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from torch import nn
ACTIVATIONS = {
"relu": nn.ReLU,
"silu": nn.SiLU,
}
def FCBlock(in_dim, hidden_dim, out_dim, layers, dropout, activation="relu"):
activation = ACTIVATIONS[activation]
assert layers >= 2
sequential = [nn.Linear(in_dim, hidden_dim), activation(), nn.Dropout(dropout)]
for i in range(layers - 2):
sequential += [nn.Linear(hidden_dim, hidden_dim), activation(), nn.Dropout(dropout)]
sequential += [nn.Linear(hidden_dim, out_dim)]
return nn.Sequential(*sequential)
class GaussianSmearing(torch.nn.Module):
# used to embed the edge distances
def __init__(self, start=0.0, stop=5.0, num_gaussians=50):
super().__init__()
offset = torch.linspace(start, stop, num_gaussians)
self.coeff = -0.5 / (offset[1] - offset[0]).item() ** 2
self.register_buffer("offset", offset)
def forward(self, dist):
dist = dist.view(-1, 1) - self.offset.view(1, -1)
return torch.exp(self.coeff * torch.pow(dist, 2))
class AtomEncoder(torch.nn.Module):
def __init__(self, emb_dim, feature_dims, sigma_embed_dim, lm_embedding_dim=0):
"""
Parameters
----------
emb_dim
feature_dims
first element of feature_dims tuple is a list with the length of each categorical feature,
and the second is the number of scalar features
sigma_embed_dim
lm_embedding_dim
"""
super(AtomEncoder, self).__init__()
self.atom_embedding_list = torch.nn.ModuleList()
self.num_categorical_features = len(feature_dims[0])
self.additional_features_dim = feature_dims[1] + sigma_embed_dim + lm_embedding_dim
for i, dim in enumerate(feature_dims[0]):
emb = torch.nn.Embedding(dim, emb_dim)
torch.nn.init.xavier_uniform_(emb.weight.data)
self.atom_embedding_list.append(emb)
if self.additional_features_dim > 0:
self.additional_features_embedder = torch.nn.Linear(
self.additional_features_dim + emb_dim, emb_dim
)
def forward(self, x):
x_embedding = 0
assert x.shape[1] == self.num_categorical_features + self.additional_features_dim
for i in range(self.num_categorical_features):
x_embedding += self.atom_embedding_list[i](x[:, i].long())
if self.additional_features_dim > 0:
x_embedding = self.additional_features_embedder(
torch.cat([x_embedding, x[:, self.num_categorical_features :]], axis=1)
)
return x_embedding
class OldAtomEncoder(torch.nn.Module):
def __init__(self, emb_dim, feature_dims, sigma_embed_dim, lm_embedding_type=None):
"""
Parameters
----------
emb_dim
feature_dims
first element of feature_dims tuple is a list with the length of each categorical feature,
and the second is the number of scalar features
sigma_embed_dim
lm_embedding_type
"""
super(OldAtomEncoder, self).__init__()
self.atom_embedding_list = torch.nn.ModuleList()
self.num_categorical_features = len(feature_dims[0])
self.num_scalar_features = feature_dims[1] + sigma_embed_dim
self.lm_embedding_type = lm_embedding_type
for i, dim in enumerate(feature_dims[0]):
emb = torch.nn.Embedding(dim, emb_dim)
torch.nn.init.xavier_uniform_(emb.weight.data)
self.atom_embedding_list.append(emb)
if self.num_scalar_features > 0:
self.linear = torch.nn.Linear(self.num_scalar_features, emb_dim)
if self.lm_embedding_type is not None:
if self.lm_embedding_type == "esm":
self.lm_embedding_dim = 1280
else:
raise ValueError(
"LM Embedding type was not correctly determined. LM embedding type: ",
self.lm_embedding_type,
)
self.lm_embedding_layer = torch.nn.Linear(self.lm_embedding_dim + emb_dim, emb_dim)
def forward(self, x):
x_embedding = 0
if self.lm_embedding_type is not None:
assert (
x.shape[1]
== self.num_categorical_features + self.num_scalar_features + self.lm_embedding_dim
)
else:
assert x.shape[1] == self.num_categorical_features + self.num_scalar_features
for i in range(self.num_categorical_features):
x_embedding += self.atom_embedding_list[i](x[:, i].long())
if self.num_scalar_features > 0:
x_embedding += self.linear(
x[:, self.num_categorical_features : self.num_categorical_features + self.num_scalar_features]
)
if self.lm_embedding_type is not None:
x_embedding = self.lm_embedding_layer(
torch.cat([x_embedding, x[:, -self.lm_embedding_dim :]], axis=1)
)
return x_embedding
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