Upload DogeForCausalLM

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "SmallDoge/Doge-160M-checkpoint",
+  "_name_or_path": "./results/Doge-160M/checkpoint-15600",
   "architectures": [
     "DogeForCausalLM"
   ],
@@ -38,9 +38,8 @@
     "rope_type": "dynamic"
   },
   "rope_theta": 10000.0,
-  "tie_word_embeddings": true,
   "torch_dtype": "float32",
-  "transformers_version": "4.48.2",
+  "transformers_version": "4.48.1",
   "use_cache": true,
   "vocab_size": 32768
 }

configuration_doge.py

@@ -1,14 +1,9 @@
-#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
-#           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.
+#
+#     https://arxiv.org/abs/2412.11834
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -21,6 +16,8 @@
 # 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 configuration"""
+
 from transformers.configuration_utils import PretrainedConfig
 from transformers.modeling_rope_utils import rope_config_validation
 
@@ -28,7 +25,7 @@ from transformers.modeling_rope_utils import rope_config_validation
 class DogeConfig(PretrainedConfig):
     r"""
     This is the configuration class to store the configuration of a [`DogeModel`]. It is used to instantiate an Doge
-    model according to the specified arguments, defining the model architecture like [SmallDoge/Doge-20M](https://huggingface.co/SmallDoge/Doge-20M).
+    model according to the specified arguments, defining the model architecture like [JingzeShi/Doge-20M](https://huggingface.co/JingzeShi/Doge-20M).
 
     Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
     documentation from [`PretrainedConfig`] for more information.
@@ -36,6 +33,10 @@ class DogeConfig(PretrainedConfig):
     Args:
         vocab_size (`int`, *optional*, defaults to 32768):
             Vocabulary size of the Doge model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`DogeModel`]
+        num_channels (`int`, *optional*, defaults to 3):
+            Number of channels in the input image.
+        patch_size (`int`, *optional*, defaults to 16):
+            Patch size of Vision Transformer Embeddings.
         hidden_size (`int`, *optional*, defaults to 1024):
             Dimension of the hidden representations.
         intermediate_size (`int`, *optional*, defaults to 2048):
@@ -48,41 +49,25 @@ class DogeConfig(PretrainedConfig):
             Dropout probability for each sequence transformation and state transformation module.
         hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
             The non-linear activation function (function or string) in the decoder.
-        initializer_range (`float`, *optional*, defaults to 0.02):
-            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
-            The epsilon used by the rms normalization layers.
-        use_cache (`bool`, *optional*, defaults to `True`):
-            Whether or not the model should return the last key/values attentions (not used by all models). Only
-            relevant if `config.is_decoder=True`.
-        bos_token_id (`int`, *optional*, defaults to 0):
-            Beginning of stream token id.
-        eos_token_id (`int`, *optional*, defaults to 1):
-            End of stream token id.
-        pad_token_id (`int`, *optional*, defaults to 2):
-            Padding token id.
-        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
-            Whether to tie weight embeddings
         max_position_embeddings (`int`, *optional*, defaults to 2048):
             The maximum sequence length that this model might ever be used with.
         rope_theta (`float`, *optional*, defaults to 10000.0):
             The base period of the RoPE embeddings.
         rope_scaling (`Dict`, *optional*):
-            Dictionary containing the scaling configuration for the RoPE embeddings.
+            Dictionary containing the scaling configuration for the RoPE embeddings. 
             NOTE: if you apply new rope type and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value accordingly.
-            Doge family of small models use `{ 'rope_type': 'dynamic', 'factor': 4.0, 'original_max_position_embeddings': 2048 }` as the default value.
             Expected contents:
                 `rope_type` (`str`):
                     The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope', 'llama3'], with 'default' being the original RoPE implementation.
                 `factor` (`float`, *optional*):
-                    Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings.
+                    Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. 
                     In most scaling types, a `factor` of x will enable the model to handle sequences of length x * original maximum pre-trained length.
                 `original_max_position_embeddings` (`int`, *optional*):
-                    Used with 'dynamic', 'longrope' and 'llama3'.
+                    Used with 'dynamic', 'longrope' and 'llama3'. 
                     The original max position embeddings used during pretraining.
                 `attention_factor` (`float`, *optional*):
                     Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
-                    computation.
+                    computation. 
                     If unspecified, it defaults to value recommended by the implementation, using the `factor` field to infer the suggested value.
                 `beta_fast` (`float`, *optional*):
                     Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
@@ -91,51 +76,54 @@ class DogeConfig(PretrainedConfig):
                     Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
                     ramp function. If unspecified, it defaults to 1.
                 `short_factor` (`List[float]`, *optional*):
-                    Only used with 'longrope'. The scaling factor to be applied to short contexts (<`original_max_position_embeddings`).
+                    Only used with 'longrope'. The scaling factor to be applied to short contexts (<`original_max_position_embeddings`). 
                     Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2
                 `long_factor` (`List[float]`, *optional*):
-                    Only used with 'longrope'. The scaling factor to be applied to long contexts (<`original_max_position_embeddings`).
+                    Only used with 'longrope'. The scaling factor to be applied to long contexts (<`original_max_position_embeddings`). 
                     Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2
                 `low_freq_factor` (`float`, *optional*):
                     Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
                 `high_freq_factor` (`float`, *optional*):
                     Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        pad_token_id (`int`, *optional*, defaults to 0):
+            Padding token id.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            End of stream token id.
+        tie_word_embeddings (`bool`, *optional*, defaults to `True`):
+            Whether to tie weight embeddings
         num_attention_heads (`int`, *optional*, defaults to 8):
             Number of attention heads for each attention layer in the Transformer decoder.
-        num_key_value_heads (`int`, *optional*):
-            This is the number of key_value heads that should be used to implement Grouped Query Attention.
+        num_key_value_heads (`int`, *optional*, defaults to `None`):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. 
             If `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
-            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used.
-            When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group.
-            For more details checkout [this paper](https://arxiv.org/pdf/2305.13245.pdf).
+            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. 
+            When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. 
+            For more details checkout [this paper](https://arxiv.org/pdf/2305.13245.pdf). 
             If it is not specified, will default to `num_attention_heads`.
         attention_dropout (`float`, *optional*, defaults to 0.0):
             The dropout ratio for the attention probabilities.
-        dynamic_mask_ratio (`float`, *optional*, defaults to 0.0):
-            The ratio to control the proportion of the dynamic mask filled with the minimum value. For more details checkout [this paper](https://arxiv.org/pdf/2412.11834).
+        dynamic_mask_ratio (`float`, *optional*, defaults to 0.0, range [0, 1]):
+            The ratio to control the proportion of the dynamic mask filled with the minimum value.
         is_moe (`bool`, *optional*, defaults to `False`):
-            Whether to use the Cross Domain Mixture of Experts, if `True`, the MoE will inherit the MLP to initialize. For more details checkout [this paper](https://arxiv.org/pdf/2412.11834).
+            Whether to use the Cross Domain Mixture of Experts, if `True`, the MoE will inherit the MLP to initialize
         num_cdmoe_experts (`int`, *optional*, defaults to 16348):
-            Number of Experts for the Cross Domain Mixture of Experts.
+            Number of Private Experts for the Cross Domain Mixture of Experts. calculation formula: :math:`\text{num_cdmoe_experts} = (32 \times \text{num_cdmoe_heads})^2`
         num_cdmoe_heads (`int`, *optional*, defaults to 4):
-            Number of retrieval heads, used to mix multi-head experts.
+            Number of heads of Private Experts for the Cross Domain Mixture of Experts.
         num_cdmoe_experts_per_head (`int`, *optional*, defaults to 8):
-            Number of Experts per retrieval head, used to mix multi-head experts.
+            Number of Private Experts per head for the Cross Domain Mixture of Experts.
         expert_retrieval_size (`int`, *optional*, defaults to 64):
-            Dimension of the Expert retrieval states for calculating the dot product of query and key to determine the expert index.
-
-    ```python
-    >>> from transformers import DogeConfig, DogeModel
-
-    >>> # Initializing a Doge-320M style configuration
-    >>> configuration = DogeConfig()
-
-    >>> # Initializing a model from the Doge-320M style configuration
-    >>> model = DogeModel(configuration)
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```"""
+            Dimension of the Expert retrieval states for the Cross Domain Mixture of Experts.
+    """
 
     model_type = "doge"
     keys_to_ignore_at_inference = ["past_key_values"]
@@ -154,26 +142,33 @@ class DogeConfig(PretrainedConfig):
     def __init__(
         self,
         vocab_size=32768,
+        num_channels=3,
+        patch_size=16,
         hidden_size=1024,
         intermediate_size=2048,
         num_hidden_layers=32,
         hidden_bias=False,
         hidden_dropout=0.0,
         hidden_act="silu",
+        max_position_embeddings=2048,
+        rope_theta=10000.0,
+        rope_scaling={
+            "rope_type": "dynamic",
+            "factor": 4.0,
+            "original_max_position_embeddings": 2048,
+        },
         initializer_range=0.02,
         rms_norm_eps=1e-06,
         use_cache=True,
         bos_token_id=0,
         eos_token_id=1,
         pad_token_id=2,
-        tie_word_embeddings=False,
-        max_position_embeddings=2048,
-        rope_theta=10000.0,
-        rope_scaling=None,
+        tie_word_embeddings=True,
         num_attention_heads=8,
         num_key_value_heads=None,
         attention_dropout=0.0,
         dynamic_mask_ratio=0.0,
+        is_causal=False,
         is_moe=False,
         num_cdmoe_experts=16348,
         num_cdmoe_heads=4,
@@ -182,24 +177,29 @@ class DogeConfig(PretrainedConfig):
         **kwargs,
     ):
         self.vocab_size = vocab_size
+        self.num_channels = num_channels
+        self.patch_size = patch_size
         self.hidden_size = hidden_size
         self.intermediate_size = intermediate_size
         self.num_hidden_layers = num_hidden_layers
-
         self.hidden_bias = hidden_bias
         self.hidden_dropout = hidden_dropout
         self.hidden_act = hidden_act
-        self.initializer_range = initializer_range
-        self.rms_norm_eps = rms_norm_eps
-        self.use_cache = use_cache
-
         self.max_position_embeddings = max_position_embeddings
         self.rope_theta = rope_theta
         self.rope_scaling = rope_scaling
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id
+        self.pad_token_id = pad_token_id
+        self.tie_word_embeddings = tie_word_embeddings
         self.num_attention_heads = num_attention_heads
         self.num_key_value_heads = num_key_value_heads
         self.attention_dropout = attention_dropout
         self.dynamic_mask_ratio = dynamic_mask_ratio
+        self.is_causal = is_causal
         self.is_moe = is_moe
         self.num_cdmoe_experts = num_cdmoe_experts
         self.num_cdmoe_heads = num_cdmoe_heads

generation_config.json

@@ -3,5 +3,5 @@
   "bos_token_id": 0,
   "eos_token_id": 1,
   "pad_token_id": 2,
-  "transformers_version": "4.48.2"
+  "transformers_version": "4.48.1"
 }

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:a798eb03366281fa7a92ec90d62de141fd23bb80c6f57d5cde731c68510e4ac4
+oid sha256:737335e804c2ef54717d9fdb4c367b841712fb3de278908782cc053f9d3d66cb
 size 610825528

modeling_doge.py

@@ -1,14 +1,9 @@
-#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
-#           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.
+#
+#     https://arxiv.org/abs/2412.11834
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -21,13 +16,16 @@
 # 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
@@ -43,16 +41,18 @@ from transformers.utils import (
     LossKwargs,
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
-    is_torch_flex_attn_available,
+    is_torch_greater_or_equal,
     logging,
     replace_return_docstrings,
 )
-from transformers.utils.deprecation import deprecate_kwarg
-
 from .configuration_doge import DogeConfig
 
+try:
+    from einx import add as einx_add
+except ImportError:
+    einx_add = None
 
-if is_torch_flex_attn_available():
+if is_torch_greater_or_equal("2.5"):
     from torch.nn.attention.flex_attention import flex_attention
 
 
@@ -94,20 +94,22 @@ class Residual(nn.Module):
 
 
 class RotaryEmbedding(nn.Module):
-    def __init__(self, config: Optional[DogeConfig] = None, device=None):
+    def __init__(self, config: Optional[DogeConfig] = None):
         super().__init__()
-        # BC: "rope_type" was originally "type"
-        if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
+        self.rope_kwargs = {}
+
+        if 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, device)
+        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, **self.rope_kwargs)
         self.register_buffer("inv_freq", inv_freq, persistent=False)
         self.original_inv_freq = self.inv_freq
 
@@ -119,14 +121,13 @@ 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)
+            inv_freq, self.attention_scaling = self.rope_init_fn(
+                self.config, device, seq_len=seq_len, **self.rope_kwargs
+            )
             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
 
@@ -135,7 +136,7 @@ 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)
@@ -163,7 +164,7 @@ def rotate_half(x):
     return torch.cat((-x2, x1), dim=-1)
 
 
-def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+def apply_QK_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
     """Applies Rotary Position Embedding to the query and key tensors.
 
     Args:
@@ -175,8 +176,8 @@ def apply_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].
+            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:
@@ -191,7 +192,7 @@ def apply_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).
+    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
@@ -210,9 +211,10 @@ class DogeDynamicMaskAttention(nn.Module):
         self.layer_idx = layer_idx
         self.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.is_causal = config.is_causal
 
         self.ALL_ATTENTION_FUNCTIONS = {
             "eager": self.eager_attention_forward,
@@ -222,21 +224,33 @@ class DogeDynamicMaskAttention(nn.Module):
 
         # 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
+            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))
+        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(
@@ -256,18 +270,21 @@ 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_rotary_pos_emb(query_states, key_states, cos, sin)
+        query_states, key_states = apply_QK_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)
+        
+        dynamic_mask = None
+        if self.is_causal is False:
+            # 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))
+            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,
@@ -278,7 +295,7 @@ class DogeDynamicMaskAttention(nn.Module):
         attention_interface: Callable = self.eager_attention_forward
         if self.config._attn_implementation != "eager":
             attention_interface = self.ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
-
+        
         attn_output = attention_interface(
             query_states,
             key_states,
@@ -324,7 +341,7 @@ class DogeDynamicMaskAttention(nn.Module):
             attn_mask = attention_mask
 
         return attn_mask
-
+    
     def eager_attention_forward(
         self,
         query: torch.Tensor,
@@ -343,7 +360,7 @@ class DogeDynamicMaskAttention(nn.Module):
         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)
@@ -352,7 +369,7 @@ class DogeDynamicMaskAttention(nn.Module):
         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,
@@ -363,9 +380,6 @@ class DogeDynamicMaskAttention(nn.Module):
         dropout: float = 0.0,
         **kwargs,
     ) -> torch.Tensor:
-        key = repeat_kv(key, self.num_key_value_groups)
-        value = repeat_kv(value, self.num_key_value_groups)
-
         causal_mask = attention_mask
         if attention_mask is not None:
             causal_mask = causal_mask[:, :, :, : key.shape[-2]]
@@ -385,10 +399,11 @@ class DogeDynamicMaskAttention(nn.Module):
             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,
@@ -399,37 +414,37 @@ class DogeDynamicMaskAttention(nn.Module):
         dropout: float = 0.0,
         **kwargs,
     ) -> torch.Tensor:
-        key = repeat_kv(key, self.num_key_value_groups)
-        value = repeat_kv(value, self.num_key_value_groups)
-
         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
@@ -468,7 +483,7 @@ class DogeCDMoE(DogeMLP):
         self.keys = nn.Parameter(torch.zeros(self.num_cdmoe_heads, self.num_keys, 2, self.expert_retrieval_dim // 2))
 
         # 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(
@@ -485,10 +500,14 @@ class DogeCDMoE(DogeMLP):
 
         # get experts with the highest similarity
         (scores_x, scores_y), (indices_x, indices_y) = sim.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)
+        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)
         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)
@@ -513,7 +532,7 @@ class DogeDecoderLayer(nn.Module):
         self.pre_residual = Residual(config.hidden_size)
 
         self.post_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.feed_forward = DogeMLP(config) if not config.is_moe else DogeCDMoE(config)
+        self.feed_forward = DogeMLP(config) if config.is_moe == False else DogeCDMoE(config)
         self.post_residual = Residual(config.hidden_size)
 
     def forward(
@@ -528,6 +547,7 @@ 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)
@@ -573,8 +593,6 @@ 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,
@@ -854,7 +872,7 @@ class DogeModel(DogePreTrainedModel):
         )
 
         return causal_mask
-
+    
     @staticmethod
     def _prepare_4d_causal_attention_mask_with_cache_position(
         attention_mask: torch.Tensor = None,
@@ -895,9 +913,7 @@ class DogeModel(DogePreTrainedModel):
             min_dtype = torch.finfo(dtype).min
             causal_mask = torch.full(
                 (sequence_length, target_length),
-                fill_value=min_dtype,
-                dtype=dtype,
-                device=device,
+                fill_value=min_dtype, dtype=dtype, device=device,
             )
             if sequence_length != 1:
                 causal_mask = torch.triu(causal_mask, diagonal=1)
@@ -943,14 +959,13 @@ class DogeForCausalLM(DogePreTrainedModel, GenerationMixin):
 
     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
 
-    @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
     @add_start_docstrings_to_model_forward(DOGE_INPUTS_DOCSTRING)
     @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
     def forward(
@@ -966,7 +981,7 @@ class DogeForCausalLM(DogePreTrainedModel, GenerationMixin):
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
         cache_position: Optional[torch.LongTensor] = None,
-        logits_to_keep: int = 0,
+        num_logits_to_keep: int = 0,
         **kwargs: Unpack[KwargsForCausalLM],
     ) -> Union[Tuple, CausalLMOutputWithPast]:
         r"""
@@ -976,12 +991,10 @@ 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]`.
 
-            logits_to_keep (`int`, *optional*):
-                If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
+            num_logits_to_keep (`int`, *optional*):
+                Calculate logits for the last `num_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:
 
@@ -990,8 +1003,8 @@ class DogeForCausalLM(DogePreTrainedModel, GenerationMixin):
         ```python
          >>> from transformers import AutoTokenizer, AutoModelForCausalLM
 
-        >>> model = AutoModelForCausalLM.from_pretrained("SmallDoge/Doge-20M")
-        >>> tokenizer = AutoTokenizer.from_pretrained("SmallDoge/Doge-20M")
+        >>> model = AutoModelForCausalLM.from_pretrained("JingzeShi/Doge-20M-Instruct")
+        >>> tokenizer = AutoTokenizer.from_pretrained("JingzeShi/Doge-20M-Instruct")
 
         >>> prompt = "Hey, are you conscious? Can you talk to me?"
         >>> inputs = tokenizer(prompt, return_tensors="pt")
@@ -1023,9 +1036,9 @@ 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
-        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, :])
+        logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :])
 
         loss = None
         if labels is not None:
@@ -1044,32 +1057,111 @@ 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).
-    """,
-    DOGE_START_DOCSTRING,
+    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).
+    """
 )
 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.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
-        self.config = config
+        self.classifier = nn.Linear(config.hidden_size, self.num_labels, bias=False)
 
         # Initialize weights and apply final processing
-        self.post_init()
+        self.init_weights()
 
     def get_input_embeddings(self):
         return self.model.word_embed
@@ -1093,14 +1185,14 @@ 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
 
-        transformer_outputs = self.model(
-            input_ids,
+        outputs = self.model(
+            input_ids=input_ids,
             attention_mask=attention_mask,
             position_ids=position_ids,
             past_key_values=past_key_values,
@@ -1110,8 +1202,8 @@ class DogeForSequenceClassification(DogePreTrainedModel):
             output_hidden_states=output_hidden_states,
             return_dict=return_dict,
         )
-        hidden_states = transformer_outputs[0]
-        logits = self.score(hidden_states)
+        hidden_states = outputs[0]
+        logits = self.classifier(hidden_states)
 
         if input_ids is not None:
             batch_size = input_ids.shape[0]
@@ -1135,19 +1227,21 @@ class DogeForSequenceClassification(DogePreTrainedModel):
 
         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,) + transformer_outputs[1:]
+            output = (pooled_logits,) + 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,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
         )
-
-
-__all__ = ["DogeForCausalLM", "DogeModel", "DogePreTrainedModel", "DogeForSequenceClassification"]