DeepSeek V4.1

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()

class RMSNorm(nn.Module):
    def __init__(self, dim: int, eps: float = 1e-6):
        super().__init__()
        self.dim = dim
        self.eps = eps
        self.weight = nn.Parameter(torch.ones(dim))

    def forward(self, x: torch.Tensor):
        dtype = x.dtype
        x = x.float()
        var = x.square().mean(-1, keepdim=True)
        x = x * torch.rsqrt(var + self.eps)
        return (self.weight * x).to(dtype)

def precompute_freqs_cis(dim, seqlen, original_seq_len, base, factor, beta_fast, beta_slow) -> torch.Tensor:
    """Rotary frequencies as complex exponentials, one row per position.

    With original_seq_len > 0 this applies YaRN: dimensions whose wavelength already fits inside the
    training context keep their frequency, those far beyond it are divided by `factor`, and the
    `beta_fast`..`beta_slow` band in between is faded across with a linear ramp.
    """
    freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
    if original_seq_len > 0:
        # the dim whose wavelength completes `rotations` turns over the training context
        def corrected_dim(rotations):
            return dim * math.log(original_seq_len / (rotations * 2 * math.pi)) / (2 * math.log(base))

        low = max(math.floor(corrected_dim(beta_fast)), 0)
        high = min(math.ceil(corrected_dim(beta_slow)), dim - 1)
        ramp = ((torch.arange(dim // 2, dtype=torch.float32) - low) / max(high - low, 1e-3)).clamp(0, 1)
        smooth = 1 - ramp
        freqs = freqs / factor * (1 - smooth) + freqs * smooth

    freqs = torch.outer(torch.arange(seqlen), freqs)
    return torch.polar(torch.ones_like(freqs), freqs)


def apply_rotary_emb(x: torch.Tensor, freqs_cis: torch.Tensor, inverse: bool = False) -> torch.Tensor:
    """Rotate `x` in place, taking adjacent element pairs as complex numbers. Accepts [b, s, d] and
    [b, s, h, d]; `inverse` conjugates the rotation, which is how the attention output gets the
    query's rotation removed again so the cache can stay in one shared rotated form."""
    y = x
    x = torch.view_as_complex(x.float().unflatten(-1, (-1, 2)))
    if inverse:
        freqs_cis = freqs_cis.conj()
    if x.ndim == 3:
        freqs_cis = freqs_cis.view(1, x.size(1), x.size(-1))
    else:
        freqs_cis = freqs_cis.view(1, x.size(1), 1, x.size(-1))
    x = torch.view_as_real(x * freqs_cis).flatten(-2)
    y.copy_(x)
    return y