Enable gammaln c code (also for GPU support) for [u]int*

theano/scalar/basic_scipy.py

@@ -269,10 +269,13 @@ class GammaLn(UnaryScalarOp):
     def c_code(self, node, name, inp, out, sub):
         x, = inp
         z, = out
-        if node.inputs[0].type in float_types:
-            return """%(z)s =
-                lgamma(%(x)s);""" % locals()
-        raise NotImplementedError('only floating point is implemented')
+        # no c code for complex
+        # [u]int* will be casted to float64 before computation
+        if x.type in complex_types:
+            raise NotImplementedError(
+                'gammaln complex c code is not implemented')
+        return """%(z)s =
+            lgamma(%(x)s);""" % locals()
 gammaln = GammaLn(upgrade_to_float, name='gammaln')
 
 

theano/tensor/tests/test_basic.py

@@ -1807,7 +1807,8 @@ _good_broadcast_unary_gammaln = dict(
     empty=(np.asarray([], dtype=config.floatX),),
     int=(randint_ranged(1, 10, (2, 3)),),
     uint8=(randint_ranged(1, 6, (2, 3)).astype('uint8'),),
-    uint16=(randint_ranged(1, 10, (2, 3)).astype('uint16'),))
+    uint16=(randint_ranged(1, 10, (2, 3)).astype('uint16'),),
+    uint64=(randint_ranged(1, 10, (2, 3)).astype('uint64'),))
 _grad_broadcast_unary_gammaln = dict(
     # smaller range as our grad method does not estimate it well enough.
     normal=(rand_ranged(1e-1, 8, (2, 3)),),)