[Kernel] Support fused_moe tuning with Kimi-VL-A3B#40545
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Adds `KimiVLForConditionalGeneration` to `get_model_params` in benchmark_moe.py. Kimi-VL wraps a DeepseekV3-family text backbone inside a vision-language outer config; the MoE fields (`n_routed_experts`, `num_experts_per_tok`, `moe_intermediate_size`, `hidden_size`) live on `config.get_text_config()`, so the existing default fallback (which reads `num_local_experts` off the text_config, Mixtral-style) raises AttributeError on this architecture. Same pattern as the Qwen3VL dispatch branch immediately above, and the parallel fix vllm-project#40181 for Gemma4. Lets `benchmark_moe.py --model moonshotai/Kimi-VL-A3B-Instruct --tune` work without a model-prefix hack. Signed-off-by: Hannu Varjoranta <hannu@varjosoft.com>
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Code Review
This pull request introduces support for the KimiVLForConditionalGeneration architecture in the MoE benchmark kernel by extracting model parameters from the inner text configuration. A review comment recommends using recursion to call get_model_params on the text configuration, which would eliminate code duplication and leverage existing logic for the DeepSeek-V3 backbone.
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Purpose
Adds `KimiVLForConditionalGeneration` to `get_model_params` in `benchmarks/kernels/benchmark_moe.py`.
Kimi-VL wraps a DeepseekV3-family text backbone inside a vision-language outer config. The MoE fields (`n_routed_experts`, `num_experts_per_tok`, `moe_intermediate_size`, `hidden_size`) live on `config.get_text_config()`, so the existing default fallback — which reads `num_local_experts` off the text_config, Mixtral-style — raises `AttributeError` on this architecture.
Same pattern as the `Qwen3VLMoeForConditionalGeneration` dispatch branch immediately above, and parallel to #40181 which does the same for Gemma4.
After this PR, `benchmark_moe.py --model moonshotai/Kimi-VL-A3B-Instruct --tune` works without a `--model-prefix` hack.
Test plan
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