Spaces:
Runtime error
Runtime error
File size: 9,255 Bytes
37b9e99 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
from common.utils import HiddenData, ClassifierOutputData
from model.decoder.interaction import BaseInteraction
class LSTMEncoder(nn.Module):
"""
Encoder structure based on bidirectional LSTM.
"""
def __init__(self, embedding_dim, hidden_dim, dropout_rate):
super(LSTMEncoder, self).__init__()
# Parameter recording.
self.__embedding_dim = embedding_dim
self.__hidden_dim = hidden_dim // 2
self.__dropout_rate = dropout_rate
# Network attributes.
self.__dropout_layer = nn.Dropout(self.__dropout_rate)
self.__lstm_layer = nn.LSTM(
input_size=self.__embedding_dim,
hidden_size=self.__hidden_dim,
batch_first=True,
bidirectional=True,
dropout=self.__dropout_rate,
num_layers=1
)
def forward(self, embedded_text, seq_lens):
""" Forward process for LSTM Encoder.
(batch_size, max_sent_len)
-> (batch_size, max_sent_len, word_dim)
-> (batch_size, max_sent_len, hidden_dim)
:param embedded_text: padded and embedded input text.
:param seq_lens: is the length of original input text.
:return: is encoded word hidden vectors.
"""
# Padded_text should be instance of LongTensor.
dropout_text = self.__dropout_layer(embedded_text)
# Pack and Pad process for input of variable length.
packed_text = pack_padded_sequence(dropout_text, seq_lens.cpu(), batch_first=True, enforce_sorted=False)
lstm_hiddens, (h_last, c_last) = self.__lstm_layer(packed_text)
padded_hiddens, _ = pad_packed_sequence(lstm_hiddens, batch_first=True)
return padded_hiddens
class GraphAttentionLayer(nn.Module):
"""
Simple GAT layer, similar to https://arxiv.org/abs/1710.10903
"""
def __init__(self, in_features, out_features, dropout, alpha, concat=True):
super(GraphAttentionLayer, self).__init__()
self.dropout = dropout
self.in_features = in_features
self.out_features = out_features
self.alpha = alpha
self.concat = concat
self.W = nn.Parameter(torch.zeros(size=(in_features, out_features)))
nn.init.xavier_uniform_(self.W.data, gain=1.414)
self.a = nn.Parameter(torch.zeros(size=(2 * out_features, 1)))
nn.init.xavier_uniform_(self.a.data, gain=1.414)
self.leakyrelu = nn.LeakyReLU(self.alpha)
def forward(self, input, adj):
h = torch.matmul(input, self.W)
B, N = h.size()[0], h.size()[1]
a_input = torch.cat([h.repeat(1, 1, N).view(B, N * N, -1), h.repeat(1, N, 1)], dim=2).view(B, N, -1,
2 * self.out_features)
e = self.leakyrelu(torch.matmul(a_input, self.a).squeeze(3))
zero_vec = -9e15 * torch.ones_like(e)
attention = torch.where(adj > 0, e, zero_vec)
attention = F.softmax(attention, dim=2)
attention = F.dropout(attention, self.dropout, training=self.training)
h_prime = torch.matmul(attention, h)
if self.concat:
return F.elu(h_prime)
else:
return h_prime
class GAT(nn.Module):
def __init__(self, nfeat, nhid, nclass, dropout, alpha, nheads, nlayers=2):
"""Dense version of GAT."""
super(GAT, self).__init__()
self.dropout = dropout
self.nlayers = nlayers
self.nheads = nheads
self.attentions = [GraphAttentionLayer(nfeat, nhid, dropout=dropout, alpha=alpha, concat=True) for _ in
range(nheads)]
for i, attention in enumerate(self.attentions):
self.add_module('attention_{}'.format(i), attention)
if self.nlayers > 2:
for i in range(self.nlayers - 2):
for j in range(self.nheads):
self.add_module('attention_{}_{}'.format(i + 1, j),
GraphAttentionLayer(nhid * nheads, nhid, dropout=dropout, alpha=alpha, concat=True))
self.out_att = GraphAttentionLayer(nhid * nheads, nclass, dropout=dropout, alpha=alpha, concat=False)
def forward(self, x, adj):
x = F.dropout(x, self.dropout, training=self.training)
input = x
x = torch.cat([att(x, adj) for att in self.attentions], dim=2)
if self.nlayers > 2:
for i in range(self.nlayers - 2):
temp = []
x = F.dropout(x, self.dropout, training=self.training)
cur_input = x
for j in range(self.nheads):
temp.append(self.__getattr__('attention_{}_{}'.format(i + 1, j))(x, adj))
x = torch.cat(temp, dim=2) + cur_input
x = F.dropout(x, self.dropout, training=self.training)
x = F.elu(self.out_att(x, adj))
return x + input
def normalize_adj(mx):
"""
Row-normalize matrix D^{-1}A
torch.diag_embed: https://github.com/pytorch/pytorch/pull/12447
"""
mx = mx.float()
rowsum = mx.sum(2)
r_inv = torch.pow(rowsum, -1)
r_inv[torch.isinf(r_inv)] = 0.
r_mat_inv = torch.diag_embed(r_inv, 0)
mx = r_mat_inv.matmul(mx)
return mx
class GLGINInteraction(BaseInteraction):
def __init__(self, **config):
super().__init__(**config)
self.intent_embedding = nn.Parameter(
torch.FloatTensor(self.config["intent_label_num"], self.config["intent_embedding_dim"])) # 191, 32
nn.init.normal_(self.intent_embedding.data)
self.adj = None
self.__slot_lstm = LSTMEncoder(
self.config["input_dim"] + self.config["intent_label_num"],
config["output_dim"],
config["dropout_rate"]
)
self.__slot_graph = GAT(
config["output_dim"],
config["hidden_dim"],
config["output_dim"],
config["dropout_rate"],
config["alpha"],
config["num_heads"],
config["num_layers"])
self.__global_graph = GAT(
config["output_dim"],
config["hidden_dim"],
config["output_dim"],
config["dropout_rate"],
config["alpha"],
config["num_heads"],
config["num_layers"])
def generate_global_adj_gat(self, seq_len, index, batch, window):
global_intent_idx = [[] for i in range(batch)]
global_slot_idx = [[] for i in range(batch)]
for item in index:
global_intent_idx[item[0]].append(item[1])
for i, len in enumerate(seq_len):
global_slot_idx[i].extend(
list(range(self.config["intent_label_num"], self.config["intent_label_num"] + len)))
adj = torch.cat([torch.eye(self.config["intent_label_num"] + max(seq_len)).unsqueeze(0) for i in range(batch)])
for i in range(batch):
for j in global_intent_idx[i]:
adj[i, j, global_slot_idx[i]] = 1.
adj[i, j, global_intent_idx[i]] = 1.
for j in global_slot_idx[i]:
adj[i, j, global_intent_idx[i]] = 1.
for i in range(batch):
for j in range(self.config["intent_label_num"], self.config["intent_label_num"] + seq_len[i]):
adj[i, j, max(self.config["intent_label_num"], j - window):min(seq_len[i] + self.config["intent_label_num"], j + window + 1)] = 1.
if self.config["row_normalized"]:
adj = normalize_adj(adj)
adj = adj.to(self.intent_embedding.device)
return adj
def generate_slot_adj_gat(self, seq_len, batch, window):
slot_idx_ = [[] for i in range(batch)]
adj = torch.cat([torch.eye(max(seq_len)).unsqueeze(0) for i in range(batch)])
for i in range(batch):
for j in range(seq_len[i]):
adj[i, j, max(0, j - window):min(seq_len[i], j + window + 1)] = 1.
if self.config["row_normalized"]:
adj = normalize_adj(adj)
adj = adj.to(self.intent_embedding.device)
return adj
def forward(self, encode_hidden: HiddenData, pred_intent: ClassifierOutputData = None, intent_index=None):
seq_lens = encode_hidden.inputs.attention_mask.sum(-1)
slot_lstm_out = self.__slot_lstm(torch.cat([encode_hidden.slot_hidden, pred_intent.classifier_output], dim=-1),
seq_lens)
global_adj = self.generate_global_adj_gat(seq_lens, intent_index, len(seq_lens),
self.config["slot_graph_window"])
slot_adj = self.generate_slot_adj_gat(seq_lens, len(seq_lens), self.config["slot_graph_window"])
batch = len(seq_lens)
slot_graph_out = self.__slot_graph(slot_lstm_out, slot_adj)
intent_in = self.intent_embedding.unsqueeze(0).repeat(batch, 1, 1)
global_graph_in = torch.cat([intent_in, slot_graph_out], dim=1)
encode_hidden.update_slot_hidden_state(self.__global_graph(global_graph_in, global_adj))
return encode_hidden
|