pybind update to use new api

backends/openvino/tests/ops/base_openvino_op_test.py

@@ -6,10 +6,8 @@
 from executorch.backends.openvino.preprocess import OpenvinoBackend
 from executorch.exir import EdgeProgramManager, to_edge_transform_and_lower
 from executorch.exir.backend.backend_details import CompileSpec
+from executorch.runtime import Runtime
 
-from executorch.extension.pybindings.portable_lib import (  # @manual
-    _load_for_executorch_from_buffer,
-)
 from torch.export import export, ExportedProgram
 
 
@@ -67,8 +65,10 @@ def execute_layer_test(
                 ]
 
             # Load model from buffer and execute
-            executorch_module = _load_for_executorch_from_buffer(exec_prog.buffer)
-            outputs = executorch_module.run_method("forward", sample_inputs)
+            runtime = Runtime.get()
+            program = runtime.load_program(exec_prog.buffer)
+            method = program.load_method("forward")
+            outputs = method.execute(sample_inputs)
 
             # Compare the outputs with the reference outputs
             self.assertTrue(len(ref_output) == len(outputs))

examples/openvino/aot_optimize_and_infer.py

@@ -17,9 +17,7 @@
 from executorch.backends.openvino.quantizer import quantize_model
 from executorch.exir import EdgeProgramManager, to_edge_transform_and_lower
 from executorch.exir.backend.backend_details import CompileSpec
-from executorch.extension.pybindings.portable_lib import (  # @manual
-    _load_for_executorch_from_buffer,
-)
+from executorch.runtime import Runtime
 from sklearn.metrics import accuracy_score
 from timm.data import resolve_data_config
 from timm.data.transforms_factory import create_transform
@@ -121,17 +119,19 @@ def infer_model(
     :return: The average inference timing.
     """
     # Load model from buffer
-    executorch_module = _load_for_executorch_from_buffer(exec_prog.buffer)
+    runtime = Runtime.get()
+    program = runtime.load_program(exec_prog.buffer)
+    method = program.load_method("forward")
 
     # Execute warmup
     for _i in range(warmup_iter):
-        out = executorch_module.run_method("forward", inputs)
+        out = method.execute(inputs)
 
     # Execute inference and measure timing
     time_total = 0.0
     for _i in range(num_iter):
         time_start = time.time()
-        out = executorch_module.run_method("forward", inputs)
+        out = method.execute(inputs)
         time_end = time.time()
         time_total += time_end - time_start
 
@@ -154,15 +154,17 @@ def validate_model(
     :return: The accuracy score of the model.
     """
     # Load model from buffer
-    executorch_module = _load_for_executorch_from_buffer(exec_prog.buffer)
+    runtime = Runtime.get()
+    program = runtime.load_program(exec_prog.buffer)
+    method = program.load_method("forward")
 
     # Iterate over the dataset and run the executor
     predictions = []
     targets = []
     for _idx, data in enumerate(calibration_dataset):
         feature, target = data
         targets.extend(target)
-        out = executorch_module.run_method("forward", (feature,))
+        out = method.execute((feature,))
         predictions.extend(torch.stack(out).reshape(-1, 1000).argmax(-1))
 
     # Check accuracy