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# -*- 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,
)
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