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1e9aaa3 | 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 | from typing import List, Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PretrainedConfig, PreTrainedModel
def _get_activation(activation: str) -> nn.Module:
if activation == "prelu":
return nn.PReLU()
elif activation == "relu":
return nn.ReLU()
elif activation == "gelu":
return nn.GELU()
elif activation == "tanh":
return nn.Tanh()
raise ValueError(f"Unsupported activation: {activation!r}")
class MLP(nn.Module):
def __init__(
self,
input_dim: int,
output_dim: int = 512,
hidden_dim: Optional[List[int]] = None,
dropout: float = 0.0,
residual: bool = False,
activation: str = "prelu",
):
super().__init__()
if hidden_dim is None:
hidden_dim = [512, 512]
self.latent_dim = output_dim
self.residual = residual
self.network = nn.ModuleList()
if residual:
assert len(set(hidden_dim)) == 1, "Residual connections require all hidden dims to be equal"
for i in range(len(hidden_dim)):
if i == 0:
self.network.append(nn.Sequential(
nn.Linear(input_dim, hidden_dim[i]),
nn.BatchNorm1d(hidden_dim[i]),
_get_activation(activation),
))
else:
self.network.append(nn.Sequential(
nn.Dropout(p=dropout),
nn.Linear(hidden_dim[i - 1], hidden_dim[i]),
nn.BatchNorm1d(hidden_dim[i]),
_get_activation(activation),
))
self.network.append(nn.Linear(hidden_dim[-1], output_dim))
def forward(self, x: torch.Tensor) -> torch.Tensor:
for i, layer in enumerate(self.network):
if self.residual and (0 < i < len(self.network) - 1):
x = layer(x) + x
else:
x = layer(x)
return x
class VirtualCellDistilConfig(PretrainedConfig):
model_type = "virtual_cell_distil"
def __init__(
self,
n_genes: int = 18301,
output_dim: int = 512,
hidden_dim: Optional[List[int]] = None,
dropout: float = 0.0,
residual: bool = False,
activation: str = "prelu",
num_labels: int = 2,
classifier_dropout: float = 0.1,
**kwargs,
):
super().__init__(**kwargs)
self.n_genes = n_genes
self.output_dim = output_dim
self.hidden_dim = hidden_dim if hidden_dim is not None else [512, 512]
self.dropout = dropout
self.residual = residual
self.activation = activation
self.num_labels = num_labels
self.classifier_dropout = classifier_dropout
class VirtualCellDistilModel(PreTrainedModel):
"""Pure encoder — returns 512-d patient embeddings from bulk expression."""
config_class = VirtualCellDistilConfig
def __init__(self, config: VirtualCellDistilConfig):
super().__init__(config)
self.encoder = MLP(
input_dim=config.n_genes,
output_dim=config.output_dim,
hidden_dim=config.hidden_dim,
dropout=config.dropout,
residual=config.residual,
activation=config.activation,
)
def forward(self, input_ids: torch.Tensor, **kwargs) -> dict:
return {"embeddings": self.encoder(input_ids)}
class VirtualCellDistilForSequenceClassification(PreTrainedModel):
"""
Encoder + linear classification head.
The encoder is initialised from pretrained distilled weights.
The classification head is randomly initialised and trained on your labels.
Use ignore_mismatched_sizes=True when loading from the pretrained checkpoint.
"""
config_class = VirtualCellDistilConfig
def __init__(self, config: VirtualCellDistilConfig):
super().__init__(config)
self.encoder = MLP(
input_dim=config.n_genes,
output_dim=config.output_dim,
hidden_dim=config.hidden_dim,
dropout=config.dropout,
residual=config.residual,
activation=config.activation,
)
self.dropout = nn.Dropout(config.classifier_dropout)
self.classifier = nn.Linear(config.output_dim, config.num_labels)
def forward(
self,
input_ids: torch.Tensor,
labels: Optional[torch.Tensor] = None,
**kwargs,
) -> dict:
embeddings = self.encoder(input_ids)
logits = self.classifier(self.dropout(embeddings))
loss = None
if labels is not None:
loss = F.cross_entropy(logits, labels)
return {"loss": loss, "logits": logits, "embeddings": embeddings}
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