transfer/code/CCFM/src/model/model.py

"""
CascadedFlowModel — Main backbone for Cascaded Conditioned Flow Matching (CCFM).

Architecture mirrors LatentForcing's model_cot.py:
1. Dual-stream embedding (expression + latent) with element-wise addition
2. Conditioning via AdaLN: c = t_expr_emb + t_latent_emb + pert_emb
   - True conditioning signals: control expression + perturbation_id (available at inference)
   - scGPT latent features are an auxiliary generation target (like DINO in LatentForcing),
     NOT a conditioning signal — they are generated from noise at inference time.
3. Shared backbone (reusing scDFM's DiffPerceiver/Perceiver blocks)
4. Separate decoder heads: ExprDecoder (reused) + LatentDecoder (new)
"""

import torch
import torch.nn as nn
from torch import Tensor
from typing import Optional, Tuple

from .layers import LatentEmbedder, LatentDecoder
from .._scdfm_imports import (
    GeneadaLN,
    ContinuousValueEncoder,
    GeneEncoder,
    BatchLabelEncoder,
    TimestepEmbedder,
    ExprDecoder,
    DifferentialTransformerBlock,
    PerceiverBlock,
    DiffPerceiverBlock,
)


class CascadedFlowModel(nn.Module):
    """
    Cascaded Flow Model for single-cell perturbation prediction.

    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, scgpt_dim)  noised scGPT features (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, scgpt_dim)   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,
        # Latent-specific
        scgpt_dim: int = 512,
        bottleneck_dim: int = 128,
        dh_depth: int = 2,
    ):
        super().__init__()
        self.d_model = d_model
        self.fusion_method = fusion_method
        self.perturbation_function = perturbation_function

        # === Timestep embedders (separate for expr and latent, like model_cot.py) ===
        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),
        )

        # === Latent stream embedder (new, analogous to dino_embedder) ===
        self.latent_embedder = LatentEmbedder(scgpt_dim, bottleneck_dim, 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 (new, analogous to dh_blocks_dino + final_layer_dino) ===
        self.latent_decoder = LatentDecoder(
            d_model=d_model,
            scgpt_dim=scgpt_dim,
            dh_depth=dh_depth,
            num_heads=max(nhead // 2, 1),
            hidden_size_c=d_model,
        )

        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, scgpt_dim) noised latent features
        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)
        val_emb_1 = self.value_encoder_1(x_t)      # (B, G, d) encoder_1 = noisy target (same role as scDFM)
        val_emb_2 = self.value_encoder_2(cell_1)    # (B, G, d) encoder_2 = control (same role as scDFM)
        expr_tokens = self.fusion_layer(torch.cat([val_emb_1, val_emb_2], dim=-1)) + gene_emb  # (B, G, d)

        # === 2. Latent stream embedding (new, analogous to dino_embedder) ===
        latent_tokens = self.latent_embedder(z_t)  # (B, G, d)

        # === 3. Element-wise addition (model_cot.py line 414) ===
        x = expr_tokens + latent_tokens  # (B, G, d)

        # === 4. Conditioning vector (model_cot.py line 409) ===
        t_expr_emb = self.t_expr_embedder(t_expr)      # (B, d)
        t_latent_emb = self.t_latent_embedder(t_latent)  # (B, d)
        pert_emb = self.get_perturbation_emb(perturbation_id, cell_1=cell_1)  # (B, d)
        c = t_expr_emb + t_latent_emb + pert_emb  # (B, d)

        # === 5. Shared backbone (reused scDFM blocks) ===
        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 (reused) ===
        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 (new, analogous to dh_blocks_dino) ===
        pred_v_latent = self.latent_decoder(x, c)  # (B, G, scgpt_dim)

        return pred_v_expr, pred_v_latent