diff --git a/onnxruntime/test/python/quantization/test_op_embed_layernorm.py b/onnxruntime/test/python/quantization/test_op_embed_layernorm.py
new file mode 100644
index 0000000000..0660767ef0
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+++ b/onnxruntime/test/python/quantization/test_op_embed_layernorm.py
@@ -0,0 +1,144 @@
+#!/usr/bin/env python
+# coding: utf-8
+# -------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for
+# license information.
+# --------------------------------------------------------------------------
+
+import unittest
+import onnx
+import numpy as np
+from onnx import helper, TensorProto
+from onnxruntime.quantization import quantize_dynamic
+from op_test_utils import TestDataFeeds, check_model_correctness, check_op_type_count
+
+
+class TestOpEmbedLayerNormalization(unittest.TestCase):
+    def input_feeds_int32(self, n, name2shape):
+        input_data_list = []
+        for i in range(n):
+            inputs = {}
+            for name, shape in name2shape.items():
+                inputs.update({name: np.ones(shape).astype(np.int32)})
+            input_data_list.extend([inputs])
+
+        dr = TestDataFeeds(input_data_list)
+        return dr
+
+    def construct_model(self, batch, hidden_size, sequence_length, model_path):
+        #    <segment_ids>    <input_ids>
+        #              \        /
+        #       (EmbedLayerNormalization)
+        #               /       \
+        # <layernorm_output>  <mask_index_output>
+
+        # Inputs to EmbedLayerNormalizationNode
+        input_ids_shape = [batch, sequence_length]
+        input_ids_tensor = helper.make_tensor_value_info('input_ids', TensorProto.INT32, input_ids_shape)
+
+        segment_ids_shape = [batch, sequence_length]
+        segment_ids_tensor = helper.make_tensor_value_info('segment_ids', TensorProto.INT32, segment_ids_shape)
+
+        # EmbedLayerNormalization Node Constants and Weights:
+        word_embed_shape = [32, hidden_size]
+        word_embed_weights = np.random.random_sample(word_embed_shape).astype(dtype='float32')
+        word_embed_initializer = onnx.numpy_helper.from_array(word_embed_weights, name='word_embed')
+
+        pos_embed_shape = [16, hidden_size]
+        pos_embed_weights = np.random.random_sample(pos_embed_shape).astype(dtype='float32')
+        pos_embed_initializer = onnx.numpy_helper.from_array(pos_embed_weights, name='pos_embed')
+
+        seg_embed_shape = [2, hidden_size]
+        seg_embed_weights = np.random.random_sample(seg_embed_shape).astype(dtype='float32')
+        seg_embed_initializer = onnx.numpy_helper.from_array(seg_embed_weights, name='seg_embed')
+
+        layer_norm_weight_shape = [hidden_size]
+        layer_norm_weights = np.random.random_sample(layer_norm_weight_shape).astype(dtype='float32')
+        layer_norm_weights_initializer = onnx.numpy_helper.from_array(layer_norm_weights, name='layer_norm_weight')
+
+        layer_norm_bias_shape = [hidden_size]
+        layer_norm_bias_weights = np.random.random_sample(layer_norm_bias_shape).astype(dtype='float32')
+        layer_norm_bias_initializer = onnx.numpy_helper.from_array(layer_norm_bias_weights, name='layer_norm_bias')
+
+        # EmbedLayerNormalization Outputs:
+        layernorm_out_shape = [batch, sequence_length, hidden_size]
+        layernorm_out_tensor = helper.make_tensor_value_info('layernorm_out', TensorProto.FLOAT, layernorm_out_shape)
+
+        mask_index_out_shape = [batch]
+        mask_index_out_tensor = helper.make_tensor_value_info('mask_index_out', TensorProto.INT32, mask_index_out_shape)
+
+        # EmbedLayerNormalization Node:
+        embed_layer_norm_inputs = [
+            'input_ids', 'segment_ids', 'word_embed', 'pos_embed', 'seg_embed', 'layer_norm_weight', 'layer_norm_bias'
+        ]
+        embed_layer_norm_outputs = ['layernorm_out', 'mask_index_out']
+        embed_layer_norm_node = helper.make_node('EmbedLayerNormalization',
+                                                 embed_layer_norm_inputs,
+                                                 embed_layer_norm_outputs,
+                                                 domain='com.microsoft')
+
+        # Construct the Graph and Model:
+        nodes = [embed_layer_norm_node]
+        graph_name = 'embed_layernorm_graph'
+        inputs = [input_ids_tensor, segment_ids_tensor]
+        outputs = [layernorm_out_tensor, mask_index_out_tensor]
+        initializers = [
+            word_embed_initializer, pos_embed_initializer, seg_embed_initializer, layer_norm_weights_initializer,
+            layer_norm_bias_initializer
+        ]
+
+        graph = helper.make_graph(nodes, graph_name, inputs, outputs, initializer=initializers)
+        model = helper.make_model(graph)
+
+        onnx.save(model, model_path)
+
+    def test_quantize_batch_size_1(self):
+        batch = 1
+        hidden_size = 4
+        sequence_length = 4
+
+        model_f32_path = 'test_embed_layer_norm_unit_test_batch1.onnx'
+        model_uint8_path = 'ttest_embed_layer_norm_unit_test_batch1_uint8.onnx'
+
+        self.construct_model(batch, hidden_size, sequence_length, model_f32_path)
+
+        data_reader = self.input_feeds_int32(1, {
+            'input_ids': [batch, sequence_length],
+            'segment_ids': [batch, sequence_length]
+        })
+
+        quantize_dynamic(model_f32_path, model_uint8_path)
+
+        qnode_counts = {'DequantizeLinear': 3}
+        check_op_type_count(self, model_uint8_path, **qnode_counts)
+        data_reader.rewind()
+
+        check_model_correctness(self, model_f32_path, model_uint8_path, data_reader.get_next())
+
+    def test_quantize_batch_size_2(self):
+        batch = 2
+        hidden_size = 4
+        sequence_length = 4
+
+        model_f32_path = 'test_embed_layer_norm_unit_test_batch2.onnx'
+        model_uint8_path = 'ttest_embed_layer_norm_unit_test_batch2_uint8.onnx'
+
+        self.construct_model(batch, hidden_size, sequence_length, model_f32_path)
+
+        data_reader = self.input_feeds_int32(1, {
+            'input_ids': [batch, sequence_length],
+            'segment_ids': [batch, sequence_length]
+        })
+
+        quantize_dynamic(model_f32_path, model_uint8_path)
+
+        qnode_counts = {'DequantizeLinear': 3}
+        check_op_type_count(self, model_uint8_path, **qnode_counts)
+        data_reader.rewind()
+
+        check_model_correctness(self, model_f32_path, model_uint8_path, data_reader.get_next())
+
+
+if __name__ == '__main__':
+    unittest.main()