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The XPU branch of UnquantizedFusedMoEMethod.process_weights_after_loading runs w13/w2.transpose(-1, -2).contiguous(). When experts are CPU-offloaded via UVAOffloader they present as accelerator tensors backed by host memory (marked _vllm_is_uva_offloaded); .contiguous() then materializes a real on-device copy, silently de-offloading every expert and OOMing the card at load time, before KV profiling. Transpose offloaded weights on the host and rebuild the UVA view so they stay off-device; resident (non-offloaded) weights keep the original device path and are numerically unchanged. Re-assert the offload marker after _setup_kernel, since replace_parameter registers a fresh Parameter and drops the old one's attributes, so a later reload (e.g. RL weight update) still takes the host-transpose path. Scoped to UnquantizedMoeBackend.XPU only; a no-op for all non-offloaded and non-XPU paths. Signed-off-by: Yeonsil Yoon <yeon.sil.yoon@intel.com> Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
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Thank you for the PR. It works, tested with B70 GPU. |
Description
When serving a CPU-offloaded MoE model on Intel XPU (e.g. Qwen3.5-397B-A17B with --cpu-offload-gb), the server OOMs at load time — during process_weights_after_loading, before KV-cache profiling.
Root cause.
UVAOffloader offloads expert weights as UVA views: they report device=xpu but their storage lives in host RAM, and the parameter is stamped _vllm_is_uva_offloaded=True. The XPU branch of UnquantizedFusedMoEMethod.process_weights_after_loading then runs:
.contiguous() on a UVA view materializes a real on-device copy, silently de-offloading every expert. For a large MoE the per-layer stacked expert tensors are big enough that this transient device allocation exhausts the card and OOMs.
Fix.
For offloaded weights, transpose on the host and rebuild the UVA view so the weight stays off-device; resident (non-offloaded) weights keep the original device path and are numerically unchanged. Because _setup_kernel → replace_parameter registers a fresh Parameter and drops the old one's Python attributes, the _vllm_is_uva_offloaded marker is re-asserted afterward so a later reload (e.g. RL weight update) still takes the host-transpose path. This also lets the existing device_loading_context re-offload safety net skip a redundant copy.
The change is scoped strictly to UnquantizedMoeBackend.XPU:
Not a duplicate
The only related open PR, #54610, changes the UVA offload selection heuristic in offloader/uva.py; it does not touch the XPU MoE transpose path. This PR fixes a distinct load-time de-offload/OOM in unquantized_fused_moe_method.py.
Tests
pre-commit run --files vllm/model_executor/layers/fused_moe/unquantized_fused_moe_method.py — all hooks Passed (ruff check, ruff format, mypy 3.10, typos, SPDX, forbidden-imports, no-new-torch.cuda).
Serving (Intel Arc Pro B70, 8×, TP8), Qwen3.5-397B-A17B, --cpu-offload-gb 85:
AI assistance
AI assistance (Claude Code) was used for diagnosis, implementation, and this description. The change and its effects were reviewed and verified locally by the submitter.