File size: 7,348 Bytes
e093a4b |
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 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 |
# Copyright (C) Tahoe Therapeutics 2025. All rights reserved.
"""
Configuration class for TXModel compatible with HuggingFace Transformers
"""
from transformers import PretrainedConfig
from typing import Optional, Dict, Any
class TXConfig(PretrainedConfig):
"""
Configuration class for TXModel.
This class stores the configuration of a TXModel, which is a Transformer-based model
for genomic/biological sequence analysis.
Args:
vocab_size (int): Size of the vocabulary
d_model (int): Dimensionality of the model embeddings
n_layers (int): Number of transformer layers
n_heads (int): Number of attention heads
expansion_ratio (int): Expansion ratio for FFN
norm_scheme (str): Normalization scheme ('pre' or 'post')
transformer_activation (str): Activation function for transformer
cell_emb_style (str): Cell embedding style ('cls', 'avg-pool', 'w-pool')
pad_token_id (int): ID of the padding token
pad_value (float): Value for padding
num_bins (int): Number of bins for expression values
use_chem_token (bool): Whether to use chemical token encoder
attn_config (Dict): Attention configuration
norm_config (Dict): Normalization configuration
init_config (Dict): Initialization configuration
gene_encoder_config (Dict): Gene encoder configuration
expression_encoder_config (Dict): Expression encoder configuration
expression_decoder_config (Dict): Expression decoder configuration
mvc_config (Optional[Dict]): MVC decoder configuration
chemical_encoder_config (Optional[Dict]): Chemical encoder configuration
use_glu (bool): Whether to use GLU in FFN
return_gene_embeddings (bool): Whether to return gene embeddings
standard_scale_outputs (bool): Whether to scale outputs
"""
model_type = "tx_model"
def __init__(
self,
vocab_size: int = 30000,
d_model: int = 512,
n_layers: int = 12,
n_heads: int = 8,
expansion_ratio: int = 4,
norm_scheme: str = "pre",
transformer_activation: str = "gelu",
cell_emb_style: str = "cls",
pad_token_id: int = 0,
pad_value: float = 0.0,
num_bins: int = 51,
use_chem_token: bool = False,
attn_config: Optional[Dict] = None,
norm_config: Optional[Dict] = None,
init_config: Optional[Dict] = None,
gene_encoder_config: Optional[Dict] = None,
expression_encoder_config: Optional[Dict] = None,
expression_decoder_config: Optional[Dict] = None,
mvc_config: Optional[Dict] = None,
chemical_encoder_config: Optional[Dict] = None,
use_glu: bool = False,
return_gene_embeddings: bool = False,
standard_scale_outputs: bool = False,
keep_first_n_tokens: int = 1,
**kwargs
):
super().__init__(pad_token_id=pad_token_id, **kwargs)
self.vocab_size = vocab_size
self.d_model = d_model
self.n_layers = n_layers
self.n_heads = n_heads
self.expansion_ratio = expansion_ratio
self.norm_scheme = norm_scheme
self.transformer_activation = transformer_activation
self.cell_emb_style = cell_emb_style
self.pad_value = pad_value
self.num_bins = num_bins
self.use_chem_token = use_chem_token
self.keep_first_n_tokens = keep_first_n_tokens
self.return_gene_embeddings = return_gene_embeddings
self.standard_scale_outputs = standard_scale_outputs
self.use_glu = use_glu
# Sub-configurations
self.attn_config = attn_config or {
"attn_type": "grouped_query_attention",
"attn_pdrop": 0.0,
"attn_impl": "flash",
"use_attn_mask": False,
"qk_ln": False,
"qk_gn": False,
"clip_qkv": None,
"softmax_scale": None,
}
self.norm_config = norm_config or {
"norm_type": "low_precision_layernorm",
"eps": 1e-5,
}
self.init_config = init_config or {
"name": "kaiming_normal_",
"fan_mode": "fan_in",
"init_nonlinearity": "relu",
"init_div_is_residual": True,
"emb_init_std": None,
"emb_init_uniform_lim": None,
"init_std": None,
"init_gain": 0.0,
}
self.gene_encoder_config = gene_encoder_config or {
"use_norm": False,
}
self.expression_encoder_config = expression_encoder_config or {
"input_emb_style": "continuous",
"dropout": 0.1,
"max_value": 512,
"activation": "relu",
"use_norm": False,
}
self.expression_decoder_config = expression_decoder_config or {
"n_outputs": 1,
"n_layers": 2,
"activation": "leaky_relu",
}
self.mvc_config = mvc_config
self.chemical_encoder_config = chemical_encoder_config
@classmethod
def from_yaml_configs(cls, model_config_dict: Dict, collator_config_dict: Dict) -> "TXConfig":
"""
Create TXConfig from model_config.yml and collator_config.yml dictionaries
Args:
model_config_dict: Dictionary from model_config.yml
collator_config_dict: Dictionary from collator_config.yml
Returns:
TXConfig instance
"""
return cls(
vocab_size=model_config_dict.get("vocab_size"),
d_model=model_config_dict.get("d_model"),
n_layers=model_config_dict.get("n_layers"),
n_heads=model_config_dict.get("n_heads"),
expansion_ratio=model_config_dict.get("expansion_ratio"),
norm_scheme=model_config_dict.get("norm_scheme", "pre"),
transformer_activation=model_config_dict.get("transformer_activation", "gelu"),
cell_emb_style=model_config_dict.get("cell_emb_style", "cls"),
pad_token_id=collator_config_dict.get("pad_token_id", 0),
pad_value=collator_config_dict.get("pad_value", 0.0),
num_bins=collator_config_dict.get("num_bins", 51),
use_chem_token=collator_config_dict.get("use_chem_token", False),
attn_config=model_config_dict.get("attn_config"),
norm_config=model_config_dict.get("norm_config"),
init_config=model_config_dict.get("init_config"),
gene_encoder_config=model_config_dict.get("gene_encoder"),
expression_encoder_config=model_config_dict.get("expression_encoder"),
expression_decoder_config=model_config_dict.get("expression_decoder"),
mvc_config=model_config_dict.get("mvc"),
chemical_encoder_config=model_config_dict.get("chemical_encoder"),
use_glu=model_config_dict.get("use_glu", False),
return_gene_embeddings=model_config_dict.get("return_gene_embeddings", False),
standard_scale_outputs=model_config_dict.get("standard_scale_outputs", False),
keep_first_n_tokens=collator_config_dict.get("keep_first_n_tokens", 1),
)
|