File size: 2,673 Bytes
c977434 |
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 |
from transformers import PretrainedConfig
from typing import List
class ScgptConfig(PretrainedConfig):
model_type = "scgpt"
def __init__(
self,
ntoken: int,
d_model: int,
nhead: int,
d_hid: int,
nlayers: int,
nlayers_cls: int = 3,
n_cls: int = 1,
vocab: Any = None,
dropout: float = 0.5,
pad_token: str = "<pad>",
pad_value: int = 0,
pert_pad_id: int = 2,
do_mvc: bool = False,
do_dab: bool = False,
use_batch_labels: bool = False,
num_batch_labels: Optional[int] = None,
domain_spec_batchnorm: Union[bool, str] = False,
input_emb_style: str = "continuous",
n_input_bins: Optional[int] = None,
cell_emb_style: str = "cls",
mvc_decoder_style: str = "inner product",
ecs_threshold: float = 0.3,
explicit_zero_prob: bool = False,
use_fast_transformer: bool = False,
fast_transformer_backend: str = "flash",
pre_norm: bool = False,
use_mod: bool = False,
ntokens_mod: Optional[int] = None,
vocab_mod: Optional[Any] = None,
**kwargs,
):
#if block_type not in ["basic", "bottleneck"]:
# raise ValueError(f"`block_type` must be 'basic' or bottleneck', got {block_type}.")
#if stem_type not in ["", "deep", "deep-tiered"]:
# raise ValueError(f"`stem_type` must be '', 'deep' or 'deep-tiered', got {stem_type}.")
self.ntoken = ntoken
self.d_model = d_model
self.nhead = nhead
self.d_hid = d_hid
self.nlayers = nlayers
self.nlayers_cls = nlayers_cls
self.n_cls = n_cls
self.vocab = vocab
self.dropout = dropout
self.pad_token = pad_token
self.pad_value = pad_value
self.pert_pad_id = pert_pad_id
self.do_mvc = do_mvc
self.do_dab = do_dab
self.use_batch_labels = use_batch_labels
self.num_batch_labels = num_batch_labels
self.domain_spec_batchnorm = domain_spec_batchnorm
self.input_emb_style = input_emb_style
self.n_input_bins = n_input_bins
self.cell_emb_style = cell_emb_style
self.mvc_decoder_style = mvc_decoder_style
self.ecs_threshold = ecs_threshold
self.explicit_zero_prob = explicit_zero_prob
self.use_fast_transformer = use_fast_transformer
self.fast_transformer_backend = fast_transformer_backend
self.pre_norm = pre_norm
self.use_mod = use_mod
self.ntokens_mod = ntokens_mod
self.vocab_mod = vocab_mod
super().__init__(**kwargs) |