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Code Review
This pull request introduces a mechanism to skip the offloading of scale and zero-point parameters to the CPU when the UVAOffloader is operating in non-UVA fallback mode. This change prevents issues where cached parameter references in quantization configurations (such as Marlin MoE) become desynchronized, leading to kernel errors. The implementation includes an explicit _vllm_skip_offload marker and a name-based heuristic fallback to identify these parameters. A new test suite, tests/quantization/test_uva_scale_skip.py, has been added to verify the skipping logic across various scenarios. I have no feedback to provide as there were no review comments to assess.
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Closes the runtime failure described in vllm-project#37883 Crash 2 ("b_scales is not on GPU") for NVFP4 MoE. Root cause ---------- UVAOffloader._maybe_offload_to_cpu replaces .data on a Parameter when moving it to CPU. In non-UVA fallback mode (WSL or any platform where pinned memory is not available) the forward wrapper later swaps the module's attributes via functional_call(module.state_dict()). External Python references to the Parameter -- e.g. FusedMoEQuantConfig.w1_scale cached by the NVFP4 MoE quant config at process_weights_after_loading time -- still resolve to the same Parameter object whose .data now lives on CPU. Marlin's GEMM (and any kernel that does TORCH_CHECK(x.is_cuda())) then crashes. In genuine UVA mode the cached reference resolves to a UVA-mapped CUDA view, so the bug does not appear there. Fix --- Two-part contract: 1. vllm/model_executor/offloader/uva.py adds an opt-in marker `_vllm_skip_offload`, following the existing `_vllm_*` Parameter attribute convention (`_vllm_is_uva_offloaded` is already used in this same module; `_vllm_patched` etc. live in patch_utils). The non-UVA fallback path of `_maybe_offload_to_cpu` honours the marker and leaves marked Parameters on their original device. The check is gated on `not self.uva_offloading`, so UVA-capable hosts still offload scales (no functional or memory regression). 2. compressed_tensors_moe_w4a4_nvfp4.py sets the marker on the two Parameters that FusedMoEQuantConfig.make(...) caches by reference (`layer.w13_weight_scale` and `layer.w2_weight_scale`) immediately after process_weights_after_loading produces them. The other cached scales are plain Tensor attributes (not Parameters), so the offloader does not traverse them and they need no marker. Opt-in is explicit; there is no name-based heuristic. Other quant schemes that hit the same cached-reference issue should set the marker at the analogous site in their own process_weights_after_loading. Per-tensor scales are typically <0.1% of weight bytes, so excluding them from offload has negligible memory impact. Tests ----- tests/quantization/test_uva_scale_skip.py (16 cases): - marker constant (name + `_vllm_` convention) - marker semantics (no marker / True / False; scale-looking names without marker are NOT auto-protected -- regression guard against reintroducing a substring fallback) - offloader integration in non-UVA mode (marked scales stay on GPU, unmarked module gets full offload, user --cpu-offload-params filter does not override marker, marker protects arbitrary names) - UVA mode gating (marked scales ARE offloaded in genuine UVA mode -- the marker is non-UVA-only) - NVFP4 producer side (source grep confirming the quant config sets the marker on the two cached scale Parameters) Signed-off-by: zemf4you <radimir@zemf4you.org>
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Summary
UVAOffloader._maybe_offload_to_cpureassigns.dataon aParameterto move it to CPU. In non-UVA fallback mode (any platform whereis_pin_memory_available()returnsFalse— WSL being the common case today), the offloader replaces the module'sforwardwith a wrapper that, on each call, gathersmodule.state_dict()and reapplies it viafunctional_call.The swap only updates attributes on the module itself. Python references held outside the module — most notably the per-tensor scale
Parameters cached byFusedMoEQuantConfigatprocess_weights_after_loading()time — still resolve to the sameParameterobject whose.datais now on CPU. Marlin's GEMM (and any kernel that doesTORCH_CHECK(x.is_cuda())) then crashes with:This is Crash 2 from #37883 and reproduces with any NVFP4 / FP8 / GPTQ MoE model +
--cpu-offload-gbon a non-UVA host.In genuine UVA mode the cached reference resolves to a UVA-mapped CUDA view (
.is_cuda == True), so the same code path works there.Fix
Two-part contract: a
Parametermarker in the offloader plus opt-in from the quant config that holds external references.1. Offloader (
vllm/model_executor/offloader/uva.py). New_VLLM_SKIP_OFFLOAD_ATTR = "_vllm_skip_offload"constant. The non-UVA fallback path of_maybe_offload_to_cpuhonours the marker:The name follows vLLM's existing
_vllm_*Parameter-marker convention (_vllm_is_uva_offloadedis set elsewhere in this same module;_vllm_patched/_vllm_fxgraph_dumps_patchedlive inpatch_utils). The check is gated onnot self.uva_offloadingso UVA-capable hosts still offload scales (no memory regression).2. NVFP4 MoE quant config (
compressed_tensors_moe_w4a4_nvfp4.py). Afterprocess_weights_after_loadingproduces the kernel-formatw13_weight_scaleandw2_weight_scaleParameters that get cached inself.moe_quant_config = self.get_fused_moe_quant_config(layer), set the marker:These are the two Parameters that
FusedMoEQuantConfig.make(...)keeps a reference to (asw13_scale/w2_scale); the other cached scales (w13_scale_2,w2_scale_2,a13_scale,a2_scale) are plainTensorattributes, which the offloader does not traverse.No naming heuristic. Opt-in is explicit. Other quant schemes that hit the same cached-reference issue should set the marker at the analogous site in their own
process_weights_after_loading.Per-tensor scales are typically <0.1% of weight bytes, so excluding them from offload has negligible memory impact.
Test plan
16 cases covering:
_vllm_prefix convention)True/False/ scale-looking names without marker are NOT auto-protected — regression guard against reintroducing a substring fallback)--cpu-offload-paramsfilter does not override the marker, marker protects arbitrary parameter names)Neighbouring regressions:
tests/quantization/test_turboquant.py(119/119 passed),tests/kernels/core/test_uva.py(skipped on hosts without UVA hw, same as before this PR).End-to-end repro
Originally discovered while trying to serve
RedHatAI/Qwen3.6-35B-A3B-NVFP4(35B-A3B MoE in NVFP4, compressed-tensors quant) with--kv-cache-dtype turboquant_4bit_ncon a single RTX 3090 (24 GB, SM 8.6) under WSL2. The model is ~17 GB of weights, the practical CUDA-accessible VRAM on this configuration is ~15 GB (WDDM holds the rest), so--cpu-offload-gbis mandatory. With non-UVA fallback active (WSL),process_weights_after_loadingruns successfully but the firstprofile_runforward crashes inside Marlin's NVFP4 MoE GEMM withb_scales is not on GPU.With this PR applied (no other workarounds — no
--cpu-offload-paramsfilter, no env overrides), the model loads, profile_run completes, and the OpenAI HTTP server answersPOST /v1/completionscorrectly:Related (not duplicate)
modelopt_mixedon Ampere via Marlin redirect. Different file, different code path; does not overlap with this PR.