NXP backend: added aten.mul support (#15971)

### Summary

adds support for aten.mul operator

### Test plan

tests can be manually run using `pytest -c /dev/null
backends/nxp/tests/`

cc @robert-kalmar @JakeStevens @digantdesai

---------

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

Author: novak-vaclav

backends/nxp/backend/edge_program_converter.py

@@ -38,6 +38,7 @@
     exir_ops.edge.aten.max_pool2d.default: MaxPool2dConverter,  # noqa F405
     exir_ops.edge.aten.mean.dim: MeanDimConverter,  # noqa F405
     exir_ops.edge.aten.mm.default: MMConverter,  # noqa F405
+    exir_ops.edge.aten.mul.Tensor: MulTensorConverter,  # noqa F405
     exir_ops.edge.aten.permute_copy.default: PermuteCopyConverter,  # noqa F405
     exir_ops.edge.aten.relu.default: ReLUConverter,  # noqa F405
     exir_ops.edge.aten._softmax.default: SoftmaxConverter,  # noqa F405

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

@@ -37,6 +37,9 @@
 from executorch.backends.nxp.backend.ir.converter.node_converters.ops_converters.mm_converter import (
     MMConverter,
 )
+from executorch.backends.nxp.backend.ir.converter.node_converters.ops_converters.mul_tensor_converter import (
+    MulTensorConverter,
+)
 from executorch.backends.nxp.backend.ir.converter.node_converters.ops_converters.permute_copy_converter import (
     PermuteCopyConverter,
 )
@@ -67,27 +70,28 @@
 )
 
 __all__ = [
+    "AbsConverter",
+    "AdaptiveAvgPool2dConverter",
     "AddMMConverter",
+    "AddTensorConverter",
+    "AvgPool2dConverter",
     "CatConverter",
+    "CloneConverter",
+    "ConstantPadNDConverter",
     "ConvolutionConverter",
+    "HardTanhConverter",
+    "MaxPool2dConverter",
+    "MeanDimConverter",
     "MMConverter",
+    "MulTensorConverter",
     "PermuteCopyConverter",
-    "SoftmaxConverter",
-    "ViewCopyConverter",
-    "QDQPerTensorDequantizeConverter",
     "QDQPerChannelDequantizeConverter",
+    "QDQPerTensorDequantizeConverter",
     "QDQQuantizeConverter",
-    "ConstantPadNDConverter",
     "ReLUConverter",
-    "MeanDimConverter",
-    "MaxPool2dConverter",
-    "AvgPool2dConverter",
-    "AddTensorConverter",
-    "SubTensorConverter",
-    "CloneConverter",
-    "AbsConverter",
-    "AdaptiveAvgPool2dConverter",
-    "HardTanhConverter",
     "SigmoidConverter",
+    "SoftmaxConverter",
+    "SubTensorConverter",
     "TanhConverter",
+    "ViewCopyConverter",
 ]

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

@@ -0,0 +1,61 @@
+# Copyright 2025 NXP
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from executorch.backends.nxp.backend.ir.converter.conversion.common import (
+    node_uses_shape_broadcasting,
+)
+from executorch.backends.nxp.backend.ir.converter.node_converter import (
+    CustomDelegationOptions,
+    NodeConverter,
+)
+from executorch.backends.nxp.backend.ir.tflite_generator.builtin_options import (
+    mul_options,
+)
+from executorch.backends.nxp.backend.neutron_target_spec import NeutronTargetSpec
+from torch.fx import Node
+from torch.nn import Parameter
+
+
+class MulTensorConverter(NodeConverter):
+    @staticmethod
+    def _is_supported_on_target(
+        node: Node,
+        neutron_target_spec: NeutronTargetSpec,
+        parameters_mapping: dict[str, Parameter],
+        custom_delegation_options: CustomDelegationOptions,
+    ) -> bool:
+        if node_uses_shape_broadcasting(node):
+            # Shape broadcasting may require the addition of `Transpose` ops during conversion.
+            return False
+
+        node_shape = node.meta["val"].shape
+
+        # Check that at least one dimension is divisible by number of MACS
+        # or all dimensions are equal to one
+        # Otherwise Neutron cannot convert it
+        dim_divisible = any(s % 8 == 0 for s in node_shape) or all(
+            s == 1 for s in node_shape
+        )
+        return dim_divisible
+
+    @staticmethod
+    def _is_supported_in_IR(
+        node: Node,
+        parameters_mapping: dict[str, Parameter],
+        custom_delegation_options: CustomDelegationOptions,
+    ) -> bool:
+        if len(node.args) != 2:
+            return False
+
+        return True
+
+    # mul.Tensor Node format: (Tensor self, Tensor other, *)
+    def convert(self, node: Node):
+        """Convert 'mul_tensor' operator to NeutronIR 'Mul'."""
+        self.assert_convertible(node)
+        t_op = self._create_tflite_op_with_io_tensors(node)
+        t_op.builtin_options = mul_options.Mul()
+
+        self.builder.append_operators([t_op])

backends/nxp/neutron_partitioner.py

@@ -208,6 +208,7 @@ def tag_qdq_clusters(self, nodes: list[torch.fx.Node]):
     exir_ops.edge.aten.max_pool2d_with_indices.default: MaxPool2dConverter,  # noqa F405
     exir_ops.edge.aten.mean.dim: MeanDimConverter,  # noqa F405
     exir_ops.edge.aten.mm.default: MMConverter,  # noqa F405
+    exir_ops.edge.aten.mul.Tensor: MulTensorConverter,  # noqa F405
     exir_ops.edge.aten.permute_copy.default: PermuteCopyConverter,  # noqa F405
     exir_ops.edge.aten.relu.default: ReLUConverter,  # noqa F405
     exir_ops.edge.aten._softmax.default: SoftmaxConverter,  # noqa F405

backends/nxp/quantizer/neutron_quantizer.py

@@ -29,6 +29,7 @@
     MaxPoolPattern,
     MeanDimPattern,
     MmPattern,
+    MulTensorPattern,
     NodeArgsIdx,
     PadPattern,
     PermutePattern,
@@ -208,6 +209,7 @@ def __init__(self, neutron_target_spec: NeutronTargetSpec):
                 NeutronAtenQuantizer(MaxPoolPattern(), static_qconfig),
                 NeutronAtenQuantizer(MeanDimPattern(), static_qconfig),
                 NeutronAtenQuantizer(MmPattern(self), static_qconfig),
+                NeutronAtenQuantizer(MulTensorPattern(), static_qconfig),
                 NeutronAtenQuantizer(PadPattern(), static_qconfig),
                 NeutronAtenQuantizer(PermutePattern(), static_qconfig),
                 NeutronAtenQuantizer(ReluPattern(), static_qconfig),

backends/nxp/quantizer/patterns.py

@@ -673,6 +673,49 @@ def get_anchors(
         )
 
 
+class MulTensorPattern(QuantizationPattern):
+    """
+    Quantization pattern for Mul Tensor quantization. Accepts 1 or 2 input nodes.
+
+    Basic quantization for all inputs and output.
+    """
+
+    def partition_types(self) -> list[torch.nn.Module]:
+        return [torch.ops.aten.mul.Tensor]
+
+    def get_anchors(
+        self, gm: fx.GraphModule, fused_partition: list[fx.GraphModule]
+    ) -> PartitionAnchors | None:
+        node = fused_partition[0].nodes[-1]
+        input_nodes = node.all_input_nodes
+
+        qspec = FixedQParamsQuantizationSpec(
+            dtype=torch.int8,
+            scale=1.0 / 256.0,
+            zero_point=0,
+            quant_min=-128,
+            quant_max=127,
+            qscheme=torch.per_tensor_affine,
+        )
+
+        # The "Mul" operator in Neutron IR requires a specific scale and zero_point
+        # (defined above) for its inputs.
+        # Since these input nodes have already been annotated by their own patterns
+        # which didn't take the requirements of "Mul" into account, we need to overwrite
+        # the existing "quantization_annotation".
+        for input_node in input_nodes:
+            input_node.meta["quantization_annotation"].output_qspec = qspec
+
+        return PartitionAnchors(
+            inputs=[(node, NodeArgsIdx(0), qspec), (node, NodeArgsIdx(1), qspec)],
+            weights=[],
+            biases=[],
+            output=[
+                (node,),
+            ],
+        )
+
+
 class PadPattern(SharedSpecPattern):
     """
     Quantizer for Pad operator.