Rename GpuCAReduce to GpuCAReduceCuda and reuse the name GpuCAReduce for…

theano/sandbox/gpuarray/elemwise.py

@@ -521,8 +521,8 @@ class GpuDimShuffle(HideC, DimShuffle):
         return (4,)
 
 
-class GpuCAReduce(HideC, CAReduce):
-    """GpuCAReduce is a Reduction along some dimensions by a scalar op.
+class GpuCAReduceCuda(HideC, CAReduce):
+    """GpuCAReduceCuda is a Reduction along some dimensions by a scalar op.
 
     The dimensions along which to reduce is specified by the
     `reduce_mask` that you pass to the constructor.  The `reduce_mask`
@@ -584,12 +584,12 @@ class GpuCAReduce(HideC, CAReduce):
         ax = ''
         if self.axis is not None:
             ax = '{%s}' % (', '.join(str(x) for x in self.axis),)
-        return "GpuCAReduce{%s}%s" % (str(self.scalar_op), ax)
+        return "GpuCAReduceCuda{%s}%s" % (str(self.scalar_op), ax)
 
     def make_node(self, x):
         x = as_gpuarray_variable(x)
         assert x.dtype == "float32"
-        ret = super(GpuCAReduce, self).make_node(x)
+        ret = super(GpuCAReduceCuda, self).make_node(x)
         self = copy.copy(self)
         self.axis = ret.op.axis
         if self.reduce_mask is None:
@@ -762,7 +762,7 @@ class GpuCAReduce(HideC, CAReduce):
             scalar_op = self.scalar_op
             zero_shp = """
             PyErr_Format(PyExc_NotImplementedError,
-                         "GpuCAReduce not implemented when input shape is 0"
+                         "GpuCAReduceCuda not implemented when input shape is 0"
                          " for this scalar_op: %(scalar_op)s");
             %(fail)s;
             """ % locals()
@@ -1162,7 +1162,7 @@ class GpuCAReduce(HideC, CAReduce):
         else:
             zero_shp = """
             PyErr_Format(PyExc_NotImplementedError,
-                         "GpuCAReduce not implemented when input shape is 0 for this scalar_op");
+                         "GpuCAReduceCuda not implemented when input shape is 0 for this scalar_op");
             %(fail)s;
             """ % locals()
 
@@ -2567,3 +2567,5 @@ class GpuCAReduceCPY(GpuKernelBase, HideC, CAReduceDtype):
         else:
             output[0] = pygpu.gpuarray.array(input, copy=True,
                                              dtype=node.outputs[0].type.dtype)
+# To allow reloading old pickled files
+GpuCAReduce = GpuCAReduceCPY

theano/sandbox/gpuarray/opt.py

@@ -22,7 +22,7 @@ from theano.sandbox.gpuarray.conv import GpuConv
 from theano.sandbox.gpuarray.nnet import (GpuCrossentropySoftmaxArgmax1HotWithBias,
                                           GpuCrossentropySoftmax1HotWithBiasDx)
 from theano.sandbox.gpuarray.elemwise import (GpuElemwise, _is_scalar,
-                                              GpuDimShuffle, GpuCAReduce)
+                                              GpuDimShuffle, GpuCAReduceCuda)
 from theano.sandbox.gpuarray.subtensor import GpuIncSubtensor, GpuSubtensor
 from theano.sandbox.gpuarray.type import GpuArrayConstant
 
@@ -248,7 +248,7 @@ def local_gpua_careduce(node):
     if (isinstance(node.op.scalar_op, scalar.basic.Add) or
         isinstance(node.op.scalar_op, scalar.basic.Mul)):
         x, = node.inputs
-        greduce = GpuCAReduce(node.op.scalar_op, axis=node.op.axis)
+        greduce = GpuCAReduceCuda(node.op.scalar_op, axis=node.op.axis)
         if x.dtype != "float32":
             return
         gvar = greduce(x)
@@ -284,7 +284,7 @@ def local_gpua_careduce(node):
                     new_mask.append(reduce_mask[i])
                     new_in_shp.append(x_shape[i])
 
-            new_greduce = GpuCAReduce(new_mask, scalar_op)
+            new_greduce = GpuCAReduceCuda(new_mask, scalar_op)
             reshaped_x = x.reshape(tensor.stack(*new_in_shp))
             gpu_reshaped_x = gpu_from_host(reshaped_x)
             reshaped_gpu_inputs = [gpu_reshaped_x]

theano/sandbox/gpuarray/tests/test_elemwise.py

@@ -10,7 +10,7 @@ from theano.tensor.tests.test_elemwise import (test_Broadcast, test_DimShuffle,
 
 from theano.sandbox.gpuarray.tests.test_basic_ops import rand_gpuarray
 from theano.sandbox.gpuarray.elemwise import (GpuElemwise, GpuDimShuffle,
-                                              GpuCAReduce, GpuCAReduceCPY)
+                                              GpuCAReduceCuda, GpuCAReduceCPY)
 from theano.sandbox.gpuarray.type import GpuArrayType
 
 from pygpu.array import gpuarray
@@ -71,7 +71,7 @@ class test_GpuCAReduceCPY(test_CAReduce):
             test_CAReduce.test_infer_shape(self, dtype)
 
 
-class test_GpuCAReduce(test_GpuCAReduceCPY):
+class test_GpuCAReduceCuda(test_GpuCAReduceCPY):
     dtypes = ["float32"]
     bin_dtypes = ["uint8", "int8"]
     bin_dtypes = []
@@ -141,7 +141,7 @@ class test_GpuCAReduce(test_GpuCAReduceCPY):
 #             ((1100,2,3,4,5),[0,1,2,3,4]),((2,1100,3,4,5),[0,1,2,3,4]),((2,3,1100,4,5),[0,1,2,3,4]),((2,3,4,1100,5),[0,1,2,3,4]),((2,3,4,5,1100),[0,1,2,3,4]),#11111
 #             ((5,4,3,10,11),[1,2]),
         ]
-    op = GpuCAReduce
+    op = GpuCAReduceCuda
     reds = [scalar.add, scalar.mul]
 
     def test_perform(self):

theano/sandbox/gpuarray/tests/test_opt.py

@@ -3,7 +3,7 @@ import numpy
 import theano
 from theano.tests import unittest_tools as utt
 from theano.sandbox.gpuarray.basic_ops import GpuAlloc, GpuReshape, gpu_alloc
-from theano.sandbox.gpuarray.elemwise import GpuCAReduce
+from theano.sandbox.gpuarray.elemwise import GpuCAReduceCuda
 import theano.sandbox.gpuarray
 from theano.tests.unittest_tools import SkipTest
 
@@ -69,8 +69,8 @@ def test_sum_prod():
         res = f(val)
         utt.assert_allclose(res, val.sum())
         assert res.shape == ()
-        assert GpuCAReduce in [type(node.op)
-                               for node in f.maker.fgraph.toposort()]
+        assert GpuCAReduceCuda in [type(node.op)
+                                   for node in f.maker.fgraph.toposort()]
 
 
 def test_local_gpualloc_memset_0():