Fix failing tests due to change in join/subtensor or inconsistency between version.

theano/sandbox/cuda/basic_ops.py

@@ -3043,13 +3043,8 @@ class GpuJoin(tensor.Join, GpuOp):
         axis, tensors = axis_and_tensors[0], axis_and_tensors[1:]
         if not tensors:
             raise ValueError('Cannot join an empty list of tensors')
-        are_instances = [isinstance(x.type, CudaNdarrayType) \
-                                                for x in tensors]
-        assert numpy.all(are_instances)
-
-        # no conversion needed, we just checked everything was
-        # a CNDA var
-        as_tensor_variable_args = tensors
+        as_tensor_variable_args = [as_cuda_ndarray_variable(x)
+                                   for x in tensors]
 
         output_maker = \
                 lambda bcast: CudaNdarrayType(broadcastable=bcast)()

theano/sandbox/cuda/tests/test_basic_ops.py

@@ -1000,6 +1000,7 @@ class T_subtensor(theano.tensor.tests.test_subtensor.T_subtensor):
     adv_incsub1 = cuda.GpuAdvancedIncSubtensor1
     mode = mode_with_gpu
     dtype = 'float32'
+    type = tcn.CudaNdarrayType
     ignore_topo = (B.HostFromGpu, B.GpuFromHost, theano.compile.DeepCopyOp)
     fast_compile = False
     ops = (cuda.GpuSubtensor, cuda.GpuIncSubtensor,

theano/sandbox/gpuarray/subtensor.py

@@ -368,13 +368,19 @@ class GpuAdvancedIncSubtensor1(HideC, tensor.AdvancedIncSubtensor1):
 
         if ilist_.type.dtype[:3] not in ('int', 'uin'):
             raise TypeError('index must be integers')
-        if ilist_.type.broadcastable != (False,):
+        if ilist_.type.ndim != 1:
             raise TypeError('index must be vector')
         if x_.type.ndim == 0:
             raise TypeError('cannot index into a scalar')
-        if x_.type.broadcastable[0]:
-            # the caller should have made a copy of x len(ilist) times
-            raise TypeError('cannot index into a broadcastable dimension')
+        if y_.type.ndim > x_.type.ndim:
+            if self.set_instead_of_inc:
+                opname = 'set'
+            else:
+                opname = 'increment'
+            raise TypeError(
+                'cannot %s x subtensor with ndim=%s'
+                ' by y with ndim=%s to x subtensor with ndim=%s ' % (
+                    opname, x_.type.ndim, y_.type.ndim))
 
         return gof.Apply(self, [x_, y_, ilist_], [x_.type()])
 
@@ -459,13 +465,19 @@ class GpuAdvancedIncSubtensor1_dev20(GpuAdvancedIncSubtensor1):
 
         if ilist_.type.dtype[:3] not in ('int', 'uin'):
             raise TypeError('index must be integers')
-        if ilist_.type.broadcastable != (False,):
+        if ilist_.type.ndim != 1:
             raise TypeError('index must be vector')
         if x_.type.ndim == 0:
             raise TypeError('cannot index into a scalar')
-        if x_.type.broadcastable[0]:
-            # the caller should have made a copy of x len(ilist) times
-            raise TypeError('cannot index into a broadcastable dimension')
+        if y_.type.ndim > x_.type.ndim:
+            if self.set_instead_of_inc:
+                opname = 'set'
+            else:
+                opname = 'increment'
+            raise TypeError(
+                'cannot %s x subtensor with ndim=%s'
+                ' by y with ndim=%s to x subtensor with ndim=%s ' % (
+                    opname, x_.type.ndim, y_.type.ndim))
 
         return gof.Apply(self, [x_, y_, ilist_], [x_.type()])
 

theano/tensor/tests/test_basic.py

@@ -3713,20 +3713,20 @@ class T_Join_and_Split(unittest.TestCase):
         self.assertRaises(ValueError, f, a_val, b_val, c_val, d_val, bad_e_val)
 
     def test_infer_shape_join(self):
-        x1 = matrix()
-        x2 = matrix()
-        x3 = matrix()
-
         def get_mat(s1, s2):
             return numpy.asarray(numpy.random.uniform(size=(s1, s2)),
                                  dtype=self.floatX)
 
+        x1 = self.shared(get_mat(3, 4))
+        x2 = self.shared(get_mat(2, 4))
+        x3 = self.shared(get_mat(1, 4))
+
         # Test dim 0
         z = self.join_op(0, x1, x2, x3)
-        f = theano.function([x1, x2, x3], z.shape, mode=self.mode)
+        f = theano.function([], z.shape, mode=self.mode)
         topo = f.maker.fgraph.toposort()
 
-        out = f(get_mat(3, 4), get_mat(2, 4), get_mat(1, 4))
+        out = f()
         assert (out == [6, 4]).all()
 
         if theano.config.mode != 'FAST_COMPILE':
@@ -3735,10 +3735,12 @@ class T_Join_and_Split(unittest.TestCase):
 
         # Test dim 1
         z = self.join_op(1, x1, x2, x3)
-        f = theano.function([x1, x2, x3], z.shape, mode=self.mode)
+        f = theano.function([], z.shape, mode=self.mode)
         topo = f.maker.fgraph.toposort()
-
-        out = f(get_mat(3, 4), get_mat(3, 4), get_mat(3, 5))
+        x1.set_value(get_mat(3, 4))
+        x2.set_value(get_mat(3, 4))
+        x3.set_value(get_mat(3, 5))
+        out = f()
         assert (out == [3, 13]).all()
 
         if theano.config.mode != 'FAST_COMPILE':
@@ -3750,7 +3752,10 @@ class T_Join_and_Split(unittest.TestCase):
             self.assertRaises(ValueError, f, get_mat(3, 4), get_mat(3, 4),
                               get_mat(2, 5))
         else:
-            f(get_mat(3, 4), get_mat(3, 4), get_mat(2, 5))
+            x1.set_value(get_mat(3, 4))
+            x2.set_value(get_mat(3, 4))
+            x3.set_value(get_mat(2, 5))
+            f()
 
     def test_rebroadcast(self):
         # Regression test for a crash that used to happen when rebroadcasting.