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Using bool for mask and handling possible spaces in prediction #4

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Using bool for mask and handling possible spaces in prediction #4

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8 changes: 4 additions & 4 deletions pytorch_pretrained_bert/crf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -97,7 +97,7 @@ def _calculate_PZ(self, feats, mask):
# partition = utils.switch(partition, cur_partition, mask[idx].view(bat_size, 1).expand(bat_size, self.tagset_size)).view(bat_size, -1)
mask_idx = mask[idx, :].view(batch_size, 1).expand(batch_size, tag_size)

mask_idx = mask_idx.byte()
mask_idx = mask_idx.bool()
## effective updated partition part, only keep the partition value of mask value = 1
masked_cur_partition = cur_partition.masked_select(mask_idx)
## let mask_idx broadcastable, to disable warning
Expand Down Expand Up @@ -143,7 +143,7 @@ def _viterbi_decode(self, feats, mask):
partition_history = list()
# reverse mask (bug for mask = 1- mask, use this as alternative choice)
# mask = 1 + (-1)*mask
mask = (1 - mask.long()).byte()
mask = (1 - mask.long()).bool()
_, inivalues = next(seq_iter) # bat_size * from_target_size * to_target_size
# only need start from start_tag
partition = inivalues[:, START_TAG, :].clone().view(batch_size, tag_size) # bat_size * to_target_size
Expand Down Expand Up @@ -253,7 +253,7 @@ def _score_sentence(self, scores, mask, tags):
### need convert tags id to search from 400 positions of scores
tg_energy = torch.gather(scores.view(seq_len, batch_size, -1), 2, new_tags).view(seq_len, batch_size) # seq_len * bat_size
## mask transpose to (seq_len, batch_size)
tg_energy = tg_energy.masked_select(mask.transpose(1,0).byte())
tg_energy = tg_energy.masked_select(mask.transpose(1,0).bool())

# ## calculate the score from START_TAG to first label
# start_transition = self.transitions[START_TAG,:].view(1, tag_size).expand(batch_size, tag_size)
Expand Down Expand Up @@ -307,7 +307,7 @@ def _viterbi_decode_nbest(self, feats, mask, nbest):
partition_history = list()
## reverse mask (bug for mask = 1- mask, use this as alternative choice)
# mask = 1 + (-1)*mask
mask = (1 - mask.long()).byte()
mask = (1 - mask.long()).bool()
_, inivalues = next(seq_iter) # bat_size * from_target_size * to_target_size
# only need start from start_tag
partition = inivalues[:, START_TAG, :].clone() # bat_size * to_target_size
Expand Down
8 changes: 4 additions & 4 deletions pytorch_pretrained_bert/crf2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -97,7 +97,7 @@ def _calculate_PZ(self, feats, mask):
# partition = utils.switch(partition, cur_partition, mask[idx].view(bat_size, 1).expand(bat_size, self.tagset_size)).view(bat_size, -1)
mask_idx = mask[idx, :].view(batch_size, 1).expand(batch_size, tag_size)

mask_idx = mask_idx.byte()
mask_idx = mask_idx.bool()
## effective updated partition part, only keep the partition value of mask value = 1
masked_cur_partition = cur_partition.masked_select(mask_idx)
## let mask_idx broadcastable, to disable warning
Expand Down Expand Up @@ -143,7 +143,7 @@ def _viterbi_decode(self, feats, mask):
partition_history = list()
# reverse mask (bug for mask = 1- mask, use this as alternative choice)
# mask = 1 + (-1)*mask
mask = (1 - mask.long()).byte()
mask = (1 - mask.long()).bool()
_, inivalues = next(seq_iter) # bat_size * from_target_size * to_target_size
# only need start from start_tag
partition = inivalues[:, START_TAG, :].clone().view(batch_size, tag_size) # bat_size * to_target_size
Expand Down Expand Up @@ -253,7 +253,7 @@ def _score_sentence(self, scores, mask, tags):
### need convert tags id to search from 400 positions of scores
tg_energy = torch.gather(scores.view(seq_len, batch_size, -1), 2, new_tags).view(seq_len, batch_size) # seq_len * bat_size
## mask transpose to (seq_len, batch_size)
tg_energy = tg_energy.masked_select(mask.transpose(1,0).byte())
tg_energy = tg_energy.masked_select(mask.transpose(1,0).bool())

# ## calculate the score from START_TAG to first label
# start_transition = self.transitions[START_TAG,:].view(1, tag_size).expand(batch_size, tag_size)
Expand Down Expand Up @@ -307,7 +307,7 @@ def _viterbi_decode_nbest(self, feats, mask, nbest):
partition_history = list()
## reverse mask (bug for mask = 1- mask, use this as alternative choice)
# mask = 1 + (-1)*mask
mask = (1 - mask.long()).byte()
mask = (1 - mask.long()).bool()
_, inivalues = next(seq_iter) # bat_size * from_target_size * to_target_size
# only need start from start_tag
partition = inivalues[:, START_TAG, :].clone() # bat_size * to_target_size
Expand Down
6 changes: 3 additions & 3 deletions pytorch_pretrained_bert/modeling_transfo_xl.py
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Original file line number Diff line number Diff line change
Expand Up @@ -484,7 +484,7 @@ def __init__(self, n_head, d_model, d_head, dropout, dropatt=0,
self.r_w_bias = r_w_bias

def _parallelogram_mask(self, h, w, left=False):
mask = torch.ones((h, w)).byte()
mask = torch.ones((h, w)).bool()
m = min(h, w)
mask[:m,:m] = torch.triu(mask[:m,:m])
mask[-m:,-m:] = torch.tril(mask[-m:,-m:])
Expand Down Expand Up @@ -1184,10 +1184,10 @@ def _forward(self, dec_inp, mems=None):
else:
mask_shift_len = qlen
dec_attn_mask = (torch.triu(all_ones, 1+mlen)
+ torch.tril(all_ones, -mask_shift_len)).byte()[:, :, None] # -1
+ torch.tril(all_ones, -mask_shift_len)).bool()[:, :, None] # -1
else:
dec_attn_mask = torch.triu(
word_emb.new_ones(qlen, klen), diagonal=1+mlen).byte()[:,:,None]
word_emb.new_ones(qlen, klen), diagonal=1+mlen).bool()[:,:,None]

hids = []
if self.attn_type == 0: # default
Expand Down
2 changes: 1 addition & 1 deletion wmseg_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -259,7 +259,7 @@ def convert_examples_to_features(self, examples):
tokenizer = self.bert_tokenizer if self.bert_tokenizer is not None else self.zen_tokenizer

for (ex_index, example) in enumerate(examples):
textlist = example.text_a.split(' ')
textlist = example.text_a.replace(' ', ' ').replace(' ', ' ').replace(' ', ' ').split(' ')
labellist = example.label
tokens = []
labels = []
Expand Down