Deepcopy model to another device before export to avoid OOM (#118710)

Summary:
Prior to onnx export, the model is deepcopied to avoid modifications that may affect later performance profiling. However this increases the memory requirement on the device.
This PR modifies the script to deepcopy and export the model on another device when possible.

X-link: pytorch/pytorch#118710
Approved by: https://github.com/thiagocrepaldi

Reviewed By: clee2000

Differential Revision: D53296686

fbshipit-source-id: e764fcf3c4f15f4f8793623571a09fd1b4263898

Author: BowenBao

userbenchmark/dynamo/dynamobench/common.py

@@ -1222,6 +1222,31 @@ def __init__(
             self.model_dir / f"{model_name}_{self._COMPILER_NAME}.onnx"
         )
 
+    def _determine_deepcopy_target_device(self):
+        if current_device == "cpu":
+            target_device = "cpu"
+        else:
+            if torch.cuda.device_count() > 1:
+                # Copy to another cuda device to avoid OOM.
+                target_device = "cuda:1"
+            else:
+                target_device = "cuda"
+        return target_device
+
+    def deepcopy_model_and_inputs_to_device(self, model, example_inputs, target_device):
+        # Deepcopy model before export to avoid modification to baseline model.
+        # To avoid OOM, the model is first moved to CPU. Both models are then moved to device.
+        model_device = next(model.parameters()).device
+        model.to("cpu")
+        model_copy = copy.deepcopy(model).to(target_device)
+        model.to(model_device)
+
+        target_device_example_inputs = tree_map_only(
+            torch.Tensor, lambda x: x.to(device=target_device), example_inputs
+        )
+
+        return model_copy, target_device_example_inputs
+
     @classmethod
     def _generate_onnx_model_directory(
         cls, output_directory: str, compiler_name: str, model_name: str
@@ -1404,7 +1429,9 @@ def __init__(
     def _export(self, model, example_inputs, output_path: str, /, **kwargs) -> None:
         if self.copy_before_export:
             # Deepcopy model before export to avoid modification to baseline model.
-            model = copy.deepcopy(model)
+            model, example_inputs = self.deepcopy_model_and_inputs_to_device(
+                model, example_inputs, self._determine_deepcopy_target_device()
+            )
 
         # Hack for huggingface models (kwargs only).
         if isinstance(example_inputs, dict):
@@ -1486,7 +1513,9 @@ def _export(
     ) -> torch.onnx.ONNXProgram:
         if self.copy_before_export:
             # Deepcopy model before export to avoid modification to baseline model.
-            model = copy.deepcopy(model)
+            model, example_inputs = self.deepcopy_model_and_inputs_to_device(
+                model, example_inputs, self._determine_deepcopy_target_device()
+            )
 
         example_args, example_kwargs = _normalize_bench_inputs(example_inputs)
         options = torch.onnx.ExportOptions(dynamic_shapes=self._dynamic_shapes)
@@ -1513,6 +1542,12 @@ class OnnxModelFromDynamoAotInline(OnnxModelFromDynamo):
     def _export(
         self, model, example_inputs, output_path: str
     ) -> torch.onnx.ONNXProgram:
+        if self.copy_before_export:
+            # Deepcopy model before export to avoid modification to baseline model.
+            model, example_inputs = self.deepcopy_model_and_inputs_to_device(
+                model, example_inputs, self._determine_deepcopy_target_device()
+            )
+
         example_args, example_kwargs = _normalize_bench_inputs(example_inputs)
         options = torch.onnx.ExportOptions(dynamic_shapes=self._dynamic_shapes)
         onnx_program = torch.onnx.dynamo_export(