fixing inference to use volatile variables

Author: soumith

word_language_model/generate.py

@@ -1,8 +1,8 @@
 ###############################################################################
 # Language Modeling on Penn Tree Bank
 #
-# With the default parameters, this should achieve ~116 perplexity on the 
-# test set.
+# This file generates new sentences sampled from the language model
+#
 ###############################################################################
 
 import argparse
@@ -38,27 +38,26 @@
 with open(args.checkpoint, 'rb') as f:
     model = torch.load(f)
 
-# Waiting on https://github.com/pytorch/pytorch/issues/188
-# if args.cuda:
-#     model.cuda()
-# else:
-#     model.cpu()
+if args.cuda:
+    model.cuda()
+else:
+    model.cpu()
 
 corpus = data.Corpus(args.data)
 ntokens = corpus.dic.ntokens()
 
 hidden = model.initHidden(1)
 
-input = torch.LongTensor(1,1).fill_(math.floor(torch.rand(1)[0] * ntokens))
+input = torch.LongTensor(1,1).fill_(int(math.floor(torch.rand(1)[0] * ntokens)))
 if args.cuda:
     input = input.cuda()
 
 temperature = max(args.temperature, 1e-3)
 with open(args.outf, 'w') as outf:
     for i in range(args.nwords):
 
-        output, hidden = model(Variable(input, requires_grad=False), hidden)
-        gen = torch.multinomial(output[0].data.cpu().div(temperature).exp(), 1)[0][0] # FIXME: no multinomial on GPU?
+        output, hidden = model(Variable(input, volatile=True), hidden)
+        gen = torch.multinomial(output[0].data.div(temperature).exp().cpu(), 1)[0][0] # FIXME: multinomial is only for CPU
         input.fill_(gen)
         word = corpus.dic.idx2word[gen]
         outf.write(word)