Fix pbc conv args Final Draft

examples/LennardJones/LJ_data.py

@@ -106,7 +106,6 @@ def __init__(self, dirpath, config, dist=False, sampling=None):
             self.dataset.append(self.transform_input_to_data_object_base(filepath))
 
     def transform_input_to_data_object_base(self, filepath):
-
         # Using readline()
         file = open(filepath, "r")
 
@@ -174,6 +173,11 @@ def transform_input_to_data_object_base(self, filepath):
             .unsqueeze(0)
             .to(torch.float32),
             energy=torch.tensor(total_energy).unsqueeze(0).to(torch.float32),
+            pbc=[
+                True,
+                True,
+                True,
+            ],  # LJ example always has periodic boundary conditions
         )
 
         # Create pbc edges and lengths
@@ -205,7 +209,6 @@ def deterministic_graph_data(
     unit_cell_z_range: list = [3, 4],
     relative_maximum_atomic_displacement: float = 1e-1,
 ):
-
     comm = MPI.COMM_WORLD
     comm_size = comm.Get_size()
     comm_rank = comm.Get_rank()
@@ -330,6 +333,7 @@ def create_configuration(
     data.supercell_size = torch.diag(
         torch.tensor([supercell_size_x, supercell_size_y, supercell_size_z])
     )
+    data.pbc = [True, True, True]
 
     create_graph_connectivity_pbc = get_radius_graph_pbc(
         radius_cutoff, max_num_neighbors
@@ -379,27 +383,23 @@ class AtomicStructureHandler:
     def __init__(
         self, list_atom_types, bravais_lattice_constants, radius_cutoff, formula
     ):
-
         self.bravais_lattice_constants = bravais_lattice_constants
         self.radius_cutoff = radius_cutoff
         self.formula = formula
 
     def compute(self, data):
-
         assert data.pos.shape[0] == data.x.shape[0]
 
         interatomic_potential = torch.zeros([data.pos.shape[0], 1])
         interatomic_forces = torch.zeros([data.pos.shape[0], 3])
 
         for node_id in range(data.pos.shape[0]):
-
             neighbor_list_indices = torch.where(data.edge_index[0, :] == node_id)[
                 0
             ].tolist()
             neighbor_list = data.edge_index[1, neighbor_list_indices]
 
             for neighbor_id, edge_id in zip(neighbor_list, neighbor_list_indices):
-
                 neighbor_pos = data.pos[neighbor_id, :]
                 distance_vector = data.pos[neighbor_id, :] - data.pos[node_id, :]
 

hydragnn/models/DIMEStack.py

@@ -26,6 +26,7 @@
 from torch_geometric.utils import scatter
 
 from .Base import Base
+from hydragnn.utils.model.operations import get_edge_vectors_and_lengths
 
 
 class DIMEStack(Base):
@@ -144,23 +145,34 @@ def get_conv(self, input_dim, output_dim):
             )
 
     def _embedding(self, data):
+        super()._embedding(data)
+
         assert (
             data.pos is not None
         ), "DimeNet requires node positions (data.pos) to be set."
+
+        # Calculate triplet indices
         i, j, idx_i, idx_j, idx_k, idx_kj, idx_ji = triplets(
             data.edge_index, num_nodes=data.x.size(0)
         )
-        dist = (data.pos[i] - data.pos[j]).pow(2).sum(dim=-1).sqrt()
 
-        # Calculate angles.
-        pos_i = data.pos[idx_i]
-        pos_ji, pos_ki = data.pos[idx_j] - pos_i, data.pos[idx_k] - pos_i
+        # Calculate edge_vec and edge_dist
+        edge_vec, edge_dist = get_edge_vectors_and_lengths(
+            data.pos, data.edge_index, data.edge_shifts
+        )
+
+        # Calculate angles
+        pos_ji = edge_vec[idx_ji]
+        pos_kj = edge_vec[idx_kj]
+        pos_ki = (
+            pos_kj + pos_ji
+        )  # It's important to calculate the vectors separately and then add in case of periodic boundary conditions
         a = (pos_ji * pos_ki).sum(dim=-1)
         b = torch.cross(pos_ji, pos_ki).norm(dim=-1)
         angle = torch.atan2(b, a)
 
-        rbf = self.rbf(dist)
-        sbf = self.sbf(dist, angle, idx_kj)
+        rbf = self.rbf(edge_dist.squeeze())
+        sbf = self.sbf(edge_dist.squeeze(), angle, idx_kj)
 
         conv_args = {
             "rbf": rbf,

hydragnn/models/EGCLStack.py

@@ -16,6 +16,7 @@
 from .Base import Base
 
 from hydragnn.utils.model import unsorted_segment_mean
+from hydragnn.utils.model.operations import get_edge_vectors_and_lengths
 
 
 class EGCLStack(Base):
@@ -89,6 +90,12 @@ def get_conv(self, input_dim, output_dim, last_layer=False):
             )
 
     def _embedding(self, data):
+        super()._embedding(data)
+
+        data.edge_shifts = torch.zeros(
+            (data.edge_index.size(1), 3), device=data.edge_index.device
+        )  # Override. pbc edge shifts are currently not supported in positional update models
+
         if self.edge_dim > 0:
             conv_args = {
                 "edge_index": data.edge_index,
@@ -229,20 +236,14 @@ def coord_model(self, coord, edge_index, coord_diff, edge_feat):
         coord = coord + agg * self.coords_weight
         return coord
 
-    def coord2radial(self, edge_index, coord):
-        row, col = edge_index
-        coord_diff = coord[row] - coord[col]
-        radial = torch.sum((coord_diff) ** 2, 1).unsqueeze(1)
-
-        if self.norm_diff:
-            norm = torch.sqrt(radial) + 1
-            coord_diff = coord_diff / (norm)
-
-        return radial, coord_diff
-
     def forward(self, x, coord, edge_index, edge_attr, node_attr=None):
         row, col = edge_index
-        radial, coord_diff = self.coord2radial(edge_index, coord)
+        edge_shifts = torch.zeros(
+            (edge_index.size(1), 3), device=edge_index.device
+        )  # pbc edge shifts are currently not supported in positional update models
+        coord_diff, radial = get_edge_vectors_and_lengths(
+            coord, edge_index, edge_shifts, normalize=self.norm_diff, eps=1.0
+        )
         # Message Passing
         edge_feat = self.edge_model(x[row], x[col], radial, edge_attr)
         if self.equivariant:

hydragnn/models/MACEStack.py

@@ -64,6 +64,7 @@
     NonLinearMultiheadDecoderBlock,
     LinearMultiheadDecoderBlock,
 )
+from hydragnn.utils.model.operations import get_edge_vectors_and_lengths
 
 # Etc
 import numpy as np

hydragnn/models/PAINNStack.py

@@ -20,6 +20,7 @@
 from torch.utils.checkpoint import checkpoint
 
 from .Base import Base
+from hydragnn.utils.model.operations import get_edge_vectors_and_lengths
 
 
 class PAINNStack(Base):
@@ -125,24 +126,25 @@ def get_conv(self, input_dim, output_dim, last_layer=False):
             )
 
     def _embedding(self, data):
+        super()._embedding(data)
+
         assert (
             data.pos is not None
-        ), "PAINNNet requires node positions (data.pos) to be set."
+        ), "PAINN requires node positions (data.pos) to be set."
 
-        # Calculate relative vectors and distances
-        i, j = data.edge_index[0], data.edge_index[1]
-        diff = data.pos[i] - data.pos[j]
-        dist = diff.pow(2).sum(dim=-1).sqrt()
-        norm_diff = diff / dist.unsqueeze(-1)
+        # Get normalized edge vectors and lengths
+        norm_edge_vec, edge_dist = get_edge_vectors_and_lengths(
+            data.pos, data.edge_index, data.edge_shifts, normalize=True
+        )
 
         # Instantiate tensor to hold equivariant traits
         v = torch.zeros(data.x.size(0), 3, data.x.size(1), device=data.x.device)
         data.v = v
 
         conv_args = {
             "edge_index": data.edge_index.t().to(torch.long),
-            "diff": norm_diff,
-            "dist": dist,
+            "diff": norm_edge_vec,
+            "dist": edge_dist,
         }
 
         return data.x, data.v, conv_args
@@ -171,9 +173,8 @@ def forward(self, node_scalar, node_vector, edge, edge_diff, edge_dist):
         filter_weight = self.filter_layer(
             sinc_expansion(edge_dist, self.edge_size, self.cutoff)
         )
-        filter_weight = filter_weight * cosine_cutoff(edge_dist, self.cutoff).unsqueeze(
-            -1
-        )
+        filter_weight = filter_weight * cosine_cutoff(edge_dist, self.cutoff)
+
         scalar_out = self.scalar_message_mlp(node_scalar)
         filter_out = filter_weight * scalar_out[edge[:, 1]]
 
@@ -185,9 +186,9 @@ def forward(self, node_scalar, node_vector, edge, edge_diff, edge_dist):
 
         # num_pairs * 3 * node_size, num_pairs * node_size
         message_vector = node_vector[edge[:, 1]] * gate_state_vector.unsqueeze(1)
-        edge_vector = gate_edge_vector.unsqueeze(1) * (
-            edge_diff / edge_dist.unsqueeze(-1)
-        ).unsqueeze(-1)
+        edge_vector = gate_edge_vector.unsqueeze(1) * (edge_diff / edge_dist).unsqueeze(
+            -1
+        )
         message_vector = message_vector + edge_vector
 
         # sum message
@@ -266,9 +267,7 @@ def sinc_expansion(edge_dist: torch.Tensor, edge_size: int, cutoff: float):
     sin(n * pi * d / d_cut) / d
     """
     n = torch.arange(edge_size, device=edge_dist.device) + 1
-    return torch.sin(
-        edge_dist.unsqueeze(-1) * n * torch.pi / cutoff
-    ) / edge_dist.unsqueeze(-1)
+    return torch.sin(edge_dist * n * torch.pi / cutoff) / edge_dist
 
 
 def cosine_cutoff(edge_dist: torch.Tensor, cutoff: float):

hydragnn/models/PNAEqStack.py

@@ -32,7 +32,9 @@
 from torch_geometric.nn.aggr.scaler import DegreeScalerAggregation
 from torch_geometric.typing import Adj, OptTensor
 
+# HydraGNN
 from .Base import Base
+from hydragnn.utils.model.operations import get_edge_vectors_and_lengths
 
 
 class PNAEqStack(Base):
@@ -156,16 +158,17 @@ def get_conv(self, input_dim, output_dim, last_layer=False):
             )
 
     def _embedding(self, data):
+        super()._embedding(data)
+
         assert (
             data.pos is not None
         ), "PNAEq requires node positions (data.pos) to be set."
 
-        # Calculate relative vectors and distances
-        i, j = data.edge_index[0], data.edge_index[1]
-        diff = data.pos[i] - data.pos[j]
-        dist = diff.pow(2).sum(dim=-1).sqrt()
-        rbf = self.rbf(dist)
-        norm_diff = diff / dist.unsqueeze(-1)
+        # Edge vector and distance features
+        norm_edge_vec, edge_dist = get_edge_vectors_and_lengths(
+            data.pos, data.edge_index, data.edge_shifts, normalize=True
+        )
+        rbf = self.rbf(edge_dist.squeeze())
 
         # Instantiate tensor to hold equivariant traits
         v = torch.zeros(data.x.size(0), 3, data.x.size(1), device=data.x.device)
@@ -174,9 +177,15 @@ def _embedding(self, data):
         conv_args = {
             "edge_index": data.edge_index.t().to(torch.long),
             "edge_rbf": rbf,
-            "edge_vec": norm_diff,
+            "edge_vec": norm_edge_vec,
         }
 
+        if self.use_edge_attr:
+            assert (
+                data.edge_attr is not None
+            ), "Data must have edge attributes if use_edge_attributes is set."
+            conv_args.update({"edge_attr": data.edge_attr})
+
         return data.x, data.v, conv_args
 
 

hydragnn/models/PNAPlusStack.py

@@ -34,6 +34,7 @@
 
 # HydraGNN
 from .Base import Base
+from hydragnn.utils.model.operations import get_edge_vectors_and_lengths
 
 
 class PNAPlusStack(Base):
@@ -98,14 +99,18 @@ def get_conv(self, input_dim, output_dim):
         )
 
     def _embedding(self, data):
+        super()._embedding(data)
+
         assert (
             data.pos is not None
         ), "PNA+ requires node positions (data.pos) to be set."
 
-        j, i = data.edge_index  # j->i
-        dist = (data.pos[i] - data.pos[j]).pow(2).sum(dim=-1).sqrt()
-        rbf = self.rbf(dist)
-        # rbf = dist.unsqueeze(-1)
+        # Radial embedding
+        _, edge_dist = get_edge_vectors_and_lengths(
+            data.pos, data.edge_index, data.edge_shifts
+        )
+        rbf = self.rbf(edge_dist.squeeze())
+
         conv_args = {"edge_index": data.edge_index.to(torch.long), "rbf": rbf}
 
         if self.use_edge_attr:

hydragnn/models/SCFStack.py

@@ -18,7 +18,6 @@
 from torch_geometric.nn import Sequential as PyGSeq
 from torch_geometric.nn import MessagePassing
 from torch_geometric.nn.models.schnet import (
-    CFConv,
     GaussianSmearing,
     RadiusInteractionGraph,
     ShiftedSoftplus,
@@ -27,6 +26,7 @@
 from .Base import Base
 
 from hydragnn.utils.model import unsorted_segment_mean
+from hydragnn.utils.model.operations import get_edge_vectors_and_lengths
 
 
 class SCFStack(Base):
@@ -135,12 +135,17 @@ def get_conv(self, input_dim, output_dim, last_layer):
             )
 
     def _embedding(self, data):
+        super()._embedding(data)
+
         if (self.use_edge_attr) and (self.equivariance):
             raise Exception(
                 "For SchNet if using edge attributes, then E(3)-equivariance cannot be ensured. Please disable equivariance or edge attributes."
             )
         elif self.use_edge_attr:
             edge_index = data.edge_index
+            data.edge_shifts = torch.zeros(
+                (data.edge_index.size(1), 3), device=data.edge_index.device
+            )  # Override. pbc edge shifts are currently not supported in positional update models
             edge_weight = data.edge_attr.norm(dim=-1)
 
             conv_args = {
@@ -218,7 +223,12 @@ def forward(
         x = self.lin1(x)
 
         if self.equivariant:
-            radial, coord_diff = self.coord2radial(edge_index, pos)
+            edge_shifts = torch.zeros(
+                (edge_index.size(1), 3), device=edge_index.device
+            )  # pbc edge shifts are currently not supported in positional update models
+            coord_diff, radial = get_edge_vectors_and_lengths(
+                pos, edge_index, edge_shifts, normalize=True, eps=1.0
+            )
             pos = self.coord_model(pos, edge_index, coord_diff, W)
 
         x = self.propagate(edge_index, x=x, W=W)
@@ -230,13 +240,3 @@ def forward(
 
     def message(self, x_j: Tensor, W: Tensor) -> Tensor:
         return x_j * W
-
-    def coord2radial(self, edge_index, coord):
-        row, col = edge_index
-        coord_diff = coord[row] - coord[col]
-        radial = torch.sum((coord_diff) ** 2, 1).unsqueeze(1)
-
-        norm = torch.sqrt(radial) + 1
-        coord_diff = coord_diff / (norm)
-
-        return radial, coord_diff