[EIEX-885] Add log support using new Neutron flow

Author: irtrukhina

backends/nxp/backend/edge_program_converter.py

@@ -41,6 +41,7 @@
     exir_ops.edge.aten.convolution.default: ConvolutionConverter,  # noqa F405
     exir_ops.edge.aten.hardtanh.default: HardTanhConverter,  # noqa F405
     exir_ops.edge.aten.leaky_relu.default: LeakyReluConverter,  # noqa F405
+    exir_ops.edge.aten.log.default: LogConverter,  # noqa F405
     exir_ops.edge.aten.max_pool2d_with_indices.default: MaxPool2DWithIndicesConverter,  # noqa F405
     exir_ops.edge.aten.mean.dim: MeanDimConverter,  # noqa F405
     exir_ops.edge.aten.mm.default: MMConverter,  # noqa F405

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

@@ -40,6 +40,9 @@
 from executorch.backends.nxp.backend.ir.converter.node_converters.ops_converters.leaky_relu_converter import (
     LeakyReluConverter,
 )
+from executorch.backends.nxp.backend.ir.converter.node_converters.ops_converters.log_converter import (
+    LogConverter,
+)
 from executorch.backends.nxp.backend.ir.converter.node_converters.ops_converters.max_pool2d_with_indices_converter import (
     MaxPool2DWithIndicesConverter,
 )
@@ -111,6 +114,7 @@
     "GetItemConverter",
     "HardTanhConverter",
     "LeakyReluConverter",
+    "LogConverter",
     "MaxPool2DWithIndicesConverter",
     "MeanDimConverter",
     "MMConverter",

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

@@ -0,0 +1,59 @@
+# 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 torch
+from executorch.backends.nxp.backend.ir.converter.node_converter import (
+    CustomDelegationOptions,
+    NeutronTargetSpec,
+    NodeConverter,
+)
+from executorch.backends.nxp.backend.ir.lib.tflite.BuiltinOperator import (
+    BuiltinOperator,
+)
+from torch.fx import Node
+from torch.nn import Parameter
+
+
+class LogConverter(NodeConverter):
+
+    @staticmethod
+    def _is_supported_in_IR(
+        node: Node,
+        parameters_mapping: dict[str, Parameter],
+        custom_delegation_options: CustomDelegationOptions,
+    ) -> bool:
+        return True
+
+    @staticmethod
+    def _is_supported_on_target(
+        node: Node,
+        neutron_target_spec: NeutronTargetSpec,
+        parameters_mapping: dict[str, Parameter],
+        custom_delegation_options: CustomDelegationOptions,
+    ) -> bool:
+        # Requirements specified by the new Neutron flow documentation.
+        # Input and Output must be INT8/UINT8.
+        if not NodeConverter.uses_quantization_type_for_io(
+            node,
+            supported_types=[torch.int8, torch.uint8],
+            input_indices=[0],
+            output_indices=[0],
+        ):
+            return False
+        return True
+
+    def convert(self, node: Node):
+        """Convert the `aten.log.default` operator to Neutron IR `Log`.
+        The schema is:
+            aten::log(
+                Tensor self
+            ) -> Tensor
+        """
+
+        self.assert_convertible(node)
+
+        t_op = self._create_tflite_op_with_io_tensors(node)
+        t_op.opcode_index = self.builder.op_code_index_for_op_type(BuiltinOperator.LOG)
+
+        self.builder.append_operators([t_op])

backends/nxp/neutron_partitioner.py

@@ -214,6 +214,7 @@ def tag_qdq_clusters(self, nodes: list[torch.fx.Node]):
     exir_ops.edge.aten.convolution.default: ConvolutionConverter,  # noqa F405
     exir_ops.edge.aten.hardtanh.default: HardTanhConverter,  # noqa F405
     exir_ops.edge.aten.leaky_relu.default: LeakyReluConverter,  # noqa F405
+    exir_ops.edge.aten.log.default: LogConverter,  # noqa F405
     exir_ops.edge.aten.max_pool2d_with_indices.default: MaxPool2DWithIndicesConverter,  # noqa F405
     exir_ops.edge.aten.mean.dim: MeanDimConverter,  # noqa F405
     exir_ops.edge.aten.mm.default: MMConverter,  # noqa F405

backends/nxp/quantizer/neutron_quantizer.py

@@ -31,6 +31,7 @@
     LeakyReluInPlacePattern,
     LeakyReluPattern,
     LinearPattern,
+    LogPattern,
     MaxPool1DPattern,
     MaxPool2DPattern,
     MeanDimPattern,
@@ -275,6 +276,7 @@ def __init__(self, neutron_target_spec: NeutronTargetSpec, is_qat: bool = False)
                 OpQuantizer(LeakyReluPattern(is_qat=is_qat), static_fc_qconfig),
                 OpQuantizer(LeakyReluInPlacePattern(is_qat=is_qat), static_fc_qconfig),
                 OpQuantizer(LinearPattern(self, is_qat=is_qat), static_fc_qconfig),
+                OpQuantizer(LogPattern(is_qat=is_qat), static_qconfig),
                 OpQuantizer(MaxPool1DPattern(is_qat=is_qat), static_qconfig),
                 OpQuantizer(MaxPool2DPattern(is_qat=is_qat), static_qconfig),
                 OpQuantizer(MeanDimPattern(is_qat=is_qat), static_qconfig),

backends/nxp/quantizer/patterns.py

@@ -836,6 +836,13 @@ def get_anchors(
         )
 
 
+class LogPattern(SingleInputBasicPattern):
+    """Quantizer for the `aten.log.default` operator."""
+
+    def partition_types(self):
+        return [torch.ops.aten.log.default]
+
+
 class MaxPool1DPattern(SharedSpecPattern):
     """Quantizer for the MaxPool1D operator."""
 

backends/nxp/tests/dataset_creator.py

@@ -337,3 +337,81 @@ def _gen_samples(
 
                 bin_file_name = f"{dataset_dir}/example_{label}_{cl}_{i}_i{str(inp_idx).zfill(2)}.bin"
                 dt.tofile(bin_file_name)
+
+
+class LinearRampDatasetCreator(DatasetCreator):
+    """Dataset creator that generates deterministic linear ramp input samples.
+
+    The generated data forms a monotonic sequence where values are evenly
+    distributed between a specified range (low to high) and span the full
+    interval. The first element is equal to `low` and the last element is
+    equal to `high`, with increments depending on the total number of elements.
+    """
+
+    def __init__(self, num_samples=2, low=0.0, high=1.0):
+        self._num_samples = num_samples
+        self.low = low
+        self.high = high
+
+    def generate_samples(
+        self, dataset_dir: str, input_spec: list[ModelInputSpec]
+    ) -> tuple[str, str]:
+        assert isinstance(input_spec, list) and all(
+            isinstance(spec, ModelInputSpec) for spec in input_spec
+        ), "Input_spec must be a list of ModelInputSpec."
+
+        calibration_dir, test_dir = (
+            _get_calibration_and_testing_dataset_directory_names(dataset_dir)
+        )
+
+        if any(spec.dim_order == torch.channels_last for spec in input_spec):
+            # We will need to generate a separate testing dataset, containing the same data as is in the calibration
+            #  dataset, just permuted to channels last where necessary.
+            self._gen_samples(test_dir, input_spec)
+
+        else:
+            # Use the calibration dataset for testing as well.
+            test_dir = calibration_dir
+
+        # Make sure the calibration dataset contains contiguous tensors.
+        contiguous_input_spec = deepcopy(input_spec)
+        for spec in contiguous_input_spec:
+            spec.dim_order = torch.contiguous_format
+
+        # Generate the calibration dataset. Calibration amd testing dataset s will contain
+        #  the same data (except for the permutation).
+        self._gen_samples(calibration_dir, contiguous_input_spec)
+
+        return calibration_dir, test_dir
+
+    def _gen_samples(self, dataset_dir: str, input_spec: list[ModelInputSpec]):
+        for idx in range(self._num_samples):
+            sample_dir = dataset_dir
+
+            # Multi-input, use a subdirectory containing the inputs for each sample
+            if len(input_spec) > 1:
+                sample_dir = os.path.join(dataset_dir, f"{str(idx).zfill(4)}")
+                mkdir(sample_dir)
+
+            for spec_idx, spec in enumerate(input_spec):
+                match spec.dim_order:
+                    case torch.contiguous_format:
+                        shape = spec.shape
+                    case torch.channels_last:
+                        shape = tuple(
+                            translator.dims_to_channels_last(list(spec.shape))
+                        )
+                    case _:
+                        raise ValueError(f"Unsupported dim_order: {spec.dim_order}")
+
+                sample_vector = (
+                    np.linspace(self.low, self.high, num=np.prod(shape))
+                    .astype(torch_type_to_numpy_type(spec.dtype))
+                    .reshape(shape)
+                )
+                file_name = (
+                    f"{str(spec_idx).zfill(2)}.bin"
+                    if len(input_spec) > 1
+                    else f"{str(idx).zfill(4)}.bin"
+                )
+                sample_vector.tofile(os.path.join(sample_dir, file_name))

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

@@ -0,0 +1,96 @@
+# 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 numpy as np
+
+# noinspection PyUnusedImports
+import pytest
+import torch
+
+from executorch.backends.nxp.tests.graph_verifier import DetailedGraphVerifier
+from executorch.backends.nxp.tests.nsys_testing import lower_run_compare
+from executorch.backends.nxp.tests.ops_aliases import Clamp, Convolution, Log, Relu
+from executorch.backends.nxp.tests.use_qat import *  # noqa F403
+from executorch.backends.nxp.tests.dataset_creator import LinearRampDatasetCreator
+
+
+@pytest.fixture(autouse=True)
+def reseed_model_per_test_run():
+    torch.manual_seed(42)
+    np.random.seed(23)
+
+
+class LogModule(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x):
+        return torch.log(x)
+
+
+class ConvBlocksWithLogModule(torch.nn.Module):
+    def __init__(self, conv_in_channels: int = 3):
+        super().__init__()
+        self.block1 = torch.nn.Sequential(
+            torch.nn.Conv2d(
+                in_channels=conv_in_channels,
+                out_channels=3,
+                kernel_size=(2, 2),
+                stride=(2, 2),
+            ),
+            torch.nn.ReLU(),
+        )
+        self.block2 = torch.nn.Sequential(
+            torch.nn.Conv2d(
+                in_channels=conv_in_channels,
+                out_channels=10,
+                kernel_size=(2, 2),
+                stride=(2, 2),
+            ),
+            torch.nn.ReLU(),
+        )
+
+    def forward(self, x):
+        x = self.block1(x)
+        x = x.clamp_min(1e-6).log()
+        return self.block2(x)
+
+
+class TestLog:
+    def test__basic_nsys_inference(self, mocker):
+        # Use 256 elements so that, after quantization to uint8, the input can
+        # cover the full discrete range [0, 255].
+        # The dataset is generated as a linear float ramp and later quantized,
+        # which effectively exercises all uint8 values.
+        input_shape = (256,)
+        model = LogModule()
+        graph_verifier = DetailedGraphVerifier(
+            mocker, expected_delegated_ops={Log: 1}, expected_non_delegated_ops={}
+        )
+        lower_run_compare(
+            model,
+            input_shape,
+            graph_verifier,
+            dataset_creator=LinearRampDatasetCreator(),
+        )
+
+    def test__basic_nsys_inference__with_conv(self, mocker):
+        input_shape = (2, 3, 6, 7)
+        in_channels = input_shape[1]
+        model = ConvBlocksWithLogModule(conv_in_channels=in_channels)
+
+        # `clamp` and one `relu` ends up in the same delegated partition as `log`
+        graph_verifier = DetailedGraphVerifier(
+            mocker,
+            expected_delegated_ops={Log: 1, Relu: 1, Clamp: 1},
+            expected_non_delegated_ops={Relu: 1, Convolution: 2},
+        )
+
+        lower_run_compare(
+            model,
+            input_shape,
+            graph_verifier,
+        )

backends/nxp/tests/ops_aliases.py

@@ -26,6 +26,7 @@
 HardTanh = exir_ops.edge.aten.hardtanh.default
 HardTanh_ = exir_ops.edge.aten.hardtanh_.default
 LeakyRelu = exir_ops.edge.aten.leaky_relu.default
+Log = exir_ops.edge.aten.log.default
 MaxPool2DWithIndices = exir_ops.edge.aten.max_pool2d_with_indices.default
 MeanDim = exir_ops.edge.aten.mean.dim
 MulTensor = exir_ops.edge.aten.mul.Tensor

docs/source/backends/nxp/op-support.csv

@@ -15,6 +15,7 @@ aten.dim_order_ops._clone_dim_order.default,,, "See aten.clone.default"
 aten.div.Tensor,int8,static int8,"divisor - static tensor or scalar value, one dimension must satisfy %8 = 0 or scalar division (all dims = 1)"
 aten.hardtanh.default,int8,static int8,"supported ranges: <0,6>, <-1, 1>, <0,1>, <0,inf>"
 aten.leaky_relu.default,int8,static int8,
+aten.log.default,int8,static int8,
 aten.max_pool1d.default,int8,static int8,"dilation=1, ceil_mode=False, channels%8=0, batch_size=1, stride_h=1 or 2"
 aten.max_pool2d.default,int8,static int8,"dilation=1, ceil_mode=False, channels%8=0, batch_size=1, stride_h=1 or 2"
 aten.max_pool2d_with_indices.default,int8,static int8,"dilation=1, ceil_mode=False, channels%8=0, batch_size=1, stride_h=1 or 2"