name: 02_kda_cutlass display_name: "Kimi Delta Attention (chunk forward) via CUTLASS CuTe" precision: bf16 regime: compute # Approximate FLOPs per forward call. KDA forward (chunk form) breakdown per # token across all heads: # - intra-chunk Aqk (q @ k.T with decay): 2*BT*K # - intra-chunk Akk + cumulative invert : ~3*BT*K + BT^2 # - w = A @ (g.exp()*k): 2*BT*K # - u = A @ v: 2*BT*V # - inter-chunk q @ S: 2*K*V # - inter-chunk (Aqk @ v_i): 2*BT*V # - state update S += k.T @ v_i: 2*K*V # Aggregating with a constant of 4 (typical for chunked linear attention) gives # the formula below. Used only for roofline reporting -- not graded directly. flops_formula: "4 * B * T * H * (K*V + CHUNK_SIZE*K + CHUNK_SIZE*V)" # Bytes moved (lower bound): one read of q,k,v,g,beta and one write of o. bytes_formula: "B*T*H*K*2 + B*T*H*K*2 + B*T*H*V*2 + B*T*H*K*4 + B*T*H*2 + B*T*H*V*2" hardware: [RTX_PRO_6000] peak_tflops_key: bf16 # KDA's heavy work is bf16 @ bf16 -> fp32 GEMM-equivalent peak_bandwidth_key: dram # bf16 default tolerance from src/eval/correctness.py is 1e-2 atol/rtol; KDA's # long recurrence accumulates more error than a single GEMM, so we relax slightly. tolerance: torch.bfloat16: 0.05 # was "5e-2" -- PyYAML parses un-dotted sci-notation as string # Forbidden ops -- using any in solution.py fails correctness post-hoc. # The whole point of the problem is to write the kernel; calling FLA's existing # Triton chunk_kda is the canonical reward-hack to block. forbidden: - "fla.ops.kda" - "fla.ops.chunk_kda" - "chunk_kda" - "fused_recurrent_kda" - "naive_chunk_kda" - "naive_recurrent_kda" sota: name: "FLA chunk_kda (Triton)" url: "https://github.com/fla-org/flash-linear-attention/tree/main/fla/ops/kda" function: "fla.ops.kda.chunk_kda" deps: - "flash-linear-attention>=0.3" # Documented H100 throughput (informational, not graded). FLA's KDA Triton # kernel hits roughly 0.6-0.8x of FlashAttention-2 wall-clock on H100 at the # B=2,T=2048,H=8,K=V=128 shape (per the Kimi Linear blog / FLA benchmarks). reference_throughput_tflops_h100: null num_correct_trials: 3 num_perf_trials: 20