Minimind/model/LMConfig.py

from transformers import PretrainedConfig
from typing import List, Optional, Union


class LMConfig(PretrainedConfig):
    model_type = "minimind"

    def __init__(
            self,
            dim: int = 512,
            n_layers: int = 8,
            n_heads: int = 32,
            n_kv_heads: int = 8,
            vocab_size: int = 6400,
            hidden_dim: Optional[int] = None,
            multiple_of: int = 64,
            norm_eps: float = 1e-5,
            max_seq_len: int = 8192,
            rope_theta: float = 1e6,
            dropout: float = 0.0,
            flash_attn: bool = True,
            ####################################################
            # DB related configurations
            ####################################################
            disable_db: bool = False,  # 特殊模式：禁用数据库功能
            use_direct_semantic: bool = False,  # 是否使用直接语义匹配（替代Product Key）
            realtime_steps: int = 2000,  # 前多少步使用实时计算（后续使用渐进式缓存）
            db_intelligent_balance: bool = True,  # 是否启用智能负载均衡
            db_relevance_threshold: float = 0.7,  # 相关性阈值（第一层过滤）
            db_balance_strength: float = 0.3,  # 平衡权重的基础值
            db_momentum: float = 0.9,  # 使用频率统计的动量
            db_adaptive_weights: bool = True,  # 是否启用动态权重调整
            ####################################################
            # Here are the specific configurations of MOE
            # When use_moe is false, the following is invalid
            ####################################################
            use_moe: bool = False,
            ####################################################
            num_experts_per_tok: int = 2,
            n_routed_experts: int = 4,
            n_shared_experts: bool = True,
            scoring_func: str = 'softmax',
            aux_loss_alpha: float = 0.1,
            seq_aux: bool = True,
            norm_topk_prob: bool = True,
            ####################################################
            knowledge_num: int = 64*64,
            knowledge_length: int = 8,
            **kwargs,
    ):
        self.dim = dim
        self.n_layers = n_layers
        self.n_heads = n_heads
        self.n_kv_heads = n_kv_heads
        self.vocab_size = vocab_size
        self.hidden_dim = hidden_dim
        self.multiple_of = multiple_of
        self.norm_eps = norm_eps
        self.max_seq_len = max_seq_len
        self.rope_theta = rope_theta
        self.dropout = dropout
        self.flash_attn = flash_attn
        ####################################################
        # DB related configurations
        ####################################################
        self.disable_db = disable_db  # 设置是否禁用数据库
        self.use_direct_semantic = use_direct_semantic  # 是否使用直接语义匹配（替代Product Key）
        self.realtime_steps = realtime_steps  # 前多少步使用实时计算（后续使用渐进式缓存）
        self.db_intelligent_balance = db_intelligent_balance  # 是否启用智能负载均衡
        self.db_relevance_threshold = db_relevance_threshold  # 相关性阈值（第一层过滤）
        self.db_balance_strength = db_balance_strength  # 平衡权重的基础值
        self.db_momentum = db_momentum  # 使用频率统计的动量
        self.db_adaptive_weights = db_adaptive_weights  # 是否启用动态权重调整
        ####################################################
        # Here are the specific configurations of MOE
        # When use_moe is false, the following is invalid
        ####################################################
        self.use_moe = use_moe
        self.num_experts_per_tok = num_experts_per_tok  # 每个token选择的专家数量
        self.n_routed_experts = n_routed_experts  # 总的专家数量
        self.n_shared_experts = n_shared_experts  # 共享专家
        self.scoring_func = scoring_func  # 评分函数，默认为'softmax'
        self.aux_loss_alpha = aux_loss_alpha  # 辅助损失的alpha参数
        self.seq_aux = seq_aux  # 是否在序列级别上计算辅助损失
        self.norm_topk_prob = norm_topk_prob  # 是否标准化top-k概率
        ####################################################
        self.knowledge_num = knowledge_num
        self.knowledge_length = knowledge_length
        super().__init__(**kwargs)