"""VSA stage-3 sparse-attention Triton REFERENCE kernel (`wan22:triton64`).

`_attn_fwd_sparse` is lifted verbatim from the production
Wan2.2/wan/kernels/block_sparse_attn_triton.py. `triton_block_sparse_attn_forward`
is the canonical BSHD host launcher (TMA descriptors). This is the reference any
candidate VSA forward-attention kernel is scored against on the
`baseten-admin/wan2.2_production_values` captures. See README.md ("Scoring / loss").

Reference call (returns (o, M)):
    o, M = triton_block_sparse_attn_forward(q, k, v, topk_idx, vbs)
    # q,k,v: (1,39936,40,128) bf16 ; topk_idx: (1,40,624,78) int32 ; vbs: (624,) int32
"""
import math
import torch
from triton.tools.tensor_descriptor import TensorDescriptor
import triton
import triton.language as tl
from triton.tools.tensor_descriptor import TensorDescriptor

# Hardcoded kernel config (matches block_sparse_attn_triton.py).
_BLOCK_M = 64
_BLOCK_N = 64
_NUM_WARPS = 4
_NUM_STAGES = 3


# ──────────────────────────── SPARSE ADDITION BEGIN ───────────────────────────
# per-query KV-block count is uniform (TOPK) for every (batch, head, query-block)
# so we pass TOPK as a `tl.constexpr`... original `q2k_num` tensor argument is gone
# for speed
# note also that warp specialization slowed this down... do not implement
@triton.jit
def _attn_fwd_sparse(
        sm_scale,
        q2k_index,            # topk indices
        variable_block_sizes,
        M,
        desc_q, # TMA descriptors for Q, K, V, Out
        desc_k,
        desc_v,
        desc_o,
        Z,
        H,
        N_CTX_Q,              # S_Q
        N_CTX_KV,             # S_KV
        HEAD_DIM: tl.constexpr,  # head dimension
        BLOCK_M: tl.constexpr,   # 64
        BLOCK_N: tl.constexpr,   # 64
        TOPK: tl.constexpr):     # topk
    # 64×64 block-sparse forward kernel and BSHD-native via TMA descriptors.

    # ----- stage 1: block idx mapping -----
    q_blk = tl.program_id(0)  # Q-tile index
    off_hz = tl.program_id(1)  # fused (batch, head)
    b = off_hz // H
    h = off_hz % H # which head am i in?
    q_tiles = N_CTX_Q // BLOCK_M # S_Q / 64
    meta_base = (h * q_tiles + q_blk) # h*q_tiles + q_blk

    # kv_blocks is now compile-time-known (== TOPK)
    kv_ptr = q2k_index + meta_base * TOPK  # ptr to list of indices for this query block

    # ----- accumulators, load q tile, and arrays for running sum -----
    q_offset = q_blk * BLOCK_M
    offs_m = q_offset + tl.arange(0, BLOCK_M)
    row_prev_max = tl.full([BLOCK_M], -float("inf"), tl.float32)
    row_prev_denom_sum = tl.zeros([BLOCK_M], dtype=tl.float32) + 1.0
    acc = tl.zeros([BLOCK_M, HEAD_DIM], dtype=tl.float32)   # running unnormalized output
    qk_scale = sm_scale * 1.44269504 # 2^(x*log_2(e)*scale) = e^(x*scale) = e^(x/sqrt(D))

    # load Q tile via TMA
    q_4d = desc_q.load([b, q_offset, h, 0])
    q = q_4d.reshape([BLOCK_M, HEAD_DIM])

    # ----- sparse loop over selected K/V cubes -----
    for i in range(0, TOPK):
        kv_idx = tl.load(kv_ptr + i).to(tl.int32)
        block_size = tl.load(variable_block_sizes + kv_idx)  # real (non-pad) count
        kv_t = kv_idx * BLOCK_N  # row offset into K/V

        # TMA loads K in natural (BLOCK_N, HEAD_DIM) orientation;
        # transpose in registers via .T (free metadata flip) to get (HEAD_DIM, BLOCK_N)
        k_4d = desc_k.load([b, kv_t, h, 0])
        k = k_4d.reshape([BLOCK_N, HEAD_DIM]).T

        qk = tl.dot(q, k)
        mask = tl.arange(0, BLOCK_N) < block_size # mask pad columns
        qk = tl.where(mask[None, :], qk, -float("inf"))
        qk_normed = qk * qk_scale

        # flash attention online softmax update with curr max
        row_curr_max = tl.maximum(row_prev_max, tl.max(qk_normed, 1))
        p = tl.math.exp2(qk_normed - row_curr_max[:, None])
        row_curr_denom_sum = tl.sum(p, 1)

        # rescale running state if max changed fa style
        alpha = tl.math.exp2(row_prev_max - row_curr_max) # undoes prev max, then adds curr max. no-op (e^0=1 if no change)
        row_prev_denom_sum = row_prev_denom_sum * alpha + row_curr_denom_sum # online update denom
        acc = acc * alpha[:, None] # online update acc

        # load V tile via TMA
        v_4d = desc_v.load([b, kv_t, h, 0])
        v = v_4d.reshape([BLOCK_N, HEAD_DIM])
        acc = tl.dot(p.to(tl.bfloat16), v, acc)
        row_prev_max = row_curr_max

    # ----- epilogue -----
    row_prev_max += tl.math.log2(row_prev_denom_sum)
    acc = acc / row_prev_denom_sum[:, None] # final softmax normalization
    tl.store(M + off_hz * N_CTX_Q + offs_m, row_prev_max)

    # store output tile via TMA
    out_4d = acc.to(tl.bfloat16).reshape([1, BLOCK_M, 1, HEAD_DIM])
    desc_o.store([b, q_offset, h, 0], out_4d)

def triton_block_sparse_attn_forward(q, k, v, q2k_index, variable_block_sizes):
    """Canonical BSHD launcher for `_attn_fwd_sparse`. Returns (o, M).

    o : (B, S, H, D) bf16   attention output
    M : (B, H, S)   fp32    log-sum-exp (discarded by inference; provided for parity)
    """
    B, S_q, H, D = q.shape
    S_kv = k.shape[1]
    sm_scale = 1.0 / math.sqrt(D)
    topk = q2k_index.shape[-1]
    o = torch.empty_like(q)
    M = torch.empty((B, H, S_q), dtype=torch.float32, device=q.device)
    sq = [S_q * H * D, H * D, D, 1]
    sk = [S_kv * H * D, H * D, D, 1]
    dq = TensorDescriptor(q, shape=[B, S_q, H, D],  strides=sq, block_shape=[1, _BLOCK_M, 1, D])
    dk = TensorDescriptor(k, shape=[B, S_kv, H, D], strides=sk, block_shape=[1, _BLOCK_N, 1, D])
    dv = TensorDescriptor(v, shape=[B, S_kv, H, D], strides=sk, block_shape=[1, _BLOCK_N, 1, D])
    do = TensorDescriptor(o, shape=[B, S_q, H, D],  strides=sq, block_shape=[1, _BLOCK_M, 1, D])
    grid = (triton.cdiv(S_q, _BLOCK_M), B * H, 1)
    _attn_fwd_sparse[grid](
        sm_scale, q2k_index, variable_block_sizes, M, dq, dk, dv, do,
        B, H, S_q, S_kv, HEAD_DIM=D, BLOCK_M=_BLOCK_M, BLOCK_N=_BLOCK_N,
        TOPK=topk, num_warps=_NUM_WARPS, num_stages=_NUM_STAGES,
    )
    return o, M