"""GQA decode kernel — Case 3 (Cube-Repl × PE-SP).

Per GQA_full_deck.pptx slide 11:
  - K, V replicated across all 8 cubes (the 8× memory waste).
  - PEs SP on S_kv inside each cube: each PE attends to its
    ``S_local = S_kv / P`` slice.
  - Intra-CUBE 8-way reduce on the partial ``(m, ℓ, O)`` triple
    (row chain + col bridge over the 2×4 PE grid, same structural
    pattern as Case 4's intra-CUBE phase).
  - NO inter-CUBE communication — every cube ends with the full
    answer (8× redundant compute is the inherent cost of Case 3).
  - Designated writer: only PE 0 of CUBE 0 stores ``O`` to avoid
    8 redundant DMA writes.

Tensor layout:
  Q : (T_q, h_q · d_head) replicated on every rank.
  K : (S_kv, h_kv · d_head) with cube=replicate, pe=row_wise —
      each PE owns its (S_local, h_kv·d_head) shard within its cube.
  V : same as K.
  O : (T_q, h_q · d_head) — only PE 0 of CUBE 0 stores.

Topology / SFR:
  - ``configure_sfr_intercube_multisip`` provides the intra_* /
    E/W/N/S namespaces; only the intra_* lanes are exercised here.
"""
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_ctx_cube_repl_pe_sp_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,
    *,
    tl,
) -> None:
    """Case-3 decode: PE-SP attention; replicated KV per cube; intra-CUBE AR only."""
    G = h_q // h_kv
    S_local = S_kv // P  # each PE owns S_kv/P (cube=replicate, pe=row_wise)
    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_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)

    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)

    # ── Intra-CUBE 8-way reduce-to-PE0 (row chain + col bridge) ──
    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

    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)

    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)

    # ── Final normalise + store (designated writer: cube 0, PE 0) ──
    if pe_id == 0 and cube_id == 0:
        O_final = O_local / l_local
        tl.store(o_ptr, O_final)
