Remove arg index from the get_arg and set_arg of a torch Node (#12326)

Summary:

Instead of using the arg index, rely on the normalized kwargs dictionary for a uniform view of a node's arguments. This diff cleans up the get_arg and set_arg api so that callers do not need to additionally keep track of arg indices. This simplifies the calling code and also prevents unnecessary code maintenance in case the op signature changes thereby changing the relative positioning of the args within a node.

Reviewed By: abeakkas

Differential Revision: D77976838

Author: Ritwik Das

backends/cadence/aot/pass_utils.py

@@ -174,30 +174,53 @@ def nodes_not_adjacent_in_gm(
 
 def get_arg(
     node: torch.fx.Node,
-    arg_index: int,
     kwarg_name: str,
-    *,
-    default: torch.fx.node.Argument = None,
 ) -> torch.fx.node.Argument:
     """
-    Get the arg at arg_index or kwarg with arg_name of the node. If neither is found
-    return default.
+    Get the arg with arg_name of the node, returns default value if not set.
     """
-    if arg_index < len(node.args):
-        return node.args[arg_index]
-    elif kwarg_name in node.kwargs:
+    # Try to get the arg from kwargs first since this is faster
+    if kwarg_name in node.kwargs:
         return node.kwargs[kwarg_name]
-    else:
-        return default
+
+    # If it's not found in kwargs, try to normalize the args
+    normalized_args = node.normalized_arguments(
+        node.graph.owning_module, normalize_to_only_use_kwargs=True
+    )
+    if not normalized_args:
+        raise RuntimeError(
+            f"get_arg: Node {node} does not support normalization of arguments"
+        )
+
+    return normalized_args.kwargs[kwarg_name]
 
 
 def set_arg(
-    node: torch.fx.Node, arg_index: int, kwarg_name: str, value: torch.fx.node.Argument
+    node: torch.fx.Node, kwarg_name: str, value: torch.fx.node.Argument
 ) -> None:
     """
-    Set the arg at arg_index if it exists, otherwise set the kwarg.
+    Set the node's arg with its name to the given value.
     """
-    if arg_index < len(node.args):
-        node.update_arg(arg_index, value)
+    # Try to set the arg if it is present in kwargs first since this is faster
+    if kwarg_name in node.kwargs:
+        node.update_kwarg(kwarg_name, value)
+        return
+
+    # If it's not found in kwargs, try to normalize the args and set the arg
+    normalized_args = node.normalized_arguments(
+        node.graph.owning_module, normalize_to_only_use_kwargs=True
+    )
+    if not normalized_args:
+        raise RuntimeError(
+            f"set_arg: Node {node} does not support normalization of arguments"
+        )
+
+    kwargs = normalized_args.kwargs
+    if kwarg_name not in kwargs:
+        raise ValueError(f"set_arg: invalid arg name {kwarg_name} for node {node} used")
+
+    idx = list(kwargs.keys()).index(kwarg_name)
+    if idx < len(node.args):
+        node.update_arg(idx, value)
     else:
         node.update_kwarg(kwarg_name, value)

backends/cadence/aot/remove_ops.py

@@ -779,17 +779,17 @@ def _remove_unused_cat(self, graph_module: torch.fx.GraphModule) -> None:
         for slice_copy_node in graph_module.graph.find_nodes(
             op="call_function", target=exir_ops.edge.aten.slice_copy.Tensor
         ):
-            cat_node = cast(Node, get_arg(slice_copy_node, 0, "input"))
-            slice_dim = cast(int, get_arg(slice_copy_node, 1, "dim", default=0))
-            start_idx = cast(int, get_arg(slice_copy_node, 2, "start", default=None))
-            end_idx = cast(int, get_arg(slice_copy_node, 3, "end", default=None))
-            step = cast(int, get_arg(slice_copy_node, 4, "step", default=1))
+            cat_node = cast(Node, get_arg(slice_copy_node, "input"))
+            slice_dim = cast(int, get_arg(slice_copy_node, "dim"))
+            start_idx = cast(int, get_arg(slice_copy_node, "start"))
+            end_idx = cast(int, get_arg(slice_copy_node, "end"))
+            step = cast(int, get_arg(slice_copy_node, "step"))
 
             if cat_node.target != exir_ops.edge.aten.cat.default or step != 1:
                 continue
 
             # Make sure cat and slice happens on the same dimension.
-            cat_dim = cast(Node, get_arg(cat_node, 1, "dim", default=0))
+            cat_dim = cast(Node, get_arg(cat_node, "dim"))
             if cat_dim != slice_dim:
                 continue
 
@@ -805,14 +805,14 @@ def _remove_unused_cat(self, graph_module: torch.fx.GraphModule) -> None:
                 end_idx += cat_output_shape[cat_dim]
 
             offset = 0
-            for cat_input_node in cast(List[Node], get_arg(cat_node, 0, "tensors")):
+            for cat_input_node in cast(List[Node], get_arg(cat_node, "tensors")):
                 cat_input_shape = cat_input_node.meta["val"].shape
 
                 # Check if the slice range overlaps with the cat input range.
                 if offset <= start_idx and end_idx <= offset + cat_input_shape[cat_dim]:
                     slice_copy_node.replace_input_with(cat_node, cat_input_node)
-                    set_arg(slice_copy_node, 2, "start", start_idx - offset)
-                    set_arg(slice_copy_node, 3, "end", end_idx - offset)
+                    set_arg(slice_copy_node, "start", start_idx - offset)
+                    set_arg(slice_copy_node, "end", end_idx - offset)
                     break
 
                 offset += cat_input_shape[cat_dim]