Arm backend: Cast int64 buffers to int32 (#18234)

Cast int64 buffers using get_attr in the same way as regular
placeholders.

---------

Signed-off-by: Adrian Lundell <adrian.lundell@arm.com>

Author: AdrianLundell

backends/arm/_passes/insert_int32_casts_after_int64_placeholders.py

@@ -89,8 +89,10 @@ def _insert_placeholder_i64_to_i32_casts(self, graph_module: torch.fx.GraphModul
         modified = False
         graph = graph_module.graph
         for node in graph.nodes:
-            if node.op != "placeholder":
+            if node.op not in ("placeholder", "get_attr"):
                 continue
+            if "val" not in node.meta:
+                continue  # Ignore submodule get_attrs
             node_val = node.meta["val"]
             if not self._is_tensor_of_dtype(node_val, torch.int64):
                 continue

backends/arm/test/ops/test_clamp.py

@@ -257,7 +257,9 @@ def test_clamp_vgf_quant(test_data):
     pipeline.run()
 
 
-aten_op_tensor = "torch.ops.aten.clamp.Tensor"
+aten_op_tensor = [
+    "torch.ops.aten.clamp.Tensor",
+]
 exir_op_tensor = "executorch_exir_dialects_edge__ops_aten_clamp_Tensor"
 
 test_data_suite_tensor_FP = {
@@ -413,10 +415,22 @@ def test_clamp_tosa_INT_tensor(test_data):
     input_tensor, min_val, max_val = test_data()
     model = Clamp(min_val, max_val)
 
+    # Check that int64 inputs are cast to int32 in the tfa pipeline
+    if any(
+        t.dtype == torch.int64
+        for t in (input_tensor, min_val, max_val)
+        if isinstance(t, torch.Tensor)
+    ):
+        aten_op = aten_op_tensor + [
+            "torch.ops.dim_order_ops._to_dim_order_copy.default"
+        ]
+    else:
+        aten_op = aten_op_tensor
+
     pipeline = TosaPipelineINT[input_t](
         model,
         (input_tensor,),
-        aten_op_tensor,
+        aten_op,
         exir_op_tensor,
     )
     pipeline.run()