Arm backend: Placeholder processing now handles non-persistent buffers (#9994)

- Update process_placeholder and called functions to use _export.utils
- Add test to validate non-persistent input buffers going forward
- Remove workaround from test_llama

Signed-off-by: Iliyan Georgiev <Iliyan.Georgiev@arm.com>

Author: iliyan-georgiev-arm

backends/arm/process_node.py

@@ -15,6 +15,14 @@
 from executorch.backends.arm.tosa_mapping import TosaArg
 from executorch.backends.arm.tosa_specification import TosaSpecification
 from executorch.backends.arm.tosa_utils import getNodeArgs, tosa_shape
+from torch._export.utils import (
+    get_buffer,
+    get_lifted_tensor_constant,
+    get_param,
+    is_buffer,
+    is_lifted_tensor_constant,
+    is_param,
+)
 from torch.export.exported_program import ExportedProgram
 
 
@@ -99,8 +107,7 @@ def process_inputs_to_parameters(
             f"Failed processing parameter placeholder: {node.name}. "
             "Is the original torch function supported?"
         ) from e
-    parameter_name = edge_program.graph_signature.inputs_to_parameters[tosa_arg.name]
-    parameter_data = edge_program.state_dict[parameter_name]
+    parameter_data = get_param(edge_program, node)
 
     assert isinstance(parameter_data, torch.Tensor), "Expect Attr to be tensor"
     parameter_values = parameter_data.detach().numpy()
@@ -128,8 +135,7 @@ def process_inputs_to_buffers(
             f"Failed processing buffer placeholder: {node.name}. "
             "Is the original torch function supported?"
         ) from e
-    buffer_name = edge_program.graph_signature.inputs_to_buffers[node.name]
-    buffer_data = edge_program.state_dict[buffer_name]
+    buffer_data = get_buffer(edge_program, node)
 
     assert isinstance(buffer_data, torch.Tensor), "Expect Attr to be tensor"
     buffer_values = buffer_data.detach().numpy()
@@ -156,11 +162,8 @@ def process_inputs_to_lifted_tensor_constants(
             f"Failed processing lifted tensor constant placeholder: {node.name}. "
             "Is the original torch function supported?"
         ) from e
-    tensor_name = edge_program.graph_signature.inputs_to_lifted_tensor_constants[
-        tosa_arg.name
-    ]
-    tensor = edge_program.tensor_constants[tensor_name]
-    tensor_data = tensor.detach().numpy()
+    tensor = get_lifted_tensor_constant(edge_program, node)
+    tensor_data = tensor.detach().numpy()  # type: ignore[union-attr]
 
     tosa_graph.addConst(
         tensor_data.shape, tosa_arg.dtype, tensor_data, name=tosa_arg.name
@@ -179,11 +182,11 @@ def process_placeholder(
 
     if node.name in edge_program.graph_signature.user_inputs:
         process_inputs(node, tosa_graph, tosa_spec)
-    elif node.name in edge_program.graph_signature.inputs_to_parameters:
+    elif is_param(edge_program, node):
         process_inputs_to_parameters(node, tosa_graph, edge_program, tosa_spec)
-    elif node.name in edge_program.graph_signature.inputs_to_buffers:
+    elif is_buffer(edge_program, node):
         process_inputs_to_buffers(node, tosa_graph, edge_program)
-    elif node.name in edge_program.graph_signature.inputs_to_lifted_tensor_constants:
+    elif is_lifted_tensor_constant(edge_program, node):
         process_inputs_to_lifted_tensor_constants(node, tosa_graph, edge_program)
     elif node.name in edge_program.graph_signature.inputs_to_lifted_custom_objs:
         raise NotImplementedError(

backends/arm/test/misc/test_non_persistent_buffers.py

@@ -0,0 +1,49 @@
+# Copyright 2025 Arm Limited and/or its affiliates.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn as nn
+
+from executorch.backends.arm.test.common import parametrize
+from executorch.backends.arm.test.tester.test_pipeline import (
+    TosaPipelineBI,
+    TosaPipelineMI,
+)
+
+
+class NonPersistentBuffer(nn.Module):
+    """
+    Min code version registering a non-persistent input buffer.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.register_buffer("test_buff", torch.rand(2, 2, 2, 2), persistent=False)
+
+    def forward(self, x):
+        return x - self.test_buff
+
+
+test_input = {"input": (torch.ones(2, 2, 2, 2),)}
+
+input_t = tuple[torch.Tensor]
+
+
+@parametrize("test_data", test_input)
+def test_non_persistent_buffer_MI(test_data: input_t):
+    """
+    Test validates Arm backend handling of non-persistent buffers
+    and ensures that there are no asserts or errors when they are used.
+    """
+    TosaPipelineMI[input_t](NonPersistentBuffer(), test_data, "").run()
+
+
+@parametrize("test_data", test_input)
+def test_non_persistent_buffer_BI(test_data: input_t):
+    """
+    Test validates Arm backend handling of non-persistent buffers
+    and ensures that there are no asserts or errors when they are used.
+    """
+    TosaPipelineBI[input_t](NonPersistentBuffer(), test_data, "").run()

backends/arm/test/models/test_llama.py

@@ -79,24 +79,6 @@ def prepare_model(self):
 
         llama_model, llama_inputs, llama_meta = get_llama_model(args)
 
-        # TODO: Remove workaround since attention mask should not be persistent,
-        # it only works if input shape is always the same
-        freqs_c = "freqs_cos"
-        freqs_s = "freqs_sin"
-        for i in range(llama_model.n_layers):
-            val = llama_model.layers[i].attention.get_buffer("mask")
-            llama_model.layers[i].attention.register_buffer(
-                "mask", val, persistent=True
-            )
-            val = llama_model.layers[i].attention.rope.get_buffer(freqs_c)
-            llama_model.layers[i].attention.rope.register_buffer(
-                freqs_c, val, persistent=True
-            )
-            val = llama_model.layers[i].attention.rope.get_buffer(freqs_s)
-            llama_model.layers[i].attention.rope.register_buffer(
-                freqs_s, val, persistent=True
-            )
-
         return llama_model, llama_inputs, llama_meta
 
     def test_llama_tosa_MI(self):