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()

    def forward(self, x: torch.Tensor, start_pos: int):
        bsz, seqlen, _ = x.size()
        freqs_cis = self.freqs_cis[start_pos : start_pos + seqlen]
        rd = self.rope_head_dim

        qr = self.q_norm(self.wq_a(x))
        q = self.wq_b(qr).unflatten(-1, (self.n_local_heads, self.head_dim))
        apply_rotary_emb(q[..., -rd:], freqs_cis)

        kv, topk_idxs = self._window_kv(x, freqs_cis, start_pos)
        if self.compress_ratio:
            compress_kv, compress_idxs = self._compress_kv(x, qr, start_pos, kv.size(1))
            kv = torch.cat([kv, compress_kv], dim=1)
            topk_idxs = torch.cat([topk_idxs, compress_idxs], dim=-1)

        o = sparse_attn(q, kv, self.attn_sink, topk_idxs, self.softmax_scale)
        apply_rotary_emb(o[..., -rd:], freqs_cis, True)

        # wo_a is block-diagonal over groups (each projects only its own heads), hence einsum not
        # Linear. convert.py dequantizes it to bf16; an fp8 grouped GEMM would halve the memory.
        o = o.view(bsz, seqlen, self.n_local_groups, -1)
        wo_a = self.wo_a.weight.view(self.n_local_groups, self.o_lora_rank, -1)
        o = torch.einsum("bsgd,grd->bsgr", o, wo_a)
        x = self.wo_b(o.flatten(2))
        return x

class Indexer(torch.nn.Module):
    """Keeps the `index_topk` best compressed positions per query.

    A small side attention: fp4 query heads against one shared key per compressed position, scores
    rectified then combined by `weights_proj`. With a candidate source this is the second of two
    levels; `select_candidate_blocks` is the first.
    """

    def __init__(self, args: ModelArgs, layer_id: int):
        super().__init__()
        # the index keys are derived from the compressor's latent, so only a layer that compresses
        # its own KV can produce them; every other indexer reads them from that layer's cache
        self.owns_k = layer_id in args.kv_source_layers
        self.compress_ratio = args.compress_ratios[layer_id]
        self.is_candidate_source = layer_id == args.candidate_source_layer
        self.uses_candidates = 0 <= args.candidate_source_layer < layer_id
        self.candidate_topk_blocks = args.candidate_topk_blocks
        self.candidate_block_size = args.candidate_block_size
        self.dim = args.dim
        self.n_heads = args.index_n_heads
        self.n_local_heads = args.index_n_heads // world_size
        self.index_head_dim = args.index_head_dim
        self.rope_head_dim = args.rope_head_dim
        self.index_topk = args.index_topk
        self.q_lora_rank = args.q_lora_rank
        self.softmax_scale = self.index_head_dim**-0.5
        self.wq_b = ColumnParallelLinear(self.q_lora_rank, self.n_heads * self.index_head_dim)
        self.weights_proj = ColumnParallelLinear(self.dim, self.n_heads, dtype=torch.bfloat16)
        self.freqs_cis: torch.Tensor | None = None
        if self.owns_k:
            self.wk = Linear(args.head_dim, self.index_head_dim, dtype=torch.bfloat16)
            self.k_norm = RMSNorm(self.index_head_dim, args.norm_eps)
            self.k_cache: torch.Tensor
            self.register_buffer(
                "k_cache",
                torch.zeros(args.max_batch_size, args.max_seq_len // self.compress_ratio, args.index_head_dim),
                persistent=False,
            )

    def forward(self, x: torch.Tensor, qr: torch.Tensor, latent: torch.Tensor, start_pos: int, offset: int):
        """`latent` is this layer's RoPE-free compressed latent, None when this layer does not
        compress or when its current group is still incomplete. An index-key owner turns it into
        index keys here, which has to happen before Attention overwrites that same storage with
        the RoPE'd, quantized values."""
        assert self.freqs_cis is not None
        bsz, seqlen, _ = x.size()
        ratio, rd, end_pos = self.compress_ratio, self.rope_head_dim, start_pos + seqlen

        # latent is None while a group is still filling up, so there is nothing to publish yet
        if self.owns_k and latent is not None:
            # a latent stands for the first token of its group, so group j takes position j * ratio
            freqs = (
                self.freqs_cis[: seqlen - seqlen % ratio : ratio]
                if start_pos == 0
                else self.freqs_cis[start_pos + 1 - ratio].unsqueeze(0)
            )
            k = self.k_norm(self.wk(latent))
            apply_rotary_emb(k[..., -rd:], freqs)
            fp4_act_quant(k, fp4_block_size, True)
            self.k_cache[:bsz, start_pos // ratio : start_pos // ratio + k.size(1)] = k
            shared_attn.index_k = self.k_cache

        q = self.wq_b(qr).unflatten(-1, (self.n_local_heads, self.index_head_dim))
        apply_rotary_emb(q[..., -rd:], self.freqs_cis[start_pos:end_pos])
        fp4_act_quant(q, fp4_block_size, True)

        index_k = shared_attn.index_k[:bsz, : end_pos // ratio]
        weights = self.weights_proj(x) * (self.softmax_scale * self.n_heads**-0.5)
        index_score = torch.einsum("bshd,btd->bsht", q, index_k)
        index_score = (index_score.relu_() * weights.unsqueeze(-1)).sum(dim=2)
        if world_size > 1:
            dist.all_reduce(index_score)

        # how many compressed positions each query can see: a block becomes visible once the query
        # has passed its last token. One query per decode step, so there it is just a number.
        if start_pos == 0:
            compress_lens = (torch.arange(1, seqlen + 1, device=x.device) // ratio).unsqueeze(-1)
            index_score.masked_fill_(torch.arange(seqlen // ratio, device=x.device) >= compress_lens, -torch.inf)
        else:
            compress_lens = end_pos // ratio

        if self.is_candidate_source:
            shared_attn.candidates = select_candidate_blocks(
                index_score, compress_lens, self.candidate_topk_blocks, self.candidate_block_size
            )
        elif self.uses_candidates:
            # level two: score with our own weights, but only inside the source's candidate blocks
            index_score = index_score.masked_fill(~shared_attn.candidates, -torch.inf)

        # top-k by score, re-sorted into position order; unreachable -> -1, rest shifted by offset
        topk = min(self.index_topk, end_pos // ratio)
        idxs = index_score.topk(topk, dim=-1, sorted=False).indices.sort(dim=-1).values
        return torch.where(idxs < compress_lens, idxs + offset, -1).int()