[ROCm][quant] INC: route w4a16-sym MoE through HybridW4A16 HIP path

[mgehre-amd]

Wires INC (Intel Neural Compressor / auto-round) quantized models into the same HIP HybridW4A16 MoE path that compressed-tensors w4a16 already uses on ROCm. Auto-round emits its checkpoints in
auto_round:auto_gptq packing (same on-disk layout as compressed-tensors pack_quantized), so the only INC-specific piece is registering the parameters under the GPTQ names (w*_qweight / w*_scales / w*_qzeros) that the standard FusedMoE expert-name mapping resolves; the conversion to ExLlama-shuffled [E, N, K//8] and the HybridW4A16MoEExperts modular-kernel install are reused from compressed-tensors.

Verified on Strix Halo (gfx1151) with Intel/Qwen3.5-35B-A3B-int4-AutoRound: the _rocm_C::fused_moe_wvSplitK_int4_gemm kernel now drives the per-token MoE GEMMs on decode; non-MoE INT4 linears were already going through HybridW4A16LinearKernel via choose_mp_linear_kernel.

Changes:

• vllm/platforms/rocm.py: add "inc" to supported_quantization (the dispatcher behind it ultimately picks AWQ/GPTQ kernels through choose_mp_linear_kernel, so ROCm support is no longer unconditionally rejected at config validation).
• vllm/model_executor/layers/quantization/inc.py: in apply_awq_quant_layer / apply_gptq_quant_layer, when the gate passes (is_rocm, 4-bit, sym, group_size>0, FusedMoE, non-marlin), return the new INCHybridW4A16MoEMethod instead of falling back to the generic MoeWNA16Method.
• vllm/model_executor/layers/quantization/inc_moe.py (new): INCHybridW4A16MoEMethod registers GPTQ-named params, drops the sym qzeros (7-sentinel) before the kernel sees them, aliases to the names the helper expects, and installs the modular kernel. Originals are freed after the repack so weight memory stays at the checkpoint footprint instead of doubling.
• vllm/model_executor/layers/fused_moe/hybrid_w4a16_moe_helper.py (new): shared setup_hybrid_w4a16_moe(method, layer) extracted from compressed-tensors; called by both backends so the conversion + modular-kernel install lives in one place.
• vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/compressed_tensors_moe_wna16.py: _process_weights_hybrid_w4a16 now delegates to the shared helper.

Bench (gfx1151, Intel/Qwen3.5-35B-A3B-int4-AutoRound, synthetic-mm 640x480, ISL/OSL=100/128, conc=1, --enforce-eager):
decode 25.9 -> 36.4 tok/s (+40%), TPOT 38.7 -> 27.5 ms.
Profile confirms _rocm_C::fused_moe_wvSplitK_int4_gemm is now
on the decode hot path (was Triton MoeWNA16 before).

Purpose

Test Plan

Test Result

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