Test xnnpack with pybindings (#13133)

Make sure we run xnnpack delegated model using pybindings, in
`test_models.sh`.

Author: larryliu0820

.ci/scripts/test_model.sh

@@ -131,13 +131,13 @@ test_model_with_xnnpack() {
     return 0
   fi
 
-  # Delegation
+  # Delegation and test with pybindings
   if [[ ${WITH_QUANTIZATION} == true ]]; then
     SUFFIX="q8"
-    "${PYTHON_EXECUTABLE}" -m examples.xnnpack.aot_compiler --model_name="${MODEL_NAME}" --delegate --quantize
+    "${PYTHON_EXECUTABLE}" -m examples.xnnpack.aot_compiler --model_name="${MODEL_NAME}" --delegate --quantize --test_after_export
   else
     SUFFIX="fp32"
-    "${PYTHON_EXECUTABLE}" -m examples.xnnpack.aot_compiler --model_name="${MODEL_NAME}" --delegate
+    "${PYTHON_EXECUTABLE}" -m examples.xnnpack.aot_compiler --model_name="${MODEL_NAME}" --delegate --test_after_export
   fi
 
   OUTPUT_MODEL_PATH="${MODEL_NAME}_xnnpack_${SUFFIX}.pte"

examples/xnnpack/aot_compiler.py

@@ -61,6 +61,14 @@
         default="",
         help="Generate and save an ETRecord to the given file location",
     )
+    parser.add_argument(
+        "-t",
+        "--test_after_export",
+        action="store_true",
+        required=False,
+        default=False,
+        help="Test the pte with pybindings",
+    )
     parser.add_argument("-o", "--output_dir", default=".", help="output directory")
 
     args = parser.parse_args()
@@ -117,3 +125,24 @@
     quant_tag = "q8" if args.quantize else "fp32"
     model_name = f"{args.model_name}_xnnpack_{quant_tag}"
     save_pte_program(exec_prog, model_name, args.output_dir)
+
+    if args.test_after_export:
+        logging.info("Testing the pte with pybind")
+        from executorch.extension.pybindings.portable_lib import (
+            _load_for_executorch_from_buffer,
+        )
+
+        # Import custom ops. This requires portable_lib to be loaded first.
+        from executorch.extension.llm.custom_ops import (  # noqa: F401, F403
+            custom_ops,
+        )  # usort: skip
+
+        # Import quantized ops. This requires portable_lib to be loaded first.
+        from executorch.kernels import quantized  # usort: skip # noqa: F401, F403
+        from torch.utils._pytree import tree_flatten
+
+        m = _load_for_executorch_from_buffer(exec_prog.buffer)
+        logging.info("Successfully loaded the model")
+        flattened = tree_flatten(example_inputs)[0]
+        res = m.run_method("forward", flattened)
+        logging.info("Successfully ran the model")