Pass in strict flag to allow non-strict state_dict loading

Summary:
X-link: pytorch/tnt#691

This allows users to ignore keys in the state_dict that aren't part of
the given module, or part of the module that aren't in the state_dict.

The default value for the flag (true) keeps the status quo and what the pytorch
interface uses.

Reviewed By: anshulverma

Differential Revision: D53066198

fbshipit-source-id: 8a849f46d09d6e7d9185d589b966f7a7a089d9fc

tests/test_snapshot.py

@@ -75,6 +75,27 @@ def test_nn_sequential(tmp_path: Path) -> None:
     assert check_state_dict_eq(foo.state_dict(), bar.state_dict())
 
 
+@pytest.mark.usefixtures("toggle_batching")
+def test_strict_false(tmp_path: Path) -> None:
+    foo = torch.nn.Sequential(
+        torch.nn.Linear(128, 64),
+        torch.nn.Linear(64, 32),
+        torch.nn.Linear(32, 16),
+    )
+    bar = torch.nn.Sequential(
+        torch.nn.Linear(128, 64),
+        torch.nn.Linear(64, 32),
+        torch.nn.Linear(32, 16),
+        torch.nn.Linear(16, 8),
+    )
+    assert not check_state_dict_eq(foo.state_dict(), bar.state_dict())
+
+    expected_dict = foo.state_dict()
+    snapshot = Snapshot.take(str(tmp_path), {"foo": foo})
+    snapshot.restore({"foo": bar}, strict=False)
+    assert check_state_dict_eq(foo.state_dict(), expected_dict)
+
+
 @pytest.mark.usefixtures("toggle_batching")
 def test_adagrad(tmp_path: Path) -> None:
     model = torch.nn.Sequential(

torchsnapshot/snapshot.py

@@ -303,7 +303,7 @@ def async_take(
             unique_id=unique_id,
         )
 
-    def restore(self, app_state: AppState) -> None:
+    def restore(self, app_state: AppState, strict: bool = True) -> None:
         """
         Restores the application state from the snapshot.
 
@@ -312,6 +312,10 @@ def restore(self, app_state: AppState) -> None:
                 ``app_state`` needs to be either identical to or a subset of the
                 ``app_state`` used for :func:`Snapshot.take` when the snapshot was
                 taken.
+            strict (bool, optional): If ``True``, raises an error if the expected
+                state_dict keys in the snapshot do not match the actual keys in
+                the :class:`torch.nn.Module`. This only applies to :class:`torch.nn.Module`
+                and not other objects being restored in ``app_state``.
         """
         torch._C._log_api_usage_once("torchsnapshot.Snapshot.restore")
         self._validate_app_state(app_state)
@@ -340,6 +344,7 @@ def restore(self, app_state: AppState) -> None:
             self._load_stateful(
                 stateful_key=key,
                 stateful=app_state.get(key),
+                strict=strict,
                 storage=storage,
                 pg=pg_wrapper,
                 event_loop=event_loop,
@@ -352,6 +357,7 @@ def restore(self, app_state: AppState) -> None:
             self._load_stateful(
                 stateful_key=key,
                 stateful=stateful,
+                strict=strict,
                 storage=storage,
                 pg=pg_wrapper,
                 event_loop=event_loop,
@@ -662,6 +668,7 @@ def _load_stateful(  # noqa
         self,
         stateful_key: str,
         stateful: Optional[Stateful],
+        strict: bool,
         storage: StoragePlugin,
         pg: PGWrapper,
         event_loop: asyncio.AbstractEventLoop,
@@ -737,7 +744,11 @@ def _load_stateful(  # noqa
             flattened={k: fut.obj for k, fut in futs.items()},
             prefix=stateful_key,
         )
-        stateful.load_state_dict(state_dict)
+
+        if isinstance(stateful, torch.nn.Module):
+            stateful.load_state_dict(state_dict, strict=strict)
+        else:
+            stateful.load_state_dict(state_dict)
 
     @staticmethod
     def _write_snapshot_metadata(