deepseek-ai/DeepSeek-V3-0324

1from transformers.configuration_utils import PretrainedConfig2from transformers.utils import logging3 4logger = logging.get_logger(__name__)5 6DEEPSEEK_PRETRAINED_CONFIG_ARCHIVE_MAP = {}7class DeepseekV3Config(PretrainedConfig):8    r"""9    This is the configuration class to store the configuration of a [`DeepseekV3Model`]. It is used to instantiate an DeepSeek10    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the11    defaults will yield a similar configuration to that of the DeepSeek-V3.12 13    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the14    documentation from [`PretrainedConfig`] for more information.15 16 17    Args:18        vocab_size (`int`, *optional*, defaults to 129280):19            Vocabulary size of the Deep model. Defines the number of different tokens that can be represented by the20            `inputs_ids` passed when calling [`DeepseekV3Model`]21        hidden_size (`int`, *optional*, defaults to 4096):22            Dimension of the hidden representations.23        intermediate_size (`int`, *optional*, defaults to 11008):24            Dimension of the MLP representations.25        moe_intermediate_size (`int`, *optional*, defaults to 1407):26            Dimension of the MoE representations.27        num_hidden_layers (`int`, *optional*, defaults to 32):28            Number of hidden layers in the Transformer decoder.29        num_nextn_predict_layers (`int`, *optional*, defaults to 1):30            Number of nextn predict layers in the DeepSeekV3 Model.31        num_attention_heads (`int`, *optional*, defaults to 32):32            Number of attention heads for each attention layer in the Transformer decoder.33        n_shared_experts (`int`, *optional*, defaults to None):34            Number of shared experts, None means dense model.35        n_routed_experts (`int`, *optional*, defaults to None):36            Number of routed experts, None means dense model.37        routed_scaling_factor (`float`, *optional*, defaults to 1.0):38            Scaling factor or routed experts.39        topk_method (`str`, *optional*, defaults to `gready`):40            Topk method used in routed gate.41        n_group (`int`, *optional*, defaults to None):42            Number of groups for routed experts.43        topk_group (`int`, *optional*, defaults to None):44            Number of selected groups for each token(for each token, ensuring the selected experts is only within `topk_group` groups).45        num_experts_per_tok (`int`, *optional*, defaults to None):46            Number of selected experts, None means dense model.47        moe_layer_freq (`int`, *optional*, defaults to 1):48            The frequency of the MoE layer: one expert layer for every `moe_layer_freq - 1` dense layers.49        first_k_dense_replace (`int`, *optional*, defaults to 0):50            Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head).51                                                            \--k dense layers--/52        norm_topk_prob (`bool`, *optional*, defaults to False):53            Whether to normalize the weights of the routed experts.54        scoring_func (`str`, *optional*, defaults to 'softmax'):55            Method of computing expert weights.56        aux_loss_alpha (`float`, *optional*, defaults to 0.001):57            Auxiliary loss weight coefficient.58        seq_aux = (`bool`, *optional*, defaults to True):59            Whether to compute the auxiliary loss for each individual sample.60        num_key_value_heads (`int`, *optional*):61            This is the number of key_value heads that should be used to implement Grouped Query Attention. If62            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if63            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When64            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed65            by meanpooling all the original heads within that group. For more details checkout [this66            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to67            `num_attention_heads`.68        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):69            The non-linear activation function (function or string) in the decoder.70        max_position_embeddings (`int`, *optional*, defaults to 2048):71            The maximum sequence length that this model might ever be used with.72        initializer_range (`float`, *optional*, defaults to 0.02):73            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.74        rms_norm_eps (`float`, *optional*, defaults to 1e-06):75            The epsilon used by the rms normalization layers.76        use_cache (`bool`, *optional*, defaults to `True`):77            Whether or not the model should return the last key/values attentions (not used by all models). Only78            relevant if `config.is_decoder=True`.79        pad_token_id (`int`, *optional*):80            Padding token id.81        bos_token_id (`int`, *optional*, defaults to 1):82            Beginning of stream token id.83        eos_token_id (`int`, *optional*, defaults to 2):84            End of stream token id.85        tie_word_embeddings (`bool`, *optional*, defaults to `False`):86            Whether to tie weight embeddings87        rope_theta (`float`, *optional*, defaults to 10000.0):88            The base period of the RoPE embeddings.89        rope_scaling (`Dict`, *optional*):90            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling91            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is92            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update93            `max_position_embeddings` to the expected new maximum.94        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):95            Whether to use a bias in the query, key, value and output projection layers during self-attention.96        attention_dropout (`float`, *optional*, defaults to 0.0):97            The dropout ratio for the attention probabilities.98 99    ```python100    >>> from transformers import DeepseekV3Model, DeepseekV3Config101 102    >>> # Initializing a Deepseek-V3 style configuration103    >>> configuration = DeepseekV3Config()104 105    >>> # Accessing the model configuration106    >>> configuration = model.config107    ```"""108 109    model_type = "deepseek_v3"110    keys_to_ignore_at_inference = ["past_key_values"]111 112    def __init__(113        self,114        vocab_size=129280,115        hidden_size=7168,116        intermediate_size=18432,117        moe_intermediate_size = 2048,118        num_hidden_layers=61,119        num_nextn_predict_layers=1,120        num_attention_heads=128,121        num_key_value_heads=128,122        n_shared_experts = 1,123        n_routed_experts = 256,124        ep_size = 1,125        routed_scaling_factor = 2.5,126        kv_lora_rank = 512,127        q_lora_rank = 1536,128        qk_rope_head_dim = 64,129        v_head_dim = 128,130        qk_nope_head_dim = 128,131        topk_method = 'noaux_tc',132        n_group = 8,133        topk_group = 4,134        num_experts_per_tok = 8,135        moe_layer_freq = 1,136        first_k_dense_replace = 3,137        norm_topk_prob = True,138        scoring_func = 'sigmoid',139        hidden_act="silu",140        max_position_embeddings=4096,141        initializer_range=0.02,142        rms_norm_eps=1e-6,143        use_cache=True,144        pad_token_id=None,145        bos_token_id=0,146        eos_token_id=1,147        tie_word_embeddings=False,148        rope_theta=10000.0,149        rope_scaling=None,150        attention_bias=False,151        attention_dropout=0.0,152        **kwargs,153    ):154        self.vocab_size = vocab_size155        self.max_position_embeddings = max_position_embeddings156        self.hidden_size = hidden_size157        self.intermediate_size = intermediate_size158        self.moe_intermediate_size = moe_intermediate_size159        self.num_hidden_layers = num_hidden_layers160        self.num_nextn_predict_layers = num_nextn_predict_layers161        self.num_attention_heads = num_attention_heads162        self.n_shared_experts = n_shared_experts163        self.n_routed_experts = n_routed_experts164        self.ep_size = ep_size165        self.routed_scaling_factor = routed_scaling_factor166        self.kv_lora_rank = kv_lora_rank167        self.q_lora_rank = q_lora_rank168        self.qk_rope_head_dim = qk_rope_head_dim169        self.v_head_dim = v_head_dim170        self.qk_nope_head_dim = qk_nope_head_dim171        self.topk_method = topk_method172        self.n_group = n_group173        self.topk_group = topk_group174        self.num_experts_per_tok = num_experts_per_tok175        self.moe_layer_freq = moe_layer_freq176        self.first_k_dense_replace = first_k_dense_replace177        self.norm_topk_prob = norm_topk_prob178        self.scoring_func = scoring_func179        # for backward compatibility180        if num_key_value_heads is None:181            num_key_value_heads = num_attention_heads182 183        self.num_key_value_heads = num_key_value_heads184        self.hidden_act = hidden_act185        self.initializer_range = initializer_range186        self.rms_norm_eps = rms_norm_eps187        self.use_cache = use_cache188        self.rope_theta = rope_theta189        self.rope_scaling = rope_scaling190        self.attention_bias = attention_bias191        self.attention_dropout = attention_dropout192 193        super().__init__(194            pad_token_id=pad_token_id,195            bos_token_id=bos_token_id,196            eos_token_id=eos_token_id,197            tie_word_embeddings=tie_word_embeddings,198            **kwargs,199        )