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import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class PositionalEncoding(nn.Module):
def __init__(self, d_model: int, dropout: float = 0.1, max_len: int = 5000):
super().__init__()
self.dropout = nn.Dropout(p=dropout)
position = torch.arange(max_len).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model))
pe = torch.zeros(1, max_len, d_model) # batch_first
pe[0, :, 0::2] = torch.sin(position * div_term)
pe[0, :, 1::2] = torch.cos(position * div_term)
self.register_buffer('pe', pe)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Arguments:
x: Tensor, shape ``[batch_size x max_seq_length x embedding_dim]``
"""
x = x + self.pe[:, :x.size(1), :]
return self.dropout(x)
class SelfMHADecoder(nn.Module):
def __init__(self, emb_dim: int, num_heads: int, num_mha_layers: int, num_classes: int, dropout: float, pos_encoder: str = None, max_len: int = 100):
super().__init__()
self.num_mha_layers = num_mha_layers
# positional encoding
self.pos_encoder_type = pos_encoder
if pos_encoder == "sincos":
self.pos_encoder = PositionalEncoding(d_model=emb_dim, dropout=dropout, max_len=max_len)
# MHA blocks
mha_layer = nn.TransformerEncoderLayer(d_model=emb_dim,
nhead=num_heads,
dim_feedforward=emb_dim,
dropout=dropout,
batch_first=True)
self.mha = nn.TransformerEncoder(mha_layer, num_layers=num_mha_layers)
# linear projection for adjusting out_dim to num_classes
self.out_linear = nn.Linear(emb_dim, num_classes, bias=True)
# Initializing weights
self.reset_parameters()
def reset_parameters(self):
for param in self.parameters():
stdv = 1. / math.sqrt(param.size(-1))
param.data.uniform_(-stdv, stdv)
def forward(self, edge_emb):
"""
Paramters
---------
graph_emb: torch.tensor [batch_size x max_seq_length x emb_dim]
Returns
-------
probs: torch.tensor [batch_size x max_seq_length x num_classes]
probabilities of classes
"""
#---------------
# MHA decoding
#---------------
if self.pos_encoder_type == "sincos":
edge_emb = self.pos_encoder(edge_emb)
h = self.mha(edge_emb, is_causal=True) # [batch_size x max_seq_length x emb_dim]
logits = self.out_linear(h)
probs = F.log_softmax(logits, dim=-1)
return probs |