diff --git "a/modeling_doge.py" "b/modeling_doge.py"
--- "a/modeling_doge.py"
+++ "b/modeling_doge.py"
@@ -1,1198 +1,1198 @@
-#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
-#           This file was automatically generated from src/transformers/models/doge/modular_doge.py.
-#               Do NOT edit this file manually as any edits will be overwritten by the generation of
-#             the file from the modular. If any change should be done, please apply the change to the
-#                          modular_doge.py file directly. One of our CI enforces this.
-#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
-# coding=utf-8
-# Copyright 2024 Jingze Shi and the HuggingFace Inc. team. All rights reserved.
-#
-# This code is based on the Wonderful Matrices paper implementation.
-# The Doge family of small language models is trained by Jingze Shi.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import math
-from typing import Callable, List, Optional, Tuple, Union
-
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from transformers.activations import ACT2FN
-from transformers.cache_utils import Cache, DynamicCache, StaticCache
-from transformers.generation import GenerationMixin
-from transformers.modeling_attn_mask_utils import AttentionMaskConverter
-from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
-from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS
-from transformers.modeling_utils import PreTrainedModel
-from transformers.processing_utils import Unpack
-from transformers.utils import (
-    LossKwargs,
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    is_torch_flex_attn_available,
-    logging,
-    replace_return_docstrings,
-)
-from .configuration_doge import DogeConfig
-
-if is_torch_flex_attn_available():
-    from torch.nn.attention.flex_attention import flex_attention
-
-logger = logging.get_logger(__name__)
-
-_CONFIG_FOR_DOC = "DogeConfig"
-
-
-class DogeRMSNorm(nn.Module):
-    def __init__(self, hidden_size, eps=1e-6):
-        """
-        DogeRMSNorm is equivalent to T5LayerNorm
-        """
-        super().__init__()
-        self.weight = nn.Parameter(torch.ones(hidden_size))
-        self.variance_epsilon = eps
-
-    def forward(self, hidden_states):
-        input_dtype = hidden_states.dtype
-        hidden_states = hidden_states.to(torch.float32)
-        variance = hidden_states.pow(2).mean(-1, keepdim=True)
-        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
-        return self.weight * hidden_states.to(input_dtype)
-
-    def extra_repr(self):
-        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
-
-
-class DogeResidual(nn.Module):
-    def __init__(self, hidden_size):
-        super().__init__()
-        self.weight = nn.Parameter(torch.ones(hidden_size))
-
-    def forward(self, residual_states, hidden_states):
-        return self.weight * residual_states + hidden_states
-
-    def extra_repr(self):
-        return f"{tuple(self.weight.shape)}"
-
-
-class DogeRotaryEmbedding(nn.Module):
-    def __init__(self, config: DogeConfig, device=None):
-        super().__init__()
-        # BC: "rope_type" was originally "type"
-        if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
-            self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
-        else:
-            self.rope_type = "default"
-        self.max_seq_len_cached = config.max_position_embeddings
-        self.original_max_seq_len = config.max_position_embeddings
-
-        self.config = config
-        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
-
-        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self.original_inv_freq = self.inv_freq
-
-    def _dynamic_frequency_update(self, position_ids, device):
-        """
-        dynamic RoPE layers should recompute `inv_freq` in the following situations:
-        1 - growing beyond the cached sequence length (allow scaling)
-        2 - the current sequence length is in the original scale (avoid losing precision with small sequences)
-        """
-        seq_len = torch.max(position_ids) + 1
-        if seq_len > self.max_seq_len_cached:  # growth
-            inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device, seq_len=seq_len)
-            self.register_buffer("inv_freq", inv_freq, persistent=False)  # TODO joao: may break with compilation
-            self.max_seq_len_cached = seq_len
-
-        if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len:  # reset
-            # This .to() is needed if the model has been moved to a device after being initialized (because
-            # the buffer is automatically moved, but not the original copy)
-            self.original_inv_freq = self.original_inv_freq.to(device)
-            self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
-            self.max_seq_len_cached = self.original_max_seq_len
-
-    @torch.no_grad()
-    def forward(self, x, position_ids):
-        if "dynamic" in self.rope_type:
-            self._dynamic_frequency_update(position_ids, device=x.device)
-
-        # Core RoPE block
-        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
-        position_ids_expanded = position_ids[:, None, :].float()
-        # Force float32 (see https://github.com/huggingface/transformers/pull/29285)
-        device_type = x.device.type
-        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
-        with torch.autocast(device_type=device_type, enabled=False):
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
-            emb = torch.cat((freqs, freqs), dim=-1)
-            cos = emb.cos()
-            sin = emb.sin()
-
-        # Advanced RoPE types (e.g. yarn) apply a post-processing scaling factor, equivalent to scaling attention
-        cos = cos * self.attention_scaling
-        sin = sin * self.attention_scaling
-
-        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
-
-
-def rotate_half(x):
-    """Rotates half the hidden dims of the input."""
-    x1 = x[..., : x.shape[-1] // 2]
-    x2 = x[..., x.shape[-1] // 2 :]
-    return torch.cat((-x2, x1), dim=-1)
-
-
-def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
-    """Applies Rotary Position Embedding to the query and key tensors.
-
-    Args:
-        q (`torch.Tensor`): The query tensor.
-        k (`torch.Tensor`): The key tensor.
-        cos (`torch.Tensor`): The cosine part of the rotary embedding.
-        sin (`torch.Tensor`): The sine part of the rotary embedding.
-        position_ids (`torch.Tensor`, *optional*):
-            Deprecated and unused.
-        unsqueeze_dim (`int`, *optional*, defaults to 1):
-            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
-            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
-            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
-            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
-            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
-            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
-    Returns:
-        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
-    """
-    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(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
-    """
-    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
-    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
-    """
-    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
-    if n_rep == 1:
-        return hidden_states
-    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
-    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
-
-
-def eager_attention_forward(
-    module: nn.Module,
-    query: torch.Tensor,
-    key: torch.Tensor,
-    value: torch.Tensor,
-    attention_mask: Optional[torch.Tensor],
-    scaling: float,
-    dropout: float = 0.0,
-    **kwargs,
-) -> Tuple[torch.Tensor, torch.Tensor]:
-    key_states = repeat_kv(key, module.num_key_value_groups)
-    value_states = repeat_kv(value, module.num_key_value_groups)
-
-    attn_weights = torch.matmul(query, key_states.transpose(-1, -2)) * scaling
-    if attention_mask is not None:
-        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
-        attn_weights = attn_weights + causal_mask
-
-    attn_weights = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
-    attn_weights = F.dropout(attn_weights, p=dropout, training=module.training)
-    attn_output = torch.matmul(attn_weights, value_states)
-    attn_output = attn_output.transpose(1, 2).contiguous()
-
-    return attn_output, attn_weights
-
-
-def sdpa_attention_forward(
-    module: nn.Module,
-    query: torch.Tensor,
-    key: torch.Tensor,
-    value: torch.Tensor,
-    attention_mask: Optional[torch.Tensor],
-    dropout: float = 0.0,
-    scaling: Optional[float] = None,
-    is_causal: Optional[bool] = None,
-    **kwargs,
-) -> Tuple[torch.Tensor, None]:
-    key = repeat_kv(key, module.num_key_value_groups)
-    value = repeat_kv(value, module.num_key_value_groups)
-
-    causal_mask = attention_mask
-    if attention_mask is not None:
-        causal_mask = causal_mask[:, :, :, : key.shape[-2]]
-
-    # SDPA with memory-efficient backend is bugged with non-contiguous inputs and custom attn_mask for some torch versions
-    # Reference: https://github.com/pytorch/pytorch/issues/112577.
-    query = query.contiguous()
-    key = key.contiguous()
-    value = value.contiguous()
-
-    # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
-    # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
-    if is_causal is None:
-        is_causal = causal_mask is None and query.shape[2] > 1
-
-    # Shapes (e.g. query.shape[2]) are tensors during jit tracing, resulting in `is_causal` being a tensor.
-    # We convert it to a bool for the SDPA kernel that only accepts bools.
-    if torch.jit.is_tracing() and isinstance(is_causal, torch.Tensor):
-        is_causal = is_causal.item()
-
-    # NOTE: As of pytorch 2.5.1, SDPA backward pass of cuDNN is still incorrect, so we disable cuDNN SDPA (see https://github.com/pytorch/pytorch/issues/138581)
-    torch.backends.cuda.enable_cudnn_sdp(False)
-    attn_output = F.scaled_dot_product_attention(
-        query=query,
-        key=key,
-        value=value,
-        attn_mask=causal_mask,
-        dropout_p=dropout,
-        scale=scaling,
-        is_causal=is_causal,
-    )
-    attn_output = attn_output.transpose(1, 2).contiguous()
-
-    return attn_output, None
-
-
-def flex_attention_forward(
-    module: nn.Module,
-    query: torch.Tensor,
-    key: torch.Tensor,
-    value: torch.Tensor,
-    attention_mask: Optional[torch.Tensor],
-    scaling: Optional[float] = None,
-    is_causal: Optional[bool] = None,
-    softcap: Optional[float] = None,
-    head_mask: Optional[torch.Tensor] = None,
-    **kwargs,
-) -> Tuple[torch.Tensor, torch.Tensor]:
-    causal_mask = attention_mask
-    if attention_mask is not None:
-        causal_mask = causal_mask[:, :, :, : key.shape[-2]]
-
-    if is_causal is None:
-        is_causal = causal_mask is None and query.shape[2] > 1
-
-    def causal_mod(score, batch, head, q_idx, kv_idx):
-        if softcap is not None:
-            score = softcap * torch.tanh(score / softcap)
-        if causal_mask is not None:
-            score = score + causal_mask[batch][0][q_idx][kv_idx]
-        if head_mask is not None:
-            score = score + head_mask[batch][head][0][0]
-        return score
-
-    def dynamic_mod(score, batch, head, q_idx, kv_idx):
-        if softcap is not None:
-            score = softcap * torch.tanh(score / softcap)
-        if causal_mask is not None:
-            score = score + causal_mask[batch][head][q_idx][kv_idx]
-        if head_mask is not None:
-            score = score + head_mask[batch][head][0][0]
-        return score
-
-    # TODO: flex_attention: As of pytorch 2.5.1, captured buffers that require grad are not yet supported.
-    # NOTE: So we only use flex_attention in inference mode.
-    mask_mod = causal_mod if is_causal or module.training else dynamic_mod
-
-    attn_output, attention_weights = flex_attention(
-        query=query,
-        key=key,
-        value=value,
-        score_mod=mask_mod,
-        enable_gqa=True,
-        scale=scaling,
-        # Last time checked on PyTorch == 2.5.1: Flex Attention always computes the lse regardless.
-        # For simplification, we thus always return it as no additional computations are introduced.
-        return_lse=True,
-    )
-    # lse is returned in float32
-    attention_weights = attention_weights.to(value.dtype)
-    attn_output = attn_output.transpose(1, 2).contiguous()
-
-    return attn_output, attention_weights
-
-
-ALL_ATTENTION_FUNCTIONS = {
-    "eager": eager_attention_forward,
-    "sdpa": sdpa_attention_forward,
-    "flex_attention": flex_attention_forward,
-}
-
-
-class DogeDynamicMaskAttention(nn.Module):
-    """Dynamic Mask Attention from 'Wonderful Matrices' paper."""
-
-    def __init__(self, config: DogeConfig, layer_idx: Optional[int] = None):
-        super().__init__()
-        self.config = config
-        self.layer_idx = layer_idx
-        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
-        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
-        self.scaling = self.head_dim**-0.5
-        self.attention_dropout = config.attention_dropout
-        self.dynamic_mask_ratio = config.dynamic_mask_ratio
-
-        self.q_proj = nn.Linear(
-            config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.hidden_bias
-        )
-        self.k_proj = nn.Linear(
-            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.hidden_bias
-        )
-        self.v_proj = nn.Linear(
-            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.hidden_bias
-        )
-        # dynamic mask for the QK^T attention weights matrix
-        self.A = nn.Parameter(torch.zeros(config.num_attention_heads))
-        self.dt_proj = nn.Linear(
-            config.num_key_value_heads * self.head_dim, config.num_attention_heads, bias=config.hidden_bias
-        )
-        self.o_proj = nn.Linear(
-            config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.hidden_bias
-        )
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        position_embeddings: Tuple[torch.Tensor, torch.Tensor],
-        attention_mask: Optional[torch.Tensor] = None,
-        past_key_value: Optional[Cache] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-        **kwargs,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        input_shape = hidden_states.shape[:-1]
-        hidden_shape = (*input_shape, -1, self.head_dim)
-
-        query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
-        key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
-        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
-
-        cos, sin = position_embeddings
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
-
-        if past_key_value is not None:
-            # sin and cos are specific to RoPE models; cache_position needed for the static cache
-            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
-            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
-
-        # calculate dynamic mask from value_states
-        dt_states = self.dt_proj(
-            value_states.transpose(1, 2).reshape(value_states.shape[0], value_states.shape[-2], -1)
-        )
-        dynamic_mask = torch.exp(self.A * F.softplus(dt_states)).transpose(-1, -2)
-        attn_mask = self.prepare_dynamic_mask(
-            hidden_states=hidden_states,
-            dynamic_mask=dynamic_mask,
-            dynamic_mask_ratio=self.dynamic_mask_ratio,
-            attention_mask=attention_mask,
-        )
-
-        attention_interface: Callable = eager_attention_forward
-        if self.config._attn_implementation != "eager":
-            if self.config._attn_implementation == "sdpa" and kwargs.get("output_attentions", False):
-                logger.warning_once(
-                    "`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to "
-                    'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
-                )
-            else:
-                attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
-
-        attn_output, attn_weights = attention_interface(
-            self,
-            query_states,
-            key_states,
-            value_states,
-            attention_mask=attn_mask,
-            dropout=0.0 if not self.training else self.attention_dropout,
-            scaling=self.scaling,
-            **kwargs,
-        )
-
-        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
-        attn_output = self.o_proj(attn_output)
-        return attn_output, attn_weights
-
-    def prepare_dynamic_mask(
-        self,
-        hidden_states: torch.Tensor,
-        dynamic_mask: torch.Tensor,
-        dynamic_mask_ratio: float = 0.0,
-        attention_mask: Optional[torch.Tensor] = None,
-    ):
-        """
-        Combine `dynamic_mask` with `attention_mask` to generate the final `attn_mask`.
-
-        Args:
-            hidden_states (`torch.Tensor`): The input hidden_states, used to determine the minimum value of the current input precision.
-            dynamic_mask (`torch.Tensor`): dynamic mask of shape `(batch_size, num_heads, key_sequence_length)`.
-            dynamic_mask_ratio (`float`, *optional*): Ratio from 0.0 to 1.0 used to control the proportion of the dynamic mask filled with the minimum value.
-            attention_mask (`torch.Tensor`, *optional*): attention mask of shape `(batch_size, 1, query_sequence_length, key_sequence_length)`.
-        """
-        attn_mask = None
-        if dynamic_mask is not None:
-            attn_mask = dynamic_mask[:, :, None, :]
-            if 0.0 < dynamic_mask_ratio < 1.0:
-                min_type = torch.finfo(hidden_states.dtype).min
-                num_dynamic_mask = int(attn_mask.shape[-1] * dynamic_mask_ratio)
-                if num_dynamic_mask > 0:
-                    rate_value = torch.kthvalue(attn_mask, num_dynamic_mask, dim=-1, keepdim=True).values
-                    attn_mask = attn_mask.masked_fill(attn_mask < rate_value, min_type)
-            if attention_mask is not None:
-                attn_mask = attn_mask + attention_mask[:, :, :, : attn_mask.shape[-1]]
-        else:
-            attn_mask = attention_mask
-
-        return attn_mask
-
-
-class DogeMLP(nn.Module):
-    def __init__(self, config: DogeConfig):
-        super().__init__()
-        self.hidden_dim = config.hidden_size
-        self.intermediate_dim = config.intermediate_size
-        self.act_fn = ACT2FN[config.hidden_act]
-
-        self.gate_proj = nn.Linear(self.hidden_dim, self.intermediate_dim, bias=config.hidden_bias)
-        self.up_proj = nn.Linear(self.hidden_dim, self.intermediate_dim, bias=config.hidden_bias)
-        self.down_proj = nn.Linear(self.intermediate_dim, self.hidden_dim, bias=config.hidden_bias)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        **kwargs,
-    ) -> torch.Tensor:
-        hidden_states = self.down_proj(self.act_fn(self.gate_proj(hidden_states)) * self.up_proj(hidden_states))
-        return hidden_states
-
-
-class DogeCDMoE(DogeMLP):
-    """Cross Domain Mixture of Experts from 'Wonderful Matrices' paper."""
-
-    def __init__(self, config: DogeConfig):
-        super().__init__(config)
-        self.hidden_dim = config.hidden_size
-        self.act_fn = ACT2FN[config.hidden_act]
-
-        self.expert_retrieval_dim = config.expert_retrieval_size
-        self.num_cdmoe_experts = config.num_cdmoe_experts
-        self.num_cdmoe_heads = config.num_cdmoe_heads
-        self.num_cdmoe_experts_per_head = config.num_cdmoe_experts_per_head
-        self.num_keys = int(math.sqrt(self.num_cdmoe_experts))
-
-        # queries and keys for retrieval experts
-        self.queries_proj = nn.Linear(self.hidden_dim, self.num_cdmoe_heads * self.expert_retrieval_dim, bias=False)
-        self.keys = nn.Parameter(torch.zeros(self.num_cdmoe_heads, self.expert_retrieval_dim, self.num_keys))
-
-        # experts
-        self.down_embed = nn.Embedding(self.num_cdmoe_experts, self.hidden_dim)
-        self.up_embed = nn.Embedding(self.num_cdmoe_experts, self.hidden_dim)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        **kwargs,
-    ) -> torch.Tensor:
-        bsz, seq_len, _ = hidden_states.shape
-
-        # get routing weights with queries and keys
-        queries = self.queries_proj(hidden_states)
-        queries = queries.view(2, self.num_cdmoe_heads, bsz * seq_len, -1)
-        keys = self.keys.view(2, self.num_cdmoe_heads, -1, self.num_keys)
-        routing_weights = torch.matmul(queries, keys)
-        routing_weights = routing_weights.transpose(-2, -3).view(2, bsz, seq_len, self.num_cdmoe_heads, self.num_keys)
-
-        # get experts with the highest routing weights
-        (scores_x, scores_y), (indices_x, indices_y) = routing_weights.topk(self.num_cdmoe_experts_per_head, dim=-1)
-        all_scores = scores_x.unsqueeze(-1) + scores_y.unsqueeze(-2)
-        all_scores = all_scores.view(*scores_x.shape[:-1], -1)
-        all_indices = (indices_x.unsqueeze(-1) * self.num_keys) + indices_y.unsqueeze(-2)
-        all_indices = all_indices.view(*indices_x.shape[:-1], -1)
-        scores, pk_indices = all_scores.topk(self.num_cdmoe_experts_per_head, dim=-1)
-        indices = all_indices.gather(-1, pk_indices)
-        down_embed = self.down_embed(indices)
-        up_embed = self.up_embed(indices)
-
-        # mix experts states with cross domain states
-        experts_weights = torch.sum(hidden_states[:, :, None, None, :] * down_embed, dim=-1)
-        experts_weights = self.act_fn(experts_weights) * scores.softmax(dim=-1)
-        experts_states = torch.sum(experts_weights[:, :, :, :, None] * up_embed, dim=(-2, -3))
-        hidden_states = self.down_proj(self.act_fn(self.gate_proj(hidden_states)) * self.up_proj(hidden_states))
-        hidden_states = hidden_states + experts_states
-        return hidden_states
-
-
-class DogeDecoderLayer(nn.Module):
-    def __init__(self, config: DogeConfig, layer_idx: Optional[int] = None):
-        super().__init__()
-        self.hidden_dropout = config.hidden_dropout
-
-        self.pre_layernorm = DogeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.self_attn = DogeDynamicMaskAttention(config=config, layer_idx=layer_idx)
-        self.pre_residual = DogeResidual(config.hidden_size)
-
-        self.post_layernorm = DogeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.feed_forward = DogeMLP(config) if not config.is_moe else DogeCDMoE(config)
-        self.post_residual = DogeResidual(config.hidden_size)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Cache] = None,
-        output_attentions: Optional[bool] = False,
-        use_cache: Optional[bool] = False,
-        cache_position: Optional[torch.LongTensor] = None,
-        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
-        **kwargs,
-    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
-        # sequence transformation
-        residual = hidden_states
-        hidden_states = self.pre_layernorm(hidden_states)
-        hidden_states, self_attn_weights = self.self_attn(
-            hidden_states=hidden_states,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_value=past_key_value,
-            output_attentions=output_attentions,
-            use_cache=use_cache,
-            cache_position=cache_position,
-            position_embeddings=position_embeddings,
-            **kwargs,
-        )
-        self_attn_weights = None
-        hidden_states = F.dropout(hidden_states, p=self.hidden_dropout, training=self.training)
-        hidden_states = self.pre_residual(residual, hidden_states)
-
-        # state transformation
-        residual = hidden_states
-        hidden_states = self.post_layernorm(hidden_states)
-        hidden_states = self.feed_forward(hidden_states)
-        hidden_states = F.dropout(hidden_states, p=self.hidden_dropout, training=self.training)
-        hidden_states = self.post_residual(residual, hidden_states)
-
-        outputs = (hidden_states,)
-        if output_attentions:
-            outputs += (self_attn_weights,)
-
-        return outputs
-
-
-DOGE_START_DOCSTRING = r"""
-    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
-    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
-    etc.)
-
-    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
-    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
-    and behavior.
-
-    Parameters:
-        config ([`DogeConfig`]):
-            Model configuration class with all the parameters of the model. Initializing with a config file does not
-            load the weights associated with the model, only the configuration. Check out the
-            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
-"""
-
-
-@add_start_docstrings(
-    "The bare Doge Model outputting raw hidden-states without any specific head on top.",
-    DOGE_START_DOCSTRING,
-)
-class DogePreTrainedModel(PreTrainedModel):
-    config_class = DogeConfig
-    base_model_prefix = "model"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["DogeDecoderLayer"]
-    _skip_keys_device_placement = ["past_key_values"]
-    _supports_sdpa = True
-    # _supports_flex_attn = True # TODO: enable this when flex_attention is fully supported
-    _supports_cache_class = True
-    _supports_quantized_cache = True
-    _supports_static_cache = True
-
-    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_()
-
-
-DOGE_INPUTS_DOCSTRING = r"""
-    Args:
-        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
-            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
-            it.
-
-            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            [What are input IDs?](../glossary#input-ids)
-        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-
-            [What are attention masks?](../glossary#attention-mask)
-
-            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
-            `past_key_values`).
-
-            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
-            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
-            information on the default strategy.
-
-            - 1 indicates the head is **not masked**,
-            - 0 indicates the head is **masked**.
-        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
-            config.n_positions - 1]`.
-
-            [What are position IDs?](../glossary#position-ids)
-        past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
-            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
-            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
-            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
-
-            Two formats are allowed:
-            - a [`~cache_utils.Cache`] instance, see our
-            [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache);
-            - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
-            shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
-            cache format.
-
-            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
-            legacy cache format will be returned.
-
-            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
-            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
-            of shape `(batch_size, sequence_length)`.
-        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
-            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
-            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
-            model's internal embedding lookup matrix.
-        use_cache (`bool`, *optional*):
-            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
-            `past_key_values`).
-        output_attentions (`bool`, *optional*):
-            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
-            tensors for more detail.
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-        cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
-            Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
-            this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
-            the complete sequence length.
-"""
-
-
-@add_start_docstrings(
-    "The bare Doge Model outputting raw hidden-states without any specific head on top.",
-    DOGE_START_DOCSTRING,
-)
-class DogeModel(DogePreTrainedModel):
-    """
-    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`DogeDecoderLayer`]
-
-    Args:
-        config: DogeConfig
-    """
-
-    def __init__(self, config: DogeConfig):
-        super().__init__(config)
-        self.config = config
-        self.padding_idx = config.pad_token_id
-        self.vocab_size = config.vocab_size
-
-        self.word_embed = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
-        self.rotary_emb = DogeRotaryEmbedding(config)
-        self.layers = nn.ModuleList(
-            [DogeDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
-        )
-        self.final_layernorm = DogeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.gradient_checkpointing = False
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.word_embed
-
-    def set_input_embeddings(self, value):
-        self.word_embed = value
-
-    @add_start_docstrings_to_model_forward(DOGE_INPUTS_DOCSTRING)
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-        **kwargs,
-    ) -> Union[Tuple, BaseModelOutputWithPast]:
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError("You cannot specify both input_ids and inputs_embeds")
-
-        if self.gradient_checkpointing and self.training and use_cache:
-            logger.warning_once(
-                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
-            )
-            use_cache = False
-
-        if inputs_embeds is None:
-            inputs_embeds = self.word_embed(input_ids)
-
-        if use_cache and past_key_values is None:
-            past_key_values = DynamicCache()
-
-        if cache_position is None:
-            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
-            cache_position = torch.arange(
-                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
-            )
-
-        if position_ids is None:
-            position_ids = cache_position.unsqueeze(0)
-
-        causal_mask = self._update_causal_mask(
-            attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
-        )
-
-        hidden_states = inputs_embeds
-
-        # create position embeddings to be shared across the decoder layers
-        position_embeddings = self.rotary_emb(hidden_states, position_ids)
-
-        # decoder layers
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attns = () if output_attentions else None
-
-        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
-            if output_hidden_states:
-                all_hidden_states += (hidden_states,)
-
-            if self.gradient_checkpointing and self.training:
-                layer_outputs = self._gradient_checkpointing_func(
-                    decoder_layer.__call__,
-                    hidden_states,
-                    causal_mask,
-                    position_ids,
-                    past_key_values,
-                    output_attentions,
-                    use_cache,
-                    cache_position,
-                    position_embeddings,
-                )
-            else:
-                layer_outputs = decoder_layer(
-                    hidden_states,
-                    attention_mask=causal_mask,
-                    position_ids=position_ids,
-                    past_key_value=past_key_values,
-                    output_attentions=output_attentions,
-                    use_cache=use_cache,
-                    cache_position=cache_position,
-                    position_embeddings=position_embeddings,
-                    **kwargs,
-                )
-
-            hidden_states = layer_outputs[0]
-
-            if output_attentions:
-                all_self_attns += (layer_outputs[1],)
-
-        hidden_states = self.final_layernorm(hidden_states)
-
-        # add hidden states from the last decoder layer
-        if output_hidden_states:
-            all_hidden_states += (hidden_states,)
-
-        output = BaseModelOutputWithPast(
-            last_hidden_state=hidden_states,
-            past_key_values=past_key_values if use_cache else None,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attns,
-        )
-        return output if return_dict else output.to_tuple()
-
-    def _update_causal_mask(
-        self,
-        attention_mask: torch.Tensor,
-        input_tensor: torch.Tensor,
-        cache_position: torch.Tensor,
-        past_key_values: Cache,
-        output_attentions: bool,
-    ):
-        # We have to provide attention_mask for dynamic mask computation
-        past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
-        using_static_cache = isinstance(past_key_values, StaticCache)
-
-        dtype, device = input_tensor.dtype, input_tensor.device
-        sequence_length = input_tensor.shape[1]
-        if using_static_cache:
-            target_length = past_key_values.get_max_cache_shape()
-        else:
-            target_length = (
-                attention_mask.shape[-1]
-                if isinstance(attention_mask, torch.Tensor)
-                else past_seen_tokens + sequence_length + 1
-            )
-
-        # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
-        causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
-            attention_mask,
-            sequence_length=sequence_length,
-            target_length=target_length,
-            dtype=dtype,
-            device=device,
-            cache_position=cache_position,
-            batch_size=input_tensor.shape[0],
-        )
-
-        if (
-            self.config._attn_implementation == "sdpa"
-            and attention_mask is not None
-            and attention_mask.device.type in ["cuda", "xpu"]
-            and not output_attentions
-        ):
-            # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
-            # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
-            # Details: https://github.com/pytorch/pytorch/issues/110213
-            min_dtype = torch.finfo(dtype).min
-            causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
-
-        return causal_mask
-
-    @staticmethod
-    def _prepare_4d_causal_attention_mask_with_cache_position(
-        attention_mask: torch.Tensor,
-        sequence_length: int,
-        target_length: int,
-        dtype: torch.dtype,
-        device: torch.device,
-        cache_position: torch.Tensor,
-        batch_size: int,
-        **kwargs,
-    ):
-        """
-        Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
-        `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
-
-        Args:
-            attention_mask (`torch.Tensor`):
-                A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
-                `(batch_size, 1, query_length, key_value_length)`.
-            sequence_length (`int`):
-                The sequence length being processed.
-            target_length (`int`):
-                The target length: when generating with static cache, the mask should be as long as the static cache,
-                to account for the 0 padding, the part of the cache that is not filled yet.
-            dtype (`torch.dtype`):
-                The dtype to use for the 4D attention mask.
-            device (`torch.device`):
-                The device to plcae the 4D attention mask on.
-            cache_position (`torch.Tensor`):
-                Indices depicting the position of the input sequence tokens in the sequence.
-            batch_size (`torch.Tensor`):
-                Batch size.
-        """
-        if attention_mask is not None and attention_mask.dim() == 4:
-            # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
-            causal_mask = attention_mask
-        else:
-            min_dtype = torch.finfo(dtype).min
-            causal_mask = torch.full(
-                (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device
-            )
-            if sequence_length != 1:
-                causal_mask = torch.triu(causal_mask, diagonal=1)
-            causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
-            causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
-            if attention_mask is not None:
-                causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
-                mask_length = attention_mask.shape[-1]
-                padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
-                padding_mask = padding_mask == 0
-                causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
-                    padding_mask, min_dtype
-                )
-
-        return causal_mask
-
-
-class DogeForCausalLM(DogePreTrainedModel, GenerationMixin):
-    _tied_weights_keys = ["lm_head.weight"]
-    _tp_plan = {"lm_head": "colwise_rep"}
-
-    def __init__(self, config: DogeConfig):
-        super().__init__(config)
-        self.config = config
-        self.model = DogeModel(config)
-        self.vocab_size = config.vocab_size
-        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.model.word_embed
-
-    def set_input_embeddings(self, value):
-        self.model.word_embed = value
-
-    def get_output_embeddings(self):
-        return self.lm_head
-
-    def set_output_embeddings(self, new_embeddings):
-        self.lm_head = new_embeddings
-
-    def get_decoder(self):
-        return self.model
-
-    def set_decoder(self, decoder):
-        self.model = decoder
-
-    @add_start_docstrings_to_model_forward(DOGE_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-        logits_to_keep: Union[int, torch.Tensor] = 0,
-        **kwargs: Unpack[LossKwargs],
-    ) -> Union[Tuple, CausalLMOutputWithPast]:
-        r"""
-        Args:
-            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
-                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
-                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
-
-            logits_to_keep (`int`, *optional*):
-                If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
-                `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
-                token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
-                If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
-                This is useful when using packed tensor format (single dimension for batch and sequence length).
-
-        Returns:
-
-        Example:
-
-        ```python
-         >>> from transformers import AutoTokenizer, AutoModelForCausalLM
-
-        >>> model = AutoModelForCausalLM.from_pretrained("SmallDoge/Doge-20M")
-        >>> tokenizer = AutoTokenizer.from_pretrained("SmallDoge/Doge-20M")
-
-        >>> prompt = "Hey, are you conscious? Can you talk to me?"
-        >>> inputs = tokenizer(prompt, return_tensors="pt")
-
-        >>> # Generate
-        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
-        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
-        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
-        ```"""
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        # decoder output consists of (dec_features, layer_state, dec_hidden, dec_attn)
-        outputs = self.model(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            cache_position=cache_position,
-            **kwargs,
-        )
-
-        hidden_states = outputs[0]
-        # only compute necessary logits, and do not upcast them to float if we are not computing the loss
-        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
-        logits = self.lm_head(hidden_states[:, slice_indices, :])
-
-        loss = None
-        if labels is not None:
-            loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.vocab_size, **kwargs)
-
-        if not return_dict:
-            output = (logits,) + outputs[1:]
-            return (loss,) + output if loss is not None else output
-
-        return CausalLMOutputWithPast(
-            loss=loss,
-            logits=logits,
-            past_key_values=outputs.past_key_values,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-    The Doge Model transformer with a sequence classification head on top (linear layer).
-
-    [`DogeForSequenceClassification`] uses the last token in order to do the classification, as other causal models
-    (e.g. GPT-2) do.
-
-    Since it does classification on the last token, it requires to know the position of the last token. If a
-    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
-    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
-    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
-    each row of the batch).
-    """,
-    DOGE_START_DOCSTRING,
-)
-class DogeForSequenceClassification(DogePreTrainedModel):
-    def __init__(self, config: DogeConfig):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-
-        self.model = DogeModel(config)
-        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
-        self.config = config
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.model.word_embed
-
-    def set_input_embeddings(self, value):
-        self.model.word_embed = value
-
-    @add_start_docstrings_to_model_forward(DOGE_INPUTS_DOCSTRING)
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
-            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
-            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        transformer_outputs = self.model(
-            input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        hidden_states = transformer_outputs[0]
-        logits = self.score(hidden_states)
-
-        if input_ids is not None:
-            batch_size = input_ids.shape[0]
-        else:
-            batch_size = inputs_embeds.shape[0]
-
-        if self.config.pad_token_id is None and batch_size != 1:
-            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
-        if self.config.pad_token_id is None:
-            last_non_pad_token = -1
-        elif input_ids is not None:
-            # To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
-            non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32)
-            token_indices = torch.arange(input_ids.shape[-1], device=logits.device)
-            last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
-        else:
-            last_non_pad_token = -1
-            logger.warning_once(
-                f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
-                "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
-            )
-
-        pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token]
-
-        loss = None
-        if labels is not None:
-            loss = self.loss_function(logits=logits, labels=labels, pooled_logits=pooled_logits, config=self.config)
-
-        if not return_dict:
-            output = (pooled_logits,) + transformer_outputs[1:]
-            return ((loss,) + output) if loss is not None else output
-
-        return SequenceClassifierOutputWithPast(
-            loss=loss,
-            logits=pooled_logits,
-            past_key_values=transformer_outputs.past_key_values,
-            hidden_states=transformer_outputs.hidden_states,
-            attentions=transformer_outputs.attentions,
-        )
-
-
-__all__ = ["DogeForCausalLM", "DogeModel", "DogePreTrainedModel", "DogeForSequenceClassification"]
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/doge/modular_doge.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_doge.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨��🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# coding=utf-8
+# Copyright 2024 Jingze Shi and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on the Wonderful Matrices paper implementation.
+# The Doge family of small language models is trained by Jingze Shi.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from typing import Callable, List, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache, StaticCache
+from transformers.generation import GenerationMixin
+from transformers.modeling_attn_mask_utils import AttentionMaskConverter
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
+from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS
+from transformers.modeling_utils import PreTrainedModel
+from transformers.processing_utils import Unpack
+from transformers.utils import (
+    LossKwargs,
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    is_torch_flex_attn_available,
+    logging,
+    replace_return_docstrings,
+)
+from .configuration_doge import DogeConfig
+
+if is_torch_flex_attn_available():
+    from torch.nn.attention.flex_attention import flex_attention
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "DogeConfig"
+
+
+class DogeRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        DogeRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+    def extra_repr(self):
+        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+class DogeResidual(nn.Module):
+    def __init__(self, hidden_size):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+
+    def forward(self, residual_states, hidden_states):
+        return self.weight * residual_states + hidden_states
+
+    def extra_repr(self):
+        return f"{tuple(self.weight.shape)}"
+
+
+class DogeRotaryEmbedding(nn.Module):
+    def __init__(self, config: DogeConfig, device=None):
+        super().__init__()
+        # BC: "rope_type" was originally "type"
+        if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
+            self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
+        else:
+            self.rope_type = "default"
+        self.max_seq_len_cached = config.max_position_embeddings
+        self.original_max_seq_len = config.max_position_embeddings
+
+        self.config = config
+        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+
+        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.original_inv_freq = self.inv_freq
+
+    def _dynamic_frequency_update(self, position_ids, device):
+        """
+        dynamic RoPE layers should recompute `inv_freq` in the following situations:
+        1 - growing beyond the cached sequence length (allow scaling)
+        2 - the current sequence length is in the original scale (avoid losing precision with small sequences)
+        """
+        seq_len = torch.max(position_ids) + 1
+        if seq_len > self.max_seq_len_cached:  # growth
+            inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device, seq_len=seq_len)
+            self.register_buffer("inv_freq", inv_freq, persistent=False)  # TODO joao: may break with compilation
+            self.max_seq_len_cached = seq_len
+
+        if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len:  # reset
+            # This .to() is needed if the model has been moved to a device after being initialized (because
+            # the buffer is automatically moved, but not the original copy)
+            self.original_inv_freq = self.original_inv_freq.to(device)
+            self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
+            self.max_seq_len_cached = self.original_max_seq_len
+
+    @torch.no_grad()
+    def forward(self, x, position_ids):
+        if "dynamic" in self.rope_type:
+            self._dynamic_frequency_update(position_ids, device=x.device)
+
+        # Core RoPE block
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
+        position_ids_expanded = position_ids[:, None, :].float()
+        # Force float32 (see https://github.com/huggingface/transformers/pull/29285)
+        device_type = x.device.type
+        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
+        with torch.autocast(device_type=device_type, enabled=False):
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos()
+            sin = emb.sin()
+
+        # Advanced RoPE types (e.g. yarn) apply a post-processing scaling factor, equivalent to scaling attention
+        cos = cos * self.attention_scaling
+        sin = sin * self.attention_scaling
+
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`, *optional*):
+            Deprecated and unused.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    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(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+def eager_attention_forward(
+    module: nn.Module,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: Optional[torch.Tensor],
+    scaling: float,
+    dropout: float = 0.0,
+    **kwargs,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    key_states = repeat_kv(key, module.num_key_value_groups)
+    value_states = repeat_kv(value, module.num_key_value_groups)
+
+    attn_weights = torch.matmul(query, key_states.transpose(-1, -2)) * scaling
+    if attention_mask is not None:
+        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+        attn_weights = attn_weights + causal_mask
+
+    attn_weights = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+    attn_weights = F.dropout(attn_weights, p=dropout, training=module.training)
+    attn_output = torch.matmul(attn_weights, value_states)
+    attn_output = attn_output.transpose(1, 2).contiguous()
+
+    return attn_output, attn_weights
+
+
+def sdpa_attention_forward(
+    module: nn.Module,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: Optional[torch.Tensor],
+    dropout: float = 0.0,
+    scaling: Optional[float] = None,
+    is_causal: Optional[bool] = None,
+    **kwargs,
+) -> Tuple[torch.Tensor, None]:
+    key = repeat_kv(key, module.num_key_value_groups)
+    value = repeat_kv(value, module.num_key_value_groups)
+
+    causal_mask = attention_mask
+    if attention_mask is not None:
+        causal_mask = causal_mask[:, :, :, : key.shape[-2]]
+
+    # SDPA with memory-efficient backend is bugged with non-contiguous inputs and custom attn_mask for some torch versions
+    # Reference: https://github.com/pytorch/pytorch/issues/112577.
+    query = query.contiguous()
+    key = key.contiguous()
+    value = value.contiguous()
+
+    # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
+    # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
+    if is_causal is None:
+        is_causal = causal_mask is None and query.shape[2] > 1
+
+    # Shapes (e.g. query.shape[2]) are tensors during jit tracing, resulting in `is_causal` being a tensor.
+    # We convert it to a bool for the SDPA kernel that only accepts bools.
+    if torch.jit.is_tracing() and isinstance(is_causal, torch.Tensor):
+        is_causal = is_causal.item()
+
+    # NOTE: As of pytorch 2.5.1, SDPA backward pass of cuDNN is still incorrect, so we disable cuDNN SDPA (see https://github.com/pytorch/pytorch/issues/138581)
+    torch.backends.cuda.enable_cudnn_sdp(False)
+    attn_output = F.scaled_dot_product_attention(
+        query=query,
+        key=key,
+        value=value,
+        attn_mask=causal_mask,
+        dropout_p=dropout,
+        scale=scaling,
+        is_causal=is_causal,
+    )
+    attn_output = attn_output.transpose(1, 2).contiguous()
+
+    return attn_output, None
+
+
+def flex_attention_forward(
+    module: nn.Module,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: Optional[torch.Tensor],
+    scaling: Optional[float] = None,
+    is_causal: Optional[bool] = None,
+    softcap: Optional[float] = None,
+    head_mask: Optional[torch.Tensor] = None,
+    **kwargs,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    causal_mask = attention_mask
+    if attention_mask is not None:
+        causal_mask = causal_mask[:, :, :, : key.shape[-2]]
+
+    if is_causal is None:
+        is_causal = causal_mask is None and query.shape[2] > 1
+
+    def causal_mod(score, batch, head, q_idx, kv_idx):
+        if softcap is not None:
+            score = softcap * torch.tanh(score / softcap)
+        if causal_mask is not None:
+            score = score + causal_mask[batch][0][q_idx][kv_idx]
+        if head_mask is not None:
+            score = score + head_mask[batch][head][0][0]
+        return score
+
+    def dynamic_mod(score, batch, head, q_idx, kv_idx):
+        if softcap is not None:
+            score = softcap * torch.tanh(score / softcap)
+        if causal_mask is not None:
+            score = score + causal_mask[batch][head][q_idx][kv_idx]
+        if head_mask is not None:
+            score = score + head_mask[batch][head][0][0]
+        return score
+
+    # TODO: flex_attention: As of pytorch 2.5.1, captured buffers that require grad are not yet supported.
+    # NOTE: So we only use flex_attention in inference mode.
+    mask_mod = causal_mod if is_causal or module.training else dynamic_mod
+
+    attn_output, attention_weights = flex_attention(
+        query=query,
+        key=key,
+        value=value,
+        score_mod=mask_mod,
+        enable_gqa=True,
+        scale=scaling,
+        # Last time checked on PyTorch == 2.5.1: Flex Attention always computes the lse regardless.
+        # For simplification, we thus always return it as no additional computations are introduced.
+        return_lse=True,
+    )
+    # lse is returned in float32
+    attention_weights = attention_weights.to(value.dtype)
+    attn_output = attn_output.transpose(1, 2).contiguous()
+
+    return attn_output, attention_weights
+
+
+ALL_ATTENTION_FUNCTIONS = {
+    "eager": eager_attention_forward,
+    "sdpa": sdpa_attention_forward,
+    "flex_attention": flex_attention_forward,
+}
+
+
+class DogeDynamicMaskAttention(nn.Module):
+    """Dynamic Mask Attention from 'Wonderful Matrices' paper."""
+
+    def __init__(self, config: DogeConfig, layer_idx: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
+        self.scaling = self.head_dim**-0.5
+        self.attention_dropout = config.attention_dropout
+        self.dynamic_mask_ratio = config.dynamic_mask_ratio
+
+        self.q_proj = nn.Linear(
+            config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.hidden_bias
+        )
+        self.k_proj = nn.Linear(
+            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.hidden_bias
+        )
+        self.v_proj = nn.Linear(
+            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.hidden_bias
+        )
+        # dynamic mask for the QK^T attention weights matrix
+        self.A = nn.Parameter(torch.zeros(config.num_attention_heads))
+        self.dt_proj = nn.Linear(
+            config.num_key_value_heads * self.head_dim, config.num_attention_heads, bias=config.hidden_bias
+        )
+        self.o_proj = nn.Linear(
+            config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.hidden_bias
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: Tuple[torch.Tensor, torch.Tensor],
+        attention_mask: Optional[torch.Tensor] = None,
+        past_key_value: Optional[Cache] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, self.head_dim)
+
+        query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+
+        cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        if past_key_value is not None:
+            # sin and cos are specific to RoPE models; cache_position needed for the static cache
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        # calculate dynamic mask from value_states
+        dt_states = self.dt_proj(
+            value_states.transpose(1, 2).reshape(value_states.shape[0], value_states.shape[-2], -1)
+        )
+        dynamic_mask = torch.exp(self.A * F.softplus(dt_states)).transpose(-1, -2)
+        attn_mask = self.prepare_dynamic_mask(
+            hidden_states=hidden_states,
+            dynamic_mask=dynamic_mask,
+            dynamic_mask_ratio=self.dynamic_mask_ratio,
+            attention_mask=attention_mask,
+        )
+
+        attention_interface: Callable = eager_attention_forward
+        if self.config._attn_implementation != "eager":
+            if self.config._attn_implementation == "sdpa" and kwargs.get("output_attentions", False):
+                logger.warning_once(
+                    "`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to "
+                    'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+                )
+            else:
+                attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+        attn_output, attn_weights = attention_interface(
+            self,
+            query_states,
+            key_states,
+            value_states,
+            attention_mask=attn_mask,
+            dropout=0.0 if not self.training else self.attention_dropout,
+            scaling=self.scaling,
+            **kwargs,
+        )
+
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+        return attn_output, attn_weights
+
+    def prepare_dynamic_mask(
+        self,
+        hidden_states: torch.Tensor,
+        dynamic_mask: torch.Tensor,
+        dynamic_mask_ratio: float = 0.0,
+        attention_mask: Optional[torch.Tensor] = None,
+    ):
+        """
+        Combine `dynamic_mask` with `attention_mask` to generate the final `attn_mask`.
+
+        Args:
+            hidden_states (`torch.Tensor`): The input hidden_states, used to determine the minimum value of the current input precision.
+            dynamic_mask (`torch.Tensor`): dynamic mask of shape `(batch_size, num_heads, key_sequence_length)`.
+            dynamic_mask_ratio (`float`, *optional*): Ratio from 0.0 to 1.0 used to control the proportion of the dynamic mask filled with the minimum value.
+            attention_mask (`torch.Tensor`, *optional*): attention mask of shape `(batch_size, 1, query_sequence_length, key_sequence_length)`.
+        """
+        attn_mask = None
+        if dynamic_mask is not None:
+            attn_mask = dynamic_mask[:, :, None, :]
+            if 0.0 < dynamic_mask_ratio < 1.0:
+                min_type = torch.finfo(hidden_states.dtype).min
+                num_dynamic_mask = int(attn_mask.shape[-1] * dynamic_mask_ratio)
+                if num_dynamic_mask > 0:
+                    rate_value = torch.kthvalue(attn_mask, num_dynamic_mask, dim=-1, keepdim=True).values
+                    attn_mask = attn_mask.masked_fill(attn_mask < rate_value, min_type)
+            if attention_mask is not None:
+                attn_mask = attn_mask + attention_mask[:, :, :, : attn_mask.shape[-1]]
+        else:
+            attn_mask = attention_mask
+
+        return attn_mask
+
+
+class DogeMLP(nn.Module):
+    def __init__(self, config: DogeConfig):
+        super().__init__()
+        self.hidden_dim = config.hidden_size
+        self.intermediate_dim = config.intermediate_size
+        self.act_fn = ACT2FN[config.hidden_act]
+
+        self.gate_proj = nn.Linear(self.hidden_dim, self.intermediate_dim, bias=config.hidden_bias)
+        self.up_proj = nn.Linear(self.hidden_dim, self.intermediate_dim, bias=config.hidden_bias)
+        self.down_proj = nn.Linear(self.intermediate_dim, self.hidden_dim, bias=config.hidden_bias)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        **kwargs,
+    ) -> torch.Tensor:
+        hidden_states = self.down_proj(self.act_fn(self.gate_proj(hidden_states)) * self.up_proj(hidden_states))
+        return hidden_states
+
+
+class DogeCDMoE(DogeMLP):
+    """Cross Domain Mixture of Experts from 'Wonderful Matrices' paper."""
+
+    def __init__(self, config: DogeConfig):
+        super().__init__(config)
+        self.hidden_dim = config.hidden_size
+        self.act_fn = ACT2FN[config.hidden_act]
+
+        self.expert_retrieval_dim = config.expert_retrieval_size
+        self.num_cdmoe_experts = config.num_cdmoe_experts
+        self.num_cdmoe_heads = config.num_cdmoe_heads
+        self.num_cdmoe_experts_per_head = config.num_cdmoe_experts_per_head
+        self.num_keys = int(math.sqrt(self.num_cdmoe_experts))
+
+        # queries and keys for retrieval experts
+        self.queries_proj = nn.Linear(self.hidden_dim, self.num_cdmoe_heads * self.expert_retrieval_dim, bias=False)
+        self.keys = nn.Parameter(torch.zeros(self.num_cdmoe_heads, self.expert_retrieval_dim, self.num_keys))
+
+        # experts
+        self.down_embed = nn.Embedding(self.num_cdmoe_experts, self.hidden_dim)
+        self.up_embed = nn.Embedding(self.num_cdmoe_experts, self.hidden_dim)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        **kwargs,
+    ) -> torch.Tensor:
+        bsz, seq_len, _ = hidden_states.shape
+
+        # get routing weights with queries and keys
+        queries = self.queries_proj(hidden_states)
+        queries = queries.view(2, self.num_cdmoe_heads, bsz * seq_len, -1)
+        keys = self.keys.view(2, self.num_cdmoe_heads, -1, self.num_keys)
+        routing_weights = torch.matmul(queries, keys)
+        routing_weights = routing_weights.transpose(-2, -3).view(2, bsz, seq_len, self.num_cdmoe_heads, self.num_keys)
+
+        # get experts with the highest routing weights
+        (scores_x, scores_y), (indices_x, indices_y) = routing_weights.topk(self.num_cdmoe_experts_per_head, dim=-1)
+        all_scores = scores_x.unsqueeze(-1) + scores_y.unsqueeze(-2)
+        all_scores = all_scores.view(*scores_x.shape[:-1], -1)
+        all_indices = (indices_x.unsqueeze(-1) * self.num_keys) + indices_y.unsqueeze(-2)
+        all_indices = all_indices.view(*indices_x.shape[:-1], -1)
+        scores, pk_indices = all_scores.topk(self.num_cdmoe_experts_per_head, dim=-1)
+        indices = all_indices.gather(-1, pk_indices)
+        down_embed = self.down_embed(indices)
+        up_embed = self.up_embed(indices)
+
+        # mix experts states with cross domain states
+        experts_weights = torch.sum(hidden_states[:, :, None, None, :] * down_embed, dim=-1)
+        experts_weights = self.act_fn(experts_weights) * scores.softmax(dim=-1)
+        experts_states = torch.sum(experts_weights[:, :, :, :, None] * up_embed, dim=(-2, -3))
+        hidden_states = self.down_proj(self.act_fn(self.gate_proj(hidden_states)) * self.up_proj(hidden_states))
+        hidden_states = hidden_states + experts_states
+        return hidden_states
+
+
+class DogeDecoderLayer(nn.Module):
+    def __init__(self, config: DogeConfig, layer_idx: Optional[int] = None):
+        super().__init__()
+        self.hidden_dropout = config.hidden_dropout
+
+        self.pre_layernorm = DogeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.self_attn = DogeDynamicMaskAttention(config=config, layer_idx=layer_idx)
+        self.pre_residual = DogeResidual(config.hidden_size)
+
+        self.post_layernorm = DogeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.feed_forward = DogeMLP(config) if not config.is_moe else DogeCDMoE(config)
+        self.post_residual = DogeResidual(config.hidden_size)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
+        **kwargs,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        # sequence transformation
+        residual = hidden_states
+        hidden_states = self.pre_layernorm(hidden_states)
+        hidden_states, self_attn_weights = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            cache_position=cache_position,
+            position_embeddings=position_embeddings,
+            **kwargs,
+        )
+        self_attn_weights = None
+        hidden_states = F.dropout(hidden_states, p=self.hidden_dropout, training=self.training)
+        hidden_states = self.pre_residual(residual, hidden_states)
+
+        # state transformation
+        residual = hidden_states
+        hidden_states = self.post_layernorm(hidden_states)
+        hidden_states = self.feed_forward(hidden_states)
+        hidden_states = F.dropout(hidden_states, p=self.hidden_dropout, training=self.training)
+        hidden_states = self.post_residual(residual, hidden_states)
+
+        outputs = (hidden_states,)
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        return outputs
+
+
+DOGE_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`DogeConfig`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+    "The bare Doge Model outputting raw hidden-states without any specific head on top.",
+    DOGE_START_DOCSTRING,
+)
+class DogePreTrainedModel(PreTrainedModel):
+    config_class = DogeConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["DogeDecoderLayer"]
+    _skip_keys_device_placement = ["past_key_values"]
+    _supports_sdpa = True
+    # _supports_flex_attn = True # TODO: enable this when flex_attention is fully supported
+    _supports_cache_class = True
+    _supports_quantized_cache = True
+    _supports_static_cache = True
+
+    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_()
+
+
+DOGE_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
+            `past_key_values`).
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
+            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
+            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+            Two formats are allowed:
+            - a [`~cache_utils.Cache`] instance, see our
+            [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache);
+            - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+            shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
+            cache format.
+
+            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
+            legacy cache format will be returned.
+
+            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
+            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
+            of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+        cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+            Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
+            this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
+            the complete sequence length.
+"""
+
+
+@add_start_docstrings(
+    "The bare Doge Model outputting raw hidden-states without any specific head on top.",
+    DOGE_START_DOCSTRING,
+)
+class DogeModel(DogePreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`DogeDecoderLayer`]
+
+    Args:
+        config: DogeConfig
+    """
+
+    def __init__(self, config: DogeConfig):
+        super().__init__(config)
+        self.config = config
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.word_embed = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
+        self.rotary_emb = DogeRotaryEmbedding(config)
+        self.layers = nn.ModuleList(
+            [DogeDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self.final_layernorm = DogeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.gradient_checkpointing = False
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.word_embed
+
+    def set_input_embeddings(self, value):
+        self.word_embed = value
+
+    @add_start_docstrings_to_model_forward(DOGE_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You cannot specify both input_ids and inputs_embeds")
+
+        if self.gradient_checkpointing and self.training and use_cache:
+            logger.warning_once(
+                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+            )
+            use_cache = False
+
+        if inputs_embeds is None:
+            inputs_embeds = self.word_embed(input_ids)
+
+        if use_cache and past_key_values is None:
+            past_key_values = DynamicCache()
+
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+            )
+
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        causal_mask = self._update_causal_mask(
+            attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
+        )
+
+        hidden_states = inputs_embeds
+
+        # create position embeddings to be shared across the decoder layers
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+
+        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    causal_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                    cache_position,
+                    position_embeddings,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=causal_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                    cache_position=cache_position,
+                    position_embeddings=position_embeddings,
+                    **kwargs,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.final_layernorm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        output = BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=past_key_values if use_cache else None,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+        return output if return_dict else output.to_tuple()
+
+    def _update_causal_mask(
+        self,
+        attention_mask: torch.Tensor,
+        input_tensor: torch.Tensor,
+        cache_position: torch.Tensor,
+        past_key_values: Cache,
+        output_attentions: bool,
+    ):
+        # We have to provide attention_mask for dynamic mask computation
+        past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+        using_static_cache = isinstance(past_key_values, StaticCache)
+
+        dtype, device = input_tensor.dtype, input_tensor.device
+        sequence_length = input_tensor.shape[1]
+        if using_static_cache:
+            target_length = past_key_values.get_max_cache_shape()
+        else:
+            target_length = (
+                attention_mask.shape[-1]
+                if isinstance(attention_mask, torch.Tensor)
+                else past_seen_tokens + sequence_length + 1
+            )
+
+        # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
+        causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
+            attention_mask,
+            sequence_length=sequence_length,
+            target_length=target_length,
+            dtype=dtype,
+            device=device,
+            cache_position=cache_position,
+            batch_size=input_tensor.shape[0],
+        )
+
+        if (
+            self.config._attn_implementation == "sdpa"
+            and attention_mask is not None
+            and attention_mask.device.type in ["cuda", "xpu"]
+            and not output_attentions
+        ):
+            # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
+            # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
+            # Details: https://github.com/pytorch/pytorch/issues/110213
+            min_dtype = torch.finfo(dtype).min
+            causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
+
+        return causal_mask
+
+    @staticmethod
+    def _prepare_4d_causal_attention_mask_with_cache_position(
+        attention_mask: torch.Tensor,
+        sequence_length: int,
+        target_length: int,
+        dtype: torch.dtype,
+        device: torch.device,
+        cache_position: torch.Tensor,
+        batch_size: int,
+        **kwargs,
+    ):
+        """
+        Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
+        `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
+
+        Args:
+            attention_mask (`torch.Tensor`):
+                A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
+                `(batch_size, 1, query_length, key_value_length)`.
+            sequence_length (`int`):
+                The sequence length being processed.
+            target_length (`int`):
+                The target length: when generating with static cache, the mask should be as long as the static cache,
+                to account for the 0 padding, the part of the cache that is not filled yet.
+            dtype (`torch.dtype`):
+                The dtype to use for the 4D attention mask.
+            device (`torch.device`):
+                The device to plcae the 4D attention mask on.
+            cache_position (`torch.Tensor`):
+                Indices depicting the position of the input sequence tokens in the sequence.
+            batch_size (`torch.Tensor`):
+                Batch size.
+        """
+        if attention_mask is not None and attention_mask.dim() == 4:
+            # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
+            causal_mask = attention_mask
+        else:
+            min_dtype = torch.finfo(dtype).min
+            causal_mask = torch.full(
+                (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device
+            )
+            if sequence_length != 1:
+                causal_mask = torch.triu(causal_mask, diagonal=1)
+            causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
+            causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
+            if attention_mask is not None:
+                causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
+                mask_length = attention_mask.shape[-1]
+                padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
+                padding_mask = padding_mask == 0
+                causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+                    padding_mask, min_dtype
+                )
+
+        return causal_mask
+
+
+class DogeForCausalLM(DogePreTrainedModel, GenerationMixin):
+    _tied_weights_keys = ["lm_head.weight"]
+    _tp_plan = {"lm_head": "colwise_rep"}
+
+    def __init__(self, config: DogeConfig):
+        super().__init__(config)
+        self.config = config
+        self.model = DogeModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.word_embed
+
+    def set_input_embeddings(self, value):
+        self.model.word_embed = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def get_decoder(self):
+        return self.model
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    @add_start_docstrings_to_model_forward(DOGE_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        logits_to_keep: Union[int, torch.Tensor] = 0,
+        **kwargs: Unpack[LossKwargs],
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+            logits_to_keep (`int`, *optional*):
+                If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
+                `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
+                token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
+                If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
+                This is useful when using packed tensor format (single dimension for batch and sequence length).
+
+        Returns:
+
+        Example:
+
+        ```python
+         >>> from transformers import AutoTokenizer, AutoModelForCausalLM
+
+        >>> model = AutoModelForCausalLM.from_pretrained("SmallDoge/Doge-20M")
+        >>> tokenizer = AutoTokenizer.from_pretrained("SmallDoge/Doge-20M")
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+        ```"""
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder output consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            cache_position=cache_position,
+            **kwargs,
+        )
+
+        hidden_states = outputs[0]
+        # only compute necessary logits, and do not upcast them to float if we are not computing the loss
+        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.vocab_size, **kwargs)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+@add_start_docstrings(
+    """
+    The Doge Model transformer with a sequence classification head on top (linear layer).
+
+    [`DogeForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+    (e.g. GPT-2) do.
+
+    Since it does classification on the last token, it requires to know the position of the last token. If a
+    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+    each row of the batch).
+    """,
+    DOGE_START_DOCSTRING,
+)
+class DogeForSequenceClassification(DogePreTrainedModel):
+    def __init__(self, config: DogeConfig):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+
+        self.model = DogeModel(config)
+        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+        self.config = config
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.word_embed
+
+    def set_input_embeddings(self, value):
+        self.model.word_embed = value
+
+    @add_start_docstrings_to_model_forward(DOGE_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        transformer_outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+        logits = self.score(hidden_states)
+
+        if input_ids is not None:
+            batch_size = input_ids.shape[0]
+        else:
+            batch_size = inputs_embeds.shape[0]
+
+        if self.config.pad_token_id is None and batch_size != 1:
+            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+        if self.config.pad_token_id is None:
+            last_non_pad_token = -1
+        elif input_ids is not None:
+            # To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
+            non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32)
+            token_indices = torch.arange(input_ids.shape[-1], device=logits.device)
+            last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
+        else:
+            last_non_pad_token = -1
+            logger.warning_once(
+                f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
+                "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
+            )
+
+        pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token]
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(logits=logits, labels=labels, pooled_logits=pooled_logits, config=self.config)
+
+        if not return_dict:
+            output = (pooled_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )
+
+
+__all__ = ["DogeForCausalLM", "DogeModel", "DogePreTrainedModel", "DogeForSequenceClassification"]