hsliuustc0106
ba5943dddb
Replace BoundedAllocatorCache's device-capacity fraction bound (25% of total memory) with a budget derived from the model topology: each attached PinnedModuleStager reports its unique staged storage bytes once, and the retention ceiling is that total times a fixed 2.0 headroom multiplier covering allocator fragmentation and transient storages freed at component boundaries. The 5% physically-free floor is unchanged. The budget therefore scales with the staged components instead of device capacity, so no user-chosen fraction is needed and behavior is identical across differently sized devices with the same model. Re-attaching the same cache does not double count; a second stager grows the budget by its own bytes. Signed-off-by: hsliuustc0106 <liuhongsheng4@huawei.com>
302 lines
12 KiB
Python
302 lines
12 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
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"""On-demand module staging backed by immutable pinned CPU storage."""
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from __future__ import annotations
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from collections.abc import Iterable
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from dataclasses import dataclass
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from itertools import chain
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from typing import Any
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import torch
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from torch import nn
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from vllm.logger import init_logger
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from vllm_omni.platforms import current_omni_platform
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from .tensor_utils import is_dtensor, set_tensor_storage
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logger = init_logger(__name__)
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# Cache budget headroom over the staged component bytes. Retained blocks
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# include allocator fragmentation and transient storages freed alongside the
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# weights at component boundaries, so the reusable payload alone under-bounds
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# the working set the policy must keep to be effective.
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_RETENTION_HEADROOM_MULTIPLIER = 2.0
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class BoundedAllocatorCache:
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"""Retain reusable allocator blocks without monopolizing device memory.
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Component offload normally calls ``empty_cache`` after every stage. That
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makes the next stage return to the device allocator even though PyTorch's
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cached blocks are immediately reusable. This policy keeps the cache while
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both of these bounds hold:
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* cached-but-unallocated memory stays within the derived budget: the
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staged component byte total reported by attached ``PinnedModuleStager``
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instances times a fixed headroom multiplier; and
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* at least 5% of device capacity is physically free.
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The budget derives from the model topology instead of a user-chosen
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fraction, so it scales with the staged components rather than with device
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capacity. Missing memory telemetry is handled conservatively by releasing
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the cache. Failure paths can force release; normal executor shutdown keeps
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its own unconditional device-cache cleanup because this policy is not
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global.
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"""
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def __init__(
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self,
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device: torch.device,
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*,
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min_free_fraction: float = 0.05,
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) -> None:
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if not 0.0 <= min_free_fraction <= 1.0:
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raise ValueError(f"min_free_fraction must be in [0, 1], got {min_free_fraction}")
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self.device = device
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self.min_free_fraction = min_free_fraction
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self._staged_bytes = 0
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def grow_retention_budget(self, staged_bytes: int) -> None:
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"""Grow the derived cache budget by a component's staged byte total."""
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if staged_bytes < 0:
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raise ValueError(f"staged_bytes must be >= 0, got {staged_bytes}")
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self._staged_bytes += int(staged_bytes)
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@property
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def budget_bytes(self) -> int:
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"""Reusable-cache ceiling: staged bytes times the headroom multiplier."""
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return int(self._staged_bytes * _RETENTION_HEADROOM_MULTIPLIER)
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def _should_release(self) -> bool:
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reserved = int(torch.accelerator.memory_reserved(self.device))
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allocated = int(torch.accelerator.memory_allocated(self.device))
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free, total = current_omni_platform.get_device_memory(self.device)
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cached = max(0, reserved - allocated)
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return cached > self.budget_bytes or free < int(total * self.min_free_fraction)
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def release_if_needed(self, *, force: bool = False) -> bool:
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"""Release cached blocks when a bound is crossed or release is forced."""
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if not force:
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try:
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if not self._should_release():
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return False
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except Exception as exc:
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# Preserve the pre-retention behavior on platforms that do not
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# expose allocator telemetry through torch.accelerator.
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logger.debug("Allocator cache telemetry unavailable; releasing cache: %s", exc)
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current_omni_platform.empty_cache()
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return True
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@dataclass(frozen=True)
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class _TensorBinding:
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target: torch.Tensor
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dtype: torch.dtype
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shape: tuple[int, ...]
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stride: tuple[int, ...]
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storage_offset: int
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@dataclass
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class _StorageGroup:
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master: torch.Tensor
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bindings: list[_TensorBinding]
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class PinnedModuleStager:
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"""Stage immutable module groups without copying device weights to CPU.
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``nn.Module.to("cpu")`` performs a device-to-host copy for every parameter
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after each forward. In inference the weights are immutable, so retain one
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pinned CPU master instead. ``load`` materializes device storage from that
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master; ``offload`` only rebinds Parameters and buffers to the master.
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A module iterable is treated as one staging group. It uses one copy stream
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and one reusable completion event. Tensors sharing storage keep their
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shapes, strides, offsets, dtypes, and aliases across every transition.
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"""
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def __init__(
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self,
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module: nn.Module | Iterable[nn.Module],
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device: torch.device,
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*,
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pin_memory: bool = True,
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copy_stream: Any | None = None,
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cache_retention: BoundedAllocatorCache | None = None,
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) -> None:
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modules = (module,) if isinstance(module, nn.Module) else tuple(module)
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if not modules or not all(isinstance(item, nn.Module) for item in modules):
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raise ValueError("PinnedModuleStager requires at least one nn.Module")
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self.device = device
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self.copy_stream = copy_stream if copy_stream is not None else current_omni_platform.Stream()
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self._ready_event = current_omni_platform.Event()
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self.cache_retention = None
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self.loaded = False
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self._groups = self._snapshot_groups(modules, pin_memory=pin_memory)
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self._device_storages: list[torch.Tensor] = []
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self._restore_masters()
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self.set_cache_retention(cache_retention)
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@staticmethod
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def _local_tensor(target: torch.Tensor) -> torch.Tensor:
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return target.to_local() if is_dtensor(target) else target
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@classmethod
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def _snapshot_groups(
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cls,
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modules: tuple[nn.Module, ...],
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*,
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pin_memory: bool,
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) -> list[_StorageGroup]:
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targets: list[torch.Tensor] = []
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seen_targets: set[int] = set()
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for target in chain.from_iterable(chain(item.parameters(), item.buffers()) for item in modules):
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if id(target) not in seen_targets:
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seen_targets.add(id(target))
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targets.append(target)
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grouped: dict[tuple[Any, ...], tuple[torch.Tensor, list[_TensorBinding]]] = {}
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for target in targets:
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local = cls._local_tensor(target)
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if local.is_meta:
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raise ValueError("PinnedModuleStager cannot snapshot a meta tensor")
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storage = local.untyped_storage()
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if storage.nbytes() == 0:
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storage_key: tuple[Any, ...] = ("empty", id(target))
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else:
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storage_key = (
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local.device.type,
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local.device.index,
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storage.data_ptr(),
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storage.nbytes(),
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)
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binding = _TensorBinding(
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target=target,
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dtype=local.dtype,
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shape=tuple(local.shape),
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stride=tuple(local.stride()),
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storage_offset=local.storage_offset(),
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)
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if storage_key not in grouped:
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grouped[storage_key] = (local, [])
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grouped[storage_key][1].append(binding)
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groups: list[_StorageGroup] = []
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for source, bindings in grouped.values():
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storage = source.untyped_storage()
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storage_view = torch.empty(0, dtype=torch.uint8, device=source.device).set_(
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storage,
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0,
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(storage.nbytes(),),
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(1,),
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)
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master = storage_view.detach() if storage_view.device.type == "cpu" else storage_view.to("cpu")
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if pin_memory and not master.is_pinned():
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master = master.pin_memory()
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groups.append(_StorageGroup(master=master, bindings=bindings))
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return groups
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@staticmethod
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def _view(backing: torch.Tensor, binding: _TensorBinding) -> torch.Tensor:
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return torch.empty(0, dtype=binding.dtype, device=backing.device).set_(
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backing.untyped_storage(),
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binding.storage_offset,
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binding.shape,
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binding.stride,
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)
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def _bind(self, storages: list[torch.Tensor]) -> None:
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for storage, group in zip(storages, self._groups):
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for binding in group.bindings:
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set_tensor_storage(binding.target, self._view(storage, binding))
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def _restore_masters(self) -> None:
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self._bind([group.master for group in self._groups])
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@property
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def staged_bytes(self) -> int:
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"""Total unique staged storage bytes (masters are uint8 byte views)."""
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return sum(group.master.numel() for group in self._groups)
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def set_cache_retention(self, cache_retention: BoundedAllocatorCache | None) -> None:
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if cache_retention is self.cache_retention:
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return
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self.cache_retention = cache_retention
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if cache_retention is not None:
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cache_retention.grow_retention_budget(self.staged_bytes)
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def _release_cache(self, *, force: bool = False) -> None:
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if self.cache_retention is None:
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current_omni_platform.empty_cache()
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else:
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self.cache_retention.release_if_needed(force=force)
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def _load_once(self) -> None:
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device_storages = [torch.empty_like(group.master, device=self.device) for group in self._groups]
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compute_stream = current_omni_platform.current_stream()
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self.copy_stream.wait_stream(compute_stream)
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with current_omni_platform.stream(self.copy_stream):
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for device_storage, group in zip(device_storages, self._groups):
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device_storage.copy_(group.master, non_blocking=group.master.is_pinned())
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self._ready_event.record(self.copy_stream)
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self._bind(device_storages)
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compute_stream.wait_event(self._ready_event)
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self._device_storages = device_storages
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self.loaded = True
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def _cleanup_failed_load(self) -> None:
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try:
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current_omni_platform.synchronize()
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except Exception:
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logger.debug("Device synchronization failed while cleaning up module staging", exc_info=True)
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self._restore_masters()
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self._device_storages.clear()
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self.loaded = False
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self._release_cache(force=True)
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def load(self) -> None:
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if self.loaded:
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return
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try:
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self._load_once()
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except torch.OutOfMemoryError:
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# A retained cache is normally reusable by this process, but an
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# explicit flush gives external memory pressure one bounded retry.
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self._cleanup_failed_load()
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try:
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self._load_once()
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except BaseException:
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self._cleanup_failed_load()
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raise
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except BaseException:
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self._cleanup_failed_load()
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raise
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def offload(self) -> None:
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if not self.loaded:
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return
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# The module has completed on the compute stream. Synchronize once at
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# the stage boundary, then discard device storage without any D2H copy.
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try:
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current_omni_platform.synchronize()
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self._restore_masters()
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except BaseException:
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self._device_storages.clear()
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self.loaded = False
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self._release_cache(force=True)
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raise
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self._device_storages.clear()
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self.loaded = False
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self._release_cache()
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__all__ = ["BoundedAllocatorCache", "PinnedModuleStager"]
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