add initial structure

docs/source/en/_toctree.yml

@@ -672,6 +672,8 @@
         title: DepthPro
       - local: model_doc/deta
         title: DETA
+      - local: model_doc/deepseek_v2
+        title: DeepSeekV2
       - local: model_doc/detr
         title: DETR
       - local: model_doc/dinat

docs/source/en/index.md

@@ -125,6 +125,7 @@ Flax), PyTorch, and/or TensorFlow.
 |                [Depth Anything](model_doc/depth_anything)                |       ✅        |         ❌         |      ❌      |
 |                     [DepthPro](model_doc/depth_pro)                      |       ✅        |         ❌         |      ❌      |
 |                          [DETA](model_doc/deta)                          |       ✅        |         ❌         |      ❌      |
+|                          [DeepSeekV2](model_doc/deepseek_v2.md)          |       ✅        |         ❌         |      ❌      |
 |                          [DETR](model_doc/detr)                          |       ✅        |         ❌         |      ❌      |
 |                      [DialoGPT](model_doc/dialogpt)                      |       ✅        |         ✅         |      ✅      |
 |                     [DiffLlama](model_doc/diffllama)                     |       ✅        |         ❌         |      ❌      |

src/transformers/models/auto/configuration_auto.py

@@ -93,6 +93,7 @@
         ("depth_anything", "DepthAnythingConfig"),
         ("depth_pro", "DepthProConfig"),
         ("deta", "DetaConfig"),
+        ("deepseek_v2", "DeepseekV2Config"),
         ("detr", "DetrConfig"),
         ("diffllama", "DiffLlamaConfig"),
         ("dinat", "DinatConfig"),

src/transformers/models/auto/modeling_auto.py

@@ -91,6 +91,7 @@
         ("deit", "DeiTModel"),
         ("depth_pro", "DepthProModel"),
         ("deta", "DetaModel"),
+        ("deepseek_v2", "DeepseekV2Model"),
         ("detr", "DetrModel"),
         ("diffllama", "DiffLlamaModel"),
         ("dinat", "DinatModel"),
@@ -508,6 +509,7 @@
         ("cpmant", "CpmAntForCausalLM"),
         ("ctrl", "CTRLLMHeadModel"),
         ("data2vec-text", "Data2VecTextForCausalLM"),
+        ("deepseek_v2", "DeepseekV2ForCausalLM"),
         ("dbrx", "DbrxForCausalLM"),
         ("diffllama", "DiffLlamaForCausalLM"),
         ("electra", "ElectraForCausalLM"),
@@ -996,6 +998,7 @@
         ("data2vec-text", "Data2VecTextForSequenceClassification"),
         ("deberta", "DebertaForSequenceClassification"),
         ("deberta-v2", "DebertaV2ForSequenceClassification"),
+        ("deepseek_v2", "DeepseekV2ForSequenceClassification"),
         ("diffllama", "DiffLlamaForSequenceClassification"),
         ("distilbert", "DistilBertForSequenceClassification"),
         ("electra", "ElectraForSequenceClassification"),
@@ -1094,6 +1097,7 @@
         ("data2vec-text", "Data2VecTextForQuestionAnswering"),
         ("deberta", "DebertaForQuestionAnswering"),
         ("deberta-v2", "DebertaV2ForQuestionAnswering"),
+        ("deepseek_v2", "DeepseekV2ForQuestionAnswering"),
         ("diffllama", "DiffLlamaForQuestionAnswering"),
         ("distilbert", "DistilBertForQuestionAnswering"),
         ("electra", "ElectraForQuestionAnswering"),
@@ -1192,6 +1196,7 @@
         ("data2vec-text", "Data2VecTextForTokenClassification"),
         ("deberta", "DebertaForTokenClassification"),
         ("deberta-v2", "DebertaV2ForTokenClassification"),
+        ("deepseek_v2", "DeepseekV2ForTokenClassification"),
         ("diffllama", "DiffLlamaForTokenClassification"),
         ("distilbert", "DistilBertForTokenClassification"),
         ("electra", "ElectraForTokenClassification"),

src/transformers/models/deepseek_v2/__init__.py

@@ -0,0 +1,29 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+    from .configuration_deepsek_v2 import *
+    from .modeling_deepsek_v2 import *
+else:
+    import sys
+
+    _file = globals()["__file__"]
+    sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)

src/transformers/models/deepseek_v2/modular_deepseek_v2.py

@@ -0,0 +1,201 @@
+# coding=utf-8
+# Copyright 2025 Baidu Inc and The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from transformers.models.qwen2_moe.modeling_qwen2_moe import Qwen2MoeModel, Qwen2MoeDecoderLayer, Qwen2MoeRMSNorm
+from transformers.models.qwen2_moe.configuration_qwen2_moe import Qwen2MoeConfig
+from torch import nn
+
+
+class DeepseekV2Config(Qwen2MoeConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`DeepseekV2Model`]. It is used to instantiate an DeepSeek
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the DeepSeek-V2.
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+    Args:
+        vocab_size (`int`, *optional*, defaults to 102400):
+            Vocabulary size of the Deep model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`DeepseekV2Model`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            Dimension of the MLP representations.
+        moe_intermediate_size (`int`, *optional*, defaults to 1407):
+            Dimension of the MoE representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        n_shared_experts (`int`, *optional*, defaults to None):
+            Number of shared experts, None means dense model.
+        n_routed_experts (`int`, *optional*, defaults to None):
+            Number of routed experts, None means dense model.
+        routed_scaling_factor (`float`, *optional*, defaults to 1.0):
+            Scaling factor or routed experts.
+        topk_method (`str`, *optional*, defaults to `gready`):
+            Topk method used in routed gate.
+        n_group (`int`, *optional*, defaults to None):
+            Number of groups for routed experts.
+        topk_group (`int`, *optional*, defaults to None):
+            Number of selected groups for each token(for each token, ensuring the selected experts is only within `topk_group` groups).
+        num_experts_per_tok (`int`, *optional*, defaults to None):
+            Number of selected experts, None means dense model.
+        moe_layer_freq (`int`, *optional*, defaults to 1):
+            The frequency of the MoE layer: one expert layer for every `moe_layer_freq - 1` dense layers.
+        first_k_dense_replace (`int`, *optional*, defaults to 0):
+            Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head).
+                                                            \--k dense layers--/
+        norm_topk_prob (`bool`, *optional*, defaults to False):
+            Whether to normalize the weights of the routed experts.
+        aux_loss_alpha (`float`, *optional*, defaults to 0.001):
+            Auxiliary loss weight coefficient.
+        seq_aux = (`bool`, *optional*, defaults to True):
+            Whether to compute the auxiliary loss for each individual sample.
+        num_key_value_heads (`int`, *optional*):
+            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). If it is not specified, will default to
+            `num_attention_heads`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
+            The maximum sequence length that this model might ever be used with.
+        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*):
+            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.
+        pretraining_tp (`int`, *optional*, defaults to 1):
+            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
+            document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
+            necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
+            issue](https://github.com/pytorch/pytorch/issues/76232).
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        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. Currently supports two scaling
+            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            `max_position_embeddings` to the expected new maximum.
+        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
+            Whether to use a bias in the query, key, value and output projection layers during self-attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+    ```python
+    >>> from transformers import DeepseekV2Model, DeepseekV2Config
+    >>> # Initializing a Deepseek-V2 style configuration
+    >>> configuration = DeepseekV2Config()
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "deepseek_v2"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+            self, 
+            attention_bias=False, 
+            aux_loss_alpha=0.001, 
+            bos_token_id=100000,
+            eos_token_id=100001,
+            first_k_dense_replace=0,
+            kv_lora_rank=512,
+            moe_layer_freq=1,
+            n_group=None,
+            n_routed_experts=None,
+            n_shared_experts=None,
+            pretraining_tp=1,
+            qk_nope_head_dim=128,
+            qk_rope_head_dim=64,
+            routed_scaling_factor=1.0,
+            seq_aux=True,
+            topk_group=None,
+            topk_method="greedy",
+            v_head_dim=128, 
+            **super_kwargs):
+        super().__init__(**super_kwargs)
+
+        del self.use_sliding_window
+        del self.sliding_window
+        del self.max_window_layers
+        del self.decoder_sparse_step
+        del self.shared_expert_intermediate_size
+        del self.output_router_logits
+        del self.router_aux_loss_coef
+        del self.mlp_only_layers
+
+        self.attention_bias = attention_bias
+        self.aux_loss_alpha = aux_loss_alpha
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id
+        self.first_k_dense_replace = first_k_dense_replace
+        self.kv_lora_rank = kv_lora_rank
+        self.moe_layer_freq = moe_layer_freq
+        self.n_group = n_group
+        self.n_routed_experts = n_routed_experts
+        self.n_shared_experts = n_shared_experts
+        self.pretraining_tp = pretraining_tp
+        self.qk_nope_head_dim = qk_nope_head_dim
+        self.qk_rope_head_dim = qk_rope_head_dim
+        self.routed_scaling_factor = routed_scaling_factor
+        self.seq_aux = seq_aux
+        self.topk_group = topk_group
+        self.topk_method = topk_method
+        self.v_head_dim = v_head_dim
+
+
+class DeepseekV2DecoderLayer(Qwen2MoeDecoderLayer):
+    pass
+
+
+class DeepseekV2RMSNorm(Qwen2MoeRMSNorm):
+    pass
+
+
+class DeepseekV2Model(Qwen2MoeModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`DeepseekV2DecoderLayer`]
+    Args:
+        config: DeepseekV2Config
+    """
+
+    def __init__(self, config: DeepseekV2Config):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList(
+            [DeepseekV2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
+        self.norm = DeepseekV2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()