configuration_scgpt.py

from transformers import PretrainedConfig
from typing import List, Any, Optional, Union


class ScgptConfig(PretrainedConfig):
    model_type = "scgpt"

    def __init__(
        self,
        ntoken: int = 60697,
        d_model: int = 512,
        nhead: int = 8,
        d_hid: int = 512,
        nlayers: int = 12,
        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)