Realization.Nvidia.SM86.AttentionHeadLayoutSM86

coppelius S5 rung 4b (D24): the ATTENTION HEAD LAYOUT kernels at coppelius's geometry — sequence 1024, 8 heads of 64 (half width 32), model width 512, so the fused QKV projection is a 1024 × 1536 fp32 row-major matrix whose row is [Q heads 0..7 | K heads 0..7 | V heads 0..7], each head 64 wide. The tensor-core GEMM (Realization.Nvidia.SM86.GEMM.HMMA.*) computes C = A · Bᵀ with A (M × K) and B (N × K) both fp16 row-major. So per head h: scores_h = Q_h · K_hᵀ A = Q_h (1024 × 64), B = K_h (1024 × 64) out_h = P_h · V_h A = P_h (1024 × 1024), B = V_hᵀ (64 × 1024) and the layout kernels here produce exactly those operands: QK ROTARY: for every position p, head h and component c < 32 the HF half-split rotation (Llama): q'[c] = q[c]·cos − q[c+32]·sin, q'[c+32] = q[c+32]·cos + q[c]·sin, with cos/sin tables of 1024 × 32 fp32 (angle p · theta^(−2c/64)); the same for K; both written fp16 head-major [head][position][64]. Grid 1024 (one block per position), block 128: thread = head (tid >> 4) × component pair (tid & 15 → c = 2g, 2g+1), four fp32 loads per plane, two packed half2 stores per plane. VALUE TRANSPOSE: V written fp16 as [head][component][position]. Grid 512 (one block per PAIR of positions, so a thread packs two adjacent positions into one half2 store), block 512: thread = head (tid >> 6) × component (tid & 63). Constant bank 0 (64-bit pointers): 0x160 first output (Q or V), 0x168 second output (K; unused by the value kernel), 0x170 QKV input, 0x178 cosine table, 0x180 sine table. No host fallback: the image is emitted by the sovereign encoder or the build refuses with a stable code.

38family AttentionHeadLayoutSM86Kind : Type 0
39constructor AttentionHeadLayoutQKRotary
40constructor AttentionHeadLayoutValueTranspose
41constructor AttentionHeadLayoutHeadToToken
42constructor AttentionHeadLayoutTokenToHead
43constructor AttentionHeadLayoutInverseRoPEQKVMerge