I need you to write the up-projection of a top-K MoE FFN with grouped GEMM and fused SwiGLU for the RTX PRO 6000 (SM120 Blackwell, GDDR7, 1.8 TB/s). The reference implementation is in reference.py and your kernel goes in solution.py. You can make whatever mess you want in this directory — scratch files, intermediate kernels, build artifacts, profiling traces — but the final answer has to be in solution.py with the same Model, get_inputs, and get_init_inputs interface as reference.py.

The op is: per expert e, compute h_e = silu(x_e @ W_gate[e]) * (x_e @ W_up[e]) where x_e is the slice of permuted hidden states routed to expert e. Inputs are hidden_states of shape (T_perm, H) bf16 (already permuted into expert order, T_perm = T_total * K), expert_offsets of shape (E+1,) int32 (prefix sums of token counts per expert, so expert e owns rows [offsets[e]:offsets[e+1]]), and per-expert weights W_gate and W_up each of shape (E, H, I) bf16. You return gated_up of shape (T_perm, I) bf16. Correctness tolerance is 0.02 abs/rel on bf16. The shapes you have to handle are T_total=32768 H=4096 I=1536 E=128 K=8 (the headline sonic-moe configuration, 32K tokens, 128 experts, top-8), T_total=4096 H=2048 I=1024 E=64 K=4 (smaller / faster iteration), and T_total=16384 H=2048 I=4096 E=64 K=8 (intermediate-heavy aspect ratio that forces larger N tiles).

This needs to be a real custom kernel — the whole point is to implement the variable-length grouped GEMM and fuse SwiGLU into the epilogue, not to dispatch to a vendor library. Don't import or call torch.matmul, torch.bmm, torch.nn.functional.linear, F.linear, or anything from sonic_moe. Try CUTLASS grouped GEMM (the intended path), Triton with a grouped-GEMM autotune, CUDA C++ via load_inline, or inline PTX — your call. Anything you're uncertain about, look up PTX docs, clone CUTLASS or sonic-moe or quack-kernels or other reference repos, read library headers, and investigate.

Your flywheel is implement, profile (ncu, nsys, torch.profiler — whatever's useful) and time it with benchmark.py, verify correctness by running `python check.py` and reading the output, then iterate. Don't substitute your own one-off correctness snippets for check.py — it iterates over every shape, your spot-check almost certainly won't. If `python check.py` hasn't printed PASS, you're not done. Take as long as you need to actually push the number up.