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import torch.nn as nn
import torch.nn.functional as F
import math
from torch_scatter import scatter
from torch_scatter.composite import scatter_softmax
from torch_geometric.utils import to_dense_adj, dense_to_sparse
from einops import rearrange, repeat
from diffcsp.common.data_utils import lattice_params_to_matrix_torch, get_pbc_distances, radius_graph_pbc, frac_to_cart_coords, repeat_blocks
MAX_ATOMIC_NUM=100
class SinusoidsEmbedding(nn.Module):
def __init__(self, n_frequencies = 10, n_space = 3):
super().__init__()
self.n_frequencies = n_frequencies
self.n_space = n_space
self.frequencies = 2 * math.pi * torch.arange(self.n_frequencies)
self.dim = self.n_frequencies * 2 * self.n_space
def forward(self, x):
emb = x.unsqueeze(-1) * self.frequencies[None, None, :].to(x.device)
emb = emb.reshape(-1, self.n_frequencies * self.n_space)
emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
return emb.detach()
class CSPLayer(nn.Module):
""" Message passing layer for cspnet."""
def __init__(
self,
hidden_dim=128,
act_fn=nn.SiLU(),
dis_emb=None,
ln=False,
ip=True
):
super(CSPLayer, self).__init__()
self.dis_dim = 3
self.dis_emb = dis_emb
self.ip = True
if dis_emb is not None:
self.dis_dim = dis_emb.dim
self.edge_mlp = nn.Sequential(
nn.Linear(hidden_dim * 2 + 9 + self.dis_dim, hidden_dim),
act_fn,
nn.Linear(hidden_dim, hidden_dim),
act_fn)
self.node_mlp = nn.Sequential(
nn.Linear(hidden_dim * 2, hidden_dim),
act_fn,
nn.Linear(hidden_dim, hidden_dim),
act_fn)
self.ln = ln
if self.ln:
self.layer_norm = nn.LayerNorm(hidden_dim)
def edge_model(self, node_features, frac_coords, lattices, edge_index, edge2graph, frac_diff = None):
hi, hj = node_features[edge_index[0]], node_features[edge_index[1]]
if frac_diff is None:
xi, xj = frac_coords[edge_index[0]], frac_coords[edge_index[1]]
frac_diff = (xj - xi) % 1.
if self.dis_emb is not None:
frac_diff = self.dis_emb(frac_diff)
if self.ip:
lattice_ips = lattices @ lattices.transpose(-1,-2)
else:
lattice_ips = lattices
lattice_ips_flatten = lattice_ips.view(-1, 9)
lattice_ips_flatten_edges = lattice_ips_flatten[edge2graph]
edges_input = torch.cat([hi, hj, lattice_ips_flatten_edges, frac_diff], dim=1)
edge_features = self.edge_mlp(edges_input)
return edge_features
def node_model(self, node_features, edge_features, edge_index):
agg = scatter(edge_features, edge_index[0], dim = 0, reduce='mean', dim_size=node_features.shape[0])
agg = torch.cat([node_features, agg], dim = 1)
out = self.node_mlp(agg)
return out
def forward(self, node_features, frac_coords, lattices, edge_index, edge2graph, frac_diff = None):
node_input = node_features
if self.ln:
node_features = self.layer_norm(node_input)
edge_features = self.edge_model(node_features, frac_coords, lattices, edge_index, edge2graph, frac_diff)
node_output = self.node_model(node_features, edge_features, edge_index)
return node_input + node_output
class CSPNet(nn.Module):
def __init__(
self,
hidden_dim = 128,
latent_dim = 256,
num_layers = 4,
max_atoms = 100,
act_fn = 'silu',
dis_emb = 'sin',
num_freqs = 10,
edge_style = 'fc',
cutoff = 6.0,
max_neighbors = 20,
ln = False,
ip = True,
smooth = False,
pred_type = False
):
super(CSPNet, self).__init__()
self.ip = ip
self.smooth = smooth
if self.smooth:
self.node_embedding = nn.Linear(max_atoms, hidden_dim)
else:
self.node_embedding = nn.Embedding(max_atoms, hidden_dim)
self.atom_latent_emb = nn.Linear(hidden_dim + latent_dim, hidden_dim)
if act_fn == 'silu':
self.act_fn = nn.SiLU()
if dis_emb == 'sin':
self.dis_emb = SinusoidsEmbedding(n_frequencies = num_freqs)
elif dis_emb == 'none':
self.dis_emb = None
for i in range(0, num_layers):
self.add_module(
"csp_layer_%d" % i, CSPLayer(hidden_dim, self.act_fn, self.dis_emb, ln=ln, ip=ip)
)
self.num_layers = num_layers
self.coord_out = nn.Linear(hidden_dim, 3, bias = False)
self.lattice_out = nn.Linear(hidden_dim, 9, bias = False)
self.cutoff = cutoff
self.max_neighbors = max_neighbors
self.pred_type = pred_type
self.ln = ln
self.edge_style = edge_style
if self.ln:
self.final_layer_norm = nn.LayerNorm(hidden_dim)
if self.pred_type:
self.type_out = nn.Linear(hidden_dim, MAX_ATOMIC_NUM)
def select_symmetric_edges(self, tensor, mask, reorder_idx, inverse_neg):
# Mask out counter-edges
tensor_directed = tensor[mask]
# Concatenate counter-edges after normal edges
sign = 1 - 2 * inverse_neg
tensor_cat = torch.cat([tensor_directed, sign * tensor_directed])
# Reorder everything so the edges of every image are consecutive
tensor_ordered = tensor_cat[reorder_idx]
return tensor_ordered
def reorder_symmetric_edges(
self, edge_index, cell_offsets, neighbors, edge_vector
):
"""
Reorder edges to make finding counter-directional edges easier.
Some edges are only present in one direction in the data,
since every atom has a maximum number of neighbors. Since we only use i->j
edges here, we lose some j->i edges and add others by
making it symmetric.
We could fix this by merging edge_index with its counter-edges,
including the cell_offsets, and then running torch.unique.
But this does not seem worth it.
"""
# Generate mask
mask_sep_atoms = edge_index[0] < edge_index[1]
# Distinguish edges between the same (periodic) atom by ordering the cells
cell_earlier = (
(cell_offsets[:, 0] < 0)
| ((cell_offsets[:, 0] == 0) & (cell_offsets[:, 1] < 0))
| (
(cell_offsets[:, 0] == 0)
& (cell_offsets[:, 1] == 0)
& (cell_offsets[:, 2] < 0)
)
)
mask_same_atoms = edge_index[0] == edge_index[1]
mask_same_atoms &= cell_earlier
mask = mask_sep_atoms | mask_same_atoms
# Mask out counter-edges
edge_index_new = edge_index[mask[None, :].expand(2, -1)].view(2, -1)
# Concatenate counter-edges after normal edges
edge_index_cat = torch.cat(
[
edge_index_new,
torch.stack([edge_index_new[1], edge_index_new[0]], dim=0),
],
dim=1,
)
# Count remaining edges per image
batch_edge = torch.repeat_interleave(
torch.arange(neighbors.size(0), device=edge_index.device),
neighbors,
)
batch_edge = batch_edge[mask]
neighbors_new = 2 * torch.bincount(
batch_edge, minlength=neighbors.size(0)
)
# Create indexing array
edge_reorder_idx = repeat_blocks(
neighbors_new // 2,
repeats=2,
continuous_indexing=True,
repeat_inc=edge_index_new.size(1),
)
# Reorder everything so the edges of every image are consecutive
edge_index_new = edge_index_cat[:, edge_reorder_idx]
cell_offsets_new = self.select_symmetric_edges(
cell_offsets, mask, edge_reorder_idx, True
)
edge_vector_new = self.select_symmetric_edges(
edge_vector, mask, edge_reorder_idx, True
)
return (
edge_index_new,
cell_offsets_new,
neighbors_new,
edge_vector_new,
)
def gen_edges(self, num_atoms, frac_coords, lattices, node2graph):
if self.edge_style == 'fc':
lis = [torch.ones(n,n, device=num_atoms.device) for n in num_atoms]
fc_graph = torch.block_diag(*lis)
fc_edges, _ = dense_to_sparse(fc_graph)
return fc_edges, (frac_coords[fc_edges[1]] - frac_coords[fc_edges[0]]) % 1.
elif self.edge_style == 'knn':
lattice_nodes = lattices[node2graph]
cart_coords = torch.einsum('bi,bij->bj', frac_coords, lattice_nodes)
edge_index, to_jimages, num_bonds = radius_graph_pbc(
cart_coords, None, None, num_atoms, self.cutoff, self.max_neighbors,
device=num_atoms.device, lattices=lattices)
j_index, i_index = edge_index
distance_vectors = frac_coords[j_index] - frac_coords[i_index]
distance_vectors += to_jimages.float()
edge_index_new, _, _, edge_vector_new = self.reorder_symmetric_edges(edge_index, to_jimages, num_bonds, distance_vectors)
return edge_index_new, -edge_vector_new
def forward(self, t, atom_types, frac_coords, lattices, num_atoms, node2graph):
edges, frac_diff = self.gen_edges(num_atoms, frac_coords, lattices, node2graph)
edge2graph = node2graph[edges[0]]
if self.smooth:
node_features = self.node_embedding(atom_types)
else:
node_features = self.node_embedding(atom_types - 1)
t_per_atom = t.repeat_interleave(num_atoms, dim=0)
node_features = torch.cat([node_features, t_per_atom], dim=1)
node_features = self.atom_latent_emb(node_features)
for i in range(0, self.num_layers):
node_features = self._modules["csp_layer_%d" % i](node_features, frac_coords, lattices, edges, edge2graph, frac_diff = frac_diff)
if self.ln:
node_features = self.final_layer_norm(node_features)
coord_out = self.coord_out(node_features)
graph_features = scatter(node_features, node2graph, dim = 0, reduce = 'mean')
lattice_out = self.lattice_out(graph_features)
lattice_out = lattice_out.view(-1, 3, 3)
if self.ip:
lattice_out = torch.einsum('bij,bjk->bik', lattice_out, lattices)
if self.pred_type:
type_out = self.type_out(node_features)
return lattice_out, coord_out, type_out
return lattice_out, coord_out
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