DeepSeek V4.1

class Block(nn.Module):
    """A block whose residual stream is `hc_mult` parallel copies (Hyper-Connections).

    Attention and FFN each sit between `hc_pre` (collapse the copies into one sublayer input) and
    `hc_post` (expand back out, mixing the residual in through `comb`). `hc_mixes` derives all three
    coefficient sets from the stream itself, `comb` made doubly stochastic by Sinkhorn.

    The coefficients a sublayer computes are used by the *next* one -- see `forward`.
    """

    attention_cls = Attention

    def __init__(
        self,
        layer_id: int,
        args: ModelArgs,
        engram_layout: EngramLayout | None = None,
    ):
        super().__init__()
        self.layer_id = layer_id
        self.norm_eps = args.norm_eps
        self.attn = self.attention_cls(layer_id, args)
        self.ffn = MoE(layer_id, args)
        self.engram = None
        if engram_layout is not None and layer_id in engram_layout.layer_ids:
            self.engram = Engram(args, layer_id, engram_layout)
        self.attn_norm = RMSNorm(args.dim, self.norm_eps)
        self.ffn_norm = RMSNorm(args.dim, self.norm_eps)
        self.hc_mult = hc_mult = args.hc_mult
        self.hc_sinkhorn_iters = args.hc_sinkhorn_iters
        self.hc_eps = args.hc_eps
        mix_hc = (2 + hc_mult) * hc_mult
        hc_dim = hc_mult * args.dim
        with set_dtype(torch.float32):
            self.hc_attn_fn = nn.Parameter(torch.empty(mix_hc, hc_dim))
            self.hc_ffn_fn = nn.Parameter(torch.empty(mix_hc, hc_dim))
            self.hc_attn_base = nn.Parameter(torch.empty(mix_hc))
            self.hc_ffn_base = nn.Parameter(torch.empty(mix_hc))
            self.hc_attn_scale = nn.Parameter(torch.empty(3))
            self.hc_ffn_scale = nn.Parameter(torch.empty(3))

    def forward(
        self,
        x: torch.Tensor,
        start_pos: int,
        pre_mix: torch.Tensor,
        image_mask: torch.Tensor | None,
        *attn_args,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        """`pre_mix` collapses the hc_mult copies down to one input for this block's attention. Each
        sub-block's own `hc_mixes` produces the mix for the *next* one, so attention uses what the
        previous layer's FFN produced and the FFN uses what this attention produced.

        image_mask: [b, s] bool, True inside image spans (selects the VL routing bias)."""
        residual = x
        attn_pre, attn_post, attn_comb = self.hc_mixes(x, self.hc_attn_fn, self.hc_attn_scale, self.hc_attn_base)
        x = self.hc_pre(x, pre_mix)
        x = self.attn_norm(x)
        x = self.attn(x, start_pos, *attn_args)
        x = self.hc_post(x, residual, attn_post, attn_comb)

        residual = x
        ffn_pre, ffn_post, ffn_comb = self.hc_mixes(x, self.hc_ffn_fn, self.hc_ffn_scale, self.hc_ffn_base)
        x = self.hc_pre(x, attn_pre)
        x = self.ffn_norm(x)
        x = self.ffn(x, image_mask)
        x = self.hc_post(x, residual, ffn_post, ffn_comb)
        return x, ffn_pre

    def hc_mixes(self, x: torch.Tensor, hc_fn: torch.Tensor, hc_scale: torch.Tensor, hc_base: torch.Tensor):
        """x: [b,s,hc,d], hc_fn: [mix_hc, hc*d], hc_scale: [3], hc_base: [mix_hc]. Returns the
        pre / post / comb coefficients, split out of one projection of the flattened stream."""
        # normalized over the whole flattened hc*d stream, one statistic per token
        x = x.flatten(2).float()
        rsqrt = torch.rsqrt(x.square().mean(-1, keepdim=True) + self.norm_eps)
        mixes = F.linear(x, hc_fn) * rsqrt
        return hc_split_sinkhorn(mixes, hc_scale, hc_base, self.hc_mult, self.hc_sinkhorn_iters, self.hc_eps)

    def hc_pre(self, x: torch.Tensor, pre_mix: torch.Tensor):
        """Collapse the hc copies into one, weighted by pre_mix. [b,s,hc,d] x [b,s,hc] -> [b,s,d]"""
        y = torch.sum(pre_mix.unsqueeze(-1) * x.float(), dim=2)
        return y.to(x.dtype)

    def hc_post(self, x: torch.Tensor, residual: torch.Tensor, post: torch.Tensor, comb: torch.Tensor):
        """Expand the sublayer output back to hc copies and mix the residual in through `comb`.
        x: [b,s,d], residual: [b,s,hc,d], post: [b,s,hc], comb: [b,s,hc,hc] -> [b,s,hc,d]"""
        y = post.unsqueeze(-1) * x.unsqueeze(-2) + torch.sum(comb.unsqueeze(-1) * residual.unsqueeze(-2), dim=2)
        return y.type_as(x)

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)

    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

{
    "vocab_size": 129280,
    "dim": 5120,
    "moe_inter_dim": 2304,
    "n_layers": 40,
    "n_mtp_layers": 3,
    "dspark_block_size": 5,
    "dspark_noise_token_id": 128799,
    "dspark_target_layer_ids": [37, 38, 39],
    "dspark_markov_rank": 256,
    "dspark_n_routed_experts": 128,
    "dspark_n_activated_experts": 3,
    "n_heads": 64,
    "n_routed_experts": 384,
    "n_shared_experts": 1,
    "n_activated_experts": 6,
    "score_func": "sqrtsoftplus",
    "route_scale": 1.5,
    "swiglu_limit": 10.0,
    "q_lora_rank": 1280,
    "head_dim": 512,
    "rope_head_dim": 64,
    "norm_eps": 1e-20,
    "o_groups": 8,
    "o_lora_rank": 1024,
    "window_size": 128,
    "kv_source_layers": [2, 8, 14, 20],
    "index_source_layers": [2, 8, 14, 20, 24, 28, 32, 36],
    "original_seq_len": 65536,
    "rope_theta": 10000,
    "rope_factor": 16,
    "beta_fast": 32,
    "beta_slow": 1,
    "index_n_heads": 32,
    "index_head_dim": 128,
    "index_topk": 512,
    "candidate_source_layer": 20,
    "candidate_topk_blocks": 2048,
    "candidate_block_size": 8,
    "hc_mult": 4,
    "hc_sinkhorn_iters": 20,
    "hc_eps": 1e-06,
    "engram_layer_ids": [1, 14],
    "engram_vocab_size": 16000000,
    "engram_num_embeddings": [384006168, 384016682],
    "engram_max_ngram_size": 4,
    "engram_pad_id": 2,
    "engram_compressed_vocab_size": 99092,
    "dtype": "fp8",
    "expert_dtype": "fp4",
    "compress_rope_theta": 160000,
    "compress_ratios": [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
    "vision_n_layers": 32,
    "vision_dim": 1024,
    "vision_n_heads": 16,
    "vision_inter_dim": 2816,
    "vision_patch_size": 14,
    "vision_downsample_ratio": 3,
    "vision_max_n_token": 1024,
    "vision_min_pixels": 295936,
    "vision_max_wh_ratio": null,
    "image_token_id": 129264,
    "engram_n_heads": 8,
    "engram_head_dim": 256,
    "vision_rope_theta": 10000
}