Upload DogeForCausalLM

config.json

@@ -6,7 +6,7 @@
   "attention_dropout": 0.0,
   "auto_map": {
     "AutoConfig": "configuration_doge.DogeConfig",
-    "AutoModelForCausalLM": "modeling_old_doge.DogeForCausalLM"
+    "AutoModelForCausalLM": "modeling_doge.DogeForCausalLM"
   },
   "bos_token_id": 0,
   "dynamic_mask_ratio": 0.0,

modeling_doge.py

@@ -1,9 +1,14 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           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.
-#
-#     https://arxiv.org/abs/2412.11834
+# 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.
@@ -16,24 +21,19 @@
 # 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.
-"""PyTorch Doge model."""
 
 import math
 from typing import Callable, List, Optional, Tuple, Union
 
 import torch
 import torch.nn.functional as F
-import torch.utils.checkpoint
 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_outputs import (
-    BaseModelOutputWithPast,
-    CausalLMOutputWithPast,
-    SequenceClassifierOutputWithPast,
-)
+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
@@ -41,30 +41,24 @@ from transformers.utils import (
     LossKwargs,
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
-    is_torch_greater_or_equal,
+    is_torch_flex_attn_available,
     logging,
     replace_return_docstrings,
 )
 from .configuration_doge import DogeConfig
 
-try:
-    from einx import add as einx_add
-except ImportError:
-    einx_add = None
-
-if is_torch_greater_or_equal("2.5"):
+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 RMSNorm(nn.Module):
+class DogeRMSNorm(nn.Module):
     def __init__(self, hidden_size, eps=1e-6):
         """
-        RMSNorm is equivalent to T5LayerNorm
+        DogeRMSNorm is equivalent to T5LayerNorm
         """
         super().__init__()
         self.weight = nn.Parameter(torch.ones(hidden_size))
@@ -81,7 +75,7 @@ class RMSNorm(nn.Module):
         return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
 
 
-class Residual(nn.Module):
+class DogeResidual(nn.Module):
     def __init__(self, hidden_size):
         super().__init__()
         self.weight = nn.Parameter(torch.ones(hidden_size))
@@ -93,23 +87,21 @@ class Residual(nn.Module):
         return f"{tuple(self.weight.shape)}"
 
 
-class RotaryEmbedding(nn.Module):
-    def __init__(self, config: Optional[DogeConfig] = None):
+class DogeRotaryEmbedding(nn.Module):
+    def __init__(self, config: DogeConfig, device=None):
         super().__init__()
-        self.rope_kwargs = {}
-
-        if config.rope_scaling is not None:
+        # 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.base = config.rope_theta
 
         self.config = config
         self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
 
-        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, **self.rope_kwargs)
+        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
 
@@ -121,13 +113,14 @@ class RotaryEmbedding(nn.Module):
         """
         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.rope_kwargs
-            )
+            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
 
@@ -136,7 +129,7 @@ class RotaryEmbedding(nn.Module):
         if "dynamic" in self.rope_type:
             self._dynamic_frequency_update(position_ids, device=x.device)
 
-        # core RoPE block
+        # 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)
@@ -156,15 +149,13 @@ class RotaryEmbedding(nn.Module):
 
 
 def rotate_half(x):
-    """
-    Rotates half the hidden dims of the input.
-    """
+    """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_QK_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_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:
@@ -176,10 +167,11 @@ def apply_QK_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
             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.
+            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.
     """
@@ -192,8 +184,8 @@ def apply_QK_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
 
 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)
+    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:
@@ -202,6 +194,148 @@ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
     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."""
 
@@ -209,47 +343,28 @@ class DogeDynamicMaskAttention(nn.Module):
         super().__init__()
         self.config = config
         self.layer_idx = layer_idx
-        self.head_dim = config.hidden_size // config.num_attention_heads
+        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.scaling = self.head_dim**-0.5
         self.attention_dropout = config.attention_dropout
         self.dynamic_mask_ratio = config.dynamic_mask_ratio
 
-        self.ALL_ATTENTION_FUNCTIONS = {
-            "eager": self.eager_attention_forward,
-            "flex_attention": self.flex_attention_forward,
-            "sdpa": self.sdpa_attention_forward,
-        }
-
-        # Q K V O projections
         self.q_proj = nn.Linear(
-            config.hidden_size,
-            config.num_attention_heads * self.head_dim,
-            bias=config.hidden_bias
+            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
+            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 score matrix
-        self.A = nn.Parameter(
-            torch.zeros(config.num_attention_heads)
+            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
+            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
+            config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.hidden_bias
         )
 
     def forward(
@@ -260,7 +375,7 @@ class DogeDynamicMaskAttention(nn.Module):
         past_key_value: Optional[Cache] = None,
         cache_position: Optional[torch.LongTensor] = None,
         **kwargs,
-    ) -> Tuple[torch.Tensor, Optional[Cache]]:
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
         input_shape = hidden_states.shape[:-1]
         hidden_shape = (*input_shape, -1, self.head_dim)
 
@@ -269,7 +384,7 @@ class DogeDynamicMaskAttention(nn.Module):
         value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
 
         cos, sin = position_embeddings
-        query_states, key_states = apply_QK_rotary_pos_emb(query_states, key_states, cos, sin)
+        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
@@ -277,9 +392,9 @@ class DogeDynamicMaskAttention(nn.Module):
             key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
 
         # calculate dynamic mask from value_states
-        # NOTE: If these weights are not trained in causal mode, a mask of all ones will be returned, which will not affect the training results of causal mode
-        # TODO: The main reason for setting causal mode is that the Flex Attention kernel does not yet support score_mod functions with learnable parameters. However, we can continue training from the causal checkpoint later.
-        dt_states = self.dt_proj(value_states.transpose(1, 2).reshape(value_states.shape[0], value_states.shape[-2], -1))
+        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,
@@ -288,11 +403,18 @@ class DogeDynamicMaskAttention(nn.Module):
             attention_mask=attention_mask,
         )
 
-        attention_interface: Callable = self.eager_attention_forward
+        attention_interface: Callable = eager_attention_forward
         if self.config._attn_implementation != "eager":
-            attention_interface = self.ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
-        
-        attn_output = attention_interface(
+            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,
@@ -304,7 +426,7 @@ class DogeDynamicMaskAttention(nn.Module):
 
         attn_output = attn_output.reshape(*input_shape, -1).contiguous()
         attn_output = self.o_proj(attn_output)
-        return attn_output
+        return attn_output, attn_weights
 
     def prepare_dynamic_mask(
         self,
@@ -337,110 +459,9 @@ class DogeDynamicMaskAttention(nn.Module):
             attn_mask = attention_mask
 
         return attn_mask
-    
-    def eager_attention_forward(
-        self,
-        query: torch.Tensor,
-        key: torch.Tensor,
-        value: torch.Tensor,
-        attention_mask: Optional[torch.Tensor],
-        scaling: float,
-        dropout: float = 0.0,
-        **kwargs,
-    ) -> torch.Tensor:
-        key_states = repeat_kv(key, self.num_key_value_groups)
-        value_states = repeat_kv(value, self.num_key_value_groups)
-
-        # compute attention scores matrix
-        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
-        
-        # upcast attention scores to fp32
-        attn_weights = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
-        attn_weights = F.dropout(attn_weights, p=dropout, training=self.training)
-
-        # apply attention scores to value states
-        attn_output = torch.matmul(attn_weights, value_states)
-        attn_output = attn_output.transpose(1, 2).contiguous()
-        return attn_output
-    
-    def sdpa_attention_forward(
-        self,
-        query: torch.Tensor,
-        key: torch.Tensor,
-        value: torch.Tensor,
-        attention_mask: Optional[torch.Tensor],
-        scaling: float,
-        dropout: float = 0.0,
-        **kwargs,
-    ) -> torch.Tensor:
-        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()
-
-        # NOTE: As of pytorch 2.5.1, cuDNN's SDPA backward pass 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,
-            key,
-            value,
-            attn_mask=causal_mask,
-            dropout_p=dropout,
-            scale=scaling,
-            enable_gqa=True,
-        )
-        attn_output = attn_output.transpose(1, 2).contiguous()
-        return attn_output
-    
-    def flex_attention_forward(
-        self,
-        query: torch.Tensor,
-        key: torch.Tensor,
-        value: torch.Tensor,
-        attention_mask: Optional[torch.Tensor],
-        scaling: float,
-        dropout: float = 0.0,
-        **kwargs,
-    ) -> torch.Tensor:
-        causal_mask = attention_mask
-        if attention_mask is not None:
-            causal_mask = causal_mask[:, :, :, : key.shape[-2]]
-
-        # 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.
-
-        def causal_mod(score, batch, head, q_idx, kv_idx):
-            score = score + causal_mask[batch][0][q_idx][kv_idx]
-            return score
-        
-        def dynamic_mod(score, batch, head, q_idx, kv_idx):
-            score = score + causal_mask[batch][head][q_idx][kv_idx]
-            return score
-        
-        mask_mod = causal_mod if self.is_causal else dynamic_mod
-        
-        attn_output = flex_attention(
-            query,
-            key,
-            value,
-            score_mod=mask_mod,
-            scale=scaling,
-            enable_gqa=True,
-        )
-        attn_output = attn_output.transpose(1, 2).contiguous()
-        return attn_output
 
 
 class DogeMLP(nn.Module):
-
     def __init__(self, config: DogeConfig):
         super().__init__()
         self.hidden_dim = config.hidden_size
@@ -475,11 +496,11 @@ class DogeCDMoE(DogeMLP):
         self.num_keys = int(math.sqrt(self.num_cdmoe_experts))
 
         # queries and keys for retrieval experts
-        self.queries = 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.num_keys, 2, self.expert_retrieval_dim // 2))
+        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.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(
@@ -489,30 +510,28 @@ class DogeCDMoE(DogeMLP):
     ) -> torch.Tensor:
         bsz, seq_len, _ = hidden_states.shape
 
-        # get similarity with queries and keys
-        queries = self.queries(hidden_states)
-        queries = queries.view(bsz, seq_len, 2, self.num_cdmoe_heads, -1).permute(2, 0, 1, 3, 4)
-        sim = torch.einsum("p b t h n, h k p n -> p b t h k", queries, self.keys)
-
-        # get experts with the highest similarity
-        (scores_x, scores_y), (indices_x, indices_y) = sim.topk(self.num_cdmoe_experts_per_head, dim=-1)
-        if einx_add is not None:
-            all_scores = einx_add("... i, ... j -> ... (i j)", scores_x, scores_y)
-            all_indices = einx_add("... i, ... j -> ... (i j)", indices_x * self.num_keys, indices_y)
-        else:
-            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)
+        # 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.einsum("b t d, b t h k d -> b t h k", hidden_states, down_embed)
+        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.einsum("b t h k, b t h k d -> b t d", experts_weights, up_embed)
+        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
@@ -523,13 +542,13 @@ class DogeDecoderLayer(nn.Module):
         super().__init__()
         self.hidden_dropout = config.hidden_dropout
 
-        self.pre_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        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 = Residual(config.hidden_size)
+        self.pre_residual = DogeResidual(config.hidden_size)
 
-        self.post_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.feed_forward = DogeMLP(config) if config.is_moe == False else DogeCDMoE(config)
-        self.post_residual = Residual(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,
@@ -543,15 +562,16 @@ class DogeDecoderLayer(nn.Module):
         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.self_attn(
+        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,
@@ -589,6 +609,8 @@ DOGE_START_DOCSTRING = r"""
             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,
@@ -600,7 +622,7 @@ class DogePreTrainedModel(PreTrainedModel):
     _no_split_modules = ["DogeDecoderLayer"]
     _skip_keys_device_placement = ["past_key_values"]
     _supports_sdpa = True
-    # _supports_flex_attn = 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
@@ -711,11 +733,11 @@ class DogeModel(DogePreTrainedModel):
         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 = RotaryEmbedding(config)
+        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 = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.final_layernorm = DogeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
         self.gradient_checkpointing = False
 
         # Initialize weights and apply final processing
@@ -842,9 +864,27 @@ class DogeModel(DogePreTrainedModel):
         past_key_values: Cache,
         output_attentions: bool,
     ):
+        if self.config._attn_implementation == "flash_attention_2":
+            if attention_mask is not None and (attention_mask == 0.0).any():
+                return attention_mask
+            return None
+
+        # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
+        # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
+        # to infer the attention mask.
         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)
 
+        # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
+        if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
+            if AttentionMaskConverter._ignore_causal_mask_sdpa(
+                attention_mask,
+                inputs_embeds=input_tensor,
+                past_key_values_length=past_seen_tokens,
+                is_training=self.training,
+            ):
+                return None
+
         dtype, device = input_tensor.dtype, input_tensor.device
         sequence_length = input_tensor.shape[1]
         if using_static_cache:
@@ -856,9 +896,9 @@ class DogeModel(DogePreTrainedModel):
                 else past_seen_tokens + sequence_length + 1
             )
 
-        # in case the provided `attention` mask is 2D, we generate a causal mask here (4D).
+        # 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=attention_mask,
+            attention_mask,
             sequence_length=sequence_length,
             target_length=target_length,
             dtype=dtype,
@@ -867,17 +907,29 @@ class DogeModel(DogePreTrainedModel):
             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 = None,
-        sequence_length: int = None,
-        target_length: int = None,
-        dtype: torch.dtype = None,
-        device: torch.device = None,
-        cache_position: torch.Tensor = None,
-        batch_size: int = None,
+        attention_mask: torch.Tensor,
+        sequence_length: int,
+        target_length: int,
+        dtype: torch.dtype,
+        device: torch.device,
+        cache_position: torch.Tensor,
+        batch_size: int,
         **kwargs,
     ):
         """
@@ -908,8 +960,7 @@ class DogeModel(DogePreTrainedModel):
         else:
             min_dtype = torch.finfo(dtype).min
             causal_mask = torch.full(
-                (sequence_length, target_length),
-                fill_value=min_dtype, dtype=dtype, device=device,
+                (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device
             )
             if sequence_length != 1:
                 causal_mask = torch.triu(causal_mask, diagonal=1)
@@ -927,9 +978,6 @@ class DogeModel(DogePreTrainedModel):
         return causal_mask
 
 
-class KwargsForCausalLM(LossKwargs): ...
-
-
 class DogeForCausalLM(DogePreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
     _tp_plan = {"lm_head": "colwise_rep"}
@@ -955,7 +1003,7 @@ class DogeForCausalLM(DogePreTrainedModel, GenerationMixin):
 
     def set_output_embeddings(self, new_embeddings):
         self.lm_head = new_embeddings
-    
+
     def get_decoder(self):
         return self.model
 
@@ -977,8 +1025,8 @@ class DogeForCausalLM(DogePreTrainedModel, GenerationMixin):
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
         cache_position: Optional[torch.LongTensor] = None,
-        num_logits_to_keep: int = 0,
-        **kwargs: Unpack[KwargsForCausalLM],
+        logits_to_keep: Union[int, torch.Tensor] = 0,
+        **kwargs: Unpack[LossKwargs],
     ) -> Union[Tuple, CausalLMOutputWithPast]:
         r"""
         Args:
@@ -987,10 +1035,12 @@ class DogeForCausalLM(DogePreTrainedModel, GenerationMixin):
                 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]`.
 
-            num_logits_to_keep (`int`, *optional*):
-                Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all
+            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:
 
@@ -999,8 +1049,8 @@ class DogeForCausalLM(DogePreTrainedModel, GenerationMixin):
         ```python
          >>> from transformers import AutoTokenizer, AutoModelForCausalLM
 
-        >>> model = AutoModelForCausalLM.from_pretrained("JingzeShi/Doge-20M-Instruct")
-        >>> tokenizer = AutoTokenizer.from_pretrained("JingzeShi/Doge-20M-Instruct")
+        >>> 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")
@@ -1032,9 +1082,9 @@ class DogeForCausalLM(DogePreTrainedModel, GenerationMixin):
         )
 
         hidden_states = outputs[0]
-
         # only compute necessary logits, and do not upcast them to float if we are not computing the loss
-        logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :])
+        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:
@@ -1053,111 +1103,32 @@ class DogeForCausalLM(DogePreTrainedModel, GenerationMixin):
         )
 
 
-class DogePatchEmbedding(nn.Module):
-    """
-    This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial `hidden_states` of shape `(batch_size, seq_len, hidden_size)` to be consumed by a Transformer.
-    """
-
-    def __init__(self, config: DogeConfig):
-        super().__init__()
-
-        self.num_channels = config.num_channels
-        self.patch_size = config.patch_size
-        self.hidden_dim = config.hidden_size
-
-        self.sequence_proj = nn.Conv2d(self.num_channels, self.hidden_dim, kernel_size=self.patch_size, stride=self.patch_size)
-        self.state_proj = nn.Linear(self.hidden_dim, self.hidden_dim, bias=config.hidden_bias)
-
-    def forward(
-        self,
-        pixel_values: torch.Tensor,
-    ) -> torch.Tensor:
-        image_embedding = self.sequence_proj(pixel_values).flatten(2).transpose(1, 2)
-        image_embedding = self.state_proj(image_embedding)
-        return image_embedding
-
-
-class DogeForCausalVLM(DogeForCausalLM):
-    _tied_weights_keys = ["lm_head.weight"]
-
-    def __init__(self, config: DogeConfig):
-        super().__init__(config)
-        self.config = config
-        self.pixel_embed = DogePatchEmbedding(config)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-    
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        pixel_values: torch.FloatTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[torch.Tensor] = 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,
-        num_logits_to_keep: int = 0,
-        **loss_kwargs,
-    ) -> Union[Tuple, CausalLMOutputWithPast]:
-        # TODO: @wubingheng111: refer to Llava for implementating the forward method
-        ...
-    
-    def prepare_inputs_for_generation(
-        self,
-        input_ids=None,
-        pixel_values=None,
-        past_key_values=None,
-        input_embeds=None,
-        attention_mask=None,
-        cache_position=None,
-        num_logits_to_keep=None,
-        **kwargs,
-    ):
-        model_inputs = self.model.prepare_inputs_for_generation(
-            input_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=input_embeds,
-            attention_mask=attention_mask,
-            cache_position=cache_position,
-            num_logits_to_keep=num_logits_to_keep,
-            **kwargs,
-        )
-
-        if cache_position[0] == 0:
-            model_inputs["pixel_values"] = pixel_values
-
-        return model_inputs
-
-
 @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.
+    [`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).
-    """
+    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.config = config
         self.num_labels = config.num_labels
 
         self.model = DogeModel(config)
-        self.classifier = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+        self.config = config
 
         # Initialize weights and apply final processing
-        self.init_weights()
+        self.post_init()
 
     def get_input_embeddings(self):
         return self.model.word_embed
@@ -1181,14 +1152,14 @@ class DogeForSequenceClassification(DogePreTrainedModel):
     ) -> 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).
+            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
 
-        outputs = self.model(
-            input_ids=input_ids,
+        transformer_outputs = self.model(
+            input_ids,
             attention_mask=attention_mask,
             position_ids=position_ids,
             past_key_values=past_key_values,
@@ -1198,8 +1169,8 @@ class DogeForSequenceClassification(DogePreTrainedModel):
             output_hidden_states=output_hidden_states,
             return_dict=return_dict,
         )
-        hidden_states = outputs[0]
-        logits = self.classifier(hidden_states)
+        hidden_states = transformer_outputs[0]
+        logits = self.score(hidden_states)
 
         if input_ids is not None:
             batch_size = input_ids.shape[0]
@@ -1209,37 +1180,36 @@ class DogeForSequenceClassification(DogePreTrainedModel):
         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:
-            sequence_lengths = -1
+            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:
-            if input_ids is not None:
-                # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
-                sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
-                sequence_lengths = sequence_lengths % input_ids.shape[-1]
-                sequence_lengths = sequence_lengths.to(logits.device)
-            else:
-                sequence_lengths = -1
+            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), sequence_lengths]
+        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,
-            )
+            loss = self.loss_function(logits=logits, labels=labels, pooled_logits=pooled_logits, config=self.config)
 
         if not return_dict:
-            output = (pooled_logits,) + outputs[1:]
+            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=outputs.past_key_values,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
         )
 
+
 __all__ = ["DogeForCausalLM", "DogeModel", "DogePreTrainedModel", "DogeForSequenceClassification"]