[Bugfix] Stop baking NVFP4 weight_scale_2 into b12x MoE block scales by yichengj0 · Pull Request #48536 · vllm-project/vllm

yichengj0 · 2026-07-13T22:20:36Z

Purpose

The SM12x b12x MoE path (RTX Pro 6000 / DGX Spark) distorts NVFP4 weights at load time:

The FlashInfer kernel's w1_alpha does two jobs: FC1 activation-quant global scale and post-GEMM multiplier. The checkpoint's per-expert weight scale (weight_scale_2, ~2e-5) would wreck activation quantization if passed as w1_alpha, so the b12x experts class bakes it into the e4m3 weight block scales instead.
The bake pushes nearly all block-scale bytes into the 3-bit e4m3 subnormal range and noticeably perturbs the dequantized weights. Marlin applies the same scale in full precision and is unaffected.

FlashInfer added a separate input_global_scale argument for the activation-quant job (flashinfer-ai/flashinfer#3932, shipped in v0.6.17). With it, this PR:

Skips the bake and keeps the checkpoint's block scales as loaded. weight_scale_2 reaches the kernel as an exact fp32 multiplier; no extra wiring is needed because g1_alphas/g2_alphas already alias the scale_2 parameters.
Passes input_global_scale=1.0 so FC1 activation quantization behaves exactly as before.

Depends on flashinfer-ai/flashinfer#3932, which shipped in FlashInfer v0.6.17 — the version vLLM pins on main since #52681. The kernel call passes input_global_scale unconditionally; a FlashInfer build without it rejects the argument loudly at call time rather than producing wrong results.

Test Plan

End-to-end accuracy and token-efficiency evals on GB10 (SM121) with an NVFP4 MoE checkpoint, against the Marlin backend as baseline, identical serving settings across configs.
Weight-parity check on captured kernel inputs: the kernel receives the checkpoint's pristine block scales and exact per-expert alphas (verified via FlashInfer tensor dumps).

Test Result

With this patch plus the FlashInfer-side kernel fixes in flashinfer-ai/flashinfer#3932, the b12x W4A4 excess token usage vs Marlin drops substantially and accuracy stays on par. The weight-parity check passes.

🤖 Generated with Claude Code

github-actions · 2026-07-13T22:20:44Z

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…ale from w1_alpha The kernels use w1_alpha for two jobs: FC1 input-quantization global scale and post-GEMM multiplier. The checkpoint's tiny per-expert weight scale (~2e-5) cannot be passed directly, so integrators bake it into the e4m3 weight block scales, pushing nearly all scale bytes into the 3-bit subnormal range (~17% average weight distortion on Qwen3.6-35B-A3B-NVFP4). The new keyword-only argument takes over the input-quant job, so w1_alpha can carry the exact fp32 weight scale and the block scales stay as loaded. The dispatch folds input_global_scale into the epilogue multiplier internally (the kernel quantizes x/gs and multiplies by alpha only, so alpha must carry the factor); B12xMoEWrapper caches the folded per-expert tensor so nothing is allocated per call or inside CUDA graph capture. Defaults to the legacy dual-use behavior when omitted. Paired integrator change: vllm-project/vllm#48536. AI-assisted (Claude Code). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

…le to decouple weight and activation scales (#3932) ## 📌 Description b12x W4A4 serving on GB10/SM121 used far more reasoning tokens than Marlin at equal benchmark scores. The drift traces to two bugs in the quantization helpers and one API limitation: - The quantizer decoded very small (subnormal) e4m3 block scales with the wrong formula, up to 4.5x off, while the tensor core decodes the same byte correctly. - The precise quantization path (`fast_math=False`) inverted the pack multiplier and quantized everything to zero. The default fast path is unaffected, which is why it went unnoticed. - `w1_alpha` does two jobs: input-quantization scale and output multiplier. Integrators therefore cannot pass the checkpoint's tiny weight scale directly and must bake it into the e4m3 block scales, which distorts the weights. Fixes: - Decode e4m3 scale bytes with the hardware conversion instead of the manual formula. It is exact for every value, including subnormals, and cheaper. - Correct the precise-path pack multiplier to match the fast path. - Apply both fixes to the duplicated helpers in `moe_w4a16_fp4_helpers.py` (unused today, but a trap). - Add an optional `input_global_scale` argument that takes over the input-quantization job, so `w1_alpha` can carry the exact weight scale and the block scales stay as loaded. It is folded into the output multiplier internally; omitting it keeps the old behavior exactly. - Add `--b12x_quant_mode {nvfp4,w4a16}` to the b12x benchmark routine (previously W4A4-only) and record the mode in the CSV output. ## 🔍 Related Issues Paired integrator change: vllm-project/vllm#48536 uses `input_global_scale` to stop the weight-scale baking. It probes for the argument at runtime, so the two PRs can merge in either order. ## 🚀 Pull Request Checklist Thank you for contributing to FlashInfer! Before we review your pull request, please make sure the following items are complete. ### ✅ Pre-commit Checks - [x] I have installed `pre-commit` by running `pip install pre-commit` (or used your preferred method). - [x] I have installed the hooks with `pre-commit install`. - [x] I have run the hooks manually with `pre-commit run --all-files` and fixed any reported issues. ## 🧪 Tests - [x] Tests have been added or updated as needed. - [x] All tests are passing (`unittest`, etc.). On GB10 (SM121): - Existing `tests/moe/test_b12x_fused_moe.py` numeric, w4a16, and activation tests pass; they exercise the rewritten decode on every quantized block. - A `fast_math=False` check now matches the fast path to 0.5% relative error (all zero before the fix). - New test `test_input_global_scale_decouples_weight_alpha` covers the decoupled path and back-compat. - Fixed a flaky threshold in `test_functional_vs_wrapper_output`: both APIs run the same kernel, so the only difference is run-to-run noise, and the test now bounds it by measured noise instead of a fixed constant. ## Reviewer Notes - The default fast-math quantization path is numerically unchanged; only `fast_math=False` and subnormal scale-byte decoding change behavior. - The internal fold exists because the kernel divides the input by the scale and multiplies the output by `w1_alpha` only, so `w1_alpha` has to carry the scale back. The wrapper caches the folded tensor, so nothing is allocated per call or during CUDA graph capture. - The relaxed consistency threshold does not weaken accuracy coverage: that test only checks the two APIs agree, and correctness against references is covered by the other tests. 🤖 Generated with [Claude Code](https://claude.com/claude-code)  ## Summary by CodeRabbit - **New Features** - Added `--b12x_quant_mode` (nvfp4/w4a16) for B12x fused MoE benchmarking. - Added optional `input_global_scale` to control FC1 input quantization in functional, wrapper, and trace flows (shared or per-expert). - **Bug Fixes** - Improved FP8 E4M3→FP32 conversion using the hardware decode path, including safer reciprocal handling for zero. - Corrected FP4 quantization scaling/reciprocal computations. - **Benchmarks** - Added `cold_l2_cache` to benchmark outputs; CUDA graph runs adjust it when using `w4a16`. - **Tests** - Added numeric regression for `input_global_scale` decoupling and wrapper folding/cache behavior; relaxed FP4 nondeterminism-sensitive comparisons.  --------- Co-authored-by: Claude Fable 5 <noreply@anthropic.com>

The SM12x b12x MoE path bakes the per-expert weight global scale (weight_scale_2, ~2e-5) into the e4m3 weight block scales because the kernel's w1_alpha doubles as the FC1 activation-quant global scale and cannot carry the tiny value directly. The bake pushes nearly all block scale bytes into the 3-bit e4m3 subnormal range and perturbs the dequantized weights by ~17% on average vs applying weight_scale_2 in float (measured on Qwen3.6-35B-A3B-NVFP4); Marlin applies the scale in full precision and is unaffected. FlashInfer's b12x API gained a separate input_global_scale argument (flashinfer-ai/flashinfer#3932) that decouples the activation-quant scale from w1_alpha. Drop the bake: block scales stay as loaded, weight_scale_2 reaches the kernel as an exact fp32 w1_alpha/w2_alpha (g1/g2_alphas already alias the scale_2 parameters), and FC1 activation quantization keeps global scale 1.0 via the new argument. Requires a FlashInfer release containing input_global_scale; land together with the flashinfer-python pin bump. On GB10 (SM121) with Qwen3.6-35B-A3B-NVFP4, combined with the FlashInfer-side kernel fixes, this cuts the b12x W4A4 excess reasoning tokens vs Marlin from +18.7% to +6.2% on GPQA and from +46% to +14% on SciCode. AI-assisted (Claude Code). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Signed-off-by: yichengj0 <yichengj@nvidia.com>

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mergify Bot added nvidia bug Something isn't working labels Jul 13, 2026

github-project-automation Bot added this to NVIDIA Jul 13, 2026

yichengj0 force-pushed the b12x-unbake-weight-scale-2 branch from bbe2754 to 73a1251 Compare July 13, 2026 22:35

yichengj0 changed the title ~~[Bugfix] Stop baking NVFP4 weight_scale_2 into b12x MoE block scales when FlashInfer supports input_global_scale~~ [Bugfix] Stop baking NVFP4 weight_scale_2 into b12x MoE block scales Jul 13, 2026

yichengj0 mentioned this pull request Jul 16, 2026

fix(b12x): correct fp4 quantization numerics and add input_global_scale to decouple weight and activation scales flashinfer-ai/flashinfer#3932

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dumko2001 mentioned this pull request Jul 22, 2026

[Bugfix] Share FlashInfer B12x MoE workspace across layers #48698

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robbiemu mentioned this pull request Jul 22, 2026

[FlashInfer] Add SM12x b12x NVFP4 MoE support (Laguna S 2.1 + DFlash working in NVFP4 on SM121) sgl-project/sglang#32119

Open

yichengj0 force-pushed the b12x-unbake-weight-scale-2 branch from 73a1251 to fd22137 Compare August 20, 2026 01:57

yichengj0 marked this pull request as ready for review August 20, 2026 01:57

yichengj0 requested review from mgoin, pavanimajety and zyongye as code owners August 20, 2026 01:57

claude Bot reviewed Aug 20, 2026

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[Bugfix] Stop baking NVFP4 weight_scale_2 into b12x MoE block scales - #48536

[Bugfix] Stop baking NVFP4 weight_scale_2 into b12x MoE block scales#48536
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