Rename torch.onnx.ExportOutput* to ONNXProgram* (#112263)

Summary:
Since PyTorch 2.1, torch.export API was introduced and the term "export"
got overloaded due to the already existing torch.onnx.export API.

The torch.onnx.dynamo_export API was introduced on pyTorch 2.0 and it
exposed a torch.onnx.ExportOutput which now can be confused with
torch.export.export output

To prevent such ambiguity and standardize names around the new
torch.export.ExportedProgram, this PR renames torch.onnx.ExportOutput to
torch.onnx.ONNXProgram

X-link: pytorch/pytorch#112263
Approved by: https://github.com/BowenBao
ghstack dependencies: #112444

Reviewed By: PaliC

Differential Revision: D51057229

fbshipit-source-id: f43c1fa8d1820ad69df61ac9f8f84d5ec3995fbe

Author: Thiago Crepaldi

userbenchmark/dynamo/dynamobench/common.py

@@ -1527,31 +1527,31 @@ class OnnxModelFromDynamo(OnnxModel):
     def __init__(self, output_directory, model, example_inputs, dynamic_shapes: bool):
         super().__init__(output_directory, model, example_inputs, dynamic_shapes)
         self._dynamic_shapes = dynamic_shapes
-        self._export_output = self._export(model, example_inputs, self.model_path)
+        self._onnx_program = self._export(model, example_inputs, self.model_path)
         # Clear the model proto to save memory.
-        # The model proto is saved to disk and no longer needed from `export_output`.
-        # `export_output` is kept for i/o adapter usage.
-        self._export_output.model_proto.Clear()
+        # The model proto is saved to disk and no longer needed from `onnx_program`.
+        # `onnx_program` is kept for i/o adapter usage.
+        self._onnx_program.model_proto.Clear()
         self.onnx_session = self._init_ort_session(self.model_path)
 
     def _export(
         self, model, example_inputs, output_path: str
-    ) -> torch.onnx.ExportOutput:
+    ) -> torch.onnx.ONNXProgram:
         example_args, example_kwargs = _normalize_bench_inputs(example_inputs)
         options = torch.onnx.ExportOptions(dynamic_shapes=self._dynamic_shapes)
-        export_output = torch.onnx.dynamo_export(
+        onnx_program = torch.onnx.dynamo_export(
             model, *example_args, **example_kwargs, export_options=options
         )
 
-        export_output.save(output_path)
-        return export_output
+        onnx_program.save(output_path)
+        return onnx_program
 
     def format_pt_inputs(self, pt_inputs):
         pt_args, pt_kwargs = _normalize_bench_inputs(pt_inputs)
-        return self._export_output.adapt_torch_inputs_to_onnx(*pt_args, **pt_kwargs)
+        return self._onnx_program.adapt_torch_inputs_to_onnx(*pt_args, **pt_kwargs)
 
     def format_pt_outputs(self, pt_outputs):
-        return self._export_output.adapt_torch_outputs_to_onnx(pt_outputs)
+        return self._onnx_program.adapt_torch_outputs_to_onnx(pt_outputs)
 
 
 class OnnxModelFromDynamoAotInline(OnnxModelFromDynamo):
@@ -1561,10 +1561,10 @@ class OnnxModelFromDynamoAotInline(OnnxModelFromDynamo):
 
     def _export(
         self, model, example_inputs, output_path: str
-    ) -> torch.onnx.ExportOutput:
+    ) -> torch.onnx.ONNXProgram:
         example_args, example_kwargs = _normalize_bench_inputs(example_inputs)
         options = torch.onnx.ExportOptions(dynamic_shapes=self._dynamic_shapes)
-        export_output = torch.onnx.dynamo_export(
+        onnx_program = torch.onnx.dynamo_export(
             model, *example_args, **example_kwargs, export_options=options
         )
         # Apply AOT inline post export.
@@ -1575,12 +1575,12 @@ def _export(
         # Workaround for inliner not supporting with models larger than 2GB.
         # Save model to disk first separating out external data,
         # and load back without external data for inliner to work on.
-        model_proto = export_output.model_proto
+        model_proto = onnx_program.model_proto
         onnx.save_model(model_proto, output_path, save_as_external_data=True)
         model_proto = onnx.load(output_path, load_external_data=False)
         model_proto = onnx.inliner.inline_local_functions(model_proto)
         onnx.save_model(model_proto, output_path)
-        return export_output
+        return onnx_program
 
 
 class _OnnxPatch:
@@ -1786,7 +1786,7 @@ def run_n_iterations_onnx(model, inputs, n=2):
             return outputs
         except exporter.OnnxExporterError as e:
             # `torch.onnx.dynamo_export` raises error that encloses diagnostics.
-            diagnostic_context = e.export_output.diagnostic_context
+            diagnostic_context = e.onnx_program.diagnostic_context
             for parsed_error in parser.parse_diagnostic_context(diagnostic_context):
                 output_csv(
                     output_error_filename, parsed_error.headers, parsed_error.row