[inductor] Fix ModularIndexing assumptions (pytorch#152993)

Fixes pytorch#151198.

Since the result of ModularIndexing can be zero due to the modulo
operation, we should not make any assumption about ModularIndexing
being positive

Pull Request resolved: pytorch#152993
Approved by: https://github.com/yf225

Author: isuruf

test/inductor/test_indexing.py

@@ -205,6 +205,13 @@ def test_modular_indexing_pairs_not_merged(self):
         self.assertEqual(expr2, actual)
         self.assertNotEqual(ModularIndexing(x, 1, b), actual)
 
+    def test_modular_indexing_positive(self):
+        x = sympy.Symbol("x", integer=True, positive=True)
+        expr = ModularIndexing(x, 1, 1024) - 1
+        expr2 = abs(expr)
+
+        self.assertNotEqual(expr2, expr)
+
     def test_expand_floor_div_skipped(self):
         sizevars = SizeVarAllocator()
         x = sympy.Symbol("x", integer=True, positive=True)

torch/utils/_sympy/functions.py

@@ -363,10 +363,6 @@ def _eval_is_nonnegative(self) -> Optional[bool]:
         p, q = self.args[:2]
         return fuzzy_eq(p.is_nonnegative, q.is_nonnegative)  # type: ignore[attr-defined]
 
-    def _eval_is_positive(self) -> Optional[bool]:
-        p, q = self.args[:2]
-        return fuzzy_eq(p.is_positive, q.is_positive)  # type: ignore[attr-defined]
-
 
 class Where(sympy.Function):
     """