# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project # Adapted from # https://github.com/sgl-project/sglang/blob/main/python/sglang/srt/utils/offloader.py """Prefetch-based CPU offloading with async prefetching. Uses static buffers and event-based stream forking for torch.compile + CUDA graph compatibility. Events allow the copy stream to join CUDA graph captures, ensuring H2D copies are properly captured. """ from abc import ABC, abstractmethod from collections.abc import Generator from dataclasses import dataclass from typing import Any import torch import torch.nn as nn # Import prefetch_ops to register custom ops at module load time import vllm.model_executor.offloader.prefetch_ops # noqa: F401 from vllm.logger import init_logger from vllm.model_executor.offloader.base import BaseOffloader from vllm.utils.platform_utils import is_pin_memory_available logger = init_logger(__name__) @dataclass class ParamInfo: """Metadata about an offloaded parameter.""" name: str shape: tuple[int, ...] stride: tuple[int, ...] dtype: torch.dtype @property def key(self) -> tuple[str, tuple[int, ...], tuple[int, ...], torch.dtype]: """Unique key for buffer pool grouping. Includes parameter name to prevent different parameters with the same shape from sharing buffers within the same layer. Parameters with the same name across different layers will share buffers (via slots). Includes stride because parameters with same shape but different strides need separate buffers to preserve memory layout. """ return (self.name, self.shape, self.stride, self.dtype) @property def num_bytes(self) -> int: """Size in bytes.""" numel = 1 for dim in self.shape: numel *= dim return numel * torch.finfo(self.dtype).bits // 8 class StaticBufferPool: """Pre-allocated GPU buffer pool for offloaded parameters. Allocates slot_capacity copies of each unique parameter (name, shape, stride, dtype), allowing for double/triple buffering during prefetch. Buffer slots are reused circularly: layer N uses slot (N % slot_capacity). The key includes parameter name to prevent different parameters within the same layer from sharing buffers. Parameters with the same name across different layers share buffers via the slot mechanism. """ def __init__( self, param_infos: list[ParamInfo], slot_capacity: int, device: torch.device, ): self.slot_capacity = slot_capacity self.total_bytes = 0 self._device = device # Group by (shape, stride, dtype) - only allocate unique combinations unique_params: dict[tuple, ParamInfo] = {} for info in param_infos: if info.key not in unique_params: unique_params[info.key] = info # Allocate buffers: key -> list of tensors (one per slot) self._buffers: dict[tuple, list[torch.Tensor]] = {} for key, info in unique_params.items(): slot_tensors = [] for _ in range(slot_capacity): # Use empty_strided to preserve parameter's memory layout buf = torch.empty_strided( size=info.shape, stride=info.stride, dtype=info.dtype, device=device, ) slot_tensors.append(buf) self.total_bytes += info.num_bytes self._buffers[key] = slot_tensors logger.debug( "[StaticBufferPool] Allocated %d unique (name, shape, stride, dtype), " "%d slots each, total %.4f GB", len(unique_params), slot_capacity, self.total_bytes / 1e9, ) def get_buffer( self, name: str, shape: tuple[int, ...], stride: tuple[int, ...], dtype: torch.dtype, slot_idx: int, ) -> torch.Tensor: """Get a static buffer for the given name/shape/stride/dtype/slot.""" key = (name, shape, stride, dtype) return self._buffers[key][slot_idx % self.slot_capacity] class PrefetchOffloader(BaseOffloader): """Prefetching-based offloader with group-based layer selection. Groups layers and uses async H2D prefetch to hide transfer latency. Uses static buffers and stream synchronization for torch.compile and CUDA graph compatibility. Args: group_size: Group every N layers together. num_in_group: Offload this many layers per group (last N of each group). prefetch_step: Number of layers to prefetch ahead. mode: Offload mode ("cpu" is currently supported). """ def __init__( self, group_size: int, num_in_group: int, prefetch_step: int, offload_params: set[str] | None = None, mode: str = "cpu", ): self.group_size = group_size self.num_in_group = num_in_group self.prefetch_step = prefetch_step self.offload_params = offload_params or set() self.mode = mode # Copy stream for async H2D transfers self.copy_stream = torch.cuda.Stream() # Module offloaders and buffer pool (populated in wrap_modules/post_init) self.module_offloaders: list[_ModuleOffloader] = [] self.buffer_pool: StaticBufferPool | None = None self.total_offloaded_bytes = 0 def wrap_modules( self, modules_generator: Generator[nn.Module, None, None], ) -> list[nn.Module]: """Wrap modules with prefetch offloading logic.""" assert len(self.module_offloaders) == 0, ( "wrap_modules should only be called once" ) all_modules = [] offload_modules = [] for module_index, module in enumerate(modules_generator): all_modules.append(module) # Select layers to offload based on group pattern # Offload last num_in_group layers of each group_size if module_index % self.group_size >= self.group_size - self.num_in_group: if self.offload_params: whitelist = [ name for name, _ in module.named_parameters() if any(f".{p}." in f".{name}." for p in self.offload_params) ] else: whitelist = [name for name, _ in module.named_parameters()] if not whitelist: continue # skip layers with no matching params offload_modules.append(module) self.module_offloaders.append( _ModuleOffloader( mode=self.mode, module=module, copy_stream=self.copy_stream, whitelist_param_names=whitelist, layer_idx=len(self.module_offloaders), ) ) for index, module in enumerate(offload_modules): self._hook_module_forward(index, module) return all_modules def _hook_module_forward(self, index: int, module: nn.Module): """Hook module's forward with torch.compile-compatible sync.""" original_forward = module.forward def forward(*args, **kwargs): # Temporarily restore original forward to avoid recursion module.forward = original_forward # Wait for this layer's prefetch to complete # mutates_args on input_tensor creates data dependency for torch.compile input_tensor = args[0] if args else kwargs.get("hidden_states") torch.ops.vllm.wait_prefetch(input_tensor, index) # No parameter swapping needed - parameters already point to # GPU static buffers (set in assign_static_buffer) output = original_forward(*args, **kwargs) # Start prefetch for next layer (circular) # mutates_args on output_tensor creates ordering dependency next_index = (index + self.prefetch_step) % len(self.module_offloaders) # Handle tuple output (e.g., (hidden_states, residual)) if isinstance(output, tuple): torch.ops.vllm.start_prefetch(output[0], next_index) else: torch.ops.vllm.start_prefetch(output, next_index) # No explicit offload needed - static buffers are reused implicitly # Restore hooked forward module.forward = forward return output module.forward = forward def _wait_for_layer(self, layer_idx: int): """Called by custom op - wait for copy to complete. Synchronization strategy: - During CUDA graph capture: use event-based wait (graph-compatible) - Outside capture (warmup/eager): use wait_stream (more robust) During capture, we skip wait for pre-capture prefetches because: 1. sync_before_graph_capture() ensures pre-capture work is complete 2. We can't wait on pre-capture events during capture (isolation error) """ offloader = self.module_offloaders[layer_idx] if torch.cuda.is_current_stream_capturing(): # During capture, skip wait for pre-capture prefetches. # sync_before_graph_capture() ensures pre-capture work is complete. if not offloader._prefetch_in_capture: return # Event-based wait for in-capture prefetches (graph-compatible) torch.cuda.current_stream().wait_event(offloader._copy_done_event) # Mark that this prefetch has been waited on (joined). offloader._prefetch_in_capture = False else: if offloader._event_valid_for_eager: # Use per-layer event to only wait for THIS layer's copy, # allowing other layers' prefetches to run concurrently. torch.cuda.current_stream().wait_event(offloader._copy_done_event) else: # Event not usable (unrecorded or recorded during capture). # Fall back to wait_stream to drain all copy_stream work. torch.cuda.current_stream().wait_stream(self.copy_stream) def sync_prev_onload(self): """Sync previous onload operations. Ensures any H2D copies in flight on copy_stream complete before the compute stream continues. Call this before CUDA graph capture/replay or when synchronization is needed. """ torch.cuda.current_stream().wait_stream(self.copy_stream) def _start_prefetch(self, layer_idx: int): """Called by custom op - start async copy to static buffer.""" offloader = self.module_offloaders[layer_idx] offloader.start_onload_to_static() def join_after_forward(self): """Join copy_stream after model forward completes. Call this after the model forward pass but before CUDA graph capture ends. This ensures copy_stream is rejoined for any prefetches started during the forward pass. We join ALL layers that have _prefetch_in_capture=True, meaning their prefetch was started during capture but not yet waited on (joined). This handles both full and piecewise cudagraph modes correctly: - Full mode: joins layers 0..prefetch_step-1 (prefetched by last layers) - Piecewise mode: joins only layers prefetched by THIS subgraph's layers """ if not self.module_offloaders: return # Join all layers whose prefetch was started in capture but not waited on for offloader in self.module_offloaders: if offloader._prefetch_in_capture: torch.cuda.current_stream().wait_event(offloader._copy_done_event) offloader._prefetch_in_capture = False def post_init(self): """Allocate static buffer pool and start initial prefetches. Note: Parameters have already been offloaded to CPU during wrap_modules() (in _CpuParamOffloader.__init__), so GPU memory is available for the static buffer pool. """ # Sync CPU storage with current param.data BEFORE collecting param info. # This is needed because process_weights_after_loading may have: # 1. Transformed weights (quantization, transpose, etc.) # 2. Created new CPU tensors via device_loading_context # Our _cpu_storage would be stale otherwise. for offloader in self.module_offloaders: offloader.sync_cpu_storage() # Collect parameter info (now using synced CPU storage) param_infos: list[ParamInfo] = [] device: torch.device | None = None for offloader in self.module_offloaders: param_infos.extend(offloader.get_param_infos()) if device is None: device = offloader.device if device is None: # No modules to offload return # Allocate static buffer pool self.buffer_pool = StaticBufferPool( param_infos=param_infos, slot_capacity=self.prefetch_step, device=device, ) # Assign buffer slots and point parameters to GPU buffers for idx, offloader in enumerate(self.module_offloaders): slot_idx = idx % self.prefetch_step offloader.assign_buffer_slot(self.buffer_pool, slot_idx) # Collect offloaded bytes for offloader in self.module_offloaders: offloader.post_init() self.total_offloaded_bytes += offloader.offloaded_bytes logger.info_once( f"[PrefetchOffloader] Initialized {len(self.module_offloaders)} modules. " f"Total GPU memory saved: {self.total_offloaded_bytes / 1e9:.4f} GB, " f"Static buffer pool: {self.buffer_pool.total_bytes / 1e9:.4f} GB " f"(group_size={self.group_size}, num_in_group={self.num_in_group}, " f"prefetch_step={self.prefetch_step}, mode={self.mode})" ) # Start initial prefetches for i in range(min(self.prefetch_step, len(self.module_offloaders))): self.module_offloaders[i].start_onload_to_static() class _ModuleOffloader: """Manages offloading for a single module. Uses static buffers from a shared pool instead of dynamic allocation. """ def __init__( self, mode: str, module: nn.Module, copy_stream: torch.cuda.Stream, whitelist_param_names: list[str], layer_idx: int, ): self.mode = mode self.module = module self.device = next(module.parameters()).device self.copy_stream = copy_stream self.layer_idx = layer_idx self.offloaded_bytes = 0 # Event to signal when H2D copy to static buffer is complete. # Used for per-layer synchronization (both eager and capture modes). self._copy_done_event = torch.cuda.Event() # Track whether _copy_done_event is valid for eager-mode wait_event. # False when: (1) never recorded, or (2) last recorded during a # cudagraph capture (events become invalid after capture ends). # In these cases we fall back to wait_stream. self._event_valid_for_eager = False # Track if last prefetch was started during CUDA graph capture. # Used to skip wait_event during capture for pre-capture prefetches. self._prefetch_in_capture = False assert self.device != torch.device("cpu"), ( "Module parameters should not already be on CPU " "(offloader handles CPU placement)" ) # Buffer pool and slot (assigned in assign_buffer_slot) self._buffer_pool: StaticBufferPool | None = None self._buffer_slot_idx: int = 0 param_dict = dict(self.module.named_parameters()) assert all(name in param_dict for name in whitelist_param_names), ( f"Whitelist params {whitelist_param_names} not found in module params " f"{list(param_dict.keys())}" ) self._param_offloaders = { name: _BaseParamOffloader.create(mode, module=module, param_name=name) for name in whitelist_param_names } def post_init(self): """Collect total offloaded bytes (offloading already done in __init__).""" for param_offloader in self._param_offloaders.values(): param_offloader.post_init() self.offloaded_bytes += param_offloader.offloaded_bytes def sync_cpu_storage(self): """Sync CPU storage with current param.data. Called after process_weights_after_loading to ensure _cpu_storage contains the final processed weights, not stale pre-loading data. """ for param_offloader in self._param_offloaders.values(): param_offloader.sync_cpu_storage() def get_param_infos(self) -> list[ParamInfo]: """Get parameter metadata for buffer pool allocation. Note: sync_cpu_storage() must be called before this method to ensure _cpu_storage reflects the final processed weights (after quantization). """ infos = [] for name, offloader in self._param_offloaders.items(): cpu_storage = offloader._cpu_storage assert cpu_storage is not None, "CPU storage not initialized" infos.append( ParamInfo( name=name, shape=tuple(cpu_storage.shape), stride=tuple(cpu_storage.stride()), dtype=cpu_storage.dtype, ) ) return infos def assign_buffer_slot(self, pool: StaticBufferPool, slot_idx: int): """Assign this module to a buffer slot in the pool. Also assigns static GPU buffers to each parameter offloader, which moves the parameter data to point to the GPU buffer. """ self._buffer_pool = pool self._buffer_slot_idx = slot_idx # Assign static buffers to parameters # Use CPU storage shape/stride/dtype since param.data is now empty for name, offloader in self._param_offloaders.items(): cpu_storage = offloader._cpu_storage assert cpu_storage is not None, "CPU storage not initialized" buffer = pool.get_buffer( name=name, shape=tuple(cpu_storage.shape), stride=tuple(cpu_storage.stride()), dtype=cpu_storage.dtype, slot_idx=slot_idx, ) offloader.assign_static_buffer(buffer) def start_onload_to_static(self): """Start async copy from CPU storage to GPU buffer. Uses event-based forking to join copy_stream to CUDA graph capture. This ensures H2D copies are properly captured when recording a graph. IMPORTANT: We must wait for the compute stream before copying, because the previous layer's forward may still be using the buffer (GPU ops are async). Without this sync, we could overwrite the buffer while it's being read. """ assert self._buffer_pool is not None, "Buffer pool not assigned" # Track if this prefetch is being captured (for _wait_for_layer logic) self._prefetch_in_capture = torch.cuda.is_current_stream_capturing() # Fork: record event on compute stream, copy_stream waits on it # This joins copy_stream to any active CUDA graph capture fork_event = torch.cuda.Event() torch.cuda.current_stream().record_event(fork_event) self.copy_stream.wait_event(fork_event) with torch.cuda.stream(self.copy_stream): for name, offloader in self._param_offloaders.items(): cpu_storage = offloader._cpu_storage gpu_buffer = offloader._gpu_buffer assert cpu_storage is not None, "CPU storage not initialized" assert gpu_buffer is not None, "GPU buffer not assigned" assert not is_pin_memory_available() or cpu_storage.is_pinned(), ( f"CPU storage for {name} is not pinned! " "non_blocking=True H2D copy from non-pinned memory " "causes stream synchronization that breaks " "event-based fork synchronization." ) gpu_buffer.copy_(cpu_storage, non_blocking=True) # Record completion event for _wait_for_layer to use self._copy_done_event.record(self.copy_stream) # Event is only valid for eager wait_event if recorded outside capture. # Events recorded during capture become invalid after capture ends. self._event_valid_for_eager = not torch.cuda.is_current_stream_capturing() class _BaseParamOffloader(ABC): """Base class for parameter offloading strategies.""" # CPU storage for offloaded parameters (set by subclasses) _cpu_storage: torch.Tensor | None # GPU buffer reference (set by subclasses when using static buffers) _gpu_buffer: torch.Tensor | None @staticmethod def create(mode: str, **kwargs) -> "_BaseParamOffloader": """Factory method to create appropriate offloader for mode.""" if mode == "cpu": return _CpuParamOffloader(**kwargs) else: raise ValueError(f"Unknown offload mode: {mode}") def __init__(self, module: nn.Module, param_name: str): self._module = module self._param_name = param_name self.offloaded_bytes = 0 self._cpu_storage = None self._gpu_buffer = None @property def _param(self) -> nn.Parameter: """Get the parameter being offloaded. Supports dotted names (e.g. 'self_attn.qkv_proj.weight') by traversing the module hierarchy. """ obj: Any = self._module for attr in self._param_name.split("."): obj = getattr(obj, attr) return obj def post_init(self): """Initialize offloading (move parameter to storage).""" return @abstractmethod def sync_cpu_storage(self) -> None: """Sync CPU storage with current param.data. Called after process_weights_after_loading to update _cpu_storage with the final processed weights. """ pass @abstractmethod def assign_static_buffer(self, gpu_buffer: torch.Tensor) -> None: """Point parameter data to GPU static buffer.""" pass class _CpuParamOffloader(_BaseParamOffloader): """Offload parameter to pinned CPU memory. Uses GPU static buffers as the actual parameter, with CPU storage kept separately. This ensures torch.compile sees GPU tensors at trace time. The offloading happens in two phases: 1. __init__() - copies GPU data to CPU, frees GPU memory immediately 2. assign_static_buffer() - points param.data to GPU static buffer """ def __init__(self, module: nn.Module, param_name: str): super().__init__(module, param_name) self._cpu_storage: torch.Tensor | None = None self._gpu_buffer: torch.Tensor | None = None # Store reference to GPU buffer # Offload to CPU immediately to free GPU memory during model loading self._offload_to_cpu_internal() def _offload_to_cpu_internal(self): """Copy parameter data to pinned CPU storage and free GPU memory. This replaces param.data with CPU storage, allowing weight loading to continue writing to CPU memory. GPU memory is freed when the original GPU tensor is garbage collected. """ param = self._param pin_memory = is_pin_memory_available() # Create pinned CPU storage and copy current GPU data self._cpu_storage = torch.empty_strided( size=param.data.size(), stride=param.data.stride(), dtype=param.data.dtype, layout=param.data.layout, device="cpu", pin_memory=pin_memory, ) self._cpu_storage.copy_(param.data) self.offloaded_bytes = ( self._cpu_storage.numel() * self._cpu_storage.element_size() ) # Point param.data to CPU storage - this allows weight loading to work # and frees GPU memory when the original GPU tensor is garbage collected param.data = self._cpu_storage def _update_cpu_storage_from_param(self) -> None: """Update _cpu_storage from current param.data, ensuring pinned memory. After process_weights_after_loading, device_loading_context creates non-pinned CPU tensors via `p.data = p.data.to("cpu")`. Using non-pinned memory with `copy_(src, non_blocking=True)` causes CUDA to perform a stream synchronization before the copy, breaking the event-based fork synchronization and potentially allowing the copy to overwrite the GPU buffer while the compute stream still reads it. This method ensures _cpu_storage always uses pinned memory when available, re-pinning if necessary. """ param = self._param if param.data.device.type == "cpu": if is_pin_memory_available() and not param.data.is_pinned(): pinned = torch.empty_strided( size=param.data.size(), stride=param.data.stride(), dtype=param.data.dtype, layout=param.data.layout, device="cpu", pin_memory=True, ) pinned.copy_(param.data) self._cpu_storage = pinned else: self._cpu_storage = param.data else: # param.data is on GPU - copy to existing CPU storage assert self._cpu_storage is not None self._cpu_storage.copy_(param.data) def assign_static_buffer(self, gpu_buffer: torch.Tensor) -> None: """Point parameter data to GPU static buffer. This is called after weight loading AND process_weights_after_loading complete. At this point: - param.data may have been replaced by device_loading_context (which creates new CPU tensors after quantization processing) - We need to update _cpu_storage to point to current param.data so that prefetch copies the processed weights, not stale data - Then point param.data to the GPU buffer for torch.compile """ assert self._cpu_storage is not None, ( "_offload_to_cpu_internal() must be called before assign_static_buffer()" ) # Get current parameter (may have been replaced by # process_weights_after_loading) param = self._param # Update _cpu_storage to current param.data. This is critical because: # 1. process_weights_after_loading may transform weights (quantization) # 2. device_loading_context creates NEW CPU tensors when moving back # 3. Our old _cpu_storage would have pre-processed or stale data self._update_cpu_storage_from_param() # Store reference to GPU buffer for use in start_onload self._gpu_buffer = gpu_buffer # Point parameter to static GPU buffer - this is what torch.compile sees param.data = gpu_buffer def sync_cpu_storage(self) -> None: """Sync CPU storage with current param.data. Called after process_weights_after_loading to update _cpu_storage with the final processed weights. This is critical because: 1. process_weights_after_loading may transform weights (quantization) 2. device_loading_context creates NEW CPU tensors when moving back 3. Our old _cpu_storage would have pre-processed or stale data """ self._update_cpu_storage_from_param() def post_init(self): """No-op: offloading done in offload_to_cpu/assign_static_buffer.""" pass