Skip to content

Revert "Arm backend: Make composable_quantizer default" #20982

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
digantdesai wants to merge 1 commit into
base: main
Choose a base branch
from
Open

Revert "Arm backend: Make composable_quantizer default" #20982

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion backends/arm/_passes/normalize_while_initial_args_pass.py
View file
Open in desktop
Original file line number Diff line number Diff 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 number Diff line number Diff 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 Down Expand 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 number Diff line number Diff 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 Down Expand 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 Down Expand 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 number Diff line number Diff 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 Down Expand 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 number Diff line number Diff 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