# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # 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. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # Copyright 2025 Jingze Shi and the HuggingFace Inc. team. All rights reserved. # # The Doge family of small language models is trained by SmallDoge 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 ...configuration_utils import PreTrainedConfig from ...modeling_rope_utils import RopeParameters 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-320M](https://huggingface.co/SmallDoge/Doge-320M). 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 32768): Vocabulary size of the Doge2 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`DogeModel`] hidden_size (`int`, *optional*, defaults to 1024): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 2048): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 32): Number of hidden layers in the Transformer decoder. hidden_dropout (`float`, *optional*, defaults to 0.0): 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`. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether the model's input and output word embeddings should be tied. max_position_embeddings (`int`, *optional*, defaults to 2048): The maximum sequence length that this model might ever be used with. rope_parameters (`RopeParameters`, *optional*): Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE with longer `max_position_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. 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://huggingface.co/papers/2305.13245). If it is not specified, will default to `num_attention_heads`. 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. mlp_bias (`bool`, *optional*, defaults to `False`): Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers. sliding_window (`int`, *optional*): Sliding window attention window size. If not specified, will default to `None`. keep_window_size (`int`, *optional*, defaults to 2048): The window size of tokens that are not dynamically masked, and dynamic masking is only performed when the sequence length exceeds this 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. num_experts (`int`, *optional*, defaults to 16384): Number of routed experts in the model. This is only used when `is_moe=True`. num_experts_per_tok (`int`, *optional*, defaults to 64): Number of selected experts to route per-token. norm_topk_prob (`bool`, *optional*, defaults to `False`): Whether to normalize the topk probabilities. output_router_logits (`bool`, *optional*, defaults to `False`): Whether or not the router logits should be returned by the model. Enabling this will also allow the model to output the auxiliary loss, including load balancing loss and router z-loss. router_aux_loss_coef (`float`, *optional*, defaults to 0.001): The aux loss factor for the total loss. pad_token_id (`int`, *optional*): Padding token id. bos_token_id (`int`, *optional*): Beginning of stream token id. eos_token_id (`int`, *optional*): End of stream token id. ```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 ```""" model_type = "doge" keys_to_ignore_at_inference = ["past_key_values"] # Default tensor parallel plan for base model `DogeModel` base_model_tp_plan = { "layers.*.self_attn.q_proj": "colwise", "layers.*.self_attn.k_proj": "colwise", "layers.*.self_attn.v_proj": "colwise", "layers.*.self_attn.dt_proj": "rowwise", "layers.*.self_attn.o_proj": "rowwise", "layers.*.mlp.gate_proj": "colwise", "layers.*.mlp.up_proj": "colwise", "layers.*.mlp.down_proj": "rowwise", "layers.*.mlp.router_gate": "colwise_gather_output", "layers.*.mlp.down_embed": "rowwise_split_input", "layers.*.mlp.up_embed": "rowwise_split_input", } base_model_pp_plan = { "embed_tokens": (["input_ids"], ["inputs_embeds"]), "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), "norm": (["hidden_states"], ["hidden_states"]), } def __init__( self, vocab_size: int | None = 32768, hidden_size: int | None = 1024, intermediate_size: int | None = 2048, num_hidden_layers: int | None = 32, hidden_dropout: float | None = 0.0, hidden_act: str | None = "silu", initializer_range: float | None = 0.02, rms_norm_eps: int | None = 1e-06, use_cache: bool | None = True, tie_word_embeddings: bool | None = False, max_position_embeddings: int | None = 2048, rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None, num_attention_heads: int | None = 8, num_key_value_heads: int | None = None, attention_bias: bool | None = False, attention_dropout: float | None = 0.0, mlp_bias: bool | None = False, sliding_window: int | None = None, keep_window_size: int | None = 2048, is_moe: bool | None = False, num_experts: int | None = 16384, num_experts_per_tok: int | None = 64, norm_topk_prob: bool | None = False, output_router_logits: bool | None = False, router_aux_loss_coef: float | None = 0.001, pad_token_id: int | None = None, bos_token_id: int | None = None, eos_token_id: int | None = None, **kwargs, ): self.vocab_size = vocab_size self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers 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.num_attention_heads = num_attention_heads self.num_key_value_heads = num_key_value_heads self.attention_bias = attention_bias self.attention_dropout = attention_dropout self.mlp_bias = mlp_bias self.sliding_window = sliding_window self.keep_window_size = keep_window_size self.is_moe = is_moe self.num_experts = num_experts self.num_experts_per_tok = num_experts_per_tok self.norm_topk_prob = norm_topk_prob self.output_router_logits = output_router_logits self.router_aux_loss_coef = router_aux_loss_coef self.tie_word_embeddings = tie_word_embeddings self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id self.rope_parameters = rope_parameters # for backward compatibility if num_key_value_heads is None: self.num_key_value_heads = num_attention_heads super().__init__(**kwargs) __all__ = ["DogeConfig"]