#9628: Update addalpha_bw test file to test default value and update …

…golden function

tests/ttnn/unit_tests/operations/backward/test_backward_addalpha.py

@@ -31,6 +31,28 @@ def test_bw_addalpha(input_shapes, alpha, device):
     assert status
 
 
+@pytest.mark.parametrize(
+    "input_shapes",
+    (
+        (torch.Size([1, 1, 32, 32])),
+        (torch.Size([1, 1, 320, 384])),
+        (torch.Size([1, 3, 320, 384])),
+    ),
+)
+def test_bw_addalpha_wo_alpha(input_shapes, device):
+    in_data, input_tensor = data_gen_with_range(input_shapes, -100, 100, device, True)
+    other_data, other_tensor = data_gen_with_range(input_shapes, -100, 100, device, True)
+    grad_data, grad_tensor = data_gen_with_range(input_shapes, -100, 100, device)
+
+    tt_output_tensor_on_device = ttnn.addalpha_bw(grad_tensor, input_tensor, other_tensor)
+
+    golden_function = ttnn.get_golden_function(ttnn.addalpha_bw)
+    golden_tensor = golden_function(grad_data, in_data, other_data)
+
+    status = compare_pcc(tt_output_tensor_on_device, golden_tensor)
+    assert status
+
+
 @pytest.mark.parametrize(
     "input_shapes",
     (
@@ -65,14 +87,50 @@ def test_bw_addalpha_with_opt_output(input_shapes, alpha, device, are_required_o
         queue_id=cq_id,
     )
 
-    in_data.retain_grad()
-    other_data.retain_grad()
+    golden_function = ttnn.get_golden_function(ttnn.addalpha_bw)
+    golden_tensor = golden_function(grad_data, in_data, other_data, alpha)
+
+    status = True
+    for i in range(len(are_required_outputs)):
+        if are_required_outputs[i]:
+            status = status & compare_pcc([tt_output_tensor_on_device[i]], [golden_tensor[i]])
+    assert status
+
+
+@pytest.mark.parametrize(
+    "input_shapes",
+    (
+        (torch.Size([1, 1, 32, 32])),
+        (torch.Size([1, 1, 320, 384])),
+        (torch.Size([1, 3, 320, 384])),
+    ),
+)
+@pytest.mark.parametrize("are_required_outputs", [[True, True], [True, False], [False, True], [False, False]])
+def test_bw_addalpha_with_opt_output_wo_alpha(input_shapes, device, are_required_outputs):
+    in_data, input_tensor = data_gen_with_range(input_shapes, -100, 100, device, True)
+    other_data, other_tensor = data_gen_with_range(input_shapes, -90, 100, device, True)
+    grad_data, grad_tensor = data_gen_with_range(input_shapes, -70, 90, device)
+    input_grad = None
+    other_grad = None
 
-    pyt_y = torch.add(in_data, other_data, alpha=alpha)
+    if are_required_outputs[0]:
+        _, input_grad = data_gen_with_range(input_shapes, -1, 1, device)
+    if are_required_outputs[1]:
+        _, other_grad = data_gen_with_range(input_shapes, -1, 1, device)
 
-    pyt_y.backward(gradient=grad_data)
+    cq_id = 0
+    tt_output_tensor_on_device = ttnn.addalpha_bw(
+        grad_tensor,
+        input_tensor,
+        other_tensor,
+        are_required_outputs=are_required_outputs,
+        input_a_grad=input_grad,
+        input_b_grad=other_grad,
+        queue_id=cq_id,
+    )
 
-    golden_tensor = [in_data.grad, other_data.grad]
+    golden_function = ttnn.get_golden_function(ttnn.addalpha_bw)
+    golden_tensor = golden_function(grad_data, in_data, other_data)
 
     status = True
     for i in range(len(are_required_outputs)):

ttnn/ttnn/operations/binary_backward.py

@@ -181,7 +181,7 @@ def _golden_function_comparison_ops(torch_op, grad_tensor, input_tensor_a, input
 
 ttnn.attach_golden_function(
     ttnn.addalpha_bw,
-    golden_function=lambda grad, a, b, alpha, *args, **kwargs: _golden_function_backward_with_float(
+    golden_function=lambda grad, a, b, alpha=None, *args, **kwargs: _golden_function_backward_with_float(
         torch.add, grad, a, b, alpha, *args, **kwargs
     ),
 )