Merged

theano/scalar/basic.py

@@ -851,9 +851,9 @@ class BinaryBitOp(BinaryScalarOp):
         return [None, None]
 
 class OR(BinaryBitOp):
-    identity = False
+    identity = 0
     commutative = True
-    associative = False
+    associative = True
     def impl(self, x, y):
         return x | y
     def c_code(self, node, name, (x, y), (z, ), sub):
@@ -861,9 +861,9 @@ class OR(BinaryBitOp):
 or_ = OR()
 
 class XOR(BinaryBitOp):
-    identity = False
+    identity = 0
     commutative = True
-    associative = False
+    associative = True
     def impl(self, x, y):
         return x ^ y
     def c_code(self, node, name, (x, y), (z, ), sub):
@@ -871,9 +871,9 @@ class XOR(BinaryBitOp):
 xor = XOR()
 
 class AND(BinaryBitOp):
-    identity = False
+    identity = 1
     commutative = True
-    associative = False
+    associative = True
     def impl(self, x, y):
         return x & y
     def c_code(self, node, name, (x, y), (z, ), sub):
@@ -881,7 +881,6 @@ class AND(BinaryBitOp):
 and_ = AND()
 
 class Invert(UnaryBitOp):
-    identity = False
     def impl(self, x):
         return ~x
     def c_code(self, node, name, (x,), (z, ), sub):

theano/tensor/tests/test_elemwise.py

@@ -172,7 +172,7 @@ class test_CAReduce(unittest.TestCase):
     def setUp(self):
         unittest_tools.seed_rng()
 
-    def with_linker(self, linker, scalar_op = add):
+    def with_linker(self, linker, scalar_op = add, dtype="floatX"):
         for xsh, tosum in [((5, 6), None),
                            ((5, 6), (0, 1)),
                            ((5, 6), (0, )),
@@ -188,11 +188,17 @@ class test_CAReduce(unittest.TestCase):
                            ((5, 0), ()),
                            ((), None),
                            ((), ())]:
-            x = TensorType('float64', [(entry == 1) for entry in xsh])('x')
+            if dtype == "floatX":
+                dtype = theano.config.floatX
+            x = TensorType(dtype, [(entry == 1) for entry in xsh])('x')
             e = CAReduce(scalar_op, axis = tosum)(x)
             if tosum is None: tosum = range(len(xsh))
             f = copy(linker).accept(Env([x], [e])).make_function()
             xv = numpy.asarray(numpy.random.rand(*xsh))
+            if dtype.startswith('float'):
+                xv = numpy.asarray(xv,dtype=dtype)
+            else:
+                xv = numpy.asarray(xv<0.5,dtype=dtype)
             zv = xv
             numpy_raised = False
             if len(tosum)>1 and any([a<0 for a in tosum]):
@@ -224,10 +230,17 @@ class test_CAReduce(unittest.TestCase):
                     numpy_raised=True
             elif scalar_op == or_:
                 for axis in reversed(sorted(tosum)):
-                    zv = numpy.any(zv, axis)
+                    zv = numpy.bitwise_or.reduce(zv, axis)
             elif scalar_op == and_:
                 for axis in reversed(sorted(tosum)):
-                    zv = numpy.all(zv, axis)
+                    zv = numpy.bitwise_and.reduce(zv, axis)
+            elif scalar_op == xor:
+                # There is no identity value for the xor function
+                # So we can't support shape of dimensions 0.
+                if numpy.prod(zv.shape)==0:
+                    continue
+                for axis in reversed(sorted(tosum)):
+                    zv = numpy.bitwise_xor.reduce(zv, axis)
             else:
                 raise Exception("Test for CAReduce with scalar_op %s not implemented"%str(scalar_op))
             if scalar_op in [maximum,minimum] and numpy_raised:
@@ -238,13 +251,16 @@ class test_CAReduce(unittest.TestCase):
                 else:
                     self.fail()
             else:
+                #numpy.{all,any} return bool type.
+                if scalar_op in [and_, or_]:
+                    zv = numpy.asarray(zv, dtype=dtype)
                 self.assertTrue((numpy.abs(f(xv) - zv) < 1e-10).all())
 
 
             #test CAReduce.infer_shape
             #the Shape op don't implement c_code!
             if isinstance(linker,gof.PerformLinker):
-                x = TensorType('float64', [(entry == 1) for entry in xsh])('x')
+                x = TensorType(dtype, [(entry == 1) for entry in xsh])('x')
                 e = CAReduce(scalar_op, axis = tosum)(x)
                 if tosum is None: tosum = range(len(xsh))
                 f = copy(linker).accept(Env([x], [e.shape])).make_function()
@@ -256,20 +272,18 @@ class test_CAReduce(unittest.TestCase):
         self.with_linker(gof.PerformLinker(), mul)
         self.with_linker(gof.PerformLinker(), maximum)
         self.with_linker(gof.PerformLinker(), minimum)
-        #need other dtype then real
-        #self.with_linker(gof.PerformLinker(), or_)
-        #self.with_linker(gof.PerformLinker(), and_)
+        self.with_linker(gof.PerformLinker(), or_, dtype='int8')
+        self.with_linker(gof.PerformLinker(), and_, dtype='int8')
+        self.with_linker(gof.PerformLinker(), xor, dtype='int8')
 
     def test_c(self):
         self.with_linker(gof.CLinker(), add)
         self.with_linker(gof.CLinker(), mul)
         self.with_linker(gof.CLinker(), maximum)
         self.with_linker(gof.CLinker(), minimum)
-
-        #need other dtype then real
-        #no c_code for or_, and_
-        #self.with_linker(gof.CLinker(), or_)
-        #self.with_linker(gof.CLinker(), and_)
+        self.with_linker(gof.CLinker(), or_, dtype='int8')
+        self.with_linker(gof.CLinker(), and_, dtype='int8')
+        self.with_linker(gof.CLinker(), xor, dtype='int8')
 
 
 class test_Prod(unittest.TestCase):