修复bug

fastNLP/core/utils/tqdm_progress.py

@@ -47,7 +47,7 @@ class TqdmProgress(metaclass=Singleton):
     def __init__(self):
         self.bars = {}
 
-    def add_task(self, iterable=None, description=None, total=None, leave=False,
+    def add_task(self, description=None, total=None, leave=False,
                  ncols=None, mininterval=0.1, maxinterval=10.0, miniters=None,
                  ascii=None, visible=True, unit='it', unit_scale=False,
                  dynamic_ncols=False, smoothing=0.3, bar_format=None, initial=0,
@@ -57,7 +57,6 @@ class TqdmProgress(metaclass=Singleton):
         主要就模仿了 tqdm bar 的创建，为了和 FRichProgress 的接口尽量统一，将 desc 重名为了 description，以及 disable 专为了
         visible 。
 
-        :param iterable:
         :param description:
         :param total:
         :param leave:
@@ -96,7 +95,7 @@ class TqdmProgress(metaclass=Singleton):
         else:
             file = sys.stdout
 
-        bar = tqdm(iterable=iterable, desc=description, total=total, leave=leave, file=file,
+        bar = tqdm(iterable=None, desc=description, total=total, leave=leave, file=file,
                  ncols=ncols, mininterval=mininterval, maxinterval=maxinterval, miniters=miniters,
                  ascii=ascii, disable=not visible, unit=unit, unit_scale=unit_scale,
                  dynamic_ncols=dynamic_ncols, smoothing=smoothing, bar_format=bar_format, initial=initial,

fastNLP/embeddings/torch/char_embedding.py

@@ -17,6 +17,8 @@ if _NEED_IMPORT_TORCH:
     import torch.nn as nn
     import torch.nn.functional as F
     from torch.nn import LSTM
+    import torch.nn.utils.rnn as rnn
+
 
 from .embedding import TokenEmbedding
 from .static_embedding import StaticEmbedding
@@ -270,8 +272,7 @@ class LSTMCharEmbedding(TokenEmbedding):
         chars = self.char_embedding(chars)  # batch_size x max_len x max_word_len x embed_size
         chars = self.dropout(chars)
         reshaped_chars = chars.reshape(batch_size * max_len, max_word_len, -1)
-        char_seq_len = chars_masks.eq(False).sum(dim=-1).reshape(batch_size * max_len)
-        lstm_chars = self.lstm(reshaped_chars, char_seq_len)[0].reshape(batch_size, max_len, max_word_len, -1)
+        lstm_chars = self.lstm(reshaped_chars, None)[0].reshape(batch_size, max_len, max_word_len, -1)
         # B x M x M x H
         
         lstm_chars = self.activation(lstm_chars)