Update on "[Executorch][target recipes] Add target based recipes for lowering models to a target device"

This diff introduces multi backend/ target based recipes to lower a model with very less code. Target recipes provide pre-configured backend recipes to use them and retarget if needed.

See RFC: #13732

## Usage

```
from executorch.export import export, ExportRecipe, IOSTargetRecipeType

# CoreML + XNNPACK
coreml_xnnpack_recipe = ExportRecipe.get_recipe(IOSTargetRecipeType.IOS_ARM64_COREML_FP32)
session = export(model, coreml_xnnpack_recipe, example_inputs)
session.save_pte_file("model.pte")
```

## Advanced usage
one can directly use `ExportRecipe.combine_recipes()` to combine specific backend recipes.
```
recipe1 = ExportRecipe.get_recipe(AndroidRecipeType.XYZ)
recipe2 = ExportRecipe.get_recipe(XNNPackRecipeType.FP32)
combined_recipe = ExportRecipe.combine(
                     [recipe1, recipe2],
                     recipe_name="multi_backend_coreml_xnnpack_fp32"
                  )
session = export(model, combined_recipe, example_inputs)
```

Fixes: #13732

Differential Revision: [D81297451](https://our.internmc.facebook.com/intern/diff/D81297451/)

[ghstack-poisoned]

Author: abhinaykukkadapu

export/multi_backend_recipes/multi_backend_recipe_provider.py

@@ -11,12 +11,12 @@
 for optimized deployment on specific targets.
 """
 
+import logging
 from typing import Any, Optional, Sequence
 
 from executorch.export import BackendRecipeProvider, ExportRecipe, RecipeType
 
 from .target_recipe_types import IOSTargetRecipeType, TargetRecipeType
-import logging
 
 
 class MultiBackendRecipeProvider(BackendRecipeProvider):

export/multi_backend_recipes/target_recipe_types.py

@@ -76,7 +76,6 @@ class IOSTargetRecipeType(TargetRecipeType):
     # No xnnpack fallback for quantization as coreml uses torch.ao quantizer vs xnnpack uses torchao quantizer
     IOS_ARM64_COREML_INT8_STATIC = "ios-arm64-coreml-int8-static"
 
-
     @classmethod
     def get_target_platform(cls) -> str:
         return "ios"

export/multi_backend_recipes/tests/test_multi_backend_target_recipes.py

@@ -16,6 +16,7 @@
 )
 from executorch.runtime import Runtime
 
+
 class TestMultiBackendTargetRecipes(unittest.TestCase):
     """Test multi-backend target recipes that combine CoreML and XNNPACK."""
 
@@ -86,11 +87,15 @@ def forward(self, x, y):
             executorch_program.execution_plan[0].delegates[2].id,
             "XnnpackBackend",
         )
-        
+
         et_runtime: Runtime = Runtime.get()
         backend_registry = et_runtime.backend_registry
-        logging.info(f"backends registered: {et_runtime.backend_registry.registered_backend_names}")
-        if backend_registry.is_available("CoreMLBackend") and backend_registry.is_available("XnnpackBackend"):
+        logging.info(
+            f"backends registered: {et_runtime.backend_registry.registered_backend_names}"
+        )
+        if backend_registry.is_available(
+            "CoreMLBackend"
+        ) and backend_registry.is_available("XnnpackBackend"):
             logging.info("Running with CoreML and XNNPACK backends")
             et_output = session.run_method("forward", *example_inputs[0])
             logging.info(f"et output {et_output}")

export/recipe.py

@@ -217,9 +217,9 @@ def combine(
         return cls._combine_recipes(recipes, recipe_name)
 
     @classmethod
-    def _combine_recipes(
+    def _combine_recipes(  # noqa: C901
         cls, backend_recipes: List["ExportRecipe"], recipe_name: Optional[str] = None
-    ) -> "ExportRecipe": # noqa: C901
+    ) -> "ExportRecipe":
         """
         Util to combine multiple backend recipes into a single multi-backend recipe.