XXX -> TODO and fix GpuArrayType.filter logic.

theano/sandbox/gpuarray/__init__.py

@@ -34,11 +34,13 @@ from type import (GpuArrayType, GpuArrayVariable, GpuArrayConstant,
 def init_dev(dev):
     import globals
     if dev.startswith('cuda'):
+        # format is cuda<devnum>
         globals.kind = 'cuda'
         devnum = int(dev[4:])
     elif dev.startswith('opencl'):
+        # format is opencl<platnum>:<devnum>
         globals.kind = 'opencl'
-        devspec = dev[7:]
+        devspec = dev[6:]
         plat, dev = devspec.split(':')
         devnum = int(dev)|(int(plat)<<16)
     else:
@@ -51,7 +53,7 @@ if pygpu:
         if (config.device.startswith('cuda') or
             config.device.startswith('opencl')):
             init_dev(config.device)
-            # XXX add optimization tags here (when we will have some)
+            # TODO add optimization tags here (when we will have some)
             import theano.compile
             theano.compile.shared_constructor(gpuarray_shared_constructor)
         elif config.gpuarray.init_device != '':

theano/sandbox/gpuarray/basic_ops.py

@@ -15,7 +15,7 @@ from type import GpuArrayType
 def as_gpuarray_variable(x):
     if hasattr(x, '_as_GpuArrayVariable'):
         return x._as_GpuArrayVariable()
-    # XXX: we need to have the cuda -> gpu path taken care of.
+    # TODO we need to have the cuda -> gpu path taken care of.
     tensor_x = tensor.as_tensor_variable(x)
     return gpu_from_host(tensor_x)
 

theano/sandbox/gpuarray/type.py

@@ -61,14 +61,14 @@ class GpuArrayType(Type):
                                   kind=self.kind, context=self.context,
                                   ndmin=len(self.broadcastable))
         else:
-            if isinstance(data, gpuarray.GpuArray):
-                up_dtype = scalar.upcast(self.dtype, data.dtype)
-                if up_dtype == self.dtype:
-                    data = data.astype(self.dtype)
-                else:
-                    raise TypeError("%s cannot store a value of dtype %s "
-                                    "without risking loss of precision." %
-                                    (self, data.dtype))
+            up_dtype = scalar.upcast(self.dtype, data.dtype)
+            if up_dtype == self.dtype:
+                data = gpuarray.array(data, dtype=self.typecode, copy=False,
+                                      kind=self.kind, context=self.context)
+            else:
+                raise TypeError("%s cannot store a value of dtype %s "
+                                "without risking loss of precision." %
+                                (self, data.dtype))
 
         if self.ndim != data.ndim:
             raise TypeError("Wrong number of dimensions: expected %s, "