Make CudaNdarray DimShuffle return error as Tensor instead of returning bad value.

theano/sandbox/cuda/cuda_ndarray.cu

@@ -2980,11 +2980,17 @@ CudaNdarray_dimshuffle(CudaNdarray * self, unsigned int len, const int * pattern
             newdims[i] = 1;
             newstrides[i] = 0;
         }
+	else if(dims_taken[pattern[i]])
+	{
+	  PyErr_SetString(PyExc_ValueError, "Cudandarray_dimshuffle: The same input dimension may not appear twice in the list of output dimensions");
+	  free(newdims);
+	  return -1;
+	}
         else
         {
             if ((dims_taken[pattern[i]]) || (pattern[i]>= self->nd))
             {
-                PyErr_SetString(PyExc_ValueError, "invalid pattern for Cudandarray_dimshuffle");
+                PyErr_SetString(PyExc_ValueError, "Cudandarray_dimshuffle: invalid pattern for Cudandarray_dimshuffle");
                 free(newdims);
                 return -1;
             }
@@ -2993,6 +2999,16 @@ CudaNdarray_dimshuffle(CudaNdarray * self, unsigned int len, const int * pattern
             dims_taken[pattern[i]] = 1;
         }
     }
+    //Check if we dropped not broadcastable dims
+    for (int i = 0; i < self->nd; ++i)
+    {
+        if (dims_taken[i]==0 && CudaNdarray_HOST_DIMS(self)[i]!=1)
+        {
+	    PyErr_SetString(PyExc_ValueError, "Cudandarray_dimshuffle: You cannot drop a non-broadcastable dimension.");
+	    free(newdims);
+	    return -1;
+        }
+    }
     //swap this structure in for the one in self, and sync to the card
     if (CudaNdarray_set_nd(self, len))
     {

theano/sandbox/cuda/tests/test_cuda_ndarray.py

+import time, copy, sys, unittest
+
+
 from theano.tests import unittest_tools as utt
-import time, copy, sys
 import theano
 import theano.sandbox.cuda as cuda_ndarray
 # Skip test if cuda_ndarray is not available.
@@ -154,14 +156,64 @@ def test_nvcc_bug():
     assert numpy.allclose(a+a, numpy.asarray(c))
     assert numpy.allclose(a, numpy.asarray(d))
 
-def test_dimshuffle():
+class test_DimShuffle(unittest.TestCase):
+    def test_dimshuffle(self):
         utt.seed_rng()
         rng = numpy.random.RandomState(utt.fetch_seed())
 
-    a = theano._asarray(rng.randn(3,11), dtype='float32')
+        # 2d -> 0d
+        a = theano._asarray(rng.randn(1,1), dtype='float32')
+        b = cuda_ndarray.CudaNdarray(a)
+        assert numpy.allclose(numpy.transpose(a), cuda_ndarray.dimshuffle(b,()))
+
+        # Test when we drop a axis that don't have shape 1
+        a = theano._asarray(rng.randn(2,1), dtype='float32')
+        b = cuda_ndarray.CudaNdarray(a)
+        self.assertRaises(ValueError, cuda_ndarray.dimshuffle, b,())
+
+        # Test that we can't take a dimensions multiple time
+        a = theano._asarray(rng.randn(2,1), dtype='float32')
+        b = cuda_ndarray.CudaNdarray(a)
+        self.assertRaises(ValueError, cuda_ndarray.dimshuffle, b,(1,1))
+
+        # 1d
+        a = theano._asarray(rng.randn(3,), dtype='float32')
         b = cuda_ndarray.CudaNdarray(a)
+        assert numpy.allclose(numpy.transpose(a), cuda_ndarray.dimshuffle(b,(0,)))
+        assert numpy.allclose(a[None,:,None], cuda_ndarray.dimshuffle(b,(-1,0,-1)))
 
+        # 2d
+        a = theano._asarray(rng.randn(3,11), dtype='float32')
+        b = cuda_ndarray.CudaNdarray(a)
         assert numpy.allclose(numpy.transpose(a), cuda_ndarray.dimshuffle(b,(1,0)))
+        assert numpy.allclose(numpy.transpose(a)[None,:,None,:,None], cuda_ndarray.dimshuffle(b,(-1,1,-1,0,-1)))
+
+        # 2d -> 1d
+        a = theano._asarray(rng.randn(1,11), dtype='float32')
+        b = cuda_ndarray.CudaNdarray(a)
+        assert numpy.allclose(a[:,], cuda_ndarray.dimshuffle(b,(1,)))
+        a = theano._asarray(rng.randn(11,1), dtype='float32')
+        b = cuda_ndarray.CudaNdarray(a)
+        assert numpy.allclose(a.reshape((11,)), cuda_ndarray.dimshuffle(b,(0,)))
+
+        # 3d
+        a = theano._asarray(rng.randn(3,4,5), dtype='float32')
+        b = cuda_ndarray.CudaNdarray(a)
+        assert numpy.allclose(a, cuda_ndarray.dimshuffle(b,(0,1,2)))
+        assert numpy.allclose(numpy.swapaxes(a,0,1), cuda_ndarray.dimshuffle(b,(1,0,2)))
+        assert numpy.allclose(numpy.swapaxes(a,0,2), cuda_ndarray.dimshuffle(b,(2,1,0)))
+        assert numpy.allclose(numpy.swapaxes(a,1,2), cuda_ndarray.dimshuffle(b,(0,2,1)))
+        assert numpy.allclose(numpy.swapaxes(a,1,2)[None,:,None,:,:,None], cuda_ndarray.dimshuffle(b,(-1,0,-1,2,1,-1)))
+
+        # 4d
+        a = theano._asarray(rng.randn(3,11,4,5), dtype='float32')
+        b = cuda_ndarray.CudaNdarray(a)
+        assert numpy.allclose(numpy.swapaxes(a,0,1), cuda_ndarray.dimshuffle(b,(1,0,2,3)))
+        assert numpy.allclose(numpy.swapaxes(a,0,2), cuda_ndarray.dimshuffle(b,(2,1,0,3)))
+        assert numpy.allclose(numpy.swapaxes(a,0,3), cuda_ndarray.dimshuffle(b,(3,1,2,0)))
+        assert numpy.allclose(numpy.swapaxes(a,0,3), cuda_ndarray.dimshuffle(b,(3,1,2,0)))
+        assert numpy.allclose(numpy.swapaxes(a,0,3)[None,:,None,:,:,:], cuda_ndarray.dimshuffle(b,(-1,3,-1,1,2,0)))
+
 
 def test_dot():
     print >>sys.stdout, 'starting test_dot'