GPU support added

src/disentangle.py

@@ -57,6 +57,7 @@
 EPOCHS = args.epochs
 DROP = args.drop
 MAX_DIST = args.max_dist
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 def header(args, out=sys.stdout):
     head_text = "# "+ time.ctime(time.time())
@@ -446,31 +447,32 @@ def __init__(self):
                 pretrained.append(vector)
             NWORDS = len(self.id_to_token)
             DIM_WORDS = len(pretrained[0])
-            self.pEmbedding = nn.Embedding.from_pretrained(torch.tensor(pretrained))
+            self.pEmbedding = nn.Embedding.from_pretrained(torch.tensor(pretrained)).to(DEVICE)
             input_size += 4 * DIM_WORDS
 
         # Create hidden layers
         self.hidden = []
-        self.hidden.append(nn.Linear(input_size, HIDDEN))
+        self.hidden.append(nn.Linear(input_size, HIDDEN, device=DEVICE))
         for i in range(args.layers - 1):
-            self.hidden.append(nn.Linear(HIDDEN, HIDDEN))
-        self.final_sum = nn.Linear(HIDDEN, 1)
+            self.hidden.append(nn.Linear(HIDDEN, HIDDEN, device=DEVICE))
+        self.final_sum = nn.Linear(HIDDEN, 1, device=DEVICE)
 
     def forward(self, query, options, gold, lengths, query_no):
         if len(options) == 1:
             return None, 0
         final = []
         if args.word_vectors:
-            qvecs = self.pEmbedding(torch.tensor(query))
+            qvecs = self.pEmbedding(torch.tensor(query, device=DEVICE))
             qvec_max = torch.max(qvecs, 0)[0]
             qvec_mean = torch.mean(qvecs, 0)
         for otext, features in options:
-            inputs = torch.tensor(features)
+            inputs = torch.tensor(features, device=DEVICE)
             if args.word_vectors:
-                ovecs = self.pEmbedding(torch.tensor(otext))
+                ovecs = self.pEmbedding(torch.tensor(otext, device=DEVICE))
                 ovec_max = torch.max(ovecs, 0)[0]
                 ovec_mean = torch.mean(ovecs, 0)
-                inputs = torch.cat([inputs, qvec_max, qvec_mean, ovec_max, ovec_mean])
+                inputs = torch.cat((inputs, qvec_max, qvec_mean, ovec_max, ovec_mean))
+
             if args.drop > 0:
                 inputs = F.dropout(inputs, args.drop)
             h = inputs
@@ -500,9 +502,9 @@ def forward(self, query, options, gold, lengths, query_no):
         dense_gold = []
         for i in range(len(options)):
             dense_gold.append(1.0 / len(gold) if i in gold else 0.0)
-        answer = torch.tensor(dense_gold)
+        answer = torch.tensor(dense_gold, device=DEVICE)
         loss = torch.dot(answer, nll)
-        predicted_link = np.argmax(final.data.numpy())
+        predicted_link = torch.argmax(final)
         return loss, predicted_link
 
     def get_ids(self, words):