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CascadedFlowModel for grn_att_only.
Key changes from grn_ccfm:
- d_model=128 (from 512)
- Latent target: raw Δ_attn (B, G, G) instead of Δ_attn @ gene_emb (B, G, 512)
- Latent encoder: z_t @ gene_emb (shared GeneEncoder) instead of LatentEmbedder
- Latent decoder: BilinearLatentDecoder (Q@K^T) instead of LatentDecoder (AdaLN + MLP)
"""
import torch
import torch.nn as nn
from torch import Tensor
from typing import Optional, Tuple
from .layers import BilinearLatentDecoder
from .._scdfm_imports import (
GeneadaLN,
ContinuousValueEncoder,
GeneEncoder,
BatchLabelEncoder,
TimestepEmbedder,
ExprDecoder,
DifferentialTransformerBlock,
PerceiverBlock,
DiffPerceiverBlock,
)
class CascadedFlowModel(nn.Module):
"""
Cascaded Flow Model with raw attention delta latent target.
Inputs:
gene_id: (B, G) gene token IDs
cell_1: (B, G) source (control) expression
x_t: (B, G) noised target expression (expression flow)
z_t: (B, G, G) noised raw attention delta (latent flow)
t_expr: (B,) expression flow timestep
t_latent: (B,) latent flow timestep
perturbation_id: (B, 2) perturbation token IDs
Outputs:
pred_v_expr: (B, G) predicted expression velocity
pred_v_latent: (B, G, G) predicted latent velocity
"""
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,
bilinear_head_dim: int = 128,
):
super().__init__()
self.d_model = d_model
self.fusion_method = fusion_method
self.perturbation_function = perturbation_function
# === Timestep embedders (separate for expr and latent) ===
self.t_expr_embedder = TimestepEmbedder(d_model)
self.t_latent_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.use_perturbation_interaction = use_perturbation_interaction
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)
])
# === Expression decoder head (reused from scDFM) ===
self.final_layer = ExprDecoder(d_model, explicit_zero_prob=False, use_batch_labels=True)
# === Latent decoder head: BilinearLatentDecoder (Q@K^T -> (B,G,G)) ===
self.latent_decoder = BilinearLatentDecoder(d_model, bilinear_head_dim)
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)
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) # (B, d)
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)
cell_1: Tensor, # (B, G) source expression
x_t: Tensor, # (B, G) noised expression
z_t: Tensor, # (B, G, G) noised raw attention delta
t_expr: Tensor, # (B,)
t_latent: Tensor, # (B,)
perturbation_id: Optional[Tensor] = None, # (B, 2)
) -> Tuple[Tensor, Tensor]:
if t_expr.dim() == 0:
t_expr = t_expr.repeat(cell_1.size(0))
if t_latent.dim() == 0:
t_latent = t_latent.repeat(cell_1.size(0))
# === 1. Expression stream embedding (aligned with scDFM) ===
gene_emb = self.encoder(gene_id) # (B, G, d_model)
val_emb_1 = self.value_encoder_1(x_t)
val_emb_2 = self.value_encoder_2(cell_1) + gene_emb
expr_tokens = self.fusion_layer(torch.cat([val_emb_1, val_emb_2], dim=-1)) + gene_emb
# === 2. Latent stream: z_t @ gene_emb (shared GeneEncoder) ===
# z_t: (B, G, G), gene_emb: (B, G, d_model) -> latent_tokens: (B, G, d_model)
# Scale by 1/sqrt(G) to control variance (analogous to attention scaling)
latent_tokens = torch.bmm(z_t, gene_emb) * (z_t.size(-1) ** -0.5)
# === 3. Element-wise addition ===
x = expr_tokens + latent_tokens # (B, G, d_model)
# === 4. Conditioning vector ===
t_expr_emb = self.t_expr_embedder(t_expr)
t_latent_emb = self.t_latent_embedder(t_latent)
pert_emb = self.get_perturbation_emb(perturbation_id, cell_1=cell_1)
c = t_expr_emb + t_latent_emb + pert_emb
# === 5. 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)
# === 6a. Expression decoder head ===
x_with_pert = torch.cat([x, pert_emb[:, None, :].expand(-1, x.size(1), -1)], dim=-1)
pred_v_expr = self.final_layer(x_with_pert)["pred"] # (B, G)
# === 6b. Latent decoder head: Q@K^T -> (B, G, G) ===
pred_v_latent = self.latent_decoder(x) # (B, G, G)
return pred_v_expr, pred_v_latent
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