make the GpuElemwise return the same error as Elemwise when the shape mismatch.…

theano/sandbox/cuda/elemwise.py

@@ -37,7 +37,7 @@ def get_str_list_logical_scalar(node, value_str='ii_i%i_value', data_str='ii_i%i
 class NaiveAlgo(object):
     verbose = 0 # 1, 2 or 3 for more verbose output.
     cache_version = ()
-    cache_version = ('debug', 11, verbose)
+    cache_version = ('debug', 12, verbose)
 
     def __init__(self, scalar_op, sync=True):
         """ 
@@ -855,7 +855,7 @@ nd_collapse_[i]=0;
             if ((!(broadcasts_%(iname)s[i] && CudaNdarray_HOST_DIMS(%(iname)s)[i] == 1))&& (dims[i] != CudaNdarray_HOST_DIMS(%(iname)s)[i]))
             {
                 //std::cerr << "C_CODE %(opname)s checking input %(iname)s failed\\n";
-                PyErr_Format(PyExc_TypeError, "GpuElemwise input has incompatible dim[%%i] == %%i, where output has size %%i",
+                PyErr_Format(PyExc_ValueError, "GpuElemwise. Input dimension mis-match.[%%i] == %%i, where output has size %%i",
                     i,
                     CudaNdarray_HOST_DIMS(%(iname)s)[i],
                     dims[i]

theano/sandbox/cuda/tests/test_basic_ops.py

@@ -213,10 +213,15 @@ def test_elemwise_bad_broadcast():
     x = cuda.fmatrix('x')
     y = cuda.fmatrix('y')
 
-    f = theano.function([x, y], x * y)
+    f = theano.function([x, y], x * y, mode=mode_with_gpu)
+    print f.maker.env.toposort()
+    assert len(f.maker.env.toposort())==2
+    assert isinstance(f.maker.env.toposort()[0].op, cuda.GpuElemwise)
+    assert f.maker.env.toposort()[1].op==cuda.host_from_gpu
+
     try:
         f(rand_cuda_ndarray((10, 3)), rand_cuda_ndarray((10, 1)))
-    except TypeError:
+    except ValueError:
         pass
     else:
         raise Exception("Theano should have raised an error")