iP jSSKrSSKJr SSKJr SSKJrJrJrJ r J r SSK r SSK J r SSKJrJr SSKJr SSK Jr SS KJr SS KJr SS KJrJr SS KJrJr SS KJrJ r J!r! SSK"J#r#J$r$ S\\RJS\&4Sjr'S\&4Sjr(S\\S\ \ RR\ RR44Sjr*S\ R&RVS\\4Sjr,S\ \S\&4Sjr-S\ R&R\S\\4Sjr/S#S\\R\-S\\0\ RRS4S \$S!\&S\R\4 S"jjr1g)$N) OrderedDict)Iterable)AnyDictListTupleType)FuseBatchNormWithLinearPass)(AddSimulatedLinearBatchNormFusionQATPass+RemoveSimulatedLinearBatchNormFusionQATPass)QuantizeFusedConvBnBiasAtenPass)fx) OpOverload)ExportedProgram)check_subgraphs_connectedSourcePartition)move_exported_model_to_evalObserverOrFakeQuantize) convert_pt2e prepare_pt2eprepare_qat_pt2e)Q_ANNOTATION_KEY QuantizernodesreturncSnUHCnU=(d7 [UR;=(a UR[RnME U$)NF)rmeta _annotated)r annotatednodes f/var/www/html/ai-image-ml/venv/lib/python3.13/site-packages/executorch/backends/nxp/quantizer/utils.py is_annotatedr"-sGI  ) Tdii8H.I.T.T  cUSSHFn[UR5S:wa g[URSR5S:wdMF g g)zO Checks if each partition other than the last does not have any outside users. NFrT)len output_nodesusers)fused_partitionsource_partitions r!no_outside_usersr,6sT,CR0 ,, - 2 ,,Q/55 6! ; 1 r# obs_or_fqscUSR5upUSR5up2X-n[R"U[RS9nXE4$)Nrr&)dtype)calculate_qparamstorch zeros_likeint64)r- act_scale_ weight_scale bias_scalebias_zero_points r!get_bias_qparamsr9BsSa=224LI m557OL)J&&zEO  &&r#graphwanted_original_aten_opc0nURH|nURRS5=ncM#USnURU;aM:UR US05nUR UR /5nUR U5 M~ S[[RRS[S[4Sjn0n UR5H-upU R5Vs/sH ox"Xz5PM snX'M/ U $s snf)a( Args: graph: The graph we want to partition wanted_original_aten_op: List of original_aten ops (OpOverload) Returns: Dictionary mapping aten ops that were given to a list of SourcePartitions that correspond to the list of nodes that were decomposed from the given aten ops. source_fn_stackr%r&r module_typerc [5n[5n[5nUHnURHFn[U[RR 5(dM.X`;dM5UR U5 MH URS:XaUR U5 URR5HnXp;dM UR U5 M M [UU[U5[U5[U55$)Nget_attr) setargs isinstancer1rNodeaddopr)keysrlist)rr> input_nodesr(paramsr argusers r!make_partition7get_aten_node_target_partitions..make_partitionmse u Dyyc588==11c6FOOC(!ww*$ 4  )$ $$T**       L   r#)rrgettarget setdefaultnameappendrr1rrDr ritemsvalues) r:r;modulesr source_fn_st source_fn diff_modules partitionrMretkvs r!get_aten_node_target_partitionsr^Ls;=G  !IIMM*;< , (* !!1!8!8!:;!))+@A& !) , ,  # #I .' r#modelcalibration_inputs. quantizeris_qatc[U[5(aUR5nU(a1[X5n[ 5"U5R n[ U5nO [X5nUHnU"U6 M U(a4[5"U5R n[5"U5R n[U5n[SS9"U5R nU$)a&Quantize the provided model. :param model: Aten model (or it's GraphModule representation) to quantize. :param calibration_inputs: Either a tuple of calibration input tensors where each element corresponds to a model input. Or an iterator over such tuples. :param quantizer: Quantizer to use. :param is_qat: Whether quantization is done using Quantization Aware Training (QAT) or not. Note: In QAT mode, training is not performed. Only calibration (in eval mode) is done. :return: Quantized GraphModule. T)default_zero_bias) rCrmodulerr graph_modulerrr r rr )rqrrrsrtmdatas r!calibrate_and_quantizer{s$%))  U . 4 6q 9 F F ' *  *" 4# 7 9! < I I ' )! , 9 9QA'$?BOOA Hr#)F)2rf collectionsrcollections.abcrtypingrrrrr r1Dexecutorch.backends.nxp.aten_passes.fuse_batch_norm_with_linear_passr Eexecutorch.backends.nxp.aten_passes.simulated_linear_bn_fusion_passesr r >executorch.backends.transforms.quantize_fused_convbn_bias_passr r torch._opsr torch.exportr*torch.fx.passes.utils.source_matcher_utilsrrtorchao.quantization.pt2err'torchao.quantization.pt2e.quantize_pt2errr-torchao.quantization.pt2e.quantizer.quantizerrrrDboolr"r,Tensorr9Graphr^rb GraphModulerptupler{r#r!rsZ#$// !( VRWW $  '+,' 5<< %&'@ 88>>@!*-@Fu_'=$ #Y0 ' R^^ +'  u||S'8!9:' '   ' ^^ ' r#