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from transformers import PretrainedConfig
from typing import List
class LMConfig(PretrainedConfig):
model_type = "minimind"
def __init__(
self,
dim: int = 512,
n_layers: int = 8,
n_heads: int = 8,
n_kv_heads: int = 2,
vocab_size: int = 25, # 6400
hidden_dim: int = None,
logit_dim : int = 9,
multiple_of: int = 64,
norm_eps: float = 1e-5,
max_seq_len: int = 8192,
rope_theta: int = 1e6,
dropout: float = 0.0,
flash_attn: bool = True,
####################################################
# 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,
padding_idx:int = None,
**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.logit_dim = logit_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
####################################################
# 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
####################################################
# Here are the specific configurations of input layer
####################################################
self.padding_idx = padding_idx
super().__init__(**kwargs)
class LMaoTaoConfig(PretrainedConfig):
model_type = "minimind"
def __init__(
self,
dim: int = 256,
n_layers: int = 8,
n_heads: int = 8,
n_kv_heads: int = 2,
# vocab_size: int = 32, # 6400
vocab_size: int = 9, # 6400
hidden_dim: int = None,
logit_dim : int = 32,
multiple_of: int = 64,
norm_eps: float = 1e-5,
max_seq_len: int = 8192,
rope_theta: int = 1e6,
dropout: float = 0.0,
flash_attn: bool = True,
####################################################
# 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,
padding_idx:int = None,
####################################################
### user defined parameters
####################################################
aa_vocab_size: int = 22, # 20 AA + * + pad
species_size: int = 5, # 5 species
truncated_size: int = 5, # full,head,tail,boundary,middle
continuous_features_dim = 3, # data['off_start'],data['off_end'],data['full_len'] log(x+1)
head_codon_num:int = 100,
**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.logit_dim = logit_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
####################################################
# 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
####################################################
# Here are the specific configurations of input layer
####################################################
self.padding_idx = padding_idx
####################################################
### user defined parameters
####################################################
self.aa_vocab_size = aa_vocab_size
self.species_size = species_size
self.truncated_size = truncated_size
self.continuous_features_dim = continuous_features_dim
self.head_codon_num = head_codon_num
super().__init__(**kwargs) |