"""GQA fused-attention decode kernel — long context (ADR-0060).

Each rank holds an ``S_local = S_kv / (C·P)`` slice of K, V and the full
Q (replicated). The local attention is computed via an S_kv-axis tile
sweep (ADR-0063 §A.2) so per-rank scratch is bounded by ``TILE_S_KV``
regardless of ``S_local``. The partial ``(m, ℓ, O)`` is then reduced
via a 2-level pattern (intra-CUBE row+col, then inter-CUBE), and the
root writes the final output.

Inter-CUBE reduce (selected by the ``sub_w`` launch arg):
  - ``sub_w == 0`` (default): 1D chain along ``W``, root at CUBE 0.
    Used for ``C ∈ {1, 4}`` panels.
  - ``sub_w >= 2``: ADR-0060 §4.2 prescribed **lrab-adapted center-root
    mesh reduce** over a ``sub_w × sub_h`` sub-mesh (``sub_h = C //
    sub_w``). Phase 1 row reduce converges at ``root_col = sub_w//2``;
    Phase 2 col reduce on ``root_col`` converges at
    ``root_row = sub_h//2``. Root cube id:
    ``root_row * sub_w + root_col``. Used for the C=8 single-KV-group
    LLaMA-3.1-70B target (sub_w=4, sub_h=2, root=cube 6).
    Requires ``sub_w >= 2 and sub_h >= 2`` — degenerate 1×N / N×1
    layouts must use the 1D-chain path with ``sub_w=0``.

Topology / SFR:
  - Requires ``configure_sfr_intercube_multisip`` when ``P > 1`` or
    ``C > 1`` (provides disjoint ``intra_*`` and ``E/W/N/S`` namespaces).
  - Intra-CUBE PEs are arranged as a 2×4 grid (no wrap).
  - Inter-CUBE CUBEs: 1D row (``sub_w == 0``) or rectangular sub-mesh
    of the SIP's 4×4 CUBE mesh starting at origin (0, 0) with
    ``sub_w == mesh_w`` (``sub_w >= 2``).

Layout caveats:
  - GEMMs use ``tl.dot`` (no composite epilogue / ``softmax_scale``).
  - K is loaded as ``(d_head, S_local)`` via byte-conserving reshape of
    the deployed ``(S_local, h_kv·d_head)`` slice — correct for zero /
    symmetric inputs (ADR-0060 §3 reshape-not-transpose caveat).
"""
from __future__ import annotations


TILE_S_KV = 1024  # ADR-0063 §A.2 S_kv-axis tile sweep (per-tile width).


def _merge_running(m_local, l_local, O_local, m_other, l_other, O_other, *, tl):
    """Online-softmax merge of two partial ``(m, ℓ, O)`` triples."""
    m_new = tl.maximum(m_local, m_other)
    scale_old = tl.exp(m_local - m_new)
    scale_new = tl.exp(m_other - m_new)
    l_new = l_local * scale_old + l_other * scale_new
    O_new = O_local * scale_old + O_other * scale_new
    return m_new, l_new, O_new


def gqa_attention_decode_long_kernel(
    q_ptr: int,
    k_ptr: int,
    v_ptr: int,
    o_ptr: int,
    T_q: int,
    S_kv: int,
    h_q: int,
    h_kv: int,
    d_head: int,
    C: int,
    P: int,
    sub_w: int = 0,
    *,
    tl,
) -> None:
    """GQA decode with M-fold + S_kv tile sweep + 2-level reduce-to-root.

    Tensor layout:
      Q : (T_q, h_q · d_head) replicated on every rank; loaded as
          (G·T_q, d_head) — byte-conserving and math-correct for T_q=1.
      K : (S_kv, h_kv · d_head) sharded row_wise by (cube, pe); each rank
          loads its (d_head, S_local) slice via byte-conserving reshape.
      V : (S_kv, h_kv · d_head) sharded row_wise by (cube, pe); each rank
          loads its (S_local, d_head) slice.
      O : (T_q, h_q · d_head) — only PE 0 of the root CUBE stores
          (CUBE 0 for ``sub_w=0``; geometric center cube for ``sub_w>=2``).
    """
    if sub_w > 0:
        sub_h = C // sub_w
        if sub_w < 2 or sub_h < 2 or sub_w * sub_h != C:
            raise ValueError(
                f"sub_w={sub_w} requires sub_w>=2 and sub_h>=2 and "
                f"sub_w*sub_h==C; got sub_h={sub_h}, C={C}. "
                "Use sub_w=0 for the 1D-chain path."
            )
        root_col = sub_w // 2
        root_row = sub_h // 2
        root_cube = root_row * sub_w + root_col
    else:
        sub_h = 0
        root_col = 0
        root_row = 0
        root_cube = 0

    G = h_q // h_kv
    n_ranks = C * P
    S_local = S_kv // n_ranks
    pe_id = tl.program_id(axis=0)
    cube_id = tl.program_id(axis=1)

    # ── Local attention (S_kv-axis tile sweep, ADR-0063 §A.2) ──
    Q = tl.load(q_ptr, shape=(G * T_q, d_head), dtype="f16")
    n_tiles = (S_local + TILE_S_KV - 1) // TILE_S_KV
    KV_ROW_BYTES = d_head * 2  # f16

    # Tile 0: establishes persistent (m_local, l_local, O_local).
    #
    # Cannot be folded into the Tiles 1..N loop (kernbench-only limitation):
    #   - persistent (m, ℓ, O) must live OUTSIDE ``tl.scratch_scope``,
    #     otherwise scope teardown discards them before the next tile's
    #     merge can read them;
    #   - kernbench has no scratch-backed initializer — ``tl.zeros`` /
    #     ``tl.full`` return addr=0 handles with no backing storage, so
    #     they cannot be overwritten via ``tl.copy_to`` to seed (-inf, 0, 0).
    # So Tile 0 computes the initial running state directly; Tiles 1..N
    # fold into it. Triton port: limitation does not apply (SSA tensors
    # stay live across iterations) — a single unified loop suffices.
    tile_s0 = min(TILE_S_KV, S_local)
    K_T = tl.load(k_ptr, shape=(d_head, tile_s0), dtype="f16")
    V = tl.load(v_ptr, shape=(tile_s0, d_head), dtype="f16")
    scores = tl.dot(Q, K_T)
    m_local = tl.max(scores, axis=-1)
    centered = scores - m_local
    exp_scores = tl.exp(centered)
    l_local = tl.sum(exp_scores, axis=-1)
    O_local = tl.dot(exp_scores, V)

    # Tiles 1..n_tiles-1: fold into running state via online-softmax merge.
    # Triton port: drop the ``with tl.scratch_scope():`` line and replace
    # each ``copy_to`` with a Python rebind.
    for tile_idx in range(1, n_tiles):
        tile_start = tile_idx * TILE_S_KV
        tile_s = min(TILE_S_KV, S_local - tile_start)
        with tl.scratch_scope():
            K_T_t = tl.load(k_ptr + tile_start * KV_ROW_BYTES,
                            shape=(d_head, tile_s), dtype="f16")
            V_t = tl.load(v_ptr + tile_start * KV_ROW_BYTES,
                          shape=(tile_s, d_head), dtype="f16")
            scores_t = tl.dot(Q, K_T_t)
            m_tile = tl.max(scores_t, axis=-1)
            centered_t = scores_t - m_tile
            exp_scores_t = tl.exp(centered_t)
            l_tile = tl.sum(exp_scores_t, axis=-1)
            O_tile = tl.dot(exp_scores_t, V_t)
            m_new, l_new, O_new = _merge_running(
                m_local, l_local, O_local, m_tile, l_tile, O_tile, tl=tl,
            )
            tl.copy_to(m_local, m_new)
            tl.copy_to(l_local, l_new)
            tl.copy_to(O_local, O_new)

    # ── Communication: chain reduce-to-root at PE 0 of CUBE 0 ──
    PE_GRID_COLS = 4
    pe_col = pe_id % PE_GRID_COLS
    pe_row = pe_id // PE_GRID_COLS
    pe_cols_used = min(PE_GRID_COLS, P)
    pe_rows_used = (P + PE_GRID_COLS - 1) // PE_GRID_COLS

    # Level-2 row chain (intra-CUBE, along intra_W, leftward).
    if pe_cols_used > 1:
        if pe_col < pe_cols_used - 1:
            with tl.scratch_scope():
                m_other = tl.recv(dir="intra_E", shape=m_local.shape, dtype="f16")
                l_other = tl.recv(dir="intra_E", shape=l_local.shape, dtype="f16")
                O_other = tl.recv(dir="intra_E", shape=O_local.shape, dtype="f16")
                m_new, l_new, O_new = _merge_running(
                    m_local, l_local, O_local, m_other, l_other, O_other, tl=tl,
                )
                tl.copy_to(m_local, m_new)
                tl.copy_to(l_local, l_new)
                tl.copy_to(O_local, O_new)
        if pe_col > 0:
            tl.send(dir="intra_W", src=m_local)
            tl.send(dir="intra_W", src=l_local)
            tl.send(dir="intra_W", src=O_local)

    # Level-2 col bridge (intra-CUBE, along intra_N, row-1 → row-0).
    if pe_col == 0 and pe_rows_used > 1:
        if pe_row < pe_rows_used - 1:
            with tl.scratch_scope():
                m_other = tl.recv(dir="intra_S", shape=m_local.shape, dtype="f16")
                l_other = tl.recv(dir="intra_S", shape=l_local.shape, dtype="f16")
                O_other = tl.recv(dir="intra_S", shape=O_local.shape, dtype="f16")
                m_new, l_new, O_new = _merge_running(
                    m_local, l_local, O_local, m_other, l_other, O_other, tl=tl,
                )
                tl.copy_to(m_local, m_new)
                tl.copy_to(l_local, l_new)
                tl.copy_to(O_local, O_new)
        if pe_row > 0:
            tl.send(dir="intra_N", src=m_local)
            tl.send(dir="intra_N", src=l_local)
            tl.send(dir="intra_N", src=O_local)

    # Level-1 inter-CUBE reduce (only PE 0 of each CUBE participates).
    if pe_id == 0 and C > 1 and sub_w == 0:
        # 1D chain along W, leftward; root at CUBE 0.
        if cube_id < C - 1:
            with tl.scratch_scope():
                m_other = tl.recv(dir="E", shape=m_local.shape, dtype="f16")
                l_other = tl.recv(dir="E", shape=l_local.shape, dtype="f16")
                O_other = tl.recv(dir="E", shape=O_local.shape, dtype="f16")
                m_new, l_new, O_new = _merge_running(
                    m_local, l_local, O_local, m_other, l_other, O_other, tl=tl,
                )
                tl.copy_to(m_local, m_new)
                tl.copy_to(l_local, l_new)
                tl.copy_to(O_local, O_new)
        if cube_id > 0:
            tl.send(dir="W", src=m_local)
            tl.send(dir="W", src=l_local)
            tl.send(dir="W", src=O_local)
    elif pe_id == 0 and sub_w > 0:
        # ── ADR-0060 §4.2 lrab-adapted center-root mesh reduce ──
        # Adapts Phases 1-2 of lrab_hierarchical_allreduce.py:
        # bidirectional row reduce converges at root_col, bidirectional
        # col reduce on root_col converges at root_row. Reduce-only
        # (Phases 3-5 dropped: no inter-SIP, no broadcast — attention
        # needs the answer at one rank). Plain ``+`` replaced with the
        # log-sum-exp ``_merge_running`` for (m, ℓ, O).
        row = cube_id // sub_w
        col = cube_id % sub_w

        # Phase 1: row reduce — converge at col == root_col.
        if col == 0 and root_col > 0:
            tl.send(dir="E", src=m_local)
            tl.send(dir="E", src=l_local)
            tl.send(dir="E", src=O_local)
        elif 0 < col < root_col:
            with tl.scratch_scope():
                m_other = tl.recv(dir="W", shape=m_local.shape, dtype="f16")
                l_other = tl.recv(dir="W", shape=l_local.shape, dtype="f16")
                O_other = tl.recv(dir="W", shape=O_local.shape, dtype="f16")
                m_new, l_new, O_new = _merge_running(
                    m_local, l_local, O_local, m_other, l_other, O_other, tl=tl,
                )
                tl.copy_to(m_local, m_new)
                tl.copy_to(l_local, l_new)
                tl.copy_to(O_local, O_new)
            tl.send(dir="E", src=m_local)
            tl.send(dir="E", src=l_local)
            tl.send(dir="E", src=O_local)
        elif col == root_col:
            if root_col > 0:
                with tl.scratch_scope():
                    m_other = tl.recv(dir="W", shape=m_local.shape, dtype="f16")
                    l_other = tl.recv(dir="W", shape=l_local.shape, dtype="f16")
                    O_other = tl.recv(dir="W", shape=O_local.shape, dtype="f16")
                    m_new, l_new, O_new = _merge_running(
                        m_local, l_local, O_local, m_other, l_other, O_other, tl=tl,
                    )
                    tl.copy_to(m_local, m_new)
                    tl.copy_to(l_local, l_new)
                    tl.copy_to(O_local, O_new)
            if sub_w - 1 > root_col:
                with tl.scratch_scope():
                    m_other = tl.recv(dir="E", shape=m_local.shape, dtype="f16")
                    l_other = tl.recv(dir="E", shape=l_local.shape, dtype="f16")
                    O_other = tl.recv(dir="E", shape=O_local.shape, dtype="f16")
                    m_new, l_new, O_new = _merge_running(
                        m_local, l_local, O_local, m_other, l_other, O_other, tl=tl,
                    )
                    tl.copy_to(m_local, m_new)
                    tl.copy_to(l_local, l_new)
                    tl.copy_to(O_local, O_new)
        elif root_col < col < sub_w - 1:
            with tl.scratch_scope():
                m_other = tl.recv(dir="E", shape=m_local.shape, dtype="f16")
                l_other = tl.recv(dir="E", shape=l_local.shape, dtype="f16")
                O_other = tl.recv(dir="E", shape=O_local.shape, dtype="f16")
                m_new, l_new, O_new = _merge_running(
                    m_local, l_local, O_local, m_other, l_other, O_other, tl=tl,
                )
                tl.copy_to(m_local, m_new)
                tl.copy_to(l_local, l_new)
                tl.copy_to(O_local, O_new)
            tl.send(dir="W", src=m_local)
            tl.send(dir="W", src=l_local)
            tl.send(dir="W", src=O_local)
        elif col == sub_w - 1 and sub_w - 1 > root_col:
            tl.send(dir="W", src=m_local)
            tl.send(dir="W", src=l_local)
            tl.send(dir="W", src=O_local)

        # Phase 2: col reduce on col == root_col — converge at row == root_row.
        if col == root_col:
            if row == 0 and root_row > 0:
                tl.send(dir="S", src=m_local)
                tl.send(dir="S", src=l_local)
                tl.send(dir="S", src=O_local)
            elif 0 < row < root_row:
                with tl.scratch_scope():
                    m_other = tl.recv(dir="N", shape=m_local.shape, dtype="f16")
                    l_other = tl.recv(dir="N", shape=l_local.shape, dtype="f16")
                    O_other = tl.recv(dir="N", shape=O_local.shape, dtype="f16")
                    m_new, l_new, O_new = _merge_running(
                        m_local, l_local, O_local, m_other, l_other, O_other, tl=tl,
                    )
                    tl.copy_to(m_local, m_new)
                    tl.copy_to(l_local, l_new)
                    tl.copy_to(O_local, O_new)
                tl.send(dir="S", src=m_local)
                tl.send(dir="S", src=l_local)
                tl.send(dir="S", src=O_local)
            elif row == root_row:
                if root_row > 0:
                    with tl.scratch_scope():
                        m_other = tl.recv(dir="N", shape=m_local.shape, dtype="f16")
                        l_other = tl.recv(dir="N", shape=l_local.shape, dtype="f16")
                        O_other = tl.recv(dir="N", shape=O_local.shape, dtype="f16")
                        m_new, l_new, O_new = _merge_running(
                            m_local, l_local, O_local, m_other, l_other, O_other, tl=tl,
                        )
                        tl.copy_to(m_local, m_new)
                        tl.copy_to(l_local, l_new)
                        tl.copy_to(O_local, O_new)
                if sub_h - 1 > root_row:
                    with tl.scratch_scope():
                        m_other = tl.recv(dir="S", shape=m_local.shape, dtype="f16")
                        l_other = tl.recv(dir="S", shape=l_local.shape, dtype="f16")
                        O_other = tl.recv(dir="S", shape=O_local.shape, dtype="f16")
                        m_new, l_new, O_new = _merge_running(
                            m_local, l_local, O_local, m_other, l_other, O_other, tl=tl,
                        )
                        tl.copy_to(m_local, m_new)
                        tl.copy_to(l_local, l_new)
                        tl.copy_to(O_local, O_new)
            elif root_row < row < sub_h - 1:
                with tl.scratch_scope():
                    m_other = tl.recv(dir="S", shape=m_local.shape, dtype="f16")
                    l_other = tl.recv(dir="S", shape=l_local.shape, dtype="f16")
                    O_other = tl.recv(dir="S", shape=O_local.shape, dtype="f16")
                    m_new, l_new, O_new = _merge_running(
                        m_local, l_local, O_local, m_other, l_other, O_other, tl=tl,
                    )
                    tl.copy_to(m_local, m_new)
                    tl.copy_to(l_local, l_new)
                    tl.copy_to(O_local, O_new)
                tl.send(dir="N", src=m_local)
                tl.send(dir="N", src=l_local)
                tl.send(dir="N", src=O_local)
            elif row == sub_h - 1 and sub_h - 1 > root_row:
                tl.send(dir="N", src=m_local)
                tl.send(dir="N", src=l_local)
                tl.send(dir="N", src=O_local)

    # ── Final normalise + store (root only) ──
    if pe_id == 0 and cube_id == root_cube:
        O_final = O_local / l_local
        tl.store(o_ptr, O_final)