name: 06_sonic_moe_swiglu
display_name: "Sonic-MoE up-projection (Grouped GEMM + SwiGLU)"
precision: bf16
regime: compute
hardware: [RTX_PRO_6000]
peak_tflops_key: bf16
peak_bandwidth_key: dram

# Dense-equivalent FLOPs: gate GEMM + up GEMM + SwiGLU (negligible elementwise).
# Per token: 2*H*I FMAs for gate, 2*H*I for up => 2 * T_total * H * (2*I).
# (Each of T_total tokens visits K experts, but total work = T_total * K * (2*H*I*2)
# only if you count routing. The standard MoE FLOPs convention counts only the
# active per-token compute: T_total tokens * 2 * (2*I) * H. We follow that.)
flops_formula: "2 * T_total * H * (2 * I)"

# Bytes moved (approximate, lower bound):
#   read hidden (T_perm = T_total*K rows of H bf16) + read 2 weight matrices per
#   expert (E * H * 2*I bf16) + write output (T_perm rows of I bf16).
bytes_formula: "T_total*K*H*2 + E*H*(2*I)*2 + T_total*K*I*2"

tolerance:
  bfloat16: 0.02

# Forbidden ops -- agent must write the grouped GEMM + fused SwiGLU themselves.
# - torch.matmul / torch.bmm / F.linear: cuBLAS dispatch, defeats the point.
# - sonic_moe imports: vendor-call cheating; the SOTA is graded separately.
forbidden:
  - "torch.matmul"
  - "torch.bmm"
  - "torch.nn.functional.linear"
  - "F.linear"
  - "from sonic_moe"
  - "import sonic_moe"

sota:
  name: "Sonic-MoE up-projection (Tri Dao)"
  url: "https://github.com/Dao-AILab/sonic-moe"
  function: "sonic_moe.fused_moe_up"
  deps:
    - "sonic-moe>=0.1.2"   # requires Python>=3.12, sm_120 support in-progress
    - "quack-kernels"      # CuTeDSL grouped GEMM that sonic-moe dispatches to
  # Documented H100 paper number for this configuration (informational, not graded
  # live on SM120). Sonic-MoE reports 1.87-4.04x over ScatterMoE/MoMoE on H100.
  reference_throughput_tflops_h100: 480

num_correct_trials: 3
num_perf_trials: 20