dynamic_shapes + retrace exported program (#110276)

avikchaudhuri · pytorchmergebot · commit 359c2a53f59a · 2023-09-29T21:06:46.000Z
An `ExportedProgram`'s `__call__` signature is different from the original module, so `dynamic_shapes` that follow the original signature would fail when applied to re-export an `ExportedProgram`. This PR fixes this issue, in other words, the original `dynamic_shapes` should now work when re-exporting. Differential Revision: D49764011 Pull Request resolved: #110276 Approved by: https://github.com/tugsbayasgalan
diff --git a/test/export/test_export.py b/test/export/test_export.py
@@ -1255,9 +1255,7 @@ def forward(self, x):
             torch._dynamo.exc.UserError,
             "Cannot provide constraints for already exported program.",
         ):
-            _ = torch.export.export(
-                exported, (inp,), dynamic_shapes={"args": [{0: dim0_x}]}
-            )
+            _ = torch.export.export(exported, (inp,), dynamic_shapes={"x": {0: dim0_x}})
         # Reexported program should still work for dynamic shapes.
         reexported = torch.export.export(exported, (inp,))
         self.assertTrue(reexported(torch.ones(7, 5)), Foo()(torch.ones(7, 5)))
diff --git a/torch/_export/__init__.py b/torch/_export/__init__.py
@@ -96,9 +96,8 @@ def export__RC__(
     kwargs = kwargs if kwargs is not None else {}
 
     from collections.abc import Mapping, Sequence
-    from typing import get_origin, get_args
 
-    def assoc_zip(combined_args, dynamic_shapes):
+    def tree_zip(combined_args, dynamic_shapes):
         if isinstance(combined_args, (tuple, list)):
             if not isinstance(dynamic_shapes, Sequence):
                 raise UserError(
@@ -112,7 +111,7 @@ def assoc_zip(combined_args, dynamic_shapes):
                     f"Expected {dynamic_shapes} to have {len(combined_args)} items",
                 )
             for i, shape in enumerate(dynamic_shapes):
-                yield from assoc_zip(combined_args[i], shape)
+                yield from tree_zip(combined_args[i], shape)
         elif isinstance(combined_args, dict):
             if not isinstance(dynamic_shapes, Mapping):
                 raise UserError(
@@ -126,7 +125,7 @@ def assoc_zip(combined_args, dynamic_shapes):
                     f"Expected {dynamic_shapes} to have {len(combined_args)} items",
                 )
             for k, shape in dynamic_shapes.items():
-                yield from assoc_zip(combined_args[k], shape)
+                yield from tree_zip(combined_args[k], shape)
         elif dataclasses.is_dataclass(combined_args):
             if not type(dynamic_shapes) == type(combined_args):
                 raise UserError(
@@ -135,7 +134,7 @@ def assoc_zip(combined_args, dynamic_shapes):
                     f"got {dynamic_shapes} instead",
                 )
             for f in dataclasses.fields(combined_args):
-                yield from assoc_zip(getattr(combined_args, f.name), getattr(dynamic_shapes, f.name))
+                yield from tree_zip(getattr(combined_args, f.name), getattr(dynamic_shapes, f.name))
         elif isinstance(combined_args, torch.Tensor):
             yield (combined_args, dynamic_shapes)
         else:
@@ -188,11 +187,17 @@ def update_symbols(tensor, shape):
                     "try None instead",
                 )
 
-    import inspect
-    signature = inspect.signature(f.forward) if isinstance(f, torch.nn.Module) else inspect.signature(f)
-    combined_args = signature.bind(*args, **kwargs).arguments
+    if isinstance(f, ExportedProgram):
+        combined_args = {
+            k: args[i] if i < len(args) else kwargs[k]
+            for i, k in enumerate(dynamic_shapes)
+        }
+    else:
+        import inspect
+        signature = inspect.signature(f.forward) if isinstance(f, torch.nn.Module) else inspect.signature(f)
+        combined_args = signature.bind(*args, **kwargs).arguments
 
-    for tensor, shape in assoc_zip(combined_args, dynamic_shapes):
+    for tensor, shape in tree_zip(combined_args, dynamic_shapes):
         update_symbols(tensor, shape)
 
     constraints = []
