaf_level",)])
+    def test_block_level_offloading_with_parameter_only_module_group(self, offload_type: str):
+        if torch.device(torch_device).type not in ["cuda", "xpu"]:
+            return
+
+        def apply_layer_output_tracker_hook(model: DummyModelWithLayerNorm):
+            for name, module in model.named_modules():
+                registry = HookRegistry.check_if_exists_or_initialize(module)
+                hook = LayerOutputTrackerHook()
+                registry.register_hook(hook, "layer_output_tracker")
+
+        model_ref = DummyModelWithLayerNorm(128, 256, 128, 2)
+        model = DummyModelWithLayerNorm(128, 256, 128, 2)
+
+        model.load_state_dict(model_ref.state_dict(), strict=True)
+
+        model_ref.to(torch_device)
+        model.enable_group_offload(torch_device, offload_type=offload_type, num_blocks_per_group=1, use_stream=True)
+
+        apply_layer_output_tracker_hook(model_ref)
+        apply_layer_output_tracker_hook(model)
+
+        x = torch.randn(2, 128).to(torch_device)
+
+        out_ref = model_ref(x)
+        out = model(x)
+        self.assertTrue(torch.allclose(out_ref, out, atol=1e-5), "Outputs do not match.")
+
+        num_repeats = 4
+        for i in range(num_repeats):
+            out_ref = model_ref(x)
+            out = model(x)
+
+        self.assertTrue(torch.allclose(out_ref, out, atol=1e-5), "Outputs do not match after multiple invocations.")
+
+        for (ref_name, ref_module), (name, module) in zip(model_ref.named_modules(), model.named_modules()):
+            assert ref_name == name
+            ref_outputs = (
+                HookRegistry.check_if_exists_or_initialize(ref_module).get_hook("layer_output_tracker").outputs
+            )
+            outputs = HookRegistry.check_if_exists_or_initialize(module).get_hook("layer_output_tracker").outputs
+            cumulated_absmax = 0.0
+            for i in range(len(outputs)):
+                diff = ref_outputs[0] - outputs[i]
+                absdiff = diff.abs()
+                absmax = absdiff.max().item()
+                cumulated_absmax += absmax
+            self.assertLess(
+                cumulated_absmax, 1e-5, f"Output differences for {name} exceeded threshold: {cumulated_absmax:.5f}"
+            )