updated dist_autograd and dist_optim to be functional per API change

Author: osalpekar

distributed/rpc/rnn/main.py

@@ -51,15 +51,15 @@ def get_next_batch():
     for epoch in range(10):
         # create distributed autograd context
         for data, target in get_next_batch():
-            with dist_autograd.context():
+            with dist_autograd.context() as context_id:
                 hidden[0].detach_()
                 hidden[1].detach_()
                 output, hidden = model(data, hidden)
                 loss = criterion(output, target)
                 # run distributed backward pass
-                dist_autograd.backward([loss])
+                dist_autograd.backward(context_id, [loss])
                 # run distributed optimizer
-                opt.step()
+                opt.step(context_id)
                 # not necessary to zero grads as each iteration creates a different
                 # distributed autograd context which hosts different grads
         print("Training epoch {}".format(epoch))