[Kernel][MoE] Add A100 tuned config for E=64,N=1408 (Kimi-VL-A3B, GLM-4.5 Air)

[varjoranta]

Purpose

Adds a tuned fused_moe config for E=64, N=1408 on NVIDIA A100-SXM4-80GB.

This is the MoE shape used by:

• moonshotai/Kimi-VL-A3B-Instruct (DeepseekV3 text backbone)
• zai-org/GLM-4.5-Air

Prior to this PR, the only tuned config for E=64, N=1408 was for B200 (from PR #26818). On A100, these models fell through to get_default_config().

Benchmark (bf16, hidden=2048, top_k=6, 1×A100 80GB SXM4)

M	default µs	tuned µs	speedup
1	183.5	183.5	1.00×¹
4	266.2	266.2	1.00×¹
16	542.6	523.3	1.04×
64	698.1	677.6	1.03×
256	769.5	717.0	1.07×
1024	1022.5	929.9	1.10×
4096	2919.0	2636.6	1.11×

¹ M=1, 2, 4, 8 entries mirror get_default_config(...) verbatim — the default heuristic was already near-optimal for decode-sized batches and the tuner didn't beat it. Keeping the entries (rather than removing) avoids try_get_optimal_moe_config nearest-match picking the M=16 config for tiny batches.

Wins grow with batch size: 1.07–1.11× at prefill-sized M≥256, small (3-4%) at mid range, parity at decode.

How it was generated

Standalone tuner using the same reduced search space as benchmarks/kernels/benchmark_moe.py (BLOCK_SIZE_M∈{16,32,64,128}, BLOCK_SIZE_N∈{32,64,128}, BLOCK_SIZE_K∈{64,128}, GROUP_SIZE_M∈{1,16,32}, num_warps∈{4,8}, num_stages∈{2,3,4}) — 432 configs × 18 batch sizes, benchmarked via triton.testing.do_bench(warmup=5, rep=30). Total sweep ~15 min on a single A100.

I didn't use benchmark_moe.py directly because get_model_params doesn't yet handle Kimi-VL's nested text_config (same pattern as the Gemma4 issue addressed in #40181). The standalone tuner bypasses model-config parsing by hardcoding the shape.

Triton version recorded: 3.6.0.

Test plan

• Generated on 1× A100 80GB SXM4, CUDA 13.0, torch 2.10.0+cu130, triton 3.6.0
• Validated config loads cleanly via get_moe_configs(E=64, N=1408, dtype=None, block_n=0, block_k=0)
• Benchmarked tuned vs default across M ∈ {1, 4, 16, 64, 256, 1024, 4096}
• Addressed gemini-code-assist review: M=1,2,4,8 entries now match default heuristic

[claude]

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[gemini-code-assist]

Code Review

This pull request introduces a new Triton configuration file for fused MoE layers on NVIDIA A100-SXM4-80GB GPUs, specifically for models with 64 experts and an intermediate dimension of 1408. Feedback indicates that the configurations for small batch sizes (M=1 to M=8) show a performance regression compared to default heuristics and should be updated or removed to maintain performance parity for decode-heavy workloads.

[varjoranta]

Thanks for the review — good catch. Fixed and force-pushed as a single clean commit (1ad298c).

The M=1, 2, 4, 8 entries now mirror what get_default_config(...) returns for bf16: BLOCK_SIZE_M=16, BLOCK_SIZE_N=64, BLOCK_SIZE_K=128, GROUP_SIZE_M=1, num_warps=4, num_stages=4. That way decode-sized requests see no regression, and the tuning wins at M≥16 (1.04× → 1.11× up to M=4096) are preserved.

[gaby]

Ping @mgoin @pavanimajety