[Executorch][Export][3/N] Modularize export recipes

backends/xnnpack/recipes/xnnpack_recipe_provider.py

@@ -27,6 +27,7 @@
 from executorch.export import (
     BackendRecipeProvider,
     ExportRecipe,
+    LoweringRecipe,
     QuantizationRecipe,
     RecipeType,
 )
@@ -88,12 +89,19 @@ def create_recipe(
             )
         return None
 
+    def _get_xnnpack_lowering_recipe(
+        self, precision_type: Optional[ConfigPrecisionType] = None
+    ) -> LoweringRecipe:
+        return LoweringRecipe(
+            partitioners=[XnnpackPartitioner(precision_type=precision_type)],
+            edge_compile_config=get_xnnpack_edge_compile_config(),
+        )
+
     def _build_fp32_recipe(self, recipe_type: RecipeType) -> ExportRecipe:
         return ExportRecipe(
             name=recipe_type.value,
-            edge_compile_config=get_xnnpack_edge_compile_config(),
+            lowering_recipe=self._get_xnnpack_lowering_recipe(),
             executorch_backend_config=get_xnnpack_executorch_backend_config(),
-            partitioners=[XnnpackPartitioner()],
         )
 
     def _build_quantized_recipe(
@@ -120,9 +128,8 @@ def _build_quantized_recipe(
         return ExportRecipe(
             name=recipe_type.value,
             quantization_recipe=quant_recipe,
-            edge_compile_config=get_xnnpack_edge_compile_config(),
+            lowering_recipe=self._get_xnnpack_lowering_recipe(precision_type),
             executorch_backend_config=get_xnnpack_executorch_backend_config(),
-            partitioners=[XnnpackPartitioner(config_precision=precision_type)],
         )
 
     def _build_int8da_intx_weight_recipe(
@@ -150,9 +157,8 @@ def _build_int8da_intx_weight_recipe(
         return ExportRecipe(
             name=recipe_type.value,
             quantization_recipe=quant_recipe,
-            edge_compile_config=get_xnnpack_edge_compile_config(),
+            lowering_recipe=self._get_xnnpack_lowering_recipe(),
             executorch_backend_config=get_xnnpack_executorch_backend_config(),
-            partitioners=[XnnpackPartitioner()],
         )
 
     def _validate_recipe_kwargs(self, recipe_type: RecipeType, **kwargs: Any) -> None:

export/__init__.py

@@ -15,13 +15,15 @@
 """
 
 from .export import export, ExportSession
-from .recipe import ExportRecipe, QuantizationRecipe, RecipeType
+from .recipe import ExportRecipe, LoweringRecipe, QuantizationRecipe, RecipeType
 from .recipe_provider import BackendRecipeProvider
 from .recipe_registry import recipe_registry
-
+from .stages import StageType
 
 __all__ = [
+    "StageType",
     "ExportRecipe",
+    "LoweringRecipe",
     "QuantizationRecipe",
     "ExportSession",
     "export",

export/export.py

@@ -16,7 +16,7 @@
 from tabulate import tabulate
 from torch import nn
 
-from .recipe import ExportRecipe, QuantizationRecipe
+from .recipe import ExportRecipe, LoweringRecipe, QuantizationRecipe
 from .stages import (
     EdgeTransformAndLowerStage,
     ExecutorchStage,
@@ -145,6 +145,10 @@ def __init__(
             self._export_recipe.quantization_recipe
         )
 
+        self._lowering_recipe: Optional[LoweringRecipe] = (
+            self._export_recipe.lowering_recipe
+        )
+
         # Stages to run
         self._pipeline_stages = pipeline_stages or self._get_default_pipeline()
 
@@ -189,18 +193,42 @@ def _build_default_stages(self) -> Dict[StageType, Stage]:
                 stage = TorchExportStage(pre_edge_passes)
             elif stage_type == StageType.TO_EDGE_TRANSFORM_AND_LOWER:
                 stage = EdgeTransformAndLowerStage(
-                    partitioners=self._export_recipe.partitioners,
-                    transform_passes=self._export_recipe.edge_transform_passes,
-                    compile_config=self._export_recipe.edge_compile_config,
+                    partitioners=(
+                        self._lowering_recipe.partitioners
+                        if self._lowering_recipe
+                        else None
+                    ),
+                    transform_passes=(
+                        self._lowering_recipe.edge_transform_passes
+                        if self._lowering_recipe
+                        else None
+                    ),
+                    compile_config=(
+                        self._lowering_recipe.edge_compile_config
+                        if self._lowering_recipe
+                        else None
+                    ),
                 )
             elif stage_type == StageType.TO_EDGE:
                 stage = ToEdgeStage(
-                    edge_compile_config=self._export_recipe.edge_compile_config
+                    edge_compile_config=(
+                        self._lowering_recipe.edge_compile_config
+                        if self._lowering_recipe
+                        else None
+                    ),
                 )
             elif stage_type == StageType.TO_BACKEND:
                 stage = ToBackendStage(
-                    partitioners=self._export_recipe.partitioners,
-                    transform_passes=self._export_recipe.edge_transform_passes,
+                    partitioners=(
+                        self._lowering_recipe.partitioners
+                        if self._lowering_recipe
+                        else None
+                    ),
+                    transform_passes=(
+                        self._lowering_recipe.edge_transform_passes
+                        if self._lowering_recipe
+                        else None
+                    ),
                 )
             elif stage_type == StageType.TO_EXECUTORCH:
                 stage = ExecutorchStage(self._export_recipe.executorch_backend_config)

export/recipe.py

@@ -87,6 +87,26 @@ def get_quantizers(self) -> Optional[List[Quantizer]]:
         return self.quantizers
 
 
+@dataclass
+class LoweringRecipe:
+    """
+    Configuration recipe for lowering and partitioning.
+
+    This class holds the configuration parameters for lowering a model
+    to backend-specific representations.
+
+    Attributes:
+        partitioners: Optional list of partitioners for model partitioning
+        edge_transform_passes: Optional sequence of transformation passes to apply
+        edge_compile_config: Optional edge compilation configuration
+    """
+
+    partitioners: Optional[List[Partitioner]] = None
+    edge_transform_passes: Optional[Sequence[PassType]] = None
+    # pyre-ignore[11]: Type not defined
+    edge_compile_config: Optional[EdgeCompileConfig] = None
+
+
 @experimental(
     "This API and all of its related functionality such as ExportSession and ExportRecipe are experimental."
 )
@@ -101,27 +121,19 @@ class ExportRecipe:
     Attributes:
         name: Optional name for the recipe
         quantization_recipe: Optional quantization recipe for model quantization
-        edge_compile_config: Optional edge compilation configuration
+        lowering_recipe: Optional lowering recipe for model lowering and partitioning
+        executorch_backend_config: Optional backend configuration for ExecuTorch
         pre_edge_transform_passes: Optional function to apply transformation passes
                                   before edge lowering
-        edge_transform_passes: Optional sequence of transformation passes to apply
-                              during edge lowering
-        transform_check_ir_validity: Whether to check IR validity during transformation
-        partitioners: Optional list of partitioners for model partitioning
-        executorch_backend_config: Optional backend configuration for ExecuTorch
         mode: Export mode (debug or release)
     """
 
     name: Optional[str] = None
     quantization_recipe: Optional[QuantizationRecipe] = None
-    # pyre-ignore[11]: Type not defined
-    edge_compile_config: Optional[EdgeCompileConfig] = None
-    pre_edge_transform_passes: Optional[Sequence[PassType]] = None
-    edge_transform_passes: Optional[Sequence[PassType]] = None
-    transform_check_ir_validity: bool = True
-    partitioners: Optional[List[Partitioner]] = None
+    lowering_recipe: Optional[LoweringRecipe] = None
     # pyre-ignore[11]: Type not defined
     executorch_backend_config: Optional[ExecutorchBackendConfig] = None
+    pre_edge_transform_passes: Optional[Sequence[PassType]] = None
     mode: Mode = Mode.RELEASE
 
     @classmethod

export/tests/test_export_session.py

@@ -11,6 +11,7 @@
 
 import torch
 from executorch.export import ExportRecipe, ExportSession
+from executorch.export.recipe import LoweringRecipe, QuantizationRecipe
 from executorch.export.stages import PipelineArtifact, StageType
 
 
@@ -329,3 +330,49 @@ def test_save_to_pte_invalid_name(self) -> None:
 
         with self.assertRaises(AssertionError):
             session.save_to_pte(None)  # pyre-ignore
+
+
+class TestExportSessionPipelineBuilding(unittest.TestCase):
+    """Test pipeline building and stage configuration."""
+
+    def setUp(self) -> None:
+        self.model = SimpleTestModel()
+        self.example_inputs = [(torch.randn(2, 10),)]
+
+    def test_pipeline_building_with_all_recipes(self) -> None:
+        """Test pipeline building with quantization and lowering recipes."""
+        # Create comprehensive recipes
+        quant_recipe = QuantizationRecipe(
+            ao_base_config=[Mock()],
+            quantizers=[Mock()],
+        )
+        lowering_recipe = LoweringRecipe(
+            partitioners=[Mock()],
+            edge_transform_passes=[Mock()],
+            edge_compile_config=Mock(),
+        )
+        recipe = ExportRecipe(
+            name="comprehensive_test",
+            quantization_recipe=quant_recipe,
+            lowering_recipe=lowering_recipe,
+            pre_edge_transform_passes=[Mock()],
+            executorch_backend_config=Mock(),
+        )
+
+        session = ExportSession(
+            model=self.model,
+            example_inputs=self.example_inputs,
+            export_recipe=recipe,
+        )
+
+        registered_stages = session.get_all_registered_stages()
+
+        self.assertEqual(len(registered_stages), 5)
+        expected_types = [
+            StageType.SOURCE_TRANSFORM,
+            StageType.QUANTIZE,
+            StageType.TORCH_EXPORT,
+            StageType.TO_EDGE_TRANSFORM_AND_LOWER,
+            StageType.TO_EXECUTORCH,
+        ]
+        self.assertListEqual(list(registered_stages.keys()), expected_types)