bpo-28685: Optimize sorted() list.sort() with type-specialized comparisons (#582)

Lib/test/test_sort.py

@@ -260,6 +260,120 @@ class TestDecorateSortUndecorate(unittest.TestCase):
         self.assertEqual(data, copy2)
 
 #==============================================================================
+def check_against_PyObject_RichCompareBool(self, L):
+    ## The idea here is to exploit the fact that unsafe_tuple_compare uses
+    ## PyObject_RichCompareBool for the second elements of tuples. So we have,
+    ## for (most) L, sorted(L) == [y[1] for y in sorted([(0,x) for x in L])]
+    ## This will work as long as __eq__ => not __lt__ for all the objects in L,
+    ## which holds for all the types used below.
+    ##
+    ## Testing this way ensures that the optimized implementation remains consistent
+    ## with the naive implementation, even if changes are made to any of the
+    ## richcompares.
+    ##
+    ## This function tests sorting for three lists (it randomly shuffles each one):
+    ##                        1. L
+    ##                        2. [(x,) for x in L]
+    ##                        3. [((x,),) for x in L]
+
+    random.seed(0)
+    random.shuffle(L)
+    L_1 = L[:]
+    L_2 = [(x,) for x in L]
+    L_3 = [((x,),) for x in L]
+    for L in [L_1, L_2, L_3]:
+        optimized = sorted(L)
+        reference = [y[1] for y in sorted([(0,x) for x in L])]
+        for (opt, ref) in zip(optimized, reference):
+            self.assertIs(opt, ref)
+            #note: not assertEqual! We want to ensure *identical* behavior.
+
+class TestOptimizedCompares(unittest.TestCase):
+    def test_safe_object_compare(self):
+        heterogeneous_lists = [[0, 'foo'],
+                               [0.0, 'foo'],
+                               [('foo',), 'foo']]
+        for L in heterogeneous_lists:
+            self.assertRaises(TypeError, L.sort)
+            self.assertRaises(TypeError, [(x,) for x in L].sort)
+            self.assertRaises(TypeError, [((x,),) for x in L].sort)
+
+        float_int_lists = [[1,1.1],
+                           [1<<70,1.1],
+                           [1.1,1],
+                           [1.1,1<<70]]
+        for L in float_int_lists:
+            check_against_PyObject_RichCompareBool(self, L)
+
+    def test_unsafe_object_compare(self):
+
+        # This test is by ppperry. It ensures that unsafe_object_compare is
+        # verifying ms->key_richcompare == tp->richcompare before comparing.
+
+        class WackyComparator(int):
+            def __lt__(self, other):
+                elem.__class__ = WackyList2
+                return int.__lt__(self, other)
+
+        class WackyList1(list):
+            pass
+
+        class WackyList2(list):
+            def __lt__(self, other):
+                raise ValueError
+
+        L = [WackyList1([WackyComparator(i), i]) for i in range(10)]
+        elem = L[-1]
+        with self.assertRaises(ValueError):
+            L.sort()
+
+        L = [WackyList1([WackyComparator(i), i]) for i in range(10)]
+        elem = L[-1]
+        with self.assertRaises(ValueError):
+            [(x,) for x in L].sort()
+
+        # The following test is also by ppperry. It ensures that
+        # unsafe_object_compare handles Py_NotImplemented appropriately.
+        class PointlessComparator:
+            def __lt__(self, other):
+                return NotImplemented
+        L = [PointlessComparator(), PointlessComparator()]
+        self.assertRaises(TypeError, L.sort)
+        self.assertRaises(TypeError, [(x,) for x in L].sort)
+
+        # The following tests go through various types that would trigger
+        # ms->key_compare = unsafe_object_compare
+        lists = [list(range(100)) + [(1<<70)],
+                 [str(x) for x in range(100)] + ['\uffff'],
+                 [bytes(x) for x in range(100)],
+                 [cmp_to_key(lambda x,y: x<y)(x) for x in range(100)]]
+        for L in lists:
+            check_against_PyObject_RichCompareBool(self, L)
+
+    def test_unsafe_latin_compare(self):
+        check_against_PyObject_RichCompareBool(self, [str(x) for
+                                                      x in range(100)])
+
+    def test_unsafe_long_compare(self):
+        check_against_PyObject_RichCompareBool(self, [x for
+                                                      x in range(100)])
+
+    def test_unsafe_float_compare(self):
+        check_against_PyObject_RichCompareBool(self, [float(x) for
+                                                      x in range(100)])
+
+    def test_unsafe_tuple_compare(self):
+        # This test was suggested by Tim Peters. It verifies that the tuple
+        # comparison respects the current tuple compare semantics, which do not
+        # guarantee that x < x <=> (x,) < (x,)
+        #
+        # Note that we don't have to put anything in tuples here, because
+        # the check function does a tuple test automatically.
+
+        check_against_PyObject_RichCompareBool(self, [float('nan')]*100)
+        check_against_PyObject_RichCompareBool(self, [float('nan') for
+                                                      _ in range(100)])
+#==============================================================================
 
 if __name__ == "__main__":
     unittest.main()

Misc/ACKS

@@ -554,6 +554,7 @@ Tiago Gonçalves
 Chris Gonnerman
 Shelley Gooch
 David Goodger
+Elliot Gorokhovsky
 Hans de Graaff
 Tim Graham
 Kim Gräsman

Misc/NEWS.d/next/Core and Builtins/2018-01-28-15-09-33.bpo-28685.cHThLM.rst

@@ -0,0 +1,2 @@
+Optimize list.sort() and sorted() by using type specialized comparisons when
+possible.

Objects/listsort.txt

@@ -753,3 +753,11 @@ example, with the region of uncertainty B[4], B[5], B[6], there are 4
 locations:  before B[4], between B[4] and B[5], between B[5] and B[6], and
 after B[6].  In general, across 2**(k-1)-1 elements, there are 2**(k-1)
 locations.  That's why k-1 binary searches are necessary and sufficient.
+
+OPTIMIZATION OF INDIVIDUAL COMPARISONS
+As noted above, even the simplest Python comparison triggers a large pile of
+C-level pointer dereferences, conditionals, and function calls.  This can be
+partially mitigated by pre-scanning the data to determine whether the data is
+homogenous with respect to type.  If so, it is sometimes possible to
+substitute faster type-specific comparisons for the slower, generic
+PyObject_RichCompareBool.