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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 model.decoder.interaction.base_interaction import BaseInteraction |
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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 AGIFInteraction(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.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_adj_gat(self, index, batch, intent_label_num): |
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intent_idx_ = [[torch.tensor(0)] for i in range(batch)] |
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for item in index: |
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intent_idx_[item[0]].append(item[1] + 1) |
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intent_idx = intent_idx_ |
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self.adj = torch.cat([torch.eye(intent_label_num + 1).unsqueeze(0) for i in range(batch)]) |
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for i in range(batch): |
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for j in intent_idx[i]: |
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self.adj[i, j, intent_idx[i]] = 1. |
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if self.config["row_normalized"]: |
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self.adj = normalize_adj(self.adj) |
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self.adj = self.adj.to(self.intent_embedding.device) |
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def forward(self, encode_hidden, **interaction_args): |
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if self.adj is None or interaction_args["sent_id"] == 0: |
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self.generate_adj_gat(interaction_args["intent_index"], interaction_args["batch_size"], interaction_args["intent_label_num"]) |
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lstm_out = torch.cat((encode_hidden, |
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self.intent_embedding.unsqueeze(0).repeat(encode_hidden.shape[0], 1, 1)), dim=1) |
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return self.graph(lstm_out, self.adj[interaction_args["sent_id"]]) |
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