pytorch1.2版本中新增boolTensor类型，所有的masked_fill必须为ByteTensor类型的索引,修改fastNLP以适配

fastNLP/embeddings/bert_embedding.py

@@ -115,7 +115,7 @@ class BertEmbedding(ContextualEmbedding):
                 if self._word_sep_index:  # 不能drop sep
                     sep_mask = words.eq(self._word_sep_index)
                 mask = torch.ones_like(words).float() * self.word_dropout
-                mask = torch.bernoulli(mask).byte()  # dropout_word越大，越多位置为1
+                mask = torch.bernoulli(mask).eq(1)  # dropout_word越大，越多位置为1
                 words = words.masked_fill(mask, self._word_unk_index)
                 if self._word_sep_index:
                     words.masked_fill_(sep_mask, self._word_sep_index)
@@ -252,7 +252,7 @@ class BertWordPieceEncoder(nn.Module):
                 if self._word_sep_index:  # 不能drop sep
                     sep_mask = words.eq(self._wordpiece_unk_index)
                 mask = torch.ones_like(words).float() * self.word_dropout
-                mask = torch.bernoulli(mask).byte()  # dropout_word越大，越多位置为1
+                mask = torch.bernoulli(mask).eq(1)  # dropout_word越大，越多位置为1
                 words = words.masked_fill(mask, self._word_unk_index)
                 if self._word_sep_index:
                     words.masked_fill_(sep_mask, self._wordpiece_unk_index)

fastNLP/embeddings/embedding.py

@@ -63,7 +63,7 @@ class Embedding(nn.Module):
         """
         if self.word_dropout>0 and self.training:
             mask = torch.ones_like(words).float() * self.word_dropout
-            mask = torch.bernoulli(mask).byte()  # dropout_word越大，越多位置为1
+            mask = torch.bernoulli(mask).eq(1)  # dropout_word越大，越多位置为1
             words = words.masked_fill(mask, self.unk_index)
         words = self.embed(words)
         return self.dropout(words)
@@ -135,7 +135,7 @@ class TokenEmbedding(nn.Module):
         """
         if self.word_dropout > 0 and self.training:
             mask = torch.ones_like(words).float() * self.word_dropout
-            mask = torch.bernoulli(mask).byte()  # dropout_word越大，越多位置为1
+            mask = torch.bernoulli(mask).eq(1)  # dropout_word越大，越多位置为1
             words = words.masked_fill(mask, self._word_unk_index)
         return words
 

fastNLP/models/biaffine_parser.py

@@ -150,7 +150,7 @@ class GraphParser(BaseModel):
         """
         _, seq_len, _ = arc_matrix.shape
         matrix = arc_matrix + torch.diag(arc_matrix.new(seq_len).fill_(-np.inf))
-        flip_mask = (mask == 0).byte()
+        flip_mask = mask.eq(0)
         matrix.masked_fill_(flip_mask.unsqueeze(1), -np.inf)
         _, heads = torch.max(matrix, dim=2)
         if mask is not None:

fastNLP/modules/decoder/crf.py

@@ -210,7 +210,7 @@ class ConditionalRandomField(nn.Module):
             trans_score = self.trans_m.view(1, n_tags, n_tags)
             tmp = alpha.view(batch_size, n_tags, 1) + emit_score + trans_score
             alpha = torch.logsumexp(tmp, 1).masked_fill(flip_mask[i].view(batch_size, 1), 0) + \
-                    alpha.masked_fill(mask[i].byte().view(batch_size, 1), 0)
+                    alpha.masked_fill(mask[i].eq(1).view(batch_size, 1), 0)
 
         if self.include_start_end_trans:
             alpha = alpha + self.end_scores.view(1, -1)
@@ -230,7 +230,7 @@ class ConditionalRandomField(nn.Module):
         seq_idx = torch.arange(seq_len, dtype=torch.long, device=logits.device)
 
         # trans_socre [L-1, B]
-        mask = mask.byte()
+        mask = mask.eq(1)
         flip_mask = mask.eq(0)
         trans_score = self.trans_m[tags[:seq_len - 1], tags[1:]].masked_fill(flip_mask[1:, :], 0)
         # emit_score [L, B]
@@ -278,7 +278,7 @@ class ConditionalRandomField(nn.Module):
         """
         batch_size, seq_len, n_tags = logits.size()
         logits = logits.transpose(0, 1).data  # L, B, H
-        mask = mask.transpose(0, 1).data.byte()  # L, B
+        mask = mask.transpose(0, 1).data.eq(1)  # L, B
 
         # dp
         vpath = logits.new_zeros((seq_len, batch_size, n_tags), dtype=torch.long)

fastNLP/modules/decoder/utils.py

@@ -27,7 +27,7 @@ def viterbi_decode(logits, transitions, mask=None, unpad=False):
             "compatible."
     logits = logits.transpose(0, 1).data  # L, B, H
     if mask is not None:
-        mask = mask.transpose(0, 1).data.byte()  # L, B
+        mask = mask.transpose(0, 1).data.eq(1)  # L, B
     else:
         mask = logits.new_ones((seq_len, batch_size), dtype=torch.uint8)