make use of the new maximum and minimum op to make smaller ARange.infer_shape graph.

theano/tensor/basic.py

@@ -3175,9 +3175,10 @@ class ARange(Op):
             if is_constant_value(start, 0):
                 return [(cast(stop, 'int64'),)]
             else:
-                return [(theano.tensor.max([cast(stop-start, 'int64'),0]),)]
+                return [(maximum(cast(stop-start, 'int64'),0),)]
         else:
-            return [(theano.tensor.max([cast(ceil(cast((stop-start),'float64')/step),'int64'),0]),)]
+            return [(maximum(cast(ceil(cast((stop-start),'float64')
+                                                     /step),'int64'),0),)]
 
     def perform(self, node, (start, stop, step), (out,)):
         start = start.item()

theano/tensor/tests/test_basic.py

@@ -2075,8 +2075,9 @@ class TestARange(unittest.TestCase):
         start, stop, step = iscalars('start', 'stop', 'step')
         out = arange(start, stop, step)
         f = function([start, stop, step], out.shape, mode=compile.mode.get_default_mode().excluding('fusion'))
-        assert len(f.maker.env.toposort())==12
-#ungly graph...        [DimShuffle{x}(step), DimShuffle{x}(start), DimShuffle{x}(stop), Elemwise{Sub{output_types_preference=transfer_type{0}}}[(0, 0)](DimShuffle{x}.0, DimShuffle{x}.0), Elemwise{Cast{float64}}(Elemwise{Sub{output_types_preference=transfer_type{0}}}[(0, 0)].0), Elemwise{TrueDiv{output_types_preference=transfer_type{0}}}[(0, 0)](Elemwise{Cast{float64}}.0, DimShuffle{x}.0), Rebroadcast{0}(Elemwise{TrueDiv{output_types_preference=transfer_type{0}}}[(0, 0)].0), Elemwise{Ceil{output_types_preference=transfer_type{0}}}[(0, 0)](Rebroadcast{0}.0), Elemwise{Cast{int64}}(Elemwise{Ceil{output_types_preference=transfer_type{0}}}[(0, 0)].0), <theano.tensor.basic.Join object at 0x1fb1d10>(0, Elemwise{Cast{int64}}.0, [0]), MaxAndArgmax(<theano.tensor.basic.Join object at 0x1fb1d10>.0, [0]), MakeVector(max)]
+        assert len(f.maker.env.toposort())==7
+#7 [Elemwise{sub,no_inplace}(stop, start), Elemwise{Cast{float64}}(Elemwise{sub,no_inplace}.0), Elemwise{TrueDiv{output_types_preference=transfer_type{0}}}[(0, 0)](Elemwise{Cast{float64}}.0, step), Elemwise{Ceil{output_types_preference=transfer_type{0}}}[(0, 0)](Elemwise{TrueDiv{output_types_preference=transfer_type{0}}}[(0, 0)].0), Elemwise{Cast{int64}}(Elemwise{Ceil{output_types_preference=transfer_type{0}}}[(0, 0)].0), Elemwise{Maximum{output_types_preference=transfer_type{0}}}[(0, 0)](Elemwise{Cast{int64}}.0, 0), MakeVector(Elemwise{Maximum{output_types_preference=transfer_type{0}}}[(0, 0)].0)]
+
         assert out.dtype == start.type.dtype
         assert numpy.all(f(0,5,1) == len(numpy.arange(0,5,1)))
         assert numpy.all(f(2,11,4) == len(numpy.arange(2,11,4)))
@@ -2087,9 +2088,8 @@ class TestARange(unittest.TestCase):
 
         out = arange(start, stop, 1)
         f = function([start, stop], out.shape, mode=compile.mode.get_default_mode().excluding('fusion'))
-        assert len(f.maker.env.toposort())==8
-#ungly graph...        [DimShuffle{x}(start), DimShuffle{x}(stop), Elemwise{Sub{output_types_preference=transfer_type{0}}}[(0, 0)](DimShuffle{x}.0, DimShuffle{x}.0), Rebroadcast{0}(Elemwise{Sub{output_types_preference=transfer_type{0}}}[(0, 0)].0), Elemwise{Cast{int64}}(Rebroadcast{0}.0), <theano.tensor.basic.Join object at 0x1fb1d10>(0, Elemwise{Cast{int64}}.0, [0]), MaxAndArgmax(<theano.tensor.basic.Join object at 0x1fb1d10>.0, [0]), MakeVector(max)]
-
+        assert len(f.maker.env.toposort())==4
+#4 [Elemwise{sub,no_inplace}(stop, start), Elemwise{Cast{int64}}(Elemwise{sub,no_inplace}.0), Elemwise{Maximum{output_types_preference=transfer_type{0}}}[(0, 0)](Elemwise{Cast{int64}}.0, 0), MakeVector(Elemwise{Maximum{output_types_preference=transfer_type{0}}}[(0, 0)].0)]
         assert out.dtype == start.type.dtype
         assert numpy.all(f(0,5) == len(numpy.arange(0,5)))
         assert numpy.all(f(2,11) == len(numpy.arange(2,11)))