Fix the E8M0 scale computation in the MXFP4 (W4A4) MOE CUTLASS kernel

[xin3he]

Purpose

Fix the E8M0 scale computation in the MXFP4 (W4A4) MOE CUTLASS kernel, which
currently produces severely incorrect quantization results.

Related PR: #37463

Root Cause

The original code computes the E8M0 scale factor via:

// Old (buggy):
float SFValue = vecMax * reciprocal_approximate_ftz(6.0f);  // vecMax / 6.0
uint32_t tmp = reinterpret_cast<uint32_t&>(SFValue) >> 23;
fp8SFVal = tmp & 0xff;  // scale_exp = biased_exponent(vecMax / 6.0)

This is mathematically wrong because:

floor(log2(a / b)) != floor(log2(a)) - floor(log2(b))

Dividing by 6.0 before extracting the exponent shifts the mantissa boundary,
causing the resulting exponent to be 1 too small for a large fraction of inputs.
When the scale is too small, vecMax / scale far exceeds 6.0 (the E2M1 max),
and the block-max value saturates -- destroying information.

vecMax	Old: biased_exp(vecMax/6)	Correct: biased_exp(vecMax)-2	Ratio (old)	Ratio (fixed)
2.7	125 (scale=0.25)	126 (scale=0.5)	10.8	5.4
5.5	126 (scale=0.5)	127 (scale=1.0)	11.0	5.5
1.2	124 (scale=0.125)	125 (scale=0.25)	9.6	4.8

Fix: Exponent-First with 0.75-Threshold Rounding

The new implementation extracts the exponent directly from vecMax, applies a
rounding bias, then subtracts 2:

uint32_t max_bits = __float_as_uint(vecMax);
// Round up exponent when mantissa >= 0.75
uint32_t rounded_bits = (max_bits + (1u << 21)) & 0xFF800000u;
uint32_t biased_exp = (rounded_bits >> 23) & 0xFFu;
uint32_t scale_exp = (biased_exp > 2u) ? (biased_exp - 2u) : 0u;

Origin: Matching the Quantization Toolchain

This rounding scheme (VAL_TO_ADD = 1 << (mantissa_bits - 2)) is used by upstream quantization libraries:

• compressed-tensors (quantization/utils/mxfp_utils.py):

  VAL_TO_ADD = 1 << (mantissa - FP4_E2M1_DATA.mantissa - 1)  # 1<<21 for fp32
  block_max_uint = torch.bitwise_and(x + VAL_TO_ADD, SIGN_EXPONENT_MASK)
  scale_exp = 127 + torch.floor(torch.log2(block_max)) - 2

The CUDA kernel must match these implementations to ensure consistency
between offline weight quantization and online activation quantization.

Why This Helps Precision (Not Strict OCP MX)

This is not the strict OCP MX spec. The differences:

Approach	Formula	Clipping zone	Behavior
Strict OCP MX	biased_exp(vecMax) - 2	mantissa in [0.5, 1.0) -> ratio in [6, 8)	50% of max values clip
This fix (threshold 0.75)	biased_exp(round_0.75(vecMax)) - 2	mantissa in [0.5, 0.75) -> ratio in [6, 7)	25% of max values clip
Threshold 0.5 (1<<22)	biased_exp(round_0.5(vecMax)) - 2	never clips	scale often too large

The 0.75 threshold is a precision-optimized tradeoff:

1. Avoids premature scale increase: For mantissa in [0.5, 0.75), the code
   keeps the smaller scale rather than doubling it. If the scale doubles, ALL
   values in the 16-element block lose half their effective resolution.

2. Tolerates mild clipping of block-max: The ratio falls in [6.0, 7.0),
   so vecMax clips to 6.0 -- at most ~14% error on a single value. This is a
   better tradeoff than halving the precision of all 16 values.

3. Prevents extreme clipping: For mantissa >= 0.75 (ratio would be >= 7.0),
   the exponent rounds up, preventing excessive saturation.

In summary: the dynamic range is effectively extended to [0, 7*scale) with
graceful saturation at 6.0, avoiding the scale growing too early which
would compress the representable range for the entire block.

Other Fixes

• Input contiguity: Added .contiguous() for FlashInfer quantization path
  to prevent incorrect results from non-contiguous tensors.

Test Plan

• UT:

CUDA_VISIBLE_DEVICES=7 .venv/bin/python -m pytest tests/kernels/moe/test_mxfp4_moe.py -v

• Real model:
  Verified on Kimi MXFP4 model
  https://huggingface.co/INCModel/Kimi-K2.5-MXFP4-AutoRound

Test Result

Command (B200):

CUDA_VISIBLE_DEVICES=4,5,6,7 lm_eval --model vllm     --model_args pretrained=/workspace/models/moonshotai/Kimi-K2.6,tensor_parallel_size=4,data_parallel_size=1,max_model_len=8192,trust_remote_code=True --tasks gsm8k,mmlu,piqa,hellaswag  --batch_size auto

lm_eval on B200 (Kimi-K2.6, TP=4):

Config	gsm8k	mmlu	piqa	hellaswag	avg
FP16 weights (W4A16)	0.948	0.889	0.852	0.773	0.866
MXFP4 W4A4 (old, broken)	0.809	0.808	0.809	0.653	0.770
MXFP4 W4A4 (this fix)	0.921	0.874	0.842	0.744	0.845

Recovers ~78% of the quantization accuracy loss.

cc @dsikka @mgoin @yiliu30 @wenhuach21 @thuang6 @hshen14

---

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[gemini-code-assist]

Code Review

This pull request updates the MXFP4 quantization logic to align with the OCP MX specification, specifically implementing E8M0 scale computation and ensuring bit-exact scaling through exact division. It also adds extensive tests for scale correctness, reconstruction accuracy, and saturation rates, and ensures input contiguity in the FlashInfer quantization path. Feedback suggests that the rounding threshold for E8M0 scale computation is suboptimal and identifies a scaling mismatch in the NVFP4 path where the scale factor is not updated to its dequantized value.

[hshen14]

@dsikka @mgoin Please help review. Thanks.

[jikunshang]

can you run this case provided by #37463

vllm serve nm-testing/Qwen3-30B-A3B-MXFP4A16
python tests/evals/gsm8k/gsm8k_eval.py

[mergify]

Hi @xin3he, the pre-commit checks have failed. Please run:

uv pip install pre-commit>=4.5.1
pre-commit install
pre-commit run --all-files

Then, commit the changes and push to your branch.

For future commits, pre-commit will run automatically on changed files before each commit.

Tip

Is mypy failing?

mypy is run differently in CI. If the failure is related to this check, please use the following command to run it locally:

# For mypy (substitute "3.10" with the failing version if needed)
pre-commit run --hook-stage manual mypy-3.10

[xin3he]

Thanks @jikunshang , Here is the additional test:

vllm serve nm-testing/Qwen3-30B-A3B-MXFP4A16
python tests/evals/gsm8k/gsm8k_eval.py

Results:
Accuracy: 0.857
Invalid responses: 0.000
Total latency: 21.832 s
Questions per second: 60.415
Total output tokens: 154938
Output tokens per second: 7096.714

vs results of the original w4a4 kernel from #37463

Results:
Accuracy: 0.835
Invalid responses: 0.000
Total latency: 24.276 s
Questions per second: 54.333
Total output tokens: 150954
Output tokens per second: 6218.226

[jikunshang]

thanks, please fix pre-commit issue.

[xin3he]

@brian-dellabetta Here I have submitted a PR aimed at resolving the MXFP4 precision issues related to LLMC. I would be very grateful if you could assist in moving this PR forward.

[jikunshang]

LGTM since accuracy got improved.
@mgoin @yewentao256 @zyongye please take a look, thanks!

[xin3he]

Seems like those failures are not related to this PR.

[xin3he]

@jikunshang
I'd like to get your help about the CI failure. There are two failures:

• amd-entrypoints-integration-api-server-openai it passes in my local env.
• kernels-fusedmoe-layer-test-2-b200s It seems like a timeout issue.

I had merged main to trigger the CI many times to avoid the random issue, but the random issues continue happens.
The failures are different in each re-run.

[mgoin]

Great work!