move gpu join test to the cpu/gpu join test and remove duplicate test.

theano/sandbox/cuda/tests/test_basic_ops.py

@@ -646,36 +646,6 @@ def test_hostfromgpu_shape_i():
 # -----------------------------------------------------------------------
 
 import theano.sandbox.cuda as cuda_ndarray
-from theano.sandbox.cuda.basic_ops import gpu_join, GpuDimShuffle
-
-def test_gpujoin_preserves_broadcasting():
-    _a = numpy.asarray([[1,2],[3,4]],dtype='float32')
-    _b = numpy.asarray([[5,6,7],[8,9,10]],dtype='float32')
-    a = tcn.shared_constructor(_a)
-    b = tcn.shared_constructor(_b)
-
-    # [0,0] : the two original dims were non-broadcastable
-    # [1,x,0]: new order and broadcastability
-    gpu_dimshuffle = GpuDimShuffle([0,0], [1,'x',0])
-
-    a_shuffled = gpu_dimshuffle(a)
-    b_shuffled = gpu_dimshuffle(b)
-
-    c = gpu_join(0,a_shuffled,b_shuffled)
-
-    assert c.type.broadcastable == (False,True,False)
-
-    f = theano.function([], c, mode=mode_with_gpu)
-
-    res = f()
-
-    a_reshaped = numpy.asarray([[[1,3]],[[2,4]]], dtype='float32')
-    b_reshaped = numpy.asarray([[[5,8]],[[6,9]],[[7,10]]], dtype='float32')
-
-    concat = numpy.concatenate([a_reshaped,b_reshaped], axis=0)
-
-    assert numpy.all(res == concat)
-
 
 def test_gpujoin_assert_cndas():
     # this will end up being an ndarray, as it's float64
@@ -683,7 +653,7 @@ def test_gpujoin_assert_cndas():
     a = theano.shared(_a)
 
     try:
-        c = gpu_join(1,a)
+        c = cuda.basic_ops.gpu_join(1,a)
         # can't "assert False" here, as we want the assertion
         # error from gpu_join
     except AssertionError: