# -*- coding: utf-8 -*- from typing import Optional from transformers.configuration_utils import PretrainedConfig class DynamicAlibiConfig(PretrainedConfig): model_type = 'dynamic_alibi' keys_to_ignore_at_inference = ['past_key_values'] def __init__( self, vocab_size: int = 32000, hidden_size: int = 2048, hidden_ratio: Optional[int] = 4, intermediate_size: Optional[int] = None, num_hidden_layers: int = 24, num_heads: int = 32, num_kv_heads: int = None, hidden_act: str = "swish", window_size: Optional[int] = None, max_position_embeddings: int = 2048, initializer_range: float = 0.02, elementwise_affine: Optional[bool] = True, norm_eps: float = 1e-6, use_cache: bool = True, pad_token_id: int = None, bos_token_id: int = 1, eos_token_id: int = 2, tie_word_embeddings: bool = False, attention_bias: bool = False, fuse_norm: bool = True, fuse_cross_entropy: bool = True, rope_base: float = 500000.0, use_rope: bool = False, use_alibi: bool = True, # 🆕 动态ALiBi参数 use_dynamic_alibi: bool = False, alibi_num_epochs: int = 10, alibi_initial_slope: float = 1.0, alibi_decay_rate: float = 0.6, **kwargs, ): self.vocab_size = vocab_size self.hidden_size = hidden_size self.hidden_ratio = hidden_ratio self.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers self.num_heads = num_heads self.num_kv_heads = num_kv_heads self.window_size = window_size self.max_position_embeddings = max_position_embeddings self.hidden_act = hidden_act self.initializer_range = initializer_range self.elementwise_affine = elementwise_affine self.norm_eps = norm_eps self.use_cache = use_cache self.attention_bias = attention_bias self.fuse_cross_entropy = fuse_cross_entropy self.fuse_norm = fuse_norm self.rope_base = rope_base self.use_rope = use_rope self.use_alibi = use_alibi # 🆕 动态ALiBi配置 self.use_dynamic_alibi = use_dynamic_alibi self.alibi_num_epochs = alibi_num_epochs self.alibi_initial_slope = alibi_initial_slope self.alibi_decay_rate = alibi_decay_rate 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, )