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from __future__ import annotations
from typing import List, Optional, Literal
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.data import Batch
from src.conv import build_gnn_encoder, GNNEncoder
def get_activation(name: str) -> nn.Module:
name = name.lower()
if name == "relu":
return nn.ReLU()
if name == "gelu":
return nn.GELU()
if name == "silu":
return nn.SiLU()
if name in ("leaky_relu", "lrelu"):
return nn.LeakyReLU(0.1)
raise ValueError(f"Unknown activation: {name}")
class FiLM(nn.Module):
"""
Simple FiLM: gamma, beta from condition vector; apply to features as (1+gamma)*h + beta
"""
def __init__(self, feat_dim: int, cond_dim: int):
super().__init__()
self.gamma = nn.Linear(cond_dim, feat_dim)
self.beta = nn.Linear(cond_dim, feat_dim)
def forward(self, h: torch.Tensor, cond: torch.Tensor) -> torch.Tensor:
g = self.gamma(cond)
b = self.beta(cond)
return (1.0 + g) * h + b
class TaskHead(nn.Module):
"""
Per-task MLP head. Input is concatenation of [graph_embed, optional task_embed].
Outputs either a mean only (scalar) or mean+logvar (heteroscedastic).
"""
def __init__(
self,
in_dim: int,
hidden_dim: int = 512,
depth: int = 2,
act: str = "relu",
dropout: float = 0.0,
heteroscedastic: bool = False,
):
super().__init__()
layers: List[nn.Module] = []
d = in_dim
for _ in range(depth):
layers.append(nn.Linear(d, hidden_dim))
layers.append(get_activation(act))
if dropout > 0:
layers.append(nn.Dropout(dropout))
d = hidden_dim
out_dim = 2 if heteroscedastic else 1
layers.append(nn.Linear(d, out_dim))
self.net = nn.Sequential(*layers)
self.hetero = heteroscedastic
def forward(self, z: torch.Tensor) -> torch.Tensor:
# returns [B, 1] or [B, 2] where [...,0] is mean and [...,1] is logvar if heteroscedastic
return self.net(z)
class MultiTaskMultiFidelityModel(nn.Module):
"""
General multi-task, multi-fidelity GNN.
- Any number of tasks (properties) via T = len(task_names)
- Any number of fidelities via num_fids
- Fidelity conditioning with an embedding and FiLM on the graph embedding
- Optional task embeddings concatenated into each task head input
- Single forward returning predictions [B, T] (means); if heteroscedastic, also returns log-variances
Expected input Batch fields (PyG):
- x : [N_nodes, F_node]
- edge_index : [2, N_edges]
- edge_attr : [N_edges, F_edge] (required if gnn_type="gine")
- batch : [N_nodes]
- fid_idx : [B] or [B, 1] long; integer fidelity per graph
Notes:
- Targets should already be normalized outside the model; apply inverse transform for plots.
- Loss weighting/equal-importance and curriculum happen in the trainer, not here.
"""
def __init__(
self,
in_dim_node: int,
in_dim_edge: int,
task_names: List[str],
num_fids: int,
gnn_type: Literal["gine", "gin", "gcn"] = "gine",
gnn_emb_dim: int = 256,
gnn_layers: int = 5,
gnn_norm: Literal["batch", "layer", "none"] = "batch",
gnn_readout: Literal["mean", "sum", "max"] = "mean",
gnn_act: str = "relu",
gnn_dropout: float = 0.0,
gnn_residual: bool = True,
# Fidelity conditioning
fid_emb_dim: int = 64,
use_film: bool = True,
# Task conditioning
use_task_embed: bool = True,
task_emb_dim: int = 32,
# Heads
head_hidden: int = 512,
head_depth: int = 2,
head_act: str = "relu",
head_dropout: float = 0.0,
heteroscedastic: bool = False,
# Optional homoscedastic task uncertainty (used in loss, kept here for checkpoint parity)
use_task_uncertainty: bool = False,
# Embedding regularization (used via regularization_loss)
fid_emb_l2: float = 0.0,
task_emb_l2: float = 0.0,
):
super().__init__()
self.task_names = list(task_names)
self.num_tasks = len(task_names)
self.num_fids = int(num_fids)
self.hetero = heteroscedastic
self.fid_emb_l2 = float(fid_emb_l2)
self.task_emb_l2 = float(task_emb_l2)
self.use_film = use_film
self.use_task_embed = use_task_embed
# Optional learned homoscedastic uncertainty per task (trainer may use it)
self.use_task_uncertainty = bool(use_task_uncertainty)
if self.use_task_uncertainty:
self.task_log_sigma2 = nn.Parameter(torch.zeros(self.num_tasks))
else:
self.task_log_sigma2 = None
# Encoder
self.encoder: GNNEncoder = build_gnn_encoder(
in_dim_node=in_dim_node,
emb_dim=gnn_emb_dim,
num_layers=gnn_layers,
gnn_type=gnn_type,
in_dim_edge=in_dim_edge,
act=gnn_act,
dropout=gnn_dropout,
residual=gnn_residual,
norm=gnn_norm,
readout=gnn_readout,
)
# Fidelity embedding + FiLM
self.fid_embed = nn.Embedding(self.num_fids, fid_emb_dim) if fid_emb_dim > 0 else None
self.film = FiLM(gnn_emb_dim, fid_emb_dim) if (use_film and fid_emb_dim > 0) else None
# --- Compute the true feature dim sent to heads ---
# If FiLM is ON: g stays [B, gnn_emb_dim]
# If FiLM is OFF but fid_embed exists: we CONCAT c → g becomes [B, gnn_emb_dim + fid_emb_dim]
self.gnn_out_dim = gnn_emb_dim + (fid_emb_dim if (self.fid_embed is not None and self.film is None) else 0)
# Task embeddings
self.task_embed = nn.Embedding(self.num_tasks, task_emb_dim) if (use_task_embed and task_emb_dim > 0) else None
# Per-task heads
head_in_dim = self.gnn_out_dim + (task_emb_dim if self.task_embed is not None else 0)
self.heads = nn.ModuleList([
TaskHead(
in_dim=head_in_dim,
hidden_dim=head_hidden,
depth=head_depth,
act=head_act,
dropout=head_dropout,
heteroscedastic=heteroscedastic,
) for _ in range(self.num_tasks)
])
def reset_parameters(self):
if self.fid_embed is not None:
nn.init.normal_(self.fid_embed.weight, mean=0.0, std=0.02)
if self.task_embed is not None:
nn.init.normal_(self.task_embed.weight, mean=0.0, std=0.02)
# Encoder/heads rely on their internal initializations.
def forward(self, data: Batch) -> dict:
"""
Returns:
{
"pred": [B, T] means,
"logvar": [B, T] optional if heteroscedastic,
"h": [B, D] graph embedding after FiLM (useful for diagnostics).
}
"""
x, edge_index = data.x, data.edge_index
edge_attr = getattr(data, "edge_attr", None)
batch = data.batch
if edge_attr is None and hasattr(self.encoder, "gnn_type") and self.encoder.gnn_type == "gine":
raise ValueError("GINE encoder requires edge_attr, but Batch.edge_attr is None.")
# Graph embedding
g = self.encoder(x, edge_index, edge_attr, batch) # [B, D]
# Fidelity conditioning
fid_idx = data.fid_idx.view(-1).long() # [B]
if self.fid_embed is not None:
c = self.fid_embed(fid_idx) # [B, C]
if self.film is not None:
g = self.film(g, c) # [B, D]
else:
g = torch.cat([g, c], dim=-1)
# Per-task heads
preds: List[torch.Tensor] = []
logvars: Optional[List[torch.Tensor]] = [] if self.hetero else None
for t_idx, head in enumerate(self.heads):
if self.task_embed is not None:
tvec = self.task_embed.weight[t_idx].unsqueeze(0).expand(g.size(0), -1)
z = torch.cat([g, tvec], dim=-1)
else:
z = g
out = head(z) # [B, 1] or [B, 2]
if self.hetero:
mu = out[..., 0:1]
lv = out[..., 1:2]
preds.append(mu)
logvars.append(lv) # type: ignore[arg-type]
else:
preds.append(out)
pred = torch.cat(preds, dim=-1) # [B, T]
result = {"pred": pred, "h": g}
if self.hetero and logvars is not None:
result["logvar"] = torch.cat(logvars, dim=-1) # [B, T]
return result
def regularization_loss(self) -> torch.Tensor:
"""
Optional small L2 on embeddings to keep them bounded.
"""
device = next(self.parameters()).device
reg = torch.zeros([], device=device)
if self.fid_embed is not None and self.fid_emb_l2 > 0:
reg = reg + self.fid_emb_l2 * (self.fid_embed.weight.pow(2).mean())
if self.task_embed is not None and self.task_emb_l2 > 0:
reg = reg + self.task_emb_l2 * (self.task_embed.weight.pow(2).mean())
return reg
def build_model(
*,
in_dim_node: int,
in_dim_edge: int,
task_names: List[str],
num_fids: int,
gnn_type: Literal["gine", "gin", "gcn"] = "gine",
gnn_emb_dim: int = 256,
gnn_layers: int = 5,
gnn_norm: Literal["batch", "layer", "none"] = "batch",
gnn_readout: Literal["mean", "sum", "max"] = "mean",
gnn_act: str = "relu",
gnn_dropout: float = 0.0,
gnn_residual: bool = True,
fid_emb_dim: int = 64,
use_film: bool = True,
use_task_embed: bool = True,
task_emb_dim: int = 32,
head_hidden: int = 512,
use_task_uncertainty: bool = False,
head_depth: int = 2,
head_act: str = "relu",
head_dropout: float = 0.0,
heteroscedastic: bool = False,
fid_emb_l2: float = 0.0,
task_emb_l2: float = 0.0,
) -> MultiTaskMultiFidelityModel:
"""
Factory to construct the multi-task, multi-fidelity model with a consistent API.
"""
return MultiTaskMultiFidelityModel(
in_dim_node=in_dim_node,
in_dim_edge=in_dim_edge,
task_names=task_names,
num_fids=num_fids,
gnn_type=gnn_type,
gnn_emb_dim=gnn_emb_dim,
gnn_layers=gnn_layers,
gnn_norm=gnn_norm,
gnn_readout=gnn_readout,
gnn_act=gnn_act,
gnn_dropout=gnn_dropout,
gnn_residual=gnn_residual,
fid_emb_dim=fid_emb_dim,
use_film=use_film,
use_task_embed=use_task_embed,
task_emb_dim=task_emb_dim,
head_hidden=head_hidden,
head_depth=head_depth,
head_act=head_act,
head_dropout=head_dropout,
heteroscedastic=heteroscedastic,
fid_emb_l2=fid_emb_l2,
task_emb_l2=task_emb_l2,
use_task_uncertainty=use_task_uncertainty,
)
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