o hZ@srddlmZmZddlmZmZddlmZmZm Z m Z m Z m Z ddl Z ddl mZddlmZddlmZddlmZgd Ze je je je je jgZe je je je je j gZ!Gd d d eZ"ed d d Gddde"Z#ed d d Gddde"Z$ e e eefefZ%de%_&ed d d Gddde"Z'ed d d Gddde"Z(eGdddZ)GdddeZ*dS))ABCabstractmethod) dataclassfield)CallableDictListOptionalTupleUnionN)Tensor)ObserverOrFakeQuantize)"_ObserverOrFakeQuantizeConstructor)Node) QuantizerQuantizationSpecBaseQuantizationSpecFixedQParamsQuantizationSpec EdgeOrNodeSharedQuantizationSpecDerivedQuantizationSpecQuantizationAnnotationc@seZdZdZdS)rzBase class for different types of quantization specs that allows users to specify how to quantize a Tensor (input/output of a Node) in the model N)__name__ __module__ __qualname____doc__rr_/var/www/html/ai/venv/lib/python3.10/site-packages/torch/ao/quantization/quantizer/quantizer.pyr!srT)eqfrozenc@szeZdZUdZejed<eed<dZe e ed<dZ e e ed<dZ e ej ed<dZ e e ed<d Zeed <d d ZdS) rzQuantization spec for common operators that allows user to specify how to quantize a Tensor, this includes dtype, quant_min, quant_max etc. dtypeobserver_or_fake_quant_ctrN quant_min quant_maxqschemech_axisF is_dynamiccCs|jtvrtd|jd|jdur+|jdur+|j|jkr+td|jd|jd|jdur>|jtvr>td|jd|jdurL|jdkrNtddSdS)NzUnsupported dtype .z quant_min z must be <= quant_max zUnsupported qscheme rzCh_axis is < 0.) r SUPPORTED_DTYPES TypeErrorr"r# ValueErrorr$SUPPORTED_QSCHEMESr%)selfrrr __post_init__;s    zQuantizationSpec.__post_init__)rrrrtorchr __annotations__rr"r intr#r$r%r&boolr-rrrrr)s    rc@sZeZdZUejed<eed<eed<dZe eed<dZ e eed<dZ e ej ed<dS)rr scale zero_pointNr"r#r$) rrrr.r r/floatr0r"r r#r$rrrrrTs rz)torch.ao.quantization.quantizer.quantizerc@seZdZUdZeed<dS)rzg Quantization spec for the Tensors whose quantization parameters are shared with other Tensors edge_or_nodeN)rrrrrr/rrrrrhs  rc@seZdZUdZeeed<eeege e e ffed<e j ed<dZ eeed<dZeeed<dZee jed<dZeeed <dS) rz^Quantization spec for the Tensors whose quantization parameters are derived from other Tensors derived_fromderive_qparams_fnr Nr"r#r$r%)rrrrrrr/rr r r r.r r"r r0r#r$r%rrrrrqs   rc@sHeZdZUdZeedZeee fe d<dZ e e e d<dZ ee d<dS)rzHow are input arguemnt or output should be quantized, expressed as QuantizationSpec, this corresponds to how a Tensor in the operator Graph is observed (PTQ) or fake quantized (QAT) )default_factoryinput_qspec_mapN output_qspecF _annotated)rrrrrdictr9rrrr/r:r r;r1rrrrr~s rc@sDeZdZedejjdejjfddZedejjddfddZdS)rmodelreturncCdSNrr,r=rrrannotatezQuantizer.annotateNcCr?r@rrArrrvalidaterCzQuantizer.validate) rrrrr.fx GraphModulerBrDrrrrrs r)+abcrr dataclassesrrtypingrrrr r r r.r torch.ao.quantizationr torch.ao.quantization.qconfigrtorch.fxr__all__uint8int8int32float16float32r(per_tensor_affineper_tensor_symmetricper_channel_affineper_channel_symmetric per_channel_affine_float_qparamsr+rrrrrrrrrrrrrs>        *