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from transformers.configuration_utils import PretrainedConfig
from loguru import logger as logging
class BuddyGPTConfig(PretrainedConfig):
""" TinyLLM 配置文件
"""
model_type = "buddygpt"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=151669,
hidden_size=4096,
intermediate_size=4096,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=None,
hidden_act="silu",
num_seq_len=2048,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=None,
bos_token_id=None,
eos_token_id=None,
tie_word_embeddings=False,
rope_theta=10000.0,
attention_dropout=0.0,
_attn_implementation="sdpa",
q_lora_rank: int = 16,
qk_rope_head_dim: int = 4,
kv_lora_rank: int = 16,
v_head_dim: int = 16,
qk_nope_head_dim: int = 12,
n_expert=None,
n_expert_per_token=2,
n_group=2,
n_topk_group=1,
norm_topk_prob=True,
routed_scaling_factor=0.2,
scoring_func='sigmoid',
topk_method='noaux_tc',
moe_intermediate_size=10,
n_shared_experts=2,
**kwargs,
):
self.vocab_size = vocab_size
self.num_seq_len = num_seq_len
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
# 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.use_cache = use_cache
self.rope_theta = rope_theta
self.attention_dropout = attention_dropout
self._attn_implementation = _attn_implementation
# mla
self.q_lora_rank = q_lora_rank
self.qk_rope_head_dim = qk_rope_head_dim
self.kv_lora_rank = kv_lora_rank
self.v_head_dim = v_head_dim
self.qk_nope_head_dim = qk_nope_head_dim
# moe
self.n_expert = n_expert
self.n_expert_per_token = n_expert_per_token
self.n_group = n_group
self.n_topk_group = n_topk_group
self.norm_topk_prob = norm_topk_prob
self.routed_scaling_factor=routed_scaling_factor
self.scoring_func = scoring_func
self.topk_method = topk_method
self.moe_intermediate_size = moe_intermediate_size
self.n_shared_experts = n_shared_experts
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
) |