File size: 8,154 Bytes
9f5e507 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 | """
SBModel — Anisotropic Schrödinger Bridge model.
Shared backbone with scDFM, dual output heads (velocity + score),
plus AnisotropicSigmaNet for per-gene diffusion coefficients.
"""
import torch
import torch.nn as nn
from torch import Tensor
from typing import Optional, Tuple
from .layers import AnisotropicSigmaNet, ScoreDecoder
from .._scdfm_imports import (
GeneadaLN,
ContinuousValueEncoder,
GeneEncoder,
BatchLabelEncoder,
TimestepEmbedder,
ExprDecoder,
DifferentialTransformerBlock,
PerceiverBlock,
DiffPerceiverBlock,
)
class SBModel(nn.Module):
"""
Anisotropic Schrödinger Bridge model.
forward(gene_id, cell_1, x_t, t, perturbation_id)
→ (pred_velocity, pred_score, sigma_g)
- pred_velocity: (B, G) PF-ODE velocity (target = x_T - x₀)
- pred_score: (B, G) score function (target = conditional score)
- sigma_g: (B, G) per-gene diffusion coefficient in [σ_min, σ_max]
"""
def __init__(
self,
ntoken: int = 6000,
d_model: int = 128,
nhead: int = 8,
d_hid: int = 512,
nlayers: int = 4,
dropout: float = 0.1,
fusion_method: str = "differential_perceiver",
perturbation_function: str = "crisper",
use_perturbation_interaction: bool = True,
mask_path: str = None,
# Sigma net params
sigma_min: float = 0.01,
sigma_max: float = 2.0,
sigma_init: float = 0.5,
sigma_hidden_dim: int = 256,
sigma_num_layers: int = 2,
# Score decoder params
score_head_depth: int = 2,
use_score: bool = True,
):
super().__init__()
self.d_model = d_model
self.fusion_method = fusion_method
self.perturbation_function = perturbation_function
self.use_score = use_score
# === Timestep embedder (single, not cascaded) ===
self.t_embedder = TimestepEmbedder(d_model)
# === Perturbation embedder ===
self.perturbation_embedder = BatchLabelEncoder(ntoken, d_model)
# === Expression stream (reused from scDFM) ===
self.value_encoder_1 = ContinuousValueEncoder(d_model, dropout)
self.value_encoder_2 = ContinuousValueEncoder(d_model, dropout)
self.encoder = GeneEncoder(
ntoken, d_model,
use_perturbation_interaction=use_perturbation_interaction,
mask_path=mask_path,
)
self.fusion_layer = nn.Sequential(
nn.Linear(2 * d_model, d_model),
nn.GELU(),
nn.Linear(d_model, d_model),
nn.LayerNorm(d_model),
)
# === Shared backbone blocks ===
if fusion_method == "differential_transformer":
self.blocks = nn.ModuleList([
DifferentialTransformerBlock(d_model, nhead, i, mlp_ratio=4.0)
for i in range(nlayers)
])
elif fusion_method == "differential_perceiver":
self.blocks = nn.ModuleList([
DiffPerceiverBlock(d_model, nhead, i, mlp_ratio=4.0)
for i in range(nlayers)
])
elif fusion_method == "perceiver":
self.blocks = nn.ModuleList([
PerceiverBlock(d_model, d_model, heads=nhead, mlp_ratio=4.0, dropout=0.1)
for _ in range(nlayers)
])
else:
raise ValueError(f"Invalid fusion method: {fusion_method}")
# === Per-layer gene AdaLN + adapter ===
self.gene_adaLN = nn.ModuleList([
GeneadaLN(d_model, dropout) for _ in range(nlayers)
])
self.adapter_layer = nn.ModuleList([
nn.Sequential(
nn.Linear(2 * d_model, d_model),
nn.LeakyReLU(),
nn.Dropout(dropout),
nn.Linear(d_model, d_model),
nn.LeakyReLU(),
)
for _ in range(nlayers)
])
# === Velocity decoder head (reused ExprDecoder from scDFM) ===
self.final_layer = ExprDecoder(d_model, explicit_zero_prob=False, use_batch_labels=True)
# === Score decoder head (NEW) ===
if use_score:
self.score_decoder = ScoreDecoder(d_model, depth=score_head_depth)
# === Anisotropic sigma network (NEW, independent of backbone) ===
self.sigma_net = AnisotropicSigmaNet(
d_model=d_model,
hidden_dim=sigma_hidden_dim,
num_layers=sigma_num_layers,
sigma_min=sigma_min,
sigma_max=sigma_max,
sigma_init=sigma_init,
)
self.initialize_weights()
def initialize_weights(self):
def _basic_init(module):
if isinstance(module, nn.Linear):
torch.nn.init.xavier_uniform_(module.weight)
if module.bias is not None:
nn.init.constant_(module.bias, 0)
self.apply(_basic_init)
# Re-initialize sigma bias after global init
self.sigma_net._init_bias(self.sigma_net.sigma_min +
(self.sigma_net.sigma_max - self.sigma_net.sigma_min) * 0.5)
def get_perturbation_emb(
self,
perturbation_id: Optional[Tensor] = None,
perturbation_emb: Optional[Tensor] = None,
cell_1: Optional[Tensor] = None,
) -> Tensor:
"""Get perturbation embedding, replicating scDFM logic."""
assert perturbation_emb is None or perturbation_id is None
if perturbation_id is not None:
if self.perturbation_function == "crisper":
perturbation_emb = self.encoder(perturbation_id)
else:
perturbation_emb = self.perturbation_embedder(perturbation_id)
perturbation_emb = perturbation_emb.mean(1)
elif perturbation_emb is not None:
perturbation_emb = perturbation_emb.to(cell_1.device, dtype=cell_1.dtype)
if perturbation_emb.dim() == 1:
perturbation_emb = perturbation_emb.unsqueeze(0)
if perturbation_emb.size(0) == 1:
perturbation_emb = perturbation_emb.expand(cell_1.shape[0], -1).contiguous()
perturbation_emb = self.perturbation_embedder.enc_norm(perturbation_emb)
return perturbation_emb
def forward(
self,
gene_id: Tensor, # (B, G) gene token IDs
cell_1: Tensor, # (B, G) source expression
x_t: Tensor, # (B, G) noised target expression
t: Tensor, # (B,) timestep
perturbation_id: Optional[Tensor] = None,
) -> Tuple[Tensor, Optional[Tensor], Tensor]:
if t.dim() == 0:
t = t.repeat(cell_1.size(0))
# 1. Expression embedding (aligned with scDFM)
gene_emb = self.encoder(gene_id) # (B, G, d)
val_emb_1 = self.value_encoder_1(x_t)
val_emb_2 = self.value_encoder_2(cell_1) + gene_emb
x = self.fusion_layer(torch.cat([val_emb_1, val_emb_2], dim=-1)) + gene_emb
# 2. Conditioning vector (single t, no cascaded)
t_emb = self.t_embedder(t)
pert_emb = self.get_perturbation_emb(perturbation_id, cell_1=cell_1)
c = t_emb + pert_emb
# 3. Shared backbone
for i, block in enumerate(self.blocks):
x = self.gene_adaLN[i](gene_emb, x)
pert_exp = pert_emb[:, None, :].expand(-1, x.size(1), -1)
x = torch.cat([x, pert_exp], dim=-1)
x = self.adapter_layer[i](x)
x = block(x, val_emb_2, c)
# 4a. Velocity head
x_with_pert = torch.cat([x, pert_emb[:, None, :].expand(-1, x.size(1), -1)], dim=-1)
pred_velocity = self.final_layer(x_with_pert)["pred"] # (B, G)
# 4b. Score head
pred_score = None
if self.use_score:
pred_score = self.score_decoder(x, pert_emb) # (B, G)
# 4c. Sigma (independent of backbone, only depends on pert_emb, t, gene_emb)
sigma_g = self.sigma_net(pert_emb, t, gene_emb) # (B, G)
return pred_velocity, pred_score, sigma_g
|