Refactor clone_dim_order_support to existing clone_support file

Author: keyprocedure

backends/arm/operator_support/__init__.py

@@ -6,7 +6,6 @@
 # pyre-unsafe
 
 from . import (  # noqa
-    clone_dim_order_support,
     clone_support,
     convolution_support,
     embedding_support,

backends/arm/operator_support/clone_dim_order_support.py

@@ -5,6 +5,7 @@
 
 import logging
 
+import torch
 import torch.fx as fx
 from executorch.backends.arm.operator_support.tosa_supported_operators import (
     register_tosa_support_check,
@@ -18,7 +19,7 @@
 
 @register_tosa_support_check
 class CloneSupported(SupportedTOSAOperatorCheck):
-    targets = [exir_ops.edge.aten.clone.default]
+    targets = [exir_ops.edge.dim_order_ops._clone_dim_order.default]
 
     tosa_specs = [
         TosaSpecification.create_from_string("TOSA-1.0+INT"),
@@ -28,10 +29,62 @@ class CloneSupported(SupportedTOSAOperatorCheck):
     def is_node_tosa_supported(
         self, node: fx.Node, tosa_spec: TosaSpecification
     ) -> bool:
+        if node.target not in self.targets:
+            self.reporter.report_reject(node, f"Target {node.target} is not supported.")
+            return False
 
         input_node = node.args[0]
         if not isinstance(input_node, fx.Node):
             self.reporter.report_reject(node, "Non tensor clones are not supported")
             return False
 
+        # Check input node
+        if len(node.all_input_nodes) != 1:
+            self.reporter.report_reject(
+                node, f"Expected 1 input node, got {len(node.all_input_nodes)}"
+            )
+            return False
+
+        input_val = node.all_input_nodes[0].meta["val"]
+        if not isinstance(input_val, torch._subclasses.FakeTensor):
+            self.reporter.report_reject(node, "Expected input to be a FakeTensor.")
+            return False
+
+        input_dtype = input_val.dtype
+
+        # Check output node
+        output_val = node.meta["val"]
+        if not isinstance(output_val, torch._subclasses.FakeTensor):
+            self.reporter.report_reject(node, "Expected output to be a FakeTensor.")
+            return False
+
+        if output_val.dtype != input_dtype:
+            self.reporter.report_reject(
+                node,
+                f"Input dtype {input_val.dtype} does not match {output_val.dtype}.",
+            )
+            return False
+
+        # Check memory format
+        if "memory_format" in node.kwargs:
+            if node.kwargs["memory_format"] in (torch.preserve_format,):
+                self.reporter.report_reject(
+                    node,
+                    f"Argument 'memory_format' is not supported for "
+                    f"{node.target} right now.",
+                )
+                return False
+
+        # Check dim_order
+        if "dim_order" in node.kwargs:
+            dim_order = node.kwargs["dim_order"]
+            # pyre-ignore[6]
+            if dim_order != list(range(len(dim_order))):  # type: ignore[arg-type]
+                self.reporter.report_reject(
+                    node,
+                    f"Argument {dim_order=} is not supported for "
+                    f"{node.target} right now.",
+                )
+                return False
+
         return True