fix

pytorch/mem_transformer.py

@@ -90,7 +90,7 @@ class MultiHeadPositionwiseFF(nn.Module):
 
         assert d_model % n_head == 0
         self.n_head = n_head
-        d_head = d_model / n_head
+        d_head = d_model // n_head
         self.d_head = d_head
         self.d_model = d_model
         self.d_inner = d_inner
@@ -138,7 +138,7 @@ class MultiHeadPositionwiseFF(nn.Module):
 
         attn_vec = torch.einsum('ibnh,ndh->ibnd', (attn_score, self.v_weight)) + self.v_bias
 
-        attn_vec = attn_vec.view(inp.size(0), inp.size(1), self.d_model)
+        attn_vec = attn_vec.contiguous().view(inp.size(0), inp.size(1), self.d_model)
         core_out = self.o_net(attn_vec)
         core_out = self.dropout(core_out)
 
@@ -987,7 +987,7 @@ class MemTransformerLM(nn.Module):
                 self.out_layer.bias, target, pred_hid, self.sampler)
             loss = -F.log_softmax(logit, -1)[:, :, 0]
         else:
-            loss = self.crit(pred_hid.view(-1, pred_hid.size(-1)), target.view(-1))
+            loss = self.crit(pred_hid.view(-1, pred_hid.size(-1)), target.contiguous().view(-1))
             loss = loss.view(tgt_len, -1)
 
         if new_mems is None: