[ROCm][Quantization] Enable moe_wna16 on ROCm via Triton fallback

[brucechanglongxu]

moe_wna16 (W4A16/W8A16 MoE quantization, used by GPTQ/AWQ-quantized Mixtral, DeepSeek, etc.) is blocked on ROCm by two issues:

1. Not in RocmPlatform.supported_quantization — platform verification rejects it outright.

2. Even if you bypass that, should_moe_wna16_use_cuda() in fused_moe.py returns True on ROCm because it checks current_platform.is_cuda(), which returns True for ROCm under the current platform model. This routes into invoke_fused_moe_wna16_cuda_kernel → ops.moe_wna16_gemm, a CUDA-only C++ op that isn't registered on ROCm builds. The Triton fallback path (invoke_fused_moe_wna16_triton_kernel) would work fine but never gets reached.

The fix is two lines:

• Add "moe_wna16" to supported_quantization in vllm/platforms/rocm.py
• Add and not current_platform.is_rocm() to should_moe_wna16_use_cuda() in vllm/model_executor/layers/fused_moe/fused_moe.py so the Triton kernel is used instead

The linear layers within moe_wna16 models already handle ROCm correctly — check_marlin_supports_layer() in marlin_utils.py returns False on ROCm (line 213-214), so MoeWNA16Config.get_quant_method() falls through to the non-Marlin AWQ/GPTQ paths which have working ROCm support via Exllama/Conch.

The Triton WNA16 MoE kernel is the same one used on CUDA when should_moe_wna16_use_cuda() returns false (W8A16 case, or large batch sizes), so it's well-exercised in existing CI.

[gemini-code-assist]

Code Review

This pull request enables moe_wna16 quantization on ROCm by ensuring the Triton fallback kernel is used instead of the CUDA-only one, and by adding moe_wna16 to the list of supported quantization methods for the ROCm platform. The changes are correct and address the issue. I have one suggestion to make the platform check more direct and robust.

[gemini-code-assist]

To make the platform check more direct and robust against potential inconsistencies in is_cuda() behavior across environments, consider using current_platform.device_name == 'cuda'. This directly checks for the CUDA platform and is less prone to misinterpretation.

Suggested change

	current_platform.is_cuda()
	and not current_platform.is_rocm()
	current_platform.device_name == "cuda"