Create configuration_scgpt.py

configuration_scgpt.py

@@ -0,0 +1,75 @@
+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)