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RegFMModel: scDFM backbone + RegulatoryHead + VelocityGate.
Parameter names for backbone components match ori_scDFM exactly,
enabling direct weight loading from scDFM baseline checkpoints.
"""
import torch
import torch.nn as nn
from src._scdfm_imports import (
GeneEncoder,
ContinuousValueEncoder,
GeneadaLN,
BatchLabelEncoder,
TimestepEmbedder,
ExprDecoder,
DiffPerceiverBlock,
DifferentialTransformerBlock,
PerceiverBlock,
)
from src.model.layers import RegulatoryHead, VelocityGate
class RegFMModel(nn.Module):
"""
Regulatory Flow Matching model.
Forward returns:
v: (B, G) — final velocity = α·v_reg + (1-α)·v_int
R: (B, G, G) — predicted interaction matrix (for L_reg supervision)
"""
def __init__(
self,
ntoken: int = 512,
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,
# RegFM-specific
d_r: int = 32,
gate_init_bias: float = -3.0,
):
super().__init__()
self.perturbation_function = perturbation_function
self._gate_init_bias = gate_init_bias
# === Backbone (parameter names match ori_scDFM for warm start) ===
self.encoder = GeneEncoder(
ntoken, d_model,
use_perturbation_interaction=use_perturbation_interaction,
mask_path=mask_path,
)
self.value_encoder_1 = ContinuousValueEncoder(d_model, dropout)
self.value_encoder_2 = ContinuousValueEncoder(d_model, dropout)
self.fusion_layer = nn.Sequential(
nn.Linear(2 * d_model, d_model),
nn.GELU(),
nn.Linear(d_model, d_model),
nn.LayerNorm(d_model),
)
self.t_embedder = TimestepEmbedder(d_model)
self.perturbation_embedder = BatchLabelEncoder(ntoken, d_model)
# Transformer blocks
if 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 == "differential_transformer":
self.blocks = nn.ModuleList(
[DifferentialTransformerBlock(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"Unknown fusion_method: {fusion_method}")
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)
])
# predict_p auxiliary head (kept for compatibility)
self.p_mask_embed = nn.Parameter(torch.randn(d_model))
self.p_head = nn.Sequential(nn.LayerNorm(d_model), nn.Linear(d_model, d_model))
# === v_int head (ExprDecoder, same as scDFM's self.final_layer) ===
self.final_layer = ExprDecoder(d_model, explicit_zero_prob=False, use_batch_labels=True)
# === RegFM additions ===
self.reg_head = RegulatoryHead(d_model, d_r)
self.velocity_gate = VelocityGate(d_model, gate_init_bias)
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-apply gate bias after global init
nn.init.zeros_(self.velocity_gate.mlp[-1].weight)
nn.init.constant_(self.velocity_gate.mlp[-1].bias, self._gate_init_bias)
def get_perturbation_emb(self, perturbation_id=None, perturbation_emb=None,
cell_1=None, use_mask: bool = False):
"""Identical to scDFM model.get_perturbation_emb for compatibility."""
if use_mask:
B = cell_1.size(0)
return self.p_mask_embed[None, :].expand(B, -1).to(cell_1.device, dtype=cell_1.dtype)
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, cell_1, t, cell_2,
perturbation_id=None, gene_id_all=None,
perturbation_emb=None, mode="predict_y"):
"""
Args:
gene_id: (B, G) vocab-encoded gene IDs
cell_1: (B, G) noised target expression x_t
t: (B,) or scalar — flow timestep
cell_2: (B, G) source/control expression
perturbation_id: (B, 2) perturbation condition IDs
gene_id_all: unused (kept for API compatibility)
perturbation_emb: optional precomputed perturbation embedding
mode: "predict_y" (default) or "predict_p"
Returns:
if mode == "predict_y": (v, R)
v: (B, G) — gated velocity: α·v_reg + (1-α)·v_int
R: (B, G, G) — predicted interaction matrix
if mode == "predict_p": (B, d_model)
"""
if t.dim() == 0:
t = t.repeat(cell_1.size(0))
# --- Backbone (identical to scDFM) ---
gene_emb = self.encoder(gene_id) # (B, G, d_model)
value_emb_1 = self.value_encoder_1(cell_1) + gene_emb # x_t encoding
value_emb_2 = self.value_encoder_2(cell_2) + gene_emb # source encoding
value_emb = torch.cat([value_emb_1, value_emb_2], dim=-1)
value_emb = self.fusion_layer(value_emb) # (B, G, d_model)
t_emb = self.t_embedder(t) # (B, d_model)
pert_emb = self.get_perturbation_emb(perturbation_id, perturbation_emb, cell_1)
x = value_emb
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, value_emb_2, t_emb)
h = x # backbone output: (B, G, d_model)
# --- predict_p mode (auxiliary task, unchanged) ---
if mode == "predict_p":
return self.p_head(h.mean(dim=1))
# --- v_int: intrinsic velocity (same as scDFM) ---
x_dec = torch.cat([h, pert_emb[:, None, :].expand(-1, h.size(1), -1)], dim=-1)
v_int = self.final_layer(x_dec)["pred"] # (B, G)
# --- v_reg, R: regulatory velocity (RegFM) ---
v_reg, R = self.reg_head(h) # (B, G), (B, G, G)
# --- Gated mixing ---
alpha = self.velocity_gate(h, pert_emb, t_emb) # (B, G)
v = alpha * v_reg + (1.0 - alpha) * v_int # (B, G)
return v, R
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