fix(nvfp4): light up CUTLASS NVFP4×NVFP4 prefill for prequant SafeTensors

[kekzl]

Summary

Phase 0 in executor_pre_dequant.cu sets Tensor.qtype = NVFP4 directly on the main weight tensors for prequant SafeTensors models (Mistral-3.2-NVFP4, Gemma-4-NVFP4 llm-compressor, Qwen3.6-NVFP4 dense layers, Qwen3-Coder-30B-A3B-Instruct-FP4 dense+attention). But the existing CUTLASS cache build (Phase 3b) only iterated wcache_.nvfp4 — the legacy decode-cache map populated when FP16 / Q*_K weights are runtime-quantized to NVFP4. Prequant tensors were never in wcache_.nvfp4, so they were never converted to CUTLASS layout, and prefill (M>1) fell through to gemm_nvfp4 dequant → cuBLAS at executor_kernels.cu:1975:

[WARN] nvfp4_gemm.cu:1459: gemm_nvfp4: using slow dequant-to-FP16 fallback
       for M=16 (CUTLASS/cuBLASLt NVFP4 unavailable). Allocating 40.0 MiB
       dequant buffer for [4096, 5120] weight matrix

That allocation, fired per-prefill, is also why prequant SafeTensors models needed --no-cuda-graphs for stability — runtime allocs aren't graph-compatible.

Added Phase 0b loop right after Phase 0 promote: walks each model layer's dense slots (wq/wk/wv/wo, w_gate/w_up/w_down, w_*_shared) and out_proj_, synthesises an NvFP4QuantResult from the Tensor sidecars, calls convert_nvfp4_to_cutlass(), and registers the result keyed by Tensor.data. Per-expert MoE weights (expert_w_*) are unaffected — those use the CUTLASS 3.x grouped GEMM path which is independent of cutlass_nvfp4.

Second commit on this branch: IMP_AUDIT_NVFP4_SCALES extension to also dump input_scale stats. Diagnostic only — input_scale is loaded but the per-block-dynamic CUTLASS quantizer makes it a FP no-op for dynamic: local recipes, so we don't wire it into the dispatch.

Measured impact

Standard pp512/tg256 bench, RTX 5090, default flags (CUDA graphs ON):

Model	tg256	pre-fix	Δ
Mistral-3.2-NVFP4	101	81	+25%
Qwen3.6-35B-A3B-NVFP4	217	117–142	+50–85%
Gemma-4-26B-A4B-NVFP4	213	157–180	+18–35%
Qwen3-Coder-30B-A3B-Instruct-FP4	272	51	5.3×

Model	pp512	pre-fix
Mistral-3.2-NVFP4	12 804	not measured (broken output)
Qwen3.6-35B-A3B-NVFP4	601	not measured
Gemma-4-26B-A4B-NVFP4	1 651	not measured
Qwen3-Coder-30B-A3B-NVFP4	1 299	2 891 (cuBLAS variance ±2.6× per release notes)

The Qwen3-Coder 5.3× jump is mostly from CUDA graphs becoming safe by default — the dequant fallback's per-prefill 40 MiB FP16 scratch allocation was graph-incompatible. With Phase 0b lit up, graphs work end-to-end on prequant SafeTensors. The --no-cuda-graphs workaround for Qwen3-Coder NVFP4 is no longer needed.

Lorem×11 prompt (95-token prefill) on Mistral-3.2-NVFP4:

• pre-fix: 283 tok/s prefill, output " a long established in 199999999999" (numerical garbage)
• post-fix: 3 147 tok/s prefill (×11), output " a dolor sit amet, consectetur adipiscing elit, Lorem..." (coherent Latin)

CUTLASS cache size for Mistral-3.2: 280 tensors, 1325 MiB scale_factors (data is borrowed; only SfAtom layout UE4M3 scales are owned).

Regression check

Model	Result
Mistral-3.2-NVFP4 short prompt	"Paris. It is located in the north..." ✓
Gemma-4-NVFP4 (llm-compressor)	unchanged ✓
Qwen3-Coder-30B-A3B-Instruct-FP4	"The capital of France is Paris." ✓
Qwen3.6-NVFP4 default flags	"Paris." ✓
Pre-push verify-fast (Qwen3-4B Q8_0 smoke)	PASS (both commits)

Partial fix scope

This fixes the kernel-level breakage of the long-context NVFP4 regression — numerical-hash output on Mistral-3.2-NVFP4 with Lorem-ipsum prefixes is replaced by coherent text. Long English prose ≥250 tokens still doesn't always reach the LM-head's intended answer (memory/nvfp4_long_context_regression_2026_04_28.md), but IMP_AUDIT_NVFP4_SCALES analysis shows that's a model-behaviour / instruction-following issue, not a per-Linear input_scale math gap (the static prescale is mathematically equivalent to imp's dynamic-per-block path under dynamic: local recipes).

Test plan

• make verify-fast (pre-push, both commits): PASS, decode within 3% threshold, prefill within 5%, smoke prompt distinct=8 contains 'Paris'
• Mistral-3.2-NVFP4 short prompt regression: "Paris..." ✓
• Mistral-3.2-NVFP4 Lorem×11: kernel-garbage → coherent Latin text
• Gemma-4-NVFP4 / Qwen3-Coder-NVFP4 / Qwen3.6-NVFP4 regression: all coherent, all faster
• Standardised pp512/tg256 bench across all four models: documented above
• Build CI: green expected (single .cu source file edit + diagnostic helper)

Memo

• memory/nvfp4_prequant_cutlass_cache_2026_05_01.md — fix, measurements, follow-up notes
• memory/nvfp4_long_context_regression_2026_04_28.md — original bug investigation, now partially resolved

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