[Executorch][Export][1/n] Add exported program and GraphModule as inputs, validation and tests

export/export.py

@@ -13,9 +13,10 @@
 from executorch.exir.program import ExecutorchProgramManager
 from executorch.exir.schema import Program
 from executorch.extension.export_util.utils import save_pte_program
-from executorch.runtime import Runtime, Verification
 from tabulate import tabulate
 from torch import nn
+from torch.export import ExportedProgram
+from torch.fx import GraphModule
 
 from .recipe import ExportRecipe, LoweringRecipe, QuantizationRecipe
 from .stages import (
@@ -36,11 +37,22 @@
     "This API and all of its related functionality such as ExportSession and ExportRecipe are experimental."
 )
 def export(
-    model: Union[nn.Module, Dict[str, nn.Module]],
-    example_inputs: Union[
-        List[tuple[torch.Tensor, ...]], Dict[str, List[tuple[torch.Tensor, ...]]]
+    model: Union[
+        nn.Module,
+        Dict[str, nn.Module],
+        GraphModule,
+        Dict[str, GraphModule],
+        ExportedProgram,
+        Dict[str, ExportedProgram],
+        str,
     ],
-    export_recipe: ExportRecipe,
+    example_inputs: Optional[
+        Union[
+            List[tuple[torch.Tensor, ...]],
+            Dict[str, List[tuple[torch.Tensor, ...]]],
+        ]
+    ] = None,
+    export_recipe: ExportRecipe = None,
     name: Optional[str] = None,
     dynamic_shapes: Optional[Union[Any, Dict[str, Any]]] = None,
     constant_methods: Optional[Union[Dict[str, Callable]]] = None,
@@ -54,10 +66,16 @@ def export(
     optionally run the export process in one step.
 
     Args:
-        model: The PyTorch model(s) to export, either a single model or a dictionary
-              mapping method names to models
+        model: The PyTorch model(s) to export. Can be:
+              - nn.Module or Dict[str, nn.Module]: Eager PyTorch model(s)
+              - GraphModule or Dict[str, GraphModule]: Quantized model(s) (e.g., from prepare/convert)
+              - ExportedProgram or Dict[str, ExportedProgram]: Already exported model(s)
+              - str: Path to load an ExportedProgram from disk
         example_inputs: Example inputs for the model(s), either a list of input tuples
-                      or a dictionary mapping method names to lists of input tuples
+                      or a dictionary mapping method names to lists of input tuples.
+                      First sample (index 0) is used for torch.export.export() to export the model.
+                      All samples are used as calibration dataset in PT2E Quantize stage.
+                      Optional when model is ExportedProgram (not needed).
         export_recipe: Contains the configuration for the export process
         name: Optional name for the export
         dynamic_shapes: Optional dynamic shape specifications
@@ -99,11 +117,22 @@ class ExportSession:
 
     def __init__(
         self,
-        model: Union[nn.Module, Dict[str, nn.Module]],
-        example_inputs: Union[
-            List[tuple[torch.Tensor, ...]], Dict[str, List[tuple[torch.Tensor, ...]]]
+        model: Union[
+            nn.Module,
+            Dict[str, nn.Module],
+            GraphModule,
+            Dict[str, GraphModule],
+            ExportedProgram,
+            Dict[str, ExportedProgram],
+            str,
         ],
-        export_recipe: ExportRecipe,
+        example_inputs: Optional[
+            Union[
+                List[tuple[torch.Tensor, ...]],
+                Dict[str, List[tuple[torch.Tensor, ...]]],
+            ]
+        ] = None,
+        export_recipe: ExportRecipe = None,
         name: Optional[str] = None,
         dynamic_shapes: Optional[Union[Any, Dict[str, Any]]] = None,
         constant_methods: Optional[Union[Dict[str, Callable]]] = None,
@@ -114,26 +143,52 @@ def __init__(
         Initialize the ExportSession with model, inputs, and recipe.
 
         Args:
-            model: The PyTorch model(s) to export, either a single model or a dictionary
-                  mapping method names to models
+            model: The PyTorch model(s) to export. Can be:
+                  - nn.Module or Dict[str, nn.Module]: Eager PyTorch model(s)
+                  - GraphModule or Dict[str, GraphModule]: Quantized model(s)
+                  - ExportedProgram or Dict[str, ExportedProgram]: Already exported model(s)
+                  - str: Path to load an ExportedProgram from disk
             example_inputs: Example inputs for the model(s), either a list of input tuples
-                          or a dictionary mapping method names to lists of input tuples
+                          or a dictionary mapping method names to lists of input tuples.
+                          First sample (index 0) is used for torch.export.export() to export the model.
+                          All samples are used as calibration dataset in PT2E Quantize stage,
+                          Optional when model is ExportedProgram (not needed).
             export_recipe: Contains the configuration for the export process
             name: Optional name for the export
             dynamic_shapes: Optional dynamic shape specifications
             constant_methods: Optional dictionary of constant methods
             artifact_dir: Optional directory to store artifacts
             generate_etrecord: Optional flag to generate an etrecord
         """
+        # Load model from file if string path provided
+        if isinstance(model, str):
+            model = torch.export.load(model)
+            logging.info(f"Loaded ExportedProgram from {model}")
+
+        # Detect input model type to determine which stages to skip
+        self._input_model_type = self._detect_model_type(model)
+
         # Standardize model to dictionary format
         self._model = model if isinstance(model, dict) else {"forward": model}
 
-        # Standardize example_inputs to dictionary format
-        self._example_inputs = (
-            example_inputs
-            if isinstance(example_inputs, dict)
-            else {"forward": example_inputs}
-        )
+        # Validate and standardize example_inputs to dictionary format
+        # example_inputs not required for ExportedProgram input
+        if self._input_model_type == "ExportedProgram":
+            self._example_inputs = example_inputs or {}
+            if isinstance(self._example_inputs, list):
+                self._example_inputs = {"forward": self._example_inputs}
+        else:
+            # For nn.Module and GraphModule, example_inputs are required
+            if example_inputs is None:
+                raise ValueError(
+                    f"example_inputs are required when model is {self._input_model_type}. "
+                    f"Only ExportedProgram inputs can omit example_inputs."
+                )
+            self._example_inputs = (
+                example_inputs
+                if isinstance(example_inputs, dict)
+                else {"forward": example_inputs}
+            )
 
         # Standardize dynamic_shapes to dictionary format
         self._dynamic_shapes = {}
@@ -176,14 +231,64 @@ def __init__(
 
         self._stage_to_artifacts: Dict[StageType, PipelineArtifact] = {}
 
+    def _detect_model_type(
+        self, model: Union[nn.Module, GraphModule, ExportedProgram, Dict]
+    ) -> str:
+        """
+        Detect the type of input model.
+
+        Args:
+            model: Input model in various formats
+
+        Returns:
+            String indicating the model type: "nn.Module", "GraphModule", or "ExportedProgram"
+        """
+        # Handle dict (multi-method) - check first value
+        if isinstance(model, dict):
+            first_value = next(iter(model.values()))
+            return self._detect_model_type(first_value)
+
+        # Detect single model type
+        if isinstance(model, ExportedProgram):
+            return "ExportedProgram"
+        elif isinstance(model, GraphModule):
+            return "GraphModule"
+        elif isinstance(model, nn.Module):
+            return "nn.Module"
+        else:
+            raise TypeError(f"Unsupported model type: {type(model)}")
+
     def _get_default_pipeline(self) -> List[StageType]:
-        return [
-            StageType.SOURCE_TRANSFORM,  # Optional stage, returns original model if quant recipe is invalid
-            StageType.QUANTIZE,  # Optional stage, returns original model if quant recipe is invalid
-            StageType.TORCH_EXPORT,
-            StageType.TO_EDGE_TRANSFORM_AND_LOWER,
-            StageType.TO_EXECUTORCH,
-        ]
+        """
+        Get default pipeline stages based on input model type.
+
+        Returns:
+            List of stages appropriate for the input model type
+        """
+        stages = []
+
+        # Add quantization stages only for eager nn.Module
+        if self._input_model_type == "nn.Module":
+            stages.extend(
+                [
+                    StageType.SOURCE_TRANSFORM,  # Optional stage, returns original model if quant recipe is invalid
+                    StageType.QUANTIZE,  # Optional stage, returns original model if quant recipe is invalid
+                ]
+            )
+
+        # Add torch export stage if not already exported
+        if self._input_model_type != "ExportedProgram":
+            stages.append(StageType.TORCH_EXPORT)
+
+        # Always include edge and executorch stages
+        stages.extend(
+            [
+                StageType.TO_EDGE_TRANSFORM_AND_LOWER,
+                StageType.TO_EXECUTORCH,
+            ]
+        )
+
+        return stages
 
     def _build_stages(self, stages: List[StageType]) -> Dict[StageType, Stage]:
         """Build the stage registry from the given stages."""
@@ -259,6 +364,34 @@ def _validate_pipeline_sequence(
         if not stages:
             raise ValueError("Pipeline stages cannot be empty")
 
+        # Validate pipeline compatibility with input model type
+        if self._input_model_type == "GraphModule":
+            # GraphModule input should not run quantization stages
+            incompatible_stages = {StageType.SOURCE_TRANSFORM, StageType.QUANTIZE}
+            found_incompatible = set(stages) & incompatible_stages
+            if found_incompatible:
+                stage_names = ", ".join(s.name for s in found_incompatible)
+                raise ValueError(
+                    f"Cannot run {stage_names} stage(s) with GraphModule input. "
+                    f"GraphModule is already quantized. "
+                    f"Remove {stage_names} from pipeline_stages or use nn.Module input."
+                )
+        elif self._input_model_type == "ExportedProgram":
+            # ExportedProgram input should not run quantization or torch export stages
+            incompatible_stages = {
+                StageType.SOURCE_TRANSFORM,
+                StageType.QUANTIZE,
+                StageType.TORCH_EXPORT,
+            }
+            found_incompatible = set(stages) & incompatible_stages
+            if found_incompatible:
+                stage_names = ", ".join(s.name for s in found_incompatible)
+                raise ValueError(
+                    f"Cannot run {stage_names} stage(s) with ExportedProgram input. "
+                    f"ExportedProgram is already exported. "
+                    f"Remove {stage_names} from pipeline_stages or use nn.Module/GraphModule input."
+                )
+
         # Validate that the first stage can start a pipeline
         first_stage = stages[0]
         first_stage_instance = self._stage_registry.get(first_stage)
@@ -436,6 +569,9 @@ def run_method(
         Raises:
             RuntimeError: If the method cannot be loaded
         """
+        # Lazy import to avoid forcing portable_lib dependency at module load time
+        from executorch.runtime import Runtime, Verification
+
         et_runtime = Runtime.get()
         program = et_runtime.load_program(
             self.get_pte_buffer(), verification=Verification.Minimal

export/stages.py

@@ -179,6 +179,7 @@ def __init__(
         ) = None,
         compile_config: Optional[Any] = None,
     ) -> None:
+        super().__init__()
         self._partitioners = partitioners
         self._transform_passes = transform_passes
         self._compile_config = compile_config
@@ -206,7 +207,7 @@ def valid_predecessor_stages(self) -> List["StageType"]:
 
     @property
     def can_start_pipeline(self) -> bool:
-        return False
+        return True
 
     def run(self, artifact: PipelineArtifact) -> None:
         """
@@ -271,7 +272,10 @@ def stage_type(self) -> str:
 
     @property
     def valid_predecessor_stages(self) -> List["StageType"]:
-        return [StageType.TO_EDGE_TRANSFORM_AND_LOWER, StageType.TO_BACKEND]
+        return [
+            StageType.TO_EDGE_TRANSFORM_AND_LOWER,
+            StageType.TO_BACKEND,
+        ]
 
     @property
     def can_start_pipeline(self) -> bool:
@@ -468,7 +472,7 @@ def valid_predecessor_stages(self) -> List["StageType"]:
 
     @property
     def can_start_pipeline(self) -> bool:
-        return False
+        return True
 
     def run(self, artifact: PipelineArtifact) -> None:
         """