Hexu ZhaoandClaude Opus 5
10bdf5e076
`load` took the host copies before checking which tensors were missing from the device, so with `vae_cpu_offload=False` — where the loader builds the VAEs on CUDA and `unload` is never called — it pulled about 11 GB per rank back over PCIe once and kept a pinned mirror of it for the life of the process, for a module that never leaves the device. It now asks what is absent first and returns early when nothing is. The offloaded path is unchanged: weights still round-trip and come back bit identical. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
89 lines
3.6 KiB
Python
89 lines
3.6 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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"""CPU offload for frozen inference modules, without the device-to-host copy.
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``module.to("cpu")`` allocates fresh host storage and copies every parameter
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and buffer back over PCIe. For a module that is only ever run under
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``torch.no_grad()`` the weights on the device are byte-identical to the ones
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that were copied in, so that copy is pure waste: ``unload`` points each
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tensor's ``.data`` back at the host copy it came from and lets the device
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storage go.
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``load`` keeps one host copy per tensor, made the first time the module is
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loaded, and copies it to the device. Net: one host-to-device copy per request
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and no device-to-host copy at all, with the module living on the host between
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requests exactly as before. Numerics are untouched -- the device tensors are
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byte copies of the same weights -- and peak host memory can only go down,
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because one buffer is reused instead of a new one being allocated per cycle.
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The host copies are pinned when ``pin`` is set (``--pin-cpu-memory``, on by
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default), which is what lets the remaining host-to-device copy run at full
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PCIe speed instead of being staged through a bounce buffer. That trades the
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module's size in non-pageable host memory for the bandwidth; ``pin=False``
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keeps the copies pageable and everything else the same.
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"""
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from __future__ import annotations
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import torch
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from torch import nn
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from fastvideo.logger import init_logger
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from fastvideo.utils import is_pin_memory_available
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logger = init_logger(__name__)
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_HOST_ATTR = "_frozen_offload_host"
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def _tensors(module: nn.Module):
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yield from module.named_parameters(recurse=True)
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for name, buf in module.named_buffers(recurse=True):
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yield "buffer:" + name, buf
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def _host_copies(module: nn.Module, pin: bool) -> dict[str, torch.Tensor]:
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host = getattr(module, _HOST_ATTR, None)
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if host is not None:
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return host
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host, total = {}, 0
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for name, t in _tensors(module):
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src = t.data if t.data.device.type == "cpu" else t.data.to("cpu")
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if pin and not src.is_pinned():
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src = src.pin_memory()
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host[name] = src
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total += src.numel() * src.element_size()
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setattr(module, _HOST_ATTR, host)
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logger.info("frozen offload: holding %.2f GB of %s on the host (%s)", total / 1e9, type(module).__name__,
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"pinned" if pin else "pageable")
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return host
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def load(module: nn.Module, device: torch.device, pin: bool = True) -> nn.Module:
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"""Put a frozen ``module`` on ``device``, copying from its host copies."""
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if device.type != "cuda":
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return module.to(device)
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missing = [(name, t) for name, t in _tensors(module) if t.data.device != device]
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if not missing:
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# Already resident, which is what ``vae_cpu_offload=False`` leaves. Taking
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# host copies here would pull the whole module back over PCIe once and keep
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# a pinned mirror of it for the life of the process, for a module that never
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# leaves the device.
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return module
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host = _host_copies(module, pin and is_pin_memory_available())
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for name, t in missing:
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src = host[name]
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dst = torch.empty_like(src, device=device)
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dst.copy_(src, non_blocking=src.is_pinned())
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t.data = dst
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return module
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def unload(module: nn.Module) -> nn.Module:
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"""Point a frozen ``module`` back at its host copies; no device-to-host copy."""
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host = getattr(module, _HOST_ATTR, None)
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if host is None:
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return module.to("cpu")
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for name, t in _tensors(module):
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if t.data.device.type != "cpu":
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t.data = host[name]
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return module
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