SubTensor: make perform faster when idx_list contains only constants (thank you Pascal).

theano/tensor/basic.py

@@ -2133,6 +2133,7 @@ class Subtensor(Op):
 
     def __init__(self, idx_list):
         self.idx_list = map(self.convert, idx_list)
+        self.perform_cache_cdata = None
 
     @staticmethod
     def my_as_scalar(a):
@@ -2172,21 +2173,38 @@ class Subtensor(Op):
 
     def perform(self, node, inputs, (out, )):
         x = inputs[0]
+
+        # The subtensor (or idx_list) does not depend on the inputs.
+        # (and cdata was cached on initial call)
+        if self.perform_cache_cdata is not None:
+            out[0] = numpy.asarray(x.__getitem__(self.perform_cache_cdata))
+            return
+
         indices = list(reversed(inputs[1:]))
 
-        def convert(entry):
-            if isinstance(entry, gof.Type):
-                return indices.pop()
-            elif isinstance(entry, slice):
-                return slice(convert(entry.start),
+        # The subtensor (or idx_list) does not depend on the inputs.
+        # (first call caches cdata here)
+        if len(indices) == 0:
+            cdata = tuple(self.idx_list)
+            if len(cdata) == 1:
+                cdata = cdata[0]
+            self.perform_cache_cdata = cdata
+        # General case
+        else:
+            def convert(entry):
+                if isinstance(entry, gof.Type):
+                    return indices.pop()
+                elif isinstance(entry, slice):
+                    return slice(convert(entry.start),
                              convert(entry.stop),
                              convert(entry.step))
-            else:
-                return entry
+                else:
+                    return entry
+            cdata = tuple(map(convert, self.idx_list))
+
+            if len(cdata) == 1:
+                cdata = cdata[0]
 
-        cdata = tuple(map(convert, self.idx_list))
-        if len(cdata) == 1:
-            cdata = cdata[0]
         out[0] = numpy.asarray(x.__getitem__(cdata))
 
     def infer_shape(self, node, shapes):