NXB backend: Add recipes for Neutron backend lowering.

Author: MartinPavella

backends/nxp/edge_passes/neutron_edge_pass_manager.py

@@ -17,7 +17,7 @@
 from executorch.backends.nxp.edge_passes.remove_as_strided_copy_nodes import (
     RemoveUselessAsStridedCopyNodes,
 )
-from torch.fx.passes.infra.pass_manager import PassManager
+from executorch.exir.pass_manager import PassManager
 
 
 class NeutronEdgePassManager(PassManager):

backends/nxp/recipes/nxp_recipe_provider.py

@@ -0,0 +1,353 @@
+# 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 logging
+from dataclasses import dataclass
+from functools import partial
+from typing import Any, Callable, cast, Iterable, Optional, Sequence
+
+import torch
+
+from executorch.backends.nxp.backend.custom_delegation_options import (
+    CustomDelegationOptions,
+)
+from executorch.backends.nxp.backend.neutron_target_spec import NeutronTargetSpec
+from executorch.backends.nxp.edge_passes.neutron_edge_pass import NeutronEdgePass
+from executorch.backends.nxp.edge_passes.neutron_edge_pass_manager import (
+    NeutronEdgePassManager,
+)
+from executorch.backends.nxp.edge_passes.remove_additional_quantize_dequantize_nodes_pass import (
+    RemoveAdditionalQDQClustersPass,
+)
+from executorch.backends.nxp.edge_passes.remove_io_quant_ops_pass import (
+    RemoveIOQuantOpsPass,
+)
+from executorch.backends.nxp.neutron_partitioner import NeutronPartitioner
+from executorch.backends.nxp.nxp_backend import (
+    core_aten_ops_exception_list,
+    generate_neutron_compile_spec,
+)
+from executorch.backends.nxp.quantizer.utils import calibrate_and_quantize
+from executorch.backends.nxp.recipes.nxp_recipe_types import NXP_BACKEND, NXPRecipeType
+from executorch.backends.nxp.tests.executorch_pipeline import (
+    _get_default_quantizer,
+    get_random_calibration_inputs,
+    GetCalibrationInputsFn,
+    handle_kernel_selection,
+    ModelInputSpec,
+    to_model_input_spec,
+)
+from executorch.exir import EdgeCompileConfig, EdgeProgramManager, ExportedProgram
+
+from executorch.exir.backend.compile_spec_schema import CompileSpec
+from executorch.exir.backend.partitioner import Partitioner
+from executorch.export import (
+    BackendRecipeProvider,
+    ExportRecipe,
+    LoweringRecipe,
+    QuantizationRecipe,
+    RecipeType,
+)
+from torchao.quantization.pt2e.quantizer import Quantizer
+
+
+class NeutronEdgePassManagerWrapper:
+    def __init__(self, passes: list[NeutronEdgePass] | None = None):
+        self.neutron_edge_pass_manager = NeutronEdgePassManager(passes)
+
+    def __call__(self, s: str, epm: EdgeProgramManager) -> NeutronEdgePassManager:
+        return self.neutron_edge_pass_manager
+
+
+NEUTRON_RECIPE_CONFIG_KEY = "neutron_recipe_config"
+
+
+@dataclass
+class NeutronRecipeConfig:
+    """Configuration shared by all NXP recipe types.
+
+    Parameters that vary the *type* of export (PTQ vs QAT, delegate vs no-delegate)
+    are expressed by choosing a different NXPRecipeType rather than by flags here.
+
+    Attributes:
+        input_spec: Model input description. Accepts a single shape tuple, a list of
+                    shape tuples (one per input), or a list of ModelInputSpec objects.
+        target: Neutron hardware target string. Default: "imxrt700".
+        operators_not_to_delegate: Optional list of op names excluded from NPU delegation.
+        intermediates_dir: Optional directory to dump intermediate compilation artefacts.
+        get_quantizer_fn: Optional factory that returns a custom Quantizer. When None,
+                          the default NeutronQuantizer is used.
+        get_calibration_inputs_fn: Optional function that, given the input_spec, returns
+                                   calibration input samples. When None, random inputs are
+                                   used.
+        train_fn: QAT training callback. Required when using INT8_QAT_NEUTRON.
+        custom_delegation_options: Optional fine-grained control over which ops are
+                                   delegated. Default: CustomDelegationOptions().
+        remove_quant_io_ops: If True, remove quantize/dequantize ops at the IO boundary
+                             (useful for integer-IO deployments).
+        use_quant_state_dict: If False, the post-quantization parameter values are not
+                              passed to NeutronPartitioner.
+        use_neutron_for_format_conversion: Whether Neutron handles data-format conversion.
+        fetch_constants_to_sram: Place constant tensors in SRAM on the target.
+        dump_kernel_selection_code: Emit kernel-selection files after compilation.
+        use_profiling: Enable execution profiling / ETRecord generation.
+    """
+
+    input_spec: Iterable[ModelInputSpec] | tuple[int, ...] | list[tuple[int, ...]]
+    target: str = "imxrt700"
+    operators_not_to_delegate: list[str] = None
+    intermediates_dir: str | None = None
+    get_quantizer_fn: Callable[[], Quantizer] | None = None
+    get_calibration_inputs_fn: GetCalibrationInputsFn | None = None
+    train_fn: Callable[[torch.fx.GraphModule], None] | None = None
+    custom_delegation_options: CustomDelegationOptions | None = None
+    remove_quant_io_ops: bool = False
+    use_quant_state_dict: bool = True
+    use_neutron_for_format_conversion: bool = True
+    fetch_constants_to_sram: bool = False
+    dump_kernel_selection_code: bool = False
+    use_profiling: bool = False
+
+
+class NXPRecipeProvider(BackendRecipeProvider):
+
+    @property
+    def backend_name(self) -> str:
+        return NXP_BACKEND
+
+    def get_supported_recipes(self) -> Sequence[RecipeType]:
+        return list(NXPRecipeType)
+
+    def create_recipe(
+        self, recipe_type: RecipeType, **kwargs: Any
+    ) -> Optional[ExportRecipe]:
+        if recipe_type not in self.get_supported_recipes():
+            logging.warning(f"NXP backend: Recipe `{recipe_type}` is not valid.")
+            return None
+
+        rc = cast(NeutronRecipeConfig, kwargs.get(NEUTRON_RECIPE_CONFIG_KEY))
+        if rc is None:
+            raise KeyError(
+                f"NXP backend: create_recipe() requires `{NEUTRON_RECIPE_CONFIG_KEY}=<NeutronRecipeConfig>`."
+            )
+
+        if rc.custom_delegation_options is None:
+            rc.custom_delegation_options = CustomDelegationOptions()
+
+        rc.input_spec = to_model_input_spec(rc.input_spec)
+
+        match recipe_type:
+            case NXPRecipeType.INT8_PTQ_NEUTRON:
+                return self._build_recipe(recipe_type, rc, is_qat=False, delegate=True)
+            case NXPRecipeType.INT8_QAT_NEUTRON:
+                if rc.train_fn is None:
+                    raise ValueError(
+                        "NXP backend: INT8_QAT_NEUTRON requires train_fn in NeutronRecipeConfig."
+                    )
+                return self._build_recipe(recipe_type, rc, is_qat=True, delegate=True)
+            case NXPRecipeType.INT8_PTQ_NO_DELEGATE:
+                return self._build_recipe(recipe_type, rc, is_qat=False, delegate=False)
+            case _:
+                raise NotImplementedError(
+                    f"NXP backend: Recipe `{recipe_type}` is not supported."
+                )
+
+    def _build_recipe(
+        self,
+        recipe_type: NXPRecipeType,
+        rc: NeutronRecipeConfig,
+        *,
+        is_qat: bool,
+        delegate: bool,
+    ) -> ExportRecipe:
+        neutron_target_spec = NeutronTargetSpec(rc.target)
+
+        if rc.get_quantizer_fn is None:
+            rc.get_quantizer_fn = partial(
+                _get_default_quantizer, neutron_target_spec, is_qat
+            )
+
+        quantization_recipe = _build_quantization_recipe(rc, is_qat)
+        compile_spec = generate_neutron_compile_spec(
+            rc.target,
+            intermediates_dir=rc.intermediates_dir,
+            operators_not_to_delegate=rc.operators_not_to_delegate,
+            use_neutron_for_format_conversion=rc.use_neutron_for_format_conversion,
+            fetch_constants_to_sram=rc.fetch_constants_to_sram,
+            dump_kernel_selection_code=rc.dump_kernel_selection_code,
+            use_profiling=rc.use_profiling,
+        )
+        lowering_recipe = _build_lowering_recipe(
+            compile_spec, neutron_target_spec, rc, delegate=delegate
+        )
+
+        return ExportRecipe(
+            name=recipe_type.value,
+            quantization_recipe=quantization_recipe,
+            lowering_recipe=lowering_recipe,
+        )
+
+
+# ---------------------------------------------------------------------------
+# Module-level builder helpers
+# ---------------------------------------------------------------------------
+
+
+def _build_quantization_recipe(
+    rc: NeutronRecipeConfig, is_qat: bool
+) -> QuantizationRecipe:
+    """Build the QuantizationRecipe for PTQ or QAT.
+
+    PTQ uses the standard QuantizeStage flow (prepare_pt2e -> calibrate -> convert_pt2e)
+    driven by calibration_inputs_fn.  quantize_fn is intentionally left None so that
+    multiple PTQ recipes can be combined with ExportRecipe.combine().
+
+    QAT requires a non-standard sequence (BN-fusion passes interleaved with training),
+    so it delegates to calibrate_and_quantize() via quantize_fn.  A recipe with
+    quantize_fn set cannot be combined with other recipes.
+    """
+    _input_spec = rc.input_spec
+    _calibration_fn = rc.get_calibration_inputs_fn or get_random_calibration_inputs
+    _quantizer = rc.get_quantizer_fn()
+
+    if is_qat:
+        _train_fn = rc.train_fn
+
+        def _quantize_fn(  # noqa: E306
+            graph_module: torch.fx.GraphModule,
+        ) -> torch.fx.GraphModule:
+            return calibrate_and_quantize(
+                model=graph_module,
+                calibration_inputs=_calibration_fn(_input_spec),
+                quantizer=_quantizer,
+                is_qat=True,
+                train_fn=_train_fn,
+            )
+
+        return QuantizationRecipe(
+            quantizers=[_quantizer],
+            quantize_fn=_quantize_fn,
+        )
+    else:
+
+        def _calibration_inputs_fn() -> Iterable:
+            return _calibration_fn(_input_spec)
+
+        return QuantizationRecipe(
+            quantizers=[_quantizer],
+            calibration_inputs_fn=_calibration_inputs_fn,
+        )
+
+
+def _build_lowering_recipe(
+    compile_spec: list[CompileSpec],
+    neutron_target_spec: NeutronTargetSpec,
+    rc: NeutronRecipeConfig,
+    *,
+    delegate: bool,
+) -> LoweringRecipe:
+    """Build the LoweringRecipe, optionally including NPU delegation."""
+    partitioners = _build_partitioners(compile_spec, neutron_target_spec, rc, delegate)
+    pre_partitioning_callback = _build_pre_partitioning_callback(rc)
+    post_partitioning_transforms = _build_post_partitioning_transforms(rc)
+
+    # The edge pass manager must be wrapped: EdgeTransformAndLowerStage calls
+    # edge_transform_passes with (method_name, ep) and expects a PassManager back.
+    return LoweringRecipe(
+        partitioners=partitioners,
+        edge_transform_passes=[NeutronEdgePassManagerWrapper()],
+        edge_compile_config=EdgeCompileConfig(
+            _check_ir_validity=False,
+            _core_aten_ops_exception_list=core_aten_ops_exception_list,
+        ),
+        pre_partitioning_callback=pre_partitioning_callback,
+        post_partitioning_transforms=post_partitioning_transforms,
+    )
+
+
+def _build_partitioners(
+    compile_spec: list[CompileSpec],
+    neutron_target_spec: NeutronTargetSpec,
+    rc: NeutronRecipeConfig,
+    delegate: bool,
+) -> list:
+    """Create the NeutronPartitioner list. Empty when delegate=False."""
+    if not delegate:
+        return []
+    return [
+        NeutronPartitioner(
+            compile_spec,
+            neutron_target_spec,
+            rc.custom_delegation_options,
+            preserve_ops=[torch.ops.aten.prelu.default],
+        )
+    ]
+
+
+def _build_pre_partitioning_callback(rc: NeutronRecipeConfig):
+    """Return a callback that assigns the post-quantization state_dict to NeutronPartitioner.
+
+    NeutronPartitioner requires static parameter data. Since the partitioner is instantiated
+    during recipe creation (before model data is available), assignment is deferred to a
+    callback invoked just before partitioning.
+    """
+    _use_quant_state_dict = rc.use_quant_state_dict
+
+    def _callback(
+        _partitioners: list[Partitioner] | None,
+        programs: dict[str, ExportedProgram],
+    ) -> None:
+        if _partitioners is None:
+            return
+
+        if _use_quant_state_dict:
+            post_quant_state_dict: dict | None = {}
+            for _, program in programs.items():
+                post_quant_state_dict.update(program.state_dict)
+        else:
+            post_quant_state_dict = None
+
+        for _partitioner in _partitioners:
+            if isinstance(_partitioner, NeutronPartitioner):
+                _partitioner.post_quantization_state_dict = post_quant_state_dict
+
+    return _callback
+
+
+def _build_post_partitioning_transforms(rc: NeutronRecipeConfig) -> list:
+    """Build the list of post-partitioning EdgeProgramManager transforms.
+
+    These mirror what the imperative pipeline did after to_edge_transform_and_lower:
+      - RemoveIOQuantOpsPass (optional, when remove_quant_io_ops=True)
+      - RemoveAdditionalQDQClustersPass (always applied)
+      - handle_kernel_selection side-effect (optional, when dump_kernel_selection_code=True)
+    """
+    transforms = []
+
+    if rc.remove_quant_io_ops:
+
+        def _remove_io_quant_ops(epm: EdgeProgramManager) -> EdgeProgramManager:
+            return epm.transform([RemoveIOQuantOpsPass(edge_program_manager=epm)])
+
+        transforms.append(_remove_io_quant_ops)
+
+    def _remove_additional_qdq_clusters(epm: EdgeProgramManager) -> EdgeProgramManager:
+        return epm.transform(
+            NeutronEdgePassManager([RemoveAdditionalQDQClustersPass()])
+        )
+
+    transforms.append(_remove_additional_qdq_clusters)
+
+    if rc.dump_kernel_selection_code:
+
+        def _handle_kernel_selection_transform(
+            epm: EdgeProgramManager,
+        ) -> EdgeProgramManager:
+            handle_kernel_selection()
+            return epm
+
+        transforms.append(_handle_kernel_selection_transform)
+
+    return transforms

backends/nxp/recipes/nxp_recipe_types.py

@@ -0,0 +1,35 @@
+# 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.
+
+from executorch.export import RecipeType
+
+
+NXP_BACKEND: str = "nxp"
+
+
+class NXPRecipeType(RecipeType):
+    """NXP-specific recipe types for Neutron NPU export.
+
+    Choose the recipe that matches your intended export configuration:
+      - INT8_PTQ_NEUTRON: standard post-training quantization, delegates to Neutron NPU.
+      - INT8_QAT_NEUTRON: quantization-aware training flow, delegates to Neutron NPU.
+      - INT8_PTQ_NO_DELEGATE: PTQ without NPU delegation (useful for debugging or CPU-only deployment).
+    """
+
+    # INT8 static PTQ (weights + activations). Calibration dataset required.
+    # Applicable operators are delegated to the Neutron NPU.
+    INT8_PTQ_NEUTRON = "nxp_int8_ptq_neutron"
+
+    # INT8 QAT flow. Requires train_fn in NeutronRecipeConfig.
+    # Applicable operators are delegated to the Neutron NPU.
+    INT8_QAT_NEUTRON = "nxp_int8_qat_neutron"
+
+    # INT8 PTQ without NPU delegation. Produces a quantized graph that runs on CPU.
+    # Useful for accuracy evaluation or debugging before enabling delegation.
+    INT8_PTQ_NO_DELEGATE = "nxp_int8_ptq_no_delegate"
+
+    @classmethod
+    def get_backend_name(cls) -> str:
+        return NXP_BACKEND