Revert "Arm backend: Make composable_quantizer default"#20982

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Merged
rascani merged 1 commit into from
Jul 17, 2026
Merged

Revert "Arm backend: Make composable_quantizer default" #20982

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2 changes: 1 addition & 1 deletion backends/arm/_passes/normalize_while_initial_args_pass.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -62,7 +62,7 @@ def _connect_to_output(
(placeholder,),
)
cloned_placeholders.append(clone)
clone.meta = placeholder.meta.copy()
clone.meta = placeholder.meta
output_node = body_module.graph.output_node()
output_values = output_node.args[0]
if not isinstance(output_values, tuple):
Expand Down
4 changes: 2 additions & 2 deletions backends/arm/public_api_manifests/api_manifest_running.toml
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Original file line numberDiff line numberDiff line change
Expand Up@@ -62,7 +62,7 @@ signature = "EthosUPartitioner.register_custom_partition_op(self, op: torch._ops

[python.EthosUQuantizer]
kind = "class"
signature = "EthosUQuantizer(compile_spec: 'EthosUCompileSpec', use_composable_quantizer: 'bool' = True) -> 'None'"
signature = "EthosUQuantizer(compile_spec: 'EthosUCompileSpec', use_composable_quantizer: 'bool' = False) -> 'None'"

[python.EthosUQuantizer.annotate]
kind = "function"
Expand DownExpand Up@@ -150,7 +150,7 @@ signature = "VgfPartitioner.register_custom_partition_op(self, op: torch._ops.Op

[python.VgfQuantizer]
kind = "class"
signature = "VgfQuantizer(compile_spec: 'VgfCompileSpec', use_composable_quantizer: 'bool' = True) -> 'None'"
signature = "VgfQuantizer(compile_spec: 'VgfCompileSpec', use_composable_quantizer: 'bool' = False) -> 'None'"

[python.VgfQuantizer.annotate]
kind = "function"
Expand Down
138 changes: 28 additions & 110 deletions backends/arm/quantizer/arm_quantizer.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -493,23 +493,21 @@ class TOSAQuantizer(Quantizer):
"""Manage quantization annotations for TOSA-compatible backends.

.. warning::
The composable quantizer is now the default implementation. Setting
``use_composable_quantizer=False`` is deprecated and will be removed in
two minor releases.
Setting ``use_composable_quantizer=True`` enables an experimental API
surface that may change without notice.

"""

def __init__(
self,
compile_spec_or_tosa_spec,
use_composable_quantizer: bool = True,
use_composable_quantizer: bool = False,
) -> None:
"""Create a TOSA quantizer from a TOSA spec or Arm compile spec.

.. warning::
The composable quantizer is now the default implementation.
Setting ``use_composable_quantizer=False`` is deprecated and will
be removed in two minor releases.
Setting ``use_composable_quantizer=True`` enables an experimental
API surface that may change without notice.

"""
self.use_composable_quantizer = use_composable_quantizer
Expand All@@ -521,45 +519,10 @@ def __init__(
self.quantizer = _TOSAQuantizerV2(compile_spec_or_tosa_spec)
else:
logger.info(
"Using deprecated legacy quantizer implementation in the arm backend. Setting use_composable_quantizer=False will be removed in two minor releases. See https://github.com/pytorch/executorch/issues/17701"
"Using default quantizer in the arm backend. This quantizer is planned to be replaced by the composable quantizer implementation in the future, see https://github.com/pytorch/executorch/issues/17701"
)
self.quantizer = _TOSAQuantizerV1(compile_spec_or_tosa_spec)

@staticmethod
def _validate_optional_quantization_config(
config_name: str, value: object, value_description: str = "value"
) -> None:
if value is not None and not isinstance(value, QuantizationConfig):
raise TypeError(
f"{config_name} {value_description} must be "
"QuantizationConfig or None, "
f"got {type(value).__name__}."
)

@staticmethod
def _validate_config_dict(
config_name: str,
value: object,
is_valid_key: Callable[[object], bool],
key_description: str,
) -> Dict[Any, Optional[QuantizationConfig]]:
if not isinstance(value, dict):
raise TypeError(
f"{config_name} must be a dict, got {type(value).__name__}."
)

for key, quantization_config in value.items():
if not is_valid_key(key):
raise TypeError(
f"{config_name} keys must be {key_description}, "
f"got {type(key).__name__}."
)
TOSAQuantizer._validate_optional_quantization_config(
config_name, quantization_config, "values"
)

return value

@property
def tosa_spec(self):
"""Return the TOSA specification used by the active quantizer."""
Expand All@@ -577,11 +540,12 @@ def global_config(self):

@global_config.setter
def global_config(self, value: Optional[QuantizationConfig]) -> None:
self._validate_optional_quantization_config("global_config", value)
if isinstance(self.quantizer, _TOSAQuantizerV1):
self.quantizer.global_config = value
else:
self.quantizer.set_global(value)
raise NotImplementedError(
"Composable quantizer does not allow setting global_config directly. Please use set_global() instead."
)

@property
def io_config(self):
Expand All@@ -595,12 +559,12 @@ def io_config(self):

@io_config.setter
def io_config(self, value: Optional[QuantizationConfig]) -> None:
self._validate_optional_quantization_config("io_config", value)
if isinstance(self.quantizer, _TOSAQuantizerV1):
self.quantizer.io_config = value
else:
self.quantizer.clear_io_config()
self.quantizer.set_io(value)
raise NotImplementedError(
"Composable quantizer does not allow setting io_config directly. Please use set_io() instead."
)

@property
def module_type_config(self):
Expand All@@ -616,18 +580,12 @@ def module_type_config(self):
def module_type_config(
self, value: Dict[Callable, Optional[QuantizationConfig]]
) -> None:
module_type_config = self._validate_config_dict(
"module_type_config",
value,
callable,
"callable",
)
if isinstance(self.quantizer, _TOSAQuantizerV1):
self.quantizer.module_type_config = module_type_config
self.quantizer.module_type_config = value
else:
self.quantizer.clear_module_type_config()
for module_type, quantization_config in module_type_config.items():
self.quantizer.set_module_type(module_type, quantization_config)
raise NotImplementedError(
"Composable quantizer does not allow setting module_type_config directly. Please use set_module_type() instead."
)

@property
def module_name_config(self):
Expand All@@ -643,18 +601,12 @@ def module_name_config(self):
def module_name_config(
self, value: Dict[str, Optional[QuantizationConfig]]
) -> None:
module_name_config = self._validate_config_dict(
"module_name_config",
value,
lambda key: isinstance(key, str),
"str",
)
if isinstance(self.quantizer, _TOSAQuantizerV1):
self.quantizer.module_name_config = module_name_config
self.quantizer.module_name_config = value
else:
self.quantizer.clear_module_name_config()
for module_name, quantization_config in module_name_config.items():
self.quantizer.set_module_name(module_name, quantization_config)
raise NotImplementedError(
"Composable quantizer does not allow setting module_name_config directly. Please use set_module_name() instead."
)

def set_global(
self, quantization_config: Optional[QuantizationConfig]
Expand DownExpand Up@@ -1179,30 +1131,6 @@ def quantizers(self, value: List[Quantizer]) -> None:
"""Update quantizers without accessing self._quantizers directly."""
self._quantizers = value

def _remove_quantizers_by_node_finder_type(
self, node_finder_types: type[NodeFinder] | tuple[type[NodeFinder], ...]
) -> None:
self._quantizers = [
quantizer
for quantizer in self._quantizers
if not (
isinstance(quantizer, PatternQuantizer)
and isinstance(quantizer.node_finder, node_finder_types)
)
]

def clear_module_type_config(self) -> _TOSAQuantizerV2:
self._remove_quantizers_by_node_finder_type(ModuleTypeNodeFinder)
return self

def clear_module_name_config(self) -> _TOSAQuantizerV2:
self._remove_quantizers_by_node_finder_type(ModuleNameNodeFinder)
return self

def clear_io_config(self) -> _TOSAQuantizerV2:
self._remove_quantizers_by_node_finder_type((InputNodeFinder, OutputNodeFinder))
return self

def annotate(self, model):
reporter = QuantizerReporter(self.quantizers, "FINAL QUANTIZATION REPORT")
model = super().annotate(model)
Expand DownExpand Up@@ -1356,25 +1284,20 @@ class EthosUQuantizer(TOSAQuantizer):
"""Quantizer supported by the Arm Ethos-U backend.

.. warning::
The composable quantizer is now the default implementation. Setting
``use_composable_quantizer=False`` is deprecated and will be removed in
two minor releases.
Setting ``use_composable_quantizer=True`` enables an experimental API
surface that may change without notice.

Args:
compile_spec (EthosUCompileSpec): Backend compile specification for
Ethos-U targets.
use_composable_quantizer (bool): Whether to use the composable
quantizer implementation. Setting this to ``False`` is deprecated
and will be removed in two minor releases. See
[issue #17701](https://github.com/pytorch/executorch/issues/17701)
for details.
use_composable_quantizer (bool): Whether to use the composable quantizer implementation. See https://github.com/pytorch/executorch/issues/17701" for details.

"""

def __init__(
self,
compile_spec: EthosUCompileSpec,
use_composable_quantizer: bool = True,
use_composable_quantizer: bool = False,
) -> None:
super().__init__(compile_spec, use_composable_quantizer)

Expand All@@ -1383,24 +1306,19 @@ class VgfQuantizer(TOSAQuantizer):
"""Quantizer supported by the Arm Vgf backend.

.. warning::
The composable quantizer is now the default implementation. Setting
``use_composable_quantizer=False`` is deprecated and will be removed in
two minor releases.
Setting ``use_composable_quantizer=True`` enables an experimental API
surface that may change without notice.

Args:
compile_spec (VgfCompileSpec): Backend compile specification for Vgf
targets.
use_composable_quantizer (bool): Whether to use the composable
quantizer implementation. Setting this to ``False`` is deprecated
and will be removed in two minor releases. See
[issue #17701](https://github.com/pytorch/executorch/issues/17701)
for details.
use_composable_quantizer (bool): Whether to use the composable quantizer implementation. See https://github.com/pytorch/executorch/issues/17701" for details.

"""

def __init__(
self,
compile_spec: VgfCompileSpec,
use_composable_quantizer: bool = True,
use_composable_quantizer: bool = False,
) -> None:
super().__init__(compile_spec, use_composable_quantizer)
3 changes: 1 addition & 2 deletions backends/arm/quantizer/quantization_config.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -356,7 +356,7 @@ def get_output_act_qspec(

If node is a pooling or upsample operator, returns a shared quantization spec.
If no weight spec is configured, return ``None``.
If node is a `to.dtype` operator, returns a fixed quantization spec if the input is integer and the output is float32.
If node is a `to.dtype` operator, returns a fixed quantization spec if the input is integer and the output is floating-point.

"""

Expand DownExpand Up@@ -391,7 +391,6 @@ def get_output_act_qspec(
isinstance(input_val, torch.Tensor)
and isinstance(output_val, torch.Tensor)
and CastCheck.is_integer_to_float(input_val.dtype, output_val.dtype)
and output_val.dtype == torch.float32
):
return FixedQParamsQuantizationSpec(
dtype=input_val.dtype,
Expand Down
1 change: 0 additions & 1 deletion backends/arm/test/misc/test_quant_custom_meta.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -105,6 +105,5 @@ def test_quantized_to_float_transition_tosa_INT_FP(fp_extension: bool):
)
pipeline.quantizer.set_module_type(torch.nn.Sigmoid, None) # type: ignore
pipeline.quantizer.set_module_type(torch.nn.Conv1d, None) # type: ignore
pipeline.quantizer.set_io(None) # type: ignore

pipeline.run()
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Revert "Arm backend: Make composable_quantizer default"#20982

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Merged
rascani merged 1 commit into from
Jul 17, 2026
Merged

Revert "Arm backend: Make composable_quantizer default" #20982

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2 changes: 1 addition & 1 deletion backends/arm/_passes/normalize_while_initial_args_pass.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -62,7 +62,7 @@ def _connect_to_output(
(placeholder,),
)
cloned_placeholders.append(clone)
clone.meta = placeholder.meta.copy()
clone.meta = placeholder.meta
output_node = body_module.graph.output_node()
output_values = output_node.args[0]
if not isinstance(output_values, tuple):
Expand Down
4 changes: 2 additions & 2 deletions backends/arm/public_api_manifests/api_manifest_running.toml
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -62,7 +62,7 @@ signature = "EthosUPartitioner.register_custom_partition_op(self, op: torch._ops

[python.EthosUQuantizer]
kind = "class"
signature = "EthosUQuantizer(compile_spec: 'EthosUCompileSpec', use_composable_quantizer: 'bool' = True) -> 'None'"
signature = "EthosUQuantizer(compile_spec: 'EthosUCompileSpec', use_composable_quantizer: 'bool' = False) -> 'None'"

[python.EthosUQuantizer.annotate]
kind = "function"
Expand DownExpand Up@@ -150,7 +150,7 @@ signature = "VgfPartitioner.register_custom_partition_op(self, op: torch._ops.Op

[python.VgfQuantizer]
kind = "class"
signature = "VgfQuantizer(compile_spec: 'VgfCompileSpec', use_composable_quantizer: 'bool' = True) -> 'None'"
signature = "VgfQuantizer(compile_spec: 'VgfCompileSpec', use_composable_quantizer: 'bool' = False) -> 'None'"

[python.VgfQuantizer.annotate]
kind = "function"
Expand Down
138 changes: 28 additions & 110 deletions backends/arm/quantizer/arm_quantizer.py
View file
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Original file line numberDiff line numberDiff line change
Expand Up@@ -493,23 +493,21 @@ class TOSAQuantizer(Quantizer):
"""Manage quantization annotations for TOSA-compatible backends.

.. warning::
The composable quantizer is now the default implementation. Setting
``use_composable_quantizer=False`` is deprecated and will be removed in
two minor releases.
Setting ``use_composable_quantizer=True`` enables an experimental API
surface that may change without notice.

"""

def __init__(
self,
compile_spec_or_tosa_spec,
use_composable_quantizer: bool = True,
use_composable_quantizer: bool = False,
) -> None:
"""Create a TOSA quantizer from a TOSA spec or Arm compile spec.

.. warning::
The composable quantizer is now the default implementation.
Setting ``use_composable_quantizer=False`` is deprecated and will
be removed in two minor releases.
Setting ``use_composable_quantizer=True`` enables an experimental
API surface that may change without notice.

"""
self.use_composable_quantizer = use_composable_quantizer
Expand All@@ -521,45 +519,10 @@ def __init__(
self.quantizer = _TOSAQuantizerV2(compile_spec_or_tosa_spec)
else:
logger.info(
"Using deprecated legacy quantizer implementation in the arm backend. Setting use_composable_quantizer=False will be removed in two minor releases. See https://github.com/pytorch/executorch/issues/17701"
"Using default quantizer in the arm backend. This quantizer is planned to be replaced by the composable quantizer implementation in the future, see https://github.com/pytorch/executorch/issues/17701"
)
self.quantizer = _TOSAQuantizerV1(compile_spec_or_tosa_spec)

@staticmethod
def _validate_optional_quantization_config(
config_name: str, value: object, value_description: str = "value"
) -> None:
if value is not None and not isinstance(value, QuantizationConfig):
raise TypeError(
f"{config_name} {value_description} must be "
"QuantizationConfig or None, "
f"got {type(value).__name__}."
)

@staticmethod
def _validate_config_dict(
config_name: str,
value: object,
is_valid_key: Callable[[object], bool],
key_description: str,
) -> Dict[Any, Optional[QuantizationConfig]]:
if not isinstance(value, dict):
raise TypeError(
f"{config_name} must be a dict, got {type(value).__name__}."
)

for key, quantization_config in value.items():
if not is_valid_key(key):
raise TypeError(
f"{config_name} keys must be {key_description}, "
f"got {type(key).__name__}."
)
TOSAQuantizer._validate_optional_quantization_config(
config_name, quantization_config, "values"
)

return value

@property
def tosa_spec(self):
"""Return the TOSA specification used by the active quantizer."""
Expand All@@ -577,11 +540,12 @@ def global_config(self):

@global_config.setter
def global_config(self, value: Optional[QuantizationConfig]) -> None:
self._validate_optional_quantization_config("global_config", value)
if isinstance(self.quantizer, _TOSAQuantizerV1):
self.quantizer.global_config = value
else:
self.quantizer.set_global(value)
raise NotImplementedError(
"Composable quantizer does not allow setting global_config directly. Please use set_global() instead."
)

@property
def io_config(self):
Expand All@@ -595,12 +559,12 @@ def io_config(self):

@io_config.setter
def io_config(self, value: Optional[QuantizationConfig]) -> None:
self._validate_optional_quantization_config("io_config", value)
if isinstance(self.quantizer, _TOSAQuantizerV1):
self.quantizer.io_config = value
else:
self.quantizer.clear_io_config()
self.quantizer.set_io(value)
raise NotImplementedError(
"Composable quantizer does not allow setting io_config directly. Please use set_io() instead."
)

@property
def module_type_config(self):
Expand All@@ -616,18 +580,12 @@ def module_type_config(self):
def module_type_config(
self, value: Dict[Callable, Optional[QuantizationConfig]]
) -> None:
module_type_config = self._validate_config_dict(
"module_type_config",
value,
callable,
"callable",
)
if isinstance(self.quantizer, _TOSAQuantizerV1):
self.quantizer.module_type_config = module_type_config
self.quantizer.module_type_config = value
else:
self.quantizer.clear_module_type_config()
for module_type, quantization_config in module_type_config.items():
self.quantizer.set_module_type(module_type, quantization_config)
raise NotImplementedError(
"Composable quantizer does not allow setting module_type_config directly. Please use set_module_type() instead."
)

@property
def module_name_config(self):
Expand All@@ -643,18 +601,12 @@ def module_name_config(self):
def module_name_config(
self, value: Dict[str, Optional[QuantizationConfig]]
) -> None:
module_name_config = self._validate_config_dict(
"module_name_config",
value,
lambda key: isinstance(key, str),
"str",
)
if isinstance(self.quantizer, _TOSAQuantizerV1):
self.quantizer.module_name_config = module_name_config
self.quantizer.module_name_config = value
else:
self.quantizer.clear_module_name_config()
for module_name, quantization_config in module_name_config.items():
self.quantizer.set_module_name(module_name, quantization_config)
raise NotImplementedError(
"Composable quantizer does not allow setting module_name_config directly. Please use set_module_name() instead."
)

def set_global(
self, quantization_config: Optional[QuantizationConfig]
Expand DownExpand Up@@ -1179,30 +1131,6 @@ def quantizers(self, value: List[Quantizer]) -> None:
"""Update quantizers without accessing self._quantizers directly."""
self._quantizers = value

def _remove_quantizers_by_node_finder_type(
self, node_finder_types: type[NodeFinder] | tuple[type[NodeFinder], ...]
) -> None:
self._quantizers = [
quantizer
for quantizer in self._quantizers
if not (
isinstance(quantizer, PatternQuantizer)
and isinstance(quantizer.node_finder, node_finder_types)
)
]

def clear_module_type_config(self) -> _TOSAQuantizerV2:
self._remove_quantizers_by_node_finder_type(ModuleTypeNodeFinder)
return self

def clear_module_name_config(self) -> _TOSAQuantizerV2:
self._remove_quantizers_by_node_finder_type(ModuleNameNodeFinder)
return self

def clear_io_config(self) -> _TOSAQuantizerV2:
self._remove_quantizers_by_node_finder_type((InputNodeFinder, OutputNodeFinder))
return self

def annotate(self, model):
reporter = QuantizerReporter(self.quantizers, "FINAL QUANTIZATION REPORT")
model = super().annotate(model)
Expand DownExpand Up@@ -1356,25 +1284,20 @@ class EthosUQuantizer(TOSAQuantizer):
"""Quantizer supported by the Arm Ethos-U backend.

.. warning::
The composable quantizer is now the default implementation. Setting
``use_composable_quantizer=False`` is deprecated and will be removed in
two minor releases.
Setting ``use_composable_quantizer=True`` enables an experimental API
surface that may change without notice.

Args:
compile_spec (EthosUCompileSpec): Backend compile specification for
Ethos-U targets.
use_composable_quantizer (bool): Whether to use the composable
quantizer implementation. Setting this to ``False`` is deprecated
and will be removed in two minor releases. See
[issue #17701](https://github.com/pytorch/executorch/issues/17701)
for details.
use_composable_quantizer (bool): Whether to use the composable quantizer implementation. See https://github.com/pytorch/executorch/issues/17701" for details.

"""

def __init__(
self,
compile_spec: EthosUCompileSpec,
use_composable_quantizer: bool = True,
use_composable_quantizer: bool = False,
) -> None:
super().__init__(compile_spec, use_composable_quantizer)

Expand All@@ -1383,24 +1306,19 @@ class VgfQuantizer(TOSAQuantizer):
"""Quantizer supported by the Arm Vgf backend.

.. warning::
The composable quantizer is now the default implementation. Setting
``use_composable_quantizer=False`` is deprecated and will be removed in
two minor releases.
Setting ``use_composable_quantizer=True`` enables an experimental API
surface that may change without notice.

Args:
compile_spec (VgfCompileSpec): Backend compile specification for Vgf
targets.
use_composable_quantizer (bool): Whether to use the composable
quantizer implementation. Setting this to ``False`` is deprecated
and will be removed in two minor releases. See
[issue #17701](https://github.com/pytorch/executorch/issues/17701)
for details.
use_composable_quantizer (bool): Whether to use the composable quantizer implementation. See https://github.com/pytorch/executorch/issues/17701" for details.

"""

def __init__(
self,
compile_spec: VgfCompileSpec,
use_composable_quantizer: bool = True,
use_composable_quantizer: bool = False,
) -> None:
super().__init__(compile_spec, use_composable_quantizer)
3 changes: 1 addition & 2 deletions backends/arm/quantizer/quantization_config.py
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Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -356,7 +356,7 @@ def get_output_act_qspec(

If node is a pooling or upsample operator, returns a shared quantization spec.
If no weight spec is configured, return ``None``.
If node is a `to.dtype` operator, returns a fixed quantization spec if the input is integer and the output is float32.
If node is a `to.dtype` operator, returns a fixed quantization spec if the input is integer and the output is floating-point.

"""

Expand DownExpand Up@@ -391,7 +391,6 @@ def get_output_act_qspec(
isinstance(input_val, torch.Tensor)
and isinstance(output_val, torch.Tensor)
and CastCheck.is_integer_to_float(input_val.dtype, output_val.dtype)
and output_val.dtype == torch.float32
):
return FixedQParamsQuantizationSpec(
dtype=input_val.dtype,
Expand Down
1 change: 0 additions & 1 deletion backends/arm/test/misc/test_quant_custom_meta.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -105,6 +105,5 @@ def test_quantized_to_float_transition_tosa_INT_FP(fp_extension: bool):
)
pipeline.quantizer.set_module_type(torch.nn.Sigmoid, None) # type: ignore
pipeline.quantizer.set_module_type(torch.nn.Conv1d, None) # type: ignore
pipeline.quantizer.set_io(None) # type: ignore

pipeline.run()
Loading
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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix"); } } catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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Revert "Arm backend: Make composable_quantizer default"#20982

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Merged
rascani merged 1 commit into from
Jul 17, 2026
Merged

Revert "Arm backend: Make composable_quantizer default" #20982

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2 changes: 1 addition & 1 deletion backends/arm/_passes/normalize_while_initial_args_pass.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -62,7 +62,7 @@ def _connect_to_output(
(placeholder,),
)
cloned_placeholders.append(clone)
clone.meta = placeholder.meta.copy()
clone.meta = placeholder.meta
output_node = body_module.graph.output_node()
output_values = output_node.args[0]
if not isinstance(output_values, tuple):
Expand Down
4 changes: 2 additions & 2 deletions backends/arm/public_api_manifests/api_manifest_running.toml
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -62,7 +62,7 @@ signature = "EthosUPartitioner.register_custom_partition_op(self, op: torch._ops

[python.EthosUQuantizer]
kind = "class"
signature = "EthosUQuantizer(compile_spec: 'EthosUCompileSpec', use_composable_quantizer: 'bool' = True) -> 'None'"
signature = "EthosUQuantizer(compile_spec: 'EthosUCompileSpec', use_composable_quantizer: 'bool' = False) -> 'None'"

[python.EthosUQuantizer.annotate]
kind = "function"
Expand DownExpand Up@@ -150,7 +150,7 @@ signature = "VgfPartitioner.register_custom_partition_op(self, op: torch._ops.Op

[python.VgfQuantizer]
kind = "class"
signature = "VgfQuantizer(compile_spec: 'VgfCompileSpec', use_composable_quantizer: 'bool' = True) -> 'None'"
signature = "VgfQuantizer(compile_spec: 'VgfCompileSpec', use_composable_quantizer: 'bool' = False) -> 'None'"

[python.VgfQuantizer.annotate]
kind = "function"
Expand Down
138 changes: 28 additions & 110 deletions backends/arm/quantizer/arm_quantizer.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -493,23 +493,21 @@ class TOSAQuantizer(Quantizer):
"""Manage quantization annotations for TOSA-compatible backends.

.. warning::
The composable quantizer is now the default implementation. Setting
``use_composable_quantizer=False`` is deprecated and will be removed in
two minor releases.
Setting ``use_composable_quantizer=True`` enables an experimental API
surface that may change without notice.

"""

def __init__(
self,
compile_spec_or_tosa_spec,
use_composable_quantizer: bool = True,
use_composable_quantizer: bool = False,
) -> None:
"""Create a TOSA quantizer from a TOSA spec or Arm compile spec.

.. warning::
The composable quantizer is now the default implementation.
Setting ``use_composable_quantizer=False`` is deprecated and will
be removed in two minor releases.
Setting ``use_composable_quantizer=True`` enables an experimental
API surface that may change without notice.

"""
self.use_composable_quantizer = use_composable_quantizer
Expand All@@ -521,45 +519,10 @@ def __init__(
self.quantizer = _TOSAQuantizerV2(compile_spec_or_tosa_spec)
else:
logger.info(
"Using deprecated legacy quantizer implementation in the arm backend. Setting use_composable_quantizer=False will be removed in two minor releases. See https://github.com/pytorch/executorch/issues/17701"
"Using default quantizer in the arm backend. This quantizer is planned to be replaced by the composable quantizer implementation in the future, see https://github.com/pytorch/executorch/issues/17701"
)
self.quantizer = _TOSAQuantizerV1(compile_spec_or_tosa_spec)

@staticmethod
def _validate_optional_quantization_config(
config_name: str, value: object, value_description: str = "value"
) -> None:
if value is not None and not isinstance(value, QuantizationConfig):
raise TypeError(
f"{config_name} {value_description} must be "
"QuantizationConfig or None, "
f"got {type(value).__name__}."
)

@staticmethod
def _validate_config_dict(
config_name: str,
value: object,
is_valid_key: Callable[[object], bool],
key_description: str,
) -> Dict[Any, Optional[QuantizationConfig]]:
if not isinstance(value, dict):
raise TypeError(
f"{config_name} must be a dict, got {type(value).__name__}."
)

for key, quantization_config in value.items():
if not is_valid_key(key):
raise TypeError(
f"{config_name} keys must be {key_description}, "
f"got {type(key).__name__}."
)
TOSAQuantizer._validate_optional_quantization_config(
config_name, quantization_config, "values"
)

return value

@property
def tosa_spec(self):
"""Return the TOSA specification used by the active quantizer."""
Expand All@@ -577,11 +540,12 @@ def global_config(self):

@global_config.setter
def global_config(self, value: Optional[QuantizationConfig]) -> None:
self._validate_optional_quantization_config("global_config", value)
if isinstance(self.quantizer, _TOSAQuantizerV1):
self.quantizer.global_config = value
else:
self.quantizer.set_global(value)
raise NotImplementedError(
"Composable quantizer does not allow setting global_config directly. Please use set_global() instead."
)

@property
def io_config(self):
Expand All@@ -595,12 +559,12 @@ def io_config(self):

@io_config.setter
def io_config(self, value: Optional[QuantizationConfig]) -> None:
self._validate_optional_quantization_config("io_config", value)
if isinstance(self.quantizer, _TOSAQuantizerV1):
self.quantizer.io_config = value
else:
self.quantizer.clear_io_config()
self.quantizer.set_io(value)
raise NotImplementedError(
"Composable quantizer does not allow setting io_config directly. Please use set_io() instead."
)

@property
def module_type_config(self):
Expand All@@ -616,18 +580,12 @@ def module_type_config(self):
def module_type_config(
self, value: Dict[Callable, Optional[QuantizationConfig]]
) -> None:
module_type_config = self._validate_config_dict(
"module_type_config",
value,
callable,
"callable",
)
if isinstance(self.quantizer, _TOSAQuantizerV1):
self.quantizer.module_type_config = module_type_config
self.quantizer.module_type_config = value
else:
self.quantizer.clear_module_type_config()
for module_type, quantization_config in module_type_config.items():
self.quantizer.set_module_type(module_type, quantization_config)
raise NotImplementedError(
"Composable quantizer does not allow setting module_type_config directly. Please use set_module_type() instead."
)

@property
def module_name_config(self):
Expand All@@ -643,18 +601,12 @@ def module_name_config(self):
def module_name_config(
self, value: Dict[str, Optional[QuantizationConfig]]
) -> None:
module_name_config = self._validate_config_dict(
"module_name_config",
value,
lambda key: isinstance(key, str),
"str",
)
if isinstance(self.quantizer, _TOSAQuantizerV1):
self.quantizer.module_name_config = module_name_config
self.quantizer.module_name_config = value
else:
self.quantizer.clear_module_name_config()
for module_name, quantization_config in module_name_config.items():
self.quantizer.set_module_name(module_name, quantization_config)
raise NotImplementedError(
"Composable quantizer does not allow setting module_name_config directly. Please use set_module_name() instead."
)

def set_global(
self, quantization_config: Optional[QuantizationConfig]
Expand DownExpand Up@@ -1179,30 +1131,6 @@ def quantizers(self, value: List[Quantizer]) -> None:
"""Update quantizers without accessing self._quantizers directly."""
self._quantizers = value

def _remove_quantizers_by_node_finder_type(
self, node_finder_types: type[NodeFinder] | tuple[type[NodeFinder], ...]
) -> None:
self._quantizers = [
quantizer
for quantizer in self._quantizers
if not (
isinstance(quantizer, PatternQuantizer)
and isinstance(quantizer.node_finder, node_finder_types)
)
]

def clear_module_type_config(self) -> _TOSAQuantizerV2:
self._remove_quantizers_by_node_finder_type(ModuleTypeNodeFinder)
return self

def clear_module_name_config(self) -> _TOSAQuantizerV2:
self._remove_quantizers_by_node_finder_type(ModuleNameNodeFinder)
return self

def clear_io_config(self) -> _TOSAQuantizerV2:
self._remove_quantizers_by_node_finder_type((InputNodeFinder, OutputNodeFinder))
return self

def annotate(self, model):
reporter = QuantizerReporter(self.quantizers, "FINAL QUANTIZATION REPORT")
model = super().annotate(model)
Expand DownExpand Up@@ -1356,25 +1284,20 @@ class EthosUQuantizer(TOSAQuantizer):
"""Quantizer supported by the Arm Ethos-U backend.

.. warning::
The composable quantizer is now the default implementation. Setting
``use_composable_quantizer=False`` is deprecated and will be removed in
two minor releases.
Setting ``use_composable_quantizer=True`` enables an experimental API
surface that may change without notice.

Args:
compile_spec (EthosUCompileSpec): Backend compile specification for
Ethos-U targets.
use_composable_quantizer (bool): Whether to use the composable
quantizer implementation. Setting this to ``False`` is deprecated
and will be removed in two minor releases. See
[issue #17701](https://github.com/pytorch/executorch/issues/17701)
for details.
use_composable_quantizer (bool): Whether to use the composable quantizer implementation. See https://github.com/pytorch/executorch/issues/17701" for details.

"""

def __init__(
self,
compile_spec: EthosUCompileSpec,
use_composable_quantizer: bool = True,
use_composable_quantizer: bool = False,
) -> None:
super().__init__(compile_spec, use_composable_quantizer)

Expand All@@ -1383,24 +1306,19 @@ class VgfQuantizer(TOSAQuantizer):
"""Quantizer supported by the Arm Vgf backend.

.. warning::
The composable quantizer is now the default implementation. Setting
``use_composable_quantizer=False`` is deprecated and will be removed in
two minor releases.
Setting ``use_composable_quantizer=True`` enables an experimental API
surface that may change without notice.

Args:
compile_spec (VgfCompileSpec): Backend compile specification for Vgf
targets.
use_composable_quantizer (bool): Whether to use the composable
quantizer implementation. Setting this to ``False`` is deprecated
and will be removed in two minor releases. See
[issue #17701](https://github.com/pytorch/executorch/issues/17701)
for details.
use_composable_quantizer (bool): Whether to use the composable quantizer implementation. See https://github.com/pytorch/executorch/issues/17701" for details.

"""

def __init__(
self,
compile_spec: VgfCompileSpec,
use_composable_quantizer: bool = True,
use_composable_quantizer: bool = False,
) -> None:
super().__init__(compile_spec, use_composable_quantizer)
3 changes: 1 addition & 2 deletions backends/arm/quantizer/quantization_config.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -356,7 +356,7 @@ def get_output_act_qspec(

If node is a pooling or upsample operator, returns a shared quantization spec.
If no weight spec is configured, return ``None``.
If node is a `to.dtype` operator, returns a fixed quantization spec if the input is integer and the output is float32.
If node is a `to.dtype` operator, returns a fixed quantization spec if the input is integer and the output is floating-point.

"""

Expand DownExpand Up@@ -391,7 +391,6 @@ def get_output_act_qspec(
isinstance(input_val, torch.Tensor)
and isinstance(output_val, torch.Tensor)
and CastCheck.is_integer_to_float(input_val.dtype, output_val.dtype)
and output_val.dtype == torch.float32
):
return FixedQParamsQuantizationSpec(
dtype=input_val.dtype,
Expand Down
1 change: 0 additions & 1 deletion backends/arm/test/misc/test_quant_custom_meta.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -105,6 +105,5 @@ def test_quantized_to_float_transition_tosa_INT_FP(fp_extension: bool):
)
pipeline.quantizer.set_module_type(torch.nn.Sigmoid, None) # type: ignore
pipeline.quantizer.set_module_type(torch.nn.Conv1d, None) # type: ignore
pipeline.quantizer.set_io(None) # type: ignore

pipeline.run()
Loading
Loading
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length \u003e 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms"); } } catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
Skip to content

Revert "Arm backend: Make composable_quantizer default"#20982

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Merged
rascani merged 1 commit into from
Jul 17, 2026
Merged

Revert "Arm backend: Make composable_quantizer default" #20982

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2 changes: 1 addition & 1 deletion backends/arm/_passes/normalize_while_initial_args_pass.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -62,7 +62,7 @@ def _connect_to_output(
(placeholder,),
)
cloned_placeholders.append(clone)
clone.meta = placeholder.meta.copy()
clone.meta = placeholder.meta
output_node = body_module.graph.output_node()
output_values = output_node.args[0]
if not isinstance(output_values, tuple):
Expand Down
4 changes: 2 additions & 2 deletions backends/arm/public_api_manifests/api_manifest_running.toml
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -62,7 +62,7 @@ signature = "EthosUPartitioner.register_custom_partition_op(self, op: torch._ops

[python.EthosUQuantizer]
kind = "class"
signature = "EthosUQuantizer(compile_spec: 'EthosUCompileSpec', use_composable_quantizer: 'bool' = True) -> 'None'"
signature = "EthosUQuantizer(compile_spec: 'EthosUCompileSpec', use_composable_quantizer: 'bool' = False) -> 'None'"

[python.EthosUQuantizer.annotate]
kind = "function"
Expand DownExpand Up@@ -150,7 +150,7 @@ signature = "VgfPartitioner.register_custom_partition_op(self, op: torch._ops.Op

[python.VgfQuantizer]
kind = "class"
signature = "VgfQuantizer(compile_spec: 'VgfCompileSpec', use_composable_quantizer: 'bool' = True) -> 'None'"
signature = "VgfQuantizer(compile_spec: 'VgfCompileSpec', use_composable_quantizer: 'bool' = False) -> 'None'"

[python.VgfQuantizer.annotate]
kind = "function"
Expand Down
138 changes: 28 additions & 110 deletions backends/arm/quantizer/arm_quantizer.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -493,23 +493,21 @@ class TOSAQuantizer(Quantizer):
"""Manage quantization annotations for TOSA-compatible backends.

.. warning::
The composable quantizer is now the default implementation. Setting
``use_composable_quantizer=False`` is deprecated and will be removed in
two minor releases.
Setting ``use_composable_quantizer=True`` enables an experimental API
surface that may change without notice.

"""

def __init__(
self,
compile_spec_or_tosa_spec,
use_composable_quantizer: bool = True,
use_composable_quantizer: bool = False,
) -> None:
"""Create a TOSA quantizer from a TOSA spec or Arm compile spec.

.. warning::
The composable quantizer is now the default implementation.
Setting ``use_composable_quantizer=False`` is deprecated and will
be removed in two minor releases.
Setting ``use_composable_quantizer=True`` enables an experimental
API surface that may change without notice.

"""
self.use_composable_quantizer = use_composable_quantizer
Expand All@@ -521,45 +519,10 @@ def __init__(
self.quantizer = _TOSAQuantizerV2(compile_spec_or_tosa_spec)
else:
logger.info(
"Using deprecated legacy quantizer implementation in the arm backend. Setting use_composable_quantizer=False will be removed in two minor releases. See https://github.com/pytorch/executorch/issues/17701"
"Using default quantizer in the arm backend. This quantizer is planned to be replaced by the composable quantizer implementation in the future, see https://github.com/pytorch/executorch/issues/17701"
)
self.quantizer = _TOSAQuantizerV1(compile_spec_or_tosa_spec)

@staticmethod
def _validate_optional_quantization_config(
config_name: str, value: object, value_description: str = "value"
) -> None:
if value is not None and not isinstance(value, QuantizationConfig):
raise TypeError(
f"{config_name} {value_description} must be "
"QuantizationConfig or None, "
f"got {type(value).__name__}."
)

@staticmethod
def _validate_config_dict(
config_name: str,
value: object,
is_valid_key: Callable[[object], bool],
key_description: str,
) -> Dict[Any, Optional[QuantizationConfig]]:
if not isinstance(value, dict):
raise TypeError(
f"{config_name} must be a dict, got {type(value).__name__}."
)

for key, quantization_config in value.items():
if not is_valid_key(key):
raise TypeError(
f"{config_name} keys must be {key_description}, "
f"got {type(key).__name__}."
)
TOSAQuantizer._validate_optional_quantization_config(
config_name, quantization_config, "values"
)

return value

@property
def tosa_spec(self):
"""Return the TOSA specification used by the active quantizer."""
Expand All@@ -577,11 +540,12 @@ def global_config(self):

@global_config.setter
def global_config(self, value: Optional[QuantizationConfig]) -> None:
self._validate_optional_quantization_config("global_config", value)
if isinstance(self.quantizer, _TOSAQuantizerV1):
self.quantizer.global_config = value
else:
self.quantizer.set_global(value)
raise NotImplementedError(
"Composable quantizer does not allow setting global_config directly. Please use set_global() instead."
)

@property
def io_config(self):
Expand All@@ -595,12 +559,12 @@ def io_config(self):

@io_config.setter
def io_config(self, value: Optional[QuantizationConfig]) -> None:
self._validate_optional_quantization_config("io_config", value)
if isinstance(self.quantizer, _TOSAQuantizerV1):
self.quantizer.io_config = value
else:
self.quantizer.clear_io_config()
self.quantizer.set_io(value)
raise NotImplementedError(
"Composable quantizer does not allow setting io_config directly. Please use set_io() instead."
)

@property
def module_type_config(self):
Expand All@@ -616,18 +580,12 @@ def module_type_config(self):
def module_type_config(
self, value: Dict[Callable, Optional[QuantizationConfig]]
) -> None:
module_type_config = self._validate_config_dict(
"module_type_config",
value,
callable,
"callable",
)
if isinstance(self.quantizer, _TOSAQuantizerV1):
self.quantizer.module_type_config = module_type_config
self.quantizer.module_type_config = value
else:
self.quantizer.clear_module_type_config()
for module_type, quantization_config in module_type_config.items():
self.quantizer.set_module_type(module_type, quantization_config)
raise NotImplementedError(
"Composable quantizer does not allow setting module_type_config directly. Please use set_module_type() instead."
)

@property
def module_name_config(self):
Expand All@@ -643,18 +601,12 @@ def module_name_config(self):
def module_name_config(
self, value: Dict[str, Optional[QuantizationConfig]]
) -> None:
module_name_config = self._validate_config_dict(
"module_name_config",
value,
lambda key: isinstance(key, str),
"str",
)
if isinstance(self.quantizer, _TOSAQuantizerV1):
self.quantizer.module_name_config = module_name_config
self.quantizer.module_name_config = value
else:
self.quantizer.clear_module_name_config()
for module_name, quantization_config in module_name_config.items():
self.quantizer.set_module_name(module_name, quantization_config)
raise NotImplementedError(
"Composable quantizer does not allow setting module_name_config directly. Please use set_module_name() instead."
)

def set_global(
self, quantization_config: Optional[QuantizationConfig]
Expand DownExpand Up@@ -1179,30 +1131,6 @@ def quantizers(self, value: List[Quantizer]) -> None:
"""Update quantizers without accessing self._quantizers directly."""
self._quantizers = value

def _remove_quantizers_by_node_finder_type(
self, node_finder_types: type[NodeFinder] | tuple[type[NodeFinder], ...]
) -> None:
self._quantizers = [
quantizer
for quantizer in self._quantizers
if not (
isinstance(quantizer, PatternQuantizer)
and isinstance(quantizer.node_finder, node_finder_types)
)
]

def clear_module_type_config(self) -> _TOSAQuantizerV2:
self._remove_quantizers_by_node_finder_type(ModuleTypeNodeFinder)
return self

def clear_module_name_config(self) -> _TOSAQuantizerV2:
self._remove_quantizers_by_node_finder_type(ModuleNameNodeFinder)
return self

def clear_io_config(self) -> _TOSAQuantizerV2:
self._remove_quantizers_by_node_finder_type((InputNodeFinder, OutputNodeFinder))
return self

def annotate(self, model):
reporter = QuantizerReporter(self.quantizers, "FINAL QUANTIZATION REPORT")
model = super().annotate(model)
Expand DownExpand Up@@ -1356,25 +1284,20 @@ class EthosUQuantizer(TOSAQuantizer):
"""Quantizer supported by the Arm Ethos-U backend.

.. warning::
The composable quantizer is now the default implementation. Setting
``use_composable_quantizer=False`` is deprecated and will be removed in
two minor releases.
Setting ``use_composable_quantizer=True`` enables an experimental API
surface that may change without notice.

Args:
compile_spec (EthosUCompileSpec): Backend compile specification for
Ethos-U targets.
use_composable_quantizer (bool): Whether to use the composable
quantizer implementation. Setting this to ``False`` is deprecated
and will be removed in two minor releases. See
[issue #17701](https://github.com/pytorch/executorch/issues/17701)
for details.
use_composable_quantizer (bool): Whether to use the composable quantizer implementation. See https://github.com/pytorch/executorch/issues/17701" for details.

"""

def __init__(
self,
compile_spec: EthosUCompileSpec,
use_composable_quantizer: bool = True,
use_composable_quantizer: bool = False,
) -> None:
super().__init__(compile_spec, use_composable_quantizer)

Expand All@@ -1383,24 +1306,19 @@ class VgfQuantizer(TOSAQuantizer):
"""Quantizer supported by the Arm Vgf backend.

.. warning::
The composable quantizer is now the default implementation. Setting
``use_composable_quantizer=False`` is deprecated and will be removed in
two minor releases.
Setting ``use_composable_quantizer=True`` enables an experimental API
surface that may change without notice.

Args:
compile_spec (VgfCompileSpec): Backend compile specification for Vgf
targets.
use_composable_quantizer (bool): Whether to use the composable
quantizer implementation. Setting this to ``False`` is deprecated
and will be removed in two minor releases. See
[issue #17701](https://github.com/pytorch/executorch/issues/17701)
for details.
use_composable_quantizer (bool): Whether to use the composable quantizer implementation. See https://github.com/pytorch/executorch/issues/17701" for details.

"""

def __init__(
self,
compile_spec: VgfCompileSpec,
use_composable_quantizer: bool = True,
use_composable_quantizer: bool = False,
) -> None:
super().__init__(compile_spec, use_composable_quantizer)
3 changes: 1 addition & 2 deletions backends/arm/quantizer/quantization_config.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -356,7 +356,7 @@ def get_output_act_qspec(

If node is a pooling or upsample operator, returns a shared quantization spec.
If no weight spec is configured, return ``None``.
If node is a `to.dtype` operator, returns a fixed quantization spec if the input is integer and the output is float32.
If node is a `to.dtype` operator, returns a fixed quantization spec if the input is integer and the output is floating-point.

"""

Expand DownExpand Up@@ -391,7 +391,6 @@ def get_output_act_qspec(
isinstance(input_val, torch.Tensor)
and isinstance(output_val, torch.Tensor)
and CastCheck.is_integer_to_float(input_val.dtype, output_val.dtype)
and output_val.dtype == torch.float32
):
return FixedQParamsQuantizationSpec(
dtype=input_val.dtype,
Expand Down
1 change: 0 additions & 1 deletion backends/arm/test/misc/test_quant_custom_meta.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -105,6 +105,5 @@ def test_quantized_to_float_transition_tosa_INT_FP(fp_extension: bool):
)
pipeline.quantizer.set_module_type(torch.nn.Sigmoid, None) # type: ignore
pipeline.quantizer.set_module_type(torch.nn.Conv1d, None) # type: ignore
pipeline.quantizer.set_io(None) # type: ignore

pipeline.run()
Loading
Loading
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links"); } } catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); } })(); (function(){ try { var __m = "youtube.com"; var __re = new RegExp('^' + "youtube\\.com" + '
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Revert "Arm backend: Make composable_quantizer default"#20982

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Merged
rascani merged 1 commit into from
Jul 17, 2026
Merged

Revert "Arm backend: Make composable_quantizer default" #20982

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2 changes: 1 addition & 1 deletion backends/arm/_passes/normalize_while_initial_args_pass.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -62,7 +62,7 @@ def _connect_to_output(
(placeholder,),
)
cloned_placeholders.append(clone)
clone.meta = placeholder.meta.copy()
clone.meta = placeholder.meta
output_node = body_module.graph.output_node()
output_values = output_node.args[0]
if not isinstance(output_values, tuple):
Expand Down
4 changes: 2 additions & 2 deletions backends/arm/public_api_manifests/api_manifest_running.toml
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -62,7 +62,7 @@ signature = "EthosUPartitioner.register_custom_partition_op(self, op: torch._ops

[python.EthosUQuantizer]
kind = "class"
signature = "EthosUQuantizer(compile_spec: 'EthosUCompileSpec', use_composable_quantizer: 'bool' = True) -> 'None'"
signature = "EthosUQuantizer(compile_spec: 'EthosUCompileSpec', use_composable_quantizer: 'bool' = False) -> 'None'"

[python.EthosUQuantizer.annotate]
kind = "function"
Expand DownExpand Up@@ -150,7 +150,7 @@ signature = "VgfPartitioner.register_custom_partition_op(self, op: torch._ops.Op

[python.VgfQuantizer]
kind = "class"
signature = "VgfQuantizer(compile_spec: 'VgfCompileSpec', use_composable_quantizer: 'bool' = True) -> 'None'"
signature = "VgfQuantizer(compile_spec: 'VgfCompileSpec', use_composable_quantizer: 'bool' = False) -> 'None'"

[python.VgfQuantizer.annotate]
kind = "function"
Expand Down
138 changes: 28 additions & 110 deletions backends/arm/quantizer/arm_quantizer.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -493,23 +493,21 @@ class TOSAQuantizer(Quantizer):
"""Manage quantization annotations for TOSA-compatible backends.

.. warning::
The composable quantizer is now the default implementation. Setting
``use_composable_quantizer=False`` is deprecated and will be removed in
two minor releases.
Setting ``use_composable_quantizer=True`` enables an experimental API
surface that may change without notice.

"""

def __init__(
self,
compile_spec_or_tosa_spec,
use_composable_quantizer: bool = True,
use_composable_quantizer: bool = False,
) -> None:
"""Create a TOSA quantizer from a TOSA spec or Arm compile spec.

.. warning::
The composable quantizer is now the default implementation.
Setting ``use_composable_quantizer=False`` is deprecated and will
be removed in two minor releases.
Setting ``use_composable_quantizer=True`` enables an experimental
API surface that may change without notice.

"""
self.use_composable_quantizer = use_composable_quantizer
Expand All@@ -521,45 +519,10 @@ def __init__(
self.quantizer = _TOSAQuantizerV2(compile_spec_or_tosa_spec)
else:
logger.info(
"Using deprecated legacy quantizer implementation in the arm backend. Setting use_composable_quantizer=False will be removed in two minor releases. See https://github.com/pytorch/executorch/issues/17701"
"Using default quantizer in the arm backend. This quantizer is planned to be replaced by the composable quantizer implementation in the future, see https://github.com/pytorch/executorch/issues/17701"
)
self.quantizer = _TOSAQuantizerV1(compile_spec_or_tosa_spec)

@staticmethod
def _validate_optional_quantization_config(
config_name: str, value: object, value_description: str = "value"
) -> None:
if value is not None and not isinstance(value, QuantizationConfig):
raise TypeError(
f"{config_name} {value_description} must be "
"QuantizationConfig or None, "
f"got {type(value).__name__}."
)

@staticmethod
def _validate_config_dict(
config_name: str,
value: object,
is_valid_key: Callable[[object], bool],
key_description: str,
) -> Dict[Any, Optional[QuantizationConfig]]:
if not isinstance(value, dict):
raise TypeError(
f"{config_name} must be a dict, got {type(value).__name__}."
)

for key, quantization_config in value.items():
if not is_valid_key(key):
raise TypeError(
f"{config_name} keys must be {key_description}, "
f"got {type(key).__name__}."
)
TOSAQuantizer._validate_optional_quantization_config(
config_name, quantization_config, "values"
)

return value

@property
def tosa_spec(self):
"""Return the TOSA specification used by the active quantizer."""
Expand All@@ -577,11 +540,12 @@ def global_config(self):

@global_config.setter
def global_config(self, value: Optional[QuantizationConfig]) -> None:
self._validate_optional_quantization_config("global_config", value)
if isinstance(self.quantizer, _TOSAQuantizerV1):
self.quantizer.global_config = value
else:
self.quantizer.set_global(value)
raise NotImplementedError(
"Composable quantizer does not allow setting global_config directly. Please use set_global() instead."
)

@property
def io_config(self):
Expand All@@ -595,12 +559,12 @@ def io_config(self):

@io_config.setter
def io_config(self, value: Optional[QuantizationConfig]) -> None:
self._validate_optional_quantization_config("io_config", value)
if isinstance(self.quantizer, _TOSAQuantizerV1):
self.quantizer.io_config = value
else:
self.quantizer.clear_io_config()
self.quantizer.set_io(value)
raise NotImplementedError(
"Composable quantizer does not allow setting io_config directly. Please use set_io() instead."
)

@property
def module_type_config(self):
Expand All@@ -616,18 +580,12 @@ def module_type_config(self):
def module_type_config(
self, value: Dict[Callable, Optional[QuantizationConfig]]
) -> None:
module_type_config = self._validate_config_dict(
"module_type_config",
value,
callable,
"callable",
)
if isinstance(self.quantizer, _TOSAQuantizerV1):
self.quantizer.module_type_config = module_type_config
self.quantizer.module_type_config = value
else:
self.quantizer.clear_module_type_config()
for module_type, quantization_config in module_type_config.items():
self.quantizer.set_module_type(module_type, quantization_config)
raise NotImplementedError(
"Composable quantizer does not allow setting module_type_config directly. Please use set_module_type() instead."
)

@property
def module_name_config(self):
Expand All@@ -643,18 +601,12 @@ def module_name_config(self):
def module_name_config(
self, value: Dict[str, Optional[QuantizationConfig]]
) -> None:
module_name_config = self._validate_config_dict(
"module_name_config",
value,
lambda key: isinstance(key, str),
"str",
)
if isinstance(self.quantizer, _TOSAQuantizerV1):
self.quantizer.module_name_config = module_name_config
self.quantizer.module_name_config = value
else:
self.quantizer.clear_module_name_config()
for module_name, quantization_config in module_name_config.items():
self.quantizer.set_module_name(module_name, quantization_config)
raise NotImplementedError(
"Composable quantizer does not allow setting module_name_config directly. Please use set_module_name() instead."
)

def set_global(
self, quantization_config: Optional[QuantizationConfig]
Expand DownExpand Up@@ -1179,30 +1131,6 @@ def quantizers(self, value: List[Quantizer]) -> None:
"""Update quantizers without accessing self._quantizers directly."""
self._quantizers = value

def _remove_quantizers_by_node_finder_type(
self, node_finder_types: type[NodeFinder] | tuple[type[NodeFinder], ...]
) -> None:
self._quantizers = [
quantizer
for quantizer in self._quantizers
if not (
isinstance(quantizer, PatternQuantizer)
and isinstance(quantizer.node_finder, node_finder_types)
)
]

def clear_module_type_config(self) -> _TOSAQuantizerV2:
self._remove_quantizers_by_node_finder_type(ModuleTypeNodeFinder)
return self

def clear_module_name_config(self) -> _TOSAQuantizerV2:
self._remove_quantizers_by_node_finder_type(ModuleNameNodeFinder)
return self

def clear_io_config(self) -> _TOSAQuantizerV2:
self._remove_quantizers_by_node_finder_type((InputNodeFinder, OutputNodeFinder))
return self

def annotate(self, model):
reporter = QuantizerReporter(self.quantizers, "FINAL QUANTIZATION REPORT")
model = super().annotate(model)
Expand DownExpand Up@@ -1356,25 +1284,20 @@ class EthosUQuantizer(TOSAQuantizer):
"""Quantizer supported by the Arm Ethos-U backend.

.. warning::
The composable quantizer is now the default implementation. Setting
``use_composable_quantizer=False`` is deprecated and will be removed in
two minor releases.
Setting ``use_composable_quantizer=True`` enables an experimental API
surface that may change without notice.

Args:
compile_spec (EthosUCompileSpec): Backend compile specification for
Ethos-U targets.
use_composable_quantizer (bool): Whether to use the composable
quantizer implementation. Setting this to ``False`` is deprecated
and will be removed in two minor releases. See
[issue #17701](https://github.com/pytorch/executorch/issues/17701)
for details.
use_composable_quantizer (bool): Whether to use the composable quantizer implementation. See https://github.com/pytorch/executorch/issues/17701" for details.

"""

def __init__(
self,
compile_spec: EthosUCompileSpec,
use_composable_quantizer: bool = True,
use_composable_quantizer: bool = False,
) -> None:
super().__init__(compile_spec, use_composable_quantizer)

Expand All@@ -1383,24 +1306,19 @@ class VgfQuantizer(TOSAQuantizer):
"""Quantizer supported by the Arm Vgf backend.

.. warning::
The composable quantizer is now the default implementation. Setting
``use_composable_quantizer=False`` is deprecated and will be removed in
two minor releases.
Setting ``use_composable_quantizer=True`` enables an experimental API
surface that may change without notice.

Args:
compile_spec (VgfCompileSpec): Backend compile specification for Vgf
targets.
use_composable_quantizer (bool): Whether to use the composable
quantizer implementation. Setting this to ``False`` is deprecated
and will be removed in two minor releases. See
[issue #17701](https://github.com/pytorch/executorch/issues/17701)
for details.
use_composable_quantizer (bool): Whether to use the composable quantizer implementation. See https://github.com/pytorch/executorch/issues/17701" for details.

"""

def __init__(
self,
compile_spec: VgfCompileSpec,
use_composable_quantizer: bool = True,
use_composable_quantizer: bool = False,
) -> None:
super().__init__(compile_spec, use_composable_quantizer)
3 changes: 1 addition & 2 deletions backends/arm/quantizer/quantization_config.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -356,7 +356,7 @@ def get_output_act_qspec(

If node is a pooling or upsample operator, returns a shared quantization spec.
If no weight spec is configured, return ``None``.
If node is a `to.dtype` operator, returns a fixed quantization spec if the input is integer and the output is float32.
If node is a `to.dtype` operator, returns a fixed quantization spec if the input is integer and the output is floating-point.

"""

Expand DownExpand Up@@ -391,7 +391,6 @@ def get_output_act_qspec(
isinstance(input_val, torch.Tensor)
and isinstance(output_val, torch.Tensor)
and CastCheck.is_integer_to_float(input_val.dtype, output_val.dtype)
and output_val.dtype == torch.float32
):
return FixedQParamsQuantizationSpec(
dtype=input_val.dtype,
Expand Down
1 change: 0 additions & 1 deletion backends/arm/test/misc/test_quant_custom_meta.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -105,6 +105,5 @@ def test_quantized_to_float_transition_tosa_INT_FP(fp_extension: bool):
)
pipeline.quantizer.set_module_type(torch.nn.Sigmoid, None) # type: ignore
pipeline.quantizer.set_module_type(torch.nn.Conv1d, None) # type: ignore
pipeline.quantizer.set_io(None) # type: ignore

pipeline.run()
Loading
Loading
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default"); } } catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
Skip to content

Revert "Arm backend: Make composable_quantizer default"#20982

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Merged
rascani merged 1 commit into from
Jul 17, 2026
Merged

Revert "Arm backend: Make composable_quantizer default" #20982

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2 changes: 1 addition & 1 deletion backends/arm/_passes/normalize_while_initial_args_pass.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -62,7 +62,7 @@ def _connect_to_output(
(placeholder,),
)
cloned_placeholders.append(clone)
clone.meta = placeholder.meta.copy()
clone.meta = placeholder.meta
output_node = body_module.graph.output_node()
output_values = output_node.args[0]
if not isinstance(output_values, tuple):
Expand Down
4 changes: 2 additions & 2 deletions backends/arm/public_api_manifests/api_manifest_running.toml
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -62,7 +62,7 @@ signature = "EthosUPartitioner.register_custom_partition_op(self, op: torch._ops

[python.EthosUQuantizer]
kind = "class"
signature = "EthosUQuantizer(compile_spec: 'EthosUCompileSpec', use_composable_quantizer: 'bool' = True) -> 'None'"
signature = "EthosUQuantizer(compile_spec: 'EthosUCompileSpec', use_composable_quantizer: 'bool' = False) -> 'None'"

[python.EthosUQuantizer.annotate]
kind = "function"
Expand DownExpand Up@@ -150,7 +150,7 @@ signature = "VgfPartitioner.register_custom_partition_op(self, op: torch._ops.Op

[python.VgfQuantizer]
kind = "class"
signature = "VgfQuantizer(compile_spec: 'VgfCompileSpec', use_composable_quantizer: 'bool' = True) -> 'None'"
signature = "VgfQuantizer(compile_spec: 'VgfCompileSpec', use_composable_quantizer: 'bool' = False) -> 'None'"

[python.VgfQuantizer.annotate]
kind = "function"
Expand Down
138 changes: 28 additions & 110 deletions backends/arm/quantizer/arm_quantizer.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -493,23 +493,21 @@ class TOSAQuantizer(Quantizer):
"""Manage quantization annotations for TOSA-compatible backends.

.. warning::
The composable quantizer is now the default implementation. Setting
``use_composable_quantizer=False`` is deprecated and will be removed in
two minor releases.
Setting ``use_composable_quantizer=True`` enables an experimental API
surface that may change without notice.

"""

def __init__(
self,
compile_spec_or_tosa_spec,
use_composable_quantizer: bool = True,
use_composable_quantizer: bool = False,
) -> None:
"""Create a TOSA quantizer from a TOSA spec or Arm compile spec.

.. warning::
The composable quantizer is now the default implementation.
Setting ``use_composable_quantizer=False`` is deprecated and will
be removed in two minor releases.
Setting ``use_composable_quantizer=True`` enables an experimental
API surface that may change without notice.

"""
self.use_composable_quantizer = use_composable_quantizer
Expand All@@ -521,45 +519,10 @@ def __init__(
self.quantizer = _TOSAQuantizerV2(compile_spec_or_tosa_spec)
else:
logger.info(
"Using deprecated legacy quantizer implementation in the arm backend. Setting use_composable_quantizer=False will be removed in two minor releases. See https://github.com/pytorch/executorch/issues/17701"
"Using default quantizer in the arm backend. This quantizer is planned to be replaced by the composable quantizer implementation in the future, see https://github.com/pytorch/executorch/issues/17701"
)
self.quantizer = _TOSAQuantizerV1(compile_spec_or_tosa_spec)

@staticmethod
def _validate_optional_quantization_config(
config_name: str, value: object, value_description: str = "value"
) -> None:
if value is not None and not isinstance(value, QuantizationConfig):
raise TypeError(
f"{config_name} {value_description} must be "
"QuantizationConfig or None, "
f"got {type(value).__name__}."
)

@staticmethod
def _validate_config_dict(
config_name: str,
value: object,
is_valid_key: Callable[[object], bool],
key_description: str,
) -> Dict[Any, Optional[QuantizationConfig]]:
if not isinstance(value, dict):
raise TypeError(
f"{config_name} must be a dict, got {type(value).__name__}."
)

for key, quantization_config in value.items():
if not is_valid_key(key):
raise TypeError(
f"{config_name} keys must be {key_description}, "
f"got {type(key).__name__}."
)
TOSAQuantizer._validate_optional_quantization_config(
config_name, quantization_config, "values"
)

return value

@property
def tosa_spec(self):
"""Return the TOSA specification used by the active quantizer."""
Expand All@@ -577,11 +540,12 @@ def global_config(self):

@global_config.setter
def global_config(self, value: Optional[QuantizationConfig]) -> None:
self._validate_optional_quantization_config("global_config", value)
if isinstance(self.quantizer, _TOSAQuantizerV1):
self.quantizer.global_config = value
else:
self.quantizer.set_global(value)
raise NotImplementedError(
"Composable quantizer does not allow setting global_config directly. Please use set_global() instead."
)

@property
def io_config(self):
Expand All@@ -595,12 +559,12 @@ def io_config(self):

@io_config.setter
def io_config(self, value: Optional[QuantizationConfig]) -> None:
self._validate_optional_quantization_config("io_config", value)
if isinstance(self.quantizer, _TOSAQuantizerV1):
self.quantizer.io_config = value
else:
self.quantizer.clear_io_config()
self.quantizer.set_io(value)
raise NotImplementedError(
"Composable quantizer does not allow setting io_config directly. Please use set_io() instead."
)

@property
def module_type_config(self):
Expand All@@ -616,18 +580,12 @@ def module_type_config(self):
def module_type_config(
self, value: Dict[Callable, Optional[QuantizationConfig]]
) -> None:
module_type_config = self._validate_config_dict(
"module_type_config",
value,
callable,
"callable",
)
if isinstance(self.quantizer, _TOSAQuantizerV1):
self.quantizer.module_type_config = module_type_config
self.quantizer.module_type_config = value
else:
self.quantizer.clear_module_type_config()
for module_type, quantization_config in module_type_config.items():
self.quantizer.set_module_type(module_type, quantization_config)
raise NotImplementedError(
"Composable quantizer does not allow setting module_type_config directly. Please use set_module_type() instead."
)

@property
def module_name_config(self):
Expand All@@ -643,18 +601,12 @@ def module_name_config(self):
def module_name_config(
self, value: Dict[str, Optional[QuantizationConfig]]
) -> None:
module_name_config = self._validate_config_dict(
"module_name_config",
value,
lambda key: isinstance(key, str),
"str",
)
if isinstance(self.quantizer, _TOSAQuantizerV1):
self.quantizer.module_name_config = module_name_config
self.quantizer.module_name_config = value
else:
self.quantizer.clear_module_name_config()
for module_name, quantization_config in module_name_config.items():
self.quantizer.set_module_name(module_name, quantization_config)
raise NotImplementedError(
"Composable quantizer does not allow setting module_name_config directly. Please use set_module_name() instead."
)

def set_global(
self, quantization_config: Optional[QuantizationConfig]
Expand DownExpand Up@@ -1179,30 +1131,6 @@ def quantizers(self, value: List[Quantizer]) -> None:
"""Update quantizers without accessing self._quantizers directly."""
self._quantizers = value

def _remove_quantizers_by_node_finder_type(
self, node_finder_types: type[NodeFinder] | tuple[type[NodeFinder], ...]
) -> None:
self._quantizers = [
quantizer
for quantizer in self._quantizers
if not (
isinstance(quantizer, PatternQuantizer)
and isinstance(quantizer.node_finder, node_finder_types)
)
]

def clear_module_type_config(self) -> _TOSAQuantizerV2:
self._remove_quantizers_by_node_finder_type(ModuleTypeNodeFinder)
return self

def clear_module_name_config(self) -> _TOSAQuantizerV2:
self._remove_quantizers_by_node_finder_type(ModuleNameNodeFinder)
return self

def clear_io_config(self) -> _TOSAQuantizerV2:
self._remove_quantizers_by_node_finder_type((InputNodeFinder, OutputNodeFinder))
return self

def annotate(self, model):
reporter = QuantizerReporter(self.quantizers, "FINAL QUANTIZATION REPORT")
model = super().annotate(model)
Expand DownExpand Up@@ -1356,25 +1284,20 @@ class EthosUQuantizer(TOSAQuantizer):
"""Quantizer supported by the Arm Ethos-U backend.

.. warning::
The composable quantizer is now the default implementation. Setting
``use_composable_quantizer=False`` is deprecated and will be removed in
two minor releases.
Setting ``use_composable_quantizer=True`` enables an experimental API
surface that may change without notice.

Args:
compile_spec (EthosUCompileSpec): Backend compile specification for
Ethos-U targets.
use_composable_quantizer (bool): Whether to use the composable
quantizer implementation. Setting this to ``False`` is deprecated
and will be removed in two minor releases. See
[issue #17701](https://github.com/pytorch/executorch/issues/17701)
for details.
use_composable_quantizer (bool): Whether to use the composable quantizer implementation. See https://github.com/pytorch/executorch/issues/17701" for details.

"""

def __init__(
self,
compile_spec: EthosUCompileSpec,
use_composable_quantizer: bool = True,
use_composable_quantizer: bool = False,
) -> None:
super().__init__(compile_spec, use_composable_quantizer)

Expand All@@ -1383,24 +1306,19 @@ class VgfQuantizer(TOSAQuantizer):
"""Quantizer supported by the Arm Vgf backend.

.. warning::
The composable quantizer is now the default implementation. Setting
``use_composable_quantizer=False`` is deprecated and will be removed in
two minor releases.
Setting ``use_composable_quantizer=True`` enables an experimental API
surface that may change without notice.

Args:
compile_spec (VgfCompileSpec): Backend compile specification for Vgf
targets.
use_composable_quantizer (bool): Whether to use the composable
quantizer implementation. Setting this to ``False`` is deprecated
and will be removed in two minor releases. See
[issue #17701](https://github.com/pytorch/executorch/issues/17701)
for details.
use_composable_quantizer (bool): Whether to use the composable quantizer implementation. See https://github.com/pytorch/executorch/issues/17701" for details.

"""

def __init__(
self,
compile_spec: VgfCompileSpec,
use_composable_quantizer: bool = True,
use_composable_quantizer: bool = False,
) -> None:
super().__init__(compile_spec, use_composable_quantizer)
3 changes: 1 addition & 2 deletions backends/arm/quantizer/quantization_config.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -356,7 +356,7 @@ def get_output_act_qspec(

If node is a pooling or upsample operator, returns a shared quantization spec.
If no weight spec is configured, return ``None``.
If node is a `to.dtype` operator, returns a fixed quantization spec if the input is integer and the output is float32.
If node is a `to.dtype` operator, returns a fixed quantization spec if the input is integer and the output is floating-point.

"""

Expand DownExpand Up@@ -391,7 +391,6 @@ def get_output_act_qspec(
isinstance(input_val, torch.Tensor)
and isinstance(output_val, torch.Tensor)
and CastCheck.is_integer_to_float(input_val.dtype, output_val.dtype)
and output_val.dtype == torch.float32
):
return FixedQParamsQuantizationSpec(
dtype=input_val.dtype,
Expand Down
1 change: 0 additions & 1 deletion backends/arm/test/misc/test_quant_custom_meta.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -105,6 +105,5 @@ def test_quantized_to_float_transition_tosa_INT_FP(fp_extension: bool):
)
pipeline.quantizer.set_module_type(torch.nn.Sigmoid, None) # type: ignore
pipeline.quantizer.set_module_type(torch.nn.Conv1d, None) # type: ignore
pipeline.quantizer.set_io(None) # type: ignore

pipeline.run()
Loading
Loading
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Revert "Arm backend: Make composable_quantizer default"#20982

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Merged
rascani merged 1 commit into from
Jul 17, 2026
Merged

Revert "Arm backend: Make composable_quantizer default" #20982

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2 changes: 1 addition & 1 deletion backends/arm/_passes/normalize_while_initial_args_pass.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -62,7 +62,7 @@ def _connect_to_output(
(placeholder,),
)
cloned_placeholders.append(clone)
clone.meta = placeholder.meta.copy()
clone.meta = placeholder.meta
output_node = body_module.graph.output_node()
output_values = output_node.args[0]
if not isinstance(output_values, tuple):
Expand Down
4 changes: 2 additions & 2 deletions backends/arm/public_api_manifests/api_manifest_running.toml
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -62,7 +62,7 @@ signature = "EthosUPartitioner.register_custom_partition_op(self, op: torch._ops

[python.EthosUQuantizer]
kind = "class"
signature = "EthosUQuantizer(compile_spec: 'EthosUCompileSpec', use_composable_quantizer: 'bool' = True) -> 'None'"
signature = "EthosUQuantizer(compile_spec: 'EthosUCompileSpec', use_composable_quantizer: 'bool' = False) -> 'None'"

[python.EthosUQuantizer.annotate]
kind = "function"
Expand DownExpand Up@@ -150,7 +150,7 @@ signature = "VgfPartitioner.register_custom_partition_op(self, op: torch._ops.Op

[python.VgfQuantizer]
kind = "class"
signature = "VgfQuantizer(compile_spec: 'VgfCompileSpec', use_composable_quantizer: 'bool' = True) -> 'None'"
signature = "VgfQuantizer(compile_spec: 'VgfCompileSpec', use_composable_quantizer: 'bool' = False) -> 'None'"

[python.VgfQuantizer.annotate]
kind = "function"
Expand Down
138 changes: 28 additions & 110 deletions backends/arm/quantizer/arm_quantizer.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -493,23 +493,21 @@ class TOSAQuantizer(Quantizer):
"""Manage quantization annotations for TOSA-compatible backends.

.. warning::
The composable quantizer is now the default implementation. Setting
``use_composable_quantizer=False`` is deprecated and will be removed in
two minor releases.
Setting ``use_composable_quantizer=True`` enables an experimental API
surface that may change without notice.

"""

def __init__(
self,
compile_spec_or_tosa_spec,
use_composable_quantizer: bool = True,
use_composable_quantizer: bool = False,
) -> None:
"""Create a TOSA quantizer from a TOSA spec or Arm compile spec.

.. warning::
The composable quantizer is now the default implementation.
Setting ``use_composable_quantizer=False`` is deprecated and will
be removed in two minor releases.
Setting ``use_composable_quantizer=True`` enables an experimental
API surface that may change without notice.

"""
self.use_composable_quantizer = use_composable_quantizer
Expand All@@ -521,45 +519,10 @@ def __init__(
self.quantizer = _TOSAQuantizerV2(compile_spec_or_tosa_spec)
else:
logger.info(
"Using deprecated legacy quantizer implementation in the arm backend. Setting use_composable_quantizer=False will be removed in two minor releases. See https://github.com/pytorch/executorch/issues/17701"
"Using default quantizer in the arm backend. This quantizer is planned to be replaced by the composable quantizer implementation in the future, see https://github.com/pytorch/executorch/issues/17701"
)
self.quantizer = _TOSAQuantizerV1(compile_spec_or_tosa_spec)

@staticmethod
def _validate_optional_quantization_config(
config_name: str, value: object, value_description: str = "value"
) -> None:
if value is not None and not isinstance(value, QuantizationConfig):
raise TypeError(
f"{config_name} {value_description} must be "
"QuantizationConfig or None, "
f"got {type(value).__name__}."
)

@staticmethod
def _validate_config_dict(
config_name: str,
value: object,
is_valid_key: Callable[[object], bool],
key_description: str,
) -> Dict[Any, Optional[QuantizationConfig]]:
if not isinstance(value, dict):
raise TypeError(
f"{config_name} must be a dict, got {type(value).__name__}."
)

for key, quantization_config in value.items():
if not is_valid_key(key):
raise TypeError(
f"{config_name} keys must be {key_description}, "
f"got {type(key).__name__}."
)
TOSAQuantizer._validate_optional_quantization_config(
config_name, quantization_config, "values"
)

return value

@property
def tosa_spec(self):
"""Return the TOSA specification used by the active quantizer."""
Expand All@@ -577,11 +540,12 @@ def global_config(self):

@global_config.setter
def global_config(self, value: Optional[QuantizationConfig]) -> None:
self._validate_optional_quantization_config("global_config", value)
if isinstance(self.quantizer, _TOSAQuantizerV1):
self.quantizer.global_config = value
else:
self.quantizer.set_global(value)
raise NotImplementedError(
"Composable quantizer does not allow setting global_config directly. Please use set_global() instead."
)

@property
def io_config(self):
Expand All@@ -595,12 +559,12 @@ def io_config(self):

@io_config.setter
def io_config(self, value: Optional[QuantizationConfig]) -> None:
self._validate_optional_quantization_config("io_config", value)
if isinstance(self.quantizer, _TOSAQuantizerV1):
self.quantizer.io_config = value
else:
self.quantizer.clear_io_config()
self.quantizer.set_io(value)
raise NotImplementedError(
"Composable quantizer does not allow setting io_config directly. Please use set_io() instead."
)

@property
def module_type_config(self):
Expand All@@ -616,18 +580,12 @@ def module_type_config(self):
def module_type_config(
self, value: Dict[Callable, Optional[QuantizationConfig]]
) -> None:
module_type_config = self._validate_config_dict(
"module_type_config",
value,
callable,
"callable",
)
if isinstance(self.quantizer, _TOSAQuantizerV1):
self.quantizer.module_type_config = module_type_config
self.quantizer.module_type_config = value
else:
self.quantizer.clear_module_type_config()
for module_type, quantization_config in module_type_config.items():
self.quantizer.set_module_type(module_type, quantization_config)
raise NotImplementedError(
"Composable quantizer does not allow setting module_type_config directly. Please use set_module_type() instead."
)

@property
def module_name_config(self):
Expand All@@ -643,18 +601,12 @@ def module_name_config(self):
def module_name_config(
self, value: Dict[str, Optional[QuantizationConfig]]
) -> None:
module_name_config = self._validate_config_dict(
"module_name_config",
value,
lambda key: isinstance(key, str),
"str",
)
if isinstance(self.quantizer, _TOSAQuantizerV1):
self.quantizer.module_name_config = module_name_config
self.quantizer.module_name_config = value
else:
self.quantizer.clear_module_name_config()
for module_name, quantization_config in module_name_config.items():
self.quantizer.set_module_name(module_name, quantization_config)
raise NotImplementedError(
"Composable quantizer does not allow setting module_name_config directly. Please use set_module_name() instead."
)

def set_global(
self, quantization_config: Optional[QuantizationConfig]
Expand DownExpand Up@@ -1179,30 +1131,6 @@ def quantizers(self, value: List[Quantizer]) -> None:
"""Update quantizers without accessing self._quantizers directly."""
self._quantizers = value

def _remove_quantizers_by_node_finder_type(
self, node_finder_types: type[NodeFinder] | tuple[type[NodeFinder], ...]
) -> None:
self._quantizers = [
quantizer
for quantizer in self._quantizers
if not (
isinstance(quantizer, PatternQuantizer)
and isinstance(quantizer.node_finder, node_finder_types)
)
]

def clear_module_type_config(self) -> _TOSAQuantizerV2:
self._remove_quantizers_by_node_finder_type(ModuleTypeNodeFinder)
return self

def clear_module_name_config(self) -> _TOSAQuantizerV2:
self._remove_quantizers_by_node_finder_type(ModuleNameNodeFinder)
return self

def clear_io_config(self) -> _TOSAQuantizerV2:
self._remove_quantizers_by_node_finder_type((InputNodeFinder, OutputNodeFinder))
return self

def annotate(self, model):
reporter = QuantizerReporter(self.quantizers, "FINAL QUANTIZATION REPORT")
model = super().annotate(model)
Expand DownExpand Up@@ -1356,25 +1284,20 @@ class EthosUQuantizer(TOSAQuantizer):
"""Quantizer supported by the Arm Ethos-U backend.

.. warning::
The composable quantizer is now the default implementation. Setting
``use_composable_quantizer=False`` is deprecated and will be removed in
two minor releases.
Setting ``use_composable_quantizer=True`` enables an experimental API
surface that may change without notice.

Args:
compile_spec (EthosUCompileSpec): Backend compile specification for
Ethos-U targets.
use_composable_quantizer (bool): Whether to use the composable
quantizer implementation. Setting this to ``False`` is deprecated
and will be removed in two minor releases. See
[issue #17701](https://github.com/pytorch/executorch/issues/17701)
for details.
use_composable_quantizer (bool): Whether to use the composable quantizer implementation. See https://github.com/pytorch/executorch/issues/17701" for details.

"""

def __init__(
self,
compile_spec: EthosUCompileSpec,
use_composable_quantizer: bool = True,
use_composable_quantizer: bool = False,
) -> None:
super().__init__(compile_spec, use_composable_quantizer)

Expand All@@ -1383,24 +1306,19 @@ class VgfQuantizer(TOSAQuantizer):
"""Quantizer supported by the Arm Vgf backend.

.. warning::
The composable quantizer is now the default implementation. Setting
``use_composable_quantizer=False`` is deprecated and will be removed in
two minor releases.
Setting ``use_composable_quantizer=True`` enables an experimental API
surface that may change without notice.

Args:
compile_spec (VgfCompileSpec): Backend compile specification for Vgf
targets.
use_composable_quantizer (bool): Whether to use the composable
quantizer implementation. Setting this to ``False`` is deprecated
and will be removed in two minor releases. See
[issue #17701](https://github.com/pytorch/executorch/issues/17701)
for details.
use_composable_quantizer (bool): Whether to use the composable quantizer implementation. See https://github.com/pytorch/executorch/issues/17701" for details.

"""

def __init__(
self,
compile_spec: VgfCompileSpec,
use_composable_quantizer: bool = True,
use_composable_quantizer: bool = False,
) -> None:
super().__init__(compile_spec, use_composable_quantizer)
3 changes: 1 addition & 2 deletions backends/arm/quantizer/quantization_config.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -356,7 +356,7 @@ def get_output_act_qspec(

If node is a pooling or upsample operator, returns a shared quantization spec.
If no weight spec is configured, return ``None``.
If node is a `to.dtype` operator, returns a fixed quantization spec if the input is integer and the output is float32.
If node is a `to.dtype` operator, returns a fixed quantization spec if the input is integer and the output is floating-point.

"""

Expand DownExpand Up@@ -391,7 +391,6 @@ def get_output_act_qspec(
isinstance(input_val, torch.Tensor)
and isinstance(output_val, torch.Tensor)
and CastCheck.is_integer_to_float(input_val.dtype, output_val.dtype)
and output_val.dtype == torch.float32
):
return FixedQParamsQuantizationSpec(
dtype=input_val.dtype,
Expand Down
1 change: 0 additions & 1 deletion backends/arm/test/misc/test_quant_custom_meta.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -105,6 +105,5 @@ def test_quantized_to_float_transition_tosa_INT_FP(fp_extension: bool):
)
pipeline.quantizer.set_module_type(torch.nn.Sigmoid, None) # type: ignore
pipeline.quantizer.set_module_type(torch.nn.Conv1d, None) # type: ignore
pipeline.quantizer.set_io(None) # type: ignore

pipeline.run()
Loading
Loading
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode"); } } catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); } })(); })();
Skip to content

Revert "Arm backend: Make composable_quantizer default"#20982

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Merged
rascani merged 1 commit into from
Jul 17, 2026
Merged

Revert "Arm backend: Make composable_quantizer default" #20982

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2 changes: 1 addition & 1 deletion backends/arm/_passes/normalize_while_initial_args_pass.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -62,7 +62,7 @@ def _connect_to_output(
(placeholder,),
)
cloned_placeholders.append(clone)
clone.meta = placeholder.meta.copy()
clone.meta = placeholder.meta
output_node = body_module.graph.output_node()
output_values = output_node.args[0]
if not isinstance(output_values, tuple):
Expand Down
4 changes: 2 additions & 2 deletions backends/arm/public_api_manifests/api_manifest_running.toml
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -62,7 +62,7 @@ signature = "EthosUPartitioner.register_custom_partition_op(self, op: torch._ops

[python.EthosUQuantizer]
kind = "class"
signature = "EthosUQuantizer(compile_spec: 'EthosUCompileSpec', use_composable_quantizer: 'bool' = True) -> 'None'"
signature = "EthosUQuantizer(compile_spec: 'EthosUCompileSpec', use_composable_quantizer: 'bool' = False) -> 'None'"

[python.EthosUQuantizer.annotate]
kind = "function"
Expand DownExpand Up@@ -150,7 +150,7 @@ signature = "VgfPartitioner.register_custom_partition_op(self, op: torch._ops.Op

[python.VgfQuantizer]
kind = "class"
signature = "VgfQuantizer(compile_spec: 'VgfCompileSpec', use_composable_quantizer: 'bool' = True) -> 'None'"
signature = "VgfQuantizer(compile_spec: 'VgfCompileSpec', use_composable_quantizer: 'bool' = False) -> 'None'"

[python.VgfQuantizer.annotate]
kind = "function"
Expand Down
138 changes: 28 additions & 110 deletions backends/arm/quantizer/arm_quantizer.py
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Expand Up@@ -493,23 +493,21 @@ class TOSAQuantizer(Quantizer):
"""Manage quantization annotations for TOSA-compatible backends.

.. warning::
The composable quantizer is now the default implementation. Setting
``use_composable_quantizer=False`` is deprecated and will be removed in
two minor releases.
Setting ``use_composable_quantizer=True`` enables an experimental API
surface that may change without notice.

"""

def __init__(
self,
compile_spec_or_tosa_spec,
use_composable_quantizer: bool = True,
use_composable_quantizer: bool = False,
) -> None:
"""Create a TOSA quantizer from a TOSA spec or Arm compile spec.

.. warning::
The composable quantizer is now the default implementation.
Setting ``use_composable_quantizer=False`` is deprecated and will
be removed in two minor releases.
Setting ``use_composable_quantizer=True`` enables an experimental
API surface that may change without notice.

"""
self.use_composable_quantizer = use_composable_quantizer
Expand All@@ -521,45 +519,10 @@ def __init__(
self.quantizer = _TOSAQuantizerV2(compile_spec_or_tosa_spec)
else:
logger.info(
"Using deprecated legacy quantizer implementation in the arm backend. Setting use_composable_quantizer=False will be removed in two minor releases. See https://github.com/pytorch/executorch/issues/17701"
"Using default quantizer in the arm backend. This quantizer is planned to be replaced by the composable quantizer implementation in the future, see https://github.com/pytorch/executorch/issues/17701"
)
self.quantizer = _TOSAQuantizerV1(compile_spec_or_tosa_spec)

@staticmethod
def _validate_optional_quantization_config(
config_name: str, value: object, value_description: str = "value"
) -> None:
if value is not None and not isinstance(value, QuantizationConfig):
raise TypeError(
f"{config_name} {value_description} must be "
"QuantizationConfig or None, "
f"got {type(value).__name__}."
)

@staticmethod
def _validate_config_dict(
config_name: str,
value: object,
is_valid_key: Callable[[object], bool],
key_description: str,
) -> Dict[Any, Optional[QuantizationConfig]]:
if not isinstance(value, dict):
raise TypeError(
f"{config_name} must be a dict, got {type(value).__name__}."
)

for key, quantization_config in value.items():
if not is_valid_key(key):
raise TypeError(
f"{config_name} keys must be {key_description}, "
f"got {type(key).__name__}."
)
TOSAQuantizer._validate_optional_quantization_config(
config_name, quantization_config, "values"
)

return value

@property
def tosa_spec(self):
"""Return the TOSA specification used by the active quantizer."""
Expand All@@ -577,11 +540,12 @@ def global_config(self):

@global_config.setter
def global_config(self, value: Optional[QuantizationConfig]) -> None:
self._validate_optional_quantization_config("global_config", value)
if isinstance(self.quantizer, _TOSAQuantizerV1):
self.quantizer.global_config = value
else:
self.quantizer.set_global(value)
raise NotImplementedError(
"Composable quantizer does not allow setting global_config directly. Please use set_global() instead."
)

@property
def io_config(self):
Expand All@@ -595,12 +559,12 @@ def io_config(self):

@io_config.setter
def io_config(self, value: Optional[QuantizationConfig]) -> None:
self._validate_optional_quantization_config("io_config", value)
if isinstance(self.quantizer, _TOSAQuantizerV1):
self.quantizer.io_config = value
else:
self.quantizer.clear_io_config()
self.quantizer.set_io(value)
raise NotImplementedError(
"Composable quantizer does not allow setting io_config directly. Please use set_io() instead."
)

@property
def module_type_config(self):
Expand All@@ -616,18 +580,12 @@ def module_type_config(self):
def module_type_config(
self, value: Dict[Callable, Optional[QuantizationConfig]]
) -> None:
module_type_config = self._validate_config_dict(
"module_type_config",
value,
callable,
"callable",
)
if isinstance(self.quantizer, _TOSAQuantizerV1):
self.quantizer.module_type_config = module_type_config
self.quantizer.module_type_config = value
else:
self.quantizer.clear_module_type_config()
for module_type, quantization_config in module_type_config.items():
self.quantizer.set_module_type(module_type, quantization_config)
raise NotImplementedError(
"Composable quantizer does not allow setting module_type_config directly. Please use set_module_type() instead."
)

@property
def module_name_config(self):
Expand All@@ -643,18 +601,12 @@ def module_name_config(self):
def module_name_config(
self, value: Dict[str, Optional[QuantizationConfig]]
) -> None:
module_name_config = self._validate_config_dict(
"module_name_config",
value,
lambda key: isinstance(key, str),
"str",
)
if isinstance(self.quantizer, _TOSAQuantizerV1):
self.quantizer.module_name_config = module_name_config
self.quantizer.module_name_config = value
else:
self.quantizer.clear_module_name_config()
for module_name, quantization_config in module_name_config.items():
self.quantizer.set_module_name(module_name, quantization_config)
raise NotImplementedError(
"Composable quantizer does not allow setting module_name_config directly. Please use set_module_name() instead."
)

def set_global(
self, quantization_config: Optional[QuantizationConfig]
Expand DownExpand Up@@ -1179,30 +1131,6 @@ def quantizers(self, value: List[Quantizer]) -> None:
"""Update quantizers without accessing self._quantizers directly."""
self._quantizers = value

def _remove_quantizers_by_node_finder_type(
self, node_finder_types: type[NodeFinder] | tuple[type[NodeFinder], ...]
) -> None:
self._quantizers = [
quantizer
for quantizer in self._quantizers
if not (
isinstance(quantizer, PatternQuantizer)
and isinstance(quantizer.node_finder, node_finder_types)
)
]

def clear_module_type_config(self) -> _TOSAQuantizerV2:
self._remove_quantizers_by_node_finder_type(ModuleTypeNodeFinder)
return self

def clear_module_name_config(self) -> _TOSAQuantizerV2:
self._remove_quantizers_by_node_finder_type(ModuleNameNodeFinder)
return self

def clear_io_config(self) -> _TOSAQuantizerV2:
self._remove_quantizers_by_node_finder_type((InputNodeFinder, OutputNodeFinder))
return self

def annotate(self, model):
reporter = QuantizerReporter(self.quantizers, "FINAL QUANTIZATION REPORT")
model = super().annotate(model)
Expand DownExpand Up@@ -1356,25 +1284,20 @@ class EthosUQuantizer(TOSAQuantizer):
"""Quantizer supported by the Arm Ethos-U backend.

.. warning::
The composable quantizer is now the default implementation. Setting
``use_composable_quantizer=False`` is deprecated and will be removed in
two minor releases.
Setting ``use_composable_quantizer=True`` enables an experimental API
surface that may change without notice.

Args:
compile_spec (EthosUCompileSpec): Backend compile specification for
Ethos-U targets.
use_composable_quantizer (bool): Whether to use the composable
quantizer implementation. Setting this to ``False`` is deprecated
and will be removed in two minor releases. See
[issue #17701](https://github.com/pytorch/executorch/issues/17701)
for details.
use_composable_quantizer (bool): Whether to use the composable quantizer implementation. See https://github.com/pytorch/executorch/issues/17701" for details.

"""

def __init__(
self,
compile_spec: EthosUCompileSpec,
use_composable_quantizer: bool = True,
use_composable_quantizer: bool = False,
) -> None:
super().__init__(compile_spec, use_composable_quantizer)

Expand All@@ -1383,24 +1306,19 @@ class VgfQuantizer(TOSAQuantizer):
"""Quantizer supported by the Arm Vgf backend.

.. warning::
The composable quantizer is now the default implementation. Setting
``use_composable_quantizer=False`` is deprecated and will be removed in
two minor releases.
Setting ``use_composable_quantizer=True`` enables an experimental API
surface that may change without notice.

Args:
compile_spec (VgfCompileSpec): Backend compile specification for Vgf
targets.
use_composable_quantizer (bool): Whether to use the composable
quantizer implementation. Setting this to ``False`` is deprecated
and will be removed in two minor releases. See
[issue #17701](https://github.com/pytorch/executorch/issues/17701)
for details.
use_composable_quantizer (bool): Whether to use the composable quantizer implementation. See https://github.com/pytorch/executorch/issues/17701" for details.

"""

def __init__(
self,
compile_spec: VgfCompileSpec,
use_composable_quantizer: bool = True,
use_composable_quantizer: bool = False,
) -> None:
super().__init__(compile_spec, use_composable_quantizer)
3 changes: 1 addition & 2 deletions backends/arm/quantizer/quantization_config.py
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Expand Up@@ -356,7 +356,7 @@ def get_output_act_qspec(

If node is a pooling or upsample operator, returns a shared quantization spec.
If no weight spec is configured, return ``None``.
If node is a `to.dtype` operator, returns a fixed quantization spec if the input is integer and the output is float32.
If node is a `to.dtype` operator, returns a fixed quantization spec if the input is integer and the output is floating-point.

"""

Expand DownExpand Up@@ -391,7 +391,6 @@ def get_output_act_qspec(
isinstance(input_val, torch.Tensor)
and isinstance(output_val, torch.Tensor)
and CastCheck.is_integer_to_float(input_val.dtype, output_val.dtype)
and output_val.dtype == torch.float32
):
return FixedQParamsQuantizationSpec(
dtype=input_val.dtype,
Expand Down
1 change: 0 additions & 1 deletion backends/arm/test/misc/test_quant_custom_meta.py
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Expand Up@@ -105,6 +105,5 @@ def test_quantized_to_float_transition_tosa_INT_FP(fp_extension: bool):
)
pipeline.quantizer.set_module_type(torch.nn.Sigmoid, None) # type: ignore
pipeline.quantizer.set_module_type(torch.nn.Conv1d, None) # type: ignore
pipeline.quantizer.set_io(None) # type: ignore

pipeline.run()
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