[Bugfix] Reject CUTLASS block-scaled FP8 when N is not a multiple of 128

Online block-wise FP8 (--quantization fp8_per_block) selected
CutlassFp8BlockScaledMMKernel for layers whose output dim N is not a
multiple of the weight block size (128). The CUTLASS c3x block-scaled FP8
GEMM (sm90/sm100/sm120) tiles weight scales at a fixed (128,128)
granularity and its dispatch only special-cases small/unaligned M
(swap_ab); it has no kernel for a partial N tile, so
gemm_op.can_implement() rejects the problem and vLLM aborts at runtime
with "cutlass_gemm_caller ... Invalid status". Since
CUTLASS_BLOCK_FP8_SUPPORTED is True and can_implement() did not check the
shape, auto-selection picked CUTLASS and crashed instead of falling back.

Add the N/K block-alignment check to can_implement() so selection falls
back to a supporting kernel (e.g. Triton). Selection is per-layer, so
aligned layers keep the CUTLASS path. Repro: Qwen3.5 GDN
linear_attn.in_proj_a/b weights are [16, 1024] (N=16) on sm_120.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

Author: sw072

vllm/model_executor/kernels/linear/scaled_mm/cutlass.py

@@ -307,6 +307,31 @@ def can_implement(cls, config: FP8ScaledMMLinearLayerConfig):
                 "Supports only dynamic per token group activation "
                 "quantization with group_shape=(1,128).",
             )
+
+        # The CUTLASS block-scaled FP8 GEMM (sm90/sm100/sm120) tiles the weight
+        # scale factors with a fixed (128, 128) granularity along (N, K) and
+        # provides no kernel for a partial N tile (the swap_ab dispatch only
+        # handles small/unaligned M). If N is not a multiple of the weight
+        # block size, CUTLASS' gemm_op.can_implement() rejects the problem at
+        # runtime and raises "Invalid status". Report this here so kernel
+        # selection can fall back to a kernel that supports it (e.g. Triton)
+        # instead of crashing. See e.g. Qwen3.5 GDN in_proj layers (N=16).
+        weight_group_shape = config.weight_quant_key.scale.group_shape
+        n, k = config.weight_shape
+        if weight_group_shape.row > 0 and n % weight_group_shape.row != 0:
+            return (
+                False,
+                f"CUTLASS block-scaled FP8 requires the output dim N ({n}) to "
+                f"be a multiple of the weight block size "
+                f"({weight_group_shape.row}).",
+            )
+        if weight_group_shape.col > 0 and k % weight_group_shape.col != 0:
+            return (
+                False,
+                f"CUTLASS block-scaled FP8 requires the input dim K ({k}) to "
+                f"be a multiple of the weight block size "
+                f"({weight_group_shape.col}).",
+            )
         return True, None
 
     def apply_block_scaled_mm(