Add tests of tensorsolve for type upcast.

theano/tensor/nlinalg.py

@@ -821,7 +821,7 @@ class TensorSolve(Op):
 
 def tensorsolve(a, b, axes=None):
     """
-    Theano utilization of numpy.linalg.tensorsolve
+    Theano utilization of numpy.linalg.tensorsolve. Does not run on GPU!
 
     Solve the tensor equation ``a x = b`` for x.
     It is assumed that all indices of `x` are summed over in the product,

theano/tensor/tests/test_nlinalg.py

@@ -165,6 +165,7 @@ def test_svd():
 
 def test_tensorsolve():
     rng = numpy.random.RandomState(utt.fetch_seed())
+
     A = tensor.tensor4("A", dtype=theano.config.floatX)
     B = tensor.matrix("B", dtype=theano.config.floatX)
     X = tensorsolve(A, B)
@@ -179,6 +180,34 @@ def test_tensorsolve():
     t_x = fn(a, b)
     assert _allclose(n_x, t_x)
 
+    # check the type upcast now
+    C = tensor.tensor4("C", dtype='float32')
+    D = tensor.matrix("D", dtype='float64')
+    Y = tensorsolve(C, D)
+    fn = function([C, D], [Y])
+
+    c = numpy.eye(2*3*4).astype('float32')
+    c.shape = (2*3, 4, 2, 3*4)
+    d = rng.rand(2*3, 4).astype('float64')
+    n_y = numpy.linalg.tensorsolve(c, d)
+    t_y = fn(c, d)
+    assert _allclose(n_y, t_y)
+    assert n_y.dtype == Y.dtype
+
+    # check the type upcast now
+    E = tensor.tensor4("E", dtype='int32')
+    F = tensor.matrix("F", dtype='float64')
+    Z = tensorsolve(E, F)
+    fn = function([E, F], [Z])
+
+    e = numpy.eye(2*3*4).astype('int32')
+    e.shape = (2*3, 4, 2, 3*4)
+    f = rng.rand(2*3, 4).astype('float64')
+    n_z = numpy.linalg.tensorsolve(e, f)
+    t_z = fn(e, f)
+    assert _allclose(n_z, t_z)
+    assert n_z.dtype == Z.dtype
+
 
 def test_inverse_singular():
     singular = numpy.array([[1, 0, 0]] + [[0, 1, 0]] * 2,