SYLARandlishunyang12
1c2a81f6d8
Signed-off-by: lishunyang12 <lishunyang03@gmail.com> Signed-off-by: lishunyang12 <lishunyang12@163.com> Co-authored-by: lishunyang12 <lishunyang03@gmail.com>
90 lines
3.3 KiB
Python
90 lines
3.3 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""On-demand module staging backed by an immutable pinned CPU snapshot."""
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from __future__ import annotations
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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_omni.platforms import current_omni_platform
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from .tensor_utils import set_tensor_storage
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class PinnedModuleStager:
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"""Stage an immutable module without copying device weights back 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 fresh device tensors from
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that master; ``offload`` only rebinds the Parameters/Buffers to it and
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releases the device allocations.
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"""
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def __init__(
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self,
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module: 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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) -> None:
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self.device = device
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self.copy_stream = copy_stream or current_omni_platform.Stream()
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self.loaded = False
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self._entries: list[tuple[torch.Tensor, torch.Tensor]] = []
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self._device_tensors: list[torch.Tensor] = []
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# parameters()/buffers() remove duplicate objects by default, which
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# preserves tied weights and buffer aliases when their storage moves.
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for target in chain(module.parameters(), module.buffers()):
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master = target.detach()
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if master.device.type != "cpu":
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master = master.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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set_tensor_storage(target, master)
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self._entries.append((target, master))
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def load(self) -> None:
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if self.loaded:
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return
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# Allocate on the compute stream so the caching allocator can reuse
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# memory released by the preceding encoder/DiT stage.
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device_tensors = [torch.empty_like(master, device=self.device) for _, master in self._entries]
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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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ready = current_omni_platform.Event()
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with current_omni_platform.stream(self.copy_stream):
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for device_tensor, (_, master) in zip(device_tensors, self._entries):
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device_tensor.copy_(master, non_blocking=master.is_pinned())
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ready.record(self.copy_stream)
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for device_tensor, (target, _) in zip(device_tensors, self._entries):
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set_tensor_storage(target, device_tensor)
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compute_stream.wait_event(ready)
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self._device_tensors = device_tensors
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self.loaded = True
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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 release HBM by rebinding to the unchanged
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# host masters. No device-to-host transfer is necessary.
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current_omni_platform.synchronize()
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for target, master in self._entries:
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set_tensor_storage(target, master)
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self._device_tensors.clear()
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self.loaded = False
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current_omni_platform.empty_cache()
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__all__ = ["PinnedModuleStager"]
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