[ruocheng-nv]

Description

Split out of #20878 (GLM-5.3-Flash FP8 on Hopper) so MoE owners can review it on its own. The other changes are in
#21179 (Python) and #21178 (C++).

On SM90, FP8 block-scale MoE runs through the fp8_block_scales CUTLASS leaf (DeepgemmCudaHopperFp8BlockScalesImpl).
Its grouped DeepGEMM kernel walks every expert group on one persistent CTA per SM. At decode batch sizes most experts are
empty, so the GEMMs stay far from the bandwidth bound.

The leaf's run_moe now runs Triton FP8-activation kernels when all of these hold: a plain SwiGLU (optionally clamped by
a scalar), no expert bias, no LoRA, BF16 input, no caller-provided output buffer and at most 32 tokens per rank.
Otherwise it keeps the fused op. The Triton path consists of:

• per-1x128 FP8 activation quantization and FP8 x FP8 GEMMs with 128x128 weight scales; the grid covers only the expert
  blocks the batch can occupy;
• SwiGLU fused with the FP8 quantization of the FC2 input;
• a single-program moe_align for up to 128 (token, expert) pairs, and a top-k sum kernel.

moe_align_block_size also accepts expert counts that are not a power of two. This also applies to the SM120 Triton
path.

For MoE owners

• This leaf's id names the DeepGEMM grouped GEMM, while the small-batch path is a Triton kernel. Please say if you would
  rather have the small-batch path as its own identity.
• The leaf serves every FP8 block-scale MoE on SM90 (e.g. DeepSeek-V3/R1 FP8), so those models also take this path at
  decode sizes. So far it has only been measured on GLM-5.3-Flash (TP4, EP1).
• With expert parallelism, (token, expert) pairs owned by other ranks keep a clamped local expert id with zero weight,
  as in the SM120 Triton path, so they are still computed.

Performance

H200, 288 experts, top-8, hidden 4096, 512 intermediate per rank (CUDA graph), full MoE: 4 tokens 64.7 us, 1 token
28.7 us. On the grouped DeepGEMM path the FC1/FC2 GEMMs alone take 53/47 us and 43/36 us.

GLM-5.3-Flash FP8, H200 TP4, MTP3, ISL 13312 / OSL 33, concurrency 1: decode step 14.4 -> 11.7 ms, 184 -> 225.5 output
tok/s per user; GSM8K unchanged. Measured on the #20878 branch (older main); not yet re-measured on this branch.

Test Coverage

• test_triton_fp8_block_scale.py (added to l0_h100.yml):
  • FP8-activation MoE against an FP32 reference with 1x128 FP8-rounded activations: no clamp, clamp, clamp after SiLU;
    1 to 24 tokens; a non-power-of-two expert count.
  • moe_align (single-program and multi-kernel) for non-power-of-two expert counts.

PR Checklist

• PR description clearly explains what and why.
• Test cases are provided for new code paths.
• No API changes.