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from typing import Optional, Union
from torch_geometric.typing import OptTensor, PairTensor, PairOptTensor
import torch
from torch import Tensor
from torch.nn import Linear
from torch_scatter import scatter
from torch_geometric.nn.conv import MessagePassing
import torch.nn as nn
import dgl
import dgl.function as fn
import numpy as np
from dgl.nn import EdgeWeightNorm
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch_cmspepr
from src.layers.GravNetConv3 import knn_per_graph
def src_dot_dst(src_field, dst_field, out_field):
def func(edges):
return {
out_field: (edges.src[src_field] * edges.dst[dst_field]).sum(
-1, keepdim=True
)
}
return func
def src_dot_distance(src_field, dst_field, out_field):
def func(edges):
dij = (edges.src[src_field] - edges.dst[dst_field]).pow(2).sum(-1, keepdim=True)
edge_weight = torch.sqrt(dij + 1e-6)
edge_weight = torch.exp(-torch.square(dij))
return {out_field: edge_weight}
return func
def scaled_exp(field, scale_constant):
def func(edges):
# clamp for softmax numerical stability
return {field: torch.exp((edges.data[field] / scale_constant).clamp(-5, 5))}
return func
def score_dij(field):
def func(edges):
# clamp for softmax numerical stability
return {field: edges.data["score"].view(-1) * edges.data["dij"].view(-1)}
return func
class MultiHeadAttentionLayer(nn.Module):
def __init__(self, in_dim, out_dim, num_heads, use_bias):
super().__init__()
self.out_dim = out_dim
self.num_heads = num_heads
if use_bias:
self.Q = nn.Linear(in_dim, out_dim * num_heads, bias=True)
self.K = nn.Linear(in_dim, out_dim * num_heads, bias=True)
self.V = nn.Linear(in_dim, out_dim * num_heads, bias=True)
else:
self.Q = nn.Linear(in_dim, out_dim * num_heads, bias=False)
self.K = nn.Linear(in_dim, out_dim * num_heads, bias=False)
self.V = nn.Linear(in_dim, out_dim * num_heads, bias=False)
def propagate_attention(self, g):
# Compute attention score
g.apply_edges(src_dot_dst("K_h", "Q_h", "score")) # , edges)
g.apply_edges(scaled_exp("score", np.sqrt(self.out_dim)))
g.apply_edges(src_dot_distance("s_l", "s_l", "dij"))
g.apply_edges(score_dij("news"))
# Send weighted values to target nodes
eids = g.edges()
g.send_and_recv(eids, fn.u_mul_e("V_h", "news", "V_h"), fn.sum("V_h", "wV"))
g.send_and_recv(eids, fn.copy_e("score", "score"), fn.sum("score", "z"))
def forward(self, g, h):
Q_h = self.Q(h)
K_h = self.K(h)
V_h = self.V(h)
# Reshaping into [num_nodes, num_heads, feat_dim] to
# get projections for multi-head attention
g.ndata["Q_h"] = Q_h.view(-1, self.num_heads, self.out_dim)
g.ndata["K_h"] = K_h.view(-1, self.num_heads, self.out_dim)
g.ndata["V_h"] = V_h.view(-1, self.num_heads, self.out_dim)
self.propagate_attention(g)
g.ndata["z"] = g.ndata["z"].tile((1, 1, self.out_dim))
mask_empty = g.ndata["z"] > 0
head_out = g.ndata["wV"]
head_out[mask_empty] = head_out[mask_empty] / (g.ndata["z"][mask_empty])
g.ndata["z"] = g.ndata["z"][:, :, 0].view(
g.ndata["wV"].shape[0], self.num_heads, 1
)
return head_out
class GraphTransformerLayer(nn.Module):
"""
Param:
"""
def __init__(
self,
in_dim,
out_dim,
num_heads,
k,
dropout=0.0,
layer_norm=False,
batch_norm=True,
residual=False,
use_bias=False,
):
super().__init__()
self.in_channels = in_dim
self.out_channels = out_dim
self.num_heads = num_heads
self.dropout = dropout
self.residual = residual
self.layer_norm = layer_norm
self.batch_norm = batch_norm
self.k = k
space_dimensions = 3
self.lin_s = Linear(self.in_channels, space_dimensions, bias=False)
self.lin_h = Linear(self.in_channels, self.out_channels)
self.lin = Linear(self.in_channels + self.out_channels, self.out_channels)
self.attention = MultiHeadAttentionLayer(
in_dim, out_dim // num_heads, num_heads, use_bias
)
self.O = nn.Linear(out_dim, out_dim)
if self.layer_norm:
self.layer_norm1 = nn.LayerNorm(out_dim)
if self.batch_norm:
self.batch_norm1 = nn.BatchNorm1d(out_dim)
# FFN
self.FFN_layer1 = nn.Linear(out_dim, out_dim * 2)
self.FFN_layer2 = nn.Linear(out_dim * 2, out_dim)
if self.layer_norm:
self.layer_norm2 = nn.LayerNorm(out_dim)
if self.batch_norm:
self.batch_norm2 = nn.BatchNorm1d(out_dim)
def forward(self, g, h):
h_l = self.lin_h(h)
s_l = self.lin_s(h)
graph = knn_per_graph(g, s_l, self.k)
graph.ndata["s_l"] = s_l
h_in1 = h_l # for first residual connection
# multi-head attention out
attn_out = self.attention(graph, h)
h = attn_out.view(-1, self.out_channels)
h = F.dropout(h, self.dropout, training=self.training)
h = self.O(h)
h = self.lin(torch.cat((h_l, h), dim=1))
if self.residual:
h = h_in1 + h # residual connection
if self.layer_norm:
h = self.layer_norm1(h)
if self.batch_norm:
h = self.batch_norm1(h)
h_in2 = h # for second residual connection
# FFN
h = self.FFN_layer1(h)
h = F.relu(h)
h = F.dropout(h, self.dropout, training=self.training)
h = self.FFN_layer2(h)
if self.residual:
h = h_in2 + h # residual connection
if self.layer_norm:
h = self.layer_norm2(h)
if self.batch_norm:
h = self.batch_norm2(h)
return h, s_l
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