def decode_dynamic(self, dec_input_var, encoder_hidden, encoder_outputs):
"""
Decoding policy 2: feeding the previous prediction as a target
:param dec_input_var: ground truth labels (used only to get the shape, not for decoding)
:param encoder_hidden: the last hidden state of the Encoder RNN; (num_layers * num_directions) x B x enc_dim
:param encoder_outputs: SL x B x enc_dim
:return:
"""
dec_len = dec_input_var.size()[0]
batch_size = dec_input_var.size()[1]
dec_hidden = encoder_hidden
dec_input = cuda_if_gpu(
Variable(torch.LongTensor([BOS_ID] * batch_size)))
predicted_logits = cuda_if_gpu(
Variable(torch.zeros(dec_len, batch_size, self.tgt_vocab_size)))
# Dynamic decoding: feed the previous prediction as the next input
for di in range(dec_len):
prev_y = self.embedding_mat(dec_input) # B x E
# prev_y = self.dropout(prev_y)
dec_output, dec_hidden, attn_weights = self.decoder(
prev_y, dec_hidden, encoder_outputs)
# shapes:
# - dec_output: B, TV
# - dec_hidden: 1, B, dec_dim
# - attn_weights: B x SL
predicted_logits[di] = dec_output
topval, topidx = dec_output.data.topk(1)
dec_input = cuda_if_gpu(
Variable(torch.LongTensor(topidx.squeeze().cpu().numpy())))
return predicted_logits
def decode_teacher(self, dec_input_var, encoder_hidden, encoder_outputs):
"""
Decoding policy 1: feeding the ground truth label as a target
:param dec_input_var: ground truth labels
:param encoder_hidden: the last hidden state of the Encoder RNN; (num_layers * num_directions) x B x enc_dim
:param encoder_outputs: SL x B x enc_dim
:return:
"""
dec_len = dec_input_var.size()[0]
batch_size = dec_input_var.size()[1]
dec_hidden = encoder_hidden
dec_input = cuda_if_gpu(
Variable(torch.LongTensor([BOS_ID] * batch_size)))
predicted_logits = cuda_if_gpu(
Variable(torch.zeros(dec_len, batch_size, self.tgt_vocab_size)))
# Teacher forcing: feed the target as the next input
for di in range(dec_len):
prev_y = self.embedding_mat(
dec_input
) # embedding lookup of a vector of length = B; result: B x E
# prev_y = self.dropout(prev_y) # apply dropout
dec_output, dec_hidden, attn_weights = self.decoder(
prev_y, dec_hidden, encoder_outputs)
# shapes:
# - dec_output: B, TV
# - dec_hidden: 1, B, dec_dim
# - attn_weights: B x SL
predicted_logits[di] = dec_output # store this step's outputs
dec_input = dec_input_var[di] # next input
return predicted_logits
def predict(self, input_var):
# Embedding lookup
encoder_input_embedded = self.embedding_lookup(input_var)
# Encode
encoder_outputs, encoder_hidden = self.encoder(encoder_input_embedded)
# Decode
dec_ids, attn_w = [], []
curr_token_id = BOS_ID
curr_dec_idx = 0
dec_input_var = cuda_if_gpu(Variable(torch.LongTensor([curr_token_id
])))
dec_hidden = encoder_hidden[:1] # 1 x B x enc_dim
while (curr_token_id != EOS_ID and curr_dec_idx <= self.max_tgt_len):
prev_y = self.embedding_mat(dec_input_var)
# prev_y = self.dropout(prev_y)
decoder_output, dec_hidden, decoder_attention = self.decoder(
prev_y, dec_hidden, encoder_outputs)
attn_w.append(decoder_attention.data)
topval, topidx = decoder_output.data.topk(1)
curr_token_id = topidx[0][0]
dec_ids.append(curr_token_id)
dec_input_var = cuda_if_gpu(
Variable(torch.LongTensor([curr_token_id])))
curr_dec_idx += 1
return dec_ids, attn_w
def train_step(self, model, datum):
datum = [
cuda_if_gpu(Variable(torch.LongTensor(t)).transpose(0, 1))
for t in datum
] # [SL x B, TL x B]
logits = model.forward(datum) # TL x B x TV
loss_var = self.calc_loss(
logits, datum) # have to compute log_logits, since using NLL loss
return loss_var
def init_hidden(self, batch_size):
hidden = Variable(
torch.zeros(self.rnn.num_layers * self.num_directions, batch_size,
self.rnn.hidden_size))
return cuda_if_gpu(hidden)
def predict_beam(self, input_var, beam_size=5, length_normalization_factor=0.0, length_normalization_const=5.):
from components.model.Beam import Sequence, TopN
partial_sequences = TopN(beam_size)
complete_sequences = TopN(beam_size)
# Embedding lookup
encoder_input_embedded = self.embedding_lookup(input_var)
# Encode
encoder_outputs, encoder_hidden = self.encoder(encoder_input_embedded)
curr_token_id = BOS_ID
dec_input_var = cuda_if_gpu(Variable(torch.LongTensor([curr_token_id])))
dec_hidden = encoder_hidden[:1] # 1 x B x enc_dim
prev_y = self.embedding_mat(dec_input_var)
decoder_output, dec_hidden, decoder_attention = self.decoder(prev_y, dec_hidden, encoder_outputs) # decode the logprobs, words
logprobs, words = decoder_output.data.topk( beam_size )
logprobs, words = logprobs[0], words[0]
for k in range(beam_size):
seq = Sequence(
output=[curr_token_id] + [words[k]],
state=dec_hidden,
logprob=logprobs[k],
score=logprobs[k],
attention=None)
partial_sequences.push(seq)
for i in range(self.max_tgt_len):
partial_sequences_list = partial_sequences.extract()
seqs = [x.output for x in partial_sequences_list]
partial_sequences.reset()
flattened_partial = [s for s in partial_sequences_list]
input_feed = [c.output[-1] for c in flattened_partial]
state_feed = [c.state for c in flattened_partial]
# print(input_feed)
if len(input_feed) == 0:
break
words_, logprobs_, states_ = [], [], []
for input_feed_, state_feed_ in zip(input_feed, state_feed):
dec_input_var = cuda_if_gpu(Variable(torch.LongTensor([input_feed_])))
prev_y = self.embedding_mat(dec_input_var)
decoder_output, dec_hidden, decoder_attention = self.decoder(prev_y, state_feed_, encoder_outputs)
logprobs, words = decoder_output.data.topk( beam_size + 1 )
logprobs, words = logprobs[0], words[0]
# print("dec_input_var, words", words )
words_.append(words), logprobs_.append(logprobs), states_.append( dec_hidden )
idx = 0
# print(len(seqs), words_)
for partial in partial_sequences_list:
dec_hidden = states_[idx]
attention = None
k, num_hyp = 0, 0
while num_hyp < beam_size:
w = words_[idx].data[k]
# print("w", w.data.item())
output = partial.output + [w]
# print(idx, "output", output, w.data.item() == EOS_ID)
logprob = partial.logprob + logprobs_[idx][k]
score = logprob
k += 1
num_hyp += 1
if w.data.item() == EOS_ID:
if length_normalization_factor > 0:
L = length_normalization_const
length_penalty = (L + len(output)) / (L + 1)
score /= length_penalty ** length_normalization_factor
beam = Sequence(output, dec_hidden,
logprob, score, attention)
complete_sequences.push(beam)
num_hyp -= 1 # we can fit another hypotheses as this one is over
else:
beam = Sequence(output, dec_hidden, logprob, score, attention)
partial_sequences.push(beam)
idx += 1
seqs = complete_sequences.extract(sort=True)
dec_ids, score = [], []
for seq in seqs:
dec_ids.append( seq.output[1:] )
score.append( seq.score.data.item() )
# print(seq.output, seq.score, seq.logprob)
# print(dec_ids, score)
# exit()
return dec_ids, score
def predict_dis(self, input_var, input_dis, alpha=1.0):
encoder_input_embedded = self.embedding_lookup(input_var)
encoder_outputs, encoder_hidden = self.encoder(encoder_input_embedded)
dec_hidden = encoder_hidden[:1]
# use the prepared first distractor input as the distractor input
choose_idx, dis_embed = 0, self.embedding_lookup(input_dis[0])
dis_o, dis_h = self.encoder(dis_embed)
dis_dec_h, dis_enc_o = dis_h[:1], dis_o
cur_simi = sum(int(x==y) for x, y in zip(input_var, input_dis[0]))
""" # choose the most similar of different distractor from the prepared inputs (not necessary)
for cur_id, cur_dis in enumerate(input_dis[1:]):
dis_embed = self.embedding_lookup( cur_dis )
dis_o, dis_h = self.encoder(dis_embed)
cur_dec_h = dis_h[:1]
tmp_simi = sum(int(x==y) for x, y in zip(input_var, cur_dis))
#tmp_simi = torch.dot( dec_hidden.view(-1), cur_dec_h.view(-1) ).item()
#if tmp_simi > cur_simi:
# dis_dec_h, dis_enc_o = cur_dec_h, dis_o
# choose_idx = cur_id + 1
# cur_simi = tmp_simi
if tmp_simi > cur_simi:
#if tmp_simi < cur_simi:
dis_dec_h, dis_enc_o = cur_dec_h, dis_o
choose_idx = cur_id + 1
cur_simi = tmp_simi
#"""
real_simi = torch.dot( dec_hidden.view(-1), dec_hidden.view(-1) ).item()
cur_m = cuda_if_gpu( torch.FloatTensor( [ real_simi / (real_simi + cur_simi), cur_simi / (real_simi + cur_simi)] ) )
cur_m = cur_m.unsqueeze(0)
curr_token_id = BOS_ID
dec_input_var = cuda_if_gpu(Variable(torch.LongTensor([curr_token_id])))
dec_ids, attn_w = [], []
curr_dec_idx = 0
while (curr_token_id != EOS_ID and curr_dec_idx <= self.max_tgt_len):
prev_y = self.embedding_mat(dec_input_var)
decoder_output, dec_hidden, decoder_attention = self.decoder(prev_y, dec_hidden, encoder_outputs)
attn_w.append(decoder_attention.data)
dis_o, dis_dec_h, dis_a = self.decoder(prev_y, dis_dec_h, dis_enc_o)
#print(decoder_output.shape, dis_o.shape)
s0_p = torch.cat( [decoder_output.unsqueeze(-1), dis_o.unsqueeze(-1)], dim=-1)
gold_p = decoder_output + torch.log(cur_m[0][0])
dis_p = dis_o + torch.log( cur_m[0][1] )
s0_tmp = torch.cat( [gold_p.unsqueeze(-1), dis_p.unsqueeze(-1)], dim=-1)
l0_p = F.log_softmax( s0_tmp, dim=-1 )
l1_p = s0_p + l0_p
s1_p = s0_p + alpha * l0_p
l1_p = F.softmax(l1_p, dim=-1)
s1_p = F.softmax(s1_p, dim=1)
lp_ = torch.log(s1_p[:, :, 0] + 1e-8)
_, p_id = lp_.data.topk(k=1, dim=-1)
p_id, l1_p, cur_m = p_id.cuda().long(), l1_p.cuda(), cur_m.cuda()
try:
cur_m = cuda_if_gpu( torch.FloatTensor([l1_p[:, :, 0][0][w], l1_p[:, :, 1][0][w]]) )
cur_m = cur_m.unsqueeze(0)
cur_m /= torch.sum(cur_m, dim=-1).view(-1, 1)
except:
cur_m = cur_m
#print(cur_m)
curr_token_id = p_id[0][0]
dec_ids.append(curr_token_id)
dec_input_var = cuda_if_gpu(Variable(torch.LongTensor([curr_token_id])))
curr_dec_idx += 1
#exit()
return dec_ids, attn_w
