from transformers.configuration_utils import PretrainedConfig from transformers.utils import logging from typing import Optional, Union logger = logging.get_logger(__name__) DEEPSEEK_PRETRAINED_CONFIG_ARCHIVE_MAP = {} class DeepseekV3Config(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`DeepseekV3Model`]. 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-V3. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Copy from https://huggingface.co/deepseek-ai/DeepSeek-V3/blob/main/configuration_deepseek.py Args: vocab_size (`int`, *optional*, defaults to 129280): Vocabulary size of the Deep model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`DeepseekV3Model`] 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_nextn_predict_layers (`int`, *optional*, defaults to 1): Number of nextn predict layers in the DeepSeekV3 Model. 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. scoring_func (`str`, *optional*, defaults to 'softmax'): Method of computing expert weights. 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 DeepseekV3Model, DeepseekV3Config >>> # Initializing a Deepseek-V3 style configuration >>> configuration = DeepseekV3Config() >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "deepseek_v3" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, vocab_size=129280, hidden_size=7168, intermediate_size=18432, moe_intermediate_size=2048, num_hidden_layers=61, num_nextn_predict_layers=1, num_attention_heads=128, num_key_value_heads=128, n_shared_experts=1, n_routed_experts=256, ep_size=1, routed_scaling_factor=2.5, kv_lora_rank=512, q_lora_rank=1536, qk_rope_head_dim=64, v_head_dim=128, qk_nope_head_dim=128, topk_method="noaux_tc", n_group=8, topk_group=4, num_experts_per_tok=8, moe_layer_freq=1, first_k_dense_replace=3, norm_topk_prob=True, scoring_func="sigmoid", aux_loss_alpha=0.001, seq_aux=True, hidden_act="silu", max_position_embeddings=4096, initializer_range=0.02, rms_norm_eps=1e-6, use_cache=True, pad_token_id=None, bos_token_id=0, eos_token_id=1, pretraining_tp=1, tie_word_embeddings=False, rope_theta=10000.0, rope_scaling=None, attention_bias=False, attention_dropout=0.0, **kwargs, ): self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.moe_intermediate_size = moe_intermediate_size self.num_hidden_layers = num_hidden_layers self.num_nextn_predict_layers = num_nextn_predict_layers self.num_attention_heads = num_attention_heads self.n_shared_experts = n_shared_experts self.n_routed_experts = n_routed_experts self.ep_size = ep_size self.routed_scaling_factor = routed_scaling_factor self.kv_lora_rank = kv_lora_rank self.q_lora_rank = q_lora_rank self.qk_rope_head_dim = qk_rope_head_dim self.v_head_dim = v_head_dim self.qk_nope_head_dim = qk_nope_head_dim self.topk_method = topk_method self.n_group = n_group self.topk_group = topk_group self.num_experts_per_tok = num_experts_per_tok self.moe_layer_freq = moe_layer_freq self.first_k_dense_replace = first_k_dense_replace self.norm_topk_prob = norm_topk_prob self.scoring_func = scoring_func self.aux_loss_alpha = aux_loss_alpha self.seq_aux = seq_aux # for backward compatibility if num_key_value_heads is None: num_key_value_heads = num_attention_heads self.num_key_value_heads = num_key_value_heads self.hidden_act = hidden_act self.initializer_range = initializer_range self.rms_norm_eps = rms_norm_eps self.pretraining_tp = pretraining_tp self.use_cache = use_cache self.rope_theta = rope_theta self.rope_scaling = rope_scaling self.attention_bias = attention_bias self.attention_dropout = attention_dropout super().__init__( pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs, ) class MoonViTConfig(PretrainedConfig): model_type = "moonvit" def __init__( self, patch_size: int = 14, init_pos_emb_height: int = 64, init_pos_emb_width: int = 64, num_attention_heads: int = 16, num_hidden_layers: int = 27, hidden_size: int = 1152, intermediate_size: int = 4304, merge_kernel_size: tuple[int, int] = (2, 2), **kwargs, ): super().__init__(**kwargs) self.patch_size = patch_size # Positional embedding config self.init_pos_emb_height = init_pos_emb_height self.init_pos_emb_width = init_pos_emb_width # Transformer config self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.hidden_size = hidden_size self.intermediate_size = intermediate_size # Patch merger config self.merge_kernel_size = merge_kernel_size class KimiVLConfig(PretrainedConfig): model_type = "kimi_vl" def __init__( self, vision_config: Optional[Union[dict, MoonViTConfig]] = None, text_config: Optional[Union[dict, DeepseekV3Config]] = None, ignore_index: int = -100, media_placeholder_token_id: int = 163605, pad_token_id: int = 0, **kwargs, ): if vision_config is None: vision_config = MoonViTConfig() elif isinstance(vision_config, dict): vision_config = MoonViTConfig(**vision_config) self.vision_config = vision_config if text_config is None: text_config = DeepseekV3Config() elif isinstance(text_config, dict): text_config = DeepseekV3Config(**text_config) self.text_config = text_config self.ignore_index = ignore_index self.media_placeholder_token_id = media_placeholder_token_id attn_implementation = kwargs.get("attn_implementation") if attn_implementation is not None: if attn_implementation in ["eager", "flash_attention_2"]: self._attn_implementation = attn_implementation self.vision_config._attn_implementation = attn_implementation self.text_config._attn_implementation = attn_implementation else: raise ValueError( f"Invalid attention implementation: {attn_implementation}" ) super().__init__(pad_token_id=pad_token_id, **kwargs)