Fixes based on review comments.

Author: MartinPavella

backends/nxp/backend/ir/converter/node_converters/ops_converters/addmm_converter.py

@@ -51,6 +51,15 @@ def _is_supported_in_IR(
             # As these cases seem rare, conversion is not implemented for the time being.
             return False
 
+        # The `aten.addmm` operator allows any bias shape, as long as it is broadcastable with the result of the matrix
+        #  multiplication. That means it supports 4 different shapes: [N, P], [1, P], [P], [1] (provided the MM result
+        #  has shape [N, P]). Out of these 4, Neutron IR allows only [1, P] and [P], both of which are supported on
+        #  Neutron.
+        bias_shape = list(node.args[BIAS_IDX].meta["val"].shape)
+        _, p = node.meta["val"].shape
+        if bias_shape not in [[1, p], [p]]:
+            return False
+
         return True
 
     @staticmethod

backends/nxp/tests/generic_tests/test_integration.py

@@ -1,4 +1,4 @@
-# Copyright 2024-2025 NXP
+# Copyright 2024-2026 NXP
 #
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.

backends/nxp/tests/ir/converter/node_converter/test_addmm_converter.py

@@ -86,6 +86,34 @@ def test__from_addmm(self, mocker, request, use_qat, input_shape: tuple[int, ...
         model = AddmmModule(input_shape[-1])
         self.assert_delegated(model, input_shape, mocker, request, use_qat=use_qat)
 
+    @pytest.mark.parametrize(
+        "bias_shape",
+        [
+            (1, 7),
+            (7,),
+        ],
+        ids=lambda s: f"bias_shape = {s}",
+    )
+    def test__from_addmm__bias_shapes__supported(
+        self, mocker, request, use_qat, bias_shape: tuple[int, ...]
+    ):
+        input_shape = (3, 11)
+        model = AddmmModule(input_shape[-1], bias_shape=bias_shape)
+        self.assert_delegated(model, input_shape, mocker, request, use_qat=use_qat)
+
+    @pytest.mark.parametrize(
+        "bias_shape",
+        [
+            (3, 7),
+            (1,),
+        ],
+        ids=lambda s: f"bias_shape = {s}",
+    )
+    def test__from_addmm__bias_shapes__unsupported(self, bias_shape: tuple[int, ...]):
+        input_shape = (3, 11)
+        model = AddmmModule(input_shape[-1], bias_shape=bias_shape)
+        self.assert_not_delegated(model, input_shape)
+
     def test__from_addmm__unsupported_alpha(self):
         input_shape = (1, 8)
         model = AddmmModule(input_shape[-1], alpha=0.42)

backends/nxp/tests/models.py

@@ -258,10 +258,13 @@ def __init__(
         out_channels: int = 7,
         alpha: float | None = None,
         beta: float | None = None,
+        bias_shape=None,
     ):
+        if bias_shape is None:
+            bias_shape = (out_channels,)
         super().__init__()
         self.weight = torch.nn.Parameter(torch.empty(in_channels, out_channels))
-        self.bias = torch.nn.Parameter(torch.empty(out_channels))
+        self.bias = torch.nn.Parameter(torch.empty(bias_shape))
         torch.nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
         fan_in, _ = torch.nn.init._calculate_fan_in_and_fan_out(self.weight)
         bound = 1 / math.sqrt(fan_in)

backends/nxp/tests/use_qat.py

@@ -1,3 +1,8 @@
+# Copyright 2026 NXP
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
 import pytest