+ "ݳj0<^RIt^RIt^RIt^RIt^RIt^RIHt^RIHtH t ^RI H t H t H t HtHtHtHt^RIt^RIHt^RIHt^RIHtHtHtHt^RIHt^R IHt^R IH t H!t!^R I"H#t#H$t$H%t%H&t&H't'H(t(H)t)^R I*H+t+^R I,H-t-^RI.H/t/H0t0^RI1H2t2^RI3H4t4H5t5^RI6H7t7H8t8H9t9^RI:H;t;Ht>^RI?H@t@^RIAHBtB^RICHDtD^RIEHFtF^RIGHHtHHItI^RIJHKtKHLtLHMtMHNtNHOtO^RIPHQtQ^RIRHStS]H!4tTRRltU]!RR44tVRR ltWR!R"ltX!R#R$]4tYR#)%N)Mapping) dataclassfield)AnyCallableDictListOptionalSetTuple)nn)Module)DTYPE_SIZE_COLUMNExecutionConfigMODULE_FEATURE_COLUMN LoopProcessor NamedModule BaseQModel)DEVICESUPPORTS_MODULE_TYPES)PROCESS_LOG_LAYERPROCESS_LOG_MODULEPROCESS_LOG_NAMEPROCESS_LOG_TIMEPROCESS_USED_MEMORYQUANT_LOG_LOSSQUANT_LOG_NSAMPLES) AwqGEMMLinear) AwqGEMVLinear)AwqGEMVFastLinear LLMAwqLinear)AwqTorchLinear) apply_clip apply_scale)append_str_prefix get_op_nameget_op_by_name)FORMATMETHODQuantizeConfigresolve_quant_format)normalize_seq_mask)ctx) get_device)normalize_fallback) setup_loggerlog_time_block) find_modulesget_module_by_name_prefixmove_tocreate_quant_module pack_module)parent_module_lock)CPUcXV^8dQhR\PR\P/#modulereturn)r rtorchdevice)formats"_/home/luca/screen-checking/.venv/lib/python3.14/site-packages/gptqmodel/looper/awq_processor.py __annotate__rD,s"   ell c\VRR4pVe\P!V4#\ V4# \\\ 3dL#i;i)zNPrefer a module's planned target device, then fall back to its current device. target_deviceN)getattrr@rA TypeError RuntimeError ValueErrorr0)r>rGs& rC_resolve_module_exec_devicerL,sTFOT:M  << . . f <4   s4A  A ca]tRt^8toRt]!]R7tRt]!] R7t Rt Rt Rt ]!] R7t]!]P R7tV3RltRtVtR#)_AWQLayerStatezITracks subset progress and cached state needed to quantize one AWQ layer.)default_factoryNFc<V^8dQh/S[S[S[3,;R&S[S[,;R&S[S[,;R&S[S[PP,;R&S[S[ ,;R&S[ ;R&S[S[,;R&S[ P;R&#) r=modules subset_totalprocessed_subsets layer_moduleprevious_weight_scale quantizedpending_moduleslock) rstrrr intr r@r rfloatbool threadingLock)rB __classdict__s"rCrD_AWQLayerState.__annotate__8s#{" #A  3-&  3x< 588??+2$E?1X: ..@rE)__name__ __module__ __qualname____firstlineno____doc__rdictrQrRsetrSrTrUrVrWr]r^rX__annotate_func____static_attributes____classdictcell__)r_s@rCrNrN8sQS&+D&AG"&L"'"<.2L-1I %c :O @DrErNcV^8dQhR\\P,R\R\\ P \3,/#r=layers group_sizer?)r r LinearrZr r@Tensor)rBs"rCrDrDFs<77 O77 5<< 7rEc V'g \R4hV^,PP4pVP^,pV^8dTMTpW4,^8wd\RV RV R24hVPP \ 8XEd=.p^pVFpVPP4pVP^,V8wd'\RV RVP^, RV R24hVPVP\PR 74WhP^,, pK \P!V^R 7pVP4PVRV4pVP^R R 7p VPV P!R 44VPWc4P#^R 7V3#\P$!V\PVPR7p ^pVFpVPP4pVP^,V8wd'\RV RVP^, RV R24hVP^,p VP4p V PV RV4p V P^R R 7p V PV P!R 44WP#^\PR7, p Wk, pK W3#)zIAccumulates normalized per-channel weight sums across a group of linears.zBExpected at least one linear layer to compute the AWQ weight mean.zExpected in_features (z!) to be divisible by group_size (z).z/Expected consistent in_features across layers (z), got z for layer .dtypedimTrwkeepdimgư>rurA)rwru)rKweightdetachshaperAtyper:appendtor@float32catabsreshapeamaxdiv_add_sumzeros)rnro first_weight num_channelseffective_group_sizeweights row_countlayerr| group_scalew_sumrows weight_abs weight_views&& rC_accumulate_awq_weight_meanrFs ]^^!9##**,L%%a(L)3a:\*a/$\N2SThSiik l  3& E\\((*F||A,. El^T!<<?+;ugQ@ NN6995==99 : a (I7*%%i5IJkkak6  K$$T*+~~i6::q:A9LL KK EMM,BUBU VEI$$& <<?l *A,P||A'{5'<  ||AZZ\  ((r3GH !&&1d&; ))$/0 AU]];;   rEczV^8dQhR\\P,R\R\P /#rm)r r rprZr@rq)rBs"rCrDrDs2 6 6 O 6 6 \\ 6rEc*\W4wr#V^,PP4pV^8Xd?\P!VP ^,VP VPR7#W#, PVP 4#)zLReturns the average normalized per-channel weight magnitude for AWQ scaling.rz) rr|r}r@rr~rurAr)rnrorrrs&& rC_compute_awq_weight_meanrsu 36FE!9##**,LA~{{<--a0 8J8JS_SfSfgg   ! !,"4"4 55rEcaa]tRt^toRt]V3RlRl4tRtV3RlV3RllltV3RlR ltRuV3R lR llt V3R lR lt Rt V3RlRlt V3RlRlt RuV3RlRlltV3RlRltV3RltV3RltV3RlRltV3RlRltRR/V3RlRlltV3R lR!ltV3R"lR#ltV3R$lR%lt]V3R&lR'l4t]V3R(lR)l4t]V3R*lR+l4t]V3R,lR-l4tV3R.lR/lt]V3R0lR1l4t]V3R2lR3l4t]V3R4lR5l4t]V3R6lR7l4t V3R8lR9lt!V3R:lR;lt"V3R<lR=lt#V3R>lR?lt$RuV3R@lRAllt%V3RBlRClt&V3RDlRElt'V3RFlRGlt(V3RHlRIlt)]*PV!4R/3V3RJlRKll4t,]*PV!4RL4t-]*PV!4RvV3RMlRNll4t.V3ROlRPlt/]*PV!4V3RQlRRl4t0/3V3RSlRTllt1]*PV!4V3RUlRVl4t2]*PV!4V3RWlRXl4t3]*PV!4V3RYlRZl4t4V3R[lR\lt5V3R]lR^lt6R_t7RuV3R`lRallt8V3RblRclt9V3RdlRelt:RwV3RflRgllt;V3RhlRiltV3RmlRnlt?]@V3RolRpl4tAV3RqlRrltBRstCVtDV;tE#)x AWQProcessorzHCaptures activations and quantizes layers with the AWQ scaling workflow.c:<V^8dQhRS[RS[P/#)r= named_moduler?)rr r)rBr_s"rCrDAWQProcessor.__annotate__s##+#"))#rEc VPPR4p\V\P4'dV#VP #)zDReturn the dense module view AWQ should use for weight quantization.quant_source_module)stateget isinstancer rr>)r quant_sources& rCresolve_quant_source_module(AWQProcessor.resolve_quant_source_modules>$))--.CD lBII . . """rEFNct<V^8dQhRS[RS[S[,RS[S[,RS[RS[RS[RS[S[,/#)r=qcfgcalibration_concat_sizecalibration_sort batch_size require_fwdcalculate_w_wq_diffcalibration_concat_separator)r,r rZrYr\)rBr_s"rCrDrshG&G&G& "*# G&#3-G&G&G&"G&'/smG&rEc  8<\SV`VVVVVV VV\V RRRR7R7 Wn.Vn^Vn^VnWpn/Vn\P!4Vn \P!4Vn Wn\VPRR4p \!VP"VP$4VnT ;'gVP'VP"4VnWnRVnRVnRVn/Vn/Vn/Vn\94Vn/Vn\P!4Vn/Vn RVn!VPE4VP2PGRR4VPHVn$R#)zAInitializes AWQ processing, layer tracking, and kernel selection.T)rfwd_replay_after_processsubset_forward_early_stopenable_activation_capture) tokenizerr calibrationrrrprepare_dataset_funcrexecution_configqlinear_kernelNattention_maski@)%super__init__rr avg_lossesnsamples_nsamples_total_quant_batch_size _layer_statesr]r^_layer_states_locklocal_scale_context gptq_modelrHr-rBmethod!_select_qlinear_kernel_for_formatrmodelr%max_chunk_memory duo_scaling_module_forward_kwargs_feature_stats_scale_feature_by_modulerh_feature_task_names_awq_feature_kwargs _rotary_lock _rotary_cache_rotary_source_id_initialize_sample_counts setdefaultfallback)selfrrrrrrrrrrrr model_kernel __class__s&&&&&&&&&&&&& rCrAWQProcessor.__init__st" #$;-)E!5!,')-*.*.   "$7   !+8:"+.."2'oo/$t0@$G *4;; D *aad.T.TUYU`U`.a !3 ?A#9;8:%-0U >@ %NN,3504 &&( ##../?F  rEcD<V^8dQhRS[S[P,/#r=r?)r r r)rBr_s"rCrDrs UU(299"5UrEc VPP'd0\VPVPP.4wrV#\ \ VPRVP4RR4#)zDReturns the model rotary module used to refresh position embeddings.r rotary_embN)rrotary_embeddingr5rrH)rrotary_s& rC_get_root_rotaryAWQProcessor._get_root_rotarysX ?? + + +1$**t?_?_>`aIFMwtzz7DJJ?tTTrEc<V^8dQhRS[S[P,RS[S[P,RS[S[P,/#)r=rrr?)r r rr@rA)rBr_s"rCrDrsH  ")))< QVQ]Q]H^ jrsxsskA rEc VfV#\\VRR4RR4pVeV#\V4# \dTu#i;i)zGResolves the effective device for a rotary module or falls back safely.Ninv_freqrA)rHr0 Exception)rrr rotary_devices&&& rC_get_rotary_deviceAWQProcessor._get_rotary_devicesU >O D A8TR  $  f% % O s 1 AAcn<V^8dQhRS[S[P,RS[S[P,/#)r=rGr?)r r@rAr r)rBr_s"rCrDrs0$$HU\\4J$xXZXaXaOb$rEc VP4pVeVfV#\P!V4pVPV4V8XdV#\ V4pVP ;_uu_4VP4pVf RRR4R#\ V4pVPV8wd!VPP4W@nVPV4V8Xd VuuRRR4#VPPV4pVf7\P!V4p\WQR7WRJdWPPV&VuuRRR4# \dTpL:i;i +'giR#;i)zBReturns a rotary module copy materialized on the requested device.NrA)rr@rArrYridrrclearrcopydeepcopyrr6)rrGr cache_key source_idcacheds&& rC_get_rotary_for_device#AWQProcessor._get_rotary_for_devicesE&&( >]2M ]3  " "6 *m ;M &    **,F~  6 I%%2""((*)2&&&v.-? ''++I6F~$!]]62F5'4:&&y11 "!$#F$#  s=(EAE&EE E EEEE E+ c \R4h)zIRejects dataset replacement because AWQ capture is fixed at construction.z5AWQProcessor's calibration_dataset cannot be modified)NotImplementedError)rcalibration_datasets&&rCset_calibration_dataset$AWQProcessor.set_calibration_datasets""YZZrEc <V^8dQhRS[/#r)r\)rBr_s"rCrDr$s  d rEc d\VPRR4p\V\4'dV\P8H#\V\ P 4'dVPR8H#\V\4'd+VPR^4^,P4R8H#R#)zFReturn whether this processor is quantizing for an Ascend NPU runtime.rANnpu:F) rHrrrNPUr@rArrYsplitlower)rrAs& rC_quant_device_is_npu!AWQProcessor._quant_device_is_npu$sHd3 ff % %VZZ' ' fell + +;;%' ' fc " "<<Q'*002e; ;rEc <V^8dQhRS[/#)r= format_value)r*)rBr_s"rCrDr0sUUfUrEc \V\4'g \V4MTpV\P8Xd#VP4'd\#\ #VP4'd\ RV R24hV\P8Xd\#V\P8Xd\#V\P8Xd\#\ RV 24h)zEMaps the resolved AWQ format to its concrete quantized linear kernel.zjNPU AWQ quantization requires FORMAT.GEMM so the AwqTorchLinear runtime can run on NPU; actual format is `z`.z)METHOD.AWQ does not support this FORMAT: ) rr*GEMMrr$r rKGEMVr! GEMV_FASTr"LLM_AWQr#)rrfmts&& rCr.AWQProcessor._select_qlinear_kernel_for_format0s+5\6*J*Jf\"P\ &++ ((**%%  $ $ & &%%(E-  &++  &"" "$ $ &..  D\NSTTrEc0<V^8dQhRS[S[,/#)r= module_name)r rY)rBr_s"rCrDrHs E E8C= ErEc HV'dVPPVRR4MRp\T;'gVPPVPP4pW0P8Xd\ VP RR4pVeV#VPV4#)zCResolves the AWQ kernel after applying any dynamic format override.rBNr)r dynamic_getr-rBrrHrr)rr format_override target_formatrs&& rC_resolve_qlinear_kernel$AWQProcessor._resolve_qlinear_kernelHsQ\$))// XtLae,_-P-P @P@PRVR[R[RbRbc KK '"4??4DdKL'##55mDDrEc&<V^8dQhRS[RS[/#)r= layer_indexr?rZrN)rBr_s"rCrDrTsCNrEc VP;_uu_4VPPV4pVf\4pW PV&RRR4V# +'giX#;i)zDReturns the mutable tracking state for a specific transformer layer.N)rrrrN)rrrs&& rC_get_layer_stateAWQProcessor._get_layer_stateTs] $ $ $&&**;7E}&(27"";/ %  % $  s 8A A( c<\SV`4pRVPRVPRVPRVP /VR&V#)rrnsamples_totalquant_batch_sizeawq)rcontinuation_state_dictrrrrrrrs& rCr$AWQProcessor.continuation_state_dict^sJ/1 $//  d22  6 6  e  rEc<\SV`V4VR,R,VnVR,R,VnVR,R,VnVR,R,VnR#)rrrrrN)rload_continuation_state_dictrrrrrs&&rCr!)AWQProcessor.load_continuation_state_dicthsT ,U3,|4e Z0 $U|,<=!&u.@!ArEc<V^8dQhRR/#r=r?Nra)rBr_s"rCrDros%%4%rEc ^p\VRR4pVf^Vn^VnR#VFp\V\4'gKVP R4pVfK2\V\ P4'd<VP4^8:d V^, pKrWP^,, pKV\V4, pK \VR^4;'gTpWPnWPnR# \dT^, pKi;i)z>Computes sample and token totals from the calibration dataset.rN input_idstotal_calibration_tokens) rHrrrrgrr@rqrwr~lenrI)rtotaldatasetrowr& total_tokenss& rCr&AWQProcessor._initialize_sample_countsos$ 5t< ?#$D DM Cc4(( ,I )U\\22==?a'QJE__Q//ES^+E"t%?CLLu +$ !QJEs4C//DDcL<V^8dQhRS[RS[S[S[S[3,,/#)r=r r?)rYr rr)rBr_s"rCrDrs)sxSRUX?WrEc \VPRR4p\V4'gR#V!V4pVfR#\V\4'g'\ RV: R\ V4P R24h\V4#)z@Return an optional model-specific activation aggregation policy.awq_input_feature_aggregationNz@awq_input_feature_aggregation must return a mapping or None for z, got rs) rHrcallablerrrIrrbrg)rr selectorpolicys&& rC_feature_aggregation_policy(AWQProcessor._feature_aggregation_policys4??,KTR!!+& >&'**"oVDL,A,A+B!E F|rE dedupe_batchcD<V^8dQhRS[RS[PRS[RR/#)r=r featurer6r?N)rYr@rqr\)rBr_s"rCrDrs=9F9F9F9F  9F  9FrEc VP4pRpV'dVeVP;_uu_4VPPV4pVfR.R./pW`PV&VP R\ 44pWG9d RRR4R#VP V4RpRRR4VPV4pVP4^8:dVP^4pVPPR8wd VP4P4pMVP4pVP;_uu_4VPPV4pVfR.R./pW`PV&V'dIVeEV'g=VP R\ 44pWG9d RRR4R#VP V4VP R.4pVP!V4VP R.4P!V4RRR4R# +'giELz;i \dT'dzTP;_uu_4TPPT4pTe*TPR\ 44PT4RRR4h +'gih;ihi;i +'giR#;i)zFF{S  Y E  YYscAHH.A0H*H*;J; J; !J;7AJ; H' *J8=J8A J# J8# J3 . J8; K cN<V^8dQhRS[PRS[P/#)r=r8r?r@rq)rBr_s"rCrDrs#%,,5<<rEc \\VRR4RR4p\P!V4'd.\P!V4'dVP^8wdV#VP VP \P R7pVP4^8d\VPR,4\VP48XdKVP!R.VPR,O5!pW2PR4,P4#VP4^8XdLVP^,VP48Xd&WPR4,P4#V#)aRemove padded rows from an activation captured by the current worker. Regular module hooks normally receive ``[B, S, H]`` inputs, while the all-expert calibration path flattens the same input to ``[B * S, H]``. Handle both layouts so root and expert statistics exclude the same padding rows. Inputs that cannot be matched unambiguously to the active mask retain their existing behavior. _mask_tlsNvaluerAru:Nr=N:r=NNr{) rHr@ is_tensorndimrrAr\rwtupler~r contiguousnumel)rr8 keep_mask flatteneds&& rCrA/AWQProcessor._apply_active_keep_mask_to_featuresGD+tT"T?W]]2->?I..r23>>@ @ ;;=A '--"2ioo6G"G,,R01<<> >rEc><V^8dQhRS[RS[PRR/#)r=r r8r?N)rYr@rq)rBr_s"rCrDrs,LLLu||LX\LrEc a\VRR4'dR#VPV4pV'dVPRR4'gR#V R2oVP;_uu_4\;QJd)V3RlVP 4F 'gK RM RM!V3RlVP 44'g RRR4R#RRR4VP WRR7R# +'giL%;i) a=Capture one pointwise MoE-root input per calibration batch when requested. MoE layers are normally hooked only at expert projections. Model-specific AWQ scale groups may instead need the shared pre-router input. The policy gate keeps this opt-in so sequence-sensitive models retain their existing replay contract. coverage_onlyFN capture_rootrsc3D<"TFqPS4xK R#5iN) startswith).0name task_prefixs& rC =AWQProcessor.record_moe_root_input_feature..sK {33 s T)r6)rHr4rrXanyr?rH)rr r8r3ras&&& @rCrecord_moe_root_input_feature*AWQProcessor.record_moe_root_input_features 4% 0 0 11+>VZZ>> % Q' YYY3K K333K KKKYK "";d"KYs#*C!'C!8C!! C1 cB<V^8dQhRS[RS[RS[S[,RS[/#)r= moe_blockmoe_block_namehandlesr?)rrYr rr\)rBr_s"rCrDr s7c   rEc aaSPP'gR#SPS4pV'dVPRR4'gR#VV3RlpVP VP V44R#)a7Register AWQ's shared MoE-root capture before the block executes. AWQ is the only lifecycle that consumes this shared pointwise feature. Keep the activation-capture and model-policy gates here, alongside the recorder they protect, so the generic subset executor remains unaware of AWQ-specific state and math. Fr[cN<V'dSPSV^,4R#R#rN)re)_moduler:rirs&&rCcapture_moe_root_inputKAWQProcessor.register_moe_root_capture_hook..capture_moe_root_inputs22>6!9MrET)rrr4rrregister_forward_pre_hook)rrhrirj root_policyrosf&f& rCregister_moe_root_capture_hook+AWQProcessor.register_moe_root_capture_hook sc$$>>>66~F +//.%"H"H N y::;QRSrEcJ<V^8dQhRS[S[P,RS[/#r=tensorsr?)r r@rqr\)rBr_s"rCrDr&s$   4 +=  $  rEc aV'gR#V^,o\;QJdV3RlV4F 'dK R# R#!V3RlV44#)z=Return whether cached tensors share the same non-batch shape.Fc3<"TFUpVP4SP48H;'d(VPR,SPR,8HxKW R#5i)NNN)rwr~)r_tensorfirsts& rCrb9AWQProcessor._can_concat_batch_tensors..-sJ ! JJLEIIK ' O OFLL,< B,O O!s ,A 0A T)all)rwr}s&@rC_can_concat_batch_tensors&AWQProcessor._can_concat_batch_tensors%sO s ! ss s s !   rEcJ<V^8dQhRS[S[P,RS[/#rv)r r@rqrZ)rBr_s"rCrDr3s(II4 #5I#IrEc .pVFypVP4^8dK\VPR,4pV^8:dK?\VP4V,4pV^8gKhVP V4K{ V'g^#\ V4p\ V4p\V4p\V\ Wg44#)aDerive a pointwise sample budget from observed calibration batches. Use one largest-batch equivalent, raised to the number of non-empty batches so every contributing batch can retain at least one row. This adapts to actual batch size, sequence lengths, and expert occupancy while limiting any increase beyond the largest observed activation batch to the minimum needed for one-row-per-batch coverage. r{) rwrZr~rTrrmaxr(min)rw row_countsr|hiddenr raw_tokens largest_batchcontributing_batchess& rC_token_row_budgetAWQProcessor._token_row_budget2s Fzz|ab)*F{v||~/0Dax!!$'_ J "::s=GHHrEcz<V^8dQhRS[S[P,RS[RS[S[PS[3,/#)r=rw max_tokensr?)r r@rqrZr )rBr_s"rCrDrQsJmEmEell#mEmE u||S ! mErEc   aaV'g\P!^4^3#V^8:d\RV R24h\V^,PR ,4p.p^pVFpVP 4^8g"\VPR ,4V8wd&\RV R\ VP4 R24hVPR V4pV\VP^,4, pVP^,^8gKVPV4K V^8XdV^,P^^V34^3#\WA4pWt8Xd*\P!V^R7P^4V3#VUu.uFp\VP^,4NK! p p\V4p Wz8dV^8Xd^.p M>\V4U u.uF)p \W^, ,V^, ,4NK+ p p \P!V 4U u.uFqV ,R,NK pp \P!V^R7P^4V3#^.V ,pWz, pV^8dV Uu.uFpVV,V, NK upoSUu.uFp\V4NK upo\#S4FwppVV;;,V, uu&K V\%V4, p\'\V 4VV3RlR7pV^8dWR pVFCpV^8:dM;VV,V V,8gK#VV;;,^, uu&V^,pR pKE V'dK\.p\)W?V 4Fwppp\VV4pV^8:dKVV8XdVPV4K6V^8Xd(\P*!^\P,R 7pMB\P.!V\P,R 7V^, ,V^, ,pVPVP1^V44K \P!V^R7P^4V3#uupiuup iuup iuupiuupi) aPack ragged pointwise activations into deterministic bounded token rows. The returned tensor always has shape ``[1, retained_tokens, hidden]``. Whenever ``max_tokens`` is at least the number of non-empty cached batches, every non-empty calibration batch contributes at least one row; the remaining budget is distributed proportionally to batch length (largest remainder, ties broken by batch order) and rows are sampled evenly inside each batch. This avoids the previous latest-batch-only bias without allocating a full concatenated activation tensor first. When ``max_tokens`` is smaller than the batch count, one row is taken from each of ``max_tokens`` evenly spaced batches instead. z/AWQ token-row max_tokens must be positive, got rszMAWQ token-row aggregation requires tensors with one common hidden dimension (z ); got shape=rvNr{Nc8<SV,SV,, )V3#r]ra)indexexactfloorss&rC/AWQProcessor._pack_token_rows..sU5\F5M%A#BE"JrEkeyFTrtr{)r@emptyrKrZr~rwrRrr new_emptyrrrBr(rangergfromkeys enumeratersortedziprint64arange index_select)rwrrrr,r|rVretained_tokens tensor_rowslengths batch_count batch_idsibatch_idselectedquotas remaininglengthsharer floor_shareleftoverorder distributedquota local_indicesrrs&$ @@rC_pack_token_rowsAWQProcessor._pack_token_rowsPs (;;q>1$ $ ?NzlZ[\] ]WQZ%%b)* Fzz|a3v||B'7#8F#B ))/ eFLL>Q=RRSUr62I C  23 3Lq!A% I& 1 1:''Av7: :l7  *99Tq)33A6 D D@DE3{((+,E$i  (!#C #?331_-/A2EFG3<@==;ST;SxXr**;SHT99X1-77:LH H{"#1 q=ELMW6Vi',66WME.34eUc%je4F&/&7"{u , '8&V4Hk"JEQ,# "E1}e}wu~5u *  A &* ##{*-dG*D &Kv&Ez ,z % AU[[ A LLekk:z# # OOK44Q F G+E yyq)33A6 DDAFUN4s%Q+/Q0Q5>Q:Q?c6<V^8dQhRS[S[,RS[/#)r=valuesr?)r r)rBr_s"rCrDrstCySrEc VUu.uF qfKVNK ppV'gR#V^,p\P!V4'dVUu.uF#p\P!V4'gK!VNK% pp\V4\V48wd VR,#\P V4'd\P !V^R7#VR,#VR,#uupiuupi)aMerge cached per-batch metadata while preserving feature alignment. When every tensor has the same trailing shape, we concatenate on dim 0 so the metadata still lines up with a concatenated feature tensor. When sequence lengths differ across batches, the cached metadata no longer shares a common shape. Real AWQ repros hit cases like `attention_mask` `[1, 1, 423, 423]` plus `[1, 1, 36, 36]` and `position_ids` `[1, 423]` plus `[1, 36]`. Those values cannot be concatenated into one batch-aligned tensor, so we return the most recent non-None value. `_layer_input_features()` makes the same choice for the corresponding feature tensor by keeping `tensors[-1]`. Nrvr{)r@rPr(rrr)rrNnon_noner}rws& rC_concat_batch_metadata#AWQProcessor._concat_batch_metadatas (.CveEEvC  ??5 ! !*0K&EOOE4Juu&GK7|s8},|#55g>>yya00B< |D LsCC C!-C!c\<V^8dQhRS[S[S[,,RS[S[S[3,/#)r=r;r?)r r rZrrYr)rBr_s"rCrDrs1??Xc]@S?X\]`be]eXf?rEc  \\VR/44p\VRR4pVfV#VUu.uFqDfKV^8gK\V4NK ppV'gV#\VRR4;'g.pV'dCVUu.uFpV\V48gKWd,NK ppVP V4pVeWR&\VRR4;'g.p V 'dCVUu.uFpV\V 48gKW,NK p pVP V 4p V eWR&\VRR4;'g.p V 'dVUu.uFpV\V 48gKW,NK p pV 'd/pV UUu0uFpVP 4FpVkK K pppVF=pTP V Uu.uFqP V4NK up4pVfK8VVV&K? VPV4V#uupiuupiuupiuupiuuppiuupi)atBuild kwargs aligned to the captured feature batches. This helper mirrors how `_layer_input_features()` collapses cached activations. If all selected metadata tensors share the same trailing dimensions we concatenate them. If sequence lengths differ across batches, we keep the most recent metadata entry so it stays aligned with the most recent feature tensor fallback. r inputs_cacheNattention_masksr position_idslayer_input_kwargs)rgrHrZr(rkeysrupdate)rr;feature_kwargscacheidx valid_indicesrselected_masks merged_maskr selected_pos merged_pos input_kwargsselected_kwargsmerged_input_kwargskwargsrall_keys merged_values&& rC"_feature_kwargs_from_batch_indices/AWQProcessor._feature_kwargs_from_batch_indicessR*.gd/0und;AAr )(C\**" !!(  44\BJ%1;~.u&:DAGGR )(C\**" !!(  68##2"1%{{},"1 $C#'#>#>7FGVCG$L$/3?+C0 $ %%&9:a\ Hs@G0G0G0?G5 G5 G:7 G:G? G?5"H.H ch<V^8dQhRS[PRS[PRS[P/#r= batch_tensorrUr?rK)rBr_s"rCrDr!s1::ELL:U\\:V[VbVb:rEc \VP^,pTpVP^,^8Xd-V^8d&VP!V.VPR,O5!p\V4Uu.uFqCWAV,3,NK pp\P!V^R7P ^4#uupi)z:Pack per-batch row tensors into one kept-token row tensor.rzrv)r~expandrr@rrB)rrUbatchsourcerD packed_rowss&& rC_pack_kept_token_rows"AWQProcessor._pack_kept_token_rows s" <<?a EAI]]5<6<<+;>IS3swwy~~'(I>\ P%P    a E 2 11,JJrEcl<V^8dQhRS[PRS[S[S[3,RS[S[S[3,/#)r=inp module_kwargsr?)r@rqrrYr)rBr_s"rCrDrdsBWW \\WCH~W c3h WrEc :\V4pVPR4p\P!V4'd2VP 4^8g\P!V4'gV#VP 4^8d\ VP ^,4M^pVP 4^8d\ VP ^,4M\ VP ^,4pV^8wgVP^8dV#\V4pTeTP^8wdT#\ TP\PR7P4P44pTP ^,^8g;'gNTP R,T8H;'d0TP^8;'gTP R,^T39p Y8wg T 'EgTp TP ^,^8XEdTP^8Xd'TP R,T8dTRRT13,p MTP^8XdZTP R,T8dBTP R,^8XgTP R,T8dTRRT1RT13,p MxTP^8XdhTP R,T8dPTP R,^8XdTRRRT13,p M(TP R,T8dTRRT1RT13,p YJd'TPRR4TPRR4T#YR&TPR4p \P!T 4'd:T P^8d)T P R,T8dT RRT13,TR&T#TPYG4TR&TPR4p \P!T 4'dTP!Y4TR&T# \dTu#i;i) a%Align masks and positions to packed feature tensors used during AWQ replay. AWQ capture hooks can flatten kept tokens from multiple batch rows into one replay tensor shaped like `[1, kept_tokens, hidden]`. The original cached kwargs still describe the pre-packed batch layout, for example an attention mask shaped `[B, 1, S, S]` or position ids shaped `[B, S]`. Passing those batched kwargs to replay produces shape mismatches in attention because the replay activations no longer have the original batch and sequence structure. When the packed token count matches the keep-mask derived from the cached attention mask, we rebuild `attention_mask` and `position_ids` to match the packed layout. If the counts do not match, we drop those kwargs rather than pass incompatible shapes into attention replay. rNrt.rrr{r)rgrr@rPrwrZr~rQr.rrrrrpoprr) rrraligned_kwargsr input_batch input_seqrUrmask_seq_matches cropped_maskrs &&& rC_align_module_kwargs_to_input*AWQProcessor._align_module_kwargs_to_inputds*m,'++,<=s##swwy1}EOON<[<[! ! ,/779>c#))A,'q ),aC ! %S1=N ! ~22Q6! ! "*>:I   ! 3! !EKK8<<>CCEF *//2a7    $ 1 X X$$q(VVN,@,@,DI,V   "*:*:*L##A&!+!&&!+0D0DR0HI0U#1#z z/#BL#((A-.2F2Fr2Ji2W%++B/148L8LR8PT]8]'5c:I:z z6Q'R #((A-.2F2Fr2Ji2W%++B/14'5c2z z6I'J '--b1Y>'5c:I:z z6Q'R -""#3T:"">48%%/;+ ,)--n=L|,,1B1Ba1GLL^L^_aLbfoLo1=c:I:o1N~.! !+/+P+PQ_+k'(%)).9 ??< ( (-1-P-PQ]-iN> *e "! ! "s P PPc\<V^8dQhRS[S[S[S[3,,RS[S[,/#)r=previous_subsetr?)r rrYrr[)rBr_s"rCrDrs200htCQ\L\G]>^0cklqcr0rEc  V'gR#.pVP4Fp\VPRR4pVfK \P!4;_uu_4VP \ VP4P4P4P444RRR4K V'gR#\ \V4\V4, 4# +'giK;i)zOEstimates the average weight scale of the previous subset for reuse heuristics.Nr|) rrHr>r@no_gradrr[r}rmeanrrr()rrrrr|s&& rC_capture_previous_subset_scale+AWQProcessor._capture_previous_subset_scales +224L\00(DAF~ eFMMO$7$7$9$>$>$@$E$E$GHI! 5S[3v;.// !s AC'' C8 cP<V^8dQhRS[RS[S[S[P3,/#)r=rr?)rNrrYr@rq)rBr_s"rCrDrs-@@>@d3 CT>U@rEc  a/p/p/p\VP4pVUu0uFqfPR^4^,kK pp\V4FcpWP9gKW9gKVP V4p V 'gK8V P RR4'gKRVPV4Ke \V4Vn VEFpVPP V4;'g/p V P R.4p V P R.4p V 'g+\P!^4W&&/W6&RRR^R ^R ^/WF&K\V 4\V 48Xd\;QJdR V 4F 'dK RM R M !R V 44'dF\\W4R R7p V Uu.uF q^,NK p pV Uu.uF q^,NK p pVP V4p T ;'g/P R4pVR8XdT ;'g/P R4pVe \V4MVP!V 4pV^8Xd=Vf9V ^,P#^^V ^,P$R,34W&&^pMVP'V VR7wW&&p/W6&\W&,P$R,4pW&,.V R&R.V R&RRRVR VR \V 4RV/WF&EMVR9d\)RV: RV: R24hVP+V 4'd\P,!V ^R7W&&VP/V 4W6&W&,.V R&R.V R&\1\W&,P$R,4^4o\W&,P34S,4pRRRVR VR \V 4/WF&MV R,W&&V 'd V R,MRpVP/V.4W6&W&,.V R&V.V R&\1\W&,P$R,4^4o\5V3RlV 44p\W&,P34S,4pRRRVR VR \V 4/WF&W&,fEKW&,P34^8gEKEK W0nW@nV#uupiuupiuupi)alCollapse cached per-batch inputs into one replay tensor per module. Model-specific pointwise policies pack variable-length batches into bounded token rows, so every calibration batch contributes to AWQ scale statistics. Generic sequence-sensitive modules retain the legacy behavior: concatenate equal shapes, otherwise keep the latest feature and aligned kwargs. rsr[Fr:r;moderrrbatchesc3B"TFqRJ;'dV^8xK R#5ir]ra)r_rs& rCrb5AWQProcessor._layer_input_features..s"TmUT)88eqj8msTc&\V^,4#)r)rZ)rs&rCr4AWQProcessor._layer_input_features..s Sa\rEr token_rowsrN)rrz)Unsupported AWQ feature aggregation mode z for rvc3d<"TF%p\VP4S,4xK' R#5ir])rZrT)r_r|rs& rCrbr>s$ UW6V\\^v%=!>!>Ws-0 latest_batchr{r)Nr)listrQrrr?r4rrrhrr@rr(rrrZrrr~rrKrrrrrTrrr)rrfeaturesr feature_stats feature_namesr`rootsrootr3rFrwr;orderedraggregation_modeconfigured_max_tokensrrrtokenslast_batch_indexrs&& @rC_layer_input_features"AWQProcessor._layer_input_featuress-/4635 U]]+ 3@A=4C#A&&=A5MDzz!d&?99$?6fjj??!((. " $'}#5 !DJJNN4(..BE*/))Hb*AG16?B1OM!&Q')$G !%qq ' # M"c'l2CTmTCCCTmTTT  /16= =WTaW =/67wt77w755d;F & "11&9 </)/2(:(:<(H%-8-.//8 ?'<'D%,QZ%9%91aAQAQRTAU:V%WHN!"J151F1F#-2G2.HNJ(*$"%hn&:&:2&>"?#+>"2h*.o&L *%s7| * ' #"8 ?@P?ST(!%//88!&7!:'+'N'N}']$#+>"2h*.o&S!5!5b!9:A>X^113v=>G &%vs7| ' #")8E=#44 '+'N'NP`Oa'b$#+>"2h*:);o&S!5!5b!9:A> UW UU "%hn&:&:&<&F"GN *%s7| ' # ~)hn.B.B.Dq.HA"N$2 +eBJ!>7s!S5S Sc<<V^8dQhRS[S[S[3,RS[/#)r= feature_statr?)rrYrrZ)rBr_s"rCrDrRs$$$gc3h6G$C$rEc VPR4R8Xd%\VPR^44pV^8dV#VP#)zDReturn the sample count exposed through the legacy quantization log.rrr)rrZr)rr%rs&& rC_feature_nsamples_for_log&AWQProcessor._feature_nsamples_for_logRsI   F #| 3!,"2"23Da"HIO"&&###rEcJ<V^8dQhRS[RS[S[S[,,RR/#)r=rfallback_task_namesr?N)rNr r rY)rBr_s"rCrDr[s2777&c#h/7  7rEc <RVnVPP4VPP4RVnVP P4RVnRVnVP;'gT;'g \4pVP;_uu_4VFIpVPPVR4pV'gK)RV9gK2VR,P4KK RRR4\4Vn/Vn /Vn/Vn\!VP"R4'd\%VP"R4\!VP"R4'd\%VP"R4R#R# +'giL;i)z>Release all per-layer AWQ state after a successful completion.TNr:r prev_scale)rVrQrrWrTrSrRrUrrhrXr?rrrrhasattrrdelattr)rrr* task_namesr` task_entrys&&& rC_finalize_quantized_layer&AWQProcessor._finalize_quantized_layer[s5  ##%! %%'!&*#--OO2E2N2N YYY"!ZZ^^D$7 :(j"8x(..0# $'5  (*%#% 4&& 6 6 D'' 7 4&& 5 5 D'' 6 6Ys''F F F  F c0<V^8dQhRS[RS[RS[RR/#)r=rrreasonr?N)rZrNrY)rBr_s"rCrDr{s9UUUU U  UrEc  RpVPP4UUu/uF.wrV\V!V4\\44'gK,WVbK0 ppp\ P RVV4V'dVPV.R7M\ P RV4VPV\V4R7R#uuppi)zIFalls back to direct quantization when AWQ scaling cannot proceed safely.cJ\V\4'd VP#T#z)mods&rCunwrap5AWQProcessor._quantize_layer_fallback..unwraps",C!=!=3:: F3 FrEz8AWQProcessor: layer %s using fallback quantization (%s). scales_listz named_childss&&&& rC_quantize_layer_fallback%AWQProcessor._quantize_layer_fallback{s G!& 3 3 5 5 &.%0E*FG DL 5  F      \r  : KKVXc d &&u#lBS&T# s )B?B?c<V^8dQhRR/#r$ra)rBr_s"rCrDrs?0?0D?0rEc \VRR4pVfR#/p\VRR4'dVPR ,pW2R&MRVR&VP4p\VRR4'dVPR ,MRpRp\VRR4'd%VPR ,pV'd V^,pVEeVEeTpVP 4^8XdVP ^4pVP 4^8dVP^,MVP^,p VP 4^8dVP^,M^p VPVP4pVPWHP4p V e#VPV 8wdVPV 4pVe5VPR ,V 8XdVPVP4p MQ\P!WP\PR7p V P ^4PV R 4p WR&VeV!W4p WR&M MVeWRR&\VR R4'd=VP&R ,;'g/pVP)4F wppVVV&K W nR# \ d-p\"P%R T TPT4Rp?LRp?ii;i) zKRefreshes cached kwargs such as masks and rotary embeddings for AWQ search.rNrrr layer_inputsrOposition_embeddingszVAWQProcessor: failed to refresh rotary position_embeddings (seq_len=%s, device=%s): %srr{)rHrrrrHrwrBr~rrArrr@rlongrrr@debugrr?r)rr refreshedmaskr pos_ids_cache hidden_cache last_inputs x_for_rotaryseq_lenrrGpos_for_rotarypeexc latest_kwargsrrNs& rC"_refresh_forward_kwargs_from_cache/AWQProcessor._refresh_forward_kwargs_from_cachesnd3 = -/ 5+T 2 2((,D*.& '*.I& '&&(29%QU2V2V**2.\`  5.$ / /,,R0K*1~  ,":'L!Q&+55a8 /;/?/?/AQ/Fl((+LL^L^_`LaG-9-=-=-?1-DL&&q)!E001D1DEF 33FQ>QY^YcYc!d!/!9!9!!>BM+113 U!& #4'0##!II6$++ s< J)) K 4"KK cf<V^8dQhRS[S[,RS[S[S[P3,RS[/#)r= layer_names input_featr?)r rYrr@rqr\)rBr_s"rCrDrs;$ $ #Y$ ell*+$   $ rEc ^RIHp^pVFpVPV4pVeVP4^8XdR#VPPV/4pVPR4R8Xd#RV9dV\ VR,4, pKRV9dV\ VR,4, pKVP 4^8dVPR ,M^pWFP4\V^4,, pK \VRR4;'g VPp T!VP\V4V 'd\V 44#R4#) zJReturns whether a scaling group lacks enough captured activations for AWQ.)should_use_fallbackNTrrrrr'r{) utils.fallbackr]rrTrrZrwr~rrHrrr[) rrZr[r]captured_tokensr`featr%rexpected_tokenss &&& rC_should_fallback_group#AWQProcessor._should_fallback_groups) 9D>>$'D|tzz|q0..224#AA' *"$(BDIaaTMaMa" MM / "&5E/ "  <@  rEc*<V^8dQhRS[RS[RR/#)r=rrr?Nr)rBr_s"rCrDrs,@U@U3@U~@U$@UrEc  TVP'dR#VPpVfVP'dx\\ VPP 444pVP PR^4^,p\VPPV4wr6W2nVPpVf\RV 24h\PRV\VP4\VP4VP \#VPP%44R,4VP'V4p/VnVP+4U U u.uFwrV P-4^8XgKV NK p p p V 'd"VP.'g\RV R24hRp VPP+4U U u/uF.wr\1V !V 4\3\444'gK,WbK0 pp p \7VP84p\7\;VR /44VR &\=VP>R V4\=VP>R VP@4VPPCVVV4pV'g5\PER V4VPGV\IV4R 7R#.pVFjp\7V4pVPKR4;'gTpVPKR4;'gTpVPMVV4VR&VPOV4Kl .p.p\I4pVEFpVPKR4;'g.pVPKR4pVUu.uFDp\1V\PPRPT4'd \WVV4M \YV4NKF ppVP.'d,VP[VV4'dVP]V4KV'd V^,MRpVVJp\1V\RP^4'd\1V\RP^4'dV'gVP`Pb^,VP`Pb^,8wdr\WVV4p\WVV4p \PgRVV\3VP`Pb4V \3VP`Pb44EKVU u.uF qV9gK V NK p!p V!'dVPOVV!34EKVPOV4EK V'd)\PgRV\V4VR,4TpVP+4U U u/uFwr\iV 4V bK p"p p VFpVPkRR4p#V#V9d*V"PK\iVPKR444p#V#fKKVPKR4;'g.FSp\1V\PPRPT4'd \WVV4M \YV4p$V#VP(V$&KU K V'g=V'g5\PERV4VPGV\IV4R 7R#.p%VR,FpVPKR4;'g.pVR,Uu.uFDp\1V\PPRPT4'd \WVV4M \YV4NKF p&pV%POV&4K \PRV\V4V%4VUu.uFpVPl!V3/VBNK p'p\oVV'VR7\T'\WTPT4R,4p'TP'd~TPTTP+4U U0u/uFwp p0T T9gKT T0PbK up0p T4p1\TT14\T1\WTPT4R,4p1TU2u/uFp2T2T9gK T2TT2,bK p3p2TP+4U U0u/uFwp p0Y9gKT T9gKT T0bK pp p0TPTT'4T3'dE\PER&T\T34\#T34R',4TPT3.R(7TPGT\IT4R 7R#uup p iuup p iuupi \dd\YT4pELi;i \dd\YT4p ELi;iuup iuup p iuupiuupi \Edp(.p)T'EFwp*pp+p,RT*RRR.RR/p\qTT*4pM \ddRpMi;iTe\;TRR4MRp-\1T-\PPr4'd\3T-Pb4TR&M%TfR TR&M\uT4Pv R!2TR&.p.TFp/\qTT/4pM% \ddT.POT/R 34K2i;i\;TRR4p-\1T-\PPr4'd)T.POT/\3T-Pb434KT.POT/\uT4Pv R!234K T.TR&\yT+R"4'd5\{T+P-44TR&M \dd R#TR&Mi;iT)POT4EK \P}R$TT(T)R%,4hRp(?(ii;iuup0p iuup2iuup0p i))zDRuns the AWQ scaling, clipping, and quantization flow for one layer.Nrsz=AWQProcessor: unable to resolve layer module for layer index zjAWQProcessor: layer %s tracking %s modules before quantization (subsets processed=%s/%s); first modules=%s:NNz.unwrap's *![99188 @q @rErrr,zZAWQProcessor: no module configuration generated for layer index %s; skipping quantization.r>module2inspectrrnprev_opzsAWQProcessor: layer %s skipping scaling group due to dimension mismatch prev_op=%s shape=%s first_layer=%s shape=%szUAWQProcessor: layer %s skipping %d scaling groups due to missing features (sample=%s):NrN_input_feature_namerz_AWQProcessor: no valid scaling groups for layer %s after filtering; marking layer as quantized.:NrNz=AWQProcessor: layer %s sanitized %d scaling groups; sample=%s)input_feat_dictprev prev_shape layer_shapes scale_elemsr|missingz (no weight)rTunknownzXAWQProcessor: apply_scale failed at layer %s with %s. Shape summary (first 5 groups): %s:NNz4AWQProcessor: layer %s fallback quant %d modules: %s:NNr<)DrVrTrQnextiterr full_namersplitr5rrrJr@infor(rSrRrrr"rr?rTrrrRrrgrrHsetattrrrUawq_get_modules_for_scalingrAr1rhr_sanitize_kwargsrr@r rr(rYrbrrpr|r~rrKrr_search_best_scaler&r)rqrrbr-rZerrorr'r%_search_best_clipr>rB)4rrrrT sample_module layer_pathrlayer_module_refr[r`r|rrr:r>rCmodule_kwargs_global module_configsanitized_module_configrFinspect_module entry_kwargsfiltered_module_configskipped_groupsfallback_namescfg layers_sample prev_modulerrZfirst_layer_module same_module prev_name first_namemissing_layersfeature_name_by_id feature_name layer_name sample_groups layers_namesr=rU debug_entries prev_op_namescales_lossr|rplnamenamed clip_listnfallback_named_childss4&&& rC_quantize_layerAWQProcessor._quantize_layers  ??? ))  EMMM emm&:&:&M&0077Q?BJ78M8MzZOL!-  --  #!^_j^klm m  x    '' (    ##% &r *  //6 (*%,6,<,<,>V,>LD&,,.TUBU44,>V 4===NwiX*+   A!& 3 3 5 5 &.%0E*FG DL 5  $D$?$?@6: D/ 47 23 ##]K@##\53N3NOCC     KKl   * *5c,FW * X .0"EKE"YY'78LL*CGGH-33M''),K+*E9C5%((//8Z8Z ,e4`cdi`jj* }}}!Tdd{NU%%{N&CD " ) )# .W+Z  IIgN#r"  #9CMCSCSCUVCU<4bj$.CUV*C77#8$?L:-155b6HI #*00b0"%99 0%8U =I--j9 1 +'~ KKq   * *5c,FW * X  *2..CGGH-33MERSUEVWEV|AJuV[V^V^VeVeDfDfK(8%@lopulvvEVLW   ./  K  ' (   1 0  # #$4 > >0  ,  (+z RX(   $4 5 ; ???.. 7C7I7I7Kj7K et[iOi#u||#7KjI ' 3)DJJ(89C?I$! #L  A|A # ! 8D7I7I7KB7K etOa fjsAgA e 7K B {3 KKF)**+B/     2  C &&u#lBS&TGW V&!1 #K 0I1!9!$%7!8J9VW2W * MSUWdfjk'"01A<"PK '"&K'ALAXh=^bfell33*/ *=E,' (*3E,'-1+->-G-G,H *UE,'! (E!'56F'N $!$++UI,>? !%\8TBF!&%,,77$++UE&,,4G,HI$++UtL7I7R7R6SS_4`,ab))5n%67++9/26<<>/Bm,$9/8m,9$$U+A=HD IIjb!   U* fk! Bs)d?d?)eeA e + e8 e-f ,f -fA ff'f n n / n=n n$0n$9n$e*)e*-ff n+n gn g ng  A;n i)(n)j n j Bn(mnmnm9nnc^<V^8dQhRS[S[P,RS[P/#)r=rnr)r r rpr@rq)rBr_s"rCrDrs1\ \ O \  \ rEc  Vf\V4^8XgQhV^,pRV9dVPR4\VPRR4p\ W0PP 4pVP 4PR VPR ,4p V P^4p V P^4p \P!.\PR7P4p T p \VPW,,4p\!W4p\#V^4p\P$!V \PVP&R7p\)^W4FSp\!VV,V 4pV VVpVP+\P4P-^R7pVV, pKU W, P+VP.4p?VP1We4p\VR/4pVP1VV4pVP34FwppVP5VV4K VP7VV4pV'd\9\P:!44;_uu_4VP=WEV4Uu.uFpVP?\P@!VP.4P \P@!VP.4P"4PC4PE4NK ppRRR4MVP+\GVPI44P&4p\9\P:!44;_uu_4VPKWEV4pRRR4XP?\P@!VP.4P \P@!VP.4P"4pVPMWHVWSXV4wpp\OW4\QVUu.uFp\OVV4NK up4VV3#uupi +'giL_;i +'giL;iuupi) z;Searches the best per-channel AWQ scale for a module group.N use_cache scale_search_chunked_activationsTrtrzrvrr{))r(rrHrrrorviewr~sizer@r|r element_sizerZrrrrrArrrrur}r?rrr/inference_mode_iter_module_forward_outputscliprr}r=rv parameters_module_forward_compute_best_scaler(rR)rr>rkrnrrjruse_chunked_scale_searchw_meaninp_flat num_elementsr float32_sizeelement_size_bytes chunk_sizex_sumrendchunk chunk_sumx_meanr global_kwargsglobal_allowed_kwargsrrNoutput fp16_output best_scaleslossrhs&&&&&&& rCr~AWQProcessor._search_best_scales  !v;!# ###AYN & JJ{ # $+4996XZ^#_ *&))2F2FG 779>>"ciim4}}Q' }}Q' ||Bemm<IIK )..3E3TUV 2 Q'  L cjjQq,3Aa*nl3CQsOE/333:I Y E 4&**3995 --fE &>C $ 5 5m^ T/557JC  $ $S% 08::3 N #U))+,,#'"C"CCYf"g"gKK FLL 9 = =u{{6< __R)9)9")=> '':+;+;A+>JO :++A..@A ;Y;/ IIk!naZ 8*1~3q8b 1~ -222((~ )>? ((7 ]]6* Q=89CM)C!G}+DEA%%',,2t,rr{)r~rrorrwr@isnanrsymraminbitsrroundclamp_rr full_like) rrrrrmax_intmin_intrrs && rCpseudo_quantize_tensor#AWQProcessor.pseudo_quantize_tensorsgg 99  ! #r?TYY%9%99Q> ^-P[\^P_O`aEFJFOFOFZFZE[[]A^ ^> "dii223Auuw!||{{1~!!#q(((yy}}}ffDf1GffDf1G'!+GG'..4.87BFkk'"233;;GME EKK 3e;WNQVVAJJ{1~r2Eeegllq$l7Gmmm-GDIINNQ./!3Gdiinnq012G&F EKK 3WFOA{{6"&&(A---{{1~!!#q((([^R0 99===OOFAdiinnq6H0I,IJE IIk "%rEcR<V^8dQhRS[PRS[PRR/#)r=srcdstr?NrK)rBr_s"rCrDrs+%"%" %"5<<%"TX%"rEc pVPpVPP^8dNVPRVPP4pVPRVPP4pM*VP V^,R4pVP V4pVPP 'EgVP^RR7pVP^RR7p^VPP,^, p^p Wg, PRR7V, p \P!Wz, 4)PW4p VPV4VPV 4\P!WUR7VP!V 4VPW4VP#V 4VP%V 4R#VP'4P^RR7PRR7p^VPP^, ,^, p^VPP^, ,)p Wh, p VPV4VPV 4\P!WUR7VPW4VP%V 4R#)zNWrites pseudo-quantized values into a destination tensor without reallocating.TrxrrrNr{)r~rrorr reshape_asrrrrrr@rrcopy_rrsub_mul_r) rrr org_shapesrc_viewdst_viewrrrrrrs &&& rCr)AWQProcessor._pseudo_quantize_tensor_intos II 99  ! #xxDII$8$89HxxDII$8$89H{{9Q<4H~~h/Hyy}}}mm4m8Gmm4m8G499>>)A-GG'..4.87BFkk'"233;;GME NN8 $ MM& ! KK / MM% OOG - MM% MM& !lln))a)>DDDNGDIINNQ./!3Gdiinnq012G&F NN8 $ MM& ! KK / OOG - MM& !rEc<V^8dQhRS[PRS[PRS[PRS[PPRS[S[P ,RS[PRS[/#)r=xrrrj linears2scalerr)r@rqr rr rpr)rBr_s"rCrDrszv6v6 <<v6 v6 v6  v6 BII v6\\v6v6rEc  Z ^p.p R p Rp \R4p VU u/uFVp WPP4P\P 4P 4P4bKX pp \VP44PpVPR 4PV4pVPR 4PV4p\VPRR4pVeV\V4,p\VP RR4p\#V4EFpVV, pVP$'dGVP'V4VP'^V, 4R,, P)RR7pM0VP'V4P)RR7PR 4pVVP+4VP-4,P/4, pVP^R 4PV4p^V\P0!V4&^V\P2!V4&VF_p V PP5V4VP7V PV P4V PP9V4Ka V'Ed9Rp^p\;VP=W4VP?WV44FwppVPA\PB!VPD4P,\PB!VPD4P*4pVPVPVPDR 7pVV, P4pVVP'^4PG4PI4, pVVPK4, pK V\+V^4, pMVPMWV4pVPA\PB!VPD4P,\PB!VPD4P*4pVPOVVV4pV PQV4VV 8dTp Tp VPS4p VF]p V PPUW,PV PPV PPDR 74K_ EK VF]p V PPUW,PV PPV PPDR 74K_ VPW4V R 8Xd\XP[V 4\h\P2!V 4PG4^8XgQV 4hV P4P 4V 3#uup i \dTPpELv\dTPpELi;i) a Compute loss and select best scales L(s) = || Q(W * s) (s^-1 * X) - W * X || Q: weight quantization function | pseudo_quantize_tensor(W * s) X: inputs from calib dataset | X W: original weights in FP16 | layer s: per channel scaling factor | s^-1 * X Ninfr,rTg-C6?rrOr{).r[r|r}rr@rr=rSrvrrA StopIterationrrrHrrrrrrrrsqrtisinfrr rrr_iter_reference_output_chunksrrrrurrrTr _compute_lossrrrrr@rK)rrrrrjrrrrhistory best_ratior best_errorfcorig_weights_cpurAprev_scale_hintrratior scales_view total_losstotal_elements ref_chunk int_w_outputdiffrs&&&&&&&& rCr AWQProcessor._compute_best_scales&  5\ $; $    "%%emm488:EEG G# ;  .3356==F R##F+R##F+!$"5"5|TJ  &eO44F $+4996XZ^#_ 6]EFNE **U+vzz!e)/Dt/KLSSX\S]E*00T0:??Cvzz|fjjl:@@BBF ++a,//7K+,F5;;v& '*+F5;;v& ' $ {+11"))RYYG {+$ ('! !"/266qF55aP0+I|$0#4#4U[[ASAS5T5X5XZ_ZeZefrfxfxZyZ}Z}#~L ) L4G4G|OaOa bI% 4;;=D$((1+//"3"8"8"::J"djjl2N0"C$:: $33AvN +00\=O=O1P1T1TV[VaVabnbtbtVuVyVyz ))+|VL NN4 j ! " $lln #  0 4 7 7ryy?O?OWYW`W`WfWf 7 gh$m#t B IIOO,033299;K;KSUS\S\SbSb3c d    IIg O{{;'++-2?K?2!!#''):55y;  XXF XXF s$AW17#W66X*X* X*)X*ch<V^8dQhRS[PRS[PRS[P/#)r=rr%rA)r@rqrA)rBr_s"rCrDrs5 ,,LL rEc :RpVPR4pVPR4pVP^4pVP4pVPV^,,p \ W4p \ P !WY4p \ P !Wi4p \W4FmwrV PV4V PV4, P4P^4P4P4pWN, pKo WG,pV#)zEComputes chunked mean-squared reconstruction loss under a memory cap.rr{) rrrrrr@rrrr[rrr)rrr%rArfp16_output_flatint_w_output_flatrrr fp16_chunks int_w_chunks fp16_chunk int_w_chunk chunk_losss&&&& rCrAWQProcessor._compute_losss&++B/(--b1',,Q/ (557**/AA/EF 2 kk"2? {{#4A (+;'E #J$--/+..2HHOOQUUVWX\\^cceJ  D(F  rEc<V^8dQhRS[PRS[PPRS[RS[P/#)r=rr>rr?r@rqr rr)rBr_s"rCrDrs= ) )\\ )+088?? )KO )  )rEc \VPWV44pV'g\P!V4#\ V4^8Xd V^,#\P !V^R7#)zHRuns a module forward with sanitized kwargs and optional micro-batching.rv)rrr@rr(r)rrr>routputss&&&& rCrAWQProcessor._module_forwardsV t88MRS##A& & w<1 1: yya((rEch<V^8dQhRS[PRS[PPRS[/#)r=rr>rr3)rBr_s"rCrDrs6//\\/+088??/KO/rEc #  aa"\V4pRp\VP44Pp\ VP44EFwrV\V\P4'd/Ve)VPV8wdVPV4W5&KPKRKT\V\ \34'gKr.pRpVFmp \V \P4'd:Ve6V PV8wd%VPV PV44RpK\VPV 4Ko V'gK\V4!V4W5&EK Rp Rp \P !VP"4Pp RV 9p RV 9p VP)4^8Xd"V 'g V 'dVP+^4pRp RpVP)4^8dPVP,^,pVP)4^8dVP,^,MVP,^,p M^pVP,^,p VP/4p\1VP2RR4;'dRV9pV EezVEeuV 'EdlV'EgcTP5T;'g VP4pTP7Y;'g VP4pV 'dVP9R4MRpV 'gRpVeVP,R ,V 8wd\P:!T T;'g VP\P<R7pVP)4^8d"VP+^4P?VR 4pV 'dVVR&TpM2TPT;'g VP4pV 'dVVR&VfTMVPV4pV!VV4VR&MV 'dV e}RV9dv\P:!Y;'g VP\P<R7pVP)4^8d"VP+^4P?VR 4pVVR&VP@'dVP@^8d VP@MRpVe.VP)4^8XgVP,^,V8:dWVe#VPV8wdVPV4MTpV!V3/VBp\V\4'd V^,pVxR#VP)4^8d\CVP,^,4M^oVV3Rlo^p\PD!VV^R 7FpVe#VPV8wdVPV4MTpVP4UUu/uF!wrVVS!VVVP,^,4bK# pppV!V3/VBp\V\4'd V^,pVxVVP,^,, pK R# \dRpEL\ dRpEL*i;i \$\&3dELi;iuuppi5i) zGYields forward outputs chunk-by-chunk to reduce peak activation memory.NFTrrI)awq_preserve_explicit_position_embeddingsrOcF<\V\P4'd6VP^8d#VP^,S8Xd WW,#V#\V\ \ 34'd*VUu.uF pS!W1V4NK pp\V4!V4#V#uupi)zBSlices batch-shaped kwargs to match a micro-batched forward chunk.)rr@rqrQr~rrRr)valstartrrsliced _slice_valuefull_batch_sizes&&& rCr>?AWQProcessor._iter_module_forward_outputs.._slice_value's#u||,,88arrGrrN convertedchangedrsupports_position_idssupports_position_embeddingsrBrR batch_dimrpreserve_position_embeddingsrpos_ids pos_valuesrSrQeffective_quant_batch_size x_forward module_output batch_offset x_partialpartial_kwargspartial_outputr>r?s&&&& @@rCr)AWQProcessor._iter_module_forward_outputss{ ]+   ! !2!2!45<"\\'-:S:S188[`[e[ef 557aM.1!+!(M,K,KW^^!L(4BM.1 - 51144 ;NL39,3WM/ 0 "w':~Ub?bg6O6OqxxW\WaWabJuuw!|'11!4;;IrJ ,6M. )?C?U?U?UZ^ZpZpstZtT%;%;z~" ' .uuw!|wwqz77/ >M-// -a 0   -.UUWq[#aggaj/a  Q(BJI7D7PU^UeUeivUv ]3}FI#0"5"5"7"7JC\%yq7IJJ"7 $I@@N.%00!/!2 IOOA. .LKW ! M ! M !2I&   dsZ#YBZA)?|#rEc  a\P!4pVP4EF-wpoRSP R2pVPV4VP S4p\ V\ V4R7SPPR4p^pVPPP^,p \V\4'dA\VPR^4;'g^4p\VPRV 44p VPPp V'd=V PV P^,V4p \ P"!W3^R7p VP%V 4wrpV'dV RR V 13,p VP&P(^8dZ\+^\,P.!WP&P(, 44pV RR V13,p VeVRR V13,pMV RR V 13,p VeVRR V 13,pSP1R V R V/4? ?VP2'dSPPP4\ P68XdVPPV , pMYVPPP9\ P:R 7V P9\ P:R 7, pSPP=R V/4SPP=RV /4\V\4'dSPP?RR 4WPn \P!4V, pR pVFLwpppp\@;QJdV3RlV4F 'gK RM RM!V3RlV44'gKJTpM VfRpMVR pVPBPSP/4pVPDPSPSP4pVPBPVV4pVPGV4p/\HVP4b\JSPLb\NSPb\PVPSS4b\TVPWS4b\XVb\ZV bR\VPR^44bR\VPR^44bR\VPR^44bRVPRR4bRVbR\VPR^44bR\VPR^44bR \VPR^44bR!VPRR4b\\VR" b\^VPa4/CpVPb;_uu_4VPdPgR#SPL R$SP 24VPhPgV4R R R 4VPkV4EK0 R # +'giL(;i)%zEPseudo-quantizes selected linears and stages AWQ tensors for packing.z Quantizing z in layerr tp_pad_infopad_colsoriginal_columnsrvrNq_scalesq_zerosrt w_wq_diffwqc3B<"TFpSPV9xK R#5ir])r`)r_rrs& rCrb+AWQProcessor.apply_quant..sU:|((J6sTFznot applicablez.10factivation_raw_tokensractivation_retained_tokensractivation_batchesractivation_aggregationrrs scale_featurescale_raw_tokensscale_retained_tokens scale_batchesscale_aggregationz.3fzlayer--)6timer?r` draw_progressrr6rLrrr|datar~rrgrZ new_zerosr@rrrrormathceilstream_state_payload_to_cpurrufloat16rrrrrdrrr'rrrrrmodule_feature_summaryrmodule_dtype_size_summaryrrrrdevice_memory_reportrX module_namesrr@ log_new_row)rrr= start_timer` base_title linear_layertp_infor[ original_colsweight_for_quantpadr`rr valid_groupsr_durationavg_loss_valuerrZr loss_summaryactivation_statrg scale_statretained_samplesstatrs&&& @rCrBAWQProcessor.apply_quantTsYY[ "/"5"5"7 D,&|'8'8&9CJ   z *;;LIL L)D\)R S"((,,];GH(//44::1=M'4((w{{:q9>>Q? #GKK0BM$R S +2277 &001A1G1G1JHU#(99.>-D!#L $ ; ; ! B>M>)*99''!+#&q$))MIIDXDX4X*Y#ZL#A} }$45F( %a,&6 7#A~ ~$56F( %a-&7 8  4 4u  '''&&++11U]]B , 3 3 8 82 =I!- 3 3 8 8 ; ;%-- ; PSUSXSX_d_l_lSXSm mI""))+    % %b' '4((""&&}d;')   $yy{Z/H!N+6';43UU333UUUU%)N,7 %/ "0!6 #1155!!O!99==!!!!M,,00J $==jI  $))+!<#;#;#L$5$5&t'B'B<'P  "4#A#A,#O   #'7&8(_-@-@q-Q)R-c/2E2EFWYZ2[.\%c/*=*=i*K&L)/*=*=fi*P #C |Q(G$H(Z^^%>%@)D,!!((6,2J2J1K1\M^M^L_)`a%   T "q#8fs *AY Y+ c \P!VP4Pp/pVP 4FwrVWS9gK WdV&K V#)a0 Remove the arguments that are not supported in the module's forward pass to avoid breaking behaviour between different versions of transformers. Args: inputs_kwargs (`dict`): The input dictionary to pass to the model layer module (`torch.nn.Module`): Target module to quantize. )rArBrCrr?)r inputs_kwargsr>module_signaturesanitized_kwargskvs&&& rCr}AWQProcessor._sanitize_kwargssP#,,V^^<GG!'')DA$&'#* rEc <V^8dQhRS[/#)r=r>r)rBr_s"rCrDrs$/$/$/rEc VPPVPR7RJdR#\W PP4VnVP VP 4pVP;_uu_4WPVP&VPPR4pVPf VeWTn VP;'gTpVP'g:Ve6\V4pVPP!VP#44VPP%VP4RRR4VP;_uu_4VPP'R^4^,pVP)V4p V 'd9V PRR4'd!VP*P-VR.R./4VP*PVP4p V fR./VP*VP&MV P-R.4RRR4R# \d/pELPi;i +'giEL;i +'giR#;i) zNRegisters a module with its layer state and initializes input capture buckets.)rFNrTrsr[r:r;)rrrxr1rrrrXrQr`rrrTrWr4rrrr@rr4r?r) rr>rr layer_statereffective_layer all_linears root_namerrrFs &&&, rC preprocessAWQProcessor.preprocesss 99 F,<,< = F +8YY5G5GH ++F,>,>?    /5   ,%||//? ''/4D4P+;()66JJ:JO...?3N%"."?K++22;3C3C3EF  ' ' + +FKK 8YYY ))#q1!4I::9EK{~uEE %%i(BQS1TUJJNN6;;/E}+3R. 6;;'  2.Y !%"$K%  YYsRAII4I9 IAI.AI,A7I, IIII I) , I= c&<V^8dQhRS[RS[/#r<)rr\)rBr_s"rCrDr s6666rEc XVPPVPR4'*#)zAReports whether preprocessing excluded this module from AWQ work.F)r?rr`)rr>s&&rC is_skippedAWQProcessor.is_skipped s ::>>&++u555rEc<V^8dQhRS[RS[S[S[S[P R3,S[P .R3,/#)r=r`r?.N)rYrrr r@rq)rBr_s"rCrDr sC   65WZIZC[]b]i]i:jlp:p1q rEc aaRVV3RllpV#)zFReturns the forward hook that caches module input activations for AWQ.crV^8dQhR\\PR3,R\P/#)r=r.r)r r@rq)rBs"rCrD7AWQProcessor.pre_process_fwd_hook..__annotate__ s, 6 6eELL#$56 6U\\ 6rEc<V'gR#Tp\V\\34'dV'd V^,pSPSV4R#)zDRecords the module input tensor for later AWQ scale and clip search.N)rrRrrH)r>rrr8r`rs&&& rChook/AWQProcessor.pre_process_fwd_hook..hook s<G'E4=11g!!*  & &tW 5rEra)rr`rsff rCpre_process_fwd_hook!AWQProcessor.pre_process_fwd_hook s 6 6 rEc <V^8dQhRS[RS[PRS[S[S[S[3,,RS[S[S[S[3,,RS[S[,RS[S[,/#)r=r>rAsubsetr subset_indexrR)rr@rAr rrYrZ)rBr_s"rCrDr sq595959 59c;./0 59 "$sK'7"89 59 sm 59sm59rEc FVP4VPpVPV4pVP;_uu_4VehVPP V4VP f?VP4F*p V PPR4p V fK$WnM VeWhn VeVPPV4Ve&VPPVP4V'dVP!V4VnVPp VP'd4VPP%VPP'44p VP('*;'de\+VP4;'dHV '*;'d9VPRJ;'g#\-VP4VP8p V 'dVP/Wx4RRR4R# +'giR#;i)zGAccumulates subset progress and triggers layer quantization when ready.NrT)rWrrrXrQrrTrrrrRrSr@rWrCr`r rU intersectionrrVr\r(r) rr>rArrrrRrr candidaterpending_in_scopeshould_quantizes &&&&&&& rCprocessAWQProcessor.process s //1(( %%k2 ZZZ! $$V,%%-%+]]_ +4??+>+>~+N(+71A.! &5 '%1"'''++L9!%%--fkk:.2.Q.QRa.b+ %44 }}}#(#8#8#E#EemmFXFXFZ#[ OO#'((&&$.JJ5223u7I7II $$[8MZZZs?AH$A#H3Hr)rr)rBr_s"rCrDrV s!!!Z!rEc (VPV4R#)z7Delegates AWQ module packing to the shared pack helper.N)r8)rr>rrs&&&,rCsubmodule_finalizeAWQProcessor.submodule_finalizeV s  rEc * VP4VP;_uu_4VP'd>VPP R4P \ 4VPnVPP RR4VPP RR4VPPR4f\RVP4VPP R4P4pVPP R4P4pRRR4XP\ 8XgQhXP\ 8XgQhVPVP4pVPVPP P#VP4/p\%VR\%VR R 44p\%VR\%VR R44p\%VPR R4pVe\&P(!4MRp \+R \,VR 7;_uu_4\/V4;_uu_4\1VPVVP2P4VP2P6VP2P8VP2P:VPP VVP2P<VP2PVPP>VP2P@VPBRVPPDR7RRR4RRR4Ve3V e/VPGR\&P(!4V , VR7VPP P#VP4p \IW4'dVPV /M/p Ve\&P(!4MRp \+R\,VR 7;_uu_4\/V4;_uu_4\KVPV VVRVVVPVP2R7 p RRR4RRR4VeGV eATPGR\&P(!4V , T RX ;'gR R2R7R#R#R# +'giEL;i +'giEL;i +'giEL;i +'giL;i +'giL;i)zLCreates the AWQ quantized module and packs saved scales/zero-points into it.r`rNr_r^moddddr]rxr`quant_region_timerr7)loggerr F)r` linear_clsrdesc_actdynamicror> submodulerrA lm_head_name pack_dtyperBregister_buffersrusubmodule_finalize_create)rpack) r`qModulesr]r^q_g_idxrnquant_linear_clsrXquantize_configsubmodule_finalize_packz [zmodule.pack_original])& stream_syncrXrrrrr:r|rorprintrxrrArrr get_submodulerHrm perf_counterr3r@r9r7r runtime_bitsrrrorlm_headrrBrurecordrr8)rr>r^r]rrn module_label parent_keytimer create_startqmoduler pack_start packer_labels&& rCr8AWQProcessor.pack_module[ s iii'''%+\\%5%5d%;%>%>s%C " LL  S$ ' LL  [$ /|| *2h 0 01ll&&y1779G||'' 399;H~~$$$#%%%778H8HI""DOO$9$9$G$GHXHX$YZv{GFFB4OP V['&&$2OP )=tD.3.?t((*T %(  $J//#))///!YY// II--#yy33??00$ 99++!%!8!8#yy33;;%* ----0  .  !9 LL+!!#l2#   //''55f6F6FG'44  w '  -2,=T&&(4 (  $J//*))%%# !%5$(II  0  "  !7 LL)!!#j0&r,*P*P:P)QQRS  "8 Yii60//    T0/   sUD R3:SC7S ST$3S/ T3 S  S  S S, / S? :T T c <V^8dQhRS[/#)r=rr)rBr_s"rCrDr s00j0rEc x<RVn\PVPn\ SV`!RRV/VBR#)zDMarks the model as AWQ-quantized and runs shared finalization logic.TrNra)rVr+AWQrrrfinalize)rrrrs&&,rCrAWQProcessor.finalize s3'-zz$ /u//rEc&<V^8dQhRS[RS[/#)r=processor_indexr?)rZr\)rBr_s"rCrDr s#$rEc 8VPf \R4hR#)zJEnsures AWQ received calibration data before the quantization loop starts.z5GPTQProcessor's calibration_dataset must be provided.T)rrK)rrs&&rCverify_calibration_dataset'AWQProcessor.verify_calibration_dataset s  # # +TU UrEc <V^8dQhRS[/#r)rY)rBr_s"rCrDr sSrEc R#)zAReturns the processor label used in logs and lifecycle reporting.rra)clss&rCr`AWQProcessor.name srEc&<V^8dQhRS[RS[/#)r=r`r?)rYr\)rBr_s"rCrDr s``3`4`rEc VPPV4;'g/pVPR.4pVRJ;'dK\V4^8;'d5\;QJdRV4F 'dK R# R#!RV44#)zDReports whether a module has any non-empty captured AWQ activations.r:Nc3F"TFqP4^8xK R#5irm)rT)r_ts& rCrb6AWQProcessor.has_captured_input_ids.. s?_W^RS A W^s!FT)r?rr(r)rr`rFrws&& rChas_captured_input_ids#AWQProcessor.has_captured_input_ids sw t$**&+ii"&=d"__s7|a'7__CC?_W^?_CC_C_C?_W^?_<__rE)rrrrrrrrrrrrrr%rrrrrBrrrrr)TFNr])g?i)NNNNN)Frbrcrdrerf staticmethodrrrrrrrrrrrr!rr4rHrArersrrrrrrrrrrr r"r'r1rDrWrbrr@rr~rrrrrrrrrrBr}rrrrrr8rr classmethodr`rrjrk __classcell__)rr_s@@rCrrsR##G&G&RUU  $$L[  UU0 E EB%%> 9F # 9F9Fv0LL08    II:mEmE^rs    #(BBB ll-9hhh77N9EZZVV0%/7ll3n   A A  A7t 6D%`=D%`rE