dflash: pass missing NVFP4 scales to attention operations

[JamePeng]

I tested the DFlash2 draft model provided by the https://huggingface.co/maurienne-ai/Qwen3.8-27B-DFlash2-NVFP4-RTNcal project. I found that the DFlash2 BF16 and Q8 versions performed normally, but the DFlash2 NVFP4 draft models produced almost no accepted speculative tokens because the Q, K, V, and output projection scales were not passed to the corresponding graph operations. I eventually traced the issue to a missing scale parameter pass in dflash.cpp; adding the missing code allowed it to compile and run correctly.

Overview

Pass the missing NVFP4 weight scales to the DFlash graph operations:

• K and V projections in KV injection
• Q, K, and V projections in decoder attention
• Output projections in both attention paths

Without these scales, the NVFP4 draft model generated incorrect draft tokens, resulting in a near-zero acceptance rate and slower speculative decoding.

Additional information

Benchmark configuration:

• GPU: NVIDIA GeForce RTX 5090
• Target model: Qwen3.8-27B-Q5_K_M
• Draft model: Qwen3.8-27b-DFlash2-NVFP4
• Full GPU offload and flash attention
• Greedy sampling with a fixed seed
• Maximum draft length: 7 tokens
• Single run with a fixed prompt

Generated tokens	Master PR	Before acceptance	After Patch	After acceptance	DFlash improvement
128	34.4 t/s	0.12%	93.1 t/s	36.33%	2.71x
512	35.7 t/s	0.17%	97.6 t/s	25.59%	2.73x
1024	38.4 t/s	0.39%	119.0 t/s	33.10%	3.10x

Compared with ordinary decoding from the patched build, DFlash achieved speedups of 1.65x, 1.62x, and 1.93x for 128, 512, and 1024 generated tokens, respectively.

Requirements

• I have read and agree with the contributing guidelines
• AI usage disclosure: Codex + Local Agent Tool

Logs

Master PR(10689)
master pr.txt

Patched PR
after_patch.txt