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# Copyright 2024 the Llamole team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import global_add_pool, global_max_pool
from torch_geometric.nn import MessagePassing
import json
class GraphCLIP(nn.Module):
def __init__(
self,
graph_num_layer,
graph_hidden_size,
dropout,
model_config,
):
super().__init__()
self.model_config = model_config
self.hidden_size = graph_hidden_size
self.molecule_encoder = GNNEncoder(num_layer=graph_num_layer, hidden_size=graph_hidden_size, drop_ratio=dropout)
self.molecule_projection = ProjectionHead(embedding_dim=graph_hidden_size, projection_dim=graph_hidden_size, dropout=dropout)
def forward(self, x, edge_index, edge_attr, batch):
molecule_features = self.molecule_encoder(x, edge_index, edge_attr, batch)
molecule_embeddings = self.molecule_projection(molecule_features)
molecule_embeddings = molecule_embeddings / molecule_embeddings.norm(dim=-1, keepdim=True)
return molecule_embeddings
def save_pretrained(self, output_dir):
"""
Save the molecule encoder, projection models, and model_config to the output directory.
"""
if not os.path.exists(output_dir):
os.makedirs(output_dir)
molecule_path = os.path.join(output_dir, 'model.pt')
proj_path = molecule_path.replace('model', 'model_proj')
config_path = os.path.join(output_dir, 'model_config.json')
torch.save(self.molecule_encoder.state_dict(), molecule_path)
torch.save(self.molecule_projection.state_dict(), proj_path)
# Save model_config to JSON file
with open(config_path, 'w') as f:
json.dump(self.model_config, f, indent=2)
def disable_grads(self):
"""
Disable gradients for all parameters in the model.
"""
for param in self.parameters():
param.requires_grad = False
def init_model(self, model_path, verbose=True):
molecule_path = os.path.join(model_path, 'model.pt')
proj_path = molecule_path.replace('model', 'model_proj')
if os.path.exists(molecule_path):
self.molecule_encoder.load_state_dict(torch.load(molecule_path, map_location='cpu', weights_only=False))
else:
raise FileNotFoundError(f"Molecule encoder file not found: {molecule_path}")
if os.path.exists(proj_path):
self.molecule_projection.load_state_dict(torch.load(proj_path, map_location='cpu', weights_only=False))
else:
raise FileNotFoundError(f"Molecule projection file not found: {proj_path}")
if verbose:
print('GraphCLIP Models initialized.')
print('Molecule model:\n', self.molecule_encoder)
print('Molecule projection:\n', self.molecule_projection)
class GNNEncoder(nn.Module):
def __init__(self, num_layer, hidden_size, drop_ratio):
super(GNNEncoder, self).__init__()
self.num_layer = num_layer
self.drop_ratio = drop_ratio
if self.num_layer < 2:
raise ValueError("Number of GNN layers must be greater than 1.")
self.atom_encoder = nn.Embedding(118, hidden_size)
### set the initial virtual node embedding to 0.
self.virtualnode_embedding = nn.Embedding(1, hidden_size)
nn.init.constant_(self.virtualnode_embedding.weight.data, 0)
### List of GNNs
self.convs = nn.ModuleList()
self.norms = nn.ModuleList()
self.mlp_virtualnode_list = nn.ModuleList()
for layer in range(num_layer):
self.convs.append(GINConv(hidden_size, drop_ratio))
self.norms.append(nn.LayerNorm(hidden_size, elementwise_affine=True))
if layer < num_layer - 1:
self.mlp_virtualnode_list.append(nn.Sequential(nn.Linear(hidden_size, 4*hidden_size), nn.LayerNorm(4*hidden_size), nn.GELU(), nn.Dropout(drop_ratio), \
nn.Linear(4*hidden_size, hidden_size)))
def initialize_weights(self):
# Initialize transformer layers:
def _basic_init(module):
if isinstance(module, nn.Linear):
torch.nn.init.xavier_uniform_(module.weight)
if module.bias is not None:
nn.init.constant_(module.bias, 0)
self.apply(_basic_init)
def forward(self, x, edge_index, edge_attr, batch):
### virtual node embeddings for graphs
virtualnode_embedding = self.virtualnode_embedding(torch.zeros(batch[-1].item() + 1).to(edge_index.dtype).to(edge_index.device))
h_list = [self.atom_encoder(x)]
for layer in range(self.num_layer):
### add message from virtual nodes to graph nodes
h_list[layer] = h_list[layer] + virtualnode_embedding[batch]
### Message passing among graph nodes
h = self.convs[layer](h_list[layer], edge_index, edge_attr)
h = self.norms[layer](h)
if layer < self.num_layer - 1:
h = F.gelu(h)
h = F.dropout(h, self.drop_ratio, training = self.training)
h = h + h_list[layer]
h_list.append(h)
if layer < self.num_layer - 1:
### add message from graph nodes to virtual nodes
virtual_pool = global_max_pool(h_list[layer], batch)
virtualnode_embedding = virtualnode_embedding + F.dropout(self.mlp_virtualnode_list[layer](virtual_pool), self.drop_ratio, training = self.training)
h_node = h_list[-1]
h_graph = global_add_pool(h_node, batch)
return h_graph
class GINConv(MessagePassing):
def __init__(self, hidden_size, drop_ratio):
'''
hidden_size (int)
'''
super(GINConv, self).__init__(aggr = "add")
self.mlp = nn.Sequential(nn.Linear(hidden_size, 4*hidden_size), nn.LayerNorm(4*hidden_size), nn.GELU(), nn.Dropout(drop_ratio), nn.Linear(4*hidden_size, hidden_size))
self.eps = torch.nn.Parameter(torch.Tensor([0]))
self.bond_encoder = nn.Embedding(5, hidden_size)
def forward(self, x, edge_index, edge_attr):
edge_embedding = self.bond_encoder(edge_attr)
out = self.mlp((1 + self.eps) *x + self.propagate(edge_index, x=x, edge_attr=edge_embedding))
return out
def message(self, x_j, edge_attr):
return F.gelu(x_j + edge_attr)
def update(self, aggr_out):
return aggr_out
class ProjectionHead(nn.Module):
def __init__(
self,
embedding_dim,
projection_dim,
dropout,
act_layer=nn.GELU,
hidden_features=None,
bias=True
):
super().__init__()
projection_dim = projection_dim or embedding_dim
hidden_features = hidden_features or embedding_dim
linear_layer = nn.Linear
self.fc1 = linear_layer(embedding_dim, hidden_features, bias=bias)
self.norm1 = nn.LayerNorm(hidden_features)
self.act = act_layer()
self.drop1 = nn.Dropout(dropout)
self.fc2 = linear_layer(hidden_features, projection_dim, bias=bias)
def forward(self, x):
x = self.fc1(x)
x = self.norm1(x)
x = self.act(x)
x = self.drop1(x)
x = self.fc2(x)
return x
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