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kag.py
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kag.py
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import torch
import math
import torch.nn as nn
import torch.nn.functional as F
from transformers import RobertaModel
from torch.nn.utils.rnn import pad_sequence
from model import mask_logic, CausePredictor
class MultiHeadAttention(nn.Module):
def __init__(self, emb_dim, nhead, dropout=0.1, attn_mask=False):
super(MultiHeadAttention, self).__init__()
self.attn_mask = attn_mask
self.nhead = nhead
self.head_dim = emb_dim // nhead
self.q_proj_weight = nn.Parameter(torch.empty(emb_dim, emb_dim), requires_grad=True)
self.k_proj_weight = nn.Parameter(torch.empty(emb_dim, emb_dim), requires_grad=True)
self.v_proj_weight = nn.Parameter(torch.empty(emb_dim, emb_dim), requires_grad=True)
self.o_proj = nn.Linear(emb_dim, emb_dim, bias=False)
self.dropout = dropout
self._reset_parameter()
def _reset_parameter(self):
nn.init.xavier_uniform_(self.q_proj_weight)
nn.init.xavier_uniform_(self.k_proj_weight)
nn.init.xavier_uniform_(self.v_proj_weight)
nn.init.xavier_uniform_(self.o_proj.weight)
def forward(self, q, k, v, mask):
src_len = q.size(0)
tgt_len = k.size(0)
assert src_len == tgt_len, "length of query does not equal length of key"
scaling = float(self.head_dim) ** -0.5
query = F.linear(q, self.q_proj_weight)
key = F.linear(k, self.k_proj_weight)
value = F.linear(v, self.v_proj_weight)
# (n_head, s_len, h_dim)
query = query.contiguous().view(src_len, self.nhead, self.head_dim).transpose(0, 1)
key = key.contiguous().view(src_len, self.nhead, self.head_dim).transpose(0, 1)
value = value.contiguous().view(src_len, self.nhead, self.head_dim).transpose(0, 1)
# q*k
attn_weight = torch.matmul(query, key.transpose(1, 2))
attn_weight = attn_weight * scaling
if mask is not None:
# [1, s_len, s_len]
mask = mask.unsqueeze(0).expand(self.nhead, src_len, tgt_len)
attn_weight = torch.masked_fill(attn_weight, mask, -1e30)
# (n_head, src_len, tgt_len)
attn_score = F.softmax(attn_weight, dim=-1)
if self.attn_mask:
attmask = mask.eq(False).to(torch.float).unsqueeze(0)
attn_score = attn_score * attmask
attn_score = F.dropout(attn_score, p=self.dropout, training=self.training)
attn_output = torch.matmul(attn_score, value)
# (n_head, src_len, h_dim) -> (src_len, n_head, h_dim) -> (src_len, emb_dim)
attn_output = attn_output.transpose(0, 1).contiguous().view(src_len, -1)
output = F.linear(attn_output, self.o_proj.weight)
return output
class TransformerLayer(nn.Module):
def __init__(self, d_model, nhead, dim_feedforward, dropout, attn_mask=False):
super(TransformerLayer, self).__init__()
self.attention = MultiHeadAttention(d_model, nhead, dropout, attn_mask)
self.linear1 = nn.Linear(d_model, dim_feedforward)
self.dropout = nn.Dropout(dropout)
self.linear2 = nn.Linear(dim_feedforward, d_model)
self.norm1 = nn.LayerNorm(d_model)
self.norm2 = nn.LayerNorm(d_model)
self.dropout1 = nn.Dropout(dropout)
self.dropout2 = nn.Dropout(dropout)
def forward(self, src, src_mask):
src2 = self.attention(src, src, src, src_mask)
ss = src + self.dropout1(src2)
ss = self.norm1(ss)
ss2 = self.linear2(self.dropout(F.relu(self.linear1(ss))))
ss = ss + self.dropout2(ss2)
ss = self.norm2(ss)
return ss
class UtterEncoder3(nn.Module):
def __init__(self, model_size, utter_dim, num_layers, nhead, ff_dim, att_dropout):
super(UtterEncoder3, self).__init__()
self.encoder = RobertaModel.from_pretrained('roberta-'+model_size)
if model_size == 'base':
bert_dim = 768
else:
bert_dim = 1024
self.trm_layers = nn.ModuleList()
for i in range(num_layers):
layer = TransformerLayer(utter_dim, nhead, ff_dim, att_dropout)
self.trm_layers.append(layer)
self.num_layers = num_layers
self.attn = nn.Linear(utter_dim, 1)
self.utter_mapping = nn.Linear(bert_dim, utter_dim)
def forward(self, conv_utterance, attention_mask):
processed_output = []
doc_output = []
for cutt, amsk in zip(conv_utterance, attention_mask):
output_data = self.encoder(cutt, attention_mask=amsk).last_hidden_state
pooler_output = torch.max(output_data, dim=1)[0]
# [num_utter, dim]
mapped_output = self.utter_mapping(pooler_output)
src_mask = torch.ones((mapped_output.shape[0], mapped_output.shape[0])).tril(0).eq(0)
src_mask = src_mask.to(mapped_output.device)
for i in range(self.num_layers):
mapped_output = self.trm_layers[i](mapped_output, src_mask)
processed_output.append(mapped_output)
attention_weight = torch.softmax(self.attn(mapped_output), dim=0)
doc_output.append(torch.matmul(attention_weight.transpose(0, 1), mapped_output))
return processed_output, doc_output
class EdgeGCN(nn.Module):
def __init__(self, in_features, out_features):
super(EdgeGCN, self).__init__()
# num_bases=2=num_relations, no base is applied according to dgl.
self.in_features = in_features
self.sequence_weight = nn.Linear(in_features, out_features, False)
self.knowledge_weight = nn.Linear(in_features, out_features, False)
self.self_loop_weight = nn.Linear(in_features, out_features, False)
def norm(self, adj):
# directed graph
deg_inv = (adj.sum(-1) + 1e-10).pow(-1)
adj = adj * deg_inv.unsqueeze(2)
return adj
def forward(self, utt_emb, edge_rep, binary_knowledge_adj, sequence_adj):
# [batch_size, seq_len, dim]
zi = self.knowledge_weight(utt_emb)
# [batch_size, 1, seq_len, dim] + [batch_size, seq_len, seq_len, dim]
query = self.knowledge_weight(utt_emb.unsqueeze(1) + edge_rep)
# [batch_size, seq_len, 1, dim]
key = zi.unsqueeze(2)
value = zi
knowledge_adj = (query * key).sum(3) / math.sqrt(self.in_features)
knowledge_adj = mask_logic(knowledge_adj, binary_knowledge_adj)
# [batch_size, seq_len, seq_len]
knowledge_adj = torch.softmax(knowledge_adj, dim=1) * binary_knowledge_adj
zi = torch.matmul(knowledge_adj.transpose(1, 2), value)
si = self.sequence_weight(utt_emb)
sequence_adj = self.norm(sequence_adj)
si = torch.matmul(sequence_adj, si)
li = self.sequence_weight(utt_emb)
x = zi + si + li
return F.selu(x)
class Kag(nn.Module):
def __init__(self, model_size, utter_dim, num_layers, nhead, trm_num_layers,
add_emotion=True, emotion_emb=None, emotion_dim=200):
super(Kag, self).__init__()
self.utter_dim = utter_dim
self.utter_encoder = UtterEncoder3(model_size, utter_dim, trm_num_layers, nhead, utter_dim, 0.1)
self.know_mapping = nn.Linear(768, utter_dim)
self.layers = nn.ModuleList()
self.add_emotion = add_emotion
self.num_layers = num_layers
if add_emotion:
self.emotion_embedding = nn.Embedding(emotion_emb.shape[0], emotion_emb.shape[1], _weight=emotion_emb)
self.emotion_lin = nn.Linear(emotion_emb.shape[1], emotion_dim)
self.emotion_mapping = nn.Linear(emotion_dim+utter_dim, utter_dim)
else:
self.emotion_embedding = None
self.emotion_lin = None
self.emotion_mapping = None
for i in range(num_layers):
layer = EdgeGCN(utter_dim, utter_dim)
self.layers.append(layer)
self.classifier = CausePredictor(utter_dim, utter_dim)
def forward(self, utterance_text, attention_mask, conv_len, knowledge_text,
batch_know_len, know_mask, know_len, know_adj, seq_adj, mask, emotion_label):
conv_output, doc_output = self.utter_encoder(utterance_text, attention_mask)
utter_emb = pad_sequence(conv_output, batch_first=True)
if self.add_emotion:
emo_emb = self.emotion_lin(self.emotion_embedding(emotion_label))
utter_emb = self.emotion_mapping(torch.cat([utter_emb, emo_emb], dim=-1))
batch_size, seq_len, utter_dim = utter_emb.shape
know_emb = self.utter_encoder.encoder(knowledge_text, know_mask).last_hidden_state
know_emb = self.know_mapping(torch.max(know_emb, dim=1)[0])
know_emb = torch.split(know_emb, batch_know_len, 0)
know_attn = [torch.zeros(1, self.utter_dim).to(utter_emb.device)]
for kemb, demb, klen in zip(know_emb, doc_output, know_len):
# kemb: [num_know, dim]; demb: [1, dim]
attn = (demb * kemb).sum(1).split(klen)
kemb_s = kemb.split(klen)
for a, k in zip(attn, kemb_s):
# [1, num_know]
a = torch.softmax(a.unsqueeze(0), dim=1)
k = torch.matmul(a, k)
know_attn.append(k)
# [num_know, dim]
know_attn = torch.cat(know_attn, dim=0)
# [batch_size, seq_len, seq_len]
# [batch_size*seq_len*seq_len, dim]
edge_rep = torch.index_select(know_attn, 0, know_adj.flatten())
edge_rep = edge_rep.contiguous().view(batch_size, seq_len, seq_len, utter_dim)
binary_know_adj = torch.clamp(know_adj.clone().float(), max=1)
gcn_output = utter_emb
for i in range(self.num_layers):
gcn_output = self.layers[i](gcn_output, edge_rep, binary_know_adj, seq_adj)
logits = self.classifier(gcn_output, mask)
return logits