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model.py
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model.py
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"""
SOM-DST
Copyright (c) 2020-present NAVER Corp.
MIT license
"""
import torch
import torch.nn as nn
from pytorch_transformers.modeling_bert import BertPreTrainedModel, BertModel
class SomDST(BertPreTrainedModel):
def __init__(self, config, n_op, n_domain, update_id, exclude_domain=False):
super(SomDST, self).__init__(config)
self.hidden_size = config.hidden_size
self.encoder = Encoder(config, n_op, n_domain, update_id, exclude_domain)
self.decoder = Decoder(config, self.encoder.bert.embeddings.word_embeddings.weight)
self.apply(self.init_weights)
def forward(self, input_ids, token_type_ids,
state_positions, attention_mask,
max_value, op_ids=None, max_update=None, teacher=None):
enc_outputs = self.encoder(input_ids=input_ids,
token_type_ids=token_type_ids,
state_positions=state_positions,
attention_mask=attention_mask,
op_ids=op_ids,
max_update=max_update)
domain_scores, state_scores, decoder_inputs, sequence_output, pooled_output = enc_outputs
gen_scores = self.decoder(input_ids, decoder_inputs, sequence_output,
pooled_output, max_value, teacher)
return domain_scores, state_scores, gen_scores
class Encoder(nn.Module):
def __init__(self, config, n_op, n_domain, update_id, exclude_domain=False):
super(Encoder, self).__init__()
self.hidden_size = config.hidden_size
self.exclude_domain = exclude_domain
self.bert = BertModel(config)
self.dropout = nn.Dropout(config.dropout)
self.action_cls = nn.Linear(config.hidden_size, n_op)
if self.exclude_domain is not True:
self.domain_cls = nn.Linear(config.hidden_size, n_domain)
self.n_op = n_op
self.n_domain = n_domain
self.update_id = update_id
def forward(self, input_ids, token_type_ids,
state_positions, attention_mask,
op_ids=None, max_update=None):
bert_outputs = self.bert(input_ids, token_type_ids, attention_mask)
sequence_output, pooled_output = bert_outputs[:2]
state_pos = state_positions[:, :, None].expand(-1, -1, sequence_output.size(-1))
state_output = torch.gather(sequence_output, 1, state_pos)
state_scores = self.action_cls(self.dropout(state_output)) # B,J,4
if self.exclude_domain:
domain_scores = torch.zeros(1, device=input_ids.device) # dummy
else:
domain_scores = self.domain_cls(self.dropout(pooled_output))
batch_size = state_scores.size(0)
if op_ids is None:
op_ids = state_scores.view(-1, self.n_op).max(-1)[-1].view(batch_size, -1)
if max_update is None:
max_update = op_ids.eq(self.update_id).sum(-1).max().item()
gathered = []
for b, a in zip(state_output, op_ids.eq(self.update_id)): # update
if a.sum().item() != 0:
v = b.masked_select(a.unsqueeze(-1)).view(1, -1, self.hidden_size)
n = v.size(1)
gap = max_update - n
if gap > 0:
zeros = torch.zeros(1, 1*gap, self.hidden_size, device=input_ids.device)
v = torch.cat([v, zeros], 1)
else:
v = torch.zeros(1, max_update, self.hidden_size, device=input_ids.device)
gathered.append(v)
decoder_inputs = torch.cat(gathered)
return domain_scores, state_scores, decoder_inputs, sequence_output, pooled_output.unsqueeze(0)
class Decoder(nn.Module):
def __init__(self, config, bert_model_embedding_weights):
super(Decoder, self).__init__()
self.pad_idx = 0
self.hidden_size = config.hidden_size
self.vocab_size = config.vocab_size
self.embed = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=self.pad_idx)
self.embed.weight = bert_model_embedding_weights
self.gru = nn.GRU(config.hidden_size, config.hidden_size, 1, batch_first=True)
self.w_gen = nn.Linear(config.hidden_size*3, 1)
self.sigmoid = nn.Sigmoid()
self.dropout = nn.Dropout(config.dropout)
for n, p in self.gru.named_parameters():
if 'weight' in n:
p.data.normal_(mean=0.0, std=config.initializer_range)
def forward(self, x, decoder_input, encoder_output, hidden, max_len, teacher=None):
mask = x.eq(self.pad_idx)
batch_size, n_update, _ = decoder_input.size() # B,J',5 # long
state_in = decoder_input
all_point_outputs = torch.zeros(n_update, batch_size, max_len, self.vocab_size).to(x.device)
result_dict = {}
for j in range(n_update):
w = state_in[:, j].unsqueeze(1) # B,1,D
slot_value = []
for k in range(max_len):
w = self.dropout(w)
_, hidden = self.gru(w, hidden) # 1,B,D
# B,T,D * B,D,1 => B,T
attn_e = torch.bmm(encoder_output, hidden.permute(1, 2, 0)) # B,T,1
attn_e = attn_e.squeeze(-1).masked_fill(mask, -1e9)
attn_history = nn.functional.softmax(attn_e, -1) # B,T
# B,D * D,V => B,V
attn_v = torch.matmul(hidden.squeeze(0), self.embed.weight.transpose(0, 1)) # B,V
attn_vocab = nn.functional.softmax(attn_v, -1)
# B,1,T * B,T,D => B,1,D
context = torch.bmm(attn_history.unsqueeze(1), encoder_output) # B,1,D
p_gen = self.sigmoid(self.w_gen(torch.cat([w, hidden.transpose(0, 1), context], -1))) # B,1
p_gen = p_gen.squeeze(-1)
p_context_ptr = torch.zeros_like(attn_vocab).to(x.device)
p_context_ptr.scatter_add_(1, x, attn_history) # copy B,V
p_final = p_gen * attn_vocab + (1 - p_gen) * p_context_ptr # B,V
_, w_idx = p_final.max(-1)
slot_value.append([ww.tolist() for ww in w_idx])
if teacher is not None:
w = self.embed(teacher[:, j, k]).unsqueeze(1)
else:
w = self.embed(w_idx).unsqueeze(1) # B,1,D
all_point_outputs[j, :, k, :] = p_final
return all_point_outputs.transpose(0, 1)