Add layer_norm e2e test with stash_type (#399)

`python run.py --mode=cl-onnx-iree -v --torchtolinalg -t
layer_norm_test`

alt_e2eshark/onnx_tests/operators/layer_norm.py

@@ -0,0 +1,34 @@
+# Copyright 2024 Advanced Micro Devices, Inc.
+#
+# Licensed under the Apache License v2.0 with LLVM Exceptions.
+# See https://llvm.org/LICENSE.txt for license information.
+# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+from onnx import TensorProto
+from onnx.helper import make_node, make_tensor_value_info
+
+from ..helper_classes import BuildAModel
+from e2e_testing.registry import register_test
+
+class LayerNormalizationModel(BuildAModel):
+    def construct_i_o_value_info(self):
+        X = make_tensor_value_info("X", TensorProto.FLOAT16, [2, 3, 5])
+        Scale = make_tensor_value_info("Scale", TensorProto.FLOAT16, [5])
+        B = make_tensor_value_info("B", TensorProto.FLOAT16, [5])
+        Y = make_tensor_value_info("Y", TensorProto.FLOAT16, [2, 3, 5])
+        Mean = make_tensor_value_info("Mean", TensorProto.FLOAT, [2, 3, 1])
+        InvStdDev = make_tensor_value_info("InvStdDev", TensorProto.FLOAT, [2, 3, 1])
+        self.input_vi = [X, Scale, B]
+        self.output_vi = [Y, Mean, InvStdDev]
+
+    def construct_nodes(self):
+        layer_norm_node = make_node(
+            op_type="LayerNormalization",
+            inputs=["X", "Scale", "B"],
+            outputs=["Y", "Mean", "InvStdDev"],
+            axis=-1,
+            epsilon=1e-05,
+            stash_type=1,
+        )
+        self.node_list = [layer_norm_node]
+
+register_test(LayerNormalizationModel, "layer_norm_test")

alt_e2eshark/onnx_tests/operators/model.py

@@ -14,3 +14,4 @@
 from .conv import *
 from .convtranspose import *
 from .tfidf_vectorizer import *
+from .layer_norm import *