lintrunner -a

.github/workflows/lint.yml

@@ -59,7 +59,7 @@ jobs:
         run: |
           set -e -x
           python -m pip install --user -r requirements-dev.txt
-          python -m pip install --user -r lintrunner
+          python -m pip install --user lintrunner
           lintrunner init
       - name: Run lintrunner on all files
         run: |

orttraining/orttraining/python/training/artifacts.py

@@ -185,10 +185,13 @@ def build(self, *inputs_to_loss):
         logging.info("Custom op library provided: %s", custom_op_library)
         custom_op_library_path = pathlib.Path(custom_op_library)
 
-    with onnxblock.base(loaded_model, model_path), (
-        onnxblock.custom_op_library(custom_op_library_path)
-        if custom_op_library is not None
-        else contextlib.nullcontext()
+    with (
+        onnxblock.base(loaded_model, model_path),
+        (
+            onnxblock.custom_op_library(custom_op_library_path)
+            if custom_op_library is not None
+            else contextlib.nullcontext()
+        ),
     ):
         _ = training_block(*[output.name for output in loaded_model.graph.output])
         training_model, eval_model = training_block.to_model_proto()

orttraining/orttraining/python/training/ortmodule/_graph_transition_manager.py

@@ -867,8 +867,9 @@ def _get_exported_model(
         assert model_info_for_export.export_mode is not None, "Please use a concrete instance of ExecutionManager"
 
         try:
-            with torch.no_grad(), stage3_export_context(
-                enable_zero_stage3_support, stage3_param_handle, flattened_module
+            with (
+                torch.no_grad(),
+                stage3_export_context(enable_zero_stage3_support, stage3_param_handle, flattened_module),
             ):
                 required_export_kwargs = {
                     "input_names": model_info_for_export.onnx_graph_input_names,  # did not contains parameters as its input yet