Upload modeling_caca.py with huggingface_hub

modeling_caca.py

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+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from transformers import PreTrainedModel
+from transformers.generation import GenerationMixin
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
+
+from configuration_caca import CacaConfig
+
+# --- NORM & MLP ---
+class CacaRMSNorm(nn.Module):
+    def __init__(self, dim, eps=1e-6):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.zeros(dim))
+
+    def _norm(self, x):
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+    def forward(self, x):
+        out = self._norm(x.float())
+        out = out * (1.0 + self.weight.float())
+        return out.type_as(x)
+
+class CacaMLP(nn.Module):
+
+    def __init__(self, config: CacaConfig, intermediate_size=None):
+        super().__init__()
+        inter = intermediate_size or config.intermediate_size
+        self.gate_proj = nn.Linear(config.hidden_size, inter, bias=False)
+        self.up_proj = nn.Linear(config.hidden_size, inter, bias=False)
+        self.down_proj = nn.Linear(inter, config.hidden_size, bias=False)
+        self.act_fn = nn.SiLU() if config.hidden_activation == "silu" else nn.GELU(approximate="tanh")
+        self.dropout = nn.Dropout(config.hidden_dropout)
+
+    def forward(self, x):
+        return self.dropout(self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)))
+
+# --- ROTARY EMBEDDING — default / linear / dynamic / YaRN ---
+class CacaRotaryEmbedding(nn.Module):
+    def __init__(self, config: CacaConfig, dim: int, device=None):
+        super().__init__()
+        self.config = config
+        self.dim = dim
+
+        rope_params = getattr(config, "rope_parameters", None) or {}
+        self.rope_type = rope_params.get("rope_type", "default")
+        self.base = rope_params.get("rope_theta", getattr(config, "rope_theta", 10000.0))
+        self.factor = rope_params.get("factor", 1.0)
+        self.original_max_pos = rope_params.get(
+            "original_max_position_embeddings", config.max_position_embeddings
+        )
+        self.beta_fast = rope_params.get("beta_fast", 32)
+        self.beta_slow = rope_params.get("beta_slow", 1)
+        self.mscale = rope_params.get("mscale", 1.0)
+
+        if self.rope_type == "yarn":
+            inv_freq, self.attention_scaling = self._yarn_inv_freq(device)
+        else:
+            inv_freq = 1.0 / (self.base ** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim))
+            self.attention_scaling = 1.0
+
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.max_seq_len_cached = config.max_position_embeddings
+
+    def _yarn_find_correction_dim(self, num_rot):
+        return (self.dim * math.log(self.original_max_pos / (num_rot * 2 * math.pi))) / (2 * math.log(self.base))
+
+    def _yarn_inv_freq(self, device):
+        dim = self.dim
+        pos_freqs = self.base ** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim)
+        inv_freq_extrapolation = 1.0 / pos_freqs
+        inv_freq_interpolation = 1.0 / (self.factor * pos_freqs)
+
+        low = max(math.floor(self._yarn_find_correction_dim(self.beta_fast)), 0)
+        high = min(math.ceil(self._yarn_find_correction_dim(self.beta_slow)), dim - 1)
+
+        ramp = torch.linspace(0, 1, dim // 2, device=device)
+        ramp = torch.clamp((ramp * dim - low) / max(high - low, 1e-3), 0, 1)
+        inv_freq_mask = 1.0 - ramp
+
+        inv_freq = inv_freq_interpolation * (1 - inv_freq_mask) + inv_freq_extrapolation * inv_freq_mask
+
+        mscale = 0.1 * math.log(self.factor) + 1.0 if self.factor > 1 else 1.0
+        return inv_freq, mscale
+
+    @torch.no_grad()
+    def forward(self, x, position_ids):
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
+        pos_expanded = position_ids[:, None, :].float()
+        freqs = (inv_freq_expanded @ pos_expanded).transpose(1, 2)
+        emb = torch.cat((freqs, freqs), dim=-1)
+        cos = emb.cos() * self.attention_scaling
+        sin = emb.sin() * self.attention_scaling
+        return cos.to(x.dtype), sin.to(x.dtype)
+
+def rotate_half(x):
+    x1, x2 = x[..., : x.shape[-1] // 2], x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1):
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+def repeat_kv(x, n_rep):
+    if n_rep == 1:
+        return x
+    b, h, s, d = x.shape
+    x = x[:, :, None, :, :].expand(b, h, n_rep, s, d)
+    return x.reshape(b, h * n_rep, s, d)
+
+# --- CACHE — sederhana ---
+class SimpleCache:
+
+    def __init__(self):
+        self.entries = {}
+
+    def update(self, layer_idx, *tensors):
+        if layer_idx not in self.entries:
+            self.entries[layer_idx] = list(tensors)
+        else:
+            self.entries[layer_idx] = [
+                torch.cat([old, new], dim=-2) for old, new in zip(self.entries[layer_idx], tensors)
+            ]
+        return self.entries[layer_idx]
+
+    def get_seq_length(self, layer_idx=0):
+        if layer_idx not in self.entries:
+            return 0
+        return self.entries[layer_idx][0].shape[-2]
+
+# --- ATTENTION — GQA (use_mla=False) ---
+class CacaGQAAttention(nn.Module):
+    def __init__(self, config: CacaConfig, layer_idx: int):
+        super().__init__()
+        self.layer_idx = layer_idx
+        self.head_dim = config.head_dim
+        self.num_heads = config.num_attention_heads
+        self.num_kv_heads = config.num_key_value_heads
+        self.num_kv_groups = self.num_heads // self.num_kv_heads
+        self.scaling = config.query_pre_attn_scalar ** -0.5
+        self.attn_dropout = config.attention_dropout
+        self.attn_softcap = config.attn_logit_softcapping
+        self.sliding_window = config.sliding_window if config.layer_types[layer_idx] == "sliding_attention" else None
+
+        self.q_proj = nn.Linear(config.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
+        self.k_proj = nn.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=config.attention_bias)
+        self.v_proj = nn.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=config.attention_bias)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, config.hidden_size, bias=config.attention_bias)
+
+        self.use_qk_norm = config.use_qk_norm
+        if self.use_qk_norm:
+            self.q_norm = CacaRMSNorm(self.head_dim, config.rms_norm_eps)
+            self.k_norm = CacaRMSNorm(self.head_dim, config.rms_norm_eps)
+
+        self.rotary_emb = CacaRotaryEmbedding(config, dim=self.head_dim)
+
+    def forward(self, hidden_states, attention_mask, position_ids, cache=None, **kwargs):
+        b, seq_len, _ = hidden_states.shape
+        shape = (b, seq_len, -1, self.head_dim)
+
+        q = self.q_proj(hidden_states).view(shape)
+        k = self.k_proj(hidden_states).view(shape)
+        v = self.v_proj(hidden_states).view(shape).transpose(1, 2)
+
+        if self.use_qk_norm:
+            q, k = self.q_norm(q), self.k_norm(k)
+
+        q, k = q.transpose(1, 2), k.transpose(1, 2)
+
+        cos, sin = self.rotary_emb(hidden_states, position_ids)
+        q, k = apply_rotary_pos_emb(q, k, cos, sin)
+
+        if cache is not None:
+            k, v = cache.update(self.layer_idx, k, v)
+
+        k = repeat_kv(k, self.num_kv_groups)
+        v = repeat_kv(v, self.num_kv_groups)
+
+        attn_weights = torch.matmul(q, k.transpose(2, 3)) * self.scaling
+        if self.attn_softcap is not None:
+            attn_weights = torch.tanh(attn_weights / self.attn_softcap) * self.attn_softcap
+        if attention_mask is not None:
+            attn_weights = attn_weights + attention_mask[:, :, :, : k.shape[-2]]
+
+        attn_weights = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(q.dtype)
+        attn_weights = F.dropout(attn_weights, p=self.attn_dropout, training=self.training)
+        attn_output = torch.matmul(attn_weights, v)
+
+        attn_output = attn_output.transpose(1, 2).contiguous().reshape(b, seq_len, -1)
+        return self.o_proj(attn_output)
+
+# --- ATTENTION — MLA ---
+class CacaMLAAttention(nn.Module):
+    def __init__(self, config: CacaConfig, layer_idx: int):
+        super().__init__()
+        self.layer_idx = layer_idx
+        self.num_heads = config.num_attention_heads
+        self.q_lora_rank = config.q_lora_rank
+        self.kv_lora_rank = config.kv_lora_rank
+        self.qk_nope_head_dim = config.qk_nope_head_dim
+        self.qk_rope_head_dim = config.qk_rope_head_dim
+        self.v_head_dim = config.v_head_dim
+        self.q_head_dim = self.qk_nope_head_dim + self.qk_rope_head_dim
+
+        self.scaling = self.q_head_dim ** -0.5
+        self.attn_dropout = config.attention_dropout
+        self.attn_softcap = config.attn_logit_softcapping
+        self.sliding_window = config.sliding_window if config.layer_types[layer_idx] == "sliding_attention" else None
+
+        if self.q_lora_rank > 0:
+            self.q_a_proj = nn.Linear(config.hidden_size, self.q_lora_rank, bias=False)
+            self.q_a_norm = CacaRMSNorm(self.q_lora_rank, config.rms_norm_eps)
+            self.q_b_proj = nn.Linear(self.q_lora_rank, self.num_heads * self.q_head_dim, bias=False)
+        else:
+            self.q_proj = nn.Linear(config.hidden_size, self.num_heads * self.q_head_dim, bias=False)
+
+        self.kv_a_proj_with_mqa = nn.Linear(
+            config.hidden_size, self.kv_lora_rank + self.qk_rope_head_dim, bias=False
+        )
+        self.kv_a_norm = CacaRMSNorm(self.kv_lora_rank, config.rms_norm_eps)
+        self.kv_b_proj = nn.Linear(
+            self.kv_lora_rank, self.num_heads * (self.qk_nope_head_dim + self.v_head_dim), bias=False
+        )
+
+        self.o_proj = nn.Linear(self.num_heads * self.v_head_dim, config.hidden_size, bias=False)
+
+        self.use_qk_norm = config.use_qk_norm
+        if self.use_qk_norm:
+            self.q_nope_norm = CacaRMSNorm(self.qk_nope_head_dim, config.rms_norm_eps)
+            self.k_nope_norm = CacaRMSNorm(self.qk_nope_head_dim, config.rms_norm_eps)
+
+        self.rotary_emb = CacaRotaryEmbedding(config, dim=self.qk_rope_head_dim)
+
+    def forward(self, hidden_states, attention_mask, position_ids, cache=None, **kwargs):
+        b, seq_len, _ = hidden_states.shape
+
+        if self.q_lora_rank > 0:
+            q = self.q_b_proj(self.q_a_norm(self.q_a_proj(hidden_states)))
+        else:
+            q = self.q_proj(hidden_states)
+        q = q.view(b, seq_len, self.num_heads, self.q_head_dim).transpose(1, 2)
+        q_nope, q_rope = q.split([self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
+
+        kv_a = self.kv_a_proj_with_mqa(hidden_states)
+        kv_a, k_rope = kv_a.split([self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
+        kv_a = self.kv_a_norm(kv_a)
+        k_rope = k_rope.view(b, seq_len, 1, self.qk_rope_head_dim).transpose(1, 2)
+
+        if cache is not None:
+            kv_a_seq, k_rope_seq = cache.update(self.layer_idx, kv_a.unsqueeze(1), k_rope)
+            kv_a = kv_a_seq.squeeze(1)
+        else:
+            kv_a_seq, k_rope_seq = kv_a.unsqueeze(1), k_rope
+
+        kv = self.kv_b_proj(kv_a_seq.squeeze(1) if cache is None else cache.entries[self.layer_idx][0].squeeze(1))
+        kv_len = kv.shape[1]
+        kv = kv.view(b, kv_len, self.num_heads, self.qk_nope_head_dim + self.v_head_dim).transpose(1, 2)
+        k_nope, value = kv.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)
+
+        if self.use_qk_norm:
+            q_nope = self.q_nope_norm(q_nope)
+            k_nope = self.k_nope_norm(k_nope)
+
+        cos, sin = self.rotary_emb(hidden_states, position_ids)
+        q_rope, k_rope_seq = apply_rotary_pos_emb(q_rope, k_rope_seq, cos, sin)
+
+        k_rope_expanded = k_rope_seq.expand(-1, self.num_heads, -1, -1)
+
+        q_full = torch.cat([q_nope, q_rope], dim=-1)
+        k_full = torch.cat([k_nope, k_rope_expanded], dim=-1)
+
+        attn_weights = torch.matmul(q_full, k_full.transpose(2, 3)) * self.scaling
+        if self.attn_softcap is not None:
+            attn_weights = torch.tanh(attn_weights / self.attn_softcap) * self.attn_softcap
+        if attention_mask is not None:
+            attn_weights = attn_weights + attention_mask[:, :, :, :kv_len]
+
+        attn_weights = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(q_full.dtype)
+        attn_weights = F.dropout(attn_weights, p=self.attn_dropout, training=self.training)
+        attn_output = torch.matmul(attn_weights, value)
+
+        attn_output = attn_output.transpose(1, 2).contiguous().reshape(b, seq_len, -1)
+        return self.o_proj(attn_output)
+
+# --- DECODER LAYER ---
+class CacaDecoderLayer(nn.Module):
+    def __init__(self, config: CacaConfig, layer_idx: int):
+        super().__init__()
+        self.self_attn = CacaMLAAttention(config, layer_idx) if config.use_mla else CacaGQAAttention(config, layer_idx)
+        self.mlp = CacaMLP(config)
+        self.input_layernorm = CacaRMSNorm(config.hidden_size, config.rms_norm_eps)
+        self.post_attention_layernorm = CacaRMSNorm(config.hidden_size, config.rms_norm_eps)
+        self.pre_feedforward_layernorm = CacaRMSNorm(config.hidden_size, config.rms_norm_eps)
+        self.post_feedforward_layernorm = CacaRMSNorm(config.hidden_size, config.rms_norm_eps)
+        self.residual_dropout = nn.Dropout(config.hidden_dropout)
+
+    def forward(self, hidden_states, attention_mask, position_ids, cache=None, **kwargs):
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        hidden_states = self.self_attn(hidden_states, attention_mask, position_ids, cache)
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = residual + self.residual_dropout(hidden_states)
+
+        residual = hidden_states
+        hidden_states = self.pre_feedforward_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = self.post_feedforward_layernorm(hidden_states)
+        hidden_states = residual + self.residual_dropout(hidden_states)
+        return hidden_states
+
+# --- MASK UTILS ---
+def build_attention_mask(attention_mask, seq_len, past_len, sliding_window, dtype, device):
+    min_val = torch.finfo(dtype).min
+    query_pos = torch.arange(past_len, past_len + seq_len, device=device)[:, None]
+    key_pos = torch.arange(past_len + seq_len, device=device)[None, :]
+
+    causal = key_pos > query_pos
+    mask = torch.zeros((seq_len, past_len + seq_len), dtype=dtype, device=device)
+    mask.masked_fill_(causal, min_val)
+
+    if sliding_window is not None:
+        too_far = key_pos <= (query_pos - sliding_window)
+        mask.masked_fill_(too_far, min_val)
+
+    mask = mask[None, None, :, :]
+    if attention_mask is not None:
+        pad = (1.0 - attention_mask[:, None, None, :].to(dtype)) * min_val
+        mask = mask + pad
+    return mask
+
+# --- PRETRAINED BASE ---
+class CacaPreTrainedModel(PreTrainedModel):
+    config_class = CacaConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["CacaDecoderLayer"]
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+# --- MODEL BODY ---
+class CacaModel(CacaPreTrainedModel):
+    def __init__(self, config: CacaConfig):
+        super().__init__(config)
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, config.pad_token_id)
+        self.embedding_dropout = nn.Dropout(config.embedding_dropout)
+        self.layers = nn.ModuleList([CacaDecoderLayer(config, i) for i in range(config.num_hidden_layers)])
+        self.norm = CacaRMSNorm(config.hidden_size, config.rms_norm_eps)
+        self.hidden_scale = config.hidden_size ** 0.5
+
+        self.post_init()
+
+    def forward(self, input_ids, attention_mask=None, position_ids=None, cache=None, use_cache=None, **kwargs):
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        b, seq_len = input_ids.shape
+
+        if use_cache and cache is None:
+            cache = SimpleCache()
+        past_len = cache.get_seq_length(0) if cache is not None else 0
+
+        if position_ids is None:
+            position_ids = torch.arange(past_len, past_len + seq_len, device=input_ids.device)[None, :].expand(b, -1)
+
+        hidden_states = self.embed_tokens(input_ids) * self.hidden_scale
+        hidden_states = self.embedding_dropout(hidden_states)
+
+        full_mask = build_attention_mask(attention_mask, seq_len, past_len, None, hidden_states.dtype, hidden_states.device)
+        sliding_mask = build_attention_mask(
+            attention_mask, seq_len, past_len, self.config.sliding_window, hidden_states.dtype, hidden_states.device
+        )
+
+        for layer in self.layers:
+            mask = sliding_mask if layer.self_attn.sliding_window is not None else full_mask
+            if self.gradient_checkpointing and self.training:
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    layer, hidden_states, mask, position_ids, cache, use_reentrant=False
+                )
+            else:
+                hidden_states = layer(hidden_states, mask, position_ids, cache)
+
+        hidden_states = self.norm(hidden_states)
+        return BaseModelOutputWithPast(last_hidden_state=hidden_states, past_key_values=cache)
+
+# --- MULTI-TOKEN PREDICTION MODULE ---
+class CacaMTPModule(nn.Module):
+
+    def __init__(self, config: CacaConfig):
+        super().__init__()
+        self.norm_prev = CacaRMSNorm(config.hidden_size, config.rms_norm_eps)
+        self.norm_emb = CacaRMSNorm(config.hidden_size, config.rms_norm_eps)
+        self.combine_proj = nn.Linear(config.hidden_size * 2, config.hidden_size, bias=False)
+        self.decoder_layer = CacaDecoderLayer(config, layer_idx=0)
+
+    def forward(self, prev_hidden, target_embeds, attention_mask, position_ids):
+        combined = self.combine_proj(torch.cat([self.norm_prev(prev_hidden), self.norm_emb(target_embeds)], dim=-1))
+        return self.decoder_layer(combined, attention_mask, position_ids, cache=None)
+
+# --- CAUSAL LM HEAD ---
+class CacaForCausalLM(CacaPreTrainedModel, GenerationMixin):
+    _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"}
+
+    def __init__(self, config: CacaConfig):
+        super().__init__(config)
+        self.model = CacaModel(config)
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        self.mtp_modules = nn.ModuleList(
+            [CacaMTPModule(config) for _ in range(config.num_mtp_tokens)]
+        ) if config.num_mtp_tokens > 0 else None
+
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def forward(
+        self, input_ids, attention_mask=None, position_ids=None, labels=None,
+        cache=None, use_cache=None, logits_to_keep=0, **kwargs,
+    ):
+        outputs = self.model(input_ids, attention_mask, position_ids, cache, use_cache)
+        hidden_states = outputs.last_hidden_state
+
+        slice_idx = slice(-logits_to_keep, None) if logits_to_keep else slice(None)
+        logits = self.lm_head(hidden_states[:, slice_idx, :])
+
+        if self.config.final_logit_softcapping is not None:
+            cap = self.config.final_logit_softcapping
+            logits = torch.tanh(logits / cap) * cap
+
+        loss = None
+        if labels is not None:
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            main_loss = F.cross_entropy(
+                shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1), ignore_index=-100
+            )
+            loss = main_loss
+
+            if self.mtp_modules is not None:
+                mtp_loss_total = 0.0
+                prev_hidden = hidden_states
+                b, seq_len = input_ids.shape
+                pos_ids = position_ids if position_ids is not None else torch.arange(seq_len, device=input_ids.device)[None, :].expand(b, -1)
+
+                for k, mtp in enumerate(self.mtp_modules, start=1):
+                    if seq_len - k <= 1:
+                        break
+                    target_ids = input_ids[:, k:]
+                    target_embeds = self.model.embed_tokens(target_ids) * self.model.hidden_scale
+                    aligned_prev = prev_hidden[:, : target_ids.shape[1], :]
+                    aligned_mask = None  
+                    
+                    mtp_hidden = mtp(aligned_prev, target_embeds, aligned_mask, pos_ids[:, : target_ids.shape[1]])
+                    mtp_logits = self.lm_head(mtp_hidden)
+
+                    mtp_labels = labels[:, k + 1 :]
+                    mtp_logits_trimmed = mtp_logits[:, : mtp_labels.shape[1], :]
+                    if mtp_labels.shape[1] > 0:
+                        mtp_loss = F.cross_entropy(
+                            mtp_logits_trimmed.reshape(-1, mtp_logits_trimmed.size(-1)),
+                            mtp_labels.reshape(-1),
+                            ignore_index=-100,
+                        )
+                        mtp_loss_total = mtp_loss_total + mtp_loss
+                    prev_hidden = mtp_hidden
+
+                if isinstance(mtp_loss_total, torch.Tensor):
+                    loss = main_loss + self.config.mtp_loss_weight * mtp_loss_total
+
+        return CausalLMOutputWithPast(loss=loss, logits=logits, past_key_values=outputs.past_key_values)
+
+    def prepare_inputs_for_generation(self, input_ids, cache=None, attention_mask=None, **kwargs):
+        if cache is not None and cache.get_seq_length(0) > 0:
+            input_ids = input_ids[:, -1:]
+        return {"input_ids": input_ids, "attention_mask": attention_mask, "cache": cache, "use_cache": True, "logits_to_keep": 1}
+
+
+# --- AUTO-REGISTER ---
+CacaConfig.register_for_auto_class()
+CacaModel.register_for_auto_class("AutoModel")
+CacaForCausalLM.register_for_auto_class("AutoModelForCausalLM")