correct handling of int/complex dtypes in IncSubtensor.grad

theano/tensor/subtensor.py

@@ -1471,36 +1471,46 @@ class IncSubtensor(Op):
         x, y = inputs[:2]
         idx_list = inputs[2:]
 
-        if self.set_instead_of_inc:
-            gx = set_subtensor(
-                Subtensor(idx_list=self.idx_list)(g_output, *idx_list),
-                theano.tensor.zeros_like(y))
+        if x.dtype in theano.tensor.discrete_dtypes:
+            # The output dtype is the same as x
+            gx = x.zeros_like(dtype=theano.config.floatX)
+            if y.dtype in theano.tensor.discrete_dtypes:
+                gy = y.zeros_like(dtype=theano.config.floatX)
+            else:
+                gy = y.zeros_like()
+        elif x.dtype in theano.tensor.complex_dtypes:
+            raise NotImplementedError("No support for complex grad yet")
         else:
-            gx = g_output
-        gy = Subtensor(idx_list=self.idx_list)(g_output, *idx_list)
-        if gy.broadcastable != y.broadcastable:
-            y_dim_added = gy.ndim - y.ndim
-            y_broad = (True,) * y_dim_added + y.broadcastable
-            assert sum(gy.broadcastable) < sum(y_broad)
-            axis_to_sum = []
-            for i in range(gy.ndim):
-                if gy.broadcastable[i] is False and y_broad[i] is True:
-                    axis_to_sum.append(i)
-                elif (gy.broadcastable[i] is True and
-                      y_broad[i] is False):
-                    # This mean that Theano where able to infer that
-                    # gy.shape[i] is 1, so y.shape[i] is 1, but we
-                    # didn't know it. It is fine.
-                    pass
-                else:
-                    assert gy.broadcastable[i] == y_broad[i]
-            gy = gy.sum(axis=axis_to_sum, keepdims=True)
-            if gy.ndim != y.ndim:
-                assert gy.ndim > y.ndim
-                for i in range(y_dim_added):
-                    assert gy.broadcastable[i]
-                gy = gy.dimshuffle(*range(y_dim_added, gy.ndim))
-            assert gy.broadcastable == y.broadcastable
+            if self.set_instead_of_inc:
+                gx = set_subtensor(
+                    Subtensor(idx_list=self.idx_list)(g_output, *idx_list),
+                    theano.tensor.zeros_like(y))
+            else:
+                gx = g_output
+            gy = Subtensor(idx_list=self.idx_list)(g_output, *idx_list)
+            if gy.broadcastable != y.broadcastable:
+                y_dim_added = gy.ndim - y.ndim
+                y_broad = (True,) * y_dim_added + y.broadcastable
+                assert sum(gy.broadcastable) < sum(y_broad)
+                axis_to_sum = []
+                for i in range(gy.ndim):
+                    if gy.broadcastable[i] is False and y_broad[i] is True:
+                        axis_to_sum.append(i)
+                    elif (gy.broadcastable[i] is True and
+                          y_broad[i] is False):
+                        # This mean that Theano where able to infer that
+                        # gy.shape[i] is 1, so y.shape[i] is 1, but we
+                        # didn't know it. It is fine.
+                        pass
+                    else:
+                        assert gy.broadcastable[i] == y_broad[i]
+                gy = gy.sum(axis=axis_to_sum, keepdims=True)
+                if gy.ndim != y.ndim:
+                    assert gy.ndim > y.ndim
+                    for i in range(y_dim_added):
+                        assert gy.broadcastable[i]
+                    gy = gy.dimshuffle(*range(y_dim_added, gy.ndim))
+                assert gy.broadcastable == y.broadcastable
 
         return [gx, gy] + [DisconnectedType()()] * len(idx_list)