#13373: Update golden function

tests/sweep_framework/sweeps/eltwise/binary/div/div_tensor_pytorch2.py

@@ -164,7 +164,8 @@ def run(
             partial(torch_random, low=-100, high=100, dtype=torch.float32), input_b_dtype
         )(other)
 
-    torch_output_tensor = torch.div(torch_input_tensor_a, torch_other_tensor)
+    golden_function = ttnn.get_golden_function(ttnn.div)
+    torch_output_tensor = golden_function(torch_input_tensor_a, torch_other_tensor)
 
     input_tensor_a = ttnn.from_torch(
         torch_input_tensor_a,

tests/sweep_framework/sweeps/eltwise/binary/lt/lt_scalar_pytorch2.py

@@ -54,7 +54,8 @@ def run(
         partial(torch_random, low=-100, high=100, dtype=torch.float32), input_a_dtype
     )(input_specs["shape"])
 
-    torch_output_tensor = torch.lt(torch_input_tensor_a, input_specs["other"])
+    golden_function = ttnn.get_golden_function(ttnn.lt)
+    torch_output_tensor = golden_function(torch_input_tensor_a, input_specs["other"])
 
     input_tensor_a = ttnn.from_torch(
         torch_input_tensor_a,

tests/sweep_framework/sweeps/eltwise/binary/lt/lt_tensor_pytorch2.py

@@ -54,7 +54,9 @@ def run(
     torch_input_tensor_b = gen_func_with_cast_tt(
         partial(torch_random, low=-100, high=100, dtype=torch.float32), input_b_dtype
     )(input_shape_b)
-    torch_output_tensor = torch.lt(torch_input_tensor_a, torch_input_tensor_b)
+
+    golden_function = ttnn.get_golden_function(ttnn.lt)
+    torch_output_tensor = golden_function(torch_input_tensor_a, torch_input_tensor_b)
 
     input_tensor_a = ttnn.from_torch(
         torch_input_tensor_a,

tests/sweep_framework/sweeps/eltwise/binary/multiply/multiply_scalar_pytorch2.py

@@ -50,7 +50,8 @@ def run(
         partial(torch_random, low=-100, high=100, dtype=torch.float32), input_a_dtype
     )(input_specs["shape"])
 
-    torch_output_tensor = torch.mul(torch_input_tensor_a, input_specs["other"])
+    golden_function = ttnn.get_golden_function(ttnn.mul)
+    torch_output_tensor = golden_function(torch_input_tensor_a, input_specs["other"])
 
     input_tensor_a = ttnn.from_torch(
         torch_input_tensor_a,

tests/sweep_framework/sweeps/eltwise/binary/ne/ne_scalar_pytorch2.py

@@ -55,7 +55,8 @@ def run(
         partial(torch_random, low=-100, high=100, dtype=torch.float32), input_a_dtype
     )(input_specs["shape"])
 
-    torch_output_tensor = torch.ne(torch_input_tensor_a, input_specs["other"])
+    golden_function = ttnn.get_golden_function(ttnn.ne)
+    torch_output_tensor = golden_function(torch_input_tensor_a, input_specs["other"])
 
     input_tensor_a = ttnn.from_torch(
         torch_input_tensor_a,

tests/sweep_framework/sweeps/eltwise/binary/subtract/subtract_tensor_pytorch2.py

@@ -143,7 +143,8 @@ def run(
             partial(torch_random, low=-100, high=100, dtype=torch.float32), input_b_dtype
         )(other)
 
-    torch_output_tensor = torch.sub(torch_input_tensor_a, torch_other_tensor)
+    golden_function = ttnn.get_golden_function(ttnn.sub)
+    torch_output_tensor = golden_function(torch_input_tensor_a, torch_other_tensor)
 
     input_tensor_a = ttnn.from_torch(
         torch_input_tensor_a,

tests/sweep_framework/sweeps/eltwise/unary/clamp/clamp_pytorch2.py

@@ -104,7 +104,8 @@ def run(
     min_val = input_specs.get("min", None)
     max_val = input_specs.get("max", None)
 
-    torch_output_tensor = torch.clamp(torch_input_tensor_a, min_val, max_val)
+    golden_function = ttnn.get_golden_function(ttnn.clamp)
+    torch_output_tensor = golden_function(torch_input_tensor_a, min_val, max_val)
 
     input_tensor_a = ttnn.from_torch(
         torch_input_tensor_a,

tests/sweep_framework/sweeps/eltwise/unary/clamp_min/clamp_min_pytorch2.py

@@ -57,7 +57,8 @@ def run(
         partial(torch_random, low=-100, high=100, dtype=torch.float32), input_a_dtype
     )(input_specs["shape"])
 
-    torch_output_tensor = torch.clamp(torch_input_tensor_a, input_specs["min"])
+    golden_function = ttnn.get_golden_function(ttnn.clamp)
+    torch_output_tensor = golden_function(torch_input_tensor_a, input_specs["min"])
 
     input_tensor_a = ttnn.from_torch(
         torch_input_tensor_a,

tests/sweep_framework/sweeps/eltwise/unary/hardswish/hardswish_pytorch2.py

@@ -74,7 +74,8 @@ def run(
         partial(torch_random, low=-100, high=100, dtype=torch.float32), input_dtype
     )(input_shape)
 
-    torch_output_tensor = torch.nn.functional.hardswish(torch_input_tensor_a)
+    golden_function = ttnn.get_golden_function(ttnn.hardswish)
+    torch_output_tensor = golden_function(torch_input_tensor_a)
 
     input_tensor_a = ttnn.from_torch(
         torch_input_tensor_a,

tests/sweep_framework/sweeps/eltwise/unary/hardtanh/hardtanh_pytorch2.py

@@ -162,7 +162,8 @@ def run(
     min_val = input_specs.get("min_val")
     max_val = input_specs.get("max_val")
 
-    torch_output_tensor = torch.nn.functional.hardtanh(torch_input_tensor_a, min_val, max_val)
+    golden_function = ttnn.get_golden_function(ttnn.hardtanh)
+    torch_output_tensor = golden_function(torch_input_tensor_a, min_val, max_val)
 
     input_tensor_a = ttnn.from_torch(
         torch_input_tensor_a,

tests/sweep_framework/sweeps/eltwise/unary/logical_not/logical_not_pytorch2.py

@@ -48,7 +48,8 @@ def run(
         partial(torch_random, low=-100, high=100, dtype=torch.float32), input_dtype
     )(input_shape)
 
-    torch_output_tensor = torch.logical_not(torch_input_tensor_a)
+    golden_function = ttnn.get_golden_function(ttnn.logical_not)
+    torch_output_tensor = golden_function(torch_input_tensor_a)
 
     input_tensor_a = ttnn.from_torch(
         torch_input_tensor_a,

tests/sweep_framework/sweeps/eltwise/unary/neg/neg_pytorch2.py

@@ -55,7 +55,8 @@ def run(
         partial(torch_random, low=-100, high=100, dtype=torch.float32), input_dtype
     )(input_shape)
 
-    torch_output_tensor = torch.neg(torch_input_tensor_a)
+    golden_function = ttnn.get_golden_function(ttnn.neg)
+    torch_output_tensor = golden_function(torch_input_tensor_a)
 
     input_tensor_a = ttnn.from_torch(
         torch_input_tensor_a,