[Bugfix][Quantization] Reject NVFP4 checkpoints with missing global scales (linear + MoE) - #54444
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…cales (linear + MoE) Rebased onto the vllm-project#49381 ModelOpt LinearMethod redesign. NVFP4 per-tensor global scales (weight_scale_2 / input_scale) were allocated with torch.empty and never validated, so a checkpoint that omits one folds uninitialized memory into the dequant math and silently corrupts the layer instead of failing. Linear: the generic ModelOptLinearMethod builds NVFP4 weights via the KNvfp4Static / KNvfp4Dynamic QuantKey schemes. NaN-init their global scales and reject any scale still carrying NaN / zero / non-finite values in process(), before it is folded into the runtime global scale. W4A16 only builds the weight scheme (activation key is None), so only weight_scale_2 is required there; the input scale is dropped as before. MoE: ModelOptNvFp4FusedMoE gets the same NaN-init plus a per-expert check that validates only the scales the selected backend consumes. HUMMING and MARLIN drop the input scales (and MARLIN serves the W4A16 MoE path whose checkpoints legitimately omit them), so only the weight global scales are required for them; every other (W4A4) backend folds both input scales in. Signed-off-by: Aditya Jha <4adityajha@gmail.com>
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Diff is one file, +68/-7. Ready for another look @mgoin @yewentao256. |
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WalkthroughThe ModelOpt NVFP4 MoE and linear paths now initialize global scales with NaN and reject missing, zero, or non-finite scales before processing. ChangesNVFP4 scale validation
Estimated code review effort: 3 (Moderate) | ~20 minutes Merge Risk: ⚪ Minimal · up to NVFP4 loading now fails early for missing, zero, or non-finite consumed scales, preventing invalid checkpoint data from silently affecting inference. The change is ready to merge. 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
✨ Finishing Touches 💡 1🛠️ Fix failing CI checks 💡
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pavanimajety
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I don't believe this is the right approach. We should ideally throw an error when the checkpoint doesn't have a parameter that the params_dict expects.
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Purpose
ModelOpt NVFP4 allocates its per-tensor global scales with
torch.emptyon both the linear methods (ModelOptNvFp4LinearMethod,ModelOptNvFp4W4A16LinearMethod) and the fused experts (ModelOptNvFp4FusedMoE). If a checkpoint never populates one of those scales, the parameter keeps whatever happened to be in that memory.process_weights_after_loadingthen folds the leftover value straight into the dequant math, thealphaon the W4A4 path, the Marlin global scale on W4A16, or the per-expert alphas in the MoE path. When that value lands on zero or a non finite number the layer is silently corrupted and the model produces garbage, with nothing raised at load time to point at the cause.This covers both halves of that class of issue in one place, the linear methods and the fused experts, so an incomplete NVFP4 checkpoint fails loudly at load instead of quietly.
What changes
torch.empty, on both the linear methods and the four MoE per-expert scales, so a scale the checkpoint never loaded is detectable rather than arbitrary.ValueErroris raised, naming the parameter and the affected output partitions (linear) or expert ids (MoE), when it is zero or non finite. On linear this lives inprocess_weights_after_loading, on MoE in a new_validate_loaded_expert_scalescalled from the same place.input_scaleis left alone because it is a placeholder that is always discarded before the kernel runs and is legitimately absent for weight only checkpoints. On the MoE side the weight only backends (HUMMING, MARLIN) never read an input scale, and FLASHINFER_B12X folds w2 differently, so validating those would reject valid inputs.The MoE side is carried over from #45320 by Wei-Cheng Chiu, credited as a co-author on the commit. Consolidating both sides here keeps the two methods from drifting and avoids two PRs touching the same file.
Test
No unit test is added. The changes are small and confined to the load path. I exercised the validation predicates directly on CPU: well formed scales pass, and forcing a scale to zero, NaN, or inf raises with the expected parameter and partition or expert ids reported, for both the linear methods and each MoE backend's consumed-scale set.
This PR was written with AI assistance. I reviewed every changed line, confirmed the consumed-scale exclusions against each code path, and take responsibility for the contribution.