#14099: ttnn.clip, clamp interface to follow Pytorch (#14127)

#14099: ttnn.clip interface to follow Pytorch

tests/tt_eager/python_api_testing/sweep_tests/tt_lib_ops.py

@@ -1288,7 +1288,7 @@ def clip(
     **kwargs,
 ):
     t0 = setup_tt_tensor(x, device, layout[0], input_mem_config[0], dtype[0])
-    t1 = ttnn.clip(t0, min=low, max=high, memory_config=output_mem_config)
+    t1 = ttnn.clip(t0, low, high, memory_config=output_mem_config)
 
     return tt2torch_tensor(t1)
 

tests/ttnn/unit_tests/operations/eltwise/test_activation.py

@@ -349,11 +349,11 @@ def run_activation_test_scalarBC_key(device, h, w, scalar1, scalar2, ttnn_functi
     torch_input_tensor_a = torch.rand((h, w), dtype=torch.bfloat16)
     golden_function = ttnn.get_golden_function(ttnn_function)
 
-    torch_output_tensor = golden_function(torch_input_tensor_a, min=scalar1, max=scalar2)
+    torch_output_tensor = golden_function(torch_input_tensor_a, scalar1, scalar2)
 
     input_tensor_a = ttnn.from_torch(torch_input_tensor_a, layout=ttnn.TILE_LAYOUT, device=device)
 
-    output_tensor = ttnn_function(input_tensor_a, min=scalar1, max=scalar2)
+    output_tensor = ttnn_function(input_tensor_a, scalar1, scalar2)
     output_tensor = ttnn.to_layout(output_tensor, ttnn.ROW_MAJOR_LAYOUT)
     output_tensor = ttnn.from_device(output_tensor)
     output_tensor = ttnn.to_torch(output_tensor)

tests/ttnn/unit_tests/operations/eltwise/test_composite.py

@@ -112,12 +112,12 @@ def test_unary_composite_clamp_ttnn(input_shapes, min, max, device):
     in_data1, input_tensor1 = data_gen_with_range(input_shapes, -100, 100, device)
     if min is None and max is None:
         with pytest.raises(RuntimeError, match="Only one of 'min' or 'max' can be None. Please provide one value"):
-            ttnn.clamp(input_tensor1, min=min, max=max)
+            ttnn.clamp(input_tensor1, min, max)
         assert True
     else:
-        output_tensor = ttnn.clamp(input_tensor1, min=min, max=max)
+        output_tensor = ttnn.clamp(input_tensor1, min, max)
         golden_function = ttnn.get_golden_function(ttnn.clamp)
-        golden_tensor = golden_function(in_data1, min=min, max=max)
+        golden_tensor = golden_function(in_data1, min, max)
         comp_pass = compare_pcc([output_tensor], [golden_tensor])
         assert comp_pass
 
@@ -149,12 +149,12 @@ def test_unary_composite_clip_ttnn(input_shapes, min, max, device):
     in_data1, input_tensor1 = data_gen_with_range(input_shapes, -100, 100, device)
     if min is None and max is None:
         with pytest.raises(RuntimeError, match="Only one of 'min' or 'max' can be None. Please provide one value"):
-            ttnn.clip(input_tensor1, min=min, max=max)
+            ttnn.clip(input_tensor1, min, max)
         assert True
     else:
-        output_tensor = ttnn.clip(input_tensor1, min=min, max=max)
+        output_tensor = ttnn.clip(input_tensor1, min, max)
         golden_function = ttnn.get_golden_function(ttnn.clip)
-        golden_tensor = golden_function(in_data1, min=min, max=max)
+        golden_tensor = golden_function(in_data1, min, max)
         comp_pass = compare_pcc([output_tensor], [golden_tensor])
         assert comp_pass
 

ttnn/cpp/ttnn/operations/eltwise/unary/unary_pybind.hpp

@@ -67,9 +67,9 @@ void bind_unary_composite_optional_floats_with_default(py::module& module, const
                 return self(input_tensor, parameter_a, parameter_b, memory_config);
             },
             py::arg("input_tensor"),
-            py::kw_only(),
             py::arg(parameter_name_a.c_str()) = parameter_a_value,
             py::arg(parameter_name_b.c_str()) = parameter_b_value,
+            py::kw_only(),
             py::arg("memory_config") = std::nullopt});
 }
 

ttnn/ttnn/operations/unary.py

@@ -289,7 +289,7 @@ def _golden_function_polygamma(input_tensor_a, k, *args, **kwargs):
 def _golden_function_clamp(input_tensor_a, min=None, max=None, *args, **kwargs):
     import torch
 
-    return torch.clamp(input=input_tensor_a, min=min, max=max)
+    return torch.clamp(input_tensor_a, min, max)
 
 
 ttnn.attach_golden_function(ttnn.clamp, golden_function=_golden_function_clamp)
@@ -298,7 +298,7 @@ def _golden_function_clamp(input_tensor_a, min=None, max=None, *args, **kwargs):
 def _golden_function_clip(input_tensor_a, min=None, max=None, *args, **kwargs):
     import torch
 
-    return torch.clip(input=input_tensor_a, min=min, max=max)
+    return torch.clip(input_tensor_a, min, max)
 
 
 ttnn.attach_golden_function(ttnn.clip, golden_function=_golden_function_clip)