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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence |
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from common.utils import HiddenData, ClassifierOutputData |
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from model.decoder.interaction import BaseInteraction |
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class LSTMEncoder(nn.Module): |
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""" |
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Encoder structure based on bidirectional LSTM. |
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""" |
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def __init__(self, embedding_dim, hidden_dim, dropout_rate): |
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super(LSTMEncoder, self).__init__() |
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self.__embedding_dim = embedding_dim |
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self.__hidden_dim = hidden_dim // 2 |
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self.__dropout_rate = dropout_rate |
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self.__dropout_layer = nn.Dropout(self.__dropout_rate) |
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self.__lstm_layer = nn.LSTM( |
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input_size=self.__embedding_dim, |
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hidden_size=self.__hidden_dim, |
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batch_first=True, |
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bidirectional=True, |
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dropout=self.__dropout_rate, |
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num_layers=1 |
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) |
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def forward(self, embedded_text, seq_lens): |
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""" Forward process for LSTM Encoder. |
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(batch_size, max_sent_len) |
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-> (batch_size, max_sent_len, word_dim) |
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-> (batch_size, max_sent_len, hidden_dim) |
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:param embedded_text: padded and embedded input text. |
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:param seq_lens: is the length of original input text. |
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:return: is encoded word hidden vectors. |
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""" |
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dropout_text = self.__dropout_layer(embedded_text) |
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packed_text = pack_padded_sequence(dropout_text, seq_lens.cpu(), batch_first=True, enforce_sorted=False) |
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lstm_hiddens, (h_last, c_last) = self.__lstm_layer(packed_text) |
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padded_hiddens, _ = pad_packed_sequence(lstm_hiddens, batch_first=True) |
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return padded_hiddens |
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class GraphAttentionLayer(nn.Module): |
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""" |
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Simple GAT layer, similar to https://arxiv.org/abs/1710.10903 |
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""" |
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def __init__(self, in_features, out_features, dropout, alpha, concat=True): |
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super(GraphAttentionLayer, self).__init__() |
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self.dropout = dropout |
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self.in_features = in_features |
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self.out_features = out_features |
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self.alpha = alpha |
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self.concat = concat |
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self.W = nn.Parameter(torch.zeros(size=(in_features, out_features))) |
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nn.init.xavier_uniform_(self.W.data, gain=1.414) |
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self.a = nn.Parameter(torch.zeros(size=(2 * out_features, 1))) |
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nn.init.xavier_uniform_(self.a.data, gain=1.414) |
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self.leakyrelu = nn.LeakyReLU(self.alpha) |
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def forward(self, input, adj): |
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h = torch.matmul(input, self.W) |
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B, N = h.size()[0], h.size()[1] |
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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, |
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2 * self.out_features) |
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e = self.leakyrelu(torch.matmul(a_input, self.a).squeeze(3)) |
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zero_vec = -9e15 * torch.ones_like(e) |
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attention = torch.where(adj > 0, e, zero_vec) |
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attention = F.softmax(attention, dim=2) |
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attention = F.dropout(attention, self.dropout, training=self.training) |
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h_prime = torch.matmul(attention, h) |
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if self.concat: |
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return F.elu(h_prime) |
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else: |
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return h_prime |
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class GAT(nn.Module): |
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def __init__(self, nfeat, nhid, nclass, dropout, alpha, nheads, nlayers=2): |
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"""Dense version of GAT.""" |
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super(GAT, self).__init__() |
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self.dropout = dropout |
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self.nlayers = nlayers |
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self.nheads = nheads |
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self.attentions = [GraphAttentionLayer(nfeat, nhid, dropout=dropout, alpha=alpha, concat=True) for _ in |
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range(nheads)] |
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for i, attention in enumerate(self.attentions): |
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self.add_module('attention_{}'.format(i), attention) |
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if self.nlayers > 2: |
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for i in range(self.nlayers - 2): |
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for j in range(self.nheads): |
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self.add_module('attention_{}_{}'.format(i + 1, j), |
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GraphAttentionLayer(nhid * nheads, nhid, dropout=dropout, alpha=alpha, concat=True)) |
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self.out_att = GraphAttentionLayer(nhid * nheads, nclass, dropout=dropout, alpha=alpha, concat=False) |
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def forward(self, x, adj): |
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x = F.dropout(x, self.dropout, training=self.training) |
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input = x |
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x = torch.cat([att(x, adj) for att in self.attentions], dim=2) |
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if self.nlayers > 2: |
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for i in range(self.nlayers - 2): |
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temp = [] |
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x = F.dropout(x, self.dropout, training=self.training) |
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cur_input = x |
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for j in range(self.nheads): |
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temp.append(self.__getattr__('attention_{}_{}'.format(i + 1, j))(x, adj)) |
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x = torch.cat(temp, dim=2) + cur_input |
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x = F.dropout(x, self.dropout, training=self.training) |
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x = F.elu(self.out_att(x, adj)) |
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return x + input |
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def normalize_adj(mx): |
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""" |
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Row-normalize matrix D^{-1}A |
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torch.diag_embed: https://github.com/pytorch/pytorch/pull/12447 |
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""" |
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mx = mx.float() |
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rowsum = mx.sum(2) |
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r_inv = torch.pow(rowsum, -1) |
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r_inv[torch.isinf(r_inv)] = 0. |
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r_mat_inv = torch.diag_embed(r_inv, 0) |
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mx = r_mat_inv.matmul(mx) |
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return mx |
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class GLGINInteraction(BaseInteraction): |
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def __init__(self, **config): |
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super().__init__(**config) |
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self.intent_embedding = nn.Parameter( |
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torch.FloatTensor(self.config["intent_label_num"], self.config["intent_embedding_dim"])) |
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nn.init.normal_(self.intent_embedding.data) |
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self.adj = None |
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self.__slot_lstm = LSTMEncoder( |
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self.config["input_dim"] + self.config["intent_label_num"], |
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config["output_dim"], |
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config["dropout_rate"] |
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) |
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self.__slot_graph = GAT( |
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config["output_dim"], |
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config["hidden_dim"], |
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config["output_dim"], |
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config["dropout_rate"], |
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config["alpha"], |
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config["num_heads"], |
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config["num_layers"]) |
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self.__global_graph = GAT( |
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config["output_dim"], |
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config["hidden_dim"], |
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config["output_dim"], |
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config["dropout_rate"], |
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config["alpha"], |
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config["num_heads"], |
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config["num_layers"]) |
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def generate_global_adj_gat(self, seq_len, index, batch, window): |
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global_intent_idx = [[] for i in range(batch)] |
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global_slot_idx = [[] for i in range(batch)] |
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for item in index: |
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global_intent_idx[item[0]].append(item[1]) |
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for i, len in enumerate(seq_len): |
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global_slot_idx[i].extend( |
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list(range(self.config["intent_label_num"], self.config["intent_label_num"] + len))) |
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adj = torch.cat([torch.eye(self.config["intent_label_num"] + max(seq_len)).unsqueeze(0) for i in range(batch)]) |
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for i in range(batch): |
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for j in global_intent_idx[i]: |
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adj[i, j, global_slot_idx[i]] = 1. |
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adj[i, j, global_intent_idx[i]] = 1. |
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for j in global_slot_idx[i]: |
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adj[i, j, global_intent_idx[i]] = 1. |
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for i in range(batch): |
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for j in range(self.config["intent_label_num"], self.config["intent_label_num"] + seq_len[i]): |
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adj[i, j, max(self.config["intent_label_num"], j - window):min(seq_len[i] + self.config["intent_label_num"], j + window + 1)] = 1. |
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if self.config["row_normalized"]: |
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adj = normalize_adj(adj) |
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adj = adj.to(self.intent_embedding.device) |
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return adj |
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def generate_slot_adj_gat(self, seq_len, batch, window): |
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slot_idx_ = [[] for i in range(batch)] |
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adj = torch.cat([torch.eye(max(seq_len)).unsqueeze(0) for i in range(batch)]) |
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for i in range(batch): |
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for j in range(seq_len[i]): |
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adj[i, j, max(0, j - window):min(seq_len[i], j + window + 1)] = 1. |
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if self.config["row_normalized"]: |
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adj = normalize_adj(adj) |
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adj = adj.to(self.intent_embedding.device) |
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return adj |
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def forward(self, encode_hidden: HiddenData, pred_intent: ClassifierOutputData = None, intent_index=None): |
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seq_lens = encode_hidden.inputs.attention_mask.sum(-1) |
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slot_lstm_out = self.__slot_lstm(torch.cat([encode_hidden.slot_hidden, pred_intent.classifier_output], dim=-1), |
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seq_lens) |
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global_adj = self.generate_global_adj_gat(seq_lens, intent_index, len(seq_lens), |
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self.config["slot_graph_window"]) |
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slot_adj = self.generate_slot_adj_gat(seq_lens, len(seq_lens), self.config["slot_graph_window"]) |
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batch = len(seq_lens) |
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slot_graph_out = self.__slot_graph(slot_lstm_out, slot_adj) |
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intent_in = self.intent_embedding.unsqueeze(0).repeat(batch, 1, 1) |
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global_graph_in = torch.cat([intent_in, slot_graph_out], dim=1) |
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encode_hidden.update_slot_hidden_state(self.__global_graph(global_graph_in, global_adj)) |
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return encode_hidden |
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