oneDNN Quantized GEMM on Xe2 (Lunar Lake/LNL, Battlemage/BMG)

Use this skill when implementing, optimizing, or debugging quantized GEMM kernels using oneDNN on Intel Xe2 (Lunar Lake/LNL, Battlemage/BMG) or newer Intel XPU. Xe2 is the GPU architecture; LNL and BMG are product names. Covers FP16/BF16 x FP8_E4M3 with per-N scale, FP16 x FP8 with block-wise scale along K, FP16 x INT4 (U4) with block-wise scale + zero-point, 2D block quantization emulation via repeat_interleave, bias fusion, and the critical API differences between set_scales_mask (JIT) vs set_scales (ref fallback). Use whenever the user mentions oneDNN FP8 GEMM, quantized matmul, W8A16, W4A16, per-N scale, block-wise FP8, block-wise INT4, 2D block quantization, or dnnl matmul primitive on Intel GPU.