Added test for many broadcastable patterns with dot

theano/tensor/tests/test_basic.py

@@ -1604,6 +1604,54 @@ class t_dot(unittest.TestCase):
         #utt.verify_grad(dot, [self.rand(), self.rand(2)])
         #utt.verify_grad(dot, [self.rand(), self.rand(2,5)])
 
+    def test_broadcastable_patterns(self):
+
+        #
+        # These examples hsould all work because we broadcastable or no, all dimensions of all
+        # results have size 1.
+        #
+        def val_for(r):
+
+            if r.ndim == 0:
+                return numpy.asarray(1.1, dtype=r.dtype)
+            if r.ndim == 1:
+                return numpy.asarray([1.2], dtype=r.dtype)
+            elif r.ndim == 2:
+                return numpy.asarray([[1.3]], dtype=r.dtype)
+            raise ValueError()
+
+        failures = []
+        for dtype0 in ('float32', 'float64', 'complex64', 'complex128'):
+            for dtype1 in ('float32', 'float64', 'complex64', 'complex128'):
+                for bc0 in ((True,), (False,), (True, True), (True, False), (False, True),
+                        (False,False)):
+                    for bc1 in ((True,), (False,), (True, True), (True, False), (False, True),
+                            (False,False)):
+
+                        x = TensorType(dtype=dtype0, broadcastable=bc0)()
+                        y = TensorType(dtype=dtype1, broadcastable=bc1)()
+                        z = dot(x,y)
+                        t = TensorType(dtype=dtype0, broadcastable=z.broadcastable)()
+
+                        rval =  z * 3 + 2*t
+                        if rval.type.dtype.startswith('complex'):
+                            # there is a problem with complex numbers right now
+                            # Elemwise code doesn't compile when both precisions of complex
+                            # numbers are used in the same file because the operators
+                            # aren't declared properly.
+                            failures.append((dtype0, dtype1, bc0, bc1))
+                            continue
+                        f = function([x,y,t], rval)
+                        xval = val_for(x)
+                        yval = val_for(y)
+                        tval = val_for(t)
+
+                        f(xval, yval, tval) #debugmode checks result
+        #if failures:
+            #print failures
+        assert not failures
+
+
 class T_tensorfromscalar(unittest.TestCase):
     def test0(self):
         s = scal.constant(56)