Shortcuts

Source code for torch.ao.quantization.fx.custom_config

from __future__ import annotations
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple, Type

from torch.ao.quantization import QConfigMapping
from torch.ao.quantization.backend_config import BackendConfig
from torch.ao.quantization.quant_type import QuantType, _quant_type_from_str, quant_type_to_str


__all__ = [
    "ConvertCustomConfig",
    "FuseCustomConfig",
    "PrepareCustomConfig",
    "StandaloneModuleConfigEntry",
]


# TODO: replace all usages with these constants
STANDALONE_MODULE_NAME_DICT_KEY = "standalone_module_name"
STANDALONE_MODULE_CLASS_DICT_KEY = "standalone_module_class"
FLOAT_TO_OBSERVED_DICT_KEY = "float_to_observed_custom_module_class"
OBSERVED_TO_QUANTIZED_DICT_KEY = "observed_to_quantized_custom_module_class"
NON_TRACEABLE_MODULE_NAME_DICT_KEY = "non_traceable_module_name"
NON_TRACEABLE_MODULE_CLASS_DICT_KEY = "non_traceable_module_class"
INPUT_QUANTIZED_INDEXES_DICT_KEY = "input_quantized_idxs"
OUTPUT_QUANTIZED_INDEXES_DICT_KEY = "output_quantized_idxs"
PRESERVED_ATTRIBUTES_DICT_KEY = "preserved_attributes"


[docs]@dataclass class StandaloneModuleConfigEntry: # qconfig_mapping for the prepare function called in the submodule, # None means use qconfig from parent qconfig_mapping qconfig_mapping: Optional[QConfigMapping] example_inputs: Tuple[Any, ...] prepare_custom_config: Optional[PrepareCustomConfig] backend_config: Optional[BackendConfig]
[docs]class PrepareCustomConfig: """ Custom configuration for :func:`~torch.ao.quantization.quantize_fx.prepare_fx` and :func:`~torch.ao.quantization.quantize_fx.prepare_qat_fx`. Example usage:: prepare_custom_config = PrepareCustomConfig() \ .set_standalone_module_name("module1", qconfig_mapping, example_inputs, \ child_prepare_custom_config, backend_config) \ .set_standalone_module_class(MyStandaloneModule, qconfig_mapping, example_inputs, \ child_prepare_custom_config, backend_config) \ .set_float_to_observed_mapping(FloatCustomModule, ObservedCustomModule) \ .set_non_traceable_module_names(["module2", "module3"]) \ .set_non_traceable_module_classes([NonTraceableModule1, NonTraceableModule2]) \ .set_input_quantized_indexes([0]) \ .set_output_quantized_indexes([0]) \ .set_preserved_attributes(["attr1", "attr2"]) """ def __init__(self): self.standalone_module_names: Dict[str, StandaloneModuleConfigEntry] = {} self.standalone_module_classes: Dict[Type, StandaloneModuleConfigEntry] = {} self.float_to_observed_mapping: Dict[QuantType, Dict[Type, Type]] = {} self.non_traceable_module_names: List[str] = [] self.non_traceable_module_classes: List[Type] = [] self.input_quantized_indexes: List[int] = [] self.output_quantized_indexes: List[int] = [] self.preserved_attributes: List[str] = []
[docs] def set_standalone_module_name( self, module_name: str, qconfig_mapping: Optional[QConfigMapping], example_inputs: Tuple[Any, ...], prepare_custom_config: Optional[PrepareCustomConfig], backend_config: Optional[BackendConfig]) -> PrepareCustomConfig: """ Set the configuration for running a standalone module identified by ``module_name``. If ``qconfig_mapping`` is None, the parent ``qconfig_mapping`` will be used instead. If ``prepare_custom_config`` is None, an empty ``PrepareCustomConfig`` will be used. If ``backend_config`` is None, the parent ``backend_config`` will be used instead. """ self.standalone_module_names[module_name] = \ StandaloneModuleConfigEntry(qconfig_mapping, example_inputs, prepare_custom_config, backend_config) return self
[docs] def set_standalone_module_class( self, module_class: Type, qconfig_mapping: Optional[QConfigMapping], example_inputs: Tuple[Any, ...], prepare_custom_config: Optional[PrepareCustomConfig], backend_config: Optional[BackendConfig]) -> PrepareCustomConfig: """ Set the configuration for running a standalone module identified by ``module_class``. If ``qconfig_mapping`` is None, the parent ``qconfig_mapping`` will be used instead. If ``prepare_custom_config`` is None, an empty ``PrepareCustomConfig`` will be used. If ``backend_config`` is None, the parent ``backend_config`` will be used instead. """ self.standalone_module_classes[module_class] = \ StandaloneModuleConfigEntry(qconfig_mapping, example_inputs, prepare_custom_config, backend_config) return self
[docs] def set_float_to_observed_mapping( self, float_class: Type, observed_class: Type, quant_type: QuantType = QuantType.STATIC) -> PrepareCustomConfig: """ Set the mapping from a custom float module class to a custom observed module class. The observed module class must have a ``from_float`` class method that converts the float module class to the observed module class. This is currently only supported for static quantization. """ if quant_type != QuantType.STATIC: raise ValueError("set_float_to_observed_mapping is currently only supported for static quantization") if quant_type not in self.float_to_observed_mapping: self.float_to_observed_mapping[quant_type] = {} self.float_to_observed_mapping[quant_type][float_class] = observed_class return self
[docs] def set_non_traceable_module_names(self, module_names: List[str]) -> PrepareCustomConfig: """ Set the modules that are not symbolically traceable, identified by name. """ self.non_traceable_module_names = module_names return self
[docs] def set_non_traceable_module_classes(self, module_classes: List[Type]) -> PrepareCustomConfig: """ Set the modules that are not symbolically traceable, identified by class. """ self.non_traceable_module_classes = module_classes return self
[docs] def set_input_quantized_indexes(self, indexes: List[int]) -> PrepareCustomConfig: """ Set the indexes of the inputs of the graph that should be quantized. Inputs are otherwise assumed to be in fp32 by default instead. """ self.input_quantized_indexes = indexes return self
[docs] def set_output_quantized_indexes(self, indexes: List[int]) -> PrepareCustomConfig: """ Set the indexes of the outputs of the graph that should be quantized. Outputs are otherwise assumed to be in fp32 by default instead. """ self.output_quantized_indexes = indexes return self
[docs] def set_preserved_attributes(self, attributes: List[str]) -> PrepareCustomConfig: """ Set the names of the attributes that will persist in the graph module even if they are not used in the model's ``forward`` method. """ self.preserved_attributes = attributes return self
# TODO: remove this
[docs] @classmethod def from_dict(cls, prepare_custom_config_dict: Dict[str, Any]) -> PrepareCustomConfig: """ Create a ``PrepareCustomConfig`` from a dictionary with the following items: "standalone_module_name": a list of (module_name, qconfig_mapping, example_inputs, child_prepare_custom_config, backend_config) tuples "standalone_module_class" a list of (module_class, qconfig_mapping, example_inputs, child_prepare_custom_config, backend_config) tuples "float_to_observed_custom_module_class": a nested dictionary mapping from quantization mode to an inner mapping from float module classes to observed module classes, e.g. {"static": {FloatCustomModule: ObservedCustomModule}} "non_traceable_module_name": a list of modules names that are not symbolically traceable "non_traceable_module_class": a list of module classes that are not symbolically traceable "input_quantized_idxs": a list of indexes of graph inputs that should be quantized "output_quantized_idxs": a list of indexes of graph outputs that should be quantized "preserved_attributes": a list of attributes that persist even if they are not used in ``forward`` This function is primarily for backward compatibility and may be removed in the future. """ def _get_qconfig_mapping(obj: Any, dict_key: str) -> Optional[QConfigMapping]: """ Convert the given object into a QConfigMapping if possible, else throw an exception. """ if isinstance(obj, QConfigMapping) or obj is None: return obj if isinstance(obj, Dict): return QConfigMapping.from_dict(obj) raise ValueError("Expected QConfigMapping in prepare_custom_config_dict[\"%s\"], got '%s'" % (dict_key, type(obj))) def _get_prepare_custom_config(obj: Any, dict_key: str) -> Optional[PrepareCustomConfig]: """ Convert the given object into a PrepareCustomConfig if possible, else throw an exception. """ if isinstance(obj, PrepareCustomConfig) or obj is None: return obj if isinstance(obj, Dict): return PrepareCustomConfig.from_dict(obj) raise ValueError("Expected PrepareCustomConfig in prepare_custom_config_dict[\"%s\"], got '%s'" % (dict_key, type(obj))) def _get_backend_config(obj: Any, dict_key: str) -> Optional[BackendConfig]: """ Convert the given object into a BackendConfig if possible, else throw an exception. """ if isinstance(obj, BackendConfig) or obj is None: return obj if isinstance(obj, Dict): return BackendConfig.from_dict(obj) raise ValueError("Expected BackendConfig in prepare_custom_config_dict[\"%s\"], got '%s'" % (dict_key, type(obj))) conf = cls() for (module_name, qconfig_dict, example_inputs, _prepare_custom_config_dict, backend_config_dict) in\ prepare_custom_config_dict.get(STANDALONE_MODULE_NAME_DICT_KEY, []): qconfig_mapping = _get_qconfig_mapping(qconfig_dict, STANDALONE_MODULE_NAME_DICT_KEY) prepare_custom_config = _get_prepare_custom_config(_prepare_custom_config_dict, STANDALONE_MODULE_NAME_DICT_KEY) backend_config = _get_backend_config(backend_config_dict, STANDALONE_MODULE_NAME_DICT_KEY) conf.set_standalone_module_name( module_name, qconfig_mapping, example_inputs, prepare_custom_config, backend_config) for (module_class, qconfig_dict, example_inputs, _prepare_custom_config_dict, backend_config_dict) in\ prepare_custom_config_dict.get(STANDALONE_MODULE_CLASS_DICT_KEY, []): qconfig_mapping = _get_qconfig_mapping(qconfig_dict, STANDALONE_MODULE_CLASS_DICT_KEY) prepare_custom_config = _get_prepare_custom_config(_prepare_custom_config_dict, STANDALONE_MODULE_CLASS_DICT_KEY) backend_config = _get_backend_config(backend_config_dict, STANDALONE_MODULE_CLASS_DICT_KEY) conf.set_standalone_module_class( module_class, qconfig_mapping, example_inputs, prepare_custom_config, backend_config) for quant_type_name, custom_module_mapping in prepare_custom_config_dict.get(FLOAT_TO_OBSERVED_DICT_KEY, {}).items(): quant_type = _quant_type_from_str(quant_type_name) for float_class, observed_class in custom_module_mapping.items(): conf.set_float_to_observed_mapping(float_class, observed_class, quant_type) conf.set_non_traceable_module_names(prepare_custom_config_dict.get(NON_TRACEABLE_MODULE_NAME_DICT_KEY, [])) conf.set_non_traceable_module_classes(prepare_custom_config_dict.get(NON_TRACEABLE_MODULE_CLASS_DICT_KEY, [])) conf.set_input_quantized_indexes(prepare_custom_config_dict.get(INPUT_QUANTIZED_INDEXES_DICT_KEY, [])) conf.set_output_quantized_indexes(prepare_custom_config_dict.get(OUTPUT_QUANTIZED_INDEXES_DICT_KEY, [])) conf.set_preserved_attributes(prepare_custom_config_dict.get(PRESERVED_ATTRIBUTES_DICT_KEY, [])) return conf
[docs] def to_dict(self) -> Dict[str, Any]: """ Convert this ``PrepareCustomConfig`` to a dictionary with the items described in :func:`~torch.ao.quantization.fx.custom_config.PrepareCustomConfig.from_dict`. """ def _make_tuple(key: Any, e: StandaloneModuleConfigEntry): qconfig_dict = e.qconfig_mapping.to_dict() if e.qconfig_mapping else None prepare_custom_config_dict = e.prepare_custom_config.to_dict() if e.prepare_custom_config else None return (key, qconfig_dict, e.example_inputs, prepare_custom_config_dict, e.backend_config) d: Dict[str, Any] = {} for module_name, sm_config_entry in self.standalone_module_names.items(): if STANDALONE_MODULE_NAME_DICT_KEY not in d: d[STANDALONE_MODULE_NAME_DICT_KEY] = [] d[STANDALONE_MODULE_NAME_DICT_KEY].append(_make_tuple(module_name, sm_config_entry)) for module_class, sm_config_entry in self.standalone_module_classes.items(): if STANDALONE_MODULE_CLASS_DICT_KEY not in d: d[STANDALONE_MODULE_CLASS_DICT_KEY] = [] d[STANDALONE_MODULE_CLASS_DICT_KEY].append(_make_tuple(module_class, sm_config_entry)) for quant_type, float_to_observed_mapping in self.float_to_observed_mapping.items(): if FLOAT_TO_OBSERVED_DICT_KEY not in d: d[FLOAT_TO_OBSERVED_DICT_KEY] = {} d[FLOAT_TO_OBSERVED_DICT_KEY][quant_type_to_str(quant_type)] = float_to_observed_mapping if len(self.non_traceable_module_names) > 0: d[NON_TRACEABLE_MODULE_NAME_DICT_KEY] = self.non_traceable_module_names if len(self.non_traceable_module_classes) > 0: d[NON_TRACEABLE_MODULE_CLASS_DICT_KEY] = self.non_traceable_module_classes if len(self.input_quantized_indexes) > 0: d[INPUT_QUANTIZED_INDEXES_DICT_KEY] = self.input_quantized_indexes if len(self.output_quantized_indexes) > 0: d[OUTPUT_QUANTIZED_INDEXES_DICT_KEY] = self.output_quantized_indexes if len(self.preserved_attributes) > 0: d[PRESERVED_ATTRIBUTES_DICT_KEY] = self.preserved_attributes return d
[docs]class ConvertCustomConfig: """ Custom configuration for :func:`~torch.ao.quantization.quantize_fx.convert_fx`. Example usage:: convert_custom_config = ConvertCustomConfig() \ .set_observed_to_quantized_mapping(ObservedCustomModule, QuantizedCustomModule) \ .set_preserved_attributes(["attr1", "attr2"]) """ def __init__(self): self.observed_to_quantized_mapping: Dict[QuantType, Dict[Type, Type]] = {} self.preserved_attributes: List[str] = []
[docs] def set_observed_to_quantized_mapping( self, observed_class: Type, quantized_class: Type, quant_type: QuantType = QuantType.STATIC) -> ConvertCustomConfig: """ Set the mapping from a custom observed module class to a custom quantized module class. The quantized module class must have a ``from_observed`` class method that converts the observed module class to the quantized module class. """ if quant_type not in self.observed_to_quantized_mapping: self.observed_to_quantized_mapping[quant_type] = {} self.observed_to_quantized_mapping[quant_type][observed_class] = quantized_class return self
[docs] def set_preserved_attributes(self, attributes: List[str]) -> ConvertCustomConfig: """ Set the names of the attributes that will persist in the graph module even if they are not used in the model's ``forward`` method. """ self.preserved_attributes = attributes return self
# TODO: remove this
[docs] @classmethod def from_dict(cls, convert_custom_config_dict: Dict[str, Any]) -> ConvertCustomConfig: """ Create a ``ConvertCustomConfig`` from a dictionary with the following items: "observed_to_quantized_custom_module_class": a nested dictionary mapping from quantization mode to an inner mapping from observed module classes to quantized module classes, e.g.:: { "static": {FloatCustomModule: ObservedCustomModule}, "dynamic": {FloatCustomModule: ObservedCustomModule}, "weight_only": {FloatCustomModule: ObservedCustomModule} } "preserved_attributes": a list of attributes that persist even if they are not used in ``forward`` This function is primarily for backward compatibility and may be removed in the future. """ conf = cls() for quant_type_name, custom_module_mapping in convert_custom_config_dict.get(OBSERVED_TO_QUANTIZED_DICT_KEY, {}).items(): quant_type = _quant_type_from_str(quant_type_name) for observed_class, quantized_class in custom_module_mapping.items(): conf.set_observed_to_quantized_mapping(observed_class, quantized_class, quant_type) conf.set_preserved_attributes(convert_custom_config_dict.get(PRESERVED_ATTRIBUTES_DICT_KEY, [])) return conf
[docs] def to_dict(self) -> Dict[str, Any]: """ Convert this ``ConvertCustomConfig`` to a dictionary with the items described in :func:`~torch.ao.quantization.fx.custom_config.ConvertCustomConfig.from_dict`. """ d: Dict[str, Any] = {} for quant_type, observed_to_quantized_mapping in self.observed_to_quantized_mapping.items(): if OBSERVED_TO_QUANTIZED_DICT_KEY not in d: d[OBSERVED_TO_QUANTIZED_DICT_KEY] = {} d[OBSERVED_TO_QUANTIZED_DICT_KEY][quant_type_to_str(quant_type)] = observed_to_quantized_mapping if len(self.preserved_attributes) > 0: d[PRESERVED_ATTRIBUTES_DICT_KEY] = self.preserved_attributes return d
[docs]class FuseCustomConfig: """ Custom configuration for :func:`~torch.ao.quantization.quantize_fx.fuse_fx`. Example usage:: fuse_custom_config = FuseCustomConfig().set_preserved_attributes(["attr1", "attr2"]) """ def __init__(self): self.preserved_attributes: List[str] = []
[docs] def set_preserved_attributes(self, attributes: List[str]) -> FuseCustomConfig: """ Set the names of the attributes that will persist in the graph module even if they are not used in the model's ``forward`` method. """ self.preserved_attributes = attributes return self
# TODO: remove this
[docs] @classmethod def from_dict(cls, fuse_custom_config_dict: Dict[str, Any]) -> FuseCustomConfig: """ Create a ``ConvertCustomConfig`` from a dictionary with the following items: "preserved_attributes": a list of attributes that persist even if they are not used in ``forward`` This function is primarily for backward compatibility and may be removed in the future. """ conf = cls() conf.set_preserved_attributes(fuse_custom_config_dict.get(PRESERVED_ATTRIBUTES_DICT_KEY, [])) return conf
[docs] def to_dict(self) -> Dict[str, Any]: """ Convert this ``FuseCustomConfig`` to a dictionary with the items described in :func:`~torch.ao.quantization.fx.custom_config.ConvertCustomConfig.from_dict`. """ d: Dict[str, Any] = {} if len(self.preserved_attributes) > 0: d[PRESERVED_ATTRIBUTES_DICT_KEY] = self.preserved_attributes return d

Docs

Access comprehensive developer documentation for PyTorch

View Docs

Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

Resources

Find development resources and get your questions answered

View Resources