Make a gpu test move computation to the GPU.

theano/gpuarray/tests/test_elemwise.py

@@ -13,7 +13,7 @@ from .config import mode_with_gpu, mode_without_gpu, test_ctx_name
 from .test_basic_ops import rand_gpuarray
 from ..elemwise import (GpuElemwise, GpuDimShuffle,
                         GpuCAReduceCuda, GpuCAReduceCPY, GpuErfinv, GpuErfcinv)
-from ..type import GpuArrayType, get_context
+from ..type import GpuArrayType, get_context, gpuarray_shared_constructor
 
 from pygpu import ndgpuarray as gpuarray
 
@@ -40,16 +40,21 @@ def test_elemwise_pow():
         for dtype_exp in dtypes:
 
             # Compile a gpu function with the specified dtypes
-            base = theano.tensor.vector(dtype=dtype_base)
-            exp = theano.tensor.vector(dtype=dtype_exp)
-            output = base ** exp
-            f = theano.function([base, exp], output)
-
             base_val = np.random.randint(0, 5, size=10).astype(dtype_base)
             exp_val = np.random.randint(0, 3, size=10).astype(dtype_exp)
 
+            base = theano.tensor.vector(dtype=dtype_base)
+            exp = gpuarray_shared_constructor(exp_val)
+            output = base ** exp
+            f = theano.function([base], output, mode=mode_with_gpu)
+            theano.printing.debugprint(f)
+            # We don't transfer to the GPU when the output dtype is int*
+            n = len([n for n in f.maker.fgraph.apply_nodes
+                     if isinstance(n.op, GpuElemwise)])
+            assert n == int("float" in output.dtype)
+
             # Call the function to make sure the output is valid
-            out = f(base_val, exp_val)
+            out = f(base_val)
             expected_out = base_val ** exp_val
             assert_allclose(out, expected_out)