Make torch_geometric models compatible with export (#123403)

Summary:
X-link: pytorch/pytorch#123403
Approved by: https://github.com/angelayi

Reviewed By: clee2000

Differential Revision: D55828744

Pulled By: tugsbayasgalan

fbshipit-source-id: 2ae53d8fbeb722c2c44b614bd6806d2b036ec97e

userbenchmark/dynamo/dynamobench/common.py

@@ -1135,12 +1135,12 @@ def load(cls, model, example_inputs, device):
                 example_outputs = copy.deepcopy(model)(*example_args, **example_kwargs)
             _register_dataclass_output_as_pytree(example_outputs)
 
-            # TODO(angelayi): change this to predispatch
-            gm = torch.export._trace._export_to_torch_ir(
+            gm = torch.export._trace._export(
                 model,
                 example_args,
                 example_kwargs,
-            )
+                pre_dispatch=True,
+            ).module()
             with torch.no_grad():
                 so_path = torch._inductor.aot_compile(
                     gm, example_args, example_kwargs

userbenchmark/dynamo/dynamobench/torchbench.py

@@ -25,6 +25,20 @@
 torch.backends.cuda.matmul.allow_tf32 = True
 
 
+def _reassign_parameters(model):
+    # torch_geometric models register parameter as tensors due to
+    # https://github.com/pyg-team/pytorch_geometric/blob/master/torch_geometric/nn/dense/linear.py#L158-L168
+    # Since it is unusual thing to do, we just reassign them to parameters
+    def state_dict_hook(module, destination, prefix, local_metadata):
+        for name, param in module.named_parameters():
+            if isinstance(destination[name], torch.Tensor) and not isinstance(
+                destination[name], torch.nn.Parameter
+            ):
+                destination[name] = torch.nn.Parameter(destination[name])
+
+    model._register_state_dict_hook(state_dict_hook)
+
+
 def setup_torchbench_cwd():
     original_dir = abspath(os.getcwd())
 
@@ -265,6 +279,14 @@ def load_model(
                 extra_args=extra_args,
             )
         model, example_inputs = benchmark.get_module()
+        if model_name in [
+            "basic_gnn_edgecnn",
+            "basic_gnn_gcn",
+            "basic_gnn_sage",
+            "basic_gnn_gin",
+        ]:
+            _reassign_parameters(model)
+
         # Models that must be in train mode while training
         if is_training and (
             not use_eval_mode or model_name in self._config["only_training"]