# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import torch from torch import nn from torch.distributed._tensor import DTensor # type: ignore[attr-defined] from vllm.logger import init_logger from vllm_omni.diffusion.hooks import HookRegistry, ModelHook from vllm_omni.platforms import current_omni_platform from .base import OffloadBackend, OffloadConfig from .module_collector import ModuleDiscovery logger = init_logger(__name__) class SequentialOffloadHook(ModelHook): """Hook for sequential offloading with mutual exclusion on encoder and DiT modules. To be used as a model-level (or "component-level") of CPU offloading method; When a module's forward is called, this hook offloads target modules to CPU and loads the current module to GPU. """ _HOOK_NAME = "sequential_offload" def __init__( self, offload_targets: list[nn.Module], device: torch.device, pin_memory: bool = True, use_hsdp: bool = False, ): # Modules to offload to CPU before this module runs self.offload_targets = offload_targets self.device = device self.pin_memory = pin_memory self.use_hsdp = use_hsdp @staticmethod def _move_params( module: nn.Module, target_device: torch.device, *, non_blocking: bool = False, pin_memory: bool = False, ) -> None: """Move module parameters and buffers to device. This cls method specifically prevents recursion device movement, E.g., Cache-DiT CachedBlocks has attr `transformer` as a ref to original transformer blocks, thus `module.to(device)` will fail for recursion calling, refer to https://github.com/vipshop/cache-dit/blob/v1.2.3/src/cache_dit/caching/cache_blocks/__init__.py#L83 """ for p in module.parameters(): if p.data.device != target_device: data = p.data.to(target_device, non_blocking=non_blocking) if pin_memory and target_device.type == "cpu" and not isinstance(data, DTensor): data = data.pin_memory() p.data = data for b in module.buffers(): if b.device != target_device: data = b.data.to(target_device, non_blocking=non_blocking) if pin_memory and target_device.type == "cpu" and not isinstance(data, DTensor): data = data.pin_memory() b.data = data def _to_cpu(self, module: nn.Module) -> None: try: param = next(module.parameters()) except StopIteration: return if param.device.type == "cpu": return # XPU's allocator doesn't respect stream dependencies in empty_cache, # so non-blocking copies can race with cache eviction. Use blocking # copies on XPU to avoid NULL pointer errors during DMA. non_blocking = not self.use_hsdp and not current_omni_platform.is_xpu() self._move_params( module, torch.device("cpu"), non_blocking=non_blocking, pin_memory=self.pin_memory, ) current_omni_platform.empty_cache() def _to_gpu(self, module: nn.Module) -> None: try: if next(module.parameters()).device == self.device: return except StopIteration: return self._move_params(module, self.device, non_blocking=False) def pre_forward(self, module: nn.Module, *args, **kwargs) -> tuple[tuple, dict]: # Offload target modules to CPU for target in self.offload_targets: self._to_cpu(target) # Load current module to GPU self._to_gpu(module) current_omni_platform.synchronize() logger.debug( "Swapped: %s -> CPU, %s -> %s, free memory: %.4f GB", [t.__class__.__name__ for t in self.offload_targets], module.__class__.__name__, f"{self.device.type}:{self.device.index}", current_omni_platform.get_free_memory() / 1024 / 1024 / 1024, ) return args, kwargs def apply_sequential_offload( dit_modules: list[nn.Module], encoder_modules: list[nn.Module], device: torch.device, pin_memory: bool = True, use_hsdp: bool = False, ) -> None: """Apply sequential offloading hooks to DiT and encoder modules. Registers hooks on modules to implement mutual-exclusion GPU allocation. - Before DiT runs, encoders are offloaded to CPU. - Before encoders run, DiT is offloaded to CPU. Args: dit_modules: DiT/transformer modules to register hooks on encoder_modules: Encoder modules to register hooks on device: Target GPU device for loading pin_memory: Whether to pin CPU memory for faster transfers use_hsdp: Whether HSDP is enabled (affects non_blocking behavior) Example: >>> apply_sequential_offload( ... dit_modules=[pipeline.transformer], ... encoder_modules=[pipeline.text_encoder, pipeline.vae], ... device=torch.device("cuda:0"), ... ) >>> # Modules of pipeline now automatically swap between CPU and GPU """ # Register hooks on DiT modules (offload encoders AND other DiTs when a DiT runs) for i, dit_mod in enumerate(dit_modules): other_dits = [d for j, d in enumerate(dit_modules) if j != i] registry = HookRegistry.get_or_create(dit_mod) hook = SequentialOffloadHook( offload_targets=encoder_modules + other_dits, device=device, pin_memory=pin_memory, use_hsdp=use_hsdp, ) registry.register_hook(SequentialOffloadHook._HOOK_NAME, hook) logger.debug("Registered offload hook for %s", dit_mod.__class__.__name__) # Register hooks on encoders (offload DiTs when encoder runs) for enc in encoder_modules: registry = HookRegistry.get_or_create(enc) hook = SequentialOffloadHook( offload_targets=dit_modules, device=device, pin_memory=pin_memory, use_hsdp=use_hsdp, ) registry.register_hook(SequentialOffloadHook._HOOK_NAME, hook) logger.debug("Registered offload hook for %s", enc.__class__.__name__) def remove_sequential_offload(modules: list[nn.Module]) -> None: """Remove sequential offloading hooks from modules. Args: modules: Modules to remove hooks from Example: >>> all_modules = [*dit_modules, *encoder_modules] >>> remove_sequential_offload(all_modules) """ for module in modules: registry: HookRegistry | None = getattr(module, "_hook_registry", None) if registry is not None: registry.remove_hook(SequentialOffloadHook._HOOK_NAME) logger.debug("Removed offload hook from %s", module.__class__.__name__) class ModelLevelOffloadBackend(OffloadBackend): """Model-level (sequential) offloading backend. Uses SequentialOffloadHook registered via HookRegistry for automatic module swapping. """ def __init__(self, config: OffloadConfig, device: torch.device): super().__init__(config, device) self._offload_modules: list[nn.Module] = [] # Track modules with hooks def enable(self, pipeline: nn.Module) -> None: if self.enabled: logger.warning("ModelLevelOffloadBackend already enabled") return modules = ModuleDiscovery.discover(pipeline) if not modules.dits: logger.warning("No DiT/transformer modules found, skipping model-level offloading") return if not modules.encoders: logger.warning("No encoder modules found, skipping model-level offloading") return # Move encoders to GPU for enc in modules.encoders: enc.to(self.device) # Move VAE(s) to GPU if available for vae in modules.vaes: try: vae.to(self.device, non_blocking=True) except Exception as exc: logger.debug("Failed to move VAE to GPU: %s", exc) # Pin resident modules on GPU (small hot submodules called inside the DiT loop). for res, name in zip(modules.resident_modules, modules.resident_names): try: res.to(self.device) except Exception as exc: logger.warning("Failed to move resident module '%s' to GPU: %s", name, exc) # Apply sequential offloading hooks apply_sequential_offload( dit_modules=modules.dits, encoder_modules=modules.encoders, device=self.device, pin_memory=self.config.pin_cpu_memory, use_hsdp=self.config.use_hsdp, ) # Track modules for cleanup self._offload_modules = [*modules.dits, *modules.encoders] self.enabled = True logger.info( "Model-level offloading enabled: %s <-> %s (mutual exclusion)%s", ", ".join(modules.dit_names), ", ".join(modules.encoder_names), f"; resident on GPU: {', '.join(modules.resident_names)}" if modules.resident_names else "", ) def disable(self) -> None: if not self.enabled: return remove_sequential_offload(self._offload_modules) self._offload_modules.clear() self.enabled = False logger.info("Model-level offloading disabled")