DeepSeek V4.1

def get_window_topk_idxs(window_size: int, bsz: int, seqlen: int, start_pos: int):
    """Which sliding-window cache slots each query attends to; -1 marks a slot holding nothing.

    The cache is a ring of `window_size` slots. Prefill needs one row per query, each seeing its own
    causal window. A decode step has a single query that sees the whole ring, listed oldest first.
    Order within a row does not matter to `sparse_attn`, which handles every slot independently.
    """
    if start_pos == 0:
        end = torch.arange(seqlen).unsqueeze(1)
        idxs = (end - window_size + 1).clamp(0) + torch.arange(min(seqlen, window_size))
        idxs = torch.where(idxs > end, -1, idxs)  # before the sequence started
    else:
        oldest = start_pos % window_size + 1
        idxs = torch.cat([torch.arange(oldest, window_size), torch.arange(oldest)])
        idxs = torch.where(idxs > start_pos, -1, idxs)  # ring still filling
    # sparse_attn needs real [b, m, topk] int32 memory, hence the materializing expand
    return idxs.int().unsqueeze(0).expand(bsz, -1, -1).contiguous()

@tilelang.jit(pass_configs=pass_configs)
def sparse_attn_kernel(h: int, d: int, scale=None):
    """Sparse multi-head attention via index gathering + online softmax (FlashAttention-style).
    For each (batch, seq_pos), gathers top-k KV positions by index, computes attention
    with numerically stable running max/sum, and includes a learnable attn_sink bias."""
    b = T.symbolic("b")
    m = T.symbolic("m")
    n = T.symbolic("n")
    topk = T.symbolic("topk")
    if scale is None:
        scale = (1.0 / d) ** 0.5

    num_stages = 2
    threads = 256
    block = 64
    num_blocks = tilelang.cdiv(topk, block)

    @T.prim_func
    def sparse_attn_kernel_(
        q: T.Tensor[(b, m, h, d), BF16],
        kv: T.Tensor[(b, n, d), BF16],
        o: T.Tensor[(b, m, h, d), BF16],
        attn_sink: T.Tensor[(h,), FP32],
        topk_idxs: T.Tensor[(b, m, topk), INT32],
    ):
        with T.Kernel(m, b, threads=threads) as (bx, by):
            q_shared = T.alloc_shared((h, d), BF16)
            kv_shared = T.alloc_shared((block, d), BF16)
            o_shared = T.alloc_shared((h, d), BF16)
            acc_s_cast = T.alloc_shared((h, block), BF16)

            idxs = T.alloc_fragment(block, INT32)
            acc_s = T.alloc_fragment((h, block), FP32)
            acc_o = T.alloc_fragment((h, d), FP32)
            scores_max = T.alloc_fragment(h, FP32)
            scores_max_prev = T.alloc_fragment(h, FP32)
            scores_scale = T.alloc_fragment(h, FP32)
            scores_sum = T.alloc_fragment(h, FP32)
            sum_exp = T.alloc_fragment(h, FP32)

            T.clear(acc_o)
            T.clear(sum_exp)
            # Finite lower bound instead of -inf: a row with no valid index (all -1) would otherwise
            # produce exp(-inf - (-inf)) = NaN. With a finite bound such rows yield an all-zero output,
            # matching the training kernel's convention.
            T.fill(scores_max, -1e30)
            T.copy(q[by, bx, :, :], q_shared)

            for t in T.Pipelined(num_blocks, num_stages=num_stages):
                for i in T.Parallel(block):
                    idxs[i] = T.if_then_else(t * block + i < topk, topk_idxs[by, bx, t * block + i], -1)
                for i, j in T.Parallel(block, d):
                    kv_shared[i, j] = T.if_then_else(idxs[i] != -1, kv[by, idxs[i], j], 0)
                for i, j in T.Parallel(h, block):
                    acc_s[i, j] = T.if_then_else(idxs[j] != -1, 0, -T.infinity(FP32))
                T.gemm(q_shared, kv_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow)
                for i, j in T.Parallel(h, block):
                    acc_s[i, j] *= scale
                T.copy(scores_max, scores_max_prev)
                T.reduce_max(acc_s, scores_max, dim=1, clear=False)
                for i in T.Parallel(h):
                    scores_scale[i] = T.exp(scores_max_prev[i] - scores_max[i])
                for i, j in T.Parallel(h, block):
                    acc_s[i, j] = T.exp(acc_s[i, j] - scores_max[i])
                T.reduce_sum(acc_s, scores_sum, dim=1)
                for i in T.Parallel(h):
                    sum_exp[i] = sum_exp[i] * scores_scale[i] + scores_sum[i]
                T.copy(acc_s, acc_s_cast)
                for i, j in T.Parallel(h, d):
                    acc_o[i, j] *= scores_scale[i]
                T.gemm(acc_s_cast, kv_shared, acc_o, policy=T.GemmWarpPolicy.FullRow)

            for i in T.Parallel(h):
                sum_exp[i] += T.exp(attn_sink[i] - scores_max[i])
            for i, j in T.Parallel(h, d):
                acc_o[i, j] /= sum_exp[i]
            T.copy(acc_o, o_shared)
            T.copy(o_shared, o[by, bx, :, :])

    return sparse_attn_kernel_


def sparse_attn(q: torch.Tensor, kv: torch.Tensor, attn_sink: torch.Tensor, topk_idxs: torch.Tensor, softmax_scale: float) -> torch.Tensor:
    b, s, h, d = q.size()
    # Pad heads to 16 for kernel efficiency (stripped after)
    if h < 16:
        q = torch.cat([q, q.new_zeros(b, s, 16 - h, d)], dim=2)
        attn_sink = torch.cat([attn_sink, attn_sink.new_zeros(16 - h)])
    o = torch.empty_like(q)
    kernel = sparse_attn_kernel(q.size(2), d, softmax_scale)
    kernel(q, kv, o, attn_sink, topk_idxs)
    if h < 16:
        o = o.narrow(2, 0, h).contiguous()
    return o

class Attention(nn.Module):
    """Latent attention over two KV sources at once, concatenated into one `sparse_attn` call: a
    sliding window of raw KV, plus -- when compress_ratio > 0 -- `index_topk` compressed positions
    reaching further back. Q and the output projection are both low-rank, the latter grouped.

    compress_ratio > 0 does not mean the layer compresses its own KV: only kv_source_layers do,
    the rest read that same cache.
    """

    def __init__(self, layer_id: int, args: ModelArgs):
        super().__init__()
        self.layer_id = layer_id
        self.dim = args.dim
        self.n_heads = args.n_heads
        self.n_local_heads = args.n_heads // world_size
        self.q_lora_rank = args.q_lora_rank
        self.o_lora_rank = args.o_lora_rank
        self.head_dim = args.head_dim
        self.rope_head_dim = args.rope_head_dim
        self.nope_head_dim = args.head_dim - args.rope_head_dim
        self.n_groups = args.o_groups
        self.n_local_groups = self.n_groups // world_size
        self.window_size = args.window_size
        self.compress_ratio = args.compress_ratios[layer_id]
        self.eps = args.norm_eps

        self.attn_sink = nn.Parameter(torch.empty(self.n_local_heads, dtype=torch.float32))
        self.wq_a = Linear(self.dim, self.q_lora_rank)
        self.q_norm = RMSNorm(self.q_lora_rank, self.eps)
        self.wq_b = ColumnParallelLinear(self.q_lora_rank, self.n_heads * self.head_dim)
        self.wkv = Linear(self.dim, self.head_dim)
        self.kv_norm = RMSNorm(self.head_dim, self.eps)
        self.wo_a = ColumnParallelLinear(
            self.n_heads * self.head_dim // self.n_groups,
            self.n_groups * args.o_lora_rank,
            dtype=torch.bfloat16,
        )
        self.wo_b = RowParallelLinear(self.n_groups * args.o_lora_rank, self.dim)
        self.softmax_scale = self.head_dim**-0.5

        is_backbone = layer_id < args.n_layers
        self.is_kv_source = is_backbone and layer_id in args.kv_source_layers
        self.is_index_source = is_backbone and layer_id in args.index_source_layers
        self.compressor: Compressor | None = None
        self.indexer: Indexer | None = None
        if self.is_kv_source:
            self.compressor = Compressor(args, layer_id)
        if self.is_index_source:
            self.indexer = Indexer(args, layer_id)

        self.window_kv_cache: torch.Tensor
        self.register_buffer(
            "window_kv_cache",
            torch.zeros(args.max_batch_size, args.window_size, self.head_dim),
            persistent=False,
        )
        if self.is_kv_source:
            self.compress_kv_cache: torch.Tensor
            self.register_buffer(
                "compress_kv_cache",
                torch.zeros(
                    args.max_batch_size,
                    args.max_seq_len // self.compress_ratio,
                    self.head_dim,
                ),
                persistent=False,
            )
        if self.compress_ratio:
            original_seq_len, rope_theta = (
                args.original_seq_len,
                args.compress_rope_theta,
            )
        else:
            # disable YaRN and use base rope_theta in pure sliding-window attention
            original_seq_len, rope_theta = 0, args.rope_theta
        freqs_cis = precompute_freqs_cis(
            self.rope_head_dim,
            args.max_seq_len,
            original_seq_len,
            rope_theta,
            args.rope_factor,
            args.beta_fast,
            args.beta_slow,
        )
        self.freqs_cis: torch.Tensor
        self.register_buffer("freqs_cis", freqs_cis, persistent=False)