Arm backend: Fix get_module_name_filter (#15910)

Update TosaQuantizer to use get_module_name_filter from torchao. Adds
new tests to validate that set_module_name works as intended.

Fixes #15870



cc @freddan80 @per @zingo @digantdesai

Signed-off-by: Oscar Andersson <[email protected]>

Author: oscarandersson8218

backends/arm/quantizer/arm_quantizer.py

@@ -46,6 +46,7 @@
 from torchao.quantization.pt2e.quantizer import (
     annotate_input_qspec_map,
     annotate_output_qspec,
+    get_module_name_filter,
     QuantizationSpec,
     Quantizer,
 )
@@ -248,33 +249,6 @@ def get_symmetric_a16w8_quantization_config(
 """
 
 
-def _get_module_name_filter(module_name: str) -> NodeFilterType:
-    """Get the module_name_filter function for a given module name, the filter accepts
-    a node and checks if the node comes from a module that has certain module name
-
-    For example:
-        node: linear_op = call_function[...](...)  # comes from a module with name blocks.sub.linear1
-
-    >> module_name_filter = _get_module_name_filter("blocks.sub")
-    >> print(module_name_filter(node))
-    True  # the node is from "blocks.sub" based on the fully qualified name "blocks.sub.linear1"
-    """
-
-    name_start = len("L['self'].")
-
-    def module_name_filter(n: Node) -> bool:
-        # node_stack example: {
-        #    'L__self___sub': ("L['self'].sub", <class '....Sub'>),
-        #    'L__self___sub_linear': ("L['self'].sub.linear", <class 'torch.nn.modules.linear.Linear'>)
-        # }
-        # get_attr nodes doesn't have nn_module_stack?
-        nn_module_stack = n.meta.get("nn_module_stack", {})
-        names = [name[name_start:] for name, _ in nn_module_stack.values()]
-        return module_name in names
-
-    return module_name_filter
-
-
 def _get_module_type_filter(tp: Callable) -> NodeFilterType:
     """Get the module_type_filter function for a given module type, the filter accepts
     a node and checks if the node comes from a module that has certain module type
@@ -306,7 +280,7 @@ def _get_not_module_type_or_name_filter(
     tp_list: List[Callable], module_name_list: List[str]
 ) -> NodeFilterType:
     module_type_filters = [_get_module_type_filter(tp) for tp in tp_list]
-    module_name_list_filters = [_get_module_name_filter(m) for m in module_name_list]
+    module_name_list_filters = [get_module_name_filter(m) for m in module_name_list]
 
     def not_module_type_or_name_filter(n: Node) -> bool:
         return not any(f(n) for f in module_type_filters + module_name_list_filters)
@@ -455,7 +429,7 @@ def _annotate_for_static_quantization_config(
         module_name_list = list(self.module_name_config.keys())
         for module_name, config in self.module_name_config.items():
             self._annotate_all_static_patterns(
-                model, config, _get_module_name_filter(module_name)
+                model, config, get_module_name_filter(module_name)
             )
 
         tp_list = list(self.module_type_config.keys())

backends/arm/test/quantizer/test_set_module_name.py

@@ -0,0 +1,158 @@
+# 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
+from executorch.backends.arm.quantizer import (
+    get_symmetric_a16w8_quantization_config,
+    get_symmetric_quantization_config,
+    is_annotated,
+    QuantizationConfig,
+    TOSAQuantizer,
+)
+from executorch.backends.arm.quantizer.quantization_config import QuantizationSpec
+from executorch.backends.arm.tosa import TosaSpecification
+from torchao.quantization.pt2e.quantize_pt2e import convert_pt2e, prepare_pt2e
+
+DQ_PER_CHANNEL = torch.ops.quantized_decomposed.dequantize_per_channel.default
+DQ_PER_TENSOR = torch.ops.quantized_decomposed.dequantize_per_tensor.default
+Q_PER_TENSOR = torch.ops.quantized_decomposed.quantize_per_tensor.default
+
+
+class ConvModel(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv0 = torch.nn.Conv2d(
+            3,
+            16,
+            kernel_size=4,
+        )
+        self.conv1 = torch.nn.Conv2d(16, 32, kernel_size=3, bias=False)
+        self.conv2 = torch.nn.Conv2d(32, 64, kernel_size=3)
+
+    def forward(self, x):
+        x = self.conv0(x)
+        x = torch.sigmoid(x)
+        x = self.conv1(x)
+        x = torch.tanh(x)
+        x = self.conv2(x)
+        return x
+
+
+test_inputs = (torch.randn(1, 3, 64, 64),)
+
+
+def validate_per_tensor_quant(node: torch.fx.Node, qspec: QuantizationSpec):
+    _, _, zero_point, qmin, qmax, dtype = node.args
+    if qspec.qscheme == torch.per_tensor_symmetric:
+        assert (
+            zero_point == 0
+        ), f"Zero point {zero_point} is not zero for symmetric quantization"
+    assert (
+        qmin == qspec.quant_min
+    ), f"Quant min {qmin} does not match expected {qspec.quant_min}"
+    assert (
+        qmax == qspec.quant_max
+    ), f"Quant max {qmax} does not match expected {qspec.quant_max}"
+    assert dtype == qspec.dtype, f"Dtype {dtype} does not match expected {qspec.dtype}"
+
+
+def validate_per_channel_quant(node: torch.fx.Node, qspec: QuantizationSpec):
+    _, _, _, channel_axis, qmin, qmax, dtype = node.args
+    assert (
+        channel_axis == qspec.ch_axis
+    ), f"Channel axis {channel_axis} does not match expected {qspec.ch_axis}"
+    assert (
+        qmin == qspec.quant_min
+    ), f"Quant min {qmin} does not match expected {qspec.quant_min}"
+    assert (
+        qmax == qspec.quant_max
+    ), f"Quant max {qmax} does not match expected {qspec.quant_max}"
+    assert dtype == qspec.dtype, f"Dtype {dtype} does not match expected {qspec.dtype}"
+
+
+def validate_input(input_node: torch.fx.Node, qspec: QuantizationSpec | None):
+    if qspec is None:
+        return
+
+    per_channel = qspec.qscheme == torch.per_channel_symmetric
+    expected_dequant_op = DQ_PER_CHANNEL if per_channel else DQ_PER_TENSOR
+    assert (
+        input_node.target == expected_dequant_op
+    ), f"Input node {input_node} is not quantized as expected"
+    if per_channel:
+        validate_per_channel_quant(input_node, qspec)
+    else:
+        validate_per_tensor_quant(input_node, qspec)
+
+
+def validate_output(node: torch.fx.Node, qspec: QuantizationSpec | None):
+    if qspec is None:
+        return
+    users = list(node.users)
+    assert len(users) == 1, f"Node {node} should have exactly one user"
+    assert (
+        users[0].target == Q_PER_TENSOR
+    ), f"Output node {users[0]} is not quantized as expected"
+    validate_per_tensor_quant(users[0], qspec)
+
+
+def validate_node(
+    node: torch.fx.Node, quantization_config: QuantizationConfig | None
+) -> None:
+    if quantization_config is None:
+        assert not is_annotated(node), f"Node {node} is unexpectedly annotated"
+        return
+
+    assert is_annotated(node), f"Node {node} is not annotated"
+    input_qspec = quantization_config.get_input_act_qspec()
+    output_qspec = quantization_config.get_output_act_qspec()
+    weight_qspec = quantization_config.get_weight_qspec()
+
+    if len(node.all_input_nodes) == 3:
+        input_node, weight_node, bias_node = node.all_input_nodes
+        bias_qspec = quantization_config.get_bias_qspec(node)
+        validate_input(bias_node, bias_qspec)
+    else:
+        input_node, weight_node = node.all_input_nodes
+
+    validate_input(input_node, input_qspec)
+    validate_input(weight_node, weight_qspec)
+    validate_output(node, output_qspec)
+
+
+def test_set_module_name() -> None:
+    model = ConvModel()
+    model.eval()
+
+    # Set up quantizer with different configs for different modules
+    tosa_spec = TosaSpecification.create_from_string("TOSA-1.0+INT")
+    quantizer = TOSAQuantizer(tosa_spec)
+    int8_config = get_symmetric_quantization_config(is_per_channel=False)
+    a16w8_config = get_symmetric_a16w8_quantization_config()
+    # Set module-specific configurations but don't set global config to test that
+    # only specified modules are quantized
+    quantizer.set_module_name("conv0", int8_config)
+    quantizer.set_module_name("conv1", a16w8_config)
+
+    # Export model
+    exported_model = torch.export.export(model, test_inputs)
+
+    # Prepare, calibrate and convert model
+    prepared_model = prepare_pt2e(exported_model.module(), quantizer)
+    prepared_model(*test_inputs)
+    converted_model = convert_pt2e(prepared_model)
+
+    validate_node(
+        [node for node in converted_model.graph.nodes if node.name == "conv2d"][0],
+        int8_config,
+    )
+    validate_node(
+        [node for node in converted_model.graph.nodes if node.name == "conv2d_1"][0],
+        a16w8_config,
+    )
+    validate_node(
+        [node for node in converted_model.graph.nodes if node.name == "conv2d_2"][0],
+        None,
+    )