Optimize out aten.clone ops

Since we do not alter its layout, it looks good to skip `aten.clone` ops.

tests/lowering/creation/test_clone.py

@@ -38,9 +38,6 @@ def test_clone_from_arg(device, input_shapes):
     result_after = m.forward(*inputs)
     option._out_fx_graphs[0].print_tabular()
 
-    # Check the graph has be rewritten and contain ttnn ops
-    nodes = list(option._out_fx_graphs[0].nodes)
-    assert [node.target for node in nodes].count(torch_ttnn.target_wrappers.clone) == 1
     # Check inference result
     assert torch.allclose(result_before, result_after)
 
@@ -63,8 +60,6 @@ def test_clone_from_node(device, input_shapes):
     # Check the graph has be rewritten and contain ttnn ops
     nodes = list(option._out_fx_graphs[0].nodes)
     target = [node.target for node in nodes]
-    assert target.count(torch_ttnn.target_wrappers.clone) == 1
-    clone_arg_0 = nodes[target.index(torch_ttnn.target_wrappers.clone)].args[0].target
-    assert isinstance(clone_arg_0, ttnn.decorators.FastOperation) or isinstance(clone_arg_0, ttnn.decorators.Operation)
+    assert target.count("call_function") == 0
     # Check inference result
     assert torch.allclose(result_before, result_after)

torch_ttnn/passes/lowering/add_data_move_pass.py

@@ -33,7 +33,6 @@ def is_function_call(node) -> bool:
     ttnn.atan,
     ttnn.atan2,  # binary
     ttnn.atanh,
-    #  ttnn.clone,  in target_wrappers
     ttnn.cos,
     ttnn.cosh,
     ttnn.erf,
@@ -124,7 +123,7 @@ def is_function_call(node) -> bool:
     ttnn.to_layout,
 ]
 
-TTNN_TARGET_WRAPPERS = [target_wrappers.clone, target_wrappers.repeat]
+TTNN_TARGET_WRAPPERS = [target_wrappers.repeat]
 
 TTNN_NORM_OPS = [
     ttnn.group_norm,

torch_ttnn/passes/lowering/target_wrappers.py

@@ -2,11 +2,6 @@
 import torch
 
 
-@torch.fx.wrap
-def clone(t):
-    return ttnn.clone(t, memory_config=t.memory_config(), dtype=t.dtype)
-
-
 @torch.fx.wrap
 def repeat(t, sizes):
     return ttnn.repeat(t, ttnn.Shape(sizes))

torch_ttnn/passes/lowering/to_tt_pass.py

@@ -292,6 +292,9 @@ def call_function(self, target, args, kwargs):
         ############################################################
         # Data movement
         ############################################################
+        if target == torch.ops.aten.clone.default:
+            return args[0]
+
         if target == torch.ops.aten.permute.default:
             return self.call_function_prop_meta(ttnn.permute, args, kwargs)
 
@@ -364,12 +367,6 @@ def rewrite_node(node):
             args = node.args
             kwargs = node.kwargs
 
-            if node.target == torch.ops.aten.clone.default:
-                arg_metadata = node.meta["val"]
-                ttnn_dtype = torch_dtype_to_ttnn_dtype(arg_metadata.dtype)
-                # Add additional logic to choose the appropriate memory_config type: DRAM or L1
-                return g.call_function(target_wrappers.clone, args=(args[0],))
-
             if node.target == torch.ops.aten.native_layer_norm.default:
                 new_node = g.call_function(
                     ttnn.layer_norm,