[ThSSKJrJr SSKJrJr SSKJrJrJ r SSK r SSK J r SSK J r SSKJr SSKJr /S Qr"S S \5r\"S S S 9"SS\55r\"S S S 9"SS\55r\ \\\4\4rS\l\"S S S 9"SS\55r\"S S S 9"SS\55r\"SS55r"SS\5rg))ABCabstractmethod) dataclassfield)CallableOptionalUnionN)Tensor)ObserverOrFakeQuantize)"_ObserverOrFakeQuantizeConstructor)Node) QuantizerQuantizationSpecBaseQuantizationSpecFixedQParamsQuantizationSpec EdgeOrNodeSharedQuantizationSpecDerivedQuantizationSpecQuantizationAnnotationc\rSrSrSrSrg)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____firstlineno____doc____static_attributes__ra/var/www/auris/envauris/lib/python3.13/site-packages/torch/ao/quantization/quantizer/quantizer.pyrrsrrT)eqfrozenc\rSrSr%Sr\R \S'\\S'Sr \ \ \S'Sr \ \ \S'Sr \ \R\S'Sr\ \ \S 'S r\\S 'S rS rg)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_dynamicc URbMURb@URUR:a&[SURSURS35eURbURS:a [S5egg)Nz quant_min z must be <= quant_max .rzCh_axis is < 0.)r'r( ValueErrorr*)selfs r __post_init__QuantizationSpec.__post_init__1s~ NN &*/T^^,,B4>>BRRST  << # q(8./ /)9 #rr)rrrrrtorchr%__annotations__r r'rintr(r)r*r+boolr0rrrr rrsh ;; !CB#Ix}##Ix}#'+GXemm $+!GXc]!J0rrc\rSrSr%\R \S'\\S'\\S'Sr \ \\S'Sr \ \\S'Sr \ \R\S'S r \\S 'S rg) rCr%scale zero_pointNr'r(r)Fr+r)rrrrr2r%r3floatr4r'rr(r)r+r5rrrr rrCsO ;; LO#Ix}##Ix}#'+GXemm $+Jrrz)torch.ao.quantization.quantizer.quantizerc$\rSrSr%Sr\\S'Srg)rXz_ Quantization spec for the Tensors whose quantization parameters are shared with other Tensors edge_or_noderN)rrrrrrr3rrrr rrXs rrc\rSrSr%Sr\\\S'\\\ /\ \ \ 44\S'\ R\S'Sr\\\S'Sr\\\S'Sr\\ R$\S 'Sr\\\S 'S r\\S 'S rg)rbz^Quantization spec for the Tensors whose quantization parameters are derived from other Tensors derived_fromderive_qparams_fnr%Nr'r(r)r*Fr+r)rrrrrlistrr3rr tupler r2r%r'rr4r(r)r*r+r5rrrr rrbshz""&ffn@U UVV ;;#Ix}##Ix}#'+GXemm $+!GXc]!Jrrcp\rSrSr%Sr\"\S9r\\\ \ 4\ S'Sr \ \ \ S'Sr \\ S'S r\\ S 'S rg) rpzHow are input arguemnt or output should be quantized, expressed as QuantizationSpec, this corresponds to how a Tensor in the operator Graph is observed (PTQ) or fake quantized (QAT) )default_factoryinput_qspec_mapN output_qspecTallow_implicit_sharingF _annotatedr)rrrrrrdictrGr rrr3rHrIr5rJrrrr rrps[ CHCOT$)= >>? 48L(/07 $(D'Jrrcn\rSrSrS\R R S\R R 4Sjr\S\R R S\R R 4Sj5r \S\R R SS4Sj5r S\R R S\ \ \ 4SS4S jrS rg) rmodelreturncU$)aAllows for user defined transforms to run before annotating the graph. This allows quantizer to allow quantizing part of the model that are otherwise not quantizable. For example quantizer can a) decompose a compound operator like scaled dot product attention, into bmm and softmax if quantizer knows how to quantize bmm/softmax but not sdpa or b) transform scalars to tensor to allow quantizing scalares. Note: this is an optional method rr/rNs r transform_for_annotation"Quantizer.transform_for_annotations  rcgNrrQs r annotateQuantizer.annotate rNcgrUrrQs r validateQuantizer.validaterXredge_or_node_to_obs_or_fqcg)aA callback that will be called after the observers or fake quants are created for each sharing group, but before they are inserted into the graph. The callback can be used to make final quantization adjustments, such as enforcing specific scale and zero point on model input or output. Args: * `model`: the graph module being prepared. * `edge_or_node_to_obs_or_fq`: a dictionary mapping each annotated edge and node to the corresponding observer or fake quant object. Note that multiple edges and/or nodes can map to the same observer / fake quant instance if they were annotated with SharedQuantizationSpec. This dictionary can be modified by the callback. Nr)r/rNr\s r prepare_obs_or_fq_callback$Quantizer.prepare_obs_or_fq_callbacks$ rr)rrrrr2fx GraphModulerRrrVrZrKrr r^rrrr rrs XX))       ehh22 uxx7K7K   ehh22 t  xx##$( 4J(J#K  rr)abcrr dataclassesrrtypingrrr r2r torch.ao.quantizationr torch.ao.quantization.qconfigr torch.fxr __all__rrrrCrrrrrrrrr ris #(,, 8L 3  d4  0+ 0! 0F d4 #7! 5t$d* + C  d4 1! d4  2 !   0,,r