Make AdvancedSubtensor1 reuse preallocated output.

theano/tensor/basic.py

@@ -4880,7 +4880,11 @@ class AdvancedSubtensor1(Op):
         x, i = inp
         out, = out_
         # Copy always implied by numpy advanced indexing semantic.
-        out[0] = x[i]
+        if out[0] is not None and out[0].shape==(len(i),)+x.shape[1:]:
+            o = out[0]
+        else:
+            o = None
+        out[0] = x.take(i, axis=0, out=o)
 
     def grad(self, inputs, grads):
         gz, = grads

theano/tensor/tests/test_basic.py

@@ -1809,6 +1809,8 @@ class T_subtensor(unittest.TestCase):
         self.inc_sub = inc_sub
         self.adv_sub1 = adv_sub1
         self.adv_incsub1 = adv_incsub1
+        if mode is None:
+            mode = theano.compile.mode.get_default_mode()
         self.mode = mode
         self.dtype = dtype
         self.ignore_topo = ignore_topo
@@ -2093,6 +2095,19 @@ class T_subtensor(unittest.TestCase):
             self.assertTrue(val.ndim == data.ndim)
             self.assertTrue(numpy.allclose(val, good), (val, good))
 
+            # Test reuse of output memory
+            if isinstance(self.adv_sub1,tensor.AdvancedSubtensor1):
+                op = self.adv_sub1()
+                # When idx is a TensorConstant.
+                if hasattr(idx, "data"):
+                    idx = idx.data
+                test_out = [[None]]
+                op.perform(None, [data, idx],test_out)
+                out1 = test_out[0][0]
+                op.perform(None, [data, idx],test_out)
+                out2 = test_out[0][0]
+                assert out1 is out2
+
     def test_err_invalid_list(self):
         n = self.shared(numpy.asarray(5, dtype=self.dtype))
         self.assertRaises(TypeError, n.__getitem__, [0,0])