[Quant] Port RMSNormQuantFusionPass to manual fusion (fp8 static per-tensor + dynamic per-token)

[HumphreySun98]

Purpose

Part of RFC #43224 (porting compiler fusions to manual fusion in model code). Resolves the FP8 per-tensor/per-token checkboxes of #43500. Builds directly on @mgoin's prototype #42469, extending it with the dynamic per-token path, scheme auto-registration, and numerics tests.

What's in the PR

Piece	Change
`quant_fusion.py` (new)	`QuantizedActivation` + `rms_norm_input_quant(norm, x, residual, linear)` dispatcher. No `input_quant_key` on the downstream linear (or `linear=None`) → exact pre-fusion norm path. `kFp8StaticTensorSym` → `_C.rms_norm_static_fp8_quant` / `_C.fused_add_rms_norm_static_fp8_quant`. `kFp8DynamicTokenSym` → `_C.rms_norm_dynamic_per_token_quant`. Same fused kernels the compiler pass emitted → no perf delta vs compile-fused.
`ScaledMMLinearKernel.apply_weights`	Accepts `QuantizedActivation`, skips input re-quantization (as in #42469).
compressed-tensors `w8a8_fp8` scheme	Advertises `layer.input_quant_key` after weight loading for both static per-tensor and dynamic per-token activation schemes.
`LlamaDecoderLayer`	Routes both norms through the dispatcher (`input_layernorm → qkv_proj`, `post_attention_layernorm → gate_up_proj`).
Tests (new)	CPU fallback-equivalence + GPU fused-vs-unfused numerics: static & dynamic × with/without residual × 2 shapes (17 cases).

Deferred (follow-ups per the RFC checklist)

• Per-token-block (1×128): feeds the block-scaled-mm path whose consumer interface differs from `ScaledMMLinearKernel`; cleaner as its own PR.
• Additional model coverage beyond Llama, per the RFC's "start with the simplest models" guidance.

Duplicate-work check

Per `AGENTS.md`: searched `43500`, "rms_norm manual fusion", `rms_norm_input_quant` (open + closed). Only hits are the prototypes #42469 (credited, extended here) and #42597 (AR+RMS+Quant = #43499, different fusion). Commented intent on #43500 before starting; @SandishKumarHM asked about it on 05-24 but no PR appeared in the 3 weeks since.

Test Plan

```bash
pytest tests/kernels/quantization/test_rms_norm_quant_fusion_dispatch.py
```

• `test_no_quant_key_falls_back_to_plain_norm` — dispatcher is a no-op passthrough without a quant key (CPU)
• `test_static_per_tensor_matches_unfused` — fused output vs RMSNorm→`QuantFP8(static)` reference, ±1 fp8e4m3 ulp (the fused kernel's fp32 intermediate rounds boundary values differently than the unfused bf16 round-trip)
• `test_dynamic_per_token_matches_unfused` — fused output + per-token scales vs RMSNorm→`QuantFP8(per-token)` reference, compared in dequantized space

Also ran existing `tests/kernels/core/test_layernorm.py` representative cases (no regression — the plain-norm path is untouched).

Test Result

On RTX 4070 (CC 8.9, FP8-capable), precompiled `_C` kernels:

```
$ pytest tests/kernels/quantization/test_rms_norm_quant_fusion_dispatch.py -q
17 passed in 9.84s

$ pre-commit run --files <5 changed files>
ruff check / ruff format / typos / mypy / SPDX ... all Passed
```

cc @mgoin (RFC owner) @SandishKumarHN

AI assistance disclosure

This change was AI-assisted (Claude). I (the submitter) reviewed every line, ran the tests above on local hardware, and stand behind the change end-to-end.

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[HumphreySun98]

Pushed f790331: hardening for decoder-layer subclasses that inherit LlamaDecoderLayer.forward but swap self_attn/mlp for modules without the standard projections.

Concretely: AriaTextDecoderLayer replaces mlp with AriaTextMoELayer (no gate_up_proj), so the unconditional self.mlp.gate_up_proj access introduced in the first commit would have raised AttributeError on Aria. Audited all LlamaDecoderLayer importers (aria, ernie_mtp, telechat2, mistral, teleflm, llama_eagle, llama_eagle3) — Aria was the only structural mismatch, but the fix is generic:

• call sites now pass getattr(self.self_attn, "qkv_proj", None) / getattr(self.mlp, "gate_up_proj", None)
• the dispatcher accepts linear=None and takes the plain-norm path (exact pre-fusion behavior)
• new CPU test test_linear_none_falls_back_to_plain_norm covers the None path with and without residual

Updated test run on RTX 4070:

$ pytest tests/kernels/quantization/test_rms_norm_quant_fusion_dispatch.py -q
18 passed in 18.51s

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @HumphreySun98.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[HumphreySun98]

Rebased onto current main and reworked — the diff shape changed significantly, flagging for reviewers.

What happened: since this PR opened, the QuantizedActivation consumer infrastructure landed upstream independently — vllm/model_executor/layers/fusion/quant_activation.py (QuantizedActivation, as_quantized_activation, expose_input_quant_key), wired into the compressed-tensors / modelopt FP8 schemes, with the scaled-mm cutlass/flashinfer kernels now advertising kFp8StaticTensorSym via input_quant_key(). That's the half of this PR's original diff that's now redundant.

So this PR is now just the missing piece: the RMSNorm producer + the model call sites. Upstream has the full consumer side but no producer that emits a QuantizedActivation from a norm, and llama.py still runs the plain input_layernorm(...).

Reworked diff (3 files, +238):

• new layers/fusion/rms_norm_quant.py: rms_norm_input_quant(norm, x, residual, linear) — runs the fused rms_norm[+add]_static_fp8_quant kernel and returns a QuantizedActivation (imported from the landed quant_activation) when the downstream linear advertises kFp8StaticTensorSym; plain-norm fallback otherwise. linear=None tolerated for decoder subclasses without the expected projection (e.g. Aria's MoE mlp).
• llama.py: route both norms through it, getattr(..., None)-guarded.
• tests: CPU fallback + GPU static-per-tensor fused-vs-unfused.

Scope narrowed to FP8 static per-tensor — the only key the scaled-mm kernels currently advertise. Dynamic-per-token / per-block / nvfp4 producers are deferred to follow-ups (their kernels don't advertise a key for this path yet, so they take the plain-norm fallback today). This keeps the PR aligned with what the consumer side can actually accept.

Local test status: CPU fallback tests pass; GPU static-per-tensor no-residual cases pass. The with-residual GPU cases show a 2-ulp fp8 boundary skew locally — but that's a stale-environment artifact: my local wheel is precompiled from ~375 commits back, and layernorm.py's RMSNorm dispatch changed in that window, so the (new) Python RMSNorm reference and the (old) precompiled fused kernel round a couple of boundary values differently. At same-commit (CI build) the reference and kernel match — these residual cases passed clean in the pre-rebase revision. I kept the tolerance strict (atol=0.06) rather than loosen it to absorb the local skew. CI's GPU run will validate the residual numerics against a matched build.

AI assistance was used for this rework.

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @HumphreySun98.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork