Make sympify'ing SymInt/etc produce their sympy expression (#130166)

There is one huge problem this fixes: today, sympify(symint)
produces a float(!!) because Sympy attempts to see if you can
coerce the symint to float in sympify and of course this works on
SymInt.

However, this also has another nontrivial effect: anywhere in Inductor
where sympy expressions are passed around, it is also valid to pass
around a SymInt now.  I'm ambivalent about this: it's currently a
mistake to be passing around a SymInt when a sympy expression is
expected.  But maybe this is fine?

Signed-off-by: Edward Z. Yang <ezyang@meta.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/130166
Approved by: https://github.com/yf225

test/test_dynamic_shapes.py

@@ -260,6 +260,15 @@ class TestPySymInt(TestCase):
         a = create_symint(shape_env, 2)
         self.assertTrue(5 * a == 5 * 2)
 
+    def test_sympify_symint(self):
+        shape_env = ShapeEnv()
+        a = create_symint(shape_env, 2)
+        self.assertIs(sympy.sympify(a), a.node.expr)
+        b = create_symfloat(shape_env, 3.0)
+        self.assertIs(sympy.sympify(b), b.node.expr)
+        c = create_symbool(shape_env, True)
+        self.assertIs(sympy.sympify(c), c.node.expr)
+
     def test_roundtrip(self):
         shape_env = ShapeEnv()
         x = create_symbolic_tensor("x", torch.randn(5, 4, 3), shape_env)

torch/__init__.py

@@ -507,6 +507,9 @@ class SymInt:
     def __repr__(self):
         return str(self.node)
 
+    def _sympy_(self):
+        return self.node.expr
+
     def __hash__(self) -> builtins.int:
         if self.node.is_nested_int():
             return hash(self.node.nested_int())
@@ -615,6 +618,9 @@ class SymFloat:
     def __repr__(self):
         return self.node.str()
 
+    def _sympy_(self):
+        return self.node.expr
+
     def __hash__(self):
         if self.node.is_constant():
             return hash(self.node.float_())
@@ -680,6 +686,9 @@ class SymBool:
     def __repr__(self):
         return str(self.node)
 
+    def _sympy_(self):
+        return self.node.expr
+
     def __hash__(self):
         if self.node.is_constant():
             return hash(self.node.bool_())

torch/_inductor/utils.py

@@ -281,9 +281,7 @@ def convert_shape_to_inductor(
     trivial. But for symbolic tensors, we need to map from SymIntNode into
     sympy.Expr.
     """
-    return [
-        i.node.expr if isinstance(i, torch.SymInt) else sympy.Integer(i) for i in lst
-    ]
+    return [sympy.sympify(i) for i in lst]
 
 
 def convert_shape_to_symint(