Revert "Add TorchAO wrapper config to allow filter_fn for quantize_ (#13264)" and "Add coreml quant recipes (#13265)"

This reverts commit 0a7cea8 and
310a05d. It appears that #13264 broke
unittest jobs and #13265 depends on it.


ghstack-source-id: 6a7d30c
ghstack-comment-id: 3184642863
Pull-Request: #13374

Author: swolchok

backends/apple/coreml/TARGETS

@@ -120,7 +120,6 @@ runtime.python_test(
         "test/*.py",
     ]),
     deps = [
-        "fbsource//third-party/pypi/coremltools:coremltools",
         "fbsource//third-party/pypi/pytest:pytest",
         ":partitioner",
         ":quantizer",

backends/apple/coreml/recipes/coreml_recipe_provider.py

@@ -6,7 +6,6 @@
 from typing import Any, Optional, Sequence
 
 import coremltools as ct
-import torch
 
 from executorch.backends.apple.coreml.compiler import CoreMLBackend
 from executorch.backends.apple.coreml.partition.coreml_partitioner import (
@@ -19,15 +18,11 @@
 
 from executorch.exir import EdgeCompileConfig
 from executorch.export import (
-    AOQuantizationConfig,
     BackendRecipeProvider,
     ExportRecipe,
     LoweringRecipe,
-    QuantizationRecipe,
     RecipeType,
 )
-from torchao.quantization.granularity import PerAxis, PerGroup
-from torchao.quantization.quant_api import IntxWeightOnlyConfig
 
 
 class CoreMLRecipeProvider(BackendRecipeProvider):
@@ -55,321 +50,66 @@ def create_recipe(
         # Validate kwargs
         self._validate_recipe_kwargs(recipe_type, **kwargs)
 
+        # Parse recipe type to get precision and compute unit
+        precision = None
         if recipe_type == CoreMLRecipeType.FP32:
-            return self._build_fp_recipe(recipe_type, ct.precision.FLOAT32, **kwargs)
+            precision = ct.precision.FLOAT32
         elif recipe_type == CoreMLRecipeType.FP16:
-            return self._build_fp_recipe(recipe_type, ct.precision.FLOAT16, **kwargs)
-        elif recipe_type == CoreMLRecipeType.PT2E_INT8_STATIC:
-            return self._build_pt2e_quantized_recipe(
-                recipe_type, activation_dtype=torch.quint8, **kwargs
-            )
-        elif recipe_type == CoreMLRecipeType.PT2E_INT8_WEIGHT_ONLY:
-            return self._build_pt2e_quantized_recipe(
-                recipe_type, activation_dtype=torch.float32, **kwargs
-            )
-        elif recipe_type == CoreMLRecipeType.TORCHAO_INT4_WEIGHT_ONLY_PER_CHANNEL:
-            return self._build_torchao_quantized_recipe(
-                recipe_type,
-                weight_dtype=torch.int4,
-                is_per_channel=True,
-                **kwargs,
-            )
-        elif recipe_type == CoreMLRecipeType.TORCHAO_INT4_WEIGHT_ONLY_PER_GROUP:
-            group_size = kwargs.pop("group_size", 32)
-            return self._build_torchao_quantized_recipe(
-                recipe_type,
-                weight_dtype=torch.int4,
-                is_per_channel=False,
-                group_size=group_size,
-                **kwargs,
-            )
-        elif recipe_type == CoreMLRecipeType.TORCHAO_INT8_WEIGHT_ONLY_PER_CHANNEL:
-            return self._build_torchao_quantized_recipe(
-                recipe_type, weight_dtype=torch.int8, is_per_channel=True, **kwargs
-            )
-        elif recipe_type == CoreMLRecipeType.TORCHAO_INT8_WEIGHT_ONLY_PER_GROUP:
-            group_size = kwargs.pop("group_size", 32)
-            return self._build_torchao_quantized_recipe(
-                recipe_type,
-                weight_dtype=torch.int8,
-                is_per_channel=False,
-                group_size=group_size,
-                **kwargs,
-            )
-        elif recipe_type == CoreMLRecipeType.CODEBOOK_WEIGHT_ONLY:
-            bits = kwargs.pop("bits")
-            block_size = kwargs.pop("block_size")
-            return self._build_codebook_quantized_recipe(
-                recipe_type, bits=bits, block_size=block_size, **kwargs
-            )
+            precision = ct.precision.FLOAT16
 
-        return None
+        if precision is None:
+            raise ValueError(f"Unknown precision for recipe: {recipe_type.value}")
 
-    def _validate_recipe_kwargs(self, recipe_type: RecipeType, **kwargs: Any) -> None:
-        """Validate kwargs for each recipe type"""
-        expected_keys = self._get_expected_keys(recipe_type)
+        return self._build_recipe(recipe_type, precision, **kwargs)
 
+    def _validate_recipe_kwargs(self, recipe_type: RecipeType, **kwargs: Any) -> None:
+        if not kwargs:
+            return
+        expected_keys = {"minimum_deployment_target", "compute_unit"}
         unexpected = set(kwargs.keys()) - expected_keys
         if unexpected:
             raise ValueError(
-                f"Recipe '{recipe_type.value}' received unexpected parameters: {list(unexpected)}"
+                f"CoreML Recipes only accept 'minimum_deployment_target' or 'compute_unit' as parameter. "
+                f"Unexpected parameters: {list(unexpected)}"
             )
-
-        self._validate_base_parameters(kwargs)
-        self._validate_group_size_parameter(recipe_type, kwargs)
-        self._validate_codebook_parameters(recipe_type, kwargs)
-
-    def _get_expected_keys(self, recipe_type: RecipeType) -> set:
-        """Get expected parameter keys for a recipe type"""
-        common_keys = {"minimum_deployment_target", "compute_unit"}
-
-        if recipe_type in [
-            CoreMLRecipeType.TORCHAO_INT4_WEIGHT_ONLY_PER_GROUP,
-            CoreMLRecipeType.TORCHAO_INT8_WEIGHT_ONLY_PER_GROUP,
-        ]:
-            return common_keys | {"group_size", "filter_fn"}
-        elif recipe_type in [
-            CoreMLRecipeType.TORCHAO_INT4_WEIGHT_ONLY_PER_CHANNEL,
-            CoreMLRecipeType.TORCHAO_INT8_WEIGHT_ONLY_PER_CHANNEL,
-        ]:
-            return common_keys | {"filter_fn"}
-        elif recipe_type == CoreMLRecipeType.CODEBOOK_WEIGHT_ONLY:
-            return common_keys | {"bits", "block_size", "filter_fn"}
-        else:
-            return common_keys
-
-    def _validate_base_parameters(self, kwargs: Any) -> None:
-        """Validate minimum_deployment_target and compute_unit parameters"""
         if "minimum_deployment_target" in kwargs:
             minimum_deployment_target = kwargs["minimum_deployment_target"]
             if not isinstance(minimum_deployment_target, ct.target):
                 raise ValueError(
                     f"Parameter 'minimum_deployment_target' must be an enum of type ct.target, got {type(minimum_deployment_target)}"
                 )
-
         if "compute_unit" in kwargs:
             compute_unit = kwargs["compute_unit"]
             if not isinstance(compute_unit, ct.ComputeUnit):
                 raise ValueError(
                     f"Parameter 'compute_unit' must be an enum of type ct.ComputeUnit, got {type(compute_unit)}"
                 )
 
-    def _validate_group_size_parameter(
-        self, recipe_type: RecipeType, kwargs: Any
-    ) -> None:
-        """Validate group_size parameter for applicable recipe types"""
-        if (
-            recipe_type
-            in [
-                CoreMLRecipeType.TORCHAO_INT4_WEIGHT_ONLY_PER_GROUP,
-                CoreMLRecipeType.TORCHAO_INT8_WEIGHT_ONLY_PER_GROUP,
-            ]
-            and "group_size" in kwargs
-        ):
-            group_size = kwargs["group_size"]
-            if not isinstance(group_size, int):
-                raise ValueError(
-                    f"Parameter 'group_size' must be an integer, got {type(group_size).__name__}: {group_size}"
-                )
-            if group_size <= 0:
-                raise ValueError(
-                    f"Parameter 'group_size' must be positive, got: {group_size}"
-                )
-
-    def _validate_codebook_parameters(
-        self, recipe_type: RecipeType, kwargs: Any
-    ) -> None:
-        """Validate bits and block_size parameters for codebook recipe type"""
-        if recipe_type != CoreMLRecipeType.CODEBOOK_WEIGHT_ONLY:
-            return
-
-        # Both bits and block_size must be present
-        if not ("bits" in kwargs and "block_size" in kwargs):
-            raise ValueError(
-                "Parameters 'bits' and 'block_size' must be present for codebook recipes"
-            )
-
-        if "bits" in kwargs:
-            bits = kwargs["bits"]
-            if not isinstance(bits, int):
-                raise ValueError(
-                    f"Parameter 'bits' must be an integer, got {type(bits).__name__}: {bits}"
-                )
-            if not (1 <= bits <= 8):
-                raise ValueError(
-                    f"Parameter 'bits' must be between 1 and 8, got: {bits}"
-                )
-
-        if "block_size" in kwargs:
-            block_size = kwargs["block_size"]
-            if not isinstance(block_size, list):
-                raise ValueError(
-                    f"Parameter 'block_size' must be a list, got {type(block_size).__name__}: {block_size}"
-                )
-
-    def _validate_and_set_deployment_target(
-        self, kwargs: Any, min_target: ct.target, quantization_type: str
-    ) -> None:
-        """Validate or set minimum deployment target for quantization recipes"""
-        minimum_deployment_target = kwargs.get("minimum_deployment_target", None)
-        if minimum_deployment_target and minimum_deployment_target < min_target:
-            raise ValueError(
-                f"minimum_deployment_target must be {str(min_target)} or higher for {quantization_type} quantization"
-            )
-        else:
-            # Default to the minimum target for this quantization type
-            kwargs["minimum_deployment_target"] = min_target
-
-    def _build_fp_recipe(
+    def _build_recipe(
         self,
         recipe_type: RecipeType,
         precision: ct.precision,
         **kwargs: Any,
     ) -> ExportRecipe:
-        """Build FP32/FP16 recipe"""
         lowering_recipe = self._get_coreml_lowering_recipe(
             compute_precision=precision,
             **kwargs,
         )
 
         return ExportRecipe(
             name=recipe_type.value,
-            lowering_recipe=lowering_recipe,
-        )
-
-    def _build_pt2e_quantized_recipe(
-        self,
-        recipe_type: RecipeType,
-        activation_dtype: torch.dtype,
-        **kwargs: Any,
-    ) -> ExportRecipe:
-        """Build PT2E-based quantization recipe"""
-        from executorch.backends.apple.coreml.quantizer import CoreMLQuantizer
-
-        self._validate_and_set_deployment_target(kwargs, ct.target.iOS17, "pt2e")
-
-        # Validate activation_dtype
-        assert activation_dtype in [
-            torch.quint8,
-            torch.float32,
-        ], f"activation_dtype must be torch.quint8 or torch.float32, got {activation_dtype}"
-
-        # Create quantization config
-        config = ct.optimize.torch.quantization.LinearQuantizerConfig(
-            global_config=ct.optimize.torch.quantization.ModuleLinearQuantizerConfig(
-                quantization_scheme="symmetric",
-                activation_dtype=activation_dtype,
-                weight_dtype=torch.qint8,
-                weight_per_channel=True,
-            )
-        )
-
-        quantizer = CoreMLQuantizer(config)
-        quantization_recipe = QuantizationRecipe(quantizers=[quantizer])
-
-        lowering_recipe = self._get_coreml_lowering_recipe(**kwargs)
-
-        return ExportRecipe(
-            name=recipe_type.value,
-            quantization_recipe=quantization_recipe,
-            lowering_recipe=lowering_recipe,
-        )
-
-    def _build_torchao_quantized_recipe(
-        self,
-        recipe_type: RecipeType,
-        weight_dtype: torch.dtype,
-        is_per_channel: bool,
-        group_size: int = 32,
-        **kwargs: Any,
-    ) -> ExportRecipe:
-        """Build TorchAO-based quantization recipe"""
-        if is_per_channel:
-            weight_granularity = PerAxis(axis=0)
-        else:
-            weight_granularity = PerGroup(group_size=group_size)
-
-        # Use user-provided filter_fn if provided
-        filter_fn = kwargs.get("filter_fn", None)
-        config = AOQuantizationConfig(
-            ao_base_config=IntxWeightOnlyConfig(
-                weight_dtype=weight_dtype,
-                granularity=weight_granularity,
-            ),
-            filter_fn=filter_fn,
-        )
-
-        quantization_recipe = QuantizationRecipe(
-            quantizers=None,
-            ao_quantization_configs=[config],
-        )
-
-        # override minimum_deployment_target to ios18 for torchao (GH issue #13122)
-        self._validate_and_set_deployment_target(kwargs, ct.target.iOS18, "torchao")
-        lowering_recipe = self._get_coreml_lowering_recipe(**kwargs)
-
-        return ExportRecipe(
-            name=recipe_type.value,
-            quantization_recipe=quantization_recipe,
-            lowering_recipe=lowering_recipe,
-        )
-
-    def _build_codebook_quantized_recipe(
-        self,
-        recipe_type: RecipeType,
-        bits: int,
-        block_size: list,
-        **kwargs: Any,
-    ) -> ExportRecipe:
-        """Build codebook/palettization quantization recipe"""
-        from torchao.prototype.quantization.codebook_coreml import (
-            CodebookWeightOnlyConfig,
-        )
-
-        self._validate_and_set_deployment_target(kwargs, ct.target.iOS18, "codebook")
-
-        # Get the appropriate dtype (torch.uint1 through torch.uint8)
-        dtype = getattr(torch, f"uint{bits}")
-
-        # Use user-provided filter_fn or default to Linear/Embedding layers
-        filter_fn = kwargs.get(
-            "filter_fn",
-            lambda m, fqn: (
-                isinstance(m, torch.nn.Embedding) or isinstance(m, torch.nn.Linear)
-            ),
-        )
-
-        config = AOQuantizationConfig(
-            ao_base_config=CodebookWeightOnlyConfig(
-                dtype=dtype,
-                block_size=block_size,
-            ),
-            filter_fn=filter_fn,
-        )
-
-        quantization_recipe = QuantizationRecipe(
-            quantizers=None,
-            ao_quantization_configs=[config],
-        )
-
-        lowering_recipe = self._get_coreml_lowering_recipe(**kwargs)
-
-        return ExportRecipe(
-            name=recipe_type.value,
-            quantization_recipe=quantization_recipe,
+            quantization_recipe=None,  # TODO - add quantization recipe
             lowering_recipe=lowering_recipe,
         )
 
     def _get_coreml_lowering_recipe(
         self,
-        compute_precision: ct.precision = ct.precision.FLOAT16,
+        compute_precision: ct.precision,
         **kwargs: Any,
     ) -> LoweringRecipe:
-        """Get CoreML lowering recipe with optional precision"""
         compile_specs = CoreMLBackend.generate_compile_specs(
             compute_precision=compute_precision,
-            compute_unit=kwargs.get("compute_unit", ct.ComputeUnit.ALL),
-            minimum_deployment_target=kwargs.get("minimum_deployment_target", None),
+            **kwargs,
         )
 
         minimum_deployment_target = kwargs.get("minimum_deployment_target", None)