diff --git a/orttraining/orttraining/core/graph/gradient_builder.cc b/orttraining/orttraining/core/graph/gradient_builder.cc
index aac803a59110a..9d22d2fa3ce2a 100755
--- a/orttraining/orttraining/core/graph/gradient_builder.cc
+++ b/orttraining/orttraining/core/graph/gradient_builder.cc
@@ -1795,11 +1795,11 @@ IMPLEMENT_GRADIENT_BUILDER(GetExternalGradient) {
 
     std::vector<ArgDef> output_args;
     for (size_t output_index = 0; output_index < node_def.outputs.size(); ++output_index) {
-      const auto& output = node_def.outputs[output_index];
       if (!IsGradientRequiredForSrcNodeInput(output_index)) {
         output_args.emplace_back(ArgDef());
         continue;
       }
+      const auto& output = node_def.outputs[output_index];
       if (output.find("GI(") == 0) {
         size_t index = static_cast<size_t>(std::stoi(output.substr(3, output.length() - 4)));
         output_args.emplace_back(GI(index));
diff --git a/orttraining/orttraining/python/training/ortmodule/_custom_gradient_registry.py b/orttraining/orttraining/python/training/ortmodule/_custom_gradient_registry.py
index c9a8f819e8975..00c969cb40844 100644
--- a/orttraining/orttraining/python/training/ortmodule/_custom_gradient_registry.py
+++ b/orttraining/orttraining/python/training/ortmodule/_custom_gradient_registry.py
@@ -286,7 +286,12 @@ def upsample_bicubic2d_gradient():
     @register_gradient("org.pytorch.aten", "ATen", "_scaled_dot_product_efficient_attention", "")
     def scaled_dot_product_attention_gradient():
         return [
-            ("Constant", [], ["grad_input_mask"], {"value": {"value": [1, 1, 1, 1], "dtype": "int", "is_tensor": True}}),
+            (
+                "Constant",
+                [],
+                ["grad_input_mask"],
+                {"value": {"value": [1, 1, 1, 1], "dtype": "int", "is_tensor": True}},
+            ),
             (
                 ("ATen", "org.pytorch.aten"),
                 [
diff --git a/orttraining/orttraining/python/training/ortmodule/_custom_op_symbolic_registry.py b/orttraining/orttraining/python/training/ortmodule/_custom_op_symbolic_registry.py
index 28aca54023bfd..e21a93b4fdfee 100644
--- a/orttraining/orttraining/python/training/ortmodule/_custom_op_symbolic_registry.py
+++ b/orttraining/orttraining/python/training/ortmodule/_custom_op_symbolic_registry.py
@@ -3,8 +3,8 @@
 # Licensed under the MIT License.
 # --------------------------------------------------------------------------
 
-from typing import Callable
 import os
+from typing import Callable
 
 import torch
 import torch.onnx.symbolic_helper as sym_help
@@ -971,6 +971,7 @@ def softmax(g, input, dim, dtype=None):
 
     return softmax
 
+
 ATEN_SDPA_FALLBACK = os.getenv("ORTMODULE_ATEN_SDPA_FALLBACK", None)
 if ATEN_SDPA_FALLBACK:
     # based on the following internal PyTorch kernel for efficient attention:
diff --git a/orttraining/orttraining/test/python/orttraining_test_ortmodule_api.py b/orttraining/orttraining/test/python/orttraining_test_ortmodule_api.py
index ae5737e804fa3..75a62577de5da 100644
--- a/orttraining/orttraining/test/python/orttraining_test_ortmodule_api.py
+++ b/orttraining/orttraining/test/python/orttraining_test_ortmodule_api.py
@@ -6981,7 +6981,7 @@ def run_step(model, inputs):
 
     mem_eff_attn_nodes = 0
     for node in onnx_nodes:
-        if ("ATen" in node.name) and ("scaled_dot_product_attention" in node.attributes.operator):
+        if "_scaled_dot_product_efficient_attention" in node.attributes.operator:
             mem_eff_attn_nodes += 1
 
     assert mem_eff_attn_nodes > 0, "No mem_eff_attn nodes are found"