DeepSeek V4.1

class Compressor(nn.Module):
    """Pools `compress_ratio` consecutive tokens into one KV latent with a learned softmax gate.

    Returns the latent before RoPE, or None while a group is still filling up -- so during decode it
    only yields every `compress_ratio` steps, holding the partial group in `kv_state`/`score_state`.
    Pre-RoPE is deliberate: the indexer needs the unrotated form, so Attention rotates afterwards.
    """

    def __init__(self, args: ModelArgs, layer_id: int):
        super().__init__()
        compress_ratio = args.compress_ratios[layer_id]
        head_dim = args.head_dim
        self.compress_ratio = compress_ratio
        self.head_dim = head_dim
        self.norm = RMSNorm(head_dim, args.norm_eps)
        # ratio 1 is a plain projection, so it stays in the checkpoint's bf16; the softmax pooling
        # above ratio 1 runs in fp32, so those weights are promoted to fp32 to match
        self.wkv = Linear(args.dim, head_dim, dtype=torch.float32 if compress_ratio > 1 else torch.bfloat16)
        if compress_ratio > 1:
            self.wgate = Linear(args.dim, head_dim, dtype=torch.float32)
            # tail of an incomplete group, carried across decode steps
            self.kv_state: torch.Tensor
            self.score_state: torch.Tensor
            state_shape = (args.max_batch_size, compress_ratio, head_dim)
            self.register_buffer("kv_state", torch.zeros(state_shape, dtype=torch.float32), persistent=False)
            self.register_buffer(
                "score_state", torch.full(state_shape, -torch.inf, dtype=torch.float32), persistent=False
            )

    def forward(self, x: torch.Tensor, start_pos: int) -> torch.Tensor | None:
        bsz, seqlen, _ = x.size()
        ratio, dtype = self.compress_ratio, x.dtype
        if ratio == 1:  # one token per group: nothing to pool, so no gate and no fp32
            return self.norm(self.wkv(x))

        x = x.float()
        kv, score = self.wkv(x), self.wgate(x)
        if start_pos == 0:
            should_compress = seqlen >= ratio
            remainder = seqlen % ratio
            cutoff = seqlen - remainder
            if remainder:  # trailing partial group waits in the state
                kv, self.kv_state[:bsz, :remainder] = kv.split([cutoff, remainder], dim=1)
                score, self.score_state[:bsz, :remainder] = score.split([cutoff, remainder], dim=1)
            kv = kv.unflatten(1, (-1, ratio))
            score = score.unflatten(1, (-1, ratio))
            kv = (kv * score.softmax(dim=2)).sum(dim=2)
        else:  # one token per step: fill a slot, and pool only when the group just completed
            should_compress = (start_pos + 1) % ratio == 0
            slot = start_pos % ratio
            self.kv_state[:bsz, slot] = kv.squeeze(1)
            self.score_state[:bsz, slot] = score.squeeze(1)
            if should_compress:
                kv = (self.kv_state[:bsz] * self.score_state[:bsz].softmax(dim=1)).sum(dim=1, keepdim=True)
        if not should_compress:
            return None
        return self.norm(kv.to(dtype))

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)