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80692f2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | 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
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