updating tests and functions by upcasting int8 indices

doc/library/tensor/basic.txt

@@ -1122,17 +1122,10 @@ Theano indexing with a "mask" (incorrect approach):
 
    >>> t = theano.tensor.arange(9).reshape((3,3))
    >>> t[t > 4].eval()  # an array with shape (3, 3, 3)
-   array([[[0, 1, 2],
-           [0, 1, 2],
-           [0, 1, 2]],
-   <BLANKLINE>
-          [[0, 1, 2],
-           [0, 1, 2],
-           [3, 4, 5]],
-   <BLANKLINE>
-          [[3, 4, 5],
-           [3, 4, 5],
-           [3, 4, 5]]])
+
+   Traceback (most recent call last):
+     ...
+   TypeError: TensorType does not support boolean mask for indexing such as tensor[x==0]. If you are indexing on purpose with an int8, please cast it to int32
 
 Getting a Theano result like NumPy:
 

theano/tensor/extra_ops.py

@@ -696,12 +696,14 @@ def bincount(x, weights=None, minlength=None, assert_nonneg=False):
     if minlength is not None:
         max_value = theano.tensor.maximum(max_value, minlength)
 
+    # Note: we do not use inc_subtensor(out[x], ...) in the following lines,
+    # since out[x] raises an exception if the indices (x) are int8.
     if weights is None:
         out = theano.tensor.zeros([max_value], dtype=x.dtype)
-        out = theano.tensor.inc_subtensor(out[x], 1)
+        out = theano.tensor.advanced_inc_subtensor1(out, 1, x)
     else:
         out = theano.tensor.zeros([max_value], dtype=weights.dtype)
-        out = theano.tensor.inc_subtensor(out[x], weights)
+        out = theano.tensor.advanced_inc_subtensor1(out, weights, x)
     return out
 
 

theano/tensor/tests/test_basic.py

@@ -6988,7 +6988,7 @@ class T_get_scalar_constant_value(unittest.TestCase):
         assert get_scalar_constant_value(mv[0]) == 1
         assert get_scalar_constant_value(mv[1]) == 2
         assert get_scalar_constant_value(mv[2]) == 3
-        assert get_scalar_constant_value(mv[numpy.int8(0)]) == 1
+        assert get_scalar_constant_value(mv[numpy.int32(0)]) == 1
         assert get_scalar_constant_value(mv[numpy.int64(1)]) == 2
         assert get_scalar_constant_value(mv[numpy.uint(2)]) == 3
         t = theano.scalar.Scalar('int64')