Fix pyre errors

Author: jesszzzz

.pyre_configuration

@@ -17,6 +17,7 @@
   "search_path": [
     "stubs"
   ],
+  "show_error_traces": true,
   "strict": true,
   "version": "0.0.101732536891"
 }

torchx/examples/apps/lightning/data.py

@@ -64,18 +64,15 @@ def __len__(self) -> int:
 # our trainer and other components that need to load data.
 
 
-# pyre-fixme[13]: Attribute `test_ds` is never initialized.
-# pyre-fixme[13]: Attribute `train_ds` is never initialized.
-# pyre-fixme[13]: Attribute `val_ds` is never initialized.
 class TinyImageNetDataModule(pl.LightningDataModule):
     """
     TinyImageNetDataModule is a pytorch LightningDataModule for the tiny
     imagenet dataset.
     """
 
-    train_ds: ImageFolderSamplesDataset
-    val_ds: ImageFolderSamplesDataset
-    test_ds: ImageFolderSamplesDataset
+    train_ds: ImageFolderSamplesDataset  # pyre-fixme[13]: Attribute `train_ds` is never initialized.
+    val_ds: ImageFolderSamplesDataset  # pyre-fixme[13]: Attribute `val_ds` is never initialized.
+    test_ds: ImageFolderSamplesDataset  # pyre-fixme[13]: Attribute `test_ds` is never initialized.
 
     def __init__(
         self, data_dir: str, batch_size: int = 16, num_samples: Optional[int] = None

torchx/examples/apps/tracker/main.py

@@ -99,6 +99,7 @@ def test(
         for data, target in test_loader:
             data, target = data.to(device), target.to(device)
             output = model(data)
+            # pyre-fixme[58] Assuming F.nll_loss(...).item() is a number
             test_loss += F.nll_loss(
                 output, target, reduction="sum"
             ).item()  # sum up batch loss

torchx/pipelines/kfp/adapter.py

@@ -51,7 +51,7 @@ def component_spec_from_app(app: api.AppDef) -> Tuple[str, api.Role]:
     role = app.roles[0]
     assert (
         role.num_replicas == 1
-    ), f"KFP adapter only supports one replica, got {app.num_replicas}"
+    ), f"KFP adapter only supports one replica, got {app.num_replicas}"  # pyre-fixme[16] Assume num_replicas is available on app
 
     command = [role.entrypoint, *role.args]
 

torchx/schedulers/aws_batch_scheduler.py

@@ -809,6 +809,7 @@ def _stream_events(
                     startFromHead=True,
                     **args,
                 )
+            # pyre-fixme[66] Assume this ResourceNotFoundException extends BaseException
             except self._log_client.exceptions.ResourceNotFoundException:
                 return []  # noqa: B901
             if response["nextForwardToken"] == next_token:

torchx/schedulers/aws_sagemaker_scheduler.py

@@ -261,9 +261,9 @@ def _submit_dryrun(
                 raise ValueError(
                     f"{key} is controlled by aws_sagemaker_scheduler and is set to {job_def[key]}"
                 )
-            value = cfg.get(key)  # pyre-ignore[26]
+            value = cfg.get(key)
             if value is not None:
-                job_def[key] = value
+                job_def[key] = value  # pyre-ignore[6]
 
         req = AWSSageMakerJob(
             job_name=job_name,

torchx/schedulers/ray/ray_driver.py

@@ -116,7 +116,9 @@ def load_actor_json(filename: str) -> List[RayActor]:
         return actors
 
 
-def create_placement_group_async(replicas: List[RayActor]) -> PlacementGroup:
+def create_placement_group_async(
+    replicas: List[RayActor],
+) -> PlacementGroup:  # pyre-ignore[11]
     """return a placement group reference, the corresponding placement group could be scheduled or pending"""
     bundles = []
     for replica in replicas: