File size: 2,575 Bytes
b3abc18 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 |
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
from typing import Optional,Tuple,List
class DCPythiaConfig(PretrainedConfig):
model_type = "dcpythia"
'''
DCPythiaConfig is a config class for DCPythia, which is adpated from https://github.com/pytorch-labs/gpt-fast/blob/main/model.py#L21
'''
def __init__(
self,
block_size: int = 2048,
vocab_size: int = 32000,
n_layer: int = 32,
n_head: int = 32,
dim: int = 2560,
intermediate_size: int = None,
n_local_heads: int = -1,
head_dim: int = 64,
rope_base: float = 10000,
norm_eps: float = 1e-5,
use_gradient_checkpointing: bool = False,
is_training: bool = False,
q_chunk_size: int = 128,
use_dcmha: bool = True,
use_qk_norm: bool = False ,
window_size: Optional[int] = 256,
window_type: Optional[str] = None,
query_wise: bool = False,
pad_token_id: Optional[int]= None,
use_parallel_residual: bool =True,
use_linear_bias: bool = True,
rotary_pct: float = 0.25,
bos_token_id: int =1,
eos_token_id: int =2,
tie_word_embeddings: bool =False,
**kwargs,
):
self.block_size=block_size
self.vocab_size=vocab_size
self.n_layer=n_layer
self.n_head=n_head
self.dim=dim
self.intermediate_size=intermediate_size
self.n_local_heads=n_local_heads
self.head_dim=head_dim
self.rope_base=rope_base
self.norm_eps=norm_eps
self.use_gradient_checkpointing=use_gradient_checkpointing
self.is_training=is_training
self.q_chunk_size=q_chunk_size
self.use_dcmha=use_dcmha
self.use_qk_norm=use_qk_norm
self.window_size=window_size
self.window_type=window_type
self.query_wise=query_wise
self.use_parallel_residual = use_parallel_residual
self.use_linear_bias = use_linear_bias
self.rotary_pct = rotary_pct
# post init
if self.n_local_heads == -1:
self.n_local_heads = self.n_head
if self.intermediate_size is None:
self.intermediate_size = 4 * self.dim
self.head_dim = self.dim // self.n_head
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,
)
|