Arm backend: Add fold_quantize option to tester

backends/arm/quantizer/arm_quantizer.py

@@ -665,6 +665,7 @@ def quantize_with_submodules(
         model: GraphModule,
         calibration_samples: list[tuple],
         is_qat: bool = False,
+        fold_quantize: bool = True,
     ):
         """Quantizes a GraphModule in a way such that conditional submodules are
         handled properly.
@@ -680,6 +681,8 @@ def quantize_with_submodules(
                 model with submodules, at least one sample per code path is
                 needed.
             is_qat (bool): Whether to do quantization aware training or not.
+            fold_quantize (bool): Enables or disables constant folding when quantization
+                is completed.
 
         Returns:
             GraphModule: The quantized model.
@@ -694,8 +697,11 @@ def quantize_with_submodules(
             prepared(*inp)
 
         for name, submodule, _ in self._get_submodules_not_handled_by_torchao(prepared):
-            prepared.set_submodule(name, convert_pt2e(submodule), strict=True)
-        converted = convert_pt2e(prepared)
+            prepared.set_submodule(
+                name, convert_pt2e(submodule, fold_quantize=fold_quantize), strict=True
+            )
+        converted = convert_pt2e(prepared, fold_quantize=fold_quantize)
+
         return converted
 
 

backends/arm/test/tester/quantize.py

@@ -1,9 +1,9 @@
-# Copyright 2025 Arm Limited and/or its affiliates.
+# Copyright 2025-2026 Arm Limited and/or its affiliates.
 #
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
-from typing import Optional, Tuple
+from typing import Any, Optional, Sequence, Tuple
 
 import torch
 from executorch.backends.arm.quantizer import TOSAQuantizer
@@ -14,9 +14,29 @@
 )
 
 from torch.export import export
+from torchao.quantization.pt2e.quantizer import Quantizer
 
 
 class ArmQuantize(Quantize):
+    def __init__(
+        self,
+        quantizer: Optional[Quantizer] = None,
+        quantization_config: Optional[Any] = None,
+        calibrate: bool = True,
+        calibration_samples: Optional[Sequence[Any]] = None,
+        is_qat: Optional[bool] = False,
+        set_global: bool = True,
+        fold_quantize: bool = True,
+    ):
+        super().__init__(
+            quantizer,
+            quantization_config,
+            calibrate,
+            calibration_samples,
+            is_qat,
+            set_global,
+        )
+        self.fold_quantize = fold_quantize
 
     def run(
         self, artifact: torch.nn.Module, inputs: Optional[Tuple[torch.Tensor]]
@@ -31,11 +51,11 @@ def run(
 
         if self.calibration_samples is not None:
             converted = self.quantizer.quantize_with_submodules(
-                captured_graph, self.calibration_samples, bool(self.is_qat)  # type: ignore
+                captured_graph, self.calibration_samples, bool(self.is_qat), self.fold_quantize  # type: ignore
             )
         else:
             converted = self.quantizer.quantize_with_submodules(
-                captured_graph, [inputs], bool(self.is_qat)
+                captured_graph, [inputs], bool(self.is_qat), self.fold_quantize
             )
 
         DuplicateDynamicQuantChainPass()(converted)

backends/arm/test/tester/test_pipeline.py

@@ -425,6 +425,7 @@ def __init__(
         tosa_version: Optional[str] = "1.0",
         tosa_extensions: Optional[List[str]] = None,
         epsilon: float = 2**-16,
+        fold_quantize: bool = True,
     ):
         if tosa_extensions is None:
             tosa_extensions = []
@@ -450,7 +451,9 @@ def __init__(
             )
         if symmetric_io_quantization:
             quantizer.set_io(quantization_config)
-        quant_stage = Quantize(quantizer, quantization_config)
+        quant_stage = Quantize(
+            quantizer, quantization_config, fold_quantize=fold_quantize
+        )
 
         super().__init__(
             module,
@@ -622,6 +625,7 @@ def __init__(
         rtol: float = 1e-03,
         qtol: int = 1,
         epsilon: float = 2**-12,
+        fold_quantize: bool = True,
     ):
         super().__init__(
             module,
@@ -644,7 +648,9 @@ def __init__(
             )
         if symmetric_io_quantization:
             quantizer.set_io(quantization_config)
-        quant_stage = Quantize(quantizer, quantization_config)
+        quant_stage = Quantize(
+            quantizer, quantization_config, fold_quantize=fold_quantize
+        )
 
         self.add_stage(self.tester.quantize, quant_stage, pos=0)
 
@@ -720,6 +726,7 @@ def __init__(
         rtol: float = 1e-03,
         qtol: int = 1,
         epsilon: float = 2**-12,
+        fold_quantize: bool = True,
     ):
         compile_spec = common.get_u55_compile_spec(
             custom_path=custom_path,
@@ -740,6 +747,7 @@ def __init__(
             rtol=rtol,
             qtol=qtol,
             epsilon=epsilon,
+            fold_quantize=fold_quantize,
         )
 
 
@@ -777,6 +785,7 @@ def __init__(
         rtol: float = 1e-03,
         qtol: int = 1,
         epsilon: float = 2**-12,
+        fold_quantize: bool = True,
     ):
         compile_spec = common.get_u85_compile_spec(
             custom_path=custom_path,
@@ -797,6 +806,7 @@ def __init__(
             rtol=rtol,
             qtol=qtol,
             epsilon=epsilon,
+            fold_quantize=fold_quantize,
         )
 
 
@@ -982,6 +992,7 @@ def __init__(
         input_qspecs: Optional[Dict[QuantizationSpec | None, int]] = None,
         output_qspecs: Optional[Dict[QuantizationSpec | None, int]] = None,
         custom_path: Optional[str] = None,
+        fold_quantize: bool = True,
     ):
         tosa_spec = quantizer.tosa_spec
         compile_spec = common.get_tosa_compile_spec(tosa_spec, custom_path=custom_path)
@@ -994,7 +1005,7 @@ def __init__(
             use_to_edge_transform_and_lower=True,
         )
         # TODO sort out typing
-        quant_stage = Quantize(quantizer, quantization_config=quantizer.global_config)  # type: ignore[arg-type]
+        quant_stage = Quantize(quantizer, quantization_config=quantizer.global_config, fold_quantize=fold_quantize)  # type: ignore[arg-type]
         self.add_stage(self.tester.quantize, quant_stage, pos=0)
 
         # Delete most of the pipeline
@@ -1126,6 +1137,7 @@ def __init__(
         tosa_version: str | None = None,
         tosa_extensions: Optional[List[str]] = None,
         tosa_spec: TosaSpecification | str | None = None,
+        fold_quantize: bool = True,
     ):
         if tosa_spec is None:
             if tosa_version is None:
@@ -1169,7 +1181,9 @@ def __init__(
             )
             if symmetric_io_quantization:
                 quantizer.set_io(quantization_config)
-            quant_stage = Quantize(quantizer, quantization_config)
+            quant_stage = Quantize(
+                quantizer, quantization_config, fold_quantize=fold_quantize
+            )
 
             self.add_stage(self.tester.quantize, quant_stage, pos=0)