feat(moe): add remaining CUTLASS unified MoE runners

docs/design_docs/flashinfer_moe_api.md

@@ -42,18 +42,19 @@ config = MoEConfig(
     ),
     quant=QuantConfig(QuantDtype.FP4, QuantGranularity.BlockScale),
     experts=ExpertConfig(intermediate_size=2048, local_num_experts=32),
-    backends=[TrtllmFp4Config(extra_backend_params...), CutlassConfig(extra_backend_params...)],
+    backends=[TrtllmFp4Config(extra_backend_params...), CutlassNvfp4Config()],
 )
 # --- Find possible backends ---
 backends = MoELayer.find_backends(**config)
-# this contains {"trtllm_fp4":TrtllmFp4Config(), "cutlass_fp4":CutlassConfig()}
-# or {"trtllm_fp4":"unsupported reason...", "cutlass_fp4":CutlassConfig()}
+# this contains {"trtllm_fp4":TrtllmFp4Config(), "cutlass_nvfp4":CutlassNvfp4Config()}
+# or {"trtllm_fp4":"unsupported reason...", "cutlass_nvfp4":CutlassNvfp4Config()}
 # more modification to the backends' parameters could be done here
-backends=["trtllm_fp4":TrtllmFp4Config(extra_backend_params...),"cutlass_fp4":CutlassConfig(extra_backend_params...)]
+backends=["trtllm_fp4":TrtllmFp4Config(extra_backend_params...),"cutlass_nvfp4":CutlassNvfp4Config()]
 # --- Prepare Inputs Data ---
 weight_pack = MoEWeightPack()
 # the data is possibly obtained through helper functions then added here
-weight_pack.prepare_for("trtllm_fp4", trtllm_weights) weight_pack.prepare_for("cutlass_fp4", cutlass_weights)
+weight_pack.prepare_for("trtllm_fp4", trtllm_weights)
+weight_pack.prepare_for("cutlass_nvfp4", CutlassNvfp4Config.prepare_weights(...))
 act_pack = MoEActivationPack(
     hidden_states_q=cute_dsl_data["x"],
     hidden_states_scale=x_sf,
@@ -120,9 +121,8 @@ Individual backend configs provided in an ordered list. The autotuner or heurist
 # Single backend
 backends = [TrtllmFp4Config()]
 # Multiple candidates — autotuner or heuristic picks best
-backends = [TrtllmFp4Config(), TrtllmFp8BlockConfig(), CutlassConfig()]
+backends = [TrtllmFp4Config(), CutlassNvfp4Config()]
 # | is associative, returns BackendOptions
-# CutlassConfig is always the universal fallback
 ```
 
 Each backend config declares its own preconditions:
@@ -131,11 +131,11 @@ Each backend config declares its own preconditions:
 class TrtllmFp4Config:
     @classmethod
     def supported(cls, arch: int) -> bool:
-        return arch >= 90  # Hopper+
-class CutlassConfig:
+        return arch in (100, 103, 107)
+class CutlassNvfp4Config:
     @classmethod
     def supported(cls, arch: int) -> bool:
-        return True  # universal fallback
+        return arch in (100, 103, 107, 110, 120, 121)
 ```
 
 ### 3.3 MoEConfig — \*\*unpack protocol
@@ -147,7 +147,7 @@ config = MoEConfig(
     routing=RoutingConfig(num_experts=256, top_k=8, method=RoutingMethodType.DeepSeekV3),
     quant=QuantConfig(QuantDtype.FP4, QuantGranularity.BlockScale),
     experts=ExpertConfig(intermediate_size=2048, local_num_experts=32),
-    backends=[TrtllmFp4Config(), CutlassConfig()],
+    backends=[TrtllmFp4Config(), CutlassNvfp4Config()],
 )
 # Unpack into any call accepting these kwargs
 output = moe_layer(tensors, **config)
@@ -258,7 +258,7 @@ repro.benchmark_all()
 # FinalizeConfig    0.11ms
 # Total             3.13ms
 # Isolate backend to narrow regression
-repro.isolate_backend(CutlassConfig())
+repro.isolate_backend(CutlassNvfp4Config())
 repro.isolate_backend(TrtllmFp4Config())
 ```
 
@@ -297,7 +297,7 @@ flashinfer/
       __init__.py     # BACKEND_REGISTRY, DEFAULT_PRIORITY
       trtllm_fp4.py   # TrtllmFp4Config + adapter
       trtllm_fp8.py   # Fp8Block + Fp8PerTensor + adapters
-      cutlass.py      # CutlassConfig + adapter
+      cutlass.py      # CutlassBf16 / W4A16 / Nvfp4 / Fp8 / Mxfp8 / W4A8 / Humming + adapters
     repro.py          # MoERepro
     tensors.py        # MoETensors, Gemm1Tensors, Gemm2Tensors
 ```
@@ -689,7 +689,8 @@ out = layer(act, weights)            # subsequent calls: cached winner dispatch
 Key mechanisms (and where they live):
 
 - **Two packs, two lifetimes.** `MoEWeightPack` holds long-lived, backend-native weight materializations keyed by `backend_key` (`prepare_for` / `get_view`); `MoEActivationPack` carries per-call pre-routed activations. This is the concrete answer to reviewers' "backends need different weight preprocessing" concern (C29–C32): each backend stores its own view, none is hidden from the caller.
-- **First-class prep.** `TrtllmFp4Config.prepare_weights(...)` / `CuteDslConfig.prepare_weights(...)` (backed by `flashinfer/fused_moe/prepare.py`) turn canonical bf16 weights into the native views (C6/C7).
+- **First-class prep.** `TrtllmFp4Config.prepare_weights(...)` / `CuteDslConfig.prepare_weights(...)` / `CutlassNvfp4Config.prepare_weights(...)` (and the other quant-specific `Cutlass*Config.prepare_weights` helpers, backed by `flashinfer/fused_moe/prepare.py`) turn canonical bf16 weights into the native views (C6/C7). CUTLASS NVFP4 uses swizzled `fp4_quantize` scales, not the TRTLLM shuffle / BlockMajorK path. CUTLASS FP8 / MXFP8 / W4A8 / Humming likewise keep unshuffled or mixed-input layouts distinct from TRTLLM. Each quant mode uses its matching `Cutlass*Config` / `Cutlass*Runner`; there is no quant-neutral CUTLASS fallback.
+- **Breaking change — `CutlassConfig` removed.** The deprecated, unregistered `CutlassConfig` placeholder is gone. It was never a runnable `MoELayer` backend (`supported()` always returned false; it was not in `_BACKEND_RUNNERS`). Import, annotate, serialize, or feature-detect a quant-specific type instead (`CutlassBf16Config`, `CutlassNvfp4Config`, `CutlassFp8PerTensorConfig`, `CutlassFp8BlockConfig`, `CutlassMxfp8Config`, `CutlassMxfp8Mxfp4Config`, `CutlassW4A16Config`, `CutlassW4A8Config`, `CutlassHummingConfig`). Historical **Anchor:** / CR1 quotes earlier in this document still mention `CutlassConfig` as review history, not current API.
 - **Two-stage cross-backend autotune** (`MoELayer._select_winner`, runners' delegation): for each candidate, the `AutoTuner.choose_one` picks the best *within-backend tactic* (each backend tuned in its own native input schema), then `bench_gpu_time` compares the candidates at their winning tactics and the fastest backend is dispatched. A single `choose_one` over both runners is not possible because their input schemas differ — hence the explicit two stages.
 - **Winner caching is per token-bucket** (`map_to_hybrid_bucket`): reusing one `MoELayer` across token counts re-selects per bucket; `winner_backend` reports the most-recent choice and `reset_winner()` clears the cache.
 - **Fail-fast scope** (`MoELayer._validate_mvp_scope`): non-NVFP4 quant or non-Swiglu activation raises `NotImplementedError` at construction.

flashinfer/fused_moe/__init__.py

@@ -21,9 +21,15 @@
     B12xW4A16Config,
     BackendOptions,
     CuteDslConfig,
-    CutlassConfig,
     CutlassBf16Config,
+    CutlassFp8BlockConfig,
+    CutlassFp8PerTensorConfig,
+    CutlassHummingConfig,
+    CutlassMxfp8Config,
+    CutlassMxfp8Mxfp4Config,
+    CutlassNvfp4Config,
     CutlassW4A16Config,
+    CutlassW4A8Config,
     ExecutionConfig,
     ExpertConfig,
     MoEActivationPack,
@@ -49,7 +55,14 @@
     B12xNvfp4Runner,
     B12xW4A16Runner,
     CutlassBf16Runner,
+    CutlassFp8BlockRunner,
+    CutlassFp8PerTensorRunner,
+    CutlassHummingRunner,
+    CutlassMxfp8Mxfp4Runner,
+    CutlassMxfp8Runner,
+    CutlassNvfp4Runner,
     CutlassW4A16Runner,
+    CutlassW4A8Runner,
     CuteDslNvfp4Runner,
     TrtllmBf16RoutedRunner,
     TrtllmFp4RoutedRunner,
@@ -151,15 +164,28 @@
     "ActivationConfig",
     "B12xNvfp4Config",
     "B12xNvfp4Runner",
-    "CutlassBf16Runner",
-    "CutlassW4A16Runner",
     "B12xW4A16Config",
     "B12xW4A16Runner",
     "BackendOptions",
     "CuteDslConfig",
-    "CutlassConfig",
     "CutlassBf16Config",
+    "CutlassBf16Runner",
+    "CutlassFp8BlockConfig",
+    "CutlassFp8BlockRunner",
+    "CutlassFp8PerTensorConfig",
+    "CutlassFp8PerTensorRunner",
+    "CutlassHummingConfig",
+    "CutlassHummingRunner",
+    "CutlassMxfp8Config",
+    "CutlassMxfp8Mxfp4Config",
+    "CutlassMxfp8Mxfp4Runner",
+    "CutlassMxfp8Runner",
+    "CutlassNvfp4Config",
+    "CutlassNvfp4Runner",
     "CutlassW4A16Config",
+    "CutlassW4A16Runner",
+    "CutlassW4A8Config",
+    "CutlassW4A8Runner",
     "ExecutionConfig",
     "ExpertConfig",
     "CuteDslNvfp4Runner",