Update fx importer testing route to simplify input handling.

Signed-off-by: zjgarvey <[email protected]>

Author: zjgarvey

projects/e2e/torch_mlir_e2e_test/configs/fx_importer_backend.py

@@ -132,6 +132,7 @@ def invoke_func(*torch_inputs):
         return result
 
     def _export_run(self, artifact: torch.nn.Module, trace: Trace) -> Trace:
+        artifact.eval()
         result: Trace = []
         for item in trace:
             prog: ExportedProgram = torch.export.export(
@@ -141,6 +142,7 @@ def _export_run(self, artifact: torch.nn.Module, trace: Trace) -> Trace:
                 prog,
                 output_type=self._output_type,
                 func_name=artifact.__class__.__name__,
+                experimental_support_mutation=True,
                 # While the current e2e tests don't exercise symbolic shapes,
                 # enabling this here ensures they don't regress either.
                 import_symbolic_shape_expressions=True,
@@ -149,14 +151,7 @@ def _export_run(self, artifact: torch.nn.Module, trace: Trace) -> Trace:
             )
             module = self._backend.compile(module)
             backend_module = self._backend.load(module)
-            params = {
-                # **dict(artifact.named_parameters(remove_duplicate=False)),
-                **dict(artifact.named_buffers(remove_duplicate=False)),
-            }
-            params_flat, params_spec = pytree.tree_flatten(params)
-            params_flat = list(params_flat)
-            with torch.no_grad():
-                numpy_inputs = recursively_convert_to_numpy(params_flat + item.inputs)
+            numpy_inputs = recursively_convert_to_numpy(item.inputs)
             outputs = getattr(backend_module, artifact.__class__.__name__)(
                 *numpy_inputs
             )

projects/e2e/torch_mlir_e2e_test/configs/utils.py

@@ -11,7 +11,7 @@
 
 def recursively_convert_to_numpy(o: Any):
     if isinstance(o, torch.Tensor):
-        return o.numpy()
+        return o.detach().numpy()
     if isinstance(o, tuple):
         return tuple(recursively_convert_to_numpy(x) for x in o)
     if isinstance(o, list):