#include "attn_decode_split_kv.cuh"
#include "attn_entry_utils.cuh"

#ifndef ASTRAI_NO_MMA
#include "attn_decode_split_kv_mma.cuh"
#endif

// Scalar fallback: one warp per query head, split-KV across grid.z.
static void launch_scalar_decode(AttentionParams<bf16>& p) {
    int group_size = p.q_head / p.kv_head;
    int chunks_total = (p.kv_len + DC_CHUNK - 1) / DC_CHUNK;
    p.num_splits = compute_num_splits(p.batch * p.kv_head, chunks_total);
    alloc_split_partials(p);

    size_t smem = DC_CHUNK * p.head_dim * sizeof(bf16);
    attn_decode_split_kv_kernel<<<dim3(p.batch * p.kv_head, 1, p.num_splits), dim3(32, group_size), smem>>>(p);
    attn_decode_combine_kernel<<<p.batch * p.q_head, p.head_dim>>>(p);
}

#ifndef ASTRAI_NO_MMA
// MMA head-packing requires G <= 16 (BR=16 rows). sm_80+ tensor-core
// + cp.async wins even at G=1 (decode is memory-bound, not compute-bound).
// STAGES=2 (double-buffer) for D<=128 (smem 16 KB); STAGES=1 for D=256
// (double-buffer would be 32 KB, near the 48 KB static cap — keep single
// to preserve occupancy).
template <int HEAD_DIM, int BC, int STAGES = (HEAD_DIM <= 128) ? 2 : 1>
static void launch_mma_decode(AttentionParams<bf16>& p) {
    int tiles_total = (p.kv_len + BC - 1) / BC;
    p.num_splits = compute_num_splits(p.batch * p.kv_head, tiles_total);
    alloc_split_partials(p);

    attn_decode_split_kv_mma_kernel<HEAD_DIM, BC, STAGES><<<dim3(p.kv_head, p.batch, p.num_splits), 32>>>(p);
    attn_decode_combine_kernel<<<p.batch * p.q_head, p.head_dim>>>(p);
}
#endif

template <int HEAD_DIM>
static void dispatch_decode(AttentionParams<bf16>& p) {
#ifndef ASTRAI_NO_MMA
    int G = p.q_head / p.kv_head;
    if (!p.use_mask && G >= 1 && G <= 16) {
        launch_mma_decode<HEAD_DIM, 32>(p);
        return;
    }
#endif
    launch_scalar_decode(p);
}

torch::Tensor attn_decode(
    torch::Tensor q,
    torch::Tensor k,
    torch::Tensor v,
    c10::optional<torch::Tensor> mask,
    int64_t causal_offset,
    double scale,
    int64_t layout
) {
    AttentionParams<bf16> p;
    attn_pack_params(q, k, v, mask, causal_offset, scale, layout, p);
    TORCH_CHECK(p.q_len == 1, "Q seq_len must be 1");
    TORCH_CHECK(p.head_dim % 32 == 0, "head_dim must be multiple of 32");

    // O matches Q's original layout
    auto O = torch::empty_strided(q.sizes(), q.strides(), q.options());
    auto O_view = (layout == 1) ? O.transpose(1, 2) : O;
    p.o = (bf16*)O_view.data_ptr();

    dispatch_head_dim(p.head_dim, [&]<int D>() { dispatch_decode<D>(p); });
    return O;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("attn_decode", &attn_decode,
        py::arg("q"),
        py::arg("k"),
        py::arg("v"),
        py::arg("mask") = py::none(),
        py::arg("causal_offset") = -1,
        py::arg("scale") = 0.0,
        py::arg("layout") = 0,
        "GQA decode (tensor-core head-packing on sm_80+, scalar fallback)");
}