Turn on type-checking in torch.fx.experimental.symbolic_shapes (#136972)

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

Pull Request resolved: https://github.com/pytorch/pytorch/pull/136972
Approved by: https://github.com/Skylion007
ghstack dependencies: #136934, #136935

mypy_plugins/sympy_mypy_plugin.py

@@ -5,10 +5,18 @@ from mypy.types import NoneType, UnionType
 
 class SympyPlugin(Plugin):
     def get_base_class_hook(self, fullname: str):
+        # TODO: This apparently never worked
         if fullname == "sympy.core.basic.Basic":
             return add_assumptions
         return None
 
+    def get_attribute_hook(self, fullname: str):
+        if fullname == "sympy.core.basic.Basic.free_symbols":
+            return lambda ctx: ctx.api.named_generic_type(
+                "builtins.set", [ctx.api.named_type("sympy.Symbol")]
+            )
+        return None
+
 
 def add_assumptions(ctx) -> None:
     # Generated by list(sys.modules['sympy.core.assumptions']._assume_defined)

torch/_dynamo/output_graph.py

@@ -660,7 +660,7 @@ class OutputGraph:
 
         assert arg.fake_tensor is not None
 
-        def bind_symint(s, prop):
+        def bind_symint(s: torch.SymInt, prop):
             if not (is_symbolic(s) and isinstance(s.node.expr, sympy.Symbol)):
                 return
             s0 = s.node.expr
@@ -677,6 +677,7 @@ class OutputGraph:
                 source=prop,
             )
             set_example_value(proxy.node, s)
+            assert isinstance(s, torch.SymInt)
             proxy.node.meta["grapharg"] = GraphArg(
                 prop,
                 s,

torch/_guards.py

@@ -154,7 +154,7 @@ class GuardBuilderBase:
 
 @dataclasses.dataclass(frozen=True)
 class SLoc:
-    framework_loc: Union[traceback.FrameSummary, str]
+    framework_loc: Optional[Union[traceback.FrameSummary, str]]
     maybe_user_loc: Optional[str]
 
     def __str__(self):
@@ -170,7 +170,7 @@ class SLoc:
 
 
 class ShapeGuard(NamedTuple):
-    expr: sympy.Expr
+    expr: sympy.logic.boolalg.Boolean
     sloc: SLoc
 
 

torch/_inductor/codegen/wrapper.py

@@ -2044,7 +2044,9 @@ class PythonWrapperCodegen(CodeGen):
             if isinstance(x, int):
                 return x
             val = V.graph._shape_env._maybe_evaluate_static(x)
-            return int(val)
+            if val is None:
+                return val
+            return int(val)  # type: ignore[call-overload]
         except Exception:
             return None
 

torch/_inductor/graph.py

@@ -45,6 +45,7 @@ from torch.fx.experimental.symbolic_shapes import (
     resolve_unbacked_bindings,
     RuntimeAssert,
     ShapeEnv,
+    SympyBoolean,
     SymTypes,
 )
 from torch.fx.graph import Graph
@@ -352,7 +353,7 @@ class GraphLowering(torch.fx.Interpreter):
         shape_env.freeze_runtime_asserts()
         # We're going to mutate ras_by_symbol as we finish generating them
         self.ras_by_symbol: Dict[
-            sympy.Symbol, List[RuntimeAssert]
+            Optional[sympy.Symbol], List[RuntimeAssert]
         ] = shape_env.deferred_runtime_asserts.copy()
         self.bound_unbacked_symbols: OrderedSet[sympy.Symbol] = OrderedSet()
         self.sizevars = SizeVarAllocator(shape_env)
@@ -1595,7 +1596,7 @@ class GraphLowering(torch.fx.Interpreter):
             # This is all doable, it just hasn't been done yet.
             shape_env = V.graph.sizevars.shape_env
 
-            def make_assert(expr: Expr, msg: str) -> None:
+            def make_assert(expr: SympyBoolean, msg: str) -> None:
                 assert_op = ir.AssertScalar(expr, msg)
                 self.register_buffer(assert_op, set_name=True)
                 self.register_operation(assert_op)
@@ -1634,6 +1635,7 @@ class GraphLowering(torch.fx.Interpreter):
             unbacked_bindings = resolve_unbacked_bindings(
                 V.graph.sizevars.shape_env, n.meta.get("unbacked_bindings", {})
             )
+            assert unbacked_bindings is not None
             # When we do lowering, it is possible we reallocate unbacked SymInts.
             # So we need to line up the unbacked SymInts when performing the test
             # here

torch/_inductor/ir.py

@@ -5913,9 +5913,11 @@ class FallbackKernel(ExternKernelAlloc):
 
     def get_unbacked_symbol_defs(self) -> OrderedSet[sympy.Symbol]:
         if unbacked_bindings := getattr(self, "unbacked_bindings", None):
-            return resolve_unbacked_bindings(
+            resolved = resolve_unbacked_bindings(
                 V.graph.sizevars.shape_env, unbacked_bindings
-            ).keys()
+            )
+            assert resolved is not None
+            return resolved.keys()  # type: ignore[return-value]
         else:
             return OrderedSet()
 

torch/_inductor/lowering.py

@@ -2676,6 +2676,7 @@ def _local_scalar_dense(data):
     unbacked_bindings = resolve_unbacked_bindings(
         V.graph.sizevars.shape_env, V.graph.current_node.meta["unbacked_bindings"]
     )
+    assert unbacked_bindings is not None
     assert len(unbacked_bindings) == 1, unbacked_bindings
     # NB: Have to be very careful here.  V.graph.current_node.meta["val"]
     # seemingly also contains a symbol which you want to do binding for,

torch/fx/passes/runtime_assert.py

@@ -346,11 +346,12 @@ def insert_deferred_runtime_asserts(
                 # this guards against deleting calls that produce unbacked bindings we haven't yet seen.
                 # in this case looking at sym_expr.free_symbols might not be enough, if the example value has a hint
                 # (is backed), but produces an unbacked symbol. In this case keep the node alive.
+                resolved_unbacked_bindings = resolve_unbacked_bindings(
+                    shape_env, node.meta.get("unbacked_bindings", {})
+                )
+                assert resolved_unbacked_bindings is not None
                 new_unbacked_bindings = (
-                    resolve_unbacked_bindings(
+                    resolved_unbacked_bindings.keys() - expr_to_proxy.keys()
-                        shape_env, node.meta.get("unbacked_bindings", {})
-                    ).keys()
-                    - expr_to_proxy.keys()
                 )
 
                 # maybe re-reify expression, replace current node

torch/utils/_python_dispatch.py

@@ -314,6 +314,17 @@ class TensorWithFlatten(Protocol):
     def stride(self, dim: int) -> int:
         ...
 
+    @overload
+    def size(self, dim: None = None) -> Tuple[int, ...]:
+        ...
+
+    @overload
+    def size(self, dim: int) -> int:
+        ...
+
+    def storage_offset(self) -> int:
+        ...
+
     def dim(self) -> int:
         ...
 

torch/utils/_sympy/solve.py

@@ -43,7 +43,7 @@ def try_solve(
     thing: sympy.Basic,
     trials: int = 5,
     floordiv_inequality: bool = True,
-) -> Optional[Tuple[sympy.Rel, sympy.Basic]]:
+) -> Optional[Tuple[sympy.Rel, sympy.Expr]]:
     mirror = mirror_rel_op(type(expr))
 
     # Ignore unsupported expressions: