fix non-strict placeholder naming with kwargs (pytorch#144278)

Fixes pytorch#143732

Differential Revision: [D67872055](https://our.internmc.facebook.com/intern/diff/D67872055/)
Pull Request resolved: pytorch#144278
Approved by: https://github.com/yushangdi, https://github.com/pianpwk

Author: avikchaudhuri

test/export/test_export.py

@@ -9752,6 +9752,47 @@ def forward(self, x):
     return (foo_functional,)""",
         )
 
+    def test_placeholder_naming_order(self):
+        # See https://github.com/pytorch/pytorch/issues/143732
+
+        class Mod(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.layer1 = torch.nn.Linear(3, 16)
+                self.layer2 = torch.nn.Linear(3, 32)
+
+            def forward(self, x1, x2, flag=True):
+                x1o = self.layer1(x1)
+                x2o = self.layer2(x2)
+                return torch.cat([x1o, x2o], dim=1)
+
+        mod = Mod()
+        args = (torch.rand(1, 3),)
+        kwargs = {"flag": False, "x2": torch.rand(1, 3)}
+        ep = export(mod, args, kwargs)
+
+        # check that graph is behaviorally correct
+        self.assertTrue(
+            torch.allclose(ep.module()(*args, **kwargs), mod(*args, **kwargs))
+        )
+
+        # check that graph input names are as expected
+        self.assertEqual(ep.graph_signature.user_inputs, ("x1", False, "x2"))
+
+    def test_placeholder_naming_order_variadic(self):
+        class Mod(torch.nn.Module):
+            def forward(self, a, b, c, **kwargs):
+                return a - b + c * kwargs["d"]
+
+        mod = Mod()
+        args = (torch.randn(3),)
+        kwargs = {"c": torch.randn(3), "b": torch.randn(3), "d": torch.randn(3)}
+        ep = export(mod, args, kwargs)
+        self.assertTrue(
+            torch.allclose(ep.module()(*args, **kwargs), mod(*args, **kwargs))
+        )
+        self.assertEqual(ep.graph_signature.user_inputs, ("a", "c", "b", "d"))
+
     def test_placeholder_naming_collisions(self):
         # test collisions between nested user inputs
         class Foo(torch.nn.Module):

torch/_export/utils.py

@@ -725,7 +725,11 @@ def _bind_signature_to_inputs(mod, fake_args, fake_kwargs):
     else:
         sig = inspect.signature(mod.forward)
 
-    return sig.bind(*fake_args, **fake_kwargs).arguments
+    # Rather than binding both fake_args and fake_kwargs to sig names, we
+    # (partially) bind only fake_args, while reusing fake_kwarg names. This
+    # ensures that fake_kwargs do not get reordered, which is important to
+    # match flattened user inputs.
+    return {**sig.bind_partial(*fake_args).arguments, **fake_kwargs}
 
 
 def _name_hoo_subgraph_placeholders(gm: torch.fx.GraphModule) -> None: