add module and first model
This commit is contained in:
Filippo Olivo
@@ -4,6 +4,7 @@ from lightning import LightningDataModule
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from datasets import load_dataset
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from torch_geometric.data import Data
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from torch_geometric.loader import DataLoader
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from torch_geometric.utils import to_undirected
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class GraphDataModule(LightningDataModule):
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@@ -34,7 +35,7 @@ class GraphDataModule(LightningDataModule):
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self.split_name
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]
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edge_index = torch.tensor(
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self.geometry["edge_index"][0], dtype=torch.int32
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self.geometry["edge_index"][0], dtype=torch.int64
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)
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pos = torch.tensor(self.geometry["points"][0], dtype=torch.float32)[
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:, :2
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@@ -51,23 +52,29 @@ class GraphDataModule(LightningDataModule):
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]
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def _build_dataset(
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self, conductivity, boundary_vales, temperature, edge_index, pos
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):
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input_ = torch.stack([conductivity, boundary_vales], dim=1)
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self,
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conductivity: torch.Tensor,
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boundary_vales: torch.Tensor,
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temperature: torch.Tensor,
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edge_index: torch.Tensor,
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pos: torch.Tensor,
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) -> Data:
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edge_index = to_undirected(edge_index, num_nodes=pos.size(0))
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edge_attr = pos[edge_index[0]] - pos[edge_index[1]]
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edge_attr = torch.cat(
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[edge_attr, torch.norm(edge_attr, dim=1).unsqueeze(-1)], dim=1
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)
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return Data(
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x=input_,
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x=boundary_vales.unsqueeze(-1),
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c=conductivity.unsqueeze(-1),
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edge_index=edge_index,
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pos=pos,
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edge_attr=edge_attr,
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y=temperature,
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y=temperature.unsqueeze(-1),
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)
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def setup(self, stage=None):
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def setup(self, stage: str = None):
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n = len(self.data)
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train_end = int(n * self.train_size)
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val_end = train_end + int(n * self.val_size)
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@@ -78,13 +85,13 @@ class GraphDataModule(LightningDataModule):
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if stage == "test" or stage is None:
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self.test_data = self.data[val_end:]
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def train_dataloader(self):
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def train_dataloader(self) -> DataLoader:
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return DataLoader(
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self.train_data, batch_size=self.batch_size, shuffle=True
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)
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def val_dataloader(self):
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def val_dataloader(self) -> DataLoader:
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return DataLoader(self.val_data, batch_size=self.batch_size)
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def test_dataloader(self):
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def test_dataloader(self) -> DataLoader:
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return DataLoader(self.test_data, batch_size=self.batch_size)
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108
ThermalSolver/model/local_gno.py
Normal file
108
ThermalSolver/model/local_gno.py
Normal file
@@ -0,0 +1,108 @@
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import torch
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from torch import nn
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from torch_geometric.nn import MessagePassing
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# ---- FiLM that starts as identity and normalizes the target ----
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class FiLM(nn.Module):
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def __init__(self, c_ch, h_ch):
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super().__init__()
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self.net = nn.Sequential(
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nn.Linear(c_ch, 2*h_ch),
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nn.SiLU(),
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nn.Linear(2*h_ch, 2*h_ch)
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)
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# init to identity: gamma≈0 (so 1+gamma=1), beta=0
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nn.init.zeros_(self.net[-1].weight)
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nn.init.zeros_(self.net[-1].bias)
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self.norm = nn.LayerNorm(h_ch)
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def forward(self, h, c):
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gb = self.net(c)
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gamma, beta = gb.chunk(2, dim=-1)
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return (1 + gamma) * self.norm(h) + beta
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class ConditionalGNOBlock(MessagePassing):
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"""
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Message passing with FiLM applied to the MESSAGE m_ij,
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using edge context c_ij = (c_i + c_j)/2.
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"""
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def __init__(self, hidden_ch, edge_ch=0, aggr="mean"):
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super().__init__(aggr=aggr, node_dim=0)
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self.pre_norm = nn.LayerNorm(hidden_ch)
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# raw message builder
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self.msg = nn.Sequential(
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nn.Linear(2*hidden_ch + edge_ch, 2*hidden_ch),
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nn.SiLU(),
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nn.Linear(2*hidden_ch, hidden_ch)
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)
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# FiLM over the message (per-edge)
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self.film_msg = FiLM(c_ch=hidden_ch, h_ch=hidden_ch)
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# node update with residual
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self.update_mlp = nn.Sequential(
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nn.Linear(2*hidden_ch, hidden_ch),
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nn.SiLU(),
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nn.Linear(hidden_ch, hidden_ch)
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)
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def forward(self, x, c, edge_index, edge_attr=None):
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# pre-norm helps stability
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x_in = x
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x = self.pre_norm(x)
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m = self.propagate(edge_index, x=x, c=c, edge_attr=edge_attr)
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out = self.update_mlp(torch.cat([x_in, m], dim=-1))
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return x_in + out # residual
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def message(self, x_i, x_j, c_i, c_j, edge_attr):
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if edge_attr is not None:
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m_in = torch.cat([x_i, x_j, edge_attr], dim=-1)
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else:
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m_in = torch.cat([x_i, x_j], dim=-1)
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m_raw = self.msg(m_in)
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# edge conditioning: simple mean
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c_ctx = 0.5 * (c_i + c_j)
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m = self.film_msg(m_raw, c_ctx)
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return m
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class GatingGNO(nn.Module):
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"""
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In:
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x : [N, Cx] (e.g., u or features to predict from)
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c : [N, Cf] (conditioning field, e.g., conductivity)
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Out:
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y : [N, out_ch]
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"""
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def __init__(self, x_ch_node, f_ch_node, hidden, layers, edge_ch=0, out_ch=1):
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super().__init__()
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self.encoder_x = nn.Sequential(
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nn.Linear(x_ch_node, hidden // 2),
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nn.SiLU(),
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nn.Linear(hidden // 2, hidden),
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)
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self.encoder_c = nn.Sequential(
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nn.Linear(f_ch_node, hidden // 2),
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nn.SiLU(),
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nn.Linear(hidden // 2, hidden),
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)
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self.blocks = nn.ModuleList(
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[ConditionalGNOBlock(hidden_ch=hidden, edge_ch=edge_ch) for _ in range(layers)]
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)
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self.dec = nn.Sequential(
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nn.LayerNorm(hidden),
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nn.SiLU(),
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nn.Linear(hidden, out_ch)
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)
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def forward(self, x, c, edge_index, edge_attr=None):
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x = self.encoder_x(x) # [N,H]
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c = self.encoder_c(c) # [N,H]
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for blk in self.blocks:
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x = blk(x, c, edge_index, edge_attr=edge_attr)
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return self.dec(x)
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74
ThermalSolver/module.py
Normal file
74
ThermalSolver/module.py
Normal file
@@ -0,0 +1,74 @@
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import torch
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from lightning import LightningModule
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from torch_geometric.data import Batch
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class GraphSolver(LightningModule):
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def __init__(self, model: torch.nn.Module, loss: torch.nn.Module = None, unrolling_steps: int = 10):
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super().__init__()
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self.model = model
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self.loss = loss if loss is not None else torch.nn.MSELoss()
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self.unrolling_steps = unrolling_steps
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def forward(
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self,
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x: torch.Tensor,
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c: torch.Tensor,
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edge_index: torch.Tensor,
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edge_attr: torch.Tensor,
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):
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return self.model(x, c, edge_index, edge_attr)
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def _compute_loss_train(self, x, x_prev, y):
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return self.loss(x, y) + self.loss(x, x_prev)
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def _compute_loss(self, x, y):
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return self.loss(x, y)
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def _preprocess_batch(self, batch: Batch):
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return batch.x, batch.y, batch.c, batch.edge_index, batch.edge_attr
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def _log_loss(self, loss, batch, stage: str):
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self.log(
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f"{stage}_loss",
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loss,
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on_step=False,
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on_epoch=True,
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prog_bar=True,
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batch_size=int(batch.num_graphs),
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)
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return loss
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def training_step(self, batch: Batch, _):
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x, y, c, edge_index, edge_attr = self._preprocess_batch(batch)
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for _ in range(self.unrolling_steps):
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x_prev = x.detach()
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x = self(x_prev, c, edge_index=edge_index, edge_attr=edge_attr)
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loss = self.loss(x, y)
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self._log_loss(loss, batch, "train")
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return loss
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def validation_step(self, batch: Batch, _):
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x, y, c, edge_index, edge_attr = self._preprocess_batch(batch)
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for _ in range(self.unrolling_steps):
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x_prev = x.detach()
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x = self(x_prev, c, edge_index=edge_index, edge_attr=edge_attr)
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loss = self.loss(x, x_prev)
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if loss < 1e-5:
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break
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loss = self._compute_loss(x, y)
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self._log_loss(loss, batch, "val")
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return loss
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def test_step(self, batch: Batch, _):
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x, y, c, edge_index, edge_attr = self._preprocess_batch(batch)
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for _ in range(self.unrolling_steps):
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x_prev = x.detach()
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x = self(x_prev, c, edge_index=edge_index, edge_attr=edge_attr)
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loss = self._compute_loss(x, y)
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self._log_loss(loss, batch, "test")
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return loss
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def configure_optimizers(self):
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optimizer = torch.optim.Adam(self.parameters(), lr=5e-3)
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return optimizer
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