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model.py
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model.py
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import numpy as np
from typing import List, Tuple
import os
import const
import json
import math
import torch.nn as nn
import torch
import torch.nn.functional as F
# named SOTA + dec_attn + rel_attn
# torch_bool
try:
torch_bool = torch.bool
except:
torch_bool = torch.uint8
class Encoder(nn.Module):
def __init__(self, config: const.Config, embedding: nn.modules.sparse.Embedding) -> None:
super(Encoder, self).__init__()
self.config = config
#----------------------------------------------------------------------
from torch.nn import TransformerEncoder, TransformerEncoderLayer
self.model_type = 'Transformer'
self.src_mask = None
#self.pos_encoder = PositionalEncoding(ninp, dropout)
encoder_layers = TransformerEncoderLayer(d_model=100, nhead=10, dim_feedforward=200, dropout=0.3)
self.transformer_encoder = TransformerEncoder(encoder_layers, num_layers=2)
#-------------------------------------------------------------------------
self.hidden_size = config.encoder_num_units
self.emb_size = config.embedding_dim
self.words_number = config.words_number
self.maxlen = config.max_sentence_length
self.dropout = nn.Dropout(0.3)
self.embedding = embedding
self.cell_name = config.cell_name
if config.cell_name == 'gru':
self.rnn = nn.GRU(self.emb_size, self.hidden_size, bidirectional=True, batch_first=True)
elif config.cell_name == 'lstm':
self.rnn = nn.LSTM(self.emb_size, self.hidden_size, bidirectional=True, batch_first=True)
else:
raise ValueError('cell name should be gru/lstm!')
def forward(self, sentence: torch.Tensor, lengths: List[int]) -> Tuple[torch.Tensor, torch.Tensor]:
embedded = self.embedding(sentence)
embedded = self.transformer_encoder(embedded)
# embedded = self.dropout(embedded)
if lengths:
embedded = nn.utils.rnn.pack_padded_sequence(
embedded, lengths=lengths, batch_first=True)
output, hidden = self.rnn(embedded)
#print('@@@@@@jeong_enc_result_hidden', np.array(hidden).size)
if lengths:
output, _ = nn.utils.rnn.pad_packed_sequence(output, total_length=self.maxlen, batch_first=True)
output = (lambda a: sum(a)/2)(torch.split(output, self.hidden_size, dim=2))
if self.cell_name == 'gru':
hidden = (lambda a: sum(a)/2)(torch.split(hidden, 1, dim=0))
elif self.cell_name == 'lstm':
hidden = tuple(map(lambda state: sum(torch.split(state, 1, dim=0))/2, hidden))
# hidden = (lambda a: sum(a)/2)(torch.split(hidden, 1, dim=0))
return output, hidden
class Decoder(nn.Module):
def __init__(self, config: const.Config, embedding: nn.modules.sparse.Embedding, device) -> None:
super(Decoder, self).__init__()
self.device = device
self.config = config
self.dropout = nn.Dropout(0.3)
self.cell_name = config.cell_name
self.decoder_type = config.decoder_type
self.hidden_size = config.decoder_num_units
self.emb_size = config.embedding_dim
self.words_number = config.words_number
self.maxlen = config.max_sentence_length
self.decodelen = config.decoder_output_max_length
#self.relation_eos = config.relation_number
self.relation_number = config.relation_number
self.word_embedding = embedding
self.relation_embedding = nn.Embedding(config.relation_number + 1, config.embedding_dim)
self.sos_embedding = nn.Embedding(1, config.embedding_dim)
self.combine_inputs = nn.Linear(self.hidden_size + self.emb_size, self.emb_size)
self.combine_inputs2 = nn.Linear(self.hidden_size + self.hidden_size, self.hidden_size)#jeong
self.combine_outputs = nn.Linear(self.emb_size + self.emb_size, self.emb_size)#jeong_1
self.attn = nn.Linear(self.hidden_size * 2, 1)
if self.cell_name == 'gru':
self.rnn = nn.GRU(self.emb_size, self.hidden_size, batch_first=True)
elif self.cell_name == 'lstm':
self.rnn = nn.LSTM(self.emb_size, self.hidden_size, batch_first=True, dropout=0.3)
self.do_eos = nn.Linear(self.hidden_size, 1)
self.do_predict = nn.Linear(self.hidden_size, self.relation_number)
self.k_size = 3
self.channel = 100
self.combine_rel_inputs = nn.Linear(self.channel + self.emb_size, self.emb_size)
self.do_rel_eos = nn.Linear(self.channel, 1)
self.do_rel_predict = nn.Linear(self.channel, self.relation_number)
self.fuse = nn.Linear(self.hidden_size * 2, 100)
self.do_copy_linear = nn.Linear(100, 1)
#----------------------------------------------------------------------------jeong_calc_new
self.linear_ctx = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
self.linear_query = nn.Linear(self.hidden_size, self.hidden_size, bias=True)
self.v = nn.Linear(self.hidden_size, 1)
#----------------------------------------------------------------------------
def calc_context(self, decoder_state: torch.Tensor, encoder_outputs: torch.Tensor) -> torch.Tensor:
# decoder_state.size() == torch.Size([1, 100, 1000])
# -> torch.Size([100, 1, 1000]) -> torch.Size([100, 80, 1000]) -cat-> torch.Size([100, 80, 2000])
attn_weight = torch.cat((decoder_state.permute(1, 0, 2).expand_as(encoder_outputs), encoder_outputs), dim=2)
attn_weight = F.softmax((self.attn(attn_weight)), dim=1)
attn_applied = torch.bmm(attn_weight.permute(0, 2, 1), encoder_outputs).squeeze(1)
return attn_applied
def calc_context_new(self, decoder_state: torch.Tensor, encoder_outputs: torch.Tensor) -> torch.Tensor:
#print('@@@@@@@@@@@@@@@@jeong_calc_dec',decoder_state.size()) #size([1,100,1000]
#print('@@@@@@@@@@@@@@@@jeong_calc_enc', encoder_outputs.size()) #size([100,80,1000]
decoder_state = decoder_state.permute(1,0,2) #size([100,1,1000]
decoder_state = decoder_state.repeat(1, encoder_outputs.size()[1], 1)
#print('@@@@@@@@@@@@@@@@jeong_calc_dec2', decoder_state.size()) #size([100,80,1000]
uh = self.linear_ctx(encoder_outputs)
wq = self.linear_query(decoder_state)
wquh = torch.tanh(wq + uh)
attn_weights = self.v(wquh).squeeze()
#attn_weights.data.masked_fill_(src_mask.data, -float("inf"))
attn_weights = F.softmax(attn_weights, dim=-1)
# print(attn_weights)
# print(src_mask)
# print(torch.sum(attn_weights != attn_weights).any())
# print('-----')
# if torch.sum(attn_weights != attn_weights).any() > 0:
# exit()
attn_applied = torch.bmm(attn_weights.unsqueeze(1), encoder_outputs).squeeze()
#print('@@@@@@@@@@@@@@@@jeong_calc_context',attn_applied.size())#size([100,1000])
return attn_applied
def extract_relation(self, decoder_state: torch.Tensor, encoder_outputs: torch.Tensor) -> torch.Tensor:
# decoder_state.size() == torch.Size([1, 100, 1000])
# -> torch.Size([100, 1, 1000]) -> torch.Size([100, 80, 1000])
decoder_state = decoder_state.permute(1, 0, 2).expand_as(encoder_outputs)
# torch.Size([100, 80, 1000]) -> torch.Size([100, 1000, 80])
decoder_state = decoder_state.permute(0, 2, 1)
attned_encoder_output = torch.bmm(encoder_outputs, decoder_state) # [100, 80, 80]
conv_emb = attned_encoder_output.unsqueeze(dim=1) # [100,1,80,80]
conv1 = torch.nn.Conv1d(in_channels=1, out_channels=self.channel, kernel_size=(self.k_size, self.maxlen),
stride=1).to(self.device)
conv_emb = conv1(conv_emb) # [100,10,76,1]
conv_emb = conv_emb.squeeze(dim=-1) # [100,10,76]
conv_emb = F.relu(conv_emb)
max_pooling = torch.nn.MaxPool2d((1, self.maxlen - (self.k_size -1)), stride=1)
conv_emb = max_pooling(conv_emb) #[100,10,1]
conv_emb = self.dropout(conv_emb)
#print('@@@jeong_conv_emb', conv_emb.size()) # [100, 10, 1]
conv_emb = conv_emb.squeeze(dim=-1)
#print('@@@jeong_conv_emb',conv_emb.size()) #[100, 100]
return conv_emb
def do_copy(self, output: torch.Tensor, encoder_outputs: torch.Tensor) -> torch.Tensor:
out = torch.cat((output.unsqueeze(1).expand_as(encoder_outputs), encoder_outputs), dim=2)
out = F.selu(self.fuse(F.selu(out)))
out = self.do_copy_linear(out).squeeze(2)
# out = (self.do_copy_linear(out).squeeze(2))
return out
def _decode_step(self, rnn_cell: nn.modules,
emb: torch.Tensor,
decoder_state: torch.Tensor,
encoder_outputs: torch.Tensor,
first_entity_mask: torch.Tensor,
dec_states,
t) \
-> Tuple[Tuple[torch.Tensor, torch.Tensor], torch.Tensor]:
if self.cell_name == 'gru':
decoder_state_h = decoder_state
elif self.cell_name == 'lstm':
decoder_state_h = decoder_state[0]
#print('@@jeong_decoder_state_h', decoder_state_h.size())
#print('@@jeong_decoder_state', decoder_state.size())
#print('@@jeong_decoder_state[0]', decoder_state[0].size())
#print('@@jeong_decoder_state[1]', decoder_state[1].size())
else:
raise ValueError('cell name should be lstm or gru')
#print('@@jeong_decoder_state', decoder_state_h.size())
#print('@@jeong_encoder_state', encoder_outputs.size())
if t != 0:
dec_states = dec_states
dec_states.append(decoder_state_h.squeeze(0))
dec_states = torch.stack(dec_states, dim=1)
decoder_state_h = self.calc_context_new(decoder_state_h, dec_states)
decoder_state_h = decoder_state_h.unsqueeze(0)
#print('@@jeong_decoder_state_new', decoder_state_h.size())
context = self.calc_context_new(decoder_state_h, encoder_outputs)
if t%3==0:
rel_context = self.extract_relation(decoder_state_h, encoder_outputs)
output = self.combine_rel_inputs(torch.cat((emb, rel_context), dim=1))
else:
output = self.combine_inputs(torch.cat((emb, context), dim=1))
output, decoder_state = rnn_cell(output.unsqueeze(1), decoder_state)
output = output.squeeze()
# eos_logits = F.selu(self.do_eos(output))
# predict_logits = F.selu(self.do_predict(output))
'''
if t % 3 == 0:
eos_logits = (self.do_eos(output))
predict_logits = (self.do_predict(output))
rel_logits, rel_eos_logits = self.extract_relation(decoder_state_h, encoder_outputs)
eos_logits += rel_eos_logits
predict_logits += rel_logits
predict_logits = F.log_softmax(torch.cat((predict_logits, eos_logits), dim=1), dim=1)
else:
eos_logits = (self.do_eos(output))
predict_logits = (self.do_predict(output))
predict_logits = F.log_softmax(torch.cat((predict_logits, eos_logits), dim=1), dim=1)
'''
eos_logits = (self.do_eos(output))
predict_logits = (self.do_predict(output))
predict_logits = F.log_softmax(torch.cat((predict_logits, eos_logits), dim=1), dim=1)
'''
if t % 3 == 0:
rel_logits, rel_eos_logits = self.extract_relation(decoder_state_h, encoder_outputs)
#rel_predict_logits = F.log_softmax(torch.cat((rel_logits, rel_eos_logits), dim=1), dim=1)
#predict_logits += rel_predict_logits
'''
copy_logits = self.do_copy(output, encoder_outputs)
copy_logits = torch.cat((copy_logits, eos_logits), dim=1)
copy_logits = F.log_softmax(copy_logits, dim=1)
# # bug fix
# copy_logits = torch.cat((copy_logits, eos_logits), dim=1)
# first_entity_mask = torch.cat((first_entity_mask, torch.ones_like(eos_logits)), dim=1)
#
# copy_logits = F.softmax(copy_logits, dim=1)
# copy_logits = copy_logits * first_entity_mask
# copy_logits = torch.clamp(copy_logits, 1e-10, 1.)
# copy_logits = torch.log(copy_logits)
return (predict_logits, copy_logits), decoder_state
def forward(self, *input):
raise NotImplementedError('abstract method!')
class MultiDecoder(Decoder):
def __init__(self, config: const.Config, embedding: nn.modules.sparse.Embedding, device) \
-> None:
super(MultiDecoder, self).__init__(config=config, embedding=embedding, device=device)
self.decoder_cell_number = config.decoder_output_max_length // 3
if self.cell_name == 'lstm':
self.rnns = nn.ModuleList([nn.LSTM(self.emb_size, self.hidden_size, batch_first=True)
for _ in range(self.decoder_cell_number)])
elif self.cell_name == 'gru':
self.rnns = nn.ModuleList([nn.GRU(self.emb_size, self.hidden_size, batch_first=True)
for _ in range(self.decoder_cell_number)])
else:
raise NameError('lstm or gru!')
def forward(self, sentence: torch.Tensor, decoder_state: torch.Tensor, encoder_outputs: torch.Tensor) \
-> Tuple[List[torch.Tensor], List[torch.Tensor]]:
# sos = go = 0
pred_action_list = []
pred_logits_list = []
go = torch.zeros(sentence.size()[0], dtype=torch.int64).to(self.device)
output = self.sos_embedding(go)
rel_output = None #jeong_1
first_entity_mask = torch.ones(go.size()[0], self.maxlen).to(self.device)
if self.cell_name == 'gru':
previous_state = torch.zeros_like(decoder_state)
elif self.cell_name == 'lstm':
previous_state = tuple(map(torch.zeros_like, decoder_state))
encoder_state = decoder_state
for decoder in self.rnns:
if self.cell_name == 'gru':
decoder_state = (encoder_state + previous_state) / 2
elif self.cell_name == 'lstm':
decoder_state = ((encoder_state[0] + previous_state[0])/2, (encoder_state[1] + previous_state[1])/2)
for t in range(3):
bag, decoder_state = self._decode_step(decoder, output, decoder_state, encoder_outputs, first_entity_mask)
predict_logits, copy_logits = bag
if t % 3 == 0:
action_logits = predict_logits
else:
action_logits = copy_logits
max_action = torch.argmax(action_logits, dim=1).detach()
pred_action_list.append(max_action)
pred_logits_list.append(action_logits)
# next time step
if t % 3 == 0:
output = max_action
rel_output = self.relation_embedding(output)
output = self.relation_embedding(output)
else:
copy_index = torch.zeros_like(sentence).scatter_(1, max_action.unsqueeze(1), 1).to(torch_bool)
output = sentence[copy_index]
#output = self.word_embedding(output)
# jeong_1 ->
output = torch.cat((rel_output, self.word_embedding(output)),dim=-1)
output = self.combine_outputs(output)
if t % 3 == 1:
first_entity_mask = torch.ones(go.size()[0], self.maxlen + 1).to(self.device)
index = torch.zeros_like(first_entity_mask).scatter_(1, max_action.unsqueeze(1), 1).to(torch_bool)
first_entity_mask[index] = 0
first_entity_mask = first_entity_mask[:, :-1]
else:
first_entity_mask = torch.ones(go.size()[0], self.maxlen).to(self.device)
previous_state = decoder_state
return pred_action_list, pred_logits_list
class OneDecoder(Decoder):
def __init__(self, config: const.Config, embedding: nn.modules.sparse.Embedding, device) \
-> None:
super(OneDecoder, self).__init__(config=config, embedding=embedding, device=device)
def forward(self, sentence: torch.Tensor, decoder_state: torch.Tensor, encoder_outputs: torch.Tensor) \
-> Tuple[List[torch.Tensor], List[torch.Tensor]]:
# sos = go = 0
pred_action_list = []
pred_logits_list = []
go = torch.zeros(sentence.size()[0], dtype=torch.int64).to(self.device)
output = self.sos_embedding(go)
rel_output = None # jeong_1
first_entity_mask = torch.ones(go.size()[0], self.maxlen).to(self.device)
raw_outputs = []
raw_outputs.append(decoder_state[0].squeeze(0))
for t in range(self.decodelen):
bag, decoder_state = self._decode_step(self.rnn, output, decoder_state, encoder_outputs, first_entity_mask, raw_outputs ,t)
raw_outputs.append(decoder_state[0].squeeze(0))
predict_logits, copy_logits = bag
if t % 3 == 0:
action_logits = predict_logits
else:
action_logits = copy_logits
max_action = torch.argmax(action_logits, dim=1).detach()
pred_action_list.append(max_action)
pred_logits_list.append(action_logits)
# next time step
if t % 3 == 0:
output = max_action
rel_output = self.relation_embedding(output)
output = self.relation_embedding(output)
# --------------------------------------------------------
elif t % 3 == 1:
copy_index = torch.zeros_like(sentence).scatter_(1, max_action.unsqueeze(1), 1).to(torch_bool)
output = sentence[copy_index]
# jeong_1 ->
# output = self.word_embedding(output)
output = torch.cat((rel_output, self.word_embedding(output)), dim=-1)
output = self.combine_outputs(output)
else:
copy_index = torch.zeros_like(sentence).scatter_(1, max_action.unsqueeze(1), 1).to(torch_bool)
output = sentence[copy_index]
output = self.word_embedding(output)
# -------------------------------------------------------
if t % 3 == 1:
first_entity_mask = torch.ones(go.size()[0], self.maxlen + 1).to(self.device)
index = torch.zeros_like(first_entity_mask).scatter_(1, max_action.unsqueeze(1), 1).to(torch_bool)
first_entity_mask[index] = 0
first_entity_mask = first_entity_mask[:, :-1]
else:
first_entity_mask = torch.ones(go.size()[0], self.maxlen).to(self.device)
return pred_action_list, pred_logits_list
class Seq2seq(nn.Module):
def __init__(self, config: const.Config, device, load_emb=False, update_emb=True):
super(Seq2seq, self).__init__()
self.device = device
self.config = config
self.emb_size = config.embedding_dim
self.words_number = config.words_number
self.maxlen = config.max_sentence_length
self.word_embedding = nn.Embedding(self.words_number + 1, self.emb_size)
if load_emb:
self.load_pretrain_emb(config)
self.word_embedding.weight.requires_grad = update_emb
self.encoder = Encoder(config, embedding=self.word_embedding)
if config.decoder_type == 'one':
self.decoder = OneDecoder(config, embedding=self.word_embedding, device=device)
elif config.decoder_type == 'multi':
self.decoder = MultiDecoder(config, embedding=self.word_embedding, device=device)
else:
raise ValueError('decoder type one/multi!!')
self.to(self.device)
def load_pretrain_emb(self, config: const.Config) -> None:
if os.path.isfile(config.words_id2vector_filename):
# logger.info('Word Embedding init from %s' % config.words_id2vector_filename)
print('load_embedding!')
words_id2vec = json.load(open(config.words_id2vector_filename, 'r'))
words_vectors = [0] * (len(words_id2vec) + 1)
for i, key in enumerate(words_id2vec):
words_vectors[int(key)] = words_id2vec[key]
# words_vectors[len(words_id2vec) + 1] = [0] * len(words_id2vec[key])
words_vectors[len(words_id2vec)] = [0] * len(words_id2vec[key])
self.word_embedding.weight.data.copy_(torch.from_numpy(np.array(words_vectors)))
def forward(self, sentence: torch.Tensor, sentence_eos: torch.Tensor, lengths: List[int]) \
-> Tuple[torch.Tensor, torch.Tensor]:
o, h = self.encoder(sentence, lengths)
pred_action_list, pred_logits_list = self.decoder(sentence=sentence_eos, decoder_state=h, encoder_outputs=o)
return pred_action_list, pred_logits_list