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import torch
import torch.nn as nn
import torch.nn.functional as F
import pdb
class IntraGraphAttention(nn.Module):
def __init__(self, d_node, d_edge, num_heads, negative_slope=0.2):
super(IntraGraphAttention, self).__init__()
assert d_node % num_heads == 0, "d_node must be divisible by num_heads"
assert d_edge % num_heads == 0, "d_edge must be divisible by num_heads"
self.num_heads = num_heads
self.d_k = d_node // num_heads
self.d_edge_head = d_edge // num_heads
self.Wn = nn.Linear(d_node, d_node)
self.Wh = nn.Linear(self.d_k, self.d_k)
self.We = nn.Linear(d_edge, d_edge)
self.Wn_2 = nn.Linear(d_node, d_node)
self.We_2 = nn.Linear(d_edge, d_edge)
self.attn_linear = nn.Linear(self.d_k * 2 + self.d_edge_head, 1, bias=False)
self.edge_linear = nn.Linear(self.d_k * 2 + self.d_edge_head, self.d_edge_head)
self.out_proj_node = nn.Linear(d_node, d_node)
self.out_proj_edge = nn.Linear(d_edge, d_edge)
self.leaky_relu = nn.LeakyReLU(negative_slope)
def forward(self, node_representation, edge_representation):
# node_representation: (B, L, d_node)
# edge_representation: (B, L, L, d_edge)
# pdb.set_trace()
B, L, d_node = node_representation.size()
d_edge = edge_representation.size(-1)
# Multi-head projection
node_proj = self.Wn(node_representation).view(B, L, self.num_heads, self.d_k) # (B, L, num_heads, d_k)
edge_proj = self.We(edge_representation).view(B, L, L, self.num_heads, self.d_edge_head) # (B, L, L, num_heads, d_edge_head)
# Node representation update
new_node_representation = self.single_head_attention_node(node_proj, edge_proj)
concatenated_node_rep = new_node_representation.view(B, L, -1) # Shape: (B, L, num_heads * d_k)
new_node_representation = self.out_proj_node(concatenated_node_rep)
# Edge representation update
node_proj_2 = self.Wn_2(new_node_representation).view(B, L, self.num_heads, self.d_k) # (B, L, num_heads, d_k)
edge_proj_2 = self.We_2(edge_representation).view(B, L, L, self.num_heads, self.d_edge_head) # (B, L, L, num_heads, d_edge_head)
new_edge_representation = self.single_head_attention_edge(node_proj_2, edge_proj_2)
concatenated_edge_rep = new_edge_representation.view(B, L, L, -1) # Shape: (B, L, L, num_heads * d_edge_head)
new_edge_representation = self.out_proj_edge(concatenated_edge_rep)
return new_node_representation, new_edge_representation
def single_head_attention_node(self, node_representation, edge_representation):
B, L, num_heads, d_k = node_representation.size()
d_edge_head = edge_representation.size(-1)
hi = node_representation.unsqueeze(2) # shape: (B, L, 1, num_heads, d_k)
hj = node_representation.unsqueeze(1) # shape: (B, 1, L, num_heads, d_k)
hi_hj_concat = torch.cat([hi.expand(-1, -1, L, -1, -1),
hj.expand(-1, L, -1, -1, -1),
edge_representation], dim=-1) # shape: (B, L, L, num_heads, 2*d_k + d_edge_head)
attention_scores = self.attn_linear(hi_hj_concat).squeeze(-1) # shape: (B, L, L, num_heads)
# Mask the diagonal (self-attention) by setting it to a large negative value
mask = torch.eye(L).bool().unsqueeze(0).unsqueeze(-1).to(node_representation.device) # shape: (1, L, L, 1)
attention_scores.masked_fill_(mask, float('-inf'))
attention_probs = F.softmax(self.leaky_relu(attention_scores), dim=2) # shape: (B, L, L, num_heads)
# Aggregating features correctly along the L dimension
node_representation_Wh = self.Wh(node_representation) # shape: (B, L, num_heads, d_k)
node_representation_Wh = node_representation_Wh.permute(0, 2, 1, 3) # shape: (B, num_heads, L, d_k)
aggregated_features = torch.matmul(attention_probs.permute(0, 3, 1, 2), node_representation_Wh) # shape: (B, num_heads, L, d_k)
aggregated_features = aggregated_features.permute(0, 2, 1, 3) # shape: (B, L, num_heads, d_k)
new_node_representation = node_representation + self.leaky_relu(aggregated_features) # shape: (B, L, num_heads, d_k)
return new_node_representation
def single_head_attention_edge(self, node_representation, edge_representation):
# Update edge representation
B, L, num_heads, d_k = node_representation.size()
d_edge_head = edge_representation.size(-1)
hi = node_representation.unsqueeze(2) # shape: (B, L, 1, num_heads, d_k)
hj = node_representation.unsqueeze(1) # shape: (B, 1, L, num_heads, d_k)
hi_hj_concat = torch.cat([edge_representation, hi.expand(-1, -1, L, -1, -1), hj.expand(-1, L, -1, -1, -1)], dim=-1) # shape: (B, L, L, num_heads, 2*d_k + d_edge_head)
new_edge_representation = self.edge_linear(hi_hj_concat) # shape: (B, L, L, num_heads, d_edge_head)
return new_edge_representation
class DiffEmbeddingLayer(nn.Module):
def __init__(self, d_node):
super(DiffEmbeddingLayer, self).__init__()
self.W_delta = nn.Linear(d_node, d_node)
def forward(self, wt_node, mut_node):
delta_h = mut_node - wt_node # (B, L, d_node)
diff_vec = torch.relu(self.W_delta(delta_h)) # (B, L, d_node)
return diff_vec
class MIM(nn.Module):
def __init__(self, d_node, d_edge, d_diff, num_heads, negative_slope=0.2):
super(MIM, self).__init__()
assert d_node % num_heads == 0, "d_node must be divisible by num_heads"
assert d_edge % num_heads == 0, "d_edge must be divisible by num_heads"
assert d_diff % num_heads == 0, "d_diff must be divisible by num_heads"
self.num_heads = num_heads
self.d_k = d_node // num_heads
self.d_edge_head = d_edge // num_heads
self.d_diff_head = d_diff // num_heads
self.Wn = nn.Linear(d_node, d_node)
self.Wh = nn.Linear(self.d_k, self.d_k)
self.We = nn.Linear(d_edge, d_edge)
self.Wn_2 = nn.Linear(d_node, d_node)
self.We_2 = nn.Linear(d_edge, d_edge)
self.Wd = nn.Linear(d_diff, d_diff)
self.Wd_2 = nn.Linear(d_diff, d_diff)
self.attn_linear = nn.Linear(self.d_k * 2 + self.d_edge_head + 2 * self.d_diff_head, 1, bias=False)
self.edge_linear = nn.Linear(self.d_k * 2 + self.d_edge_head + 2 * self.d_diff_head, self.d_edge_head)
self.out_proj_node = nn.Linear(d_node, d_node)
self.out_proj_edge = nn.Linear(d_edge, d_edge)
self.leaky_relu = nn.LeakyReLU(negative_slope)
def forward(self, node_representation, edge_representation, diff_vec):
# node_representation: (B, L, d_node)
# edge_representation: (B, L, L, d_edge)
# diff_vec: (B, L, d_diff)
B, L, d_node = node_representation.size()
d_edge = edge_representation.size(-1)
# Multi-head projection
node_proj = self.Wn(node_representation).view(B, L, self.num_heads, self.d_k) # (B, L, num_heads, d_k)
edge_proj = self.We(edge_representation).view(B, L, L, self.num_heads, self.d_edge_head) # (B, L, L, num_heads, d_edge_head)
diff_proj = self.Wd(diff_vec).view(B, L, self.num_heads, self.d_diff_head) # (B, L, num_heads, d_diff_head)
# Node representation update
new_node_representation = self.single_head_attention_node(node_proj, edge_proj, diff_proj)
concatenated_node_rep = new_node_representation.view(B, L, -1) # Shape: (B, L, num_heads * d_k)
new_node_representation = self.out_proj_node(concatenated_node_rep)
# Edge representation update
node_proj_2 = self.Wn_2(new_node_representation).view(B, L, self.num_heads, self.d_k) # (B, L, num_heads, d_k)
edge_proj_2 = self.We_2(edge_representation).view(B, L, L, self.num_heads, self.d_edge_head) # (B, L, L, num_heads, d_edge_head)
diff_proj_2 = self.Wd_2(diff_vec).view(B, L, self.num_heads, self.d_diff_head) # (B, L, num_heads, d_diff_head)
new_edge_representation = self.single_head_attention_edge(node_proj_2, edge_proj_2, diff_proj_2)
concatenated_edge_rep = new_edge_representation.view(B, L, L, -1) # Shape: (B, L, L, num_heads * d_edge_head)
new_edge_representation = self.out_proj_edge(concatenated_edge_rep)
return new_node_representation, new_edge_representation
def single_head_attention_node(self, node_representation, edge_representation, diff_vec):
# Update node representation
B, L, num_heads, d_k = node_representation.size()
d_edge_head = edge_representation.size(-1)
d_diff_head = diff_vec.size(-1)
hi = node_representation.unsqueeze(2) # shape: (B, L, 1, num_heads, d_k)
hj = node_representation.unsqueeze(1) # shape: (B, 1, L, num_heads, d_k)
diff_i = diff_vec.unsqueeze(2) # shape: (B, L, 1, num_heads, d_diff_head)
diff_j = diff_vec.unsqueeze(1) # shape: (B, 1, L, num_heads, d_diff_head)
hi_hj_concat = torch.cat([
hi.expand(-1, -1, L, -1, -1),
hj.expand(-1, L, -1, -1, -1),
edge_representation,
diff_i.expand(-1, -1, L, -1, -1),
diff_j.expand(-1, L, -1, -1, -1)
], dim=-1) # shape: (B, L, L, num_heads, 2*d_k + d_edge_head + 2*d_diff_head)
attention_scores = self.attn_linear(hi_hj_concat).squeeze(-1) # shape: (B, L, L, num_heads)
# Mask the diagonal (self-attention) by setting it to a large negative value
mask = torch.eye(L).bool().unsqueeze(0).unsqueeze(-1).to(node_representation.device) # shape: (1, L, L, 1)
attention_scores.masked_fill_(mask, float('-inf'))
attention_probs = F.softmax(self.leaky_relu(attention_scores), dim=2) # shape: (B, L, L, num_heads)
# Aggregating features correctly along the L dimension
node_representation_Wh = self.Wh(node_representation) # shape: (B, L, num_heads, d_k)
node_representation_Wh = node_representation_Wh.permute(0, 2, 1, 3) # shape: (B, num_heads, L, d_k)
aggregated_features = torch.matmul(attention_probs.permute(0, 3, 1, 2), node_representation_Wh) # shape: (B, num_heads, L, d_k)
aggregated_features = aggregated_features.permute(0, 2, 1, 3) # shape: (B, L, num_heads, d_k)
new_node_representation = node_representation + self.leaky_relu(aggregated_features) # shape: (B, L, num_heads, d_k)
return new_node_representation
def single_head_attention_edge(self, node_representation, edge_representation, diff_vec):
# Update edge representation
B, L, num_heads, d_k = node_representation.size()
d_edge_head = edge_representation.size(-1)
d_diff_head = diff_vec.size(-1)
hi = node_representation.unsqueeze(2) # shape: (B, L, 1, num_heads, d_k)
hj = node_representation.unsqueeze(1) # shape: (B, 1, L, num_heads, d_k)
diff_i = diff_vec.unsqueeze(2) # shape: (B, L, 1, num_heads, d_diff_head)
diff_j = diff_vec.unsqueeze(1) # shape: (B, 1, L, num_heads, d_diff_head)
hi_hj_concat = torch.cat([edge_representation,
hi.expand(-1, -1, L, -1, -1),
hj.expand(-1, L, -1, -1, -1),
diff_i.expand(-1, -1, L, -1, -1),
diff_j.expand(-1, L, -1, -1, -1)], dim=-1) # shape: (B, L, L, num_heads, 2*d_k + d_edge_head + 2*d_diff_head)
new_edge_representation = self.edge_linear(hi_hj_concat) # shape: (B, L, L, num_heads, d_edge_head)
return new_edge_representation
class CrossGraphAttention(nn.Module):
def __init__(self, d_node, d_cross_edge, d_diff, num_heads, negative_slope=0.2):
super(CrossGraphAttention, self).__init__()
assert d_node % num_heads == 0, "d_node must be divisible by num_heads"
assert d_cross_edge % num_heads == 0, "d_edge must be divisible by num_heads"
assert d_diff % num_heads == 0, "d_diff must be divisible by num_heads"
self.num_heads = num_heads
self.d_k = d_node // num_heads
self.d_edge_head = d_cross_edge // num_heads
self.d_diff_head = d_diff // num_heads
self.Wn = nn.Linear(d_node, d_node)
self.Wh = nn.Linear(self.d_k, self.d_k)
self.We = nn.Linear(d_cross_edge, d_cross_edge)
self.Wn_2 = nn.Linear(d_node, d_node)
self.We_2 = nn.Linear(d_cross_edge, d_cross_edge)
self.Wd = nn.Linear(d_diff, d_diff)
self.Wd_2 = nn.Linear(d_diff, d_diff)
self.attn_linear_target = nn.Linear(self.d_k * 2 + self.d_edge_head + self.d_diff_head, 1, bias=False)
self.attn_linear_binder = nn.Linear(self.d_k * 2 + self.d_edge_head, 1, bias=False)
self.edge_linear = nn.Linear(self.d_k * 2 + self.d_edge_head + self.d_diff_head, self.d_edge_head)
self.out_proj_node = nn.Linear(d_node, d_node)
self.out_proj_edge = nn.Linear(d_cross_edge, d_cross_edge)
self.leaky_relu = nn.LeakyReLU(negative_slope)
def forward(self, target_representation, binder_representation, edge_representation, diff_vec):
B, L1, d_node = target_representation.size()
L2 = binder_representation.size()[1]
d_edge = edge_representation.size(-1)
# pdb.set_trace()
# Multi-head projection
target_proj = self.Wn(target_representation).view(B, L1, self.num_heads, self.d_k)
binder_proj = self.Wn(binder_representation).view(B, L2, self.num_heads, self.d_k)
edge_proj = self.We(edge_representation).view(B, L1, L2, self.num_heads, self.d_edge_head)
diff_proj = self.Wd(diff_vec).view(B, L1, self.num_heads, self.d_diff_head)
# Edge representation update
new_edge_representation = self.single_head_attention_edge(target_proj, binder_proj, edge_proj, diff_proj)
concatenated_edge_rep = new_edge_representation.view(B, L1, L2, -1)
new_edge_representation = self.out_proj_edge(concatenated_edge_rep)
# Node representation update
target_proj_2 = self.Wn_2(target_representation).view(B, L1, self.num_heads, self.d_k)
binder_proj_2 = self.Wn_2(binder_representation).view(B, L2, self.num_heads, self.d_k)
edge_proj_2 = self.We_2(new_edge_representation).view(B, L1, L2, self.num_heads, self.d_edge_head)
diff_proj_2 = self.Wd_2(diff_vec).view(B, L1, self.num_heads, self.d_diff_head)
new_target_representation = self.single_head_attention_target(target_proj_2, binder_proj_2, edge_proj_2, diff_proj_2)
new_binder_representation = self.single_head_attention_binder(binder_proj_2, target_proj_2, edge_proj_2)
concatenated_target_rep = new_target_representation.view(B, L1, -1)
new_target_representation = self.out_proj_node(concatenated_target_rep)
concatenated_binder_rep = new_binder_representation.view(B, L2, -1)
new_binder_representation = self.out_proj_node(concatenated_binder_rep)
return new_target_representation, new_binder_representation, new_edge_representation
def single_head_attention_target(self, target_representation, binder_representation, edge_representation, diff_vec):
# Update target node representation
# pdb.set_trace()
B, L1, num_heads, d_k = target_representation.size()
L2 = binder_representation.size(1)
d_edge_head = edge_representation.size(-1)
d_diff_head = diff_vec.size(-1)
hi = target_representation.unsqueeze(2) # shape: (B, L1, 1, num_heads, d_k)
hj = binder_representation.unsqueeze(1) # shape: (B, 1, L2, num_heads, d_k)
diff_i = diff_vec.unsqueeze(2) # shape: (B, L1, 1, num_heads, d_diff_head)
# Concatenate hi, hj, edge_representation, and diff_i
hi_hj_concat = torch.cat([
hi.expand(-1, -1, L2, -1, -1),
hj.expand(-1, L1, -1, -1, -1),
edge_representation,
diff_i.expand(-1, -1, L2, -1, -1)
], dim=-1) # shape: (B, L1, L2, num_heads, 2*d_k + d_edge_head + d_diff_head)
# Calculate attention scores
attention_scores = self.attn_linear_target(hi_hj_concat).squeeze(-1) # shape: (B, L1, L2, num_heads)
attention_probs = F.softmax(self.leaky_relu(attention_scores), dim=2) # shape: (B, L1, L2, num_heads)
# Aggregating features correctly along the L2 dimension
binder_representation_Wh = self.Wh(binder_representation) # shape: (B, L2, num_heads, d_k)
binder_representation_Wh = binder_representation_Wh.permute(0, 2, 1, 3) # shape: (B, num_heads, L2, d_k)
aggregated_features = torch.matmul(attention_probs.permute(0, 3, 1, 2), binder_representation_Wh) # shape: (B, num_heads, L1, d_k)
aggregated_features = aggregated_features.permute(0, 2, 1, 3) # shape: (B, L1, num_heads, d_k)
# Update target representation
new_target_representation = target_representation + self.leaky_relu(aggregated_features) # shape: (B, L1, num_heads, d_k)
return new_target_representation
def single_head_attention_binder(self, target_representation, binder_representation, edge_representation):
# Update target node representation
# pdb.set_trace()
B, L1, num_heads, d_k = target_representation.size()
L2 = binder_representation.size(1)
d_edge_head = edge_representation.size(-1)
hi = target_representation.unsqueeze(2) # shape: (B, L1, 1, num_heads, d_k)
hj = binder_representation.unsqueeze(1) # shape: (B, 1, L2, num_heads, d_k)
edge_representation = edge_representation.transpose(1,2)
# Concatenate hi, hj, edge_representation, and diff_i
hi_hj_concat = torch.cat([
hi.expand(-1, -1, L2, -1, -1),
hj.expand(-1, L1, -1, -1, -1),
edge_representation,
], dim=-1) # shape: (B, L1, L2, num_heads, 2*d_k + d_edge_head)
# Calculate attention scores
attention_scores = self.attn_linear_binder(hi_hj_concat).squeeze(-1) # shape: (B, L1, L2, num_heads)
attention_probs = F.softmax(self.leaky_relu(attention_scores), dim=2) # shape: (B, L1, L2, num_heads)
# Aggregating features correctly along the L2 dimension
binder_representation_Wh = self.Wh(binder_representation) # shape: (B, L2, num_heads, d_k)
binder_representation_Wh = binder_representation_Wh.permute(0, 2, 1, 3) # shape: (B, num_heads, L2, d_k)
aggregated_features = torch.matmul(attention_probs.permute(0, 3, 1, 2), binder_representation_Wh) # shape: (B, num_heads, L1, d_k)
aggregated_features = aggregated_features.permute(0, 2, 1, 3) # shape: (B, L1, num_heads, d_k)
# Update target representation
new_target_representation = target_representation + self.leaky_relu(aggregated_features) # shape: (B, L1, num_heads, d_k)
return new_target_representation
def single_head_attention_edge(self, target_representation, binder_representation, edge_representation, diff_vec):
# Update edge representation
# pdb.set_trace()
B, L1, num_heads, d_k = target_representation.size()
L2 = binder_representation.size(1)
d_edge_head = edge_representation.size(-1)
d_diff_head = diff_vec.size(-1)
hi = target_representation.unsqueeze(2) # shape: (B, L1, 1, num_heads, d_k)
hj = binder_representation.unsqueeze(1) # shape: (B, 1, L2, num_heads, d_k)
diff_i = diff_vec.unsqueeze(2) # shape: (B, L1, 1, num_heads, d_diff_head)
hi_hj_concat = torch.cat([edge_representation,
hi.expand(-1, -1, L2, -1, -1),
hj.expand(-1, L1, -1, -1, -1),
diff_i.expand(-1, -1, L2, -1, -1)], dim=-1) # shape: (B, L1, L2, num_heads, 2*d_k + d_edge_head + d_diff_head)
new_edge_representation = self.edge_linear(hi_hj_concat) # shape: (B, L1, L2, num_heads, d_edge_head)
return new_edge_representation
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