Standalone test that verifies refresh_moe_lora_merge_from_pristine
produces the same W_inf as the textbook reference
``W_ref[e] = W_pristine[e] + scaling * B[e] @ A[e]`` for both standard
(E, 2I, H) and transposed (E, H, 2I) stacked expert layouts, single and
multi-adapter, fp32 + bf16. All 8 cases bitwise-match.
Also benchmarks the batched torch.baddbmm path against (a) a per-expert
torch.addmm loop of equivalent arithmetic and (b) the dense-layer
torch.addmm used by refresh_lora_merge_from_pristine. At Qwen3-30B-A3B
MoE shapes on B200: baddbmm is 5-9.6x faster than the addmm loop; its
per-expert amortized cost is 3.6-7.1x cheaper than the dense addmm
baseline — baddbmm amortizes kernel launch and grid setup across
E=128 experts in one kernel call.
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