Upload CrossDNA 8.1M (model files only)

8.1M/config.json

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+{
+  "_name_or_path": "CrossDNA-pretrain",
+  "model_type": "crossdna",
+  "architectures": ["CrossDNAForMaskedLM"],
+
+  "auto_map": {
+    "AutoConfig": "configuration_crossdna.CrossDNAConfig",
+    "AutoModelForMaskedLM": "modeling_crossdna.CrossDNAForMaskedLM",
+    "AutoTokenizer": "tokenization_crossdna.CrossDNATokenizer"
+  },
+
+  "torch_dtype": "float32",
+
+  "alphabet_size": 5,
+  "d_model": 128,
+  "block_size": 1024,
+  "depth": 6,
+  "drop_path_rates": [0.0, 0.05],
+  "dropout": 0.15,
+
+  "pretrain": true,
+  "for_representation": false,
+  "use_s_scan": true,
+  "use_bridge": true,
+  "use_mem": false,
+  "use_rc_kl": false,
+  "use_barlow": false,
+  "use_tv": false,
+
+  "sem_max_weight": 0.12,
+  "sem_warmup_steps": 10000,
+  "aux_ce_weight": 0.0,
+  "gate_freeze_steps": 5000,
+  "detach_gate": false,
+  "gate_sup_weight": 0.02,
+  "gate_sup_warmup_steps": 500,
+  "gate_temp": 2.0,
+
+  "transformer_cfg": {
+    "hidden_size": 128,
+    "norm_eps": 1e-5,
+    "max_position_embeddings": 1024,
+    "hidden_ratio": 4.0,
+    "hidden_act": "swish",
+    "fuse_swiglu": true,
+    "attn": {
+      "num_heads": 8,
+      "num_kv_heads": 8,
+      "qkv_bias": false,
+      "window_size": 2048,
+      "rope_theta": 10000
+    }
+  },
+  "comba_cfg": {
+    "hidden_size": 128,
+    "expand_v": 1,
+    "head_dim": 64,
+    "num_heads": 8,
+    "use_gate": true,
+    "mode": "chunk",
+    "use_short_conv": true,
+    "correction_factor": 0.02,
+    "conv_size": 4,
+    "norm_eps": 1e-5
+  },
+
+  "pad_token_id": 4,
+  "bos_token_id": 2,
+  "sep_token_id": 1,
+  "cls_token_id": 0,
+  "mask_token_id": 3,
+
+  "vocab_size": 5
+}

8.1M/configuration_crossdna.py

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+from transformers import PretrainedConfig
+
+class CrossDNAConfig(PretrainedConfig):
+    model_type = "crossdna"
+
+    def __init__(
+        self,
+        
+        alphabet_size=5,
+        d_model=128,
+        block_size=1024,
+        depth=6,
+        drop_path_rates=(0.0, 0.05),
+        dropout=0.15,
+
+        pretrain=True,
+        for_representation=False,
+        use_s_scan=True,
+        use_bridge=True,
+        use_mem=False,
+        use_rc_kl=False,
+        use_barlow=False,
+        use_tv=False,
+
+        sem_max_weight=0.12,
+        sem_warmup_steps=10000,
+        aux_ce_weight=0.0,
+        gate_freeze_steps=5000,
+        detach_gate=False,
+        gate_sup_weight=0.02,
+        gate_sup_warmup_steps=500,
+        gate_temp=2.0,
+
+        
+        transformer_cfg=None,
+        comba_cfg=None,
+
+        
+        pad_token_id=4,   # [PAD]
+        bos_token_id=2,   # [BOS]
+        sep_token_id=1,   # [SEP]
+        cls_token_id=0,   # [CLS]
+        mask_token_id=3,  # [MASK]
+        **kwargs
+    ):
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            **kwargs
+        )
+        self.alphabet_size = alphabet_size
+        self.d_model = d_model
+        self.block_size = block_size
+        self.depth = depth
+        self.drop_path_rates = list(drop_path_rates) if drop_path_rates is not None else None
+        self.dropout = dropout
+
+        self.pretrain = pretrain
+        self.for_representation = for_representation
+        self.use_s_scan = use_s_scan
+        self.use_bridge = use_bridge
+        self.use_mem = use_mem
+        self.use_rc_kl = use_rc_kl
+        self.use_barlow = use_barlow
+        self.use_tv = use_tv
+
+        self.sem_max_weight = sem_max_weight
+        self.sem_warmup_steps = sem_warmup_steps
+        self.aux_ce_weight = aux_ce_weight
+        self.gate_freeze_steps = gate_freeze_steps
+        self.detach_gate = detach_gate
+        self.gate_sup_weight = gate_sup_weight
+        self.gate_sup_warmup_steps = gate_sup_warmup_steps
+        self.gate_temp = gate_temp
+
+        self.transformer_cfg = transformer_cfg or {
+            "hidden_size": d_model,
+            "norm_eps": 1e-5,
+            "max_position_embeddings": 1024,  # 会被dataset.max_length替换；此处给默认
+            "hidden_ratio": 4.0,
+            "hidden_act": "swish",
+            "fuse_swiglu": True,
+            "attn": {
+                "num_heads": 8,
+                "num_kv_heads": 8,
+                "qkv_bias": False,
+                "window_size": 2048,
+                "rope_theta": 10000
+            }
+        }
+        self.comba_cfg = comba_cfg or {
+            "hidden_size": d_model,
+            "expand_v": 1,
+            "head_dim": 64,
+            "num_heads": 8,
+            "use_gate": True,
+            "mode": "chunk",
+            "use_short_conv": True,
+            "correction_factor": 0.02,
+            "conv_size": 4,
+            "norm_eps": 1e-5,
+        }
+
+        # 方便AutoModel推断 vocab_size
+        self.vocab_size = self.alphabet_size

8.1M/model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:886fc527a7103aa27d4f7f41626a9a283f400983e91d5e09247243d79b1dac63
+size 64417904

8.1M/modeling_crossdna.py

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+
+import math
+import torch
+import copy
+import torch.nn as nn
+from torch import amp
+import torch.nn.functional as F
+from functools import partial
+from contextlib import contextmanager
+from collections import namedtuple
+from typing import Dict, Optional, Tuple, Any
+
+from transformers import PreTrainedModel
+from transformers.modeling_outputs import MaskedLMOutput
+
+# 编译ckpt到bin文件时候打开注释。
+import os as _os, torch as _torch
+if _os.environ.get("DISABLE_TORCH_COMPILE", "1") == "1" and hasattr(_torch, "compile"):
+    def _no_compile(fn=None, *args, **kwargs):
+        if fn is None:
+            def deco(f): return f
+            return deco
+        return fn
+    _torch.compile = _no_compile
+print("torch.compile =>", torch.compile)
+
+
+from fla.layers import comba
+from fla.layers.attn import Attention
+from fla.modules import GatedMLP as SambaMLP
+from fla.modules import RMSNorm
+from torch.cuda.amp import autocast
+
+
+try:
+    # 在 Transformers 的动态包  transformers_modules.<hash>.*  环境下走相对导入
+    from .configuration_crossdna import CrossDNAConfig
+except ImportError:
+    # 直接本地运行（不是通过 from_pretrained 动态加载）时也能跑
+    from configuration_crossdna import CrossDNAConfig
+
+
+
+# ========================
+# Utils
+# ========================
+def complement(seq: torch.Tensor) -> torch.Tensor:
+    # A=0, C=1, G=2, T=3, N=4
+    comp = 3 - seq
+    comp[seq == 4] = 4
+    return comp
+
+
+def reverse_complement(seq: torch.Tensor) -> torch.Tensor:
+    comp = complement(seq)
+    return torch.flip(comp, dims=[1])
+
+
+def make_complement_perm(C=5, device=None, dtype=torch.float32):
+    # A=0,C=1,G=2,T=3,N=4  ->  T,A,C,G,N
+    perm = torch.tensor([3, 0, 2, 1, 4], device=device)
+    P = torch.zeros(C, C, device=device, dtype=dtype)
+    P[torch.arange(C, device=device), perm] = 1.0
+    return P, perm
+
+
+def ensure_finite(x: torch.Tensor, name: str):
+    # 只检查，不“吃掉”数值问题
+    if not torch.isfinite(x).all():
+        raise FloatingPointError(f"Non-finite values detected in {name}")
+    return x
+
+
+def linear_warmup_weight(step: int, warmup_steps: int, max_w: float):
+    if warmup_steps <= 0:
+        return max_w
+    if step <= 0:
+        return 0.0
+    if step >= warmup_steps:
+        return max_w
+    return max_w * (step / warmup_steps)
+
+
+def preferred_amp_dtype():
+    try:
+        if torch.cuda.is_available() and torch.cuda.is_bf16_supported():
+            return torch.bfloat16
+    except Exception:
+        pass
+    return torch.float16
+
+
+# ========================
+# RC 一致性（可选损失）
+# ========================
+def rc_consistency_kl(logits_A, logits_B_fwd, P, tau: float = 1.0, eps: float = 1e-6):
+    zA = logits_A.float() / tau
+    zB = logits_B_fwd.float() / tau
+    pA = F.softmax(zA, dim=-1)
+    logpA = F.log_softmax(zA, dim=-1)
+    pB = F.softmax(zB, dim=-1)
+    pB_comp = torch.matmul(pB, P.t()).clamp_min(eps)
+    logpB_comp = pB_comp.log()
+    kl = (pA * (logpA - logpB_comp)).sum(dim=-1).mean()
+    return kl * (tau * tau)
+
+
+def rc_consistency_bidirectional_stopgrad(logits_A, logits_B_fwd, P, tau: float = 1.5, eps: float = 1e-6):
+    zA = logits_A.float() / tau
+    zB = logits_B_fwd.float() / tau
+    with torch.no_grad():
+        pB_t = torch.matmul(F.softmax(zB, dim=-1), P.t()).clamp_min(eps)
+        logpB_t = pB_t.log()
+    loss_A = F.kl_div(F.log_softmax(zA, dim=-1), logpB_t, reduction="batchmean", log_target=True)
+    with torch.no_grad():
+        pA_t = torch.matmul(F.softmax(zA, dim=-1), P.t()).clamp_min(eps)
+        logpA_t = pA_t.log()
+    loss_B = F.kl_div(F.log_softmax(zB, dim=-1), logpA_t, reduction="batchmean", log_target=True)
+    return 0.5 * (tau * tau) * (loss_A + loss_B)
+
+
+# ========================
+# Barlow & TV（可选）
+# ========================
+def barlow_strand_loss_v2(z1, z2, λ_off=0.04, λ_diag=0.04, eps=1e-3):
+    """稳定 Barlow：方差项 + 对角/非对角，z1,z2:[B,L,H] 已对齐到正向"""
+    B, L, H = z1.shape
+    n = B * L
+    z1 = z1.reshape(n, H)
+    z2 = z2.reshape(n, H)
+
+    def _std(z):
+        var = z.var(dim=0, unbiased=False)
+        return torch.sqrt(var + eps)
+
+    std1, std2 = _std(z1), _std(z2)
+    var_term = (F.relu(1 - std1).pow(2).mean() + F.relu(1 - std2).pow(2).mean())
+
+    z1 = (z1 - z1.mean(0)) / (std1 + eps)
+    z2 = (z2 - z2.mean(0)) / (std2 + eps)
+    c = (z1.t() @ z2) / max(1, n)  # [H,H]
+    diag = torch.diagonal(c)
+    off = c - torch.diag_embed(diag)
+    cov = λ_diag * (1 - diag).pow(2).mean() + λ_off * off.pow(2).mean()
+    return var_term + cov
+
+
+def tv_mixed(h: torch.Tensor):
+    """一阶 L1 + 二阶 L2，总体平滑；h:[B,L,H]"""
+    d1 = h[:, 1:, :] - h[:, :-1, :]
+    d2 = d1[:, 1:, :] - d1[:, :-1, :]
+    return d1.abs().mean() + d2.pow(2).mean()
+
+class Mlp(nn.Module):
+    """两层线性 + GELU，可返回 residual"""
+
+    def __init__(self, input_dimension, hidden_dimension=None, output_dimension=None,
+                 activation=F.gelu, return_residual=False):
+        super().__init__()
+        self.return_residual = return_residual
+        hd = hidden_dimension or input_dimension
+        od = output_dimension or input_dimension
+        self.linear1 = nn.Linear(input_dimension, hd)
+        self.activation = activation
+        self.linear2 = nn.Linear(hd, od)
+
+    def forward(self, x: torch.Tensor):
+        h = self.activation(self.linear1(x))
+        y = self.linear2(h)
+        return (y, x) if self.return_residual else y
+
+
+def create_comba_cls(comba_kwargs=None, device=None, dtype=None):
+    """安全工厂：返回一个可调用的 Comba 构造器（partial）"""
+    factory_kwargs = {}
+    if device is not None:
+        factory_kwargs["device"] = device
+    if dtype is not None:
+        factory_kwargs["dtype"] = dtype
+    try:
+        base_kwargs = dict(comba_kwargs or {})
+        mixer_cls = partial(comba.Comba, **base_kwargs, **factory_kwargs)
+    except ImportError:
+        class FallbackComba(nn.Module):
+            def forward(self, x, *args, **kwargs):
+                return x
+        mixer_cls = lambda *args, **kwargs: FallbackComba()
+    return mixer_cls
+
+
+# ========================
+# SWA block（去手动 FP16，统一 AMP）
+# ========================
+class SlidingWindowAttention(nn.Module):
+    """
+    RMSNorm -> Sliding-window Attention -> Residual -> RMSNorm -> Gated MLP -> Residual
+    """
+
+    def __init__(self, config: Any):
+        super().__init__()
+
+        
+        if isinstance(config, dict):
+            c = config
+        else:
+            try:
+                c = vars(config)  
+            except Exception as e:
+                raise TypeError(f"transformer_cfg must be dict-like, got {type(config)}") from e
+
+        attn_cfg = c["attn"]
+       
+        self.mixer_norm = RMSNorm(hidden_size=c["hidden_size"], eps=c.get("norm_eps", 1e-5))
+        self.mixer = Attention(
+            hidden_size=c["hidden_size"],
+            num_heads=attn_cfg["num_heads"],
+            num_kv_heads=attn_cfg["num_kv_heads"],
+            qkv_bias=attn_cfg["qkv_bias"],
+            window_size=attn_cfg["window_size"],
+            rope_theta=attn_cfg["rope_theta"],
+            max_position_embeddings=c["max_position_embeddings"]
+        )
+        self.mlp_norm = RMSNorm(c["hidden_size"], eps=c.get("norm_eps", 1e-5))
+        self.mlp = SambaMLP(
+            hidden_size=c["hidden_size"],
+            hidden_ratio=c["hidden_ratio"],
+            hidden_act=c["hidden_act"],
+            fuse_swiglu=c["fuse_swiglu"]
+        )
+        self.pre_scale = 1.0 / math.sqrt(2.0)
+
+    def forward(self, hidden_states: torch.Tensor,
+                cache_params: Optional[Any] = None, **kwargs) -> Tuple[torch.Tensor, Any]:
+        residual = hidden_states
+        x = self.mixer_norm(hidden_states)
+
+        amp_dtype = preferred_amp_dtype()
+        with amp.autocast("cuda", enabled=True, dtype=amp_dtype):
+            x_scaled = x * self.pre_scale
+            attn_out, _, cache_params = self.mixer(
+                hidden_states=x_scaled,
+                past_key_values=cache_params,
+                **kwargs
+            )
+            attn_out = attn_out / self.pre_scale
+
+        ensure_finite(attn_out, "attention_out")
+        h = residual + attn_out.to(x.dtype)
+
+        residual = h
+        x = self.mlp_norm(h)
+        with amp.autocast("cuda", enabled=True, dtype=amp_dtype):
+            x = self.mlp(x, **kwargs)
+        h = residual + x
+        ensure_finite(h, "block_output")
+        return h, cache_params
+
+
+# ========================
+# Enhanced Hybrid Core (Comba + SWA + gating)
+# ========================
+class EnhancedHybridCore(nn.Module):
+    def __init__(self, hidden_dim, comba_cfg, transformer_cfg, layer_idx=0, device=None, dtype=None):
+        super().__init__()
+        self.comba_cls = create_comba_cls(comba_kwargs=comba_cfg, device=device, dtype=dtype)
+        try:
+            self.comba = self.comba_cls(layer_idx=layer_idx)
+        except TypeError:
+            self.comba = self.comba_cls()
+        self.transformer = SlidingWindowAttention(config=transformer_cfg)
+        self.gate = nn.Linear(hidden_dim * 2, hidden_dim)
+        self.out_norm = nn.LayerNorm(hidden_dim)
+
+    @staticmethod
+    def _first(x):
+        return x[0] if isinstance(x, tuple) else x
+
+    def forward(self, x):
+        # x: [B, l, H]
+        m_out = self._first(self.comba(x))
+        t_out, _ = self.transformer(m_out)
+        concat = torch.cat([m_out, t_out], dim=-1)
+        g = torch.sigmoid(self.gate(concat))
+        fused = g * t_out + (1 - g) * m_out
+        y = self.out_norm(fused)
+        ensure_finite(y, "EnhancedHybridCore.out")
+        return y
+
+
+# ========================
+# Deep Branch —— 与原版一致
+# ========================
+class DeepEnhancedBranch(nn.Module):
+    def __init__(self, hidden_dim: int, comba_cfg: Dict | None, transformer_cfg: Any, depth: int = 4,
+                 drop_path_rates=None, *, device=None, dtype=None):
+        super().__init__()
+        self.layers = nn.ModuleList()
+        if drop_path_rates is None:
+            rates = [0.05 * (i / max(1, depth - 1)) for i in range(depth)]
+        elif isinstance(drop_path_rates, (float, int)):
+            rates = [float(drop_path_rates)] * depth
+        else:
+            rates = list(drop_path_rates) + [drop_path_rates[-1]] * (depth - len(drop_path_rates))
+        for i in range(depth):
+            layer_cfg = transformer_cfg.copy()
+            layer_cfg["drop_path_prob"] = rates[i]
+            self.layers.append(EnhancedHybridCore(hidden_dim, comba_cfg, layer_cfg, i, device, dtype))
+        self.output_norm = nn.LayerNorm(hidden_dim)
+
+    def forward(self, x: torch.Tensor):  # x:[B,l,H]
+        for layer in self.layers:
+            x = layer(x)
+        y = self.output_norm(x)
+        ensure_finite(y, "DeepEnhancedBranch.out")
+        return y
+
+
+# ========================
+# TokenBridge：多尺度膨胀卷积聚合 + 按位线性交换 + 门控
+# ========================
+class TokenBridge(nn.Module):
+    """
+    轻量高效：用 depthwise dilated conv 把对侧分支的邻域/远邻上下文“揉”到当前位置，再做按位线性交换与门控注入。
+    要求 xA/xB 已对齐到正向坐标。
+    """
+    def __init__(self, hidden_dim: int, dropout: float = 0.0,
+                 kernel_size: int = 9, dilations=(1, 2, 4, 8, 16),
+                 use_global_token: bool = True):
+        super().__init__()
+        h = hidden_dim
+        pad = lambda d: d * (kernel_size // 2)
+
+        # 对侧分支的多尺度上下文 B→A
+        self.dw_B = nn.ModuleList([
+            nn.Conv1d(h, h, kernel_size, padding=pad(d), dilation=d,
+                      groups=h, bias=False) for d in dilations
+        ])
+        self.mix_B = nn.Conv1d(h * len(dilations), h, 1)
+
+        # 对侧分支的多尺度上下文 A→B
+        self.dw_A = nn.ModuleList([
+            nn.Conv1d(h, h, kernel_size, padding=pad(d), dilation=d,
+                      groups=h, bias=False) for d in dilations
+        ])
+        self.mix_A = nn.Conv1d(h * len(dilations), h, 1)
+
+        # 本地按位交换（线性）
+        self.proj_B2A = nn.Linear(h, h)
+        self.proj_A2B = nn.Linear(h, h)
+
+        # 可选：极廉价“全局 token”（均值）广播
+        self.use_global_token = use_global_token
+        if use_global_token:
+            self.glb_B2A = nn.Linear(h, h)
+            self.glb_A2B = nn.Linear(h, h)
+
+        # 门控（逐 token、逐通道）
+        self.gate = nn.Linear(h * 4, h * 2)  # -> [gA, gB]
+        self.dropout = nn.Dropout(dropout)
+        self.normA = nn.LayerNorm(h)
+        self.normB = nn.LayerNorm(h)
+
+    @staticmethod
+    def _agg(x: torch.Tensor, branches: nn.ModuleList, mix: nn.Module) -> torch.Tensor:
+        # x:[B,L,H] -> [B,L,H]
+        xch = x.transpose(1, 2)                         # [B,H,L]
+        ys = [conv(xch) for conv in branches]           # list of [B,H,L]
+        y = torch.cat(ys, dim=1)                        # [B,H*len,L]
+        y = mix(y).transpose(1, 2).contiguous()         # [B,L,H]
+        return y
+
+    def forward(self, xA: torch.Tensor, xB: torch.Tensor):
+        # 1) 多尺度聚合对侧上下文
+        ctxB = self._agg(xB, self.dw_B, self.mix_B)     # B 的多尺度上下文，用于注入 A
+        ctxA = self._agg(xA, self.dw_A, self.mix_A)     # A 的多尺度上下文，用于注入 B
+
+        # 2) 按位线性交换（叠加对侧上下文）
+        locA = self.proj_B2A(xB + ctxB)                 # B→A
+        locB = self.proj_A2B(xA + ctxA)                 # A→B
+
+        # 3) 可选：极廉价全局 token（均值）广播
+        if self.use_global_token:
+            gB = self.glb_B2A(xB.mean(dim=1, keepdim=True))  # [B,1,H]
+            gA = self.glb_A2B(xA.mean(dim=1, keepdim=True))  # [B,1,H]
+            locA = locA + gB.expand(-1, xA.size(1), -1)
+            locB = locB + gA.expand(-1, xB.size(1), -1)
+
+        # 4) 门控注入
+        z = torch.cat([xA, xB, xA - xB, xA * xB], dim=-1)    # [B,L,4H]
+        gA, gB = self.gate(z).chunk(2, dim=-1)
+        gA = torch.sigmoid(gA)
+        gB = torch.sigmoid(gB)
+
+        yA = self.normA(xA + self.dropout(gA * locA))
+        yB = self.normB(xB + self.dropout(gB * locB))
+
+        ensure_finite(yA, "TokenBridgeLite.A")
+        ensure_finite(yB, "TokenBridgeLite.B")
+        return yA, yB
+
+
+# ========================
+# Semantic Preservation Loss
+# ========================
+def semantic_preservation_loss(R_plus: torch.Tensor, H_S_plus: torch.Tensor,
+                               λ_recon: float = 1.0, λ_local: float = 0.5, λ_global: float = 0.2):
+    recon = F.mse_loss(H_S_plus, R_plus)
+    if R_plus.size(1) >= 2:
+        d_ref = R_plus[:, 1:] - R_plus[:, :-1]
+        d_S = H_S_plus[:, 1:] - H_S_plus[:, :-1]
+        local = F.mse_loss(d_S, d_ref)
+    else:
+        local = torch.tensor(0., device=R_plus.device)
+
+    def gram_norm(x):
+        G = torch.einsum("b i d, b j d -> b i j", x, x)
+        return G / (G.norm(dim=(1, 2), keepdim=True) + 1e-6)
+
+    glob = F.mse_loss(gram_norm(H_S_plus), gram_norm(R_plus))
+    return λ_recon * recon + λ_local * local + λ_global * glob
+
+
+
+
+
+@contextmanager
+def eval_mode(*modules):
+    states = [m.training for m in modules]
+    try:
+        for m in modules: m.eval()
+        yield
+    finally:
+        for m, s in zip(modules, states): m.train(s)
+
+
+class SSScanDNAHybridModel(nn.Module):
+    """
+    S-scan 分块内存优化版：
+    - 按块就地完成：分支→(必要时翻转)→桥接→投影→门控融合→(可选 final_conv)→输出
+    - 预训练仅拼接小体量 logits / 掩码；微调则按块收集 fused 并在末尾拼成整段
+    - 语义保持 teacher 也按块按需计算，避免整段 _run_branch 占显存
+    """
+
+    def __init__(
+        self,
+        alphabet_size=5,
+        d_model=128,
+        block_size=2048,
+        comba_cfg=None,
+        transformer_cfg=None,
+        depth=4,
+        drop_path_rates=None,
+        pretrain=False,
+        for_representation=False,
+        use_final_conv=False,
+
+        use_s_scan: bool = True,
+        use_mem: bool = False,
+        use_rc_kl: bool = False,
+        use_barlow: bool = False,
+        use_tv: bool = False,
+
+        sem_max_weight: float = 0.2,
+        sem_warmup_steps: int = 3000,
+
+        rc_max_weight: float = 0.2,
+        rc_warmup_steps: int = 2000,
+        rc_tau: float = 1.5,
+        rc_bidirectional_stopgrad: bool = True,
+
+        aux_ce_weight: float = 0.1,
+
+        gate_freeze_steps: int = 1000,
+        detach_gate: bool = False,
+        gate_sup_weight: float = 0.005,
+        gate_sup_warmup_steps: int = 500,
+        gate_temp: float = 2.0,
+
+        dropout=0.1,
+
+        use_ema_teacher: bool = True,
+        ema_decay: float = 0.999,
+        auto_update_ema_in_forward: bool = True,
+
+        use_bridge: bool = True,
+        bridge_dropout: float = 0.0,
+    ):
+        super().__init__()
+        self.alphabet_size = alphabet_size
+        self.pretrain = pretrain
+        self.for_representation = for_representation
+        self.block_size = block_size
+        self.use_final_conv = use_final_conv
+        self.d_model = d_model
+
+        # 训练步计数
+        self.register_buffer("g_step", torch.zeros(1, dtype=torch.long))
+
+        # 输入映射（两条管线）
+        self.linear = nn.Conv1d(alphabet_size, d_model, kernel_size=9, padding=4)
+        self.rc_linear = nn.Conv1d(alphabet_size, d_model, kernel_size=9, padding=4)
+
+        # 双分支
+        self.branchA_core = DeepEnhancedBranch(
+            hidden_dim=d_model, comba_cfg=comba_cfg, transformer_cfg=transformer_cfg,
+            depth=depth, drop_path_rates=drop_path_rates
+        )
+        self.branchB_core = DeepEnhancedBranch(
+            hidden_dim=d_model, comba_cfg=comba_cfg, transformer_cfg=transformer_cfg,
+            depth=depth, drop_path_rates=drop_path_rates
+        )
+
+        # TokenBridge
+        self.use_bridge = use_bridge
+        if self.use_bridge:
+            self.bridge = TokenBridge(d_model, dropout=bridge_dropout)
+
+        # EMA teacher
+        self.use_ema_teacher = use_ema_teacher
+        self.ema_decay = ema_decay
+        self.auto_update_ema_in_forward = auto_update_ema_in_forward
+        if self.use_ema_teacher:
+            self.branchA_core_ema = copy.deepcopy(self.branchA_core)
+            self.branchB_core_ema = copy.deepcopy(self.branchB_core)
+            for p in self.branchA_core_ema.parameters(): p.requires_grad_(False)
+            for p in self.branchB_core_ema.parameters(): p.requires_grad_(False)
+            if self.use_bridge:
+                self.bridge_ema = copy.deepcopy(self.bridge)
+                for p in self.bridge_ema.parameters(): p.requires_grad_(False)
+
+        # 末端投影 + 融合
+        self.proj_A = Mlp(d_model, d_model * 2, d_model, activation=F.gelu, return_residual=True)
+        self.proj_B = Mlp(d_model, d_model * 2, d_model, activation=F.gelu, return_residual=True)
+        self.gate_fuse = nn.Linear(2 * d_model, d_model)
+        self.out_linear = nn.Linear(d_model, alphabet_size)
+        self.dropout = nn.Dropout(dropout)
+
+        # RC 置换矩阵
+        P_comp, _ = make_complement_perm(self.alphabet_size)
+        self.register_buffer("P_comp", P_comp)
+
+        # 开关 & 调度
+        self.use_s_scan = use_s_scan
+        self.use_rc_kl = use_rc_kl
+        self.use_barlow = use_barlow
+        self.use_tv = use_tv
+        self.sem_max_weight = sem_max_weight
+        self.sem_warmup_steps = sem_warmup_steps
+        self.rc_max_weight = rc_max_weight
+        self.rc_warmup_steps = rc_warmup_steps
+        self.rc_tau = rc_tau
+        self.rc_bidirectional_stopgrad = rc_bidirectional_stopgrad
+        self.aux_ce_weight = aux_ce_weight
+        self.gate_freeze_steps = gate_freeze_steps
+        self.detach_gate = detach_gate
+        self.gate_sup_weight = gate_sup_weight
+        self.gate_sup_warmup_steps = gate_sup_warmup_steps
+        self.gate_temp = gate_temp
+
+        if use_final_conv:
+            self.final_conv = nn.Conv1d(d_model, d_model, kernel_size=3, padding=1)
+
+    @torch.no_grad()
+    def update_ema(self):
+        if not getattr(self, "use_ema_teacher", False): return
+        d = float(getattr(self, "ema_decay", 0.999))
+        for m_ema, m in [(self.branchA_core_ema, self.branchA_core),
+                         (self.branchB_core_ema, self.branchB_core)]:
+            for p_ema, p in zip(m_ema.parameters(), m.parameters()):
+                p_ema.data.lerp_(p.data, 1.0 - d)
+        if getattr(self, "use_bridge", False) and hasattr(self, "bridge_ema"):
+            for p_ema, p in zip(self.bridge_ema.parameters(), self.bridge.parameters()):
+                p_ema.data.lerp_(p.data, 1.0 - d)
+
+    # 保持原签名；不使用 return_embedding 作为控制变量
+    def forward(self, seq, t=None, cls=None, return_embedding=False, state=None):
+        step = int(self.g_step.item())
+        if self.training:
+            self.g_step += 1
+
+        if self.pretrain:
+            mask = seq[1]
+            seq = seq[0]
+        else:
+            mask = None
+        
+        mn, mx = int(seq.min()), int(seq.max())
+        assert 0 <= mn and mx < self.alphabet_size, f"seq ids out of range: [{mn}, {mx}] vs alpha={self.alphabet_size}"
+
+        # # ===== 输入 one-hot -> conv1d -> [B,L,H] =====
+        rc_seq = reverse_complement(seq)
+        seq_oh = F.one_hot(seq, num_classes=self.alphabet_size).float()
+        rc_oh = F.one_hot(rc_seq, num_classes=self.alphabet_size).float()
+        
+
+        h = F.gelu(self.linear(seq_oh.permute(0, 2, 1)))       # [B,H,L]
+        rc_h = F.gelu(self.rc_linear(rc_oh.permute(0, 2, 1)))  # [B,H,L]
+        feat = self.dropout(h).permute(0, 2, 1)                # [B,L,H]
+        rc_feat = self.dropout(rc_h).permute(0, 2, 1)
+
+        fused = None  # 用于微调返回
+
+        # ===== 主干：S-scan / 非 S-scan =====
+        if self.use_s_scan:
+            B, L, H = feat.shape
+            l = self.block_size
+            K = (L + l - 1) // l
+
+            # 是否收集各类输出（减少不必要的拼接/驻留）
+            collect_fused = bool(self.for_representation)
+            collect_logits = (not self.for_representation) or self.pretrain
+            collect_ab_logits = (self.pretrain or self.use_rc_kl)
+
+            fused_chunks = [] if collect_fused else None
+            logits_chunks = [] if collect_logits else None
+            logitsA_chunks = [] if collect_ab_logits else None
+            logitsB_chunks = [] if collect_ab_logits else None
+            maskA_list, maskB_list = [], []
+
+            total_aux = feat.new_zeros([])  # 仅预训练使用
+            mem_A = mem_B = None
+
+            for t_block in range(K):
+                start = t_block * l
+                end = min(start + l, L)
+
+                X_fwd = feat[:, start:end, :]
+                X_rc  = rc_feat[:, start:end, :]
+
+                if (t_block % 2) == 0:
+                    X_A, X_B = X_fwd, X_rc
+                    rev_A, rev_B = False, True
+                    maskA_rc_blk = torch.zeros(B, end - start, dtype=torch.bool, device=feat.device)
+                    maskB_rc_blk = torch.ones (B, end - start, dtype=torch.bool, device=feat.device)
+                else:
+                    X_A, X_B = X_rc, X_fwd
+                    rev_A, rev_B = True, False
+                    maskA_rc_blk = torch.ones (B, end - start, dtype=torch.bool, device=feat.device)
+                    maskB_rc_blk = torch.zeros(B, end - start, dtype=torch.bool, device=feat.device)
+
+
+                # 分支
+                H_A = self.branchA_core(X_A)
+                H_B = self.branchB_core(X_B)
+                if rev_A: H_A = torch.flip(H_A, dims=[1])
+                if rev_B: H_B = torch.flip(H_B, dims=[1])
+
+                if self.use_bridge:
+                    H_A, H_B = self.bridge(H_A, H_B)
+
+                # 投影 + 融合
+                fA, rA = self.proj_A(H_A); FA = fA + rA
+                fB, rB = self.proj_B(H_B); FB = fB + rB
+
+                gate_in_blk = torch.cat([FA, FB], dim=-1)
+                g_logits_blk = self.gate_fuse(gate_in_blk)
+                g_raw_blk = torch.sigmoid(g_logits_blk / getattr(self, "gate_temp", 1.0))
+                if step < getattr(self, "gate_freeze_steps", 0):
+                    g_blk = 0.5 * torch.ones_like(g_raw_blk)
+                else:
+                    g_blk = g_raw_blk
+                if getattr(self, "detach_gate", False):
+                    mix_blk = g_blk.detach() * FA + (1 - g_blk.detach()) * FB
+                else:
+                    mix_blk = g_blk * FA + (1 - g_blk) * FB
+                fused_blk = F.layer_norm(mix_blk, (mix_blk.size(-1),))
+                fused_blk = ensure_finite(fused_blk, "fused_blk")
+
+                if self.use_final_conv:
+                    fused_blk = self.final_conv(fused_blk.permute(0, 2, 1)).permute(0, 2, 1)
+
+                if collect_fused:
+                    fused_chunks.append(fused_blk)
+
+                if collect_logits:
+                    logits_blk = self.out_linear(fused_blk)
+                    logits_chunks.append(logits_blk)
+
+                if collect_ab_logits:
+                    logitsA_chunks.append(self.out_linear(FA))
+                    logitsB_chunks.append(self.out_linear(FB))
+
+                maskA_list.append(maskA_rc_blk)
+                maskB_list.append(maskB_rc_blk)
+
+                # ===== 预训练 aux（按块）=====
+                if self.pretrain:
+                    with torch.no_grad():
+                        A_feat_blk = F.gelu(self.linear(
+                            F.one_hot(seq[:, start:end], self.alphabet_size).float().permute(0, 2, 1)
+                        )).permute(0, 2, 1)
+                        B_feat_blk_rc = F.gelu(self.rc_linear(
+                            F.one_hot(rc_seq[:, start:end], self.alphabet_size).float().permute(0, 2, 1)
+                        )).permute(0, 2, 1)
+
+                        teacherA = self.branchA_core_ema if self.use_ema_teacher else self.branchA_core
+                        teacherB = self.branchB_core_ema if self.use_ema_teacher else self.branchB_core
+                        tbridge  = self.bridge_ema if (self.use_bridge and self.use_ema_teacher and hasattr(self, "bridge_ema")) else (self.bridge if self.use_bridge else None)
+
+                        mods = [teacherA, teacherB] + ([tbridge] if tbridge is not None else [])
+                        with eval_mode(*mods):
+                            R_plus_A_blk = teacherA(A_feat_blk)
+                            R_plus_B_blk = teacherB(A_feat_blk)
+                            if tbridge is not None:
+                                R_plus_A_blk, R_plus_B_blk = tbridge(R_plus_A_blk, R_plus_B_blk)
+
+                            R_minus_A_blk_rc = teacherA(B_feat_blk_rc)
+                            R_minus_B_blk_rc = teacherB(B_feat_blk_rc)
+                            R_minus_A_blk_fwd = torch.flip(R_minus_A_blk_rc, dims=[1])
+                            R_minus_B_blk_fwd = torch.flip(R_minus_B_blk_rc, dims=[1])
+                            if tbridge is not None:
+                                R_minus_A_blk_fwd, R_minus_B_blk_fwd = tbridge(R_minus_A_blk_fwd, R_minus_B_blk_fwd)
+
+                    R_A_teacher_blk = torch.where(maskA_rc_blk.unsqueeze(-1), R_minus_A_blk_fwd, R_plus_A_blk)
+                    R_B_teacher_blk = torch.where(maskB_rc_blk.unsqueeze(-1), R_minus_B_blk_fwd, R_plus_B_blk)
+
+                    sem_A = semantic_preservation_loss(R_A_teacher_blk, FA)
+                    sem_B = semantic_preservation_loss(R_B_teacher_blk, FB)
+                    w_sem = linear_warmup_weight(step, getattr(self, "sem_warmup_steps", 0),
+                                                 getattr(self, "sem_max_weight", 1.0))
+                    total_aux = total_aux + w_sem * (sem_A + sem_B)
+
+                    if (getattr(self, "gate_sup_weight", 0.0) > 0.0) and (step >= getattr(self, "gate_freeze_steps", 0)):
+                        g_target_blk = (~maskA_rc_blk).float().unsqueeze(-1)
+                        g_token_logits_blk = g_logits_blk.mean(dim=-1, keepdim=True) / getattr(self, "gate_temp", 1.0)
+                        w_gate = linear_warmup_weight(
+                            step - getattr(self, "gate_freeze_steps", 0),
+                            getattr(self, "gate_sup_warmup_steps", 0),
+                            getattr(self, "gate_sup_weight", 0.0),
+                        )
+                        total_aux = total_aux + w_gate * F.binary_cross_entropy_with_logits(
+                            g_token_logits_blk, g_target_blk
+                        )
+
+                    if self.use_rc_kl and getattr(self, "rc_max_weight", 0.0) > 0:
+                        if getattr(self, "rc_bidirectional_stopgrad", True):
+                            rc = rc_consistency_bidirectional_stopgrad(
+                                logitsA_chunks[-1], logitsB_chunks[-1], self.P_comp, tau=getattr(self, "rc_tau", 1.5)
+                            )
+                        else:
+                            rc = rc_consistency_kl(
+                                logitsA_chunks[-1], logitsB_chunks[-1], self.P_comp, tau=getattr(self, "rc_tau", 1.5)
+                            )
+                        w_rc = linear_warmup_weight(step, getattr(self, "rc_warmup_steps", 0),
+                                                    getattr(self, "rc_max_weight", 0.0))
+                        total_aux = total_aux + w_rc * rc
+
+                    if self.use_barlow:
+                        total_aux = total_aux + barlow_strand_loss_v2(H_A, H_B)
+                    if self.use_tv:
+                        total_aux = total_aux + tv_mixed(fused_blk)
+
+            # 拼接所需结果（并裁到 L）
+            logits = torch.cat(logits_chunks, dim=1)[:, :L, :] if collect_logits else None
+            logits_A_only = torch.cat(logitsA_chunks, dim=1)[:, :L, :] if collect_ab_logits else None
+            logits_B_only = torch.cat(logitsB_chunks, dim=1)[:, :L, :] if collect_ab_logits else None
+            mask_A_rc = torch.cat(maskA_list, dim=1)[:, :L]
+            mask_B_rc = torch.cat(maskB_list, dim=1)[:, :L]
+            if collect_fused:
+                fused = torch.cat(fused_chunks, dim=1)[:, :L, :]
+
+        else:
+            # 非 S-scan：整段走一遍（短序列或推理场景）
+            H_A = self.branchA_core(feat)
+            H_Br = self.branchB_core(rc_feat)
+            R_A = H_A
+            R_B = torch.flip(H_Br, dims=[1])
+
+            if self.use_bridge:
+                R_A, R_B = self.bridge(R_A, R_B)
+
+            fA, rA = self.proj_A(R_A); FA = fA + rA
+            fB, rB = self.proj_B(R_B); FB = fB + rB
+
+            gate_in = torch.cat([FA, FB], dim=-1)
+            g_logits = self.gate_fuse(gate_in)
+            g_raw = torch.sigmoid(g_logits / getattr(self, "gate_temp", 1.0))
+            if step < getattr(self, "gate_freeze_steps", 0):
+                g = 0.5 * torch.ones_like(g_raw)
+            else:
+                g = g_raw
+            if getattr(self, "detach_gate", False):
+                mix = g.detach() * FA + (1 - g.detach()) * FB
+            else:
+                mix = g * FA + (1 - g) * FB
+            fused = F.layer_norm(mix, (mix.size(-1),))
+            fused = ensure_finite(fused, "fused")
+            if self.use_final_conv:
+                fused = self.final_conv(fused.permute(0, 2, 1)).permute(0, 2, 1)
+
+            logits = self.out_linear(fused) if (not self.for_representation or self.pretrain) else None
+            logits_A_only = self.out_linear(FA) if (self.pretrain or self.use_rc_kl) else None
+            logits_B_only = self.out_linear(FB) if (self.pretrain or self.use_rc_kl) else None
+            mask_A_rc = torch.zeros(FA.size()[:2], dtype=torch.bool, device=FA.device)
+            mask_B_rc = torch.zeros_like(mask_A_rc)
+            total_aux = logits.new_zeros(()) if self.pretrain else None
+
+        # ===== 按原行为返回 =====
+        if self.for_representation:
+            # 微调时不做 EMA/aux，不更新 teacher
+            return fused, None
+
+        if self.training and self.use_ema_teacher and self.auto_update_ema_in_forward:
+            self.update_ema()
+
+        current_step = int(step)
+
+        if self.pretrain:
+            # 兼容原有 loss：确保 A/B logits 存在
+            if logits_A_only is None: logits_A_only = self.out_linear(FA)
+            if logits_B_only is None: logits_B_only = self.out_linear(FB)
+            HybridOutput = namedtuple("HybridOutput", ["logits"])
+            return HybridOutput(
+                logits=(logits,
+                        mask,
+                        total_aux,
+                        logits_A_only.detach(),
+                        logits_B_only.detach(),
+                        mask_A_rc.detach(),
+                        mask_B_rc.detach(),
+                        current_step)
+            ), None
+
+        return logits, None
+
+    @property
+    def d_output(self):
+        if getattr(self, "d_model", None) is None:
+            raise NotImplementedError("SequenceModule instantiation must set d_output")
+        return self.d_model
+
+
+
+class CrossDNAForMaskedLM(PreTrainedModel):
+    config_class = CrossDNAConfig
+    base_model_prefix = "backbone"
+
+    def __init__(self, config: CrossDNAConfig):
+        super().__init__(config)
+        self.config = config
+
+        
+        self.backbone = SSScanDNAHybridModel(
+            alphabet_size=config.alphabet_size,
+            d_model=config.d_model,
+            block_size=config.block_size,
+            comba_cfg=config.comba_cfg,
+            transformer_cfg=config.transformer_cfg,
+            depth=config.depth,
+            drop_path_rates=config.drop_path_rates,
+            pretrain=config.pretrain,
+            for_representation=config.for_representation,
+            use_s_scan=config.use_s_scan,
+            use_mem=config.use_mem,
+            use_rc_kl=config.use_rc_kl,
+            use_barlow=config.use_barlow,
+            use_tv=config.use_tv,
+            sem_max_weight=config.sem_max_weight,
+            sem_warmup_steps=config.sem_warmup_steps,
+            aux_ce_weight=config.aux_ce_weight,
+            gate_freeze_steps=config.gate_freeze_steps,
+            detach_gate=config.detach_gate,
+            gate_sup_weight=config.gate_sup_weight,
+            gate_sup_warmup_steps=config.gate_sup_warmup_steps,
+            gate_temp=config.gate_temp,
+            dropout=config.dropout,
+            use_bridge=config.use_bridge,
+            bridge_dropout=0.0,
+        )
+        
+        self.post_init()
+
+    @property
+    def mask_token_id(self) -> int:
+        return getattr(self.config, "mask_token_id", 3)
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor,
+        attention_mask: Optional[torch.Tensor] = None,  # 未使用
+        labels: Optional[torch.LongTensor] = None,
+        **kwargs
+    ) -> MaskedLMOutput:
+        """
+        input_ids: [B, L]，取值范围 0..alphabet_size-1（A=0,C=1,G=2,T=3,N=4）
+        labels:    [B, L]，MLM标签；非mask位置应为 -100（忽略）
+        """
+
+        # 计算MLM mask：优先用 labels!=-100；否则 fallback 到 input_ids==mask_token_id
+        if labels is not None:
+            mlm_mask = (labels != -100)
+        else:
+            mlm_mask = (input_ids == self.mask_token_id)
+
+        if self.config.pretrain:
+            # 预训练路径：你的backbone在pretrain=True时期望 (seq, mask)
+            outputs, _ = self.backbone((input_ids, mlm_mask))
+            # outputs[0] 是 namedtuple HybridOutput，取 logits[0] 为主 logits
+            logits = outputs.logits[0]  # [B, L, vocab_size]
+        else:
+            logits, _ = self.backbone(input_ids)  # [B, L, vocab_size]
+
+        loss = None
+        if labels is not None:
+            # 标准MLM损失：仅在 labels != -100 的位置计算交叉熵
+            vocab = self.config.alphabet_size
+            logits_2d = logits.view(-1, vocab)
+            labels_1d = labels.view(-1)
+            loss = F.cross_entropy(logits_2d, labels_1d, ignore_index=-100)
+
+        return MaskedLMOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=None,
+            attentions=None,
+        )

8.1M/special_tokens_map.json

@@ -0,0 +1,9 @@
+{
+  "bos_token": "[BOS]",
+  "cls_token": "[CLS]",
+  "eos_token": "[SEP]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

8.1M/tokenization_crossdna.py

@@ -0,0 +1,159 @@
+""" 
+CharacterTokenzier for Hugging Face Transformers.
+This is heavily inspired from CanineTokenizer in transformers package.
+"""
+import json
+import os
+from pathlib import Path
+from typing import Dict, List, Optional, Sequence, Union
+
+from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
+
+
+class CrossDNATokenizer(PreTrainedTokenizer):
+    def __init__(self, characters: Sequence[str], model_max_length: int, padding_side: str='left', **kwargs):
+        """Character tokenizer for Hugging Face transformers.
+        Args:
+            characters (Sequence[str]): List of desired characters. Any character which
+                is not included in this list will be replaced by a special token called
+                [UNK] with id=6. Following are list of all of the special tokens with
+                their corresponding ids:
+                    "[CLS]": 0
+                    "[SEP]": 1
+                    "[BOS]": 2
+                    "[MASK]": 3
+                    "[PAD]": 4
+                    "[RESERVED]": 5
+                    "[UNK]": 6
+                an id (starting at 7) will be assigned to each character.
+            model_max_length (int): Model maximum sequence length.
+        """
+        self.characters = characters
+        self.model_max_length = model_max_length
+
+        self._vocab_str_to_int = {
+            "[CLS]": 0,
+            "[SEP]": 1,
+            "[BOS]": 2,
+            "[MASK]": 3,
+            "[PAD]": 4,
+            "[RESERVED]": 5,
+            "[UNK]": 6,
+            **{ch: i + 7 for i, ch in enumerate(characters)},
+        }
+        self._vocab_int_to_str = {v: k for k, v in self._vocab_str_to_int.items()}
+
+
+        bos_token = AddedToken("[BOS]", lstrip=False, rstrip=False)
+        eos_token = AddedToken("[SEP]", lstrip=False, rstrip=False)
+        sep_token = AddedToken("[SEP]", lstrip=False, rstrip=False)
+        cls_token = AddedToken("[CLS]", lstrip=False, rstrip=False)
+        pad_token = AddedToken("[PAD]", lstrip=False, rstrip=False)
+        unk_token = AddedToken("[UNK]", lstrip=False, rstrip=False)
+
+        mask_token = AddedToken("[MASK]", lstrip=True, rstrip=False)
+
+        if "add_special_tokens" in kwargs:
+            kwargs.pop("add_special_tokens")
+
+        super().__init__(
+            bos_token=bos_token,
+            eos_token=sep_token,
+            sep_token=sep_token,
+            cls_token=cls_token,
+            pad_token=pad_token,
+            mask_token=mask_token,
+            unk_token=unk_token,
+            add_prefix_space=False,
+            model_max_length=model_max_length,
+            padding_side=padding_side,
+            **kwargs,
+        )
+
+    def __len__(self):
+        return len(self._vocab_str_to_int)
+
+    @property
+    def vocab_size(self) -> int:
+        return len(self._vocab_str_to_int)
+
+    def get_vocab(self):
+        """返回token到id的字典（HuggingFace标准接口）"""
+        return self._vocab_str_to_int
+
+    def _tokenize(self, text: str) -> List[str]:
+        return list(text)
+
+    def _convert_token_to_id(self, token: str) -> int:
+        return self._vocab_str_to_int.get(token, self._vocab_str_to_int["[UNK]"])
+
+    def _convert_id_to_token(self, index: int) -> str:
+        return self._vocab_int_to_str[index]
+
+    def convert_tokens_to_string(self, tokens):
+        return "".join(tokens)
+
+    def build_inputs_with_special_tokens(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        sep = [self.sep_token_id]
+        # cls = [self.cls_token_id]
+        result = token_ids_0 + sep
+        if token_ids_1 is not None:
+            result += token_ids_1 + sep
+        return result
+
+    def get_special_tokens_mask(
+        self,
+        token_ids_0: List[int],
+        token_ids_1: Optional[List[int]] = None,
+        already_has_special_tokens: bool = False,
+    ) -> List[int]:
+        if already_has_special_tokens:
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0,
+                token_ids_1=token_ids_1,
+                already_has_special_tokens=True,
+            )
+
+        result = ([0] * len(token_ids_0)) + [1]
+        if token_ids_1 is not None:
+            result += ([0] * len(token_ids_1)) + [1]
+        return result
+
+    def create_token_type_ids_from_sequences(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        sep = [self.sep_token_id]
+        cls = [self.cls_token_id]
+
+        result = len(cls + token_ids_0 + sep) * [0]
+        if token_ids_1 is not None:
+            result += len(token_ids_1 + sep) * [1]
+        return result
+
+    def get_config(self) -> Dict:
+        return {
+            "char_ords": [ord(ch) for ch in self.characters],
+            "model_max_length": self.model_max_length,
+        }
+
+    @classmethod
+    def from_config(cls, config: Dict) -> "CrossDNATokenizer":
+        cfg = {}
+        cfg["characters"] = [chr(i) for i in config["char_ords"]]
+        cfg["model_max_length"] = config["model_max_length"]
+        return cls(**cfg)
+
+    def save_pretrained(self, save_directory: Union[str, os.PathLike], **kwargs):
+        cfg_file = Path(save_directory) / "tokenizer_config.json"
+        cfg = self.get_config()
+        with open(cfg_file, "w") as f:
+            json.dump(cfg, f, indent=4)
+
+    @classmethod
+    def from_pretrained(cls, save_directory: Union[str, os.PathLike], **kwargs):
+        cfg_file = Path(save_directory) / "tokenizer_config.json"
+        with open(cfg_file) as f:
+            cfg = json.load(f)
+        return cls.from_config(cfg)

8.1M/tokenizer_config.json

@@ -0,0 +1,32 @@
+{
+  "char_ords": [65, 67, 71, 84, 78], 
+  "model_max_length": 1024,
+
+  "add_prefix_space": false,
+  "padding_side": "left",
+
+  "bos_token": "[BOS]",
+  "eos_token": "[SEP]",
+  "sep_token": "[SEP]",
+  "cls_token": "[CLS]",
+  "pad_token": "[PAD]",
+  "mask_token": "[MASK]",
+  "unk_token": "[UNK]",
+
+  "added_tokens_decoder": {
+    "0":  { "content": "[CLS]", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true },
+    "1":  { "content": "[SEP]", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true },
+    "2":  { "content": "[BOS]", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true },
+    "3":  { "content": "[MASK]", "lstrip": true,  "normalized": false, "rstrip": false, "single_word": false, "special": true },
+    "4":  { "content": "[PAD]", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true },
+    "6":  { "content": "[UNK]", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true }
+  },
+
+  "tokenizer_class": "CrossDNATokenizer",
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenization_crossdna.CrossDNATokenizer",
+      null
+    ]
+  }
+}