Fix a bug where pack_padded_sequence would take a cuda tensor instead…

… of a CPU one (#81)

* Fix a bug where pack_padded_sequence would take a cuda tensor instead of a CPU one

* Release 0.3.7c

pie/models/embedding.py

@@ -115,7 +115,7 @@ def forward(self, char, nchars, nwords):
         char, nchars = char[:, sort], nchars[sort]
         if isinstance(self.rnn, nn.RNNBase):
             outs, emb = self.rnn(
-                nn.utils.rnn.pack_padded_sequence(char, nchars), hidden)
+                nn.utils.rnn.pack_padded_sequence(char, nchars.cpu()), hidden)
             outs, _ = nn.utils.rnn.pad_packed_sequence(outs)
             if isinstance(emb, tuple):
                 emb, _ = emb

pie/models/encoder.py

@@ -38,15 +38,15 @@ def forward(self, inp, lengths, hidden=None, only_last=False, return_hidden=Fals
 
         _, sort = torch.sort(lengths, descending=True)
         _, unsort = sort.sort()
-        inp = nn.utils.rnn.pack_padded_sequence(inp[:, sort], lengths[sort])
+        inp = nn.utils.rnn.pack_padded_sequence(inp[:, sort], lengths[sort].cpu())
         outs, hiddens = [], []
 
         for layer, rnn in enumerate(self.rnn):
             # apply dropout only in between layers (not on the output)
             if layer > 0:
                 inp, lengths = nn.utils.rnn.pad_packed_sequence(inp)
                 inp = torch_utils.sequential_dropout(inp, self.dropout, self.training)
-                inp = nn.utils.rnn.pack_padded_sequence(inp, lengths)
+                inp = nn.utils.rnn.pack_padded_sequence(inp, lengths.cpu())
             # run layer
             louts, lhidden = rnn(inp, hidden[layer])
             # unpack

setup.py

@@ -19,7 +19,7 @@
 URL = 'https://github.com/emanjavacas/pie'
 AUTHOR = 'Enrique Manjavacas; Mike Kestemont; Thibault Clerice'
 REQUIRES_PYTHON = '>=3.6.0'
-VERSION = "0.3.7b"
+VERSION = "0.3.7c"
 
 # What packages are required for this module to be executed?