[bugfix] 针对pytorch1.3.0版本bug的补丁

fastNLP/modules/decoder/utils.py

@@ -42,7 +42,7 @@ def viterbi_decode(logits, transitions, mask=None, unpad=False):
         score = prev_score + trans_score + cur_score
         best_score, best_dst = score.max(1)
         vpath[i] = best_dst
-        vscore = best_score.masked_fill(mask[i].eq(0).view(batch_size, 1), 0) + \
+        vscore = best_score.masked_fill(mask[i].eq(False).view(batch_size, 1), 0) + \
                  vscore.masked_fill(mask[i].view(batch_size, 1), 0)
 
     # backtrace

fastNLP/modules/encoder/conv_maxpool.py

@@ -80,7 +80,7 @@ class ConvMaxpool(nn.Module):
         xs = [self.activation(conv(x)) for conv in self.convs]  # [[N,C,L], ...]
         if mask is not None:
             mask = mask.unsqueeze(1)  # B x 1 x L
-            xs = [x.masked_fill_(mask.eq(0), float('-inf')) for x in xs]
+            xs = [x.masked_fill_(mask.eq(False), float('-inf')) for x in xs]
         # max-pooling
         xs = [F.max_pool1d(input=i, kernel_size=i.size(2)).squeeze(2)
               for i in xs]  # [[N, C], ...]

reproduction/multi-criteria-cws/models.py

@@ -158,7 +158,7 @@ class CWSModel(nn.Module):
         else:
             if tags is not None:
                 out = out.contiguous().view(-1, self.tag_size)
-                tags = tags.data.masked_fill_(mask == 0, -100).view(-1)
+                tags = tags.data.masked_fill_(mask.eq(False), -100).view(-1)
                 loss = self.loss_f(out, tags)
                 return {"loss": loss}
             else:

reproduction/multi-criteria-cws/transformer.py

@@ -18,7 +18,7 @@ def subsequent_mask(size):
     "Mask out subsequent positions."
     attn_shape = (1, size, size)
     subsequent_mask = np.triu(np.ones(attn_shape), k=1).astype("uint8")
-    return torch.from_numpy(subsequent_mask) == 0
+    return torch.from_numpy(subsequent_mask).eq(False)
 
 
 def attention(query, key, value, mask=None, dropout=None):

reproduction/sequence_labelling/cws/model/module.py

@@ -37,8 +37,8 @@ class SemiCRFShiftRelay(nn.Module):
         # 当前时刻结束的分数是多少
         scores = logits.new_zeros(batch_size, max_len+1)
         # golden的分数
-        gold_scores = relay_logits[:, 0].masked_fill(relay_mask[:, 0].eq(0), 0) + \
-                                logits[:, 0, 0].masked_fill(end_seg_mask[:, 0].eq(0), 0)
+        gold_scores = relay_logits[:, 0].masked_fill(relay_mask[:, 0].eq(False), 0) + \
+                                logits[:, 0, 0].masked_fill(end_seg_mask[:, 0].eq(False), 0)
         # 初始化
         scores[:, 1] = logits[:, 0, 0]
         batch_i = torch.arange(batch_size).to(logits.device).long()
@@ -67,8 +67,8 @@ class SemiCRFShiftRelay(nn.Module):
 
             # 计算golden
             seg_i = relay_target[:, t] # batch_size
-            gold_segment_scores = logits[:, t][(batch_i, seg_i)].masked_fill(end_seg_mask[:, t].eq(0), 0) # batch_size, 后向从0到L长度的segment的分数
-            relay_score = relay_logits[:, t].masked_fill(relay_mask[:, t].eq(0), 0)
+            gold_segment_scores = logits[:, t][(batch_i, seg_i)].masked_fill(end_seg_mask[:, t].eq(False), 0) # batch_size, 后向从0到L长度的segment的分数
+            relay_score = relay_logits[:, t].masked_fill(relay_mask[:, t].eq(False), 0)
             gold_scores = gold_scores + relay_score + gold_segment_scores
         all_scores = scores.gather(dim=1, index=seq_len.unsqueeze(1)).squeeze(1) # batch_size
         return all_scores - gold_scores

reproduction/sequence_labelling/ner/model/dilated_cnn.py

@@ -106,7 +106,7 @@ class IDCNN(nn.Module):
             if self.crf is not None and target is not None:
                 loss = self.crf(y.transpose(1, 2), t, mask)
             else:
-                y.masked_fill_((mask == 0)[:,None,:], -100)
+                y.masked_fill_((mask.eq(False))[:,None,:], -100)
                 # f_mask = mask.float()
                 # t = f_mask * t + (1-f_mask) * -100
                 loss = F.cross_entropy(y, t, ignore_index=-100)