#pragma once
#include "attn_common.cuh"
#include "attn_mma_utils.cuh"

// Split-K (FlashDecoding) tensor-core decode via GQA head-packing.
//
// Decode has q_len == 1, so S = q @ K^T is a GEMV per head — no tensor-core work
// on its own. But GQA gives us G = q_head / kv_head query heads that all share
// one kv_head. We pack those G heads into the M=16 rows of mma.sync.m16n8k16,
// turning G independent GEMVs into a single GEMM that reuses each loaded K/V tile
// across all G heads (K/V load is the decode bottleneck, so the reuse is the win,
// not the flops). The KV sequence is partitioned across gridDim.z blocks so that
// a decode with only batch*kv_head independent tasks can fill all SMs. Each
// (batch, kv_head, split) block computes an UN-normalised partial (Oacc, m, l)
// over its KV slice; the combine kernel below reduces across splits. Fixes the
// "grid too small" bottleneck (0.04 waves/SM → many blocks) for long-context,
// small-batch decode.
//
// Partial layout (float, contiguous):
//   o_part : [batch, q_head, num_splits, HEAD_DIM]
//   ml_part: [batch, q_head, num_splits, 2]  (m, l)
//
// Optimizations:
//   - cp.async global→shared for K/V (bypasses registers, cuts instruction count)
//   - XOR swizzle (swiz_col): LD=HEAD_DIM, zero waste, no bank conflicts
//   - pre-scaled Q: Q scaled during load, softmax skips per-tile multiply
//   - single-buffer: keeps smem small for high occupancy
template <int HEAD_DIM, int BC>
__global__ void attn_decode_split_kv_mma_kernel(AttentionParams p) {
    constexpr int BR = 16;
    constexpr int KD = HEAD_DIM / 16;
    constexpr int NC8 = BC / 8;
    constexpr int KT2 = BC / 16;
    constexpr int DN8 = HEAD_DIM / 8;
    constexpr int LD = HEAD_DIM;
    constexpr int SWIZ_MASK = (HEAD_DIM >= 64) ? 7 : (HEAD_DIM / 8 - 1);

    const int lane = threadIdx.x;
    const int gid = lane >> 2;
    const int tid4 = lane & 3;

    const int kv_head = blockIdx.x;
    const int batch = blockIdx.y;
    const int split = blockIdx.z;
    const int G = p.q_head / p.kv_head;
    const int q_head0 = kv_head * G;

    __shared__ __align__(16) bf16 sK[BC * HEAD_DIM];
    __shared__ __align__(16) bf16 sV[BC * HEAD_DIM];
    __shared__ __align__(16) bf16 sQ[BR * HEAD_DIM];

    bf16 scale_bf16 = __float2bfloat16(p.scale);
    for (int i = lane; i < BR * HEAD_DIM; i += 32) {
        int r = i / HEAD_DIM, d = i % HEAD_DIM;
        bf16 val = __float2bfloat16(0.0f);
        if (r < G) {
            int qh = q_head0 + r;
            val = p.q[(batch * p.q_head + qh) * HEAD_DIM + d];
        }
        sQ[r * LD + swiz_col(d, r, SWIZ_MASK)] = __hmul(val, scale_bf16);
    }
    __syncwarp();

    unsigned Qa[KD][4];
    int qrow_l = (lane & 7) + (lane & 8);
    int qcol_l = (lane & 16) ? 8 : 0;
#pragma unroll
    for (int kt = 0; kt < KD; kt++)
        ldmatrix_x4(Qa[kt], &sQ[qrow_l * LD + swiz_col(kt * 16 + qcol_l, qrow_l, SWIZ_MASK)]);

    float Oacc[DN8][4];
#pragma unroll
    for (int j = 0; j < DN8; j++)
        Oacc[j][0] = Oacc[j][1] = Oacc[j][2] = Oacc[j][3] = 0.0f;
    float m0 = -FLT_MAX, m1 = -FLT_MAX, l0 = 0.0f, l1 = 0.0f;

    const int kv_base = (batch * p.kv_head + kv_head) * p.kv_len * HEAD_DIM;
    const int mask_base = batch * p.kv_len;
    const int tiles_total = (p.kv_len + BC - 1) / BC;
    const int tiles_per_split = (tiles_total + p.num_splits - 1) / p.num_splits;
    const int ti_begin = split * tiles_per_split;
    const int ti_end = min(tiles_total, ti_begin + tiles_per_split);
    const int has_mask = p.use_mask && p.mask;

    for (int ti = ti_begin; ti < ti_end; ti++) {
        int kv0 = ti * BC;

        bool full_tile = (kv0 + BC <= p.kv_len);
        if (full_tile) {
            constexpr int VEC = 8;
            int total = BC * HEAD_DIM;
#pragma unroll
            for (int i = lane * VEC; i < total; i += 32 * VEC) {
                int r = i / HEAD_DIM, d = i % HEAD_DIM;
                int kc = kv0 + r;
                cp_async_16(&sK[r * LD + swiz_col(d, r, SWIZ_MASK)],
                            &p.k[kv_base + kc * HEAD_DIM + d]);
                cp_async_16(&sV[r * LD + swiz_col(d, r, SWIZ_MASK)],
                            &p.v[kv_base + kc * HEAD_DIM + d]);
            }
            cp_async_commit();
            cp_async_wait_all();
        } else {
            for (int i = lane; i < BC * HEAD_DIM; i += 32) {
                int r = i / HEAD_DIM, d = i % HEAD_DIM;
                int kc = kv0 + r;
                bf16 z = __float2bfloat16(0.0f);
                sK[r * LD + swiz_col(d, r, SWIZ_MASK)] =
                    (kc < p.kv_len) ? p.k[kv_base + kc * HEAD_DIM + d] : z;
                sV[r * LD + swiz_col(d, r, SWIZ_MASK)] =
                    (kc < p.kv_len) ? p.v[kv_base + kc * HEAD_DIM + d] : z;
            }
        }
        __syncwarp();

        float Sacc[NC8][4];
        mma_compute_scores<KD, NC8>(Qa, sK, LD, SWIZ_MASK, lane, Sacc);

        int maxc = p.is_causal ? min(p.kv_len, p.causal_offset + 1) : p.kv_len;
        mma_softmax_tile<NC8, DN8>(kv0, maxc, maxc,
                                    mask_base, p.mask, has_mask,
                                    Sacc, Oacc, m0, m1, l0, l1, lane);

        mma_pv_accumulate<DN8, KT2>(Sacc, sV, LD, SWIZ_MASK, lane, Oacc);
        __syncwarp();
    }

    // ---- write UN-normalised partials for this split ----
    auto split_slot = [&](int h) -> size_t {
        size_t bh = (size_t)batch * p.q_head + h;
        return bh * p.num_splits + split;
    };
#pragma unroll
    for (int dn8 = 0; dn8 < DN8; dn8++) {
        int d = dn8 * 8 + 2 * tid4;
        int r0 = gid, r1 = gid + 8;
        if (r0 < G) {
            int h = q_head0 + r0;
            float* op = p.o_part + split_slot(h) * HEAD_DIM;
            op[d] = Oacc[dn8][0];
            op[d + 1] = Oacc[dn8][1];
        }
        if (r1 < G) {
            int h = q_head0 + r1;
            float* op = p.o_part + split_slot(h) * HEAD_DIM;
            op[d] = Oacc[dn8][2];
            op[d + 1] = Oacc[dn8][3];
        }
    }
    if (tid4 == 0) {
        int r0 = gid, r1 = gid + 8;
        if (r0 < G) {
            int h = q_head0 + r0;
            float* mp = p.ml_part + split_slot(h) * 2;
            mp[0] = m0; mp[1] = l0;
        }
        if (r1 < G) {
            int h = q_head0 + r1;
            float* mp = p.ml_part + split_slot(h) * 2;
            mp[0] = m1; mp[1] = l1;
        }
    }
}