Merge pull request #3902 from abergeron/fix_filter

theano/compile/pfunc.py

@@ -195,7 +195,7 @@ def rebuild_collect_shared(outputs,
                            store_into,
                            store_into.type,
                            update_val,
-                           update_val.type))
+                           getattr(update_val, 'type', None)))
             err_sug = ('If the difference is related to the broadcast pattern,'
                        ' you can call the'
                        ' tensor.unbroadcast(var, axis_to_unbroadcast[, ...])'

theano/sandbox/gpuarray/tests/test_type.py

@@ -3,6 +3,7 @@ import numpy
 import theano
 from theano.compile import DeepCopyOp
 
+from .config import test_ctx_name
 from .test_basic_ops import rand_gpuarray
 
 from ..type import GpuArrayType
@@ -37,3 +38,8 @@ def test_specify_shape():
     g = GpuArrayType(dtype='float32', broadcastable=(False,))('g')
     f = theano.function([g], theano.tensor.specify_shape(g, [20]))
     f(a)
+
+
+def test_filter_float():
+    s = theano.shared(numpy.array(0.0, dtype='float32'), target=test_ctx_name)
+    theano.function([], updates=[(s, 0.0)])

theano/sandbox/gpuarray/type.py

@@ -189,13 +189,13 @@ class GpuArrayType(Type):
             if self.context != data.context:
                 raise TypeError("data context does not match type context")
             # fallthrough to ndim check
-            elif (allow_downcast or
-                  (allow_downcast is None and
-                   type(data) == float and
-                   self.dtype == config.floatX)):
-                data = gpuarray.array(data, dtype=self.typecode, copy=False,
-                                      ndmin=len(self.broadcastable),
-                                      context=self.context)
+        elif (allow_downcast or
+              (allow_downcast is None and
+               type(data) == float and
+               self.dtype == config.floatX)):
+            data = gpuarray.array(data, dtype=self.typecode, copy=False,
+                                  ndmin=len(self.broadcastable),
+                                  context=self.context)
         else:
             if not hasattr(data, 'dtype'):
                 # This is to convert objects that don't have a dtype