diff --git a/debug/accuracy_tools/api_accuracy_checker/run_ut/run_ut.py b/debug/accuracy_tools/api_accuracy_checker/run_ut/run_ut.py
index cf9bfe29bdfd054db96ae878813c099892430bbb..c5d4daada698acedc4d3854a5b6f415c0ee4fb75 100644
--- a/debug/accuracy_tools/api_accuracy_checker/run_ut/run_ut.py
+++ b/debug/accuracy_tools/api_accuracy_checker/run_ut/run_ut.py
@@ -233,28 +233,30 @@ def run_backward(api_full_name, args, backward_content, grad_index, device_args,
     backward_args = backward_content[api_full_name]
     grad = gen_args(backward_args)[0]
     cpu_grad, _ = generate_cpu_params(grad, {}, False)
-    if grad_index is not None:
-        out[grad_index].backward(cpu_grad)
-    elif isinstance(out, (list, tuple)):
-        raise NotImplementedError("Multiple backward is not supported.")
-    else:
-        out.backward(cpu_grad)
-    args_grad = []
-    for arg in args:
-        if isinstance(arg, torch.Tensor):
-            args_grad.append(arg.grad)
-    grad_out = args_grad
-    device_grad = grad.clone().detach().to(current_device)
-    if grad_index is not None:
-        device_out[grad_index].backward(device_grad)
+    if isinstance(out, (list, tuple)):
+        grad_out = [None] * len(out)
+        device_grad_out = [None] * len(device_out)
     else:
-        device_out.backward(device_grad)
-    device_args_grad = []
-    for arg in device_args:
-        if isinstance(arg, torch.Tensor):
-            device_args_grad.append(arg.grad)
-    device_grad_out = device_args_grad
-    return grad_out, device_grad_out, grad, device_grad
+        if grad_index is not None:
+            out[grad_index].backward(cpu_grad)
+            device_out[grad_index].backward(grad.clone().detach().to(current_device))
+        else:
+            out.backward(cpu_grad)
+            device_out.backward(grad.clone().detach().to(current_device))
+        
+        args_grad = []
+        for arg in args:
+            if isinstance(arg, torch.Tensor):
+                args_grad.append(arg.grad)
+        grad_out = args_grad
+        
+        device_args_grad = []
+        for arg in device_args:
+            if isinstance(arg, torch.Tensor):
+                device_args_grad.append(arg.grad)
+        device_grad_out = device_args_grad
+    
+    return grad_out, device_grad_out, grad, cpu_grad
 
 
 def initialize_save_error_data():