diff --git a/onnxruntime/test/python/transformers/test_gqa_cpu.py b/onnxruntime/test/python/transformers/test_gqa_cpu.py
index f42be8ead29ee..77b4b326bf645 100644
--- a/onnxruntime/test/python/transformers/test_gqa_cpu.py
+++ b/onnxruntime/test/python/transformers/test_gqa_cpu.py
@@ -157,7 +157,6 @@ def create_group_query_attention_graph_prompt(
     packed=False,
     softcap=0.0,
     use_smooth_softmax=False,
-    node_name=None,
 ):
     past_kv_seqlen = config.buffer_sequence_length if share_buffer else 0
     present_kv_seqlen = config.buffer_sequence_length if share_buffer else config.kv_sequence_length
@@ -176,7 +175,7 @@ def create_group_query_attention_graph_prompt(
                 "sin_cache" if rotary else "",
             ],
             ["output", "present_key", "present_value"],
-            name=node_name,
+            "GroupQueryAttention_0",
             num_heads=config.num_heads,
             kv_num_heads=config.kv_num_heads,
             local_window_size=local_window_size,
@@ -187,7 +186,6 @@ def create_group_query_attention_graph_prompt(
             # is_past_bsnh=1 if past_kv_format == Formats.BSNH else 0,
             # kv_share_buffer=1 if share_buffer else 0,
             domain="com.microsoft",
-
         ),
     ]
 
@@ -689,7 +687,6 @@ def gqa_prompt_func(
     rotary_interleaved=False,
     softcap=0.0,
     use_smooth_softmax=False,
-    node_name=None,
 ):
     onnx_model_str = create_group_query_attention_graph_prompt(
         config,
@@ -701,7 +698,6 @@ def gqa_prompt_func(
         packed=new_k is None,
         softcap=softcap,
         use_smooth_softmax=use_smooth_softmax,
-        node_name=node_name,
     )
     q = torch.reshape(q, (config.batch_size, config.q_sequence_length, -1))
     past_k = k.clone() if share_buffer else None
@@ -718,7 +714,6 @@ def gqa_prompt_func(
             "total_sequence_length": torch.tensor([config.q_sequence_length], dtype=torch.int32).detach().cpu().numpy(),
         }
         sess_options = SessionOptions()
-        sess_options.enable_profiling = True
         ort_session = InferenceSession(onnx_model_str, sess_options, providers=["CPUExecutionProvider"])
         io_binding = ort_session.io_binding()
         if new_k is not None:
@@ -752,11 +747,6 @@ def gqa_prompt_func(
         ort_output, present_k, present_v = io_binding.copy_outputs_to_cpu()
         ort_output = numpy.array(ort_output)
         output = torch.tensor(ort_output)
-
-        if sess_options.enable_profiling:
-            profile_file = ort_session.end_profiling()
-            print(f"Profiling data saved to: {profile_file}")
-
         return output, present_k, present_v
     else:
         ort_inputs = {
@@ -1076,7 +1066,6 @@ def parity_check_gqa_prompt(
     packed=False,
     softcap=0.0,
     use_smooth_softmax=False,
-    node_name=None,
     rtol=RTOL,
     atol=ATOL,
 ):
@@ -1196,7 +1185,6 @@ def parity_check_gqa_prompt(
 
     # Flash function
     if packed:
-        node_name = "packed_"
         packed_qkv = torch.concatenate([q, new_k, new_v], dim=2)
         out, present_k, present_v = gqa_prompt_func(
             packed_qkv,
@@ -1214,7 +1202,6 @@ def parity_check_gqa_prompt(
             rotary_interleaved,
             softcap,
             use_smooth_softmax=use_smooth_softmax,
-            node_name=node_name,
         )
     else:
         out, present_k, present_v = gqa_prompt_func(
@@ -1940,14 +1927,6 @@ def test_gqa_no_past(self):
                                 for packed in [False, True]:
                                     for softcap in [0.0, 50.0]:
                                         for use_smooth_softmax in [False, True]:
-                                            node_name = (
-                                                ("packed_" if packed else "") +
-                                                ("rotary_" if rotary else "") +
-                                                ("rotary_interleaved_" if rotary_interleaved else "") +
-                                                "softcap_" + str(softcap) + "_" +
-                                                "smooth_softmax_" + str(use_smooth_softmax) + "_" +
-                                                 "b_" + str(b) + "_sq_" + str(sq) + "_skv_" + str(skv) + "_n_" + str(n) + "_n2_" + str(n2) + "_h_" + str(h)
-                                            )
                                             config = PromptConfig(b, sq, skv, sq + skv + 8, n, n2, h)
                                             past_kv_format = Formats.BNSH
                                             all_close = parity_check_gqa_prompt(
@@ -1959,7 +1938,6 @@ def test_gqa_no_past(self):
                                                 packed=packed,
                                                 softcap=softcap,
                                                 use_smooth_softmax=use_smooth_softmax,
-                                                node_name=node_name,
                                             )
                                             self.assertTrue(all_close)
                                             all_close = parity_check_gqa_prompt_no_buff(